wehub-resource-sync
84 lines
2.5 KiB
Python
84 lines
2.5 KiB
Python
# Copyright (c) 2022 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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import paddle.nn.functional as F
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class BF16EmbeddingTest(unittest.TestCase):
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def setUp(self):
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self.batch_size = 30
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self.vocab_size = 1024
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self.hidden_size = 512
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self.seed = 10
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def run_main(self, dtype):
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ids, weight, dout = self.gen_random()
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origin_dtype = weight.dtype
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weight_cast = weight.astype(dtype)
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out = F.embedding(ids, weight_cast)
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dout = dout.astype(out.dtype)
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dweight = paddle.autograd.grad(out, weight, dout)
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return (
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out.astype(origin_dtype).numpy(),
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dweight[0].astype(origin_dtype).numpy(),
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)
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def gen_random(self):
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np.random.seed(self.seed)
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weight = np.random.random([self.vocab_size, self.hidden_size]).astype(
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'float32'
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)
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ids = np.random.randint(
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low=0, high=self.vocab_size, size=[self.batch_size]
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)
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dout = np.random.random([self.batch_size, self.hidden_size]).astype(
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'float32'
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)
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weight = paddle.to_tensor(weight)
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weight.stop_gradient = False
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ids = paddle.to_tensor(ids)
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dout = paddle.to_tensor(dout)
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return ids, weight, dout
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def test_main(self):
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if (
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not (paddle.is_compiled_with_cuda() or is_custom_device())
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or paddle.is_compiled_with_rocm()
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):
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return
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ret1 = self.run_main('float32')
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ret2 = self.run_main('bfloat16')
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self.assertEqual(len(ret1), len(ret2))
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for i, (r1, r2) in enumerate(zip(ret1, ret2)):
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np.testing.assert_allclose(r1, r2, atol=1e-3, rtol=1e-2)
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class BF16EmbeddingTestOddHiddenSize(BF16EmbeddingTest):
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def setUp(self):
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self.batch_size = 30
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self.vocab_size = 511
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self.hidden_size = 512
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self.seed = 20
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if __name__ == "__main__":
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unittest.main()
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