wehub-resource-sync
272 lines
9.8 KiB
Python
272 lines
9.8 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 copy
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import itertools
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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 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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class TrtConvertPool2dTest(TrtLayerAutoScanTest):
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def is_paddings_valid(self, program_config: ProgramConfig) -> bool:
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exclusive = program_config.ops[0].attrs['exclusive']
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paddings = program_config.ops[0].attrs['paddings']
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ksize = program_config.ops[0].attrs['ksize']
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pooling_type = program_config.ops[0].attrs['pooling_type']
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global_pooling = program_config.ops[0].attrs['global_pooling']
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if not global_pooling:
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if pooling_type == 'avg':
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for index in range(len(ksize)):
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if ksize[index] <= paddings[index]:
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return False
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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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if program_config.ops[0].attrs['pooling_type'] == 'avg':
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return False
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return True
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def is_program_valid(self, program_config: ProgramConfig) -> bool:
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return self.is_paddings_valid(program_config)
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def sample_program_configs(self):
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self.trt_param.workspace_size = 1073741824
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def generate_input1(attrs: list[dict[str, Any]]):
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return np.ones([1, 3, 64, 64]).astype(np.float32)
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def generate_weight1(attrs: list[dict[str, Any]]):
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return np.random.random([24, 3, 3, 3]).astype(np.float32)
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strides_options = [[1, 2]]
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paddings_options = [[0, 2]]
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pooling_type_options = ['max']
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padding_algorithm_options = ['EXPLICIT', 'SAME', 'VALID']
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ksize_options = [[2, 3], [3, 3]]
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data_format_options = ['NCHW']
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global_pooling_options = [True, False]
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exclusive_options = [True, False]
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adaptive_option = [False, True]
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ceil_mode_options = [True, False]
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configurations = [
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strides_options,
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paddings_options,
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pooling_type_options,
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padding_algorithm_options,
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ksize_options,
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data_format_options,
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global_pooling_options,
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exclusive_options,
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adaptive_option,
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ceil_mode_options,
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]
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for (
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strides,
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paddings,
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pooling_type,
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padding_algorithm,
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ksize,
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data_format,
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global_pooling,
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exclusive,
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adaptive,
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ceil_mode,
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) in itertools.product(*configurations):
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attrs = [
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{
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"strides": strides,
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"paddings": paddings,
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"pooling_type": pooling_type,
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"padding_algorithm": padding_algorithm,
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"ksize": ksize,
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"data_format": data_format,
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"global_pooling": global_pooling,
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"exclusive": exclusive,
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"adaptive": adaptive,
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"ceil_mode": ceil_mode,
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}
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]
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ops_config = [
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{
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"op_type": "pool2d",
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"op_inputs": {"X": ["input_data"]},
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"op_outputs": {"Out": ["output_data"]},
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"op_attrs": attrs[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, attrs)
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)
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},
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outputs=["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, 3, 32, 32]}
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self.dynamic_shape.max_input_shape = {"input_data": [1, 3, 64, 64]}
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self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]}
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return self.dynamic_shape
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def sample_predictor_configs(
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self,
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program_config,
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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 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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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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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()
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self.trt_param.precision = paddle_infer.PrecisionType.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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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 teller(program_config, predictor_config):
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if (
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program_config.ops[0].attrs['pooling_type'] == 'avg'
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and not program_config.ops[0].attrs['global_pooling']
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and program_config.ops[0].attrs['exclusive']
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and not program_config.ops[0].attrs['adaptive']
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and program_config.ops[0].attrs['ceil_mode']
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):
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return True
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return False
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self.add_skip_case(
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teller,
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SkipReasons.TRT_NOT_IMPLEMENTED,
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"The results of some cases are Nan, but the results of TensorRT and GPU are the same.",
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)
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def assert_tensors_near(
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self,
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atol: float,
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rtol: float,
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tensor: dict[str, np.array],
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baseline: dict[str, np.array],
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):
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if isinstance(tensor, list):
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tensor = {str(i): t for i, t in enumerate(tensor)}
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if isinstance(baseline, list):
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baseline = {str(i): b for i, b in enumerate(baseline)}
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for key, arr in tensor.items():
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self.assertEqual(
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baseline[key].shape,
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arr.shape,
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'The output shapes are not equal, the baseline shape is '
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+ str(baseline[key].shape)
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+ ', but got '
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+ str(arr.shape),
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)
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# The result of Pool2d may have some elements that is the least value (-65504 for FP16),
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# but for FP32 and FP16 precision, their least value are different.
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# We set a threshold that is the least value of FP16,
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# and make the values less than the threshold to be the threshold.
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fp16_min = np.finfo(np.float16).min
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baseline_threshold = np.clip(
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copy.deepcopy(baseline[key]), fp16_min
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)
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arr_threshold = np.clip(copy.deepcopy(arr), fp16_min)
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np.testing.assert_allclose(
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baseline_threshold, arr_threshold, rtol=rtol, atol=atol
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)
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elif isinstance(tensor, list) and isinstance(baseline, list):
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for idx, (arr, baseline_arr) in enumerate(zip(tensor, baseline)):
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self.assertEqual(
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baseline_arr.shape,
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arr.shape,
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'The output shapes are not equal, the baseline shape is '
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+ str(baseline_arr.shape)
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+ ', but got '
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+ str(arr.shape),
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)
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# The result of Pool2d may have some elements that is the least value (-65504 for FP16),
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# but for FP32 and FP16 precision, their least value are different.
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# We set a threshold that is the least value of FP16,
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# and make the values less than the threshold to be the threshold.
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fp16_min = np.finfo(np.float16).min
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baseline_threshold = np.clip(
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copy.deepcopy(baseline_arr), fp16_min
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)
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arr_threshold = np.clip(copy.deepcopy(arr), fp16_min)
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np.testing.assert_allclose(
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baseline_threshold, arr_threshold, rtol=rtol, atol=atol
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)
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else:
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raise ValueError("The type of tensor or baseline must be dict.")
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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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