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提交 3be8ffad 编写于 作者: J JiabinYang
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test=develop, polish code and merge conflict

上级 1bf2face
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Showing with 10 addition and 320 deletion
paddle/fluid/framework/operator.cc
浏览文件 @ 3be8ffad
......@@ -1073,7 +1073,8 @@ Scope* OperatorWithKernel::PrepareData(
proto::VarType::Type OperatorWithKernel::IndicateDataType(
const ExecutionContext& ctx) const {
int data_type = -1;
proto::VarType::Type defaut_data_type = static_cast<proto::VarType::Type>(-1);
proto::VarType::Type data_type = defaut_data_type;
for (auto& input : this->inputs_) {
const std::vector<const Variable*> vars = ctx.MultiInputVar(input.first);
for (size_t i = 0; i < vars.size(); ++i) {
......@@ -1090,18 +1091,19 @@ proto::VarType::Type OperatorWithKernel::IndicateDataType(
if (t != nullptr) {
PADDLE_ENFORCE(t->IsInitialized(), "Input %s(%lu)is not initialized",
input.first, i);
int tmp = static_cast<int>(t->type());
proto::VarType::Type tmp = t->type();
PADDLE_ENFORCE(
tmp == data_type || data_type == -1,
tmp == data_type || data_type == defaut_data_type,
"DataType of Paddle Op %s must be the same. Get (%d) != (%d)",
Type(), data_type, tmp);
Type(), DataTypeToString(data_type), DataTypeToString(tmp));
data_type = tmp;
}
}
}
}
PADDLE_ENFORCE(data_type != -1, "DataType should be indicated by input");
return static_cast<proto::VarType::Type>(data_type);
PADDLE_ENFORCE(data_type != defaut_data_type,
"DataType should be indicated by input");
return data_type;
}
OpKernelType OperatorWithKernel::GetExpectedKernelType(
......
paddle/fluid/framework/tensor_impl.h
浏览文件 @ 3be8ffad
......@@ -25,7 +25,8 @@ inline const T* Tensor::data() const {
check_memory_size();
bool valid =
std::is_same<T, void>::value || type_ == DataTypeTrait<T>::DataType;
PADDLE_ENFORCE(valid, "Tensor holds the wrong type, it holds %d", type_);
PADDLE_ENFORCE(valid, "Tensor holds the wrong type, it holds %d",
DataTypeToString(type_));
return reinterpret_cast<const T*>(
reinterpret_cast<uintptr_t>(holder_->ptr()) + offset_);
......
python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py 已删除 100644 → 0
浏览文件 @ 1bf2face
# Copyright (c) 2018 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
from paddle.fluid.backward import append_backward
class SimpleLSTMRNN(fluid.imperative.Layer):
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, init_hidden=None, init_cell=None):
self.weight_1_arr = []
self.weight_2_arr = []
self.bias_arr = []
self.hidden_array = []
self.cell_array = []
self.mask_array = []
for i in range(self._num_layers):
weight_1 = fluid.layers.create_parameter(
shape=[self._hidden_size * 2, self._hidden_size * 4],
dtype="float32",
name="fc_weight1_" + str(i),
default_initializer=fluid.initializer.UniformInitializer(
low=-self._init_scale, high=self._init_scale))
self.weight_1_arr.append(weight_1)
bias_1 = fluid.layers.create_parameter(
[self._hidden_size * 4],
dtype="float32",
name="fc_bias1_" + str(i),
default_initializer=fluid.initializer.Constant(0.0))
self.bias_arr.append(bias_1)
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])
pre_hidden = fluid.layers.reshape(
pre_hidden, shape=[-1, self._hidden_size])
pre_cell = fluid.layers.reshape(
pre_cell, shape=[-1, self._hidden_size])
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(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]
nn = fluid.layers.concat([self.input, pre_hidden], 1)
gate_input = fluid.layers.matmul(x=nn, y=weight_1)
gate_input = fluid.layers.elementwise_add(gate_input, bias)
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):
def __init__(self,
hidden_size,
vocab_size,
num_layers=2,
num_steps=20,
init_scale=0.1,
dropout=None):
super(PtbModel, self).__init__()
self.hidden_size = hidden_size
self.vocab_size = vocab_size
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)
self.embedding = EMBEDDING(
size=[vocab_size, hidden_size],
dtype='float32',
is_sparse=False,
param_attr=fluid.ParamAttr(
name='embedding_para',
initializer=fluid.initializer.UniformInitializer(
low=-init_scale, high=init_scale)))
def _build_once(self, input, label, init_hidden, init_cell):
self.softmax_weight = fluid.layers.create_parameter(
[self.hidden_size, self.vocab_size],
dtype="float32",
name="softmax_weight",
default_initializer=fluid.initializer.UniformInitializer(
low=-self.init_scale, high=self.init_scale))
self.softmax_bias = fluid.layers.create_parameter(
[self.vocab_size],
dtype="float32",
name='softmax_bias',
default_initializer=fluid.initializer.UniformInitializer(
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])
x_emb = self.embedding(input)
x_emb = fluid.layers.reshape(
x_emb, shape=[-1, self.num_steps, self.hidden_size])
if self.dropout is not None and self.dropout > 0.0:
x_emb = fluid.layers.dropout(
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(
rnn_out, shape=[-1, self.num_steps, self.hidden_size])
projection = fluid.layers.reshape(rnn_out, self.softmax_weight)
projection = fluid.layers.elementwise_add(projection, self.softmax_bias)
projection = fluid.layers.reshape(
projection, shape=[-1, self.vocab_size])
projection = fluid.layers.reshape(
projection, 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)
loss.permissions = True
return loss, last_hidden, last_cell
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=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()
python/paddle/fluid/tests/unittests/test_imperative_split.py 已删除 100644 → 0
浏览文件 @ 1bf2face
# 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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