Add LpB kernel patches for Qwen3.5 dense models (27B, 9B)
Loop-over-B custom GEMV kernels for expanding projections (N > K): gate_proj, up_proj, down_proj, in_proj_qkv, in_proj_z, out_proj, q_proj. These reduce S>1 verification cost from ~7ms/token to ~3ms/token, critical for speculative decoding speedup. Auto-detected for model_type=qwen3_5 (dense models like 27B, 9B). MoE models (qwen3_5_moe) use the existing batched fused patches instead. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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dmcc73
@@ -37,3 +37,9 @@ def maybe_apply_patches(model: nn.Module, model_path: Path) -> None:
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logger.info("Detected Qwen3.5 MoE model, applying batched fused kernel patches")
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apply_qwen35_batched_fused_patches(model)
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elif model_type == "qwen3_5":
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from .qwen3_5.lpb_patch import apply_lpb_patches
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logger.info("Detected Qwen3.5 dense model, applying LpB kernel patches")
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apply_lpb_patches(model, batch_size=4)
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@@ -0,0 +1,135 @@
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#!/usr/bin/env python3
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"""Isolated loop-over-B GEMV kernel for quantized matmul.
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Extracts the loop-over-B pattern from batched_fused_gdn_projections_8bit
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but without any epilogues — pure Y = X @ dequant(W)^T output.
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For comparing our GEMV approach against MLX's affine_qmv_fast on
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an isolated QuantizedLinear operation (e.g., in_proj_qkv: N=8192, K=2048).
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TG: (32, 2, 1) = 64 threads = 2 SGs.
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Each SG: 4 output rows.
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B loop inside row loop for low register pressure (R = 4B + 5).
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Usage:
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from custom_qmv_loop_over_b import custom_qmv_loop_over_b
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y = custom_qmv_loop_over_b(x, w, scales, biases, M=8, N=8192, K=2048)
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"""
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import mlx.core as mx
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def ceil_div(a, b):
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return (a + b - 1) // b
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def _gen_custom_qmv_source(M_val, N_val, K_val, group_size=64):
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gs = group_size
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sc_stride = 256 // gs
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slid_div = gs // 8
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K_groups = K_val // gs
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B = M_val # batch size = M
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return f"""
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const int RESULTS_PER_SG = 4;
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const int VALUES_PER_THREAD = 8;
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const int BLOCK_SIZE = 256;
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const int K = {K_val};
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const int N = {N_val};
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const int M = {M_val};
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const int K_groups = {K_groups};
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const int SC_STRIDE = {sc_stride};
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const int SLID_DIV = {slid_div};
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uint3 tgid = threadgroup_position_in_grid;
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uint sgid = simdgroup_index_in_threadgroup;
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uint slid = thread_index_in_simdgroup;
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int tg = tgid.y;
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int out_row = tg * 8 + sgid * RESULTS_PER_SG;
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if (out_row >= N) return;
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// Weight pointers
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const device uint8_t* ws = (const device uint8_t*)w + (long)out_row * K + slid * VALUES_PER_THREAD;
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const device bfloat16_t* sc = (const device bfloat16_t*)scales + (long)out_row * K_groups + slid / SLID_DIV;
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const device bfloat16_t* bi = (const device bfloat16_t*)biases + (long)out_row * K_groups + slid / SLID_DIV;
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// Result accumulators: 4 rows × B batches
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float result[{4 * B}];
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for (int i = 0; i < {4 * B}; i++) result[i] = 0;
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int x_base = slid * VALUES_PER_THREAD;
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// K-loop: loop over B inside row loop
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for (int k_off = 0; k_off < K; k_off += BLOCK_SIZE) {{
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for (int row = 0; row < RESULTS_PER_SG; row++) {{
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const device uint8_t* wl = ws + row * K;
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float s_val = float(sc[row * K_groups]);
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float b_val = float(bi[row * K_groups]);
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for (int b = 0; b < {B}; b++) {{
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float accum = 0, xsum = 0;
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for (int i = 0; i < VALUES_PER_THREAD; i++) {{
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float xi = float(((const device bfloat16_t*)x)[b * K + x_base + i]);
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accum += xi * float(wl[i]);
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xsum += xi;
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}}
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result[b * 4 + row] += s_val * accum + xsum * b_val;
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}}
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}}
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ws += BLOCK_SIZE; sc += SC_STRIDE; bi += SC_STRIDE; x_base += BLOCK_SIZE;
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}}
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// Reduction
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for (int i = 0; i < {4 * B}; i++) result[i] = simd_sum(result[i]);
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// Write output (bf16)
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if (slid < 4u) {{
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for (int b = 0; b < {B}; b++) {{
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int r = out_row + (int)slid;
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if (r < N) {{
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y[b * N + r] = static_cast<bfloat16_t>(result[b * 4 + slid]);
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}}
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}}
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}}
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"""
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_custom_qmv_cache = {}
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def custom_qmv_loop_over_b(x, w, scales, biases, M, N, K, group_size=64):
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"""Loop-over-B GEMV for quantized matmul.
