wehub-resource-sync
1202 lines
45 KiB
Python
1202 lines
45 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import pickle
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import tempfile
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import unittest
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import numpy as np
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from op_test import get_device_place, is_custom_device
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from test_imperative_base import new_program_scope
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import paddle
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from paddle import base
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from paddle.base import core, framework
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from paddle.framework import in_pir_mode
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from paddle.optimizer import Adam
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paddle.enable_static()
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class SimpleLSTMRNN(paddle.nn.Layer):
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def __init__(
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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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):
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super().__init__()
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self._hidden_size = hidden_size
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self._num_layers = num_layers
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self._init_scale = init_scale
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self._dropout = dropout
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self._input = None
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self._num_steps = num_steps
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self.cell_array = []
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self.hidden_array = []
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self.weight_1_arr = []
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self.weight_2_arr = []
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self.bias_arr = []
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self.mask_array = []
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for i in range(self._num_layers):
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weight_1 = self.create_parameter(
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attr=base.ParamAttr(
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initializer=paddle.nn.initializer.Uniform(
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low=-self._init_scale, high=self._init_scale
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)
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),
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shape=[self._hidden_size * 2, self._hidden_size * 4],
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dtype="float32",
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default_initializer=paddle.nn.initializer.Uniform(
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low=-self._init_scale, high=self._init_scale
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),
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)
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self.weight_1_arr.append(self.add_parameter(f'w_{i}', weight_1))
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bias_1 = self.create_parameter(
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attr=base.ParamAttr(
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initializer=paddle.nn.initializer.Uniform(
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low=-self._init_scale, high=self._init_scale
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)
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),
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shape=[self._hidden_size * 4],
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dtype="float32",
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default_initializer=paddle.nn.initializer.Constant(0.0),
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)
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self.bias_arr.append(self.add_parameter(f'b_{i}', bias_1))
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def forward(self, input_embedding, init_hidden=None, init_cell=None):
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self.cell_array = []
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self.hidden_array = []
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for i in range(self._num_layers):
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pre_hidden = paddle.slice(
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init_hidden, axes=[0], starts=[i], ends=[i + 1]
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)
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pre_cell = paddle.slice(
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init_cell, axes=[0], starts=[i], ends=[i + 1]
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)
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pre_hidden = paddle.reshape(
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pre_hidden, shape=[-1, self._hidden_size]
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)
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pre_cell = paddle.reshape(pre_cell, shape=[-1, self._hidden_size])
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self.hidden_array.append(pre_hidden)
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self.cell_array.append(pre_cell)
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res = []
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for index in range(self._num_steps):
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self._input = paddle.slice(
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input_embedding, axes=[1], starts=[index], ends=[index + 1]
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)
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self._input = paddle.reshape(
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self._input, shape=[-1, self._hidden_size]
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)
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for k in range(self._num_layers):
