493 lines
18 KiB
Python
493 lines
18 KiB
Python
"""
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Copyright 2023-2024 SGLang Team
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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"""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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import warnings
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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 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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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 (
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get_tp_group,
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init_distributed_environment,
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initialize_model_parallel,
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)
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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.schedule_batch import ScheduleBatch, global_server_args_dict
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from sglang.srt.mem_cache.memory_pool import (
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MHATokenToKVPool,
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MLATokenToKVPool,
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ReqToTokenPool,
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)
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from sglang.srt.model_config import AttentionArch
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from sglang.srt.model_executor.forward_batch_info import ForwardMode, InputMetadata
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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_generation_model,
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is_llama3_405b_fp8_head_16,
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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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monkey_patch_vllm_qvk_linear_loader,
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)
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logger = logging.getLogger(__name__)
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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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global_server_args_dict.update(
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{
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"disable_flashinfer": server_args.disable_flashinfer,
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"disable_flashinfer_sampling": server_args.disable_flashinfer_sampling,
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"attention_reduce_in_fp32": server_args.attention_reduce_in_fp32,
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"enable_mla": server_args.enable_mla,
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}
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)
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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={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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self.tp_group = get_tp_group()
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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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# Load the model and create memory pool
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self.load_model()
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self.init_memory_pool(
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total_gpu_memory,
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server_args.max_num_reqs,
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server_args.max_total_tokens,
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)
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self.init_cublas()
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self.init_flashinfer()
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if self.is_generation:
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# FIXME Currently, cuda graph only capture decode steps, which only exists in causal models
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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={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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monkey_patch_vllm_dummy_weight_loader()
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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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if is_llama3_405b_fp8_head_16(self.model_config) and self.tp_size <= 8:
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# A temporary hack to fix the num_heads for meta-llama/Meta-Llama-3.1-405B-FP8 checkpoints
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self.model_config.hf_config.num_key_value_heads = 8
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vllm_model_config.hf_config.num_key_value_heads = 8
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monkey_patch_vllm_qvk_linear_loader()
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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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if (
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self.server_args.efficient_weight_load
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and "llama" in self.server_args.model_path.lower()
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and self.server_args.quantization == "fp8"
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):
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from sglang.srt.model_loader.model_loader import get_model
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else:
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from vllm.model_executor.model_loader import get_model
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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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self.sliding_window_size = (
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self.model.get_window_size()
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if hasattr(self.model, "get_window_size")
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else None
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)
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self.is_generation = is_generation_model(
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self.model_config.hf_config.architectures
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)
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logger.info(
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f"[gpu={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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if (
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self.model_config.attention_arch == AttentionArch.MLA
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and self.server_args.enable_mla
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):
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cell_size = (
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(self.model_config.kv_lora_rank + self.model_config.qk_rope_head_dim)
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* self.model_config.num_hidden_layers
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* torch._utils._element_size(self.dtype)
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)
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else:
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cell_size = (
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self.model_config.get_num_kv_heads(self.tp_size)
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* self.model_config.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(
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self, total_gpu_memory, max_num_reqs=None, max_total_tokens=None
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):
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self.max_total_num_tokens = self.profile_max_num_token(total_gpu_memory)
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if max_total_tokens is not None:
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if max_total_tokens > self.max_total_num_tokens:
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warnings.warn(
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f"max_total_tokens={max_total_tokens} is larger than the profiled value "
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f"{self.max_total_num_tokens}. "
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f"Use the profiled value instead."
