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Paddle
提交 b4a32eaf 编写于 作者: Q Qiao Longfei
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into optimize-sum-seq-pooling-op 

test=develop
上级 936926aa af91d41a
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Showing with 901 addition and 400 deletion
cmake/inference_lib.cmake
浏览文件 @ b4a32eaf
......@@ -18,7 +18,7 @@ function(copy TARGET)
set(oneValueArgs "")
set(multiValueArgs SRCS DSTS DEPS)
cmake_parse_arguments(copy_lib "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(inference_lib_dist_dep ${TARGET} ${inference_lib_dist_dep} PARENT_SCOPE)
set(fluid_lib_dist_dep ${TARGET} ${fluid_lib_dist_dep} PARENT_SCOPE)
list(LENGTH copy_lib_SRCS copy_lib_SRCS_len)
list(LENGTH copy_lib_DSTS copy_lib_DSTS_len)
......@@ -185,7 +185,8 @@ copy(cmake_cache
SRCS ${CMAKE_CURRENT_BINARY_DIR}/CMakeCache.txt
DSTS ${FLUID_INSTALL_DIR})
add_custom_target(inference_lib_dist DEPENDS ${inference_lib_dist_dep})
# This command generates a complete fluid library for both train and inference
add_custom_target(fluid_lib_dist DEPENDS ${fluid_lib_dist_dep})
# paddle fluid version
execute_process(
......
paddle/fluid/API.spec
浏览文件 @ b4a32eaf
......@@ -127,6 +127,7 @@ paddle.fluid.layers.relu ArgSpec(args=['x', 'name'], varargs=None, keywords=None
paddle.fluid.layers.log ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.margin_rank_loss ArgSpec(args=['label', 'left', 'right', 'margin', 'name'], varargs=None, keywords=None, defaults=(0.1, None))
paddle.fluid.layers.elu ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(1.0, None))
paddle.fluid.layers.relu6 ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(6.0, None))
paddle.fluid.layers.pow ArgSpec(args=['x', 'factor', 'name'], varargs=None, keywords=None, defaults=(1.0, None))
......
paddle/fluid/CMakeLists.txt
浏览文件 @ b4a32eaf
......@@ -12,6 +12,5 @@ endif(NOT WIN32)
if(WITH_INFERENCE)
# NOTE: please add subdirectory inference at last.
add_subdirectory(inference)
add_subdirectory(train)
endif()
add_subdirectory(train)
paddle/fluid/framework/details/op_handle_base.h
浏览文件 @ b4a32eaf
......@@ -64,7 +64,8 @@ class OpHandleBase {
virtual bool IsMultiDeviceTransfer() { return false; }
const platform::DeviceContext *DeviceContext(platform::Place place) {
return dev_ctxes_[place];
auto it = dev_ctxes_.find(place);
return it != dev_ctxes_.end() ? it->second : nullptr;
}
void SetDeviceContext(platform::Place place, platform::DeviceContext *ctx_) {
......
paddle/fluid/framework/executor.cc
浏览文件 @ b4a32eaf
......@@ -46,6 +46,41 @@ ExecutorPrepareContext::~ExecutorPrepareContext() {
VLOG(5) << "destroy ExecutorPrepareContext";
}
template <typename RefCntMap>
static void DeleteUnusedTensors(const Scope& scope, const OperatorBase* op,
GarbageCollector<Tensor>* gc,
RefCntMap* ref_cnts) {
std::unordered_set<Tensor*> erase_tensors;
auto handler = [&](const VariableNameMap& name_map) {
for (auto& name_pair : name_map) {
for (auto& name : name_pair.second) {
auto it = ref_cnts->find(name);
if (it == ref_cnts->end()) continue;
if ((it->second)-- == 1) {
auto* var = scope.FindVar(name);
if (var != nullptr) {
VLOG(10) << "Erase tensor \'" << name << "\'";
if (var->IsType<LoDTensor>()) {
erase_tensors.insert(var->GetMutable<LoDTensor>());
} else if (var->IsType<SelectedRows>()) {
erase_tensors.insert(
var->GetMutable<SelectedRows>()->mutable_value());
}
}
}
}
}
};
handler(op->Inputs());
handler(op->Outputs());
if (!erase_tensors.empty()) {
gc->Add(erase_tensors);
}
}
Executor::Executor(const platform::Place& place) : place_(place) {}
void Executor::Close() {
......@@ -331,9 +366,13 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
}
int64_t max_memory_size = GetEagerDeletionThreshold();
std::unique_ptr<GarbageCollector<Tensor>> gc;
if (max_memory_size >= 0) {
// WhileOp would set keep_kids to false
// WhileGradOp would need the scopes created in WhileOp
// Perhaps, we should not perform eager deletion in WhileOp
// The scopes and variables created by WhileOp would be deleted
// in WhileGradOp.
if (max_memory_size >= 0 && !keep_kids) {
ctx->ResetReferenceCount();
#ifdef PADDLE_WITH_CUDA
if (platform::is_gpu_place(place_)) {
......@@ -352,45 +391,8 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
op->Run(*local_scope, place_);
if (gc != nullptr) {
std::vector<std::string> erase_vars;
for (auto& input : op->Inputs()) {
for (auto& input_name : input.second) {
auto it = ctx->cur_ref_cnts_.find(input_name);
if (it == ctx->cur_ref_cnts_.end()) continue;
if (it->second == 1) { // should delete it
erase_vars.emplace_back(input_name);
ctx->cur_ref_cnts_.erase(input_name);
} else {
--(it->second);
}
}
}
for (auto& output : op->Outputs()) {
for (auto& output_name : output.second) {
auto it = ctx->cur_ref_cnts_.find(output_name);
if (it == ctx->cur_ref_cnts_.end()) continue;
if (it->second == 1) {
erase_vars.emplace_back(output_name);
ctx->cur_ref_cnts_.erase(output_name);
} else {
--(it->second);
}
}
}
if (!erase_vars.empty()) {
std::vector<framework::LoDTensor*> erase_tensors;
for (auto& name : erase_vars) {
auto* var = local_scope->FindVar(name);
if (var == nullptr) continue;
if (var->IsType<framework::LoDTensor>()) {
auto* tensor = var->GetMutable<framework::LoDTensor>();
erase_tensors.push_back(tensor);
}
}
if (!erase_tensors.empty()) gc->Add(erase_tensors);
}
DeleteUnusedTensors(*local_scope, op.get(), gc.get(),
&(ctx->cur_ref_cnts_));
}
if (FLAGS_benchmark) {
......
paddle/fluid/framework/executor.h
浏览文件 @ b4a32eaf
......@@ -32,38 +32,32 @@ template <typename T>
std::unordered_map<std::string, T> GetNonPersistableReferenceCount(
const ProgramDesc& prog, size_t block_id) {
auto& block = prog.Block(block_id);
std::unordered_set<std::string> ignored_vars;
std::unordered_map<std::string, T> ref_cnts;
for (auto var_desc : block.AllVars()) {
auto type = var_desc->Proto()->type().type();
if (type != proto::VarType::LOD_TENSOR || var_desc->Persistable()) {
ignored_vars.insert(var_desc->Name()); // ignore persistable vars
}
}
for (auto op_desc : block.AllOps()) {
for (auto& input : op_desc->Inputs()) {
for (auto& input_name : input.second) {
if (!ignored_vars.count(input_name)) {
if (ref_cnts.count(input_name))
++ref_cnts[input_name];
else
ref_cnts[input_name] = 1;
auto update_ref_cnts = [&](OpDesc* op_desc, const VariableNameMap& name_map) {
for (auto& name_pair : name_map) {
for (auto& name : name_pair.second) {
auto* var_desc = block.FindVar(name);
if (var_desc == nullptr || var_desc->Persistable()) continue;
auto type = var_desc->Proto()->type().type();
if (type != proto::VarType::LOD_TENSOR &&
type != proto::VarType::SELECTED_ROWS) {
continue;
}
}
}
for (auto& output : op_desc->Outputs()) {
for (auto output_name : output.second) {
if (!ignored_vars.count(output_name)) {
if (ref_cnts.count(output_name))
++ref_cnts[output_name];
else
ref_cnts[output_name] = 1;
auto it = ref_cnts.find(name);
if (it != ref_cnts.end()) {
++it->second;
} else {
ref_cnts[name] = 1;
}
}
}
};
for (auto op_desc : block.AllOps()) {
update_ref_cnts(op_desc, op_desc->Inputs());
update_ref_cnts(op_desc, op_desc->Outputs());
}
return ref_cnts;
}
......
