wehub-resource-sync
268 lines
9.5 KiB
Python
268 lines
9.5 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
|
|
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from __future__ import annotations
|
|
|
|
import unittest
|
|
from functools import partial
|
|
from typing import Any
|
|
|
|
import numpy as np
|
|
from program_config import ProgramConfig, TensorConfig
|
|
from trt_layer_auto_scan_test import TrtLayerAutoScanTest
|
|
|
|
import paddle.inference as paddle_infer
|
|
|
|
|
|
class TrtConvertLayerNormTest(TrtLayerAutoScanTest):
|
|
def is_program_valid(self, program_config: ProgramConfig) -> bool:
|
|
inputs = program_config.inputs
|
|
weights = program_config.weights
|
|
attrs = [
|
|
program_config.ops[i].attrs for i in range(len(program_config.ops))
|
|
]
|
|
|
|
if attrs[0]['epsilon'] < 0 or attrs[0]['epsilon'] > 0.001:
|
|
return False
|
|
if attrs[0]['begin_norm_axis'] <= 0 or attrs[0]['begin_norm_axis'] >= (
|
|
len(inputs['input_data'].shape) - 1
|
|
):
|
|
return False
|
|
|
|
return True
|
|
|
|
def sample_program_configs(self):
|
|
def generate_input1(attrs: list[dict[str, Any]], shape_input):
|
|
return np.random.random(shape_input).astype(np.float32)
|
|
|
|
def generate_input2(attrs: list[dict[str, Any]], shape_input):
|
|
begin = attrs[0]["begin_norm_axis"]
|
|
sum = 1
|
|
for x in range(begin, len(shape_input)):
|
|
sum *= shape_input[x]
|
|
return np.ones([sum]).astype(np.float32)
|
|
|
|
for epsilon in [0.0005, -1, 1]:
|
|
for begin_norm_axis in [1, 0, -1, 2, 3]:
|
|
dics = [
|
|
{"epsilon": epsilon, "begin_norm_axis": begin_norm_axis},
|
|
{},
|
|
]
|
|
|
|
ops_config = [
|
|
{
|
|
"op_type": "layer_norm",
|
|
"op_inputs": {
|
|
"X": ["input_data"],
|
|
"Scale": ["scale_data"],
|
|
"Bias": ["bias_data"],
|
|
},
|
|
"op_outputs": {
|
|
"Y": ["y_data"],
|
|
"Mean": ["saved_mean_data"],
|
|
"Variance": ["saved_variance_data"],
|
|
},
|
|
"op_attrs": dics[0],
|
|
}
|
|
]
|
|
ops = self.generate_op_config(ops_config)
|
|
shape_input = [1, 3, 64, 64]
|
|
program_config = ProgramConfig(
|
|
ops=ops,
|
|
weights={
|
|
"bias_data": TensorConfig(
|
|
data_gen=partial(generate_input2, dics, shape_input)
|
|
),
|
|
"scale_data": TensorConfig(
|
|
data_gen=partial(generate_input2, dics, shape_input)
|
|
),
|
|
},
|
|
inputs={
|
|
"input_data": TensorConfig(
|
|
data_gen=partial(generate_input1, dics, shape_input)
|
|
)
|
|
},
|
|
outputs=["y_data"],
|
|
)
|
|
|
|
yield program_config
|
|
|
|
def sample_predictor_configs(
|
|
self, program_config
|
|
) -> tuple[paddle_infer.Config, list[int], float]:
|
|
def generate_dynamic_shape(attrs):
|
|
self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 32, 32]}
|
|
self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]}
|
|
self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]}
|
|
|
|
def clear_dynamic_shape():
|
|
self.dynamic_shape.min_input_shape = {}
|
|
self.dynamic_shape.max_input_shape = {}
|
|
self.dynamic_shape.opt_input_shape = {}
|
|
|
|
def generate_trt_nodes_num(attrs, dynamic_shape):
|
|
inputs = program_config.inputs
|
|
# if not dynamic_shape:
|
|
# if attrs[0]["begin_norm_axis"] >= len(inputs["input_data"].shape) - 1:
|
|
# print ("iiiiiii")
|
|
# return 0, 3
|
|
return 1, 2
|
|
|
|
attrs = [
|
|
program_config.ops[i].attrs for i in range(len(program_config.ops))
|
|
]
|
|
|
|
# for dynamic_shape
|
|
generate_dynamic_shape(attrs)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Float32
|
|
program_config.set_input_type(np.float32)
|
|
yield (
|
|
self.create_inference_config(),
|
|
generate_trt_nodes_num(attrs, True),
|
|
1e-5,
|
|
