[XPU] Set consistent default KV cache layout (#24745)

Signed-off-by: NickLucche <nlucches@redhat.com>

vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py

@@ -56,9 +56,9 @@ except ImportError:
     logger.warning("NIXL is not available")
     NixlWrapper = None
 
-# Supported xPUs and types of kv transfer buffer.
+# Supported platforms and types of kv transfer buffer.
-# {xPU: tuple of supported kv buffer types}
+# {device: tuple of supported kv buffer types}
-_NIXL_SUPPORTED_XPUS = {
+_NIXL_SUPPORTED_DEVICE = {
     "cuda": ("cuda", ),
     "tpu": ("cpu", ),
     "xpu": ("cpu", ),
@@ -458,9 +458,9 @@ class NixlConnectorWorker:
         self.device_type = current_platform.device_type
         self.kv_buffer_device: str = \
             vllm_config.kv_transfer_config.kv_buffer_device
-        if self.device_type not in _NIXL_SUPPORTED_XPUS:
+        if self.device_type not in _NIXL_SUPPORTED_DEVICE:
             raise RuntimeError(f"{self.device_type} is not supported.")
-        elif self.kv_buffer_device not in _NIXL_SUPPORTED_XPUS[
+        elif self.kv_buffer_device not in _NIXL_SUPPORTED_DEVICE[
                 self.device_type]:
             raise RuntimeError(
                 f"{self.device_type} with {self.kv_buffer_device} kv_buffer "
@@ -468,7 +468,7 @@ class NixlConnectorWorker:
         self.device_kv_caches: dict[str, torch.Tensor] = {}
 
         # cpu kv buffer for xfer
-        # used when xPU memory can not be registered under nixl
+        # used when device memory can not be registered under nixl
         self.host_xfer_buffers: dict[str, torch.Tensor] = {}
         self.use_host_buffer = self.kv_buffer_device == "cpu"
         if self.kv_buffer_device == "cuda":
@@ -927,6 +927,9 @@ class NixlConnectorWorker:
             if tp_ratio > 1:
                 # Heterogeneous TP expects same kv_cache_layout.
                 assert nixl_agent_meta.kv_cache_layout == self.kv_cache_layout
+                if self.device_type == "xpu":
+                    raise ValueError(
+                        "Heterogeneous TP is not supported on XPU")
 
             assert nixl_agent_meta.block_len == self.block_len * tp_ratio, (
                 "Remote P worker KV layer cache must be of shape [2, N, "

vllm/platforms/xpu.py

@@ -9,6 +9,7 @@ import torch
 import vllm.envs as envs
 from vllm.logger import init_logger
 from vllm.utils import DEFAULT_MAX_NUM_BATCHED_TOKENS
+from vllm.v1.attention.backends.utils import set_kv_cache_layout
 
 from .interface import DeviceCapability, Platform, PlatformEnum, _Backend
 
@@ -164,12 +165,9 @@ class XPUPlatform(Platform):
                 vllm_config.scheduler_config.max_model_len,
                 DEFAULT_MAX_NUM_BATCHED_TOKENS)
 
-        if (envs.VLLM_KV_CACHE_LAYOUT is None
+        set_kv_cache_layout("NHD")
-                or envs.VLLM_KV_CACHE_LAYOUT != "NHD"):
+        logger.info("Setting VLLM_KV_CACHE_LAYOUT to 'NHD' for XPU; "
-            os.environ["VLLM_KV_CACHE_LAYOUT"] = "NHD"
+                    "only NHD layout is supported by XPU attention kernels.")
-            logger.info(
-                "Setting VLLM_KV_CACHE_LAYOUT to 'NHD' for XPU; "
-                "only NHD layout is supported by XPU attention kernels.")
 
     @classmethod
     def is_pin_memory_available(cls):

vllm/v1/attention/backends/utils.py

@@ -5,8 +5,8 @@ import enum
 import functools
 from abc import abstractmethod
 from dataclasses import dataclass, fields, make_dataclass
-from typing import (TYPE_CHECKING, Any, ClassVar, Generic, Optional, Protocol,
+from typing import (TYPE_CHECKING, Any, ClassVar, Generic, Literal, Optional,
-                    TypeVar)
+                    Protocol, TypeVar, Union, get_args)
 
 import numpy as np
 import torch
@@ -30,7 +30,12 @@ from vllm.logger import init_logger
 from vllm.v1.kv_cache_interface import AttentionSpec
 
 logger = init_logger(__name__)
-_KV_CACHE_LAYOUT_OVERRIDE = None
+KVCacheLayoutType = Literal["NHD", "HND"]
+_KV_CACHE_LAYOUT_OVERRIDE: Union[KVCacheLayoutType, None] = None
+
+
+def is_valid_kv_cache_layout(value: str) -> bool:
+    return value in get_args(KVCacheLayoutType)
 
 
 @dataclass
@@ -296,12 +301,13 @@ def get_kv_cache_layout():
     if cache_layout is None:
         cache_layout = get_kv_connector_cache_layout()
     else:
+        assert is_valid_kv_cache_layout(cache_layout)
         logger.info_once("`VLLM_KV_CACHE_LAYOUT` environment variable " \
         "detected. Setting KV cache layout to %s.", cache_layout)
     return cache_layout
 
 
-def set_kv_cache_layout(cache_layout: str):
+def set_kv_cache_layout(cache_layout: KVCacheLayoutType):
     global _KV_CACHE_LAYOUT_OVERRIDE
     _KV_CACHE_LAYOUT_OVERRIDE = cache_layout