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PaddleDetection
提交 ce7e503c 编写于 作者: X Xin Pan
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refactor to avoid scope. 

test=develop
上级 0238a3bb
隐藏空白更改
内联 并排
Showing with 347 addition and 199 deletion
paddle/fluid/framework/operator.cc
浏览文件 @ ce7e503c
......@@ -180,6 +180,11 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
VLOG(3) << place << " " << DebugStringEx(&scope);
}
void OperatorBase::Run(const RuntimeContext& ctx,
const platform::Place& place) {
RunImpl(ctx, place);
}
bool OperatorBase::HasInputs(const std::string& name) const {
return inputs_.find(name) != inputs_.end();
}
......@@ -954,6 +959,51 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
}
}
void OperatorWithKernel::RunImpl(const RuntimeContext& ctx,
const platform::Place& place) const {
Scope scope;
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto* dev_ctx = pool.Get(place);
// check if op[type] has kernel registered.
auto& all_op_kernels = AllOpKernels();
auto kernels_iter = all_op_kernels.find(type_);
if (kernels_iter == all_op_kernels.end()) {
PADDLE_THROW(
"There are no kernels which are registered in the %s operator.", type_);
}
OpKernelMap& kernels = kernels_iter->second;
auto expected_kernel_key = this->GetExpectedKernelType(
ExecutionContext(*this, scope, *dev_ctx, ctx));
VLOG(3) << "expected_kernel_key:" << expected_kernel_key;
auto kernel_iter = kernels.find(expected_kernel_key);
#ifdef PADDLE_WITH_MKLDNN
// workaround for missing MKLDNN kernel when FLAGS_use_mkldnn env var is set
if (kernel_iter == kernels.end() &&
expected_kernel_key.library_type_ == LibraryType::kMKLDNN) {
VLOG(3) << "missing MKLDNN kernel: fallbacking to PLAIN one";
expected_kernel_key.library_type_ = LibraryType::kPlain;
expected_kernel_key.data_layout_ = DataLayout::kAnyLayout;
kernel_iter = kernels.find(expected_kernel_key);
}
#endif
if (kernel_iter == kernels.end()) {
PADDLE_THROW("op %s does not have kernel for %s", type_,
KernelTypeToString(expected_kernel_key));
}
if (!(expected_kernel_key.place_ == dev_ctx->GetPlace())) {
dev_ctx = pool.Get(expected_kernel_key.place_);
}
RuntimeInferShapeContext infer_shape_ctx(*this, scope, ctx);
this->InferShape(&infer_shape_ctx);
kernel_iter->second(ExecutionContext(*this, scope, *dev_ctx, ctx));
}
void OperatorWithKernel::TransferInplaceVarsBack(
const Scope& scope, const std::vector<std::string>& inplace_vars,
const Scope& transfer_scope) const {
......@@ -1041,12 +1091,9 @@ Scope* OperatorWithKernel::PrepareData(
proto::VarType::Type OperatorWithKernel::IndicateDataType(
const ExecutionContext& ctx) const {
auto& scope = ctx.scope();
int data_type = -1;
std::string last_input_name;
for (auto& input : this->inputs_) {
for (auto& ipt_name : input.second) {
auto* var = scope.FindVar(ipt_name);
for (const Variable* var : ctx.MultiInputVar(input.first)) {
if (var != nullptr) {
const Tensor* t = nullptr;
if (var->IsType<Tensor>()) {
......@@ -1062,10 +1109,9 @@ proto::VarType::Type OperatorWithKernel::IndicateDataType(
int tmp = static_cast<int>(t->type());
PADDLE_ENFORCE(
tmp == data_type || data_type == -1,
"DataType of Paddle Op %s must be the same. Get %s(%d) != %s(%d)",
Type(), last_input_name, data_type, ipt_name, tmp);
"DataType of Paddle Op %s must be the same. Get (%d) != (%d)",
Type(), data_type, tmp);
data_type = tmp;
last_input_name = ipt_name;
}
}
}
......
paddle/fluid/framework/operator.h
浏览文件 @ ce7e503c
......@@ -81,6 +81,10 @@ class RuntimeContext {
RuntimeContext(const VariableNameMap& innames,
const VariableNameMap& outnames, const Scope& scope);
RuntimeContext(const VariableValueMap& invars,
const VariableValueMap& outvars)
: inputs(invars), outputs(outvars) {}
VariableValueMap inputs;
VariableValueMap outputs;
};
......@@ -101,6 +105,7 @@ class OperatorBase {
/// Executor will call this interface function to Run an op.
