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[V0 deprecation] Remove VLLM_USE_V1 usage in platform and v1 module (#27798)

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Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
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wangxiyuan 2025-11-01 18:17:45 +08:00 committed by GitHub
parent 799ce45cc1
commit 30a14b034f
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8 changed files with 128 additions and 204 deletions
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190
vllm/platforms/cuda.py
View File

@ -276,17 +276,12 @@ class CudaPlatformBase(Platform):
"FLASHMLA, FLASH_ATTN_MLA, or TRITON_MLA. Alternatively, set "
"VLLM_MLA_DISABLE=1 to disable MLA for this model."
)
if not use_v1:
raise RuntimeError(
"MLA attention backends require the V1 engine. "
"Set VLLM_USE_V1=1 to enable them."
)
from vllm.attention.ops.flashmla import is_flashmla_dense_supported
from vllm.attention.utils.fa_utils import flash_attn_supports_mla
if use_sparse:
logger.info_once("Using Sparse MLA backend on V1 engine.")
logger.info_once("Using Sparse MLA backend.")
return (
"vllm.v1.attention.backends.mla.flashmla_sparse."
"FlashMLASparseBackend"
@ -313,15 +308,13 @@ class CudaPlatformBase(Platform):
)
if use_cutlassmla:
logger.info_once(
"Using Cutlass MLA backend on V1 engine.", scope="local"
)
logger.info_once("Using Cutlass MLA backend.", scope="local")
return "vllm.v1.attention.backends.mla.cutlass_mla.CutlassMLABackend"
if use_flashinfermla:
from vllm.v1.attention.backends.utils import set_kv_cache_layout
set_kv_cache_layout("HND")
logger.info_once("Using FlashInfer MLA backend on V1 engine.")
logger.info_once("Using FlashInfer MLA backend.")
return (
"vllm.v1.attention.backends.mla.flashinfer_mla.FlashInferMLABackend"
)
@ -333,116 +326,107 @@ class CudaPlatformBase(Platform):
block_size,
)
else:
logger.info_once("Using FlashMLA backend on V1 engine.")
logger.info_once("Using FlashMLA backend.")
return "vllm.v1.attention.backends.mla.flashmla.FlashMLABackend"
if use_flashattn:
logger.info_once("Using FlashAttention MLA backend on V1 engine.")
logger.info_once("Using FlashAttention MLA backend.")
return (
"vllm.v1.attention.backends.mla.flashattn_mla.FlashAttnMLABackend"
)
if use_triton:
logger.info_once("Using Triton MLA backend on V1 engine.")
logger.info_once("Using Triton MLA backend.")
return "vllm.v1.attention.backends.mla.triton_mla.TritonMLABackend"
if use_v1:
FLASHINFER_V1 = "vllm.v1.attention.backends.flashinfer.FlashInferBackend" # noqa: E501
FLEX_ATTENTION_V1 = (
"vllm.v1.attention.backends.flex_attention.FlexAttentionBackend" # noqa: E501
)
TRITON_ATTN = (
"vllm.v1.attention.backends.triton_attn.TritonAttentionBackend" # noqa: E501
)
FLASH_ATTN_V1 = (
"vllm.v1.attention.backends.flash_attn.FlashAttentionBackend" # noqa: E501
)
TREE_ATTN_V1 = "vllm.v1.attention.backends.tree_attn.TreeAttentionBackend" # noqa: E501
XFORMERS_V1 = "vllm.v1.attention.backends.xformers.XFormersAttentionBackend" # noqa: E501
use_fp8_kv_cache = kv_cache_dtype is not None and kv_cache_dtype.startswith(
"fp8"
)
FLASHINFER_V1 = "vllm.v1.attention.backends.flashinfer.FlashInferBackend" # noqa: E501
FLEX_ATTENTION_V1 = (
"vllm.v1.attention.backends.flex_attention.FlexAttentionBackend" # noqa: E501
)
TRITON_ATTN = "vllm.v1.attention.backends.triton_attn.TritonAttentionBackend" # noqa: E501
FLASH_ATTN_V1 = "vllm.v1.attention.backends.flash_attn.FlashAttentionBackend" # noqa: E501
TREE_ATTN_V1 = "vllm.v1.attention.backends.tree_attn.TreeAttentionBackend" # noqa: E501
XFORMERS_V1 = "vllm.v1.attention.backends.xformers.XFormersAttentionBackend" # noqa: E501
if selected_backend == _Backend.FLASHINFER:
logger.info_once("Using FlashInfer backend on V1 engine.")
if cls.has_device_capability(100):
from vllm.v1.attention.backends.utils import set_kv_cache_layout
use_fp8_kv_cache = kv_cache_dtype is not None and kv_cache_dtype.startswith(
"fp8"
)
if selected_backend == _Backend.FLASHINFER:
logger.info_once("Using FlashInfer backend.")
if cls.has_device_capability(100):
from vllm.v1.attention.backends.utils import set_kv_cache_layout
set_kv_cache_layout("HND")
return FLASHINFER_V1
elif selected_backend == _Backend.FLEX_ATTENTION:
logger.info_once("Using FlexAttention backend.")
return FLEX_ATTENTION_V1
elif selected_backend == _Backend.TRITON_ATTN:
logger.info_once("Using Triton backend.")
return TRITON_ATTN
elif selected_backend == _Backend.FLASH_ATTN:
