65bf2ac165
This PR combines prepare_prompt and prepare_decode into a single API. This PR also coelsce the attn metadata for prefill/decode to a single class and allow to slice them when running attn backend. It also refactors subquery_start_loc which was not refactored in the previous PR
1027 lines
44 KiB
Python
1027 lines
44 KiB
Python
import time
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from typing import Dict, List, NamedTuple, Optional, Set, Tuple, Union
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import numpy as np
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import torch
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import torch.nn as nn
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from vllm.attention import AttentionMetadata, get_attn_backend
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from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
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ModelConfig, ParallelConfig, SchedulerConfig,
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VisionLanguageConfig)
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from vllm.distributed import broadcast_tensor_dict
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from vllm.distributed.communication_op import graph_capture, graph_mode
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from vllm.logger import init_logger
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from vllm.lora.layers import LoRAMapping
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from vllm.lora.request import LoRARequest
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from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager
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from vllm.model_executor import SamplingMetadata
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from vllm.model_executor.model_loader import get_model
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from vllm.sampling_params import SamplingParams
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from vllm.sequence import (MultiModalData, SamplerOutput, SequenceData,
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SequenceGroupMetadata)
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from vllm.utils import (CudaMemoryProfiler, get_kv_cache_torch_dtype, is_hip,
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is_pin_memory_available, make_tensor_with_pad)
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logger = init_logger(__name__)
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_PAD_SLOT_ID = -1
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LORA_WARMUP_RANK = 8
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_BATCH_SIZE_ALIGNMENT = 8
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# Capture graphs for token size 1, 2, 4, 8, 16, 24, 32, 40, ..., 256.
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# NOTE: _get_graph_batch_size needs to be updated if this list is changed.
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_BATCH_SIZES_TO_CAPTURE = [1, 2, 4] + [
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_BATCH_SIZE_ALIGNMENT * i for i in range(1, 33)
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]
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class ModelInput(NamedTuple):
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input_tokens: torch.Tensor
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input_positions: torch.Tensor
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attn_metadata: Optional[AttentionMetadata]
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seq_lens: List[int]
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query_lens: List[int]
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lora_mapping: Optional[LoRAMapping]
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lora_requests: Set[LoRARequest]
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multi_modal_input: Optional[torch.Tensor]
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slot_mapping: torch.Tensor
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num_prefill_tokens: int
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num_decode_tokens: int
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num_prefills: int
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@classmethod
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def empty(cls, device):
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return ModelInput(
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input_tokens=torch.empty(0, device=device),
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input_positions=torch.empty(0, device=device),
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attn_metadata=None,
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seq_lens=[],
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query_lens=[],
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lora_mapping=None,
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lora_requests=set(),
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multi_modal_input=None,
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slot_mapping=torch.empty(0, device=device),
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num_prefill_tokens=0,
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num_decode_tokens=0,
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num_prefills=0,
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)
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class ModelRunner:
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def __init__(
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self,
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model_config: ModelConfig,
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parallel_config: ParallelConfig,
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scheduler_config: SchedulerConfig,
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device_config: DeviceConfig,
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cache_config: CacheConfig,
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load_config: LoadConfig,
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lora_config: Optional[LoRAConfig],
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kv_cache_dtype: Optional[str] = "auto",
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is_driver_worker: bool = False,
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vision_language_config: Optional[VisionLanguageConfig] = None,
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):
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self.model_config = model_config
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self.parallel_config = parallel_config
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self.scheduler_config = scheduler_config
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self.device_config = device_config
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self.cache_config = cache_config
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self.lora_config = lora_config
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self.load_config = load_config
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self.is_driver_worker = is_driver_worker
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self.vision_language_config = vision_language_config
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self.device = self.device_config.device
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self.pin_memory = is_pin_memory_available()
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self.kv_cache_dtype = kv_cache_dtype
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self.sliding_window = model_config.get_sliding_window()
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self.block_size = cache_config.block_size
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self.max_seq_len_to_capture = self.model_config.max_seq_len_to_capture
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self.graph_runners: Dict[int, CUDAGraphRunner] = {}
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self.graph_memory_pool: Optional[Tuple[
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int, int]] = None # Set during graph capture.
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# When using CUDA graph, the input block tables must be padded to
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# max_seq_len_to_capture. However, creating the block table in
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# Python can be expensive. To optimize this, we cache the block table
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# in numpy and only copy the actual input content at every iteration.
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# The shape of the cached block table will be
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# (max batch size to capture, max context len to capture / block size).
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self.graph_block_tables = np.zeros(
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(max(_BATCH_SIZES_TO_CAPTURE), self.get_max_block_per_batch()),
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dtype=np.int32)
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self.attn_backend = get_attn_backend(
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self.model_config.get_num_attention_heads(self.parallel_config),
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self.model_config.get_head_size(),
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self.model_config.get_num_kv_heads(self.parallel_config),
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self.model_config.get_sliding_window(),
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self.model_config.dtype,
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self.kv_cache_dtype,
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self.block_size,
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)
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# Lazy initialization
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self.model: nn.Module # Set after load_model
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# Set if the backend is flashinfer.
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self.flashinfer_workspace_buffer: torch.Tensor
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# Set after load_model.
