import dataclasses import gc import inspect import itertools import time import warnings import weakref from dataclasses import dataclass from typing import (TYPE_CHECKING, Any, Callable, Dict, List, Optional, Set, Tuple, Type, TypeVar, Union) import numpy as np import torch import torch.distributed import torch.nn as nn import vllm.envs as envs from vllm.attention import AttentionMetadata, get_attn_backend from vllm.attention.backends.abstract import AttentionState from vllm.attention.backends.utils import CommonAttentionState from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig, ModelConfig, ObservabilityConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig) from vllm.core.scheduler import SchedulerOutputs from vllm.distributed import get_pp_group from vllm.distributed.parallel_state import graph_capture from vllm.inputs import INPUT_REGISTRY, InputRegistry from vllm.logger import init_logger from vllm.lora.layers import LoRAMapping from vllm.lora.request import LoRARequest from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager from vllm.model_executor import SamplingMetadata, SamplingMetadataCache from vllm.model_executor.layers.sampler import SamplerOutput from vllm.model_executor.model_loader import get_model from vllm.model_executor.model_loader.tensorizer import TensorizerConfig from vllm.model_executor.models.interfaces import (supports_lora, supports_multimodal) from vllm.model_executor.models.utils import set_cpu_offload_max_bytes from vllm.multimodal import (MULTIMODAL_REGISTRY, BatchedTensorInputs, MultiModalInputs, MultiModalRegistry) from vllm.prompt_adapter.layers import PromptAdapterMapping from vllm.prompt_adapter.request import PromptAdapterRequest from vllm.prompt_adapter.worker_manager import ( LRUCacheWorkerPromptAdapterManager) from vllm.sampling_params import SamplingParams from vllm.sequence import IntermediateTensors, SequenceGroupMetadata from vllm.utils import (CudaMemoryProfiler, PyObjectCache, async_tensor_h2d, flatten_2d_lists, is_hip, is_pin_memory_available, supports_dynamo) from vllm.worker.model_runner_base import ( ModelRunnerBase, ModelRunnerInputBase, ModelRunnerInputBuilderBase, _add_attn_metadata_broadcastable_dict, _add_sampling_metadata_broadcastable_dict, _init_attn_metadata_from_tensor_dict, _init_sampling_metadata_from_tensor_dict) if TYPE_CHECKING: from vllm.attention.backends.abstract import AttentionBackend logger = init_logger(__name__) LORA_WARMUP_RANK = 8 _BATCH_SIZE_ALIGNMENT = 8 # all the token sizes that **can** be captured by cudagraph. # they can be arbitrarily large. # currently it includes: 1, 2, 4, 8, 16, 24, 32, 40, ..., 8192. # the actual sizes to capture will be determined by the model, # depending on the model's max_num_seqs. # NOTE: _get_graph_batch_size needs to be updated if this list is changed. _BATCH_SIZES_TO_CAPTURE = [1, 2, 4] + [ _BATCH_SIZE_ALIGNMENT * i for i in range(1, 1025) ] _NUM_WARMUP_ITERS = 2 TModelInputForGPU = TypeVar('TModelInputForGPU', bound="ModelInputForGPU") @dataclass(frozen=True) class ModelInputForGPU(ModelRunnerInputBase): """ This base class contains metadata needed for the base model forward pass but not metadata for possible additional steps, e.g., sampling. Model runners that run additional steps should subclass this method to add additional fields. """ input_tokens: Optional[torch.Tensor] = None input_positions: Optional[torch.Tensor] = None seq_lens: Optional[List[int]] = None query_lens: Optional[List[int]] = None lora_mapping: Optional["LoRAMapping"] = None lora_requests: Optional[Set[LoRARequest]] = None attn_metadata: Optional["AttentionMetadata"] = None prompt_adapter_mapping: Optional[PromptAdapterMapping] = None prompt_adapter_requests: Optional[Set[PromptAdapterRequest]] = None multi_modal_kwargs: Optional[BatchedTensorInputs] = None request_ids_to_seq_ids: Optional[Dict[str, List[int]]] = None finished_requests_ids: Optional[List[str]] = None virtual_engine: int = 0 async_callback: Optional[Callable] = None seq_group_metadata_list: Optional[List[SequenceGroupMetadata]] = None scheduler_outputs: Optional[SchedulerOutputs] = None def as_broadcastable_tensor_dict(self) -> Dict[str, Any]: tensor_dict = { "input_tokens": self.input_tokens, "input_positions": self.input_positions, "lora_requests": self.lora_requests, "lora_mapping": self.lora_mapping, "multi_modal_kwargs": self.multi_modal_kwargs, "prompt_adapter_mapping": self.prompt_adapter_mapping, "prompt_adapter_requests": self.prompt_adapter_requests, "virtual_engine": self.virtual_engine, "request_ids_to_seq_ids": self.request_ids_to_seq_ids, "finished_requests_ids": self.finished_requests_ids, } _add_attn_metadata_broadcastable_dict(tensor_dict, self.attn_metadata) return tensor_dict @classmethod def from_broadcasted_tensor_dict( cls: Type[TModelInputForGPU], tensor_dict: Dict[str, Any], attn_backend: Optional["AttentionBackend"] = None, ) -> TModelInputForGPU: if attn_backend is not None: tensor_dict = _init_attn_metadata_from_tensor_dict( attn_backend, tensor_dict) return cls(**tensor_dict) @dataclass(frozen=True) class ModelInputForGPUWithSamplingMetadata(ModelInputForGPU): """ Used by the ModelRunner. """ sampling_metadata: Optional["SamplingMetadata"] = None # Used for speculative decoding. We do not broadcast it because it is only # used by the driver worker. is_prompt: Optional[bool] = None def as_broadcastable_tensor_dict(self) -> Dict[str, Any]: tensor_dict = { "input_tokens": self.input_tokens, "input_positions": self.input_positions, "lora_requests": self.lora_requests, "lora_mapping": self.lora_mapping, "multi_modal_kwargs": self.multi_modal_kwargs, "prompt_adapter_mapping": self.prompt_adapter_mapping, "prompt_adapter_requests": self.prompt_adapter_requests, "virtual_engine": self.virtual_engine, "request_ids_to_seq_ids": self.request_ids_to_seq_ids, "finished_requests_ids": self.finished_requests_ids, } _add_attn_metadata_broadcastable_dict(tensor_dict, self.attn_metadata) _add_sampling_metadata_broadcastable_dict(tensor_dict, self.sampling_metadata) return tensor_dict @classmethod def from_broadcasted_tensor_dict( cls, tensor_dict: Dict[str, Any], attn_backend: Optional["AttentionBackend"] = None, ) -> "ModelInputForGPUWithSamplingMetadata": tensor_dict = _init_sampling_metadata_from_tensor_dict(tensor_dict) if attn_backend is not None: tensor_dict = _init_attn_metadata_from_tensor_dict( attn_backend, tensor_dict) return cls(**tensor_dict) class