# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import dataclasses import gc import itertools import time from collections import defaultdict from collections.abc import Iterator from contextlib import contextmanager from typing import TYPE_CHECKING, Any, Optional, Union, cast import numpy as np import torch import torch.distributed import torch.nn as nn from tqdm import tqdm import vllm.envs as envs from vllm.attention import Attention, AttentionType from vllm.attention.backends.abstract import AttentionBackend from vllm.attention.layers.chunked_local_attention import ChunkedLocalAttention from vllm.compilation.counter import compilation_counter from vllm.compilation.cuda_graph import CUDAGraphWrapper from vllm.compilation.monitor import set_cudagraph_capturing_enabled from vllm.config import (CompilationLevel, CUDAGraphMode, VllmConfig, get_layers_from_vllm_config, update_config) from vllm.distributed.eplb.eplb_state import EplbState from vllm.distributed.kv_transfer import (get_kv_transfer_group, has_kv_transfer_group) from vllm.distributed.parallel_state import ( get_pp_group, get_tp_group, graph_capture, is_global_first_rank, prepare_communication_buffer_for_model) from vllm.forward_context import (BatchDescriptor, DPMetadata, set_forward_context) from vllm.logger import init_logger from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaBase from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding from vllm.model_executor.model_loader import TensorizerLoader, get_model_loader from vllm.model_executor.models.interfaces import (is_mixture_of_experts, supports_eagle3, supports_transcription) from vllm.model_executor.models.interfaces_base import ( VllmModelForPooling, is_pooling_model, is_text_generation_model) from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.inputs import (BatchedTensorInputs, MultiModalKwargsItem, PlaceholderRange) from vllm.multimodal.utils import group_mm_kwargs_by_modality from vllm.pooling_params import PoolingParams from vllm.sampling_params import SamplingType from vllm.sequence import IntermediateTensors, PoolerOutput from vllm.tasks import GenerationTask, PoolingTask, SupportedTask from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler, GiB_bytes, LazyLoader, cdiv, check_use_alibi, get_dtype_size, is_pin_memory_available, round_up, supports_dynamo) from vllm.v1.attention.backends.mamba_selectors import get_mamba_attn_backend from vllm.v1.attention.backends.utils import ( AttentionCGSupport, AttentionMetadataBuilder, CommonAttentionMetadata, make_kv_sharing_fast_prefill_attention_metadata, reorder_batch_to_split_decodes_and_prefills) from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher from vllm.v1.kv_cache_interface import (AttentionSpec, ChunkedLocalAttentionSpec, FullAttentionSpec, KVCacheConfig, KVCacheSpec, MambaSpec, SlidingWindowSpec) from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, DraftTokenIds, LogprobsTensors, ModelRunnerOutput) from vllm.v1.pool.metadata import PoolingMetadata from vllm.v1.sample.logits_processor import LogitsProcessors, build_logitsprocs from vllm.v1.sample.metadata import SamplingMetadata from vllm.v1.sample.rejection_sampler import RejectionSampler from vllm.v1.sample.sampler import Sampler from vllm.v1.spec_decode.eagle import EagleProposer from vllm.v1.spec_decode.medusa import MedusaProposer from vllm.v1.spec_decode.metadata import SpecDecodeMetadata from vllm.v1.spec_decode.ngram_proposer import NgramProposer from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch from vllm.v1.worker.kv_connector_model_runner_mixin import ( KVConnectorModelRunnerMixin, KVConnectorOutput) from vllm.v1.worker.lora_model_runner_mixin import LoRAModelRunnerMixin from .utils import (AttentionGroup, MultiModalBudget, bind_kv_cache, gather_mm_placeholders, initialize_kv_cache_for_kv_sharing, sanity_check_mm_encoder_outputs, scatter_mm_placeholders) if TYPE_CHECKING: import xgrammar as xgr import xgrammar.kernels.apply_token_bitmask_inplace_torch_compile as xgr_torch_compile # noqa: E501 from vllm.model_executor.model_loader.tensorizer import TensorizerConfig from vllm.v1.core.sched.output import SchedulerOutput else: xgr = LazyLoader("xgr", globals(), "xgrammar") xgr_torch_compile = LazyLoader( "xgr_torch_compile", globals(), "xgrammar.kernels.apply_token_bitmask_inplace_torch_compile") logger = init_logger(__name__) class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin): def __init__( self, vllm_config: VllmConfig, device: torch.device, ): self.vllm_config = vllm_config self.model_config = vllm_config.model_config self.cache_config = vllm_config.cache_config self.compilation_config = vllm_config.compilation_config self.lora_config = vllm_config.lora_config self.load_config = vllm_config.load_config self.parallel_config = vllm_config.parallel_config self.scheduler_config = vllm_config.scheduler_config self.speculative_config = vllm_config.speculative_config self.observability_config = vllm_config.observability_config from vllm.model_executor.models.utils import set_cpu_offload_max_bytes set_cpu_offload_max_bytes( int(self.cache_config.cpu_offload_gb * 1024**3)) model_config = self.model_config cache_config = self.cache_config scheduler_config = self.scheduler_config parallel_config = self.parallel_config self.device = device self.pin_memory = is_pin_memory_available() self.dtype = self.model_config.dtype if cache_config.cache_dtype == "auto": self.kv_cache_dtype = self.dtype else: self.kv_cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[ cache_config.cache_dtype] self.is_pooling_model = model_config.pooler_config is not None self.is_encoder_only_model = False self.is_multimodal_raw_input_supported = ( model_config.is_multimodal_raw_input_supported) self.max_model_len = model_config.max_model_len self.max_num_tokens = scheduler_config.max_num_batched_tokens self.max_num_reqs = scheduler_config.max_num_seqs # Model-related. self.num_query_heads = model_config.get_num_attention_heads( parallel_config) self.hidden_size = model_config.get_hidden_size() self.attention_chunk_size = model_config.attention_chunk_size self.cascade_attn_enabled = not self.model_config.disable_cascade_attn # Multi-modal data support self.mm_registry = MULTIMODAL_REGISTRY self.uses_mrope = model_config.uses_mrope self.supports_mm_inputs = self.mm_registry.supports_multimodal_inputs( model_config) # Sampler self.sampler = Sampler(logprobs_mode=self.model_config.logprobs_mode) self.eplb_state: Optional[EplbState] = None """ State of the expert parallelism load balancer. Will be lazily initialized when the model is loaded. """ # Lazy initializations # self.model: nn.Module # Set after load_model # Initialize in initialize_kv_cache self.kv_caches: list[torch.Tensor] = [] # indexes: [kv_cache_group_id][attn_group] self.attn_groups: list[list[AttentionGroup]] = [] # self.kv_cache_config: KVCacheConfig # req_id -> (input_id -> encoder_output) self.encoder_cache: dict[str, dict[int, torch.Tensor]] = {} self.use_aux_hidden_state_outputs = False # Set up speculative decoding. # NOTE(Jiayi): currently we put the entire draft model on # the last PP rank. This is not ideal if there are many # layers in the draft model. if self.speculative_config and get_pp_group().is_last_rank: if self.speculative_config.method == "ngram": self.drafter = NgramProposer(self.vllm_config) elif self.speculative_config.use_eagle(): self.drafter = EagleProposer(self.vllm_config, self.device, self) # type: ignore if self.speculative_config.method == "eagle3": self.use_aux_hidden_state_outputs = True elif self.speculative_config.method == "medusa": self.drafter = MedusaProposer( vllm_config=self.vllm_config, device=self.device) # type: ignore else: raise ValueError("Unknown speculative decoding method: " f"{self.speculative_config.method}") self.rejection_sampler = RejectionSampler() # Request states. self.requests: dict[str, CachedRequestState] = {} # Input Batch # NOTE(Chen): Ideally, we should initialize the input batch inside # `initialize_kv_cache` based on the kv cache config. However, as in # https://github.com/vllm-project/vllm/pull/18298, due to some unknown # reasons, we have to initialize the input batch before `load_model`, # quantization + weight offloading will fail otherwise. As a temporary # solution, we initialize the input batch here, and re-initialize it # in `initialize_kv_cache` if the block_sizes here is different from # the block_sizes in the kv cache config. self.input_batch = InputBatch( max_num_reqs=self.max_num_reqs, max_model_len=self.max_model_len, max_num_batched_tokens=self.max_num_tokens, device=self.device, pin_memory=self.pin_memory, vocab_size=self.model_config.get_vocab_size(), block_sizes=[self.cache_config.block_size], is_spec_decode=bool(self.vllm_config.speculative_config), logitsprocs=build_logitsprocs( self.vllm_config, self.device, self.pin_memory, self.is_pooling_model, self.vllm_config.model_config.logits_processors), is_pooling_model=self.is_pooling_model, ) # TODO(woosuk): Provide an option to tune the max cudagraph batch size. # The convention is different. # self.cudagraph_batch_sizes sorts in ascending order. # The batch sizes in the config are in descending order. if self.compilation_config.cudagraph_capture_sizes and \ self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE: self.cudagraph_batch_sizes = list( reversed(self.compilation_config.cudagraph_capture_sizes)) # Cache the device properties. self._init_device_properties() # Persistent buffers for CUDA graphs. self.input_ids = torch.zeros(self.max_num_tokens, dtype=torch.int32, device=self.device) self.positions = torch.zeros(self.max_num_tokens, dtype=torch.int64, device=self.device) self.query_start_loc = torch.zeros(self.max_num_reqs + 1, dtype=torch.int32, device=self.device) self.seq_lens = torch.zeros(self.max_num_reqs, dtype=torch.int32, device=self.device) self.slot_mapping = torch.zeros(self.max_num_tokens, dtype=torch.int64, device=self.device) # None in the first PP rank. The rest are set after load_model. self.intermediate_tensors: Optional[IntermediateTensors] = None # Only relevant for models using M-RoPE (e.g, Qwen2-VL) if self.uses_mrope: # NOTE: `mrope_positions` is implemented with one additional dummy # position on purpose to make it non-contiguous so that it can work # with torch compile. # See detailed explanation in https://github.com/vllm-project/vllm/pull/12128#discussion_r1926431923 # NOTE: When M-RoPE is enabled, position ids are 3D regardless of # the modality of inputs. For text-only inputs, each dimension has # identical position IDs, making M-RoPE functionally equivalent to # 1D-RoPE. # See page 5 of https://arxiv.org/abs/2409.12191 self.mrope_positions = torch.zeros((3, self.max_num_tokens + 1), dtype=torch.int64, device=self.device) self.mrope_positions_cpu = torch.zeros( (3, self.max_num_tokens + 1), dtype=torch.int64, device="cpu", pin_memory=self.pin_memory) self.mrope_positions_np = self.mrope_positions_cpu.numpy() # Only relevant for models using ALiBi (e.g, MPT) self.use_alibi = check_use_alibi(model_config) self.inputs_embeds = torch.zeros( (self.max_num_tokens, self.hidden_size), dtype=self.dtype, device=self.device) # OPTIMIZATION: Cache the tensors rather than creating them every step. # Keep in int64 to avoid overflow with long context self.arange_np = np.arange(max(self.max_num_reqs + 1, self.max_model_len, self.max_num_tokens), dtype=np.int64) # NOTE(woosuk): These tensors are "stateless", i.e., they are literally # a faster version of creating a new tensor every time. Thus, we should # not make any assumptions about the values in these tensors. self.input_ids_cpu = torch.zeros(self.max_num_tokens, dtype=torch.int32, device="cpu", pin_memory=self.pin_memory) self.positions_cpu = torch.zeros(self.max_num_tokens, dtype=torch.int64, device="cpu", pin_memory=self.pin_memory) self.positions_np = self.positions_cpu.numpy() self.query_start_loc_cpu = torch.zeros(self.max_num_reqs + 1, dtype=torch.int32, device="cpu", pin_memory=self.pin_memory) self.query_start_loc_np = self.query_start_loc_cpu.numpy() self.seq_lens_cpu = torch.zeros(self.max_num_reqs, dtype=torch.int32, device="cpu", pin_memory=self.pin_memory) self.seq_lens_np = self.seq_lens_cpu.numpy() # Layer pairings for cross-layer KV sharing. # If an Attention layer `layer_name` is in the keys of this dict, it # means this layer will perform attention using the keys and values # from the KV cache of `shared_kv_cache_layers[layer_name]`. self.shared_kv_cache_layers: dict[str, str] = {} self.kv_sharing_fast_prefill_eligible_layers: set[str] = set() self.kv_sharing_fast_prefill_logits_indices = None if self.cache_config.kv_sharing_fast_prefill: self.kv_sharing_fast_prefill_logits_indices = torch.zeros( self.max_num_tokens, dtype=torch.int32, device=self.device) self.uniform_decode_query_len = 1 if not self.speculative_config else \ 1 + self.speculative_config.num_speculative_tokens # Cudagraph dispatcher for runtime cudagraph dispatching. self.cudagraph_dispatcher = CudagraphDispatcher(self.vllm_config) self.mm_budget = (MultiModalBudget( self.model_config, self.scheduler_config, self.mm_registry, max_model_len=self.max_model_len, max_num_reqs=self.max_num_reqs, ) if self.supports_mm_inputs \ else None) self.reorder_batch_threshold: Optional[int] = None # Cached outputs. self._draft_token_ids: Optional[Union[list[list[int]], torch.Tensor]] = None def _init_model_kwargs(self, num_tokens: int): model_kwargs = dict[str, Any]() num_reqs = self.input_batch.num_reqs num_pooling_reqs = len(self.input_batch.pooling_params) if num_pooling_reqs == 0: return model_kwargs pooling_params = self.input_batch.pooling_metadata.pooling_params assert num_pooling_reqs == num_reqs token_type_id_requests = dict[int, Any]() for i, param in enumerate(pooling_params): if param.extra_kwargs is not None and \ (token_types := param.extra_kwargs.get( "compressed_token_type_ids")) is not None: token_type_id_requests[i] = token_types if len(token_type_id_requests) == 0: return model_kwargs seq_lens = self.seq_lens[:num_reqs] token_type_ids = [] for i in range(num_reqs): pos = token_type_id_requests.get(i, seq_lens[i]) ids = (torch.arange(seq_lens[i]) >= pos).int() token_type_ids.append(ids) model_kwargs["token_type_ids"] = torch.concat(token_type_ids).to( device=self.device) return model_kwargs