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提交 e02cbf35 编写于 作者: Y Yancey1989
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Merge branch 'develop' of github.com:PaddlePaddle/Paddle into fix_pserver_sub_blocks

上级 5c7d6a55 6e1c48d1
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benchmark/fluid/Dockerfile
浏览文件 @ e02cbf35
FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04
# Use UBUNTU_MIRROR can speed up apt-get speed.
# ARG UBUNTU_MIRROR
# RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com/ubuntu#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi'
RUN apt-get update && apt-get install -y python python-pip iputils-ping libgtk2.0-dev wget vim net-tools iftop python-opencv
RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.7 /usr/lib/libcudnn.so && ln -s /usr/lib/x86_64-linux-gnu/libnccl.so.2 /usr/lib/libnccl.so
RUN pip install -U pip
RUN pip install -U kubernetes paddlepaddle
# IMPORTANT:
# Add "ENV http_proxy=http://ip:port" if your download is slow, and don't forget to unset it at runtime.
# exmaple: unset http_proxy && unset https_proxy && python fluid_benchmark.py ...
RUN pip install -U pip
RUN pip install -U kubernetes paddlepaddle
RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.cifar.train10()\npaddle.dataset.flowers.fetch()" | python'
RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.mnist.train()\npaddle.dataset.mnist.test()\npaddle.dataset.imdb.fetch()" | python'
......@@ -14,9 +21,11 @@ RUN pip uninstall -y paddlepaddle && mkdir /workspace
ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/paddle_k8s /usr/bin
ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/k8s_tools.py /root
RUN chmod +x /usr/bin/paddle_k8s
ADD *.whl /
RUN pip install /*.whl && rm -f /*.whl && chmod +x /usr/bin/paddle_k8s
RUN pip install /*.whl && rm -f /*.whl
ENV LD_LIBRARY_PATH=/usr/local/lib
ADD fluid_benchmark.py recordio_converter.py models/ /workspace/
ADD fluid_benchmark.py recordio_converter.py args.py recordio_converter.py run.sh run_fluid_benchmark.sh /workspace/
ADD models/ /workspace/models/
benchmark/fluid/fluid_benchmark.py
浏览文件 @ e02cbf35
......@@ -97,7 +97,7 @@ def dist_transpile(trainer_id, args):
return train_program, fluid.default_startup_program()
else:
raise ValueError(
'TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
'PADDLE_TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
)
......@@ -264,8 +264,6 @@ def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
break
else:
loss, = exe.run([avg_loss.name], feed=feeder.feed(data))
if args.update_method == "pserver":
exe.bcast_params()
if args.use_reader_op:
num_samples += args.batch_size * args.gpus
else:
......@@ -301,9 +299,18 @@ def print_train_time(start_time, end_time, num_samples):
(num_samples, train_elapsed, examples_per_sec))
def print_paddle_envs():
print('----------- Configuration envs -----------')
for k in os.environ:
if "PADDLE_" in k:
print "ENV %s:%s" % (k, os.environ[k])
print('------------------------------------------------')
def main():
args = parse_args()
print_arguments(args)
print_paddle_envs()
# the unique trainer id, starting from 0, needed by trainer
# only
......
benchmark/fluid/kube_gen_job.py
浏览文件 @ e02cbf35
......@@ -17,6 +17,7 @@ import copy
import argparse
import random
import os
import copy
from kube_templates import pserver, trainer, envs
......@@ -108,10 +109,9 @@ def gen_job():
tn_container["ports"][0]["containerPort"] = spreadport
envs.append({"name": "PADDLE_JOB_NAME", "value": args.jobname})
envs.append({"name": "TRAINERS", "value": str(args.trainers)})
envs.append({"name": "PSERVERS", "value": str(args.pservers)})
envs.append({"name": "PADDLE_TRAINERS", "value": str(args.trainers)})
envs.append({"name": "PADDLE_PSERVERS", "value": str(args.pservers)})
envs.append({"name": "ENTRY", "value": args.entry})
envs.append({"name": "PADDLE_INIT_PORT", "value": str(args.port)})
envs.append({"name": "PADDLE_PSERVER_PORT", "value": str(args.port)})
# NOTE: these directories below are cluster specific, please modify
# this settings before you run on your own cluster.
......@@ -166,17 +166,23 @@ def gen_job():
tn["spec"]["template"]["spec"]["volumes"] = volumes
tn_container["volumeMounts"] = volumeMounts
ps_container["env"] = envs
ps_container["env"].append({"name": "TRAINING_ROLE", "value": "PSERVER"})
ps_container["env"] = copy.deepcopy(envs)
ps_container["env"].append({
"name": "PADDLE_TRAINING_ROLE",
"value": "PSERVER"
})
tn_container["env"] = envs
if args.disttype == "pserver":
tn_container["env"].append({
"name": "TRAINING_ROLE",
"name": "PADDLE_TRAINING_ROLE",
"value": "TRAINER"
})
elif args.disttype == "nccl2" or args.disttype == "local":
# NCCL2 have no training role, set to plain WORKER
tn_container["env"].append({"name": "TRAINING_ROLE", "value": "WORKER"})
tn_container["env"].append({
"name": "PADDLE_TRAINING_ROLE",
"value": "WORKER"
})
os.mkdir(args.jobname)
if args.disttype == "pserver":
......
doc/fluid/api/gen_doc.sh
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#!/bin/bash
python gen_doc.py layers --submodules control_flow device io nn ops tensor detection learning_rate_scheduler metric > layers.rst
for module in data_feeder clip metrics executor initializer io nets optimizer param_attr profiler regularizer
for module in data_feeder clip metrics executor initializer io nets optimizer param_attr profiler regularizer transpiler
do
python gen_doc.py ${module} > ${module}.rst
done
doc/fluid/api/transpiler.rst 0 → 100644
浏览文件 @ e02cbf35
.. THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
!DO NOT EDIT THIS FILE MANUALLY!
==========
transpiler
==========
DistributeTranspiler
--------------------
.. autoclass:: paddle.fluid.transpiler.DistributeTranspiler
:members:
:noindex:
InferenceTranspiler
-------------------
.. autoclass:: paddle.fluid.transpiler.InferenceTranspiler
:members:
:noindex:
memory_optimize
---------------
.. autofunction:: paddle.fluid.transpiler.memory_optimize
:noindex:
release_memory
--------------
.. autofunction:: paddle.fluid.transpiler.release_memory
:noindex:
HashName
--------
.. autoclass:: paddle.fluid.transpiler.HashName
:members:
:noindex:
RoundRobin
----------
.. autoclass:: paddle.fluid.transpiler.RoundRobin
:members:
:noindex:
doc/fluid/howto/cluster/fluid_cluster_train_cn.md
浏览文件 @ e02cbf35
......@@ -168,13 +168,13 @@ cd /paddle/python/paddle/fluid/tests/book
第二步，启动Parameter Server：
```bash
PADDLE_INIT_PORT=6174 PADDLE_INIT_PSERVERS=192.168.1.2 TRAINERS=2 POD_IP=192.168.1.2 PADDLE_INIT_TRAINER_ID=1 TRAINING_ROLE=PSERVER python test_fit_a_line.py
PADDLE_PSERVER_PORT=6174 PADDLE_PSERVER_IPS=192.168.1.2 PADDLE_TRAINERS=2 PADDLE_CURRENT_IP=192.168.1.2 PADDLE_TRAINER_ID=1 PADDLE_TRAINING_ROLE=PSERVER python test_fit_a_line.py
```
执行命令后请等待出现提示： ```Server listening on 192.168.1.2:6174 ```, 表示Paramter Server已经正常启动。
第三步，启动Trainer：
```bash
PADDLE_INIT_PORT=6174 PADDLE_INIT_PSERVERS=192.168.1.3 TRAINERS=2 POD_IP=192.168.1.3 PADDLE_INIT_TRAINER_ID=1 TRAINING_ROLE=TRAINER python test_fit_a_line.py
PADDLE_PSERVER_PORT=6174 PADDLE_PSERVER_IPS=192.168.1.3 PADDLE_TRAINERS=2 PADDLE_CURRENT_IPP=192.168.1.3 PADDLE_TRAINER_ID=1 PADDLE_TRAINING_ROLE=TRAINER python test_fit_a_line.py
```
由于我们定义的Trainer的数量是2个，因此需要在另外一个计算节点上再启动一个Trainer。
......
doc/fluid/howto/cluster/fluid_recordio.md
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......@@ -114,8 +114,8 @@ def gen_train_list(file_pattern, trainers, trainer_id):
ret_list.append(f)
return ret_list
trainers = int(os.getenv("TRAINERS"))
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
trainers = int(os.getenv("PADDLE_TRAINERS"))
trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
data_file = fluid.layers.io.open_files(
filenames=gen_train_list("./mnist-[0-9]*.recordio", 2, 0),
thread_num=1,
......
doc/fluid/howto/inference/build_and_install_lib_cn.rst
浏览文件 @ e02cbf35
......@@ -13,6 +13,7 @@ cpu_noavx_openblas `fluid.tgz <https://guest:@paddleci.ngrok.io/repository
cuda7.5_cudnn5_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/fluid.tgz>`_
cuda8.0_cudnn5_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/fluid.tgz>`_
cuda8.0_cudnn7_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/fluid.tgz>`_
cuda9.0_cudnn7_avx_mkl `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda90cudnn7avxMkl/.lastSuccessful/fluid.tgz>`_
====================== ========================================
从源码编译
......
paddle/contrib/inference/demo/simple_on_word2vec.cc
浏览文件 @ e02cbf35
......@@ -40,10 +40,9 @@ void Main(bool use_gpu) {
//# 2. Prepare input.
int64_t data[4] = {1, 2, 3, 4};
PaddleBuf buf{.data = data, .length = sizeof(data)};
PaddleTensor tensor{.name = "",
.shape = std::vector<int>({4, 1}),
.data = buf,
.data = PaddleBuf(data, sizeof(data)),
.dtype = PaddleDType::INT64};
// For simplicity, we set all the slots with the same data.
......@@ -55,14 +54,12 @@ void Main(bool use_gpu) {
//# 4. Get output.
ASSERT_EQ(outputs.size(), 1UL);
LOG(INFO) << "output buffer size: " << outputs.front().data.length;
const size_t num_elements = outputs.front().data.length / sizeof(float);
LOG(INFO) << "output buffer size: " << outputs.front().data.length();
const size_t num_elements = outputs.front().data.length() / sizeof(float);
// The outputs' buffers are in CPU memory.
for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
LOG(INFO) << static_cast<float*>(outputs.front().data.data)[i];
LOG(INFO) << static_cast<float*>(outputs.front().data.data())[i];
}
// TODO(Superjomn): this is should be free automatically
free(outputs[0].data.data);
}
}
......@@ -86,10 +83,9 @@ void MainThreads(int num_threads, bool use_gpu) {
for (int batch_id = 0; batch_id < num_batches; ++batch_id) {
// 2. Dummy Input Data
int64_t data[4] = {1, 2, 3, 4};
PaddleBuf buf{.data = data, .length = sizeof(data)};
PaddleTensor tensor{.name = "",
.shape = std::vector<int>({4, 1}),
.data = buf,
.data = PaddleBuf(data, sizeof(data)),
.dtype = PaddleDType::INT64};
std::vector<PaddleTensor> inputs(4, tensor);
std::vector<PaddleTensor> outputs;
......@@ -99,13 +95,13 @@ void MainThreads(int num_threads, bool use_gpu) {
// 4. Get output.
ASSERT_EQ(outputs.size(), 1UL);
LOG(INFO) << "TID: " << tid << ", "
<< "output buffer size: " << outputs.front().data.length;
const size_t num_elements = outputs.front().data.length / sizeof(float);
<< "output buffer size: " << outputs.front().data.length();
const size_t num_elements =
outputs.front().data.length() / sizeof(float);
// The outputs' buffers are in CPU memory.
for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
LOG(INFO) << static_cast<float*>(outputs.front().data.data)[i];
LOG(INFO) << static_cast<float*>(outputs.front().data.data())[i];
}
free(outputs[0].data.data);
}
});
}
......
paddle/contrib/inference/paddle_inference_api.cc
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......@@ -13,3 +13,53 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/contrib/inference/paddle_inference_api.h"
namespace paddle {
PaddleBuf::PaddleBuf(PaddleBuf&& other)
: data_(other.data_),
length_(other.length_),
memory_owned_(other.memory_owned_) {
other.memory_owned_ = false;
other.data_ = nullptr;
other.length_ = 0;
}
PaddleBuf::PaddleBuf(const PaddleBuf& other) { *this = other; }
PaddleBuf& PaddleBuf::operator=(const PaddleBuf& other) {
// only the buffer with external memory can be copied
assert(!other.memory_owned_);
data_ = other.data_;
length_ = other.length_;
memory_owned_ = other.memory_owned_;
return *this;
}
void PaddleBuf::Resize(size_t length) {
// Only the owned memory can be reset, the external memory can't be changed.
if (length_ == length) return;
assert(memory_owned_);
Free();
data_ = new char[length];
length_ = length;
memory_owned_ = true;
}
void PaddleBuf::Reset(void* data, size_t length) {
Free();
memory_owned_ = false;
data_ = data;
length_ = length;
}
void PaddleBuf::Free() {
if (memory_owned_ && data_) {
assert(length_ > 0);
delete static_cast<char*>(data_);
data_ = nullptr;
length_ = 0;
}
}
} // namespace paddle
\ No newline at end of file
paddle/contrib/inference/paddle_inference_api.h
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......@@ -21,6 +21,7 @@ limitations under the License. */
#pragma once
#include <cassert>
#include <memory>
#include <string>
#include <vector>
......@@ -32,12 +33,38 @@ enum PaddleDType {
INT64,
};
struct PaddleBuf {
void* data; // pointer to the data memory.
size_t length; // number of memory bytes.
class PaddleBuf {
public:
PaddleBuf() = default;
PaddleBuf(PaddleBuf&& other);
// Copy only available when memory is managed externally.
explicit PaddleBuf(const PaddleBuf&);
PaddleBuf& operator=(const PaddleBuf&);
// Do not own the memory.