ModelInputForGPUBuilder(ModelRunnerInputBuilderBase[ModelInputForGPU]): """Build ModelInputForGPU from SequenceGroupMetadata.""" # Note: ideally we would be using a dataclass(kw_only=True) # here, so that this can be subclassed easily, # but kw_only is not supported in python<3.10. class InterDataForSeqGroup: """Intermediate data for the current sequence group.""" def simple_reinit(self): self.input_tokens[0].clear() # type: ignore self.input_positions[0].clear() # type: ignore self.seq_lens[0] = 0 # type: ignore self.orig_seq_lens[0] = 0 # type: ignore self.query_lens[0] = 0 # type: ignore self.context_lens[0] = 0 # type: ignore self.curr_sliding_window_blocks[0] = 0 # type: ignore self.lora_index_mapping.clear() # type: ignore self.lora_prompt_mapping.clear() # type: ignore self.lora_requests.clear() # type: ignore self.prompt_adapter_index_mapping.clear() # type: ignore self.prompt_adapter_prompt_mapping.clear() # type: ignore def __init__( self, *, # From sequence group metadata. request_id: str, seq_ids: List[int], is_prompt: bool, block_tables: Optional[Dict[int, List[int]]], computed_block_nums: List[int], n_seqs: int = 0, # Input tokens and positions. input_tokens: Optional[List[List[int]]] = None, input_positions: Optional[List[List[int]]] = None, # The sequence length (may be capped to the sliding window). seq_lens: Optional[List[int]] = None, # The original sequence length (before applying sliding window). # This is used to compute slot mapping. orig_seq_lens: Optional[List[int]] = None, # The query length. query_lens: Optional[List[int]] = None, # The number of tokens that are already computed. context_lens: Optional[List[int]] = None, # The current sliding window block. curr_sliding_window_blocks: Optional[List[int]] = None, # LoRA inputs. lora_index_mapping: Optional[List[List[int]]] = None, lora_prompt_mapping: Optional[List[List[int]]] = None, lora_requests: Optional[Set[LoRARequest]] = None, # Prompt adapter inputs. prompt_adapter_index_mapping: Optional[List[int]] = None, prompt_adapter_prompt_mapping: Optional[List[int]] = None, prompt_adapter_request: Optional[PromptAdapterRequest] = None, # Multi-modal inputs. multi_modal_inputs: Optional[MultiModalInputs] = None, # Whether the prefix cache is hit (prefill only). prefix_cache_hit: bool = False, reinit: bool = False, reinit_use_defaults: bool = False, ): if reinit: assert len(self.seq_ids) == len(seq_ids) # type: ignore for i, seq_id in enumerate(seq_ids): self.seq_ids[i] = seq_id # type: ignore else: self.seq_ids = seq_ids self.request_id = request_id self.is_prompt = is_prompt self.block_tables = block_tables self.computed_block_nums = computed_block_nums self.n_seqs = n_seqs if reinit: if len(self.seq_ids) == 1 and reinit_use_defaults: self.simple_reinit() else: if input_tokens: self.input_tokens = input_tokens else: for seq_id in range(len(self.seq_ids)): self.input_tokens[seq_id].clear() if input_positions: self.input_positions = input_positions else: for seq_id in range(len(self.seq_ids)): self.input_positions[seq_id].clear() if seq_lens: self.seq_lens = seq_lens else: for seq_id in range(len(self.seq_ids)): self.seq_lens[seq_id] = 0 if orig_seq_lens: self.orig_seq_lens = orig_seq_lens else: for seq_id in range(len(self.seq_ids)): self.orig_seq_lens[seq_id] = 0 if query_lens: self.query_lens = query_lens else: for seq_id in range(len(self.seq_ids)): self.query_lens[seq_id] = 0 if context_lens: self.context_lens = context_lens else: for seq_id in range(len(self.seq_ids)): self.context_lens[seq_id] = 0 if curr_sliding_window_blocks: self.curr_sliding_window_blocks = \ curr_sliding_window_blocks else: for seq_id in range(len(self.seq_ids)): self.curr_sliding_window_blocks[seq_id] = 0 if lora_index_mapping: self.lora_index_mapping = lora_index_mapping else: self.lora_index_mapping.clear() if lora_prompt_mapping: self.lora_prompt_mapping = lora_prompt_mapping else: self.lora_prompt_mapping.clear() if lora_requests: self.lora_requests = lora_requests else: self.lora_requests.clear() if prompt_adapter_index_mapping: self.prompt_adapter_index_mapping = \ prompt_adapter_index_mapping else: self.prompt_adapter_index_mapping.clear() if prompt_adapter_prompt_mapping: self.prompt_adapter_prompt_mapping = \ prompt_adapter_prompt_mapping else: self.prompt_adapter_prompt_mapping.clear() else: self.input_tokens = input_tokens or [] self.input_positions = input_positions or [] self.seq_lens = seq_lens or [] self.orig_seq_lens = orig_seq_lens or [] self.query_lens = query_lens or [] self.context_lens = context_lens or [] self.curr_sliding_window_blocks = \ curr_sliding_window_blocks or [] self.lora_index_mapping = lora_index_mapping or [] self.lora_prompt_mapping = lora_prompt_mapping or [] self.lora_requests = lora_requests or set() self.prompt_adapter_index_mapping = ( prompt_adapter_index_mapping or []) self.prompt_adapter_prompt_mapping = ( prompt_adapter_prompt_mapping or []) self.prompt_adapter_request = prompt_adapter_request self.multi_modal_inputs = multi_modal_inputs self.prefix_cache_hit = prefix_cache_hit self.n_seqs = len(self.seq_ids) if not reinit: self.__post_init__() def __post_init__(self): self.n_seqs = len(self.seq_ids) self.input_tokens = [[] for _ in range(self.n_seqs)] self.input_positions = [[] for _ in range(self.n_seqs)] self.seq_lens = [0] * self.n_seqs self.orig_seq_lens = [0] * self.n_seqs self.query_lens = [0] * self.n_seqs self.context_lens = [0] * self.n_seqs self.curr_sliding_window_blocks = [0] * self.n_seqs self.lora_index_mapping = [] self.lora_prompt_mapping = [] def gen_inter_data_builder(self, num_seqs: int): return lambda: ModelInputForGPUBuilder.InterDataForSeqGroup( request_id="", seq_ids=[0] * num_seqs, is_prompt=True, block_tables=None, computed_block_nums=[]) def init_cached_inter_data(self, *args, **kwargs): assert len(args) == 0 assert "seq_ids" in kwargs seq_ids = kwargs["seq_ids"] num_seqs = len(seq_ids) # The inter-data cache is per model_runner inter_data_cache = self.runner.inter_data_cache if num_seqs not in inter_data_cache: inter_data_cache[num_seqs] = PyObjectCache( self.gen_inter_data_builder(num_seqs)) obj = inter_data_cache[num_seqs].get_object() obj.__init__(*args, **kwargs) return obj def reset_cached_inter_data(self): for cache in self.runner.inter_data_cache.values(): cache.reset() def __init__(self, runner: "GPUModelRunnerBase", finished_requests_ids: Optional[List[str]] = None): super().