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eager_generator.cc
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eager_generator.cc
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// Copyright (c) 2021 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.
#include <algorithm>
#include <fstream>
#include <iostream>
#include <string>
#include <unordered_set>
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/pybind/op_function_generator.h"
#include "paddle/fluid/pybind/pybind.h"
#include "paddle/fluid/string/string_helper.h"
// phi
#include "paddle/phi/kernels/declarations.h"
#define NUM_CREATED_DUP_INPUTS 4
namespace paddle {
namespace framework {
// To handle append_op at python-level
std::unordered_map<std::string, std::vector<std::string>>
core_ops_legacy_returns_info = {};
std::unordered_map<std::string, std::vector<std::string>>
core_ops_legacy_args_info = {};
std::unordered_map<std::string, std::vector<std::string>>
core_ops_legacy_args_type_info = {};
/* --- Static maps to handle corner cases --- */
static std::unordered_map<std::string, paddle::framework::AttributeMap>
operators_with_attrs = {};
static std::unordered_set<std::string> ops_to_fill_zero_for_empty_grads = {
"split", "rnn"};
/* --- Black Ops list that's NO NEED to apply code generation --- */
static std::unordered_set<std::string> black_ops_list = {"run_program",
"fused_gate_attention",
"fused_feedforward",
"fused_attention",
"fused_gemm_epilogue"};
static std::string LegalizeVariableName(const std::string& var_name) {
std::string ret = var_name;
std::replace(ret.begin(), ret.end(), '-', '_'); // replace all '-' to '_'
std::replace(ret.begin(), ret.end(), '@', '_'); // replace all '-' to '_'
return ret;
}
static std::string LegalizeVarName(const std::string& var_name) {
std::string ret = var_name;
std::replace(ret.begin(), ret.end(), '@', '_'); // replace all '-' to '_'
return ret;
}
static std::string HandleDynamicGradAttributes(const std::string& fwd_op_type,
const std::string& attrs_name) {
std::string additional_grad_attrs_str = "";
if (fwd_op_type == "sum") {
const char* GRAD_ATTRS_TEMPLATE = " %s[\"%s\"] = %s;\n";
additional_grad_attrs_str = paddle::string::Sprintf(
GRAD_ATTRS_TEMPLATE, attrs_name, "scale", "float(1.0)");
additional_grad_attrs_str += paddle::string::Sprintf(
GRAD_ATTRS_TEMPLATE, attrs_name, "bias", "float(0.0f)");
additional_grad_attrs_str += paddle::string::Sprintf(
GRAD_ATTRS_TEMPLATE, attrs_name, "bias_after_scale", "bool(true)");
} else if (fwd_op_type == "scale") {
const char* GRAD_ATTRS_TEMPLATE = " %s[\"%s\"] = %s;\n";
additional_grad_attrs_str += paddle::string::Sprintf(
GRAD_ATTRS_TEMPLATE, attrs_name, "bias", "float(0.0f)");
additional_grad_attrs_str += paddle::string::Sprintf(
GRAD_ATTRS_TEMPLATE, attrs_name, "bias_after_scale", "bool(true)");
}
return additional_grad_attrs_str;
}
static void PrepareAttrMapForOps() {
// Handle "fused_elemwise_add_activation"
std::vector<std::string> functor_list = {"a", "b"};
operators_with_attrs["fused_elemwise_add_activation"] = {};
operators_with_attrs["fused_elemwise_add_activation"]["functor_list"] =
functor_list;
// Handle "fused_elemwise_activation"
operators_with_attrs["fused_elemwise_activation"] = {};
operators_with_attrs["fused_elemwise_activation"]["functor_list"] =
functor_list;
// Handle "reverse"
std::vector<int> axis = {0};
operators_with_attrs["reverse"] = {};
operators_with_attrs["reverse"]["axis"] = axis;
// Handle "flip"
operators_with_attrs["flip"] = {};
operators_with_attrs["flip"]["axis"] = axis;
// Handle "cast"
operators_with_attrs["cast"] = {};
operators_with_attrs["cast"]["out_dtype"] = 5;
operators_with_attrs["cast"]["in_dtype"] = 5;
// Handle "transfer_dtype"
operators_with_attrs["transfer_dtype"] = {};
operators_with_attrs["transfer_dtype"]["out_dtype"] = 5;
operators_with_attrs["transfer_dtype"]["in_dtype"] = 5;
// Handle "c_split"
operators_with_attrs["c_split"] = {};
operators_with_attrs["c_split"]["nranks"] = 1;
