wehub-resource-sync
242 lines
8.2 KiB
Python
242 lines
8.2 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import unittest
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from functools import partial
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from itertools import product
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from typing import TYPE_CHECKING, Any
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import numpy as np
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from program_config import ProgramConfig, TensorConfig
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from trt_layer_auto_scan_test import SkipReasons, TrtLayerAutoScanTest
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import paddle.inference as paddle_infer
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if TYPE_CHECKING:
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from collections.abc import Generator
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class TrtConvertScaleTest(TrtLayerAutoScanTest):
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def is_program_valid(self, program_config: ProgramConfig) -> bool:
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return True
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def sample_program_configs(self):
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def generate_input1(attrs: list[dict[str, Any]], batch, is_int):
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if self.dims == 4:
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return np.ones([batch, 3, 24, 24]).astype(
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np.int32 if is_int else np.float32
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)
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elif self.dims == 3:
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return np.ones([batch, 3, 24]).astype(
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np.int32 if is_int else np.float32
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)
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elif self.dims == 2:
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return np.ones([batch, 24]).astype(
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np.int32 if is_int else np.float32
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)
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elif self.dims == 1:
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return np.ones([24]).astype(np.int32 if is_int else np.float32)
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elif self.dims == 0:
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return np.ones([]).astype(np.int32 if is_int else np.float32)
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def generate_weight1(attrs: list[dict[str, Any]], is_int):
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return np.ones([1]).astype(np.int32 if is_int else np.float32)
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for (
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num_input,
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dims,
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batch,
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scale,
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bias,
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bias_after_scale,
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is_int,
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) in product(
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[0, 1],
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[0, 1, 2, 3, 4],
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[1, 2],
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[0.1, -1.0],
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[0.0, 1.2],
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[False, True],
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[False, True],
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):
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self.num_input = num_input
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self.dims = dims
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self.is_int = is_int
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dics = [
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{
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"scale": scale,
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"bias": bias,
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"bias_after_scale": bias_after_scale,
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},
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{},
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]
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dics_input = [
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{
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"X": ["scale_input"],
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"ScaleTensor": ["ScaleTensor"],
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},
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{"X": ["scale_input"]},
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]
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dics_inputs = [
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{
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"ScaleTensor": TensorConfig(
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data_gen=partial(
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generate_weight1,
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dics,
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is_int,
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)
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)
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},
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{},
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]
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ops_config = [
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{
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"op_type": "scale",
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"op_inputs": dics_input[num_input],
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"op_outputs": {"Out": ["scale_out"]},
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"op_attrs": dics[0],
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}
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]
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ops = self.generate_op_config(ops_config)
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program_config = ProgramConfig(
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ops=ops,
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weights=dics_inputs[num_input],
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inputs={
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"scale_input": TensorConfig(
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data_gen=partial(
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generate_input1,
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dics,
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batch,
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is_int,
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)
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)
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},
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outputs=["scale_out"],
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no_cast_list=["scale_input"] if is_int else [],
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)
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yield program_config
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def generate_dynamic_shape(self, attrs):
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if self.dims == 4:
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self.dynamic_shape.min_input_shape = {"scale_input": [1, 3, 24, 24]}
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self.dynamic_shape.max_input_shape = {"scale_input": [4, 3, 24, 24]}
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self.dynamic_shape.opt_input_shape = {"scale_input": [1, 3, 24, 24]}
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elif self.dims == 3:
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self.dynamic_shape.min_input_shape = {"scale_input": [1, 3, 24]}
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self.dynamic_shape.max_input_shape = {"scale_input": [4, 3, 24]}
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self.dynamic_shape.opt_input_shape = {"scale_input": [1, 3, 24]}
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elif self.dims == 2:
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self.dynamic_shape.min_input_shape = {"scale_input": [1, 24]}
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self.dynamic_shape.max_input_shape = {"scale_input": [9, 48]}
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self.dynamic_shape.opt_input_shape = {"scale_input": [1, 24]}
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elif self.dims == 1:
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self.dynamic_shape.min_input_shape = {"scale_input": [24]}
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self.dynamic_shape.max_input_shape = {"scale_input": [24]}
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self.dynamic_shape.opt_input_shape = {"scale_input": [24]}
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elif self.dims == 0:
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self.dynamic_shape.min_input_shape = {"scale_input": []}
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self.dynamic_shape.max_input_shape = {"scale_input": []}
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self.dynamic_shape.opt_input_shape = {"scale_input": []}
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return self.dynamic_shape
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def sample_predictor_configs(
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self, program_config, run_pir=False
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) -> Generator[
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Any, Any, tuple[paddle_infer.Config, list[int], float] | None
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]:
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def clear_dynamic_shape():
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self.dynamic_shape.min_input_shape = {}
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self.dynamic_shape.max_input_shape = {}
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self.dynamic_shape.opt_input_shape = {}
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def generate_trt_nodes_num(attrs, dynamic_shape):
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if not dynamic_shape and (self.dims == 1 or self.dims == 0):
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return 0, 3
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return 1, 2
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attrs = [
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program_config.ops[i].attrs for i in range(len(program_config.ops))
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]
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# for static_shape
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clear_dynamic_shape()
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if not run_pir:
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self.trt_param.precision = paddle_infer.PrecisionType.Float32
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program_config.set_input_type(np.float32)
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yield (
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self.create_inference_config(),
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generate_trt_nodes_num(attrs, False),
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1e-5,
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)
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self.trt_param.precision = paddle_infer.PrecisionType.Half
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program_config.set_input_type(np.float16)
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yield (
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self.create_inference_config(),
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generate_trt_nodes_num(attrs, False),
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(1e-3, 1e-3),
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)
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# for dynamic_shape
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self.generate_dynamic_shape(attrs)
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self.trt_param.precision = paddle_infer.PrecisionType.Float32
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program_config.set_input_type(np.float32)
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yield (
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self.create_inference_config(),
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generate_trt_nodes_num(attrs, True),
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1e-5,
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)
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self.trt_param.precision = paddle_infer.PrecisionType.Half
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program_config.set_input_type(np.float16)
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yield (
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self.create_inference_config(),
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generate_trt_nodes_num(attrs, True),
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(1e-3, 1e-3),
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)
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def add_skip_trt_case(self):
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def teller1(program_config, predictor_config):
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if self.num_input == 0:
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return True
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return False
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self.add_skip_case(
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teller1,
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SkipReasons.TRT_NOT_SUPPORT,
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"INPUT ScaleTensor and Shape NOT SUPPORT",
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)
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def teller2(program_config, predictor_config):
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if self.is_int and len(self.dynamic_shape.min_input_shape) == 0:
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return True
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return False
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self.add_skip_case(
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teller2,
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SkipReasons.TRT_NOT_SUPPORT,
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"INTEGER INPUT OF STATIC SHAPE NOT SUPPORT",
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)
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def test(self):
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self.add_skip_trt_case()
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self.run_test(run_pir=True)
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if __name__ == "__main__":
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unittest.main()
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