[Feature][Perf] Support Selective CPU Weight Offloading

vllm/config/cache.py

@@ -101,6 +101,17 @@ class CacheConfig:
     Note that this requires fast CPU-GPU interconnect, as part of the model is
     loaded from CPU memory to GPU memory on the fly in each model forward pass.
     """
+    cpu_offload_params: set[str] = Field(default_factory=set)
+    """ The set of parameter name segments to target for CPU offloading.
+    Unmatched parameters are not offloaded. If this set is empty, parameters
+    are offloaded non-selectively until the memory limit defined by
+    `cpu_offload_gb` is reached.
+    Examples:
+        - For parameter name "mlp.experts.w2_weight":
+            - "experts" or "experts.w2_weight" will match.
+            - "expert" or "w2" will NOT match (must be exact segments).
+    This allows distinguishing parameters like "w2_weight" and "w2_weight_scale".
+    """
     calculate_kv_scales: bool = False
     """This enables dynamic calculation of `k_scale` and `v_scale` when
     kv_cache_dtype is fp8. If `False`, the scales will be loaded from the model

vllm/engine/arg_utils.py

@@ -434,6 +434,7 @@ class EngineArgs:
     disable_cascade_attn: bool = ModelConfig.disable_cascade_attn
     swap_space: float = CacheConfig.swap_space
     cpu_offload_gb: float = CacheConfig.cpu_offload_gb
+    cpu_offload_params: set[str] = get_field(CacheConfig, "cpu_offload_params")
     gpu_memory_utilization: float = CacheConfig.gpu_memory_utilization
     kv_cache_memory_bytes: int | None = CacheConfig.kv_cache_memory_bytes
     max_num_batched_tokens: int | None = None
@@ -942,6 +943,9 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             "--prefix-caching-hash-algo", **cache_kwargs["prefix_caching_hash_algo"]
         )
         cache_group.add_argument("--cpu-offload-gb", **cache_kwargs["cpu_offload_gb"])
+        cache_group.add_argument(
+            "--cpu-offload-params", **cache_kwargs["cpu_offload_params"]
+        )
         cache_group.add_argument(
             "--calculate-kv-scales", **cache_kwargs["calculate_kv_scales"]
         )
@@ -1453,6 +1457,7 @@ def create_engine_config(
             enable_prefix_caching=self.enable_prefix_caching,
             prefix_caching_hash_algo=self.prefix_caching_hash_algo,
             cpu_offload_gb=self.cpu_offload_gb,
+            cpu_offload_params=self.cpu_offload_params,
             calculate_kv_scales=self.calculate_kv_scales,
             kv_sharing_fast_prefill=self.kv_sharing_fast_prefill,
             mamba_cache_dtype=self.mamba_cache_dtype,

vllm/model_executor/models/utils.py

@@ -31,6 +31,7 @@
 from vllm.multimodal import NestedTensors
 from vllm.sequence import IntermediateTensors
 from vllm.utils.math_utils import cdiv
+from vllm.utils.mem_utils import format_gib
 from vllm.utils.platform_utils import (
     is_pin_memory_available,
     is_uva_available,
@@ -613,6 +614,7 @@ def forward(self, *args, **kwargs):
 
 _CPU_OFFLOAD_BYTES = 0
 _CPU_OFFLOAD_MAX_BYTES = 0
+_CPU_OFFLOAD_PARAMS = set()
 
 
 def set_cpu_offload_max_bytes(max_bytes: int) -> None:
@@ -621,6 +623,11 @@ def set_cpu_offload_max_bytes(max_bytes: int) -> None:
     _CPU_OFFLOAD_MAX_BYTES = max_bytes
 
 
+def set_cpu_offload_params(params: set[str]) -> None:
+    global _CPU_OFFLOAD_PARAMS
+    _CPU_OFFLOAD_PARAMS = params
+
+
 def maybe_offload_to_cpu(module: torch.nn.Module) -> torch.nn.Module:
     if (params := next(module.parameters(), None)) is None:
         return module
@@ -642,12 +649,23 @@ def maybe_offload_to_cpu(module: torch.nn.Module) -> torch.nn.Module:
     # offload parameters to CPU
     # use pin_memory if possible, which helps cudagraph capture speed
     offloaded_parameters = False
-    for p in module.parameters():
+    for name, p in module.named_parameters():
         if _CPU_OFFLOAD_BYTES >= _CPU_OFFLOAD_MAX_BYTES:
             # we use per-parameter offloading
             # one module might have some parameters offloaded and some not
             break
 
+        if _CPU_OFFLOAD_PARAMS:
+            # Check if parameter belongs to the offloading set
+            # Add dots here to ensure we match full segments only
+            # e.g., "experts.w2_weight" matches "mlp.experts.w2_weight" but not
+            # "mlp.experts.w2_weight_scale"
+            should_offload = any(
+                f".{param}." in f".{name}." for param in _CPU_OFFLOAD_PARAMS
+            )
+            if not should_offload:
+                continue
+
         cpu_data = p.data.to(device="cpu")
         if pin_memory:
             cpu_data = cpu_data.pin_memory()
@@ -708,6 +726,10 @@ def make_layers(
         ]
         + [PPMissingLayer() for _ in range(end_layer, num_hidden_layers)]
     )
+    if _CPU_OFFLOAD_MAX_BYTES > 0:
+        logger.info(
+            "Total CPU offloaded parameters: %s GBs", format_gib(_CPU_OFFLOAD_BYTES)
+        )
     return start_layer, end_layer, modules
 
 

vllm/v1/worker/gpu_model_runner.py

@@ -345,9 +345,13 @@ def __init__(
         self.speculative_config = vllm_config.speculative_config
         self.observability_config = vllm_config.observability_config
 
-        from vllm.model_executor.models.utils import set_cpu_offload_max_bytes
+        from vllm.model_executor.models.utils import (
+            set_cpu_offload_max_bytes,
+            set_cpu_offload_params,
+        )
 
         set_cpu_offload_max_bytes(int(self.cache_config.cpu_offload_gb * 1024**3))
+        set_cpu_offload_params(self.cache_config.cpu_offload_params)
 
         model_config = self.model_config
         cache_config = self.cache_config