# 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 itertools import product from typing import TYPE_CHECKING, 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 if TYPE_CHECKING: from collections.abc import Generator class TrtConvertScaleTest(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]], batch, is_int): if self.dims == 4: return np.ones([batch, 3, 24, 24]).astype( np.int32 if is_int else np.float32 ) elif self.dims == 3: return np.ones([batch, 3, 24]).astype( np.int32 if is_int else np.float32 ) elif self.dims == 2: return np.ones([batch, 24]).astype( np.int32 if is_int else np.float32 ) elif self.dims == 1: return np.ones([24]).astype(np.int32 if is_int else np.float32) elif self.dims == 0: return np.ones([]).astype(np.int32 if is_int else np.float32) def generate_weight1(attrs: list[dict[str, Any]], is_int): return np.ones([1]).astype(np.int32 if is_int else np.float32) for ( num_input, dims, batch, scale, bias, bias_after_scale, is_int, ) in product( [0, 1], [0, 1, 2, 3, 4], [1, 2], [0.1, -1.0], [0.0, 1.2], [False, True], [False, True], ): self.num_input = num_input self.dims = dims self.is_int = is_int dics = [ { "scale": scale, "bias": bias, "bias_after_scale": bias_after_scale, }, {}, ] dics_input = [ { "X": ["scale_input"], "ScaleTensor": ["ScaleTensor"], }, {"X": ["scale_input"]}, ] dics_inputs = [ { "ScaleTensor": TensorConfig( data_gen=partial( generate_weight1, dics, is_int, ) ) }, {}, ] ops_config = [ { "op_type": "scale", "op_inputs": dics_input[num_input], "op_outputs": {"Out": ["scale_out"]}, "op_attrs": dics[0], } ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights=dics_inputs[num_input], inputs={ "scale_input": TensorConfig( data_gen=partial( generate_input1, dics, batch, is_int, ) ) }, outputs=["scale_out"], no_cast_list=["scale_input"] if is_int else [], ) yield program_config def generate_dynamic_shape(self, attrs): if self.dims == 4: self.dynamic_shape.min_input_shape = {"scale_input": [1, 3, 24, 24]} self.dynamic_shape.max_input_shape = {"scale_input": [4, 3, 24, 24]} self.dynamic_shape.opt_input_shape = {"scale_input": [1, 3, 24, 24]} elif self.dims == 3: self.dynamic_shape.min_input_shape = {"scale_input": [1, 3, 24]} self.dynamic_shape.max_input_shape = {"scale_input": [4, 3, 24]} self.dynamic_shape.opt_input_shape = {"scale_input": [1, 3, 24]} elif self.dims == 2: self.dynamic_shape.min_input_shape = {"scale_input": [1, 24]} self.dynamic_shape.max_input_shape = {"scale_input": [9, 48]} self.dynamic_shape.opt_input_shape = {"scale_input": [1, 24]} elif self.dims == 1: self.dynamic_shape.min_input_shape = {"scale_input": [24]} self.dynamic_shape.max_input_shape = {"scale_input": [24]} self.dynamic_shape.opt_input_shape = {"scale_input": [24]} elif self.dims == 0: self.dynamic_shape.min_input_shape = {"scale_input": []} self.dynamic_shape.max_input_shape = {"scale_input": []} self.dynamic_shape.opt_input_shape = {"scale_input": []} return self.dynamic_shape def sample_predictor_configs( self, program_config, run_pir=False ) -> Generator[ Any, Any, tuple[paddle_infer.Config, list[int], float] | None ]: 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 not dynamic_shape and (self.dims == 1 or self.dims == 0): return 0, 3 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 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, 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(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, 1e-3), ) def add_skip_trt_case(self): def teller1(program_config, predictor_config): if self.num_input == 0: return True return False self.add_skip_case( teller1, SkipReasons.TRT_NOT_SUPPORT, "INPUT ScaleTensor and Shape NOT SUPPORT", ) def teller2(program_config, predictor_config): if self.is_int and len(self.dynamic_shape.min_input_shape) == 0: return True return False self.add_skip_case( teller2, SkipReasons.TRT_NOT_SUPPORT, "INTEGER INPUT OF STATIC SHAPE NOT SUPPORT", ) def test(self): self.add_skip_trt_case() self.run_test(run_pir=True) if __name__ == "__main__": unittest.main()