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docs/build/html/structmlx_1_1steel_1_1_layout2_d-members.html create mode 100644 docs/build/html/structmlx_1_1steel_1_1_layout2_d.html create mode 100644 docs/build/html/structmlx_1_1steel_1_1_shape2_d-members.html rename docs/build/html/{struct_m_l_x_scaled_dot_product_attention_params.html => structmlx_1_1steel_1_1_shape2_d.html} (50%) diff --git a/docs/build/html/.buildinfo b/docs/build/html/.buildinfo index b92778bc..c20792bb 100644 --- a/docs/build/html/.buildinfo +++ b/docs/build/html/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: aa9c5e5c24909e63ee7546fee2f7474d +config: 8880bb5f0a2c9a353db73959d72b9edf tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/build/html/_sources/dev/extensions.rst b/docs/build/html/_sources/dev/extensions.rst index ecb41846..196f8bf6 100644 --- a/docs/build/html/_sources/dev/extensions.rst +++ b/docs/build/html/_sources/dev/extensions.rst @@ -494,7 +494,7 @@ below. // Prepare to encode kernel auto& compute_encoder = d.get_command_encoder(s.index); - compute_encoder->setComputePipelineState(kernel); + compute_encoder.set_compute_pipeline_state(kernel); // Kernel parameters are registered with buffer indices corresponding to // those in the kernel declaration at axpby.metal @@ -509,14 +509,14 @@ below. compute_encoder.set_output_array(out, 2); // Encode alpha and beta - compute_encoder->setBytes(&alpha_, sizeof(float), 3); - compute_encoder->setBytes(&beta_, sizeof(float), 4); + compute_encoder.set_bytes(alpha_, 3); + compute_encoder.set_bytes(beta_, 4); // Encode shape, strides and ndim - compute_encoder->setBytes(x.shape().data(), ndim * sizeof(int), 5); - compute_encoder->setBytes(x.strides().data(), ndim * sizeof(size_t), 6); - compute_encoder->setBytes(y.strides().data(), ndim * sizeof(size_t), 7); - compute_encoder->setBytes(&ndim, sizeof(int), 8); + compute_encoder.set_vector_bytes(x.shape(), 5); + compute_encoder.set_vector_bytes(x.strides(), 6); + compute_encoder.set_bytes(y.strides(), 7); + compute_encoder.set_bytes(ndim, 8); // We launch 1 thread for each input and make sure that the number of // threads in any given threadgroup is not higher than the max allowed @@ -530,7 +530,7 @@ below. // Launch the grid with the given number of threads divided among // the given threadgroups - compute_encoder.dispatchThreads(grid_dims, group_dims); + compute_encoder.dispatch_threads(grid_dims, group_dims); } We can now call the :meth:`axpby` operation on both the CPU and the GPU! diff --git a/docs/build/html/_sources/install.rst b/docs/build/html/_sources/install.rst index 7c68942f..1566d1f2 100644 --- a/docs/build/html/_sources/install.rst +++ b/docs/build/html/_sources/install.rst @@ -209,7 +209,7 @@ Metal library by run-time compiling kernels the first time they are used in MLX on a given machine. Note run-time compilation incurs a cold-start cost which can be anwywhere from a few hundred millisecond to a few seconds depending on the application. Once a kernel is compiled, it will be cached by the system. The -Metal kernel cache persists accross reboots. +Metal kernel cache persists across reboots. Troubleshooting ^^^^^^^^^^^^^^^ diff --git a/docs/build/html/_sources/python/_autosummary/mlx.core.fast.affine_quantize.rst b/docs/build/html/_sources/python/_autosummary/mlx.core.fast.affine_quantize.rst deleted file mode 100644 index 9daa5e11..00000000 --- a/docs/build/html/_sources/python/_autosummary/mlx.core.fast.affine_quantize.rst +++ /dev/null @@ -1,6 +0,0 @@ -mlx.core.fast.affine\_quantize -============================== - -.. currentmodule:: mlx.core.fast - -.. autofunction:: affine_quantize \ No newline at end of file diff --git a/docs/build/html/_sources/python/fast.rst b/docs/build/html/_sources/python/fast.rst index 30ade264..f78f4056 100644 --- a/docs/build/html/_sources/python/fast.rst +++ b/docs/build/html/_sources/python/fast.rst @@ -12,5 +12,4 @@ Fast layer_norm rope scaled_dot_product_attention - affine_quantize metal_kernel diff --git a/docs/build/html/_sources/python/nn/_autosummary/mlx.nn.AvgPool3d.rst b/docs/build/html/_sources/python/nn/_autosummary/mlx.nn.AvgPool3d.rst new file mode 100644 index 00000000..71dc765d --- /dev/null +++ b/docs/build/html/_sources/python/nn/_autosummary/mlx.nn.AvgPool3d.rst @@ -0,0 +1,16 @@ +mlx.nn.AvgPool3d +================ + +.. currentmodule:: mlx.nn + +.. autoclass:: AvgPool3d + + + + + .. rubric:: Methods + + .. autosummary:: + + + diff --git a/docs/build/html/_sources/python/nn/_autosummary/mlx.nn.MaxPool3d.rst b/docs/build/html/_sources/python/nn/_autosummary/mlx.nn.MaxPool3d.rst new file mode 100644 index 00000000..2656eef3 --- /dev/null +++ b/docs/build/html/_sources/python/nn/_autosummary/mlx.nn.MaxPool3d.rst @@ -0,0 +1,16 @@ +mlx.nn.MaxPool3d +================ + +.. currentmodule:: mlx.nn + +.. autoclass:: MaxPool3d + + + + + .. rubric:: Methods + + .. autosummary:: + + + diff --git a/docs/build/html/_sources/python/nn/layers.rst b/docs/build/html/_sources/python/nn/layers.rst index fc24d410..4eb14b08 100644 --- a/docs/build/html/_sources/python/nn/layers.rst +++ b/docs/build/html/_sources/python/nn/layers.rst @@ -12,6 +12,7 @@ Layers ALiBi AvgPool1d AvgPool2d + AvgPool3d BatchNorm CELU Conv1d @@ -41,6 +42,7 @@ Layers LSTM MaxPool1d MaxPool2d + MaxPool3d Mish MultiHeadAttention PReLU diff --git a/docs/build/html/_sources/usage/function_transforms.rst b/docs/build/html/_sources/usage/function_transforms.rst index 9769fcea..045c36c9 100644 --- a/docs/build/html/_sources/usage/function_transforms.rst +++ b/docs/build/html/_sources/usage/function_transforms.rst @@ -184,8 +184,8 @@ Let's time these two different versions: print(timeit.timeit(lambda: mx.eval(naive_add(xs, ys)), number=100)) print(timeit.timeit(lambda: mx.eval(vmap_add(xs, ys)), number=100)) -On an M1 Max the naive version takes in total ``0.390`` seconds whereas the -vectorized version takes only ``0.025`` seconds, more than ten times faster. +On an M1 Max the naive version takes in total ``5.639`` seconds whereas the +vectorized version takes only ``0.024`` seconds, more than 200 times faster. Of course, this operation is quite contrived. A better approach is to simply do ``xs + ys.T``, but for more complex functions :func:`vmap` can be quite handy. diff --git a/docs/build/html/_static/documentation_options.js b/docs/build/html/_static/documentation_options.js index a531c3d5..75c0fd73 100644 --- a/docs/build/html/_static/documentation_options.js +++ b/docs/build/html/_static/documentation_options.js @@ -1,5 +1,5 @@ const DOCUMENTATION_OPTIONS = { - VERSION: '0.20.0', + VERSION: '0.21.0', LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/docs/build/html/annotated.html b/docs/build/html/annotated.html index 87141e0a..cbd46ef8 100644 --- a/docs/build/html/annotated.html +++ b/docs/build/html/annotated.html @@ -236,129 +236,135 @@ $(function(){ initResizable(false); });  Ccomplex64_t  CConcatenate  CConjugate - CContiguousIterator - CConvolution - CCopy - CCos - CCosh - CCustomTransforms - CDefaultContiguousReduce - CDefaultStridedReduce - CDepends - CDevice - CDivide - CDivMod - CDtype - CEigh - CEqual - CErf - CErfInv - CEvent - CExp - CExpm1 - CFFT - CFloor - CFull - CGather - CGatherMM - CGatherQMM - CGreater - CGreaterEqual - CHadamard - CImag - CInverse - CLess - CLessEqual - CLoad - CLog - CLog1p - CLogAddExp - CLogicalAnd - CLogicalNot - CLogicalOr - CMatmul - CMaximum - CMinimum - CMultiply - CNegative - CNodeNamer - CNotEqual - CNumberOfElements - CPad - CPartition - CPower - CPrimitive - CPrintFormatter - CQRF - CQuantizedMatmul - CRandomBits - CReal - CReduce - CReductionPlan - CRemainder - CReshape - CRound - CScan - CScatter - CSelect - CSigmoid - CSign - CSin - CSinh - CSlice - CSliceUpdate - CSoftmax - CSort - CSplit - CSqrt - CSquare - CStopGradient - CStream - CStreamContext - CSubtract - CSVD - CTan - CTanh - CTranspose - CTypeToDtype - CUnaryPrimitive - CUniform - CView + CContiguous + CContiguousIterator + CConvolution + CCopy + CCos + CCosh + CCustomTransforms + CDefaultContiguousReduce + CDefaultStridedReduce + CDepends + CDevice + CDivide + CDivMod + CDtype + CEigh + CEqual + CErf + CErfInv + CEvent + CExp + CExpm1 + CFFT + CFloor + CFull + CGather + CGatherMM + CGatherQMM + CGreater + CGreaterEqual + CHadamard + CImag + CInverse + CLess + CLessEqual + CLoad + CLog + CLog1p + CLogAddExp + CLogicalAnd + CLogicalNot + CLogicalOr + CMatmul + CMaximum + CMinimum + CMultiply + CNegative + CNodeNamer + CNotEqual + CNumberOfElements + CPad + CPartition + CPower + CPrimitive + CPrintFormatter + CQRF + CQuantizedMatmul + CRandomBits + CReal + CReduce + CReductionPlan + CRemainder + CReshape + CRound + CScan + CScatter + CSelect + CSigmoid + CSign + CSin + CSinh + CSlice + CSliceUpdate + CSoftmax + CSort + CSplit + CSqrt + CSquare + CStopGradient + CStream + CStreamContext + CSubtract + CSVD + CTan + CTanh + CTranspose + CTypeToDtype + CUnaryPrimitive + CUniform + CView  ►Nsteel  CAccumHelper - CBaseMMAFrag - CBaseMMAFrag< T, 8, 8 > - ►CBlockLoader - CReadVector - CBlockMMA - CBlockSwizzle - CChannelHelper - CChannelHelper< 1 > - CChannelHelper< 2 > - CChannelHelper< 3 > - CChannelHelper< 4 > - CConv2DGeneralBaseInfo - CConv2DGeneralJumpParams - CConv2DInputBlockLoaderGeneral - CConv2DInputBlockLoaderLargeFilter - CConv2DInputBlockLoaderSmallChannels - CConv2DInputBlockLoaderSmallFilter - CConv2DWeightBlockLoader - CConv2DWeightBlockLoaderGeneral - CConv2DWeightBlockLoaderSmallChannels - CGEMMAddMMParams - CGEMMKernel - CGEMMParams - CGEMMSpiltKParams - CImplicitGemmConv2DParams - Cintegral_constant - Cis_integral - Cis_integral< integral_constant< T, v > > - CLoopAlignment - CMMATile - CTransformAdd - CTransformAxpby - CTransformNone + CAttnParams + CBaseMMAFrag + CBaseMMAFrag< T, 8, 8 > + ►CBlockLoader + CReadVector + CBlockLoaderT + CBlockMMA + CBlockSwizzle + CChannelHelper + CChannelHelper< 1 > + CChannelHelper< 2 > + CChannelHelper< 3 > + CChannelHelper< 4 > + CConv2DGeneralBaseInfo + CConv2DGeneralJumpParams + CConv2DInputBlockLoaderGeneral + CConv2DInputBlockLoaderLargeFilter + CConv2DInputBlockLoaderSmallChannels + CConv2DInputBlockLoaderSmallFilter + CConv2DWeightBlockLoader + CConv2DWeightBlockLoaderGeneral + CConv2DWeightBlockLoaderSmallChannels + CCShape + CGEMMAddMMParams + CGEMMKernel + CGEMMParams + CGEMMSpiltKParams + CImplicitGemmConv2DParams + Cintegral_constant + Cis_integral + Cis_integral< integral_constant< T, v > > + CLayout2D + CLoopAlignment + CMMATile + CShape2D + CTransformAdd + CTransformAxpby + CTransformNone  ►Npocketfft  ►Ndetail  ►Nthreading @@ -423,88 +429,93 @@ $(function(){ initResizable(false); });  CCumSum  CDivide  CDivMod - CEqual - CErf - CErfInv - CExp - CExpm1 - CFloor - CFloorDivide - CGEMVKernel - CGEMVTKernelVector matrix multiplication - CGreater - CGreaterEqual - CImag - CIndices - CKernelMergeSort - CKernelMultiBlockMergeSort - CLeftShift - CLess - CLessEqual - CLessThan - CLimits - CLimits< bfloat16_t > - CLimits< bool > - CLimits< complex64_t > - CLimits< float > - CLimits< half > - CLimits< int16_t > - CLimits< int32_t > - CLimits< int64_t > - CLimits< int8_t > - CLimits< uint16_t > - CLimits< uint32_t > - CLimits< uint64_t > - CLimits< uint8_t > - CLog - CLog10 - CLog1p - CLog2 - CLogAddExp - CLogicalAnd - CLogicalNot - CLogicalOr - Clooped_elem_to_loc - Clooped_elem_to_loc< 0, offset_t > - Clooped_elem_to_loc< 1, offset_t > - CMax - CMaximum - CMin - CMinimum - Cmlx_atomic - Cmlx_atomic< T, enable_if_t< is_metal_atomic< T > > > - CMLXConvParams - CMLXFastAttentionParams - CMLXScaledDotProductAttentionParams - CMultiply - CNaNEqual - CNegative - CNone - CNotEqual - COr - CPower - CProd - CQuantizedBlockLoader - CReadWriter - CReal - CRemainder - CRightShift - CRound - CRsqrt - CScaleOp - CSelect - CSigmoid - CSign - CSin - CSinh - CSqrt - CSquare - CSubtract - CSum - CTan - CTanh - CThreadPool - CThreadSort + CDivOp + CEqual + CErf + CErfInv + CExp + CExpm1 + CExpSubOp + CFloor + CFloorDivide + CGEMVKernel + CGEMVTKernelVector matrix multiplication + CGreater + CGreaterEqual + CImag + CIndices + CKernelMergeSort + CKernelMultiBlockMergeSort + CLeftShift + CLess + CLessEqual + CLessThan + CLimits + CLimits< bfloat16_t > + CLimits< bool > + CLimits< complex64_t > + CLimits< float > + CLimits< half > + CLimits< int16_t > + CLimits< int32_t > + CLimits< int64_t > + CLimits< int8_t > + CLimits< uint16_t > + CLimits< uint32_t > + CLimits< uint64_t > + CLimits< uint8_t > + CLog + CLog10 + CLog1p + CLog2 + CLogAddExp + CLogicalAnd + CLogicalNot + CLogicalOr + CLoopedElemToLoc + CLoopedElemToLoc< 1, OffsetT, false > + CLoopedElemToLoc< 1, OffsetT, true > + CMax + CMaximum + CMaxOp + CMin + CMinimum + Cmlx_atomic + Cmlx_atomic< T, enable_if_t< is_metal_atomic< T > > > + CMLXConvParams + CMulOp + CMultiply + CNaNEqual + CNegative + CNone + CNotEqual + COr + CPower + CProd + CQuantizedBlockLoader + CReadWriter + CReal + CRemainder + CRightShift + CRound + CRsqrt + CScaleOp + CSelect + CSigmoid + CSign + CSin + CSinh + CSqrt + CSquare + CSubOp + CSubtract + CSum + CSumOp + CTan + CTanh + CThreadPool + CThreadSort + CTransformScale diff --git a/docs/build/html/atomic_8h_source.html b/docs/build/html/atomic_8h_source.html index 796c0a05..a5c220e8 100644 --- a/docs/build/html/atomic_8h_source.html +++ b/docs/build/html/atomic_8h_source.html @@ -472,9 +472,9 @@ $(function(){ initResizable(false); });
mlx_atomic_fetch_mul_explicit
METAL_FUNC void mlx_atomic_fetch_mul_explicit(device mlx_atomic< T > *object, T val, size_t offset)
Definition atomic.h:91
op
Op op
Definition binary.h:129
mlx::core::identity
array identity(int n, Dtype dtype, StreamOrDevice s={})
Create a square matrix of shape (n,n) of zeros, and ones in the major diagonal.
-
metal
Definition bf16.h:265
-
metal::min
METAL_FUNC bfloat16_t min(bfloat16_t x, bfloat16_t y)
Definition bf16_math.h:234
-
metal::max
METAL_FUNC bfloat16_t max(bfloat16_t x, bfloat16_t y)
Definition bf16_math.h:234
+
metal
Definition bf16_math.h:226
+
metal::min
METAL_FUNC bfloat16_t min(bfloat16_t x, bfloat16_t y)
Definition bf16_math.h:232
+
metal::max
METAL_FUNC bfloat16_t max(bfloat16_t x, bfloat16_t y)
Definition bf16_math.h:232
mlx::core::distributed::init
Group init(bool strict=false)
Initialize the distributed backend and return the group containing all discoverable processes.
mlx::core::random::bits
array bits(const std::vector< int > &shape, int width, const std::optional< array > &key=std::nullopt, StreamOrDevice s={})
Generate an array with type uint32 filled with random bits.
mlx_atomic< T, enable_if_t< is_metal_atomic< T > > >::val
atomic< T > val
Definition atomic.h:31
diff --git a/docs/build/html/attn_2loader_8h.html b/docs/build/html/attn_2loader_8h.html new file mode 100644 index 00000000..882286ae --- /dev/null +++ b/docs/build/html/attn_2loader_8h.html @@ -0,0 +1,126 @@ + + + + + + + +MLX: mlx/backend/metal/kernels/steel/attn/loader.h File Reference + + + + + + + + + + + + + +
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loader.h File Reference
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#include "mlx/backend/metal/kernels/steel/defines.h"
+
+

Go to the source code of this file.

+ + + + + + + + + + +

+Classes

struct  mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
 
struct  mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >::ReadVector
 
struct  mlx::steel::CShape< R, C >
 
struct  mlx::steel::BlockLoaderT< T, BROWS, BCOLS, kDstStrRow, kDstStrCol, reduction_dim, tgp_size, n_reads, TCOLS, TROWS >
 
+ + + + + +

+Namespaces

namespace  mlx
 
namespace  mlx::steel
 
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loader.h
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+Go to the documentation of this file.
1// Copyright © 2024 Apple Inc.
+
2
+
3#pragma once
+
4
+
5#include "mlx/backend/metal/kernels/steel/defines.h"
+
6
+
8// Loading helper
+
10
+
11namespace mlx {
+
12namespace steel {
+
13
+
14template <
+
15 typename T,
+
16 short BROWS,
+
17 short BCOLS,
+
18 short dst_ld,
+
19 short reduction_dim,
+
20 short tgp_size,
+
21 short alignment = 1,
+
22 short n_reads = (BCOLS * BROWS) / (tgp_size),
+
23 short TCOLS = BCOLS / n_reads,
+
24 short TROWS = tgp_size / TCOLS>
+
25struct BlockLoader {
+
26 STEEL_CONST short n_rows = (BROWS + TROWS - 1) / TROWS;
+
27 STEEL_CONST short vec_size = n_reads;
+
28
+
29 // Leading dimension for src
+
30 const int src_ld;
+
31 const int tile_stride;
+
32
+
33 // Thread location indices
+
34 const short thread_idx;
+
35 const short bi;
+
36 const short bj;
+
37
+
38 // threadgroup and device memory
+
39 threadgroup T* dst;
+
40 const device T* src;
+
41
+
42 struct alignas(alignment * sizeof(T)) ReadVector {
+
43 uint8_t v[sizeof(T) * vec_size];
+
44 };
+
45
+
46 /* Constructor */
+
+
47 METAL_FUNC BlockLoader(
+
48 const device T* src_,
+
49 const int src_ld_,
+
50 threadgroup T* dst_,
+
51 ushort simd_group_id [[simdgroup_index_in_threadgroup]],
+
52 ushort simd_lane_id [[thread_index_in_simdgroup]])
+
53 : src_ld(src_ld_),
+
54 tile_stride(reduction_dim ? BCOLS : BROWS * src_ld),
+
55 thread_idx(simd_group_id * 32 + simd_lane_id),
+
56 bi(thread_idx / TCOLS),
+
57 bj(vec_size * (thread_idx % TCOLS)),
+
58 dst(dst_ + bi * dst_ld + bj),
+
59 src(src_ + bi * src_ld + bj) {}
+
+
60
+
61 /* Apply operation to threadgroup without bound checking */
+
62 template <typename UnaryOp>
+
+
63 METAL_FUNC void apply_inplace_op(thread const UnaryOp& op) const {
+
64 STEEL_PRAGMA_UNROLL
+
65 for (short i = 0; i < BROWS; i += TROWS) {
+
66 STEEL_PRAGMA_UNROLL
+
67 for (short j = 0; j < vec_size; j++) {
+
68 dst[i * dst_ld + j] = op.apply(dst[i * dst_ld + j]);
+
69 }
+
70 }
+
71 }
+
+
72
+
73 /* Load from device memory into threadgroup memory - without bound checking */
+
+
74 METAL_FUNC void load_unsafe() const {
+
75 STEEL_PRAGMA_UNROLL
+
76 for (short i = 0; i < BROWS; i += TROWS) {
+
77 *((threadgroup ReadVector*)(&dst[i * dst_ld])) =
+
78 *((const device ReadVector*)(&src[i * src_ld]));
+
79 }
+
80 }
+
+
81
+
82 /* Load from device memory into threadgroup memory - with bound checking */
+
+
83 METAL_FUNC void load_safe(short2 src_tile_dim) const {
+
84 src_tile_dim = src_tile_dim - short2(bj, bi);
+
85
+
86 // Skip loading if thread has no valid reads
+
87 if (src_tile_dim.x <= 0 || src_tile_dim.y <= 0) {
+
88 STEEL_PRAGMA_UNROLL
+
89 for (short i = 0; i < BROWS; i += TROWS) {
+
90 STEEL_PRAGMA_UNROLL
+
91 for (short j = 0; j < vec_size; j++) {
+
92 dst[i * dst_ld + j] = T(0);
+
93 }
+
94 }
+
95 return;
+
96 }
+
97
+
98 // Use fast thread memory for bound checks
+
99 bool tmp_idx[vec_size];
+
100 T tmp_val[vec_size];
+
101
+
102 STEEL_PRAGMA_UNROLL
+
103 for (short i = 0; i < BROWS; i += TROWS) {
+
104 // Make sure tmp_idx only contains valid indices
+
105 STEEL_PRAGMA_UNROLL
+
106 for (short j = 0; j < vec_size; j++) {
+
107 tmp_idx[j] = (i < src_tile_dim.y) && (j < src_tile_dim.x);
+
108 }
+
109
+
110 // Read valid indices into tmp_val
+
111 STEEL_PRAGMA_UNROLL
+
112 for (short j = 0; j < vec_size; j++) {
+
113 tmp_val[j] = src[(tmp_idx[j] ? i * src_ld + j : 0)];
+
114 }
+
115
+
116 // Zero out uneeded values
+
117 STEEL_PRAGMA_UNROLL
+
118 for (short j = 0; j < vec_size; j++) {
+
119 tmp_val[j] = tmp_idx[j] ? tmp_val[j] : T(0);
+
120 }
+
121
+
122 // Copy values to threadgroup memory
+
123 STEEL_PRAGMA_UNROLL
+
124 for (short j = 0; j < vec_size; j++) {
+
125 dst[i * dst_ld + j] = tmp_val[j];
+
126 }
+
127 }
+
128 }
+
+
129
+
130 /* Iteration helper */
+
+
131 METAL_FUNC void next() {
+
132 src += tile_stride;
+
133 }
+
+
134};
+
135
+
136template <int R, int C>
+
+
137struct CShape {
+
138 STEEL_CONST int kRows = R;
+
139 STEEL_CONST int kCols = C;
+
140};
+
+
141
+
142template <
+
143 typename T,
+
144 short BROWS,
+
145 short BCOLS,
+
146 short kDstStrRow,
+
147 short kDstStrCol,
+
148 short reduction_dim,
+
149 short tgp_size,
+
150 short n_reads = (BCOLS * BROWS) / (tgp_size),
+
151 short TCOLS = BCOLS / n_reads,
+
152 short TROWS = tgp_size / TCOLS>
+
+
153struct BlockLoaderT {
+
154 STEEL_CONST short n_rows = (BROWS + TROWS - 1) / TROWS;
+
155 STEEL_CONST short vec_size = n_reads;
+
156
+
157 // Leading dimension for src
+
158 const int src_ld;
+
159 const int tile_stride;
+
160
+
161 // Thread location indices
+
162 const short thread_idx;
+
163 const short bi;
+
164 const short bj;
+
165
+
166 // threadgroup and device memory
+
167 threadgroup T* dst;
+
168 const device T* src;
+
169
+
170 /* Constructor */
+
+
171 METAL_FUNC BlockLoaderT(
+
172 const device T* src_,
+
173 const int src_ld_,
+
174 threadgroup T* dst_,
+
175 ushort simd_group_id [[simdgroup_index_in_threadgroup]],
+
176 ushort simd_lane_id [[thread_index_in_simdgroup]])
+
177 : src_ld(src_ld_),
+
178 tile_stride(reduction_dim ? BCOLS : BROWS * src_ld),
+
179 thread_idx(simd_group_id * 32 + simd_lane_id),
+
180 bi(thread_idx / TCOLS),
+
181 bj(vec_size * (thread_idx % TCOLS)),
+
182 dst(dst_ + bi * kDstStrRow + bj * kDstStrCol),
+
183 src(src_ + bi * src_ld + bj) {}
+
+
184
+
185 /* Apply operation to threadgroup without bound checking */
+
186 template <typename UnaryOp>
+
+
187 METAL_FUNC void apply_inplace_op(thread const UnaryOp& op) const {
+
188 STEEL_PRAGMA_UNROLL
+
189 for (short i = 0; i < BROWS; i += TROWS) {
+
190 STEEL_PRAGMA_UNROLL
+
191 for (short j = 0; j < vec_size; j++) {
+
192 dst[i * kDstStrRow + j * kDstStrCol] =
+
193 op.apply(dst[i * kDstStrRow + j * kDstStrCol]);
+
194 }
+
195 }
+
196 }
+
+
197
+
198 /* Load from device memory into threadgroup memory - without bound checking */
+
+
199 METAL_FUNC void load_unsafe() const {
+
200 STEEL_PRAGMA_UNROLL
+
201 for (short i = 0; i < BROWS; i += TROWS) {
+
202 STEEL_PRAGMA_UNROLL
+
203 for (short j = 0; j < vec_size; j++) {
+
204 dst[i * kDstStrRow + j * kDstStrCol] = src[i * src_ld + j];
+
205 }
+
206 }
+
207 }
+
+
208
+
209 /* Load from device memory into threadgroup memory - with bound checking */
+
+
210 METAL_FUNC void load_safe(short2 src_tile_dim) const {
+
211 src_tile_dim = src_tile_dim - short2(bj, bi);
+
212
+
213 // Skip loading if thread has no valid reads
+
214 if (src_tile_dim.x <= 0 || src_tile_dim.y <= 0) {
+
215 STEEL_PRAGMA_UNROLL
+
216 for (short i = 0; i < BROWS; i += TROWS) {
+
217 STEEL_PRAGMA_UNROLL
+
218 for (short j = 0; j < vec_size; j++) {
+
219 dst[i * kDstStrRow + j * kDstStrCol] = T(0);
+
220 }
+
221 }
+
222 return;
+
223 }
+
224
+
225 // Use fast thread memory for bound checks
+
226 bool tmp_idx[vec_size];
+
227 T tmp_val[vec_size];
+
228
+
229 STEEL_PRAGMA_UNROLL
+
230 for (short i = 0; i < BROWS; i += TROWS) {
+
231 // Make sure tmp_idx only contains valid indices
+
232 STEEL_PRAGMA_UNROLL
+
233 for (short j = 0; j < vec_size; j++) {
+
234 tmp_idx[j] = (i < src_tile_dim.y) && (j < src_tile_dim.x);
+
235 }
+
236
+
237 // Read valid indices into tmp_val
+
238 STEEL_PRAGMA_UNROLL
+
239 for (short j = 0; j < vec_size; j++) {
+
240 tmp_val[j] = src[(tmp_idx[j] ? i * src_ld + j : 0)];
+
241 }
+
242
+
243 // Zero out uneeded values
+
244 STEEL_PRAGMA_UNROLL
+
245 for (short j = 0; j < vec_size; j++) {
+
246 tmp_val[j] = tmp_idx[j] ? tmp_val[j] : T(0);
+
247 }
+
248
+
249 // Copy values to threadgroup memory
+
250 STEEL_PRAGMA_UNROLL
+
251 for (short j = 0; j < vec_size; j++) {
+
252 dst[i * kDstStrRow + j * kDstStrCol] = tmp_val[j];
+
253 }
+
254 }
+
255 }
+
+
256
+
257 /* Iteration helper */
+
+
258 METAL_FUNC void next() {
+
259 src += tile_stride;
+
260 }
+
+
261};
+
+
262
+
263} // namespace steel
+
264} // namespace mlx
+
op
Op op
Definition binary.h:129
+
mlx
Definition allocator.h:7
+ +
STEEL_PRAGMA_UNROLL
#define STEEL_PRAGMA_UNROLL
Definition defines.h:4
+
STEEL_CONST
#define STEEL_CONST
Definition defines.h:3
+ +
mlx::steel::BlockLoader::ReadVector::v
uint8_t v[sizeof(T) *vec_size]
Definition loader.h:43
+
mlx::steel::BlockLoader::thread_idx
const short thread_idx
Definition loader.h:34
+
mlx::steel::BlockLoader::BlockLoader
METAL_FUNC BlockLoader(const device T *src_, const int src_ld_, threadgroup T *dst_, ushort simd_group_id, ushort simd_lane_id)
Definition loader.h:47
+
mlx::steel::BlockLoader::vec_size
STEEL_CONST short vec_size
Definition loader.h:27
+
mlx::steel::BlockLoader::next
METAL_FUNC void next()
Definition loader.h:131
+
mlx::steel::BlockLoader::load_unsafe
METAL_FUNC void load_unsafe() const
Definition loader.h:74
+
mlx::steel::BlockLoader::bj
const short bj
Definition loader.h:36
+
mlx::steel::BlockLoader::n_rows
STEEL_CONST short n_rows
Definition loader.h:26
+
mlx::steel::BlockLoader::bi
const short bi
Definition loader.h:35
+
mlx::steel::BlockLoader::src_ld
const int src_ld
Definition loader.h:30
+
mlx::steel::BlockLoader::tile_stride
const int tile_stride
Definition loader.h:31
+
mlx::steel::BlockLoader::load_safe
METAL_FUNC void load_safe(short2 src_tile_dim) const
Definition loader.h:83
+
mlx::steel::BlockLoader::src
const device T * src
Definition loader.h:40
+
mlx::steel::BlockLoader::apply_inplace_op
METAL_FUNC void apply_inplace_op(thread const UnaryOp &op) const
Definition loader.h:63
+
mlx::steel::BlockLoader::dst
threadgroup T * dst
Definition loader.h:39
+
mlx::steel::BlockLoaderT
Definition loader.h:153
+
mlx::steel::BlockLoaderT::BlockLoaderT
METAL_FUNC BlockLoaderT(const device T *src_, const int src_ld_, threadgroup T *dst_, ushort simd_group_id, ushort simd_lane_id)
Definition loader.h:171
+
mlx::steel::BlockLoaderT::n_rows
STEEL_CONST short n_rows
Definition loader.h:154
+
mlx::steel::BlockLoaderT::apply_inplace_op
METAL_FUNC void apply_inplace_op(thread const UnaryOp &op) const
Definition loader.h:187
+
mlx::steel::BlockLoaderT::tile_stride
const int tile_stride
Definition loader.h:159
+
mlx::steel::BlockLoaderT::next
METAL_FUNC void next()
Definition loader.h:258
+
mlx::steel::BlockLoaderT::bi
const short bi
Definition loader.h:163
+
mlx::steel::BlockLoaderT::dst
threadgroup T * dst
Definition loader.h:167
+
mlx::steel::BlockLoaderT::src
const device T * src
Definition loader.h:168
+
mlx::steel::BlockLoaderT::vec_size
STEEL_CONST short vec_size
Definition loader.h:155
+
mlx::steel::BlockLoaderT::load_safe
METAL_FUNC void load_safe(short2 src_tile_dim) const
Definition loader.h:210
+
mlx::steel::BlockLoaderT::bj
const short bj
Definition loader.h:164
+
mlx::steel::BlockLoaderT::load_unsafe
METAL_FUNC void load_unsafe() const
Definition loader.h:199
+
mlx::steel::BlockLoaderT::src_ld
const int src_ld
Definition loader.h:158
+
mlx::steel::BlockLoaderT::thread_idx
const short thread_idx
Definition loader.h:162
+
mlx::steel::CShape
Definition loader.h:137
+
mlx::steel::CShape::kCols
STEEL_CONST int kCols
Definition loader.h:139
+
mlx::steel::CShape::kRows
STEEL_CONST int kRows
Definition loader.h:138
+
+ +
+Generated by doxygen 1.12.0 +
+
+ + diff --git a/docs/build/html/attn_2mma_8h.html b/docs/build/html/attn_2mma_8h.html new file mode 100644 index 00000000..9c7def40 --- /dev/null +++ b/docs/build/html/attn_2mma_8h.html @@ -0,0 +1,142 @@ + + + + + + + +MLX: mlx/backend/metal/kernels/steel/attn/mma.h File Reference + + + + + + + + + + + + + +
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+Classes | +Namespaces | +Functions
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mma.h File Reference
+
+
+
#include <metal_simdgroup>
+#include <metal_simdgroup_matrix>
+#include <metal_stdlib>
+#include "mlx/backend/metal/kernels/steel/attn/transforms.h"
+#include "mlx/backend/metal/kernels/steel/defines.h"
+#include "mlx/backend/metal/kernels/steel/utils/integral_constant.h"
+
+

Go to the source code of this file.

+ + + + + + + + + + + + + + +

+Classes

struct  mlx::steel::Shape2D< RInt, CInt >
 
struct  mlx::steel::Layout2D< Shape, Layout >
 
struct  mlx::steel::BaseMMAFrag< T, kFragRows_, kFragCols_ >
 
struct  mlx::steel::BaseMMAFrag< T, 8, 8 >
 
struct  mlx::steel::MMATile< T, kTileRows_, kTileCols_, MMAFrag_ >
 
struct  mlx::steel::BlockMMA< T, U, BM, BN, BK, WM, WN, transpose_a, transpose_b, lda_tgp, ldb_tgp, AccumType, Epilogue >
 
+ + + + + +

+Namespaces

namespace  mlx
 
namespace  mlx::steel
 
+ + + + +

+Functions

template<typename T , typename U , int M, int N, int K>
METAL_FUNC void mlx::steel::tile_matmad (thread MMATile< T, M, N > &D, thread MMATile< U, M, K > &A, thread MMATile< U, K, N > &B, thread MMATile< T, M, N > &C)
 
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+Generated by doxygen 1.12.0 +
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+ + diff --git a/docs/build/html/attn_2mma_8h_source.html b/docs/build/html/attn_2mma_8h_source.html new file mode 100644 index 00000000..156ab79e --- /dev/null +++ b/docs/build/html/attn_2mma_8h_source.html @@ -0,0 +1,986 @@ + + + + + + + +MLX: mlx/backend/metal/kernels/steel/attn/mma.h Source File + + + + + + + + + + + + + +
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mma.h
+
+
+Go to the documentation of this file.
1// Copyright © 2024 Apple Inc.
+
2
+
3#pragma once
+
4
+
5#include <metal_simdgroup>
+
6#include <metal_simdgroup_matrix>
+
7#include <metal_stdlib>
+
8
+
9#include "mlx/backend/metal/kernels/steel/attn/transforms.h"
+
10#include "mlx/backend/metal/kernels/steel/defines.h"
+
11#include "mlx/backend/metal/kernels/steel/utils/integral_constant.h"
+
12
+
13using namespace metal;
+
14
+
16// MMA helper
+
18
+
19namespace mlx {
+
20namespace steel {
+
21
+
22template <typename RInt, typename CInt>
+
+
23struct Shape2D {
+
24 RInt r;
+
25 CInt c;
+
26
+
27 Shape2D(RInt r_, CInt c_) : r(r_), c(c_) {}
+
28};
+
+
29
+
30template <typename Shape, typename Layout>
+
+
31struct Layout2D {
+
32 Shape shape;
+
33 Layout layout;
+
34};
+
+
35
+
36template <typename T, int kFragRows_, int kFragCols_>
+
37struct BaseMMAFrag {
+
38 static_assert(
+
39 kFragRows_ == 8,
+
40 "Only 8 x 8 fragment matrices are currently supported");
+
41 static_assert(
+
42 kFragCols_ == 8,
+
43 "Only 8 x 8 fragment matrices are currently supported");
+
44};
+
45
+
46template <typename T>
+
47struct BaseMMAFrag<T, 8, 8> {
+
48 STEEL_CONST int kFragRows = 8;
+
49 STEEL_CONST int kFragCols = 8;
+
50
+
51 STEEL_CONST int kElemsPerFrag = (kFragRows * kFragCols) / 32;
+
52
+
53 STEEL_CONST int kElemRows = 1;
+
54 STEEL_CONST int kElemCols = 2;
+
55
+
56 static_assert(
+
57 kElemRows * kElemCols == kElemsPerFrag,
+
58 "MMAFrag shape is not consistent with MMAFrag size");
+
59
+
60 typedef metal::simdgroup_matrix<T, kFragRows, kFragCols> mat_type;
+
61 typedef metal::vec<T, kElemsPerFrag> frag_type;
+
62 typedef metal::vec<T, kElemRows> row_frag_type;
+
63 typedef metal::vec<T, kElemCols> col_frag_type;
+
64
+
+
65 METAL_FUNC static constexpr short2 get_coord(ushort simd_lane_id
+
66 [[thread_index_in_simdgroup]]) {
+
67 const short qid = simd_lane_id / 4;
+
68 const short fm = (qid & 4) + ((simd_lane_id / 2) % 4);
+
69 const short fn = (qid & 2) * 2 + (simd_lane_id % 2) * 2;
+
70 return short2{fn, fm};
+
71 }
+
+
72
+
73 template <typename SrcPtrType, typename StrX, typename StrY>
+
74 METAL_FUNC static constexpr void
+
+
75 load(thread frag_type& dst, SrcPtrType src, StrX str_x, StrY str_y) {
+
76 STEEL_PRAGMA_UNROLL
+
77 for (short i = 0; i < kElemRows; i++) {
+
78 STEEL_PRAGMA_UNROLL
+
79 for (short j = 0; j < kElemCols; j++) {
+
80 dst[i * kElemCols + j] = static_cast<T>(src[i * str_x + j * str_y]);
+
81 }
+
82 }
+
83 }
+
+
84
+
85 template <
+
86 typename SrcPtrType,
+
87 typename StrX,
+
88 typename StrY,
+
89 typename LimX,
+
90 typename LimY,
+
91 typename OffX,
+
92 typename OffY>
+
+
93 METAL_FUNC static constexpr void load_safe(
+
94 thread frag_type& dst,
+
95 SrcPtrType src,
+
96 StrX str_x,
+
97 StrY str_y,
+
98 LimX lim_x,
+
99 LimY lim_y,
+
100 OffX off_x = Int<0>{},
+
101 OffY off_y = Int<0>{}) {
+
102 STEEL_PRAGMA_UNROLL
+
103 for (short i = 0; i < kElemRows; i++) {
+
104 STEEL_PRAGMA_UNROLL
+
105 for (short j = 0; j < kElemCols; j++) {
+
106 if ((off_x + i) < lim_x && (off_y + j) < lim_y) {
+
107 dst[i * kElemCols + j] =
+
108 static_cast<T>(src[(off_x + i) * str_x + (off_x + j) * str_y]);
+
109 } else {
+
110 dst[i * kElemCols + j] = T(0);
+
111 }
+
112 }
+
113 }
+
114 }
+
+
115
+
116 template <typename DstPtrType, typename StrX, typename StrY>
+
117 METAL_FUNC static constexpr void
+
+
118 store(const thread frag_type& src, DstPtrType dst, StrX str_x, StrY str_y) {
+
119 using U = pointer_element_t<DstPtrType>;
+
120
+
121 STEEL_PRAGMA_UNROLL
+
122 for (short i = 0; i < kElemRows; i++) {
+
123 STEEL_PRAGMA_UNROLL
+
124 for (short j = 0; j < kElemCols; j++) {
+
125 dst[i * str_x + j * str_y] = static_cast<U>(src[i * kElemCols + j]);
+
126 }
+
127 }
+
128 }
+
+
129
+
130 template <
+
131 typename DstPtrType,
+
132 typename StrX,
+
133 typename StrY,
+
134 typename LimX,
+
135 typename LimY,
+
136 typename OffX,
+
137 typename OffY>
+
+
138 METAL_FUNC static constexpr void store_safe(
+
139 const thread frag_type& src,
+
140 DstPtrType dst,
+
141 StrX str_x,
+
142 StrY str_y,
+
143 LimX lim_x,
+
144 LimY lim_y,
+
145 OffX off_x = Int<0>{},
+
146 OffY off_y = Int<0>{}) {
+
147 using U = pointer_element_t<DstPtrType>;
+
148
+
149 STEEL_PRAGMA_UNROLL
+
150 for (short i = 0; i < kElemRows; i++) {
+
151 STEEL_PRAGMA_UNROLL
+
152 for (short j = 0; j < kElemCols; j++) {
+
153 if ((off_x + i) < lim_x && (off_y + j) < lim_y) {
+
154 dst[(off_x + i) * str_x + (off_y + j) * str_y] =
+
155 static_cast<U>(src[i * kElemCols + j]);
+
156 }
+
157 }
+
158 }
+
159 }
+
+
160
+
+
161 METAL_FUNC static constexpr void mma(
+
162 thread frag_type& D,
+
163 thread frag_type& A,
+
164 thread frag_type& B,
+
165 thread frag_type& C) {
+
166 mat_type D_mat;
+
167 mat_type A_mat;
+
168 mat_type B_mat;
+
169 mat_type C_mat;
+
170
+
171 reinterpret_cast<thread frag_type&>(A_mat.thread_elements()) = A;
+
172 reinterpret_cast<thread frag_type&>(B_mat.thread_elements()) = B;
+
173 reinterpret_cast<thread frag_type&>(C_mat.thread_elements()) = C;
+
174
+
175 mma(D_mat, A_mat, B_mat, C_mat);
+
176
+
177 D = reinterpret_cast<thread frag_type&>(D_mat.thread_elements());
+
178 }
+
+
179
+
+
180 METAL_FUNC static constexpr void mma(
+
181 thread mat_type& D,
+
182 thread mat_type& A,
+
183 thread mat_type& B,
+
184 thread mat_type& C) {
+
185 simdgroup_multiply_accumulate(D, A, B, C);
+
186 }
+
+
187
+
188 template <typename Op>
+
+
189 METAL_FUNC static constexpr void row_reduce(
+
190 thread const frag_type& inp_vals,
+
191 thread T* reduced_vals) {
+
192 T thr_reduce = Op::apply(inp_vals.x, inp_vals.y);
+
193
+
194 T qgr_reduce = simd_shuffle_xor(thr_reduce, ushort(1));
+
195 qgr_reduce = Op::apply(thr_reduce, qgr_reduce);
+
196
+
197 T sgr_reduce = simd_shuffle_xor(qgr_reduce, ushort(8));
+
198 sgr_reduce = Op::apply(qgr_reduce, sgr_reduce);
+
199
+
200 reduced_vals[0] = Op::apply(reduced_vals[0], sgr_reduce);
+
201 }
+
+
202
+
203 template <typename Op>
+
+
204 METAL_FUNC static constexpr void row_bin_op(
+
205 thread frag_type& inp_vals,
+
206 thread T* row_vals) {
+
207 STEEL_PRAGMA_UNROLL
+
208 for (short i = 0; i < kElemRows; i++) {
+
209 STEEL_PRAGMA_UNROLL
+
210 for (short j = 0; j < kElemCols; j++) {
+
211 inp_vals[i * kElemCols + j] =
+
212 Op::apply(inp_vals[i * kElemCols + j], row_vals[i]);
+
213 }
+
214 }
+
215 }
+
+
216};
+
217
+
218template <
+
219 typename T,
+
220 int kTileRows_,
+
221 int kTileCols_,
+
222 class MMAFrag_ = BaseMMAFrag<T, 8, 8>>
+
223struct MMATile {
+
224 using MMAFrag_t = MMAFrag_;
+
225 using elem_type = T;
+
226 STEEL_CONST int kFragRows = MMAFrag_t::kFragRows;
+
227 STEEL_CONST int kFragCols = MMAFrag_t::kFragCols;
+
228 STEEL_CONST int kElemsPerFrag = MMAFrag_t::kElemsPerFrag;
+
229
+
230 STEEL_CONST int kTileRows = kTileRows_;
+
231 STEEL_CONST int kTileCols = kTileCols_;
+
232
+
233 STEEL_CONST int kRows = kTileRows * kFragRows;
+
234 STEEL_CONST int kCols = kTileCols * kFragCols;
+
235
+
236 STEEL_CONST int kNumFrags = kTileRows * kTileCols;
+
237 STEEL_CONST int kElemsPerTile = kNumFrags * kElemsPerFrag;
+
238
+
239 STEEL_CONST int kRowsPerThread = kTileRows * MMAFrag_t::kElemRows;
+
240 STEEL_CONST int kColsPerThread = kTileCols * MMAFrag_t::kElemCols;
+
241
+
242 typedef typename MMAFrag_t::mat_type mat_type;
+
243 typedef typename MMAFrag_t::frag_type frag_type;
+
244
+
245 frag_type val_frags[kNumFrags] = {frag_type(0)};
+
246
+
247 METAL_FUNC MMATile() thread {}
+
248
+
+
249 METAL_FUNC constexpr void clear() {
+
250 STEEL_PRAGMA_UNROLL
+
251 for (short i = 0; i < kNumFrags; ++i) {
+
252 val_frags[i] = frag_type(0);
+
253 }
+
254 }
+
+
255
+
+
256 METAL_FUNC constexpr thread frag_type& frag_at(const short i, const short j) {
+
257 return val_frags[i * kTileCols + j];
+
258 }
+
+
259
+
+
260 METAL_FUNC constexpr const thread frag_type& frag_at(
+
261 const short i,
+
262 const short j) const {
+
263 return val_frags[i * kTileCols + j];
+
264 }
+
+
265
+
+
266 METAL_FUNC mat_type mat_at(const short i, const short j) {
+
267 mat_type val_mat;
+
268 STEEL_PRAGMA_UNROLL
+
269 for (short ii = 0; ii < kElemsPerFrag; ++ii) {
+
270 val_mat.thread_elements()[ii] = frag_at(i, j)[ii];
+
271 }
+
272 return val_mat;
+
273 }
+
+
274
+
+
275 METAL_FUNC thread elem_type* elems() {
+
276 return reinterpret_cast<thread elem_type*>(val_frags);
+
277 }
+
+
278
+
+
279 METAL_FUNC const thread elem_type* elems() const {
+
280 return reinterpret_cast<const thread elem_type*>(val_frags);
+
281 }
+
+
282
+
283 template <typename Op>
+
+
284 METAL_FUNC void row_reduce(thread T vals[kRowsPerThread]) const {
+
285 STEEL_PRAGMA_UNROLL
+
286 for (short i = 0; i < kTileRows; ++i) {
+
287 STEEL_PRAGMA_UNROLL
+
288 for (short j = 0; j < kTileCols; ++j) {
+
289 MMAFrag_t::template row_reduce<Op>(
+
290 frag_at(i, j), &vals[i * MMAFrag_t::kElemRows]);
+
291 }
+
292 }
+
293 }
+
+
294
+
295 template <typename Op>
+
+
296 METAL_FUNC void row_bin_op(thread T vals[kRowsPerThread]) {
+
297 STEEL_PRAGMA_UNROLL
+
298 for (short i = 0; i < kTileRows; ++i) {
+
299 STEEL_PRAGMA_UNROLL
+
300 for (short j = 0; j < kTileCols; ++j) {
+
301 MMAFrag_t::template row_bin_op<Op>(
+
302 frag_at(i, j), &vals[i * MMAFrag_t::kElemRows]);
+
303 }
+
304 }
+
305 }
+
+
306
+
307 template <typename U, int w_x, int w_y, int str_x, int str_y>
+
+
308 METAL_FUNC void load(const threadgroup U* src) {
+
309 STEEL_PRAGMA_UNROLL
+
310 for (short i = 0; i < kTileRows; ++i) {
+
311 STEEL_PRAGMA_UNROLL
+
312 for (short j = 0; j < kTileCols; ++j) {
+
313 MMAFrag_t::load(
+
314 frag_at(i, j),
+
315 &(
+
316 src[(i * kFragRows) * w_x * str_x +
+
317 (j * kFragCols) * w_y * str_y]),
+
318 Int<str_x>{},
+
319 Int<str_y>{});
+
320 }
+
321 }
+
322 }
+
+
323
+
324 template <typename U, int w_x, int w_y, int str_x, int str_y>
+
+
325 METAL_FUNC void store(threadgroup U* dst) const {
+
326 STEEL_PRAGMA_UNROLL
+
327 for (short i = 0; i < kTileRows; ++i) {
+
328 STEEL_PRAGMA_UNROLL
+
329 for (short j = 0; j < kTileCols; ++j) {
+
330 MMAFrag_t::store(
+
331 frag_at(i, j),
+
332 &(
+
333 dst[(i * kFragRows) * w_x * str_x +
+
334 (j * kFragCols) * w_y * str_y]),
+
335 Int<str_x>{},
+
336 Int<str_y>{});
+
337 }
+
338 }
+
339 }
+
+
340
+
341 template <typename U, int w_x, int w_y>
+
+
342 METAL_FUNC void load(const device U* src, const int ld) {
+
343 STEEL_PRAGMA_UNROLL
+
344 for (short i = 0; i < kTileRows; ++i) {
+
345 STEEL_PRAGMA_UNROLL
+
346 for (short j = 0; j < kTileCols; ++j) {
+
347 MMAFrag_t::load(
+
348 frag_at(i, j),
+
349 &(src[(i * kFragRows) * w_x * ld + (j * kFragCols) * w_y]),
+
350 ld,
+
351 Int<1>{});
+
352 }
+
353 }
+
354 }
+
+
355
+
356 template <typename U, int w_x, int w_y>
+
+
357 METAL_FUNC void store(device U* dst, const int ld) const {
+
358 STEEL_PRAGMA_UNROLL
+
359 for (short i = 0; i < kTileRows; ++i) {
+
360 STEEL_PRAGMA_UNROLL
+
361 for (short j = 0; j < kTileCols; ++j) {
+
362 MMAFrag_t::store(
+
363 frag_at(i, j),
+
364 &(dst[(i * kFragRows) * w_x * ld + (j * kFragCols) * w_y]),
+
365 ld,
+
366 Int<1>{});
+
367 }
+
368 }
+
369 }
+
+
370
+
371 template <typename U, int w_x, int w_y>
+
372 METAL_FUNC void
+
+
373 load_safe(const device U* src, const int ld, const short2 src_tile_dims) {
+
374 STEEL_PRAGMA_UNROLL
+
375 for (int i = 0; i < kTileRows; ++i) {
+
376 STEEL_PRAGMA_UNROLL
+
377 for (int j = 0; j < kTileCols; ++j) {
+
378 MMAFrag_t::load_safe(
+
379 frag_at(i, j),
+
380 src,
+
381 ld,
+
382 Int<1>{},
+
383 src_tile_dims.y,
+
384 src_tile_dims.x,
+
385 (i * kFragRows) * w_x,
+
386 (j * kFragCols) * w_y);
+
387 }
+
388 }
+
389 }
+
+
390
+
391 template <typename U, int w_x, int w_y>
+
392 METAL_FUNC void
+
+
393 store_safe(device U* dst, const int ld, const short2 dst_tile_dims) const {
+
394 STEEL_PRAGMA_UNROLL
+
395 for (int i = 0; i < kTileRows; ++i) {
+
396 STEEL_PRAGMA_UNROLL
+
397 for (int j = 0; j < kTileCols; ++j) {
+
398 MMAFrag_t::store_safe(
+
399 frag_at(i, j),
+
400 dst,
+
401 ld,
+
402 Int<1>{},
+
403 dst_tile_dims.y,
+
404 dst_tile_dims.x,
+
405 (i * kFragRows) * w_x,
+
406 (j * kFragCols) * w_y);
+
407 }
+
408 }
+
409 }
+
+
410};
+
411
+
412template <typename T, typename U, int M, int N, int K>
+
+
413METAL_FUNC void tile_matmad(
+
414 thread MMATile<T, M, N>& D,
+
415 thread MMATile<U, M, K>& A,
+
416 thread MMATile<U, K, N>& B,
+
417 thread MMATile<T, M, N>& C) {
+
418 STEEL_PRAGMA_UNROLL
+
419 for (short k = 0; k < K; ++k) {
+
420 STEEL_PRAGMA_UNROLL
+
421 for (short m = 0; m < M; ++m) {
+
422 STEEL_PRAGMA_UNROLL
+
423 for (short n = 0; n < N; ++n) {
+
424 short n_serp = (m % 2) ? (N - 1 - n) : n;
+
425 MMATile<T, M, N>::MMAFrag_t::mma(
+
426 D.frag_at(m, n_serp),
+
427 A.frag_at(m, k),
+
428 B.frag_at(k, n_serp),
+
429 C.frag_at(m, n_serp));
+
430 }
+
431 }
+
432 }
+
433}
+
+
434
+
435template <
+
436 typename T,
+
437 typename U,
+
438 int BM,
+
439 int BN,
+
440 int BK,
+
441 int WM,
+
442 int WN,
+
443 bool transpose_a,
+
444 bool transpose_b,
+
445 short lda_tgp,
+
446 short ldb_tgp,
+
447 typename AccumType = float,
+
448 typename Epilogue = TransformNone<U, AccumType>>
+
449struct BlockMMA {
+
450 // MMAFrag size
+
451 STEEL_CONST short kFragSize = 8;
+
452 using MMAFrag_acc_t = BaseMMAFrag<AccumType, kFragSize, kFragSize>;
+
453
+
454 // Warp tile simdgroup matrix strides along M
+
455 STEEL_CONST short TM_stride = kFragSize * WM;
+
456 // Warp tile simdgroup matrix strides along M
+
457 STEEL_CONST short TN_stride = kFragSize * WN;
+
458
+
459 // Warp tile size along M
+
460 STEEL_CONST short TM = BM / TM_stride;
+
461 // Warp tile size along N
+
462 STEEL_CONST short TN = BN / TN_stride;
+
463
+
464 // Threadgroup A strides
+
465 STEEL_CONST short A_str_m = transpose_a ? 1 : lda_tgp; // M
+
466 STEEL_CONST short A_str_k = transpose_a ? lda_tgp : 1; // K
+
467
+
468 // Threadgroup B strides
+
469 STEEL_CONST short B_str_k = transpose_b ? 1 : ldb_tgp; // K
+
470 STEEL_CONST short B_str_n = transpose_b ? ldb_tgp : 1; // N
+
471
+
472 // Threadgroup strides along K
+
473 STEEL_CONST short tile_stride_a = kFragSize * A_str_k;
+
474 STEEL_CONST short tile_stride_b = kFragSize * B_str_k;
+
475
+
476 // Simdgroup matrices
+
477 MMATile<AccumType, TM, 1, MMAFrag_acc_t> Atile;
+
478 MMATile<AccumType, 1, TN, MMAFrag_acc_t> Btile;
+
479 MMATile<AccumType, TM, TN, MMAFrag_acc_t> Ctile;
+
480
+
481 // Offsets within threadgroup
+
482 short sm;
+
483 short sn;
+
484
+
485 short As_offset;
+
486 short Bs_offset;
+
487
+
488 /* Constructor */
+
+
489 METAL_FUNC BlockMMA(
+
490 ushort simd_group_id [[simdgroup_index_in_threadgroup]],
+
491 ushort simd_lane_id [[thread_index_in_simdgroup]]) {
+
492 // Determine thread position in simdgroup matrix
+
493 short tm = kFragSize * (simd_group_id / WN);
+
494 short tn = kFragSize * (simd_group_id % WN);
+
495
+
496 short2 simd_coord = MMAFrag_acc_t::get_coord(simd_lane_id);
+
497 sm = simd_coord.y;
+
498 sn = simd_coord.x;
+
499
+
500 // Determine thread and simdgroup offset
+
501 As_offset = (tm + sm) * A_str_m + (sn)*A_str_k; // M, K
+
502 Bs_offset = (sm)*B_str_k + (tn + sn) * B_str_n; // K, N
+
503
+
504 sm += tm;
+
505 sn += tn;
+
506 }
+
+
507
+
508 /* (BM, BK) X (BK, BN) multiply accumulate function */
+
+
509 METAL_FUNC void mma(const threadgroup T* As, const threadgroup T* Bs) {
+
510 // Adjust for simdgroup and thread location
+
511 As += As_offset;
+
512 Bs += Bs_offset;
+
513
+
514 // Iterate over BK in blocks of kFragSize
+
515 STEEL_PRAGMA_UNROLL
+
516 for (short kk = 0; kk < BK; kk += kFragSize) {
+
517 simdgroup_barrier(mem_flags::mem_none);
+
518
+
519 Atile.template load<T, WM, 1, A_str_m, A_str_k>(As);
+
520
+
521 simdgroup_barrier(mem_flags::mem_none);
+
522
+
523 Btile.template load<T, 1, WN, B_str_k, B_str_n>(Bs);
+
524
+
525 simdgroup_barrier(mem_flags::mem_none);
+
526
+
527 tile_matmad(Ctile, Atile, Btile, Ctile);
+
528
+
529 // Progress to next simdgroup tile
+
530 As += tile_stride_a;
+
531 Bs += tile_stride_b;
+
532 }
+
533 }
+
+
534
+
535 /* Store results from simdgroup_matrix results into device memory */
+
+
536 METAL_FUNC void store_result(device U* D, const int ldd) {
+
537 // Apply epilogue
+
538 STEEL_PRAGMA_UNROLL
+
539 for (short i = 0; i < decltype(Ctile)::kElemsPerTile; i++) {
+
540 Ctile.elems()[i] = Epilogue::apply(Ctile.elems()[i]);
+
541 }
+
542
+
543 // Adjust for simdgroup and thread location
+
544 D += sm * ldd + sn;
+
545
+
546 Ctile.template store<U, WM, WN>(D, ldd);
+
547 }
+
+
548
+
549 METAL_FUNC void
+
+
550 store_result_safe(device U* D, const int ldd, short2 dst_tile_dims) {
+
551 // Apply epilogue
+
552 STEEL_PRAGMA_UNROLL
+
553 for (short i = 0; i < decltype(Ctile)::kElemsPerTile; i++) {
+
554 Ctile.elems()[i] = Epilogue::apply(Ctile.elems()[i]);
+
555 }
+
556
+
557 // Adjust for simdgroup and thread location
+
558 D += sm * ldd + sn;
+
559 dst_tile_dims -= short2(sn, sm);
+
560
+
561 if (dst_tile_dims.x <= 0 || dst_tile_dims.y <= 0)
+
562 return;
+
563
+
564 Ctile.template store_safe<U, WM, WN>(D, ldd, dst_tile_dims);
+
565 }
+
+
566
+
567 /* Apply epilogue */
+
568 template <typename UnaryEpilogue>
+
+
569 METAL_FUNC void apply_epilogue(thread const UnaryEpilogue& epilogue_op) {
+
570 // Loop over all simdgroup tiles
+
571 STEEL_PRAGMA_UNROLL
+
572 for (short i = 0; i < decltype(Ctile)::kElemsPerTile; i++) {
+
573 Ctile.elems()[i] = epilogue_op.apply(Ctile.elems()[i]);
+
574 }
+
575 }
+
+
576
+
577 /* Apply epilogue */
+
578 template <typename BinaryEpilogue>
+
+
579 METAL_FUNC void apply_epilogue(
+
580 const device U* C,
+
581 const int ldc,
+
582 const int fdc,
+
583 thread const BinaryEpilogue& epilogue_op) {
+
584 // Adjust for simdgroup and thread location
+
585 C += (sm)*ldc + (sn)*fdc;
+
586
+
587 // Loop over all simdgroup tiles
+
588 STEEL_PRAGMA_UNROLL
+
589 for (short i = 0; i < TM; i++) {
+
590 STEEL_PRAGMA_UNROLL
+
591 for (short j = 0; j < TN; j++) {
+
592 // Get accumulated result and associated offset in C
+
593 thread auto& accum = Ctile.frag_at(i, j);
+
594 int offset_c = (i * TM_stride) * ldc + (j * TN_stride) * fdc;
+
595
+
596 // Apply epilogue
+
597 STEEL_PRAGMA_UNROLL
+
598 for (short k = 0; k < decltype(Ctile)::kElemsPerFrag; k++) {
+
599 accum[k] = epilogue_op.apply(accum[k], C[offset_c + k * fdc]);
+
600 }
+
601 }
+
602 }
+
603 }
+
+
604
+
605 /* Apply epilogue */
+
606 template <typename BinaryEpilogue>
+
+
607 METAL_FUNC void apply_epilogue_safe(
+
608 const device U* C,
+
609 const int ldc,
+
610 const int fdc,
+
611 short2 dst_tile_dims,
+
612 thread const BinaryEpilogue& epilogue_op) {
+
613 // Adjust for simdgroup and thread location
+
614 C += (sm)*ldc + (sn)*fdc;
+
615 dst_tile_dims -= short2(sn, sm);
+
616
+
617 if (dst_tile_dims.x <= 0 || dst_tile_dims.y <= 0)
+
618 return;
+
619
+
620 // Loop over all simdgroup tiles
+
621 STEEL_PRAGMA_UNROLL
+
622 for (short i = 0; i < TM; i++) {
+
623 STEEL_PRAGMA_UNROLL
+
624 for (short j = 0; j < TN; j++) {
+
625 // Get accumulated result and associated offset in C
+
626 thread auto& accum = Ctile.frag_at(i, j);
+
627 int offset_c = (i * TM_stride) * ldc + (j * TN_stride) * fdc;
+
628
+
629 constexpr short kelems = decltype(Ctile)::kElemsPerFrag;
+
630
+
631 // Read C
+
632 U c_elems[kelems] = {0};
+
633
+
634 STEEL_PRAGMA_UNROLL
+
635 for (short k = 0; k < kelems; k++) {
+
636 if ((j * TN_stride + k) < dst_tile_dims.x) {
+
637 c_elems[k] = C[offset_c + k * fdc];
+
638 }
+
639 }
+
640
+
641 // Apply epilogue
+
642 STEEL_PRAGMA_UNROLL
+
643 for (short k = 0; k < kelems; k++) {
+
644 accum[k] = epilogue_op.apply(accum[k], c_elems[k]);
+
645 }
+
646 }
+
647 }
+
648 }
+
+
649
+
650 /* Store results from simdgroup_matrix results into device memory */
+
+
651 METAL_FUNC void store_result(
+
652 device U* D,
+
653 const int ldd,
+
654 const device U* C,
+
655 const int ldc,
+
656 const int fdc,
+
657 thread const Epilogue& epilogue_op) const {
+
658 // Adjust for simdgroup and thread location
+
659 C += (sm)*ldc + (sn)*fdc;
+
660 D += (sm)*ldd + sn;
+
661
+
662 constexpr short kelems = decltype(Ctile)::kElemsPerFrag;
+
663
+
664 // Loop over all simdgroup tiles
+
665 STEEL_PRAGMA_UNROLL
+
666 for (short i = 0; i < TM; i++) {
+
667 STEEL_PRAGMA_UNROLL
+
668 for (short j = 0; j < TN; j++) {
+
669 // Get accumulated result and associated offset in C
+
670 thread const auto& accum = Ctile.frag_at(i, j);
+
671 int offset_c = (i * TM_stride) * ldc + (j * TN_stride) * fdc;
+
672 int offset_d = (i * TM_stride) * ldd + (j * TN_stride);
+
673
+
674 // Apply epilogue
+
675 STEEL_PRAGMA_UNROLL
+
676 for (short k = 0; k < kelems; k++) {
+
677 D[offset_d + k] = epilogue_op.apply(accum[k], C[offset_c + k * fdc]);
+
678 }
+
679 }
+
680 }
+
681 }
+
+
682
+
+
683 METAL_FUNC void store_result_safe(
+
684 device U* D,
+
685 const int ldd,
+
686 const device U* C,
+
687 const int ldc,
+
688 const int fdc,
+
689 short2 dst_tile_dims,
+
690 thread const Epilogue& epilogue_op) const {
+
691 // Adjust for simdgroup and thread location
+
692 C += (sm)*ldc + (sn)*fdc;
+
693 D += (sm)*ldd + sn;
+
694 dst_tile_dims -= short2(sn, sm);
+
695
+
696 if (dst_tile_dims.x <= 0 || dst_tile_dims.y <= 0)
+
697 return;
+
698
+
699 constexpr short kelems = decltype(Ctile)::kElemsPerFrag;
+
700
+
701 STEEL_PRAGMA_UNROLL
+
702 for (int i = 0; i < TM; i++) {
+
703 if (i * TM_stride < dst_tile_dims.y) {
+
704 STEEL_PRAGMA_UNROLL
+
705 for (int j = 0; j < TN; j++) {
+
706 // Get accumulated result and associated offset in C
+
707 thread const auto& accum = Ctile.frag_at(i, j);
+
708 int offset_c = (i * TM_stride) * ldc + (j * TN_stride) * fdc;
+
709 int offset_d = (i * TM_stride) * ldd + (j * TN_stride);
+
710
+
711 // Apply epilogue
+
712 STEEL_PRAGMA_UNROLL
+
713 for (short k = 0; k < kelems; k++) {
+
714 if ((j * TN_stride + k) < dst_tile_dims.x) {
+
715 D[offset_d + k] =
+
716 epilogue_op.apply(accum[k], C[offset_c + k * fdc]);
+
717 }
+
718 }
+
719 }
+
720 }
+
721 }
+
722 }
+
+
723};
+
724
+
725} // namespace steel
+
726} // namespace mlx
+ +
integral_constant.h
+
metal
Definition bf16_math.h:226
+
metal::simd_shuffle_xor
METAL_FUNC bfloat16_t simd_shuffle_xor(bfloat16_t data, ushort mask)
Definition bf16_math.h:377
+
metal::pointer_element_t
typename pointer_element< remove_cv_t< T > >::type pointer_element_t
Definition type_traits.h:51
+
mlx::steel::tile_matmad
METAL_FUNC void tile_matmad(thread MMATile< T, M, N > &D, thread MMATile< U, M, K > &A, thread MMATile< U, K, N > &B, thread MMATile< T, M, N > &C)
Definition mma.h:413
+
mlx
Definition allocator.h:7
+ +
STEEL_PRAGMA_UNROLL
#define STEEL_PRAGMA_UNROLL
Definition defines.h:4
+
STEEL_CONST
#define STEEL_CONST
Definition defines.h:3
+
mlx::steel::BaseMMAFrag< T, 8, 8 >::mma
static METAL_FUNC constexpr void mma(thread mat_type &D, thread mat_type &A, thread mat_type &B, thread mat_type &C)
Definition mma.h:180
+
mlx::steel::BaseMMAFrag< T, 8, 8 >::store_safe
static METAL_FUNC constexpr void store_safe(const thread frag_type &src, DstPtrType dst, StrX str_x, StrY str_y, LimX lim_x, LimY lim_y, OffX off_x=Int< 0 >{}, OffY off_y=Int< 0 >{})
Definition mma.h:138
+
mlx::steel::BaseMMAFrag< T, 8, 8 >::row_bin_op
static METAL_FUNC constexpr void row_bin_op(thread frag_type &inp_vals, thread T *row_vals)
Definition mma.h:204
+
mlx::steel::BaseMMAFrag< T, 8, 8 >::row_reduce
static METAL_FUNC constexpr void row_reduce(thread const frag_type &inp_vals, thread T *reduced_vals)
Definition mma.h:189
+
mlx::steel::BaseMMAFrag< T, 8, 8 >::row_frag_type
metal::vec< T, kElemRows > row_frag_type
Definition mma.h:62
+
mlx::steel::BaseMMAFrag< T, 8, 8 >::get_coord
static METAL_FUNC constexpr short2 get_coord(ushort simd_lane_id)
Definition mma.h:65
+
mlx::steel::BaseMMAFrag< T, 8, 8 >::mma
static METAL_FUNC constexpr void mma(thread frag_type &D, thread frag_type &A, thread frag_type &B, thread frag_type &C)
Definition mma.h:161
+
mlx::steel::BaseMMAFrag< T, 8, 8 >::mat_type
metal::simdgroup_matrix< T, kFragRows, kFragCols > mat_type
Definition mma.h:60
+
mlx::steel::BaseMMAFrag< T, 8, 8 >::frag_type
metal::vec< T, kElemsPerFrag > frag_type
Definition mma.h:61
+
mlx::steel::BaseMMAFrag< T, 8, 8 >::store
static METAL_FUNC constexpr void store(const thread frag_type &src, DstPtrType dst, StrX str_x, StrY str_y)
Definition mma.h:118
+
mlx::steel::BaseMMAFrag< T, 8, 8 >::col_frag_type
metal::vec< T, kElemCols > col_frag_type
Definition mma.h:63
+
mlx::steel::BaseMMAFrag< T, 8, 8 >::load
static METAL_FUNC constexpr void load(thread frag_type &dst, SrcPtrType src, StrX str_x, StrY str_y)
Definition mma.h:75
+
mlx::steel::BaseMMAFrag< T, 8, 8 >::load_safe
static METAL_FUNC constexpr void load_safe(thread frag_type &dst, SrcPtrType src, StrX str_x, StrY str_y, LimX lim_x, LimY lim_y, OffX off_x=Int< 0 >{}, OffY off_y=Int< 0 >{})
Definition mma.h:93
+
mlx::steel::BaseMMAFrag
Definition mma.h:23
+
mlx::steel::BlockMMA::store_result
METAL_FUNC void store_result(device U *D, const int ldd)
Definition mma.h:536
+
mlx::steel::BlockMMA::store_result_safe
METAL_FUNC void store_result_safe(device U *D, const int ldd, short2 dst_tile_dims)
Definition mma.h:550
+
mlx::steel::BlockMMA::As_offset
short As_offset
Definition mma.h:485
+
mlx::steel::BlockMMA::Ctile
MMATile< AccumType, TM, TN, MMAFrag_acc_t > Ctile
Definition mma.h:479
+
mlx::steel::BlockMMA::A_str_k
STEEL_CONST short A_str_k
Definition mma.h:466
+
mlx::steel::BlockMMA::Btile
MMATile< AccumType, 1, TN, MMAFrag_acc_t > Btile
Definition mma.h:478
+
mlx::steel::BlockMMA::Atile
MMATile< AccumType, TM, 1, MMAFrag_acc_t > Atile
Definition mma.h:477
+
mlx::steel::BlockMMA::B_str_n
STEEL_CONST short B_str_n
Definition mma.h:470
+
mlx::steel::BlockMMA::TM_stride
STEEL_CONST short TM_stride
Definition mma.h:455
+
mlx::steel::BlockMMA::mma
METAL_FUNC void mma(const threadgroup T *As, const threadgroup T *Bs)
Definition mma.h:509
+
mlx::steel::BlockMMA::TN
STEEL_CONST short TN
Definition mma.h:462
+
mlx::steel::BlockMMA::store_result_safe
METAL_FUNC void store_result_safe(device U *D, const int ldd, const device U *C, const int ldc, const int fdc, short2 dst_tile_dims, thread const Epilogue &epilogue_op) const
Definition mma.h:683
+
mlx::steel::BlockMMA::store_result
METAL_FUNC void store_result(device U *D, const int ldd, const device U *C, const int ldc, const int fdc, thread const Epilogue &epilogue_op) const
Definition mma.h:651
+
mlx::steel::BlockMMA::apply_epilogue
METAL_FUNC void apply_epilogue(const device U *C, const int ldc, const int fdc, thread const BinaryEpilogue &epilogue_op)
Definition mma.h:579
+
mlx::steel::BlockMMA::TN_stride
STEEL_CONST short TN_stride
Definition mma.h:457
+
mlx::steel::BlockMMA::tile_stride_a
STEEL_CONST short tile_stride_a
Definition mma.h:473
+
mlx::steel::BlockMMA::Bs_offset
short Bs_offset
Definition mma.h:486
+
mlx::steel::BlockMMA::apply_epilogue_safe
METAL_FUNC void apply_epilogue_safe(const device U *C, const int ldc, const int fdc, short2 dst_tile_dims, thread const BinaryEpilogue &epilogue_op)
Definition mma.h:607
+
mlx::steel::BlockMMA::BlockMMA
METAL_FUNC BlockMMA(ushort simd_group_id, ushort simd_lane_id)
Definition mma.h:489
+
mlx::steel::BlockMMA::B_str_k
STEEL_CONST short B_str_k
Definition mma.h:469
+
mlx::steel::BlockMMA::sm
short sm
Definition mma.h:482
+
mlx::steel::BlockMMA::A_str_m
STEEL_CONST short A_str_m
Definition mma.h:465
+
mlx::steel::BlockMMA::TM
STEEL_CONST short TM
Definition mma.h:460
+
mlx::steel::BlockMMA::sn
short sn
Definition mma.h:483
+
mlx::steel::BlockMMA::tile_stride_b
STEEL_CONST short tile_stride_b
Definition mma.h:474
+
mlx::steel::BlockMMA::kFragSize
STEEL_CONST short kFragSize
Definition mma.h:451
+
mlx::steel::BlockMMA::apply_epilogue
METAL_FUNC void apply_epilogue(thread const UnaryEpilogue &epilogue_op)
Definition mma.h:569
+
mlx::steel::Layout2D
Definition mma.h:31
+
mlx::steel::Layout2D::shape
Shape shape
Definition mma.h:32
+
mlx::steel::Layout2D::layout
Layout layout
Definition mma.h:33
+
mlx::steel::MMATile
Definition mma.h:178
+
mlx::steel::MMATile::frag_at
METAL_FUNC constexpr thread frag_type & frag_at(const short i, const short j)
Definition mma.h:256
+
mlx::steel::MMATile::kTileRows
STEEL_CONST int kTileRows
Definition mma.h:230
+
mlx::steel::MMATile::kColsPerThread
STEEL_CONST int kColsPerThread
Definition mma.h:240
+
mlx::steel::MMATile::mat_type
MMAFrag_t::mat_type mat_type
Definition mma.h:242
+
mlx::steel::MMATile::store
METAL_FUNC void store(threadgroup U *dst) const
Definition mma.h:325
+
mlx::steel::MMATile::mat_at
METAL_FUNC mat_type mat_at(const short i, const short j)
Definition mma.h:266
+
mlx::steel::MMATile::row_bin_op
METAL_FUNC void row_bin_op(thread T vals[kRowsPerThread])
Definition mma.h:296
+
mlx::steel::MMATile::kTileCols
STEEL_CONST int kTileCols
Definition mma.h:231
+
mlx::steel::MMATile::store_safe
METAL_FUNC void store_safe(device U *dst, const int ld, const short2 dst_tile_dims) const
Definition mma.h:393
+
mlx::steel::MMATile::kFragRows
STEEL_CONST int kFragRows
Definition mma.h:226
+
mlx::steel::MMATile::kRowsPerThread
STEEL_CONST int kRowsPerThread
Definition mma.h:239
+
mlx::steel::MMATile::kRows
STEEL_CONST int kRows
Definition mma.h:233
+
mlx::steel::MMATile::val_frags
frag_type val_frags[kNumFrags]
Definition mma.h:245
+
mlx::steel::MMATile::MMAFrag_t
MMAFrag_ MMAFrag_t
Definition mma.h:224
+
mlx::steel::MMATile::store
METAL_FUNC void store(device U *dst, const int ld) const
Definition mma.h:357
+
mlx::steel::MMATile::elem_type
T elem_type
Definition mma.h:225
+
mlx::steel::MMATile::elems
METAL_FUNC thread elem_type * elems()
Definition mma.h:275
+
mlx::steel::MMATile::kCols
STEEL_CONST int kCols
Definition mma.h:234
+
mlx::steel::MMATile::kElemsPerTile
STEEL_CONST int kElemsPerTile
Definition mma.h:237
+
mlx::steel::MMATile::row_reduce
METAL_FUNC void row_reduce(thread T vals[kRowsPerThread]) const
Definition mma.h:284
+
mlx::steel::MMATile::load_safe
METAL_FUNC void load_safe(const device U *src, const int ld, const short2 src_tile_dims)
Definition mma.h:373
+
mlx::steel::MMATile::MMATile
METAL_FUNC MMATile() thread
Definition mma.h:247
+
mlx::steel::MMATile::load
METAL_FUNC void load(const threadgroup U *src)
Definition mma.h:308
+
mlx::steel::MMATile::clear
METAL_FUNC constexpr void clear()
Definition mma.h:249
+
mlx::steel::MMATile::load
METAL_FUNC void load(const device U *src, const int ld)
Definition mma.h:342
+
mlx::steel::MMATile::frag_type
MMAFrag_t::frag_type frag_type
Definition mma.h:243
+
mlx::steel::MMATile::kFragCols
STEEL_CONST int kFragCols
Definition mma.h:227
+
mlx::steel::MMATile::frag_at
METAL_FUNC constexpr const thread frag_type & frag_at(const short i, const short j) const
Definition mma.h:260
+
mlx::steel::MMATile::elems
METAL_FUNC const thread elem_type * elems() const
Definition mma.h:279
+
mlx::steel::MMATile::kNumFrags
STEEL_CONST int kNumFrags
Definition mma.h:236
+
mlx::steel::MMATile::kElemsPerFrag
STEEL_CONST int kElemsPerFrag
Definition mma.h:228
+
mlx::steel::Shape2D
Definition mma.h:23
+
mlx::steel::Shape2D::Shape2D
Shape2D(RInt r_, CInt c_)
Definition mma.h:27
+
mlx::steel::Shape2D::r
RInt r
Definition mma.h:24
+
mlx::steel::Shape2D::c
CInt c
Definition mma.h:25
+
mlx::steel::integral_constant
Definition integral_constant.h:18
+
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+Generated by doxygen 1.12.0 +
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+ + diff --git a/docs/build/html/attn_2params_8h.html b/docs/build/html/attn_2params_8h.html new file mode 100644 index 00000000..e7fe51aa --- /dev/null +++ b/docs/build/html/attn_2params_8h.html @@ -0,0 +1,119 @@ + + + + + + + +MLX: mlx/backend/metal/kernels/steel/attn/params.h File Reference + + + + + + + + + + + + + +
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struct  mlx::steel::AttnParams
 
+ + + + + +

+Namespaces

namespace  mlx
 
namespace  mlx::steel
 
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params.h
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+Go to the documentation of this file.
1// Copyright © 2024 Apple Inc.
+
2
+
3#pragma once
+
4
+
6// Attn param classes
+
8
+
9namespace mlx {
+
10namespace steel {
+
11
+
+
12struct AttnParams {
+
13 int B;
+
14 int H;
+
15 int D;
+
16
+
17 int qL;
+
18 int kL;
+
19
+
20 int gqa_factor;
+
21 float scale;
+
22
+
23 int NQ;
+
24 int NK;
+
25
+
26 int NQ_aligned;
+
27 int NK_aligned;
+
28
+
29 size_t Q_strides[3];
+
30 size_t K_strides[3];
+
31 size_t V_strides[3];
+
32 size_t O_strides[3];
+
33};
+
+
34
+
35} // namespace steel
+
36} // namespace mlx
+
mlx
Definition allocator.h:7
+
mlx::steel::AttnParams
Definition params.h:12
+
mlx::steel::AttnParams::K_strides
size_t K_strides[3]
Key strides (B, H, L, D = 1)
Definition params.h:30
+
mlx::steel::AttnParams::D
int D
Head Dim.
Definition params.h:15
+
mlx::steel::AttnParams::B
int B
Batch Size.
Definition params.h:13
+
mlx::steel::AttnParams::O_strides
size_t O_strides[3]
Output strides (B, H, L, D = 1)
Definition params.h:32
+
mlx::steel::AttnParams::gqa_factor
int gqa_factor
Group Query factor.
Definition params.h:20
+
mlx::steel::AttnParams::H
int H
Heads.
Definition params.h:14
+
mlx::steel::AttnParams::NQ
int NQ
Number of query blocks.
Definition params.h:23
+
mlx::steel::AttnParams::kL
int kL
Key Sequence Length.
Definition params.h:18
+
mlx::steel::AttnParams::NQ_aligned
int NQ_aligned
Number of full query blocks.
Definition params.h:26
+
mlx::steel::AttnParams::qL
int qL
Query Sequence Length.
Definition params.h:17
+
mlx::steel::AttnParams::NK
int NK
Number of key/value blocks.
Definition params.h:24
+
mlx::steel::AttnParams::Q_strides
size_t Q_strides[3]
Query strides (B, H, L, D = 1)
Definition params.h:29
+
mlx::steel::AttnParams::NK_aligned
int NK_aligned
Number of full key/value blocks.
Definition params.h:27
+
mlx::steel::AttnParams::V_strides
size_t V_strides[3]
Value strides (B, H, L, D = 1)
Definition params.h:31
+
mlx::steel::AttnParams::scale
float scale
Attention scale.
Definition params.h:21
+
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+
#include "mlx/backend/metal/kernels/steel/attn/loader.h"
+#include "mlx/backend/metal/kernels/steel/attn/mma.h"
+#include "mlx/backend/metal/kernels/steel/attn/params.h"
+#include "mlx/backend/metal/kernels/steel/attn/transforms.h"
+#include "mlx/backend/metal/kernels/steel/gemm/params.h"
+#include "mlx/backend/metal/kernels/steel/utils.h"
+
+

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+ + + + + + +

+Classes

struct  mlx::steel::LoopAlignment< M_aligned, N_aligned, K_aligned >
 
struct  mlx::steel::GEMMKernel< T, U, BM, BN, BK, WM, WN, transpose_a, transpose_b, MN_aligned, K_aligned, AccumType, Epilogue >
 
+ + + + + +

+Namespaces

namespace  mlx
 
namespace  mlx::steel
 
+
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attn.h
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1// Copyright © 2024 Apple Inc.
+
2
+
3#pragma once
+
4
+
5#include "mlx/backend/metal/kernels/steel/attn/loader.h"
+
6#include "mlx/backend/metal/kernels/steel/attn/mma.h"
+
7#include "mlx/backend/metal/kernels/steel/attn/params.h"
+
8#include "mlx/backend/metal/kernels/steel/attn/transforms.h"
+
9#include "mlx/backend/metal/kernels/steel/gemm/params.h"
+
10#include "mlx/backend/metal/kernels/steel/utils.h"
+
11
+
12using namespace metal;
+
13
+
15// GEMM kernel class
+
17
+
18namespace mlx {
+
+
19namespace steel {
+
20
+
21template <bool M_aligned, bool N_aligned, bool K_aligned>
+
22struct LoopAlignment {};
+
23
+
24template <
+
25 typename T,
+
26 typename U,
+
27 int BM,
+
28 int BN,
+
29 int BK,
+
30 int WM,
+
31 int WN,
+
32 bool transpose_a,
+
33 bool transpose_b,
+
34 bool MN_aligned,
+
35 bool K_aligned,
+
36 typename AccumType = typename AccumHelper<T>::accum_type,
+
37 typename Epilogue = TransformNone<U, AccumType>>
+
38struct GEMMKernel {
+
39 STEEL_CONST short tgp_padding_a = 16 / sizeof(T);
+
40 STEEL_CONST short tgp_padding_b = 16 / sizeof(T);
+
41 STEEL_CONST short tgp_mem_size_a =
+
42 transpose_a ? BK * (BM + tgp_padding_a) : BM * (BK + tgp_padding_a);
+
43 STEEL_CONST short tgp_mem_size_b =
+
44 transpose_b ? BN * (BK + tgp_padding_b) : BK * (BN + tgp_padding_b);
+
45 STEEL_CONST short tgp_mem_size = tgp_mem_size_a + tgp_mem_size_b;
+
46
+
47 STEEL_CONST short tgp_size = WM * WN * 32;
+
48
+
49 using loader_a_t = BlockLoader<
+
50 T,
+
51 transpose_a ? BK : BM,
+
52 transpose_a ? BM : BK,
+
53 transpose_a ? BM + tgp_padding_a : BK + tgp_padding_a,
+
54 !transpose_a,
+
55 tgp_size>;
+
56 using loader_b_t = BlockLoader<
+
57 T,
+
58 transpose_b ? BN : BK,
+
59 transpose_b ? BK : BN,
+
60 transpose_b ? BK + tgp_padding_b : BN + tgp_padding_b,
+
61 transpose_b,
+
62 tgp_size>;
+
63 using mma_t = BlockMMA<
+
64 T,
+
65 U,
+
66 BM,
+
67 BN,
+
68 BK,
+
69 WM,
+
70 WN,
+
71 transpose_a,
+
72 transpose_b,
+
73 transpose_a ? BM + tgp_padding_a : BK + tgp_padding_a,
+
74 transpose_b ? BK + tgp_padding_b : BN + tgp_padding_b,
+
75 AccumType,
+
76 Epilogue>;
+
77
+
78 /* Main kernel function */
+
79 template <bool M_aligned, bool N_aligned, bool K_aligned_>
+
+
80 static METAL_FUNC void gemm_loop(
+
81 threadgroup T* As [[threadgroup(0)]],
+
82 threadgroup T* Bs [[threadgroup(1)]],
+
83 const int gemm_k_iterations,
+
84 thread loader_a_t& loader_a,
+
85 thread loader_b_t& loader_b,
+
86 thread mma_t& mma_op,
+
87 thread const short& tgp_bm,
+
88 thread const short& tgp_bn,
+
89 thread const short& lbk,
+
90 LoopAlignment<M_aligned, N_aligned, K_aligned_> l = {}) {
+
91 // Appease the compiler
+
92 (void)l;
+
93
+
94 short2 tile_dims_A = transpose_a ? short2(tgp_bm, BK) : short2(BK, tgp_bm);
+
95
+
96 short2 tile_dims_B = transpose_b ? short2(BK, tgp_bn) : short2(tgp_bn, BK);
+
97
+
98 for (int k = 0; k < gemm_k_iterations; k++) {
+
99 threadgroup_barrier(mem_flags::mem_threadgroup);
+
100 // Load elements into threadgroup
+
101 if (M_aligned) {
+
102 loader_a.load_unsafe();
+
103 } else {
+
104 loader_a.load_safe(tile_dims_A);
+
105 }
+
106
+
107 if (N_aligned) {
+
108 loader_b.load_unsafe();
+
109 } else {
+
110 loader_b.load_safe(tile_dims_B);
+
111 }
+
112
+
113 threadgroup_barrier(mem_flags::mem_threadgroup);
+
114
+
115 // Multiply and accumulate threadgroup elements
+
116 mma_op.mma(As, Bs);
+
117
+
118 // Prepare for next iteration
+
119 loader_a.next();
+
120 loader_b.next();
+
121 }
+
122
+
123 if (!K_aligned_) {
+
124 threadgroup_barrier(mem_flags::mem_threadgroup);
+
125
+
126 short2 tile_dims_A_last =
+
127 transpose_a ? short2(tgp_bm, lbk) : short2(lbk, tgp_bm);
+
128 short2 tile_dims_B_last =
+
129 transpose_b ? short2(lbk, tgp_bn) : short2(tgp_bn, lbk);
+
130
+
131 loader_a.load_safe(tile_dims_A_last);
+
132 loader_b.load_safe(tile_dims_B_last);
+
133
+
134 threadgroup_barrier(mem_flags::mem_threadgroup);
+
135
+
136 mma_op.mma(As, Bs);
+
137 }
+
138 }
+
+
139
+
140 /* Main kernel function */
+
+
141 static METAL_FUNC void run(
+
142 const device T* A [[buffer(0)]],
+
143 const device T* B [[buffer(1)]],
+
144 device U* D [[buffer(2)]],
+
145 const constant GEMMParams* params [[buffer(3)]],
+
146 threadgroup T* As [[threadgroup(0)]],
+
147 threadgroup T* Bs [[threadgroup(1)]],
+
148 uint simd_lane_id [[thread_index_in_simdgroup]],
+
149 uint simd_group_id [[simdgroup_index_in_threadgroup]],
+
150 uint3 tid [[threadgroup_position_in_grid]],
+
151 uint3 lid [[thread_position_in_threadgroup]]) {
+
152 // Pacifying compiler
+
153 (void)lid;
+
154
+
155 const int tid_y = ((tid.y) << params->swizzle_log) +
+
156 ((tid.x) & ((1 << params->swizzle_log) - 1));
+
157 const int tid_x = (tid.x) >> params->swizzle_log;
+
158
+
159 if (params->tiles_n <= tid_x || params->tiles_m <= tid_y) {
+
160 return;
+
161 }
+
162
+
163 threadgroup_barrier(mem_flags::mem_none);
+
164
+
165 // Find block in A, B, C
+
166 const int c_row = tid_y * BM;
+
167 const int c_col = tid_x * BN;
+
168 const size_t c_row_long = size_t(c_row);
+
169 const size_t c_col_long = size_t(c_col);
+
170
+
171 A += transpose_a ? c_row_long : c_row_long * params->lda;
+
172 B += transpose_b ? c_col_long * params->ldb : c_col_long;
+
173 D += c_row_long * params->ldd + c_col_long;
+
174
+
175 // Prepare threadgroup loading operations
+
176 thread loader_a_t loader_a(A, params->lda, As, simd_group_id, simd_lane_id);
+
177 thread loader_b_t loader_b(B, params->ldb, Bs, simd_group_id, simd_lane_id);
+
178
+
179 // Prepare threadgroup mma operation
+
180 thread mma_t mma_op(simd_group_id, simd_lane_id);
+
181
+
182 int gemm_k_iterations = params->gemm_k_iterations_aligned;
+
183
+
185 // MNK aligned loop
+
186 if (MN_aligned) {
+
187 for (int k = 0; k < gemm_k_iterations; k++) {
+
188 threadgroup_barrier(mem_flags::mem_threadgroup);
+
189 // Load elements into threadgroup
+
190 loader_a.load_unsafe();
+
191 loader_b.load_unsafe();
+
192
+
193 threadgroup_barrier(mem_flags::mem_threadgroup);
+
194
+
195 // Multiply and accumulate threadgroup elements
+
196 mma_op.mma(As, Bs);
+
197
+
198 // Prepare for next iteration
+
199 loader_a.next();
+
200 loader_b.next();
+
201 }
+
202
+
203 threadgroup_barrier(mem_flags::mem_none);
+
204
+
205 // Loop tail
+
206 if (!K_aligned) {
+
207 int lbk = params->K - params->gemm_k_iterations_aligned * BK;
+
208 short2 tile_dims_A = transpose_a ? short2(BM, lbk) : short2(lbk, BM);
+
209 short2 tile_dims_B = transpose_b ? short2(lbk, BN) : short2(BN, lbk);
+
210
+
211 loader_a.load_safe(tile_dims_A);
+
212 loader_b.load_safe(tile_dims_B);
+
213
+
214 threadgroup_barrier(mem_flags::mem_threadgroup);
+
215
+
216 mma_op.mma(As, Bs);
+
217 }
+
218
+
219 // Store results to device memory
+
220 mma_op.store_result(D, params->ldd);
+
221 return;
+
222
+
223 }
+
225 // MN unaligned loop
+
226 else { // Loop over K - unaligned case
+
227 short tgp_bm = min(BM, params->M - c_row);
+
228 short tgp_bn = min(BN, params->N - c_col);
+
229 short leftover_bk = params->K - params->gemm_k_iterations_aligned * BK;
+
230
+
231 if (tgp_bm == BM && tgp_bn == BN) {
+
232 gemm_loop<true, true, K_aligned>(
+
233 As,
+
234 Bs,
+
235 gemm_k_iterations,
+
236 loader_a,
+
237 loader_b,
+
238 mma_op,
+
239 tgp_bm,
+
240 tgp_bn,
+
241 leftover_bk);
+
242
+
243 mma_op.store_result(D, params->ldd);
+
244 return;
+
245
+
246 } else if (tgp_bn == BN) {
+
247 gemm_loop<false, true, K_aligned>(
+
248 As,
+
249 Bs,
+
250 gemm_k_iterations,
+
251 loader_a,
+
252 loader_b,
+
253 mma_op,
+
254 tgp_bm,
+
255 tgp_bn,
+
256 leftover_bk);
+
257
+
258 mma_op.store_result_safe(D, params->ldd, short2(tgp_bn, tgp_bm));
+
259 return;
+
260
+
261 } else if (tgp_bm == BM) {
+
262 gemm_loop<true, false, K_aligned>(
+
263 As,
+
264 Bs,
+
265 gemm_k_iterations,
+
266 loader_a,
+
267 loader_b,
+
268 mma_op,
+
269 tgp_bm,
+
270 tgp_bn,
+
271 leftover_bk);
+
272
+
273 mma_op.store_result_safe(D, params->ldd, short2(tgp_bn, tgp_bm));
+
274 return;
+
275
+
276 } else {
+
277 gemm_loop<false, false, K_aligned>(
+
278 As,
+
279 Bs,
+
280 gemm_k_iterations,
+
281 loader_a,
+
282 loader_b,
+
283 mma_op,
+
284 tgp_bm,
+
285 tgp_bn,
+
286 leftover_bk);
+
287
+
288 mma_op.store_result_safe(D, params->ldd, short2(tgp_bn, tgp_bm));
+
289 return;
+
290 }
+
291 }
+
292 }
+
+
293};
+
294
+
295} // namespace steel
+
+
296} // namespace mlx
+ + + + + + +
metal
Definition bf16_math.h:226
+
metal::min
METAL_FUNC bfloat16_t min(bfloat16_t x, bfloat16_t y)
Definition bf16_math.h:232
+
mlx
Definition allocator.h:7
+
STEEL_CONST
#define STEEL_CONST
Definition defines.h:3
+
mlx::steel::AccumHelper::accum_type
float accum_type
Definition transforms.h:57
+
mlx::steel::BlockLoader
Definition loader.h:25
+
mlx::steel::BlockMMA
Definition mma.h:377
+
mlx::steel::GEMMKernel
Definition gemm.h:37
+
mlx::steel::GEMMKernel::run
static METAL_FUNC void run(const device T *A, const device T *B, device U *D, const constant GEMMParams *params, threadgroup T *As, threadgroup T *Bs, uint simd_lane_id, uint simd_group_id, uint3 tid, uint3 lid)
Definition attn.h:141
+
mlx::steel::GEMMKernel::tgp_mem_size_b
STEEL_CONST short tgp_mem_size_b
Definition attn.h:43
+
mlx::steel::GEMMKernel::tgp_mem_size
STEEL_CONST short tgp_mem_size
Definition attn.h:45
+
mlx::steel::GEMMKernel::gemm_loop
static METAL_FUNC void gemm_loop(threadgroup T *As, threadgroup T *Bs, const int gemm_k_iterations, thread loader_a_t &loader_a, thread loader_b_t &loader_b, thread mma_t &mma_op, thread const short &tgp_bm, thread const short &tgp_bn, thread const short &lbk, LoopAlignment< M_aligned, N_aligned, K_aligned_ > l={})
Definition attn.h:80
+
mlx::steel::GEMMKernel::tgp_size
STEEL_CONST short tgp_size
Definition attn.h:47
+
mlx::steel::GEMMKernel::tgp_mem_size_a
STEEL_CONST short tgp_mem_size_a
Definition attn.h:41
+
mlx::steel::GEMMKernel::tgp_padding_b
STEEL_CONST short tgp_padding_b
Definition attn.h:40
+
mlx::steel::GEMMKernel::tgp_padding_a
STEEL_CONST short tgp_padding_a
Definition attn.h:39
+
mlx::steel::GEMMParams
Definition params.h:12
+
mlx::steel::LoopAlignment
Definition gemm.h:21
+
mlx::steel::TransformNone
Definition transforms.h:15
+
+ +
+Generated by doxygen 1.12.0 +
+
+ + diff --git a/docs/build/html/backend_2common_2utils_8h.html b/docs/build/html/backend_2common_2utils_8h.html index e5c00a23..f09f5481 100644 --- a/docs/build/html/backend_2common_2utils_8h.html +++ b/docs/build/html/backend_2common_2utils_8h.html @@ -142,6 +142,10 @@ Functions   bool mlx::core::is_donatable (const array &in, const array &out)   +void mlx::core::move_or_copy (const array &in, array &out) +  +void mlx::core::move_or_copy (const array &in, array &out, const std::vector< size_t > &strides, array::Flags flags, size_t data_size, size_t offset=0) +  diff --git a/docs/build/html/backend_2common_2utils_8h_source.html b/docs/build/html/backend_2common_2utils_8h_source.html index 0e6718d7..be28e148 100644 --- a/docs/build/html/backend_2common_2utils_8h_source.html +++ b/docs/build/html/backend_2common_2utils_8h_source.html @@ -297,7 +297,16 @@ $(function(){ initResizable(false); });
179}
180
-
181} // namespace mlx::core
+
181void move_or_copy(const array& in, array& out);
+
182void move_or_copy(
+
183 const array& in,
+
184 array& out,
+
185 const std::vector<size_t>& strides,
+
186 array::Flags flags,
+
187 size_t data_size,
+
188 size_t offset = 0);
+
189
+
190} // namespace mlx::core
array.h
mlx::core::array
Definition array.h:20
mlx::core::array::flags
const Flags & flags() const
Get the Flags bit-field.
Definition array.h:302
@@ -312,6 +321,7 @@ $(function(){ initResizable(false); });
mlx::core::collapse_contiguous_dims
std::tuple< std::vector< int >, std::vector< std::vector< int64_t > > > collapse_contiguous_dims(const std::vector< int > &shape, const std::vector< std::vector< int64_t > > &strides, int64_t size_cap=std::numeric_limits< int32_t >::max())
mlx::core::check_contiguity
auto check_contiguity(const std::vector< int > &shape, const std::vector< StrideT > &strides)
Definition utils.h:151
mlx::core::elem_to_loc
StrideT elem_to_loc(int elem, const std::vector< int > &shape, const std::vector< StrideT > &strides)
Definition utils.h:12
+
mlx::core::move_or_copy
void move_or_copy(const array &in, array &out)
mlx::core::is_donatable
bool is_donatable(const array &in, const array &out)
Definition utils.h:174
mlx::core::enable_for_arrays_t
typename std::enable_if_t< is_arrays_v< T... > > enable_for_arrays_t
Definition array.h:611
mlx::core::ContiguousIterator
Definition utils.h:89
@@ -322,6 +332,7 @@ $(function(){ initResizable(false); });
mlx::core::ContiguousIterator::ContiguousIterator
ContiguousIterator()
Definition utils.h:120
mlx::core::ContiguousIterator::ContiguousIterator
ContiguousIterator(const array &a)
Definition utils.h:122
mlx::core::ContiguousIterator::step
void step()
Definition utils.h:90
+
mlx::core::array::Flags
Definition array.h:221
mlx::core::array::Flags::row_contiguous
bool row_contiguous
Definition array.h:233
diff --git a/docs/build/html/backend_2metal_2device_8h_source.html b/docs/build/html/backend_2metal_2device_8h_source.html index bb0b3537..7cf8c375 100644 --- a/docs/build/html/backend_2metal_2device_8h_source.html +++ b/docs/build/html/backend_2metal_2device_8h_source.html @@ -160,225 +160,264 @@ $(function(){ initResizable(false); });
59 };
60
-
-
61 MTL::ComputeCommandEncoder* operator->() {
-
62 return enc_;
-
63 }
+
61 void set_input_array(const array& a, int idx, int64_t offset = 0);
+
62 void set_output_array(array& a, int idx, int64_t offset = 0);
+
63 void dispatch_threadgroups(MTL::Size grid_dims, MTL::Size group_dims);
+
64 void dispatch_threads(MTL::Size grid_dims, MTL::Size group_dims);
+
65 void maybeInsertBarrier();
+
66
+
+
67 void set_compute_pipeline_state(MTL::ComputePipelineState* kernel) {
+
68 enc_->setComputePipelineState(kernel);
+
69 }
-
64
-
65 void set_input_array(const array& a, int idx, int64_t offset = 0);
-
66 void set_output_array(array& a, int idx, int64_t offset = 0);
-
67 void dispatchThreadgroups(MTL::Size grid_dims, MTL::Size group_dims);
-
68 void dispatchThreads(MTL::Size grid_dims, MTL::Size group_dims);
-
69 void maybeInsertBarrier();
70
-
71 ConcurrentContext start_concurrent() {
-
72 return ConcurrentContext(*this);
+
71 void wait_for_fence(MTL::Fence* fence) {
+
72 enc_->waitForFence(fence);
73 }
-
74 ~CommandEncoder();
-
75
-
76 // Inputs to all kernels in the encoder including temporaries
-
-
77 std::unordered_set<const void*>& inputs() {
-
78 return all_inputs_;
-
79 };
+
74
+
+
75 void update_fence(MTL::Fence* fence) {
+
76 enc_->updateFence(fence);
+
77 }
-
80
-
81 // Outputs of all kernels in the encoder including temporaries
-
-
82 std::unordered_set<const void*> outputs() {
-
83 return all_outputs_;
-
84 };
+
78
+
79 template <typename T>
+
+
80 void set_vector_bytes(const std::vector<T>& vec, size_t nelems, int idx) {
+
81 enc_->setBytes(vec.data(), nelems * sizeof(T), idx);
+
82 }
-
85
-
86 private:
-
87 MTL::ComputeCommandEncoder* enc_;
-
88 bool needs_barrier_{false};
-
89 bool concurrent_{false};
-
90 std::unordered_set<MTL::Resource*> prev_outputs_;
-
91 std::unordered_set<MTL::Resource*> next_outputs_;
-
92 std::unordered_set<MTL::Resource*> concurrent_outputs_;
-
93 std::unordered_set<const void*> all_inputs_;
-
94 std::unordered_set<const void*> all_outputs_;
-
95};
+
83 template <typename T>
+
+
84 void set_vector_bytes(const std::vector<T>& vec, int idx) {
+
85 return set_vector_bytes(vec, vec.size(), idx);
+
86 }
-
96
-
-
97struct Fence {
-
98 Fence(MTL::Fence* fence) : fence(fence) {}
-
-
99 ~Fence() {
-
100 fence->release();
-
101 }
+
87
+
88 template <typename T>
+
+
89 void set_bytes(const T* v, int n, int idx) {
+
90 return enc_->setBytes(v, n * sizeof(T), idx);
+
91 }
-
102 MTL::Fence* fence;
-
103};
+
92
+
93 template <typename T>
+
+
94 void set_bytes(const T& v, int idx) {
+
95 return enc_->setBytes(&v, sizeof(T), idx);
+
96 }
-
104
-
-
105struct DeviceStream {
-
106 DeviceStream(MTL::CommandQueue* queue) : queue(queue) {};
-
-
107 ~DeviceStream() {
-
108 queue->release();
-
109 if (buffer != nullptr) {
-
110 buffer->release();
-
111 }
-
112 };
+
97
+
+
98 ConcurrentContext start_concurrent() {
+
99 return ConcurrentContext(*this);
+
100 }
+
+
101 ~CommandEncoder();
+
102
+
103 // Inputs to all kernels in the encoder including temporaries
+
+
104 std::unordered_set<const void*>& inputs() {
+
105 return all_inputs_;
+
106 };
+
+
107
+
108 // Outputs of all kernels in the encoder including temporaries
+
+
109 std::unordered_set<const void*> outputs() {
+
110 return all_outputs_;
+
111 };
+
+
112
+
113 private:
+
114 MTL::ComputeCommandEncoder* enc_;
+
115 bool needs_barrier_{false};
+
116 bool concurrent_{false};
+
117 std::unordered_set<MTL::Resource*> prev_outputs_;
+
118 std::unordered_set<MTL::Resource*> next_outputs_;
+
119 std::unordered_set<MTL::Resource*> concurrent_outputs_;
+
120 std::unordered_set<const void*> all_inputs_;
+
121 std::unordered_set<const void*> all_outputs_;
+
122};
-
113 MTL::CommandQueue* queue;
-
114 // A map of prior command encoder outputs to their corresponding fence
-
115 std::unordered_map<const void*, std::shared_ptr<Fence>> outputs;
-
116 // Used to allow thread-safe access to the outputs map
-
117 std::mutex fence_mtx;
-
118
-
119 // The buffer and buffer op count are updated
-
120 // between command buffers
-
121 MTL::CommandBuffer* buffer{nullptr};
-
122 int buffer_ops{0};
123
-
124 // The command encoder, fence, and temporaries are updated between command
-
125 // encoders
-
126 std::unique_ptr<CommandEncoder> encoder{nullptr};
-
127 std::shared_ptr<Fence> fence;
-
128 std::vector<array> temporaries;
-
129};
+
+
124struct Fence {
+
125 Fence(MTL::Fence* fence) : fence(fence) {}
+
+
126 ~Fence() {
+
127 fence->release();
+
128 }
-
130
-
-
131class Device {
-
132 public:
-
133 Device();
-
134 Device(const Device&) = delete;
-
135 Device& operator=(const Device&) = delete;
-
136 ~Device();
-
137
-
-
138 MTL::Device* mtl_device() {
-
139 return device_;
-
140 };
+
129 MTL::Fence* fence;
+
130};
-
141
-
-
142 const std::string& get_architecture() {
-
143 return arch_;
-
144 }
+
131
+
+
132struct DeviceStream {
+
133 DeviceStream(MTL::CommandQueue* queue) : queue(queue) {};
+
+
134 ~DeviceStream() {
+
135 queue->release();
+
136 if (buffer != nullptr) {
+
137 buffer->release();
+
138 }
+
139 };
+
140 MTL::CommandQueue* queue;
+
141 // A map of prior command encoder outputs to their corresponding fence
+
142 std::unordered_map<const void*, std::shared_ptr<Fence>> outputs;
+
143 // Used to allow thread-safe access to the outputs map
+
144 std::mutex fence_mtx;
145
-
146 void new_queue(int index);
-
147 MTL::CommandBuffer* get_command_buffer(int index);
-
148 int get_command_buffer_ops(int index);
-
149 void increment_command_buffer_ops(int index);
-
150 void commit_command_buffer(int index);
-
151 CommandEncoder& get_command_encoder(int index);
-
152 void end_encoding(int index);
-
153
-
154 void register_library(
-
155 const std::string& lib_name,
-
156 const std::string& lib_path);
+
146 // The buffer and buffer op count are updated
+
147 // between command buffers
+
148 MTL::CommandBuffer* buffer{nullptr};
+
149 int buffer_ops{0};
+
150
+
151 // The command encoder, fence, and temporaries are updated between command
+
152 // encoders
+
153 std::unique_ptr<CommandEncoder> encoder{nullptr};
+
154 std::shared_ptr<Fence> fence;
+
155 std::vector<array> temporaries;
+
156};
+
157
-
158 // Note, this should remain in the header so that it is not dynamically
-
159 // linked
-
-
160 void register_library(const std::string& lib_name) {
-
161 if (auto it = library_map_.find(lib_name); it == library_map_.end()) {
-
162 register_library(lib_name, get_colocated_mtllib_path(lib_name));
-
163 }
-
164 }
+
+
158class Device {
+
159 public:
+
160 Device();
+
161 Device(const Device&) = delete;
+
162 Device& operator=(const Device&) = delete;
+
163 ~Device();
+
164
+
+
165 MTL::Device* mtl_device() {
+
166 return device_;
+
167 };
+
+
168
+
+
169 const std::string& get_architecture() {
+
170 return arch_;
+
171 }
+
+
172
+
173 void new_queue(int index);
+
174 MTL::CommandBuffer* get_command_buffer(int index);
+
175 int get_command_buffer_ops(int index);
+
176 void increment_command_buffer_ops(int index);
+
177 void commit_command_buffer(int index);
+
178 CommandEncoder& get_command_encoder(int index);
+
179 void end_encoding(int index);
+
180
+
181 void register_library(
+
182 const std::string& lib_name,
+
183 const std::string& lib_path);
+
184
+
185 // Note, this should remain in the header so that it is not dynamically
+
186 // linked
+
+
187 void register_library(const std::string& lib_name) {
+
188 if (auto it = library_map_.find(lib_name); it == library_map_.end()) {
+
189 register_library(lib_name, get_colocated_mtllib_path(lib_name));
+
190 }
+
191 }
-
165
-
166 MTL::Library* get_library(
-
167 const std::string& name,
-
168 const std::function<std::string(void)>& builder);
-
169
-
170 MTL::ComputePipelineState* get_kernel(
-
171 const std::string& base_name,
-
172 MTL::Library* mtl_lib,
-
173 const std::string& hash_name = "",
-
174 const MTLFCList& func_consts = {},
-
175 const std::vector<MTL::Function*>& linked_functions = {});
-
176
-
177 MTL::ComputePipelineState* get_kernel(
-
178 const std::string& base_name,
-
179 const std::string& lib_name = "mlx",
-
180 const std::string& hash_name = "",
-
181 const MTLFCList& func_consts = {},
-
182 const std::vector<MTL::Function*>& linked_functions = {});
-
183
-
184 MTL::ArgumentEncoder* argument_encoder(
-
185 const std::vector<MTL::ArgumentDescriptor*>& arg_descs) const;
-
186
-
187 // Record temporary arrays for the given stream index
-
188 void add_temporary(array arr, int index);
-
189 void add_temporaries(std::vector<array> arrays, int index);
-
190
-
191 void set_residency_set(const MTL::ResidencySet* residency_set);
192
-
193 private:
-
194 DeviceStream& get_stream_(int index) {
-
195 return stream_map_.find(index)->second;
-
196 }
-
197 MTL::Library* get_library_cache_(const std::string& name);
-
198
-
199 MTL::Library* get_library_(const std::string& name);
-
200 MTL::Library* build_library_(const std::string& source_string);
-
201
-
202 MTL::Function* get_function_(const std::string& name, MTL::Library* mtl_lib);
+
193 MTL::Library* get_library(
+
194 const std::string& name,
+
195 const std::function<std::string(void)>& builder);
+
196
+
197 MTL::ComputePipelineState* get_kernel(
+
198 const std::string& base_name,
+
199 MTL::Library* mtl_lib,
+
200 const std::string& hash_name = "",
+
201 const MTLFCList& func_consts = {},
+
202 const std::vector<MTL::Function*>& linked_functions = {});
203
-
204 MTL::Function* get_function_(
-
205 const std::string& name,
-
206 const std::string& specialized_name,
-
207 const MTLFCList& func_consts,
-
208 MTL::Library* mtl_lib);
-
209
-
210 MTL::LinkedFunctions* get_linked_functions_(
-
211 const std::vector<MTL::Function*>& funcs);
-
212
-
213 MTL::ComputePipelineState* get_kernel_(
-
214 const std::string& name,
-
215 const MTL::Function* mtl_function);
-
216
-
217 MTL::ComputePipelineState* get_kernel_(
-
218 const std::string& name,
-
219 const MTL::Function* mtl_function,
-
220 const MTL::LinkedFunctions* linked_functions);
-
221
-
222 MTL::ComputePipelineState* get_kernel_(
-
223 const std::string& base_name,
-
224 MTL::Library* mtl_lib,
-
225 const std::string& hash_name,
-
226 const MTLFCList& func_consts = {},
-
227 const std::vector<MTL::Function*>& linked_functions = {});
+
204 MTL::ComputePipelineState* get_kernel(
+
205 const std::string& base_name,
+
206 const std::string& lib_name = "mlx",
+
207 const std::string& hash_name = "",
+
208 const MTLFCList& func_consts = {},
+
209 const std::vector<MTL::Function*>& linked_functions = {});
+
210
+
211 MTL::ArgumentEncoder* argument_encoder(
+
212 const std::vector<MTL::ArgumentDescriptor*>& arg_descs) const;
+
213
+
214 // Record temporary arrays for the given stream index
+
215 void add_temporary(array arr, int index);
+
216 void add_temporaries(std::vector<array> arrays, int index);
+
217
+
218 void set_residency_set(const MTL::ResidencySet* residency_set);
+
219
+
220 private:
+
221 DeviceStream& get_stream_(int index) {
+
222 return stream_map_.find(index)->second;
+
223 }
+
224 MTL::Library* get_library_cache_(const std::string& name);
+
225
+
226 MTL::Library* get_library_(const std::string& name);
+
227 MTL::Library* build_library_(const std::string& source_string);
228
-
229 MTL::Device* device_;
-
230 std::unordered_map<int32_t, DeviceStream> stream_map_;
-
231
-
232 std::shared_mutex kernel_mtx_;
-
233 std::unordered_map<std::string, MTL::ComputePipelineState*> kernel_map_;
-
234
-
235 std::shared_mutex library_mtx_;
-
236 std::unordered_map<std::string, MTL::Library*> library_map_;
-
237 const MTL::ResidencySet* residency_set_{nullptr};
-
238 std::string arch_;
-
239};
+
229 MTL::Function* get_function_(const std::string& name, MTL::Library* mtl_lib);
+
230
+
231 MTL::Function* get_function_(
+
232 const std::string& name,
+
233 const std::string& specialized_name,
+
234 const MTLFCList& func_consts,
+
235 MTL::Library* mtl_lib);
+
236
+
237 MTL::LinkedFunctions* get_linked_functions_(
+
238 const std::vector<MTL::Function*>& funcs);
+
239
+
240 MTL::ComputePipelineState* get_kernel_(
+
241 const std::string& name,
+
242 const MTL::Function* mtl_function);
+
243
+
244 MTL::ComputePipelineState* get_kernel_(
+
245 const std::string& name,
+
246 const MTL::Function* mtl_function,
+
247 const MTL::LinkedFunctions* linked_functions);
+
248
+
249 MTL::ComputePipelineState* get_kernel_(
+
250 const std::string& base_name,
+
251 MTL::Library* mtl_lib,
+
252 const std::string& hash_name,
+
253 const MTLFCList& func_consts = {},
+
254 const std::vector<MTL::Function*>& linked_functions = {});
+
255
+
256 MTL::Device* device_;
+
257 std::unordered_map<int32_t, DeviceStream> stream_map_;
+
258
+
259 std::shared_mutex kernel_mtx_;
+
260 std::unordered_map<std::string, MTL::ComputePipelineState*> kernel_map_;
+
261
+
262 std::shared_mutex library_mtx_;
+
263 std::unordered_map<std::string, MTL::Library*> library_map_;
+
264 const MTL::ResidencySet* residency_set_{nullptr};
+
265 std::string arch_;
+
266};
-
240
-
241Device& device(mlx::core::Device);
-
242
-
243} // namespace mlx::core::metal
+
267
+
268Device& device(mlx::core::Device);
+
269
+
270} // namespace mlx::core::metal
mlx::core::array
Definition array.h:20
-
mlx::core::metal::Device
Definition device.h:131
+
mlx::core::metal::Device
Definition device.h:158
mlx::core::metal::Device::set_residency_set
void set_residency_set(const MTL::ResidencySet *residency_set)
mlx::core::metal::Device::get_command_buffer_ops
int get_command_buffer_ops(int index)
-
mlx::core::metal::Device::mtl_device
MTL::Device * mtl_device()
Definition device.h:138
+
mlx::core::metal::Device::mtl_device
MTL::Device * mtl_device()
Definition device.h:165
mlx::core::metal::Device::register_library
void register_library(const std::string &lib_name, const std::string &lib_path)
mlx::core::metal::Device::get_command_buffer
MTL::CommandBuffer * get_command_buffer(int index)
mlx::core::metal::Device::end_encoding
void end_encoding(int index)
-
mlx::core::metal::Device::get_architecture
const std::string & get_architecture()
Definition device.h:142
+
mlx::core::metal::Device::get_architecture
const std::string & get_architecture()
Definition device.h:169
mlx::core::metal::Device::get_kernel
MTL::ComputePipelineState * get_kernel(const std::string &base_name, MTL::Library *mtl_lib, const std::string &hash_name="", const MTLFCList &func_consts={}, const std::vector< MTL::Function * > &linked_functions={})
mlx::core::metal::Device::argument_encoder
MTL::ArgumentEncoder * argument_encoder(const std::vector< MTL::ArgumentDescriptor * > &arg_descs) const
mlx::core::metal::Device::add_temporaries
void add_temporaries(std::vector< array > arrays, int index)
@@ -386,7 +425,7 @@ $(function(){ initResizable(false); });
mlx::core::metal::Device::increment_command_buffer_ops
void increment_command_buffer_ops(int index)
mlx::core::metal::Device::new_queue
void new_queue(int index)
mlx::core::metal::Device::commit_command_buffer
void commit_command_buffer(int index)
-
mlx::core::metal::Device::register_library
void register_library(const std::string &lib_name)
Definition device.h:160
+
mlx::core::metal::Device::register_library
void register_library(const std::string &lib_name)
Definition device.h:187
mlx::core::metal::Device::Device
Device(const Device &)=delete
mlx::core::metal::Device::add_temporary
void add_temporary(array arr, int index)
mlx::core::metal::Device::operator=
Device & operator=(const Device &)=delete
@@ -403,34 +442,40 @@ $(function(){ initResizable(false); });
mlx::core::metal::CommandEncoder::ConcurrentContext::ConcurrentContext
ConcurrentContext(CommandEncoder &enc)
Definition device.h:47
mlx::core::metal::CommandEncoder
Definition device.h:41
-
mlx::core::metal::CommandEncoder::dispatchThreads
void dispatchThreads(MTL::Size grid_dims, MTL::Size group_dims)
+
mlx::core::metal::CommandEncoder::dispatch_threads
void dispatch_threads(MTL::Size grid_dims, MTL::Size group_dims)
mlx::core::metal::CommandEncoder::CommandEncoder
CommandEncoder(MTL::CommandBuffer *cbuf)
-
mlx::core::metal::CommandEncoder::inputs
std::unordered_set< const void * > & inputs()
Definition device.h:77
+
mlx::core::metal::CommandEncoder::inputs
std::unordered_set< const void * > & inputs()
Definition device.h:104
mlx::core::metal::CommandEncoder::operator=
CommandEncoder & operator=(const CommandEncoder &)=delete
-
mlx::core::metal::CommandEncoder::start_concurrent
ConcurrentContext start_concurrent()
Definition device.h:71
+
mlx::core::metal::CommandEncoder::start_concurrent
ConcurrentContext start_concurrent()
Definition device.h:98
+
mlx::core::metal::CommandEncoder::set_vector_bytes
void set_vector_bytes(const std::vector< T > &vec, size_t nelems, int idx)
Definition device.h:80
mlx::core::metal::CommandEncoder::set_output_array
void set_output_array(array &a, int idx, int64_t offset=0)
-
mlx::core::metal::CommandEncoder::dispatchThreadgroups
void dispatchThreadgroups(MTL::Size grid_dims, MTL::Size group_dims)
+
mlx::core::metal::CommandEncoder::set_compute_pipeline_state
void set_compute_pipeline_state(MTL::ComputePipelineState *kernel)
Definition device.h:67
+
mlx::core::metal::CommandEncoder::set_vector_bytes
void set_vector_bytes(const std::vector< T > &vec, int idx)
Definition device.h:84
+
mlx::core::metal::CommandEncoder::dispatch_threadgroups
void dispatch_threadgroups(MTL::Size grid_dims, MTL::Size group_dims)
-
mlx::core::metal::CommandEncoder::operator->
MTL::ComputeCommandEncoder * operator->()
Definition device.h:61
+
mlx::core::metal::CommandEncoder::set_bytes
void set_bytes(const T *v, int n, int idx)
Definition device.h:89
mlx::core::metal::CommandEncoder::set_input_array
void set_input_array(const array &a, int idx, int64_t offset=0)
+
mlx::core::metal::CommandEncoder::set_bytes
void set_bytes(const T &v, int idx)
Definition device.h:94
mlx::core::metal::CommandEncoder::CommandEncoder
CommandEncoder(const CommandEncoder &)=delete
-
mlx::core::metal::CommandEncoder::outputs
std::unordered_set< const void * > outputs()
Definition device.h:82
-
mlx::core::metal::DeviceStream
Definition device.h:105
-
mlx::core::metal::DeviceStream::~DeviceStream
~DeviceStream()
Definition device.h:107
-
mlx::core::metal::DeviceStream::outputs
std::unordered_map< const void *, std::shared_ptr< Fence > > outputs
Definition device.h:115
-
mlx::core::metal::DeviceStream::DeviceStream
DeviceStream(MTL::CommandQueue *queue)
Definition device.h:106
-
mlx::core::metal::DeviceStream::encoder
std::unique_ptr< CommandEncoder > encoder
Definition device.h:126
-
mlx::core::metal::DeviceStream::fence_mtx
std::mutex fence_mtx
Definition device.h:117
-
mlx::core::metal::DeviceStream::queue
MTL::CommandQueue * queue
Definition device.h:113
-
mlx::core::metal::DeviceStream::fence
std::shared_ptr< Fence > fence
Definition device.h:127
-
mlx::core::metal::DeviceStream::buffer
MTL::CommandBuffer * buffer
Definition device.h:121
-
mlx::core::metal::DeviceStream::buffer_ops
int buffer_ops
Definition device.h:122
-
mlx::core::metal::DeviceStream::temporaries
std::vector< array > temporaries
Definition device.h:128
-
mlx::core::metal::Fence
Definition device.h:97
-
mlx::core::metal::Fence::Fence
Fence(MTL::Fence *fence)
Definition device.h:98
-
mlx::core::metal::Fence::~Fence
~Fence()
Definition device.h:99
-
mlx::core::metal::Fence::fence
MTL::Fence * fence
Definition device.h:102
+
mlx::core::metal::CommandEncoder::update_fence
void update_fence(MTL::Fence *fence)
Definition device.h:75
+
mlx::core::metal::CommandEncoder::outputs
std::unordered_set< const void * > outputs()
Definition device.h:109
+
mlx::core::metal::CommandEncoder::wait_for_fence
void wait_for_fence(MTL::Fence *fence)
Definition device.h:71
+
mlx::core::metal::DeviceStream
Definition device.h:132
+
mlx::core::metal::DeviceStream::~DeviceStream
~DeviceStream()
Definition device.h:134
+
mlx::core::metal::DeviceStream::outputs
std::unordered_map< const void *, std::shared_ptr< Fence > > outputs
Definition device.h:142
+
mlx::core::metal::DeviceStream::DeviceStream
DeviceStream(MTL::CommandQueue *queue)
Definition device.h:133
+
mlx::core::metal::DeviceStream::encoder
std::unique_ptr< CommandEncoder > encoder
Definition device.h:153
+
mlx::core::metal::DeviceStream::fence_mtx
std::mutex fence_mtx
Definition device.h:144
+
mlx::core::metal::DeviceStream::queue
MTL::CommandQueue * queue
Definition device.h:140
+
mlx::core::metal::DeviceStream::fence
std::shared_ptr< Fence > fence
Definition device.h:154
+
mlx::core::metal::DeviceStream::buffer
MTL::CommandBuffer * buffer
Definition device.h:148
+
mlx::core::metal::DeviceStream::buffer_ops
int buffer_ops
Definition device.h:149
+
mlx::core::metal::DeviceStream::temporaries
std::vector< array > temporaries
Definition device.h:155
+
mlx::core::metal::Fence
Definition device.h:124
+
mlx::core::metal::Fence::Fence
Fence(MTL::Fence *fence)
Definition device.h:125
+
mlx::core::metal::Fence::~Fence
~Fence()
Definition device.h:126
+
mlx::core::metal::Fence::fence
MTL::Fence * fence
Definition device.h:129
diff --git a/docs/build/html/backend_2metal_2kernels_2complex_8h_source.html b/docs/build/html/backend_2metal_2kernels_2complex_8h_source.html index 87aa7be4..9f5645c7 100644 --- a/docs/build/html/backend_2metal_2kernels_2complex_8h_source.html +++ b/docs/build/html/backend_2metal_2kernels_2complex_8h_source.html @@ -268,7 +268,7 @@ $(function(){ initResizable(false); });
operator+
constexpr complex64_t operator+(complex64_t a, complex64_t b)
Definition complex.h:104
operator/
constexpr complex64_t operator/(complex64_t a, complex64_t b)
Definition complex.h:116
operator<=
constexpr bool operator<=(complex64_t a, complex64_t b)
Definition complex.h:92
-
metal
Definition bf16.h:265
+
metal
Definition bf16_math.h:226
complex64_t
Definition complex.h:20
complex64_t::complex64_t
constexpr complex64_t(T x) const ant
Definition complex.h:48
complex64_t::complex64_t
constexpr complex64_t()
Definition complex.h:26
diff --git a/docs/build/html/backend_2metal_2kernels_2fft_8h_source.html b/docs/build/html/backend_2metal_2kernels_2fft_8h_source.html index 80b3a9cb..85d097d0 100644 --- a/docs/build/html/backend_2metal_2kernels_2fft_8h_source.html +++ b/docs/build/html/backend_2metal_2kernels_2fft_8h_source.html @@ -629,8 +629,8 @@ $(function(){ initResizable(false); });
rader_5_steps_
static constant constexpr const int rader_5_steps_
Definition fft.h:44
mlx::core::real
array real(const array &a, StreamOrDevice s={})
radix_func
METAL_FUNC void radix_func(thread float *x)
Definition hadamard.h:11
-
metal
Definition bf16.h:265
-
metal::min
METAL_FUNC bfloat16_t min(bfloat16_t x, bfloat16_t y)
Definition bf16_math.h:234
+
metal
Definition bf16_math.h:226
+
metal::min
METAL_FUNC bfloat16_t min(bfloat16_t x, bfloat16_t y)
Definition bf16_math.h:232
radix5
METAL_FUNC void radix5(thread float2 *x, thread float2 *y)
Definition radix.h:69
radix4
METAL_FUNC void radix4(thread float2 *x, thread float2 *y)
Definition radix.h:56
diff --git a/docs/build/html/backend_2metal_2kernels_2jit_2bf16_8h.html b/docs/build/html/backend_2metal_2kernels_2jit_2bf16_8h.html new file mode 100644 index 00000000..563d1b30 --- /dev/null +++ b/docs/build/html/backend_2metal_2kernels_2jit_2bf16_8h.html @@ -0,0 +1,160 @@ + + + + + + + +MLX: mlx/backend/metal/kernels/jit/bf16.h File Reference + + + + + + + + + + + + + +
+
+ + + + + + +
+
MLX +
+
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+ + + + + + + + + + +
+
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+
+
+
+
+
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+
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+
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+
+
+
+
+ + +
+
+
+
+Macros
+
bf16.h File Reference
+
+
+
#include "mlx/backend/metal/kernels/metal_3_1/bf16.h"
+#include "mlx/backend/metal/kernels/metal_3_0/bf16.h"
+
+

Go to the source code of this file.

