[Hardware][intel GPU] bump up ipex version to 2.3 #8365
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@WoosukKwon @youkaichao please take a review, thanks! |
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@jikunshang Thanks for the update! LGTM. Left a few minor comments.
| def gelu_tanh_and_mul(out: torch.Tensor, x: torch.Tensor) -> None: | ||
| x1, x2 = ipex_ops._reshape_activation_tensor(x) | ||
| ipex.llm.functional.gelu_mul(x1, x2, out, "tanh") | ||
| ipex.llm.functional.gelu_and_mul(x, out) |
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It seems you use the same function for gelu and gelu tanh. Is this intended? This might cause subtle accuracy drop for some models.
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yes, they are same kernel in ipex implementation. cc @ganyi1996ppo
| def gelu_fast(x: torch.Tensor) -> torch.Tensor: | ||
| return torch.nn.functional.gelu(x) | ||
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| @staticmethod | ||
| def gelu_new(out: torch.Tensor, x: torch.Tensor) -> None: | ||
| out.copy_(torch.nn.functional.gelu(x)) | ||
| def gelu_new(x: torch.Tensor) -> torch.Tensor: | ||
| return torch.nn.functional.gelu(x) |
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Same for gelu fast and gelu new.
| ipex.llm.functional.varlen_attention(query.contiguous(), | ||
| key.contiguous(), | ||
| value.contiguous(), out, |
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contiguous will cause extra memory copy overhead. Is this intended?
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yes. Ipex 2.3 require qkv here to be contiguous for this API.
Co-authored-by: Yan Ma <[email protected]> Signed-off-by: Alvant <[email protected]>
Co-authored-by: Yan Ma <[email protected]> Signed-off-by: Amit Garg <[email protected]>
Co-authored-by: Yan Ma <[email protected]> Signed-off-by: LeiWang1999 <[email protected]>
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ipex-xpu release 2.3 version recently, this pr update ipex-xpu dependency to latest. Also fix below issues:
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