fix missing removal

Signed-off-by: Zhuohan Li <zhuohan123@gmail.com>

tests/utils_/test_utils.py

@@ -24,7 +24,6 @@ from vllm.transformers_utils.detokenizer_utils import convert_ids_list_to_tokens
 from vllm.utils import (
     FlexibleArgumentParser,
     MemorySnapshot,
-    bind_kv_cache,
     common_broadcastable_dtype,
     current_stream,
     get_open_port,
@@ -343,87 +342,6 @@ def test_memory_profiling():
     lib.cudaFree(handle2)
 
 
-def test_bind_kv_cache():
-    from vllm.attention import Attention
-
-    ctx = {
-        "layers.0.self_attn": Attention(32, 128, 0.1),
-        "layers.1.self_attn": Attention(32, 128, 0.1),
-        "layers.2.self_attn": Attention(32, 128, 0.1),
-        "layers.3.self_attn": Attention(32, 128, 0.1),
-    }
-    kv_cache = [
-        torch.zeros((1,)),
-        torch.zeros((1,)),
-        torch.zeros((1,)),
-        torch.zeros((1,)),
-    ]
-    bind_kv_cache(ctx, [kv_cache])
-    assert ctx["layers.0.self_attn"].kv_cache[0] is kv_cache[0]
-    assert ctx["layers.1.self_attn"].kv_cache[0] is kv_cache[1]
-    assert ctx["layers.2.self_attn"].kv_cache[0] is kv_cache[2]
-    assert ctx["layers.3.self_attn"].kv_cache[0] is kv_cache[3]
-
-
-def test_bind_kv_cache_kv_sharing():
-    from vllm.attention import Attention
-
-    ctx = {
-        "layers.0.self_attn": Attention(32, 128, 0.1),
-        "layers.1.self_attn": Attention(32, 128, 0.1),
-        "layers.2.self_attn": Attention(32, 128, 0.1),
-        "layers.3.self_attn": Attention(32, 128, 0.1),
-    }
-    kv_cache = [
-        torch.zeros((1,)),
-        torch.zeros((1,)),
-        torch.zeros((1,)),
-        torch.zeros((1,)),
-    ]
-    shared_kv_cache_layers = {
-        "layers.2.self_attn": "layers.1.self_attn",
-        "layers.3.self_attn": "layers.0.self_attn",
-    }
-    bind_kv_cache(ctx, [kv_cache], shared_kv_cache_layers)
-    assert ctx["layers.0.self_attn"].kv_cache[0] is kv_cache[0]
-    assert ctx["layers.1.self_attn"].kv_cache[0] is kv_cache[1]
-    assert ctx["layers.2.self_attn"].kv_cache[0] is kv_cache[1]
-    assert ctx["layers.3.self_attn"].kv_cache[0] is kv_cache[0]
-
-
-def test_bind_kv_cache_non_attention():
-    from vllm.attention import Attention
-
-    # example from Jamba PP=2
-    ctx = {
-        "model.layers.20.attn": Attention(32, 128, 0.1),
-        "model.layers.28.attn": Attention(32, 128, 0.1),
-    }
-    kv_cache = [
-        torch.zeros((1,)),
-        torch.zeros((1,)),
-    ]
-    bind_kv_cache(ctx, [kv_cache])
-    assert ctx["model.layers.20.attn"].kv_cache[0] is kv_cache[0]
-    assert ctx["model.layers.28.attn"].kv_cache[0] is kv_cache[1]
-
-
-def test_bind_kv_cache_pp():
-    with patch("vllm.utils.cuda_device_count_stateless", lambda: 2):
-        # this test runs with 1 GPU, but we simulate 2 GPUs
-        cfg = VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=2))
-    with set_current_vllm_config(cfg):
-        from vllm.attention import Attention
-
-        ctx = {
-            "layers.0.self_attn": Attention(32, 128, 0.1),
-        }
-        kv_cache = [[torch.zeros((1,))], [torch.zeros((1,))]]
-        bind_kv_cache(ctx, kv_cache)
-        assert ctx["layers.0.self_attn"].kv_cache[0] is kv_cache[0][0]
-        assert ctx["layers.0.self_attn"].kv_cache[1] is kv_cache[1][0]
-
-
 @pytest.mark.parametrize(
     ("src_dtype", "tgt_dtype", "expected_result"),
     [

