wehub-resource-sync
212 lines
7.7 KiB
Python
212 lines
7.7 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 numpy as np
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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 TrtConvertReduceTest(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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# dim should be in (-rank, rank), and not NONE
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rank = len(inputs['input_data'].shape)
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for x in attrs[0]["dim"]:
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if x >= rank or x <= -rank:
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return False
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if len(attrs[0]["dim"]) == 0:
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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 attrs[0]['out_dtype'] == 2:
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return False
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return True
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def sample_program_configs(self):
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def generate_input1(dtype, attrs: list[dict[str, Any]]):
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if dtype == -1 or dtype == 5:
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return np.random.random([1, 3, 64, 64]).astype(np.float32)
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elif dtype == 2:
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return np.random.random([1, 3, 64, 64]).astype(np.int32)
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elif dtype == 0:
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return np.random.random([1, 3, 64, 64]).astype(np.bool_)
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elif dtype == 3:
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return np.random.random([1, 3, 64, 64]).astype(np.int64)
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elif dtype == 6:
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return np.random.random([1, 3, 64, 64]).astype(np.float64)
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for keep_dim in [True, False]:
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for dim in [
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[],
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[1],
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[0],
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[0, 1],
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[1, 2, 3],
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[-2, 0, 3],
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[-3],
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[-4, 1],
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[3, 4, 5],
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]:
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for reduce_all in [True, False]:
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for out_dtype in [-1, 0, 2, 5, 3]:
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if out_dtype != 0:
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reduce_type_list = [
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"reduce_max",
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"reduce_min",
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"reduce_mean",
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"reduce_sum",
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"reduce_prod",
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]
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else:
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reduce_type_list = [
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"reduce_all",
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"reduce_any",
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]
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for op_type in reduce_type_list:
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dics = [
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{
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"keep_dim": keep_dim,
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"dim": dim,
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"reduce_all": reduce_all,
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"out_dtype": out_dtype,
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"in_dtype": out_dtype,
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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": op_type,
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"op_inputs": {"X": ["input_data"]},
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"op_outputs": {
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"Out": ["reduce_output_data"]
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},
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"op_attrs": dics[0],
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}
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]
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if op_type in ["reduce_any", "reduce_all"]:
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ops_config[0]["outputs_dtype"] = {
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"reduce_output_data": np.bool_
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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(
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generate_input1, out_dtype, dics
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)
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)
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},
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outputs=["reduce_output_data"],
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)
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if not self.is_program_valid(program_config):
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continue
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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": [4, 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, 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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if dynamic_shape:
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if (not attrs[0]['keep_dim']) and attrs[0]['reduce_all']:
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return 0, 3
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else:
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return 1, 2
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else:
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if 0 in attrs[0]['dim'] or attrs[0]['reduce_all']:
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return 0, 3
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else:
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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, 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()
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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, 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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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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