// The implementation should be written at RunImpl
void Run(const Scope& scope, const platform::Place& place);
void Run(const RuntimeContext& ctx, const platform::Place& place);
// FIXME(typhoonzero): this is only used for recv_op to stop event_loop.
virtual void Stop() {}
......@@ -167,6 +172,9 @@ class OperatorBase {
void CheckAllInputOutputSet() const;
virtual void RunImpl(const Scope& scope,
const platform::Place& place) const = 0;
virtual void RunImpl(const RuntimeContext& ctx,
const platform::Place& place) const {}
};
class ExecutionContext {
......@@ -458,6 +466,8 @@ class OperatorWithKernel : public OperatorBase {
// same.
proto::VarType::Type IndicateDataType(const ExecutionContext& ctx) const;
void RunImpl(const Scope& scope, const platform::Place& place) const final;
void RunImpl(const RuntimeContext& ctx,
const platform::Place& place) const final;
/**
* Transfer data from scope to a transfered scope. If there is no data need to
......
paddle/fluid/imperative/layer.cc
浏览文件 @ ce7e503c
......@@ -31,6 +31,11 @@ using framework::Variable;
void AddTo(Variable* src, Variable* dst) {
framework::LoDTensor* dst_tensor = dst->GetMutable<framework::LoDTensor>();
framework::LoDTensor* src_tensor = src->GetMutable<framework::LoDTensor>();
VLOG(3) << "apply var grad " << src_tensor->data<float>()[0] << " "
<< src_tensor->data<float>()[1] << " "
<< src_tensor->data<float>()[2];
PADDLE_ENFORCE(dst_tensor->numel() == src_tensor->numel(), "%lld vs %lld",
dst_tensor->numel(), src_tensor->numel());
float* dst_data = dst_tensor->mutable_data<float>(platform::CPUPlace());
......@@ -38,16 +43,28 @@ void AddTo(Variable* src, Variable* dst) {
for (size_t i = 0; i < src_tensor->numel(); ++i) {
dst_data[i] += src_data[i];
}
VLOG(3) << "apply var dst grad " << dst_tensor->data<float>()[0] << " "
<< dst_tensor->data<float>()[1] << " "
<< dst_tensor->data<float>()[2];
}
class Autograd {
public:
explicit Autograd(framework::Scope* scope) : scope_(scope) {}
Autograd() {}
void RunBackward(VarBase* var) {
PADDLE_ENFORCE(var->pre_op_->op_desc_);
// TODO(panyx0718): Only create for vars that "require_grad"
(*var->pre_op_->output_vars_)[var->pre_op_out_idx_]->grads_ = var->grads_;
LOG(ERROR) << reinterpret_cast<void*>(var->grads_) << " vs "
<< reinterpret_cast<void*>(
var->pre_op_
->output_vars_[var->pre_op_out_name_]
[var->pre_op_out_idx_]
->grads_);
var->pre_op_->output_vars_[var->pre_op_out_name_][var->pre_op_out_idx_]
->grads_->GetMutable<framework::LoDTensor>()
->ShareDataWith(var->grads_->Get<framework::LoDTensor>());
std::deque<OpBase*> ready;
ready.push_back(var->pre_op_);
......@@ -57,18 +74,23 @@ class Autograd {
while (!ready.empty()) {
OpBase* ready_op = ready.front();
ready.pop_front();
std::vector<Variable*> input_grads = ready_op->ApplyGrad(scope_);
for (size_t i = 0; i < input_grads.size(); ++i) {
if (!input_grads[i]) continue;
OpBase* pre_op = ready_op->pre_ops_->at(i);
if (!pre_op) continue;
dep_counts[pre_op] -= 1;
PADDLE_ENFORCE(dep_counts[pre_op] >= 0);
bool pre_op_ready = dep_counts[pre_op] == 0;
if (pre_op_ready) {
ready.push_back(pre_op);
std::map<std::string, std::vector<VarBase*>> input_grads =
ready_op->ApplyGrad();
