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https://git.datalinker.icu/vllm-project/vllm.git
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125 lines
4.8 KiB
Python
125 lines
4.8 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 TYPE_CHECKING, Any
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import torch
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import torch.nn as nn
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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.v1.attention.backends.cpu_attn import TorchSDPAMetadataBuilderV1
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from vllm.v1.worker.gpu_model_runner import GPUModelRunner
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if TYPE_CHECKING:
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from vllm.v1.core.sched.output import SchedulerOutput
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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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super().__init__(vllm_config, device)
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assert device == torch.device("cpu")
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assert self.speculative_config is None, "spec decode is not supported."
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self.use_cuda_graph = False
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self.cascade_attn_enabled = False
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self._postprocess_tenosrs()
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def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
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"""
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Update the order of requests in the batch based on the attention
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backend's needs. For example, some attention backends (namely MLA) may
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want to separate requests based on if the attention computation will be
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compute-bound or memory-bound.
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Args:
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scheduler_output: The scheduler output.
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"""
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# Attention free models have zero kv_cache_goups, however models
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# like Mamba are also attention free but use the kv_cache for
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# keeping its internal state. This is why we check the number
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# of kv_cache groups instead of solely checking
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# for self.model_config.is_attention_free.
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if len(self.kv_cache_config.kv_cache_groups) == 0:
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return
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if len(self.kv_cache_config.kv_cache_groups) > 1:
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raise ValueError("Multiple KVCacheGroups is not"
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"currently supported with CPU model runner.")
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assert type(self.attn_groups[0]
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[0].metadata_builder) is TorchSDPAMetadataBuilderV1
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self.attn_groups[0][0].metadata_builder.reorder_batch(
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self.input_batch, scheduler_output)
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def _postprocess_tenosrs(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,
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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 cpu_tensor is not None and device_tensor is not None:
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assert isinstance(cpu_tensor, torch.Tensor)
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assert isinstance(device_tensor, torch.Tensor)
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setattr(obj, device_attr_name, cpu_tensor)
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for k, v in vars(self).items():
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if k.endswith("_cpu") and isinstance(v, torch.Tensor):
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replace_tensor(self, k, k[:-4])
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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 k, v in vars(block_table).items():
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if k.endswith("_cpu") and isinstance(v, torch.Tensor):
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replace_tensor(block_table, k, k[:-4])
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def load_model(self, eep_scale_up: bool = False) -> None:
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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.model_config,
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self.scheduler_config,
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self.lora_config, self.device)
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def get_model(self) -> nn.Module:
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return self.model
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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._dummy_run(max(16, self.max_num_reqs))
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logger.info("Warming up done.")
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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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@contextmanager
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def _set_global_compilation_settings(config: VllmConfig):
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import torch._inductor.config
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inductor_config = config.compilation_config.inductor_compile_config
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try:
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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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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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