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https://github.com/ml-explore/mlx.git
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446 lines
17 KiB
Python
446 lines
17 KiB
Python
import unittest
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from itertools import permutations
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import math
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import mlx.core as mx
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import numpy as np
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import mlx_tests
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class TestBlas(mlx_tests.MLXTestCase):
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@property
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def dtypes(self):
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return ["float32", "float16"] if mx.metal.is_available() else ["float32"]
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def __gemm_test(
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self,
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shape_a,
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shape_b,
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np_dtype=np.float32,
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f_np_a=lambda x: x,
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f_np_b=lambda x: x,
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f_mx_a=lambda x: x,
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f_mx_b=lambda x: x,
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):
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with self.subTest(
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dtype=np.dtype(np_dtype).name, shape_a=shape_a, shape_b=shape_b
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):
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np.random.seed(42)
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scale = max(np.sum(shape_a), 128)
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a_np = np.random.normal(0.0, 1.0 / scale, shape_a).astype(np_dtype)
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b_np = np.random.normal(0.0, 1.0 / scale, shape_b).astype(np_dtype)
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a_mx = mx.array(a_np)
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b_mx = mx.array(b_np)
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a_np = f_np_a(a_np.astype(np.float32))
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b_np = f_np_b(b_np.astype(np.float32))
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a_mx = f_mx_a(a_mx)
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b_mx = f_mx_b(b_mx)
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out_npy = a_np @ b_np
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out_mlx = a_mx @ b_mx
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self.assertListEqual(list(out_npy.shape), list(out_mlx.shape))
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self.assertTrue(np.allclose(out_mlx, out_npy.astype(np_dtype), atol=1e-5))
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def test_matmul_unaligned(self):
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if not mx.metal.is_available():
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return
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for dtype in self.dtypes:
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np_dtype = getattr(np, dtype)
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base_shapes = [4, 8, 16, 32, 64, 128]
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pertubations = [-2, -1, 0, 1, 2]
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for dim in base_shapes:
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for p in pertubations:
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shape_a = (dim + p, dim + p)
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shape_b = (dim + p, dim + p)
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self.__gemm_test(shape_a, shape_b, np_dtype)
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def test_matmul_shapes(self):
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if not mx.metal.is_available():
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return
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shapes = [
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(1, 2, 1, 1),
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(1, 1, 2, 1),
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(3, 23, 457, 3),
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]
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if mx.default_device() == mx.gpu:
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shapes += [
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(16, 768, 768, 128),
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]
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for dtype in self.dtypes:
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np_dtype = getattr(np, dtype)
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for B, M, N, K in shapes:
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with self.subTest(tranpose="nn"):
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shape_a = (B, M, K)
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shape_b = (B, K, N)
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self.__gemm_test(shape_a, shape_b, np_dtype)
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with self.subTest(tranpose="nt"):
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shape_a = (B, M, K)
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shape_b = (B, N, K)
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self.__gemm_test(
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shape_a,
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shape_b,
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np_dtype,
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f_np_b=lambda x: np.transpose(x, (0, 2, 1)),
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f_mx_b=lambda x: mx.transpose(x, (0, 2, 1)),
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)
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with self.subTest(tranpose="tn"):
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shape_a = (B, K, M)
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shape_b = (B, K, N)
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self.__gemm_test(
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shape_a,
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shape_b,
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np_dtype,
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f_np_a=lambda x: np.transpose(x, (0, 2, 1)),
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f_mx_a=lambda x: mx.transpose(x, (0, 2, 1)),
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)
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with self.subTest(tranpose="tt"):
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shape_a = (B, K, M)
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shape_b = (B, N, K)
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self.__gemm_test(
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shape_a,