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)
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self.max_total_num_tokens = min(self.max_total_num_tokens, max_total_tokens)
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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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if max_num_reqs is None:
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max_num_reqs = min(
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max(
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int(
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self.max_total_num_tokens / self.model_config.context_len * 512
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),
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2048,
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),
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5120,
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)
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self.req_to_token_pool = ReqToTokenPool(
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max_num_reqs,
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self.model_config.context_len + 8,
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)
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if (
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self.model_config.attention_arch == AttentionArch.MLA
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and self.server_args.enable_mla
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):
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self.token_to_kv_pool = MLATokenToKVPool(
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self.max_total_num_tokens,
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dtype=self.dtype,
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kv_lora_rank=self.model_config.kv_lora_rank,
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qk_rope_head_dim=self.model_config.qk_rope_head_dim,
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layer_num=self.model_config.num_hidden_layers,
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)
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logger.info("using MLA Triton implementaion, flashinfer is disabled")
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# FIXME: temporarily only Triton MLA is supported
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self.server_args.disable_flashinfer = True
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else:
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self.token_to_kv_pool = MHATokenToKVPool(
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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={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_flashinfer(self):
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if self.server_args.disable_flashinfer:
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assert (
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self.sliding_window_size is None
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), "turn on flashinfer to support window attention"
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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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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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if self.sliding_window_size is None:
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self.flashinfer_workspace_buffer = torch.empty(
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global_config.flashinfer_workspace_size,
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dtype=torch.uint8,
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device="cuda",
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)
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self.flashinfer_prefill_wrapper_ragged = (
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BatchPrefillWithRaggedKVCacheWrapper(
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self.flashinfer_workspace_buffer, "NHD"
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)
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)
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self.flashinfer_prefill_wrapper_paged = BatchPrefillWithPagedKVCacheWrapper(
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self.flashinfer_workspace_buffer, "NHD"
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)
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self.flashinfer_decode_wrapper = BatchDecodeWithPagedKVCacheWrapper(
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self.flashinfer_workspace_buffer,
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"NHD",
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use_tensor_cores=use_tensor_cores,
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)
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else:
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self.flashinfer_workspace_buffer = torch.empty(
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global_config.flashinfer_workspace_size,
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dtype=torch.uint8,
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device="cuda",
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)
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self.flashinfer_prefill_wrapper_ragged = (
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BatchPrefillWithRaggedKVCacheWrapper(
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self.flashinfer_workspace_buffer, "NHD"
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)
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)
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self.flashinfer_prefill_wrapper_paged = []
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self.flashinfer_decode_wrapper = []
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for i in range(2):
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self.flashinfer_prefill_wrapper_paged.append(
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BatchPrefillWithPagedKVCacheWrapper(
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self.flashinfer_workspace_buffer, "NHD"
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)
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)
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self.flashinfer_decode_wrapper.append(
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BatchDecodeWithPagedKVCacheWrapper(
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self.flashinfer_workspace_buffer,
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"NHD",
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use_tensor_cores=use_tensor_cores,
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)
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)
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def init_cuda_graphs(self):
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from sglang.srt.model_executor.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(
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f"[gpu={self.gpu_id}] Capture cuda graph begin. This can take up to several minutes."
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)
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batch_size_list = [1, 2, 4] + [i * 8 for i in range(1, 17)]
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self.cuda_graph_runner = CudaGraphRunner(
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self,
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max_batch_size_to_capture=max(batch_size_list),
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use_torch_compile=self.server_args.enable_torch_compile,
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)
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try:
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self.cuda_graph_runner.capture(batch_size_list)
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except RuntimeError as e:
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raise Exception(
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f"Capture cuda graph failed: {e}\n"
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"Possible solutions:\n"
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"1. disable torch compile by not using --enable-torch-compile\n"
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"2. disable cuda graph by --disable-cuda-graph\n"
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"3. set --mem-fraction-static to a smaller value\n"
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"Open an issue on GitHub https://github.com/sgl-project/sglang/issues/new/choose \n"
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)
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@torch.inference_mode()
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def forward_decode(self, batch: ScheduleBatch):
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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.from_schedule_batch(
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self,
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batch,
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ForwardMode.DECODE,
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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: ScheduleBatch):
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input_metadata = InputMetadata.from_schedule_batch(
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self,
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batch,
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forward_mode=ForwardMode.EXTEND,
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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: ScheduleBatch):
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input_metadata = InputMetadata.from_schedule_batch(
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self,
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batch,
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forward_mode=ForwardMode.EXTEND,
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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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input_metadata.pixel_values,
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input_metadata.image_sizes,
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input_metadata.image_offsets,
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)
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def forward(self, batch: ScheduleBatch, 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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assert tmp.__name__ not in model_arch_name_to_cls
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model_arch_name_to_cls[tmp.__name__] = tmp
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else:
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assert entry.__name__ not in model_arch_name_to_cls
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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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|
assert remap[0] not in model_arch_name_to_cls
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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}. "
|
|
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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