Sync from v0.13

vllm/model_executor/layers/mamba/abstract.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from abc import abstractmethod
+from collections.abc import Iterable
+
+import torch
+
+from vllm.attention.backends.abstract import AttentionBackend
+from vllm.attention.selector import get_mamba_attn_backend
+from vllm.config import VllmConfig
+from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
+from vllm.v1.kv_cache_interface import KVCacheSpec, MambaSpec
+
+
+class MambaBase(AttentionLayerBase):
+    """
+    Base class for Mamba-like layers which support the v1 engine.
+    Inherit from this class if you implement a custom layer.
+    """
+
+    # Contains the KV cache (mamba state) for the layer
+    # in the shape specified by `self.get_state_shape`.
+    kv_cache: tuple[torch.Tensor, ...]
+
+    @abstractmethod
+    def get_state_shape(self) -> Iterable[tuple[int, ...]]:
+        """
+        Defines the shape of the state.
+        For mamba layers this is usually a (conv_state, ssm_state) tuple.
+        In this case, returns (conv_state_shape, ssm_state_shape).
+        """
+        pass
+
+    @property
+    @abstractmethod
+    def mamba_type(self) -> str:
+        pass
+
+    @abstractmethod
+    def get_state_dtype(self) -> tuple[torch.dtype, ...]:
+        pass
+
+    def get_kv_cache_spec(self, vllm_config: VllmConfig) -> KVCacheSpec | None:
+        if (
+            vllm_config.speculative_config is not None
+            and vllm_config.model_config.hf_config.model_type not in ["qwen3_next"]
+        ):
+            raise NotImplementedError(
+                "Mamba with speculative decoding is not supported yet."
+            )
+        mamba_block_size = vllm_config.cache_config.mamba_block_size
+        page_size_padded = vllm_config.cache_config.mamba_page_size_padded
+        return MambaSpec(
+            shapes=self.get_state_shape(),
+            dtypes=self.get_state_dtype(),
+            block_size=mamba_block_size,
+            page_size_padded=page_size_padded,
+            mamba_type=self.mamba_type,
+            num_speculative_blocks=(
+                vllm_config.speculative_config.num_speculative_tokens
+                if vllm_config.speculative_config
+                else 0
+            ),
+        )
+
+    def get_attn_backend(self) -> type[AttentionBackend]:
+        """Get the attention backend class for this Mamba layer."""
+        return get_mamba_attn_backend(self.mamba_type)