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shape_b,
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np_dtype,
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f_np_a=lambda x: np.transpose(x, (0, 2, 1)),
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f_mx_a=lambda x: mx.transpose(x, (0, 2, 1)),
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f_np_b=lambda x: np.transpose(x, (0, 2, 1)),
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f_mx_b=lambda x: mx.transpose(x, (0, 2, 1)),
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)
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def test_matmul(self):
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# Note: so far, matmul only works with floating-point types
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a = mx.array([[1.0, 2.0], [3.0, 4.0]])
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b = mx.array([[0.0, -1.0], [-3.0, 3.0]])
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expected = [[-6.0, 5.0], [-12.0, 9.0]]
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self.assertEqual((a @ b).tolist(), expected)
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self.assertEqual(mx.matmul(a, b).tolist(), expected)
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# Transposed matmul
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np.random.seed(0)
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a_npy = np.random.normal(0.0, 1.0 / 128, (128, 16)).astype(np.float32)
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b_npy = np.random.normal(0.0, 1.0 / 128, (128, 16)).astype(np.float32)
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c_npy = a_npy @ np.transpose(b_npy, (1, 0))
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d_npy = np.transpose(a_npy, (1, 0)) @ b_npy
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a_mlx = mx.array(a_npy)
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b_mlx = mx.array(b_npy)
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c_mlx = a_mlx @ mx.transpose(b_mlx, (1, 0))
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d_mlx = mx.transpose(a_mlx, (1, 0)) @ b_mlx
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self.assertListEqual(list(c_npy.shape), list(c_mlx.shape))
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self.assertListEqual(list(d_npy.shape), list(d_mlx.shape))
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self.assertTrue(np.allclose(c_mlx, c_npy, atol=1e-6))
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self.assertTrue(np.allclose(d_mlx, d_npy, atol=1e-6))
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def test_matmul_dtypes(self):
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for dt in self.dtypes:
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a_npy = np.random.normal(0.0, 1.0 / 256, (16, 16, 16)).astype(
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getattr(np, dt)
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)
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b_npy = np.random.normal(0.0, 1.0 / 256, (16, 16, 16)).astype(
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getattr(np, dt)
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)
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a_mlx = mx.array(a_npy)
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b_mlx = mx.array(b_npy)
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c_npy = np.matmul(a_npy, b_npy, dtype=getattr(np, dt))
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c_mlx = a_mlx @ b_mlx
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self.assertTrue(np.allclose(c_mlx, c_npy, atol=1e-6))
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def test_matmul_batched(self):
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np.random.seed(0)
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# Batched matmul
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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, (32, 16, 16)).astype(np.float32)
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c_npy = a_npy @ b_npy
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a_mlx = mx.array(a_npy)
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b_mlx = mx.array(b_npy)
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c_mlx = a_mlx @ b_mlx
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self.assertListEqual(list(c_npy.shape), list(c_mlx.shape))
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self.assertTrue(np.allclose(c_mlx, c_npy, atol=1e-6))
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# Batched and transposed matmul
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b_npy = np.random.normal(0.0, 1.0 / 128, (32, 128, 16)).astype(np.float32)
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c_npy = a_npy @ np.transpose(b_npy, (0, 2, 1))
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b_mlx = mx.array(b_npy)
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c_mlx = a_mlx @ mx.transpose(b_mlx, (0, 2, 1))
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self.assertListEqual(list(c_npy.shape), list(c_mlx.shape))
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self.assertTrue(np.allclose(c_mlx, c_npy, atol=1e-6))
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# Batched matmul with simple broadast
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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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c_npy = a_npy @ b_npy
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a_mlx = mx.array(a_npy)
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b_mlx = mx.array(b_npy)
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c_mlx = a_mlx @ b_mlx
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self.assertListEqual(list(c_npy.shape), list(c_mlx.shape))
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self.assertTrue(np.allclose(c_mlx, c_npy, atol=1e-6))
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# Both operands broadcasted
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d_npy = np.broadcast_to(b_npy, (5, 16, 16))
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d_mlx = mx.broadcast_to(b_mlx, (5, 16, 16))
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e_npy = d_npy @ d_npy
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e_mlx = d_mlx @ d_mlx
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self.assertListEqual(list(e_npy.shape), list(e_mlx.shape))
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self.assertTrue(np.allclose(e_mlx, e_npy, atol=1e-6))
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# Batched and transposed matmul with simple broadast
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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, (128, 16)).astype(np.float32)
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a_mlx = mx.array(a_npy)
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b_mlx = mx.array(b_npy)
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c_npy = a_npy @ np.transpose(b_npy, (1, 0))
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c_mlx = a_mlx @ mx.transpose(b_mlx, (1, 0))
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self.assertListEqual(list(c_npy.shape), list(c_mlx.shape))
