wehub-resource-sync
470 lines
17 KiB
Python
470 lines
17 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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from utils import DyGraphProgramDescTracerTestHelper
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import paddle
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from paddle import base
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from paddle.autograd.backward_utils import ValueDict
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from paddle.base import core
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from paddle.nn import Embedding
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def create_parameter_mapping(startup_program, main_program):
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startup_params = {}
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main_params = {}
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parameter_mapping = ValueDict()
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for op in startup_program.global_block().ops:
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if op.name() == "builtin.set_parameter":
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name = op.attrs()["parameter_name"]
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param = op.operand(0).source()
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startup_params[name] = param
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for op in main_program.global_block().ops:
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if op.name() == "builtin.parameter":
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name = op.attrs()["parameter_name"]
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param = op.result(0)
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main_params[name] = param
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assert len(startup_params) == len(main_params)
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for name, startup_param in startup_params.items():
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assert name in main_params
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main_param = main_params[name]
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parameter_mapping[main_param] = startup_param
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return parameter_mapping
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class SimpleLSTMRNN(paddle.nn.Layer):
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def __init__(
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self, hidden_size, num_steps, num_layers=2, init_scale=0.1, dropout=None
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):
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super().__init__()
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self._hidden_size = hidden_size
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self._num_layers = num_layers
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self._init_scale = init_scale
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self._dropout = dropout
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self._input = None
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self._num_steps = num_steps
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self.cell_array = []
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self.hidden_array = []
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self._create_parameter()
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def _create_parameter(self):
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self.weight_1_arr = []
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self.weight_2_arr = []
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self.bias_arr = []
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self.mask_array = []
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for i in range(self._num_layers):
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weight_1 = self.create_parameter(
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attr=base.ParamAttr(
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initializer=paddle.nn.initializer.Uniform(
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low=-self._init_scale, high=self._init_scale
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)
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),
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shape=[self._hidden_size * 2, self._hidden_size * 4],
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dtype="float32",
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default_initializer=paddle.nn.initializer.Uniform(
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low=-self._init_scale, high=self._init_scale
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),
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)
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self.weight_1_arr.append(self.add_parameter(f'w_{i}', weight_1))
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bias_1 = self.create_parameter(
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attr=base.ParamAttr(
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initializer=paddle.nn.initializer.Uniform(
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low=-self._init_scale, high=self._init_scale
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)
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),
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shape=[self._hidden_size * 4],
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dtype="float32",
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default_initializer=paddle.nn.initializer.Constant(0.0),
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)
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self.bias_arr.append(self.add_parameter(f'b_{i}', bias_1))
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def forward(self, input_embedding, init_hidden=None, init_cell=None):
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self.cell_array = []
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self.hidden_array = []
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for i in range(self._num_layers):
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pre_hidden = paddle.slice(
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init_hidden, axes=[0], starts=[i], ends=[i + 1]
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)
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pre_cell = paddle.slice(
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init_cell, axes=[0], starts=[i], ends=[i + 1]
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)
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pre_hidden = paddle.reshape(
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pre_hidden, shape=[-1, self._hidden_size]
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)
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pre_cell = paddle.reshape(pre_cell, shape=[-1, self._hidden_size])
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self.hidden_array.append(pre_hidden)
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self.cell_array.append(pre_cell)
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res = []
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for index in range(self._num_steps):
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self._input = paddle.slice(
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input_embedding, axes=[1], starts=[index], ends=[index + 1]
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)
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self._input = paddle.reshape(
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self._input, shape=[-1, self._hidden_size]
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)
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for k in range(self._num_layers):
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pre_hidden = self.hidden_array[k]
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pre_cell = self.cell_array[k]
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weight_1 = self.weight_1_arr[k]
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bias = self.bias_arr[k]
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nn = paddle.concat([self._input, pre_hidden], 1)
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gate_input = paddle.matmul(x=nn, y=weight_1)
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gate_input = paddle.add(gate_input, bias)
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i, j, f, o = paddle.split(
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gate_input, num_or_sections=4, axis=-1
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)
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c = pre_cell * paddle.nn.functional.sigmoid(
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f
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) + paddle.nn.functional.sigmoid(i) * paddle.tanh(j)
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m = paddle.tanh(c) * paddle.nn.functional.sigmoid(o)
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self.hidden_array[k] = m
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self.cell_array[k] = c
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self._input = m
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if self._dropout is not None and self._dropout > 0.0:
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self._input = paddle.nn.functional.dropout(
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self._input,
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p=self._dropout,
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mode='upscale_in_train',
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)
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res.append(
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paddle.reshape(self._input, shape=[1, -1, self._hidden_size])
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)
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real_res = paddle.concat(res, 0)
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real_res = paddle.transpose(x=real_res, perm=[1, 0, 2])
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last_hidden = paddle.concat(self.hidden_array, 1)
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last_hidden = paddle.reshape(
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last_hidden, shape=[-1, self._num_layers, self._hidden_size]
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)
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last_hidden = paddle.transpose(x=last_hidden, perm=[1, 0, 2])
