wehub-resource-sync
499 lines
16 KiB
Python
499 lines
16 KiB
Python
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
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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 unittest
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import numpy as np
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import paddle
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from paddle import base
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from paddle.base.executor import Executor
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os.environ["CPU_NUM"] = "1"
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class RNNConfig:
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def __init__(self, model_type, rnn_model):
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self.model_type = model_type
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self.rnn_model = rnn_model
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self.vocab_size = 10000
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if self.model_type == "test":
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self.num_layers = 1
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self.batch_size = 2
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self.hidden_size = 10
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self.num_steps = 3
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self.init_scale = 0.1
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self.max_grad_norm = 5.0
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self.epoch_start_decay = 1
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self.max_epoch = 1
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self.dropout = 0.0
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self.lr_decay = 0.5
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self.base_learning_rate = 1.0
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elif self.model_type == "small":
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self.num_layers = 2
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self.batch_size = 20
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self.hidden_size = 200
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self.num_steps = 20
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self.init_scale = 0.1
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self.max_grad_norm = 5.0
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self.epoch_start_decay = 4
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self.max_epoch = 13
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self.dropout = 0.0
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self.lr_decay = 0.5
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self.base_learning_rate = 1.0
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elif self.model_type == "medium":
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self.num_layers = 2
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self.batch_size = 20
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self.hidden_size = 650
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self.num_steps = 35
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self.init_scale = 0.05
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self.max_grad_norm = 5.0
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self.epoch_start_decay = 6
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self.max_epoch = 39
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self.dropout = 0.5
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self.lr_decay = 0.8
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self.base_learning_rate = 1.0
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elif self.model_type == "large":
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self.num_layers = 2
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self.batch_size = 20
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self.hidden_size = 1500
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self.num_steps = 35
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self.init_scale = 0.04
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self.max_grad_norm = 10.0
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self.epoch_start_decay = 14
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self.max_epoch = 55
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self.dropout = 0.65
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self.lr_decay = 1.0 / 1.15
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self.base_learning_rate = 1.0
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else:
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raise ValueError('Unsupported model_type.')
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if rnn_model not in ('static', 'cudnn'):
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raise ValueError('Unsupported rnn_model.')
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self.batch_size = 12
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self.max_epoch = 3
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self.random_seed = 123
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# Fake data reader for test
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class Reader:
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def get_data_iter(self, rnn_config):
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for i in range(rnn_config.max_epoch):
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x = np.zeros(
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shape=(rnn_config.batch_size, rnn_config.num_steps),
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dtype='int64',
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)
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y = np.ones(
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shape=(rnn_config.batch_size, rnn_config.num_steps),
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dtype='int64',
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)
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yield (x, y)
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# Model from PaddleNLP/models/language_model/lm_model.py in Paddle Models repo
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def lm_model(
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hidden_size,
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vocab_size,
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batch_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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rnn_model='static',
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):
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def encoder_static(
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input_embedding, len=3, init_hidden=None, init_cell=None
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):
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weight_1_arr = []
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weight_2_arr = []
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bias_arr = []
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hidden_array = []
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cell_array = []
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mask_array = []
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for i in range(num_layers):
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weight_1 = paddle.create_parameter(
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[hidden_size * 2, hidden_size * 4],
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dtype="float32",
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name="fc_weight1_" + str(i),
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default_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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weight_1_arr.append(weight_1)
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bias_1 = paddle.create_parameter(
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[hidden_size * 4],
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dtype="float32",
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name="fc_bias1_" + str(i),
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default_initializer=paddle.nn.initializer.Constant(0.0),
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)
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bias_arr.append(bias_1)
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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(pre_hidden, shape=[-1, hidden_size])
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pre_cell = paddle.reshape(pre_cell, shape=[-1, hidden_size])
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hidden_array.append(pre_hidden)
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cell_array.append(pre_cell)
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res = []
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sliced_inputs = paddle.split(
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input_embedding, num_or_sections=len, axis=1
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)
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for index in range(len):
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input = sliced_inputs[index]
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input = paddle.reshape(input, shape=[-1, hidden_size])
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for k in range(num_layers):
