wehub-resource-sync
163 lines
5.9 KiB
Python
163 lines
5.9 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import unittest
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from functools import partial
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from typing import Any
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import numpy as np
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from program_config import ProgramConfig, TensorConfig
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from trt_layer_auto_scan_test import TrtLayerAutoScanTest
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import paddle.inference as paddle_infer
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class 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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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 len(inputs['in_data'].shape) <= max(self.axes):
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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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for dims in [4]:
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for batch in [4]:
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for axes in [[2], [2, 3], [-1]]:
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for attr_axis in [True, False]:
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self.batch = batch
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self.dims = dims
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self.axes = axes
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dics = [{"axes": []}]
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if attr_axis:
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dics[0]["axes"] = axes
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ops_config = [
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{
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"op_type": "squeeze2",
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"op_inputs": {"X": ["in_data"]},
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"op_outputs": {
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"Out": ["out_data"],
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"XShape": ["XShape_data"],
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},
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"op_attrs": dics[0],
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}
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]
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# new_axes is the update of axes
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new_axes = list(axes)
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for i in range(len(new_axes)):
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if new_axes[i] < 0:
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new_axes[i] += dims
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if max(new_axes) >= dims:
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continue
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# generate input data
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self.input_shape = [1] * dims
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for i in range(dims):
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self.input_shape[i] = np.random.randint(1, 20)
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def generate_input1(attrs: list[dict[str, Any]], batch):
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self.input_shape[0] = batch
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for i in new_axes:
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self.input_shape[i] = 1
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return np.random.random(self.input_shape).astype(
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np.float32
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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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"in_data": 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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outputs=["out_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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max_shape = list(self.input_shape)
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min_shape = list(self.input_shape)
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opt_shape = list(self.input_shape)
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self.dynamic_shape.min_input_shape = {"in_data": min_shape}
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self.dynamic_shape.max_input_shape = {"in_data": max_shape}
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self.dynamic_shape.opt_input_shape = {"in_data": opt_shape}
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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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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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self.trt_param.max_batch_size = 9
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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(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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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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