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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into develop

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fix-typo
zchen0211 7 years ago
parent 8d3324d707 0318f47e1c
commit a0acfc6a62
31 changed files with 360 additions and 138 deletions

10
paddle/framework/attribute.cc
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@ -19,7 +19,7 @@ limitations under the License. */
namespace paddle {
namespace framework {
Attribute GetAttrValue(const OpDesc::Attr& attr_desc, ProgramDesc* program) {
Attribute GetAttrValue(const OpDesc::Attr& attr_desc) {
switch (attr_desc.type()) {
case framework::AttrType::BOOLEAN: {
return attr_desc.b();
@ -61,13 +61,9 @@ Attribute GetAttrValue(const OpDesc::Attr& attr_desc, ProgramDesc* program) {
}
return val;
}
case framework::AttrType::BLOCK: {
PADDLE_ENFORCE(program != nullptr,
"Need to specify ProgramDesc when get a block attr");
return program->mutable_blocks(attr_desc.block_idx());
}
default:
PADDLE_THROW("Unsupport attr type %d", attr_desc.type());
}
PADDLE_ENFORCE(false, "Unknown OpDesc::AttrDesc::type !");
return boost::blank();
}

2
paddle/framework/attribute.h
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@ -32,7 +32,7 @@ inline AttrType AttrTypeID() {
return static_cast<AttrType>(tmp.which() - 1);
}
Attribute GetAttrValue(const OpDesc::Attr& attr_desc, ProgramDesc* desc);
Attribute GetAttrValue(const OpDesc::Attr& attr_desc);
class AttrReader {
public:

35
paddle/framework/backward.cc
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@ -18,6 +18,7 @@
#include <deque>
#include <list>
#include <memory>
#include <unordered_set>
#include "paddle/framework/block_desc.h"
#include "paddle/framework/op_registry.h"
@ -285,6 +286,15 @@ static bool AllGradInSet(const std::vector<std::string>& names,
return true;
}
static std::string FwdName(const std::string& grad_name) {
auto pos = grad_name.find("@GRAD");
if (pos == std::string::npos) {
return "";
} else {
return grad_name.substr(0, pos);
}
}
static void CreateGradVarInBlock(
size_t grad_op_start_index,
const std::unordered_map<std::string, std::string>& param_name_map,
@ -294,6 +304,7 @@ static void CreateGradVarInBlock(
for (size_t op_index = grad_op_start_index; op_index < ops.size();
++op_index) {
bool need_infer_shape = false;
std::unordered_set<std::string> new_vars;
ForEachVarName(ops[op_index]->Outputs(),
[&](const std::string& grad_var_name) {
if (block_desc->HasVar(grad_var_name)) {
@ -301,8 +312,7 @@ static void CreateGradVarInBlock(
}
need_infer_shape = true;
auto var = block_desc->Var(grad_var_name);
// FIXME(qiao) infer the datatype
var->SetDataType(framework::DataType::FP32);
new_vars.insert(var->Name());
auto it = param_name_map.find(grad_var_name);
if (it == param_name_map.end()) {
return false;
@ -316,6 +326,21 @@ static void CreateGradVarInBlock(
});
if (need_infer_shape) {
ops[op_index]->InferVarType(block_desc);
for (auto& arg : ops[op_index]->OutputArgumentNames()) {
if (new_vars.find(arg) == new_vars.end()) {
continue;
}
auto pname = FwdName(arg);
auto* param = block_desc->FindVar(pname);
auto* grad = block_desc->FindVar(arg);
if (param == nullptr) {
LOG(WARNING) << "Cannot find forward variable of " << arg
<< ". Set its gradient to FP32";
grad->SetDataType(DataType::FP32);
} else {
grad->SetDataType(param->GetDataType());
}
}
ops[op_index]->InferShape(*block_desc);
}
}
@ -368,7 +393,7 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
ProgramDescBind& program_desc, int block_idx,
std::unordered_set<std::string>* no_grad_vars,
std::unordered_map<std::string, std::string>* grad_to_var) {
BlockDescBind* cur_block = program_desc.Block(block_idx);
BlockDescBind* cur_block = program_desc.MutableBlock(block_idx);
std::vector<OpDescBind*> op_descs = cur_block->AllOps();
std::unordered_map<std::string, std::vector<size_t>> dup_out_ops;
size_t grad_desc_idx = 0;
@ -443,7 +468,7 @@ ParamGradInfoMap AppendBackward(
}
const int root_block_idx = 0;
auto root_block = program_desc.Block(root_block_idx);
auto root_block = program_desc.MutableBlock(root_block_idx);
// insert fill one op for target
// TODO(qiao) add some check to the target.
@ -492,7 +517,7 @@ ParamGradInfoMap AppendBackward(
CreateGradVarInBlock(forward_op_num, grad_to_var, root_block, &retv);
for (size_t block_index = forward_block_num;
block_index < program_desc.Size(); ++block_index) {
CreateGradVarInBlock(0, grad_to_var, program_desc.Block(block_index),
CreateGradVarInBlock(0, grad_to_var, program_desc.MutableBlock(block_index),
&retv);
}
return retv;