paddle/fluid/framework/ir/conv_bn_fuse_pass.cc
浏览文件 @ b4a32eaf
......@@ -44,89 +44,6 @@ namespace ir {
GET_IR_NODE_FROM_SUBGRAPH(bn_saved_mean, bn_saved_mean, pattern_name); \
GET_IR_NODE_FROM_SUBGRAPH(bn_saved_variance, bn_saved_variance, pattern_name)
template <typename UnaryOperation>
LoDTensor tensor_apply(const LoDTensor& vec, UnaryOperation f) {
LoDTensor vec_y;
vec_y.Resize(vec.dims());
const float* x = vec.data<float>();
float* y = vec_y.mutable_data<float>(platform::CPUPlace());
for (int64_t i = 0; i < vec.numel(); i++) {
y[i] = f(x[i]);
}
return vec_y;
}
void tensor_apply_inplace(LoDTensor* vec, float (*f)(float)) {
float* data = vec->mutable_data<float>(platform::CPUPlace());
for (int64_t i = 0; i < vec->numel(); i++) {
data[i] = f(data[i]);
}
}
template <typename BinaryOperation>
LoDTensor tensor_apply_eltwise(const LoDTensor& vec_a, const LoDTensor& vec_b,
BinaryOperation f) {
PADDLE_ENFORCE_EQ(vec_a.dims(), vec_b.dims());
LoDTensor vec_y;
vec_y.Resize(vec_a.dims());
const float* a = vec_a.data<float>();
const float* b = vec_b.data<float>();
float* y = vec_y.mutable_data<float>(platform::CPUPlace());
for (int64_t i = 0; i < vec_a.numel(); i++) {
y[i] = f(a[i], b[i]);
}
return vec_y;
}
template <typename BinaryOperation>
LoDTensor tensor_apply_eltwise_broadcast(const LoDTensor& vec_a,
const LoDTensor& vec_b,
BinaryOperation f) {
PADDLE_ENFORCE_EQ(vec_a.dims().size(), 2);
PADDLE_ENFORCE_EQ(vec_b.dims().size(), 2);
PADDLE_ENFORCE_EQ(vec_a.dims()[0], vec_b.dims()[0]);
PADDLE_ENFORCE_EQ(vec_b.dims()[1], 1);
LoDTensor vec_y;
vec_y.Resize(vec_a.dims());
const float* a = vec_a.data<float>();
const float* b = vec_b.data<float>();
float* y = vec_y.mutable_data<float>(platform::CPUPlace());
size_t a_height = vec_a.dims()[0];
size_t a_width = vec_a.dims()[1];
for (size_t h = 0; h < a_height; h++) {
for (size_t w = 0; w < a_width; ++w) {
*(y++) = f(*(a++), b[h]);
}
}
return vec_y;
}
// reshape to two dimensions {A, B * C * ...}
void make_tensor_2d(LoDTensor* tensor_to_reshape) {
auto dims_count = tensor_to_reshape->dims().size();
PADDLE_ENFORCE_GT(dims_count, 0);
int size2 = 1;
for (int i = 1; i < dims_count; i++) {
size2 *= tensor_to_reshape->dims()[i];
}
tensor_to_reshape->Resize(make_ddim({tensor_to_reshape->dims()[0], size2}));
}
void recompute_conv_weights(LoDTensor* weights, LoDTensor* tmp) {
// remember the weights tensor shape {A, B, C, ...}
auto weights_shape = weights->dims();
// reduce the weights to 2d {A, B * C * ...}
make_tensor_2d(weights);
// make tmp tensor 2d by adding 1 as second dim {A, 1}
make_tensor_2d(tmp);
*weights =
tensor_apply_eltwise_broadcast(*weights, *tmp, std::multiplies<float>());
// reshape weights to the original dims {A, B, C, ...}
weights->Resize(weights_shape);
}
void recompute_bias_and_weights(const Scope* scope,
ir::Node* conv_weight, //
const ir::Node& bn_scale, //
......@@ -135,6 +52,13 @@ void recompute_bias_and_weights(const Scope* scope,
const ir::Node& bn_variance, //
LoDTensor* eltwise_y_in_tensor, //
float epsilon) {
using EigenVectorArrayMap =
Eigen::Map<Eigen::Array<float, Eigen::Dynamic, 1>>;
using ConstEigenVectorArrayMap =
Eigen::Map<const Eigen::Array<float, Eigen::Dynamic, 1>>;
using EigenMatrixArrayMap = Eigen::Map<
Eigen::Array<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>;
// Re-compute bias of conv2d from BN
PADDLE_ENFORCE_EQ(eltwise_y_in_tensor->dims(), bn_bias_tensor.dims());
......@@ -143,31 +67,38 @@ void recompute_bias_and_weights(const Scope* scope,
scope->FindVar(bn_variance.Name())->GetMutable<LoDTensor>();
auto* mean_tensor = scope->FindVar(bn_mean.Name())->GetMutable<LoDTensor>();
auto std_tensor = LoDTensor();
std_tensor.Resize(bn_bias_tensor.dims());
std_tensor =
tensor_apply(*variance_tensor, [&](float x) { return x + epsilon; });
ConstEigenVectorArrayMap scale_array(scale_tensor->data<float>(),
scale_tensor->numel(), 1);
EigenVectorArrayMap variance_array(
variance_tensor->mutable_data<float>(platform::CPUPlace()),
variance_tensor->numel(), 1);
ConstEigenVectorArrayMap mean_array(mean_tensor->data<float>(),
mean_tensor->numel(), 1);
ConstEigenVectorArrayMap bn_bias_array(bn_bias_tensor.data<float>(),
bn_bias_tensor.numel(), 1);
using EigenVectorArrayMap =
Eigen::Map<Eigen::Array<float, Eigen::Dynamic, 1>>;
// variance will not be used anymore, so make it std_array and then tmp_array
variance_array += epsilon;
variance_array = variance_array.sqrt();
variance_array = scale_array / variance_array;
EigenVectorArrayMap eltwise_y_in_array(
eltwise_y_in_tensor->mutable_data<float>(platform::CPUPlace()),
eltwise_y_in_tensor->numel(), 1);
EigenVectorArrayMap std_vec(
std_tensor.mutable_data<float>(platform::CPUPlace()), std_tensor.numel(),
1);
std_vec = std_vec.sqrt();
auto tmp_tensor =
tensor_apply_eltwise(*scale_tensor, std_tensor, std::divides<float>());
auto tensor_minus = tensor_apply_eltwise(*eltwise_y_in_tensor, *mean_tensor,
std::minus<float>());
auto tensor_mul =
tensor_apply_eltwise(tensor_minus, tmp_tensor, std::multiplies<float>());
*eltwise_y_in_tensor =
tensor_apply_eltwise(tensor_mul, bn_bias_tensor, std::plus<float>());
eltwise_y_in_array =
((eltwise_y_in_array - mean_array) * variance_array) + bn_bias_array;
// Re-compute weight of conv2d from BN
auto* current_param =
scope->FindVar(conv_weight->Name())->GetMutable<LoDTensor>();
recompute_conv_weights(current_param, &tmp_tensor);
auto* weights = scope->FindVar(conv_weight->Name())->GetMutable<LoDTensor>();
auto weights_shape = weights->dims();
auto weights_shape_2d = flatten_to_2d(weights_shape, 1);
EigenMatrixArrayMap weights_array_2d(
weights->mutable_data<float>(platform::CPUPlace()), weights_shape_2d[0],
weights_shape_2d[1]);
weights_array_2d.colwise() *= variance_array;
}
std::unique_ptr<ir::Graph> ConvBNFusePass::ApplyImpl(
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ b4a32eaf
......@@ -307,6 +307,10 @@ ParallelExecutor::~ParallelExecutor() {
}
}
}
// member_ must be destructed before gcs_ since the destructor of
// ReferenceCountOpHandle use raw pointers of gcs_ inside.
member_.reset();
}
} // namespace framework
......
paddle/fluid/framework/parallel_executor.h
浏览文件 @ b4a32eaf
......@@ -75,7 +75,7 @@ class ParallelExecutor {
private:
void BCastParamsToDevices(const std::unordered_set<std::string> &vars) const;
ParallelExecutorPrivate *member_;
std::unique_ptr<ParallelExecutorPrivate> member_;
#ifdef PADDLE_WITH_CUDA
// ref_cnts_ is only initialized when ParallelExecutor constructs, and then
......
paddle/fluid/framework/scope.cc
浏览文件 @ b4a32eaf
......@@ -49,18 +49,18 @@ int64_t GetEagerDeletionThreshold() {
Scope::~Scope() { DropKids(); }
Scope& Scope::NewScope() const {
std::unique_lock<std::mutex> lock(mutex_);
std::lock_guard<std::mutex> lock(mutex_);
kids_.push_back(new Scope(this));
return *kids_.back();
}
Variable* Scope::Var(const std::string& name) {
std::unique_lock<std::mutex> lock(mutex_);
std::lock_guard<std::mutex> lock(mutex_);
return VarInternal(name);
}
Variable* Scope::Var(std::string* name) {
std::unique_lock<std::mutex> lock(mutex_);
std::lock_guard<std::mutex> lock(mutex_);
auto new_name = string::Sprintf("%p.%d", this, vars_.size());
if (name != nullptr) {
*name = new_name;
......@@ -69,29 +69,34 @@ Variable* Scope::Var(std::string* name) {
}
Variable* Scope::FindVar(const std::string& name) const {
std::unique_lock<std::mutex> lock(mutex_);
std::lock_guard<std::mutex> lock(mutex_);
return FindVarInternal(name);
}
Variable* Scope::FindLocalVar(const std::string& name) const {
std::lock_guard<std::mutex> lock(mutex_);
return FindVarLocally(name);
}
const Scope* Scope::FindScope(const Variable* var) const {
std::unique_lock<std::mutex> lock(mutex_);
std::lock_guard<std::mutex> lock(mutex_);
return FindScopeInternal(var);
}
void Scope::DropKids() {
std::unique_lock<std::mutex> lock(mutex_);
std::lock_guard<std::mutex> lock(mutex_);
for (Scope* s : kids_) delete s;
kids_.clear();
}
bool Scope::HasKid(const Scope* scope) const {
std::unique_lock<std::mutex> lock(mutex_);
std::lock_guard<std::mutex> lock(mutex_);
auto it = std::find(this->kids_.begin(), this->kids_.end(), scope);
return it != this->kids_.end();
}
std::vector<std::string> Scope::LocalVarNames() const {
std::unique_lock<std::mutex> lock(mutex_);
std::lock_guard<std::mutex> lock(mutex_);
std::vector<std::string> known_vars;
known_vars.reserve(this->vars_.size());
for (auto& p : vars_) {
......@@ -101,7 +106,7 @@ std::vector<std::string> Scope::LocalVarNames() const {
}
void Scope::DeleteScope(Scope* scope) const {
std::unique_lock<std::mutex> lock(mutex_);
std::lock_guard<std::mutex> lock(mutex_);
auto it = std::find(this->kids_.begin(), this->kids_.end(), scope);
PADDLE_ENFORCE(it != this->kids_.end(), "Cannot find %p as kid scope", scope);
this->kids_.erase(it);
......@@ -114,7 +119,7 @@ void Scope::DeleteScope(Scope* scope) const {
}
void Scope::EraseVars(const std::vector<std::string>& var_names) {
std::unique_lock<std::mutex> lock(mutex_);
std::lock_guard<std::mutex> lock(mutex_);
std::set<std::string> var_set(var_names.begin(), var_names.end());
for (auto it = vars_.begin(); it != vars_.end();) {
if (var_set.find(it->first) != var_set.end()) {
......@@ -127,12 +132,12 @@ void Scope::EraseVars(const std::vector<std::string>& var_names) {
void Scope::Rename(const std::string& origin_name,
const std::string& new_name) const {
std::unique_lock<std::mutex> lock(mutex_);
std::lock_guard<std::mutex> lock(mutex_);
RenameInternal(origin_name, new_name);
}
std::string Scope::Rename(const std::string& origin_name) const {
std::unique_lock<std::mutex> lock(mutex_);
std::lock_guard<std::mutex> lock(mutex_);
auto new_name = string::Sprintf("%p.%d", this, vars_.size());
RenameInternal(origin_name, new_name);
return new_name;
......