}
/* --- Helper Objects --- */
class ForwardGenerationInfo {
public:
const std::string& GetOpType() const { return op_type_; }
void SetOpType(const std::string& op_type) { op_type_ = op_type; }
const std::unordered_map<std::string, size_t>& GetFwdInputsNamePosMap()
const {
return fwd_inputs_name_pos_map_;
}
std::unordered_map<std::string, size_t>* GetMutableFwdInputsNamePosMap() {
return &fwd_inputs_name_pos_map_;
}
const std::unordered_map<std::string, size_t>& GetFwdOutputsNamePosMap()
const {
return fwd_outputs_name_pos_map_;
}
std::unordered_map<std::string, size_t>* GetMutableFwdOutputsNamePosMap() {
return &fwd_outputs_name_pos_map_;
}
const std::vector<proto::OpProto::Var>& GetInVars() const { return in_vars_; }
std::vector<proto::OpProto::Var>* GetMutableInVars() { return &in_vars_; }
const std::vector<proto::OpProto::Var>& GetOutVars() const {
return out_vars_;
}
std::vector<proto::OpProto::Var>* GetMutableOutVars() { return &out_vars_; }
private:
std::string op_type_;
std::unordered_map<std::string, size_t> fwd_inputs_name_pos_map_;
std::unordered_map<std::string, size_t> fwd_outputs_name_pos_map_;
std::vector<proto::OpProto::Var> in_vars_;
std::vector<proto::OpProto::Var> out_vars_;
};
class GradNodeGenerationInfo {
class OpBaseGenerationInfo {
public:
const std::string& GetOpBaseType() const { return op_base_type_; }
void SetOpBaseType(const std::string& op_type) { op_base_type_ = op_type; }
const std::map<std::string, std::string>& GetGradOutsSlotnameMap() const {
return grad_outs_slotname_map_;
}
std::map<std::string, std::string>* GetMutableGradOutsSlotnameMap() {
return &grad_outs_slotname_map_;
}
const std::map<std::string, std::string>& GetGradInsFwdSlotnameMap() const {
return grad_ins_fwd_slotname_map_;
}
std::map<std::string, std::string>* GetMutableGradInsFwdSlotnameMap() {
return &grad_ins_fwd_slotname_map_;
}
const std::map<std::string, std::string>& GetGradInsGradSlotnameMap()
const {
return grad_ins_grad_slotname_map_;
}
std::map<std::string, std::string>* GetMutableGradInsGradSlotnameMap() {
return &grad_ins_grad_slotname_map_;
}
const std::map<
std::string,
std::vector<std::shared_ptr<paddle::imperative::VariableWrapper>>>&
GetGradIns() const {
return grad_ins_;
}
std::map<std::string,
std::vector<std::shared_ptr<paddle::imperative::VariableWrapper>>>*
GetMutableGradIns() {
return &grad_ins_;
}
const std::map<
std::string,
std::vector<std::shared_ptr<paddle::imperative::VariableWrapper>>>&
GetGradOuts() const {
return grad_outs_;
}
std::map<std::string,
std::vector<std::shared_ptr<paddle::imperative::VariableWrapper>>>*
GetMutableGradOuts() {
return &grad_outs_;
}
const paddle::framework::AttributeMap& GetGradAttrs() const {
return grad_attrs_;
}
paddle::framework::AttributeMap* GetMutableGradAttrs() {
return &grad_attrs_;
}
const std::unordered_set<std::string>& GetNoNeedBufferInputs() const {
return no_need_buffer_ins_;
}
std::unordered_set<std::string>* GetMutableNoNeedBufferInputs() {
return &no_need_buffer_ins_;
}
const std::unordered_map<std::string, std::string>& GetBackwardInplaceMap()
const {
return backward_inplace_map_;
}
std::unordered_map<std::string, std::string>*
GetMutableBackwardInplaceMap() {
return &backward_inplace_map_;
}
private:
std::string op_base_type_;
std::map<std::string, std::string> grad_outs_slotname_map_;
std::map<std::string, std::string> grad_ins_fwd_slotname_map_;
std::map<std::string, std::string> grad_ins_grad_slotname_map_;
std::map<std::string,
std::vector<std::shared_ptr<paddle::imperative::VariableWrapper>>>
grad_ins_;
std::map<std::string,
std::vector<std::shared_ptr<paddle::imperative::VariableWrapper>>>
grad_outs_;
paddle::framework::AttributeMap grad_attrs_;
std::unordered_set<std::string> no_need_buffer_ins_;
std::unordered_map<std::string, std::string> backward_inplace_map_;
};
public:
const std::string& GetFwdOpType() const { return fwd_op_type_; }
