824 lines
32 KiB
Python
824 lines
32 KiB
Python
# 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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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 gc
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import json
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import logging
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import time
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from typing import List, Optional, Tuple
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import torch
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import torch.distributed as dist
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from sglang.srt.configs.device_config import DeviceConfig
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from sglang.srt.configs.load_config import LoadConfig
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from sglang.srt.configs.model_config import AttentionArch, ModelConfig
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from sglang.srt.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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set_custom_all_reduce,
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)
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from sglang.srt.distributed.parallel_state import monkey_patch_vllm_parallel_state
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from sglang.srt.layers.attention.double_sparsity_backend import DoubleSparseAttnBackend
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from sglang.srt.layers.attention.flashinfer_backend import FlashInferAttnBackend
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from sglang.srt.layers.attention.torch_native_backend import TorchNativeAttnBackend
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from sglang.srt.layers.attention.triton_backend import TritonAttnBackend
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from sglang.srt.layers.dp_attention import (
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get_attention_tp_group,
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get_attention_tp_size,
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initialize_dp_attention,
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)
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from sglang.srt.layers.logits_processor import LogitsProcessorOutput
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from sglang.srt.layers.sampler import Sampler
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from sglang.srt.layers.torchao_utils import apply_torchao_config_to_model
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from sglang.srt.lora.lora_manager import LoRAManager
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from sglang.srt.managers.schedule_batch import global_server_args_dict
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from sglang.srt.mem_cache.memory_pool import (
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DoubleSparseTokenToKVPool,
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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_executor.forward_batch_info import ForwardBatch
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from sglang.srt.model_loader import get_model
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from sglang.srt.server_args import ServerArgs
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from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
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from sglang.srt.torch_memory_saver_adapter import TorchMemorySaverAdapter
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from sglang.srt.utils import (
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enable_show_time_cost,
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get_available_gpu_memory,
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init_custom_process_group,
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is_cuda,
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is_hip,
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monkey_patch_p2p_access_check,
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monkey_patch_vllm_gguf_config,
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set_cpu_offload_max_bytes,
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)
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logger = logging.getLogger(__name__)
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class ModelRunner:
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"""ModelRunner runs the forward passes of the models."""
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def __init__(
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self,
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model_config: ModelConfig,
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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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is_draft_worker: bool = False,
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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.device = server_args.device
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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.dist_port = nccl_port
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self.server_args = server_args
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self.is_draft_worker = is_draft_worker
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self.is_generation = model_config.is_generation
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self.is_multimodal = model_config.is_multimodal
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self.should_log = tp_rank == 0
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self.spec_algorithm = SpeculativeAlgorithm.from_string(
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server_args.speculative_algorithm
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)
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# Model-specific adjustment
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if (
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self.model_config.attention_arch == AttentionArch.MLA
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and not self.server_args.disable_mla
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):
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# TODO: add MLA optimization on CPU
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if self.server_args.device != "cpu":
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logger.info("MLA optimization is turned on. Use triton backend.")
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self.server_args.attention_backend = "triton"
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if self.server_args.enable_double_sparsity:
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logger.info(
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"Double sparsity optimization is turned on. Use triton backend without CUDA graph."
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)
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self.server_args.attention_backend = "triton"
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self.server_args.disable_cuda_graph = True
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if self.server_args.ds_heavy_channel_type is None:
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raise ValueError(
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"Please specify the heavy channel type for double sparsity optimization."
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)
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self.init_double_sparsity_channel_config(
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self.server_args.ds_heavy_channel_type
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)
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if self.is_multimodal:
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self.mem_fraction_static *= 0.95
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logger.info(
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f"Automatically reduce --mem-fraction-static to {self.mem_fraction_static:.3f} "
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f"because this is a multimodal model."
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)
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if self.model_config.hf_config.architectures == [
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"MllamaForConditionalGeneration"
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]:
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logger.info("Automatically turn off --chunked-prefill-size for mllama.")
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server_args.chunked_prefill_size = -1
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if self.model_config.hf_config.architectures == [
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"Qwen2VLForConditionalGeneration"
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]:
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# TODO: qwen2-vl does not support radix cache now, set disable_radix_cache=True automatically
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logger.info(
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"Automatically turn off --chunked-prefill-size and disable radix cache for qwen2-vl."
