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1523 lines
65 KiB
1523 lines
65 KiB
# 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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from __future__ import print_function
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import sys
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import unittest
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.dygraph.nn import Embedding
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import paddle.fluid.framework as framework
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from paddle.fluid.optimizer import Adam
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from paddle.fluid.dygraph.base import to_variable
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from test_imperative_base import new_program_scope
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from paddle.fluid.executor import global_scope
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import numpy as np
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import six
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import pickle
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import os
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import errno
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class SimpleLSTMRNN(fluid.Layer):
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def __init__(self,
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name_scope,
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hidden_size,
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num_steps,
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num_layers=2,
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init_scale=0.1,
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dropout=None):
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super(SimpleLSTMRNN, self).__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.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=fluid.ParamAttr(
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initializer=fluid.initializer.UniformInitializer(
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low=-self._init_scale, high=self._init_scale)),
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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=fluid.initializer.UniformInitializer(
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low=-self._init_scale, high=self._init_scale))
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self.weight_1_arr.append(self.add_parameter('w_%d' % i, weight_1))
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bias_1 = self.create_parameter(
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attr=fluid.ParamAttr(
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initializer=fluid.initializer.UniformInitializer(
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low=-self._init_scale, high=self._init_scale)),
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shape=[self._hidden_size * 4],
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dtype="float32",
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default_initializer=fluid.initializer.Constant(0.0))
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self.bias_arr.append(self.add_parameter('b_%d' % 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 = fluid.layers.slice(
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init_hidden, axes=[0], starts=[i], ends=[i + 1])
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pre_cell = fluid.layers.slice(
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init_cell, axes=[0], starts=[i], ends=[i + 1])
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pre_hidden = fluid.layers.reshape(
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pre_hidden, shape=[-1, self._hidden_size])
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pre_cell = fluid.layers.reshape(
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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 = fluid.layers.slice(
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input_embedding, axes=[1], starts=[index], ends=[index + 1])
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self._input = fluid.layers.reshape(
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self._input, shape=[-1, self._hidden_size])
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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 = fluid.layers.concat([self._input, pre_hidden], 1)
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gate_input = fluid.layers.matmul(x=nn, y=weight_1)
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gate_input = fluid.layers.elementwise_add(gate_input, bias)
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i, j, f, o = fluid.layers.split(
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gate_input, num_or_sections=4, dim=-1)
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c = pre_cell * fluid.layers.sigmoid(f) + fluid.layers.sigmoid(
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i) * fluid.layers.tanh(j)
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m = fluid.layers.tanh(c) * fluid.layers.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 = fluid.layers.dropout(
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self._input,
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dropout_prob=self._dropout,
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dropout_implementation='upscale_in_train')
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res.append(
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fluid.layers.reshape(
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self._input, shape=[1, -1, self._hidden_size]))
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real_res = fluid.layers.concat(res, 0)
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real_res = fluid.layers.transpose(x=real_res, perm=[1, 0, 2])
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last_hidden = fluid.layers.concat(self.hidden_array, 1)
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last_hidden = fluid.layers.reshape(
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last_hidden, shape=[-1, self._num_layers, self._hidden_size])
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last_hidden = fluid.layers.transpose(x=last_hidden, perm=[1, 0, 2])
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last_cell = fluid.layers.concat(self.cell_array, 1)
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last_cell = fluid.layers.reshape(
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last_cell, shape=[-1, self._num_layers, self._hidden_size])
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last_cell = fluid.layers.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(fluid.Layer):
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def __init__(self,
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name_scope,