def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None: """ Update the order of requests in the batch based on the attention backend's needs. For example, some attention backends (namely MLA) may want to separate requests based on if the attention computation will be compute-bound or memory-bound. Args: scheduler_output: The scheduler output. """ # Attention free models have zero kv_cache_goups, however models # like Mamba are also attention free but use the kv_cache for # keeping its internal state. This is why we check the number # of kv_cache groups instead of solely checking # for self.model_config.is_attention_free. if len(self.kv_cache_config.kv_cache_groups) == 0: return if self.reorder_batch_threshold is not None: reorder_batch_to_split_decodes_and_prefills( self.input_batch, scheduler_output, decode_threshold=self.reorder_batch_threshold) # Note: used for model runner override. def _init_device_properties(self) -> None: """Initialize attributes from torch.cuda.get_device_properties """ self.device_properties = torch.cuda.get_device_properties(self.device) self.num_sms = self.device_properties.multi_processor_count # Note: used for model runner override. def _sync_device(self) -> None: torch.cuda.synchronize() def _update_states(self, scheduler_output: "SchedulerOutput") -> None: """Update the cached states and the persistent batch with the scheduler output. The updated states are used by the `_prepare_inputs` function to create the input GPU tensors for the model. The SamplingMetadata is updated and copied to the GPU if there is a new/resumed/paused/finished request in the batch. """ # Remove finished requests from the cached states. for req_id in scheduler_output.finished_req_ids: self.requests.pop(req_id, None) self.encoder_cache.pop(req_id, None) # Remove the finished requests from the persistent batch. # NOTE(woosuk): There could be an edge case where finished_req_ids and # scheduled_req_ids overlap. This happens when a request is aborted and # then resubmitted with the same ID. In this case, we treat them as two # distinct requests - clearing the cached states for the first request # and handling the second as a new request. for req_id in scheduler_output.finished_req_ids: self.input_batch.remove_request(req_id) # Free the cached encoder outputs. for req_id, input_id in scheduler_output.free_encoder_input_ids: encoder_outputs = self.encoder_cache.get(req_id) if encoder_outputs is not None: encoder_outputs.pop(input_id, None) if not encoder_outputs: self.encoder_cache.pop(req_id, None) # Remove the unscheduled requests from the persistent batch. # NOTE(woosuk): The unscheduled requests are either preempted requests # or running requests that are not scheduled in this step. We remove # them from the persistent batch but keep their cached states since # they will be scheduled again sometime in the future. scheduled_req_ids = scheduler_output.num_scheduled_tokens.keys() cached_req_ids = self.input_batch.req_id_to_index.keys() unscheduled_req_ids = cached_req_ids - scheduled_req_ids # NOTE(woosuk): The persistent batch optimization assumes that # consecutive batches contain mostly the same requests. If batches # have low request overlap (e.g., alternating between two distinct # sets of requests), this optimization becomes very inefficient. for req_id in unscheduled_req_ids: self.input_batch.remove_request(req_id) req_ids_to_add: list[str] = [] # Add new requests to the cached states. for new_req_data in scheduler_output.scheduled_new_reqs: req_id = new_req_data.req_id sampling_params = new_req_data.sampling_params pooling_params = new_req_data.pooling_params if sampling_params and \ sampling_params.sampling_type == SamplingType.RANDOM_SEED: generator = torch.Generator(device=self.device) generator.manual_seed(sampling_params.seed) else: generator = None if pooling_params: assert (task := pooling_params.task) is not None, ( "You did not set `task` in the API") model = cast(VllmModelForPooling, self.get_model()) to_update = model.pooler.get_pooling_updates(task) to_update.apply(pooling_params) self.requests[req_id] = CachedRequestState( req_id=req_id, prompt_token_ids=new_req_data.prompt_token_ids, mm_kwargs=new_req_data.mm_kwargs, mm_positions=new_req_data.mm_positions, sampling_params=sampling_params, pooling_params=pooling_params, generator=generator, block_ids=new_req_data.block_ids, num_computed_tokens=new_req_data.num_computed_tokens, output_token_ids=[], lora_request=new_req_data.lora_request, ) # Only relevant for models using M-RoPE (e.g, Qwen2-VL) if self.uses_mrope: image_grid_thw = [] video_grid_thw = [] second_per_grid_ts = [] audio_feature_lengths = [] use_audio_in_video = False for mm_item in self.requests[req_id].mm_kwargs: mm_input = mm_item.get_data() if mm_input.get("image_grid_thw") is not None: image_grid_thw.append( mm_input["image_grid_thw"].tolist()) if mm_input.get("video_grid_thw") is not None: video_grid_thw.append( mm_input["video_grid_thw"].tolist()) if mm_input.get("second_per_grid_ts") is not None: second_per_grid_ts.append( mm_input["second_per_grid_ts"]) if mm_input.get("audio_feature_lengths") is not None: audio_feature_lengths.append( mm_input["audio_feature_lengths"]) if mm_input.get("use_audio_in_video") is True: use_audio_in_video = True hf_config = self.model_config.hf_config self.requests[req_id].mrope_positions, \ self.requests[req_id].mrope_position_delta = \ MRotaryEmbedding.get_input_positions_tensor( self.requests[req_id].prompt_token_ids, hf_config=hf_config, image_grid_thw=image_grid_thw, video_grid_thw=video_grid_thw, second_per_grid_ts=second_per_grid_ts, audio_feature_lengths=audio_feature_lengths, use_audio_in_video=use_audio_in_video, ) req_ids_to_add.append(req_id) # Update the states of the running/resumed requests. is_last_rank = get_pp_group().is_last_rank req_data = scheduler_output.scheduled_cached_reqs for i, req_id in enumerate(req_data.req_ids): req_state = self.requests[req_id] num_computed_tokens = req_data.num_computed_tokens[i] new_block_ids = req_data.new_block_ids[i] resumed_from_preemption = req_data.resumed_from_preemption[i] # Update the cached states. req_state.num_computed_tokens = num_computed_tokens if not is_last_rank: # When using PP, the scheduler sends the sampled tokens back, # because there's no direct communication between the first- # stage worker and the last-stage worker. new_token_ids = req_data.new_token_ids[i] # Add the sampled token(s) from the previous step (if any). # This doesn't include "unverified" tokens like spec tokens. num_new_tokens = (num_computed_tokens + len(new_token_ids) - req_state.num_tokens) if num_new_tokens == 1: # Avoid slicing list in most common case. req_state.output_token_ids.append(new_token_ids[-1]) elif num_new_tokens > 0: req_state.output_token_ids.extend( new_token_ids[-num_new_tokens:]) # Update the block IDs. if not resumed_from_preemption: # Append the new blocks to the existing block IDs. for block_ids, new_ids in zip(req_state.block_ids, new_block_ids): block_ids.extend(new_ids) else: # The request is resumed from preemption. # Replace the existing block IDs with the new ones. req_state.block_ids = new_block_ids req_index = self.input_batch.req_id_to_index.get(req_id) if req_index is None: # The request is not in the persistent batch. # The request was either preempted and resumed later, or was not # scheduled in the previous step and needs to be added again. req_ids_to_add.append(req_id) continue # Update the persistent batch. self.input_batch.num_computed_tokens_cpu[req_index] = ( num_computed_tokens) self.input_batch.block_table.append_row(new_block_ids, req_index) # For the last rank, we don't need to update the token_ids_cpu # because the sampled tokens are already cached. if not is_last_rank: # Add new_token_ids to token_ids_cpu. start_token_index = num_computed_tokens end_token_index = num_computed_tokens + len(new_token_ids) self.input_batch.token_ids_cpu[ req_index, start_token_index:end_token_index] = new_token_ids self.input_batch.num_tokens_no_spec[ req_index] = end_token_index self.input_batch.num_tokens[req_index] = end_token_index # Add spec_token_ids to token_ids_cpu. spec_token_ids = ( scheduler_output.scheduled_spec_decode_tokens.get(req_id, ())) if spec_token_ids: num_spec_tokens = len(spec_token_ids) start_index = self.input_batch.num_tokens_no_spec[req_index] end_token_index = start_index + num_spec_tokens self.input_batch.token_ids_cpu[ req_index, start_index:end_token_index] = spec_token_ids # NOTE(woosuk): `num_tokens` here may include spec tokens. self.input_batch.num_tokens[req_index] += num_spec_tokens # Add the new or resumed requests to the persistent batch. # The smaller empty indices are filled first. for req_id in req_ids_to_add: req_state = self.requests[req_id] self.input_batch.add_request(req_state) # Condense the batched states if there are gaps left by removed requests self.input_batch.condense() # Allow attention backend to reorder the batch, potentially self._may_reorder_batch(scheduler_output) # Refresh batch metadata with any pending updates. self.input_batch.refresh_metadata() def _extract_mm_kwargs( self, scheduler_output: "SchedulerOutput", ) -> BatchedTensorInputs: if self.is_multimodal_raw_input_supported: # noqa: SIM102 if scheduler_output: mm_kwargs = list[MultiModalKwargsItem]() for req in scheduler_output.scheduled_new_reqs: req_mm_kwargs = req.mm_kwargs if not isinstance(req_mm_kwargs, list): req_mm_kwargs = list(req_mm_kwargs) mm_kwargs.extend(req_mm_kwargs) # Input all modalities at once mm_kwargs_combined: BatchedTensorInputs = {} for _, _, mm_kwargs_group in group_mm_kwargs_by_modality( mm_kwargs, device=self.device, pin_memory=self.pin_memory, ): mm_kwargs_combined.update(mm_kwargs_group) return mm_kwargs_combined return {} def _dummy_mm_kwargs(self, num_seqs: int) -> BatchedTensorInputs: if self.is_multimodal_raw_input_supported: mm_budget = self.mm_budget assert mm_budget is not None dummy_modality, _ = mm_budget.get_modality_with_max_tokens() return self._get_mm_dummy_batch(dummy_modality, num_seqs) return {} def _get_cumsum_and_arange( self, num_tokens: np.ndarray, cumsum_dtype: Optional[np.dtype] = None, ) -> tuple[np.ndarray, np.ndarray]: """Get the cumulative sum and batched arange of the given array. # E.g., [2, 5, 3] -> ([2, 7, 10], [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]) # Equivalent to but faster than: # np.concatenate([np.arange(n) for n in num_tokens]) """ # Step 1. [2, 5, 3] -> [2, 7, 10] cu_num_tokens = np.cumsum(num_tokens, dtype=cumsum_dtype) total_num_tokens = cu_num_tokens[-1] # Step 2. [2, 7, 10] -> [0, 0, 2, 2, 2, 2, 2, 7, 7, 7] cumsums_offsets = np.repeat(cu_num_tokens - num_tokens, num_tokens) # Step 3. [0, 1, 0, 1, 2, 3, 4, 0, 1, 2] arange = self.arange_np[:total_num_tokens] - cumsums_offsets return cu_num_tokens, arange def _prepare_inputs( self, scheduler_output: "SchedulerOutput", ) -> tuple[dict[str, Any], torch.Tensor, Optional[SpecDecodeMetadata], np.ndarray, Optional[CommonAttentionMetadata], int]: """ :return: tuple[ attn_metadata: layer-to-attention_metadata mapping, logits_indices, spec_decode_metadata ] """ total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens assert total_num_scheduled_tokens > 0 num_reqs = self.input_batch.num_reqs assert num_reqs > 0 # OPTIMIZATION: Start copying the block table first. # This way, we can overlap the copy with the following CPU operations. self.input_batch.block_table.commit_block_table(num_reqs) # Get the number of scheduled tokens for each request. req_ids = self.input_batch.req_ids tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids] num_scheduled_tokens = np.array(tokens, dtype=np.int32) max_num_scheduled_tokens = max(tokens) # Get request indices. # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2] req_indices = np.repeat(self.arange_np[:num_reqs], num_scheduled_tokens) # cu_num_tokens: [2, 5, 3] -> [2, 7, 10] # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2] cu_num_tokens, arange = self._get_cumsum_and_arange( num_scheduled_tokens) # Get positions. positions_np = self.positions_np[:total_num_scheduled_tokens] np.add(self.input_batch.num_computed_tokens_cpu[req_indices], arange, out=positions_np) # Calculate M-RoPE positions. # Only relevant for models using M-RoPE (e.g, Qwen2-VL) if self.uses_mrope: self._calc_mrope_positions(scheduler_output) # Get token indices. # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2] # -> [0, 1, M, M + 1, M + 2, M + 3, M + 4, 2 * M, 2 * M + 1, 2 * M + 2] # where M is the max_model_len. token_indices = (positions_np + req_indices * self.input_batch.token_ids_cpu.shape[1]) # NOTE(woosuk): We use torch.index_select instead of np.take here # because torch.index_select is much faster than np.take for large # tensors. torch.index_select(self.input_batch.token_ids_cpu_tensor.flatten(), 0, torch.from_numpy(token_indices), out=self.input_ids_cpu[:total_num_scheduled_tokens]) self.input_batch.block_table.compute_slot_mapping( req_indices, positions_np) self.input_batch.block_table.commit_slot_mapping( total_num_scheduled_tokens) # Prepare the attention metadata. self.query_start_loc_np[0] = 0 self.query_start_loc_np[1:num_reqs + 1] = cu_num_tokens # Note: pad query_start_loc to be non-decreasing, as kernels # like FlashAttention requires that self.query_start_loc_np[num_reqs + 1:].fill(cu_num_tokens[-1]) self.query_start_loc.copy_(self.query_start_loc_cpu, non_blocking=True) query_start_loc = self.query_start_loc[:num_reqs + 1] self.seq_lens_np[:num_reqs] = ( self.