tests/v1/kv_connector/unit/test_nixl_connector.py

@@ -382,7 +382,6 @@ class TestNixlHandshake:
             dummy_ctx = ForwardContext(
                 no_compile_layers={},
                 attn_metadata={},
-                virtual_engine=0,
             )
             _before_load = time.perf_counter()
             connector.start_load_kv(dummy_ctx)
@@ -450,7 +449,6 @@ class TestNixlHandshake:
             dummy_ctx = ForwardContext(
                 no_compile_layers={},
                 attn_metadata={},
-                virtual_engine=0,
             )
             _before_load = time.perf_counter()
             connector.start_load_kv(dummy_ctx)
@@ -506,7 +504,6 @@ class TestNixlHandshake:
             dummy_ctx = ForwardContext(
                 no_compile_layers={},
                 attn_metadata={},
-                virtual_engine=0,
             )
             _before_load = time.perf_counter()
             connector.start_load_kv(dummy_ctx)
@@ -666,7 +663,6 @@ def test_kv_connector_stats(dist_init):
     dummy_ctx = ForwardContext(
         no_compile_layers={},
         attn_metadata={},
-        virtual_engine=0,
     )
     connector.start_load_kv(dummy_ctx)
 
@@ -1241,7 +1237,6 @@ def test_aborted_request_removed_from_worker_in_batch(dist_init):
     dummy_ctx = ForwardContext(
         no_compile_layers={},
         attn_metadata={},
-        virtual_engine=0,
     )
     connector.start_load_kv(dummy_ctx)
 
@@ -1344,7 +1339,6 @@ def test_handshake_failure_returns_finished(dist_init):
     dummy_ctx = ForwardContext(
         no_compile_layers={},
         attn_metadata={},
-        virtual_engine=0,
     )
     connector.start_load_kv(dummy_ctx)
 
@@ -1393,7 +1387,6 @@ def test_transfer_setup_failure_returns_finished(dist_init):
     dummy_ctx = ForwardContext(
         no_compile_layers={},
         attn_metadata={},
-        virtual_engine=0,
     )
     connector.start_load_kv(dummy_ctx)
 

tests/v1/kv_connector/unit/test_offloading_connector.py

@@ -179,7 +179,7 @@ class RequestRunner:
         self._block_hasher = get_request_block_hasher(gpu_block_size, sha256)
 
         self._dummy_ctx: ForwardContext = ForwardContext(
-            no_compile_layers={}, attn_metadata={}, virtual_engine=0
+            no_compile_layers={}, attn_metadata={}
         )
 
     def new_request(self, token_ids: list[int]):

vllm/attention/layer.py

@@ -272,14 +272,9 @@ class Attention(nn.Module, AttentionLayerBase):
         self.kv_sharing_target_layer_name = kv_sharing_target_layer_name
 
         # use a placeholder kv cache tensor during init, which will be replaced
-        # by bind_kv_cache
-        # this variable will not be accessed if use_direct_call is True
-        self.kv_cache = [
-            torch.tensor([])
-            for _ in range(
-                get_current_vllm_config().parallel_config.pipeline_parallel_size
-            )
-        ]
+        # by bind_kv_cache this variable will not be accessed if use_direct_call
+        # is True
+        self.kv_cache = torch.tensor([])
 
         try:
             self.q_range = torch.tensor(envs.Q_SCALE_CONSTANT, dtype=torch.float32)
@@ -361,9 +356,9 @@ class Attention(nn.Module, AttentionLayerBase):
                 attn_metadata = forward_context.attn_metadata
                 if isinstance(attn_metadata, dict):
                     attn_metadata = attn_metadata[self.layer_name]
-                self_kv_cache = self.kv_cache[forward_context.virtual_engine]
+
                 self.impl.forward(
-                    self, query, key, value, self_kv_cache, attn_metadata, output=output
+                    self, query, key, value, self.kv_cache, attn_metadata, output=output
                 )
             else:
                 torch.ops.vllm.unified_attention_with_output(
@@ -376,9 +371,9 @@ class Attention(nn.Module, AttentionLayerBase):
                 attn_metadata = forward_context.attn_metadata
                 if isinstance(attn_metadata, dict):
                     attn_metadata = attn_metadata[self.layer_name]
-                self_kv_cache = self.kv_cache[forward_context.virtual_engine]
+
                 return self.impl.forward(
-                    self, query, key, value, self_kv_cache, attn_metadata
+                    self, query, key, value, self.kv_cache, attn_metadata
                 )
             else:
                 return torch.ops.vllm.unified_attention(
@@ -644,12 +639,7 @@ class MLAAttention(nn.Module, AttentionLayerBase):
             raise ValueError(f"Duplicate layer name: {prefix}")
         compilation_config.static_forward_context[prefix] = self
 