logger.info_once("Using Flash Attention backend.")
return FLASH_ATTN_V1
elif selected_backend == _Backend.TREE_ATTN:
logger.info_once("Using Tree Attention backend.")
return TREE_ATTN_V1
elif selected_backend == _Backend.XFORMERS:
logger.info_once("Using XFormers backend.")
return XFORMERS_V1
from vllm.attention.selector import is_attn_backend_supported
# Default backends for V1 engine
# Prefer FlashInfer for Blackwell GPUs if installed
if cls.is_device_capability(100):
if is_default_backend_supported := is_attn_backend_supported(
FLASHINFER_V1, head_size, dtype
):
from vllm.v1.attention.backends.utils import set_kv_cache_layout
logger.info_once(
"Using FlashInfer backend with HND KV cache layout on "
"V1 engine by default for Blackwell (SM 10.0) GPUs."
)
set_kv_cache_layout("HND")
set_kv_cache_layout("HND")
return FLASHINFER_V1
elif selected_backend == _Backend.FLEX_ATTENTION:
logger.info_once("Using FlexAttention backend on V1 engine.")
return FLEX_ATTENTION_V1
elif selected_backend == _Backend.TRITON_ATTN:
logger.info_once("Using Triton backend on V1 engine.")
if not is_default_backend_supported.can_import:
logger.warning_once(
"FlashInfer failed to import on Blackwell (SM 10.0) GPUs; "
"it is recommended to install FlashInfer for better "
"performance."
)
# FlashAttention is the default for SM 8.0+ GPUs
if cls.has_device_capability(80):
if (has_sink or use_fp8_kv_cache) and not cls.is_device_capability(90):
logger.info_once("Using Triton backend.")
return TRITON_ATTN
elif selected_backend == _Backend.FLASH_ATTN:
logger.info_once("Using Flash Attention backend on V1 engine.")
elif is_default_backend_supported := is_attn_backend_supported(
FLASH_ATTN_V1, head_size, dtype, allow_import_error=False
):
logger.info_once("Using Flash Attention backend.")
return FLASH_ATTN_V1
elif selected_backend == _Backend.TREE_ATTN:
logger.info_once("Using Tree Attention backend on V1 engine.")
return TREE_ATTN_V1
elif selected_backend == _Backend.XFORMERS:
logger.info_once("Using XFormers backend on V1 engine.")
return XFORMERS_V1
from vllm.attention.selector import is_attn_backend_supported
# Default backends for V1 engine
# Prefer FlashInfer for Blackwell GPUs if installed
if cls.is_device_capability(100):
if is_default_backend_supported := is_attn_backend_supported(
FLASHINFER_V1, head_size, dtype
):
from vllm.v1.attention.backends.utils import set_kv_cache_layout
logger.info_once(
"Using FlashInfer backend with HND KV cache layout on "
"V1 engine by default for Blackwell (SM 10.0) GPUs."
)
set_kv_cache_layout("HND")
return FLASHINFER_V1
if not is_default_backend_supported.can_import:
logger.warning_once(
"FlashInfer failed to import for V1 engine on "
"Blackwell (SM 10.0) GPUs; it is recommended to "
"install FlashInfer for better performance."
)
# FlashAttention is the default for SM 8.0+ GPUs
if cls.has_device_capability(80):
if (has_sink or use_fp8_kv_cache) and not cls.is_device_capability(90):
logger.info_once("Using Triton backend on V1 engine.")
return TRITON_ATTN
elif is_default_backend_supported := is_attn_backend_supported(
FLASH_ATTN_V1, head_size, dtype, allow_import_error=False
):
logger.info_once("Using Flash Attention backend on V1 engine.")
return FLASH_ATTN_V1
# FlexAttention is the default for older GPUs
else:
logger.info_once("Using FlexAttention backend on V1 engine.")
return FLEX_ATTENTION_V1
assert not is_default_backend_supported
use_flex_attention_reason = {}
if not is_default_backend_supported.head_size:
use_flex_attention_reason["head_size"] = head_size
if not is_default_backend_supported.dtype:
use_flex_attention_reason["dtype"] = dtype
logger.info_once(
"Using FlexAttention backend for %s on V1 engine.",
", ".join(f"{k}={v}" for k, v in use_flex_attention_reason.items()),
)
# FlexAttention is the default for older GPUs
else:
logger.info_once("Using FlexAttention backend.")
return FLEX_ATTENTION_V1
raise RuntimeError(
"V0 attention backends have been removed. Set VLLM_USE_V1=1 "
"to select a supported backend."
assert not is_default_backend_supported
use_flex_attention_reason = {}
if not is_default_backend_supported.head_size:
use_flex_attention_reason["head_size"] = head_size
if not is_default_backend_supported.dtype:
use_flex_attention_reason["dtype"] = dtype
logger.info_once(
"Using FlexAttention backend for %s.",
", ".join(f"{k}={v}" for k, v in use_flex_attention_reason.items()),
)
return FLEX_ATTENTION_V1
@classmethod
def get_punica_wrapper(cls) -> str:

