wehub-resource-sync
122 lines
4.5 KiB
Python
122 lines
4.5 KiB
Python
# Copyright (c) 2018 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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from op_test import get_device_place, is_custom_device
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from test_imperative_base import new_program_scope
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import paddle
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from paddle import base
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from paddle.base import core
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class RecurrentTest(paddle.nn.Layer):
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def __init__(self, name_scope):
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super().__init__(name_scope)
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def forward(self, in1, in2):
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out = paddle.matmul(in1, in2)
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sum_out = paddle.sum(out)
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return sum_out, out
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class TestRecurrentFeed(unittest.TestCase):
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def test_recurrent_feed(self):
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seed = 90
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original_np1 = np.arange(1, 5).reshape(2, 2).astype("float32")
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original_np2 = np.arange(5, 9).reshape(2, 2).astype("float32")
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with base.dygraph.guard():
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paddle.seed(seed)
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original_in1 = paddle.to_tensor(original_np1)
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original_in2 = paddle.to_tensor(original_np2)
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original_in1.stop_gradient = False
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original_in2.stop_gradient = False
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rt = RecurrentTest("RecurrentTest")
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for i in range(3):
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sum_out, out = rt(original_in1, original_in2)
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out.retain_grads()
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original_in1 = out
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sum_out_value = sum_out.numpy()
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sum_out.backward()
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dyout = out.gradient()
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original_in1.stop_gradient = True
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rt.clear_gradients()
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with base.dygraph.guard():
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paddle.seed(seed)
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original_in1 = paddle.to_tensor(original_np1)
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original_in2 = paddle.to_tensor(original_np2)
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original_in1.stop_gradient = False
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original_in2.stop_gradient = False
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rt = RecurrentTest("RecurrentTest")
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for i in range(3):
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sum_out, out = rt(original_in1, original_in2)
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out.retain_grads()
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original_in1 = out
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eager_sum_out_value = sum_out.numpy()
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sum_out.backward()
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eager_dyout = out.gradient()
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original_in1.stop_gradient = True
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rt.clear_gradients()
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with new_program_scope():
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paddle.seed(seed)
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in1 = paddle.static.data(name="inp1", shape=[2, 2])
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in1.stop_gradient = False
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in2 = paddle.static.data(name="inp2", shape=[2, 2])
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in2.stop_gradient = False
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rt1 = RecurrentTest("RecurrentTest")
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static_sum_out, static_out = rt1(in1, in2)
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static_out.persistable = True
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exe = base.Executor(
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base.CPUPlace()
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if not (core.is_compiled_with_cuda() or is_custom_device())
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else get_device_place()
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)
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if paddle.framework.use_pir_api():
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grad_list = paddle.static.append_backward(static_sum_out)
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_, static_dout = grad_list[-1]
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else:
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base.backward.append_backward(static_sum_out)
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static_dout = (
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base.default_main_program()
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.block(0)
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._find_var_recursive(static_out.name + "@GRAD")
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)
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fetch_list = [static_sum_out, static_out, static_dout]
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for i in range(3):
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out = exe.run(
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base.default_main_program(),
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feed={"inp1": original_np1, "inp2": original_np2},
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fetch_list=fetch_list,
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)
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static_out_value = out[1]
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static_sum_out = out[0]
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static_dout = out[2]
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original_np1 = static_out_value
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np.testing.assert_array_equal(static_sum_out, sum_out_value)
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np.testing.assert_array_equal(static_sum_out, eager_sum_out_value)
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np.testing.assert_array_equal(static_dout, dyout)
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np.testing.assert_array_equal(static_dout, eager_dyout)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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