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Args:
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x: (M, K) bfloat16 input
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w: (N, K/4) uint32 packed 8-bit weights
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scales: (N, K/gs) bfloat16
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biases: (N, K/gs) bfloat16
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M, N, K: dimensions
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Returns:
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y: (M, N) bfloat16
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"""
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key = (M, N, K, group_size)
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if key not in _custom_qmv_cache:
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_custom_qmv_cache[key] = mx.fast.metal_kernel(
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name=f"custom_qmv_loop_b_M{M}_N{N}_K{K}",
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input_names=["x", "w", "scales", "biases"],
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output_names=["y"],
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source=_gen_custom_qmv_source(M, N, K, group_size),
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)
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kern = _custom_qmv_cache[key]
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n_tg = ceil_div(N, 8)
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result = kern(
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inputs=[x, w, scales, biases],
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output_shapes=[(M * N,)],
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output_dtypes=[mx.bfloat16],
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grid=(32, n_tg * 2, 1),
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threadgroup=(32, 2, 1),
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)
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return result[0].reshape(M, N)
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@@ -0,0 +1,108 @@
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#!/usr/bin/env python3
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"""Loop-over-B patches for Qwen3.5-27B dense model.
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Replaces vanilla QuantizedLinear calls with custom loop-over-B GEMV
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for projections where N > K (expanding projections). Falls back to
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vanilla for N <= K (contracting projections like down_proj, o_proj).
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Usage:
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from lpb_patch import apply_lpb_patches
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apply_lpb_patches(model, batch_size=4)
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"""
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import mlx.core as mx
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import mlx.nn as nn
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from .custom_qmv_loop_over_b import custom_qmv_loop_over_b
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def _make_lpb_forward(original_module, N, K, BS, GS=64):
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"""Create a patched forward that uses loop-over-B."""
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w = original_module.weight
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s = original_module.scales
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b = original_module.biases
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MAX_M = 16 # Max total tokens (B*S) for custom kernel; above this use vanilla
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def forward(self_unused, x):
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# Use LpB for small M=B*S. Large prefill falls back to vanilla.
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M_total = 1
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for d in x.shape[:-1]:
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M_total *= d
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if M_total > MAX_M:
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return original_module(x)
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orig_shape = x.shape
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x_2d = x.reshape(-1, K)
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M = x_2d.shape[0]
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y = custom_qmv_loop_over_b(x_2d, w, s, b, M, N, K, GS)
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return y.reshape(*orig_shape[:-1], N)
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return forward
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def apply_lpb_patches(model, batch_size=4):
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"""Patch all expanding QuantizedLinear projections with loop-over-B.
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Only patches projections where N > K (expanding):
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- gate_proj, up_proj (17408 > 5120)
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- in_proj_qkv (10240 > 5120)
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- in_proj_z (6144 > 5120)
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- q_proj (12288 > 5120)
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Skips N <= K projections (down_proj, o_proj, k_proj, v_proj)
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where vanilla is already efficient.
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"""
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inner = getattr(model, 'model', None) or model.language_model.model
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patched = 0
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for li, layer in enumerate(inner.layers):
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# MLP: gate_proj, up_proj (N=17408, K=5120)
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mlp = layer.mlp
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for proj_name in ['gate_proj', 'up_proj', 'down_proj']:
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proj = getattr(mlp, proj_name)
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if isinstance(proj, nn.QuantizedLinear):
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N = proj.weight.shape[0] # output dim
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K_packed = proj.weight.shape[1]
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K = K_packed * 4 # 8-bit: 4 values per uint32
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setattr(mlp, proj_name, type('LpBLinear', (), {
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'__call__': _make_lpb_forward(proj, N, K, batch_size),
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'weight': proj.weight,
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'scales': proj.scales,
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'biases': proj.biases,
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})())
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patched += 1
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# Attention projections
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if layer.is_linear:
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attn = layer.linear_attn
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for proj_name in ['in_proj_qkv', 'in_proj_z', 'out_proj']:
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if hasattr(attn, proj_name):
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proj = getattr(attn, proj_name)
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if isinstance(proj, nn.QuantizedLinear):
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N = proj.weight.shape[0]
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K = proj.weight.shape[1] * 4
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setattr(attn, proj_name, type('LpBLinear', (), {
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'__call__': _make_lpb_forward(proj, N, K, batch_size),
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'weight': proj.weight,
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'scales': proj.scales,
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'biases': proj.biases,
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})())
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patched += 1
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else:
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attn = layer.self_attn
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for proj_name in ['q_proj', 'o_proj']:
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if hasattr(attn, proj_name):
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proj = getattr(attn, proj_name)
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if isinstance(proj, nn.QuantizedLinear):
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N = proj.weight.shape[0]
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K = proj.weight.shape[1] * 4
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setattr(attn, proj_name, type('LpBLinear', (), {
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'__call__': _make_lpb_forward(proj, N, K, batch_size),
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'weight': proj.weight,
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'scales': proj.scales,
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'biases': proj.biases,
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})())
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patched += 1
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print(f" Patched {patched} projections with loop-over-B")
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return patched
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