+ + + + + + + + +

+Macros

#define jit_if   #if
 
#define jit_else   #else
 
#define jit_endif   #endif
 
+

Macro Definition Documentation

+ +

◆ jit_else

+ +
+
+ + + + +
#define jit_else   #else
+
+ +
+
+ +

◆ jit_endif

+ +
+
+ + + + +
#define jit_endif   #endif
+
+ +
+
+ +

◆ jit_if

+ +
+
+ + + + +
#define jit_if   #if
+
+ +
+
+
+ +
+Generated by doxygen 1.12.0 +
+
+ + diff --git a/docs/build/html/backend_2metal_2kernels_2jit_2bf16_8h_source.html b/docs/build/html/backend_2metal_2kernels_2jit_2bf16_8h_source.html new file mode 100644 index 00000000..fd142865 --- /dev/null +++ b/docs/build/html/backend_2metal_2kernels_2jit_2bf16_8h_source.html @@ -0,0 +1,121 @@ + + + + + + + +MLX: mlx/backend/metal/kernels/jit/bf16.h Source File + + + + + + + + + + + + + +
+
+ + + + + + +
+
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bf16.h
+
+
+Go to the documentation of this file.
1// Copyright © 2024 Apple Inc.
+
2
+
3// clang-format off
+
4#define jit_if #if
+
5#define jit_else #else
+
6#define jit_endif #endif
+
7
+
8jit_if (__METAL_VERSION__ >= 310)
+
9
+
10#include "mlx/backend/metal/kernels/metal_3_1/bf16.h"
+
11
+
12jit_else
+
13
+
14#include "mlx/backend/metal/kernels/metal_3_0/bf16.h"
+
15
+
16jit_endif // clang-format on
+
jit_else
#define jit_else
Definition bf16.h:5
+
jit_endif
#define jit_endif
Definition bf16.h:6
+
jit_if
#define jit_if
Definition bf16.h:4
+
bf16.h
+
+ +
+Generated by doxygen 1.12.0 +
+
+ + diff --git a/docs/build/html/backend_2metal_2kernels_2bf16_8h.html b/docs/build/html/backend_2metal_2kernels_2metal__3__0_2bf16_8h.html similarity index 99% rename from docs/build/html/backend_2metal_2kernels_2bf16_8h.html rename to docs/build/html/backend_2metal_2kernels_2metal__3__0_2bf16_8h.html index 9d74af68..76a2309e 100644 --- a/docs/build/html/backend_2metal_2kernels_2bf16_8h.html +++ b/docs/build/html/backend_2metal_2kernels_2metal__3__0_2bf16_8h.html @@ -5,7 +5,7 @@ -MLX: mlx/backend/metal/kernels/bf16.h File Reference +MLX: mlx/backend/metal/kernels/metal_3_0/bf16.h File Reference @@ -83,7 +83,7 @@ $(function(){ initResizable(false); });
@@ -99,9 +99,8 @@ $(function(){ initResizable(false); });
#include <metal_stdlib>
-#include "mlx/backend/metal/kernels/bf16_math.h"
-

Go to the source code of this file.

+

Go to the source code of this file.

@@ -821,6 +820,10 @@ Functions + + + +

Classes

 
METAL_FUNC bool metal::isnan (_MLX_BFloat16 x)
 
uint16_t bfloat16_to_uint16 (const bfloat16_t x)
 
bfloat16_t uint16_to_bfloat16 (const uint16_t x)
 
@@ -858,8 +861,8 @@ Variables
bfloat_binop_helper(_op_, _operator_, _MLX_BFloat16, uint32_t, float); \
bfloat_binop_helper(_op_, _operator_, _MLX_BFloat16, int64_t, float); \
bfloat_binop_helper(_op_, _operator_, _MLX_BFloat16, uint64_t, float);
-
bfloat_binop_base
#define bfloat_binop_base(__op__, __operator__, otype, atype, btype, ctype)
Definition bf16.h:141
-
_MLX_BFloat16
Definition bf16.h:54
+
bfloat_binop_base
#define bfloat_binop_base(__op__, __operator__, otype, atype, btype, ctype)
Definition bf16.h:135
+
_MLX_BFloat16
Definition bf16.h:48
@@ -996,7 +999,7 @@ Variables
bfloat_inplace_op_addr_space_helper(-, operator-=, itype); \
bfloat_inplace_op_addr_space_helper(*, operator*=, itype); \
bfloat_inplace_op_addr_space_helper(/, operator/=, itype);
-
bfloat_inplace_op_addr_space_helper
#define bfloat_inplace_op_addr_space_helper(__op__, __operator__, itype)
Definition bf16.h:209
+
bfloat_inplace_op_addr_space_helper
#define bfloat_inplace_op_addr_space_helper(__op__, __operator__, itype)
Definition bf16.h:203
@@ -1021,7 +1024,7 @@ VariablesValue:
bfloat_inplace_op_helper(__op__, __operator__, device); \
bfloat_inplace_op_helper(__op__, __operator__, thread); \
bfloat_inplace_op_helper(__op__, __operator__, threadgroup);
-
bfloat_inplace_op_helper
#define bfloat_inplace_op_helper(__op__, __operator__, itype, addr_space)
Definition bf16.h:197
+
bfloat_inplace_op_helper
#define bfloat_inplace_op_helper(__op__, __operator__, itype, addr_space)
Definition bf16.h:191
@@ -1142,6 +1145,31 @@ Variables

Function Documentation

+ +

◆ bfloat16_to_uint16()

+ +
+
+

Variables

+ + + + +
+ + + + + + + +
uint16_t bfloat16_to_uint16 (const bfloat16_t x)
+ 		+inline
+
+ +
+

◆ bfloat_bits_to_float()

@@ -10901,6 +10929,31 @@ Variables
+
+
+ +

◆ uint16_to_bfloat16()

+ +
+
+ + + + + +
+ + + + + + + +
bfloat16_t uint16_to_bfloat16 (const uint16_t x)
+ 		+inline
+
+

Variable Documentation

diff --git a/docs/build/html/backend_2metal_2kernels_2bf16_8h_source.html b/docs/build/html/backend_2metal_2kernels_2metal__3__0_2bf16_8h_source.html similarity index 60% rename from docs/build/html/backend_2metal_2kernels_2bf16_8h_source.html rename to docs/build/html/backend_2metal_2kernels_2metal__3__0_2bf16_8h_source.html index c29e3c42..9c4dd6bc 100644 --- a/docs/build/html/backend_2metal_2kernels_2bf16_8h_source.html +++ b/docs/build/html/backend_2metal_2kernels_2metal__3__0_2bf16_8h_source.html @@ -5,7 +5,7 @@ -MLX: mlx/backend/metal/kernels/bf16.h Source File +MLX: mlx/backend/metal/kernels/metal_3_0/bf16.h Source File @@ -83,7 +83,7 @@ $(function(){ initResizable(false); });
@@ -91,7 +91,7 @@ $(function(){ initResizable(false); });
bf16.h
-Go to the documentation of this file.
1// Copyright © 2023 Apple Inc.
+Go to the documentation of this file.
1// Copyright © 2023 Apple Inc.
2
3#pragma once
4
@@ -99,395 +99,394 @@ $(function(){ initResizable(false); });
6
7using namespace metal;
8
-
9#if (MLX_METAL_VERSION >= 310) || (__METAL_VERSION__ >= 310)
-
10
-
11typedef bfloat bfloat16_t;
+
10// Helpers
12
-
13#else
-
14
-
16// Helpers
-
18
-
-
19constexpr METAL_FUNC uint16_t float_to_bfloat_bits(float x) {
-
20 // Check for nan
-
21 if ((as_type<uint32_t>(x) & ~_fp_encoding_traits<float>::sign_mask) >
-
22 _fp_encoding_traits<float>::inf_mask) {
-
23 return uint16_t(as_type<uint32_t>(0x7FC0));
-
24 }
-
25 // Take bits
-
26 uint32_t float_bits = as_type<uint32_t>(x);
-
27
-
28 // Round to nearest even
-
29 float_bits += ((float_bits >> 16) & 1) + as_type<uint32_t>(0x7FFF);
-
30
-
31 // Take upper 16 bits
-
32 return float_bits >> 16;
-
33}
+
+
13constexpr METAL_FUNC uint16_t float_to_bfloat_bits(float x) {
+
14 // Check for nan
+
15 if ((as_type<uint32_t>(x) & ~_fp_encoding_traits<float>::sign_mask) >
+
16 _fp_encoding_traits<float>::inf_mask) {
+
17 return uint16_t(as_type<uint32_t>(0x7FC0));
+
18 }
+
19 // Take bits
+
20 uint32_t float_bits = as_type<uint32_t>(x);
+
21
+
22 // Round to nearest even
+
23 float_bits += ((float_bits >> 16) & 1) + as_type<uint32_t>(0x7FFF);
+
24
+
25 // Take upper 16 bits
+
26 return float_bits >> 16;
+
27}
-
34
-
-
35constexpr METAL_FUNC float bfloat_bits_to_float(uint16_t x) {
-
36 // Upper 16 bits are the data and lower 16 bits are 0s
-
37 return as_type<float>((uint32_t)x << 16);
-
38}
+
28
+
+
29constexpr METAL_FUNC float bfloat_bits_to_float(uint16_t x) {
+
30 // Upper 16 bits are the data and lower 16 bits are 0s
+
31 return as_type<float>((uint32_t)x << 16);
+
32}
+
33
+
34struct _MLX_BFloat16;
+
35
+
36template <typename T>
+
37static constexpr constant bool can_convert_to_bfloat =
+
38 !is_same_v<T, _MLX_BFloat16> && is_convertible_v<T, float>;
39
-
40struct _MLX_BFloat16;
-
41
-
42template <typename T>
-
43static constexpr constant bool can_convert_to_bfloat =
-
44 !is_same_v<T, _MLX_BFloat16> && is_convertible_v<T, float>;
-
45
-
46template <typename T>
-
47static constexpr constant bool can_convert_from_bfloat =
-
48 !is_same_v<T, _MLX_BFloat16> && is_convertible_v<float, T>;
-
49
-
51// Bfloat struct
-
53
-
-
54struct _MLX_BFloat16 {
-
56 // Constructors
-
57 uint16_t bits_;
-
58 _MLX_BFloat16() thread = default;
-
59 _MLX_BFloat16() threadgroup = default;
-
60 _MLX_BFloat16() device = default;
-
61 _MLX_BFloat16() constant = default;
-
62
-
63 struct bits_to_bfloat_struct {};
-
-
64 static constexpr METAL_FUNC bits_to_bfloat_struct bits_to_bfloat() {
-
65 return bits_to_bfloat_struct();
-
66 }
+
40template <typename T>
+
41static constexpr constant bool can_convert_from_bfloat =
+
42 !is_same_v<T, _MLX_BFloat16> && is_convertible_v<float, T>;
+
43
+
45// Bfloat struct
+
47
+
+
48struct _MLX_BFloat16 {
+
50 // Constructors
+
51 uint16_t bits_;
+
52 _MLX_BFloat16() thread = default;
+
53 _MLX_BFloat16() threadgroup = default;
+
54 _MLX_BFloat16() device = default;
+
55 _MLX_BFloat16() constant = default;
+
56
+
57 struct bits_to_bfloat_struct {};
+
+
58 static constexpr METAL_FUNC bits_to_bfloat_struct bits_to_bfloat() {
+
59 return bits_to_bfloat_struct();
+
60 }
-
-
67 constexpr METAL_FUNC _MLX_BFloat16(uint16_t bits, bits_to_bfloat_struct)
-
68 : bits_(bits) {}
+
+
61 constexpr METAL_FUNC _MLX_BFloat16(uint16_t bits, bits_to_bfloat_struct)
+
62 : bits_(bits) {}
+
+
63
+
65 // Conversions to bfloat
+
66
+
67 template <
+
68 typename T,
+
69 typename = typename enable_if<can_convert_to_bfloat<T>>::type>
+
+
70 constexpr METAL_FUNC _MLX_BFloat16(T x) thread
+
71 : bits_(float_to_bfloat_bits(static_cast<float>(x))) {}
-
69
-
71 // Conversions to bfloat
72
73 template <
74 typename T,
75 typename = typename enable_if<can_convert_to_bfloat<T>>::type>
-
76 constexpr METAL_FUNC _MLX_BFloat16(T x) thread
-
77 : bits_(float_to_bfloat_bits(static_cast<float>(x))) {}
+
76 constexpr METAL_FUNC _MLX_BFloat16(T x) threadgroup
+
77 : bits_(float_to_bfloat_bits(static_cast<float>(x))) {}
78
79 template <
80 typename T,
81 typename = typename enable_if<can_convert_to_bfloat<T>>::type>
-
82 constexpr METAL_FUNC _MLX_BFloat16(T x) threadgroup
-
83 : bits_(float_to_bfloat_bits(static_cast<float>(x))) {}
+
82 constexpr METAL_FUNC _MLX_BFloat16(T x) device
+
83 : bits_(float_to_bfloat_bits(static_cast<float>(x))) {}
84
85 template <
86 typename T,
87 typename = typename enable_if<can_convert_to_bfloat<T>>::type>
-
88 constexpr METAL_FUNC _MLX_BFloat16(T x) device
-
89 : bits_(float_to_bfloat_bits(static_cast<float>(x))) {}
+
88 constexpr METAL_FUNC _MLX_BFloat16(T x) constant
+
89 : bits_(float_to_bfloat_bits(static_cast<float>(x))) {}
90
-
91 template <
-
92 typename T,
-
93 typename = typename enable_if<can_convert_to_bfloat<T>>::type>
-
-
94 constexpr METAL_FUNC _MLX_BFloat16(T x) constant
-
95 : bits_(float_to_bfloat_bits(static_cast<float>(x))) {}
+
92 // Conversions from bfloat
+
93
+
94 template <
+
95 typename T,
+
96 typename = typename enable_if<can_convert_from_bfloat<T>>::type>
+
+
97 constexpr METAL_FUNC operator T() const thread {
+
98 return static_cast<T>(bfloat_bits_to_float(bits_));
+
99 }
-
96
-
98 // Conversions from bfloat
-
99
-
100 template <
-
101 typename T,
-
102 typename = typename enable_if<can_convert_from_bfloat<T>>::type>
-
-
103 constexpr METAL_FUNC operator T() const thread {
-
104 return static_cast<T>(bfloat_bits_to_float(bits_));
-
105 }
+
100
+
101 template <
+
102 typename T,
+
103 typename = typename enable_if<can_convert_from_bfloat<T>>::type>
+
+
104 constexpr METAL_FUNC operator T() const threadgroup {
+
105 return static_cast<T>(bfloat_bits_to_float(bits_));
+
106 }
-
106
-
107 template <
-
108 typename T,
-
109 typename = typename enable_if<can_convert_from_bfloat<T>>::type>
-
-
110 constexpr METAL_FUNC operator T() const threadgroup {
-
111 return static_cast<T>(bfloat_bits_to_float(bits_));
-
112 }
+
107
+
108 template <
+
109 typename T,
+
110 typename = typename enable_if<can_convert_from_bfloat<T>>::type>
+
+
111 constexpr METAL_FUNC operator T() const device {
+
112 return static_cast<T>(bfloat_bits_to_float(bits_));
+
113 }
-
113
-
114 template <
-
115 typename T,
-
116 typename = typename enable_if<can_convert_from_bfloat<T>>::type>
-
-
117 constexpr METAL_FUNC operator T() const device {
-
118 return static_cast<T>(bfloat_bits_to_float(bits_));
-
119 }
+
114
+
115 template <
+
116 typename T,
+
117 typename = typename enable_if<can_convert_from_bfloat<T>>::type>
+
+
118 constexpr METAL_FUNC operator T() const constant {
+
119 return static_cast<T>(bfloat_bits_to_float(bits_));
+
120 }
-
120
-
121 template <
-
122 typename T,
-
123 typename = typename enable_if<can_convert_from_bfloat<T>>::type>
-
-
124 constexpr METAL_FUNC operator T() const constant {
-
125 return static_cast<T>(bfloat_bits_to_float(bits_));
-
126 }
+
121};
-
127};
+
122
+
124// Bfloat operators
+
126
+
128// Unary ops
+
+
129constexpr METAL_FUNC _MLX_BFloat16 operator-(_MLX_BFloat16 x) {
+
130 return -static_cast<float>(x);
+
131}
-
128
-
130// Bfloat operators
132
-
134// Unary ops
-
-
135constexpr METAL_FUNC _MLX_BFloat16 operator-(_MLX_BFloat16 x) {
-
136 return -static_cast<float>(x);
-
137}
+
134// Binary operators
+
+
135#define bfloat_binop_base(__op__, __operator__, otype, atype, btype, ctype) \
+
136 constexpr METAL_FUNC otype __operator__(atype lhs, btype rhs) { \
+
137 return static_cast<ctype>(lhs) __op__ static_cast<ctype>(rhs); \
+
138 }
-
138
-
140// Binary operators
-
-
141#define bfloat_binop_base(__op__, __operator__, otype, atype, btype, ctype) \
-
142 constexpr METAL_FUNC otype __operator__(atype lhs, btype rhs) { \
-
143 return static_cast<ctype>(lhs) __op__ static_cast<ctype>(rhs); \
-
144 }
+
139
+
+
140#define bfloat_binop_helper(__op__, __operator__, otype, itype, ctype) \
+
141 constexpr METAL_FUNC otype __operator__(_MLX_BFloat16 lhs, itype rhs) { \
+
142 return static_cast<ctype>(lhs) __op__ static_cast<ctype>(rhs); \
+
143 } \
+
144 constexpr METAL_FUNC otype __operator__(itype lhs, _MLX_BFloat16 rhs) { \
+
145 return static_cast<ctype>(lhs) __op__ static_cast<ctype>(rhs); \
+
146 }
-
145
-
-
146#define bfloat_binop_helper(__op__, __operator__, otype, itype, ctype) \
-
147 constexpr METAL_FUNC otype __operator__(_MLX_BFloat16 lhs, itype rhs) { \
-
148 return static_cast<ctype>(lhs) __op__ static_cast<ctype>(rhs); \
-
149 } \
-
150 constexpr METAL_FUNC otype __operator__(itype lhs, _MLX_BFloat16 rhs) { \
-
151 return static_cast<ctype>(lhs) __op__ static_cast<ctype>(rhs); \
-
152 }
+
147
+
149// Arithmetic Operators
+
+
150#define bfloat_binop(_op_, _operator_) \
+
151 bfloat_binop_base( \
+
152 _op_, _operator_, _MLX_BFloat16, _MLX_BFloat16, _MLX_BFloat16, float); \
+
153 bfloat_binop_helper(_op_, _operator_, float, float, float); \
+
154 bfloat_binop_helper(_op_, _operator_, float, half, float); \
+
155 bfloat_binop_helper(_op_, _operator_, _MLX_BFloat16, int32_t, float); \
+
156 bfloat_binop_helper(_op_, _operator_, _MLX_BFloat16, uint32_t, float); \
+
157 bfloat_binop_helper(_op_, _operator_, _MLX_BFloat16, int64_t, float); \
+
158 bfloat_binop_helper(_op_, _operator_, _MLX_BFloat16, uint64_t, float);
-
153
-
155// Arithmetic Operators
-
-
156#define bfloat_binop(_op_, _operator_) \
-
157 bfloat_binop_base( \
-
158 _op_, _operator_, _MLX_BFloat16, _MLX_BFloat16, _MLX_BFloat16, float); \
-
159 bfloat_binop_helper(_op_, _operator_, float, float, float); \
-
160 bfloat_binop_helper(_op_, _operator_, float, half, float); \
-
161 bfloat_binop_helper(_op_, _operator_, _MLX_BFloat16, int32_t, float); \
-
162 bfloat_binop_helper(_op_, _operator_, _MLX_BFloat16, uint32_t, float); \
-
163 bfloat_binop_helper(_op_, _operator_, _MLX_BFloat16, int64_t, float); \
-
164 bfloat_binop_helper(_op_, _operator_, _MLX_BFloat16, uint64_t, float);
+
159
+
160bfloat_binop(+, operator+);
+
161bfloat_binop(-, operator-);
+
162bfloat_binop(*, operator*);
+
163bfloat_binop(/, operator/);
+
164
+
166// Comparison ops
+
+
167#define bfloat_compop(__op__, __operator__) \
+
168 bfloat_binop_base( \
+
169 __op__, __operator__, bool, _MLX_BFloat16, _MLX_BFloat16, float); \
+
170 bfloat_binop_helper(__op__, __operator__, bool, float, float); \
+
171 bfloat_binop_helper(__op__, __operator__, bool, half, float); \
+
172 bfloat_binop_helper(__op__, __operator__, bool, int32_t, float); \
+
173 bfloat_binop_helper(__op__, __operator__, bool, uint32_t, float); \
+
174 bfloat_binop_helper(__op__, __operator__, bool, int64_t, float); \
+
175 bfloat_binop_helper(__op__, __operator__, bool, uint64_t, float);
-
165
-
166bfloat_binop(+, operator+);
-
167bfloat_binop(-, operator-);
-
168bfloat_binop(*, operator*);
-
169bfloat_binop(/, operator/);
-
170
-
172// Comparison ops
-
-
173#define bfloat_compop(__op__, __operator__) \
-
174 bfloat_binop_base( \
-
175 __op__, __operator__, bool, _MLX_BFloat16, _MLX_BFloat16, float); \
-
176 bfloat_binop_helper(__op__, __operator__, bool, float, float); \
-
177 bfloat_binop_helper(__op__, __operator__, bool, half, float); \
-
178 bfloat_binop_helper(__op__, __operator__, bool, int32_t, float); \
-
179 bfloat_binop_helper(__op__, __operator__, bool, uint32_t, float); \
-
180 bfloat_binop_helper(__op__, __operator__, bool, int64_t, float); \
-
181 bfloat_binop_helper(__op__, __operator__, bool, uint64_t, float);
+
176
+
177bfloat_compop(>, operator>);
+
178bfloat_compop(<, operator<);
+
179bfloat_compop(>=, operator>=);
+
180bfloat_compop(<=, operator<=);
+
181bfloat_compop(==, operator==);
+
182bfloat_compop(!=, operator!=);
+
183
+
184#undef bfloat_compop
+
185#undef bfloat_binop_base
+
186#undef bfloat_binop_helper
+
187#undef bfloat_binop
+
188
+
190// Inplace Operators
+
+
191#define bfloat_inplace_op_helper(__op__, __operator__, itype, addr_space) \
+
192 constexpr METAL_FUNC addr_space _MLX_BFloat16& __operator__( \
+
193 addr_space _MLX_BFloat16& lhs, itype rhs) { \
+
194 lhs = static_cast<float>(lhs) __op__ static_cast<float>(rhs); \
+
195 return lhs; \
+
196 } \
+
197 constexpr METAL_FUNC addr_space itype& __operator__( \
+
198 addr_space itype& lhs, _MLX_BFloat16 rhs) { \
+
199 lhs = static_cast<float>(lhs) __op__ static_cast<float>(rhs); \
+
200 return lhs; \
+
201 }
-
182
-
183bfloat_compop(>, operator>);
-
184bfloat_compop(<, operator<);
-
185bfloat_compop(>=, operator>=);
-
186bfloat_compop(<=, operator<=);
-
187bfloat_compop(==, operator==);
-
188bfloat_compop(!=, operator!=);
-
189
-
190#undef bfloat_compop
-
191#undef bfloat_binop_base
-
192#undef bfloat_binop_helper
-
193#undef bfloat_binop
-
194
-
196// Inplace Operators
-
-
197#define bfloat_inplace_op_helper(__op__, __operator__, itype, addr_space) \
-
198 constexpr METAL_FUNC addr_space _MLX_BFloat16& __operator__( \
-
199 addr_space _MLX_BFloat16& lhs, itype rhs) { \
-
200 lhs = static_cast<float>(lhs) __op__ static_cast<float>(rhs); \
-
201 return lhs; \
-
202 } \
-
203 constexpr METAL_FUNC addr_space itype& __operator__( \
-
204 addr_space itype& lhs, _MLX_BFloat16 rhs) { \
-
205 lhs = static_cast<float>(lhs) __op__ static_cast<float>(rhs); \
-
206 return lhs; \
-
207 }
+
202
+
+
203#define bfloat_inplace_op_addr_space_helper(__op__, __operator__, itype) \
+
204 bfloat_inplace_op_helper(__op__, __operator__, itype, device); \
+
205 bfloat_inplace_op_helper(__op__, __operator__, itype, thread); \
+
206 bfloat_inplace_op_helper(__op__, __operator__, itype, threadgroup);
-
208
-
-
209#define bfloat_inplace_op_addr_space_helper(__op__, __operator__, itype) \
-
210 bfloat_inplace_op_helper(__op__, __operator__, itype, device); \
-
211 bfloat_inplace_op_helper(__op__, __operator__, itype, thread); \
-
212 bfloat_inplace_op_helper(__op__, __operator__, itype, threadgroup);
+
207
+
+
208#define bfloat_inplace_op(itype) \
+
209 bfloat_inplace_op_addr_space_helper(+, operator+=, itype); \
+
210 bfloat_inplace_op_addr_space_helper(-, operator-=, itype); \
+
211 bfloat_inplace_op_addr_space_helper(*, operator*=, itype); \
+
212 bfloat_inplace_op_addr_space_helper(/, operator/=, itype);
213
-
-
214#define bfloat_inplace_op(itype) \
-
215 bfloat_inplace_op_addr_space_helper(+, operator+=, itype); \
-
216 bfloat_inplace_op_addr_space_helper(-, operator-=, itype); \
-
217 bfloat_inplace_op_addr_space_helper(*, operator*=, itype); \
-
218 bfloat_inplace_op_addr_space_helper(/, operator/=, itype);
-
-
219
-
220bfloat_inplace_op(float);
-
221bfloat_inplace_op(half);
-
222bfloat_inplace_op(int16_t);
-
223bfloat_inplace_op(int32_t);
-
224bfloat_inplace_op(int64_t);
-
225bfloat_inplace_op(uint16_t);
-
226bfloat_inplace_op(uint32_t);
-
227bfloat_inplace_op(uint64_t);
-
228
-
229#undef bfloat_inplace_op_helper
-
230#undef bfloat_inplace_op_addr_space_helper
-
231#undef bfloat_inplace_op
-
232
-
233#define bfloat_inplace_op_helper(__op__, __operator__, addr_space) \
-
234 constexpr METAL_FUNC addr_space _MLX_BFloat16& __operator__( \
-
235 addr_space _MLX_BFloat16& lhs, _MLX_BFloat16 rhs) { \
-
236 lhs = static_cast<float>(lhs) __op__ static_cast<float>(rhs); \
-
237 return lhs; \
-
238 }
-
239
-
240#define bfloat_inplace_op_addr_space_helper(__op__, __operator__) \
-
241 bfloat_inplace_op_helper(__op__, __operator__, device); \
-
242 bfloat_inplace_op_helper(__op__, __operator__, thread); \
-
243 bfloat_inplace_op_helper(__op__, __operator__, threadgroup);
-
244
-
245bfloat_inplace_op_addr_space_helper(+, operator+=);
-
246bfloat_inplace_op_addr_space_helper(-, operator-=);
-
247bfloat_inplace_op_addr_space_helper(*, operator*=);
-
248bfloat_inplace_op_addr_space_helper(/, operator/=);
-
249
-
250#undef bfloat_inplace_op_helper
-
251#undef bfloat_inplace_op_addr_space_helper
+
214bfloat_inplace_op(float);
+
215bfloat_inplace_op(half);
+
216bfloat_inplace_op(int16_t);
+
217bfloat_inplace_op(int32_t);
+
218bfloat_inplace_op(int64_t);
+
219bfloat_inplace_op(uint16_t);
+
220bfloat_inplace_op(uint32_t);
+
221bfloat_inplace_op(uint64_t);
+
222
+
223#undef bfloat_inplace_op_helper
+
224#undef bfloat_inplace_op_addr_space_helper
+
225#undef bfloat_inplace_op
+
226
+
227#define bfloat_inplace_op_helper(__op__, __operator__, addr_space) \
+
228 constexpr METAL_FUNC addr_space _MLX_BFloat16& __operator__( \
+
229 addr_space _MLX_BFloat16& lhs, _MLX_BFloat16 rhs) { \
+
230 lhs = static_cast<float>(lhs) __op__ static_cast<float>(rhs); \
+
231 return lhs; \
+
232 }
+
233
+
234#define bfloat_inplace_op_addr_space_helper(__op__, __operator__) \
+
235 bfloat_inplace_op_helper(__op__, __operator__, device); \
+
236 bfloat_inplace_op_helper(__op__, __operator__, thread); \
+
237 bfloat_inplace_op_helper(__op__, __operator__, threadgroup);
+
238
+
239bfloat_inplace_op_addr_space_helper(+, operator+=);
+
240bfloat_inplace_op_addr_space_helper(-, operator-=);
+
241bfloat_inplace_op_addr_space_helper(*, operator*=);
+
242bfloat_inplace_op_addr_space_helper(/, operator/=);
+
243
+
244#undef bfloat_inplace_op_helper
+
245#undef bfloat_inplace_op_addr_space_helper
+
246
+
248// Bfloat typedef
+
250
+
251typedef struct _MLX_BFloat16 bfloat16_t;
252
-
254// Bfloat typedef
+
254// Bfloat numeric limits
256
-
257typedef struct _MLX_BFloat16 bfloat16_t;
+
257#pragma METAL internals : enable
258
-
260// Bfloat numeric limits
-
262
-
263#pragma METAL internals : enable
-
264
-
-
265namespace metal {
-
266
-
267template <>
-
-
268struct _numeric_limits_impl<bfloat16_t> : _fp_numeric_limits_impl_base {
-
269 static constexpr constant int digits = 8;
-
270 static constexpr constant int digits10 = 2;
-
271 static constexpr constant int max_digits10 = 4;
-
272 static constexpr constant int radix = 2;
-
273 static constexpr constant int min_exponent = -125;
-
274 static constexpr constant int min_exponent10 = -37;
-
275 static constexpr constant int max_exponent = 128;
-
276 static constexpr constant int max_exponent10 = 38;
-
277
+
259namespace metal {
+
260
+
261template <>
+
+
262struct _numeric_limits_impl<bfloat16_t> : _fp_numeric_limits_impl_base {
+
263 static constexpr constant int digits = 8;
+
264 static constexpr constant int digits10 = 2;
+
265 static constexpr constant int max_digits10 = 4;
+
266 static constexpr constant int radix = 2;
+
267 static constexpr constant int min_exponent = -125;
+
268 static constexpr constant int min_exponent10 = -37;
+
269 static constexpr constant int max_exponent = 128;
+
270 static constexpr constant int max_exponent10 = 38;
+
271
+
+
272 static constexpr bfloat16_t min() {
+
273 return _MLX_BFloat16(0x0080, _MLX_BFloat16::bits_to_bfloat());
+
274 }
+
+
+
275 static constexpr bfloat16_t lowest() {
+
276 return _MLX_BFloat16(0xFF7F, _MLX_BFloat16::bits_to_bfloat());
+
277 }
+
-
278 static constexpr bfloat16_t min() {
-
279 return _MLX_BFloat16(0x0080, _MLX_BFloat16::bits_to_bfloat());
+
278 static constexpr bfloat16_t max() {
+
279 return _MLX_BFloat16(0x7F7F, _MLX_BFloat16::bits_to_bfloat());
280 }
-
281 static constexpr bfloat16_t lowest() {
-
282 return _MLX_BFloat16(0xFF7F, _MLX_BFloat16::bits_to_bfloat());
+
281 static constexpr bfloat16_t epsilon() {
+
282 return _MLX_BFloat16(0x3C00, _MLX_BFloat16::bits_to_bfloat());
283 }
-
284 static constexpr bfloat16_t max() {
-
285 return _MLX_BFloat16(0x7F7F, _MLX_BFloat16::bits_to_bfloat());
+
284 static constexpr bfloat16_t round_error() {
+
285 return _MLX_BFloat16(0x3F00, _MLX_BFloat16::bits_to_bfloat());
286 }
-
287 static constexpr bfloat16_t epsilon() {
-
288 return _MLX_BFloat16(0x3C00, _MLX_BFloat16::bits_to_bfloat());
+
287 static constexpr bfloat16_t infinity() {
+
288 return _MLX_BFloat16(0x7F80, _MLX_BFloat16::bits_to_bfloat());
289 }
-
290 static constexpr bfloat16_t round_error() {
-
291 return _MLX_BFloat16(0x3F00, _MLX_BFloat16::bits_to_bfloat());
+
290 static constexpr bfloat16_t quiet_NaN() {
+
291 return _MLX_BFloat16(0x7FC0, _MLX_BFloat16::bits_to_bfloat());
292 }
-
293 static constexpr bfloat16_t infinity() {
+
293 static constexpr bfloat16_t signaling_NaN() {
294 return _MLX_BFloat16(0x7F80, _MLX_BFloat16::bits_to_bfloat());
295 }
-
296 static constexpr bfloat16_t quiet_NaN() {
-
297 return _MLX_BFloat16(0x7FC0, _MLX_BFloat16::bits_to_bfloat());
+
296 static constexpr bfloat16_t denorm_min() {
+
297 return _MLX_BFloat16(0x0001, _MLX_BFloat16::bits_to_bfloat());
298 }
-
-
299 static constexpr bfloat16_t signaling_NaN() {
-
300 return _MLX_BFloat16(0x7F80, _MLX_BFloat16::bits_to_bfloat());
-
301 }
+
299};
-
-
302 static constexpr bfloat16_t denorm_min() {
-
303 return _MLX_BFloat16(0x0001, _MLX_BFloat16::bits_to_bfloat());
-
304 }
-
-
305};
+
300
+
+
301METAL_FUNC bool isnan(_MLX_BFloat16 x) {
+
302 return x != x;
+
303}
+
304
+
305} // namespace metal
306
-
-
307METAL_FUNC bool isnan(_MLX_BFloat16 x) {
-
308 return x != x;
-
309}
+
307#pragma METAL internals : disable
+
+
308inline uint16_t bfloat16_to_uint16(const bfloat16_t x) {
+
309 return x.bits_;
+
310}
-
310
-
311} // namespace metal
+
311
+
+
312inline bfloat16_t uint16_to_bfloat16(const uint16_t x) {
+
313 return _MLX_BFloat16(x, _MLX_BFloat16::bits_to_bfloat());
+
314}
-
312
-
313#pragma METAL internals : disable
-
314
-
315#endif
-
316
-
317#include "mlx/backend/metal/kernels/bf16_math.h"
-
float_to_bfloat_bits
constexpr METAL_FUNC uint16_t float_to_bfloat_bits(float x)
Definition bf16.h:19
-
bfloat_compop
#define bfloat_compop(__op__, __operator__)
Definition bf16.h:173
-
bfloat_bits_to_float
constexpr METAL_FUNC float bfloat_bits_to_float(uint16_t x)
Definition bf16.h:35
-
bfloat_inplace_op
#define bfloat_inplace_op(itype)
Definition bf16.h:214
-
operator-
constexpr METAL_FUNC _MLX_BFloat16 operator-(_MLX_BFloat16 x)
Definition bf16.h:135
-
bfloat_binop
#define bfloat_binop(_op_, _operator_)
Definition bf16.h:156
-
bfloat16_t
struct _MLX_BFloat16 bfloat16_t
Definition bf16.h:257
-
can_convert_from_bfloat
static constexpr constant bool can_convert_from_bfloat
Definition bf16.h:47
-
can_convert_to_bfloat
static constexpr constant bool can_convert_to_bfloat
Definition bf16.h:43
-
bfloat_inplace_op_addr_space_helper
#define bfloat_inplace_op_addr_space_helper(__op__, __operator__, itype)
Definition bf16.h:209
-
bf16_math.h
-
metal
Definition bf16.h:265
-
metal::isnan
METAL_FUNC bool isnan(_MLX_BFloat16 x)
Definition bf16.h:307
-
_MLX_BFloat16::bits_to_bfloat_struct
Definition bf16.h:63
-
_MLX_BFloat16
Definition bf16.h:54
-
_MLX_BFloat16::_MLX_BFloat16
constexpr METAL_FUNC _MLX_BFloat16(T x) thread
Definition bf16.h:76
-
_MLX_BFloat16::bits_
uint16_t bits_
Definition bf16.h:57
-
_MLX_BFloat16::_MLX_BFloat16
constexpr METAL_FUNC _MLX_BFloat16(uint16_t bits, bits_to_bfloat_struct)
Definition bf16.h:67
-
_MLX_BFloat16::bits_to_bfloat
static constexpr METAL_FUNC bits_to_bfloat_struct bits_to_bfloat()
Definition bf16.h:64
+
bfloat16_to_uint16
uint16_t bfloat16_to_uint16(const bfloat16_t x)
Definition bf16.h:308
+
float_to_bfloat_bits
constexpr METAL_FUNC uint16_t float_to_bfloat_bits(float x)
Definition bf16.h:13
+
bfloat_compop
#define bfloat_compop(__op__, __operator__)
Definition bf16.h:167
+
bfloat_bits_to_float
constexpr METAL_FUNC float bfloat_bits_to_float(uint16_t x)
Definition bf16.h:29
+
bfloat_inplace_op
#define bfloat_inplace_op(itype)
Definition bf16.h:208
+
operator-
constexpr METAL_FUNC _MLX_BFloat16 operator-(_MLX_BFloat16 x)
Definition bf16.h:129
+
bfloat_binop
#define bfloat_binop(_op_, _operator_)
Definition bf16.h:150
+
can_convert_from_bfloat
static constexpr constant bool can_convert_from_bfloat
Definition bf16.h:41
+
uint16_to_bfloat16
bfloat16_t uint16_to_bfloat16(const uint16_t x)
Definition bf16.h:312
+
can_convert_to_bfloat
static constexpr constant bool can_convert_to_bfloat
Definition bf16.h:37
+
bfloat_inplace_op_addr_space_helper
#define bfloat_inplace_op_addr_space_helper(__op__, __operator__, itype)
Definition bf16.h:203
+
metal
Definition bf16_math.h:226
+
metal::isnan
METAL_FUNC bool isnan(_MLX_BFloat16 x)
Definition bf16.h:301
+
_MLX_BFloat16::bits_to_bfloat_struct
Definition bf16.h:57
+
_MLX_BFloat16
Definition bf16.h:48
+
_MLX_BFloat16::_MLX_BFloat16
constexpr METAL_FUNC _MLX_BFloat16(T x) thread
Definition bf16.h:70
+
_MLX_BFloat16::bits_
uint16_t bits_
Definition bf16.h:51
+
_MLX_BFloat16::_MLX_BFloat16
constexpr METAL_FUNC _MLX_BFloat16(uint16_t bits, bits_to_bfloat_struct)
Definition bf16.h:61
+
_MLX_BFloat16::bits_to_bfloat
static constexpr METAL_FUNC bits_to_bfloat_struct bits_to_bfloat()
Definition bf16.h:58
_MLX_BFloat16::_MLX_BFloat16
_MLX_BFloat16() thread=default
-
_MLX_BFloat16::_MLX_BFloat16
constexpr METAL_FUNC _MLX_BFloat16(T x) device
Definition bf16.h:88
-
_MLX_BFloat16::_MLX_BFloat16
constexpr METAL_FUNC _MLX_BFloat16(T x) threadgroup
Definition bf16.h:82
-
_MLX_BFloat16::_MLX_BFloat16
constexpr METAL_FUNC _MLX_BFloat16(T x) const ant
Definition bf16.h:94
-
metal::_numeric_limits_impl< bfloat16_t >::infinity
static constexpr bfloat16_t infinity()
Definition bf16.h:293
-
metal::_numeric_limits_impl< bfloat16_t >::denorm_min
static constexpr bfloat16_t denorm_min()
Definition bf16.h:302
-
metal::_numeric_limits_impl< bfloat16_t >::max
static constexpr bfloat16_t max()
Definition bf16.h:284
-
metal::_numeric_limits_impl< bfloat16_t >::epsilon
static constexpr bfloat16_t epsilon()
Definition bf16.h:287
-
metal::_numeric_limits_impl< bfloat16_t >::signaling_NaN
static constexpr bfloat16_t signaling_NaN()
Definition bf16.h:299
-
metal::_numeric_limits_impl< bfloat16_t >::min
static constexpr bfloat16_t min()
Definition bf16.h:278
-
metal::_numeric_limits_impl< bfloat16_t >::lowest
static constexpr bfloat16_t lowest()
Definition bf16.h:281
-
metal::_numeric_limits_impl< bfloat16_t >::quiet_NaN
static constexpr bfloat16_t quiet_NaN()
Definition bf16.h:296
-
metal::_numeric_limits_impl< bfloat16_t >::round_error
static constexpr bfloat16_t round_error()
Definition bf16.h:290
+
_MLX_BFloat16::_MLX_BFloat16
constexpr METAL_FUNC _MLX_BFloat16(T x) device
Definition bf16.h:82
+
_MLX_BFloat16::_MLX_BFloat16
constexpr METAL_FUNC _MLX_BFloat16(T x) threadgroup
Definition bf16.h:76
+
_MLX_BFloat16::_MLX_BFloat16
constexpr METAL_FUNC _MLX_BFloat16(T x) const ant
Definition bf16.h:88
+
metal::_numeric_limits_impl< bfloat16_t >::infinity
static constexpr bfloat16_t infinity()
Definition bf16.h:287
+
metal::_numeric_limits_impl< bfloat16_t >::denorm_min
static constexpr bfloat16_t denorm_min()
Definition bf16.h:296
+
metal::_numeric_limits_impl< bfloat16_t >::max
static constexpr bfloat16_t max()
Definition bf16.h:278
+
metal::_numeric_limits_impl< bfloat16_t >::epsilon
static constexpr bfloat16_t epsilon()
Definition bf16.h:281
+
metal::_numeric_limits_impl< bfloat16_t >::signaling_NaN
static constexpr bfloat16_t signaling_NaN()
Definition bf16.h:293
+
metal::_numeric_limits_impl< bfloat16_t >::min
static constexpr bfloat16_t min()
Definition bf16.h:272
+
metal::_numeric_limits_impl< bfloat16_t >::lowest
static constexpr bfloat16_t lowest()
Definition bf16.h:275
+
metal::_numeric_limits_impl< bfloat16_t >::quiet_NaN
static constexpr bfloat16_t quiet_NaN()
Definition bf16.h:290
+
metal::_numeric_limits_impl< bfloat16_t >::round_error
static constexpr bfloat16_t round_error()
Definition bf16.h:284
diff --git a/docs/build/html/backend_2metal_2kernels_2metal__3__1_2bf16_8h.html b/docs/build/html/backend_2metal_2kernels_2metal__3__1_2bf16_8h.html new file mode 100644 index 00000000..628c1311 --- /dev/null +++ b/docs/build/html/backend_2metal_2kernels_2metal__3__1_2bf16_8h.html @@ -0,0 +1,186 @@ + + + + + + + +MLX: mlx/backend/metal/kernels/metal_3_1/bf16.h File Reference + + + + + + + + + + + + + +
+
+ + + + + + +
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+
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+
+
+
#include <metal_stdlib>
+
+

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+ + + + +

+Typedefs

typedef bfloat bfloat16_t
 
+ + + + + +

+Functions

uint16_t bfloat16_to_uint16 (const bfloat16_t x)
 
bfloat16_t uint16_to_bfloat16 (const uint16_t x)
 
+

Typedef Documentation

+ +

◆ bfloat16_t

+ +
+
+ + + + +
typedef bfloat bfloat16_t
+
+ +
+
+

Function Documentation

+ +

◆ bfloat16_to_uint16()

+ +
+
+ + + + + +
+ + + + + + + +
uint16_t bfloat16_to_uint16 (const bfloat16_t x)
+ 		+inline
+
+ +
+
+ +

◆ uint16_to_bfloat16()

+ +
+
+ + + + + +
+ + + + + + + +
bfloat16_t uint16_to_bfloat16 (const uint16_t x)
+ 		+inline
+
+ +
+
+
+ +
+Generated by doxygen 1.12.0 +
+
+ + diff --git a/docs/build/html/backend_2metal_2kernels_2metal__3__1_2bf16_8h_source.html b/docs/build/html/backend_2metal_2kernels_2metal__3__1_2bf16_8h_source.html new file mode 100644 index 00000000..8f980990 --- /dev/null +++ b/docs/build/html/backend_2metal_2kernels_2metal__3__1_2bf16_8h_source.html @@ -0,0 +1,126 @@ + + + + + + + +MLX: mlx/backend/metal/kernels/metal_3_1/bf16.h Source File + + + + + + + + + + + + + +
+
+ + + + + + +
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bf16.h
+
+
+Go to the documentation of this file.
1// Copyright © 2023 Apple Inc.
+
2
+
3#pragma once
+
4
+
5#include <metal_stdlib>
+
6
+
7using namespace metal;
+
8
+
9typedef bfloat bfloat16_t;
+
+
10inline uint16_t bfloat16_to_uint16(const bfloat16_t x) {
+
11 return as_type<uint16_t>(x);
+
12}
+
+
13
+
+
14inline bfloat16_t uint16_to_bfloat16(const uint16_t x) {
+
15 return as_type<bfloat16_t>(x);
+
16}
+
+
bfloat16_to_uint16
uint16_t bfloat16_to_uint16(const bfloat16_t x)
Definition bf16.h:10
+
bfloat16_t
bfloat bfloat16_t
Definition bf16.h:9
+
uint16_to_bfloat16
bfloat16_t uint16_to_bfloat16(const uint16_t x)
Definition bf16.h:14
+
metal
Definition bf16_math.h:226
+
_MLX_BFloat16
Definition bf16.h:48
+
+ +
+Generated by doxygen 1.12.0 +
+
+ + diff --git a/docs/build/html/backend_2metal_2kernels_2reduction_2ops_8h.html b/docs/build/html/backend_2metal_2kernels_2reduction_2ops_8h.html index e22c9695..7feb2846 100644 --- a/docs/build/html/backend_2metal_2kernels_2reduction_2ops_8h.html +++ b/docs/build/html/backend_2metal_2kernels_2reduction_2ops_8h.html @@ -157,7 +157,7 @@ Variables
return val; \
}
simd_size
static constant constexpr const uint8_t simd_size
Definition ops.h:22
-
simd_shuffle_down
uint64_t simd_shuffle_down(uint64_t data, uint16_t delta)
Definition utils.h:305
+
simd_shuffle_down
uint64_t simd_shuffle_down(uint64_t data, uint16_t delta)
Definition utils.h:346
diff --git a/docs/build/html/backend_2metal_2kernels_2reduction_2ops_8h_source.html b/docs/build/html/backend_2metal_2kernels_2reduction_2ops_8h_source.html index 3bd0aa0c..35b73c8b 100644 --- a/docs/build/html/backend_2metal_2kernels_2reduction_2ops_8h_source.html +++ b/docs/build/html/backend_2metal_2kernels_2reduction_2ops_8h_source.html @@ -325,7 +325,7 @@ $(function(){ initResizable(false); });
simd_size
static constant constexpr const uint8_t simd_size
Definition ops.h:22
DEFINE_SIMD_REDUCE
#define DEFINE_SIMD_REDUCE()
Definition ops.h:8
And
Definition ops.h:37
-
Limits
Definition utils.h:17
+
Limits
Definition utils.h:23
Max
Definition ops.h:185
Max::a
b a
Definition ops.h:202
Min
Definition ops.h:163
diff --git a/docs/build/html/backend_2metal_2kernels_2steel_2attn_2transforms_8h.html b/docs/build/html/backend_2metal_2kernels_2steel_2attn_2transforms_8h.html new file mode 100644 index 00000000..9e8b79eb --- /dev/null +++ b/docs/build/html/backend_2metal_2kernels_2steel_2attn_2transforms_8h.html @@ -0,0 +1,128 @@ + + + + + + + +MLX: mlx/backend/metal/kernels/steel/attn/transforms.h File Reference + + + + + + + + + + + + + +
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#include "mlx/backend/metal/kernels/steel/utils.h"
+
+

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+ + + + + + + + + + + + +

+Classes

struct  mlx::steel::TransformNone< OutT, InT >
 
struct  mlx::steel::TransformAdd< OutT, InT >
 
struct  mlx::steel::TransformAxpby< OutT, InT >
 
struct  mlx::steel::AccumHelper< T >
 
struct  mlx::steel::BlockSwizzle
 
+ + + + + +

+Namespaces

namespace  mlx
 
namespace  mlx::steel
 
+
+ +
+Generated by doxygen 1.12.0 +
+
+ + diff --git a/docs/build/html/backend_2metal_2kernels_2steel_2attn_2transforms_8h_source.html b/docs/build/html/backend_2metal_2kernels_2steel_2attn_2transforms_8h_source.html new file mode 100644 index 00000000..38d8120a --- /dev/null +++ b/docs/build/html/backend_2metal_2kernels_2steel_2attn_2transforms_8h_source.html @@ -0,0 +1,201 @@ + + + + + + + +MLX: mlx/backend/metal/kernels/steel/attn/transforms.h Source File + + + + + + + + + + + + + +
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1// Copyright © 2024 Apple Inc.
+
2
+
3#pragma once
+
4
+
5#include "mlx/backend/metal/kernels/steel/utils.h"
+
6
+
8// Transforms and Epilogues
+
10
+
11namespace mlx {
+
12namespace steel {
+
13
+
14template <typename OutT, typename InT>
+
15struct TransformNone {
+
+
16 static METAL_FUNC OutT apply(InT x) {
+
17 return static_cast<OutT>(x);
+
18 }
+
+
19
+
+
20 static METAL_FUNC OutT apply(InT x, OutT) {
+
21 return static_cast<OutT>(x);
+
22 }
+
+
23};
+
24
+
25template <typename OutT, typename InT>
+
26struct TransformAdd {
+
27 TransformAdd(const float, const float) {}
+
28
+
+
29 static METAL_FUNC OutT apply(InT x) {
+
30 return static_cast<OutT>(x);
+
31 }
+
+
32
+
+
33 static METAL_FUNC OutT apply(InT x, OutT c) {
+
34 return static_cast<OutT>(x) + c;
+
35 }
+
+
36};
+
37
+
38template <typename OutT, typename InT>
+
39struct TransformAxpby {
+
40 const float alpha;
+
41 const float beta;
+
42
+
+
43 TransformAxpby(const float alpha_, const float beta_)
+
44 : alpha(alpha_), beta(beta_) {}
+
+
45
+
+
46 static METAL_FUNC OutT apply(InT x) {
+
47 return static_cast<OutT>(x);
+
48 }
+
+
49
+
+
50 METAL_FUNC OutT apply(InT x, OutT c) const {
+
51 return static_cast<OutT>(x * alpha + (beta * c));
+
52 }
+
+
53};
+
54
+
55template <typename T>
+
56struct AccumHelper {
+
57 typedef float accum_type;
+
58};
+
59
+
60struct BlockSwizzle {
+
61 static METAL_FUNC int2
+
+
62 swizzle(uint3 tid [[threadgroup_position_in_grid]], const int swizzle_log) {
+
63 const int tid_x = (tid.x) >> swizzle_log;
+
64 const int tid_y =
+
65 ((tid.y) << swizzle_log) + ((tid.x) & ((1 << swizzle_log) - 1));
+
66 return int2(tid_x, tid_y);
+
67 }
+
+
68};
+
69
+
70} // namespace steel
+
71} // namespace mlx
+ +
mlx
Definition allocator.h:7
+
mlx::steel::AccumHelper::accum_type
float accum_type
Definition transforms.h:57
+
mlx::steel::BlockSwizzle
Definition transforms.h:60
+
mlx::steel::BlockSwizzle::swizzle
static METAL_FUNC int2 swizzle(uint3 tid, const int swizzle_log)
Definition transforms.h:62
+
mlx::steel::TransformAdd::apply
static METAL_FUNC OutT apply(InT x, OutT c)
Definition transforms.h:33
+
mlx::steel::TransformAdd::TransformAdd
TransformAdd(const float, const float)
Definition transforms.h:27
+
mlx::steel::TransformAdd::apply
static METAL_FUNC OutT apply(InT x)
Definition transforms.h:29
+
mlx::steel::TransformAxpby::apply
static METAL_FUNC OutT apply(InT x)
Definition transforms.h:46
+
mlx::steel::TransformAxpby::beta
const float beta
Definition transforms.h:41
+
mlx::steel::TransformAxpby::apply
METAL_FUNC OutT apply(InT x, OutT c) const
Definition transforms.h:50
+
mlx::steel::TransformAxpby::alpha
const float alpha
Definition transforms.h:40
+
mlx::steel::TransformAxpby::TransformAxpby
TransformAxpby(const float alpha_, const float beta_)
Definition transforms.h:43
+
mlx::steel::TransformNone::apply
static METAL_FUNC OutT apply(InT x)
Definition transforms.h:16
+
mlx::steel::TransformNone::apply
static METAL_FUNC OutT apply(InT x, OutT)
Definition transforms.h:20
+
+ +
+Generated by doxygen 1.12.0 +
+
+ + diff --git a/docs/build/html/backend_2metal_2kernels_2steel_2gemm_2transforms_8h_source.html b/docs/build/html/backend_2metal_2kernels_2steel_2gemm_2transforms_8h_source.html index e5a0a3b9..c8295ad7 100644 --- a/docs/build/html/backend_2metal_2kernels_2steel_2gemm_2transforms_8h_source.html +++ b/docs/build/html/backend_2metal_2kernels_2steel_2gemm_2transforms_8h_source.html @@ -141,8 +141,8 @@ $(function(){ initResizable(false); });
38template <typename OutT, typename InT>
39struct TransformAxpby {
-
40 const float alpha;
-
41 const float beta;
+
40 const float alpha;
+
41 const float beta;
42
43 TransformAxpby(const float alpha_, const float beta_)
@@ -166,7 +166,7 @@ $(function(){ initResizable(false); });
55template <typename T>
56struct AccumHelper {
-
57 typedef float accum_type;
+
57 typedef float accum_type;
58};
59
@@ -189,7 +189,7 @@ $(function(){ initResizable(false); });
mlx
Definition allocator.h:7
mlx::steel::AccumHelper
Definition transforms.h:56
-
mlx::steel::AccumHelper::accum_type
float accum_type
Definition transforms.h:57
+
mlx::steel::AccumHelper::accum_type
float accum_type
Definition transforms.h:57
mlx::steel::BlockSwizzle
Definition transforms.h:60
mlx::steel::BlockSwizzle::swizzle
static METAL_FUNC int2 swizzle(uint3 tid, const int swizzle_log)
Definition transforms.h:62
mlx::steel::TransformAdd
Definition transforms.h:26
diff --git a/docs/build/html/backend_2metal_2kernels_2utils_8h.html b/docs/build/html/backend_2metal_2kernels_2utils_8h.html index 39067084..90774282 100644 --- a/docs/build/html/backend_2metal_2kernels_2utils_8h.html +++ b/docs/build/html/backend_2metal_2kernels_2utils_8h.html @@ -97,7 +97,8 @@ $(function(){ initResizable(false); });
#include <metal_math>
-#include "mlx/backend/metal/kernels/bf16.h"
+#include "bf16.h"
+#include "mlx/backend/metal/kernels/bf16_math.h"
#include "mlx/backend/metal/kernels/complex.h"
#include "mlx/backend/metal/kernels/defines.h"
@@ -133,11 +134,11 @@ Classes   struct  Limits< complex64_t >   -struct  looped_elem_to_loc< dim, offset_t > +struct  LoopedElemToLoc< DIM, OffsetT, General >   -struct  looped_elem_to_loc< 1, offset_t > +struct  LoopedElemToLoc< 1, OffsetT, true >   -struct  looped_elem_to_loc< 0, offset_t > +struct  LoopedElemToLoc< 1, OffsetT, false >  

@@ -156,36 +157,37 @@ Typedefs

- - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + - + @@ -244,11 +246,11 @@ Functions
static constexpr constant type finite_min = \
metal::numeric_limits<type>::min(); \
};
-
Limits
Definition utils.h:17
-
Limits::max
static const constant U max
Definition utils.h:18
-
Limits::finite_max
static const constant U finite_max
Definition utils.h:20
-
Limits::min
static const constant U min
Definition utils.h:19
-
Limits::finite_min
static const constant U finite_min
Definition utils.h:21
+
Limits
Definition utils.h:23
+
Limits::max
static const constant U max
Definition utils.h:24
+
Limits::finite_max
static const constant U finite_max
Definition utils.h:26
+
Limits::min
static const constant U min
Definition utils.h:25
+
Limits::finite_min
static const constant U finite_min
Definition utils.h:27
@@ -343,18 +345,18 @@ template<typename T , typename U > - -

◆ elem_to_loc() [1/3]

+ +

◆ elem_to_loc() [1/3]

-template<typename stride_t >
+template<typename StrideT , typename IdxT = StrideT>

Functions

template<typename stride_t >
METAL_FUNC stride_t elem_to_loc (uint elem, constant const int *shape, constant const stride_t *strides, int ndim)
 
template<typename stride_t >
METAL_FUNC stride_t elem_to_loc (stride_t elem, constant const int *shape, constant const stride_t *strides, int ndim)
 
template<typename stride_t >
METAL_FUNC stride_t elem_to_loc (uint3 elem, constant const int *shape, constant const stride_t *strides, int ndim)
 
template<typename stride_t >
METAL_FUNC stride_t elem_to_loc_1 (uint elem, constant const stride_t &stride)
 
template<typename stride_t >
METAL_FUNC stride_t elem_to_loc_2 (uint2 elem, constant const stride_t strides[2])
 
template<typename stride_t >
METAL_FUNC stride_t elem_to_loc_3 (uint3 elem, constant const stride_t strides[3])
 
template<typename stride_t >
METAL_FUNC ulong2 elem_to_loc_2_nd (uint3 elem, constant const int *shape, constant const stride_t *a_strides, constant const stride_t *b_strides, int ndim)
 
METAL_FUNC ulong3 elem_to_loc_3_nd (uint3 elem, constant const int *shape, constant const size_t *a_strides, constant const size_t *b_strides, constant const size_t *c_strides, int ndim)
 
template<typename StrideT , typename IdxT = StrideT>
METAL_FUNC IdxT elem_to_loc (uint elem, constant const int *shape, constant const StrideT *strides, int ndim)
 
template<typename StrideT , typename IdxT = StrideT>
METAL_FUNC IdxT elem_to_loc (StrideT elem, constant const int *shape, constant const StrideT *strides, int ndim)
 
template<typename StrideT , typename IdxT = StrideT>
METAL_FUNC IdxT elem_to_loc (uint3 elem, constant const int *shape, constant const StrideT *strides, int ndim)
 
template<typename StrideT , typename IdxT = StrideT>
METAL_FUNC IdxT elem_to_loc_1 (uint elem, constant const StrideT &stride)
 
template<typename StrideT , typename IdxT = StrideT>
METAL_FUNC IdxT elem_to_loc_2 (uint2 elem, constant const StrideT strides[2])
 
template<typename StrideT , typename IdxT = StrideT>
METAL_FUNC IdxT elem_to_loc_3 (uint3 elem, constant const StrideT strides[3])
 
template<typename StrideT , typename IdxT = StrideT>
METAL_FUNC vec< IdxT, 2 > elem_to_loc_2_nd (uint3 elem, constant const int *shape, constant const StrideT *a_strides, constant const StrideT *b_strides, int ndim)
 
template<typename IdxT = size_t>
METAL_FUNC vec< IdxT, 3 > elem_to_loc_3_nd (uint3 elem, constant const int *shape, constant const size_t *a_strides, constant const size_t *b_strides, constant const size_t *c_strides, int ndim)
 
template<typename T , typename U >
T ceildiv (T N, U M)
 Compute ceil((float)N/(float)M)
 
float log1p (float x)
 
bfloat16_t log1p (bfloat16_t x)
bfloat16_t log1p (bfloat16_t x)
 
uint64_t simd_shuffle_down (uint64_t data, uint16_t delta)
 
- + - + @@ -364,7 +366,7 @@ template<typename stride_t > - + @@ -376,16 +378,16 @@ template<typename stride_t > - -

◆ elem_to_loc() [2/3]

+ +

◆ elem_to_loc() [2/3]

-template<typename stride_t >
+template<typename StrideT , typename IdxT = StrideT>
METAL_FUNC stride_t elem_to_loc METAL_FUNC IdxT elem_to_loc (stride_t elem, StrideT elem,
constant const stride_t * strides, constant const StrideT * strides,
- + @@ -397,7 +399,7 @@ template<typename stride_t > - + @@ -409,16 +411,16 @@ template<typename stride_t > - -

◆ elem_to_loc() [3/3]

+ +

◆ elem_to_loc() [3/3]

-template<typename stride_t >
+template<typename StrideT , typename IdxT = StrideT>
METAL_FUNC stride_t elem_to_loc METAL_FUNC IdxT elem_to_loc ( uint elem,
constant const stride_t * strides, constant const StrideT * strides,
- + @@ -430,7 +432,7 @@ template<typename stride_t > - + @@ -442,62 +444,62 @@ template<typename stride_t > - -

◆ elem_to_loc_1()

+ +

◆ elem_to_loc_1()

-template<typename stride_t >
+template<typename StrideT , typename IdxT = StrideT>
METAL_FUNC stride_t elem_to_loc METAL_FUNC IdxT elem_to_loc ( uint3 elem,
constant const stride_t * strides, constant const StrideT * strides,
- + - +
METAL_FUNC stride_t elem_to_loc_1 METAL_FUNC IdxT elem_to_loc_1 ( uint elem,
constant const stride_t & stride )constant const StrideT & stride )
- -

◆ elem_to_loc_2()

+ +

◆ elem_to_loc_2()

-template<typename stride_t >
+template<typename StrideT , typename IdxT = StrideT>
- + - +
METAL_FUNC stride_t elem_to_loc_2 METAL_FUNC IdxT elem_to_loc_2 ( uint2 elem,
constant const stride_t strides[2] )constant const StrideT strides[2] )
- -

◆ elem_to_loc_2_nd()

+ +

◆ elem_to_loc_2_nd()

-template<typename stride_t >
+template<typename StrideT , typename IdxT = StrideT>
- + @@ -509,12 +511,12 @@ template<typename stride_t > - + - + @@ -526,37 +528,39 @@ template<typename stride_t > - -

◆ elem_to_loc_3()

+ +

◆ elem_to_loc_3()

-template<typename stride_t >
+template<typename StrideT , typename IdxT = StrideT>
METAL_FUNC ulong2 elem_to_loc_2_nd METAL_FUNC vec< IdxT, 2 > elem_to_loc_2_nd ( uint3 elem,
constant const stride_t * a_strides, constant const StrideT * a_strides,
constant const stride_t * b_strides, constant const StrideT * b_strides,
- + - +
METAL_FUNC stride_t elem_to_loc_3 METAL_FUNC IdxT elem_to_loc_3 ( uint3 elem,
constant const stride_t strides[3] )constant const StrideT strides[3] )
- -

◆ elem_to_loc_3_nd()

+ +

◆ elem_to_loc_3_nd()

+
+template<typename IdxT = size_t>
- + @@ -600,9 +604,9 @@ template<typename stride_t >
METAL_FUNC ulong3 elem_to_loc_3_nd METAL_FUNC vec< IdxT, 3 > elem_to_loc_3_nd ( uint3 elem,
- + - +
bfloat16_t log1p bfloat16_t log1p (bfloat16_t x)bfloat16_t x)
diff --git a/docs/build/html/backend_2metal_2kernels_2utils_8h_source.html b/docs/build/html/backend_2metal_2kernels_2utils_8h_source.html index ddb1325c..66c8011c 100644 --- a/docs/build/html/backend_2metal_2kernels_2utils_8h_source.html +++ b/docs/build/html/backend_2metal_2kernels_2utils_8h_source.html @@ -96,497 +96,546 @@ $(function(){ initResizable(false); });
3#pragma once
4
5#include <metal_math>
-
6#include "mlx/backend/metal/kernels/bf16.h"
-
7#include "mlx/backend/metal/kernels/complex.h"
-
8#include "mlx/backend/metal/kernels/defines.h"
-
9
-
10typedef half float16_t;
+
6
+
7// The correct bf16.h is included based on the metal version
+
8// by giving the correct path to -I during compilation
+
9// e.g. mlx/backend/metal/kernels/metal_3_0/ for Metal 3.0
+
10#include "bf16.h"
11
-
13// Type limits utils
+
12#include "mlx/backend/metal/kernels/bf16_math.h"
+
13#include "mlx/backend/metal/kernels/complex.h"
+
14#include "mlx/backend/metal/kernels/defines.h"
15
-
16template <typename U>
-
-
17struct Limits {
-
18 static const constant U max = metal::numeric_limits<U>::max();
-
19 static const constant U min = metal::numeric_limits<U>::min();
-
20 static const constant U finite_max = metal::numeric_limits<U>::max();
-
21 static const constant U finite_min = metal::numeric_limits<U>::min();
-
22};
+
16typedef half float16_t;
+
17
+
19// Type limits utils
+
21
+
22template <typename U>
+
+
23struct Limits {
+
24 static const constant U max = metal::numeric_limits<U>::max();
+
25 static const constant U min = metal::numeric_limits<U>::min();
+
26 static const constant U finite_max = metal::numeric_limits<U>::max();
+
27 static const constant U finite_min = metal::numeric_limits<U>::min();
+
28};
-
23
-
-
24#define instantiate_default_limit(type) \
-
25 template <> \
-
26 struct Limits<type> { \
-
27 static constexpr constant type max = metal::numeric_limits<type>::max(); \
-
28 static constexpr constant type min = metal::numeric_limits<type>::min(); \
-
29 static constexpr constant type finite_max = \
-
30 metal::numeric_limits<type>::max(); \
-
31 static constexpr constant type finite_min = \
-
32 metal::numeric_limits<type>::min(); \
-
33 };
+
29
+
+
30#define instantiate_default_limit(type) \
+
31 template <> \
+
32 struct Limits<type> { \
+
33 static constexpr constant type max = metal::numeric_limits<type>::max(); \
+
34 static constexpr constant type min = metal::numeric_limits<type>::min(); \
+
35 static constexpr constant type finite_max = \
+
36 metal::numeric_limits<type>::max(); \
+
37 static constexpr constant type finite_min = \
+
38 metal::numeric_limits<type>::min(); \
+
39 };
-
34
-
35instantiate_default_limit(uint8_t);
-
36instantiate_default_limit(uint16_t);
-
37instantiate_default_limit(uint32_t);
-
38instantiate_default_limit(uint64_t);
-
39instantiate_default_limit(int8_t);
-
40instantiate_default_limit(int16_t);
-
41instantiate_default_limit(int32_t);
-
42instantiate_default_limit(int64_t);
-
43
-
-
44#define instantiate_float_limit(type) \
-
45 template <> \
-
46 struct Limits<type> { \
-
47 static constexpr constant type max = \
-
48 metal::numeric_limits<type>::infinity(); \
-
49 static constexpr constant type min = \
-
50 -metal::numeric_limits<type>::infinity(); \
-
51 static constexpr constant type finite_max = \
-
52 metal::numeric_limits<type>::max(); \
-
53 static constexpr constant type finite_min = \
-
54 -metal::numeric_limits<type>::max(); \
-
55 };
-
-
56
-
57instantiate_float_limit(half);
-
58instantiate_float_limit(float);
-
59instantiate_float_limit(bfloat16_t);
-
60
-
61template <>
-
-
62struct Limits<bool> {
-
63 static constexpr constant bool max = true;
-
64 static constexpr constant bool min = false;
-
65};
+
40
+
41instantiate_default_limit(uint8_t);
+
42instantiate_default_limit(uint16_t);
+
43instantiate_default_limit(uint32_t);
+
44instantiate_default_limit(uint64_t);
+
45instantiate_default_limit(int8_t);
+
46instantiate_default_limit(int16_t);
+
47instantiate_default_limit(int32_t);
+
48instantiate_default_limit(int64_t);
+
49
+
+
50#define instantiate_float_limit(type) \
+
51 template <> \
+
52 struct Limits<type> { \
+
53 static constexpr constant type max = \
+
54 metal::numeric_limits<type>::infinity(); \
+
55 static constexpr constant type min = \
+
56 -metal::numeric_limits<type>::infinity(); \
+
57 static constexpr constant type finite_max = \
+
58 metal::numeric_limits<type>::max(); \
+
59 static constexpr constant type finite_min = \
+
60 -metal::numeric_limits<type>::max(); \
+
61 };
+
62
+
63instantiate_float_limit(half);
+
64instantiate_float_limit(float);
+
65instantiate_float_limit(bfloat16_t);
66
67template <>
-
68struct Limits<complex64_t> {
-
69 static constexpr constant complex64_t max = complex64_t(
-
70 metal::numeric_limits<float>::infinity(),
-
71 metal::numeric_limits<float>::infinity());
-
72 static constexpr constant complex64_t min = complex64_t(
-
73 -metal::numeric_limits<float>::infinity(),
-
74 -metal::numeric_limits<float>::infinity());
-
75};
+
68struct Limits<bool> {
+
69 static constexpr constant bool max = true;
+
70 static constexpr constant bool min = false;
+
71};
+
+
72
+
73template <>
+
+
74struct Limits<complex64_t> {
+
75 static constexpr constant complex64_t max = complex64_t(
+
76 metal::numeric_limits<float>::infinity(),
+
77 metal::numeric_limits<float>::infinity());
+
78 static constexpr constant complex64_t min = complex64_t(
+
79 -metal::numeric_limits<float>::infinity(),
+
80 -metal::numeric_limits<float>::infinity());
+
81};
-
76
-
78// Indexing utils
-
80
-
81#define MLX_MTL_PRAGMA_UNROLL _Pragma("clang loop unroll(full)")
82
-
84// Single Array with generic dims
-
85
-
86template <typename stride_t>
-
-
87METAL_FUNC stride_t elem_to_loc(
-
88 uint elem,
-
89 constant const int* shape,
-
90 constant const stride_t* strides,
-
91 int ndim) {
-
92 stride_t loc = 0;
-
93 for (int i = ndim - 1; i >= 0 && elem > 0; --i) {
-
94 loc += (elem % shape[i]) * strides[i];
-
95 elem /= shape[i];
-
96 }
-
97 return loc;
-
98}
+
84// Indexing utils
+
86
+
87#define MLX_MTL_PRAGMA_UNROLL _Pragma("clang loop unroll(full)")
+
88
+
90// Single Array with generic dims
+
91
+
92template <typename StrideT, typename IdxT = StrideT>
+
+
93METAL_FUNC IdxT elem_to_loc(
+
94 uint elem,
+
95 constant const int* shape,
+
96 constant const StrideT* strides,
+
97 int ndim) {
+
98 IdxT loc = 0;
+
99 for (int i = ndim - 1; i >= 0 && elem > 0; --i) {
+
100 loc += (elem % shape[i]) * IdxT(strides[i]);
+
101 elem /= shape[i];
+
102 }
+
103 return loc;
+
104}
-
99
-
100template <typename stride_t>
-
-
101METAL_FUNC stride_t elem_to_loc(
-
102 stride_t elem,
-
103 constant const int* shape,
-
104 constant const stride_t* strides,
-
105 int ndim) {
-
106 stride_t loc = 0;
-
107 for (int i = ndim - 1; i >= 0 && elem > 0; --i) {
-
108 loc += (elem % shape[i]) * strides[i];
-
109 elem /= shape[i];
-
110 }
-
111 return loc;
-
112}
+
105
+
106template <typename StrideT, typename IdxT = StrideT>
+
+
107METAL_FUNC IdxT elem_to_loc(
+
108 StrideT elem,
+
109 constant const int* shape,
+
110 constant const StrideT* strides,
+
111 int ndim) {
+
112 IdxT loc = 0;
+
113 for (int i = ndim - 1; i >= 0 && elem > 0; --i) {
+
114 loc += (elem % shape[i]) * IdxT(strides[i]);
+
115 elem /= shape[i];
+
116 }
+
117 return loc;
+
118}
-
113
-
114// Non templated version to handle arbitrary dims
-
115template <typename stride_t>
-
-
116METAL_FUNC stride_t elem_to_loc(
-
117 uint3 elem,
-
118 constant const int* shape,
-
119 constant const stride_t* strides,
-
120 int ndim) {
-
121 stride_t loc = elem.x * strides[ndim - 1] + elem.y * strides[ndim - 2];
-
122 for (int d = ndim - 3; d >= 0; --d) {
-
123 loc += (elem.z % shape[d]) * strides[d];
-
124 elem.z /= shape[d];
-
125 }
-
126 return loc;
-
127}
+
119
+
120// Non templated version to handle arbitrary dims
+
121template <typename StrideT, typename IdxT = StrideT>
+
+
122METAL_FUNC IdxT elem_to_loc(
+
123 uint3 elem,
+
124 constant const int* shape,
+
125 constant const StrideT* strides,
+
126 int ndim) {
+
127 IdxT loc =
+
128 elem.x * IdxT(strides[ndim - 1]) + elem.y * IdxT(strides[ndim - 2]);
+
129 for (int d = ndim - 3; d >= 0; --d) {
+
130 loc += (elem.z % shape[d]) * IdxT(strides[d]);
+
131 elem.z /= shape[d];
+
132 }
+
133 return loc;
+
134}
-
128
-
130// Single Array with fixed N dims
-
131
-
132template <typename stride_t>
-
-
133METAL_FUNC stride_t elem_to_loc_1(uint elem, constant const stride_t& stride) {
-
134 return elem * stride;
-
135}
+
135
+
137// Single Array with fixed N dims
+
138
+
139template <typename StrideT, typename IdxT = StrideT>
+
+
140METAL_FUNC IdxT elem_to_loc_1(uint elem, constant const StrideT& stride) {
+
141 return elem * IdxT(stride);
+
142}
-
136
-
137template <typename stride_t>
-
138METAL_FUNC stride_t
-
-
139elem_to_loc_2(uint2 elem, constant const stride_t strides[2]) {
-
140 return elem.x * strides[1] + elem.y * strides[0];
-
141}
-
-
142
-
143template <typename stride_t>
-
144METAL_FUNC stride_t
+
143
+
144template <typename StrideT, typename IdxT = StrideT>
-
145elem_to_loc_3(uint3 elem, constant const stride_t strides[3]) {
-
146 return elem.x * strides[2] + elem.y * strides[1] + elem.z * strides[0];
+
145METAL_FUNC IdxT elem_to_loc_2(uint2 elem, constant const StrideT strides[2]) {
+
146 return elem.x * IdxT(strides[1]) + elem.y * IdxT(strides[0]);
147}
148
-
150// Multiple Arrays with generic dims
-
151
-
152template <typename stride_t>
-
-
153METAL_FUNC ulong2 elem_to_loc_2_nd(
-
154 uint3 elem,
-
155 constant const int* shape,
-
156 constant const stride_t* a_strides,
-
157 constant const stride_t* b_strides,
-
158 int ndim) {
-
159 ulong2 loc = {
-
160 ulong(elem.x * a_strides[ndim - 1] + elem.y * a_strides[ndim - 2]),
-
161 ulong(elem.x * b_strides[ndim - 1] + elem.y * b_strides[ndim - 2])};
-
162 for (int d = ndim - 3; d >= 0; --d) {
-
163 uint l = elem.z % shape[d];
-
164 loc.x += l * a_strides[d];
-
165 loc.y += l * b_strides[d];
-
166 elem.z /= shape[d];
-
167 }
-
168 return loc;
-
169}
+
149template <typename StrideT, typename IdxT = StrideT>
+
+
150METAL_FUNC IdxT elem_to_loc_3(uint3 elem, constant const StrideT strides[3]) {
+
151 return elem.x * IdxT(strides[2]) + elem.y * IdxT(strides[1]) +
+
152 elem.z * IdxT(strides[0]);
+
153}
-
170
-
-
171METAL_FUNC ulong3 elem_to_loc_3_nd(
-
172 uint3 elem,
-
173 constant const int* shape,
-
174 constant const size_t* a_strides,
-
175 constant const size_t* b_strides,
-
176 constant const size_t* c_strides,
-
177 int ndim) {
-
178 ulong3 loc = {
-
179 elem.x * a_strides[ndim - 1] + elem.y * a_strides[ndim - 2],
-
180 elem.x * b_strides[ndim - 1] + elem.y * b_strides[ndim - 2],
-
181 elem.x * c_strides[ndim - 1] + elem.y * c_strides[ndim - 2]};
-
182 for (int d = ndim - 3; d >= 0; --d) {
-
183 uint l = elem.z % shape[d];
-
184 loc.x += l * a_strides[d];
-
185 loc.y += l * b_strides[d];
-
186 loc.z += l * c_strides[d];
-
187 elem.z /= shape[d];
-
188 }
-
189 return loc;
-
190}
+
154
+
156// Multiple Arrays with generic dims
+
157
+
158template <typename StrideT, typename IdxT = StrideT>
+
+
159METAL_FUNC vec<IdxT, 2> elem_to_loc_2_nd(
+
160 uint3 elem,
+
161 constant const int* shape,
+
162 constant const StrideT* a_strides,
+
163 constant const StrideT* b_strides,
+
164 int ndim) {
+
165 vec<IdxT, 2> loc = {
+
166 IdxT(
+
167 elem.x * IdxT(a_strides[ndim - 1]) +
+
168 IdxT(elem.y) * IdxT(a_strides[ndim - 2])),
+
169 IdxT(
+
170 elem.x * IdxT(b_strides[ndim - 1]) +
+
171 elem.y * IdxT(b_strides[ndim - 2]))};
+
172 for (int d = ndim - 3; d >= 0; --d) {
+
173 uint l = elem.z % shape[d];
+
174 loc.x += l * IdxT(a_strides[d]);
+
175 loc.y += l * IdxT(b_strides[d]);
+
176 elem.z /= shape[d];
+
177 }
+
178 return loc;
+
179}
-
191
-
193// Elem to loc in a loop utils
-
195
-
196template <int dim, typename offset_t = size_t>
-
-
197struct looped_elem_to_loc {
-
198 looped_elem_to_loc<dim - 1, offset_t> inner_looper;
-
199 offset_t offset{0};
-
200 int index{0};
-
201
-
-
202 void next(const constant int* shape, const constant size_t* strides) {
-
203 index++;
-
204 offset += strides[dim - 1];
-
205
-
206 if (index >= shape[dim - 1]) {
-
207 index = 0;
-
208 inner_looper.next(shape, strides);
-
209 offset = inner_looper.offset;
-
210 }
-
211 }
+
180
+
181template <typename IdxT = size_t>
+
+
182METAL_FUNC vec<IdxT, 3> elem_to_loc_3_nd(
+
183 uint3 elem,
+
184 constant const int* shape,
+
185 constant const size_t* a_strides,
+
186 constant const size_t* b_strides,
+
187 constant const size_t* c_strides,
+
188 int ndim) {
+
189 vec<IdxT, 3> loc = {
+
190 elem.x * IdxT(a_strides[ndim - 1]) + elem.y * IdxT(a_strides[ndim - 2]),
+
191 elem.x * IdxT(b_strides[ndim - 1]) + elem.y * IdxT(b_strides[ndim - 2]),
+
192 elem.x * IdxT(c_strides[ndim - 1]) + elem.y * IdxT(c_strides[ndim - 2])};
+
193 for (int d = ndim - 3; d >= 0; --d) {
+
194 uint l = elem.z % shape[d];
+
195 loc.x += l * IdxT(a_strides[d]);
+
196 loc.y += l * IdxT(b_strides[d]);
+
197 loc.z += l * IdxT(c_strides[d]);
+
198 elem.z /= shape[d];
+
199 }
+
200 return loc;
+
201}
-
212
-
-
213 void next(int n, const constant int* shape, const constant size_t* strides) {
-
214 index += n;
-
215 offset += n * strides[dim - 1];
-
216
-
217 if (index >= shape[dim - 1]) {
-
218 int extra = index - shape[dim - 1];
-
219 index = 0;
-
220 inner_looper.next(shape, strides);
-
221 offset = inner_looper.offset;
-
222 if (extra > 0) {
-
223 next(extra, shape, strides);
-
224 }
-
225 }
-
226 }
+
202
+
204// Elem to loc in a loop utils
+
206
+
207template <int DIM, typename OffsetT = size_t, bool General = true>
+
+
208struct LoopedElemToLoc {
+
209 int dim;
+
210 LoopedElemToLoc<DIM - 1, OffsetT, General> inner_looper;
+
211 OffsetT offset{0};
+
212 int index{0};
+
213
+
214 LoopedElemToLoc(int dim) : dim(dim), inner_looper(dim - 1) {}
+
215
+
+
216 void next(const constant int* shape, const constant size_t* strides) {
+
217 if (dim == 0) {
+
218 return;
+
219 }
+
220 index++;
+
221 offset += OffsetT(strides[dim - 1]);
+
222 if (index >= shape[dim - 1]) {
+
223 index = 0;
+
224 inner_looper.next(shape, strides);
+
225 offset = inner_looper.offset;
+
226 }
+
227 }
-
227
-
228 offset_t
+
228
-
229 location(offset_t, const constant int*, const constant size_t*, int) {
-
230 return offset;
-
231 }
-
-
232};
-
-
233
-
234template <typename offset_t>
-
-
235struct looped_elem_to_loc<1, offset_t> {
-
236 offset_t offset{0};
-
237
-
-
238 void next(const constant int*, const constant size_t* strides) {
-
239 offset += strides[0];
-
240 }
-
-
241
-
-
242 void next(int n, const constant int*, const constant size_t* strides) {
-
243 offset += n * strides[0];
-
244 }
-
-
245
-
246 offset_t
-
-
247 location(offset_t, const constant int*, const constant size_t*, int) {
-
248 return offset;
-
249 }
-
-
250};
+
229 void next(int n, const constant int* shape, const constant size_t* strides) {
+
230 if (dim == 0) {
+
231 return;
+
232 }
+
233 index += n;
+
234 offset += n * OffsetT(strides[dim - 1]);
+
235
+
236 if (index >= shape[dim - 1]) {
+
237 int extra = index - shape[dim - 1];
+
238 if (extra >= shape[dim - 1]) {
+
239 inner_looper.next(1 + extra / shape[dim - 1], shape, strides);
+
240 extra = extra % shape[dim - 1];
+
241 } else {
+
242 inner_looper.next(shape, strides);
+
243 }
+
244 index = 0;
+
245 offset = inner_looper.offset;
+
246 if (extra > 0) {
+
247 next(extra, shape, strides);
+
248 }
+
249 }
+
250 }
251
-
252template <typename offset_t>
-
-
253struct looped_elem_to_loc<0, offset_t> {
-
254 void next(const constant int*, const constant size_t*) {}
-
255 void next(int, const constant int*, const constant size_t*) {}
+
+
252 OffsetT location() {
+
253 return offset;
+
254 }
+
+
255};
+
256
-
-
257 offset_t location(
-
258 offset_t idx,
-
259 const constant int* shape,
-
260 const constant size_t* strides,
-
261 int ndim) {
-
262 return elem_to_loc(idx, shape, strides, ndim);
-
263 }
+
257template <typename OffsetT>
+
+
258struct LoopedElemToLoc<1, OffsetT, true> {
+
259 int dim;
+
260 OffsetT offset{0};
+
261 uint index{0};
+
262
+
263 LoopedElemToLoc(int dim) : dim(dim) {}
+
264
+
+
265 void next(const constant int* shape, const constant size_t* strides) {
+
266 index++;
+
267 if (dim > 1) {
+
268 offset = elem_to_loc<size_t, OffsetT>(index, shape, strides, dim);
+
269 } else {
+
270 offset += OffsetT(strides[0]);
+
271 }
+
272 }
-
264};
-
-
265
-
267// Calculation utils
-
269
-
271template <typename T, typename U>
-
-
272inline T ceildiv(T N, U M) {
-
273 return (N + M - 1) / M;
-
274}
-
-
275
-
276// https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html#1202
-
-
277inline float log1p(float x) {
-
278 float xp1 = 1.0f + x;
-
279 if (xp1 == Limits<float>::max) {
-
280 return Limits<float>::max;
+
273
+
+
274 void next(int n, const constant int* shape, const constant size_t* strides) {
+
275 index += n;
+
276 if (dim > 1) {
+
277 offset = elem_to_loc<size_t, OffsetT>(index, shape, strides, dim);
+
278 } else {
+
279 offset = index * OffsetT(strides[0]);
+
280 }
281 }
-
282 if (xp1 == 1.0f) {
-
283 return x;
-
284 }
-
285
-
286 return x * (metal::log(xp1) / (xp1 - 1.0f));
-
287}
-
288
-
-
289inline bfloat16_t log1p(bfloat16_t x) {
-
290 float xp1 = 1.0f + static_cast<float>(x);
-
291 if (xp1 == Limits<float>::max) {
-
292 return Limits<bfloat16_t>::max;
-
293 }
-
294 if (xp1 == 1.0f) {
-
295 return x;
+
282
+
+
283 OffsetT location() {
+
284 return offset;
+
285 }
+
+
286};
+
+
287
+
288template <typename OffsetT>
+
+
289struct LoopedElemToLoc<1, OffsetT, false> {
+
290 OffsetT offset{0};
+
291
+
292 LoopedElemToLoc(int) {}
+
293
+
+
294 void next(const constant int*, const constant size_t* strides) {
+
295 offset += OffsetT(strides[0]);
296 }
+
297
-
298 return bfloat16_t(x * (metal::log(xp1) / (xp1 - 1.0f)));
-
299}
+
+
298 void next(int n, const constant int*, const constant size_t* strides) {
+
299 offset += n * OffsetT(strides[0]);
+
300 }
-
300
-
302// SIMD shuffle ops
-
304
-
-
305inline uint64_t simd_shuffle_down(uint64_t data, uint16_t delta) {
-
306 return as_type<uint64_t>(
-
307 metal::simd_shuffle_down(as_type<uint2>(data), delta));
-
308}
+
301
+
+
302 OffsetT location() {
+
303 return offset;
+
304 }
-
309
-
-
310inline int64_t simd_shuffle_down(int64_t data, uint16_t delta) {
-
311 return as_type<int64_t>(
-
312 metal::simd_shuffle_down(as_type<uint2>(data), delta));
-
313}
+
305};
-
314
-
-
315inline bool simd_shuffle_down(bool data, uint16_t delta) {
-
316 return simd_shuffle_down(static_cast<uint32_t>(data), delta);
-
317}
+
306
+
308// Calculation utils
+
310
+
312template <typename T, typename U>
+
+
313inline T ceildiv(T N, U M) {
+
314 return (N + M - 1) / M;
+
315}
-
318
-
-
319inline complex64_t simd_shuffle_down(complex64_t data, uint16_t delta) {
-
320 return complex64_t(
-
321 simd_shuffle_down(data.real, delta), simd_shuffle_down(data.imag, delta));
-
322}
+
316
+
317// https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html#1202
+
+
318inline float log1p(float x) {
+
319 float xp1 = 1.0f + x;
+
320 if (xp1 == Limits<float>::max) {
+
321 return Limits<float>::max;
+
322 }
+
323 if (xp1 == 1.0f) {
+
324 return x;
+
325 }
+
326
+
327 return x * (metal::log(xp1) / (xp1 - 1.0f));
+
328}
-
323
-
-
324inline uint64_t simd_shuffle_up(uint64_t data, uint16_t delta) {
-
325 return as_type<uint64_t>(metal::simd_shuffle_up(as_type<uint2>(data), delta));
-
326}
+
329
+
+
330inline bfloat16_t log1p(bfloat16_t x) {
+
331 float xp1 = 1.0f + static_cast<float>(x);
+
332 if (xp1 == Limits<float>::max) {
+
333 return Limits<bfloat16_t>::max;
+
334 }
+
335 if (xp1 == 1.0f) {
+
336 return x;
+
337 }
+
338
+
339 return bfloat16_t(x * (metal::log(xp1) / (xp1 - 1.0f)));
+
340}
-
327
-
-
328inline int64_t simd_shuffle_up(int64_t data, uint16_t delta) {
-
329 return as_type<int64_t>(metal::simd_shuffle_up(as_type<uint2>(data), delta));
-
330}
+
341
+
343// SIMD shuffle ops
+
345
+
+
346inline uint64_t simd_shuffle_down(uint64_t data, uint16_t delta) {
+
347 return as_type<uint64_t>(
+
348 metal::simd_shuffle_down(as_type<uint2>(data), delta));
+
349}
-
331
-
-
332inline bool simd_shuffle_up(bool data, uint16_t delta) {
-
333 return simd_shuffle_up(static_cast<uint32_t>(data), delta);
-
334}
+
350
+
+
351inline int64_t simd_shuffle_down(int64_t data, uint16_t delta) {
+
352 return as_type<int64_t>(
+
353 metal::simd_shuffle_down(as_type<uint2>(data), delta));
+
354}
-
335
-
-
336inline complex64_t simd_shuffle_up(complex64_t data, uint16_t delta) {
-
337 return complex64_t(
-
338 simd_shuffle_up(data.real, delta), simd_shuffle_up(data.imag, delta));
-
339}
+
355
+
+
356inline bool simd_shuffle_down(bool data, uint16_t delta) {
+
357 return simd_shuffle_down(static_cast<uint32_t>(data), delta);
+
358}
-
340
-
341inline uint64_t
-
-
342simd_shuffle_and_fill_up(uint64_t data, uint64_t filling, uint16_t delta) {
-
343 return as_type<uint64_t>(metal::simd_shuffle_and_fill_up(
-
344 as_type<uint2>(data), as_type<uint2>(filling), delta));
-
345}
+
359
+
+
360inline complex64_t simd_shuffle_down(complex64_t data, uint16_t delta) {
+
361 return complex64_t(
+
362 simd_shuffle_down(data.real, delta), simd_shuffle_down(data.imag, delta));
+
363}
-
346
-
347inline int64_t
-
-
348simd_shuffle_and_fill_up(int64_t data, int64_t filling, uint16_t delta) {
-
349 return as_type<int64_t>(metal::simd_shuffle_and_fill_up(
-
350 as_type<uint2>(data), as_type<uint2>(filling), delta));
-
351}
+
364
+
+
365inline uint64_t simd_shuffle_up(uint64_t data, uint16_t delta) {
+
366 return as_type<uint64_t>(metal::simd_shuffle_up(as_type<uint2>(data), delta));
+
367}
-
352
-
-
353inline bool simd_shuffle_and_fill_up(bool data, bool filling, uint16_t delta) {
-
354 return simd_shuffle_and_fill_up(
-
355 static_cast<uint32_t>(data), static_cast<uint32_t>(filling), delta);
-
356}
+
368
+
+
369inline int64_t simd_shuffle_up(int64_t data, uint16_t delta) {
+
370 return as_type<int64_t>(metal::simd_shuffle_up(as_type<uint2>(data), delta));
+
371}
-
357
-
-
358inline complex64_t simd_shuffle_and_fill_up(
-
359 complex64_t data,
-
360 complex64_t filling,
-
361 uint16_t delta) {
-
362 return complex64_t(
-
363 simd_shuffle_and_fill_up(data.real, filling.real, delta),
-
364 simd_shuffle_and_fill_up(data.imag, filling.imag, delta));
-
365}
+
372
+
+
373inline bool simd_shuffle_up(bool data, uint16_t delta) {
+
374 return simd_shuffle_up(static_cast<uint32_t>(data), delta);
+
375}
-
366
-
-
367inline uint64_t simd_shuffle(uint64_t data, uint16_t lane) {
-
368 return as_type<uint64_t>(metal::simd_shuffle(as_type<uint2>(data), lane));
-
369}
+
376
+
+
377inline complex64_t simd_shuffle_up(complex64_t data, uint16_t delta) {
+
378 return complex64_t(
+
379 simd_shuffle_up(data.real, delta), simd_shuffle_up(data.imag, delta));
+
380}
-
370
-
-
371inline int64_t simd_shuffle(int64_t data, uint16_t lane) {
-
372 return as_type<int64_t>(metal::simd_shuffle(as_type<uint2>(data), lane));
-
373}
+
381
+
382inline uint64_t
+
+
383simd_shuffle_and_fill_up(uint64_t data, uint64_t filling, uint16_t delta) {
+
384 return as_type<uint64_t>(metal::simd_shuffle_and_fill_up(
+
385 as_type<uint2>(data), as_type<uint2>(filling), delta));
+
386}
-
374
-
-
375inline bool simd_shuffle(bool data, uint16_t lane) {
-
376 return simd_shuffle(static_cast<uint32_t>(data), lane);
-
377}
+
387
+
388inline int64_t
+
+
389simd_shuffle_and_fill_up(int64_t data, int64_t filling, uint16_t delta) {
+
390 return as_type<int64_t>(metal::simd_shuffle_and_fill_up(
+
391 as_type<uint2>(data), as_type<uint2>(filling), delta));
+
392}
-
378
-
-
379inline complex64_t simd_shuffle(complex64_t data, uint16_t lane) {
-
380 return complex64_t(
-
381 simd_shuffle(data.real, lane), simd_shuffle(data.imag, lane));
-
382}
+
393
+
+
394inline bool simd_shuffle_and_fill_up(bool data, bool filling, uint16_t delta) {
+
395 return simd_shuffle_and_fill_up(
+
396 static_cast<uint32_t>(data), static_cast<uint32_t>(filling), delta);
+
397}
+
+
398
+
+
399inline complex64_t simd_shuffle_and_fill_up(
+
400 complex64_t data,
+
401 complex64_t filling,
+
402 uint16_t delta) {
+
403 return complex64_t(
+
404 simd_shuffle_and_fill_up(data.real, filling.real, delta),
+
405 simd_shuffle_and_fill_up(data.imag, filling.imag, delta));
+
406}
+
+
407
+
+
408inline uint64_t simd_shuffle(uint64_t data, uint16_t lane) {
+
409 return as_type<uint64_t>(metal::simd_shuffle(as_type<uint2>(data), lane));
+
410}
+
+
411
+
+
412inline int64_t simd_shuffle(int64_t data, uint16_t lane) {
+
413 return as_type<int64_t>(metal::simd_shuffle(as_type<uint2>(data), lane));
+
414}
+
+
415
+
+
416inline bool simd_shuffle(bool data, uint16_t lane) {
+
417 return simd_shuffle(static_cast<uint32_t>(data), lane);
+
418}
+
+
419
+
+
420inline complex64_t simd_shuffle(complex64_t data, uint16_t lane) {
+
421 return complex64_t(
+
422 simd_shuffle(data.real, lane), simd_shuffle(data.imag, lane));
+
423}
next
BufferHolder * next
Definition allocator.h:38
- -
bfloat16_t
struct _MLX_BFloat16 bfloat16_t
Definition bf16.h:257
-
elem_to_loc_2_nd
METAL_FUNC ulong2 elem_to_loc_2_nd(uint3 elem, constant const int *shape, constant const stride_t *a_strides, constant const stride_t *b_strides, int ndim)
Definition utils.h:153
-
elem_to_loc_1
METAL_FUNC stride_t elem_to_loc_1(uint elem, constant const stride_t &stride)
Definition utils.h:133
-
instantiate_float_limit
#define instantiate_float_limit(type)
Definition utils.h:44
-
log1p
float log1p(float x)
Definition utils.h:277
-
elem_to_loc_3
METAL_FUNC stride_t elem_to_loc_3(uint3 elem, constant const stride_t strides[3])
Definition utils.h:145
-
elem_to_loc_3_nd
METAL_FUNC ulong3 elem_to_loc_3_nd(uint3 elem, constant const int *shape, constant const size_t *a_strides, constant const size_t *b_strides, constant const size_t *c_strides, int ndim)
Definition utils.h:171
-
ceildiv
T ceildiv(T N, U M)
Compute ceil((float)N/(float)M)
Definition utils.h:272
-
elem_to_loc
METAL_FUNC stride_t elem_to_loc(uint elem, constant const int *shape, constant const stride_t *strides, int ndim)
Definition utils.h:87
-
instantiate_default_limit
#define instantiate_default_limit(type)
Definition utils.h:24
-
float16_t
half float16_t
Definition utils.h:10
-
elem_to_loc_2
METAL_FUNC stride_t elem_to_loc_2(uint2 elem, constant const stride_t strides[2])
Definition utils.h:139
+
bfloat16_t
struct _MLX_BFloat16 bfloat16_t
Definition bf16.h:251
+
instantiate_float_limit
#define instantiate_float_limit(type)
Definition utils.h:50
+
elem_to_loc
METAL_FUNC IdxT elem_to_loc(uint elem, constant const int *shape, constant const StrideT *strides, int ndim)
Definition utils.h:93
+
log1p
float log1p(float x)
Definition utils.h:318
+
elem_to_loc_2
METAL_FUNC IdxT elem_to_loc_2(uint2 elem, constant const StrideT strides[2])
Definition utils.h:145
+
elem_to_loc_3
METAL_FUNC IdxT elem_to_loc_3(uint3 elem, constant const StrideT strides[3])
Definition utils.h:150
+
elem_to_loc_3_nd
METAL_FUNC vec< IdxT, 3 > elem_to_loc_3_nd(uint3 elem, constant const int *shape, constant const size_t *a_strides, constant const size_t *b_strides, constant const size_t *c_strides, int ndim)
Definition utils.h:182
+
elem_to_loc_2_nd
METAL_FUNC vec< IdxT, 2 > elem_to_loc_2_nd(uint3 elem, constant const int *shape, constant const StrideT *a_strides, constant const StrideT *b_strides, int ndim)
Definition utils.h:159
+
ceildiv
T ceildiv(T N, U M)
Compute ceil((float)N/(float)M)
Definition utils.h:313
+
instantiate_default_limit
#define instantiate_default_limit(type)
Definition utils.h:30
+
elem_to_loc_1
METAL_FUNC IdxT elem_to_loc_1(uint elem, constant const StrideT &stride)
Definition utils.h:140
+
float16_t
half float16_t
Definition utils.h:16
+
bf16_math.h
-
metal::simd_shuffle_and_fill_up
METAL_FUNC bfloat16_t simd_shuffle_and_fill_up(bfloat16_t data, bfloat16_t filling_data, ushort delta, ushort modulo)
Definition bf16_math.h:391
-
metal::simd_shuffle
METAL_FUNC bfloat16_t simd_shuffle(bfloat16_t data, ushort simd_lane_id)
Definition bf16_math.h:391
-
metal::log
METAL_FUNC bfloat16_t log(bfloat16_t x)
Definition bf16_math.h:234
-
metal::simd_shuffle_down
METAL_FUNC bfloat16_t simd_shuffle_down(bfloat16_t data, ushort delta)
Definition bf16_math.h:391
-
metal::simd_shuffle_up
METAL_FUNC bfloat16_t simd_shuffle_up(bfloat16_t data, ushort delta)
Definition bf16_math.h:391
-
pocketfft::detail::stride_t
std::vector< ptrdiff_t > stride_t
Definition pocketfft.h:103
-
_MLX_BFloat16
Definition bf16.h:54
-
Limits
Definition utils.h:17
-
Limits::max
static const constant U max
Definition utils.h:18
-
Limits::finite_max
static const constant U finite_max
Definition utils.h:20
-
Limits::min
static const constant U min
Definition utils.h:19
-
Limits::finite_min
static const constant U finite_min
Definition utils.h:21
+
metal::simd_shuffle_and_fill_up
METAL_FUNC bfloat16_t simd_shuffle_and_fill_up(bfloat16_t data, bfloat16_t filling_data, ushort delta, ushort modulo)
Definition bf16_math.h:377
+
metal::simd_shuffle
METAL_FUNC bfloat16_t simd_shuffle(bfloat16_t data, ushort simd_lane_id)
Definition bf16_math.h:377
+
metal::log
METAL_FUNC bfloat16_t log(bfloat16_t x)
Definition bf16_math.h:232
+
metal::simd_shuffle_down
METAL_FUNC bfloat16_t simd_shuffle_down(bfloat16_t data, ushort delta)
Definition bf16_math.h:377
+
metal::simd_shuffle_up
METAL_FUNC bfloat16_t simd_shuffle_up(bfloat16_t data, ushort delta)
Definition bf16_math.h:377
+
_MLX_BFloat16
Definition bf16.h:48
+
Limits
Definition utils.h:23
+
Limits::max
static const constant U max
Definition utils.h:24
+
Limits::finite_max
static const constant U finite_max
Definition utils.h:26
+
Limits::min
static const constant U min
Definition utils.h:25
+
Limits::finite_min
static const constant U finite_min
Definition utils.h:27
+
LoopedElemToLoc< 1, OffsetT, false >::next
void next(const constant int *, const constant size_t *strides)
Definition utils.h:294
+
LoopedElemToLoc< 1, OffsetT, false >::LoopedElemToLoc
LoopedElemToLoc(int)
Definition utils.h:292
+
LoopedElemToLoc< 1, OffsetT, false >::location
OffsetT location()
Definition utils.h:302
+
LoopedElemToLoc< 1, OffsetT, false >::next
void next(int n, const constant int *, const constant size_t *strides)
Definition utils.h:298
+
LoopedElemToLoc< 1, OffsetT, true >::location
OffsetT location()
Definition utils.h:283
+
LoopedElemToLoc< 1, OffsetT, true >::dim
int dim
Definition utils.h:259
+
LoopedElemToLoc< 1, OffsetT, true >::next
void next(int n, const constant int *shape, const constant size_t *strides)
Definition utils.h:274
+
LoopedElemToLoc< 1, OffsetT, true >::LoopedElemToLoc
LoopedElemToLoc(int dim)
Definition utils.h:263
+
LoopedElemToLoc< 1, OffsetT, true >::next
void next(const constant int *shape, const constant size_t *strides)
Definition utils.h:265
+
LoopedElemToLoc
Definition utils.h:208
+
LoopedElemToLoc::next
void next(const constant int *shape, const constant size_t *strides)
Definition utils.h:216
+
LoopedElemToLoc::LoopedElemToLoc
LoopedElemToLoc(int dim)
Definition utils.h:214
+
LoopedElemToLoc::next
void next(int n, const constant int *shape, const constant size_t *strides)
Definition utils.h:229
+
LoopedElemToLoc::inner_looper
LoopedElemToLoc< DIM - 1, OffsetT, General > inner_looper
Definition utils.h:210
+
LoopedElemToLoc::location
OffsetT location()
Definition utils.h:252
+
LoopedElemToLoc::index
int index
Definition utils.h:212
+
LoopedElemToLoc::offset
OffsetT offset
Definition utils.h:211
+
LoopedElemToLoc::dim
int dim
Definition utils.h:209
complex64_t
Definition complex.h:20
complex64_t::imag
float imag
Definition complex.h:22
complex64_t::real
float real
Definition complex.h:21
-
looped_elem_to_loc< 0, offset_t >::next
void next(int, const constant int *, const constant size_t *)
Definition utils.h:255
-
looped_elem_to_loc< 0, offset_t >::location
offset_t location(offset_t idx, const constant int *shape, const constant size_t *strides, int ndim)
Definition utils.h:257
-
looped_elem_to_loc< 0, offset_t >::next
void next(const constant int *, const constant size_t *)
Definition utils.h:254
-
looped_elem_to_loc< 1, offset_t >::location
offset_t location(offset_t, const constant int *, const constant size_t *, int)
Definition utils.h:247
-
looped_elem_to_loc< 1, offset_t >::next
void next(const constant int *, const constant size_t *strides)
Definition utils.h:238
-
looped_elem_to_loc< 1, offset_t >::next
void next(int n, const constant int *, const constant size_t *strides)
Definition utils.h:242
-
looped_elem_to_loc
Definition utils.h:197
-
looped_elem_to_loc::next
void next(const constant int *shape, const constant size_t *strides)
Definition utils.h:202
-
looped_elem_to_loc::offset
offset_t offset
Definition utils.h:199
-
looped_elem_to_loc::index
int index
Definition utils.h:200
-
looped_elem_to_loc::inner_looper
looped_elem_to_loc< dim - 1, offset_t > inner_looper
Definition utils.h:198
-
looped_elem_to_loc::location
offset_t location(offset_t, const constant int *, const constant size_t *, int)
Definition utils.h:229
-
looped_elem_to_loc::next
void next(int n, const constant int *shape, const constant size_t *strides)
Definition utils.h:213
diff --git a/docs/build/html/backend_2metal_2utils_8h.html b/docs/build/html/backend_2metal_2utils_8h.html index 9fc5d1f1..93f04c83 100644 --- a/docs/build/html/backend_2metal_2utils_8h.html +++ b/docs/build/html/backend_2metal_2utils_8h.html @@ -109,12 +109,8 @@ Namespaces
- - - - - - + + @@ -131,6 +127,12 @@ Functions + + + + + +

Functions

template<typename T >
void mlx::core::set_vector_bytes (CommandEncoder &enc, const std::vector< T > &vec, size_t nelems, int idx)
 
template<typename T >
void mlx::core::set_vector_bytes (CommandEncoder &enc, const std::vector< T > &vec, int idx)
 
std::string mlx::core::type_to_name (const Dtype &t)
 
std::string mlx::core::type_to_name (const array &a)
 
MTL::Size mlx::core::get_block_dims (int dim0, int dim1, int dim2, int pow2=10)
 
std::string mlx::core::get_primitive_string (Primitive *primitive)
 
template<typename T >
void mlx::core::concatenate (std::string &acc, T first)
 
template<typename T , typename... Args>
void mlx::core::concatenate (std::string &acc, T first, Args... args)
 
diff --git a/docs/build/html/backend_2metal_2utils_8h_source.html b/docs/build/html/backend_2metal_2utils_8h_source.html index 0795ecb3..32b2bae6 100644 --- a/docs/build/html/backend_2metal_2utils_8h_source.html +++ b/docs/build/html/backend_2metal_2utils_8h_source.html @@ -101,87 +101,82 @@ $(function(){ initResizable(false); });
8
9namespace mlx::core {
10
-
11using metal::CommandEncoder;
-
12
-
13template <typename T>
-
-
14inline void set_vector_bytes(
-
15 CommandEncoder& enc,
-
16 const std::vector<T>& vec,
-
17 size_t nelems,
-
18 int idx) {
-
19 enc->setBytes(vec.data(), nelems * sizeof(T), idx);
-
20}
+
11std::string type_to_name(const Dtype& t);
+
12std::string type_to_name(const array& a);
+
13
+
14// Compute the thread block dimensions which fit the given
+
15// input dimensions.
+
16// - The thread block dimensions will be powers of two
+
17// - The thread block size will be less than 2^pow2
+
18MTL::Size get_block_dims(int dim0, int dim1, int dim2, int pow2 = 10);
+
19
+
20// Computes a 2D grid where each element is < UINT_MAX
+
21// Assumes:
+
22// - overall size (product of non-broadcasted dimensions) is < UINT_MAX^2
+
23// - shape and strides correspond to a contiguous (no holes) but
+
24// possibly broadcasted array
+
25MTL::Size get_2d_grid_dims(
+
26 const std::vector<int>& shape,
+
27 const std::vector<size_t>& strides);
+
28
+
29// Same as above but we do an implicit division with divisor.
+
30// Basically, equivalent to factorizing
+
31// Prod(s \forall s in shape if strides[s] > 0) / divisor.
+
32MTL::Size get_2d_grid_dims(
+
33 const std::vector<int>& shape,
+
34 const std::vector<size_t>& strides,
+
35 size_t divisor);
+
36
+
+
37inline NS::String* make_string(std::ostringstream& os) {
+
38 std::string string = os.str();
+
39 return NS::String::string(string.c_str(), NS::UTF8StringEncoding);
+
40}
-
21
-
22template <typename T>
-
23inline void
-
-
24set_vector_bytes(CommandEncoder& enc, const std::vector<T>& vec, int idx) {
-
25 return set_vector_bytes(enc, vec, vec.size(), idx);
-
26}
+
41
+
+
42inline void debug_set_stream_queue_label(MTL::CommandQueue* queue, int index) {
+
43#ifdef MLX_METAL_DEBUG
+
44 std::ostringstream label;
+
45 label << "Stream " << index;
+
46 queue->setLabel(make_string(label));
+
47#endif
+
48}
-
27
-
28std::string type_to_name(const array& a);
-
29
-
30// Compute the thread block dimensions which fit the given
-
31// input dimensions.
-
32// - The thread block dimensions will be powers of two
-
33// - The thread block size will be less than 2^pow2
-
34MTL::Size get_block_dims(int dim0, int dim1, int dim2, int pow2 = 10);
-
35
-
36// Computes a 2D grid where each element is < UINT_MAX
-
37// Assumes:
-
38// - overall size (product of non-broadcasted dimensions) is < UINT_MAX^2
-
39// - shape and strides correspond to a contiguous (no holes) but
-
40// possibly broadcasted array
-
41MTL::Size get_2d_grid_dims(
-
42 const std::vector<int>& shape,
-
43 const std::vector<size_t>& strides);
-
44
-
45// Same as above but we do an implicit division with divisor.
-
46// Basically, equivalent to factorizing
-
47// Prod(s \forall s in shape if strides[s] > 0) / divisor.
-
48MTL::Size get_2d_grid_dims(
-
49 const std::vector<int>& shape,
-
50 const std::vector<size_t>& strides,
-
51 size_t divisor);
-
52
-
-
53inline NS::String* make_string(std::ostringstream& os) {
-
54 std::string string = os.str();
-
55 return NS::String::string(string.c_str(), NS::UTF8StringEncoding);
-
56}
+
49
+
+
50inline void debug_set_primitive_buffer_label(
+
51 MTL::CommandBuffer* command_buffer,
+
52 Primitive& primitive) {
+
53#ifdef MLX_METAL_DEBUG
+
54 std::ostringstream label;
+
55 if (auto cbuf_label = command_buffer->label(); cbuf_label) {
+
56 label << cbuf_label->utf8String();
+
57 }
+
58 primitive.print(label);
+
59 command_buffer->setLabel(make_string(label));
+
60#endif
+
61}
-
57
-
-
58inline void debug_set_stream_queue_label(MTL::CommandQueue* queue, int index) {
-
59#ifdef MLX_METAL_DEBUG
-
60 std::ostringstream label;
-
61 label << "Stream " << index;
-
62 queue->setLabel(make_string(label));
-
63#endif
-
64}
-
-
65
+
62
+
63std::string get_primitive_string(Primitive* primitive);
+
64
+
65template <typename T>
-
66inline void debug_set_primitive_buffer_label(
-
67 MTL::CommandBuffer* command_buffer,
-
68 Primitive& primitive) {
-
69#ifdef MLX_METAL_DEBUG
-
70 std::ostringstream label;
-
71 if (auto cbuf_label = command_buffer->label(); cbuf_label) {
-
72 label << cbuf_label->utf8String();
-
73 }
-
74 primitive.print(label);
-
75 command_buffer->setLabel(make_string(label));
-
76#endif
-
77}
+
66void concatenate(std::string& acc, T first) {
+
67 acc += first;
+
68}
-
78
-
79std::string get_primitive_string(Primitive* primitive);
-
80
-
81} // namespace mlx::core
+
69
+
70template <typename T, typename... Args>
+
+
71void concatenate(std::string& acc, T first, Args... args) {
+
72 acc += first;
+
73 concatenate(acc, args...);
+
74}
+
+
75
+
76} // namespace mlx::core
mlx::core::Primitive
Definition primitives.h:48
@@ -189,15 +184,15 @@ $(function(){ initResizable(false); });
mlx::core::array
Definition array.h:20
mlx::core
Definition allocator.h:7
mlx::core::get_block_dims
MTL::Size get_block_dims(int dim0, int dim1, int dim2, int pow2=10)
-
mlx::core::debug_set_primitive_buffer_label
void debug_set_primitive_buffer_label(MTL::CommandBuffer *command_buffer, Primitive &primitive)
Definition utils.h:66
-
mlx::core::set_vector_bytes
void set_vector_bytes(CommandEncoder &enc, const std::vector< T > &vec, size_t nelems, int idx)
Definition utils.h:14
-
mlx::core::debug_set_stream_queue_label
void debug_set_stream_queue_label(MTL::CommandQueue *queue, int index)
Definition utils.h:58
+
mlx::core::debug_set_primitive_buffer_label
void debug_set_primitive_buffer_label(MTL::CommandBuffer *command_buffer, Primitive &primitive)
Definition utils.h:50
+
mlx::core::concatenate
void concatenate(std::string &acc, T first)
Definition utils.h:66
+
mlx::core::debug_set_stream_queue_label
void debug_set_stream_queue_label(MTL::CommandQueue *queue, int index)
Definition utils.h:42
mlx::core::get_2d_grid_dims
MTL::Size get_2d_grid_dims(const std::vector< int > &shape, const std::vector< size_t > &strides)
mlx::core::get_primitive_string
std::string get_primitive_string(Primitive *primitive)
-
mlx::core::make_string
NS::String * make_string(std::ostringstream &os)
Definition utils.h:53
-
mlx::core::type_to_name
std::string type_to_name(const array &a)
+
mlx::core::make_string
NS::String * make_string(std::ostringstream &os)
Definition utils.h:37
+
mlx::core::type_to_name
std::string type_to_name(const Dtype &t)
-
mlx::core::metal::CommandEncoder
Definition device.h:41
+
mlx::core::Dtype
Definition dtype.h:13
diff --git a/docs/build/html/bf16__math_8h.html b/docs/build/html/bf16__math_8h.html index 4d649701..da7db647 100644 --- a/docs/build/html/bf16__math_8h.html +++ b/docs/build/html/bf16__math_8h.html @@ -95,8 +95,7 @@ $(function(){ initResizable(false); });
bf16_math.h File Reference
-
#include "mlx/backend/metal/kernels/bf16.h"
-
+

Go to the source code of this file.

