/
run_program_op_node.h
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/
run_program_op_node.h
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "paddle/fluid/eager/api/utils/global_utils.h"
#include "paddle/fluid/eager/grad_node_info.h"
#include "paddle/fluid/eager/tensor_wrapper.h"
#include "paddle/fluid/operators/run_program_op.h"
#include "paddle/fluid/platform/enforce.h"
namespace details {
using Tensor = paddle::experimental::Tensor;
static std::vector<Tensor> DereferenceTensors(
const std::vector<Tensor *> &tensor_ptr) {
std::vector<Tensor> res;
for (auto *t : tensor_ptr) {
res.emplace_back(*t);
}
return res;
}
static std::vector<std::string> GetTensorsName(const std::vector<Tensor> &ins) {
std::vector<std::string> in_names;
for (auto &in_t : ins) {
in_names.emplace_back(in_t.name());
}
return in_names;
}
static std::vector<std::string> GetTensorsName(
const std::vector<Tensor *> &ins) {
std::vector<std::string> in_names;
for (auto *in_t : ins) {
in_names.emplace_back(in_t->name());
}
return in_names;
}
static void CheckInputVarStatus(const Tensor &tensor) {
PADDLE_ENFORCE_EQ(tensor.defined() && tensor.is_dense_tensor(), true,
paddle::platform::errors::InvalidArgument(
"The input tensor %s of "
"RunProgram(Grad)Op holds "
"wrong type. Expect type is DenseTensor.",
tensor.name()));
PADDLE_ENFORCE_EQ(tensor.initialized(), true,
paddle::platform::errors::InvalidArgument(
"The tensor in input tensor %s of "
"RunProgram(Grad)Op "
"is not initialized.",
tensor.name()));
}
static void CheckOutputVarStatus(const paddle::framework::Variable &src_var,
const Tensor &dst_tensor) {
auto name = dst_tensor.name();
PADDLE_ENFORCE_EQ(dst_tensor.defined(), true,
paddle::platform::errors::InvalidArgument(
"dst_tensor shall be defined."));
if (dst_tensor.is_dense_tensor()) {
auto &src_tensor = src_var.Get<phi::DenseTensor>();
PADDLE_ENFORCE_EQ(phi::DenseTensor::classof(&src_tensor), true,
paddle::platform::errors::InvalidArgument(
"The output tensor %s get from "
"RunProgram(Grad)Op's internal scope holds "
"wrong type. Expect type is DenseTensor",
name));
PADDLE_ENFORCE_EQ(src_tensor.initialized(), true,
paddle::platform::errors::InvalidArgument(
"The tensor in output tensor %s get from "
"RunProgram(Grad)Op's internal "
"scope is not initialized.",
name));
} else if (dst_tensor.is_selected_rows()) {
auto &src_tensor = src_var.Get<phi::SelectedRows>();
PADDLE_ENFORCE_EQ(phi::SelectedRows::classof(&src_tensor), true,
paddle::platform::errors::InvalidArgument(
"The output tensodfr %s get from "
"RunProgram(Grad)Op's internal scope holds "
"wrong type. Expect type is SelectedRows",
name));
PADDLE_ENFORCE_EQ(src_tensor.initialized(), true,
paddle::platform::errors::InvalidArgument(
"The tensor in output tensor %s get from "
"RunProgram(Grad)Op's "
"internal scope is not initialized.",
name));
} else {
PADDLE_THROW(paddle::platform::errors::InvalidArgument(
"The RunProgram(Grad)Op only support output "
"variable of type LoDTensor or SelectedRows",
name));
}
}
static void ShareTensorsIntoScope(const std::vector<Tensor> &tensors,
paddle::framework::Scope *scope) {
for (size_t i = 0; i < tensors.size(); ++i) {
auto name = tensors[i].name();
if (name == "Fake_var" || !tensors[i].initialized()) {
continue;
}
auto *var = scope->Var(name);
CheckInputVarStatus(tensors[i]);
// share tensor
auto tensor_base = tensors[i].impl();
if (phi::DenseTensor::classof(tensor_base.get())) {
auto *dst_tensor = var->GetMutable<phi::DenseTensor>();
auto t = std::dynamic_pointer_cast<phi::DenseTensor>(tensor_base);
*dst_tensor = *t;
} else if (phi::SelectedRows::classof(tensor_base.get())) {
auto *dst_tensor = var->GetMutable<phi::SelectedRows>();
auto t = std::dynamic_pointer_cast<phi::SelectedRows>(tensor_base);
*dst_tensor = *t;
}
}
}
static void ShareTensorsFromScope(
const std::vector<Tensor *> &tensors,
const paddle::framework::BlockDesc &global_block,
paddle::framework::Scope *scope) {
for (size_t i = 0; i < tensors.size(); ++i) {
// NOTE: In case of setting out_tmp.stop_gradient = True in model code, all
// parameters before generating out_tmp have no @GRAD, it will raise error
// because we can't find them in scope. So we skip sharing these vars or
// var@GRAD if they don't appear in global block.
