import contextlib import time from typing import Dict, List, Optional, Set, Tuple import numpy as np import torch import torch.nn as nn from vllm.attention import AttentionMetadata, get_attn_backend from vllm.config import (DeviceConfig, LoRAConfig, ModelConfig, ParallelConfig, SchedulerConfig, VisionLanguageConfig) 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.model_executor.parallel_utils import custom_all_reduce, pynccl_utils from vllm.model_executor.parallel_utils.communication_op import ( broadcast_tensor_dict) from vllm.model_executor.parallel_utils.parallel_state import ( with_pynccl_for_all_reduce) from vllm.sampling_params import SamplingParams, SamplingType from vllm.sequence import (MultiModalData, SamplerOutput, SequenceData, SequenceGroupMetadata) from vllm.utils import (CudaMemoryProfiler, async_tensor_h2d, is_pin_memory_available, make_tensor_with_pad, maybe_expand_dim) 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 ModelRunner: def __init__( self, model_config: ModelConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, 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.lora_config = lora_config self.is_driver_worker = is_driver_worker # model_config can be None in tests/samplers/test_sampler.py. # FIXME(woosuk): This is a hack to make the tests work. Refactor this. self.sliding_window = (model_config.get_sliding_window() if model_config is not None else None) self.device_config = (device_config if device_config is not None else DeviceConfig()) self.device = self.device_config.device self.model = None self.block_size = None # Set after initial profiling. self.lora_manager = None self.graph_runners: Dict[int, CUDAGraphRunner] = {} self.graph_memory_pool = None # Set during graph capture. self.max_context_len_to_capture = ( self.model_config.max_context_len_to_capture if self.model_config is not None else 0) # When using CUDA graph, the input block tables must be padded to # max_context_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 = None # Set after initial profiling. self.pin_memory = is_pin_memory_available() self.kv_cache_dtype = kv_cache_dtype self.vision_language_config = vision_language_config self.attn_backend = get_attn_backend( self.model_config.dtype if model_config is not None else None) def load_model(self) -> None: with CudaMemoryProfiler() as m: self.model = get_model( self.model_config, self.device_config, lora_config=self.lora_config, vision_language_config=self.vision_language_config, parallel_config=self.parallel_config, scheduler_config=self.scheduler_config) self.model_memory_usage = m.consumed_memory logger.info(f"Loading model weights took " f"{self.model_memory_usage / float(2**30):.4f} GB") 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) self.model = self.lora_manager.create_lora_manager(self.model) def set_block_size(self, block_size: int) -> None: self.block_size = block_size self.graph_block_tables = np.zeros( (max(_BATCH_SIZES_TO_CAPTURE), self.get_max_block_per_batch()), dtype=np.int32) def get_max_block_per_batch(self) -> int: block_size = self.block_size return (self.max_context_len_to_capture + block_size - 1) // block_size def _prepare_prompt( self, seq_group_metadata_list: List[SequenceGroupMetadata], ) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata, List[int], List[int], List[int], List[int], Set[LoRARequest], torch.Tensor]: assert len(seq_group_metadata_list) > 0 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() prompt_lens: List[int] = [] context_lens: List[int] = [] subquery_lens: List[int] = [] prefix_block_tables: List[List[int]] = [] multi_modal_input_list: List[torch.Tensor] = [] for seq_group_metadata in seq_group_metadata_list: assert seq_group_metadata.is_prompt seq_ids = list(seq_group_metadata.seq_data.keys()) assert len(seq_ids) == 1 seq_id = seq_ids[0] computed_block_nums = seq_group_metadata.computed_block_nums if (self.scheduler_config is not None and self.scheduler_config.chunked_prefill_enabled and computed_block_nums is not None): raise RuntimeError( "chunked prefill cannot be used with prefix caching " "now.") token_chunk_size = seq_group_metadata.token_chunk_size seq_data = seq_group_metadata.seq_data[seq_id] computed_len = seq_data.get_num_computed_tokens() # We should use get_len here because in case of preemption # it contains output tokens. prefill_end = min(seq_data.get_len(), computed_len + token_chunk_size) # TODO(sang): Rename it after chunked prefill is introduced. prompt_tokens = seq_data.get_token_ids()[computed_len:prefill_end] prompt_len = len(prompt_tokens) # Right now, the prefill_end is always same as the length of # sequence. However, once chunked prefill is introduced, this # assumption can be changed. assert prefill_end == seq_data.get_len() prompt_lens.append(prompt_len) # NOTE: This only works for oooooooxxx style attention. if computed_block_nums is not None and len( computed_block_nums) > 0 and self.sliding_window is None: # Prefix is not supported with sliding_window computed_len = len(computed_block_nums) * self.block_size prompt_tokens = prompt_tokens[computed_len:] prefix_block_tables.append(computed_block_nums) else: prefix_block_tables.append([]) # Right now, prefill start is always 0. However, this # assumption can be changed once chunked prefill is introduced. assert computed_len == 0 # actual prompt lens context_lens.append(computed_len) subquery_lens.append(prompt_len - computed_len) input_tokens.extend(prompt_tokens) # NOTE(woosuk): Here we assume that the first token in the prompt # is always the first token in the sequence. input_positions.extend(list(range(computed_len, prefill_end))) lora_id = seq_group_metadata.lora_int_id if lora_id > 0: lora_requests.add(seq_group_metadata.lora_request) lora_index_mapping += [lora_id] * (prompt_len - computed_len) lora_prompt_mapping.extend( [lora_id] * (prompt_len - computed_len if 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 seq_group_metadata.block_tables is None: # During memory profiling, the block tables are not initialized # yet. In this case, we just use a dummy slot mapping. slot_mapping.extend([_PAD_SLOT_ID] * prompt_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, prompt_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: assert computed_len == 0, ( "Prefix caching is currently not supported with " "sliding window attention") start_idx = max(0, prompt_len - self.sliding_window) for i in range(computed_len, prefill_end): 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) max_subquery_len = max(subquery_lens) max_prompt_len = max(prompt_lens) num_prompt_tokens = len(input_tokens) assert max_subquery_len > 0 input_tokens = torch.tensor(input_tokens, dtype=torch.long, device=self.device) input_positions = torch.tensor(input_positions, dtype=torch.long, device=self.device) slot_mapping = torch.tensor(slot_mapping, dtype=torch.long, device=self.device) lora_index_mapping = lora_index_mapping 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 # Prepare prefix block tables max_prompt_block_table_len = max(len(t) for t in prefix_block_tables) block_tables = make_tensor_with_pad( prefix_block_tables, max_len=max_prompt_block_table_len, pad=0, dtype=torch.int, device=self.device, ) # Query length can be shorter than key (i.e., prompt) when prefill # is chunked or prefix cached. subquery_lens_tensor = torch.tensor(subquery_lens, dtype=torch.long, device=self.device) subquery_start_loc = torch.zeros(subquery_lens_tensor.shape[0] + 1, dtype=torch.int32, device=self.device) prompt_lens_tensor = torch.tensor(prompt_lens, dtype=torch.long, device=self.device) seq_start_loc = torch.zeros(prompt_lens_tensor.shape[0] + 1, dtype=torch.int32, device=self.device) torch.cumsum(subquery_lens_tensor, dim=0, dtype=subquery_start_loc.dtype, out=subquery_start_loc[1:]) torch.cumsum(prompt_lens_tensor, dim=0, dtype=seq_start_loc.dtype, out=seq_start_loc[1:]) attn_metadata = self.attn_backend.make_metadata( is_prompt=True, slot_mapping=slot_mapping, prompt_lens=prompt_lens, prompt_lens_tensor=prompt_lens_tensor, num_prompt_tokens=num_prompt_tokens, num_generation_tokens=0, max_subquery_len=max_subquery_len, max_context_len=None, max_prompt_len=max_prompt_len, subquery_start_loc=subquery_start_loc, seq_start_loc=seq_start_loc, context_lens=context_lens_tensor, block_tables=block_tables, use_cuda_graph=False, kv_cache_dtype=self.kv_cache_dtype, ) return (input_tokens, input_positions, attn_metadata, prompt_lens, subquery_lens, lora_index_mapping, lora_prompt_mapping, lora_requests, multi_modal_input) def _prepare_decode( self, seq_group_metadata_list: List[SequenceGroupMetadata], ) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata, List[int], List[int], Set[LoRARequest]]: assert len(seq_group_metadata_list) > 0 input_tokens: List[int] = [] input_positions: List[int] = [] slot_mapping: List[int] = [] context_lens: List[int] = [] block_tables: List[List[int]] = [] lora_index_mapping: List[int] = [] lora_prompt_mapping: List[int] = [] lora_requests: Set[LoRARequest] = set() for seq_group_metadata in seq_group_metadata_list: assert not seq_group_metadata.is_prompt assert seq_group_metadata.token_chunk_size == 1 seq_ids = list(seq_group_metadata.seq_data.keys()) lora_id = seq_group_metadata.lora_int_id if lora_id > 0: lora_requests.add(seq_group_metadata.lora_request) for seq_id in seq_ids: seq_data = seq_group_metadata.seq_data[seq_id] generation_token = seq_data.get_last_token_id() input_tokens.append(generation_token) seq_len = seq_data.get_len() position = seq_len - 1 input_positions.append(position) context_len = seq_len if self.sliding_window is None else min( seq_len, self.sliding_window) context_lens.append(context_len) block_table = seq_group_metadata.block_tables[seq_id] block_number = block_table[position // self.block_size] block_offset = position % self.block_size slot = block_number * self.block_size + block_offset slot_mapping.append(slot) lora_index_mapping.append(lora_id) lora_prompt_mapping.append(lora_id) if self.sliding_window is not None: sliding_window_blocks = (self.sliding_window // self.block_size) block_table = block_table[-sliding_window_blocks:] block_tables.append(block_table) # vLLM uses cuda graph only for decoding requests. # See `capture_model` API for more details. # For decoding requests, batch_size == input_tokens. batch_size = len(input_tokens) max_context_len = max(context_lens) use_captured_graph = ( not self.model_config.enforce_eager and batch_size <= _BATCH_SIZES_TO_CAPTURE[-1] and max_context_len <= self.max_context_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) context_lens.append(1) block_tables.append([]) lora_index_mapping.append(0) batch_size = graph_batch_size input_tokens = torch.tensor(input_tokens, dtype=torch.long, device=self.device) input_positions = torch.tensor(input_positions, dtype=torch.long, device=self.device) slot_mapping = torch.tensor(slot_mapping, dtype=torch.long, device=self.device) context_lens = torch.tensor(context_lens, dtype=torch.int, device=self.device) if use_captured_graph: # When using cuda-graph all these tensors should be # padded. assert context_lens.shape[0] == input_tokens.shape[0] assert context_lens.shape[0] == input_positions.shape[0] assert context_lens.shape[0] == slot_mapping.shape[0] # 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, ) attn_metadata = self.attn_backend.make_metadata( is_prompt=False, slot_mapping=slot_mapping, prompt_lens=None, prompt_lens_tensor=None, num_prompt_tokens=0, num_generation_tokens=len(input_tokens), max_subquery_len=None, max_context_len=max_context_len, max_prompt_len=None, subquery_start_loc=None, seq_start_loc=None, context_lens=context_lens, block_tables=block_tables, use_cuda_graph=use_captured_graph, kv_cache_dtype=self.kv_cache_dtype, ) return (input_tokens, input_positions, attn_metadata, lora_index_mapping, lora_prompt_mapping, lora_requests) def _prepare_sample( self, seq_group_metadata_list: List[SequenceGroupMetadata], prompt_lens: List[int], subquery_lens: Optional[List[int]], ) -> SamplingMetadata: seq_groups: List[Tuple[List[int], SamplingParams]] = [] selected_token_indices: List[int] = [] generators: List[torch.Generator] = [] selected_token_start_idx = 0 categorized_sample_indices = {t: [] for t in SamplingType} categorized_sample_indices_start_idx = 0 categorized_sampled_token_indices_start_idx = 0 for i, seq_group_metadata in enumerate(seq_group_metadata_list): seq_ids = list(seq_group_metadata.seq_data.keys()) sampling_params = seq_group_metadata.sampling_params seq_groups.append((seq_ids, sampling_params)) if seq_group_metadata.is_prompt: assert len(seq_ids) == 1 assert subquery_lens is not None subquery_len = subquery_lens[i] if sampling_params.prompt_logprobs is not None: # NOTE: prompt token positions do not need sample, skip categorized_sample_indices_start_idx += subquery_len - 1 categorized_sample_indices[ sampling_params.sampling_type].append([ categorized_sample_indices_start_idx, categorized_sampled_token_indices_start_idx ]) categorized_sample_indices_start_idx += 1 categorized_sampled_token_indices_start_idx += 1 if sampling_params.prompt_logprobs is not None: selected_token_indices.extend( range(selected_token_start_idx, selected_token_start_idx + subquery_len - 1)) selected_token_indices.append(selected_token_start_idx + subquery_len - 1) selected_token_start_idx += subquery_len if sampling_params.seed is not None: seq_group_metadata.state.generator = torch.Generator( device=self.device).manual_seed(sampling_params.seed) else: num_seqs = len(seq_ids) selected_token_indices.extend( range(selected_token_start_idx, selected_token_start_idx + num_seqs)) selected_token_start_idx += num_seqs categorized_sample_indices[ sampling_params.sampling_type].extend( zip( range( categorized_sample_indices_start_idx, categorized_sample_indices_start_idx + num_seqs), range( categorized_sampled_token_indices_start_idx, categorized_sampled_token_indices_start_idx + num_seqs))) categorized_sample_indices_start_idx += num_seqs categorized_sampled_token_indices_start_idx += num_seqs if sampling_params.seed is not None: generators.append(seq_group_metadata.state.generator) selected_token_indices = async_tensor_h2d(selected_token_indices, dtype=torch.long, target_device=self.device, pin_memory=self.pin_memory) categorized_sample_indices = { t: maybe_expand_dim( async_tensor_h2d(seq_ids, dtype=torch.int, target_device=self.device, pin_memory=self.pin_memory), 2, 2) for t, seq_ids in categorized_sample_indices.items() } seq_data: Dict[int, SequenceData] = {} for seq_group_metadata in seq_group_metadata_list: seq_data.update(seq_group_metadata.seq_data) sampling_metadata = SamplingMetadata( seq_groups=seq_groups, seq_data=seq_data, prompt_lens=prompt_lens, selected_token_indices=selected_token_indices, categorized_sample_indices=categorized_sample_indices, generators=generators, ) return sampling_metadata def prepare_input_tensors( self, seq_group_metadata_list: Optional[List[SequenceGroupMetadata]], ) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata, SamplingMetadata, Set[int], LoRAMapping, torch.Tensor]: if self.is_driver_worker: # NOTE: We assume that all sequences in the group are all prompts or # all decodes. is_prompt = seq_group_metadata_list[0].is_prompt # Prepare input tensors. if is_prompt: (input_tokens, input_positions, attn_metadata, prompt_lens, subquery_lens, lora_index_mapping, lora_prompt_mapping, lora_requests, multi_modal_input ) = self._prepare_prompt(seq_group_metadata_list) else: (input_tokens, input_positions, attn_metadata, lora_index_mapping, lora_prompt_mapping, lora_requests) = self._prepare_decode(seq_group_metadata_list) prompt_lens = [] subquery_lens = None multi_modal_input = None sampling_metadata = self._prepare_sample(seq_group_metadata_list, prompt_lens, subquery_lens) if self.lora_config: lora_mapping = LoRAMapping( lora_index_mapping, lora_prompt_mapping, ) else: lora_mapping = None # Broadcast the metadata. 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, } 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") attn_metadata = self.attn_backend.make_metadata(**metadata_dict) sampling_metadata = SamplingMetadata( seq_groups=None, seq_data=None, prompt_lens=None, selected_token_indices=selected_token_indices, categorized_sample_indices=None, generators=None, perform_sampling=False, ) 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) # Execute the model. if attn_metadata.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 sampling_metadata.perform_sampling: 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: 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) -> bool: if not self.lora_manager: raise RuntimeError("LoRA is not enabled.") return self.lora_manager.remove_all_loras() def set_active_loras(self, lora_requests: List[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. """ # NOTE(woosuk): This is a hack to ensure that the NCCL backend is never # deleted before the CUDA graphs. self.pynccl_backend = pynccl_utils.get_nccl_backend() 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) context_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 ] # NOTE(woosuk): There are 3 backends for all-reduce: custom all-reduce # kernel, pynccl, and PyTorch NCCL. When using CUDA graph, we use # either custom all-reduce kernel or pynccl. When not using CUDA # graph, we use either custom all-reduce kernel or PyTorch NCCL. # We always prioritize using custom all-reduce kernel but fall back # to PyTorch or pynccl if it is disabled or not supported. with custom_all_reduce.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( is_prompt=False, slot_mapping=slot_mapping[:batch_size], prompt_lens=None, prompt_lens_tensor=None, num_prompt_tokens=0, num_generation_tokens=batch_size, max_subquery_len=None, max_context_len=self.max_context_len_to_capture, max_prompt_len=None, subquery_start_loc=None, seq_start_loc=None, context_lens=context_lens[:batch_size], block_tables=block_tables[:batch_size], use_cuda_graph=True, kv_cache_dtype=self.kv_cache_dtype, ) 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(f"Graph capturing finished in {elapsed_time:.0f} secs.") 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.graph = None self.input_buffers: Dict[str, torch.Tensor] = {} self.output_buffers: Dict[str, torch.Tensor] = {} 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 _maybe_pynccl(): 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 _maybe_pynccl(): 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, "context_lens": attn_metadata.context_lens, "block_tables": attn_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["context_lens"].copy_(attn_metadata.context_lens, non_blocking=True) self.input_buffers["block_tables"].copy_(attn_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) @contextlib.contextmanager def _maybe_pynccl(): if pynccl_utils.is_initialized( ) and not custom_all_reduce.is_initialized(): with with_pynccl_for_all_reduce(): yield else: yield 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