diff --git a/paddle/fluid/inference/api/analysis_predictor.cc b/paddle/fluid/inference/api/analysis_predictor.cc index 5e787394bce256ef5e7bfe5415262ae940be8189..541c53c8dae6484bb805b4b2ad535482eec2aee4 100644 --- a/paddle/fluid/inference/api/analysis_predictor.cc +++ b/paddle/fluid/inference/api/analysis_predictor.cc @@ -2089,6 +2089,7 @@ USE_TRT_CONVERTER(top_k) USE_TRT_CONVERTER(top_k_v2) USE_TRT_CONVERTER(squeeze2) USE_TRT_CONVERTER(unsqueeze2) +USE_TRT_CONVERTER(fill_constant) USE_TRT_CONVERTER(fused_token_prune) #if PADDLE_WITH_CUSPARSELT && IS_TRT_VERSION_GE(8000) USE_TRT_CONVERTER(sparse_fc) diff --git a/paddle/fluid/inference/tensorrt/convert/CMakeLists.txt b/paddle/fluid/inference/tensorrt/convert/CMakeLists.txt index ca91df902a9a1469207a9e6dca8a03b2a86319dd..519daba2747d4d5e0dbccf429ffe283f47f7272e 100644 --- a/paddle/fluid/inference/tensorrt/convert/CMakeLists.txt +++ b/paddle/fluid/inference/tensorrt/convert/CMakeLists.txt @@ -69,6 +69,7 @@ list( top_k_op.cc squeeze2_op.cc unsqueeze2_op.cc + fill_constant_op.cc fused_token_prune_op.cc) if(CUSPARSELT_FOUND AND ${TENSORRT_MAJOR_VERSION} GREATER_EQUAL 8) diff --git a/paddle/fluid/inference/tensorrt/convert/fill_constant_op.cc b/paddle/fluid/inference/tensorrt/convert/fill_constant_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..53eb3f2c89732952cc62e9afe6f6c736420e74a1 --- /dev/null +++ b/paddle/fluid/inference/tensorrt/convert/fill_constant_op.cc @@ -0,0 +1,71 @@ +/* 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. */ + +#include "paddle/fluid/inference/tensorrt/convert/op_converter.h" + +namespace paddle { +namespace inference { +namespace tensorrt { + +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"; + + 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 shape = + BOOST_GET_CONST(std::vector, op_desc.GetAttr("shape")); + std::unique_ptr 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(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(tmp_ptr); + } else if (dtype == 5) { // float + auto* tmp_ptr = out_tensor->mutable_data(platform::CPUPlace()); + for (int64_t i = 0; i < out_tensor->numel(); i++) + tmp_ptr[i] = std::stof(str_value); + trt_data = static_cast(tmp_ptr); + } + + trt_num = static_cast(out_tensor->numel()); + engine_->SetWeights("fill_constant_value", std::move(out_tensor)); + TensorRTEngine::Weight weight{trt_dtype, trt_data, trt_num}; + + 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); + } +}; + +} // namespace tensorrt +} // namespace inference +} // namespace paddle + +REGISTER_TRT_OP_CONVERTER(fill_constant, FillConstantOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/reshape_op.cc b/paddle/fluid/inference/tensorrt/convert/reshape_op.cc index 00ee5503cc2e21d138095a14d6a84be3c3e334a9..eec881eae8e188ffb67f236b2d5d2617cb667973 100644 --- a/paddle/fluid/inference/tensorrt/convert/reshape_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/reshape_op.cc @@ -35,14 +35,29 @@ class ReshapeOpConverter : public OpConverter { framework::OpDesc op_desc(op, nullptr); // Declare inputs auto* input = engine_->GetITensor(op_desc.Input("X")[0]); + std::vector shape = BOOST_GET_CONST(std::vector, op_desc.GetAttr("shape")); int nbDims_num = shape.size(); nvinfer1::Dims reshape_dim; - if (engine_->with_dynamic_shape()) { // running the TRT Dynamic Shape mode - reshape_dim.nbDims = nbDims_num; - for (int i = 0; i < nbDims_num; ++i) { - reshape_dim.d[i] = shape[i]; + nvinfer1::ITensor* real_shape_tensor = nullptr; + std::vector concat_inputs; + bool one_input = false; + if (engine_->with_dynamic_shape()) { + if (op_desc.Inputs().find("ShapeTensor") != op_desc.Inputs().end() && + op_desc.Input("ShapeTensor").size() > 0) { + for (auto name : op_desc.Input("ShapeTensor")) + concat_inputs.push_back(engine_->GetITensor(name)); + real_shape_tensor = Concat(concat_inputs); + } else