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fuse_multi_transformer_layer_pass.cc
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fuse_multi_transformer_layer_pass.cc
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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.
#include "paddle/fluid/framework/ir/fuse_multi_transformer_layer_pass.h"
#include <string>
#include "paddle/fluid/framework/convert_utils.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_version_registry.h"
namespace paddle {
namespace framework {
class Scope;
} // namespace framework
} // namespace paddle
namespace paddle {
namespace framework {
namespace ir {
namespace patterns {
std::unordered_map<std::string, std::string>
MultiTransformerLayerPattern::operator()(bool enable_int8,
int num_fused_op,
bool is_decoder) {
std::string fused_multi_transformer_name =
enable_int8 ? "fused_multi_transformer_int8" : "fused_multi_transformer";
std::unordered_map<std::string, std::string> node_reprs;
// x0 and src_mask is unqiue input of subgraph
auto* x0 = pattern->NewNode(x0_repr());
x0->assert_is_op_input(fused_multi_transformer_name, "X")->AsInput();
auto* src_mask = pattern->NewNode(src_mask_repr());
src_mask->assert_is_op_input(fused_multi_transformer_name, "SrcMask")
->AsInput();
for (int i = 0; i < num_fused_op; ++i) {
auto fuse_op_repr =
PDNodeName(name_scope_, repr_, id_, "fuse_op_" + std::to_string(i));
node_reprs["fuse_op_" + std::to_string(i)] = fuse_op_repr;
auto* fused_multi_transformer =
pattern->NewNode(fuse_op_repr)
->assert_is_op(fused_multi_transformer_name);
auto out_repr =
PDNodeName(name_scope_, repr_, id_, "out_" + std::to_string(i));
node_reprs["out_" + std::to_string(i)] = out_repr;
auto* out = pattern->NewNode(out_repr)->assert_is_op_output(
fused_multi_transformer_name, "Out");
if (is_decoder) {
auto shape_repr =
PDNodeName(name_scope_, repr_, id_, "shape_" + std::to_string(i));
node_reprs["shape_" + std::to_string(i)] = shape_repr;
auto* shape = pattern->NewNode(shape_repr)->assert_is_op("shape");
auto shape_out_repr =
PDNodeName(name_scope_, repr_, id_, "shape_out_" + std::to_string(i));
node_reprs["shape_out_" + std::to_string(i)] = shape_out_repr;
auto* shape_out =
pattern->NewNode(shape_out_repr)->assert_is_op_output("shape", "Out");
shape->LinksFrom({src_mask}).LinksTo({shape_out});
auto slice_repr =
PDNodeName(name_scope_, repr_, id_, "slice_" + std::to_string(i));
node_reprs["slice_" + std::to_string(i)] = slice_repr;
auto* slice = pattern->NewNode(slice_repr)->assert_is_op("slice");
auto slice_out_repr =
PDNodeName(name_scope_, repr_, id_, "slice_out_" + std::to_string(i));
node_reprs["slice_out_" + std::to_string(i)] = slice_out_repr;
auto* slice_out =
pattern->NewNode(slice_out_repr)->assert_is_op_output("slice", "Out");
slice->LinksFrom({shape_out}).LinksTo({slice_out});
fused_multi_transformer->LinksFrom({x0, src_mask, slice_out})
.LinksTo({out});
} else {
auto cache_kv_repr =
PDNodeName(name_scope_, repr_, id_, "cache_kv_" + std::to_string(i));
node_reprs["cache_kv_" + std::to_string(i)] = cache_kv_repr;
auto* cache_kv = pattern->NewNode(cache_kv_repr);
cache_kv->assert_is_op_input(fused_multi_transformer_name, "CacheKV");
cache_kv->AsInput();
auto fill_const_op_repr =
PDNodeName(name_scope_, repr_, id_, "fill_op_" + std::to_string(i));