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self.lora_manager: Optional[LRUCacheWorkerLoRAManager] = None
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def load_model(self) -> None:
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with CudaMemoryProfiler() as m:
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self.model = get_model(
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model_config=self.model_config,
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device_config=self.device_config,
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load_config=self.load_config,
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lora_config=self.lora_config,
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vision_language_config=self.vision_language_config,
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parallel_config=self.parallel_config,
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scheduler_config=self.scheduler_config,
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cache_config=self.cache_config,
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)
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self.model_memory_usage = m.consumed_memory
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logger.info("Loading model weights took %.4f GB",
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self.model_memory_usage / float(2**30))
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if self.lora_config:
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assert hasattr(self.model, "supported_lora_modules"
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) and self.model.supported_lora_modules, (
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"Model does not support LoRA")
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assert hasattr(
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self.model,
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"embedding_modules"), "Model does not have embedding_modules"
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assert hasattr(self.model, "embedding_padding_modules"
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), "Model does not have embedding_padding_modules"
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self.lora_manager = LRUCacheWorkerLoRAManager(
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self.scheduler_config.max_num_seqs,
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self.scheduler_config.max_num_batched_tokens, self.vocab_size,
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self.lora_config, self.device, self.model.embedding_modules,
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self.model.embedding_padding_modules)
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self.model = self.lora_manager.create_lora_manager(self.model)
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if self.kv_cache_dtype == "fp8" and is_hip():
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# Currently scaled KV cache is only enabled on ROCm
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if self.model_config.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.model_config.quantization_param_path)
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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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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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elif self.model_config.quantization_param_path is not None:
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logger.warning("KV cache scaling factors provided, "
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"but the KV cache data type is not FP8. "
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"KV cache scaling factors will not be used.")
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def get_max_block_per_batch(self) -> int:
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block_size = self.block_size
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return (self.max_seq_len_to_capture + block_size - 1) // block_size
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def _prepare_model_input(
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self,
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seq_group_metadata_list: List[SequenceGroupMetadata],
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) -> ModelInput:
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"""Prepare the model input based on a given sequence group.
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The API assumes seq_group_metadata_list is sorted by prefill -> decode.
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The result tensors and data structure also batches input in prefill
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-> decode order. For example,
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- input_tokens[:num_prefill_tokens] contains prefill tokens.
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- input_tokens[num_prefill_tokens:] contains decode tokens.
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If cuda graph is required, this API automatically pads inputs.
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"""
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input_tokens: List[int] = []
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input_positions: List[int] = []
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slot_mapping: List[int] = []
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lora_index_mapping: List[int] = []
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lora_prompt_mapping: List[int] = []
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lora_requests: Set[LoRARequest] = set()
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seq_lens: List[int] = []
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prefill_seq_lens: List[int] = []
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decode_seq_lens: List[int] = []
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context_lens: List[int] = []
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query_lens: List[int] = []
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block_tables: List[List[int]] = []
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multi_modal_input_list: List[torch.Tensor] = []
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decode_only = True
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num_prefills = 0
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num_prefill_tokens = 0
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num_decode_tokens = 0
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# The following fields are only for flashinfer
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# Please follow https://docs.flashinfer.ai/tutorials/kv_layout.html#page-layout
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# for the precise definition of the following fields.
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# An example:
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# request 1, page indices [0, 5, 8]
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# request 2, page indices [1, 6, 7]
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# request 3, page indices [3, 4]
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# paged_kv_indices is a concatenation of page indices of all requests:
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# [0, 5, 8, 1, 6, 7, 3, 4]
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# paged_kv_indptr is used to index into paged_kv_indices:
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# [0, 3, 6, 8]
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paged_kv_indices: List[int] = []
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# 0 at the beginning of paged_kv_indptr indicates the start of the
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# first request’s page indices in the paged_kv_indices list.
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paged_kv_indptr: List[int] = [0]
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# paged_kv_last_page_len is the length of the last page of each request
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paged_kv_last_page_len: List[int] = []
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if len(seq_group_metadata_list) == 0:
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return ModelInput.empty(self.device)
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for seq_group_metadata in seq_group_metadata_list:
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seq_ids = list(seq_group_metadata.seq_data.keys())
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is_prompt = seq_group_metadata.is_prompt
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for seq_id in seq_ids:
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computed_block_nums = seq_group_metadata.computed_block_nums
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if (self.scheduler_config is not None
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and self.scheduler_config.chunked_prefill_enabled
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and not (computed_block_nums is None
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or computed_block_nums == [])):
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raise RuntimeError(
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"chunked prefill cannot be used with prefix caching "
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"now.")
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seq_data = seq_group_metadata.seq_data[seq_id]
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if is_prompt:
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context_len = seq_data.get_num_computed_tokens()
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else:
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# get_num_computed_tokens is incorrect for spec decoding.
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# So, we should have a special logic here.
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# TODO(sang): Fix it.
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context_len = seq_data.get_len() - 1
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seq_len = min(
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seq_data.get_len(),
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context_len + seq_group_metadata.token_chunk_size)
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if is_prompt:
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tokens = seq_data.get_token_ids()[context_len:seq_len]
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else:
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# Optimization. get_token_ids requires the entire copy of
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# tokens.
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tokens = [seq_data.get_last_token_id()]
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# Prefix cache was hit.
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# Prefix is not supported with sliding_window
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prefix_cache_hit = (computed_block_nums is not None
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and len(computed_block_nums) > 0
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and self.sliding_window is None
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and is_prompt)
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# TODO(sang): Combine chunked prefill and prefix caching by
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# only allowing multiple of block_size chunk size.
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# NOTE: This only works for oooooooxxx style attention.
