# 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 copy import itertools import unittest from functools import partial from typing import Any 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 TrtConvertPool2dTest(TrtLayerAutoScanTest): def is_paddings_valid(self, program_config: ProgramConfig) -> bool: exclusive = program_config.ops[0].attrs['exclusive'] paddings = program_config.ops[0].attrs['paddings'] ksize = program_config.ops[0].attrs['ksize'] pooling_type = program_config.ops[0].attrs['pooling_type'] global_pooling = program_config.ops[0].attrs['global_pooling'] if not global_pooling: if pooling_type == 'avg': for index in range(len(ksize)): if ksize[index] <= paddings[index]: return False ver = paddle_infer.get_trt_compile_version() if ver[0] * 1000 + ver[1] * 100 + ver[0] * 10 < 7000: if program_config.ops[0].attrs['pooling_type'] == 'avg': return False return True def is_program_valid(self, program_config: ProgramConfig) -> bool: return self.is_paddings_valid(program_config) def sample_program_configs(self): self.trt_param.workspace_size = 1073741824 def generate_input1(attrs: list[dict[str, Any]]): return np.ones([1, 3, 64, 64]).astype(np.float32) def generate_weight1(attrs: list[dict[str, Any]]): return np.random.random([24, 3, 3, 3]).astype(np.float32) strides_options = [[1, 2]] paddings_options = [[0, 2]] pooling_type_options = ['max'] padding_algorithm_options = ['EXPLICIT', 'SAME', 'VALID'] ksize_options = [[2, 3], [3, 3]] data_format_options = ['NCHW'] global_pooling_options = [True, False] exclusive_options = [True, False] adaptive_option = [False, True] ceil_mode_options = [True, False] configurations = [ strides_options, paddings_options, pooling_type_options, padding_algorithm_options, ksize_options, data_format_options, global_pooling_options, exclusive_options, adaptive_option, ceil_mode_options, ] for ( strides, paddings, pooling_type, padding_algorithm, ksize, data_format, global_pooling, exclusive, adaptive, ceil_mode, ) in itertools.product(*configurations): attrs = [ { "strides": strides, "paddings": paddings, "pooling_type": pooling_type, "padding_algorithm": padding_algorithm, "ksize": ksize, "data_format": data_format, "global_pooling": global_pooling, "exclusive": exclusive, "adaptive": adaptive, "ceil_mode": ceil_mode, } ] ops_config = [ { "op_type": "pool2d", "op_inputs": {"X": ["input_data"]}, "op_outputs": {"Out": ["output_data"]}, "op_attrs": attrs[0], } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data": TensorConfig( data_gen=partial(generate_input1, attrs) ) }, outputs=["output_data"], ) yield program_config def generate_dynamic_shape(self): self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 32, 32]} self.dynamic_shape.max_input_shape = {"input_data": [1, 3, 64, 64]} self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]} 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 static_shape clear_dynamic_shape() if not run_pir: 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-3, 1e-3), ) # for dynamic_shape self.generate_dynamic_shape() 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-3, 1e-3), ) def add_skip_trt_case(self): def teller(program_config, predictor_config): if ( program_config.ops[0].attrs['pooling_type'] == 'avg' and not program_config.ops[0].attrs['global_pooling'] and program_config.ops[0].attrs['exclusive'] and not program_config.ops[0].attrs['adaptive'] and program_config.ops[0].attrs['ceil_mode'] ): return True return False self.add_skip_case( teller, SkipReasons.TRT_NOT_IMPLEMENTED, "The results of some cases are Nan, but the results of TensorRT and GPU are the same.", ) def assert_tensors_near( self, atol: float, rtol: float, tensor: dict[str, np.array], baseline: dict[str, np.array], ): if isinstance(tensor, list): tensor = {str(i): t for i, t in enumerate(tensor)} if isinstance(baseline, list): baseline = {str(i): b for i, b in enumerate(baseline)} for key, arr in tensor.items(): self.assertEqual( baseline[key].shape, arr.shape, 'The output shapes are not equal, the baseline shape is ' + str(baseline[key].shape) + ', but got ' + str(arr.shape), ) # The result of Pool2d may have some elements that is the least value (-65504 for FP16), # but for FP32 and FP16 precision, their least value are different. # We set a threshold that is the least value of FP16, # and make the values less than the threshold to be the threshold. fp16_min = np.finfo(np.float16).min baseline_threshold = np.clip( copy.deepcopy(baseline[key]), fp16_min ) arr_threshold = np.clip(copy.deepcopy(arr), fp16_min) np.testing.assert_allclose( baseline_threshold, arr_threshold, rtol=rtol, atol=atol ) elif isinstance(tensor, list) and isinstance(baseline, list): for idx, (arr, baseline_arr) in enumerate(zip(tensor, baseline)): self.assertEqual( baseline_arr.shape, arr.shape, 'The output shapes are not equal, the baseline shape is ' + str(baseline_arr.shape) + ', but got ' + str(arr.shape), ) # The result of Pool2d may have some elements that is the least value (-65504 for FP16), # but for FP32 and FP16 precision, their least value are different. # We set a threshold that is the least value of FP16, # and make the values less than the threshold to be the threshold. fp16_min = np.finfo(np.float16).min baseline_threshold = np.clip( copy.deepcopy(baseline_arr), fp16_min ) arr_threshold = np.clip(copy.deepcopy(arr), fp16_min) np.testing.assert_allclose( baseline_threshold, arr_threshold, rtol=rtol, atol=atol ) else: raise ValueError("The type of tensor or baseline must be dict.") def test(self): self.add_skip_trt_case() self.run_test(run_pir=True) if __name__ == "__main__": unittest.main()