- - - -

@@ -116,379 +115,357 @@ Macros

 
#define instantiate_metal_simd_reduction_funcs(itype, otype, ctype)
 
#define bfloat16_to_uint16(x)
 
#define uint16_to_bfloat16(x)
 
- + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +

Functions

METAL_FUNC bfloat16_t metal::abs (bfloat16_t x)
METAL_FUNC bfloat16_t metal::abs (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::acos (bfloat16_t x)
METAL_FUNC bfloat16_t metal::acos (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::acosh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::acosh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::asin (bfloat16_t x)
METAL_FUNC bfloat16_t metal::asin (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::asinh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::asinh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::atan (bfloat16_t y_over_x)
METAL_FUNC bfloat16_t metal::atan (bfloat16_t y_over_x)
 
METAL_FUNC bfloat16_t metal::atan2 (bfloat16_t y, bfloat16_t x)
METAL_FUNC bfloat16_t metal::atan2 (bfloat16_t y, bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::atanh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::atanh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::ceil (bfloat16_t x)
METAL_FUNC bfloat16_t metal::ceil (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::cos (bfloat16_t x)
METAL_FUNC bfloat16_t metal::cos (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::cosh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::cosh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::cospi (bfloat16_t x)
METAL_FUNC bfloat16_t metal::cospi (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::divide (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::divide (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::exp (bfloat16_t x)
METAL_FUNC bfloat16_t metal::exp (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::exp10 (bfloat16_t x)
METAL_FUNC bfloat16_t metal::exp10 (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::exp2 (bfloat16_t x)
METAL_FUNC bfloat16_t metal::exp2 (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fabs (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fabs (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fdim (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::fdim (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::floor (bfloat16_t x)
METAL_FUNC bfloat16_t metal::floor (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fma (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::fma (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::fmax (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::fmax (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::fmax3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::fmax3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::fmedian3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::fmedian3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::fmin (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::fmin (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::fmin3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::fmin3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::fmod (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::fmod (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::fract (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fract (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::frexp (bfloat16_t x, thread int &exp)
METAL_FUNC bfloat16_t metal::frexp (bfloat16_t x, thread int &exp)
 
METAL_FUNC bfloat16_t metal::ldexp (bfloat16_t x, int k)
METAL_FUNC bfloat16_t metal::ldexp (bfloat16_t x, int k)
 
METAL_FUNC bfloat16_t metal::log (bfloat16_t x)
METAL_FUNC bfloat16_t metal::log (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::log10 (bfloat16_t x)
METAL_FUNC bfloat16_t metal::log10 (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::log2 (bfloat16_t x)
METAL_FUNC bfloat16_t metal::log2 (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::max (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::max (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::max3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::max3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::median3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::median3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::min (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::min (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::min3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::min3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::nextafter (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::nextafter (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::pow (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::pow (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::powr (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::powr (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::rint (bfloat16_t x)
METAL_FUNC bfloat16_t metal::rint (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::round (bfloat16_t x)
METAL_FUNC bfloat16_t metal::round (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::rsqrt (bfloat16_t x)
METAL_FUNC bfloat16_t metal::rsqrt (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::sin (bfloat16_t x)
METAL_FUNC bfloat16_t metal::sin (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::sinh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::sinh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::sinpi (bfloat16_t x)
METAL_FUNC bfloat16_t metal::sinpi (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::sqrt (bfloat16_t x)
METAL_FUNC bfloat16_t metal::sqrt (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::tan (bfloat16_t x)
METAL_FUNC bfloat16_t metal::tan (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::tanh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::tanh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::tanpi (bfloat16_t x)
METAL_FUNC bfloat16_t metal::tanpi (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::trunc (bfloat16_t x)
METAL_FUNC bfloat16_t metal::trunc (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::abs (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::abs (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::acos (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::acos (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::acosh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::acosh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::asin (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::asin (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::asinh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::asinh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::atan (bfloat16_t y_over_x)
METAL_FUNC bfloat16_t metal::fast::atan (bfloat16_t y_over_x)
 
METAL_FUNC bfloat16_t metal::fast::atan2 (bfloat16_t y, bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::atan2 (bfloat16_t y, bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::atanh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::atanh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::ceil (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::ceil (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::cos (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::cos (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::cosh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::cosh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::cospi (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::cospi (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::divide (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::fast::divide (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::fast::exp (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::exp (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::exp10 (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::exp10 (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::exp2 (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::exp2 (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::fabs (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::fabs (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::fdim (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::fast::fdim (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::fast::floor (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::floor (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::fma (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::fast::fma (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::fast::fmax (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::fast::fmax (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::fast::fmax3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::fast::fmax3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::fast::fmedian3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::fast::fmedian3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::fast::fmin (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::fast::fmin (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::fast::fmin3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::fast::fmin3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::fast::fmod (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::fast::fmod (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::fast::fract (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::fract (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::frexp (bfloat16_t x, thread int &exp)
METAL_FUNC bfloat16_t metal::fast::frexp (bfloat16_t x, thread int &exp)
 
METAL_FUNC bfloat16_t metal::fast::ldexp (bfloat16_t x, int k)
METAL_FUNC bfloat16_t metal::fast::ldexp (bfloat16_t x, int k)
 
METAL_FUNC bfloat16_t metal::fast::log (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::log (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::log10 (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::log10 (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::log2 (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::log2 (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::max (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::fast::max (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::fast::max3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::fast::max3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::fast::median3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::fast::median3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::fast::min (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::fast::min (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::fast::min3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::fast::min3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::fast::nextafter (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::fast::nextafter (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::fast::pow (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::fast::pow (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::fast::powr (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::fast::powr (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::fast::rint (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::rint (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::round (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::round (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::rsqrt (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::rsqrt (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::sin (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::sin (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::sinh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::sinh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::sinpi (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::sinpi (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::sqrt (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::sqrt (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::tan (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::tan (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::tanh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::tanh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::tanpi (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::tanpi (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::fast::trunc (bfloat16_t x)
METAL_FUNC bfloat16_t metal::fast::trunc (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::abs (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::abs (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::acos (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::acos (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::acosh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::acosh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::asin (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::asin (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::asinh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::asinh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::atan (bfloat16_t y_over_x)
METAL_FUNC bfloat16_t metal::precise::atan (bfloat16_t y_over_x)
 
METAL_FUNC bfloat16_t metal::precise::atan2 (bfloat16_t y, bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::atan2 (bfloat16_t y, bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::atanh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::atanh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::ceil (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::ceil (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::cos (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::cos (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::cosh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::cosh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::cospi (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::cospi (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::divide (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::precise::divide (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::precise::exp (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::exp (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::exp10 (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::exp10 (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::exp2 (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::exp2 (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::fabs (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::fabs (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::fdim (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::precise::fdim (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::precise::floor (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::floor (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::fma (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::precise::fma (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::precise::fmax (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::precise::fmax (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::precise::fmax3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::precise::fmax3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::precise::fmedian3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::precise::fmedian3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::precise::fmin (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::precise::fmin (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::precise::fmin3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::precise::fmin3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::precise::fmod (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::precise::fmod (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::precise::fract (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::fract (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::frexp (bfloat16_t x, thread int &exp)
METAL_FUNC bfloat16_t metal::precise::frexp (bfloat16_t x, thread int &exp)
 
METAL_FUNC bfloat16_t metal::precise::ldexp (bfloat16_t x, int k)
METAL_FUNC bfloat16_t metal::precise::ldexp (bfloat16_t x, int k)
 
METAL_FUNC bfloat16_t metal::precise::log (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::log (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::log10 (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::log10 (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::log2 (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::log2 (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::max (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::precise::max (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::precise::max3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::precise::max3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::precise::median3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::precise::median3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::precise::min (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::precise::min (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::precise::min3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
METAL_FUNC bfloat16_t metal::precise::min3 (bfloat16_t x, bfloat16_t y, bfloat16_t z)
 
METAL_FUNC bfloat16_t metal::precise::nextafter (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::precise::nextafter (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::precise::pow (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::precise::pow (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::precise::powr (bfloat16_t x, bfloat16_t y)
METAL_FUNC bfloat16_t metal::precise::powr (bfloat16_t x, bfloat16_t y)
 
METAL_FUNC bfloat16_t metal::precise::rint (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::rint (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::round (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::round (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::rsqrt (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::rsqrt (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::sin (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::sin (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::sinh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::sinh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::sinpi (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::sinpi (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::sqrt (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::sqrt (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::tan (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::tan (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::tanh (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::tanh (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::tanpi (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::tanpi (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::precise::trunc (bfloat16_t x)
METAL_FUNC bfloat16_t metal::precise::trunc (bfloat16_t x)
 
METAL_FUNC bfloat16_t metal::simd_broadcast (bfloat16_t data, ushort broadcast_lane_id)
METAL_FUNC bfloat16_t metal::simd_broadcast (bfloat16_t data, ushort broadcast_lane_id)
 
METAL_FUNC bfloat16_t metal::simd_shuffle (bfloat16_t data, ushort simd_lane_id)
METAL_FUNC bfloat16_t metal::simd_shuffle (bfloat16_t data, ushort simd_lane_id)
 
METAL_FUNC bfloat16_t metal::simd_shuffle_and_fill_down (bfloat16_t data, bfloat16_t filling_data, ushort delta, ushort modulo)
METAL_FUNC bfloat16_t metal::simd_shuffle_and_fill_down (bfloat16_t data, bfloat16_t filling_data, ushort delta, ushort modulo)
 
METAL_FUNC bfloat16_t metal::simd_shuffle_and_fill_down (bfloat16_t data, bfloat16_t filling_data, ushort delta)
METAL_FUNC bfloat16_t metal::simd_shuffle_and_fill_down (bfloat16_t data, bfloat16_t filling_data, ushort delta)
 
METAL_FUNC bfloat16_t metal::simd_shuffle_and_fill_up (bfloat16_t data, bfloat16_t filling_data, ushort delta, ushort modulo)
METAL_FUNC bfloat16_t metal::simd_shuffle_and_fill_up (bfloat16_t data, bfloat16_t filling_data, ushort delta, ushort modulo)
 
METAL_FUNC bfloat16_t metal::simd_shuffle_and_fill_up (bfloat16_t data, bfloat16_t filling_data, ushort delta)
METAL_FUNC bfloat16_t metal::simd_shuffle_and_fill_up (bfloat16_t data, bfloat16_t filling_data, ushort delta)
 
METAL_FUNC bfloat16_t metal::simd_shuffle_down (bfloat16_t data, ushort delta)
METAL_FUNC bfloat16_t metal::simd_shuffle_down (bfloat16_t data, ushort delta)
 
METAL_FUNC bfloat16_t metal::simd_shuffle_rotate_down (bfloat16_t data, ushort delta)
METAL_FUNC bfloat16_t metal::simd_shuffle_rotate_down (bfloat16_t data, ushort delta)
 
METAL_FUNC bfloat16_t metal::simd_shuffle_rotate_up (bfloat16_t data, ushort delta)
METAL_FUNC bfloat16_t metal::simd_shuffle_rotate_up (bfloat16_t data, ushort delta)
 
METAL_FUNC bfloat16_t metal::simd_shuffle_up (bfloat16_t data, ushort delta)
METAL_FUNC bfloat16_t metal::simd_shuffle_up (bfloat16_t data, ushort delta)
 
METAL_FUNC bfloat16_t metal::simd_shuffle_xor (bfloat16_t data, ushort mask)
METAL_FUNC bfloat16_t metal::simd_shuffle_xor (bfloat16_t data, ushort mask)
 
METAL_FUNC bfloat16_t metal::simd_max (bfloat16_t data)
METAL_FUNC bfloat16_t metal::simd_max (bfloat16_t data)
 
METAL_FUNC bfloat16_t metal::simd_min (bfloat16_t data)
METAL_FUNC bfloat16_t metal::simd_min (bfloat16_t data)
 
METAL_FUNC bfloat16_t metal::simd_prefix_exclusive_product (bfloat16_t data)
METAL_FUNC bfloat16_t metal::simd_prefix_exclusive_product (bfloat16_t data)
 
METAL_FUNC bfloat16_t metal::simd_prefix_exclusive_sum (bfloat16_t data)
METAL_FUNC bfloat16_t metal::simd_prefix_exclusive_sum (bfloat16_t data)
 
METAL_FUNC bfloat16_t metal::simd_prefix_inclusive_product (bfloat16_t data)
METAL_FUNC bfloat16_t metal::simd_prefix_inclusive_product (bfloat16_t data)
 
METAL_FUNC bfloat16_t metal::simd_prefix_inclusive_sum (bfloat16_t data)
METAL_FUNC bfloat16_t metal::simd_prefix_inclusive_sum (bfloat16_t data)
 
METAL_FUNC bfloat16_t metal::simd_product (bfloat16_t data)
METAL_FUNC bfloat16_t metal::simd_product (bfloat16_t data)
 
METAL_FUNC bfloat16_t metal::simd_sum (bfloat16_t data)
METAL_FUNC bfloat16_t metal::simd_sum (bfloat16_t data)
 
METAL_FUNC bfloat16_t metal::simd_xor (bfloat16_t data)
METAL_FUNC bfloat16_t metal::simd_xor (bfloat16_t data)
 

Macro Definition Documentation

- -

◆ bfloat16_to_uint16

- -
-
- - - - - - - -
#define bfloat16_to_uint16( x)
-
-Value:
x.bits_
-
-
-

◆ instantiate_metal_math_funcs

@@ -580,26 +557,6 @@ Functions
-
-
- -

◆ uint16_to_bfloat16

- -
-
- - - - - - - -
#define uint16_to_bfloat16( x)
-
-Value:
_MLX_BFloat16(x, _MLX_BFloat16::bits_to_bfloat())
-
_MLX_BFloat16
Definition bf16.h:54
-
_MLX_BFloat16::bits_to_bfloat
static constexpr METAL_FUNC bits_to_bfloat_struct bits_to_bfloat()
Definition bf16.h:64
-
diff --git a/docs/build/html/bf16__math_8h_source.html b/docs/build/html/bf16__math_8h_source.html index c7b8985d..e060315d 100644 --- a/docs/build/html/bf16__math_8h_source.html +++ b/docs/build/html/bf16__math_8h_source.html @@ -95,408 +95,395 @@ $(function(){ initResizable(false); });
2
3#pragma once
4
-
5#include "mlx/backend/metal/kernels/bf16.h"
-
6
-
8// Metal math for bfloat16
-
10
-
11/*
+
6// Metal math for bfloat16
+
8
+
9/*
+
10
+
11Following the Metal Shading Language Specification (Metal 3.1)
12
-
13Following the Metal Shading Language Specification (Metal 3.1)
-
14
-
15"bfloat is an extended itypeing point type that only allows implicit conversion
-
16 to a type of greater itypeing point rank. While bfloat can be implicitly
-
17 converted to itype, it cannot be implicitly converted to half, and neither
-
18 itype nor half can be implicitly converted to bfloat."
-
19
-
20Further, as far as I can tell, the stdlib math/simd functions are not defined
-
21for bfloat and calling with an argument of type bfloat will result in that
-
22argument getting implicitly converted to itype which then returns an output
-
23that is (likely) a itype which cannot be implicitly converted into a bfloat
-
24
-
25This leads to situations where
-
26bfloat a = 5.0bf;
-
27bfloat b = metal::abs(a); // this will throw an error since abs return itype
-
28bfloat c = static_cast<bfloat>(metal::abs(a)); // this is fine
-
29
-
30For the moment, I will be adding overloaded instantiations of the math
-
31functions to accordingly automatically handle the casting
-
32
-
33*/
-
34
-
-
35#define instantiate_metal_math_funcs(itype, otype, ctype, mfast) \
-
36 \
-
37 METAL_FUNC otype abs(itype x) { \
-
38 return static_cast<otype>(__metal_fabs(static_cast<ctype>(x), mfast)); \
-
39 } \
-
40 METAL_FUNC otype acos(itype x) { \
-
41 return static_cast<otype>(__metal_acos(static_cast<ctype>(x), mfast)); \
-
42 } \
-
43 METAL_FUNC otype acosh(itype x) { \
-
44 return static_cast<otype>(__metal_acosh(static_cast<ctype>(x), mfast)); \
-
45 } \
-
46 METAL_FUNC otype asin(itype x) { \
-
47 return static_cast<otype>(__metal_asin(static_cast<ctype>(x), mfast)); \
-
48 } \
-
49 METAL_FUNC otype asinh(itype x) { \
-
50 return static_cast<otype>(__metal_asinh(static_cast<ctype>(x), mfast)); \
-
51 } \
-
52 METAL_FUNC otype atan(itype y_over_x) { \
-
53 return static_cast<otype>( \
-
54 __metal_atan(static_cast<ctype>(y_over_x), mfast)); \
-
55 } \
-
56 METAL_FUNC otype atan2(itype y, itype x) { \
-
57 return static_cast<otype>( \
-
58 __metal_atan2(static_cast<ctype>(y), static_cast<ctype>(x), mfast)); \
-
59 } \
-
60 METAL_FUNC otype atanh(itype x) { \
-
61 return static_cast<otype>(__metal_atanh(static_cast<ctype>(x), mfast)); \
-
62 } \
-
63 METAL_FUNC otype ceil(itype x) { \
-
64 return static_cast<otype>(__metal_ceil(static_cast<ctype>(x), mfast)); \
-
65 } \
-
66 METAL_FUNC otype cos(itype x) { \
-
67 return static_cast<otype>(__metal_cos(static_cast<ctype>(x), mfast)); \
-
68 } \
-
69 METAL_FUNC otype cosh(itype x) { \
-
70 return static_cast<otype>(__metal_cosh(static_cast<ctype>(x), mfast)); \
-
71 } \
-
72 METAL_FUNC otype cospi(itype x) { \
-
73 return static_cast<otype>(__metal_cospi(static_cast<ctype>(x), mfast)); \
-
74 } \
-
75 METAL_FUNC otype divide(itype x, itype y) { \
-
76 return static_cast<otype>( \
-
77 __metal_divide(static_cast<ctype>(x), static_cast<ctype>(y), mfast)); \
-
78 } \
-
79 METAL_FUNC otype exp(itype x) { \
-
80 return static_cast<otype>(__metal_exp(static_cast<ctype>(x), mfast)); \
-
81 } \
-
82 METAL_FUNC otype exp10(itype x) { \
-
83 return static_cast<otype>(__metal_exp10(static_cast<ctype>(x), mfast)); \
-
84 } \
-
85 METAL_FUNC otype exp2(itype x) { \
-
86 return static_cast<otype>(__metal_exp2(static_cast<ctype>(x), mfast)); \
-
87 } \
-
88 METAL_FUNC otype fabs(itype x) { \
-
89 return static_cast<otype>(__metal_fabs(static_cast<ctype>(x), mfast)); \
-
90 } \
-
91 METAL_FUNC otype fdim(itype x, itype y) { \
-
92 ctype t = static_cast<ctype>(x - y); \
-
93 return static_cast<otype>(select(t, ctype(0), t < ctype(0) || x == y)); \
-
94 } \
-
95 METAL_FUNC otype floor(itype x) { \
-
96 return static_cast<otype>(__metal_floor(static_cast<ctype>(x), mfast)); \
-
97 } \
-
98 METAL_FUNC otype fma(itype x, itype y, itype z) { \
-
99 return static_cast<otype>(__metal_fma( \
-
100 static_cast<ctype>(x), static_cast<ctype>(y), static_cast<ctype>(z))); \
-
101 } \
-
102 METAL_FUNC otype fmax(itype x, itype y) { \
-
103 return static_cast<otype>( \
-
104 __metal_fmax(static_cast<ctype>(x), static_cast<ctype>(y), mfast)); \
-
105 } \
-
106 METAL_FUNC otype fmax3(itype x, itype y, itype z) { \
-
107 return static_cast<otype>(__metal_fmax3( \
-
108 static_cast<ctype>(x), \
-
109 static_cast<ctype>(y), \
-
110 static_cast<ctype>(z), \
-
111 mfast)); \
-
112 } \
-
113 METAL_FUNC otype fmedian3(itype x, itype y, itype z) { \
-
114 return static_cast<otype>(__metal_fmedian3( \
-
115 static_cast<ctype>(x), \
-
116 static_cast<ctype>(y), \
-
117 static_cast<ctype>(z), \
-
118 mfast)); \
-
119 } \
-
120 METAL_FUNC otype fmin(itype x, itype y) { \
-
121 return static_cast<otype>( \
-
122 __metal_fmin(static_cast<ctype>(x), static_cast<ctype>(y), mfast)); \
-
123 } \
-
124 METAL_FUNC otype fmin3(itype x, itype y, itype z) { \
-
125 return static_cast<otype>(__metal_fmin3( \
-
126 static_cast<ctype>(x), \
-
127 static_cast<ctype>(y), \
-
128 static_cast<ctype>(z), \
-
129 mfast)); \
-
130 } \
-
131 METAL_FUNC otype fmod(itype x, itype y) { \
-
132 return static_cast<otype>( \
-
133 __metal_fmod(static_cast<ctype>(x), static_cast<ctype>(y), mfast)); \
-
134 } \
-
135 METAL_FUNC otype fract(itype x) { \
-
136 return static_cast<otype>(__metal_fract(static_cast<ctype>(x), mfast)); \
-
137 } \
-
138 METAL_FUNC otype frexp(itype x, thread int& exp) { \
-
139 return static_cast<otype>(__metal_frexp(static_cast<ctype>(x), &exp)); \
-
140 } \
-
141 METAL_FUNC otype ldexp(itype x, int k) { \
-
142 return static_cast<otype>(__metal_ldexp(static_cast<ctype>(x), k, mfast)); \
-
143 } \
-
144 METAL_FUNC otype log(itype x) { \
-
145 return static_cast<otype>(__metal_log(static_cast<ctype>(x), mfast)); \
-
146 } \
-
147 METAL_FUNC otype log10(itype x) { \
-
148 return static_cast<otype>(__metal_log10(static_cast<ctype>(x), mfast)); \
-
149 } \
-
150 METAL_FUNC otype log2(itype x) { \
-
151 return static_cast<otype>(__metal_log2(static_cast<ctype>(x), mfast)); \
-
152 } \
-
153 METAL_FUNC otype max(itype x, itype y) { \
-
154 return static_cast<otype>( \
-
155 __metal_fmax(static_cast<ctype>(x), static_cast<ctype>(y), mfast)); \
-
156 } \
-
157 METAL_FUNC otype max3(itype x, itype y, itype z) { \
-
158 return static_cast<otype>(__metal_fmax3( \
-
159 static_cast<ctype>(x), \
-
160 static_cast<ctype>(y), \
-
161 static_cast<ctype>(z), \
-
162 mfast)); \
-
163 } \
-
164 METAL_FUNC otype median3(itype x, itype y, itype z) { \
-
165 return static_cast<otype>(__metal_fmedian3( \
-
166 static_cast<ctype>(x), \
-
167 static_cast<ctype>(y), \
-
168 static_cast<ctype>(z), \
-
169 mfast)); \
-
170 } \
-
171 METAL_FUNC otype min(itype x, itype y) { \
-
172 return static_cast<otype>( \
-
173 __metal_fmin(static_cast<ctype>(x), static_cast<ctype>(y), mfast)); \
-
174 } \
-
175 METAL_FUNC otype min3(itype x, itype y, itype z) { \
-
176 return static_cast<otype>(__metal_fmin3( \
-
177 static_cast<ctype>(x), \
-
178 static_cast<ctype>(y), \
-
179 static_cast<ctype>(z), \
-
180 mfast)); \
-
181 } \
-
182 METAL_FUNC otype nextafter(itype x, itype y) { \
-
183 return static_cast<otype>( \
-
184 __metal_nextafter(static_cast<ctype>(x), static_cast<ctype>(y))); \
-
185 } \
-
186 METAL_FUNC otype pow(itype x, itype y) { \
-
187 return static_cast<otype>( \
-
188 __metal_pow(static_cast<ctype>(x), static_cast<ctype>(y), mfast)); \
-
189 } \
-
190 METAL_FUNC otype powr(itype x, itype y) { \
-
191 return static_cast<otype>( \
-
192 __metal_powr(static_cast<ctype>(x), static_cast<ctype>(y), mfast)); \
-
193 } \
-
194 METAL_FUNC otype rint(itype x) { \
-
195 return static_cast<otype>(__metal_rint(static_cast<ctype>(x), mfast)); \
-
196 } \
-
197 METAL_FUNC otype round(itype x) { \
-
198 return static_cast<otype>(__metal_round(static_cast<ctype>(x), mfast)); \
-
199 } \
-
200 METAL_FUNC otype rsqrt(itype x) { \
-
201 return static_cast<otype>(__metal_rsqrt(static_cast<ctype>(x), mfast)); \
-
202 } \
-
203 METAL_FUNC otype sin(itype x) { \
-
204 return static_cast<otype>(__metal_sin(static_cast<ctype>(x), mfast)); \
-
205 } \
-
206 METAL_FUNC otype sinh(itype x) { \
-
207 return static_cast<otype>(__metal_sinh(static_cast<ctype>(x), mfast)); \
-
208 } \
-
209 METAL_FUNC otype sinpi(itype x) { \
-
210 return static_cast<otype>(__metal_sinpi(static_cast<ctype>(x), mfast)); \
-
211 } \
-
212 METAL_FUNC otype sqrt(itype x) { \
-
213 return static_cast<otype>(__metal_sqrt(static_cast<ctype>(x), mfast)); \
-
214 } \
-
215 METAL_FUNC otype tan(itype x) { \
-
216 return static_cast<otype>(__metal_tan(static_cast<ctype>(x), mfast)); \
-
217 } \
-
218 METAL_FUNC otype tanh(itype x) { \
-
219 return static_cast<otype>(__metal_tanh(static_cast<ctype>(x), mfast)); \
-
220 } \
-
221 METAL_FUNC otype tanpi(itype x) { \
-
222 return static_cast<otype>(__metal_tanpi(static_cast<ctype>(x), mfast)); \
-
223 } \
-
224 METAL_FUNC otype trunc(itype x) { \
-
225 return static_cast<otype>(__metal_trunc(static_cast<ctype>(x), mfast)); \
-
226 }
+
13"bfloat is an extended itypeing point type that only allows implicit conversion
+
14 to a type of greater itypeing point rank. While bfloat can be implicitly
+
15 converted to itype, it cannot be implicitly converted to half, and neither
+
16 itype nor half can be implicitly converted to bfloat."
+
17
+
18Further, as far as I can tell, the stdlib math/simd functions are not defined
+
19for bfloat and calling with an argument of type bfloat will result in that
+
20argument getting implicitly converted to itype which then returns an output
+
21that is (likely) a itype which cannot be implicitly converted into a bfloat
+
22
+
23This leads to situations where
+
24bfloat a = 5.0bf;
+
25bfloat b = metal::abs(a); // this will throw an error since abs return itype
+
26bfloat c = static_cast<bfloat>(metal::abs(a)); // this is fine
+
27
+
28For the moment, I will be adding overloaded instantiations of the math
+
29functions to accordingly automatically handle the casting
+
30
+
31*/
+
32
+
+
33#define instantiate_metal_math_funcs(itype, otype, ctype, mfast) \
+
34 \
+
35 METAL_FUNC otype abs(itype x) { \
+
36 return static_cast<otype>(__metal_fabs(static_cast<ctype>(x), mfast)); \
+
37 } \
+
38 METAL_FUNC otype acos(itype x) { \
+
39 return static_cast<otype>(__metal_acos(static_cast<ctype>(x), mfast)); \
+
40 } \
+
41 METAL_FUNC otype acosh(itype x) { \
+
42 return static_cast<otype>(__metal_acosh(static_cast<ctype>(x), mfast)); \
+
43 } \
+
44 METAL_FUNC otype asin(itype x) { \
+
45 return static_cast<otype>(__metal_asin(static_cast<ctype>(x), mfast)); \
+
46 } \
+
47 METAL_FUNC otype asinh(itype x) { \
+
48 return static_cast<otype>(__metal_asinh(static_cast<ctype>(x), mfast)); \
+
49 } \
+
50 METAL_FUNC otype atan(itype y_over_x) { \
+
51 return static_cast<otype>( \
+
52 __metal_atan(static_cast<ctype>(y_over_x), mfast)); \
+
53 } \
+
54 METAL_FUNC otype atan2(itype y, itype x) { \
+
55 return static_cast<otype>( \
+
56 __metal_atan2(static_cast<ctype>(y), static_cast<ctype>(x), mfast)); \
+
57 } \
+
58 METAL_FUNC otype atanh(itype x) { \
+
59 return static_cast<otype>(__metal_atanh(static_cast<ctype>(x), mfast)); \
+
60 } \
+
61 METAL_FUNC otype ceil(itype x) { \
+
62 return static_cast<otype>(__metal_ceil(static_cast<ctype>(x), mfast)); \
+
63 } \
+
64 METAL_FUNC otype cos(itype x) { \
+
65 return static_cast<otype>(__metal_cos(static_cast<ctype>(x), mfast)); \
+
66 } \
+
67 METAL_FUNC otype cosh(itype x) { \
+
68 return static_cast<otype>(__metal_cosh(static_cast<ctype>(x), mfast)); \
+
69 } \
+
70 METAL_FUNC otype cospi(itype x) { \
+
71 return static_cast<otype>(__metal_cospi(static_cast<ctype>(x), mfast)); \
+
72 } \
+
73 METAL_FUNC otype divide(itype x, itype y) { \
+
74 return static_cast<otype>( \
+
75 __metal_divide(static_cast<ctype>(x), static_cast<ctype>(y), mfast)); \
+
76 } \
+
77 METAL_FUNC otype exp(itype x) { \
+
78 return static_cast<otype>(__metal_exp(static_cast<ctype>(x), mfast)); \
+
79 } \
+
80 METAL_FUNC otype exp10(itype x) { \
+
81 return static_cast<otype>(__metal_exp10(static_cast<ctype>(x), mfast)); \
+
82 } \
+
83 METAL_FUNC otype exp2(itype x) { \
+
84 return static_cast<otype>(__metal_exp2(static_cast<ctype>(x), mfast)); \
+
85 } \
+
86 METAL_FUNC otype fabs(itype x) { \
+
87 return static_cast<otype>(__metal_fabs(static_cast<ctype>(x), mfast)); \
+
88 } \
+
89 METAL_FUNC otype fdim(itype x, itype y) { \
+
90 ctype t = static_cast<ctype>(x - y); \
+
91 return static_cast<otype>(select(t, ctype(0), t < ctype(0) || x == y)); \
+
92 } \
+
93 METAL_FUNC otype floor(itype x) { \
+
94 return static_cast<otype>(__metal_floor(static_cast<ctype>(x), mfast)); \
+
95 } \
+
96 METAL_FUNC otype fma(itype x, itype y, itype z) { \
+
97 return static_cast<otype>(__metal_fma( \
+
98 static_cast<ctype>(x), static_cast<ctype>(y), static_cast<ctype>(z))); \
+
99 } \
+
100 METAL_FUNC otype fmax(itype x, itype y) { \
+
101 return static_cast<otype>( \
+
102 __metal_fmax(static_cast<ctype>(x), static_cast<ctype>(y), mfast)); \
+
103 } \
+
104 METAL_FUNC otype fmax3(itype x, itype y, itype z) { \
+
105 return static_cast<otype>(__metal_fmax3( \
+
106 static_cast<ctype>(x), \
+
107 static_cast<ctype>(y), \
+
108 static_cast<ctype>(z), \
+
109 mfast)); \
+
110 } \
+
111 METAL_FUNC otype fmedian3(itype x, itype y, itype z) { \
+
112 return static_cast<otype>(__metal_fmedian3( \
+
113 static_cast<ctype>(x), \
+
114 static_cast<ctype>(y), \
+
115 static_cast<ctype>(z), \
+
116 mfast)); \
+
117 } \
+
118 METAL_FUNC otype fmin(itype x, itype y) { \
+
119 return static_cast<otype>( \
+
120 __metal_fmin(static_cast<ctype>(x), static_cast<ctype>(y), mfast)); \
+
121 } \
+
122 METAL_FUNC otype fmin3(itype x, itype y, itype z) { \
+
123 return static_cast<otype>(__metal_fmin3( \
+
124 static_cast<ctype>(x), \
+
125 static_cast<ctype>(y), \
+
126 static_cast<ctype>(z), \
+
127 mfast)); \
+
128 } \
+
129 METAL_FUNC otype fmod(itype x, itype y) { \
+
130 return static_cast<otype>( \
+
131 __metal_fmod(static_cast<ctype>(x), static_cast<ctype>(y), mfast)); \
+
132 } \
+
133 METAL_FUNC otype fract(itype x) { \
+
134 return static_cast<otype>(__metal_fract(static_cast<ctype>(x), mfast)); \
+
135 } \
+
136 METAL_FUNC otype frexp(itype x, thread int& exp) { \
+
137 return static_cast<otype>(__metal_frexp(static_cast<ctype>(x), &exp)); \
+
138 } \
+
139 METAL_FUNC otype ldexp(itype x, int k) { \
+
140 return static_cast<otype>(__metal_ldexp(static_cast<ctype>(x), k, mfast)); \
+
141 } \
+
142 METAL_FUNC otype log(itype x) { \
+
143 return static_cast<otype>(__metal_log(static_cast<ctype>(x), mfast)); \
+
144 } \
+
145 METAL_FUNC otype log10(itype x) { \
+
146 return static_cast<otype>(__metal_log10(static_cast<ctype>(x), mfast)); \
+
147 } \
+
148 METAL_FUNC otype log2(itype x) { \
+
149 return static_cast<otype>(__metal_log2(static_cast<ctype>(x), mfast)); \
+
150 } \
+
151 METAL_FUNC otype max(itype x, itype y) { \
+
152 return static_cast<otype>( \
+
153 __metal_fmax(static_cast<ctype>(x), static_cast<ctype>(y), mfast)); \
+
154 } \
+
155 METAL_FUNC otype max3(itype x, itype y, itype z) { \
+
156 return static_cast<otype>(__metal_fmax3( \
+
157 static_cast<ctype>(x), \
+
158 static_cast<ctype>(y), \
+
159 static_cast<ctype>(z), \
+
160 mfast)); \
+
161 } \
+
162 METAL_FUNC otype median3(itype x, itype y, itype z) { \
+
163 return static_cast<otype>(__metal_fmedian3( \
+
164 static_cast<ctype>(x), \
+
165 static_cast<ctype>(y), \
+
166 static_cast<ctype>(z), \
+
167 mfast)); \
+
168 } \
+
169 METAL_FUNC otype min(itype x, itype y) { \
+
170 return static_cast<otype>( \
+
171 __metal_fmin(static_cast<ctype>(x), static_cast<ctype>(y), mfast)); \
+
172 } \
+
173 METAL_FUNC otype min3(itype x, itype y, itype z) { \
+
174 return static_cast<otype>(__metal_fmin3( \
+
175 static_cast<ctype>(x), \
+
176 static_cast<ctype>(y), \
+
177 static_cast<ctype>(z), \
+
178 mfast)); \
+
179 } \
+
180 METAL_FUNC otype nextafter(itype x, itype y) { \
+
181 return static_cast<otype>( \
+
182 __metal_nextafter(static_cast<ctype>(x), static_cast<ctype>(y))); \
+
183 } \
+
184 METAL_FUNC otype pow(itype x, itype y) { \
+
185 return static_cast<otype>( \
+
186 __metal_pow(static_cast<ctype>(x), static_cast<ctype>(y), mfast)); \
+
187 } \
+
188 METAL_FUNC otype powr(itype x, itype y) { \
+
189 return static_cast<otype>( \
+
190 __metal_powr(static_cast<ctype>(x), static_cast<ctype>(y), mfast)); \
+
191 } \
+
192 METAL_FUNC otype rint(itype x) { \
+
193 return static_cast<otype>(__metal_rint(static_cast<ctype>(x), mfast)); \
+
194 } \
+
195 METAL_FUNC otype round(itype x) { \
+
196 return static_cast<otype>(__metal_round(static_cast<ctype>(x), mfast)); \
+
197 } \
+
198 METAL_FUNC otype rsqrt(itype x) { \
+
199 return static_cast<otype>(__metal_rsqrt(static_cast<ctype>(x), mfast)); \
+
200 } \
+
201 METAL_FUNC otype sin(itype x) { \
+
202 return static_cast<otype>(__metal_sin(static_cast<ctype>(x), mfast)); \
+
203 } \
+
204 METAL_FUNC otype sinh(itype x) { \
+
205 return static_cast<otype>(__metal_sinh(static_cast<ctype>(x), mfast)); \
+
206 } \
+
207 METAL_FUNC otype sinpi(itype x) { \
+
208 return static_cast<otype>(__metal_sinpi(static_cast<ctype>(x), mfast)); \
+
209 } \
+
210 METAL_FUNC otype sqrt(itype x) { \
+
211 return static_cast<otype>(__metal_sqrt(static_cast<ctype>(x), mfast)); \
+
212 } \
+
213 METAL_FUNC otype tan(itype x) { \
+
214 return static_cast<otype>(__metal_tan(static_cast<ctype>(x), mfast)); \
+
215 } \
+
216 METAL_FUNC otype tanh(itype x) { \
+
217 return static_cast<otype>(__metal_tanh(static_cast<ctype>(x), mfast)); \
+
218 } \
+
219 METAL_FUNC otype tanpi(itype x) { \
+
220 return static_cast<otype>(__metal_tanpi(static_cast<ctype>(x), mfast)); \
+
221 } \
+
222 METAL_FUNC otype trunc(itype x) { \
+
223 return static_cast<otype>(__metal_trunc(static_cast<ctype>(x), mfast)); \
+
224 }
+
225
+
+
226namespace metal {
227
-
228namespace metal {
-
229
-
230instantiate_metal_math_funcs(
-
231 bfloat16_t,
-
232 bfloat16_t,
-
233 float,
-
234 __METAL_MAYBE_FAST_MATH__);
+
228instantiate_metal_math_funcs(
+
229 bfloat16_t,
+
230 bfloat16_t,
+
231 float,
+
232 __METAL_MAYBE_FAST_MATH__);
+
233
+
+
234namespace fast {
235
-
-
236namespace fast {
-
237
-
238instantiate_metal_math_funcs(
-
239 bfloat16_t,
-
240 bfloat16_t,
-
241 float,
-
242 __METAL_FAST_MATH__);
-
243
-
244} // namespace fast
+
236instantiate_metal_math_funcs(
+
237 bfloat16_t,
+
238 bfloat16_t,
+
239 float,
+
240 __METAL_FAST_MATH__);
+
241
+
242} // namespace fast
+
243
+
+
244namespace precise {
245
-
-
246namespace precise {
-
247
-
248instantiate_metal_math_funcs(
-
249 bfloat16_t,
-
250 bfloat16_t,
-
251 float,
-
252 __METAL_PRECISE_MATH__);
+
246instantiate_metal_math_funcs(
+
247 bfloat16_t,
+
248 bfloat16_t,
+
249 float,
+
250 __METAL_PRECISE_MATH__);
+
251
+
252} // namespace precise
+
253
-
254} // namespace precise
+
254} // namespace metal
255
-
256} // namespace metal
-
257
-
259// Metal simd for bfloat16
-
261
-
262#define instantiate_metal_simd_comm_funcs( \
-
263 itype, otype, ctype, itype_to_ctype, ctype_to_otype) \
-
264 \
-
265 METAL_FUNC otype simd_broadcast(itype data, ushort broadcast_lane_id) { \
-
266 return ctype_to_otype( \
-
267 __metal_simd_broadcast(itype_to_ctype(data), broadcast_lane_id)); \
-
268 } \
-
269 \
-
270 METAL_FUNC otype simd_shuffle(itype data, ushort simd_lane_id) { \
-
271 return ctype_to_otype( \
-
272 __metal_simd_shuffle(itype_to_ctype(data), simd_lane_id)); \
-
273 } \
-
274 \
-
275 METAL_FUNC otype simd_shuffle_and_fill_down( \
-
276 itype data, itype filling_data, ushort delta, ushort modulo) { \
-
277 return ctype_to_otype(__metal_simd_shuffle_and_fill_down( \
-
278 itype_to_ctype(data), itype_to_ctype(filling_data), delta, modulo)); \
-
279 } \
-
280 \
-
281 METAL_FUNC otype simd_shuffle_and_fill_down( \
-
282 itype data, itype filling_data, ushort delta) { \
-
283 return ctype_to_otype(__metal_simd_shuffle_and_fill_down( \
-
284 itype_to_ctype(data), \
-
285 itype_to_ctype(filling_data), \
-
286 delta, \
-
287 __metal_get_simdgroup_size(ushort()))); \
-
288 } \
-
289 \
-
290 METAL_FUNC otype simd_shuffle_and_fill_up( \
-
291 itype data, itype filling_data, ushort delta, ushort modulo) { \
-
292 return ctype_to_otype(__metal_simd_shuffle_and_fill_up( \
-
293 itype_to_ctype(data), itype_to_ctype(filling_data), delta, modulo)); \
-
294 } \
-
295 \
-
296 METAL_FUNC otype simd_shuffle_and_fill_up( \
-
297 itype data, itype filling_data, ushort delta) { \
-
298 return ctype_to_otype(__metal_simd_shuffle_and_fill_up( \
-
299 itype_to_ctype(data), \
-
300 itype_to_ctype(filling_data), \
-
301 delta, \
-
302 __metal_get_simdgroup_size(ushort()))); \
-
303 } \
-
304 \
-
305 METAL_FUNC otype simd_shuffle_down(itype data, ushort delta) { \
-
306 return ctype_to_otype( \
-
307 __metal_simd_shuffle_down(itype_to_ctype(data), delta)); \
-
308 } \
-
309 \
-
310 METAL_FUNC otype simd_shuffle_rotate_down(itype data, ushort delta) { \
-
311 return ctype_to_otype( \
-
312 __metal_simd_shuffle_rotate_down(itype_to_ctype(data), delta)); \
-
313 } \
-
314 \
-
315 METAL_FUNC otype simd_shuffle_rotate_up(itype data, ushort delta) { \
-
316 return ctype_to_otype( \
-
317 __metal_simd_shuffle_rotate_up(itype_to_ctype(data), delta)); \
-
318 } \
-
319 \
-
320 METAL_FUNC otype simd_shuffle_up(itype data, ushort delta) { \
-
321 return ctype_to_otype( \
-
322 __metal_simd_shuffle_up(itype_to_ctype(data), delta)); \
-
323 } \
-
324 \
-
325 METAL_FUNC otype simd_shuffle_xor(itype data, ushort mask) { \
-
326 return ctype_to_otype( \
-
327 __metal_simd_shuffle_xor(itype_to_ctype(data), mask)); \
-
328 }
-
329
-
-
330#define instantiate_metal_simd_reduction_funcs(itype, otype, ctype) \
-
331 \
-
332 METAL_FUNC otype simd_max(itype data) { \
-
333 return static_cast<otype>(__metal_simd_max(static_cast<ctype>(data))); \
-
334 } \
-
335 \
-
336 METAL_FUNC otype simd_min(itype data) { \
-
337 return static_cast<otype>(__metal_simd_min(static_cast<ctype>(data))); \
-
338 } \
-
339 \
-
340 METAL_FUNC otype simd_prefix_exclusive_product(itype data) { \
-
341 return static_cast<otype>( \
-
342 __metal_simd_prefix_exclusive_product(static_cast<ctype>(data))); \
-
343 } \
-
344 \
-
345 METAL_FUNC otype simd_prefix_exclusive_sum(itype data) { \
-
346 return static_cast<otype>( \
-
347 __metal_simd_prefix_exclusive_sum(static_cast<ctype>(data))); \
-
348 } \
-
349 \
-
350 METAL_FUNC otype simd_prefix_inclusive_product(itype data) { \
-
351 return static_cast<otype>( \
-
352 __metal_simd_prefix_inclusive_product(static_cast<ctype>(data))); \
-
353 } \
-
354 \
-
355 METAL_FUNC otype simd_prefix_inclusive_sum(itype data) { \
-
356 return static_cast<otype>( \
-
357 __metal_simd_prefix_inclusive_sum(static_cast<ctype>(data))); \
-
358 } \
-
359 \
-
360 METAL_FUNC otype simd_product(itype data) { \
-
361 return static_cast<otype>(__metal_simd_product(static_cast<ctype>(data))); \
-
362 } \
-
363 \
-
364 METAL_FUNC otype simd_sum(itype data) { \
-
365 return static_cast<otype>(__metal_simd_sum(static_cast<ctype>(data))); \
-
366 } \
-
367 \
-
368 METAL_FUNC otype simd_xor(itype data) { \
-
369 return static_cast<otype>(__metal_simd_xor(static_cast<ctype>(data))); \
-
370 }
+
257// Metal simd for bfloat16
+
259
+
260#define instantiate_metal_simd_comm_funcs( \
+
261 itype, otype, ctype, itype_to_ctype, ctype_to_otype) \
+
262 \
+
263 METAL_FUNC otype simd_broadcast(itype data, ushort broadcast_lane_id) { \
+
264 return ctype_to_otype( \
+
265 __metal_simd_broadcast(itype_to_ctype(data), broadcast_lane_id)); \
+
266 } \
+
267 \
+
268 METAL_FUNC otype simd_shuffle(itype data, ushort simd_lane_id) { \
+
269 return ctype_to_otype( \
+
270 __metal_simd_shuffle(itype_to_ctype(data), simd_lane_id)); \
+
271 } \
+
272 \
+
273 METAL_FUNC otype simd_shuffle_and_fill_down( \
+
274 itype data, itype filling_data, ushort delta, ushort modulo) { \
+
275 return ctype_to_otype(__metal_simd_shuffle_and_fill_down( \
+
276 itype_to_ctype(data), itype_to_ctype(filling_data), delta, modulo)); \
+
277 } \
+
278 \
+
279 METAL_FUNC otype simd_shuffle_and_fill_down( \
+
280 itype data, itype filling_data, ushort delta) { \
+
281 return ctype_to_otype(__metal_simd_shuffle_and_fill_down( \
+
282 itype_to_ctype(data), \
+
283 itype_to_ctype(filling_data), \
+
284 delta, \
+
285 __metal_get_simdgroup_size(ushort()))); \
+
286 } \
+
287 \
+
288 METAL_FUNC otype simd_shuffle_and_fill_up( \
+
289 itype data, itype filling_data, ushort delta, ushort modulo) { \
+
290 return ctype_to_otype(__metal_simd_shuffle_and_fill_up( \
+
291 itype_to_ctype(data), itype_to_ctype(filling_data), delta, modulo)); \
+
292 } \
+
293 \
+
294 METAL_FUNC otype simd_shuffle_and_fill_up( \
+
295 itype data, itype filling_data, ushort delta) { \
+
296 return ctype_to_otype(__metal_simd_shuffle_and_fill_up( \
+
297 itype_to_ctype(data), \
+
298 itype_to_ctype(filling_data), \
+
299 delta, \
+
300 __metal_get_simdgroup_size(ushort()))); \
+
301 } \
+
302 \
+
303 METAL_FUNC otype simd_shuffle_down(itype data, ushort delta) { \
+
304 return ctype_to_otype( \
+
305 __metal_simd_shuffle_down(itype_to_ctype(data), delta)); \
+
306 } \
+
307 \
+
308 METAL_FUNC otype simd_shuffle_rotate_down(itype data, ushort delta) { \
+
309 return ctype_to_otype( \
+
310 __metal_simd_shuffle_rotate_down(itype_to_ctype(data), delta)); \
+
311 } \
+
312 \
+
313 METAL_FUNC otype simd_shuffle_rotate_up(itype data, ushort delta) { \
+
314 return ctype_to_otype( \
+
315 __metal_simd_shuffle_rotate_up(itype_to_ctype(data), delta)); \
+
316 } \
+
317 \
+
318 METAL_FUNC otype simd_shuffle_up(itype data, ushort delta) { \
+
319 return ctype_to_otype( \
+
320 __metal_simd_shuffle_up(itype_to_ctype(data), delta)); \
+
321 } \
+
322 \
+
323 METAL_FUNC otype simd_shuffle_xor(itype data, ushort mask) { \
+
324 return ctype_to_otype( \
+
325 __metal_simd_shuffle_xor(itype_to_ctype(data), mask)); \
+
326 }
+
327
+
+
328#define instantiate_metal_simd_reduction_funcs(itype, otype, ctype) \
+
329 \
+
330 METAL_FUNC otype simd_max(itype data) { \
+
331 return static_cast<otype>(__metal_simd_max(static_cast<ctype>(data))); \
+
332 } \
+
333 \
+
334 METAL_FUNC otype simd_min(itype data) { \
+
335 return static_cast<otype>(__metal_simd_min(static_cast<ctype>(data))); \
+
336 } \
+
337 \
+
338 METAL_FUNC otype simd_prefix_exclusive_product(itype data) { \
+
339 return static_cast<otype>( \
+
340 __metal_simd_prefix_exclusive_product(static_cast<ctype>(data))); \
+
341 } \
+
342 \
+
343 METAL_FUNC otype simd_prefix_exclusive_sum(itype data) { \
+
344 return static_cast<otype>( \
+
345 __metal_simd_prefix_exclusive_sum(static_cast<ctype>(data))); \
+
346 } \
+
347 \
+
348 METAL_FUNC otype simd_prefix_inclusive_product(itype data) { \
+
349 return static_cast<otype>( \
+
350 __metal_simd_prefix_inclusive_product(static_cast<ctype>(data))); \
+
351 } \
+
352 \
+
353 METAL_FUNC otype simd_prefix_inclusive_sum(itype data) { \
+
354 return static_cast<otype>( \
+
355 __metal_simd_prefix_inclusive_sum(static_cast<ctype>(data))); \
+
356 } \
+
357 \
+
358 METAL_FUNC otype simd_product(itype data) { \
+
359 return static_cast<otype>(__metal_simd_product(static_cast<ctype>(data))); \
+
360 } \
+
361 \
+
362 METAL_FUNC otype simd_sum(itype data) { \
+
363 return static_cast<otype>(__metal_simd_sum(static_cast<ctype>(data))); \
+
364 } \
+
365 \
+
366 METAL_FUNC otype simd_xor(itype data) { \
+
367 return static_cast<otype>(__metal_simd_xor(static_cast<ctype>(data))); \
+
368 }
+
369
+
370namespace metal {
371
-
372#if (MLX_METAL_VERSION >= 310) || (__METAL_VERSION__ >= 310)
-
373
-
374#define bfloat16_to_uint16(x) as_type<uint16_t>(x)
-
375#define uint16_to_bfloat16(x) as_type<bfloat16_t>(x)
-
376
-
377#else
-
378
-
379#define bfloat16_to_uint16(x) x.bits_
-
380#define uint16_to_bfloat16(x) _MLX_BFloat16(x, _MLX_BFloat16::bits_to_bfloat())
-
381
-
382#endif
-
383
-
384namespace metal {
-
385
-
386instantiate_metal_simd_comm_funcs(
-
387 bfloat16_t,
-
388 bfloat16_t,
-
389 uint16_t,
-
390 bfloat16_to_uint16,
-
391 uint16_to_bfloat16);
-
392instantiate_metal_simd_reduction_funcs(bfloat16_t, bfloat16_t, float);
-
393
-
394} // namespace metal
- -
uint16_to_bfloat16
#define uint16_to_bfloat16(x)
Definition bf16_math.h:380
-
instantiate_metal_simd_reduction_funcs
#define instantiate_metal_simd_reduction_funcs(itype, otype, ctype)
Definition bf16_math.h:330
-
bfloat16_to_uint16
#define bfloat16_to_uint16(x)
Definition bf16_math.h:379
-
instantiate_metal_math_funcs
#define instantiate_metal_math_funcs(itype, otype, ctype, mfast)
Definition bf16_math.h:35
-
instantiate_metal_simd_comm_funcs
#define instantiate_metal_simd_comm_funcs( itype, otype, ctype, itype_to_ctype, ctype_to_otype)
Definition bf16_math.h:262
-
metal
Definition bf16.h:265
-
_MLX_BFloat16
Definition bf16.h:54
+
372instantiate_metal_simd_comm_funcs(
+
373 bfloat16_t,
+
374 bfloat16_t,
+
375 uint16_t,
+
376 bfloat16_to_uint16,
+
377 uint16_to_bfloat16);
+
378instantiate_metal_simd_reduction_funcs(bfloat16_t, bfloat16_t, float);
+
379
+
380} // namespace metal
+
bfloat16_to_uint16
uint16_t bfloat16_to_uint16(const bfloat16_t x)
Definition bf16.h:308
+
uint16_to_bfloat16
bfloat16_t uint16_to_bfloat16(const uint16_t x)
Definition bf16.h:312
+
instantiate_metal_simd_reduction_funcs
#define instantiate_metal_simd_reduction_funcs(itype, otype, ctype)
Definition bf16_math.h:328
+
instantiate_metal_math_funcs
#define instantiate_metal_math_funcs(itype, otype, ctype, mfast)
Definition bf16_math.h:33
+
instantiate_metal_simd_comm_funcs
#define instantiate_metal_simd_comm_funcs( itype, otype, ctype, itype_to_ctype, ctype_to_otype)
Definition bf16_math.h:260
+
metal
Definition bf16_math.h:226
+
_MLX_BFloat16
Definition bf16.h:48
diff --git a/docs/build/html/binary__ops_8h_source.html b/docs/build/html/binary__ops_8h_source.html index 7731805a..5216c820 100644 --- a/docs/build/html/binary__ops_8h_source.html +++ b/docs/build/html/binary__ops_8h_source.html @@ -519,18 +519,18 @@ $(function(){ initResizable(false); });
296};
-
log1p
float log1p(float x)
Definition utils.h:277
-
metal::precise::atan2
METAL_FUNC bfloat16_t atan2(bfloat16_t y, bfloat16_t x)
Definition bf16_math.h:252
-
metal::atan2
METAL_FUNC bfloat16_t atan2(bfloat16_t y, bfloat16_t x)
Definition bf16_math.h:234
-
metal::cos
METAL_FUNC bfloat16_t cos(bfloat16_t x)
Definition bf16_math.h:234
-
metal::log
METAL_FUNC bfloat16_t log(bfloat16_t x)
Definition bf16_math.h:234
-
metal::sin
METAL_FUNC bfloat16_t sin(bfloat16_t x)
Definition bf16_math.h:234
-
metal::min
METAL_FUNC bfloat16_t min(bfloat16_t x, bfloat16_t y)
Definition bf16_math.h:234
-
metal::isnan
METAL_FUNC bool isnan(_MLX_BFloat16 x)
Definition bf16.h:307
-
metal::max
METAL_FUNC bfloat16_t max(bfloat16_t x, bfloat16_t y)
Definition bf16_math.h:234
-
metal::exp
METAL_FUNC bfloat16_t exp(bfloat16_t x)
Definition bf16_math.h:234
-
metal::pow
METAL_FUNC bfloat16_t pow(bfloat16_t x, bfloat16_t y)
Definition bf16_math.h:234
-
_MLX_BFloat16
Definition bf16.h:54
+
log1p
float log1p(float x)
Definition utils.h:318
+
metal::precise::atan2
METAL_FUNC bfloat16_t atan2(bfloat16_t y, bfloat16_t x)
Definition bf16_math.h:250
+
metal::atan2
METAL_FUNC bfloat16_t atan2(bfloat16_t y, bfloat16_t x)
Definition bf16_math.h:232
+
metal::cos
METAL_FUNC bfloat16_t cos(bfloat16_t x)
Definition bf16_math.h:232
+
metal::log
METAL_FUNC bfloat16_t log(bfloat16_t x)
Definition bf16_math.h:232
+
metal::sin
METAL_FUNC bfloat16_t sin(bfloat16_t x)
Definition bf16_math.h:232
+
metal::min
METAL_FUNC bfloat16_t min(bfloat16_t x, bfloat16_t y)
Definition bf16_math.h:232
+
metal::isnan
METAL_FUNC bool isnan(_MLX_BFloat16 x)
Definition bf16.h:301
+
metal::max
METAL_FUNC bfloat16_t max(bfloat16_t x, bfloat16_t y)
Definition bf16_math.h:232
+
metal::exp
METAL_FUNC bfloat16_t exp(bfloat16_t x)
Definition bf16_math.h:232
+
metal::pow
METAL_FUNC bfloat16_t pow(bfloat16_t x, bfloat16_t y)
Definition bf16_math.h:232
+
_MLX_BFloat16
Definition bf16.h:48
Add
Definition binary_ops.h:8
Add::operator()
T operator()(T x, T y)
Definition binary_ops.h:10
ArcTan2
Definition binary_ops.h:284
diff --git a/docs/build/html/classes.html b/docs/build/html/classes.html index 31672a75..77d6a0d1 100644 --- a/docs/build/html/classes.html +++ b/docs/build/html/classes.html @@ -91,19 +91,19 @@ $(function(){ initResizable(false); });
A
-
Abs
Abs (mlx::core)
Abs (mlx::core::detail)
AccumHelper (mlx::steel)
Add
Add (mlx::core)
Add (mlx::core::detail)
add_vec (pocketfft::detail)
add_vec< cmplx< T > > (pocketfft::detail)
AddMM (mlx::core)
AffineQuantize (mlx::core::fast)
aligned_allocator (pocketfft::detail::threading)
AllGather (mlx::core::distributed)
Allocator (mlx::core::allocator)
AllReduce (mlx::core::distributed)
And
Arange (mlx::core)
ArcCos
ArcCos (mlx::core)
ArcCos (mlx::core::detail)
ArcCosh
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diff --git a/docs/build/html/classmlx_1_1core_1_1_contiguous-members.html b/docs/build/html/classmlx_1_1core_1_1_contiguous-members.html new file mode 100644 index 00000000..49a90d86 --- /dev/null +++ b/docs/build/html/classmlx_1_1core_1_1_contiguous-members.html @@ -0,0 +1,129 @@ + + + + + + + +MLX: Member List + + + + + + + + + + + + + +
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mlx::core::Contiguous Member List
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This is the complete list of members for mlx::core::Contiguous, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + +
Contiguous(Stream stream, bool allow_col_major)mlx::core::Contiguousinlineexplicit
device()mlx::core::Primitiveinline
eval_cpu(const std::vector< array > &inputs, array &out) overridemlx::core::Contiguousvirtual
mlx::core::UnaryPrimitive::eval_cpu(const std::vector< array > &inputs, std::vector< array > &outputs) overridemlx::core::UnaryPrimitiveinlinevirtual
eval_gpu(const std::vector< array > &inputs, array &out) overridemlx::core::Contiguousvirtual
mlx::core::UnaryPrimitive::eval_gpu(const std::vector< array > &inputs, std::vector< array > &outputs) overridemlx::core::UnaryPrimitiveinlinevirtual
is_equivalent(const Primitive &other) const overridemlx::core::Contiguousvirtual
jvp(const std::vector< array > &primals, const std::vector< array > &tangents, const std::vector< int > &argnums) overridemlx::core::Contiguousvirtual
operator=(const UnaryPrimitive &other)=deletemlx::core::UnaryPrimitive
operator=(UnaryPrimitive &&other)=deletemlx::core::UnaryPrimitive
mlx::core::Primitive::operator=(const Primitive &other)=deletemlx::core::Primitive
mlx::core::Primitive::operator=(Primitive &&other)=deletemlx::core::Primitive
output_shapes(const std::vector< array > &inputs) overridemlx::core::Contiguousinlinevirtual
Primitive(Stream stream)mlx::core::Primitiveinlineexplicit
Primitive(const Primitive &other)=deletemlx::core::Primitive
Primitive(Primitive &&other)=deletemlx::core::Primitive
print(std::ostream &os) overridemlx::core::Contiguousinlinevirtual
stream()mlx::core::Primitiveinline
UnaryPrimitive(Stream stream)mlx::core::UnaryPrimitiveinlineexplicit
UnaryPrimitive(const UnaryPrimitive &other)=deletemlx::core::UnaryPrimitive
UnaryPrimitive(UnaryPrimitive &&other)=deletemlx::core::UnaryPrimitive
vjp(const std::vector< array > &primals, const std::vector< array > &cotangents, const std::vector< int > &argnums, const std::vector< array > &outputs) overridemlx::core::Contiguousvirtual
vmap(const std::vector< array > &inputs, const std::vector< int > &axes) overridemlx::core::Contiguousvirtual
~Primitive()=defaultmlx::core::Primitivevirtual
~UnaryPrimitive()=defaultmlx::core::UnaryPrimitivevirtual
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+ + diff --git a/docs/build/html/classmlx_1_1core_1_1_contiguous.html b/docs/build/html/classmlx_1_1core_1_1_contiguous.html new file mode 100644 index 00000000..98a3d0d2 --- /dev/null +++ b/docs/build/html/classmlx_1_1core_1_1_contiguous.html @@ -0,0 +1,481 @@ + + + + + + + +MLX: mlx::core::Contiguous Class Reference + + + + + + + + + + + + + +
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mlx::core::Contiguous Class Reference
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#include <primitives.h>

+
+Inheritance diagram for mlx::core::Contiguous:
+
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+ + +mlx::core::UnaryPrimitive +mlx::core::Primitive + +
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+Public Member Functions

 Contiguous (Stream stream, bool allow_col_major)
 
void eval_cpu (const std::vector< array > &inputs, array &out) override
 
void eval_gpu (const std::vector< array > &inputs, array &out) override
 
virtual std::pair< std::vector< array >, std::vector< int > > vmap (const std::vector< array > &inputs, const std::vector< int > &axes) override
 The primitive must know how to vectorize itself across the given axes.
 
std::vector< array > jvp (const std::vector< array > &primals, const std::vector< array > &tangents, const std::vector< int > &argnums) override
 The Jacobian-vector product.
 
std::vector< array > vjp (const std::vector< array > &primals, const std::vector< array > &cotangents, const std::vector< int > &argnums, const std::vector< array > &outputs) override
 The vector-Jacobian product.
 
void print (std::ostream &os) override
 Print the primitive.
 
std::vector< std::vector< int > > output_shapes (const std::vector< array > &inputs) override
 Get the output shapes of the primitive.
 
bool is_equivalent (const Primitive &other) const override
 Equivalence check defaults to false unless overridden by the primitive.
 
- Public Member Functions inherited from mlx::core::UnaryPrimitive
 UnaryPrimitive (Stream stream)
 An abstract base class for a primitive with a single output.
 
void eval_cpu (const std::vector< array > &inputs, std::vector< array > &outputs) override
 A primitive must know how to evaluate itself on the CPU/GPU for the given inputs and populate the output arrays.
 
void eval_gpu (const std::vector< array > &inputs, std::vector< array > &outputs) override
 
virtual ~UnaryPrimitive ()=default
 
 UnaryPrimitive (const UnaryPrimitive &other)=delete
 
 UnaryPrimitive (UnaryPrimitive &&other)=delete
 
UnaryPrimitive & operator= (const UnaryPrimitive &other)=delete
 
UnaryPrimitive & operator= (UnaryPrimitive &&other)=delete
 
- Public Member Functions inherited from mlx::core::Primitive
 Primitive (Stream stream)
 
const Device & device ()
 The device the primitive will run on.
 
const Stream & stream ()
 The stream the primitive will run on.
 
virtual ~Primitive ()=default
 
 Primitive (const Primitive &other)=delete
 
 Primitive (Primitive &&other)=delete
 
Primitive & operator= (const Primitive &other)=delete
 
Primitive & operator= (Primitive &&other)=delete
 
+

Constructor & Destructor Documentation

+ +

◆ Contiguous()

+ +
+
+ + + + + +
+ + + + + + + + + + + +
mlx::core::Contiguous::Contiguous (Stream stream,
bool allow_col_major )
+ 		+inlineexplicit
+
+ +
+
+

Member Function Documentation

+ +

◆ eval_cpu()

+ +
+
+ + + + + +
+ + + + + + + + + + + +
void mlx::core::Contiguous::eval_cpu (const std::vector< array > & inputs,
array & out )
+ 		+overridevirtual
+
+ +

Implements mlx::core::UnaryPrimitive.

+ +
+
+ +

◆ eval_gpu()

+ +
+
+ + + + + +
+ + + + + + + + + + + +
void mlx::core::Contiguous::eval_gpu (const std::vector< array > & inputs,
array & out )
+ 		+overridevirtual
+
+ +

Implements mlx::core::UnaryPrimitive.

+ +
+
+ +

◆ is_equivalent()

+ +
+
+ + + + + +
+ + + + + + + +
bool mlx::core::Contiguous::is_equivalent (const Primitive & other) const
+ 		+overridevirtual
+
+ +

Equivalence check defaults to false unless overridden by the primitive.

+ +

Reimplemented from mlx::core::Primitive.

+ +
+
+ +

◆ jvp()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + +
std::vector< array > mlx::core::Contiguous::jvp (const std::vector< array > & primals,
const std::vector< array > & tangents,
const std::vector< int > & argnums )
+ 		+overridevirtual
+
+ +

The Jacobian-vector product.

+ +

Reimplemented from mlx::core::Primitive.

+ +
+
+ +

◆ output_shapes()

+ +
+
+ + + + + +
+ + + + + + + +
std::vector< std::vector< int > > mlx::core::Contiguous::output_shapes (const std::vector< array > & inputs)
+ 		+inlineoverridevirtual
+
+ +

Get the output shapes of the primitive.

+

This is not required to be implemented by derived classes, in which case it will throw.

+ +

Reimplemented from mlx::core::Primitive.

+ +
+
+ +

◆ print()

+ +
+
+ + + + + +
+ + + + + + + +
void mlx::core::Contiguous::print (std::ostream & os)
+ 		+inlineoverridevirtual
+
+ +

Print the primitive.

+ +

Implements mlx::core::Primitive.

+ +
+
+ +

◆ vjp()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + +
std::vector< array > mlx::core::Contiguous::vjp (const std::vector< array > & primals,
const std::vector< array > & cotangents,
const std::vector< int > & argnums,
const std::vector< array > & outputs )
+ 		+overridevirtual
+
+ +

The vector-Jacobian product.

+ +

Reimplemented from mlx::core::Primitive.

+ +
+
+ +

◆ vmap()

+ +
+
+ + + + + +
+ + + + + + + + + + + +
virtual std::pair< std::vector< array >, std::vector< int > > mlx::core::Contiguous::vmap (const std::vector< array > & inputs,
const std::vector< int > & axes )
+ 		+overridevirtual
+
+ +

The primitive must know how to vectorize itself across the given axes.

+

The output is a pair containing the output arrays representing the vectorized computation and the axes which corresponds to the vectorized dimensions of each output.

+ +

Reimplemented from mlx::core::Primitive.

+ +
+
+
The documentation for this class was generated from the following file: +
+ +
+Generated by doxygen 1.12.0 +
+
+ + diff --git a/docs/build/html/classmlx_1_1core_1_1_contiguous.png b/docs/build/html/classmlx_1_1core_1_1_contiguous.png new file mode 100644 index 0000000000000000000000000000000000000000..13ba3febe5b2964e1387ebe0c32bd51531f92ff2 GIT binary patch literal 924 zcmeAS@N?(olHy`uVBq!ia0vp^(}1{xgBeH~F+Z{fQqloFA+G=b{|7Q(y!l$%e`vXd zfo6fk^fNCWJa_;UH$5A+1SrQ@666=m0OW&#In(Sb3=GWbo-U3d6^w7^zAk#Lz{57Z ze`e3U|DFAhe=)IHd-=_}w9@L$5pl)!ttwPf!D$ngD2HFw+AOn5QFZH;y`@wB?g`s|+JEb`Z7b3|wc7nx z-*HsGnsxiv!f7gVA6!}$_3uXXStYw`TH9Myw%RP-s++M_c>80&$jKqSuSDZ-bx!({ z+%iq2k`wC6)R?t%iskqk$_tq#`VF_{ZCBiT?8>Y)ZeOHAw9Awl=3ij?AQZ(A=fr)$ zJA<);Q)wk#=om0z;$~2wN-^F=)^*a0F8~?sVe<*n@@qKEp|MnQ|`6jIS>Z$i)v(L?5 zQg+4owD(cx>C%69yo&nn`Si}NsMIqHrGkP^FN{jx7@aR0|NL&0FaLG!KrPw(3Z8Ex z#B0vqzN+>8xaU@@y`i^rdw;ZqZGXBwaBI!;!tm}Nv%+@j%4OzqN4?+hwqk#M#KXO} z+HLP%T-9~qCNNaW=KYcXv9SOA>9G5&zV5pE{%45yrG8;xJW?-&yfl}7RG2xl*DYh_ zOke~U8|#Zl`TxFGK4E6$(UPaF;`NKK$=vs;$~BByyJg>lnt&&8`hIlWdKRSHH+O`*TvH^{X>amlmzOw)*56Dc`=VT{A@&hp)Y~c%_@sX64eY zRs6P}EwlIjzpOR;Ppa;7rCCd>V!~FxUAuhiyx1eFp7yW5v108-^{DLiSGUK#T$^;d zv@B`o;%l3Kz54W^R{fp5_kw!CvuF0TrptX%T)Ry8>>0fuAzQ2Ns~T?>uDied)|ofU lIY2&moV9eyrGh`qNy}zkynb}5F)$x7c)I$ztaD0e0svz$%6b3* literal 0 HcmV?d00001 diff --git a/docs/build/html/classmlx_1_1core_1_1_primitive.html b/docs/build/html/classmlx_1_1core_1_1_primitive.html index b59585a8..0a127787 100644 --- a/docs/build/html/classmlx_1_1core_1_1_primitive.html +++ b/docs/build/html/classmlx_1_1core_1_1_primitive.html @@ -380,7 +380,7 @@ Public Member Functions

Equivalence check defaults to false unless overridden by the primitive.

-

Reimplemented in mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::Arange, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsStrided, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::BlockMaskedMM, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Compiled, mlx::core::Concatenate, mlx::core::Conjugate, mlx::core::Convolution, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::Divide, mlx::core::DivMod, mlx::core::Eigh, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::fast::ScaledDotProductAttention, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherMM, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Less, mlx::core::LessEqual, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::QuantizedMatmul, mlx::core::RandomBits, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Split, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::Tan, mlx::core::Tanh, mlx::core::Transpose, mlx::core::Uniform, and mlx::core::View.

+

Reimplemented in mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::Arange, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsStrided, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::BlockMaskedMM, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Compiled, mlx::core::Concatenate, mlx::core::Conjugate, mlx::core::Contiguous, mlx::core::Convolution, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::Divide, mlx::core::DivMod, mlx::core::Eigh, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::fast::ScaledDotProductAttention, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherMM, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Less, mlx::core::LessEqual, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::QuantizedMatmul, mlx::core::RandomBits, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Split, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::Tan, mlx::core::Tanh, mlx::core::Transpose, mlx::core::Uniform, and mlx::core::View.

@@ -418,7 +418,7 @@ Public Member Functions

The Jacobian-vector product.

-

Reimplemented in mlx::core::Abs, mlx::core::Add, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::AsStrided, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Compiled, mlx::core::Concatenate, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::CustomTransforms, mlx::core::distributed::AllGather, mlx::core::distributed::AllReduce, mlx::core::Divide, mlx::core::DivMod, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::fast::Custom, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Less, mlx::core::LessEqual, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::QuantizedMatmul, mlx::core::Real, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Split, mlx::core::Sqrt, mlx::core::Square, mlx::core::Subtract, mlx::core::Tan, mlx::core::Tanh, and mlx::core::Transpose.

+

Reimplemented in mlx::core::Abs, mlx::core::Add, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::AsStrided, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Compiled, mlx::core::Concatenate, mlx::core::Contiguous, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::CustomTransforms, mlx::core::distributed::AllGather, mlx::core::distributed::AllReduce, mlx::core::Divide, mlx::core::DivMod, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::fast::Custom, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Less, mlx::core::LessEqual, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::QuantizedMatmul, mlx::core::Real, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Split, mlx::core::Sqrt, mlx::core::Square, mlx::core::Subtract, mlx::core::Tan, mlx::core::Tanh, and mlx::core::Transpose.

@@ -498,7 +498,7 @@ Public Member Functions

Get the output shapes of the primitive.

This is not required to be implemented by derived classes, in which case it will throw.

-

Reimplemented in mlx::core::Abs, mlx::core::Add, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::Ceil, mlx::core::Compiled, mlx::core::Conjugate, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::Divide, mlx::core::DivMod, mlx::core::Eigh, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::Floor, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Less, mlx::core::LessEqual, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Partition, mlx::core::Power, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Round, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Softmax, mlx::core::Sort, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::Tan, and mlx::core::Tanh.

+

Reimplemented in mlx::core::Abs, mlx::core::Add, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::Ceil, mlx::core::Compiled, mlx::core::Conjugate, mlx::core::Contiguous, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::Divide, mlx::core::DivMod, mlx::core::Eigh, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::Floor, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Less, mlx::core::LessEqual, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Partition, mlx::core::Power, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Round, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Softmax, mlx::core::Sort, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::Tan, and mlx::core::Tanh.

@@ -527,7 +527,7 @@ Public Member Functions

Print the primitive.

-

Implemented in mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::Arange, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsStrided, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::BlockMaskedMM, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Cholesky, mlx::core::Compiled, mlx::core::Concatenate, mlx::core::Conjugate, mlx::core::Convolution, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::CustomTransforms, mlx::core::Depends, mlx::core::distributed::AllReduce, mlx::core::Divide, mlx::core::DivMod, mlx::core::Eigh, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherMM, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Inverse, mlx::core::Less, mlx::core::LessEqual, mlx::core::Load, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::QRF, mlx::core::QuantizedMatmul, mlx::core::RandomBits, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Split, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::SVD, mlx::core::Tan, mlx::core::Tanh, mlx::core::Transpose, mlx::core::Uniform, and mlx::core::View.

+

Implemented in mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::Arange, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsStrided, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::BlockMaskedMM, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Cholesky, mlx::core::Compiled, mlx::core::Concatenate, mlx::core::Conjugate, mlx::core::Contiguous, mlx::core::Convolution, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::CustomTransforms, mlx::core::Depends, mlx::core::distributed::AllReduce, mlx::core::Divide, mlx::core::DivMod, mlx::core::Eigh, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherMM, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Inverse, mlx::core::Less, mlx::core::LessEqual, mlx::core::Load, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::QRF, mlx::core::QuantizedMatmul, mlx::core::RandomBits, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Split, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::SVD, mlx::core::Tan, mlx::core::Tanh, mlx::core::Transpose, mlx::core::Uniform, and mlx::core::View.

@@ -597,7 +597,7 @@ Public Member Functions

The vector-Jacobian product.

-

Reimplemented in mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::AsStrided, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::BlockMaskedMM, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Compiled, mlx::core::Concatenate, mlx::core::Convolution, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::CustomTransforms, mlx::core::Depends, mlx::core::distributed::AllGather, mlx::core::distributed::AllReduce, mlx::core::Divide, mlx::core::DivMod, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::fast::Custom, mlx::core::fast::LayerNorm, mlx::core::fast::RMSNorm, mlx::core::fast::RoPE, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherMM, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Less, mlx::core::LessEqual, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::QuantizedMatmul, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Split, mlx::core::Sqrt, mlx::core::Square, mlx::core::Subtract, mlx::core::Tan, mlx::core::Tanh, and mlx::core::Transpose.

+

Reimplemented in mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::AsStrided, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::BlockMaskedMM, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Compiled, mlx::core::Concatenate, mlx::core::Contiguous, mlx::core::Convolution, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::CustomTransforms, mlx::core::Depends, mlx::core::distributed::AllGather, mlx::core::distributed::AllReduce, mlx::core::Divide, mlx::core::DivMod, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::fast::Custom, mlx::core::fast::LayerNorm, mlx::core::fast::RMSNorm, mlx::core::fast::RoPE, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherMM, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Less, mlx::core::LessEqual, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::QuantizedMatmul, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Split, mlx::core::Sqrt, mlx::core::Square, mlx::core::Subtract, mlx::core::Tan, mlx::core::Tanh, and mlx::core::Transpose.

@@ -631,7 +631,7 @@ Public Member Functions

The primitive must know how to vectorize itself across the given axes.

The output is a pair containing the output arrays representing the vectorized computation and the axes which corresponds to the vectorized dimensions of each output.

-

Reimplemented in mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Cholesky, mlx::core::Compiled, mlx::core::Concatenate, mlx::core::Conjugate, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::CustomTransforms, mlx::core::distributed::AllGather, mlx::core::distributed::AllReduce, mlx::core::distributed::Send, mlx::core::Divide, mlx::core::DivMod, mlx::core::Eigh, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::fast::Custom, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Inverse, mlx::core::Less, mlx::core::LessEqual, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::QuantizedMatmul, mlx::core::RandomBits, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Split, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::SVD, mlx::core::Tan, mlx::core::Tanh, mlx::core::Transpose, mlx::core::Uniform, and mlx::core::View.

+

Reimplemented in mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Cholesky, mlx::core::Compiled, mlx::core::Concatenate, mlx::core::Conjugate, mlx::core::Contiguous, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::CustomTransforms, mlx::core::distributed::AllGather, mlx::core::distributed::AllReduce, mlx::core::distributed::Send, mlx::core::Divide, mlx::core::DivMod, mlx::core::Eigh, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::fast::Custom, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Inverse, mlx::core::Less, mlx::core::LessEqual, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::QuantizedMatmul, mlx::core::RandomBits, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Split, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::SVD, mlx::core::Tan, mlx::core::Tanh, mlx::core::Transpose, mlx::core::Uniform, and mlx::core::View.

diff --git a/docs/build/html/classmlx_1_1core_1_1_unary_primitive.html b/docs/build/html/classmlx_1_1core_1_1_unary_primitive.html index 8335de9d..610953b5 100644 --- a/docs/build/html/classmlx_1_1core_1_1_unary_primitive.html +++ b/docs/build/html/classmlx_1_1core_1_1_unary_primitive.html @@ -126,73 +126,74 @@ Inheritance diagram for mlx::core::UnaryPrimitive:
mlx::core::Cholesky mlx::core::Concatenate mlx::core::Conjugate -mlx::core::Convolution -mlx::core::Copy -mlx::core::Cos -mlx::core::Cosh -mlx::core::Divide -mlx::core::Equal -mlx::core::Erf -mlx::core::ErfInv -mlx::core::Exp -mlx::core::Expm1 -mlx::core::FFT -mlx::core::Floor -mlx::core::Full -mlx::core::Gather -mlx::core::GatherMM -mlx::core::GatherQMM -mlx::core::Greater -mlx::core::GreaterEqual -mlx::core::Hadamard -mlx::core::Imag -mlx::core::Inverse -mlx::core::Less -mlx::core::LessEqual -mlx::core::Load -mlx::core::Log -mlx::core::Log1p -mlx::core::LogAddExp -mlx::core::LogicalAnd -mlx::core::LogicalNot -mlx::core::LogicalOr -mlx::core::Matmul -mlx::core::Maximum -mlx::core::Minimum -mlx::core::Multiply -mlx::core::Negative -mlx::core::NotEqual -mlx::core::NumberOfElements -mlx::core::Pad -mlx::core::Partition -mlx::core::Power -mlx::core::QuantizedMatmul -mlx::core::RandomBits -mlx::core::Real -mlx::core::Reduce -mlx::core::Remainder -mlx::core::Reshape -mlx::core::Round -mlx::core::Scan -mlx::core::Scatter -mlx::core::Select -mlx::core::Sigmoid -mlx::core::Sign -mlx::core::Sin -mlx::core::Sinh -mlx::core::Slice -mlx::core::SliceUpdate -mlx::core::Softmax -mlx::core::Sort -mlx::core::Sqrt -mlx::core::Square -mlx::core::StopGradient -mlx::core::Subtract -mlx::core::Tan -mlx::core::Tanh -mlx::core::Transpose -mlx::core::Uniform -mlx::core::View +mlx::core::Contiguous +mlx::core::Convolution +mlx::core::Copy +mlx::core::Cos +mlx::core::Cosh +mlx::core::Divide +mlx::core::Equal +mlx::core::Erf +mlx::core::ErfInv +mlx::core::Exp +mlx::core::Expm1 +mlx::core::FFT +mlx::core::Floor +mlx::core::Full +mlx::core::Gather +mlx::core::GatherMM +mlx::core::GatherQMM +mlx::core::Greater +mlx::core::GreaterEqual +mlx::core::Hadamard +mlx::core::Imag +mlx::core::Inverse +mlx::core::Less +mlx::core::LessEqual +mlx::core::Load +mlx::core::Log +mlx::core::Log1p +mlx::core::LogAddExp +mlx::core::LogicalAnd +mlx::core::LogicalNot +mlx::core::LogicalOr +mlx::core::Matmul +mlx::core::Maximum +mlx::core::Minimum +mlx::core::Multiply +mlx::core::Negative +mlx::core::NotEqual +mlx::core::NumberOfElements +mlx::core::Pad +mlx::core::Partition +mlx::core::Power +mlx::core::QuantizedMatmul +mlx::core::RandomBits +mlx::core::Real +mlx::core::Reduce +mlx::core::Remainder +mlx::core::Reshape +mlx::core::Round +mlx::core::Scan +mlx::core::Scatter +mlx::core::Select +mlx::core::Sigmoid +mlx::core::Sign +mlx::core::Sin +mlx::core::Sinh +mlx::core::Slice +mlx::core::SliceUpdate +mlx::core::Softmax +mlx::core::Sort +mlx::core::Sqrt +mlx::core::Square +mlx::core::StopGradient +mlx::core::Subtract +mlx::core::Tan +mlx::core::Tanh +mlx::core::Transpose +mlx::core::Uniform +mlx::core::View
@@ -389,7 +390,7 @@ Public Member Functions
-

Implemented in mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::Arange, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsStrided, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::BlockMaskedMM, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Cholesky, mlx::core::Concatenate, mlx::core::Conjugate, mlx::core::Convolution, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::Divide, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherMM, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Inverse, mlx::core::Less, mlx::core::LessEqual, mlx::core::Load, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::QuantizedMatmul, mlx::core::RandomBits, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::Tan, mlx::core::Tanh, mlx::core::Transpose, mlx::core::Uniform, and mlx::core::View.

+

Implemented in mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::Arange, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsStrided, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::BlockMaskedMM, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Cholesky, mlx::core::Concatenate, mlx::core::Conjugate, mlx::core::Contiguous, mlx::core::Convolution, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::Divide, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherMM, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Inverse, mlx::core::Less, mlx::core::LessEqual, mlx::core::Load, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::QuantizedMatmul, mlx::core::RandomBits, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::Tan, mlx::core::Tanh, mlx::core::Transpose, mlx::core::Uniform, and mlx::core::View.

@@ -454,7 +455,7 @@ Public Member Functions
-

Implemented in mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::Arange, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsStrided, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::BlockMaskedMM, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Cholesky, mlx::core::Concatenate, mlx::core::Conjugate, mlx::core::Convolution, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::Divide, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherMM, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Inverse, mlx::core::Less, mlx::core::LessEqual, mlx::core::Load, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::QuantizedMatmul, mlx::core::RandomBits, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::Tan, mlx::core::Tanh, mlx::core::Transpose, mlx::core::Uniform, and mlx::core::View.

+

Implemented in mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::Arange, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsStrided, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::BlockMaskedMM, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Cholesky, mlx::core::Concatenate, mlx::core::Conjugate, mlx::core::Contiguous, mlx::core::Convolution, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::Divide, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherMM, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Inverse, mlx::core::Less, mlx::core::LessEqual, mlx::core::Load, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::QuantizedMatmul, mlx::core::RandomBits, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::Tan, mlx::core::Tanh, mlx::core::Transpose, mlx::core::Uniform, and mlx::core::View.

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a/docs/build/html/classmlx_1_1core_1_1fast_1_1_affine_quantize-members.html +++ b/docs/build/html/classmlx_1_1core_1_1fast_1_1_affine_quantize-members.html @@ -98,7 +98,7 @@ $(function(){ initResizable(false); }); Custom(Stream stream, std::function< std::vector< array >(std::vector< array >)> fallback)mlx::core::fast::Custominlineexplicit DEFINE_PRINT(AffineQuantize)mlx::core::fast::AffineQuantize device()mlx::core::Primitiveinline - eval_cpu(const std::vector< array > &inputs, std::vector< array > &outputs) overridemlx::core::fast::AffineQuantizeinlinevirtual + eval_cpu(const std::vector< array > &inputs, std::vector< array > &outputs) overridemlx::core::fast::AffineQuantizevirtual eval_gpu(const std::vector< array > &inputs, std::vector< array > &outputs) overridemlx::core::fast::AffineQuantizevirtual is_equivalent(const Primitive &other) constmlx::core::Primitiveinlinevirtual jvp(const std::vector< array > &primals, const std::vector< array > &tangents, const std::vector< int > &argnums) overridemlx::core::fast::Customvirtual diff --git a/docs/build/html/classmlx_1_1core_1_1fast_1_1_affine_quantize.html b/docs/build/html/classmlx_1_1core_1_1fast_1_1_affine_quantize.html index 940fe29c..ca6128e8 100644 --- a/docs/build/html/classmlx_1_1core_1_1fast_1_1_affine_quantize.html +++ b/docs/build/html/classmlx_1_1core_1_1fast_1_1_affine_quantize.html @@ -244,7 +244,7 @@ Public Member Functions -inlineoverridevirtual +overridevirtual
diff --git a/docs/build/html/compiled_8h.html b/docs/build/html/compiled_8h.html index 0f20b792..5727d969 100644 --- a/docs/build/html/compiled_8h.html +++ b/docs/build/html/compiled_8h.html @@ -132,7 +132,7 @@ Functions   bool mlx::core::compiled_check_contiguity (const std::vector< array > &inputs, const std::vector< int > &shape)   -void mlx::core::compiled_allocate_outputs (const std::vector< array > &inputs, std::vector< array > &outputs, const std::vector< array > &inputs_, const std::unordered_set< uintptr_t > &constant_ids_, bool contiguous, bool move_buffers=false) +void mlx::core::compiled_allocate_outputs (const std::vector< array > &inputs, std::vector< array > &outputs, const std::vector< array > &inputs_, const std::unordered_set< uintptr_t > &constant_ids_, bool contiguous, bool move_buffers=false)  
diff --git a/docs/build/html/compiled_8h_source.html b/docs/build/html/compiled_8h_source.html index 020b90fd..ba238def 100644 --- a/docs/build/html/compiled_8h_source.html +++ b/docs/build/html/compiled_8h_source.html @@ -167,20 +167,21 @@ $(function(){ initResizable(false); });
64 std::vector<array>& outputs,
65 const std::vector<array>& inputs_,
66 const std::unordered_set<uintptr_t>& constant_ids_,
-
67 bool contiguous,
+
67 bool contiguous,
68 bool move_buffers = false);
69
70} // namespace mlx::core
mlx::core::AsType
Definition primitives.h:418
mlx::core::Broadcast
Definition primitives.h:528
-
mlx::core::Copy
Definition primitives.h:683
+
mlx::core::Copy
Definition primitives.h:702
mlx::core::Primitive
Definition primitives.h:48
-
mlx::core::StopGradient
Definition primitives.h:2015
+
mlx::core::StopGradient
Definition primitives.h:2034
mlx::core::array
Definition array.h:20
mlx::core::array::ndim
size_t ndim() const
The number of dimensions of the array.
Definition array.h:94
mlx::core::array::item
T item()
Get the value from a scalar array.
Definition array.h:535
mlx::core::array::dtype
Dtype dtype() const
Get the arrays data type.
Definition array.h:127
+
mlx::core::contiguous
array contiguous(const array &a, bool allow_col_major=false, StreamOrDevice s={})
mlx::core
Definition allocator.h:7
mlx::core::print_complex_constant
void print_complex_constant(std::ostream &os, const array &x)
Definition compiled.h:40
mlx::core::compiled_check_contiguity
bool compiled_check_contiguity(const std::vector< array > &inputs, const std::vector< int > &shape)
diff --git a/docs/build/html/conv_8h.html b/docs/build/html/conv_8h.html index f673ac74..f35a8c3e 100644 --- a/docs/build/html/conv_8h.html +++ b/docs/build/html/conv_8h.html @@ -95,7 +95,7 @@ $(function(){ initResizable(false); }); #include "mlx/backend/metal/kernels/steel/utils.h"
#include "mlx/backend/metal/kernels/steel/conv/loader.h"
#include "mlx/backend/metal/kernels/steel/conv/params.h"
-#include "mlx/backend/metal/kernels/steel/gemm/mma.h"
+#include "mlx/backend/metal/kernels/steel/gemm/mma.h"

Go to the source code of this file.

diff --git a/docs/build/html/conv_8h_source.html b/docs/build/html/conv_8h_source.html index df66e331..17bfc4bc 100644 --- a/docs/build/html/conv_8h_source.html +++ b/docs/build/html/conv_8h_source.html @@ -100,16 +100,16 @@ $(function(){ initResizable(false); });
7
8#include "mlx/backend/metal/kernels/steel/conv/loader.h"
9#include "mlx/backend/metal/kernels/steel/conv/params.h"
-
10#include "mlx/backend/metal/kernels/steel/gemm/mma.h"
+
10#include "mlx/backend/metal/kernels/steel/gemm/mma.h"
11
12using namespace metal;
13using namespace mlx::steel;
- -
metal
Definition bf16.h:265
-
mlx::steel
Definition loader_channel_l.h:14
+ +
metal
Definition bf16_math.h:226
+
mlx::steel
Definition attn.h:19
diff --git a/docs/build/html/cpp/ops.html b/docs/build/html/cpp/ops.html index f8e1ac23..71ad8482 100644 --- a/docs/build/html/cpp/ops.html +++ b/docs/build/html/cpp/ops.html @@ -8,7 +8,7 @@ - Operations — MLX 0.20.0 documentation + Operations — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • @@ -1157,6 +1158,7 @@
  • roll()
  • real()
  • imag()
    • +
  • contiguous()
    • @@ -2966,6 +2968,11 @@ array imag(const array &a, StreamOrDevice s = {})#
    +
    +
    +array contiguous(const array &a, bool allow_col_major = false, StreamOrDevice s = {})#
    +
    + @@ -3322,6 +3329,7 @@
  • roll()
  • real()
  • imag()
    • +
  • contiguous()
    • diff --git a/docs/build/html/dev/custom_metal_kernels.html b/docs/build/html/dev/custom_metal_kernels.html index 41ee05ff..c342bdf5 100644 --- a/docs/build/html/dev/custom_metal_kernels.html +++ b/docs/build/html/dev/custom_metal_kernels.html @@ -8,7 +8,7 @@ - Custom Metal Kernels — MLX 0.20.0 documentation + Custom Metal Kernels — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -50,7 +50,7 @@ - + @@ -129,8 +129,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -443,7 +443,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -520,6 +519,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -549,6 +549,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/dev/extensions.html b/docs/build/html/dev/extensions.html index 61d2c14e..0268bea8 100644 --- a/docs/build/html/dev/extensions.html +++ b/docs/build/html/dev/extensions.html @@ -8,7 +8,7 @@ - Custom Extensions in MLX — MLX 0.20.0 documentation + Custom Extensions in MLX — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • @@ -1342,7 +1343,7 @@ below.

    // Prepare to encode kernel auto& compute_encoder = d.get_command_encoder(s.index); - compute_encoder->setComputePipelineState(kernel); + compute_encoder.set_compute_pipeline_state(kernel); // Kernel parameters are registered with buffer indices corresponding to // those in the kernel declaration at axpby.metal @@ -1357,14 +1358,14 @@ below.

    compute_encoder.set_output_array(out, 2); // Encode alpha and beta - compute_encoder->setBytes(&alpha_, sizeof(float), 3); - compute_encoder->setBytes(&beta_, sizeof(float), 4); + compute_encoder.set_bytes(alpha_, 3); + compute_encoder.set_bytes(beta_, 4); // Encode shape, strides and ndim - compute_encoder->setBytes(x.shape().data(), ndim * sizeof(int), 5); - compute_encoder->setBytes(x.strides().data(), ndim * sizeof(size_t), 6); - compute_encoder->setBytes(y.strides().data(), ndim * sizeof(size_t), 7); - compute_encoder->setBytes(&ndim, sizeof(int), 8); + compute_encoder.set_vector_bytes(x.shape(), 5); + compute_encoder.set_vector_bytes(x.strides(), 6); + compute_encoder.set_bytes(y.strides(), 7); + compute_encoder.set_bytes(ndim, 8); // We launch 1 thread for each input and make sure that the number of // threads in any given threadgroup is not higher than the max allowed @@ -1378,7 +1379,7 @@ below.

    // Launch the grid with the given number of threads divided among // the given threadgroups - compute_encoder.dispatchThreads(grid_dims, group_dims); + compute_encoder.dispatch_threads(grid_dims, group_dims); } diff --git a/docs/build/html/dev/metal_debugger.html b/docs/build/html/dev/metal_debugger.html index 31fe3dfb..d7ef311b 100644 --- a/docs/build/html/dev/metal_debugger.html +++ b/docs/build/html/dev/metal_debugger.html @@ -8,7 +8,7 @@ - Metal Debugger — MLX 0.20.0 documentation + Metal Debugger — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/scaled__dot__product__attention__params_8h.html b/docs/build/html/dir_5aea41cce495e77a0857a0aecf063e33.html similarity index 81% rename from docs/build/html/scaled__dot__product__attention__params_8h.html rename to docs/build/html/dir_5aea41cce495e77a0857a0aecf063e33.html index c2695c4c..2001241f 100644 --- a/docs/build/html/scaled__dot__product__attention__params_8h.html +++ b/docs/build/html/dir_5aea41cce495e77a0857a0aecf063e33.html @@ -5,7 +5,7 @@ -MLX: mlx/backend/metal/kernels/scaled_dot_product_attention_params.h File Reference +MLX: mlx/backend/metal/kernels/steel/attn/kernels Directory Reference @@ -83,24 +83,18 @@ $(function(){ initResizable(false); });
    -
    -Classes
    -
    scaled_dot_product_attention_params.h File Reference
    +
    kernels Directory Reference
    - -

    Go to the source code of this file.

