# 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. import unittest from functools import partial from typing import Any, Dict, List import numpy as np from program_config import ProgramConfig, TensorConfig from trt_layer_auto_scan_test import SkipReasons, TrtLayerAutoScanTest import paddle.inference as paddle_infer 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 if self.dims == 2: if self.batch != 3: return False if len(attrs[0]['sections']) != 0 and attrs[0]['axis'] == 0: if self.dims != 2 or self.batch != 3: return False return True def sample_program_configs(self): def generate_input1(attrs: List[Dict[str, Any]], 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) 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 - 1, 3 - 1, 24 - 1] } self.dynamic_shape.max_input_shape = { "split_input": [9, 3 + 1, 3 + 1, 24 + 1] } 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 - 1, 24 - 1] } self.dynamic_shape.max_input_shape = { "split_input": [9, 3 + 1, 24 + 1] } self.dynamic_shape.opt_input_shape = {"split_input": [1, 3, 24]} elif self.dims == 2: self.dynamic_shape.min_input_shape = {"split_input": [3, 24]} self.dynamic_shape.max_input_shape = {"split_input": [3, 24]} self.dynamic_shape.opt_input_shape = {"split_input": [3, 24]} elif self.dims == 1: self.dynamic_shape.min_input_shape = {"split_input": [24 - 1]} self.dynamic_shape.max_input_shape = {"split_input": [24 + 1]} 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 dynamic_shape: return 1, 3 else: if attrs[0]['axis'] != 0: return 1, 3 else: return 0, 4 else: if dynamic_shape: return 1, 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 program_config.set_input_type(np.float32) yield self.create_inference_config(), generate_trt_nodes_num( attrs, False ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half program_config.set_input_type(np.float16) yield self.create_inference_config(), generate_trt_nodes_num( attrs, False ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 program_config.set_input_type(np.float32) yield self.create_inference_config(), generate_trt_nodes_num( attrs, True ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half program_config.set_input_type(np.float16) yield self.create_inference_config(), generate_trt_nodes_num( attrs, True ), 1e-3 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 test(self): self.add_skip_trt_case() self.run_test() class TrtConvertSplitTest2(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]]): return np.random.random([3, 3, 3, 24]).astype(np.float32) for sections in [ [-1, -1, -1], [1, 1, 1], ]: for num in [0]: for axis in [0, 1]: dics = [ { "sections": sections, "num": num, "axis": axis, } ] dics_intput = [ { "X": ["split_input"], "SectionsTensorList": [ "shapeT1_data", "shapeT2_data", "shapeT3_data", ], }, ] ops_config = [ { "op_type": "fill_constant", "op_inputs": {}, "op_outputs": {"Out": ["shapeT1_data"]}, "op_attrs": { "dtype": 2, "str_value": "1", "shape": [1], }, }, { "op_type": "fill_constant", "op_inputs": {}, "op_outputs": {"Out": ["shapeT2_data"]}, "op_attrs": { "dtype": 2, "str_value": "1", "shape": [1], }, }, { "op_type": "fill_constant", "op_inputs": {}, "op_outputs": {"Out": ["shapeT3_data"]}, "op_attrs": { "dtype": 2, "str_value": "1", "shape": [1], }, }, { "op_type": "split", "op_inputs": dics_intput[0], "op_outputs": { "Out": [ "output_var0", "output_var1", "output_var2", ] }, "op_attrs": dics[0], }, ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "split_input": TensorConfig( data_gen=partial(generate_input1, dics) ) }, outputs=["output_var0", "output_var1", "output_var2"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): 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": [3, 3, 3, 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: return 1, 4 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 dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 program_config.set_input_type(np.float32) yield self.create_inference_config(), generate_trt_nodes_num( attrs, True ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half program_config.set_input_type(np.float16) yield self.create_inference_config(), generate_trt_nodes_num( attrs, True ), 1e-3 def add_skip_trt_case(self): pass def test(self): self.add_skip_trt_case() self.run_test() if __name__ == "__main__": unittest.main()