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Fix conv module bug (#5245)
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* fix bert backward bug

* fix argwhere module bug

* align dropout module with torch

* fix acosh ci bug

* fix conv group large input channel error

Co-authored-by: oneflow-ci-bot <69100618+oneflow-ci-bot@users.noreply.github.com>
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@BBuf @oneflow-ci-bot
BBuf and oneflow-ci-bot committed Jun 21, 2021
1 parent 50f32b6 commit 582677f
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13 changes: 12 additions & 1 deletion oneflow/python/nn/modules/conv.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -221,6 +221,7 @@ def __init__(
self.weight = flow.nn.Parameter(
flow.Tensor(out_channels, in_channels // groups, *kernel_size)
)
self.out_channel_groups = out_channels // groups
self.bias = None
self._bias_add_op = None
if bias:
Expand Down Expand Up @@ -280,7 +281,17 @@ def forward(self, x):
out_list = []
for i in range(len(in_split_list)):
out_list.append(
self._cpu_op(in_split_list[i], self.weight[i : i + 1, :, :, :])[0]
self._cpu_op(
in_split_list[i],
self.weight[
i
* self.out_channel_groups : (i + 1)
* self.out_channel_groups,
:,
:,
:,
],
)[0]
)
res = flow.experimental.cat(out_list, dim=in_channel_axis)
else:
Expand Down
285 changes: 285 additions & 0 deletions oneflow/python/test/modules/test_conv.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -14,8 +14,12 @@
limitations under the License.
"""
import unittest
from collections import OrderedDict

import numpy as np

import oneflow.experimental as flow
from test_util import GenArgList

test_conv2d_weight = np.array(
[
Expand Down Expand Up @@ -1202,6 +1206,278 @@ def _test_conv2d_backward(
)