auto &name = tensors[i]->name();
if (name == paddle::framework::kEmptyVarName || name == "Fake_var" ||
!global_block.HasVar(name)) {
VLOG(2) << "find tensor name is " << name << ", skip it!";
continue;
}
// NOTE: Here skip not found var is dangerous, if a bug is caused here,
// the result is grad calculation error, which will be very hidden!
auto *var = scope->FindVar(name);
PADDLE_ENFORCE_NOT_NULL(var, paddle::platform::errors::NotFound(
"The output tensor %s is not in "
"RunProgram(Grad)Op'"
"s internal scope.",
name));
CheckOutputVarStatus(*var, *tensors[i]);
// share tensor
if (var->IsType<phi::DenseTensor>()) {
auto &src_tensor = var->Get<phi::DenseTensor>();
auto *dst_tensor = const_cast<phi::DenseTensor *>(
dynamic_cast<const phi::DenseTensor *>(tensors[i]->impl().get()));
VLOG(2) << "share " << name << " from scope";
*dst_tensor = src_tensor;
} else if (var->IsType<phi::SelectedRows>()) {
auto &src_tensor = var->Get<phi::SelectedRows>();
auto *dst_tensor = const_cast<phi::SelectedRows *>(
dynamic_cast<const phi::SelectedRows *>(tensors[i]->impl().get()));
*dst_tensor = src_tensor;
}
}
}
} // namespace details
inline void RunProgramAPI(
const std::vector<paddle::experimental::Tensor> &x,
const std::vector<paddle::experimental::Tensor> ¶ms,
std::vector<paddle::experimental::Tensor *> &out, // NOLINT
std::vector<paddle::framework::Scope *> &step_scope, // NOLINT
std::vector<paddle::experimental::Tensor *> &dout, // NOLINT
const paddle::framework::AttributeMap &attrs) {
VLOG(2) << "RunProgramOpKernel Compute";
auto start_op_index = BOOST_GET_CONST(int64_t, attrs.at("start_op_index"));
auto end_op_index = BOOST_GET_CONST(int64_t, attrs.at("end_op_index"));
// In the original run_program OP, the default value of the is_test
// attribute is false, we should check if there is is_test parameter
// in attrs
auto is_test = false;
if (attrs.count("is_test")) {
is_test = BOOST_GET_CONST(bool, attrs.at("is_test"));
}
auto program_id = BOOST_GET_CONST(int64_t, attrs.at("program_id"));
// NOTE(chenweihang): In order not to add new variable type, use vector
// here. Originally, here can use scope directly.
auto *out_scope_vec = &step_scope;
PADDLE_ENFORCE_EQ(
out_scope_vec->size(), 1,
paddle::platform::errors::InvalidArgument(
"The OutScope of RunProgramGradOp should only hold one scope."));
// Step 2. prepare executor and init persistable variables
// NOTE(Aurelius84): While training some models, forward can be called many
// times and then apply backpropagation all at once, such as Reinforcement
// Learning. Tensor data in multi-step training should be saved into single
// scope separately. Otherwise, the gradients can be miscalculated because
// always using the Tensor data of the last step in forward.