14
paddle/framework/backward_test.cc
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@ -499,7 +499,7 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
TEST(Backward, simple_single_op) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
f::OpDescBind *op = block->AppendOp();
op->SetType("rowwise_add");
@ -535,7 +535,7 @@ TEST(Backward, simple_single_op) {
TEST(Backward, default_attribute) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
f::OpDescBind *op = block->AppendOp();
op->SetType("mul");
op->SetInput("X", {"x"});
@ -561,7 +561,7 @@ TEST(Backward, default_attribute) {
TEST(Backward, simple_mult_op) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
f::OpDescBind *op1 = block->AppendOp();
op1->SetType("rowwise_add");
op1->SetInput("X", {"x1"});
@ -644,7 +644,7 @@ TEST(Backward, simple_mult_op) {
TEST(Backward, intermedia_var_no_grad) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
f::OpDescBind *op1 = block->AppendOp();
op1->SetType("rowwise_add");
op1->SetInput("X", {"x1"});
@ -714,7 +714,7 @@ TEST(Backward, intermedia_var_no_grad) {
TEST(Backward, var_no_grad) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
f::OpDescBind *op1 = block->AppendOp();
op1->SetType("mult_in_out");
op1->SetInput("X", {"x1"});
@ -790,7 +790,7 @@ TEST(Backward, var_no_grad) {
TEST(Backward, shared_var) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
f::OpDescBind *op1 = block->AppendOp();
op1->SetType("rowwise_add");
op1->SetInput("X", {"x1"});
@ -880,7 +880,7 @@ TEST(Backward, shared_var) {
TEST(Backward, half_backward) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
auto *op1 = block->AppendOp();
op1->SetType("minus");
op1->SetInput("X", {"a"});

2
paddle/framework/block_desc.cc
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@ -113,7 +113,7 @@ BlockDescBind *BlockDescBind::ParentBlock() const {
if (this->desc_->parent_idx() == kNoneBlockIndex) {
return nullptr;
}
return prog_->Block(static_cast<size_t>(this->desc_->parent_idx()));
return prog_->MutableBlock(static_cast<size_t>(this->desc_->parent_idx()));
}
BlockDesc *BlockDescBind::Proto() {

