# 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 __future__ import annotations import unittest from functools import partial from typing import Any import hypothesis.strategies as st import numpy as np from hypothesis import given from program_config import ProgramConfig, TensorConfig from trt_layer_auto_scan_test import TrtLayerAutoScanTest import paddle.inference as paddle_infer class TrtConvertTileTest(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] for x in attrs[0]['repeat_times']: if x <= 0: return False return True def sample_program_configs(self, *args, **kwargs): def generate_input1(attrs: list[dict[str, Any]]): return np.ones([1, 2]).astype(np.float32) dics = [{"repeat_times": kwargs.get('repeat_times', [1])}] ops_config = [ { "op_type": "tile", "op_inputs": {"X": ["input_data"]}, "op_outputs": {"Out": ["tile_output_data"]}, "op_attrs": dics[0], } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data": TensorConfig( data_gen=partial(generate_input1, dics) ) }, outputs=["tile_output_data"], ) yield program_config def generate_dynamic_shape(self): self.dynamic_shape.min_input_shape = {"input_data": [1, 2]} self.dynamic_shape.max_input_shape = {"input_data": [4, 3]} self.dynamic_shape.opt_input_shape = {"input_data": [1, 3]} return self.dynamic_shape def sample_predictor_configs( self, program_config, run_pir=False ) -> tuple[paddle_infer.Config, list[int], float]: 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): ver = paddle_infer.get_trt_compile_version() if ver[0] * 1000 + ver[1] * 100 + ver[0] * 10 >= 7000: return 1, 2 else: return 0, 3 attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] # for dynamic_shape self.generate_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, 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, ) @given(repeat_times=st.sampled_from([[1], [1, 2], [0, 3]])) def test(self, *args, **kwargs): self.run_test(run_pir=True) class TrtConvertTileTest2(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.ones([1, 2]).astype(np.float32) dics = [{}] dics_input = [ {"X": ["tile_input"], "RepeatTimes": ["repeat_times"]}, ] ops_config = [ { "op_type": "fill_constant", "op_inputs": {}, "op_outputs": {"Out": ["repeat_times"]}, "op_attrs": { "dtype": 2, "str_value": "1", "value": 1, "shape": [1], }, }, { "op_type": "tile", "op_inputs": dics_input[0], "op_outputs": {"Out": ["tile_out"]}, "op_attrs": dics[0], }, ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "tile_input": TensorConfig( data_gen=partial(generate_input1, dics) ) }, outputs=["tile_out"], ) yield program_config def generate_dynamic_shape(self): self.dynamic_shape.min_input_shape = {"tile_input": [1, 2]} self.dynamic_shape.max_input_shape = {"tile_input": [4, 3]} self.dynamic_shape.opt_input_shape = {"tile_input": [1, 2]} return self.dynamic_shape def sample_predictor_configs( self, program_config, run_pir=False ) -> tuple[paddle_infer.Config, list[int], float]: 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)) ] # for dynamic_shape self.generate_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, 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(run_pir=True) class TrtConvertTileTest3(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] return True def sample_program_configs(self): def generate_input1(attrs: list[dict[str, Any]]): return np.ones([1, 2]).astype(np.float32) dics = [{}] dics_input = [ { "X": ["tile_input"], "repeat_times_tensor": ["repeat_times1", "repeat_times2"], }, ] ops_config = [ { "op_type": "fill_constant", "op_inputs": {}, "op_outputs": {"Out": ["repeat_times1"]}, "op_attrs": { "dtype": 2, "str_value": "10", "value": 10, "shape": [1], }, }, { "op_type": "fill_constant", "op_inputs": {}, "op_outputs": {"Out": ["repeat_times2"]}, "op_attrs": { "dtype": 2, "str_value": "12", "value": 12, "shape": [1], }, }, { "op_type": "tile", "op_inputs": dics_input[0], "op_outputs": {"Out": ["tile_out"]}, "op_attrs": dics[0], }, ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "tile_input": TensorConfig( data_gen=partial(generate_input1, dics) ) }, outputs=["tile_out"], ) yield program_config def generate_dynamic_shape(self): self.dynamic_shape.min_input_shape = {"tile_input": [1, 2]} self.dynamic_shape.max_input_shape = {"tile_input": [4, 3]} self.dynamic_shape.opt_input_shape = {"tile_input": [1, 2]} return self.dynamic_shape def sample_predictor_configs( self, program_config, run_pir=False ) -> tuple[paddle_infer.Config, list[int], float]: def clear_dynamic_shape(): # 清空最小输入形状 self.dynamic_shape.min_input_shape = {} # 清空最大输入形状 self.dynamic_shape.max_input_shape = {} self.dynamic_shape.opt_input_shape = {} def generate_trt_nodes_num(attrs, dynamic_shape): return 1, 2 attrs = [ # 清空最优输入形状 program_config.ops[i].attrs for i in range(len(program_config.ops)) ] # for dynamic_shape self.generate_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, 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(run_pir=True) if __name__ == "__main__": unittest.main()