9
vllm/platforms/interface.py
View File

@ -467,14 +467,7 @@ class Platform:
"""
Whether to use allgather in LogitsProcessor to gather the logits.
"""
import vllm.envs as envs
from vllm.config import get_current_vllm_config
parallel_config = get_current_vllm_config().parallel_config
return (
envs.VLLM_USE_V1
or parallel_config.distributed_executor_backend == "external_launcher"
)
return True
@classmethod
def use_custom_allreduce(cls) -> bool:

84
vllm/platforms/rocm.py
View File

@ -149,7 +149,7 @@ def use_rocm_custom_paged_attention(
# disabled due to observed numerical discrepancy.
if ON_GFX9:
return (
(not envs.VLLM_USE_V1 or sliding_window == 0 or sliding_window == (-1, -1))
(sliding_window == 0 or sliding_window == (-1, -1))
and (qtype == torch.half or qtype == torch.bfloat16)
and (head_size == 64 or head_size == 128)
and (block_size == 16 or block_size == 32)
@ -163,11 +163,7 @@ def use_rocm_custom_paged_attention(
else:
return (
ON_GFX11_GFX12
and (
not envs.VLLM_USE_V1
or sliding_window == 0
or sliding_window == (-1, -1)
)
and (sliding_window == 0 or sliding_window == (-1, -1))
and (qtype == torch.half or qtype == torch.bfloat16)
and head_size == 128
and block_size == 16
@ -236,12 +232,6 @@ class RocmPlatform(Platform):
if use_sparse:
raise NotImplementedError("Sparse Attention is not supported on ROCm.")
if use_mla:
if not use_v1:
raise RuntimeError(
"MLA attention backends require the V1 engine. "
"Set VLLM_USE_V1=1 to enable them."
)
from vllm.v1.attention.backends.mla.rocm_aiter_mla import (
is_aiter_mla_enabled,
)
@ -255,7 +245,7 @@ class RocmPlatform(Platform):
if selected_backend == _Backend.TRITON_MLA:
if block_size != 1:
logger.info_once("Using Triton MLA backend on V1 engine.")
logger.info_once("Using Triton MLA backend.")
return "vllm.v1.attention.backends.mla.triton_mla.TritonMLABackend"
raise ValueError(
f" The selected backend, {selected_backend.name},"
@ -263,7 +253,7 @@ class RocmPlatform(Platform):
)
if selected_backend == _Backend.ROCM_AITER_MLA:
if block_size == 1:
logger.info("Using AITER MLA backend on V1 engine.")
logger.info("Using AITER MLA backend.")
return (
"vllm.v1.attention.backends.mla.rocm_aiter_mla.AiterMLABackend" # noqa: E501
)
@ -277,41 +267,33 @@ class RocmPlatform(Platform):
f"is not MLA type while requested for MLA backend."
)
if envs.VLLM_USE_V1:
if selected_backend == _Backend.FLEX_ATTENTION:
logger.info("Using FlexAttention backend on V1 engine.")
return "vllm.v1.attention.backends.flex_attention.FlexAttentionBackend"
if (
envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_MHA and on_gfx9()
) or selected_backend == _Backend.ROCM_AITER_FA:
logger.info("Using Aiter Flash Attention backend on V1 engine.")
return (
"vllm.v1.attention.backends."
"rocm_aiter_fa.AiterFlashAttentionBackend"
)
if (
envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION
) or selected_backend == _Backend.ROCM_AITER_UNIFIED_ATTN:
logger.info("Using Aiter Unified Attention backend on V1 engine.")
return (
"vllm.v1.attention.backends."
"rocm_aiter_unified_attn.RocmAiterUnifiedAttentionBackend"
)
if (
envs.VLLM_V1_USE_PREFILL_DECODE_ATTENTION
or selected_backend == _Backend.ROCM_ATTN
):
# rocm specific backend, with aiter and/or
# triton prefix-prefill
logger.info("Using Rocm Attention backend on V1 engine.")
return "vllm.v1.attention.backends.rocm_attn.RocmAttentionBackend"
# default case, using triton unified attention
logger.info("Using Triton Attention backend on V1 engine.")
return "vllm.v1.attention.backends.triton_attn.TritonAttentionBackend"
raise RuntimeError(
"V0 attention backends have been removed. Set VLLM_USE_V1=1 "
"to select a supported backend."
)
if selected_backend == _Backend.FLEX_ATTENTION:
logger.info("Using FlexAttention backend.")
return "vllm.v1.attention.backends.flex_attention.FlexAttentionBackend"
if (
envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_MHA and on_gfx9()
) or selected_backend == _Backend.ROCM_AITER_FA:
logger.info("Using Aiter Flash Attention backend.")
return "vllm.v1.attention.backends.rocm_aiter_fa.AiterFlashAttentionBackend"
if (
envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION
) or selected_backend == _Backend.ROCM_AITER_UNIFIED_ATTN:
logger.info("Using Aiter Unified Attention backend.")
return (
"vllm.v1.attention.backends."
"rocm_aiter_unified_attn.RocmAiterUnifiedAttentionBackend"
)
if (
envs.VLLM_V1_USE_PREFILL_DECODE_ATTENTION
or selected_backend == _Backend.ROCM_ATTN
):
# rocm specific backend, with aiter and/or
# triton prefix-prefill
logger.info("Using Rocm Attention backend.")
return "vllm.v1.attention.backends.rocm_attn.RocmAttentionBackend"
# default case, using triton unified attention
logger.info("Using Triton Attention backend.")
return "vllm.v1.attention.backends.triton_attn.TritonAttentionBackend"
@classmethod
def set_device(cls, device: torch.device) -> None:
@ -372,7 +354,6 @@ class RocmPlatform(Platform):
parallel_config = vllm_config.parallel_config
is_eager_execution = compilation_config == CUDAGraphMode.NONE
use_v1 = envs.VLLM_USE_V1
use_aiter_rms_norm = (
envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_RMSNORM
)
@ -384,8 +365,7 @@ class RocmPlatform(Platform):
parallel_config.worker_cls = "vllm.v1.worker.gpu_worker.Worker"
# Aiter rms norm perform best when CUDA Graph capture is enabled.
if (
use_v1
and use_aiter_rms_norm
use_aiter_rms_norm
and not is_eager_execution
and "-rms_norm" not in compilation_config.custom_ops
):

4
vllm/platforms/tpu.py
View File

@ -204,10 +204,6 @@ class TpuPlatform(Platform):
def get_device_communicator_cls(cls) -> str:
return "vllm.distributed.device_communicators.tpu_communicator.TpuCommunicator" # noqa
@classmethod
def use_all_gather(cls) -> bool:
return True
@classmethod
def validate_request(
cls,

9
vllm/platforms/xpu.py
View File

@ -66,16 +66,13 @@ class XPUPlatform(Platform):
if use_sparse:
raise NotImplementedError("Sparse Attention is not supported on XPU.")
use_v1 = envs.VLLM_USE_V1
if not use_v1:
raise ValueError("XPU backend only supports V1.")
TRITON_ATTN = "vllm.v1.attention.backends.triton_attn.TritonAttentionBackend" # noqa: E501
FLASH_ATTN = "vllm.v1.attention.backends.flash_attn.FlashAttentionBackend" # noqa: E501
if selected_backend == _Backend.TRITON_ATTN:
logger.info_once("Using Triton backend on V1 engine.")
logger.info_once("Using Triton backend.")
return TRITON_ATTN
elif selected_backend == _Backend.FLASH_ATTN:
logger.info_once("Using Flash Attention backend on V1 engine.")
logger.info_once("Using Flash Attention backend.")
return FLASH_ATTN
elif selected_backend:
raise ValueError(
@ -83,7 +80,7 @@ class XPUPlatform(Platform):
f"with use_v1: {use_v1} use_mla: {use_mla}"
)
logger.info("Using Flash Attention backend on V1 engine.")
logger.info("Using Flash Attention backend.")
return "vllm.v1.attention.backends.flash_attn.FlashAttentionBackend"
@classmethod

16
vllm/v1/engine/async_llm.py
View File

@ -88,14 +88,6 @@ class AsyncLLM(EngineClient):
Returns:
None
"""
if not envs.VLLM_USE_V1:
raise ValueError(
"Using V1 AsyncLLMEngine, but envs.VLLM_USE_V1=False. "
"This should not happen. As a workaround, try using "
"AsyncLLMEngine.from_vllm_config(...) or explicitly set "
"VLLM_USE_V1=0 or 1 and report this issue on Github."
)
# Ensure we can serialize custom transformer configs
maybe_register_config_serialize_by_value()
@ -206,14 +198,6 @@ class AsyncLLM(EngineClient):
client_index: int = 0,
disable_log_requests: bool = True, # Deprecated, will be removed
) -> "AsyncLLM":
if not envs.VLLM_USE_V1:
raise ValueError(
"Using V1 AsyncLLMEngine, but envs.VLLM_USE_V1=False. "
"This should not happen. As a workaround, try using "
"AsyncLLMEngine.from_vllm_config(...) or explicitly set "
"VLLM_USE_V1=0 or 1 and report this issue on Github."
)
# Create the LLMEngine.
return cls(
vllm_config=vllm_config,

11
vllm/v1/engine/llm_engine.py
View File

@ -58,18 +58,9 @@ class LLMEngine:
use_cached_outputs: bool = False,
multiprocess_mode: bool = False,
) -> None:
if not envs.VLLM_USE_V1:
raise ValueError(
"Using V1 LLMEngine, but envs.VLLM_USE_V1=False. "
"This should not happen. As a workaround, try using "
"LLMEngine.from_vllm_config(...) or explicitly set "
"VLLM_USE_V1=0 or 1 and report this issue on Github."
)
if stat_loggers is not None:
raise NotImplementedError(
"Passing StatLoggers to LLMEngine in V1 is not yet supported. "
"Set VLLM_USE_V1=0 and file and issue on Github."
"Passing StatLoggers to LLMEngine is not yet supported."
)
self.vllm_config = vllm_config

9
vllm/v1/executor/uniproc_executor.py
View File

@ -124,11 +124,10 @@ class ExecutorWithExternalLauncher(UniProcExecutor):
def _init_executor(self) -> None:
"""Initialize the worker and load the model."""
if envs.VLLM_USE_V1:
assert not envs.VLLM_ENABLE_V1_MULTIPROCESSING, (
"To get deterministic execution in V1, "
"please set VLLM_ENABLE_V1_MULTIPROCESSING=0"
)
assert not envs.VLLM_ENABLE_V1_MULTIPROCESSING, (
"To get deterministic execution, "
"please set VLLM_ENABLE_V1_MULTIPROCESSING=0"
)
super()._init_executor()
def _distributed_args(self) -> tuple[str, int, int]: