d779835a11
Signed-off-by: Wu, Xiaochang <xiaochang.wu@intel.com> Signed-off-by: Xiaochang Wu <xiaochang.wu@intel.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Co-authored-by: Kunshang Ji <kunshang.ji@intel.com>
83 lines
2.9 KiB
Python
83 lines
2.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
import torch
|
|
|
|
from vllm.model_executor.layers.quantization.utils.mxfp8_utils import (
|
|
xpu_mxfp8_quantize as quant_mxfp8,
|
|
)
|
|
from vllm.model_executor.utils import replace_parameter
|
|
from vllm.platforms import current_platform
|
|
|
|
from .Mxfp8LinearKernel import Mxfp8LinearKernel, Mxfp8LinearLayerConfig
|
|
|
|
|
|
class XPUMxFp8LinearKernel(Mxfp8LinearKernel):
|
|
"""MXFP8 W8A8 GEMM on XPU."""
|
|
|
|
@classmethod
|
|
def is_supported(
|
|
cls, compute_capability: int | None = None
|
|
) -> tuple[bool, str | None]:
|
|
if not current_platform.is_xpu():
|
|
return False, "XPUMxFp8 only support on XPU"
|
|
return True, None
|
|
|
|
@classmethod
|
|
def can_implement(cls, c: Mxfp8LinearLayerConfig) -> tuple[bool, str | None]:
|
|
return True, None
|
|
|
|
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
|
|
# Checkpoint scale is [N, K//32] (one E8M0 scale per 32 elements).
|
|
# oneDNN fp8_gemm requires contiguous [K//32, N] layout. Store the
|
|
# transposed contiguous buffer as a .t() view so that:
|
|
# - dequantization consumers still see the checkpoint shape
|
|
# - apply_weights recovers the oneDNN layout via .t() at zero cost
|
|
weight_scale = layer.weight_scale.view(torch.float8_e8m0fnu)
|
|
scale_kn = weight_scale.data.t().contiguous()
|
|
replace_parameter(layer, "weight_scale", scale_kn.t())
|
|
|
|
if getattr(layer, "is_bmm", False):
|
|
self._prepare_bmm_params(layer, scale_kn)
|
|
|
|
def _prepare_bmm_params(
|
|
self, layer: torch.nn.Module, scale_kn: torch.Tensor
|
|
) -> None:
|
|
"""Precompute batched weight and scale for grouped fp8_bmm (e.g. wo_a).
|
|
|
|
Splits scale [K//32, N_total] into [G, K//32, N_per_group] and weight
|
|
[N_total, K] into contiguous [G, K, N_per_group] for batch GEMM.
|
|
"""
|
|
batch = layer.bmm_batch_size
|
|
k_blocks, n_blocks = scale_kn.shape
|
|
layer.bmm_scale = (
|
|
scale_kn.reshape(k_blocks, batch, n_blocks // batch)
|
|
.permute(1, 0, 2)
|
|
.contiguous()
|
|
)
|
|
w = layer.weight
|
|
n_total, k = w.shape
|
|
layer.bmm_weight = (
|
|
w.reshape(batch, n_total // batch, k).permute(0, 2, 1).contiguous()
|
|
)
|
|
|
|
def apply_weights(
|
|
self,
|
|
layer: torch.nn.Module,
|
|
x: torch.Tensor,
|
|
bias: torch.Tensor | None = None,
|
|
) -> torch.Tensor:
|
|
out_dtype = x.dtype
|
|
x_fp8, x_scale = quant_mxfp8(x)
|
|
# Weight is [N, K]; .t() gives a [K, N] view without copying.
|
|
# Scale is stored as [N, K//32]; .t() recovers the contiguous
|
|
# [K//32, N] buffer that oneDNN expects.
|
|
return torch.ops._xpu_C.fp8_gemm(
|
|
x_fp8,
|
|
layer.weight.t(),
|
|
out_dtype,
|
|
x_scale,
|
|
layer.weight_scale.t(),
|
|
bias,
|
|
)
|