354 lines
13 KiB
Python
354 lines
13 KiB
Python
"""ModelRunner runs the forward passes of the models."""
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import importlib
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import importlib.resources
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import logging
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import pkgutil
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from functools import lru_cache
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from typing import Optional, Type
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import torch
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import torch.nn as nn
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from vllm.config import DeviceConfig, LoadConfig
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from vllm.config import ModelConfig as VllmModelConfig
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from vllm.distributed import init_distributed_environment, initialize_model_parallel
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from vllm.model_executor.model_loader import get_model
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from vllm.model_executor.models import ModelRegistry
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from sglang.global_config import global_config
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from sglang.srt.managers.controller.infer_batch import Batch, ForwardMode, InputMetadata, global_server_args_dict
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from sglang.srt.memory_pool import ReqToTokenPool, TokenToKVPool
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from sglang.srt.server_args import ServerArgs
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from sglang.srt.utils import (
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get_available_gpu_memory,
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is_multimodal_model,
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monkey_patch_vllm_dummy_weight_loader,
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monkey_patch_vllm_p2p_access_check,
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)
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logger = logging.getLogger("srt.model_runner")
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class ModelRunner:
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def __init__(
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self,
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model_config,
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mem_fraction_static: float,
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gpu_id: int,
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tp_rank: int,
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tp_size: int,
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nccl_port: int,
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server_args: ServerArgs,
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):
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# Parse args
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self.model_config = model_config
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self.mem_fraction_static = mem_fraction_static
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self.gpu_id = gpu_id
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self.tp_rank = tp_rank
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self.tp_size = tp_size
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self.nccl_port = nccl_port
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self.server_args = server_args
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self.is_multimodal_model = is_multimodal_model(self.model_config)
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monkey_patch_vllm_dummy_weight_loader()
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# Init torch distributed
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torch.cuda.set_device(self.gpu_id)
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logger.info(f"[gpu_id={self.gpu_id}] Init nccl begin.")
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if not server_args.enable_p2p_check:
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monkey_patch_vllm_p2p_access_check(self.gpu_id)
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if server_args.nccl_init_addr:
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nccl_init_method = f"tcp://{server_args.nccl_init_addr}"
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else:
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nccl_init_method = f"tcp://127.0.0.1:{self.nccl_port}"
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init_distributed_environment(
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backend="nccl",
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world_size=self.tp_size,
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rank=self.tp_rank,
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local_rank=self.gpu_id,
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distributed_init_method=nccl_init_method,
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)
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initialize_model_parallel(tensor_model_parallel_size=self.tp_size)
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total_gpu_memory = get_available_gpu_memory(
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self.gpu_id, distributed=self.tp_size > 1
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)
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if self.tp_size > 1:
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total_local_gpu_memory = get_available_gpu_memory(self.gpu_id)
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if total_local_gpu_memory < total_gpu_memory * 0.9:
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raise ValueError(
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"The memory capacity is unbalanced. Some GPUs may be occupied by other processes."
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)
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# Set some global args
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global_server_args_dict["disable_flashinfer"] = server_args.disable_flashinfer
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global_server_args_dict["attention_reduce_in_fp32"] = server_args.attention_reduce_in_fp32
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# Load the model and create memory pool
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self.load_model()
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self.init_memory_pool(total_gpu_memory)
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self.init_cublas()
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self.init_flash_infer()
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# Capture cuda graphs
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self.init_cuda_graphs()
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def load_model(self):
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logger.info(
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f"[gpu_id={self.gpu_id}] Load weight begin. "
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f"avail mem={get_available_gpu_memory(self.gpu_id):.2f} GB"
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)
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device_config = DeviceConfig()
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load_config = LoadConfig(load_format=self.server_args.load_format)
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vllm_model_config = VllmModelConfig(
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model=self.server_args.model_path,
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quantization=self.server_args.quantization,
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tokenizer=None,
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tokenizer_mode=None,
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trust_remote_code=self.server_args.trust_remote_code,
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dtype=self.server_args.dtype,
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seed=42,
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skip_tokenizer_init=True,
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)
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self.dtype = vllm_model_config.dtype
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if self.model_config.model_overide_args is not None:
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vllm_model_config.hf_config.update(self.model_config.model_overide_args)
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self.model = get_model(
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model_config=vllm_model_config,
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device_config=device_config,
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load_config=load_config,
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lora_config=None,
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multimodal_config=None,
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parallel_config=None,
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scheduler_config=None,
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cache_config=None,
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)
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logger.info(
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f"[gpu_id={self.gpu_id}] Load weight end. "
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f"type={type(self.model).__name__}, "
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f"dtype={self.dtype}, "
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f"avail mem={get_available_gpu_memory(self.gpu_id):.2f} GB"
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)
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def profile_max_num_token(self, total_gpu_memory):
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available_gpu_memory = get_available_gpu_memory(
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self.gpu_id, distributed=self.tp_size > 1
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)
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head_dim = self.model_config.head_dim
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head_num = self.model_config.get_num_kv_heads(self.tp_size)
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cell_size = (
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head_num
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* head_dim
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* self.model_config.num_hidden_layers
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* 2
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* torch._utils._element_size(self.dtype)
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)
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rest_memory = available_gpu_memory - total_gpu_memory * (
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1 - self.mem_fraction_static
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)
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max_num_token = int(rest_memory * (1 << 30) // cell_size)
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return max_num_token
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def init_memory_pool(self, total_gpu_memory):
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self.max_total_num_tokens = self.profile_max_num_token(total_gpu_memory)
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if self.max_total_num_tokens <= 0:
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raise RuntimeError(
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"Not enough memory. Please try to increase --mem-fraction-static."
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)
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self.req_to_token_pool = ReqToTokenPool(
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int(self.max_total_num_tokens / self.model_config.context_len * 256),
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self.model_config.context_len + 8,
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)
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self.token_to_kv_pool = TokenToKVPool(
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self.max_total_num_tokens,
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dtype=self.dtype,
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head_num=self.model_config.get_num_kv_heads(self.tp_size),
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head_dim=self.model_config.head_dim,
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layer_num=self.model_config.num_hidden_layers,
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)
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logger.info(
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f"[gpu_id={self.gpu_id}] Memory pool end. "
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f"avail mem={get_available_gpu_memory(self.gpu_id):.2f} GB"
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)
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def init_cublas(self):
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"""We need to run a small matmul to init cublas. Otherwise, it will raise some errors later."""
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dtype = torch.float16
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device = "cuda"
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a = torch.ones((16, 16), dtype=dtype, device=device)
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b = torch.ones((16, 16), dtype=dtype, device=device)
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c = a @ b
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return c
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def init_flash_infer(self):
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if self.server_args.disable_flashinfer:
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self.flashinfer_prefill_wrapper_ragged = None
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self.flashinfer_prefill_wrapper_paged = None
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self.flashinfer_decode_wrapper = None
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return
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from flashinfer import (
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BatchDecodeWithPagedKVCacheWrapper,
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BatchPrefillWithPagedKVCacheWrapper,
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BatchPrefillWithRaggedKVCacheWrapper,
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)
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from flashinfer.decode import _grouped_size_compiled_for_decode_kernels
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if not _grouped_size_compiled_for_decode_kernels(
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self.model_config.num_attention_heads // self.tp_size,
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self.model_config.get_num_kv_heads(self.tp_size),
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):
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use_tensor_cores = True
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else:
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use_tensor_cores = False
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self.flashinfer_workspace_buffers = torch.empty(
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2, global_config.flashinfer_workspace_size, dtype=torch.uint8, device="cuda"
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)
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self.flashinfer_prefill_wrapper_ragged = BatchPrefillWithRaggedKVCacheWrapper(
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self.flashinfer_workspace_buffers[0], "NHD"
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)
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self.flashinfer_prefill_wrapper_paged = BatchPrefillWithPagedKVCacheWrapper(
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self.flashinfer_workspace_buffers[1], "NHD"
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)
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self.flashinfer_decode_wrapper = BatchDecodeWithPagedKVCacheWrapper(
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self.flashinfer_workspace_buffers[0], "NHD", use_tensor_cores=use_tensor_cores
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)
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def init_cuda_graphs(self):
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from sglang.srt.managers.controller.cuda_graph_runner import CudaGraphRunner
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if self.server_args.disable_cuda_graph or self.server_args.disable_flashinfer:
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self.cuda_graph_runner = None
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return
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logger.info(f"[gpu_id={self.gpu_id}] Capture cuda graph begin.")