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pre_hidden = hidden_array[k]
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pre_cell = cell_array[k]
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weight_1 = weight_1_arr[k]
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bias = bias_arr[k]
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nn = paddle.concat([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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hidden_array[k] = m
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cell_array[k] = c
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input = m
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if dropout is not None and dropout > 0.0:
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input = paddle.nn.functional.dropout(
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input,
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p=dropout,
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mode='upscale_in_train',
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)
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res.append(input)
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last_hidden = paddle.concat(hidden_array, 1)
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last_hidden = paddle.reshape(
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last_hidden, shape=[-1, num_layers, 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(cell_array, 1)
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last_cell = paddle.reshape(
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last_cell, shape=[-1, num_layers, 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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real_res = paddle.concat(res, 0)
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real_res = paddle.reshape(real_res, shape=[len, -1, hidden_size])
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real_res = paddle.transpose(x=real_res, perm=[1, 0, 2])
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return real_res, last_hidden, last_cell
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batch_size_each = batch_size
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x = paddle.static.data(
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name="x", shape=[batch_size_each, num_steps, 1], dtype='int64'
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)
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y = paddle.static.data(
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name="y", shape=[batch_size_each * num_steps, 1], dtype='int64'
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)
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init_hidden = paddle.static.data(
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name="init_hidden",
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shape=[num_layers, batch_size_each, hidden_size],
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dtype='float32',
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)
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init_cell = paddle.static.data(
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name="init_cell",
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shape=[num_layers, batch_size_each, hidden_size],
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dtype='float32',
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)
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init_cell.persistable = True
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init_hidden.persistable = True
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init_hidden_reshape = paddle.reshape(
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init_hidden, shape=[num_layers, -1, hidden_size]
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)
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init_cell_reshape = paddle.reshape(
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init_cell, shape=[num_layers, -1, hidden_size]
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)
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if paddle.framework.in_pir_mode():
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Emb = paddle.nn.Embedding(
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vocab_size,
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hidden_size,
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sparse=False,
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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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x_emb = Emb(x)
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else:
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x_emb = paddle.static.nn.embedding(
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input=x,
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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=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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x_emb = paddle.reshape(x_emb, shape=[-1, num_steps, hidden_size])
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if dropout is not None and dropout > 0.0:
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x_emb = paddle.nn.functional.dropout(
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x_emb,
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p=dropout,
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mode='upscale_in_train',
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)
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if rnn_model == "static":
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rnn_out, last_hidden, last_cell = encoder_static(
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x_emb,
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len=num_steps,
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init_hidden=init_hidden_reshape,
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init_cell=init_cell_reshape,
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)
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else:
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print("type not support")
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return
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rnn_out = paddle.reshape(rnn_out, shape=[-1, num_steps, hidden_size])
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softmax_weight = paddle.create_parameter(
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[hidden_size, vocab_size],
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dtype="float32",
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name="softmax_weight",
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default_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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softmax_bias = paddle.create_parameter(
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[vocab_size],
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dtype="float32",
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name='softmax_bias',
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default_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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projection = paddle.matmul(rnn_out, softmax_weight)
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projection = paddle.add(projection, softmax_bias)
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projection = paddle.reshape(projection, shape=[-1, vocab_size])
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loss = paddle.nn.functional.softmax_with_cross_entropy(
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logits=projection, label=y, soft_label=False
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)
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loss = paddle.reshape(loss, shape=[-1, num_steps])
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loss = paddle.mean(loss, axis=[0])
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loss = paddle.sum(loss)
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loss.persistable = True
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last_cell.persistable = True
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last_hidden.persistable = True
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# This will feed last_hidden, last_cell to init_hidden, init_cell, which
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# can be used directly in next batch. This can avoid the fetching of
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# last_hidden and last_cell and feeding of init_hidden and init_cell in
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# each training step.
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paddle.assign(last_cell, output=init_cell)
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paddle.assign(last_hidden, output=init_hidden)
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feeding_list = [x, y, init_hidden, init_cell]
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return loss, last_hidden, last_cell, feeding_list
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class PaddingRNNTestBase(unittest.TestCase):
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def setUp(self):
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self.reader = Reader()
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self.device_count = 1
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# The default build_strategy used for PaddingRNN.
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# You can change it in set_customed_config.