paddle/fluid/framework/scope.h
浏览文件 @ b4a32eaf
......@@ -63,6 +63,11 @@ class Scope {
/// Caller doesn't own the returned Variable.
Variable* FindVar(const std::string& name) const;
/// Find a variable in the current scope.
/// Return nullptr if cannot find.
/// Caller doesn't own the returned Variable.
Variable* FindLocalVar(const std::string& name) const;
const Scope* parent() const { return parent_; }
/// Find the scope or an ancestor scope that contains the given variable.
......
paddle/fluid/operators/adadelta_op.cc
浏览文件 @ b4a32eaf
......@@ -18,6 +18,7 @@ namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
class AdadeltaOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -31,6 +32,16 @@ class AdadeltaOp : public framework::OperatorWithKernel {
"Input(AvgSquaredGrad) of AdadeltaOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("AvgSquaredUpdate"),
"Input(AvgSquaredUpdate) of AdadeltaOp should not be null.");
PADDLE_ENFORCE(
ctx->GetInputsVarType("Param").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
PADDLE_ENFORCE(
ctx->GetInputsVarType("Grad").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Grad").front(), ctx->GetInputsVarType("Grad").front());
PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
"Output(ParamOut) of AdadeltaOp should not be null.");
......@@ -56,6 +67,7 @@ class AdadeltaOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("AvgSquaredGradOut", param_dim);
ctx->SetOutputDim("AvgSquaredUpdateOut", param_dim);
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
auto input_data_type =
......
paddle/fluid/operators/adadelta_op.h
浏览文件 @ b4a32eaf
......@@ -23,6 +23,17 @@ template <typename DeviceContext, typename T>
class AdadeltaOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const auto* param_var = ctx.InputVar("Param");
PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.Inputs("Param").front(), param_var->Type().name());
const auto* grad_var = ctx.InputVar("Grad");
PADDLE_ENFORCE(grad_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.Inputs("Grad").front(), grad_var->Type().name());
auto param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
auto avg_squared_grad_out_tensor =
ctx.Output<framework::Tensor>("AvgSquaredGradOut");
......
paddle/fluid/operators/adagrad_op.h
浏览文件 @ b4a32eaf
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
......@@ -21,25 +22,31 @@ namespace operators {
template <typename DeviceContext, typename T>
struct SparseAdagradFunctor {
void operator()(const DeviceContext& context,
const framework::SelectedRows& grad,
const framework::Tensor& learning_rate, T epsilon,
framework::Tensor* moment, framework::Tensor* param);
void operator()(const DeviceContext &context,
const framework::SelectedRows &grad,
const framework::Tensor &learning_rate, T epsilon,
framework::Tensor *moment, framework::Tensor *param);
};
template <typename DeviceContext, typename T>
class AdagradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
auto* moment_out_tensor = ctx.Output<framework::Tensor>("MomentOut");
void Compute(const framework::ExecutionContext &ctx) const override {
const auto *param_var = ctx.InputVar("Param");
PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.Inputs("Param").front(), param_var->Type().name());
auto *param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
auto *moment_out_tensor = ctx.Output<framework::Tensor>("MomentOut");
param_out_tensor->mutable_data<T>(ctx.GetPlace());
moment_out_tensor->mutable_data<T>(ctx.GetPlace());
T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
auto* grad_var = ctx.InputVar("Grad");
auto *grad_var = ctx.InputVar("Grad");
if (grad_var->IsType<framework::LoDTensor>()) {
auto param = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Param"));
......@@ -47,16 +54,16 @@ class AdagradOpKernel : public framework::OpKernel<T> {
*ctx.Input<framework::Tensor>("Grad"));
auto moment = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Moment"));
auto* learning_rate = ctx.Input<framework::Tensor>("LearningRate");
auto *learning_rate = ctx.Input<framework::Tensor>("LearningRate");
auto param_out = framework::EigenVector<T>::Flatten(*param_out_tensor);
auto moment_out = framework::EigenVector<T>::Flatten(*moment_out_tensor);
auto* place = ctx.template device_context<DeviceContext>().eigen_device();
auto *place = ctx.template device_context<DeviceContext>().eigen_device();
moment_out.device(*place) = moment + grad * grad;
Eigen::DSizes<int, 1> m_dsize(moment_out_tensor->numel());
if (platform::is_cpu_place(ctx.GetPlace())) {
auto* lr = learning_rate->data<T>();
auto *lr = learning_rate->data<T>();
param_out.device(*place) =
param - lr[0] * grad / (moment_out.sqrt() + epsilon);
} else {
......@@ -66,10 +73,10 @@ class AdagradOpKernel : public framework::OpKernel<T> {
lr.broadcast(m_dsize) * grad / (moment_out.sqrt() + epsilon);
}
} else if (grad_var->IsType<framework::SelectedRows>()) {
auto* param_tensor = ctx.Input<framework::Tensor>("Param");
auto *param_tensor = ctx.Input<framework::Tensor>("Param");
PADDLE_ENFORCE_EQ(param_tensor, param_out_tensor);
auto* moment_tensor = ctx.Input<framework::Tensor>("Moment");
auto *moment_tensor = ctx.Input<framework::Tensor>("Moment");
PADDLE_ENFORCE_EQ(moment_tensor, moment_out_tensor);
SparseAdagradFunctor<DeviceContext, T> functor;
......
paddle/fluid/operators/adam_op.h
浏览文件 @ b4a32eaf
......@@ -18,6 +18,7 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/algorithm.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
#include "paddle/fluid/platform/for_range.h"
......@@ -199,23 +200,9 @@ struct SparseAdamFunctor {
row_numel_(row_numel),
row_count_(row_count) {}
inline HOSTDEVICE int64_t BinarySearchInRows(int64_t row) const {
int64_t beg = 0, end = row_count_ - 1;
while (beg <= end) {
auto mid = ((beg + end) >> 1);
if (rows_[mid] == row)
return mid;
else if (rows_[mid] < row)
beg = mid + 1;
else
end = mid - 1;
}
return -1;
}
inline HOSTDEVICE void operator()(size_t i) const {
int64_t row = i / row_numel_;
auto row_idx = BinarySearchInRows(row);
auto row_idx =
math::BinarySearch<int64_t>(rows_, row_count_, i / row_numel_);
T g = row_idx >= 0 ? grad_[row_idx * row_numel_ + i % row_numel_] : 0;
// The following code is the same as dense
......@@ -244,6 +231,12 @@ template <typename DeviceContext, typename T>
class AdamOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const auto* param_var = ctx.InputVar("Param");
PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.Inputs("Param").front(), param_var->Type().name());
using paddle::framework::LoDTensor;
using paddle::operators::detail::Ref;
......
paddle/fluid/operators/adamax_op.cc
浏览文件 @ b4a32eaf
......@@ -35,6 +35,16 @@ class AdamaxOp : public framework::OperatorWithKernel {
"Input(LearningRate) of AdamaxOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Beta1Pow"),
"Input(Beta1Pow) of AdamaxOp should not be null.");
PADDLE_ENFORCE(
ctx->GetInputsVarType("Param").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
PADDLE_ENFORCE(
ctx->GetInputsVarType("Grad").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Grad").front(), ctx->GetInputsVarType("Grad").front());
PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
"Output(ParamOut) of AdamaxOp should not be null.");
......
paddle/fluid/operators/adamax_op.h
浏览文件 @ b4a32eaf
......@@ -23,6 +23,17 @@ template <typename DeviceContext, typename T>
class AdamaxOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const auto* param_var = ctx.InputVar("Param");
PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.Inputs("Param").front(), param_var->Type().name());
const auto* grad_var = ctx.InputVar("Grad");
PADDLE_ENFORCE(grad_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.Inputs("Grad").front(), grad_var->Type().name());
auto param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
auto moment_out_tensor = ctx.Output<framework::Tensor>("MomentOut");
auto inf_norm_out_tensor = ctx.Output<framework::Tensor>("InfNormOut");
......
