mirror of
https://github.com/ml-explore/mlx.git
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447 lines
15 KiB
Python
447 lines
15 KiB
Python
# Copyright © 2023 Apple Inc.
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import math
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import unittest
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from itertools import permutations
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import mlx.core as mx
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import mlx_tests
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import numpy as np
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try:
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import torch
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import torch.nn.functional as F
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has_torch = True
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except ImportError as e:
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has_torch = False
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class TestConv(mlx_tests.MLXTestCase):
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def test_numpy_conv(self):
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for dtype in (
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"float16",
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"float32",
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):
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np_dtype = getattr(np, dtype)
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for M, N, mode in (
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(1, 1, "full"),
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(25, 5, "full"),
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(24, 5, "same"),
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(24, 4, "same"),
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(24, 4, "valid"),
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(4, 24, "full"),
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(5, 25, "same"),
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(4, 25, "valid"),
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):
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with self.subTest(dtype=dtype, M=M, N=N, mode=mode):
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atol = 1e-6 if dtype == "float32" else 1e-5
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a_np = np.random.rand(M).astype(np_dtype)
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v_np = np.random.rand(N).astype(np_dtype)
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a_mx = mx.array(a_np)
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v_mx = mx.array(v_np)
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c_np = np.convolve(a_np, v_np, mode=mode)
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c_mx = mx.convolve(a_mx, v_mx, mode=mode)
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self.assertListEqual(list(c_mx.shape), list(c_np.shape))
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self.assertTrue(np.allclose(c_mx, c_np, atol=atol))
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@unittest.skipIf(not has_torch, "requires Torch")
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def test_torch_conv_1D(self):
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def run_conv1D(
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N,
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C,
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O,
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iH,
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kH,
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stride,
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padding,
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dilation=1,
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groups=1,
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dtype="float32",
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atol=1e-5,
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):
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with self.subTest(
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dtype=dtype,
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N=N,
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C=C,
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O=O,
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iH=iH,
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kH=kH,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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):
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np_dtype = getattr(np, dtype)
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np.random.seed(0)
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in_np = np.random.normal(0, 1.0 / C, (N, iH, C)).astype(np_dtype)
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wt_np = np.random.normal(0, 1.0 / C, (O, kH, C)).astype(np_dtype)
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in_mx, wt_mx = map(mx.array, (in_np, wt_np))
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in_pt, wt_pt = map(
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lambda x: torch.from_numpy(x.transpose(0, 2, 1)), (in_np, wt_np)
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)
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out_mx = mx.conv1d(
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in_mx,
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wt_mx,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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)
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out_pt = torch.conv1d(
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in_pt,
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wt_pt,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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)
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out_pt = torch.transpose(out_pt, 2, 1)
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self.assertListEqual(list(out_pt.shape), out_mx.shape)
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self.assertTrue(np.allclose(out_pt.numpy(), out_mx, atol=atol))
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for dtype in ("float32",):
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for N, C, O in (
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(1, 1, 1),
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(1, 6, 1),
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(1, 1, 6),
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(4, 32, 64),
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):
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for iH, kH, stride, padding in (
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(1, 1, 1, 0),
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(3, 3, 1, 0),
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(31, 5, 5, 2),
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):
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run_conv1D(N, C, O, iH, kH, stride, padding, dtype=dtype)
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# Strided inputs tests
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for tpose_in, tpose_wt in (
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((0, 2, 1), (0, 1, 2)),
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((0, 2, 1), (0, 2, 1)),
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):
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with self.subTest(name="strided", tpose_in=tpose_in, tpose_wt=tpose_wt):
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in_np = np.random.normal(0, 1.0 / 16, (16, 16, 16)).astype(np.float32)
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wt_np = np.random.normal(0, 1.0 / 16, (16, 16, 16)).astype(np.float32)
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in_mx, wt_mx = map(mx.array, (in_np, wt_np))
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in_mx_t = mx.transpose(in_mx, tpose_in)
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wt_mx_t = mx.transpose(wt_mx, tpose_wt)
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out_mx = mx.conv1d(in_mx_t, wt_mx_t)
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in_pt, wt_pt = map(
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lambda x: torch.from_numpy(x.transpose(0, 2, 1)),
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(in_np.transpose(tpose_in), wt_np.transpose(tpose_wt)),
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)
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out_pt = torch.conv1d(in_pt, wt_pt)
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out_pt = torch.transpose(out_pt, 2, 1)
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self.assertListEqual(list(out_pt.shape), out_mx.shape)