    - - - - + +

    -Classes

    struct  MLXFastAttentionParams
     
    struct  MLXScaledDotProductAttentionParams

    +Files

     steel_attention.h
     
    diff --git a/docs/build/html/dir_6768c99e6145fb9510ccdb40db8ede25.html b/docs/build/html/dir_6768c99e6145fb9510ccdb40db8ede25.html index 2c23d663..4bc85b3b 100644 --- a/docs/build/html/dir_6768c99e6145fb9510ccdb40db8ede25.html +++ b/docs/build/html/dir_6768c99e6145fb9510ccdb40db8ede25.html @@ -103,7 +103,7 @@ Files    loader.h   - mma.h + mma.h    params.h   diff --git a/docs/build/html/dir_70a37effa88bcbd6b791977fa1e64356.html b/docs/build/html/dir_70a37effa88bcbd6b791977fa1e64356.html index 85eb93c5..e88fa763 100644 --- a/docs/build/html/dir_70a37effa88bcbd6b791977fa1e64356.html +++ b/docs/build/html/dir_70a37effa88bcbd6b791977fa1e64356.html @@ -96,6 +96,12 @@ $(function(){ initResizable(false); }); Directories  fft   + jit +  + metal_3_0 +  + metal_3_1 +   reduction    steel @@ -107,8 +113,6 @@ Files    atomic.h   - bf16.h -   bf16_math.h    binary.h @@ -143,8 +147,6 @@ Files    reduce_utils.h   - scaled_dot_product_attention_params.h -   scan.h    scatter.h diff --git a/docs/build/html/dir_76215a6c54e2b67053e723fc2395583c.html b/docs/build/html/dir_76215a6c54e2b67053e723fc2395583c.html index a10d315e..374387cf 100644 --- a/docs/build/html/dir_76215a6c54e2b67053e723fc2395583c.html +++ b/docs/build/html/dir_76215a6c54e2b67053e723fc2395583c.html @@ -94,6 +94,8 @@ $(function(){ initResizable(false); }); + + diff --git a/docs/build/html/dir_83367edb60e23ad59b1a493d8c883287.html b/docs/build/html/dir_83367edb60e23ad59b1a493d8c883287.html new file mode 100644 index 00000000..7383c68c --- /dev/null +++ b/docs/build/html/dir_83367edb60e23ad59b1a493d8c883287.html @@ -0,0 +1,107 @@ + + + + + + + +MLX: mlx/backend/metal/kernels/metal_3_1 Directory Reference + + + + + + + + + + + + + +
    +
    +

    Directories

     attn
     
     conv
     
     gemm
    + + + + + +
    +
    MLX +
    +
    +
    + + + + + + + + + + +
    +
    + + +
    +
    +
    +
    +
    +
    Loading...
    +
    Searching...
    +
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    +
    +
    +
    +
    + + + +
    +
    +
    metal_3_1 Directory Reference
    +
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    erfinv
    float erfinv(float a)
    Definition erf.h:42
    erf
    float erf(float a)
    Definition erf.h:11
    -
    metal::log
    METAL_FUNC bfloat16_t log(bfloat16_t x)
    Definition bf16_math.h:234
    -
    metal::fma
    METAL_FUNC bfloat16_t fma(bfloat16_t x, bfloat16_t y, bfloat16_t z)
    Definition bf16_math.h:234
    -
    metal::abs
    METAL_FUNC bfloat16_t abs(bfloat16_t x)
    Definition bf16_math.h:234
    -
    metal::exp
    METAL_FUNC bfloat16_t exp(bfloat16_t x)
    Definition bf16_math.h:234
    +
    metal::log
    METAL_FUNC bfloat16_t log(bfloat16_t x)
    Definition bf16_math.h:232
    +
    metal::fma
    METAL_FUNC bfloat16_t fma(bfloat16_t x, bfloat16_t y, bfloat16_t z)
    Definition bf16_math.h:232
    +
    metal::abs
    METAL_FUNC bfloat16_t abs(bfloat16_t x)
    Definition bf16_math.h:232
    +
    metal::exp
    METAL_FUNC bfloat16_t exp(bfloat16_t x)
    Definition bf16_math.h:232
    u
    uint32_t u
    Definition bf16.h:17
    diff --git a/docs/build/html/examples/linear_regression.html b/docs/build/html/examples/linear_regression.html index f5f704fa..0cb7ddf6 100644 --- a/docs/build/html/examples/linear_regression.html +++ b/docs/build/html/examples/linear_regression.html @@ -8,7 +8,7 @@ - Linear Regression — MLX 0.20.0 documentation + Linear Regression — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/examples/llama-inference.html b/docs/build/html/examples/llama-inference.html index fe42132f..286a271a 100644 --- a/docs/build/html/examples/llama-inference.html +++ b/docs/build/html/examples/llama-inference.html @@ -8,7 +8,7 @@ - LLM inference — MLX 0.20.0 documentation + LLM inference — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/examples/mlp.html b/docs/build/html/examples/mlp.html index e30b0d5e..254db7d8 100644 --- a/docs/build/html/examples/mlp.html +++ b/docs/build/html/examples/mlp.html @@ -8,7 +8,7 @@ - Multi-Layer Perceptron — MLX 0.20.0 documentation + Multi-Layer Perceptron — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/fast_8h.html b/docs/build/html/fast_8h.html index 990ee371..f584102d 100644 --- a/docs/build/html/fast_8h.html +++ b/docs/build/html/fast_8h.html @@ -129,8 +129,6 @@ Functions   std::tuple< array, array, array > mlx::core::fast::affine_quantize (const array &w, int group_size=64, int bits=4, StreamOrDevice s={})   -array mlx::core::fast::affine_quantize (const array &w, const array &scales, const array &biases, int group_size=64, int bits=4, StreamOrDevice s={}) -  array mlx::core::fast::affine_dequantize (const array &w, const array &scales, const array &biases, int group_size=64, int bits=4, StreamOrDevice s={})   MetalKernelFunction mlx::core::fast::metal_kernel (const std::string &name, const std::vector< std::string > &input_names, const std::vector< std::string > &output_names, const std::string &source, const std::string &header="", bool ensure_row_contiguous=true, bool atomic_outputs=false) diff --git a/docs/build/html/fast_8h_source.html b/docs/build/html/fast_8h_source.html index 90326ce0..3a1a368a 100644 --- a/docs/build/html/fast_8h_source.html +++ b/docs/build/html/fast_8h_source.html @@ -140,7 +140,7 @@ $(function(){ initResizable(false); });
    47 int bits = 4,
    48 StreamOrDevice s = {});
    49
    -
    50array affine_quantize(
    +
    50array affine_dequantize(
    51 const array& w,
    52 const array& scales,
    53 const array& biases,
    @@ -148,47 +148,39 @@ $(function(){ initResizable(false); });
    55 int bits = 4,
    56 StreamOrDevice s = {});
    57
    -
    58array affine_dequantize(
    -
    59 const array& w,
    -
    60 const array& scales,
    -
    61 const array& biases,
    -
    62 int group_size = 64,
    -
    63 int bits = 4,
    -
    64 StreamOrDevice s = {});
    -
    65
    -
    66typedef std::variant<int, bool, Dtype> TemplateArg;
    -
    67
    -
    68typedef std::function<std::vector<array>(
    -
    69 const std::vector<array>&,
    -
    70 const std::vector<std::vector<int>>&,
    -
    71 const std::vector<Dtype>&,
    -
    72 std::tuple<int, int, int>,
    -
    73 std::tuple<int, int, int>,
    -
    74 std::vector<std::pair<std::string, TemplateArg>>,
    -
    75 std::optional<float>,
    -
    76 bool,
    -
    77 StreamOrDevice)>
    -
    78 MetalKernelFunction;
    -
    79
    -
    80MetalKernelFunction metal_kernel(
    -
    81 const std::string& name,
    -
    82 const std::vector<std::string>& input_names,
    -
    83 const std::vector<std::string>& output_names,
    -
    84 const std::string& source,
    -
    85 const std::string& header = "",
    -
    86 bool ensure_row_contiguous = true,
    -
    87 bool atomic_outputs = false);
    -
    88
    -
    89} // namespace mlx::core::fast
    +
    58typedef std::variant<int, bool, Dtype> TemplateArg;
    +
    59
    +
    60typedef std::function<std::vector<array>(
    +
    61 const std::vector<array>&,
    +
    62 const std::vector<std::vector<int>>&,
    +
    63 const std::vector<Dtype>&,
    +
    64 std::tuple<int, int, int>,
    +
    65 std::tuple<int, int, int>,
    +
    66 std::vector<std::pair<std::string, TemplateArg>>,
    +
    67 std::optional<float>,
    +
    68 bool,
    +
    69 StreamOrDevice)>
    +
    70 MetalKernelFunction;
    +
    71
    +
    72MetalKernelFunction metal_kernel(
    +
    73 const std::string& name,
    +
    74 const std::vector<std::string>& input_names,
    +
    75 const std::vector<std::string>& output_names,
    +
    76 const std::string& source,
    +
    77 const std::string& header = "",
    +
    78 bool ensure_row_contiguous = true,
    +
    79 bool atomic_outputs = false);
    +
    80
    +
    81} // namespace mlx::core::fast
    mlx::core::array
    Definition array.h:20
    mlx::core::fast
    Definition fast.h:9
    mlx::core::fast::layer_norm
    array layer_norm(const array &x, const std::optional< array > &weight, const std::optional< array > &bias, float eps, StreamOrDevice s={})
    -
    mlx::core::fast::MetalKernelFunction
    std::function< std::vector< array >(const std::vector< array > &, const std::vector< std::vector< int > > &, const std::vector< Dtype > &, std::tuple< int, int, int >, std::tuple< int, int, int >, std::vector< std::pair< std::string, TemplateArg > >, std::optional< float >, bool, StreamOrDevice)> MetalKernelFunction
    Definition fast.h:78
    +
    mlx::core::fast::MetalKernelFunction
    std::function< std::vector< array >(const std::vector< array > &, const std::vector< std::vector< int > > &, const std::vector< Dtype > &, std::tuple< int, int, int >, std::tuple< int, int, int >, std::vector< std::pair< std::string, TemplateArg > >, std::optional< float >, bool, StreamOrDevice)> MetalKernelFunction
    Definition fast.h:70
    mlx::core::fast::affine_dequantize
    array affine_dequantize(const array &w, const array &scales, const array &biases, int group_size=64, int bits=4, StreamOrDevice s={})
    mlx::core::fast::scaled_dot_product_attention
    array scaled_dot_product_attention(const array &queries, const array &keys, const array &values, const float scale, const std::optional< array > &mask=std::nullopt, const std::optional< int > memory_efficient_threshold=std::nullopt, StreamOrDevice s={})
    Computes: O = softmax(Q @ K.T) @ V.
    mlx::core::fast::rope
    array rope(const array &x, int dims, bool traditional, std::optional< float > base, float scale, int offset, const std::optional< array > &freqs=std::nullopt, StreamOrDevice s={})
    -
    mlx::core::fast::TemplateArg
    std::variant< int, bool, Dtype > TemplateArg
    Definition fast.h:66
    +
    mlx::core::fast::TemplateArg
    std::variant< int, bool, Dtype > TemplateArg
    Definition fast.h:58
    mlx::core::fast::affine_quantize
    std::tuple< array, array, array > affine_quantize(const array &w, int group_size=64, int bits=4, StreamOrDevice s={})
    mlx::core::fast::metal_kernel
    MetalKernelFunction metal_kernel(const std::string &name, const std::vector< std::string > &input_names, const std::vector< std::string > &output_names, const std::string &source, const std::string &header="", bool ensure_row_contiguous=true, bool atomic_outputs=false)
    mlx::core::fast::rms_norm
    array rms_norm(const array &x, const array &weight, float eps, StreamOrDevice s={})
    diff --git a/docs/build/html/fast__primitives_8h_source.html b/docs/build/html/fast__primitives_8h_source.html index 5780e0cd..68274176 100644 --- a/docs/build/html/fast__primitives_8h_source.html +++ b/docs/build/html/fast__primitives_8h_source.html @@ -365,87 +365,83 @@ $(function(){ initResizable(false); });
    228 dequantize_(dequantize) {}
    229
    -
    -
    230 void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
    -
    231 override {
    -
    232 throw std::runtime_error("NYI");
    -
    233 }
    -
    -
    234
    -
    235 void eval_gpu(const std::vector<array>& inputs, std::vector<array>& outputs)
    -
    236 override;
    +
    230 void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
    +
    231 override;
    +
    232
    +
    233 void eval_gpu(const std::vector<array>& inputs, std::vector<array>& outputs)
    +
    234 override;
    +
    235
    +
    236 DEFINE_PRINT(AffineQuantize);
    237
    -
    238 DEFINE_PRINT(AffineQuantize);
    -
    239
    -
    240 private:
    -
    241 std::function<std::vector<array>(std::vector<array>)> fallback_;
    -
    242 int group_size_;
    -
    243 int bits_;
    -
    244 bool dequantize_;
    -
    245};
    +
    238 private:
    +
    239 std::function<std::vector<array>(std::vector<array>)> fallback_;
    +
    240 int group_size_;
    +
    241 int bits_;
    +
    242 bool dequantize_;
    +
    243};
    -
    246
    -
    -
    247struct CustomKernelShapeInfo {
    -
    248 bool shape = false;
    -
    249 bool strides = false;
    -
    250 bool ndim = false;
    -
    251};
    +
    244
    +
    +
    245struct CustomKernelShapeInfo {
    +
    246 bool shape = false;
    +
    247 bool strides = false;
    +
    248 bool ndim = false;
    +
    249};
    -
    252
    -
    -
    253class CustomKernel : public Primitive {
    -
    254 public:
    -
    -
    255 CustomKernel(
    -
    256 Stream stream,
    -
    257 std::string name,
    -
    258 std::string source,
    -
    259 std::tuple<int, int, int> grid,
    -
    260 std::tuple<int, int, int> threadgroup,
    -
    261 std::vector<CustomKernelShapeInfo> shape_infos,
    -
    262 bool ensure_row_contiguous,
    -
    263 std::optional<float> init_value)
    -
    264 : Primitive(stream),
    -
    265 source_(std::move(source)),
    -
    266 name_(std::move(name)),
    -
    267 grid_(grid),
    -
    268 threadgroup_(threadgroup),
    -
    269 shape_infos_(std::move(shape_infos)),
    -
    270 ensure_row_contiguous_(ensure_row_contiguous),
    -
    271 init_value_(init_value) {}
    +
    250
    +
    +
    251class CustomKernel : public Primitive {
    +
    252 public:
    +
    +
    253 CustomKernel(
    +
    254 Stream stream,
    +
    255 std::string name,
    +
    256 std::string source,
    +
    257 std::tuple<int, int, int> grid,
    +
    258 std::tuple<int, int, int> threadgroup,
    +
    259 std::vector<CustomKernelShapeInfo> shape_infos,
    +
    260 bool ensure_row_contiguous,
    +
    261 std::optional<float> init_value)
    +
    262 : Primitive(stream),
    +
    263 source_(std::move(source)),
    +
    264 name_(std::move(name)),
    +
    265 grid_(grid),
    +
    266 threadgroup_(threadgroup),
    +
    267 shape_infos_(std::move(shape_infos)),
    +
    268 ensure_row_contiguous_(ensure_row_contiguous),
    +
    269 init_value_(init_value) {}
    -
    272
    -
    -
    273 void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
    -
    274 override {
    -
    275 throw std::runtime_error("Custom Metal kernels only run on GPU.");
    -
    276 }
    +
    270
    +
    +
    271 void eval_cpu(const std::vector<array>& inputs, std::vector<array>& outputs)
    +
    272 override {
    +
    273 throw std::runtime_error("Custom Metal kernels only run on GPU.");
    +
    274 }
    -
    277
    -
    278 void eval_gpu(const std::vector<array>& inputs, std::vector<array>& outputs)
    -
    279 override;
    +
    275
    +
    276 void eval_gpu(const std::vector<array>& inputs, std::vector<array>& outputs)
    +
    277 override;
    +
    278
    +
    279 DEFINE_PRINT(CustomKernel);
    280
    -
    281 DEFINE_PRINT(CustomKernel);
    -
    282
    -
    283 private:
    -
    284 std::string source_;
    -
    285 std::string name_;
    -
    286 std::tuple<int, int, int> grid_;
    -
    287 std::tuple<int, int, int> threadgroup_;
    -
    288 std::vector<CustomKernelShapeInfo> shape_infos_;
    -
    289 bool ensure_row_contiguous_;
    -
    290 std::optional<float> init_value_;
    -
    291};
    +
    281 private:
    +
    282 std::string source_;
    +
    283 std::string name_;
    +
    284 std::tuple<int, int, int> grid_;
    +
    285 std::tuple<int, int, int> threadgroup_;
    +
    286 std::vector<CustomKernelShapeInfo> shape_infos_;
    +
    287 bool ensure_row_contiguous_;
    +
    288 std::optional<float> init_value_;
    +
    289};
    -
    292
    -
    293} // namespace mlx::core::fast
    +
    290
    +
    291} // namespace mlx::core::fast
    mlx::core::Primitive
    Definition primitives.h:48
    mlx::core::Primitive::stream
    const Stream & stream()
    The stream the primitive will run on.
    Definition primitives.h:58
    mlx::core::Primitive::is_equivalent
    virtual bool is_equivalent(const Primitive &other) const
    Equivalence check defaults to false unless overridden by the primitive.
    Definition primitives.h:107
    mlx::core::array
    Definition array.h:20
    mlx::core::fast::AffineQuantize
    Definition fast_primitives.h:217
    -
    mlx::core::fast::AffineQuantize::eval_cpu
    void eval_cpu(const std::vector< array > &inputs, std::vector< array > &outputs) override
    A primitive must know how to evaluate itself on the CPU/GPU for the given inputs and populate the out...
    Definition fast_primitives.h:230
    +
    mlx::core::fast::AffineQuantize::eval_cpu
    void eval_cpu(const std::vector< array > &inputs, std::vector< array > &outputs) override
    A primitive must know how to evaluate itself on the CPU/GPU for the given inputs and populate the out...
    mlx::core::fast::AffineQuantize::eval_gpu
    void eval_gpu(const std::vector< array > &inputs, std::vector< array > &outputs) override
    mlx::core::fast::AffineQuantize::AffineQuantize
    AffineQuantize(Stream stream, std::function< std::vector< array >(std::vector< array >)> fallback, int group_size, int bits, bool dequantize)
    Definition fast_primitives.h:219
    @@ -454,11 +450,11 @@ $(function(){ initResizable(false); });
    mlx::core::fast::Custom::vjp
    virtual std::vector< array > vjp(const std::vector< array > &primals, const std::vector< array > &cotangents, const std::vector< int > &argnums, const std::vector< array > &outputs) override
    The vector-Jacobian product.
    mlx::core::fast::Custom::vmap
    virtual std::pair< std::vector< array >, std::vector< int > > vmap(const std::vector< array > &inputs, const std::vector< int > &axes) override
    The primitive must know how to vectorize itself across the given axes.
    mlx::core::fast::Custom::jvp
    virtual std::vector< array > jvp(const std::vector< array > &primals, const std::vector< array > &tangents, const std::vector< int > &argnums) override
    The Jacobian-vector product.
    -
    mlx::core::fast::CustomKernel
    Definition fast_primitives.h:253
    +
    mlx::core::fast::CustomKernel
    Definition fast_primitives.h:251
    mlx::core::fast::CustomKernel::eval_gpu
    void eval_gpu(const std::vector< array > &inputs, std::vector< array > &outputs) override
    -
    mlx::core::fast::CustomKernel::eval_cpu
    void eval_cpu(const std::vector< array > &inputs, std::vector< array > &outputs) override
    A primitive must know how to evaluate itself on the CPU/GPU for the given inputs and populate the out...
    Definition fast_primitives.h:273
    -
    mlx::core::fast::CustomKernel::CustomKernel
    CustomKernel(Stream stream, std::string name, std::string source, std::tuple< int, int, int > grid, std::tuple< int, int, int > threadgroup, std::vector< CustomKernelShapeInfo > shape_infos, bool ensure_row_contiguous, std::optional< float > init_value)
    Definition fast_primitives.h:255
    +
    mlx::core::fast::CustomKernel::eval_cpu
    void eval_cpu(const std::vector< array > &inputs, std::vector< array > &outputs) override
    A primitive must know how to evaluate itself on the CPU/GPU for the given inputs and populate the out...
    Definition fast_primitives.h:271
    +
    mlx::core::fast::CustomKernel::CustomKernel
    CustomKernel(Stream stream, std::string name, std::string source, std::tuple< int, int, int > grid, std::tuple< int, int, int > threadgroup, std::vector< CustomKernelShapeInfo > shape_infos, bool ensure_row_contiguous, std::optional< float > init_value)
    Definition fast_primitives.h:253
    mlx::core::fast::LayerNorm
    Definition fast_primitives.h:90
    mlx::core::fast::LayerNorm::DEFINE_PRINT
    DEFINE_PRINT(LayerNorm) bool is_equivalent(const Primitive &other) const override
    mlx::core::fast::LayerNorm::LayerNorm
    LayerNorm(Stream stream, std::function< std::vector< array >(std::vector< array >)> fallback, float eps)
    Definition fast_primitives.h:92
    @@ -499,10 +495,10 @@ $(function(){ initResizable(false); });
    mlx::core::fast
    Definition fast.h:9
    mlx::core::Stream
    Definition stream.h:9
    -
    mlx::core::fast::CustomKernelShapeInfo
    Definition fast_primitives.h:247
    -
    mlx::core::fast::CustomKernelShapeInfo::strides
    bool strides
    Definition fast_primitives.h:249
    -
    mlx::core::fast::CustomKernelShapeInfo::shape
    bool shape
    Definition fast_primitives.h:248
    -
    mlx::core::fast::CustomKernelShapeInfo::ndim
    bool ndim
    Definition fast_primitives.h:250
    +
    mlx::core::fast::CustomKernelShapeInfo
    Definition fast_primitives.h:245
    +
    mlx::core::fast::CustomKernelShapeInfo::strides
    bool strides
    Definition fast_primitives.h:247
    +
    mlx::core::fast::CustomKernelShapeInfo::shape
    bool shape
    Definition fast_primitives.h:246
    +
    mlx::core::fast::CustomKernelShapeInfo::ndim
    bool ndim
    Definition fast_primitives.h:248
    diff --git a/docs/build/html/files.html b/docs/build/html/files.html index 70a9ad5c..a7b33eee 100644 --- a/docs/build/html/files.html +++ b/docs/build/html/files.html @@ -125,70 +125,82 @@ $(function(){ initResizable(false); });  ► fft  radix.h  readwrite.h - ► reduction - ops.h - reduce_all.h - reduce_col.h - reduce_init.h - reduce_row.h - ► steel - ► conv - ► kernels - steel_conv.h - steel_conv_general.h - ► loaders - loader_channel_l.h - loader_channel_n.h - loader_general.h - conv.h - loader.h - params.h - ► gemm - ► kernels - steel_gemm_fused.h - steel_gemm_masked.h - steel_gemm_splitk.h - gemm.h - loader.h - mma.h - params.h - transforms.h - ► utils - integral_constant.h - type_traits.h - defines.h - utils.h - arange.h - atomic.h - bf16.h - bf16_math.h - binary.h - binary_ops.h - binary_two.h - complex.h - copy.h - defines.h - erf.h - expm1f.h - fft.h - gather.h - gemv_masked.h - hadamard.h - indexing.h - quantized.h - reduce.h - reduce_utils.h - scaled_dot_product_attention_params.h - scan.h - scatter.h - sdpa_vector.h - softmax.h - sort.h - ternary.h - ternary_ops.h - unary.h - unary_ops.h - utils.h + ► jit + bf16.h + ► metal_3_0 + bf16.h + ► metal_3_1 + bf16.h + ► reduction + ops.h + reduce_all.h + reduce_col.h + reduce_init.h + reduce_row.h + ► steel + ► attn + ► kernels + steel_attention.h + attn.h + loader.h + mma.h + params.h + transforms.h + ► conv + ► kernels + steel_conv.h + steel_conv_general.h + ► loaders + loader_channel_l.h + loader_channel_n.h + loader_general.h + conv.h + loader.h + params.h + ► gemm + ► kernels + steel_gemm_fused.h + steel_gemm_masked.h + steel_gemm_splitk.h + gemm.h + loader.h + mma.h + params.h + transforms.h + ► utils + integral_constant.h + type_traits.h + defines.h + utils.h + arange.h + atomic.h + bf16_math.h + binary.h + binary_ops.h + binary_two.h + complex.h + copy.h + defines.h + erf.h + expm1f.h + fft.h + gather.h + gemv_masked.h + hadamard.h + indexing.h + quantized.h + reduce.h + reduce_utils.h + scan.h + scatter.h + sdpa_vector.h + softmax.h + sort.h + ternary.h + ternary_ops.h + unary.h + unary_ops.h + utils.h  allocator.h  binary.h  copy.h diff --git a/docs/build/html/functions_a.html b/docs/build/html/functions_a.html index 32ebacc4..c6c45164 100644 --- a/docs/build/html/functions_a.html +++ b/docs/build/html/functions_a.html @@ -91,7 +91,7 @@ $(function(){ initResizable(false); });
  • A_str_k : mlx::steel::BlockMMA< T, U, BM, BN, BK, WM, WN, transpose_a, transpose_b, lda_tgp, ldb_tgp, AccumType, Epilogue >
  • A_str_m : mlx::steel::BlockMMA< T, U, BM, BN, BK, WM, WN, transpose_a, transpose_b, lda_tgp, ldb_tgp, AccumType, Epilogue >
  • Abs() : mlx::core::Abs
    • -
  • accum_type : mlx::steel::AccumHelper< T >
    • +
  • accum_type : mlx::steel::AccumHelper< T >
  • Add() : mlx::core::Add
  • add_temporaries() : mlx::core::metal::Device
  • add_temporary() : mlx::core::metal::Device
    • @@ -108,12 +108,12 @@ $(function(){ initResizable(false); });
  • Allocator() : mlx::core::allocator::Allocator
  • allocator : mlx::core::allocator::CommonAllocator, mlx::core::metal::MetalAllocator
  • AllReduce() : mlx::core::distributed::AllReduce
    • -
  • alpha : mlx::steel::GEMMAddMMParams, mlx::steel::TransformAxpby< OutT, InT >, MLXFastAttentionParams
    • +
  • alpha : mlx::steel::GEMMAddMMParams, mlx::steel::TransformAxpby< OutT, InT >
  • And : mlx::core::BitwiseBinary, mlx::core::distributed::AllReduce, mlx::core::Reduce
    • -
  • apply() : mlx::steel::TransformAdd< OutT, InT >, mlx::steel::TransformAxpby< OutT, InT >, mlx::steel::TransformNone< OutT, InT >, ScaleOp< OutT, InT >
    • +
  • apply() : DivOp, ExpSubOp, MaxOp, mlx::steel::TransformAdd< OutT, InT >, mlx::steel::TransformAxpby< OutT, InT >, mlx::steel::TransformNone< OutT, InT >, MulOp, ScaleOp< OutT, InT >, SubOp, SumOp, TransformScale< T >
  • apply_epilogue() : mlx::steel::BlockMMA< T, U, BM, BN, BK, WM, WN, transpose_a, transpose_b, lda_tgp, ldb_tgp, AccumType, Epilogue >
  • apply_epilogue_safe() : mlx::steel::BlockMMA< T, U, BM, BN, BK, WM, WN, transpose_a, transpose_b, lda_tgp, ldb_tgp, AccumType, Epilogue >
    • -
  • apply_inplace_op() : mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    • +
  • apply_inplace_op() : mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >, mlx::steel::BlockLoaderT< T, BROWS, BCOLS, kDstStrRow, kDstStrCol, reduction_dim, tgp_size, n_reads, TCOLS, TROWS >
  • Arange() : mlx::core::Arange
  • ArcCos() : mlx::core::ArcCos
  • ArcCosh() : mlx::core::ArcCosh
    • @@ -135,7 +135,7 @@ $(function(){ initResizable(false); });
  • As_offset : mlx::steel::BlockMMA< T, U, BM, BN, BK, WM, WN, transpose_a, transpose_b, lda_tgp, ldb_tgp, AccumType, Epilogue >
  • AsStrided() : mlx::core::AsStrided
  • AsType() : mlx::core::AsType
    • -
  • Atile : mlx::steel::BlockMMA< T, U, BM, BN, BK, WM, WN, transpose_a, transpose_b, lda_tgp, ldb_tgp, AccumType, Epilogue >
    • +
  • Atile : mlx::steel::BlockMMA< T, U, BM, BN, BK, WM, WN, transpose_a, transpose_b, lda_tgp, ldb_tgp, AccumType, Epilogue >
  • atomic_update() : None
  • attach_event() : mlx::core::array
  • available : mlx::core::array
    • diff --git a/docs/build/html/functions_b.html b/docs/build/html/functions_b.html index f1cfde39..6eeb537f 100644 --- a/docs/build/html/functions_b.html +++ b/docs/build/html/functions_b.html @@ -87,35 +87,33 @@ $(function(){ initResizable(false); });
    Here is a list of all class members with links to the classes they belong to:

    - b -

    diff --git a/docs/build/html/functions_c.html b/docs/build/html/functions_c.html index 72eac8b7..1396ec6b 100644 --- a/docs/build/html/functions_c.html +++ b/docs/build/html/functions_c.html @@ -88,6 +88,7 @@ $(function(){ initResizable(false); });

    - c -

    diff --git a/docs/build/html/functions_func_l.html b/docs/build/html/functions_func_l.html index b787da34..be7c06ba 100644 --- a/docs/build/html/functions_func_l.html +++ b/docs/build/html/functions_func_l.html @@ -101,16 +101,17 @@ $(function(){ initResizable(false); });
  • Load() : mlx::core::Load
  • load() : mlx::steel::BaseMMAFrag< T, 8, 8 >, mlx::steel::MMATile< T, kTileRows_, kTileCols_, MMAFrag_ >, ReadWriter< in_T, out_T, step, four_step_real >
  • load_padded() : ReadWriter< in_T, out_T, step, four_step_real >
    • -
  • load_safe() : GEMVKernel< T, out_mask_t, op_mask_t, BM, BN, SM, SN, TM, TN >, mlx::steel::BaseMMAFrag< T, 8, 8 >, mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >, mlx::steel::MMATile< T, kTileRows_, kTileCols_, MMAFrag_ >, QuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    • +
  • load_safe() : GEMVKernel< T, out_mask_t, op_mask_t, BM, BN, SM, SN, TM, TN >, mlx::steel::BaseMMAFrag< T, 8, 8 >, mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >, mlx::steel::BlockLoaderT< T, BROWS, BCOLS, kDstStrRow, kDstStrCol, reduction_dim, tgp_size, n_reads, TCOLS, TROWS >, mlx::steel::MMATile< T, kTileRows_, kTileCols_, MMAFrag_ >, QuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
  • load_strided() : ReadWriter< in_T, out_T, step, four_step_real >
    • -
  • load_unsafe() : GEMVKernel< T, out_mask_t, op_mask_t, BM, BN, SM, SN, TM, TN >, mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >, mlx::steel::Conv2DInputBlockLoaderGeneral< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DInputBlockLoaderLargeFilter< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DInputBlockLoaderSmallChannels< T, BM, BN, BK, tgp_size, n_channels, tgp_padding >, mlx::steel::Conv2DInputBlockLoaderSmallFilter< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DWeightBlockLoader< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DWeightBlockLoaderGeneral< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DWeightBlockLoaderSmallChannels< T, BM, BN, BK, tgp_size, n_channels, tgp_padding >, QuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    • -
  • location() : looped_elem_to_loc< dim, offset_t >, looped_elem_to_loc< 0, offset_t >, looped_elem_to_loc< 1, offset_t >
    • +
  • load_unsafe() : GEMVKernel< T, out_mask_t, op_mask_t, BM, BN, SM, SN, TM, TN >, mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >, mlx::steel::BlockLoaderT< T, BROWS, BCOLS, kDstStrRow, kDstStrCol, reduction_dim, tgp_size, n_reads, TCOLS, TROWS >, mlx::steel::Conv2DInputBlockLoaderGeneral< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DInputBlockLoaderLargeFilter< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DInputBlockLoaderSmallChannels< T, BM, BN, BK, tgp_size, n_channels, tgp_padding >, mlx::steel::Conv2DInputBlockLoaderSmallFilter< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DWeightBlockLoader< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DWeightBlockLoaderGeneral< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DWeightBlockLoaderSmallChannels< T, BM, BN, BK, tgp_size, n_channels, tgp_padding >, QuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    • +
  • location() : LoopedElemToLoc< DIM, OffsetT, General >, LoopedElemToLoc< 1, OffsetT, false >, LoopedElemToLoc< 1, OffsetT, true >
  • Log() : mlx::core::Log
  • Log1p() : mlx::core::Log1p
  • LogAddExp() : mlx::core::LogAddExp
  • LogicalAnd() : mlx::core::LogicalAnd
  • LogicalNot() : mlx::core::LogicalNot
  • LogicalOr() : mlx::core::LogicalOr
    • +
  • LoopedElemToLoc() : LoopedElemToLoc< DIM, OffsetT, General >, LoopedElemToLoc< 1, OffsetT, false >, LoopedElemToLoc< 1, OffsetT, true >
  • lowest() : metal::_numeric_limits_impl< bfloat16_t >
    • diff --git a/docs/build/html/functions_func_n.html b/docs/build/html/functions_func_n.html index df574bf2..70032b09 100644 --- a/docs/build/html/functions_func_n.html +++ b/docs/build/html/functions_func_n.html @@ -94,7 +94,7 @@ $(function(){ initResizable(false); });
  • Negative() : mlx::core::Negative
  • new_queue() : mlx::core::metal::Device
  • new_stream() : mlx::core::scheduler::Scheduler
    • -
  • next() : looped_elem_to_loc< dim, offset_t >, looped_elem_to_loc< 0, offset_t >, looped_elem_to_loc< 1, offset_t >, mlx::core::random::KeySequence, mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >, mlx::steel::Conv2DInputBlockLoaderGeneral< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DInputBlockLoaderLargeFilter< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DInputBlockLoaderSmallChannels< T, BM, BN, BK, tgp_size, n_channels, tgp_padding >, mlx::steel::Conv2DInputBlockLoaderSmallFilter< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DWeightBlockLoader< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DWeightBlockLoaderGeneral< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DWeightBlockLoaderSmallChannels< T, BM, BN, BK, tgp_size, n_channels, tgp_padding >, QuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    • +
  • next() : LoopedElemToLoc< DIM, OffsetT, General >, LoopedElemToLoc< 1, OffsetT, false >, LoopedElemToLoc< 1, OffsetT, true >, mlx::core::random::KeySequence, mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >, mlx::steel::BlockLoaderT< T, BROWS, BCOLS, kDstStrRow, kDstStrCol, reduction_dim, tgp_size, n_reads, TCOLS, TROWS >, mlx::steel::Conv2DInputBlockLoaderGeneral< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DInputBlockLoaderLargeFilter< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DInputBlockLoaderSmallChannels< T, BM, BN, BK, tgp_size, n_channels, tgp_padding >, mlx::steel::Conv2DInputBlockLoaderSmallFilter< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DWeightBlockLoader< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DWeightBlockLoaderGeneral< T, BM, BN, BK, tgp_size, tgp_padding >, mlx::steel::Conv2DWeightBlockLoaderSmallChannels< T, BM, BN, BK, tgp_size, n_channels, tgp_padding >, QuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
  • NotEqual() : mlx::core::NotEqual
  • notify_new_task() : mlx::core::scheduler::Scheduler
  • notify_task_completion() : mlx::core::scheduler::Scheduler
    • diff --git a/docs/build/html/functions_func_o.html b/docs/build/html/functions_func_o.html index e605ad36..0df9ec53 100644 --- a/docs/build/html/functions_func_o.html +++ b/docs/build/html/functions_func_o.html @@ -103,11 +103,10 @@ $(function(){ initResizable(false); });
  • operator+=() : pocketfft::detail::cmplx< T >
  • operator-() : pocketfft::detail::cmplx< T >
  • operator-=() : pocketfft::detail::cmplx< T >
    • -
  • operator->() : mlx::core::metal::CommandEncoder
  • operator=() : mlx::core::_MLX_BFloat16, mlx::core::_MLX_Float16, mlx::core::allocator::Allocator, mlx::core::array::Data, mlx::core::array, mlx::core::metal::CommandEncoder, mlx::core::metal::Device, mlx::core::metal::ResidencySet, mlx::core::Primitive, mlx::core::scheduler::Scheduler, mlx::core::UnaryPrimitive
  • operator[]() : pocketfft::detail::arr< T >, pocketfft::detail::cndarr< T >, pocketfft::detail::ndarr< T >, pocketfft::detail::sincos_2pibyn< T >
  • out_of_bounds() : ReadWriter< in_T, out_T, step, four_step_real >
    • -
  • output_shapes() : mlx::core::Abs, mlx::core::Add, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::Ceil, mlx::core::Compiled, mlx::core::Conjugate, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::Divide, mlx::core::DivMod, mlx::core::Eigh, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::Floor, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Less, mlx::core::LessEqual, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Partition, mlx::core::Power, mlx::core::Primitive, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Round, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Softmax, mlx::core::Sort, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::Tan, mlx::core::Tanh
    • +
  • output_shapes() : mlx::core::Abs, mlx::core::Add, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::Ceil, mlx::core::Compiled, mlx::core::Conjugate, mlx::core::Contiguous, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::Divide, mlx::core::DivMod, mlx::core::Eigh, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::Floor, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Less, mlx::core::LessEqual, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Partition, mlx::core::Power, mlx::core::Primitive, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Round, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Softmax, mlx::core::Sort, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::Tan, mlx::core::Tanh
  • outputs() : mlx::core::array, mlx::core::metal::CommandEncoder
  • overwrite_descriptor() : mlx::core::array
    • diff --git a/docs/build/html/functions_func_p.html b/docs/build/html/functions_func_p.html index fda8c19d..e3626ae4 100644 --- a/docs/build/html/functions_func_p.html +++ b/docs/build/html/functions_func_p.html @@ -99,7 +99,7 @@ $(function(){ initResizable(false); });
  • primitive() : mlx::core::array
  • primitive_id() : mlx::core::array
  • primitive_ptr() : mlx::core::array
    • -
  • print() : mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::Arange, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsStrided, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::BlockMaskedMM, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Cholesky, mlx::core::Compiled, mlx::core::Concatenate, mlx::core::Conjugate, mlx::core::Convolution, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::CustomTransforms, mlx::core::Depends, mlx::core::distributed::AllReduce, mlx::core::Divide, mlx::core::DivMod, mlx::core::Eigh, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherMM, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Inverse, mlx::core::Less, mlx::core::LessEqual, mlx::core::Load, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::Primitive, mlx::core::PrintFormatter, mlx::core::QRF, mlx::core::QuantizedMatmul, mlx::core::RandomBits, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Split, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::SVD, mlx::core::Tan, mlx::core::Tanh, mlx::core::Transpose, mlx::core::Uniform, mlx::core::View
    • +
  • print() : mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::Arange, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsStrided, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::BlockMaskedMM, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Cholesky, mlx::core::Compiled, mlx::core::Concatenate, mlx::core::Conjugate, mlx::core::Contiguous, mlx::core::Convolution, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::CustomTransforms, mlx::core::Depends, mlx::core::distributed::AllReduce, mlx::core::Divide, mlx::core::DivMod, mlx::core::Eigh, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherMM, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Inverse, mlx::core::Less, mlx::core::LessEqual, mlx::core::Load, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::Primitive, mlx::core::PrintFormatter, mlx::core::QRF, mlx::core::QuantizedMatmul, mlx::core::RandomBits, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Split, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::SVD, mlx::core::Tan, mlx::core::Tanh, mlx::core::Transpose, mlx::core::Uniform, mlx::core::View
  • prod() : pocketfft::detail::util
  • ptr() : mlx::core::allocator::Buffer
  • push() : pocketfft::detail::threading::concurrent_queue< T >
    • diff --git a/docs/build/html/functions_func_q.html b/docs/build/html/functions_func_q.html index f09c3145..a5868be0 100644 --- a/docs/build/html/functions_func_q.html +++ b/docs/build/html/functions_func_q.html @@ -88,7 +88,7 @@ $(function(){ initResizable(false); });

    - q -

    diff --git a/docs/build/html/functions_func_r.html b/docs/build/html/functions_func_r.html index 56f88041..d0276be4 100644 --- a/docs/build/html/functions_func_r.html +++ b/docs/build/html/functions_func_r.html @@ -117,6 +117,8 @@ $(function(){ initResizable(false); });
  • RoPE() : mlx::core::fast::RoPE
  • Round() : mlx::core::Round
  • round_error() : metal::_numeric_limits_impl< bfloat16_t >
    • +
  • row_bin_op() : mlx::steel::BaseMMAFrag< T, 8, 8 >, mlx::steel::MMATile< T, kTileRows_, kTileCols_, MMAFrag_ >
    • +
  • row_reduce() : mlx::steel::BaseMMAFrag< T, 8, 8 >, mlx::steel::MMATile< T, kTileRows_, kTileCols_, MMAFrag_ >
  • run() : GEMVKernel< T, out_mask_t, op_mask_t, BM, BN, SM, SN, TM, TN >, GEMVTKernel< T, out_mask_t, op_mask_t, BM, BN, SM, SN, TM, TN >, mlx::steel::GEMMKernel< T, U, BM, BN, BK, WM, WN, transpose_a, transpose_b, MN_aligned, K_aligned, AccumType, Epilogue >
    • diff --git a/docs/build/html/functions_func_s.html b/docs/build/html/functions_func_s.html index e99403c1..fffb9cd7 100644 --- a/docs/build/html/functions_func_s.html +++ b/docs/build/html/functions_func_s.html @@ -97,7 +97,9 @@ $(function(){ initResizable(false); });
  • Select() : mlx::core::Select
  • Send() : mlx::core::distributed::Send
  • Set() : pocketfft::detail::cmplx< T >
    • +
  • set_bytes() : mlx::core::metal::CommandEncoder
  • set_cache_limit() : mlx::core::metal::MetalAllocator
    • +
  • set_compute_pipeline_state() : mlx::core::metal::CommandEncoder
  • set_data() : mlx::core::array
  • set_default_stream() : mlx::core::scheduler::Scheduler
  • set_input_array() : mlx::core::metal::CommandEncoder
    • @@ -108,8 +110,10 @@ $(function(){ initResizable(false); });
  • set_status() : mlx::core::array
  • set_tracer() : mlx::core::array
  • set_value() : mlx::core::Event
    • +
  • set_vector_bytes() : mlx::core::metal::CommandEncoder
  • set_wired_limit() : mlx::core::metal::MetalAllocator
  • shape() : mlx::core::array, pocketfft::detail::arr_info
    • +
  • Shape2D() : mlx::steel::Shape2D< RInt, CInt >
  • shutdown() : pocketfft::detail::threading::thread_pool
  • siblings() : mlx::core::array
  • Sigmoid() : mlx::core::Sigmoid
    • diff --git a/docs/build/html/functions_func_t.html b/docs/build/html/functions_func_t.html index 6e8b1ebb..7eece817 100644 --- a/docs/build/html/functions_func_t.html +++ b/docs/build/html/functions_func_t.html @@ -100,6 +100,7 @@ $(function(){ initResizable(false); });
  • ThreadPool() : ThreadPool
  • TransformAdd() : mlx::steel::TransformAdd< OutT, InT >
  • TransformAxpby() : mlx::steel::TransformAxpby< OutT, InT >
    • +
  • TransformScale() : TransformScale< T >
  • Transpose() : mlx::core::Transpose
  • try_pop() : pocketfft::detail::threading::concurrent_queue< T >
    • diff --git a/docs/build/html/functions_func_u.html b/docs/build/html/functions_func_u.html index 09d0138c..d8106c7e 100644 --- a/docs/build/html/functions_func_u.html +++ b/docs/build/html/functions_func_u.html @@ -89,6 +89,7 @@ $(function(){ initResizable(false); });

    - u -

    diff --git a/docs/build/html/functions_func_v.html b/docs/build/html/functions_func_v.html index 33d877a0..c51ad5fb 100644 --- a/docs/build/html/functions_func_v.html +++ b/docs/build/html/functions_func_v.html @@ -91,8 +91,8 @@ $(function(){ initResizable(false); });
  • valid() : mlx::core::Event
  • value() : mlx::core::Event
  • View() : mlx::core::View
    • -
  • vjp() : mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::AsStrided, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::BlockMaskedMM, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Compiled, mlx::core::Concatenate, mlx::core::Convolution, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::CustomTransforms, mlx::core::Depends, mlx::core::distributed::AllGather, mlx::core::distributed::AllReduce, mlx::core::Divide, mlx::core::DivMod, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::fast::Custom, mlx::core::fast::LayerNorm, mlx::core::fast::RMSNorm, mlx::core::fast::RoPE, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherMM, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Less, mlx::core::LessEqual, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::Primitive, mlx::core::QuantizedMatmul, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Split, mlx::core::Sqrt, mlx::core::Square, mlx::core::Subtract, mlx::core::Tan, mlx::core::Tanh, mlx::core::Transpose
    • -
  • vmap() : mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Cholesky, mlx::core::Compiled, mlx::core::Concatenate, mlx::core::Conjugate, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::CustomTransforms, mlx::core::distributed::AllGather, mlx::core::distributed::AllReduce, mlx::core::distributed::Send, mlx::core::Divide, mlx::core::DivMod, mlx::core::Eigh, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::fast::Custom, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Inverse, mlx::core::Less, mlx::core::LessEqual, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::Primitive, mlx::core::QuantizedMatmul, mlx::core::RandomBits, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Split, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::SVD, mlx::core::Tan, mlx::core::Tanh, mlx::core::Transpose, mlx::core::Uniform, mlx::core::View
    • +
  • vjp() : mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::AsStrided, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::BlockMaskedMM, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Compiled, mlx::core::Concatenate, mlx::core::Contiguous, mlx::core::Convolution, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::CustomTransforms, mlx::core::Depends, mlx::core::distributed::AllGather, mlx::core::distributed::AllReduce, mlx::core::Divide, mlx::core::DivMod, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::fast::Custom, mlx::core::fast::LayerNorm, mlx::core::fast::RMSNorm, mlx::core::fast::RoPE, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherMM, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Less, mlx::core::LessEqual, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::Primitive, mlx::core::QuantizedMatmul, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Split, mlx::core::Sqrt, mlx::core::Square, mlx::core::Subtract, mlx::core::Tan, mlx::core::Tanh, mlx::core::Transpose
    • +
  • vmap() : mlx::core::Abs, mlx::core::Add, mlx::core::AddMM, mlx::core::ArcCos, mlx::core::ArcCosh, mlx::core::ArcSin, mlx::core::ArcSinh, mlx::core::ArcTan2, mlx::core::ArcTan, mlx::core::ArcTanh, mlx::core::ArgPartition, mlx::core::ArgReduce, mlx::core::ArgSort, mlx::core::AsType, mlx::core::BitwiseBinary, mlx::core::Broadcast, mlx::core::Ceil, mlx::core::Cholesky, mlx::core::Compiled, mlx::core::Concatenate, mlx::core::Conjugate, mlx::core::Contiguous, mlx::core::Copy, mlx::core::Cos, mlx::core::Cosh, mlx::core::CustomTransforms, mlx::core::distributed::AllGather, mlx::core::distributed::AllReduce, mlx::core::distributed::Send, mlx::core::Divide, mlx::core::DivMod, mlx::core::Eigh, mlx::core::Equal, mlx::core::Erf, mlx::core::ErfInv, mlx::core::Exp, mlx::core::Expm1, mlx::core::fast::Custom, mlx::core::FFT, mlx::core::Floor, mlx::core::Full, mlx::core::Gather, mlx::core::GatherQMM, mlx::core::Greater, mlx::core::GreaterEqual, mlx::core::Hadamard, mlx::core::Imag, mlx::core::Inverse, mlx::core::Less, mlx::core::LessEqual, mlx::core::Log1p, mlx::core::Log, mlx::core::LogAddExp, mlx::core::LogicalAnd, mlx::core::LogicalNot, mlx::core::LogicalOr, mlx::core::Matmul, mlx::core::Maximum, mlx::core::Minimum, mlx::core::Multiply, mlx::core::Negative, mlx::core::NotEqual, mlx::core::NumberOfElements, mlx::core::Pad, mlx::core::Partition, mlx::core::Power, mlx::core::Primitive, mlx::core::QuantizedMatmul, mlx::core::RandomBits, mlx::core::Real, mlx::core::Reduce, mlx::core::Remainder, mlx::core::Reshape, mlx::core::Round, mlx::core::Scan, mlx::core::Scatter, mlx::core::Select, mlx::core::Sigmoid, mlx::core::Sign, mlx::core::Sin, mlx::core::Sinh, mlx::core::Slice, mlx::core::SliceUpdate, mlx::core::Softmax, mlx::core::Sort, mlx::core::Split, mlx::core::Sqrt, mlx::core::Square, mlx::core::StopGradient, mlx::core::Subtract, mlx::core::SVD, mlx::core::Tan, mlx::core::Tanh, mlx::core::Transpose, mlx::core::Uniform, mlx::core::View
    • diff --git a/docs/build/html/functions_func_w.html b/docs/build/html/functions_func_w.html index f7fb19db..f5ad13fd 100644 --- a/docs/build/html/functions_func_w.html +++ b/docs/build/html/functions_func_w.html @@ -88,6 +88,7 @@ $(function(){ initResizable(false); });

    - w -

    diff --git a/docs/build/html/functions_v.html b/docs/build/html/functions_v.html index 44ca8b80..724da0a5 100644 --- a/docs/build/html/functions_v.html +++ b/docs/build/html/functions_v.html @@ -87,18 +87,19 @@ $(function(){ initResizable(false); });
    Here is a list of all class members with links to the classes they belong to:

    - v -

    diff --git a/docs/build/html/functions_vars.html b/docs/build/html/functions_vars.html index 9be45cb9..e0e70e22 100644 --- a/docs/build/html/functions_vars.html +++ b/docs/build/html/functions_vars.html @@ -94,9 +94,9 @@ $(function(){ initResizable(false); });
  • adj_out_h : mlx::steel::Conv2DGeneralJumpParams
  • adj_out_hw : mlx::steel::Conv2DGeneralJumpParams
  • adj_out_w : mlx::steel::Conv2DGeneralJumpParams
    • -
  • alpha : mlx::steel::GEMMAddMMParams, mlx::steel::TransformAxpby< OutT, InT >, MLXFastAttentionParams
    • +
  • alpha : mlx::steel::GEMMAddMMParams, mlx::steel::TransformAxpby< OutT, InT >
  • As_offset : mlx::steel::BlockMMA< T, U, BM, BN, BK, WM, WN, transpose_a, transpose_b, lda_tgp, ldb_tgp, AccumType, Epilogue >
    • -
  • Atile : mlx::steel::BlockMMA< T, U, BM, BN, BK, WM, WN, transpose_a, transpose_b, lda_tgp, ldb_tgp, AccumType, Epilogue >
    • +
  • Atile : mlx::steel::BlockMMA< T, U, BM, BN, BK, WM, WN, transpose_a, transpose_b, lda_tgp, ldb_tgp, AccumType, Epilogue >
    • diff --git a/docs/build/html/functions_vars_b.html b/docs/build/html/functions_vars_b.html index 6880a2f5..cdb0253b 100644 --- a/docs/build/html/functions_vars_b.html +++ b/docs/build/html/functions_vars_b.html @@ -87,37 +87,35 @@ $(function(){ initResizable(false); });
    Here is a list of all variables with links to the classes they belong to:

    - b -

    diff --git a/docs/build/html/functions_vars_c.html b/docs/build/html/functions_vars_c.html index 05419120..b39005e3 100644 --- a/docs/build/html/functions_vars_c.html +++ b/docs/build/html/functions_vars_c.html @@ -88,13 +88,14 @@ $(function(){ initResizable(false); });

    - c -

    diff --git a/docs/build/html/functions_vars_d.html b/docs/build/html/functions_vars_d.html index bb3dad61..2dae6285 100644 --- a/docs/build/html/functions_vars_d.html +++ b/docs/build/html/functions_vars_d.html @@ -87,12 +87,14 @@ $(function(){ initResizable(false); });
    Here is a list of all variables with links to the classes they belong to:

    - d -

    diff --git a/docs/build/html/functions_vars_g.html b/docs/build/html/functions_vars_g.html index fc9d6c8b..d5050957 100644 --- a/docs/build/html/functions_vars_g.html +++ b/docs/build/html/functions_vars_g.html @@ -88,11 +88,10 @@ $(function(){ initResizable(false); });

    - g -

    @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.arange.html b/docs/build/html/python/_autosummary/mlx.core.arange.html index 7734e041..3766992e 100644 --- a/docs/build/html/python/_autosummary/mlx.core.arange.html +++ b/docs/build/html/python/_autosummary/mlx.core.arange.html @@ -8,7 +8,7 @@ - mlx.core.arange — MLX 0.20.0 documentation + mlx.core.arange — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.arccos.html b/docs/build/html/python/_autosummary/mlx.core.arccos.html index 4a0c9f62..59ae6cdb 100644 --- a/docs/build/html/python/_autosummary/mlx.core.arccos.html +++ b/docs/build/html/python/_autosummary/mlx.core.arccos.html @@ -8,7 +8,7 @@ - mlx.core.arccos — MLX 0.20.0 documentation + mlx.core.arccos — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.arccosh.html b/docs/build/html/python/_autosummary/mlx.core.arccosh.html index 01ee928e..aa0ab59d 100644 --- a/docs/build/html/python/_autosummary/mlx.core.arccosh.html +++ b/docs/build/html/python/_autosummary/mlx.core.arccosh.html @@ -8,7 +8,7 @@ - mlx.core.arccosh — MLX 0.20.0 documentation + mlx.core.arccosh — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.arcsin.html b/docs/build/html/python/_autosummary/mlx.core.arcsin.html index 51d90ad5..4b5d7b57 100644 --- a/docs/build/html/python/_autosummary/mlx.core.arcsin.html +++ b/docs/build/html/python/_autosummary/mlx.core.arcsin.html @@ -8,7 +8,7 @@ - mlx.core.arcsin — MLX 0.20.0 documentation + mlx.core.arcsin — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.arcsinh.html b/docs/build/html/python/_autosummary/mlx.core.arcsinh.html index 55429324..0d0ef608 100644 --- a/docs/build/html/python/_autosummary/mlx.core.arcsinh.html +++ b/docs/build/html/python/_autosummary/mlx.core.arcsinh.html @@ -8,7 +8,7 @@ - mlx.core.arcsinh — MLX 0.20.0 documentation + mlx.core.arcsinh — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.arctan.html b/docs/build/html/python/_autosummary/mlx.core.arctan.html index 672285f5..eac1b8fd 100644 --- a/docs/build/html/python/_autosummary/mlx.core.arctan.html +++ b/docs/build/html/python/_autosummary/mlx.core.arctan.html @@ -8,7 +8,7 @@ - mlx.core.arctan — MLX 0.20.0 documentation + mlx.core.arctan — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.arctan2.html b/docs/build/html/python/_autosummary/mlx.core.arctan2.html index b41fdcaa..c93c3289 100644 --- a/docs/build/html/python/_autosummary/mlx.core.arctan2.html +++ b/docs/build/html/python/_autosummary/mlx.core.arctan2.html @@ -8,7 +8,7 @@ - mlx.core.arctan2 — MLX 0.20.0 documentation + mlx.core.arctan2 — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.arctanh.html b/docs/build/html/python/_autosummary/mlx.core.arctanh.html index 3c23cfcd..22b8db91 100644 --- a/docs/build/html/python/_autosummary/mlx.core.arctanh.html +++ b/docs/build/html/python/_autosummary/mlx.core.arctanh.html @@ -8,7 +8,7 @@ - mlx.core.arctanh — MLX 0.20.0 documentation + mlx.core.arctanh — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • @@ -521,6 +520,7 @@
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • +
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    • -
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    • +
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    • -
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    • +
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    • -
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    • +
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • -
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    • +
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    • -
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    • +
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • +
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  • mlx.nn.PReLU
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    • -
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    • +
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • +
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • +
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • -
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    • @@ -521,6 +520,7 @@
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    • -
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    • +
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    • -
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    • +
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    • -
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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  • mlx.nn.PReLU
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • +
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  • mlx.nn.PReLU
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  • mlx.core.fast.layer_norm
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    • -
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  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
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    • diff --git a/docs/build/html/python/_autosummary/mlx.core.as_strided.html b/docs/build/html/python/_autosummary/mlx.core.as_strided.html index 1f1ab296..b230f595 100644 --- a/docs/build/html/python/_autosummary/mlx.core.as_strided.html +++ b/docs/build/html/python/_autosummary/mlx.core.as_strided.html @@ -8,7 +8,7 @@ - mlx.core.as_strided — MLX 0.20.0 documentation + mlx.core.as_strided — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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    • -
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  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • +
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    • -
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    • -
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    • -
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    • -
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  • mlx.nn.PReLU
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    • -
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    • @@ -521,6 +520,7 @@
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    mlx.core.fast.affine_quantize#

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    -affine_quantize(w: array, /, scales: array, biases: array, group_size: int = 64, bits: int = 4, *, stream: None | Stream | Device = None) → array#
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    Quantize the matrix w using the provided scales and -biases and the group_size and bits configuration.

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    Formally, given the notation in quantize(), we compute -\(w_i\) from \(\hat{w_i}\) and corresponding \(s\) and -\(\beta\) as follows

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    -\[w_i = s (\hat{w_i} + \beta)\]
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    Parameters:
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    • w (array) – Matrix to be quantize

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    • scales (array) – The scales to use per group_size elements of w

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    • biases (array) – The biases to use per group_size elements of w

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    • group_size (int, optional) – The size of the group in w that shares a -scale and bias. (default: 64)

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    • bits (int, optional) – The number of bits occupied by each element in -w. (default: 4)

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    The quantized version of w

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    -

    array

    -
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    - -
    - - -
    - - - - - - - - -
    - - - - -
    - - -
    -
    - Contents -
    -
    - -
    - - -
    -
    - -
    - -
    - -

    -By MLX Contributors -

    - -
    - -
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    - - © Copyright 2023, MLX Contributors. -
    - -

    - -
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    - - \ No newline at end of file diff --git a/docs/build/html/python/_autosummary/mlx.core.fast.layer_norm.html b/docs/build/html/python/_autosummary/mlx.core.fast.layer_norm.html index 97287596..3f04d8c8 100644 --- a/docs/build/html/python/_autosummary/mlx.core.fast.layer_norm.html +++ b/docs/build/html/python/_autosummary/mlx.core.fast.layer_norm.html @@ -8,7 +8,7 @@ - mlx.core.fast.layer_norm — MLX 0.20.0 documentation + mlx.core.fast.layer_norm — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.fast.metal_kernel.html b/docs/build/html/python/_autosummary/mlx.core.fast.metal_kernel.html index 0a1c7d6c..727c2f48 100644 --- a/docs/build/html/python/_autosummary/mlx.core.fast.metal_kernel.html +++ b/docs/build/html/python/_autosummary/mlx.core.fast.metal_kernel.html @@ -8,7 +8,7 @@ - mlx.core.fast.metal_kernel — MLX 0.20.0 documentation + mlx.core.fast.metal_kernel — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -48,10 +48,10 @@ - + - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • @@ -937,12 +938,12 @@ e.g. device

    previous

    -

    mlx.core.fast.affine_quantize

    +

    mlx.core.fast.scaled_dot_product_attention

     - mlx.core.fast.rms_norm — MLX 0.20.0 documentation + mlx.core.fast.rms_norm — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home
    @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.fast.rope.html b/docs/build/html/python/_autosummary/mlx.core.fast.rope.html index 2c6c22d8..67698d35 100644 --- a/docs/build/html/python/_autosummary/mlx.core.fast.rope.html +++ b/docs/build/html/python/_autosummary/mlx.core.fast.rope.html @@ -8,7 +8,7 @@ - mlx.core.fast.rope — MLX 0.20.0 documentation + mlx.core.fast.rope — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.fast.scaled_dot_product_attention.html b/docs/build/html/python/_autosummary/mlx.core.fast.scaled_dot_product_attention.html index 1a8ec936..17c65c44 100644 --- a/docs/build/html/python/_autosummary/mlx.core.fast.scaled_dot_product_attention.html +++ b/docs/build/html/python/_autosummary/mlx.core.fast.scaled_dot_product_attention.html @@ -8,7 +8,7 @@ - mlx.core.fast.scaled_dot_product_attention — MLX 0.20.0 documentation + mlx.core.fast.scaled_dot_product_attention — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -47,11 +47,11 @@ - + - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • @@ -877,14 +878,25 @@ the input precision.

    Note: For Grouped Query Attention and Multi-Query Attention, the k and v inputs should not be pre-tiled to match q.

    +

    In the following the dimensions are given by:

    +
      +

    • B: The batch size.

      • +

    • N_q: The number of query heads.

      • +

    • N_kv: The number of key and value heads.

      • +

    • T_q: The number of queries per example.

      • +

    • T_kv: The number of keys and values per example.

      • +

    • D: The per-head dimension.

      • +
    Parameters:
      -

    • q (array) – Input query array.

      • -

    • k (array) – Input keys array.

      • -

    • v (array) – Input values array.

      • +

    • q (array) – Queries with shape [B, N_q, T_q, D].

      • +

    • k (array) – Keys with shape [B, N_kv, T_kv, D].

      • +

    • v (array) – Values with shape [B, N_kv, T_kv, D].

    • scale (float) – Scale for queries (typically 1.0 / sqrt(q.shape(-1))

      • -

    • mask (array, optional) – An additive mask to apply to the query-key scores.

      • +

    • mask (array, optional) – An additive mask to apply to the query-key +scores. The mask can have at most 4 dimensions and must be +broadcast-compatible with the shape [B, N, T_q, T_kv].

    Returns:
    @@ -919,11 +931,11 @@ and v

    next

    -

    mlx.core.fast.affine_quantize

    +

    mlx.core.fast.metal_kernel

     diff --git a/docs/build/html/python/_autosummary/mlx.core.fft.fft.html b/docs/build/html/python/_autosummary/mlx.core.fft.fft.html index 6fa04349..b040467e 100644 --- a/docs/build/html/python/_autosummary/mlx.core.fft.fft.html +++ b/docs/build/html/python/_autosummary/mlx.core.fft.fft.html @@ -8,7 +8,7 @@ - mlx.core.fft.fft — MLX 0.20.0 documentation + mlx.core.fft.fft — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.fft.fft2.html b/docs/build/html/python/_autosummary/mlx.core.fft.fft2.html index ab9b7a00..3f91a21e 100644 --- a/docs/build/html/python/_autosummary/mlx.core.fft.fft2.html +++ b/docs/build/html/python/_autosummary/mlx.core.fft.fft2.html @@ -8,7 +8,7 @@ - mlx.core.fft.fft2 — MLX 0.20.0 documentation + mlx.core.fft.fft2 — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.fft.fftn.html b/docs/build/html/python/_autosummary/mlx.core.fft.fftn.html index 41210f04..d165e232 100644 --- a/docs/build/html/python/_autosummary/mlx.core.fft.fftn.html +++ b/docs/build/html/python/_autosummary/mlx.core.fft.fftn.html @@ -8,7 +8,7 @@ - mlx.core.fft.fftn — MLX 0.20.0 documentation + mlx.core.fft.fftn — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.fft.ifft.html b/docs/build/html/python/_autosummary/mlx.core.fft.ifft.html index 64718c95..7e633dcc 100644 --- a/docs/build/html/python/_autosummary/mlx.core.fft.ifft.html +++ b/docs/build/html/python/_autosummary/mlx.core.fft.ifft.html @@ -8,7 +8,7 @@ - mlx.core.fft.ifft — MLX 0.20.0 documentation + mlx.core.fft.ifft — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.fft.ifft2.html b/docs/build/html/python/_autosummary/mlx.core.fft.ifft2.html index 77277593..750a8a98 100644 --- a/docs/build/html/python/_autosummary/mlx.core.fft.ifft2.html +++ b/docs/build/html/python/_autosummary/mlx.core.fft.ifft2.html @@ -8,7 +8,7 @@ - mlx.core.fft.ifft2 — MLX 0.20.0 documentation + mlx.core.fft.ifft2 — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.fft.ifftn.html b/docs/build/html/python/_autosummary/mlx.core.fft.ifftn.html index 40868024..f5e5b2f2 100644 --- a/docs/build/html/python/_autosummary/mlx.core.fft.ifftn.html +++ b/docs/build/html/python/_autosummary/mlx.core.fft.ifftn.html @@ -8,7 +8,7 @@ - mlx.core.fft.ifftn — MLX 0.20.0 documentation + mlx.core.fft.ifftn — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.fft.irfft.html b/docs/build/html/python/_autosummary/mlx.core.fft.irfft.html index d60e5561..dec9d88a 100644 --- a/docs/build/html/python/_autosummary/mlx.core.fft.irfft.html +++ b/docs/build/html/python/_autosummary/mlx.core.fft.irfft.html @@ -8,7 +8,7 @@ - mlx.core.fft.irfft — MLX 0.20.0 documentation + mlx.core.fft.irfft — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.fft.irfft2.html b/docs/build/html/python/_autosummary/mlx.core.fft.irfft2.html index acba21bc..5e61afe7 100644 --- a/docs/build/html/python/_autosummary/mlx.core.fft.irfft2.html +++ b/docs/build/html/python/_autosummary/mlx.core.fft.irfft2.html @@ -8,7 +8,7 @@ - mlx.core.fft.irfft2 — MLX 0.20.0 documentation + mlx.core.fft.irfft2 — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
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    • -
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    • -
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • @@ -550,6 +550,7 @@
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • @@ -550,6 +550,7 @@
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  • mlx.nn.PReLU
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • -
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    • +
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    • -
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    • -
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    • -
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    • -
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  • mlx.nn.PReLU
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    • -
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    • -
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    • -
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    • -
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    • +
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    • -
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    • -
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  • mlx.core.fast.layer_norm
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    • -
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    • -
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    • -
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    • +
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    • -
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    • -
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • -
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    • @@ -521,6 +520,7 @@
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  • mlx.core.fast.layer_norm
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    • -
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    • -
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    • -
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    • -
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    • @@ -521,6 +520,7 @@
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    • @@ -550,6 +550,7 @@
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    • -
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    • +
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    • +
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    • -
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    • +
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.PReLU
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
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  • mlx.core.fast.layer_norm
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    • -
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  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.set_default_device.html b/docs/build/html/python/_autosummary/mlx.core.set_default_device.html index c97ec5a0..3d9e5dda 100644 --- a/docs/build/html/python/_autosummary/mlx.core.set_default_device.html +++ b/docs/build/html/python/_autosummary/mlx.core.set_default_device.html @@ -8,7 +8,7 @@ - mlx.core.set_default_device — MLX 0.20.0 documentation + mlx.core.set_default_device — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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  • mlx.core.fast.scaled_dot_product_attention
    • -
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  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.set_default_stream.html b/docs/build/html/python/_autosummary/mlx.core.set_default_stream.html index dfedfe67..3837bce6 100644 --- a/docs/build/html/python/_autosummary/mlx.core.set_default_stream.html +++ b/docs/build/html/python/_autosummary/mlx.core.set_default_stream.html @@ -8,7 +8,7 @@ - mlx.core.set_default_stream — MLX 0.20.0 documentation + mlx.core.set_default_stream — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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  • mlx.core.fast.scaled_dot_product_attention
    • -
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  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.sigmoid.html b/docs/build/html/python/_autosummary/mlx.core.sigmoid.html index 33ef7190..e440fb68 100644 --- a/docs/build/html/python/_autosummary/mlx.core.sigmoid.html +++ b/docs/build/html/python/_autosummary/mlx.core.sigmoid.html @@ -8,7 +8,7 @@ - mlx.core.sigmoid — MLX 0.20.0 documentation + mlx.core.sigmoid — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
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  • mlx.core.fast.scaled_dot_product_attention
    • -
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  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
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    • +
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    • @@ -551,6 +551,7 @@
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    • +
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  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.sign.html b/docs/build/html/python/_autosummary/mlx.core.sign.html index c7c7fcee..326d2b7a 100644 --- a/docs/build/html/python/_autosummary/mlx.core.sign.html +++ b/docs/build/html/python/_autosummary/mlx.core.sign.html @@ -8,7 +8,7 @@ - mlx.core.sign — MLX 0.20.0 documentation + mlx.core.sign — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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  • mlx.core.fast.scaled_dot_product_attention
    • -
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  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
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  • mlx.nn.AvgPool2d
    • +
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    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
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    • +
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  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.sin.html b/docs/build/html/python/_autosummary/mlx.core.sin.html index 04802634..ea1bf29b 100644 --- a/docs/build/html/python/_autosummary/mlx.core.sin.html +++ b/docs/build/html/python/_autosummary/mlx.core.sin.html @@ -8,7 +8,7 @@ - mlx.core.sin — MLX 0.20.0 documentation + mlx.core.sin — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
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    • -
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    • +
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • +
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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    • diff --git a/docs/build/html/python/_autosummary/mlx.core.split.html b/docs/build/html/python/_autosummary/mlx.core.split.html index 089ca9ab..d4d5a2ad 100644 --- a/docs/build/html/python/_autosummary/mlx.core.split.html +++ b/docs/build/html/python/_autosummary/mlx.core.split.html @@ -8,7 +8,7 @@ - mlx.core.split — MLX 0.20.0 documentation + mlx.core.split — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.sqrt.html b/docs/build/html/python/_autosummary/mlx.core.sqrt.html index 17b71396..049f15b9 100644 --- a/docs/build/html/python/_autosummary/mlx.core.sqrt.html +++ b/docs/build/html/python/_autosummary/mlx.core.sqrt.html @@ -8,7 +8,7 @@ - mlx.core.sqrt — MLX 0.20.0 documentation + mlx.core.sqrt — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
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    • diff --git a/docs/build/html/python/_autosummary/mlx.core.square.html b/docs/build/html/python/_autosummary/mlx.core.square.html index 2c1744cc..afd87c2b 100644 --- a/docs/build/html/python/_autosummary/mlx.core.square.html +++ b/docs/build/html/python/_autosummary/mlx.core.square.html @@ -8,7 +8,7 @@ - mlx.core.square — MLX 0.20.0 documentation + mlx.core.square — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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    • diff --git a/docs/build/html/python/_autosummary/mlx.core.squeeze.html b/docs/build/html/python/_autosummary/mlx.core.squeeze.html index bc7872dd..b931d91c 100644 --- a/docs/build/html/python/_autosummary/mlx.core.squeeze.html +++ b/docs/build/html/python/_autosummary/mlx.core.squeeze.html @@ -8,7 +8,7 @@ - mlx.core.squeeze — MLX 0.20.0 documentation + mlx.core.squeeze — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.stack.html b/docs/build/html/python/_autosummary/mlx.core.stack.html index 5c0abc8d..de1b8e82 100644 --- a/docs/build/html/python/_autosummary/mlx.core.stack.html +++ b/docs/build/html/python/_autosummary/mlx.core.stack.html @@ -8,7 +8,7 @@ - mlx.core.stack — MLX 0.20.0 documentation + mlx.core.stack — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
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    • diff --git a/docs/build/html/python/_autosummary/mlx.core.stop_gradient.html b/docs/build/html/python/_autosummary/mlx.core.stop_gradient.html index 17902432..aed67e20 100644 --- a/docs/build/html/python/_autosummary/mlx.core.stop_gradient.html +++ b/docs/build/html/python/_autosummary/mlx.core.stop_gradient.html @@ -8,7 +8,7 @@ - mlx.core.stop_gradient — MLX 0.20.0 documentation + mlx.core.stop_gradient — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.stream.html b/docs/build/html/python/_autosummary/mlx.core.stream.html index 4873de9f..5240a01f 100644 --- a/docs/build/html/python/_autosummary/mlx.core.stream.html +++ b/docs/build/html/python/_autosummary/mlx.core.stream.html @@ -8,7 +8,7 @@ - mlx.core.stream — MLX 0.20.0 documentation + mlx.core.stream — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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    • -
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  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.sum.html b/docs/build/html/python/_autosummary/mlx.core.sum.html index 634b87a0..c4ad7a65 100644 --- a/docs/build/html/python/_autosummary/mlx.core.sum.html +++ b/docs/build/html/python/_autosummary/mlx.core.sum.html @@ -8,7 +8,7 @@ - mlx.core.sum — MLX 0.20.0 documentation + mlx.core.sum — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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    • -
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  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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  • mlx.nn.MaxPool2d
    • +
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  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.swapaxes.html b/docs/build/html/python/_autosummary/mlx.core.swapaxes.html index c8d04d95..2e0de6a7 100644 --- a/docs/build/html/python/_autosummary/mlx.core.swapaxes.html +++ b/docs/build/html/python/_autosummary/mlx.core.swapaxes.html @@ -8,7 +8,7 @@ - mlx.core.swapaxes — MLX 0.20.0 documentation + mlx.core.swapaxes — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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  • mlx.core.fast.scaled_dot_product_attention
    • -
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  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
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    • +
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    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
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  • mlx.nn.MaxPool2d
    • +
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  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.core.synchronize.html b/docs/build/html/python/_autosummary/mlx.core.synchronize.html index d9fadf6f..cfa0d2b8 100644 --- a/docs/build/html/python/_autosummary/mlx.core.synchronize.html +++ b/docs/build/html/python/_autosummary/mlx.core.synchronize.html @@ -8,7 +8,7 @@ - mlx.core.synchronize — MLX 0.20.0 documentation + mlx.core.synchronize — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
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    • -
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • +
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • +
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • +
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • +
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • +
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  • mlx.nn.MultiHeadAttention
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
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    • +
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
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  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.utils.tree_reduce.html b/docs/build/html/python/_autosummary/mlx.utils.tree_reduce.html index a0ba468d..3570b426 100644 --- a/docs/build/html/python/_autosummary/mlx.utils.tree_reduce.html +++ b/docs/build/html/python/_autosummary/mlx.utils.tree_reduce.html @@ -8,7 +8,7 @@ - mlx.utils.tree_reduce — MLX 0.20.0 documentation + mlx.utils.tree_reduce — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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  • mlx.core.fast.scaled_dot_product_attention
    • -
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    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/mlx.utils.tree_unflatten.html b/docs/build/html/python/_autosummary/mlx.utils.tree_unflatten.html index b9d1b59c..61309f4f 100644 --- a/docs/build/html/python/_autosummary/mlx.utils.tree_unflatten.html +++ b/docs/build/html/python/_autosummary/mlx.utils.tree_unflatten.html @@ -8,7 +8,7 @@ - mlx.utils.tree_unflatten — MLX 0.20.0 documentation + mlx.utils.tree_unflatten — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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  • mlx.core.fast.scaled_dot_product_attention
    • -
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  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/_autosummary/stream_class.html b/docs/build/html/python/_autosummary/stream_class.html index c277ce61..6600af16 100644 --- a/docs/build/html/python/_autosummary/stream_class.html +++ b/docs/build/html/python/_autosummary/stream_class.html @@ -8,7 +8,7 @@ - mlx.core.Stream — MLX 0.20.0 documentation + mlx.core.Stream — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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  • mlx.core.fast.scaled_dot_product_attention
    • -
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  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/array.html b/docs/build/html/python/array.html index 0609bafd..64bd5bb4 100644 --- a/docs/build/html/python/array.html +++ b/docs/build/html/python/array.html @@ -8,7 +8,7 @@ - Array — MLX 0.20.0 documentation + Array — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/data_types.html b/docs/build/html/python/data_types.html index 11d36e95..f3d76807 100644 --- a/docs/build/html/python/data_types.html +++ b/docs/build/html/python/data_types.html @@ -8,7 +8,7 @@ - Data Types — MLX 0.20.0 documentation + Data Types — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/devices_and_streams.html b/docs/build/html/python/devices_and_streams.html index 2c708cfc..e7a82819 100644 --- a/docs/build/html/python/devices_and_streams.html +++ b/docs/build/html/python/devices_and_streams.html @@ -8,7 +8,7 @@ - Devices and Streams — MLX 0.20.0 documentation + Devices and Streams — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/distributed.html b/docs/build/html/python/distributed.html index e7990efd..55787a80 100644 --- a/docs/build/html/python/distributed.html +++ b/docs/build/html/python/distributed.html @@ -8,7 +8,7 @@ - Distributed Communication — MLX 0.20.0 documentation + Distributed Communication — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/fast.html b/docs/build/html/python/fast.html index f296cc90..c27b9023 100644 --- a/docs/build/html/python/fast.html +++ b/docs/build/html/python/fast.html @@ -8,7 +8,7 @@ - Fast — MLX 0.20.0 documentation + Fast — MLX 0.21.0 documentation @@ -39,11 +39,10 @@ - + - @@ -52,7 +51,7 @@ - + @@ -131,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • @@ -868,10 +868,7 @@

    scaled_dot_product_attention(q, k, v, *, scale)

    A fast implementation of multi-head attention: O = softmax(Q @ K.T, dim=-1) @ V.

    -

    affine_quantize(w, /, scales, biases[, ...])

    -

    Quantize the matrix w using the provided scales and biases and the group_size and bits configuration.

    - -

    metal_kernel(name, input_names, ...[, ...])

    +

    metal_kernel(name, input_names, ...[, ...])

    A jit-compiled custom Metal kernel defined from a source string.

    diff --git a/docs/build/html/python/fft.html b/docs/build/html/python/fft.html index 3ada9a8e..256538c7 100644 --- a/docs/build/html/python/fft.html +++ b/docs/build/html/python/fft.html @@ -8,7 +8,7 @@ - FFT — MLX 0.20.0 documentation + FFT — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/linalg.html b/docs/build/html/python/linalg.html index ba425dbf..8c27548b 100644 --- a/docs/build/html/python/linalg.html +++ b/docs/build/html/python/linalg.html @@ -8,7 +8,7 @@ - Linear Algebra — MLX 0.20.0 documentation + Linear Algebra — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/metal.html b/docs/build/html/python/metal.html index a9ef8111..eae7ebda 100644 --- a/docs/build/html/python/metal.html +++ b/docs/build/html/python/metal.html @@ -8,7 +8,7 @@ - Metal — MLX 0.20.0 documentation + Metal — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn.html b/docs/build/html/python/nn.html index 3c1b19ff..afa53bd0 100644 --- a/docs/build/html/python/nn.html +++ b/docs/build/html/python/nn.html @@ -8,7 +8,7 @@ - Neural Networks — MLX 0.20.0 documentation + Neural Networks — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • @@ -1121,6 +1122,10 @@ parameters as the first argument to the function returned by
  • AvgPool2d
    • +
  • mlx.nn.AvgPool3d +
  • mlx.nn.BatchNorm @@ -1237,6 +1242,10 @@ parameters as the first argument to the function returned by
  • MaxPool2d
    • +
  • mlx.nn.MaxPool3d +
  • mlx.nn.Mish diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.ALiBi.html b/docs/build/html/python/nn/_autosummary/mlx.nn.ALiBi.html index aaf20607..48e8650d 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.ALiBi.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.ALiBi.html @@ -8,7 +8,7 @@ - mlx.nn.ALiBi — MLX 0.20.0 documentation + mlx.nn.ALiBi — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.AvgPool1d.html b/docs/build/html/python/nn/_autosummary/mlx.nn.AvgPool1d.html index 1a1be97b..762afeb6 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.AvgPool1d.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.AvgPool1d.html @@ -8,7 +8,7 @@ - mlx.nn.AvgPool1d — MLX 0.20.0 documentation + mlx.nn.AvgPool1d — MLX 0.21.0 documentation @@ -39,11 +39,10 @@ - + - @@ -52,7 +51,7 @@ - + @@ -131,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • @@ -868,16 +868,8 @@
    class AvgPool1d(kernel_size: int | Tuple[int], stride: int | Tuple[int] | None = None, padding: int | Tuple[int] = 0)#

    Applies 1-dimensional average pooling.

    -

    Assuming an input of shape \((N, L, C)\) and kernel_size is -\(k\), the output is a tensor of shape \((N, L_{out}, C)\), given -by:

    -
    -
    -\[\text{out}(N_i, t, C_j) = \frac{1}{k} \sum_{m=0, \ldots, k - 1} - \text{input}(N_i, \text{stride} \times t + m, C_j),\]
    -
    -

    where \(L_{out} = \left\lfloor \frac{L + 2 \times \text{padding} - -\text{kernel\_size}}{\text{stride}}\right\rfloor + 1\).

    +

    Spatially downsamples the input by taking the average of a sliding window +of size kernel_size and sliding stride stride.

    Parameters:
      diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.AvgPool2d.html b/docs/build/html/python/nn/_autosummary/mlx.nn.AvgPool2d.html index 0f3ac611..4ecff76d 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.AvgPool2d.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.AvgPool2d.html @@ -8,7 +8,7 @@ - mlx.nn.AvgPool2d — MLX 0.20.0 documentation + mlx.nn.AvgPool2d — MLX 0.21.0 documentation @@ -39,20 +39,19 @@ - + - - + - + @@ -131,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +444,6 @@
    • mlx.core.fast.layer_norm
    • mlx.core.fast.rope
    • mlx.core.fast.scaled_dot_product_attention
      • -
    • mlx.core.fast.affine_quantize
    • mlx.core.fast.metal_kernel
    @@ -522,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • @@ -868,26 +868,15 @@
    class AvgPool2d(kernel_size: int | Tuple[int, int], stride: int | Tuple[int, int] | None = None, padding: int | Tuple[int, int] | None = 0)#

    Applies 2-dimensional average pooling.

    -

    Assuming an input of shape \((N, H, W, C)\) and kernel_size is -\((k_H, k_W)\), the output is a tensor of shape \((N, H_{out}, -W_{out}, C)\), given by:

    -
    -\[\begin{split}\begin{aligned} - \text{out}(N_i, h, w, C_j) = & \frac{1}{k_H k_W} \sum_{m=0, \ldots, k_H-1} \sum_{n=0, \ldots, k_W-1} \\ - & \text{input}(N_i, \text{stride[0]} \times h + m, - \text{stride[1]} \times w + n, C_j), -\end{aligned}\end{split}\]
    -

    where \(H_{out} = \left\lfloor\frac{H + 2 * \text{padding[0]} - \text{kernel\_size[0]}}{\text{stride[0]}}\right\rfloor + 1\), -\(W_{out} = \left\lfloor\frac{W + 2 * \text{padding[1]} - \text{kernel\_size[1]}}{\text{stride[1]}}\right\rfloor + 1\).

    -

    The parameters kernel_size, stride, padding, can either be:

    -
    -
      +

      Spatially downsamples the input by taking the average of a sliding window +of size kernel_size and sliding stride stride.

      +

      The parameters kernel_size, stride, and padding can either be:

      +

      • a single int – in which case the same value is used for both the -height and width axis;

        • +height and width axis.

      • a tuple of two int s – in which case, the first int is used for the height axis, the second int for the width axis.

      -
    Parameters:
    @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • @@ -929,12 +930,12 @@ running mean and variance. Default: <

    previous

    -

    mlx.nn.AvgPool2d

    +

    mlx.nn.AvgPool3d

     - mlx.nn.CELU — MLX 0.20.0 documentation + mlx.nn.CELU — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home
    @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Conv1d.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Conv1d.html index 818eea49..a8129fe8 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Conv1d.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Conv1d.html @@ -8,7 +8,7 @@ - mlx.nn.Conv1d — MLX 0.20.0 documentation + mlx.nn.Conv1d — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Conv2d.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Conv2d.html index f348dd7a..99c2f5b2 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Conv2d.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Conv2d.html @@ -8,7 +8,7 @@ - mlx.nn.Conv2d — MLX 0.20.0 documentation + mlx.nn.Conv2d — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • @@ -865,7 +866,7 @@

    mlx.nn.Conv2d#

    -class Conv2d(in_channels: int, out_channels: int, kernel_size: int | tuple, stride: int | tuple = 1, padding: int | tuple = 0, dilation: int | tuple = 1, bias: bool = True)#
    +class Conv2d(in_channels: int, out_channels: int, kernel_size: int | tuple, stride: int | tuple = 1, padding: int | tuple = 0, dilation: int | tuple = 1, groups: int = 1, bias: bool = True)#

    Applies a 2-dimensional convolution over the multi-channel input image.

    The channels are expected to be last i.e. the input shape should be NHWC where:

      @@ -885,6 +886,8 @@ applying the filter. Default:

      padding (int or tuple, optional) – How many positions to 0-pad the input with. Default: 0.

    • dilation (int or tuple, optional) – The dilation of the convolution.

      • +

    • groups (int, optional) – The number of groups for the convolution. +Default: 1.

    • bias (bool, optional) – If True add a learnable bias to the output. Default: True

    diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Conv3d.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Conv3d.html index a8c5a859..4cd2ff2b 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Conv3d.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Conv3d.html @@ -8,7 +8,7 @@ - mlx.nn.Conv3d — MLX 0.20.0 documentation + mlx.nn.Conv3d — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.ConvTranspose1d.html b/docs/build/html/python/nn/_autosummary/mlx.nn.ConvTranspose1d.html index 4b96b967..dd149a60 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.ConvTranspose1d.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.ConvTranspose1d.html @@ -8,7 +8,7 @@ - mlx.nn.ConvTranspose1d — MLX 0.20.0 documentation + mlx.nn.ConvTranspose1d — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.ConvTranspose2d.html b/docs/build/html/python/nn/_autosummary/mlx.nn.ConvTranspose2d.html index 71cb5249..70e38947 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.ConvTranspose2d.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.ConvTranspose2d.html @@ -8,7 +8,7 @@ - mlx.nn.ConvTranspose2d — MLX 0.20.0 documentation + mlx.nn.ConvTranspose2d — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.ConvTranspose3d.html b/docs/build/html/python/nn/_autosummary/mlx.nn.ConvTranspose3d.html index c870efc0..798ce3d7 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.ConvTranspose3d.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.ConvTranspose3d.html @@ -8,7 +8,7 @@ - mlx.nn.ConvTranspose3d — MLX 0.20.0 documentation + mlx.nn.ConvTranspose3d — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Dropout.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Dropout.html index 66cf6cf0..85bcffd5 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Dropout.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Dropout.html @@ -8,7 +8,7 @@ - mlx.nn.Dropout — MLX 0.20.0 documentation + mlx.nn.Dropout — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Dropout2d.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Dropout2d.html index fbe6f453..2a5fa07c 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Dropout2d.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Dropout2d.html @@ -8,7 +8,7 @@ - mlx.nn.Dropout2d — MLX 0.20.0 documentation + mlx.nn.Dropout2d — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Dropout3d.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Dropout3d.html index 6cca8ff1..76d0ab41 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Dropout3d.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Dropout3d.html @@ -8,7 +8,7 @@ - mlx.nn.Dropout3d — MLX 0.20.0 documentation + mlx.nn.Dropout3d — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.ELU.html b/docs/build/html/python/nn/_autosummary/mlx.nn.ELU.html index 6ad6cf84..13bc9464 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.ELU.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.ELU.html @@ -8,7 +8,7 @@ - mlx.nn.ELU — MLX 0.20.0 documentation + mlx.nn.ELU — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Embedding.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Embedding.html index 3d3728c3..aecf5c76 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Embedding.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Embedding.html @@ -8,7 +8,7 @@ - mlx.nn.Embedding — MLX 0.20.0 documentation + mlx.nn.Embedding — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.GELU.html b/docs/build/html/python/nn/_autosummary/mlx.nn.GELU.html index 935b65de..5f48216c 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.GELU.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.GELU.html @@ -8,7 +8,7 @@ - mlx.nn.GELU — MLX 0.20.0 documentation + mlx.nn.GELU — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.GLU.html b/docs/build/html/python/nn/_autosummary/mlx.nn.GLU.html index 247d82f2..0782c664 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.GLU.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.GLU.html @@ -8,7 +8,7 @@ - mlx.nn.GLU — MLX 0.20.0 documentation + mlx.nn.GLU — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.GRU.html b/docs/build/html/python/nn/_autosummary/mlx.nn.GRU.html index cbcf0a61..774445ca 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.GRU.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.GRU.html @@ -8,7 +8,7 @@ - mlx.nn.GRU — MLX 0.20.0 documentation + mlx.nn.GRU — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
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  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
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  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.GroupNorm.html b/docs/build/html/python/nn/_autosummary/mlx.nn.GroupNorm.html index 70216bd4..22f18d7d 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.GroupNorm.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.GroupNorm.html @@ -8,7 +8,7 @@ - mlx.nn.GroupNorm — MLX 0.20.0 documentation + mlx.nn.GroupNorm — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.HardShrink.html b/docs/build/html/python/nn/_autosummary/mlx.nn.HardShrink.html index 61f2a228..aa37d505 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.HardShrink.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.HardShrink.html @@ -8,7 +8,7 @@ - mlx.nn.HardShrink — MLX 0.20.0 documentation + mlx.nn.HardShrink — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.HardTanh.html b/docs/build/html/python/nn/_autosummary/mlx.nn.HardTanh.html index 144403ca..89e9fa0a 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.HardTanh.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.HardTanh.html @@ -8,7 +8,7 @@ - mlx.nn.HardTanh — MLX 0.20.0 documentation + mlx.nn.HardTanh — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Hardswish.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Hardswish.html index ef845846..28aa440e 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Hardswish.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Hardswish.html @@ -8,7 +8,7 @@ - mlx.nn.Hardswish — MLX 0.20.0 documentation + mlx.nn.Hardswish — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.InstanceNorm.html b/docs/build/html/python/nn/_autosummary/mlx.nn.InstanceNorm.html index b8a42a84..c2dfe360 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.InstanceNorm.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.InstanceNorm.html @@ -8,7 +8,7 @@ - mlx.nn.InstanceNorm — MLX 0.20.0 documentation + mlx.nn.InstanceNorm — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.LSTM.html b/docs/build/html/python/nn/_autosummary/mlx.nn.LSTM.html index b89a1105..ae82553f 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.LSTM.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.LSTM.html @@ -8,7 +8,7 @@ - mlx.nn.LSTM — MLX 0.20.0 documentation + mlx.nn.LSTM — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.LayerNorm.html b/docs/build/html/python/nn/_autosummary/mlx.nn.LayerNorm.html index dd7a0214..70f7b85a 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.LayerNorm.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.LayerNorm.html @@ -8,7 +8,7 @@ - mlx.nn.LayerNorm — MLX 0.20.0 documentation + mlx.nn.LayerNorm — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.LeakyReLU.html b/docs/build/html/python/nn/_autosummary/mlx.nn.LeakyReLU.html index 704d1a17..abc49b8c 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.LeakyReLU.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.LeakyReLU.html @@ -8,7 +8,7 @@ - mlx.nn.LeakyReLU — MLX 0.20.0 documentation + mlx.nn.LeakyReLU — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Linear.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Linear.html index 92ff8f4d..9844d84a 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Linear.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Linear.html @@ -8,7 +8,7 @@ - mlx.nn.Linear — MLX 0.20.0 documentation + mlx.nn.Linear — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.LogSigmoid.html b/docs/build/html/python/nn/_autosummary/mlx.nn.LogSigmoid.html index 7ea70fd2..36ee5b93 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.LogSigmoid.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.LogSigmoid.html @@ -8,7 +8,7 @@ - mlx.nn.LogSigmoid — MLX 0.20.0 documentation + mlx.nn.LogSigmoid — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.LogSoftmax.html b/docs/build/html/python/nn/_autosummary/mlx.nn.LogSoftmax.html index 9e76efb3..41ee622d 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.LogSoftmax.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.LogSoftmax.html @@ -8,7 +8,7 @@ - mlx.nn.LogSoftmax — MLX 0.20.0 documentation + mlx.nn.LogSoftmax — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.MaxPool1d.html b/docs/build/html/python/nn/_autosummary/mlx.nn.MaxPool1d.html index 174653ac..e4966cb1 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.MaxPool1d.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.MaxPool1d.html @@ -8,7 +8,7 @@ - mlx.nn.MaxPool1d — MLX 0.20.0 documentation + mlx.nn.MaxPool1d — MLX 0.21.0 documentation @@ -39,11 +39,10 @@ - + - @@ -52,7 +51,7 @@ - + @@ -131,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • @@ -868,16 +868,8 @@
    class MaxPool1d(kernel_size: int | Tuple[int], stride: int | Tuple[int] | None = None, padding: int | Tuple[int] = 0)#

    Applies 1-dimensional max pooling.

    -

    Assuming an input of shape \((N, L, C)\) and kernel_size is -\(k\), the output is a tensor of shape \((N, L_{out}, C)\), given -by:

    -
    -
    -\[\text{out}(N_i, t, C_j) = \max_{m=0, \ldots, k - 1} - \text{input}(N_i, \text{stride} \times t + m, C_j),\]
    -
    -

    where \(L_{out} = \left\lfloor \frac{L + 2 \times \text{padding} - -\text{kernel\_size}}{\text{stride}}\right\rfloor + 1\).

    +

    Spatially downsamples the input by taking the maximum of a sliding window +of size kernel_size and sliding stride stride.

    Parameters:
      diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.MaxPool2d.html b/docs/build/html/python/nn/_autosummary/mlx.nn.MaxPool2d.html index f45b3cd0..6b940f16 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.MaxPool2d.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.MaxPool2d.html @@ -8,7 +8,7 @@ - mlx.nn.MaxPool2d — MLX 0.20.0 documentation + mlx.nn.MaxPool2d — MLX 0.21.0 documentation @@ -39,20 +39,19 @@ - + - - + - + @@ -131,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +444,6 @@
    • mlx.core.fast.layer_norm
    • mlx.core.fast.rope
    • mlx.core.fast.scaled_dot_product_attention
      • -
    • mlx.core.fast.affine_quantize
    • mlx.core.fast.metal_kernel
    @@ -522,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • @@ -868,26 +868,15 @@
    class MaxPool2d(kernel_size: int | Tuple[int, int], stride: int | Tuple[int, int] | None = None, padding: int | Tuple[int, int] | None = 0)#

    Applies 2-dimensional max pooling.

    -

    Assuming an input of shape \((N, H, W, C)\) and kernel_size is -\((k_H, k_W)\), the output is a tensor of shape \((N, H_{out}, -W_{out}, C)\), given by:

    -
    -\[\begin{split}\begin{aligned} - \text{out}(N_i, h, w, C_j) = & \max_{m=0, \ldots, k_H-1} \max_{n=0, \ldots, k_W-1} \\ - & \text{input}(N_i, \text{stride[0]} \times h + m, - \text{stride[1]} \times w + n, C_j), -\end{aligned}\end{split}\]
    -

    where \(H_{out} = \left\lfloor\frac{H + 2 * \text{padding[0]} - \text{kernel\_size[0]}}{\text{stride[0]}}\right\rfloor + 1\), -\(W_{out} = \left\lfloor\frac{W + 2 * \text{padding[1]} - \text{kernel\_size[1]}}{\text{stride[1]}}\right\rfloor + 1\).

    -

    The parameters kernel_size, stride, padding, can either be:

    -
    -
      +

      Spatially downsamples the input by taking the maximum of a sliding window +of size kernel_size and sliding stride stride.

      +

      The parameters kernel_size, stride, and padding can either be:

      +

      • a single int – in which case the same value is used for both the -height and width axis;

        • +height and width axis.

      • a tuple of two int s – in which case, the first int is used for the height axis, the second int for the width axis.

      -
    Parameters:
    @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
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  • mlx.nn.BatchNorm
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    • @@ -551,6 +551,7 @@
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    • +
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  • mlx.nn.PReLU
    • @@ -893,12 +894,12 @@

    previous

    -

    mlx.nn.MaxPool2d

    +

    mlx.nn.MaxPool3d

     - mlx.nn.Module.apply — MLX 0.20.0 documentation + mlx.nn.Module.apply — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home
    @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
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    • @@ -521,6 +520,7 @@
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  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.apply_to_modules.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.apply_to_modules.html index afcf06dd..4b9d3b5d 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.apply_to_modules.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.apply_to_modules.html @@ -8,7 +8,7 @@ - mlx.nn.Module.apply_to_modules — MLX 0.20.0 documentation + mlx.nn.Module.apply_to_modules — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
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    • -
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    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
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    • +
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  • mlx.nn.BatchNorm
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    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
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    • +
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  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.children.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.children.html index 9db4ac9b..b000446e 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.children.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.children.html @@ -8,7 +8,7 @@ - mlx.nn.Module.children — MLX 0.20.0 documentation + mlx.nn.Module.children — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
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    • -
  • mlx.core.fast.affine_quantize
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    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.eval.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.eval.html index fc68eebb..7227830d 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.eval.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.eval.html @@ -8,7 +8,7 @@ - mlx.nn.Module.eval — MLX 0.20.0 documentation + mlx.nn.Module.eval — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
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    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.filter_and_map.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.filter_and_map.html index 8f14e3eb..b5dd1e4e 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.filter_and_map.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.filter_and_map.html @@ -8,7 +8,7 @@ - mlx.nn.Module.filter_and_map — MLX 0.20.0 documentation + mlx.nn.Module.filter_and_map — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
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  • mlx.nn.MaxPool1d
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    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.freeze.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.freeze.html index 1033693e..83221a7c 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.freeze.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.freeze.html @@ -8,7 +8,7 @@ - mlx.nn.Module.freeze — MLX 0.20.0 documentation + mlx.nn.Module.freeze — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
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  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
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  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.leaf_modules.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.leaf_modules.html index 2ed01993..799c3718 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.leaf_modules.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.leaf_modules.html @@ -8,7 +8,7 @@ - mlx.nn.Module.leaf_modules — MLX 0.20.0 documentation + mlx.nn.Module.leaf_modules — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.load_weights.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.load_weights.html index a698e2ad..f8c3a25a 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.load_weights.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.load_weights.html @@ -8,7 +8,7 @@ - mlx.nn.Module.load_weights — MLX 0.20.0 documentation + mlx.nn.Module.load_weights — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
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  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.modules.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.modules.html index c114d99f..48d4b619 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.modules.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.modules.html @@ -8,7 +8,7 @@ - mlx.nn.Module.modules — MLX 0.20.0 documentation + mlx.nn.Module.modules — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.named_modules.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.named_modules.html index d3202cc4..a334c79d 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.named_modules.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.named_modules.html @@ -8,7 +8,7 @@ - mlx.nn.Module.named_modules — MLX 0.20.0 documentation + mlx.nn.Module.named_modules — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
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  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.parameters.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.parameters.html index bef2a961..62825d8b 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.parameters.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.parameters.html @@ -8,7 +8,7 @@ - mlx.nn.Module.parameters — MLX 0.20.0 documentation + mlx.nn.Module.parameters — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.save_weights.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.save_weights.html index 338a37bd..6e011538 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.save_weights.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.save_weights.html @@ -8,7 +8,7 @@ - mlx.nn.Module.save_weights — MLX 0.20.0 documentation + mlx.nn.Module.save_weights — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
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  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
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  • mlx.nn.MaxPool2d
    • +
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  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.set_dtype.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.set_dtype.html index a4eb5b94..499f7de5 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.set_dtype.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.set_dtype.html @@ -8,7 +8,7 @@ - mlx.nn.Module.set_dtype — MLX 0.20.0 documentation + mlx.nn.Module.set_dtype — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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    • -
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  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
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    • @@ -550,6 +550,7 @@
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  • mlx.nn.MaxPool2d
    • +
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  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.state.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.state.html index 6c5361b5..902f7b62 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.state.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.state.html @@ -8,7 +8,7 @@ - mlx.nn.Module.state — MLX 0.20.0 documentation + mlx.nn.Module.state — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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  • mlx.core.fast.scaled_dot_product_attention
    • -
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  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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    • +
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    • +
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  • mlx.core.fast.layer_norm
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    • +
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    • +
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  • mlx.core.fast.layer_norm
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    • +
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
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  • mlx.core.fast.layer_norm
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    • -
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    • +
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.update_modules.html b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.update_modules.html index d90e7f80..e213b30a 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.Module.update_modules.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.Module.update_modules.html @@ -8,7 +8,7 @@ - mlx.nn.Module.update_modules — MLX 0.20.0 documentation + mlx.nn.Module.update_modules — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.MultiHeadAttention.html b/docs/build/html/python/nn/_autosummary/mlx.nn.MultiHeadAttention.html index 449c0fe6..0951d216 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.MultiHeadAttention.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.MultiHeadAttention.html @@ -8,7 +8,7 @@ - mlx.nn.MultiHeadAttention — MLX 0.20.0 documentation + mlx.nn.MultiHeadAttention — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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  • mlx.core.fast.scaled_dot_product_attention
    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.PReLU.html b/docs/build/html/python/nn/_autosummary/mlx.nn.PReLU.html index 1449d66b..3836d66f 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.PReLU.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.PReLU.html @@ -8,7 +8,7 @@ - mlx.nn.PReLU — MLX 0.20.0 documentation + mlx.nn.PReLU — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
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  • mlx.core.fast.scaled_dot_product_attention
    • -
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    • @@ -522,6 +521,7 @@
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    • +
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    • @@ -551,6 +551,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.QuantizedEmbedding.html b/docs/build/html/python/nn/_autosummary/mlx.nn.QuantizedEmbedding.html index ba0ff618..66d4abb8 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.QuantizedEmbedding.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.QuantizedEmbedding.html @@ -8,7 +8,7 @@ - mlx.nn.QuantizedEmbedding — MLX 0.20.0 documentation + mlx.nn.QuantizedEmbedding — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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  • mlx.core.fast.scaled_dot_product_attention
    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.QuantizedLinear.html b/docs/build/html/python/nn/_autosummary/mlx.nn.QuantizedLinear.html index 4a6ab03a..d26ff979 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.QuantizedLinear.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.QuantizedLinear.html @@ -8,7 +8,7 @@ - mlx.nn.QuantizedLinear — MLX 0.20.0 documentation + mlx.nn.QuantizedLinear — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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  • mlx.core.fast.scaled_dot_product_attention
    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.RMSNorm.html b/docs/build/html/python/nn/_autosummary/mlx.nn.RMSNorm.html index 8b3a11ac..76b5531d 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.RMSNorm.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.RMSNorm.html @@ -8,7 +8,7 @@ - mlx.nn.RMSNorm — MLX 0.20.0 documentation + mlx.nn.RMSNorm — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
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    • -
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    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
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    • +
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    • @@ -551,6 +551,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.RNN.html b/docs/build/html/python/nn/_autosummary/mlx.nn.RNN.html index a4ee9e21..29e0c2d6 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.RNN.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.RNN.html @@ -8,7 +8,7 @@ - mlx.nn.RNN — MLX 0.20.0 documentation + mlx.nn.RNN — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
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    • -
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  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
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    • +
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    • @@ -551,6 +551,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.ReLU.html b/docs/build/html/python/nn/_autosummary/mlx.nn.ReLU.html index f64346d7..0ec51dd8 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.ReLU.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.ReLU.html @@ -8,7 +8,7 @@ - mlx.nn.ReLU — MLX 0.20.0 documentation + mlx.nn.ReLU — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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  • mlx.core.fast.scaled_dot_product_attention
    • -
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    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary/mlx.nn.ReLU6.html b/docs/build/html/python/nn/_autosummary/mlx.nn.ReLU6.html index 43c2042b..a28ac22e 100644 --- a/docs/build/html/python/nn/_autosummary/mlx.nn.ReLU6.html +++ b/docs/build/html/python/nn/_autosummary/mlx.nn.ReLU6.html @@ -8,7 +8,7 @@ - mlx.nn.ReLU6 — MLX 0.20.0 documentation + mlx.nn.ReLU6 — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
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    • -
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  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
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    • +
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
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  • mlx.core.fast.layer_norm
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    • -
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    • -
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    • +
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  • mlx.core.fast.layer_norm
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    • -
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    • +
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    • -
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  • mlx.core.fast.layer_norm
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    • -
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    • +
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    • -
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    • -
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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    • +
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    • +
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    • -
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    • @@ -522,6 +521,7 @@
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    • +
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    • +
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    • -
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    • @@ -522,6 +521,7 @@
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    • +
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    • -
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    • @@ -522,6 +521,7 @@
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    • +
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    • @@ -551,6 +551,7 @@
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    • +
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    • -
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    • +
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    • @@ -551,6 +551,7 @@
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    • +
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    • -
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    • +
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    • -
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    • +
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • -
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    • @@ -522,6 +521,7 @@
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    • +
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  • mlx.nn.PReLU
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • @@ -550,6 +550,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary_functions/mlx.nn.gelu.html b/docs/build/html/python/nn/_autosummary_functions/mlx.nn.gelu.html index e6fdf4a4..68726795 100644 --- a/docs/build/html/python/nn/_autosummary_functions/mlx.nn.gelu.html +++ b/docs/build/html/python/nn/_autosummary_functions/mlx.nn.gelu.html @@ -8,7 +8,7 @@ - mlx.nn.gelu — MLX 0.20.0 documentation + mlx.nn.gelu — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
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    • -
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    • @@ -522,6 +521,7 @@
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    • @@ -551,6 +551,7 @@
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    • +
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  • mlx.nn.PReLU
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    • -
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    • @@ -522,6 +521,7 @@
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    • @@ -551,6 +551,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
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  • mlx.core.fast.layer_norm
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    • -
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  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
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    • @@ -551,6 +551,7 @@
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    • +
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
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    • -
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    • @@ -522,6 +521,7 @@
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    • @@ -551,6 +551,7 @@
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    • -
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    • -
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  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary_functions/mlx.nn.softmin.html b/docs/build/html/python/nn/_autosummary_functions/mlx.nn.softmin.html index 004fa6c3..a33c6627 100644 --- a/docs/build/html/python/nn/_autosummary_functions/mlx.nn.softmin.html +++ b/docs/build/html/python/nn/_autosummary_functions/mlx.nn.softmin.html @@ -8,7 +8,7 @@ - mlx.nn.softmin — MLX 0.20.0 documentation + mlx.nn.softmin — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary_functions/mlx.nn.softplus.html b/docs/build/html/python/nn/_autosummary_functions/mlx.nn.softplus.html index 05b431fc..c786292b 100644 --- a/docs/build/html/python/nn/_autosummary_functions/mlx.nn.softplus.html +++ b/docs/build/html/python/nn/_autosummary_functions/mlx.nn.softplus.html @@ -8,7 +8,7 @@ - mlx.nn.softplus — MLX 0.20.0 documentation + mlx.nn.softplus — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary_functions/mlx.nn.softshrink.html b/docs/build/html/python/nn/_autosummary_functions/mlx.nn.softshrink.html index 727fd2fa..3fe36507 100644 --- a/docs/build/html/python/nn/_autosummary_functions/mlx.nn.softshrink.html +++ b/docs/build/html/python/nn/_autosummary_functions/mlx.nn.softshrink.html @@ -8,7 +8,7 @@ - mlx.nn.softshrink — MLX 0.20.0 documentation + mlx.nn.softshrink — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary_functions/mlx.nn.step.html b/docs/build/html/python/nn/_autosummary_functions/mlx.nn.step.html index 706e78f2..ea4bccaa 100644 --- a/docs/build/html/python/nn/_autosummary_functions/mlx.nn.step.html +++ b/docs/build/html/python/nn/_autosummary_functions/mlx.nn.step.html @@ -8,7 +8,7 @@ - mlx.nn.step — MLX 0.20.0 documentation + mlx.nn.step — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/_autosummary_functions/mlx.nn.tanh.html b/docs/build/html/python/nn/_autosummary_functions/mlx.nn.tanh.html index 3211a175..d7bf4d49 100644 --- a/docs/build/html/python/nn/_autosummary_functions/mlx.nn.tanh.html +++ b/docs/build/html/python/nn/_autosummary_functions/mlx.nn.tanh.html @@ -8,7 +8,7 @@ - mlx.nn.tanh — MLX 0.20.0 documentation + mlx.nn.tanh — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/functions.html b/docs/build/html/python/nn/functions.html index 063f25e9..6bcb3e2d 100644 --- a/docs/build/html/python/nn/functions.html +++ b/docs/build/html/python/nn/functions.html @@ -8,7 +8,7 @@ - Functions — MLX 0.20.0 documentation + Functions — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/init.html b/docs/build/html/python/nn/init.html index aae6a250..67d36960 100644 --- a/docs/build/html/python/nn/init.html +++ b/docs/build/html/python/nn/init.html @@ -8,7 +8,7 @@ - Initializers — MLX 0.20.0 documentation + Initializers — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/layers.html b/docs/build/html/python/nn/layers.html index 6e5de370..5724cd06 100644 --- a/docs/build/html/python/nn/layers.html +++ b/docs/build/html/python/nn/layers.html @@ -8,7 +8,7 @@ - Layers — MLX 0.20.0 documentation + Layers — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • @@ -865,93 +866,99 @@

    AvgPool2d(kernel_size[, stride, padding])

    Applies 2-dimensional average pooling.

    -

    BatchNorm(num_features[, eps, momentum, ...])

    +

    AvgPool3d(kernel_size[, stride, padding])

    +

    Applies 3-dimensional average pooling.

    + +

    BatchNorm(num_features[, eps, momentum, ...])

    Applies Batch Normalization over a 2D or 3D input.

    -

    CELU([alpha])

    +

    CELU([alpha])

    Applies the Continuously Differentiable Exponential Linear Unit.

    -

    Conv1d(in_channels, out_channels, kernel_size)

    +

    Conv1d(in_channels, out_channels, kernel_size)

    Applies a 1-dimensional convolution over the multi-channel input sequence.

    -

    Conv2d(in_channels, out_channels, kernel_size)

    +

    Conv2d(in_channels, out_channels, kernel_size)

    Applies a 2-dimensional convolution over the multi-channel input image.

    -

    Conv3d(in_channels, out_channels, kernel_size)

    +

    Conv3d(in_channels, out_channels, kernel_size)

    Applies a 3-dimensional convolution over the multi-channel input image.

    -

    ConvTranspose1d(in_channels, out_channels, ...)

    +

    ConvTranspose1d(in_channels, out_channels, ...)

    Applies a 1-dimensional transposed convolution over the multi-channel input sequence.

    -

    ConvTranspose2d(in_channels, out_channels, ...)

    +

    ConvTranspose2d(in_channels, out_channels, ...)

    Applies a 2-dimensional transposed convolution over the multi-channel input image.

    -

    ConvTranspose3d(in_channels, out_channels, ...)

    +

    ConvTranspose3d(in_channels, out_channels, ...)

    Applies a 3-dimensional transposed convolution over the multi-channel input image.

    -

    Dropout([p])

    +

    Dropout([p])

    Randomly zero a portion of the elements during training.

    -

    Dropout2d([p])

    +

    Dropout2d([p])

    Apply 2D channel-wise dropout during training.

    -

    Dropout3d([p])

    +

    Dropout3d([p])

    Apply 3D channel-wise dropout during training.

    -

    Embedding(num_embeddings, dims)

    +

    Embedding(num_embeddings, dims)

    Implements a simple lookup table that maps each input integer to a high-dimensional vector.

    -

    ELU([alpha])

    +

    ELU([alpha])

    Applies the Exponential Linear Unit.

    -

    GELU([approx])

    +

    GELU([approx])

    Applies the Gaussian Error Linear Units.

    -

    GLU([axis])

    +

    GLU([axis])

    Applies the gated linear unit function.

    -

    GroupNorm(num_groups, dims[, eps, affine, ...])

    +

    GroupNorm(num_groups, dims[, eps, affine, ...])

    Applies Group Normalization [1] to the inputs.

    -

    GRU(input_size, hidden_size[, bias])

    +

    GRU(input_size, hidden_size[, bias])

    A gated recurrent unit (GRU) RNN layer.

    -

    HardShrink()

    +

    HardShrink()

    Applies the HardShrink function.

    -

    HardTanh()

    +

    HardTanh()

    Applies the HardTanh function.

    -

    Hardswish()

    +

    Hardswish()

    Applies the hardswish function, element-wise.

    -

    InstanceNorm(dims[, eps, affine])

    +

    InstanceNorm(dims[, eps, affine])

    Applies instance normalization [1] on the inputs.

    -

    LayerNorm(dims[, eps, affine, bias])

    +

    LayerNorm(dims[, eps, affine, bias])

    Applies layer normalization [1] on the inputs.

    -

    LeakyReLU([negative_slope])

    +

    LeakyReLU([negative_slope])

    Applies the Leaky Rectified Linear Unit.

    -

    Linear(input_dims, output_dims[, bias])

    +

    Linear(input_dims, output_dims[, bias])

    Applies an affine transformation to the input.

    -

    LogSigmoid()

    +

    LogSigmoid()

    Applies the Log Sigmoid function.

    -

    LogSoftmax()

    +

    LogSoftmax()

    Applies the Log Softmax function.

    -

    LSTM(input_size, hidden_size[, bias])

    +

    LSTM(input_size, hidden_size[, bias])

    An LSTM recurrent layer.

    -

    MaxPool1d(kernel_size[, stride, padding])

    +

    MaxPool1d(kernel_size[, stride, padding])

    Applies 1-dimensional max pooling.

    -

    MaxPool2d(kernel_size[, stride, padding])

    +

    MaxPool2d(kernel_size[, stride, padding])

    Applies 2-dimensional max pooling.

    +

    MaxPool3d(kernel_size[, stride, padding])

    +

    Applies 3-dimensional max pooling.