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self.assertTrue(np.allclose(c_mlx, c_npy, atol=1e-6))
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# Matmul with vector
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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,)).astype(np.float32)
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a_mlx = mx.array(a_npy)
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b_mlx = mx.array(b_npy)
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c_npy = a_npy @ b_npy
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c_mlx = a_mlx @ b_mlx
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self.assertListEqual(list(c_npy.shape), list(c_mlx.shape))
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self.assertTrue(np.allclose(c_mlx, c_npy, atol=1e-6))
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# Test Multiheaded attention style matmul
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a_npy = np.random.normal(0.0, 1.0 / 128, (64, 16, 4, 32)).astype(np.float32)
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b_npy = np.random.normal(0.0, 1.0 / 128, (64, 16, 4, 32)).astype(np.float32)
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a_mlx = mx.array(a_npy)
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b_mlx = mx.array(b_npy)
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a_npy = np.transpose(a_npy, (0, 2, 1, 3))
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b_npy = np.transpose(b_npy, (0, 2, 1, 3))
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a_mlx = mx.transpose(a_mlx, (0, 2, 1, 3))
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b_mlx = mx.transpose(b_mlx, (0, 2, 1, 3))
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c_npy = a_npy @ np.transpose(b_npy, (0, 1, 3, 2))
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c_mlx = a_mlx @ mx.transpose(b_mlx, (0, 1, 3, 2))
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self.assertListEqual(list(c_npy.shape), list(c_mlx.shape))
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self.assertTrue(np.allclose(c_mlx, c_npy, atol=1e-6))
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def __gemv_test(
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self,
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shape_mat,
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shape_vec,
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np_dtype=np.float32,
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mat_first=True,
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np_mat_f=lambda x: x,
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np_vec_f=lambda x: x,
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mlx_mat_f=lambda x: x,
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mlx_vec_f=lambda x: x,
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):
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with self.subTest(shape=shape_mat):
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np.random.seed(42)
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scale = max(np.sum(shape_mat), 32)
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mat_npy = np.random.normal(0.0, 1.0 / scale, shape_mat).astype(np_dtype)
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vec_npy = np.random.normal(0.0, 1.0 / scale, shape_vec).astype(np_dtype)
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mat_mlx = mx.array(mat_npy)
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vec_mlx = mx.array(vec_npy)
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mat_npy = np_mat_f(mat_npy)
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vec_npy = np_vec_f(vec_npy)
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mat_mlx = mlx_mat_f(mat_mlx)
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vec_mlx = mlx_vec_f(vec_mlx)
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if mat_first:
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out_npy = mat_npy @ vec_npy
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out_mlx = mat_mlx @ vec_mlx
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else:
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out_npy = vec_npy @ mat_npy
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out_mlx = vec_mlx @ mat_mlx
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self.assertListEqual(list(out_npy.shape), list(out_mlx.shape))
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self.assertTrue(np.allclose(out_mlx, out_npy, atol=1e-5))
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def test_matrix_vector(self):
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for dtype in self.dtypes:
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with self.subTest(dtype=dtype):
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np_dtype = getattr(np, dtype)
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# Basic square matrix test
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self.__gemv_test(
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shape_mat=(64, 64), shape_vec=(64, 1), np_dtype=np_dtype
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)
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self.__gemv_test(
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shape_mat=(64, 64),
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shape_vec=(64, 1),
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np_dtype=np_dtype,
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mat_first=False,
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np_vec_f=lambda x: np.transpose(x, (1, 0)),
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mlx_vec_f=lambda x: mx.transpose(x, (1, 0)),
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)
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# Vector matrix product with aligned and unaligned shapes
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for in_len_base, out_len_base in (
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(2, 2),
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(32, 32),
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(64, 64),
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(2048, 2048),
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):
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for mi in (-1, 0, 1):
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for mj in (-1, 0, 1):
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# Vec mat
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shape_mat = (in_len_base + mi, out_len_base + mj)
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shape_vec = (1, in_len_base + mi)
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self.__gemv_test(
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shape_mat, shape_vec, mat_first=False, np_dtype=np_dtype
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)
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# Mat vec
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shape_mat = (out_len_base + mj, in_len_base + mi)
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shape_vec = (in_len_base + mi, 1)
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self.__gemv_test(
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shape_mat, shape_vec, mat_first=True, np_dtype=np_dtype