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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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dropout=None):
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super(PtbModel, self).__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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self.full_name(),
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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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self.embedding = Embedding(
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size=[vocab_size, hidden_size],
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dtype='float32',
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is_sparse=False,
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param_attr=fluid.ParamAttr(
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name='embedding_para',
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initializer=fluid.initializer.UniformInitializer(
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low=-init_scale, high=init_scale)))
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self.softmax_weight = self.create_parameter(
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attr=fluid.ParamAttr(),
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shape=[self.hidden_size, self.vocab_size],
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dtype="float32",
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default_initializer=fluid.initializer.UniformInitializer(
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low=-self.init_scale, high=self.init_scale))
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self.softmax_bias = self.create_parameter(
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attr=fluid.ParamAttr(),
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shape=[self.vocab_size],
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dtype="float32",
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default_initializer=fluid.initializer.UniformInitializer(
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low=-self.init_scale, high=self.init_scale))
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def forward(self, input, label, init_hidden, init_cell):
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init_h = fluid.layers.reshape(
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init_hidden, shape=[self.num_layers, -1, self.hidden_size])
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init_c = fluid.layers.reshape(
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init_cell, shape=[self.num_layers, -1, self.hidden_size])
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x_emb = self.embedding(input)
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x_emb = fluid.layers.reshape(
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x_emb, shape=[-1, self.num_steps, self.hidden_size])
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if self.dropout is not None and self.dropout > 0.0:
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x_emb = fluid.layers.dropout(
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x_emb,
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dropout_prob=self.drop_out,
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dropout_implementation='upscale_in_train')
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rnn_out, last_hidden, last_cell = self.simple_lstm_rnn(x_emb, init_h,
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init_c)
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rnn_out = fluid.layers.reshape(
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rnn_out, shape=[-1, self.num_steps, self.hidden_size])
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projection = fluid.layers.matmul(rnn_out, self.softmax_weight)
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projection = fluid.layers.elementwise_add(projection, self.softmax_bias)
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projection = fluid.layers.reshape(
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projection, shape=[-1, self.vocab_size])
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loss = fluid.layers.softmax_with_cross_entropy(
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logits=projection, label=label, soft_label=False)
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loss = fluid.layers.reshape(loss, shape=[-1, self.num_steps])
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loss = fluid.layers.reduce_mean(loss, dim=[0])
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loss = fluid.layers.reduce_sum(loss)
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return loss, last_hidden, last_cell
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class TestSaveLoadBase(unittest.TestCase):
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def test_ptb_rnn_cpu_float32(self):
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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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with new_program_scope():
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fluid.default_startup_program().random_seed = seed
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fluid.default_main_program().random_seed = seed
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ptb_model = PtbModel(
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"ptb_model",
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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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place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
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) else fluid.CUDAPlace(0)
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exe = fluid.Executor(place)
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sgd = Adam(learning_rate=1e-3)
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x = fluid.layers.data(
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name="x", shape=[-1, num_steps], dtype='int64')
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y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32')
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init_hidden = fluid.layers.data(
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name="init_hidden", shape=[1], dtype='float32')
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init_cell = fluid.layers.data(
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name="init_cell", shape=[1], dtype='float32')
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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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sgd.minimize(static_loss)
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static_param_updated = dict()
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static_param_init = dict()
