258 lines
9.6 KiB
Python
258 lines
9.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from contextlib import contextmanager
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from typing import Any
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import torch
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import torch.nn as nn
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import vllm.utils.cpu_triton_utils as cpu_tl
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from vllm.config import VllmConfig
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from vllm.logger import init_logger
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from vllm.model_executor.model_loader import get_model
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from vllm.tracing import instrument
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from vllm.v1.core.sched.output import SchedulerOutput
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from vllm.v1.kv_cache_interface import KVCacheConfig
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from vllm.v1.utils import CpuGpuBuffer
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from vllm.v1.worker.gpu_model_runner import GPUModelRunner
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logger = init_logger(__name__)
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class CPUModelRunner(GPUModelRunner):
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def __init__(self, vllm_config: VllmConfig, device: torch.device):
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# avoid calling accelerator APIs for methods inherited from super class
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_set_torch_accelerator_to_noop()
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with _torch_cuda_wrapper():
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super().__init__(vllm_config, device)
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assert device == torch.device("cpu")
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# Note: speculative decoding is now supported on CPU with C++ native impls
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self.use_cuda_graph = False
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self.cascade_attn_enabled = False
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self._postprocess_tensors()
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self._postprocess_triton()
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def _postprocess_tensors(self) -> None:
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# Note: replace device tensors with cpu tensors
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def replace_tensor(obj: Any, cpu_attr_name: str, device_attr_name) -> None:
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cpu_tensor = getattr(obj, cpu_attr_name, None)
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device_tensor = getattr(obj, device_attr_name, None)
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if isinstance(cpu_tensor, torch.Tensor) and isinstance(
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device_tensor, torch.Tensor
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):
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setattr(obj, device_attr_name, cpu_tensor)
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for v in vars(self).values():
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if isinstance(v, CpuGpuBuffer):
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v.gpu = v.cpu
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for k, v in vars(self.input_batch).items():
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if k.endswith("_cpu_tensor") and isinstance(v, torch.Tensor):
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replace_tensor(self.input_batch, k, k[:-11])
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for block_table in self.input_batch.block_table.block_tables:
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for v in vars(block_table).values():
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if isinstance(v, CpuGpuBuffer):
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v.gpu = v.cpu
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def _postprocess_triton(self) -> None:
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import vllm.v1.worker.block_table
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vllm.v1.worker.block_table._compute_slot_mapping_kernel = (
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cpu_tl.compute_slot_mapping_kernel
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)
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# Speculative decoding fallbacks
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import vllm.v1.sample.rejection_sampler
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import vllm.v1.spec_decode.llm_base_proposer
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import vllm.v1.spec_decode.utils
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vllm.v1.spec_decode.llm_base_proposer.eagle_prepare_inputs_padded_kernel = (
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cpu_tl.eagle_prepare_inputs_padded_kernel
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)
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vllm.v1.spec_decode.llm_base_proposer.eagle_prepare_next_token_padded_kernel = (
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cpu_tl.eagle_prepare_next_token_padded_kernel
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)
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vllm.v1.spec_decode.llm_base_proposer.copy_and_expand_eagle_inputs_kernel = (
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cpu_tl.copy_and_expand_eagle_inputs_kernel
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)
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vllm.v1.spec_decode.utils.eagle_step_slot_mapping_metadata_kernel = (
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cpu_tl.eagle_step_slot_mapping_metadata_kernel
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)
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vllm.v1.sample.rejection_sampler.rejection_greedy_sample_kernel = (
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cpu_tl.rejection_greedy_sample_kernel
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)
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vllm.v1.sample.rejection_sampler.rejection_random_sample_kernel = (
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cpu_tl.rejection_random_sample_kernel
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)
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vllm.v1.sample.rejection_sampler.expand_kernel = cpu_tl.expand_kernel
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vllm.v1.sample.rejection_sampler.sample_recovered_tokens_kernel = (
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cpu_tl.sample_recovered_tokens_kernel
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)
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@instrument(span_name="Loading (CPU)")
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def load_model(self, load_dummy_weights: bool = False) -> None:
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if load_dummy_weights:
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raise ValueError(
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"Loading dummy weights (needed for elastic EP scale-up) "
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"Is not supported by the CPU Model Runner."
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)
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logger.info("Starting to load model %s...", self.model_config.model)
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self.model = get_model(vllm_config=self.vllm_config)
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if self.lora_config:
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self.model = self.load_lora_model(self.model, self.vllm_config, self.device)
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if hasattr(self, "drafter"):
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logger.info_once("Loading drafter model...")
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self.drafter.load_model(self.model)
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def get_model(self) -> nn.Module:
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return self.model
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@instrument(span_name="Warmup (CPU)")
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def warming_up_model(self) -> None:
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logger.info("Warming up model for the compilation...")
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# Only generate graph for the generic shape
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with _set_global_compilation_settings(self.vllm_config):
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self.profile_run()
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logger.info("Warming up done.")
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def initialize_kv_cache(
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self,
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kv_cache_config: KVCacheConfig,
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is_profiling: bool = False,
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) -> None:
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super().initialize_kv_cache(kv_cache_config, is_profiling)
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if self.speculative_config:
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if self.speculative_config.use_eagle():
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logger.info("EAGLE drafter KV cache initialized for CPU backend")
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elif self.speculative_config.uses_draft_model():
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logger.info("Draft model KV cache initialized for CPU backend")
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def _init_device_properties(self) -> None:
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pass
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def _sync_device(self) -> None:
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pass
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def _zero_block_ids(self, block_ids: list[int]) -> None:
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# CPU attention assigns -INF to logits at invalid positions,
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# so stale KV cache data never affects computation.
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pass
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# =========================================================================
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# CPU-safe overrides for speculative decoding methods
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# These methods override GPU-specific implementations that use CUDA streams
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# =========================================================================
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def _copy_draft_token_ids_to_cpu(
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self, scheduler_output: "SchedulerOutput", zeros_only: bool = False
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) -> None:
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"""CPU-safe version: no async copy needed, tensors already on CPU."""
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if self.use_async_scheduling and not (
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scheduler_output.has_structured_output_requests
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or self.input_batch.sampling_metadata.output_token_ids
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):
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return
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self._draft_token_req_ids = self.input_batch.req_ids.copy()
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draft_token_ids: torch.Tensor = self._draft_token_ids
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if not torch.is_tensor(draft_token_ids):
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return
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num_reqs = draft_token_ids.shape[0]
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if self.draft_token_ids_cpu is not None:
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if not zeros_only:
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self.draft_token_ids_cpu[:num_reqs].copy_(draft_token_ids)
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else:
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self.draft_token_ids_cpu[:num_reqs] = 0
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def _get_draft_token_ids_cpu(self) -> tuple[list[list[int]], list[str]]:
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"""CPU-safe version: no event synchronization needed."""
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if isinstance(self._draft_token_ids, list):
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return self._draft_token_ids, self.input_batch.req_ids
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req_ids = self._draft_token_req_ids
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if req_ids is None:
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return [], []
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if self.draft_token_ids_cpu is not None:
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return self.draft_token_ids_cpu[: len(req_ids)].tolist(), req_ids
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return [], []
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def _copy_valid_sampled_token_count(
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self, next_token_ids: torch.Tensor, valid_sampled_tokens_count: torch.Tensor
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) -> None:
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"""CPU-safe version: direct copy without CUDA streams."""
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if self.valid_sampled_token_count_cpu is None:
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return
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counts = valid_sampled_tokens_count
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counts_cpu = self.valid_sampled_token_count_cpu
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counts_cpu[: counts.shape[0]].copy_(counts)
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self.input_batch.prev_sampled_token_ids = next_token_ids.unsqueeze(1)
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def _get_valid_sampled_token_count(self) -> list[int]:
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"""CPU-safe version: no event synchronization needed."""
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prev_sampled_token_ids = self.input_batch.prev_sampled_token_ids
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if prev_sampled_token_ids is None:
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return []
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counts_cpu = self.valid_sampled_token_count_cpu
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if counts_cpu is None:
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return []
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return counts_cpu[: prev_sampled_token_ids.shape[0]].tolist()
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def _to_list(self, sampled_token_ids: torch.Tensor) -> list[list[int]]:
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"""CPU-safe version: direct tolist() without CUDA events."""
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return sampled_token_ids.tolist()
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@contextmanager
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def _torch_cuda_wrapper():
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class _EventPlaceholder:
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def __init__(self, *args, **kwargs) -> None:
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self.record = lambda: None
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self.synchronize = lambda: None
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class _StreamPlaceholder:
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def __init__(self, *args, **kwargs) -> None:
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pass
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cuda_event = torch.Event
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cuda_stream = torch.cuda.Stream
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try:
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torch.Event = _EventPlaceholder
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torch.cuda.Stream = _StreamPlaceholder
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yield
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finally:
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torch.Event = cuda_event
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torch.cuda.Stream = cuda_stream
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@contextmanager
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def _set_global_compilation_settings(config: VllmConfig):
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import torch._inductor.config as torch_inductor_config
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inductor_config = config.compilation_config.inductor_compile_config
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# Note: The MKLDNN and CPPGEMM backend requires freezing parameters.
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freezing_value = torch_inductor_config.freezing
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try:
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if inductor_config.get("max_autotune", False):
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torch_inductor_config.freezing = True
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yield
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finally:
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torch_inductor_config.freezing = freezing_value
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def _set_torch_accelerator_to_noop() -> None:
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def noop(*args: Any, **kwargs: Any) -> None:
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pass
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torch.accelerator.synchronize = noop
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torch.accelerator.empty_cache = noop
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