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pre_hidden = self.hidden_array[k]
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pre_cell = self.cell_array[k]
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weight_1 = self.weight_1_arr[k]
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bias = self.bias_arr[k]
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nn = paddle.concat([self._input, pre_hidden], 1)
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gate_input = paddle.matmul(x=nn, y=weight_1)
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gate_input = paddle.add(gate_input, bias)
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i, j, f, o = paddle.split(
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gate_input, num_or_sections=4, axis=-1
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)
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c = pre_cell * paddle.nn.functional.sigmoid(
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f
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) + paddle.nn.functional.sigmoid(i) * paddle.tanh(j)
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m = paddle.tanh(c) * paddle.nn.functional.sigmoid(o)
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self.hidden_array[k] = m
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self.cell_array[k] = c
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self._input = m
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if self._dropout is not None and self._dropout > 0.0:
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self._input = paddle.nn.functional.dropout(
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self._input,
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p=self._dropout,
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mode='upscale_in_train',
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)
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res.append(
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paddle.reshape(self._input, shape=[1, -1, self._hidden_size])
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)
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real_res = paddle.concat(res, 0)
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real_res = paddle.transpose(x=real_res, perm=[1, 0, 2])
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last_hidden = paddle.concat(self.hidden_array, 1)
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last_hidden = paddle.reshape(
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last_hidden, shape=[-1, self._num_layers, self._hidden_size]
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)
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last_hidden = paddle.transpose(x=last_hidden, perm=[1, 0, 2])
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last_cell = paddle.concat(self.cell_array, 1)
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last_cell = paddle.reshape(
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last_cell, shape=[-1, self._num_layers, self._hidden_size]
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)
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last_cell = paddle.transpose(x=last_cell, perm=[1, 0, 2])
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return real_res, last_hidden, last_cell
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class PtbModel(paddle.nn.Layer):
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def __init__(
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self,
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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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):
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super().__init__()
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self.hidden_size = hidden_size
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self.vocab_size = vocab_size
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self.init_scale = init_scale
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self.num_layers = num_layers
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self.num_steps = num_steps
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self.dropout = dropout
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self.simple_lstm_rnn = SimpleLSTMRNN(
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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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)
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self.embedding = paddle.nn.Embedding(
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num_embeddings=vocab_size,
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embedding_dim=hidden_size,
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weight_attr=base.ParamAttr(
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name='embedding_para',
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initializer=paddle.nn.initializer.Uniform(
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low=-init_scale, high=init_scale
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),
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),
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)
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self.softmax_weight = self.create_parameter(
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attr=base.ParamAttr(),
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shape=[self.hidden_size, self.vocab_size],
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dtype="float32",
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default_initializer=paddle.nn.initializer.Uniform(
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low=-self.init_scale, high=self.init_scale
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),
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)
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self.softmax_bias = self.create_parameter(
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attr=base.ParamAttr(),
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shape=[self.vocab_size],
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dtype="float32",
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default_initializer=paddle.nn.initializer.Uniform(
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low=-self.init_scale, high=self.init_scale