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens) # Fill unused with 0 for full cuda graph mode. self.seq_lens_np[num_reqs:].fill(0) self.seq_lens.copy_(self.seq_lens_cpu, non_blocking=True) seq_lens = self.seq_lens[:num_reqs] # Copy the tensors to the GPU. self.input_ids[:total_num_scheduled_tokens].copy_( self.input_ids_cpu[:total_num_scheduled_tokens], non_blocking=True) if self.uses_mrope: # Only relevant for models using M-RoPE (e.g, Qwen2-VL) self.mrope_positions[:, :total_num_scheduled_tokens].copy_( self.mrope_positions_cpu[:, :total_num_scheduled_tokens], non_blocking=True) else: # Common case (1D positions) self.positions[:total_num_scheduled_tokens].copy_( self.positions_cpu[:total_num_scheduled_tokens], non_blocking=True) use_spec_decode = len( scheduler_output.scheduled_spec_decode_tokens) > 0 if not use_spec_decode: # NOTE(woosuk): Due to chunked prefills, the batch may contain # partial requests. While we should not sample any token # from these partial requests, we do so for simplicity. # We will ignore the sampled tokens from the partial requests. # TODO: Support prompt logprobs. logits_indices = query_start_loc[1:] - 1 spec_decode_metadata = None else: # Get the number of draft tokens for each request. # Iterate over the dictionary rather than all requests since not all # requests have draft tokens. num_draft_tokens = np.zeros(num_reqs, dtype=np.int32) for req_id, draft_token_ids in ( scheduler_output.scheduled_spec_decode_tokens.items()): req_idx = self.input_batch.req_id_to_index[req_id] num_draft_tokens[req_idx] = len(draft_token_ids) spec_decode_metadata = self._calc_spec_decode_metadata( num_draft_tokens, cu_num_tokens) logits_indices = spec_decode_metadata.logits_indices logits_indices_padded = None if self.cache_config.kv_sharing_fast_prefill: assert self.kv_sharing_fast_prefill_logits_indices is not None num_logits = logits_indices.shape[0] assert num_logits > 0 self.kv_sharing_fast_prefill_logits_indices[:num_logits].copy_( logits_indices) # There might have leftover indices in logits_indices[num_logits:] # from previous iterations, whose values may be greater than the # batch size in the current iteration. To ensure indices are always # valid, we fill the padded indices with the last index. self.kv_sharing_fast_prefill_logits_indices[num_logits:].fill_( logits_indices[-1].item()) if (self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE and num_logits <= self.cudagraph_batch_sizes[-1]): # Use piecewise CUDA graphs. # Add padding to the batch size. num_logits_padded = self.vllm_config.pad_for_cudagraph( num_logits) else: num_logits_padded = num_logits logits_indices_padded = ( self.kv_sharing_fast_prefill_logits_indices[:num_logits_padded] ) attn_metadata: dict[str, Any] = {} # Prepare encoder attention metadata separately # (encoder layers are not in KV cache groups) if self.is_encoder_only_model: per_layer_metadata = \ self._build_encoder_only_attn_metadata( scheduler_output) # Add encoder attention metadata for all encoder layers attention_layers = get_layers_from_vllm_config( self.vllm_config, Attention) for layer_name, attn_module in attention_layers.items(): if attn_module.attn_type == AttentionType.ENCODER_ONLY: common_attn_metadata, encoder_attn_metadata =\ per_layer_metadata[layer_name] attn_metadata[layer_name] = encoder_attn_metadata # Used in the below loop. query_start_loc_cpu = self.query_start_loc_cpu[:num_reqs + 1] seq_lens_cpu = self.seq_lens_cpu[:num_reqs] num_computed_tokens_cpu = ( self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs]) spec_decode_common_attn_metadata = None # Prepare the attention metadata for each KV cache group and make layers # in the same group share the same metadata. for kv_cache_group_id, kv_cache_group_spec in enumerate( self.kv_cache_config.kv_cache_groups): blk_table = self.input_batch.block_table[kv_cache_group_id] blk_table_tensor = blk_table.get_device_tensor()[:num_reqs] slot_mapping = blk_table.slot_mapping[:total_num_scheduled_tokens] # Fill unused with -1. Needed for reshape_and_cache in full cuda # graph mode. blk_table.slot_mapping[total_num_scheduled_tokens:].fill_(-1) common_attn_metadata = CommonAttentionMetadata( query_start_loc=query_start_loc, query_start_loc_cpu=query_start_loc_cpu, seq_lens=seq_lens, seq_lens_cpu=seq_lens_cpu, num_computed_tokens_cpu=num_computed_tokens_cpu, num_reqs=num_reqs, num_actual_tokens=total_num_scheduled_tokens, max_query_len=max_num_scheduled_tokens, block_table_tensor=blk_table_tensor, slot_mapping=slot_mapping, causal=True, ) if self.speculative_config and \ spec_decode_common_attn_metadata is None: spec_decode_common_attn_metadata = common_attn_metadata for attn_group in self.attn_groups[kv_cache_group_id]: # Prepare for cascade attention if enabled & beneficial. common_prefix_len = 0 builder = attn_group.metadata_builder if self.cascade_attn_enabled: common_prefix_len = self._compute_cascade_attn_prefix_len( num_scheduled_tokens, scheduler_output. num_common_prefix_blocks[kv_cache_group_id], kv_cache_group_spec.kv_cache_spec, builder, ) attn_metadata_i = (builder.build( common_prefix_len=common_prefix_len, common_attn_metadata=common_attn_metadata, )) fast_prefill_metadata = attn_metadata_i if (self.cache_config.kv_sharing_fast_prefill and self.kv_sharing_fast_prefill_eligible_layers): # Dynamically create a a dataclass type that inherits # from attention metadata type but includes additional # fields logits_indices_padded and num_logits_indices # which are required for prefill truncation fast_prefill_metadata_type = ( make_kv_sharing_fast_prefill_attention_metadata( metadata_cls=type(attn_metadata_i), )) fast_prefill_metadata = fast_prefill_metadata_type( **dataclasses.asdict(attn_metadata_i), logits_indices_padded=logits_indices_padded, num_logits_indices=logits_indices.size(0), ) for layer_name in attn_group.layer_names: if (self.cache_config.kv_sharing_fast_prefill and layer_name in self.kv_sharing_fast_prefill_eligible_layers): attn_metadata[layer_name] = fast_prefill_metadata continue attn_metadata[layer_name] = attn_metadata_i # Hot-Swap lora model if self.lora_config: self.set_active_loras(self.input_batch, num_scheduled_tokens) return (attn_metadata, logits_indices, spec_decode_metadata, num_scheduled_tokens, spec_decode_common_attn_metadata, max_num_scheduled_tokens) def _compute_cascade_attn_prefix_len( self, num_scheduled_tokens: np.ndarray, num_common_prefix_blocks: int, kv_cache_spec: KVCacheSpec, attn_metadata_builder: AttentionMetadataBuilder, ) -> int: """Compute the length of the common prefix for cascade attention. NOTE(woosuk): The common prefix length returned by this function represents the length used specifically for cascade attention, not the actual number of tokens shared between requests. When cascade attention is disabled (use_cascade=False), this function returns 0 even if requests share common tokens. Additionally, the common prefix length is truncated to a multiple of the block size and may be further truncated due to implementation details explained below. Args: num_scheduled_tokens: Number of tokens scheduled per request. num_common_prefix_blocks: Number of shared KV cache blocks. Returns: int: Length of common prefix in tokens. """ common_prefix_len = num_common_prefix_blocks * kv_cache_spec.block_size if common_prefix_len == 0: # Common case. return 0 # NOTE(woosuk): Cascade attention uses two attention kernels: one # for the common prefix and the other for the rest. For the first # kernel, we concatenate all the query tokens (possibly from # different requests) and treat them as if they are from the same # request. Then, we use bi-directional attention to process the # common prefix in the KV cache. Importantly, this means that the # first kernel does not do any masking. # Consider the following example: # Request 1's input query: [D, E, X] # Request 1's kv cache: [A, B, C, D, E, X] # Request 1's num_computed_tokens: 3 (i.e., [A, B, C]) # Request 2's input query: [E, Y] # Request 2's kv cache: [A, B, C, D, E, Y] # Request 2's num_computed_tokens: 4 (i.e., [A, B, C, D]) # If we use [A, B, C, D, E] as the common prefix, then the # first kernel will compute the bi-directional attention between # input query [D, E, X, E, Y] and common prefix [A, B, C, D, E]. # However, this is wrong because D in Request 1 should not attend to # E in the common prefix (i.e., we need masking). # To avoid this, [A, B, C, D] should be the common prefix. # That is, the common prefix should be capped by the minimum # num_computed_tokens among the requests, and plus one to include # the first token of the query. # In practice, we use [A, B, C] as the common prefix, instead of # [A, B, C, D] (i.e., the common prefix is capped by the minimum # num_computed_tokens, without plus one). # This is because of an implementation detail: We want to always # use two kernels for cascade attention. Let's imagine: # Request 3's input query: [D] # Request 3's kv cache: [A, B, C, D] # Request 3's num_computed_tokens: 3 (i.e., [A, B, C]) # If we use [A, B, C, D] as the common prefix for Request 1-3, # then Request 3 will be processed only by the first kernel, # and the second kernel will get an empty input. While this is not # a fundamental problem, our current implementation does not support # this case. num_reqs = len(num_scheduled_tokens) common_prefix_len = min( common_prefix_len, self.input_batch.num_computed_tokens_cpu[:num_reqs].min()) # common_prefix_len should be a multiple of the block size. common_prefix_len = (common_prefix_len // kv_cache_spec.block_size * kv_cache_spec.block_size) use_sliding_window = (isinstance(kv_cache_spec, SlidingWindowSpec) or (isinstance(kv_cache_spec, FullAttentionSpec) and kv_cache_spec.sliding_window is not None)) use_local_attention = ( isinstance(kv_cache_spec, ChunkedLocalAttentionSpec) or (isinstance(kv_cache_spec, FullAttentionSpec) and kv_cache_spec.attention_chunk_size is not None)) assert isinstance(kv_cache_spec, AttentionSpec) use_cascade = attn_metadata_builder.use_cascade_attention( common_prefix_len=common_prefix_len, query_lens=num_scheduled_tokens, num_query_heads=self.num_query_heads, num_kv_heads=kv_cache_spec.num_kv_heads, use_alibi=self.use_alibi, use_sliding_window=use_sliding_window, use_local_attention=use_local_attention, num_sms=self.num_sms, ) return common_prefix_len if use_cascade else 0 def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"): mrope_pos_ptr = 0 for index, req_id in enumerate(self.input_batch.req_ids): req = self.requests[req_id] assert req.mrope_positions is not None num_computed_tokens = \ self.input_batch.num_computed_tokens_cpu[index] num_scheduled_tokens = \ scheduler_output.num_scheduled_tokens[req_id] num_prompt_tokens = len(req.prompt_token_ids) if num_computed_tokens + num_scheduled_tokens > num_prompt_tokens: prompt_part_len = max(0, num_prompt_tokens - num_computed_tokens) completion_part_len = max( 0, num_scheduled_tokens - prompt_part_len) else: prompt_part_len = num_scheduled_tokens completion_part_len = 0 assert num_scheduled_tokens == prompt_part_len + completion_part_len if prompt_part_len > 0: # prompt's mrope_positions are pre-computed dst_start = mrope_pos_ptr dst_end = mrope_pos_ptr + prompt_part_len src_start = num_computed_tokens src_end = num_computed_tokens + prompt_part_len self.mrope_positions_cpu[:, dst_start:dst_end] = \ req.mrope_positions[:,src_start:src_end] mrope_pos_ptr += prompt_part_len if completion_part_len > 0: # compute completion's mrope_positions on-the-fly dst_start = mrope_pos_ptr dst_end = mrope_pos_ptr + completion_part_len MRotaryEmbedding.get_next_input_positions_tensor( out=self.mrope_positions_np, out_offset=dst_start, mrope_position_delta=req.mrope_position_delta, context_len=num_computed_tokens + prompt_part_len, num_new_tokens=completion_part_len, ) mrope_pos_ptr += completion_part_len def _calc_spec_decode_metadata( self, num_draft_tokens: np.ndarray, cu_num_scheduled_tokens: np.ndarray, ) -> SpecDecodeMetadata: # Inputs: # cu_num_scheduled_tokens: [ 4, 104, 107, 207, 209] # num_draft_tokens: [ 3, 0, 2, 0, 1] # Outputs: # cu_num_draft_tokens: [ 3, 3, 5, 5, 6] # logits_indices: [ 0, 1, 2, 3, 103, 104, 105, 106, # 206, 207, 208] # target_logits_indices: [ 0, 1, 2, 5, 6, 9] # bonus_logits_indices: [ 3, 4, 7, 8, 10] # Compute the logits indices. # [4, 1, 3, 1, 2] num_sampled_tokens = num_draft_tokens + 1 # Step 1. cu_num_sampled_tokens: [4, 5, 8, 9, 11] # arange: [0, 1, 2, 3, 0, 0, 1, 2, 0, 0, 1] cu_num_sampled_tokens, arange = self._get_cumsum_and_arange( num_sampled_tokens, cumsum_dtype=np.int32) # Step 2. [0, 0, 0, 0, 103, 104, 104, 104, 206, 207, 207] logits_indices = np.repeat( cu_num_scheduled_tokens - num_sampled_tokens, num_sampled_tokens) # Step 3. [0, 1, 2, 3, 103, 104, 105, 106, 206, 207, 208] logits_indices += arange # Compute the bonus logits indices. bonus_logits_indices = cu_num_sampled_tokens - 1 # Compute the draft logits indices. # cu_num_draft_tokens: [3, 3, 5, 5, 6] # arange: [0, 1, 2, 0, 1, 0] cu_num_draft_tokens, arange = self._get_cumsum_and_arange( num_draft_tokens, cumsum_dtype=np.int32) # [0, 0, 0, 5, 5, 9] target_logits_indices = np.repeat( cu_num_sampled_tokens - num_sampled_tokens, num_draft_tokens) # [0, 1, 2, 5, 6, 9] target_logits_indices += arange # TODO: Optimize the CPU -> GPU copy. cu_num_draft_tokens = torch.from_numpy(cu_num_draft_tokens).to( self.device, non_blocking=True) logits_indices = torch.from_numpy(logits_indices).to(self.device, non_blocking=True) target_logits_indices = torch.from_numpy(target_logits_indices).to( self.device, non_blocking=True) bonus_logits_indices = torch.from_numpy(bonus_logits_indices).to( self.device, non_blocking=True) # Compute the draft token ids. # draft_token_indices: [ 1, 2, 3, 105, 106, 208] draft_token_ids = self.input_ids[logits_indices] draft_token_ids = draft_token_ids[target_logits_indices + 1] metadata = SpecDecodeMetadata( draft_token_ids=draft_token_ids, num_draft_tokens=num_draft_tokens.tolist(), cu_num_draft_tokens=cu_num_draft_tokens, target_logits_indices=target_logits_indices, bonus_logits_indices=bonus_logits_indices, logits_indices=logits_indices, ) return metadata def _execute_mm_encoder(self, scheduler_output: "SchedulerOutput"): scheduled_encoder_inputs = scheduler_output.scheduled_encoder_inputs if not scheduled_encoder_inputs: return # Batch the multi-modal inputs. mm_kwargs = list[MultiModalKwargsItem]() req_ids_pos = list[tuple[str, int, PlaceholderRange]]() for req_id, encoder_input_ids in scheduled_encoder_inputs.items(): req_state = self.requests[req_id] for mm_input_id in encoder_input_ids: mm_kwargs.append(req_state.mm_kwargs[mm_input_id]) req_ids_pos.append( (req_id, mm_input_id, req_state.mm_positions[mm_input_id])) # Batch mm inputs as much as we can: if a request in the batch has # multiple modalities or a different modality than the previous one, # we process it separately to preserve item order. # FIXME(ywang96): This is a hacky way to deal with multiple modalities # in the same batch while still being able to benefit from batching # multimodal inputs. The proper solution should be reordering the # encoder outputs. encoder_outputs = [] for _, num_items, mm_kwargs_group in