27
paddle/framework/executor.cc
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@ -73,33 +73,32 @@ static void CreateTensor(Variable* var, VarDesc::VarType var_type) {
}
}
void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id) {
void Executor::Run(const ProgramDescBind& pdesc, Scope* scope, int block_id) {
// TODO(tonyyang-svail):
// - only runs on the first device (i.e. no interdevice communication)
// - will change to use multiple blocks for RNN op and Cond Op
PADDLE_ENFORCE_GT(pdesc.blocks_size(), block_id);
auto& block = pdesc.blocks(block_id);
PADDLE_ENFORCE_LT(block_id, pdesc.Size());
auto& block = pdesc.Block(block_id);
auto& device = device_contexts_[0];
Scope& local_scope = scope->NewScope();
for (auto& var : block.vars()) {
if (var.persistable()) {
auto* ptr = scope->Var(var.name());
CreateTensor(ptr, var.type());
VLOG(3) << "Create Variable " << var.name()
for (auto& var : block.AllVars()) {
if (var->Persistable()) {
auto* ptr = scope->Var(var->Name());
CreateTensor(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " global, which pointer is " << ptr;
} else {
auto* ptr = local_scope.Var(var.name());
CreateTensor(ptr, var.type());
VLOG(3) << "Create Variable " << var.name()
auto* ptr = local_scope.Var(var->Name());
CreateTensor(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " locally, which pointer is " << ptr;
}
}
for (auto& op_desc : block.ops()) {
auto op = paddle::framework::OpRegistry::CreateOp(
op_desc, const_cast<ProgramDesc*>(&pdesc));
for (auto& op_desc : block.AllOps()) {
auto op = paddle::framework::OpRegistry::CreateOp(*op_desc);
op->Run(local_scope, *device);
}

4
paddle/framework/executor.h
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@ -14,8 +14,8 @@ limitations under the License. */
#pragma once
#include "paddle/framework/framework.pb.h"
#include "paddle/framework/op_info.h"
#include "paddle/framework/program_desc.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/tensor.h"
@ -34,7 +34,7 @@ class Executor {
* ProgramDesc
* Scope
*/
void Run(const ProgramDesc&, Scope*, int);
void Run(const ProgramDescBind&, Scope*, int);
private:
std::vector<platform::DeviceContext*> device_contexts_;

28
paddle/framework/op_desc.cc
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@ -52,6 +52,22 @@ class CompileTimeInferShapeContext : public InferShapeContext {
const std::vector<std::string> &Outputs(
const std::string &name) const override;
void ShareLoD(const std::string &in, const std::string &out, size_t i = 0,
size_t j = 0) const override {
PADDLE_ENFORCE_LT(i, Inputs(in).size());
PADDLE_ENFORCE_LT(j, Outputs(out).size());
auto *in_var = block_.FindVarRecursive(Inputs(in)[i]);
auto *out_var = block_.FindVarRecursive(Outputs(out)[j]);
if (in_var->GetType() != VarDesc::LOD_TENSOR) {
VLOG(3) << "input " << in << "is not LodTensor";
return;
}
PADDLE_ENFORCE_EQ(in_var->GetType(), VarDesc::LOD_TENSOR,
"The %d-th output of Output(%s) must be LoDTensor.", j,
out);
in_var->SetLoDLevel(out_var->GetLodLevel());
}
private:
DDim GetDim(const std::string &name) const override;
@ -98,7 +114,12 @@ OpDescBind::OpDescBind(const OpDesc &desc, ProgramDescBind *prog)
// restore attrs_
for (const OpDesc::Attr &attr : desc_.attrs()) {
std::string attr_name = attr.name();
attrs_[attr_name] = GetAttrValue(attr, prog->Proto());
if (attr.type() != AttrType::BLOCK) {
attrs_[attr_name] = GetAttrValue(attr);
} else {
auto bid = attr.block_idx();
attrs_[attr_name] = prog->MutableBlock(bid);
}
}
}
@ -172,8 +193,7 @@ void OpDescBind::SetAttr(const std::string &name, const Attribute &v) {
}
void OpDescBind::SetBlockAttr(const std::string &name, BlockDescBind &block) {
BlockDesc *desc = block.Proto();
this->attrs_[name] = desc;
this->attrs_[name] = &block;
need_update_ = true;
}
@ -192,7 +212,7 @@ Attribute OpDescBind::GetAttr(const std::string &name) const {
int OpDescBind::GetBlockAttr(const std::string &name) const {
auto it = attrs_.find(name);
PADDLE_ENFORCE(it != attrs_.end(), "Attribute %s is not found", name);
return boost::get<BlockDesc *>(it->second)->idx();
return boost::get<BlockDescBind *>(it->second)->ID();
}
const std::unordered_map<std::string, Attribute> &OpDescBind::GetAttrMap()