    +

    Mish()

    Applies the Mish function, element-wise.

    diff --git a/docs/build/html/python/nn/losses.html b/docs/build/html/python/nn/losses.html index 40c6b94d..16b007d2 100644 --- a/docs/build/html/python/nn/losses.html +++ b/docs/build/html/python/nn/losses.html @@ -8,7 +8,7 @@ - Loss Functions — MLX 0.20.0 documentation + Loss Functions — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/nn/module.html b/docs/build/html/python/nn/module.html index 68ba455f..157a7758 100644 --- a/docs/build/html/python/nn/module.html +++ b/docs/build/html/python/nn/module.html @@ -8,7 +8,7 @@ - Module — MLX 0.20.0 documentation + Module — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/ops.html b/docs/build/html/python/ops.html index 3f77dd8b..539ded7d 100644 --- a/docs/build/html/python/ops.html +++ b/docs/build/html/python/ops.html @@ -8,7 +8,7 @@ - Operations — MLX 0.20.0 documentation + Operations — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/optimizers.html b/docs/build/html/python/optimizers.html index 27367403..c512a138 100644 --- a/docs/build/html/python/optimizers.html +++ b/docs/build/html/python/optimizers.html @@ -8,7 +8,7 @@ - Optimizers — MLX 0.20.0 documentation + Optimizers — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
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    • -
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    • @@ -522,6 +521,7 @@
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    • +
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    • -
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    • @@ -522,6 +521,7 @@
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    • +
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    • @@ -551,6 +551,7 @@
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    • +
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    • -
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    • @@ -522,6 +521,7 @@
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    • +
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    • -
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    • +
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    • -
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    • @@ -522,6 +521,7 @@
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    • +
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    • -
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    • @@ -522,6 +521,7 @@
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    • +
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    • -
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    • -
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    • +
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    • -
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    • @@ -521,6 +520,7 @@
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    • +
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    • -
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    • @@ -521,6 +520,7 @@
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    • -
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    • @@ -522,6 +521,7 @@
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    • -
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    • @@ -522,6 +521,7 @@
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    • @@ -551,6 +551,7 @@
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.cosine_decay.html b/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.cosine_decay.html index 654c4976..c46a28db 100644 --- a/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.cosine_decay.html +++ b/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.cosine_decay.html @@ -8,7 +8,7 @@ - mlx.optimizers.cosine_decay — MLX 0.20.0 documentation + mlx.optimizers.cosine_decay — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
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    • -
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    • @@ -521,6 +520,7 @@
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  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.exponential_decay.html b/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.exponential_decay.html index d150ccb4..21f7f355 100644 --- a/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.exponential_decay.html +++ b/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.exponential_decay.html @@ -8,7 +8,7 @@ - mlx.optimizers.exponential_decay — MLX 0.20.0 documentation + mlx.optimizers.exponential_decay — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.join_schedules.html b/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.join_schedules.html index 931e6e29..3df4fbea 100644 --- a/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.join_schedules.html +++ b/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.join_schedules.html @@ -8,7 +8,7 @@ - mlx.optimizers.join_schedules — MLX 0.20.0 documentation + mlx.optimizers.join_schedules — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.linear_schedule.html b/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.linear_schedule.html index e9aec67b..807bed1f 100644 --- a/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.linear_schedule.html +++ b/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.linear_schedule.html @@ -8,7 +8,7 @@ - mlx.optimizers.linear_schedule — MLX 0.20.0 documentation + mlx.optimizers.linear_schedule — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.step_decay.html b/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.step_decay.html index 0f20abc6..3b9c5b4e 100644 --- a/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.step_decay.html +++ b/docs/build/html/python/optimizers/_autosummary/mlx.optimizers.step_decay.html @@ -8,7 +8,7 @@ - mlx.optimizers.step_decay — MLX 0.20.0 documentation + mlx.optimizers.step_decay — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/optimizers/common_optimizers.html b/docs/build/html/python/optimizers/common_optimizers.html index 4e40764b..6dc07a35 100644 --- a/docs/build/html/python/optimizers/common_optimizers.html +++ b/docs/build/html/python/optimizers/common_optimizers.html @@ -8,7 +8,7 @@ - Common Optimizers — MLX 0.20.0 documentation + Common Optimizers — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/optimizers/optimizer.html b/docs/build/html/python/optimizers/optimizer.html index 4e080517..5aa49958 100644 --- a/docs/build/html/python/optimizers/optimizer.html +++ b/docs/build/html/python/optimizers/optimizer.html @@ -8,7 +8,7 @@ - Optimizer — MLX 0.20.0 documentation + Optimizer — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/optimizers/schedulers.html b/docs/build/html/python/optimizers/schedulers.html index c38b9e94..e5c19102 100644 --- a/docs/build/html/python/optimizers/schedulers.html +++ b/docs/build/html/python/optimizers/schedulers.html @@ -8,7 +8,7 @@ - Schedulers — MLX 0.20.0 documentation + Schedulers — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -52,7 +52,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/random.html b/docs/build/html/python/random.html index 82efd6af..2d606a54 100644 --- a/docs/build/html/python/random.html +++ b/docs/build/html/python/random.html @@ -8,7 +8,7 @@ - Random — MLX 0.20.0 documentation + Random — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/transforms.html b/docs/build/html/python/transforms.html index 4ee6f244..ccedbe16 100644 --- a/docs/build/html/python/transforms.html +++ b/docs/build/html/python/transforms.html @@ -8,7 +8,7 @@ - Transforms — MLX 0.20.0 documentation + Transforms — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/python/tree_utils.html b/docs/build/html/python/tree_utils.html index 233c7296..be8a0239 100644 --- a/docs/build/html/python/tree_utils.html +++ b/docs/build/html/python/tree_utils.html @@ -8,7 +8,7 @@ - Tree Utils — MLX 0.20.0 documentation + Tree Utils — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/quantized_8h.html b/docs/build/html/quantized_8h.html index c0e18903..15c39827 100644 --- a/docs/build/html/quantized_8h.html +++ b/docs/build/html/quantized_8h.html @@ -194,9 +194,6 @@ Functions template<typename T , const int group_size, const int bits> void affine_quantize (const device T *w, device uint8_t *out, device T *scales, device T *biases, uint2 index, uint2 grid_dim)   -template<typename T , const int group_size, const int bits> -void affine_quantize_scales_biases (const device T *w, const device T *scales, const device T *biases, device uint8_t *out, uint2 index, uint2 grid_dim) -  template<typename T , const int group_size, const int bits> void affine_dequantize (const device uint8_t *w, const device T *scales, const device T *biases, device T *out, uint2 index, uint2 grid_dim)   @@ -514,49 +511,6 @@ template<typename T , const int group_size, const int bits>
    -
    - - -

    ◆ affine_quantize_scales_biases()

    - -
    -
    -
    -template<typename T , const int group_size, const int bits>
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    void affine_quantize_scales_biases (const device T * w,
    const device T * scales,
    const device T * biases,
    device uint8_t * out,
    uint2 index,
    uint2 grid_dim )
    -
    -
    diff --git a/docs/build/html/quantized_8h_source.html b/docs/build/html/quantized_8h_source.html index 0a08db75..4562088e 100644 --- a/docs/build/html/quantized_8h_source.html +++ b/docs/build/html/quantized_8h_source.html @@ -107,8 +107,8 @@ $(function(){ initResizable(false); });
    14inline U load_vector(const device T* x, thread U* x_thread) {
    15 static_assert(
    -
    16 bits == 2 || bits == 4 || bits == 8,
    -
    17 "Template undefined for bits not in {2, 4, 8}");
    +
    16 bits == 2 || bits == 3 || bits == 4 || bits == 6 || bits == 8,
    +
    17 "Template undefined for bits not in {2, 3, 4, 6, 8}");
    18
    19 U sum = 0;
    20
    @@ -122,1803 +122,1797 @@ $(function(){ initResizable(false); });
    28 }
    29 }
    30
    -
    31 else if (bits == 4) {
    -
    32 for (int i = 0; i < values_per_thread; i += 4) {
    -
    33 sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3];
    -
    34 x_thread[i] = x[i];
    -
    35 x_thread[i + 1] = x[i + 1] / 16.0f;
    -
    36 x_thread[i + 2] = x[i + 2] / 256.0f;
    -
    37 x_thread[i + 3] = x[i + 3] / 4096.0f;
    -
    38 }
    -
    39 }
    -
    40
    -
    41 else if (bits == 8) {
    -
    42 for (int i = 0; i < values_per_thread; i++) {
    -
    43 sum += x[i];
    -
    44 x_thread[i] = x[i];
    -
    45 }
    -
    46 }
    -
    47
    -
    48 return sum;
    -
    49}
    -
    -
    50
    -
    51template <typename T, typename U, int values_per_thread, int bits>
    -
    -
    52inline U load_vector_safe(const device T* x, thread U* x_thread, int N) {
    -
    53 static_assert(
    -
    54 bits == 2 || bits == 4 || bits == 8,
    -
    55 "Template undefined for bits not in {2, 4, 8}");
    -
    56
    -
    57 U sum = 0;
    -
    58
    -
    59 if (bits == 2) {
    -
    60 for (int i = 0; i < N; i += 4) {
    -
    61 sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3];
    -
    62 x_thread[i] = x[i];
    -
    63 x_thread[i + 1] = x[i + 1] / 4.0f;
    -
    64 x_thread[i + 2] = x[i + 2] / 16.0f;
    -
    65 x_thread[i + 3] = x[i + 3] / 64.0f;
    -
    66 }
    -
    67 for (int i = N; i < values_per_thread; i++) {
    -
    68 x_thread[i] = 0;
    -
    69 }
    -
    70 }
    -
    71
    -
    72 else if (bits == 4) {
    -
    73 for (int i = 0; i < N; i += 4) {
    -
    74 sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3];
    -
    75 x_thread[i] = x[i];
    -
    76 x_thread[i + 1] = x[i + 1] / 16.0f;
    -
    77 x_thread[i + 2] = x[i + 2] / 256.0f;
    -
    78 x_thread[i + 3] = x[i + 3] / 4096.0f;
    -
    79 }
    -
    80 for (int i = N; i < values_per_thread; i++) {
    -
    81 x_thread[i] = 0;
    -
    82 }
    -
    83 }
    -
    84
    -
    85 else if (bits == 8) {
    -
    86 for (int i = 0; i < N; i++) {
    -
    87 sum += x[i];
    -
    88 x_thread[i] = x[i];
    -
    89 }
    -
    90 for (int i = N; i < values_per_thread; i++) {
    -
    91 x_thread[i] = 0;
    -
    92 }
    -
    93 }
    -
    94
    -
    95 return sum;
    -
    96}
    -
    -
    97
    -
    98template <typename U, int values_per_thread, int bits>
    -
    -
    99inline U qdot(
    -
    100 const device uint8_t* w,
    -
    101 const thread U* x_thread,
    -
    102 U scale,
    -
    103 U bias,
    -
    104 U sum) {
    -
    105 static_assert(
    -
    106 bits == 2 || bits == 4 || bits == 8,
    -
    107 "Template undefined for bits not in {2, 4, 8}");
    -
    108
    -
    109 U accum = 0;
    -
    110
    -
    111 if (bits == 2) {
    -
    112 for (int i = 0; i < (values_per_thread / 4); i++) {
    -
    113 accum +=
    -
    114 (x_thread[4 * i] * (w[i] & 0x03) +
    -
    115 x_thread[4 * i + 1] * (w[i] & 0x0c) +
    -
    116 x_thread[4 * i + 2] * (w[i] & 0x30) +
    -
    117 x_thread[4 * i + 3] * (w[i] & 0xc0));
    -
    118 }
    -
    119 }
    -
    120
    -
    121 else if (bits == 4) {
    -
    122 const device uint16_t* ws = (const device uint16_t*)w;
    -
    123 for (int i = 0; i < (values_per_thread / 4); i++) {
    -
    124 accum +=
    -
    125 (x_thread[4 * i] * (ws[i] & 0x000f) +
    -
    126 x_thread[4 * i + 1] * (ws[i] & 0x00f0) +
    -
    127 x_thread[4 * i + 2] * (ws[i] & 0x0f00) +
    -
    128 x_thread[4 * i + 3] * (ws[i] & 0xf000));
    -
    129 }
    -
    130 }
    -
    131
    -
    132 else if (bits == 8) {
    -
    133 for (int i = 0; i < values_per_thread; i++) {
    -
    134 accum += x_thread[i] * w[i];
    -
    135 }
    -
    136 }
    -
    137
    -
    138 return scale * accum + sum * bias;
    -
    139}
    +
    31 else if (bits == 3) {
    +
    32 for (int i = 0; i < values_per_thread; i += 8) {
    +
    33 sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3] + x[i + 4] + x[i + 5] +
    +
    34 x[i + 6] + x[i + 7];
    +
    35 x_thread[i] = x[i];
    +
    36 x_thread[i + 1] = x[i + 1] / 8.0f;
    +
    37 x_thread[i + 2] = x[i + 2] / 64.0f;
    +
    38 x_thread[i + 3] = x[i + 3] / 2.0f;
    +
    39 x_thread[i + 4] = x[i + 4] / 16.0f;
    +
    40 x_thread[i + 5] = x[i + 5] / 128.0f;
    +
    41 x_thread[i + 6] = x[i + 6] / 4.0f;
    +
    42 x_thread[i + 7] = x[i + 7] / 32.0f;
    +
    43 }
    +
    44 }
    +
    45
    +
    46 else if (bits == 4) {
    +
    47 for (int i = 0; i < values_per_thread; i += 4) {
    +
    48 sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3];
    +
    49 x_thread[i] = x[i];
    +
    50 x_thread[i + 1] = x[i + 1] / 16.0f;
    +
    51 x_thread[i + 2] = x[i + 2] / 256.0f;
    +
    52 x_thread[i + 3] = x[i + 3] / 4096.0f;
    +
    53 }
    +
    54 }
    +
    55
    +
    56 else if (bits == 6) {
    +
    57 for (int i = 0; i < values_per_thread; i += 4) {
    +
    58 sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3];
    +
    59 x_thread[i] = x[i];
    +
    60 x_thread[i + 1] = x[i + 1] / 64.0f;
    +
    61 x_thread[i + 2] = x[i + 2] / 16.0f;
    +
    62 x_thread[i + 3] = x[i + 3] / 4.0f;
    +
    63 }
    +
    64 }
    +
    65
    +
    66 else if (bits == 8) {
    +
    67 for (int i = 0; i < values_per_thread; i++) {
    +
    68 sum += x[i];
    +
    69 x_thread[i] = x[i];
    +
    70 }
    +
    71 }
    +
    72
    +
    73 return sum;
    +
    74}
    +
    75
    +
    76template <typename T, typename U, int values_per_thread, int bits>
    +
    +
    77inline U load_vector_safe(const device T* x, thread U* x_thread, int N) {
    +
    78 static_assert(
    +
    79 bits == 2 || bits == 3 || bits == 4 || bits == 6 || bits == 8,
    +
    80 "Template undefined for bits not in {2, 3, 4, 6, 8}");
    +
    81
    +
    82 U sum = 0;
    +
    83
    +
    84 if (bits == 2) {
    +
    85 for (int i = 0; i < N; i += 4) {
    +
    86 sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3];
    +
    87 x_thread[i] = x[i];
    +
    88 x_thread[i + 1] = x[i + 1] / 4.0f;
    +
    89 x_thread[i + 2] = x[i + 2] / 16.0f;
    +
    90 x_thread[i + 3] = x[i + 3] / 64.0f;
    +
    91 }
    +
    92 }
    +
    93
    +
    94 else if (bits == 3) {
    +
    95 for (int i = 0; i < N; i += 8) {
    +
    96 sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3] + x[i + 4] + x[i + 5] +
    +
    97 x[i + 6] + x[i + 7];
    +
    98
    +
    99 x_thread[i] = x[i];
    +
    100 x_thread[i + 1] = x[i + 1] / 8.0f;
    +
    101 x_thread[i + 2] = x[i + 2] / 64.0f;
    +
    102 x_thread[i + 3] = x[i + 3] / 2.0f;
    +
    103 x_thread[i + 4] = x[i + 4] / 16.0f;
    +
    104 x_thread[i + 5] = x[i + 5] / 128.0f;
    +
    105 x_thread[i + 6] = x[i + 6] / 4.0f;
    +
    106 x_thread[i + 7] = x[i + 7] / 32.0f;
    +
    107 }
    +
    108 }
    +
    109
    +
    110 else if (bits == 4) {
    +
    111 for (int i = 0; i < N; i += 4) {
    +
    112 sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3];
    +
    113 x_thread[i] = x[i];
    +
    114 x_thread[i + 1] = x[i + 1] / 16.0f;
    +
    115 x_thread[i + 2] = x[i + 2] / 256.0f;
    +
    116 x_thread[i + 3] = x[i + 3] / 4096.0f;
    +
    117 }
    +
    118 }
    +
    119
    +
    120 else if (bits == 6) {
    +
    121 for (int i = 0; i < N; i += 4) {
    +
    122 sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3];
    +
    123 x_thread[i] = x[i];
    +
    124 x_thread[i + 1] = x[i + 1] / 64.0f;
    +
    125 x_thread[i + 2] = x[i + 2] / 16.0f;
    +
    126 x_thread[i + 3] = x[i + 3] / 4.0f;
    +
    127 }
    +
    128 }
    +
    129
    +
    130 else if (bits == 8) {
    +
    131 for (int i = 0; i < N; i++) {
    +
    132 sum += x[i];
    +
    133 x_thread[i] = x[i];
    +
    134 }
    +
    135 }
    +
    136
    +
    137 for (int i = N; i < values_per_thread; i++) {
    +
    138 x_thread[i] = 0;
    +
    139 }
    140
    -
    141template <typename U, int values_per_thread, int bits>
    -
    -
    142inline U qdot_safe(
    -
    143 const device uint8_t* w,
    -
    144 const thread U* x_thread,
    -
    145 U scale,
    -
    146 U bias,
    -
    147 U sum,
    -
    148 int N) {
    -
    149 static_assert(
    -
    150 bits == 2 || bits == 4 || bits == 8,
    -
    151 "Template undefined for bits not in {2, 4, 8}");
    -
    152
    -
    153 U accum = 0;
    +
    141 return sum;
    +
    142}
    +
    +
    143
    +
    144template <typename U, int values_per_thread, int bits>
    +
    +
    145inline U qdot(
    +
    146 const device uint8_t* w,
    +
    147 const thread U* x_thread,
    +
    148 U scale,
    +
    149 U bias,
    +
    150 U sum) {
    +
    151 static_assert(
    +
    152 bits == 2 || bits == 3 || bits == 4 || bits == 6 || bits == 8,
    +
    153 "Template undefined for bits not in {2, 3, 4, 6, 8}");
    154
    -
    155 if (bits == 2) {
    -
    156 for (int i = 0; i < (N / 4); i++) {
    -
    157 accum +=
    -
    158 (x_thread[4 * i] * (w[i] & 0x03) +
    -
    159 x_thread[4 * i + 1] * (w[i] & 0x0c) +
    -
    160 x_thread[4 * i + 2] * (w[i] & 0x30) +
    -
    161 x_thread[4 * i + 3] * (w[i] & 0xc0));
    -
    162 }
    -
    163 }
    -
    164
    -
    165 else if (bits == 4) {
    -
    166 const device uint16_t* ws = (const device uint16_t*)w;
    -
    167 for (int i = 0; i < (N / 4); i++) {
    -
    168 accum +=
    -
    169 (x_thread[4 * i] * (ws[i] & 0x000f) +
    -
    170 x_thread[4 * i + 1] * (ws[i] & 0x00f0) +
    -
    171 x_thread[4 * i + 2] * (ws[i] & 0x0f00) +
    -
    172 x_thread[4 * i + 3] * (ws[i] & 0xf000));
    -
    173 }
    -
    174 }
    -
    175
    -
    176 else if (bits == 8) {
    -
    177 for (int i = 0; i < N; i++) {
    -
    178 accum += x_thread[i] * w[i];
    -
    179 }
    -
    180 }
    +
    155 U accum = 0;
    +
    156
    +
    157 if (bits == 2) {
    +
    158 for (int i = 0; i < (values_per_thread / 4); i++) {
    +
    159 accum +=
    +
    160 (x_thread[4 * i] * (w[i] & 0x03) +
    +
    161 x_thread[4 * i + 1] * (w[i] & 0x0c) +
    +
    162 x_thread[4 * i + 2] * (w[i] & 0x30) +
    +
    163 x_thread[4 * i + 3] * (w[i] & 0xc0));
    +
    164 }
    +
    165 }
    +
    166
    +
    167 else if (bits == 3) {
    +
    168 for (int i = 0; i < (values_per_thread / 8); i++) {
    +
    169 x_thread += 8 * i;
    +
    170 w += 3 * i;
    +
    171
    +
    172 accum += (w[0] & 0x07) * x_thread[0];
    +
    173 accum += (w[0] & 0x38) * x_thread[1];
    +
    174 accum += (w[0] & 0xc0) * x_thread[2];
    +
    175 accum += (w[1] & 0x01) * (x_thread[2] * 256.0f);
    +
    176
    +
    177 accum += (w[1] & 0x0e) * x_thread[3];
    +
    178 accum += (w[1] & 0x70) * x_thread[4];
    +
    179 accum += (w[1] & 0x80) * x_thread[5];
    +
    180 accum += (w[2] & 0x03) * (x_thread[5] * 256.0f);
    181
    -
    182 return scale * accum + sum * bias;
    -
    183}
    +
    182 accum += (w[2] & 0x1c) * x_thread[6];
    +
    183 accum += (w[2] & 0xe0) * x_thread[7];
    +
    184 }
    +
    185 }
    +
    186
    +
    187 else if (bits == 4) {
    +
    188 const device uint16_t* ws = (const device uint16_t*)w;
    +
    189 for (int i = 0; i < (values_per_thread / 4); i++) {
    +
    190 accum +=
    +
    191 (x_thread[4 * i] * (ws[i] & 0x000f) +
    +
    192 x_thread[4 * i + 1] * (ws[i] & 0x00f0) +
    +
    193 x_thread[4 * i + 2] * (ws[i] & 0x0f00) +
    +
    194 x_thread[4 * i + 3] * (ws[i] & 0xf000));
    +
    195 }
    +
    196 }
    +
    197
    +
    198 else if (bits == 6) {
    +
    199 for (int i = 0; i < (values_per_thread / 4); i++) {
    +
    200 x_thread += 4 * i;
    +
    201 w += 3 * i;
    +
    202
    +
    203 accum += (w[0] & 0x3f) * x_thread[0];
    +
    204
    +
    205 accum += (w[0] & 0xc0) * x_thread[1];
    +
    206 accum += (w[1] & 0x0f) * (x_thread[1] * 256.0f);
    +
    207
    +
    208 accum += (w[1] & 0xf0) * x_thread[2];
    +
    209 accum += (w[2] & 0x03) * (x_thread[2] * 256.0f);
    +
    210
    +
    211 accum += (w[2] & 0xfc) * x_thread[3];
    +
    212 }
    +
    213 }
    +
    214
    +
    215 else if (bits == 8) {
    +
    216 for (int i = 0; i < values_per_thread; i++) {
    +
    217 accum += x_thread[i] * w[i];
    +
    218 }
    +
    219 }
    +
    220
    +
    221 return scale * accum + sum * bias;
    +
    222}
    -
    184
    -
    185template <typename U, int values_per_thread, int bits>
    -
    186inline void
    -
    -
    187qouter(const thread uint8_t* w, U x, U scale, U bias, thread U* result) {
    -
    188 static_assert(
    -
    189 bits == 2 || bits == 4 || bits == 8,
    -
    190 "Template undefined for bits not in {2, 4, 8}");
    -
    191
    -
    192 if (bits == 2) {
    -
    193 U s[4] = {scale, scale / 4.0f, scale / 16.0f, scale / 64.0f};
    -
    194 for (int i = 0; i < (values_per_thread / 4); i++) {
    -
    195 result[4 * i] += x * (s[0] * (w[i] & 0x03) + bias);
    -
    196 result[4 * i + 1] += x * (s[1] * (w[i] & 0x0c) + bias);
    -
    197 result[4 * i + 2] += x * (s[2] * (w[i] & 0x30) + bias);
    -
    198 result[4 * i + 3] += x * (s[3] * (w[i] & 0xc0) + bias);
    -
    199 }
    -
    200 }
    -
    201
    -
    202 else if (bits == 4) {
    -
    203 U s[2] = {scale, scale / 16.0f};
    -
    204 for (int i = 0; i < (values_per_thread / 2); i++) {
    -
    205 result[2 * i] += x * (s[0] * (w[i] & 0x0f) + bias);
    -
    206 result[2 * i + 1] += x * (s[1] * (w[i] & 0xf0) + bias);
    -
    207 }
    -
    208 }
    -
    209
    -
    210 else if (bits == 8) {
    -
    211 for (int i = 0; i < values_per_thread; i++) {
    -
    212 result[i] += x * (scale * w[i] + bias);
    -
    213 }
    -
    214 }
    -
    215}
    -
    -
    216
    -
    217template <typename U, int N, int bits>
    -
    218inline void
    -
    -
    219dequantize(const device uint8_t* w, U scale, U bias, threadgroup U* w_local) {
    -
    220 static_assert(
    -
    221 bits == 2 || bits == 4 || bits == 8,
    -
    222 "Template undefined for bits not in {2, 4, 8}");
    223
    -
    224 if (bits == 2) {
    -
    225 U s[4] = {
    -
    226 scale,
    -
    227 scale / static_cast<U>(4.0f),
    -
    228 scale / static_cast<U>(16.0f),
    -
    229 scale / static_cast<U>(64.0f)};
    -
    230 for (int i = 0; i < (N / 4); i++) {
    -
    231 w_local[4 * i] = s[0] * (w[i] & 0x03) + bias;
    -
    232 w_local[4 * i + 1] = s[1] * (w[i] & 0x0c) + bias;
    -
    233 w_local[4 * i + 2] = s[2] * (w[i] & 0x30) + bias;
    -
    234 w_local[4 * i + 3] = s[3] * (w[i] & 0xc0) + bias;
    -
    235 }
    -
    236 }
    +
    224template <typename U, int values_per_thread, int bits>
    +
    +
    225inline U qdot_safe(
    +
    226 const device uint8_t* w,
    +
    227 const thread U* x_thread,
    +
    228 U scale,
    +
    229 U bias,
    +
    230 U sum,
    +
    231 int N) {
    +
    232 static_assert(
    +
    233 bits == 2 || bits == 3 || bits == 4 || bits == 6 || bits == 8,
    +
    234 "Template undefined for bits not in {2, 3, 4, 6, 8}");
    +
    235
    +
    236 U accum = 0;
    237
    -
    238 else if (bits == 4) {
    -
    239 U s[2] = {scale, scale / static_cast<U>(16.0f)};
    -
    240 for (int i = 0; i < (N / 2); i++) {
    -
    241 w_local[2 * i] = s[0] * (w[i] & 0x0f) + bias;
    -
    242 w_local[2 * i + 1] = s[1] * (w[i] & 0xf0) + bias;
    -
    243 }
    -
    244 }
    -
    245
    -
    246 else if (bits == 8) {
    -
    247 for (int i = 0; i < N; i++) {
    -
    248 w_local[i] = scale * w[i] + bias;
    -
    249 }
    -
    250 }
    -
    251}
    -
    +
    238 if (bits == 2) {
    +
    239 for (int i = 0; i < (N / 4); i++) {
    +
    240 accum +=
    +
    241 (x_thread[4 * i] * (w[i] & 0x03) +
    +
    242 x_thread[4 * i + 1] * (w[i] & 0x0c) +
    +
    243 x_thread[4 * i + 2] * (w[i] & 0x30) +
    +
    244 x_thread[4 * i + 3] * (w[i] & 0xc0));
    +
    245 }
    +
    246 }
    +
    247
    +
    248 else if (bits == 3) {
    +
    249 for (int i = 0; i < (N / 8); i++) {
    +
    250 x_thread += 8 * i;
    +
    251 w += 3 * i;
    252
    -
    253template <
    -
    254 typename T,
    -
    255 short BROWS,
    -
    256 short BCOLS,
    -
    257 short dst_ld,
    -
    258 short reduction_dim,
    -
    259 short tgp_size,
    -
    260 short group_size,
    -
    261 short bits>
    -
    -
    262struct QuantizedBlockLoader {
    -
    263 static_assert(
    -
    264 BCOLS <= group_size,
    -
    265 "The group size should be larger than the columns");
    -
    266 static_assert(
    -
    267 group_size % BCOLS == 0,
    -
    268 "The group size should be divisible by the columns");
    -
    269 static_assert(
    -
    270 bits == 2 || bits == 4 || bits == 8,
    -
    271 "Template undefined for bits not in {2, 4, 8}");
    -
    272
    -
    273 MLX_MTL_CONST short pack_factor = 32 / bits;
    -
    274 MLX_MTL_CONST short BCOLS_PACKED = BCOLS / pack_factor;
    -
    275 MLX_MTL_CONST short n_reads =
    -
    276 (BCOLS_PACKED * BROWS < tgp_size) ? 1 : (BCOLS_PACKED * BROWS) / tgp_size;
    -
    277 MLX_MTL_CONST short group_steps = group_size / BCOLS;
    +
    253 accum += (w[0] & 0x07) * x_thread[0];
    +
    254 accum += (w[0] & 0x38) * x_thread[1];
    +
    255 accum += (w[0] & 0xc0) * x_thread[2];
    +
    256 accum += (w[1] & 0x01) * (x_thread[2] * 256.0f);
    +
    257
    +
    258 accum += (w[1] & 0x0e) * x_thread[3];
    +
    259 accum += (w[1] & 0x70) * x_thread[4];
    +
    260 accum += (w[1] & 0x80) * x_thread[5];
    +
    261 accum += (w[2] & 0x03) * (x_thread[5] * 256.0f);
    +
    262
    +
    263 accum += (w[2] & 0x1c) * x_thread[6];
    +
    264 accum += (w[2] & 0xe0) * x_thread[7];
    +
    265 }
    +
    266 }
    +
    267
    +
    268 else if (bits == 4) {
    +
    269 const device uint16_t* ws = (const device uint16_t*)w;
    +
    270 for (int i = 0; i < (N / 4); i++) {
    +
    271 accum +=
    +
    272 (x_thread[4 * i] * (ws[i] & 0x000f) +
    +
    273 x_thread[4 * i + 1] * (ws[i] & 0x00f0) +
    +
    274 x_thread[4 * i + 2] * (ws[i] & 0x0f00) +
    +
    275 x_thread[4 * i + 3] * (ws[i] & 0xf000));
    +
    276 }
    +
    277 }
    278
    -
    279 const int src_ld;
    -
    280 const int tile_stride;
    -
    281 short group_step_cnt;
    -
    282 const int group_stride;
    +
    279 else if (bits == 6) {
    +
    280 for (int i = 0; i < (N / 4); i++) {
    +
    281 x_thread += 4 * i;
    +
    282 w += 3 * i;
    283
    -
    284 const short thread_idx;
    -
    285 const short bi;
    -
    286 const short bj;
    -
    287
    -
    288 threadgroup T* dst;
    -
    289 const device uint32_t* src;
    -
    290 const device T* scales;
    -
    291 const device T* biases;
    -
    292
    -
    -
    293 QuantizedBlockLoader(
    -
    294 const device uint32_t* src_,
    -
    295 const device T* scales_,
    -
    296 const device T* biases_,
    -
    297 const int src_ld_,
    -
    298 threadgroup T* dst_,
    -
    299 ushort simd_group_id [[simdgroup_index_in_threadgroup]],
    -
    300 ushort simd_lane_id [[thread_index_in_simdgroup]])
    -
    301 : src_ld(src_ld_),
    -
    302 tile_stride(
    -
    303 reduction_dim ? BCOLS_PACKED : BROWS * src_ld / pack_factor),
    -
    304 group_step_cnt(0),
    -
    305 group_stride(BROWS * src_ld / group_size),
    -
    306 thread_idx(simd_group_id * 32 + simd_lane_id),
    -
    307 bi(n_reads * thread_idx / BCOLS_PACKED),
    -
    308 bj((n_reads * thread_idx) % BCOLS_PACKED),
    -
    309 dst(dst_ + bi * dst_ld + bj * pack_factor),
    -
    310 src(src_ + bi * src_ld / pack_factor + bj),
    -
    311 scales(scales_ + bi * src_ld / group_size),
    -
    312 biases(biases_ + bi * src_ld / group_size) {}
    -
    -
    313
    -
    -
    314 void load_unsafe() const {
    -
    315 if (BCOLS_PACKED * BROWS < tgp_size && bi >= BROWS) {
    -
    316 return;
    -
    317 }
    -
    318
    -
    319 T scale = *scales;
    -
    320 T bias = *biases;
    -
    321 for (int i = 0; i < n_reads; i++) {
    -
    322 dequantize<T, pack_factor, bits>(
    -
    323 (device uint8_t*)(src + i), scale, bias, dst + i * pack_factor);
    -
    324 }
    -
    325 }
    -
    -
    326
    -
    -
    327 void load_safe(short2 src_tile_dim) const {
    -
    328 if (BCOLS_PACKED * BROWS < tgp_size && bi >= BROWS) {
    -
    329 return;
    -
    330 }
    -
    331
    -
    332 if (reduction_dim == 1 && bi >= src_tile_dim.y) {
    -
    333 for (int i = 0; i < n_reads * pack_factor; i++) {
    -
    334 dst[i] = T(0);
    -
    335 }
    -
    336 return;
    -
    337 }
    -
    338
    -
    339 if (reduction_dim == 0 && bi >= src_tile_dim.x) {
    -
    340 for (int i = 0; i < n_reads * pack_factor; i++) {
    -
    341 dst[i] = T(0);
    -
    342 }
    -
    343 return;
    -
    344 }
    -
    345
    -
    346 T scale = *scales;
    -
    347 T bias = *biases;
    -
    348 for (int i = 0; i < n_reads; i++) {
    -
    349 dequantize<T, pack_factor, bits>(
    -
    350 (device uint8_t*)(src + i), scale, bias, dst + i * pack_factor);
    -
    351 }
    -
    352 }
    +
    284 accum += (w[0] & 0x3f) * x_thread[0];
    +
    285
    +
    286 accum += (w[0] & 0xc0) * x_thread[1];
    +
    287 accum += (w[1] & 0x0f) * (x_thread[1] * 256.0f);
    +
    288
    +
    289 accum += (w[1] & 0xf0) * x_thread[2];
    +
    290 accum += (w[2] & 0x03) * (x_thread[2] * 256.0f);
    +
    291
    +
    292 accum += (w[2] & 0xfc) * x_thread[3];
    +
    293 }
    +
    294 }
    +
    295
    +
    296 else if (bits == 8) {
    +
    297 for (int i = 0; i < N; i++) {
    +
    298 accum += x_thread[i] * w[i];
    +
    299 }
    +
    300 }
    +
    301
    +
    302 return scale * accum + sum * bias;
    +
    303}
    +
    304
    +
    305template <typename U, int values_per_thread, int bits>
    +
    306inline void
    +
    +
    307qouter(const thread uint8_t* w, U x, U scale, U bias, thread U* result) {
    +
    308 static_assert(
    +
    309 bits == 2 || bits == 3 || bits == 4 || bits == 6 || bits == 8,
    +
    310 "Template undefined for bits not in {2, 3, 4, 6, 8}");
    +
    311
    +
    312 if (bits == 2) {
    +
    313 U s[4] = {scale, scale / 4.0f, scale / 16.0f, scale / 64.0f};
    +
    314 for (int i = 0; i < (values_per_thread / 4); i++) {
    +
    315 result[4 * i] += x * (s[0] * (w[i] & 0x03) + bias);
    +
    316 result[4 * i + 1] += x * (s[1] * (w[i] & 0x0c) + bias);
    +
    317 result[4 * i + 2] += x * (s[2] * (w[i] & 0x30) + bias);
    +
    318 result[4 * i + 3] += x * (s[3] * (w[i] & 0xc0) + bias);
    +
    319 }
    +
    320 }
    +
    321
    +
    322 else if (bits == 3) {
    +
    323 for (int i = 0; i < (values_per_thread / 8); i++) {
    +
    324 uint8_t w0 = w[3 * i];
    +
    325 uint8_t w1 = w[3 * i + 1];
    +
    326 uint8_t w2 = w[3 * i + 2];
    +
    327
    +
    328 result[8 * i] += x * ((w0 & 0x7) * scale + bias);
    +
    329 result[8 * i + 1] += x * (((w0 & 0x38) >> 3) * scale + bias);
    +
    330 result[8 * i + 2] +=
    +
    331 x * ((((w0 & 0xc0) >> 6) + ((w1 & 0x1) << 2)) * scale + bias);
    +
    332 result[8 * i + 3] += x * (((w1 & 0xe) >> 1) * scale + bias);
    +
    333 result[8 * i + 4] += x * (((w1 & 0x70) >> 4) * scale + bias);
    +
    334 result[8 * i + 5] +=
    +
    335 x * ((((w1 & 0x80) >> 7) + ((w2 & 0x3) << 1)) * scale + bias);
    +
    336 result[8 * i + 6] += x * (((w2 & 0x1c) >> 2) * scale + bias);
    +
    337 result[8 * i + 7] += x * (((w2 & 0xe0) >> 5) * scale + bias);
    +
    338 }
    +
    339 }
    +
    340
    +
    341 else if (bits == 4) {
    +
    342 U s[2] = {scale, scale / 16.0f};
    +
    343 for (int i = 0; i < (values_per_thread / 2); i++) {
    +
    344 result[2 * i] += x * (s[0] * (w[i] & 0x0f) + bias);
    +
    345 result[2 * i + 1] += x * (s[1] * (w[i] & 0xf0) + bias);
    +
    346 }
    +
    347
    +
    348 } else if (bits == 6) {
    +
    349 for (int i = 0; i < (values_per_thread / 4); i++) {
    +
    350 uint8_t w0 = w[3 * i];
    +
    351 uint8_t w1 = w[3 * i + 1];
    +
    352 uint8_t w2 = w[3 * i + 2];
    353
    -
    -
    354 void next() {
    -
    355 src += tile_stride;
    -
    356 if (reduction_dim == 1) {
    -
    357 if (group_steps > 1) {
    -
    358 group_step_cnt++;
    -
    359 if (group_step_cnt == group_steps) {
    -
    360 group_step_cnt = 0;
    -
    361 scales++;
    -
    362 biases++;
    -
    363 }
    -
    364 } else {
    -
    365 scales++;
    -
    366 biases++;
    -
    367 }
    -
    368 } else {
    -
    369 scales += group_stride;
    -
    370 biases += group_stride;
    -
    371 }
    -
    372 }
    +
    354 result[4 * i] += x * ((w0 & 0x3f) * scale + bias);
    +
    355 result[4 * i + 1] +=
    +
    356 x * ((((w0 >> 6) & 0x03) + ((w1 & 0x0f) << 2)) * scale + bias);
    +
    357 result[4 * i + 2] +=
    +
    358 x * ((((w1 >> 4) & 0x0f) + ((w2 & 0x03) << 4)) * scale + bias);
    +
    359 result[4 * i + 3] += x * (((w2 >> 2) & 0x3f) * scale + bias);
    +
    360 }
    +
    361 }
    +
    362
    +
    363 else if (bits == 8) {
    +
    364 for (int i = 0; i < values_per_thread; i++) {
    +
    365 result[i] += x * (scale * w[i] + bias);
    +
    366 }
    +
    367 }
    +
    368}
    -
    373};
    -
    -
    374
    -
    375template <typename T, int group_size, int bits, int D>
    -
    -
    376METAL_FUNC void qmv_quad_impl(
    -
    377 const device uint32_t* w,
    -
    378 const device T* scales,
    -
    379 const device T* biases,
    -
    380 const device T* x,
    -
    381 device T* y,
    -
    382 constant int& in_vec_size,
    -
    383 const constant int& out_vec_size,
    -
    384 uint3 tid [[threadgroup_position_in_grid]],
    -
    385 uint quad_gid [[quadgroup_index_in_threadgroup]],
    -
    386 uint quad_lid [[thread_index_in_quadgroup]]) {
    -
    387 constexpr int quads_per_simd = SIMD_SIZE / QUAD_SIZE;
    -
    388 constexpr int pack_factor = 32 / bits;
    -
    389 constexpr int values_per_thread = D / QUAD_SIZE;
    -
    390 constexpr int packs_per_thread = values_per_thread / pack_factor;
    -
    391 constexpr int scale_step_per_thread = group_size / values_per_thread;
    -
    392 constexpr int results_per_quadgroup = 8;
    -
    393
    -
    394 typedef float U;
    +
    369
    +
    370template <typename U, int N, int bits>
    +
    371inline void
    +
    +
    372dequantize(const device uint8_t* w, U scale, U bias, threadgroup U* w_local) {
    +
    373 static_assert(
    +
    374 bits == 2 || bits == 3 || bits == 4 || bits == 6 || bits == 8,
    +
    375 "Template undefined for bits not in {2, 3, 4, 6, 8}");
    +
    376
    +
    377 if (bits == 2) {
    +
    378 U s[4] = {
    +
    379 scale,
    +
    380 scale / static_cast<U>(4.0f),
    +
    381 scale / static_cast<U>(16.0f),
    +
    382 scale / static_cast<U>(64.0f)};
    +
    383 for (int i = 0; i < (N / 4); i++) {
    +
    384 w_local[4 * i] = s[0] * (w[i] & 0x03) + bias;
    +
    385 w_local[4 * i + 1] = s[1] * (w[i] & 0x0c) + bias;
    +
    386 w_local[4 * i + 2] = s[2] * (w[i] & 0x30) + bias;
    +
    387 w_local[4 * i + 3] = s[3] * (w[i] & 0xc0) + bias;
    +
    388 }
    +
    389 }
    +
    390
    +
    391 else if (bits == 3) {
    +
    392 for (int i = 0; i < (N / 8); i++) {
    +
    393 w_local += 8 * i;
    +
    394 w += 3 * i;
    395
    -
    396 thread U x_thread[values_per_thread];
    -
    397 thread U result[results_per_quadgroup] = {0};
    -
    398
    -
    399 // Adjust positions
    -
    400 const int in_vec_size_w = in_vec_size / pack_factor;
    -
    401 const int in_vec_size_g = in_vec_size / group_size;
    -
    402 const int out_row = tid.x * quads_per_simd * results_per_quadgroup + quad_gid;
    -
    403
    -
    404 w += out_row * in_vec_size_w + quad_lid * packs_per_thread;
    -
    405 scales += out_row * in_vec_size_g + quad_lid / scale_step_per_thread;
    -
    406 biases += out_row * in_vec_size_g + quad_lid / scale_step_per_thread;
    -
    407 x += tid.y * in_vec_size + quad_lid * values_per_thread;
    -
    408 y += tid.y * out_vec_size + out_row;
    -
    409
    -
    410 U sum = load_vector<T, U, values_per_thread, bits>(x, x_thread);
    -
    411
    -
    412 for (int row = 0; row < results_per_quadgroup; row++) {
    -
    413 const device uint8_t* wl =
    -
    414 (const device uint8_t*)(w + row * in_vec_size_w * quads_per_simd);
    -
    415 const device T* sl = scales + row * in_vec_size_g * quads_per_simd;
    -
    416 const device T* bl = biases + row * in_vec_size_g * quads_per_simd;
    -
    417
    -
    418 U s = sl[0];
    -
    419 U b = bl[0];
    -
    420 if (row * quads_per_simd + out_row < out_vec_size) {
    -
    421 result[row] += qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);
    -
    422 }
    -
    423 }
    -
    424
    -
    425 for (int row = 0; row < results_per_quadgroup; row++) {
    -
    426 result[row] = quad_sum(result[row]);
    -
    427 if (quad_lid == 0 && row * quads_per_simd + out_row < out_vec_size) {
    -
    428 y[row * quads_per_simd] = static_cast<T>(result[row]);
    -
    429 }
    -
    430 }
    -
    431}
    +
    396 w_local[0] = (w[0] & 0x7) * scale + bias;
    +
    397 w_local[1] = ((w[0] & 0x38) >> 3) * scale + bias;
    +
    398 w_local[2] = (((w[0] & 0xc0) >> 6) + ((w[1] & 0x1) << 2)) * scale + bias;
    +
    399 w_local[3] = ((w[1] & 0xe) >> 1) * scale + bias;
    +
    400 w_local[4] = ((w[1] & 0x70) >> 4) * scale + bias;
    +
    401 w_local[5] = (((w[1] & 0x80) >> 7) + ((w[2] & 0x3) << 1)) * scale + bias;
    +
    402 w_local[6] = ((w[2] & 0x1c) >> 2) * scale + bias;
    +
    403 w_local[7] = ((w[2] & 0xe0) >> 5) * scale + bias;
    +
    404 }
    +
    405 }
    +
    406
    +
    407 else if (bits == 4) {
    +
    408 U s[2] = {scale, scale / static_cast<U>(16.0f)};
    +
    409 for (int i = 0; i < (N / 2); i++) {
    +
    410 w_local[2 * i] = s[0] * (w[i] & 0x0f) + bias;
    +
    411 w_local[2 * i + 1] = s[1] * (w[i] & 0xf0) + bias;
    +
    412 }
    +
    413 }
    +
    414
    +
    415 else if (bits == 6) {
    +
    416 for (int i = 0; i < (N / 4); i++) {
    +
    417 w_local += 4 * i;
    +
    418 w += 3 * i;
    +
    419
    +
    420 w_local[0] = (w[0] & 0x3f) * scale + bias;
    +
    421 w_local[1] = (((w[0] >> 6) & 0x03) + ((w[1] & 0x0f) << 2)) * scale + bias;
    +
    422 w_local[2] = (((w[1] >> 4) & 0x0f) + ((w[2] & 0x03) << 4)) * scale + bias;
    +
    423 w_local[3] = ((w[2] >> 2) & 0x3f) * scale + bias;
    +
    424 }
    +
    425 }
    +
    426
    +
    427 else if (bits == 8) {
    +
    428 for (int i = 0; i < N; i++) {
    +
    429 w_local[i] = scale * w[i] + bias;
    +
    430 }
    +
    431 }
    +
    432}
    -
    432
    -
    433template <typename T, int group_size, int bits>
    -
    -
    434METAL_FUNC void qmv_fast_impl(
    -
    435 const device uint32_t* w,
    -
    436 const device T* scales,
    -
    437 const device T* biases,
    -
    438 const device T* x,
    -
    439 device T* y,
    -
    440 const constant int& in_vec_size,
    -
    441 const constant int& out_vec_size,
    -
    442 uint3 tid [[threadgroup_position_in_grid]],
    -
    443 uint simd_gid [[simdgroup_index_in_threadgroup]],
    -
    444 uint simd_lid [[thread_index_in_simdgroup]]) {
    -
    445 constexpr int packs_per_thread = bits > 2 ? 2 : 1;
    -
    446 constexpr int num_simdgroups = 2;
    -
    447 constexpr int results_per_simdgroup = 4;
    -
    448 constexpr int pack_factor = 32 / bits;
    -
    449 constexpr int values_per_thread = pack_factor * packs_per_thread;
    -
    450 constexpr int block_size = values_per_thread * SIMD_SIZE;
    -
    451 constexpr int scale_step_per_thread = group_size / values_per_thread;
    -
    452
    -
    453 typedef float U;
    -
    454
    -
    455 thread U x_thread[values_per_thread];
    -
    456 thread U result[results_per_simdgroup] = {0};
    -
    457
    -
    458 // Adjust positions
    -
    459 const int in_vec_size_w = in_vec_size / pack_factor;
    -
    460 const int in_vec_size_g = in_vec_size / group_size;
    -
    461 const int out_row = tid.x * (num_simdgroups * results_per_simdgroup) +
    -
    462 simd_gid * results_per_simdgroup;
    -
    463 w += out_row * in_vec_size_w + simd_lid * packs_per_thread;
    -
    464 scales += out_row * in_vec_size_g + simd_lid / scale_step_per_thread;
    -
    465 biases += out_row * in_vec_size_g + simd_lid / scale_step_per_thread;
    -
    466 x += tid.y * in_vec_size + simd_lid * values_per_thread;
    -
    467 y += tid.y * out_vec_size + out_row;
    -
    468
    -
    469 for (int k = 0; k < in_vec_size; k += block_size) {
    -
    470 U sum = load_vector<T, U, values_per_thread, bits>(x, x_thread);
    -
    471
    -
    472 for (int row = 0; row < results_per_simdgroup; row++) {
    -
    473 const device uint8_t* wl =
    -
    474 (const device uint8_t*)(w + row * in_vec_size_w);
    -
    475 const device T* sl = scales + row * in_vec_size_g;
    -
    476 const device T* bl = biases + row * in_vec_size_g;
    -
    477
    -
    478 U s = sl[0];
    -
    479 U b = bl[0];
    -
    480 result[row] += qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);
    -
    481 }
    -
    482
    -
    483 w += block_size / pack_factor;
    -
    484 scales += block_size / group_size;
    -
    485 biases += block_size / group_size;
    -
    486 x += block_size;
    -
    487 }
    -
    488
    -
    489 for (int row = 0; row < results_per_simdgroup; row++) {
    -
    490 result[row] = simd_sum(result[row]);
    -
    491 if (simd_lid == 0) {
    -
    492 y[row] = static_cast<T>(result[row]);
    -
    493 }
    -
    494 }
    -
    495}
    +
    433
    +
    434template <
    +
    435 typename T,
    +
    436 short BROWS,
    +
    437 short BCOLS,
    +
    438 short dst_ld,
    +
    439 short reduction_dim,
    +
    440 short tgp_size,
    +
    441 short group_size,
    +
    442 short bits>
    +
    +
    443struct QuantizedBlockLoader {
    +
    444 static_assert(
    +
    445 BCOLS <= group_size,
    +
    446 "The group size should be larger than the columns");
    +
    447 static_assert(
    +
    448 group_size % BCOLS == 0,
    +
    449 "The group size should be divisible by the columns");
    +
    450 static_assert(
    +
    451 bits == 2 || bits == 3 || bits == 4 || bits == 6 || bits == 8,
    +
    452 "Template undefined for bits not in {2, 3, 4, 6, 8}");
    +
    453
    +
    454 MLX_MTL_CONST short pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 8 / bits;
    +
    455 MLX_MTL_CONST short bytes_per_pack = (bits == 3 || bits == 6) ? 3 : 1;
    +
    456 MLX_MTL_CONST short BCOLS_PACKED = BCOLS / pack_factor;
    +
    457 MLX_MTL_CONST short n_reads =
    +
    458 (BCOLS_PACKED * BROWS < tgp_size) ? 1 : (BCOLS_PACKED * BROWS) / tgp_size;
    +
    459 MLX_MTL_CONST short group_steps = group_size / BCOLS;
    +
    460
    +
    461 const int src_ld;
    +
    462 const int tile_stride;
    +
    463 short group_step_cnt;
    +
    464 const int group_stride;
    +
    465
    +
    466 const short thread_idx;
    +
    467 const short bi;
    +
    468 const short bj;
    +
    469
    +
    470 threadgroup T* dst;
    +
    471 const device uint8_t* src;
    +
    472 const device T* scales;
    +
    473 const device T* biases;
    +
    474
    +
    +
    475 QuantizedBlockLoader(
    +
    476 const device uint8_t* src_,
    +
    477 const device T* scales_,
    +
    478 const device T* biases_,
    +
    479 const int src_ld_,
    +
    480 threadgroup T* dst_,
    +
    481 ushort simd_group_id [[simdgroup_index_in_threadgroup]],
    +
    482 ushort simd_lane_id [[thread_index_in_simdgroup]])
    +
    483 : src_ld(src_ld_),
    +
    484 tile_stride(
    +
    485 reduction_dim ? BCOLS_PACKED * bytes_per_pack
    +
    486 : BROWS * src_ld * bytes_per_pack / pack_factor),
    +
    487 group_step_cnt(0),
    +
    488 group_stride(BROWS * src_ld / group_size),
    +
    489 thread_idx(simd_group_id * 32 + simd_lane_id),
    +
    490 bi(n_reads * thread_idx / BCOLS_PACKED),
    +
    491 bj((n_reads * thread_idx) % BCOLS_PACKED),
    +
    492 dst(dst_ + bi * dst_ld + bj * pack_factor),
    +
    493 src(src_ + bi * src_ld * bytes_per_pack / pack_factor +
    +
    494 bj * bytes_per_pack),
    +
    495 scales(scales_ + bi * src_ld / group_size),
    +
    496 biases(biases_ + bi * src_ld / group_size) {}
    -
    496
    -
    497template <typename T, int group_size, int bits>
    +
    497
    -
    498METAL_FUNC void qmv_impl(
    -
    499 const device uint32_t* w,
    -
    500 const device T* scales,
    -
    501 const device T* biases,
    -
    502 const device T* x,
    -
    503 device T* y,
    -
    504 const constant int& in_vec_size,
    -
    505 const constant int& out_vec_size,
    -
    506 uint3 tid [[threadgroup_position_in_grid]],
    -
    507 uint simd_gid [[simdgroup_index_in_threadgroup]],
    -
    508 uint simd_lid [[thread_index_in_simdgroup]]) {
    -
    509 constexpr int num_simdgroups = 2;
    -
    510 constexpr int results_per_simdgroup = 4;
    -
    511 constexpr int packs_per_thread = 1;
    -
    512 constexpr int pack_factor = 32 / bits;
    -
    513 constexpr int values_per_thread = pack_factor * packs_per_thread;
    -
    514 constexpr int block_size = values_per_thread * SIMD_SIZE;
    -
    515 constexpr int scale_step_per_thread = group_size / values_per_thread;
    -
    516
    -
    517 typedef float U;
    -
    518
    -
    519 thread U x_thread[values_per_thread];
    -
    520 thread U result[results_per_simdgroup] = {0};
    -
    521
    -
    522 // Adjust positions
    -
    523 const int in_vec_size_w = in_vec_size / pack_factor;
    -
    524 const int in_vec_size_g = in_vec_size / group_size;
    -
    525 const int out_row = tid.x * (num_simdgroups * results_per_simdgroup) +
    -
    526 simd_gid * results_per_simdgroup;
    -
    527 const int used_out_row = min(out_vec_size - results_per_simdgroup, out_row);
    -
    528
    -
    529 if (out_row >= out_vec_size) {
    -
    530 return;
    -
    531 }
    -
    532
    -
    533 // In this case we need to properly guard all our reads because there isn't
    -
    534 // even 1 tile in the matrix
    -
    535 if (out_vec_size < (num_simdgroups * results_per_simdgroup)) {
    -
    536 w += out_row * in_vec_size_w + simd_lid * packs_per_thread;
    -
    537 scales += out_row * in_vec_size_g + simd_lid / scale_step_per_thread;
    -
    538 biases += out_row * in_vec_size_g + simd_lid / scale_step_per_thread;
    -
    539 x += tid.y * in_vec_size + simd_lid * values_per_thread;
    -
    540 y += tid.y * out_vec_size + out_row;
    -
    541
    -
    542 int k = 0;
    -
    543 for (; k < in_vec_size - block_size; k += block_size) {
    -
    544 U sum = load_vector<T, U, values_per_thread, bits>(x, x_thread);
    -
    545
    -
    546 for (int row = 0; out_row + row < out_vec_size; row++) {
    -
    547 const device uint8_t* wl =
    -
    548 (const device uint8_t*)(w + row * in_vec_size_w);
    -
    549 const device T* sl = scales + row * in_vec_size_g;
    -
    550 const device T* bl = biases + row * in_vec_size_g;
    -
    551
    -
    552 U s = sl[0];
    -
    553 U b = bl[0];
    -
    554 result[row] +=
    -
    555 qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);
    -
    556 }
    -
    557
    -
    558 w += block_size / pack_factor;
    -
    559 scales += block_size / group_size;
    -
    560 biases += block_size / group_size;
    -
    561 x += block_size;
    -
    562 }
    -
    563 const int remaining = clamp(
    -
    564 static_cast<int>(in_vec_size - k - simd_lid * values_per_thread),
    -
    565 0,
    -
    566 values_per_thread);
    -
    567 U sum =
    -
    568 load_vector_safe<T, U, values_per_thread, bits>(x, x_thread, remaining);
    -
    569
    -
    570 for (int row = 0; out_row + row < out_vec_size; row++) {
    -
    571 const device uint8_t* wl =
    -
    572 (const device uint8_t*)(w + row * in_vec_size_w);
    -
    573 const device T* sl = scales + row * in_vec_size_g;
    -
    574 const device T* bl = biases + row * in_vec_size_g;
    -
    575
    -
    576 U s = sl[0];
    -
    577 U b = bl[0];
    -
    578 result[row] += qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);
    -
    579 }
    +
    498 void load_unsafe() const {
    +
    499 if (BCOLS_PACKED * BROWS < tgp_size && bi >= BROWS) {
    +
    500 return;
    +
    501 }
    +
    502
    +
    503 T scale = *scales;
    +
    504 T bias = *biases;
    +
    505 for (int i = 0; i < n_reads; i++) {
    +
    506 dequantize<T, pack_factor, bits>(
    +
    507 src + i * bytes_per_pack, scale, bias, dst + i * pack_factor);
    +
    508 }
    +
    509 }
    +
    +
    510
    +
    +
    511 void load_safe(short2 src_tile_dim) const {
    +
    512 if (BCOLS_PACKED * BROWS < tgp_size && bi >= BROWS) {
    +
    513 return;
    +
    514 }
    +
    515
    +
    516 if (reduction_dim == 1 && bi >= src_tile_dim.y) {
    +
    517 for (int i = 0; i < n_reads * pack_factor; i++) {
    +
    518 dst[i] = T(0);
    +
    519 }
    +
    520 return;
    +
    521 }
    +
    522
    +
    523 if (reduction_dim == 0 && bi >= src_tile_dim.x) {
    +
    524 for (int i = 0; i < n_reads * pack_factor; i++) {
    +
    525 dst[i] = T(0);
    +
    526 }
    +
    527 return;
    +
    528 }
    +
    529
    +
    530 T scale = *scales;
    +
    531 T bias = *biases;
    +
    532 for (int i = 0; i < n_reads; i++) {
    +
    533 dequantize<T, pack_factor, bits>(
    +
    534 (device uint8_t*)(src + i * bytes_per_pack),
    +
    535 scale,
    +
    536 bias,
    +
    537 dst + i * pack_factor);
    +
    538 }
    +
    539 }
    +
    +
    540
    +
    +
    541 void next() {
    +
    542 src += tile_stride;
    +
    543 if (reduction_dim == 1) {
    +
    544 if (group_steps > 1) {
    +
    545 group_step_cnt++;
    +
    546 if (group_step_cnt == group_steps) {
    +
    547 group_step_cnt = 0;
    +
    548 scales++;
    +
    549 biases++;
    +
    550 }
    +
    551 } else {
    +
    552 scales++;
    +
    553 biases++;
    +
    554 }
    +
    555 } else {
    +
    556 scales += group_stride;
    +
    557 biases += group_stride;
    +
    558 }
    +
    559 }
    +
    +
    560};
    +
    +
    561
    +
    562template <typename T, int group_size, int bits, int D>
    +
    +
    563METAL_FUNC void qmv_quad_impl(
    +
    564 const device uint32_t* w,
    +
    565 const device T* scales,
    +
    566 const device T* biases,
    +
    567 const device T* x,
    +
    568 device T* y,
    +
    569 constant int& in_vec_size,
    +
    570 const constant int& out_vec_size,
    +
    571 uint3 tid [[threadgroup_position_in_grid]],
    +
    572 uint quad_gid [[quadgroup_index_in_threadgroup]],
    +
    573 uint quad_lid [[thread_index_in_quadgroup]]) {
    +
    574 constexpr int quads_per_simd = SIMD_SIZE / QUAD_SIZE;
    +
    575 constexpr int pack_factor = 32 / bits;
    +
    576 constexpr int values_per_thread = D / QUAD_SIZE;
    +
    577 constexpr int packs_per_thread = values_per_thread / pack_factor;
    +
    578 constexpr int scale_step_per_thread = group_size / values_per_thread;
    +
    579 constexpr int results_per_quadgroup = 8;
    580
    -
    581 for (int row = 0; out_row + row < out_vec_size; row++) {
    -
    582 result[row] = simd_sum(result[row]);
    -
    583 if (simd_lid == 0) {
    -
    584 y[row] = static_cast<T>(result[row]);
    -
    585 }
    -
    586 }
    -
    587 }
    -
    588
    -
    589 // In this case the last tile is moved back to redo some output values
    -
    590 else {
    -
    591 w += used_out_row * in_vec_size_w + simd_lid * packs_per_thread;
    -
    592 scales += used_out_row * in_vec_size_g + simd_lid / scale_step_per_thread;
    -
    593 biases += used_out_row * in_vec_size_g + simd_lid / scale_step_per_thread;
    -
    594 x += tid.y * in_vec_size + simd_lid * values_per_thread;
    -
    595 y += tid.y * out_vec_size + used_out_row;
    +
    581 typedef float U;
    +
    582
    +
    583 thread U x_thread[values_per_thread];
    +
    584 thread U result[results_per_quadgroup] = {0};
    +
    585
    +
    586 // Adjust positions
    +
    587 const int in_vec_size_w = in_vec_size / pack_factor;
    +
    588 const int in_vec_size_g = in_vec_size / group_size;
    +
    589 const int out_row = tid.x * quads_per_simd * results_per_quadgroup + quad_gid;
    +
    590
    +
    591 w += out_row * in_vec_size_w + quad_lid * packs_per_thread;
    +
    592 scales += out_row * in_vec_size_g + quad_lid / scale_step_per_thread;
    +
    593 biases += out_row * in_vec_size_g + quad_lid / scale_step_per_thread;
    +
    594 x += tid.y * in_vec_size + quad_lid * values_per_thread;
    +
    595 y += tid.y * out_vec_size + out_row;
    596
    -
    597 int k = 0;
    -
    598 for (; k < in_vec_size - block_size; k += block_size) {
    -
    599 U sum = load_vector<T, U, values_per_thread, bits>(x, x_thread);
    -
    600
    -
    601 for (int row = 0; row < results_per_simdgroup; row++) {
    -
    602 const device uint8_t* wl =
    -
    603 (const device uint8_t*)(w + row * in_vec_size_w);
    -
    604 const device T* sl = scales + row * in_vec_size_g;
    -
    605 const device T* bl = biases + row * in_vec_size_g;
    -
    606
    -
    607 U s = sl[0];
    -
    608 U b = bl[0];
    -
    609 result[row] +=
    -
    610 qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);
    -
    611 }
    -
    612
    -
    613 w += block_size / pack_factor;
    -
    614 scales += block_size / group_size;
    -
    615 biases += block_size / group_size;
    -
    616 x += block_size;
    -
    617 }
    -
    618 const int remaining = clamp(
    -
    619 static_cast<int>(in_vec_size - k - simd_lid * values_per_thread),
    -
    620 0,
    -
    621 values_per_thread);
    -
    622 U sum =
    -
    623 load_vector_safe<T, U, values_per_thread, bits>(x, x_thread, remaining);
    -
    624
    -
    625 for (int row = 0; row < results_per_simdgroup; row++) {
    -
    626 const device uint8_t* wl =
    -
    627 (const device uint8_t*)(w + row * in_vec_size_w);
    -
    628 const device T* sl = scales + row * in_vec_size_g;
    -
    629 const device T* bl = biases + row * in_vec_size_g;
    -
    630
    -
    631 U s = sl[0];
    -
    632 U b = bl[0];
    -
    633 result[row] += qdot_safe<U, values_per_thread, bits>(
    -
    634 wl, x_thread, s, b, sum, remaining);
    -
    635 }
    -
    636
    -
    637 for (int row = 0; row < results_per_simdgroup; row++) {
    -
    638 result[row] = simd_sum(result[row]);
    -
    639 if (simd_lid == 0) {
    -
    640 y[row] = static_cast<T>(result[row]);
    -
    641 }
    -
    642 }
    -
    643 }
    -
    644}
    +
    597 U sum = load_vector<T, U, values_per_thread, bits>(x, x_thread);
    +
    598
    +
    599 for (int row = 0; row < results_per_quadgroup; row++) {
    +
    600 auto wl = (const device uint8_t*)(w + row * in_vec_size_w * quads_per_simd);
    +
    601 const device T* sl = scales + row * in_vec_size_g * quads_per_simd;
    +
    602 const device T* bl = biases + row * in_vec_size_g * quads_per_simd;
    +
    603
    +
    604 U s = sl[0];
    +
    605 U b = bl[0];
    +
    606 if (row * quads_per_simd + out_row < out_vec_size) {
    +
    607 result[row] += qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);
    +
    608 }
    +
    609 }
    +
    610
    +
    611 for (int row = 0; row < results_per_quadgroup; row++) {
    +
    612 result[row] = quad_sum(result[row]);
    +
    613 if (quad_lid == 0 && row * quads_per_simd + out_row < out_vec_size) {
    +
    614 y[row * quads_per_simd] = static_cast<T>(result[row]);
    +
    615 }
    +
    616 }
    +
    617}
    -
    645
    -
    646template <typename T, const int group_size, const int bits>
    -
    -
    647METAL_FUNC void qvm_impl(
    -
    648 const device uint32_t* w,
    -
    649 const device T* scales,
    -
    650 const device T* biases,
    -
    651 const device T* x,
    -
    652 device T* y,
    -
    653 const int in_vec_size,
    -
    654 const int out_vec_size,
    -
    655 uint3 tid [[threadgroup_position_in_grid]],
    -
    656 uint simd_gid [[simdgroup_index_in_threadgroup]],
    -
    657 uint simd_lid [[thread_index_in_simdgroup]]) {
    -
    658 constexpr int num_simdgroups = 2;
    -
    659 constexpr int pack_factor = 32 / bits;
    -
    660 constexpr int tn = 32 / pack_factor;
    -
    661 constexpr int blocksize = SIMD_SIZE;
    +
    618
    +
    619template <typename T, int group_size, int bits>
    +
    +
    620METAL_FUNC void qmv_fast_impl(
    +
    621 const device uint32_t* w,
    +
    622 const device T* scales,
    +
    623 const device T* biases,
    +
    624 const device T* x,
    +
    625 device T* y,
    +
    626 const constant int& in_vec_size,
    +
    627 const constant int& out_vec_size,
    +
    628 uint3 tid [[threadgroup_position_in_grid]],
    +
    629 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    630 uint simd_lid [[thread_index_in_simdgroup]]) {
    +
    631 constexpr int power_of_2_bits = (bits & (bits - 1)) == 0;
    +
    632 constexpr int packs_per_thread = bits == 2 ? 1 : 2;
    +
    633 constexpr int num_simdgroups = 2;
    +
    634 constexpr int results_per_simdgroup = 4;
    +
    635 constexpr int pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 32 / bits;
    +
    636 constexpr int bytes_per_pack = power_of_2_bits ? 4 : 3;
    +
    637 constexpr int values_per_thread = pack_factor * packs_per_thread;
    +
    638 constexpr int block_size = values_per_thread * SIMD_SIZE;
    +
    639 constexpr int scale_step_per_thread = group_size / values_per_thread;
    +
    640
    +
    641 const device uint8_t* ws = (const device uint8_t*)w;
    +
    642
    +
    643 typedef float U;
    +
    644
    +
    645 thread U x_thread[values_per_thread];
    +
    646 thread U result[results_per_simdgroup] = {0};
    +
    647
    +
    648 // Adjust positions
    +
    649 const int in_vec_size_w = in_vec_size * bytes_per_pack / pack_factor;
    +
    650 const int in_vec_size_g = in_vec_size / group_size;
    +
    651 const int out_row = tid.x * (num_simdgroups * results_per_simdgroup) +
    +
    652 simd_gid * results_per_simdgroup;
    +
    653
    +
    654 ws += out_row * in_vec_size_w + simd_lid * packs_per_thread * bytes_per_pack;
    +
    655 scales += out_row * in_vec_size_g + simd_lid / scale_step_per_thread;
    +
    656 biases += out_row * in_vec_size_g + simd_lid / scale_step_per_thread;
    +
    657 x += tid.y * in_vec_size + simd_lid * values_per_thread;
    +
    658 y += tid.y * out_vec_size + out_row;
    +
    659
    +
    660 for (int k = 0; k < in_vec_size; k += block_size) {
    +
    661 U sum = load_vector<T, U, values_per_thread, bits>(x, x_thread);
    662
    -
    663 typedef float U;
    -
    664 typedef struct {
    -
    665 uint32_t wi[tn];
    -
    666 } vec_w;
    +
    663 for (int row = 0; row < results_per_simdgroup; row++) {
    +
    664 auto wl = (const device uint8_t*)(ws + row * in_vec_size_w);
    +
    665 const device T* sl = scales + row * in_vec_size_g;
    +
    666 const device T* bl = biases + row * in_vec_size_g;
    667
    -
    668 thread vec_w w_local;
    -
    669 thread U result[tn * pack_factor] = {0};
    -
    670 thread U scale = 1;
    -
    671 thread U bias = 0;
    -
    672 thread U x_local = 0;
    -
    673
    -
    674 // Adjust positions
    -
    675 const int out_vec_size_w = out_vec_size / pack_factor;
    -
    676 const int out_vec_size_g = out_vec_size / group_size;
    -
    677 int out_col =
    -
    678 tid.x * (num_simdgroups * pack_factor * tn) + simd_gid * pack_factor * tn;
    -
    679 w += out_col / pack_factor + simd_lid * out_vec_size_w;
    -
    680 scales += out_col / group_size + simd_lid * out_vec_size_g;
    -
    681 biases += out_col / group_size + simd_lid * out_vec_size_g;
    -
    682 x += tid.y * in_vec_size + simd_lid;
    -
    683 y += tid.y * out_vec_size + out_col;
    -
    684
    -
    685 if (out_col >= out_vec_size) {
    -
    686 return;
    -
    687 }
    -
    688
    -
    689 // Loop over in_vec in blocks of blocksize
    -
    690 int remaining = in_vec_size % blocksize;
    -
    691 if (remaining == 0) {
    -
    692 for (int i = 0; i < in_vec_size; i += blocksize) {
    -
    693 x_local = *x;
    -
    694 scale = *scales;
    -
    695 bias = *biases;
    -
    696 w_local = *((device vec_w*)w);
    -
    697
    -
    698 qouter<U, tn * pack_factor, bits>(
    -
    699 (thread uint8_t*)&w_local, x_local, scale, bias, result);
    -
    700
    -
    701 x += blocksize;
    -
    702 scales += blocksize * out_vec_size_g;
    -
    703 biases += blocksize * out_vec_size_g;
    -
    704 w += blocksize * out_vec_size_w;
    -
    705 }
    -
    706 } else {
    -
    707 for (int i = blocksize; i < in_vec_size; i += blocksize) {
    -
    708 x_local = *x;
    -
    709 scale = *scales;
    -
    710 bias = *biases;
    -
    711 w_local = *((device vec_w*)w);
    -
    712
    -
    713 qouter<U, tn * pack_factor, bits>(
    -
    714 (thread uint8_t*)&w_local, x_local, scale, bias, result);
    -
    715
    -
    716 x += blocksize;
    -
    717 scales += blocksize * out_vec_size_g;
    -
    718 biases += blocksize * out_vec_size_g;
    -
    719 w += blocksize * out_vec_size_w;
    -
    720 }
    -
    721 if (static_cast<int>(simd_lid) < remaining) {
    -
    722 x_local = *x;
    -
    723 scale = *scales;
    -
    724 bias = *biases;
    -
    725 w_local = *((device vec_w*)w);
    -
    726 } else {
    -
    727 x_local = 0;
    -
    728 scale = 0;
    -
    729 bias = 0;
    -
    730 }
    -
    731 qouter<U, tn * pack_factor, bits>(
    -
    732 (thread uint8_t*)&w_local, x_local, scale, bias, result);
    -
    733 }
    -
    734
    -
    735// Accumulate in the simdgroup
    -
    736#pragma clang loop unroll(full)
    -
    737 for (int k = 0; k < tn * pack_factor; k++) {
    -
    738 result[k] = simd_sum(result[k]);
    -
    739 }
    -
    740
    -
    741 // Store the result
    -
    742 if (simd_lid == 0) {
    -
    743#pragma clang loop unroll(full)
    -
    744 for (int k = 0; k < tn * pack_factor; k++) {
    -
    745 y[k] = static_cast<T>(result[k]);
    -
    746 }
    -
    747 }
    -
    748}
    +
    668 U s = sl[0];
    +
    669 U b = bl[0];
    +
    670 result[row] += qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);
    +
    671 }
    +
    672
    +
    673 ws += block_size * bytes_per_pack / pack_factor;
    +
    674 scales += block_size / group_size;
    +
    675 biases += block_size / group_size;
    +
    676 x += block_size;
    +
    677 }
    +
    678
    +
    679 for (int row = 0; row < results_per_simdgroup; row++) {
    +
    680 result[row] = simd_sum(result[row]);
    +
    681 if (simd_lid == 0) {
    +
    682 y[row] = static_cast<T>(result[row]);
    +
    683 }
    +
    684 }
    +
    685}
    -
    749
    -
    750template <
    -
    751 typename T,
    -
    752 const int group_size,
    -
    753 const int bits,
    -
    754 const bool aligned_N,
    -
    755 const int BM = 32,
    -
    756 const int BK = 32,
    -
    757 const int BN = 32>
    -
    -
    758METAL_FUNC void qmm_t_impl(
    -
    759 const device uint32_t* w,
    -
    760 const device T* scales,
    -
    761 const device T* biases,
    -
    762 const device T* x,
    -
    763 device T* y,
    -
    764 threadgroup T* Xs,
    -
    765 threadgroup T* Ws,
    -
    766 const constant int& K,
    -
    767 const constant int& N,
    -
    768 const constant int& M,
    -
    769 uint3 tid [[threadgroup_position_in_grid]],
    -
    770 uint lid [[thread_index_in_threadgroup]],
    -
    771 uint simd_gid [[simdgroup_index_in_threadgroup]],
    -
    772 uint simd_lid [[thread_index_in_simdgroup]]) {
    -
    773 static_assert(BK >= SIMD_SIZE, "BK should be larger than SIMD_SIZE");
    -
    774 static_assert(BK % SIMD_SIZE == 0, "BK should be divisible by SIMD_SIZE");
    -
    775
    -
    776 (void)lid;
    -
    777
    -
    778 constexpr int WM = 2;
    -
    779 constexpr int WN = 2;
    -
    780 constexpr int pack_factor = 32 / bits;
    -
    781 constexpr int BK_padded = (BK + 16 / sizeof(T));
    -
    782
    -
    783 // Instantiate the appropriate BlockMMA and Loader
    -
    784 using mma_t = mlx::steel::
    -
    785 BlockMMA<T, T, BM, BN, BK, WM, WN, false, true, BK_padded, BK_padded>;
    -
    786 using loader_x_t =
    -
    787 mlx::steel::BlockLoader<T, BM, BK, BK_padded, 1, WM * WN * SIMD_SIZE>;
    -
    788 using loader_w_t = QuantizedBlockLoader<
    -
    789 T,
    -
    790 BN,
    -
    791 BK,
    -
    792 BK_padded,
    -
    793 1,
    -
    794 WM * WN * SIMD_SIZE,
    -
    795 group_size,
    -
    796 bits>;
    +
    686
    +
    687template <typename T, int group_size, int bits>
    +
    +
    688METAL_FUNC void qmv_impl(
    +
    689 const device uint32_t* w,
    +
    690 const device T* scales,
    +
    691 const device T* biases,
    +
    692 const device T* x,
    +
    693 device T* y,
    +
    694 const constant int& in_vec_size,
    +
    695 const constant int& out_vec_size,
    +
    696 uint3 tid [[threadgroup_position_in_grid]],
    +
    697 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    698 uint simd_lid [[thread_index_in_simdgroup]]) {
    +
    699 constexpr int power_of_2_bits = (bits & (bits - 1)) == 0;
    +
    700 constexpr int num_simdgroups = 2;
    +
    701 constexpr int results_per_simdgroup = 4;
    +
    702 constexpr int packs_per_thread = 1;
    +
    703 constexpr int pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 32 / bits;
    +
    704 constexpr int bytes_per_pack = power_of_2_bits ? 4 : 3;
    +
    705 constexpr int values_per_thread = pack_factor * packs_per_thread;
    +
    706 constexpr int block_size = values_per_thread * SIMD_SIZE;
    +
    707 constexpr int scale_step_per_thread = group_size / values_per_thread;
    +
    708
    +
    709 const device uint8_t* ws = (const device uint8_t*)w;
    +
    710
    +
    711 typedef float U;
    +
    712
    +
    713 thread U x_thread[values_per_thread];
    +
    714 thread U result[results_per_simdgroup] = {0};
    +
    715
    +
    716 // Adjust positions
    +
    717 const int in_vec_size_w = in_vec_size * bytes_per_pack / pack_factor;
    +
    718 const int in_vec_size_g = in_vec_size / group_size;
    +
    719 const int out_row = tid.x * (num_simdgroups * results_per_simdgroup) +
    +
    720 simd_gid * results_per_simdgroup;
    +
    721 const int used_out_row = min(out_vec_size - results_per_simdgroup, out_row);
    +
    722
    +
    723 if (out_row >= out_vec_size) {
    +
    724 return;
    +
    725 }
    +
    726
    +
    727 // In this case we need to properly guard all our reads because there isn't
    +
    728 // even 1 tile in the matrix
    +
    729 if (out_vec_size < (num_simdgroups * results_per_simdgroup)) {
    +
    730 ws +=
    +
    731 out_row * in_vec_size_w + simd_lid * packs_per_thread * bytes_per_pack;
    +
    732 scales += out_row * in_vec_size_g + simd_lid / scale_step_per_thread;
    +
    733 biases += out_row * in_vec_size_g + simd_lid / scale_step_per_thread;
    +
    734 x += tid.y * in_vec_size + simd_lid * values_per_thread;
    +
    735 y += tid.y * out_vec_size + out_row;
    +
    736
    +
    737 int k = 0;
    +
    738 for (; k < in_vec_size - block_size; k += block_size) {
    +
    739 U sum = load_vector<T, U, values_per_thread, bits>(x, x_thread);
    +
    740
    +
    741 for (int row = 0; out_row + row < out_vec_size; row++) {
    +
    742 auto wl = (const device uint8_t*)(ws + row * in_vec_size_w);
    +
    743 const device T* sl = scales + row * in_vec_size_g;
    +
    744 const device T* bl = biases + row * in_vec_size_g;
    +
    745
    +
    746 U s = sl[0];
    +
    747 U b = bl[0];
    +
    748 result[row] +=
    +
    749 qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);
    +
    750 }
    +
    751
    +
    752 ws += block_size * bytes_per_pack / pack_factor;
    +
    753 scales += block_size / group_size;
    +
    754 biases += block_size / group_size;
    +
    755 x += block_size;
    +
    756 }
    +
    757 const int remaining = clamp(
    +
    758 static_cast<int>(in_vec_size - k - simd_lid * values_per_thread),
    +
    759 0,
    +
    760 values_per_thread);
    +
    761 if (remaining > 0) {
    +
    762 U sum = load_vector_safe<T, U, values_per_thread, bits>(
    +
    763 x, x_thread, remaining);
    +
    764
    +
    765 for (int row = 0; out_row + row < out_vec_size; row++) {
    +
    766 auto wl = (const device uint8_t*)(ws + row * in_vec_size_w);
    +
    767 const device T* sl = scales + row * in_vec_size_g;
    +
    768 const device T* bl = biases + row * in_vec_size_g;
    +
    769
    +
    770 U s = sl[0];
    +
    771 U b = bl[0];
    +
    772 result[row] +=
    +
    773 qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);
    +
    774 }
    +
    775 }
    +
    776
    +
    777 for (int row = 0; out_row + row < out_vec_size; row++) {
    +
    778 result[row] = simd_sum(result[row]);
    +
    779 if (simd_lid == 0) {
    +
    780 y[row] = static_cast<T>(result[row]);
    +
    781 }
    +
    782 }
    +
    783 }
    +
    784
    +
    785 // In this case the last tile is moved back to redo some output values
    +
    786 else {
    +
    787 ws += used_out_row * in_vec_size_w +
    +
    788 simd_lid * packs_per_thread * bytes_per_pack;
    +
    789 scales += used_out_row * in_vec_size_g + simd_lid / scale_step_per_thread;
    +
    790 biases += used_out_row * in_vec_size_g + simd_lid / scale_step_per_thread;
    +
    791 x += tid.y * in_vec_size + simd_lid * values_per_thread;
    +
    792 y += tid.y * out_vec_size + used_out_row;
    +
    793
    +
    794 int k = 0;
    +
    795 for (; k < in_vec_size - block_size; k += block_size) {
    +
    796 U sum = load_vector<T, U, values_per_thread, bits>(x, x_thread);
    797
    -
    798 // Set the block
    -
    799 const int K_w = K / pack_factor;
    -
    800 const int K_g = K / group_size;
    -
    801 const int y_row = tid.y * BM;
    -
    802 const int y_col = tid.x * BN;
    -
    803
    -
    804 x += y_row * K;
    -
    805 w += y_col * K_w;
    -
    806 scales += y_col * K_g;
    -
    807 biases += y_col * K_g;
    -
    808 y += y_row * N + y_col;
    -
    809
    -
    810 // Make the x loader and mma operation
    -
    811 const short num_els = min(BM, M - y_row);
    -
    812 const short num_outs = min(BN, N - y_col);
    -
    813 loader_x_t loader_x(x, K, Xs, simd_gid, simd_lid);
    -
    814 loader_w_t loader_w(w, scales, biases, K, Ws, simd_gid, simd_lid);
    -
    815 mma_t mma_op(simd_gid, simd_lid);
    -
    816
    -
    817 if (num_els < BM) {
    -
    818 if (!aligned_N && num_outs < BN) {
    -
    819 for (int k = 0; k < K; k += BK) {
    -
    820 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    821 loader_x.load_safe(short2(BK, num_els));
    -
    822 loader_w.load_safe(short2(BK, num_outs));
    -
    823 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    824 mma_op.mma(Xs, Ws);
    -
    825 loader_x.next();
    -
    826 loader_w.next();
    -
    827 }
    -
    828 } else {
    -
    829 for (int k = 0; k < K; k += BK) {
    -
    830 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    831 loader_x.load_safe(short2(BK, num_els));
    -
    832 loader_w.load_unsafe();
    -
    833 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    834 mma_op.mma(Xs, Ws);
    -
    835 loader_x.next();
    -
    836 loader_w.next();
    +
    798 for (int row = 0; row < results_per_simdgroup; row++) {
    +
    799 auto wl = (const device uint8_t*)(ws + row * in_vec_size_w);
    +
    800 const device T* sl = scales + row * in_vec_size_g;
    +
    801 const device T* bl = biases + row * in_vec_size_g;
    +
    802
    +
    803 U s = sl[0];
    +
    804 U b = bl[0];
    +
    805 result[row] +=
    +
    806 qdot<U, values_per_thread, bits>(wl, x_thread, s, b, sum);
    +
    807 }
    +
    808
    +
    809 ws += block_size * bytes_per_pack / pack_factor;
    +
    810 scales += block_size / group_size;
    +
    811 biases += block_size / group_size;
    +
    812 x += block_size;
    +
    813 }
    +
    814 const int remaining = clamp(
    +
    815 static_cast<int>(in_vec_size - k - simd_lid * values_per_thread),
    +
    816 0,
    +
    817 values_per_thread);
    +
    818 if (remaining > 0) {
    +
    819 U sum = load_vector_safe<T, U, values_per_thread, bits>(
    +
    820 x, x_thread, remaining);
    +
    821
    +
    822 for (int row = 0; row < results_per_simdgroup; row++) {
    +
    823 auto wl = (const device uint8_t*)(ws + row * in_vec_size_w);
    +
    824 const device T* sl = scales + row * in_vec_size_g;
    +
    825 const device T* bl = biases + row * in_vec_size_g;
    +
    826
    +
    827 U s = sl[0];
    +
    828 U b = bl[0];
    +
    829 result[row] += qdot_safe<U, values_per_thread, bits>(
    +
    830 wl, x_thread, s, b, sum, remaining);
    +
    831 }
    +
    832 }
    +
    833 for (int row = 0; row < results_per_simdgroup; row++) {
    +
    834 result[row] = simd_sum(result[row]);
    +
    835 if (simd_lid == 0) {
    +
    836 y[row] = static_cast<T>(result[row]);
    837 }
    838 }
    -
    839 } else {
    -
    840 if (!aligned_N && num_outs < BN) {
    -
    841 for (int k = 0; k < K; k += BK) {
    -
    842 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    843 loader_x.load_unsafe();
    -
    844 loader_w.load_safe(short2(BK, num_outs));
    -
    845 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    846 mma_op.mma(Xs, Ws);
    -
    847 loader_x.next();
    -
    848 loader_w.next();
    -
    849 }
    -
    850 } else {
    -
    851 for (int k = 0; k < K; k += BK) {
    -
    852 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    853 loader_x.load_unsafe();
    -
    854 loader_w.load_unsafe();
    -
    855 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    856 mma_op.mma(Xs, Ws);
    -
    857 loader_x.next();
    -
    858 loader_w.next();
    -
    859 }
    -
    860 }
    -
    861 }
    +
    839 }
    +
    840}
    +
    +
    841
    +
    842template <typename T, const int group_size, const int bits>
    +
    +
    843METAL_FUNC void qvm_impl(
    +
    844 const device uint32_t* w,
    +
    845 const device T* scales,
    +
    846 const device T* biases,
    +
    847 const device T* x,
    +
    848 device T* y,
    +
    849 const int in_vec_size,
    +
    850 const int out_vec_size,
    +
    851 uint3 tid [[threadgroup_position_in_grid]],
    +
    852 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    853 uint simd_lid [[thread_index_in_simdgroup]]) {
    +
    854 constexpr int power_of_2_bits = (bits & (bits - 1)) == 0;
    +
    855 constexpr int num_simdgroups = 2;
    +
    856 constexpr int pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 32 / bits;
    +
    857 constexpr int bytes_per_pack = power_of_2_bits ? 4 : 3;
    +
    858 constexpr int tn = 32 / pack_factor;
    +
    859 constexpr int block_size = SIMD_SIZE;
    +
    860
    +
    861 const device uint8_t* ws = (const device uint8_t*)w;
    862
    -
    863 // Store results to device memory
    -
    864 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    865 if (num_els < BM || num_outs < BN) {
    -
    866 mma_op.store_result_safe(y, N, short2(num_outs, num_els));
    -
    867 } else {
    -
    868 mma_op.store_result(y, N);
    -
    869 }
    -
    870}
    -
    -
    871
    -
    872template <
    -
    873 typename T,
    -
    874 const int group_size,
    -
    875 const int bits,
    -
    876 const int BM = 32,
    -
    877 const int BK = 32,
    -
    878 const int BN = 32>
    -
    -
    879METAL_FUNC void qmm_n_impl(
    -
    880 const device uint32_t* w,
    -
    881 const device T* scales,
    -
    882 const device T* biases,
    -
    883 const device T* x,
    -
    884 device T* y,
    -
    885 threadgroup T* Xs,
    -
    886 threadgroup T* Ws,
    -
    887 const constant int& K,
    -
    888 const constant int& N,
    -
    889 const constant int& M,
    -
    890 uint3 tid [[threadgroup_position_in_grid]],
    -
    891 uint lid [[thread_index_in_threadgroup]],
    -
    892 uint simd_gid [[simdgroup_index_in_threadgroup]],
    -
    893 uint simd_lid [[thread_index_in_simdgroup]]) {
    -
    894 static_assert(BK >= SIMD_SIZE, "BK should be larger than SIMD_SIZE");
    -
    895 static_assert(BK % SIMD_SIZE == 0, "BK should be divisible by SIMD_SIZE");
    -
    896
    -
    897 (void)lid;
    +
    863 typedef float U;
    +
    864 typedef struct {
    +
    865 uint8_t wi[tn * bytes_per_pack];
    +
    866 } vec_w;
    +
    867
    +
    868 thread vec_w w_local;
    +
    869 thread U result[tn * pack_factor] = {0};
    +
    870 thread U scale = 1;
    +
    871 thread U bias = 0;
    +
    872 thread U x_local = 0;
    +
    873
    +
    874 // Adjust positions
    +
    875 const int out_vec_size_w = out_vec_size * bytes_per_pack / pack_factor;
    +
    876 const int out_vec_size_g = out_vec_size / group_size;
    +
    877 int out_col = pack_factor * tn * (tid.x * num_simdgroups + simd_gid);
    +
    878 ws += out_col * bytes_per_pack / pack_factor + simd_lid * out_vec_size_w;
    +
    879 scales += out_col / group_size + simd_lid * out_vec_size_g;
    +
    880 biases += out_col / group_size + simd_lid * out_vec_size_g;
    +
    881 x += tid.y * in_vec_size + simd_lid;
    +
    882 y += tid.y * out_vec_size + out_col;
    +
    883
    +
    884 if (out_col >= out_vec_size) {
    +
    885 return;
    +
    886 }
    +
    887
    +
    888 // Loop over in_vec in blocks of block_size
    +
    889 int remaining = in_vec_size % block_size;
    +
    890 if (remaining == 0) {
    +
    891 for (int i = 0; i < in_vec_size; i += block_size) {
    +
    892 x_local = *x;
    +
    893 scale = *scales;
    +
    894 bias = *biases;
    +
    895 w_local = *((device vec_w*)ws);
    +
    896 qouter<U, tn * pack_factor, bits>(
    +
    897 (thread uint8_t*)&w_local, x_local, scale, bias, result);
    898
    -
    899 constexpr int WM = 2;
    -
    900 constexpr int WN = 2;
    -
    901 constexpr int pack_factor = 32 / bits;
    -
    902 constexpr int BK_padded = (BK + 16 / sizeof(T));
    -
    903 constexpr int BN_padded = (BN + 16 / sizeof(T));
    -
    904
    -
    905 // Instantiate the appropriate BlockMMA and Loader
    -
    906 using mma_t = mlx::steel::
    -
    907 BlockMMA<T, T, BM, BN, BK, WM, WN, false, false, BK_padded, BN_padded>;
    -
    908 using loader_x_t = mlx::steel::
    -
    909 BlockLoader<T, BM, BK, BK_padded, 1, WM * WN * SIMD_SIZE, 1, 4>;
    -
    910 using loader_w_t = QuantizedBlockLoader<
    -
    911 T,
    -
    912 BK,
    -
    913 BN,
    -
    914 BN_padded,
    -
    915 0,
    -
    916 WM * WN * SIMD_SIZE,
    -
    917 group_size,
    -
    918 bits>;
    -
    919
    -
    920 // Set the block
    -
    921 const int y_row = tid.y * BM;
    -
    922 const int y_col = tid.x * BN;
    -
    923 x += y_row * K;
    -
    924 w += y_col / pack_factor;
    -
    925 scales += y_col / group_size;
    -
    926 biases += y_col / group_size;
    -
    927 y += y_row * N + y_col;
    -
    928
    -
    929 // Make the x loader and mma operation
    -
    930 const short num_els = min(BM, M - y_row);
    -
    931 loader_x_t loader_x(x, K, Xs, simd_gid, simd_lid);
    -
    932 loader_w_t loader_w(w, scales, biases, N, Ws, simd_gid, simd_lid);
    -
    933 mma_t mma_op(simd_gid, simd_lid);
    -
    934
    -
    935 if (num_els < BM) {
    -
    936 if ((K % BK) != 0) {
    -
    937 const int k_blocks = K / BK;
    -
    938 for (int k = 0; k < k_blocks; k++) {
    -
    939 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    940 loader_x.load_safe(short2(BK, num_els));
    -
    941 loader_w.load_unsafe();
    -
    942 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    943 mma_op.mma(Xs, Ws);
    -
    944 loader_x.next();
    -
    945 loader_w.next();
    -
    946 }
    -
    947 const short num_k = K - k_blocks * BK;
    -
    948 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    949 loader_x.load_safe(short2(num_k, num_els));
    -
    950 loader_w.load_safe(short2(BN, num_k));
    -
    951 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    952 mma_op.mma(Xs, Ws);
    -
    953 } else {
    -
    954 for (int k = 0; k < K; k += BK) {
    -
    955 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    956 loader_x.load_safe(short2(BK, num_els));
    -
    957 loader_w.load_unsafe();
    -
    958 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    959 mma_op.mma(Xs, Ws);
    -
    960 loader_x.next();
    -
    961 loader_w.next();
    -
    962 }
    -
    963 }
    -
    964 } else {
    -
    965 if ((K % BK) != 0) {
    -
    966 const int k_blocks = K / BK;
    -
    967 for (int k = 0; k < k_blocks; k++) {
    -
    968 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    969 loader_x.load_unsafe();
    -
    970 loader_w.load_unsafe();
    -
    971 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    972 mma_op.mma(Xs, Ws);
    -
    973 loader_x.next();
    -
    974 loader_w.next();
    -
    975 }
    -
    976 const short num_k = K - k_blocks * BK;
    -
    977 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    978 loader_x.load_safe(short2(num_k, BM));
    -
    979 loader_w.load_safe(short2(BN, num_k));
    -
    980 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    981 mma_op.mma(Xs, Ws);
    -
    982 } else {
    -
    983 for (int k = 0; k < K; k += BK) {
    -
    984 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    985 loader_x.load_unsafe();
    -
    986 loader_w.load_unsafe();
    -
    987 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    988 mma_op.mma(Xs, Ws);
    -
    989 loader_x.next();
    -
    990 loader_w.next();
    -
    991 }
    -
    992 }
    -
    993 }
    -
    994
    -
    995 // Store results to device memory
    -
    996 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    997 if (num_els < BM) {
    -
    998 mma_op.store_result_safe(y, N, short2(BN, num_els));
    -
    999 } else {
    -
    1000 mma_op.store_result(y, N);
    -
    1001 }
    -
    1002}
    +
    899 x += block_size;
    +
    900 scales += block_size * out_vec_size_g;
    +
    901 biases += block_size * out_vec_size_g;
    +
    902 ws += block_size * out_vec_size_w;
    +
    903 }
    +
    904 } else {
    +
    905 for (int i = block_size; i < in_vec_size; i += block_size) {
    +
    906 x_local = *x;
    +
    907 scale = *scales;
    +
    908 bias = *biases;
    +
    909 w_local = *((device vec_w*)ws);
    +
    910
    +
    911 qouter<U, tn * pack_factor, bits>(
    +
    912 (thread uint8_t*)&w_local, x_local, scale, bias, result);
    +
    913
    +
    914 x += block_size;
    +
    915 scales += block_size * out_vec_size_g;
    +
    916 biases += block_size * out_vec_size_g;
    +
    917 ws += block_size * out_vec_size_w;
    +
    918 }
    +
    919 if (static_cast<int>(simd_lid) < remaining) {
    +
    920 x_local = *x;
    +
    921 scale = *scales;
    +
    922 bias = *biases;
    +
    923 w_local = *((device vec_w*)ws);
    +
    924 } else {
    +
    925 x_local = 0;
    +
    926 scale = 0;
    +
    927 bias = 0;
    +
    928 }
    +
    929 qouter<U, tn * pack_factor, bits>(
    +
    930 (thread uint8_t*)&w_local, x_local, scale, bias, result);
    +
    931 }
    +
    932
    +
    933// Accumulate in the simdgroup
    +
    934#pragma clang loop unroll(full)
    +
    935 for (int k = 0; k < tn * pack_factor; k++) {
    +
    936 result[k] = simd_sum(result[k]);
    +
    937 }
    +
    938
    +
    939 // Store the result
    +
    940 if (simd_lid == 0) {
    +
    941#pragma clang loop unroll(full)
    +
    942 for (int k = 0; k < tn * pack_factor; k++) {
    +
    943 y[k] = static_cast<T>(result[k]);
    +
    944 }
    +
    945 }
    +
    946}
    -
    1003
    -
    1004template <typename T>
    -
    -
    1005METAL_FUNC void adjust_matrix_offsets(
    -
    1006 const device T*& x,
    -
    1007 const device uint32_t*& w,
    -
    1008 const device T*& scales,
    -
    1009 const device T*& biases,
    -
    1010 device T*& y,
    -
    1011 int output_stride,
    -
    1012 const constant int& x_batch_ndims,
    -
    1013 const constant int* x_shape,
    -
    1014 const constant size_t* x_strides,
    -
    1015 const constant int& w_batch_ndims,
    -
    1016 const constant int* w_shape,
    -
    1017 const constant size_t* w_strides,
    -
    1018 const constant size_t* s_strides,
    -
    1019 const constant size_t* b_strides,
    -
    1020 uint3 tid [[threadgroup_position_in_grid]]) {
    -
    1021 // Set the input/output matrices
    -
    1022 uint32_t x_idx = tid.z;
    -
    1023 uint32_t w_idx = tid.z;
    -
    1024 if (x_batch_ndims == 1) {
    -
    1025 x += x_idx * x_strides[0];
    -
    1026 } else {
    -
    1027 x += elem_to_loc(x_idx, x_shape, x_strides, x_batch_ndims);
    -
    1028 }
    -
    1029 if (w_batch_ndims == 1) {
    -
    1030 w += w_idx * w_strides[0];
    -
    1031 scales += w_idx * s_strides[0];
    -
    1032 biases += w_idx * b_strides[0];
    -
    1033 } else {
    -
    1034 ulong3 idx = elem_to_loc_broadcast(
    -
    1035 w_idx, w_shape, w_strides, s_strides, b_strides, w_batch_ndims);
    -
    1036 w += idx.x;
    -
    1037 scales += idx.y;
    -
    1038 biases += idx.z;
    -
    1039 }
    -
    1040 y += tid.z * output_stride;
    -
    1041}
    +
    947
    +
    948template <
    +
    949 typename T,
    +
    950 const int group_size,
    +
    951 const int bits,
    +
    952 const bool aligned_N,
    +
    953 const int BM = 32,
    +
    954 const int BK = 32,
    +
    955 const int BN = 32>
    +
    +
    956METAL_FUNC void qmm_t_impl(
    +
    957 const device uint32_t* w,
    +
    958 const device T* scales,
    +
    959 const device T* biases,
    +
    960 const device T* x,
    +
    961 device T* y,
    +
    962 threadgroup T* Xs,
    +
    963 threadgroup T* Ws,
    +
    964 const constant int& K,
    +
    965 const constant int& N,
    +
    966 const constant int& M,
    +
    967 uint3 tid [[threadgroup_position_in_grid]],
    +
    968 uint lid [[thread_index_in_threadgroup]],
    +
    969 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    970 uint simd_lid [[thread_index_in_simdgroup]]) {
    +
    971 static_assert(BK >= SIMD_SIZE, "BK should be larger than SIMD_SIZE");
    +
    972 static_assert(BK % SIMD_SIZE == 0, "BK should be divisible by SIMD_SIZE");
    +
    973
    +
    974 (void)lid;
    +
    975
    +
    976 constexpr int WM = 2;
    +
    977 constexpr int WN = 2;
    +
    978 constexpr int pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 8 / bits;
    +
    979 constexpr int BK_padded = (BK + 16 / sizeof(T));
    +
    980 constexpr int bytes_per_pack = (bits == 3 || bits == 6) ? 3 : 1;
    +
    981
    +
    982 // Instantiate the appropriate BlockMMA and Loader
    +
    983 using mma_t = mlx::steel::
    +
    984 BlockMMA<T, T, BM, BN, BK, WM, WN, false, true, BK_padded, BK_padded>;
    +
    985 using loader_x_t =
    +
    986 mlx::steel::BlockLoader<T, BM, BK, BK_padded, 1, WM * WN * SIMD_SIZE>;
    +
    987 using loader_w_t = QuantizedBlockLoader<
    +
    988 T,
    +
    989 BN,
    +
    990 BK,
    +
    991 BK_padded,
    +
    992 1,
    +
    993 WM * WN * SIMD_SIZE,
    +
    994 group_size,
    +
    995 bits>;
    +
    996
    +
    997 // Set the block
    +
    998 const int K_w = K * bytes_per_pack / pack_factor;
    +
    999 const int K_g = K / group_size;
    +
    1000 const int y_row = tid.y * BM;
    +
    1001 const int y_col = tid.x * BN;
    +
    1002
    +
    1003 auto wl = (const device uint8_t*)w;
    +
    1004
    +
    1005 x += y_row * K;
    +
    1006 wl += y_col * K_w;
    +
    1007 scales += y_col * K_g;
    +
    1008 biases += y_col * K_g;
    +
    1009 y += y_row * N + y_col;
    +
    1010
    +
    1011 // Make the x loader and mma operation
    +
    1012 const short num_els = min(BM, M - y_row);
    +
    1013 const short num_outs = min(BN, N - y_col);
    +
    1014 loader_x_t loader_x(x, K, Xs, simd_gid, simd_lid);
    +
    1015 loader_w_t loader_w(wl, scales, biases, K, Ws, simd_gid, simd_lid);
    +
    1016 mma_t mma_op(simd_gid, simd_lid);
    +
    1017
    +
    1018 if (num_els < BM) {
    +
    1019 if (!aligned_N && num_outs < BN) {
    +
    1020 for (int k = 0; k < K; k += BK) {
    +
    1021 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1022 loader_x.load_safe(short2(BK, num_els));
    +
    1023 loader_w.load_safe(short2(BK, num_outs));
    +
    1024 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1025 mma_op.mma(Xs, Ws);
    +
    1026 loader_x.next();
    +
    1027 loader_w.next();
    +
    1028 }
    +
    1029 } else {
    +
    1030 for (int k = 0; k < K; k += BK) {
    +
    1031 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1032 loader_x.load_safe(short2(BK, num_els));
    +
    1033 loader_w.load_unsafe();
    +
    1034 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1035 mma_op.mma(Xs, Ws);
    +
    1036 loader_x.next();
    +
    1037 loader_w.next();
    +
    1038 }
    +
    1039 }
    +
    1040 } else {
    +
    1041 if (!aligned_N && num_outs < BN) {
    +
    1042 for (int k = 0; k < K; k += BK) {
    +
    1043 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1044 loader_x.load_unsafe();
    +
    1045 loader_w.load_safe(short2(BK, num_outs));
    +
    1046 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1047 mma_op.mma(Xs, Ws);
    +
    1048 loader_x.next();
    +
    1049 loader_w.next();
    +
    1050 }
    +
    1051 } else {
    +
    1052 for (int k = 0; k < K; k += BK) {
    +
    1053 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1054 loader_x.load_unsafe();
    +
    1055 loader_w.load_unsafe();
    +
    1056 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1057
    +
    1058 mma_op.mma(Xs, Ws);
    +
    1059 loader_x.next();
    +
    1060 loader_w.next();
    +
    1061 }
    +
    1062 }
    +
    1063 }
    +
    1064
    +
    1065 // Store results to device memory
    +
    1066 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1067 if (num_els < BM || num_outs < BN) {
    +
    1068 mma_op.store_result_safe(y, N, short2(num_outs, num_els));
    +
    1069 } else {
    +
    1070 mma_op.store_result(y, N);
    +
    1071 }
    +
    1072}
    -
    1042
    -
    1043template <typename T>
    -
    -
    1044METAL_FUNC void adjust_matrix_offsets(
    -
    1045 const device T*& x,
    -
    1046 const device uint32_t*& w,
    -
    1047 const device T*& scales,
    -
    1048 const device T*& biases,
    -
    1049 const device uint32_t* lhs_indices,
    -
    1050 const device uint32_t* rhs_indices,
    -
    1051 device T*& y,
    -
    1052 int output_stride,
    -
    1053 const constant int& batch_ndims,
    -
    1054 const constant int* batch_shape,
    -
    1055 const constant size_t* lhs_strides,
    -
    1056 const constant size_t* rhs_strides,
    -
    1057 const constant int& x_batch_ndims,
    -
    1058 const constant int* x_shape,
    -
    1059 const constant size_t* x_strides,
    -
    1060 const constant int& w_batch_ndims,
    -
    1061 const constant int* w_shape,
    -
    1062 const constant size_t* w_strides,
    -
    1063 const constant size_t* s_strides,
    -
    1064 const constant size_t* b_strides,
    -
    1065 uint3 tid [[threadgroup_position_in_grid]]) {
    -
    1066 // Set the input/output matrices
    -
    1067 uint32_t x_idx;
    -
    1068 uint32_t w_idx;
    -
    1069 if (batch_ndims == 1) {
    -
    1070 x_idx = lhs_indices[tid.z * lhs_strides[0]];
    -
    1071 w_idx = rhs_indices[tid.z * rhs_strides[0]];
    -
    1072 } else {
    -
    1073 ulong2 idx = elem_to_loc_broadcast(
    -
    1074 tid.z, batch_shape, lhs_strides, rhs_strides, batch_ndims);
    -
    1075 x_idx = lhs_indices[idx.x];
    -
    1076 w_idx = rhs_indices[idx.y];
    -
    1077 }
    -
    1078 if (x_batch_ndims == 1) {
    -
    1079 x += x_idx * x_strides[0];
    -
    1080 } else {
    -
    1081 x += elem_to_loc(x_idx, x_shape, x_strides, x_batch_ndims);
    -
    1082 }
    -
    1083 if (w_batch_ndims == 1) {
    -
    1084 w += w_idx * w_strides[0];
    -
    1085 scales += w_idx * s_strides[0];
    -
    1086 biases += w_idx * b_strides[0];
    -
    1087 } else {
    -
    1088 ulong3 idx = elem_to_loc_broadcast(
    -
    1089 w_idx, w_shape, w_strides, s_strides, b_strides, w_batch_ndims);
    -
    1090 w += idx.x;
    -
    1091 scales += idx.y;
    -
    1092 biases += idx.z;
    -
    1093 }
    -
    1094 y += tid.z * output_stride;
    -
    1095}
    +
    1073
    +
    1074template <
    +
    1075 typename T,
    +
    1076 const int group_size,
    +
    1077 const int bits,
    +
    1078 const int BM = 32,
    +
    1079 const int BK = 32,
    +
    1080 const int BN = 32>
    +
    +
    1081METAL_FUNC void qmm_n_impl(
    +
    1082 const device uint32_t* w,
    +
    1083 const device T* scales,
    +
    1084 const device T* biases,
    +
    1085 const device T* x,
    +
    1086 device T* y,
    +
    1087 threadgroup T* Xs,
    +
    1088 threadgroup T* Ws,
    +
    1089 const constant int& K,
    +
    1090 const constant int& N,
    +
    1091 const constant int& M,
    +
    1092 uint3 tid [[threadgroup_position_in_grid]],
    +
    1093 uint lid [[thread_index_in_threadgroup]],
    +
    1094 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    1095 uint simd_lid [[thread_index_in_simdgroup]]) {
    +
    1096 static_assert(BK >= SIMD_SIZE, "BK should be larger than SIMD_SIZE");
    +
    1097 static_assert(BK % SIMD_SIZE == 0, "BK should be divisible by SIMD_SIZE");
    +
    1098
    +
    1099 (void)lid;
    +
    1100
    +
    1101 constexpr int WM = 2;
    +
    1102 constexpr int WN = 2;
    +
    1103 constexpr int pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 8 / bits;
    +
    1104 constexpr int BK_padded = (BK + 16 / sizeof(T));
    +
    1105 constexpr int BN_padded = (BN + 16 / sizeof(T));
    +
    1106 constexpr int power_of_2_bits = (bits & (bits - 1)) == 0;
    +
    1107 constexpr int bytes_per_pack = power_of_2_bits ? 1 : 3;
    +
    1108
    +
    1109 // Instantiate the appropriate BlockMMA and Loader
    +
    1110 using mma_t = mlx::steel::
    +
    1111 BlockMMA<T, T, BM, BN, BK, WM, WN, false, false, BK_padded, BN_padded>;
    +
    1112 using loader_x_t = mlx::steel::
    +
    1113 BlockLoader<T, BM, BK, BK_padded, 1, WM * WN * SIMD_SIZE, 1, 4>;
    +
    1114 using loader_w_t = QuantizedBlockLoader<
    +
    1115 T,
    +
    1116 BK,
    +
    1117 BN,
    +
    1118 BN_padded,
    +
    1119 0,
    +
    1120 WM * WN * SIMD_SIZE,
    +
    1121 group_size,
    +
    1122 bits>;
    +
    1123
    +
    1124 auto wl = (const device uint8_t*)w;
    +
    1125
    +
    1126 // Set the block
    +
    1127 const int y_row = tid.y * BM;
    +
    1128 const int y_col = tid.x * BN;
    +
    1129 x += y_row * K;
    +
    1130 wl += y_col * bytes_per_pack / pack_factor;
    +
    1131 scales += y_col / group_size;
    +
    1132 biases += y_col / group_size;
    +
    1133 y += y_row * N + y_col;
    +
    1134
    +
    1135 // Make the x loader and mma operation
    +
    1136 const short num_els = min(BM, M - y_row);
    +
    1137 loader_x_t loader_x(x, K, Xs, simd_gid, simd_lid);
    +
    1138 loader_w_t loader_w(wl, scales, biases, N, Ws, simd_gid, simd_lid);
    +
    1139 mma_t mma_op(simd_gid, simd_lid);
    +
    1140
    +
    1141 if (num_els < BM) {
    +
    1142 if ((K % BK) != 0) {
    +
    1143 const int k_blocks = K / BK;
    +
    1144 for (int k = 0; k < k_blocks; k++) {
    +
    1145 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1146 loader_x.load_safe(short2(BK, num_els));
    +
    1147 loader_w.load_unsafe();
    +
    1148 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1149 mma_op.mma(Xs, Ws);
    +
    1150 loader_x.next();
    +
    1151 loader_w.next();
    +
    1152 }
    +
    1153 const short num_k = K - k_blocks * BK;
    +
    1154 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1155 loader_x.load_safe(short2(num_k, num_els));
    +
    1156 loader_w.load_safe(short2(BN, num_k));
    +
    1157 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1158 mma_op.mma(Xs, Ws);
    +
    1159 } else {
    +
    1160 for (int k = 0; k < K; k += BK) {
    +
    1161 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1162 loader_x.load_safe(short2(BK, num_els));
    +
    1163 loader_w.load_unsafe();
    +
    1164 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1165 mma_op.mma(Xs, Ws);
    +
    1166 loader_x.next();
    +
    1167 loader_w.next();
    +
    1168 }
    +
    1169 }
    +
    1170 } else {
    +
    1171 if ((K % BK) != 0) {
    +
    1172 const int k_blocks = K / BK;
    +
    1173 for (int k = 0; k < k_blocks; k++) {
    +
    1174 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1175 loader_x.load_unsafe();
    +
    1176 loader_w.load_unsafe();
    +
    1177 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1178 mma_op.mma(Xs, Ws);
    +
    1179 loader_x.next();
    +
    1180 loader_w.next();
    +
    1181 }
    +
    1182 const short num_k = K - k_blocks * BK;
    +
    1183 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1184 loader_x.load_safe(short2(num_k, BM));
    +
    1185 loader_w.load_safe(short2(BN, num_k));
    +
    1186 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1187 mma_op.mma(Xs, Ws);
    +
    1188 } else {
    +
    1189 for (int k = 0; k < K; k += BK) {
    +
    1190 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1191 loader_x.load_unsafe();
    +
    1192 loader_w.load_unsafe();
    +
    1193 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1194 mma_op.mma(Xs, Ws);
    +
    1195 loader_x.next();
    +
    1196 loader_w.next();
    +
    1197 }
    +
    1198 }
    +
    1199 }
    +
    1200
    +
    1201 // Store results to device memory
    +
    1202 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    1203 if (num_els < BM) {
    +
    1204 mma_op.store_result_safe(y, N, short2(BN, num_els));
    +
    1205 } else {
    +
    1206 mma_op.store_result(y, N);
    +
    1207 }
    +
    1208}
    -
    1096
    -
    1097template <typename T, int group_size, int bits, int D, bool batched>
    -
    -
    1098[[kernel]] void qmv_quad(
    -
    1099 const device uint32_t* w [[buffer(0)]],
    -
    1100 const device T* scales [[buffer(1)]],
    -
    1101 const device T* biases [[buffer(2)]],
    -
    1102 const device T* x [[buffer(3)]],
    -
    1103 device T* y [[buffer(4)]],
    -
    1104 const constant int& in_vec_size [[buffer(5)]],
    -
    1105 const constant int& out_vec_size [[buffer(6)]],
    -
    1106 const constant int& x_batch_ndims [[buffer(7)]],
    -
    1107 const constant int* x_shape [[buffer(8)]],
    -
    1108 const constant size_t* x_strides [[buffer(9)]],
    -
    1109 const constant int& w_batch_ndims [[buffer(10)]],
    -
    1110 const constant int* w_shape [[buffer(11)]],
    -
    1111 const constant size_t* w_strides [[buffer(12)]],
    -
    1112 const constant size_t* s_strides [[buffer(13)]],
    -
    1113 const constant size_t* b_strides [[buffer(14)]],
    -
    1114 uint3 tid [[threadgroup_position_in_grid]],
    -
    1115 uint quad_gid [[quadgroup_index_in_threadgroup]],
    -
    1116 uint quad_lid [[thread_index_in_quadgroup]]) {
    -
    1117 if (batched) {
    -
    1118 adjust_matrix_offsets<T>(
    -
    1119 x,
    -
    1120 w,
    -
    1121 scales,
    -
    1122 biases,
    -
    1123 y,
    -
    1124 out_vec_size,
    -
    1125 x_batch_ndims,
    -
    1126 x_shape,
    -
    1127 x_strides,
    -
    1128 w_batch_ndims,
    -
    1129 w_shape,
    -
    1130 w_strides,
    -
    1131 s_strides,
    -
    1132 b_strides,
    -
    1133 tid);
    -
    1134 }
    -
    1135 qmv_quad_impl<T, group_size, bits, D>(
    -
    1136 w,
    -
    1137 scales,
    -
    1138 biases,
    -
    1139 x,
    -
    1140 y,
    -
    1141 in_vec_size,
    -
    1142 out_vec_size,
    -
    1143 tid,
    -
    1144 quad_gid,
    -
    1145 quad_lid);
    -
    1146}
    +
    1209
    +
    1210template <typename T>
    +
    +
    1211METAL_FUNC void adjust_matrix_offsets(
    +
    1212 const device T*& x,
    +
    1213 const device uint32_t*& w,
    +
    1214 const device T*& scales,
    +
    1215 const device T*& biases,
    +
    1216 device T*& y,
    +
    1217 int output_stride,
    +
    1218 const constant int& x_batch_ndims,
    +
    1219 const constant int* x_shape,
    +
    1220 const constant size_t* x_strides,
    +
    1221 const constant int& w_batch_ndims,
    +
    1222 const constant int* w_shape,
    +
    1223 const constant size_t* w_strides,
    +
    1224 const constant size_t* s_strides,
    +
    1225 const constant size_t* b_strides,
    +
    1226 uint3 tid [[threadgroup_position_in_grid]]) {
    +
    1227 // Set the input/output matrices
    +
    1228 uint32_t x_idx = tid.z;
    +
    1229 uint32_t w_idx = tid.z;
    +
    1230 if (x_batch_ndims == 1) {
    +
    1231 x += x_idx * x_strides[0];
    +
    1232 } else {
    +
    1233 x += elem_to_loc(x_idx, x_shape, x_strides, x_batch_ndims);
    +
    1234 }
    +
    1235 if (w_batch_ndims == 1) {
    +
    1236 w += w_idx * w_strides[0];
    +
    1237 scales += w_idx * s_strides[0];
    +
    1238 biases += w_idx * b_strides[0];
    +
    1239 } else {
    +
    1240 ulong3 idx = elem_to_loc_broadcast(
    +
    1241 w_idx, w_shape, w_strides, s_strides, b_strides, w_batch_ndims);
    +
    1242 w += idx.x;
    +
    1243 scales += idx.y;
    +
    1244 biases += idx.z;
    +
    1245 }
    +
    1246 y += tid.z * output_stride;
    +
    1247}
    -
    1147
    -
    1148template <typename T, int group_size, int bits, bool batched>
    -
    -
    1149[[kernel]] void qmv_fast(
    -
    1150 const device uint32_t* w [[buffer(0)]],
    -
    1151 const device T* scales [[buffer(1)]],
    -
    1152 const device T* biases [[buffer(2)]],
    -
    1153 const device T* x [[buffer(3)]],
    -
    1154 device T* y [[buffer(4)]],
    -
    1155 const constant int& in_vec_size [[buffer(5)]],
    -
    1156 const constant int& out_vec_size [[buffer(6)]],
    -
    1157 const constant int& x_batch_ndims [[buffer(7)]],
    -
    1158 const constant int* x_shape [[buffer(8)]],
    -
    1159 const constant size_t* x_strides [[buffer(9)]],
    -
    1160 const constant int& w_batch_ndims [[buffer(10)]],
    -
    1161 const constant int* w_shape [[buffer(11)]],
    -
    1162 const constant size_t* w_strides [[buffer(12)]],
    -
    1163 const constant size_t* s_strides [[buffer(13)]],
    -
    1164 const constant size_t* b_strides [[buffer(14)]],
    -
    1165 uint3 tid [[threadgroup_position_in_grid]],
    -
    1166 uint simd_gid [[simdgroup_index_in_threadgroup]],
    -
    1167 uint simd_lid [[thread_index_in_simdgroup]]) {
    -
    1168 if (batched) {
    -
    1169 adjust_matrix_offsets<T>(
    -
    1170 x,
    -
    1171 w,
    -
    1172 scales,
    -
    1173 biases,
    -
    1174 y,
    -
    1175 out_vec_size,
    -
    1176 x_batch_ndims,
    -
    1177 x_shape,
    -
    1178 x_strides,
    -
    1179 w_batch_ndims,
    -
    1180 w_shape,
    -
    1181 w_strides,
    -
    1182 s_strides,
    -
    1183 b_strides,
    -
    1184 tid);
    -
    1185 }
    -
    1186 qmv_fast_impl<T, group_size, bits>(
    -
    1187 w,
    -
    1188 scales,
    -
    1189 biases,
    -
    1190 x,
    -
    1191 y,
    -
    1192 in_vec_size,
    -
    1193 out_vec_size,
    -
    1194 tid,
    -
    1195 simd_gid,
    -
    1196 simd_lid);
    -
    1197}
    +
    1248
    +
    1249template <typename T>
    +
    +
    1250METAL_FUNC void adjust_matrix_offsets(
    +
    1251 const device T*& x,
    +
    1252 const device uint32_t*& w,
    +
    1253 const device T*& scales,
    +
    1254 const device T*& biases,
    +
    1255 const device uint32_t* lhs_indices,
    +
    1256 const device uint32_t* rhs_indices,
    +
    1257 device T*& y,
    +
    1258 int output_stride,
    +
    1259 const constant int& batch_ndims,
    +
    1260 const constant int* batch_shape,
    +
    1261 const constant size_t* lhs_strides,
    +
    1262 const constant size_t* rhs_strides,
    +
    1263 const constant int& x_batch_ndims,
    +
    1264 const constant int* x_shape,
    +
    1265 const constant size_t* x_strides,
    +
    1266 const constant int& w_batch_ndims,
    +
    1267 const constant int* w_shape,
    +
    1268 const constant size_t* w_strides,
    +
    1269 const constant size_t* s_strides,
    +
    1270 const constant size_t* b_strides,
    +
    1271 uint3 tid [[threadgroup_position_in_grid]]) {
    +
    1272 // Set the input/output matrices
    +
    1273 uint32_t x_idx;
    +
    1274 uint32_t w_idx;
    +
    1275 if (batch_ndims == 1) {
    +
    1276 x_idx = lhs_indices[tid.z * lhs_strides[0]];
    +
    1277 w_idx = rhs_indices[tid.z * rhs_strides[0]];
    +
    1278 } else {
    +
    1279 ulong2 idx = elem_to_loc_broadcast(
    +
    1280 tid.z, batch_shape, lhs_strides, rhs_strides, batch_ndims);
    +
    1281 x_idx = lhs_indices[idx.x];
    +
    1282 w_idx = rhs_indices[idx.y];
    +
    1283 }
    +
    1284 if (x_batch_ndims == 1) {
    +
    1285 x += x_idx * x_strides[0];
    +
    1286 } else {
    +
    1287 x += elem_to_loc(x_idx, x_shape, x_strides, x_batch_ndims);
    +
    1288 }
    +
    1289 if (w_batch_ndims == 1) {
    +
    1290 w += w_idx * w_strides[0];
    +
    1291 scales += w_idx * s_strides[0];
    +
    1292 biases += w_idx * b_strides[0];
    +
    1293 } else {
    +
    1294 ulong3 idx = elem_to_loc_broadcast(
    +
    1295 w_idx, w_shape, w_strides, s_strides, b_strides, w_batch_ndims);
    +
    1296 w += idx.x;
    +
    1297 scales += idx.y;
    +
    1298 biases += idx.z;
    +
    1299 }
    +
    1300 y += tid.z * output_stride;
    +
    1301}
    -
    1198
    -
    1199template <typename T, const int group_size, const int bits, bool batched>
    -
    -
    1200[[kernel]] void qmv(
    -
    1201 const device uint32_t* w [[buffer(0)]],
    -
    1202 const device T* scales [[buffer(1)]],
    -
    1203 const device T* biases [[buffer(2)]],
    -
    1204 const device T* x [[buffer(3)]],
    -
    1205 device T* y [[buffer(4)]],
    -
    1206 const constant int& in_vec_size [[buffer(5)]],
    -
    1207 const constant int& out_vec_size [[buffer(6)]],
    -
    1208 const constant int& x_batch_ndims [[buffer(7)]],
    -
    1209 const constant int* x_shape [[buffer(8)]],
    -
    1210 const constant size_t* x_strides [[buffer(9)]],
    -
    1211 const constant int& w_batch_ndims [[buffer(10)]],
    -
    1212 const constant int* w_shape [[buffer(11)]],
    -
    1213 const constant size_t* w_strides [[buffer(12)]],
    -
    1214 const constant size_t* s_strides [[buffer(13)]],
    -
    1215 const constant size_t* b_strides [[buffer(14)]],
    -
    1216 uint3 tid [[threadgroup_position_in_grid]],
    -
    1217 uint simd_gid [[simdgroup_index_in_threadgroup]],
    -
    1218 uint simd_lid [[thread_index_in_simdgroup]]) {
    -
    1219 if (batched) {
    -
    1220 adjust_matrix_offsets<T>(
    -
    1221 x,
    -
    1222 w,
    -
    1223 scales,
    -
    1224 biases,
    -
    1225 y,
    -
    1226 out_vec_size,
    -
    1227 x_batch_ndims,
    -
    1228 x_shape,
    -
    1229 x_strides,
    -
    1230 w_batch_ndims,
    -
    1231 w_shape,
    -
    1232 w_strides,
    -
    1233 s_strides,
    -
    1234 b_strides,
    -
    1235 tid);
    -
    1236 }
    -
    1237 qmv_impl<T, group_size, bits>(
    -
    1238 w,
    -
    1239 scales,
    -
    1240 biases,
    -
    1241 x,
    -
    1242 y,
    -
    1243 in_vec_size,
    -
    1244 out_vec_size,
    -
    1245 tid,
    -
    1246 simd_gid,
    -
    1247 simd_lid);
    -
    1248}
    +
    1302
    +
    1303template <typename T, int group_size, int bits, int D, bool batched>
    +
    +
    1304[[kernel]] void qmv_quad(
    +
    1305 const device uint32_t* w [[buffer(0)]],
    +
    1306 const device T* scales [[buffer(1)]],
    +
    1307 const device T* biases [[buffer(2)]],
    +
    1308 const device T* x [[buffer(3)]],
    +
    1309 device T* y [[buffer(4)]],
    +
    1310 const constant int& in_vec_size [[buffer(5)]],
    +
    1311 const constant int& out_vec_size [[buffer(6)]],
    +
    1312 const constant int& x_batch_ndims [[buffer(7)]],
    +
    1313 const constant int* x_shape [[buffer(8)]],
    +
    1314 const constant size_t* x_strides [[buffer(9)]],
    +
    1315 const constant int& w_batch_ndims [[buffer(10)]],
    +
    1316 const constant int* w_shape [[buffer(11)]],
    +
    1317 const constant size_t* w_strides [[buffer(12)]],
    +
    1318 const constant size_t* s_strides [[buffer(13)]],
    +
    1319 const constant size_t* b_strides [[buffer(14)]],
    +
    1320 uint3 tid [[threadgroup_position_in_grid]],
    +
    1321 uint quad_gid [[quadgroup_index_in_threadgroup]],
    +
    1322 uint quad_lid [[thread_index_in_quadgroup]]) {
    +
    1323 if (batched) {
    +
    1324 adjust_matrix_offsets<T>(
    +
    1325 x,
    +
    1326 w,
    +
    1327 scales,
    +
    1328 biases,
    +
    1329 y,
    +
    1330 out_vec_size,
    +
    1331 x_batch_ndims,
    +
    1332 x_shape,
    +
    1333 x_strides,
    +
    1334 w_batch_ndims,
    +
    1335 w_shape,
    +
    1336 w_strides,
    +
    1337 s_strides,
    +
    1338 b_strides,
    +
    1339 tid);
    +
    1340 }
    +
    1341 qmv_quad_impl<T, group_size, bits, D>(
    +
    1342 w,
    +
    1343 scales,
    +
    1344 biases,
    +
    1345 x,
    +
    1346 y,
    +
    1347 in_vec_size,
    +
    1348 out_vec_size,
    +
    1349 tid,
    +
    1350 quad_gid,
    +
    1351 quad_lid);
    +
    1352}
    -
    1249
    -
    1250template <typename T, const int group_size, const int bits, bool batched>
    -
    -
    1251[[kernel]] void qvm(
    -
    1252 const device uint32_t* w [[buffer(0)]],
    -
    1253 const device T* scales [[buffer(1)]],
    -
    1254 const device T* biases [[buffer(2)]],
    -
    1255 const device T* x [[buffer(3)]],
    -
    1256 device T* y [[buffer(4)]],
    -
    1257 const constant int& in_vec_size [[buffer(5)]],
    -
    1258 const constant int& out_vec_size [[buffer(6)]],
    -
    1259 const constant int& x_batch_ndims [[buffer(7)]],
    -
    1260 const constant int* x_shape [[buffer(8)]],
    -
    1261 const constant size_t* x_strides [[buffer(9)]],
    -
    1262 const constant int& w_batch_ndims [[buffer(10)]],
    -
    1263 const constant int* w_shape [[buffer(11)]],
    -
    1264 const constant size_t* w_strides [[buffer(12)]],
    -
    1265 const constant size_t* s_strides [[buffer(13)]],
    -
    1266 const constant size_t* b_strides [[buffer(14)]],
    -
    1267 uint3 tid [[threadgroup_position_in_grid]],
    -
    1268 uint simd_gid [[simdgroup_index_in_threadgroup]],
    -
    1269 uint simd_lid [[thread_index_in_simdgroup]]) {
    -
    1270 if (batched) {
    -
    1271 adjust_matrix_offsets<T>(
    -
    1272 x,
    -
    1273 w,
    -
    1274 scales,
    -
    1275 biases,
    -
    1276 y,
    -
    1277 out_vec_size,
    -
    1278 x_batch_ndims,
    -
    1279 x_shape,
    -
    1280 x_strides,
    -
    1281 w_batch_ndims,
    -
    1282 w_shape,
    -
    1283 w_strides,
    -
    1284 s_strides,
    -
    1285 b_strides,
    -
    1286 tid);
    -
    1287 }
    -
    1288 qvm_impl<T, group_size, bits>(
    -
    1289 w,
    -
    1290 scales,
    -
    1291 biases,
    -
    1292 x,
    -
    1293 y,
    -
    1294 in_vec_size,
    -
    1295 out_vec_size,
    -
    1296 tid,
    -
    1297 simd_gid,
    -
    1298 simd_lid);
    -
    1299}
    +
    1353
    +
    1354template <typename T, int group_size, int bits, bool batched>
    +
    +
    1355[[kernel]] void qmv_fast(
    +
    1356 const device uint32_t* w [[buffer(0)]],
    +
    1357 const device T* scales [[buffer(1)]],
    +
    1358 const device T* biases [[buffer(2)]],
    +
    1359 const device T* x [[buffer(3)]],
    +
    1360 device T* y [[buffer(4)]],
    +
    1361 const constant int& in_vec_size [[buffer(5)]],
    +
    1362 const constant int& out_vec_size [[buffer(6)]],
    +
    1363 const constant int& x_batch_ndims [[buffer(7)]],
    +
    1364 const constant int* x_shape [[buffer(8)]],
    +
    1365 const constant size_t* x_strides [[buffer(9)]],
    +
    1366 const constant int& w_batch_ndims [[buffer(10)]],
    +
    1367 const constant int* w_shape [[buffer(11)]],
    +
    1368 const constant size_t* w_strides [[buffer(12)]],
    +
    1369 const constant size_t* s_strides [[buffer(13)]],
    +
    1370 const constant size_t* b_strides [[buffer(14)]],
    +
    1371 uint3 tid [[threadgroup_position_in_grid]],
    +
    1372 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    1373 uint simd_lid [[thread_index_in_simdgroup]]) {
    +
    1374 if (batched) {
    +
    1375 adjust_matrix_offsets<T>(
    +
    1376 x,
    +
    1377 w,
    +
    1378 scales,
    +
    1379 biases,
    +
    1380 y,
    +
    1381 out_vec_size,
    +
    1382 x_batch_ndims,
    +
    1383 x_shape,
    +
    1384 x_strides,
    +
    1385 w_batch_ndims,
    +
    1386 w_shape,
    +
    1387 w_strides,
    +
    1388 s_strides,
    +
    1389 b_strides,
    +
    1390 tid);
    +
    1391 }
    +
    1392 qmv_fast_impl<T, group_size, bits>(
    +
    1393 w,
    +
    1394 scales,
    +
    1395 biases,
    +
    1396 x,
    +
    1397 y,
    +
    1398 in_vec_size,
    +
    1399 out_vec_size,
    +
    1400 tid,
    +
    1401 simd_gid,
    +
    1402 simd_lid);
    +
    1403}
    -
    1300
    -
    1301template <typename T, const int group_size, const int bits, int split_k = 32>
    -
    -
    1302[[kernel]] void qvm_split_k(
    -
    1303 const device uint32_t* w [[buffer(0)]],
    -
    1304 const device T* scales [[buffer(1)]],
    -
    1305 const device T* biases [[buffer(2)]],
    -
    1306 const device T* x [[buffer(3)]],
    -
    1307 device T* y [[buffer(4)]],
    -
    1308 const constant int& in_vec_size [[buffer(5)]],
    -
    1309 const constant int& out_vec_size [[buffer(6)]],
    -
    1310 const constant int& x_batch_ndims [[buffer(7)]],
    -
    1311 const constant int* x_shape [[buffer(8)]],
    -
    1312 const constant size_t* x_strides [[buffer(9)]],
    -
    1313 const constant int& w_batch_ndims [[buffer(10)]],
    -
    1314 const constant int* w_shape [[buffer(11)]],
    -
    1315 const constant size_t* w_strides [[buffer(12)]],
    -
    1316 const constant size_t* s_strides [[buffer(13)]],
    -
    1317 const constant size_t* b_strides [[buffer(14)]],
    -
    1318 const constant int& final_block_size [[buffer(15)]],
    -
    1319 uint3 tid [[threadgroup_position_in_grid]],
    -
    1320 uint simd_gid [[simdgroup_index_in_threadgroup]],
    -
    1321 uint simd_lid [[thread_index_in_simdgroup]]) {
    -
    1322 adjust_matrix_offsets<T>(
    -
    1323 x,
    -
    1324 w,
    -
    1325 scales,
    -
    1326 biases,
    -
    1327 y,
    -
    1328 out_vec_size,
    -
    1329 x_batch_ndims,
    -
    1330 x_shape,
    -
    1331 x_strides,
    -
    1332 w_batch_ndims,
    -
    1333 w_shape,
    -
    1334 w_strides,
    -
    1335 s_strides,
    -
    1336 b_strides,
    -
    1337 tid);
    -
    1338
    -
    1339 // When (in_vec_size % split_k != 0) the final block needs to be smaller
    -
    1340 int in_vec_size_adj =
    -
    1341 tid.z % split_k == split_k - 1 ? final_block_size : in_vec_size;
    -
    1342
    -
    1343 qvm_impl<T, group_size, bits>(
    -
    1344 w,
    -
    1345 scales,
    -
    1346 biases,
    -
    1347 x,
    -
    1348 y,
    -
    1349 in_vec_size_adj,
    -
    1350 out_vec_size,
    -
    1351 tid,
    -
    1352 simd_gid,
    -
    1353 simd_lid);
    -
    1354}
    +
    1404
    +
    1405template <typename T, const int group_size, const int bits, bool batched>
    +
    +
    1406[[kernel]] void qmv(
    +
    1407 const device uint32_t* w [[buffer(0)]],
    +
    1408 const device T* scales [[buffer(1)]],
    +
    1409 const device T* biases [[buffer(2)]],
    +
    1410 const device T* x [[buffer(3)]],
    +
    1411 device T* y [[buffer(4)]],
    +
    1412 const constant int& in_vec_size [[buffer(5)]],
    +
    1413 const constant int& out_vec_size [[buffer(6)]],
    +
    1414 const constant int& x_batch_ndims [[buffer(7)]],
    +
    1415 const constant int* x_shape [[buffer(8)]],
    +
    1416 const constant size_t* x_strides [[buffer(9)]],
    +
    1417 const constant int& w_batch_ndims [[buffer(10)]],
    +
    1418 const constant int* w_shape [[buffer(11)]],
    +
    1419 const constant size_t* w_strides [[buffer(12)]],
    +
    1420 const constant size_t* s_strides [[buffer(13)]],
    +
    1421 const constant size_t* b_strides [[buffer(14)]],
    +
    1422 uint3 tid [[threadgroup_position_in_grid]],
    +
    1423 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    1424 uint simd_lid [[thread_index_in_simdgroup]]) {
    +
    1425 if (batched) {
    +
    1426 adjust_matrix_offsets<T>(
    +
    1427 x,
    +
    1428 w,
    +
    1429 scales,
    +
    1430 biases,
    +
    1431 y,
    +
    1432 out_vec_size,
    +
    1433 x_batch_ndims,
    +
    1434 x_shape,
    +
    1435 x_strides,
    +
    1436 w_batch_ndims,
    +
    1437 w_shape,
    +
    1438 w_strides,
    +
    1439 s_strides,
    +
    1440 b_strides,
    +
    1441 tid);
    +
    1442 }
    +
    1443 qmv_impl<T, group_size, bits>(
    +
    1444 w,
    +
    1445 scales,
    +
    1446 biases,
    +
    1447 x,
    +
    1448 y,
    +
    1449 in_vec_size,
    +
    1450 out_vec_size,
    +
    1451 tid,
    +
    1452 simd_gid,
    +
    1453 simd_lid);
    +
    1454}
    -
    1355
    -
    1356template <
    -
    1357 typename T,
    -
    1358 const int group_size,
    -
    1359 const int bits,
    -
    1360 const bool aligned_N,
    -
    1361 const bool batched,
    -
    1362 const int BM = 32,
    -
    1363 const int BK = 32,
    -
    1364 const int BN = 32>
    -
    -
    1365[[kernel]] void qmm_t(
    -
    1366 const device uint32_t* w [[buffer(0)]],
    -
    1367 const device T* scales [[buffer(1)]],
    -
    1368 const device T* biases [[buffer(2)]],
    -
    1369 const device T* x [[buffer(3)]],
    -
    1370 device T* y [[buffer(4)]],
    -
    1371 const constant int& K [[buffer(5)]],
    -
    1372 const constant int& N [[buffer(6)]],
    -
    1373 const constant int& M [[buffer(7)]],
    -
    1374 const constant int& x_batch_ndims [[buffer(8)]],
    -
    1375 const constant int* x_shape [[buffer(9)]],
    -
    1376 const constant size_t* x_strides [[buffer(10)]],
    -
    1377 const constant int& w_batch_ndims [[buffer(11)]],
    -
    1378 const constant int* w_shape [[buffer(12)]],
    -
    1379 const constant size_t* w_strides [[buffer(13)]],
    -
    1380 const constant size_t* s_strides [[buffer(14)]],
    -
    1381 const constant size_t* b_strides [[buffer(15)]],
    -
    1382 uint3 tid [[threadgroup_position_in_grid]],
    -
    1383 uint lid [[thread_index_in_threadgroup]],
    -
    1384 uint simd_gid [[simdgroup_index_in_threadgroup]],
    -
    1385 uint simd_lid [[thread_index_in_simdgroup]]) {
    -
    1386 (void)lid;
    -
    1387
    -
    1388 constexpr int BK_padded = (BK + 16 / sizeof(T));
    -
    1389
    -
    1390 threadgroup T Xs[BM * BK_padded];
    -
    1391 threadgroup T Ws[BN * BK_padded];
    -
    1392
    -
    1393 if (batched) {
    -
    1394 adjust_matrix_offsets<T>(
    -
    1395 x,
    -
    1396 w,
    -
    1397 scales,
    -
    1398 biases,
    -
    1399 y,
    -
    1400 M * N,
    -
    1401 x_batch_ndims,
    -
    1402 x_shape,
    -
    1403 x_strides,
    -
    1404 w_batch_ndims,
    -
    1405 w_shape,
    -
    1406 w_strides,
    -
    1407 s_strides,
    -
    1408 b_strides,
    -
    1409 tid);
    -
    1410 }
    -
    1411 qmm_t_impl<T, group_size, bits, aligned_N, BM, BK, BN>(
    -
    1412 w, scales, biases, x, y, Xs, Ws, K, N, M, tid, lid, simd_gid, simd_lid);
    -
    1413}
    +
    1455
    +
    1456template <typename T, const int group_size, const int bits, bool batched>
    +
    +
    1457[[kernel]] void qvm(
    +
    1458 const device uint32_t* w [[buffer(0)]],
    +
    1459 const device T* scales [[buffer(1)]],
    +
    1460 const device T* biases [[buffer(2)]],
    +
    1461 const device T* x [[buffer(3)]],
    +
    1462 device T* y [[buffer(4)]],
    +
    1463 const constant int& in_vec_size [[buffer(5)]],
    +
    1464 const constant int& out_vec_size [[buffer(6)]],
    +
    1465 const constant int& x_batch_ndims [[buffer(7)]],
    +
    1466 const constant int* x_shape [[buffer(8)]],
    +
    1467 const constant size_t* x_strides [[buffer(9)]],
    +
    1468 const constant int& w_batch_ndims [[buffer(10)]],
    +
    1469 const constant int* w_shape [[buffer(11)]],
    +
    1470 const constant size_t* w_strides [[buffer(12)]],
    +
    1471 const constant size_t* s_strides [[buffer(13)]],
    +
    1472 const constant size_t* b_strides [[buffer(14)]],
    +
    1473 uint3 tid [[threadgroup_position_in_grid]],
    +
    1474 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    1475 uint simd_lid [[thread_index_in_simdgroup]]) {
    +
    1476 if (batched) {
    +
    1477 adjust_matrix_offsets<T>(
    +
    1478 x,
    +
    1479 w,
    +
    1480 scales,
    +
    1481 biases,
    +
    1482 y,
    +
    1483 out_vec_size,
    +
    1484 x_batch_ndims,
    +
    1485 x_shape,
    +
    1486 x_strides,
    +
    1487 w_batch_ndims,
    +
    1488 w_shape,
    +
    1489 w_strides,
    +
    1490 s_strides,
    +
    1491 b_strides,
    +
    1492 tid);
    +
    1493 }
    +
    1494 qvm_impl<T, group_size, bits>(
    +
    1495 w,
    +
    1496 scales,
    +
    1497 biases,
    +
    1498 x,
    +
    1499 y,
    +
    1500 in_vec_size,
    +
    1501 out_vec_size,
    +
    1502 tid,
    +
    1503 simd_gid,
    +
    1504 simd_lid);
    +
    1505}
    -
    1414
    -
    1415template <
    -
    1416 typename T,
    -
    1417 const int group_size,
    -
    1418 const int bits,
    -
    1419 const bool batched,
    -
    1420 const int BM = 32,
    -
    1421 const int BK = 32,
    -
    1422 const int BN = 32>
    -
    -
    1423[[kernel]] void qmm_n(
    -
    1424 const device uint32_t* w [[buffer(0)]],
    -
    1425 const device T* scales [[buffer(1)]],
    -
    1426 const device T* biases [[buffer(2)]],
    -
    1427 const device T* x [[buffer(3)]],
    -
    1428 device T* y [[buffer(4)]],
    -
    1429 const constant int& K [[buffer(5)]],
    -
    1430 const constant int& N [[buffer(6)]],
    -
    1431 const constant int& M [[buffer(7)]],
    -
    1432 const constant int& x_batch_ndims [[buffer(8)]],
    -
    1433 const constant int* x_shape [[buffer(9)]],
    -
    1434 const constant size_t* x_strides [[buffer(10)]],
    -
    1435 const constant int& w_batch_ndims [[buffer(11)]],
    -
    1436 const constant int* w_shape [[buffer(12)]],
    -
    1437 const constant size_t* w_strides [[buffer(13)]],
    -
    1438 const constant size_t* s_strides [[buffer(14)]],
    -
    1439 const constant size_t* b_strides [[buffer(15)]],
    -
    1440 uint3 tid [[threadgroup_position_in_grid]],
    -
    1441 uint lid [[thread_index_in_threadgroup]],
    -
    1442 uint simd_gid [[simdgroup_index_in_threadgroup]],
    -
    1443 uint simd_lid [[thread_index_in_simdgroup]]) {
    -
    1444 (void)lid;
    -
    1445
    -
    1446 constexpr int BK_padded = (BK + 16 / sizeof(T));
    -
    1447 constexpr int BN_padded = (BN + 16 / sizeof(T));
    -
    1448
    -
    1449 threadgroup T Xs[BM * BK_padded];
    -
    1450 threadgroup T Ws[BK * BN_padded];
    -
    1451
    -
    1452 if (batched) {
    -
    1453 adjust_matrix_offsets<T>(
    -
    1454 x,
    -
    1455 w,
    -
    1456 scales,
    -
    1457 biases,
    -
    1458 y,
    -
    1459 M * N,
    -
    1460 x_batch_ndims,
    -
    1461 x_shape,
    -
    1462 x_strides,
    -
    1463 w_batch_ndims,
    -
    1464 w_shape,
    -
    1465 w_strides,
    -
    1466 s_strides,
    -
    1467 b_strides,
    -
    1468 tid);
    -
    1469 }
    -
    1470
    -
    1471 qmm_n_impl<T, group_size, bits, BM, BK, BN>(
    -
    1472 w, scales, biases, x, y, Xs, Ws, K, N, M, tid, lid, simd_gid, simd_lid);
    -
    1473}
    +
    1506
    +
    1507template <typename T, const int group_size, const int bits, int split_k = 32>
    +
    +
    1508[[kernel]] void qvm_split_k(
    +
    1509 const device uint32_t* w [[buffer(0)]],
    +
    1510 const device T* scales [[buffer(1)]],
    +
    1511 const device T* biases [[buffer(2)]],
    +
    1512 const device T* x [[buffer(3)]],
    +
    1513 device T* y [[buffer(4)]],
    +
    1514 const constant int& in_vec_size [[buffer(5)]],
    +
    1515 const constant int& out_vec_size [[buffer(6)]],
    +
    1516 const constant int& x_batch_ndims [[buffer(7)]],
    +
    1517 const constant int* x_shape [[buffer(8)]],
    +
    1518 const constant size_t* x_strides [[buffer(9)]],
    +
    1519 const constant int& w_batch_ndims [[buffer(10)]],
    +
    1520 const constant int* w_shape [[buffer(11)]],
    +
    1521 const constant size_t* w_strides [[buffer(12)]],
    +
    1522 const constant size_t* s_strides [[buffer(13)]],
    +
    1523 const constant size_t* b_strides [[buffer(14)]],
    +
    1524 const constant int& final_block_size [[buffer(15)]],
    +
    1525 uint3 tid [[threadgroup_position_in_grid]],
    +
    1526 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    1527 uint simd_lid [[thread_index_in_simdgroup]]) {
    +
    1528 adjust_matrix_offsets<T>(
    +
    1529 x,
    +
    1530 w,
    +
    1531 scales,
    +
    1532 biases,
    +
    1533 y,
    +
    1534 out_vec_size,
    +
    1535 x_batch_ndims,
    +
    1536 x_shape,
    +
    1537 x_strides,
    +
    1538 w_batch_ndims,
    +
    1539 w_shape,
    +
    1540 w_strides,
    +
    1541 s_strides,
    +
    1542 b_strides,
    +
    1543 tid);
    +
    1544
    +
    1545 // When (in_vec_size % split_k != 0) the final block needs to be smaller
    +
    1546 int in_vec_size_adj =
    +
    1547 tid.z % split_k == split_k - 1 ? final_block_size : in_vec_size;
    +
    1548
    +
    1549 qvm_impl<T, group_size, bits>(
    +
    1550 w,
    +
    1551 scales,
    +
    1552 biases,
    +
    1553 x,
    +
    1554 y,
    +
    1555 in_vec_size_adj,
    +
    1556 out_vec_size,
    +
    1557 tid,
    +
    1558 simd_gid,
    +
    1559 simd_lid);
    +
    1560}
    -
    1474
    -
    1475template <typename T, int group_size, int bits>
    -
    -
    1476[[kernel]] void bs_qmv_fast(
    -
    1477 const device uint32_t* w [[buffer(0)]],
    -
    1478 const device T* scales [[buffer(1)]],
    -
    1479 const device T* biases [[buffer(2)]],
    -
    1480 const device T* x [[buffer(3)]],
    -
    1481 device T* y [[buffer(4)]],
    -
    1482 const constant int& in_vec_size [[buffer(5)]],
    -
    1483 const constant int& out_vec_size [[buffer(6)]],
    -
    1484 const constant int& x_batch_ndims [[buffer(7)]],
    -
    1485 const constant int* x_shape [[buffer(8)]],
    -
    1486 const constant size_t* x_strides [[buffer(9)]],
    -
    1487 const constant int& w_batch_ndims [[buffer(10)]],
    -
    1488 const constant int* w_shape [[buffer(11)]],
    -
    1489 const constant size_t* w_strides [[buffer(12)]],
    -
    1490 const constant size_t* s_strides [[buffer(13)]],
    -
    1491 const constant size_t* b_strides [[buffer(14)]],
    -
    1492 const constant int& batch_ndims [[buffer(15)]],
    -
    1493 const constant int* batch_shape [[buffer(16)]],
    -
    1494 const device uint32_t* lhs_indices [[buffer(17)]],
    -
    1495 const device uint32_t* rhs_indices [[buffer(18)]],
    -
    1496 const constant size_t* lhs_strides [[buffer(19)]],
    -
    1497 const constant size_t* rhs_strides [[buffer(20)]],
    -
    1498 uint3 tid [[threadgroup_position_in_grid]],
    -
    1499 uint simd_gid [[simdgroup_index_in_threadgroup]],
    -
    1500 uint simd_lid [[thread_index_in_simdgroup]]) {
    -
    1501 adjust_matrix_offsets<T>(
    -
    1502 x,
    -
    1503 w,
    -
    1504 scales,
    -
    1505 biases,
    -
    1506 lhs_indices,
    -
    1507 rhs_indices,
    -
    1508 y,
    -
    1509 out_vec_size,
    -
    1510 batch_ndims,
    -
    1511 batch_shape,
    -
    1512 lhs_strides,
    -
    1513 rhs_strides,
    -
    1514 x_batch_ndims,
    -
    1515 x_shape,
    -
    1516 x_strides,
    -
    1517 w_batch_ndims,
    -
    1518 w_shape,
    -
    1519 w_strides,
    -
    1520 s_strides,
    -
    1521 b_strides,
    -
    1522 tid);
    -
    1523 qmv_fast_impl<T, group_size, bits>(
    -
    1524 w,
    -
    1525 scales,
    -
    1526 biases,
    -
    1527 x,
    -
    1528 y,
    -
    1529 in_vec_size,
    -
    1530 out_vec_size,
    -
    1531 tid,
    -
    1532 simd_gid,
    -
    1533 simd_lid);
    -
    1534}
    -
    -
    1535
    -
    1536template <typename T, int group_size, int bits>
    -
    -
    1537[[kernel]] void bs_qmv(
    -
    1538 const device uint32_t* w [[buffer(0)]],
    -
    1539 const device T* scales [[buffer(1)]],
    -
    1540 const device T* biases [[buffer(2)]],
    -
    1541 const device T* x [[buffer(3)]],
    -
    1542 device T* y [[buffer(4)]],
    -
    1543 const constant int& in_vec_size [[buffer(5)]],
    -
    1544 const constant int& out_vec_size [[buffer(6)]],
    -
    1545 const constant int& x_batch_ndims [[buffer(7)]],
    -
    1546 const constant int* x_shape [[buffer(8)]],
    -
    1547 const constant size_t* x_strides [[buffer(9)]],
    -
    1548 const constant int& w_batch_ndims [[buffer(10)]],
    -
    1549 const constant int* w_shape [[buffer(11)]],
    -
    1550 const constant size_t* w_strides [[buffer(12)]],
    -
    1551 const constant size_t* s_strides [[buffer(13)]],
    -
    1552 const constant size_t* b_strides [[buffer(14)]],
    -
    1553 const constant int& batch_ndims [[buffer(15)]],
    -
    1554 const constant int* batch_shape [[buffer(16)]],
    -
    1555 const device uint32_t* lhs_indices [[buffer(17)]],
    -
    1556 const device uint32_t* rhs_indices [[buffer(18)]],
    -
    1557 const constant size_t* lhs_strides [[buffer(19)]],
    -
    1558 const constant size_t* rhs_strides [[buffer(20)]],
    -
    1559 uint3 tid [[threadgroup_position_in_grid]],
    -
    1560 uint simd_gid [[simdgroup_index_in_threadgroup]],
    -
    1561 uint simd_lid [[thread_index_in_simdgroup]]) {
    -
    1562 adjust_matrix_offsets<T>(
    -
    1563 x,
    -
    1564 w,
    -
    1565 scales,
    -
    1566 biases,
    -
    1567 lhs_indices,
    -
    1568 rhs_indices,
    -
    1569 y,
    -
    1570 out_vec_size,
    -
    1571 batch_ndims,
    -
    1572 batch_shape,
    -
    1573 lhs_strides,
    -
    1574 rhs_strides,
    -
    1575 x_batch_ndims,
    -
    1576 x_shape,
    -
    1577 x_strides,
    -
    1578 w_batch_ndims,
    -
    1579 w_shape,
    -
    1580 w_strides,
    -
    1581 s_strides,
    -
    1582 b_strides,
    -
    1583 tid);
    -
    1584 qmv_impl<T, group_size, bits>(
    -
    1585 w,
    -
    1586 scales,
    -
    1587 biases,
    -
    1588 x,
    -
    1589 y,
    -
    1590 in_vec_size,
    -
    1591 out_vec_size,
    -
    1592 tid,
    -
    1593 simd_gid,
    -
    1594 simd_lid);
    -
    1595}
    -
    -
    1596
    -
    1597template <typename T, int group_size, int bits>
    -
    -
    1598[[kernel]] void bs_qvm(
    -
    1599 const device uint32_t* w [[buffer(0)]],
    -
    1600 const device T* scales [[buffer(1)]],
    -
    1601 const device T* biases [[buffer(2)]],
    -
    1602 const device T* x [[buffer(3)]],
    -
    1603 device T* y [[buffer(4)]],
    -
    1604 const constant int& in_vec_size [[buffer(5)]],
    -
    1605 const constant int& out_vec_size [[buffer(6)]],
    -
    1606 const constant int& x_batch_ndims [[buffer(7)]],
    -
    1607 const constant int* x_shape [[buffer(8)]],
    -
    1608 const constant size_t* x_strides [[buffer(9)]],
    -
    1609 const constant int& w_batch_ndims [[buffer(10)]],
    -
    1610 const constant int* w_shape [[buffer(11)]],
    -
    1611 const constant size_t* w_strides [[buffer(12)]],
    -
    1612 const constant size_t* s_strides [[buffer(13)]],
    -
    1613 const constant size_t* b_strides [[buffer(14)]],
    -
    1614 const constant int& batch_ndims [[buffer(15)]],
    -
    1615 const constant int* batch_shape [[buffer(16)]],
    -
    1616 const device uint32_t* lhs_indices [[buffer(17)]],
    -
    1617 const device uint32_t* rhs_indices [[buffer(18)]],
    -
    1618 const constant size_t* lhs_strides [[buffer(19)]],
    -
    1619 const constant size_t* rhs_strides [[buffer(20)]],
    -
    1620 uint3 tid [[threadgroup_position_in_grid]],
    -
    1621 uint simd_gid [[simdgroup_index_in_threadgroup]],
    -
    1622 uint simd_lid [[thread_index_in_simdgroup]]) {
    -
    1623 adjust_matrix_offsets<T>(
    -
    1624 x,
    -
    1625 w,
    -
    1626 scales,
    -
    1627 biases,
    -
    1628 lhs_indices,
    -
    1629 rhs_indices,
    -
    1630 y,
    -
    1631 out_vec_size,
    -
    1632 batch_ndims,
    -
    1633 batch_shape,
    -
    1634 lhs_strides,
    -
    1635 rhs_strides,
    -
    1636 x_batch_ndims,
    -
    1637 x_shape,
    -
    1638 x_strides,
    -
    1639 w_batch_ndims,
    -
    1640 w_shape,
    -
    1641 w_strides,
    -
    1642 s_strides,
    -
    1643 b_strides,
    -
    1644 tid);
    -
    1645 qvm_impl<T, group_size, bits>(
    -
    1646 w,
    -
    1647 scales,
    -
    1648 biases,
    -
    1649 x,
    -
    1650 y,
    -
    1651 in_vec_size,
    -
    1652 out_vec_size,
    -
    1653 tid,
    -
    1654 simd_gid,
    -
    1655 simd_lid);
    -
    1656}
    +
    1561
    +
    1562template <
    +
    1563 typename T,
    +
    1564 const int group_size,
    +
    1565 const int bits,
    +
    1566 const bool aligned_N,
    +
    1567 const bool batched,
    +
    1568 const int BM = 32,
    +
    1569 const int BK = 32,
    +
    1570 const int BN = 32>
    +
    +
    1571[[kernel]] void qmm_t(
    +
    1572 const device uint32_t* w [[buffer(0)]],
    +
    1573 const device T* scales [[buffer(1)]],
    +
    1574 const device T* biases [[buffer(2)]],
    +
    1575 const device T* x [[buffer(3)]],
    +
    1576 device T* y [[buffer(4)]],
    +
    1577 const constant int& K [[buffer(5)]],
    +
    1578 const constant int& N [[buffer(6)]],
    +
    1579 const constant int& M [[buffer(7)]],
    +
    1580 const constant int& x_batch_ndims [[buffer(8)]],
    +
    1581 const constant int* x_shape [[buffer(9)]],
    +
    1582 const constant size_t* x_strides [[buffer(10)]],
    +
    1583 const constant int& w_batch_ndims [[buffer(11)]],
    +
    1584 const constant int* w_shape [[buffer(12)]],
    +
    1585 const constant size_t* w_strides [[buffer(13)]],
    +
    1586 const constant size_t* s_strides [[buffer(14)]],
    +
    1587 const constant size_t* b_strides [[buffer(15)]],
    +
    1588 uint3 tid [[threadgroup_position_in_grid]],
    +
    1589 uint lid [[thread_index_in_threadgroup]],
    +
    1590 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    1591 uint simd_lid [[thread_index_in_simdgroup]]) {
    +
    1592 (void)lid;
    +
    1593
    +
    1594 constexpr int BK_padded = (BK + 16 / sizeof(T));
    +
    1595
    +
    1596 threadgroup T Xs[BM * BK_padded];
    +
    1597 threadgroup T Ws[BN * BK_padded];
    +
    1598
    +
    1599 if (batched) {
    +
    1600 adjust_matrix_offsets<T>(
    +
    1601 x,
    +
    1602 w,
    +
    1603 scales,
    +
    1604 biases,
    +
    1605 y,
    +
    1606 M * N,
    +
    1607 x_batch_ndims,
    +
    1608 x_shape,
    +
    1609 x_strides,
    +
    1610 w_batch_ndims,
    +
    1611 w_shape,
    +
    1612 w_strides,
    +
    1613 s_strides,
    +
    1614 b_strides,
    +
    1615 tid);
    +
    1616 }
    +
    1617 qmm_t_impl<T, group_size, bits, aligned_N, BM, BK, BN>(
    +
    1618 w, scales, biases, x, y, Xs, Ws, K, N, M, tid, lid, simd_gid, simd_lid);
    +
    1619}
    +
    1620
    +
    1621template <
    +
    1622 typename T,
    +
    1623 const int group_size,
    +
    1624 const int bits,
    +
    1625 const bool batched,
    +
    1626 const int BM = 32,
    +
    1627 const int BK = 32,
    +
    1628 const int BN = 32>
    +
    +
    1629[[kernel]] void qmm_n(
    +
    1630 const device uint32_t* w [[buffer(0)]],
    +
    1631 const device T* scales [[buffer(1)]],
    +
    1632 const device T* biases [[buffer(2)]],
    +
    1633 const device T* x [[buffer(3)]],
    +
    1634 device T* y [[buffer(4)]],
    +
    1635 const constant int& K [[buffer(5)]],
    +
    1636 const constant int& N [[buffer(6)]],
    +
    1637 const constant int& M [[buffer(7)]],
    +
    1638 const constant int& x_batch_ndims [[buffer(8)]],
    +
    1639 const constant int* x_shape [[buffer(9)]],
    +
    1640 const constant size_t* x_strides [[buffer(10)]],
    +
    1641 const constant int& w_batch_ndims [[buffer(11)]],
    +
    1642 const constant int* w_shape [[buffer(12)]],
    +
    1643 const constant size_t* w_strides [[buffer(13)]],
    +
    1644 const constant size_t* s_strides [[buffer(14)]],
    +
    1645 const constant size_t* b_strides [[buffer(15)]],
    +
    1646 uint3 tid [[threadgroup_position_in_grid]],
    +
    1647 uint lid [[thread_index_in_threadgroup]],
    +
    1648 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    1649 uint simd_lid [[thread_index_in_simdgroup]]) {
    +
    1650 (void)lid;
    +
    1651
    +
    1652 constexpr int BK_padded = (BK + 16 / sizeof(T));
    +
    1653 constexpr int BN_padded = (BN + 16 / sizeof(T));
    +
    1654
    +
    1655 threadgroup T Xs[BM * BK_padded];
    +
    1656 threadgroup T Ws[BK * BN_padded];
    1657
    -
    1658template <
    -
    1659 typename T,
    -
    1660 const int group_size,
    -
    1661 const int bits,
    -
    1662 const bool aligned_N,
    -
    1663 const int BM = 32,
    -
    1664 const int BK = 32,
    -
    1665 const int BN = 32>
    -
    -
    1666[[kernel]] void bs_qmm_t(
    -
    1667 const device uint32_t* w [[buffer(0)]],
    -
    1668 const device T* scales [[buffer(1)]],
    -
    1669 const device T* biases [[buffer(2)]],
    -
    1670 const device T* x [[buffer(3)]],
    -
    1671 device T* y [[buffer(4)]],
    -
    1672 const constant int& K [[buffer(5)]],
    -
    1673 const constant int& N [[buffer(6)]],
    -
    1674 const constant int& M [[buffer(7)]],
    -
    1675 const constant int& x_batch_ndims [[buffer(8)]],
    -
    1676 const constant int* x_shape [[buffer(9)]],
    -
    1677 const constant size_t* x_strides [[buffer(10)]],
    -
    1678 const constant int& w_batch_ndims [[buffer(11)]],
    -
    1679 const constant int* w_shape [[buffer(12)]],
    -
    1680 const constant size_t* w_strides [[buffer(13)]],
    -
    1681 const constant size_t* s_strides [[buffer(14)]],
    -
    1682 const constant size_t* b_strides [[buffer(15)]],
    -
    1683 const constant int& batch_ndims [[buffer(16)]],
    -
    1684 const constant int* batch_shape [[buffer(17)]],
    -
    1685 const device uint32_t* lhs_indices [[buffer(18)]],
    -
    1686 const device uint32_t* rhs_indices [[buffer(19)]],
    -
    1687 const constant size_t* lhs_strides [[buffer(20)]],
    -
    1688 const constant size_t* rhs_strides [[buffer(21)]],
    -
    1689 uint3 tid [[threadgroup_position_in_grid]],
    -
    1690 uint lid [[thread_index_in_threadgroup]],
    -
    1691 uint simd_gid [[simdgroup_index_in_threadgroup]],
    -
    1692 uint simd_lid [[thread_index_in_simdgroup]]) {
    -
    1693 (void)lid;
    -
    1694
    -
    1695 constexpr int BK_padded = (BK + 16 / sizeof(T));
    -
    1696
    -
    1697 threadgroup T Xs[BM * BK_padded];
    -
    1698 threadgroup T Ws[BN * BK_padded];
    -
    1699
    -
    1700 adjust_matrix_offsets<T>(
    -
    1701 x,
    -
    1702 w,
    -
    1703 scales,
    -
    1704 biases,
    -
    1705 lhs_indices,
    -
    1706 rhs_indices,
    -
    1707 y,
    -
    1708 M * N,
    -
    1709 batch_ndims,
    -
    1710 batch_shape,
    -
    1711 lhs_strides,
    -
    1712 rhs_strides,
    -
    1713 x_batch_ndims,
    -
    1714 x_shape,
    -
    1715 x_strides,
    -
    1716 w_batch_ndims,
    -
    1717 w_shape,
    -
    1718 w_strides,
    -
    1719 s_strides,
    -
    1720 b_strides,
    -
    1721 tid);
    -
    1722 qmm_t_impl<T, group_size, bits, aligned_N, BM, BK, BN>(
    -
    1723 w, scales, biases, x, y, Xs, Ws, K, N, M, tid, lid, simd_gid, simd_lid);
    -
    1724}
    +
    1658 if (batched) {
    +
    1659 adjust_matrix_offsets<T>(
    +
    1660 x,
    +
    1661 w,
    +
    1662 scales,
    +
    1663 biases,
    +
    1664 y,
    +
    1665 M * N,
    +
    1666 x_batch_ndims,
    +
    1667 x_shape,
    +
    1668 x_strides,
    +
    1669 w_batch_ndims,
    +
    1670 w_shape,
    +
    1671 w_strides,
    +
    1672 s_strides,
    +
    1673 b_strides,
    +
    1674 tid);
    +
    1675 }
    +
    1676
    +
    1677 qmm_n_impl<T, group_size, bits, BM, BK, BN>(
    +
    1678 w, scales, biases, x, y, Xs, Ws, K, N, M, tid, lid, simd_gid, simd_lid);
    +
    1679}
    -
    1725
    -
    1726template <
    -
    1727 typename T,
    -
    1728 const int group_size,
    -
    1729 const int bits,
    -
    1730 const int BM = 32,
    -
    1731 const int BK = 32,
    -
    1732 const int BN = 32>
    -
    -
    1733[[kernel]] void bs_qmm_n(
    -
    1734 const device uint32_t* w [[buffer(0)]],
    -
    1735 const device T* scales [[buffer(1)]],
    -
    1736 const device T* biases [[buffer(2)]],
    -
    1737 const device T* x [[buffer(3)]],
    -
    1738 device T* y [[buffer(4)]],
    -
    1739 const constant int& K [[buffer(5)]],
    -
    1740 const constant int& N [[buffer(6)]],
    -
    1741 const constant int& M [[buffer(7)]],
    -
    1742 const constant int& x_batch_ndims [[buffer(8)]],
    -
    1743 const constant int* x_shape [[buffer(9)]],
    -
    1744 const constant size_t* x_strides [[buffer(10)]],
    -
    1745 const constant int& w_batch_ndims [[buffer(11)]],
    -
    1746 const constant int* w_shape [[buffer(12)]],
    -
    1747 const constant size_t* w_strides [[buffer(13)]],
    -
    1748 const constant size_t* s_strides [[buffer(14)]],
    -
    1749 const constant size_t* b_strides [[buffer(15)]],
    -
    1750 const constant int& batch_ndims [[buffer(16)]],
    -
    1751 const constant int* batch_shape [[buffer(17)]],
    -
    1752 const device uint32_t* lhs_indices [[buffer(18)]],
    -
    1753 const device uint32_t* rhs_indices [[buffer(19)]],
    -
    1754 const constant size_t* lhs_strides [[buffer(20)]],
    -
    1755 const constant size_t* rhs_strides [[buffer(21)]],
    -
    1756 uint3 tid [[threadgroup_position_in_grid]],
    -
    1757 uint lid [[thread_index_in_threadgroup]],
    -
    1758 uint simd_gid [[simdgroup_index_in_threadgroup]],
    -
    1759 uint simd_lid [[thread_index_in_simdgroup]]) {
    -
    1760 (void)lid;
    -
    1761
    -
    1762 constexpr int BK_padded = (BK + 16 / sizeof(T));
    -
    1763 constexpr int BN_padded = (BN + 16 / sizeof(T));
    -
    1764
    -
    1765 threadgroup T Xs[BM * BK_padded];
    -
    1766 threadgroup T Ws[BK * BN_padded];
    -
    1767
    +
    1680
    +
    1681template <typename T, int group_size, int bits>
    +
    +
    1682[[kernel]] void bs_qmv_fast(
    +
    1683 const device uint32_t* w [[buffer(0)]],
    +
    1684 const device T* scales [[buffer(1)]],
    +
    1685 const device T* biases [[buffer(2)]],
    +
    1686 const device T* x [[buffer(3)]],
    +
    1687 device T* y [[buffer(4)]],
    +
    1688 const constant int& in_vec_size [[buffer(5)]],
    +
    1689 const constant int& out_vec_size [[buffer(6)]],
    +
    1690 const constant int& x_batch_ndims [[buffer(7)]],
    +
    1691 const constant int* x_shape [[buffer(8)]],
    +
    1692 const constant size_t* x_strides [[buffer(9)]],
    +
    1693 const constant int& w_batch_ndims [[buffer(10)]],
    +
    1694 const constant int* w_shape [[buffer(11)]],
    +
    1695 const constant size_t* w_strides [[buffer(12)]],
    +
    1696 const constant size_t* s_strides [[buffer(13)]],
    +
    1697 const constant size_t* b_strides [[buffer(14)]],
    +
    1698 const constant int& batch_ndims [[buffer(15)]],
    +
    1699 const constant int* batch_shape [[buffer(16)]],
    +
    1700 const device uint32_t* lhs_indices [[buffer(17)]],
    +
    1701 const device uint32_t* rhs_indices [[buffer(18)]],
    +
    1702 const constant size_t* lhs_strides [[buffer(19)]],
    +
    1703 const constant size_t* rhs_strides [[buffer(20)]],
    +
    1704 uint3 tid [[threadgroup_position_in_grid]],
    +
    1705 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    1706 uint simd_lid [[thread_index_in_simdgroup]]) {
    +
    1707 adjust_matrix_offsets<T>(
    +
    1708 x,
    +
    1709 w,
    +
    1710 scales,
    +
    1711 biases,
    +
    1712 lhs_indices,
    +
    1713 rhs_indices,
    +
    1714 y,
    +
    1715 out_vec_size,
    +
    1716 batch_ndims,
    +
    1717 batch_shape,
    +
    1718 lhs_strides,
    +
    1719 rhs_strides,
    +
    1720 x_batch_ndims,
    +
    1721 x_shape,
    +
    1722 x_strides,
    +
    1723 w_batch_ndims,
    +
    1724 w_shape,
    +
    1725 w_strides,
    +
    1726 s_strides,
    +
    1727 b_strides,
    +
    1728 tid);
    +
    1729 qmv_fast_impl<T, group_size, bits>(
    +
    1730 w,
    +
    1731 scales,
    +
    1732 biases,
    +
    1733 x,
    +
    1734 y,
    +
    1735 in_vec_size,
    +
    1736 out_vec_size,
    +
    1737 tid,
    +
    1738 simd_gid,
    +
    1739 simd_lid);
    +
    1740}
    +
    +
    1741
    +
    1742template <typename T, int group_size, int bits>
    +
    +
    1743[[kernel]] void bs_qmv(
    +
    1744 const device uint32_t* w [[buffer(0)]],
    +
    1745 const device T* scales [[buffer(1)]],
    +
    1746 const device T* biases [[buffer(2)]],
    +
    1747 const device T* x [[buffer(3)]],
    +
    1748 device T* y [[buffer(4)]],
    +
    1749 const constant int& in_vec_size [[buffer(5)]],
    +
    1750 const constant int& out_vec_size [[buffer(6)]],
    +
    1751 const constant int& x_batch_ndims [[buffer(7)]],
    +
    1752 const constant int* x_shape [[buffer(8)]],
    +
    1753 const constant size_t* x_strides [[buffer(9)]],
    +
    1754 const constant int& w_batch_ndims [[buffer(10)]],
    +
    1755 const constant int* w_shape [[buffer(11)]],
    +
    1756 const constant size_t* w_strides [[buffer(12)]],
    +
    1757 const constant size_t* s_strides [[buffer(13)]],
    +
    1758 const constant size_t* b_strides [[buffer(14)]],
    +
    1759 const constant int& batch_ndims [[buffer(15)]],
    +
    1760 const constant int* batch_shape [[buffer(16)]],
    +
    1761 const device uint32_t* lhs_indices [[buffer(17)]],
    +
    1762 const device uint32_t* rhs_indices [[buffer(18)]],
    +
    1763 const constant size_t* lhs_strides [[buffer(19)]],
    +
    1764 const constant size_t* rhs_strides [[buffer(20)]],
    +
    1765 uint3 tid [[threadgroup_position_in_grid]],
    +
    1766 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    1767 uint simd_lid [[thread_index_in_simdgroup]]) {
    1768 adjust_matrix_offsets<T>(
    1769 x,
    1770 w,
    @@ -1927,7 +1921,7 @@ $(function(){ initResizable(false); });
    1773 lhs_indices,
    1774 rhs_indices,
    1775 y,
    -
    1776 M * N,
    +
    1776 out_vec_size,
    1777 batch_ndims,
    1778 batch_shape,
    1779 lhs_strides,
    @@ -1941,229 +1935,442 @@ $(function(){ initResizable(false); });
    1787 s_strides,
    1788 b_strides,
    1789 tid);
    -
    1790 qmm_n_impl<T, group_size, bits, BM, BK, BN>(
    -
    1791 w, scales, biases, x, y, Xs, Ws, K, N, M, tid, lid, simd_gid, simd_lid);
    -
    1792}
    +
    1790 qmv_impl<T, group_size, bits>(
    +
    1791 w,
    +
    1792 scales,
    +
    1793 biases,
    +
    1794 x,
    +
    1795 y,
    +
    1796 in_vec_size,
    +
    1797 out_vec_size,
    +
    1798 tid,
    +
    1799 simd_gid,
    +
    1800 simd_lid);
    +
    1801}
    -
    1793
    -
    1794template <typename T, const int group_size, const int bits>
    -
    -
    1795[[kernel]] void affine_quantize(
    -
    1796 const device T* w [[buffer(0)]],
    -
    1797 device uint8_t* out [[buffer(1)]],
    -
    1798 device T* scales [[buffer(2)]],
    -
    1799 device T* biases [[buffer(3)]],
    -
    1800 uint2 index [[thread_position_in_grid]],
    -
    1801 uint2 grid_dim [[threads_per_grid]]) {
    -
    1802 constexpr T eps = T(1e-7);
    -
    1803 constexpr int simd_size = 32;
    -
    1804 constexpr int uint8_bits = 8;
    -
    1805 constexpr T n_bins = (1 << bits) - 1;
    -
    1806 constexpr int packs_per_int = uint8_bits / bits;
    -
    1807 constexpr int values_per_reduce = group_size / simd_size;
    -
    1808 constexpr int writes_per_reduce = packs_per_int / values_per_reduce;
    -
    1809 constexpr int writes_per_pack =
    -
    1810 writes_per_reduce > 1 ? 1 : values_per_reduce / packs_per_int;
    -
    1811
    -
    1812 static_assert(
    -
    1813 group_size % simd_size == 0,
    -
    1814 "Group size must be divisible by simd size.");
    -
    1815
    -
    1816 size_t offset = index.x + grid_dim.x * size_t(index.y);
    -
    1817 size_t in_index = offset * values_per_reduce;
    -
    1818 size_t out_index = offset * writes_per_pack;
    -
    1819
    -
    1820 T w_thread[values_per_reduce];
    -
    1821 T w_min = Limits<T>::max;
    -
    1822 T w_max = 0;
    -
    1823
    -
    1824#pragma clang loop unroll(full)
    -
    1825 for (int i = 0; i < values_per_reduce; i++) {
    -
    1826 T val = w[in_index + i];
    -
    1827 w_thread[i] = val;
    -
    1828 w_min = min(w_min, val);
    -
    1829 w_max = max(w_max, val);
    -
    1830 }
    -
    1831
    -
    1832 w_min = simd_min(w_min);
    -
    1833 w_max = simd_max(w_max);
    -
    1834
    -
    1835 T scale = max((w_max - w_min) / n_bins, eps);
    -
    1836 bool side = abs(w_min) > abs(w_max);
    -
    1837 scale = side ? scale : -scale;
    -
    1838 T edge = side ? w_min : w_max;
    -
    1839 T q0 = round(edge / scale);
    -
    1840 bool at_zero = q0 == 0.0f;
    -
    1841 scale = at_zero ? scale : edge / q0;
    -
    1842 T bias = at_zero ? T(0) : edge;
    -
    1843
    -
    1844 // Write out the scales and biases
    -
    1845 size_t gindex = in_index / group_size;
    -
    1846 if (in_index % group_size == 0) {
    -
    1847 scales[gindex] = scale;
    -
    1848 biases[gindex] = bias;
    -
    1849 }
    -
    1850
    -
    1851 uint8_t output = 0;
    -
    1852#pragma clang loop unroll(full)
    -
    1853 for (int i = 0; i < values_per_reduce; i++) {
    -
    1854 uint8_t val = min(round((w_thread[i] - bias) / scale), n_bins);
    -
    1855 if (bits == 8) {
    -
    1856 output = val;
    -
    1857 } else {
    -
    1858 output += val << (bits * (i % packs_per_int));
    -
    1859 }
    -
    1860
    -
    1861 if (packs_per_int < values_per_reduce &&
    -
    1862 i % packs_per_int == packs_per_int - 1) {
    -
    1863 out[out_index + i / packs_per_int] = output;
    -
    1864 output = 0;
    -
    1865 } else {
    -
    1866#pragma clang loop unroll(full)
    -
    1867 for (int j = 0; j < writes_per_reduce - 1; j++) {
    -
    1868 uint8_t sval = simd_shuffle_down(val, j + 1);
    -
    1869 output += sval << (bits * (values_per_reduce + j + i));
    -
    1870 }
    -
    1871 }
    -
    1872 }
    -
    1873 if (writes_per_reduce > 0 && out_index % writes_per_reduce == 0) {
    -
    1874 out[out_index / writes_per_reduce] = output;
    -
    1875 }
    -
    1876}
    +
    1802
    +
    1803template <typename T, int group_size, int bits>
    +
    +
    1804[[kernel]] void bs_qvm(
    +
    1805 const device uint32_t* w [[buffer(0)]],
    +
    1806 const device T* scales [[buffer(1)]],
    +
    1807 const device T* biases [[buffer(2)]],
    +
    1808 const device T* x [[buffer(3)]],
    +
    1809 device T* y [[buffer(4)]],
    +
    1810 const constant int& in_vec_size [[buffer(5)]],
    +
    1811 const constant int& out_vec_size [[buffer(6)]],
    +
    1812 const constant int& x_batch_ndims [[buffer(7)]],
    +
    1813 const constant int* x_shape [[buffer(8)]],
    +
    1814 const constant size_t* x_strides [[buffer(9)]],
    +
    1815 const constant int& w_batch_ndims [[buffer(10)]],
    +
    1816 const constant int* w_shape [[buffer(11)]],
    +
    1817 const constant size_t* w_strides [[buffer(12)]],
    +
    1818 const constant size_t* s_strides [[buffer(13)]],
    +
    1819 const constant size_t* b_strides [[buffer(14)]],
    +
    1820 const constant int& batch_ndims [[buffer(15)]],
    +
    1821 const constant int* batch_shape [[buffer(16)]],
    +
    1822 const device uint32_t* lhs_indices [[buffer(17)]],
    +
    1823 const device uint32_t* rhs_indices [[buffer(18)]],
    +
    1824 const constant size_t* lhs_strides [[buffer(19)]],
    +
    1825 const constant size_t* rhs_strides [[buffer(20)]],
    +
    1826 uint3 tid [[threadgroup_position_in_grid]],
    +
    1827 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    1828 uint simd_lid [[thread_index_in_simdgroup]]) {
    +
    1829 adjust_matrix_offsets<T>(
    +
    1830 x,
    +
    1831 w,
    +
    1832 scales,
    +
    1833 biases,
    +
    1834 lhs_indices,
    +
    1835 rhs_indices,
    +
    1836 y,
    +
    1837 out_vec_size,
    +
    1838 batch_ndims,
    +
    1839 batch_shape,
    +
    1840 lhs_strides,
    +
    1841 rhs_strides,
    +
    1842 x_batch_ndims,
    +
    1843 x_shape,
    +
    1844 x_strides,
    +
    1845 w_batch_ndims,
    +
    1846 w_shape,
    +
    1847 w_strides,
    +
    1848 s_strides,
    +
    1849 b_strides,
    +
    1850 tid);
    +
    1851 qvm_impl<T, group_size, bits>(
    +
    1852 w,
    +
    1853 scales,
    +
    1854 biases,
    +
    1855 x,
    +
    1856 y,
    +
    1857 in_vec_size,
    +
    1858 out_vec_size,
    +
    1859 tid,
    +
    1860 simd_gid,
    +
    1861 simd_lid);
    +
    1862}
    -
    1877
    -
    1878template <typename T, const int group_size, const int bits>
    -
    -
    1879[[kernel]] void affine_quantize_scales_biases(
    -
    1880 const device T* w [[buffer(0)]],
    -
    1881 const device T* scales [[buffer(1)]],
    -
    1882 const device T* biases [[buffer(2)]],
    -
    1883 device uint8_t* out [[buffer(3)]],
    -
    1884 uint2 index [[thread_position_in_grid]],
    -
    1885 uint2 grid_dim [[threads_per_grid]]) {
    -
    1886 constexpr int uint8_bits = 8;
    -
    1887 constexpr int packs_per_int = uint8_bits / bits;
    -
    1888 constexpr T n_bins = (1 << bits) - 1;
    -
    1889
    -
    1890 size_t offset = index.x + grid_dim.x * size_t(index.y);
    -
    1891 size_t in_index = offset * packs_per_int;
    -
    1892 size_t gindex = in_index / group_size;
    -
    1893
    -
    1894 T scale = scales[gindex];
    -
    1895 T bias = biases[gindex];
    -
    1896
    -
    1897 uint8_t output = 0;
    -
    1898#pragma clang loop unroll(full)
    -
    1899 for (int i = 0; i < packs_per_int; i++) {
    -
    1900 uint8_t val = min(round((w[in_index + i] - bias) / scale), n_bins);
    -
    1901 if (bits == 8) {
    -
    1902 output = val;
    -
    1903 } else {
    -
    1904 output += val << (bits * i);
    -
    1905 }
    -
    1906 }
    -
    1907 out[offset] = output;
    -
    1908}
    +
    1863
    +
    1864template <
    +
    1865 typename T,
    +
    1866 const int group_size,
    +
    1867 const int bits,
    +
    1868 const bool aligned_N,
    +
    1869 const int BM = 32,
    +
    1870 const int BK = 32,
    +
    1871 const int BN = 32>
    +
    +
    1872[[kernel]] void bs_qmm_t(
    +
    1873 const device uint32_t* w [[buffer(0)]],
    +
    1874 const device T* scales [[buffer(1)]],
    +
    1875 const device T* biases [[buffer(2)]],
    +
    1876 const device T* x [[buffer(3)]],
    +
    1877 device T* y [[buffer(4)]],
    +
    1878 const constant int& K [[buffer(5)]],
    +
    1879 const constant int& N [[buffer(6)]],
    +
    1880 const constant int& M [[buffer(7)]],
    +
    1881 const constant int& x_batch_ndims [[buffer(8)]],
    +
    1882 const constant int* x_shape [[buffer(9)]],
    +
    1883 const constant size_t* x_strides [[buffer(10)]],
    +
    1884 const constant int& w_batch_ndims [[buffer(11)]],
    +
    1885 const constant int* w_shape [[buffer(12)]],
    +
    1886 const constant size_t* w_strides [[buffer(13)]],
    +
    1887 const constant size_t* s_strides [[buffer(14)]],
    +
    1888 const constant size_t* b_strides [[buffer(15)]],
    +
    1889 const constant int& batch_ndims [[buffer(16)]],
    +
    1890 const constant int* batch_shape [[buffer(17)]],
    +
    1891 const device uint32_t* lhs_indices [[buffer(18)]],
    +
    1892 const device uint32_t* rhs_indices [[buffer(19)]],
    +
    1893 const constant size_t* lhs_strides [[buffer(20)]],
    +
    1894 const constant size_t* rhs_strides [[buffer(21)]],
    +
    1895 uint3 tid [[threadgroup_position_in_grid]],
    +
    1896 uint lid [[thread_index_in_threadgroup]],
    +
    1897 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    1898 uint simd_lid [[thread_index_in_simdgroup]]) {
    +
    1899 (void)lid;
    +
    1900
    +
    1901 constexpr int BK_padded = (BK + 16 / sizeof(T));
    +
    1902
    +
    1903 threadgroup T Xs[BM * BK_padded];
    +
    1904 threadgroup T Ws[BN * BK_padded];
    +
    1905
    +
    1906 adjust_matrix_offsets<T>(
    +
    1907 x,
    +
    1908 w,
    +
    1909 scales,
    +
    1910 biases,
    +
    1911 lhs_indices,
    +
    1912 rhs_indices,
    +
    1913 y,
    +
    1914 M * N,
    +
    1915 batch_ndims,
    +
    1916 batch_shape,
    +
    1917 lhs_strides,
    +
    1918 rhs_strides,
    +
    1919 x_batch_ndims,
    +
    1920 x_shape,
    +
    1921 x_strides,
    +
    1922 w_batch_ndims,
    +
    1923 w_shape,
    +
    1924 w_strides,
    +
    1925 s_strides,
    +
    1926 b_strides,
    +
    1927 tid);
    +
    1928 qmm_t_impl<T, group_size, bits, aligned_N, BM, BK, BN>(
    +
    1929 w, scales, biases, x, y, Xs, Ws, K, N, M, tid, lid, simd_gid, simd_lid);
    +
    1930}
    -
    1909
    -
    1910template <typename T, const int group_size, const int bits>
    -
    -
    1911[[kernel]] void affine_dequantize(
    -
    1912 const device uint8_t* w [[buffer(0)]],
    -
    1913 const device T* scales [[buffer(1)]],
    -
    1914 const device T* biases [[buffer(2)]],
    -
    1915 device T* out [[buffer(3)]],
    -
    1916 uint2 index [[thread_position_in_grid]],
    -
    1917 uint2 grid_dim [[threads_per_grid]]) {
    -
    1918 constexpr int uint8_bits = 8;
    -
    1919 constexpr int packs_per_int = uint8_bits / bits;
    -
    1920
    -
    1921 size_t offset = index.x + grid_dim.x * size_t(index.y);
    -
    1922 size_t oindex = offset * packs_per_int;
    -
    1923 size_t gindex = oindex / group_size;
    -
    1924 T scale = scales[gindex];
    -
    1925 T bias = biases[gindex];
    -
    1926 uint val = w[offset];
    -
    1927
    -
    1928#pragma clang loop unroll(full)
    -
    1929 for (int i = 0; i < packs_per_int; i++) {
    -
    1930 uint8_t d;
    -
    1931 if (bits == 2) {
    -
    1932 d = (val >> (bits * i)) & 0x03;
    -
    1933 } else if (bits == 4) {
    -
    1934 d = (val >> (bits * i)) & 0x0f;
    -
    1935 } else if (bits == 8) {
    -
    1936 d = val;
    -
    1937 }
    -
    1938 out[oindex + i] = scale * d + bias;
    -
    1939 }
    -
    1940}
    +
    1931
    +
    1932template <
    +
    1933 typename T,
    +
    1934 const int group_size,
    +
    1935 const int bits,
    +
    1936 const int BM = 32,
    +
    1937 const int BK = 32,
    +
    1938 const int BN = 32>
    +
    +
    1939[[kernel]] void bs_qmm_n(
    +
    1940 const device uint32_t* w [[buffer(0)]],
    +
    1941 const device T* scales [[buffer(1)]],
    +
    1942 const device T* biases [[buffer(2)]],
    +
    1943 const device T* x [[buffer(3)]],
    +
    1944 device T* y [[buffer(4)]],
    +
    1945 const constant int& K [[buffer(5)]],
    +
    1946 const constant int& N [[buffer(6)]],
    +
    1947 const constant int& M [[buffer(7)]],
    +
    1948 const constant int& x_batch_ndims [[buffer(8)]],
    +
    1949 const constant int* x_shape [[buffer(9)]],
    +
    1950 const constant size_t* x_strides [[buffer(10)]],
    +
    1951 const constant int& w_batch_ndims [[buffer(11)]],
    +
    1952 const constant int* w_shape [[buffer(12)]],
    +
    1953 const constant size_t* w_strides [[buffer(13)]],
    +
    1954 const constant size_t* s_strides [[buffer(14)]],
    +
    1955 const constant size_t* b_strides [[buffer(15)]],
    +
    1956 const constant int& batch_ndims [[buffer(16)]],
    +
    1957 const constant int* batch_shape [[buffer(17)]],
    +
    1958 const device uint32_t* lhs_indices [[buffer(18)]],
    +
    1959 const device uint32_t* rhs_indices [[buffer(19)]],
    +
    1960 const constant size_t* lhs_strides [[buffer(20)]],
    +
    1961 const constant size_t* rhs_strides [[buffer(21)]],
    +
    1962 uint3 tid [[threadgroup_position_in_grid]],
    +
    1963 uint lid [[thread_index_in_threadgroup]],
    +
    1964 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    1965 uint simd_lid [[thread_index_in_simdgroup]]) {
    +
    1966 (void)lid;
    +
    1967
    +
    1968 constexpr int BK_padded = (BK + 16 / sizeof(T));
    +
    1969 constexpr int BN_padded = (BN + 16 / sizeof(T));
    +
    1970
    +
    1971 threadgroup T Xs[BM * BK_padded];
    +
    1972 threadgroup T Ws[BK * BN_padded];
    +
    1973
    +
    1974 adjust_matrix_offsets<T>(
    +
    1975 x,
    +
    1976 w,
    +
    1977 scales,
    +
    1978 biases,
    +
    1979 lhs_indices,
    +
    1980 rhs_indices,
    +
    1981 y,
    +
    1982 M * N,
    +
    1983 batch_ndims,
    +
    1984 batch_shape,
    +
    1985 lhs_strides,
    +
    1986 rhs_strides,
    +
    1987 x_batch_ndims,
    +
    1988 x_shape,
    +
    1989 x_strides,
    +
    1990 w_batch_ndims,
    +
    1991 w_shape,
    +
    1992 w_strides,
    +
    1993 s_strides,
    +
    1994 b_strides,
    +
    1995 tid);
    +
    1996 qmm_n_impl<T, group_size, bits, BM, BK, BN>(
    +
    1997 w, scales, biases, x, y, Xs, Ws, K, N, M, tid, lid, simd_gid, simd_lid);
    +
    1998}
    +
    +
    1999
    +
    2000template <typename T, const int group_size, const int bits>
    +
    +
    2001[[kernel]] void affine_quantize(
    +
    2002 const device T* w [[buffer(0)]],
    +
    2003 device uint8_t* out [[buffer(1)]],
    +
    2004 device T* scales [[buffer(2)]],
    +
    2005 device T* biases [[buffer(3)]],
    +
    2006 uint2 index [[thread_position_in_grid]],
    +
    2007 uint2 grid_dim [[threads_per_grid]]) {
    +
    2008 constexpr T eps = T(1e-7);
    +
    2009 constexpr int simd_size = 32;
    +
    2010 constexpr T n_bins = (1 << bits) - 1;
    +
    2011 constexpr int packs_per_int = bits == 3 ? 8 : bits == 6 ? 4 : 8 / bits;
    +
    2012 constexpr int values_per_reduce = group_size / simd_size;
    +
    2013 constexpr int writes_per_reduce = packs_per_int / values_per_reduce;
    +
    2014 constexpr int writes_per_pack =
    +
    2015 writes_per_reduce > 1 ? 1 : values_per_reduce / packs_per_int;
    +
    2016 constexpr int power_of_2_bits = (bits & (bits - 1)) == 0;
    +
    2017 constexpr int bytes_per_pack = power_of_2_bits ? 1 : 3;
    +
    2018
    +
    2019 static_assert(
    +
    2020 group_size % simd_size == 0,
    +
    2021 "Group size must be divisible by simd size.");
    +
    2022
    +
    2023 size_t offset = index.x + grid_dim.x * size_t(index.y);
    +
    2024 size_t in_index = offset * values_per_reduce;
    +
    2025 size_t out_index = power_of_2_bits
    +
    2026 ? offset * writes_per_pack
    +
    2027 : offset * bytes_per_pack / writes_per_reduce;
    +
    2028
    +
    2029 T w_thread[values_per_reduce];
    +
    2030 T w_min = Limits<T>::max;
    +
    2031 T w_max = 0;
    +
    2032
    +
    2033#pragma clang loop unroll(full)
    +
    2034 for (int i = 0; i < values_per_reduce; i++) {
    +
    2035 T val = w[in_index + i];
    +
    2036 w_thread[i] = val;
    +
    2037 w_min = min(w_min, val);
    +
    2038 w_max = max(w_max, val);
    +
    2039 }
    +
    2040
    +
    2041 w_min = simd_min(w_min);
    +
    2042 w_max = simd_max(w_max);
    +
    2043
    +
    2044 T scale = max((w_max - w_min) / n_bins, eps);
    +
    2045 bool side = abs(w_min) > abs(w_max);
    +
    2046 scale = side ? scale : -scale;
    +
    2047 T edge = side ? w_min : w_max;
    +
    2048 T q0 = round(edge / scale);
    +
    2049 bool at_zero = q0 == 0.0f;
    +
    2050 scale = at_zero ? scale : edge / q0;
    +
    2051 T bias = at_zero ? T(0) : edge;
    +
    2052
    +
    2053 // Write out the scales and biases
    +
    2054 size_t gindex = in_index / group_size;
    +
    2055 if (in_index % group_size == 0) {
    +
    2056 scales[gindex] = scale;
    +
    2057 biases[gindex] = bias;
    +
    2058 }
    +
    2059
    +
    2060 // We accumulate 3 bytes worth for 3/6 bit so we need a uint32_t
    +
    2061 uint32_t output = 0;
    +
    2062
    +
    2063#pragma clang loop unroll(full)
    +
    2064 for (int i = 0; i < values_per_reduce; i++) {
    +
    2065 uint8_t val = min(round((w_thread[i] - bias) / scale), n_bins);
    +
    2066 if (bits == 8) {
    +
    2067 output = val;
    +
    2068 } else {
    +
    2069 output += val << (bits * (i % packs_per_int));
    +
    2070 }
    +
    2071
    +
    2072 if (packs_per_int < values_per_reduce &&
    +
    2073 i % packs_per_int == packs_per_int - 1) {
    +
    2074 out[out_index + i / packs_per_int] = output;
    +
    2075 output = 0;
    +
    2076 } else {
    +
    2077#pragma clang loop unroll(full)
    +
    2078 for (int j = 1; j < writes_per_reduce; j++) {
    +
    2079 uint8_t sval = simd_shuffle_down(val, j);
    +
    2080 output += sval << (bits * (j * values_per_reduce + i));
    +
    2081 }
    +
    2082 }
    +
    2083 }
    +
    2084 if (bits == 3 || bits == 6) {
    +
    2085 if (in_index % packs_per_int == 0 && out_index % bytes_per_pack == 0) {
    +
    2086 out[out_index] = output & 0xff;
    +
    2087 out[out_index + 1] = (output & 0xff00) >> 8;
    +
    2088 out[out_index + 2] = (output & 0xff0000) >> 16;
    +
    2089 }
    +
    2090 } else {
    +
    2091 if (writes_per_reduce > 0 && out_index % writes_per_reduce == 0) {
    +
    2092 out[out_index / writes_per_reduce] = output;
    +
    2093 }
    +
    2094 }
    +
    2095}
    +
    +
    2096
    +
    2097template <typename T, const int group_size, const int bits>
    +
    +
    2098[[kernel]] void affine_dequantize(
    +
    2099 const device uint8_t* w [[buffer(0)]],
    +
    2100 const device T* scales [[buffer(1)]],
    +
    2101 const device T* biases [[buffer(2)]],
    +
    2102 device T* out [[buffer(3)]],
    +
    2103 uint2 index [[thread_position_in_grid]],
    +
    2104 uint2 grid_dim [[threads_per_grid]]) {
    +
    2105 constexpr int packs_per_int = bits == 3 ? 8 : bits == 6 ? 4 : 8 / bits;
    +
    2106 constexpr int power_of_2_bits = (bits & (bits - 1)) == 0;
    +
    2107 constexpr int bytes_per_pack = power_of_2_bits ? 1 : 3;
    +
    2108
    +
    2109 size_t offset = index.x + grid_dim.x * size_t(index.y);
    +
    2110 size_t oindex = offset * packs_per_int;
    +
    2111 size_t gindex = oindex / group_size;
    +
    2112 T scale = scales[gindex];
    +
    2113 T bias = biases[gindex];
    +
    2114
    +
    2115 out += oindex;
    +
    2116
    +
    2117 if (bits == 3) {
    +
    2118 w += offset * bytes_per_pack;
    +
    2119 out[0] = (w[0] & 0x7) * scale + bias;
    +
    2120 out[1] = ((w[0] & 0x38) >> 3) * scale + bias;
    +
    2121 out[2] = (((w[0] & 0xc0) >> 6) + ((w[1] & 0x1) << 2)) * scale + bias;
    +
    2122 out[3] = ((w[1] & 0xe) >> 1) * scale + bias;
    +
    2123 out[4] = ((w[1] & 0x70) >> 4) * scale + bias;
    +
    2124 out[5] = (((w[1] & 0x80) >> 7) + ((w[2] & 0x3) << 1)) * scale + bias;
    +
    2125 out[6] = ((w[2] & 0x1c) >> 2) * scale + bias;
    +
    2126 out[7] = ((w[2] & 0xe0) >> 5) * scale + bias;
    +
    2127
    +
    2128 } else if (bits == 6) {
    +
    2129 w += offset * bytes_per_pack;
    +
    2130 out[0] = (w[0] & 0x3f) * scale + bias;
    +
    2131 out[1] = (((w[0] >> 6) & 0x03) + ((w[1] & 0x0f) << 2)) * scale + bias;
    +
    2132 out[2] = (((w[1] >> 4) & 0x0f) + ((w[2] & 0x03) << 4)) * scale + bias;
    +
    2133 out[3] = ((w[2] >> 2) & 0x3f) * scale + bias;
    +
    2134 } else {
    +
    2135 uint val = w[offset];
    +
    2136#pragma clang loop unroll(full)
    +
    2137 for (int i = 0; i < packs_per_int; i++) {
    +
    2138 uint8_t d;
    +
    2139 if (bits == 2) {
    +
    2140 d = (val >> (bits * i)) & 0x03;
    +
    2141 } else if (bits == 4) {
    +
    2142 d = (val >> (bits * i)) & 0x0f;
    +
    2143 } else if (bits == 8) {
    +
    2144 d = val;
    +
    2145 }
    +
    2146 out[i] = scale * d + bias;
    +
    2147 }
    +
    2148 }
    +
    2149}
    simd_size
    static constant constexpr const uint8_t simd_size
    Definition ops.h:22
    elem_to_loc_broadcast
    METAL_FUNC ulong2 elem_to_loc_broadcast(uint elem, constant const int *shape, constant const size_t *a_strides, constant const size_t *b_strides, int ndim)
    Definition utils.h:7
    -
    elem_to_loc
    METAL_FUNC stride_t elem_to_loc(uint elem, constant const int *shape, constant const stride_t *strides, int ndim)
    Definition utils.h:87
    -
    metal
    Definition bf16.h:265
    -
    metal::simd_max
    METAL_FUNC bfloat16_t simd_max(bfloat16_t data)
    Definition bf16_math.h:392
    -
    metal::round
    METAL_FUNC bfloat16_t round(bfloat16_t x)
    Definition bf16_math.h:234
    -
    metal::min
    METAL_FUNC bfloat16_t min(bfloat16_t x, bfloat16_t y)
    Definition bf16_math.h:234
    -
    metal::simd_sum
    METAL_FUNC bfloat16_t simd_sum(bfloat16_t data)
    Definition bf16_math.h:392
    -
    metal::max
    METAL_FUNC bfloat16_t max(bfloat16_t x, bfloat16_t y)
    Definition bf16_math.h:234
    -
    metal::abs
    METAL_FUNC bfloat16_t abs(bfloat16_t x)
    Definition bf16_math.h:234
    -
    metal::simd_min
    METAL_FUNC bfloat16_t simd_min(bfloat16_t data)
    Definition bf16_math.h:392
    -
    metal::simd_shuffle_down
    METAL_FUNC bfloat16_t simd_shuffle_down(bfloat16_t data, ushort delta)
    Definition bf16_math.h:391
    +
    elem_to_loc
    METAL_FUNC IdxT elem_to_loc(uint elem, constant const int *shape, constant const StrideT *strides, int ndim)
    Definition utils.h:93
    +
    metal
    Definition bf16_math.h:226
    +
    metal::simd_max
    METAL_FUNC bfloat16_t simd_max(bfloat16_t data)
    Definition bf16_math.h:378
    +
    metal::round
    METAL_FUNC bfloat16_t round(bfloat16_t x)
    Definition bf16_math.h:232
    +
    metal::min
    METAL_FUNC bfloat16_t min(bfloat16_t x, bfloat16_t y)
    Definition bf16_math.h:232
    +
    metal::simd_sum
    METAL_FUNC bfloat16_t simd_sum(bfloat16_t data)
    Definition bf16_math.h:378
    +
    metal::max
    METAL_FUNC bfloat16_t max(bfloat16_t x, bfloat16_t y)
    Definition bf16_math.h:232
    +
    metal::abs
    METAL_FUNC bfloat16_t abs(bfloat16_t x)
    Definition bf16_math.h:232
    +
    metal::simd_min
    METAL_FUNC bfloat16_t simd_min(bfloat16_t data)
    Definition bf16_math.h:378
    +
    metal::simd_shuffle_down
    METAL_FUNC bfloat16_t simd_shuffle_down(bfloat16_t data, ushort delta)
    Definition bf16_math.h:377
    mlx::core::random::bits
    array bits(const std::vector< int > &shape, int width, const std::optional< array > &key=std::nullopt, StreamOrDevice s={})
    Generate an array with type uint32 filled with random bits.
    MLX_MTL_CONST
    #define MLX_MTL_CONST
    Definition quantized.h:8
    -
    qdot_safe
    U qdot_safe(const device uint8_t *w, const thread U *x_thread, U scale, U bias, U sum, int N)
    Definition quantized.h:142
    -
    qmm_n_impl
    METAL_FUNC void qmm_n_impl(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, threadgroup T *Xs, threadgroup T *Ws, const constant int &K, const constant int &N, const constant int &M, uint3 tid, uint lid, uint simd_gid, uint simd_lid)
    Definition quantized.h:879
    -
    qvm_impl
    METAL_FUNC void qvm_impl(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const int in_vec_size, const int out_vec_size, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:647
    -
    bs_qmm_n
    void bs_qmm_n(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &K, const constant int &N, const constant int &M, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, const constant int &batch_ndims, const constant int *batch_shape, const device uint32_t *lhs_indices, const device uint32_t *rhs_indices, const constant size_t *lhs_strides, const constant size_t *rhs_strides, uint3 tid, uint lid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1733
    -
    qmm_n
    void qmm_n(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &K, const constant int &N, const constant int &M, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid, uint lid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1423
    -
    affine_quantize
    void affine_quantize(const device T *w, device uint8_t *out, device T *scales, device T *biases, uint2 index, uint2 grid_dim)
    Definition quantized.h:1795
    -
    bs_qmv_fast
    void bs_qmv_fast(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, const constant int &batch_ndims, const constant int *batch_shape, const device uint32_t *lhs_indices, const device uint32_t *rhs_indices, const constant size_t *lhs_strides, const constant size_t *rhs_strides, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1476
    -
    affine_dequantize
    void affine_dequantize(const device uint8_t *w, const device T *scales, const device T *biases, device T *out, uint2 index, uint2 grid_dim)
    Definition quantized.h:1911
    +
    qdot_safe
    U qdot_safe(const device uint8_t *w, const thread U *x_thread, U scale, U bias, U sum, int N)
    Definition quantized.h:225
    +
    qmm_n_impl
    METAL_FUNC void qmm_n_impl(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, threadgroup T *Xs, threadgroup T *Ws, const constant int &K, const constant int &N, const constant int &M, uint3 tid, uint lid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1081
    +
    qvm_impl
    METAL_FUNC void qvm_impl(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const int in_vec_size, const int out_vec_size, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:843
    +
    bs_qmm_n
    void bs_qmm_n(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &K, const constant int &N, const constant int &M, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, const constant int &batch_ndims, const constant int *batch_shape, const device uint32_t *lhs_indices, const device uint32_t *rhs_indices, const constant size_t *lhs_strides, const constant size_t *rhs_strides, uint3 tid, uint lid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1939
    +
    qmm_n
    void qmm_n(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &K, const constant int &N, const constant int &M, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid, uint lid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1629
    +
    affine_quantize
    void affine_quantize(const device T *w, device uint8_t *out, device T *scales, device T *biases, uint2 index, uint2 grid_dim)
    Definition quantized.h:2001
    +
    bs_qmv_fast
    void bs_qmv_fast(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, const constant int &batch_ndims, const constant int *batch_shape, const device uint32_t *lhs_indices, const device uint32_t *rhs_indices, const constant size_t *lhs_strides, const constant size_t *rhs_strides, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1682
    +
    affine_dequantize
    void affine_dequantize(const device uint8_t *w, const device T *scales, const device T *biases, device T *out, uint2 index, uint2 grid_dim)
    Definition quantized.h:2098
    SIMD_SIZE
    static constant constexpr const int SIMD_SIZE
    Definition quantized.h:10
    -
    qmv
    void qmv(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1200
    -
    bs_qvm
    void bs_qvm(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, const constant int &batch_ndims, const constant int *batch_shape, const device uint32_t *lhs_indices, const device uint32_t *rhs_indices, const constant size_t *lhs_strides, const constant size_t *rhs_strides, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1598
    -
    affine_quantize_scales_biases
    void affine_quantize_scales_biases(const device T *w, const device T *scales, const device T *biases, device uint8_t *out, uint2 index, uint2 grid_dim)
    Definition quantized.h:1879
    -
    qmv_fast
    void qmv_fast(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1149
    -
    qmv_quad
    void qmv_quad(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid, uint quad_gid, uint quad_lid)
    Definition quantized.h:1098
    +
    qmv
    void qmv(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1406
    +
    bs_qvm
    void bs_qvm(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, const constant int &batch_ndims, const constant int *batch_shape, const device uint32_t *lhs_indices, const device uint32_t *rhs_indices, const constant size_t *lhs_strides, const constant size_t *rhs_strides, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1804
    +
    qmv_fast
    void qmv_fast(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1355
    +
    qmv_quad
    void qmv_quad(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid, uint quad_gid, uint quad_lid)
    Definition quantized.h:1304
    QUAD_SIZE
    static constant constexpr const int QUAD_SIZE
    Definition quantized.h:11
    load_vector
    U load_vector(const device T *x, thread U *x_thread)
    Definition quantized.h:14
    -
    qmv_impl
    METAL_FUNC void qmv_impl(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:498
    -
    load_vector_safe
    U load_vector_safe(const device T *x, thread U *x_thread, int N)
    Definition quantized.h:52
    -
    bs_qmm_t
    void bs_qmm_t(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &K, const constant int &N, const constant int &M, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, const constant int &batch_ndims, const constant int *batch_shape, const device uint32_t *lhs_indices, const device uint32_t *rhs_indices, const constant size_t *lhs_strides, const constant size_t *rhs_strides, uint3 tid, uint lid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1666
    -
    qdot
    U qdot(const device uint8_t *w, const thread U *x_thread, U scale, U bias, U sum)
    Definition quantized.h:99
    -
    qvm_split_k
    void qvm_split_k(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, const constant int &final_block_size, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1302
    -
    qmv_fast_impl
    METAL_FUNC void qmv_fast_impl(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:434
    -
    qmm_t
    void qmm_t(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &K, const constant int &N, const constant int &M, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid, uint lid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1365
    -
    adjust_matrix_offsets
    METAL_FUNC void adjust_matrix_offsets(const device T *&x, const device uint32_t *&w, const device T *&scales, const device T *&biases, device T *&y, int output_stride, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid)
    Definition quantized.h:1005
    -
    bs_qmv
    void bs_qmv(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, const constant int &batch_ndims, const constant int *batch_shape, const device uint32_t *lhs_indices, const device uint32_t *rhs_indices, const constant size_t *lhs_strides, const constant size_t *rhs_strides, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1537
    -
    qmv_quad_impl
    METAL_FUNC void qmv_quad_impl(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, constant int &in_vec_size, const constant int &out_vec_size, uint3 tid, uint quad_gid, uint quad_lid)
    Definition quantized.h:376
    -
    qvm
    void qvm(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1251
    -
    qouter
    void qouter(const thread uint8_t *w, U x, U scale, U bias, thread U *result)
    Definition quantized.h:187
    -
    dequantize
    void dequantize(const device uint8_t *w, U scale, U bias, threadgroup U *w_local)
    Definition quantized.h:219
    -
    qmm_t_impl
    METAL_FUNC void qmm_t_impl(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, threadgroup T *Xs, threadgroup T *Ws, const constant int &K, const constant int &N, const constant int &M, uint3 tid, uint lid, uint simd_gid, uint simd_lid)
    Definition quantized.h:758
    -
    Limits
    Definition utils.h:17
    -
    QuantizedBlockLoader
    Definition quantized.h:262
    -
    QuantizedBlockLoader::group_stride
    const int group_stride
    Definition quantized.h:282
    -
    QuantizedBlockLoader::BCOLS_PACKED
    static constant constexpr const short BCOLS_PACKED
    Definition quantized.h:274
    -
    QuantizedBlockLoader::biases
    const device T * biases
    Definition quantized.h:291
    -
    QuantizedBlockLoader::group_step_cnt
    short group_step_cnt
    Definition quantized.h:281
    -
    QuantizedBlockLoader::group_steps
    static constant constexpr const short group_steps
    Definition quantized.h:277
    -
    QuantizedBlockLoader::thread_idx
    const short thread_idx
    Definition quantized.h:284
    -
    QuantizedBlockLoader::scales
    const device T * scales
    Definition quantized.h:290
    -
    QuantizedBlockLoader::n_reads
    static constant constexpr const short n_reads
    Definition quantized.h:275
    -
    QuantizedBlockLoader::next
    void next()
    Definition quantized.h:354
    -
    QuantizedBlockLoader::load_safe
    void load_safe(short2 src_tile_dim) const
    Definition quantized.h:327
    -
    QuantizedBlockLoader::src_ld
    const int src_ld
    Definition quantized.h:279
    -
    QuantizedBlockLoader::bi
    const short bi
    Definition quantized.h:285
    -
    QuantizedBlockLoader::load_unsafe
    void load_unsafe() const
    Definition quantized.h:314
    -
    QuantizedBlockLoader::pack_factor
    static constant constexpr const short pack_factor
    Definition quantized.h:273
    -
    QuantizedBlockLoader::dst
    threadgroup T * dst
    Definition quantized.h:288
    -
    QuantizedBlockLoader::tile_stride
    const int tile_stride
    Definition quantized.h:280
    -
    QuantizedBlockLoader::src
    const device uint32_t * src
    Definition quantized.h:289
    -
    QuantizedBlockLoader::bj
    const short bj
    Definition quantized.h:286
    -
    QuantizedBlockLoader::QuantizedBlockLoader
    QuantizedBlockLoader(const device uint32_t *src_, const device T *scales_, const device T *biases_, const int src_ld_, threadgroup T *dst_, ushort simd_group_id, ushort simd_lane_id)
    Definition quantized.h:293
    +
    qmv_impl
    METAL_FUNC void qmv_impl(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:688
    +
    load_vector_safe
    U load_vector_safe(const device T *x, thread U *x_thread, int N)
    Definition quantized.h:77
    +
    bs_qmm_t
    void bs_qmm_t(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &K, const constant int &N, const constant int &M, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, const constant int &batch_ndims, const constant int *batch_shape, const device uint32_t *lhs_indices, const device uint32_t *rhs_indices, const constant size_t *lhs_strides, const constant size_t *rhs_strides, uint3 tid, uint lid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1872
    +
    qdot
    U qdot(const device uint8_t *w, const thread U *x_thread, U scale, U bias, U sum)
    Definition quantized.h:145
    +
    qvm_split_k
    void qvm_split_k(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, const constant int &final_block_size, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1508
    +
    qmv_fast_impl
    METAL_FUNC void qmv_fast_impl(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:620
    +
    qmm_t
    void qmm_t(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &K, const constant int &N, const constant int &M, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid, uint lid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1571
    +
    adjust_matrix_offsets
    METAL_FUNC void adjust_matrix_offsets(const device T *&x, const device uint32_t *&w, const device T *&scales, const device T *&biases, device T *&y, int output_stride, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid)
    Definition quantized.h:1211
    +
    bs_qmv
    void bs_qmv(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, const constant int &batch_ndims, const constant int *batch_shape, const device uint32_t *lhs_indices, const device uint32_t *rhs_indices, const constant size_t *lhs_strides, const constant size_t *rhs_strides, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1743
    +
    qmv_quad_impl
    METAL_FUNC void qmv_quad_impl(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, constant int &in_vec_size, const constant int &out_vec_size, uint3 tid, uint quad_gid, uint quad_lid)
    Definition quantized.h:563
    +
    qvm
    void qvm(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, const constant int &in_vec_size, const constant int &out_vec_size, const constant int &x_batch_ndims, const constant int *x_shape, const constant size_t *x_strides, const constant int &w_batch_ndims, const constant int *w_shape, const constant size_t *w_strides, const constant size_t *s_strides, const constant size_t *b_strides, uint3 tid, uint simd_gid, uint simd_lid)
    Definition quantized.h:1457
    +
    qouter
    void qouter(const thread uint8_t *w, U x, U scale, U bias, thread U *result)
    Definition quantized.h:307
    +
    dequantize
    void dequantize(const device uint8_t *w, U scale, U bias, threadgroup U *w_local)
    Definition quantized.h:372
    +
    qmm_t_impl
    METAL_FUNC void qmm_t_impl(const device uint32_t *w, const device T *scales, const device T *biases, const device T *x, device T *y, threadgroup T *Xs, threadgroup T *Ws, const constant int &K, const constant int &N, const constant int &M, uint3 tid, uint lid, uint simd_gid, uint simd_lid)
    Definition quantized.h:956
    +
    Limits
    Definition utils.h:23
    +
    QuantizedBlockLoader
    Definition quantized.h:443
    +
    QuantizedBlockLoader::group_stride
    const int group_stride
    Definition quantized.h:464
    +
    QuantizedBlockLoader::BCOLS_PACKED
    static constant constexpr const short BCOLS_PACKED
    Definition quantized.h:456
    +
    QuantizedBlockLoader::biases
    const device T * biases
    Definition quantized.h:473
    +
    QuantizedBlockLoader::group_step_cnt
    short group_step_cnt
    Definition quantized.h:463
    +
    QuantizedBlockLoader::group_steps
    static constant constexpr const short group_steps
    Definition quantized.h:459
    +
    QuantizedBlockLoader::thread_idx
    const short thread_idx
    Definition quantized.h:466
    +
    QuantizedBlockLoader::QuantizedBlockLoader
    QuantizedBlockLoader(const device uint8_t *src_, const device T *scales_, const device T *biases_, const int src_ld_, threadgroup T *dst_, ushort simd_group_id, ushort simd_lane_id)
    Definition quantized.h:475
    +
    QuantizedBlockLoader::scales
    const device T * scales
    Definition quantized.h:472
    +
    QuantizedBlockLoader::n_reads
    static constant constexpr const short n_reads
    Definition quantized.h:457
    +
    QuantizedBlockLoader::next
    void next()
    Definition quantized.h:541
    +
    QuantizedBlockLoader::load_safe
    void load_safe(short2 src_tile_dim) const
    Definition quantized.h:511
    +
    QuantizedBlockLoader::src_ld
    const int src_ld
    Definition quantized.h:461
    +
    QuantizedBlockLoader::bi
    const short bi
    Definition quantized.h:467
    +
    QuantizedBlockLoader::load_unsafe
    void load_unsafe() const
    Definition quantized.h:498
    +
    QuantizedBlockLoader::pack_factor
    static constant constexpr const short pack_factor
    Definition quantized.h:454
    +
    QuantizedBlockLoader::dst
    threadgroup T * dst
    Definition quantized.h:470
    +
    QuantizedBlockLoader::src
    const device uint8_t * src
    Definition quantized.h:471
    +
    QuantizedBlockLoader::tile_stride
    const int tile_stride
    Definition quantized.h:462
    +
    QuantizedBlockLoader::bytes_per_pack
    static constant constexpr const short bytes_per_pack
    Definition quantized.h:455
    +
    QuantizedBlockLoader::bj
    const short bj
    Definition quantized.h:468
    mlx::steel::BlockLoader
    Definition loader.h:25
    diff --git a/docs/build/html/radix_8h_source.html b/docs/build/html/radix_8h_source.html index 22d91ce0..8ec8e64b 100644 --- a/docs/build/html/radix_8h_source.html +++ b/docs/build/html/radix_8h_source.html @@ -447,8 +447,8 @@ $(function(){ initResizable(false); });
    327 y[2] = x[12] * inv + x[0];
    328}
    -
    metal::fast::sin
    METAL_FUNC bfloat16_t sin(bfloat16_t x)
    Definition bf16_math.h:242
    -
    metal::fast::cos
    METAL_FUNC bfloat16_t cos(bfloat16_t x)
    Definition bf16_math.h:242
    +
    metal::fast::sin
    METAL_FUNC bfloat16_t sin(bfloat16_t x)
    Definition bf16_math.h:240
    +
    metal::fast::cos
    METAL_FUNC bfloat16_t cos(bfloat16_t x)
    Definition bf16_math.h:240
    radix5
    METAL_FUNC void radix5(thread float2 *x, thread float2 *y)
    Definition radix.h:69
    complex_mul_conj
    METAL_FUNC float2 complex_mul_conj(float2 a, float2 b)
    Definition radix.h:24
    radix4
    METAL_FUNC void radix4(thread float2 *x, thread float2 *y)
    Definition radix.h:56
    diff --git a/docs/build/html/readwrite_8h_source.html b/docs/build/html/readwrite_8h_source.html index 9b6e62dd..39ba91ca 100644 --- a/docs/build/html/readwrite_8h_source.html +++ b/docs/build/html/readwrite_8h_source.html @@ -778,8 +778,8 @@ $(function(){ initResizable(false); });
    buf
    MTL::Buffer * buf
    Definition allocator.h:39
    inv_
    static constant constexpr const bool inv_
    Definition fft.h:23
    elems_per_thread_
    static constant constexpr const int elems_per_thread_
    Definition fft.h:25
    -
    metal
    Definition bf16.h:265
    -
    metal::min
    METAL_FUNC bfloat16_t min(bfloat16_t x, bfloat16_t y)
    Definition bf16_math.h:234
    +
    metal
    Definition bf16_math.h:226
    +
    metal::min
    METAL_FUNC bfloat16_t min(bfloat16_t x, bfloat16_t y)
    Definition bf16_math.h:232
    complex_mul
    METAL_FUNC float2 complex_mul(float2 a, float2 b)
    Definition radix.h:19
    get_twiddle
    METAL_FUNC float2 get_twiddle(int k, int p)
    Definition radix.h:29
    diff --git a/docs/build/html/reduce__all_8h.html b/docs/build/html/reduce__all_8h.html index 1b115a39..ed33e10f 100644 --- a/docs/build/html/reduce__all_8h.html +++ b/docs/build/html/reduce__all_8h.html @@ -98,18 +98,18 @@ $(function(){ initResizable(false); }); - - - + + +