void SetFwdOpType(const std::string& op_type) { fwd_op_type_ = op_type; }
bool GenerateForwardOnly() const { return generate_forward_only_; }
void SetGenerateForwardOnly(bool generate_forward_only) {
generate_forward_only_ = generate_forward_only;
}
const std::vector<OpBaseGenerationInfo>& GetOpBaseInfos() const {
return op_base_infos_;
}
std::vector<OpBaseGenerationInfo>* GetMutableOpBaseInfos() {
return &op_base_infos_;
}
private:
std::string fwd_op_type_;
bool generate_forward_only_ = false;
std::vector<OpBaseGenerationInfo> op_base_infos_;
};
/* --- Helper Functions --- */
static std::string AttrTypeToString(const proto::AttrType& type) {
std::string ret;
switch (type) {
case (proto::AttrType::INT): {
ret = "int";
break;
}
case (proto::AttrType::FLOAT): {
ret = "float";
break;
}
case (proto::AttrType::STRING): {
ret = "std::string&";
break;
}
case (proto::AttrType::INTS): {
ret = "std::vector<int>&";
break;
}
case (proto::AttrType::FLOATS): {
ret = "std::vector<float>&";
break;
}
case (proto::AttrType::STRINGS): {
ret = "std::vector<std::string>&";
break;
}
case (proto::AttrType::BOOLEAN): {
ret = "bool";
break;
}
case (proto::AttrType::BOOLEANS): {
ret = "std::vector<bool>&";
break;
}
case (proto::AttrType::LONG): {
ret = "int64_t";
break;
}
case (proto::AttrType::LONGS): {
ret = "std::vector<int64_t>&";
break;
}
case (proto::AttrType::BLOCK): {
ret = "paddle::framework::BlockDesc*";
break;
}
case (proto::AttrType::BLOCKS): {
ret = "std::vector<paddle::framework::BlockDesc*>&";
break;
}
case (proto::AttrType::FLOAT64S): {
ret = "std::vector<double>&";
break;
}
default: {
PADDLE_THROW(platform::errors::Fatal(
"AttrType of type paddle::variant only supports specific data types."
"However, detected unrecognized AttrType: %d",
type));
}
}
return ret;
}
template <typename T, bool IsVector>
static typename std::enable_if<IsVector, std::string>::type GetAttrValue(
const framework::Attribute& attr) {
std::string val = "";
val += "{";
for (auto x : PADDLE_GET_CONST(std::vector<T>, attr)) {
val += std::to_string(x) + ",";
}
if (val.size() > 1) val.pop_back();
val += "}";
return val;
}
template <typename T, bool IsVector>
static typename std::enable_if<!IsVector, std::string>::type GetAttrValue(
const framework::Attribute& attr) {
return std::to_string(PADDLE_GET_CONST(T, attr));
}
static std::pair<std::string, std::string> GetAttrType(
const framework::Attribute& attr, bool is_arg) {
std::string ret = "";
std::string val = "";
size_t variant_pos = attr.index();
switch (variant_pos) {
case (1): {
ret = "int";
val = GetAttrValue<int, false>(attr);
break;
}
case (2): {
ret = "float";
val = GetAttrValue<float, false>(attr);
break;
}
case (3): {
ret = "std::string";
if (is_arg) ret += "&";
val = "\"" + PADDLE_GET_CONST(std::string, attr) + "\"";
break;
}
case (4): {
ret = "std::vector<int>";
if (is_arg) ret += "&";
val = GetAttrValue<int, true>(attr);
break;
}
case (5): {
ret = "std::vector<float>";
if (is_arg) ret += "&";
val = GetAttrValue<float, true>(attr);
break;
}
case (6): {
ret = "std::vector<std::string>";
if (is_arg) ret += "&";
val += "{";
for (auto x : PADDLE_GET_CONST(std::vector<std::string>, attr)) {
val += "\"" + x + "\"" + ",";
}
if (val.size() > 1) val.pop_back();
val += "};";
break;
}
case (7): {
ret = "bool";
val = GetAttrValue<bool, false>(attr);
break;
}
case (8): {
ret = "std::vector<bool>";
if (is_arg) ret += "&";
val = GetAttrValue<bool, true>(attr);
break;
}
case (9): {
ret = "BlockDesc*";
break;
}
case (10): {
ret = "int64_t";
val = GetAttrValue<int64_t, false>(attr);
break;
}
case (11): {
ret = "std::vector<BlockDesc*>";
if (is_arg) ret += "&";
break;
}
case (12): {
ret = "std::vector<int64_t>";
if (is_arg) ret += "&";
val = GetAttrValue<int64_t, true>(attr);
break;
}
case (13): {
ret = "std::vector<double>";
if (is_arg) ret += "&";
val = GetAttrValue<double, true>(attr);
break;
}
default: {
PADDLE_THROW(platform::errors::Fatal(
"AttrType of type paddle::variant only supports specific data types."