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last_cell = paddle.concat(self.cell_array, 1)
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last_cell = paddle.reshape(
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last_cell, shape=[-1, self._num_layers, self._hidden_size]
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)
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last_cell = paddle.transpose(x=last_cell, perm=[1, 0, 2])
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return real_res, last_hidden, last_cell
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class PtbModel(paddle.nn.Layer):
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def __init__(
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self,
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hidden_size,
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vocab_size,
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num_layers=2,
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num_steps=20,
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init_scale=0.1,
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is_sparse=False,
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dropout=None,
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):
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super().__init__()
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self.hidden_size = hidden_size
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self.vocab_size = vocab_size
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self.init_scale = init_scale
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self.num_layers = num_layers
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self.num_steps = num_steps
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self.dropout = dropout
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self.simple_lstm_rnn = SimpleLSTMRNN(
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hidden_size,
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num_steps,
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num_layers=num_layers,
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init_scale=init_scale,
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dropout=dropout,
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)
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self.embedding = Embedding(
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vocab_size,
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hidden_size,
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sparse=is_sparse,
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weight_attr=base.ParamAttr(
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name='embedding_para',
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initializer=paddle.nn.initializer.Uniform(
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low=-init_scale, high=init_scale
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),
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),
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)
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self.softmax_weight = self.create_parameter(
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attr=base.ParamAttr(),
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shape=[self.hidden_size, self.vocab_size],
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dtype="float32",
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default_initializer=paddle.nn.initializer.Uniform(
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low=-self.init_scale, high=self.init_scale
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),
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)
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self.softmax_bias = self.create_parameter(
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attr=base.ParamAttr(),
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shape=[self.vocab_size],
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dtype="float32",
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default_initializer=paddle.nn.initializer.Uniform(
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low=-self.init_scale, high=self.init_scale
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),
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)
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def forward(self, input, label, init_hidden, init_cell):
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init_h = paddle.reshape(
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init_hidden, shape=[self.num_layers, -1, self.hidden_size]
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)
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init_c = paddle.reshape(
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init_cell, shape=[self.num_layers, -1, self.hidden_size]
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)
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x_emb = self.embedding(input)
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x_emb = paddle.reshape(
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x_emb, shape=[-1, self.num_steps, self.hidden_size]
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)
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if self.dropout is not None and self.dropout > 0.0:
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x_emb = paddle.nn.functional.dropout(
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x_emb,
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p=self.drop_out,
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mode='upscale_in_train',
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)
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rnn_out, last_hidden, last_cell = self.simple_lstm_rnn(
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x_emb, init_h, init_c
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)
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rnn_out = paddle.reshape(
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rnn_out, shape=[-1, self.num_steps, self.hidden_size]
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)
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projection = paddle.matmul(rnn_out, self.softmax_weight)
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projection = paddle.add(projection, self.softmax_bias)
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projection = paddle.reshape(projection, shape=[-1, self.vocab_size])
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loss = paddle.nn.functional.softmax_with_cross_entropy(
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logits=projection, label=label, soft_label=False
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)
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loss = paddle.reshape(loss, shape=[-1, self.num_steps])
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loss = paddle.mean(loss, axis=[0])
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loss = paddle.sum(loss)
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return loss, last_hidden, last_cell
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class TestDygraphPtbRnn(unittest.TestCase):
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def test_ptb_rnn(self):
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for is_sparse in [True, False]:
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self.ptb_rnn_cpu_float32(is_sparse)
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def ptb_rnn_cpu_float32(self, is_sparse):
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seed = 90
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hidden_size = 10
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vocab_size = 1000
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num_layers = 1
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num_steps = 3
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init_scale = 0.1
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batch_size = 4
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batch_num = 200
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traced_layer = None
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with base.dygraph.guard():
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paddle.seed(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(seed)
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paddle.framework.random._manual_program_seed(seed)
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else:
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paddle.framework.random._manual_program_seed(seed)
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# TODO: marsyang1993 Change seed to
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ptb_model = PtbModel(
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hidden_size=hidden_size,
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vocab_size=vocab_size,
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num_layers=num_layers,
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num_steps=num_steps,
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init_scale=init_scale,
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is_sparse=is_sparse,
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)
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sgd = paddle.optimizer.SGD(
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learning_rate=1e-3, parameters=ptb_model.parameters()
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)
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dy_param_updated = {}
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dy_param_init = {}
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dy_loss = None
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last_hidden = None
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last_cell = None
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helper = DyGraphProgramDescTracerTestHelper(self)
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program = None
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for i in range(batch_num):
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x_data = np.arange(12).reshape(4, 3).astype('int64')
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y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
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y_data = y_data.reshape((-1, 1))
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init_hidden_data = np.zeros(
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(num_layers, batch_size, hidden_size), dtype='float32'
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)