paddle/fluid/operators/decayed_adagrad_op.cc
浏览文件 @ b4a32eaf
......@@ -32,6 +32,16 @@ class DecayedAdagradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(
ctx->HasInput("LearningRate"),
"Input(LearningRate) of DecayedAdagradOp should not be null.");
PADDLE_ENFORCE(
ctx->GetInputsVarType("Param").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
PADDLE_ENFORCE(
ctx->GetInputsVarType("Grad").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Grad").front(), ctx->GetInputsVarType("Grad").front());
PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
"Output(ParamOut) of DecayedAdagradOp should not be null.");
......
paddle/fluid/operators/decayed_adagrad_op.h
浏览文件 @ b4a32eaf
......@@ -23,6 +23,17 @@ template <typename DeviceContext, typename T>
class DecayedAdagradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const auto* param_var = ctx.InputVar("Param");
PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.Inputs("Param").front(), param_var->Type().name());
const auto* grad_var = ctx.InputVar("Grad");
PADDLE_ENFORCE(grad_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.Inputs("Grad").front(), grad_var->Type().name());
auto param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
auto moment_out_tensor = ctx.Output<framework::Tensor>("MomentOut");
......
paddle/fluid/operators/ftrl_op.cc
浏览文件 @ b4a32eaf
......@@ -34,6 +34,16 @@ class FTRLOp : public framework::OperatorWithKernel {
"Input(Grad) of FTRL should not be null.");
PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
"Input(LearningRate) of FTRL should not be null.");
PADDLE_ENFORCE(
ctx->GetInputsVarType("Param").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
PADDLE_ENFORCE(
ctx->GetInputsVarType("Grad").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Grad").front(), ctx->GetInputsVarType("Grad").front());
PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
"Output(ParamOut) of FTRL should not be null.");
......
paddle/fluid/operators/ftrl_op.h
浏览文件 @ b4a32eaf
......@@ -28,6 +28,17 @@ template <typename DeviceContext, typename T>
class FTRLOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const auto* param_var = ctx.InputVar("Param");
PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.Inputs("Param").front(), param_var->Type().name());
const auto* grad_var = ctx.InputVar("Grad");
PADDLE_ENFORCE(grad_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.Inputs("Grad").front(), grad_var->Type().name());
auto* param_out = ctx.Output<Tensor>("ParamOut");
auto* sq_accum_out = ctx.Output<Tensor>("SquaredAccumOut");
auto* lin_accum_out = ctx.Output<Tensor>("LinearAccumOut");
......
paddle/fluid/operators/math/algorithm.h 0 → 100644
浏览文件 @ b4a32eaf
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <algorithm>
#include <cstdint> // for int64_t
#include <numeric>
#include "paddle/fluid/platform/hostdevice.h"
namespace paddle {
namespace operators {
namespace math {
template <typename T>
HOSTDEVICE inline int64_t BinarySearch(const T *x, int64_t num, const T &val) {
int64_t beg = 0, end = num - 1;
while (beg <= end) {
auto mid = ((beg + end) >> 1);
if (x[mid] == val)
return mid;
else if (x[mid] < val)
beg = mid + 1;
else
end = mid - 1;
}
return -1;
}
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/selected_rows_functor.cc
浏览文件 @ b4a32eaf
......@@ -15,6 +15,7 @@ limitations under the License. */
#include <set>
#include <unordered_map>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
namespace paddle {
......
paddle/fluid/operators/math/selected_rows_functor.h
浏览文件 @ b4a32eaf
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <map>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
......
paddle/fluid/operators/math/sequence_pooling.cc
浏览文件 @ b4a32eaf
......@@ -12,9 +12,11 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/math/sequence_pooling.h"
#include <string>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/sequence_pooling.h"
namespace paddle {
namespace operators {
......@@ -180,6 +182,7 @@ class SequencePoolFunctor<platform::CPUDeviceContext, T> {
}
auto lod = input.lod()[0];
auto& place = *context.eigen_device();
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
Tensor in_t =
input.Slice(static_cast<int>(lod[i]), static_cast<int>(lod[i + 1]));
......@@ -191,7 +194,14 @@ class SequencePoolFunctor<platform::CPUDeviceContext, T> {
if (pooltype == "AVERAGE") {
out_e.device(place) = in_e.mean(Eigen::array<int, 1>({{0}}));
} else if (pooltype == "SUM") {
out_e.device(place) = in_e.sum(Eigen::array<int, 1>({{0}}));
if (h > 0) {
const T* in_data = in_t.data<T>();
T* out_data = out_t.mutable_data<T>(context.GetPlace());
blas.VCOPY(w, in_data, out_data);
for (int64_t r = 1; r != h; ++r) {
blas.AXPY(w, 1., in_data + r * w, out_data);
}
}
} else if (pooltype == "SQRT") {
out_e.device(place) = in_e.sum(Eigen::array<int, 1>({{0}})) /
std::sqrt(static_cast<T>(h));
......@@ -223,6 +233,7 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
}
auto lod = in_grad->lod()[0];
auto& place = *context.eigen_device();
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
auto in_g_t = in_grad->Slice(static_cast<int>(lod[i]),
static_cast<int>(lod[i + 1]));
......@@ -237,7 +248,11 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
if (pooltype == "AVERAGE") {
in_g_e.device(place) = (out_g_e / static_cast<T>(h)).broadcast(bcast);
} else if (pooltype == "SUM") {
in_g_e.device(place) = (out_g_e).broadcast(bcast);
const T* out_g_data = out_g_t.data<T>();
T* in_g_data = in_g_t.mutable_data<T>(context.GetPlace());
for (int r = 0; r != h; ++r) {
blas.VCOPY(w, out_g_data, in_g_data + r * w);
}
} else if (pooltype == "SQRT") {
in_g_e.device(place) =
(out_g_e / std::sqrt(static_cast<T>(h))).broadcast(bcast);
......
paddle/fluid/operators/momentum_op.cc
浏览文件 @ b4a32eaf
......@@ -33,6 +33,11 @@ class MomentumOp : public framework::OperatorWithKernel {
"Input(velocity) of Momentum should not be null.");
PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
"Input(LearningRate) of Momentum should not be null.");
PADDLE_ENFORCE(
ctx->GetInputsVarType("Param").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
"Output(ParamOut) of Momentum should not be null.");
......
paddle/fluid/operators/momentum_op.cu
浏览文件 @ b4a32eaf
......@@ -46,6 +46,17 @@ template <typename T>
class MomentumOpCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const auto* param_var = ctx.InputVar("Param");
PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.Inputs("Param").front(), param_var->Type().name());
const auto* grad_var = ctx.InputVar("Grad");
PADDLE_ENFORCE(grad_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.Inputs("Grad").front(), grad_var->Type().name());
auto param_out = ctx.Output<framework::Tensor>("ParamOut");
auto velocity_out = ctx.Output<framework::Tensor>("VelocityOut");
auto param = ctx.Input<framework::Tensor>("Param");
......
paddle/fluid/operators/momentum_op.h
浏览文件 @ b4a32eaf
......@@ -23,6 +23,12 @@ template <typename T>
class MomentumOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const auto* param_var = ctx.InputVar("Param");
PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.Inputs("Param").front(), param_var->Type().name());
auto param_out = ctx.Output<framework::Tensor>("ParamOut");
auto velocity_out = ctx.Output<framework::Tensor>("VelocityOut");
auto param = ctx.Input<framework::Tensor>("Param");
......
paddle/fluid/operators/reader/blocking_queue.h
浏览文件 @ b4a32eaf
......@@ -31,8 +31,8 @@ class BlockingQueue {
// is a workaround and a simplified version of framework::Channel as it
// doesn't support GPU and it implements on buffered blocking queue.
public:
explicit BlockingQueue(size_t capacity)
: capacity_(capacity), closed_(false) {
explicit BlockingQueue(size_t capacity, bool speed_test_mode = false)
: capacity_(capacity), speed_test_mode_(speed_test_mode), closed_(false) {
PADDLE_ENFORCE_GT(
capacity_, 0,
"The capacity of a reader::BlockingQueue must be greater than 0.");
......@@ -72,7 +72,9 @@ class BlockingQueue {
if (!queue_.empty()) {
PADDLE_ENFORCE_NOT_NULL(elem);
*elem = queue_.front();
queue_.pop_front();
if (LIKELY(!speed_test_mode_)) {
queue_.pop_front();
}
send_cv_.notify_one();
return true;
} else {
......@@ -114,6 +116,7 @@ class BlockingQueue {
private:
size_t capacity_;
bool speed_test_mode_;
bool closed_;
std::deque<T> queue_;
......
paddle/fluid/operators/reader/lod_tensor_blocking_queue.h
浏览文件 @ b4a32eaf
......@@ -33,8 +33,9 @@ class LoDTensorBlockingQueue {
private:
LoDTensorBlockingQueue(size_t capacity,
const std::vector<framework::DDim>& dims)
: queue_(capacity), dims_(dims) {}
const std::vector<framework::DDim>& dims,
bool speed_test_mode = false)
: queue_(capacity, speed_test_mode), dims_(dims) {}
public:
bool Push(const std::vector<framework::LoDTensor>& lod_tensor_vec) {
......@@ -69,11 +70,12 @@ class LoDTensorBlockingQueue {
class LoDTensorBlockingQueueHolder {
public:
void InitOnce(size_t capacity, const std::vector<framework::DDim>& dims) {
void InitOnce(size_t capacity, const std::vector<framework::DDim>& dims,
bool speed_test_mode = false) {
PADDLE_ENFORCE(
queue_ == nullptr,
"LoDTensorBlockingQueueHolder::InitOnce() can only be called once");
queue_.reset(new LoDTensorBlockingQueue(capacity, dims));
queue_.reset(new LoDTensorBlockingQueue(capacity, dims, speed_test_mode));
}
inline const std::shared_ptr<LoDTensorBlockingQueue>& GetQueue() const {
......