PaddleBuf(void* data, size_t length)
: data_(data), length_(length), memory_owned_{false} {}
// Own memory.
PaddleBuf(size_t length)
: data_(new char[length]), length_(length), memory_owned_(true) {}
// Resize to `length` bytes.
void Resize(size_t length);
// Reset to external memory.
void Reset(void* data, size_t length);
bool empty() const { return length_ == 0; }
void* data() const { return data_; }
size_t length() const { return length_; }
~PaddleBuf() { Free(); }
private:
void Free();
void* data_{nullptr}; // pointer to the data memory.
size_t length_{0}; // number of memory bytes.
bool memory_owned_{true};
};
struct PaddleTensor {
PaddleTensor() = default;
std::string name; // variable name.
std::vector<int> shape;
// TODO(Superjomn) for LoD support, add a vector<vector<int>> field if needed.
......@@ -67,8 +94,9 @@ class PaddlePredictor {
// Predict an record.
// The caller should be responsible for allocating and releasing the memory of
// `inputs`. `inputs` should be alive until Run returns. caller should be
// responsible for releasing the memory of `output_data`.
// `inputs`. `inputs` should be available until Run returns. Caller should be
// responsible for the output tensor's buffer, either allocated or passed from
// outside.
virtual bool Run(const std::vector<PaddleTensor>& inputs,
std::vector<PaddleTensor>* output_data) = 0;
......
paddle/contrib/inference/paddle_inference_api_anakin_engine.cc
浏览文件 @ e02cbf35
......@@ -48,7 +48,7 @@ bool PaddleInferenceAnakinPredictor::Run(
auto d_tensor_in_p = executor_.get_in(input.name);
float *d_data_p = d_tensor_in_p->mutable_data();
if (cudaMemcpy(d_data_p,
static_cast<float *>(input.data.data),
static_cast<float *>(input.data.data()),
d_tensor_in_p->valid_size() * sizeof(float),
cudaMemcpyHostToDevice) != 0) {
LOG(ERROR) << "copy data from CPU to GPU error";
......@@ -65,8 +65,11 @@ bool PaddleInferenceAnakinPredictor::Run(
for (auto &output : *output_data) {
auto *tensor = executor_.get_out(output.name);
output.shape = tensor->shape();
if (output.data.length() < tensor->valid_size() * sizeof(float)) {
output.data.Resize(tensor->valid_size() * sizeof(float));
}
// Copy data from GPU -> CPU
if (cudaMemcpy(output.data.data,
if (cudaMemcpy(output.data.data(),
tensor->mutable_data(),
tensor->valid_size() * sizeof(float),
cudaMemcpyDeviceToHost) != 0) {
......
paddle/contrib/inference/paddle_inference_api_anakin_engine_tester.cc
浏览文件 @ e02cbf35
......@@ -37,28 +37,26 @@ TEST(inference, anakin) {
float data[1 * 3 * 224 * 224] = {1.0f};
PaddleBuf buf{.data = data, .length = sizeof(data)};
PaddleTensor tensor{.name = "input_0",
.shape = std::vector<int>({1, 3, 224, 224}),
.data = buf,
.data = PaddleBuf(data, sizeof(data)),
.dtype = PaddleDType::FLOAT32};
// For simplicity, we set all the slots with the same data.
std::vector<PaddleTensor> paddle_tensor_feeds(1, tensor);
std::vector<PaddleTensor> paddle_tensor_feeds;
paddle_tensor_feeds.emplace_back(std::move(tensor));
float data_out[1000];
PaddleBuf buf_out{.data = data_out, .length = sizeof(data)};
PaddleTensor tensor_out{.name = "prob_out",
.shape = std::vector<int>({1000, 1}),
.data = buf_out,
.data = PaddleBuf(),
.dtype = PaddleDType::FLOAT32};
std::vector<PaddleTensor> outputs(1, tensor_out);
std::vector<PaddleTensor> outputs;
outputs.emplace_back(std::move(tensor_out));
ASSERT_TRUE(predictor->Run(paddle_tensor_feeds, &outputs));
float* data_o = static_cast<float*>(outputs[0].data.data);
float* data_o = static_cast<float*>(outputs[0].data.data());
for (size_t j = 0; j < 1000; ++j) {
LOG(INFO) << "output[" << j << "]: " << data_o[j];
}
......
paddle/contrib/inference/paddle_inference_api_impl.cc
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......@@ -178,8 +178,8 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
// TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
std::memcpy(static_cast<void *>(input_ptr),
inputs[i].data.data,
inputs[i].data.length);
inputs[i].data.data(),
inputs[i].data.length());
feeds->push_back(input);
}
return true;
......@@ -241,10 +241,11 @@ bool NativePaddlePredictor::GetFetch(
}
outputs->at(i).shape = shape;
outputs->at(i).data.length = sizeof(float) * data.size();
outputs->at(i).data.data = malloc(outputs->at(i).data.length);
std::memcpy(
outputs->at(i).data.data, data.data(), outputs->at(i).data.length);
auto &buffer = outputs->at(i).data;
if (buffer.empty() || buffer.length() < sizeof(float) * data.size()) {
buffer.Resize(sizeof(float) * data.size());
}
std::memcpy(buffer.data(), data.data(), buffer.length());
outputs->at(i).dtype = PaddleDType::FLOAT32;
// TODO(panyx0718): support other types? fill tensor name? avoid a copy.
}
......
paddle/contrib/inference/test_paddle_inference_api_impl.cc
浏览文件 @ e02cbf35
......@@ -27,13 +27,12 @@ namespace paddle {
PaddleTensor LodTensorToPaddleTensor(framework::LoDTensor* t) {
PaddleTensor pt;
pt.data.data = t->data<void>();
if (t->type() == typeid(int64_t)) {
pt.data.length = t->numel() * sizeof(int64_t);
pt.data.Reset(t->data<void>(), t->numel() * sizeof(int64_t));
pt.dtype = PaddleDType::INT64;
} else if (t->type() == typeid(float)) {
pt.data.length = t->numel() * sizeof(float);
pt.data.Reset(t->data<void>(), t->numel() * sizeof(float));
pt.dtype = PaddleDType::FLOAT32;
} else {
LOG(FATAL) << "unsupported type.";
......@@ -79,8 +78,8 @@ void MainWord2Vec(bool use_gpu) {
std::vector<PaddleTensor> outputs;
ASSERT_TRUE(predictor->Run(paddle_tensor_feeds, &outputs));
ASSERT_EQ(outputs.size(), 1UL);
size_t len = outputs[0].data.length;
float* data = static_cast<float*>(outputs[0].data.data);
size_t len = outputs[0].data.length();
float* data = static_cast<float*>(outputs[0].data.data());
for (size_t j = 0; j < len / sizeof(float); ++j) {
ASSERT_LT(data[j], 1.0);
ASSERT_GT(data[j], -1.0);
......@@ -103,8 +102,6 @@ void MainWord2Vec(bool use_gpu) {
EXPECT_LT(lod_data[i] - data[i], 1e-3);
EXPECT_GT(lod_data[i] - data[i], -1e-3);
}
free(outputs[0].data.data);
}
void MainImageClassification(bool use_gpu) {
......@@ -143,13 +140,12 @@ void MainImageClassification(bool use_gpu) {
std::vector<PaddleTensor> outputs;
ASSERT_TRUE(predictor->Run(paddle_tensor_feeds, &outputs));
ASSERT_EQ(outputs.size(), 1UL);
size_t len = outputs[0].data.length;
float* data = static_cast<float*>(outputs[0].data.data);
size_t len = outputs[0].data.length();
float* data = static_cast<float*>(outputs[0].data.data());
float* lod_data = output1.data<float>();
for (size_t j = 0; j < len / sizeof(float); ++j) {
EXPECT_NEAR(lod_data[j], data[j], 1e-3);
}
free(data);
}
void MainThreadsWord2Vec(bool use_gpu) {
......@@ -192,8 +188,8 @@ void MainThreadsWord2Vec(bool use_gpu) {
// check outputs range
ASSERT_EQ(local_outputs.size(), 1UL);
const size_t len = local_outputs[0].data.length;
float* data = static_cast<float*>(local_outputs[0].data.data);
const size_t len = local_outputs[0].data.length();
float* data = static_cast<float*>(local_outputs[0].data.data());
for (size_t j = 0; j < len / sizeof(float); ++j) {
ASSERT_LT(data[j], 1.0);
ASSERT_GT(data[j], -1.0);
......@@ -205,7 +201,6 @@ void MainThreadsWord2Vec(bool use_gpu) {
for (int i = 0; i < refs[tid].numel(); ++i) {
EXPECT_NEAR(ref_data[i], data[i], 1e-3);
}
free(data);
});
}
for (int i = 0; i < num_jobs; ++i) {
......@@ -251,14 +246,13 @@ void MainThreadsImageClassification(bool use_gpu) {
// check outputs correctness
ASSERT_EQ(local_outputs.size(), 1UL);
const size_t len = local_outputs[0].data.length;
float* data = static_cast<float*>(local_outputs[0].data.data);
const size_t len = local_outputs[0].data.length();
float* data = static_cast<float*>(local_outputs[0].data.data());
float* ref_data = refs[tid].data<float>();
EXPECT_EQ(refs[tid].numel(), len / sizeof(float));
for (int i = 0; i < refs[tid].numel(); ++i) {
EXPECT_NEAR(ref_data[i], data[i], 1e-3);
}
free(data);
});
}
for (int i = 0; i < num_jobs; ++i) {
......
paddle/fluid/framework/executor.cc
浏览文件 @ e02cbf35
......@@ -321,7 +321,8 @@ std::vector<std::shared_ptr<ExecutorPrepareContext>> Executor::Prepare(
}
void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
bool create_local_scope, bool create_vars) {
bool create_local_scope, bool create_vars,
bool keep_kids) {
Scope* local_scope = scope;
if (create_vars) {
if (create_local_scope) {
......@@ -344,12 +345,20 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
}
}
platform::DeviceContextPool::Instance().Get(place_)->Wait();
if (create_vars && create_local_scope) {
if (local_scope != scope) {
scope->DeleteScope(local_scope);
} else {
// Delete the local scopes created in operators.
scope->DropKids();
if (!keep_kids) {
// By default, we should delete all kid scopes after run executor because
// some operators may create local scope when running, such as while_op.
// But when while_op also create a local executor to run it's sub block,
// the sub scopes it created should not be dropped immediately, because
// while_grad_op will use some variables created during while_op run, so
// we need to keep the kids and wait for the outer executor to drop them.
scope->DropKids();
}
}
if (FLAGS_benchmark) {
VLOG(2) << "-------------------------------------------------------";
VLOG(2) << "Memory used after deleting local scope: "
......
paddle/fluid/framework/executor.h
浏览文件 @ e02cbf35
......@@ -78,7 +78,7 @@ class Executor {
void RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
bool create_local_scope = true,
bool create_vars = true);
bool create_vars = true, bool keep_kids = false);
void RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
std::map<std::string, const LoDTensor*>* feed_targets,
......
paddle/fluid/inference/analysis/tensorrt_subgraph_pass.cc
浏览文件 @ e02cbf35
......@@ -27,7 +27,7 @@ void TensorRTSubGraphPass::Run(DataFlowGraph *graph) {
SubGraphFuse(graph, node_inside_subgraph_teller_);
}
} // analysis
} // inference
} // namespace analysis
} // namespace inference
} // paddle
} // namespace paddle
paddle/fluid/operators/activation_op.cc
浏览文件 @ e02cbf35
......@@ -143,7 +143,7 @@ $$out = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
__attribute__((unused)) constexpr char TanhShrinkDoc[] = R"DOC(
TanhShrink Activation Operator.
$$out = x - \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
$$out = x - \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
)DOC";
......@@ -385,7 +385,7 @@ class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
STanh Activation Operator.
$$out = b * \frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
$$out = b * \\frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
)DOC");
}
......
paddle/fluid/operators/batch_norm_mkldnn_op.cc
浏览文件 @ e02cbf35
......@@ -21,8 +21,6 @@ namespace operators {
using batch_norm_bwd = mkldnn::batch_normalization_backward;
using batch_norm_fwd = mkldnn::batch_normalization_forward;
using framework::DataLayout;
using framework::Tensor;
using mkldnn::memory;
using mkldnn::primitive;
using mkldnn::reorder;
......@@ -31,18 +29,6 @@ using paddle::platform::MKLDNNDeviceContext;
using paddle::platform::MKLDNNMemDesc;
using platform::to_void_cast;
template <typename T>
using EigenArrayMap =
Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using ConstEigenArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
template <typename T>
using ConstEigenVectorArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
namespace {
template <typename T>
struct bn_type_traits {
......
paddle/fluid/operators/batch_norm_op.cc
浏览文件 @ e02cbf35
......@@ -22,22 +22,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using DataLayout = framework::DataLayout;
template <typename T>
using EigenArrayMap =
Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using ConstEigenArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
template <typename T>
using ConstEigenVectorArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
class BatchNormOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......
paddle/fluid/operators/batch_norm_op.h
浏览文件 @ e02cbf35
......@@ -19,6 +19,22 @@ limitations under the License. */
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using DataLayout = framework::DataLayout;
template <typename T>
using EigenArrayMap =
Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using ConstEigenArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
template <typename T>
using ConstEigenVectorArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
template <typename DeviceContext, typename T>
class BatchNormKernel : public framework::OpKernel<T> {
public:
......