8
paddle/framework/op_registry.cc
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@ -43,13 +43,15 @@ static VariableNameMap ConvertOpDescVarsToVarNameMap(
return ret_val;
}
std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const OpDesc& op_desc,
ProgramDesc* program) {
std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const OpDesc& op_desc) {
VLOG(1) << "CreateOp directly from OpDesc is deprecated. It should only be"
"used in unit tests. Use CreateOp(const OpDescBind& op_desc) "
"instead.";
VariableNameMap inputs = ConvertOpDescVarsToVarNameMap(op_desc.inputs());
VariableNameMap outputs = ConvertOpDescVarsToVarNameMap(op_desc.outputs());
AttributeMap attrs;
for (auto& attr : op_desc.attrs()) {
attrs[attr.name()] = GetAttrValue(attr, program);
attrs[attr.name()] = GetAttrValue(attr);
}
return CreateOp(op_desc.type(), inputs, outputs, attrs);

3
paddle/framework/op_registry.h
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@ -77,8 +77,7 @@ class OpRegistry {
const VariableNameMap& outputs,
AttributeMap attrs);
static std::unique_ptr<OperatorBase> CreateOp(const OpDesc& op_desc,
ProgramDesc* program);
static std::unique_ptr<OperatorBase> CreateOp(const OpDesc& op_desc);
static std::unique_ptr<OperatorBase> CreateOp(const OpDescBind& op_desc);
};

12
paddle/framework/op_registry_test.cc
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@ -74,7 +74,7 @@ TEST(OpRegistry, CreateOp) {
attr->set_type(paddle::framework::AttrType::FLOAT);
attr->set_f(scale);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
paddle::framework::Scope scope;
paddle::platform::CPUDeviceContext dev_ctx;
op->Run(scope, dev_ctx);
@ -95,7 +95,7 @@ TEST(OpRegistry, IllegalAttr) {
bool caught = false;
try {
paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
paddle::framework::OpRegistry::CreateOp(op_desc);
} catch (paddle::platform::EnforceNotMet err) {
caught = true;
std::string msg = "larger_than check fail";
@ -115,7 +115,7 @@ TEST(OpRegistry, DefaultValue) {
ASSERT_TRUE(op_desc.IsInitialized());
auto op = paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
paddle::framework::Scope scope;
paddle::platform::CPUDeviceContext dev_ctx;
op->Run(scope, dev_ctx);
@ -131,7 +131,7 @@ TEST(OpRegistry, CustomChecker) {
// attr 'test_attr' is not set
bool caught = false;
try {
paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
paddle::framework::OpRegistry::CreateOp(op_desc);
} catch (paddle::platform::EnforceNotMet err) {
caught = true;
std::string msg = "Attribute 'test_attr' is required!";
@ -149,7 +149,7 @@ TEST(OpRegistry, CustomChecker) {
attr->set_i(3);
caught = false;
try {
paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
paddle::framework::OpRegistry::CreateOp(op_desc);
} catch (paddle::platform::EnforceNotMet err) {
caught = true;
std::string msg = "'test_attr' must be even!";
@ -166,7 +166,7 @@ TEST(OpRegistry, CustomChecker) {
attr->set_name("test_attr");
attr->set_type(paddle::framework::AttrType::INT);
attr->set_i(4);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
paddle::platform::CPUDeviceContext dev_ctx;
paddle::framework::Scope scope;
op->Run(scope, dev_ctx);

14
paddle/framework/operator.cc
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@ -351,6 +351,20 @@ class RuntimeInferShapeContext : public InferShapeContext {
return op_.Outputs(name);
}
void ShareLoD(const std::string& in, const std::string& out, size_t i = 0,
size_t j = 0) const override {
PADDLE_ENFORCE_LT(i, Inputs(in).size());
PADDLE_ENFORCE_LT(j, Outputs(out).size());
Variable* in_var = scope_.FindVar(Inputs(in)[i]);
Variable* out_var = scope_.FindVar(Outputs(out)[j]);
if (!in_var->IsType<LoDTensor>()) return;
PADDLE_ENFORCE(out_var->IsType<LoDTensor>(),
"The %d-th output of Output(%s) must be LoDTensor.", j, out);
auto in_tensor = in_var->Get<LoDTensor>();
auto* out_tensor = out_var->GetMutable<LoDTensor>();
out_tensor->set_lod(in_tensor.lod());
}
private:
DDim GetDim(const std::string& name) const override {
Variable* var = scope_.FindVar(name);