"However, detected unrecognized AttrType: %d",
variant_pos));
}
}
return {ret, val};
}
static void SlotNameMatching(
const std::map<
std::string,
std::vector<std::shared_ptr<paddle::imperative::VariableWrapper>>>&
grad_map,
const std::map<
std::string,
std::vector<std::shared_ptr<paddle::imperative::VariableWrapper>>>&
fwd_ins,
const std::map<
std::string,
std::vector<std::shared_ptr<paddle::imperative::VariableWrapper>>>&
fwd_outs,
std::map<std::string, std::string>* grad_fwd_slotname_map_ptr,
std::map<std::string, std::string>* grad_grad_slotname_map_ptr) {
std::map<std::string, std::string>& grad_fwd_slotname_map =
*grad_fwd_slotname_map_ptr;
std::map<std::string, std::string>& grad_grad_slotname_map =
*grad_grad_slotname_map_ptr;
for (const auto& iter : grad_map) {
const std::string& grad_slot_name = iter.first;
const std::vector<std::shared_ptr<paddle::imperative::VariableWrapper>>&
grad_vars = iter.second;
// Find matching fwd_slot_name
bool found_matching = false;
for (const std::shared_ptr<paddle::imperative::VariableWrapper>& grad_var :
grad_vars) {
for (const auto& fwd_iter : fwd_ins) {
const std::string& fwd_slot_name = fwd_iter.first;
const std::vector<std::shared_ptr<paddle::imperative::VariableWrapper>>&
fwd_vars = fwd_iter.second;
for (const std::shared_ptr<paddle::imperative::VariableWrapper>&
fwd_var : fwd_vars) {
if (grad_var == fwd_var) {
if (grad_fwd_slotname_map.count(grad_slot_name) &&
grad_fwd_slotname_map[grad_slot_name] != fwd_slot_name) {
PADDLE_THROW(platform::errors::Fatal(
"Detected mismatched slot names."
"grad_slot_name %s matches both %s and %s fwd_slot_name",
grad_slot_name,
grad_fwd_slotname_map[grad_slot_name],
fwd_slot_name));
}
grad_fwd_slotname_map[grad_slot_name] = fwd_slot_name;
found_matching = true;
}
if (fwd_var->GetGradVar() && grad_var == fwd_var->GetGradVar()) {
if (grad_grad_slotname_map.count(grad_slot_name) &&
grad_grad_slotname_map[grad_slot_name] != fwd_slot_name) {
PADDLE_THROW(platform::errors::Fatal(
"Detected mismatched slot names."
"grad_slot_name %s matches both %s and %s fwd_slot_name",
grad_slot_name,
grad_grad_slotname_map[grad_slot_name],
fwd_slot_name));
}
grad_grad_slotname_map[grad_slot_name] = fwd_slot_name;
found_matching = true;
}
}
}
for (const auto& fwd_iter : fwd_outs) {
const std::string& fwd_slot_name = fwd_iter.first;
const std::vector<std::shared_ptr<paddle::imperative::VariableWrapper>>&
fwd_vars = fwd_iter.second;
for (const std::shared_ptr<paddle::imperative::VariableWrapper>&
fwd_var : fwd_vars) {
if (grad_var == fwd_var) {
if (grad_fwd_slotname_map.count(grad_slot_name) &&
grad_fwd_slotname_map[grad_slot_name] != fwd_slot_name) {
PADDLE_THROW(platform::errors::Fatal(
"Detected mismatched slot names"
"grad_slot_name %s matches both %s and %s fwd_slot_name",
grad_slot_name,
grad_fwd_slotname_map[grad_slot_name],
fwd_slot_name));
}
grad_fwd_slotname_map[grad_slot_name] = fwd_slot_name;
found_matching = true;
}
if (fwd_var->GetGradVar() && grad_var == fwd_var->GetGradVar()) {
if (grad_grad_slotname_map.count(grad_slot_name) &&
grad_grad_slotname_map[grad_slot_name] != fwd_slot_name) {
PADDLE_THROW(platform::errors::Fatal(
"Detected mismatched slot names."