paddle/fluid/operators/bilinear_interp_op.cc
浏览文件 @ e02cbf35
......@@ -110,6 +110,7 @@ REGISTER_OPERATOR(bilinear_interp, ops::BilinearInterpOp,
ops::BilinearInterpOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(bilinear_interp_grad, ops::BilinearInterpOpGrad);
REGISTER_OP_CPU_KERNEL(bilinear_interp, ops::BilinearInterpKernel<float>);
REGISTER_OP_CPU_KERNEL(bilinear_interp, ops::BilinearInterpKernel<float>,
ops::BilinearInterpKernel<uint8_t>);
REGISTER_OP_CPU_KERNEL(bilinear_interp_grad,
ops::BilinearInterpGradKernel<float>);
paddle/fluid/operators/bilinear_interp_op.h
浏览文件 @ e02cbf35
......@@ -46,8 +46,10 @@ class BilinearInterpKernel : public framework::OpKernel<T> {
int in_chw = channels * in_hw;
int out_chw = channels * out_hw;
T ratio_h = (out_h > 1) ? static_cast<T>(in_h - 1) / (out_h - 1) : 0.f;
T ratio_w = (out_w > 1) ? static_cast<T>(in_w - 1) / (out_w - 1) : 0.f;
float ratio_h =
(out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
float ratio_w =
(out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
if (in_h == out_h && in_w == out_w) {
memcpy(output, input, input_t->numel() * sizeof(T));
......@@ -56,24 +58,24 @@ class BilinearInterpKernel : public framework::OpKernel<T> {
for (int i = 0; i < out_h; ++i) { // loop for images
int h = ratio_h * i;
int hid = (h < in_h - 1) ? 1 : 0;
T h1lambda = ratio_h * i - h;
T h2lambda = 1 - h1lambda;
float h1lambda = ratio_h * i - h;
float h2lambda = 1.f - h1lambda;
for (int j = 0; j < out_w; ++j) {
int w = ratio_w * j;
int wid = (w < in_w - 1) ? 1 : 0;
T w1lambda = ratio_w * j - w;
T w2lambda = 1 - w1lambda;
float w1lambda = ratio_w * j - w;
float w2lambda = 1.f - w1lambda;
// calculate four position for bilinear interpolation
const T* in_pos = &input[k * in_chw + h * in_w + w];
T* out_pos = &output[k * out_chw + i * out_w + j];
for (int c = 0; c < channels; ++c) { // loop for channels
// bilinear interpolation
out_pos[0] =
out_pos[0] = static_cast<T>(
h2lambda * (w2lambda * in_pos[0] + w1lambda * in_pos[wid]) +
h1lambda * (w2lambda * in_pos[hid * in_w] +
w1lambda * in_pos[hid * in_w + wid]);
w1lambda * in_pos[hid * in_w + wid]));
in_pos += in_hw;
out_pos += out_hw;
}
......@@ -117,8 +119,10 @@ class BilinearInterpGradKernel : public framework::OpKernel<T> {
int in_chw = channels * in_hw;
int out_chw = channels * out_hw;
T ratio_h = (out_h > 1) ? static_cast<T>(in_h - 1) / (out_h - 1) : 0.f;
T ratio_w = (out_w > 1) ? static_cast<T>(in_w - 1) / (out_w - 1) : 0.f;
float ratio_h =
(out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
float ratio_w =
(out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
if (in_h == out_h && in_w == out_w) {
memcpy(d_input, d_output, d_input_t->numel() * sizeof(T));
......@@ -127,22 +131,24 @@ class BilinearInterpGradKernel : public framework::OpKernel<T> {
for (int i = 0; i < out_h; ++i) { // loop for images
int h = ratio_h * i;
int hid = (h < in_h - 1) ? 1 : 0;
T h1lambda = ratio_h * i - h;
T h2lambda = 1 - h1lambda;
float h1lambda = ratio_h * i - h;
float h2lambda = 1 - h1lambda;
for (int j = 0; j < out_w; ++j) {
int w = ratio_w * j;
int wid = (w < in_w - 1) ? 1 : 0;
T w1lambda = ratio_w * j - w;
T w2lambda = 1 - w1lambda;
float w1lambda = ratio_w * j - w;
float w2lambda = 1 - w1lambda;
T* in_pos = &d_input[k * in_chw + h * in_w + w];
const T* out_pos = &d_output[k * out_chw + i * out_w + j];
for (int c = 0; c < channels; ++c) { // loop for channels
in_pos[0] += h2lambda * w2lambda * out_pos[0];
in_pos[wid] += h2lambda * w1lambda * out_pos[0];
in_pos[hid * in_w] += h1lambda * w2lambda * out_pos[0];
in_pos[hid * in_w + wid] += h1lambda * w1lambda * out_pos[0];
in_pos[0] += static_cast<T>(h2lambda * w2lambda * out_pos[0]);
in_pos[wid] += static_cast<T>(h2lambda * w1lambda * out_pos[0]);
in_pos[hid * in_w] +=
static_cast<T>(h1lambda * w2lambda * out_pos[0]);
in_pos[hid * in_w + wid] +=
static_cast<T>(h1lambda * w1lambda * out_pos[0]);
in_pos += in_hw;
out_pos += out_hw;
}
......
paddle/fluid/operators/logical_op.cc
浏览文件 @ e02cbf35
......@@ -146,6 +146,6 @@ REGISTER_UNARY_LOGICAL_OP(logical_not, "$$Out = !X$$");
REGISTER_UNARY_LOGICAL_KERNEL(logical_not, CPU,
paddle::operators::LogicalNotFunctor);
REGISTER_BINARY_LOGICAL_OP(logical_xor,
"$$Out = (X || Y) \\, \\&\\& \\, !(X \\&\\& Y)$$");
"$$Out = (X || Y) \\&\\& !(X \\&\\& Y)$$");
REGISTER_BINARY_LOGICAL_KERNEL(logical_xor, CPU,
paddle::operators::LogicalXorFunctor);
paddle/fluid/operators/math/concat.cu
浏览文件 @ e02cbf35
......@@ -209,7 +209,7 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
outputs_cols[0] = 0;
for (int i = 0; i < o_num; ++i) {
int t_col = outputs->at(i)->numel() / out_row;
int t_col = ref_inputs.at(i)->numel() / out_row;
if (sameShape) {
if (t_col != out0_col) sameShape = false;
}
......
paddle/fluid/operators/math/math_function.cc
浏览文件 @ e02cbf35
......@@ -30,6 +30,7 @@ template struct SetConstant<platform::CPUDeviceContext, double>;
template struct SetConstant<platform::CPUDeviceContext, int>;
template struct SetConstant<platform::CPUDeviceContext, int64_t>;
template struct SetConstant<platform::CPUDeviceContext, bool>;
template struct SetConstant<platform::CPUDeviceContext, uint8_t>;
#define DEFINE_CPU_TRANS(RANK) \
template struct Transpose<platform::CPUDeviceContext, platform::float16, \
......
paddle/fluid/operators/tensorrt_engine_op.cc
浏览文件 @ e02cbf35
......@@ -14,11 +14,14 @@
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/operators/tensorrt_engine_op.h"
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/engine.h"
#include "paddle/fluid/inference/utils/singleton.h"
#include "paddle/fluid/operators/tensorrt_engine_op.h"
namespace paddle {
namespace operators {
......
paddle/fluid/operators/tensorrt_engine_op.h
浏览文件 @ e02cbf35
......@@ -16,10 +16,12 @@
#ifdef PADDLE_WITH_CUDA
#include <string>
#include <vector>
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/inference/analysis/helper.h"
#include "paddle/fluid/inference/tensorrt/engine.h"
#include "paddle/fluid/inference/tensorrt/engine.h"
namespace paddle {
namespace operators {
......
paddle/fluid/operators/tensorrt_engine_op_test.cc
浏览文件 @ e02cbf35
......@@ -179,7 +179,6 @@ void Execute(int batch_size, int input_dim, int output_dim, int nlayers = 1) {
const std::string& z_name, bool x_created,
const shape_t& x_shape, const shape_t& y_shape,
const shape_t& z_shape) {
LOG(INFO) << "create fc op";
auto* fc = block_desc.AppendOp();
fc->SetType("mul");
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ e02cbf35
......@@ -159,6 +159,11 @@ PYBIND11_PLUGIN(core) {
new (&instance) LoDTensor(new_offset_lod);
})
.def("__init__", [](LoDTensor &instance) { new (&instance) LoDTensor(); })
// We implement offset based LOD in C++ while we use length based with
// Python API. So we changed set_lod to set_recursive_sequence_lengths to
// avoid misuse.
// The discussion is here:
// https://github.com/PaddlePaddle/Paddle/issues/10855
.def("set_lod",
[](LoDTensor &self, const std::vector<std::vector<size_t>> &lod) {
// the input lod is offset-based level-of-detail info
......@@ -199,6 +204,7 @@ PYBIND11_PLUGIN(core) {
std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
return new_lod;
})
// Set above comments of set_lod.
.def("recursive_sequence_lengths",
[](LoDTensor &self) -> std::vector<std::vector<size_t>> {
// output the length-based lod info
......
paddle/fluid/pybind/tensor_py.h
浏览文件 @ e02cbf35
......@@ -97,7 +97,7 @@ struct CastToPyBufferImpl<true, I, ARGS...> {
inline pybind11::buffer_info CastToPyBuffer(const framework::Tensor &tensor) {
auto buffer_info =
details::CastToPyBufferImpl<true, 0, float, int, double, int64_t, bool,
platform::float16>()(tensor);
uint8_t, platform::float16>()(tensor);
return buffer_info;
}
......
python/paddle/fluid/__init__.py
浏览文件 @ e02cbf35
......@@ -44,7 +44,7 @@ import metrics
import transpiler
from param_attr import ParamAttr, WeightNormParamAttr
from data_feeder import DataFeeder
from core import LoDTensor, CPUPlace, CUDAPlace, CUDAPinnedPlace
from core import LoDTensor, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope
from transpiler import DistributeTranspiler, InferenceTranspiler, \
memory_optimize, release_memory
from concurrency import (Go, make_channel, channel_send, channel_recv,
......@@ -83,6 +83,7 @@ __all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + \
'profiler',
'unique_name',
'recordio_writer',
'Scope',
]
......
python/paddle/fluid/average.py
浏览文件 @ e02cbf35
......@@ -36,6 +36,25 @@ def _is_number_or_matrix_(var):
class WeightedAverage(object):
"""
Calculate weighted average.
The average calculating is accomplished via Python totally.
They do not change Paddle's Program, nor do anything to
modify NN model's configuration. They are completely
wrappers of Python functions.
Examples:
.. code-block:: python
avg = fluid.average.WeightedAverage()
avg.add(value=2.0, weight=1)
avg.add(value=4.0, weight=2)
avg.eval()
# The result is 3.333333333.
# For (2.0 * 1 + 4.0 * 2) / (1 + 2) = 3.333333333
"""
def __init__(self):
warnings.warn(
"The %s is deprecated, please use fluid.metrics.Accuracy instead." %
......
python/paddle/fluid/backward.py
浏览文件 @ e02cbf35
......@@ -147,7 +147,7 @@ def _addup_repetitive_outputs_(op_descs):
else:
if len(renamed_vars[var_name]) == 1:
new_name = var_name + "@RENAME@" + \
str(var_rename_count[var_name])
str(var_rename_count[var_name])
var_rename_count[var_name] += 1
# rename original var_name
renamed_vars[var_name][0] = new_name
......@@ -155,7 +155,7 @@ def _addup_repetitive_outputs_(op_descs):
_rename_arg_(pending_sum_ops, var_name, new_name)
new_name = var_name + "@RENAME@" + \
str(var_rename_count[var_name])
str(var_rename_count[var_name])
var_rename_count[var_name] += 1
op_desc.rename_output(var_name, new_name)
renamed_vars[var_name].append(new_name)
......@@ -435,18 +435,65 @@ def _get_stop_gradients_(program):
def append_backward(loss, parameter_list=None, no_grad_set=None,
callbacks=None):
"""
Append backward part to main_program
Append backward part to main_program.
Args:
loss(Variable): The variable generated by cost function.
parameter_list(list[string]): Parameters that need to be updated by
optimizer. If None, it means all parameters need to be updated.
no_grad_set(set): Variables that have no gradients in Block 0.
All variables with `step_gradient=True` from all blocks will be
automatically added.
A complete neural network training is made up of forward and backward
propagation. However, when we configure a network, we only need to
specify its forwrd part. The backward part is generated automatically
according to the forward part by this function.
Return:
(list[(Variable,Variable)]): list of (parameter, gradient) pair.
In most cases, users do not need to invoke this function manually. It
will be automatically invoked by the optimizer's `minimize` function.
Args:
loss(Variable): The loss variable of the network.
parameter_list(list[string]|None): Names of parameters that need
to be updated by optimizers.
If it is None, all parameters
will be updated.
Default: None
no_grad_set(set|None): Variables in the Block 0 whose gradients
should be ignored. All variables with
`step_gradient=True` from all blocks will
be automatically added into this set.
Default: None
callbacks(list[callable object]|None): The callbacks are used for
doing some custom jobs during
backward part building. All
callable objects in it will
be invoked once each time a
new gradient operator is added
into the program. The callable
object must has two input
parameters: 'block' and 'context'.
The 'block' is the block which
the new gradient operator will
be added to. The 'context' is a
map, whose keys are gradient
variable names and values are
corresponding original variables.
In addition to this, the 'context'
has another special key-value pair:
the key is string '__current_op_desc__'
and the value is the op_desc of the
gradient operator who has just
triggered the callable object.
Returns:
list[(Variable,Variable)]: Pairs of parameter and its
corresponding gradients. The key is the parameter and the
value is gradient variable.
Raises:
AssertionError: If `loss` is not an instance of Variable.
