merger paddle develop

port
tangwei12 7 years ago
commit fb7e479188

@ -76,7 +76,8 @@ RUN easy_install -U pip && \
pip install sphinx-rtd-theme==0.1.9 recommonmark
RUN pip install pre-commit 'ipython==5.3.0' && \
pip install 'ipykernel==4.6.0' 'jupyter==1.0.0'
pip install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip install opencv-python
#For docstring checker
RUN pip install pylint pytest astroid isort

@ -54,7 +54,7 @@ ExternalProject_Add(
${EXTERNAL_PROJECT_LOG_ARGS}
DEPENDS ${MKLDNN_DEPENDS}
GIT_REPOSITORY "https://github.com/01org/mkl-dnn.git"
GIT_TAG "db3424ad44901513c03a1ea31ccaacdf633fbe9f"
GIT_TAG "a29d8487a63afca3d5b8c5bbdbb473cf8ccc6e51"
PREFIX ${MKLDNN_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}

@ -213,3 +213,12 @@ virtualenv本身也是Python的一个包可以用pip进行安装
保存并关闭文件。
这样每次打开终端时就会自动启动名为paddle的Python环境了。
10. 通过pip安装的PaddlePaddle在 :code:`import paddle.fluid` 报找不到 :code:`libmkldnn.so`:code:`libmklml_intel.so`
------------------------------------------------------------------------------------------
出现这种问题的原因是在导入 :code:`paddle.fluid` 时需要加载 :code:`libmkldnn.so`:code:`libmklml_intel.so`
但是系统没有找到该文件。一般通过pip安装PaddlePaddle时会将 :code:`libmkldnn.so`:code:`libmklml_intel.so`
拷贝到 :code:`/usr/local/lib` 路径下,所以解决办法是将该路径加到 :code:`LD_LIBRARY_PATH` 环境变量下,
即: :code:`export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH`
**注意**如果是在虚拟环境中安装PaddlePaddle :code:`libmkldnn.so`:code:`libmklml_intel.so` 可能不在 :code:`/usr/local/lib` 路径下。

@ -0,0 +1,59 @@
# Inference High-level APIs
This document describes the high-level inference APIs one can use to easily deploy a Paddle model for an application.
The APIs are described in `paddle_inference_api.h`, just one header file, and two libaries `libpaddle_fluid.so` and `libpaddle_fluid_api.so` are needed.
## PaddleTensor
We provide the `PaddleTensor` data structure is to give a general tensor interface.
The definition is
```c++
struct PaddleTensor {
std::string name; // variable name.
std::vector<int> shape;
PaddleBuf data; // blob of data.
PaddleDType dtype;
};
```
The data is stored in a continuous memory `PaddleBuf`, and tensor's data type is specified by a `PaddleDType`.
The `name` field is used to specify the name of input variable,
that is important when there are multiple inputs and need to distiuish which variable to set.
## engine
The inference APIs has two different underlying implementation, currently there are two valid engines:
- the native engine, which is consists of the native operators and framework,
- the Anakin engine, which is a Anakin library embeded.
The native engine takes a native Paddle model as input, and supports any model that trained by Paddle,
but the Anakin engine can only take the Anakin model as input(user need to manully transform the format first) and currently not all Paddle models are supported.
```c++
enum class PaddleEngineKind {
kNative = 0, // Use the native Fluid facility.
kAnakin, // Use Anakin for inference.
};
```
## PaddlePredictor and how to create one
The main interface is `PaddlePredictor`, there are following methods
- `bool Run(const std::vector<PaddleTensor>& inputs, std::vector<PaddleTensor>* output_data)`
- take inputs and output `output_data`
- `Clone` to clone a predictor from an existing one, with model parameter shared.
There is a factory method to help create a predictor, and the user takes the ownership of this object.
```c++
template <typename ConfigT, PaddleEngineKind engine = PaddleEngineKind::kNative>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT& config);
```
By specifying the engine kind and config, one can get an specific implementation.
## Reference
- [paddle_inference_api.h](./paddle_inference_api.h)
- [demos](./demo)

@ -109,8 +109,7 @@ class PaddlePredictor {
// The common configs for all the predictors.
struct Config {
std::string model_dir; // path to the model directory.
bool enable_engine{false}; // Enable to execute (part of) the model on
std::string model_dir; // path to the model directory.
};
};

