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 loguru import logger from aphrodite.attention import AttentionMetadata, get_attn_backend from aphrodite.common.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig, ModelConfig, ParallelConfig, SchedulerConfig, VisionLanguageConfig) from aphrodite.common.sampling_params import SamplingParams from aphrodite.common.sequence import (MultiModalData, SamplerOutput, SequenceData, SequenceGroupMetadata) from aphrodite.common.utils import (CudaMemoryProfiler, get_kv_cache_torch_dtype, is_hip, is_pin_memory_available, make_tensor_with_pad) from aphrodite.distributed import broadcast_tensor_dict from aphrodite.distributed.communication_op import graph_capture from aphrodite.distributed.parallel_state import ( get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size) from aphrodite.lora.layers import LoRAMapping from aphrodite.lora.request import LoRARequest from aphrodite.lora.worker_manager import LRUCacheWorkerLoRAManager from aphrodite.modeling import SamplingMetadata from aphrodite.modeling.model_loader import get_model _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 PrepareDecodeMetadata(NamedTuple): input_tokens: List[int] input_positions: List[int] attn_metadata: Optional[AttentionMetadata] lora_index_mapping: List[int] lora_prompt_mapping: List[int] lora_requests: Set[LoRARequest] slot_mapping: List[int] @classmethod def empty(cls): return PrepareDecodeMetadata( input_tokens=[], input_positions=[], attn_metadata=None, lora_index_mapping=[], lora_prompt_mapping=[], lora_requests=set(), slot_mapping=[], ) 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: # measure the time it takes to load the model start_time = time.time() 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, ) end_time = time.time() self.model_memory_usage = m.consumed_memory tp = get_tensor_model_parallel_world_size() rank = get_tensor_model_parallel_rank() total_time = end_time - start_time if tp > 1: logger.info( f"Rank {rank}: Model weights loaded in {total_time:.2f} secs.") if rank == 0: logger.info( "Memory usage: " f"{self.model_memory_usage / float(2**30):.2f} GiB x {tp} =" f" {self.model_memory_usage * tp / float(2**30):.2f} GiB") else: logger.info(f"Model weights loaded in {total_time:.2f} seconds.") logger.info("Memory usage: " f"{self.model_memory_usage / float(2**30):.2f} GiB") 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 " f"{self.model_config.quantization_param_path}") else: raise RuntimeError("Using FP8 KV cache and scaling factors" " provided but model " f"{self.model.__class__} does not " "support loading scaling factors.") 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 aphrodite.modeling.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) 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: 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) # TODO: 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:] if self.attn_backend.get_name() == "flash-attn": # NOTE: For flash-attn, the block table should # include the entries for the incoming prefill tokens. # TODO: 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 self.sliding_window is not None: # chunked prefill doesn't support sliding window. assert (not self.scheduler_config. chunked_prefill_enabled) sliding_window_blocks = (self.sliding_window // self.block_size) block_table = block_table[-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) # TODO: 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): seq_len = min(seq_len, self.sliding_window) context_len = seq_len - 1 seq_lens.append(seq_len) context_lens.append(context_len) query_len = seq_len - 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(seq_len) if lora_id > 0: lora_requests.add(seq_group_metadata.lora_request) lora_index_mapping += [lora_id] * (seq_len - context_len) lora_prompt_mapping.extend( [lora_id] * (seq_len - context_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 context_len == 0, ( "Prefix caching is currently not supported with " "sliding window attention") # It is an optimization. When it is decoding, it is always # 0. When prefill, we use it to not write slots to kv cache # to save memory. start_idx = max(0, query_len - self.sliding_window) for i in range(context_len, seq_len): if i < start_idx: 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. # Aphrodite uses cuda graph only for decoding requests. use_captured_graph = ( decode_only and not self.model_config.enforce_eager and batch_size <= _BATCH_SIZES_TO_CAPTURE[-1] and max_decode_seq_len <= self.max_seq_len_to_capture) if use_captured_graph: graph_batch_size = _get_graph_batch_size(batch_size) assert graph_batch_size >= batch_size for _ in range(graph_batch_size - batch_size): input_tokens.append(0) input_positions.append(0) slot_mapping.append(_PAD_SLOT_ID) seq_lens.append(1) block_tables.append([]) lora_index_mapping.append(0) batch_size = graph_batch_size num_decode_tokens = batch_size if use_captured_graph: # The shape of graph_block_tables is # [max batch size, max context len // block size]. input_block_tables = self.graph_block_tables[:batch_size] for i, block_table in enumerate(block_tables): if block_table: input_block_tables[i, :len(block_table)] = block_table