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if prefix_cache_hit:
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assert computed_block_nums is not None
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context_len = len(computed_block_nums) * self.block_size
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tokens = tokens[context_len:]
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if self.attn_backend.get_name() == "flash-attn":
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# NOTE(woosuk): For flash-attn, the block table should
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# include the entries for the incoming prefill tokens.
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# TODO(woosuk): This is a temporary fix. We should
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# provide a unified interface for different backends.
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block_table = seq_group_metadata.block_tables[seq_id]
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else:
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block_table = computed_block_nums
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elif (self.scheduler_config.chunked_prefill_enabled
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or not is_prompt):
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if seq_group_metadata.block_tables is not None:
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# chunked prefill or decode
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block_table = seq_group_metadata.block_tables[seq_id]
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if self.sliding_window is not None:
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# chunked prefill doesn't support sliding window.
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assert (not self.scheduler_config.
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chunked_prefill_enabled)
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sliding_window_blocks = (self.sliding_window //
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self.block_size)
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block_table = block_table[-sliding_window_blocks:]
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if self.attn_backend.get_name() == "flashinfer":
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paged_kv_indices.extend(block_table)
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paged_kv_indptr.append(paged_kv_indptr[-1] +
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len(block_table))
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last_page_len = seq_data.get_len(
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) % self.block_size
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if last_page_len == 0:
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last_page_len = self.block_size
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paged_kv_last_page_len.append(last_page_len)
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else:
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# Only happens when memory profiling runs.
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block_table = []
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else:
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# Prefill without chunked prefill or memory profiling.
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block_table = []
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block_tables.append(block_table)
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# TODO(sang): This is a hack to make sliding window work with
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# paged attn. We can remove it if we make paged attn kernel
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# to properly handle slinding window attn.
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if (self.sliding_window is not None and not is_prompt):
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seq_len = min(seq_len, self.sliding_window)
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context_len = seq_len - 1
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seq_lens.append(seq_len)
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context_lens.append(context_len)
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query_len = seq_len - context_len
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query_lens.append(query_len)
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input_tokens.extend(tokens)
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input_positions.extend(list(range(context_len, seq_len)))
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lora_id = seq_group_metadata.lora_int_id
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if is_prompt:
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assert len(seq_ids) == 1
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num_prefills += 1
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num_prefill_tokens += len(tokens)
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decode_only = False
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prefill_seq_lens.append(seq_len)
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else:
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assert query_len == 1, (
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"seq_len: {}, context_len: {}, query_len: {}".format(
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seq_len, context_len, query_len))
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num_decode_tokens += query_len
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decode_seq_lens.append(seq_len)
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if lora_id > 0:
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lora_requests.add(seq_group_metadata.lora_request)
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lora_index_mapping += [lora_id] * (seq_len - context_len)
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lora_prompt_mapping.extend(
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[lora_id] *
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(seq_len -
|
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context_len if seq_group_metadata.sampling_params
|
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and seq_group_metadata.sampling_params.prompt_logprobs
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else 1))
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|
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if seq_group_metadata.multi_modal_data:
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multi_modal_input_list.append(
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seq_group_metadata.multi_modal_data.data)
|
||
|
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if _is_block_tables_empty(seq_group_metadata.block_tables):
|
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# During memory profiling, the block tables are not
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# initialized yet. In this case, we just use a dummy
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# slot mapping.
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# In embeddings, the block tables are {seq_id: None}.
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slot_mapping.extend([_PAD_SLOT_ID] * seq_len)
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continue
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||
|
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# Compute the slot mapping.
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||
block_table = seq_group_metadata.block_tables[seq_id]
|
||
|
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# Mask the [0, start_idx) tokens of the prompt with
|
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# _PAD_SLOT_ID, where start_idx is max(0, seq_len -
|
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# sliding_window). For example, if the prompt len is 10,
|
||
# sliding window is 8, and block size is 4, the first two
|
||
# tokens are masked and the slot mapping will be
|
||
# [-1, -1, 2, 3, 4, 5, 6, 7, 0, 1].
|
||
start_idx = 0
|
||
if self.sliding_window is not None:
|
||
if is_prompt:
|
||
assert context_len == 0, (
|
||
"Prefix caching is currently not supported with "
|
||
"sliding window attention")
|
||
# It is an optimization. When it is decoding, it is always
|
||
# 0. When prefill, we use it to not write slots to kv cache
|
||
# to save memory.
|
||
start_idx = max(0, query_len - self.sliding_window)
|
||
|
||
for i in range(context_len, seq_len):
|
||
if i < start_idx:
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||
slot_mapping.append(_PAD_SLOT_ID)
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||
continue
|
||
|
||
block_number = block_table[i // self.block_size]
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||
block_offset = i % self.block_size
|
||
slot = block_number * self.block_size + block_offset
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||
slot_mapping.append(slot)
|
||
|
||
batch_size = len(input_tokens)
|
||
max_query_len = max(query_lens)
|
||
max_prefill_seq_len = max(prefill_seq_lens, default=0)
|
||
max_decode_seq_len = max(decode_seq_lens, default=0)
|
||
|
||
# If cuda graph can be used, pad tensors accordingly.
|
||
# See `capture_model` API for more details.
|
||
# vLLM uses cuda graph only for decoding requests.
|
||
use_captured_graph = (
|
||
decode_only and not self.model_config.enforce_eager
|
||
and batch_size <= _BATCH_SIZES_TO_CAPTURE[-1]
|
||
and max_decode_seq_len <= self.max_seq_len_to_capture)
|
||
if use_captured_graph:
|
||
graph_batch_size = _get_graph_batch_size(batch_size)
|
||
assert graph_batch_size >= batch_size
|
||
for _ in range(graph_batch_size - batch_size):
|
||
input_tokens.append(0)
|
||
input_positions.append(0)
|
||
slot_mapping.append(_PAD_SLOT_ID)
|
||
seq_lens.append(1)
|
||
block_tables.append([])
|
||
lora_index_mapping.append(0)
|
||
batch_size = graph_batch_size
|
||
num_decode_tokens = batch_size
|
||
|
||
if use_captured_graph:
|
||
# The shape of graph_block_tables is
|
||
# [max batch size, max context len // block size].
|
||
input_block_tables = self.graph_block_tables[:batch_size]
|
||
for i, block_table in enumerate(block_tables):
|
||
if block_table:
|
||
input_block_tables[i, :len(block_table)] = block_table
|
||
block_tables = torch.tensor(input_block_tables, device=self.device)
|
||
else:
|
||
max_block_table_len = max(
|
||
len(block_table) for block_table in block_tables)
|
||
block_tables = make_tensor_with_pad(
|
||
block_tables,
|
||
max_len=max_block_table_len,
|
||
pad=0,
|
||
dtype=torch.int,
|
||
device=self.device,
|
||
)
|
||
assert max_query_len > 0, ("query_lens: {}".format(query_lens))
|
||
|
||
context_lens_tensor = torch.tensor(context_lens,
|
||
dtype=torch.int,
|
||
device=self.device)
|
||
|
||
if multi_modal_input_list:
|
||
assert self.vision_language_config, (
|
||
"Multi-modal inputs are only supported by "
|
||
"vision language models.")
|
||
multi_modal_input = torch.cat(multi_modal_input_list,
|
||
dim=0).to(self.device)
|
||
else:
|
||
multi_modal_input = None
|
||
|
||
seq_lens_tensor = torch.tensor(seq_lens,
|
||
dtype=torch.int,
|
||
device=self.device)
|
||
query_lens_tensor = torch.tensor(query_lens,
|
||
dtype=torch.long,
|
||
device=self.device)
|
||
query_start_loc = torch.zeros(query_lens_tensor.shape[0] + 1,
|
||
dtype=torch.int32,
|
||
device=self.device)
|
||
|
||
seq_lens_tensor = torch.tensor(seq_lens,
|
||
dtype=torch.int,
|
||
device=self.device)
|
||
seq_start_loc = torch.zeros(seq_lens_tensor.shape[0] + 1,
|
||
dtype=torch.int32,
|
||
device=self.device)
|
||
|
||
torch.cumsum(query_lens_tensor,
|
||
dim=0,
|
||
dtype=query_start_loc.dtype,
|
||
out=query_start_loc[1:])
|
||
|
||
torch.cumsum(seq_lens_tensor,
|
||
dim=0,
|
||
dtype=seq_start_loc.dtype,
|
||
out=seq_start_loc[1:])
|
||
|
||
input_tokens_tensor = torch.tensor(input_tokens,
|
||
dtype=torch.long,
|
||
device=self.device)
|
||
input_positions_tensor = torch.tensor(input_positions,
|
||
dtype=torch.long,
|
||
device=self.device)
|
||
slot_mapping_tensor = torch.tensor(slot_mapping,
|
||
dtype=torch.long,
|
||
device=self.device)
|
||
|
||
if self.attn_backend.get_name() == "flashinfer":
|
||
if not hasattr(self, "flashinfer_workspace_buffer"):
|
||
# Allocate 16MB workspace buffer
|
||
# Follow the example of flashinfer: https://docs.flashinfer.ai/api/python/decode.html
|
||
self.flashinfer_workspace_buffer = torch.empty(
|
||
16 * 1024 * 1024, dtype=torch.uint8, device=self.device)
|
||
paged_kv_indptr_tensor = torch.tensor(paged_kv_indptr,
|
||
dtype=torch.int,
|
||
device=self.device)
|
||
paged_kv_indices_tensor = torch.tensor(paged_kv_indices,
|
||
dtype=torch.int,
|
||
device=self.device)
|
||
paged_kv_last_page_len_tensor = torch.tensor(
|
||
paged_kv_last_page_len, dtype=torch.int, device=self.device)
|
||
kv_cache_dtype = get_kv_cache_torch_dtype(self.kv_cache_dtype,
|
||
self.model_config.dtype)
|
||
attn_metadata = self.attn_backend.make_metadata(
|
||
num_prefills=num_prefills,
|
||
slot_mapping=slot_mapping_tensor,
|
||
num_prefill_tokens=num_prefill_tokens,
|
||
num_decode_tokens=num_decode_tokens,
|
||
use_cuda_graph=False,
|
||
max_prefill_seq_len=max_prefill_seq_len,
|
||
block_tables=block_tables,
|
||
workspace_buffer=self.flashinfer_workspace_buffer,
|
||
paged_kv_indptr=paged_kv_indptr_tensor,
|
||
paged_kv_indices=paged_kv_indices_tensor,
|
||
