Option to JIT steel gemm / conv (#1139)
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-3
@@ -3968,9 +3968,9 @@ void init_ops(nb::module_& m) {
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a (array): Input array or scalar.
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b (array): Input array or scalar.
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block_size (int): Size of blocks to be masked. Must be ``32`` or ``64`` (default: ``64``)
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mask_out (array, optional): Boolean mask for output (default: ``None``)
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mask_lhs (array, optional): Boolean mask for a (default: ``None``)
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mask_rhs (array, optional): Boolean mask for b (default: ``None``)
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mask_out (array, optional): Mask for output (default: ``None``)
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mask_lhs (array, optional): Mask for a (default: ``None``)
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mask_rhs (array, optional): Mask for b (default: ``None``)
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)pbdoc");
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m.def(
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@@ -556,8 +556,7 @@ class TestBlas(mlx_tests.MLXTestCase):
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self.assertListEqual(list(d_npy.shape), list(d_mlx.shape))
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self.assertTrue(np.allclose(d_mlx, d_npy, atol=1e-5))
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# # Batched matmul with simple broadcast
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# Batched matmul with simple broadcast
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a_npy = np.random.normal(0.0, 1.0 / 128, (32, 128, 16)).astype(np.float32)
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b_npy = np.random.normal(0.0, 1.0 / 128, (16, 16)).astype(np.float32)
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@@ -573,7 +572,6 @@ class TestBlas(mlx_tests.MLXTestCase):
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self.assertListEqual(list(d_npy.shape), list(d_mlx.shape))
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self.assertTrue(np.allclose(d_mlx, d_npy, atol=1e-5))
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# Matmul with vector
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a_npy = np.random.normal(0.0, 1.0 / 128, (16,)).astype(np.float32)
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b_npy = np.random.normal(0.0, 1.0 / 128, (32, 16, 128)).astype(np.float32)
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