wehub-resource-sync
139 lines
5.0 KiB
Python
139 lines
5.0 KiB
Python
# Copyright (c) 2024 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 unittest
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import numpy as np
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from op_test import get_device, get_places
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import paddle
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from paddle import base
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class TestAsStrided(unittest.TestCase):
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def setUp(self):
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self.shape = [32, 32]
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self.typelist = ['float32', 'float64', 'int32', 'int64', 'float16']
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self.places = get_places()
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if base.core.is_compiled_with_cuda():
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self.places.append(base.CUDAPinnedPlace())
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def test_as_strided_forward(self):
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for idx, p in enumerate(self.places):
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if idx == 0:
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paddle.set_device('cpu')
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else:
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paddle.set_device(get_device())
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for dtype in self.typelist:
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x_np = np.random.random(self.shape).astype(dtype)
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x = paddle.to_tensor(x_np, place=p)
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a = paddle.as_strided(x, shape=(3, 4), stride=(32, 1))
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np.testing.assert_allclose(a.numpy(), x_np[:3, :4])
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def test_as_strided_backward(self):
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for idx, p in enumerate(self.places):
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if idx == 0:
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paddle.set_device('cpu')
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else:
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paddle.set_device(get_device())
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for dtype in self.typelist:
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x_np = np.random.random(self.shape).astype(dtype)
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x = paddle.to_tensor(x_np, place=p)
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x.stop_gradient = False
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a = paddle.as_strided(x, shape=(3,), stride=(1,))
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b = a * 2
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b.retain_grads()
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loss = b.sum()
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loss.backward()
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self.assertEqual((b.grad.numpy() == 1).all().item(), True)
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class TestAsStrided_ZeroSize(unittest.TestCase):
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def setUp(self):
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self.places = get_places()
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def test_as_strided_forward(self):
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for place in self.places:
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with base.dygraph.guard(place):
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a = paddle.to_tensor(
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np.random.random([0, 32]).astype('float32')
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)
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a.stop_gradient = False
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b = paddle.as_strided(a, shape=(0, 4), stride=(32, 1))
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np.testing.assert_equal(b.shape, [0, 4])
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b.backward(paddle.ones_like(b))
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np.testing.assert_equal(a.grad.shape, [0, 32])
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def test_as_strided_error(self):
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for place in self.places:
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with base.dygraph.guard(place):
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self.assertRaises(
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ValueError,
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paddle.as_strided,
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x=paddle.to_tensor(
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np.random.random([0, 32]).astype('float32')
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),
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shape=[3, 4],
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stride=[32, 1],
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)
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class TestAsStridedAlias(unittest.TestCase):
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def test_as_strided_alias(self):
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self.shape = [32, 32]
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self.typelist = ['float32', 'float64', 'int32', 'int64', 'float16']
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with base.dygraph.guard():
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for dtype in self.typelist:
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x_np = np.random.random(self.shape).astype(dtype)
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x = paddle.to_tensor(x_np)
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shape = (3, 4)
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stride = (32, 1)
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offset = 0
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# 1. Standard call (Benchmark)
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out_ref = paddle.as_strided(
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x, shape=shape, stride=stride, offset=offset
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)
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# 2. Test alias: input -> x
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out_input = paddle.as_strided(
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input=x, shape=shape, stride=stride, offset=offset
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)
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np.testing.assert_array_equal(
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out_ref.numpy(), out_input.numpy()
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)
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# 3. Test alias: size -> shape
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out_size = paddle.as_strided(
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x=x, size=shape, stride=stride, offset=offset
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)
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np.testing.assert_array_equal(out_ref.numpy(), out_size.numpy())
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# 4. Test alias: storage_offset -> offset
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out_offset = paddle.as_strided(
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x=x, shape=shape, stride=stride, storage_offset=offset
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)
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np.testing.assert_array_equal(
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out_ref.numpy(), out_offset.numpy()
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)
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# 5. Test both aliases: input -> x, shape -> repeat_times
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out_both = paddle.as_strided(
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input=x, size=shape, stride=stride, storage_offset=offset
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)
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np.testing.assert_array_equal(out_ref.numpy(), out_both.numpy())
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if __name__ == '__main__':
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unittest.main()
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