def _test_conv2d_large_in_channel(test_case, device):
np_arr = np.array(
[
[
[
[
0.6206631238581714,
-1.1225329393404626,
0.8407155480700242,
-0.6845162855236345,
],
[
-0.5186484633906412,
0.10420735184519186,
-0.1711568947473012,
0.5168640476046483,
],
[
-0.12429464919764661,
0.050277779246134253,
-1.0144501797426606,
-2.184600444658526,
],
[
0.28918126931309923,
-0.822872663244595,
0.44019150436683663,
-1.0247720130825562,
],
],
[
[
0.7786504412818226,
-0.7501839068078657,
-0.8187283189941765,
-1.1116653569170698,
],
[
0.18085524152316743,
-1.3461349607476678,
1.142505437476448,
-0.000649619704040145,
],
[
0.03160672782674317,
-0.006318157449953413,
1.2218487782604377,
0.15903027907930234,
],
[
1.5857011815642381,
0.6656477116332891,
-0.04036621813223574,
-0.3427168687988546,
],
],
[
[
-1.1774346070102524,
1.6195241269303395,
-0.36185552303441965,
-1.1382193113192487,
],
[
0.08061907334568702,
1.5025447613238763,
-1.1591348706634745,
1.6449050139676873,
],
[
1.1539915649822392,
-2.414624939646017,
0.3056063774849572,
1.1920089257083162,
],
[
0.7623012858982319,
-0.01685314742940813,
-1.096666898224702,
-0.4406476137098582,
],
],
[
[
0.9383797282214235,
-1.1075876842796508,
-0.4420913825139058,
-1.0736097610655628,
],
[
-0.3101376466546291,
1.6578227745160954,
-0.6225454278031398,
0.6831188620748697,
],
[
0.00743800968372913,
-0.8089158949698473,
2.08084287836801,
0.721204366332351,
],
[
0.5694701823297723,
0.031519314469744895,
-0.5041680957766629,
-0.4738588233094669,
],
],
]
]
)
input = flow.Tensor(
np_arr, dtype=flow.float32, device=flow.device(device), requires_grad=True
)
weight = np.array(
[
[
[
[0.06456436216831207, -0.10852358490228653, -0.21638715267181396],
[-0.2279110550880432, 0.1476770043373108, 0.19457484781742096],
[0.05026858672499657, 0.10818571597337723, 0.02056501805782318],
],
[
[0.205095112323761, 0.1488947868347168, -0.2344113141298294],
[0.1684819906949997, -0.21986986696720123, 0.1082606166601181],
[-0.1528974026441574, 0.17120417952537537, 0.01954500749707222],
],
],
[
[
[-0.09441672265529633, -0.03644559532403946, -0.22235223650932312],
[-0.1771145612001419, 0.08043312281370163, 0.06938580423593521],
[0.054393064230680466, -0.05483492836356163, 0.23438701033592224],
],
[
[0.22666795551776886, 0.0874653309583664, 0.07092718034982681],
[0.08883464336395264, -0.052362944930791855, -0.1720171570777893],
[0.10441060364246368, 0.011952142231166363, -0.0894528403878212],
],
],
]
)
m = flow.nn.Conv2d(4, 2, 3, groups=2, bias=False)
m.weight = flow.nn.Parameter(flow.Tensor(weight), requires_grad=True)
m = m.to(device)
output = m(input)
np_out = [
[
[
[0.7666134238243103, -0.3961866497993469],
[-0.656266987323761, -1.1613956689834595],
],
[
[0.3077264130115509, -0.42817503213882446],
[-0.5761325359344482, 0.1300736665725708],
],
]
]
test_case.assertTrue(np.allclose(output.numpy(), np_out, 1e-6, 1e-6))
output = output.sum()
output.backward()
np_grad = [
[
[
[
0.06456436216831207,
-0.04395922273397446,
-0.3249107301235199,
-0.21638715267181396,
],
[
-0.16334669291973114,
-0.12419328093528748,
0.017341122031211853,
-0.021812304854393005,
],
[
-0.17764246463775635,
0.07822024822235107,
0.47100257873535156,
0.21513986587524414,
],
[
0.05026858672499657,
0.1584542989730835,
0.128750741481781,
0.02056501805782318,
],
],
[
[
0.205095112323761,
0.3539898991584778,
-0.08551652729511261,
-0.2344113141298294,
],
[
0.3735771179199219,
0.30260205268859863,
-0.19712577760219574,
-0.1261506974697113,
],
[
0.015584588050842285,
-0.03308109939098358,
0.07913993299007416,
0.12780562043190002,
],
[
-0.1528974026441574,
0.018306776881217957,
0.1907491832971573,
0.01954500749707222,
],
],
[
[
-0.09441672265529633,
-0.13086232542991638,
-0.258797824382782,
-0.22235223650932312,
],
[
-0.27153128385543823,
-0.22754377126693726,
-0.10897888988256454,
-0.1529664397239685,
],
[
-0.12272149324417114,
-0.09712330251932144,
0.32937100529670715,
0.30377280712127686,
],
[
0.054393064230680466,
-0.00044186413288116455,
0.1795520782470703,
0.23438701033592224,
],
],
[
[
0.22666795551776886,
0.31413328647613525,
0.1583925187587738,
0.07092718034982681,
],
[
0.3155025839805603,
0.35060498118400574,
-0.06598758697509766,
-0.1010899767279625,
],
[
0.19324524700641632,
0.1528344452381134,
-0.301880806684494,
-0.2614699900150299,
],
[
0.10441060364246368,
0.11636274307966232,
-0.07750070095062256,
-0.0894528403878212,
],
],
]
]
test_case.assertTrue(np.allclose(input.grad.numpy(), np_grad, 1e-6, 1e-6))


@unittest.skipIf(
not flow.unittest.env.eager_execution_enabled(),
".numpy() doesn't work in lazy mode",
Expand Down Expand Up @@ -1401,6 +1677,15 @@ def test_conv2d_dilation_backward(test_case):
device=device,
)

def test_large_channel_group_conv(test_case):
arg_dict = OrderedDict()
arg_dict["test_fun"] = [
_test_conv2d_large_in_channel,
]
arg_dict["device"] = ["cuda", "cpu"]
for arg in GenArgList(arg_dict):
arg[0](test_case, *arg[1:])


if __name__ == "__main__":
unittest.main()

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