VLOG(3) << "after apply grad";
for (auto it : input_grads) {
const std::vector<VarBase*>& ingrads = it.second;
for (size_t i = 0; i < ingrads.size(); ++i) {
if (!ingrads[i]) continue;
OpBase* pre_op = (*ready_op->pre_ops_)[it.first][i];
if (!pre_op) continue;
dep_counts[pre_op] -= 1;
PADDLE_ENFORCE(dep_counts[pre_op] >= 0);
bool pre_op_ready = dep_counts[pre_op] == 0;
if (pre_op_ready) {
ready.push_back(pre_op);
}
}
}
}
......@@ -85,26 +107,25 @@ class Autograd {
while (!queue.empty()) {
OpBase* candidate = queue.front();
queue.pop_front();
for (OpBase* pre_op : *(candidate->pre_ops_)) {
if (!pre_op) continue;
if (visited.find(pre_op) == visited.end()) {
visited.insert(pre_op);
queue.push_back(pre_op);
for (auto it : *(candidate->pre_ops_)) {
for (OpBase* pre_op : it.second) {
if (!pre_op) continue;
if (visited.find(pre_op) == visited.end()) {
visited.insert(pre_op);
queue.push_back(pre_op);
}
ret[pre_op] += 1;
}
ret[pre_op] += 1;
}
}
return ret;
}
framework::Scope* scope_;
};
framework::Variable* CreateVariable(const std::string& name,
const framework::DDim& dim, float val,
framework::Scope* scope,
bool random_name = true) {
void CreateVariable(const std::string& name, const framework::DDim& dim,
float val, bool random_name, framework::Variable* var) {
if (var->IsInitialized()) return;
std::string varname = name;
if (random_name) {
std::mt19937 rng;
......@@ -116,12 +137,9 @@ framework::Variable* CreateVariable(const std::string& name,
}
VLOG(3) << "creating var " << varname;
framework::Variable* var = scope->Var(varname);
framework::LoDTensor* tensor = var->GetMutable<framework::LoDTensor>();
float* data = tensor->mutable_data<float>(dim, platform::CPUPlace());
std::fill(data, data + tensor->numel(), val);
return var;
}
framework::LoDTensor& VarBase::Grad() {
......@@ -129,94 +147,56 @@ framework::LoDTensor& VarBase::Grad() {
return *grads_->GetMutable<framework::LoDTensor>();
}
void VarBase::ApplyGrad(framework::Scope* scope, Variable* grad) {
VLOG(3) << "apply var grad " << var_desc_->Name() << " "
<< grad->Get<framework::LoDTensor>().data<float>()[0];
if (!grads_) {
grads_ =
CreateVariable(string::Sprintf("%s@IGrad", var_desc_->Name()),
var_->Get<framework::LoDTensor>().dims(), 0.0, scope);
std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
if (!grad_op_desc_) {
VLOG(3) << "op with no grad: " << op_desc_->Type();
return {};
}
AddTo(grad, grads_);
VLOG(3) << "grad_ after apply var grad " << var_desc_->Name() << " "
<< grads_->Get<framework::LoDTensor>().data<float>()[0];
}
std::vector<Variable*> OpBase::ApplyGrad(framework::Scope* scope) {
VLOG(3) << "op grad " << grad_op_desc_->Type();
for (const std::string& grad_invar : grad_op_desc_->InputArgumentNames()) {
if (grad_to_var_->find(grad_invar) == grad_to_var_->end()) {
// grad op inputs can be forward inputs, so not in grad_to_var.
continue;
}
VLOG(3) << "op grad in var " << grad_invar;
block_->FindRecursiveOrCreateVar(grad_invar);
framework::Variable* var = scope->Var(grad_invar);
const std::string& invar = grad_to_var_->at(grad_invar);
for (VarBase* varbase : *output_vars_) {
// Use the accumulated grads_ by sharing the input with grads_.
