wehub-resource-sync
75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
# Copyright (c) 2024 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 unittest
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import numpy as np
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import paddle
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from paddle.autograd.ir_backward import grad
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from paddle.decomposition import decomp
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paddle.enable_static()
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class TestPrimMode(unittest.TestCase):
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def setUp(self):
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np.random.seed(2023)
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self.shape_x = [32, 32]
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self.shape_y = [32, 32]
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self.x = np.random.random(self.shape_x).astype("float32")
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self.y = np.random.random(self.shape_y).astype("float32")
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def base_net(self, flag=None):
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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x = paddle.static.data('x', self.shape_x, dtype='float32')
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y = paddle.static.data('y', self.shape_y, dtype='float32')
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x.stop_gradient = False
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y.stop_gradient = False
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x1 = paddle.sin(x)
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y1 = paddle.cos(y)
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y3 = paddle.matmul(x1, y1)
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tmp1 = paddle.concat((x1, y1, y3))
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tmp1 = paddle.slice(tmp1, axes=[1], starts=[0], ends=[2])
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tmp2 = paddle.mean(tmp1)
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sum_out = paddle.sin(tmp2)
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gradients = grad(sum_out, (x, y))
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if flag == "prim":
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with decomp.prim_guard():
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decomp.decompose_dist_program(main_program)
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exe = paddle.static.Executor()
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[fwd, dx, dy] = exe.run(
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feed={'x': self.x, 'y': self.y}, fetch_list=[sum_out, gradients]
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)
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whole_ops = [op.name() for op in main_program.global_block().ops]
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if flag == "prim":
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assert 'pd_op.concat_grad' not in whole_ops
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else:
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assert 'pd_op.concat_grad' in whole_ops
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return fwd, dx, dy
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def test_prim_all(self):
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paddle.base.core._set_prim_backward_blacklist("sin_grad", "cos_grad")
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res_ref = self.base_net()
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res = self.base_net("prim")
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for ref, actual in zip(res_ref, res):
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np.testing.assert_allclose(ref, actual, rtol=1e-6, atol=1e-6)
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if __name__ == "__main__":
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unittest.main()
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