"grad_slot_name %s matches both %s and %s fwd_slot_name",
grad_slot_name,
grad_grad_slotname_map[grad_slot_name],
fwd_slot_name));
}
grad_grad_slotname_map[grad_slot_name] = fwd_slot_name;
found_matching = true;
}
}
}
}
if (!found_matching) {
PADDLE_THROW(platform::errors::Fatal(
"Detected mismatched slot names."
"Found no matching fwd_slot_name for grad_slot_name: %s",
grad_slot_name));
} else {
std::string fwd_slot_name = grad_grad_slotname_map.count(grad_slot_name)
? grad_grad_slotname_map[grad_slot_name]
: grad_fwd_slotname_map[grad_slot_name];
VLOG(6) << "Found matching fwd_slot_name: " << fwd_slot_name
<< " for grad_slot_name: " << grad_slot_name;
}
}
}
static bool CheckOpProto(proto::OpProto* op_proto) {
if (op_proto == nullptr) {
return false;
}
const std::string& op_type = op_proto->type();
// Skip ooerator which is not inherit form OperatorWithKernel, like while,
// since only OperatorWithKernel can run in dygraph mode.
auto& all_kernels = paddle::framework::OperatorWithKernel::AllOpKernels();
if (!all_kernels.count(op_type) &&
!phi::KernelFactory::Instance().HasCompatiblePhiKernel(op_type)) {
return false;
}
// Only handle matmul_v2 for now
VLOG(1) << "------ Analyzing Op ------: " << op_type;
return true;
}
static bool BeSameAsInput(const std::string& output_name,
const std::set<std::string>& input_names) {
if (output_name.size() < 4) {
return false;
}
if (output_name.substr(output_name.size() - 3, 3) == "Out") {
if (input_names.count(output_name.substr(0, output_name.size() - 3))) {
return true;
}
}
return false;
}
/* --------------------------------------- */
/* --------- Preprocess Ins/Outs --------- */
/* --------------------------------------- */
static void PurifyForwardOpProto(const proto::OpProto& op_proto,
ForwardGenerationInfo* fwd_info) {
// Op Name
const std::string op_name = op_proto.type();
auto* in_vars = fwd_info->GetMutableInVars();
auto* out_vars = fwd_info->GetMutableOutVars();
auto* fwd_inputs_name_pos_map = fwd_info->GetMutableFwdInputsNamePosMap();
auto* fwd_outputs_name_pos_map = fwd_info->GetMutableFwdOutputsNamePosMap();
// Handle dispensable inputs
for (const proto::OpProto::Var& input : op_proto.inputs()) {
std::string input_name = input.name();
// Delete dispensable tensor unless specified in op_ins_map
if (input.dispensable()) {
if (!op_ins_map.count(op_name) ||
!op_ins_map[op_name].count(input_name)) {
VLOG(6) << "Removing Dispensable Input: " << input_name;
// in_vars
auto iter = in_vars->begin();
for (iter = in_vars->begin(); iter != in_vars->end(); iter++) {
if (iter->name() == input_name) {
break;
}
}
in_vars->erase(iter);
}
}
}
for (const proto::OpProto::Var& output : op_proto.outputs()) {
std::string output_name = output.name();
// Delete dispensable tensor unless specified in op_outs_map
if (output.dispensable()) {
if (!op_outs_map.count(op_name) ||
!op_outs_map[op_name].count(output_name)) {
VLOG(6) << "Removing Dispensable Output: " << output_name;
// out_vars
auto iter = out_vars->begin();
for (iter = out_vars->begin(); iter != out_vars->end(); iter++) {
if (iter->name() == output_name) {
break;
}
}
out_vars->erase(iter);
}
}
}
/* ------ Maping forward slot name to fwd position ------ */
size_t in_pos = 0;
for (const auto& var : *in_vars) {
VLOG(6) << "Mapping input tensor: " << var.name()
<< " To position: " << in_pos;
(*fwd_inputs_name_pos_map)[var.name()] = in_pos;
in_pos++;
}
size_t out_pos = 0;
for (const auto& var : *out_vars) {
VLOG(6) << "Mapping output tensor: " << var.name()
<< " To position: " << out_pos;
(*fwd_outputs_name_pos_map)[var.name()] = out_pos;
out_pos++;
}
}
static void PurifyGradNodeGenerationInfo(const proto::OpProto& op_proto,
GradNodeGenerationInfo* bwd_info) {
auto* op_base_infos = bwd_info->GetMutableOpBaseInfos();
for (auto& iter : *op_base_infos) {
std::map<std::string, std::string>* grad_outs_slotname_map =
iter.GetMutableGradOutsSlotnameMap();
std::map<std::string, std::string>* grad_ins_fwd_slotname_map =
iter.GetMutableGradInsFwdSlotnameMap();
std::map<std::string, std::string>* grad_ins_grad_slotname_map =