if (varbase->var_desc_->Name() == invar) {
var->GetMutable<framework::LoDTensor>()->ShareDataWith(
varbase->grads_->Get<framework::LoDTensor>());
break;
}
std::map<std::string, std::vector<framework::Variable*>> grad_outputs;
for (auto it : grad_output_vars_) {
auto& outputs = grad_outputs[it.first];
for (size_t i = 0; i < it.second.size(); ++i) {
outputs.push_back(new framework::Variable());
outputs.back()->GetMutable<framework::LoDTensor>();
/*
auto& accum_grad_t = it.second[i]->Get<framework::LoDTensor>();
Variable* grad_var = outputs.back();
float* data = grad_var->GetMutable<framework::LoDTensor>()
->mutable_data<float>(accum_grad_t.dims(), platform::CPUPlace());
std::fill(data, data + accum_grad_t.numel(), 0.0);*/
}
}
for (const std::string& outvar : grad_op_desc_->OutputArgumentNames()) {
VLOG(3) << "grad outvar " << outvar;
block_->FindRecursiveOrCreateVar(outvar);
framework::Variable* var = scope->Var(outvar);
if (!var->IsInitialized()) {
framework::VarDesc* var_desc = block_->FindVar(outvar);
if (var_desc->GetType() == framework::proto::VarType::LOD_TENSOR) {
var->GetMutable<framework::LoDTensor>();
} else {
LOG(ERROR) << "tracer doesn't support yet";
}
}
}
grad_op_desc_->InferShape(*block_);
framework::RuntimeContext ctx(grad_input_vars_, grad_outputs);
// grad_op_desc_->InferShape(*block_);
grad_op_desc_->InferVarType(block_);
std::unique_ptr<framework::OperatorBase> opbase =
framework::OpRegistry::CreateOp(*grad_op_desc_);
opbase->Run(*scope, platform::CPUPlace());
// `ret` matches exactly with `input_vars_` of forward op.
std::vector<Variable*> ret;
for (size_t i = 0; i < input_vars_->size(); ++i) {
bool found = false;
VarBase* origin_var = (*input_vars_)[i];
for (const std::string& outvar : grad_op_desc_->OutputArgumentNames()) {
Variable* var = scope->FindVar(outvar);
std::string orig_var = grad_to_var_->at(outvar);
if (origin_var->var_desc_->Name() != orig_var) {
continue;
}
VLOG(3) << "apply grad " << outvar << " with origin " << orig_var;
origin_var->ApplyGrad(scope, var);
found = true;
ret.push_back(var);
// TODO(panyx0718): There might be another outvar with the same name.
// In that case, it doesn't matter the first one or the second one is
// used.
break;
}
if (!found) {
ret.push_back(nullptr);
opbase->Run(ctx, platform::CPUPlace());
for (auto it : grad_output_vars_) {
auto& outputs = grad_outputs[it.first];
auto& origin_outputs = it.second;
for (size_t i = 0; i < outputs.size(); ++i) {
framework::Variable* orig_grad = origin_outputs[i];
AddTo(outputs[i], orig_grad);
VLOG(3) << "done add to " << grad_op_desc_->Outputs().at(it.first)[i];
}
}
return ret;
return input_vars_;
}
void VarBase::RunBackward(framework::Scope* scope) {
grads_ = CreateVariable(framework::GradVarName(var_desc_->Name()),
var_->Get<framework::LoDTensor>().dims(), 1.0, scope,
false);
void VarBase::RunBackward() {
auto grads_t = grads_->GetMutable<framework::LoDTensor>();
float* data = grads_t->mutable_data<float>(platform::CPUPlace());
std::fill(data, data + grads_t->numel(), 1.0);
if (!pre_op_) return;
Autograd(scope).RunBackward(this);
Autograd().RunBackward(this);
}
} // namespace imperative
......
paddle/fluid/imperative/layer.h
浏览文件 @ ce7e503c
......@@ -14,11 +14,11 @@
#pragma once
#include <map>
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/var_desc.h"
#include "paddle/fluid/platform/enforce.h"
......@@ -33,18 +33,26 @@ class VarBase {
: pre_op_(nullptr),
pre_op_out_idx_(-1),
var_desc_(nullptr),
var_(nullptr),
grads_(nullptr) {}
virtual ~VarBase() {}
void ApplyGrad(framework::Scope* scope, framework::Variable* grad);
var_(new framework::Variable()),
grads_(new framework::Variable()) {}
virtual ~VarBase() {
if (var_) {
delete var_;
var_ = nullptr;
}
if (grads_) {
delete grads_;
grads_ = nullptr;
}
}
void RunBackward(framework::Scope* scope);
void RunBackward();
framework::LoDTensor& Grad();
OpBase* pre_op_;
std::string pre_op_out_name_;
int pre_op_out_idx_;
framework::VarDesc* var_desc_;
......@@ -55,17 +63,12 @@ class VarBase {
class OpBase {
public:
OpBase()
: input_vars_(new std::vector<VarBase*>()),
output_vars_(new std::vector<VarBase*>()),
pre_ops_(new std::vector<OpBase*>()),
pre_ops_out_idx_(new std::vector<int>()),
: pre_ops_(new std::map<std::string, std::vector<OpBase*>>()),
pre_ops_out_idx_(new std::map<std::string, std::vector<int>>()),
op_desc_(nullptr),
grad_op_desc_(nullptr) {}
virtual ~OpBase() {
delete input_vars_;
delete output_vars_;
delete pre_ops_;
delete pre_ops_out_idx_;
......@@ -73,16 +76,18 @@ class OpBase {
if (grad_to_var_) delete grad_to_var_;
}
std::vector<framework::Variable*> ApplyGrad(framework::Scope* scope);
std::map<std::string, std::vector<VarBase*>> ApplyGrad();
std::vector<VarBase*>* input_vars_;
std::vector<VarBase*>* output_vars_;
std::vector<OpBase*>* pre_ops_;
std::vector<int>* pre_ops_out_idx_;
std::map<std::string, std::vector<VarBase*>> input_vars_;
std::map<std::string, std::vector<VarBase*>> output_vars_;
std::map<std::string, std::vector<OpBase*>>* pre_ops_;
std::map<std::string, std::vector<int>>* pre_ops_out_idx_;
framework::OpDesc* op_desc_;
framework::OpDesc* grad_op_desc_;
std::unordered_map<std::string, std::string>* grad_to_var_;
std::map<std::string, std::vector<framework::Variable*>> grad_input_vars_;
std::map<std::string, std::vector<framework::Variable*>> grad_output_vars_;
framework::BlockDesc* block_;
};
......
