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[Attention][V1] Toggle for v1 attention backend #18275

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[Attention][V1] Toggle for v1 attention backend #18275

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ac2b381
Toggle for v1 attention
gshtras May 16, 2025
9540f72
Caching the env variable in the __init__
gshtras May 19, 2025
dc0f0d1
Merge remote-tracking branch 'origin/main' into attention_toggle_upst…
gshtras May 19, 2025
f82da97
Better naming and logic extracted to a variable
gshtras May 22, 2025
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4 changes: 2 additions & 2 deletions vllm/attention/ops/chunked_prefill_paged_decode.py
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Original file line number Diff line number Diff line change
Expand Up @@ -264,8 +264,8 @@ def chunked_prefill_paged_decode(
# Conversion of FP8 Tensor from uint8 storage to
# appropriate torch.dtype for interpretation by Triton
if "fp8" in kv_cache_dtype:
assert key_cache.dtype == torch.uint8
assert value_cache.dtype == torch.uint8
assert key_cache.dtype in [torch.uint8, current_platform.fp8_dtype()]
assert value_cache.dtype in [torch.uint8, current_platform.fp8_dtype()]

if kv_cache_dtype in ("fp8", "fp8_e4m3"):
target_dtype = current_platform.fp8_dtype()
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4 changes: 2 additions & 2 deletions vllm/attention/ops/prefix_prefill.py
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Expand Up @@ -744,8 +744,8 @@ def context_attention_fwd(q,
# Conversion of FP8 Tensor from uint8 storage to
# appropriate torch.dtype for interpretation by Triton
if "fp8" in kv_cache_dtype:
assert (k_cache.dtype == torch.uint8)
assert (v_cache.dtype == torch.uint8)
assert k_cache.dtype in [torch.uint8, current_platform.fp8_dtype()]
assert v_cache.dtype in [torch.uint8, current_platform.fp8_dtype()]

if kv_cache_dtype in ("fp8", "fp8_e4m3"):
target_dtype = current_platform.fp8_dtype()
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12 changes: 10 additions & 2 deletions vllm/envs.py
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Expand Up @@ -15,6 +15,7 @@
VLLM_NCCL_SO_PATH: Optional[str] = None
LD_LIBRARY_PATH: Optional[str] = None
VLLM_USE_TRITON_FLASH_ATTN: bool = False
VLLM_V1_USE_PREFILL_DECODE_ATTENTION: bool = False
VLLM_FLASH_ATTN_VERSION: Optional[int] = None
LOCAL_RANK: int = 0
CUDA_VISIBLE_DEVICES: Optional[str] = None
Expand Down Expand Up @@ -289,6 +290,13 @@ def get_vllm_port() -> Optional[int]:
lambda: (os.environ.get("VLLM_USE_TRITON_FLASH_ATTN", "True").lower() in
("true", "1")),

# Use separate prefill and decode kernels for V1 attention instead of
# the unified triton kernel.
"VLLM_V1_USE_PREFILL_DECODE_ATTENTION":
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Nit: VLLM_V1_TRITON_ATTN_FORCE_PREFILL_DECODE sounds slightly more accurate to me, but feel free to use the name that works best in your eyes

lambda:
(os.getenv("VLLM_V1_USE_PREFILL_DECODE_ATTENTION", "False").lower() in
("true", "1")),

# Force vllm to use a specific flash-attention version (2 or 3), only valid
# when using the flash-attention backend.
"VLLM_FLASH_ATTN_VERSION":
Expand Down Expand Up @@ -322,8 +330,8 @@ def get_vllm_port() -> Optional[int]:

# Whether to log responses from API Server for debugging
"VLLM_DEBUG_LOG_API_SERVER_RESPONSE":
lambda: os.environ.get("VLLM_DEBUG_LOG_API_SERVER_RESPONSE", "False").
lower() == "true",
lambda: os.environ.get("VLLM_DEBUG_LOG_API_SERVER_RESPONSE", "False"
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Nit: accidental change?

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ruff or yapf change, reformatting the whole file now results in this

).lower() == "true",

# S3 access information, used for tensorizer to load model from S3
"S3_ACCESS_KEY_ID":
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6 changes: 4 additions & 2 deletions vllm/v1/attention/backends/triton_attn.py
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Expand Up @@ -5,6 +5,7 @@
import torch

from vllm import _custom_ops as ops
from vllm import envs
from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
AttentionMetadata, AttentionType)
from vllm.attention.ops.chunked_prefill_paged_decode import (
Expand Down Expand Up @@ -126,6 +127,7 @@ def __init__(
"TritonAttentionImpl")

self.fp8_dtype = current_platform.fp8_dtype()
self.use_prefill_decode_attn = envs.VLLM_V1_USE_PREFILL_DECODE_ATTENTION
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Suggested change
self.use_prefill_decode_attn = envs.VLLM_V1_USE_PREFILL_DECODE_ATTENTION
self.force_prefill_decode_attn = envs. VLLM_V1_TRITON_ATTN_FORCE_PREFILL_DECODE


def forward(
self,
Expand Down Expand Up @@ -166,8 +168,8 @@ def forward(
# performance to make sure it does not introduce any overhead.

num_queries_per_kv = query.shape[1] // key.shape[1]
use_prefill_decode_attn = (num_queries_per_kv &
(num_queries_per_kv - 1)) != 0
use_prefill_decode_attn = self.use_prefill_decode_attn or (
(num_queries_per_kv & (num_queries_per_kv - 1)) != 0)
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Could you extract this into a bool (like is_num_q_pow2)


num_actual_tokens = attn_metadata.num_actual_tokens

Expand Down