paged_kv_last_page_len=paged_kv_last_page_len_tensor,
|
||
num_qo_heads=self.model_config.get_num_attention_heads(
|
||
self.parallel_config),
|
||
num_kv_heads=self.model_config.get_num_kv_heads(
|
||
self.parallel_config),
|
||
head_dim=self.model_config.get_head_size(),
|
||
page_size=16,
|
||
seq_start_loc=seq_start_loc,
|
||
data_type=kv_cache_dtype)
|
||
else:
|
||
attn_metadata = self.attn_backend.make_metadata(
|
||
num_prefills=num_prefills,
|
||
slot_mapping=slot_mapping_tensor,
|
||
num_prefill_tokens=num_prefill_tokens,
|
||
num_decode_tokens=num_decode_tokens,
|
||
seq_lens=seq_lens,
|
||
seq_lens_tensor=seq_lens_tensor,
|
||
max_query_len=max_query_len,
|
||
max_prefill_seq_len=max_prefill_seq_len,
|
||
max_decode_seq_len=max_decode_seq_len,
|
||
query_start_loc=query_start_loc,
|
||
seq_start_loc=seq_start_loc,
|
||
context_lens_tensor=context_lens_tensor,
|
||
block_tables=block_tables,
|
||
use_cuda_graph=use_captured_graph,
|
||
)
|
||
|
||
if self.lora_config:
|
||
lora_mapping = LoRAMapping(
|
||
lora_index_mapping,
|
||
lora_prompt_mapping,
|
||
)
|
||
else:
|
||
lora_mapping = None
|
||
|
||
return ModelInput(
|
||
input_tokens=input_tokens_tensor,
|
||
input_positions=input_positions_tensor,
|
||
attn_metadata=attn_metadata,
|
||
seq_lens=seq_lens,
|
||
query_lens=query_lens,
|
||
lora_mapping=lora_mapping,
|
||
lora_requests=lora_requests,
|
||
multi_modal_input=multi_modal_input,
|
||
slot_mapping=slot_mapping_tensor,
|
||
num_prefill_tokens=num_prefill_tokens,
|
||
num_decode_tokens=num_decode_tokens,
|
||
num_prefills=num_prefills,
|
||
)
|
||
|
||
def prepare_input_tensors(
|
||
self,
|
||
seq_group_metadata_list: List[SequenceGroupMetadata],
|
||
) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata, SamplingMetadata,
|
||
Set[LoRARequest], LoRAMapping, torch.Tensor]:
|
||
if self.is_driver_worker:
|
||
# Prepare input tensors.
|
||
(
|
||
input_tokens,
|
||
input_positions,
|
||
attn_metadata,
|
||
seq_lens,
|
||
query_lens,
|
||
lora_mapping,
|
||
lora_requests,
|
||
multi_modal_input,
|
||
slot_mapping,
|
||
num_prefill_tokens,
|
||
num_decode_tokens,
|
||
num_prefills,
|
||
) = self._prepare_model_input(seq_group_metadata_list)
|
||
sampling_metadata = SamplingMetadata.prepare(
|
||
seq_group_metadata_list, seq_lens, query_lens, self.device,
|
||
self.pin_memory)
|
||
|
||
metadata_dict = {
|
||
"input_tokens": input_tokens,
|
||
"input_positions": input_positions,
|
||
"selected_token_indices":
|
||
sampling_metadata.selected_token_indices,
|
||
"lora_requests": lora_requests,
|
||
"lora_mapping": lora_mapping,
|
||
"multi_modal_input": multi_modal_input,
|
||
"num_prefill_tokens": num_prefill_tokens,
|
||
"num_decode_tokens": num_decode_tokens,
|
||
"slot_mapping": slot_mapping,
|
||
"num_prefills": num_prefills,
|
||
}
|
||
if attn_metadata:
|
||
metadata_dict.update(attn_metadata.asdict_zerocopy())
|
||
broadcast_tensor_dict(metadata_dict, src=0)
|
||
else:
|
||
metadata_dict = broadcast_tensor_dict(src=0)
|
||
input_tokens = metadata_dict.pop("input_tokens")
|
||
input_positions = metadata_dict.pop("input_positions")
|
||
selected_token_indices = metadata_dict.pop(
|
||
"selected_token_indices")
|
||
lora_mapping = metadata_dict.pop("lora_mapping")
|
||
lora_requests = metadata_dict.pop("lora_requests")
|
||
multi_modal_input = metadata_dict.pop("multi_modal_input")
|
||
if metadata_dict:
|
||
attn_metadata = self.attn_backend.make_metadata(
|
||
**metadata_dict)
|
||
else:
|
||
attn_metadata = None
|
||
sampling_metadata = SamplingMetadata(
|
||
seq_groups=None,
|
||
selected_token_indices=selected_token_indices,
|
||
categorized_sample_indices=None,
|
||
num_prompts=0,
|
||
)
|
||
|
||
return (input_tokens, input_positions, attn_metadata,
|
||
sampling_metadata, lora_requests, lora_mapping,
|
||
multi_modal_input)
|
||
|
||
@torch.inference_mode()
|
||
def execute_model(
|
||
self,
|
||
seq_group_metadata_list: List[SequenceGroupMetadata],
|
||
kv_caches: List[torch.Tensor],
|
||
) -> Optional[SamplerOutput]:
|
||
(input_tokens, input_positions, attn_metadata, sampling_metadata,
|
||
lora_requests, lora_mapping, multi_modal_input
|
||
) = self.prepare_input_tensors(seq_group_metadata_list)
|
||
|
||
if self.lora_config:
|
||
self.set_active_loras(lora_requests, lora_mapping)
