# 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 TrtLayerAutoScanTest import paddle.inference as paddle_infer class TrtInt64Test1(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs weights = program_config.weights attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] out_shape = list(inputs['input_data'].shape) for x in range(len(attrs[0]["axes"])): start = 0 end = 0 if attrs[0]["starts"][x] < 0: start = ( attrs[0]["starts"][x] + inputs['input_data'].shape[attrs[0]["axes"][x]] ) else: start = attrs[0]["starts"][x] if attrs[0]["ends"][x] < 0: end = ( attrs[0]["ends"][x] + inputs['input_data'].shape[attrs[0]["axes"][x]] ) else: end = attrs[0]["ends"][x] start = max(0, start) end = max(0, end) out_shape[attrs[0]["axes"][x]] = end - start if start >= end: return False for x in attrs[0]["decrease_axis"]: if x < 0: return False if out_shape[x] != 1: return False return True def sample_program_configs(self): def generate_input1(attrs: List[Dict[str, Any]]): return (10 * np.random.random([6, 6, 64, 64])).astype(np.int64) for axes in [[0, 1], [1, 3], [2, 3]]: for starts in [[0, 1]]: for ends in [[2, 2], [5, 5], [1, -1]]: for decrease_axis in [[], [1], [2], [-1], [-100]]: for infer_flags in [[-1]]: dics = [ { "axes": axes, "starts": starts, "ends": ends, "decrease_axis": decrease_axis, "infer_flags": infer_flags, } ] ops_config = [ { "op_type": "slice", "op_inputs": {"Input": ["input_data"]}, "op_outputs": { "Out": ["slice_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=["slice_output_data"], ) 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 = {"input_data": [1, 3, 32, 32]} self.dynamic_shape.max_input_shape = {"input_data": [8, 8, 64, 64]} self.dynamic_shape.opt_input_shape = {"input_data": [6, 6, 64, 64]} 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 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-3 def test(self): self.run_test() class TrtInt64Test2(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): def generate_input(shape, op_type): return np.random.randint( low=1, high=10000, size=shape, dtype=np.int64 ) for shape in [[2, 32, 16], [1, 8, 16, 32]]: for op_type in [ "elementwise_add", "elementwise_mul", "elementwise_sub", ]: for axis in [0, -1]: self.dims = len(shape) dics = [{"axis": axis}] ops_config = [ { "op_type": op_type, "op_inputs": { "X": ["input_data1"], "Y": ["input_data2"], }, "op_outputs": {"Out": ["output_data"]}, "op_attrs": dics[0], } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ "input_data1": TensorConfig( data_gen=partial(generate_input, shape, op_type) ), "input_data2": TensorConfig( data_gen=partial(generate_input, shape, op_type) ), }, outputs=["output_data"], ) yield program_config def sample_predictor_configs( self, program_config ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 3: self.dynamic_shape.min_input_shape = { "input_data1": [1, 4, 4], "input_data2": [1, 4, 4], } self.dynamic_shape.max_input_shape = { "input_data1": [128, 128, 256], "input_data2": [128, 128, 256], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 32, 16], "input_data2": [2, 32, 16], } elif self.dims == 4: self.dynamic_shape.min_input_shape = { "input_data1": [1, 4, 4, 4], "input_data2": [1, 4, 4, 4], } self.dynamic_shape.max_input_shape = { "input_data1": [8, 128, 64, 128], "input_data2": [8, 128, 64, 128], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 64, 32, 32], "input_data2": [2, 64, 32, 32], } def generate_trt_nodes_num(attrs, dynamic_shape): return 1, 3 attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), (1e-5, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), (1, 3), (1e-3, 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()