support fp8 quant vllm ir on xpu

[xinyu-intel]

Purpose

impl quant ir ops in xpu_kernels

Test Plan

Test Result

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[BadrBasowid]

@xinyu-intel thanks for your effort, but i’m having some difficulty understanding the motivation behind this PR. From what I can see, the XPU file appears to redefine the same operations that already exist under vllm_c, with only a minor addition for the dynamic_group_quant_fp8 op:

if use_ue8m0:
    x_s = x_s.to(torch.float8_e8m0fnu)

It seems that this additional logic could be added directly into the existing vllm_c implementation rather than introducing a separate definition.

Additionally, I noticed that all IR ops have been removed from XPU’s IrOpPriorityConfig. As a result, it appears that neither the vllm_c ops nor the newly added code would be invoked.

Could you please clarify the intended purpose and design rationale?

[xinyu-intel]

@xinyu-intel thanks for your effort, but i’m having some difficulty understanding the motivation behind this PR. From what I can see, the XPU file appears to redefine the same operations that already exist under vllm_c, with only a minor addition for the dynamic_group_quant_fp8 op:

if use_ue8m0:
    x_s = x_s.to(torch.float8_e8m0fnu)

It seems that this additional logic could be added directly into the existing vllm_c implementation rather than introducing a separate definition.

Additionally, I noticed that all IR ops have been removed from XPU’s IrOpPriorityConfig. As a result, it appears that neither the vllm_c ops nor the newly added code would be invoked.

Could you please clarify the intended purpose and design rationale?

Hi, thanks for the comments. By default, we will dispatch the implementation to native under compile mode and xpu_kernels under eager mode for ir ops. Regarding the vllm_c, exactly, most of our implementations are align with that of vllm_c. However, xpu is not cudealike device and if we want to reuse vllm_c, we need relax the check in vllm_c to cover both cudaalike and xpu device. I think the pros to reuse vllm_c can be reduce the redundant code but the cons will be more maintenance effort. Which one you suggest? cc @ProExpertProg @tjtanaa @jikunshang

[tjtanaa]

@ProExpertProg What's the initial design for this op where we still define rmsnorm in xpu_ops.py when torch.ops._C is used. Shouldn't it be categorized as vllm_C?

[ProExpertProg]

@BadrBasowid @tjtanaa I told XPU folks to define their own kernels. The reason is that they do use torch.ops._C but these kernels don't come from the _C.so CUDA kernel library, but instead from their own definitions OOT. They just used the _C namespace for easier integration into vLLM. Now that we have vLLM IR, that can be the extension point, and they no longer need to exactly match the _C kernel interface. That's also what we did with rms_norm and fused_add_rms_norm.

[jikunshang]

@ProExpertProg thanks for your explanation. Just want to clarify position of vllm-xpu-kernels. Start from day 1 of building vllm-xpu-kernels, our goal is providing 100% API level compatible with vllm cuda custom kernel. We also have a _C.so, it provides torch.ops._C compatible, it will be installed automatically with vllm-xpu. My feeling is it is same to _C.so on ROCm platform, the only difference is ROCm could share some of cuda kernel code, while ROCm still use their own compile toolchain.
I agree that xpu no longer need to exactly match the _C kernel interface. If we start building vllm-xpu-kernel after vllm ir happen, we may choose to not match.
While current concern from our side is xpu need to add register code for each ir op in xpu_ops.py(otherwise it will fall backe to native impl), register code may be duplicate to vllm_c.py, developer who adding new ir may forget xpu, cause some potential break or regression and we may not find it in time. that's why we contribute to this branch.

[ProExpertProg]

@jikunshang thank for the context! Just to clarify, you prefer separate xpu_ops instead of relying vllm_c, is that right?

[jikunshang]

@ProExpertProg honestly I prefer relying vllm_c for xpu, this could save some xpu effort.

[ProExpertProg]

Sure, that's fine, we can do that instead then. @BadrBasowid let's include xpu in the support check for quant

[jikunshang]

much appreciate!

[xinyu-intel]

@ProExpertProg @jikunshang thank you for the comments, I'll update the PR.

[BadrBasowid]

Thanks @ProExpertProg @jikunshang @xinyu-intel, this conversation cleared everything up. Looking forward to your updates.

[xinyu-intel]

updated, pls review again.

[BadrBasowid]

LGTM