Skip to content

P
PaddleDetection

s920243400 / PaddleDetection
与 Fork 源项目一致

Fork自 PaddlePaddle / PaddleDetection

通知 2
Star 0
Fork 0
P
PaddleDetection
提交 706f3839 编写于 作者: Y yuyang18
浏览文件
操作

Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add-none-layers-api-doc

上级 fe5de04b 9988f8ec
隐藏空白更改
内联 并排
Showing with 855 addition and 236 deletion
cmake/external/mkldnn.cmake
浏览文件 @ 706f3839
......@@ -45,7 +45,8 @@ IF(${CBLAS_PROVIDER} STREQUAL "MKLML")
ELSE()
MESSAGE(FATAL_ERROR "Should enable MKLML when build MKLDNN")
ENDIF()
SET(MKLDNN_FLAG "-Wno-error=strict-overflow -Wno-error=unused-result -Wno-unused-result")
SET(MKLDNN_FLAG "-Wno-error=strict-overflow -Wno-error=unused-result")
SET(MKLDNN_FLAG "${MKLDNN_FLAG} -Wno-unused-result -Wno-unused-value")
SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} ${MKLDNN_FLAG}")
SET(MKLDNN_CXXFLAG "${CMAKE_CXX_FLAGS} ${MKLDNN_FLAG}")
ExternalProject_Add(
......
paddle/fluid/framework/executor.cc
浏览文件 @ 706f3839
......@@ -295,13 +295,14 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
std::unique_ptr<ExecutorPrepareContext> Executor::Prepare(
const ProgramDesc& program, int block_id) {
auto* ctx = new ExecutorPrepareContext(program, block_id);
std::unique_ptr<ExecutorPrepareContext> ctx(
new ExecutorPrepareContext(program, block_id));
PADDLE_ENFORCE_LT(static_cast<size_t>(block_id), program.Size());
auto& block = program.Block(block_id);
for (auto& op_desc : block.AllOps()) {
ctx->ops_.push_back(OpRegistry::CreateOp(*op_desc));
}
return std::unique_ptr<ExecutorPrepareContext>(ctx);
return ctx;
}
std::vector<std::shared_ptr<ExecutorPrepareContext>> Executor::Prepare(
......
paddle/fluid/operators/activation_mkldnn_op.cc
浏览文件 @ 706f3839
......@@ -12,16 +12,20 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "mkldnn.hpp"
#include "paddle/fluid/operators/activation_op.h"
#include "paddle/fluid/operators/mkldnn_activation_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
namespace paddle {
namespace operators {
using paddle::framework::Tensor;
using paddle::platform::MKLDNNDeviceContext;
using framework::DataLayout;
using framework::Tensor;
using mkldnn::memory;
using mkldnn::primitive;
using mkldnn::stream;
using platform::GetMKLDNNFormat;
using platform::MKLDNNDeviceContext;
using platform::to_void_cast;
namespace {
std::string gethash(const mkldnn::memory::dims &operand_dims,
......@@ -35,188 +39,260 @@ std::string gethash(const mkldnn::memory::dims &operand_dims,
};
return dim2str(operand_dims) + std::to_string(algorithm);
}
} // namespace
template <typename Functor>
class MKLDNNActivationKernel
: public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
const auto *x = ctx.Input<Tensor>("X");
PADDLE_ENFORCE(x->layout() == DataLayout::kMKLDNN &&
x->format() != memory::format::format_undef,
"Wrong layout/format set for Input x tensor");
Functor functor;
auto attrs = functor.GetAttrs();
for (auto &attr : attrs) {
*attr.second = ctx.Attr<float>(attr.first);
}
functor(ctx);
}
};
template <typename T, typename ExecContext>
void eltwise_forward(const ExecContext &ctx, mkldnn::algorithm algorithm,
const T alpha = 0, const T beta = 0) {
template <typename Functor>
class MKLDNNActivationGradKernel
: public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
const auto *diff_y = ctx.Input<Tensor>(framework::GradVarName("Out"));
PADDLE_ENFORCE(diff_y->layout() == DataLayout::kMKLDNN &&
diff_y->format() != memory::format::format_undef,
"Wrong layout/format set for Input OutGrad tensor");
Functor functor;
auto attrs = functor.GetAttrs();
for (auto &attr : attrs) {
*attr.second = ctx.Attr<float>(attr.first);
}
functor(ctx);
}
};
template <typename T>
void eltwise_forward(const framework::ExecutionContext &ctx,
mkldnn::algorithm algorithm, const T alpha = 0,
const T beta = 0) {
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
const auto &mkldnn_engine = dev_ctx.GetEngine();
// get buffers
const auto *src = ctx.template Input<Tensor>("X");
const auto *src_data = src->template data<T>();
const auto *x = ctx.Input<Tensor>("X");
auto *y = ctx.Output<Tensor>("Out");
auto *dst = ctx.template Output<Tensor>("Out");
T *dst_data = dst->template mutable_data<T>(ctx.GetPlace());
const T *x_data = x->data<T>();
T *y_data = y->mutable_data<T>(ctx.GetPlace());
// get memory dim
PADDLE_ENFORCE(src->dims().size() == 2 || src->dims().size() == 4,
PADDLE_ENFORCE(x->dims().size() == 2 || x->dims().size() == 4,
"Input dim must be with 2 or 4");
std::vector<int> src_tz = framework::vectorize2int(src->dims());
std::vector<int> src_tz = framework::vectorize2int(x->dims());
auto src_format =
src_tz.size() == 2 ? mkldnn::memory::format::nc : x->format();
const std::string key = gethash(src_tz, algorithm);
const std::string key_src_data =
key + ctx.op().Output("Out") + "@eltwise_fwd_src_data";
const std::string key_src_mem = key + "@eltwise_fwd_src_mem";
const std::string key_dst_mem = key + "@eltwise_fwd_dst_mem";
const std::string key_fwd = key + "@eltwise_fwd";
const std::string key_src_layout =
key + ctx.op().Output("Out") + "@eltwise_fwd_src_layout";
const std::string key_with_layout = key + std::to_string(src_format);
const std::string key_src_mem = key_with_layout + "@eltwise_fwd_src_mem";
const std::string key_dst_mem = key_with_layout + "@eltwise_fwd_dst_mem";
const std::string key_fwd = key_with_layout + "@eltwise_fwd";
const std::string key_fwd_pd = key_with_layout + "@eltwise_fwd_pd";
// save input data and layout to be referred in backward path
auto p_src_data = std::make_shared<const T *>(x_data);
dev_ctx.SetBlob(key_src_data, p_src_data);
auto p_src_layout = std::make_shared<memory::format>(src_format);
dev_ctx.SetBlob(key_src_layout, p_src_layout);
auto p_fwd = std::static_pointer_cast<mkldnn::eltwise_forward>(
dev_ctx.GetBlob(key_fwd));
// save input data to be referred in backward path
auto p_src_data = std::make_shared<const T *>(src_data);
dev_ctx.SetBlob(key_src_data, p_src_data);
std::shared_ptr<memory> dst_memory;
if (p_fwd == nullptr) {
// create memory description
auto data_md = src_tz.size() == 2
? platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nc)
: platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
// create memory primitives
auto p_src_mem = std::make_shared<mkldnn::memory>(mkldnn::memory(
{data_md, mkldnn_engine}, platform::to_void_cast(src_data)));
dev_ctx.SetBlob(key_src_mem, p_src_mem);
