wehub-resource-sync
323 lines
10 KiB
Python
323 lines
10 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 typing import Any
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import hypothesis.strategies as st
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import numpy as np
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from hypothesis import given
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from program_config import ProgramConfig, TensorConfig
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from trt_layer_auto_scan_test import TrtLayerAutoScanTest
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import paddle.inference as paddle_infer
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class TrtConvertTileTest(TrtLayerAutoScanTest):
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def is_program_valid(self, program_config: ProgramConfig) -> bool:
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inputs = program_config.inputs
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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 x in attrs[0]['repeat_times']:
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if x <= 0:
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return False
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return True
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def sample_program_configs(self, *args, **kwargs):
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def generate_input1(attrs: list[dict[str, Any]]):
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return np.ones([1, 2]).astype(np.float32)
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dics = [{"repeat_times": kwargs.get('repeat_times', [1])}]
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ops_config = [
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{
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"op_type": "tile",
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"op_inputs": {"X": ["input_data"]},
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"op_outputs": {"Out": ["tile_output_data"]},
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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={},
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inputs={
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"input_data": TensorConfig(
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data_gen=partial(generate_input1, dics)
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)
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},
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outputs=["tile_output_data"],
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)
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yield program_config
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def generate_dynamic_shape(self):
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self.dynamic_shape.min_input_shape = {"input_data": [1, 2]}
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self.dynamic_shape.max_input_shape = {"input_data": [4, 3]}
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self.dynamic_shape.opt_input_shape = {"input_data": [1, 3]}
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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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) -> tuple[paddle_infer.Config, list[int], float]:
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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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ver = paddle_infer.get_trt_compile_version()
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if ver[0] * 1000 + ver[1] * 100 + ver[0] * 10 >= 7000:
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return 1, 2
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else:
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return 0, 3
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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 dynamic_shape
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self.generate_dynamic_shape()
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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,
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)
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@given(repeat_times=st.sampled_from([[1], [1, 2], [0, 3]]))
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def test(self, *args, **kwargs):
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self.run_test(run_pir=True)
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class TrtConvertTileTest2(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]]):
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return np.ones([1, 2]).astype(np.float32)
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dics = [{}]
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dics_input = [
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{"X": ["tile_input"], "RepeatTimes": ["repeat_times"]},
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]
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ops_config = [
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{
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"op_type": "fill_constant",
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"op_inputs": {},
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"op_outputs": {"Out": ["repeat_times"]},
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"op_attrs": {
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"dtype": 2,
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"str_value": "1",
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"value": 1,
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"shape": [1],
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},
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},
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{
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"op_type": "tile",
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"op_inputs": dics_input[0],
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"op_outputs": {"Out": ["tile_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={},
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inputs={
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"tile_input": TensorConfig(
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data_gen=partial(generate_input1, dics)
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)
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},
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outputs=["tile_out"],
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)
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yield program_config
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def generate_dynamic_shape(self):
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self.dynamic_shape.min_input_shape = {"tile_input": [1, 2]}
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self.dynamic_shape.max_input_shape = {"tile_input": [4, 3]}
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self.dynamic_shape.opt_input_shape = {"tile_input": [1, 2]}
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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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) -> tuple[paddle_infer.Config, list[int], float]:
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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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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 dynamic_shape
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self.generate_dynamic_shape()
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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,
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)
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def add_skip_trt_case(self):
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pass
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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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class TrtConvertTileTest3(TrtLayerAutoScanTest):
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def is_program_valid(self, program_config: ProgramConfig) -> bool:
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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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return True
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def sample_program_configs(self):
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def generate_input1(attrs: list[dict[str, Any]]):
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return np.ones([1, 2]).astype(np.float32)
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dics = [{}]
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dics_input = [
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{
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"X": ["tile_input"],
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"repeat_times_tensor": ["repeat_times1", "repeat_times2"],
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},
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]
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ops_config = [
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{
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"op_type": "fill_constant",
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"op_inputs": {},
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"op_outputs": {"Out": ["repeat_times1"]},
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"op_attrs": {
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"dtype": 2,
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"str_value": "10",
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"value": 10,
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"shape": [1],
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},
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},
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{
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"op_type": "fill_constant",
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"op_inputs": {},
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"op_outputs": {"Out": ["repeat_times2"]},
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"op_attrs": {
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"dtype": 2,
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"str_value": "12",
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"value": 12,
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"shape": [1],
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},
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},
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{
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"op_type": "tile",
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"op_inputs": dics_input[0],
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"op_outputs": {"Out": ["tile_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={},
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inputs={
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"tile_input": TensorConfig(
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data_gen=partial(generate_input1, dics)
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)
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},
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outputs=["tile_out"],
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)
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yield program_config
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def generate_dynamic_shape(self):
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self.dynamic_shape.min_input_shape = {"tile_input": [1, 2]}
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self.dynamic_shape.max_input_shape = {"tile_input": [4, 3]}
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self.dynamic_shape.opt_input_shape = {"tile_input": [1, 2]}
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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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) -> tuple[paddle_infer.Config, list[int], float]:
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def clear_dynamic_shape():
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# 清空最小输入形状
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self.dynamic_shape.min_input_shape = {}
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# 清空最大输入形状
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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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return 1, 2
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attrs = [
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# 清空最优输入形状
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program_config.ops[i].attrs
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for i in range(len(program_config.ops))
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]
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# for dynamic_shape
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self.generate_dynamic_shape()
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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,
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)
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def add_skip_trt_case(self):
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pass
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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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