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test_auto_scan_logical_ops.py
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executable file
·159 lines (134 loc) · 4.67 KB
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# 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 auto_scan_test import OPConvertAutoScanTest, BaseNet
import hypothesis.strategies as st
import unittest
import paddle
op_api_map = {
"greater_equal": paddle.greater_equal,
"equal": paddle.equal,
"not_equal": paddle.not_equal,
"greater_than": paddle.greater_than,
"logical_and": paddle.logical_and,
"logical_or": paddle.logical_or,
"logical_xor": paddle.logical_xor,
"less_equal": paddle.less_equal,
"less_than": paddle.less_than,
}
opset_version_map = {
"greater_equal": [12, 15],
"equal": [11, 15],
"not_equal": [11, 15],
"greater_than": [11, 15],
"logical_and": [7, 15],
"logical_or": [7, 12, 15],
"logical_xor": [7, 15],
"less_equal": [12, 15],
"less_than": [9, 15],
}
class Net(BaseNet):
def forward(self, inputs1, inputs2):
if self.config["op_names"] in ["logical_and", "logical_or", "logical_xor"]:
inputs1 = inputs1.astype("bool")
inputs2 = inputs2.astype("bool")
if (
self.config["op_names"]
in ["greater_equal", "greater_than", "less_equal", "less_than"]
and inputs1.dtype == paddle.bool
):
inputs1 = inputs1.astype("int32")
inputs2 = inputs2.astype("int32")
x = op_api_map[self.config["op_names"]](inputs1, inputs2)
return x
class TestLogicopsConvert(OPConvertAutoScanTest):
"""
api: logic ops
OPset version:
"""
def sample_convert_config(self, draw):
input1_shape = draw(
st.lists(st.integers(min_value=10, max_value=20), min_size=0, max_size=4)
)
if len(input1_shape) > 0:
if draw(st.booleans()):
# [N * N] + [N]
input2_shape = [input1_shape[-1]]
elif draw(st.booleans()):
# [N * N] + [N * N]
input2_shape = input1_shape
elif draw(st.booleans()):
# [N * N] + [1]
input2_shape = [1]
else:
# [N * N] + []
input2_shape = []
else:
if draw(st.booleans()):
# [] + []
input2_shape = input1_shape
else:
# [] + [N * N]
input2_shape = draw(
st.lists(
st.integers(min_value=10, max_value=20), min_size=1, max_size=4
)
)
dtype = draw(st.sampled_from(["float32", "int32", "int64", "bool"]))
config = {
"op_names": ["elementwise_add"],
"test_data_shapes": [input1_shape, input2_shape],
"test_data_types": [[dtype], [dtype]],
"opset_version": [7, 9, 15],
"input_spec_shape": [],
}
models = list()
op_names = list()
opset_versions = list()
for op_name, i in op_api_map.items():
config["op_names"] = op_name
models.append(Net(config))
op_names.append(op_name)
opset_versions.append(opset_version_map[op_name])
config["op_names"] = op_names
config["opset_version"] = opset_versions
return (config, models)
def test(self):
self.run_and_statis(max_examples=30, max_duration=-1)
class NetNot(BaseNet):
def forward(self, inputs):
x = paddle.logical_not(inputs)
return x.astype("float32")
class TestLogicNotConvert(OPConvertAutoScanTest):
"""
api: logical_not ops
OPset version: 7, 9, 15
"""
def sample_convert_config(self, draw):
input1_shape = draw(
st.lists(st.integers(min_value=10, max_value=20), min_size=0, max_size=4)
)
dtype = "bool"
config = {
"op_names": ["logical_not"],
"test_data_shapes": [input1_shape],
"test_data_types": [[dtype]],
"opset_version": [7, 9, 15],
"input_spec_shape": [],
}
model = NetNot(config)
return (config, model)
def test(self):
self.run_and_statis(max_examples=30, max_duration=-1)
if __name__ == "__main__":
unittest.main()