Examples:
.. code-block:: python
# network configuration code
# ...
avg_loss = fluid.layers.mean(loss)
param_grad_list = fluid.backward.append_backward(loss=avg_loss)
"""
assert isinstance(loss, framework.Variable)
......
python/paddle/fluid/data_feeder.py
浏览文件 @ e02cbf35
......@@ -29,6 +29,13 @@ class DataToLoDTensorConverter(object):
self.place = place
self.lod_level = lod_level
self.shape = shape
negtive_count = 0
for s in self.shape:
if s < 0:
negtive_count += 1
if negtive_count > 1:
self.shape = None
break
if dtype == core.VarDesc.VarType.FP32:
self.dtype = 'float32'
elif dtype == core.VarDesc.VarType.INT64:
......@@ -61,7 +68,9 @@ class DataToLoDTensorConverter(object):
self._feed_impl_(each_data, lod[1:], lod_level - 1)
def done(self):
arr = numpy.array(self.data, dtype=self.dtype).reshape(self.shape)
arr = numpy.array(self.data, dtype=self.dtype)
if self.shape:
arr = arr.reshape(self.shape)
t = core.LoDTensor()
t.set(arr, self.place)
if self.lod_level > 0:
......@@ -70,6 +79,61 @@ class DataToLoDTensorConverter(object):
class DataFeeder(object):
"""
DataFeeder converts the data that returned by a reader into a data
structure that can feed into Executor and ParallelExecutor. The reader
usually returns a list of mini-batch data entries. Each data entry in
the list is one sample. Each sample is a list or a tuple with one
feature or multiple features.
The simple usage shows below:
.. code-block:: python
place = fluid.CPUPlace()
img = fluid.layers.data(name='image', shape=[1, 28, 28])
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
feeder = fluid.DataFeeder([img, label], fluid.CPUPlace())
result = feeder.feed([([0] * 784, [9]), ([1] * 784, [1])])
If you want to feed data into GPU side separately in advance when you
use multi-GPU to train a model, you can use `decorate_reader` function.
.. code-block:: python
place=fluid.CUDAPlace(0)
feeder = fluid.DataFeeder(place=place, feed_list=[data, label])
reader = feeder.decorate_reader(
paddle.batch(flowers.train(), batch_size=16))
Args:
feed_list(list): The Variables or Variables'name that will
feed into model.
place(Place): place indicates feed data into CPU or GPU, if you want to
feed data into GPU, please using `fluid.CUDAPlace(i)` (`i` represents
the GPU id), or if you want to feed data into CPU, please using
`fluid.CPUPlace()`.
program(Program): The Program that will feed data into, if program
is None, it will use default_main_program(). Default None.
Raises:
ValueError: If some Variable is not in this Program.
Examples:
.. code-block:: python
# ...
place = fluid.CPUPlace()
feed_list = [
main_program.global_block().var(var_name) for var_name in feed_vars_name
] # feed_vars_name is a list of variables' name.
feeder = fluid.DataFeeder(feed_list, place)
for data in reader():
outs = exe.run(program=main_program,
feed=feeder.feed(data))
"""
def __init__(self, feed_list, place, program=None):
self.feed_dtypes = []
self.feed_names = []
......@@ -99,6 +163,16 @@ class DataFeeder(object):
self.place = place
def feed(self, iterable):
"""
According to feed_list and iterable, converters the input into
a data structure that can feed into Executor and ParallelExecutor.
Args:
iterable(list|tuple): the input data.
Returns:
dict: the result of conversion.
"""
converter = []
for lod_level, shape, dtype in six.zip(
self.feed_lod_level, self.feed_shapes, self.feed_dtypes):
......@@ -121,6 +195,20 @@ class DataFeeder(object):
return ret_dict
def feed_parallel(self, iterable, num_places=None):
"""
Takes multiple mini-batches. Each mini-batch will be feed on each
device in advance.
Args:
iterable(list|tuple): the input data.
num_places(int): the number of devices. Default None.
Returns:
dict: the result of conversion.
Notes:
The number of devices and number of mini-batches must be same.
"""
if isinstance(self.place, core.CUDAPlace):
places = [
core.CUDAPlace(i)
......@@ -159,6 +247,24 @@ class DataFeeder(object):
multi_devices,
num_places=None,
drop_last=True):
"""
Converter the input data into a data that returned by reader into
multiple mini-batches. Each mini-batch will be feed on each device.
Args:
reader(fun): the input data.
multi_devices(bool): the number of places. Default None.
num_places(int): the number of places. Default None.
drop_last(bool): the number of places. Default None.
Returns:
dict: the result of conversion.
Raises:
ValueError: If drop_last is False and the data batch which cannot
fit for devices.
"""
def __reader_creator__():
if not multi_devices:
for item in reader():
......
python/paddle/fluid/executor.py
浏览文件 @ e02cbf35
......@@ -25,6 +25,13 @@ g_scope = core.Scope()
def global_scope():
"""
Get the global/default scope instance. There are a lot of APIs use
:code:`global_scope` as its default value, e.g., :code:`Executor.run`
Returns:
Scope: The global/default scope instance.
"""
return g_scope
......@@ -37,6 +44,19 @@ def switch_scope(scope):
@contextlib.contextmanager
def scope_guard(scope):
"""
Change the global/default scope instance by Python `with` statement. All
variable in runtime will assigned to the new scope.
Examples:
>>> import paddle.fluid as fluid
>>> new_scope = fluid.Scope()
>>> with fluid.scope_guard(new_scope):
>>> ...
Args:
scope: The new global/default scope.
"""
ex = switch_scope(scope)
yield
switch_scope(ex)
......@@ -135,14 +155,18 @@ def has_fetch_operators(block, fetch_targets, fetch_holder_name):
def fetch_var(name, scope=None, return_numpy=True):
"""
Fetch the value of the variable with the given name from the given scope
Fetch the value of the variable with the given name from the
given scope.
Args:
name(str): name of the variable. Typically, only persistable variables
can be found in the scope used for running the program.
scope(core.Scope|None): scope object. It should be the scope where
you pass to Executor.run() when running your program.
If None, global_scope() will be used.
return_numpy(bool): whether convert the tensor to numpy.ndarray
If None, global_scope() will be used. Default None.
return_numpy(bool): whether convert the tensor to numpy.ndarray.
Default True.
Returns:
LodTensor|numpy.ndarray
"""
......
python/paddle/fluid/framework.py
浏览文件 @ e02cbf35
此差异已折叠。
python/paddle/fluid/io.py
浏览文件 @ e02cbf35
此差异已折叠。
python/paddle/fluid/layers/control_flow.py
浏览文件 @ e02cbf35
......@@ -185,12 +185,14 @@ def Print(input,
Returns:
Variable: Output tensor, same data with input tensor.
Examples:
.. code-block:: python
value = some_layer(...)
Print(value, summarize=10,
message="The content of some_layer: ")
value = some_layer(...)
Print(value, summarize=10,
message="The content of some_layer: ")
'''
helper = LayerHelper('print', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
......@@ -1201,6 +1203,31 @@ class ConditionalBlockGuard(BlockGuard):
class ConditionalBlock(object):
'''
**ConditionalBlock**
ConditionalBlock is an operator that bind a block to a specific condition,
if the condition matches, the corresponding block will be executed.
Args:
inputs (Variable): bool conditions.
is_scalar_condition (bool): whether the branch is controled by a scalar.
name(str): name of this ConditionalBlock.
Examples:
.. code-block:: python
cond = layers.less_than(x=label, y=limit)
true_image, false_image = layers.split_lod_tensor(
input=image, mask=cond)
true_cond = layers.ConditionalBlock([true_image])
with true_cond.block():
...
with false_cond.block():
...
'''
def __init__(self, inputs, is_scalar_condition=False, name=None):
for each_input in inputs:
if not isinstance(each_input, Variable):
......
python/paddle/fluid/layers/nn.py
浏览文件 @ e02cbf35
......@@ -2678,18 +2678,35 @@ def sequence_expand(x, y, ref_level=-1, name=None):
def beam_search(pre_ids, ids, scores, beam_size, end_id, level=0):
'''
**beam search**
This function implements the beam search algorithm.
Beam search is a classical algorithm for selecting candidate words
in a machine translation task.
Refer to `Beam search <https://en.wikipedia.org/wiki/Beam_search>`_
for more details.
Args:
pre_ids (Variable): ${pre_ids_comment}
ids (Variable): ${ids_comment}
scores (Variable): ${scores_comment}
beam_size (int): ${beam_size_comment}
end_id (int): ${end_id_comment}
level (int): ${level_comment}
pre_ids (Variable): ids in previous step.
ids (Variable): a LoDTensor of shape of [None,k]
scores (Variable): a LoDTensor that has the same shape and LoD with `ids`
beam_size (int): beam size for beam search
end_id (int): the token id which indicates the end of a sequence
level (int): the level of LoDTensor
Returns:
tuple: a tuple of beam_search output variables: selected_ids, selected_scores
tuple: a tuple of beam_search output variables: `selected_ids`, `selected_scores`
Examples:
.. code-block:: python
# current_score is a Tensor of shape (num_batch_size, embed_size), which
# consists score of each candidate word.
topk_scores, topk_indices = pd.topk(current_score, k=50)
selected_ids, selected_scores = pd.beam_search(
pre_ids, topk_indices, topk_scores, beam_size, end_id=10, level=0)
'''
helper = LayerHelper('beam_search', **locals())
score_type = scores.dtype
......
python/paddle/fluid/lod_tensor.py
浏览文件 @ e02cbf35
......@@ -19,33 +19,41 @@ __all__ = ['create_lod_tensor', 'create_random_int_lodtensor']
def create_lod_tensor(data, lod, place):
"""Create a lod tensor from a numpy array, a list, or an existing lod tensor.
"""
Create a lod tensor from a numpy array, a list, or an existing lod tensor.
Create a lod tensor by doing the following:
1. Check that the length-based input lod is valid.
2. Convert the length-based lod to a offset-based LoD.
3. Copy the data from a numpy array, a list or a existing lod tensor to
3. Copy the data from a numpy array, a list or a existing lod tensor to
CPU or GPU device (based on input place).
4. Set the level of detail (LoD) using the offset-based LoD.
Use example:
Suppose we want LoDTensor to hold data for sequences of word, where each word is
represented by an integer. If we want to create a LoDTensor to represent two
sentences, one of 2 words, and one of 3 words.
Examples:
Then 'data' can be a numpy array of integers with shape (5, 1).
'lod' will be [[2, 3]], indicating the length(# of words) in each sentence.
This length-based input lod [[2, 3]] will be converted to offset-based lod [[0, 2, 5]]
inside the function call.
Suppose we want LoDTensor to hold data for sequences of word, where each
word is represented by an integer. If we want to create a LoDTensor to
represent two sentences, one of 2 words, and one of 3 words.
Please refer to
github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/design/concepts/lod_tensor.md
for more details regarding LoD.
Then :code:`data` can be a numpy array of integers with shape (5, 1).
:code:`lod` will be [[2, 3]], indicating the length(# of words) in each
sentence. This length-based input lod [[2, 3]] will be converted to
offset-based lod [[0, 2, 5]] inside the function call.
Please reference :ref:`api_guide_low_level_lod_tensor` for more details
regarding LoD.
Args:
data: a numpy array or a LoDTensor or a list holding the data to be copied.
lod: a list of lists indicating the length-based LoD info specified by the user.
place: CPU or GPU place indicating where the data in the new LoDTensor will be stored.
data(numpy.ndarray|list|LoDTensor): a numpy array or a LoDTensor or a
list holding the data to be copied.
lod(list): a list of lists indicating the length-based LoD info
specified by the user.
place(Place): CPU or GPU place indicating where the data in the new
LoDTensor will be stored.
Returns:
A fluid LoDTensor object with tensor data and lod info.
......@@ -77,31 +85,38 @@ def create_lod_tensor(data, lod, place):
def create_random_int_lodtensor(lod, base_shape, place, low, high):
"""Create a LoDTensor containing random integers.
"""
Create a LoDTensor containing random integers.
This function is frequently used in the book examples. So we revised it based on
the new create_lod_tensor API and put it here in the lod_tensor module to simplify
the code.
This function is frequently used in the book examples. So we revised it
based on the new create_lod_tensor API and put it here in the lod_tensor
module to simplify the code.
The function does the following:
1. Calculate the overall shape of the LoDTensor based on the length-based 'lod' input
and the shape of the basic element in 'base_shape'.
1. Calculate the overall shape of the LoDTensor based on the length-based
:code:`lod` input and the shape of the basic element in
:code:`base_shape`.
2. Create a numpy array of this shape.
3. Create the LoDTensor using create_lod_tensor API.
Suppose we want LoDTensor to hold data for sequences of word, where each word is
represented by an integer. If we want to create a LoDTensor to represent two
sentences, one of 2 words, and one of 3 words. Then 'base_shape' is [1], input
length-based 'lod' is [[2, 3]]. Then the overall shape of the LoDTensor would be
[5, 1], holding 5 words for two sentences.
Suppose we want LoDTensor to hold data for sequences of word, where each
word is represented by an integer. If we want to create a LoDTensor to
represent two sentences, one of 2 words, and one of 3 words. Then
'base_shape' is [1], input length-based 'lod' is [[2, 3]]. Then the overall
shape of the LoDTensor would be [5, 1], holding 5 words for two sentences.
Args:
data: a numpy array or a LoDTensor holding the data to be copied.
lod: a list of lists indicating the length-based LoD info specified by the user.
base_shape: the shape of the basic element to be held by the LoDTensor.
place: CPU or GPU place indicating where the data in the new LoDTensor will be stored.
low: the lower bound of the random integers.
high: the upper bound of the random integers.
lod(list): a list of lists indicating the length-based LoD info
specified by the user.
base_shape(list): the shape of the basic element to be held by the
LoDTensor.
place(Place): CPU or GPU place indicating where the data in the new
LoDTensor will be stored.
low(int): the lower bound of the random integers.
high(int): the upper bound of the random integers.