__init__() # Compute functions for each sequence in a sequence group. # WARNING: The order of the functions matters! self.per_seq_compute_fns = [ self._compute_lens, self._compute_for_prefix_cache_hit, self._compute_for_sliding_window, self._compute_lora_input, ] # Compute functions for each sequence group. # WARNING: The order of the functions matters! self.per_seq_group_compute_fns = [ self._compute_prompt_adapter_input, self._compute_multi_modal_input, ] self.runner = runner self.model_input_cls = self.runner._model_input_cls self.attn_backend = self.runner.attn_backend self.scheduler_config = self.runner.scheduler_config self.sliding_window = self.runner.sliding_window self.block_size = self.runner.block_size self.enable_lora = self.runner.lora_config is not None self.enable_prompt_adapter = (self.runner.prompt_adapter_config is not None) self.multi_modal_input_mapper = self.runner.multi_modal_input_mapper self.finished_requests_ids = finished_requests_ids self.decode_only = True # Intermediate data (data in CPU before going to GPU) for # the current sequence group. self.inter_data_list: List[ ModelInputForGPUBuilder.InterDataForSeqGroup] = [] # Attention metadata inputs. self.attn_metadata_builder = self.attn_backend.make_metadata_builder( weakref.proxy(self)) # Engine/Model configurations. self.chunked_prefill_enabled = ( self.scheduler_config is not None and self.scheduler_config.chunked_prefill_enabled) if self.sliding_window is not None: self.sliding_window_blocks = ( self.sliding_window + self.block_size - 1) // self.block_size self.block_aligned_sliding_window = \ self.sliding_window_blocks * self.block_size def _compute_lens(self, inter_data: InterDataForSeqGroup, seq_idx: int, seq_group_metadata: SequenceGroupMetadata): """Compute context length, sequence length and tokens for the given sequence data. """ seq_data = seq_group_metadata.seq_data[inter_data.seq_ids[seq_idx]] token_chunk_size = seq_group_metadata.token_chunk_size # Compute context length (the number of tokens that are # already computed) and sequence length (total number of tokens). seq_len = seq_data.get_len() if inter_data.is_prompt: context_len = seq_data.get_num_computed_tokens() else: # get_num_computed_tokens is incorrect for spec decoding. # So, we should have a special logic here. # TODO(sang): Fix it. context_len = seq_len - 1 seq_len = min(seq_len, context_len + token_chunk_size) # Compute tokens. if inter_data.is_prompt: tokens = seq_data.get_token_ids() if context_len != 0 or seq_len < len(tokens): tokens = tokens[context_len:seq_len] else: # Optimization. get_token_ids requires the entire copy of # tokens. tokens = seq_data.get_last_token_id() inter_data.seq_lens[seq_idx] = seq_len inter_data.orig_seq_lens[seq_idx] = seq_len inter_data.context_lens[seq_idx] = context_len if isinstance(tokens, list): inter_data.input_tokens[seq_idx].extend(tokens) else: inter_data.input_tokens[seq_idx].append(tokens) if (seq_len - context_len) == 1: inter_data.input_positions[seq_idx].append(seq_len - 1) else: inter_data.input_positions[seq_idx].extend( range(context_len, seq_len)) inter_data.query_lens[ seq_idx] = seq_len - context_len if inter_data.is_prompt else 1 def _compute_for_prefix_cache_hit( self, inter_data: InterDataForSeqGroup, seq_idx: int, seq_group_metadata: SequenceGroupMetadata): """Check if hit prefix cache (i.e., some blocks are already computed). If hit, update input tokens and positions to only compute the remaining blocks. """ computed_block_nums = inter_data.computed_block_nums # Note that prefix caching does not support sliding window. prefix_cache_hit = (computed_block_nums is not None and len(computed_block_nums) > 0 and self.sliding_window is None and inter_data.is_prompt) inter_data.prefix_cache_hit = prefix_cache_hit if not prefix_cache_hit: return assert computed_block_nums is not None # The cache hit prompt tokens in this sequence. Note that # this may be larger than the sequence length if chunked # prefill is enabled. prefix_cache_len = len(computed_block_nums) * self.block_size # The number of so far computed prompt tokens in this sequence. context_len = inter_data.context_lens[seq_idx] # The total number of prompt tokens in this sequence. # When chunked prefill is enabled, this is the token number of # computed chunks + current chunk. seq_len = inter_data.seq_lens[seq_idx] if prefix_cache_len <= context_len: # We already passed the cache hit region, # so do normal computation. pass elif context_len < prefix_cache_len < seq_len: # Partial hit. Compute the missing part. uncomputed_start = prefix_cache_len - context_len inter_data.input_tokens[seq_idx] = inter_data.input_tokens[ seq_idx][uncomputed_start:] inter_data.input_positions[seq_idx] = inter_data.input_positions[ seq_idx][uncomputed_start:] context_len = prefix_cache_len inter_data.context_lens[seq_idx] = context_len inter_data.query_lens[ seq_idx] = inter_data.seq_lens[seq_idx] - context_len elif seq_len <= prefix_cache_len: # Full hit. Only compute the last token to avoid # erroneous behavior. FIXME: Ideally we should directly # mark all tokens as computed in the scheduler and do not # schedule this sequence, so this case should not happen. inter_data.input_tokens[seq_idx] = inter_data.input_tokens[ seq_idx][-1:] inter_data.input_positions[seq_idx] = inter_data.input_positions[ seq_idx][-1:] inter_data.query_lens[seq_idx] = 1 inter_data.context_lens[seq_idx] = inter_data.seq_lens[seq_idx] - 1 def _compute_for_sliding_window(self, inter_data: InterDataForSeqGroup, seq_idx: int, seq_group_metadata: SequenceGroupMetadata): """Update seq_len and curr_sliding_window_block for the given sequence data (only required by decoding) if sliding window is enabled. """ curr_sliding_window_block = 0 sliding_seq_len = inter_data.seq_lens[seq_idx] if not inter_data.is_prompt and self.sliding_window is not None: # TODO(sang): This is a hack to make sliding window work with # paged attn. We can remove it if we make paged attn kernel # to properly handle slinding window attn. curr_sliding_window_block = self.sliding_window_blocks if self.scheduler_config.use_v2_block_manager: # number of elements in last block suff_len = inter_data.seq_lens[seq_idx] % self.block_size sliding_seq_len = min( inter_data.seq_lens[seq_idx], self.block_aligned_sliding_window + suff_len) if suff_len > 0: curr_sliding_window_block += 1 else: sliding_seq_len = min(inter_data.seq_lens[seq_idx], self.sliding_window) inter_data.curr_sliding_window_blocks[ seq_idx] = curr_sliding_window_block