auto p_dst_mem = std::make_shared<mkldnn::memory>(mkldnn::memory(
{data_md, mkldnn_engine}, platform::to_void_cast(dst_data)));
dev_ctx.SetBlob(key_dst_mem, p_dst_mem);
auto fwd_desc = mkldnn::eltwise_forward::desc(
mkldnn::prop_kind::forward_training, algorithm, data_md, alpha, beta);
auto p_fwd_pd = std::make_shared<mkldnn::eltwise_forward::primitive_desc>(
fwd_desc, mkldnn_engine);
const std::string key_fwd_pd = key + "eltwise_fwd_pd";
dev_ctx.SetBlob(key_fwd_pd, p_fwd_pd);
p_fwd = std::make_shared<mkldnn::eltwise_forward>(
*p_fwd_pd, *(p_src_mem.get()), *(p_dst_mem.get()));
// create mkldnn memory for input X
auto src_md = platform::MKLDNNMemDesc(
src_tz, platform::MKLDNNGetDataType<T>(), src_format);
auto src_memory = std::shared_ptr<memory>(
new memory({src_md, mkldnn_engine}, to_void_cast(x_data)));
// save src_memory to be referred in backward path
dev_ctx.SetBlob(key_src_mem, src_memory);
// create primitive descriptor for activation forward and save it
auto forward_desc = mkldnn::eltwise_forward::desc(
mkldnn::prop_kind::forward_training, algorithm,
src_memory->get_primitive_desc().desc(), alpha, beta);
auto forward_pd = std::make_shared<mkldnn::eltwise_forward::primitive_desc>(
forward_desc, mkldnn_engine);
// save prim desc into global device context to be referred in backward path
dev_ctx.SetBlob(key_fwd_pd, forward_pd);
// create mkldnn memory for output y
dst_memory =
std::make_shared<memory>(forward_pd->dst_primitive_desc(), y_data);
dev_ctx.SetBlob(key_dst_mem, dst_memory);
// create activation primitive
p_fwd = std::make_shared<mkldnn::eltwise_forward>(*forward_pd, *src_memory,
*dst_memory);
dev_ctx.SetBlob(key_fwd, p_fwd);
} else {
// primitives already exist
auto p_src_mem =
auto src_memory =
std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_src_mem));
PADDLE_ENFORCE(p_src_mem != nullptr,
"Fail to find eltwise p_src_mem in device context.");
auto p_dst_mem =
PADDLE_ENFORCE(src_memory != nullptr,
"Fail to find eltwise src_memory in device context.");
dst_memory =
std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_dst_mem));
PADDLE_ENFORCE(p_dst_mem != nullptr,
"Fail to find eltwise p_src_mem in device context.");
PADDLE_ENFORCE(dst_memory != nullptr,
"Fail to find eltwise dst_memory in device context.");
p_src_mem->set_data_handle(platform::to_void_reinterpret_cast(src_data));
p_dst_mem->set_data_handle(dst_data);
src_memory->set_data_handle(platform::to_void_cast(x_data));
dst_memory->set_data_handle(y_data);
}
// push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline = {*(p_fwd.get())};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
std::vector<primitive> pipeline;
pipeline.push_back(*p_fwd);
stream(stream::kind::eager).submit(pipeline).wait();
y->set_layout(DataLayout::kMKLDNN);
y->set_format(GetMKLDNNFormat(*dst_memory));
}
template <typename T, typename ExecContext>
void eltwise_grad(const ExecContext &ctx, mkldnn::algorithm algorithm,
const T alpha = 0, const T beta = 0) {
template <typename T>
void eltwise_grad(const framework::ExecutionContext &ctx,
mkldnn::algorithm algorithm, const T alpha = 0,
const T beta = 0) {
auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
const auto &mkldnn_engine = dev_ctx.GetEngine();
// get buffers
const auto *out = ctx.template Input<Tensor>("Out");
auto *dout = ctx.template Input<Tensor>(framework::GradVarName("Out"));
const auto *diff_dst = dout->template data<T>();
const auto *diff_y = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto *diff_x = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *dx =
ctx.template Output<framework::Tensor>(framework::GradVarName("X"));
const T *diff_src = dx->template mutable_data<T>(ctx.GetPlace());
const T *diff_y_data = diff_y->data<T>();
T *diff_x_data = diff_x->mutable_data<T>(ctx.GetPlace());
// get memory dim
std::vector<int> src_tz = framework::vectorize2int(out->dims());
std::vector<int> diff_dst_tz = framework::vectorize2int(diff_y->dims());
const std::string key = gethash(src_tz, algorithm);
const std::string key_diff_src_mem = key + "@eltwise_diff_src_mem";
const std::string key_diff_dst_mem = key + "@eltwise_diff_dst_mem";
const std::string key_grad = key + "@eltwise_grad";
auto diff_y_format =
diff_dst_tz.size() == 2 ? mkldnn::memory::format::nc : diff_y->format();
const std::string key = gethash(diff_dst_tz, algorithm);
const std::string key_src_data =
key + ctx.op().Input("Out") + "@eltwise_fwd_src_data";
const std::string key_src_layout =
key + ctx.op().Input("Out") + "@eltwise_fwd_src_layout";
const auto p_src_layout =
std::static_pointer_cast<memory::format>(dev_ctx.GetBlob(key_src_layout));
const std::string key_src_mem =
key + std::to_string(*p_src_layout) + "@eltwise_fwd_src_mem";
const std::string key_fwd_pd =
key + std::to_string(*p_src_layout) + "@eltwise_fwd_pd";
const std::string key_with_layouts =
key + std::to_string(*p_src_layout) + "-" + std::to_string(diff_y_format);
const std::string key_diff_src_mem =
key_with_layouts + "@eltwise_diff_src_mem";
const std::string key_diff_dst_mem =
key_with_layouts + "@eltwise_diff_dst_mem";
const std::string key_grad = key_with_layouts + "@eltwise_grad";
const auto p_src_data =
std::static_pointer_cast<T *>(dev_ctx.GetBlob(key_src_data));
const std::string key_src_mem = key + "@eltwise_fwd_src_mem";
auto p_src_mem =
auto src_memory =
std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_src_mem));
p_src_mem->set_data_handle(*p_src_data.get());
PADDLE_ENFORCE(src_memory != nullptr,
"Fail to find src_memory in device context");
src_memory->set_data_handle(*p_src_data.get());
std::shared_ptr<memory> diff_src_memory;
auto p_grad = std::static_pointer_cast<mkldnn::eltwise_forward::primitive>(
auto p_grad = std::static_pointer_cast<mkldnn::eltwise_backward>(
dev_ctx.GetBlob(key_grad));
if (p_grad == nullptr) {
// create memory description
auto data_md = src_tz.size() == 2
? platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nc)
: platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
// create memory primitives
std::shared_ptr<void> p_diff_src_mem =
std::make_shared<mkldnn::memory>(mkldnn::memory(
{data_md, mkldnn_engine}, platform::to_void_cast(diff_src)));
dev_ctx.SetBlob(key_diff_src_mem, p_diff_src_mem);
std::shared_ptr<void> p_diff_dst_mem =
std::make_shared<mkldnn::memory>(mkldnn::memory(
{data_md, mkldnn_engine}, platform::to_void_cast(diff_dst)));
dev_ctx.SetBlob(key_diff_dst_mem, p_diff_dst_mem);
auto bwd_desc = mkldnn::eltwise_backward::desc(algorithm, data_md, data_md,
alpha, beta);
const std::string key_fwd_pd = key + "eltwise_fwd_pd";
auto *p_fwd_pd = static_cast<mkldnn::eltwise_forward::primitive_desc *>(
dev_ctx.GetBlob(key_fwd_pd).get());
auto eltwise_bwd_prim_desc = mkldnn::eltwise_backward::primitive_desc(
bwd_desc, mkldnn_engine, *p_fwd_pd);
// create mkldnn memory for input diff_y
auto diff_dst_md = platform::MKLDNNMemDesc(
diff_dst_tz, platform::MKLDNNGetDataType<T>(), diff_y_format);
auto diff_dst_memory = std::shared_ptr<memory>(
new memory({diff_dst_md, mkldnn_engine}, to_void_cast(diff_y_data)));
dev_ctx.SetBlob(key_diff_dst_mem, diff_dst_memory);
// retrieve eltwise primitive desc from device context
auto forward_pd =
std::static_pointer_cast<mkldnn::eltwise_forward::primitive_desc>(
dev_ctx.GetBlob(key_fwd_pd));
PADDLE_ENFORCE(forward_pd != nullptr,
"Fail to find eltwise_fwd_pd in device context");
// ceate primitive descriptor for activation backward
auto backward_desc = mkldnn::eltwise_backward::desc(
algorithm, diff_dst_memory->get_primitive_desc().desc(),
src_memory->get_primitive_desc().desc(), alpha, beta);
auto backward_pd = mkldnn::eltwise_backward::primitive_desc(
backward_desc, mkldnn_engine, *forward_pd);
// create mkldnn memory for output diff_src
diff_src_memory = std::make_shared<memory>(
backward_pd.diff_src_primitive_desc(), diff_x_data);
dev_ctx.SetBlob(key_diff_src_mem, diff_src_memory);
// create activation backward primitive
p_grad = std::make_shared<mkldnn::eltwise_backward>(
eltwise_bwd_prim_desc, *static_cast<mkldnn::memory *>(p_src_mem.get()),
*(static_cast<mkldnn::memory *>(p_diff_dst_mem.get())),
*(static_cast<mkldnn::memory *>(p_diff_src_mem.get())));
backward_pd, *src_memory, *diff_dst_memory, *diff_src_memory);
dev_ctx.SetBlob(key_grad, p_grad);
} else {
// primitives already exist
auto p_diff_src_mem = std::static_pointer_cast<mkldnn::memory>(
diff_src_memory = std::static_pointer_cast<mkldnn::memory>(
dev_ctx.GetBlob(key_diff_src_mem));
auto p_diff_dst_mem = std::static_pointer_cast<mkldnn::memory>(
auto diff_dst_memory = std::static_pointer_cast<mkldnn::memory>(
dev_ctx.GetBlob(key_diff_dst_mem));
p_diff_src_mem->set_data_handle(
platform::to_void_reinterpret_cast(diff_src));
p_diff_dst_mem->set_data_handle(
platform::to_void_reinterpret_cast(diff_dst));
diff_src_memory->set_data_handle(
platform::to_void_reinterpret_cast(diff_x_data));
diff_dst_memory->set_data_handle(
platform::to_void_reinterpret_cast(diff_y_data));
}
// push primitive to stream and wait until it's executed
std::vector<mkldnn::primitive> pipeline = {*(p_grad.get())};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
std::vector<primitive> pipeline;
pipeline.push_back(*p_grad);
stream(stream::kind::eager).submit(pipeline).wait();
diff_x->set_layout(DataLayout::kMKLDNN);
diff_x->set_format(GetMKLDNNFormat(*diff_src_memory));
}
} // anonymous namespace
template <typename T, mkldnn::algorithm algorithm>
struct MKLDNNActivationFunc : public BaseActivationFunctor<T> {
template <typename ExecContext>
void operator()(const ExecContext &ctx) const {
void operator()(const framework::ExecutionContext &ctx) const {
eltwise_forward<T>(ctx, algorithm);
}
};
template <typename T, mkldnn::algorithm algorithm>
struct MKLDNNActivationGradFunc : public BaseActivationFunctor<T> {
template <typename ExecContext>
void operator()(const ExecContext &ctx) const {
void operator()(const framework::ExecutionContext &ctx) const {
eltwise_grad<T>(ctx, algorithm);
}
};
......
paddle/fluid/operators/activation_op.cc
浏览文件 @ 706f3839
......@@ -19,18 +19,20 @@ limitations under the License. */
namespace paddle {
namespace operators {
#define REGISTER_ACTIVATION_OP_MAKER(OP_NAME, OP_COMMENT) \
class OP_NAME##OpMaker \
: public ::paddle::framework::OpProtoAndCheckerMaker { \
public: \
void Make() override { \
AddInput("X", "Input of " #OP_NAME " operator"); \
AddOutput("Out", "Output of " #OP_NAME " operator").Reuse("X"); \
AddAttr<bool>("use_mkldnn", \
"(default false) Only used in mkldnn kernel") \
.SetDefault(false); \
AddComment(OP_COMMENT); \
} \
using paddle::framework::Tensor;
#define REGISTER_ACTIVATION_OP_MAKER(OP_NAME, OP_COMMENT) \
class OP_NAME##OpMaker \
: public ::paddle::framework::OpProtoAndCheckerMaker { \
public: \
void Make() override { \
AddInput("X", "Input of " #OP_NAME " operator"); \
AddOutput("Out", "Output of " #OP_NAME " operator").Reuse("X"); \
AddAttr<bool>("use_mkldnn", \
"(bool, default false) Only used in mkldnn kernel") \
.SetDefault(false); \
AddComment(#OP_COMMENT); \
} \
}
#define REGISTER_ACTIVATION_OP_GRAD_MAKER(OP_NAME, KERNEL_TYPE) \
......@@ -58,7 +60,6 @@ framework::OpKernelType GetKernelType(const framework::ExecutionContext& ctx,
const framework::OperatorWithKernel& oper,
const std::string& name) {
framework::LibraryType library{framework::LibraryType::kPlain};
framework::DataLayout layout = framework::DataLayout::kAnyLayout;
#ifdef PADDLE_WITH_MKLDNN
auto it = oper.Attrs().find("use_mkldnn");
......@@ -82,6 +83,7 @@ class ActivationOp : public framework::OperatorWithKernel {
ctx->ShareLoD("X", /*->*/ "Out");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return GetKernelType(ctx, *this, "X");
......@@ -96,6 +98,7 @@ class ActivationOpGrad : public framework::OperatorWithKernel {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Out"));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return GetKernelType(ctx, *this, "Out");
......
paddle/fluid/operators/concat_op.h
浏览文件 @ 706f3839
......@@ -60,34 +60,45 @@ template <typename DeviceContext, typename T>
class ConcatGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* in = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* out_grad =
ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto out_var_names = ctx.Outputs(framework::GradVarName("X"));
auto outs = ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
// get output tensor that the name is not kEmptyVarName
std::vector<framework::Tensor*> outputs;
for (size_t j = 0; j < outs.size(); ++j) {
if (out_var_names[j] != framework::kEmptyVarName) {
outs[j]->mutable_data<T>(ctx.GetPlace());
outputs.push_back(outs[j]);
} else {
outputs.push_back(nullptr);
}
}
// Sometimes direct copies will be faster, this maybe need deeply analysis.
