wehub-resource-sync
404 lines
14 KiB
Python
404 lines
14 KiB
Python
# Copyright (c) 2022 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 paddle
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paddle.set_default_dtype("float64")
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import unittest
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import numpy as np
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from paddle import base
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paddle.enable_static()
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bidirectional_list = ["bidirectional", "bidirect"]
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class TestSimpleRNN(unittest.TestCase):
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def __init__(self, time_major=True, direction="forward", place="cpu"):
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super().__init__("runTest")
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self.time_major = time_major
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self.direction = direction
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self.num_directions = 2 if direction in bidirectional_list else 1
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self.place = place
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self.batch_size = 4
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self.input_size = 16
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self.hidden_size = 16
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self.seq_len = 12
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self.seed = 1234
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def setUp(self):
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# Since `set_device` is global, set `set_device` in `setUp` rather than
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# `__init__` to avoid using an error device set by another test case.
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place = paddle.set_device(self.place)
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paddle.disable_static(self.place)
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paddle.seed(self.seed)
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if paddle.framework.use_pir_api():
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with paddle.pir_utils.OldIrGuard():
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# Note: dygraph use self.main_program.global_block().create_parameter(), it's need manual seed to old Program
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paddle.framework.random._manual_program_seed(self.seed)
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paddle.framework.random._manual_program_seed(self.seed)
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else:
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paddle.framework.random._manual_program_seed(self.seed)
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cell_dy = paddle.nn.SimpleRNNCell(self.input_size, self.hidden_size)
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self.rnn_net = paddle.nn.RNN(cell_dy, time_major=self.time_major)
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paddle.enable_static()
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with paddle.base.unique_name.guard():
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(
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main_program=main_program, startup_program=startup_program
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):
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paddle.seed(self.seed)
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paddle.framework.random._manual_program_seed(self.seed)
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self.exe = base.Executor(
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base.CPUPlace()
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if self.place == "cpu"
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else base.CUDAPlace(0)
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)
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rnn_in_data = paddle.static.data(
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"x",
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[None, self.batch_size, self.hidden_size],
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dtype="float64",
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)
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pre_h_data = paddle.static.data(
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"pre_h",
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[self.batch_size, self.hidden_size],
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dtype="float64",
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)
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seq_len_data = paddle.static.data(
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"seq_len", [self.batch_size], dtype="int64"
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)
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cell_st = paddle.nn.SimpleRNNCell(
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self.input_size, self.hidden_size
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)
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self.rnn_st = paddle.nn.RNN(cell_st, time_major=self.time_major)
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st_out, st_last_h = self.rnn_st(
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rnn_in_data, pre_h_data, sequence_length=seq_len_data
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)
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self.fetch_list = [st_out, st_last_h]
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self.exe.run(paddle.static.default_startup_program())
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self.main_program = paddle.static.default_main_program()
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paddle.disable_static(self.place)
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def test_base(self, test_seq_len=False):
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x = np.random.randn(12, 4, 16)
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if not self.time_major:
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x = np.transpose(x, [1, 0, 2])
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prev_h = np.random.randn(4, 16)
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paddle.disable_static(self.place)
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if test_seq_len:
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seq_len = np.array([9, 10, 8, 12], "int64")
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else:
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seq_len = np.array([12, 12, 12, 12], "int64")
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y1, h1 = self.rnn_net(
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paddle.to_tensor(x),
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paddle.to_tensor(prev_h),
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sequence_length=paddle.to_tensor(seq_len),
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)
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paddle.enable_static()
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out = self.exe.run(
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self.main_program,
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feed={"x": x, "pre_h": prev_h, "seq_len": seq_len},
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fetch_list=[self.fetch_list],
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)
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y2, h2 = out
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np.testing.assert_allclose(y1.numpy(), y2, atol=1e-8, rtol=1e-5)
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np.testing.assert_allclose(h1.numpy(), h2, atol=1e-8, rtol=1e-5)
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def runTest(self):
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self.test_base()
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self.test_base(True)
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class TestGRU(unittest.TestCase):
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def __init__(self, time_major=True, direction="forward", place="cpu"):
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super().__init__("runTest")
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self.time_major = time_major
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self.direction = direction
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self.num_directions = 2 if direction in bidirectional_list else 1
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self.place = place
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self.batch_size = 4
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self.input_size = 16
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self.hidden_size = 16
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self.seq_len = 12
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self.seed = 1234
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def setUp(self):
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# Since `set_device` is global, set `set_device` in `setUp` rather than
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# `__init__` to avoid using an error device set by another test case.
