diff --git a/paddle/fluid/inference/tensorrt/op_teller.cc b/paddle/fluid/inference/tensorrt/op_teller.cc index d5655c5f87de3e28a98ea8c6312cd9e5de5b5358..23f7a48382a8a975f366177181ec0aaf1e0b3c18 100644 --- a/paddle/fluid/inference/tensorrt/op_teller.cc +++ b/paddle/fluid/inference/tensorrt/op_teller.cc @@ -305,6 +305,12 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, } else { if (axis <= 0) return false; } + auto concat_inputs = desc.Inputs(); + if (concat_inputs.find("AxisTensor") != concat_inputs.end()) { + if (desc.Input("AxisTensor").size() >= 1) { + return false; + } + } } } if (op_type == "transpose2" || op_type == "transpose") { diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_concat.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_concat.py new file mode 100644 index 0000000000000000000000000000000000000000..25e96787dd1329f36a818f1aeade933220a1e698 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_concat.py @@ -0,0 +1,326 @@ +# 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 TrtConvertConcatTest(TrtLayerAutoScanTest): + def is_program_valid(self, program_config: ProgramConfig) -> bool: + inputs = program_config.inputs + weights = program_config.weights + outputs = program_config.outputs + + attrs = [ + program_config.ops[i].attrs + for i in range(len(program_config.ops)) + ] + #The input dimension should be less than or equal to the set axis. + if len(inputs['concat_input1'].shape) <= attrs[0]['axis']: + return False + + return True + + def sample_program_configs(self): + def generate_input1(attrs: List[Dict[str, Any]], batch): + if self.dims == 4: + return np.ones([batch, 3, 24, 24]).astype(np.float32) + elif self.dims == 3: + return np.ones([batch, 3, 24]).astype(np.float32) + elif self.dims == 2: + return np.ones([batch, 24]).astype(np.float32) + elif self.dims == 1: + return np.ones([24]).astype(np.float32) + + def generate_input2(attrs: List[Dict[str, Any]], batch): + if self.dims == 4: + return np.ones([batch, 3, 24, 24]).astype(np.float32) + elif self.dims == 3: + return np.ones([batch, 3, 24]).astype(np.float32) + elif self.dims == 2: + return np.ones([batch, 24]).astype(np.float32) + elif self.dims == 1: + return np.ones([24]).astype(np.float32) + + def generate_input3(attrs: List[Dict[str, Any]], batch): + if self.dims == 4: + return np.ones([batch, 3, 24, 24]).astype(np.float32) + elif self.dims == 3: + return np.ones([batch, 3, 24]).astype(np.float32) + elif self.dims == 2: + return np.ones([batch, 24]).astype(np.float32) + elif self.dims == 1: + return np.ones([24]).astype(np.float32) + + def generate_weight1(attrs: List[Dict[str, Any]]): + return np.zeros([1]).astype(np.int32) + + for dims in [1, 2, 3, 4]: + for num_input in [0, 1]: + for batch in [1, 2, 4]: + for axis in [-1, 0, 1, 2, 3]: + self.num_input = num_input + self.dims = dims + dics = [{"axis": axis}, {}] + dics_intput = [{ + "X": [ + "concat_input1", "concat_input2", + "concat_input3" + ], + "AxisTensor": ["AxisTensor"], + }, { + "X": [ + "concat_input1", "concat_input2", + "concat_input3" + ] + }] + dics_inputs = [{ + "concat_input1": TensorConfig(data_gen=partial( + generate_input1, dics, batch)), + "concat_input2": TensorConfig(data_gen=partial( + generate_input2, dics, batch)), + "concat_input3": TensorConfig(data_gen=partial( + generate_input3, dics, batch)), + "AxisTensor": TensorConfig(data_gen=partial( + generate_weight1, dics)) + }, { + "concat_input1": TensorConfig(data_gen=partial( + generate_input1, dics, batch)), + "concat_input2": TensorConfig(data_gen=partial( + generate_input2, dics, batch)), + "concat_input3": TensorConfig(data_gen=partial( + generate_input3, dics, batch)) + }] + ops_config = [{ + "op_type": "concat", + "op_inputs": dics_intput[num_input], + "op_outputs": { + "Out": ["concat_output"] + }, + "op_attrs": dics[0] + }] + ops = self.generate_op_config(ops_config) + program_config = ProgramConfig( + ops=ops, + weights={}, + inputs=dics_inputs[num_input], + outputs=["concat_output"]) + + yield program_config + + def sample_predictor_configs( + self, program_config) -> (paddle_infer.Config, List[int], float): + def generate_dynamic_shape(attrs): + if self.num_input == 0: + if self.dims == 4: + self.dynamic_shape.min_input_shape = { + "concat_input1": [1, 3, 24, 24], + "concat_input2": [1, 3, 24, 24], + "concat_input3": [1, 3, 24, 24], + "AxisTensor": [1] + } + self.dynamic_shape.max_input_shape = { + "concat_input1": [4, 3, 48, 48], + "concat_input2": [4, 3, 48, 48], + "concat_input3": [4, 3, 48, 48], + "AxisTensor": [1] + } + self.dynamic_shape.opt_input_shape = { + "concat_input1": [1, 3, 24, 24], + "concat_input2": [1, 3, 24, 24], + "concat_input3": [1, 3, 24, 24], + "AxisTensor": [1] + } + elif self.dims == 3: + self.dynamic_shape.min_input_shape = { + "concat_input1": [1, 3, 24], + "concat_input2": [1, 3, 24], + "concat_input3": [1, 3, 24], + "AxisTensor": [1] + } + self.dynamic_shape.max_input_shape = { + "concat_input1": [4, 12, 48], + "concat_input2": [4, 12, 48], + "concat_input3": [4, 12, 48], + "AxisTensor": [1] + } + self.dynamic_shape.opt_input_shape = { + "concat_input1": [1, 3, 24], + "concat_input2": [1, 3, 24], + "concat_input3": [1, 3, 24], + "AxisTensor": [1] + } + elif self.dims == 2: + self.dynamic_shape.min_input_shape = { + "concat_input1": [1, 24], + "concat_input2": [1, 24], + "concat_input3": [1, 24], + "AxisTensor": [1] + } + self.dynamic_shape.max_input_shape = { + "concat_input1": [4, 48], + "concat_input2": [4, 48], + "concat_input3": [4, 48], + "AxisTensor": [1] + } + self.dynamic_shape.opt_input_shape = { + "concat_input1": [1, 24], + "concat_input2": [1, 24], + "concat_input3": [1, 24], + "AxisTensor": [1] + } + elif self.dims == 1: + self.dynamic_shape.min_input_shape = { + "concat_input1": [24], + "concat_input2": [24], + "concat_input3": [24], + "AxisTensor": [0] + } + self.dynamic_shape.max_input_shape = { + "concat_input1": [48], + "concat_input2": [48], + "concat_input3": [48], + "AxisTensor": [0] + } + self.dynamic_shape.opt_input_shape = { + "concat_input1": [24], + "concat_input2": [24], + "concat_input3": [24], + "AxisTensor": [0] + } + elif self.num_input == 1: + if self.dims == 4: + self.dynamic_shape.min_input_shape = { + "concat_input1": [1, 3, 24, 24], + "concat_input2": [1, 3, 24, 24], + "concat_input3": [1, 3, 24, 24], + } + self.dynamic_shape.max_input_shape = { + "concat_input1": [4, 3, 48, 48], + "concat_input2": [4, 3, 48, 48], + "concat_input3": [4, 3, 48, 48] + } + self.dynamic_shape.opt_input_shape = { + "concat_input1": [1, 3, 24, 24], + "concat_input2": [1, 3, 24, 24], + "concat_input3": [1, 3, 24, 24] + } + elif self.dims == 3: + self.dynamic_shape.min_input_shape = { + "concat_input1": [1, 3, 24], + "concat_input2": [1, 3, 24], + "concat_input3": [1, 3, 24] + } + self.dynamic_shape.max_input_shape = { + "concat_input1": [4, 12, 48], + "concat_input2": [4, 12, 48], + "concat_input3": [4, 12, 48] + } + self.dynamic_shape.opt_input_shape = { + "concat_input1": [1, 3, 24], + "concat_input2": [1, 3, 24], + "concat_input3": [1, 3, 24] + } + elif self.dims == 2: + self.dynamic_shape.min_input_shape = { + "concat_input1": [1, 24], + "concat_input2": [1, 24], + "concat_input3": [1, 24] + } + self.dynamic_shape.max_input_shape = { + "concat_input1": [4, 48], + "concat_input2": [4, 48], + "concat_input3": [4, 48] + } + self.dynamic_shape.opt_input_shape = { + "concat_input1": [1, 24], + "concat_input2": [1, 24], + "concat_input3": [1, 24] + } + elif self.dims == 1: + self.dynamic_shape.min_input_shape = { + "concat_input1": [24], + "concat_input2": [24], + "concat_input3": [24] + } + self.dynamic_shape.max_input_shape = { + "concat_input1": [48], + "concat_input2": [48], + "concat_input3": [48] + } + self.dynamic_shape.opt_input_shape = { + "concat_input1": [24], + "concat_input2": [24], + "concat_input3": [24] + } + + 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 dynamic_shape == True: + if attrs[0]['axis'] >= 0: + return 1, 4 + else: + return 0, 5 + else: + if attrs[0]['axis'] > 0: + return 1, 4 + else: + return 0, 5 + + attrs = [ + program_config.ops[i].attrs + for i in range(len(program_config.ops)) + ] + # 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.inputs) == 4: + return True + return False + + self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT, + "INPUT AxisTensor NOT SUPPORT") + + def test(self): + self.add_skip_trt_case() + self.run_test() + + +if __name__ == "__main__": + unittest.main()