inter_data.seq_lens[seq_idx] = sliding_seq_len def _compute_lora_input(self, inter_data: InterDataForSeqGroup, seq_idx: int, seq_group_metadata: SequenceGroupMetadata): """If LoRA is enabled, compute LoRA index and prompt mapping.""" if not self.enable_lora: return lora_id = seq_group_metadata.lora_int_id if lora_id > 0: inter_data.lora_requests.add(seq_group_metadata.lora_request) query_len = inter_data.query_lens[seq_idx] inter_data.lora_index_mapping.append([lora_id] * query_len) inter_data.lora_prompt_mapping.append( [lora_id] * (query_len if seq_group_metadata.sampling_params and seq_group_metadata.sampling_params.prompt_logprobs is not None else 1)) def _compute_prompt_adapter_input( self, inter_data: InterDataForSeqGroup, seq_group_metadata: SequenceGroupMetadata): """If prompt adapter is enabled, compute index and prompt mapping. """ # Note that when is_prompt=True, we expect only one sequence # in the group. if not self.enable_prompt_adapter: return prompt_adapter_id = seq_group_metadata.prompt_adapter_id if prompt_adapter_id <= 0 or not inter_data.is_prompt: return # We expect only one sequence in the group when is_prompt=True. assert inter_data.n_seqs == 1 query_len = inter_data.query_lens[0] inter_data.prompt_adapter_request = ( seq_group_metadata.prompt_adapter_request) num_tokens = seq_group_metadata.prompt_adapter_num_virtual_tokens inter_data.prompt_adapter_index_mapping = [ prompt_adapter_id ] * num_tokens + [0] * (query_len - num_tokens) inter_data.prompt_adapter_prompt_mapping = [prompt_adapter_id] * ( query_len if seq_group_metadata.sampling_params and seq_group_metadata.sampling_params.prompt_logprobs else 1) def _compute_multi_modal_input(self, inter_data: InterDataForSeqGroup, seq_group_metadata: SequenceGroupMetadata): """If multi-modal data is given, add it to the input.""" mm_data = seq_group_metadata.multi_modal_data if not mm_data: return mm_kwargs = self.multi_modal_input_mapper(mm_data) inter_data.multi_modal_inputs = mm_kwargs def add_seq_group(self, seq_group_metadata: SequenceGroupMetadata): """Add a sequence group to the builder.""" seq_ids = seq_group_metadata.seq_data.keys() n_seqs = len(seq_ids) is_prompt = seq_group_metadata.is_prompt if is_prompt: assert n_seqs == 1 self.decode_only = False inter_data = self.init_cached_inter_data( request_id=seq_group_metadata.request_id, seq_ids=seq_ids, is_prompt=is_prompt, block_tables=seq_group_metadata.block_tables, computed_block_nums=seq_group_metadata.computed_block_nums, reinit=True, reinit_use_defaults=True) self.inter_data_list.append(inter_data) for seq_idx in range(n_seqs): for per_seq_fn in self.per_seq_compute_fns: per_seq_fn(inter_data, seq_idx, seq_group_metadata) for per_seq_group_fn in self.per_seq_group_compute_fns: per_seq_group_fn(inter_data, seq_group_metadata) def _use_captured_graph(self, batch_size: int, max_decode_seq_len: int) -> bool: return (self.decode_only and not self.runner.model_config.enforce_eager and batch_size <= self.runner.max_batchsize_to_capture and max_decode_seq_len <= self.runner.max_seq_len_to_capture) def build(self) -> ModelInputForGPU: """Finalize the builder intermediate data and create on-device tensors. """ # Combine and flatten intermediate data. input_tokens = [] for inter_data in self.inter_data_list: for cur_input_tokens in inter_data.input_tokens: input_tokens.extend(cur_input_tokens) if not input_tokens: # This may happen when all prefill requests hit # prefix caching and there is no decode request. return self.model_input_cls() input_positions = [] for inter_data in self.inter_data_list: for cur_input_positions in inter_data.input_positions: input_positions.extend(cur_input_positions) seq_lens = [] max_decode_seq_len = 0 for inter_data in self.inter_data_list: seq_lens.extend(inter_data.seq_lens) if not inter_data.is_prompt: max_decode_seq_len = max(max_decode_seq_len, max(inter_data.seq_lens)) query_lens = [] for inter_data in self.inter_data_list: query_lens.extend(inter_data.query_lens) # Mapping from request IDs to sequence IDs. Used for Jamba models # that manages the cache by itself. request_ids_to_seq_ids = { data.request_id: data.seq_ids for data in self.inter_data_list } batch_size = len(input_tokens) use_captured_graph = self._use_captured_graph(batch_size, max_decode_seq_len) # If cuda graph can be used, pad tensors accordingly. # See `capture_model` API for more details. # vLLM uses cuda graph only for decoding requests. cuda_graph_pad_size = -1 if use_captured_graph: graph_batch_size = _get_graph_batch_size(batch_size) assert graph_batch_size >= batch_size cuda_graph_pad_size = graph_batch_size - batch_size batch_size = graph_batch_size # Tokens and positions. if cuda_graph_pad_size: input_tokens.extend(itertools.repeat(0, cuda_graph_pad_size)) input_positions.extend(itertools.repeat(0, cuda_graph_pad_size)) assert self.runner.device is not None input_tokens_tensor = async_tensor_h2d(input_tokens, torch.long, self.runner.device, self.runner.pin_memory) input_positions_tensor = async_tensor_h2d(input_positions, torch.long, self.runner.device, self.runner.pin_memory) # Sequence and query lengths. if cuda_graph_pad_size: seq_lens.extend(itertools.repeat(1, cuda_graph_pad_size)) # Attention metadata. attn_metadata = self.attn_metadata_builder.build( seq_lens, query_lens, cuda_graph_pad_size, batch_size) # LoRA data. lora_requests = set() lora_mapping = None if self.enable_lora: lora_requests = set(r for data in self.inter_data_list for r in data.lora_requests) lora_index_mapping = flatten_2d_lists([ flatten_2d_lists(inter_data.lora_index_mapping) for inter_data in self.inter_data_list ]) if cuda_graph_pad_size: lora_index_mapping.extend( itertools.repeat(0, cuda_graph_pad_size)) lora_prompt_mapping = flatten_2d_lists([ flatten_2d_lists(inter_data.lora_prompt_mapping) for inter_data in self.inter_data_list ]) lora_mapping = LoRAMapping( **dict(index_mapping=lora_index_mapping, prompt_mapping=lora_prompt_mapping, is_prefill=not self.decode_only)) # Prompt adapter data. prompt_adapter_requests: Set[PromptAdapterRequest] = set() prompt_adapter_mapping = None if self.enable_prompt_adapter: prompt_adapter_requests = set( data.prompt_adapter_request for data in self.inter_data_list if data.prompt_adapter_request is not None) prompt_adapter_index_mapping = flatten_2d_lists([ inter_data.prompt_adapter_index_mapping for inter_data in self.inter_data_list ]) if cuda_graph_pad_size: prompt_adapter_index_mapping.extend( itertools.repeat(0, cuda_graph_pad_size)) prompt_adapter_prompt_mapping = flatten_2d_lists([ inter_data.prompt_adapter_prompt_mapping for inter_data in self.inter_data_list ]) prompt_adapter_mapping = PromptAdapterMapping( prompt_adapter_index_mapping, prompt_adapter_prompt_mapping, ) # Multi-modal data. multi_modal_inputs_list = [ data.multi_modal_inputs for data in self.inter_data_list if data.multi_modal_inputs is not None ] multi_modal_kwargs = MultiModalInputs.batch(multi_modal_inputs_list) return self.model_input_cls( 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_kwargs=multi_modal_kwargs, request_ids_to_seq_ids=request_ids_to_seq_ids, finished_requests_ids=self.finished_requests_ids, prompt_adapter_mapping=prompt_adapter_mapping, prompt_adapter_requests=prompt_adapter_requests) class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]): """ Helper class for shared methods between GPU model runners. """ _model_input_cls: Type[TModelInputForGPU] _builder_cls: Type[ModelInputForGPUBuilder] def __init__( self, model_config: ModelConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, cache_config: CacheConfig, load_config: LoadConfig, lora_config: Optional[LoRAConfig], kv_cache_dtype: Optional[str] = "auto", is_driver_worker: bool = False, prompt_adapter_config: Optional[PromptAdapterConfig] = None, return_hidden_states: bool = False, observability_config: Optional[ObservabilityConfig] = None, input_registry: InputRegistry = INPUT_REGISTRY, mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY, ): self.model_config = model_config self.parallel_config = parallel_config self.scheduler_config = scheduler_config self.device_config = device_config self.cache_config = cache_config self.lora_config = lora_config self.load_config = load_config self.is_driver_worker = is_driver_worker self.prompt_adapter_config = prompt_adapter_config self.return_hidden_states = return_hidden_states self.observability_config = observability_config self.device = self.device_config.device self.pin_memory = is_pin_memory_available() self.kv_cache_dtype = kv_cache_dtype self.sliding_window = model_config.get_sliding_window() self.block_size = cache_config.block_size self.max_seq_len_to_capture = self.model_config.max_seq_len_to_capture self.max_batchsize_to_capture = _get_max_graph_batch_size( self.scheduler_config.max_num_seqs) self.graph_runners: List[Dict[int, CUDAGraphRunner]] = [ {} for _ in range(self.parallel_config.pipeline_parallel_size) ] self.graph_memory_pool: Optional[Tuple[ int, int]] = None # Set during graph capture. self.has_seqlen_agnostic = model_config.contains_seqlen_agnostic_layers( parallel_config) # When using CUDA graph, the input block tables must be padded to # max_seq_len_to_capture. However, creating the block table in # Python can be expensive. To optimize this, we cache the block table # in numpy and only copy the actual input content at every iteration. # The shape of the cached block table will be # (max batch size to capture, max context len to capture / block size). self.graph_block_tables = np.zeros( (self.max_batchsize_to_capture, self.get_max_block_per_batch()), dtype=np.int32) num_attn_heads = self.model_config.get_num_attention_heads( self.parallel_config) self.attn_backend = get_attn_backend( num_attn_heads, self.model_config.get_head_size(), self.model_config.get_num_kv_heads(self.parallel_config), self.model_config.get_sliding_window(), self.model_config.dtype, self.kv_cache_dtype, self.block_size, ) if num_attn_heads else None if self.attn_backend: self.attn_state = self.attn_backend.get_state_cls()( weakref.proxy(self)) else: self.attn_state = CommonAttentionState(weakref.proxy(self)) # Multi-modal data support self.input_registry = input_registry self.mm_registry = mm_registry self.multi_modal_input_mapper = mm_registry \ .create_input_mapper(model_config) self.mm_registry.init_mm_limits_per_prompt(self.model_config) # Lazy initialization self.model: nn.Module # Set after load_model # Set after load_model. self.lora_manager: Optional[LRUCacheWorkerLoRAManager] = None self.prompt_adapter_manager: LRUCacheWorkerPromptAdapterManager = None set_cpu_offload_max_bytes( int(self.cache_config.cpu_offload_gb * 1024**3)) # Used to cache python objects self.inter_data_cache: Dict[int, PyObjectCache] = {} self.sampling_metadata_cache: SamplingMetadataCache = \ SamplingMetadataCache() def load_model(self) -> None: logger.info("Starting to load model %s...", self.model_config.model) with CudaMemoryProfiler() as m: self.model = get_model(model_config=self.model_config, device_config=self.device_config, load_config=self.load_config, lora_config=self.lora_config, parallel_config=self.parallel_config, scheduler_config=self.scheduler_config, cache_config=self.cache_config) self.model_memory_usage = m.consumed_memory logger.info("Loading model weights took %.4f GB", self.model_memory_usage / float(2**30)) if self.lora_config: assert supports_lora(self.model), "Model does not support LoRA" assert not supports_multimodal( self.model ), "To be tested: Multi-modal model with LoRA settings." self.lora_manager = LRUCacheWorkerLoRAManager( self.scheduler_config.max_num_seqs, self.scheduler_config.max_num_batched_tokens, self.vocab_size, self.lora_config, self.device, self.model.embedding_modules, self.model.embedding_padding_modules, max_position_embeddings=self.model.config. max_position_embeddings, ) self.model = self.lora_manager.create_lora_manager(self.model) if self.prompt_adapter_config: self.prompt_adapter_manager = LRUCacheWorkerPromptAdapterManager( self.scheduler_config.max_num_seqs, self.scheduler_config.max_num_batched_tokens, self.device, self.prompt_adapter_config) self.model = ( self.prompt_adapter_manager.create_prompt_adapter_manager( self.model)) if self.kv_cache_dtype == "fp8" and is_hip(): # Currently only ROCm accepts kv-cache scaling factors # via quantization_param_path and this will be deprecated # in the future. if self.model_config.quantization_param_path is not None: if callable(getattr(self.model, "load_kv_cache_scales", None)): warnings.warn( "Loading kv cache scaling factor from JSON is " "deprecated and will be removed. Please include " "kv cache scaling factors in the model checkpoint.", FutureWarning, stacklevel=2) self.model.load_kv_cache_scales( self.model_config.quantization_param_path) logger.info("Loaded KV cache scaling factors from %s", self.model_config.quantization_param_path) else: raise RuntimeError( "Using FP8 KV cache and scaling factors provided but " "model %s does not support loading scaling factors.", self.model.