[XPU] support Triton Attention backend on Intel GPU (#24149)

Signed-off-by: Kunshang Ji <kunshang.ji@intel.com>

vllm/v1/attention/backends/triton_attn.py

@@ -7,7 +7,6 @@ from typing import ClassVar, Optional
 
 import torch
 
-from vllm import _custom_ops as ops
 from vllm import envs
 from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
                                               AttentionMetadata, AttentionType)
@@ -23,6 +22,11 @@ from vllm.v1.attention.backends.utils import (AttentionCGSupport,
                                               CommonAttentionMetadata)
 from vllm.v1.kv_cache_interface import AttentionSpec
 
+if current_platform.is_cuda_alike():
+    from vllm import _custom_ops as ops
+elif current_platform.is_xpu():
+    from vllm._ipex_ops import ipex_ops as ops
+
 logger = init_logger(__name__)
 
 
@@ -337,7 +341,7 @@ class TritonAttentionImpl(AttentionImpl):
                     layer._v_scale,
                 )
             else:
-                torch.ops._C_cache_ops.reshape_and_cache_flash(
+                ops.reshape_and_cache_flash(
                     key,
                     value,
                     key_cache,
@@ -354,9 +358,10 @@ class TritonAttentionImpl(AttentionImpl):
             num_tokens, num_heads, head_size = query.shape
             assert layer._q_scale == 1.0, \
                 "A non 1.0 q_scale is not currently supported."
-            if not current_platform.is_rocm():
-                # Skip Q quantization on ROCm, since dequantizing back to
-                # f32 in the attention kernel is not supported.
+            if current_platform.is_cuda():
+                # Skip Q quantization on ROCm and XPU, enable this on cuda
+                # only, since dequantizing back to f32 in the attention kernel
+                # is not supported.
                 query, _ = ops.scaled_fp8_quant(
                     query.reshape(
                         (num_tokens, num_heads * head_size)).contiguous(),