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out = exe.run(framework.default_startup_program())
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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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init_cell_data = np.zeros(
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(num_layers, batch_size, hidden_size), dtype='float32')
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fetch_list = [static_loss, static_last_hidden, static_last_cell]
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out = exe.run(fluid.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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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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# get value before save
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main_program = framework.default_main_program()
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base_map = {}
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for var in main_program.list_vars():
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if isinstance(var, framework.Parameter) or var.persistable:
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t = np.array(fluid.global_scope().find_var(var.name)
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.get_tensor())
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# make sure all the paramerter or optimizer var have been update
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self.assertTrue(np.sum(np.abs(t)) != 0)
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base_map[var.name] = t
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fluid.save(main_program, "./test_1")
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# set var to zero
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for var in main_program.list_vars():
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if isinstance(var, framework.Parameter) or var.persistable:
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ten = fluid.global_scope().find_var(var.name).get_tensor()
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ten.set(np.zeros_like(np.array(ten)), place)
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new_t = np.array(fluid.global_scope().find_var(var.name)
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.get_tensor())
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# make sure all the paramerter or optimizer var have been set to zero
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self.assertTrue(np.sum(np.abs(new_t)) == 0)
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fluid.load(main_program, "./test_1.pdparams", exe)
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for var in main_program.list_vars():
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if isinstance(var, framework.Parameter) or var.persistable:
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new_t = np.array(fluid.global_scope().find_var(var.name)
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.get_tensor())
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base_t = base_map[var.name]
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self.assertTrue(np.array_equal(new_t, base_t))
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class TestSaveLoadPartial(unittest.TestCase):
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def test_ptb_rnn_cpu_float32(self):
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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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with new_program_scope():
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fluid.default_startup_program().random_seed = seed
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fluid.default_main_program().random_seed = seed
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ptb_model = PtbModel(
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"ptb_model",
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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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place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
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) else fluid.CUDAPlace(0)
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exe = fluid.Executor(place)
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sgd = Adam(learning_rate=1e-3)
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x = fluid.layers.data(
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name="x", shape=[-1, num_steps], dtype='int64')
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y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32')
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init_hidden = fluid.layers.data(
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name="init_hidden", shape=[1], dtype='float32')
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init_cell = fluid.layers.data(
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name="init_cell", shape=[1], dtype='float32')
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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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test_program = fluid.default_main_program().clone(for_test=True)
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add_1 = fluid.layers.fc(static_last_hidden,
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size=hidden_size,
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num_flatten_dims=2,
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bias_attr=False)
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sgd.minimize(static_loss)
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static_param_updated = dict()
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static_param_init = dict()
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out = exe.run(framework.default_startup_program())
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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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init_cell_data = np.zeros(
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(num_layers, batch_size, hidden_size), dtype='float32')
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fetch_list = [static_loss, static_last_hidden, static_last_cell]
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out = exe.run(fluid.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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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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# get value before save
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main_program = framework.default_main_program()
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base_map = {}
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for var in main_program.list_vars():
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if isinstance(var, framework.Parameter) or var.persistable:
