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Gy-Lu
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Feb 16, 2023
tests/test_fastnn/test_softmax.py
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| for seq_ in test_seq_: | ||
| for dtype in test_dtype: | ||
| sample_input = torch.rand(batch_, chunk_, head_, seq_, | ||
| sample_input = torch.rand(batch, batch_, chunk_, head_, seq_, |
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Oh, without the batch dimension it would work correctly as well
Shenggan
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Feb 18, 2023
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I found it would fail when the batch dimension comes to 2. |
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Update: |
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Support another batch dimension for softmax. In training or batch inference, we may add a batch dimension as the first dimension of some tensors. However, we use the third dimension(
tensor.shape[2]) as thehead_dim, which would be influenced. In this pr, I modify it totensor.shape[-3]to solve this problem. CUDA kernel is modified as well.Enable test_atten_core, this test is skipped by default and never be used.