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revert-15470-feature/imperative
JiabinYang 6 years ago
parent cddecad701
commit e3a8929cf8
4 changed files with 159 additions and 61 deletions
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4
paddle/fluid/inference/utils/CMakeLists.txt
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@ -1,4 +1,4 @@
cc_library(benchmark SRCS benchmark.cc DEPS enforce)
cc_test(test_benchmark SRCS benchmark_tester.cc DEPS benchmark)
cc_binary(visualizer SRCS visualizer.cc DEPS analysis
paddle_pass_builder ir_pass_manager pass graph_viz_pass analysis_passes)
#cc_binary(visualizer SRCS visualizer.cc DEPS analysis
# paddle_pass_builder ir_pass_manager pass graph_viz_pass analysis_passes)

2
python/paddle/fluid/imperative/nn.py
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@ -295,7 +295,7 @@ class EMBEDDING(layers.Layer):
self._param_attr = param_attr
self._dtype = dtype
self._remote_prefetch = self.is_sparse and (not self.is_distributed)
self._remote_prefetch = self._is_sparse and (not self._is_distributed)
if self._remote_prefetch:
assert self._is_sparse is True and self._is_distributed is False

166
python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py
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@ -18,23 +18,28 @@ import unittest
import paddle.fluid as fluid
from paddle.fluid.imperative.nn import EMBEDDING
import paddle.fluid.framework as framework
import paddle.fluid.optimizer as optimizer
from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.imperative.base import to_variable
import numpy as np
from paddle.fluid.backward import append_backward
class SimpleLSTMRNN(fluid.imperative.Layer):
def __init__(self, hidden_size, num_layers=2, init_scale=0.1, dropout=None):
def __init__(self,
hidden_size,
num_steps,
num_layers=2,
init_scale=0.1,
dropout=None):
super(SimpleLSTMRNN, self).__init__()
self._hidden_size = hidden_size
self._num_layers = num_layers
self._init_scale = init_scale
self._dropout = dropout
self.input = None
self.num_steps = num_steps
def _build_once(self,
input_embedding,
seq_len,
init_hidden=None,
init_cell=None):
def _build_once(self, input_embedding, init_hidden=None, init_cell=None):
self.weight_1_arr = []
self.weight_2_arr = []
self.bias_arr = []
@ -57,7 +62,7 @@ class SimpleLSTMRNN(fluid.imperative.Layer):
default_initializer=fluid.initializer.Constant(0.0))
self.bias_arr.append(bias_1)
pre_hidden = self.layers.slice(
pre_hidden = fluid.layers.slice(
init_hidden, axes=[0], starts=[i], ends=[i + 1])
pre_cell = fluid.layers.slice(
init_cell, axes=[0], starts=[i], ends=[i + 1])
@ -65,22 +70,20 @@ class SimpleLSTMRNN(fluid.imperative.Layer):
pre_hidden, shape=[-1, self._hidden_size])
pre_cell = fluid.layers.reshape(
pre_cell, shape=[-1, self._hidden_size])
fluid.hidden_array.append(pre_hidden)
fluid.cell_array.append(pre_cell)
def forward(self,
input_embedding,
seq_len,
init_hidden=None,
init_cell=None):
self.hidden_array.append(pre_hidden)
self.cell_array.append(pre_cell)
def forward(self, input_embedding, init_hidden=None, init_cell=None):
res = []
for index in range(seq_len):
for index in range(self.num_steps):
self.input = fluid.layers.slice(
input_embedding, axes=[1], starts=[index], ends=[index + 1])
self.input = fluid.layers.reshape(
self.input, shape=[-1, self._hidden_size])
for k in range(self._num_layers):
pre_hidden = self.hidden_array[k]
print("pre_hidden shape is:{}".format(pre_hidden.shape))
print("input shape is:{}".format(self.input.shape))
pre_cell = self.cell_array[k]
weight_1 = self.weight_1_arr[k]
bias = self.bias_arr[k]
@ -89,38 +92,41 @@ class SimpleLSTMRNN(fluid.imperative.Layer):
gate_input = fluid.layers.matmul(x=nn, y=weight_1)
gate_input = fluid.layers.elementwise_add(gate_input, bias)
i, j, f, o = fluid.layers.split(
gate_input, num_or_sections=4, dim=-1)
c = pre_cell * fluid.layers.sigmoid(f) + fluid.layers.sigmoid(
i) * fluid.layers.tanh(j)
m = fluid.layers.tanh(c) * fluid.layers.sigmoid(o)
self.hidden_array[k] = m
self.cell_array[k] = c
self.input = m
if self.dropout is not None and self.dropout > 0.0:
self.input = fluid.layers.dropout(
self.input,
dropout_prob=self.dropout,
dropout_implementation='upscale_in_train')
res.append(
fluid.layers.reshape(
input, shape=[1, -1, self._hidden_size]))
real_res = fluid.layers.concat(res, 0)
real_res = fluid.layers.transpose(x=real_res, perm=[1, 0, 2])
last_hidden = fluid.layers.concat(self.hidden_array, 1)
last_hidden = fluid.layers.reshape(
last_hidden, shape=[-1, self._num_layers, self._hidden_size])
last_hidden = fluid.layers.transpose(x=last_hidden, perm=[1, 0, 2])
last_cell = fluid.layers.concat(self.cell_array, 1)
last_cell = fluid.layers.reshape(
last_cell, shape=[-1, self._num_layers, self._hidden_size])
last_cell = fluid.layers.transpose(x=last_cell, perm=[1, 0, 2])
return real_res, last_hidden, last_cell
print("gate_input shape is: {}".format(gate_input.shape))
print("gate_input value is :{}".format(gate_input._numpy()))
print("gate_input desc is :{}".format(gate_input))
