import time import warnings from typing import Dict, List, NamedTuple, Optional, Set, Tuple, Union import numpy as np import torch import torch.nn as nn from vllm.attention import AttentionMetadata, get_attn_backend from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig, ModelConfig, ParallelConfig, SchedulerConfig, VisionLanguageConfig) from vllm.distributed import broadcast_tensor_dict from vllm.distributed.communication_op import graph_capture 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 from vllm.model_executor.model_loader import get_model from vllm.sampling_params import SamplingParams from vllm.sequence import (MultiModalData, SamplerOutput, SequenceData, SequenceGroupMetadata) from vllm.utils import (CudaMemoryProfiler, get_kv_cache_torch_dtype, is_hip, is_pin_memory_available, make_tensor_with_pad) logger = init_logger(__name__) _PAD_SLOT_ID = -1 LORA_WARMUP_RANK = 8 _BATCH_SIZE_ALIGNMENT = 8 # Capture graphs for token size 1, 2, 4, 8, 16, 24, 32, 40, ..., 256. # 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, 33) ] class ModelInput(NamedTuple): input_tokens: torch.Tensor input_positions: torch.Tensor attn_metadata: Optional[AttentionMetadata] seq_lens: List[int] query_lens: List[int] lora_mapping: Optional[LoRAMapping] lora_requests: Set[LoRARequest] multi_modal_input: Optional[torch.Tensor] slot_mapping: torch.Tensor num_prefill_tokens: int num_decode_tokens: int num_prefills: int @classmethod def empty(cls, device): return ModelInput( input_tokens=torch.empty(0, device=device), input_positions=torch.empty(0, device=device), attn_metadata=None, seq_lens=[], query_lens=[], lora_mapping=None, lora_requests=set(), multi_modal_input=None, slot_mapping=torch.empty(0, device=device), num_prefill_tokens=0, num_decode_tokens=0, num_prefills=0, ) class ModelRunner: 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, vision_language_config: Optional[VisionLanguageConfig] = None, ): 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.vision_language_config = vision_language_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.graph_runners: Dict[int, CUDAGraphRunner] = {} self.graph_memory_pool: Optional[Tuple[ int, int]] = None # Set during graph capture. # 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( (max(_BATCH_SIZES_TO_CAPTURE), self.get_max_block_per_batch()), dtype=np.int32) self.attn_backend = get_attn_backend( self.model_config.get_num_attention_heads(self.parallel_config), 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, ) # Lazy initialization self.model: nn.Module # Set after load_model # Set if the backend is flashinfer. self.flashinfer_workspace_buffer: torch.Tensor # Set after load_model. self.lora_manager: Optional[LRUCacheWorkerLoRAManager] = None def load_model(self) -> None: 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, vision_language_config=self.vision_language_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 hasattr(self.model, "supported_lora_modules" ) and self.model.supported_lora_modules, ( "Model does not support LoRA") assert hasattr( self.model, "embedding_modules"), "Model does not have embedding_modules" assert hasattr(self.model, "embedding_padding_modules" ), "Model does not have embedding_padding_modules" 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.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!") 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 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( self, seq_group_metadata_list: List[SequenceGroupMetadata], ) -> ModelInput: """Prepare the model input based on a given sequence group. 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. """ input_tokens: List[int] = [] input_positions: List[int] = [] slot_mapping: List[int] = [] lora_index_mapping: List[int] = [] lora_prompt_mapping: List[int] = [] lora_requests: Set[LoRARequest] = set() seq_lens: List[int] = [] prefill_seq_lens: List[int] = [] decode_seq_lens: List[int] = [] context_lens: List[int] = [] query_lens: List[int] = [] block_tables: List[List[int]] = [] multi_modal_input_list: List[torch.Tensor] = [] decode_only = True num_prefills = 0 num_prefill_tokens = 0 num_decode_tokens = 0 # The following fields are only for flashinfer # Please follow https://docs.flashinfer.ai/tutorials/kv_layout.html#page-layout # for the precise definition of the following fields. # An example: # request 1, page indices [0, 5, 8] # request 2, page indices [1, 6, 7] # request 3, page indices [3, 4] # paged_kv_indices is a concatenation of page indices of all requests: # [0, 5, 8, 1, 6, 7, 3, 4] # paged_kv_indptr is used to index into paged_kv_indices: # [0, 3, 6, 8] paged_kv_indices: List[int] = [] # 0 at the beginning of paged_kv_indptr indicates the start of the # first request’s page indices in the paged_kv_indices list. paged_kv_indptr: List[int] = [0] # paged_kv_last_page_len is the length of the last page of each request paged_kv_last_page_len: List[int] = [] if len(seq_group_metadata_list) == 0: return ModelInput.empty(self.device) if self.sliding_window is not None: sliding_window_blocks = (self.sliding_window + self.block_size - 1) // self.block_size block_aligned_sliding_window = \ sliding_window_blocks * self.block_size for seq_group_metadata in seq_group_metadata_list: seq_ids = list(seq_group_metadata.seq_data.keys()) is_prompt = seq_group_metadata.is_prompt for seq_id in seq_ids: computed_block_nums = seq_group_metadata.computed_block_nums if (self.scheduler_config is not None and self.scheduler_config.chunked_prefill_enabled and not (computed_block_nums is None or computed_block_nums == [])): raise RuntimeError( "chunked prefill cannot be used with prefix caching " "now.") seq_data = seq_group_metadata.seq_data[seq_id] if 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_data.get_len() - 1 seq_len = min( seq_data.get_len(), context_len + seq_group_metadata.token_chunk_size) if is_prompt: tokens = seq_data.get_token_ids()[context_len:seq_len] else: # Optimization. get_token_ids requires the entire copy of # tokens. tokens = [seq_data.get_last_token_id()] # Prefix cache was hit. # Prefix is not supported with sliding_window prefix_cache_hit = (computed_block_nums is not None and len(computed_block_nums) > 0 and self.sliding_window is None and is_prompt) # These are seq_len/context_len capped to the sliding window. # They are passed to decode kernel. # We still need original seq_len/context_len to compute slot # mapping (and input position) below. curr_sliding_window_blocks = None sliding_seq_len = seq_len sliding_context_len = context_len # 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. if (self.sliding_window is not None and not is_prompt): curr_sliding_window_blocks = sliding_window_blocks if self.scheduler_config.use_v2_block_manager: # number of elements in last block suff_len = seq_len % self.block_size sliding_seq_len = min( seq_len, block_aligned_sliding_window + suff_len) if suff_len > 0: curr_sliding_window_blocks += 1 else: sliding_seq_len = min(seq_len, self.sliding_window) sliding_context_len = sliding_seq_len - 1 # TODO(sang): Combine chunked prefill and prefix caching by # only allowing multiple of block_size chunk size. # NOTE: This only works for oooooooxxx style attention. if prefix_cache_hit: assert computed_block_nums is not None context_len = len(computed_block_nums) * self.block_size tokens = tokens[context_len:] # need to think what to set it to when we have both sliding # window and prefix caching... assert self.sliding_window is None, \ "Prefix caching is not supported with sliding window" sliding_context_len = context_len if self.attn_backend.get_name() == "flash-attn": # NOTE(woosuk): For flash-attn, the block table should # include the entries for the incoming prefill tokens. # TODO(woosuk): This is a temporary fix. We should # provide a unified interface for different backends. block_table = seq_group_metadata.block_tables[seq_id] else: block_table = computed_block_nums elif (self.scheduler_config.chunked_prefill_enabled or not is_prompt): if seq_group_metadata.block_tables is not None: # chunked prefill or decode block_table = seq_group_metadata.block_tables[seq_id] if curr_sliding_window_blocks is not None: block_table = block_table[ -curr_sliding_window_blocks:] if self.attn_backend.get_name() == "flashinfer": paged_kv_indices.extend(block_table) paged_kv_indptr.append(paged_kv_indptr[-1] + len(block_table)) last_page_len = seq_data.get_len( ) % self.block_size if last_page_len == 0: last_page_len = self.block_size paged_kv_last_page_len.append(last_page_len) else: # Only happens when memory profiling runs. block_table = [] else: # Prefill without chunked prefill or memory profiling. block_table = [] block_tables.append(block_table) seq_lens.append(sliding_seq_len) context_lens.append(sliding_context_len) query_len = sliding_seq_len - sliding_context_len query_lens.append(query_len) input_tokens.extend(tokens) input_positions.extend(list(range(context_len, seq_len))) lora_id = seq_group_metadata.lora_int_id if is_prompt: assert len(seq_ids) == 1 num_prefills += 1 num_prefill_tokens += len(tokens) decode_only = False prefill_seq_lens.append(seq_len) else: assert query_len == 1, ( "seq_len: {}, context_len: {}, query_len: {}".format( seq_len, context_len, query_len)) num_decode_tokens += query_len decode_seq_lens.append(sliding_seq_len) if lora_id > 0: lora_requests.add(seq_group_metadata.lora_request) lora_index_mapping += [lora_id] * query_len lora_prompt_mapping.extend( [lora_id] * (query_len if seq_group_metadata.sampling_params and seq_group_metadata.sampling_params.prompt_logprobs else 1)) if seq_group_metadata.multi_modal_data: multi_modal_input_list.append( seq_group_metadata.multi_modal_data.data) if _is_block_tables_empty(seq_group_metadata.block_tables): # During memory profiling, the block tables are not # initialized yet. In this case, we just use a dummy # slot mapping. # In embeddings, the block tables are {seq_id: None}. slot_mapping.extend([_PAD_SLOT_ID] * seq_len) continue # Compute the slot mapping. block_table = seq_group_metadata.block_tables[seq_id] # Mask the [0, start_idx) tokens of the prompt with # _PAD_SLOT_ID, where start_idx is max(0, seq_len - # 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 self.scheduler_config.use_v2_block_manager \ or context_len == 0, ( "Prefix caching is currently not supported with " "sliding window attention in V1 block manager") # 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: slot_mapping.append(_PAD_SLOT_ID) continue block_number = block_table[i // self.block_size] block_offset = i % self.block_size slot = block_number * self.block_size + block_offset 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 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: Optional[List[SequenceGroupMetadata]], ) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata, SamplingMetadata, Set[LoRARequest], LoRAMapping, torch.Tensor]: if self.is_driver_worker: assert seq_group_metadata_list is not None # 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: Optional[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() as graph_capture_context: # 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, stream=graph_capture_context.stream, ) 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) @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: Optional[Tuple[int, int]], stream: torch.cuda.Stream, **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). self.model( input_ids, positions, kv_caches, attn_metadata, **kwargs, ) torch.cuda.synchronize() # Capture the graph. self._graph = torch.cuda.CUDAGraph() with torch.cuda.graph(self._graph, pool=memory_pool, stream=stream): 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