From 29e89f6dee24514f6473fa7799e812a688a7754f Mon Sep 17 00:00:00 2001 From: xiaoxiaohehe001 <49090790+xiaoxiaohehe001@users.noreply.github.com> Date: Tue, 14 Sep 2021 16:48:37 +0800 Subject: [PATCH] [Paddle Inference]Add reshape op TRT converter unittest. (#35166) * add_reshape_teller * add_reshape_teller * add_reshape_teller * add_reshape_teller * add_reshape_teller * add_reshape_teller * add_reshape_teller * add_reshape_teller * add_reshape_teller * add_reshape_teller * add_reshape_teller * add_reshape_teller * add_reshape_teller * add_reshape_teller * add_reshape_teller --- paddle/fluid/inference/tensorrt/op_teller.cc | 3 +- .../ir/inference/test_trt_convert_reshape.py | 205 ++++++++++++++++++ 2 files changed, 207 insertions(+), 1 deletion(-) create mode 100644 python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_reshape.py diff --git a/paddle/fluid/inference/tensorrt/op_teller.cc b/paddle/fluid/inference/tensorrt/op_teller.cc index cf7f312625e..d0e5e346e73 100644 --- a/paddle/fluid/inference/tensorrt/op_teller.cc +++ b/paddle/fluid/inference/tensorrt/op_teller.cc @@ -839,7 +839,8 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, std::vector shape = BOOST_GET_CONST(std::vector, desc.GetAttr("shape")); if (shape.size() >= nvinfer1::Dims::MAX_DIMS) return false; - if (!with_dynamic_shape && shape[0] == -1) return false; + if (!with_dynamic_shape && (shape[0] == -1 || shape.size() == 1)) + return false; } if (op_type == "reduce_sum" || op_type == "reduce_mean") { diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_reshape.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_reshape.py new file mode 100644 index 00000000000..cf7ab11c35d --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_reshape.py @@ -0,0 +1,205 @@ +# 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. + +from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons +from program_config import TensorConfig, ProgramConfig +import numpy as np +import paddle.inference as paddle_infer +from functools import partial +from typing import Optional, List, Callable, Dict, Any, Set + + +class TrtConvertReshapeTest(TrtLayerAutoScanTest): + def is_program_valid(self, program_config: ProgramConfig) -> bool: + attrs = [ + program_config.ops[i].attrs + for i in range(len(program_config.ops)) + ] + if self.dims == 1: + if len(attrs[0]['shape']) != 1: + return False + + #To test if the shape contains 0 + if len(attrs[0]['shape']) == 3: + if attrs[0]['shape'][1] == 0: + if self.dims != 3: + return False + + if len(attrs[0]['shape']) == 4: + if attrs[0]['shape'][2] == 0: + if self.dims != 4: + return False + + return True + + def sample_program_configs(self): + def generate_input1(attrs: List[Dict[str, Any]]): + if self.dims == 4: + return np.ones([1, 2, 4, 6]).astype(np.float32) + elif self.dims == 3: + return np.ones([1, 8, 6]).astype(np.float32) + elif self.dims == 2: + return np.ones([1, 48]).astype(np.float32) + elif self.dims == 1: + return np.ones([48]).astype(np.float32) + + def generate_weight1(attrs: List[Dict[str, Any]]): + return np.array([1, 48]).astype(np.int32) + + def generate_shapeT1_data(attrs: List[Dict[str, Any]]): + return np.array([2]).astype(np.int32) + + def generate_shapeT2_data(attrs: List[Dict[str, Any]]): + return np.array([24]).astype(np.int32) + + for dims in [4, 3, 2, 1]: + for num_input in [0, 1, 2, 3]: + for shape in [[1, 6, 8], [1, 2, 4, 6], [1, 1, 0, 12], + [1, 0, 6], [1, -1, 12], [2, -1], [3, 16], + [3, 4, 4], [48]]: + dics = [{"shape": shape, }, {}] + self.num_input = num_input + self.dims = dims + dics_intput = [{ + "X": ["reshape_input"], + "Shape": ["shape_data"], + "ShapeTensor": ["shapeT1_data", "shapeT2_data"], + }, { + "X": ["reshape_input"], + "Shape": ["shape_data"], + }, { + "X": ["reshape_input"], + "ShapeTensor": ["shapeT1_data", "shapeT2_data"], + }, { + "X": ["reshape_input"] + }] + + dics_weight = [{ + "shape_data": + TensorConfig(data_gen=partial(generate_weight1, dics)), + "shapeT1_data": TensorConfig(data_gen=partial( + generate_shapeT1_data, dics)), + "shapeT2_data": TensorConfig(data_gen=partial( + generate_shapeT2_data, dics)) + }, { + "shape_data": + TensorConfig(data_gen=partial(generate_weight1, dics)) + }, { + "shapeT1_data": TensorConfig(data_gen=partial( + generate_shapeT1_data, dics)), + "shapeT2_data": TensorConfig(data_gen=partial( + generate_shapeT2_data, dics)) + }, {}] + + ops_config = [{ + "op_type": "reshape", + "op_inputs": dics_intput[num_input], + "op_outputs": { + "Out": ["reshape_out"] + }, + "op_attrs": dics[0] + }] + ops = self.generate_op_config(ops_config) + program_config = ProgramConfig( + ops=ops, + weights=dics_weight[num_input], + inputs={ + "reshape_input": TensorConfig(data_gen=partial( + generate_input1, dics)) + }, + outputs=["reshape_out"]) + + yield program_config + + 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 = { + "reshape_input": [1, 2, 4, 6] + } + self.dynamic_shape.max_input_shape = { + "reshape_input": [4, 2, 4, 6] + } + self.dynamic_shape.opt_input_shape = { + "reshape_input": [1, 2, 4, 6] + } + elif self.dims == 3: + self.dynamic_shape.min_input_shape = { + "reshape_input": [1, 8, 6] + } + self.dynamic_shape.max_input_shape = { + "reshape_input": [4, 8, 6] + } + self.dynamic_shape.opt_input_shape = { + "reshape_input": [1, 8, 6] + } + elif self.dims == 2: + self.dynamic_shape.min_input_shape = {"reshape_input": [1, 48]} + self.dynamic_shape.max_input_shape = {"reshape_input": [4, 48]} + self.dynamic_shape.opt_input_shape = {"reshape_input": [1, 48]} + elif self.dims == 1: + self.dynamic_shape.min_input_shape = {"reshape_input": [48]} + self.dynamic_shape.max_input_shape = {"reshape_input": [48]} + self.dynamic_shape.opt_input_shape = {"reshape_input": [48]} + + def clear_dynamic_shape(): + self.dynamic_shape.min_input_shape = {} + self.dynamic_shape.max_input_shape = {} + self.dynamic_shape.opt_input_shape = {} + + def generate_trt_nodes_num(attrs, dynamic_shape): + return 1, 2 + + attrs = [ + program_config.ops[i].attrs + for i in range(len(program_config.ops)) + ] + if attrs[0]['shape'][0] > 1 and len(attrs[0]['shape']) > 1: + pass + else: + # 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 + + # 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 + + def add_skip_trt_case(self): + def teller1(program_config, predictor_config): + if len(program_config.weights) >= 1: + return True + return False + + self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT, + "INPUT ShapeTensor and Shape NOT SUPPORT") + + def test(self): + self.add_skip_trt_case() + self.run_test() + + +if __name__ == "__main__": + unittest.main() -- GitLab