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),
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)
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def forward(self, input, label, init_hidden, init_cell):
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init_h = paddle.reshape(
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init_hidden, shape=[self.num_layers, -1, self.hidden_size]
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)
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init_c = paddle.reshape(
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init_cell, shape=[self.num_layers, -1, self.hidden_size]
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)
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# NPU 'tok_k' kernel only support `int32` dtype, so cast `input` from `int64` to `int32`.
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input = paddle.cast(input, "int32")
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x_emb = self.embedding(input)
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x_emb = paddle.reshape(
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x_emb, shape=[-1, self.num_steps, self.hidden_size]
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)
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if self.dropout is not None and self.dropout > 0.0:
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x_emb = paddle.nn.functional.dropout(
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x_emb,
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p=self.drop_out,
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mode='upscale_in_train',
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)
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rnn_out, last_hidden, last_cell = self.simple_lstm_rnn(
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x_emb, init_h, init_c
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)
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rnn_out = paddle.reshape(
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rnn_out, shape=[-1, self.num_steps, self.hidden_size]
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)
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projection = paddle.matmul(rnn_out, self.softmax_weight)
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projection = paddle.add(projection, self.softmax_bias)
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projection = paddle.reshape(projection, shape=[-1, self.vocab_size])
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loss = paddle.nn.functional.softmax_with_cross_entropy(
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logits=projection, label=label, soft_label=False
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)
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loss = paddle.reshape(loss, shape=[-1, self.num_steps])
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loss = paddle.mean(loss, axis=[0])
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loss = paddle.sum(loss)
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return loss, last_hidden, last_cell
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class TestSaveLoadBase(unittest.TestCase):
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def set_place(self):
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return (
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base.CPUPlace()
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if not (core.is_compiled_with_cuda() or is_custom_device())
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else get_device_place()
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)
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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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temp_dir = tempfile.TemporaryDirectory()
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with new_program_scope():
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paddle.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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)
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place = self.set_place()
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exe = base.Executor(place)
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sgd = Adam(learning_rate=1e-3)
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x = paddle.static.data(
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name="x", shape=[-1, num_steps], dtype='int64'
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)
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y = paddle.static.data(name="y", shape=[-1, 1], dtype='float32')
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init_hidden = paddle.static.data(
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name="init_hidden", shape=[-1, 1], dtype='float32'
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)
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init_cell = paddle.static.data(
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name="init_cell", shape=[-1, 1], dtype='float32'
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)
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if not in_pir_mode():
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x.desc.set_need_check_feed(False)
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y.desc.set_need_check_feed(False)
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init_hidden.desc.set_need_check_feed(False)
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init_cell.desc.set_need_check_feed(False)
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static_loss, static_last_hidden, static_last_cell = ptb_model(
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x, y, init_hidden, init_cell
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)
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sgd.minimize(static_loss)
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static_param_updated = {}
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static_param_init = {}
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out = exe.run(paddle.static.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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)
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init_cell_data = np.zeros(