group_mm_kwargs_by_modality( mm_kwargs, device=self.device, pin_memory=self.pin_memory, ): # Run the encoder. # `curr_group_outputs` is either of the following: # 1. A tensor of shape (num_items, feature_size, hidden_size) # in case feature_size is fixed across all multimodal items. # 2. A list or tuple (length: num_items) of tensors, each of shape # (feature_size, hidden_size) in case the feature size is dynamic # depending on the input multimodal items. curr_group_outputs = self.model.get_multimodal_embeddings( **mm_kwargs_group) sanity_check_mm_encoder_outputs( curr_group_outputs, expected_num_items=num_items, ) for output in curr_group_outputs: encoder_outputs.append(output) # Cache the encoder outputs. for (req_id, input_id, pos_info), output in zip( req_ids_pos, encoder_outputs, ): if req_id not in self.encoder_cache: self.encoder_cache[req_id] = {} self.encoder_cache[req_id][input_id] = scatter_mm_placeholders( output, is_embed=pos_info.is_embed, ) def _gather_mm_embeddings( self, scheduler_output: "SchedulerOutput", shift_computed_tokens: int = 0, ) -> list[torch.Tensor]: mm_embeds: list[torch.Tensor] = [] for req_id in self.input_batch.req_ids: num_scheduled_tokens = scheduler_output.num_scheduled_tokens[ req_id] req_state = self.requests[req_id] num_computed_tokens = \ req_state.num_computed_tokens + shift_computed_tokens mm_positions = req_state.mm_positions for i, pos_info in enumerate(mm_positions): start_pos = pos_info.offset num_encoder_tokens = pos_info.length # The encoder output is needed if the two ranges overlap: # [num_computed_tokens, # num_computed_tokens + num_scheduled_tokens) and # [start_pos, start_pos + num_encoder_tokens) if start_pos >= num_computed_tokens + num_scheduled_tokens: # The encoder output is not needed in this step. break if start_pos + num_encoder_tokens <= num_computed_tokens: # The encoder output is already processed and stored # in the decoder's KV cache. continue start_idx = max(num_computed_tokens - start_pos, 0) end_idx = min( num_computed_tokens - start_pos + num_scheduled_tokens, num_encoder_tokens) assert start_idx < end_idx assert req_id in self.encoder_cache assert i in self.encoder_cache[req_id] encoder_output = self.encoder_cache[req_id][i] if (is_embed := pos_info.is_embed) is not None: is_embed = is_embed[start_idx:end_idx] mm_embeds_item = gather_mm_placeholders( encoder_output[start_idx:end_idx], is_embed=is_embed, ) mm_embeds.append(mm_embeds_item) return mm_embeds def get_model(self) -> nn.Module: # get raw model out of the cudagraph wrapper. if isinstance(self.model, CUDAGraphWrapper): return self.model.unwrap() return self.model def get_supported_generation_tasks(self) -> list[GenerationTask]: model = self.get_model() supported_tasks = list[GenerationTask]() if is_text_generation_model(model): supported_tasks.append("generate") if supports_transcription(model): if model.supports_transcription_only: return ["transcription"] supported_tasks.append("transcription") return supported_tasks def get_supported_pooling_tasks(self) -> list[PoolingTask]: model = self.get_model() if not is_pooling_model(model): return [] supported_tasks = list(model.pooler.get_supported_tasks()) if (self.scheduler_config.chunked_prefill_enabled and "encode" in supported_tasks): supported_tasks.remove("encode") logger.info_once("Chunked prefill is not supported with " "encode task which using ALL pooling. " "Please turn off chunked prefill by " "`--no-enable-chunked-prefill` before using it.") return supported_tasks def get_supported_tasks(self) -> tuple[SupportedTask, ...]: tasks = list[SupportedTask]() if self.model_config.runner_type == "generate": tasks.extend(self.get_supported_generation_tasks()) if self.model_config.runner_type == "pooling": tasks.extend(self.get_supported_pooling_tasks()) return tuple(tasks) def apply_grammar_bitmask( self, scheduler_output: "SchedulerOutput", logits: torch.Tensor, ): grammar_bitmask = scheduler_output.grammar_bitmask if grammar_bitmask is None: return # We receive the structured output bitmask from the scheduler, # compacted to contain bitmasks only for structured output requests. # The order of the requests in the bitmask is not guaranteed to be the # same as the order of the requests in the gpu runner's batch. We need # to sort the bitmask to match the order of the requests used here. # Get the batch indices of the structured output requests. # Keep track of the number of speculative tokens scheduled for every # request in the batch, as the logit indices are offset by this amount. struct_out_req_batch_indices: dict[str, int] = {} cumulative_offset = 0 seq = sorted(self.input_batch.req_id_to_index.items(), key=lambda x: x[1]) for req_id, batch_index in seq: logit_index = batch_index + cumulative_offset cumulative_offset += len( scheduler_output.scheduled_spec_decode_tokens.get(req_id, [])) if req_id in scheduler_output.structured_output_request_ids: struct_out_req_batch_indices[req_id] = logit_index out_indices = [] # Reorder the bitmask to match the order of the requests in the batch. sorted_bitmask = np.full(shape=(logits.shape[0], grammar_bitmask.shape[1]), fill_value=-1, dtype=grammar_bitmask.dtype) cumulative_index = 0 seq = sorted(scheduler_output.structured_output_request_ids.items(), key=lambda x: x[1]) for req_id, _ in seq: logit_index = struct_out_req_batch_indices[req_id] num_spec_tokens = len( scheduler_output.scheduled_spec_decode_tokens.get(req_id, [])) for i in range(1 + num_spec_tokens): sorted_bitmask[logit_index + i] = \ grammar_bitmask[cumulative_index + i] out_indices.append(logit_index + i) cumulative_index += 1 + num_spec_tokens grammar_bitmask = sorted_bitmask # If the length of out indices and the logits have the same shape # we don't need to pass indices to the kernel, # since the bitmask is already aligned with the logits. skip_out_indices = len(out_indices) == logits.shape[0] # Serialization of np.ndarray is much more efficient than a tensor, # so we receive it in that format. grammar_bitmask = torch.from_numpy(grammar_bitmask).contiguous() # Force use of the torch.compile implementation from xgrammar to work # around issues with the Triton kernel in concurrent structured output # scenarios. See PR #19565 and issues #19493, #18376 for details. xgr_torch_compile.apply_token_bitmask_inplace_torch_compile( logits, grammar_bitmask.to(self.device, non_blocking=True), indices=out_indices if not skip_out_indices else None, ) def sync_and_slice_intermediate_tensors( self, num_tokens: int, intermediate_tensors: IntermediateTensors, sync_self: bool) -> IntermediateTensors: assert self.intermediate_tensors is not None tp = self.vllm_config.parallel_config.tensor_parallel_size enabled_sp = self.compilation_config.pass_config. \ enable_sequence_parallelism if enabled_sp: # When sequence parallelism is enabled, we always pad num_tokens # to be a multiple of tensor_parallel_size (tp) earlier assert num_tokens % tp == 0 is_residual_scattered = tp > 1 and enabled_sp \ and num_tokens % tp == 0 # When sequence parallelism is enabled, the "residual" tensor is sharded # across tensor parallel ranks, so each rank only needs its own slice. if sync_self: assert intermediate_tensors is not None for k, v in intermediate_tensors.items(): is_scattered = k == "residual" and is_residual_scattered copy_len = num_tokens // tp if is_scattered else \ num_tokens self.intermediate_tensors[k][:copy_len].copy_( v[:copy_len], non_blocking=True) return IntermediateTensors({ k: v[:num_tokens // tp] if k == "residual" and is_residual_scattered else v[:num_tokens] for k, v in self.intermediate_tensors.items() }) def eplb_step(self, is_dummy: bool = False, is_profile: bool = False) -> None: """ Step for the EPLB (Expert Parallelism Load Balancing) state. """ if not self.parallel_config.enable_eplb: return assert self.eplb_state is not None model = self.get_model() assert is_mixture_of_experts(model) self.eplb_state.step( model, is_dummy, is_profile, log_stats=self.parallel_config.eplb_log_balancedness, ) def get_dp_padding(self, num_tokens: int) -> tuple[int, Optional[torch.Tensor]]: dp_size = self.vllm_config.parallel_config.data_parallel_size dp_rank = self.vllm_config.parallel_config.data_parallel_rank # For DP: Don't pad when setting enforce_eager. # This lets us set enforce_eager on the prefiller in a P/D setup and # still use CUDA graphs (enabled by this padding) on the decoder. # # TODO(tms) : There are many cases where padding is enabled for # prefills, causing unnecessary and excessive padding of activations. if dp_size == 1 or self.vllm_config.model_config.enforce_eager: # Early exit. return 0, None num_tokens_across_dp = DPMetadata.num_tokens_across_dp( num_tokens, dp_size, dp_rank) max_tokens_across_dp_cpu = torch.max(num_tokens_across_dp).item() num_tokens_after_padding = torch.tensor([max_tokens_across_dp_cpu] * dp_size, device="cpu", dtype=torch.int32) return max_tokens_across_dp_cpu - num_tokens, num_tokens_after_padding def _pool( self, hidden_states: torch.Tensor, num_scheduled_tokens: int, num_scheduled_tokens_np: np.ndarray, kv_connector_output: Optional[KVConnectorOutput], ) -> ModelRunnerOutput: assert self.input_batch.num_reqs ==\ len(self.input_batch.pooling_params), \ "Either all or none of the requests in" \ " a batch must be pooling request" extracted_hidden_states = list( torch.split(hidden_states[:num_scheduled_tokens], num_scheduled_tokens_np.tolist())) pooling_metadata = self.input_batch.pooling_metadata raw_pooler_output = self.model.pooler( hidden_states=extracted_hidden_states, pooling_metadata=pooling_metadata) pooler_output: list[Optional[torch.Tensor]] = [] seq_lens = self.seq_lens[:self.input_batch.num_reqs] for raw_output, seq_len, prompt_len in zip( raw_pooler_output, seq_lens, pooling_metadata.prompt_lens): if seq_len == prompt_len: pooler_output.append(raw_output.data.cpu()) else: pooler_output.append(None) return ModelRunnerOutput( req_ids=self.input_batch.req_ids, req_id_to_index=self.input_batch.req_id_to_index, sampled_token_ids=[], logprobs=None, prompt_logprobs_dict={}, pooler_output=pooler_output, kv_connector_output=kv_connector_output, ) @torch.inference_mode() def execute_model( self, scheduler_output: "SchedulerOutput", intermediate_tensors: Optional[IntermediateTensors] = None, ) -> Union[ModelRunnerOutput, IntermediateTensors]: self._update_states(scheduler_output) if not scheduler_output.total_num_scheduled_tokens: if not has_kv_transfer_group(): # Return empty ModelRunnerOutput if there's no work to do. return EMPTY_MODEL_RUNNER_OUTPUT return self.kv_connector_no_forward(scheduler_output, self.vllm_config) # Prepare the decoder inputs. (attn_metadata, logits_indices, spec_decode_metadata, num_scheduled_tokens_np, spec_decode_common_attn_metadata, max_query_len) = (self._prepare_inputs(scheduler_output)) num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens if (self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]): # Use CUDA graphs. # Add padding to the batch size. num_input_tokens = self.vllm_config.pad_for_cudagraph( num_scheduled_tokens) else: # Eager mode. # Pad tokens to multiple of tensor_parallel_size when # enabled collective fusion for SP tp_size = self.vllm_config.parallel_config.tensor_parallel_size if self.compilation_config.pass_config. \ enable_sequence_parallelism and tp_size > 1: num_input_tokens = round_up(num_scheduled_tokens, tp_size) else: num_input_tokens = num_scheduled_tokens # Padding for DP num_pad, num_tokens_across_dp = self.get_dp_padding(num_input_tokens) num_input_tokens += num_pad # _prepare_inputs may reorder the batch, so we must gather multi # modal outputs after that to ensure the correct order if self.supports_mm_inputs: # Run the multimodal encoder if any. self._execute_mm_encoder(scheduler_output) mm_embeds = self._gather_mm_embeddings(scheduler_output) else: mm_embeds = [] if self.supports_mm_inputs and get_pp_group().is_first_rank: # NOTE(woosuk): To unify token ids and soft tokens (vision # embeddings), we always use embeddings (rather than token ids) # as input to the multimodal model, even when the input is text. inputs_embeds_scheduled = self.model.get_input_embeddings( input_ids=self.input_ids[:num_scheduled_tokens], multimodal_embeddings=mm_embeds or None, ) # TODO(woosuk): Avoid the copy. Optimize. self.inputs_embeds[:num_scheduled_tokens].copy_( inputs_embeds_scheduled) input_ids = None inputs_embeds = self.inputs_embeds[:num_input_tokens] model_kwargs = { **self._init_model_kwargs(num_scheduled_tokens), **self._extract_mm_kwargs(scheduler_output), } else: # For text-only models, we use token ids as input. # While it is possible to use embeddings as input just like the # multimodal models, it is not desirable for performance since # then the embedding layer is not included in the CUDA graph. input_ids = self.input_ids[:num_input_tokens] inputs_embeds = None model_kwargs = self._init_model_kwargs(num_input_tokens) if self.uses_mrope: positions = self.mrope_positions[:, :num_input_tokens] else: positions = self.positions[:num_input_tokens] if get_pp_group().is_first_rank: intermediate_tensors = None else: intermediate_tensors = self.sync_and_slice_intermediate_tensors( num_input_tokens, intermediate_tensors, True) uniform_decode = (max_query_len == self.uniform_decode_query_len) and ( num_scheduled_tokens == self.input_batch.num_reqs * max_query_len) batch_descriptor = BatchDescriptor(num_tokens=num_input_tokens, uniform_decode=uniform_decode) cudagraph_runtime_mode, batch_descriptor = \ self.cudagraph_dispatcher.dispatch(batch_descriptor) # Run the model. # Use persistent buffers for CUDA graphs. with set_forward_context( attn_metadata, self.vllm_config, num_tokens=num_input_tokens, num_tokens_across_dp=num_tokens_across_dp, cudagraph_runtime_mode=cudagraph_runtime_mode, batch_descriptor=batch_descriptor, ), self.maybe_get_kv_connector_output( scheduler_output) as kv_connector_output: model_output = self.model( input_ids=input_ids, positions=positions, intermediate_tensors=intermediate_tensors, inputs_embeds=inputs_embeds, **model_kwargs, ) if self.use_aux_hidden_state_outputs: hidden_states, aux_hidden_states = model_output else: hidden_states = model_output aux_hidden_states = None # Broadcast PP output for external_launcher (torchrun) # to make sure we are synced across pp ranks # TODO: Support overlapping mirco-batches # https://github.com/vllm-project/vllm/issues/18019 