-        self.kv_cache = [
-            torch.tensor([])
-            for _ in range(
-                get_current_vllm_config().parallel_config.pipeline_parallel_size
-            )
-        ]
+        self.kv_cache = torch.tensor([])
 
         # Align with Attention's scale attributes for MLA backends.
 
@@ -688,7 +678,6 @@ class MLAAttention(nn.Module, AttentionLayerBase):
             attn_metadata = forward_context.attn_metadata
             if isinstance(attn_metadata, dict):
                 attn_metadata = attn_metadata[self.layer_name]
-            self_kv_cache = self.kv_cache[forward_context.virtual_engine]
 
             # Mirror Attention.forward scale calculation path
             if self.calculate_kv_scales and getattr(
@@ -703,14 +692,14 @@ class MLAAttention(nn.Module, AttentionLayerBase):
                     q,
                     kv_c_normed,
                     k_pe,
-                    self_kv_cache,
+                    self.kv_cache,
                     attn_metadata,
                     output=output,
                 )
                 return output
             else:
                 return self.impl.forward(
-                    self, q, kv_c_normed, k_pe, self_kv_cache, attn_metadata
+                    self, q, kv_c_normed, k_pe, self.kv_cache, attn_metadata
                 )
         else:
             if self.attn_backend.accept_output_buffer:
@@ -785,7 +774,7 @@ def wait_for_kv_layer_from_connector(layer_name: str):
 
 def maybe_save_kv_layer_to_connector(
     layer_name: str,
-    kv_cache_layer: list[torch.Tensor],
+    kv_cache_layer: torch.Tensor,
 ):
     if not has_kv_transfer_group() or not is_v1_kv_transfer_group():
         return
@@ -851,10 +840,9 @@ def unified_attention(
     if isinstance(attn_metadata, dict):
         attn_metadata = attn_metadata[layer_name]
     self = forward_context.no_compile_layers[layer_name]
-    kv_cache = self.kv_cache[forward_context.virtual_engine]
-    output = self.impl.forward(self, query, key, value, kv_cache, attn_metadata)
+    output = self.impl.forward(self, query, key, value, self.kv_cache, attn_metadata)
 
-    maybe_save_kv_layer_to_connector(layer_name, kv_cache)
+    maybe_save_kv_layer_to_connector(layer_name, self.kv_cache)
     return output
 
 
@@ -889,20 +877,19 @@ def unified_attention_with_output(
     if isinstance(attn_metadata, dict):
         attn_metadata = attn_metadata[layer_name]
     self = forward_context.no_compile_layers[layer_name]
-    kv_cache = self.kv_cache[forward_context.virtual_engine]
     self.impl.forward(
         self,
         query,
         key,
         value,
-        kv_cache,
+        self.kv_cache,
         attn_metadata,
         output=output,
         output_scale=output_scale,
         output_block_scale=output_block_scale,
     )
 
-    maybe_save_kv_layer_to_connector(layer_name, kv_cache)
+    maybe_save_kv_layer_to_connector(layer_name, self.kv_cache)
 
 
 def unified_attention_with_output_fake(
@@ -938,10 +925,9 @@ def unified_mla_attention(
     if isinstance(attn_metadata, dict):
         attn_metadata = attn_metadata[layer_name]
     self: MLAAttention = forward_context.no_compile_layers[layer_name]
-    kv_cache = self.kv_cache[forward_context.virtual_engine]
-    output = self.impl.forward(self, q, kv_c_normed, k_pe, kv_cache, attn_metadata)
+    output = self.impl.forward(self, q, kv_c_normed, k_pe, self.kv_cache, attn_metadata)
 
-    maybe_save_kv_layer_to_connector(layer_name, kv_cache)
+    maybe_save_kv_layer_to_connector(layer_name, self.kv_cache)
     return output
 
 
@@ -978,20 +964,19 @@ def unified_mla_attention_with_output(
     if isinstance(attn_metadata, dict):
         attn_metadata = attn_metadata[layer_name]
     self: MLAAttention = forward_context.no_compile_layers[layer_name]
-    kv_cache = self.kv_cache[forward_context.virtual_engine]
     self.impl.forward(
         self,
         q,
         kv_c_normed,
         k_pe,
-        kv_cache,
+        self.kv_cache,
         attn_metadata,
         output=output,
         output_scale=output_scale,
         output_block_scale=output_block_scale,
     )
 