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(num_layers, batch_size, hidden_size), dtype='float32'
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)
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fetch_list = [static_loss, static_last_hidden, static_last_cell]
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out = exe.run(
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paddle.static.default_main_program(),
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feed={
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"x": x_data,
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"y": y_data,
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"init_hidden": init_hidden_data,
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"init_cell": init_cell_data,
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},
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fetch_list=fetch_list,
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)
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static_loss_value = out[0]
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static_last_hidden_value = out[1]
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static_last_cell_value = out[2]
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# get value before save
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main_program = paddle.static.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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if (
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in_pir_mode()
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and var.get_defining_op().name() == "pd_op.fetch"
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):
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continue
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t = np.array(
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base.global_scope().find_var(var.name).get_tensor()
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)
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# make sure all the parameter 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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paddle.static.save(
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main_program, os.path.join(temp_dir.name, "test_1")
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)
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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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if (
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in_pir_mode()
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and var.get_defining_op().name() == "pd_op.fetch"
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):
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continue
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ten = base.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(
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base.global_scope().find_var(var.name).get_tensor()
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)
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# make sure all the parameter 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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paddle.static.load(
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main_program,
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os.path.join(temp_dir.name, "test_1.pdparams"),
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exe,
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)
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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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if (
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in_pir_mode()
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and var.get_defining_op().name() == "pd_op.fetch"
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):
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continue
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new_t = np.array(
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base.global_scope().find_var(var.name).get_tensor()
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)
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base_t = base_map[var.name]
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np.testing.assert_array_equal(new_t, base_t)
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temp_dir.cleanup()
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class TestSaveLoadPartial(unittest.TestCase):
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def set_place(self):
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return (
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base.CPUPlace()
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if not (core.is_compiled_with_cuda() or is_custom_device())
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else get_device_place()
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)
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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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temp_dir = tempfile.TemporaryDirectory()
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with new_program_scope():
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paddle.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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)
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place = self.set_place()
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exe = base.Executor(place)
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sgd = Adam(learning_rate=1e-3)
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x = paddle.static.data(
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name="x", shape=[-1, num_steps], dtype='int64'
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)
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y = paddle.static.data(name="y", shape=[-1, 1], dtype='float32')
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init_hidden = paddle.static.data(
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name="init_hidden", shape=[-1, 1], dtype='float32'
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)
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init_cell = paddle.static.data(
