Support varlen FlashAttention #23
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baoleai
reviewed
Sep 20, 2024
| if softmax_scale is None: | ||
| softmax_scale = q.shape[-1]**(-0.5) | ||
| assert isinstance(window_size, tuple) and len(window_size) == 2 | ||
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| softmax_lse, out, rng_state = torch_xla._XLAC._flash_attention_forward( |
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SPMDFlashAttnVarlenXla and FlashAttnVarlenQKVPackedXla also need to be updated.
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Done. But I found no UT for them.
anw90
reviewed
Oct 8, 2024
tests/ops/test_flash_attn.py
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| @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16]) | ||
| @pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"]) | ||
| @pytest.mark.parametrize("deterministic", [False, True]) | ||
| @pytest.mark.parametrize("dtype", [torch.bfloat16]) |
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Why are we removing so many test options?
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| class FlashAttention2(nn.Module): |
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What does the FlashAttention2 mean? Does FA2 stand for it, or is the 2 just a magic number?
| dk_xla, | ||
| dv_xla, | ||
| ) = torch.autograd.grad(ret_xla, (q_xla, k_xla, v_xla), g_xla) | ||
| ta.mark_step() |
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