paddle/fluid/imperative/tracer.h
浏览文件 @ ce7e503c
......@@ -41,6 +41,14 @@ void CreateGradOp(const framework::OpDesc& op_desc,
*grad_op_desc = grad_op_descs[0].release();
}
void InitVar(framework::Variable* var, framework::Variable* grad_var) {
auto& var_t = var->Get<framework::LoDTensor>();
float* data =
grad_var->GetMutable<framework::LoDTensor>()->mutable_data<float>(
var_t.dims(), platform::CPUPlace());
std::fill(data, data + var_t.numel(), 0.0);
}
class Tracer {
public:
explicit Tracer(framework::BlockDesc* root_block,
......@@ -53,10 +61,13 @@ class Tracer {
virtual ~Tracer() { delete root_scope_; }
void Trace(OpBase* op, const std::vector<VarBase*>& inputs,
const std::vector<VarBase*>& outputs,
void Trace(OpBase* op,
const std::map<std::string, std::vector<VarBase*>>& inputs,
const std::map<std::string, std::vector<VarBase*>>& outputs,
framework::BlockDesc* block) {
framework::Scope* scope = GetScope(block);
// framework::Scope* scope = GetScope(block);
std::map<std::string, VarBase*> vars;
framework::OpDesc* op_desc = op->op_desc_;
VLOG(3) << "tracer tracing " << op_desc->Type();
op_desc->InferShape(*block);
......@@ -64,48 +75,60 @@ class Tracer {
std::unique_ptr<framework::OperatorBase> op_base =
framework::OpRegistry::CreateOp(*op_desc);
*op->input_vars_ = inputs;
for (VarBase* input : inputs) {
const std::string vname = input->var_desc_->Name();
framework::Variable* var = scope->Var(vname);
input->var_ = var;
if (!var->IsInitialized()) {
framework::VarDesc* var_desc = block->FindVar(vname);
if (var_desc->GetType() == framework::proto::VarType::LOD_TENSOR) {
var->GetMutable<framework::LoDTensor>();
framework::VariableValueMap invars_map;
framework::VariableValueMap outvars_map;
op->input_vars_ = inputs;
for (auto it : op->input_vars_) {
auto& invars = invars_map[it.first];
for (VarBase* inp : it.second) {
PADDLE_ENFORCE_NOT_NULL(inp->var_, "op %s input %s nullptr",
op->op_desc_->Type(), inp->var_desc_->Name());
invars.push_back(inp->var_);
vars[inp->var_desc_->Name()] = inp;
if (inp->pre_op_) {
(*op->pre_ops_)[it.first].push_back(inp->pre_op_);
(*op->pre_ops_out_idx_)[it.first].push_back(inp->pre_op_out_idx_);
} else {
LOG(ERROR) << "tracer doesn't support yet";
(*op->pre_ops_)[it.first].push_back(nullptr);
}
VLOG(3) << "input vname " << inp->var_desc_->Name() << " "
<< inp->var_->Get<framework::LoDTensor>().dims().size()
<< reinterpret_cast<void*>(inp->var_);
}
if (input->pre_op_) {
op->pre_ops_->push_back(input->pre_op_);
op->pre_ops_out_idx_->push_back(input->pre_op_out_idx_);
} else {
op->pre_ops_->push_back(nullptr);
}
VLOG(3) << "input vname " << vname << " "
<< var->Get<framework::LoDTensor>().dims().size();
}
*op->output_vars_ = outputs;