if (axis == 0 && outs.size() < 10) {
size_t input_offset = 0;
auto in_stride = framework::stride_numel(in->dims());
const auto in_stride = framework::stride_numel(out_grad->dims());
for (auto& out : outs) {
out->mutable_data<T>(ctx.GetPlace());
auto out_stride = framework::stride_numel(out->dims());
StridedNumelCopyWithAxis<T>(ctx.device_context(), axis, out->data<T>(),
out_stride, in->data<T>() + input_offset,
in_stride, out_stride[axis]);
for (size_t i = 0; i < outs.size(); ++i) {
auto out_stride = framework::stride_numel(ins[i]->dims());
auto* out = outputs[i];
if (out != nullptr) {
StridedNumelCopyWithAxis<T>(
ctx.device_context(), axis, out->data<T>(), out_stride,
out_grad->data<T>() + input_offset, in_stride, out_stride[axis]);
}
input_offset += out_stride[axis];
}
} else {
std::vector<framework::Tensor> outputs(outs.size());
for (size_t j = 0; j < outs.size(); ++j) {
outs[j]->mutable_data<T>(ctx.GetPlace());
outputs[j] = *outs[j];
}
auto& dev_ctx = ctx.template device_context<DeviceContext>();
paddle::operators::math::ConcatGradFunctor<DeviceContext, T>
concat_grad_functor;
concat_grad_functor(dev_ctx, *in, static_cast<int>(axis), &outputs);
concat_grad_functor(dev_ctx, *out_grad, ins, static_cast<int>(axis),
&outputs);
}
}
};
......
paddle/fluid/operators/detection_map_op.cc
浏览文件 @ 706f3839
......@@ -175,12 +175,12 @@ class DetectionMAPOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Detection mAP evaluate operator.
The general steps are as follows. First, calculate the true positive and
false positive according to the input of detection and labels, then
calculate the mAP evaluate value.
Supporting '11 point' and 'integral' mAP algorithm. Please get more information
from the following articles:
https://sanchom.wordpress.com/tag/average-precision/
https://arxiv.org/abs/1512.02325
false positive according to the input of detection and labels, then
calculate the mAP evaluate value.
Supporting '11 point' and 'integral' mAP algorithm. Please get more information
from the following articles:
https://sanchom.wordpress.com/tag/average-precision/
https://arxiv.org/abs/1512.02325
)DOC");
}
......
paddle/fluid/operators/math/concat.cc
浏览文件 @ 706f3839
......@@ -70,35 +70,40 @@ template <typename T>
class ConcatGradFunctor<platform::CPUDeviceContext, T> {
public:
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& input, const int axis,
std::vector<framework::Tensor>* outputs) {
const framework::Tensor& input,
const std::vector<const framework::Tensor*>& ref_inputs,
const int axis, std::vector<framework::Tensor*>* outputs) {
// TODO(zcd): Add input data validity checking
int num = outputs->size();
size_t num = outputs->size();
int input_rows = 1;
auto dim_0 = outputs->at(0).dims();
auto dim_0 = ref_inputs[0]->dims();
for (int i = 0; i < axis; ++i) {
input_rows *= dim_0[i];
}
int input_cols = 0;
std::vector<int64_t> output_cols(outputs->size());
for (int i = 0; i < num; ++i) {
int t_cols = outputs->at(i).numel() / input_rows;
for (size_t i = 0; i < num; ++i) {
int t_cols = ref_inputs[i]->numel() / input_rows;
input_cols += t_cols;
output_cols[i] = t_cols;
}
auto cpu_place = boost::get<platform::CPUPlace>(context.GetPlace());
// computation
for (int k = 0; k < input_rows; ++k) {
for (size_t k = 0; k < input_rows; ++k) {
const T* src_ptr = input.data<T>() + k * input_cols;
int col_idx = 0;
for (int j = 0; j < num; ++j) {
int col_len = output_cols[j];
T* dst_ptr = outputs->at(j).data<T>() + k * col_len;
memory::Copy(cpu_place, dst_ptr, cpu_place, src_ptr + col_idx,
sizeof(T) * col_len);
auto* out_tensor = outputs->at(j);
if (out_tensor != nullptr) {
T* dst_ptr = out_tensor->data<T>() + k * col_len;
memory::Copy(cpu_place, dst_ptr, cpu_place, src_ptr + col_idx,
sizeof(T) * col_len);
}
col_idx += col_len;
}
}
......
paddle/fluid/operators/math/concat.cu
浏览文件 @ 706f3839
......@@ -102,10 +102,12 @@ __global__ void KernelConcatGrad(const T* input_data, const int in_row,
int local_col = tid_x - curr_offset;
int segment_width = curr_col_offset - curr_offset;
T* output_ptr = outputs_data[curr_segment];
int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
for (; tid_y < in_row; tid_y += blockDim.y * gridDim.y)
output_ptr[tid_y * segment_width + local_col] =
input_data[tid_y * in_col + tid_x];
if (output_ptr != nullptr) {
int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
for (; tid_y < in_row; tid_y += blockDim.y * gridDim.y)
output_ptr[tid_y * segment_width + local_col] =
input_data[tid_y * in_col + tid_x];
}
}
}
......@@ -118,10 +120,12 @@ __global__ void KernelConcatGrad(const T* input_data, const int in_row,
int split = tid_x / fixed_out_col;
int in_offset = tid_x - split * fixed_out_col;
T* output_ptr = outputs_data[split];
int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
for (; tid_y < in_row; tid_y += blockDim.y * gridDim.y)
output_ptr[tid_y * fixed_out_col + in_offset] =
input_data[tid_y * in_col + tid_x];
if (output_ptr != nullptr) {
int tid_y = blockIdx.y * blockDim.y + threadIdx.y;
for (; tid_y < in_row; tid_y += blockDim.y * gridDim.y)
output_ptr[tid_y * fixed_out_col + in_offset] =
input_data[tid_y * in_col + tid_x];
}
}
}
......@@ -203,17 +207,18 @@ template <typename T>
class ConcatGradFunctor<platform::CUDADeviceContext, T> {
public:
void operator()(const platform::CUDADeviceContext& context,
const framework::Tensor& input, const int axis,
std::vector<framework::Tensor>* outputs) {
const framework::Tensor& input,
const std::vector<const framework::Tensor*>& ref_inputs,
const int axis, std::vector<framework::Tensor*>* outputs) {
// TODO(zcd): Add input data validity checking
int o_num = outputs->size();
int out_row = 1;
auto dim_0 = outputs->at(0).dims();
auto dim_0 = ref_inputs[0]->dims();
for (int i = 0; i < axis; ++i) {
out_row *= dim_0[i];
}
int out_col = outputs->at(0).numel() / out_row;
int out0_col = ref_inputs[0]->numel() / out_row;
int in_col = 0, in_row = out_row;
bool sameShape = true;
......@@ -223,13 +228,17 @@ 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 = outputs->at(i)->numel() / out_row;
if (sameShape) {
if (t_col != out_col) sameShape = false;
if (t_col != out0_col) sameShape = false;
}
in_col += t_col;
outputs_cols[i + 1] = in_col;
outputs_ptr[i] = outputs->at(i).data<T>();
if (outputs->at(i) != nullptr) {
outputs_ptr[i] = outputs->at(i)->data<T>();
} else {
outputs_ptr[i] = nullptr;
}
}
T** dev_out_gpu_data =
......@@ -255,7 +264,7 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
if (sameShape) {
KernelConcatGrad<<<grid_size, block_size, 0, context.stream()>>>(
input.data<T>(), in_row, in_col, out_col, dev_out_gpu_data);
input.data<T>(), in_row, in_col, out0_col, dev_out_gpu_data);
} else {
const int* dev_outs_col_data = outputs_cols.CUDAData(context.GetPlace());
KernelConcatGrad<<<grid_size, block_size, 0, context.stream()>>>(
......
