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add exp,log trt converter (PaddlePaddle#42655)
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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. */ | ||
|
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| #include <NvInfer.h> | ||
| #include <string> | ||
| #include "glog/logging.h" | ||
| #include "paddle/fluid/framework/op_desc.h" | ||
| #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" | ||
| #include "paddle/fluid/inference/tensorrt/engine.h" | ||
| #include "paddle/fluid/inference/tensorrt/helper.h" | ||
| #include "paddle/fluid/platform/enforce.h" | ||
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| namespace paddle { | ||
| namespace framework { | ||
| class Scope; | ||
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| namespace proto { | ||
| class OpDesc; | ||
| } // namespace proto | ||
| } // namespace framework | ||
| } // namespace paddle | ||
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| namespace paddle { | ||
| namespace inference { | ||
| namespace tensorrt { | ||
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| class UnaryOpConverter : public OpConverter { | ||
| public: | ||
| UnaryOpConverter() {} | ||
| void operator()(const framework::proto::OpDesc& op, | ||
| const framework::Scope& scope, bool test_mode) override { | ||
| // Here the two nullptr looks strange, that's because the | ||
| // framework::OpDesc's constructor is strange. | ||
| framework::OpDesc op_desc(op, nullptr); | ||
| VLOG(3) << "convert a fluid unary op to tensorrt unary layer whose " | ||
| "type is " | ||
| << op_type_; | ||
| nvinfer1::ITensor* input_tensor = | ||
| engine_->GetITensor(op_desc.Input("X")[0]); | ||
| auto op_pair = ops.find(op_type_); | ||
| nvinfer1::IUnaryLayer* layer = | ||
| TRT_ENGINE_ADD_LAYER(engine_, Unary, *input_tensor, op_pair->second); | ||
| auto output_name = op_desc.Output("Out")[0]; | ||
| RreplenishLayerAndOutput(layer, op_type_, {output_name}, test_mode); | ||
| } | ||
|
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| protected: | ||
| std::string op_type_; | ||
| static const std::unordered_map<std::string, nvinfer1::UnaryOperation> ops; | ||
| }; | ||
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| const std::unordered_map<std::string, nvinfer1::UnaryOperation> | ||
| UnaryOpConverter::ops = { | ||
| {"exp", nvinfer1::UnaryOperation::kEXP}, | ||
| {"log", nvinfer1::UnaryOperation::kLOG}, | ||
| }; | ||
|
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| class ExpOpConverter : public UnaryOpConverter { | ||
| public: | ||
| ExpOpConverter() { op_type_ = "exp"; } | ||
| }; | ||
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| class LogOpConverter : public UnaryOpConverter { | ||
| public: | ||
| LogOpConverter() { op_type_ = "log"; } | ||
| }; | ||
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| } // namespace tensorrt | ||
| } // namespace inference | ||
| } // namespace paddle | ||
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| REGISTER_TRT_OP_CONVERTER(exp, ExpOpConverter); | ||
| REGISTER_TRT_OP_CONVERTER(log, LogOpConverter); |
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132 changes: 132 additions & 0 deletions
132
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_unary.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,132 @@ | ||
| # 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. | ||
|
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| from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons | ||
| from program_config import TensorConfig, ProgramConfig | ||
| import unittest | ||
| import numpy as np | ||
| import paddle.inference as paddle_infer | ||
| from functools import partial | ||
| from typing import Optional, List, Callable, Dict, Any, Set | ||
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| class TrtConvertActivationTest(TrtLayerAutoScanTest): | ||
| def is_program_valid(self, program_config: ProgramConfig) -> bool: | ||
| return True | ||
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| def sample_program_configs(self): | ||
