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71 changes: 71 additions & 0 deletions
71
paddle/fluid/inference/tensorrt/convert/fill_constant_op.cc
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| /* Copyright (c) 2018 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 FillConstantOpConverter : public OpConverter { | ||
| public: | ||
| void operator()(const framework::proto::OpDesc& op, | ||
| const framework::Scope& scope, | ||
| bool test_mode) override { | ||
| VLOG(4) | ||
| << "convert a fluid fill_constant op to tensorrt fill_constant layer"; | ||
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| framework::OpDesc op_desc(op, nullptr); | ||
| int dtype = BOOST_GET_CONST(int, op_desc.GetAttr("dtype")); | ||
| std::string str_value = | ||
| BOOST_GET_CONST(std::string, op_desc.GetAttr("str_value")); | ||
| std::vector<int64_t> shape = | ||
| BOOST_GET_CONST(std::vector<int64_t>, op_desc.GetAttr("shape")); | ||
| std::unique_ptr<framework::Tensor> out_tensor(new framework::Tensor()); | ||
| out_tensor->Resize(phi::make_ddim(shape)); | ||
| nvinfer1::DataType trt_dtype = nvinfer1::DataType::kFLOAT; | ||
| void* trt_data = nullptr; | ||
| size_t trt_num; | ||
| if (dtype == 2 || dtype == 3) { // int, int64 | ||
| auto* tmp_ptr = out_tensor->mutable_data<int>(platform::CPUPlace()); | ||
| for (int64_t i = 0; i < out_tensor->numel(); i++) | ||
| tmp_ptr[i] = std::stoi(str_value); | ||
| trt_dtype = nvinfer1::DataType::kINT32; | ||
| trt_data = static_cast<void*>(tmp_ptr); | ||
| } else if (dtype == 5) { // float | ||
| auto* tmp_ptr = out_tensor->mutable_data<float>(platform::CPUPlace()); | ||
| for (int64_t i = 0; i < out_tensor->numel(); i++) | ||
| tmp_ptr[i] = std::stof(str_value); | ||
| trt_data = static_cast<void*>(tmp_ptr); | ||
| } else { | ||
| } | ||
| trt_num = static_cast<size_t>(out_tensor->numel()); | ||
| engine_->SetWeights("fill_constant_value", std::move(out_tensor)); | ||
| TensorRTEngine::Weight weight{trt_dtype, trt_data, trt_num}; | ||
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| nvinfer1::Dims trt_in_shape; | ||
| trt_in_shape.nbDims = shape.size(); | ||
| for (size_t i = 0; i < shape.size(); i++) trt_in_shape.d[i] = shape[i]; | ||
| nvinfer1::ILayer* layer = | ||
| TRT_ENGINE_ADD_LAYER(engine_, Constant, trt_in_shape, weight.get()); | ||
| auto output_name = op_desc.Output("Out")[0]; | ||
| RreplenishLayerAndOutput(layer, "fill_constant", {output_name}, test_mode); | ||
| } | ||
| }; | ||
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| } // namespace tensorrt | ||
| } // namespace inference | ||
| } // namespace paddle | ||
|
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| REGISTER_TRT_OP_CONVERTER(fill_constant, FillConstantOpConverter); |
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142 changes: 142 additions & 0 deletions
142
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_fill_constant.py
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,142 @@ | ||
| # Copyright (c) 2021 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 TrtConvertSplitTest(TrtLayerAutoScanTest): | ||
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| def is_program_valid(self, program_config: ProgramConfig) -> bool: | ||
| return True | ||
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| def sample_program_configs(self): | ||
|
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| def generate_value_data(attrs: List[Dict[str, Any]]): | ||
| return np.array([1]).astype(np.int32) | ||
|
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| def generate_shape_data(attrs: List[Dict[str, Any]]): | ||
| return np.array([4, 23]).astype(np.int32) | ||
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| def generate_shapelist_data(attrs: List[Dict[str, Any]]): | ||
| return np.array([4]).astype(np.int32) | ||
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| for shape in [[2, 3, 4]]: | ||
| for num_input in [0, 1, 2, 3]: | ||
| for dtype in [5, 2, 3]: | ||
| for str_value in ["2", "23", "-1"]: | ||
| self.num_input = num_input | ||
| dics = [{ | ||
| "str_value": str_value, | ||
| "shape": shape, | ||
| "dtype": dtype | ||
| }, { | ||
| "axis": -1 | ||
| }] | ||
| dics_intput = [{ | ||
| "ValueTensor": ["value_data"] | ||
| }, { | ||
| "ShapeTensor": ["shape_data"], | ||
| }, { | ||
| "ShapeTensorList": ["shapeT1_data", "shapeT2_data"], | ||
| }, {}] | ||
| ops_config = [ | ||
| { | ||
| "op_type": "fill_constant", | ||
| "op_inputs": dics_intput[num_input], | ||
| "op_outputs": { | ||
| "Out": ["out_data"], | ||
| }, | ||
| "op_attrs": dics[0] | ||
| }, | ||
| ] | ||
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| def generate_input(): | ||
| return np.random.random([1, 1]).astype(np.float32) | ||
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| ops = self.generate_op_config(ops_config) | ||
| program_config = ProgramConfig( | ||
| ops=ops, | ||
| weights={}, | ||
| inputs={ | ||
| "value_data": | ||
| TensorConfig(data_gen=partial( | ||
| generate_value_data, dics)), | ||
| "shape_data": | ||
| TensorConfig(data_gen=partial( | ||
| generate_shape_data, dics)), | ||
| "shapeT1_data": | ||
| TensorConfig(data_gen=partial( | ||
| generate_shapelist_data, dics)), | ||
| "shapeT2_data": | ||
| TensorConfig(data_gen=partial( | ||
| generate_shapelist_data, dics)), | ||
| }, | ||
| outputs=["out_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): | ||
|
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| def generate_dynamic_shape(attrs): | ||
| self.input_shape = [1, 1] | ||
| max_shape = list(self.input_shape) | ||
| min_shape = list(self.input_shape) | ||
| opt_shape = list(self.input_shape) | ||
| for i in range(len(self.input_shape)): | ||
| max_shape[i] = max_shape[i] + 1 | ||
| self.dynamic_shape.min_input_shape = {"Y_data": min_shape} | ||
| self.dynamic_shape.max_input_shape = {"Y_data": max_shape} | ||
| self.dynamic_shape.opt_input_shape = {"Y_data": opt_shape} | ||
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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.num_input < 3): | ||
| return 0, 6 | ||
| return 1, 5 | ||
|
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| attrs = [ | ||
| program_config.ops[i].attrs for i in range(len(program_config.ops)) | ||
| ] | ||
| # Don't test static shape | ||
|
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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 add_skip_trt_case(self): | ||
| pass | ||
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| def test(self): | ||
| self.add_skip_trt_case() | ||
| self.run_test() | ||
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| if __name__ == "__main__": | ||
| unittest.main() |
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