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)
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def test_matrix_vector_batched(self):
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for dtype in self.dtypes:
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with self.subTest(dtype=dtype):
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np_dtype = getattr(np, dtype)
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# Batched mat vec
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for shape_mat, shape_vec in (
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((32, 128, 64), (32, 64, 1)),
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((128, 64), (32, 64, 1)),
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((32, 128, 64), (64, 1)),
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):
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self.__gemv_test(
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shape_mat, shape_vec, mat_first=True, np_dtype=np_dtype
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)
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# Batched vec mat
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for shape_vec, shape_mat in (
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((32, 1, 128), (32, 128, 64)),
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((32, 1, 128), (128, 64)),
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((1, 128), (32, 128, 64)),
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):
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self.__gemv_test(
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shape_mat, shape_vec, mat_first=False, np_dtype=np_dtype
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)
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def test_matrix_vector_broadcast(self):
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for dtype in self.dtypes:
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with self.subTest(dtype=dtype):
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np_dtype = getattr(np, dtype)
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# Different broadcasts mat vec
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for shape_mat, shape_vec in (
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((32, 64, 64), (32, 64, 1)),
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((64, 64), (32, 64, 1)),
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((32, 64, 64), (64, 1)),
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):
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self.__gemv_test(
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shape_mat=(64, 64),
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shape_vec=(64, 1),
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np_dtype=np_dtype,
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np_mat_f=(lambda mat_npy: np.broadcast_to(mat_npy, shape_mat)),
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np_vec_f=(lambda vec_npy: np.broadcast_to(vec_npy, shape_vec)),
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mlx_mat_f=(lambda mat_mlx: mx.broadcast_to(mat_mlx, shape_mat)),
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mlx_vec_f=(lambda vec_mlx: mx.broadcast_to(vec_mlx, shape_vec)),
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)
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# Different broadcasts vec mat
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for shape_vec, shape_mat in (
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((32, 1, 64), (32, 64, 64)),
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((32, 1, 64), (64, 64)),
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((1, 64), (32, 64, 64)),
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):
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self.__gemv_test(
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shape_mat=(64, 64),
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shape_vec=(1, 64),
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np_dtype=np_dtype,
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mat_first=False,
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np_mat_f=lambda mat_npy: np.broadcast_to(mat_npy, shape_mat),
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np_vec_f=lambda vec_npy: np.broadcast_to(vec_npy, shape_vec),
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mlx_mat_f=lambda mat_mlx: mx.broadcast_to(mat_mlx, shape_mat),
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mlx_vec_f=lambda vec_mlx: mx.broadcast_to(vec_mlx, shape_vec),
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)
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def test_matrix_vector_edgecases(self):
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for dtype in self.dtypes:
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with self.subTest(dtype=dtype):
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np_dtype = getattr(np, dtype)
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for in_vec_len in np.arange(1, 5):
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for out_vec_len in np.arange(1, 5):
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for batch_size in np.arange(1, 5):
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with self.subTest(
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problem_shape=(batch_size, in_vec_len, out_vec_len)
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):
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# Matrix vector
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with self.subTest(transpose=False):
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a_npy = np.ones(
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(batch_size, out_vec_len, in_vec_len),
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dtype=np_dtype,
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)
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b_npy = np.ones(
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(batch_size, in_vec_len, 1), dtype=np_dtype
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)
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for i in range(batch_size):
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b_npy[i] *= i + 1.0
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a_mlx, b_mlx = map(mx.array, [a_npy, b_npy])
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c_npy = a_npy @ b_npy
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c_mlx = a_mlx @ b_mlx
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self.assertListEqual(
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list(c_npy.shape), list(c_mlx.shape)
|
|
)
|
|
self.assertTrue(np.array_equal(c_mlx, c_npy))
|
|
|
|
# Vector matrix
|
|
with self.subTest(transpose=True):
|
|
a_npy = np.ones(
|
|
(batch_size, out_vec_len, in_vec_len),
|
|
dtype=np_dtype,
|
|
)
|
|
b_npy = np.ones(
|
|
(batch_size, 1, out_vec_len), dtype=np_dtype
|
|
)
|
|
for i in range(batch_size):
|
|
b_npy[i] *= i + 1.0
|
|
|
|
a_mlx, b_mlx = map(mx.array, [a_npy, b_npy])
|
|
c_npy = b_npy @ a_npy
|
|
c_mlx = b_mlx @ a_mlx
|
|
|
|
self.assertListEqual(
|
|
list(c_npy.shape), list(c_mlx.shape)
|
|
)
|
|
self.assertTrue(np.array_equal(c_mlx, c_npy))
|