broadcast_pp_output = \ self.parallel_config.distributed_executor_backend \ == "external_launcher" and len(get_pp_group().ranks) > 0 if not get_pp_group().is_last_rank: # For mid-pipeline stages, return the hidden states. assert isinstance(hidden_states, IntermediateTensors) if not broadcast_pp_output: hidden_states.kv_connector_output = kv_connector_output return hidden_states get_pp_group().send_tensor_dict(hidden_states.tensors, all_gather_group=get_tp_group()) logits = None else: if self.input_batch.pooling_params: return self._pool(hidden_states, num_scheduled_tokens, num_scheduled_tokens_np, kv_connector_output) sample_hidden_states = hidden_states[logits_indices] logits = self.model.compute_logits(sample_hidden_states, None) if broadcast_pp_output: model_output_broadcast_data = { "logits": logits.contiguous(), } if logits is not None else {} model_output_broadcast_data = get_pp_group().broadcast_tensor_dict( model_output_broadcast_data, src=len(get_pp_group().ranks) - 1) assert model_output_broadcast_data is not None logits = model_output_broadcast_data["logits"] # Apply structured output bitmasks if present if scheduler_output.grammar_bitmask is not None: self.apply_grammar_bitmask(scheduler_output, logits) # Sample the next token and get logprobs if needed. sampling_metadata = self.input_batch.sampling_metadata if spec_decode_metadata is None: sampler_output = self.sampler( logits=logits, sampling_metadata=sampling_metadata, ) else: # When indexing with a tensor (bonus_logits_indices), PyTorch # creates a new tensor with separate storage from the original # logits tensor. This means any in-place operations on bonus_logits # won't affect the original logits tensor. assert logits is not None bonus_logits = logits[spec_decode_metadata.bonus_logits_indices] sampler_output = self.sampler( logits=bonus_logits, sampling_metadata=sampling_metadata, ) bonus_token_ids = sampler_output.sampled_token_ids # Just like `bonus_logits`, `target_logits` is a new tensor with # separate storage from the original `logits` tensor. Therefore, # it is safe to update `target_logits` in place. target_logits = logits[spec_decode_metadata.target_logits_indices] output_token_ids = self.rejection_sampler( spec_decode_metadata, None, # draft_probs target_logits, bonus_token_ids, sampling_metadata, ) sampler_output.sampled_token_ids = output_token_ids num_nans_in_logits = {} if envs.VLLM_COMPUTE_NANS_IN_LOGITS: num_nans_in_logits = self._get_nans_in_logits(logits) # TODO(woosuk): The following loop can be slow since it iterates over # the requests one by one. Optimize. discard_sampled_tokens_req_indices = [] for i, req_id in enumerate(self.input_batch.req_ids): req_state = self.requests[req_id] seq_len = (req_state.num_computed_tokens + scheduler_output.num_scheduled_tokens[req_id]) if seq_len < req_state.num_tokens: # Ignore the sampled token for partial prefills. # Rewind the generator state as if the token was not sampled. # This relies on cuda-specific torch-internal impl details generator = self.input_batch.generators.get(i) if generator is not None: generator.set_offset(generator.get_offset() - 4) # Record the index of the request that should not be sampled, # so that we could clear the sampled tokens before returning. discard_sampled_tokens_req_indices.append(i) # NOTE: GPU -> CPU Sync happens here. # Move as many CPU operations as possible before this sync point. logprobs_tensors = sampler_output.logprobs_tensors logprobs_lists = logprobs_tensors.tolists() \ if logprobs_tensors is not None else None # Compute prompt logprobs if needed. prompt_logprobs_dict = self._get_prompt_logprobs_dict( hidden_states[:num_scheduled_tokens], scheduler_output.num_scheduled_tokens, ) # Get the valid generated tokens. sampled_token_ids = sampler_output.sampled_token_ids max_gen_len = sampled_token_ids.shape[-1] if max_gen_len == 1: # No spec decode tokens. valid_sampled_token_ids = sampled_token_ids.tolist() else: # Includes spec decode tokens. valid_sampled_token_ids = self.rejection_sampler.parse_output( sampled_token_ids, self.input_batch.vocab_size, ) # Mask out the sampled tokens that should not be sampled. for i in discard_sampled_tokens_req_indices: valid_sampled_token_ids[i].clear() # Cache the sampled tokens in the model runner, so that the scheduler # doesn't need to send them back. # NOTE(woosuk): As an exception, when using PP, the scheduler sends # the sampled tokens back, because there's no direct communication # between the first-stage worker and the last-stage worker. for req_idx, sampled_ids in enumerate(valid_sampled_token_ids): if not sampled_ids: continue start_idx = self.input_batch.num_tokens_no_spec[req_idx] end_idx = start_idx + len(sampled_ids) assert end_idx <= self.max_model_len, ( "Sampled token IDs exceed the max model length. " f"Total number of tokens: {end_idx} > max_model_len: " f"{self.max_model_len}") self.input_batch.token_ids_cpu[req_idx, start_idx:end_idx] = sampled_ids self.input_batch.num_tokens_no_spec[req_idx] = end_idx self.input_batch.num_tokens[req_idx] = end_idx req_id = self.input_batch.req_ids[req_idx] req_state = self.requests[req_id] req_state.output_token_ids.extend(sampled_ids) if self.speculative_config: assert spec_decode_common_attn_metadata is not None self._draft_token_ids = self.propose_draft_token_ids( scheduler_output, valid_sampled_token_ids, sampling_metadata, hidden_states, sample_hidden_states, aux_hidden_states, spec_decode_metadata, spec_decode_common_attn_metadata, ) self.eplb_step() return ModelRunnerOutput( req_ids=self.input_batch.req_ids, req_id_to_index=self.input_batch.req_id_to_index, sampled_token_ids=valid_sampled_token_ids, logprobs=logprobs_lists, prompt_logprobs_dict=prompt_logprobs_dict, pooler_output=[], kv_connector_output=kv_connector_output, num_nans_in_logits=num_nans_in_logits, ) def take_draft_token_ids(self) -> Optional[DraftTokenIds]: if self._draft_token_ids is None: return None req_ids = self.input_batch.req_ids if isinstance(self._draft_token_ids, torch.Tensor): draft_token_ids = self._draft_token_ids.tolist() else: draft_token_ids = self._draft_token_ids self._draft_token_ids = None return DraftTokenIds(req_ids, draft_token_ids) def propose_draft_token_ids( self, scheduler_output: "SchedulerOutput", sampled_token_ids: list[list[int]], sampling_metadata: SamplingMetadata, hidden_states: torch.Tensor, sample_hidden_states: torch.Tensor, aux_hidden_states: Optional[torch.Tensor], spec_decode_metadata: Optional[SpecDecodeMetadata], common_attn_metadata: CommonAttentionMetadata, ) -> Union[list[list[int]], torch.Tensor]: num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens if self.speculative_config.method == "ngram": assert isinstance(self.drafter, NgramProposer) draft_token_ids = self.propose_ngram_draft_token_ids( sampled_token_ids) elif self.speculative_config.method == "medusa": assert isinstance(self.drafter, MedusaProposer) if sample_hidden_states.shape[0] == len(sampled_token_ids): # The input to the target model does not include draft tokens. hidden_states = sample_hidden_states else: indices = [] offset = 0 for num_draft, tokens in zip( spec_decode_metadata.num_draft_tokens, sampled_token_ids): indices.append(offset + len(tokens) - 1) offset += num_draft + 1 indices = torch.tensor(indices, device=self.device) hidden_states = sample_hidden_states[indices] draft_token_ids = self.drafter.propose( target_hidden_states=hidden_states, sampling_metadata=sampling_metadata, ) elif self.speculative_config.use_eagle(): assert isinstance(self.drafter, EagleProposer) # TODO(woosuk): Refactor the loop. next_token_ids: list[int] = [] for i, token_ids in enumerate(sampled_token_ids): if token_ids: # Common case. next_token_id = token_ids[-1] else: # Partial prefill (rare case). # Get the next token id from the request state. req_id = self.input_batch.req_ids[i] req_state = self.requests[req_id] seq_len = (req_state.num_computed_tokens + scheduler_output.num_scheduled_tokens[req_id]) next_token_id = req_state.get_token_id(seq_len) next_token_ids.append(next_token_id) next_token_ids = torch.tensor(next_token_ids, dtype=torch.int32, device=self.device) if spec_decode_metadata is None: # input_ids can be None for multimodal models. target_token_ids = self.input_ids[:num_scheduled_tokens] # TODO(woosuk): Support M-RoPE. target_positions = self.positions[:num_scheduled_tokens] if self.use_aux_hidden_state_outputs: target_hidden_states = torch.cat( [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1) else: target_hidden_states = hidden_states[:num_scheduled_tokens] else: # TODO(woosuk): Refactor this. num_draft_tokens = spec_decode_metadata.num_draft_tokens num_rejected_tokens = [ n + 1 - len(sampled_token_ids[i]) if n > 0 else 0 for i, n in enumerate(num_draft_tokens) ] num_rejected_tokens_cpu = torch.tensor(num_rejected_tokens, dtype=torch.int32) common_attn_metadata, token_indices =\ self.drafter.prepare_inputs( common_attn_metadata, num_rejected_tokens_cpu) target_token_ids = self.input_ids[token_indices] # TODO(woosuk): Support M-RoPE. target_positions = self.positions[token_indices] if self.use_aux_hidden_state_outputs: target_hidden_states = torch.cat( [h[token_indices] for h in aux_hidden_states], dim=-1) else: target_hidden_states = hidden_states[token_indices] mm_embeds = None if self.supports_mm_inputs: mm_embeds = self._gather_mm_embeddings(scheduler_output, shift_computed_tokens=1) draft_token_ids = self.drafter.propose( target_token_ids=target_token_ids, target_positions=target_positions, target_hidden_states=target_hidden_states, next_token_ids=next_token_ids, sampling_metadata=sampling_metadata, common_attn_metadata=common_attn_metadata, mm_embeds=mm_embeds, ) return draft_token_ids def propose_ngram_draft_token_ids( self, sampled_token_ids: list[list[int]], ) -> list[list[int]]: # TODO(woosuk): Optimize. draft_token_ids: list[list[int]] = [] for i, sampled_ids in enumerate(sampled_token_ids): num_sampled_ids = len(sampled_ids) if not num_sampled_ids: # Skip speculative decoding. draft_token_ids.append([]) continue # Skip requests that require sampling parameters that are not # supported with speculative decoding. req_id = self.input_batch.req_ids[i] if req_id in self.input_batch.spec_decode_unsupported_reqs: draft_token_ids.append([]) continue num_tokens = self.input_batch.num_tokens_no_spec[i] if num_tokens >= self.max_model_len: # Skip requests that have already reached the max model length. draft_token_ids.append([]) continue drafter_output = self.drafter.propose( self.input_batch.token_ids_cpu[i, :num_tokens]) if drafter_output is None or len(drafter_output) == 0: draft_token_ids.append([]) else: draft_token_ids.append(drafter_output.tolist()) return draft_token_ids def update_config(self, overrides: dict[str, Any]) -> None: allowed_config_names = {"load_config", "model_config"} for config_name, config_overrides in overrides.items(): assert config_name in allowed_config_names, \ f"Config `{config_name}` not supported. " \ f"Allowed configs: {allowed_config_names}" config = getattr(self, config_name) new_config = update_config(config, config_overrides) setattr(self, config_name, new_config) def load_model(self, eep_scale_up: bool = False) -> None: """ Args: eep_scale_up: the model loading is for elastic EP scale up. """ logger.info("Starting to load model %s...", self.model_config.model) if eep_scale_up: from vllm.distributed.parallel_state import get_ep_group num_local_physical_experts = torch.empty(1, dtype=torch.int32, device="cpu") torch.distributed.broadcast(num_local_physical_experts, group=get_ep_group().cpu_group, group_src=0) num_local_physical_experts = int(num_local_physical_experts.item()) new_ep_size = get_ep_group().world_size global_expert_load, old_global_expert_indices = ( EplbState.recv_state()) num_logical_experts = global_expert_load.shape[1] self.parallel_config.num_redundant_experts = ( num_local_physical_experts * new_ep_size - num_logical_experts) assert old_global_expert_indices.shape[ 1] % num_local_physical_experts == 0 old_ep_size = old_global_expert_indices.shape[ 1] // num_local_physical_experts rank_mapping = { old_ep_rank: old_ep_rank for old_ep_rank in range(old_ep_size) } else: global_expert_load = None old_global_expert_indices = None rank_mapping = None with DeviceMemoryProfiler() as m: time_before_load = time.perf_counter() model_loader = get_model_loader(self.load_config) logger.info("Loading model from scratch...") self.model = model_loader.load_model( vllm_config=self.vllm_config, model_config=self.model_config) if self.lora_config: self.model = self.load_lora_model(self.model, self.model_config, self.scheduler_config, self.lora_config, self.device) if hasattr(self, "drafter"): logger.info("Loading drafter model...") self.drafter.load_model(self.model) if self.use_aux_hidden_state_outputs: if supports_eagle3(self.model): self.model.set_aux_hidden_state_layers( self.model.get_eagle3_aux_hidden_state_layers()) else: raise RuntimeError( "Model does not support EAGLE3 interface but " "aux_hidden_state_outputs was requested") time_after_load = time.perf_counter() self.model_memory_usage = m.consumed_memory logger.info("Model loading took %.4f GiB and %.6f seconds", self.model_memory_usage / GiB_bytes, time_after_load - time_before_load) prepare_communication_buffer_for_model(self.model) if is_mixture_of_experts( self.model) and self.parallel_config.enable_eplb: logger.info("EPLB is enabled for model %s.", self.model_config.model) self.eplb_state = EplbState.build( self.model, self.device, self.parallel_config, global_expert_load, old_global_expert_indices, rank_mapping, ) if ( self.vllm_config.compilation_config.level == \ CompilationLevel.DYNAMO_AS_IS and supports_dynamo() ): backend = self.vllm_config.compilation_config.init_backend( self.vllm_config) compilation_counter.dynamo_as_is_count += 1 self.model.compile( fullgraph=envs.VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE, backend=backend) return # for other compilation levels, cudagraph behavior is controlled by # CudagraphWraper and CudagraphDispatcher of vllm. # wrap the model with full cudagraph wrapper if needed. if self.compilation_config.cudagraph_mode.has_full_cudagraphs(): self.model = CUDAGraphWrapper(self.model, self.vllm_config, runtime_mode=CUDAGraphMode.FULL) def reload_weights(self) -> None: assert getattr(self, "model", None) is not