paddle/fluid/operators/math/concat.h
浏览文件 @ 706f3839
......@@ -57,7 +57,8 @@ template <typename DeviceContext, typename T>
class ConcatGradFunctor {
public:
void operator()(const DeviceContext& context, const framework::Tensor& input,
const int axis, std::vector<framework::Tensor>* outputs);
const std::vector<const framework::Tensor*>& ref_inputs,
const int axis, std::vector<framework::Tensor*>* outputs);
};
} // namespace math
......
paddle/fluid/platform/cpu_info.cc
浏览文件 @ 706f3839
......@@ -28,9 +28,15 @@ DEFINE_double(fraction_of_cpu_memory_to_use, 1,
"Default use 100% of CPU memory for PaddlePaddle,"
"reserve the rest for page tables, etc");
DEFINE_uint64(
initial_cpu_memory_in_mb, 500,
"Default initial 500MB of CPU memory for PaddlePaddle, in MD unit.");
DEFINE_uint64(initial_cpu_memory_in_mb,
#ifdef PADDLE_WITH_MKLDNN
/* Aligned with mozga-intel, MKLDNN need at least 5000 MB
* to obtain the best performance*/
5000,
#else
500,
#endif
"Initial CPU memory for PaddlePaddle, in MD unit.");
DEFINE_double(
fraction_of_cuda_pinned_memory_to_use, 0.5,
......@@ -59,10 +65,7 @@ inline size_t CpuTotalPhysicalMemory() {
size_t CpuMaxAllocSize() {
// For distributed systems, it requires configuring and limiting
// the fraction of memory to use.
return std::min(
static_cast<size_t>(FLAGS_fraction_of_cpu_memory_to_use *
CpuTotalPhysicalMemory()),
static_cast<size_t>(FLAGS_initial_cpu_memory_in_mb * 1 << 20));
return FLAGS_fraction_of_cpu_memory_to_use * CpuTotalPhysicalMemory();
}
size_t CpuMinChunkSize() {
......@@ -71,8 +74,11 @@ size_t CpuMinChunkSize() {
}
size_t CpuMaxChunkSize() {
// Allow to allocate the maximum chunk size is roughly 3% of CPU memory.
return CpuMaxAllocSize() / 32;
// Allow to allocate the maximum chunk size is roughly 3% of CPU memory,
// or the initial_cpu_memory_in_mb.
return std::min(
static_cast<size_t>(CpuMaxAllocSize() / 32),
static_cast<size_t>(FLAGS_initial_cpu_memory_in_mb * 1 << 20));
}
size_t CUDAPinnedMaxAllocSize() {
......
paddle/testing/paddle_gtest_main.cc
浏览文件 @ 706f3839
......@@ -30,8 +30,9 @@ int main(int argc, char** argv) {
new_argv.push_back(
strdup("--tryfromenv=fraction_of_gpu_memory_to_use,use_pinned_memory"));
#else
new_argv.push_back(strdup("--tryfromenv=use_pinned_memory,use_mkldnn"));
new_argv.push_back(strdup("--undefok=use_mkldnn"));
new_argv.push_back(strdup(
"--tryfromenv=use_pinned_memory,use_mkldnn,initial_cpu_memory_in_mb"));
new_argv.push_back(strdup("--undefok=use_mkldnn,initial_cpu_memory_in_mb"));
#endif
int new_argc = static_cast<int>(new_argv.size());
char** new_argv_address = new_argv.data();
......
python/paddle/fluid/__init__.py
浏览文件 @ 706f3839
......@@ -117,7 +117,7 @@ def __bootstrap__():
read_env_flags = [
'use_pinned_memory', 'check_nan_inf', 'benchmark', 'warpctc_dir',
'eager_delete_scope', 'use_mkldnn'
'eager_delete_scope', 'use_mkldnn', 'initial_cpu_memory_in_mb'
]
if core.is_compiled_with_cuda():
read_env_flags += [
......
python/paddle/fluid/framework.py
浏览文件 @ 706f3839
......@@ -644,7 +644,13 @@ class Operator(object):
def set_attr(self, name, val):
self.attrs[name] = val
self.desc.set_attr(name, val)
if isinstance(val, Block):
self.desc.set_block_attr(name, val.desc)
elif isinstance(val, core.BlockDesc) or \
isinstance(val, core.ProgramDesc):
self.desc.set_serialized_attr(name, val.serialize_to_string())
else:
self.desc.set_attr(name, val)
@property
def attr_names(self):
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 706f3839
......@@ -16,7 +16,7 @@ All layers just related to the detection neural network.
"""
from layer_function_generator import generate_layer_fn
from layer_function_generator import autodoc
from layer_function_generator import autodoc, templatedoc
from ..layer_helper import LayerHelper
import tensor
import nn
......@@ -155,7 +155,7 @@ def detection_output(loc,
return nmsed_outs
@autodoc()
@templatedoc()
def detection_map(detect_res,
label,
class_num,
......@@ -166,6 +166,47 @@ def detection_map(detect_res,
input_states=None,
out_states=None,
ap_version='integral'):
"""
${comment}
Args:
detect_res: ${detect_res_comment}
label: ${label_comment}
class_num: ${class_num_comment}
background_label: ${background_label_comment}
overlap_threshold: ${overlap_threshold_comment}
evaluate_difficult: ${evaluate_difficult_comment}
has_state: ${has_state_comment}
input_states: If not None, It contains 3 elements:
1. pos_count ${pos_count_comment}.
2. true_pos ${true_pos_comment}.
3. false_pos ${false_pos_comment}.
out_states: If not None, it contains 3 elements.
1. accum_pos_count ${accum_pos_count_comment}.
2. accum_true_pos ${accum_true_pos_comment}.
3. accum_false_pos ${accum_false_pos_comment}.
ap_version: ${ap_type_comment}
Returns:
${map_comment}
Examples:
.. code-block:: python
detect_res = fluid.layers.data(
name='detect_res',
shape=[10, 6],
append_batch_size=False,
dtype='float32')
label = fluid.layers.data(
name='label',
shape=[10, 6],
append_batch_size=False,
dtype='float32')
map_out = fluid.layers.detection_map(detect_res, label, 21)
"""
helper = LayerHelper("detection_map", **locals())
def __create_var(type):
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 706f3839
......@@ -93,6 +93,7 @@ __all__ = [
'mean_iou',
'relu',
'log',
'crop',
]
......@@ -5003,3 +5004,101 @@ def mean_iou(input, label, num_classes):
},
attrs={"num_classes": num_classes})
return out_mean_iou, out_wrong, out_correct
def crop(x, shape=None, offsets=None, name=None):
"""
Crop input into output, as specified by offsets and shape.
.. code-block:: text
* Case 1:
Given
X = [[0, 1, 2, 0, 0]
[0, 3, 4, 0, 0]
[0, 0, 0, 0, 0]],
and
shape = [2, 2],
offsets = [0, 1],
output is:
Out = [[1, 2],
[3, 4]].