Returns:
A fluid LoDTensor object with tensor data and lod info.
......
python/paddle/fluid/metrics.py
浏览文件 @ e02cbf35
......@@ -325,14 +325,14 @@ class Auc(MetricBase):
"""
def __init__(self, name, curve='ROC', num_thresholds=200):
super(MetricBase, self).__init__(name, curve, num_thresholds)
super(Auc, self).__init__(name=name)
self._curve = curve
self._num_thresholds = num_thresholds
self._epsilon = 1e-6
self.tp_list = np.ndarray((num_thresholds, ))
self.fn_list = np.ndarray((num_thresholds, ))
self.tn_list = np.ndarray((num_thresholds, ))
self.fp_list = np.ndarray((num_thresholds, ))
self.tp_list = np.zeros((num_thresholds, ))
self.fn_list = np.zeros((num_thresholds, ))
self.tn_list = np.zeros((num_thresholds, ))
self.fp_list = np.zeros((num_thresholds, ))
def update(self, labels, predictions, axis=1):
if not _is_numpy_(labels):
......@@ -350,12 +350,12 @@ class Auc(MetricBase):
tp, fn, tn, fp = 0, 0, 0, 0
for i, lbl in enumerate(labels):
if lbl:
if predictions[i, 0] >= thresh:
if predictions[i, 1] >= thresh:
tp += 1
else:
fn += 1
else:
if predictions[i, 0] >= thresh:
if predictions[i, 1] >= thresh:
fp += 1
else:
tn += 1
......
python/paddle/fluid/nets.py
浏览文件 @ e02cbf35
......@@ -26,16 +26,87 @@ def simple_img_conv_pool(input,
filter_size,
pool_size,
pool_stride,
act,
param_attr=None,
pool_padding=0,
pool_type='max',
global_pooling=False,
conv_stride=1,
conv_padding=0,
conv_dilation=1,
conv_groups=1,
param_attr=None,
bias_attr=None,
act=None,
use_cudnn=True,
use_mkldnn=False):
"""
The simple_img_conv_pool is composed with one Convolution2d and one Pool2d.
Args:
input (Variable): The input image with [N, C, H, W] format.
num_filters(int): The number of filter. It is as same as the output
feature channel.
filter_size (int|list|tuple): The filter size. If filter_size is a list or
tuple, it must contain two integers, (filter_size_H, filter_size_W). Otherwise,
the filter_size_H = filter_size_W = filter_size.
pool_size (int|list|tuple): The pooling size of Pool2d layer. If pool_size
is a list or tuple, it must contain two integers, (pool_size_H, pool_size_W).
Otherwise, the pool_size_H = pool_size_W = pool_size.
pool_stride (int|list|tuple): The pooling stride of Pool2d layer. If pool_stride
is a list or tuple, it must contain two integers, (pooling_stride_H, pooling_stride_W).
Otherwise, the pooling_stride_H = pooling_stride_W = pool_stride.
pool_padding (int|list|tuple): The padding of Pool2d layer. If pool_padding is a list or
tuple, it must contain two integers, (pool_padding_H, pool_padding_W).
Otherwise, the pool_padding_H = pool_padding_W = pool_padding. Default 0.
pool_type (str): Pooling type can be :math:`max` for max-pooling and :math:`avg` for
average-pooling. Default :math:`max`.
global_pooling (bool): Whether to use the global pooling. If global_pooling = true,
pool_size and pool_padding while be ignored. Default False
conv_stride (int|list|tuple): The stride size of the Conv2d Layer. If stride is a
list or tuple, it must contain two integers, (conv_stride_H, conv_stride_W). Otherwise,
the conv_stride_H = conv_stride_W = conv_stride. Default: conv_stride = 1.
conv_padding (int|list|tuple): The padding size of the Conv2d Layer. If padding is
a list or tuple, it must contain two integers, (conv_padding_H, conv_padding_W).
Otherwise, the conv_padding_H = conv_padding_W = conv_padding. Default: conv_padding = 0.
conv_dilation (int|list|tuple): The dilation size of the Conv2d Layer. If dilation is
a list or tuple, it must contain two integers, (conv_dilation_H, conv_dilation_W).
Otherwise, the conv_dilation_H = conv_dilation_W = conv_dilation. Default: conv_dilation = 1.
conv_groups (int): The groups number of the Conv2d Layer. According to grouped
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
the first half of the filters is only connected to the first half
of the input channels, while the second half of the filters is only
connected to the second half of the input channels. Default: groups=1
param_attr (ParamAttr): The parameters to the Conv2d Layer. Default: None
bias_attr (ParamAttr): Bias parameter for the Conv2d layer. Default: None
act (str): Activation type for Conv2d. Default: None
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
use_mkldnn (bool): Use mkldnn kernels or not, it is valid only when compiled
with mkldnn library. Default: False
Return:
Variable: The result of input after Convolution2d and Pool2d.
Examples:
.. code-block:: python
img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
conv_pool = fluid.nets.simple_img_conv_pool(input=img,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
"""
conv_out = layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=conv_stride,
padding=conv_padding,
dilation=conv_dilation,
groups=conv_groups,
param_attr=param_attr,
bias_attr=bias_attr,
act=act,
use_cudnn=use_cudnn,
use_mkldnn=use_mkldnn)
......@@ -45,6 +116,8 @@ def simple_img_conv_pool(input,
pool_size=pool_size,
pool_type=pool_type,
pool_stride=pool_stride,
pool_padding=pool_padding,
global_pooling=global_pooling,
use_cudnn=use_cudnn,
use_mkldnn=use_mkldnn)
return pool_out
......@@ -60,11 +133,65 @@ def img_conv_group(input,
conv_with_batchnorm=False,
conv_batchnorm_drop_rate=0.0,
pool_stride=1,
pool_type=None,
pool_type="max",
use_cudnn=True,
use_mkldnn=False):
"""
Image Convolution Group, Used for vgg net.
The Image Convolution Group is composed of Convolution2d, BatchNorm, DropOut,
and Pool2d. According to the input arguments, img_conv_group will do serials of
computation for Input using Convolution2d, BatchNorm, DropOut, and pass the last
result to Pool2d.
Args:
input (Variable): The input image with [N, C, H, W] format.
conv_num_filter(list|tuple): Indicates the numbers of filter of this group.
pool_size (int|list|tuple): The pooling size of Pool2d Layer. If pool_size
is a list or tuple, it must contain two integers, (pool_size_H, pool_size_W).
Otherwise, the pool_size_H = pool_size_W = pool_size.
conv_padding (int|list|tuple): The padding size of the Conv2d Layer. If padding is
a list or tuple, its length must be equal to the length of conv_num_filter.
Otherwise the conv_padding of all Conv2d Layers are the same. Default 1.
conv_filter_size (int|list|tuple): The filter size. If filter_size is a list or
tuple, its length must be equal to the length of conv_num_filter.
Otherwise the conv_filter_size of all Conv2d Layers are the same. Default 3.
conv_act (str): Activation type for Conv2d Layer that is not followed by BatchNorm.
Default: None.
param_attr (ParamAttr): The parameters to the Conv2d Layer. Default: None
conv_with_batchnorm (bool|list): Indicates whether to use BatchNorm after Conv2d Layer.
If conv_with_batchnorm is a list, its length must be equal to the length of
conv_num_filter. Otherwise, conv_with_batchnorm indicates whether all the
Conv2d Layer follows a BatchNorm. Default False.
conv_batchnorm_drop_rate (float|list): Indicates the drop_rate of Dropout Layer
after BatchNorm. If conv_batchnorm_drop_rate is a list, its length must be
equal to the length of conv_num_filter. Otherwise, drop_rate of all Dropout
Layers is conv_batchnorm_drop_rate. Default 0.0.
pool_stride (int|list|tuple): The pooling stride of Pool2d layer. If pool_stride
is a list or tuple, it must contain two integers, (pooling_stride_H,
pooling_stride_W). Otherwise, the pooling_stride_H = pooling_stride_W = pool_stride.
Default 1.
pool_type (str): Pooling type can be :math:`max` for max-pooling and :math:`avg` for
average-pooling. Default :math:`max`.
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
use_mkldnn (bool): Use mkldnn kernels or not, it is valid only when compiled
with mkldnn library. Default: False
Return:
Variable: The final result after serial computation using Convolution2d,
BatchNorm, DropOut, and Pool2d.
Examples:
.. code-block:: python
img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
conv_pool = fluid.nets.img_conv_group(input=img,
num_channels=3,
conv_padding=1,
conv_num_filter=[3, 3],
conv_filter_size=3,
conv_act="relu",
pool_size=2,
pool_stride=2)
"""
tmp = input
assert isinstance(conv_num_filter, list) or \
......@@ -74,6 +201,7 @@ def img_conv_group(input,
if not hasattr(obj, '__len__'):
return [obj] * len(conv_num_filter)
else:
assert len(obj) == len(conv_num_filter)
return obj
conv_padding = __extend_list__(conv_padding)
......@@ -119,6 +247,39 @@ def sequence_conv_pool(input,
param_attr=None,
act="sigmoid",
pool_type="max"):
"""
The sequence_conv_pool is composed with Sequence Convolution and Pooling.
Args:
input (Variable): The input of sequence_conv, which supports variable-time
length input sequence. The underlying of input is a matrix with shape
(T, N), where T is the total time steps in this mini-batch and N is
the input_hidden_size
num_filters(int): The number of filter.
filter_size (int): The filter size.
param_attr (ParamAttr): The parameters to the Sequence_conv Layer. Default: None.
act (str): Activation type for Sequence_conv Layer. Default: "sigmoid".
pool_type (str): Pooling type can be :math:`max` for max-pooling, :math:`average` for
average-pooling, :math:`sum` for sum-pooling, :math:`sqrt` for sqrt-pooling.
Default :math:`max`.
Return:
Variable: The final result after Sequence Convolution and Pooling.
Examples:
.. code-block:: python
input_dim = len(word_dict)
emb_dim = 128
hid_dim = 512
data = fluid.layers.data( ame="words", shape=[1], dtype="int64", lod_level=1)
emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim], is_sparse=True)
seq_conv = fluid.nets.sequence_conv_pool(input=emb,
num_filters=hid_dim,
filter_size=3,
act="tanh",
pool_type="sqrt")
"""
conv_out = layers.sequence_conv(
input=input,
num_filters=num_filters,
......@@ -132,9 +293,9 @@ def sequence_conv_pool(input,
def glu(input, dim=-1):
"""
The gated linear unit composed by split, sigmoid activation and elementwise
multiplication. Specifically, Split the input into two equal sized parts
:math:`a` and :math:`b` along the given dimension and then compute as
The Gated Linear Units(GLU) composed by split, sigmoid activation and element-wise
multiplication. Specifically, Split the input into two equal sized parts,
:math:`a` and :math:`b`, along the given dimension and then compute as
following:
.. math::
......@@ -147,16 +308,16 @@ def glu(input, dim=-1):
Args:
input (Variable): The input variable which is a Tensor or LoDTensor.
dim (int): The dimension along which to split. If :math:`dim < 0`, the
dimension to split along is :math:`rank(input) + dim`.
dimension to split along is :math:`rank(input) + dim`. Default -1.
Returns:
Variable: The Tensor variable with half the size of input.
Variable: Variable with half the size of input.
Examples:
.. code-block:: python
# x is a Tensor variable with shape [3, 6, 9]
fluid.nets.glu(input=x, dim=1) # shape of output: [3, 3, 9]
data = fluid.layers.data(name="words", shape=[3, 6, 9], dtype="float32")
output = fluid.nets.glu(input=data, dim=1) # shape of output: [3, 3, 9]
"""
a, b = layers.split(input, num_or_sections=2, dim=dim)
......@@ -189,40 +350,48 @@ def scaled_dot_product_attention(queries,
<https://arxiv.org/pdf/1706.03762.pdf>`_.
Args:
queries (Variable): The input variable which should be a 3-D Tensor.
keys (Variable): The input variable which should be a 3-D Tensor.
values (Variable): The input variable which should be a 3-D Tensor.
num_heads (int): Head number to compute the scaled dot product
attention. Default value is 1.
attention. Default: 1.
dropout_rate (float): The dropout rate to drop the attention weight.
Default value is 0.
Default: 0.0.
Returns:
Variable: A 3-D Tensor computed by multi-head scaled dot product \
attention.
Variable: A 3-D Tensor computed by multi-head scaled dot product\
attention.
Raises:
ValueError: If input queries, keys, values are not 3-D Tensors.
NOTE:
NOTES:
1. When num_heads > 1, three linear projections are learned respectively
to map input queries, keys and values into queries', keys' and values'.
queries', keys' and values' have the same shapes with queries, keys
and values.
1. When num_heads == 1, scaled_dot_product_attention has no learnable
parameters.
to map input queries, keys and values into queries', keys' and values'.
queries', keys' and values' have the same shapes with queries, keys
and values.
2. When num_heads == 1, scaled_dot_product_attention has no learnable
parameters.
Examples:
.. code-block:: python
# Suppose q, k, v are Tensors with the following shape:
# q: [3, 5, 9], k: [3, 6, 9], v: [3, 6, 10]
contexts = fluid.nets.scaled_dot_product_attention(q, k, v)
queries = fluid.layers.data(name="queries",
shape=[3, 5, 9],
dtype="float32",
append_batch_size=False)
queries.stop_gradient = False
keys = fluid.layers.data(name="keys",
shape=[3, 6, 9],
dtype="float32",
append_batch_size=False)
keys.stop_gradient = False
values = fluid.layers.data(name="values",
shape=[3, 6, 10],
dtype="float32",
append_batch_size=False)
values.stop_gradient = False
contexts = fluid.nets.scaled_dot_product_attention(queries, keys, values)
contexts.shape # [3, 5, 10]
"""
if not (len(queries.shape) == len(keys.shape) == len(values.shape) == 3):
......
python/paddle/fluid/parallel_executor.py
浏览文件 @ e02cbf35
......@@ -27,6 +27,40 @@ BuildStrategy = core.ParallelExecutor.BuildStrategy
class ParallelExecutor(object):
"""
ParallelExecutor can run program in parallel.