node_reprs["fill_op_" + std::to_string(i)] = fill_const_op_repr;
auto fill_const_op = pattern->NewNode(fill_const_op_repr)
->assert_is_op("fill_constant_batch_size_like");
fused_multi_transformer->LinksFrom({x0, src_mask, cache_kv})
.LinksTo({out});
fill_const_op->LinksFrom({x0}).LinksTo({cache_kv});
}
x0 = out;
}
x0->AsOutput();
return node_reprs;
}
} // namespace patterns
inline void MergeInput(OpDesc* op,
const std::vector<VariableNameMap>& input_name_maps,
const std::string& input_name) {
std::vector<std::string> tmp = input_name_maps[0].at(input_name);
for (size_t i = 1; i < input_name_maps.size(); ++i) {
tmp.insert(tmp.end(),
input_name_maps[i].at(input_name).begin(),
input_name_maps[i].at(input_name).end());
}
op->SetInput(input_name, tmp);
}
template <typename T>
inline void MergeAttrs(const std::vector<OpDesc*>& ops,
const std::string& attr_name) {
std::vector<T> res;
for (size_t i = 0; i < ops.size(); ++i) {
auto scale_vec =
PADDLE_GET_CONST(std::vector<T>, ops[i]->GetAttr(attr_name));
res.insert(res.end(), scale_vec.begin(), scale_vec.end());
}
ops[0]->SetAttr(attr_name, res);
}
int FuseMultiTransformerLayerPass::BuildFusion(Graph* graph,
const std::string& name_scope,
Scope* scope) const {
GraphPatternDetector gpd;
auto* pattern = gpd.mutable_pattern();
// TODO(wufeisheng): Get enable_int8 attr from graph after
// fused_multi_transformer pass with int8 merged
bool enable_int8 = false;
int num_fuse_op = 0;
bool is_decoder = false;
if (graph->Has(kFusedMultiTransformerEncoderFusionCount)) {
num_fuse_op = graph->Get<int>(kFusedMultiTransformerEncoderFusionCount);
is_decoder = false;
} else if (graph->Has(kFusedMultiTransformerDecoderFusionCount)) {
num_fuse_op = graph->Get<int>(kFusedMultiTransformerDecoderFusionCount);
is_decoder = true;
}
if (num_fuse_op == 0) {
VLOG(4) << "fuse_multi_transformer_layer_pass will be skipped "
"cause num_fuse_op is not been set or set to 0";
return 0;
}
if (!is_decoder) {
VLOG(4) << "fuse_multi_transformer_layer_pass will match encoder pattern";
} else {
VLOG(4) << "fuse_multi_transformer_layer_pass will match decoder pattern";
}
patterns::MultiTransformerLayerPattern multi_layer_pattern(pattern,
name_scope);
auto node_reprs = multi_layer_pattern(enable_int8, num_fuse_op, is_decoder);
int fusion_count{0};
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* graph) {
///////////////////
//// Get nodes ////
///////////////////
GET_IR_NODE_FROM_SUBGRAPH(src_mask, src_mask, multi_layer_pattern);
GET_IR_NODE_FROM_SUBGRAPH(x0, x0, multi_layer_pattern);
std::vector<Node*> fuse_op_nodes;
std::vector<Node*> out_nodes;
std::vector<std::string> unused_node_prefixes = {
"shape_", "shape_out_", "slice_", "slice_out_"};
std::vector<Node*> unused_nodes;
std::vector<OpDesc*> fuse_op_descs;
std::vector<VariableNameMap> fuse_op_input_var_name_maps;
std::vector<VariableNameMap> fuse_op_output_var_name_maps;
for (int i = 0; i < num_fuse_op; ++i) {
PDNode* fuse_op_pdnode =
multi_layer_pattern.PatternBase::pattern->RetrieveNode(
node_reprs["fuse_op_" + std::to_string(i)]);
Node* fuse_op_node = subgraph.at(fuse_op_pdnode);
fuse_op_nodes.push_back(fuse_op_node);
fuse_op_descs.push_back(fuse_op_node->Op());
fuse_op_input_var_name_maps.emplace_back(fuse_op_node->Op()->Inputs());
fuse_op_output_var_name_maps.emplace_back(fuse_op_node->Op()->Outputs());
PDNode* out_pdnode =