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self.assertTrue(np.allclose(out_pt.numpy(), out_mx, atol=1e-5))
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@unittest.skipIf(not has_torch, "requires Torch")
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def test_torch_conv_1D_grad(self):
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def run_conv1D_grad(
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N,
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C,
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O,
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iH,
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kH,
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stride,
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padding,
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dilation=1,
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groups=1,
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dtype="float32",
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atol=1e-5,
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):
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with self.subTest(
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dtype=dtype,
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N=N,
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C=C,
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O=O,
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iH=iH,
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kH=kH,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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):
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np_dtype = getattr(np, dtype)
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np.random.seed(0)
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oH = 1 + ((iH + 2 * padding - dilation * (kH - 1) - 1) // stride)
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in_np = np.random.normal(0, 1.0 / C, (N, iH, C)).astype(np_dtype)
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wt_np = np.random.normal(0, 1.0 / C, (O, kH, C)).astype(np_dtype)
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ct_np = np.random.normal(0, 1.0 / C, (N, oH, O)).astype(np_dtype)
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in_mx, wt_mx, ct_mx = map(mx.array, (in_np, wt_np, ct_np))
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in_pt, wt_pt, ct_pt = map(
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lambda x: torch.from_numpy(x.transpose(0, 2, 1)),
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(in_np, wt_np, ct_np),
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)
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def f(a, b):
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return mx.conv1d(
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a,
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b,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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)
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_, outs_mx = mx.vjp(
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f,
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[
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in_mx,
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wt_mx,
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],
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[
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ct_mx,
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],
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)
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pt_grad_in = F.grad.conv1d_input(
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in_pt.shape,
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wt_pt,
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ct_pt,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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)
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pt_grad_wt = F.grad.conv1d_weight(
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in_pt,
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wt_pt.shape,
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ct_pt,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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)
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pt_grad_in = torch.transpose(pt_grad_in, 2, 1).numpy()
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pt_grad_wt = torch.transpose(pt_grad_wt, 2, 1).numpy()
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mx_grad_in, mx_grad_wt = outs_mx
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self.assertListEqual(list(pt_grad_in.shape), mx_grad_in.shape)
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self.assertListEqual(list(in_mx.shape), mx_grad_in.shape)
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self.assertTrue(np.allclose(pt_grad_in, mx_grad_in, atol=atol))
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self.assertListEqual(list(pt_grad_wt.shape), mx_grad_wt.shape)
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self.assertListEqual(list(wt_mx.shape), mx_grad_wt.shape)
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self.assertTrue(np.allclose(pt_grad_wt, mx_grad_wt, atol=atol))
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for dtype in ("float32",):
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for N, C, O in (
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(1, 1, 1),
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(1, 6, 1),
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(1, 1, 6),
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(4, 32, 64),
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):
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for iH, kH, stride, padding in (
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(1, 1, 1, 0),
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(3, 3, 1, 0),
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(31, 5, 5, 2),
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):
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run_conv1D_grad(N, C, O, iH, kH, stride, padding, dtype=dtype)
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@unittest.skipIf(not has_torch, "requires Torch")
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def test_torch_conv_2D(self):
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def run_conv2D(
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N,
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C,
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O,
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idim,
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kdim,
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stride,
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padding,
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dilation=(1, 1),
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groups=1,
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dtype="float32",
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atol=1e-5,
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):
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with self.subTest(
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dtype=dtype,
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N=N,
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C=C,
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O=O,
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idim=idim,
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kdim=kdim,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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):
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np_dtype = getattr(np, dtype)
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np.random.seed(0)
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iH, iW = idim
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kH, kW = kdim
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scale = 1.0 / math.sqrt(kH * kW * C)
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in_np = np.random.normal(0.0, scale, (N, iH, iW, C)).astype(np_dtype)
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wt_np = np.random.normal(0.0, 1.0, (O, kH, kW, C)).astype(np_dtype)
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in_mx, wt_mx = map(mx.array, (in_np, wt_np))
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in_pt, wt_pt = map(
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lambda x: torch.from_numpy(x.transpose(0, 3, 1, 2)).to("cpu"),
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(in_np, wt_np),
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)
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out_mx = mx.conv2d(
