mirror of
https://github.com/ml-explore/mlx.git
synced 2025-11-01 00:28:11 +08:00
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 @@
|
||||
# Copyright © 2023 Apple Inc.
|
||||
# Copyright © 2023-2024 Apple Inc.
|
||||
|
||||
import math
|
||||
import unittest
|
||||
@@ -388,13 +388,8 @@ class TestConv(mlx_tests.MLXTestCase):
|
||||
|
||||
_, outs_mx = mx.vjp(
|
||||
f,
|
||||
[
|
||||
in_mx,
|
||||
wt_mx,
|
||||
],
|
||||
[
|
||||
ct_mx,
|
||||
],
|
||||
[in_mx, wt_mx],
|
||||
[ct_mx],
|
||||
)
|
||||
pt_grad_in = F.grad.conv1d_input(
|
||||
in_pt.shape,
|
||||
@@ -428,18 +423,218 @@ class TestConv(mlx_tests.MLXTestCase):
|
||||
self.assertTrue(np.allclose(pt_grad_wt, mx_grad_wt, atol=atol))
|
||||
|
||||
for dtype in ("float32",):
|
||||
for N, C, O in (
|
||||
(1, 1, 1),
|
||||
(1, 6, 1),
|
||||
(1, 1, 6),
|
||||
(4, 32, 64),
|
||||
):
|
||||
for idim, kdim, stride, padding in (
|
||||
((1, 1), (1, 1), (1, 1), (0, 0)),
|
||||
((3, 3), (3, 1), (1, 1), (0, 0)),
|
||||
((31, 31), (5, 5), (5, 5), (2, 2)),
|
||||
for N, C, O in ((1, 1, 1), (1, 6, 1), (1, 1, 6), (4, 32, 64), (4, 16, 32)):
|
||||
for idim, kdim, stride, padding, dilation in (
|
||||
((1, 1), (1, 1), (1, 1), (0, 0), (1, 1)),
|
||||
((3, 3), (3, 1), (1, 1), (0, 0), (1, 1)),
|
||||
((31, 31), (5, 5), (5, 5), (2, 2), (1, 1)),
|
||||
((32, 32), (3, 3), (2, 2), (1, 1), (1, 1)),
|
||||
((31, 31), (5, 5), (5, 5), (2, 2), (3, 2)),
|
||||
((32, 32), (3, 3), (2, 2), (1, 1), (3, 2)),
|
||||
):
|
||||
run_conv2D_grad(N, C, O, idim, kdim, stride, padding, dtype=dtype)
|
||||
run_conv2D_grad(
|
||||
N, C, O, idim, kdim, stride, padding, dilation, dtype=dtype
|
||||
)
|
||||
|
||||
def __conv_general_test(
|
||||
self,
|
||||
in_shape,
|
||||
wt_shape,
|
||||
stride=1,
|
||||
padding=0,
|
||||
kernel_dilation=1,
|
||||
input_dilation=1,
|
||||
groups=1,
|
||||
flip=False,
|
||||
np_dtype=np.float32,
|
||||
atol=1e-5,
|
||||
):
|
||||
|
||||
with self.subTest(
|
||||
in_shape=in_shape,
|
||||
wt_shape=wt_shape,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
kernel_dilation=kernel_dilation,
|
||||
input_dilation=input_dilation,
|
||||
groups=groups,
|
||||
flip=flip,
|
||||
np_dtype=np_dtype,
|
||||
):
|
||||
|
||||
scale = 1.0 / math.sqrt(np.prod(wt_shape[1:]))
|
||||
in_np = np.random.normal(0.0, scale, in_shape).astype(np_dtype)
|
||||
wt_np = np.random.normal(0.0, scale, wt_shape).astype(np_dtype)
|
||||
|
||||
in_mx, wt_mx = map(mx.array, (in_np, wt_np))
|
||||
|
||||
in_pt, wt_pt = map(
|
||||
lambda x: torch.from_numpy(np.moveaxis(x, -1, 1)).to("cpu"),
|
||||
(in_np, wt_np),
|
||||
)
|
||||
|
||||
out_mx = mx.conv_general(
|
||||
in_mx,
|
||||
wt_mx,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
kernel_dilation=kernel_dilation,
|
||||
input_dilation=input_dilation,
|
||||
groups=groups,
|
||||
flip=flip,
|
||||
)
|
||||
|
||||
def conv_general_pt(
|
||||
inp, wt, stride, padding, kernel_dilation, input_dilation, groups, flip
|
||||
):
|
||||
|
||||
C = inp.size()[1]
|
||||
ndim = inp.ndim - 2
|
||||
map_ints = lambda x: [x] * ndim if isinstance(x, int) else x
|
||||
|
||||
stride, padding, kernel_dilation, input_dilation = map(
|
||||
map_ints, (stride, padding, kernel_dilation, input_dilation)
|
||||
)
|
||||
|
||||