multi_layer_pattern.PatternBase::pattern->RetrieveNode(
node_reprs["out_" + std::to_string(i)]);
out_nodes.push_back(subgraph.at(out_pdnode));
// fill_const op use x0 as input
if (!is_decoder && i != 0) {
PDNode* fill_op_pdnode =
multi_layer_pattern.PatternBase::pattern->RetrieveNode(
node_reprs["fill_op_" + std::to_string(i)]);
Node* fill_op_node = subgraph.at(fill_op_pdnode);
fill_op_node->Op()->SetInput("Input", {x0->Name()});
IR_NODE_UNLINK(out_nodes[i - 1], fill_op_node);
IR_NODE_LINK_TO(x0, fill_op_node);
} else if (is_decoder && i != 0) {
for (const auto& unused_node_prefix : unused_node_prefixes) {
PDNode* unused_pdnode =
multi_layer_pattern.PatternBase::pattern->RetrieveNode(
node_reprs[unused_node_prefix + std::to_string(i)]);
Node* unused_node = subgraph.at(unused_pdnode);
unused_nodes.push_back(unused_node);
}
}
}
///////////////
//// Merge ////
///////////////
// Merge inputs
std::vector<std::string> inputs_names = {"CacheKV",
"FFN1Bias",
"FFN1Weight",
"FFN2Bias",
"FFN2Weight",
"FFNLnBias",
"FFNLnScale",
"LnBias",
"LnScale",
"OutLinearBias",
"OutLinearW",
"QKVBias",
"QKVW"};
for (const auto& input_name : inputs_names) {
MergeInput(fuse_op_descs[0], fuse_op_input_var_name_maps, input_name);
}
// Merge outputs
fuse_op_descs[0]->SetOutput(
"Out", fuse_op_output_var_name_maps[num_fuse_op - 1]["Out"]);
auto& merged_cache_kv_out_names =
fuse_op_output_var_name_maps[0]["CacheKVOut"];
for (int i = 1; i < num_fuse_op; ++i) {
const auto& out_var_names = fuse_op_output_var_name_maps[i]["CacheKVOut"];
merged_cache_kv_out_names.insert(merged_cache_kv_out_names.end(),
out_var_names.begin(),
out_var_names.end());
}
fuse_op_descs[0]->SetOutput("CacheKVOut", merged_cache_kv_out_names);
////////////////
//// ReLink ////
////////////////
// Before relink, out nodes (0 -> num_layer-1) should be removed
std::unordered_set<const Node*> marked_out_nodes(out_nodes.begin(),
out_nodes.end() - 1);
GraphSafeRemoveNodes(graph, marked_out_nodes);
// Relink all input nodes of fused_multi_transformer ops to the first op
auto& merged_inputs = fuse_op_nodes[0]->inputs;
for (int i = 1; i < num_fuse_op; ++i) {
merged_inputs.insert(merged_inputs.end(),
fuse_op_nodes[i]->inputs.begin(),
fuse_op_nodes[i]->inputs.end());
}
// Relink fuse op -> out
IR_NODE_UNLINK(fuse_op_nodes[num_fuse_op - 1], out_nodes[num_fuse_op - 1]);
IR_NODE_LINK_TO(fuse_op_nodes[0], out_nodes[num_fuse_op - 1]);
/////////////////////////////
//// Delete unused nodes ////
/////////////////////////////
// Delete fused_multi_transformer op expect for the first one
std::unordered_set<const Node*> marked_fuse_op_nodes(
fuse_op_nodes.begin() + 1, fuse_op_nodes.end());
if (is_decoder) {
marked_fuse_op_nodes.insert(unused_nodes.begin(), unused_nodes.end());
}
GraphSafeRemoveNodes(graph, marked_fuse_op_nodes);
++fusion_count;
};
gpd(graph, handler);
return fusion_count;
}
void FuseMultiTransformerLayerPass::ApplyImpl(Graph* graph) const {
FusePassBase::Init(name_scope_, graph);
auto* scope = param_scope();
PADDLE_ENFORCE_NOT_NULL(
scope,
platform::errors::Fatal("During the fuse_multi_transformer_layer pass, "
"The scope should not be null."));
int fusion_count = BuildFusion(graph, name_scope_, scope);
AddStatis(fusion_count);
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(fuse_multi_transformer_layer_pass,
paddle::framework::ir::FuseMultiTransformerLayerPass);