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928 lines
35 KiB
928 lines
35 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 os
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import unittest
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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, Linear
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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 paddle.fluid.dygraph.learning_rate_scheduler import LearningRateDecay
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from test_imperative_base import new_program_scope
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import numpy as np
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import six
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import paddle
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class SimpleLSTMRNN(fluid.Layer):
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def __init__(self,
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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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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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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 TestDygraphPtbRnn(unittest.TestCase):
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def setUp(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 fluid.dygraph.guard():
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paddle.seed(seed)
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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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bd = []
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lr_arr = [1.0]
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# this a fake lr decay strategy
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for i in range(1, 10):
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bd.append(100 * i)
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new_lr = 1.0
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lr_arr.append(new_lr)
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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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adam = Adam(
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learning_rate=fluid.layers.piecewise_decay(
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boundaries=bd, values=lr_arr),
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parameter_list=ptb_model.parameters())
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dy_param_updated = dict()
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dy_param_init = dict()
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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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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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init_cell_data = np.zeros(
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(num_layers, batch_size, hidden_size), dtype='float32')
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x = to_variable(x_data)
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y = to_variable(y_data)
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init_hidden = to_variable(init_hidden_data)
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init_cell = to_variable(init_cell_data)
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dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
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init_cell)
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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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adam.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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# check optimizer
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self.opti_dict = adam.state_dict()
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self.base_opti = {}
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for k, v in self.opti_dict.items():
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if isinstance(v, core.VarBase):
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self.base_opti[v.name] = v.numpy()
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self.assertTrue(np.sum(np.abs(v.numpy())) != 0)
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else:
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self.base_opti[k] = v
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fluid.save_dygraph(self.opti_dict, "./test_dy")
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self.state_dict = ptb_model.state_dict()
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self.model_base = {}
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for k, v in self.state_dict.items():