__class__) else: logger.warning( "Using FP8 KV cache but no scaling factors " "provided. Defaulting to scaling factors of 1.0. " "This may lead to less accurate results!") if envs.VLLM_TEST_DYNAMO_GRAPH_CAPTURE and supports_dynamo(): self.model = torch.compile(self.model, fullgraph=True, backend="eager") def save_sharded_state( self, path: str, pattern: Optional[str] = None, max_size: Optional[int] = None, ) -> None: from vllm.model_executor.model_loader.loader import ShardedStateLoader ShardedStateLoader.save_model( self.model, path, pattern=pattern, max_size=max_size, ) def save_tensorized_model( self, tensorizer_config: TensorizerConfig, ) -> None: from vllm.model_executor.model_loader.loader import TensorizerLoader TensorizerLoader.save_model( self.model, tensorizer_config=tensorizer_config, ) def get_max_block_per_batch(self) -> int: block_size = self.block_size return (self.max_seq_len_to_capture + block_size - 1) // block_size def _prepare_model_input_tensors( self, seq_group_metadata_list: List[SequenceGroupMetadata], finished_requests_ids: Optional[List[str]] = None ) -> TModelInputForGPU: """Helper method to prepare the model input based on a given sequence group. Prepares metadata needed for the base model forward pass but not metadata for possible additional steps, e.g., sampling. The API assumes seq_group_metadata_list is sorted by prefill -> decode. The result tensors and data structure also batches input in prefill -> decode order. For example, - input_tokens[:num_prefill_tokens] contains prefill tokens. - input_tokens[num_prefill_tokens:] contains decode tokens. If cuda graph is required, this API automatically pads inputs. """ builder = self._builder_cls(weakref.proxy(self), finished_requests_ids) for seq_group_metadata in seq_group_metadata_list: builder.add_seq_group(seq_group_metadata) builder.reset_cached_inter_data() return builder.build() # type: ignore @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: List[LoRARequest] = [] dummy_lora_requests_per_seq: List[LoRARequest] = [] 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_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 multi-modal 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. max_mm_tokens = self.mm_registry.get_max_multimodal_tokens( self.model_config) if max_mm_tokens > 0: max_num_seqs_orig = max_num_seqs max_num_seqs = min(max_num_seqs, max_num_batched_tokens // max_mm_tokens) if max_num_seqs < 1: expr = (f"min({max_num_seqs_orig}, " f"{max_num_batched_tokens} // {max_mm_tokens})") logger.warning( "Computed max_num_seqs (%s) to be less than 1. " "Setting it to the minimum value of 1.", expr) max_num_seqs = 1 batch_size = 0 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)) batch_size += seq_len seq_data, dummy_multi_modal_data = self.input_registry \ .dummy_data_for_profiling(self.model_config, seq_len, self.mm_registry) 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=dummy_multi_modal_data, ) 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 finished_requests_ids = [seq.request_id for seq in seqs] model_input = self.prepare_model_input( seqs, finished_requests_ids=finished_requests_ids) intermediate_tensors = None if not get_pp_group().is_first_rank: intermediate_tensors = self.model.make_empty_intermediate_tensors( batch_size=batch_size, dtype=self.model_config.dtype, device=self.device) self.execute_model(model_input, kv_caches, intermediate_tensors) 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_adapters() 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_adapters(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_adapter(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_adapter(lora_id) def pin_lora(self, lora_id: int) -> bool: if not self.lora_manager: raise RuntimeError("LoRA is not enabled.") return self.lora_manager.pin_adapter(lora_id) def list_loras(self) -> Set[int]: if not self.lora_manager: raise RuntimeError("LoRA is not enabled.") return self.lora_manager.list_adapters() def remove_all_prompt_adapters(self): if not self.prompt_adapter_manager: raise RuntimeError("PromptAdapter is not enabled.") self.prompt_adapter_manager.remove_all_adapters() def set_active_prompt_adapters( self, prompt_adapter_requests: Set[PromptAdapterRequest], prompt_adapter_mapping: PromptAdapterMapping) -> None: if not self.prompt_adapter_manager: raise RuntimeError("PromptAdapter is not enabled.") self.prompt_adapter_manager.set_active_adapters( prompt_adapter_requests, prompt_adapter_mapping) def add_prompt_adapter( self, prompt_adapter_request: PromptAdapterRequest) -> bool: if not self.prompt_adapter_manager: raise RuntimeError("PromptAdapter is not enabled.") return self.prompt_adapter_manager.add_adapter(prompt_adapter_request) def remove_prompt_adapter(self, prompt_adapter_id: int) -> bool: if not self.prompt_adapter_manager: raise RuntimeError("PromptAdapter is not enabled.") return self.prompt_adapter_manager.remove_adapter(prompt_adapter_id) def pin_prompt_adapter(self, prompt_adapter_id: int) -> bool: if not self.prompt_adapter_manager: raise RuntimeError("PromptAdapter is not enabled.") return self.prompt_adapter_manager.pin_adapter(prompt_adapter_id) def list_prompt_adapters(self) -> Set[int]: if not self.prompt_adapter_manager: raise RuntimeError("PromptAdapter is not enabled.") return self.prompt_adapter_manager.list_adapters() @torch.inference_mode() def capture_model(self, kv_caches: List[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 = self.max_batchsize_to_capture input_tokens = torch.zeros(max_batch_size, dtype=torch.long).cuda() input_positions = torch.zeros(max_batch_size, dtype=torch.long).cuda() # Prepare dummy previous_hidden_states only if needed by the model. # This is used by draft models such as EAGLE. previous_hidden_states = None if "previous_hidden_states" in inspect.signature( self.model.forward).parameters: previous_hidden_states = torch.empty( [max_batch_size, self.model_config.get_hidden_size()], dtype=self.model_config.dtype, device=self.device) intermediate_inputs = None if not get_pp_group().is_first_rank: intermediate_inputs = self.model.make_empty_intermediate_tensors( batch_size=max_batch_size, dtype=self.model_config.dtype, device=self.device) # Prepare buffer for outputs. These will be reused for all batch sizes. # It will be filled after the first