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batch_size_list = [1, 2, 4] + [i * 8 for i in range(1, 16)]
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self.cuda_graph_runner = CudaGraphRunner(self, max_batch_size_to_capture=max(batch_size_list))
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self.cuda_graph_runner.capture(batch_size_list)
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@torch.inference_mode()
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def forward_decode(self, batch: Batch):
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if self.cuda_graph_runner and self.cuda_graph_runner.can_run(len(batch.reqs)):
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return self.cuda_graph_runner.replay(batch)
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input_metadata = InputMetadata.create(
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self,
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forward_mode=ForwardMode.DECODE,
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req_pool_indices=batch.req_pool_indices,
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seq_lens=batch.seq_lens,
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prefix_lens=batch.prefix_lens,
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position_ids_offsets=batch.position_ids_offsets,
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out_cache_loc=batch.out_cache_loc,
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out_cache_cont_start=batch.out_cache_cont_start,
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out_cache_cont_end=batch.out_cache_cont_end,
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top_logprobs_nums=batch.top_logprobs_nums,
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return_logprob=batch.return_logprob,
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)
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return self.model.forward(
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batch.input_ids, input_metadata.positions, input_metadata
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)
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@torch.inference_mode()
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def forward_extend(self, batch: Batch):
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input_metadata = InputMetadata.create(
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self,
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forward_mode=ForwardMode.EXTEND,
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req_pool_indices=batch.req_pool_indices,
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seq_lens=batch.seq_lens,
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prefix_lens=batch.prefix_lens,
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position_ids_offsets=batch.position_ids_offsets,
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out_cache_loc=batch.out_cache_loc,
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top_logprobs_nums=batch.top_logprobs_nums,
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return_logprob=batch.return_logprob,
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)
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return self.model.forward(
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batch.input_ids, input_metadata.positions, input_metadata
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)
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@torch.inference_mode()
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def forward_extend_multi_modal(self, batch: Batch):
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input_metadata = InputMetadata.create(
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self,
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forward_mode=ForwardMode.EXTEND,
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tp_size=self.tp_size,
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req_pool_indices=batch.req_pool_indices,
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seq_lens=batch.seq_lens,
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prefix_lens=batch.prefix_lens,
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position_ids_offsets=batch.position_ids_offsets,
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out_cache_loc=batch.out_cache_loc,
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top_logprobs_nums=batch.top_logprobs_nums,
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return_logprob=batch.return_logprob,
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flashinfer_prefill_wrapper_ragged=self.flashinfer_prefill_wrapper_ragged,
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flashinfer_prefill_wrapper_paged=self.flashinfer_prefill_wrapper_paged,
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flashinfer_decode_wrapper=self.flashinfer_decode_wrapper,
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)
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return self.model.forward(
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batch.input_ids,
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input_metadata.positions,
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input_metadata,
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batch.pixel_values,
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batch.image_sizes,
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batch.image_offsets,
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)
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def forward(self, batch: Batch, forward_mode: ForwardMode):
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if self.is_multimodal_model and forward_mode == ForwardMode.EXTEND:
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return self.forward_extend_multi_modal(batch)
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elif forward_mode == ForwardMode.DECODE:
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return self.forward_decode(batch)
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elif forward_mode == ForwardMode.EXTEND:
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return self.forward_extend(batch)
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else:
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raise ValueError(f"Invaid forward mode: {forward_mode}")
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@lru_cache()
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def import_model_classes():
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model_arch_name_to_cls = {}
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package_name = "sglang.srt.models"
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package = importlib.import_module(package_name)
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for _, name, ispkg in pkgutil.iter_modules(package.__path__, package_name + "."):
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if not ispkg:
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module = importlib.import_module(name)
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if hasattr(module, "EntryClass"):
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entry = module.EntryClass
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if isinstance(
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entry, list
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): # To support multiple model classes in one module
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for tmp in entry:
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model_arch_name_to_cls[tmp.__name__] = tmp
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else:
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model_arch_name_to_cls[entry.__name__] = entry
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# compat: some models such as chatglm has incorrect class set in config.json
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# usage: [ tuple("From_Entry_Class_Name": EntryClass), ]
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if hasattr(module, "EntryClassRemapping") and isinstance(
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module.EntryClassRemapping, list
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):
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for remap in module.EntryClassRemapping:
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if isinstance(remap, tuple) and len(remap) == 2:
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model_arch_name_to_cls[remap[0]] = remap[1]
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return model_arch_name_to_cls
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def load_model_cls_srt(model_arch: str) -> Optional[Type[nn.Module]]:
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model_arch_name_to_cls = import_model_classes()
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if model_arch not in model_arch_name_to_cls:
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raise ValueError(
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f"Unsupported architectures: {model_arch}. "
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f"Supported list: {list(model_arch_name_to_cls.keys())}"
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)
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return model_arch_name_to_cls[model_arch]
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# Monkey patch model loader
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setattr(ModelRegistry, "load_model_cls", load_model_cls_srt)
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