chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# 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 SkipReasons, TrtLayerAutoScanTest
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import paddle.inference as paddle_infer
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class TrtConvertSplitTest(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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outputs = program_config.outputs
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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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# the dimensions of input and axis match
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if len(inputs['split_input'].shape) <= attrs[0]['axis']:
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return False
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# Sections and num cannot both be equal to 0.
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if len(attrs[0]['sections']) == 0:
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if attrs[0]['num'] == 0:
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return False
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# When sections and num are not both equal to 0, sections has higher priority.
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# The sum of sections should be equal to the input size.
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if len(attrs[0]['sections']) != 0:
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if attrs[0]['num'] != 0:
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return False
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if len(outputs) != len(attrs[0]['sections']):
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return False
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sum = 0
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for num in attrs[0]['sections']:
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sum += num
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if sum != inputs['split_input'].shape[attrs[0]['axis']]:
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return False
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# The size of num should be equal to the input dimension.
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if attrs[0]['num'] != 0:
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if len(outputs) != attrs[0]['num']:
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return False
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# Test AxisTensor and SectionsTensorList
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if self.num_input == 0:
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if (
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self.dims == 2
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and attrs[0]['sections'] == [10, 14]
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and len(outputs) == 2
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):
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return True
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else:
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return False
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if self.dims == 2:
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if self.batch != 3:
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return False
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if len(attrs[0]['sections']) != 0 and attrs[0]['axis'] == 0:
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if self.dims != 2 or self.batch != 3:
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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]], batch):
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if self.dims == 4:
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return np.random.random([batch, 3, 3, 24]).astype(np.float32)
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elif self.dims == 3:
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return np.random.random([batch, 3, 24]).astype(np.float32)
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elif self.dims == 2:
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return np.random.random([batch, 24]).astype(np.float32)
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elif self.dims == 1:
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return np.random.random([24]).astype(np.int32)
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def generate_AxisTensor(attrs: list[dict[str, Any]]):
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return np.ones([1]).astype(np.int32)
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def generate_SectionsTensorList1(attrs: list[dict[str, Any]]):
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return np.array([10]).astype(np.int32)
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def generate_SectionsTensorList2(attrs: list[dict[str, Any]]):
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return np.array([14]).astype(np.int32)
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for (
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num_input,
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dims,
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batch,
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out,
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sections,
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num,
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axis,
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) in itertools.product(
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[0, 1],
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[1, 2, 3, 4],
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[3, 6, 9],
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[
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["output_var0", "output_var1"],
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["output_var0", "output_var1", "output_var2"],
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],
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[
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[],
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[1, 2],
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[2, 1],
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[10, 14],
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[1, 1, 1],
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[2, 2, 2],
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[3, 3, 3],
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[3, 7, 14],
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],
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[0, 3],
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[0, 1, 2, 3],
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):
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self.batch = batch
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self.num_input = num_input
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self.dims = dims
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dics = [
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{
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"sections": sections,
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"num": num,
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"axis": axis,
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},
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{},
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]
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dics_input = [
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{
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"X": ["split_input"],
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"AxisTensor": ["AxisTensor"],
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"SectionsTensorList": [
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"SectionsTensorList1",
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"SectionsTensorList2",
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],
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},
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{"X": ["split_input"]},
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]
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dics_inputs = [
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{
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"AxisTensor": TensorConfig(
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data_gen=partial(generate_AxisTensor, dics)
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),
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"SectionsTensorList1": TensorConfig(
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data_gen=partial(
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generate_SectionsTensorList1,
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dics,
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)
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),
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"SectionsTensorList2": TensorConfig(
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data_gen=partial(
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generate_SectionsTensorList2,
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dics,
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)
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),
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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": "split",
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"op_inputs": dics_input[num_input],
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"op_outputs": {"Out": 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=dics_inputs[num_input],
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inputs={
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"split_input": TensorConfig(
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data_gen=partial(generate_input1, dics, batch)
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)
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},
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outputs=out,
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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 == 4:
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self.dynamic_shape.min_input_shape = {
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"split_input": [1, 3 - 1, 3 - 1, 24 - 1]
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}
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self.dynamic_shape.max_input_shape = {
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"split_input": [9, 3 + 1, 3 + 1, 24 + 1]
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}
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self.dynamic_shape.opt_input_shape = {
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"split_input": [1, 3, 3, 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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"split_input": [1, 3 - 1, 24 - 1]
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}
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self.dynamic_shape.max_input_shape = {
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"split_input": [9, 3 + 1, 24 + 1]
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}
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self.dynamic_shape.opt_input_shape = {"split_input": [1, 3, 24]}
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elif self.dims == 2:
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self.dynamic_shape.min_input_shape = {"split_input": [3, 24]}
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self.dynamic_shape.max_input_shape = {"split_input": [3, 24]}
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self.dynamic_shape.opt_input_shape = {"split_input": [3, 24]}
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elif self.dims == 1:
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self.dynamic_shape.min_input_shape = {"split_input": [24 - 1]}
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self.dynamic_shape.max_input_shape = {"split_input": [24 + 1]}
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self.dynamic_shape.opt_input_shape = {"split_input": [24]}
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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 len(program_config.outputs) == 2:
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if dynamic_shape:
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return 1, 3
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else:
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if attrs[0]['axis'] != 0:
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return 1, 3
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else:
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return 0, 4
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else:
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if dynamic_shape:
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return 1, 4
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else:
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if attrs[0]['axis'] != 0:
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return 1, 4
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else:
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return 0, 5
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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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self.trt_param.max_batch_size = 9
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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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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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def teller1(program_config, predictor_config):
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if len(program_config.weights) == 3:
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return True
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return False
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self.add_skip_case(
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teller1,
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SkipReasons.TRT_NOT_SUPPORT,
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"INPUT AxisTensor AND SectionsTensorList NOT SUPPORT.",
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)
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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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class TrtConvertSplitTest2(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.random.random([3, 3, 3, 24]).astype(np.float32)
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for sections in [
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[-1, -1, -1],
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[1, 1, 1],
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]:
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for num in [0]:
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for axis in [0, 1]:
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dics = [
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{
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"sections": sections,
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"num": num,
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"axis": axis,
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}
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]
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dics_input = [
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{
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"X": ["split_input"],
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"SectionsTensorList": [
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"shapeT1_data",
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"shapeT2_data",
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"shapeT3_data",
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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": "fill_constant",
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"op_inputs": {},
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"op_outputs": {"Out": ["shapeT1_data"]},
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"op_attrs": {
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"dtype": 2,
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"str_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": "fill_constant",
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"op_inputs": {},
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"op_outputs": {"Out": ["shapeT2_data"]},
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"op_attrs": {
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"dtype": 2,
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"str_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": "fill_constant",
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"op_inputs": {},
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"op_outputs": {"Out": ["shapeT3_data"]},
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"op_attrs": {
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"dtype": 2,
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"str_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": "split",
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"op_inputs": dics_input[0],
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"op_outputs": {
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"Out": [
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"output_var0",
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"output_var1",
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"output_var2",
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]
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},
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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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"split_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=["output_var0", "output_var1", "output_var2"],
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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 = {"split_input": [1, 3, 3, 24]}
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self.dynamic_shape.max_input_shape = {"split_input": [9, 3, 3, 24]}
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self.dynamic_shape.opt_input_shape = {"split_input": [3, 3, 3, 24]}
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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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return 1, 4
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return 0, 5
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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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self.trt_param.max_batch_size = 9
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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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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()
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if __name__ == "__main__":
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unittest.main()
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