wehub-resource-sync
135 lines
5.2 KiB
C++
135 lines
5.2 KiB
C++
// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include <string>
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#include "paddle/fluid/framework/convert_utils.h"
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#include "paddle/fluid/framework/ir/pass.h"
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#include "paddle/fluid/framework/ir/xpu/quant_utils.h"
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#include "paddle/fluid/framework/scope.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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#define GET_IR_NODE(node_) SAFE_GET_IR_NODE_FROM_SUBGRAPH(node_, node_, pattern)
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// Get an ir::Node* from the matched subgraph.
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// var: variable.
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// arg: the argument declared by PATTERN_DECL_NODE in a pattern definition.
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// pat: the pattern object.
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#define SAFE_GET_IR_NODE_FROM_SUBGRAPH(var, arg, pat) \
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Node* var = nullptr; \
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if (pat.arg##_n()) { \
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PADDLE_ENFORCE_NE(subgraph.count(pat.arg##_n()), \
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0UL, \
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common::errors::NotFound("Node not found for PDNode %s", \
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pat.arg##_repr())); \
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var = subgraph.at(pat.arg##_n()); \
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PADDLE_ENFORCE_NOT_NULL(var, \
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common::errors::NotFound( \
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"node %s not exists in the sub-graph", #arg)); \
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}
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#define SAFE_IR_NODE_LINK_TO(a, b) \
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if (a != nullptr && b != nullptr) { \
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IR_NODE_LINK_TO(a, b) \
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}
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int ConvertActivationType(std::string act_type);
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Node* FindNodeWithName(Graph* graph, std::string name);
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std::vector<Node*> FindOpNodeByInputName(Graph* graph,
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const std::string& var_name);
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template <typename T>
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size_t HashTensor(const DenseTensor& in);
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void ConvertFromFp32ToFp16(DenseTensor* weight,
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DenseTensor* weight_max,
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bool transpose);
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template <typename Tcpu,
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typename Txpu,
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typename std::enable_if<!std::is_same<Tcpu, Txpu>::value, Tcpu>::type*
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ptr = nullptr>
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void ConvertWeightWrapper(DenseTensor* weight,
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DenseTensor* weight_max,
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DenseTensor* scale_max,
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bool transpose,
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const std::vector<float>& weight_scales,
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bool per_channel_quant) {
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if (std::is_same<Tcpu, float>::value && std::is_same<Txpu, float16>::value) {
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ConvertFromFp32ToFp16(weight, weight_max, transpose);
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} else {
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ConvertWithQuant<Tcpu, Txpu>(
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weight, weight_max, scale_max, transpose, per_channel_quant);
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}
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}
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template <typename Tcpu,
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typename Txpu,
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typename std::enable_if<std::is_same<Tcpu, Txpu>::value, Tcpu>::type*
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ptr = nullptr>
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void ConvertWeightWrapper(DenseTensor* weight,
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DenseTensor* weight_max,
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DenseTensor* scale_max,
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bool transpose,
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const std::vector<float>& weight_scales,
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bool per_channel_quant) {
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ConvertWithoutQuant<Tcpu>(
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weight, weight_max, scale_max, transpose, weight_scales);
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}
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// 1. Quant weight from fp32 to int16/int31/int8
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// 2. Weight data is in-place update.
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// 3. Generate weight max tensor
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template <typename Tcpu, typename Txpu>
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void PrepareWeight(Graph* graph,
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Scope* scope,
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BlockDesc* block,
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Node* weight,
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Node** dst_weight,
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Node** dst_weight_max,
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Node** dst_scale_max,
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bool transpose,
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const std::vector<float>& weight_scales,
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bool per_channel_quant = false);
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void PrepareBias(
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Graph* graph, Scope* scope, BlockDesc* block, Node* src, Node** dst);
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inline std::string FindOutputNameByVarName(framework::OpDesc* op,
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const std::string& searched_name) {
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std::string ret;
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for (const auto& name : op->OutputNames())
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for (const auto& output_name : op->Output(name))
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if (output_name == searched_name) ret = name;
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return ret;
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}
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inline std::string FindInputNameByVarName(framework::OpDesc* op,
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const std::string& searched_name) {
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std::string ret;
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for (const auto& name : op->InputNames())
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for (const auto& input_name : op->Input(name))
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if (input_name == searched_name) ret = name;
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return ret;
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}
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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