paddle::framework::Scope *global_inner_scope = out_scope_vec->front();
VLOG(2) << "The number of sub scopes before forward: "
<< out_scope_vec->front()->kids().size();
paddle::framework::Scope &scope = global_inner_scope->NewScope();
// share input_vars & parameters into scope
details::ShareTensorsIntoScope(x, &scope);
details::ShareTensorsIntoScope(params, &scope);
auto *global_block =
BOOST_GET_CONST(paddle::framework::BlockDesc *, attrs.at("global_block"));
const auto &place = egr::Controller::Instance().GetExpectedPlace();
if (end_op_index > start_op_index) {
auto input_names = details::GetTensorsName(x);
auto output_names = details::GetTensorsName(out);
auto dout_names = details::GetTensorsName(dout);
auto *program = global_block->Program();
auto cache_info = paddle::framework::GetExecutorInfoFromCache(
*program, place, start_op_index, end_op_index,
/*is_grad=*/false, program_id, &scope);
auto ¶llel_executor = cache_info.first;
// all out_vars are skip_eager_var
auto &skip_eager_delete_vars =
paddle::framework::ExecutorInfoCache::Instance().SkipEagerDeleteVars(
program_id, false);
if (cache_info.second /*is_new_created*/) {
parallel_executor->SkipMemoryReuse(/*scope_idx=*/0, input_names);
skip_eager_delete_vars.insert(skip_eager_delete_vars.end(),
output_names.begin(), output_names.end());
skip_eager_delete_vars.insert(skip_eager_delete_vars.end(),
dout_names.begin(), dout_names.end());
paddle::framework::details::ParseSafeEagerDeletionSkipVars(
*program, end_op_index, output_names, &skip_eager_delete_vars);
}
// Step 3. run ops
parallel_executor->RunWithoutFetch(skip_eager_delete_vars);
}
// Step 4. Get Output
details::ShareTensorsFromScope(out, *global_block, &scope);
details::ShareTensorsFromScope(dout, *global_block, &scope);
// Debug info: scope info when run end
VLOG(3) << paddle::framework::GenScopeTreeDebugInfo(out_scope_vec->front());
// Step 5. Drop all children scopes while testing.
if (is_test) {
out_scope_vec->front()->DropKids();
}
VLOG(2) << "The number of sub scopes after forward: "
<< out_scope_vec->front()->kids().size();
#ifdef PADDLE_WITH_MKLDNN
if (FLAGS_use_mkldnn) paddle::platform::DontClearMKLDNNCache(place);
#endif
}
inline void RunProgramGradAPI(
const std::vector<paddle::experimental::Tensor> &x,
const std::vector<paddle::experimental::Tensor> ¶ms,
const std::vector<paddle::experimental::Tensor> &out_grad,
const std::vector<paddle::framework::Scope *> &step_scope, // NOLINT
const paddle::framework::AttributeMap &attrs,
std::vector<paddle::experimental::Tensor *> &x_grad, // NOLINT
std::vector<paddle::experimental::Tensor *> ¶ms_grad // NOLINT
) {
// if all output vars are set to stop_gradient, grad op no need to executed
if (x_grad.empty() && params_grad.empty()) return;
auto *global_block =
BOOST_GET_CONST(paddle::framework::BlockDesc *, attrs.at("global_block"));
auto orig_end_op_index = BOOST_GET_CONST(int64_t, attrs.at("end_op_index"));
auto program_id = BOOST_GET_CONST(int64_t, attrs.at("program_id"));
// NOTE: skip `shape` and `fill_constant` op created by
// fluid.backward.gradients, one forward output will generate one `shape`
// and `fill_constant`
int64_t start_op_index = orig_end_op_index + (out_grad.size() * 2);
int64_t end_op_index = global_block->OpSize();
auto *out_scope_vec = &step_scope;
PADDLE_ENFORCE_EQ(