-    maybe_save_kv_layer_to_connector(layer_name, kv_cache)
+    maybe_save_kv_layer_to_connector(layer_name, self.kv_cache)
 
 
 def unified_mla_attention_with_output_fake(

vllm/distributed/kv_transfer/kv_connector/v1/p2p/p2p_nccl_connector.py

@@ -200,12 +200,10 @@ class P2pNcclConnector(KVConnectorBase_V1):
                 # Only process layers that have kv_cache
                 # attribute (attention layers) Skip non-attention
                 # layers like FusedMoE
-                kv_cache = getattr(layer, "kv_cache", None)
-                if kv_cache is None:
+                layer = getattr(layer, "kv_cache", None)
+                if layer is None:
                     continue
 
-                layer = kv_cache[forward_context.virtual_engine]
-
                 kv_cache = self.p2p_nccl_engine.recv_tensor(
                     request.request_id + "#" + layer_name, remote_address
                 )

vllm/distributed/kv_transfer/kv_connector/v1/shared_storage_connector.py

@@ -174,12 +174,10 @@ class SharedStorageConnector(KVConnectorBase_V1):
                 # Only process layers that have kv_cache
                 # attribute (attention layers) Skip non-attention
                 # layers like FusedMoE/MLP etc.
-                kv_cache_attr = getattr(layer, "kv_cache", None)
-                if kv_cache_attr is None:
+                kv_cache_layer = getattr(layer, "kv_cache", None)
+                if kv_cache_layer is None:
                     continue
 
-                kv_cache_layer = kv_cache_attr[forward_context.virtual_engine]
-
                 filename = self._generate_filename_debug(
                     layer_name, request.token_ids, request.mm_hashes
                 )

vllm/forward_context.py

@@ -37,7 +37,7 @@ class BatchDescriptor(NamedTuple):
     num_tokens: int
     uniform_decode: bool = False
     """
-    False can also be used for an uniform decode batch to dispatch to the 
+    False can also be used for an uniform decode batch to dispatch to the
     cudagraph supporting non-uniform batches.
     """
 
@@ -179,8 +179,8 @@ class ForwardContext:
     # copy from vllm_config.compilation_config.static_forward_context
     no_compile_layers: dict[str, Any]
     """
-    Type AttentionMetadata for v0, 
-    Type Dict[str, AttentionMetadata] for v1, map from layer_name of each 
+    Type AttentionMetadata for v0,
+    Type Dict[str, AttentionMetadata] for v1, map from layer_name of each
     attention layer to its attention metadata
     Type List[Dict[str, AttentionMetadata]] for DBO. List of size two, one
     for each microbatch.
@@ -191,8 +191,6 @@ class ForwardContext:
         dict[str, "AttentionMetadata"],
         list[dict[str, "AttentionMetadata"]],
     ]
-    # TODO: remove after making all virtual_engines share the same kv cache
-    virtual_engine: int  # set dynamically for each forward pass
     # set dynamically for each forward pass
     dp_metadata: DPMetadata | None = None
     # determine the cudagraph style at runtime to be FULL, PIECEWISE, or NONE.
@@ -223,7 +221,6 @@ def get_forward_context() -> ForwardContext:
 def create_forward_context(
     attn_metadata: Any,
     vllm_config: VllmConfig,
-    virtual_engine: int = 0,
     dp_metadata: DPMetadata | None = None,
     cudagraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
     batch_descriptor: BatchDescriptor | None = None,
@@ -231,7 +228,6 @@ def create_forward_context(
 ):
     return ForwardContext(
         no_compile_layers=vllm_config.compilation_config.static_forward_context,
-        virtual_engine=virtual_engine,
         attn_metadata=attn_metadata,
         dp_metadata=dp_metadata,
         cudagraph_runtime_mode=cudagraph_runtime_mode,
@@ -259,7 +255,6 @@ def override_forward_context(forward_context: ForwardContext | None):
 def set_forward_context(
     attn_metadata: Any,
     vllm_config: VllmConfig,
-    virtual_engine: int = 0,
     num_tokens: int | None = None,
     num_tokens_across_dp: torch.Tensor | None = None,
     cudagraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
@@ -305,7 +300,6 @@ def set_forward_context(
     forward_context = create_forward_context(
         attn_metadata,
         vllm_config,
-        virtual_engine,
         dp_metadata,
         cudagraph_runtime_mode,
         batch_descriptor,