for (size_t i = 0; i < outputs.size(); ++i) {
const std::string vname = outputs[i]->var_desc_->Name();
framework::Variable* var = scope->Var(vname);
if (!var->IsInitialized()) {
framework::VarDesc* var_desc = block->FindVar(vname);
op->output_vars_ = outputs;
for (auto it : op->output_vars_) {
auto& outvars = outvars_map[it.first];
const std::vector<VarBase*>& outputs = it.second;
for (size_t i = 0; i < outputs.size(); ++i) {
VarBase* out = outputs[i];
outvars.push_back(out->var_);
vars[out->var_desc_->Name()] = out;
framework::VarDesc* var_desc = block->FindVar(out->var_desc_->Name());
if (var_desc->GetType() == framework::proto::VarType::LOD_TENSOR) {
var->GetMutable<framework::LoDTensor>();
out->var_->GetMutable<framework::LoDTensor>();
} else {
LOG(ERROR) << "tracer doesn't support yet";
}
out->pre_op_ = op;
out->pre_op_out_name_ = it.first;
out->pre_op_out_idx_ = i;
VLOG(3) << "output vname " << out->var_desc_->Name() << " "
<< out->var_->Get<framework::LoDTensor>().dims().size() << " "
<< reinterpret_cast<void*>(out->var_) << " "
<< out->var_->IsInitialized();
}
outputs[i]->var_ = var;
outputs[i]->pre_op_ = op;
outputs[i]->pre_op_out_idx_ = i;
}
VLOG(3) << "tracer running " << op_desc->Type();
op_base->Run(*scope, platform::CPUPlace());
framework::RuntimeContext ctx(invars_map, outvars_map);
op_base->Run(ctx, platform::CPUPlace());
if (block == startup_block_) {
op->grad_op_desc_ = nullptr;
op->grad_to_var_ = nullptr;
......@@ -115,6 +138,39 @@ class Tracer {
CreateGradOp(*op_desc, {}, {block}, &grad_op_desc, grad_to_var);
op->grad_op_desc_ = grad_op_desc;
op->grad_to_var_ = grad_to_var;
for (auto it : grad_op_desc->Inputs()) {
auto& grad_in_vars = op->grad_input_vars_[it.first];
for (const std::string& grad_invar : it.second) {
block->FindRecursiveOrCreateVar(grad_invar);
auto var_it = op->grad_to_var_->find(grad_invar);
if (var_it == op->grad_to_var_->end()) {
auto fwd_var_it = vars.find(grad_invar);
PADDLE_ENFORCE(fwd_var_it != vars.end());
grad_in_vars.push_back(fwd_var_it->second->var_);
} else {
VarBase* var = vars[var_it->second];
if (!var->grads_->IsInitialized()) {
InitVar(var->var_, var->grads_);
}
grad_in_vars.push_back(var->grads_);
}
}
}
for (auto it : grad_op_desc->Outputs()) {
auto& grad_out_vars = op->grad_output_vars_[it.first];
for (const std::string& grad_outvar : it.second) {
block->FindRecursiveOrCreateVar(grad_outvar);
auto var_it = op->grad_to_var_->find(grad_outvar);
PADDLE_ENFORCE(var_it != op->grad_to_var_->end());
VarBase* var = vars[var_it->second];
if (!var->grads_->IsInitialized()) {
InitVar(var->var_, var->grads_);
}
LOG(ERROR) << grad_outvar << " map to " << var->var_desc_->Name();
grad_out_vars.push_back(var->grads_);
}
}
}
op->block_ = block;
}
......