out_scope_vec->size(), 1,
paddle::platform::errors::InvalidArgument(
"The OutScope of RunProgramGradOp should only hold one scope."));
paddle::framework::Scope *global_inner_scope = out_scope_vec->front();
auto sub_scope_num = global_inner_scope->kids().size();
VLOG(2) << "The number of sub scopes before backward: " << sub_scope_num;
PADDLE_ENFORCE_GT(sub_scope_num, 0,
paddle::platform::errors::InvalidArgument(
"The OutScope of RunProgramGradOp should hold at "
"least one sub scope."));
auto &scope = *(global_inner_scope->kids().front());
const auto &place = egr::Controller::Instance().GetExpectedPlace();
if (end_op_index > start_op_index) {
auto out_grad_names = details::GetTensorsName(out_grad);
// NOTE: after PR22939 [Add double grad] merged, the grad op maker's
// SetOutput will set to None if the input var stop_gradient=True,
// it will cause an NotFound error when ctx.OutputNames() is called
std::vector<std::string> x_grad_names;
std::vector<std::string> param_grad_names;
if (!x_grad.empty()) {
x_grad_names = details::GetTensorsName(x_grad);
}
if (!params_grad.empty()) {
param_grad_names = details::GetTensorsName(params_grad);
}
// Step 2. prepare executor and scope
auto *program = global_block->Program();
auto cache_info = paddle::framework::GetExecutorInfoFromCache(
*program, place, start_op_index, end_op_index,
/*is_grad*/ true, program_id, &scope);
auto ¶llel_executor = cache_info.first;
auto &skip_eager_delete_vars =
paddle::framework::ExecutorInfoCache::Instance().SkipEagerDeleteVars(
program_id, true);
if (cache_info.second /*is_new_created*/) {
parallel_executor->SkipMemoryReuse(/*scope_idx=*/0, out_grad_names);
skip_eager_delete_vars.insert(skip_eager_delete_vars.end(),
x_grad_names.begin(), x_grad_names.end());
paddle::framework::details::AppendSkipDeletionVars(
param_grad_names, &skip_eager_delete_vars);
}
details::ShareTensorsIntoScope(out_grad, &scope);
// Debug info: scope info when run end
VLOG(3) << paddle::framework::GenScopeTreeDebugInfo(out_scope_vec->front());
// Step 3. run ops
parallel_executor->RunWithoutFetch(
/*skip_eager_delete_vars=*/skip_eager_delete_vars);
}
// Step 4. get outputs
details::ShareTensorsFromScope(x_grad, *global_block, &scope);
details::ShareTensorsFromScope(params_grad, *global_block, &scope);
// Step5. drop current scope
global_inner_scope->DeleteScope(&scope);
VLOG(2) << "The number of sub scopes after backward: "
<< global_inner_scope->kids().size();
}
class GradNodeRunProgram : public egr::GradNodeBase {
public:
GradNodeRunProgram(size_t bwd_in_slot_num, size_t bwd_out_slot_num)
: egr::GradNodeBase(bwd_in_slot_num, bwd_out_slot_num) {}
~GradNodeRunProgram() override = default;
// Functor: perform backward computations
virtual paddle::small_vector<std::vector<paddle::experimental::Tensor>,
egr::kSlotSmallVectorSize>
operator()(paddle::small_vector<std::vector<paddle::experimental::Tensor>,
egr::kSlotSmallVectorSize> &grads, // NOLINT
bool create_graph,
bool is_new_grad) override {
VLOG(3) << "Running Eager Backward Node: GradNodeRunProgram";
paddle::small_vector<std::vector<paddle::experimental::Tensor>,
egr::kSlotSmallVectorSize>
hooked_grads = GradNodeRunProgram::ApplyGradientHooks(grads);
PADDLE_ENFORCE_EQ(hooked_grads.size(), 1,
paddle::platform::errors::InvalidArgument(
"The hooked_grads.size() of RunProgramGradOp should "