@ -73,6 +73,9 @@ void BroadcastOpHandle::RunImpl() {
int root_id = boost::get<platform::CUDAPlace>(in_tensor.place()).device;
std::vector<std::function<void()>> broadcast_calls;
int type = platform::ToNCCLDataType(in_tensor.type());
size_t numel = static_cast<size_t>(in_tensor.numel());
for (auto out_var_handle : out_var_handles) {
Variable *out_var = var_scopes.at(out_var_handle->scope_idx_)
->FindVar(out_var_handle->name_);
@ -87,13 +90,11 @@ void BroadcastOpHandle::RunImpl() {
send_recv_buffer = const_cast<void *>(in_tensor.data<void>());
out_handle = out_var_handle;
} else {
send_recv_buffer =
VariableVisitor::GetMutableTensor(out_var).mutable_data(
out_var_handle->place_);
send_recv_buffer = VariableVisitor::GetMutableTensor(out_var)
.Resize(in_tensor.dims())
.mutable_data(out_var_handle->place_);
}
int type = platform::ToNCCLDataType(in_tensor.type());
size_t numel = static_cast<size_t>(in_tensor.numel());
broadcast_calls.emplace_back(
[send_recv_buffer, numel, type, root_id, &nccl_ctx] {
PADDLE_ENFORCE(platform::dynload::ncclBcast(

@ -351,7 +351,7 @@ void MultiDevSSAGraphBuilder::InsertAllReduceOp(SSAGraph *result,
auto &prev_grad = vars.back();
op_handle->AddInput(prev_grad.get());
auto var = new VarHandle(vars.size() - 1, i, og, p);
auto var = new VarHandle(vars.size(), i, og, p);
vars.emplace_back(var);
op_handle->AddOutput(var);
}
@ -447,8 +447,7 @@ VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(SSAGraph *result,
op_handle->AddInput(prev_grad.get());
}
auto &vars = result->vars_[dst_dev_id][og];
auto var =
new VarHandle(vars.size() - 1, dst_dev_id, og, places_[dst_dev_id]);
auto var = new VarHandle(vars.size(), dst_dev_id, og, places_[dst_dev_id]);
vars.emplace_back(var);
op_handle->AddOutput(var);
return var;

@ -47,7 +47,7 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
#endif
std::unique_ptr<SSAGraph> Build(const ProgramDesc &program) const override;
int GetVarDeviceID(const std::string &varname) const;
int GetVarDeviceID(const std::string &varname) const override;
private:
void CreateOpHandleIOs(SSAGraph *result, const OpDesc &op,

@ -11,8 +11,8 @@
// 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/framework/details/op_handle_base.h"
#include <map>
namespace paddle {
namespace framework {
@ -122,11 +122,16 @@ void OpHandleBase::RunAndRecordEvent(const std::function<void()> &callback) {
#ifdef PADDLE_WITH_CUDA
if (!events_.empty()) { // Use event
std::function<void()> method = callback;
// NOTE(zcd): device context must be ordered here because RecordEvent
// will use a mutex to ensure the safe of multi-threads.
std::map<platform::DeviceContext *, platform::Place> ordered_ctxes;
for (auto &p : dev_ctxes_) {
ordered_ctxes.emplace(p.second, p.first);
}
for (auto &p : ordered_ctxes) {
method = [method, p, this]() {
static_cast<platform::CUDADeviceContext *>(p.second)->RecordEvent(
events_.at(boost::get<platform::CUDAPlace>(p.first).device),
static_cast<platform::CUDADeviceContext *>(p.first)->RecordEvent(
events_.at(boost::get<platform::CUDAPlace>(p.second).device),
method);
};
}

@ -27,6 +27,7 @@ enum AttrType {
BOOLEANS = 7;
BLOCK = 8;
LONG = 9;
BLOCKS = 10;
}
// OpDesc describes an instance of a C++ framework::OperatorBase
@ -46,6 +47,7 @@ message OpDesc {
repeated bool bools = 11;
optional int32 block_idx = 12;
optional int64 l = 13;
repeated int32 blocks_idx = 14;
};
message Var {

@ -51,8 +51,6 @@ std::ostream &operator<<(std::ostream &os, const LoD &lod) {
}
std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
PADDLE_ENFORCE(t.type().hash_code() == typeid(float).hash_code());
if (!platform::is_cpu_place(t.place())) {
LoDTensor tt;
framework::TensorCopy(t, platform::CPUPlace(), &tt);
@ -70,7 +68,13 @@ std::ostream &operator<<(std::ostream &os, const LoDTensor &t) {
// only print first ten elements
int64_t size = t.numel() < 10 ? t.numel() : 10;
for (int64_t i = 0; i < size; ++i) {
os << t.data<float>()[i] << " ";
if (t.type().hash_code() == typeid(float).hash_code()) {
os << t.data<float>()[i] << " ";
} else if (t.type().hash_code() == typeid(int64_t).hash_code()) {
os << t.data<int64_t>()[i] << " ";
} else {
PADDLE_THROW("LoDTensor data type not in [float, int64_t]");
}
}
return os;