6
paddle/framework/operator_test.cc
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@ -83,7 +83,7 @@ TEST(OperatorBase, all) {
paddle::platform::CPUDeviceContext device_context;
paddle::framework::Scope scope;
auto op = paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
scope.Var("OUT1");
ASSERT_EQ(paddle::framework::op_run_num, 0);
op->Run(scope, device_context);
@ -208,7 +208,7 @@ TEST(OpKernel, all) {
paddle::platform::CPUDeviceContext cpu_device_context;
paddle::framework::Scope scope;
auto op = paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
ASSERT_EQ(paddle::framework::cpu_kernel_run_num, 0);
op->Run(scope, cpu_device_context);
ASSERT_EQ(paddle::framework::cpu_kernel_run_num, 1);
@ -244,7 +244,7 @@ TEST(OpKernel, multi_inputs) {
scope.Var("y0")->GetMutable<LoDTensor>();
scope.Var("y1")->GetMutable<LoDTensor>();
auto op = paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
op->Run(scope, cpu_device_context);
}

4
paddle/framework/program_desc.h
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@ -37,7 +37,9 @@ class ProgramDescBind {
BlockDescBind *AppendBlock(const BlockDescBind &parent);
BlockDescBind *Block(size_t idx) { return blocks_[idx].get(); }
BlockDescBind *MutableBlock(size_t idx) { return blocks_[idx].get(); }
const BlockDescBind &Block(size_t idx) const { return *blocks_[idx]; }
size_t Size() const { return blocks_.size(); }

8
paddle/framework/program_desc_test.cc
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@ -20,7 +20,7 @@ namespace paddle {
namespace framework {
TEST(ProgramDesc, copy_ctor) {
ProgramDescBind program;
auto* global_block = program.Block(0);
auto* global_block = program.MutableBlock(0);
auto* x = global_block->Var("X");
x->SetType(VarDesc_VarType_LOD_TENSOR);
x->SetLoDLevel(0);
@ -44,7 +44,7 @@ TEST(ProgramDesc, copy_ctor) {
ProgramDescBind program_copy(program);
auto* global_block_copy = program_copy.Block(0);
auto* global_block_copy = program_copy.MutableBlock(0);
ASSERT_NE(global_block, global_block_copy);
auto assert_same_var = [&](const std::string& name, VarDescBind* var_before) {
@ -82,7 +82,7 @@ TEST(ProgramDesc, copy_ctor) {
TEST(ProgramDescBind, serialize_and_deserialize) {
ProgramDescBind program_origin;
auto* global_block = program_origin.Block(0);
auto* global_block = program_origin.MutableBlock(0);
auto* x = global_block->Var("X");
x->SetType(VarDesc_VarType_LOD_TENSOR);
x->SetLoDLevel(0);
@ -108,7 +108,7 @@ TEST(ProgramDescBind, serialize_and_deserialize) {
program_origin.Proto()->SerializeToString(&binary_str);
ProgramDescBind program_restored(binary_str);
auto* global_block_restored = program_restored.Block(0);
auto* global_block_restored = program_restored.MutableBlock(0);
ASSERT_NE(global_block, global_block_restored);
auto assert_same_var = [&](const std::string& name, VarDescBind* var_before) {