* Case 2:
Given
X = [[0, 1, 2, 5, 0]
[0, 3, 4, 6, 0]
[0, 0, 0, 0, 0]],
and shape is tensor
shape = [[0, 0, 0]
[0, 0, 0]]
and
offsets = [0, 1],
output is:
Out = [[1, 2, 5],
[3, 4, 6]].
Args:
x (Variable): The input tensor variable.
shape (Variable|list/tuple of integer): The output shape is specified
by `shape`, which can a Variable or a list/tupe of integer.
If a tensor Variable, it's rank must be the same as `x`. This way
is suitable for the case that the output shape may be changed each
iteration. If a list/tupe of integer, it's length must be the same
as the rank of `x`
offsets (Variable|list/tuple of integer|None): Specifies the copping
offsets at each dimension. It can be a Variable or or a list/tupe
of integer. If a tensor Variable, it's rank must be the same as `x`.
This way is suitable for the case that the offsets may be changed
each iteration. If a list/tupe of integer, it's length must be the
same as the rank of `x`. If None, the offsets are 0 at each
dimension.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The cropped tensor variable.
Raises:
ValueError: If shape is not a list, tuple or Variable.
Examples:
.. code-block:: python
x = fluid.layers.data(name="x", shape=[3, 5], dtype="float32")
y = fluid.layers.data(name="y", shape=[2, 3], dtype="float32")
crop = fluid.layers.crop(x, shape=y)
# or
z = fluid.layers.data(name="z", shape=[3, 5], dtype="float32")
crop = fluid.layers.crop(z, shape=[2, 3])
"""
helper = LayerHelper('crop', **locals())
if not (isinstance(shape, list) or isinstance(shape, tuple) or \
isinstance(shape, Variable)):
raise ValueError("The shape should be a list, tuple or Variable.")
if offsets is None:
offsets = [0] * len(x.shape)
out = helper.create_tmp_variable(x.dtype)
ipts = {'X': x}
attrs = {}
if isinstance(shape, Variable):
ipts['Y'] = shape
else:
attrs['shape'] = shape
if isinstance(offsets, Variable):
ipts['Offsets'] = offsets
else:
attrs['offsets'] = offsets
helper.append_op(
type='crop',
inputs=ipts,
outputs={'Out': out},
attrs=None if len(attrs) == 0 else attrs)
return out
python/paddle/fluid/optimizer.py
浏览文件 @ 706f3839
......@@ -26,10 +26,10 @@ from clip import append_gradient_clip_ops, error_clip_callback
from contextlib import contextmanager
__all__ = [
'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad',
'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad', 'Ftrl',
'SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer', 'AdamOptimizer',
'AdamaxOptimizer', 'DecayedAdagradOptimizer', 'AdadeltaOptimizer',
'RMSPropOptimizer', 'Adadelta', 'ModelAverage', 'Optimizer'
'AdamaxOptimizer', 'DecayedAdagradOptimizer', 'RMSPropOptimizer',
'FtrlOptimizer', 'Adadelta', 'ModelAverage', 'Optimizer', 'RMSPropOptimizer'
]
......@@ -794,7 +794,7 @@ class AdadeltaOptimizer(Optimizer):
E(dx_t^2) &= \\rho * E(dx_{t-1}^2) + (1-\\rho) * (-g*learning\\_rate)^2
Args:
learning_rate(float): global leraning rate
learning_rate(float): global learning rate
rho(float): rho in equation
epsilon(float): epsilon in equation
......@@ -895,9 +895,9 @@ class RMSPropOptimizer(Optimizer):
Args:
learning_rate(float): global leraning rate.
rho(float): rho is :math:`\\rho` in equation, set 0.95 by default.
epsilon(float): :math:`\\epsilon` in equation is smoothing term to
learning_rate(float): global learning rate.
rho(float): rho is :math: `\\rho` in equation, set 0.95 by default.
epsilon(float): :math: `\\epsilon` in equation is smoothing term to
avoid division by zero, set 1e-6 by default.
momentum(float): :math:`\\beta` in equation is the momentum term,
set 0.0 by default.
......@@ -976,6 +976,113 @@ class RMSPropOptimizer(Optimizer):
return rmsprop_op
class FtrlOptimizer(Optimizer):
"""
FTRL (Follow The Regularized Leader) Optimizer.
The paper that proposed Follow The Regularized Leader (FTRL):
(https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf)
.. math::
&new\_accum = squared\_accum + grad^2
&if (lr\_power == -0.5):
&\quad linear\_accum += grad - \\frac{\\sqrt{new\_accum} - \\sqrt{squared\_accum}}{learning\_rate * param}
&else:
&\quad linear\_accum += grad - \\frac{new\_accum^{-lr\_power} - accum^{-lr\_power}}{learning\_rate * param}
&x = l1 * sign(linear\_accum) - linear\_accum
&if (lr\_power == -0.5):
&\quad y = \\frac{\\sqrt{new\_accum}}{learning\_rate} + (2 * l2)
&\quad pre\_shrink = \\frac{x}{y}
&\quad param = (abs(linear\_accum) > l1).select(pre\_shrink, 0.0)
&else:
&\quad y = \\frac{new\_accum^{-lr\_power}}{learning\_rate} + (2 * l2)
&\quad pre\_shrink = \\frac{x}{y}
&\quad param = (abs(linear\_accum) > l1).select(pre\_shrink, 0.0)
&squared\_accum += grad^2
Args:
learning_rate (float|Variable): global learning rate.
l1 (float):
l2 (float):
lr_power (float):
Raises:
ValueError: If learning_rate, rho, epsilon, momentum are None.
Examples:
.. code-block:: python
optimizer = fluid.optimizer.Ftrl(0.0001)
_, params_grads = optimizer.minimize(cost)
"""
_squared_acc_str = "squared"
_linear_acc_str = "linear"
def __init__(self, learning_rate, l1=0.0, l2=0.0, lr_power=-0.5, **kwargs):
super(FtrlOptimizer, self).__init__(
learning_rate=learning_rate, **kwargs)
if learning_rate is None:
raise ValueError("learning_rate is not set.")
self.type = "ftrl"
self._l1 = l1
self._l2 = l2
self._lr_power = lr_power
def _create_accumulators(self, block, parameters):
if not isinstance(block, framework.Block):
raise TypeError("block is not instance of framework.Block.")
for p in parameters:
self._add_accumulator(self._squared_acc_str, p)
self._add_accumulator(self._linear_acc_str, p)
def _append_optimize_op(self, block, param_and_grad):
if not isinstance(block, framework.Block):
raise TypeError("block is not instance of framework.Block.")
squared_acc = self._get_accumulator(self._squared_acc_str,
param_and_grad[0])
linear_acc = self._get_accumulator(self._linear_acc_str,
param_and_grad[0])
ftrl_op = block.append_op(
type=self.type,
inputs={
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"SquaredAccumulator": squared_acc,
"LinearAccumulator": linear_acc,
"LearningRate": self._create_param_lr(param_and_grad),
},
outputs={
"ParamOut": param_and_grad[0],
"SquaredAccumOut": squared_acc,
"LinearAccumOut": linear_acc
},
attrs={"l1": self._l1,
"l2": self._l1,
"lr_power": self._lr_power})
return ftrl_op
# We short the class name, since users will use the optimizer with the package
# name. The sample code:
#
......@@ -992,6 +1099,7 @@ Adamax = AdamaxOptimizer
DecayedAdagrad = DecayedAdagradOptimizer
Adadelta = AdadeltaOptimizer
RMSProp = RMSPropOptimizer
Ftrl = FtrlOptimizer
class ModelAverage(Optimizer):
......
python/paddle/fluid/profiler.py
浏览文件 @ 706f3839
......@@ -42,6 +42,9 @@ def cuda_profiler(output_file, output_mode=None, config=None):
counters/options for profiling by `config` argument. The default config
is ['gpustarttimestamp', 'gpustarttimestamp', 'gridsize3d',
'threadblocksize', 'streamid', 'enableonstart 0', 'conckerneltrace'].