@ -26,6 +26,20 @@
namespace paddle {
namespace framework {
TEST(LoD, PrintLoDTensor) {
LoDTensor tensor1;
tensor1.mutable_data<float>(platform::CPUPlace());
tensor1.data<float>()[0] = 0.2;
tensor1.data<float>()[1] = 0.5;
LOG(INFO) << tensor1;
LoDTensor tensor2;
tensor2.mutable_data<int64_t>(platform::CPUPlace());
tensor2.data<int64_t>()[0] = 1;
tensor2.data<int64_t>()[1] = 2;
LOG(INFO) << tensor2;
}
TEST(LoD, data) {
LoD lod{{0, 1, 2}};
lod.push_back({0, 2, 4, 5});
@ -37,7 +51,7 @@ TEST(LoD, data) {
}
}
TEST(LodExpand, test) {
TEST(LoD, ExpandLoD) {
LoD lod{{0, 2}};
LoDTensor tensor;
tensor.set_lod(lod);

@ -211,6 +211,12 @@ void OpDesc::SetBlockAttr(const std::string &name, BlockDesc *block) {
need_update_ = true;
}
void OpDesc::SetBlocksAttr(const std::string &name,
std::vector<BlockDesc *> blocks) {
this->attrs_[name] = blocks;
need_update_ = true;
}
void OpDesc::SetAttrMap(
const std::unordered_map<std::string, Attribute> &attr_map) {
attrs_ = attr_map;
@ -305,6 +311,13 @@ struct SetAttrDescVisitor : public boost::static_visitor<void> {
void operator()(const std::vector<bool> &v) const {
VectorToRepeated(v, attr_->mutable_bools());
}
void operator()(const std::vector<BlockDesc *> &v) const {
std::vector<int> blocks_idx;
for (auto blk : v) {
blocks_idx.push_back(blk->ID());
}
VectorToRepeated(blocks_idx, attr_->mutable_blocks_idx());
}
void operator()(BlockDesc *desc) const { attr_->set_block_idx(desc->ID()); }
void operator()(int64_t v) const { attr_->set_l(v); }
void operator()(boost::blank) const { PADDLE_THROW("Unexpected branch"); }

@ -77,6 +77,8 @@ class OpDesc {
void SetBlockAttr(const std::string &name, BlockDesc *block);
void SetBlocksAttr(const std::string &name, std::vector<BlockDesc *> blocks);
Attribute GetAttr(const std::string &name) const;
Attribute GetNullableAttr(const std::string &name) const;

@ -121,7 +121,7 @@ ParallelExecutor::ParallelExecutor(
#endif
}
builder_ = std::move(builder_factory.Create());
builder_ = builder_factory.Create();
member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, places,
builder_->Build(main_program)));

@ -35,7 +35,8 @@ using VariableNameMap = std::map<std::string, std::vector<std::string>>;
using Attribute =
boost::variant<boost::blank, int, float, std::string, std::vector<int>,
std::vector<float>, std::vector<std::string>, bool,
std::vector<bool>, BlockDesc*, int64_t>;
std::vector<bool>, BlockDesc*, int64_t,
std::vector<BlockDesc*>>;
using AttributeMap = std::unordered_map<std::string, Attribute>;

@ -18,6 +18,7 @@ limitations under the License. */
#include <limits>
#include "glog/logging.h" // For VLOG
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/operators/distributed/request_handler.h"
#include "paddle/fluid/platform/profiler.h"
@ -75,6 +76,9 @@ bool GRPCClient::AsyncSendVar(const std::string& ep,
var_h.scope = p_scope;
var_h.name = var_name_val;
var_h.ctx = p_ctx;
var_h.method = "Send";
VLOG(3) << var_h.String() << " begin";
// stub context
SendProcessor* s = new SendProcessor(ch);
@ -129,6 +133,9 @@ bool GRPCClient::AsyncGetVar(const std::string& ep,
var_h.scope = p_scope;
var_h.name = var_name_val;
var_h.ctx = p_ctx;
var_h.method = "Get";
VLOG(3) << var_h.String() << " begin";
// stub context
GetProcessor* s = new GetProcessor(ch);
@ -172,6 +179,9 @@ bool GRPCClient::AsyncPrefetchVar(const std::string& ep,
var_h.scope = p_scope;
var_h.name = out_var_name_val;
var_h.ctx = p_ctx;
var_h.method = "Prefetch";
VLOG(3) << var_h.String() << " begin";
// stub context
GetProcessor* s = new GetProcessor(ch);
@ -260,10 +270,11 @@ void GRPCClient::Proceed() {
GPR_ASSERT(ok);
PADDLE_ENFORCE(c);
if (c->status_.ok()) {
VLOG(3) << c->var_h_.String() << " process";
c->Process();
} else {
LOG(FATAL) << "var: " << c->var_h_.String()
<< " grpc error:" << c->status_.error_message();
LOG(FATAL) << c->var_h_.String()
<< " meets grpc error:" << c->status_.error_message();
}
delete c;
{

