wehub-resource-sync
240 lines
8.7 KiB
Python
240 lines
8.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import sys
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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.kv_cache_interface import FullAttentionSpec, 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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from vllm.triton_utils import HAS_TRITON
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if HAS_TRITON:
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logger.info(
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"Triton-CPU backend is available; skipping C++ monkey-patches "
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"for Triton kernels."
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)
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return
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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 as 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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spec_decode_utils.copy_and_expand_dflash_inputs_kernel = (
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cpu_tl.copy_and_expand_dflash_inputs_kernel
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)
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dflash_module = sys.modules.get("vllm.v1.spec_decode.dflash")
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if dflash_module is not None:
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dflash_kernel_name = "copy_and_expand_dflash_inputs_kernel"
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setattr(
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dflash_module,
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dflash_kernel_name,
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cpu_tl.copy_and_expand_dflash_inputs_kernel,
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)
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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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import vllm.v1.worker.mamba_utils
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vllm.v1.worker.mamba_utils.batch_memcpy_kernel = cpu_tl.batch_memcpy_kernel
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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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self._setup_eagle3_aux_hidden_state_outputs()
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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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# Zero full-attention blocks to prevent stale data corruption on partial writes.
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# Encoder-only (runner-only) layers are not FullAttentionSpec, so the
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# spec filter below already excludes them; no runner-only skip needed.
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seen_ptrs: set[int] = set()
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for group in self.kv_cache_config.kv_cache_groups:
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if not isinstance(group.kv_cache_spec, FullAttentionSpec):
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continue
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for layer_name in group.layer_names:
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ctx = self.compilation_config.static_forward_context.get(layer_name)
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if ctx is None:
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continue
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kv = ctx.kv_cache
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if not isinstance(kv, torch.Tensor):
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continue
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if kv.data_ptr() in seen_ptrs:
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continue
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seen_ptrs.add(kv.data_ptr())
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for block_id in block_ids:
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kv[block_id].zero_()
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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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self.wait_stream = lambda *a, **kw: None
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self.device = torch.device("cpu")
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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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