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)
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server_args.chunked_prefill_size = -1
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server_args.disable_radix_cache = True
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# Global vars
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if server_args.show_time_cost:
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enable_show_time_cost()
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if server_args.disable_outlines_disk_cache:
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from outlines.caching import disable_cache
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disable_cache()
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global_server_args_dict.update(
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{
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"attention_backend": server_args.attention_backend,
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"sampling_backend": server_args.sampling_backend,
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"triton_attention_reduce_in_fp32": server_args.triton_attention_reduce_in_fp32,
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"disable_mla": server_args.disable_mla,
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"torchao_config": server_args.torchao_config,
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"enable_nan_detection": server_args.enable_nan_detection,
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"enable_dp_attention": server_args.enable_dp_attention,
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"enable_ep_moe": server_args.enable_ep_moe,
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"device": server_args.device,
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}
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)
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set_cpu_offload_max_bytes(int(server_args.cpu_offload_gb * 1024**3))
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# Get memory before model loading
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min_per_gpu_memory = self.init_torch_distributed()
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self.memory_saver_adapter = TorchMemorySaverAdapter.create(
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enable=self.server_args.enable_memory_saver
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)
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# Load the model
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self.sampler = Sampler()
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self.load_model()
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# Apply torchao quantization
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torchao_applied = getattr(self.model, "torchao_applied", False)
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# In layered loading, torchao may have been applied
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if not torchao_applied:
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apply_torchao_config_to_model(
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self.model, global_server_args_dict["torchao_config"]
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)
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# Apply torch TP if the model supports it
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supports_torch_tp = getattr(self.model, "supports_torch_tp", False)
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if self.tp_size > 1 and supports_torch_tp:
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self.apply_torch_tp()
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self.torch_tp_applied = True
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else:
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self.torch_tp_applied = False
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# Init memory pool and attention backends
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if server_args.lora_paths is not None:
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self.init_lora_manager()
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self.init_memory_pool(
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min_per_gpu_memory,
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server_args.max_running_requests,
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server_args.max_total_tokens,
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)
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if self.device == "cuda":
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self.init_cublas()
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self.init_attention_backend()
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self.init_cuda_graphs()
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else:
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self.cuda_graph_runner = None
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self.init_attention_backend()
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def init_torch_distributed(self):
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logger.info("Init torch distributed begin.")
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# Init torch distributed
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torch.get_device_module(self.device).set_device(self.gpu_id)
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if self.device == "cuda":
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backend = "nccl"
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elif self.device == "xpu":
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# TODO(liangan1): Just use gloo to bypass the initilization fail
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# Need to use xccl for xpu backend in the future
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backend = "gloo"
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elif self.device == "hpu":
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backend = "hccl"
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elif self.device == "cpu":
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backend = "gloo"
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if not self.server_args.enable_p2p_check:
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monkey_patch_p2p_access_check()
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if self.server_args.dist_init_addr:
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dist_init_method = f"tcp://{self.server_args.dist_init_addr}"
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else:
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dist_init_method = f"tcp://127.0.0.1:{self.dist_port}"
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set_custom_all_reduce(not self.server_args.disable_custom_all_reduce)
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if not self.is_draft_worker:
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# Only initialize the distributed environment on the target model worker.
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init_distributed_environment(
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backend=backend,
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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=dist_init_method,
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)
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initialize_model_parallel(tensor_model_parallel_size=self.tp_size)
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initialize_dp_attention(
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enable_dp_attention=self.server_args.enable_dp_attention,
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tp_rank=self.tp_rank,
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tp_size=self.tp_size,
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dp_size=self.server_args.dp_size,
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)
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min_per_gpu_memory = get_available_gpu_memory(
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self.device, self.gpu_id, distributed=self.tp_size > 1
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)
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self.tp_group = get_tp_group()
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self.attention_tp_group = get_attention_tp_group()
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# Check memory for tensor parallelism
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if self.tp_size > 1:
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local_gpu_memory = get_available_gpu_memory(self.device, self.gpu_id)
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if min_per_gpu_memory < local_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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return min_per_gpu_memory
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def load_model(self):
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logger.info(
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f"Load weight begin. avail mem={get_available_gpu_memory(self.device, self.gpu_id):.2f} GB"
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)
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# This can reduce thread conflicts and speed up weight loading.