None, \ "Cannot reload weights before model is loaded." model_loader = get_model_loader(self.load_config) logger.info("Reloading weights inplace...") model = self.get_model() model_loader.load_weights(model, model_config=self.model_config) def save_tensorized_model( self, tensorizer_config: "TensorizerConfig", ) -> None: model = self.get_model() TensorizerLoader.save_model( model, tensorizer_config=tensorizer_config, model_config=self.model_config, ) def _get_prompt_logprobs_dict( self, hidden_states: torch.Tensor, num_scheduled_tokens: dict[str, int], ) -> dict[str, Optional[LogprobsTensors]]: num_prompt_logprobs_dict = self.input_batch.num_prompt_logprobs if not num_prompt_logprobs_dict: return {} in_progress_dict = self.input_batch.in_progress_prompt_logprobs_cpu prompt_logprobs_dict: dict[str, Optional[LogprobsTensors]] = {} # Since prompt logprobs are a rare feature, prioritize simple, # maintainable loop over optimal performance. completed_prefill_reqs = [] for req_id, num_prompt_logprobs in num_prompt_logprobs_dict.items(): num_tokens = num_scheduled_tokens[req_id] # Get metadata for this request. request = self.requests[req_id] num_prompt_tokens = len(request.prompt_token_ids) prompt_token_ids = torch.tensor(request.prompt_token_ids).to( self.device, non_blocking=True) # Set up target LogprobsTensors object. logprobs_tensors = in_progress_dict.get(req_id) if not logprobs_tensors: # Create empty logprobs CPU tensors for the entire prompt. # If chunked, we'll copy in slice by slice. logprobs_tensors = LogprobsTensors.empty_cpu( num_prompt_tokens - 1, num_prompt_logprobs + 1) in_progress_dict[req_id] = logprobs_tensors # Determine number of logits to retrieve. start_idx = request.num_computed_tokens start_tok = start_idx + 1 num_remaining_tokens = num_prompt_tokens - start_tok if num_tokens <= num_remaining_tokens: # This is a chunk, more tokens remain. # In the == case, there are no more prompt logprobs to produce # but we want to defer returning them to the next step where we # have new generated tokens to return. num_logits = num_tokens else: # This is the last chunk of prompt tokens to return. num_logits = num_remaining_tokens completed_prefill_reqs.append(req_id) prompt_logprobs_dict[req_id] = logprobs_tensors if num_logits <= 0: # This can happen for the final chunk if we prefilled exactly # (num_prompt_tokens - 1) tokens for this request in the prior # step. There are no more prompt logprobs to produce. continue # Get the logits corresponding to this req's prompt tokens. # If this is a partial request (i.e. chunked prefill), # then there is prompt logprob generated for each index. req_idx = self.input_batch.req_id_to_index[req_id] offset = self.query_start_loc_np[req_idx].item() prompt_hidden_states = hidden_states[offset:offset + num_logits] logits = self.model.compute_logits(prompt_hidden_states, None) # Get the "target" tokens for each index. For prompt at index i, # the token at prompt index i+1 is the "sampled" token we want # to gather the logprob for. tgt_token_ids = prompt_token_ids[start_tok:start_tok + num_logits] # Compute prompt logprobs. logprobs = self.sampler.compute_logprobs(logits) token_ids, logprobs, ranks = self.sampler.gather_logprobs( logprobs, num_prompt_logprobs, tgt_token_ids) # Transfer GPU->CPU async. chunk_slice = slice(start_idx, start_idx + num_logits) logprobs_tensors.logprob_token_ids[chunk_slice].copy_( token_ids, non_blocking=True) logprobs_tensors.logprobs[chunk_slice].copy_(logprobs, non_blocking=True) logprobs_tensors.selected_token_ranks[chunk_slice].copy_( ranks, non_blocking=True) # Remove requests that have completed prefill from the batch # num_prompt_logprobs_dict. for req_id in completed_prefill_reqs: del num_prompt_logprobs_dict[req_id] del in_progress_dict[req_id] # Must synchronize the non-blocking GPU->CPU transfers. if prompt_logprobs_dict: self._sync_device() return prompt_logprobs_dict def _get_nans_in_logits( self, logits: Optional[torch.Tensor], ) -> dict[str, int]: try: if logits is None: return {req_id: 0 for req_id in self.input_batch.req_ids} num_nans_in_logits = {} num_nans_for_index = logits.isnan().sum(dim=-1).cpu().numpy() for req_id in self.input_batch.req_ids: req_index = self.input_batch.req_id_to_index[req_id] num_nans_in_logits[req_id] = ( int(num_nans_for_index[req_index]) if num_nans_for_index is not None and req_index < logits.shape[0] else 0) return num_nans_in_logits except IndexError: return {} @contextmanager def maybe_randomize_inputs(self, input_ids: torch.Tensor): """ Randomize input_ids if VLLM_RANDOMIZE_DP_DUMMY_INPUTS is set. This is to help balance expert-selection - during profile_run - during DP rank dummy run """ dp_size = self.vllm_config.parallel_config.data_parallel_size randomize_inputs = envs.VLLM_RANDOMIZE_DP_DUMMY_INPUTS and dp_size > 1 if not randomize_inputs: yield else: import functools @functools.cache def rand_input_ids() -> torch.Tensor: return torch.randint_like( self.input_ids, low=0, high=self.model_config.get_vocab_size(), dtype=input_ids.dtype) logger.debug_once("Randomizing dummy data for DP Rank") input_ids.copy_(rand_input_ids()[:input_ids.size(0)], non_blocking=True) yield input_ids.fill_(0) def _get_mm_dummy_batch( self, modality: str, max_items_per_batch: int, ) -> BatchedTensorInputs: """Dummy data for profiling and precompiling multimodal models.""" dummy_decoder_data = self.mm_registry.get_decoder_dummy_data( model_config=self.model_config, seq_len=self.max_num_tokens, mm_counts={modality: 1}, ) dummy_mm_data = dummy_decoder_data.multi_modal_data # Result in the maximum GPU consumption of the model dummy_mm_item = dummy_mm_data[modality][0] dummy_mm_items = [dummy_mm_item] * max_items_per_batch return next(mm_kwargs_group for _, _, mm_kwargs_group in group_mm_kwargs_by_modality( dummy_mm_items, device=self.device, pin_memory=self.pin_memory, )) @torch.inference_mode() def _dummy_run( self, num_tokens: int, cudagraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE, force_attention: bool = False, uniform_decode: bool = False, skip_eplb: bool = False, is_profile: bool = False, ) -> tuple[torch.Tensor, torch.Tensor]: """ Run a dummy forward pass to warm up/profile run or capture the CUDA graph for the model. Args: num_tokens: Number of tokens to run the dummy forward pass. cudagraph_runtime_mode: used to control the behavior. - CUDAGraphMode.NONE: No cudagraph, for warm up and profile run - CUDAGraphMode.PIECEWISE: Piecewise cudagraph. - CUDAGraphMode.FULL: Full cudagraph, attention metadata is needed. force_attention: If True, always create attention metadata. Used to warm up attention backend when mode is NONE. uniform_decode: If True, the batch is a uniform decode batch. skip_eplb: If True, skip EPLB state update. is_profile: If True, this is a profile run. """ assert cudagraph_runtime_mode in { CUDAGraphMode.NONE, CUDAGraphMode.PIECEWISE, CUDAGraphMode.FULL } # Padding for DP num_pad, num_tokens_across_dp = self.get_dp_padding(num_tokens) num_tokens += num_pad # If cudagraph_mode.decode_mode() == FULL and # cudagraph_mode.seperate_routine(). This means that we are using # different graphs and/or modes for mixed prefill-decode batches vs. # uniform decode batches. A uniform decode batch means that all # requests have identical query length, except a potential virtual # request (shorter) in the batch account for padding. # Uniform decode batch could either be common pure decode, where # max_query_len == 1, or speculative decode, where # max_query_len == 1 + num_spec_decode_tokens. # When setting max_query_len = 1, we switch to and capture the optimized # routine of FA2 for pure decode, i.e., Flashdecode + an optimization # for GQA/MQA. max_query_len = self.uniform_decode_query_len if uniform_decode else \ num_tokens # Set num_scheduled_tokens based on num_tokens and max_num_seqs # for dummy run with LoRA so that the num_reqs collectively # has num_tokens in total. assert num_tokens <= self.scheduler_config.max_num_batched_tokens max_num_reqs = self.scheduler_config.max_num_seqs if uniform_decode: num_reqs = cdiv(num_tokens, max_query_len) assert num_reqs <= max_num_reqs, \ "Do not capture num_reqs > max_num_reqs for uniform batch" num_scheduled_tokens_list = [max_query_len] * num_reqs if num_tokens % max_query_len != 0: num_scheduled_tokens_list[-1] = num_tokens % max_query_len else: num_reqs = min(num_tokens, max_num_reqs) min_tokens_per_req = num_tokens // num_reqs num_scheduled_tokens_list = [min_tokens_per_req] * num_reqs num_scheduled_tokens_list[-1] += num_tokens % num_reqs assert sum(num_scheduled_tokens_list) == num_tokens assert len(num_scheduled_tokens_list) == num_reqs num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32) attn_metadata: Optional[dict[str, Any]] = None # If force_attention is True, we always capture attention. Otherwise, # it only happens for cudagraph_runtime_mode=FULL. if force_attention or cudagraph_runtime_mode == \ CUDAGraphMode.FULL: attn_metadata = {} # Make sure max_model_len is used at the graph capture time. self.seq_lens_np[:num_reqs] = self.max_model_len self.seq_lens_np[num_reqs:] = 0 self.seq_lens[:num_reqs].copy_(self.seq_lens_cpu[:num_reqs], non_blocking=True) for kv_cache_group_id, kv_cache_group_spec in enumerate( self.kv_cache_config.kv_cache_groups): common_attn_metadata = CommonAttentionMetadata( query_start_loc=self.query_start_loc[:num_reqs + 1], query_start_loc_cpu=self.query_start_loc_cpu[:num_reqs + 1], seq_lens=self.seq_lens[:num_reqs], seq_lens_cpu=self.seq_lens_cpu[:num_reqs], num_computed_tokens_cpu=self.input_batch. num_computed_tokens_cpu_tensor[:num_reqs], num_reqs=num_reqs, num_actual_tokens=num_tokens, max_query_len=max_query_len, block_table_tensor=self.input_batch.block_table[ kv_cache_group_id].get_device_tensor()[:num_reqs], slot_mapping=self.input_batch. block_table[kv_cache_group_id].slot_mapping[:num_tokens], causal=True) for attn_group in self.attn_groups[kv_cache_group_id]: attn_metadata_i = attn_group.metadata_builder\ .build_for_cudagraph_capture(common_attn_metadata) for layer_name in kv_cache_group_spec.layer_names: attn_metadata[layer_name] = attn_metadata_i with self.maybe_dummy_run_with_lora(self.lora_config, num_scheduled_tokens): if self.supports_mm_inputs: input_ids = None inputs_embeds = self.inputs_embeds[:num_tokens] model_kwargs = { **self._init_model_kwargs(num_tokens), **self._dummy_mm_kwargs(num_reqs), } else: input_ids = self.input_ids[:num_tokens] inputs_embeds = None model_kwargs = self._init_model_kwargs(num_tokens) if self.uses_mrope: positions = self.mrope_positions[:, :num_tokens] else: positions = self.positions[:num_tokens] if get_pp_group().is_first_rank: intermediate_tensors = None else: if self.intermediate_tensors is None: self.intermediate_tensors = ( self.model.make_empty_intermediate_tensors( batch_size=self.max_num_tokens, dtype=self.model_config.dtype, device=self.device)) intermediate_tensors = self.sync_and_slice_intermediate_tensors( num_tokens, None, False) if cudagraph_runtime_mode == CUDAGraphMode.NONE: batch_descriptor = None else: # filter out the valid batch descriptor _cg_mode, batch_descriptor = \ self.cudagraph_dispatcher.dispatch( BatchDescriptor(num_tokens=num_tokens, uniform_decode=uniform_decode)) # sanity check assert cudagraph_runtime_mode == _cg_mode, ( f"Cudagraph runtime mode mismatch at dummy_run. " f"Expected {_cg_mode}, but got {cudagraph_runtime_mode}.") with self.maybe_randomize_inputs(input_ids), set_forward_context( attn_metadata, self.vllm_config, num_tokens=num_tokens, num_tokens_across_dp=num_tokens_across_dp, cudagraph_runtime_mode=cudagraph_runtime_mode, batch_descriptor=batch_descriptor): outputs = self.model( input_ids=input_ids, positions=positions, intermediate_tensors=intermediate_tensors, inputs_embeds=inputs_embeds, **model_kwargs, ) if self.use_aux_hidden_state_outputs: hidden_states, _ = outputs else: hidden_states = outputs if self.speculative_config and self.speculative_config.use_eagle(): assert isinstance(self.drafter, EagleProposer) self.drafter.dummy_run(num_tokens) # This is necessary to avoid blocking DP. # For dummy runs, we typically skip EPLB since we don't have any real # requests to process. # However, in DP settings, there may be cases when some DP ranks do # not have any requests to process, so they're executing dummy batches. # In such cases, we still have to trigger EPLB to make sure # ranks execute the rearrangement in synchronization. if not skip_eplb: self.eplb_step(is_dummy=True, is_profile=is_profile) logit_indices = np.cumsum(num_scheduled_tokens) - 1 return hidden_states, hidden_states[logit_indices] @torch.inference_mode() def _dummy_sampler_run( self, hidden_states: torch.Tensor, ) -> torch.Tensor: # The dummy hidden states may contain special values, # like `inf` or `nan`. # To avoid breaking the sampler, we use a random tensor here instead. hidden_states = torch.rand_like(hidden_states) logits = self.model.compute_logits(hidden_states, None) num_reqs = logits.size(0) dummy_tensors = lambda v: torch.full( (num_reqs, ), v, device=self.device) dummy_metadata = SamplingMetadata( temperature=dummy_tensors(0.5), all_greedy=False, all_random=False, top_p=dummy_tensors(0.9), top_k=dummy_tensors(logits.size(1) - 1), generators={}, max_num_logprobs=None, no_penalties=True, prompt_token_ids=None, frequency_penalties=dummy_tensors(0.1), presence_penalties=dummy_tensors(0.1), repetition_penalties=dummy_tensors(0.1), output_token_ids=[[] for _ in range(num_reqs)], allowed_token_ids_mask=None, bad_words_token_ids={}, logitsprocs=LogitsProcessors(), ) try: sampler_output = self.sampler(logits=logits, sampling_metadata=dummy_metadata) except RuntimeError as e: if 'out of memory' in str(e): raise RuntimeError( "CUDA out of memory occurred when warming up sampler with " f"{num_reqs} dummy requests. Please try lowering " "`max_num_seqs` or `gpu_memory_utilization` when " "initializing the engine.") from e else: raise e if self.speculative_config: draft_token_ids = [[0] for _ in range(num_reqs)] dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy( draft_token_ids, self.device) num_tokens = sum(len(ids) for ids in draft_token_ids) # draft_probs = torch.randn( # num_tokens, logits.shape[-1], device=self.device, # dtype=logits.dtype) draft_probs = None target_logits = torch.randn(num_tokens, logits.shape[-1], device=self.device, dtype=logits.dtype) # NOTE(woosuk): Here, we