    Functions

    template<typename T , typename U , typename Op , int N_READS = REDUCE_N_READS>
    void all_reduce (const device T *in, device U *out, const constant size_t &in_size, const constant size_t &row_size, uint3 gid, uint3 lid, uint3 lsize, uint simd_per_group, uint simd_lane_id, uint simd_group_id)
     
    template<typename T , typename U , typename Op , typename IdxT = int64_t, int N_READS = REDUCE_N_READS>
    void all_reduce (const device T *in, device U *out, const constant size_t &in_size, const constant size_t &row_size, uint3 gid, uint3 lid, uint3 lsize, uint simd_per_group, uint simd_lane_id, uint simd_group_id)
     

    Function Documentation

    - -

    ◆ all_reduce()

    + +

    ◆ all_reduce()

    -template<typename T , typename U , typename Op , int N_READS = REDUCE_N_READS>
    +template<typename T , typename U , typename Op , typename IdxT = int64_t, int N_READS = REDUCE_N_READS>
    diff --git a/docs/build/html/reduce__all_8h_source.html b/docs/build/html/reduce__all_8h_source.html index 013cf714..34ab309f 100644 --- a/docs/build/html/reduce__all_8h_source.html +++ b/docs/build/html/reduce__all_8h_source.html @@ -93,70 +93,76 @@ $(function(){ initResizable(false); });
    Go to the documentation of this file.
    1// Copyright © 2023-2024 Apple Inc.
    2
    -
    3template <typename T, typename U, typename Op, int N_READS = REDUCE_N_READS>
    -
    -
    4[[kernel]] void all_reduce(
    -
    5 const device T* in [[buffer(0)]],
    -
    6 device U* out [[buffer(1)]],
    -
    7 const constant size_t& in_size [[buffer(2)]],
    -
    8 const constant size_t& row_size [[buffer(3)]],
    -
    9 uint3 gid [[threadgroup_position_in_grid]],
    -
    10 uint3 lid [[thread_position_in_threadgroup]],
    -
    11 uint3 lsize [[threads_per_threadgroup]],
    -
    12 uint simd_per_group [[simdgroups_per_threadgroup]],
    -
    13 uint simd_lane_id [[thread_index_in_simdgroup]],
    -
    14 uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
    -
    15 Op op;
    -
    16 threadgroup U shared_vals[simd_size];
    -
    17
    -
    18 U total = Op::init;
    -
    19 int64_t start_idx = gid.y * row_size;
    -
    20 int64_t actual_row =
    -
    21 (start_idx + row_size <= in_size) ? row_size : in_size - start_idx;
    -
    22 int64_t blocks = actual_row / (lsize.x * N_READS);
    -
    23 int extra = actual_row - blocks * (lsize.x * N_READS);
    -
    24 extra -= lid.x * N_READS;
    -
    25 start_idx += lid.x * N_READS;
    -
    26 in += start_idx;
    -
    27
    -
    28 if (extra >= N_READS) {
    -
    29 blocks++;
    -
    30 extra = 0;
    -
    31 }
    +
    3template <
    +
    4 typename T,
    +
    5 typename U,
    +
    6 typename Op,
    +
    7 typename IdxT = int64_t,
    +
    8 int N_READS = REDUCE_N_READS>
    +
    +
    9[[kernel]] void all_reduce(
    +
    10 const device T* in [[buffer(0)]],
    +
    11 device U* out [[buffer(1)]],
    +
    12 const constant size_t& in_size [[buffer(2)]],
    +
    13 const constant size_t& row_size [[buffer(3)]],
    +
    14 uint3 gid [[threadgroup_position_in_grid]],
    +
    15 uint3 lid [[thread_position_in_threadgroup]],
    +
    16 uint3 lsize [[threads_per_threadgroup]],
    +
    17 uint simd_per_group [[simdgroups_per_threadgroup]],
    +
    18 uint simd_lane_id [[thread_index_in_simdgroup]],
    +
    19 uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
    +
    20 Op op;
    +
    21 threadgroup U shared_vals[simd_size];
    +
    22
    +
    23 U total = Op::init;
    +
    24 IdxT start_idx = gid.y * IdxT(row_size);
    +
    25 IdxT actual_row =
    +
    26 (start_idx + row_size <= in_size) ? row_size : in_size - start_idx;
    +
    27 IdxT blocks = actual_row / (lsize.x * N_READS);
    +
    28 int extra = actual_row - blocks * (lsize.x * N_READS);
    +
    29 extra -= lid.x * N_READS;
    +
    30 start_idx += lid.x * N_READS;
    +
    31 in += start_idx;
    32
    -
    33 for (int64_t b = 0; b < blocks; b++) {
    -
    34 for (int i = 0; i < N_READS; i++) {
    -
    35 total = op(static_cast<U>(in[i]), total);
    -
    36 }
    -
    37 in += lsize.x * N_READS;
    -
    38 }
    -
    39 if (extra > 0) {
    -
    40 for (int i = 0; i < extra; i++) {
    -
    41 total = op(static_cast<U>(in[i]), total);
    -
    42 }
    +
    33 if (extra >= N_READS) {
    +
    34 blocks++;
    +
    35 extra = 0;
    +
    36 }
    +
    37
    +
    38 for (IdxT b = 0; b < blocks; b++) {
    +
    39 for (int i = 0; i < N_READS; i++) {
    +
    40 total = op(static_cast<U>(in[i]), total);
    +
    41 }
    +
    42 in += lsize.x * N_READS;
    43 }
    -
    44
    -
    45 // Reduction within simd group
    -
    46 total = op.simd_reduce(total);
    -
    47 if (simd_per_group > 1) {
    -
    48 if (simd_lane_id == 0) {
    -
    49 shared_vals[simd_group_id] = total;
    -
    50 }
    -
    51
    -
    52 // Reduction within thread group
    -
    53 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    54 total = lid.x < simd_per_group ? shared_vals[lid.x] : op.init;
    -
    55 total = op.simd_reduce(total);
    -
    56 }
    -
    57
    -
    58 if (lid.x == 0) {
    -
    59 out[gid.y] = total;
    -
    60 }
    -
    61}
    +
    44 if (extra > 0) {
    +
    45 for (int i = 0; i < extra; i++) {
    +
    46 total = op(static_cast<U>(in[i]), total);
    +
    47 }
    +
    48 }
    +
    49
    +
    50 // Reduction within simd group
    +
    51 total = op.simd_reduce(total);
    +
    52 if (simd_per_group > 1) {
    +
    53 if (simd_lane_id == 0) {
    +
    54 shared_vals[simd_group_id] = total;
    +
    55 }
    +
    56
    +
    57 // Reduction within thread group
    +
    58 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    59 total = lid.x < simd_per_group ? shared_vals[lid.x] : op.init;
    +
    60 total = op.simd_reduce(total);
    +
    61 }
    +
    62
    +
    63 if (lid.x == 0) {
    +
    64 out[gid.y] = total;
    +
    65 }
    +
    66}
    simd_size
    static constant constexpr const uint8_t simd_size
    Definition ops.h:22
    op
    Op op
    Definition binary.h:129
    -
    all_reduce
    void all_reduce(const device T *in, device U *out, const constant size_t &in_size, const constant size_t &row_size, uint3 gid, uint3 lid, uint3 lsize, uint simd_per_group, uint simd_lane_id, uint simd_group_id)
    Definition reduce_all.h:4
    +
    REDUCE_N_READS
    static constexpr int REDUCE_N_READS
    Definition defines.h:12
    +
    all_reduce
    void all_reduce(const device T *in, device U *out, const constant size_t &in_size, const constant size_t &row_size, uint3 gid, uint3 lid, uint3 lsize, uint simd_per_group, uint simd_lane_id, uint simd_group_id)
    Definition reduce_all.h:9
    diff --git a/docs/build/html/reduce__col_8h.html b/docs/build/html/reduce__col_8h.html index b7dda2ed..92d3040f 100644 --- a/docs/build/html/reduce__col_8h.html +++ b/docs/build/html/reduce__col_8h.html @@ -98,28 +98,28 @@ $(function(){ initResizable(false); });
    void all_reduce
    - - - - - - - - - - - - - + + + + + + + + + + + + +

    Functions

    template<typename T , typename U , typename Op , int NDIMS>
    void col_reduce_small (const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &reduction_stride, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, const constant size_t &non_col_reductions, uint3 gid, uint3 gsize, uint3 lid, uint3 lsize)
     
    template<typename T , typename U , typename Op , int NDIMS>
    void col_reduce_longcolumn (const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &reduction_stride, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, const constant size_t &non_col_reductions, const constant size_t &out_size, uint3 gid, uint3 gsize, uint3 lid, uint3 lsize)
     
    template<typename T , typename U , typename Op , int NDIMS, int BM, int BN>
    void col_reduce_looped (const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &reduction_stride, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, const constant size_t &non_col_reductions, uint3 gid, uint3 gsize, uint simd_lane_id, uint simd_group_id)
     Our approach is the following simple looped approach:
     
    template<typename T , typename U , typename Op , int NDIMS, int BM, int BN>
    void col_reduce_2pass (const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &reduction_stride, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, const constant size_t &non_col_reductions, const constant size_t &out_size, uint3 gid, uint3 gsize, uint simd_lane_id, uint simd_group_id)
     
    template<typename T , typename U , typename Op , typename IdxT , int NDIMS>
    void col_reduce_small (const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &reduction_stride, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, const constant size_t &non_col_reductions, uint3 gid, uint3 gsize, uint3 lid, uint3 lsize)
     
    template<typename T , typename U , typename Op , typename IdxT , int NDIMS>
    void col_reduce_longcolumn (const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &reduction_stride, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, const constant size_t &non_col_reductions, const constant size_t &out_size, uint3 gid, uint3 gsize, uint3 lid, uint3 lsize)
     
    template<typename T , typename U , typename Op , typename IdxT , int NDIMS, int BM, int BN>
    void col_reduce_looped (const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &reduction_stride, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, const constant size_t &non_col_reductions, uint3 gid, uint3 gsize, uint simd_lane_id, uint simd_group_id)
     Our approach is the following simple looped approach:
     
    template<typename T , typename U , typename Op , typename IdxT , int NDIMS, int BM, int BN>
    void col_reduce_2pass (const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &reduction_stride, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, const constant size_t &non_col_reductions, const constant size_t &out_size, uint3 gid, uint3 gsize, uint simd_lane_id, uint simd_group_id)
     

    Function Documentation

    - -

    ◆ col_reduce_2pass()

    + +

    ◆ col_reduce_2pass()

    -template<typename T , typename U , typename Op , int NDIMS, int BM, int BN>
    +template<typename T , typename U , typename Op , typename IdxT , int NDIMS, int BM, int BN>
    @@ -206,13 +206,13 @@ template<typename T , typename U , typename Op , int NDIMS, int BM, int BN> - -

    ◆ col_reduce_longcolumn()

    + +

    ◆ col_reduce_longcolumn()

    -template<typename T , typename U , typename Op , int NDIMS>
    +template<typename T , typename U , typename Op , typename IdxT , int NDIMS>
    void col_reduce_2pass
    @@ -299,13 +299,13 @@ template<typename T , typename U , typename Op , int NDIMS> - -

    ◆ col_reduce_looped()

    + +

    ◆ col_reduce_looped()

    -template<typename T , typename U , typename Op , int NDIMS, int BM, int BN>
    +template<typename T , typename U , typename Op , typename IdxT , int NDIMS, int BM, int BN>
    void col_reduce_longcolumn
    @@ -396,13 +396,13 @@ template<typename T , typename U , typename Op , int NDIMS, int BM, int BN> - -

    ◆ col_reduce_small()

    + +

    ◆ col_reduce_small()