Args:
use_cuda (bool): Whether to use CUDA or not.
loss_name (str): The loss name must set in training. Default None.
main_program (Program): The program that need to run, if not provided,
then default_main_program will be used. Default None.
share_vars_from(ParallelExecutor): If provied, it will share variables
from the specified ParallelExecutor. Default None.
num_trainers(int): If greater than 1, NCCL will be initialized with
multiple rank of nodes, each node should have same number of GPUs.
Distributed training will be enabled then. Default 1.
trainer_id(int: Must use together with num_trainers. trainer_id is the
"rank" of current node starts from 0. Default 0.
Returns:
ParallelExecutor: The initialized ParallelExecutor object.
Raises:
TypeError: If share_vars_from is provided, but not ParallelExecutor object.
Examples:
.. code-block:: python
train_exe = fluid.ParallelExecutor(use_cuda=True, loss_name=loss.name)
test_exe = fluid.ParallelExecutor(use_cuda=True,
main_program=test_program,
share_vars_from=train_exe)
train_loss, = train_exe.run([loss.name], feed=feed_dict)
test_loss, = test_exe.run([loss.name], feed=feed_dict)
"""
def __init__(self,
use_cuda,
loss_name=None,
......@@ -37,42 +71,6 @@ class ParallelExecutor(object):
num_trainers=1,
trainer_id=0,
**kwargs):
"""
ParallelExecutor can run program in parallel.
Args:
use_cuda(bool): Whether to use CUDA or not.
loss_name(str, default None): The loss name must set in training.
main_program(Program, default None): The program that need to run,
if not provided, then default_main_program will be used.
share_vars_from(ParallelExecutor, default None): If provied,
it will share variables from the specified ParallelExecutor.
num_trainers(int, default 1): If greater than 1, NCCL will be
initialized with multpile rank of nodes, each node should have
same number of GPUs. Distributed training will be enabled then.
trainer_id(int, default 0): Must use together with num_trainers.
trainer_id is the "rank" of current node starts from 0.
Returns:
A ParallelExecutor object.
Raises:
TypeError: If share_vars_from is provided, but not ParallelExecutor
object.
Examples:
.. code-block:: python
train_exe = fluid.ParallelExecutor(
use_cuda=True, loss_name=loss.name)
test_exe = fluid.ParallelExecutor(
use_cuda=True,
main_program=test_program,
share_vars_from=train_exe)
train_loss, = train_exe.run([loss.name], feed=feed_dict)
test_loss, = test_exe.run([loss.name], feed=feed_dict)
"""
if len(kwargs) != 0:
err_msg = ""
for key in kwargs:
......@@ -131,10 +129,16 @@ class ParallelExecutor(object):
main = main_program
main = main if main else framework.default_main_program()
scope = executor.global_scope()
# FIXME(Yancey1989): it's a temporary approach to determinate the distribute
# train program, call self.bcast_param() at the end of each mini-batch.
self.is_dist = True if "recv" in [
op.type for op in main.global_block().ops
] else False
if share_vars_from and not isinstance(share_vars_from,
ParallelExecutor):
raise TypeError("share_vars_from must be ParallelExecutor.")
local_scopes = share_vars_from.executor.local_scopes(
) if share_vars_from else []
......@@ -166,12 +170,14 @@ class ParallelExecutor(object):
element in the list will be copied to each device directly.
For example, if the feed is a dict:
>>> exe = ParallelExecutor()
>>> # the image will be splitted into devices. If there is two devices
>>> # each device will process an image with shape (24, 1, 28, 28)
>>> exe.run(feed={'image': numpy.random.random(size=(48, 1, 28, 28))})
For example, if the feed is a list:
>>> exe = ParallelExecutor()
>>> # each device will process each element in the list.
>>> # the 1st device will process an image with shape (48, 1, 28, 28)
......@@ -182,18 +188,40 @@ class ParallelExecutor(object):
>>> {"image": numpy.random.random(size=(32, 1, 28, 28))},
>>> ])
Args:
fetch_list(list): The fetched variable names
feed(list|dict|None): The feed variables. If the feed is a dict,
tensors in that dict will be splitted into each devices. If
the feed is a list, each element of the list will be copied
to each device.
to each device. Default None.
feed_dict: Alias for feed parameter, for backward compatibility.
This parameter is deprecated.
This parameter has been deprecated. Default None.
Returns:
List: The fetched result list.
Raises:
ValueError: If the feed is a list, but its length is not equal the
length of active places, or its element's is not dict.
NOTES:
1. If the feed's type is dict, the number of data that feeds to
ParallelExecutor must be bigger than active places. Otherwise,
it will throw exception from C++ side. Special attention should be
paid to check whether the last batch of the dataset is bigger
than active places.
2. If active places are more than one, the fetch results for each
variable is a list, and each element of this list is the variable of
respective active place.
Returns: fetched result list.
Examples:
.. code-block:: python
pe = fluid.ParallelExecutor(use_cuda=use_cuda,
loss_name=avg_cost.name,
main_program=fluid.default_main_program())
loss = pe.run(feed=feeder.feed(cur_batch),
fetch_list=[avg_cost.name]))
"""
if feed is None and feed_dict is not None:
feed = feed_dict
......@@ -238,9 +266,17 @@ class ParallelExecutor(object):
fetch_var_name = '@FETCHED_VAR_NAME@'
self.executor.run(fetch_list, fetch_var_name)
arr = self.scope.find_var(fetch_var_name).get_lod_tensor_array()
if self.is_dist:
self.bcast_params()
return [arr[i] for i in range(len(arr))]
def bcast_params(self):
"""
Broadcast the parameters to other devices. It is used during
distributed training.
"""
self.executor.bcast_params(set(self.persistable_vars))
@property
......
python/paddle/fluid/param_attr.py
浏览文件 @ e02cbf35
......@@ -22,6 +22,35 @@ __all__ = [
class ParamAttr(object):
"""
Parameter attributes object. To fine-tuning network training process, user
can set parameter's attributes to control training details. Such as learning rate,
regularization, trainable, do_model_average and the method to initialize param.
Args:
name(str): The parameter's name. Default None.
initializer(Initializer): The method to initial this parameter. Default None.
learning_rate(float): The parameter's learning rate. The learning rate when
optimize is :math:`global\_lr * parameter\_lr * scheduler\_factor`.
Default 1.0.
regularizer(WeightDecayRegularizer): Regularization factor. Default None.
trainable(bool): Whether this parameter is trainable. Default True.
gradient_clip(BaseGradientClipAttr): The method to clip this parameter's
gradient. Default None.
do_model_average(bool): Whether this parameter should do model average.
Default False.
Examples:
.. code-block:: python
w_param_attrs = fluid.ParamAttr(name="fc_weight",
learning_rate=0.5,
regularizer=fluid.L2Decay(1.0),
trainable=True)
y_predict = fluid.layers.fc(input=x, size=10, param_attr=w_param_attrs)
"""
def __init__(self,
name=None,
initializer=None,
......@@ -29,7 +58,7 @@ class ParamAttr(object):
regularizer=None,
trainable=True,
gradient_clip=None,
do_model_average=None):
do_model_average=False):
self.name = name
self.initializer = initializer
self.learning_rate = learning_rate
......@@ -39,6 +68,16 @@ class ParamAttr(object):
self.model_average = do_model_average
def set_default_initializer(self, initializer):
"""
Set the default initializer, the initializer should be Constant,
Uniform, Normal, Xavier, MSRA.
Args:
initializer(Initializer): the initializer to set.
Returns:
None
"""
if initializer is None:
if self.initializer is None:
raise ValueError("ParamAttr.initializer is not set")
......@@ -50,13 +89,45 @@ class ParamAttr(object):
self.initializer = initializer
def set_default_param_initializer(self):
"""
Set the default initializer for the parameter with Xavier.
Args:
None.
Returns:
None.
"""
self.set_default_initializer(Xavier())
def set_default_bias_initializer(self):
"""
Set the default initializer for the bias with Constant(0.0).
Args:
None.
Returns:
None.
"""
self.set_default_initializer(Constant(0.0))
@staticmethod
def to_attr(arg):
"""
Create ParamAttr[s].
Args:
arg: Arguments to initialize ParamAttr[s]. arg's type can be
str, Initializer, float, WeightDecayRegularizer, BaseGradientClipAttr,
bool, ParamAttr, or a list of above type.
Returns:
ParamAttr[s]: ParamAttr[s] initialized with arg.
Raises:
arg can not initialize a ParamAttr.
"""
if arg is None:
return ParamAttr()
elif isinstance(arg, list) or isinstance(arg, tuple):
......@@ -75,6 +146,15 @@ class ParamAttr(object):
raise TypeError("{0} cast to ParamAttr".format(type(arg)))
def to_kwargs(self, with_initializer=False):
"""
Returns the attributes of this parameter.
Args:
with_initializer(bool): Whether to add initializer attr.
Returns:
Parameter attributes(map): The attributes of this parameter.
"""
kwargs = {
'name': self.name,
'optimize_attr': {
......@@ -92,9 +172,27 @@ class ParamAttr(object):
class WeightNormParamAttr(ParamAttr):
"""
Used for weight normalization. Any field in ParamAttr can also be set here.
Besides, an extra field dim can be set to indicate the dimension except
which to normalize.
Used for weight Norm. Weight Norm is a reparameterization of the weight vectors
in a neural network that decouples the length of those weight vectors from
their direction. Weight Norm has been implemented as discussed in this
paper: `Weight Normalization: A Simple Reparameterization to Accelerate
Training of Deep Neural Networks
<https://arxiv.org/pdf/1602.07868.pdf>`_.
Args:
dim(list): The parameter's name. Default None.
kwargs: Any field in ParamAttr. Default None.
Examples:
.. code-block:: python
data = fluid.layers.data(name="data", shape=[3, 32, 32], dtype="float32")
fc = fluid.layers.fc(input=data,
size=1000,
param_attr=WeightNormParamAttr(
dim=None,
name='weight_norm_param'))
"""
# List to record the parameters reparameterized by weight normalization.
# If these parameters are treated as Variable rather than Parameter,
......
python/paddle/fluid/recordio_writer.py
浏览文件 @ e02cbf35
......@@ -36,6 +36,45 @@ def convert_reader_to_recordio_file(
compressor=core.RecordIOWriter.Compressor.Snappy,
max_num_records=1000,
feed_order=None):
"""
Convert a Python Reader to a recordio file.
Please see :ref:`api_guide_python_reader` and :ref:`api_guide_reader_op` for
details.
Examples:
>>> import paddle.fluid as fluid
>>> import paddle.dataset.mnist as mnist
>>> import paddle
>>>
>>> tmp_program = fluid.Program()
>>> with fluid.program_guard(tmp_program):
>>> img = fluid.layers.data(name='img', shape=[784])
>>> label = fluid.layers.data(name='label', shape=[1], dtype='int64')
>>> feeder = fluid.DataFeeder(feed_list=[img, label], place=fluid.CPUPlace())
>>> # mnist.recordio will be generated in current directory
>>> fluid.recordio_writer.convert_reader_to_recordio_file(
>>> filename="mnist.recordio",
>>> reader_creator=paddle.batch(mnist.train(), batch_size=32),
>>> feeder=feeder)
Args:
filename(str): The recordio filename.
reader_creator(callable): The Python Reader Creator. See
:ref:`api_guide_python_reader`.
feeder(DataFeeder): The DataFeeder instance. Used to convert
:code:`reader_creator` to :code: `lod_tensor`
compressor: Must in fluid.core.RecordIOWriter.Compressor.Snappy or
fluid.core.RecordIOWriter.Compressor.NoCompress. Use :code:`Snappy`
by default.
max_num_records(int): Maximum number of records in one chuck. Each record
is each return value from reader function
feed_order(list): The order of variable names that the reader returns
Returns:
int: the number of record that saved.
"""
if feed_order is None:
feed_order = feeder.feed_names
counter = 0
......@@ -58,6 +97,17 @@ def convert_reader_to_recordio_files(
compressor=core.RecordIOWriter.Compressor.Snappy,
max_num_records=1000,
feed_order=None):
"""
convert a python reader to many recordio files.
This API is basically same as :code:`convert_reader_to_recordio_file`,
instead of it will create many recordio files. Each file contains at
most :code:`batch_per_file` records.
Please reference
:ref:`api_fluid_recordio_writer_convert_reader_to_recordio_file` for more
details.