vllm/model_executor/layers/mamba/linear_attn.py

@@ -328,7 +328,7 @@ class MiniMaxText01LinearAttention(nn.Module, MambaBase):
         qkvact = qkvact.view((qkv.shape[0], self.tp_heads, -1))
         q, k, v = torch.split(qkvact, [self.head_dim] * 3, dim=-1)
         if attn_metadata is not None:
-            kv_cache = self.kv_cache[forward_context.virtual_engine][0]
+            kv_cache = self.kv_cache[0]
             state_indices_tensor = attn_metadata.state_indices_tensor
 
             num_prefills = getattr(attn_metadata, "num_prefills", 0)

vllm/model_executor/layers/mamba/mamba_mixer.py

@@ -248,9 +248,8 @@ class MambaMixer(MambaBase, CustomOp):
             assert isinstance(mamba1_metadata, Mamba1AttentionMetadata)
             query_start_loc = mamba1_metadata.query_start_loc
             state_indices_tensor = mamba1_metadata.state_indices_tensor
-            self_kv_cache = self.kv_cache[forward_context.virtual_engine]
-            conv_state = self_kv_cache[0].transpose(-1, -2)
-            ssm_state = self_kv_cache[1]
+            conv_state = self.kv_cache[0].transpose(-1, -2)
+            ssm_state = self.kv_cache[1]
             has_initial_states = mamba1_metadata.has_initial_states
             num_padded_decodes = mamba1_metadata.num_padded_decodes
 

vllm/model_executor/layers/mamba/mamba_mixer2.py

@@ -511,10 +511,9 @@ class MambaMixer2(MambaBase, CustomOp):
             assert isinstance(attn_metadata, dict)
             attn_metadata = attn_metadata[self.prefix]
             assert isinstance(attn_metadata, Mamba2AttentionMetadata)
-            self_kv_cache = self.kv_cache[forward_context.virtual_engine]
             # conv_state = (..., dim, width-1) yet contiguous along 'dim'
-            conv_state = self_kv_cache[0].transpose(-1, -2)
-            ssm_state = self_kv_cache[1]
+            conv_state = self.kv_cache[0].transpose(-1, -2)
+            ssm_state = self.kv_cache[1]
             state_indices_tensor = attn_metadata.state_indices_tensor
             has_initial_states_p = attn_metadata.has_initial_states_p
             prep_initial_states = attn_metadata.prep_initial_states

vllm/model_executor/layers/mamba/short_conv.py

@@ -118,8 +118,7 @@ class ShortConv(MambaBase, CustomOp):
             assert isinstance(attn_metadata, dict)
             attn_metadata = attn_metadata[self.prefix]
             assert isinstance(attn_metadata, ShortConvAttentionMetadata)
-            self_kv_cache = self.kv_cache[forward_context.virtual_engine]
-            conv_state = self_kv_cache[0].transpose(-1, -2)
+            conv_state = self.kv_cache[0].transpose(-1, -2)
             state_indices_tensor = attn_metadata.state_indices_tensor
             has_initial_states_p = attn_metadata.has_initial_states_p
 

vllm/model_executor/models/deepseek_v2.py

@@ -471,7 +471,7 @@ class DeepseekV32IndexerCache(torch.nn.Module, AttentionLayerBase):
         self, head_dim: int, dtype: torch.dtype, prefix: str, cache_config: CacheConfig
     ):
         super().__init__()
-        self.kv_cache = [torch.tensor([])]
+        self.kv_cache = torch.tensor([])
         self.head_dim = head_dim
         self.prefix = prefix
         self.cache_config = cache_config

vllm/model_executor/models/plamo2.py

@@ -258,10 +258,9 @@ class Plamo2MambaMixer(MambaBase, CustomOp):
             assert isinstance(attn_metadata, dict)
             attn_metadata = attn_metadata[self.prefix]
             assert isinstance(attn_metadata, Mamba2AttentionMetadata)
-            self_kv_cache = self.kv_cache[forward_context.virtual_engine]
             # conv_state = (..., dim, width-1) yet contiguous along 'dim'
-            conv_state = self_kv_cache[0].transpose(-1, -2)
-            ssm_state = self_kv_cache[1]
+            conv_state = self.kv_cache[0].transpose(-1, -2)
+            ssm_state = self.kv_cache[1]
             state_indices_tensor = attn_metadata.state_indices_tensor
             has_initial_states_p = attn_metadata.has_initial_states_p
             prep_initial_states = attn_metadata.prep_initial_states

