[bugfix] adapt to new implemented get_kv_cache_spec in cpuoffload connector (#4311)

### What this PR does / why we need it?
func `get_kv_cache_spec` in model_runner changed a lot and caused error
in cpuoffloading connector which is copied from model_runner, this PR
adapts to new implemented `get_kv_cache_spec` to fix it.

### How was this patch tested?

- vLLM version: v0.11.0
- vLLM main:
2918c1b49c

Signed-off-by: lidenghui <lidenghui1110@gmail.com>

vllm_ascend/distributed/cpu_offload_connector.py

@@ -10,18 +10,20 @@ from typing import TYPE_CHECKING, Any, Optional, Sequence
 
 import torch
 from vllm.attention.backends.abstract import AttentionType
-from vllm.attention.layer import Attention
+from vllm.attention.layer import Attention, MLAAttention
-from vllm.config import VllmConfig
+from vllm.config import VllmConfig, get_layers_from_vllm_config
+from vllm.distributed.ec_transfer import get_ec_transfer, has_ec_transfer
 from vllm.distributed.kv_transfer.kv_connector.v1.base import (
     KVConnectorBase_V1, KVConnectorMetadata, KVConnectorRole)
 from vllm.distributed.parallel_state import get_pp_group, get_tp_group
 from vllm.logger import logger
-from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
+from vllm.model_executor.layers.mamba.abstract import MambaBase
+from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheSpec,
-                                        MLAAttentionSpec)
+                                        MambaSpec, MLAAttentionSpec)
 
-from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.distributed.cpu_offload_manager.metadata import (
     MetadataServer, MetadataServerProc, MLAConfig)
 
@@ -435,41 +437,92 @@ class CPUOffloadingConnectorWorker:
             save_block_mapping.clear()
 
 
-# Copied from vllm_ascend/worker/model_runner_v1.py.
+# copied and modified from vllm_ascend/worker/model_runner_v1.py
 def get_kv_cache_spec(vllm_config: VllmConfig) -> dict[str, KVCacheSpec]:
-    forward_ctx = vllm_config.compilation_config.static_forward_context
+    """
+    Generates the KVCacheSpec by parsing the kv cache format from each
+    Attention module in the static forward context.
+    Returns:
+        KVCacheSpec: A dictionary mapping layer names to their KV cache
+        format. Layers that do not need KV cache are not included.
+    """
+    if has_ec_transfer() and get_ec_transfer().is_producer:
+        return {}
+
     block_size = vllm_config.cache_config.block_size
     use_mla = vllm_config.model_config.use_mla
-    ascend_config = get_ascend_config()
+    use_sparse = hasattr(vllm_config.model_config.hf_config, "index_topk")
-    use_sfa = ascend_config.use_sfa
+    if vllm_config.cache_config.cache_dtype == "auto":
-    kv_cache_spec: dict[str, KVCacheSpec] = {}
+        kv_cache_dtype = vllm_config.model_config.dtype
-    for layer_name, attn_module in forward_ctx.items():
-        if isinstance(attn_module, FusedMoE):
-            continue
-        assert isinstance(attn_module, Attention)
-        if attn_module.attn_type == AttentionType.DECODER:
-            if use_mla and not use_sfa:
-                kv_cache_spec[layer_name] = MLAAttentionSpec(
-                    block_size=block_size,
-                    num_kv_heads=attn_module.num_kv_heads,
-                    head_size=attn_module.head_size,
-                    dtype=attn_module.dtype,
-                    cache_dtype_str=vllm_config.cache_config.cache_dtype)
     else:
-                # TODO(cmq): This is a hack way to fix deepseek kvcache when
+        kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[
-                # using DSA. Fix the spec in vLLM is a finnal way.
+            vllm_config.cache_config.cache_dtype]
+    kv_cache_spec: dict[str, KVCacheSpec] = {}
+    attn_layers = get_layers_from_vllm_config(vllm_config, AttentionLayerBase)
+    for layer_name, attn_module in attn_layers.items():
+        if isinstance(attn_module, Attention):
+            # TODO: Support other attention modules, e.g., cross-attention
+            # TODO(lucas): move the attention specs into the model layers like
+            # the attention backends
+            if attn_module.attn_type == AttentionType.DECODER:
                 kv_cache_spec[layer_name] = FullAttentionSpec(
                     block_size=block_size,
                     num_kv_heads=attn_module.num_kv_heads,
                     head_size=attn_module.head_size,
-                    dtype=attn_module.dtype)
+                    dtype=kv_cache_dtype)
-
             elif attn_module.attn_type in (AttentionType.ENCODER,
                                            AttentionType.ENCODER_ONLY):
+                # encoder-only attention does not need KV cache.
                 continue
             elif attn_module.attn_type == AttentionType.ENCODER_DECODER:
                 raise NotImplementedError
             else:
                 raise ValueError(
                     f"Unknown attention type: {attn_module.attn_type}")
+
+        elif isinstance(attn_module, MLAAttention):
+            if use_mla and not use_sparse:
+                kv_cache_spec[layer_name] = MLAAttentionSpec(
+                    block_size=block_size,
+                    num_kv_heads=1,
+                    head_size=attn_module.head_size,
+                    dtype=kv_cache_dtype,
+                    cache_dtype_str=vllm_config.cache_config.cache_dtype)
+            else:
+                # TODO(cmq): This is a hack way to fix deepseek kvcache when
+                # using DSA. Fix the spec in vLLM is a finnal way.
+                kv_cache_spec[layer_name] = FullAttentionSpec(
+                    block_size=block_size,
+                    num_kv_heads=1,
+                    head_size=attn_module.head_size,
+                    dtype=kv_cache_dtype)
+
+    mamba_layers = get_layers_from_vllm_config(vllm_config, MambaBase)
+    if len(mamba_layers) > 0:
+        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.")
+        if vllm_config.cache_config.enable_prefix_caching:
+            raise NotImplementedError(
+                "Prefix caching is not supported for Mamba yet.")
+        max_model_len = vllm_config.model_config.max_model_len
+
+        page_size_padded = (vllm_config.cache_config.mamba_page_size_padded)
+
+        # Set block_size to max_model_len, so that mamba model will always
+        # have only one block in the KV cache.
+        for layer_name, mamba_module in mamba_layers.items():
+            kv_cache_spec[layer_name] = MambaSpec(
+                shapes=mamba_module.get_state_shape(),
+                dtypes=mamba_module.get_state_dtype(),
+                block_size=max_model_len,
+                page_size_padded=page_size_padded,
+                mamba_type=mamba_module.mamba_type,
+                num_speculative_blocks=(
+                    vllm_config.speculative_config.num_speculative_tokens
+                    if vllm_config.speculative_config else 0),
+            )
+
     return kv_cache_spec

vllm_ascend/distributed/cpu_offload_manager/metadata.py

@@ -12,7 +12,7 @@ from vllm.config import KVTransferConfig, VllmConfig
 from vllm.logger import logger
 from vllm.utils.network_utils import make_zmq_socket
 from vllm.utils.torch_utils import get_dtype_size
-from vllm.v1.kv_cache_interface import AttentionSpec
+from vllm.v1.kv_cache_interface import AttentionSpec, MLAAttentionSpec
 
 from vllm_ascend.distributed.cpu_offload_manager.cpu_kv_cache_manager import \
     CPUKVCacheManager
@@ -140,14 +140,15 @@ class MetadataServer:
             layer.page_size_bytes == any.page_size_bytes
             for any in kv_cache_specs.values()
         ])
+        use_mla = isinstance(layer, MLAAttentionSpec)
         # mla shares the same kv cache among different tp
-        if layer.use_mla:
+        if use_mla:
             tp_rank = 0
         if (pp_rank, tp_rank) in self.shared_memory:
             return self.shared_memory[(pp_rank, tp_rank)]
         available_memory = self.available_memory
         shared_memory_dict = {}
-        if layer.use_mla:
+        if use_mla:
             available_memory //= self.pipeline_parallel_size
             available_memory //= len(kv_cache_specs)
             num_blocks = available_memory // layer.page_size_bytes
@@ -165,7 +166,7 @@ class MetadataServer:
             shared_memory_dict[
                 layer_name] = MetadataServer._safe_create_shared_memory(
                     f"cpu_kv_cache_{pp_rank}_{tp_rank}_{layer_name}", nbytes)
-        if layer.use_mla:
+        if use_mla:
             assert mla_config is not None
             assert layer.head_size == mla_config.rope_dim + mla_config.nope_dim
             self.shared_memory[(pp_rank,