if (op_desc.Inputs().find("Shape") != op_desc.Inputs().end() && + op_desc.Input("Shape").size() > 0) { + real_shape_tensor = engine_->GetITensor(op_desc.Input("Shape")[0]); + } else { + reshape_dim.nbDims = nbDims_num; + for (int i = 0; i < nbDims_num; ++i) { + reshape_dim.d[i] = shape[i]; + } + one_input = true; } } else { // running the TRT Static Shape mode reshape_dim.nbDims = nbDims_num - 1; @@ -51,7 +66,10 @@ class ReshapeOpConverter : public OpConverter { } } auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input); - layer->setReshapeDimensions(reshape_dim); + if (!engine_->with_dynamic_shape() || one_input) + layer->setReshapeDimensions(reshape_dim); + else + layer->setInput(1, *real_shape_tensor); auto output_name = op_desc.Output("Out")[0]; RreplenishLayerAndOutput(layer, "reshape", {output_name}, test_mode); } diff --git a/paddle/fluid/inference/tensorrt/op_teller.cc b/paddle/fluid/inference/tensorrt/op_teller.cc index eaef331356575f53b7f4b1178524360de02c6a91..05d0b41f14e1f4cd39d17138ca7280f0b06da250 100644 --- a/paddle/fluid/inference/tensorrt/op_teller.cc +++ b/paddle/fluid/inference/tensorrt/op_teller.cc @@ -169,6 +169,7 @@ struct SimpleOpTypeSetTeller : public Teller { "transformer_input_convert", "recover_padding", "remove_padding", + "fill_constant", "squeeze2", "unsqueeze2"}; std::unordered_set teller_set{ @@ -274,6 +275,7 @@ struct SimpleOpTypeSetTeller : public Teller { "transformer_input_convert", "recover_padding", "remove_padding", + "fill_constant", "squeeze2", "unsqueeze2", "fused_token_prune"}; @@ -1448,6 +1450,27 @@ bool OpTeller::Tell(const framework::ir::Node* node, } } + if (op_type == "fill_constant") { + auto fill_constant_inputs = desc.Inputs(); + if (fill_constant_inputs.find("ValueTensor") != + fill_constant_inputs.end()) { + if (desc.Input("ValueTensor").size()) return false; + } + if (fill_constant_inputs.find("ShapeTensor") != + fill_constant_inputs.end()) { + if (desc.Input("ShapeTensor").size()) return false; + } + if (fill_constant_inputs.find("ShapeTensorList") != + fill_constant_inputs.end()) { + if (desc.Input("ShapeTensorList").size()) return false; + } + int dtype = BOOST_GET_CONST(int, desc.GetAttr("dtype")); + // only support int32, int64, float32 + if (!(dtype == 2 || dtype == 3 || dtype == 5)) { + return false; + } + } + if (op_type == "instance_norm") { if (with_dynamic_shape) { VLOG(3) << "trt instance_norm op does not support dynamic shape "; @@ -1801,6 +1824,9 @@ bool OpTeller::Tell(const framework::ir::Node* node, } if (op_type == "reshape" || op_type == "reshape2") { + if (with_dynamic_shape) { + return true; + } if (!desc.HasAttr("shape")) { return false; } diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_fill_constant.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_fill_constant.py new file mode 100644 index 0000000000000000000000000000000000000000..84ee70782acc2840d85d172071a90e1464120367 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_fill_constant.py @@ -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. + +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 + + +class TrtConvertSplitTest(TrtLayerAutoScanTest): + + def is_program_valid(self, program_config: ProgramConfig) -> bool: + return True + + def sample_program_configs(self): + + def generate_value_data(attrs: List[Dict[str, Any]]): + return np.array([1]).astype(np.int32) + + def generate_shape_data(attrs: List[Dict[str, Any]]): + return np.array([4, 23]).astype(np.int32) + + def generate_shapelist_data(attrs: List[Dict[str, Any]]): + return np.array([4]).astype(np.int32) + + 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] + }, + ] + + def generate_input(): + return np.random.random([1, 1]).astype(np.float32) + + 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"]) + + yield program_config + + def sample_predictor_configs( + self, program_config) -> (paddle_infer.Config, List[int], float): + + 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} + + 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): + if (self.num_input < 3): + return 0, 6 + return 1, 5 + + attrs = [ + program_config.ops[i].attrs for i in range(len(program_config.ops)) + ] + # Don't test static shape + + # 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): + pass + + def test(self): + self.add_skip_trt_case() + self.run_test() + + +if __name__ == "__main__": + unittest.main() 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 index e05a78e66b9002c061589bbd0f13930da391fec2..7902a35a9a6b47dd5aa9bb4b5b40918fdb809a25 100644 --- 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 @@ -48,12 +48,16 @@ class TrtConvertReshapeTest(TrtLayerAutoScanTest): def generate_input1(attrs: List[Dict[str, Any]]): if self.dims == 4: + self.input_shape = [1, 2, 4, 6] return np.ones([1, 2, 4, 6]).astype(np.float32) elif self.dims == 3: + self.input_shape = [1, 8, 6] return np.ones([1, 8, 6]).astype(np.float32) elif self.dims == 2: + self.input_shape = [1, 48] return np.ones([1, 48]).astype(np.float32) elif self.dims == 1: + self.input_shape = [48] return np.ones([48]).astype(np.float32) def generate_weight1(attrs: List[Dict[str, Any]]): @@ -66,69 +70,36 @@ class TrtConvertReshapeTest(TrtLayerAutoScanTest): 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 = [{ + 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], + [-1, 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"]) + }, + ] + self.dims = dims + dics_intput = [{"X": ["reshape_input"]}] + + ops_config = [{ + "op_type": "reshape", + "op_inputs": dics_intput[0], + "op_outputs": { + "Out": ["reshape_out"] + }, + "op_attrs": dics[0] + }] + ops = self.generate_op_config(ops_config) + program_config = ProgramConfig( + ops=ops, + weights={}, + inputs={ + "reshape_input": + TensorConfig(data_gen=partial(generate_input1, dics)) + }, + outputs=["reshape_out"]) - yield program_config + yield program_config def sample_predictor_configs( self, program_config) -> (paddle_infer.Config, List[int], float): @@ -169,22 +140,31 @@ class TrtConvertReshapeTest(TrtLayerAutoScanTest): self.dynamic_shape.opt_input_shape = {} def generate_trt_nodes_num(attrs, dynamic_shape): + # in static shape mode, here is consistent with op_teller.cc + if (not dynamic_shape): + if (attrs[0]['shape'][0] == 0): + return 1, 2 + elif (len(attrs[0]['shape']) == 1): + return 0, 3 + elif (np.prod(attrs[0]['shape'][1:]) == np.prod( + self.input_shape[1:])): + return 1, 2 + else: + return 0, 3 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 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) @@ -196,14 +176,243 @@ class TrtConvertReshapeTest(TrtLayerAutoScanTest): attrs, True), 1e-5 def add_skip_trt_case(self): + pass + + def test(self): + self.add_skip_trt_case() + self.run_test() + + +# reshape having three inputs. +class TrtConvertReshapeTest2(TrtLayerAutoScanTest): + + def is_program_valid(self, program_config: ProgramConfig) -> bool: + return True + + def sample_program_configs(self): - def teller1(program_config, predictor_config): - if len(program_config.weights) >= 1: - return True - return False + def generate_input1(attrs: List[Dict[str, Any]]): + if self.dims == 4: + return np.random.random([1, 2, 4, 6]).astype(np.float32) + elif self.dims == 3: + return np.random.random([1, 8, 6]).astype(np.float32) + elif self.dims == 2: + return np.random.random([1, 48]).astype(np.float32) + elif self.dims == 1: + return np.random.random([48]).astype(np.float32) - self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT, - "INPUT ShapeTensor and Shape NOT SUPPORT") + for dims in [4, 3, 2, 1]: + for shape in [[-1, 48]]: + dics = [{ + "shape": shape, + }, {}] + self.dims = dims + dics_intput = [ + { + "X": ["reshape_input"], + "ShapeTensor": ["shapeT1_data", "shapeT2_data"], + }, + ] + ops_config = [ + { + "op_type": "fill_constant", + "op_inputs": {}, + "op_outputs": { + "Out": ["shapeT1_data"] + }, + "op_attrs": { + "dtype": 2, + "str_value": "2", + "shape": [1], + }, + }, + { + "op_type": "fill_constant", + "op_inputs": {}, + "op_outputs": { + "Out": ["shapeT2_data"] + }, + "op_attrs": { + "dtype": 2, + "str_value": "24", + "shape": [1], + }, + }, + { + "op_type": "reshape", + "op_inputs": dics_intput[0], + "op_outputs": { + "Out": ["reshape_out"] + }, + "op_attrs": dics[0] + }, + ] + ops = self.generate_op_config(ops_config) + program_config = ProgramConfig( + ops=ops, + weights={}, + 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(): + 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]} + + # for dynamic_shape + generate_dynamic_shape() + self.trt_param.precision = paddle_infer.PrecisionType.Float32 + yield self.create_inference_config(), (1, 2), 1e-5 + self.trt_param.precision = paddle_infer.PrecisionType.Half + yield self.create_inference_config(), (1, 2), 1e-5 + + def add_skip_trt_case(self): + pass + + def test(self): + self.add_skip_trt_case() + self.run_test() + + +# reshape having 2 inputs. +class TrtConvertReshapeTest3(TrtLayerAutoScanTest): + + def is_program_valid(self, program_config: ProgramConfig) -> bool: + return True + + def sample_program_configs(self): + + def generate_input1(attrs: List[Dict[str, Any]]): + if self.dims == 4: + return np.random.random([1, 2, 12, 6]).astype(np.float32) + elif self.dims == 3: + return np.random.random([1, 8, 18]).astype(np.float32) + elif self.dims == 2: + return np.random.random([1, 144]).astype(np.float32) + elif self.dims == 1: + return np.random.random([144]).astype(np.float32) + + for dims in [4, 3, 2, 1]: + for shape in [[-1, 144]]: + dics = [{ + "shape": shape, + }, {}] + self.dims = dims + dics_intput = [ + { + "X": ["reshape_input"], + "shape_data": ["shape_data"], + }, + ] + ops_config = [ + { + "op_type": "fill_constant", + "op_inputs": {}, + "op_outputs": { + "Out": ["shape_data"] + }, + "op_attrs": { + "dtype": 2, + "str_value": "12", + "shape": [2], + }, + }, + { + "op_type": "reshape", + "op_inputs": dics_intput[0], + "op_outputs": { + "Out": ["reshape_out"] + }, + "op_attrs": dics[0] + }, + ] + ops = self.generate_op_config(ops_config) + program_config = ProgramConfig( + ops=ops, + weights={}, + 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(): + if self.dims == 4: + self.dynamic_shape.min_input_shape = { + "reshape_input": [1, 2, 12, 6] + } + self.dynamic_shape.max_input_shape = { + "reshape_input": [4, 2, 12, 6] + } + self.dynamic_shape.opt_input_shape = { + "reshape_input": [1, 2, 12, 6] + } + elif self.dims == 3: + self.dynamic_shape.min_input_shape = { + "reshape_input": [1, 8, 18] + } + self.dynamic_shape.max_input_shape = { + "reshape_input": [4, 8, 18] + } + self.dynamic_shape.opt_input_shape = { + "reshape_input": [1, 8, 18] + } + elif self.dims == 2: + self.dynamic_shape.min_input_shape = {"reshape_input": [1, 144]} + self.dynamic_shape.max_input_shape = {"reshape_input": [4, 144]} + self.dynamic_shape.opt_input_shape = {"reshape_input": [1, 144]} + elif self.dims == 1: + self.dynamic_shape.min_input_shape = {"reshape_input": [144]} + self.dynamic_shape.max_input_shape = {"reshape_input": [144]} + self.dynamic_shape.opt_input_shape = {"reshape_input": [144]} + + # for dynamic_shape + generate_dynamic_shape() + self.trt_param.precision = paddle_infer.PrecisionType.Float32 + yield self.create_inference_config(), (1, 2), 1e-5 + self.trt_param.precision = paddle_infer.PrecisionType.Half + yield self.create_inference_config(), (1, 2), 1e-5 + + def add_skip_trt_case(self): + pass def test(self): self.add_skip_trt_case()