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init_cell_data = np.zeros(
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(num_layers, batch_size, hidden_size), dtype='float32'
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)
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x = paddle.to_tensor(x_data)
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y = paddle.to_tensor(y_data)
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init_hidden = paddle.to_tensor(init_hidden_data)
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init_cell = paddle.to_tensor(init_cell_data)
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outs = ptb_model(x, y, init_hidden, init_cell)
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dy_loss, last_hidden, last_cell = outs
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if i == 0:
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for param in ptb_model.parameters():
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dy_param_init[param.name] = param.numpy()
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dy_loss.backward()
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sgd.minimize(dy_loss)
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ptb_model.clear_gradients()
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if i == batch_num - 1:
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for param in ptb_model.parameters():
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dy_param_updated[param.name] = param.numpy()
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dy_loss_value = dy_loss.numpy()
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dy_last_cell_value = last_cell.numpy()
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dy_last_hidden_value = last_hidden.numpy()
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paddle.enable_static()
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with new_program_scope():
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paddle.seed(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(seed)
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paddle.framework.random._manual_program_seed(seed)
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else:
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paddle.framework.random._manual_program_seed(seed)
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ptb_model = PtbModel(
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hidden_size=hidden_size,
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vocab_size=vocab_size,
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num_layers=num_layers,
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num_steps=num_steps,
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init_scale=init_scale,
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is_sparse=is_sparse,
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)
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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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sgd = paddle.optimizer.SGD(learning_rate=1e-3)
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x = paddle.static.data(
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name="x", shape=[-1, num_steps], dtype='int64'
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)
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y = paddle.static.data(name="y", shape=[-1, 1], dtype='float32')
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init_hidden = paddle.static.data(
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name="init_hidden", shape=[-1, 1], dtype='float32'
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)
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init_cell = paddle.static.data(
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name="init_cell", shape=[-1, 1], dtype='float32'
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)
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if not paddle.framework.use_pir_api():
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x.desc.set_need_check_feed(False)
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y.desc.set_need_check_feed(False)
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init_hidden.desc.set_need_check_feed(False)
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init_cell.desc.set_need_check_feed(False)
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static_loss, static_last_hidden, static_last_cell = ptb_model(
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x, y, init_hidden, init_cell
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)
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sgd.minimize(static_loss)
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static_param_updated = {}
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static_param_init = {}
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static_param_name_list = []
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static_params = []
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for param in ptb_model.parameters():
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static_param_name_list.append(param.name)
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static_params.append(param)
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if paddle.framework.use_pir_api():
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parameter_mapping = create_parameter_mapping(
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paddle.static.default_startup_program(),
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paddle.static.default_main_program(),
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)
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startup_params = [
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parameter_mapping[param] for param in static_params
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]
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else:
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startup_params = static_params
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out = exe.run(
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paddle.static.default_startup_program(),
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fetch_list=startup_params,
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)
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for i in range(len(static_params)):
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param_name = static_param_name_list[i]
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static_param_init[param_name] = out[i]
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static_loss_value = None
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static_last_cell_value = None
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static_last_hidden_value = None
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for i in range(batch_num):
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x_data = np.arange(12).reshape(4, 3).astype('int64')
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y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
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x_data = x_data.reshape((-1, num_steps, 1))
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y_data = y_data.reshape((-1, 1))
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init_hidden_data = np.zeros(
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(num_layers, batch_size, hidden_size), dtype='float32'
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)
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init_cell_data = np.zeros(
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(num_layers, batch_size, hidden_size), dtype='float32'
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)
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fetch_list = [static_loss, static_last_hidden, static_last_cell]
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fetch_list.extend(static_params)
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out = exe.run(
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base.default_main_program(),
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feed={
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"x": x_data,
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"y": y_data,
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"init_hidden": init_hidden_data,
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"init_cell": init_cell_data,
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},
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fetch_list=fetch_list,
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)
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static_loss_value = out[0]
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static_last_hidden_value = out[1]
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static_last_cell_value = out[2]
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if i == batch_num - 1:
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for k in range(3, len(out)):
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static_param_updated[static_param_name_list[k - 3]] = (
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out[k]
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)
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np.testing.assert_array_equal(static_loss_value, dy_loss_value)
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np.testing.assert_array_equal(
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static_last_cell_value, dy_last_cell_value
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)
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np.testing.assert_array_equal(
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static_last_hidden_value, dy_last_hidden_value
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)
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for key, value in static_param_init.items():
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np.testing.assert_array_equal(value, dy_param_init[key])
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for key, value in static_param_updated.items():
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np.testing.assert_allclose(
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value, dy_param_updated[key], atol=1e-10, rtol=1e-6
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)
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paddle.disable_static()
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if __name__ == '__main__':
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unittest.main()
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