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if self.device != "cpu":
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torch.set_num_threads(1)
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if self.device == "cuda":
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if torch.cuda.get_device_capability()[0] < 8:
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logger.info(
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"Compute capability below sm80. Use float16 due to lack of bfloat16 support."
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)
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self.server_args.dtype = "float16"
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self.model_config.dtype = torch.float16
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if torch.cuda.get_device_capability()[1] < 5:
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raise RuntimeError("SGLang only supports sm75 and above.")
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# Prepare the model config
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self.load_config = LoadConfig(
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load_format=self.server_args.load_format,
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download_dir=self.server_args.download_dir,
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)
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if self.server_args.load_format == "gguf":
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monkey_patch_vllm_gguf_config()
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# Load the model
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# Remove monkey_patch when linear.py quant remove dependencies with vllm
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monkey_patch_vllm_parallel_state()
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with self.memory_saver_adapter.region():
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self.model = get_model(
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model_config=self.model_config,
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load_config=self.load_config,
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device_config=DeviceConfig(self.device),
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)
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monkey_patch_vllm_parallel_state(reverse=True)
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if self.server_args.kv_cache_dtype == "fp8_e4m3":
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if self.server_args.quantization_param_path is not None:
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if callable(getattr(self.model, "load_kv_cache_scales", None)):
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self.model.load_kv_cache_scales(
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self.server_args.quantization_param_path
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)
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logger.info(
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"Loaded KV cache scaling factors from %s",
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self.server_args.quantization_param_path,
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)
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else:
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raise RuntimeError(
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"Using FP8 KV cache and scaling factors provided but "
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"model %s does not support loading scaling factors.",
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self.model.__class__,
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)
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else:
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logger.warning(
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"Using FP8 KV cache but no scaling factors "
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"provided. Defaulting to scaling factors of 1.0. "
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"This may lead to less accurate results!"
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)
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# Parse other args
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self.sliding_window_size = (
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self.model.get_attention_sliding_window_size()
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if hasattr(self.model, "get_attention_sliding_window_size")
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else None
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)
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self.dtype = self.model_config.dtype
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logger.info(
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f"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.device, self.gpu_id):.2f} GB"
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)
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def update_weights_from_disk(
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self, model_path: str, load_format: str
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) -> tuple[bool, str]:
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"""Update engine weights in-place from the disk."""
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from sglang.srt.model_loader.loader import (
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DefaultModelLoader,
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device_loading_context,
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get_model_loader,
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)
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from sglang.srt.model_loader.utils import set_default_torch_dtype
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logger.info(
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f"Update engine weights online from disk begin. "
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f"avail mem={get_available_gpu_memory(self.device, self.gpu_id):.2f} GB"
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)
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target_device = torch.device(self.device)
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self.model_config.model_path = model_path
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load_config = LoadConfig(load_format=load_format)
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# Only support vllm DefaultModelLoader for now
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loader = get_model_loader(load_config)
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if not isinstance(loader, DefaultModelLoader):
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message = f"Failed to get model loader: {loader}."
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return False, message
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def get_weight_iter(config):
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iter = loader._get_weights_iterator(
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DefaultModelLoader.Source(
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config.model_path,
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revision=config.revision,
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fall_back_to_pt=getattr(
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self.model, "fall_back_to_pt_during_load", True
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),
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)
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)
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return iter
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def model_load_weights(model, iter):
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model.load_weights(iter)
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for _, module in self.model.named_modules():
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quant_method = getattr(module, "quant_method", None)
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if quant_method is not None:
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with device_loading_context(module, target_device):
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quant_method.process_weights_after_loading(module)
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return model
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with set_default_torch_dtype(self.model_config.dtype):
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try:
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iter = get_weight_iter(self.model_config)
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except Exception as e:
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message = f"Failed to get weights iterator: {e}."
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return False, message
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try:
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model = model_load_weights(self.model, iter)
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except Exception as e:
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message = (
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f"Failed to update weights: {e}.\nRolling back to original weights."
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)
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del iter
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gc.collect()
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iter = get_weight_iter(self.model_config)
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self.model = model_load_weights(self.model, iter)
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return False, message
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self.model = model
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self.server_args.model_path = model_path
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self.server_args.load_format = load_format
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self.load_config = load_config
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logger.info("Update weights end.")
