wehub-resource-sync
156 lines
5.0 KiB
Python
156 lines
5.0 KiB
Python
# Copyright (c) 2023 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 TrtConvertQuantizeDequantizeTest(TrtLayerAutoScanTest):
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def is_program_valid(self, program_config: ProgramConfig) -> bool:
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ver = paddle_infer.get_trt_compile_version()
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# only TRT > 8.0 has quantize / dequantize layers
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if ver[0] * 1000 + ver[1] * 100 + ver[0] * 10 < 8517:
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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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self.trt_param.workspace_size = 1073741824
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def generate_input1(shape):
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return np.random.random(shape).astype(np.float32)
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def generate_add(shape):
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return np.ones(shape).astype(np.float32)
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def generate_scale():
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return np.ones([1]).astype(np.float32) + 2.521234002
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def generate_zeropoint():
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return np.zeros([1]).astype(np.float32)
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desc = [{"quant_axis": -1}]
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ops_config = [
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{
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"op_type": "quantize_linear",
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"op_inputs": {
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"X": ["input_data_1"],
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"Scale": ["scale_data_1"],
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"ZeroPoint": ["zeropoint_data_1"],
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},
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"op_outputs": {
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"Y": ["y_data_1"],
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},
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"op_attrs": desc[0],
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},
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{
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"op_type": "dequantize_linear",
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"op_inputs": {
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"X": ["y_data_1"],
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"Scale": ["scale_data_2"],
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"ZeroPoint": ["zeropoint_data_2"],
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},
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"op_outputs": {
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"Y": ["y_data_2"],
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},
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"op_attrs": desc[0],
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},
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{
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"op_type": "elementwise_add",
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"op_inputs": {
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"X": ["y_data_2"],
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"Y": ["add"],
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},
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"op_outputs": {
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"Out": ["y_data_3"],
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},
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"op_attrs": {"axis": -1},
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"outputs_dtype": {"output_data": np.float32},
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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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"scale_data_1": TensorConfig(data_gen=partial(generate_scale)),
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"zeropoint_data_1": TensorConfig(
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data_gen=partial(generate_zeropoint)
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),
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"scale_data_2": TensorConfig(data_gen=partial(generate_scale)),
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"zeropoint_data_2": TensorConfig(
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data_gen=partial(generate_zeropoint)
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),
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"add": TensorConfig(
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data_gen=partial(generate_add, [1, 8, 32, 32])
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),
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},
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inputs={
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"input_data_1": TensorConfig(
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data_gen=partial(generate_input1, [1, 8, 32, 32])
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)
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},
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outputs=["y_data_3"],
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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 = {
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"input_data_1": [1, 8, 32, 32],
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"add": [1, 8, 32, 32],
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}
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self.dynamic_shape.max_input_shape = {
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"input_data_1": [16, 8, 32, 32],
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"add": [16, 8, 32, 32],
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}
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self.dynamic_shape.opt_input_shape = {
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"input_data_1": [16, 8, 32, 32],
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"add": [16, 8, 32, 32],
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}
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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.Int8
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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-2, 1e-2),
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)
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def test(self):
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self.run_test(quant=False, explicit=True)
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if __name__ == "__main__":
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unittest.main()
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