10
paddle/framework/prune_test.cc
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@ -52,7 +52,7 @@ void AddOp(const std::string &type, const f::VariableNameMap &inputs,
TEST(Prune, one_operator) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
AddOp("one_one", {{"input", {"a"}}}, {{"output", {"b"}}}, {}, block);
@ -69,7 +69,7 @@ TEST(Prune, one_operator) {
TEST(Prune, forward) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
AddOp("one_one", {{"input", {"a"}}}, {{"output", {"b"}}}, {}, block);
AddOp("one_one", {{"input", {"b"}}}, {{"output", {"c"}}}, {}, block);
@ -88,7 +88,7 @@ TEST(Prune, forward) {
TEST(Prune, multi_input_op) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
AddOp("one_one", {{"input", {"a0"}}}, {{"output", {"b0"}}}, {}, block);
AddOp("one_one", {{"input", {"a1"}}}, {{"output", {"b1"}}}, {}, block);
@ -106,7 +106,7 @@ TEST(Prune, multi_input_op) {
TEST(Prune, multi_output_op) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
AddOp("one_two", {{"input", {"a"}}}, {{"output", {"b", "c"}}}, {}, block);
AddOp("one_one", {{"input", {"b"}}}, {{"output", {"b1"}}}, {}, block);
@ -122,7 +122,7 @@ TEST(Prune, multi_output_op) {
TEST(Prune, multi_target) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
AddOp("one_two", {{"input", {"a"}}}, {{"output", {"b", "c"}}}, {}, block);
AddOp("one_one", {{"input", {"b"}}}, {{"output", {"b1"}}}, {}, block);

3
paddle/framework/shape_inference.cc
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@ -28,9 +28,6 @@ void InferShapeContext::SetOutputsDim(
SetDims(names, dims);
}
void InferShapeContext::ShareLoD(const std::string &in, const std::string &out,
size_t i, size_t j) const {}
std::vector<framework::DDim> InferShapeContext::GetDims(
const std::vector<std::string> &names) const {
std::vector<framework::DDim> ret;

5
paddle/framework/shape_inference.h
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@ -43,9 +43,8 @@ class InferShapeContext {
virtual const std::vector<std::string> &Outputs(
const std::string &name) const = 0;
// TODO(qiao) implement this function
void ShareLoD(const std::string &in, const std::string &out, size_t i = 0,
size_t j = 0) const;
virtual void ShareLoD(const std::string &in, const std::string &out,
size_t i = 0, size_t j = 0) const = 0;
protected:
virtual framework::DDim GetDim(const std::string &name) const = 0;

2
paddle/framework/type_defs.h
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@ -36,7 +36,7 @@ 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*>;
std::vector<bool>, BlockDescBind*>;
using AttributeMap = std::unordered_map<std::string, Attribute>;

36
paddle/framework/var_type_inference_test.cc
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@ -63,41 +63,43 @@ namespace framework {
TEST(InferVarType, sum_op) {
ProgramDescBind prog;
auto *op = prog.Block(0)->AppendOp();
auto *op = prog.MutableBlock(0)->AppendOp();
op->SetType("sum");
op->SetInput("X", {"test_a", "test_b", "test_c"});
op->SetOutput("Out", {"test_out"});
prog.Block(0)->Var("test_a")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->Var("test_b")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->Var("test_c")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->Var("test_out");
prog.MutableBlock(0)->Var("test_a")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test_b")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test_c")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test_out");
op->InferVarType(prog.Block(0));
op->InferVarType(prog.MutableBlock(0));
ASSERT_EQ(VarDesc::SELECTED_ROWS, prog.Block(0)->Var("test_out")->GetType());
ASSERT_EQ(VarDesc::SELECTED_ROWS,
prog.MutableBlock(0)->Var("test_out")->GetType());
prog.Block(0)->Var("test_b")->SetType(VarDesc::LOD_TENSOR);
op->InferVarType(prog.Block(0));
ASSERT_EQ(VarDesc::LOD_TENSOR, prog.Block(0)->Var("test_out")->GetType());
prog.MutableBlock(0)->Var("test_b")->SetType(VarDesc::LOD_TENSOR);
op->InferVarType(prog.MutableBlock(0));
ASSERT_EQ(VarDesc::LOD_TENSOR,
prog.MutableBlock(0)->Var("test_out")->GetType());
}
TEST(InferVarType, sum_op_without_infer_var_type) {
ProgramDescBind prog;
auto *op = prog.Block(0)->AppendOp();
auto *op = prog.MutableBlock(0)->AppendOp();
op->SetType("sum_without_infer_var_type");
op->SetInput("X", {"test2_a", "test2_b", "test2_c"});
op->SetOutput("Out", {"test2_out"});
prog.Block(0)->Var("test2_a")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->Var("test2_b")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->Var("test2_c")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->Var("test2_out");
prog.MutableBlock(0)->Var("test2_a")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test2_b")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test2_c")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test2_out");
op->InferVarType(prog.Block(0));
op->InferVarType(prog.MutableBlock(0));
ASSERT_EQ(VarDesc_VarType_LOD_TENSOR,
prog.Block(0)->Var("test2_out")->GetType());
prog.MutableBlock(0)->Var("test2_out")->GetType());
}
} // namespace framework

