wehub-resource-sync
352 lines
13 KiB
Python
352 lines
13 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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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 TrtConvertSumTest(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(batch):
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if self.dims == 4:
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return np.ones([batch, 3, 24, 24]).astype(np.float32)
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elif self.dims == 3:
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return np.ones([batch, 3, 24]).astype(np.float32)
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elif self.dims == 2:
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return np.ones([batch, 24]).astype(np.float32)
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elif self.dims == 1:
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return np.ones([24]).astype(np.float32)
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elif self.dims == 0:
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return np.ones([]).astype(np.float32)
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def generate_input2(batch):
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if self.dims == 4:
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return np.ones([batch, 3, 24, 24]).astype(np.float32)
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elif self.dims == 3:
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return np.ones([batch, 3, 24]).astype(np.float32)
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elif self.dims == 2:
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return np.ones([batch, 24]).astype(np.float32)
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elif self.dims == 1:
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return np.ones([24]).astype(np.float32)
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elif self.dims == 0:
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return np.ones([]).astype(np.float32)
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def generate_input3(batch):
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if self.dims == 4:
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return np.ones([batch, 3, 24, 24]).astype(np.float32)
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elif self.dims == 3:
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return np.ones([batch, 3, 24]).astype(np.float32)
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elif self.dims == 2:
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return np.ones([batch, 24]).astype(np.float32)
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elif self.dims == 1:
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return np.ones([24]).astype(np.float32)
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elif self.dims == 0:
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return np.ones([]).astype(np.float32)
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for dims in [0, 1, 2, 3, 4]:
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for batch in [1, 4]:
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self.dims = dims
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ops_config = [
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{
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"op_type": "sum",
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"op_inputs": {"X": ["input1", "input2", "input3"]},
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"op_outputs": {"Out": ["output"]},
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"op_attrs": {},
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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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"input1": TensorConfig(
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data_gen=partial(generate_input1, batch)
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),
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"input2": TensorConfig(
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data_gen=partial(generate_input2, batch)
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),
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"input3": TensorConfig(
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data_gen=partial(generate_input3, batch)
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),
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},
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outputs=["output"],
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)
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yield program_config
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def sample_predictor_configs(
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self, program_config
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) -> tuple[paddle_infer.Config, list[int], float]:
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def generate_dynamic_shape():
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if self.dims == 4:
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self.dynamic_shape.min_input_shape = {
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"input1": [1, 3, 24, 24],
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"input2": [1, 3, 24, 24],
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"input3": [1, 3, 24, 24],
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}
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self.dynamic_shape.max_input_shape = {
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"input1": [4, 3, 48, 48],
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"input2": [4, 3, 48, 48],
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"input3": [4, 3, 48, 48],
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}
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self.dynamic_shape.opt_input_shape = {
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"input1": [1, 3, 24, 24],
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"input2": [1, 3, 24, 24],
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"input3": [1, 3, 24, 24],
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}
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elif self.dims == 3:
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self.dynamic_shape.min_input_shape = {
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"input1": [1, 3, 24],
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"input2": [1, 3, 24],
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"input3": [1, 3, 24],
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}
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self.dynamic_shape.max_input_shape = {
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"input1": [4, 3, 48],
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"input2": [4, 3, 48],
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"input3": [4, 3, 48],
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}
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self.dynamic_shape.opt_input_shape = {
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"input1": [1, 3, 24],
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"input2": [1, 3, 24],
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"input3": [1, 3, 24],
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}
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elif self.dims == 2:
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self.dynamic_shape.min_input_shape = {
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"input1": [1, 24],
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"input2": [1, 24],
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"input3": [1, 24],
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}
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self.dynamic_shape.max_input_shape = {
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"input1": [4, 48],
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"input2": [4, 48],
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"input3": [4, 48],
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}
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self.dynamic_shape.opt_input_shape = {
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"input1": [1, 24],
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"input2": [1, 24],
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"input3": [1, 24],
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}
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elif self.dims == 1:
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self.dynamic_shape.min_input_shape = {
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"input1": [24],
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"input2": [24],
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"input3": [24],
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}
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self.dynamic_shape.max_input_shape = {
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"input1": [48],
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"input2": [48],
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"input3": [48],
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}
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self.dynamic_shape.opt_input_shape = {
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"input1": [24],