should use int32 because the sampler uses # int32 for bonus_token_ids. If the dtype mismatches, re-compilation # will occur at runtime. bonus_token_ids = torch.zeros(num_reqs, device=self.device, dtype=torch.int32) self.rejection_sampler( dummy_spec_decode_metadata, draft_probs, target_logits, bonus_token_ids, dummy_metadata, ) return sampler_output def _dummy_pooler_run_task( self, hidden_states: torch.Tensor, task: PoolingTask, ) -> PoolerOutput: num_tokens = hidden_states.shape[0] max_num_reqs = self.scheduler_config.max_num_seqs num_reqs = min(num_tokens, max_num_reqs) min_tokens_per_req = num_tokens // num_reqs num_scheduled_tokens_list = [min_tokens_per_req] * num_reqs num_scheduled_tokens_list[-1] += num_tokens % num_reqs assert sum(num_scheduled_tokens_list) == num_tokens assert len(num_scheduled_tokens_list) == num_reqs hidden_states_list = list( torch.split(hidden_states, num_scheduled_tokens_list)) req_num_tokens = num_tokens // num_reqs dummy_prompt_lens = torch.tensor( [h.shape[0] for h in hidden_states_list], device=self.device, ) dummy_token_ids = torch.zeros((num_reqs, req_num_tokens), dtype=torch.int32, device=self.device) model = cast(VllmModelForPooling, self.get_model()) dummy_pooling_params = PoolingParams(task=task) to_update = model.pooler.get_pooling_updates(task) to_update.apply(dummy_pooling_params) dummy_metadata = PoolingMetadata( prompt_lens=dummy_prompt_lens, prompt_token_ids=dummy_token_ids, pooling_params=[dummy_pooling_params] * num_reqs, ) try: return model.pooler(hidden_states=hidden_states_list, pooling_metadata=dummy_metadata) except RuntimeError as e: if 'out of memory' in str(e): raise RuntimeError( "CUDA out of memory occurred when warming up pooler " f"({task=}) with {num_reqs} dummy requests. Please try " "lowering `max_num_seqs` or `gpu_memory_utilization` when " "initializing the engine.") from e else: raise e @torch.inference_mode() def _dummy_pooler_run( self, hidden_states: torch.Tensor, ) -> PoolerOutput: # Find the task that has the largest output for subsequent steps output_size = dict[PoolingTask, float]() for task in self.get_supported_pooling_tasks(): # Run a full batch with each task to ensure none of them OOMs output = self._dummy_pooler_run_task(hidden_states, task) output_size[task] = output.get_data_nbytes() del output # Allow GC max_task = max(output_size.items(), key=lambda x: x[1])[0] return self._dummy_pooler_run_task(hidden_states, max_task) def profile_run(self) -> None: # Profile with multimodal encoder & encoder cache. if self.supports_mm_inputs: if self.model_config.multimodal_config.skip_mm_profiling: logger.info( "Skipping memory profiling for multimodal encoder and " "encoder cache.") else: mm_budget = self.mm_budget assert mm_budget is not None # TODO: handle encoder-decoder models once we support them. if (encoder_budget := mm_budget.get_encoder_budget()) > 0: # NOTE: Currently model is profiled with a single non-text # modality with the max possible input tokens even when # it supports multiple. ( dummy_modality, max_tokens, ) = mm_budget.get_modality_with_max_tokens() ( max_mm_items_per_prompt, max_mm_items_per_batch, ) = mm_budget.get_max_items(dummy_modality, max_tokens) logger.info( "Encoder cache will be initialized with a budget of " "%s tokens, and profiled with %s %s items of the " "maximum feature size.", encoder_budget, max_mm_items_per_batch, dummy_modality, ) # Create dummy batch of multimodal inputs. batched_dummy_mm_inputs = self._get_mm_dummy_batch( dummy_modality, max_mm_items_per_batch, ) # Run multimodal encoder. dummy_encoder_outputs = \ self.model.get_multimodal_embeddings( **batched_dummy_mm_inputs) sanity_check_mm_encoder_outputs( dummy_encoder_outputs, expected_num_items=max_mm_items_per_batch, ) # Cache the dummy encoder outputs. self.encoder_cache["tmp"] = dict( enumerate(dummy_encoder_outputs)) # Add `is_profile` here to pre-allocate communication buffers hidden_states, last_hidden_states \ = self._dummy_run(self.max_num_tokens, is_profile=True) if get_pp_group().is_last_rank: if self.is_pooling_model: output = self._dummy_pooler_run(hidden_states) else: output = self._dummy_sampler_run(last_hidden_states) else: output = None self._sync_device() del hidden_states, output self.encoder_cache.clear() gc.collect() def capture_model(self) -> None: if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE: logger.warning( "Skipping CUDA graph capture. To turn on CUDA graph capture, " "ensure `cudagraph_mode` was not manually set to `NONE`") return else: self.initialize_cudagraph_capture() compilation_counter.num_gpu_runner_capture_triggers += 1 start_time = time.perf_counter() start_free_gpu_memory = torch.cuda.mem_get_info()[0] @contextmanager def freeze_gc(): # Optimize garbage collection during CUDA graph capture. # Clean up, then freeze all remaining objects from being included # in future collections. gc.collect() should_freeze = not envs.VLLM_ENABLE_CUDAGRAPH_GC if should_freeze: gc.freeze() try: yield finally: if should_freeze: gc.unfreeze() # Trigger CUDA graph capture for specific shapes. # Capture the large shapes first so that the smaller shapes # can reuse the memory pool allocated for the large shapes. set_cudagraph_capturing_enabled(True) with freeze_gc(), graph_capture(device=self.device): cudagraph_mode = self.compilation_config.cudagraph_mode if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE: cudagraph_runtime_mode = cudagraph_mode.mixed_mode() compilation_cases = list(reversed(self.cudagraph_batch_sizes)) self._capture_cudagraphs( compilation_cases, cudagraph_runtime_mode=cudagraph_runtime_mode, uniform_decode=False) # Capture full cudagraph for uniform decode batches if we have # dont already have full mixed prefill-decode cudagraphs if cudagraph_mode.decode_mode() == CUDAGraphMode.FULL and \ cudagraph_mode.separate_routine(): max_num_tokens = self.scheduler_config.max_num_seqs * \ self.uniform_decode_query_len decode_cudagraph_batch_sizes = [ x for x in self.cudagraph_batch_sizes if x <= max_num_tokens and x >= self.uniform_decode_query_len ] compilation_cases_decode = list( reversed(decode_cudagraph_batch_sizes)) self._capture_cudagraphs( compilation_cases=compilation_cases_decode, cudagraph_runtime_mode=CUDAGraphMode.FULL, uniform_decode=True) # Disable cudagraph capturing globally, so any unexpected cudagraph # capturing will be detected and raise an error after here. # Note: We don't put it into graph_capture context manager because # we may doing lazy capturing in future that still allows capturing # after here. set_cudagraph_capturing_enabled(False) end_time = time.perf_counter() end_free_gpu_memory = torch.cuda.mem_get_info()[0] elapsed_time = end_time - start_time cuda_graph_size = start_free_gpu_memory - end_free_gpu_memory # This usually takes 5~20 seconds. logger.info("Graph capturing finished in %.0f secs, took %.2f GiB", elapsed_time, cuda_graph_size / (1 << 30)) def _capture_cudagraphs(self, compilation_cases: list[int], cudagraph_runtime_mode: CUDAGraphMode, uniform_decode: bool): assert cudagraph_runtime_mode != CUDAGraphMode.NONE and \ cudagraph_runtime_mode in [CUDAGraphMode.FULL, CUDAGraphMode.PIECEWISE] # Only rank 0 should print progress bar during capture if is_global_first_rank(): compilation_cases = tqdm( compilation_cases, disable=not self.load_config.use_tqdm_on_load, desc="Capturing CUDA graphs ({}, {})".format( "decode" if uniform_decode else "mixed prefill-decode", cudagraph_runtime_mode.name)) # We skip EPLB here since we don't want to record dummy metrics for num_tokens in compilation_cases: for _ in range(self.compilation_config.cudagraph_num_of_warmups): # Use CUDAGraphRuntimeStyle.NONE (default) for warmup. # But be careful, warm up with `NONE`is orthogonal to # if we want to warm up attention or not. This is # different from the case where `FULL` implies capture # attention while `PIECEWISE` implies no attention. force_attention = ( cudagraph_runtime_mode == CUDAGraphMode.FULL) self._dummy_run(num_tokens, cudagraph_runtime_mode=CUDAGraphMode.NONE, force_attention=force_attention, uniform_decode=uniform_decode, skip_eplb=True) self._dummy_run(num_tokens, cudagraph_runtime_mode=cudagraph_runtime_mode, uniform_decode=uniform_decode, skip_eplb=True) def initialize_attn_backend(self, kv_cache_config: KVCacheConfig) -> None: """ Initialize the attention backends and attention metadata builders. """ assert len(self.attn_groups) == 0, \ "Attention backends are already initialized" attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention) def get_attn_backends_for_layers( layer_names: list[str] ) -> dict[type[AttentionBackend], list[str]]: attn_backends = {} attn_backend_layers = defaultdict(list) # Dedupe based on full class name; this is a bit safer than using # using the class itself as the key because when we create dynamic # attention backend subclasses (e.g. ChunkedLocalAttention) unless # they are cached correctly, there will be different objects per # layer. for layer_name in layer_names: attn_backend = attn_layers[layer_name].get_attn_backend() key = attn_backend.full_cls_name() attn_backends[key] = attn_backend attn_backend_layers[key].append(layer_name) return { attn_backends[k]: v for k, v in attn_backend_layers.items() } def create_attn_groups( attn_backends_map: dict[AttentionBackend, list[str]], kv_cache_spec: KVCacheSpec, ) -> list[AttentionGroup]: attn_groups: list[AttentionGroup] = [] for attn_backend, layer_names in attn_backends_map.items(): attn_metadata_builder_i = attn_backend.get_builder_cls()( kv_cache_spec, layer_names, self.vllm_config, self.device, ) attn_group = AttentionGroup(attn_backend, attn_metadata_builder_i, layer_names) attn_groups.append(attn_group) return attn_groups for kv_cache_group_spec in kv_cache_config.kv_cache_groups: kv_cache_spec = kv_cache_group_spec.kv_cache_spec if isinstance(kv_cache_spec, AttentionSpec): attn_backends = get_attn_backends_for_layers( kv_cache_group_spec.layer_names) # TODO(lucas): move `get_mamba_attn_backend` into the mamba # layers like above elif isinstance(kv_cache_spec, MambaSpec): attn_backends = { get_mamba_attn_backend(kv_cache_spec.mamba_type): kv_cache_group_spec.layer_names } else: raise ValueError( f"Unknown KV cache spec type: {type(kv_cache_spec)}") self.attn_groups.append( create_attn_groups(attn_backends, kv_cache_spec)) # Calculate reorder batch threshold (if neeeded) self.calculate_reorder_batch_threshold() if len(self.attn_groups) > 0: return # Check if model is encoder-only block_size = self.vllm_config.cache_config.block_size use_mla = self.vllm_config.model_config.use_mla attn_specs: dict[AttentionSpec, list[str]] = defaultdict(list) for layer_name, attn_module in attn_layers.items(): if attn_module.attn_type == AttentionType.ENCODER_ONLY: if attn_module.sliding_window is None: attn_spec: AttentionSpec = FullAttentionSpec( block_size=block_size, num_kv_heads=attn_module.num_kv_heads, head_size=attn_module.head_size, dtype=self.kv_cache_dtype, use_mla=use_mla) else: attn_spec = SlidingWindowSpec( block_size=block_size, num_kv_heads=attn_module.num_kv_heads, head_size=attn_module.head_size, dtype=self.kv_cache_dtype, sliding_window=attn_module.sliding_window, use_mla=use_mla) attn_specs[attn_spec].append(layer_name) else: raise ValueError("Expected only encoder-only layers") if len(attn_specs) > 0: total_layers = 0 for attn_spec, layer_names in attn_specs.items(): attn_backends = get_attn_backends_for_layers(layer_names) total_layers += len(layer_names) self.attn_groups.append( create_attn_groups(attn_backends, attn_spec)) assert total_layers == len(attn_layers), \ "All or none of the layers are expected to be encoder-only" self.is_encoder_only_model = True def initialize_cudagraph_capture(self) -> None: min_cg_support = AttentionCGSupport.ALWAYS min_cg_builder_name = None for attn_group in self._attn_group_iterator(): builder = attn_group.metadata_builder if builder.cudagraph_support.value < min_cg_support.value: min_cg_support = builder.cudagraph_support min_cg_builder_name = builder.__class__.__name__ # Flexible resolve the cudagraph mode cudagraph_mode = self.compilation_config.cudagraph_mode # check cudagraph for mixed batch is supported if cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL \ and min_cg_support != AttentionCGSupport.ALWAYS: msg = (f"CUDAGraphMode.{cudagraph_mode.name} is not supported " f"with {min_cg_builder_name} backend (support: " f"{min_cg_support})") if min_cg_support == AttentionCGSupport.NEVER: # if not supported any full cudagraphs, just raise it. msg += "; please try cudagraph_mode=PIECEWISE, and "\ "make sure compilation level is piecewise" raise ValueError(msg) # attempt to resolve the full cudagraph related mode if self.compilation_config.splitting_ops_contain_attention(): msg += "; setting cudagraph_mode=FULL_AND_PIECEWISE" cudagraph_mode = self.compilation_config.cudagraph_mode = \ CUDAGraphMode.FULL_AND_PIECEWISE else: msg += "; setting cudagraph_mode=FULL_DECODE_ONLY" cudagraph_mode = self.compilation_config.cudagraph_mode = \ CUDAGraphMode.FULL_DECODE_ONLY logger.warning(msg) # check that if we are doing spec-decode + decode full-cudagraphs it is # supported if (cudagraph_mode.decode_mode() == CUDAGraphMode.FULL and self.uniform_decode_query_len > 1 and min_cg_support.value < AttentionCGSupport.UNIFORM_BATCH.value): msg = (f"CUDAGraphMode.{cudagraph_mode.name} is not supported" f" with spec-decode for attention backend " f"{min_cg_builder_name} (support: {min_cg_support})") if self.compilation_config.splitting_ops_contain_attention(): msg += "; setting cudagraph_mode=PIECEWISE" cudagraph_mode = self.compilation_config.cudagraph_mode = \ CUDAGraphMode.PIECEWISE else: msg += "; setting cudagraph_mode=NONE" cudagraph_mode = self.compilation_config.cudagraph_mode = \ CUDAGraphMode.NONE logger.warning(msg) # double check that we can support full cudagraph if they are requested # even after automatic downgrades if cudagraph_mode.has_full_cudagraphs() \ and min_cg_support == AttentionCGSupport.NEVER: raise ValueError(f"CUDAGraphMode.