Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into stat_usage
zhouyingfeng
8 years ago
commit
766adaea7c
@ -0,0 +1,37 @@
|
||||
#!/usr/bin/python
|
||||
# Copyright (c) 2016 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.
|
||||
import sys
|
||||
import re
|
||||
import math
|
||||
|
||||
|
||||
def get_best_pass(log_filename):
|
||||
with open(log_filename, 'r') as f:
|
||||
text = f.read()
|
||||
pattern = re.compile('Test.*? cost=([0-9]+\.[0-9]+).*?pass-([0-9]+)',
|
||||
re.S)
|
||||
results = re.findall(pattern, text)
|
||||
sorted_results = sorted(results, key=lambda result: float(result[0]))
|
||||
return sorted_results[0]
|
||||
|
||||
|
||||
log_filename = sys.argv[1]
|
||||
log = get_best_pass(log_filename)
|
||||
predict_error = math.sqrt(float(log[0])) / 2
|
||||
print 'Best pass is %s, error is %s, which means predict get error as %f' % (
|
||||
log[1], log[0], predict_error)
|
||||
|
||||
evaluate_pass = "output/pass-%s" % log[1]
|
||||
print "evaluating from pass %s" % evaluate_pass
|
||||
File diff suppressed because it is too large
Load Diff
|
After Width: | Height: | Size: 232 KiB |
|
After Width: | Height: | Size: 244 KiB |
|
After Width: | Height: | Size: 225 KiB |
|
After Width: | Height: | Size: 242 KiB |
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,85 @@
|
||||
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
|
||||
|
||||
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. */
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "Function.h"
|
||||
|
||||
namespace paddle {
|
||||
|
||||
/**
|
||||
* \brief Context Projection Forward.
|
||||
*
|
||||
* \param[out] outputs output data.
|
||||
* \param[in] input input data.
|
||||
* \param[in] weight input weight.
|
||||
* \param[in] sequence input data.
|
||||
* \param[in] context_length consecutive rows for concatenation.
|
||||
* \param[in] context_start context start position.
|
||||
* \param[in] begin_pad begining pad position.
|
||||
* \param[in] is_padding whether padding 0 or not.
|
||||
*
|
||||
*/
|
||||
template <DeviceType Device>
|
||||
void ContextProjectionForward(typename MatrixT<Device>::type* output,
|
||||
const typename MatrixT<Device>::type* input,
|
||||
const typename MatrixT<Device>::type* weight,
|
||||
const typename SequenceT<Device>::type& sequence,
|
||||
size_t context_length,
|
||||
int context_start,
|
||||
size_t begin_pad);
|
||||
|
||||
/**
|
||||
* \brief Context Projection Backward.
|
||||
*
|
||||
* \param[out] outputs output gradient.
|
||||
* \param[in] input input gradient.
|
||||
* \param[in] weight input weight gradient.
|
||||
* \param[in] sequence input data.
|
||||
* \param[in] context_length consecutive rows for concatenation.
|
||||
* \param[in] context_start context start position.
|
||||
* \param[in] begin_pad begining pad position.
|
||||
* \param[in] is_padding whether padding 0 or not.