# i, j, f, o = fluid.layers.split(gate_input, num_or_sections=4, dim=-1)
# #
# # c = pre_cell * fluid.layers.sigmoid(f) + fluid.layers.sigmoid(
# # i) * fluid.layers.tanh(j)
# # m = fluid.layers.tanh(c) * fluid.layers.sigmoid(o)
# #
# # self.hidden_array[k] = m
# # self.cell_array[k] = c
# # self.input = m
# #
# # if self.dropout is not None and self.dropout > 0.0:
# # self.input = fluid.layers.dropout(
# # self.input,
# # dropout_prob=self.dropout,
# # dropout_implementation='upscale_in_train')
# #
# # res.append(
# # fluid.layers.reshape(
# # input, shape=[1, -1, self._hidden_size]))
# # real_res = fluid.layers.concat(res, 0)
# # real_res = fluid.layers.transpose(x=real_res, perm=[1, 0, 2])
# # last_hidden = fluid.layers.concat(self.hidden_array, 1)
# # last_hidden = fluid.layers.reshape(
# # last_hidden, shape=[-1, self._num_layers, self._hidden_size])
# # last_hidden = fluid.layers.transpose(x=last_hidden, perm=[1, 0, 2])
# # last_cell = fluid.layers.concat(self.cell_array, 1)
# # last_cell = fluid.layers.reshape(
# # last_cell, shape=[-1, self._num_layers, self._hidden_size])
# # last_cell = fluid.layers.transpose(x=last_cell, perm=[1, 0, 2])
# #
# return real_res, last_hidden, last_cell
return [1], [2], [3]
class PtbModel(fluid.imperative.Layer):
@ -137,8 +143,10 @@ class PtbModel(fluid.imperative.Layer):
self.init_scale = init_scale
self.num_layers = num_layers
self.num_steps = num_steps
self.dropout = dropout
self.simple_lstm_rnn = SimpleLSTMRNN(
hidden_size,
num_steps,
num_layers=num_layers,
init_scale=init_scale,
dropout=dropout)
@ -153,21 +161,23 @@ class PtbModel(fluid.imperative.Layer):
def _build_once(self, input, label, init_hidden, init_cell):
self.softmax_weight = fluid.layers.create_parameter(
[self._hidden_size, self._vocab_size],
[self.hidden_size, self.vocab_size],
dtype="float32",
name="softmax_weight",
default_initializer=fluid.initializer.UniformInitializer(
low=-self._init_scale, high=self._init_scale))
low=-self.init_scale, high=self.init_scale))
self.softmax_bias = fluid.layers.create_parameter(
[self._vocab_size],
[self.vocab_size],
dtype="float32",
name='softmax_bias',
default_initializer=fluid.initializer.UniformInitializer(
low=-self._init_scale, high=self._init_scale))
low=-self.init_scale, high=self.init_scale))
def forward(self, input, label, init_hidden, init_cell):
init_h = fluid.layers.reshape(
init_hidden, shape=[self.num_layers, -1, self.hidden_size])
init_c = fluid.layers.reshape(
init_cell, shape=[self.num_layers, -1, self.hidden_size])
@ -179,6 +189,7 @@ class PtbModel(fluid.imperative.Layer):
x_emb,
dropout_prob=self.drop_out,
dropout_implementation='upscale_in_train')
print("init_c is {}".format(init_c))
rnn_out, last_hidden, last_cell = self.simple_lstm_rnn(x_emb, init_h,
init_c)
rnn_out = fluid.layers.reshape(
@ -202,14 +213,53 @@ class PtbModel(fluid.imperative.Layer):
class TestImperativePtbRnn(unittest.TestCase):
def test_mnist_cpu_float32(self):
seed = 90
hidden_size = 10
vocab_size = 1000
num_layers = 1
num_steps = 3
init_scale = 0.1
batch_size = 4
with fluid.imperative.guard():
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
# TODO: marsyang1993 Change seed to
ptb_model = PtbModel(
hidden_size=10,
vocab_size=1000,
num_layers=1,
num_steps=3,
init_scale=0.1)
hidden_size=hidden_size,
vocab_size=vocab_size,
num_layers=num_layers,
num_steps=num_steps,
init_scale=init_scale)
sgd = SGDOptimizer(learning_rate=1e-3)
print("q")
for i in range(2):
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')
x = to_variable(x_data)
y = to_variable(y_data)
init_hidden = to_variable(init_hidden_data)
init_cell = to_variable(init_cell_data)
dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
init_cell)
dy_param_init = dict()
if i == 0:
for param in fluid.default_main_program().global_block(
).all_parameters():
dy_param_init[param.name] = param._numpy()
dy_loss._backward()
sgd.minimize(dy_loss)
dy_param_updated = dict()
for param in fluid.default_main_program().global_block(
).all_parameters():
dy_param_updated[param.name] = param._numpy()
if __name__ == '__main__':
unittest.main()

48
python/paddle/fluid/tests/unittests/test_imperative_split.py
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@ -0,0 +1,48 @@
# Copyright (c) 2019 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 unittest
import paddle.fluid as fluid
from paddle.fluid.imperative.nn import EMBEDDING
import paddle.fluid.framework as framework
from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.imperative.base import to_variable
import numpy as np
class Split_test(fluid.imperative.Layer):
def __init__(self):
super(Split_test, self).__init__()
def _build_once(self, input):
pass
def forward(self, input):
out = fluid.layers.split(input, num_or_sections=4, dim=-1)
return out
class TestImperativePtbRnn(unittest.TestCase):
def test_spilt(self):
with fluid.imperative.guard():
inp = to_variable(np.arange(160).reshape(4, 40).astype('float32'))
st = Split_test()
out = st(inp)
print(out)
if __name__ == '__main__':
unittest.main()
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