paddle/fluid/operators/fill_constant_op.cc
浏览文件 @ ce7e503c
......@@ -68,6 +68,41 @@ class FillConstantOp : public framework::OperatorBase {
auto &dev_ctx = *pool.Get(dev_place);
math::set_constant(dev_ctx, tensor, value);
}
void RunImpl(const framework::RuntimeContext &ctx,
const platform::Place &dev_place) const override {
auto data_type =
static_cast<framework::proto::VarType::Type>(Attr<int>("dtype"));
auto value = Attr<float>("value");
auto force_cpu = Attr<bool>("force_cpu");
framework::Tensor *tensor = nullptr;
auto &out_var = *ctx.outputs.at("Out")[0];
if (out_var.IsType<framework::LoDTensor>()) {
tensor = out_var.GetMutable<framework::LoDTensor>();
tensor->Resize(framework::make_ddim(Attr<std::vector<int64_t>>("shape")));
} else if (out_var.IsType<framework::SelectedRows>()) {
tensor = out_var.GetMutable<framework::SelectedRows>()->mutable_value();
tensor->Resize(framework::make_ddim(Attr<std::vector<int64_t>>("shape")));
} else {
PADDLE_THROW(
"fill constant op's output only"
"supports SelectedRows and LoDTensor");
}
if (force_cpu) {
auto cpu = platform::CPUPlace();
tensor->mutable_data(cpu, data_type);
} else {
tensor->mutable_data(dev_place, data_type);
}
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(dev_place);
math::set_constant(dev_ctx, tensor, value);
}
};
class FillConstantOpVarTypeInference : public framework::VarTypeInference {
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ ce7e503c
......@@ -124,9 +124,7 @@ PYBIND11_MODULE(core, m) {
py::class_<imperative::VarBase, PyVarBase>(m, "VarBase", R"DOC()DOC")
.def(py::init<>())
.def("_run_backward",
[](imperative::VarBase &self, framework::Scope *scope) {
self.RunBackward(scope);
})
[](imperative::VarBase &self) { self.RunBackward(); })
.def("_grad", &imperative::VarBase::Grad)
.def_property(
"desc",
......@@ -134,7 +132,13 @@ PYBIND11_MODULE(core, m) {
[](imperative::VarBase &self, framework::VarDesc *var_desc) {
self.var_desc_ = var_desc;
},
py::return_value_policy::reference);
py::return_value_policy::reference)
.def_property("var",
[](const imperative::VarBase &self) { return self.var_; },
[](imperative::VarBase &self, framework::Variable *var) {
self.var_ = var;
},
py::return_value_policy::reference);
py::class_<imperative::OpBase, PyOpBase>(m, "OpBase", R"DOC()DOC")
.def(py::init<>())
......
python/paddle/fluid/framework.py
浏览文件 @ ce7e503c
......@@ -15,6 +15,7 @@
from __future__ import print_function
import collections
from collections import defaultdict
import contextlib
import os
import re
......@@ -369,13 +370,11 @@ class Variable(object):
self._ivar.desc = self.desc
def _numpy(self):
scope = _imperative_tracer().get_scope(self.block.desc)
tensor = core.get_variable_tensor(scope, self.desc.name())
tensor = self._ivar.var.get_tensor()
return np.array(tensor)
def _backward(self):
scope = _imperative_tracer().get_scope(self.block.desc)
self._ivar._run_backward(scope)
self._ivar._run_backward()
def _gradient(self):
return np.array(self._ivar._grad())
......@@ -692,20 +691,20 @@ class Operator(object):
if _in_imperative_mode():
self.iop = core.OpBase()
self.iop.desc = self.desc
self.inputs = []
self.inputs = defaultdict(list)
if inputs is not None:
for inp in inputs.values():
if isinstance(inp, Variable):
self.inputs.append(inp)
elif isinstance(inp, list) or isinstance(inp, tuple):
self.inputs.extend(inp[:])
self.outputs = []
for k, v in six.iteritems(inputs):
if isinstance(v, Variable):
self.inputs[k].append(v._ivar)
elif isinstance(v, list) or isinstance(v, tuple):
self.inputs[k].extend([var._ivar for var in v])
self.outputs = defaultdict(list)
if outputs is not None:
for out in outputs.values():
if isinstance(out, Variable):
self.outputs.append(out)
elif isinstance(out, list) or isinstance(out, tuple):
self.outputs.extend(out[:])
for k, v in six.iteritems(outputs):
if isinstance(v, Variable):
self.outputs[k].append(v._ivar)
elif isinstance(v, list) or isinstance(v, tuple):
self.outputs[k].extend([var._ivar for var in v])
def _has_kernel(self, op_type):
return op_type not in self.OP_WITHOUT_KERNEL_SET
......@@ -1273,8 +1272,7 @@ class Block(object):
op_desc = self.desc.append_op()
op = Operator(block=self, desc=op_desc, *args, **kwargs)
if _in_imperative_mode():
_imperative_tracer().trace(op.iop, [v._ivar for v in op.inputs],
[v._ivar for v in op.outputs], self.desc)
_imperative_tracer().trace(op.iop, op.inputs, op.outputs, self.desc)
self.ops.append(op)
return op
......@@ -1325,8 +1323,7 @@ class Block(object):
op_desc = self.desc._prepend_op()
op = Operator(self, op_desc, *args, **kwargs)
if _in_imperative_mode():
_imperative_tracer().trace(op.iop, [v._ivar for v in op.inputs],
[v._ivar for v in op.outputs], self.desc)
_imperative_tracer().trace(op.iop, op.inputs, op.outputs, self.desc)
self.ops.insert(0, op)
return op
......