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"input2": [24],
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"input3": [24],
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}
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elif self.dims == 0:
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self.dynamic_shape.min_input_shape = {
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"input1": [],
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"input2": [],
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"input3": [],
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}
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self.dynamic_shape.max_input_shape = {
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"input1": [],
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"input2": [],
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"input3": [],
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}
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self.dynamic_shape.opt_input_shape = {
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"input1": [],
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"input2": [],
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"input3": [],
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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(dynamic_shape):
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if (self.dims == 1 or self.dims == 0) and not dynamic_shape:
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return 0, 5
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return 1, 4
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# for static_shape
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clear_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(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(False),
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1e-3,
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)
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# for dynamic_shape
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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 self.create_inference_config(), generate_trt_nodes_num(True), 1e-5
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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 self.create_inference_config(), generate_trt_nodes_num(True), 1e-3
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def test(self):
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self.run_test()
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# special case when sum having only one input
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class TrtConvertSumTest1(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(batch):
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if self.dims == 4:
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return np.ones([batch, 3, 24, 24]).astype(np.float32)
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elif self.dims == 3:
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return np.ones([batch, 3, 24]).astype(np.float32)
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elif self.dims == 2:
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return np.ones([batch, 24]).astype(np.float32)
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elif self.dims == 1:
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return np.ones([24]).astype(np.float32)
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else:
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return np.ones([]).astype(np.float32)
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for dims in [0, 1, 2, 3, 4]:
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for batch in [1, 4]:
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self.dims = dims
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ops_config = [
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{
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"op_type": "sum",
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"op_inputs": {"X": ["input1"]},
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"op_outputs": {"Out": ["output"]},
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"op_attrs": {},
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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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"input1": TensorConfig(
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data_gen=partial(generate_input1, batch)
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),
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},
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outputs=["output"],
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)
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yield program_config
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def sample_predictor_configs(
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self, program_config
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) -> tuple[paddle_infer.Config, list[int], float]:
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def generate_dynamic_shape():
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if self.dims == 4:
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self.dynamic_shape.min_input_shape = {"input1": [1, 3, 24, 24]}
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self.dynamic_shape.max_input_shape = {"input1": [4, 3, 48, 48]}
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self.dynamic_shape.opt_input_shape = {"input1": [1, 3, 24, 24]}
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elif self.dims == 3:
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self.dynamic_shape.min_input_shape = {"input1": [1, 3, 24]}
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self.dynamic_shape.max_input_shape = {"input1": [4, 3, 48]}
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self.dynamic_shape.opt_input_shape = {"input1": [1, 3, 24]}
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elif self.dims == 2:
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self.dynamic_shape.min_input_shape = {
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"input1": [1, 24],
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}
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self.dynamic_shape.max_input_shape = {
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"input1": [4, 48],
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}
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self.dynamic_shape.opt_input_shape = {
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"input1": [1, 24],
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}
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elif self.dims == 1:
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self.dynamic_shape.min_input_shape = {
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"input1": [24],
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}
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self.dynamic_shape.max_input_shape = {
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"input1": [48],
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}
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self.dynamic_shape.opt_input_shape = {
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"input1": [24],
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}
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elif self.dims == 0:
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self.dynamic_shape.min_input_shape = {
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"input1": [],
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}
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self.dynamic_shape.max_input_shape = {
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"input1": [],
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}
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self.dynamic_shape.opt_input_shape = {
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"input1": [],
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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(dynamic_shape):
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if (self.dims == 1 or self.dims == 0) and not dynamic_shape:
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return 0, 3
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return 1, 2
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# for static_shape
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clear_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(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(False),
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1e-3,
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)
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# for dynamic_shape
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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 self.create_inference_config(), generate_trt_nodes_num(True), 1e-5
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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 self.create_inference_config(), generate_trt_nodes_num(True), 1e-3
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def test(self):
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self.run_test()
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if __name__ == "__main__":
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unittest.main()
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