|
||
|
||
# Currently cuda graph is only supported by the decode phase.
|
||
prefill_meta = attn_metadata.prefill_metadata
|
||
decode_meta = attn_metadata.decode_metadata
|
||
if prefill_meta is None and decode_meta.use_cuda_graph:
|
||
graph_batch_size = input_tokens.shape[0]
|
||
model_executable = self.graph_runners[graph_batch_size]
|
||
else:
|
||
model_executable = self.model
|
||
execute_model_kwargs = {
|
||
"input_ids": input_tokens,
|
||
"positions": input_positions,
|
||
"kv_caches": kv_caches,
|
||
"attn_metadata": attn_metadata,
|
||
}
|
||
if self.vision_language_config:
|
||
execute_model_kwargs.update({"image_input": multi_modal_input})
|
||
hidden_states = model_executable(**execute_model_kwargs)
|
||
|
||
# Compute the logits.
|
||
logits = self.model.compute_logits(hidden_states, sampling_metadata)
|
||
|
||
# Only perform sampling in the driver worker.
|
||
if not self.is_driver_worker:
|
||
return None
|
||
|
||
# Sample the next token.
|
||
output = self.model.sample(
|
||
logits=logits,
|
||
sampling_metadata=sampling_metadata,
|
||
)
|
||
|
||
return output
|
||
|
||
@torch.inference_mode()
|
||
def profile_run(self) -> None:
|
||
# Enable top-k sampling to reflect the accurate memory usage.
|
||
sampling_params = SamplingParams(top_p=0.99, top_k=self.vocab_size - 1)
|
||
max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens
|
||
max_num_seqs = self.scheduler_config.max_num_seqs
|
||
# This represents the maximum number of different requests
|
||
# that will have unique loras, an therefore the max amount of memory
|
||
# consumption create dummy lora request copies from the lora request
|
||
# passed in, which contains a lora from the lora warmup path.
|
||
dummy_lora_requests = []
|
||
dummy_lora_requests_per_seq = []
|
||
if self.lora_config:
|
||
assert self.lora_manager is not None
|
||
with self.lora_manager.dummy_lora_cache():
|
||
for idx in range(self.lora_config.max_loras):
|
||
lora_id = idx + 1
|
||
dummy_lora_request = LoRARequest(
|
||
lora_name=f"warmup_{lora_id}",
|
||
lora_int_id=lora_id,
|
||
lora_local_path="/not/a/real/path",
|
||
)
|
||
self.lora_manager.add_dummy_lora(dummy_lora_request,
|
||
rank=LORA_WARMUP_RANK)
|
||
dummy_lora_requests.append(dummy_lora_request)
|
||
dummy_lora_requests_per_seq = [
|
||
dummy_lora_requests[idx % len(dummy_lora_requests)]
|
||
for idx in range(max_num_seqs)
|
||
]
|
||
|
||
# Profile memory usage with max_num_sequences sequences and the total
|
||
# number of tokens equal to max_num_batched_tokens.
|
||
seqs: List[SequenceGroupMetadata] = []
|
||
# Additional GPU memory may be needed for vision encoding, which needs
|
||
# to be accounted for when calculating the GPU blocks for
|
||
# vLLM blocker manager.
|
||
# To exercise the worst scenario for GPU memory consumption,
|
||
# the number of seqs (batch_size) is chosen to maximize the number
|
||
# of images processed.
|
||
if self.vision_language_config:
|
||
max_num_seqs = min(
|
||
max_num_seqs,
|
||
int(max_num_batched_tokens /
|
||
self.vision_language_config.image_feature_size))
|
||
for group_id in range(max_num_seqs):
|
||
seq_len = (max_num_batched_tokens // max_num_seqs +
|
||
(group_id < max_num_batched_tokens % max_num_seqs))
|
||
seq_data, fake_multi_modal_input = _prepare_fake_inputs(
|
||
seq_len, self.vision_language_config)
|
||
seq = SequenceGroupMetadata(
|
||
request_id=str(group_id),
|
||
is_prompt=True,
|
||
seq_data={group_id: seq_data},
|
||
sampling_params=sampling_params,
|
||
block_tables=None,
|
||
lora_request=dummy_lora_requests_per_seq[group_id]
|
||
if dummy_lora_requests_per_seq else None,
|
||
multi_modal_data=fake_multi_modal_input,
|
||
)
|
||
seqs.append(seq)
|
||
|
||
# Run the model with the dummy inputs.
|
||
num_layers = self.model_config.get_num_layers(self.parallel_config)
|
||
kv_caches = [None] * num_layers
|
||
self.execute_model(seqs, kv_caches)
|
||
torch.cuda.synchronize()
|
||
return
|
||
|
||
def remove_all_loras(self):
|
||
if not self.lora_manager:
|
||
raise RuntimeError("LoRA is not enabled.")
|
||
self.lora_manager.remove_all_loras()
|
||
|
||
def set_active_loras(self, lora_requests: Set[LoRARequest],
|
||
lora_mapping: LoRAMapping) -> None:
|
||
if not self.lora_manager:
|
||
raise RuntimeError("LoRA is not enabled.")
|
||
self.lora_manager.set_active_loras(lora_requests, lora_mapping)
|
||
|
||
def add_lora(self, lora_request: LoRARequest) -> bool:
|
||
if not self.lora_manager:
|
||
raise RuntimeError("LoRA is not enabled.")
|
||
return self.lora_manager.add_lora(lora_request)
|
||
|
||
def remove_lora(self, lora_id: int) -> bool:
|
||
if not self.lora_manager:
|
||
raise RuntimeError("LoRA is not enabled.")