@ -47,14 +47,18 @@ namespace operators {
namespace distributed {
struct VarHandle {
// RPC endpoint.
std::string ep;
const platform::DeviceContext* ctx;
const framework::Scope* scope;
// Variable name.
std::string name;
// RPC method name.
std::string method;
std::string String() const {
std::ostringstream s;
s << "name:[" << name << "] ep:[" << ep << "]";
s << method << " name:[" << name << "], ep:[" << ep << "]";
return s.str();
}
};

@ -41,6 +41,19 @@ class RequestBase {
virtual ~RequestBase() {}
virtual void Process() = 0;
std::string Status2String(const std::string& method) {
std::string status = "Process";
if (status_ == FINISH) {
status = "Finish";
}
std::ostringstream s;
s << method << " name:[" << GetReqName() << "]"
<< ", ep:[" << ctx_.peer() << "]"
<< " " << status << " using req_id:" << req_id_;
return s.str();
}
CallStatus Status() const {
std::lock_guard<std::mutex> l(status_mu_);
return status_;
@ -312,7 +325,7 @@ void AsyncGRPCServer::TryToRegisterNewOne(const std::string& rpc_name,
int req_id) {
std::unique_lock<std::mutex> lock(cq_mutex_);
if (is_shut_down_) {
VLOG(3) << "shutdown, do not TryToRegisterNewSendOne";
LOG(WARNING) << "shutdown, do not TryToRegisterNewSendOne";
return;
}
@ -348,14 +361,14 @@ void AsyncGRPCServer::HandleRequest(
bool ok = false;
while (true) {
VLOG(3) << "HandleRequest " << rpc_name << " wait next";
VLOG(4) << "HandleRequest " << rpc_name << " wait next";
if (!cq->Next(&tag, &ok)) {
VLOG(3) << "CompletionQueue " << rpc_name << " shutdown!";
break;
}
int req_id = static_cast<int>(reinterpret_cast<intptr_t>(tag));
VLOG(3) << "HandleRequest " << rpc_name << ", req_id:" << req_id
VLOG(4) << "HandleRequest " << rpc_name << ", req_id:" << req_id
<< " get next";
auto& reqs = rpc_reqs_[rpc_name];
@ -366,22 +379,21 @@ void AsyncGRPCServer::HandleRequest(
base = reqs[req_id];
}
VLOG(3) << base->Status2String(rpc_name);
// reference:
// https://github.com/tensorflow/tensorflow/issues/5596
// https://groups.google.com/forum/#!topic/grpc-io/xftlRy-IQwM
// https://groups.google.com/forum/#!topic/grpc-io/ywATt88Ef_I
if (!ok) {
LOG(WARNING) << "completion queue:" << rpc_name
<< " recv no regular event:argument name["
<< base->GetReqName() << "]";
<< " recv no regular event"
<< " context:" << base->Status2String(rpc_name);
TryToRegisterNewOne(rpc_name, req_id);
delete base;
continue;
}
VLOG(3) << "queue id:" << rpc_name << ", req_id:" << req_id
<< ", status:" << base->Status();
switch (base->Status()) {
case PROCESS: {
base->Process();

@ -76,6 +76,8 @@ bool ReadRaw(::google::protobuf::io::CodedInputStream* input,
if (total_written + size_to_write > length) {
size_to_write = length - total_written;
}
// This log is useful to see how long a internal block size is of rpc.
VLOG(7) << "copy " << size_to_write << " data to CUDAPlace";
memory::Copy(boost::get<platform::CUDAPlace>(place),
reinterpret_cast<void*>(p), cpu, data, size_to_write,
gpu_dev_ctx.stream());
@ -103,6 +105,8 @@ bool ReadRaw(::google::protobuf::io::CodedInputStream* input,
}
// TODO(gongwb): can we avoid copy?
platform::CPUPlace cpu;
// This log is useful to see how long a internal block size is of rpc.
VLOG(7) << "copy " << size_to_write << " data to CPUPlace";
memory::Copy(cpu, reinterpret_cast<void*>(p), cpu, data, size_to_write);
p += size_to_write;