paddle/fluid/operators/reader/reader_blocking_queue_test.cc
浏览文件 @ b4a32eaf
......@@ -217,3 +217,27 @@ TEST(BlockingQueue, MyClassTest) {
q.Receive(&b);
EXPECT_EQ(a.val_, b.val_);
}
TEST(BlockingQueue, speed_test_mode) {
size_t queue_size = 10;
BlockingQueue<size_t> q1(queue_size, false);
for (size_t i = 0; i < queue_size; ++i) {
q1.Send(i);
}
size_t b;
for (size_t i = 0; i < queue_size; ++i) {
q1.Receive(&b);
EXPECT_EQ(b, i);
}
EXPECT_EQ(q1.Size(), 0);
BlockingQueue<size_t> q2(queue_size, true);
for (size_t i = 0; i < queue_size; ++i) {
q2.Send(i);
}
for (size_t i = 0; i < queue_size; ++i) {
q2.Receive(&b);
EXPECT_EQ(b, 0);
}
EXPECT_EQ(q2.Size(), queue_size);
}
paddle/fluid/operators/rmsprop_op.cc
浏览文件 @ b4a32eaf
......@@ -32,6 +32,11 @@ class RmspropOp : public framework::OperatorWithKernel {
"Input(Grad) of RmspropOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Moment"),
"Input(Moment) of RmspropOp should not be null.");
PADDLE_ENFORCE(
ctx->GetInputsVarType("Param").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
"Output(param_out) of RmspropOp should not be null.");
......
paddle/fluid/operators/rmsprop_op.h
浏览文件 @ b4a32eaf
......@@ -13,66 +13,254 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <math.h>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/algorithm.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
#include "paddle/fluid/platform/for_range.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T>
struct DenseRmspropGradFunctor {
inline explicit DenseRmspropGradFunctor(const T *grad) : grad_(grad) {}
HOSTDEVICE inline T operator()(int64_t idx) const { return grad_[idx]; }
const T *grad_;
};
template <typename T>
struct SparseRmspropGradFunctor {
inline SparseRmspropGradFunctor(const T *grad, const int64_t *rows,
int64_t row_numel, int64_t row_count)
: grad_(grad),
rows_(rows),
row_numel_(row_numel),
row_count_(row_count) {}
HOSTDEVICE inline T operator()(int64_t idx) const {
auto row_idx = math::BinarySearch(rows_, row_count_, idx / row_numel_);
return row_idx >= 0 ? grad_[row_idx * row_numel_ + idx % row_numel_] : 0;
}
const T *grad_;
const int64_t *rows_;
int64_t row_numel_;
int64_t row_count_;
};
template <typename T, typename GradFunctor>
struct UncenteredRmspropFunctor {
UncenteredRmspropFunctor(T *param, T *ms, T *mom, const T *lr, T rho,
T epsilon, T momentum,
const GradFunctor &grad_functor)
: param_(param),
ms_(ms),
mom_(mom),
lr_(lr),
rho_(rho),
epsilon_(epsilon),
momentum_(momentum),
grad_functor_(grad_functor) {}
HOSTDEVICE inline void operator()(int64_t idx) const {
T g = grad_functor_(idx);
T ms_out = rho_ * ms_[idx] + (1 - rho_) * g * g;
T mom_out = momentum_ * mom_[idx] + lr_[0] * g / sqrt(ms_out + epsilon_);
param_[idx] -= mom_out;
ms_[idx] = ms_out;
mom_[idx] = mom_out;
}
T *param_;
T *ms_;
T *mom_;
const T *lr_;
T rho_;
T epsilon_;
T momentum_;
GradFunctor grad_functor_;
};
template <typename T, typename GradFunctor>
struct CenteredRmspropFunctor {
CenteredRmspropFunctor(T *param, T *ms, T *mom, T *mean_grad, const T *lr,
T rho, T epsilon, T momentum,
const GradFunctor &grad_functor)
: param_(param),
ms_(ms),
mom_(mom),
mean_grad_(mean_grad),
lr_(lr),
rho_(rho),
epsilon_(epsilon),
momentum_(momentum),
grad_functor_(grad_functor) {}
HOSTDEVICE inline void operator()(int64_t idx) const {
T g = grad_functor_(idx);
T ms_out = rho_ * ms_[idx] + (1 - rho_) * g * g;
T mg_out = rho_ * mean_grad_[idx] + (1 - rho_) * g;
T mom_out = momentum_ * mom_[idx] +
lr_[0] * g / sqrt(ms_out - mg_out * mg_out + epsilon_);
param_[idx] -= mom_out;
ms_[idx] = ms_out;
mom_[idx] = mom_out;
mean_grad_[idx] = mg_out;
}
T *param_;
T *ms_;
T *mom_;
T *mean_grad_;
const T *lr_;
T rho_;
T epsilon_;
T momentum_;
GradFunctor grad_functor_;
};
template <typename DeviceContext, typename T>
class RmspropOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* param_out = ctx.Output<Tensor>("ParamOut");
auto* moment_out = ctx.Output<Tensor>("MomentOut");
auto* mean_square_out = ctx.Output<Tensor>("MeanSquareOut");
void Compute(const framework::ExecutionContext &ctx) const override {
using LoDTensor = framework::LoDTensor;
auto *grad_var = ctx.InputVar("Grad");
auto *param_out = ctx.Output<LoDTensor>("ParamOut");
auto *moment_out = ctx.Output<LoDTensor>("MomentOut");
auto *mean_square_out = ctx.Output<LoDTensor>("MeanSquareOut");
auto grad = ctx.Input<Tensor>("Grad");
auto epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
auto rho = static_cast<T>(ctx.Attr<float>("decay"));
auto momentum = static_cast<T>(ctx.Attr<float>("momentum"));
bool centered = ctx.Attr<bool>("centered");
param_out->mutable_data<T>(ctx.GetPlace());
moment_out->mutable_data<T>(ctx.GetPlace());
mean_square_out->mutable_data<T>(ctx.GetPlace());
auto &p_tensor = *ctx.Input<LoDTensor>("Param");
auto &ms_tensor = *ctx.Input<LoDTensor>("MeanSquare");
auto &lr_tensor = *ctx.Input<LoDTensor>("LearningRate");
auto &mom_tensor = *ctx.Input<LoDTensor>("Moment");
float epsilon = ctx.Attr<float>("epsilon");
float rho = ctx.Attr<float>("decay");
float momentum = ctx.Attr<float>("momentum");
bool centered = ctx.Attr<bool>("centered");
PADDLE_ENFORCE_EQ(&p_tensor, param_out,
"Param and ParamOut must be the same Tensor");
PADDLE_ENFORCE_EQ(&mom_tensor, moment_out,
"Moment and MomentOut must be the same Tensor");
PADDLE_ENFORCE_EQ(&ms_tensor, mean_square_out,
"MeanSquare and MeanSquareOut must be the same Tensor");
auto &dev_ctx = ctx.template device_context<DeviceContext>();
size_t limit = static_cast<size_t>(ms_tensor.numel());
if (grad_var->IsType<LoDTensor>()) {
auto &grad_tensor = grad_var->Get<LoDTensor>();
if (std::is_same<DeviceContext, platform::CPUDeviceContext>::value) {
auto &place =
*ctx.template device_context<DeviceContext>().eigen_device();
auto lr_value = lr_tensor.data<T>()[0];
auto p = EigenVector<T>::Flatten(p_tensor);
auto ms = EigenVector<T>::Flatten(ms_tensor);
auto g = EigenVector<T>::Flatten(grad_tensor);
auto mom = EigenVector<T>::Flatten(mom_tensor);
auto p_out = EigenVector<T>::Flatten(*param_out);
auto mom_out = EigenVector<T>::Flatten(*moment_out);
auto ms_out = EigenVector<T>::Flatten(*mean_square_out);
ms_out.device(place) = rho * ms + (1 - rho) * g * g;
if (centered) {
auto &mg_tensor = *ctx.Input<LoDTensor>("MeanGrad");
auto mg = EigenVector<T>::Flatten(mg_tensor);
auto *mean_grad_out = ctx.Output<LoDTensor>("MeanGradOut");
PADDLE_ENFORCE(&mg_tensor, mean_grad_out,
"MeanGrad and MeanGradOut must be the same Tensor");
auto mg_out = EigenVector<T>::Flatten(*mean_grad_out);
mg_out.device(place) = rho * mg + (1 - rho) * g;
mom_out.device(place) =
momentum * mom +
lr_value * g / (ms_out - mg_out.square() + epsilon).sqrt();
} else {
mom_out.device(place) =
momentum * mom + lr_value * g / (ms_out + epsilon).sqrt();
}
p_out.device(place) = p - mom_out;
} else {
DenseRmspropGradFunctor<T> grad_func(grad_tensor.data<T>());
platform::ForRange<DeviceContext> for_range(dev_ctx, limit);
if (centered) {
auto &mg_tensor = *ctx.Input<LoDTensor>("MeanGrad");
auto *mean_grad_out = ctx.Output<LoDTensor>("MeanGradOut");
PADDLE_ENFORCE(&mg_tensor, mean_grad_out,
"MeanGrad and MeanGradOut must be the same Tensor");
for_range(CenteredRmspropFunctor<T, DenseRmspropGradFunctor<T>>(
param_out->mutable_data<T>(ctx.GetPlace()),
mean_square_out->mutable_data<T>(ctx.GetPlace()),
moment_out->mutable_data<T>(ctx.GetPlace()),
mean_grad_out->mutable_data<T>(ctx.GetPlace()),
lr_tensor.data<T>(), rho, epsilon, momentum, grad_func));
} else {
for_range(UncenteredRmspropFunctor<T, DenseRmspropGradFunctor<T>>(
param_out->mutable_data<T>(ctx.GetPlace()),
mean_square_out->mutable_data<T>(ctx.GetPlace()),
moment_out->mutable_data<T>(ctx.GetPlace()), lr_tensor.data<T>(),
rho, epsilon, momentum, grad_func));