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t = np.array(fluid.global_scope().find_var(var.name)
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.get_tensor())
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# make sure all the paramerter or optimizer var have been update
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self.assertTrue(np.sum(np.abs(t)) != 0)
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base_map[var.name] = t
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fluid.save(main_program, "./test_1")
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# set var to zero
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for var in main_program.list_vars():
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if isinstance(var, framework.Parameter) or var.persistable:
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ten = fluid.global_scope().find_var(var.name).get_tensor()
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ten.set(np.zeros_like(np.array(ten)), place)
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new_t = np.array(fluid.global_scope().find_var(var.name)
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.get_tensor())
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# make sure all the paramerter or optimizer var have been set to zero
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self.assertTrue(np.sum(np.abs(new_t)) == 0)
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fluid.load(test_program, "./test_1.pdopt", None)
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for var in test_program.list_vars():
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if isinstance(var, framework.Parameter) or var.persistable:
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new_t = np.array(fluid.global_scope().find_var(var.name)
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.get_tensor())
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base_t = base_map[var.name]
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self.assertTrue(np.array_equal(new_t, base_t))
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fluid.load(test_program, "./test_1.pdmodel", None)
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class TestSaveLoadSetStateDict(unittest.TestCase):
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def test_ptb_rnn_cpu_float32(self):
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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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with new_program_scope():
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fluid.default_startup_program().random_seed = seed
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fluid.default_main_program().random_seed = seed
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ptb_model = PtbModel(
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"ptb_model",
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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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place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
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) else fluid.CUDAPlace(0)
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exe = fluid.Executor(place)
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sgd = Adam(learning_rate=1e-3)
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x = fluid.layers.data(
|
|
name="x", shape=[-1, num_steps], dtype='int64')
|
|
y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32')
|
|
init_hidden = fluid.layers.data(
|
|
name="init_hidden", shape=[1], dtype='float32')
|
|
init_cell = fluid.layers.data(
|
|
name="init_cell", shape=[1], dtype='float32')
|
|
|
|
static_loss, static_last_hidden, static_last_cell = ptb_model(
|
|
x, y, init_hidden, init_cell)
|
|
sgd.minimize(static_loss)
|
|
static_param_updated = dict()
|
|
static_param_init = dict()
|
|
|
|
out = exe.run(framework.default_startup_program())
|
|
|
|
static_loss_value = None
|
|
static_last_cell_value = None
|
|
static_last_hidden_value = None
|
|
for i in range(batch_num):
|
|
x_data = np.arange(12).reshape(4, 3).astype('int64')
|
|
y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
|
|
x_data = x_data.reshape((-1, num_steps, 1))
|
|
y_data = y_data.reshape((-1, 1))
|
|
init_hidden_data = np.zeros(
|
|
(num_layers, batch_size, hidden_size), dtype='float32')
|
|
init_cell_data = np.zeros(
|
|
(num_layers, batch_size, hidden_size), dtype='float32')
|
|
fetch_list = [static_loss, static_last_hidden, static_last_cell]
|
|
out = exe.run(fluid.default_main_program(),
|
|
feed={
|
|
"x": x_data,
|
|
"y": y_data,
|
|
"init_hidden": init_hidden_data,
|
|
"init_cell": init_cell_data
|
|
},
|
|
fetch_list=fetch_list)
|
|
static_loss_value = out[0]
|
|
static_last_hidden_value = out[1]
|
|
static_last_cell_value = out[2]
|
|
|
|
# get value before save
|
|
main_program = framework.default_main_program()
|
|
base_map = {}
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been update
|
|
self.assertTrue(np.sum(np.abs(t)) != 0)
|
|
base_map[var.name] = t
|
|
|
|
fluid.save(main_program, "./test_1")
|
|
|
|
# set var to zero
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
ten = fluid.global_scope().find_var(var.name).get_tensor()
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been set to zero
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
fluid.load(main_program, "./test_1", exe)
|
|
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
base_t = base_map[var.name]
|
|
self.assertTrue(np.array_equal(new_t, base_t))
|
|
|
|
|
|
class TestProgramStatePartial(unittest.TestCase):
|
|
def test_ptb_rnn_cpu_float32(self):
|
|
seed = 90
|
|
hidden_size = 10
|
|
vocab_size = 1000
|
|
num_layers = 1
|
|
num_steps = 3
|
|
init_scale = 0.1
|
|
batch_size = 4
|
|
batch_num = 200
|
|
|
|
with new_program_scope():
|
|
fluid.default_startup_program().random_seed = seed
|
|
fluid.default_main_program().random_seed = seed
|
|
ptb_model = PtbModel(
|
|
"ptb_model",
|
|
hidden_size=hidden_size,
|
|
vocab_size=vocab_size,
|
|
num_layers=num_layers,
|
|
num_steps=num_steps,
|
|
init_scale=init_scale)
|
|
|
|
place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
|
|
) else fluid.CUDAPlace(0)
|
|
exe = fluid.Executor(place)
|
|
sgd = Adam(learning_rate=1e-3)
|
|
x = fluid.layers.data(
|
|
name="x", shape=[-1, num_steps], dtype='int64')
|
|
y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32')
|
|
init_hidden = fluid.layers.data(
|
|
name="init_hidden", shape=[1], dtype='float32')
|
|
init_cell = fluid.layers.data(
|
|
name="init_cell", shape=[1], dtype='float32')
|
|
|
|
static_loss, static_last_hidden, static_last_cell = ptb_model(
|
|
x, y, init_hidden, init_cell)
|
|
|
|
test_program = fluid.default_main_program().clone(for_test=True)
|
|
|
|
add_1 = fluid.layers.fc(static_last_hidden,
|
|
size=hidden_size,
|
|
num_flatten_dims=2,
|
|
bias_attr=False)
|
|
|
|
sgd.minimize(static_loss)
|
|
static_param_updated = dict()
|
|
static_param_init = dict()
|
|
|
|
out = exe.run(framework.default_startup_program())
|
|
|
|
static_loss_value = None
|
|
static_last_cell_value = None
|
|
static_last_hidden_value = None
|
|
for i in range(batch_num):
|
|
x_data = np.arange(12).reshape(4, 3).astype('int64')
|
|
y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
|
|
x_data = x_data.reshape((-1, num_steps, 1))
|
|
y_data = y_data.reshape((-1, 1))
|
|
init_hidden_data = np.zeros(
|
|
(num_layers, batch_size, hidden_size), dtype='float32')
|
|
init_cell_data = np.zeros(
|
|
(num_layers, batch_size, hidden_size), dtype='float32')
|
|
fetch_list = [static_loss, static_last_hidden, static_last_cell]
|
|
out = exe.run(fluid.default_main_program(),
|
|
feed={
|
|
"x": x_data,
|
|
"y": y_data,
|
|
"init_hidden": init_hidden_data,
|
|
"init_cell": init_cell_data
|
|
},
|
|
fetch_list=fetch_list)
|
|
static_loss_value = out[0]
|
|
static_last_hidden_value = out[1]
|
|
static_last_cell_value = out[2]
|
|
|
|
# get value before save
|
|
main_program = framework.default_main_program()
|
|
base_map = {}
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been update
|
|
self.assertTrue(np.sum(np.abs(t)) != 0)
|
|
base_map[var.name] = t
|
|
|
|
fluid.save(main_program, os.path.join('some_dir', 'test_1'))
|
|
|
|
# set var to zero
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
ten = fluid.global_scope().find_var(var.name).get_tensor()
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been set to zero
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
#fluid.load(test_program, "./test_1", None )
|
|
program_state = fluid.load_program_state(
|
|
os.path.join('some_dir', 'test_1'))
|
|
|
|
program_state_1 = fluid.load_program_state(
|
|
os.path.join('some_dir', 'test_1.pdparams'))
|
|
|
|
program_state_2 = fluid.load_program_state(
|
|
os.path.join('some_dir', 'test_1.pdopt'))
|
|
|
|
program_state_3 = fluid.load_program_state(
|
|
os.path.join('some_dir', 'test_1.pdmodel'))
|
|
|
|
fluid.set_program_state(test_program, program_state)
|
|
|
|
for var in test_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
base_t = base_map[var.name]
|
|
self.assertTrue(np.array_equal(new_t, base_t))
|
|
|
|
# check 1
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
ten = fluid.global_scope().find_var(var.name).get_tensor()
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been set to zero
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
fluid.set_program_state(test_program, program_state_1)
|
|
|
|
for var in test_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
base_t = base_map[var.name]
|
|
self.assertTrue(np.array_equal(new_t, base_t))
|
|
|
|
# check 2
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
ten = fluid.global_scope().find_var(var.name).get_tensor()
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been set to zero
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
fluid.set_program_state(test_program, program_state_2)
|
|
|
|
for var in test_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
base_t = base_map[var.name]
|
|
self.assertTrue(np.array_equal(new_t, base_t))
|
|
|
|
# check 3
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
ten = fluid.global_scope().find_var(var.name).get_tensor()
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been set to zero
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
fluid.set_program_state(test_program, program_state_3)
|
|
|
|
for var in test_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
base_t = base_map[var.name]
|
|
self.assertTrue(np.array_equal(new_t, base_t))
|
|
|
|
|
|
class TestVariableInit(unittest.TestCase):
|
|
def test_variable_init(self):
|
|
|
|
x = fluid.data(name="x", shape=[10, 10], dtype='float32')
|
|
y = fluid.layers.fc(x, 10)
|
|
z = fluid.layers.fc(y, 10)
|
|
|
|
place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
|
|
) else fluid.CUDAPlace(0)
|
|
exe = fluid.Executor(place)
|
|
exe.run(fluid.default_startup_program())
|
|
|
|
fluid.save(fluid.default_main_program(), "./test_path")
|
|
|
|
def set_var(var, ndarray):
|
|
t = var.get_tensor()
|
|
p = t._place()
|
|
if p.is_cpu_place():
|
|
place = paddle.fluid.CPUPlace()
|
|
elif p.is_cuda_pinned_place():
|
|
place = paddle.fluid.CUDAPinnedPlace()
|
|
else:
|
|
p = paddle.fluid.core.Place()
|
|
p.set_place(t._place())
|
|
place = paddle.fluid.CUDAPlace(p.gpu_device_id())
|
|
|
|
t.set(ndarray, place)
|
|
|
|
program = fluid.default_main_program()
|
|
new_scope = fluid.core.Scope()
|
|
|
|
place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
|
|
) else fluid.CUDAPlace(0)
|
|
exe = fluid.Executor(place)
|
|
parameter_list = list(
|
|
filter(fluid.io.is_parameter, program.list_vars()))
|
|
|
|
fluid.core._create_loaded_parameter(parameter_list, new_scope,
|
|
exe._default_executor)
|
|
parameter_file_name = "./test_path.pdparams"
|
|
with open(parameter_file_name, 'rb') as f:
|
|
load_dict = pickle.load(f)
|
|
|
|
for v in parameter_list:
|
|
assert v.name in load_dict, \
|
|
"Can not find [{}] in model file [{}]".format(
|
|
v.name, parameter_file_name)
|
|
new_v = new_scope.find_var(v.name)
|
|
set_var(new_v, load_dict[v.name])
|
|
|
|
opt_list = list(
|
|
filter(fluid.io.is_belong_to_optimizer, program.list_vars()))
|
|
|
|
fluid.core._create_loaded_parameter(opt_list, new_scope,
|
|
exe._default_executor)
|
|
opt_file_name = "./test_path.pdopt"
|
|
with open(opt_file_name, 'rb') as f:
|
|
load_dict = pickle.load(f)
|
|
|
|
for v in opt_list:
|
|
assert v.name in load_dict, \
|
|
"Can not find [{}] in model file [{}]".format(
|
|
v.name, opt_file_name)
|
|
|
|
new_v = new_scope.find_var(v.name)
|
|
set_var(new_v, load_dict[v.name])
|
|
|
|
base_map = {}
|
|
for var in program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been update
|
|
base_map[var.name] = t
|
|
|
|
for var in program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
new_t = np.array(new_scope.find_var(var.name).get_tensor())
|
|
base_t = base_map[var.name]
|
|
|
|
self.assertTrue(np.array_equal(new_t, base_t))
|
|
|
|
|
|
class TestLoadFromOldInterface(unittest.TestCase):
|
|
def setUp(self):
|
|
if os.path.exists("test_path.pdparams"):
|
|
os.remove("test_path.pdparams")
|
|
|
|
if os.path.exists("test_static_load_var_list.pdparams"):
|
|
os.remove("test_static_load_var_list.pdparams")
|
|
|
|
def test_load_from_old_interface(self):
|
|
seed = 90
|
|
hidden_size = 10
|
|
vocab_size = 1000
|
|
num_layers = 1
|
|
num_steps = 3
|
|
init_scale = 0.1
|
|
batch_size = 4
|
|
batch_num = 200
|
|
|
|
with new_program_scope():
|
|
fluid.default_startup_program().random_seed = seed
|
|
fluid.default_main_program().random_seed = seed
|
|
ptb_model = PtbModel(
|
|
"ptb_model",
|
|
hidden_size=hidden_size,
|
|
vocab_size=vocab_size,
|
|
num_layers=num_layers,
|
|
num_steps=num_steps,
|
|
init_scale=init_scale)
|
|
|
|
place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
|
|
) else fluid.CUDAPlace(0)
|
|
exe = fluid.Executor(place)
|
|