2
paddle/operators/dynamic_recurrent_op_test.cc
Unescape View File

@ -51,7 +51,7 @@ class RNNAlgorithmTestHelper : public ::testing::Test {
CreateGlobalVariables();
auto op_desc = CreateOpDesc();
op = paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
op = paddle::framework::OpRegistry::CreateOp(op_desc);
dop = &(dynamic_cast<DynamicRecurrentOp*>(op.get())->rnn);
InitCacheManually();
InitStepNet();

12
paddle/operators/gaussian_random_op.cc
Unescape View File

@ -45,14 +45,14 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of GaussianRandomOp should not be null.");
auto dims = ctx->Attrs().Get<std::vector<int>>("dims");
auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
std::vector<int64_t> temp;
temp.reserve(dims.size());
for (auto dim : dims) {
temp.reserve(shape.size());
for (auto dim : shape) {
temp.push_back(static_cast<int64_t>(dim));
}
PADDLE_ENFORCE(dims.size() > 0UL,
"dims can be one int or array. dims must be set.");
PADDLE_ENFORCE(shape.size() > 0UL,
"shape can be one int or array. shape must be set.");
ctx->SetOutputDim("Out", framework::make_ddim(temp));
}
@ -74,7 +74,7 @@ GaussianRandom operator.
Use to initialize tensor with gaussian random generator.
)DOC");
AddAttr<std::vector<int>>("dims", "The dimension of random tensor.");
AddAttr<std::vector<int>>("shape", "The dimension of random tensor.");
AddAttr<float>("mean", "mean of random tensor.").SetDefault(.0f);
AddAttr<float>("std", "std of random tensor.").SetDefault(1.0f);
AddAttr<int>("seed",

4
paddle/operators/lookup_table_op.cc
Unescape View File

@ -43,7 +43,7 @@ class LookupTableOp : public framework::OperatorWithKernel {
protected:
framework::DataType IndicateDataType(
const framework::ExecutionContext& ctx) const override {
return framework::ToDataType(ctx.Input<Tensor>("W")->type());
return framework::ToDataType(ctx.Input<LoDTensor>("W")->type());
}
};
@ -93,7 +93,7 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
protected:
framework::DataType IndicateDataType(
const framework::ExecutionContext& ctx) const override {
return framework::ToDataType(ctx.Input<Tensor>("W")->type());
return framework::ToDataType(ctx.Input<LoDTensor>("W")->type());
}
};

