[Bugfix] Get a specific type of layer from forward context (#17222)

Signed-off-by: Chen Zhang <zhangch99@outlook.com>

vllm/attention/backends/flashinfer.py

@@ -38,7 +38,7 @@ from vllm.attention.backends.utils import (PAD_SLOT_ID, compute_slot_mapping,
                                            is_block_tables_empty)
 from vllm.attention.layer import Attention
 from vllm.attention.ops.paged_attn import PagedAttention
-from vllm.config import VllmConfig
+from vllm.config import VllmConfig, get_layers_from_vllm_config
 from vllm.logger import init_logger
 from vllm.utils import (async_tensor_h2d, get_kv_cache_torch_dtype,
                         make_tensor_with_pad)
@@ -140,12 +140,10 @@ def get_per_layer_parameters(
     to use during `plan`.
     """
 
-    layers = vllm_config.compilation_config.static_forward_context
+    layers = get_layers_from_vllm_config(vllm_config, Attention)
     per_layer_params: Dict[str, PerLayerParameters] = {}
 
     for key, layer in layers.items():
-        assert isinstance(layer, Attention)
-
         impl = layer.impl
         assert isinstance(impl, FlashInferImpl)
 

vllm/config.py

@@ -3445,7 +3445,8 @@ class CompilationConfig(BaseModel):
     compilation_time: float = PrivateAttr
 
     # Per-model forward context
-    # Map from layer name to the attention cls
+    # Map from layer name to layer objects that need to be accessed outside
+    # model code, e.g., Attention, FusedMOE when dp_size>1.
     static_forward_context: dict[str, Any] = PrivateAttr
 
     def compute_hash(self) -> str:
@@ -4079,3 +4080,16 @@ def assert_hashable(text):
         f"vLLM tried to hash some configs that may have Python objects ids "
         f"in them. This is a bug, please file an issue. "
         f"Text being hashed: {text}")
+
+
+T = TypeVar("T")
+
+
+def get_layers_from_vllm_config(vllm_config: VllmConfig,
+                                layer_type: type[T]) -> dict[str, T]:
+    return {
+        layer_name: layer
+        for layer_name, layer in
+        vllm_config.compilation_config.static_forward_context.items()
+        if isinstance(layer, layer_type)
+    }

vllm/v1/attention/backends/flashinfer.py

@@ -14,7 +14,8 @@ import vllm.envs as envs
 from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
                                               AttentionType)
 from vllm.attention.layer import Attention
-from vllm.config import VllmConfig, get_current_vllm_config
+from vllm.config import (VllmConfig, get_current_vllm_config,
+                         get_layers_from_vllm_config)
 from vllm.logger import init_logger
 from vllm.v1.attention.backends.flash_attn import use_cascade_attention
 
@@ -81,12 +82,10 @@ def get_per_layer_parameters(
     to use during `plan`.
     """
 
-    layers = vllm_config.compilation_config.static_forward_context
+    layers = get_layers_from_vllm_config(vllm_config, Attention)
     per_layer_params: dict[str, PerLayerParameters] = {}
 
     for key, layer in layers.items():
-        assert isinstance(layer, Attention)
-
         impl = layer.impl
         assert isinstance(impl, FlashInferImpl)
 

vllm/v1/worker/gpu_model_runner.py

@@ -12,13 +12,13 @@ import torch.nn as nn
 
 from vllm.attention import AttentionType, get_attn_backend
 from vllm.attention.layer import Attention
-from vllm.config import CompilationLevel, VllmConfig
+from vllm.config import (CompilationLevel, VllmConfig,
+                         get_layers_from_vllm_config)
 from vllm.distributed.kv_transfer import (get_kv_transfer_group,
                                           has_kv_transfer_group)
 from vllm.distributed.parallel_state import get_pp_group, graph_capture
 from vllm.forward_context import set_forward_context
 from vllm.logger import init_logger
-from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
 from vllm.model_executor.model_loader import get_model
 from vllm.multimodal import MULTIMODAL_REGISTRY
@@ -1733,17 +1733,12 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             format. Layers that do not need KV cache are not included.
         """
 
-        forward_ctx = self.vllm_config.compilation_config.static_forward_context
+        layers = get_layers_from_vllm_config(self.vllm_config, Attention)
         block_size = self.vllm_config.cache_config.block_size
         use_mla = self.vllm_config.model_config.use_mla
         kv_cache_spec: dict[str, KVCacheSpec] = {}
-        for layer_name, attn_module in forward_ctx.items():
-            if isinstance(attn_module, FusedMoE):
-                continue
-
-            # TODO: Support other attention modules, e.g., sliding window,
-            # cross-attention
-            assert isinstance(attn_module, Attention)
+        for layer_name, attn_module in layers.items():
+            # TODO: Support other attention modules, e.g., cross-attention
             if attn_module.attn_type == AttentionType.DECODER:
                 if attn_module.sliding_window is not None:
                     kv_cache_spec[layer_name] = SlidingWindowSpec(

vllm/v1/worker/tpu_model_runner.py

@@ -17,7 +17,7 @@ import vllm.envs as envs
 from vllm.attention.backends.abstract import AttentionType
 from vllm.attention.layer import Attention
 from vllm.compilation.wrapper import TorchCompileWrapperWithCustomDispatcher
-from vllm.config import VllmConfig
+from vllm.config import VllmConfig, get_layers_from_vllm_config
 from vllm.forward_context import set_forward_context
 from vllm.logger import init_logger
 from vllm.model_executor.model_loader import get_model
@@ -429,11 +429,10 @@ class TPUModelRunner:
             format. Layers that do not need KV cache are not included.
         """
 
-        forward_ctx = self.vllm_config.compilation_config.static_forward_context
+        layers = get_layers_from_vllm_config(self.vllm_config, Attention)
         block_size = self.vllm_config.cache_config.block_size
         kv_cache_spec: dict[str, KVCacheSpec] = {}
-        for layer_name, attn_module in forward_ctx.items():
-            assert isinstance(attn_module, Attention)
+        for layer_name, attn_module in layers.items():
             if attn_module.attn_type == AttentionType.DECODER:
                 if attn_module.sliding_window is not None:
                     kv_cache_spec[layer_name] = SlidingWindowSpec(