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np_t = v.numpy()
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self.model_base[k] = np_t
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fluid.save_dygraph(self.state_dict, "./test_dy")
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def testLoadAndSetVarBase(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 fluid.dygraph.guard():
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paddle.seed(seed)
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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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bd = []
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lr_arr = [1.0]
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# this a fake lr decay strategy
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for i in range(1, 10):
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bd.append(100 * i)
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new_lr = 1.0
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lr_arr.append(new_lr)
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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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adam = Adam(
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learning_rate=fluid.layers.piecewise_decay(
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boundaries=bd, values=lr_arr),
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parameter_list=ptb_model.parameters())
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dy_param_updated = dict()
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dy_param_init = dict()
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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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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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init_cell_data = np.zeros(
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(num_layers, batch_size, hidden_size), dtype='float32')
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x = to_variable(x_data)
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y = to_variable(y_data)
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init_hidden = to_variable(init_hidden_data)
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init_cell = to_variable(init_cell_data)
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dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
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init_cell)
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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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adam.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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# check optimizer
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opti_dict = adam.state_dict()
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# set to zero
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for k, v in opti_dict.items():
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if isinstance(v, core.VarBase):
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np_t = v.numpy()
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var = v.value().get_tensor()
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var.set(np.zeros_like(np_t), place)
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self.assertTrue(np.sum(np.abs(v.numpy())) == 0)
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if isinstance(adam._learning_rate, LearningRateDecay):
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adam._learning_rate.step_num = 0
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para_state_dict, opti_state_dict = fluid.load_dygraph("./test_dy")
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adam.set_state_dict(opti_state_dict)
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opti_dict = adam.state_dict()
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for k, v in opti_dict.items():
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if isinstance(v, core.VarBase):
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self.assertTrue(
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np.array_equal(v.numpy(), self.base_opti[v.name]))
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else:
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self.assertEqual(v, self.base_opti[k])
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# check parameter
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state_dict = ptb_model.state_dict()
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for k, v in state_dict.items():
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np_t = v.numpy()
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var = v.value().get_tensor()