26
paddle/operators/lookup_table_op.cu
Unescape View File

@ -61,16 +61,16 @@ template <typename T>
class LookupTableCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto table_t = context.Input<Tensor>("W");
auto ids_t = context.Input<Tensor>("Ids");
auto output_t = context.Output<Tensor>("Out");
auto* table_t = context.Input<LoDTensor>("W");
auto* ids_t = context.Input<LoDTensor>("Ids");
auto* output_t = context.Output<LoDTensor>("Out");
size_t N = table_t->dims()[0];
size_t D = table_t->dims()[1];
size_t K = ids_t->numel();
auto ids = ids_t->data<int64_t>();
auto table = table_t->data<T>();
auto output = output_t->mutable_data<T>(context.GetPlace());
auto* ids = ids_t->data<int64_t>();
auto* table = table_t->data<T>();
auto* output = output_t->mutable_data<T>(context.GetPlace());
dim3 threads(128, 8);
dim3 grids(8, 1);
@ -87,9 +87,9 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& context) const override {
bool is_sparse = context.Attr<bool>("is_sparse");
if (is_sparse) {
auto* ids = context.Input<Tensor>("Ids");
auto* table = context.Input<Tensor>("W");
auto* d_output = context.Input<Tensor>(framework::GradVarName("Out"));
auto* ids = context.Input<LoDTensor>("Ids");
auto* table = context.Input<LoDTensor>("W");
auto* d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* d_table = context.Output<SelectedRows>(framework::GradVarName("W"));
auto* ids_data = ids->data<int64_t>();
@ -116,12 +116,12 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
auto* d_output_data = d_output->data<T>();
PADDLE_ENFORCE_EQ(d_table_value->dims(), d_output->dims());
memory::Copy(gpu_place, d_table_data, gpu_place, d_output_data,
d_output->numel(), stream);
d_output->numel() * sizeof(T), stream);
} else {
auto ids_t = context.Input<Tensor>("Ids");
auto d_output_t = context.Input<Tensor>(framework::GradVarName("Out"));
auto d_table_t = context.Output<Tensor>(framework::GradVarName("W"));
auto ids_t = context.Input<LoDTensor>("Ids");
auto d_output_t = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto d_table_t = context.Output<LoDTensor>(framework::GradVarName("W"));
int N = d_table_t->dims()[0];
int D = d_table_t->dims()[1];

28
paddle/operators/lookup_table_op.h
Unescape View File

@ -19,22 +19,22 @@
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using SelectedRows = framework::SelectedRows;
template <typename T>
class LookupTableKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto table_t = context.Input<Tensor>("W"); // float tensor
auto ids_t = context.Input<Tensor>("Ids"); // int tensor
auto output_t = context.Output<Tensor>("Out"); // float tensor
auto* table_t = context.Input<LoDTensor>("W"); // float tensor
auto* ids_t = context.Input<LoDTensor>("Ids"); // int tensor
auto* output_t = context.Output<LoDTensor>("Out"); // float tensor
int N = table_t->dims()[0];
int D = table_t->dims()[1];
auto ids = ids_t->data<int64_t>();
auto table = table_t->data<T>();
auto output = output_t->mutable_data<T>(context.GetPlace());
auto* ids = ids_t->data<int64_t>();
auto* table = table_t->data<T>();
auto* output = output_t->mutable_data<T>(context.GetPlace());
for (int64_t i = 0; i < ids_t->numel(); ++i) {
PADDLE_ENFORCE_LT(ids[i], N);
PADDLE_ENFORCE_GE(ids[i], 0);
@ -49,9 +49,9 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& context) const override {
bool is_sparse = context.Attr<bool>("is_sparse");
if (is_sparse) {
auto* ids = context.Input<Tensor>("Ids");
auto* table = context.Input<Tensor>("W");
auto* d_output = context.Input<Tensor>(framework::GradVarName("Out"));
auto* ids = context.Input<LoDTensor>("Ids");
auto* table = context.Input<LoDTensor>("W");
auto* d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* d_table = context.Output<SelectedRows>(framework::GradVarName("W"));
auto* ids_data = ids->data<int64_t>();
@ -76,10 +76,10 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_EQ(d_table_value->dims(), d_output->dims());
memcpy(d_table_data, d_output_data, sizeof(T) * d_output->numel());
} else {
auto* ids = context.Input<Tensor>("Ids");
auto* d_output = context.Input<Tensor>(framework::GradVarName("Out"));
auto* d_table = context.Output<Tensor>(framework::GradVarName("W"));
auto* table = context.Input<Tensor>("W");
auto* ids = context.Input<LoDTensor>("Ids");
auto* d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* d_table = context.Output<LoDTensor>(framework::GradVarName("W"));
auto* table = context.Input<LoDTensor>("W");
auto* ids_data = ids->data<int64_t>();
auto ids_dim = ids->dims();

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