iter.GetMutableGradInsGradSlotnameMap();
std::map<std::string,
std::vector<std::shared_ptr<paddle::imperative::VariableWrapper>>>*
grad_ins = iter.GetMutableGradIns();
std::map<std::string,
std::vector<std::shared_ptr<paddle::imperative::VariableWrapper>>>*
grad_outs = iter.GetMutableGradOuts();
// Op Name
const std::string op_name = op_proto.type();
// Handle dispensable inputs
for (const proto::OpProto::Var& input : op_proto.inputs()) {
std::string input_name = input.name();
// Delete dispensable tensor unless specified in op_ins_map
if (input.dispensable()) {
if (!op_ins_map.count(op_name) ||
!op_ins_map[op_name].count(input_name)) {
VLOG(6) << "Removing Dispensable Input: " << input_name;
// grad_outs_slotname_map
auto grad_outs_slotname_map_purified = *grad_outs_slotname_map;
for (const auto& iter : *grad_outs_slotname_map) {
const std::string& grad_output_name = iter.first;
const std::string& matched_input_name = iter.second;
if (matched_input_name == input_name) {
grad_outs_slotname_map_purified.erase(grad_output_name);
PADDLE_ENFORCE(
grad_outs->count(grad_output_name) > 0,
paddle::platform::errors::Fatal(
"Unable to find gradient output name in grad_outs."));
// grad_outs
grad_outs->erase(grad_output_name);
}
}
*grad_outs_slotname_map = grad_outs_slotname_map_purified;
// grad_ins_fwd_slotname_map: output as tensorwrapper
if (grad_ins_fwd_slotname_map->count(input_name))
grad_ins_fwd_slotname_map->erase(input_name);
// grad_ins: output as tensorwrapper
if (grad_ins->count(input_name)) grad_ins->erase(input_name);
}
}
}
for (const proto::OpProto::Var& output : op_proto.outputs()) {
std::string output_name = output.name();
// Delete dispensable tensor unless specified in op_outs_map
if (output.dispensable()) {
if (!op_outs_map.count(op_name) ||
!op_outs_map[op_name].count(output_name)) {
VLOG(6) << "Removing Dispensable Output: " << output_name;
// grad_ins_grad_slotname_map
auto grad_ins_grad_slotname_map_purified =
*grad_ins_grad_slotname_map;
for (const auto& iter : *grad_ins_grad_slotname_map) {
const std::string& grad_input_name = iter.first;
const std::string& matched_output_name = iter.second;
if (matched_output_name == output_name) {
grad_ins_grad_slotname_map_purified.erase(grad_input_name);
PADDLE_ENFORCE(
grad_ins->count(grad_input_name) > 0,
paddle::platform::errors::Fatal(
"Unable to find gradient input name in grad_ins."));
// grad_ins
grad_ins->erase(grad_input_name);
}
}
*grad_ins_grad_slotname_map = grad_ins_grad_slotname_map_purified;
// grad_ins_fwd_slotname_map: output as tensorwrapper
if (grad_ins_fwd_slotname_map->count(output_name))
grad_ins_fwd_slotname_map->erase(output_name);
// grad_ins: output as tensorwrapper
if (grad_ins->count(output_name)) grad_ins->erase(output_name);
}
}
}
}
}
/* -------------------------------- */
/* --------- Collect Info --------- */
/* -------------------------------- */
static void CollectForwardInformationFromOpInfo(
const paddle::framework::OpInfo& op_info, ForwardGenerationInfo* fwd_info) {
const proto::OpProto& op_proto = *op_info.proto_;
fwd_info->SetOpType(op_proto.type());
for (const proto::OpProto::Var& input : op_proto.inputs()) {
fwd_info->GetMutableInVars()->push_back(input);
}
for (const proto::OpProto::Var& output : op_proto.outputs()) {
fwd_info->GetMutableOutVars()->push_back(output);
}
}
static bool CollectGradInformationFromOpInfo(
const paddle::framework::OpInfo& op_info,
GradNodeGenerationInfo* bwd_info) {
const proto::OpProto& op_proto = *op_info.proto_;
const std::string& op_type = op_proto.type();
std::vector<int64_t> dims = {1, 1, 1, 1};
/* ------ Prepare "ins" ------ */
std::map<std::string,
std::vector<std::shared_ptr<paddle::imperative::VarBase>>>
ins;
if (op_proto.inputs().size() == 1 && op_proto.outputs().size() == 1 &&
op_proto.inputs()[0].duplicable() &&
!op_proto.outputs()[0].duplicable()) {
VLOG(6) << "Handle op with special op_bases: " << op_type;
// @special case (sum_op): for ops with single duplicable input and single
// non-duplicable output
// feed in NUM_CREATED_DUP_INPUTS inputs to detect a
// special scenario.
const std::string& in_name = op_proto.inputs()[0].name();
ins[in_name] = {};
for (size_t i = 0; i < NUM_CREATED_DUP_INPUTS; i++) {
ins[in_name].emplace_back(std::shared_ptr<paddle::imperative::VarBase>(
new paddle::imperative::VarBase("auto_" + in_name + "_" +
std::to_string(i))));
ins[in_name][i]->SetOverridedStopGradient(false);
ins[in_name][i]->MutableVar()->GetMutable<framework::LoDTensor>();
}
} else {
for (const proto::OpProto::Var& input : op_proto.inputs()) {
const std::string& in_name = input.name();
// Handle dispensable input:
// 1. At python level, dispensable input will be detected at Python-C
// interface and filled with an empty vector
// 2. At C++ level, customers should always pass an empty vector for any
// dispensable input
// 3. During further lowering, there will always be a placeholder VarBase
// in ins/outs no matter whether it's dispensable or not
// As a result, we always create input VarBase regardless of its
// dispensability.
// Handle duplicable input: list(VarBase) or VarBase
// We dont know the exact number of inputs expected,
// but we only need to identify the slot name order,
// therefore fill in 1 single input VarBase is enough in this scenario
ins[in_name] = {std::shared_ptr<paddle::imperative::VarBase>(
new paddle::imperative::VarBase("auto_" + in_name))};
ins[in_name][0]->SetOverridedStopGradient(false);
ins[in_name][0]->MutableVar()->GetMutable<framework::LoDTensor>();
}
}
VLOG(6) << "Prepared Forward Ins Map, size = " << ins.size();
/* ------ Prepare "outs" ------ */
std::map<std::string,
std::vector<std::shared_ptr<paddle::imperative::VarBase>>>
outs;
for (const proto::OpProto::Var& output : op_proto.outputs()) {
const std::string& out_name = output.name();
// We always create output VarBase regardless of its dispensability.