|
||||
*
|
||||
*/
|
||||
template <DeviceType Device>
|
||||
void ContextProjectionBackward(typename MatrixT<Device>::type* out_grad,
|
||||
typename MatrixT<Device>::type* in_grad,
|
||||
typename MatrixT<Device>::type* w_grad,
|
||||
const typename SequenceT<Device>::type& seq_vec,
|
||||
size_t context_length,
|
||||
int context_start,
|
||||
size_t begin_pad,
|
||||
bool is_padding,
|
||||
size_t total_pad);
|
||||
|
||||
template <DeviceType Device>
|
||||
void ContextProjectionBackwardData(
|
||||
typename MatrixT<Device>::type* out_grad,
|
||||
typename MatrixT<Device>::type* in_grad,
|
||||
const typename SequenceT<Device>::type& sequence,
|
||||
size_t context_length,
|
||||
int context_start);
|
||||
|
||||
template <DeviceType Device>
|
||||
void ContextProjectionBackwardWeight(
|
||||
typename MatrixT<Device>::type* out_grad,
|
||||
typename MatrixT<Device>::type* w_grad,
|
||||
const typename SequenceT<Device>::type& seq_vec,
|
||||
size_t context_length,
|
||||
int context_start,
|
||||
size_t total_pad,
|
||||
size_t begin_pad);
|
||||
|
||||
} // namespace paddle
|
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,172 @@
|
||||
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
|
||||
|
||||
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. */
|
||||
|
||||
#include <gtest/gtest.h>
|
||||
#include "FunctionTest.h"
|
||||
#include "paddle/gserver/tests/TestUtil.h"
|
||||
#include "paddle/math/Matrix.h"
|
||||
|
||||
using namespace paddle; // NOLINT
|
||||
|
||||
void testMatrixProjectionForward(int context_start,
|
||||
size_t context_length,
|
||||
bool is_padding,
|
||||
size_t batch_size,
|
||||
size_t input_dim) {
|
||||
size_t pad = std::max(0, -context_start) +
|
||||
std::max(0, (int)(context_start + context_length - 1));
|
||||
if (pad == 0) is_padding = false;
|
||||
|
||||
FunctionCompare compare("ContextProjectionForward",
|
||||
FuncConfig()
|
||||
.set("context_length", context_length)
|
||||
.set("context_start", context_start)
|
||||
.set("begin_pad", std::max(0, -context_start)));
|
||||
|
||||
CpuMatrix cpu_in(batch_size, input_dim);
|
||||
cpu_in.randomizeUniform();
|
||||
GpuMatrix gpu_in(batch_size, input_dim);
|
||||
gpu_in.copyFrom(cpu_in);
|
||||
auto cpu_weight =
|
||||
is_padding ? std::make_shared<CpuMatrix>(pad, input_dim) : nullptr;
|
||||
auto gpu_weight =
|
||||
is_padding ? std::make_shared<GpuMatrix>(pad, input_dim) : nullptr;
|
||||
if (is_padding) {
|
||||
cpu_weight->randomizeUniform();
|
||||
gpu_weight->copyFrom(*cpu_weight);
|
||||
}
|
||||
IVectorPtr cpu_seq;
|
||||
generateSequenceStartPositions(batch_size, cpu_seq);
|
||||
IVectorPtr gpu_seq = IVector::create(cpu_seq->getSize(), true);
|
||||
gpu_seq->copyFrom(*cpu_seq);
|
||||
|
||||
CpuMatrix cpu_out(batch_size, input_dim * context_length);
|
||||
GpuMatrix gpu_out(batch_size, input_dim * context_length);
|
||||
cpu_out.randomizeUniform();
|
||||
gpu_out.copyFrom(cpu_out);
|
||||
|
||||
compare.getCpuFunction()->calc(
|
||||
{Tensor(cpu_in.getData(), Dims{batch_size, input_dim}),
|
||||
Tensor(cpu_weight ? cpu_weight->getData() : nullptr,
|
||||
Dims{pad, input_dim}),
|
||||
Tensor(reinterpret_cast<real*>(cpu_seq->getData()),
|
||||
Dims{cpu_seq->getSize()})},
|
||||
{Tensor(cpu_out.getData(), Dims{batch_size, input_dim * context_length})},
|
||||
{});
|
||||
compare.getGpuFunction()->calc(
|
||||
{Tensor(gpu_in.getData(), Dims{batch_size, input_dim}),
|
||||
Tensor(gpu_weight ? gpu_weight->getData() : nullptr,
|
||||
Dims{pad, input_dim}),
|
||||
Tensor(reinterpret_cast<real*>(gpu_seq->getData()),
|
||||
Dims{gpu_seq->getSize()})},
