[Hybrid] Add mamba_block_size to Engine Args (#27289)

Signed-off-by: asafg <39553475+Josephasafg@users.noreply.github.com>

vllm/config/cache.py

@@ -5,7 +5,7 @@ import hashlib
 from dataclasses import field
 from typing import TYPE_CHECKING, Any, Literal
 
-from pydantic import Field, SkipValidation, field_validator
+from pydantic import Field, SkipValidation, field_validator, model_validator
 from pydantic.dataclasses import dataclass
 
 from vllm.config.utils import config
@@ -90,8 +90,10 @@ class CacheConfig:
     mamba_page_size_padded: int | None = None
     """ Optional override for mamba page size; used by hybrid mamba/attention
     models to ensure exact alignment with attention page size."""
-    mamba_block_size: int | None = None
-    """Size of a contiguous cache block in number of tokens for mamba cache."""
+    mamba_block_size: int | None = Field(default=None, gt=0)
+    """Size of a contiguous cache block in number of tokens for mamba cache.
+    Can be set only when prefix caching is enabled.
+    Value must be a multiple of 8 to align with causal_conv1d kernel."""
     mamba_cache_dtype: MambaDType = "auto"
     """The data type to use for the Mamba cache (both the conv as well as the
     ssm state). If set to 'auto', the data type will be inferred from the model
@@ -183,3 +185,11 @@ class CacheConfig:
             raise ValueError("Too large swap space. " + msg)
         elif cpu_memory_usage > 0.4 * total_cpu_memory:
             logger.warning("Possibly too large swap space. %s", msg)
+
+    @model_validator(mode="after")
+    def validate_mamba_block_size(self) -> "CacheConfig":
+        if self.mamba_block_size is not None and not self.enable_prefix_caching:
+            raise ValueError(
+                "--mamba-block-size can only be set with --enable-prefix-caching"
+            )
+        return self

vllm/engine/arg_utils.py

@@ -535,6 +535,7 @@ class EngineArgs:
     calculate_kv_scales: bool = CacheConfig.calculate_kv_scales
     mamba_cache_dtype: MambaDType = CacheConfig.mamba_cache_dtype
     mamba_ssm_cache_dtype: MambaDType = CacheConfig.mamba_ssm_cache_dtype
+    mamba_block_size: int | None = get_field(CacheConfig, "mamba_block_size")
 
     additional_config: dict[str, Any] = get_field(VllmConfig, "additional_config")
 
@@ -893,6 +894,9 @@ class EngineArgs:
         cache_group.add_argument(
             "--mamba-ssm-cache-dtype", **cache_kwargs["mamba_ssm_cache_dtype"]
         )
+        cache_group.add_argument(
+            "--mamba-block-size", **cache_kwargs["mamba_block_size"]
+        )
 
         # Multimodal related configs
         multimodal_kwargs = get_kwargs(MultiModalConfig)
@@ -1390,6 +1394,7 @@ class EngineArgs:
             kv_sharing_fast_prefill=self.kv_sharing_fast_prefill,
             mamba_cache_dtype=self.mamba_cache_dtype,
             mamba_ssm_cache_dtype=self.mamba_ssm_cache_dtype,
+            mamba_block_size=self.mamba_block_size,
         )
 
         ray_runtime_env = None

vllm/model_executor/models/config.py

@@ -291,9 +291,8 @@ class MambaModelConfig(VerifyAndUpdateConfig):
         model_config = vllm_config.model_config
         cache_config = vllm_config.cache_config
 
-        # Set mamba block size to max_model_len (this may get
-        # override by prefix caching logic later)
-        cache_config.mamba_block_size = model_config.max_model_len
+        if cache_config.mamba_block_size is None:
+            cache_config.mamba_block_size = model_config.max_model_len
 
         if cache_config.enable_prefix_caching:
             if model_config.supports_mamba_prefix_caching:
@@ -333,6 +332,8 @@ class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
         if not envs.VLLM_USE_V1:
             return
 
+        # Save the user input before it gets modified by MambaModelConfig
+        mamba_block_size = vllm_config.cache_config.mamba_block_size
         # Enable FULL_AND_PIECEWISE by default
         MambaModelConfig.verify_and_update_config(vllm_config)
 
@@ -386,7 +387,7 @@ class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
             # With prefix caching, select attention block size to
             # optimize for mamba kernel performance
 
-            # mamba SSD kernel uses a chunk_size, e.g. 256
+            # Mamba2 SSD kernel uses a chunk_size, e.g. 256
             # Align the block to the kernel: use lowest multiple of chunk_size
             # of attention tokens that would fit mamba_page_size:
             # e.g. for mamba page size = 788kB
@@ -404,7 +405,8 @@ class HybridAttentionMambaModelConfig(VerifyAndUpdateConfig):
             def lcm(a, b):
                 return a * b // gcd(a, b)
 
-            base_chunk_size = model_config.get_mamba_chunk_size()
+            base_chunk_size = mamba_block_size or model_config.get_mamba_chunk_size()
+
             attn_tokens_per_mamba_state = cdiv(mamba_page_size, attn_page_size_1_token)
 
             chunk_size = lcm(base_chunk_size, kernel_block_alignment_size)