@ -102,18 +102,16 @@ void ListenAndServOp::RunSyncLoop(
const std::vector<int> &prefetch_block_id_list,
const int checkpoint_point_block_id) const {
size_t num_blocks = program->Size();
auto optimize_blocks =
Attr<std::vector<framework::BlockDesc *>>(kOptimizeBlocks);
PADDLE_ENFORCE_GE(num_blocks, 2,
"server program should have at least 2 blocks");
std::vector<int> optimize_block_id_list;
for (int blkid = 1; blkid < num_blocks; ++blkid) {
if (std::find(prefetch_block_id_list.begin(), prefetch_block_id_list.end(),
blkid) == prefetch_block_id_list.end() &&
blkid != checkpoint_point_block_id) {
optimize_block_id_list.push_back(blkid);
}
std::vector<int> optimize_blocks_idx;
for (auto blk : optimize_blocks) {
optimize_blocks_idx.push_back(blk->ID());
}
auto optimize_prepared = executor->Prepare(*program, optimize_block_id_list);
auto optimize_prepared = executor->Prepare(*program, optimize_blocks_idx);
// Insert placeholder for block0 which holds current op itself.
optimize_prepared.insert(
optimize_prepared.begin(),
@ -136,14 +134,14 @@ void ListenAndServOp::RunSyncLoop(
// and this will still work.
// The optimize blocks which have the same parent ID would run parallel
// TODO(Yancey1989): need to use ParallelExecutor for future
int32_t last_parent_blkid = program->Block(1).Parent();
int32_t last_parent_blkid = optimize_blocks[0]->Parent();
std::vector<size_t> parallel_blkids;
parallel_blkids.push_back(1);
parallel_blkids.push_back(optimize_blocks[0]->ID());
double ts = GetTimestamp();
for (size_t i = 1; i < optimize_block_id_list.size(); ++i) {
for (size_t i = 1; i < optimize_blocks.size(); ++i) {
// skip the first optimize block because it is already in the
// parallel_blkids.
int blkid = optimize_block_id_list[i];
int blkid = optimize_blocks[i]->ID();
if (program->Block(blkid).Parent() != last_parent_blkid) {
ParallelExecuteBlocks(parallel_blkids, executor, optimize_prepared,
program, recv_scope);
@ -271,8 +269,11 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
rpc_service_->RegisterRPC(distributed::kRequestCheckpoint,
request_checkpoint_handler_.get());
auto *optimize_block = Attr<framework::BlockDesc *>(kOptimizeBlock);
auto *program = optimize_block->Program();
auto optimize_blocks =
Attr<std::vector<framework::BlockDesc *>>(kOptimizeBlocks);
PADDLE_ENFORCE(optimize_blocks.size() >= 1,
"optimize blocks should be 1 at least on the pserver side.");
auto *program = optimize_blocks[0]->Program();
framework::Executor executor(dev_place);
std::shared_ptr<framework::ExecutorPrepareContext> ckpt_pre_context = nullptr;
@ -358,8 +359,9 @@ class ListenAndServOpMaker : public framework::OpProtoAndCheckerMaker {
"a map from grad name to it's optimize block id")
.SetDefault({});
AddAttr<bool>("sync_mode", "if works at sync_mode or not").SetDefault(true);
AddAttr<framework::BlockDesc *>(kOptimizeBlock,
"BlockID to run on server side.");
AddAttr<std::vector<framework::BlockDesc *>>(
kOptimizeBlocks, "Optimize blocks to run on server side.")
.SetDefault({});
AddAttr<std::vector<std::string>>(kPrefetchVarNameToBlockId,
"prefetch blocks to run on server side.")
.SetDefault({});

@ -30,7 +30,7 @@ limitations under the License. */
namespace paddle {
namespace operators {
constexpr char kOptimizeBlock[] = "OptimizeBlock";
constexpr char kOptimizeBlocks[] = "optimize_blocks";
constexpr char kPrefetchVarNameToBlockId[] = "prefetch_var_name_to_block_id";
constexpr char kCheckpointBlockId[] = "checkpint_block_id";

@ -129,7 +129,10 @@ void StartServerNet(bool is_sparse, std::atomic<bool> *initialized) {
// sub program run in listen_and_serv_op, for simple test we use sum
f::ProgramDesc program;
const auto &root_block = program.Block(0);
std::vector<framework::BlockDesc *> optimize_blocks;
auto *optimize_block = program.AppendBlock(root_block);
optimize_blocks.push_back(optimize_block);
auto *prefetch_block = program.AppendBlock(root_block);
// X for server side tensors, RX for received tensors, must be of same shape.
AddOp("sum", {{"X", {"x0", "x1"}}}, {{"Out", {"Out"}}}, {}, optimize_block,
@ -139,7 +142,7 @@ void StartServerNet(bool is_sparse, std::atomic<bool> *initialized) {
attrs.insert({"Fanin", 1});
attrs.insert({"ParamList", std::vector<std::string>({"Out"})});
attrs.insert({"GradList", std::vector<std::string>({"x1"})});
attrs.insert({"OptimizeBlock", optimize_block});
attrs.insert({"optimize_blocks", optimize_blocks});
attrs.insert({"PrefetchBlock", prefetch_block});
attrs.insert({"grad_to_block_id", std::vector<std::string>({""})});
attrs.insert({"sync_mode", true});

@ -27,8 +27,81 @@ using paddle::platform::MKLDNNMemDesc;
using mkldnn::memory; // Note: paddle has also "memory" namespace
using mkldnn::primitive;
using mkldnn::softmax_forward;
using mkldnn::softmax_backward;
using mkldnn::prop_kind;
using mkldnn::stream;
using platform::to_void_cast;
class SoftmaxMKLDNNHandler : public platform::MKLDNNHandler {
public:
SoftmaxMKLDNNHandler(
std::shared_ptr<mkldnn::softmax_forward::primitive_desc> softmax_pd,
const platform::MKLDNNDeviceContext& dev_ctx, mkldnn::engine engine,
const std::string& base_key)
: platform::MKLDNNHandler(dev_ctx, engine, base_key),
softmax_pd_(softmax_pd) {}
SoftmaxMKLDNNHandler(
std::shared_ptr<mkldnn::softmax_forward::primitive_desc> softmax_pd,
std::shared_ptr<mkldnn::softmax_backward::primitive_desc> softmax_bwd_pd,
const platform::MKLDNNDeviceContext& dev_ctx, mkldnn::engine engine,
const std::string& base_key)
: platform::MKLDNNHandler(dev_ctx, engine, base_key),
softmax_pd_(softmax_pd),
softmax_bwd_pd_(softmax_bwd_pd) {
// If we are in Grad operatgor then update a key with BWD suffix to
// distinguish from FWD memory primitives
key_ += "-BWD";
}
std::shared_ptr<mkldnn::softmax_forward> AcquireSoftmax(
std::shared_ptr<mkldnn::memory> dst_memory_p,
std::shared_ptr<mkldnn::memory> src_memory_p) {
/*Generate key*/
auto prim_key = key_ + "@softmax_p";
auto softmax_p = std::static_pointer_cast<mkldnn::softmax_forward>(
dev_ctx_.GetBlob(prim_key));
PADDLE_ENFORCE((softmax_p != nullptr) || (is_reusing_ == false),
"Fail to find softmax primitive in device context");
if (softmax_p == nullptr) {
softmax_p = std::make_shared<mkldnn::softmax_forward>(
*(softmax_pd_.get()),
*(static_cast<mkldnn::memory*>(src_memory_p.get())),
*(static_cast<mkldnn::memory*>(dst_memory_p.get())));
dev_ctx_.SetBlob(prim_key, softmax_p);
} else {
is_reusing_ = true;
}
return softmax_p;
}
std::shared_ptr<mkldnn::softmax_backward> AcquireSoftmaxBackward(
std::shared_ptr<mkldnn::memory> dst_memory_p,
std::shared_ptr<mkldnn::memory> diff_dst_memory_p,
std::shared_ptr<mkldnn::memory> diff_src_memory_p) {
auto prim_key = key_ + "@softmax_bwd_p";
auto softmax_bwd_p = std::static_pointer_cast<mkldnn::softmax_backward>(
dev_ctx_.GetBlob(prim_key));
PADDLE_ENFORCE((softmax_bwd_p != nullptr) || (is_reusing_ == false),
"Fail to find softmax backward primitive in device context");
if (softmax_bwd_p == nullptr) {
softmax_bwd_p = std::make_shared<mkldnn::softmax_backward>(
*softmax_bwd_pd_, *(dst_memory_p.get()), *(diff_dst_memory_p.get()),
*(diff_src_memory_p.get()));
dev_ctx_.SetBlob(prim_key, softmax_bwd_p);
} else {
is_reusing_ = true;
}
return softmax_bwd_p;
}
private:
std::shared_ptr<mkldnn::softmax_forward::primitive_desc> softmax_pd_;
std::shared_ptr<mkldnn::softmax_backward::primitive_desc> softmax_bwd_pd_;
};
template <typename T>
class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel<T> {
@ -54,56 +127,27 @@ class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel<T> {
// Same memory descriptor to be used for input and output
memory::dims softmax_tz = {src_tz[0], src_tz[1]};
// Generate keys for storing/retriving primitives for this operator
// TODO(jczaja): Each MKLDNN operator may have diffrent hashing function
auto gethash = [](memory::dims& operand_dims) {
return std::string(std::to_string(operand_dims[0]) + "-" +
std::to_string(operand_dims[1]));
};
const std::string key = gethash(softmax_tz);
const std::string key_softmax_p = key + "@softmax_p";
const std::string key_softmax_src_mem_p = key + "@softmax_src_mem_p";
const std::string key_softmax_dst_mem_p = key + "@softmax_dst_mem_p";
std::shared_ptr<void> softmax_p = dev_ctx.GetBlob(key_softmax_p);
if (softmax_p == nullptr) {
// Currently only NC data format is supported
auto softmax_md =
MKLDNNMemDesc({softmax_tz}, memory::f32, memory::format::nc);
// Normalization is made after innermost dimension eg. C out of NC
auto softmax_desc = softmax_forward::desc(prop_kind::forward_scoring,
softmax_md, 1 /*dim: C*/);
// create memory primitives
auto softmax_src_memory_p = std::make_shared<memory>(
memory::primitive_desc{softmax_md, mkldnn_engine},
static_cast<void*>(const_cast<T*>(input_data)));
dev_ctx.SetBlob(key_softmax_src_mem_p, softmax_src_memory_p);
auto softmax_dst_memory_p = std::make_shared<memory>(
memory::primitive_desc{softmax_md, mkldnn_engine},
static_cast<void*>(output_data));
dev_ctx.SetBlob(key_softmax_dst_mem_p, softmax_dst_memory_p);
auto softmax_forward_pd =
std::make_shared<softmax_forward::primitive_desc>(softmax_desc,
mkldnn_engine);
softmax_p = std::make_shared<softmax_forward>(
*(softmax_forward_pd.get()),
*(static_cast<memory*>(softmax_src_memory_p.get())),
*(static_cast<memory*>(softmax_dst_memory_p.get())));
dev_ctx.SetBlob(key_softmax_p, softmax_p);
} else {
// Primitives already exist
auto src_memory_p = std::static_pointer_cast<memory>(
dev_ctx.GetBlob(key_softmax_src_mem_p));
PADDLE_ENFORCE(src_memory_p != nullptr,
"Fail to find softmax src mem_p in device context");
auto dst_memory_p = std::static_pointer_cast<memory>(
dev_ctx.GetBlob(key_softmax_dst_mem_p));
PADDLE_ENFORCE(dst_memory_p != nullptr,
"Fail to find softmax dst mem_p in device context");
src_memory_p->set_data_handle(
reinterpret_cast<void*>(const_cast<T*>(input_data)));
dst_memory_p->set_data_handle(output_data);
}
const std::string key =
platform::MKLDNNHandler::GetHash(softmax_tz, ctx.op().Output("Out"));
const std::string key_softmax_pd = key + "@softmax_pd";
// Currently only NC data format is supported
auto softmax_md = MKLDNNMemDesc(
{softmax_tz}, platform::MKLDNNGetDataType<T>(), memory::format::nc);
// Normalization is made after innermost dimension eg. C out of NC
auto softmax_desc = softmax_forward::desc(prop_kind::forward_scoring,
softmax_md, 1 /*dim: C*/);
auto softmax_pd = std::make_shared<mkldnn::softmax_forward::primitive_desc>(
softmax_desc, mkldnn_engine);
dev_ctx.SetBlob(key_softmax_pd, softmax_pd);
SoftmaxMKLDNNHandler handler(softmax_pd, dev_ctx, mkldnn_engine, key);
auto softmax_src_memory_p =
handler.AcquireSrcMemory(softmax_md, to_void_cast<T>(input_data));
auto softmax_dst_memory_p =
handler.AcquireDstMemory(softmax_md, to_void_cast<T>(output_data));
auto softmax_p =
handler.AcquireSoftmax(softmax_dst_memory_p, softmax_src_memory_p);
std::vector<primitive> pipeline{
*(static_cast<softmax_forward::primitive*>(softmax_p.get()))};
@ -120,6 +164,77 @@ class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel<T> {
}
};
template <typename T>
class SoftmaxMKLDNNGradKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
auto mkldnn_engine = dev_ctx.GetEngine();
const Tensor* output = ctx.Input<Tensor>("Out");
const T* dst_data = output->data<T>();
auto* dout = ctx.template Input<Tensor>(framework::GradVarName("Out"));
const auto* diff_dst_ptr = dout->template data<T>();
auto* dx =
ctx.template Output<framework::Tensor>(framework::GradVarName("X"));
T* diff_src_ptr = dx->template mutable_data<T>(ctx.GetPlace());
std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
std::vector<int> src_tz(dst_tz);
PADDLE_ENFORCE(output->dims().size() == 2UL,
"The input of softmax op must be a 2D matrix.");
// MKL-DNN does support softmax over selected axis. Having 2D Tensor,
// we will make normalization after final eg. axis: 1
PADDLE_ENFORCE(((src_tz[0] == dst_tz[0]) && (src_tz[1] == dst_tz[1])),
"Softmax input and output dimensions should match");
// Same memory descriptor to be used for input and output
memory::dims softmax_tz = {src_tz[0], src_tz[1]};
// Currently only supports NC data format
// retrieve eltwise primitive desc from device context
const std::string key =
platform::MKLDNNHandler::GetHash(softmax_tz, ctx.op().Input("Out"));
const std::string key_softmax_pd = key + "@softmax_pd";
auto softmax_pd =
std::static_pointer_cast<mkldnn::softmax_forward::primitive_desc>(
dev_ctx.GetBlob(key_softmax_pd));
PADDLE_ENFORCE(softmax_pd != nullptr,
"Fail to find softmax_pd in device context");
// TODO(jczaja): Add layouts support when there is a need to do so
// Two dimensional softmax does support NC format
auto data_softmax_md = MKLDNNMemDesc(
{softmax_tz}, platform::MKLDNNGetDataType<T>(), memory::format::nc);
auto diff_softmax_md = MKLDNNMemDesc(
{softmax_tz}, platform::MKLDNNGetDataType<T>(), memory::format::nc);
// Normalization is made after innermost dimension eg. C out of NC
auto softmax_bwd_desc =
softmax_backward::desc(diff_softmax_md, data_softmax_md, 1 /* dim: C*/);
auto softmax_bwd_pd =
std::make_shared<mkldnn::softmax_backward::primitive_desc>(
softmax_bwd_desc, mkldnn_engine, *softmax_pd);
SoftmaxMKLDNNHandler handler(softmax_pd, softmax_bwd_pd, dev_ctx,
mkldnn_engine, key);
auto dst_memory_p =
handler.AcquireDstMemory(data_softmax_md, to_void_cast<T>(dst_data));
auto diff_dst_memory_p = handler.AcquireDiffDstMemory(
diff_softmax_md, to_void_cast<T>(diff_dst_ptr));
auto diff_src_memory_p = handler.AcquireDiffSrcMemory(
diff_softmax_md, to_void_cast<T>(diff_src_ptr));
// Get primitve from device context
auto softmax_bwd_p = handler.AcquireSoftmaxBackward(
dst_memory_p, diff_dst_memory_p, diff_src_memory_p);
std::vector<primitive> pipeline{*softmax_bwd_p};
stream(stream::kind::eager).submit(pipeline).wait();
}
};
} // namespace operators
} // namespace paddle
@ -127,3 +242,5 @@ namespace ops = paddle::operators;
REGISTER_OP_KERNEL(softmax, MKLDNN, ::paddle::platform::CPUPlace,
ops::SoftmaxMKLDNNKernel<float>);
REGISTER_OP_KERNEL(softmax_grad, MKLDNN, ::paddle::platform::CPUPlace,
ops::SoftmaxMKLDNNGradKernel<float>);

@ -145,16 +145,30 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
const framework::ExecutionContext& ctx) const override {
// choose cudnn kernel if the runtime supported.
framework::LibraryType library_{framework::LibraryType::kPlain};
std::string data_format = ctx.Attr<std::string>("data_format");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
#ifdef PADDLE_WITH_CUDA
if (platform::CanCUDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kCUDNN;
}
#endif
std::string data_format = ctx.Attr<std::string>("data_format");
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
framework::StringToDataLayout(data_format), library_);
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
layout_ = framework::DataLayout::kMKLDNN;
}
#endif
auto input_data_type =
framework::ToDataType(ctx.Input<Tensor>("X")->type());
if (input_data_type == framework::proto::VarType::FP16) {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"float16 can only be used on GPU place");
}
return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout_,
library_);
}
};

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