[V1][Kernel] Flashinfer HND KV cache layout

vllm/attention/backends/flashinfer.py

@@ -2,7 +2,6 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import dataclasses
-import os
 from collections import defaultdict
 from contextlib import contextmanager
 from dataclasses import dataclass
@@ -50,8 +49,7 @@
     from vllm.worker.model_runner import (ModelInputForGPUBuilder,
                                           ModelInputForGPUWithSamplingMetadata)
 
-FLASHINFER_KV_CACHE_LAYOUT: str = os.getenv("FLASHINFER_KV_CACHE_LAYOUT",
-                                            "NHD").upper()
+FLASHINFER_KV_CACHE_LAYOUT: str = envs.VLLM_KV_CACHE_LAYOUT or "NHD"
 
 
 class FlashInferBackend(AttentionBackend):

vllm/distributed/kv_transfer/kv_connector/utils.py

@@ -3,7 +3,6 @@
 """
 KV cache helper for store.
 """
-
 import torch
 
 import vllm.envs as envs
@@ -94,15 +93,17 @@ def put_kv_to_cache(self, model_executable: torch.nn.Module, keys, values,
 
 
 def get_kv_connector_cache_layout():
+    # NOTE (NickLucche) When running disaggregated PD with NIXL, HND layout is
+    # used for faster transfer.
     vllm_config = get_current_vllm_config()
     kv_config = vllm_config.kv_transfer_config
-    if vllm_config.model_config is None:
-        logger.warning("Unable to detect current VLLM config. " \
+    if vllm_config.model_config is None or kv_config is None:
+        logger.warning_once("Unable to detect current VLLM config. " \
         "Defaulting to NHD kv cache layout.")
     else:
         use_mla = vllm_config.model_config.use_mla
         if not use_mla and kv_config.kv_connector == "NixlConnector":
-            logger.info("NixlConnector detected. Setting KV cache " \
+            logger.info_once("NixlConnector detected. Setting KV cache " \
             "layout to HND for better xfer performance.")
             return "HND"
     return "NHD"

vllm/envs.py

@@ -128,6 +128,7 @@
     VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS: int = 1
     VLLM_SLEEP_WHEN_IDLE: bool = False
     VLLM_MQ_MAX_CHUNK_BYTES_MB: int = 16
+    VLLM_KV_CACHE_LAYOUT: Optional[str] = None
 
 
 def get_default_cache_root():
@@ -879,6 +880,16 @@ def get_vllm_port() -> Optional[int]:
     # processes via zmq.
     "VLLM_MQ_MAX_CHUNK_BYTES_MB":
     lambda: int(os.getenv("VLLM_MQ_MAX_CHUNK_BYTES_MB", "16")),
+
+    # KV Cache layout used throughout vllm.
+    # Some common values are:
+    # - NHD
+    # - HND
+    # Where N=num_blocks, H=num_heads and D=head_size. The default value will
+    # leave the layout choice to the backend. Mind that backends may only
+    # implement and support a subset of all possible layouts.
+    "VLLM_KV_CACHE_LAYOUT":
+    lambda: os.getenv("VLLM_KV_CACHE_LAYOUT", None)
 }
 
 # --8<-- [end:env-vars-definition]

vllm/v1/attention/backends/flash_attn.py

@@ -16,13 +16,12 @@
 from vllm.attention.utils.fa_utils import (flash_attn_supports_fp8,
                                            get_flash_attn_version)
 from vllm.config import VllmConfig, get_layers_from_vllm_config
-from vllm.distributed.kv_transfer.kv_connector.utils import (
-    get_kv_connector_cache_layout)
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.utils import cdiv
 from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
-                                              CommonAttentionMetadata)
+                                              CommonAttentionMetadata,
+                                              get_kv_cache_layout)
 from vllm.v1.kv_cache_interface import AttentionSpec
 from vllm.v1.worker.block_table import BlockTable
 
@@ -73,16 +72,15 @@ def get_kv_cache_shape(
 
     @staticmethod
     def get_kv_cache_stride_order() -> tuple[int, ...]:
-        # NOTE When running disaggregated PD with NIXL, HND layout is used for
-        # faster transfer. `stride_order` indicates the permutation that gets
+        # `stride_order` indicates the permutation that gets
         # us from `get_kv_cache_shape` to the actual memory layout we want.
-        cache_layout = get_kv_connector_cache_layout()
+        cache_layout = get_kv_cache_layout()
         if cache_layout == "NHD":
             stride_order = (0, 1, 2, 3, 4)
         elif cache_layout == "HND":
             stride_order = (0, 1, 3, 2, 4)
         else:
-            raise ValueError("Unknown cache layout format %s.", cache_layout)
+            raise ValueError(f"Unknown cache layout format {cache_layout}.")
         return stride_order
 
 

vllm/v1/attention/backends/flashinfer.py

@@ -19,7 +19,8 @@
 from vllm.logger import init_logger
 from vllm.v1.attention.backends.flash_attn import use_cascade_attention
 from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
-                                              CommonAttentionMetadata)
+                                              CommonAttentionMetadata,
+                                              get_kv_cache_layout)
 from vllm.v1.kv_cache_interface import AttentionSpec
 from vllm.v1.worker.block_table import BlockTable
 
@@ -66,6 +67,19 @@ def get_kv_cache_shape(
     ) -> tuple[int, ...]:
         return (num_blocks, 2, block_size, num_kv_heads, head_size)
 
+    @staticmethod
+    def get_kv_cache_stride_order() -> tuple[int, ...]:
+        # `stride_order` indicates the permutation that gets us from
+        # `get_kv_cache_shape` to the actual memory layout we want.
+        cache_layout = get_kv_cache_layout()
+        if cache_layout == "NHD":
+            stride_order = (0, 1, 2, 3, 4)
+        elif cache_layout == "HND":
+            stride_order = (0, 1, 3, 2, 4)
+        else:
+            raise ValueError(f"Unknown cache layout format {cache_layout}.")
+        return stride_order
+
 
 @dataclass
 class PerLayerParameters:
@@ -290,7 +304,7 @@ def _get_workspace_buffer(self):
     def _get_prefill_wrapper(self):
         if self._prefill_wrapper is None:
             self._prefill_wrapper = BatchPrefillWithPagedKVCacheWrapper(
-                self._get_workspace_buffer(), "NHD")
+                self._get_workspace_buffer(), get_kv_cache_layout())
         return self._prefill_wrapper
 
     def _get_decode_wrapper(self):
@@ -303,14 +317,14 @@ def _get_decode_wrapper(self):
                 num_qo_heads // num_kv_heads > 4)
             self._decode_wrapper = BatchDecodeWithPagedKVCacheWrapper(
                 self._get_workspace_buffer(),
-                "NHD",
+                get_kv_cache_layout(),
                 use_tensor_cores=use_tensor_cores)
         return self._decode_wrapper
 
     def _get_cascade_wrapper(self):
         if self._cascade_wrapper is None:
             self._cascade_wrapper = MultiLevelCascadeAttentionWrapper(
-                2, self._get_workspace_buffer(), "NHD")
+                2, self._get_workspace_buffer(), get_kv_cache_layout())
         return self._cascade_wrapper
 
     def _plan(self, attn_metadata: FlashInferMetadata):
@@ -621,6 +635,7 @@ def forward(
         num_decode_tokens = attn_metadata.num_decode_tokens
         num_prefill_tokens = attn_metadata.num_prefill_tokens
 
+        stride_order = FlashInferBackend.get_kv_cache_stride_order()
         # Regular attention (common case).
         # Decodes are at the front and prefills are at the back,
         # according to reorder_batch()
@@ -635,7 +650,7 @@ def forward(
             assert prefill_wrapper._sm_scale == self.scale
             prefill_wrapper.run(
                 prefill_query,
-                kv_cache,
+                kv_cache.permute(*stride_order),
                 k_scale=layer._k_scale_float,
                 v_scale=layer._v_scale_float,
                 out=output[num_decode_tokens:],
@@ -651,7 +666,7 @@ def forward(
             assert decode_wrapper._sm_scale == self.scale
             decode_wrapper.run(
                 decode_query,
-                kv_cache,
+                kv_cache.permute(*stride_order),
                 k_scale=layer._k_scale_float,
                 v_scale=layer._v_scale_float,
                 out=output[:num_decode_tokens],

vllm/v1/attention/backends/utils.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import abc
+import functools
 from abc import abstractmethod
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, ClassVar, Generic, TypeVar
@@ -12,6 +13,13 @@
     from vllm.v1.core.sched.output import SchedulerOutput
     from vllm.v1.worker.gpu_input_batch import InputBatch
 
+import vllm.envs as envs
+from vllm.distributed.kv_transfer.kv_connector.utils import (
+    get_kv_connector_cache_layout)
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
 
 @dataclass
 class CommonAttentionMetadata:
@@ -119,3 +127,16 @@ def validate_kv_sharing_target(current_layer_name, target_layer_name,
         raise ValueError(
             error_msg +
             f"must be the same type as the current layer ({expected}).")
+
+
+@functools.lru_cache
+def get_kv_cache_layout():
+    # Override with format specified by the user.
+    cache_layout = envs.VLLM_KV_CACHE_LAYOUT
+    if cache_layout is None:
+        cache_layout = get_kv_connector_cache_layout()
+    else:
+        logger.info_once("`FLASHINFER_KV_CACHE_LAYOUT` environment variable " \
+        "detected. Setting KV cache layout to %s.", cache_layout)
+
+    return cache_layout