| def generate_input1(dims, batch, attrs: List[Dict[str, Any]]): | ||
| if dims == 1: | ||
| return np.ones([32]).astype(np.float32) | ||
| elif dims == 2: | ||
| return np.ones([3, 32]).astype(np.float32) | ||
| elif dims == 3: | ||
| return np.ones([3, 32, 32]).astype(np.float32) | ||
| else: | ||
| return np.ones([batch, 3, 32, 32]).astype(np.float32) | ||
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| for dims in [1, 2, 3, 4]: | ||
| for batch in [1, 4]: | ||
| for op_type in ["exp", "log"]: | ||
| self.dims = dims | ||
| dics = [{}] | ||
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| ops_config = [{ | ||
| "op_type": op_type, | ||
| "op_inputs": { | ||
| "X": ["input_data"] | ||
| }, | ||
| "op_outputs": { | ||
| "Out": ["output_data"] | ||
| }, | ||
| "op_attrs": dics[0] | ||
| }] | ||
| ops = self.generate_op_config(ops_config) | ||
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| program_config = ProgramConfig( | ||
| ops=ops, | ||
| weights={}, | ||
| inputs={ | ||
| "input_data": TensorConfig(data_gen=partial( | ||
| generate_input1, dims, batch, dics)) | ||
| }, | ||
| outputs=["output_data"]) | ||
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| yield program_config | ||
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| def sample_predictor_configs( | ||
| self, program_config) -> (paddle_infer.Config, List[int], float): | ||
| def generate_dynamic_shape(attrs): | ||
| if self.dims == 1: | ||
| self.dynamic_shape.min_input_shape = {"input_data": [1]} | ||
| self.dynamic_shape.max_input_shape = {"input_data": [64]} | ||
| self.dynamic_shape.opt_input_shape = {"input_data": [32]} | ||
| elif self.dims == 2: | ||
| self.dynamic_shape.min_input_shape = {"input_data": [1, 16]} | ||
| self.dynamic_shape.max_input_shape = {"input_data": [4, 32]} | ||
| self.dynamic_shape.opt_input_shape = {"input_data": [3, 32]} | ||
| elif self.dims == 3: | ||
| self.dynamic_shape.min_input_shape = {"input_data": [1, 16, 16]} | ||
| self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 32]} | ||
| self.dynamic_shape.opt_input_shape = {"input_data": [3, 32, 32]} | ||
| else: | ||
| self.dynamic_shape.min_input_shape = { | ||
| "input_data": [1, 3, 16, 16] | ||
| } | ||
| self.dynamic_shape.max_input_shape = { | ||
| "input_data": [4, 3, 32, 32] | ||
| } | ||
| self.dynamic_shape.opt_input_shape = { | ||
| "input_data": [1, 3, 32, 32] | ||
| } | ||
|
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| def clear_dynamic_shape(): | ||
| self.dynamic_shape.min_input_shape = {} | ||
| self.dynamic_shape.max_input_shape = {} | ||
| self.dynamic_shape.opt_input_shape = {} | ||
|
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| def generate_trt_nodes_num(attrs, dynamic_shape): | ||
| if self.dims == 1: | ||
| return 0, 3 | ||
| return 1, 2 | ||
|
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| attrs = [ | ||
| program_config.ops[i].attrs | ||
| for i in range(len(program_config.ops)) | ||
| ] | ||
|
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| # for static_shape | ||
| clear_dynamic_shape() | ||
| self.trt_param.precision = paddle_infer.PrecisionType.Float32 | ||
| yield self.create_inference_config(), generate_trt_nodes_num( | ||
| attrs, False), 1e-5 | ||
| self.trt_param.precision = paddle_infer.PrecisionType.Half | ||
| yield self.create_inference_config(), generate_trt_nodes_num( | ||
| attrs, False), 1e-5 | ||
|
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| # for dynamic_shape | ||
| generate_dynamic_shape(attrs) | ||
| self.trt_param.precision = paddle_infer.PrecisionType.Float32 | ||
| yield self.create_inference_config(), generate_trt_nodes_num(attrs, | ||
| True), 1e-5 | ||
| self.trt_param.precision = paddle_infer.PrecisionType.Half | ||
| yield self.create_inference_config(), generate_trt_nodes_num(attrs, | ||
| True), 1e-5 | ||
|
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| def test(self): | ||
| self.run_test() | ||
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| if __name__ == "__main__": | ||
| unittest.main() |