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self.build_strategy = base.BuildStrategy()
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self.build_strategy.enable_inplace = True
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self.build_strategy.memory_optimize = False
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self.build_strategy.fuse_all_optimizer_ops = True
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# CPU executor is used for PaddingRNN default.
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# You can change to CUDA executor in set_customed_config.
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self.exe = Executor(base.CPUPlace())
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def set_customed_config(self):
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# This function will be called before training.
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# You can override the function to set your own config.
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pass
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def _prepare_program(self, config):
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paddle.seed(config.random_seed)
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self.main_program = base.Program()
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self.startup_program = base.Program()
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with (
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base.program_guard(self.main_program, self.startup_program),
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base.unique_name.guard(),
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):
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res_vars = lm_model(
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config.hidden_size,
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config.vocab_size,
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config.batch_size,
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num_layers=config.num_layers,
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num_steps=config.num_steps,
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init_scale=config.init_scale,
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dropout=config.dropout,
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rnn_model=config.rnn_model,
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)
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(
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self.loss,
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self.last_hidden,
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self.last_cell,
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self.feed_list,
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) = res_vars
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paddle.nn.clip.set_gradient_clip(
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clip=paddle.nn.ClipGradByGlobalNorm(
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clip_norm=config.max_grad_norm
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)
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)
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optimizer = paddle.optimizer.SGD(learning_rate=1.0)
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optimizer.minimize(self.loss)
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self.exe.run(self.startup_program)
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self.train_program = self.main_program
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def _generate_init_data(self):
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init_hidden = np.zeros(
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(
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self.config.num_layers,
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self.config.batch_size,
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self.config.hidden_size,
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),
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dtype='float32',
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)
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init_cell = np.zeros(
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(
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self.config.num_layers,
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self.config.batch_size,
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self.config.hidden_size,
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),
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dtype='float32',
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)
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return init_hidden, init_cell
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def _generate_new_lr(self, epoch_id=0, device_count=1):
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new_lr = self.config.base_learning_rate * (
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self.config.lr_decay
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** max(epoch_id + 1 - self.config.epoch_start_decay, 0.0)
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)
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lr = np.ones((self.device_count), dtype='float32') * new_lr
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return lr
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def _prepare_input(
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self,
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batch,
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init_hidden=None,
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init_cell=None,
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epoch_id=0,
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with_lr=True,
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device_count=1,
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):
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x, y = batch
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x = x.reshape((-1, self.config.num_steps, 1))
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y = y.reshape((-1, 1))
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res = {}
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res['x'] = x
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res['y'] = y
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if init_hidden is not None:
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res['init_hidden'] = init_hidden
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if init_cell is not None:
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res['init_cell'] = init_cell
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if with_lr:
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res['learning_rate'] = self._generate_new_lr(epoch_id, device_count)
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return res
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def _train_an_epoch(self, epoch_id, use_program_cache=True):
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train_data_iter = self.reader.get_data_iter(self.config)
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total_loss = 0
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iters = 0
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init_hidden, init_cell = self._generate_init_data()
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ppl = np.zeros(shape=(0))
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for batch_id, batch in enumerate(train_data_iter):
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input_data_feed = self._prepare_input(
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batch,
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init_hidden=init_hidden,
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init_cell=init_cell,
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epoch_id=epoch_id,
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with_lr=True,
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device_count=self.device_count,
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)
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fetch_outs = self.exe.run(
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self.train_program,
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feed=input_data_feed,
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fetch_list=[
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self.loss,
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self.last_hidden,
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self.last_cell,
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],
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use_program_cache=use_program_cache,
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)
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cost_train = np.array(fetch_outs[0])
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init_hidden = np.array(fetch_outs[1])
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init_cell = np.array(fetch_outs[2])
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total_loss += cost_train
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iters += self.config.num_steps
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batch_ppl = np.exp(total_loss / iters)
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ppl = np.append(ppl, batch_ppl)
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return ppl
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def train(self, config, use_program_cache=True):
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self.set_customed_config()
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self.config = config
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self._prepare_program(config)
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ppl = np.zeros(shape=(0, config.batch_size))
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for epoch_id in range(config.max_epoch):
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train_ppl = self._train_an_epoch(epoch_id, use_program_cache)
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ppl = np.append(ppl, train_ppl)
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return ppl
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if __name__ == '__main__':
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unittest.main()
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