You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
127 lines
4.2 KiB
127 lines
4.2 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from __future__ import print_function
|
|
|
|
import numpy as np
|
|
|
|
import paddle
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid.dygraph.nn import Embedding
|
|
from paddle.fluid.dygraph.base import to_variable
|
|
|
|
from test_dist_base import runtime_main, TestParallelDyGraphRunnerBase
|
|
|
|
|
|
class SimpleNet(fluid.Layer):
|
|
def __init__(self,
|
|
hidden_size,
|
|
vocab_size,
|
|
num_steps=20,
|
|
init_scale=0.1,
|
|
is_sparse=False,
|
|
dtype="float32"):
|
|
super(SimpleNet, self).__init__()
|
|
self.hidden_size = hidden_size
|
|
self.vocab_size = vocab_size
|
|
self.init_scale = init_scale
|
|
self.num_steps = num_steps
|
|
self.embedding = Embedding(
|
|
size=[self.vocab_size, self.hidden_size],
|
|
dtype=dtype,
|
|
is_sparse=is_sparse,
|
|
param_attr=fluid.ParamAttr(
|
|
name='embedding_param',
|
|
initializer=fluid.initializer.UniformInitializer(
|
|
low=-init_scale, high=init_scale)))
|
|
self.softmax_weight = self.create_parameter(
|
|
attr=fluid.ParamAttr(),
|
|
shape=[self.hidden_size, self.vocab_size],
|
|
dtype=dtype,
|
|
default_initializer=fluid.initializer.UniformInitializer(
|
|
low=-self.init_scale, high=self.init_scale))
|
|
self.softmax_bias = self.create_parameter(
|
|
attr=fluid.ParamAttr(),
|
|
shape=[self.vocab_size],
|
|
dtype=dtype,
|
|
default_initializer=fluid.initializer.UniformInitializer(
|
|
low=-self.init_scale, high=self.init_scale))
|
|
|
|
def forward(self, input, label):
|
|
x_emb = self.embedding(input)
|
|
fc = fluid.layers.matmul(x_emb, self.softmax_weight)
|
|
fc = fluid.layers.elementwise_add(fc, self.softmax_bias)
|
|
projection = fluid.layers.reshape(fc, shape=[-1, self.vocab_size])
|
|
loss = fluid.layers.softmax_with_cross_entropy(
|
|
logits=projection, label=label, soft_label=False)
|
|
loss = fluid.layers.reshape(loss, shape=[-1, self.num_steps])
|
|
loss = fluid.layers.reduce_mean(loss, dim=[0])
|
|
loss = fluid.layers.reduce_sum(loss)
|
|
|
|
return loss
|
|
|
|
|
|
# global configs
|
|
batch_size = 4
|
|
batch_num = 200
|
|
hidden_size = 10
|
|
vocab_size = 1000
|
|
num_steps = 3
|
|
init_scale = 0.1
|
|
|
|
|
|
def fake_sample_reader():
|
|
def __reader__():
|
|
for i in range(batch_num):
|
|
x_data = np.arange(num_steps).astype('int64')
|
|
y_data = np.arange(1, 1 + num_steps).astype('int64')
|
|
yield x_data, y_data
|
|
|
|
return __reader__
|
|
|
|
|
|
class TestSparseEmbedding(TestParallelDyGraphRunnerBase):
|
|
def get_model(self):
|
|
model = SimpleNet(
|
|
hidden_size=hidden_size,
|
|
vocab_size=vocab_size,
|
|
num_steps=num_steps,
|
|
init_scale=init_scale,
|
|
is_sparse=True)
|
|
|
|
train_reader = paddle.batch(
|
|
fake_sample_reader(), batch_size=batch_size, drop_last=True)
|
|
|
|
optimizer = fluid.optimizer.SGD(learning_rate=0.001,
|
|
parameter_list=model.parameters())
|
|
|
|
return model, train_reader, optimizer
|
|
|
|
def run_one_loop(self, model, optimizer, batch):
|
|
x_data = np.array([x[0].reshape(3) for x in batch]).astype('int64')
|
|
y_data = np.array([x[1].reshape(3) for x in batch]).astype('int64')
|
|
x_data = x_data.reshape((-1, num_steps, 1))
|
|
y_data = y_data.reshape((-1, 1))
|
|
|
|
x = to_variable(x_data)
|
|
y = to_variable(y_data)
|
|
|
|
dy_loss = model(x, y)
|
|
|
|
return dy_loss
|
|
|
|
|
|
if __name__ == "__main__":
|
|
runtime_main(TestSparseEmbedding)
|