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[XPU]fix Kimi-VL-A3B-thinking on xpu (#29309)

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Signed-off-by: Yan Ma <yan.ma@intel.com>
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Yan Ma 2025-11-25 05:02:21 +08:00 committed by GitHub
parent 4d6afcaddc
commit 3cfa63ad99
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1 changed files with 14 additions and 6 deletions
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20
vllm/model_executor/models/moonvit.py
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@ -56,10 +56,13 @@ from transformers.utils import is_flash_attn_2_available
from vllm.model_executor.layers.conv import Conv2dLayer from vllm.model_executor.layers.conv import Conv2dLayer
from vllm.model_executor.layers.linear import ReplicatedLinear from vllm.model_executor.layers.linear import ReplicatedLinear
from vllm.model_executor.models.utils import maybe_prefix from vllm.model_executor.models.utils import maybe_prefix
from vllm.platforms import current_platform
from vllm.transformers_utils.configs.moonvit import MoonViTConfig from vllm.transformers_utils.configs.moonvit import MoonViTConfig
if is_flash_attn_2_available(): if is_flash_attn_2_available():
from flash_attn import flash_attn_varlen_func from flash_attn import flash_attn_varlen_func
elif current_platform.is_xpu():
from vllm.attention.utils.fa_utils import flash_attn_varlen_func
else: else:
flash_attn_varlen_func = None flash_attn_varlen_func = None
@ -106,10 +109,10 @@ def multihead_attention(
q, q,
k, k,
v, v,
q_cu_seqlens, cu_seqlens_q=q_cu_seqlens,
k_cu_seqlens, cu_seqlens_k=k_cu_seqlens,
max_seqlen_q, max_seqlen_q=max_seqlen_q,
max_seqlen_k, max_seqlen_k=max_seqlen_k,
causal=False, causal=False,
) )
attn_out = attn_out.flatten(start_dim=-2) attn_out = attn_out.flatten(start_dim=-2)
@ -291,7 +294,12 @@ class Rope2DPosEmb(nn.Module):
""" """
def __init__( def __init__(
self, dim: int, max_height: int, max_width: int, theta_base=10000, device="cuda" self,
dim: int,
max_height: int,
max_width: int,
theta_base=10000,
device=current_platform.device_type,
): ):
super().__init__() super().__init__()
self.dim = dim self.dim = dim
@ -437,7 +445,7 @@ class MoonVitEncoderLayer(nn.Module):
self.hidden_size_per_attention_head = self.hidden_dim // self.num_heads self.hidden_size_per_attention_head = self.hidden_dim // self.num_heads
self.attn_implementation = attn_implementation self.attn_implementation = attn_implementation
# use fa2 in vllm by default # use fa2 in vllm by default
if is_flash_attn_2_available(): if is_flash_attn_2_available() or current_platform.is_xpu():
self.attn_implementation = "flash_attention_2" self.attn_implementation = "flash_attention_2"
self.norm0 = nn.LayerNorm(hidden_dim) self.norm0 = nn.LayerNorm(hidden_dim)