// We dont know the exact number of outputs during code generation,
// however, simply identifying the slot name order would be enough
outs[out_name] = {std::shared_ptr<paddle::imperative::VarBase>(
new paddle::imperative::VarBase("auto_" + out_name))};
outs[out_name][0]->SetOverridedStopGradient(false);
outs[out_name][0]->MutableVar()->GetMutable<framework::LoDTensor>();
}
VLOG(6) << "Prepared Forward Outs Map, size = " << outs.size();
framework::AttributeMap attrs;
paddle::framework::AttributeMap default_attrs;
auto* attr_checker = op_info.Checker();
if (attr_checker) {
VLOG(6) << "Checking AttributeMap Settings";
attr_checker->Check(&attrs, true, /*only_check_exist_value=*/true);
default_attrs = attr_checker->GetDefaultAttrMap();
} else {
VLOG(6) << "Detected Null Attribute Checker, use empty default_attrs";
}
if (operators_with_attrs.count(op_type)) {
VLOG(6) << "Found operator " << op_type << " using special AttributeMap";
attrs = operators_with_attrs[op_type];
}
VLOG(6) << "Prepared Default Attributes Map, size = " << default_attrs.size();
for (const auto& iter : default_attrs) {
VLOG(6) << iter.first;
}
/* ---------------------------- */
/* --------- Backward --------- */
/* ---------------------------- */
/* ------ Fwd paddle::imperative::VariableWrapper Map ------ */
std::map<std::string,
std::vector<std::shared_ptr<paddle::imperative::VariableWrapper>>>
fwd_ins;
std::map<std::string,
std::vector<std::shared_ptr<paddle::imperative::VariableWrapper>>>
fwd_outs;
for (const auto& iter : ins) {
fwd_ins[iter.first] = {};
for (const std::shared_ptr<paddle::imperative::VarBase>& var_base :
iter.second) {
fwd_ins[iter.first].push_back(var_base->SharedVar());
}
}
for (const auto& iter : outs) {
fwd_outs[iter.first] = {};
for (const std::shared_ptr<paddle::imperative::VarBase>& var_base :
iter.second) {
fwd_outs[iter.first].push_back(var_base->SharedVar());
}
}
VLOG(6) << "Constructed Forward paddle::imperative::VariableWrapper Map";
/* ------ Run GradOpMaker ------ */
if (!op_info.dygraph_grad_op_maker_) {
VLOG(6) << op_type << " has no GradOpMaker";
bwd_info->SetGenerateForwardOnly(true);
return false;
}
std::shared_ptr<paddle::imperative::GradOpNode> grad_node =
op_info.dygraph_grad_op_maker_(
op_type, ins, outs, attrs, default_attrs, {});
if (!grad_node) {
VLOG(6) << "Got nullptr GradOpNode for " << op_type
<< " likely registered EmptyGradOpMaker";
bwd_info->SetGenerateForwardOnly(true);
return false;
}
VLOG(6) << "Prepared GradOpNode";
/* ---- Collect OpBase's op_types ---- */
bwd_info->SetFwdOpType(op_type);
auto* op_base_infos = bwd_info->GetMutableOpBaseInfos();
op_base_infos->resize(grad_node->size());
for (auto iter = grad_node->begin(); iter < grad_node->end(); iter++) {
// Each OpBase
int index = std::distance(grad_node->begin(), iter);
paddle::imperative::OpBase& op_base = *iter;
(*op_base_infos)[index].SetOpBaseType(op_base.Type());
}
/* ------ Get Grad ins/outs/attrs ---- */
VLOG(6) << "In function size: " << grad_node->size();
for (auto iter = grad_node->begin(); iter < grad_node->end(); iter++) {
int index = std::distance(grad_node->begin(), iter);
auto* op_base_grad_ins = (*op_base_infos)[index].GetMutableGradIns();
auto* op_base_grad_outs = (*op_base_infos)[index].GetMutableGradOuts();
auto* op_base_grad_attrs = (*op_base_infos)[index].GetMutableGradAttrs();
const paddle::imperative::OpBase& op_base = *iter;
const std::map<std::string, paddle::imperative::SavedVariableWrapperList>&
g_ins = op_base.GetInsMap();
const std::map<std::string, paddle::imperative::SavedVariableWrapperList>&
g_outs = op_base.GetOutsMap();
*op_base_grad_attrs = op_base.Attrs();
for (const auto& it : g_ins) {
if (!op_base_grad_ins->count(it.first))
(*op_base_grad_ins)[it.first] = {};
for (auto vw_iter = it.second.begin(); vw_iter != it.second.end();
vw_iter++) {
std::shared_ptr<paddle::imperative::VariableWrapper> vw = *vw_iter;
(*op_base_grad_ins)[it.first].push_back(vw);
VLOG(6) << "GradIns Name: " << it.first;
}
}
for (const auto& it : g_outs) {
if (!op_base_grad_outs->count(it.first))
(*op_base_grad_outs)[it.first] = {};
for (auto vw_iter = it.second.begin(); vw_iter != it.second.end();
vw_iter++) {
std::shared_ptr<paddle::imperative::VariableWrapper> vw = *vw_iter;
(*op_base_grad_outs)[it.first].push_back(vw);
VLOG(6) << "GradOuts Name: " << it.first;
}
}
auto& inferer = op_base.Info().NoNeedBufferVarsInferer();
if (inferer && !special_no_need_buffer_op_set.count(op_type)) {
*(*op_base_infos)[index].GetMutableNoNeedBufferInputs() =
inferer(g_ins, g_outs, *op_base_grad_attrs);