// Copyright (c) 2019 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/inference/tensorrt/op_teller.h" #include "paddle/fluid/framework/block_desc.h" #include "paddle/fluid/framework/var_desc.h" namespace paddle { namespace inference { namespace tensorrt { // Just tell by the op_types. struct SimpleOpTypeSetTeller : public Teller { SimpleOpTypeSetTeller() { #if IS_TRT_VERSION_GE(5130) teller_set.insert("relu6"); teller_set.insert("hard_sigmoid"); #endif #if IS_TRT_VERSION_GE(6000) teller_set.insert("fused_embedding_eltwise_layernorm"); teller_set.insert("multihead_matmul"); teller_set.insert("skip_layernorm"); teller_set.insert("slice"); #endif } bool operator()(const std::string& op_type, const framework::OpDesc& desc, bool use_no_calib_int8) override { if (use_no_calib_int8) { return int8_teller_set.count(op_type); } else { return teller_set.count(op_type); } } private: // use this set for no calib int8. std::unordered_set<std::string> int8_teller_set{"mul", "conv2d", "pool2d", "relu", "depthwise_conv2d", "softmax", "batch_norm", "elementwise_add", "leaky_relu", "fc", "relu6", "concat", "scale", "elementwise_mul", "conv2d_transpose"}; std::unordered_set<std::string> teller_set{ "mul", "matmul", "conv2d", "pool2d", "relu", "softmax", "sigmoid", "hard_swish", "depthwise_conv2d", "batch_norm", "concat", "tanh", "pad", "elementwise_add", "elementwise_mul", "dropout", "prelu", "conv2d_transpose", "leaky_relu", "fc", "shuffle_channel", "swish", "split", "instance_norm", "gelu", "layer_norm", "scale", "stack", }; }; bool OpTeller::Tell(const std::string& op_type, const framework::OpDesc& desc, bool use_no_calib_int8) { // do not support the op which is labeled the `skip_quant` if ((desc.HasAttr("namescope") && boost::get<std::string>(desc.GetAttr("op_namescope")) == "/skip_quant_2/") || desc.HasAttr("skip_quant")) return false; for (auto& teller : tellers_) { if (op_type == "pool2d" || op_type == "conv2d" || op_type == "depthwise_conv2d" || op_type == "conv2d_transpose") { std::vector<int> paddings = boost::get<std::vector<int>>(desc.GetAttr("paddings")); std::string padding_algorithm = "EXPLICIT"; if (desc.HasAttr("padding_algorithm")) padding_algorithm = boost::get<std::string>(desc.GetAttr("padding_algorithm")); if (paddings.size() > 2 || (padding_algorithm == "SAME" && op_type != "pool2d")) return false; } if (op_type == "matmul") { auto* block = desc.Block(); for (auto& param_name : desc.Inputs()) { for (auto& var_name : param_name.second) { auto* var_desc = block->FindVar(var_name); const auto shape = var_desc->GetShape(); if (shape.size() < 3) { VLOG(1) << "matmul op dims < 3 not supported in tensorrt, but got dims " << shape.size() << ", so jump it."; return false; } } } } if ((*teller)(op_type, desc, use_no_calib_int8)) return true; } return false; } OpTeller::OpTeller() { tellers_.emplace_back(new SimpleOpTypeSetTeller); } } // namespace tensorrt } // namespace inference } // namespace paddle