|
||
return self.lora_manager.remove_lora(lora_id)
|
||
|
||
def list_loras(self) -> Set[int]:
|
||
if not self.lora_manager:
|
||
raise RuntimeError("LoRA is not enabled.")
|
||
return self.lora_manager.list_loras()
|
||
|
||
@torch.inference_mode()
|
||
def capture_model(self, kv_caches: List[torch.Tensor]) -> None:
|
||
"""Cuda graph capture a model.
|
||
|
||
Note that CUDA graph's performance gain is negligible if number
|
||
of batched tokens are larger than 200. And since CUDA graph
|
||
requires fixed sized tensors, supporting large/variable batch
|
||
size requires high GPU memory overhead. Thus, vLLM only captures
|
||
decoding requests. Mixed batch (chunked prefill + decoding) or
|
||
prefill requests are not captured.
|
||
|
||
Since it is used for decoding-only, it assumes there's only 1 token
|
||
per sequence in the batch.
|
||
"""
|
||
assert not self.model_config.enforce_eager
|
||
logger.info("Capturing the model for CUDA graphs. This may lead to "
|
||
"unexpected consequences if the model is not static. To "
|
||
"run the model in eager mode, set 'enforce_eager=True' or "
|
||
"use '--enforce-eager' in the CLI.")
|
||
logger.info("CUDA graphs can take additional 1~3 GiB memory per GPU. "
|
||
"If you are running out of memory, consider decreasing "
|
||
"`gpu_memory_utilization` or enforcing eager mode. "
|
||
"You can also reduce the `max_num_seqs` as needed "
|
||
"to decrease memory usage.")
|
||
start_time = time.perf_counter()
|
||
|
||
# Prepare dummy inputs. These will be reused for all batch sizes.
|
||
max_batch_size = max(_BATCH_SIZES_TO_CAPTURE)
|
||
input_tokens = torch.zeros(max_batch_size, dtype=torch.long).cuda()
|
||
input_positions = torch.zeros(max_batch_size, dtype=torch.long).cuda()
|
||
slot_mapping = torch.empty(max_batch_size, dtype=torch.long).cuda()
|
||
slot_mapping.fill_(_PAD_SLOT_ID)
|
||
seq_lens = torch.ones(max_batch_size, dtype=torch.int32).cuda()
|
||
block_tables = torch.from_numpy(self.graph_block_tables).cuda()
|
||
|
||
graph_batch_size = _get_graph_batch_size(
|
||
self.scheduler_config.max_num_seqs)
|
||
batch_size_capture_list = [
|
||
bs for bs in _BATCH_SIZES_TO_CAPTURE if bs <= graph_batch_size
|
||
]
|
||
|
||
with graph_capture():
|
||
# NOTE: Capturing the largest batch size first may help reduce the
|
||
# memory usage of CUDA graph.
|
||
for batch_size in reversed(batch_size_capture_list):
|
||
# Create dummy attn_metadata.
|
||
attn_metadata = self.attn_backend.make_metadata(
|
||
num_prefills=0,
|
||
num_prefill_tokens=0,
|
||
num_decode_tokens=batch_size,
|
||
slot_mapping=slot_mapping[:batch_size],
|
||
seq_lens=None,
|
||
seq_lens_tensor=seq_lens[:batch_size],
|
||
max_query_len=None,
|
||
max_prefill_seq_len=0,
|
||
max_decode_seq_len=self.max_seq_len_to_capture,
|
||
query_start_loc=None,
|
||
seq_start_loc=None,
|
||
context_lens_tensor=None,
|
||
block_tables=block_tables[:batch_size],
|
||
use_cuda_graph=True,
|
||
)
|
||
|
||
if self.lora_config:
|
||
lora_mapping = LoRAMapping(
|
||
[0] * batch_size,
|
||
[0] * batch_size,
|
||
)
|
||
self.set_active_loras(set(), lora_mapping)
|
||
|
||
graph_runner = CUDAGraphRunner(self.model)
|
||
graph_runner.capture(
|
||
input_tokens[:batch_size],
|
||
input_positions[:batch_size],
|
||
kv_caches,
|
||
attn_metadata,
|
||
memory_pool=self.graph_memory_pool,
|
||
)
|
||
self.graph_memory_pool = graph_runner.graph.pool()
|
||
self.graph_runners[batch_size] = graph_runner
|
||
|
||
end_time = time.perf_counter()
|
||
elapsed_time = end_time - start_time
|
||
# This usually takes < 10 seconds.
|
||
logger.info("Graph capturing finished in %.0f secs.", elapsed_time)
|
||
|
||
def __del__(self) -> None:
|
||
# Delete the CUDA graphs before deleting the pynccl communicator.
|
||
# NOTE(woosuk): This is necessary because otherwise deadlocks can
|
||
# happen.
|
||
# FIXME(woosuk): This is a bit hacky. Find a more robust solution.
|
||
# TODO(youkaichao): when we get enough user feedback that pynccl is
|
||
# more stable than cupy, we can remove this, e.g. in v0.4.1.
|
||
self.graph_runners.clear()
|
||
self.pynccl_backend = None
|
||
|
||
@property
|
||
def vocab_size(self) -> int:
|
||
return self.model_config.get_vocab_size()
|
||
|
||
|
||
class CUDAGraphRunner:
|
||
|
||
def __init__(self, model: nn.Module):
|
||
self.model = model
|
||
self.input_buffers: Dict[str, torch.Tensor] = {}
|
||
self.output_buffers: Dict[str, torch.Tensor] = {}
|
||
|
||
self._graph: Optional[torch.cuda.CUDAGraph] = None
|
||
|
||
@property
|
||
def graph(self):
|
||
assert self._graph is not None
|
||
return self._graph
|
||
|
||
def capture(
|
||
self,
|
||
input_ids: torch.Tensor,
|
||
positions: torch.Tensor,
|
||
kv_caches: List[torch.Tensor],
|
||
attn_metadata: AttentionMetadata,
|
||
memory_pool,
|
||
**kwargs,
|
||
) -> None:
|
||
assert self._graph is None
|
||
# Run the model once without capturing the graph.
|
||
# This is to make sure that the captured graph does not include the
|
||
# kernel launches for initial benchmarking (e.g., Triton autotune).
|
||
with graph_mode():
|
||
self.model(
|
||
input_ids,
|
||
positions,
|
||
kv_caches,
|
||
attn_metadata,
|
||
**kwargs,
|
||
)
|
||
torch.cuda.synchronize()
|
||
|
||
# Capture the graph.
|
||
# NOTE(woosuk): Python 3.8 does not support multi-line with statements.
|
||
# https://stackoverflow.com/questions/31039022/python-multi-line-with-statement
|
||
self._graph = torch.cuda.CUDAGraph()
|
||
with torch.cuda.graph(self._graph, pool=memory_pool): # noqa: SIM117
|
||
with graph_mode():
|
||
hidden_states = self.model(
|
||
input_ids,
|
||
positions,
|
||
kv_caches,
|
||
attn_metadata,
|
||
**kwargs,
|
||
)
|
||
torch.cuda.synchronize()
|
||
|
||
# Save the input and output buffers.
|
||
self.input_buffers = {
|
||
"input_ids": input_ids,
|
||
"positions": positions,
|
||
"kv_caches": kv_caches,
|
||
"slot_mapping": attn_metadata.slot_mapping,
|
||
"seq_lens_tensor": attn_metadata.decode_metadata.seq_lens_tensor,
|
||
"block_tables": attn_metadata.decode_metadata.block_tables,
|
||
}
|
||
self.output_buffers = {"hidden_states": hidden_states}
|
||
return
|
||
|
||
def forward(
|
||
self,
|
||
input_ids: torch.Tensor,
|
||
positions: torch.Tensor,
|
||
kv_caches: List[torch.Tensor],
|
||
attn_metadata: AttentionMetadata,
|
||
**kwargs,
|
||
) -> torch.Tensor:
|
||
# KV caches are fixed tensors, so we don't need to copy them.
|
||
del kv_caches
|
||
|
||
# Copy the input tensors to the input buffers.
|
||
self.input_buffers["input_ids"].copy_(input_ids, non_blocking=True)
|
||
self.input_buffers["positions"].copy_(positions, non_blocking=True)
|
||
self.input_buffers["slot_mapping"].copy_(attn_metadata.slot_mapping,
|
||
non_blocking=True)
|
||
self.input_buffers["seq_lens_tensor"].copy_(
|
||
attn_metadata.decode_metadata.seq_lens_tensor, non_blocking=True)
|
||
self.input_buffers["block_tables"].copy_(
|
||
attn_metadata.decode_metadata.block_tables, non_blocking=True)
|
||
# Run the graph.
|
||
self.graph.replay()
|
||
|
||
# Return the output tensor.
|
||
return self.output_buffers["hidden_states"]
|
||
|
||
def __call__(self, *args, **kwargs):
|
||
return self.forward(*args, **kwargs)
|
||
|
||
|
||
def _get_graph_batch_size(batch_size: int) -> int:
|
||
"""Returns the padded batch size given actual batch size.
|
||
|
||
Batch sizes are 1, 2, 4, _BATCH_SIZE_ALIGNMENT,
|
||
2*_BATCH_SIZE_ALIGNMENT, 3*_BATCH_SIZE_ALIGNMENT...
|
||
"""
|
||
if batch_size <= 2:
|
||
return batch_size
|
||
elif batch_size <= 4:
|
||
return 4
|
||
else:
|
||
return ((batch_size + _BATCH_SIZE_ALIGNMENT - 1) //
|
||
_BATCH_SIZE_ALIGNMENT * _BATCH_SIZE_ALIGNMENT)
|
||
|
||
|
||
def _prepare_fake_inputs(
|
||
seq_len: int, vision_language_config: Optional[VisionLanguageConfig]):
|
||
"""Prepare fake inputs for profile run."""
|
||
if vision_language_config:
|
||
prompt_tokens = [
|
||
vision_language_config.image_token_id
|
||
] * vision_language_config.image_feature_size + [0] * (
|
||
seq_len - vision_language_config.image_feature_size)
|
||
fake_image_input = MultiModalData(
|
||
type=MultiModalData.Type.IMAGE,
|
||
data=torch.zeros(vision_language_config.image_input_shape,
|
||
dtype=torch.float16))
|
||
else:
|
||
prompt_tokens = [0] * seq_len
|
||
fake_image_input = None
|
||
return SequenceData(prompt_tokens), fake_image_input
|
||
|
||
|
||
def _is_block_tables_empty(block_tables: Union[None, Dict]):
|
||
"""
|
||
Check if block_tables is None or a dictionary with all None values.
|
||
"""
|
||
if block_tables is None:
|
||
return True
|
||
if isinstance(block_tables, dict) and all(
|
||
value is None for value in block_tables.values()):
|
||
return True
|
||
return False
|