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return True, "Succeeded to update model weights."
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def init_weights_update_group(
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self,
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master_address,
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master_port,
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rank_offset,
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world_size,
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group_name,
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backend="nccl",
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):
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"""Initialize the Torch process group for model parameter updates.
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`_model_update_group` is used in the RLHF workflow, where rank
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0 is the actor model in the training engine, and the other ranks are
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the inference engine, which is used for rollout.
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In the RLHF workflow, the training engine updates the model
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weights/parameters online, and broadcasts them to the inference
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engine through the `_model_update_group` process group.
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"""
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assert (
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torch.distributed.is_initialized()
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), "Default torch process group must be initialized"
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assert group_name != "", "Group name cannot be empty"
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rank = rank_offset + self.tp_rank
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logger.info(
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f"init custom process group: master_address={master_address}, master_port={master_port}, "
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f"rank_offset={rank_offset}, rank={rank}, world_size={world_size}, group_name={group_name}, backend={backend}"
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)
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try:
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self._model_update_group = init_custom_process_group(
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backend=backend,
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init_method=f"tcp://{master_address}:{master_port}",
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world_size=world_size,
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rank=rank,
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group_name=group_name,
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)
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dist.barrier(group=self._model_update_group, device_ids=[rank])
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return True, "Succeeded to initialize custom process group."
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except Exception as e:
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message = f"Failed to initialize custom process group: {e}."
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logger.error(message)
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return False, message
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|
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def update_weights_from_distributed(self, name, dtype, shape):
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"""
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Update specific parameter in the model weights online
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through `_model_update_group` process group.
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Args:
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name: the name of the parameter to be updated.
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dtype: the data type of the parameter to be updated.
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shape: the shape of the parameter to be updated.
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"""
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target_dtype = (
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dtype if isinstance(dtype, torch.dtype) else getattr(torch, dtype)
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)
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assert (
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self._model_update_group is not None
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|
), "model update group must be initialized"
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try:
|
|
weights = torch.empty(shape, dtype=target_dtype, device=self.device)
|
|
torch.distributed.broadcast(weights, src=0, group=self._model_update_group)
|
|
self.model.load_weights([(name, weights)])
|
|
return True, f"Succeeded to update parameter {name} online."
|
|
|
|
except Exception as e:
|
|
error_msg = (
|
|
f"Failed to update parameter online: {e}. "
|
|
f"The full weights of the ModelRunner are partially updated. "
|
|
f"Please discard the whole weights."
|
|
)
|
|
logger.error(error_msg)
|
|
return False, error_msg
|
|
|
|
def update_weights_from_tensor(self, named_tensors: List[Tuple[str, torch.Tensor]]):
|
|
self.model.load_weights(named_tensors)
|
|
return True, "Success"
|
|
|
|
def get_weights_by_name(
|
|
self, name: str, truncate_size: int = 100
|
|
) -> Optional[torch.Tensor]:
|
|
"""Get the weights of the parameter by its name. Similar to `get_parameter` in Hugging Face.
|
|
|
|
Only used for unit test with an unoptimized performance.
|
|
For optimized performance, please use torch.save and torch.load.
|
|
"""
|
|
# TODO: (chenyang) Add support for Qwen models.
|
|
try:
|
|
return self.model.get_weights_by_name(
|
|
name, truncate_size, tp_size=self.tp_size
|
|
)
|
|
except Exception as e:
|
|
logger.error(f"Error when getting parameter {name}: {e}")
|
|
return None
|
|
|
|
def init_lora_manager(self):
|
|
self.lora_manager = LoRAManager(
|
|
base_model=self.model,
|
|
lora_paths=self.server_args.lora_paths,
|
|
base_hf_config=self.model_config.hf_config,
|
|
max_loras_per_batch=self.server_args.max_loras_per_batch,
|
|
load_config=self.load_config,
|
|
dtype=self.dtype,
|
|
)
|
|
logger.info("LoRA manager ready.")
