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[Paddle-TRT] add assign op (PaddlePaddle#55426)
* [Paddle-TRT] add assign op
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,39 @@ | ||
| /* 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 "paddle/fluid/inference/tensorrt/convert/op_converter.h" | ||
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| namespace paddle { | ||
| namespace inference { | ||
| namespace tensorrt { | ||
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| class AssignOpConverter : public OpConverter { | ||
| public: | ||
| void operator()(const framework::proto::OpDesc& op, | ||
| const framework::Scope& scope, | ||
| bool test_mode) override { | ||
| VLOG(3) << "convert a assign op to tensorrt"; | ||
| framework::OpDesc op_desc(op, nullptr); | ||
| auto* input = engine_->GetITensor(op_desc.Input("X")[0]); | ||
| auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Identity, *input); | ||
| auto output_name = op_desc.Output("Out")[0]; | ||
| RreplenishLayerAndOutput(layer, "assign", {output_name}, test_mode); | ||
| } | ||
| }; | ||
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| } // namespace tensorrt | ||
| } // namespace inference | ||
| } // namespace paddle | ||
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| REGISTER_TRT_OP_CONVERTER(assign, AssignOpConverter); |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,160 @@ | ||
| # 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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| import unittest | ||
| from functools import partial | ||
| from typing import List | ||
|
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| import numpy as np | ||
| from program_config import ProgramConfig, TensorConfig | ||
| from trt_layer_auto_scan_test import TrtLayerAutoScanTest | ||
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| import paddle.inference as paddle_infer | ||
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| class TrtConvertAssignTest(TrtLayerAutoScanTest): | ||
| def is_program_valid(self, program_config: ProgramConfig) -> bool: | ||
| compile_version = paddle_infer.get_trt_compile_version() | ||
| runtime_version = paddle_infer.get_trt_runtime_version() | ||
| if ( | ||
| compile_version[0] * 1000 | ||
| + compile_version[1] * 100 | ||
| + compile_version[2] * 10 | ||
| < 8400 | ||
| ): | ||
| return False | ||
| if ( | ||
| runtime_version[0] * 1000 | ||
| + runtime_version[1] * 100 | ||
| + runtime_version[2] * 10 | ||
| < 8400 | ||
| ): | ||
| return False | ||
| return True | ||
|
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| def sample_program_configs(self): | ||
| def generate_input(type): | ||
| if self.dims == 0: | ||
| return np.ones([]).astype(type) | ||
| elif self.dims == 1: | ||
| return np.ones([1]).astype(type) | ||
| else: | ||
| return np.ones([1, 3, 64, 64]).astype(type) | ||
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| for dims in [0, 1, 4]: | ||
| self.dims = dims | ||
| for dtype in [ | ||
| np.bool_, | ||
| np.int32, | ||
| np.float32, | ||
| np.int64, | ||
| ]: | ||
| self.has_bool_dtype = dtype == np.bool_ | ||
| ops_config = [ | ||
| { | ||
| "op_type": "assign", | ||
| "op_inputs": {"X": ["input_data"]}, | ||
| "op_outputs": {"Out": ["assign_output_data0"]}, | ||
| "op_attrs": {}, | ||
| }, | ||
| { | ||
| "op_type": "assign", | ||
| "op_inputs": {"X": ["assign_output_data0"]}, | ||
| "op_outputs": {"Out": ["assign_output_data1"]}, | ||
| "op_attrs": {}, | ||
| }, | ||
| ] | ||
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| 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_input, dtype) | ||
| ) | ||
| }, | ||
| outputs=["assign_output_data1"], | ||
| ) | ||
|
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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 == 0: | ||
| self.dynamic_shape.min_input_shape = {"input_data": []} | ||
| self.dynamic_shape.max_input_shape = {"input_data": []} | ||
| self.dynamic_shape.opt_input_shape = {"input_data": []} | ||
| elif self.dims == 1: | ||
| self.dynamic_shape.min_input_shape = {"input_data": [1]} | ||
| self.dynamic_shape.max_input_shape = {"input_data": [1]} | ||
| self.dynamic_shape.opt_input_shape = {"input_data": [1]} | ||
| else: | ||
| self.dynamic_shape.min_input_shape = { | ||
| "input_data": [1, 3, 64, 64] | ||
| } | ||
| self.dynamic_shape.max_input_shape = { | ||
| "input_data": [1, 3, 64, 64] | ||
| } | ||
| self.dynamic_shape.opt_input_shape = { | ||
| "input_data": [1, 3, 64, 64] | ||
| } | ||
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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 not dynamic_shape and ( | ||
| self.has_bool_dtype or self.dims == 1 or self.dims == 0 | ||
| ): | ||
| return 0, 4 | ||
| 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-2 | ||
|
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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-2 | ||
|
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| def test(self): | ||
| self.run_test() | ||
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| if __name__ == "__main__": | ||
| unittest.main() |