[V1] Enable Mamba2 layers other than MambaMixer2 in the v1 engine (#20660)

Signed-off-by: nopperl <54780682+nopperl@users.noreply.github.com>

vllm/model_executor/layers/mamba/mamba_mixer2.py

@@ -17,6 +17,7 @@ from vllm.forward_context import get_forward_context
 from vllm.model_executor.custom_op import CustomOp
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                RowParallelLinear)
+from vllm.model_executor.layers.mamba.abstract import MambaBase
 from vllm.model_executor.layers.mamba.mamba2_metadata import (Mamba2Metadata,
                                                               update_metadata)
 from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
@@ -219,7 +220,7 @@ def mamba_v2_sharded_weight_loader(
 
 # Adapted from transformers.models.mamba.modeling_mamba.MambaMixer
 @CustomOp.register("mamba_mixer2")
-class MambaMixer2(CustomOp):
+class MambaMixer2(MambaBase, CustomOp):
     """
     Compute ∆, A, B, C, and D the state space parameters and compute
     the `contextualized_states`. A, D are input independent
@@ -231,22 +232,21 @@ class MambaMixer2(CustomOp):
     """
 
     def __init__(
-            self,
-            hidden_size: int,
-            ssm_state_size: int,
-            conv_kernel_size: int,
-            intermediate_size: int,
-            use_conv_bias: bool,
-            use_bias: bool,
-            n_groups: int = 1,
-            num_heads: int = 128,
-            head_dim: int = 64,
-            rms_norm_eps: float = 1e-5,
-            activation: str = "silu",
-            use_rms_norm: bool = True,
-            quant_config: Optional[QuantizationConfig] = None,
-            prefix: str = "",
-            chunk_size: int = -1,  # the chunk size used by v1
+        self,
+        hidden_size: int,
+        ssm_state_size: int,
+        conv_kernel_size: int,
+        intermediate_size: int,
+        use_conv_bias: bool,
+        use_bias: bool,
+        n_groups: int = 1,
+        num_heads: int = 128,
+        head_dim: int = 64,
+        rms_norm_eps: float = 1e-5,
+        activation: str = "silu",
+        use_rms_norm: bool = True,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
     ):
         super().__init__()
 
@@ -428,10 +428,7 @@ class MambaMixer2(CustomOp):
             # of Attention + v0 PP.
             # The inner tuple is (conv_state, ssm_state)
             self.kv_cache = [(torch.tensor([]), torch.tensor([]))]
-            assert chunk_size != -1, "chunk_size must be set for v1"
 
-        # NOTE: chunk_size may be -1 for models without v1 support
-        self.chunk_size = chunk_size
         self.prefix = prefix
 
     def forward_native(