[Feature] Allow configuring FlashInfer workspace size

vllm/envs.py

@@ -156,6 +156,7 @@
     VLLM_USE_FLASHINFER_MOE_FP8: bool = False
     VLLM_USE_FLASHINFER_MOE_FP4: bool = False
     VLLM_FLASHINFER_MOE_BACKEND: Literal["throughput", "latency"] = "latency"
+    VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE: int = 256 * 1024 * 1024
     VLLM_XGRAMMAR_CACHE_MB: int = 0
     VLLM_MSGPACK_ZERO_COPY_THRESHOLD: int = 256
     VLLM_ALLOW_INSECURE_SERIALIZATION: bool = False
@@ -1220,6 +1221,10 @@ def get_vllm_port() -> int | None:
     "VLLM_FLASHINFER_MOE_BACKEND": env_with_choices(
         "VLLM_FLASHINFER_MOE_BACKEND", "latency", ["throughput", "latency"]
     ),
+    # Control the workspace buffer size for the FlashInfer backend.
+    "VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE": lambda: int(
+        os.getenv("VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE", str(256 * 1024 * 1024))
+    ),
     # Control the maximum number of tokens per expert supported by the
     # NVFP4 MoE CUTLASS Kernel. This value is used to create a buffer for
     # the blockscale tensor of activations NVFP4 Quantization.
@@ -1565,6 +1570,7 @@ def compute_hash() -> str:
         "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8",
         "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS",
         "VLLM_USE_FLASHINFER_MOE_MXFP4_BF16",
+        "VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE",
         "VLLM_USE_CUDNN_PREFILL",
         "VLLM_USE_TRTLLM_RAGGED_DEEPSEEK_PREFILL",
         "VLLM_USE_TRTLLM_ATTENTION",

vllm/v1/attention/backends/flashinfer.py

@@ -16,6 +16,7 @@
 from flashinfer.prefill import trtllm_batch_context_with_kv_cache
 from flashinfer.utils import FP4Tensor
 
+from vllm import envs
 from vllm.attention.backends.abstract import (
     AttentionBackend,
     AttentionImpl,
@@ -52,7 +53,6 @@
 )
 from vllm.v1.kv_cache_interface import AttentionSpec
 
-FLASHINFER_WORKSPACE_BUFFER_SIZE = 256 * 1024 * 1024
 FLASHINFER_WORKSPACE_BUFFER_SIZE_BATCH_INVARIANT = 2048 * 1024 * 1024
 
 FP8_DTYPE = current_platform.fp8_dtype()
@@ -67,7 +67,7 @@ def _get_trtllm_gen_workspace_buffer():
     global trtllm_gen_workspace_buffer
     if trtllm_gen_workspace_buffer is None:
         trtllm_gen_workspace_buffer = torch.zeros(
-            FLASHINFER_WORKSPACE_BUFFER_SIZE, dtype=torch.uint8, device="cuda"
+            envs.VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE, dtype=torch.uint8, device="cuda"
         )
     return trtllm_gen_workspace_buffer
 
@@ -413,7 +413,7 @@ def __init__(
 
     def _get_workspace_buffer(self):
         if self._workspace_buffer is None:
-            buffer_size = FLASHINFER_WORKSPACE_BUFFER_SIZE
+            buffer_size = envs.VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE
             if vllm_is_batch_invariant():
                 buffer_size = FLASHINFER_WORKSPACE_BUFFER_SIZE_BATCH_INVARIANT
             self._workspace_buffer = torch.zeros(

vllm/v1/attention/backends/mla/common.py

@@ -196,8 +196,8 @@
 import torch
 from tqdm import tqdm
 
-import vllm.envs as envs
 from vllm import _custom_ops as ops
+from vllm import envs
 from vllm.attention.backends.abstract import (
     AttentionBackend,
     AttentionLayer,
@@ -480,12 +480,6 @@ def use_trtllm_ragged_deepseek_prefill() -> bool:
     )
 
 
-# Currently 394MB, this can be tuned based on GEMM sizes used.
-# Chosen to be the same as sglang:
-#  https://github.com/sgl-project/sglang/blob/766392c6bda2558b61ce6d1c1bfd8081a549e1f1/python/sglang/global_config.py#L37
-FLASHINFER_WORKSPACE_BUFFER_SIZE = 394 * 1024 * 1024
-
-
 class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
     """
     NOTE: Please read the comment at the top of the file before trying to
@@ -615,7 +609,9 @@ def __init__(
 
         if self._use_fi_prefill:
             self._workspace_buffer = torch.empty(
-                FLASHINFER_WORKSPACE_BUFFER_SIZE, dtype=torch.uint8, device=device
+                envs.VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE,
+                dtype=torch.uint8,
+                device=device,
             )
 
             self._fi_prefill_main: BatchPrefillWithRaggedKVCacheWrapper | None = None
@@ -627,7 +623,9 @@ def __init__(
 
         if self._use_trtllm_ragged_prefill:
             self._workspace_buffer = torch.empty(
-                FLASHINFER_WORKSPACE_BUFFER_SIZE, dtype=torch.uint8, device=device
+                envs.VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE,
+                dtype=torch.uint8,
+                device=device,
             )
 
         if self._use_cudnn_prefill: