class CPUModelRunner(GPUModelRunner):
def __init__(self, vllm_config: VllmConfig, device: torch.device):
# avoid calling accelerator APIs for methods inherited from super class
_set_torch_accelerator_to_noop()
with _torch_cuda_wrapper():
super().__init__(vllm_config, device)
assert device == torch.device("cpu")
# Note: speculative decoding is now supported on CPU with C++ native impls
self.use_cuda_graph = False
self.cascade_attn_enabled = False
self._postprocess_tensors()
def _postprocess_tensors(self) -> None:
# Note: replace device tensors with cpu tensors
def replace_tensor(obj: Any, cpu_attr_name: str, device_attr_name) -> None:
cpu_tensor = getattr(obj, cpu_attr_name, None)
device_tensor = getattr(obj, device_attr_name, None)
if isinstance(cpu_tensor, torch.Tensor) and isinstance(
device_tensor, torch.Tensor
):
setattr(obj, device_attr_name, cpu_tensor)
for v in vars(self).values():
if isinstance(v, CpuGpuBuffer):
v.gpu = v.cpu
for k, v in vars(self.input_batch).items():
if k.endswith("_cpu_tensor") and isinstance(v, torch.Tensor):
replace_tensor(self.input_batch, k, k[:-11])
for block_table in self.input_batch.block_table.block_tables:
for v in vars(block_table).values():
if isinstance(v, CpuGpuBuffer):
v.gpu = v.cpu
@instrument(span_name="Loading (CPU)")
def load_model(self, load_dummy_weights: bool = False) -> None:
if load_dummy_weights:
raise ValueError(
"Loading dummy weights (needed for elastic EP scale-up) "
"Is not supported by the CPU Model Runner."
)
logger.info("Starting to load model %s...", self.model_config.model)
self.model = get_model(vllm_config=self.vllm_config)
if self.lora_config:
self.model = self.load_lora_model(self.model, self.vllm_config, self.device)
if hasattr(self, "drafter"):
logger.info_once("Loading drafter model...")
self.drafter.load_model(self.model)
self._setup_eagle3_aux_hidden_state_outputs()
def get_model(self) -> nn.Module:
return self.model
@instrument(span_name="Warmup (CPU)")
def warming_up_model(self) -> None:
if self.vllm_config.compilation_config.mode == CompilationMode.NONE:
return
logger.info("Warming up model for the compilation...")
# Only generate graph for the generic shape
with _set_global_compilation_settings(self.vllm_config):
self.profile_run()
logger.info("Warming up done.")
def initialize_kv_cache(
self,
kv_cache_config: KVCacheConfig,
is_profiling: bool = False,
kv_cache_allocation_context: AbstractContextManager | None = None,
) -> None:
super().initialize_kv_cache(
kv_cache_config,
is_profiling,
kv_cache_allocation_context=kv_cache_allocation_context,
)
if self.speculative_config:
if self.speculative_config.use_eagle():
logger.info("EAGLE drafter KV cache initialized for CPU backend")
elif self.speculative_config.uses_draft_model():
logger.info("Draft model KV cache initialized for CPU backend")
def _init_device_properties(self) -> None:
pass
def _sync_device(self) -> None:
pass
def _zero_block_ids(self, block_ids: list[int]) -> None:
# Zero full-attention blocks to prevent stale data corruption on partial writes.
# Encoder-only (runner-only) layers are not FullAttentionSpec, so the
# spec filter below already excludes them; no runner-only skip needed.
seen_ptrs: set[int] = set()
for group in self.kv_cache_config.kv_cache_groups:
if not isinstance(group.kv_cache_spec, FullAttentionSpec):
continue
for layer_name in group.layer_names:
ctx = self.compilation_config.static_forward_context.get(layer_name)
if ctx is None:
continue
kv = ctx.kv_cache
if not isinstance(kv, torch.Tensor):
continue
if kv.data_ptr() in seen_ptrs:
continue
seen_ptrs.add(kv.data_ptr())
for block_id in block_ids:
kv[block_id].zero_()
def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
"""CPU-safe version: direct tolist() without CUDA events."""
return sampled_token_ids.tolist()