|
|
|
|
def profile_max_num_token(self, total_gpu_memory: int):
|
|
available_gpu_memory = get_available_gpu_memory(
|
|
self.device, self.gpu_id, distributed=self.tp_size > 1
|
|
)
|
|
if (
|
|
self.model_config.attention_arch == AttentionArch.MLA
|
|
and not self.server_args.disable_mla
|
|
):
|
|
cell_size = (
|
|
(self.model_config.kv_lora_rank + self.model_config.qk_rope_head_dim)
|
|
* self.model_config.num_hidden_layers
|
|
* torch._utils._element_size(self.kv_cache_dtype)
|
|
)
|
|
else:
|
|
cell_size = (
|
|
self.model_config.get_num_kv_heads(get_attention_tp_size())
|
|
* self.model_config.head_dim
|
|
* self.model_config.num_hidden_layers
|
|
* 2
|
|
* torch._utils._element_size(self.kv_cache_dtype)
|
|
)
|
|
rest_memory = available_gpu_memory - total_gpu_memory * (
|
|
1 - self.mem_fraction_static
|
|
)
|
|
max_num_token = int(rest_memory * (1 << 30) // cell_size)
|
|
return max_num_token
|
|
|
|
def init_memory_pool(
|
|
self,
|
|
total_gpu_memory: int,
|
|
max_num_reqs: Optional[int] = None,
|
|
max_total_tokens: Optional[int] = None,
|
|
):
|
|
if self.server_args.kv_cache_dtype == "auto":
|
|
self.kv_cache_dtype = self.dtype
|
|
elif self.server_args.kv_cache_dtype == "fp8_e5m2":
|
|
if is_hip(): # Using natively supported format
|
|
self.kv_cache_dtype = torch.float8_e5m2fnuz
|
|
else:
|
|
self.kv_cache_dtype = torch.float8_e5m2
|
|
elif self.server_args.kv_cache_dtype == "fp8_e4m3":
|
|
if is_cuda():
|
|
self.kv_cache_dtype = torch.float8_e4m3fn
|
|
else:
|
|
raise ValueError(
|
|
f"Unsupported kv_cache_dtype: {self.server_args.kv_cache_dtype}."
|
|
)
|
|
|
|
self.max_total_num_tokens = self.profile_max_num_token(total_gpu_memory)
|
|
|
|
if max_num_reqs is None:
|
|
max_num_reqs = min(
|
|
max(
|
|
int(
|
|
self.max_total_num_tokens / self.model_config.context_len * 512
|
|
),
|
|
2048,
|
|
),
|
|
4096,
|
|
)
|
|
|
|
if not self.spec_algorithm.is_none():
|
|
if self.is_draft_worker:
|
|
self.max_total_num_tokens = self.server_args.draft_runner_cache_size
|
|
else:
|
|
self.server_args.draft_runner_cache_size = (
|
|
self.max_total_num_tokens
|
|
+ max_num_reqs * self.server_args.speculative_num_steps
|
|
+ 100
|
|
)
|
|
|
|
if max_total_tokens is not None:
|
|
if max_total_tokens > self.max_total_num_tokens:
|
|
logging.warning(
|
|
f"max_total_tokens={max_total_tokens} is larger than the profiled value "
|
|
f"{self.max_total_num_tokens}. "
|
|
f"Use the profiled value instead."
|
|
)
|
|
self.max_total_num_tokens = min(self.max_total_num_tokens, max_total_tokens)
|
|
|
|
if self.max_total_num_tokens <= 0:
|
|
raise RuntimeError(
|
|
"Not enough memory. Please try to increase --mem-fraction-static."