sgd = Adam(learning_rate=1e-3)
|
|
x = fluid.layers.data(
|
|
name="x", shape=[-1, num_steps], dtype='int64')
|
|
y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32')
|
|
init_hidden = fluid.layers.data(
|
|
name="init_hidden", shape=[1], dtype='float32')
|
|
init_cell = fluid.layers.data(
|
|
name="init_cell", shape=[1], dtype='float32')
|
|
|
|
static_loss, static_last_hidden, static_last_cell = ptb_model(
|
|
x, y, init_hidden, init_cell)
|
|
|
|
test_clone_program = fluid.default_main_program().clone()
|
|
sgd.minimize(static_loss)
|
|
static_param_updated = dict()
|
|
static_param_init = dict()
|
|
|
|
out = exe.run(framework.default_startup_program())
|
|
|
|
static_loss_value = None
|
|
static_last_cell_value = None
|
|
static_last_hidden_value = None
|
|
for i in range(batch_num):
|
|
x_data = np.arange(12).reshape(4, 3).astype('int64')
|
|
y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
|
|
x_data = x_data.reshape((-1, num_steps, 1))
|
|
y_data = y_data.reshape((-1, 1))
|
|
init_hidden_data = np.zeros(
|
|
(num_layers, batch_size, hidden_size), dtype='float32')
|
|
init_cell_data = np.zeros(
|
|
(num_layers, batch_size, hidden_size), dtype='float32')
|
|
fetch_list = [static_loss, static_last_hidden, static_last_cell]
|
|
out = exe.run(fluid.default_main_program(),
|
|
feed={
|
|
"x": x_data,
|
|
"y": y_data,
|
|
"init_hidden": init_hidden_data,
|
|
"init_cell": init_cell_data
|
|
},
|
|
fetch_list=fetch_list)
|
|
static_loss_value = out[0]
|
|
static_last_hidden_value = out[1]
|
|
static_last_cell_value = out[2]
|
|
|
|
# get value before save
|
|
main_program = framework.default_main_program()
|
|
base_map = {}
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been update
|
|
self.assertTrue(np.sum(np.abs(t)) != 0)
|
|
base_map[var.name] = t
|
|
|
|
#fluid.save(main_program, "./test_1")
|
|
fluid.io.save_persistables(exe, "test_path", main_program)
|
|
|
|
# set var to zero
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
ten = fluid.global_scope().find_var(var.name).get_tensor()
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been set to zero
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
fluid.load(main_program, "test_path", exe)
|
|
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
base_t = base_map[var.name]
|
|
self.assertTrue(np.array_equal(new_t, base_t))
|
|
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
ten = fluid.global_scope().find_var(var.name).get_tensor()
|
|
old_shape = np.array(ten).shape
|
|
new_shape = [e + 10 for e in old_shape]
|
|
|
|
var.desc.set_shape(new_shape)
|
|
with self.assertRaises(RuntimeError):
|
|
fluid.load(main_program, "test_path", exe)
|
|
|
|
# check unused parameter
|
|
|
|
fluid.load(test_clone_program, "test_path", exe)
|
|
|
|
def test_load_from_old_interface_var_list(self):
|
|
seed = 90
|
|
hidden_size = 10
|
|
vocab_size = 1000
|
|
num_layers = 1
|
|
num_steps = 3
|
|
init_scale = 0.1
|
|
batch_size = 4
|
|
batch_num = 200
|
|
|
|
with new_program_scope():
|
|
fluid.default_startup_program().random_seed = seed
|
|
fluid.default_main_program().random_seed = seed
|
|
ptb_model = PtbModel(
|
|
"ptb_model",
|
|
hidden_size=hidden_size,
|
|
vocab_size=vocab_size,
|
|
num_layers=num_layers,
|
|
num_steps=num_steps,
|
|
init_scale=init_scale)
|
|
|
|
place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
|
|
) else fluid.CUDAPlace(0)
|
|
exe = fluid.Executor(place)
|
|
sgd = Adam(learning_rate=1e-3)
|
|
x = fluid.layers.data(
|
|
name="x", shape=[-1, num_steps], dtype='int64')
|
|
y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32')
|
|
init_hidden = fluid.layers.data(
|
|
name="init_hidden", shape=[1], dtype='float32')
|
|
init_cell = fluid.layers.data(
|
|
name="init_cell", shape=[1], dtype='float32')
|
|
|
|
static_loss, static_last_hidden, static_last_cell = ptb_model(
|
|
x, y, init_hidden, init_cell)
|
|
|
|
test_clone_program = fluid.default_main_program().clone()
|
|
sgd.minimize(static_loss)
|
|
static_param_updated = dict()
|
|
static_param_init = dict()
|
|
|
|
out = exe.run(framework.default_startup_program())
|
|
|
|
static_loss_value = None
|
|
static_last_cell_value = None
|
|
static_last_hidden_value = None
|
|
for i in range(batch_num):
|
|
x_data = np.arange(12).reshape(4, 3).astype('int64')
|
|
y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
|
|
x_data = x_data.reshape((-1, num_steps, 1))
|
|
y_data = y_data.reshape((-1, 1))
|
|
init_hidden_data = np.zeros(
|
|
(num_layers, batch_size, hidden_size), dtype='float32')
|
|
init_cell_data = np.zeros(
|
|
(num_layers, batch_size, hidden_size), dtype='float32')
|
|
fetch_list = [static_loss, static_last_hidden, static_last_cell]
|
|
out = exe.run(fluid.default_main_program(),
|
|
feed={
|
|
"x": x_data,
|
|
"y": y_data,
|
|
"init_hidden": init_hidden_data,
|
|
"init_cell": init_cell_data
|
|
},
|
|
fetch_list=fetch_list)
|
|
static_loss_value = out[0]
|
|
static_last_hidden_value = out[1]
|
|
static_last_cell_value = out[2]
|
|
|
|
# get value before save
|
|
main_program = framework.default_main_program()
|
|
base_map = {}
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been update
|
|
self.assertTrue(np.sum(np.abs(t)) != 0)
|
|
base_map[var.name] = t
|
|
|
|
#fluid.save(main_program, "./test_1")
|
|
fluid.io.save_persistables(exe, "test_static_load_var_list",
|
|
main_program)
|
|
|
|
# set var to zero
|
|
var_list = []
|
|
for i, var in enumerate(main_program.list_vars()):
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if i % 2 == 0:
|
|
var_list.append(var)
|
|
ten = fluid.global_scope().find_var(var.name).get_tensor()
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been set to zero
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
fluid.load(main_program, "test_static_load_var_list", exe, var_list)
|
|
var_list_names = [var.name for var in var_list]
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
if var.name in var_list_names:
|
|
# loaded vars
|
|
base_t = base_map[var.name]
|
|
self.assertTrue(np.array_equal(new_t, base_t))
|
|
else:
|
|
#not loaded vars
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
|
|
class TestLoadFromOldInterfaceSingleFile(unittest.TestCase):
|
|
def test_load_from_old_interface(self):
|
|
seed = 90
|
|
hidden_size = 10
|
|
vocab_size = 1000
|
|
num_layers = 1
|
|
num_steps = 3
|
|
init_scale = 0.1
|
|
batch_size = 4
|
|
batch_num = 200
|
|
|
|
with new_program_scope():
|
|
fluid.default_startup_program().random_seed = seed
|
|
fluid.default_main_program().random_seed = seed
|
|
ptb_model = PtbModel(
|
|
"ptb_model",
|
|
hidden_size=hidden_size,
|
|
vocab_size=vocab_size,
|
|
num_layers=num_layers,
|
|
num_steps=num_steps,
|
|
init_scale=init_scale)
|
|
|
|
place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
|
|
) else fluid.CUDAPlace(0)
|
|
exe = fluid.Executor(place)
|
|
sgd = Adam(learning_rate=1e-3)
|
|
x = fluid.layers.data(
|
|
name="x", shape=[-1, num_steps], dtype='int64')
|
|
y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32')
|
|
init_hidden = fluid.layers.data(
