from typing import Any, Optional, Union import torch import numpy as np from vllm import envs from vllm.distributed.kv_transfer.kv_transfer_state import get_kv_transfer_group, has_kv_transfer_group from vllm.distributed.parallel_state import get_pp_group, get_tp_group from vllm.forward_context import set_forward_context from vllm.sequence import IntermediateTensors from vllm.utils import async_tensor_h2d, round_up from vllm.v1.core.sched.output import SchedulerOutput from vllm.v1.outputs import EMPTY_MODEL_RUNNER_OUTPUT, ModelRunnerOutput from vllm.v1.sample.metadata import SamplingMetadata 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_model_runner import GPUModelRunner from vllm.zero_overhead.v1.outputs import ZeroV1ModelRunnerOutput from vllm.profiler.prof import profile from vllm.two_batch_overlap.v1.model_input_split_v1 import tbo_split_and_execute_model class V1ZeroModelRunner(GPUModelRunner): def __init__(self, vllm_config, device): super().__init__(vllm_config, device) self.last_sampled_token_ids = None self.last_sampled_req_ids = [] self.last_sampled_token_lens = [] self.last_sampler_event = torch.cuda.Event(enable_timing=False) self.last_sampler_host_tokens = None self.token_ids_cpu_fix_recode = [] self.last_draft_token_ids = None self.last_draft_host_tokens = None self.last_draft_event = torch.cuda.Event(enable_timing=False) def zero_prepare_inputs(self, scheduler_output, input_ids): req_ids = self.input_batch.req_ids update_req_indices = [] input_ids_indices = [] token_idx = 0 if self.last_draft_token_ids is not None: draft_tokens_num = self.last_draft_token_ids.shape[1] for req_id in req_ids: if req_id in self.last_sampled_req_ids: req_idx = self.last_sampled_req_ids.index(req_id) * draft_tokens_num for num_idx in range(draft_tokens_num): update_req_indices.append(req_idx + num_idx) input_ids_indices.append(token_idx + num_idx + 1) token_idx += draft_tokens_num + 1 if len(update_req_indices) > 0: update_req_indices_tensor = async_tensor_h2d(update_req_indices, torch.int32, self.device, True) input_ids_indices_tensor = async_tensor_h2d(input_ids_indices, torch.int32, self.device, True) last_draft_token_ids = self.last_draft_token_ids.flatten().to(torch.int) input_ids[input_ids_indices_tensor] = last_draft_token_ids[update_req_indices_tensor] update_req_indices = [] input_ids_indices = [] token_idx = 0 if self.last_sampled_token_ids is not None: sampled_tokens_num = self.last_sampled_token_ids.shape[1] for req_id in req_ids: if req_id in self.last_sampled_req_ids: req_idx = self.last_sampled_req_ids.index(req_id) * sampled_tokens_num update_req_indices.append(req_idx) input_ids_indices.append(token_idx) token_idx += scheduler_output.num_scheduled_tokens[req_id] if len(update_req_indices) > 0: update_req_indices_tensor = async_tensor_h2d(update_req_indices, torch.int32, self.device, True) input_ids_indices_tensor = async_tensor_h2d(input_ids_indices, torch.int32, self.device, True) last_sampled_token_ids = self.last_sampled_token_ids.flatten() for i in range(sampled_tokens_num): input_ids[input_ids_indices_tensor + i] = last_sampled_token_ids[update_req_indices_tensor + i] 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], attn_metadata: dict[str, Any], ) -> list[list[int]]: num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens if self.speculative_config.method == "ngram": assert isinstance(self.drafter, NgramProposer) spec_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] spec_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. if self.last_sampled_token_ids is not None: next_token_ids = self.last_sampled_token_ids.flatten() else: 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) # At this moment, we assume all eagle layers belong to the same KV # cache group, thus using the same attention metadata. eagle_attn_metadata = attn_metadata[ self.drafter.attn_layer_names[0]] # NOTE: deepseek_mtp uses MLA which does not have `block_table` if hasattr(eagle_attn_metadata, "block_table"): block_table = eagle_attn_metadata.block_table else: block_table = None num_rejected_tokens = None 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] target_slot_mapping = eagle_attn_metadata.slot_mapping cu_num_tokens = eagle_attn_metadata.query_start_loc 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_tensor = async_tensor_h2d( num_rejected_tokens, dtype=torch.int32, target_device=self.device, pin_memory=True) num_tokens = num_scheduled_tokens - sum(num_rejected_tokens) cu_num_tokens, token_indices = self.drafter.prepare_inputs( eagle_attn_metadata.query_start_loc, num_rejected_tokens_tensor, num_tokens, ) 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] target_slot_mapping = eagle_attn_metadata.slot_mapping[ token_indices] draft_token_ids = self.drafter.propose( target_token_ids=target_token_ids, target_positions=target_positions, target_hidden_states=target_hidden_states, target_slot_mapping=target_slot_mapping, next_token_ids=next_token_ids, cu_num_tokens=cu_num_tokens, block_table=block_table, sampling_metadata=sampling_metadata, num_rejected_tokens=num_rejected_tokens ) spec_token_ids = np.ones(draft_token_ids.shape, dtype=int).tolist() self.last_draft_token_ids = draft_token_ids self.last_draft_host_tokens = draft_token_ids.to('cpu', non_blocking=True) self.last_draft_event.record() return