|
|
)
|
|
|
|
self.req_to_token_pool = ReqToTokenPool(
|
|
size=max_num_reqs + 1,
|
|
max_context_len=self.model_config.context_len + 4,
|
|
device=self.device,
|
|
enable_memory_saver=self.server_args.enable_memory_saver,
|
|
)
|
|
if (
|
|
self.model_config.attention_arch == AttentionArch.MLA
|
|
and not self.server_args.disable_mla
|
|
):
|
|
self.token_to_kv_pool = MLATokenToKVPool(
|
|
self.max_total_num_tokens,
|
|
dtype=self.kv_cache_dtype,
|
|
kv_lora_rank=self.model_config.kv_lora_rank,
|
|
qk_rope_head_dim=self.model_config.qk_rope_head_dim,
|
|
layer_num=self.model_config.num_hidden_layers,
|
|
device=self.device,
|
|
enable_memory_saver=self.server_args.enable_memory_saver,
|
|
)
|
|
elif self.server_args.enable_double_sparsity:
|
|
self.token_to_kv_pool = DoubleSparseTokenToKVPool(
|
|
self.max_total_num_tokens,
|
|
dtype=self.kv_cache_dtype,
|
|
head_num=self.model_config.get_num_kv_heads(get_attention_tp_size()),
|
|
head_dim=self.model_config.head_dim,
|
|
layer_num=self.model_config.num_hidden_layers,
|
|
device=self.device,
|
|
heavy_channel_num=self.server_args.ds_heavy_channel_num,
|
|
enable_memory_saver=self.server_args.enable_memory_saver,
|
|
)
|
|
else:
|
|
self.token_to_kv_pool = MHATokenToKVPool(
|
|
self.max_total_num_tokens,
|
|
dtype=self.kv_cache_dtype,
|
|
head_num=self.model_config.get_num_kv_heads(get_attention_tp_size()),
|
|
head_dim=self.model_config.head_dim,
|
|
layer_num=self.model_config.num_hidden_layers,
|
|
device=self.device,
|
|
enable_memory_saver=self.server_args.enable_memory_saver,
|
|
)
|
|
logger.info(
|
|
f"Memory pool end. "
|
|
f"avail mem={get_available_gpu_memory(self.device, self.gpu_id):.2f} GB"
|
|
)
|
|
|
|
def init_cublas(self):
|
|
"""We need to run a small matmul to init cublas. Otherwise, it will raise some errors later."""
|
|
dtype = torch.float16
|
|
device = "cuda"
|
|
a = torch.ones((16, 16), dtype=dtype, device=device)
|
|
b = torch.ones((16, 16), dtype=dtype, device=device)
|
|
c = a @ b
|
|
return c
|
|
|
|
def init_attention_backend(self):
|
|
"""Init attention kernel backend."""
|
|
if self.server_args.attention_backend == "flashinfer":
|
|
self.attn_backend = FlashInferAttnBackend(self)
|
|
elif self.server_args.attention_backend == "triton":
|
|
assert self.sliding_window_size is None, (
|
|
"Window attention is not supported in the triton attention backend. "
|
|
"Please use `--attention-backend flashinfer`."
|
|
)
|
|
assert not self.model_config.is_encoder_decoder, (
|
|
"Cross attention is not supported in the triton attention backend. "
|
|
"Please use `--attention-backend flashinfer`."
|
|
)
|
|
if self.server_args.enable_double_sparsity:
|
|
self.attn_backend = DoubleSparseAttnBackend(self)
|
|
else:
|
|
self.attn_backend = TritonAttnBackend(self)
|
|
elif self.server_args.attention_backend == "torch_native":
|
|
self.attn_backend = TorchNativeAttnBackend(self)
|
|
else:
|
|
raise ValueError(
|
|
f"Invalid attention backend: {self.server_args.attention_backend}"
|
|
)
|
|
|
|
def init_double_sparsity_channel_config(self, selected_channel):
|
|
selected_channel = "." + selected_channel + "_proj"
|
|
self.sorted_channels = []
|
|
# load channel config
|
|
with open(self.server_args.ds_channel_config_path, "r") as f:
|
|
channel_config = json.load(f)
|
|
|
|
for i in range(self.model_config.num_hidden_layers):
|
|
key = "model.layers." + str(i) + ".self_attn" + selected_channel
|
|
self.sorted_channels.append(
|
|
torch.tensor(channel_config[key])[
|
|
:, : self.server_args.ds_heavy_channel_num
|
|
]
|
|
.contiguous()
|
|
.cuda()
|
|
)
|
|
|
|
def init_cuda_graphs(self):
|
|
"""Capture cuda graphs."""
|
|
from sglang.srt.model_executor.cuda_graph_runner import CudaGraphRunner
|
|
|
|
self.cuda_graph_runner = None
|
|
|
|
if not self.is_generation:
|
|
# TODO: Currently, cuda graph only captures decode steps, which only exists for generation models
|
|
return
|
|
|
|
if self.server_args.disable_cuda_graph:
|
|
return
|
|
|
|
tic = time.time()
|
|
logger.info("Capture cuda graph begin. This can take up to several minutes.")
