wehub-resource-sync
136 lines
4.7 KiB
Python
136 lines
4.7 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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import unittest
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import numpy as np
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from op_test import is_custom_device
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import paddle
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class TestAddnOp(unittest.TestCase):
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def setUp(self):
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np.random.seed(20)
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self.l = 32
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self.x_np = np.random.random([self.l, 16, 256])
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def check_main(self, x_np, dtype, axis=None, mixed_dtype=False):
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paddle.disable_static()
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x = []
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for i in range(x_np.shape[0]):
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if mixed_dtype and i == 0:
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val = paddle.to_tensor(x_np[i].astype('float32'))
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else:
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val = paddle.to_tensor(x_np[i].astype(dtype))
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val.stop_gradient = False
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x.append(val)
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y = paddle.add_n(x)
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x_g = paddle.grad(y, x)
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y_np = y.numpy().astype(dtype)
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x_g_np = []
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for val in x_g:
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x_g_np.append(val.numpy().astype(dtype))
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paddle.enable_static()
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return y_np, x_g_np
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def test_add_n_fp16(self):
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if not (paddle.is_compiled_with_cuda() or is_custom_device()):
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return
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y_np_16, x_g_np_16 = self.check_main(self.x_np, 'float16')
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y_np_32, x_g_np_32 = self.check_main(self.x_np, 'float32')
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np.testing.assert_allclose(y_np_16, y_np_32, rtol=1e-03)
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for i in range(len(x_g_np_32)):
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np.testing.assert_allclose(x_g_np_16[i], x_g_np_32[i], rtol=1e-03)
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def test_add_n_fp16_mixed_dtype(self):
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if not (paddle.is_compiled_with_cuda() or is_custom_device()):
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return
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y_np_16, x_g_np_16 = self.check_main(
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self.x_np, 'float16', mixed_dtype=True
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)
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y_np_32, x_g_np_32 = self.check_main(self.x_np, 'float32')
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np.testing.assert_allclose(y_np_16, y_np_32, rtol=1e-03)
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for i in range(len(x_g_np_32)):
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np.testing.assert_allclose(x_g_np_16[i], x_g_np_32[i], rtol=1e-03)
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def test_add_n_api(self):
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if not (paddle.is_compiled_with_cuda() or is_custom_device()):
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return
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dtypes = ['float32', 'complex64', 'complex128']
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for dtype in dtypes:
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if dtype == 'complex64' or dtype == 'complex128':
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self.x_np = (
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np.random.random([self.l, 16, 256])
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+ 1j * np.random.random([self.l, 16, 256])
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).astype(dtype)
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y_np_32, x_g_np_32 = self.check_main(self.x_np, dtype)
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y_np_gt = np.sum(self.x_np, axis=0).astype(dtype)
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np.testing.assert_allclose(y_np_32, y_np_gt, rtol=1e-06)
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class TestAddnOp_ZeroSize(unittest.TestCase):
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def setUp(self):
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np.random.seed(20)
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self.l = 2
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self.x_np = np.random.random([self.l, 0, 256])
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def check_main(self, x_np, dtype, axis=None, mixed_dtype=False):
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paddle.disable_static()
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x = []
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for i in range(x_np.shape[0]):
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if mixed_dtype and i == 0:
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val = paddle.to_tensor(x_np[i].astype('float32'))
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else:
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val = paddle.to_tensor(x_np[i].astype(dtype))
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val.stop_gradient = False
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x.append(val)
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y = paddle.add_n(x)
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x_g = paddle.grad(y, x)
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y_np = y.numpy().astype(dtype)
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x_g_np = []
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for val in x_g:
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x_g_np.append(val.numpy().astype(dtype))
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paddle.enable_static()
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return y_np, x_g_np
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def test_add_n_zerosize(self):
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if not (paddle.is_compiled_with_cuda() or is_custom_device()):
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return
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y_np_32, x_g_np_32 = self.check_main(self.x_np, 'float32')
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np.testing.assert_allclose(y_np_32.shape, [0, 256])
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for i in range(len(x_g_np_32)):
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np.testing.assert_allclose(x_g_np_32[i].shape, [0, 256])
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class TestAddnOpZeroSizeAndNonZeroSize(unittest.TestCase):
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def test_add_n_zero_size_and_non_zero_size(self):
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paddle.disable_static()
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try:
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with self.assertRaises(ValueError):
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x0 = paddle.to_tensor([], dtype='float32')
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x1 = paddle.to_tensor([1], dtype='float32')
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out = paddle.add_n([x0, x1])
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finally:
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paddle.enable_static()
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if __name__ == "__main__":
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unittest.main()
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