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name="init_cell", shape=[-1, 1], dtype='float32'
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)
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if not in_pir_mode():
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x.desc.set_need_check_feed(False)
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y.desc.set_need_check_feed(False)
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init_hidden.desc.set_need_check_feed(False)
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init_cell.desc.set_need_check_feed(False)
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static_loss, static_last_hidden, static_last_cell = ptb_model(
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x, y, init_hidden, init_cell
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)
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if in_pir_mode():
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test_program = paddle.static.default_main_program().clone()
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else:
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test_program = paddle.static.default_main_program().clone(
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for_test=True
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)
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sgd.minimize(static_loss)
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static_param_updated = {}
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static_param_init = {}
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out = exe.run(paddle.static.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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)
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init_cell_data = np.zeros(
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(num_layers, batch_size, hidden_size), dtype='float32'
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)
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fetch_list = [static_loss, static_last_hidden, static_last_cell]
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out = exe.run(
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paddle.static.default_main_program(),
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feed={
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"x": x_data,
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"y": y_data,
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"init_hidden": init_hidden_data,
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"init_cell": init_cell_data,
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},
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fetch_list=fetch_list,
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)
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static_loss_value = out[0]
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static_last_hidden_value = out[1]
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static_last_cell_value = out[2]
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|
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# get value before save
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main_program = paddle.static.default_main_program()
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base_map = {}
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for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if (
|
|
in_pir_mode()
|
|
and var.get_defining_op().name() == "pd_op.fetch"
|
|
):
|
|
continue
|
|
t = np.array(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
# make sure all the parameter or optimizer var have been update
|
|
self.assertTrue(np.sum(np.abs(t)) != 0)
|
|
base_map[var.name] = t
|
|
|
|
paddle.static.save(
|
|
main_program, os.path.join(temp_dir.name, "test_1")
|
|
)
|
|
|
|
# set var to zero
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if (
|
|
in_pir_mode()
|
|
and var.get_defining_op().name() == "pd_op.fetch"
|
|
):
|
|
continue
|
|
ten = base.global_scope().find_var(var.name).get_tensor()
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
# make sure all the parameter or optimizer var have been set to zero
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
paddle.static.load(
|
|
test_program, os.path.join(temp_dir.name, "test_1.pdopt"), None
|
|
)
|
|
|
|
for var in test_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if (
|
|
in_pir_mode()
|
|
and var.get_defining_op().name() == "pd_op.fetch"
|
|
):
|
|
continue
|
|
new_t = np.array(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
base_t = base_map[var.name]
|
|
np.testing.assert_array_equal(new_t, base_t)
|
|
paddle.static.load(
|
|
test_program,
|
|
os.path.join(temp_dir.name, "test_1.pdmodel"),
|
|
None,
|
|
)
|
|
temp_dir.cleanup()
|
|
|
|
|
|
class TestSaveLoadSetStateDict(unittest.TestCase):
|
|
def set_place(self):
|
|
return (
|
|
base.CPUPlace()
|
|
if not (core.is_compiled_with_cuda() or is_custom_device())
|
|
else get_device_place()
|
|
)
|
|
|
|
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
|
|
temp_dir = tempfile.TemporaryDirectory()
|
|
|
|
with new_program_scope():
|
|
paddle.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 = self.set_place()
|
|
exe = base.Executor(place)
|
|
sgd = Adam(learning_rate=1e-3)
|
|
x = paddle.static.data(
|
|
name="x", shape=[-1, num_steps], dtype='int64'
|
|
)
|
|
y = paddle.static.data(name="y", shape=[-1, 1], dtype='float32')
|
|
init_hidden = paddle.static.data(
|
|
name="init_hidden", shape=[-1, 1], dtype='float32'
|
|
)
|
|
init_cell = paddle.static.data(
|
|
name="init_cell", shape=[-1, 1], dtype='float32'
|
|
)
|
|
if not in_pir_mode():
|
|
x.desc.set_need_check_feed(False)
|
|
y.desc.set_need_check_feed(False)
|
|
init_hidden.desc.set_need_check_feed(False)
|
|
init_cell.desc.set_need_check_feed(False)
|
|
|
|
static_loss, static_last_hidden, static_last_cell = ptb_model(
|
|
x, y, init_hidden, init_cell
|
|
)
|
|
sgd.minimize(static_loss)
|
|
static_param_updated = {}
|
|
static_param_init = {}
|
|
|
|
out = exe.run(paddle.static.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(
|
|
paddle.static.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 = paddle.static.default_main_program()
|
|
base_map = {}
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if (
|
|
in_pir_mode()
|
|
and var.get_defining_op().name() == "pd_op.fetch"
|
|
):
|
|
continue
|
|
t = np.array(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
