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