# 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: 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 dimensions of input and axis match if len(inputs['split_input'].shape) <= attrs[0]['axis']: return False #Sections and num cannot both be equal to 0. if len(attrs[0]['sections']) == 0: if attrs[0]['num'] == 0: return False #When sections and num are not both equal to 0, sections has higher priority. #The sum of sections should be equal to the input size. if len(attrs[0]['sections']) != 0: if attrs[0]['num'] != 0: return False if len(outputs) != len(attrs[0]['sections']): return False sum = 0 for num in attrs[0]['sections']: sum += num if sum != inputs['split_input'].shape[attrs[0]['axis']]: return False #The size of num should be equal to the input dimension. if attrs[0]['num'] != 0: if len(outputs) != attrs[0]['num']: return False #Test AxisTensor and SectionsTensorList if self.num_input == 0: if self.dims == 2 and attrs[0]['sections'] == [10, 14] and len( outputs) == 2: return True else: 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, 3, 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_AxisTensor(attrs: List[Dict[str, Any]]): return np.ones([1]).astype(np.int32) def generate_SectionsTensorList1(attrs: List[Dict[str, Any]]): return np.array([10]).astype(np.int32) def generate_SectionsTensorList2(attrs: List[Dict[str, Any]]): return np.array([14]).astype(np.int32) for num_input in [0, 1]: for dims in [1, 2, 3, 4]: for batch in [3, 6, 9]: for Out in [["output_var0", "output_var1"], ["output_var0", "output_var1", "output_var2"]]: for sections in [[], [1, 2], [2, 1], [10, 14], [1, 1, 1], [2, 2, 2], [3, 3, 3], [3, 7, 14]]: for num in [0, 3]: for axis in [0, 1, 2, 3]: self.batch = batch self.num_input = num_input self.dims = dims dics = [{ "sections": sections, "num": num, "axis": axis }, {}] dics_intput = [{ "X": ["split_input"], "AxisTensor": ["AxisTensor"], "SectionsTensorList": [ "SectionsTensorList1", "SectionsTensorList2" ] }, { "X": ["split_input"] }] dics_intputs = [{ "AxisTensor": TensorConfig(data_gen=partial( generate_AxisTensor, dics)), "SectionsTensorList1": TensorConfig( data_gen=partial( generate_SectionsTensorList1, dics)), "SectionsTensorList2": TensorConfig(data_gen=partial( generate_SectionsTensorList2, dics)) }, {}] ops_config = [{ "op_type": "split", "op_inputs": dics_intput[num_input], "op_outputs": { "Out": Out }, "op_attrs": dics[0] }] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights=dics_intputs[num_input], inputs={ "split_input": TensorConfig(data_gen=partial( generate_input1, dics, batch)) }, outputs=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 = { "split_input": [1, 3, 3, 24] } self.dynamic_shape.max_input_shape = { "split_input": [9, 3, 3, 24] } self.dynamic_shape.opt_input_shape = { "split_input": [1, 3, 3, 24] } elif self.dims == 3: self.dynamic_shape.min_input_shape = {"split_input": [1, 3, 24]} self.dynamic_shape.max_input_shape = {"split_input": [9, 3, 24]} self.dynamic_shape.opt_input_shape = {"split_input": [1, 3, 24]} elif self.dims == 2: self.dynamic_shape.min_input_shape = {"split_input": [1, 24]} self.dynamic_shape.max_input_shape = {"split_input": [9, 24]} self.dynamic_shape.opt_input_shape = {"split_input": [1, 24]} elif self.dims == 1: self.dynamic_shape.min_input_shape = {"split_input": [24]} self.dynamic_shape.max_input_shape = {"split_input": [24]} self.dynamic_shape.opt_input_shape = {"split_input": [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 len(program_config.outputs) == 2: if attrs[0]['axis'] != 0: return 1, 3 else: return 0, 4 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)) ] self.trt_param.max_batch_size = 9 # 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) == 3: return True return False self.add_skip_case( teller1, SkipReasons.TRT_NOT_SUPPORT, "INPUT AxisTensor AND SectionsTensorList NOT SUPPORT.") def teller2(program_config, predictor_config): if len( program_config.inputs['split_input'].shape ) == 2 and not predictor_config.tensorrt_dynamic_shape_enabled(): return True return False self.add_skip_case( teller2, SkipReasons.TRT_NOT_IMPLEMENTED, "The output shape has diff, but we can add shuffle layer to resolve it." ) def test(self): self.add_skip_trt_case() self.run_test() if __name__ == "__main__": unittest.main()