# make sure all the parameter or optimizer var have been update
|
|
self.assertTrue(np.sum(np.abs(t)) != 0)
|
|
base_map[var.name] = t
|
|
|
|
paddle.static.save(
|
|
main_program, os.path.join(temp_dir.name, "test_1")
|
|
)
|
|
|
|
# set var to zero
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if (
|
|
in_pir_mode()
|
|
and var.get_defining_op().name() == "pd_op.fetch"
|
|
):
|
|
continue
|
|
ten = base.global_scope().find_var(var.name).get_tensor()
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
# make sure all the parameter or optimizer var have been set to zero
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
paddle.static.load(
|
|
main_program, os.path.join(temp_dir.name, "test_1"), exe
|
|
)
|
|
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if (
|
|
in_pir_mode()
|
|
and var.get_defining_op().name() == "pd_op.fetch"
|
|
):
|
|
continue
|
|
new_t = np.array(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
base_t = base_map[var.name]
|
|
np.testing.assert_array_equal(new_t, base_t)
|
|
temp_dir.cleanup()
|
|
|
|
|
|
class TestProgramStatePartial(unittest.TestCase):
|
|
def set_place(self):
|
|
return (
|
|
base.CPUPlace()
|
|
if not (core.is_compiled_with_cuda() or is_custom_device())
|
|
else get_device_place()
|
|
)
|
|
|
|
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
|
|
temp_dir = tempfile.TemporaryDirectory()
|
|
|
|
with new_program_scope():
|
|
paddle.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 = self.set_place()
|
|
exe = base.Executor(place)
|
|
sgd = Adam(learning_rate=1e-3)
|
|
x = paddle.static.data(
|
|
name="x", shape=[-1, num_steps], dtype='int64'
|
|
)
|
|
y = paddle.static.data(name="y", shape=[-1, 1], dtype='float32')
|
|
init_hidden = paddle.static.data(
|
|
name="init_hidden", shape=[-1, 1], dtype='float32'
|
|
)
|
|
init_cell = paddle.static.data(
|
|
name="init_cell", shape=[-1, 1], dtype='float32'
|
|
)
|
|
if not in_pir_mode():
|
|
x.desc.set_need_check_feed(False)
|
|
y.desc.set_need_check_feed(False)
|
|
init_hidden.desc.set_need_check_feed(False)
|
|
init_cell.desc.set_need_check_feed(False)
|
|
|
|
static_loss, static_last_hidden, static_last_cell = ptb_model(
|
|
x, y, init_hidden, init_cell
|
|
)
|
|
|
|
if in_pir_mode():
|
|
test_program = paddle.static.default_main_program().clone()
|
|
else:
|
|
test_program = paddle.static.default_main_program().clone(
|
|
for_test=True
|
|
)
|
|
|
|
sgd.minimize(static_loss)
|
|
static_param_updated = {}
|
|
static_param_init = {}
|
|
|
|
out = exe.run(paddle.static.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(
|
|
paddle.static.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 = paddle.static.default_main_program()
|
|
base_map = {}
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if (
|
|
in_pir_mode()
|
|
and var.get_defining_op().name() == "pd_op.fetch"
|
|
):
|
|
continue
|
|
t = np.array(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
# make sure all the parameter or optimizer var have been update
|
|
self.assertTrue(np.sum(np.abs(t)) != 0)
|
|
base_map[var.name] = t
|
|
|
|
paddle.static.save(
|
|
main_program, os.path.join(temp_dir.name, 'test_1')
|
|
)
|
|
|
|
# set var to zero
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if (
|
|
in_pir_mode()
|
|
and var.get_defining_op().name() == "pd_op.fetch"
|
|
):
|
|
continue
|
|
ten = base.global_scope().find_var(var.name).get_tensor()
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
# make sure all the parameter or optimizer var have been set to zero
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
# base.load(test_program, "./test_1", None )
|
|
program_state = paddle.static.load_program_state(
|
|
os.path.join(temp_dir.name, 'test_1')
|
|
)
|
|
|
|
program_state_1 = paddle.static.load_program_state(
|
|
os.path.join(temp_dir.name, 'test_1.pdparams')
|
|
)
|
|
|
|
program_state_2 = paddle.static.load_program_state(
|
|
os.path.join(temp_dir.name, 'test_1.pdopt')
|
|
)
|
|
|
|
program_state_3 = paddle.static.load_program_state(
|
|
os.path.join(temp_dir.name, 'test_1.pdmodel')
|
|
)
|
|
|
|
paddle.static.set_program_state(test_program, program_state)
|
|
|
|
for var in test_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if (
|
|
in_pir_mode()
|
|
and var.get_defining_op().name() == "pd_op.fetch"
|
|
):
|
|
continue
|
|
new_t = np.array(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
base_t = base_map[var.name]
|
|
np.testing.assert_array_equal(new_t, base_t)
|
|
|
|
# check 1
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if (
|
|
in_pir_mode()
|
|
and var.get_defining_op().name() == "pd_op.fetch"
|
|
):
|
|
continue
|
|
ten = base.global_scope().find_var(var.name).get_tensor()
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
# make sure all the parameter or optimizer var have been set to zero
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
paddle.static.set_program_state(test_program, program_state_1)
|
|
|
|
for var in test_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if (
|
|
in_pir_mode()
|
|
and var.get_defining_op().name() == "pd_op.fetch"
|
|
):
|
|
continue
|
|
new_t = np.array(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
base_t = base_map[var.name]
|
|
np.testing.assert_array_equal(new_t, base_t)
|
|
|
|
# check 2
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if (
|
|
in_pir_mode()
|
|
and var.get_defining_op().name() == "pd_op.fetch"
|
|
):
|
|
continue
|
|
ten = base.global_scope().find_var(var.name).get_tensor()
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
# make sure all the parameter or optimizer var have been set to zero
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
paddle.static.set_program_state(test_program, program_state_2)
|
|
|
|
for var in test_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if (
|
|
in_pir_mode()
|
|
and var.get_defining_op().name() == "pd_op.fetch"
|
|
):
|
|
continue
|
|
new_t = np.array(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
base_t = base_map[var.name]
|
|
np.testing.assert_array_equal(new_t, base_t)
|
|
|
|
# check 3
|
|
for var in main_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if (
|
|
in_pir_mode()
|
|
and var.get_defining_op().name() == "pd_op.fetch"
|
|
):
|
|
continue
|
|
ten = base.global_scope().find_var(var.name).get_tensor()
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
# make sure all the parameter or optimizer var have been set to zero
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