spec_token_ids @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) # Prepare the decoder inputs. (attn_metadata, attention_cuda_graphs, logits_indices, spec_decode_metadata, num_scheduled_tokens_np) = (self._prepare_inputs(scheduler_output)) num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens if (self.use_cuda_graph and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]): # Use piecewise 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.is_multimodal_model: # 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.is_multimodal_model 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. input_ids = self.input_ids[:num_scheduled_tokens] if mm_embeds: inputs_embeds = self.model.get_input_embeddings( input_ids, mm_embeds) else: inputs_embeds = self.model.get_input_embeddings(input_ids) # TODO(woosuk): Avoid the copy. Optimize. self.inputs_embeds[:num_scheduled_tokens].copy_(inputs_embeds) inputs_embeds = self.inputs_embeds[:num_input_tokens] input_ids = None 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 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) # Some attention backends only support CUDA Graphs in pure decode. # If attention doesn't support CUDA Graphs for this batch, but we # compiled with full CUDA graphs, we have to skip them entirely. skip_cuda_graphs = self.full_cuda_graph and not attention_cuda_graphs self.zero_prepare_inputs(scheduler_output, input_ids) if envs.VLLM_ENABLE_TBO and not self.use_cuda_graph: model_output, finished_sending, finished_recving = \ tbo_split_and_execute_model(self, attn_metadata, num_input_tokens, num_tokens_across_dp, input_ids, positions, inputs_embeds, scheduler_output, intermediate_tensors) else: # 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, skip_cuda_graphs=skip_cuda_graphs, ): self.maybe_setup_kv_connector(scheduler_output) model_output = self.model( input_ids=input_ids, positions=positions, intermediate_tensors=intermediate_tensors, inputs_embeds=inputs_embeds, ) self.maybe_wait_for_kv_save() finished_sending, finished_recving = ( self.get_finished_kv_transfers(scheduler_output)) 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. if not broadcast_pp_output: return hidden_states assert isinstance(hidden_states, IntermediateTensors) 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, finished_sending, finished_recving) 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, ) # Get the valid generated tokens. sampled_token_ids = sampler_output.sampled_token_ids max_gen_len = sampled_token_ids.shape[-1] fix_req_ids = None fix_sampled_token_ids = None fix_draft_token_ids = None fix_draft_req_ids = self.last_sampled_req_ids is_output_valid = False if self.speculative_config: if max_gen_len == 1: 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, ) self.last_sampler_host_tokens = None self.last_sampled_token_ids = None is_output_valid = True else: # No spec decode tokens. fix_req_ids = self.last_sampled_req_ids if self.last_sampler_host_tokens != None: self.last_sampler_event.synchronize() fix_sampled_token_ids = self.last_sampler_host_tokens.tolist() for req_idx, start_idx, end_idx in self.token_ids_cpu_fix_recode: self.input_batch.token_ids_cpu[req_idx, start_idx:end_idx] = fix_sampled_token_ids[req_idx] for req_idx, req_id in enumerate(fix_req_ids): if req_id in self.requests: req_state = self.requests[req_id] token_idx = self.last_sampled_token_lens[req_idx] req_state.output_token_ids[token_idx] = fix_sampled_token_ids[req_idx][0] self.last_sampler_host_tokens = sampled_token_ids.to('cpu', non_blocking=True) self.last_sampler_event.record() self.last_sampled_token_ids = sampled_token_ids valid_sampled_token_ids = np.ones(sampled_token_ids.shape, dtype=int).tolist() # 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. self.token_ids_cpu_fix_recode.clear() self.last_sampled_req_ids = [] self.last_sampled_token_lens = [] 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.token_ids_cpu_fix_recode.append([req_idx, start_idx, end_idx]) 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] if req_id in self.requests: req_state = self.requests[req_id] self.last_sampled_req_ids.append(req_id) self.last_sampled_token_lens.append(len(req_state.output_token_ids)) req_state.output_token_ids.extend(sampled_ids) if not self.speculative_config: # Speculative decoding is not enabled. spec_token_ids = None fix_draft_req_ids = None else: if self.last_draft_host_tokens is not None: self.last_draft_event.synchronize() fix_draft_token_ids = self.last_draft_host_tokens.tolist() spec_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, attn_metadata, ) # Clear KVConnector state after all KVs are generated. if has_kv_transfer_group(): get_kv_transfer_group().clear_connector_metadata() self.eplb_step() model_output = ZeroV1ModelRunnerOutput( 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, spec_token_ids=spec_token_ids, logprobs=logprobs_lists, prompt_logprobs_dict=prompt_logprobs_dict, pooler_output=[], finished_sending=finished_sending, finished_recving=finished_recving, num_nans_in_logits=num_nans_in_logits, fix_req_ids = fix_req_ids, fix_sampled_token_ids = fix_sampled_token_ids, fix_draft_tokens_ids = fix_draft_token_ids, fix_draft_req_ids = fix_draft_req_ids, is_output_valid=is_output_valid ) return model_output