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var.set(np.zeros_like(np_t), place)
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ptb_model.set_state_dict(stat_dict=para_state_dict)
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state_dict = ptb_model.state_dict()
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for k, v in state_dict.items():
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new_t = v.numpy()
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base_t = self.model_base[k]
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self.assertTrue(np.array_equal(new_t, base_t))
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def testSetVariable(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 fluid.dygraph.guard():
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paddle.seed(seed)
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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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bd = []
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lr_arr = [1.0]
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# this a fake lr decay strategy
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for i in range(1, 10):
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bd.append(100 * i)
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new_lr = 1.0
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lr_arr.append(new_lr)
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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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adam = Adam(
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learning_rate=fluid.layers.piecewise_decay(
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boundaries=bd, values=lr_arr),
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parameter_list=ptb_model.parameters())
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dy_param_updated = dict()
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dy_param_init = dict()
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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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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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init_cell_data = np.zeros(
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(num_layers, batch_size, hidden_size), dtype='float32')
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x = to_variable(x_data)
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y = to_variable(y_data)
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init_hidden = to_variable(init_hidden_data)
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init_cell = to_variable(init_cell_data)
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dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
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init_cell)
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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()
|
|
adam.minimize(dy_loss)
|
|
ptb_model.clear_gradients()
|
|
if i == batch_num - 1:
|
|
for param in ptb_model.parameters():
|
|
dy_param_updated[param.name] = param.numpy()
|
|
|
|
# check optimizer
|
|
opti_dict = adam.state_dict()
|
|
# set to zero
|
|
for k, v in opti_dict.items():
|
|
if isinstance(v, core.VarBase):
|
|
np_t = v.numpy()
|
|
var = v.value().get_tensor()
|
|
var.set(np.zeros_like(np_t), place)
|
|
|
|
self.assertTrue(np.sum(np.abs(v.numpy())) == 0)
|
|
|
|
if isinstance(adam._learning_rate, LearningRateDecay):
|
|
adam._learning_rate.step_num = 0
|
|
|
|
adam.set_state_dict(self.opti_dict)
|
|
opti_dict = adam.state_dict()
|
|
for k, v in opti_dict.items():
|
|
if isinstance(v, core.VarBase):
|
|
self.assertTrue(
|
|
np.array_equal(v.numpy(), self.base_opti[v.name]))
|
|
else:
|
|
self.assertEqual(v, self.base_opti[k])
|
|
|
|
# check parameter
|
|
state_dict = ptb_model.state_dict()
|
|
for k, v in state_dict.items():
|
|
np_t = v.numpy()
|
|
var = v.value().get_tensor()
|
|
|
|
var.set(np.zeros_like(np_t), place)
|
|
|
|
ptb_model.set_state_dict(self.state_dict)
|
|
|
|
state_dict = ptb_model.state_dict()
|
|
|
|
for k, v in state_dict.items():
|
|
new_t = v.numpy()
|
|
|
|
base_t = self.model_base[k]
|
|
|
|
self.assertTrue(np.array_equal(new_t, base_t))
|
|
|
|
def testSetNumpy(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 fluid.dygraph.guard():
|
|
paddle.seed(seed)
|
|
paddle.framework.random._manual_program_seed(seed)
|
|
# TODO: marsyang1993 Change seed to
|
|
ptb_model = PtbModel(
|
|
hidden_size=hidden_size,
|
|
vocab_size=vocab_size,
|
|
num_layers=num_layers,
|
|
num_steps=num_steps,
|
|
init_scale=init_scale)
|
|
|
|
bd = []
|
|
lr_arr = [1.0]
|
|
# this a fake lr decay strategy
|
|
for i in range(1, 10):
|
|
bd.append(100 * i)
|
|
new_lr = 1.0
|
|