"""
if feed_order is None:
feed_order = feeder.feed_names
f_name, f_ext = os.path.splitext(filename)
......
python/paddle/fluid/tests/book/notest_understand_sentiment.py
浏览文件 @ e02cbf35
......@@ -194,16 +194,16 @@ def train(word_dict,
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
port = os.getenv("PADDLE_PSERVER_PORT", "6174")
pserver_ips = os.getenv("PADDLE_PSERVER_IPS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
trainers = int(os.getenv("PADDLE_TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
if training_role == "PSERVER":
......
python/paddle/fluid/tests/book/test_fit_a_line.py
浏览文件 @ e02cbf35
......@@ -69,16 +69,16 @@ def train(use_cuda, save_dirname, is_local):
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
port = os.getenv("PADDLE_PSERVER_PORT", "6174")
pserver_ips = os.getenv("PADDLE_PSERVER_IPS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
trainers = int(os.getenv("PADDLE_TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
if training_role == "PSERVER":
......
python/paddle/fluid/tests/book/test_image_classification.py
浏览文件 @ e02cbf35
......@@ -178,16 +178,16 @@ def train(net_type, use_cuda, save_dirname, is_local):
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
port = os.getenv("PADDLE_PSERVER_PORT", "6174")
pserver_ips = os.getenv("PADDLE_PSERVER_IPS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
trainers = int(os.getenv("PADDLE_TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
if training_role == "PSERVER":
......
python/paddle/fluid/tests/book/test_label_semantic_roles.py
浏览文件 @ e02cbf35
......@@ -209,16 +209,16 @@ def train(use_cuda, save_dirname=None, is_local=True):
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
port = os.getenv("PADDLE_PSERVER_PORT", "6174")
pserver_ips = os.getenv("PADDLE_PSERVER_IPS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
trainers = int(os.getenv("PADDLE_TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
if training_role == "PSERVER":
......
python/paddle/fluid/tests/book/test_machine_translation.py
浏览文件 @ e02cbf35
......@@ -200,16 +200,16 @@ def train_main(use_cuda, is_sparse, is_local=True):
if is_local:
train_loop(framework.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
port = os.getenv("PADDLE_PSERVER_PORT", "6174")
pserver_ips = os.getenv("PADDLE_PSERVER_IPS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
trainers = int(os.getenv("PADDLE_TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
if training_role == "PSERVER":
......
python/paddle/fluid/tests/book/test_recognize_digits.py
浏览文件 @ e02cbf35
......@@ -151,16 +151,16 @@ def train(nn_type,
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
port = os.getenv("PADDLE_PSERVER_PORT", "6174")
pserver_ips = os.getenv("PADDLE_PSERVER_IPS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
trainers = int(os.getenv("PADDLE_TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
if training_role == "PSERVER":
......
python/paddle/fluid/tests/book/test_recommender_system.py
浏览文件 @ e02cbf35
......@@ -220,16 +220,16 @@ def train(use_cuda, save_dirname, is_local=True):
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
port = os.getenv("PADDLE_PSERVER_PORT", "6174")
pserver_ips = os.getenv("PADDLE_PSERVER_IPS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
trainers = int(os.getenv("PADDLE_TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
if training_role == "PSERVER":
......
python/paddle/fluid/tests/book/test_word2vec.py
浏览文件 @ e02cbf35
......@@ -125,16 +125,16 @@ def train(use_cuda, is_sparse, is_parallel, save_dirname, is_local=True):
if is_local:
train_loop(fluid.default_main_program())
else:
port = os.getenv("PADDLE_INIT_PORT", "6174")
pserver_ips = os.getenv("PADDLE_INIT_PSERVERS") # ip,ip...
port = os.getenv("PADDLE_PSERVER_PORT", "6174")
pserver_ips = os.getenv("PADDLE_PSERVER_IPS") # ip,ip...
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist) # ip:port,ip:port...
trainers = int(os.getenv("TRAINERS"))
trainers = int(os.getenv("PADDLE_TRAINERS"))
current_endpoint = os.getenv("POD_IP") + ":" + port
trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
training_role = os.getenv("TRAINING_ROLE", "TRAINER")
trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
t = fluid.DistributeTranspiler()
t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
if training_role == "PSERVER":
......
python/paddle/fluid/tests/unittests/test_bilinear_interp_op.py
浏览文件 @ e02cbf35
......@@ -15,6 +15,7 @@
import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid.core as core
def bilinear_interp_np(input, out_h, out_w, out_size):
......@@ -45,9 +46,9 @@ def bilinear_interp_np(input, out_h, out_w, out_size):
out[:, :, i, j] = h2lambda*(w2lambda*input[:, :, h, w] +
w1lambda*input[:, :, h, w+wid]) + \
h1lambda*(w2lambda*input[:, :, h+hid, w] +
w1lambda*input[:, :, h+hid, w+wid])
return out.astype("float32")
h1lambda*(w2lambda*input[:, :, h+hid, w] +
w1lambda*input[:, :, h+hid, w+wid])
return out.astype(input.dtype)
class TestBilinearInterpOp(OpTest):
......@@ -122,5 +123,44 @@ class TestCase6(TestBilinearInterpOp):
self.out_size = np.array([65, 129]).astype("int32")
class TestBilinearInterpOpUint8(OpTest):
def setUp(self):
self.out_size = None
self.init_test_case()
self.op_type = "bilinear_interp"
input_np = np.random.randint(
low=0, high=256, size=self.input_shape).astype("uint8")
output_np = bilinear_interp_np(input_np, self.out_h, self.out_w,
self.out_size)
self.inputs = {'X': input_np}
if self.out_size is not None:
self.inputs['OutSize'] = self.out_size
self.attrs = {'out_h': self.out_h, 'out_w': self.out_w}
self.outputs = {'Out': output_np}
def test_check_output(self):
self.check_output_with_place(place=core.CPUPlace(), atol=1)
def init_test_case(self):
self.input_shape = [1, 3, 9, 6]
self.out_h = 10
self.out_w = 9
class TestCase1Uint8(TestBilinearInterpOpUint8):
def init_test_case(self):
self.input_shape = [2, 3, 128, 64]
self.out_h = 120
self.out_w = 50
class TestCase2Uint8(TestBilinearInterpOpUint8):
def init_test_case(self):
self.input_shape = [4, 1, 7, 8]
self.out_h = 5
self.out_w = 13
self.out_size = np.array([6, 15]).astype("int32")
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/trainer.py
浏览文件 @ e02cbf35
......@@ -33,23 +33,59 @@ __all__ = [
class BeginEpochEvent(object):
"""
The begin of a training epoch.
Args:
epoch_id(int): The current epoch ID.
"""
def __init__(self, epoch_id):
self.epoch = epoch_id
class EndEpochEvent(object):
"""
The end of a training epoch.
Args:
epoch_id(int): The current epoch ID.
"""
def __init__(self, epoch_id):
self.epoch = epoch_id
class BeginStepEvent(object):
"""
The begin of a training epoch.
Args:
epoch_id(int): The current epoch ID.
step_id(int): The current step ID.
"""
def __init__(self, epoch_id, step_id):
self.epoch = epoch_id
self.step = step_id
self.fetch_metrics = True
"""
If fetch_metrics is true, the metrics will be fetched at the
EndStepEvent. Default is True.
"""
class EndStepEvent(object):
"""
The end of a training step.
Args:
epoch_id(int): The current epoch ID.
step_id(int): The current step ID.
metrics(list): A list of fetched tensor. The order of this list is same
as the :code:`train_func` returns.
"""
def __init__(self, epoch_id, step_id, metrics):
self.epoch = epoch_id
self.step = step_id
......@@ -57,6 +93,27 @@ class EndStepEvent(object):
class CheckpointConfig(object):
"""
Parameter object for :code:`fluid.io.save_checkpoint` and
:code:`fluid.Trainer`. Used to configuration how to save checkpoint.
Args:
checkpoint_dir(str): Directory path to save check point. Default is the
current directory.
max_num_checkpoints(int): The max number of local check points.
epoch_interval(int): Every number of epoch to save check point.
step_interval(int): Every number of step to save check point.
Examples:
>>> config = fluid.CheckpointConfig("./checkpoints")
>>> trainer = fluid.Trainer(train_func=train_program,
>>> place=place,
>>> optimizer_func=optimizer_func,
>>> checkpoint_config=config)
>>> trainer.train(...)
"""
def __init__(self,
checkpoint_dir=None,
max_num_checkpoints=3,
......@@ -113,11 +170,62 @@ def check_and_get_place(place):
class Trainer(object):
"""
A trainer wraps MultiGPU/MultiNode training loops and can be used to train a
simple neural network easily.
This API takes a :code:`train_func`. A :code:`train_func` is a function that
return loss as it first return value. The reset value can be fetched by
EndStepEvent.metrics
This API also takes a :code:`optimizer_func` that will return an optimizer
instance.
For example, to train a MLP for MNIST dataset, the sample program is
>>> import paddle.fluid as fluid
>>>
>>> def mlp(image, layer_sizes=[200, 100], activation="relu", num_classes=10):
>>> hidden = image
>>> for layer_size in layer_sizes:
>>> hidden = fluid.layers.fc(input=hidden, size=layer_size, act=activation)
>>> return fluid.layers.fc(input=hidden, size=num_classes, act="softmax")
>>>
>>> def train_mnist_mlp():
>>> img = fluid.layers.data(name='image', shape=[784])
>>> label = fluid.layers.data(name='label', shape=[1], dtype='int64')
>>> prediction = mlp(img)
>>> return fluid.layers.mean(fluid.layers.cross_entropy(prediction, label))
>>>
>>> def optimizer():
>>> return fluid.optimizer.Adam()
>>>
>>> trainer = Trainer(train_func=train_mnist_mlp,
>>> optimizer_func=optimizer,
>>> place=fluid.CUDAPlace(0),
>>> parallel=True)
>>>
>>> def train_callback(event):
>>> if isinstance(event, fluid.EndStepEvent):
>>> print "Epoch ID", event.epoch, "Step ID",\
>>> event.step, "AvgLoss", event.metrics[0]
>>> elif isinstance(event, fluid.EndEpochEvent):
>>> trainer.save_params("./model_{0}".format(event.epoch))
>>>
>>> trainer.train(num_epochs=100, event_handler=train_callback)
For more example, please see :ref:`api_guide_high_level_api`.
Args:
train_func(callable): A function which will return loss. The loss must be a scalar.
train_func(callable): A function which will return loss. The loss must be
a scalar tensor.
optimizer_func(callable): A function that returns an Optimizer object.
place: The device place of this trainer.
place(CUDAPlace|CPUPlace): The device place of this trainer. If
:code:`parallel=True,` all CUDA Places will be used if :code:`place`
is a :code:`CUDAPlace`.
parallel(bool): True if use multiple devices.
checkpoint_config(CheckpointConfig): Configuration about how to save
checkpoints.
"""
def __init__(self,
......@@ -129,9 +237,6 @@ class Trainer(object):
checkpoint_config=None):
self.__stop = False
self.parallel = parallel
# 1. we need to generate a framework.Program by calling
# program_func. Reference: fluid.program_guard in
# test_word2vec.py
# config for checkpoint
# only chief worker will save variables
......@@ -145,6 +250,10 @@ class Trainer(object):
self.scope = core.Scope()
# 1. we need to generate a framework.Program by calling
# program_func. Reference: fluid.program_guard in
# test_word2vec.py
self.startup_program = framework.Program()
self.train_program = framework.Program()
......@@ -277,17 +386,18 @@ class Trainer(object):
def train(self, num_epochs, event_handler, reader=None, feed_order=None):
"""
Train the model.
Start the train loop to train the model.
Args:
num_epochs: The number of epoch. An epoch will process all data in reader
event_handler: The event handler. A function with type (ev:Event)->void
reader:
feed_order: Feeding order of reader. None will following the defining
num_epochs(int): The number of epoch. An epoch will process all data in reader
event_handler(callable): The event handler. A function with type (ev:Event)->void
reader(callable): A reader creator object. See also
:ref:`api_guide_python_reader` .
feed_order(list): Feeding order of reader. None will following the defining
order in program
Returns:
None
"""
training_role = os.getenv("PADDLE_TRAINING_ROLE", "")
if training_role == "PSERVER":
......@@ -307,16 +417,24 @@ class Trainer(object):
Test the model on given test data
Args:
reader: The reader that yields test data.
feed_order: Feeding order of reader. None will following the defining
order in program
reader(callable): The reader that yields test data.
feed_order(list): Feeding order of reader. None will following the
defining order in program
"""
return self._test_by_executor(reader, feed_order,
self.train_func_outputs)
def save_params(self, param_path):
# reference: save_persistables in io.py
"""
Save all parameters into :code:`param_path`.
Args:
param_path(str): The path to save parameters.
Returns:
None
"""
with self._prog_and_scope_guard():
exe = executor.Executor(self.place)
io.save_persistables(exe, dirname=param_path)
......
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ e02cbf35
......@@ -12,14 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Transpile the program to distributed data-parallelism programs.
The main_program will be transformed to use a remote parameter server
to do parameter optimization. And the optimization graph will be put
into a parameter server program.
Use different methods to split trainable variables to different
parameter servers.
Steps to transpile trainer:
1. split variable to multiple blocks, aligned by product(dim[1:]) (width).
2. rename splited grad variables to add trainer_id suffix ".trainer_%d".