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place = paddle.set_device(self.place)
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paddle.disable_static(self.place)
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paddle.seed(self.seed)
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if paddle.framework.use_pir_api():
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with paddle.pir_utils.OldIrGuard():
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# Note: dygraph use self.main_program.global_block().create_parameter(), it's need manual seed to old Program
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paddle.framework.random._manual_program_seed(self.seed)
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paddle.framework.random._manual_program_seed(self.seed)
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else:
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paddle.framework.random._manual_program_seed(self.seed)
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cell_dy = paddle.nn.GRUCell(self.input_size, self.hidden_size)
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self.rnn_net = paddle.nn.RNN(cell_dy, time_major=self.time_major)
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paddle.enable_static()
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with paddle.base.unique_name.guard():
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(
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main_program=main_program, startup_program=startup_program
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):
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paddle.seed(self.seed)
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paddle.framework.random._manual_program_seed(self.seed)
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self.exe = base.Executor(
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base.CPUPlace()
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if self.place == "cpu"
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else base.CUDAPlace(0)
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)
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rnn_in_data = paddle.static.data(
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"x",
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[None, self.batch_size, self.hidden_size],
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dtype="float64",
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)
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pre_h_data = paddle.static.data(
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"pre_h",
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[self.batch_size, self.hidden_size],
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dtype="float64",
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)
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seq_len_data = paddle.static.data(
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"seq_len", [self.batch_size], dtype="int64"
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)
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cell_st = paddle.nn.GRUCell(self.input_size, self.hidden_size)
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self.rnn_st = paddle.nn.RNN(cell_st, time_major=self.time_major)
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st_out, st_last_h = self.rnn_st(
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rnn_in_data, pre_h_data, sequence_length=seq_len_data
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)
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self.fetch_list = [st_out, st_last_h]
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self.exe.run(paddle.static.default_startup_program())
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self.main_program = paddle.static.default_main_program()
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paddle.disable_static(self.place)
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def test_base(self, test_seq_len=False):
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x = np.random.randn(12, 4, 16)
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if not self.time_major:
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x = np.transpose(x, [1, 0, 2])
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prev_h = np.random.randn(4, 16)
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paddle.disable_static(self.place)
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if test_seq_len:
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seq_len = np.array([9, 10, 8, 12], "int64")
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else:
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seq_len = np.array([12, 12, 12, 12], "int64")
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y1, h1 = self.rnn_net(
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paddle.to_tensor(x),
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paddle.to_tensor(prev_h),
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sequence_length=paddle.to_tensor(seq_len),
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)
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paddle.enable_static()
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out = self.exe.run(
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self.main_program,
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feed={"x": x, "pre_h": prev_h, "seq_len": seq_len},
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fetch_list=[self.fetch_list],
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)
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y2, h2 = out
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np.testing.assert_allclose(y1.numpy(), y2, atol=1e-8, rtol=1e-5)
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np.testing.assert_allclose(h1.numpy(), h2, atol=1e-8, rtol=1e-5)
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def runTest(self):
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self.test_base()
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self.test_base(True)
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class TestGRUBackward(unittest.TestCase):
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def __init__(self, time_major=True, direction="forward", place="cpu"):
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super().__init__("runTest")
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self.time_major = time_major
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self.direction = direction
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self.num_directions = 2 if direction in bidirectional_list else 1
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self.place = place
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self.batch_size = 4
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self.input_size = 4
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self.hidden_size = 4
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self.seq_len = 12
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self.seed = 1234
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def setUp(self):
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# Since `set_device` is global, set `set_device` in `setUp` rather than
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# `__init__` to avoid using an error device set by another test case.
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place = paddle.set_device(self.place)
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paddle.disable_static(self.place)
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paddle.seed(self.seed)
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if paddle.framework.use_pir_api():
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with paddle.pir_utils.OldIrGuard():
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# Note: dygraph use self.main_program.global_block().create_parameter(), it's need manual seed to old Program
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paddle.framework.random._manual_program_seed(self.seed)
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paddle.framework.random._manual_program_seed(self.seed)
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else:
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paddle.framework.random._manual_program_seed(self.seed)
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cell_dy = paddle.nn.SimpleRNNCell(self.input_size, self.hidden_size)
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self.rnn_net = paddle.nn.RNN(cell_dy, time_major=self.time_major)
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paddle.enable_static()
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with paddle.base.unique_name.guard():
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(
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main_program=main_program, startup_program=startup_program
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):
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paddle.seed(self.seed)
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paddle.framework.random._manual_program_seed(self.seed)
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self.exe = paddle.base.Executor(
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base.CPUPlace()
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if self.place == "cpu"
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else base.CUDAPlace(0)
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)
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rnn_in_data = paddle.static.data(
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"x",
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[None, self.batch_size, self.hidden_size],
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dtype="float64",
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)
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pre_h_data = paddle.static.data(
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"pre_h",
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[self.batch_size, self.hidden_size],
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dtype="float64",
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)
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seq_len_data = paddle.static.data(
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"seq_len", [self.batch_size], dtype="int64"
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)
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pre_h_data.stop_gradient = False
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rnn_in_data.stop_gradient = False
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cell_st = paddle.nn.SimpleRNNCell(
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self.input_size, self.hidden_size
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)
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self.rnn_st = paddle.nn.RNN(cell_st, time_major=self.time_major)
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st_out, st_last_h = self.rnn_st(
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rnn_in_data, pre_h_data, sequence_length=seq_len_data
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)
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loss = paddle.sum(st_out)
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sgd = paddle.optimizer.SGD(0.0)
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if paddle.framework.in_pir_mode():
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rnn_in_data.persistable = True
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pre_h_data.persistable = True
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params_grads = paddle.base.backward.append_backward(loss)
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pre_h_data_grad = None
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rnn_in_data_grad = None
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for p, g in params_grads:
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if p.is_same(rnn_in_data):
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rnn_in_data_grad = g
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elif p.is_same(pre_h_data):
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pre_h_data_grad = g
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self.fetch_list = [
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st_out,
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st_last_h,
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pre_h_data_grad,
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rnn_in_data_grad,
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]
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else:
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sgd.minimize(loss)
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self.fetch_list = [
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st_out,
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st_last_h,
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"pre_h@GRAD",
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"x@GRAD",
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]
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self.exe.run(paddle.static.default_startup_program())
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self.main_program = paddle.static.default_main_program()
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paddle.disable_static(self.place)
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def test_base(self, test_seq_len=False):
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x = np.random.randn(12, 4, self.hidden_size)
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if not self.time_major:
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x = np.transpose(x, [1, 0, 2])
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prev_h = np.random.randn(4, self.hidden_size)
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paddle.disable_static(self.place)
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if test_seq_len:
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seq_len = np.array([9, 10, 8, 12], "int64")
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else:
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seq_len = np.array([12, 12, 12, 12], "int64")
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x_in = paddle.to_tensor(x)
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h_in = paddle.to_tensor(prev_h)
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x_in.stop_gradient = False
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h_in.stop_gradient = False
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y1, h1 = self.rnn_net(
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x_in,
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h_in,
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sequence_length=paddle.to_tensor(seq_len),
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)
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loss = y1.sum()
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loss.backward()
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h1_grad = h_in.gradient()
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paddle.enable_static()
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out = self.exe.run(
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self.main_program,
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feed={"x": x, "pre_h": prev_h, "seq_len": seq_len},
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fetch_list=[self.fetch_list],
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)
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y2, h2, g1, g2 = out
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np.testing.assert_allclose(h1_grad, g1, atol=1e-8, rtol=1e-5)
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self.exe._executor_cache._get_cached_program_and_executor_pir_mode.cache_clear()
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def runTest(self):
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self.test_base(True)
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self.test_base()
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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