    -template<typename T , typename U , typename Op , int NDIMS>
    +template<typename T , typename U , typename Op , typename IdxT , int NDIMS>
    void col_reduce_looped
    diff --git a/docs/build/html/reduce__col_8h_source.html b/docs/build/html/reduce__col_8h_source.html index caf4a5f8..3531ccde 100644 --- a/docs/build/html/reduce__col_8h_source.html +++ b/docs/build/html/reduce__col_8h_source.html @@ -93,9 +93,9 @@ $(function(){ initResizable(false); });
    Go to the documentation of this file.
    1// Copyright © 2023-2024 Apple Inc.
    2
    -
    3template <typename T, typename U, typename Op, int NDIMS>
    +
    3template <typename T, typename U, typename Op, typename IdxT, int NDIMS>
    -
    4[[kernel]] void col_reduce_small(
    +
    4[[kernel]] void col_reduce_small(
    5 const device T* in [[buffer(0)]],
    6 device U* out [[buffer(1)]],
    7 const constant size_t& reduction_size [[buffer(2)]],
    @@ -113,7 +113,7 @@ $(function(){ initResizable(false); });
    19 uint3 lsize [[threads_per_threadgroup]]) {
    20 constexpr int n_reads = 4;
    21 Op op;
    -
    22 looped_elem_to_loc<NDIMS> loop;
    +
    22 LoopedElemToLoc<NDIMS, IdxT, (NDIMS > 2)> loop(reduce_ndim);
    23 const device T* row;
    24
    25 U totals[n_reads];
    @@ -121,20 +121,20 @@ $(function(){ initResizable(false); });
    27 totals[i] = Op::init;
    28 }
    29
    -
    30 size_t column = size_t(gid.x) * lsize.x * n_reads + lid.x * n_reads;
    +
    30 IdxT column = IdxT(gid.x) * lsize.x * n_reads + lid.x * n_reads;
    31 if (column >= reduction_stride) {
    32 return;
    33 }
    34 bool safe = column + n_reads <= reduction_stride;
    35
    -
    36 size_t out_idx = gid.y + gsize.y * size_t(gid.z);
    -
    37 size_t in_idx = elem_to_loc(out_idx, shape, strides, ndim);
    +
    36 IdxT out_idx = gid.y + gsize.y * IdxT(gid.z);
    +
    37 IdxT in_idx = elem_to_loc<size_t, IdxT>(out_idx, shape, strides, ndim);
    38 in += in_idx + column;
    39
    -
    40 size_t total_rows = non_col_reductions * reduction_size;
    -
    41 loop.next(lid.y, reduce_shape, reduce_strides);
    -
    42 for (size_t r = lid.y; r < total_rows; r += lsize.y) {
    -
    43 row = in + loop.location(r, reduce_shape, reduce_strides, reduce_ndim);
    +
    40 IdxT total_rows = IdxT(non_col_reductions) * IdxT(reduction_size);
    +
    41 loop.next(lid.y, reduce_shape, reduce_strides);
    +
    42 for (IdxT r = lid.y; r < total_rows; r += lsize.y) {
    +
    43 row = in + loop.location();
    44 if (safe) {
    45 for (int i = 0; i < n_reads; i++) {
    46 totals[i] = op(static_cast<U>(row[i]), totals[i]);
    @@ -149,7 +149,7 @@ $(function(){ initResizable(false); });
    55 totals[i] = op(vals[i], totals[i]);
    56 }
    57 }
    -
    58 loop.next(lsize.y, reduce_shape, reduce_strides);
    +
    58 loop.next(lsize.y, reduce_shape, reduce_strides);
    59 }
    60
    61 if (lsize.y > 1) {
    @@ -174,7 +174,7 @@ $(function(){ initResizable(false); });
    80 }
    81
    82 if (lid.y == 0) {
    -
    83 out += out_idx * reduction_stride + column;
    +
    83 out += out_idx * IdxT(reduction_stride) + column;
    84 if (safe) {
    85 for (int i = 0; i < n_reads; i++) {
    86 out[i] = totals[i];
    @@ -188,9 +188,9 @@ $(function(){ initResizable(false); });
    94}
    95
    -
    96template <typename T, typename U, typename Op, int NDIMS>
    +
    96template <typename T, typename U, typename Op, typename IdxT, int NDIMS>
    -
    97[[kernel]] void col_reduce_longcolumn(
    +
    97[[kernel]] void col_reduce_longcolumn(
    98 const device T* in [[buffer(0)]],
    99 device U* out [[buffer(1)]],
    100 const constant size_t& reduction_size [[buffer(2)]],
    @@ -208,21 +208,21 @@ $(function(){ initResizable(false); });
    112 uint3 lid [[thread_position_in_threadgroup]],
    113 uint3 lsize [[threads_per_threadgroup]]) {
    114 Op op;
    -
    115 looped_elem_to_loc<NDIMS> loop;
    +
    115 LoopedElemToLoc<NDIMS, IdxT, (NDIMS > 2)> loop(reduce_ndim);
    116 const device T* row;
    117
    -
    118 size_t out_idx = gid.x + gsize.x * size_t(gid.y);
    -
    119 size_t in_idx = elem_to_loc(out_idx, shape, strides, ndim);
    +
    118 IdxT out_idx = gid.x + gsize.x * IdxT(gid.y);
    +
    119 IdxT in_idx = elem_to_loc<size_t, IdxT>(out_idx, shape, strides, ndim);
    120 in += in_idx + lid.x;
    121
    122 U total = Op::init;
    -
    123 size_t total_rows = non_col_reductions * reduction_size;
    -
    124 loop.next(gid.z * lsize.y + lid.y, reduce_shape, reduce_strides);
    -
    125 for (size_t r = gid.z * lsize.y + lid.y; r < total_rows;
    +
    123 IdxT total_rows = IdxT(non_col_reductions) * IdxT(reduction_size);
    +
    124 loop.next(gid.z * lsize.y + lid.y, reduce_shape, reduce_strides);
    +
    125 for (IdxT r = gid.z * lsize.y + lid.y; r < total_rows;
    126 r += lsize.y * gsize.z) {
    -
    127 row = in + loop.location(r, reduce_shape, reduce_strides, reduce_ndim);
    +
    127 row = in + loop.location();
    128 total = op(static_cast<U>(*row), total);
    -
    129 loop.next(lsize.y * gsize.z, reduce_shape, reduce_strides);
    +
    129 loop.next(lsize.y * gsize.z, reduce_shape, reduce_strides);
    130 }
    131
    132 threadgroup U shared_vals[32 * 32];
    @@ -232,256 +232,268 @@ $(function(){ initResizable(false); });
    136 for (uint i = 1; i < lsize.y; i++) {
    137 total = op(total, shared_vals[i * lsize.x + lid.x]);
    138 }
    -
    139 out[gid.z * out_size + out_idx * reduction_stride + lid.x] = total;
    -
    140 }
    -
    141}
    +
    139 out[gid.z * IdxT(out_size) + out_idx * IdxT(reduction_stride) + lid.x] =
    +
    140 total;
    +
    141 }
    +
    142}
    -
    142
    -
    154template <typename T, typename U, typename Op, int NDIMS, int BM, int BN>
    -
    -
    155[[kernel]] void col_reduce_looped(
    -
    156 const device T* in [[buffer(0)]],
    -
    157 device U* out [[buffer(1)]],
    -
    158 const constant size_t& reduction_size [[buffer(2)]],
    -
    159 const constant size_t& reduction_stride [[buffer(3)]],
    -
    160 const constant int* shape [[buffer(4)]],
    -
    161 const constant size_t* strides [[buffer(5)]],
    -
    162 const constant int& ndim [[buffer(6)]],
    -
    163 const constant int* reduce_shape [[buffer(7)]],
    -
    164 const constant size_t* reduce_strides [[buffer(8)]],
    -
    165 const constant int& reduce_ndim [[buffer(9)]],
    -
    166 const constant size_t& non_col_reductions [[buffer(10)]],
    -
    167 uint3 gid [[threadgroup_position_in_grid]],
    -
    168 uint3 gsize [[threadgroups_per_grid]],
    -
    169 uint simd_lane_id [[thread_index_in_simdgroup]],
    -
    170 uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
    -
    171 Op op;
    -
    172 constexpr int n_simdgroups = 8;
    -
    173 constexpr short tgp_size = n_simdgroups * simd_size;
    -
    174 constexpr short n_reads = (BM * BN) / tgp_size;
    -
    175 constexpr short n_read_blocks = BN / n_reads;
    -
    176
    -
    177 threadgroup U shared_vals[BN * BM];
    -
    178 U totals[n_reads];
    -
    179 looped_elem_to_loc<NDIMS> loop;
    -
    180 const device T* row;
    -
    181
    -
    182 for (int i = 0; i < n_reads; i++) {
    -
    183 totals[i] = Op::init;
    -
    184 }
    -
    185
    -
    186 short lid = simd_group_id * simd_size + simd_lane_id;
    -
    187 short2 offset((lid % n_read_blocks) * n_reads, lid / n_read_blocks);
    -
    188 size_t column = BN * gid.x + offset.x;
    -
    189 bool safe = column + n_reads <= reduction_stride;
    -
    190
    -
    191 size_t out_idx = gid.y + gsize.y * size_t(gid.z);
    -
    192 size_t in_idx = elem_to_loc(out_idx, shape, strides, ndim);
    -
    193 in += in_idx + column;
    -
    194
    -
    195 size_t total = non_col_reductions * reduction_size;
    -
    196 loop.next(offset.y, reduce_shape, reduce_strides);
    -
    197 for (size_t r = offset.y; r < total; r += BM) {
    -
    198 row = in + loop.location(r, reduce_shape, reduce_strides, reduce_ndim);
    -
    199
    -
    200 if (safe) {
    -
    201 for (int i = 0; i < n_reads; i++) {
    -
    202 totals[i] = op(static_cast<U>(row[i]), totals[i]);
    -
    203 }
    -
    204 } else {
    -
    205 U vals[n_reads];
    -
    206 for (int i = 0; i < n_reads; i++) {
    -
    207 vals[i] =
    -
    208 (column + i < reduction_stride) ? static_cast<U>(row[i]) : op.init;
    -
    209 }
    -
    210 for (int i = 0; i < n_reads; i++) {
    -
    211 totals[i] = op(vals[i], totals[i]);
    -
    212 }
    -
    213 }
    -
    214
    -
    215 loop.next(BM, reduce_shape, reduce_strides);
    -
    216 }
    -
    217
    -
    218 // We can use a simd reduction to accumulate across BM so each thread writes
    -
    219 // the partial output to SM and then each simdgroup does BN / n_simdgroups
    -
    220 // accumulations.
    -
    221 if (BM == 32) {
    -
    222 constexpr int n_outputs = BN / n_simdgroups;
    -
    223 static_assert(
    -
    224 BM != 32 || n_outputs == n_reads,
    -
    225 "The tile should be selected such that n_outputs == n_reads");
    -
    226 for (int i = 0; i < n_reads; i++) {
    -
    227 shared_vals[offset.y * BN + offset.x + i] = totals[i];
    -
    228 }
    -
    229 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    230 short2 out_offset(simd_group_id * n_outputs, simd_lane_id);
    -
    231 for (int i = 0; i < n_outputs; i++) {
    -
    232 totals[i] =
    -
    233 op.simd_reduce(shared_vals[out_offset.y * BN + out_offset.x + i]);
    -
    234 }
    -
    235
    -
    236 // Write the output.
    -
    237 if (simd_lane_id == 0) {
    -
    238 size_t out_column = BN * gid.x + out_offset.x;
    -
    239 out += out_idx * reduction_stride + out_column;
    -
    240 if (out_column + n_outputs <= reduction_stride) {
    -
    241 for (int i = 0; i < n_outputs; i++) {
    -
    242 out[i] = totals[i];
    -
    243 }
    -
    244 } else {
    -
    245 for (int i = 0; out_column + i < reduction_stride; i++) {
    -
    246 out[i] = totals[i];
    -
    247 }
    -
    248 }
    -
    249 }
    -
    250 }
    -
    251
    -
    252 // Each thread holds n_reads partial results. We write them all out to shared
    -
    253 // memory and threads with offset.y == 0 aggregate the columns and write the
    -
    254 // outputs.
    -
    255 else {
    -
    256 short x_block = offset.x / n_reads;
    -
    257 for (int i = 0; i < n_reads; i++) {
    -
    258 shared_vals[x_block * BM * n_reads + i * BM + offset.y] = totals[i];
    -
    259 }
    -
    260 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    261 if (offset.y == 0) {
    -
    262 for (int i = 0; i < n_reads; i++) {
    -
    263 for (int j = 1; j < BM; j++) {
    -
    264 totals[i] =
    -
    265 op(shared_vals[x_block * BM * n_reads + i * BM + j], totals[i]);
    -
    266 }
    -
    267 }
    -
    268 }
    -
    269
    -
    270 // Write the output.
    -
    271 if (offset.y == 0) {
    -
    272 out += out_idx * reduction_stride + column;
    -
    273 if (safe) {
    -
    274 for (int i = 0; i < n_reads; i++) {
    -
    275 out[i] = totals[i];
    -
    276 }
    -
    277 } else {
    -
    278 for (int i = 0; column + i < reduction_stride; i++) {
    -
    279 out[i] = totals[i];
    -
    280 }
    -
    281 }
    -
    282 }
    -
    283 }
    -
    284}
    +
    143
    +
    155template <
    +
    156 typename T,
    +
    157 typename U,
    +
    158 typename Op,
    +
    159 typename IdxT,
    +
    160 int NDIMS,
    +
    161 int BM,
    +
    162 int BN>
    +
    +
    163[[kernel]] void col_reduce_looped(
    +
    164 const device T* in [[buffer(0)]],
    +
    165 device U* out [[buffer(1)]],
    +
    166 const constant size_t& reduction_size [[buffer(2)]],
    +
    167 const constant size_t& reduction_stride [[buffer(3)]],
    +
    168 const constant int* shape [[buffer(4)]],
    +
    169 const constant size_t* strides [[buffer(5)]],
    +
    170 const constant int& ndim [[buffer(6)]],
    +
    171 const constant int* reduce_shape [[buffer(7)]],
    +
    172 const constant size_t* reduce_strides [[buffer(8)]],
    +
    173 const constant int& reduce_ndim [[buffer(9)]],
    +
    174 const constant size_t& non_col_reductions [[buffer(10)]],
    +
    175 uint3 gid [[threadgroup_position_in_grid]],
    +
    176 uint3 gsize [[threadgroups_per_grid]],
    +
    177 uint simd_lane_id [[thread_index_in_simdgroup]],
    +
    178 uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
    +
    179 Op op;
    +
    180 constexpr int n_simdgroups = 8;
    +
    181 constexpr short tgp_size = n_simdgroups * simd_size;
    +
    182 constexpr short n_reads = (BM * BN) / tgp_size;
    +
    183 constexpr short n_read_blocks = BN / n_reads;
    +
    184
    +
    185 threadgroup U shared_vals[BN * BM];
    +
    186 U totals[n_reads];
    +
    187 LoopedElemToLoc<NDIMS, IdxT, (NDIMS > 2)> loop(reduce_ndim);
    +
    188 const device T* row;
    +
    189
    +
    190 for (int i = 0; i < n_reads; i++) {
    +
    191 totals[i] = Op::init;
    +
    192 }
    +
    193
    +
    194 short lid = simd_group_id * simd_size + simd_lane_id;
    +
    195 short2 offset((lid % n_read_blocks) * n_reads, lid / n_read_blocks);
    +
    196 IdxT column = BN * gid.x + offset.x;
    +
    197 bool safe = column + n_reads <= reduction_stride;
    +
    198
    +
    199 IdxT out_idx = gid.y + gsize.y * IdxT(gid.z);
    +
    200 IdxT in_idx = elem_to_loc<size_t, IdxT>(out_idx, shape, strides, ndim);
    +
    201 in += in_idx + column;
    +
    202
    +
    203 IdxT total = IdxT(non_col_reductions) * IdxT(reduction_size);
    +
    204 loop.next(offset.y, reduce_shape, reduce_strides);
    +
    205 for (IdxT r = offset.y; r < total; r += BM) {
    +
    206 row = in + loop.location();
    +
    207
    +
    208 if (safe) {
    +
    209 for (int i = 0; i < n_reads; i++) {
    +
    210 totals[i] = op(static_cast<U>(row[i]), totals[i]);
    +
    211 }
    +
    212 } else {
    +
    213 U vals[n_reads];
    +
    214 for (int i = 0; i < n_reads; i++) {
    +
    215 vals[i] =
    +
    216 (column + i < reduction_stride) ? static_cast<U>(row[i]) : op.init;
    +
    217 }
    +
    218 for (int i = 0; i < n_reads; i++) {
    +
    219 totals[i] = op(vals[i], totals[i]);
    +
    220 }
    +
    221 }
    +
    222
    +
    223 loop.next(BM, reduce_shape, reduce_strides);
    +
    224 }
    +
    225
    +
    226 // We can use a simd reduction to accumulate across BM so each thread writes
    +
    227 // the partial output to SM and then each simdgroup does BN / n_simdgroups
    +
    228 // accumulations.
    +
    229 if (BM == 32) {
    +
    230 constexpr int n_outputs = BN / n_simdgroups;
    +
    231 static_assert(
    +
    232 BM != 32 || n_outputs == n_reads,
    +
    233 "The tile should be selected such that n_outputs == n_reads");
    +
    234 for (int i = 0; i < n_reads; i++) {
    +
    235 shared_vals[offset.y * BN + offset.x + i] = totals[i];
    +
    236 }
    +
    237 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    238 short2 out_offset(simd_group_id * n_outputs, simd_lane_id);
    +
    239 for (int i = 0; i < n_outputs; i++) {
    +
    240 totals[i] =
    +
    241 op.simd_reduce(shared_vals[out_offset.y * BN + out_offset.x + i]);
    +
    242 }
    +
    243
    +
    244 // Write the output.
    +
    245 if (simd_lane_id == 0) {
    +
    246 IdxT out_column = BN * gid.x + out_offset.x;
    +
    247 out += out_idx * IdxT(reduction_stride) + out_column;
    +
    248 if (out_column + n_outputs <= reduction_stride) {
    +
    249 for (int i = 0; i < n_outputs; i++) {
    +
    250 out[i] = totals[i];
    +
    251 }
    +
    252 } else {
    +
    253 for (int i = 0; out_column + i < reduction_stride; i++) {
    +
    254 out[i] = totals[i];
    +
    255 }
    +
    256 }
    +
    257 }
    +
    258 }
    +
    259
    +
    260 // Each thread holds n_reads partial results. We write them all out to shared
    +
    261 // memory and threads with offset.y == 0 aggregate the columns and write the
    +
    262 // outputs.
    +
    263 else {
    +
    264 short x_block = offset.x / n_reads;
    +
    265 for (int i = 0; i < n_reads; i++) {
    +
    266 shared_vals[x_block * BM * n_reads + i * BM + offset.y] = totals[i];
    +
    267 }
    +
    268 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    269 if (offset.y == 0) {
    +
    270 for (int i = 0; i < n_reads; i++) {
    +
    271 for (int j = 1; j < BM; j++) {
    +
    272 totals[i] =
    +
    273 op(shared_vals[x_block * BM * n_reads + i * BM + j], totals[i]);
    +
    274 }
    +
    275 }
    +
    276 }
    +
    277
    +
    278 // Write the output.
    +
    279 if (offset.y == 0) {
    +
    280 out += out_idx * IdxT(reduction_stride) + column;
    +
    281 if (safe) {
    +
    282 for (int i = 0; i < n_reads; i++) {
    +
    283 out[i] = totals[i];
    +
    284 }
    +
    285 } else {
    +
    286 for (int i = 0; column + i < reduction_stride; i++) {
    +
    287 out[i] = totals[i];
    +
    288 }
    +
    289 }
    +
    290 }
    +
    291 }
    +
    292}
    -
    285
    -
    286template <typename T, typename U, typename Op, int NDIMS, int BM, int BN>
    -
    -
    287[[kernel]] void col_reduce_2pass(
    -
    288 const device T* in [[buffer(0)]],
    -
    289 device U* out [[buffer(1)]],
    -
    290 const constant size_t& reduction_size [[buffer(2)]],
    -
    291 const constant size_t& reduction_stride [[buffer(3)]],
    -
    292 const constant int* shape [[buffer(4)]],
    -
    293 const constant size_t* strides [[buffer(5)]],
    -
    294 const constant int& ndim [[buffer(6)]],
    -
    295 const constant int* reduce_shape [[buffer(7)]],
    -
    296 const constant size_t* reduce_strides [[buffer(8)]],
    -
    297 const constant int& reduce_ndim [[buffer(9)]],
    -
    298 const constant size_t& non_col_reductions [[buffer(10)]],
    -
    299 const constant size_t& out_size [[buffer(11)]],
    -
    300 uint3 gid [[threadgroup_position_in_grid]],
    -
    301 uint3 gsize [[threadgroups_per_grid]],
    -
    302 uint simd_lane_id [[thread_index_in_simdgroup]],
    -
    303 uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
    -
    304 Op op;
    -
    305 constexpr int n_simdgroups = 8;
    -
    306 constexpr short tgp_size = n_simdgroups * simd_size;
    -
    307 constexpr short n_reads = (BM * BN) / tgp_size;
    -
    308 constexpr short n_read_blocks = BN / n_reads;
    -
    309 constexpr int n_outputs = BN / n_simdgroups;
    -
    310 constexpr short outer_blocks = 32;
    -
    311 static_assert(BM == 32, "BM should be equal to 32");
    -
    312
    -
    313 threadgroup U shared_vals[BN * BM];
    -
    314 U totals[n_reads];
    -
    315 looped_elem_to_loc<NDIMS> loop;
    -
    316 const device T* row;
    -
    317
    -
    318 for (int i = 0; i < n_reads; i++) {
    -
    319 totals[i] = Op::init;
    -
    320 }
    -
    321
    -
    322 short lid = simd_group_id * simd_size + simd_lane_id;
    -
    323 short2 offset((lid % n_read_blocks) * n_reads, lid / n_read_blocks);
    -
    324 size_t column = BN * gid.x + offset.x;
    -
    325 bool safe = column + n_reads <= reduction_stride;
    -
    326
    -
    327 size_t full_idx = gid.y + gsize.y * size_t(gid.z);
    -
    328 size_t block_idx = full_idx / out_size;
    -
    329 size_t out_idx = full_idx % out_size;
    -
    330 size_t in_idx = elem_to_loc(out_idx, shape, strides, ndim);
    -
    331 in += in_idx + column;
    +
    293
    +
    294template <
    +
    295 typename T,
    +
    296 typename U,
    +
    297 typename Op,
    +
    298 typename IdxT,
    +
    299 int NDIMS,
    +
    300 int BM,
    +
    301 int BN>
    +
    +
    302[[kernel]] void col_reduce_2pass(
    +
    303 const device T* in [[buffer(0)]],
    +
    304 device U* out [[buffer(1)]],
    +
    305 const constant size_t& reduction_size [[buffer(2)]],
    +
    306 const constant size_t& reduction_stride [[buffer(3)]],
    +
    307 const constant int* shape [[buffer(4)]],
    +
    308 const constant size_t* strides [[buffer(5)]],
    +
    309 const constant int& ndim [[buffer(6)]],
    +
    310 const constant int* reduce_shape [[buffer(7)]],
    +
    311 const constant size_t* reduce_strides [[buffer(8)]],
    +
    312 const constant int& reduce_ndim [[buffer(9)]],
    +
    313 const constant size_t& non_col_reductions [[buffer(10)]],
    +
    314 const constant size_t& out_size [[buffer(11)]],
    +
    315 uint3 gid [[threadgroup_position_in_grid]],
    +
    316 uint3 gsize [[threadgroups_per_grid]],
    +
    317 uint simd_lane_id [[thread_index_in_simdgroup]],
    +
    318 uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
    +
    319 Op op;
    +
    320 constexpr int n_simdgroups = 8;
    +
    321 constexpr short tgp_size = n_simdgroups * simd_size;
    +
    322 constexpr short n_reads = (BM * BN) / tgp_size;
    +
    323 constexpr short n_read_blocks = BN / n_reads;
    +
    324 constexpr int n_outputs = BN / n_simdgroups;
    +
    325 constexpr short outer_blocks = 32;
    +
    326 static_assert(BM == 32, "BM should be equal to 32");
    +
    327
    +
    328 threadgroup U shared_vals[BN * BM];
    +
    329 U totals[n_reads];
    +
    330 LoopedElemToLoc<NDIMS, IdxT, (NDIMS > 2)> loop(reduce_ndim);
    +
    331 const device T* row;
    332
    -
    333 size_t total = non_col_reductions * reduction_size;
    -
    334 loop.next(offset.y + block_idx * BM, reduce_shape, reduce_strides);
    -
    335 for (size_t r = offset.y + block_idx * BM; r < total;
    -
    336 r += outer_blocks * BM) {
    -
    337 row = in + loop.location(r, reduce_shape, reduce_strides, reduce_ndim);
    -
    338
    -
    339 if (safe) {
    -
    340 for (int i = 0; i < n_reads; i++) {
    -
    341 totals[i] = op(static_cast<U>(row[i]), totals[i]);
    -
    342 }
    -
    343 } else {
    -
    344 U vals[n_reads];
    -
    345 for (int i = 0; i < n_reads; i++) {
    -
    346 vals[i] =
    -
    347 (column + i < reduction_stride) ? static_cast<U>(row[i]) : op.init;
    -
    348 }
    -
    349 for (int i = 0; i < n_reads; i++) {
    -
    350 totals[i] = op(vals[i], totals[i]);
    -
    351 }
    -
    352 }
    -
    353
    -
    354 loop.next(outer_blocks * BM, reduce_shape, reduce_strides);
    -
    355 }
    -
    356
    -
    357 // We can use a simd reduction to accumulate across BM so each thread writes
    -
    358 // the partial output to SM and then each simdgroup does BN / n_simdgroups
    -
    359 // accumulations.
    -
    360 for (int i = 0; i < n_reads; i++) {
    -
    361 shared_vals[offset.y * BN + offset.x + i] = totals[i];
    -
    362 }
    -
    363 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    364 short2 out_offset(simd_group_id * n_outputs, simd_lane_id);
    -
    365 for (int i = 0; i < n_outputs; i++) {
    -
    366 totals[i] =
    -
    367 op.simd_reduce(shared_vals[out_offset.y * BN + out_offset.x + i]);
    -
    368 }
    -
    369
    -
    370 // Write the output.
    -
    371 if (simd_lane_id == 0) {
    -
    372 size_t out_column = BN * gid.x + out_offset.x;
    -
    373 out += full_idx * reduction_stride + out_column;
    -
    374 if (out_column + n_outputs <= reduction_stride) {
    -
    375 for (int i = 0; i < n_outputs; i++) {
    -
    376 out[i] = totals[i];
    -
    377 }
    -
    378 } else {
    -
    379 for (int i = 0; out_column + i < reduction_stride; i++) {
    -
    380 out[i] = totals[i];
    -
    381 }
    -
    382 }
    -
    383 }
    -
    384}
    +
    333 for (int i = 0; i < n_reads; i++) {
    +
    334 totals[i] = Op::init;
    +
    335 }
    +
    336
    +
    337 short lid = simd_group_id * simd_size + simd_lane_id;
    +
    338 short2 offset((lid % n_read_blocks) * n_reads, lid / n_read_blocks);
    +
    339 IdxT column = BN * gid.x + offset.x;
    +
    340 bool safe = column + n_reads <= reduction_stride;
    +
    341
    +
    342 IdxT full_idx = gid.y + gsize.y * IdxT(gid.z);
    +
    343 IdxT block_idx = full_idx / IdxT(out_size);
    +
    344 IdxT out_idx = full_idx % IdxT(out_size);
    +
    345 IdxT in_idx = elem_to_loc<size_t, IdxT>(out_idx, shape, strides, ndim);
    +
    346 in += in_idx + column;
    +
    347
    +
    348 IdxT total = IdxT(non_col_reductions) * IdxT(reduction_size);
    +
    349 loop.next(offset.y + block_idx * BM, reduce_shape, reduce_strides);
    +
    350 for (IdxT r = offset.y + block_idx * BM; r < total; r += outer_blocks * BM) {
    +
    351 row = in + loop.location();
    +
    352
    +
    353 if (safe) {
    +
    354 for (int i = 0; i < n_reads; i++) {
    +
    355 totals[i] = op(static_cast<U>(row[i]), totals[i]);
    +
    356 }
    +
    357 } else {
    +
    358 U vals[n_reads];
    +
    359 for (int i = 0; i < n_reads; i++) {
    +
    360 vals[i] =
    +
    361 (column + i < reduction_stride) ? static_cast<U>(row[i]) : op.init;
    +
    362 }
    +
    363 for (int i = 0; i < n_reads; i++) {
    +
    364 totals[i] = op(vals[i], totals[i]);
    +
    365 }
    +
    366 }
    +
    367
    +
    368 loop.next(outer_blocks * BM, reduce_shape, reduce_strides);
    +
    369 }
    +
    370
    +
    371 // We can use a simd reduction to accumulate across BM so each thread writes
    +
    372 // the partial output to SM and then each simdgroup does BN / n_simdgroups
    +
    373 // accumulations.
    +
    374 for (int i = 0; i < n_reads; i++) {
    +
    375 shared_vals[offset.y * BN + offset.x + i] = totals[i];
    +
    376 }
    +
    377 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    378 short2 out_offset(simd_group_id * n_outputs, simd_lane_id);
    +
    379 for (int i = 0; i < n_outputs; i++) {
    +
    380 totals[i] =
    +
    381 op.simd_reduce(shared_vals[out_offset.y * BN + out_offset.x + i]);
    +
    382 }
    +
    383
    +
    384 // Write the output.
    +
    385 if (simd_lane_id == 0) {
    +
    386 IdxT out_column = BN * gid.x + out_offset.x;
    +
    387 out += full_idx * IdxT(reduction_stride) + out_column;
    +
    388 if (out_column + n_outputs <= reduction_stride) {
    +
    389 for (int i = 0; i < n_outputs; i++) {
    +
    390 out[i] = totals[i];
    +
    391 }
    +
    392 } else {
    +
    393 for (int i = 0; out_column + i < reduction_stride; i++) {
    +
    394 out[i] = totals[i];
    +
    395 }
    +
    396 }
    +
    397 }
    +
    398}
    simd_size
    static constant constexpr const uint8_t simd_size
    Definition ops.h:22
    -
    elem_to_loc
    METAL_FUNC stride_t elem_to_loc(uint elem, constant const int *shape, constant const stride_t *strides, int ndim)
    Definition utils.h:87
    +
    elem_to_loc
    METAL_FUNC IdxT elem_to_loc(uint elem, constant const int *shape, constant const StrideT *strides, int ndim)
    Definition utils.h:93
    op
    Op op
    Definition binary.h:129
    -
    col_reduce_2pass
    void col_reduce_2pass(const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &reduction_stride, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, const constant size_t &non_col_reductions, const constant size_t &out_size, uint3 gid, uint3 gsize, uint simd_lane_id, uint simd_group_id)
    Definition reduce_col.h:287
    -
    col_reduce_looped
    void col_reduce_looped(const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &reduction_stride, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, const constant size_t &non_col_reductions, uint3 gid, uint3 gsize, uint simd_lane_id, uint simd_group_id)
    Our approach is the following simple looped approach:
    Definition reduce_col.h:155
    -
    col_reduce_longcolumn
    void col_reduce_longcolumn(const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &reduction_stride, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, const constant size_t &non_col_reductions, const constant size_t &out_size, uint3 gid, uint3 gsize, uint3 lid, uint3 lsize)
    Definition reduce_col.h:97
    -
    col_reduce_small
    void col_reduce_small(const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &reduction_stride, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, const constant size_t &non_col_reductions, uint3 gid, uint3 gsize, uint3 lid, uint3 lsize)
    Definition reduce_col.h:4
    -
    looped_elem_to_loc
    Definition utils.h:197
    -
    looped_elem_to_loc::next
    void next(const constant int *shape, const constant size_t *strides)
    Definition utils.h:202
    -
    looped_elem_to_loc::location
    offset_t location(offset_t, const constant int *, const constant size_t *, int)
    Definition utils.h:229
    +
    col_reduce_small
    void col_reduce_small(const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &reduction_stride, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, const constant size_t &non_col_reductions, uint3 gid, uint3 gsize, uint3 lid, uint3 lsize)
    Definition reduce_col.h:4
    +
    col_reduce_2pass
    void col_reduce_2pass(const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &reduction_stride, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, const constant size_t &non_col_reductions, const constant size_t &out_size, uint3 gid, uint3 gsize, uint simd_lane_id, uint simd_group_id)
    Definition reduce_col.h:302
    +
    col_reduce_longcolumn
    void col_reduce_longcolumn(const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &reduction_stride, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, const constant size_t &non_col_reductions, const constant size_t &out_size, uint3 gid, uint3 gsize, uint3 lid, uint3 lsize)
    Definition reduce_col.h:97
    +
    col_reduce_looped
    void col_reduce_looped(const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &reduction_stride, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, const constant size_t &non_col_reductions, uint3 gid, uint3 gsize, uint simd_lane_id, uint simd_group_id)
    Our approach is the following simple looped approach:
    Definition reduce_col.h:163
    +
    LoopedElemToLoc
    Definition utils.h:208
    diff --git a/docs/build/html/reduce__row_8h.html b/docs/build/html/reduce__row_8h.html index 15e218f0..d85dd87a 100644 --- a/docs/build/html/reduce__row_8h.html +++ b/docs/build/html/reduce__row_8h.html @@ -117,15 +117,15 @@ Functions
    - - - - - - - - - + + + + + + + + +
    void col_reduce_small
    template<typename T , typename U , typename Op , int N_READS = REDUCE_N_READS>
    METAL_FUNC void thread_reduce (thread U &total, const device T *row, int blocks, int extra)
     
    template<typename T , typename U , typename Op , int NDIMS, int N_READS = REDUCE_N_READS>
    void row_reduce_small (const device T *in, device U *out, const constant size_t &row_size, const constant size_t &non_row_reductions, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, uint simd_lane_id, uint3 gid, uint3 gsize, uint3 tid, uint3 tsize)
     
    template<typename T , typename U , typename Op , int N_READS = REDUCE_N_READS, int N_WRITES = REDUCE_N_WRITES>
    void row_reduce_simple (const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &out_size, uint3 gid, uint3 gsize, uint3 lid, uint3 lsize, uint simd_lane_id, uint simd_per_group, uint simd_group_id)
     
    template<typename T , typename U , typename Op , int NDIMS, int N_READS = REDUCE_N_READS>
    void row_reduce_looped (const device T *in, device U *out, const constant size_t &row_size, const constant size_t &non_row_reductions, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, uint3 gid, uint3 gsize, uint3 lid, uint3 lsize, uint simd_lane_id, uint simd_per_group, uint simd_group_id)
     
    template<typename T , typename U , typename Op , typename IdxT , int NDIMS, int N_READS = REDUCE_N_READS>
    void row_reduce_small (const device T *in, device U *out, const constant size_t &row_size, const constant size_t &non_row_reductions, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, uint simd_lane_id, uint3 gid, uint3 gsize, uint3 tid, uint3 tsize)
     
    template<typename T , typename U , typename Op , typename IdxT = size_t, int N_READS = REDUCE_N_READS, int N_WRITES = REDUCE_N_WRITES>
    void row_reduce_simple (const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &out_size, uint3 gid, uint3 gsize, uint3 lid, uint3 lsize, uint simd_lane_id, uint simd_per_group, uint simd_group_id)
     
    template<typename T , typename U , typename Op , typename IdxT , int NDIMS, int N_READS = REDUCE_N_READS>
    void row_reduce_looped (const device T *in, device U *out, const constant size_t &row_size, const constant size_t &non_row_reductions, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, uint3 gid, uint3 gsize, uint3 lid, uint3 lsize, uint simd_lane_id, uint simd_per_group, uint simd_group_id)
     

    Function Documentation

    @@ -289,13 +289,13 @@ template<typename T , typename U , typename Op , int N_READS = REDUCE_N_READS
    - -

    ◆ row_reduce_looped()

    + +

    ◆ row_reduce_looped()

    -template<typename T , typename U , typename Op , int NDIMS, int N_READS = REDUCE_N_READS>
    +template<typename T , typename U , typename Op , typename IdxT , int NDIMS, int N_READS = REDUCE_N_READS>
    @@ -387,13 +387,13 @@ template<typename T , typename U , typename Op , int NDIMS, int N_READS = RED - -

    ◆ row_reduce_simple()

    + +

    ◆ row_reduce_simple()

    -template<typename T , typename U , typename Op , int N_READS = REDUCE_N_READS, int N_WRITES = REDUCE_N_WRITES>
    +template<typename T , typename U , typename Op , typename IdxT = size_t, int N_READS = REDUCE_N_READS, int N_WRITES = REDUCE_N_WRITES>
    void row_reduce_looped
    @@ -455,13 +455,13 @@ template<typename T , typename U , typename Op , int N_READS = REDUCE_N_READS - -

    ◆ row_reduce_small()

    + +

    ◆ row_reduce_small()

    -template<typename T , typename U , typename Op , int NDIMS, int N_READS = REDUCE_N_READS>
    +template<typename T , typename U , typename Op , typename IdxT , int NDIMS, int N_READS = REDUCE_N_READS>
    void row_reduce_simple
    diff --git a/docs/build/html/reduce__row_8h_source.html b/docs/build/html/reduce__row_8h_source.html index d59088ed..588211d0 100644 --- a/docs/build/html/reduce__row_8h_source.html +++ b/docs/build/html/reduce__row_8h_source.html @@ -198,7 +198,7 @@ $(function(){ initResizable(false); });
    110 const device T* inputs[N_WRITES];
    111 in += lid_x * N_READS;
    112 for (int i = 0; i < N_READS; i++) {
    -
    113 inputs[i] = in + elem_to_loc(row_idx + i, shape, strides, ndim);
    +
    113 inputs[i] = in + elem_to_loc(row_idx + i, shape, strides, ndim);
    114 }
    115
    116 per_thread_row_reduce<T, U, Op, N_READS, N_WRITES>(
    @@ -283,197 +283,199 @@ $(function(){ initResizable(false); });
    193 typename T,
    194 typename U,
    195 typename Op,
    -
    196 int NDIMS,
    -
    197 int N_READS = REDUCE_N_READS>
    -
    -
    198[[kernel]] void row_reduce_small(
    -
    199 const device T* in [[buffer(0)]],
    -
    200 device U* out [[buffer(1)]],
    -
    201 const constant size_t& row_size [[buffer(2)]],
    -
    202 const constant size_t& non_row_reductions [[buffer(3)]],
    -
    203 const constant int* shape [[buffer(4)]],
    -
    204 const constant size_t* strides [[buffer(5)]],
    -
    205 const constant int& ndim [[buffer(6)]],
    -
    206 const constant int* reduce_shape [[buffer(7)]],
    -
    207 const constant size_t* reduce_strides [[buffer(8)]],
    -
    208 const constant int& reduce_ndim [[buffer(9)]],
    -
    209 uint simd_lane_id [[thread_index_in_simdgroup]],
    -
    210 uint3 gid [[threadgroup_position_in_grid]],
    -
    211 uint3 gsize [[threadgroups_per_grid]],
    -
    212 uint3 tid [[thread_position_in_grid]],
    -
    213 uint3 tsize [[threads_per_grid]]) {
    -
    214 Op op;
    -
    215
    -
    216 U total_val = Op::init;
    -
    217 looped_elem_to_loc<NDIMS> loop;
    -
    218
    -
    219 // Precompute some row reduction numbers
    -
    220 const device T* row;
    -
    221 int blocks = row_size / N_READS;
    -
    222 int extra = row_size % N_READS;
    -
    223
    -
    224 if ((non_row_reductions < 32 && row_size <= 8) || non_row_reductions <= 8) {
    -
    225 // Simple loop over non_row_reductions and reduce the row in the thread.
    -
    226 size_t out_idx = tid.x + tsize.y * size_t(tid.y);
    -
    227 in += elem_to_loc(out_idx, shape, strides, ndim);
    -
    228
    -
    229 for (uint r = 0; r < non_row_reductions; r++) {
    -
    230 row = in + loop.location(r, reduce_shape, reduce_strides, reduce_ndim);
    -
    231 thread_reduce<T, U, Op, N_READS>(total_val, row, blocks, extra);
    -
    232 loop.next(reduce_shape, reduce_strides);
    -
    233 }
    -
    234
    -
    235 out[out_idx] = total_val;
    -
    236 } else {
    -
    237 // Collaboratively reduce over non_row_reductions in the simdgroup. Each
    -
    238 // thread reduces every 32nd row and then a simple simd reduce.
    -
    239 size_t out_idx = gid.y + gsize.y * size_t(gid.z);
    -
    240 in += elem_to_loc(out_idx, shape, strides, ndim);
    -
    241
    -
    242 loop.next(simd_lane_id, reduce_shape, reduce_strides);
    -
    243
    -
    244 for (uint r = simd_lane_id; r < non_row_reductions; r += simd_size) {
    -
    245 row = in + loop.location(r, reduce_shape, reduce_strides, reduce_ndim);
    -
    246 thread_reduce<T, U, Op, N_READS>(total_val, row, blocks, extra);
    -
    247 loop.next(simd_size, reduce_shape, reduce_strides);
    -
    248 }
    -
    249
    -
    250 total_val = op.simd_reduce(total_val);
    -
    251
    -
    252 if (simd_lane_id == 0) {
    -
    253 out[out_idx] = total_val;
    -
    254 }
    -
    255 }
    -
    256}
    +
    196 typename IdxT,
    +
    197 int NDIMS,
    +
    198 int N_READS = REDUCE_N_READS>
    +
    +
    199[[kernel]] void row_reduce_small(
    +
    200 const device T* in [[buffer(0)]],
    +
    201 device U* out [[buffer(1)]],
    +
    202 const constant size_t& row_size [[buffer(2)]],
    +
    203 const constant size_t& non_row_reductions [[buffer(3)]],
    +
    204 const constant int* shape [[buffer(4)]],
    +
    205 const constant size_t* strides [[buffer(5)]],
    +
    206 const constant int& ndim [[buffer(6)]],
    +
    207 const constant int* reduce_shape [[buffer(7)]],
    +
    208 const constant size_t* reduce_strides [[buffer(8)]],
    +
    209 const constant int& reduce_ndim [[buffer(9)]],
    +
    210 uint simd_lane_id [[thread_index_in_simdgroup]],
    +
    211 uint3 gid [[threadgroup_position_in_grid]],
    +
    212 uint3 gsize [[threadgroups_per_grid]],
    +
    213 uint3 tid [[thread_position_in_grid]],
    +
    214 uint3 tsize [[threads_per_grid]]) {
    +
    215 Op op;
    +
    216
    +
    217 U total_val = Op::init;
    +
    218 LoopedElemToLoc<NDIMS, IdxT, (NDIMS > 2)> loop(reduce_ndim);
    +
    219
    +
    220 // Precompute some row reduction numbers
    +
    221 const device T* row;
    +
    222 int blocks = IdxT(row_size) / N_READS;
    +
    223 int extra = IdxT(row_size) % N_READS;
    +
    224
    +
    225 if ((non_row_reductions < 32 && row_size <= 8) || non_row_reductions <= 8) {
    +
    226 // Simple loop over non_row_reductions and reduce the row in the thread.
    +
    227 IdxT out_idx = tid.x + tsize.y * IdxT(tid.y);
    +
    228 in += elem_to_loc<size_t, IdxT>(out_idx, shape, strides, ndim);
    +
    229
    +
    230 for (uint r = 0; r < non_row_reductions; r++) {
    +
    231 row = in + loop.location();
    +
    232 thread_reduce<T, U, Op, N_READS>(total_val, row, blocks, extra);
    +
    233 loop.next(reduce_shape, reduce_strides);
    +
    234 }
    +
    235
    +
    236 out[out_idx] = total_val;
    +
    237 } else {
    +
    238 // Collaboratively reduce over non_row_reductions in the simdgroup. Each
    +
    239 // thread reduces every 32nd row and then a simple simd reduce.
    +
    240 IdxT out_idx = gid.y + gsize.y * IdxT(gid.z);
    +
    241 in += elem_to_loc<size_t, IdxT>(out_idx, shape, strides, ndim);
    +
    242
    +
    243 loop.next(simd_lane_id, reduce_shape, reduce_strides);
    +
    244
    +
    245 for (uint r = simd_lane_id; r < non_row_reductions; r += simd_size) {
    +
    246 row = in + loop.location();
    +
    247 thread_reduce<T, U, Op, N_READS>(total_val, row, blocks, extra);
    +
    248 loop.next(simd_size, reduce_shape, reduce_strides);
    +
    249 }
    +
    250
    +
    251 total_val = op.simd_reduce(total_val);
    +
    252
    +
    253 if (simd_lane_id == 0) {
    +
    254 out[out_idx] = total_val;
    +
    255 }
    +
    256 }
    +
    257}
    -
    257
    -
    258template <
    -
    259 typename T,
    -
    260 typename U,
    -
    261 typename Op,
    -
    262 int N_READS = REDUCE_N_READS,
    -
    263 int N_WRITES = REDUCE_N_WRITES>
    -
    -
    264[[kernel]] void row_reduce_simple(
    -
    265 const device T* in [[buffer(0)]],
    -
    266 device U* out [[buffer(1)]],
    -
    267 const constant size_t& reduction_size [[buffer(2)]],
    -
    268 const constant size_t& out_size [[buffer(3)]],
    -
    269 uint3 gid [[threadgroup_position_in_grid]],
    -
    270 uint3 gsize [[threadgroups_per_grid]],
    -
    271 uint3 lid [[thread_position_in_threadgroup]],
    -
    272 uint3 lsize [[threads_per_threadgroup]],
    -
    273 uint simd_lane_id [[thread_index_in_simdgroup]],
    -
    274 uint simd_per_group [[simdgroups_per_threadgroup]],
    -
    275 uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
    -
    276 threadgroup U shared_vals[simd_size * N_WRITES];
    -
    277 U totals[N_WRITES];
    -
    278
    -
    279 // Move to the row
    -
    280 size_t out_idx = N_WRITES * (gid.y + gsize.y * size_t(gid.z));
    -
    281 if (out_idx + N_WRITES > out_size) {
    -
    282 out_idx = out_size - N_WRITES;
    -
    283 }
    -
    284 in += out_idx * reduction_size;
    -
    285 out += out_idx;
    -
    286
    -
    287 // Each thread reduces across the row
    -
    288 int blocks = reduction_size / (lsize.x * N_READS);
    -
    289 int extra = reduction_size - blocks * (lsize.x * N_READS);
    -
    290 per_thread_row_reduce<T, U, Op, N_READS, N_WRITES>(
    -
    291 totals, in, reduction_size, blocks, extra, lsize.x, lid.x);
    -
    292
    -
    293 // Reduce across the threadgroup
    -
    294 threadgroup_reduce<T, U, Op, N_READS, N_WRITES>(
    -
    295 totals, shared_vals, lid, simd_lane_id, simd_per_group, simd_group_id);
    -
    296
    -
    297 // Write the output
    -
    298 if (lid.x == 0) {
    -
    299 for (int i = 0; i < N_WRITES; i++) {
    -
    300 out[i] = totals[i];
    -
    301 }
    -
    302 }
    -
    303}
    +
    258
    +
    259template <
    +
    260 typename T,
    +
    261 typename U,
    +
    262 typename Op,
    +
    263 typename IdxT = size_t,
    +
    264 int N_READS = REDUCE_N_READS,
    +
    265 int N_WRITES = REDUCE_N_WRITES>
    +
    +
    266[[kernel]] void row_reduce_simple(
    +
    267 const device T* in [[buffer(0)]],
    +
    268 device U* out [[buffer(1)]],
    +
    269 const constant size_t& reduction_size [[buffer(2)]],
    +
    270 const constant size_t& out_size [[buffer(3)]],
    +
    271 uint3 gid [[threadgroup_position_in_grid]],
    +
    272 uint3 gsize [[threadgroups_per_grid]],
    +
    273 uint3 lid [[thread_position_in_threadgroup]],
    +
    274 uint3 lsize [[threads_per_threadgroup]],
    +
    275 uint simd_lane_id [[thread_index_in_simdgroup]],
    +
    276 uint simd_per_group [[simdgroups_per_threadgroup]],
    +
    277 uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
    +
    278 threadgroup U shared_vals[simd_size * N_WRITES];
    +
    279 U totals[N_WRITES];
    +
    280
    +
    281 // Move to the row
    +
    282 IdxT out_idx = N_WRITES * (gid.y + gsize.y * IdxT(gid.z));
    +
    283 if (out_idx + N_WRITES > out_size) {
    +
    284 out_idx = out_size - N_WRITES;
    +
    285 }
    +
    286 in += out_idx * IdxT(reduction_size);
    +
    287 out += out_idx;
    +
    288
    +
    289 // Each thread reduces across the row
    +
    290 int blocks = IdxT(reduction_size) / (lsize.x * N_READS);
    +
    291 int extra = reduction_size - blocks * (lsize.x * N_READS);
    +
    292 per_thread_row_reduce<T, U, Op, N_READS, N_WRITES>(
    +
    293 totals, in, reduction_size, blocks, extra, lsize.x, lid.x);
    +
    294
    +
    295 // Reduce across the threadgroup
    +
    296 threadgroup_reduce<T, U, Op, N_READS, N_WRITES>(
    +
    297 totals, shared_vals, lid, simd_lane_id, simd_per_group, simd_group_id);
    +
    298
    +
    299 // Write the output
    +
    300 if (lid.x == 0) {
    +
    301 for (int i = 0; i < N_WRITES; i++) {
    +
    302 out[i] = totals[i];
    +
    303 }
    +
    304 }
    +
    305}
    -
    304
    -
    305template <
    -
    306 typename T,
    -
    307 typename U,
    -
    308 typename Op,
    -
    309 int NDIMS,
    -
    310 int N_READS = REDUCE_N_READS>
    -
    -
    311[[kernel]] void row_reduce_looped(
    -
    312 const device T* in [[buffer(0)]],
    -
    313 device U* out [[buffer(1)]],
    -
    314 const constant size_t& row_size [[buffer(2)]],
    -
    315 const constant size_t& non_row_reductions [[buffer(3)]],
    -
    316 const constant int* shape [[buffer(4)]],
    -
    317 const constant size_t* strides [[buffer(5)]],
    -
    318 const constant int& ndim [[buffer(6)]],
    -
    319 const constant int* reduce_shape [[buffer(7)]],
    -
    320 const constant size_t* reduce_strides [[buffer(8)]],
    -
    321 const constant int& reduce_ndim [[buffer(9)]],
    -
    322 uint3 gid [[threadgroup_position_in_grid]],
    -
    323 uint3 gsize [[threadgroups_per_grid]],
    -
    324 uint3 lid [[thread_position_in_threadgroup]],
    -
    325 uint3 lsize [[threads_per_threadgroup]],
    -
    326 uint simd_lane_id [[thread_index_in_simdgroup]],
    -
    327 uint simd_per_group [[simdgroups_per_threadgroup]],
    -
    328 uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
    -
    329 Op op;
    -
    330 threadgroup U shared_vals[simd_size];
    -
    331 U total = Op::init;
    -
    332
    -
    333 size_t out_idx = gid.y + gsize.y * size_t(gid.z);
    -
    334
    -
    335 // lid.x * N_READS breaks the per_thread_row_reduce interface a bit. Maybe it
    -
    336 // needs a small refactor.
    -
    337 in += elem_to_loc(out_idx, shape, strides, ndim) + lid.x * N_READS;
    -
    338
    -
    339 looped_elem_to_loc<NDIMS> loop;
    -
    340 const device T* row;
    -
    341 int blocks = row_size / (lsize.x * N_READS);
    -
    342 int extra = row_size - blocks * (lsize.x * N_READS);
    -
    343
    -
    344 for (size_t i = 0; i < non_row_reductions; i++) {
    -
    345 row = in + loop.location(i, reduce_shape, reduce_strides, reduce_ndim);
    -
    346
    -
    347 // Each thread reduces across the row
    -
    348 U row_total;
    -
    349 per_thread_row_reduce<T, U, Op, N_READS, 1>(
    -
    350 &row_total, &row, blocks, extra, lsize.x, lid.x);
    -
    351
    -
    352 // Aggregate across rows
    -
    353 total = op(total, row_total);
    -
    354
    -
    355 loop.next(reduce_shape, reduce_strides);
    -
    356 }
    -
    357
    -
    358 // Reduce across the threadgroup
    -
    359 threadgroup_reduce<T, U, Op, N_READS, 1>(
    -
    360 &total, shared_vals, lid, simd_lane_id, simd_per_group, simd_group_id);
    +
    306
    +
    307template <
    +
    308 typename T,
    +
    309 typename U,
    +
    310 typename Op,
    +
    311 typename IdxT,
    +
    312 int NDIMS,
    +
    313 int N_READS = REDUCE_N_READS>
    +
    +
    314[[kernel]] void row_reduce_looped(
    +
    315 const device T* in [[buffer(0)]],
    +
    316 device U* out [[buffer(1)]],
    +
    317 const constant size_t& row_size [[buffer(2)]],
    +
    318 const constant size_t& non_row_reductions [[buffer(3)]],
    +
    319 const constant int* shape [[buffer(4)]],
    +
    320 const constant size_t* strides [[buffer(5)]],
    +
    321 const constant int& ndim [[buffer(6)]],
    +
    322 const constant int* reduce_shape [[buffer(7)]],
    +
    323 const constant size_t* reduce_strides [[buffer(8)]],
    +
    324 const constant int& reduce_ndim [[buffer(9)]],
    +
    325 uint3 gid [[threadgroup_position_in_grid]],
    +
    326 uint3 gsize [[threadgroups_per_grid]],
    +
    327 uint3 lid [[thread_position_in_threadgroup]],
    +
    328 uint3 lsize [[threads_per_threadgroup]],
    +
    329 uint simd_lane_id [[thread_index_in_simdgroup]],
    +
    330 uint simd_per_group [[simdgroups_per_threadgroup]],
    +
    331 uint simd_group_id [[simdgroup_index_in_threadgroup]]) {
    +
    332 Op op;
    +
    333 threadgroup U shared_vals[simd_size];
    +
    334 U total = Op::init;
    +
    335
    +
    336 IdxT out_idx = gid.y + gsize.y * IdxT(gid.z);
    +
    337
    +
    338 // lid.x * N_READS breaks the per_thread_row_reduce interface a bit. Maybe it
    +
    339 // needs a small refactor.
    +
    340 in += elem_to_loc<size_t, IdxT>(out_idx, shape, strides, ndim) +
    +
    341 lid.x * N_READS;
    +
    342
    +
    343 LoopedElemToLoc<NDIMS, IdxT, (NDIMS > 2)> loop(reduce_ndim);
    +
    344 const device T* row;
    +
    345 int blocks = IdxT(row_size) / (lsize.x * N_READS);
    +
    346 int extra = row_size - blocks * (lsize.x * N_READS);
    +
    347
    +
    348 for (IdxT i = 0; i < non_row_reductions; i++) {
    +
    349 row = in + loop.location();
    +
    350
    +
    351 // Each thread reduces across the row
    +
    352 U row_total;
    +
    353 per_thread_row_reduce<T, U, Op, N_READS, 1>(
    +
    354 &row_total, &row, blocks, extra, lsize.x, lid.x);
    +
    355
    +
    356 // Aggregate across rows
    +
    357 total = op(total, row_total);
    +
    358
    +
    359 loop.next(reduce_shape, reduce_strides);
    +
    360 }
    361
    -
    362 // Write the output
    -
    363 if (lid.x == 0) {
    -
    364 out[out_idx] = total;
    -
    365 }
    -
    366}
    +
    362 // Reduce across the threadgroup
    +
    363 threadgroup_reduce<T, U, Op, N_READS, 1>(
    +
    364 &total, shared_vals, lid, simd_lane_id, simd_per_group, simd_group_id);
    +
    365
    +
    366 // Write the output
    +
    367 if (lid.x == 0) {
    +
    368 out[out_idx] = total;
    +
    369 }
    +
    370}
    simd_size
    static constant constexpr const uint8_t simd_size
    Definition ops.h:22
    -
    elem_to_loc
    METAL_FUNC stride_t elem_to_loc(uint elem, constant const int *shape, constant const stride_t *strides, int ndim)
    Definition utils.h:87
    +
    elem_to_loc
    METAL_FUNC IdxT elem_to_loc(uint elem, constant const int *shape, constant const StrideT *strides, int ndim)
    Definition utils.h:93
    op
    Op op
    Definition binary.h:129
    REDUCE_N_READS
    static constexpr int REDUCE_N_READS
    Definition defines.h:12
    REDUCE_N_WRITES
    static constexpr int REDUCE_N_WRITES
    Definition defines.h:13
    -
    row_reduce_small
    void row_reduce_small(const device T *in, device U *out, const constant size_t &row_size, const constant size_t &non_row_reductions, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, uint simd_lane_id, uint3 gid, uint3 gsize, uint3 tid, uint3 tsize)
    Definition reduce_row.h:198
    per_thread_row_reduce
    METAL_FUNC void per_thread_row_reduce(thread U totals[N_WRITES], const device T *inputs[N_WRITES], int blocks, int extra, uint lsize_x, uint lid_x)
    The thread group collaboratively reduces across the rows with bounds checking.
    Definition reduce_row.h:19
    threadgroup_reduce
    METAL_FUNC void threadgroup_reduce(thread U totals[N_WRITES], threadgroup U *shared_vals, uint3 lid, uint simd_lane_id, uint simd_per_group, uint simd_group_id)
    Reduce within the threadgroup.
    Definition reduce_row.h:129
    -
    row_reduce_simple
    void row_reduce_simple(const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &out_size, uint3 gid, uint3 gsize, uint3 lid, uint3 lsize, uint simd_lane_id, uint simd_per_group, uint simd_group_id)
    Definition reduce_row.h:264
    -
    row_reduce_looped
    void row_reduce_looped(const device T *in, device U *out, const constant size_t &row_size, const constant size_t &non_row_reductions, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, uint3 gid, uint3 gsize, uint3 lid, uint3 lsize, uint simd_lane_id, uint simd_per_group, uint simd_group_id)
    Definition reduce_row.h:311
    +
    row_reduce_small
    void row_reduce_small(const device T *in, device U *out, const constant size_t &row_size, const constant size_t &non_row_reductions, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, uint simd_lane_id, uint3 gid, uint3 gsize, uint3 tid, uint3 tsize)
    Definition reduce_row.h:199
    +
    row_reduce_simple
    void row_reduce_simple(const device T *in, device U *out, const constant size_t &reduction_size, const constant size_t &out_size, uint3 gid, uint3 gsize, uint3 lid, uint3 lsize, uint simd_lane_id, uint simd_per_group, uint simd_group_id)
    Definition reduce_row.h:266
    +
    row_reduce_looped
    void row_reduce_looped(const device T *in, device U *out, const constant size_t &row_size, const constant size_t &non_row_reductions, const constant int *shape, const constant size_t *strides, const constant int &ndim, const constant int *reduce_shape, const constant size_t *reduce_strides, const constant int &reduce_ndim, uint3 gid, uint3 gsize, uint3 lid, uint3 lsize, uint simd_lane_id, uint simd_per_group, uint simd_group_id)
    Definition reduce_row.h:314
    thread_reduce
    METAL_FUNC void thread_reduce(thread U &total, const device T *row, int blocks, int extra)
    Definition reduce_row.h:166
    -
    looped_elem_to_loc
    Definition utils.h:197
    -
    looped_elem_to_loc::next
    void next(const constant int *shape, const constant size_t *strides)
    Definition utils.h:202
    -
    looped_elem_to_loc::location
    offset_t location(offset_t, const constant int *, const constant size_t *, int)
    Definition utils.h:229
    +
    LoopedElemToLoc
    Definition utils.h:208
    diff --git a/docs/build/html/scaled__dot__product__attention__params_8h_source.html b/docs/build/html/scaled__dot__product__attention__params_8h_source.html deleted file mode 100644 index 2f67adea..00000000 --- a/docs/build/html/scaled__dot__product__attention__params_8h_source.html +++ /dev/null @@ -1,174 +0,0 @@ - - - - - - - -MLX: mlx/backend/metal/kernels/scaled_dot_product_attention_params.h Source File - - - - - - - - - - - - - -
    -
    -
    void row_reduce_small
    - - - - - -
    -
    MLX -
    -
    -
    - - - - - - - - - - -
    -
    - - -
    -
    -
    -
    -
    -
    Loading...
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    -
    scaled_dot_product_attention_params.h
    -
    -
    -Go to the documentation of this file.
    1//
    -
    2// scaled_dot_product_attention_params.h
    -
    3// mlx
    -
    4
    -
    5#pragma once
    -
    6
    -
    -
    7struct MLXFastAttentionParams {
    -
    8 const int M;
    -
    9 const int N;
    -
    10 const int K;
    -
    11
    -
    12 const int ldq; // ldq == ldo
    -
    13 const int ldk;
    -
    14 const int ldv;
    -
    15 const int lds;
    -
    16 const int ldo;
    -
    17
    -
    18 const int tiles_n;
    -
    19 const int tiles_m;
    -
    20
    -
    21 const int batch_stride_q;
    -
    22 const int batch_stride_k;
    -
    23 const int batch_stride_v;
    -
    24 const int batch_stride_o;
    -
    25
    -
    26 const int swizzle_log;
    -
    27 const int gemm_n_iterations_aligned;
    -
    28 const int gemm_k_iterations_aligned;
    -
    29 const int gemm_sv_m_block_iterations;
    -
    30
    -
    31 const int batch_ndim;
    -
    32 const float alpha;
    -
    33};
    -
    -
    34
    -
    -
    35struct MLXScaledDotProductAttentionParams {
    -
    36 // Associated dimensions & transposition information
    -
    37 const uint QUERY_SEQUENCE_LENGTH = 1;
    -
    38 const uint N_Q_HEADS = 32;
    -
    39 const uint N_KV_HEADS = 32;
    -
    40 const uint KV_TILES = 1;
    -
    41 const float INV_ALPHA = 0.08838834764831843f;
    -
    42};
    -
    -
    MLXFastAttentionParams
    Definition scaled_dot_product_attention_params.h:7
    -
    MLXFastAttentionParams::tiles_m
    const int tiles_m
    Definition scaled_dot_product_attention_params.h:19
    -
    MLXFastAttentionParams::batch_stride_v
    const int batch_stride_v
    Definition scaled_dot_product_attention_params.h:23
    -
    MLXFastAttentionParams::batch_stride_k
    const int batch_stride_k
    Definition scaled_dot_product_attention_params.h:22
    -
    MLXFastAttentionParams::ldk
    const int ldk
    Definition scaled_dot_product_attention_params.h:13
    -
    MLXFastAttentionParams::lds
    const int lds
    Definition scaled_dot_product_attention_params.h:15
    -
    MLXFastAttentionParams::gemm_sv_m_block_iterations
    const int gemm_sv_m_block_iterations
    Definition scaled_dot_product_attention_params.h:29
    -
    MLXFastAttentionParams::batch_stride_o
    const int batch_stride_o
    Definition scaled_dot_product_attention_params.h:24
    -
    MLXFastAttentionParams::M
    const int M
    Definition scaled_dot_product_attention_params.h:8
    -
    MLXFastAttentionParams::tiles_n
    const int tiles_n
    Definition scaled_dot_product_attention_params.h:18
    -
    MLXFastAttentionParams::swizzle_log
    const int swizzle_log
    Definition scaled_dot_product_attention_params.h:26
    -
    MLXFastAttentionParams::batch_ndim
    const int batch_ndim
    Definition scaled_dot_product_attention_params.h:31
    -
    MLXFastAttentionParams::alpha
    const float alpha
    Definition scaled_dot_product_attention_params.h:32
    -
    MLXFastAttentionParams::batch_stride_q
    const int batch_stride_q
    Definition scaled_dot_product_attention_params.h:21
    -
    MLXFastAttentionParams::ldo
    const int ldo
    Definition scaled_dot_product_attention_params.h:16
    -
    MLXFastAttentionParams::N
    const int N
    Definition scaled_dot_product_attention_params.h:9
    -
    MLXFastAttentionParams::gemm_n_iterations_aligned
    const int gemm_n_iterations_aligned
    Definition scaled_dot_product_attention_params.h:27
    -
    MLXFastAttentionParams::K
    const int K
    Definition scaled_dot_product_attention_params.h:10
    -
    MLXFastAttentionParams::gemm_k_iterations_aligned
    const int gemm_k_iterations_aligned
    Definition scaled_dot_product_attention_params.h:28
    -
    MLXFastAttentionParams::ldv
    const int ldv
    Definition scaled_dot_product_attention_params.h:14
    -
    MLXFastAttentionParams::ldq
    const int ldq
    Definition scaled_dot_product_attention_params.h:12
    -
    MLXScaledDotProductAttentionParams
    Definition scaled_dot_product_attention_params.h:35
    -
    MLXScaledDotProductAttentionParams::N_Q_HEADS
    const uint N_Q_HEADS
    Definition scaled_dot_product_attention_params.h:38
    -
    MLXScaledDotProductAttentionParams::QUERY_SEQUENCE_LENGTH
    const uint QUERY_SEQUENCE_LENGTH
    Definition scaled_dot_product_attention_params.h:37
    -
    MLXScaledDotProductAttentionParams::KV_TILES
    const uint KV_TILES
    Definition scaled_dot_product_attention_params.h:40
    -
    MLXScaledDotProductAttentionParams::N_KV_HEADS
    const uint N_KV_HEADS
    Definition scaled_dot_product_attention_params.h:39
    -
    MLXScaledDotProductAttentionParams::INV_ALPHA
    const float INV_ALPHA
    Definition scaled_dot_product_attention_params.h:41
    -
    - -
    -Generated by doxygen 1.12.0 -
    -
    - - diff --git a/docs/build/html/scan_8h.html b/docs/build/html/scan_8h.html index d4dce074..17c2b345 100644 --- a/docs/build/html/scan_8h.html +++ b/docs/build/html/scan_8h.html @@ -164,7 +164,7 @@ Functions
    val = simd_scan(val); \
    return simd_shuffle_and_fill_up(val, init, 1); \
    }
    -
    simd_shuffle_and_fill_up
    uint64_t simd_shuffle_and_fill_up(uint64_t data, uint64_t filling, uint16_t delta)
    Definition utils.h:342
    +
    simd_shuffle_and_fill_up
    uint64_t simd_shuffle_and_fill_up(uint64_t data, uint64_t filling, uint16_t delta)
    Definition utils.h:383
    diff --git a/docs/build/html/scan_8h_source.html b/docs/build/html/scan_8h_source.html index ed748db2..0aa061ea 100644 --- a/docs/build/html/scan_8h_source.html +++ b/docs/build/html/scan_8h_source.html @@ -623,9 +623,9 @@ $(function(){ initResizable(false); });
    487}
    simd_size
    static constant constexpr const uint8_t simd_size
    Definition ops.h:22
    -
    simd_shuffle_and_fill_up
    uint64_t simd_shuffle_and_fill_up(uint64_t data, uint64_t filling, uint16_t delta)
    Definition utils.h:342
    -
    simd_shuffle
    uint64_t simd_shuffle(uint64_t data, uint16_t lane)
    Definition utils.h:367
    -
    ceildiv
    T ceildiv(T N, U M)
    Compute ceil((float)N/(float)M)
    Definition utils.h:272
    +
    simd_shuffle_and_fill_up
    uint64_t simd_shuffle_and_fill_up(uint64_t data, uint64_t filling, uint16_t delta)
    Definition utils.h:383
    +
    simd_shuffle
    uint64_t simd_shuffle(uint64_t data, uint16_t lane)
    Definition utils.h:408
    +
    ceildiv
    T ceildiv(T N, U M)
    Compute ceil((float)N/(float)M)
    Definition utils.h:313
    op
    Op op
    Definition binary.h:129
    DEFINE_SIMD_SCAN
    #define DEFINE_SIMD_SCAN()
    Definition scan.h:5
    DEFINE_SIMD_EXCLUSIVE_SCAN
    #define DEFINE_SIMD_EXCLUSIVE_SCAN()
    Definition scan.h:19
    @@ -650,7 +650,7 @@ $(function(){ initResizable(false); });
    CumProd< bool >::operator()
    bool operator()(bool a, T b)
    Definition scan.h:78
    CumProd
    Definition scan.h:53
    CumSum
    Definition scan.h:32
    -
    Limits
    Definition utils.h:17
    +
    Limits
    Definition utils.h:23
    diff --git a/docs/build/html/scatter_8h.html b/docs/build/html/scatter_8h.html index 86d8336b..77f5bfc4 100644 --- a/docs/build/html/scatter_8h.html +++ b/docs/build/html/scatter_8h.html @@ -99,18 +99,18 @@ $(function(){ initResizable(false); }); - - - + + +

    Functions

    template<typename T , typename IdxT , typename Op , int NIDX, bool UPD_ROW_CONTIG, int NWORK>
    METAL_FUNC void scatter_impl (const device T *updates, device mlx_atomic< T > *out, const constant int *upd_shape, const constant size_t *upd_strides, const constant size_t &upd_ndim, const constant size_t &upd_size, const constant int *out_shape, const constant size_t *out_strides, const constant size_t &out_ndim, const constant int *axes, const constant size_t &idx_size, const thread Indices< IdxT, NIDX > &indices, uint2 gid)
     
    template<typename T , typename IdxT , typename Op , int NIDX, bool UPD_ROW_CONTIG, int NWORK, typename LocT >
    METAL_FUNC void scatter_impl (const device T *updates, device mlx_atomic< T > *out, const constant int *upd_shape, const constant size_t *upd_strides, const constant size_t &upd_ndim, const constant size_t &upd_size, const constant int *out_shape, const constant size_t *out_strides, const constant size_t &out_ndim, const constant int *axes, const constant size_t &idx_size, const thread Indices< IdxT, NIDX > &indices, uint2 gid)
     

    Function Documentation

    - -

    ◆ scatter_impl()

    + +

    ◆ scatter_impl()

    -template<typename T , typename IdxT , typename Op , int NIDX, bool UPD_ROW_CONTIG, int NWORK>
    +template<typename T , typename IdxT , typename Op , int NIDX, bool UPD_ROW_CONTIG, int NWORK, typename LocT >
    diff --git a/docs/build/html/scatter_8h_source.html b/docs/build/html/scatter_8h_source.html index 2c89521d..0a9e0f29 100644 --- a/docs/build/html/scatter_8h_source.html +++ b/docs/build/html/scatter_8h_source.html @@ -103,58 +103,61 @@ $(function(){ initResizable(false); });
    10 typename Op,
    11 int NIDX,
    12 bool UPD_ROW_CONTIG,
    -
    13 int NWORK>
    -
    -
    14METAL_FUNC void scatter_impl(
    -
    15 const device T* updates,
    -
    16 device mlx_atomic<T>* out,
    -
    17 const constant int* upd_shape,
    -
    18 const constant size_t* upd_strides,
    -
    19 const constant size_t& upd_ndim,
    -
    20 const constant size_t& upd_size,
    -
    21 const constant int* out_shape,
    -
    22 const constant size_t* out_strides,
    -
    23 const constant size_t& out_ndim,
    -
    24 const constant int* axes,
    -
    25 const constant size_t& idx_size,
    -
    26 const thread Indices<IdxT, NIDX>& indices,
    -
    27 uint2 gid [[thread_position_in_grid]]) {
    -
    28 Op op;
    -
    29
    -
    30 auto ind_idx = gid.y * NWORK;
    -
    31 size_t out_offset = 0;
    -
    32 if (upd_size > 1) {
    -
    33 out_offset =
    -
    34 elem_to_loc(gid.x, upd_shape + indices.ndim, out_strides, out_ndim);
    -
    35 }
    -
    36
    -
    37 for (int j = 0; j < NWORK && ind_idx < idx_size; ++j, ind_idx++) {
    -
    38 size_t out_idx = out_offset;
    -
    39 for (int i = 0; i < NIDX; ++i) {
    -
    40 auto idx_loc = indices.row_contiguous[i]
    -
    41 ? ind_idx
    -
    42 : elem_to_loc(
    -
    43 ind_idx,
    -
    44 &indices.shapes[indices.ndim * i],
    -
    45 &indices.strides[indices.ndim * i],
    -
    46 indices.ndim);
    -
    47 auto ax = axes[i];
    -
    48 auto idx_val = offset_neg_idx(indices.buffers[i][idx_loc], out_shape[ax]);
    -
    49 out_idx += idx_val * out_strides[ax];
    -
    50 }
    -
    51 auto upd_idx = ind_idx * upd_size + gid.x;
    -
    52 if constexpr (!UPD_ROW_CONTIG) {
    -
    53 upd_idx = elem_to_loc(upd_idx, upd_shape, upd_strides, upd_ndim);
    -
    54 }
    -
    55 op.atomic_update(out, updates[upd_idx], out_idx);
    -
    56 }
    -
    57}
    +
    13 int NWORK,
    +
    14 typename LocT>
    +
    +
    15METAL_FUNC void scatter_impl(
    +
    16 const device T* updates,
    +
    17 device mlx_atomic<T>* out,
    +
    18 const constant int* upd_shape,
    +
    19 const constant size_t* upd_strides,
    +
    20 const constant size_t& upd_ndim,
    +
    21 const constant size_t& upd_size,
    +
    22 const constant int* out_shape,
    +
    23 const constant size_t* out_strides,
    +
    24 const constant size_t& out_ndim,
    +
    25 const constant int* axes,
    +
    26 const constant size_t& idx_size,
    +
    27 const thread Indices<IdxT, NIDX>& indices,
    +
    28 uint2 gid [[thread_position_in_grid]]) {
    +
    29 Op op;
    +
    30
    +
    31 auto ind_idx = gid.y * NWORK;
    +
    32 LocT out_offset = 0;
    +
    33 if (upd_size > 1) {
    +
    34 out_offset = elem_to_loc<size_t, LocT>(
    +
    35 gid.x, upd_shape + indices.ndim, out_strides, out_ndim);
    +
    36 }
    +
    37
    +
    38 for (int j = 0; j < NWORK && ind_idx < idx_size; ++j, ind_idx++) {
    +
    39 LocT out_idx = out_offset;
    +
    40 for (int i = 0; i < NIDX; ++i) {
    +
    41 auto idx_loc = indices.row_contiguous[i]
    +
    42 ? ind_idx
    +
    43 : elem_to_loc<size_t, LocT>(
    +
    44 ind_idx,
    +
    45 &indices.shapes[indices.ndim * i],
    +
    46 &indices.strides[indices.ndim * i],
    +
    47 indices.ndim);
    +
    48 auto ax = axes[i];
    +
    49 auto idx_val = offset_neg_idx(indices.buffers[i][idx_loc], out_shape[ax]);
    +
    50 out_idx +=
    +
    51 static_cast<LocT>(idx_val) * static_cast<LocT>(out_strides[ax]);
    +
    52 }
    +
    53 auto upd_idx = ind_idx * static_cast<LocT>(upd_size) + gid.x;
    +
    54 if constexpr (!UPD_ROW_CONTIG) {
    +
    55 upd_idx =
    +
    56 elem_to_loc<size_t, LocT>(upd_idx, upd_shape, upd_strides, upd_ndim);
    +
    57 }
    +
    58 op.atomic_update(out, updates[upd_idx], out_idx);
    +
    59 }
    +
    60}
    -
    elem_to_loc
    METAL_FUNC stride_t elem_to_loc(uint elem, constant const int *shape, constant const stride_t *strides, int ndim)
    Definition utils.h:87
    +
    elem_to_loc
    METAL_FUNC IdxT elem_to_loc(uint elem, constant const int *shape, constant const StrideT *strides, int ndim)
    Definition utils.h:93
    op
    Op op
    Definition binary.h:129
    -
    offset_neg_idx
    METAL_FUNC size_t offset_neg_idx(IdxT idx, size_t size)
    Definition indexing.h:17
    -
    scatter_impl
    METAL_FUNC void scatter_impl(const device T *updates, device mlx_atomic< T > *out, const constant int *upd_shape, const constant size_t *upd_strides, const constant size_t &upd_ndim, const constant size_t &upd_size, const constant int *out_shape, const constant size_t *out_strides, const constant size_t &out_ndim, const constant int *axes, const constant size_t &idx_size, const thread Indices< IdxT, NIDX > &indices, uint2 gid)
    Definition scatter.h:14
    +
    offset_neg_idx
    METAL_FUNC size_t offset_neg_idx(IdxT idx, int size)
    Definition indexing.h:17
    +
    scatter_impl
    METAL_FUNC void scatter_impl(const device T *updates, device mlx_atomic< T > *out, const constant int *upd_shape, const constant size_t *upd_strides, const constant size_t &upd_ndim, const constant size_t &upd_size, const constant int *out_shape, const constant size_t *out_strides, const constant size_t &out_ndim, const constant int *axes, const constant size_t &idx_size, const thread Indices< IdxT, NIDX > &indices, uint2 gid)
    Definition scatter.h:15
    Indices
    Definition indexing.h:8
    mlx_atomic
    Definition atomic.h:25
    diff --git a/docs/build/html/sdpa__vector_8h.html b/docs/build/html/sdpa__vector_8h.html index daee8ad0..a809f89e 100644 --- a/docs/build/html/sdpa__vector_8h.html +++ b/docs/build/html/sdpa__vector_8h.html @@ -102,6 +102,12 @@ Functions + + + + + +
    METAL_FUNC void scatter_impl
    template<typename T , int D>
    void sdpa_vector (const device T *queries, const device T *keys, const device T *values, device T *out, const constant int &gqa_factor, const constant int &N, const constant size_t &k_stride, const constant size_t &v_stride, const constant float &scale, uint3 tid, uint simd_gid, uint simd_lid)
     
    template<typename T , int D>
    void sdpa_vector_2pass_1 (const device T *queries, const device T *keys, const device T *values, device float *out, device float *sums, device float *maxs, const constant int &gqa_factor, const constant int &N, const constant size_t &k_stride, const constant size_t &v_stride, const constant float &scale, uint3 tid, uint simd_gid, uint simd_lid)
     
    template<typename T , int D>
    void sdpa_vector_2pass_2 (const device float *partials, const device float *sums, const device float *maxs, device T *out, uint3 tid, uint simd_gid, uint simd_lid)
     

    Function Documentation

    @@ -175,6 +181,137 @@ template<typename T , int D>
    +
    +
    + +

    ◆ sdpa_vector_2pass_1()

    + +
    +
    +
    +template<typename T , int D>
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    void sdpa_vector_2pass_1 (const device T * queries,
    const device T * keys,
    const device T * values,
    device float * out,
    device float * sums,
    device float * maxs,
    const constant int & gqa_factor,
    const constant int & N,
    const constant size_t & k_stride,
    const constant size_t & v_stride,
    const constant float & scale,
    uint3 tid,
    uint simd_gid,
    uint simd_lid )
    +
    + +
    +
    + +

    ◆ sdpa_vector_2pass_2()

    + +
    +
    +
    +template<typename T , int D>
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    void sdpa_vector_2pass_2 (const device float * partials,
    const device float * sums,
    const device float * maxs,
    device T * out,
    uint3 tid,
    uint simd_gid,
    uint simd_lid )
    +
    +
    diff --git a/docs/build/html/sdpa__vector_8h_source.html b/docs/build/html/sdpa__vector_8h_source.html index 39019313..8785dfc9 100644 --- a/docs/build/html/sdpa__vector_8h_source.html +++ b/docs/build/html/sdpa__vector_8h_source.html @@ -115,106 +115,288 @@ $(function(){ initResizable(false); });
    21 constexpr int BN = 32;
    22 constexpr int BD = 32;
    23 constexpr int elem_per_thread = D / BD;
    -
    24
    -
    25 const int stride = BN * D;
    -
    26
    -
    27 typedef float U;
    -
    28
    -
    29 thread U q[elem_per_thread];
    -
    30 thread U k[elem_per_thread];
    -
    31 thread U o[elem_per_thread];
    -
    32
    -
    33 threadgroup U outputs[BN * BD];
    -
    34 threadgroup U max_scores[BN];
    -
    35 threadgroup U sum_exp_scores[BN];
    -
    36
    -
    37 // Adjust positions
    -
    38 const int head_idx = tid.y;
    -
    39 const int kv_head_idx = head_idx / gqa_factor;
    -
    40 queries += head_idx * D + simd_lid * elem_per_thread;
    -
    41 keys += kv_head_idx * k_stride + simd_gid * D + simd_lid * elem_per_thread;
    -
    42 values += kv_head_idx * v_stride + simd_gid * D + simd_lid * elem_per_thread;
    -
    43 out += head_idx * D + simd_gid * elem_per_thread;
    -
    44
    -
    45 // Read the query and 0 the output accumulator
    -
    46 for (int i = 0; i < elem_per_thread; i++) {
    -
    47 q[i] = static_cast<U>(scale) * queries[i];
    -
    48 }
    -
    49 for (int i = 0; i < elem_per_thread; i++) {
    -
    50 o[i] = 0;
    -
    51 }
    -
    52
    -
    53 U max_score = -INFINITY;
    -
    54 U sum_exp_score = 0;
    -
    55
    -
    56 // For each key
    -
    57 for (int i = simd_gid; i < N; i += BN) {
    -
    58 // Read the key
    -
    59 for (int i = 0; i < elem_per_thread; i++) {
    -
    60 k[i] = keys[i];
    -
    61 }
    -
    62
    -
    63 // Compute the i-th score
    -
    64 U score = 0;
    -
    65 for (int i = 0; i < elem_per_thread; i++) {
    -
    66 score += q[i] * k[i];
    -
    67 }
    -
    68 score = simd_sum(score);
    -
    69
    -
    70 // Update the accumulators
    -
    71 U new_max = max(max_score, score);
    -
    72 U factor = fast::exp(max_score - new_max);
    -
    73 U exp_score = fast::exp(score - new_max);
    -
    74
    -
    75 max_score = new_max;
    -
    76 sum_exp_score = sum_exp_score * factor + exp_score;
    -
    77
    -
    78 // Update the output accumulator
    -
    79 for (int i = 0; i < elem_per_thread; i++) {
    -
    80 o[i] = o[i] * factor + exp_score * values[i];
    -
    81 }
    -
    82
    -
    83 // Move the pointers to the next kv
    -
    84 keys += stride;
    -
    85 values += stride;
    -
    86 }
    -
    87 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    24 constexpr int stride = BN * D;
    +
    25
    +
    26 typedef float U;
    +
    27
    +
    28 thread U q[elem_per_thread];
    +
    29 thread U k[elem_per_thread];
    +
    30 thread U o[elem_per_thread];
    +
    31
    +
    32 threadgroup U outputs[BN * BD];
    +
    33 threadgroup U max_scores[BN];
    +
    34 threadgroup U sum_exp_scores[BN];
    +
    35
    +
    36 // Adjust positions
    +
    37 const int head_idx = tid.y;
    +
    38 const int kv_head_idx = head_idx / gqa_factor;
    +
    39 queries += head_idx * D + simd_lid * elem_per_thread;
    +
    40 keys += kv_head_idx * k_stride + simd_gid * D + simd_lid * elem_per_thread;
    +
    41 values += kv_head_idx * v_stride + simd_gid * D + simd_lid * elem_per_thread;
    +
    42 out += head_idx * D + simd_gid * elem_per_thread;
    +
    43
    +
    44 // Read the query and 0 the output accumulator
    +
    45 for (int i = 0; i < elem_per_thread; i++) {
    +
    46 q[i] = static_cast<U>(scale) * queries[i];
    +
    47 }
    +
    48 for (int i = 0; i < elem_per_thread; i++) {
    +
    49 o[i] = 0;
    +
    50 }
    +
    51
    +
    52 U max_score = -INFINITY;
    +
    53 U sum_exp_score = 0;
    +
    54
    +
    55 // For each key
    +
    56 for (int i = simd_gid; i < N; i += BN) {
    +
    57 // Read the key
    +
    58 for (int i = 0; i < elem_per_thread; i++) {
    +
    59 k[i] = keys[i];
    +
    60 }
    +
    61
    +
    62 // Compute the i-th score
    +
    63 U score = 0;
    +
    64 for (int i = 0; i < elem_per_thread; i++) {
    +
    65 score += q[i] * k[i];
    +
    66 }
    +
    67 score = simd_sum(score);
    +
    68
    +
    69 // Update the accumulators
    +
    70 U new_max = max(max_score, score);
    +
    71 U factor = fast::exp(max_score - new_max);
    +
    72 U exp_score = fast::exp(score - new_max);
    +
    73
    +
    74 max_score = new_max;
    +
    75 sum_exp_score = sum_exp_score * factor + exp_score;
    +
    76
    +
    77 // Update the output accumulator
    +
    78 for (int i = 0; i < elem_per_thread; i++) {
    +
    79 o[i] = o[i] * factor + exp_score * values[i];
    +
    80 }
    +
    81
    +
    82 // Move the pointers to the next kv
    +
    83 keys += stride;
    +
    84 values += stride;
    +
    85 }
    +
    86
    +
    87 // Each thread has a partial part of the output so we need to combine them.
    88
    -
    89 // Each thread has a partial part of the output so we need to combine them.
    -
    90
    -
    91 // First let's communicate the max and sum_exp
    -
    92 if (simd_lid == 0) {
    -
    93 max_scores[simd_gid] = max_score;
    -
    94 sum_exp_scores[simd_gid] = sum_exp_score;
    -
    95 }
    -
    96 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    97 max_score = max_scores[simd_lid];
    -
    98 U new_max = simd_max(max_score);
    -
    99 U factor = fast::exp(max_score - new_max);
    -
    100 sum_exp_score = simd_sum(sum_exp_scores[simd_lid] * factor);
    -
    101
    -
    102 // Now we need to aggregate all the outputs
    -
    103 for (int i = 0; i < elem_per_thread; i++) {
    -
    104 outputs[simd_lid * BD + simd_gid] = o[i];
    +
    89 // First let's communicate the max and sum_exp
    +
    90 if (simd_lid == 0) {
    +
    91 max_scores[simd_gid] = max_score;
    +
    92 sum_exp_scores[simd_gid] = sum_exp_score;
    +
    93 }
    +
    94 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    95 max_score = max_scores[simd_lid];
    +
    96 U new_max = simd_max(max_score);
    +
    97 U factor = fast::exp(max_score - new_max);
    +
    98 sum_exp_score = simd_sum(sum_exp_scores[simd_lid] * factor);
    +
    99
    +
    100 // Now we need to aggregate all the outputs
    +
    101 for (int i = 0; i < elem_per_thread; i++) {
    +
    102 outputs[simd_lid * BD + simd_gid] = o[i];
    +
    103 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    104 o[i] = simd_sum(outputs[simd_gid * BD + simd_lid] * factor) / sum_exp_score;
    105 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    106 o[i] = simd_sum(outputs[simd_gid * BD + simd_lid] * factor) / sum_exp_score;
    -
    107 threadgroup_barrier(mem_flags::mem_threadgroup);
    -
    108 }
    -
    109
    -
    110 // And write the output
    -
    111 if (simd_lid == 0) {
    -
    112 for (int i = 0; i < elem_per_thread; i++) {
    -
    113 out[i] = static_cast<T>(o[i]);
    -
    114 }
    -
    115 }
    -
    116}
    +
    106 }
    +
    107
    +
    108 // And write the output
    +
    109 if (simd_lid == 0) {
    +
    110 for (int i = 0; i < elem_per_thread; i++) {
    +
    111 out[i] = static_cast<T>(o[i]);
    +
    112 }
    +
    113 }
    +
    114}
    -
    metal::fast::exp
    METAL_FUNC bfloat16_t exp(bfloat16_t x)
    Definition bf16_math.h:242
    -
    metal
    Definition bf16.h:265
    -
    metal::simd_max
    METAL_FUNC bfloat16_t simd_max(bfloat16_t data)
    Definition bf16_math.h:392
    -
    metal::simd_sum
    METAL_FUNC bfloat16_t simd_sum(bfloat16_t data)
    Definition bf16_math.h:392
    -
    metal::max
    METAL_FUNC bfloat16_t max(bfloat16_t x, bfloat16_t y)
    Definition bf16_math.h:234
    +
    115
    +
    116template <typename T, int D>
    +
    +
    117[[kernel]] void sdpa_vector_2pass_1(
    +
    118 const device T* queries [[buffer(0)]],
    +
    119 const device T* keys [[buffer(1)]],
    +
    120 const device T* values [[buffer(2)]],
    +
    121 device float* out [[buffer(3)]],
    +
    122 device float* sums [[buffer(4)]],
    +
    123 device float* maxs [[buffer(5)]],
    +
    124 const constant int& gqa_factor,
    +
    125 const constant int& N,
    +
    126 const constant size_t& k_stride,
    +
    127 const constant size_t& v_stride,
    +
    128 const constant float& scale,
    +
    129 uint3 tid [[threadgroup_position_in_grid]],
    +
    130 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    131 uint simd_lid [[thread_index_in_simdgroup]]) {
    +
    132 constexpr int BN = 8;
    +
    133 constexpr int BD = 32;
    +
    134 constexpr int elem_per_thread = D / BD;
    +
    135 constexpr int stride = BN * D;
    +
    136 constexpr int blocks = 32;
    +
    137
    +
    138 typedef float U;
    +
    139
    +
    140 thread U q[elem_per_thread];
    +
    141 thread U k[elem_per_thread];
    +
    142 thread U o[elem_per_thread];
    +
    143
    +
    144 threadgroup U outputs[BN * BD];
    +
    145 threadgroup U max_scores[BN];
    +
    146 threadgroup U sum_exp_scores[BN];
    +
    147
    +
    148 // Adjust positions
    +
    149 const int block_idx = tid.z;
    +
    150 const int head_idx = tid.y;
    +
    151 const int kv_head_idx = head_idx / gqa_factor;
    +
    152 queries += head_idx * D + simd_lid * elem_per_thread;
    +
    153 keys += kv_head_idx * k_stride + (block_idx * BN + simd_gid) * D +
    +
    154 simd_lid * elem_per_thread;
    +
    155 values += kv_head_idx * v_stride + (block_idx * BN + simd_gid) * D +
    +
    156 simd_lid * elem_per_thread;
    +
    157 out += head_idx * blocks * D + block_idx * D + simd_lid * elem_per_thread;
    +
    158 sums += head_idx * blocks + block_idx;
    +
    159 maxs += head_idx * blocks + block_idx;
    +
    160
    +
    161 // Read the query and 0 the output accumulator
    +
    162 for (int i = 0; i < elem_per_thread; i++) {
    +
    163 q[i] = static_cast<U>(scale) * queries[i];
    +
    164 }
    +
    165 for (int i = 0; i < elem_per_thread; i++) {
    +
    166 o[i] = 0;
    +
    167 }
    +
    168
    +
    169 U max_score = -1e9;
    +
    170 U sum_exp_score = 0;
    +
    171
    +
    172 // For each key
    +
    173 for (int i = block_idx * BN + simd_gid; i < N; i += blocks * BN) {
    +
    174 // Read the key
    +
    175 for (int i = 0; i < elem_per_thread; i++) {
    +
    176 k[i] = keys[i];
    +
    177 }
    +
    178
    +
    179 // Compute the i-th score
    +
    180 U score = 0;
    +
    181 for (int i = 0; i < elem_per_thread; i++) {
    +
    182 score += q[i] * k[i];
    +
    183 }
    +
    184 score = simd_sum(score);
    +
    185
    +
    186 // Update the accumulators
    +
    187 U new_max = max(max_score, score);
    +
    188 U factor = fast::exp(max_score - new_max);
    +
    189 U exp_score = fast::exp(score - new_max);
    +
    190
    +
    191 max_score = new_max;
    +
    192 sum_exp_score = sum_exp_score * factor + exp_score;
    +
    193
    +
    194 // Update the output accumulator
    +
    195 for (int i = 0; i < elem_per_thread; i++) {
    +
    196 o[i] = o[i] * factor + exp_score * values[i];
    +
    197 }
    +
    198
    +
    199 // Move the pointers to the next kv
    +
    200 keys += blocks * stride;
    +
    201 values += blocks * stride;
    +
    202 }
    +
    203
    +
    204 // Each thread has a partial part of the output so we need to combine them.
    +
    205
    +
    206 // First let's communicate the max and sum_exp
    +
    207 if (simd_lid == 0) {
    +
    208 max_scores[simd_gid] = max_score;
    +
    209 sum_exp_scores[simd_gid] = sum_exp_score;
    +
    210 }
    +
    211 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    212 max_score = (simd_lid < BN) ? max_scores[simd_lid] : -1e9;
    +
    213 U new_max = simd_max(max_score);
    +
    214 U factor = fast::exp(max_score - new_max);
    +
    215 sum_exp_score = (simd_lid < BN) ? sum_exp_scores[simd_lid] : 0;
    +
    216 sum_exp_score = simd_sum(sum_exp_score * factor);
    +
    217
    +
    218 // Write the sum and new max
    +
    219 if (simd_gid == 0) {
    +
    220 sums[0] = sum_exp_score;
    +
    221 maxs[0] = new_max;
    +
    222 }
    +
    223
    +
    224 // Now we need to aggregate all the outputs
    +
    225 for (int i = 0; i < elem_per_thread; i++) {
    +
    226 outputs[simd_lid * BN + simd_gid] =
    +
    227 o[i] * fast::exp(max_scores[simd_gid] - new_max);
    +
    228 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    229
    +
    230 // And write the output
    +
    231 if (simd_gid == 0) {
    +
    232 U output = outputs[simd_lid * BN];
    +
    233 for (int j = 1; j < BN; j++) {
    +
    234 output += outputs[simd_lid * BN + j];
    +
    235 }
    +
    236 out[i] = static_cast<T>(output);
    +
    237 }
    +
    238 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    239 }
    +
    240}
    +
    +
    241
    +
    242template <typename T, int D>
    +
    +
    243[[kernel]] void sdpa_vector_2pass_2(
    +
    244 const device float* partials [[buffer(0)]],
    +
    245 const device float* sums [[buffer(1)]],
    +
    246 const device float* maxs [[buffer(2)]],
    +
    247 device T* out [[buffer(3)]],
    +
    248 uint3 tid [[threadgroup_position_in_grid]],
    +
    249 uint simd_gid [[simdgroup_index_in_threadgroup]],
    +
    250 uint simd_lid [[thread_index_in_simdgroup]]) {
    +
    251 constexpr int BN = 32;
    +
    252 constexpr int BD = 32;
    +
    253 constexpr int elem_per_thread = D / BD;
    +
    254 constexpr int blocks = 32;
    +
    255
    +
    256 typedef float U;
    +
    257
    +
    258 thread U o[elem_per_thread];
    +
    259 threadgroup U outputs[BN * BD];
    +
    260
    +
    261 // Adjust positions
    +
    262 const int head_idx = tid.y;
    +
    263 partials += head_idx * blocks * D + simd_gid * D + simd_lid * elem_per_thread;
    +
    264 sums += head_idx * blocks;
    +
    265 maxs += head_idx * blocks;
    +
    266 out += head_idx * D + simd_gid * elem_per_thread;
    +
    267
    +
    268 // First everybody reads the max and sum_exp
    +
    269 U max_score = maxs[simd_lid];
    +
    270 U new_max = simd_max(max_score);
    +
    271 U factor = fast::exp(max_score - new_max);
    +
    272 U sum_exp_score = simd_sum(sums[simd_lid] * factor);
    +
    273
    +
    274 // Now read the block into registers and then use shared memory to transpose
    +
    275 // it
    +
    276 for (int i = 0; i < elem_per_thread; i++) {
    +
    277 o[i] = partials[i];
    +
    278 }
    +
    279 for (int i = 0; i < elem_per_thread; i++) {
    +
    280 outputs[simd_lid * BD + simd_gid] = o[i];
    +
    281 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    282 o[i] = simd_sum(outputs[simd_gid * BD + simd_lid] * factor) / sum_exp_score;
    +
    283 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    284 }
    +
    285
    +
    286 // And write the output
    +
    287 if (simd_lid == 0) {
    +
    288 for (int i = 0; i < elem_per_thread; i++) {
    +
    289 out[i] = static_cast<T>(o[i]);
    +
    290 }
    +
    291 }
    +
    292}
    +
    +
    metal::fast::exp
    METAL_FUNC bfloat16_t exp(bfloat16_t x)
    Definition bf16_math.h:240
    +
    metal
    Definition bf16_math.h:226
    +
    metal::simd_max
    METAL_FUNC bfloat16_t simd_max(bfloat16_t data)
    Definition bf16_math.h:378
    +
    metal::simd_sum
    METAL_FUNC bfloat16_t simd_sum(bfloat16_t data)
    Definition bf16_math.h:378
    +
    metal::max
    METAL_FUNC bfloat16_t max(bfloat16_t x, bfloat16_t y)
    Definition bf16_math.h:232
    +
    sdpa_vector_2pass_2
    void sdpa_vector_2pass_2(const device float *partials, const device float *sums, const device float *maxs, device T *out, uint3 tid, uint simd_gid, uint simd_lid)
    Definition sdpa_vector.h:243
    sdpa_vector
    void sdpa_vector(const device T *queries, const device T *keys, const device T *values, device T *out, const constant int &gqa_factor, const constant int &N, const constant size_t &k_stride, const constant size_t &v_stride, const constant float &scale, uint3 tid, uint simd_gid, uint simd_lid)
    Definition sdpa_vector.h:8
    +
    sdpa_vector_2pass_1
    void sdpa_vector_2pass_1(const device T *queries, const device T *keys, const device T *values, device float *out, device float *sums, device float *maxs, const constant int &gqa_factor, const constant int &N, const constant size_t &k_stride, const constant size_t &v_stride, const constant float &scale, uint3 tid, uint simd_gid, uint simd_lid)
    Definition sdpa_vector.h:117
    diff --git a/docs/build/html/search.html b/docs/build/html/search.html index db738de0..d1f29637 100644 --- a/docs/build/html/search.html +++ b/docs/build/html/search.html @@ -6,7 +6,7 @@ - Search - MLX 0.20.0 documentation + Search - MLX 0.21.0 documentation @@ -37,7 +37,7 @@ - + @@ -50,7 +50,7 @@ - + @@ -131,8 +131,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home
    @@ -445,7 +445,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -522,6 +521,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -551,6 +551,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/search/all_1.js b/docs/build/html/search/all_1.js index 2feceb6c..0b76d81d 100644 --- a/docs/build/html/search/all_1.js +++ b/docs/build/html/search/all_1.js @@ -5,7 +5,7 @@ var searchData= ['a_5fstr_5fm_2',['A_str_m',['../structmlx_1_1steel_1_1_block_m_m_a.html#ab9c7f5386594497f5f4df7e59670b877',1,'mlx::steel::BlockMMA']]], ['abs_3',['Abs',['../struct_abs.html',1,'Abs'],['../classmlx_1_1core_1_1_abs.html',1,'mlx::core::Abs'],['../structmlx_1_1core_1_1detail_1_1_abs.html',1,'mlx::core::detail::Abs'],['../classmlx_1_1core_1_1_abs.html#a1247e72feb640fb562d036b2dd1ae4ad',1,'mlx::core::Abs::Abs()']]], 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402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473], "model": 5, "modul": [323, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 453], "moveaxi": [60, 222], "mpi": 481, "mse_loss": 432, "multi": 6, "multiheadattent": 379, "multipli": 223, "multivariate_norm": 244, "named_modul": 368, "nan_to_num": 224, "nbyte": 61, "ndim": 62, "neg": 225, "network": 323, "neural": 323, "new_stream": 226, "nll_loss": 433, "nn": [306, 307, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448], "norm": 188, "normal": [245, 408], "not_equ": 227, "numpi": [483, 485], "ones": 228, "ones_lik": 229, "onli": 484, "oper": [0, 2, 454], "optim": [308, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475], "option": 8, "other": 485, "outer": 230, "pad": 231, "paramet": [323, 369], "partit": 232, "perceptron": 6, "permut": 246, "place": 483, "power": 233, "prelu": [380, 437], "primit": 2, "prod": [63, 234], "pure": 480, "put": 5, "put_along_axi": 235, "python": [2, 7, 8], "pytorch": 485, "qr": 189, "quantiz": [236, 306], "quantized_matmul": 237, "quantizedembed": 381, "quantizedlinear": 382, "quick": [323, 486], "radian": 238, "randint": 247, "random": [239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 477], "read": 7, "real": 252, "reciproc": [64, 253], "recv": 125, "recv_lik": 126, "reduc": 481, "refer": 7, "regress": 4, "relu": [385, 438], "relu6": [386, 439], "remaind": 254, "remot": 481, "repeat": 255, "requir": 8, "reset_peak_memori": 214, "reshap": [65, 256], "result": 2, "rfft": 155, "rfft2": 156, "rfftn": 157, "right_shift": 257, "rms_norm": 143, "rmsnorm": 383, "rmsprop": 467, "rnn": 384, "roll": 258, "rope": [144, 387], "round": [66, 259], "rsqrt": [67, 260], "sampl": 1, "save": [261, 455, 487], "save_gguf": 262, "save_safetensor": 263, "save_weight": 370, "savez": 264, "savez_compress": 265, "scaled_dot_product_attent": 145, "schedul": 476, "script": [2, 5], "seed": 248, "selu": [388, 440], "send": 127, "sequenti": 389, "serial": 487, "set": 481, "set_cache_limit": 215, "set_default_devic": 266, "set_default_stream": 267, "set_dtyp": 371, "set_memory_limit": 216, "set_wired_limit": 217, "setuptool": 2, "sgd": 468, "shape": [1, 68], "shell": 8, "sigmoid": [268, 391, 441], "sign": 269, "silu": [390, 442], "simpl": [1, 488], "sin": [69, 270], "sinh": 271, "sinusoidalpositionalencod": 392, "size": [8, 70], "smooth_l1_loss": 434, "softmax": [272, 393, 443], "softmin": [394, 444], "softplu": [395, 445], "softshrink": [396, 446], "softsign": 397, "sort": 273, "sourc": 8, "specifi": 489, "speedup": 480, "split": [71, 249, 274], "sqrt": [72, 275], "squar": [73, 276], "squeez": [74, 277], "stack": 278, "start": [323, 481, 486], "start_captur": 218, "state": [372, 465], "std": [75, 279], "step": [398, 447], "step_decai": 473, "stop_captur": 219, "stop_gradi": 280, "stream": [281, 314, 317, 489], "stride": 1, "subtract": 282, "sum": [76, 283], "support": 316, "svd": 190, "swapax": [77, 284], "synchron": 285, "t": 31, "take": 286, "take_along_axi": 287, "tan": 288, "tanh": [289, 399, 448], "tensordot": 290, "tensorflow": 485, "tile": 291, "togeth": 5, "tolist": 78, "topk": 292, "trace": 293, "train": [373, 375, 480, 481], "trainable_paramet": 374, "transform": [2, 400, 478, 480, 482, 484, 486], "transpos": [79, 294], "tree": 479, "tree_flatten": 309, "tree_map": 310, "tree_map_with_path": 311, "tree_reduc": 312, "tree_unflatten": 313, "tri": 295, "tri_inv": 191, "tril": 296, "triplet_loss": 435, "triu": 297, "troubleshoot": 8, "truncated_norm": 250, "tune": 481, "type": 316, "unfreez": 376, "unifi": 488, "uniform": [251, 409], "up": 481, "updat": [323, 377, 466, 483], "update_modul": 378, "upsampl": 401, "us": [1, 2, 484, 489], "usag": [2, 7], "util": [309, 310, 311, 312, 313, 479], "valu": 323, "value_and_grad": [298, 307], "var": [80, 299], "vector": 482, "view": [81, 300], "vjp": [1, 301], "vmap": 302, "weight": 5, "what": 484, "when": 484, "where": 303, "why": 484, "workflow": 3, "x86": 8, "xcode": 3, "you": 484, "zero": 304, "zeros_lik": 305}}) \ No newline at end of file diff --git a/docs/build/html/sort_8h_source.html b/docs/build/html/sort_8h_source.html index 374bc71d..8fbbbc12 100644 --- a/docs/build/html/sort_8h_source.html +++ b/docs/build/html/sort_8h_source.html @@ -464,8 +464,8 @@ $(function(){ initResizable(false); });
    352 using val_t = typename sort_kernel::val_t;
    353 using idx_t = typename sort_kernel::idx_t;
    354
    -
    355 auto in_block_idx = elem_to_loc(tid.y, nc_shape, in_nc_strides, nc_dim);
    -
    356 auto out_block_idx = elem_to_loc(tid.y, nc_shape, out_nc_strides, nc_dim);
    +
    355 auto in_block_idx = elem_to_loc(tid.y, nc_shape, in_nc_strides, nc_dim);
    +
    356 auto out_block_idx = elem_to_loc(tid.y, nc_shape, out_nc_strides, nc_dim);
    357 inp += in_block_idx;
    358 out += out_block_idx;
    359
    @@ -616,7 +616,7 @@ $(function(){ initResizable(false); });
    496 BLOCK_THREADS,
    497 N_PER_THREAD>;
    498
    -
    499 auto block_idx = elem_to_loc(tid.y, nc_shape, nc_strides, nc_dim);
    +
    499 auto block_idx = elem_to_loc(tid.y, nc_shape, nc_strides, nc_dim);
    500 inp += block_idx;
    501 out_vals += tid.y * size_sorted_axis;
    502 out_idxs += tid.y * size_sorted_axis;
    @@ -818,11 +818,11 @@ $(function(){ initResizable(false); });
    694 }
    695}
    -
    elem_to_loc
    METAL_FUNC stride_t elem_to_loc(uint elem, constant const int *shape, constant const stride_t *strides, int ndim)
    Definition utils.h:87
    +
    elem_to_loc
    METAL_FUNC IdxT elem_to_loc(uint elem, constant const int *shape, constant const StrideT *strides, int ndim)
    Definition utils.h:93
    op
    Op op
    Definition binary.h:129
    -
    metal
    Definition bf16.h:265
    -
    metal::min
    METAL_FUNC bfloat16_t min(bfloat16_t x, bfloat16_t y)
    Definition bf16_math.h:234
    -
    metal::max
    METAL_FUNC bfloat16_t max(bfloat16_t x, bfloat16_t y)
    Definition bf16_math.h:234
    +
    metal
    Definition bf16_math.h:226
    +
    metal::min
    METAL_FUNC bfloat16_t min(bfloat16_t x, bfloat16_t y)
    Definition bf16_math.h:232
    +
    metal::max
    METAL_FUNC bfloat16_t max(bfloat16_t x, bfloat16_t y)
    Definition bf16_math.h:232
    MLX_MTL_CONST
    #define MLX_MTL_CONST
    Definition sort.h:3
    mb_block_partition
    void mb_block_partition(device idx_t *block_partitions, const device val_t *dev_vals, const device idx_t *dev_idxs, const constant int &size_sorted_axis, const constant int &merge_tiles, const constant int &n_blocks, uint3 tid, uint3 lid, uint3 tgp_dims)
    Definition sort.h:525
    block_sort_nc
    void block_sort_nc(const device T *inp, device U *out, const constant int &size_sorted_axis, const constant int &in_stride_sorted_axis, const constant int &out_stride_sorted_axis, const constant int &nc_dim, const constant int *nc_shape, const constant size_t *in_nc_strides, const constant size_t *out_nc_strides, uint3 tid, uint3 lid)
    Definition sort.h:338
    @@ -848,7 +848,7 @@ $(function(){ initResizable(false); });
    LessThan
    Definition sort.h:23
    LessThan::operator()
    METAL_FUNC bool operator()(T a, T b)
    Definition sort.h:26
    LessThan::init
    static constexpr constant T init
    Definition sort.h:24
    -
    Limits
    Definition utils.h:17
    +
    Limits
    Definition utils.h:23
    ThreadSort
    Definition sort.h:37
    ThreadSort::sort
    static METAL_FUNC void sort(thread val_t(&vals)[N_PER_THREAD], thread idx_t(&idxs)[N_PER_THREAD])
    Definition sort.h:38
    diff --git a/docs/build/html/steel__attention_8h.html b/docs/build/html/steel__attention_8h.html new file mode 100644 index 00000000..3fc19645 --- /dev/null +++ b/docs/build/html/steel__attention_8h.html @@ -0,0 +1,226 @@ + + + + + + + +MLX: mlx/backend/metal/kernels/steel/attn/kernels/steel_attention.h File Reference + + + + + + + + + + + + + +
    +
    + + + + + + +
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    +Classes | +Functions | +Variables
    +
    steel_attention.h File Reference
    +
    +
    + +

    Go to the source code of this file.