......@@ -117,129 +109,41 @@ def slice_variable(var_list, slice_count, min_block_size=8192):
return blocks
class DistributeTranspiler:
def _has_distributed_lookup_table(self):
# process lookup_table_op
# 1. check all lookup_table_op is distributed
# 2. check all lookup_table_op share the same table.
distributed_lookup_table_ops = []
# support only one distributed_lookup_table now
self.table_name = None
for op in self.origin_program.global_block().ops:
if op.type == LOOKUP_TABLE_TYPE:
if op.attrs['is_distributed'] is True:
if self.table_name is None:
self.table_name = op.input("W")[0]
if self.table_name != op.input("W")[0]:
raise RuntimeError("all distributed lookup_table_ops"
" should have only one table")
distributed_lookup_table_ops.append(op)
else:
if self.table_name is not None:
assert op.input("W")[0] != self.table_name
return len(distributed_lookup_table_ops) > 0
def _update_dist_lookup_table_vars(self, param_list, grad_list,
params_grads):
# TODO(wuyi): put find a way to put dist lookup table stuff all together.
# update self.table_param_grad and self.trainer_side_table_grad_list
program = self.origin_program
if self.has_distributed_lookup_table:
param_list = [
param for param in param_list if param.name != self.table_name
]
grad_list = [
grad for grad in grad_list
if grad.name != grad_var_name(self.table_name)
]
self.table_param_grad = [
param_grad for param_grad in params_grads
if param_grad[0].name == self.table_name
][0]
table_grad_var = self.table_param_grad[1]
if self.sync_mode:
self.trainer_side_table_grad_list = [
program.global_block().create_var(
name="%s.trainer_%d.pserver_%d" %
(table_grad_var.name, self.trainer_id, index),
type=table_grad_var.type,
shape=table_grad_var.shape,
dtype=table_grad_var.dtype)
for index in range(len(self.pserver_endpoints))
]
else:
self.trainer_side_table_grad_list = [
program.global_block().create_var(
name="%s.pserver_%d" % (table_grad_var.name, index),
type=table_grad_var.type,
shape=table_grad_var.shape,
dtype=table_grad_var.dtype)
for index in range(len(self.pserver_endpoints))
]
return param_list, grad_list
def _init_splited_vars(self, slice_var_up):
# update these mappings for further transpile:
# 1. param_var_mapping: param var name -> [splited params vars]
# 2. grad_var_mapping: grad var name -> [splited grads vars]
# 3. grad_param_mapping: grad.blockx -> param.blockx
# 4. param_grad_ep_mapping: ep -> {"params": [], "grads": []}
param_list = []
grad_list = []
param_grad_set = set()
for p, g in self.params_grads:
# skip parameter marked not trainable
if type(p) == Parameter and p.trainable == False:
continue
if p.name not in param_grad_set:
param_list.append(p)
param_grad_set.add(p.name)
if g.name not in param_grad_set:
grad_list.append(g)
param_grad_set.add(g.name)
param_list, grad_list = self._update_dist_lookup_table_vars(
param_list, grad_list, self.params_grads)
if slice_var_up:
# when we slice var up into blocks, we will slice the var according to
# pserver services' count. A pserver may have two or more listening ports.
grad_blocks = slice_variable(grad_list, len(self.pserver_endpoints))
param_blocks = slice_variable(param_list,
len(self.pserver_endpoints))
else:
# when we do NOT slice var up into blocks, we will always slice params
# grads into one block.
grad_blocks = slice_variable(grad_list, 1)
param_blocks = slice_variable(param_list, 1)
assert (len(grad_blocks) == len(param_blocks))
# origin_varname -> [splited_var]
self.param_var_mapping = self._create_vars_from_blocklist(
self.origin_program, param_blocks)
self.grad_var_mapping = self._create_vars_from_blocklist(
self.origin_program,
grad_blocks,
add_trainer_suffix=self.trainer_num > 1)
self.grad_param_mapping = dict()
for g, p in zip(grad_blocks, param_blocks):
g_name, g_bid, _ = g.split(":")
p_name, p_bid, _ = p.split(":")
self.grad_param_mapping[self.grad_var_mapping[g_name][int(g_bid)]] = \
self.param_var_mapping[p_name][int(p_bid)]
# create mapping of endpoint -> split var to create pserver side program
self.param_grad_ep_mapping = dict()
[
self.param_grad_ep_mapping.update({
ep: {
"params": [],
"grads": []
}
}) for ep in self.pserver_endpoints
]
class DistributeTranspiler(object):
"""
**DistributeTranspiler**
Convert the fluid program to distributed data-parallelism programs.
The main_program will be transformed to use a remote parameter server
to do parameter optimization. And the optimization graph will be put
into a parameter server program.
Examples:
.. code-block:: python
# Define your model before these codes.
port = os.getenv("PADDLE_PSERVER_PORT", "6174")
pserver_ips = os.getenv("PADDLE_PSERVER_IPS", "")
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist)
trainers = int(os.getenv("PADDLE_TRAINERS"))
current_endpoint = os.getenv("PADDLE_CURRENT_IP", "") + ":" + port
trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
role = os.getenv("PADDLE_TRAINING_ROLE")
t = distribute_transpiler.DistributeTranspiler()
t.transpile(
trainer_id, pservers=pserver_endpoints, trainers=trainers)
if role == "PSERVER":
pserver_program = t.get_pserver_program(current_endpoint)
pserver_startup_program = t.get_startup_program(current_endpoint,
pserver_program)
elif role == "TRAINER":
trainer_program = t.get_trainer_program()
"""
def transpile(self,
trainer_id,
......@@ -250,15 +154,20 @@ class DistributeTranspiler:
split_method=RoundRobin,
sync_mode=True):
"""
Run the transpiler.
Args:
trainer_id(int): one unique id for each trainer in a job.
program(Program): program to transpile, default is default_main_program
pservers(string): parameter server endpoints like "m1:6174,m2:6174"
trainers(int): total number of workers/trainers in the job
split_method(PSDispatcher): A function to determin how to split variables
to different servers equally.
sync_mode(boolean): if sync_mode is set True, it means that dist transpiler
will transpile the program into sync_mode pserver and trainer program.
trainer_id (int): id for current trainer worker, if you have
n workers, the id may range from 0 ~ n-1
program (Program|None): program to transpile,
default is fluid.default_main_program().
pservers (str): comma separated ip:port string for the pserver
list.
trainers (int): number of trainers in the distributed job.
slice_var_up (bool): Do Tensor slice for pservers, default is True.
split_method (PSDispatcher): RoundRobin or HashName can be used
try to choose the best method to balance loads for pservers.
sync_mode (bool): Do sync training or not, default is True.
"""
assert (split_method.__bases__[0] == PSDispatcher)
if program is None:
......@@ -385,6 +294,12 @@ class DistributeTranspiler:
self._split_table_grad_and_add_send_vars(program, pserver_endpoints)
def get_trainer_program(self):
"""
Get transpiled trainer side program.
Returns:
Program: trainer side program.
"""
# remove optimize ops and add a send op to main_program
delete_ops(self.origin_program.global_block(), self.optimize_ops)
# FIXME(typhoonzero): serialize once will fix error occurs when clone.
......@@ -393,17 +308,19 @@ class DistributeTranspiler:
def get_pserver_program(self, endpoint):
"""
Get pserver side program using the endpoint.
TODO(panyx0718): Revisit this assumption. what if #blocks > #pservers.
NOTE: assume blocks of the same variable is not distributed
on the same pserver, only change param/grad varnames for
trainers to fetch.
Get parameter server side program.
Args:
endpoint(string): the endpoint for the current pserver instance.
Returns(Program): the pserver program
endpoint (str): current parameter server endpoint.
Returns:
Program: the program for current parameter server to run.
"""
# TODO(panyx0718): Revisit this assumption. what if #blocks > #pservers.
# NOTE: assume blocks of the same variable is not distributed
# on the same pserver, only change param/grad varnames for
# trainers to fetch.
# step1
pserver_program = Program()
# step2: Create vars to receive vars at parameter servers.
......@@ -481,7 +398,7 @@ class DistributeTranspiler:
def __clone_lr_op_sub_block__(op, program, new_block, skip_sub_blks):
if not op.has_attr('sub_block'):
return -1
return
origin_block_desc = op.attr('sub_block')
origin_block = self.origin_program.block(origin_block_desc.id)
......@@ -587,11 +504,14 @@ class DistributeTranspiler:
Get startup program for current parameter server.
Modify operator input variables if there are variables that
were split to several blocks.
Args:
endpoint(string): the endpoint for the current pserver instance.
pserver_program(Program): the program for pserver to execute.
Returns(Program): the startup program for pserver
Args:
endpoint (str): current pserver endpoint.
pserver_program (Program): call get_pserver_program first and
pass the result here.
Returns:
Program: parameter server side startup program.
"""
s_prog = Program()
orig_s_prog = default_startup_program()
......@@ -643,6 +563,129 @@ class DistributeTranspiler:
# ====================== private transpiler functions =====================
def _has_distributed_lookup_table(self):
# process lookup_table_op
# 1. check all lookup_table_op is distributed
# 2. check all lookup_table_op share the same table.
distributed_lookup_table_ops = []
# support only one distributed_lookup_table now
self.table_name = None
for op in self.origin_program.global_block().ops:
if op.type == LOOKUP_TABLE_TYPE:
if op.attrs['is_distributed'] is True:
if self.table_name is None:
self.table_name = op.input("W")[0]
if self.table_name != op.input("W")[0]:
raise RuntimeError("all distributed lookup_table_ops"
" should have only one table")
distributed_lookup_table_ops.append(op)
else:
if self.table_name is not None:
assert op.input("W")[0] != self.table_name
return len(distributed_lookup_table_ops) > 0
def _update_dist_lookup_table_vars(self, param_list, grad_list,
params_grads):
# TODO(wuyi): put find a way to put dist lookup table stuff all together.
# update self.table_param_grad and self.trainer_side_table_grad_list
program = self.origin_program
if self.has_distributed_lookup_table:
param_list = [
param for param in param_list if param.name != self.table_name
]
grad_list = [
grad for grad in grad_list
if grad.name != grad_var_name(self.table_name)
]
self.table_param_grad = [
param_grad for param_grad in params_grads
if param_grad[0].name == self.table_name
][0]
table_grad_var = self.table_param_grad[1]
if self.sync_mode:
self.trainer_side_table_grad_list = [
program.global_block().create_var(
name="%s.trainer_%d.pserver_%d" %
(table_grad_var.name, self.trainer_id, index),
type=table_grad_var.type,
shape=table_grad_var.shape,
dtype=table_grad_var.dtype)
for index in range(len(self.pserver_endpoints))
]
else:
self.trainer_side_table_grad_list = [
program.global_block().create_var(
name="%s.pserver_%d" % (table_grad_var.name, index),
type=table_grad_var.type,
shape=table_grad_var.shape,
dtype=table_grad_var.dtype)
for index in range(len(self.pserver_endpoints))
]
return param_list, grad_list
def _init_splited_vars(self, slice_var_up):
# update these mappings for further transpile:
# 1. param_var_mapping: param var name -> [splited params vars]
# 2. grad_var_mapping: grad var name -> [splited grads vars]
# 3. grad_param_mapping: grad.blockx -> param.blockx
# 4. param_grad_ep_mapping: ep -> {"params": [], "grads": []}
param_list = []
grad_list = []
param_grad_set = set()
for p, g in self.params_grads:
# skip parameter marked not trainable
if type(p) == Parameter and p.trainable == False:
continue
if p.name not in param_grad_set:
param_list.append(p)
param_grad_set.add(p.name)
if g.name not in param_grad_set:
grad_list.append(g)
param_grad_set.add(g.name)
param_list, grad_list = self._update_dist_lookup_table_vars(
param_list, grad_list, self.params_grads)
if slice_var_up:
# when we slice var up into blocks, we will slice the var according to
# pserver services' count. A pserver may have two or more listening ports.
grad_blocks = slice_variable(grad_list, len(self.pserver_endpoints))
param_blocks = slice_variable(param_list,
len(self.pserver_endpoints))
else:
# when we do NOT slice var up into blocks, we will always slice params
# grads into one block.
grad_blocks = slice_variable(grad_list, 1)
param_blocks = slice_variable(param_list, 1)
assert (len(grad_blocks) == len(param_blocks))
# origin_varname -> [splited_var]
self.param_var_mapping = self._create_vars_from_blocklist(
self.origin_program, param_blocks)
self.grad_var_mapping = self._create_vars_from_blocklist(
self.origin_program,
grad_blocks,
add_trainer_suffix=self.trainer_num > 1)
self.grad_param_mapping = dict()
for g, p in zip(grad_blocks, param_blocks):
g_name, g_bid, _ = g.split(":")
p_name, p_bid, _ = p.split(":")
self.grad_param_mapping[self.grad_var_mapping[g_name][int(g_bid)]] = \
self.param_var_mapping[p_name][int(p_bid)]
# create mapping of endpoint -> split var to create pserver side program
self.param_grad_ep_mapping = dict()
[
self.param_grad_ep_mapping.update({
ep: {
"params": [],
"grads": []
}
}) for ep in self.pserver_endpoints
]
# transpiler function for dis lookup_table
def _replace_lookup_table_op_with_prefetch(self, program,
pserver_endpoints):
......
python/paddle/fluid/transpiler/memory_optimization_transpiler.py
浏览文件 @ e02cbf35
......@@ -383,6 +383,16 @@ def memory_optimize(input_program, skip_opt_set=None, print_log=False, level=0):
def release_memory(input_program, skip_opt_set=None):
"""
Modify the input program and insert :code:`delete_op` to early drop not used
variables. The modification will be performed inplace.
Notes: This is an experimental API and could be removed in next few
releases. Users should not use this API.
Args:
input_program(Program): The program will be inserted :code:`delete_op`.
"""
cfgs = _get_cfgs(input_program)
for cfg in cfgs:
cfg.release_memory(skip_opt_set=skip_opt_set)
python/paddle/fluid/transpiler/ps_dispatcher.py
浏览文件 @ e02cbf35
......@@ -33,15 +33,21 @@ class PSDispatcher(object):
def dispatch(self, varlist):
"""
:param varlist: a list of Variables
:return: a map of pserver endpoint -> varname
Args:
varlist(list): a list of Variables
Returns:
a map of pserver endpoint -> varname
"""
AssertionError("Interface has not been implemented.")
class HashName(PSDispatcher):
"""
Hash variable names to several endpoints
Hash variable names to several endpoints using python
"hash()" function.
Args:
pserver_endpoints (list): list of endpoint(ip:port).
"""
def __init__(self, pserver_endpoints):
......@@ -61,7 +67,11 @@ class HashName(PSDispatcher):
class RoundRobin(PSDispatcher):
"""
Distribute variables to serveral endpoints.
Distribute variables to serveral endpoints using
RondRobin<https://en.wikipedia.org/wiki/Round-robin_scheduling> method.
Args:
pserver_endpoints (list): list of endpoint(ip:port).
"""
def __init__(self, pserver_endpoints):
......
python/paddle/fluid/unique_name.py
浏览文件 @ e02cbf35
此差异已折叠。
tools/print_signatures.py 0 → 100644
浏览文件 @ e02cbf35
此差异已折叠。
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