vllm/model_executor/models/qwen3_next.py

@@ -458,9 +458,8 @@ class Qwen3NextGatedDeltaNet(nn.Module, MambaBase):
         non_spec_token_indx = attn_metadata.non_spec_token_indx
         spec_state_indices_tensor = attn_metadata.spec_state_indices_tensor  # noqa: E501
         non_spec_state_indices_tensor = attn_metadata.non_spec_state_indices_tensor  # noqa: E501
-        self_kv_cache = self.kv_cache[forward_context.virtual_engine]
-        conv_state = self_kv_cache[0].transpose(-1, -2)
-        ssm_state = self_kv_cache[1]
+        conv_state = self.kv_cache[0].transpose(-1, -2)
+        ssm_state = self.kv_cache[1]
         num_actual_tokens = attn_metadata.num_actual_tokens
         num_accepted_tokens = attn_metadata.num_accepted_tokens
 

vllm/utils/__init__.py

@@ -2013,55 +2013,6 @@ def get_mp_context():
     return multiprocessing.get_context(mp_method)
 
 
-def bind_kv_cache(
-    ctx: dict[str, Any],
-    kv_cache: list[list[torch.Tensor]],  # [virtual_engine][layer_index]
-    shared_kv_cache_layers: dict[str, str] | None = None,
-) -> None:
-    # Bind the kv_cache tensor to Attention modules, similar to
-    # ctx[layer_name].kv_cache[ve]=kv_cache[ve][extract_layer_index(layer_name)]
-    # Special things handled here:
-    # 1. Some models have non-attention layers, e.g., Jamba
-    # 2. Pipeline parallelism, each rank only has a subset of layers
-    # 3. Encoder attention has no kv cache
-    # 4. Encoder-decoder models, encoder-decoder attention and decoder-only
-    #    attention of the same layer (e.g., bart's decoder.layers.1.self_attn
-    #    and decoder.layers.1.encoder_attn) is mapped to the same kv cache
-    #    tensor
-    # 5. Some models have attention layers that share kv cache with previous
-    #    layers, this is specified through shared_kv_cache_layers
-    if shared_kv_cache_layers is None:
-        shared_kv_cache_layers = {}
-    from vllm.attention import AttentionType
-    from vllm.model_executor.models.utils import extract_layer_index
-
-    layer_need_kv_cache = [
-        layer_name
-        for layer_name in ctx
-        if (
-            hasattr(ctx[layer_name], "attn_type")
-            and ctx[layer_name].attn_type
-            in (AttentionType.DECODER, AttentionType.ENCODER_DECODER)
-        )
-        and ctx[layer_name].kv_sharing_target_layer_name is None
-    ]
-    layer_index_sorted = sorted(
-        set(extract_layer_index(layer_name) for layer_name in layer_need_kv_cache)
-    )
-    for layer_name in layer_need_kv_cache:
-        kv_cache_idx = layer_index_sorted.index(extract_layer_index(layer_name))
-        forward_ctx = ctx[layer_name]
-        assert len(forward_ctx.kv_cache) == len(kv_cache)
-        for ve, ve_kv_cache in enumerate(kv_cache):
-            forward_ctx.kv_cache[ve] = ve_kv_cache[kv_cache_idx]
-    if shared_kv_cache_layers is not None:
-        for layer_name, target_layer_name in shared_kv_cache_layers.items():
-            assert extract_layer_index(target_layer_name) < extract_layer_index(
-                layer_name
-            ), "v0 doesn't support interleaving kv sharing"
-            ctx[layer_name].kv_cache = ctx[target_layer_name].kv_cache
-
-
 def run_method(
     obj: Any,
     method: str | bytes | Callable,

vllm/v1/worker/utils.py

@@ -318,8 +318,7 @@ def bind_kv_cache(
 
     # Bind kv_caches to forward context
     for layer_name, kv_cache in kv_caches.items():
-        # NOTE: Use list because of v0 PP virtual engine.
-        forward_context[layer_name].kv_cache = [kv_cache]
+        forward_context[layer_name].kv_cache = kv_cache
 
 
 def is_residual_scattered_for_sp(