154 lines
5.2 KiB
Python
154 lines
5.2 KiB
Python
# Copyright 2019 The TensorFlow 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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# ==============================================================================
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"""Tests for gradients of (block) LSTM/GRU operations."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import functools
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import numpy as np
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gen_rnn_ops
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from tensorflow.python.ops import gradients
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import rnn_grad # pylint: disable=unused-import
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from tensorflow.python.platform import test
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class RNNGradTest(test.TestCase):
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@test_util.deprecated_graph_mode_only
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def testBlockLSTMV1V2Consistency(self):
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num_steps = 1
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batch_size = 1
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input_size = 1
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hidden_size = 8
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w = deterministic_random_uniform(
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[input_size + hidden_size, 4 * hidden_size])
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b = deterministic_random_uniform([4 * hidden_size])
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x = deterministic_random_uniform([num_steps, batch_size, input_size])
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cs_prev = h_prev = deterministic_random_uniform([batch_size, hidden_size])
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all_cs, all_h = self._lstm_block(
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functools.partial(
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gen_rnn_ops.BlockLSTM,
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forget_bias=0.0, # Disable to match V2 default.
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cell_clip=0.0), # Disable to match V2 default.
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w, b, x, cs_prev, h_prev)
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w_grad, b_grad = gradients.gradients(all_cs + all_h, [w, b])
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w_ifco, b_ifco = icfo_to_ifco(w, b)
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all_cs_ifco, all_h_ifco = self._lstm_block(
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gen_rnn_ops.BlockLSTMV2, w_ifco, b_ifco, x, cs_prev, h_prev)
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w_ifco_grad, b_ifco_grad = gradients.gradients(
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all_cs_ifco + all_h_ifco, [w_ifco, b_ifco])
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self.assertAllEqual(all_cs, all_cs_ifco)
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self.assertAllEqual(all_h, all_h_ifco)
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self.assertAllEqual(w_grad, w_ifco_grad)
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self.assertAllEqual(b_grad, b_ifco_grad)
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@test_util.deprecated_graph_mode_only
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def testLSTMBlockCell(self):
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batch_size = np.random.randint(1, 32)
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input_size = np.random.randint(1, 32)
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hidden_size = np.random.randint(1, 32)
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w = deterministic_random_uniform(
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[input_size + hidden_size, 4 * hidden_size])
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b = deterministic_random_uniform([4 * hidden_size])
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x = deterministic_random_uniform([batch_size, input_size])
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cs_prev = h_prev = deterministic_random_uniform([batch_size, hidden_size])
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w_peephole = array_ops.zeros(cs_prev.shape[1:], dtype=w.dtype)
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cs_grad = deterministic_random_uniform([batch_size, hidden_size])
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h_grad = deterministic_random_uniform([batch_size, hidden_size])
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outputs = []
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grads = []
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for use_gpu in [False, True]:
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with self.cached_session(use_gpu=use_gpu):
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output = gen_rnn_ops.lstm_block_cell(
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x=x,
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cs_prev=cs_prev,
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h_prev=h_prev,
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w=w,
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wci=w_peephole,
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wcf=w_peephole,
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wco=w_peephole,
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b=b,
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forget_bias=1.0,
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cell_clip=0.0,
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use_peephole=False)
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(i, cs, f, o, ci, co, _) = output
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grad = gen_rnn_ops.lstm_block_cell_grad(
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x=x,
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cs_prev=cs_prev,
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h_prev=h_prev,
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w=w,
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wci=w_peephole,
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wcf=w_peephole,
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wco=w_peephole,
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b=b,
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i=i,
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cs=cs,
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f=f,
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o=o,
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ci=ci,
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co=co,
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cs_grad=cs_grad,
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h_grad=h_grad,
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use_peephole=False)
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outputs.append(output)
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grads.append(grad)
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self.assertAllClose(outputs[0], outputs[1])
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self.assertAllClose(grads[0], grads[1])
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def _lstm_block(self, op, w, b, x, cs_prev, h_prev):
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w_peephole = array_ops.zeros(cs_prev.shape[1:], dtype=w.dtype)
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_, all_cs, _, _, _, _, all_h = op(
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seq_len_max=math_ops.cast(array_ops.shape(x)[0], dtypes.int64),
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x=x,
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cs_prev=cs_prev,
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h_prev=h_prev,
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w=w,
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wci=w_peephole,
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wcf=w_peephole,
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wco=w_peephole,
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b=b,
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use_peephole=False)
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return all_cs, all_h
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def deterministic_random_uniform(shape):
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return ops.convert_to_tensor(np.random.random(shape), dtype=dtypes.float32)
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def icfo_to_ifco(w, b):
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"""Convert gates' weights and biases from ICFO to IFCO layout."""
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w_i, w_c, w_f, w_o = array_ops.split(w, num_or_size_splits=4, axis=1)
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b_i, b_c, b_f, b_o = array_ops.split(b, num_or_size_splits=4)
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w_ifco = array_ops.concat([w_i, w_f, w_c, w_o], axis=1)
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b_ifco = array_ops.concat([b_i, b_f, b_c, b_o], axis=0)
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return w_ifco, b_ifco
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if __name__ == "__main__":
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test.main()
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