lr_arr.append(new_lr)
|
|
|
|
place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
|
|
) else fluid.CUDAPlace(0)
|
|
adam = Adam(
|
|
learning_rate=fluid.layers.piecewise_decay(
|
|
boundaries=bd, values=lr_arr),
|
|
parameter_list=ptb_model.parameters())
|
|
dy_param_updated = dict()
|
|
dy_param_init = dict()
|
|
dy_loss = None
|
|
last_hidden = None
|
|
last_cell = 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')
|
|
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')
|
|
x = to_variable(x_data)
|
|
y = to_variable(y_data)
|
|
init_hidden = to_variable(init_hidden_data)
|
|
init_cell = to_variable(init_cell_data)
|
|
dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
|
|
init_cell)
|
|
if i == 0:
|
|
for param in ptb_model.parameters():
|
|
dy_param_init[param.name] = param.numpy()
|
|
dy_loss.backward()
|
|
adam.minimize(dy_loss)
|
|
ptb_model.clear_gradients()
|
|
if i == batch_num - 1:
|
|
for param in ptb_model.parameters():
|
|
dy_param_updated[param.name] = param.numpy()
|
|
|
|
# check optimizer
|
|
opti_dict = adam.state_dict()
|
|
np_opti_dict = {}
|
|
# set to zero
|
|
for k, v in opti_dict.items():
|
|
if isinstance(v, core.VarBase):
|
|
np_t = v.numpy()
|
|
np_opti_dict[v.name] = np_t
|
|
var = v.value().get_tensor()
|
|
var.set(np.zeros_like(np_t), place)
|
|
self.assertTrue(np.sum(np.abs(v.numpy())) == 0)
|
|
else:
|
|
np_opti_dict[k] = v
|
|
|
|
if isinstance(adam._learning_rate, LearningRateDecay):
|
|
adam._learning_rate.step_num = 0
|
|
|
|
adam.set_state_dict(np_opti_dict)
|
|
|
|
opti_dict = adam.state_dict()
|
|
for k, v in opti_dict.items():
|
|
if isinstance(v, core.VarBase):
|
|
self.assertTrue(
|
|
np.array_equal(v.numpy(), self.base_opti[v.name]))
|
|
else:
|
|
self.assertEqual(v, self.base_opti[k])
|
|
|
|
# check parameter
|
|
state_dict = ptb_model.state_dict()
|
|
np_state_dict = {}
|
|
for k, v in state_dict.items():
|
|
np_t = v.numpy()
|
|
np_state_dict[k] = np_t
|
|
var = v.value().get_tensor()
|
|
|
|
var.set(np.zeros_like(np_t), place)
|
|
|
|
ptb_model.set_state_dict(np_state_dict)
|
|
|
|
state_dict = ptb_model.state_dict()
|
|
|
|
for k, v in state_dict.items():
|
|
new_t = v.numpy()
|
|
|
|
base_t = self.model_base[k]
|
|
|
|
self.assertTrue(np.array_equal(new_t, base_t))
|
|
|
|
def testSetVariableBeforeTrain(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 fluid.dygraph.guard():
|
|
# TODO: marsyang1993 Change seed to
|
|
ptb_model = PtbModel(
|
|
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)
|
|
adam = Adam(
|
|
learning_rate=0.0,
|
|
beta1=0.8,
|
|
beta2=0.6,
|
|
parameter_list=ptb_model.parameters())
|
|
dy_param_updated = dict()
|
|
dy_param_init = dict()
|
|
dy_loss = None
|
|
last_hidden = None
|
|
last_cell = None
|
|
|
|
adam.set_state_dict(self.opti_dict)
|
|
ptb_model.set_state_dict(self.state_dict)
|
|
|
|
for i in range(1):
|
|
x_data = np.arange(12).reshape(4, 3).astype('int64')
|
|
y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
|
|
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')
|
|
x = to_variable(x_data)
|
|
y = to_variable(y_data)
|
|
init_hidden = to_variable(init_hidden_data)
|
|
init_cell = to_variable(init_cell_data)
|
|
dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
|
|
init_cell)
|
|
|
|
dy_loss.backward()
|
|
adam.minimize(dy_loss)
|
|
ptb_model.clear_gradients()
|
|
|
|
opti_dict = adam.state_dict()
|
|
for k, v in opti_dict.items():
|
|
if k == "global_step":
|
|
self.assertTrue(
|
|
np.array_equal(v.numpy(), self.base_opti[v.name] + 1))
|
|
|
|
if k.find("beta1_pow_acc_0") > 0:
|
|
self.assertTrue(
|
|
np.array_equal(v.numpy(), self.base_opti[v.name] *
|
|
adam._beta1))
|
|
if k.find("beta2_pow_acc_0") > 0:
|
|
self.assertTrue(
|
|
np.array_equal(v.numpy(), self.base_opti[v.name] *
|
|
adam._beta2))
|
|
|
|
state_dict = ptb_model.state_dict()
|
|
|
|
for k, v in state_dict.items():
|
|
new_t = v.numpy()
|
|
|
|
base_t = self.model_base[k]
|
|
self.assertTrue(np.array_equal(new_t, base_t))
|
|
|
|
def testLoadAndSetVarBaseBeforeTrain(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 fluid.dygraph.guard():
|
|
paddle.seed(seed)
|
|
paddle.framework.random._manual_program_seed(seed)
|
|
# TODO: marsyang1993 Change seed to
|
|
ptb_model = PtbModel(
|
|
hidden_size=hidden_size,
|
|
vocab_size=vocab_size,
|
|
num_layers=num_layers,
|
|
num_steps=num_steps,
|
|
init_scale=init_scale)
|
|
|
|
bd = []
|
|
lr_arr = [0.0]
|
|
# this a fake lr decay strategy
|
|
for i in range(1, 10):
|
|
bd.append(100 * i)
|
|
# set lr to zero not update parameter
|
|
new_lr = 0.0
|
|
lr_arr.append(new_lr)
|
|
|
|
place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
|
|
) else fluid.CUDAPlace(0)
|
|
adam = Adam(
|
|
learning_rate=0.0,
|
|
beta1=0.8,
|
|
beta2=0.6,
|
|
parameter_list=ptb_model.parameters())
|
|
dy_param_updated = dict()
|
|
dy_param_init = dict()
|
|
dy_loss = None
|
|
last_hidden = None
|
|
last_cell = None
|
|
|
|
