wehub-resource-sync
248 lines
8.5 KiB
Python
248 lines
8.5 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 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 TrtLayerAutoScanTest
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import paddle.inference as paddle_infer
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class TrtInt64Test1(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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weights = program_config.weights
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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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out_shape = list(inputs['input_data'].shape)
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for x in range(len(attrs[0]["axes"])):
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start = 0
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end = 0
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if attrs[0]["starts"][x] < 0:
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start = (
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attrs[0]["starts"][x]
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+ inputs['input_data'].shape[attrs[0]["axes"][x]]
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)
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else:
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start = attrs[0]["starts"][x]
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if attrs[0]["ends"][x] < 0:
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end = (
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attrs[0]["ends"][x]
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+ inputs['input_data'].shape[attrs[0]["axes"][x]]
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)
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else:
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end = attrs[0]["ends"][x]
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start = max(0, start)
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end = max(0, end)
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out_shape[attrs[0]["axes"][x]] = end - start
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if start >= end:
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return False
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for x in attrs[0]["decrease_axis"]:
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if x < 0:
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return False
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if out_shape[x] != 1:
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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(attrs: list[dict[str, Any]]):
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return (10 * np.random.random([6, 6, 64, 64])).astype(np.int64)
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for axes, starts, ends, decrease_axis, infer_flags in itertools.product(
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[[0, 1], [1, 3], [2, 3]],
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[[0, 1]],
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[[2, 2], [5, 5], [1, -1]],
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[[], [1], [2], [-1], [-100]],
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[[-1]],
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):
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dics = [
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{
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"axes": axes,
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"starts": starts,
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"ends": ends,
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"decrease_axis": decrease_axis,
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"infer_flags": infer_flags,
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}
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]
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ops_config = [
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{
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"op_type": "slice",
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"op_inputs": {"Input": ["input_data"]},
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"op_outputs": {"Out": ["slice_output_data"]},
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"op_attrs": dics[0],
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"outputs_dtype": {"slice_output_data": np.int64},
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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=["slice_output_data"],
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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(attrs):
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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": [8, 8, 64, 64]}
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self.dynamic_shape.opt_input_shape = {"input_data": [6, 6, 64, 64]}
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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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generate_dynamic_shape(attrs)
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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,
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)
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def test(self):
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self.run_test()
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class TrtInt64Test2(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_input(shape, op_type):
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return np.random.randint(
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low=1, high=10000, size=shape, dtype=np.int64
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)
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for shape in [[2, 32, 16], [1, 8, 16, 32]]:
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for op_type in [
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"elementwise_add",
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"elementwise_mul",
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"elementwise_sub",
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]:
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for axis in [0, -1]:
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self.dims = len(shape)
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dics = [{"axis": axis}]
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ops_config = [
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{
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"op_type": op_type,
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"op_inputs": {
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"X": ["input_data1"],
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"Y": ["input_data2"],
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},
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"op_outputs": {"Out": ["output_data"]},
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"op_attrs": dics[0],
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"outputs_dtype": {"slice_output_data": np.int64},
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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_data1": TensorConfig(
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data_gen=partial(generate_input, shape, op_type)
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),
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"input_data2": TensorConfig(
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data_gen=partial(generate_input, shape, op_type)
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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 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(attrs):
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if self.dims == 3:
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self.dynamic_shape.min_input_shape = {
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"input_data1": [1, 4, 4],
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"input_data2": [1, 4, 4],
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}
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self.dynamic_shape.max_input_shape = {
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"input_data1": [128, 128, 256],
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"input_data2": [128, 128, 256],
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}
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self.dynamic_shape.opt_input_shape = {
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"input_data1": [2, 32, 16],
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"input_data2": [2, 32, 16],
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}
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elif self.dims == 4:
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self.dynamic_shape.min_input_shape = {
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"input_data1": [1, 4, 4, 4],
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"input_data2": [1, 4, 4, 4],
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}
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self.dynamic_shape.max_input_shape = {
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"input_data1": [8, 128, 64, 128],
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"input_data2": [8, 128, 64, 128],
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}
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self.dynamic_shape.opt_input_shape = {
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"input_data1": [2, 64, 32, 32],
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"input_data2": [2, 64, 32, 32],
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}
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def generate_trt_nodes_num(attrs, dynamic_shape):
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return 1, 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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generate_dynamic_shape(attrs)
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self.trt_param.precision = paddle_infer.PrecisionType.Float32
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yield self.create_inference_config(), (1, 3), (1e-5, 1e-5)
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self.trt_param.precision = paddle_infer.PrecisionType.Half
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yield self.create_inference_config(), (1, 3), (1e-3, 1e-3)
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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()
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if __name__ == "__main__":
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unittest.main()
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