Then users can use NVIDIA Visual Profiler
(https://developer.nvidia.com/nvidia-visual-profiler) tools to load this
this output file to visualize results.
Args:
output_file (string) : The output file name, the result will be
......@@ -50,6 +53,33 @@ def cuda_profiler(output_file, output_mode=None, config=None):
Comma separated values format. It should be 'kvp' or 'csv'.
config (list of string) : The profiler options and counters can refer
to "Compute Command Line Profiler User Guide".
Raises:
ValueError: If `output_mode` is not in ['kvp', 'csv'].
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
epoc = 8
dshape = [4, 3, 28, 28]
data = fluid.layers.data(name='data', shape=[3, 28, 28], dtype='float32')
conv = fluid.layers.conv2d(data, 20, 3, stride=[1, 1], padding=[1, 1])
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
output_file = 'cuda_profiler.txt'
with profiler.cuda_profiler(output_file, 'csv') as nvprof:
for i in range(epoc):
input = np.random.random(dshape).astype('float32')
exe.run(fluid.default_main_program(), feed={'data': input})
# then use NVIDIA Visual Profiler (nvvp) to load this output file
# to visualize results.
"""
if output_mode is None:
output_mode = 'csv'
......@@ -69,19 +99,52 @@ def cuda_profiler(output_file, output_mode=None, config=None):
def reset_profiler():
"""The profiler clear interface.
reset_profiler will clear the previous time record.
"""
Clear the previous time record. This interface does not work for
`fluid.profiler.cuda_profiler`, it only works for
`fluid.profiler.start_profiler`, `fluid.profiler.stop_profiler`,
and `fluid.profiler.profiler`.
Examples:
.. code-block:: python
import paddle.fluid.profiler as profiler
with profiler.profiler(state, 'total', '/tmp/profile'):
for iter in range(10):
if iter == 2:
profiler.reset_profiler()
# ...
"""
core.reset_profiler()
def start_profiler(state):
"""Enable the profiler.
"""
Enable the profiler. Uers can use `fluid.profiler.start_profiler` and
`fluid.profiler.stop_profiler` to insert the code, except the usage of
`fluid.profiler.profiler` interface.
Args:
state (string) : The profiling state, which should be 'CPU', 'GPU'
or 'All'. 'CPU' means only profile CPU. 'GPU' means profiling
GPU as well. 'All' also generates timeline.
Raises:
ValueError: If `state` is not in ['CPU', 'GPU', 'All'].
Examples:
.. code-block:: python
import paddle.fluid.profiler as profiler
profiler.start_profiler('GPU')
for iter in range(10):
if iter == 2:
profiler.reset_profiler()
# except each iteration
profiler.stop_profiler('total', '/tmp/profile')
"""
if core.is_profiler_enabled():
return
......@@ -97,7 +160,10 @@ def start_profiler(state):
def stop_profiler(sorted_key=None, profile_path='/tmp/profile'):
"""Stop the profiler.
"""
Stop the profiler. Uers can use `fluid.profiler.start_profiler` and
`fluid.profiler.stop_profiler` to insert the code, except the usage of
`fluid.profiler.profiler` interface.
Args:
sorted_key (string) : If None, the profiling results will be printed
......@@ -111,6 +177,23 @@ def stop_profiler(sorted_key=None, profile_path='/tmp/profile'):
The `ave` means sorting by the average execution time.
profile_path (string) : If state == 'All', it will write a profile
proto output file.
Raises:
ValueError: If `sorted_key` is not in
['calls', 'total', 'max', 'min', 'ave'].
Examples:
.. code-block:: python
import paddle.fluid.profiler as profiler
profiler.start_profiler('GPU')
for iter in range(10):
if iter == 2:
profiler.reset_profiler()
# except each iteration
profiler.stop_profiler('total', '/tmp/profile')
"""
if not core.is_profiler_enabled():
return
......@@ -137,7 +220,12 @@ def profiler(state, sorted_key=None, profile_path='/tmp/profile'):
Different from cuda_profiler, this profiler can be used to profile both CPU
and GPU program. By defalut, it records the CPU and GPU operator kernels,
if you want to profile other program, you can refer the profiling tutorial
to add more records.
to add more records in C++ code.
If the state == 'All', a profile proto file will be written to
`profile_path`. This file records timeline information during the execution.
Then users can visualize this file to see the timeline, please refer
https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/howto/optimization/timeline.md
Args:
state (string) : The profiling state, which should be 'CPU' or 'GPU',
......@@ -156,6 +244,25 @@ def profiler(state, sorted_key=None, profile_path='/tmp/profile'):
The `ave` means sorting by the average execution time.
profile_path (string) : If state == 'All', it will write a profile
proto output file.
Raises:
ValueError: If `state` is not in ['CPU', 'GPU', 'All']. If `sorted_key` is
not in ['calls', 'total', 'max', 'min', 'ave'].
Examples:
.. code-block:: python
import paddle.fluid.profiler as profiler
with profiler.profiler('All', 'total', '/tmp/profile') as prof:
for pass_id in range(pass_num):
for batch_id, data in enumerate(train_reader()):
exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[],
use_program_cache=True)
# ...
"""
start_profiler(state)
yield
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 706f3839
......@@ -401,6 +401,15 @@ class TestBook(unittest.TestCase):
self.assertIsNotNone(output)
print(str(program))
def test_maxout(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[3, 5], dtype="float32")
y = layers.data(name='y', shape=[2, 3], dtype="float32")
output = layers.crop(x, shape=y)
self.assertIsNotNone(output)
print(str(program))
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_optimizer.py
浏览文件 @ 706f3839
......@@ -434,5 +434,71 @@ class TestDecayedAdagradOptimizer(unittest.TestCase):
self.assertAlmostEqual(init_ops[1].attr('value'), 0.0)
class TestFtrlOptimizer(unittest.TestCase):
class MockFtrl(optimizer.FtrlOptimizer):
def get_accumulators(self):
return self._accumulators
def get_squared_str(self):
return self._squared_acc_str
def get_linear_str(self):
return self._linear_acc_str
def test_ftrl_optimizer(self):
init_program = framework.Program()
program = framework.Program()
block = program.global_block()
mul_x = block.create_parameter(
dtype="float32",
shape=[5, 10],
lod_level=0,
name="mul.x",
optimize_attr={'learning_rate': 1.1})
mul_y = block.create_var(
dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
mul_out = block.create_var(
dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
block.append_op(
type="mul",
inputs={"X": mul_x,
"Y": mul_y},
outputs={"Out": mul_out},
attrs={"x_num_col_dims": 1})
mean_out = block.create_var(
dtype="float32", shape=[1], lod_level=0, name="mean.out")
block.append_op(
type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
learning_rate = 0.01
ftrl_optimizer = self.MockFtrl(
learning_rate=learning_rate, l1=0.0, l2=0.0, lr_power=-0.5)
params_grads = append_backward(mean_out)
self.assertEqual(len(params_grads), 1)
self.assertEqual(len(ftrl_optimizer.get_accumulators()), 0)
opts = ftrl_optimizer.create_optimization_pass(params_grads, mul_out,
init_program)
self.assertEqual(len(opts), 3)
self.assertEqual([op.type for op in opts],
["fill_constant", "elementwise_mul", "ftrl"])
# Check accumulators
accumulators = ftrl_optimizer.get_accumulators()
self.assertEqual(len(accumulators), 2)
self.assertTrue(ftrl_optimizer.get_squared_str() in accumulators)
self.assertTrue(ftrl_optimizer.get_linear_str() in accumulators)
squared_acc = accumulators[ftrl_optimizer.get_squared_str()]
linear_acc = accumulators[ftrl_optimizer.get_linear_str()]
self.assertEqual(len(squared_acc), 1)
self.assertEqual(len(linear_acc), 1)
self.assertTrue(mul_x.name in squared_acc)
self.assertTrue(mul_x.name in linear_acc)
# Check init_program
init_ops = init_program.global_block().ops
self.assertEqual(len(init_ops), 3)
self.assertEqual(init_ops[0].type, "fill_constant")
self.assertAlmostEqual(init_ops[0].attr('value'), learning_rate)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 706f3839
......@@ -24,7 +24,7 @@ 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".
