wehub-resource-sync
375 lines
15 KiB
Python
375 lines
15 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 SkipReasons, TrtLayerAutoScanTest
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import paddle.inference as paddle_infer
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class TrtConvertConcatTest(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 input dimension should be less than or equal to the set axis.
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if len(inputs['concat_input1'].shape) <= attrs[0]['axis']:
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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.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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def generate_input2(attrs: list[dict[str, Any]], 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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def generate_input3(attrs: list[dict[str, Any]], 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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def generate_weight1(attrs: list[dict[str, Any]]):
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return np.zeros([1]).astype(np.int32)
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for dims in [2, 3, 4]:
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for num_input in [0, 1]:
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for batch in [1, 2, 4]:
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for axis in [-1, 0, 1, 2, 3]:
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self.num_input = num_input
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self.dims = dims
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dics = [{"axis": axis}, {}]
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dics_input = [
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{
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"X": [
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"concat_input1",
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"concat_input2",
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"concat_input3",
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],
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"AxisTensor": ["AxisTensor"],
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},
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{
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"X": [
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"concat_input1",
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"concat_input2",
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"concat_input3",
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]
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},
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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_weight1, dics)
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),
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"concat_input3": TensorConfig(
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data_gen=partial(
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generate_input3, dics, batch
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)
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),
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"concat_input2": TensorConfig(
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data_gen=partial(
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generate_input2, dics, batch
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)
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),
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"concat_input1": TensorConfig(
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data_gen=partial(
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generate_input1, dics, batch
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)
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),
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},
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{
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"concat_input3": TensorConfig(
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data_gen=partial(
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generate_input3, dics, batch
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)
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),
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"concat_input2": TensorConfig(
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data_gen=partial(
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generate_input2, dics, batch
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)
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),
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"concat_input1": TensorConfig(
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data_gen=partial(
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generate_input1, dics, batch
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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": "concat",
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"op_inputs": dics_input[num_input],
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"op_outputs": {"Out": ["concat_output"]},
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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=dics_inputs[num_input],
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outputs=["concat_output"],
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)
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yield program_config
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def generate_dynamic_shape(self, attrs):
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if self.num_input == 0:
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if self.dims == 4:
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self.dynamic_shape.min_input_shape = {
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"concat_input1": [1, 3, 24, 24],
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"concat_input2": [1, 3, 24, 24],
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"concat_input3": [1, 3, 24, 24],
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"AxisTensor": [1],
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}
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self.dynamic_shape.max_input_shape = {
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"concat_input1": [4, 3, 48, 48],
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"concat_input2": [4, 3, 48, 48],
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"concat_input3": [4, 3, 48, 48],
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"AxisTensor": [1],
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}
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self.dynamic_shape.opt_input_shape = {
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"concat_input1": [1, 3, 24, 24],
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"concat_input2": [1, 3, 24, 24],
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"concat_input3": [1, 3, 24, 24],
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"AxisTensor": [1],
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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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"concat_input1": [1, 3, 24],
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"concat_input2": [1, 3, 24],
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"concat_input3": [1, 3, 24],
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"AxisTensor": [1],
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}
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self.dynamic_shape.max_input_shape = {
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"concat_input1": [4, 12, 48],
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"concat_input2": [4, 12, 48],
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"concat_input3": [4, 12, 48],
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"AxisTensor": [1],
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}
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self.dynamic_shape.opt_input_shape = {
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"concat_input1": [1, 3, 24],
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"concat_input2": [1, 3, 24],
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"concat_input3": [1, 3, 24],
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"AxisTensor": [1],
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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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"concat_input1": [1, 24],
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"concat_input2": [1, 24],
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"concat_input3": [1, 24],
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"AxisTensor": [1],
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}
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self.dynamic_shape.max_input_shape = {
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"concat_input1": [4, 48],
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"concat_input2": [4, 48],
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"concat_input3": [4, 48],
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"AxisTensor": [1],
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}
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self.dynamic_shape.opt_input_shape = {
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"concat_input1": [1, 24],
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"concat_input2": [1, 24],
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"concat_input3": [1, 24],
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"AxisTensor": [1],
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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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"concat_input1": [24],
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"concat_input2": [24],
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"concat_input3": [24],
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"AxisTensor": [0],
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}
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self.dynamic_shape.max_input_shape = {
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"concat_input1": [48],
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"concat_input2": [48],
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"concat_input3": [48],
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"AxisTensor": [0],
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}
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self.dynamic_shape.opt_input_shape = {
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"concat_input1": [24],
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"concat_input2": [24],
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"concat_input3": [24],
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"AxisTensor": [0],
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}
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elif self.num_input == 1:
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if self.dims == 4:
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self.dynamic_shape.min_input_shape = {
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"concat_input1": [1, 3, 24, 24],
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"concat_input2": [1, 3, 24, 24],
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"concat_input3": [1, 3, 24, 24],
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}
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self.dynamic_shape.max_input_shape = {
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"concat_input1": [4, 3, 48, 48],
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"concat_input2": [4, 3, 48, 48],
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"concat_input3": [4, 3, 48, 48],
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}
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self.dynamic_shape.opt_input_shape = {
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"concat_input1": [1, 3, 24, 24],
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"concat_input2": [1, 3, 24, 24],
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"concat_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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"concat_input1": [1, 3, 24],
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"concat_input2": [1, 3, 24],
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"concat_input3": [1, 3, 24],
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}
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self.dynamic_shape.max_input_shape = {
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"concat_input1": [4, 12, 48],
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"concat_input2": [4, 12, 48],
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"concat_input3": [4, 12, 48],
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}
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self.dynamic_shape.opt_input_shape = {
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"concat_input1": [1, 3, 24],
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"concat_input2": [1, 3, 24],
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"concat_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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"concat_input1": [1, 24],
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"concat_input2": [1, 24],
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"concat_input3": [1, 24],
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}
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self.dynamic_shape.max_input_shape = {
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"concat_input1": [4, 48],
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"concat_input2": [4, 48],
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"concat_input3": [4, 48],
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}
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self.dynamic_shape.opt_input_shape = {
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"concat_input1": [1, 24],
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"concat_input2": [1, 24],
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"concat_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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"concat_input1": [24],
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"concat_input2": [24],
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"concat_input3": [24],
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}
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self.dynamic_shape.max_input_shape = {
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"concat_input1": [48],
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"concat_input2": [48],
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"concat_input3": [48],
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}
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self.dynamic_shape.opt_input_shape = {
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"concat_input1": [24],
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"concat_input2": [24],
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"concat_input3": [24],
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}
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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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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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if not run_pir:
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# for static_shape
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clear_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, 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(attrs, False),
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1e-3,
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)
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# for dynamic_shape
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self.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.inputs) == 4:
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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, SkipReasons.TRT_NOT_SUPPORT, "INPUT AxisTensor 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(run_pir=True)
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if __name__ == "__main__":
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unittest.main()
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