|
|
name="init_hidden", shape=[1], dtype='float32')
|
|
init_cell = fluid.layers.data(
|
|
name="init_cell", shape=[1], dtype='float32')
|
|
|
|
static_loss, static_last_hidden, static_last_cell = ptb_model(
|
|
x, y, init_hidden, init_cell)
|
|
sgd.minimize(static_loss)
|
|
static_param_updated = dict()
|
|
static_param_init = dict()
|
|
|
|
out = exe.run(framework.default_startup_program())
|
|
|
|
static_loss_value = None
|
|
static_last_cell_value = None
|
|
static_last_hidden_value = None
|
|
for i in range(batch_num):
|
|
x_data = np.arange(12).reshape(4, 3).astype('int64')
|
|
y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
|
|
x_data = x_data.reshape((-1, num_steps, 1))
|
|
y_data = y_data.reshape((-1, 1))
|
|
init_hidden_data = np.zeros(
|
|
(num_layers, batch_size, hidden_size), dtype='float32')
|
|
init_cell_data = np.zeros(
|
|
(num_layers, batch_size, hidden_size), dtype='float32')
|
|
fetch_list = [static_loss, static_last_hidden, static_last_cell]
|
|
out = exe.run(fluid.default_main_program(),
|
|
feed={
|
|
"x": x_data,
|
|
"y": y_data,
|
|
"init_hidden": init_hidden_data,
|
|
"init_cell": init_cell_data
|
|
},
|
|
fetch_list=fetch_list)
|
|
static_loss_value = out[0]
|
|
static_last_hidden_value = out[1]
|
|
static_last_cell_value = out[2]
|
|
|
|
# get value before save
|
|
main_program = framework.default_main_program()
|
|
base_map = {}
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been update
|
|
self.assertTrue(np.sum(np.abs(t)) != 0)
|
|
base_map[var.name] = t
|
|
|
|
#fluid.save(main_program, "./test_1")
|
|
fluid.io.save_persistables(
|
|
exe, "test_path", main_program, filename="model_single")
|
|
|
|
# set var to zero
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
ten = fluid.global_scope().find_var(var.name).get_tensor()
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been set to zero
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
file_model_path = os.path.join("test_path", "model_single")
|
|
fluid.load(main_program, file_model_path, exe,
|
|
fluid.io.get_program_persistable_vars(main_program))
|
|
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
base_t = base_map[var.name]
|
|
self.assertTrue(np.array_equal(new_t, base_t))
|
|
|
|
# test exception
|
|
# change shape
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
ten = fluid.global_scope().find_var(var.name).get_tensor()
|
|
old_shape = np.array(ten).shape
|
|
new_shape = [e + 10 for e in old_shape]
|
|
|
|
var.desc.set_shape(new_shape)
|
|
|
|
with self.assertRaises(RuntimeError):
|
|
fluid.load(main_program, file_model_path, exe,
|
|
fluid.io.get_program_persistable_vars(main_program))
|
|
|
|
fluid.io.save_params(
|
|
exe, "test_path", main_program, filename="model_single")
|
|
with self.assertRaises(RuntimeError):
|
|
fluid.load(main_program, file_model_path, exe,
|
|
fluid.io.get_program_persistable_vars(main_program))
|
|
|
|
# check when executor is None
|
|
with self.assertRaises(ValueError):
|
|
fluid.load(main_program, file_model_path, None,
|
|
fluid.io.get_program_persistable_vars(main_program))
|
|
|
|
# check when var list is None
|
|
with self.assertRaises(ValueError):
|
|
fluid.load(main_program, file_model_path, exe, None)
|
|
|
|
# check save params, load var_list = get_program_persistable_vars
|
|
with self.assertRaises(RuntimeError):
|
|
temp_var = framework.Variable(
|
|
main_program.global_block(),
|
|
shape=[1],
|
|
name="test_temp_var")
|
|
all_var_list = list(main_program.list_vars())
|
|
fluid.load(main_program, file_model_path, exe,
|
|
all_var_list + [temp_var])
|
|
|
|
|
|
class TestProgramStateOldSave(unittest.TestCase):
|
|
def test_ptb_rnn_cpu_float32(self):
|
|
seed = 90
|
|
hidden_size = 10
|
|
vocab_size = 1000
|
|
num_layers = 1
|
|
num_steps = 3
|
|
init_scale = 0.1
|
|
batch_size = 4
|
|
batch_num = 200
|
|
|
|
with new_program_scope():
|
|
fluid.default_startup_program().random_seed = seed
|
|
fluid.default_main_program().random_seed = seed
|
|
ptb_model = PtbModel(
|
|
"ptb_model",
|
|
hidden_size=hidden_size,
|
|
vocab_size=vocab_size,
|
|
num_layers=num_layers,
|
|
num_steps=num_steps,
|
|
init_scale=init_scale)
|
|
|
|
place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
|
|
) else fluid.CUDAPlace(0)
|
|
exe = fluid.Executor(place)
|
|
sgd = Adam(learning_rate=1e-3)
|
|
x = fluid.layers.data(
|
|
name="x", shape=[-1, num_steps], dtype='int64')
|
|
y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32')
|
|
init_hidden = fluid.layers.data(
|
|
name="init_hidden", shape=[1], dtype='float32')
|
|
init_cell = fluid.layers.data(
|
|
name="init_cell", shape=[1], dtype='float32')
|
|
|
|
static_loss, static_last_hidden, static_last_cell = ptb_model(
|
|
x, y, init_hidden, init_cell)
|
|
|
|
test_program = fluid.default_main_program().clone(for_test=True)
|
|
|
|
add_1 = fluid.layers.fc(static_last_hidden,
|
|
size=hidden_size,
|
|
num_flatten_dims=2,
|
|
bias_attr=False)
|
|
|
|
sgd.minimize(static_loss)
|
|
static_param_updated = dict()
|
|
static_param_init = dict()
|
|
|
|
out = exe.run(framework.default_startup_program())
|
|
|
|
static_loss_value = None
|
|
static_last_cell_value = None
|
|
static_last_hidden_value = None
|
|
for i in range(batch_num):
|
|
x_data = np.arange(12).reshape(4, 3).astype('int64')
|
|
y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
|
|
x_data = x_data.reshape((-1, num_steps, 1))
|
|
y_data = y_data.reshape((-1, 1))
|
|
init_hidden_data = np.zeros(
|
|
(num_layers, batch_size, hidden_size), dtype='float32')
|
|
init_cell_data = np.zeros(
|
|
(num_layers, batch_size, hidden_size), dtype='float32')
|
|
fetch_list = [static_loss, static_last_hidden, static_last_cell]
|
|
out = exe.run(fluid.default_main_program(),
|
|
feed={
|
|
"x": x_data,
|
|
"y": y_data,
|
|
"init_hidden": init_hidden_data,
|
|
"init_cell": init_cell_data
|
|
},
|
|
fetch_list=fetch_list)
|
|
static_loss_value = out[0]
|
|
static_last_hidden_value = out[1]
|
|
static_last_cell_value = out[2]
|
|
|
|
# get value before save
|
|
main_program = framework.default_main_program()
|
|
base_map = {}
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been update
|
|
self.assertTrue(np.sum(np.abs(t)) != 0)
|
|
base_map[var.name] = t
|
|
|
|
fluid.io.save_persistables(exe, "test_program_1", main_program)
|
|
|
|
# set var to zero
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
ten = fluid.global_scope().find_var(var.name).get_tensor()
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been set to zero
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
# case 1: load basic
|
|
program_state = fluid.load_program_state("test_program_1")
|
|
fluid.set_program_state(main_program, program_state)
|
|
self.check_in_static(main_program, base_map)
|
|
|
|
# case 2: load with no need file
|
|
def symlink_force(target, link_name):
|
|
try:
|
|
self.create_symlink(target, link_name)
|
|
except OSError as e:
|
|
if e.errno == errno.EEXIST:
|
|
os.remove(link_name)
|
|
self.create_symlink(target, link_name)
|
|
else:
|
|
raise e
|
|
|
|
