wehub-resource-sync
89 lines
2.8 KiB
Python
89 lines
2.8 KiB
Python
# Copyright (c) 2025 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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import pickle
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import unittest
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import numpy as np
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import paddle
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class Test__Reduce_EX__BASE(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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self.dtypes = [
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'bool',
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'float16',
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'bfloat16',
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'uint16',
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'float32',
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'float64',
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'int4',
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'int8',
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'int16',
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'int32',
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'int64',
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'uint8',
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]
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self.places = [paddle.CPUPlace()]
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if paddle.device.is_compiled_with_cuda():
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self.places.append(paddle.CUDAPlace(0))
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self.shape = [3, 4, 5, 6]
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def _prepare_data(self, dtype, place):
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if dtype.startswith("int") or dtype.startswith("uint"):
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tensor = paddle.randint(low=0, high=10, shape=self.shape)
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elif (
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dtype.startswith("float")
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or dtype.startswith("bfloat")
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or dtype.startswith("complex")
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):
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tensor = paddle.rand(shape=self.shape).astype(dtype)
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elif dtype.startswith("bool"):
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tensor = paddle.rand(self.shape) > 0.5
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return paddle.tensor(tensor, device=place)
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def _perform_compare(self, actual, expected):
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assert actual.shape == expected.shape
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assert actual.dtype == expected.dtype
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assert actual.place == expected.place
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assert actual.stop_gradient == expected.stop_gradient
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np.testing.assert_array_equal(actual.numpy(), expected.numpy())
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def _perform_test(self, place, dtype, pin_mem, requires_grad):
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x = paddle.tensor(self._prepare_data(dtype, place))
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x.requires_grad = requires_grad
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if pin_mem:
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x = x.pin_memory()
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data = pickle.dumps(x)
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y = pickle.loads(data)
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self._perform_compare(x, y)
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def test___reduce_ex__(self):
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for place in self.places:
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for dtype in self.dtypes:
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for pin_mem in (
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[True, False]
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if paddle.device.is_compiled_with_cuda()
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else [False]
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):
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for requires_grad in [True, False]:
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self._perform_test(place, dtype, pin_mem, requires_grad)
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if __name__ == '__main__':
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unittest.main()
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