}
}
} else if (grad_var->IsType<framework::SelectedRows>()) {
auto &grad = grad_var->Get<framework::SelectedRows>();
auto *merged_grad = const_cast<framework::Scope &>(ctx.scope())
.Var()
->GetMutable<framework::SelectedRows>();
math::scatter::MergeAdd<DeviceContext, T> merge_func;
merge_func(dev_ctx, grad, merged_grad);
platform::ForRange<DeviceContext> for_range(dev_ctx, limit);
const int64_t *rows;
#ifdef PADDLE_WITH_CUDA
if (platform::is_gpu_place(ctx.GetPlace())) {
rows = merged_grad->rows().CUDAData(ctx.GetPlace());
} else {
#endif
rows = merged_grad->rows().data();
#ifdef PADDLE_WITH_CUDA
}
#endif
auto &merged_tensor = merged_grad->value();
int64_t row_count = merged_grad->rows().size();
int64_t row_numel = merged_tensor.numel() / row_count;
SparseRmspropGradFunctor<T> grad_func(merged_tensor.data<T>(), rows,
row_numel, row_count);
auto p = EigenVector<T>::Flatten(*ctx.Input<Tensor>("Param"));
auto ms = EigenVector<T>::Flatten(*ctx.Input<Tensor>("MeanSquare"));
auto lr = EigenVector<T>::Flatten(*ctx.Input<Tensor>("LearningRate"));
auto g = EigenVector<T>::Flatten(*grad);
auto mom = EigenVector<T>::Flatten(*ctx.Input<Tensor>("Moment"));
auto p_out = EigenVector<T>::Flatten(*param_out);
auto mom_out = EigenVector<T>::Flatten(*moment_out);
auto ms_out = EigenVector<T>::Flatten(*mean_square_out);
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
Eigen::DSizes<int, 1> grad_dsize(static_cast<int>(grad->numel()));
ms_out.device(place) = rho * ms + (1 - rho) * g * g;
if (centered) {
auto mg = EigenVector<T>::Flatten(*ctx.Input<Tensor>("MeanGrad"));
auto* mean_grad_out = ctx.Output<Tensor>("MeanGradOut");
mean_grad_out->mutable_data<T>(ctx.GetPlace());
auto mg_out = EigenVector<T>::Flatten(*mean_grad_out);
mg_out.device(place) = rho * mg + (1 - rho) * g;
mom_out.device(place) = momentum * mom +
lr.broadcast(grad_dsize) * g /
(ms_out - mg_out.square() + epsilon).sqrt();
if (centered) {
auto &mg_tensor = *ctx.Input<LoDTensor>("MeanGrad");
auto *mean_grad_out = ctx.Output<LoDTensor>("MeanGradOut");
PADDLE_ENFORCE(&mg_tensor, mean_grad_out,
"MeanGrad and MeanGradOut must be the same Tensor");
for_range(CenteredRmspropFunctor<T, SparseRmspropGradFunctor<T>>(
param_out->mutable_data<T>(ctx.GetPlace()),
mean_square_out->mutable_data<T>(ctx.GetPlace()),
moment_out->mutable_data<T>(ctx.GetPlace()),
mean_grad_out->mutable_data<T>(ctx.GetPlace()), lr_tensor.data<T>(),
rho, epsilon, momentum, grad_func));
} else {
for_range(UncenteredRmspropFunctor<T, SparseRmspropGradFunctor<T>>(
param_out->mutable_data<T>(ctx.GetPlace()),
mean_square_out->mutable_data<T>(ctx.GetPlace()),
moment_out->mutable_data<T>(ctx.GetPlace()), lr_tensor.data<T>(),
rho, epsilon, momentum, grad_func));
}
} else {
mom_out.device(place) =
momentum * mom +
lr.broadcast(grad_dsize) * g / (ms_out + epsilon).sqrt();
PADDLE_THROW("RMSProp only supports LoDTensor or SelectedRows gradient");
}
p_out.device(place) = p - mom_out;
}
};
......
paddle/fluid/operators/sgd_op.cc
浏览文件 @ b4a32eaf
......@@ -21,7 +21,7 @@ class SGDOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Param"),
"Input(Param) of SGDOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Grad"),
......@@ -42,7 +42,7 @@ class SGDOp : public framework::OperatorWithKernel {
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
const framework::ExecutionContext &ctx) const override {
auto data_type = framework::GetDataTypeOfVar(ctx.InputVar("Param"));
return framework::OpKernelType(data_type, ctx.device_context());
}
......@@ -50,17 +50,20 @@ class SGDOp : public framework::OperatorWithKernel {
class SGDOpInferVarType : public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc& op_desc,
framework::BlockDesc* block) const override {
auto input_var = op_desc.Input("Param")[0];
for (auto& out_var : op_desc.Output("ParamOut")) {
if (block->FindRecursiveOrCreateVar(input_var).GetType() ==
framework::proto::VarType::SELECTED_ROWS) {
block->FindRecursiveOrCreateVar(out_var).SetType(
framework::proto::VarType::SELECTED_ROWS);
} else {
block->FindRecursiveOrCreateVar(out_var).SetType(
framework::proto::VarType::LOD_TENSOR);
void operator()(const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
auto input_var_n = op_desc.Input("Param")[0];
auto in_var_type = block->FindRecursiveOrCreateVar(input_var_n).GetType();
PADDLE_ENFORCE(in_var_type == framework::proto::VarType::SELECTED_ROWS ||
in_var_type == framework::proto::VarType::LOD_TENSOR,
"The input Var's type should be LoDtensor or SelectedRows,"
" but the received var(%s)'s type is %s",
input_var_n, in_var_type);
for (auto &out_var_n : op_desc.Output("ParamOut")) {
auto &out_var = block->FindRecursiveOrCreateVar(out_var_n);
if (out_var.GetType() != in_var_type) {
out_var.SetType(in_var_type);
}
}
}
......
paddle/fluid/operators/sgd_op.cu
浏览文件 @ b4a32eaf
......@@ -56,6 +56,12 @@ template <typename T>
class SGDOpCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const auto* param_var = ctx.InputVar("Param");
PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.Inputs("Param").front(), param_var->Type().name());
auto* param = ctx.Input<framework::Tensor>("Param");
auto* param_out = ctx.Output<framework::Tensor>("ParamOut");
auto* learning_rate = ctx.Input<framework::Tensor>("LearningRate");
......
paddle/fluid/platform/device_context.cc
浏览文件 @ b4a32eaf
......@@ -198,9 +198,9 @@ class CudnnHolder {
CUDADeviceContext::CUDADeviceContext(CUDAPlace place)
: place_(place), cudnn_holder_(nullptr) {
SetDeviceId(place_.device);
compute_capability = GetCUDAComputeCapability(place_.device);
multi_process = GetCUDAMultiProcessors(place_.device);
max_threads_per_mp = GetCUDAMaxThreadsPerMultiProcessor(place_.device);
compute_capability_ = GetCUDAComputeCapability(place_.device);
multi_process_ = GetCUDAMultiProcessors(place_.device);
max_threads_per_mp_ = GetCUDAMaxThreadsPerMultiProcessor(place_.device);
PADDLE_ENFORCE(cudaStreamCreate(&stream_));
eigen_stream_.reset(new EigenCudaStreamDevice());
eigen_stream_->Reinitialize(&stream_, place);
......@@ -211,6 +211,16 @@ CUDADeviceContext::CUDADeviceContext(CUDAPlace place)
cudnn_holder_.reset(new CudnnHolder(&stream_, place));
}
driver_version_ = GetCUDADriverVersion(place_.device);
runtime_version_ = GetCUDARuntimeVersion(place_.device);
LOG(INFO) << "device: " << place_.device
<< ", CUDA Capability: " << compute_capability_
<< ", Driver Version: " << driver_version_ / 1000 << "."
<< (driver_version_ % 100) / 10
<< ", Runtime Version: " << runtime_version_ / 1000 << "."
<< (runtime_version_ % 100) / 10;
callback_manager_.reset(new StreamCallbackManager(stream_));
}
......@@ -232,11 +242,11 @@ void CUDADeviceContext::Wait() const {
}
int CUDADeviceContext::GetComputeCapability() const {
return compute_capability;
return compute_capability_;
}
int CUDADeviceContext::GetMaxPhysicalThreadCount() const {
return multi_process * max_threads_per_mp;
return multi_process_ * max_threads_per_mp_;
}
Eigen::GpuDevice* CUDADeviceContext::eigen_device() const {
......
paddle/fluid/platform/device_context.h
浏览文件 @ b4a32eaf
......@@ -135,9 +135,11 @@ class CUDADeviceContext : public DeviceContext {
cudaStream_t stream_;
cublasHandle_t cublas_handle_;
int compute_capability;
int multi_process;
int max_threads_per_mp;
int compute_capability_;
int runtime_version_;
int driver_version_;
int multi_process_;
int max_threads_per_mp_;
mutable std::mutex mtx_;
......
paddle/fluid/platform/enforce.h
浏览文件 @ b4a32eaf
......@@ -130,6 +130,13 @@ struct EOFException : public std::exception {
#define UNLIKELY(condition) (condition == 0)
#endif
#if !defined(_WIN32)
#define LIKELY(condition) __builtin_expect(static_cast<bool>(condition), 1)
#else
// there is no equivalent intrinsics in msvc.
