wehub-resource-sync
83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import unittest
|
|
|
|
import numpy as np
|
|
|
|
import paddle
|
|
import paddle.distributed as dist
|
|
from paddle.autograd.py_layer import PyLayer
|
|
|
|
|
|
class TestNet(PyLayer):
|
|
@staticmethod
|
|
def forward(ctx, x1, x2, x3):
|
|
y1 = paddle.matmul(x1, x2, transpose_x=False, transpose_y=False)
|
|
y2 = paddle.matmul(x2, x3, transpose_x=False, transpose_y=False)
|
|
return y1, y2
|
|
|
|
@staticmethod
|
|
def backward(ctx, dy1, dy2):
|
|
return dy1, dy2, dy2
|
|
|
|
|
|
class TestPyLayerForSemiAutoParallel(unittest.TestCase):
|
|
def run_test_case(self):
|
|
mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
|
|
|
|
x1_np = np.random.random(size=[64, 32]).astype(np.float32)
|
|
x2_np = np.random.random(size=[32, 48]).astype(np.float32)
|
|
x3_np = np.random.random(size=[48, 64]).astype(np.float32)
|
|
x1 = paddle.to_tensor(x1_np)
|
|
x2 = paddle.to_tensor(x2_np)
|
|
x3 = paddle.to_tensor(x3_np)
|
|
x1.stop_gradient = False
|
|
x2.stop_gradient = False
|
|
x3.stop_gradient = False
|
|
|
|
dist_x1 = dist.shard_tensor(x1_np, mesh, [dist.Replicate()])
|
|
dist_x2 = dist.shard_tensor(x2_np, mesh, [dist.Replicate()])
|
|
dist_x3 = dist.shard_tensor(x3_np, mesh, [dist.Replicate()])
|
|
dist_x1.stop_gradient = False
|
|
dist_x2.stop_gradient = False
|
|
dist_x3.stop_gradient = False
|
|
|
|
y1, y2 = TestNet.apply(x1, x2, x3)
|
|
loss = y1.sum()
|
|
|
|
dist_y1, dist_y2 = TestNet.apply(dist_x1, dist_x2, dist_x3)
|
|
dist_loss = dist_y1.sum()
|
|
|
|
np.testing.assert_allclose(
|
|
loss.numpy(), dist_loss.numpy(), rtol=1e-04, verbose=True
|
|
)
|
|
|
|
loss.backward()
|
|
dist_loss.backward()
|
|
|
|
np.testing.assert_allclose(
|
|
x1.grad.numpy(), dist_x1.grad.numpy(), rtol=1e-04, verbose=True
|
|
)
|
|
np.testing.assert_allclose(
|
|
x2.grad.numpy(), dist_x2.grad.numpy(), rtol=1e-04, verbose=True
|
|
)
|
|
np.testing.assert_allclose(
|
|
x3.grad.numpy(), dist_x3.grad.numpy(), rtol=1e-04, verbose=True
|
|
)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
TestPyLayerForSemiAutoParallel().run_test_case()
|