orig_filepath = './test_program_1/fc_0.w_0'
|
|
symlink_filepath = './test_program_1/link_fc_0.w_0'
|
|
# create a needless link file for coverage
|
|
symlink_force(orig_filepath, symlink_filepath)
|
|
program_state = fluid.load_program_state("test_program_1")
|
|
fluid.set_program_state(main_program, program_state)
|
|
self.check_in_static(main_program, base_map)
|
|
|
|
# case 3: load with var_list
|
|
program_state = fluid.load_program_state(
|
|
"test_program_1", main_program.all_parameters())
|
|
fluid.set_program_state(main_program, program_state)
|
|
self.check_in_static(main_program, base_map)
|
|
|
|
# make sure `load_program_state` can be used in dynamic graph mode
|
|
with fluid.dygraph.guard(place):
|
|
load_state = fluid.load_program_state("test_program_1")
|
|
for k, v in load_state.items():
|
|
self.assertTrue(np.array_equal(base_map[k], v))
|
|
|
|
def create_symlink(self, target, link_name):
|
|
try:
|
|
os.symlink(target, link_name)
|
|
except AttributeError:
|
|
import ctypes
|
|
kernel_dll = ctypes.windll.LoadLibrary("kernel32.dll")
|
|
kernel_dll.CreateSymbolicLinkA(target, link_name, 0)
|
|
|
|
def check_in_static(self, main_program, base_map):
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
base_t = base_map[var.name]
|
|
self.assertTrue(np.array_equal(new_t, base_t))
|
|
|
|
|
|
class TestProgramStateOldSaveSingleModel(unittest.TestCase):
|
|
def test_ptb_rnn_cpu_float32(self):
|
|
seed = 90
|
|
hidden_size = 10
|
|
vocab_size = 1000
|
|
num_layers = 1
|
|
num_steps = 3
|
|
init_scale = 0.1
|
|
batch_size = 4
|
|
batch_num = 200
|
|
|
|
with new_program_scope():
|
|
fluid.default_startup_program().random_seed = seed
|
|
fluid.default_main_program().random_seed = seed
|
|
ptb_model = PtbModel(
|
|
"ptb_model",
|
|
hidden_size=hidden_size,
|
|
vocab_size=vocab_size,
|
|
num_layers=num_layers,
|
|
num_steps=num_steps,
|
|
init_scale=init_scale)
|
|
|
|
place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
|
|
) else fluid.CUDAPlace(0)
|
|
exe = fluid.Executor(place)
|
|
sgd = Adam(learning_rate=1e-3)
|
|
x = fluid.layers.data(
|
|
name="x", shape=[-1, num_steps], dtype='int64')
|
|
y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32')
|
|
init_hidden = fluid.layers.data(
|
|
name="init_hidden", shape=[1], dtype='float32')
|
|
init_cell = fluid.layers.data(
|
|
name="init_cell", shape=[1], dtype='float32')
|
|
|
|
static_loss, static_last_hidden, static_last_cell = ptb_model(
|
|
x, y, init_hidden, init_cell)
|
|
|
|
test_program = fluid.default_main_program().clone(for_test=True)
|
|
|
|
add_1 = fluid.layers.fc(static_last_hidden,
|
|
size=hidden_size,
|
|
num_flatten_dims=2,
|
|
bias_attr=False)
|
|
|
|
sgd.minimize(static_loss)
|
|
static_param_updated = dict()
|
|
static_param_init = dict()
|
|
|
|
out = exe.run(framework.default_startup_program())
|
|
|
|
static_loss_value = None
|
|
static_last_cell_value = None
|
|
static_last_hidden_value = None
|
|
for i in range(batch_num):
|
|
x_data = np.arange(12).reshape(4, 3).astype('int64')
|
|
y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
|
|
x_data = x_data.reshape((-1, num_steps, 1))
|
|
y_data = y_data.reshape((-1, 1))
|
|
init_hidden_data = np.zeros(
|
|
(num_layers, batch_size, hidden_size), dtype='float32')
|
|
init_cell_data = np.zeros(
|
|
(num_layers, batch_size, hidden_size), dtype='float32')
|
|
fetch_list = [static_loss, static_last_hidden, static_last_cell]
|
|
out = exe.run(fluid.default_main_program(),
|
|
feed={
|
|
"x": x_data,
|
|
"y": y_data,
|
|
"init_hidden": init_hidden_data,
|
|
"init_cell": init_cell_data
|
|
},
|
|
fetch_list=fetch_list)
|
|
static_loss_value = out[0]
|
|
static_last_hidden_value = out[1]
|
|
static_last_cell_value = out[2]
|
|
|
|
# get value before save
|
|
main_program = framework.default_main_program()
|
|
base_map = {}
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been update
|
|
self.assertTrue(np.sum(np.abs(t)) != 0)
|
|
base_map[var.name] = t
|
|
|
|
fluid.io.save_persistables(
|
|
exe, "test_program_2", main_program, filename="model_1")
|
|
|
|
# set var to zero
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
ten = fluid.global_scope().find_var(var.name).get_tensor()
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been set to zero
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
#fluid.load(test_program, "./test_1", None )
|
|
program_state = fluid.load_program_state(
|
|
os.path.join("test_program_2", "model_1"),
|
|
var_list=fluid.io.get_program_persistable_vars(main_program))
|
|
fluid.set_program_state(main_program, program_state)
|
|
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
base_t = base_map[var.name]
|
|
self.assertTrue(np.array_equal(new_t, base_t))
|
|
|
|
with self.assertRaises(ValueError):
|
|
fluid.load_program_state(
|
|
os.path.join("test_program_2", "model_1"))
|
|
|
|
with self.assertRaises(TypeError):
|
|
fluid.load_program_state(
|
|
os.path.join("test_program_2", "model_1"),
|
|
var_list=["str"])
|
|
|
|
with self.assertRaises(RuntimeError):
|
|
fluid.load_program_state(
|
|
os.path.join("test_program_2", "model_1"),
|
|
var_list=[
|
|
main_program.global_block().create_var(
|
|
name="fake_var_name", persistable=True)
|
|
])
|
|
|
|
|
|
class TestStaticSaveLoadPickle(unittest.TestCase):
|
|
def test_pickle_protocol(self):
|
|
# enable static mode
|
|
paddle.enable_static()
|
|
|
|
with new_program_scope():
|
|
# create network
|
|
x = paddle.static.data(
|
|
name="static_save_load_large_x",
|
|
shape=[None, 10],
|
|
dtype='float32')
|
|
z = paddle.static.nn.fc(x, 10, bias_attr=False)
|
|
place = paddle.CPUPlace()
|
|
exe = paddle.static.Executor(place)
|
|
exe.run(paddle.static.default_startup_program())
|
|
prog = paddle.static.default_main_program()
|
|
|
|
base_map = {}
|
|
for var in prog.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
# make sure all the paramerter or optimizer var have been update
|
|
self.assertTrue(np.sum(np.abs(t)) != 0)
|
|
base_map[var.name] = t
|
|
|
|
path = os.path.join("test_static_save_load_pickle",
|
|
"pickle_protocol")
|
|
|
|
with self.assertRaises(ValueError):
|
|
paddle.fluid.save(prog, path, 2.0)
|
|
|
|
with self.assertRaises(ValueError):
|
|
paddle.fluid.save(prog, path, 1)
|
|
|
|
with self.assertRaises(ValueError):
|
|
paddle.fluid.save(prog, path, 5)
|
|
|
|
protocols = [2, ]
|
|
if sys.version_info.major >= 3 and sys.version_info.minor >= 4:
|
|
protocols += [3, 4]
|
|
for protocol in protocols:
|
|
paddle.fluid.save(prog, path, protocol)
|
|
# set var to zero
|
|
for var in prog.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
ten = fluid.global_scope().find_var(
|
|
var.name).get_tensor()
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
paddle.fluid.load(prog, path)
|
|
|
|
for var in prog.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
new_t = np.array(fluid.global_scope().find_var(var.name)
|
|
.get_tensor())
|
|
base_t = base_map[var.name]
|
|
self.assertTrue(np.array_equal(new_t, base_t))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|