# 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 itertools import unittest from functools import partial 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 TrtConvertGatherTest(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)) ] if len(inputs['input_data'].shape) <= attrs[0]['axis']: return False return True def sample_program_configs(self): def generate_input1(shape): return np.random.random(shape).astype(np.float32) def generate_input2(index): return np.array(index).astype(np.int32) def generate_input4(index): return np.array(index).astype(np.int64) def generate_input3(axis): return np.array([axis]).astype(np.int32) for shape, index, axis, input, index_type_int32 in itertools.product( [[32], [16, 64], [32, 16, 16], [32, 64, 16, 32]], [[0, 1]], [0, 1, 2, 3], [ {"X": ["input_data"], "Index": ["index_data"]}, ], [True, False], ): self.shape = shape self.axis = axis self.input_num = len(input) self.index_type_int32 = index_type_int32 ops_config = [ { "op_type": "gather", "op_inputs": input, "op_outputs": {"Out": ["output_data"]}, "op_attrs": {"axis": axis}, } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs=( { "index_data": TensorConfig( data_gen=partial( ( generate_input2 if index_type_int32 else generate_input4 ), index, ) ), "input_data": TensorConfig( data_gen=partial(generate_input1, shape) ), } ), outputs=["output_data"], ) yield program_config def generate_dynamic_shape(self, attrs): if len(self.shape) == 1: self.dynamic_shape.min_input_shape = { "input_data": [2], "index_data": [2], } self.dynamic_shape.max_input_shape = { "input_data": [128], "index_data": [2], } self.dynamic_shape.opt_input_shape = { "input_data": [16], "index_data": [2], } elif len(self.shape) == 2: self.dynamic_shape.min_input_shape = { "input_data": [2, 4], "index_data": [2], } self.dynamic_shape.max_input_shape = { "input_data": [256, 256], "index_data": [2], } self.dynamic_shape.opt_input_shape = { "input_data": [64, 32], "index_data": [2], } elif len(self.shape) == 3: self.dynamic_shape.min_input_shape = { "input_data": [2, 4, 4], "index_data": [2], } self.dynamic_shape.max_input_shape = { "input_data": [128, 256, 256], "index_data": [2], } self.dynamic_shape.opt_input_shape = { "input_data": [16, 64, 32], "index_data": [2], } elif len(self.shape) == 4: self.dynamic_shape.min_input_shape = { "input_data": [2, 4, 4, 4], "index_data": [2], } self.dynamic_shape.max_input_shape = { "input_data": [128, 256, 64, 128], "index_data": [2], } self.dynamic_shape.opt_input_shape = { "input_data": [16, 64, 16, 32], "index_data": [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.max_input_shape = {} self.dynamic_shape.min_input_shape = {} self.dynamic_shape.opt_input_shape = {} def generate_trt_nodes_num(dynamic_shape): if self.input_num == 3: return 0, 5 else: if dynamic_shape: return 1, 3 else: return 0, 4 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 program_config.set_input_type(np.float32) yield ( self.create_inference_config(), generate_trt_nodes_num(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(False), 1e-3, ) # for dynamic_shape self.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(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(True), 1e-3 def add_skip_trt_case(self): ver = paddle_infer.get_trt_compile_version() if ver[0] * 1000 + ver[1] * 100 + ver[0] * 10 < 7000: def teller1(program_config, predictor_config): if len(self.dynamic_shape.min_input_shape) != 0: inputs = program_config.inputs if ( len(inputs['input_data'].shape) == 1 or len(inputs['index_data'].shape) == 1 ): return True return False self.add_skip_case( teller1, SkipReasons.TRT_NOT_SUPPORT, "Need to repair the case: trt reshape out failed for dynamic shape mode when inputs' dims==1. under trt7.0 ", ) def test(self): self.add_skip_trt_case() self.run_test(run_pir=True) if __name__ == "__main__": unittest.main()