python/paddle/fluid/imperative/base.py
浏览文件 @ ce7e503c
......@@ -46,8 +46,7 @@ def to_variable(value, block=None):
name=None,
shape=value.shape,
dtype=value.dtype)
scope = framework._imperative_tracer().get_scope(block.desc)
var = scope.var(py_var.name)
var = py_var._ivar.var
tensor = var.get_tensor()
tensor.set(value, core.CPUPlace())
return py_var
......
python/paddle/fluid/layer_helper.py
浏览文件 @ ce7e503c
......@@ -20,7 +20,7 @@ import six
import sys
import numpy as np
from .framework import Variable, Parameter, default_main_program, default_startup_program, dtype_is_floating
from .framework import Variable, Parameter, default_main_program, default_startup_program, dtype_is_floating, _in_imperative_mode
from . import unique_name
from paddle.fluid.initializer import Constant, Xavier
from paddle.fluid.imperative import base
......@@ -313,11 +313,20 @@ class LayerHelper(object):
param = self._create_weight_normalize(attr, shape, dtype)
WeightNormParamAttr.params_with_weight_norm.append(param)
return param
self.startup_program.global_block().create_parameter(
dtype=dtype, shape=shape, **attr._to_kwargs(with_initializer=True))
return self.main_program.global_block().create_parameter(
dtype=dtype, shape=shape, **attr._to_kwargs())
if _in_imperative_mode():
self.main_program.global_block().create_parameter(
dtype=dtype, shape=shape, **attr._to_kwargs())
return self.startup_program.global_block().create_parameter(
dtype=dtype,
shape=shape,
**attr._to_kwargs(with_initializer=True))
else:
self.startup_program.global_block().create_parameter(
dtype=dtype,
shape=shape,
**attr._to_kwargs(with_initializer=True))
return self.main_program.global_block().create_parameter(
dtype=dtype, shape=shape, **attr._to_kwargs())
def get_parameter(self, name):
param = self.main_program.global_block().var(name)
......
python/paddle/fluid/layers/nn.py
浏览文件 @ ce7e503c
......@@ -20,6 +20,7 @@ from __future__ import print_function
import numpy as np
import six
import os
import sys
import inspect
from ..layer_helper import LayerHelper
from ..initializer import Normal, Constant
......@@ -9682,6 +9683,7 @@ class FC(layers.PyLayer):
shape=param_shape,
dtype=self._dtype,
is_bias=False)
sys.stderr.write('created w: %s\n' % self._w.name)
def forward(self, inputs):
tmp = self._helper.create_variable_for_type_inference(self._dtype)
......
python/paddle/fluid/tests/unittests/test_imperative.py
浏览文件 @ ce7e503c
......@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import contextlib
import unittest
import numpy as np
......@@ -38,7 +39,9 @@ class MyLayer(fluid.imperative.PyLayer):
def forward(self, inputs):
x = fluid.layers.relu(inputs[0])
self._x_for_debug = x
return [fluid.layers.elementwise_mul(x, x)]
x = fluid.layers.elementwise_mul(x, x)
x = fluid.layers.reduce_sum(x)
return [x]
class MLP(fluid.imperative.PyLayer):
......@@ -79,10 +82,12 @@ class TestImperative(unittest.TestCase):
with new_program_scope():
inp = fluid.layers.data(
name="inp", shape=[3], append_batch_size=False)
l = MyLayer()
x = l(inp)[0]
x = fluid.layers.relu(inp)
x_for_debug = x
x = fluid.layers.elementwise_mul(x, x)
x = fluid.layers.reduce_sum(x)
param_grads = fluid.backward.append_backward(
x, parameter_list=[l._x_for_debug.name])[0]
x, parameter_list=[x_for_debug.name])[0]
exe = fluid.Executor(fluid.CPUPlace())
static_out, static_grad = exe.run(
......
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