#define LIKELY(condition) (condition != 0)
#endif
template <typename... Args>
inline typename std::enable_if<sizeof...(Args) != 0, void>::type throw_on_error(
bool stat, const Args&... args) {
......
paddle/fluid/platform/gpu_info.cc
浏览文件 @ b4a32eaf
......@@ -46,6 +46,24 @@ int GetCUDAComputeCapability(int id) {
return device_prop.major * 10 + device_prop.minor;
}
int GetCUDARuntimeVersion(int id) {
PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(), "id must less than GPU count");
int runtime_version = 0;
PADDLE_ENFORCE(cudaRuntimeGetVersion(&runtime_version),
"cudaRuntimeGetVersion failed in "
"paddle::platform::cudaRuntimeGetVersion");
return runtime_version;
}
int GetCUDADriverVersion(int id) {
PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(), "id must less than GPU count");
int driver_version = 0;
PADDLE_ENFORCE(cudaDriverGetVersion(&driver_version),
"cudaDriverGetVersion failed in "
"paddle::platform::GetCUDADriverVersion");
return driver_version;
}
int GetCUDAMultiProcessors(int id) {
PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(), "id must less than GPU count");
int count;
......
paddle/fluid/platform/gpu_info.h
浏览文件 @ b4a32eaf
......@@ -29,6 +29,12 @@ int GetCUDADeviceCount();
//! Get the compute capability of the ith GPU (format: major * 10 + minor)
int GetCUDAComputeCapability(int i);
//! Get the runtime version of the ith GPU
int GetCUDARuntimeVersion(int id);
//! Get the driver version of the ith GPU
int GetCUDADriverVersion(int id);
//! Get the MultiProcessors of the ith GPU.
int GetCUDAMultiProcessors(int i);
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ b4a32eaf
......@@ -57,6 +57,10 @@ limitations under the License. */
#include "pybind11/stl.h"
DEFINE_bool(reader_queue_speed_test_mode, false,
"If set true, the queue.pop will only get data from queue but not "
"remove the data from queue for speed testing");
// disable auto conversion to list in Python
PYBIND11_MAKE_OPAQUE(paddle::framework::LoDTensorArray);
......@@ -170,14 +174,14 @@ PYBIND11_PLUGIN(core) {
A LoDTensor X can look like the example below. It contains 2 sequences.
The first has length 2 and the second has length 3, as described by x.lod.
The first tensor dimension 6=2+3 is calculated from LoD if it's available.
The first tensor dimension 5=2+3 is calculated from LoD if it's available.
It means the total number of sequence element. In X, each element has 2
columns, hence [6, 2].
columns, hence [5, 2].
x.lod = [[2, 3]]
x.data = [[1, 2], [3, 4],
[5, 6], [7, 8], [9, 10], [11, 12]]
x.shape = [6, 2]
[5, 6], [7, 8], [9, 10]]
x.shape = [5, 2]
LoD can have multiple levels (for example, a paragraph can have multiple
sentences and a sentence can have multiple words). In the following
......@@ -380,7 +384,8 @@ All parameter, weight, gradient are variables in Paddle.
return make_ddim(shape);
});
auto *holder = var.GetMutable<LoDTensorBlockingQueueHolder>();
holder->InitOnce(capacity, dims);
holder->InitOnce(capacity, dims,
FLAGS_reader_queue_speed_test_mode);
return holder->GetQueue();
},
py::return_value_policy::copy);
......
paddle/fluid/train/demo/README.md
浏览文件 @ b4a32eaf
......@@ -15,7 +15,7 @@ cmake .. -DFLUID_INSTALL_DIR=$PADDLE_LIB \
-DWITH_MKL=OFF \
-DWITH_MKLDNN=OFF
make -j8
make -j8 inference_lib_dist
make -j8 fluid_lib_dist
```
### step 2. generate program desc
......
paddle/scripts/paddle_build.sh
浏览文件 @ b4a32eaf
......@@ -648,25 +648,25 @@ function gen_capi_package() {
fi
}
function gen_fluid_inference_lib() {
function gen_fluid_lib() {
mkdir -p ${PADDLE_ROOT}/build
cd ${PADDLE_ROOT}/build
if [[ ${WITH_C_API:-OFF} == "OFF" && ${WITH_INFERENCE:-ON} == "ON" ]] ; then
cat <<EOF
========================================
Generating fluid inference library ...
Generating fluid library for train and inference ...
========================================
EOF
cmake .. -DWITH_DISTRIBUTE=OFF
make -j `nproc` inference_lib_dist
make -j `nproc` fluid_lib_dist
fi
}
function tar_fluid_inference_lib() {
function tar_fluid_lib() {
if [[ ${WITH_C_API:-OFF} == "OFF" && ${WITH_INFERENCE:-ON} == "ON" ]] ; then
cat <<EOF
========================================
Taring fluid inference library ...
Taring fluid library for train and inference ...
========================================
EOF
cd ${PADDLE_ROOT}/build
......@@ -675,11 +675,11 @@ EOF
fi
}
function test_fluid_inference_lib() {
function test_fluid_lib() {
if [[ ${WITH_C_API:-OFF} == "OFF" && ${WITH_INFERENCE:-ON} == "ON" ]] ; then
cat <<EOF
========================================
Testing fluid inference library ...
Testing fluid library for inference ...
========================================
EOF
cd ${PADDLE_ROOT}/paddle/fluid/inference/api/demo_ci
......@@ -731,9 +731,9 @@ function main() {
;;
fluid_inference_lib)
cmake_gen ${PYTHON_ABI:-""}
gen_fluid_inference_lib
tar_fluid_inference_lib
test_fluid_inference_lib
gen_fluid_lib
tar_fluid_lib
test_fluid_lib
;;
check_style)
check_style
......@@ -744,8 +744,8 @@ function main() {
assert_api_not_changed ${PYTHON_ABI:-""}
run_test
gen_capi_package
gen_fluid_inference_lib
test_fluid_inference_lib
gen_fluid_lib
test_fluid_lib
assert_api_spec_approvals
;;
maccheck)
......
python/paddle/fluid/__init__.py
浏览文件 @ b4a32eaf
......@@ -113,7 +113,8 @@ def __bootstrap__():
'use_pinned_memory', 'check_nan_inf', 'benchmark', 'warpctc_dir',
'eager_delete_scope', 'use_mkldnn', 'initial_cpu_memory_in_mb',
'init_allocated_mem', 'free_idle_memory', 'paddle_num_threads',
"dist_threadpool_size", 'cpu_deterministic', 'eager_delete_tensor_gb'
'dist_threadpool_size', 'cpu_deterministic', 'eager_delete_tensor_gb',
'reader_queue_speed_test_mode'
]
if core.is_compiled_with_dist():
read_env_flags.append('rpc_deadline')
......
python/paddle/fluid/layers/nn.py
浏览文件 @ b4a32eaf
......@@ -107,6 +107,7 @@ __all__ = [
'log',
'crop',
'rank_loss',
'margin_rank_loss',
'elu',
'relu6',
'pow',
......@@ -5827,6 +5828,54 @@ def rank_loss(label, left, right, name=None):
return out
def margin_rank_loss(label, left, right, margin=0.1, name=None):
"""
Margin Ranking Loss Layer for ranking problem,
which compares left score and right score passed in.
The ranking loss can be defined as following equation:
.. math::
rank\_loss &= max(0, -label * (left - right) + margin)
Args:
label (Variable): Indicates whether the left is ranked higher than the right or not.
left (Variable): Ranking score for left.
right (Variable): Ranking score for right.
margin (float): Indicates the given margin.
name (str|None): A name for this layer (optional). If set None, the layer
will be named automatically.
Returns:
Variable: The ranking loss.
Raises:
ValueError: Any of label, left, and right is not a Variable.
Examples:
.. code-block:: python
label = fluid.layers.data(name="label", shape=[4, 1], dtype="float32")
left = fluid.layers.data(name="left", shape=[4, 1], dtype="float32")
right = fluid.layers.data(name="right", shape=[4, 1], dtype="float32")
out = fluid.layers.margin_rank_loss(label, left, right)
"""
helper = LayerHelper('margin_rank_loss', **locals())
if not isinstance(label, Variable):
raise ValueError("The label should be a Variable.")
if not isinstance(left, Variable):
raise ValueError("The left should be a Variable.")
if not isinstance(right, Variable):
raise ValueError("The right should be a Variable.")
out = helper.create_tmp_variable(left.dtype)
act = helper.create_tmp_variable(left.dtype)
helper.append_op(
type='margin_rank_loss',
inputs={"Label": label,
"X1": left,
"X2": right},
outputs={'Out': out,
'Activated': act},
attrs={'margin': margin})
return out
def pad2d(input,
paddings=[0, 0, 0, 0],
mode='constant',
......@@ -6290,6 +6339,7 @@ def sequence_enumerate(input, win_size, pad_value=0, name=None):
outputs={'Out': out},
attrs={'win_size': win_size,
'pad_value': pad_value})
return out
def sequence_mask(x, maxlen=None, dtype='int64', name=None):
......
python/paddle/fluid/optimizer.py
浏览文件 @ b4a32eaf
......@@ -659,6 +659,9 @@ class AdamaxOptimizer(Optimizer):
optimizer = fluid.optimizer.Adamax(learning_rate=0.2)
optimizer.minimize(cost)
Notes:
Currently, AdamaxOptimizer doesn't support sparse parameter optimization.
