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torch.empty
can create issues; use torch.zeros
For MPS, using a tensor created with `torch.empty()` can cause `torch.baddbmm()` to include NaNs in the tensor it returns, even though `beta=0`. However, with a tensor of shape [1,1,1], there should be a negligible performance difference between `torch.empty()` and `torch.zeros()` anyway, so it's better to just use `torch.zeros()` for this and avoid unnecessarily creating issues.
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@ -58,7 +58,7 @@ def _summarize_chunk(
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scale: float,
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) -> AttnChunk:
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attn_weights = torch.baddbmm(
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torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
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torch.zeros(1, 1, 1, device=query.device, dtype=query.dtype),
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query,
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key.transpose(1,2),
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alpha=scale,
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@ -121,7 +121,7 @@ def _get_attention_scores_no_kv_chunking(
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scale: float,
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) -> Tensor:
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attn_scores = torch.baddbmm(
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torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
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torch.zeros(1, 1, 1, device=query.device, dtype=query.dtype),
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query,
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key.transpose(1,2),
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alpha=scale,
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