|
||||
{Tensor(gpu_out.getData(), Dims{batch_size, input_dim * context_length})},
|
||||
{});
|
||||
|
||||
autotest::TensorCheckEqual(cpu_out, gpu_out);
|
||||
}
|
||||
|
||||
void testMatrixProjectionBackward(int context_start,
|
||||
int context_length,
|
||||
bool is_padding,
|
||||
size_t batch_size,
|
||||
size_t input_dim) {
|
||||
size_t pad = std::max(0, -context_start) +
|
||||
std::max(0, (int)(context_start + context_length - 1));
|
||||
if (pad == 0) is_padding = false;
|
||||
|
||||
FunctionCompare compare("ContextProjectionBackward",
|
||||
FuncConfig()
|
||||
.set("context_length", context_length)
|
||||
.set("context_start", context_start)
|
||||
.set("begin_pad", std::max(0, -context_start))
|
||||
.set("is_padding", is_padding)
|
||||
.set("total_pad", pad));
|
||||
|
||||
CpuMatrix cpu_in_grad(batch_size, input_dim);
|
||||
cpu_in_grad.randomizeUniform();
|
||||
GpuMatrix gpu_in_grad(batch_size, input_dim);
|
||||
gpu_in_grad.copyFrom(cpu_in_grad);
|
||||
|
||||
CpuMatrix cpu_out_grad(batch_size, input_dim * context_length);
|
||||
cpu_out_grad.randomizeUniform();
|
||||
GpuMatrix gpu_out_grad(batch_size, input_dim * context_length);
|
||||
gpu_out_grad.copyFrom(cpu_out_grad);
|
||||
|
||||
IVectorPtr cpu_seq;
|
||||
generateSequenceStartPositions(batch_size, cpu_seq);
|
||||
IVectorPtr gpu_seq = IVector::create(cpu_seq->getSize(), true);
|
||||
gpu_seq->copyFrom(*cpu_seq);
|
||||
|
||||
auto cpu_w_grad =
|
||||
is_padding ? std::make_shared<CpuMatrix>(pad, input_dim) : nullptr;
|
||||
auto gpu_w_grad =
|
||||
is_padding ? std::make_shared<GpuMatrix>(pad, input_dim) : nullptr;
|
||||
if (is_padding) {
|
||||
cpu_w_grad->randomizeUniform();
|
||||
gpu_w_grad->copyFrom(*cpu_w_grad);
|
||||
}
|
||||
|
||||
compare.getCpuFunction()->calc(
|
||||
{Tensor(cpu_in_grad.getData(), Dims{batch_size, input_dim}),
|
||||
Tensor(cpu_w_grad ? cpu_w_grad->getData() : nullptr,
|
||||
Dims{pad, input_dim}),
|
||||
Tensor(reinterpret_cast<real*>(cpu_seq->getData()),
|
||||
Dims{cpu_seq->getSize()})},
|
||||
{Tensor(cpu_out_grad.getData(),
|
||||
Dims{batch_size, input_dim * context_length})},
|
||||
{});
|
||||
|
||||
compare.getGpuFunction()->calc(
|
||||
{Tensor(gpu_in_grad.getData(), Dims{batch_size, input_dim}),
|
||||
Tensor(gpu_w_grad ? gpu_w_grad->getData() : nullptr,
|
||||
Dims{pad, input_dim}),
|
||||
Tensor(reinterpret_cast<real*>(gpu_seq->getData()),
|
||||
Dims{gpu_seq->getSize()})},
|
||||
{Tensor(gpu_out_grad.getData(),
|
||||
Dims{batch_size, input_dim * context_length})},
|
||||
{});
|
||||
|
||||
autotest::TensorCheckErr(cpu_in_grad, gpu_in_grad);
|
||||
if (is_padding) {
|
||||
autotest::TensorCheckErr(*cpu_w_grad, *gpu_w_grad);
|
||||
}
|
||||
}
|
||||
|
||||
TEST(ContextProjection, projection) {
|
||||
for (auto context_start : {-5, -3, -1, 0, 3}) {
|
||||
for (auto context_length : {1, 2, 5, 7}) {
|
||||
for (auto trainable_padding : {false, true}) {
|
||||
for (auto batch_size : {1, 2, 5, 20, 100}) {
|
||||
for (auto input_dim : {15, 32, 63, 128, 200}) {
|
||||
VLOG(3) << " context_start=" << context_start
|
||||
<< " context_length=" << context_length
|
||||
<< " trainable_padding=" << trainable_padding
|
||||
<< " batch_size=" << batch_size
|
||||
<< " input_dim=" << input_dim;
|
||||
testMatrixProjectionForward(context_start,
|
||||
context_length,
|
||||
trainable_padding,
|
||||
batch_size,
|
||||
input_dim);
|
||||
testMatrixProjectionBackward(context_start,
|
||||
context_length,
|
||||
trainable_padding,
|
||||
batch_size,
|
||||
input_dim);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in new issue