{cudagraph_mode.name} is not " f"supported with {min_cg_builder_name} backend (" f"support:{min_cg_support}) " "; please try cudagraph_mode=PIECEWISE, " "and make sure compilation level is piecewise") # Trigger cudagraph dispatching keys initialization here (after # initializing attn backends). self.cudagraph_dispatcher.initialize_cudagraph_keys( self.compilation_config.cudagraph_mode, self.uniform_decode_query_len) def calculate_reorder_batch_threshold(self) -> None: """ Check that if any backends reorder batches; that the reordering is compatible (e.g., decode threshold is the same) """ for group in self._attn_group_iterator(): attn_metadata_builder_i = group.metadata_builder # check that if any backends reorder batches; that the reordering # is compatible (e.g., decode threshold is the same) reorder_batch_threshold_i = ( attn_metadata_builder_i.reorder_batch_threshold) if reorder_batch_threshold_i is not None: if self.reorder_batch_threshold is not None: if reorder_batch_threshold_i != \ self.reorder_batch_threshold: raise ValueError( f"Attention backend reorders decodes with " f"threshold {reorder_batch_threshold_i} but other " f"backend uses threshold " f"{self.reorder_batch_threshold}") else: self.reorder_batch_threshold = reorder_batch_threshold_i def may_reinitialize_input_batch(self, kv_cache_config: KVCacheConfig) -> None: """ Re-initialize the input batch if the block sizes are different from `[self.cache_config.block_size]`. This usually happens when there are multiple KV cache groups. Args: kv_cache_config: The KV cache configuration. """ block_sizes = [ kv_cache_group.kv_cache_spec.block_size for kv_cache_group in kv_cache_config.kv_cache_groups ] if block_sizes != [self.cache_config.block_size]: assert self.cache_config.cpu_offload_gb == 0, ( "Cannot re-initialize the input batch when CPU weight " "offloading is enabled. See https://github.com/vllm-project/vllm/pull/18298 " # noqa: E501 "for more details.") self.input_batch = InputBatch( max_num_reqs=self.max_num_reqs, max_model_len=self.max_model_len, max_num_batched_tokens=self.max_num_tokens, device=self.device, pin_memory=self.pin_memory, vocab_size=self.model_config.get_vocab_size(), block_sizes=block_sizes, is_spec_decode=bool(self.vllm_config.speculative_config), logitsprocs=self.input_batch.logitsprocs, is_pooling_model=self.is_pooling_model, ) def _allocate_kv_cache_tensors( self, kv_cache_config: KVCacheConfig) -> dict[str, torch.Tensor]: """ Initializes the KV cache buffer with the correct size. The buffer needs to be reshaped to the desired shape before being used by the models. Args: kv_cache_config: The KV cache config Returns: dict[str, torch.Tensor]: A map between layer names to their corresponding memory buffer for KV cache. """ kv_cache_raw_tensors: dict[str, torch.Tensor] = {} for kv_cache_tensor in kv_cache_config.kv_cache_tensors: tensor = torch.zeros(kv_cache_tensor.size, dtype=torch.int8, device=self.device) for layer_name in kv_cache_tensor.shared_by: kv_cache_raw_tensors[layer_name] = tensor layer_names = set() for group in kv_cache_config.kv_cache_groups: layer_names.update(group.layer_names) assert layer_names == set(kv_cache_raw_tensors.keys( )), "Some layers are not correctly initialized" return kv_cache_raw_tensors def _attn_group_iterator(self) -> Iterator[AttentionGroup]: return itertools.chain.from_iterable(self.attn_groups) def _kv_cache_spec_attn_group_iterator( self) -> Iterator[tuple[KVCacheSpec, AttentionGroup]]: if not self.kv_cache_config.kv_cache_groups: return for kv_cache_spec_id, attn_groups in enumerate(self.attn_groups): for attn_group in attn_groups: yield self.kv_cache_config.kv_cache_groups[ kv_cache_spec_id].kv_cache_spec, attn_group def _reshape_kv_cache_tensors( self, kv_cache_config: KVCacheConfig, kv_cache_raw_tensors: dict[str, torch.Tensor], ) -> dict[str, torch.Tensor]: """ Reshape the KV cache tensors to the desired shape and dtype. Args: kv_cache_config: The KV cache config kv_cache_raw_tensors: The KV cache buffer of each layer, with correct size but uninitialized shape. Returns: Dict[str, torch.Tensor]: A map between layer names to their corresponding memory buffer for KV cache. """ kv_caches: dict[str, torch.Tensor] = {} has_attn, has_mamba = False, False for kv_cache_spec, group in self._kv_cache_spec_attn_group_iterator(): attn_backend = group.backend for layer_name in group.layer_names: raw_tensor = kv_cache_raw_tensors[layer_name] assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0 num_blocks = (raw_tensor.numel() // kv_cache_spec.page_size_bytes) if isinstance(kv_cache_spec, AttentionSpec): has_attn = True kv_cache_shape = attn_backend.get_kv_cache_shape( num_blocks, kv_cache_spec.block_size, kv_cache_spec.num_kv_heads, kv_cache_spec.head_size) dtype = kv_cache_spec.dtype try: kv_cache_stride_order = \ attn_backend.get_kv_cache_stride_order() assert len(kv_cache_stride_order) == len( kv_cache_shape) except (AttributeError, NotImplementedError): kv_cache_stride_order = tuple( range(len(kv_cache_shape))) # The allocation respects the backend-defined stride order # to ensure the semantic remains consistent for each # backend. We first obtain the generic kv cache shape and # then permute it according to the stride order which could # result in a non-contiguous tensor. kv_cache_shape = tuple(kv_cache_shape[i] for i in kv_cache_stride_order) # Maintain original KV shape view. inv_order = [ kv_cache_stride_order.index(i) for i in range(len(kv_cache_stride_order)) ] kv_caches[layer_name] = kv_cache_raw_tensors[ layer_name].view(dtype).view(kv_cache_shape).permute( *inv_order) elif isinstance(kv_cache_spec, MambaSpec): has_mamba = True raw_tensor = kv_cache_raw_tensors[layer_name] state_tensors = [] storage_offset_bytes = 0 for (shape, dtype) in zip(kv_cache_spec.shapes, kv_cache_spec.dtypes): dtype_size = get_dtype_size(dtype) num_element_per_page = ( kv_cache_spec.page_size_bytes // dtype_size) target_shape = (num_blocks, *shape) stride = torch.empty(target_shape).stride() target_stride = (num_element_per_page, *stride[1:]) assert storage_offset_bytes % dtype_size == 0 tensor = torch.as_strided( raw_tensor.view(dtype), size=target_shape, stride=target_stride, storage_offset=storage_offset_bytes // dtype_size, ) state_tensors.append(tensor) storage_offset_bytes += stride[0] * dtype_size kv_caches[layer_name] = state_tensors else: raise NotImplementedError if has_attn and has_mamba: self._verify_hybrid_attention_mamba_layout(kv_cache_config, kv_cache_raw_tensors) return kv_caches def _verify_hybrid_attention_mamba_layout( self, kv_cache_config: KVCacheConfig, kv_cache_raw_tensors: dict[str, torch.Tensor]) -> None: """ Verify that the KV cache memory layout is compatible for models with both attention and mamba KV cache groups. Args: kv_cache_config: The KV cache config kv_cache_raw_tensors: The KV cache buffer of each layer. """ for kv_cache_spec, group in self._kv_cache_spec_attn_group_iterator(): for layer_name in group.layer_names: raw_tensor = kv_cache_raw_tensors[layer_name] num_blocks = (raw_tensor.numel() // kv_cache_spec.page_size_bytes) if isinstance(kv_cache_spec, AttentionSpec): kv_cache_shape = group.backend.get_kv_cache_shape( num_blocks, kv_cache_spec.block_size, kv_cache_spec.num_kv_heads, kv_cache_spec.head_size) if kv_cache_shape[0] != num_blocks or kv_cache_shape[ 1] != 2: raise ValueError( "Hybrid models in V1 require an attention " "backend with kv_cache_shape=" "(num_blocks, 2, ...). Please try setting " "VLLM_ATTENTION_BACKEND=FLASHINFER") def initialize_kv_cache_tensors( self, kv_cache_config: KVCacheConfig) -> dict[str, torch.Tensor]: """ Initialize the memory buffer for KV cache. Args: kv_cache_config: The KV cache config Returns: Dict[str, torch.Tensor]: A map between layer names to their corresponding memory buffer for KV cache. """ # Initialize the memory buffer for KV cache kv_cache_raw_tensors = self._allocate_kv_cache_tensors(kv_cache_config) # Change the memory buffer to the desired shape kv_caches = self._reshape_kv_cache_tensors(kv_cache_config, kv_cache_raw_tensors) # Setup `kv_cache_config` and `kv_caches` for models # with cross-layer KV sharing if self.shared_kv_cache_layers: initialize_kv_cache_for_kv_sharing( self.shared_kv_cache_layers, kv_cache_config.kv_cache_groups, kv_caches, self.attn_groups, ) attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention) # Iterate in reversed order and add layers that re-use KV cache # e.g. in YOCO-like KV sharing setups (e.g. Gemma3n) for layer_name in reversed(attn_layers): if layer_name in self.shared_kv_cache_layers: self.kv_sharing_fast_prefill_eligible_layers.add( layer_name) else: break bind_kv_cache(kv_caches, self.compilation_config.static_forward_context, self.kv_caches) return kv_caches def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None: """ Initialize KV cache based on `kv_cache_config`. Args: kv_cache_config: Configuration for the KV cache, including the KV cache size of each layer """ self.kv_cache_config = kv_cache_config self.may_reinitialize_input_batch(kv_cache_config) self.initialize_attn_backend(kv_cache_config) kv_caches = self.initialize_kv_cache_tensors(kv_cache_config) if self.speculative_config and self.speculative_config.use_eagle(): assert isinstance(self.drafter, EagleProposer) # validate all draft model layers belong to the same kv cache # group self.drafter.validate_same_kv_cache_group(kv_cache_config) if has_kv_transfer_group(): get_kv_transfer_group().register_kv_caches(kv_caches) def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]: """ Generates the KVCacheSpec by parsing the kv cache format from each Attention module in the static forward context. Returns: KVCacheSpec: A dictionary mapping layer names to their KV cache format. Layers that do not need KV cache are not included. """ block_size = self.vllm_config.cache_config.block_size use_mla = self.vllm_config.model_config.use_mla kv_cache_spec: dict[str, KVCacheSpec] = {} attn_layers = get_layers_from_vllm_config(self.vllm_config, Attention) for layer_name, attn_module in attn_layers.items(): if (kv_tgt_layer := attn_module.kv_sharing_target_layer_name) is not None: # The layer doesn't need its own KV cache and will use that of # the target layer. We skip creating a KVCacheSpec for it, so # that KV cache management logic will act as this layer does # not exist, and doesn't allocate KV cache for the layer. This # enables the memory saving of cross-layer kv sharing, allowing # a given amount of memory to accommodate longer context lengths # or enable more requests to be processed simultaneously. self.shared_kv_cache_layers[layer_name] = kv_tgt_layer continue # TODO: Support other attention modules, e.g., cross-attention # TODO(lucas): move the attention specs into the model layers like # the attention backends if attn_module.attn_type == AttentionType.DECODER: if attn_module.sliding_window is not None: kv_cache_spec[layer_name] = SlidingWindowSpec( block_size=block_size, num_kv_heads=attn_module.num_kv_heads, head_size=attn_module.head_size, dtype=self.kv_cache_dtype, sliding_window=attn_module.sliding_window, use_mla=use_mla) elif self.attention_chunk_size is not None \ and isinstance(attn_module, ChunkedLocalAttention): kv_cache_spec[layer_name] = ChunkedLocalAttentionSpec( block_size=block_size, num_kv_heads=attn_module.num_kv_heads, head_size=attn_module.head_size, dtype=self.kv_cache_dtype, attention_chunk_size=self.attention_chunk_size, use_mla=use_mla) else: kv_cache_spec[layer_name] = FullAttentionSpec( block_size=block_size, num_kv_heads=attn_module.num_kv_heads, head_size=attn_module.head_size, dtype=self.kv_cache_dtype, use_mla=use_mla) elif attn_module.attn_type in (AttentionType.ENCODER, AttentionType.ENCODER_ONLY): # encoder-only attention does not need KV cache. continue elif attn_module.attn_type == AttentionType.ENCODER_DECODER: raise NotImplementedError else: raise ValueError( f"Unknown attention type: {attn_module.attn_type}") mamba_layers = get_layers_from_vllm_config(self.vllm_config, MambaBase) if len(mamba_layers) > 0: if self.vllm_config.speculative_config is not None: raise NotImplementedError( "Mamba with speculative decoding is not supported yet.") if self.vllm_config.cache_config.enable_prefix_caching: raise NotImplementedError( "Prefix caching is not supported for Mamba yet.") max_model_len = self.vllm_config.model_config.max_model_len page_size_padded = ( self.vllm_config.cache_config.mamba_page_size_padded) # Set block_size to max_model_len, so that mamba model will always # have only one block in the KV cache. for layer_name, mamba_module in mamba_layers.items(): kv_cache_spec[layer_name] = MambaSpec( shapes=mamba_module.get_state_shape(), dtypes=mamba_module.get_state_dtype(), block_size=max_model_len, page_size_padded=page_size_padded, mamba_type=mamba_module.mamba_type) return kv_cache_spec def _build_encoder_only_attn_metadata( self, scheduler_output: "SchedulerOutput") -> \ dict[str, tuple[CommonAttentionMetadata, Any]]: """Prepare encoder attention metadata for encoder-only models. Args: scheduler_output: Scheduler output Returns: dict[str, Any]: Encoder attention metadata """ num_reqs = self.input_batch.num_reqs total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens # Get the number of scheduled tokens for each request. req_ids = self.input_batch.req_ids tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids] max_num_scheduled_tokens = max(tokens) dummy_block_table = torch.zeros((num_reqs, 1), dtype=torch.int32, device=self.device) dummy_slot_mapping = torch.zeros((total_num_scheduled_tokens, ), dtype=torch.int32, device=self.device) group_metadata = dict[str, tuple[CommonAttentionMetadata, Any]]() for attn_group_list in self.attn_groups: assert len(attn_group_list) == 1 attn_group = attn_group_list[0] # Use the first attention metadata builder # to create encoder attention metadata builder = attn_group.metadata_builder common_metadata = CommonAttentionMetadata( query_start_loc=self.query_start_loc[:num_reqs + 1], query_start_loc_cpu=self.query_start_loc_cpu[:num_reqs + 1], seq_lens=self.seq_lens[:num_reqs], seq_lens_cpu=self.seq_lens_cpu[:num_reqs], num_computed_tokens_cpu=self.input_batch. num_computed_tokens_cpu_tensor[:num_reqs], num_reqs=num_reqs, num_actual_tokens=total_num_scheduled_tokens, max_query_len=max_num_scheduled_tokens, block_table_tensor=dummy_block_table, slot_mapping=dummy_slot_mapping, causal=False, ) metadata = builder.build( common_prefix_len=0, # No cascade for encoder common_attn_metadata=common_metadata, ) for layer_name in attn_group.layer_names: group_metadata[layer_name] = (common_metadata, metadata) return group_metadata