    + + + + + + + + + + + + + + + + +

    +Classes

    struct  TransformScale< T >
     
    struct  MaxOp
     
    struct  SumOp
     
    struct  MulOp
     
    struct  SubOp
     
    struct  ExpSubOp
     
    struct  DivOp
     
    + + + + +

    +Functions

    template<typename T , int BQ, int BK, int BD, int WM, int WN, typename AccumType = float>
    void attention (const device T *Q, const device T *K, const device T *V, device T *O, const constant AttnParams *params, uint simd_lane_id, uint simd_group_id, uint3 tid, uint3 lid)
     
    + + + + + +

    +Variables

    constant bool align_Q
     
    constant bool align_K
     
    +

    Function Documentation

    + +

    ◆ attention()

    + +
    +
    +
    +template<typename T , int BQ, int BK, int BD, int WM, int WN, typename AccumType = float>
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    void attention (const device T * Q,
    const device T * K,
    const device T * V,
    device T * O,
    const constant AttnParams * params,
    uint simd_lane_id,
    uint simd_group_id,
    uint3 tid,
    uint3 lid )
    +
    + +
    +
    +

    Variable Documentation

    + +

    ◆ align_K

    + +
    +
    + + + + +
    constant bool align_K
    +
    + +
    +
    + +

    ◆ align_Q

    + +
    +
    + + + + +
    constant bool align_Q
    +
    + +
    +
    +
    + +
    +Generated by doxygen 1.12.0 +
    +
    + + diff --git a/docs/build/html/steel__attention_8h_source.html b/docs/build/html/steel__attention_8h_source.html new file mode 100644 index 00000000..d38622d0 --- /dev/null +++ b/docs/build/html/steel__attention_8h_source.html @@ -0,0 +1,508 @@ + + + + + + + +MLX: mlx/backend/metal/kernels/steel/attn/kernels/steel_attention.h Source File + + + + + + + + + + + + + +
    +
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    steel_attention.h
    +
    +
    +Go to the documentation of this file.
    1// Copyright © 2024 Apple Inc.
    +
    2
    +
    3using namespace mlx::steel;
    +
    4
    +
    6// GEMM kernels
    +
    8
    +
    9constant bool align_Q [[function_constant(200)]];
    +
    10constant bool align_K [[function_constant(201)]];
    +
    11
    +
    12template <typename T>
    +
    +
    13struct TransformScale {
    +
    14 T scale;
    +
    15 METAL_FUNC TransformScale(T scale_) : scale(scale_) {}
    +
    16
    +
    +
    17 METAL_FUNC T apply(T x) const {
    +
    18 return scale * x;
    +
    19 }
    +
    +
    20};
    +
    +
    21
    +
    +
    22struct MaxOp {
    +
    23 template <typename T>
    +
    +
    24 METAL_FUNC static constexpr T apply(T x, T y) {
    +
    25 return metal::max(x, y);
    +
    26 }
    +
    +
    27};
    +
    +
    28
    +
    +
    29struct SumOp {
    +
    30 template <typename T>
    +
    +
    31 METAL_FUNC static constexpr T apply(T x, T y) {
    +
    32 return x + y;
    +
    33 }
    +
    +
    34};
    +
    +
    35
    +
    +
    36struct MulOp {
    +
    37 template <typename T>
    +
    +
    38 METAL_FUNC static constexpr T apply(T x, T y) {
    +
    39 return x * y;
    +
    40 }
    +
    +
    41};
    +
    +
    42
    +
    +
    43struct SubOp {
    +
    44 template <typename T>
    +
    +
    45 METAL_FUNC static constexpr T apply(T x, T y) {
    +
    46 return x - y;
    +
    47 }
    +
    +
    48};
    +
    +
    49
    +
    +
    50struct ExpSubOp {
    +
    51 template <typename T>
    +
    +
    52 METAL_FUNC static constexpr T apply(T x, T y) {
    +
    53 return fast::exp(x - y);
    +
    54 }
    +
    +
    55};
    +
    +
    56
    +
    +
    57struct DivOp {
    +
    58 template <typename T>
    +
    +
    59 METAL_FUNC static constexpr T apply(T x, T y) {
    +
    60 return x / y;
    +
    61 }
    +
    +
    62};
    +
    +
    63
    +
    64// clang-format off
    +
    65template <
    +
    66 typename T,
    +
    67 int BQ,
    +
    68 int BK,
    +
    69 int BD,
    +
    70 int WM,
    +
    71 int WN,
    +
    72 typename AccumType = float>
    +
    +
    73[[kernel, max_total_threads_per_threadgroup(WM * WN * 32)]] void attention(
    +
    74 const device T* Q [[buffer(0)]],
    +
    75 const device T* K [[buffer(1)]],
    +
    76 const device T* V [[buffer(2)]],
    +
    77 device T* O [[buffer(3)]],
    +
    78 const constant AttnParams* params [[buffer(4)]],
    +
    79 uint simd_lane_id [[thread_index_in_simdgroup]],
    +
    80 uint simd_group_id [[simdgroup_index_in_threadgroup]],
    +
    81 uint3 tid [[threadgroup_position_in_grid]],
    +
    82 uint3 lid [[thread_position_in_threadgroup]]) { // clang-format on
    +
    83
    +
    84 // Pacifying compiler
    +
    85 (void)lid;
    +
    86
    +
    87 // Move to correct block
    +
    88 ulong3 tidl{tid.x, tid.y, tid.z};
    +
    89
    +
    90 Q += tidl.z * params->Q_strides[0] + // Batch
    +
    91 tidl.y * params->Q_strides[1] + // Head
    +
    92 tidl.x * BQ * params->Q_strides[2]; // Seqeunce
    +
    93
    +
    94 ulong kv_head_idx = int(tid.y) / params->gqa_factor;
    +
    95 K += tidl.z * params->K_strides[0] + // Batch
    +
    96 kv_head_idx * params->K_strides[1]; // Head
    +
    97
    +
    98 V += tidl.z * params->V_strides[0] + // Batch
    +
    99 kv_head_idx * params->V_strides[1]; // Head
    +
    100
    +
    101 O += tidl.z * params->O_strides[0] + // Batch
    +
    102 tidl.y * params->O_strides[1] + // Head
    +
    103 tidl.x * BQ * params->O_strides[2]; // Seqeunce
    +
    104
    +
    105 // Prepare threadgroup memory
    +
    106 constexpr short padQ = 0; // 16 / sizeof(T);
    +
    107 constexpr short padK = 0; // 16 / sizeof(T);
    +
    108 constexpr short padV = 0; // 16 / sizeof(T);
    +
    109
    +
    110 constexpr short LDQ_tgp = BD + padQ;
    +
    111 constexpr short LDK_tgp = BK + padK;
    +
    112 constexpr short LDV_tgp = BD + padV;
    +
    113
    +
    114 threadgroup T Qs[BQ * (BD + padQ)];
    +
    115 threadgroup T Ks[(BK + padK) * BD];
    +
    116 threadgroup T Vs[BK * (BD + padV)];
    +
    117
    +
    118 // Prepare block loaders
    +
    119 using QBlockLoader = BlockLoaderT<
    +
    120 /* typename T = */ T,
    +
    121 /* short BROWS = */ BQ,
    +
    122 /* short BCOLS = */ BD,
    +
    123 /* short kDstStrRow = */ LDQ_tgp,
    +
    124 /* short kDstStrCol = */ 1,
    +
    125 /* short reduction_dim = */ 1,
    +
    126 /* short tgp_size = */ WM * WN * 32>;
    +
    127
    +
    128 // K is loaded in transposed
    +
    129 using KBlockLoader = BlockLoaderT<
    +
    130 /* typename T = */ T,
    +
    131 /* short BROWS = */ BK,
    +
    132 /* short BCOLS = */ BD,
    +
    133 /* short kDstStrRow = */ 1,
    +
    134 /* short kDstStrCol = */ LDK_tgp,
    +
    135 /* short reduction_dim = */ 0,
    +
    136 /* short tgp_size = */ WM * WN * 32>;
    +
    137
    +
    138 using VBlockLoader = BlockLoaderT<
    +
    139 /* typename T = */ T,
    +
    140 /* short BROWS = */ BK,
    +
    141 /* short BCOLS = */ BD,
    +
    142 /* short kDstStrRow = */ LDV_tgp,
    +
    143 /* short kDstStrCol = */ 1,
    +
    144 /* short reduction_dim = */ 0,
    +
    145 /* short tgp_size = */ WM * WN * 32>;
    +
    146
    +
    147 QBlockLoader loader_q(
    +
    148 Q, params->Q_strides[2], Qs, simd_group_id, simd_lane_id);
    +
    149 KBlockLoader loader_k(
    +
    150 K, params->K_strides[2], Ks, simd_group_id, simd_lane_id);
    +
    151 VBlockLoader loader_v(
    +
    152 V, params->V_strides[2], Vs, simd_group_id, simd_lane_id);
    +
    153
    +
    154 TransformScale<T> ts(static_cast<T>(params->scale));
    +
    155
    +
    156 // Prepare MMA tiles
    +
    157 constexpr short kFragSize = 8; // MMAFrag size
    +
    158 using MMAFrag_acc_t = BaseMMAFrag<AccumType, kFragSize, kFragSize>;
    +
    159
    +
    160 constexpr int kNWarps = WM * WN;
    +
    161 static_assert(
    +
    162 BQ >= (kNWarps * kFragSize) && BQ % (kNWarps * kFragSize) == 0,
    +
    163 "Each simdgroup must host atleast 1 simdgroup matrix along Q sequence.");
    +
    164
    +
    165 // Q seq frags per warp
    +
    166 constexpr int TQ = BQ / (kNWarps * kFragSize);
    +
    167 // KV sequence frags (all warps load the same frags)
    +
    168 constexpr int TK = BK / kFragSize;
    +
    169 // HeadDim frags (all warps load the same frags)
    +
    170 constexpr int TD = BD / kFragSize;
    +
    171
    +
    172 static_assert(TQ == 1, "Check TQ");
    +
    173
    +
    174 MMATile<AccumType, TQ, 1, MMAFrag_acc_t> Qtile;
    +
    175 MMATile<AccumType, 1, TK, MMAFrag_acc_t> Ktile;
    +
    176 MMATile<AccumType, TQ, TK, MMAFrag_acc_t> Stile;
    +
    177 MMATile<AccumType, TK, TD, MMAFrag_acc_t> Vtile;
    +
    178 MMATile<AccumType, TQ, TD, MMAFrag_acc_t> Otile;
    +
    179
    +
    180 Otile.clear();
    +
    181
    +
    182 // Prepare mma tile offsets
    +
    183 const short2 simd_coord = MMAFrag_acc_t::get_coord(simd_lane_id);
    +
    184 const short sm = simd_coord.y;
    +
    185 const short sn = simd_coord.x;
    +
    186 const short tm = kFragSize * TQ * simd_group_id;
    +
    187
    +
    188 const short Qs_offset = (tm + sm) * LDQ_tgp + sn;
    +
    189 const short Ks_offset = sm * LDK_tgp + sn;
    +
    190 const short Vs_offset = sm * LDV_tgp + sn;
    +
    191
    +
    192 constexpr short Qs_tile_stride = kFragSize;
    +
    193 constexpr short Ks_tile_stride = kFragSize * LDK_tgp;
    +
    194
    +
    195 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    196
    +
    197 // Load Q blocks apply scale
    +
    198 if (!align_Q && int(tid.x) == (params->NQ_aligned)) {
    +
    199 loader_q.load_safe(short2(BD, params->qL - params->NQ_aligned * BQ));
    +
    200 } else {
    +
    201 loader_q.load_unsafe();
    +
    202 }
    +
    203 loader_q.apply_inplace_op(ts);
    +
    204
    +
    205 // Init row reduction variables
    +
    206 constexpr short kRowsPT = decltype(Stile)::kRowsPerThread;
    +
    207
    +
    208 AccumType max_score[kRowsPT];
    +
    209 AccumType sum_score[kRowsPT] = {0};
    +
    210
    +
    211 // Init to -Inf
    +
    212 STEEL_PRAGMA_UNROLL
    +
    213 for (short i = 0; i < kRowsPT; ++i) {
    +
    214 max_score[i] = Limits<AccumType>::min;
    +
    215 }
    +
    216
    +
    217 // Loop over KV seq length
    +
    218 for (int kb = 0; kb < params->NK; kb++) {
    +
    219 // Load K block and apply scale
    +
    220 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    221 if (!align_K && kb == (params->NK_aligned)) {
    +
    222 loader_k.load_safe(short2(BD, params->kL - params->NK_aligned * BK));
    +
    223 } else {
    +
    224 loader_k.load_unsafe();
    +
    225 }
    +
    226
    +
    227 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    228
    +
    229 // Do S = Q @ K.T
    +
    230 Stile.clear();
    +
    231
    +
    232 for (short dd = 0; dd < TD; dd++) {
    +
    233 simdgroup_barrier(mem_flags::mem_none);
    +
    234
    +
    235 Qtile.template load<T, 1, 1, LDQ_tgp, 1>(
    +
    236 &Qs[Qs_offset + dd * Qs_tile_stride]);
    +
    237 Ktile.template load<T, 1, 1, LDK_tgp, 1>(
    +
    238 &Ks[Ks_offset + dd * Ks_tile_stride]);
    +
    239
    +
    240 simdgroup_barrier(mem_flags::mem_none);
    +
    241
    +
    242 tile_matmad(Stile, Qtile, Ktile, Stile);
    +
    243 }
    +
    244
    +
    245 // Mask out of length sequence
    +
    246 if (!align_K && kb == (params->NK_aligned)) {
    +
    247 using stile_t = decltype(Stile);
    +
    248 using selem_t = typename stile_t::elem_type;
    +
    249 constexpr auto neg_inf = -metal::numeric_limits<selem_t>::infinity();
    +
    250 const short lim = params->kL - params->NK_aligned * BK;
    +
    251
    +
    252 STEEL_PRAGMA_UNROLL
    +
    253 for (short i = 0; i < stile_t::kTileRows; i++) {
    +
    254 STEEL_PRAGMA_UNROLL
    +
    255 for (short j = 0; j < stile_t::kTileCols; j++) {
    +
    256 short col_pos = sn + (j * stile_t::kFragCols);
    +
    257 STEEL_PRAGMA_UNROLL
    +
    258 for (short jj = 0; jj < stile_t::MMAFrag_t::kElemCols; jj++) {
    +
    259 if ((col_pos + jj) >= lim) {
    +
    260 Stile.frag_at(i, j)[jj] = neg_inf;
    +
    261 }
    +
    262 }
    +
    263 }
    +
    264 }
    +
    265 }
    +
    266
    +
    267 simdgroup_barrier(mem_flags::mem_none);
    +
    268
    +
    269 // Load V blocks
    +
    270 if (!align_K && kb == (params->NK_aligned)) {
    +
    271 loader_v.load_safe(short2(BD, params->kL - params->NK_aligned * BK));
    +
    272 } else {
    +
    273 loader_v.load_unsafe();
    +
    274 }
    +
    275
    +
    276 // Do softmax
    +
    277
    +
    278 // Temp variables
    +
    279 AccumType new_max[kRowsPT];
    +
    280 AccumType factor[kRowsPT];
    +
    281 STEEL_PRAGMA_UNROLL
    +
    282 for (short i = 0; i < kRowsPT; ++i) {
    +
    283 new_max[i] = max_score[i];
    +
    284 }
    +
    285
    +
    286 // Row max
    +
    287 Stile.template row_reduce<MaxOp>(new_max);
    +
    288
    +
    289 // exp(Si - rowmax(Si))
    +
    290 Stile.template row_bin_op<ExpSubOp>(new_max);
    +
    291
    +
    292 // Factor exp(rowmax(Si) - rowmax(Si-1))
    +
    293 STEEL_PRAGMA_UNROLL
    +
    294 for (short i = 0; i < kRowsPT; ++i) {
    +
    295 factor[i] = fast::exp(max_score[i] - new_max[i]);
    +
    296 }
    +
    297
    +
    298 // Save max for next iteration
    +
    299 STEEL_PRAGMA_UNROLL
    +
    300 for (short i = 0; i < kRowsPT; ++i) {
    +
    301 max_score[i] = new_max[i];
    +
    302 }
    +
    303
    +
    304 // Row Sum
    +
    305 AccumType sum_score_tmp[kRowsPT] = {0};
    +
    306 Stile.template row_reduce<SumOp>(sum_score_tmp);
    +
    307
    +
    308 // Update norm
    +
    309 STEEL_PRAGMA_UNROLL
    +
    310 for (short i = 0; i < kRowsPT; ++i) {
    +
    311 sum_score[i] = sum_score[i] * factor[i] + sum_score_tmp[i];
    +
    312 }
    +
    313
    +
    314 // Update O
    +
    315 Otile.template row_bin_op<MulOp>(factor);
    +
    316
    +
    317 // Load V into registers
    +
    318 threadgroup_barrier(mem_flags::mem_threadgroup);
    +
    319 Vtile.template load<T, 1, 1, LDV_tgp, 1>(&Vs[Vs_offset]);
    +
    320
    +
    321 simdgroup_barrier(mem_flags::mem_none);
    +
    322
    +
    323 // Do O = S @ V
    +
    324 tile_matmad(Otile, Stile, Vtile, Otile);
    +
    325
    +
    326 // Prepare for next iteration
    +
    327 loader_k.next();
    +
    328 loader_v.next();
    +
    329 }
    +
    330
    +
    331 // Normalize output
    +
    332 Otile.template row_bin_op<DivOp>(sum_score);
    +
    333 threadgroup_barrier(mem_flags::mem_none);
    +
    334
    +
    335 // Store results
    +
    336 O += (tm + sm) * params->O_strides[2] + sn;
    +
    337
    +
    338 if (!align_Q && int(tid.x) == (params->NQ_aligned)) {
    +
    339 auto dst_tile_dims =
    +
    340 short2(BD - sn, params->qL - BQ * params->NQ_aligned - (tm + sm));
    +
    341
    +
    342 if (dst_tile_dims.x <= 0 || dst_tile_dims.y <= 0)
    +
    343 return;
    +
    344
    +
    345 Otile.template store_safe<T, 1, 1>(O, params->O_strides[2], dst_tile_dims);
    +
    346 } else {
    +
    347 Otile.template store<T, 1, 1>(O, params->O_strides[2]);
    +
    348 }
    +
    349}
    +
    +
    metal::max
    METAL_FUNC bfloat16_t max(bfloat16_t x, bfloat16_t y)
    Definition bf16_math.h:232
    +
    mlx::steel
    Definition attn.h:19
    +
    mlx::steel::tile_matmad
    METAL_FUNC void tile_matmad(thread MMATile< T, M, N > &D, thread MMATile< U, M, K > &A, thread MMATile< U, K, N > &B, thread MMATile< T, M, N > &C)
    Definition mma.h:413
    +
    STEEL_PRAGMA_UNROLL
    #define STEEL_PRAGMA_UNROLL
    Definition defines.h:4
    +
    align_Q
    constant bool align_Q
    Definition steel_attention.h:9
    +
    attention
    void attention(const device T *Q, const device T *K, const device T *V, device T *O, const constant AttnParams *params, uint simd_lane_id, uint simd_group_id, uint3 tid, uint3 lid)
    Definition steel_attention.h:73
    +
    align_K
    constant bool align_K
    Definition steel_attention.h:10
    +
    DivOp
    Definition steel_attention.h:57
    +
    DivOp::apply
    static METAL_FUNC constexpr T apply(T x, T y)
    Definition steel_attention.h:59
    +
    ExpSubOp
    Definition steel_attention.h:50
    +
    ExpSubOp::apply
    static METAL_FUNC constexpr T apply(T x, T y)
    Definition steel_attention.h:52
    +
    Limits
    Definition utils.h:23
    +
    MaxOp
    Definition steel_attention.h:22
    +
    MaxOp::apply
    static METAL_FUNC constexpr T apply(T x, T y)
    Definition steel_attention.h:24
    +
    MulOp
    Definition steel_attention.h:36
    +
    MulOp::apply
    static METAL_FUNC constexpr T apply(T x, T y)
    Definition steel_attention.h:38
    +
    SubOp
    Definition steel_attention.h:43
    +
    SubOp::apply
    static METAL_FUNC constexpr T apply(T x, T y)
    Definition steel_attention.h:45
    +
    SumOp
    Definition steel_attention.h:29
    +
    SumOp::apply
    static METAL_FUNC constexpr T apply(T x, T y)
    Definition steel_attention.h:31
    +
    TransformScale
    Definition steel_attention.h:13
    +
    TransformScale::apply
    METAL_FUNC T apply(T x) const
    Definition steel_attention.h:17
    +
    TransformScale::scale
    T scale
    Definition steel_attention.h:14
    +
    TransformScale::TransformScale
    METAL_FUNC TransformScale(T scale_)
    Definition steel_attention.h:15
    +
    mlx::steel::AttnParams
    Definition params.h:12
    +
    mlx::steel::BaseMMAFrag
    Definition mma.h:23
    +
    mlx::steel::BlockLoaderT
    Definition loader.h:153
    +
    mlx::steel::MMATile
    Definition mma.h:178
    +
    mlx::steel::MMATile::frag_at
    METAL_FUNC constexpr thread frag_type & frag_at(const short i, const short j)
    Definition mma.h:256
    +
    mlx::steel::MMATile::clear
    METAL_FUNC constexpr void clear()
    Definition mma.h:249
    +
    + +
    +Generated by doxygen 1.12.0 +
    +
    + + diff --git a/docs/build/html/steel__gemm__fused_8h_source.html b/docs/build/html/steel__gemm__fused_8h_source.html index d758f32b..685b0c30 100644 --- a/docs/build/html/steel__gemm__fused_8h_source.html +++ b/docs/build/html/steel__gemm__fused_8h_source.html @@ -196,7 +196,7 @@ $(function(){ initResizable(false); });
    104 if (use_out_source) {
    105 const constant size_t* indx_C_bstrides =
    106 indx_B_bstrides + params->batch_ndim;
    -
    107 auto indx_offset_C = elem_to_loc(
    +
    107 auto indx_offset_C = elem_to_loc(
    108 tid.z, batch_shape, indx_C_bstrides, params->batch_ndim);
    109 indx_C = C_indices[indx_offset_C];
    110 }
    @@ -213,18 +213,18 @@ $(function(){ initResizable(false); });
    121 int batch_ndim_A = operand_batch_ndim.x;
    122 const constant int* batch_shape_A = operand_shape;
    123 const constant size_t* batch_strides_A = operand_strides;
    -
    124 A += elem_to_loc(indx_A, batch_shape_A, batch_strides_A, batch_ndim_A);
    +
    124 A += elem_to_loc(indx_A, batch_shape_A, batch_strides_A, batch_ndim_A);
    125
    126 int batch_ndim_B = operand_batch_ndim.y;
    127 const constant int* batch_shape_B = batch_shape_A + batch_ndim_A;
    128 const constant size_t* batch_strides_B = batch_strides_A + batch_ndim_A;
    -
    129 B += elem_to_loc(indx_B, batch_shape_B, batch_strides_B, batch_ndim_B);
    +
    129 B += elem_to_loc(indx_B, batch_shape_B, batch_strides_B, batch_ndim_B);
    130
    131 if (use_out_source) {
    132 int batch_ndim_C = operand_batch_ndim.z;
    133 const constant int* batch_shape_C = batch_shape_B + batch_ndim_B;
    134 const constant size_t* batch_strides_C = batch_strides_B + batch_ndim_B;
    -
    135 C += elem_to_loc(indx_C, batch_shape_C, batch_strides_C, batch_ndim_C);
    +
    135 C += elem_to_loc(indx_C, batch_shape_C, batch_strides_C, batch_ndim_C);
    136 }
    137
    138 }
    @@ -243,7 +243,7 @@ $(function(){ initResizable(false); });
    151
    152 if (use_out_source) {
    153 const constant size_t* C_bstrides = B_bstrides + params->batch_ndim;
    -
    154 C += elem_to_loc(tid.z, batch_shape, C_bstrides, params->batch_ndim);
    +
    154 C += elem_to_loc(tid.z, batch_shape, C_bstrides, params->batch_ndim);
    155 }
    156 } else {
    157 A += params->batch_stride_a * tid.z;
    @@ -505,8 +505,8 @@ $(function(){ initResizable(false); });
    415}
    elem_to_loc_broadcast
    METAL_FUNC ulong2 elem_to_loc_broadcast(uint elem, constant const int *shape, constant const size_t *a_strides, constant const size_t *b_strides, int ndim)
    Definition utils.h:7
    -
    elem_to_loc
    METAL_FUNC stride_t elem_to_loc(uint elem, constant const int *shape, constant const stride_t *strides, int ndim)
    Definition utils.h:87
    -
    mlx::steel
    Definition loader_channel_l.h:14
    +
    elem_to_loc
    METAL_FUNC IdxT elem_to_loc(uint elem, constant const int *shape, constant const StrideT *strides, int ndim)
    Definition utils.h:93
    +
    mlx::steel
    Definition attn.h:19
    use_out_source
    constant bool use_out_source
    Definition steel_gemm_fused.h:11
    align_M
    constant bool align_M
    Definition steel_gemm_fused.h:14
    do_gather
    constant bool do_gather
    Definition steel_gemm_fused.h:18
    diff --git a/docs/build/html/steel__gemm__masked_8h_source.html b/docs/build/html/steel__gemm__masked_8h_source.html index 2b8e6003..a609873d 100644 --- a/docs/build/html/steel__gemm__masked_8h_source.html +++ b/docs/build/html/steel__gemm__masked_8h_source.html @@ -211,7 +211,7 @@ $(function(){ initResizable(false); });
    109
    110 if (params->batch_ndim > 1) {
    111 if (has_output_mask) {
    -
    112 out_mask += elem_to_loc(
    +
    112 out_mask += elem_to_loc(
    113 tid.z, batch_shape, mask_batch_strides, params->batch_ndim);
    114
    115 mask_batch_strides += params->batch_ndim;
    @@ -580,7 +580,7 @@ $(function(){ initResizable(false); });
    479 const constant size_t* mask_batch_strides =
    480 batch_strides + 2 * params->batch_ndim;
    481 out_mask +=
    -
    482 elem_to_loc(tid.z, batch_shape, mask_batch_strides, params->batch_ndim);
    +
    482 elem_to_loc(tid.z, batch_shape, mask_batch_strides, params->batch_ndim);
    483
    484 if (has_operand_mask) {
    485 const constant size_t* mask_strides_lhs =
    @@ -818,10 +818,10 @@ $(function(){ initResizable(false); });
    719}
    elem_to_loc_broadcast
    METAL_FUNC ulong2 elem_to_loc_broadcast(uint elem, constant const int *shape, constant const size_t *a_strides, constant const size_t *b_strides, int ndim)
    Definition utils.h:7
    -
    elem_to_loc
    METAL_FUNC stride_t elem_to_loc(uint elem, constant const int *shape, constant const stride_t *strides, int ndim)
    Definition utils.h:87
    -
    metal
    Definition bf16.h:265
    -
    metal::min
    METAL_FUNC bfloat16_t min(bfloat16_t x, bfloat16_t y)
    Definition bf16_math.h:234
    -
    mlx::steel
    Definition loader_channel_l.h:14
    +
    elem_to_loc
    METAL_FUNC IdxT elem_to_loc(uint elem, constant const int *shape, constant const StrideT *strides, int ndim)
    Definition utils.h:93
    +
    metal
    Definition bf16_math.h:226
    +
    metal::min
    METAL_FUNC bfloat16_t min(bfloat16_t x, bfloat16_t y)
    Definition bf16_math.h:232
    +
    mlx::steel
    Definition attn.h:19
    STEEL_PRAGMA_UNROLL
    #define STEEL_PRAGMA_UNROLL
    Definition defines.h:4
    block_masked_gemm
    void block_masked_gemm(const device T *A, const device T *B, device T *D, const constant GEMMParams *params, const constant int *batch_shape, const constant size_t *batch_strides, const device out_mask_t *out_mask, const device op_mask_t *lhs_mask, const device op_mask_t *rhs_mask, const constant int *mask_strides, uint simd_lane_id, uint simd_group_id, uint3 tid, uint3 lid)
    Definition steel_gemm_masked.h:53
    diff --git a/docs/build/html/steel__gemm__splitk_8h_source.html b/docs/build/html/steel__gemm__splitk_8h_source.html index 1a0d5936..c5844e58 100644 --- a/docs/build/html/steel__gemm__splitk_8h_source.html +++ b/docs/build/html/steel__gemm__splitk_8h_source.html @@ -321,7 +321,7 @@ $(function(){ initResizable(false); });
    227}
    op
    Op op
    Definition binary.h:129
    -
    mlx::steel
    Definition loader_channel_l.h:14
    +
    mlx::steel
    Definition attn.h:19
    gemm_splitk
    void gemm_splitk(const device T *A, const device T *B, device U *C, const constant GEMMSpiltKParams *params, uint simd_lane_id, uint simd_group_id, uint3 tid, uint3 lid)
    Definition steel_gemm_splitk.h:21
    gemm_splitk_accum
    void gemm_splitk_accum(const device AccT *C_split, device OutT *D, const constant int &k_partitions, const constant int &partition_stride, const constant int &ldd, uint2 gid)
    Definition steel_gemm_splitk.h:172
    gemm_splitk_accum_axpby
    void gemm_splitk_accum_axpby(const device AccT *C_split, device OutT *D, const constant int &k_partitions, const constant int &partition_stride, const constant int &ldd, const device OutT *C, const constant int &ldc, const constant int &fdc, const constant float &alpha, const constant float &beta, uint2 gid)
    Definition steel_gemm_splitk.h:199
    diff --git a/docs/build/html/struct___m_l_x___b_float16.html b/docs/build/html/struct___m_l_x___b_float16.html index 3a69ede2..33440915 100644 --- a/docs/build/html/struct___m_l_x___b_float16.html +++ b/docs/build/html/struct___m_l_x___b_float16.html @@ -94,7 +94,7 @@ $(function(){ initResizable(false); });
    -

    #include <bf16.h>

    +

    #include <bf16.h>

    @@ -536,7 +536,7 @@ template<typename T , typename = typename enable_if<can_convert_from_bflo
    The documentation for this struct was generated from the following file:
      -
    • mlx/backend/metal/kernels/bf16.h
      • +
    • mlx/backend/metal/kernels/metal_3_0/bf16.h
    diff --git a/docs/build/html/struct___m_l_x___b_float16_1_1bits__to__bfloat__struct.html b/docs/build/html/struct___m_l_x___b_float16_1_1bits__to__bfloat__struct.html index e340d914..06872a35 100644 --- a/docs/build/html/struct___m_l_x___b_float16_1_1bits__to__bfloat__struct.html +++ b/docs/build/html/struct___m_l_x___b_float16_1_1bits__to__bfloat__struct.html @@ -92,9 +92,9 @@ $(function(){ initResizable(false); });
    -

    #include <bf16.h>

    +

    #include <bf16.h>

    The documentation for this struct was generated from the following file:
      -
    • mlx/backend/metal/kernels/bf16.h
      • +
    • mlx/backend/metal/kernels/metal_3_0/bf16.h
    diff --git a/docs/build/html/structlooped__elem__to__loc_3_010_00_01offset__t_01_4-members.html b/docs/build/html/struct_div_op-members.html similarity index 70% rename from docs/build/html/structlooped__elem__to__loc_3_010_00_01offset__t_01_4-members.html rename to docs/build/html/struct_div_op-members.html index f77f8a50..d1a8235f 100644 --- a/docs/build/html/structlooped__elem__to__loc_3_010_00_01offset__t_01_4-members.html +++ b/docs/build/html/struct_div_op-members.html @@ -84,15 +84,13 @@ $(function(){ initResizable(false); });
    -
    looped_elem_to_loc< 0, offset_t > Member List
    +
    DivOp Member List
    -

    This is the complete list of members for looped_elem_to_loc< 0, offset_t >, including all inherited members.

    +

    This is the complete list of members for DivOp, including all inherited members.

    Classes

    - - - +
    location(offset_t idx, const constant int *shape, const constant size_t *strides, int ndim)looped_elem_to_loc< 0, offset_t >inline
    next(const constant int *, const constant size_t *)looped_elem_to_loc< 0, offset_t >inline
    next(int, const constant int *, const constant size_t *)looped_elem_to_loc< 0, offset_t >inline
    apply(T x, T y)DivOpinlinestatic
    diff --git a/docs/build/html/struct_div_op.html b/docs/build/html/struct_div_op.html new file mode 100644 index 00000000..3dfdaeb1 --- /dev/null +++ b/docs/build/html/struct_div_op.html @@ -0,0 +1,144 @@ + + + + + + + +MLX: DivOp Struct Reference + + + + + + + + + + + + + +
    +
    + + + + + + +
    +
    MLX +
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    +Static Public Member Functions | +List of all members
    +
    DivOp Struct Reference
    +
    +
    + +

    #include <steel_attention.h>

    + + + + + +

    +Static Public Member Functions

    template<typename T >
    static METAL_FUNC constexpr T apply (T x, T y)
     
    +

    Member Function Documentation

    + +

    ◆ apply()

    + +
    +
    +
    +template<typename T >
    + + + + + +
    + + + + + + + + + + + +
    static METAL_FUNC constexpr T DivOp::apply (T x,
    T y )
    +     		+inlinestaticconstexpr
    +
    + +
    +
    +
    The documentation for this struct was generated from the following file: +
    + +
    +Generated by doxygen 1.12.0 +
    +
    + + diff --git a/docs/build/html/structlooped__elem__to__loc_3_011_00_01offset__t_01_4-members.html b/docs/build/html/struct_exp_sub_op-members.html similarity index 65% rename from docs/build/html/structlooped__elem__to__loc_3_011_00_01offset__t_01_4-members.html rename to docs/build/html/struct_exp_sub_op-members.html index 159b40e1..9ef4a277 100644 --- a/docs/build/html/structlooped__elem__to__loc_3_011_00_01offset__t_01_4-members.html +++ b/docs/build/html/struct_exp_sub_op-members.html @@ -84,16 +84,13 @@ $(function(){ initResizable(false); });
    -
    looped_elem_to_loc< 1, offset_t > Member List
    +
    ExpSubOp Member List
    -

    This is the complete list of members for looped_elem_to_loc< 1, offset_t >, including all inherited members.

    +

    This is the complete list of members for ExpSubOp, including all inherited members.

    - - - - +
    location(offset_t, const constant int *, const constant size_t *, int)looped_elem_to_loc< 1, offset_t >inline
    next(const constant int *, const constant size_t *strides)looped_elem_to_loc< 1, offset_t >inline
    next(int n, const constant int *, const constant size_t *strides)looped_elem_to_loc< 1, offset_t >inline
    offsetlooped_elem_to_loc< 1, offset_t >
    apply(T x, T y)ExpSubOpinlinestatic
    diff --git a/docs/build/html/struct_exp_sub_op.html b/docs/build/html/struct_exp_sub_op.html new file mode 100644 index 00000000..03089675 --- /dev/null +++ b/docs/build/html/struct_exp_sub_op.html @@ -0,0 +1,144 @@ + + + + + + + +MLX: ExpSubOp Struct Reference + + + + + + + + + + + + + +
    +
    + + + + + + +
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    +Static Public Member Functions | +List of all members
    +
    ExpSubOp Struct Reference
    +
    +
    + +

    #include <steel_attention.h>

    + + + + + +

    +Static Public Member Functions

    template<typename T >
    static METAL_FUNC constexpr T apply (T x, T y)
     
    +

    Member Function Documentation

    + +

    ◆ apply()

    + +
    +
    +
    +template<typename T >
    + + + + + +
    + + + + + + + + + + + +
    static METAL_FUNC constexpr T ExpSubOp::apply (T x,
    T y )
    +     		+inlinestaticconstexpr
    +
    + +
    +
    +
    The documentation for this struct was generated from the following file: +
    + +
    +Generated by doxygen 1.12.0 +
    +
    + + diff --git a/docs/build/html/struct_floor_divide.html b/docs/build/html/struct_floor_divide.html index 3f033a78..4ac05b19 100644 --- a/docs/build/html/struct_floor_divide.html +++ b/docs/build/html/struct_floor_divide.html @@ -105,7 +105,7 @@ Public Member Functions half operator() (half x, half y)   template<> -bfloat16_t operator() (bfloat16_t x, bfloat16_t y) +bfloat16_t operator() (bfloat16_t x, bfloat16_t y)  

    Member Function Documentation

    @@ -121,14 +121,14 @@ template<>
    - + - + - +
    bfloat16_t FloorDivide::operator() bfloat16_t FloorDivide::operator() (bfloat16_t x, bfloat16_t x,
    bfloat16_t y )bfloat16_t y )
    diff --git a/docs/build/html/struct_limits_3_01bfloat16__t_01_4.html b/docs/build/html/struct_limits_3_01bfloat16__t_01_4.html index 3969642d..671ed35f 100644 --- a/docs/build/html/struct_limits_3_01bfloat16__t_01_4.html +++ b/docs/build/html/struct_limits_3_01bfloat16__t_01_4.html @@ -95,13 +95,13 @@ $(function(){ initResizable(false); }); - + - + - + - +

    Static Public Attributes

    static constexpr constant bfloat16_t max = metal::numeric_limits<bfloat16_t>::infinity()
    static constexpr constant bfloat16_t max = metal::numeric_limits<bfloat16_t>::infinity()
     
    static constexpr constant bfloat16_t min = -metal::numeric_limits<bfloat16_t>::infinity()
    static constexpr constant bfloat16_t min = -metal::numeric_limits<bfloat16_t>::infinity()
     
    static constexpr constant bfloat16_t finite_max = metal::numeric_limits<bfloat16_t>::max()
    static constexpr constant bfloat16_t finite_max = metal::numeric_limits<bfloat16_t>::max()
     
    static constexpr constant bfloat16_t finite_min = -metal::numeric_limits<bfloat16_t>::max()
    static constexpr constant bfloat16_t finite_min = -metal::numeric_limits<bfloat16_t>::max()
     

    Member Data Documentation

    @@ -115,7 +115,7 @@ Static Public Attributes - +
    constant bfloat16_t Limits< bfloat16_t >::finite_max = metal::numeric_limits<bfloat16_t>::max()constant bfloat16_t Limits< bfloat16_t >::finite_max = metal::numeric_limits<bfloat16_t>::max()
    @@ -137,7 +137,7 @@ Static Public Attributes - +
    constant bfloat16_t Limits< bfloat16_t >::finite_min = -metal::numeric_limits<bfloat16_t>::max()constant bfloat16_t Limits< bfloat16_t >::finite_min = -metal::numeric_limits<bfloat16_t>::max()
    @@ -159,7 +159,7 @@ Static Public Attributes - +
    constant bfloat16_t Limits< bfloat16_t >::max = metal::numeric_limits<bfloat16_t>::infinity()constant bfloat16_t Limits< bfloat16_t >::max = metal::numeric_limits<bfloat16_t>::infinity()
    @@ -181,7 +181,7 @@ Static Public Attributes - +
    constant bfloat16_t Limits< bfloat16_t >::min = -metal::numeric_limits<bfloat16_t>::infinity()constant bfloat16_t Limits< bfloat16_t >::min = -metal::numeric_limits<bfloat16_t>::infinity()
    diff --git a/docs/build/html/struct_looped_elem_to_loc-members.html b/docs/build/html/struct_looped_elem_to_loc-members.html new file mode 100644 index 00000000..eb7e0481 --- /dev/null +++ b/docs/build/html/struct_looped_elem_to_loc-members.html @@ -0,0 +1,108 @@ + + + + + + + +MLX: Member List + + + + + + + + + + + + + +
    +
    + + + + + + +
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    LoopedElemToLoc< DIM, OffsetT, General > Member List
    +
    +
    + +

    This is the complete list of members for LoopedElemToLoc< DIM, OffsetT, General >, including all inherited members.

    + + + + + + + + + +
    dimLoopedElemToLoc< DIM, OffsetT, General >
    indexLoopedElemToLoc< DIM, OffsetT, General >
    inner_looperLoopedElemToLoc< DIM, OffsetT, General >
    location()LoopedElemToLoc< DIM, OffsetT, General >inline
    LoopedElemToLoc(int dim)LoopedElemToLoc< DIM, OffsetT, General >inline
    next(const constant int *shape, const constant size_t *strides)LoopedElemToLoc< DIM, OffsetT, General >inline
    next(int n, const constant int *shape, const constant size_t *strides)LoopedElemToLoc< DIM, OffsetT, General >inline
    offsetLoopedElemToLoc< DIM, OffsetT, General >
    + +
    +Generated by doxygen 1.12.0 +
    +
    + + diff --git a/docs/build/html/struct_looped_elem_to_loc.html b/docs/build/html/struct_looped_elem_to_loc.html new file mode 100644 index 00000000..b8e76b15 --- /dev/null +++ b/docs/build/html/struct_looped_elem_to_loc.html @@ -0,0 +1,317 @@ + + + + + + + +MLX: LoopedElemToLoc< DIM, OffsetT, General > Struct Template Reference + + + + + + + + + + + + + +
    +
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    +Public Member Functions | +Public Attributes | +List of all members
    +
    LoopedElemToLoc< DIM, OffsetT, General > Struct Template Reference
    +
    +
    + +

    #include <utils.h>

    + + + + + + + + + + +

    +Public Member Functions

     LoopedElemToLoc (int dim)
     
    void next (const constant int *shape, const constant size_t *strides)
     
    void next (int n, const constant int *shape, const constant size_t *strides)
     
    OffsetT location ()
     
    + + + + + + + + + +

    +Public Attributes

    int dim
     
    LoopedElemToLoc< DIM - 1, OffsetT, General > inner_looper
     
    OffsetT offset {0}
     
    int index {0}
     
    +

    Constructor & Destructor Documentation

    + +

    ◆ LoopedElemToLoc()

    + +
    +
    +
    +template<int DIM, typename OffsetT = size_t, bool General = true>
    + + + + + +
    + + + + + + + +
    LoopedElemToLoc< DIM, OffsetT, General >::LoopedElemToLoc (int dim)
    +     		+inline
    +
    + +
    +
    +

    Member Function Documentation

    + +

    ◆ location()

    + +
    +
    +
    +template<int DIM, typename OffsetT = size_t, bool General = true>
    + + + + + +
    + + + + + + + +
    OffsetT LoopedElemToLoc< DIM, OffsetT, General >::location ()
    +     		+inline
    +
    + +
    +
    + +

    ◆ next() [1/2]

    + +
    +
    +
    +template<int DIM, typename OffsetT = size_t, bool General = true>
    + + + + + +
    + + + + + + + + + + + +
    void LoopedElemToLoc< DIM, OffsetT, General >::next (const constant int * shape,
    const constant size_t * strides )
    +     		+inline
    +
    + +
    +
    + +

    ◆ next() [2/2]

    + +
    +
    +
    +template<int DIM, typename OffsetT = size_t, bool General = true>
    + + + + + +
    + + + + + + + + + + + + + + + + +
    void LoopedElemToLoc< DIM, OffsetT, General >::next (int n,
    const constant int * shape,
    const constant size_t * strides )
    +     		+inline
    +
    + +
    +
    +

    Member Data Documentation

    + +

    ◆ dim

    + +
    +
    +
    +template<int DIM, typename OffsetT = size_t, bool General = true>
    + + + + +
    int LoopedElemToLoc< DIM, OffsetT, General >::dim
    +
    + +
    +
    + +

    ◆ index

    + +
    +
    +
    +template<int DIM, typename OffsetT = size_t, bool General = true>
    + + + + +
    int LoopedElemToLoc< DIM, OffsetT, General >::index {0}
    +
    + +
    +
    + +

    ◆ inner_looper

    + +
    +
    +
    +template<int DIM, typename OffsetT = size_t, bool General = true>
    + + + + +
    LoopedElemToLoc<DIM - 1, OffsetT, General> LoopedElemToLoc< DIM, OffsetT, General >::inner_looper
    +
    + +
    +
    + +

    ◆ offset

    + +
    +
    +
    +template<int DIM, typename OffsetT = size_t, bool General = true>
    + + + + +
    OffsetT LoopedElemToLoc< DIM, OffsetT, General >::offset {0}
    +
    + +
    +
    +
    The documentation for this struct was generated from the following file:
      +
    • mlx/backend/metal/kernels/utils.h
      • +
    +
    + +
    +Generated by doxygen 1.12.0 +
    +
    + + diff --git a/docs/build/html/structlooped__elem__to__loc-members.html b/docs/build/html/struct_looped_elem_to_loc_3_011_00_01_offset_t_00_01false_01_4-members.html similarity index 60% rename from docs/build/html/structlooped__elem__to__loc-members.html rename to docs/build/html/struct_looped_elem_to_loc_3_011_00_01_offset_t_00_01false_01_4-members.html index bf1d293f..a4d1c7be 100644 --- a/docs/build/html/structlooped__elem__to__loc-members.html +++ b/docs/build/html/struct_looped_elem_to_loc_3_011_00_01_offset_t_00_01false_01_4-members.html @@ -84,18 +84,17 @@ $(function(){ initResizable(false); });
    -
    looped_elem_to_loc< dim, offset_t > Member List
    +
    LoopedElemToLoc< 1, OffsetT, false > Member List
    -

    This is the complete list of members for looped_elem_to_loc< dim, offset_t >, including all inherited members.

    +

    This is the complete list of members for LoopedElemToLoc< 1, OffsetT, false >, including all inherited members.

    - - - - - - + + + + +
    indexlooped_elem_to_loc< dim, offset_t >
    inner_looperlooped_elem_to_loc< dim, offset_t >
    location(offset_t, const constant int *, const constant size_t *, int)looped_elem_to_loc< dim, offset_t >inline
    next(const constant int *shape, const constant size_t *strides)looped_elem_to_loc< dim, offset_t >inline
    next(int n, const constant int *shape, const constant size_t *strides)looped_elem_to_loc< dim, offset_t >inline
    offsetlooped_elem_to_loc< dim, offset_t >
    location()LoopedElemToLoc< 1, OffsetT, false >inline
    LoopedElemToLoc(int)LoopedElemToLoc< 1, OffsetT, false >inline
    next(const constant int *, const constant size_t *strides)LoopedElemToLoc< 1, OffsetT, false >inline
    next(int n, const constant int *, const constant size_t *strides)LoopedElemToLoc< 1, OffsetT, false >inline
    offsetLoopedElemToLoc< 1, OffsetT, false >
    diff --git a/docs/build/html/structlooped__elem__to__loc_3_011_00_01offset__t_01_4.html b/docs/build/html/struct_looped_elem_to_loc_3_011_00_01_offset_t_00_01false_01_4.html similarity index 61% rename from docs/build/html/structlooped__elem__to__loc_3_011_00_01offset__t_01_4.html rename to docs/build/html/struct_looped_elem_to_loc_3_011_00_01_offset_t_00_01false_01_4.html index 31a67fa6..68b15a3a 100644 --- a/docs/build/html/structlooped__elem__to__loc_3_011_00_01offset__t_01_4.html +++ b/docs/build/html/struct_looped_elem_to_loc_3_011_00_01_offset_t_00_01false_01_4.html @@ -5,7 +5,7 @@ -MLX: looped_elem_to_loc< 1, offset_t > Struct Template Reference +MLX: LoopedElemToLoc< 1, OffsetT, false > Struct Template Reference @@ -87,8 +87,8 @@ $(function(){ initResizable(false); });
    Public Member Functions | Public Attributes | -List of all members
    -
    looped_elem_to_loc< 1, offset_t > Struct Template Reference
    +List of all members
    +
    LoopedElemToLoc< 1, OffsetT, false > Struct Template Reference
    @@ -96,49 +96,37 @@ $(function(){ initResizable(false); }); - - - - - - + + + + + + + +

    Public Member Functions

    void next (const constant int *, const constant size_t *strides)
     
    void next (int n, const constant int *, const constant size_t *strides)
     
    offset_t location (offset_t, const constant int *, const constant size_t *, int)
     
     LoopedElemToLoc (int)
     
    void next (const constant int *, const constant size_t *strides)
     
    void next (int n, const constant int *, const constant size_t *strides)
     
    OffsetT location ()
     
    - - + +

    Public Attributes

    offset_t offset {0}
     
    OffsetT offset {0}
     
    -

    Member Function Documentation

    - -

    ◆ location()

    +

    Constructor & Destructor Documentation

    + +

    ◆ LoopedElemToLoc()

    -template<typename offset_t >
    +template<typename OffsetT >
    @@ -150,19 +138,47 @@ template<typename offset_t > - -

    ◆ next() [1/2]

    +

    Member Function Documentation

    + +

    ◆ location()

    -template<typename offset_t >
    +template<typename OffsetT >
    - + - - - - + - - - - - - - - - - -
    offset_t looped_elem_to_loc< 1, offset_t >::location LoopedElemToLoc< 1, OffsetT, false >::LoopedElemToLoc (offset_t ,
    int ) const constant int * ,
    const constant size_t * ,
    int  )
    + + +
    - + + + + + +
    void looped_elem_to_loc< 1, offset_t >::next OffsetT LoopedElemToLoc< 1, OffsetT, false >::location ()
    +     		+inline
    +
    + +
    +
    + +

    ◆ next() [1/2]

    + +
    +
    +
    +template<typename OffsetT >
    + + + + + +
    + + + @@ -181,19 +197,19 @@ template<typename offset_t > - -

    ◆ next() [2/2]

    + +

    ◆ next() [2/2]

    -template<typename offset_t >
    +template<typename OffsetT >
    void LoopedElemToLoc< 1, OffsetT, false >::next ( const constant int * ,
    @@ -493,8 +519,8 @@ template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction - -

    ◆ src

    + +

    ◆ src

    @@ -502,7 +528,7 @@ template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction_dim, short tgp_size, short group_size, short bits>
    - + @@ -218,16 +234,16 @@ template<typename offset_t >

    Member Data Documentation

    - -

    ◆ offset

    + +

    ◆ offset

    -template<typename offset_t >
    +template<typename OffsetT >
    void looped_elem_to_loc< 1, offset_t >::next void LoopedElemToLoc< 1, OffsetT, false >::next ( int n,
    - +
    offset_t looped_elem_to_loc< 1, offset_t >::offset {0}OffsetT LoopedElemToLoc< 1, OffsetT, false >::offset {0}
    diff --git a/docs/build/html/struct_looped_elem_to_loc_3_011_00_01_offset_t_00_01true_01_4-members.html b/docs/build/html/struct_looped_elem_to_loc_3_011_00_01_offset_t_00_01true_01_4-members.html new file mode 100644 index 00000000..f122e524 --- /dev/null +++ b/docs/build/html/struct_looped_elem_to_loc_3_011_00_01_offset_t_00_01true_01_4-members.html @@ -0,0 +1,107 @@ + + + + + + + +MLX: Member List + + + + + + + + + + + + + +
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    +
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    This is the complete list of members for LoopedElemToLoc< 1, OffsetT, true >, including all inherited members.

    + + + + + + + + +
    dimLoopedElemToLoc< 1, OffsetT, true >
    indexLoopedElemToLoc< 1, OffsetT, true >
    location()LoopedElemToLoc< 1, OffsetT, true >inline
    LoopedElemToLoc(int dim)LoopedElemToLoc< 1, OffsetT, true >inline
    next(const constant int *shape, const constant size_t *strides)LoopedElemToLoc< 1, OffsetT, true >inline
    next(int n, const constant int *shape, const constant size_t *strides)LoopedElemToLoc< 1, OffsetT, true >inline
    offsetLoopedElemToLoc< 1, OffsetT, true >
    + +
    +Generated by doxygen 1.12.0 +
    +
    + + diff --git a/docs/build/html/structlooped__elem__to__loc.html b/docs/build/html/struct_looped_elem_to_loc_3_011_00_01_offset_t_00_01true_01_4.html similarity index 56% rename from docs/build/html/structlooped__elem__to__loc.html rename to docs/build/html/struct_looped_elem_to_loc_3_011_00_01_offset_t_00_01true_01_4.html index f377b1c8..aa8215fb 100644 --- a/docs/build/html/structlooped__elem__to__loc.html +++ b/docs/build/html/struct_looped_elem_to_loc_3_011_00_01_offset_t_00_01true_01_4.html @@ -5,7 +5,7 @@ -MLX: looped_elem_to_loc< dim, offset_t > Struct Template Reference +MLX: LoopedElemToLoc< 1, OffsetT, true > Struct Template Reference @@ -87,8 +87,8 @@ $(function(){ initResizable(false); });
    Public Member Functions | Public Attributes | -List of all members
    -
    looped_elem_to_loc< dim, offset_t > Struct Template Reference
    +List of all members
    +
    LoopedElemToLoc< 1, OffsetT, true > Struct Template Reference
    @@ -96,53 +96,41 @@ $(function(){ initResizable(false); }); - - - - - - + + + + + + + +

    Public Member Functions

    void next (const constant int *shape, const constant size_t *strides)
     
    void next (int n, const constant int *shape, const constant size_t *strides)
     
    offset_t location (offset_t, const constant int *, const constant size_t *, int)
     
     LoopedElemToLoc (int dim)
     
    void next (const constant int *shape, const constant size_t *strides)
     
    void next (int n, const constant int *shape, const constant size_t *strides)
     
    OffsetT location ()
     
    - - - - - - + + + + + +

    Public Attributes

    looped_elem_to_loc< dim - 1, offset_t > inner_looper
     
    offset_t offset {0}
     
    int index {0}
     
    int dim
     
    OffsetT offset {0}
     
    uint index {0}
     
    -

    Member Function Documentation

    - -

    ◆ location()

    +

    Constructor & Destructor Documentation

    + +

    ◆ LoopedElemToLoc()

    -template<int dim, typename offset_t = size_t>
    +template<typename OffsetT >
    @@ -154,19 +142,47 @@ template<int dim, typename offset_t = size_t> - -

    ◆ next() [1/2]

    +

    Member Function Documentation

    + +

    ◆ location()

    -template<int dim, typename offset_t = size_t>
    +template<typename OffsetT >
    - + - - - - + - - - - - - - - - - -
    offset_t looped_elem_to_loc< dim, offset_t >::location LoopedElemToLoc< 1, OffsetT, true >::LoopedElemToLoc (offset_t ,
    int dim) const constant int * ,
    const constant size_t * ,
    int  )
    + + +
    - + + + + + +
    void looped_elem_to_loc< dim, offset_t >::next OffsetT LoopedElemToLoc< 1, OffsetT, true >::location ()
    +     		+inline
    +
    + +
    +
    + +

    ◆ next() [1/2]

    + +
    +
    +
    +template<typename OffsetT >
    + + + - + @@ -352,6 +354,30 @@ template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction
    + + + @@ -185,19 +201,19 @@ template<int dim, typename offset_t = size_t> - -

    ◆ next() [2/2]

    + +

    ◆ next() [2/2]

    -template<int dim, typename offset_t = size_t>
    +template<typename OffsetT >
    void LoopedElemToLoc< 1, OffsetT, true >::next ( const constant int * shape,
    - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + +
    - + @@ -222,48 +238,48 @@ template<int dim, typename offset_t = size_t>

    Member Data Documentation

    - -

    ◆ index

    + +

    ◆ dim

    -template<int dim, typename offset_t = size_t>
    +template<typename OffsetT >
    void looped_elem_to_loc< dim, offset_t >::next void LoopedElemToLoc< 1, OffsetT, true >::next ( int n,
    - +
    int looped_elem_to_loc< dim, offset_t >::index {0}int LoopedElemToLoc< 1, OffsetT, true >::dim
    - -

    ◆ inner_looper

    + +

    ◆ index

    -template<int dim, typename offset_t = size_t>
    +template<typename OffsetT >
    - +
    looped_elem_to_loc<dim - 1, offset_t> looped_elem_to_loc< dim, offset_t >::inner_looperuint LoopedElemToLoc< 1, OffsetT, true >::index {0}
    - -

    ◆ offset

    + +

    ◆ offset

    -template<int dim, typename offset_t = size_t>
    +template<typename OffsetT >
    - +
    offset_t looped_elem_to_loc< dim, offset_t >::offset {0}OffsetT LoopedElemToLoc< 1, OffsetT, true >::offset {0}
    diff --git a/docs/build/html/struct_m_l_x_fast_attention_params-members.html b/docs/build/html/struct_m_l_x_fast_attention_params-members.html deleted file mode 100644 index af8536a6..00000000 --- a/docs/build/html/struct_m_l_x_fast_attention_params-members.html +++ /dev/null @@ -1,120 +0,0 @@ - - - - - - - -MLX: Member List - - - - - - - - - - - - - -
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    This is the complete list of members for MLXFastAttentionParams, including all inherited members.

    - - - - - - - - - - - - - - - - - - - - - -
    alphaMLXFastAttentionParams
    batch_ndimMLXFastAttentionParams
    batch_stride_kMLXFastAttentionParams
    batch_stride_oMLXFastAttentionParams
    batch_stride_qMLXFastAttentionParams
    batch_stride_vMLXFastAttentionParams
    gemm_k_iterations_alignedMLXFastAttentionParams
    gemm_n_iterations_alignedMLXFastAttentionParams
    gemm_sv_m_block_iterationsMLXFastAttentionParams
    KMLXFastAttentionParams
    ldkMLXFastAttentionParams
    ldoMLXFastAttentionParams
    ldqMLXFastAttentionParams
    ldsMLXFastAttentionParams
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    MMLXFastAttentionParams
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    tiles_nMLXFastAttentionParams
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    #include <scaled_dot_product_attention_params.h>

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

    -Public Attributes

    const int M
     
    const int N
     
    const int K
     
    const int ldq
     
    const int ldk
     
    const int ldv
     
    const int lds
     
    const int ldo
     
    const int tiles_n
     
    const int tiles_m
     
    const int batch_stride_q
     
    const int batch_stride_k
     
    const int batch_stride_v
     
    const int batch_stride_o
     
    const int swizzle_log
     
    const int gemm_n_iterations_aligned
     
    const int gemm_k_iterations_aligned
     
    const int gemm_sv_m_block_iterations
     
    const int batch_ndim
     
    const float alpha
     
    -

    Member Data Documentation

    - -

    ◆ alpha

    - -
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    const float MLXFastAttentionParams::alpha
    -
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    -
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    ◆ batch_ndim

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::batch_ndim
    -
    - -
    -
    - -

    ◆ batch_stride_k

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::batch_stride_k
    -
    - -
    -
    - -

    ◆ batch_stride_o

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::batch_stride_o
    -
    - -
    -
    - -

    ◆ batch_stride_q

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::batch_stride_q
    -
    - -
    -
    - -

    ◆ batch_stride_v

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::batch_stride_v
    -
    - -
    -
    - -

    ◆ gemm_k_iterations_aligned

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::gemm_k_iterations_aligned
    -
    - -
    -
    - -

    ◆ gemm_n_iterations_aligned

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::gemm_n_iterations_aligned
    -
    - -
    -
    - -

    ◆ gemm_sv_m_block_iterations

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::gemm_sv_m_block_iterations
    -
    - -
    -
    - -

    ◆ K

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::K
    -
    - -
    -
    - -

    ◆ ldk

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::ldk
    -
    - -
    -
    - -

    ◆ ldo

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::ldo
    -
    - -
    -
    - -

    ◆ ldq

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::ldq
    -
    - -
    -
    - -

    ◆ lds

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::lds
    -
    - -
    -
    - -

    ◆ ldv

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::ldv
    -
    - -
    -
    - -

    ◆ M

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::M
    -
    - -
    -
    - -

    ◆ N

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::N
    -
    - -
    -
    - -

    ◆ swizzle_log

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::swizzle_log
    -
    - -
    -
    - -

    ◆ tiles_m

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::tiles_m
    -
    - -
    -
    - -

    ◆ tiles_n

    - -
    -
    - - - - -
    const int MLXFastAttentionParams::tiles_n
    -
    - -
    -
    -
    The documentation for this struct was generated from the following file: -
    - -
    -Generated by doxygen 1.12.0 -
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    - - diff --git a/docs/build/html/struct_max_op-members.html b/docs/build/html/struct_max_op-members.html new file mode 100644 index 00000000..9d49410a --- /dev/null +++ b/docs/build/html/struct_max_op-members.html @@ -0,0 +1,101 @@ + + + + + + + +MLX: Member List + + + + + + + + + + + + + +
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    #include <steel_attention.h>

    + + + + + +

    +Static Public Member Functions

    template<typename T >
    static METAL_FUNC constexpr T apply (T x, T y)
     
    +

    Member Function Documentation

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    ◆ apply()

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    static METAL_FUNC constexpr T MaxOp::apply (T x,
    T y )
    +     		+inlinestaticconstexpr
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    #include <steel_attention.h>

    + + + + + +

    +Static Public Member Functions

    template<typename T >
    static METAL_FUNC constexpr T apply (T x, T y)
     
    +

    Member Function Documentation

    + +

    ◆ apply()

    + +
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    +template<typename T >
    + + + + + +
    + + + + + + + + + + + +
    static METAL_FUNC constexpr T MulOp::apply (T x,
    T y )
    +     		+inlinestaticconstexpr
    +
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    The documentation for this struct was generated from the following file: +
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    + + diff --git a/docs/build/html/struct_quantized_block_loader-members.html b/docs/build/html/struct_quantized_block_loader-members.html index e0c400f3..638c4464 100644 --- a/docs/build/html/struct_quantized_block_loader-members.html +++ b/docs/build/html/struct_quantized_block_loader-members.html @@ -94,21 +94,22 @@ $(function(){ initResizable(false); });
    biQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    biasesQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    bjQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    dstQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    group_step_cntQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    group_stepsQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >static
    group_strideQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    load_safe(short2 src_tile_dim) constQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >inline
    load_unsafe() constQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >inline
    n_readsQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >static
    next()QuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >inline
    pack_factorQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >static
    QuantizedBlockLoader(const device uint32_t *src_, const device T *scales_, const device T *biases_, const int src_ld_, threadgroup T *dst_, ushort simd_group_id, ushort simd_lane_id)QuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >inline
    scalesQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    srcQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    src_ldQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    thread_idxQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    tile_strideQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    bytes_per_packQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >static
    dstQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    group_step_cntQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    group_stepsQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >static
    group_strideQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    load_safe(short2 src_tile_dim) constQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >inline
    load_unsafe() constQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >inline
    n_readsQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >static
    next()QuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >inline
    pack_factorQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >static
    QuantizedBlockLoader(const device uint8_t *src_, const device T *scales_, const device T *biases_, const int src_ld_, threadgroup T *dst_, ushort simd_group_id, ushort simd_lane_id)QuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >inline
    scalesQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    srcQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    src_ldQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    thread_idxQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    tile_strideQuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >
    diff --git a/docs/build/html/struct_quantized_block_loader.html b/docs/build/html/struct_quantized_block_loader.html index 8b1f99ea..8d94b9df 100644 --- a/docs/build/html/struct_quantized_block_loader.html +++ b/docs/build/html/struct_quantized_block_loader.html @@ -97,8 +97,8 @@ $(function(){ initResizable(false); }); - - + + @@ -124,8 +124,8 @@ Public Attributes - - + + @@ -133,8 +133,10 @@ Public Attributes

    Public Member Functions

     QuantizedBlockLoader (const device uint32_t *src_, const device T *scales_, const device T *biases_, const int src_ld_, threadgroup T *dst_, ushort simd_group_id, ushort simd_lane_id)
     
     QuantizedBlockLoader (const device uint8_t *src_, const device T *scales_, const device T *biases_, const int src_ld_, threadgroup T *dst_, ushort simd_group_id, ushort simd_lane_id)
     
    void load_unsafe () const
     
    void load_safe (short2 src_tile_dim) const
     
    threadgroup T * dst
     
    const device uint32_t * src
     
    const device uint8_t * src
     
    const device T * scales
     
    const device T * biases
    - + + + @@ -143,8 +145,8 @@ Static Public Attributes

    Static Public Attributes

    static constant constexpr const short pack_factor = 32 / bits
    static constant constexpr const short pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 8 / bits
     
    static constant constexpr const short bytes_per_pack = (bits == 3 || bits == 6) ? 3 : 1
     
    static constant constexpr const short BCOLS_PACKED = BCOLS / pack_factor
     
    static constant constexpr const short n_reads
     

    Constructor & Destructor Documentation

    - -

    ◆ QuantizedBlockLoader()

    + +

    ◆ QuantizedBlockLoader()

    @@ -157,7 +159,7 @@ template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction
    QuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >::QuantizedBlockLoader (const device uint32_t * src_, const device uint8_t * src_,
    +
    +
    + +

    ◆ bytes_per_pack

    + +
    +
    +
    +template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction_dim, short tgp_size, short group_size, short bits>
    + + + + + +
    + + + + +
    constant constexpr const short QuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >::bytes_per_pack = (bits == 3 || bits == 6) ? 3 : 1
    +     		+staticconstexpr
    +
    +
    @@ -449,7 +475,7 @@ template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction
    Initial value:
    =
    (BCOLS_PACKED * BROWS < tgp_size) ? 1 : (BCOLS_PACKED * BROWS) / tgp_size
    -
    QuantizedBlockLoader::BCOLS_PACKED
    static constant constexpr const short BCOLS_PACKED
    Definition quantized.h:274
    +
    QuantizedBlockLoader::BCOLS_PACKED
    static constant constexpr const short BCOLS_PACKED
    Definition quantized.h:456
    @@ -465,7 +491,7 @@ template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction     		- +
    constant constexpr const short QuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >::pack_factor = 32 / bitsconstant constexpr const short QuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >::pack_factor = bits == 3 ? 8 : bits == 6 ? 4 : 8 / bits
    - +
    const device uint32_t* QuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >::srcconst device uint8_t* QuantizedBlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, group_size, bits >::src
    diff --git a/docs/build/html/struct_sub_op-members.html b/docs/build/html/struct_sub_op-members.html new file mode 100644 index 00000000..4e6db215 --- /dev/null +++ b/docs/build/html/struct_sub_op-members.html @@ -0,0 +1,101 @@ + + + + + + + +MLX: Member List + + + + + + + + + + + + + +
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    This is the complete list of members for SubOp, including all inherited members.

    + + +
    apply(T x, T y)SubOpinlinestatic
    + +
    +Generated by doxygen 1.12.0 +
    +
    + + diff --git a/docs/build/html/struct_sub_op.html b/docs/build/html/struct_sub_op.html new file mode 100644 index 00000000..e89010e2 --- /dev/null +++ b/docs/build/html/struct_sub_op.html @@ -0,0 +1,144 @@ + + + + + + + +MLX: SubOp Struct Reference + + + + + + + + + + + + + +
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    SubOp Struct Reference
    +
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    + +

    #include <steel_attention.h>

    + + + + + +

    +Static Public Member Functions

    template<typename T >
    static METAL_FUNC constexpr T apply (T x, T y)
     
    +

    Member Function Documentation

    + +

    ◆ apply()

    + +
    +
    +
    +template<typename T >
    + + + + + +
    + + + + + + + + + + + +
    static METAL_FUNC constexpr T SubOp::apply (T x,
    T y )
    +     		+inlinestaticconstexpr
    +
    + +
    +
    +
    The documentation for this struct was generated from the following file: +
    + +
    +Generated by doxygen 1.12.0 +
    +
    + + diff --git a/docs/build/html/struct_sum_op-members.html b/docs/build/html/struct_sum_op-members.html new file mode 100644 index 00000000..27df88b0 --- /dev/null +++ b/docs/build/html/struct_sum_op-members.html @@ -0,0 +1,101 @@ + + + + + + + +MLX: Member List + + + + + + + + + + + + + +
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    apply(T x, T y)SumOpinlinestatic
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    +Generated by doxygen 1.12.0 +
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    + + diff --git a/docs/build/html/struct_sum_op.html b/docs/build/html/struct_sum_op.html new file mode 100644 index 00000000..2fcb18d9 --- /dev/null +++ b/docs/build/html/struct_sum_op.html @@ -0,0 +1,144 @@ + + + + + + + +MLX: SumOp Struct Reference + + + + + + + + + + + + + +
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    + +

    #include <steel_attention.h>

    + + + + + +

    +Static Public Member Functions

    template<typename T >
    static METAL_FUNC constexpr T apply (T x, T y)
     
    +

    Member Function Documentation

    + +

    ◆ apply()

    + +
    +
    +
    +template<typename T >
    + + + + + +
    + + + + + + + + + + + +
    static METAL_FUNC constexpr T SumOp::apply (T x,
    T y )
    +     		+inlinestaticconstexpr
    +
    + +
    +
    +
    The documentation for this struct was generated from the following file: +
    + +
    +Generated by doxygen 1.12.0 +
    +
    + + diff --git a/docs/build/html/struct_m_l_x_scaled_dot_product_attention_params-members.html b/docs/build/html/struct_transform_scale-members.html similarity index 68% rename from docs/build/html/struct_m_l_x_scaled_dot_product_attention_params-members.html rename to docs/build/html/struct_transform_scale-members.html index ea40d7a9..a42a65ec 100644 --- a/docs/build/html/struct_m_l_x_scaled_dot_product_attention_params-members.html +++ b/docs/build/html/struct_transform_scale-members.html @@ -84,17 +84,15 @@ $(function(){ initResizable(false); });
    -
    MLXScaledDotProductAttentionParams Member List
    +
    TransformScale< T > Member List
    -

    This is the complete list of members for MLXScaledDotProductAttentionParams, including all inherited members.

    +

    This is the complete list of members for TransformScale< T >, including all inherited members.

    - - - - - + + +
    INV_ALPHAMLXScaledDotProductAttentionParams
    KV_TILESMLXScaledDotProductAttentionParams
    N_KV_HEADSMLXScaledDotProductAttentionParams
    N_Q_HEADSMLXScaledDotProductAttentionParams
    QUERY_SEQUENCE_LENGTHMLXScaledDotProductAttentionParams
    apply(T x) constTransformScale< T >inline
    scaleTransformScale< T >
    TransformScale(T scale_)TransformScale< T >inline
    diff --git a/docs/build/html/structlooped__elem__to__loc_3_010_00_01offset__t_01_4.html b/docs/build/html/struct_transform_scale.html similarity index 50% rename from docs/build/html/structlooped__elem__to__loc_3_010_00_01offset__t_01_4.html rename to docs/build/html/struct_transform_scale.html index d1bf2a22..9432afa3 100644 --- a/docs/build/html/structlooped__elem__to__loc_3_010_00_01offset__t_01_4.html +++ b/docs/build/html/struct_transform_scale.html @@ -5,7 +5,7 @@ -MLX: looped_elem_to_loc< 0, offset_t > Struct Template Reference +MLX: TransformScale< T > Struct Template Reference @@ -86,53 +86,71 @@ $(function(){ initResizable(false); });
    Public Member Functions | -List of all members
    -
    looped_elem_to_loc< 0, offset_t > Struct Template Reference
    +Public Attributes | +List of all members
    +
    TransformScale< T > Struct Template Reference
    -

    #include <utils.h>

    +

    #include <steel_attention.h>

    - - - - - - + + + + +

    Public Member Functions

    void next (const constant int *, const constant size_t *)
     
    void next (int, const constant int *, const constant size_t *)
     
    offset_t location (offset_t idx, const constant int *shape, const constant size_t *strides, int ndim)
     
    METAL_FUNC TransformScale (T scale_)
     
    METAL_FUNC T apply (T x) const
     
    + + +

    +Public Attributes

    T scale
     
    +

    Constructor & Destructor Documentation

    + +

    ◆ TransformScale()

    + +
    +
    +
    +template<typename T >
    + + + + + +
    + + + + + + + +
    METAL_FUNC TransformScale< T >::TransformScale (T scale_)
    +     		+inline
    +
    + +
    +

    Member Function Documentation

    - -

    ◆ location()

    + +

    ◆ apply()

    -template<typename offset_t >
    +template<typename T >
    @@ -144,75 +162,25 @@ template<typename offset_t > - -

    ◆ next() [1/2]

    +

    Member Data Documentation

    + +

    ◆ scale

    -template<typename offset_t >
    -
    - + - - - - - - - - - - - - - - - - + +
    offset_t looped_elem_to_loc< 0, offset_t >::location METAL_FUNC T TransformScale< T >::apply (offset_t idx,
    const constant int * shape,
    const constant size_t * strides,
    int ndim )T x) const
    - - - - -
    +template<typename T > - - - - - - - - +
    void looped_elem_to_loc< 0, offset_t >::next (const constant int * ,
    const constant size_t *  )T TransformScale< T >::scale
    -     		-inline
    -
    - -
    -
    - -

    ◆ next() [2/2]

    - -
    -
    -
    -template<typename offset_t >
    - - - - - -
    - - - - - - - - - - - - - - - - -
    void looped_elem_to_loc< 0, offset_t >::next (int ,
    const constant int * ,
    const constant size_t *  )
    -     		-inline
    The documentation for this struct was generated from the following file: diff --git a/docs/build/html/structmetal_1_1__numeric__limits__impl_3_01bfloat16__t_01_4.html b/docs/build/html/structmetal_1_1__numeric__limits__impl_3_01bfloat16__t_01_4.html index 7ab337e4..e5093b18 100644 --- a/docs/build/html/structmetal_1_1__numeric__limits__impl_3_01bfloat16__t_01_4.html +++ b/docs/build/html/structmetal_1_1__numeric__limits__impl_3_01bfloat16__t_01_4.html @@ -96,7 +96,7 @@ $(function(){ initResizable(false); });
    -

    #include <bf16.h>

    +

    #include <bf16.h>

    Inheritance diagram for metal::_numeric_limits_impl< bfloat16_t >:
    @@ -106,23 +106,23 @@ Inheritance diagram for metal::_numeric_limits_impl< bfloat16_t >:
    - + - + - + - + - + - + - + - + - +

    Static Public Member Functions

    static constexpr bfloat16_t min ()
    static constexpr bfloat16_t min ()
     
    static constexpr bfloat16_t lowest ()
    static constexpr bfloat16_t lowest ()
     
    static constexpr bfloat16_t max ()
    static constexpr bfloat16_t max ()
     
    static constexpr bfloat16_t epsilon ()
    static constexpr bfloat16_t epsilon ()
     
    static constexpr bfloat16_t round_error ()
    static constexpr bfloat16_t round_error ()
     
    static constexpr bfloat16_t infinity ()
    static constexpr bfloat16_t infinity ()
     
    static constexpr bfloat16_t quiet_NaN ()
    static constexpr bfloat16_t quiet_NaN ()
     
    static constexpr bfloat16_t signaling_NaN ()
    static constexpr bfloat16_t signaling_NaN ()
     
    static constexpr bfloat16_t denorm_min ()
    static constexpr bfloat16_t denorm_min ()
     
    + + +

    @@ -155,7 +155,7 @@ Static Public Attributes

    - + @@ -180,7 +180,7 @@ Static Public Attributes - + + + + + + + + + + + + + + + + + +
    static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::denorm_min static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::denorm_min ( )
    - + @@ -205,7 +205,7 @@ Static Public Attributes template<typename T >
    static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::epsilon static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::epsilon ( )
    - + @@ -230,7 +230,7 @@ Static Public Attributes template<typename T >
    static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::infinity static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::infinity ( )
    - + @@ -255,7 +255,7 @@ Static Public Attributes
    static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::lowest static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::lowest ( )
    - + @@ -280,7 +280,7 @@ Static Public Attributes
    static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::max static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::max ( )
    - + @@ -305,7 +305,7 @@ Static Public Attributes
    static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::min static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::min ( )
    - + @@ -330,7 +330,7 @@ Static Public Attributes - - - - - - + + + + + + + + + + + + + + + + + + + + + + @@ -193,14 +209,14 @@ Public Member Functions

    Member Function Documentation

    - -

    ◆ dispatchThreadgroups()

    + +

    ◆ dispatch_threadgroups()

    static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::quiet_NaN static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::quiet_NaN ( )
    - + @@ -355,7 +355,7 @@ Static Public Attributes diff --git a/docs/build/html/structmlx_1_1core_1_1metal_1_1_command_encoder-members.html b/docs/build/html/structmlx_1_1core_1_1metal_1_1_command_encoder-members.html index 98160123..b6705a30 100644 --- a/docs/build/html/structmlx_1_1core_1_1metal_1_1_command_encoder-members.html +++ b/docs/build/html/structmlx_1_1core_1_1metal_1_1_command_encoder-members.html @@ -96,16 +96,22 @@ $(function(){ initResizable(false); });
    static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::round_error static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::round_error ( )
    - + @@ -381,7 +381,7 @@ Static Public Attributes @@ -403,7 +403,7 @@ Static Public Attributes @@ -425,7 +425,7 @@ Static Public Attributes @@ -447,7 +447,7 @@ Static Public Attributes @@ -469,7 +469,7 @@ Static Public Attributes @@ -491,7 +491,7 @@ Static Public Attributes @@ -513,7 +513,7 @@ Static Public Attributes @@ -535,7 +535,7 @@ Static Public Attributes @@ -548,7 +548,7 @@ Static Public Attributes
    The documentation for this struct was generated from the following file:
      -
    • mlx/backend/metal/kernels/bf16.h
      • +
    • mlx/backend/metal/kernels/metal_3_0/bf16.h
    diff --git a/docs/build/html/structmlx_1_1core_1_1_print_formatter.html b/docs/build/html/structmlx_1_1core_1_1_print_formatter.html index 03a8c1e0..448f6cf6 100644 --- a/docs/build/html/structmlx_1_1core_1_1_print_formatter.html +++ b/docs/build/html/structmlx_1_1core_1_1_print_formatter.html @@ -116,7 +116,7 @@ Public Member Functions - + @@ -146,7 +146,7 @@ Public Attributes - +
    static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::signaling_NaN static constexpr bfloat16_t metal::_numeric_limits_impl< bfloat16_t >::signaling_NaN ( )
    - +
    constant int metal::_numeric_limits_impl< bfloat16_t >::digits = 8constant int metal::_numeric_limits_impl< bfloat16_t >::digits = 8
    - +
    constant int metal::_numeric_limits_impl< bfloat16_t >::digits10 = 2constant int metal::_numeric_limits_impl< bfloat16_t >::digits10 = 2
    - +
    constant int metal::_numeric_limits_impl< bfloat16_t >::max_digits10 = 4constant int metal::_numeric_limits_impl< bfloat16_t >::max_digits10 = 4
    - +
    constant int metal::_numeric_limits_impl< bfloat16_t >::max_exponent = 128constant int metal::_numeric_limits_impl< bfloat16_t >::max_exponent = 128
    - +
    constant int metal::_numeric_limits_impl< bfloat16_t >::max_exponent10 = 38constant int metal::_numeric_limits_impl< bfloat16_t >::max_exponent10 = 38
    - +
    constant int metal::_numeric_limits_impl< bfloat16_t >::min_exponent = -125constant int metal::_numeric_limits_impl< bfloat16_t >::min_exponent = -125
    - +
    constant int metal::_numeric_limits_impl< bfloat16_t >::min_exponent10 = -37constant int metal::_numeric_limits_impl< bfloat16_t >::min_exponent10 = -37
    - +
    constant int metal::_numeric_limits_impl< bfloat16_t >::radix = 2constant int metal::_numeric_limits_impl< bfloat16_t >::radix = 2
     
    void print (std::ostream &os, float16_t val)
     
    void print (std::ostream &os, bfloat16_t val)
    void print (std::ostream &os, bfloat16_t val)
     
    void print (std::ostream &os, float val)
     
    bfloat16_t val )bfloat16_t val )
    - - + + - - - + + + + + + + + +
    CommandEncoder(MTL::CommandBuffer *cbuf)mlx::core::metal::CommandEncoder
    CommandEncoder(const CommandEncoder &)=deletemlx::core::metal::CommandEncoder
    dispatchThreadgroups(MTL::Size grid_dims, MTL::Size group_dims)mlx::core::metal::CommandEncoder
    dispatchThreads(MTL::Size grid_dims, MTL::Size group_dims)mlx::core::metal::CommandEncoder
    dispatch_threadgroups(MTL::Size grid_dims, MTL::Size group_dims)mlx::core::metal::CommandEncoder
    dispatch_threads(MTL::Size grid_dims, MTL::Size group_dims)mlx::core::metal::CommandEncoder
    inputs()mlx::core::metal::CommandEncoderinline
    maybeInsertBarrier()mlx::core::metal::CommandEncoder
    operator->()mlx::core::metal::CommandEncoderinline
    operator=(const CommandEncoder &)=deletemlx::core::metal::CommandEncoder
    outputs()mlx::core::metal::CommandEncoderinline
    operator=(const CommandEncoder &)=deletemlx::core::metal::CommandEncoder
    outputs()mlx::core::metal::CommandEncoderinline
    set_bytes(const T *v, int n, int idx)mlx::core::metal::CommandEncoderinline
    set_bytes(const T &v, int idx)mlx::core::metal::CommandEncoderinline
    set_compute_pipeline_state(MTL::ComputePipelineState *kernel)mlx::core::metal::CommandEncoderinline
    set_input_array(const array &a, int idx, int64_t offset=0)mlx::core::metal::CommandEncoder
    set_output_array(array &a, int idx, int64_t offset=0)mlx::core::metal::CommandEncoder
    set_vector_bytes(const std::vector< T > &vec, size_t nelems, int idx)mlx::core::metal::CommandEncoderinline
    set_vector_bytes(const std::vector< T > &vec, int idx)mlx::core::metal::CommandEncoderinline
    start_concurrent()mlx::core::metal::CommandEncoderinline
    update_fence(MTL::Fence *fence)mlx::core::metal::CommandEncoderinline
    wait_for_fence(MTL::Fence *fence)mlx::core::metal::CommandEncoderinline
    ~CommandEncoder()mlx::core::metal::CommandEncoder
    diff --git a/docs/build/html/structmlx_1_1core_1_1metal_1_1_command_encoder.html b/docs/build/html/structmlx_1_1core_1_1metal_1_1_command_encoder.html index ee359fde..b26bf1f8 100644 --- a/docs/build/html/structmlx_1_1core_1_1metal_1_1_command_encoder.html +++ b/docs/build/html/structmlx_1_1core_1_1metal_1_1_command_encoder.html @@ -111,18 +111,34 @@ Public Member Functions
     
    CommandEncoder & operator= (const CommandEncoder &)=delete
     
    MTL::ComputeCommandEncoder * operator-> ()
     
    void set_input_array (const array &a, int idx, int64_t offset=0)
     
    void set_output_array (array &a, int idx, int64_t offset=0)
     
    void dispatchThreadgroups (MTL::Size grid_dims, MTL::Size group_dims)
     
    void dispatchThreads (MTL::Size grid_dims, MTL::Size group_dims)
     
    void dispatch_threadgroups (MTL::Size grid_dims, MTL::Size group_dims)
     
    void dispatch_threads (MTL::Size grid_dims, MTL::Size group_dims)
     
    void maybeInsertBarrier ()
     
    void set_compute_pipeline_state (MTL::ComputePipelineState *kernel)
     
    void wait_for_fence (MTL::Fence *fence)
     
    void update_fence (MTL::Fence *fence)
     
    template<typename T >
    void set_vector_bytes (const std::vector< T > &vec, size_t nelems, int idx)
     
    template<typename T >
    void set_vector_bytes (const std::vector< T > &vec, int idx)
     
    template<typename T >
    void set_bytes (const T *v, int n, int idx)
     
    template<typename T >
    void set_bytes (const T &v, int idx)
     
    ConcurrentContext start_concurrent ()
     
     ~CommandEncoder ()
    - + @@ -214,14 +230,14 @@ Public Member Functions - -

    ◆ dispatchThreads()

    + +

    ◆ dispatch_threads()

    void mlx::core::metal::CommandEncoder::dispatchThreadgroups void mlx::core::metal::CommandEncoder::dispatch_threadgroups ( MTL::Size grid_dims,
    - + @@ -275,31 +291,6 @@ Public Member Functions
    void mlx::core::metal::CommandEncoder::dispatchThreads void mlx::core::metal::CommandEncoder::dispatch_threads ( MTL::Size grid_dims,
    -
    - - -

    ◆ operator->()

    - -
    -
    - - - - - -
    - - - - - - - -
    MTL::ComputeCommandEncoder * mlx::core::metal::CommandEncoder::operator-> ()
    -     		-inline
    -
    -
    @@ -350,6 +341,98 @@ Public Member Functions
    +
    + + +

    ◆ set_bytes() [1/2]

    + +
    +
    +
    +template<typename T >
    + + + + + +
    + + + + + + + + + + + +
    void mlx::core::metal::CommandEncoder::set_bytes (const T & v,
    int idx )
    +     		+inline
    +
    + +
    +
    + +

    ◆ set_bytes() [2/2]

    + +
    +
    +
    +template<typename T >
    + + + + + +
    + + + + + + + + + + + + + + + + +
    void mlx::core::metal::CommandEncoder::set_bytes (const T * v,
    int n,
    int idx )
    +     		+inline
    +
    + +
    +
    + +

    ◆ set_compute_pipeline_state()

    + +
    +
    + + + + + +
    + + + + + + + +
    void mlx::core::metal::CommandEncoder::set_compute_pipeline_state (MTL::ComputePipelineState * kernel)
    +     		+inline
    +
    +
    @@ -402,6 +485,73 @@ Public Member Functions
    +
    + + +

    ◆ set_vector_bytes() [1/2]

    + +
    +
    +
    +template<typename T >
    + + + + + +
    + + + + + + + + + + + +
    void mlx::core::metal::CommandEncoder::set_vector_bytes (const std::vector< T > & vec,
    int idx )
    +     		+inline
    +
    + +
    +
    + +

    ◆ set_vector_bytes() [2/2]

    + +
    +
    +
    +template<typename T >
    + + + + + +
    + + + + + + + + + + + + + + + + +
    void mlx::core::metal::CommandEncoder::set_vector_bytes (const std::vector< T > & vec,
    size_t nelems,
    int idx )
    +     		+inline
    +
    +
    @@ -427,6 +577,56 @@ Public Member Functions
    +
    + + +

    ◆ update_fence()

    + +
    +
    + + + + + +
    + + + + + + + +
    void mlx::core::metal::CommandEncoder::update_fence (MTL::Fence * fence)
    +     		+inline
    +
    + +
    +
    + +

    ◆ wait_for_fence()

    + +
    +
    + + + + + +
    + + + + + + + +
    void mlx::core::metal::CommandEncoder::wait_for_fence (MTL::Fence * fence)
    +     		+inline
    +
    +
    The documentation for this struct was generated from the following file:
      diff --git a/docs/build/html/structmlx_1_1steel_1_1_accum_helper-members.html b/docs/build/html/structmlx_1_1steel_1_1_accum_helper-members.html index 5e66f523..14021229 100644 --- a/docs/build/html/structmlx_1_1steel_1_1_accum_helper-members.html +++ b/docs/build/html/structmlx_1_1steel_1_1_accum_helper-members.html @@ -94,7 +94,7 @@ $(function(){ initResizable(false); });

      This is the complete list of members for mlx::steel::AccumHelper< T >, including all inherited members.

      - +
      accum_type typedefmlx::steel::AccumHelper< T >
      accum_type typedefmlx::steel::AccumHelper< T >
      diff --git a/docs/build/html/structmlx_1_1steel_1_1_accum_helper.html b/docs/build/html/structmlx_1_1steel_1_1_accum_helper.html index a3e3e258..bd7cc3cd 100644 --- a/docs/build/html/structmlx_1_1steel_1_1_accum_helper.html +++ b/docs/build/html/structmlx_1_1steel_1_1_accum_helper.html @@ -95,16 +95,16 @@ $(function(){ initResizable(false); });
      -

      #include <transforms.h>

      +

      #include <transforms.h>

      - - + +

      Public Types

      typedef float accum_type
       
      typedef float accum_type
       

      Member Typedef Documentation

      - -

      ◆ accum_type

      + +

      ◆ accum_type

      @@ -112,14 +112,15 @@ Public Types
    - +
    float mlx::steel::AccumHelper< T >::accum_typetypedef float mlx::steel::AccumHelper< T >::accum_type
    -
    The documentation for this struct was generated from the following file:
      +
      The documentation for this struct was generated from the following files: diff --git a/docs/build/html/structmlx_1_1steel_1_1_attn_params-members.html b/docs/build/html/structmlx_1_1steel_1_1_attn_params-members.html new file mode 100644 index 00000000..4074723b --- /dev/null +++ b/docs/build/html/structmlx_1_1steel_1_1_attn_params-members.html @@ -0,0 +1,119 @@ + + + + + + + +MLX: Member List + + + + + + + + + + + + + +
      +
      + + + + + + +
      +
      MLX +
      +
      +
      + + + + + + + + + + +
      +
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      +
      +
      Loading...
      +
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      +
      No Matches
      +
      +
      +
      +
      + + +
      +
      +
      +
      mlx::steel::AttnParams Member List
      +
      +
      + +

      This is the complete list of members for mlx::steel::AttnParams, including all inherited members.

      + + + + + + + + + + + + + + + + +
      Bmlx::steel::AttnParams
      Dmlx::steel::AttnParams
      gqa_factormlx::steel::AttnParams
      Hmlx::steel::AttnParams
      K_stridesmlx::steel::AttnParams
      kLmlx::steel::AttnParams
      NKmlx::steel::AttnParams
      NK_alignedmlx::steel::AttnParams
      NQmlx::steel::AttnParams
      NQ_alignedmlx::steel::AttnParams
      O_stridesmlx::steel::AttnParams
      Q_stridesmlx::steel::AttnParams
      qLmlx::steel::AttnParams
      scalemlx::steel::AttnParams
      V_stridesmlx::steel::AttnParams
      + +
      +Generated by doxygen 1.12.0 +
      +
      + + diff --git a/docs/build/html/structmlx_1_1steel_1_1_attn_params.html b/docs/build/html/structmlx_1_1steel_1_1_attn_params.html new file mode 100644 index 00000000..bbfa45e0 --- /dev/null +++ b/docs/build/html/structmlx_1_1steel_1_1_attn_params.html @@ -0,0 +1,399 @@ + + + + + + + +MLX: mlx::steel::AttnParams Struct Reference + + + + + + + + + + + + + +
      +
      + + + + + + +
      +
      MLX +
      +
      +
      + + + + + + + + + + +
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      Loading...
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      +
      +
      +
      +
      + + +
      +
      +
      +
      +Public Attributes | +List of all members
      +
      mlx::steel::AttnParams Struct Reference
      +
      +
      + +

      #include <params.h>

      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

      +Public Attributes

      int B
       Batch Size.
       
      int H
       Heads.
       
      int D
       Head Dim.
       
      int qL
       Query Sequence Length.
       
      int kL
       Key Sequence Length.
       
      int gqa_factor
       Group Query factor.
       
      float scale
       Attention scale.
       
      int NQ
       Number of query blocks.
       
      int NK
       Number of key/value blocks.
       
      int NQ_aligned
       Number of full query blocks.
       
      int NK_aligned
       Number of full key/value blocks.
       
      size_t Q_strides [3]
       Query strides (B, H, L, D = 1)
       
      size_t K_strides [3]
       Key strides (B, H, L, D = 1)
       
      size_t V_strides [3]
       Value strides (B, H, L, D = 1)
       
      size_t O_strides [3]
       Output strides (B, H, L, D = 1)
       
      +

      Member Data Documentation

      + +

      ◆ B

      + +
      +
      + + + + +
      int mlx::steel::AttnParams::B
      +
      + +

      Batch Size.

      + +
      +
      + +

      ◆ D

      + +
      +
      + + + + +
      int mlx::steel::AttnParams::D
      +
      + +

      Head Dim.

      + +
      +
      + +

      ◆ gqa_factor

      + +
      +
      + + + + +
      int mlx::steel::AttnParams::gqa_factor
      +
      + +

      Group Query factor.

      + +
      +
      + +

      ◆ H

      + +
      +
      + + + + +
      int mlx::steel::AttnParams::H
      +
      + +

      Heads.

      + +
      +
      + +

      ◆ K_strides

      + +
      +
      + + + + +
      size_t mlx::steel::AttnParams::K_strides[3]
      +
      + +

      Key strides (B, H, L, D = 1)

      + +
      +
      + +

      ◆ kL

      + +
      +
      + + + + +
      int mlx::steel::AttnParams::kL
      +
      + +

      Key Sequence Length.

      + +
      +
      + +

      ◆ NK

      + +
      +
      + + + + +
      int mlx::steel::AttnParams::NK
      +
      + +

      Number of key/value blocks.

      + +
      +
      + +

      ◆ NK_aligned

      + +
      +
      + + + + +
      int mlx::steel::AttnParams::NK_aligned
      +
      + +

      Number of full key/value blocks.

      + +
      +
      + +

      ◆ NQ

      + +
      +
      + + + + +
      int mlx::steel::AttnParams::NQ
      +
      + +

      Number of query blocks.

      + +
      +
      + +

      ◆ NQ_aligned

      + +
      +
      + + + + +
      int mlx::steel::AttnParams::NQ_aligned
      +
      + +

      Number of full query blocks.

      + +
      +
      + +

      ◆ O_strides

      + +
      +
      + + + + +
      size_t mlx::steel::AttnParams::O_strides[3]
      +
      + +

      Output strides (B, H, L, D = 1)

      + +
      +
      + +

      ◆ Q_strides

      + +
      +
      + + + + +
      size_t mlx::steel::AttnParams::Q_strides[3]
      +
      + +

      Query strides (B, H, L, D = 1)

      + +
      +
      + +

      ◆ qL

      + +
      +
      + + + + +
      int mlx::steel::AttnParams::qL
      +
      + +

      Query Sequence Length.

      + +
      +
      + +

      ◆ scale

      + +
      +
      + + + + +
      float mlx::steel::AttnParams::scale
      +
      + +

      Attention scale.

      + +
      +
      + +

      ◆ V_strides

      + +
      +
      + + + + +
      size_t mlx::steel::AttnParams::V_strides[3]
      +
      + +

      Value strides (B, H, L, D = 1)

      + +
      +
      +
      The documentation for this struct was generated from the following file:
        +
      • mlx/backend/metal/kernels/steel/attn/params.h
        • +
      +
      + +
      +Generated by doxygen 1.12.0 +
      +
      + + diff --git a/docs/build/html/structmlx_1_1steel_1_1_base_m_m_a_frag.html b/docs/build/html/structmlx_1_1steel_1_1_base_m_m_a_frag.html index 1258f171..d66ae2c4 100644 --- a/docs/build/html/structmlx_1_1steel_1_1_base_m_m_a_frag.html +++ b/docs/build/html/structmlx_1_1steel_1_1_base_m_m_a_frag.html @@ -92,9 +92,10 @@ $(function(){ initResizable(false); });
      -

      #include <mma.h>

      -
      The documentation for this struct was generated from the following file:
        -
      • mlx/backend/metal/kernels/steel/gemm/mma.h
        • +

        #include <mma.h>

        +
        The documentation for this struct was generated from the following files:
          +
        • mlx/backend/metal/kernels/steel/attn/mma.h
          • +
        • mlx/backend/metal/kernels/steel/gemm/mma.h
      diff --git a/docs/build/html/structmlx_1_1steel_1_1_base_m_m_a_frag_3_01_t_00_018_00_018_01_4-members.html b/docs/build/html/structmlx_1_1steel_1_1_base_m_m_a_frag_3_01_t_00_018_00_018_01_4-members.html index 7b063956..7b840ba9 100644 --- a/docs/build/html/structmlx_1_1steel_1_1_base_m_m_a_frag_3_01_t_00_018_00_018_01_4-members.html +++ b/docs/build/html/structmlx_1_1steel_1_1_base_m_m_a_frag_3_01_t_00_018_00_018_01_4-members.html @@ -94,7 +94,9 @@ $(function(){ initResizable(false); });

      This is the complete list of members for mlx::steel::BaseMMAFrag< T, 8, 8 >, including all inherited members.

      - + + + @@ -102,12 +104,21 @@ $(function(){ initResizable(false); }); + + - + + + + + + + +
      frag_type typedefmlx::steel::BaseMMAFrag< T, 8, 8 >
      col_frag_type typedefmlx::steel::BaseMMAFrag< T, 8, 8 >
      frag_type typedefmlx::steel::BaseMMAFrag< T, 8, 8 >
      get_coord(ushort simd_lane_id)mlx::steel::BaseMMAFrag< T, 8, 8 >inlinestatic
      get_coord(ushort simd_lane_id)mlx::steel::BaseMMAFrag< T, 8, 8 >inlinestatic
      kElemColsmlx::steel::BaseMMAFrag< T, 8, 8 >
      kElemRowsmlx::steel::BaseMMAFrag< T, 8, 8 >
      kFragColsmlx::steel::BaseMMAFrag< T, 8, 8 >
      kFragRowsmlx::steel::BaseMMAFrag< T, 8, 8 >
      load(thread frag_type &dst, SrcPtrType src, StrX str_x, StrY str_y)mlx::steel::BaseMMAFrag< T, 8, 8 >inlinestatic
      load(thread frag_type &dst, SrcPtrType src, StrX str_x, StrY str_y)mlx::steel::BaseMMAFrag< T, 8, 8 >inlinestatic
      load_safe(thread frag_type &dst, SrcPtrType src, StrX str_x, StrY str_y, LimX lim_x, LimY lim_y, OffX off_x=Int< 0 >{}, OffY off_y=Int< 0 >{})mlx::steel::BaseMMAFrag< T, 8, 8 >inlinestatic
      load_safe(thread frag_type &dst, SrcPtrType src, StrX str_x, StrY str_y, LimX lim_x, LimY lim_y, OffX off_x=Int< 0 >{}, OffY off_y=Int< 0 >{})mlx::steel::BaseMMAFrag< T, 8, 8 >inlinestatic
      mat_type typedefmlx::steel::BaseMMAFrag< T, 8, 8 >
      mat_type typedefmlx::steel::BaseMMAFrag< T, 8, 8 >
      mma(thread frag_type &D, thread frag_type &A, thread frag_type &B, thread frag_type &C)mlx::steel::BaseMMAFrag< T, 8, 8 >inlinestatic
      mma(thread mat_type &D, thread mat_type &A, thread mat_type &B, thread mat_type &C)mlx::steel::BaseMMAFrag< T, 8, 8 >inlinestatic
      mma(thread frag_type &D, thread frag_type &A, thread frag_type &B, thread frag_type &C)mlx::steel::BaseMMAFrag< T, 8, 8 >inlinestatic
      mma(thread mat_type &D, thread mat_type &A, thread mat_type &B, thread mat_type &C)mlx::steel::BaseMMAFrag< T, 8, 8 >inlinestatic
      row_bin_op(thread frag_type &inp_vals, thread T *row_vals)mlx::steel::BaseMMAFrag< T, 8, 8 >inlinestatic
      row_frag_type typedefmlx::steel::BaseMMAFrag< T, 8, 8 >
      row_reduce(thread const frag_type &inp_vals, thread T *reduced_vals)mlx::steel::BaseMMAFrag< T, 8, 8 >inlinestatic
      store(const thread frag_type &src, DstPtrType dst, StrX str_x, StrY str_y)mlx::steel::BaseMMAFrag< T, 8, 8 >inlinestatic
      store(const thread frag_type &src, DstPtrType dst, StrX str_x, StrY str_y)mlx::steel::BaseMMAFrag< T, 8, 8 >inlinestatic
      store_safe(const thread frag_type &src, DstPtrType dst, StrX str_x, StrY str_y, LimX lim_x, LimY lim_y, OffX off_x=Int< 0 >{}, OffY off_y=Int< 0 >{})mlx::steel::BaseMMAFrag< T, 8, 8 >inlinestatic
      store_safe(const thread frag_type &src, DstPtrType dst, StrX str_x, StrY str_y, LimX lim_x, LimY lim_y, OffX off_x=Int< 0 >{}, OffY off_y=Int< 0 >{})mlx::steel::BaseMMAFrag< T, 8, 8 >inlinestatic
      diff --git a/docs/build/html/structmlx_1_1steel_1_1_base_m_m_a_frag_3_01_t_00_018_00_018_01_4.html b/docs/build/html/structmlx_1_1steel_1_1_base_m_m_a_frag_3_01_t_00_018_00_018_01_4.html index 5c8cc5d2..9d08e5db 100644 --- a/docs/build/html/structmlx_1_1steel_1_1_base_m_m_a_frag_3_01_t_00_018_00_018_01_4.html +++ b/docs/build/html/structmlx_1_1steel_1_1_base_m_m_a_frag_3_01_t_00_018_00_018_01_4.html @@ -97,34 +97,62 @@ $(function(){ initResizable(false); });
      -

      #include <mma.h>

      +

      #include <mma.h>

      - - - - + + + + + + + +

      Public Types

      typedef metal::simdgroup_matrix< T, kFragRows, kFragCols > mat_type
       
      typedef metal::vec< T, kElemsPerFrag > frag_type
       
      typedef metal::simdgroup_matrix< T, kFragRows, kFragCols > mat_type
       
      typedef metal::vec< T, kElemsPerFrag > frag_type
       
      typedef metal::vec< T, kElemRows > row_frag_type
       
      typedef metal::vec< T, kElemCols > col_frag_type
       
      - + - + - + - + - + - + + + + + + + + + + + + + + + + + + + + + + + + +

      Static Public Member Functions

      static METAL_FUNC constexpr short2 get_coord (ushort simd_lane_id)
       
      template<typename SrcPtrType , typename StrX , typename StrY >
      static METAL_FUNC constexpr void load (thread frag_type &dst, SrcPtrType src, StrX str_x, StrY str_y)
      static METAL_FUNC constexpr void load (thread frag_type &dst, SrcPtrType src, StrX str_x, StrY str_y)
       
      template<typename SrcPtrType , typename StrX , typename StrY , typename LimX , typename LimY , typename OffX , typename OffY >
      static METAL_FUNC constexpr void load_safe (thread frag_type &dst, SrcPtrType src, StrX str_x, StrY str_y, LimX lim_x, LimY lim_y, OffX off_x=Int< 0 >{}, OffY off_y=Int< 0 >{})
      static METAL_FUNC constexpr void load_safe (thread frag_type &dst, SrcPtrType src, StrX str_x, StrY str_y, LimX lim_x, LimY lim_y, OffX off_x=Int< 0 >{}, OffY off_y=Int< 0 >{})
       
      template<typename DstPtrType , typename StrX , typename StrY >
      static METAL_FUNC constexpr void store (const thread frag_type &src, DstPtrType dst, StrX str_x, StrY str_y)
      static METAL_FUNC constexpr void store (const thread frag_type &src, DstPtrType dst, StrX str_x, StrY str_y)
       
      template<typename DstPtrType , typename StrX , typename StrY , typename LimX , typename LimY , typename OffX , typename OffY >
      static METAL_FUNC constexpr void store_safe (const thread frag_type &src, DstPtrType dst, StrX str_x, StrY str_y, LimX lim_x, LimY lim_y, OffX off_x=Int< 0 >{}, OffY off_y=Int< 0 >{})
      static METAL_FUNC constexpr void store_safe (const thread frag_type &src, DstPtrType dst, StrX str_x, StrY str_y, LimX lim_x, LimY lim_y, OffX off_x=Int< 0 >{}, OffY off_y=Int< 0 >{})
       
      static METAL_FUNC constexpr void mma (thread frag_type &D, thread frag_type &A, thread frag_type &B, thread frag_type &C)
      static METAL_FUNC constexpr void mma (thread frag_type &D, thread frag_type &A, thread frag_type &B, thread frag_type &C)
       
      static METAL_FUNC constexpr void mma (thread mat_type &D, thread mat_type &A, thread mat_type &B, thread mat_type &C)
      static METAL_FUNC constexpr void mma (thread mat_type &D, thread mat_type &A, thread mat_type &B, thread mat_type &C)
       
      template<typename Op >
      static METAL_FUNC constexpr void row_reduce (thread const frag_type &inp_vals, thread T *reduced_vals)
       
      template<typename Op >
      static METAL_FUNC constexpr void row_bin_op (thread frag_type &inp_vals, thread T *row_vals)
       
      static METAL_FUNC constexpr short2 get_coord (ushort simd_lane_id)
       
      template<typename SrcPtrType , typename StrX , typename StrY >
      static METAL_FUNC constexpr void load (thread frag_type &dst, SrcPtrType src, StrX str_x, StrY str_y)
       
      template<typename SrcPtrType , typename StrX , typename StrY , typename LimX , typename LimY , typename OffX , typename OffY >
      static METAL_FUNC constexpr void load_safe (thread frag_type &dst, SrcPtrType src, StrX str_x, StrY str_y, LimX lim_x, LimY lim_y, OffX off_x=Int< 0 >{}, OffY off_y=Int< 0 >{})
       
      template<typename DstPtrType , typename StrX , typename StrY >
      static METAL_FUNC constexpr void store (const thread frag_type &src, DstPtrType dst, StrX str_x, StrY str_y)
       
      template<typename DstPtrType , typename StrX , typename StrY , typename LimX , typename LimY , typename OffX , typename OffY >
      static METAL_FUNC constexpr void store_safe (const thread frag_type &src, DstPtrType dst, StrX str_x, StrY str_y, LimX lim_x, LimY lim_y, OffX off_x=Int< 0 >{}, OffY off_y=Int< 0 >{})
       
      static METAL_FUNC constexpr void mma (thread frag_type &D, thread frag_type &A, thread frag_type &B, thread frag_type &C)
       
      static METAL_FUNC constexpr void mma (thread mat_type &D, thread mat_type &A, thread mat_type &B, thread mat_type &C)
       

      @@ -141,8 +169,8 @@ Public Attributes

       

      Member Typedef Documentation

      - -

      ◆ frag_type

      + +

      ◆ col_frag_type

      @@ -150,15 +178,15 @@ Public Attributes
    - +
    metal::vec<T, kElemsPerFrag> mlx::steel::BaseMMAFrag< T, 8, 8 >::frag_typemetal::vec<T, kElemCols> mlx::steel::BaseMMAFrag< T, 8, 8 >::col_frag_type
    - -

    ◆ mat_type

    + +

    ◆ frag_type

    @@ -166,7 +194,39 @@ template<typename T >
    template<typename T >
    - + + +
    metal::simdgroup_matrix<T, kFragRows, kFragCols> mlx::steel::BaseMMAFrag< T, 8, 8 >::mat_typetypedef metal::vec< T, kElemsPerFrag > mlx::steel::BaseMMAFrag< T, 8, 8 >::frag_type
    +
    + +
    + + +

    ◆ mat_type

    + +
    +
    +
    +template<typename T >
    + + + + +
    typedef metal::simdgroup_matrix< T, kFragRows, kFragCols > mlx::steel::BaseMMAFrag< T, 8, 8 >::mat_type
    +
    + +
    +
    + +

    ◆ row_frag_type

    + +
    +
    +
    +template<typename T >
    + + +
    metal::vec<T, kElemRows> mlx::steel::BaseMMAFrag< T, 8, 8 >::row_frag_type
    @@ -175,7 +235,34 @@ template<typename T >

    Member Function Documentation

    -

    ◆ get_coord()

    +

    ◆ get_coord() [1/2]

    + +
    +
    +
    +template<typename T >
    + + + + + +
    + + + + + + + +
    static METAL_FUNC constexpr short2 mlx::steel::BaseMMAFrag< T, 8, 8 >::get_coord (ushort simd_lane_id)
    +     		+inlinestaticconstexpr
    +
    + +
    +
    + +

    ◆ get_coord() [2/2]

    @@ -202,7 +289,7 @@ template<typename T >
    -

    ◆ load()

    +

    ◆ load() [1/2]

    @@ -217,7 +304,50 @@ template<typename SrcPtrType , typename StrX , typename StrY >
    static METAL_FUNC constexpr void mlx::steel::BaseMMAFrag< T, 8, 8 >::load (thread frag_type & dst, thread frag_type & dst,
    SrcPtrType src,
    StrX str_x,
    StrY str_y )
    +     		+inlinestaticconstexpr
    +
    + +
    + + +

    ◆ load() [2/2]

    + +
    +
    +
    +template<typename T >
    +
    +template<typename SrcPtrType , typename StrX , typename StrY >
    + + + + + +
    + + + + + @@ -245,7 +375,7 @@ template<typename SrcPtrType , typename StrX , typename StrY > -

    ◆ load_safe()

    +

    ◆ load_safe() [1/2]

    @@ -260,7 +390,70 @@ template<typename SrcPtrType , typename StrX , typename StrY , typename LimX
    - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    static METAL_FUNC constexpr void mlx::steel::BaseMMAFrag< T, 8, 8 >::load (thread frag_type & dst,
    static METAL_FUNC constexpr void mlx::steel::BaseMMAFrag< T, 8, 8 >::load_safe (thread frag_type & dst, thread frag_type & dst,
    SrcPtrType src,
    StrX str_x,
    StrY str_y,
    LimX lim_x,
    LimY lim_y,
    OffX off_x = Int<0>{},
    OffY off_y = Int<0>{} )
    +     		+inlinestaticconstexpr
    +
    + +
    +
    + +

    ◆ load_safe() [2/2]

    + +
    +
    +
    +template<typename T >
    +
    +template<typename SrcPtrType , typename StrX , typename StrY , typename LimX , typename LimY , typename OffX , typename OffY >
    + + + + + +
    + + + + + @@ -308,7 +501,7 @@ template<typename SrcPtrType , typename StrX , typename StrY , typename LimX -

    ◆ mma() [1/2]

    +

    ◆ mma() [1/4]

    @@ -321,22 +514,63 @@ template<typename T >
    - + - + - + - + + +
    static METAL_FUNC constexpr void mlx::steel::BaseMMAFrag< T, 8, 8 >::load_safe (thread frag_type & dst,
    static METAL_FUNC constexpr void mlx::steel::BaseMMAFrag< T, 8, 8 >::mma (thread frag_type & D, thread frag_type & D,
    thread frag_type & A, thread frag_type & A,
    thread frag_type & B, thread frag_type & B,
    thread frag_type & C )thread frag_type & C )
    +     		+inlinestaticconstexpr
    +
    + +
    +
    + +

    ◆ mma() [2/4]

    + +
    +
    +
    +template<typename T >
    + + + @@ -349,7 +583,7 @@ template<typename T > -

    ◆ mma() [2/2]

    +

    ◆ mma() [3/4]

    @@ -362,22 +596,129 @@ template<typename T >
    - + - + - + - + + +
    + + + + + + + + + + + + + + + + + + + +
    static METAL_FUNC constexpr void mlx::steel::BaseMMAFrag< T, 8, 8 >::mma (thread frag_type & D,
    thread frag_type & A,
    thread frag_type & B,
    thread frag_type & C )
    static METAL_FUNC constexpr void mlx::steel::BaseMMAFrag< T, 8, 8 >::mma (thread mat_type & D, thread mat_type & D,
    thread mat_type & A, thread mat_type & A,
    thread mat_type & B, thread mat_type & B,
    thread mat_type & C )thread mat_type & C )
    +
    		+inlinestaticconstexpr
    +
    + +
    +
    + +

    ◆ mma() [4/4]

    + +
    +
    +
    +template<typename T >
    + + + + + +
    + + + + + + + + + + + + + + + + + + + + + +
    static METAL_FUNC constexpr void mlx::steel::BaseMMAFrag< T, 8, 8 >::mma (thread mat_type & D,
    thread mat_type & A,
    thread mat_type & B,
    thread mat_type & C )
    +     		+inlinestaticconstexpr
    +
    + +
    +
    + +

    ◆ row_bin_op()

    + +
    +
    +
    +template<typename T >
    +
    +template<typename Op >
    + + + + + +
    + + + + + + + + + + + +
    static METAL_FUNC constexpr void mlx::steel::BaseMMAFrag< T, 8, 8 >::row_bin_op (thread frag_type & inp_vals,
    thread T * row_vals )
    +     		+inlinestaticconstexpr
    +
    + +
    +
    + +

    ◆ row_reduce()

    + +
    +
    +
    +template<typename T >
    +
    +template<typename Op >
    + + + @@ -390,7 +731,7 @@ template<typename T > -

    ◆ store()

    +

    ◆ store() [1/2]

    @@ -405,7 +746,50 @@ template<typename DstPtrType , typename StrX , typename StrY >
    - + + + + + + + + + + + + + + + + + +
    + + + + + + + + + +
    static METAL_FUNC constexpr void mlx::steel::BaseMMAFrag< T, 8, 8 >::row_reduce (thread const frag_type & inp_vals,
    thread T * reduced_vals )
    static METAL_FUNC constexpr void mlx::steel::BaseMMAFrag< T, 8, 8 >::store (const thread frag_type & src, const thread frag_type & src,
    DstPtrType dst,
    StrX str_x,
    StrY str_y )
    + + +inlinestaticconstexpr + + +
    + +
    +
    + +

    ◆ store() [2/2]

    + +
    +
    +
    +template<typename T >
    +
    +template<typename DstPtrType , typename StrX , typename StrY >
    + + + + + +
    + + + + + @@ -433,7 +817,7 @@ template<typename DstPtrType , typename StrX , typename StrY > -

    ◆ store_safe()

    +

    ◆ store_safe() [1/2]

    @@ -448,7 +832,70 @@ template<typename DstPtrType , typename StrX , typename StrY , typename LimX
    - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    static METAL_FUNC constexpr void mlx::steel::BaseMMAFrag< T, 8, 8 >::store (const thread frag_type & src,
    static METAL_FUNC constexpr void mlx::steel::BaseMMAFrag< T, 8, 8 >::store_safe (const thread frag_type & src, const thread frag_type & src,
    DstPtrType dst,
    StrX str_x,
    StrY str_y,
    LimX lim_x,
    LimY lim_y,
    OffX off_x = Int<0>{},
    OffY off_y = Int<0>{} )
    +     		+inlinestaticconstexpr
    +
    + +
    +
    + +

    ◆ store_safe() [2/2]

    + +
    +
    +
    +template<typename T >
    +
    +template<typename DstPtrType , typename StrX , typename StrY , typename LimX , typename LimY , typename OffX , typename OffY >
    + + + template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction_dim, short tgp_size, short alignment = 1, short n_reads = (BCOLS * BROWS) / (tgp_size), short TCOLS = BCOLS / n_reads, short TROWS = tgp_size / TCOLS>
    + + + + + @@ -576,8 +1023,9 @@ template<typename T > -
    The documentation for this struct was generated from the following file:
      -
    • mlx/backend/metal/kernels/steel/gemm/mma.h
      • +
      The documentation for this struct was generated from the following files:
        +
      • mlx/backend/metal/kernels/steel/attn/mma.h
        • +
      • mlx/backend/metal/kernels/steel/gemm/mma.h
      diff --git a/docs/build/html/structmlx_1_1steel_1_1_block_loader-members.html b/docs/build/html/structmlx_1_1steel_1_1_block_loader-members.html index 469f9c8b..91595711 100644 --- a/docs/build/html/structmlx_1_1steel_1_1_block_loader-members.html +++ b/docs/build/html/structmlx_1_1steel_1_1_block_loader-members.html @@ -95,19 +95,24 @@ $(function(){ initResizable(false); });

      This is the complete list of members for mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >, including all inherited members.

    static METAL_FUNC constexpr void mlx::steel::BaseMMAFrag< T, 8, 8 >::store_safe (const thread frag_type & src,
    - - + + + + - + + + - - - - - + + + + + +
    apply_inplace_op(thread const UnaryOp &op) constmlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >inline
    bimlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    bjmlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    apply_inplace_op(thread const UnaryOp &op) constmlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >inline
    bimlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    bjmlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    BlockLoader(const device T *src_, const int src_ld_, threadgroup T *dst_, ushort simd_group_id, ushort simd_lane_id)mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >inline
    BlockLoader(const device T *src_, const int src_ld_, threadgroup T *dst_, ushort simd_group_id, ushort simd_lane_id)mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >inline
    dstmlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    dstmlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    load_safe(short2 src_tile_dim) constmlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >inline
    load_safe(short2 src_tile_dim) constmlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >inline
    load_unsafe() constmlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >inline
    load_unsafe() constmlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >inline
    n_rowsmlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    next()mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >inline
    srcmlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    src_ldmlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    thread_idxmlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    tile_stridemlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    vec_sizemlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    next()mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >inline
    srcmlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    src_ldmlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    thread_idxmlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    tile_stridemlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    vec_sizemlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >
    diff --git a/docs/build/html/structmlx_1_1steel_1_1_block_loader.html b/docs/build/html/structmlx_1_1steel_1_1_block_loader.html index 9eb13677..e66ce3ab 100644 --- a/docs/build/html/structmlx_1_1steel_1_1_block_loader.html +++ b/docs/build/html/structmlx_1_1steel_1_1_block_loader.html @@ -97,7 +97,7 @@ $(function(){ initResizable(false); });
    -

    #include <loader.h>

    +

    #include <loader.h>

    @@ -117,6 +117,17 @@ Public Member Functions + + + + + + + + + + +

    Classes

     
    METAL_FUNC void next ()
     
    METAL_FUNC BlockLoader (const device T *src_, const int src_ld_, threadgroup T *dst_, ushort simd_group_id, ushort simd_lane_id)
     
    template<typename UnaryOp >
    METAL_FUNC void apply_inplace_op (thread const UnaryOp &op) const
     
    METAL_FUNC void load_unsafe () const
     
    METAL_FUNC void load_safe (short2 src_tile_dim) const
     
    METAL_FUNC void next ()
     
    @@ -134,14 +145,60 @@ Public Attributes - - - - + + + +

    Public Attributes

     
    const short bj
     
    threadgroup T * dst
     
    const device T * src
     
    threadgroup T * dst
     
    const device T * src
     

    Constructor & Destructor Documentation

    -

    ◆ BlockLoader()

    +

    ◆ BlockLoader() [1/2]

    + +
    +
    +
    +template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction_dim, short tgp_size, short alignment = 1, short n_reads = (BCOLS * BROWS) / (tgp_size), short TCOLS = BCOLS / n_reads, short TROWS = tgp_size / TCOLS>
    + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + +
    METAL_FUNC mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >::BlockLoader (const device T * src_,
    const int src_ld_,
    threadgroup T * dst_,
    ushort simd_group_id,
    ushort simd_lane_id )
    +     		+inline
    +
    + +
    +
    + +

    ◆ BlockLoader() [2/2]

    @@ -188,7 +245,36 @@ template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction

    Member Function Documentation

    -

    ◆ apply_inplace_op()

    +

    ◆ apply_inplace_op() [1/2]

    + +
    +
    +
    +template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction_dim, short tgp_size, short alignment = 1, short n_reads = (BCOLS * BROWS) / (tgp_size), short TCOLS = BCOLS / n_reads, short TROWS = tgp_size / TCOLS>
    +
    +template<typename UnaryOp >
    + + + + + +
    + + + + + + + +
    METAL_FUNC void mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >::apply_inplace_op (thread const UnaryOp & op) const
    +     		+inline
    +
    + +
    +
    + +

    ◆ apply_inplace_op() [2/2]

    @@ -217,7 +303,34 @@ template<typename UnaryOp >
    -

    ◆ load_safe()

    +

    ◆ load_safe() [1/2]

    + +
    +
    +
    +template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction_dim, short tgp_size, short alignment = 1, short n_reads = (BCOLS * BROWS) / (tgp_size), short TCOLS = BCOLS / n_reads, short TROWS = tgp_size / TCOLS>
    + + + + + +
    + + + + + + + +
    METAL_FUNC void mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >::load_safe (short2 src_tile_dim) const
    +     		+inline
    +
    + +
    +
    + +

    ◆ load_safe() [2/2]

    @@ -244,7 +357,34 @@ template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction
    -

    ◆ load_unsafe()

    +

    ◆ load_unsafe() [1/2]

    + +
    +
    +
    +template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction_dim, short tgp_size, short alignment = 1, short n_reads = (BCOLS * BROWS) / (tgp_size), short TCOLS = BCOLS / n_reads, short TROWS = tgp_size / TCOLS>
    + + + + + +
    + + + + + + + +
    METAL_FUNC void mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >::load_unsafe () const
    +     		+inline
    +
    + +
    +
    + +

    ◆ load_unsafe() [2/2]

    @@ -271,7 +411,34 @@ template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction
    -

    ◆ next()

    +

    ◆ next() [1/2]

    + +
    +
    +
    +template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction_dim, short tgp_size, short alignment = 1, short n_reads = (BCOLS * BROWS) / (tgp_size), short TCOLS = BCOLS / n_reads, short TROWS = tgp_size / TCOLS>
    + + + + + +
    + + + + + + + +
    METAL_FUNC void mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >::next ()
    +     		+inline
    +
    + +
    +
    + +

    ◆ next() [2/2]

    @@ -330,8 +497,8 @@ template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction
    - -

    ◆ dst

    + +

    ◆ dst

    @@ -339,7 +506,7 @@ template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction_dim, short tgp_size, short alignment = 1, short n_reads = (BCOLS * BROWS) / (tgp_size), short TCOLS = BCOLS / n_reads, short TROWS = tgp_size / TCOLS>
    - +
    threadgroup T* mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >::dstthreadgroup T * mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >::dst
    @@ -362,8 +529,8 @@ template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction
    - -

    ◆ src

    + +

    ◆ src

    @@ -371,7 +538,7 @@ template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction_dim, short tgp_size, short alignment = 1, short n_reads = (BCOLS * BROWS) / (tgp_size), short TCOLS = BCOLS / n_reads, short TROWS = tgp_size / TCOLS>
    - +
    const device T* mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >::srcconst device T * mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >::src
    @@ -442,7 +609,8 @@ template<typename T , short BROWS, short BCOLS, short dst_ld, short reduction
    -
    The documentation for this struct was generated from the following file:
    - +
    uint8_t mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >::ReadVector::v[sizeof(T) *vec_size]uint8_t mlx::steel::BlockLoader< T, BROWS, BCOLS, dst_ld, reduction_dim, tgp_size, alignment, n_reads, TCOLS, TROWS >::ReadVector::v
    -
    The documentation for this struct was generated from the following file: @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/usage/lazy_evaluation.html b/docs/build/html/usage/lazy_evaluation.html index da2b8f68..6b991ad7 100644 --- a/docs/build/html/usage/lazy_evaluation.html +++ b/docs/build/html/usage/lazy_evaluation.html @@ -8,7 +8,7 @@ - Lazy Evaluation — MLX 0.20.0 documentation + Lazy Evaluation — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/usage/numpy.html b/docs/build/html/usage/numpy.html index 6593dc81..d8073873 100644 --- a/docs/build/html/usage/numpy.html +++ b/docs/build/html/usage/numpy.html @@ -8,7 +8,7 @@ - Conversion to NumPy and Other Frameworks — MLX 0.20.0 documentation + Conversion to NumPy and Other Frameworks — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/usage/quick_start.html b/docs/build/html/usage/quick_start.html index 5639e9f1..6c87d32f 100644 --- a/docs/build/html/usage/quick_start.html +++ b/docs/build/html/usage/quick_start.html @@ -8,7 +8,7 @@ - Quick Start Guide — MLX 0.20.0 documentation + Quick Start Guide — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/usage/saving_and_loading.html b/docs/build/html/usage/saving_and_loading.html index 7e44b7a8..89573984 100644 --- a/docs/build/html/usage/saving_and_loading.html +++ b/docs/build/html/usage/saving_and_loading.html @@ -8,7 +8,7 @@ - Saving and Loading Arrays — MLX 0.20.0 documentation + Saving and Loading Arrays — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/usage/unified_memory.html b/docs/build/html/usage/unified_memory.html index 1c7207dd..c32b609f 100644 --- a/docs/build/html/usage/unified_memory.html +++ b/docs/build/html/usage/unified_memory.html @@ -8,7 +8,7 @@ - Unified Memory — MLX 0.20.0 documentation + Unified Memory — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/usage/using_streams.html b/docs/build/html/usage/using_streams.html index fb91ff46..25c3fdef 100644 --- a/docs/build/html/usage/using_streams.html +++ b/docs/build/html/usage/using_streams.html @@ -8,7 +8,7 @@ - Using Streams — MLX 0.20.0 documentation + Using Streams — MLX 0.21.0 documentation @@ -39,7 +39,7 @@ - + @@ -51,7 +51,7 @@ - + @@ -130,8 +130,8 @@ - MLX 0.20.0 documentation - Home - MLX 0.20.0 documentation - Home + MLX 0.21.0 documentation - Home + MLX 0.21.0 documentation - Home @@ -444,7 +444,6 @@
  • mlx.core.fast.layer_norm
  • mlx.core.fast.rope
  • mlx.core.fast.scaled_dot_product_attention
    • -
  • mlx.core.fast.affine_quantize
  • mlx.core.fast.metal_kernel
    • @@ -521,6 +520,7 @@
  • mlx.nn.ALiBi
  • mlx.nn.AvgPool1d
  • mlx.nn.AvgPool2d
    • +
  • mlx.nn.AvgPool3d
  • mlx.nn.BatchNorm
  • mlx.nn.CELU
  • mlx.nn.Conv1d
    • @@ -550,6 +550,7 @@
  • mlx.nn.LSTM
  • mlx.nn.MaxPool1d
  • mlx.nn.MaxPool2d
    • +
  • mlx.nn.MaxPool3d
  • mlx.nn.Mish
  • mlx.nn.MultiHeadAttention
  • mlx.nn.PReLU
    • diff --git a/docs/build/html/utils_8h.html b/docs/build/html/utils_8h.html index 758422d8..228c93a0 100644 --- a/docs/build/html/utils_8h.html +++ b/docs/build/html/utils_8h.html @@ -118,6 +118,8 @@ Namespaces   namespace  mlx::core   +namespace  mlx::core::env +  @@ -166,12 +168,18 @@ Functions - + + + + + + +

    Typedefs

     
    std::ostream & mlx::core::operator<< (std::ostream &os, const float16_t &v)
     
    std::ostream & mlx::core::operator<< (std::ostream &os, const bfloat16_t &v)
    std::ostream & mlx::core::operator<< (std::ostream &os, const bfloat16_t &v)
     
    bool mlx::core::is_power_of_2 (int n)
     
    int mlx::core::next_power_of_2 (int n)
     
    int mlx::core::env::get_var (const char *name, int default_value)
     
    int mlx::core::env::bfs_max_width ()
     
    int mlx::core::env::max_ops_per_buffer ()
     
    diff --git a/docs/build/html/utils_8h_source.html b/docs/build/html/utils_8h_source.html index edd6e218..22d36ebf 100644 --- a/docs/build/html/utils_8h_source.html +++ b/docs/build/html/utils_8h_source.html @@ -230,7 +230,29 @@ $(function(){ initResizable(false); });
    121}
    122
    -
    123} // namespace mlx::core
    +
    +
    123namespace env {
    +
    124
    +
    125int get_var(const char* name, int default_value);
    +
    126
    +
    +
    127inline int bfs_max_width() {
    +
    128 static int bfs_max_width_ = get_var("MLX_BFS_MAX_WIDTH", 20);
    +
    129 return bfs_max_width_;
    +
    130}
    +
    +
    131
    +
    +
    132inline int max_ops_per_buffer() {
    +
    133 static int max_ops_per_buffer_ = get_var("MLX_MAX_OPS_PER_BUFFER", 10);
    +
    134 return max_ops_per_buffer_;
    +
    135}
    +
    +
    136
    +
    137} // namespace env
    +
    +
    138
    +
    139} // namespace mlx::core
    mlx::core::array
    Definition array.h:20
    mlx::core::array::dtype
    Dtype dtype() const
    Get the arrays data type.
    Definition array.h:127
    @@ -239,6 +261,9 @@ $(function(){ initResizable(false); });
    mlx::core::max
    array max(const array &a, bool keepdims, StreamOrDevice s={})
    The maximum of all elements of the array.
    mlx::core::min
    array min(const array &a, bool keepdims, StreamOrDevice s={})
    The minimum of all elements of the array.
    mlx::core::operator<<
    array operator<<(const array &a, const array &b)
    +
    mlx::core::env::get_var
    int get_var(const char *name, int default_value)
    +
    mlx::core::env::bfs_max_width
    int bfs_max_width()
    Definition utils.h:127
    +
    mlx::core::env::max_ops_per_buffer
    int max_ops_per_buffer()
    Definition utils.h:132
    mlx::core
    Definition allocator.h:7
    mlx::core::normalize_axis
    int normalize_axis(int axis, int ndim)
    Returns the axis normalized to be in the range [0, ndim).
    mlx::core::default_device
    const Device & default_device()
    @@ -256,7 +281,7 @@ $(function(){ initResizable(false); });
    mlx::core::set_default_stream
    void set_default_stream(Stream s)
    Make the stream the default for its device.
    mlx::core::global_formatter
    PrintFormatter global_formatter
    -
    _MLX_BFloat16
    Definition bf16.h:54
    +
    _MLX_BFloat16
    Definition bf16.h:48
    mlx::core::_MLX_Float16
    Definition fp16.h:21
    mlx::core::Device
    Definition device.h:7
    mlx::core::Dtype
    Definition dtype.h:13

    Variables