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[Paddle-TRT] add flip op (PaddlePaddle#55688)
* [Paddle-TRT] add flip op
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,83 @@ | ||
| /* Copyright (c) 2023 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 FlipOpConverter : public OpConverter { | ||
| public: | ||
| void operator()(const framework::proto::OpDesc& op, | ||
| const framework::Scope& scope, | ||
| bool test_mode) override { | ||
| VLOG(4) << "convert a flip op to tensorrt layer"; | ||
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| framework::OpDesc op_desc(op, nullptr); | ||
| // Declare inputs | ||
| auto* input = engine_->GetITensor(op_desc.Input("X")[0]); | ||
| auto input_dims = input->getDimensions(); | ||
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| // Get Attrs | ||
| std::vector<int> axis = | ||
| PADDLE_GET_CONST(std::vector<int>, op_desc.GetAttr("axis")); | ||
| for (size_t i = 0; i < axis.size(); ++i) { | ||
| axis[i] += (axis[i] < 0) ? input_dims.nbDims : 0; | ||
| } | ||
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| nvinfer1::ITensor* shape_tensor = Shape(input); | ||
| // getAxisLength default is a scalar | ||
| auto getAxisLength = [&](int axis, bool scalar = true) { | ||
| int d = input_dims.d[axis]; | ||
| if (d >= 0) { | ||
| return Add1DConstantLayer(d, "", scalar); | ||
| } else { | ||
| return GetEleTensorOfShape(shape_tensor, axis, scalar); | ||
| } | ||
| }; | ||
| for (size_t i = 0; i < axis.size(); ++i) { | ||
| auto loop = TRT_ENGINE_ADD_LAYER(engine_, Loop); | ||
| nvinfer1::ITensor* tripLimit = getAxisLength(axis[i]); | ||
| loop->addTripLimit(*tripLimit, nvinfer1::TripLimit::kCOUNT); | ||
| auto iterator = loop->addIterator(*input, axis[i], true); | ||
| std::vector<int32_t> zero_vec{0}; | ||
| std::vector<int32_t> one_vec{1}; | ||
| auto zero = Add1DConstantLayer(zero_vec); | ||
| auto one = Add1DConstantLayer(one_vec); | ||
| nvinfer1::IRecurrenceLayer* iRec = loop->addRecurrence(*zero); | ||
| nvinfer1::ITensor* iCur = iRec->getOutput(0); | ||
| auto iNext = TRT_ENGINE_ADD_LAYER(engine_, | ||
| ElementWise, | ||
| *iCur, | ||
| *one, | ||
| nvinfer1::ElementWiseOperation::kSUM); | ||
| iRec->setInput(1, *iNext->getOutput(0)); | ||
| nvinfer1::ILoopOutputLayer* loopOut = loop->addLoopOutput( | ||
| *iterator->getOutput(0), nvinfer1::LoopOutput::kCONCATENATE, axis[i]); | ||
| loopOut->setInput(1, *tripLimit); | ||
| input = loopOut->getOutput(0); | ||
| } | ||
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| auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Identity, *input); | ||
| auto output_name = op_desc.Output("Out")[0]; | ||
| RreplenishLayerAndOutput(layer, "flip", {output_name}, test_mode); | ||
| } | ||
| }; | ||
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| } // namespace tensorrt | ||
| } // namespace inference | ||
| } // namespace paddle | ||
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| REGISTER_TRT_OP_CONVERTER(flip, FlipOpConverter); |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,140 @@ | ||
| # Copyright (c) 2023 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 TrtConvertFlipTest(TrtLayerAutoScanTest): | ||
| def is_program_valid(self, program_config: ProgramConfig) -> bool: | ||
| ver = paddle_infer.get_trt_compile_version() | ||
| if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 7220: | ||
| return False | ||
| return True | ||
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| def sample_program_configs(self): | ||
| def generate_input(batch): | ||
| if self.dims == 4: | ||
| return np.random.random([batch, 3, 3, 24]).astype(np.float32) | ||
| elif self.dims == 3: | ||
| return np.random.random([batch, 3, 24]).astype(np.float32) | ||
| elif self.dims == 2: | ||
| return np.random.random([batch, 24]).astype(np.float32) | ||
| elif self.dims == 1: | ||
| return np.random.random([24]).astype(np.int32) | ||
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| def generate_axis(): | ||
| return np.arange(self.dims).tolist() | ||
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| for dims in [2, 3, 4]: | ||
| for batch in [3, 6, 9]: | ||
| self.dims = dims | ||
| axis = generate_axis() | ||
| ops_config = [ | ||
| { | ||
| "op_type": "flip", | ||
| "op_inputs": { | ||
| "X": ["input_data"], | ||
| }, | ||
| "op_outputs": {"Out": ["output_data"]}, | ||
| "op_attrs": {"axis": axis}, | ||
| } | ||
| ] | ||
| 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, batch) | ||
| ), | ||
| }, | ||
| 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 == 4: | ||
| self.dynamic_shape.min_input_shape = { | ||
| "input_data": [1, 3 - 1, 3 - 1, 24 - 1] | ||
| } | ||
| self.dynamic_shape.max_input_shape = { | ||
| "input_data": [9, 3 + 1, 3 + 1, 24 + 1] | ||
| } | ||
| self.dynamic_shape.opt_input_shape = { | ||
| "input_data": [1, 3, 3, 24] | ||
| } | ||
| elif self.dims == 3: | ||
| self.dynamic_shape.min_input_shape = { | ||
| "input_data": [1, 3 - 1, 24 - 1] | ||
| } | ||
| self.dynamic_shape.max_input_shape = { | ||
| "input_data": [9, 3 + 1, 24 + 1] | ||
| } | ||
| self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 24]} | ||
| elif self.dims == 2: | ||
| self.dynamic_shape.min_input_shape = {"input_data": [1, 24]} | ||
| self.dynamic_shape.max_input_shape = {"input_data": [9, 24]} | ||
| self.dynamic_shape.opt_input_shape = {"input_data": [1, 24]} | ||
| elif self.dims == 1: | ||
| self.dynamic_shape.min_input_shape = {"input_data": [24 - 1]} | ||
| self.dynamic_shape.max_input_shape = {"input_data": [24 + 1]} | ||
| self.dynamic_shape.opt_input_shape = {"input_data": [24]} | ||
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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): | ||
| ver = paddle_infer.get_trt_compile_version() | ||
| if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 7220: | ||
| return 0, 3 | ||
| return 1, 2 | ||
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| attrs = [ | ||
| program_config.ops[i].attrs for i in range(len(program_config.ops)) | ||
| ] | ||
| self.trt_param.max_batch_size = 9 | ||
| self.trt_param.workspace_size = 1073741824 | ||
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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-3 | ||
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| def test(self): | ||
| self.run_test() | ||
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| if __name__ == "__main__": | ||
| unittest.main() |