3. modify trainer program add split_op to each grad variable.
4. append send_op to send splited variables to server and
4. append send_op to send splited variables to server and
5. add recv_op to fetch params(splited blocks or origin param) from server.
6. append concat_op to merge splited blocks to update local weights.
......@@ -44,7 +44,7 @@ import numpy as np
from ps_dispatcher import RoundRobin, HashName, PSDispatcher
from .. import core, framework
from ..framework import Program, default_main_program, \
default_startup_program, \
default_startup_program, Block, \
Variable, Parameter, grad_var_name
from details import *
......@@ -471,7 +471,7 @@ class DistributeTranspiler:
self._append_pserver_ops(block, op, endpoint, grad_to_block_id,
self.origin_program, merged_var)
else:
self._append_pserver_non_opt_ops(block, op, endpoint)
self._append_pserver_non_opt_ops(block, op)
def __op_have_grad_input__(op):
for varname in op.input_arg_names:
......@@ -479,13 +479,39 @@ class DistributeTranspiler:
return varname
return ""
def __clone_lr_op_sub_block__(op, program, new_block):
if not op.has_attr('sub_block'):
return
origin_block_desc = op.attr('sub_block')
origin_block = self.origin_program.block(origin_block_desc.id)
assert isinstance(origin_block, Block)
# we put the new sub block to new block to follow the block
# hierarchy of the original blocks
new_sub_block = program.create_block(new_block.idx)
# clone vars
for var in origin_block.vars:
new_sub_block.clone_variable(var)
# clone ops
for op in origin_block.ops:
self._clone_lr_op(program, new_sub_block, op)
# clone sub_block of op
__clone_lr_op_sub_block__(op, program, new_sub_block)
# reset the block of op
op.set_attr('sub_block', new_sub_block)
# append lr decay ops to the child block if exists
lr_ops = self._get_lr_ops()
if len(lr_ops) > 0:
lr_decay_block = pserver_program.create_block(
pserver_program.num_blocks - 1)
for _, op in enumerate(lr_ops):
self._append_pserver_non_opt_ops(lr_decay_block, op, endpoint)
self._append_pserver_non_opt_ops(lr_decay_block, op)
# append sub blocks to pserver_program in lr_decay_op
__clone_lr_op_sub_block__(op, pserver_program, lr_decay_block)
# append op to the current block
grad_to_block_id = []
......@@ -1116,7 +1142,29 @@ class DistributeTranspiler:
break
return grad_block
def _append_pserver_non_opt_ops(self, optimize_block, opt_op, endpoint):
def _clone_lr_op(self, program, block, op):
inputs = self._get_input_map_from_op(
self.origin_program.global_block().vars, op)
for key, varlist in inputs.iteritems():
if not isinstance(varlist, list):
varlist = [varlist]
for var in varlist:
if var not in program.global_block().vars:
block.clone_variable(var)
outputs = self._get_output_map_from_op(
self.origin_program.global_block().vars, op)
for key, varlist in outputs.iteritems():
if not isinstance(varlist, list):
varlist = [varlist]
for var in varlist:
if var not in program.global_block().vars:
block.clone_variable(var)
block.append_op(
type=op.type, inputs=inputs, outputs=outputs, attrs=op.attrs)
def _append_pserver_non_opt_ops(self, optimize_block, opt_op):
program = optimize_block.program
# Append the ops for parameters that do not need to be optimized/updated
inputs = self._get_input_map_from_op(
......
python/paddle/fluid/transpiler/inference_transpiler.py
浏览文件 @ 706f3839
......@@ -19,16 +19,30 @@ from ..executor import global_scope
class InferenceTranspiler:
'''
Convert the fluid program to optimized inference program.
There are several optimizations, only fuse batch normalization is supported now.
Examples:
.. code-block:: python
# As InferenceTranspiler will modify the original program,
# please clone before use it.
inference_transpiler_program = program.clone()
t = fluid.InferenceTranspiler()
t.transpile(inference_transpiler_program, place)
'''
def transpile(self, program, place, scope=None):
'''
Transpile the program. Support only fuse batch normalization now.
:param program: program to transpile
:type program: Program
:param place: inference place
:type place: Place
:param scope: inference scope
:type scope: Scope or None
Run the transpiler.
Args:
program (Program): program to transpile
place (Place): inference place
scope (Scope|None): inference Scope
'''
if not isinstance(program, Program):
raise TypeError("program should be as Program type")
......@@ -49,36 +63,43 @@ class InferenceTranspiler:
can be integrated with them. Doing so will give us a forward acceleration,
especially in environments like mobile or embedded.
For input X:
- Conv process: X = input * W + bias
- Batch norm process: X' = (X - mean) / std
- Scale Process: Y = a * X' + b
For input :math:`X`:
- Conv process: :math:`X = input * W + bias`
- Batch norm process: :math:`X' = (X - mean) / std`
- Scale Process: :math:`Y = a * X' + b`
After fuse into one operation:
Y = (input * W + bias - mean) / std * a + b
= input * a * W / std + ((bias - mean) / std * a + b)
.. math::
Y &= (input * W + bias - mean) / std * a + b \\\\
&= input * a * W / std + ((bias - mean) / std * a + b)
The operator transformation is:
- before:
- conv->batch_norm->any_other_op (bias == 0)
- conv->elementwise_add->batch_norm->any_other_op (bias != 0)
- after:
- conv->elementwise_add->any_other_op
The transpile stages are:
1. insert elementwise_add op when bias == 0.
2. fuse the batch_norm's parameters to conv and elementwise_add operators.
3. remove batch_norm ops which are not used in any other ops.
4. adjust the input of any_other_op to be the output of elementwise_add operator.
5. remove unused variables.
:param program: program to transpile
:type program: Program
:param place: inference place
:type place: Place
:param scope: inference scope
:type scope: Scope
Args:
program (Program): program to transpile
place (Place): inference place
scope (Scope): inference Scope
'''
self.scope = scope
self.place = place
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或