paddle.static.set_program_state(test_program, program_state_3)
|
|
|
|
for var in test_program.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if (
|
|
in_pir_mode()
|
|
and var.get_defining_op().name() == "pd_op.fetch"
|
|
):
|
|
continue
|
|
new_t = np.array(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
base_t = base_map[var.name]
|
|
np.testing.assert_array_equal(new_t, base_t)
|
|
temp_dir.cleanup()
|
|
|
|
|
|
class TestVariableInit(unittest.TestCase):
|
|
def set_place(self):
|
|
return (
|
|
base.CPUPlace()
|
|
if not (core.is_compiled_with_cuda() or is_custom_device())
|
|
else get_device_place()
|
|
)
|
|
|
|
def test_variable_init(self):
|
|
x = paddle.static.data(name="x", shape=[10, 10], dtype='float32')
|
|
y = paddle.static.nn.fc(x, 10)
|
|
z = paddle.static.nn.fc(y, 10)
|
|
|
|
place = self.set_place()
|
|
exe = base.Executor(place)
|
|
exe.run(paddle.static.default_startup_program())
|
|
|
|
temp_dir = tempfile.TemporaryDirectory()
|
|
paddle.static.save(
|
|
paddle.static.default_main_program(),
|
|
os.path.join(temp_dir.name, "test_path"),
|
|
)
|
|
|
|
def set_var(var, ndarray):
|
|
t = var.get_tensor()
|
|
p = t._place()
|
|
if p.is_cpu_place():
|
|
place = paddle.base.CPUPlace()
|
|
elif p.is_cuda_pinned_place():
|
|
place = paddle.base.CUDAPinnedPlace()
|
|
else:
|
|
p = paddle.base.core.Place()
|
|
p.set_place(t._place())
|
|
place = get_device_place(p.gpu_device_id())
|
|
|
|
t.set(ndarray, place)
|
|
|
|
program = paddle.static.default_main_program()
|
|
new_scope = base.core.Scope()
|
|
|
|
place = self.set_place()
|
|
exe = base.Executor(place)
|
|
if in_pir_mode():
|
|
parameter_list = []
|
|
for var in program.list_vars():
|
|
if var.is_parameter and var.persistable:
|
|
parameter_list.append(var)
|
|
paddle.base.libpaddle.pir.create_loaded_parameter(
|
|
parameter_list, new_scope, exe._default_executor
|
|
)
|
|
else:
|
|
parameter_list = list(
|
|
filter(paddle.framework.is_parameter, program.list_vars())
|
|
)
|
|
base.core._create_loaded_parameter(
|
|
parameter_list, new_scope, exe._default_executor
|
|
)
|
|
parameter_file_name = os.path.join(temp_dir.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, (
|
|
f"Can not find [{v.name}] in model file [{parameter_file_name}]"
|
|
)
|
|
new_v = new_scope.find_var(v.name)
|
|
set_var(new_v, load_dict[v.name])
|
|
|
|
if in_pir_mode():
|
|
opt_list = []
|
|
for var in program.list_vars():
|
|
if var.persistable and not var.is_parameter:
|
|
opt_list.append(var)
|
|
paddle.base.libpaddle.pir.create_loaded_parameter(
|
|
opt_list, new_scope, exe._default_executor
|
|
)
|
|
else:
|
|
opt_list = list(
|
|
filter(
|
|
paddle.framework.io_utils.is_belong_to_optimizer,
|
|
program.list_vars(),
|
|
)
|
|
)
|
|
base.core._create_loaded_parameter(
|
|
opt_list, new_scope, exe._default_executor
|
|
)
|
|
opt_file_name = os.path.join(temp_dir.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, (
|
|
f"Can not find [{v.name}] in model file [{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(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
# make sure all the parameter 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]
|
|
|
|
np.testing.assert_array_equal(new_t, base_t)
|
|
temp_dir.cleanup()
|
|
|
|
|
|
class TestStaticSaveLoadPickle(unittest.TestCase):
|
|
def test_pickle_protocol(self):
|
|
# enable static graph 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',
|
|
)
|
|
if not in_pir_mode():
|
|
x.desc.set_need_check_feed(False)
|
|
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(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
# make sure all the parameter or optimizer var have been update
|
|
self.assertTrue(np.sum(np.abs(t)) != 0)
|
|
base_map[var.name] = t
|
|
|
|
temp_dir = tempfile.TemporaryDirectory()
|
|
path = os.path.join(
|
|
temp_dir.name, "test_static_save_load_pickle", "pickle_protocol"
|
|
)
|
|
|
|
with self.assertRaises(ValueError):
|
|
paddle.static.save(prog, path, 2.0)
|
|
|
|
with self.assertRaises(ValueError):
|
|
paddle.static.save(prog, path, 1)
|
|
|
|
with self.assertRaises(ValueError):
|
|
paddle.static.save(prog, path, 5)
|
|
|
|
protocols = [2, 3, 4]
|
|
for protocol in protocols:
|
|
paddle.static.save(prog, path, protocol)
|
|
# set var to zero
|
|
for var in prog.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if (
|
|
in_pir_mode()
|
|
and var.get_defining_op().name() == "pd_op.fetch"
|
|
):
|
|
continue
|
|
ten = (
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
ten.set(np.zeros_like(np.array(ten)), place)
|
|
|
|
new_t = np.array(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
self.assertTrue(np.sum(np.abs(new_t)) == 0)
|
|
|
|
paddle.static.load(prog, path)
|
|
|
|
for var in prog.list_vars():
|
|
if isinstance(var, framework.Parameter) or var.persistable:
|
|
if (
|
|
in_pir_mode()
|
|
and var.get_defining_op().name() == "pd_op.fetch"
|
|
):
|
|
continue
|
|
new_t = np.array(
|
|
base.global_scope().find_var(var.name).get_tensor()
|
|
)
|
|
base_t = base_map[var.name]
|
|
np.testing.assert_array_equal(new_t, base_t)
|
|
|
|
|
|
class TestSaveLoadInferenceModel(unittest.TestCase):
|
|
def setUp(self):
|
|
self.temp_dir = tempfile.TemporaryDirectory()
|
|
self.model_path = os.path.join(self.temp_dir.name, 'no_params')
|
|
|
|
def tearDown(self):
|
|
self.temp_dir.cleanup()
|
|
|
|
def test_no_params(self):
|
|
main_program = paddle.static.Program()
|
|
with paddle.static.program_guard(main_program):
|
|
x = paddle.static.data(name="x", shape=[10, 10], dtype='float32')
|
|
if not in_pir_mode():
|
|
x.desc.set_need_check_feed(False)
|
|
y = x + x
|
|
|
|
place = paddle.CPUPlace()
|
|
exe = paddle.static.Executor(place)
|
|
|
|
paddle.static.save_inference_model(self.model_path, [x], [y], exe)
|
|
|
|
[
|
|
inference_program,
|
|
feed_target_names,
|
|
fetch_targets,
|
|
] = paddle.static.load_inference_model(self.model_path, exe)
|
|
|
|
self.assertEqual(feed_target_names, ['x'])
|
|
if in_pir_mode():
|
|
self.assertEqual(fetch_targets[0].shape, [10, 10])
|
|
ops = [op.name() for op in inference_program.global_block().ops]
|
|
self.assertEqual(
|
|
ops,
|
|
[
|
|
'pd_op.data',
|
|
'pd_op.add',
|
|
'pd_op.fetch',
|
|
],
|
|
)
|
|
else:
|
|
self.assertEqual(fetch_targets[0].shape, (10, 10))
|
|
ops = [op.type for op in inference_program.block(0).ops]
|
|
self.assertEqual(ops, ['feed', 'elementwise_add', 'fetch'])
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|