|
|
self.cuda_graph_runner = CudaGraphRunner(self)
|
|
logger.info(f"Capture cuda graph end. Time elapsed: {time.time() - tic:.2f} s")
|
|
|
|
def apply_torch_tp(self):
|
|
logger.info(f"Enabling torch tensor parallelism on {self.tp_size} devices.")
|
|
from sglang.srt.model_parallel import tensor_parallel
|
|
|
|
device_mesh = torch.distributed.init_device_mesh(self.device, (self.tp_size,))
|
|
tensor_parallel(self.model, device_mesh)
|
|
|
|
def forward_decode(self, forward_batch: ForwardBatch):
|
|
self.attn_backend.init_forward_metadata(forward_batch)
|
|
return self.model.forward(
|
|
forward_batch.input_ids, forward_batch.positions, forward_batch
|
|
)
|
|
|
|
def forward_extend(self, forward_batch: ForwardBatch):
|
|
self.attn_backend.init_forward_metadata(forward_batch)
|
|
if self.is_generation:
|
|
if forward_batch.input_embeds is None:
|
|
return self.model.forward(
|
|
forward_batch.input_ids, forward_batch.positions, forward_batch
|
|
)
|
|
else:
|
|
return self.model.forward(
|
|
forward_batch.input_ids,
|
|
forward_batch.positions,
|
|
forward_batch,
|
|
input_embeds=forward_batch.input_embeds.bfloat16(),
|
|
)
|
|
else:
|
|
# Only embedding models have get_embedding parameter
|
|
return self.model.forward(
|
|
forward_batch.input_ids,
|
|
forward_batch.positions,
|
|
forward_batch,
|
|
get_embedding=True,
|
|
)
|
|
|
|
def forward_idle(self, forward_batch: ForwardBatch):
|
|
return self.model.forward(
|
|
forward_batch.input_ids, forward_batch.positions, forward_batch
|
|
)
|
|
|
|
def forward(self, forward_batch: ForwardBatch) -> LogitsProcessorOutput:
|
|
if (
|
|
forward_batch.forward_mode.is_cuda_graph()
|
|
and self.cuda_graph_runner
|
|
and self.cuda_graph_runner.can_run(forward_batch)
|
|
):
|
|
return self.cuda_graph_runner.replay(forward_batch)
|
|
|
|
if forward_batch.forward_mode.is_decode():
|
|
return self.forward_decode(forward_batch)
|
|
elif forward_batch.forward_mode.is_extend():
|
|
return self.forward_extend(forward_batch)
|
|
elif forward_batch.forward_mode.is_idle():
|
|
return self.forward_idle(forward_batch)
|
|
else:
|
|
raise ValueError(f"Invalid forward mode: {forward_batch.forward_mode}")
|
|
|
|
def sample(
|
|
self, logits_output: LogitsProcessorOutput, forward_batch: ForwardBatch
|
|
) -> torch.Tensor:
|
|
# Apply logit bias
|
|
sampling_info = forward_batch.sampling_info
|
|
if sampling_info.sampling_info_done:
|
|
# Overlap mode: the function update_regex_vocab_mask was executed
|
|
# in process_batch_result of the last batch.
|
|
if sampling_info.grammars:
|
|
sampling_info.sampling_info_done.wait()
|
|
else:
|
|
# Normal mode: Put CPU-heavy tasks here. They will be overlapped with the forward pass.
|
|
sampling_info.update_regex_vocab_mask()
|
|
sampling_info.update_penalties()
|
|
sampling_info.apply_logits_bias(logits_output.next_token_logits)
|
|
|
|
# Sample the next tokens
|
|
next_token_ids = self.sampler(
|
|
logits_output,
|
|
sampling_info,
|
|
forward_batch.return_logprob,
|
|
forward_batch.top_logprobs_nums,
|
|
)
|
|
return next_token_ids
|
|
|
|
@property
|
|
def model_is_mrope(self) -> bool:
|
|
"""Detect if the model has "mrope" rope_scaling type.
|
|
mrope requires keep "rope_deltas" between prompt and decoding phases."""
|
|
rope_scaling = getattr(self.model_config.hf_config, "rope_scaling", {})
|
|
if rope_scaling is None:
|
|
return False
|
|
return rope_scaling.get("type", None) == "mrope"
|