torch_convt_list = (
|
||||
F.conv_transpose1d,
|
||||
F.conv_transpose2d,
|
||||
F.conv_transpose3d,
|
||||
)
|
||||
torch_conv_list = (F.conv1d, F.conv2d, F.conv3d)
|
||||
|
||||
conv_f = torch_conv_list[ndim - 1]
|
||||
convt_f = torch_convt_list[ndim - 1]
|
||||
|
||||
if flip:
|
||||
wt = torch.flip(wt, tuple(np.arange(2, wt.ndim)))
|
||||
|
||||
if not np.all(input_dilation == 1):
|
||||
ones = torch.ones(
|
||||
[C]
|
||||
+ [
|
||||
1,
|
||||
]
|
||||
* (ndim + 1)
|
||||
).to(inp.dtype)
|
||||
inp = convt_f(inp, ones, stride=input_dilation, groups=C)
|
||||
|
||||
return conv_f(
|
||||
inp,
|
||||
wt,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
dilation=kernel_dilation,
|
||||
groups=groups,
|
||||
)
|
||||
|
||||
out_pt = conv_general_pt(
|
||||
in_pt,
|
||||
wt_pt,
|
||||
stride=stride,
|
||||
padding=padding,
|
||||
kernel_dilation=kernel_dilation,
|
||||
input_dilation=input_dilation,
|
||||
groups=groups,
|
||||
flip=flip,
|
||||
)
|
||||
|
||||
out_pt = np.moveaxis(out_pt.numpy(), 1, -1)
|
||||
|
||||
self.assertEqual(out_mx.shape, out_pt.shape)
|
||||
self.assertTrue(np.allclose(out_mx, out_pt, atol=atol))
|
||||
|
||||
@unittest.skipIf(not has_torch, "requires Torch")
|
||||
def test_torch_conv_general(self):
|
||||
in_shape = (2, 32, 32, 16)
|
||||
wt_shape = (32, 5, 5, 16)
|
||||
stride = (1, 1)
|
||||
padding = (2, 2)
|
||||
kernel_dilation = (2, 3)
|
||||
input_dilation = (1, 1)
|
||||
flip = False
|
||||
|
||||
self.__conv_general_test(
|
||||
in_shape,
|
||||
wt_shape,
|
||||
stride,
|
||||
padding,
|
||||
kernel_dilation,
|
||||
input_dilation,
|
||||
flip=flip,
|
||||
)
|
||||
|
||||
in_shape = (2, 32, 32, 16)
|
||||
wt_shape = (32, 5, 10, 16)
|
||||
stride = (2, 3)
|
||||
padding = (0, 0)
|
||||
kernel_dilation = (3, 2)
|
||||
input_dilation = (2, 4)
|
||||
flip = False
|
||||
|
||||
self.__conv_general_test(
|
||||
in_shape,
|
||||
wt_shape,
|
||||
stride,
|
||||
padding,
|
||||
kernel_dilation,
|
||||
input_dilation,
|
||||
flip=flip,
|
||||
)
|
||||
|
||||
in_shape = (2, 32, 32, 16)
|
||||
wt_shape = (32, 5, 10, 16)
|
||||
stride = (2, 2)
|
||||
padding = (3, 2)
|
||||
kernel_dilation = (3, 2)
|
||||
input_dilation = (2, 4)
|
||||
flip = False
|
||||
|
||||
self.__conv_general_test(
|
||||
in_shape,
|
||||
wt_shape,
|
||||
stride,
|
||||
padding,
|
||||
kernel_dilation,
|
||||
input_dilation,
|
||||
flip=flip,
|
||||
)
|
||||
|
||||
in_shape = (2, 32, 32, 16)
|
||||
wt_shape = (32, 5, 10, 16)
|
||||
stride = (2, 3)
|
||||
padding = (3, 2)
|
||||
kernel_dilation = (3, 2)
|
||||
input_dilation = (2, 5)
|
||||
flip = False
|
||||
|
||||
self.__conv_general_test(
|
||||
in_shape,
|
||||
wt_shape,
|
||||
stride,
|
||||
padding,
|
||||
kernel_dilation,
|
||||
input_dilation,
|
||||
flip=flip,
|
||||
)
|
||||
|
||||
in_shape = (2, 32, 32, 16)
|
||||
wt_shape = (32, 5, 5, 16)
|
||||
stride = (2, 3)
|
||||
padding = (0, 0)
|
||||
kernel_dilation = (3, 1)
|
||||
input_dilation = (2, 5)
|
||||
flip = True
|
||||
|
||||
self.__conv_general_test(
|
||||
in_shape,
|
||||
wt_shape,
|
||||
stride,
|
||||
padding,
|
||||
kernel_dilation,
|
||||
input_dilation,
|
||||
flip=flip,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
Reference in New Issue
Block a user