"be equal to 1."));
egr::EagerUtils::FillZeroForEmptyOptionalGradInput(&hooked_grads[0],
this->InputMeta()[0]);
VLOG(3) << "hooked_grads[0].size() : " << hooked_grads[0].size();
std::vector<paddle::experimental::Tensor> x_grad;
std::vector<paddle::experimental::Tensor> params_grad;
ConstructXGradTensors(x_, &x_grad);
ConstructParamGradTensors(params_, ¶ms_grad);
std::vector<paddle::experimental::Tensor *> x_grad_ptr;
std::vector<paddle::experimental::Tensor *> params_grad_ptr;
for (auto &i : x_grad) {
x_grad_ptr.emplace_back(&i);
}
for (auto &i : params_grad) {
if (i.defined()) {
params_grad_ptr.emplace_back(&i);
}
}
PADDLE_ENFORCE_EQ(hooked_grads[0].size(), fwd_out_names_.size(),
paddle::platform::errors::InvalidArgument(
"The hooked_grads[0].size() and "
"fwd_out_names_.size() should be equal."));
for (size_t i = 0; i < fwd_out_names_.size(); ++i) {
hooked_grads[0][i].set_name(fwd_out_names_[i] + "@GRAD");
}
RunProgramGradAPI(x_, params_, hooked_grads[0], step_scope_, attrs_,
x_grad_ptr, params_grad_ptr);
VLOG(3) << "End Eager Backward Node: GradNodeRunProgram";
return {x_grad, params_grad};
}
void ClearTensorWrappers() override { VLOG(6) << "Do nothing here now"; }
// SetAttrMap
void SetAttrMap(const paddle::framework::AttributeMap &attrs) {
attrs_ = attrs;
}
void SetFwdX(const std::vector<paddle::experimental::Tensor> &tensors) {
x_ = tensors;
}
void SetFwdParams(const std::vector<paddle::experimental::Tensor> &tensors) {
params_ = tensors;
}
void SetStepScope(const std::vector<paddle::framework::Scope *> &scopes) {
step_scope_ = scopes;
}
void SetFwdOutNames(std::vector<std::string> out_names) {
fwd_out_names_ = out_names;
}
protected:
void ConstructXGradTensors(
const std::vector<paddle::experimental::Tensor> &x,
std::vector<paddle::experimental::Tensor> *x_grad) {
// TODO(dev): Need an elegant way to determine inforamtion of grad_tensor,
// such as: name, tensor type(DenseTensor or SelectedRows).
for (auto &t : x) {
if (t.is_dense_tensor()) {
x_grad->emplace_back(std::make_shared<phi::DenseTensor>());
} else if (t.is_selected_rows()) {
x_grad->emplace_back(std::make_shared<phi::SelectedRows>());
}
x_grad->back().set_name(t.name() + "@GRAD");
}
}
void ConstructParamGradTensors(
const std::vector<paddle::experimental::Tensor> ¶m,
std::vector<paddle::experimental::Tensor> *param_grad) {
for (auto &t : param) {
auto t_grad = egr::EagerUtils::unsafe_autograd_meta(t)->Grad();
// In eager mode, the number of param_grad should be the same as
// param, so here an empty Tensor is added for the param with
// stop_gradient=True
if (!t_grad.defined()) {
param_grad->emplace_back();
} else if (t_grad.is_dense_tensor()) {
param_grad->emplace_back(std::make_shared<phi::DenseTensor>());
} else if (t_grad.is_selected_rows()) {
param_grad->emplace_back(std::make_shared<phi::SelectedRows>());
}
param_grad->back().set_name(t.name() + "@GRAD");
}
}
std::shared_ptr<GradNodeBase> Copy() const override {
auto copied_node =
std::shared_ptr<GradNodeRunProgram>(new GradNodeRunProgram(*this));
return copied_node;
}
private:
// TensorWrappers
std::vector<paddle::experimental::Tensor> x_;
std::vector<paddle::experimental::Tensor> params_;
std::vector<paddle::framework::Scope *> step_scope_;
std::vector<std::string> fwd_out_names_;
// Attribute Map
paddle::framework::AttributeMap attrs_;
};