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in_mx,
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wt_mx,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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)
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out_pt = torch.conv2d(
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in_pt,
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wt_pt,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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)
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out_pt = torch.permute(out_pt, (0, 2, 3, 1)).numpy(force=True)
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self.assertListEqual(list(out_pt.shape), list(out_mx.shape))
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self.assertTrue(np.allclose(out_pt, out_mx, atol=atol))
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for dtype in ("float32",):
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for N, C, O in (
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(1, 1, 1),
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(1, 6, 1),
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(1, 1, 6),
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(4, 32, 64),
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):
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for idim, kdim, stride, padding in (
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((1, 1), (1, 1), (1, 1), (0, 0)),
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((3, 3), (3, 1), (1, 1), (0, 0)),
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((31, 31), (5, 5), (5, 5), (2, 2)),
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):
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run_conv2D(N, C, O, idim, kdim, stride, padding, dtype=dtype)
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@unittest.skipIf(not has_torch, "requires Torch")
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def test_torch_conv_2D_grad(self):
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def run_conv2D_grad(
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N,
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C,
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O,
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idim,
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kdim,
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stride,
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padding,
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dilation=(1, 1),
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groups=1,
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dtype="float32",
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atol=1e-5,
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):
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with self.subTest(
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dtype=dtype,
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N=N,
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C=C,
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O=O,
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idim=idim,
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kdim=kdim,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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):
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np_dtype = getattr(np, dtype)
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np.random.seed(0)
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iH, iW = idim
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kH, kW = kdim
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scale = 1.0 / math.sqrt(kH * kW * C)
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oH = 1 + (
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(iH + 2 * padding[0] - dilation[0] * (kH - 1) - 1) // stride[0]
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)
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oW = 1 + (
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(iW + 2 * padding[1] - dilation[1] * (kW - 1) - 1) // stride[1]
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)
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in_np = np.random.normal(0.0, scale, (N, iH, iW, C)).astype(np_dtype)
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wt_np = np.random.normal(0.0, scale, (O, kH, kW, C)).astype(np_dtype)
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ct_np = np.random.normal(0.0, scale, (N, oH, oW, O)).astype(np_dtype)
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in_mx, wt_mx, ct_mx = map(mx.array, (in_np, wt_np, ct_np))
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in_pt, wt_pt, ct_pt = map(
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lambda x: torch.from_numpy(x.transpose(0, 3, 1, 2)).to("cpu"),
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(in_np, wt_np, ct_np),
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)
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def f(a, b):
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return mx.conv2d(
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a,
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b,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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)
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_, outs_mx = mx.vjp(
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f,
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[
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in_mx,
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wt_mx,
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],
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[
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ct_mx,
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],
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)
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pt_grad_in = F.grad.conv1d_input(
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in_pt.shape,
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wt_pt,
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ct_pt,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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)
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pt_grad_wt = F.grad.conv1d_weight(
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in_pt,
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wt_pt.shape,
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ct_pt,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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)
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pt_grad_in = torch.permute(pt_grad_in, (0, 2, 3, 1)).numpy()
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pt_grad_wt = torch.permute(pt_grad_wt, (0, 2, 3, 1)).numpy()
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mx_grad_in, mx_grad_wt = outs_mx
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self.assertListEqual(list(pt_grad_in.shape), mx_grad_in.shape)
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self.assertListEqual(list(in_mx.shape), mx_grad_in.shape)
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self.assertTrue(np.allclose(pt_grad_in, mx_grad_in, atol=atol))
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self.assertListEqual(list(pt_grad_wt.shape), mx_grad_wt.shape)
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self.assertListEqual(list(wt_mx.shape), mx_grad_wt.shape)
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self.assertTrue(np.allclose(pt_grad_wt, mx_grad_wt, atol=atol))
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for dtype in ("float32",):
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for N, C, O in (
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(1, 1, 1),
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(1, 6, 1),
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(1, 1, 6),
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(4, 32, 64),
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):
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for idim, kdim, stride, padding in (
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((1, 1), (1, 1), (1, 1), (0, 0)),
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((3, 3), (3, 1), (1, 1), (0, 0)),
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((31, 31), (5, 5), (5, 5), (2, 2)),
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):
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run_conv2D_grad(N, C, O, idim, kdim, stride, padding, dtype=dtype)
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if __name__ == "__main__":
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unittest.main()
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