)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Half
|
|
program_config.set_input_type(np.float16)
|
|
yield (
|
|
self.create_inference_config(),
|
|
generate_trt_nodes_num(attrs, True),
|
|
1e-2,
|
|
)
|
|
|
|
def test(self):
|
|
self.run_test()
|
|
|
|
|
|
class TrtConvertLayerNormTest_2(TrtLayerAutoScanTest):
|
|
def is_program_valid(self, program_config: ProgramConfig) -> bool:
|
|
inputs = program_config.inputs
|
|
weights = program_config.weights
|
|
attrs = [
|
|
program_config.ops[i].attrs for i in range(len(program_config.ops))
|
|
]
|
|
|
|
if attrs[0]['epsilon'] < 0 or attrs[0]['epsilon'] > 0.001:
|
|
return False
|
|
if attrs[0]['begin_norm_axis'] <= 0 or attrs[0]['begin_norm_axis'] >= (
|
|
len(inputs['input_data'].shape) - 1
|
|
):
|
|
return False
|
|
|
|
return True
|
|
|
|
def sample_program_configs(self):
|
|
def generate_input1(attrs: list[dict[str, Any]], shape_input):
|
|
return np.ones(shape_input).astype(np.float32)
|
|
|
|
def generate_input2(attrs: list[dict[str, Any]], shape_input):
|
|
begin = attrs[0]["begin_norm_axis"]
|
|
sum = 1
|
|
for x in range(begin, len(shape_input)):
|
|
sum *= shape_input[x]
|
|
return np.ones([sum]).astype(np.float32)
|
|
|
|
for epsilon in [0.0005, -1, 1]:
|
|
for begin_norm_axis in [1, 0, -1, 2, 3]:
|
|
dics = [
|
|
{"epsilon": epsilon, "begin_norm_axis": begin_norm_axis},
|
|
{},
|
|
]
|
|
|
|
ops_config = [
|
|
{
|
|
"op_type": "layer_norm",
|
|
"op_inputs": {
|
|
"X": ["input_data"],
|
|
"Scale": ["scale_data"],
|
|
"Bias": ["bias_data"],
|
|
},
|
|
"op_outputs": {
|
|
"Y": ["y_data"],
|
|
"Mean": ["saved_mean_data"],
|
|
"Variance": ["saved_variance_data"],
|
|
},
|
|
"op_attrs": dics[0],
|
|
}
|
|
]
|
|
ops = self.generate_op_config(ops_config)
|
|
shape_input = [2, 64, 3, 3]
|
|
program_config = ProgramConfig(
|
|
ops=ops,
|
|
weights={
|
|
"bias_data": TensorConfig(
|
|
data_gen=partial(generate_input2, dics, shape_input)
|
|
),
|
|
"scale_data": TensorConfig(
|
|
data_gen=partial(generate_input2, dics, shape_input)
|
|
),
|
|
},
|
|
inputs={
|
|
"input_data": TensorConfig(
|
|
data_gen=partial(generate_input1, dics, shape_input)
|
|
)
|
|
},
|
|
outputs=["y_data"],
|
|
)
|
|
|
|
yield program_config
|
|
|
|
def sample_predictor_configs(
|
|
self, program_config
|
|
) -> (paddle_infer.Config, list[int], float):
|
|
def generate_dynamic_shape(attrs):
|
|
self.dynamic_shape.min_input_shape = {"input_data": [1, 64, 3, 3]}
|
|
self.dynamic_shape.max_input_shape = {"input_data": [4, 64, 3, 9]}
|
|
self.dynamic_shape.opt_input_shape = {"input_data": [2, 64, 3, 3]}
|
|
|
|
def clear_dynamic_shape():
|
|
self.dynamic_shape.min_input_shape = {}
|
|
self.dynamic_shape.max_input_shape = {}
|
|
self.dynamic_shape.opt_input_shape = {}
|
|
|
|
def generate_trt_nodes_num(attrs, dynamic_shape):
|
|
inputs = program_config.inputs
|
|
# if not dynamic_shape:
|
|
# if attrs[0]["begin_norm_axis"] >= len(inputs["input_data"].shape) - 1:
|
|
# print ("iiiiiii")
|
|
# return 0, 3
|
|
return 1, 2
|
|
|
|
attrs = [
|
|
program_config.ops[i].attrs for i in range(len(program_config.ops))
|
|
]
|
|
|
|
generate_dynamic_shape(attrs)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Float32
|
|
program_config.set_input_type(np.float32)
|
|
yield (
|
|
self.create_inference_config(),
|
|
generate_trt_nodes_num(attrs, True),
|
|
1e-5,
|
|
)
|
|
self.trt_param.precision = paddle_infer.PrecisionType.Half
|
|
program_config.set_input_type(np.float16)
|
|
yield (
|
|
self.create_inference_config(),
|
|
generate_trt_nodes_num(attrs, True),
|
|
1e-2,
|
|
)
|
|
|
|
def test(self):
|
|
self.run_test()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|