block_tables = torch.tensor(input_block_tables, device=self.device) else: max_block_table_len = max( len(block_table) for block_table in block_tables) block_tables = make_tensor_with_pad( block_tables, max_len=max_block_table_len, pad=0, dtype=torch.int, device=self.device, ) assert max_query_len > 0, ("query_lens: {}".format(query_lens)) context_lens_tensor = torch.tensor(context_lens, dtype=torch.int, device=self.device) if multi_modal_input_list: assert self.vision_language_config, ( "Multi-modal inputs are only supported by " "vision language models.") multi_modal_input = torch.cat(multi_modal_input_list, dim=0).to(self.device) else: multi_modal_input = None seq_lens_tensor = torch.tensor(seq_lens, dtype=torch.int, device=self.device) query_lens_tensor = torch.tensor(query_lens, dtype=torch.long, device=self.device) query_start_loc = torch.zeros(query_lens_tensor.shape[0] + 1, dtype=torch.int32, device=self.device) seq_lens_tensor = torch.tensor(seq_lens, dtype=torch.int, device=self.device) seq_start_loc = torch.zeros(seq_lens_tensor.shape[0] + 1, dtype=torch.int32, device=self.device) torch.cumsum(query_lens_tensor, dim=0, dtype=query_start_loc.dtype, out=query_start_loc[1:]) torch.cumsum(seq_lens_tensor, dim=0, dtype=seq_start_loc.dtype, out=seq_start_loc[1:]) input_tokens_tensor = torch.tensor(input_tokens, dtype=torch.long, device=self.device) input_positions_tensor = torch.tensor(input_positions, dtype=torch.long, device=self.device) slot_mapping_tensor = torch.tensor(slot_mapping, dtype=torch.long, device=self.device) if self.attn_backend.get_name() == "flashinfer": if not hasattr(self, "flashinfer_workspace_buffer"): # Allocate 16MB workspace buffer # Follow the example of flashinfer: https://docs.flashinfer.ai/api/python/decode.html self.flashinfer_workspace_buffer = torch.empty( 16 * 1024 * 1024, dtype=torch.uint8, device=self.device) paged_kv_indptr_tensor = torch.tensor(paged_kv_indptr, dtype=torch.int, device=self.device) paged_kv_indices_tensor = torch.tensor(paged_kv_indices, dtype=torch.int, device=self.device) paged_kv_last_page_len_tensor = torch.tensor( paged_kv_last_page_len, dtype=torch.int, device=self.device) kv_cache_dtype = get_kv_cache_torch_dtype(self.kv_cache_dtype, self.model_config.dtype) attn_metadata = self.attn_backend.make_metadata( num_prefills=num_prefills, slot_mapping=slot_mapping_tensor, num_prefill_tokens=num_prefill_tokens, num_decode_tokens=num_decode_tokens, use_cuda_graph=False, max_prefill_seq_len=max_prefill_seq_len, block_tables=block_tables, workspace_buffer=self.flashinfer_workspace_buffer, paged_kv_indptr=paged_kv_indptr_tensor, paged_kv_indices=paged_kv_indices_tensor, paged_kv_last_page_len=paged_kv_last_page_len_tensor, num_qo_heads=self.model_config.get_num_attention_heads( self.parallel_config), num_kv_heads=self.model_config.get_num_kv_heads( self.parallel_config), head_dim=self.model_config.get_head_size(), page_size=16, seq_start_loc=seq_start_loc, data_type=kv_cache_dtype) else: attn_metadata = self.attn_backend.make_metadata( num_prefills=num_prefills, slot_mapping=slot_mapping_tensor, num_prefill_tokens=num_prefill_tokens, num_decode_tokens=num_decode_tokens, seq_lens=seq_lens, seq_lens_tensor=seq_lens_tensor, max_query_len=max_query_len, max_prefill_seq_len=max_prefill_seq_len, max_decode_seq_len=max_decode_seq_len, query_start_loc=query_start_loc, seq_start_loc=seq_start_loc, context_lens_tensor=context_lens_tensor, block_tables=block_tables, use_cuda_graph=use_captured_graph, ) if self.lora_config: lora_mapping = LoRAMapping( lora_index_mapping, lora_prompt_mapping, ) else: lora_mapping = None return ModelInput( input_tokens=input_tokens_tensor, input_positions=input_positions_tensor, attn_metadata=attn_metadata, seq_lens=seq_lens, query_lens=query_lens, lora_mapping=lora_mapping, lora_requests=lora_requests, multi_modal_input=multi_modal_input, slot_mapping=slot_mapping_tensor, num_prefill_tokens=num_prefill_tokens, num_decode_tokens=num_decode_tokens, num_prefills=num_prefills, ) def prepare_input_tensors( self, seq_group_metadata_list: 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 # Aphrodite 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, Aphrodite 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(f"Graph capturing finished in {elapsed_time} secs.") def __del__(self) -> None: # Delete the CUDA graphs before deleting the pynccl communicator. # NOTE: This is necessary because otherwise deadlocks can # happen. # FIXME: This is a bit hacky. Find a more robust solution. # TODO: when we get enough user feedback that pynccl is # more stable than cupy, we can remove this, e.g. in v0.4.1. self.graph_runners.clear() self.pynccl_backend = None @property def vocab_size(self) -> int: return self.model_config.get_vocab_size() class CUDAGraphRunner: def __init__(self, model: nn.Module): self.model = model self.input_buffers: Dict[str, torch.Tensor] = {} self.output_buffers: Dict[str, torch.Tensor] = {} self._graph: Optional[torch.cuda.CUDAGraph] = None @property def graph(self): assert self._graph is not None return self._graph def capture( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, memory_pool: 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