mirror of
https://github.com/ml-explore/mlx.git
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Convolution update (#651)
* Init steel conv and update Conv primitive * Update slow CPU implementation to support flipping and input dilation winograd conv routing Co-authored-by: Awni Hannun <awni@apple.com>
This commit is contained in:
Jagrit Digani
@@ -1,4 +1,4 @@
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# Copyright © 2023 Apple Inc.
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# Copyright © 2023-2024 Apple Inc.
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import math
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import unittest
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@@ -388,13 +388,8 @@ class TestConv(mlx_tests.MLXTestCase):
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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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[in_mx, wt_mx],
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[ct_mx],
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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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@@ -428,18 +423,218 @@ class TestConv(mlx_tests.MLXTestCase):
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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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for N, C, O in ((1, 1, 1), (1, 6, 1), (1, 1, 6), (4, 32, 64), (4, 16, 32)):
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for idim, kdim, stride, padding, dilation in (
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((1, 1), (1, 1), (1, 1), (0, 0), (1, 1)),
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((3, 3), (3, 1), (1, 1), (0, 0), (1, 1)),
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((31, 31), (5, 5), (5, 5), (2, 2), (1, 1)),
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((32, 32), (3, 3), (2, 2), (1, 1), (1, 1)),
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((31, 31), (5, 5), (5, 5), (2, 2), (3, 2)),
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((32, 32), (3, 3), (2, 2), (1, 1), (3, 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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run_conv2D_grad(
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N, C, O, idim, kdim, stride, padding, dilation, dtype=dtype
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)
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def __conv_general_test(
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self,
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in_shape,
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wt_shape,
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stride=1,
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padding=0,
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kernel_dilation=1,
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input_dilation=1,
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groups=1,
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flip=False,
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np_dtype=np.float32,
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atol=1e-5,
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):
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with self.subTest(
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in_shape=in_shape,
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wt_shape=wt_shape,
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stride=stride,
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padding=padding,
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kernel_dilation=kernel_dilation,
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input_dilation=input_dilation,
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groups=groups,
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flip=flip,
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np_dtype=np_dtype,
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):
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scale = 1.0 / math.sqrt(np.prod(wt_shape[1:]))
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in_np = np.random.normal(0.0, scale, in_shape).astype(np_dtype)
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wt_np = np.random.normal(0.0, scale, wt_shape).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(np.moveaxis(x, -1, 1)).to("cpu"),
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(in_np, wt_np),
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)
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out_mx = mx.conv_general(
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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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kernel_dilation=kernel_dilation,
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input_dilation=input_dilation,
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groups=groups,
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flip=flip,
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)
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def conv_general_pt(
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inp, wt, stride, padding, kernel_dilation, input_dilation, groups, flip
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):
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C = inp.size()[1]
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ndim = inp.ndim - 2
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map_ints = lambda x: [x] * ndim if isinstance(x, int) else x
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stride, padding, kernel_dilation, input_dilation = map(
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map_ints, (stride, padding, kernel_dilation, input_dilation)
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)
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torch_convt_list = (
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F.conv_transpose1d,
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F.conv_transpose2d,
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F.conv_transpose3d,
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)
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torch_conv_list = (F.conv1d, F.conv2d, F.conv3d)
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conv_f = torch_conv_list[ndim - 1]
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convt_f = torch_convt_list[ndim - 1]
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if flip:
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wt = torch.flip(wt, tuple(np.arange(2, wt.ndim)))
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if not np.all(input_dilation == 1):
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ones = torch.ones(
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[C]
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+ [
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1,
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]
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* (ndim + 1)
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).to(inp.dtype)
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inp = convt_f(inp, ones, stride=input_dilation, groups=C)
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return conv_f(
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inp,
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wt,
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stride=stride,
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padding=padding,
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dilation=kernel_dilation,
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groups=groups,
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)
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out_pt = conv_general_pt(
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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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kernel_dilation=kernel_dilation,
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input_dilation=input_dilation,
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groups=groups,
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flip=flip,
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)
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out_pt = np.moveaxis(out_pt.numpy(), 1, -1)
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self.assertEqual(out_mx.shape, out_pt.shape)
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self.assertTrue(np.allclose(out_mx, out_pt, atol=atol))
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@unittest.skipIf(not has_torch, "requires Torch")
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def test_torch_conv_general(self):
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in_shape = (2, 32, 32, 16)
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wt_shape = (32, 5, 5, 16)
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stride = (1, 1)
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padding = (2, 2)
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kernel_dilation = (2, 3)
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input_dilation = (1, 1)
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flip = False
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self.__conv_general_test(
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in_shape,
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wt_shape,
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stride,
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padding,
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kernel_dilation,
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input_dilation,
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flip=flip,
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)
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in_shape = (2, 32, 32, 16)
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wt_shape = (32, 5, 10, 16)
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stride = (2, 3)
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padding = (0, 0)
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kernel_dilation = (3, 2)
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input_dilation = (2, 4)
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flip = False
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self.__conv_general_test(
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in_shape,
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wt_shape,
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stride,
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padding,
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kernel_dilation,
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input_dilation,
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flip=flip,
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)
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in_shape = (2, 32, 32, 16)
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wt_shape = (32, 5, 10, 16)
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stride = (2, 2)
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padding = (3, 2)
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kernel_dilation = (3, 2)
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input_dilation = (2, 4)
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flip = False
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self.__conv_general_test(
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in_shape,
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wt_shape,
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stride,
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padding,
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kernel_dilation,
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input_dilation,
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flip=flip,
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)
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in_shape = (2, 32, 32, 16)
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wt_shape = (32, 5, 10, 16)
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stride = (2, 3)
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padding = (3, 2)
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kernel_dilation = (3, 2)
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input_dilation = (2, 5)
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flip = False
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self.__conv_general_test(
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in_shape,
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wt_shape,
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stride,
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padding,
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kernel_dilation,
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input_dilation,
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flip=flip,
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)
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in_shape = (2, 32, 32, 16)
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wt_shape = (32, 5, 5, 16)
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stride = (2, 3)
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padding = (0, 0)
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kernel_dilation = (3, 1)
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input_dilation = (2, 5)
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flip = True
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self.__conv_general_test(
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in_shape,
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wt_shape,
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stride,
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padding,
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kernel_dilation,
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input_dilation,
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flip=flip,
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)
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if __name__ == "__main__":
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