graph capture. hidden_or_intermediate_states: List[Optional[torch.Tensor]] = [ None ] * self.parallel_config.pipeline_parallel_size graph_batch_size = self.max_batchsize_to_capture batch_size_capture_list = [ bs for bs in _BATCH_SIZES_TO_CAPTURE if bs <= graph_batch_size ] with self.attn_state.graph_capture( max_batch_size), graph_capture() as graph_capture_context: # NOTE: Capturing the largest batch size first may help reduce the # memory usage of CUDA graph. for virtual_engine in range( self.parallel_config.pipeline_parallel_size): for batch_size in reversed(batch_size_capture_list): attn_metadata = ( self.attn_state.graph_capture_get_metadata_for_batch( batch_size)) if self.lora_config: lora_mapping = LoRAMapping( **dict(index_mapping=[0] * batch_size, prompt_mapping=[0] * batch_size, is_prefill=False)) self.set_active_loras(set(), lora_mapping) if self.prompt_adapter_config: prompt_adapter_mapping = PromptAdapterMapping( [-1] * batch_size, [-1] * batch_size, ) self.set_active_prompt_adapters( set(), prompt_adapter_mapping) graph_runner = CUDAGraphRunner( self.model, self.attn_backend.get_name(), self.attn_state.graph_clone(batch_size)) capture_inputs = { "input_ids": input_tokens[:batch_size], "positions": input_positions[:batch_size], "hidden_or_intermediate_states": hidden_or_intermediate_states[ virtual_engine] # type: ignore [:batch_size] if hidden_or_intermediate_states[virtual_engine] is not None else None, "intermediate_inputs": intermediate_inputs[:batch_size] if intermediate_inputs is not None else None, "kv_caches": kv_caches[virtual_engine], "attn_metadata": attn_metadata, "memory_pool": self.graph_memory_pool, "stream": graph_capture_context.stream } if previous_hidden_states is not None: capture_inputs[ "previous_hidden_states"] = previous_hidden_states[: batch_size] if self.has_seqlen_agnostic: # Only used by Mamba-based models CUDA graph atm (Jamba) capture_inputs.update({ "seqlen_agnostic_capture_inputs": self.model.get_seqlen_agnostic_capture_inputs( batch_size) }) graph_runner.capture(**capture_inputs) self.graph_memory_pool = graph_runner.graph.pool() self.graph_runners[virtual_engine][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) @property def vocab_size(self) -> int: return self.model_config.get_vocab_size() class ModelRunner(GPUModelRunnerBase[ModelInputForGPUWithSamplingMetadata]): """ GPU model runner with sampling step. """ _model_input_cls: Type[ModelInputForGPUWithSamplingMetadata] = ( ModelInputForGPUWithSamplingMetadata) _builder_cls: Type[ModelInputForGPUBuilder] = ModelInputForGPUBuilder def make_model_input_from_broadcasted_tensor_dict( self, tensor_dict: Dict[str, Any], ) -> ModelInputForGPUWithSamplingMetadata: model_input = \ ModelInputForGPUWithSamplingMetadata.from_broadcasted_tensor_dict( tensor_dict, attn_backend=self.attn_backend, ) return model_input def prepare_model_input( self, seq_group_metadata_list: List[SequenceGroupMetadata], virtual_engine: int = 0, finished_requests_ids: Optional[List[str]] = None, ) -> ModelInputForGPUWithSamplingMetadata: """Prepare the model input based on a given sequence group, including metadata for the sampling step. The API assumes seq_group_metadata_list is sorted by prefill -> decode. The result tensors and data structure also batches input in prefill -> decode order. For example, - input_tokens[:num_prefill_tokens] contains prefill tokens. - input_tokens[num_prefill_tokens:] contains decode tokens. If cuda graph is required, this API automatically pads inputs. """ model_input = self._prepare_model_input_tensors( seq_group_metadata_list, finished_requests_ids) if get_pp_group().is_last_rank: # Sampling metadata is only required for the final pp group generators = self.get_generators(finished_requests_ids) sampling_metadata = SamplingMetadata.prepare( seq_group_metadata_list, model_input.seq_lens, model_input.query_lens, self.device, self.pin_memory, generators, self.sampling_metadata_cache) else: sampling_metadata = None is_prompt = (seq_group_metadata_list[0].is_prompt if seq_group_metadata_list else None) return dataclasses.replace(model_input, sampling_metadata=sampling_metadata, is_prompt=is_prompt, virtual_engine=virtual_engine) @torch.inference_mode() def execute_model( self, model_input: ModelInputForGPUWithSamplingMetadata, kv_caches: List[torch.Tensor], intermediate_tensors: Optional[IntermediateTensors] = None, num_steps: int = 1, ) -> Optional[Union[List[SamplerOutput], IntermediateTensors]]: if num_steps > 1: raise ValueError("num_steps > 1 is not supported in ModelRunner") if self.lora_config: assert model_input.lora_requests is not None assert model_input.lora_mapping is not None self.set_active_loras(model_input.lora_requests, model_input.lora_mapping) if self.prompt_adapter_config: assert model_input.prompt_adapter_requests is not None assert model_input.prompt_adapter_mapping is not None self.set_active_prompt_adapters( model_input.prompt_adapter_requests, model_input.prompt_adapter_mapping) self.attn_state.begin_forward(model_input) # Currently cuda graph is only supported by the decode phase. assert model_input.attn_metadata is not None prefill_meta = model_input.attn_metadata.prefill_metadata decode_meta = model_input.attn_metadata.decode_metadata # TODO(andoorve): We can remove this once all # virtual engines share the same kv cache. virtual_engine = model_input.virtual_engine if prefill_meta is None and decode_meta.use_cuda_graph: assert model_input.input_tokens is not None graph_batch_size = model_input.input_tokens.shape[0] model_executable = self.graph_runners[virtual_engine][ graph_batch_size] else: model_executable = self.model multi_modal_kwargs = model_input.multi_modal_kwargs or {} seqlen_agnostic_kwargs = { "finished_requests_ids": model_input.finished_requests_ids, "request_ids_to_seq_ids": model_input.request_ids_to_seq_ids, } if self.has_seqlen_agnostic else {} if (self.observability_config is not None and self.observability_config.collect_model_forward_time): model_forward_start = torch.cuda.Event(enable_timing=True) model_forward_end = torch.cuda.Event(enable_timing=True) model_forward_start.record() hidden_or_intermediate_states = model_executable( input_ids=model_input.input_tokens, positions=model_input.input_positions, kv_caches=kv_caches, attn_metadata=model_input.attn_metadata, intermediate_tensors=intermediate_tensors, **MultiModalInputs.as_kwargs(multi_modal_kwargs, device=self.device), **seqlen_agnostic_kwargs) if (self.observability_config is not None and self.observability_config.collect_model_forward_time): model_forward_end.record() # Compute the logits in the last pipeline stage. if not get_pp_group().is_last_rank: if (self.is_driver_worker and hidden_or_intermediate_states is not None and isinstance(hidden_or_intermediate_states, IntermediateTensors) and self.observability_config is not None and self.observability_config.collect_model_forward_time): model_forward_end.synchronize() model_forward_time = model_forward_start.elapsed_time( model_forward_end) orig_model_forward_time = 0.0 if intermediate_tensors is not None: orig_model_forward_time = intermediate_tensors.tensors.get( "model_forward_time", torch.tensor(0.0)).item() hidden_or_intermediate_states.tensors["model_forward_time"] = ( torch.tensor(model_forward_time + orig_model_forward_time)) return hidden_or_intermediate_states logits = self.model.compute_logits(hidden_or_intermediate_states, model_input.sampling_metadata) if not self.is_driver_worker: return [] if model_input.async_callback is not None: model_input.async_callback() # Sample the next token. output: SamplerOutput = self.model.sample( logits=logits, sampling_metadata=model_input.sampling_metadata, ) if (self.observability_config is not None and self.observability_config.collect_model_forward_time and output is not None): model_forward_end.synchronize() model_forward_time = model_forward_start.elapsed_time( model_forward_end) orig_model_forward_time = 0.0 if intermediate_tensors is not None: orig_model_forward_time = intermediate_tensors.tensors.get( "model_forward_time", torch.tensor(0.0)).item() # If there are multiple workers, we are still tracking the latency # from the start time of the driver worker to the end time of the # driver worker. The model forward time will then end up covering # the communication time as well. output.model_forward_time = (orig_model_forward_time + model_forward_time) if self.return_hidden_states: # we only need to pass hidden states of most recent token assert model_input.sampling_metadata is not None indices = model_input.sampling_metadata.selected_token_indices if model_input.is_prompt: hidden_states = hidden_or_intermediate_states.index_select( 0, indices) output.prefill_hidden_states = hidden_or_intermediate_states elif decode_meta.use_cuda_graph: hidden_states = hidden_or_intermediate_states[:len(indices)] else: hidden_states = hidden_or_intermediate_states output.hidden_states = hidden_states return [output] class CUDAGraphRunner: def __init__(self, model: nn.Module, backend_name: str, attn_state: AttentionState): self.model = model self.backend_name = backend_name self.attn_state = attn_state 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, hidden_or_intermediate_states: Optional[Union[IntermediateTensors, torch.Tensor]], intermediate_inputs: Optional[IntermediateTensors], kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, memory_pool: Optional[Tuple[int, int]], stream: torch.cuda.Stream, **kwargs, ) -> Union[torch.Tensor, IntermediateTensors]: assert self._graph is None # Run the model a few times 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). # Note one iteration is not enough for torch.jit.script for _ in range(_NUM_WARMUP_ITERS): self.model( input_ids=input_ids, positions=positions, kv_caches=kv_caches, attn_metadata=attn_metadata, intermediate_tensors=intermediate_inputs, **kwargs, ) torch.cuda.synchronize() # Capture the graph. self._graph = torch.cuda.CUDAGraph() with torch.cuda.graph(self._graph, pool=memory_pool, stream=stream): output_hidden_or_intermediate_states = self.model( input_ids=input_ids, positions=positions, kv_caches=kv_caches, attn_metadata=attn_metadata, intermediate_tensors=intermediate_inputs, **kwargs, ) if hidden_or_intermediate_states is not None: if get_pp_group().is_last_rank: hidden_or_intermediate_states.copy_( output_hidden_or_intermediate_states) else: for key in hidden_or_intermediate_states.tensors: hidden_or_intermediate_states[key].copy_( output_hidden_or_intermediate_states[key]) else: hidden_or_intermediate_states = ( output_hidden_or_intermediate_states) del output_hidden_or_intermediate_states # make sure `output_hidden_states` is deleted # in the graph's memory pool gc.collect() torch.cuda.synchronize() # Save the input and output buffers. self.input_buffers = { "input_ids": input_ids, "positions": positions, "kv_caches": kv_caches, **self.attn_state.get_graph_input_buffers(attn_metadata), **kwargs, } if intermediate_inputs is not None: self.input_buffers.update(intermediate_inputs.tensors) if get_pp_group().is_last_rank: self.output_buffers = { "hidden_states": hidden_or_intermediate_states } else: self.output_buffers = hidden_or_intermediate_states return hidden_or_intermediate_states def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, intermediate_tensors: Optional[IntermediateTensors], **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.attn_state.prepare_graph_input_buffers(self.input_buffers, attn_metadata) if "seqlen_agnostic_capture_inputs" in self.input_buffers: self.model.copy_inputs_before_cuda_graphs(self.input_buffers, **kwargs) if "previous_hidden_states" in self.input_buffers: self.input_buffers["previous_hidden_states"].copy_( kwargs["previous_hidden_states"], non_blocking=True) if intermediate_tensors is not None: for key in intermediate_tensors.tensors: if key != "model_execute_time" and key != "model_forward_time": self.input_buffers[key].copy_(intermediate_tensors[key], non_blocking=True) # Run the graph. self.graph.replay() # Return the output tensor. if get_pp_group().is_last_rank: return self.output_buffers["hidden_states"] return self.output_buffers 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 _get_max_graph_batch_size(max_num_seqs: int) -> int: """ max_num_seqs: Maximum number of sequences in a batch. _BATCH_SIZES_TO_CAPTURE: all the sizes that we want to capture. pad the max_num_seqs if necessary by calling _get_graph_batch_size, which will deal with some edge cases like 1, 2, 4. if the padded size is in _BATCH_SIZES_TO_CAPTURE, return the padded size. if not, it means the padded size is larger than the largest size in _BATCH_SIZES_TO_CAPTURE, return the largest size in _BATCH_SIZES_TO_CAPTURE. """ padded_size = _get_graph_batch_size(max_num_seqs) if padded_size in _BATCH_SIZES_TO_CAPTURE: return padded_size assert padded_size > _BATCH_SIZES_TO_CAPTURE[-1] return _BATCH_SIZES_TO_CAPTURE[-1]