"""
_moment_acc_str = "moment"
_inf_norm_acc_str = "inf_norm"
......@@ -778,6 +781,9 @@ class DecayedAdagradOptimizer(Optimizer):
optimizer = fluid.optimizer.DecayedAdagrad(learning_rate=0.2)
optimizer.minimize(cost)
Notes:
Currently, DecayedAdagradOptimizer doesn't support sparse parameter optimization.
"""
_moment_acc_str = "moment"
......@@ -858,6 +864,9 @@ class AdadeltaOptimizer(Optimizer):
optimizer = fluid.optimizer.Adadelta(
learning_rate=0.0003, epsilon=1.0e-6, rho=0.95)
_, params_grads = optimizer.minimize(cost)
Notes:
Currently, AdadeltaOptimizer doesn't support sparse parameter optimization.
"""
_avg_squared_grad_acc_str = "_avg_squared_grad"
......@@ -1126,6 +1135,9 @@ class FtrlOptimizer(Optimizer):
optimizer = fluid.optimizer.Ftrl(0.0001)
_, params_grads = optimizer.minimize(cost)
Notes:
Currently, FtrlOptimizer doesn't support sparse parameter optimization.
"""
_squared_acc_str = "squared"
......
python/paddle/fluid/tests/unittests/test_rmsprop_op.py
浏览文件 @ b4a32eaf
......@@ -19,33 +19,76 @@ import unittest
import numpy as np
import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
def create_selected_rows_and_tensor(scope, place, height, row_num,
embedding_size):
sr = scope.var("@selected_rows@").get_selected_rows()
tensor = scope.var("grad").get_tensor()
rows = np.random.random_integers(
low=0, high=height - 1, size=[row_num, ]).astype('int64')
sr_val = np.random.random(size=[row_num, embedding_size]).astype('float32')
sr.set_height(height)
sr.set_rows(rows)
sr.get_tensor().set(sr_val, place)
tensor_val = np.zeros(shape=[height, embedding_size], dtype='float32')
for i in range(row_num):
row = rows[i]
tensor_val[row, :] = tensor_val[row, :] + sr_val[i, :]
tensor.set(tensor_val, place)
return tensor_val, sr_val
class TestBase(unittest.TestCase):
def setup(self, centered, epsilon=1e-6):
def setup(self,
place,
is_sparse,
centered,
size,
row_num=None,
epsilon=1e-6):
np.random.seed(5) # fix seed
self.scope = fluid.global_scope()
self.place = place
self.param_name = "param"
self.param = np.random.random((123, 321)).astype("float32")
self.param = np.random.random(size).astype("float32")
self.mean_square_name = "mean_square"
self.mean_square = np.random.random((123, 321)).astype("float32")
self.mean_square = np.random.uniform(
low=1, high=2, size=size).astype("float32")
self.mean_grad_name = "mean_grad"
self.mean_grad = np.random.random((123, 321)).astype("float32")
self.mean_grad = np.random.random(size).astype("float32")
self.lr_name = "lr"
self.learning_rate = np.array([0.01]).astype("float32")
self.grad_name = "grad"
self.grad = np.random.random((123, 321)).astype("float32")
self.is_sparse = is_sparse
if self.is_sparse:
self.grad_sr_name = "@selected_rows@"
self.grad, self.grad_sr = create_selected_rows_and_tensor(
self.scope, place, size[0], row_num, size[1])
else:
self.grad = np.random.random(size).astype("float32")
grad_tensor = self.scope.var(self.grad_name).get_tensor()
grad_tensor.set(self.grad, place)
self.moment_name = "moment"
self.moment = np.zeros((123, 321)).astype("float32")
self.moment = np.random.uniform(
low=0, high=1, size=size).astype("float32")
self.epsilon = epsilon
self.decay = 0.9
self.momentum = 0.0
self.momentum = 0.1
self.centered = centered
self.ms_out = self.decay * self.mean_square + (1 - self.decay
......@@ -61,118 +104,122 @@ class TestBase(unittest.TestCase):
self.param_out = self.param - self.moment_out
def check(self,
actual_t,
expect_t,
place,
out_name,
atol=1e-5,
equal_nan=False):
self.assertTrue(
np.allclose(
actual_t, expect_t, atol=atol, equal_nan=equal_nan),
"Output (" + out_name + ") has diff at " + str(place) + "\nExpect "
+ str(expect_t) + "\n" + "But Got" + str(actual_t))
class TestRmspropOp(TestBase):
def check_with_place(self, place, centered, epsilon):
self.setup(centered, epsilon)
scope = core.Scope()
# create and initialize Param Variable
param = scope.var(self.param_name).get_tensor()
param.set(self.param, place)
self.param_tensor = self.scope.var(self.param_name).get_tensor()
self.param_tensor.set(self.param, place)
mean_square = scope.var(self.mean_square_name).get_tensor()
mean_square.set(self.mean_square, place)
self.mean_square_tensor = self.scope.var(
self.mean_square_name).get_tensor()
self.mean_square_tensor.set(self.mean_square, place)
lr = scope.var(self.lr_name).get_tensor()
lr = self.scope.var(self.lr_name).get_tensor()
lr.set(self.learning_rate, place)
grad = scope.var(self.grad_name).get_tensor()
grad.set(self.grad, place)
self.moment_tensor = self.scope.var(self.moment_name).get_tensor()
self.moment_tensor.set(self.moment, place)
moment = scope.var(self.moment_name).get_tensor()
moment.set(self.moment, place)
if self.centered:
self.mean_grad_tensor = self.scope.var(
self.mean_grad_name).get_tensor()
self.mean_grad_tensor.set(self.mean_grad, place)
# create and run sgd operator
def check(self, actual_t, expect_t, place, out_name, atol=1e-5):
self.assertTrue(
np.allclose(
actual_t, expect_t, atol=atol),
"Output (" + out_name + ") has diff at " + str(place) + "\nExpect "
+ str(expect_t) + "\n" + "But Got" + str(actual_t))
if self.centered:
mean_grad = scope.var(self.mean_grad_name).get_tensor()
mean_grad.set(self.mean_grad, place)
rmsprop_op = Operator(
"rmsprop",
Param=self.param_name,
Grad=self.grad_name,
MeanSquare=self.mean_square_name,
MeanGrad=self.mean_grad_name,
Moment=self.moment_name,
LearningRate=self.lr_name,
ParamOut=self.param_name,
MeanSquareOut=self.mean_square_name,
MomentOut=self.moment_name,
MeanGradOut=self.mean_grad_name,
epsilon=self.epsilon,
decay=self.decay,
momentum=self.momentum,
centered=True)
else:
rmsprop_op = Operator(
"rmsprop",
Param=self.param_name,
Grad=self.grad_name,
MeanSquare=self.mean_square_name,
Moment=self.moment_name,
LearningRate=self.lr_name,
ParamOut=self.param_name,
MeanSquareOut=self.mean_square_name,
MomentOut=self.moment_name,
epsilon=self.epsilon,
decay=self.decay,
momentum=self.momentum,
centered=False)
rmsprop_op.run(scope, place)
atol = 1e-5
equal_nan = False
class TestRmspropOp(TestBase):
def check_with_place(self,
place,
is_sparse,
centered,
size,
row_num=None,
epsilon=1e-6):
self.setup(place, is_sparse, centered, size, row_num, epsilon)
self.run_and_check()
def run_and_check(self):
grad_name = self.grad_sr_name if self.is_sparse else self.grad_name
kwargs = {
'Param': self.param_name,
'Grad': grad_name,
'MeanSquare': self.mean_square_name,
'Moment': self.moment_name,
'LearningRate': self.lr_name,
'ParamOut': self.param_name,
'MeanSquareOut': self.mean_square_name,
'MomentOut': self.moment_name,
'epsilon': self.epsilon,
'decay': self.decay,
'momentum': self.momentum,
'centered': self.centered
}
if self.centered:
atol = 1e-3
equal_nan = True
kwargs['MeanGrad'] = self.mean_grad_name
kwargs['MeanGradOut'] = self.mean_grad_name
rmsprop_op = Operator('rmsprop', **kwargs)
atol = 1e-6
rmsprop_op.run(self.scope, self.place)
self.check(
np.array(mean_square), self.ms_out, place, self.mean_square_name)
np.array(self.mean_square_tensor),
self.ms_out,
self.place,
self.mean_square_name,
atol=atol)
self.check(
np.array(moment),
np.array(self.moment_tensor),
self.moment_out,
place,
self.place,
self.moment_name,
atol=atol,
equal_nan=equal_nan)
atol=atol)
self.check(
np.array(param),
np.array(self.param_tensor),
self.param_out,
place,
self.place,
self.param_name,
atol=atol,
equal_nan=equal_nan)
atol=atol)
if self.centered:
self.check(
np.array(mean_grad), self.mg_out, place, self.mean_grad_name)
np.array(self.mean_grad_tensor), self.mg_out, self.place,
self.mean_grad_name)
def test_rmsprop(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
size = (128, 320)
for place in places:
self.check_with_place(place, False, 1e-6)
self.check_with_place(place, False, 1e-10)
self.check_with_place(place, True, 1e-6)
self.check_with_place(place, True, 1e-10)
for centered in [False, True]:
with fluid.scope_guard(core.Scope()):
self.check_with_place(
place, is_sparse=False, centered=centered, size=size)
with fluid.scope_guard(core.Scope()):
self.check_with_place(
place,
is_sparse=True,
centered=centered,
row_num=512,
size=size)
with fluid.scope_guard(core.Scope()):
self.check_with_place(
place,
is_sparse=True,
centered=centered,
row_num=60,
size=size)
if __name__ == "__main__":
......
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