wehub-resource-sync
121 lines
4.2 KiB
Python
121 lines
4.2 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import platform
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import shutil
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import unittest
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import paddle
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class TestCaptureBackwardSubGraphGuard(unittest.TestCase):
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# Just run it for coverage ci and don't check the res
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def test_guard(self):
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# windows ci may have some permission issues
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if 'Windows' == platform.system() or not paddle.is_compiled_with_cuda():
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return
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import paddle.nn.functional as F
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from paddle import nn
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dump_dir_path = "_test/"
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x = paddle.randn([3, 3], dtype='float16')
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y = paddle.randn([3, 3], dtype='float32')
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z = paddle.randn([3, 3], dtype='float64')
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w = paddle.randn([3, 3], dtype='float64')
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x.stop_gradient = False
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y.stop_gradient = False
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z.stop_gradient = False
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w.stop_gradient = True
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y = y + y
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with paddle.utils.capture_backward_subgraph_guard(dump_dir_path, True):
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conv_x = paddle.randn((2, 3, 8, 8), dtype='float32')
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conv_w = paddle.randn((6, 3, 3, 3), dtype='float16')
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sync_bn_input = paddle.to_tensor(
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[[[[0.3, 0.4], [0.3, 0.07]], [[0.83, 0.37], [0.18, 0.93]]]]
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).astype('float32')
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conv_x.stop_gradient = False
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conv_w.stop_gradient = False
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sync_bn_input.stop_gradient = False
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with paddle.amp.auto_cast(enable=True):
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out1 = paddle.add_n([x, y])
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out2 = paddle.multiply(x, y)
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out6 = F.conv2d(conv_x, conv_w)
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out3 = paddle.add_n([out1, y])
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out4 = paddle.multiply(out2, z)
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out5 = paddle.multiply_(w, y)
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if paddle.is_compiled_with_cuda():
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sync_batch_norm = nn.SyncBatchNorm(2)
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hidden1 = sync_batch_norm(sync_bn_input)
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out7 = out6.sum() + hidden1.sum()
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loss = out1 + out2 + out3 + out4 + out5 + out7
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loss.backward()
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self._check_files_in_directory(dump_dir_path)
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shutil.rmtree(dump_dir_path)
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def _check_files_in_directory(self, directory):
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# Check whether the expected file exists in the directory
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entries = os.listdir(directory)
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files = [
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entry
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for entry in entries
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if os.path.isfile(os.path.join(directory, entry))
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]
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expect_keywords_in_file_name = [
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"backward_graph.dot",
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"ref_forward_graph.dot",
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"call_stack.log",
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"grad_tensors.log",
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]
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for keywords in expect_keywords_in_file_name:
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if not any(keywords in f for f in files):
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raise AssertionError(
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f"Error: File '{keywords}' not found in directory '{directory}'! "
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)
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def test_dy2st(self):
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if 'Windows' == platform.system() or not paddle.is_compiled_with_cuda():
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return
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x = paddle.randn((3, 3))
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y = paddle.randn((3, 3))
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x.stop_gradient = False
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y.stop_gradient = False
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def matmul_func(x, y):
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res = paddle.matmul(x, y)
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return res
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func = paddle.jit.to_static(matmul_func, full_graph=True)
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dump_dir_path = "./dy2st_debug"
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paddle.set_flags(
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{"FLAGS_tensor_md5_checksum_output_path": "./dy2st_md5.txt"}
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)
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with (
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paddle.utils.capture_backward_subgraph_guard(dump_dir_path, True),
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paddle.utils.capture_forward_subgraph_guard("./dy2st_subraph"),
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):
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res = func(x, y)
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z = res + x
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loss = z.sum()
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loss.backward()
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self._check_files_in_directory(dump_dir_path)
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if __name__ == "__main__":
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unittest.main()
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