state_dict, opti_dict = fluid.load_dygraph("./test_dy")
|
|
adam.set_state_dict(opti_dict)
|
|
ptb_model.set_state_dict(state_dict)
|
|
|
|
for i in range(1):
|
|
x_data = np.arange(12).reshape(4, 3).astype('int64')
|
|
y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
|
|
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')
|
|
x = to_variable(x_data)
|
|
y = to_variable(y_data)
|
|
init_hidden = to_variable(init_hidden_data)
|
|
init_cell = to_variable(init_cell_data)
|
|
dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
|
|
init_cell)
|
|
|
|
dy_loss.backward()
|
|
adam.minimize(dy_loss)
|
|
ptb_model.clear_gradients()
|
|
|
|
opti_dict = adam.state_dict()
|
|
for k, v in opti_dict.items():
|
|
if k == "global_step":
|
|
self.assertTrue(
|
|
np.array_equal(v.numpy(), self.base_opti[v.name] + 1))
|
|
|
|
if k.find("beta1_pow_acc_0") > 0:
|
|
self.assertTrue(
|
|
np.array_equal(v.numpy(), self.base_opti[v.name] *
|
|
adam._beta1))
|
|
if k.find("beta2_pow_acc_0") > 0:
|
|
self.assertTrue(
|
|
np.array_equal(v.numpy(), self.base_opti[v.name] *
|
|
adam._beta2))
|
|
|
|
# check parameter
|
|
|
|
state_dict = ptb_model.state_dict()
|
|
|
|
for k, v in state_dict.items():
|
|
new_t = v.numpy()
|
|
|
|
base_t = self.model_base[k]
|
|
self.assertTrue(np.array_equal(new_t, base_t))
|
|
|
|
def testSetNumpyBeforeTrain(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 fluid.dygraph.guard():
|
|
paddle.seed(seed)
|
|
paddle.framework.random._manual_program_seed(seed)
|
|
# TODO: marsyang1993 Change seed to
|
|
|
|
ptb_model = PtbModel(
|
|
hidden_size=hidden_size,
|
|
vocab_size=vocab_size,
|
|
num_layers=num_layers,
|
|
num_steps=num_steps,
|
|
init_scale=init_scale)
|
|
|
|
bd = []
|
|
lr_arr = [0.0]
|
|
# this a fake lr decay strategy
|
|
for i in range(1, 10):
|
|
bd.append(100 * i)
|
|
# set lr to 0.0, not update parameter
|
|
new_lr = 0.0
|
|
lr_arr.append(new_lr)
|
|
|
|
place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
|
|
) else fluid.CUDAPlace(0)
|
|
adam = Adam(
|
|
learning_rate=fluid.layers.piecewise_decay(
|
|
boundaries=bd, values=lr_arr),
|
|
beta1=0.8,
|
|
beta2=0.6,
|
|
parameter_list=ptb_model.parameters())
|
|
dy_param_updated = dict()
|
|
dy_param_init = dict()
|
|
dy_loss = None
|
|
last_hidden = None
|
|
last_cell = None
|
|
|
|
np_opti_dict = {}
|
|
np_state_dict = {}
|
|
|
|
for k, v in self.opti_dict.items():
|
|
if isinstance(v, core.VarBase):
|
|
np_opti_dict[v.name] = v.numpy()
|
|
else:
|
|
np_opti_dict[k] = v
|
|
|
|
for k, v in self.state_dict.items():
|
|
np_state_dict[k] = v.numpy()
|
|
|
|
adam.set_state_dict(np_opti_dict)
|
|
ptb_model.set_state_dict(np_state_dict)
|
|
for i in range(1):
|
|
x_data = np.arange(12).reshape(4, 3).astype('int64')
|
|
y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
|
|
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')
|
|
x = to_variable(x_data)
|
|
y = to_variable(y_data)
|
|
init_hidden = to_variable(init_hidden_data)
|
|
init_cell = to_variable(init_cell_data)
|
|
dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
|
|
init_cell)
|
|
|
|
dy_loss.backward()
|
|
adam.minimize(dy_loss)
|
|
ptb_model.clear_gradients()
|
|
|
|
opti_dict = adam.state_dict()
|
|
for k, v in opti_dict.items():
|
|
if k == "global_step":
|
|
self.assertTrue(
|
|
np.array_equal(v.numpy(), self.base_opti[v.name] + 1))
|
|
|
|
if k.find("beta1_pow_acc_0") > 0:
|
|
self.assertTrue(
|
|
np.array_equal(v.numpy(), self.base_opti[v.name] *
|
|
adam._beta1))
|
|
if k.find("beta2_pow_acc_0") > 0:
|
|
self.assertTrue(
|
|
np.array_equal(v.numpy(), self.base_opti[v.name] *
|
|
adam._beta2))
|
|
|
|
# check parameter
|
|
|
|
state_dict = ptb_model.state_dict()
|
|
|
|
for k, v in state_dict.items():
|
|
new_t = v.numpy()
|
|
|
|
base_t = self.model_base[k]
|
|
self.assertTrue(np.array_equal(new_t, base_t))
|
|
|
|
def testOnlyLoadParams(self):
|
|
with fluid.dygraph.guard():
|
|
emb = fluid.dygraph.Embedding([10, 10])
|
|
state_dict = emb.state_dict()
|
|
fluid.save_dygraph(state_dict, os.path.join('saved_dy', 'emb_dy'))
|
|
|
|
para_state_dict, opti_state_dict = fluid.load_dygraph(
|
|
os.path.join('saved_dy', 'emb_dy'))
|
|
|
|
self.assertTrue(opti_state_dict == None)
|
|
|
|
para_state_dict, opti_state_dict = fluid.load_dygraph(
|
|
os.path.join('saved_dy', 'emb_dy.pdparams'))
|
|
|
|
para_state_dict, opti_state_dict = fluid.load_dygraph(
|
|
os.path.join('saved_dy', 'emb_dy.pdopt'))
|
|
|
|
def test_load_compatible_with_keep_name_table(self):
|
|
with fluid.dygraph.guard():
|
|
emb = fluid.dygraph.Embedding([10, 10])
|
|
state_dict = emb.state_dict()
|
|
fluid.save_dygraph(state_dict, os.path.join('saved_dy', 'emb_dy'))
|
|
|
|
para_state_dict, opti_state_dict = fluid.load_dygraph(
|
|
os.path.join('saved_dy', 'emb_dy'), keep_name_table=True)
|
|
self.assertTrue(para_state_dict != None)
|
|
self.assertTrue(opti_state_dict == None)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|