from typing import List, Optional import torch from vllm import _custom_ops as ops from vllm.model_executor.layers.sampler import SamplerOutput try: from vllm.attention.backends.flash_attn import FlashAttentionMetadata except ModuleNotFoundError: # vllm_flash_attn is not installed, use the identical ROCm FA metadata from vllm.attention.backends.rocm_flash_attn import ( ROCmFlashAttentionMetadata as FlashAttentionMetadata) from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig, ModelConfig, ObservabilityConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig) from vllm.logger import init_logger from vllm.multimodal import MultiModalInputs from vllm.sequence import ExecuteModelRequest, IntermediateTensors from vllm.worker.model_runner import (ModelInputForGPUWithSamplingMetadata, ModelRunner) logger = init_logger(__name__) # A flag to enable debug prints for the updated input tensors # before each step. debug_advance_input = False # A flag to allow GPU advance step for draft model runner. # Set to False for debugging. allow_gpu_advance_step = True class TP1DraftModelRunner(ModelRunner): """Specialized model runner for speculative decoding draft model. Since the draft model always execute k forward passes consecutively to generate k speculative tokens in a single speculative decoding step, we could get rid of most CPU-GPU synchronization and data transfer overheads by keeping model input and output tensors on GPU all the time. TODOs: 1. Currently supports only flash-attn, add support for other attn_backends. 2. Support TP > 1 (this requires some designs because we do not expect any broadcasting inside execute_model). """ def __init__( self, model_config: ModelConfig, parallel_config: ParallelConfig, scheduler_config: SchedulerConfig, device_config: DeviceConfig, cache_config: CacheConfig, load_config: LoadConfig, lora_config: Optional[LoRAConfig], kv_cache_dtype: Optional[str] = "auto", is_driver_worker: bool = False, prompt_adapter_config: Optional[PromptAdapterConfig] = None, return_hidden_states: bool = False, observability_config: Optional[ObservabilityConfig] = None, ): if return_hidden_states: raise ValueError( "return_hidden_states is not supported for TP1DraftModelRunner." ) super().__init__( model_config=model_config, parallel_config=parallel_config, scheduler_config=scheduler_config, device_config=device_config, cache_config=cache_config, load_config=load_config, lora_config=lora_config, kv_cache_dtype=kv_cache_dtype, is_driver_worker=is_driver_worker, prompt_adapter_config=prompt_adapter_config, return_hidden_states=return_hidden_states, observability_config=observability_config, ) def _update_sampling_metadata(self, sampling_metadata, num_seqs, num_queries): assert sampling_metadata.num_prompts == 0 assert len(sampling_metadata.seq_groups) == num_queries assert sampling_metadata.selected_token_indices.shape == ( num_queries, ) # assert sampling_metadata.categorized_sample_indices == TODO: Add if needed # noqa: E501 # Verify that all sequences are decodes for i in range(num_queries): seq_group = sampling_metadata.seq_groups[i] assert seq_group.is_prompt is False # No prompt assert seq_group.prompt_logprob_indices == [] # No prompt assert seq_group.sample_indices == [i] # Simple assert seq_group.seq_len is None # Decode assert seq_group.query_len is None # Decode def _gpu_advance_step( self, model_input: ModelInputForGPUWithSamplingMetadata, last_output: SamplerOutput ) -> ModelInputForGPUWithSamplingMetadata: # Currently, we expect "decode mode" only assert not model_input.is_prompt # Get num_seqs num_seqs = len(model_input.seq_lens) num_queries = len(model_input.query_lens) # Get output tokens GPU tensor sampled_token_ids = last_output.sampled_token_ids assert sampled_token_ids is not None # Update attn_metadata attn_metadata = model_input.attn_metadata assert isinstance(attn_metadata, FlashAttentionMetadata) attn_metadata.advance_step(num_seqs, num_queries) # Update GPU tensors ops.advance_step(num_seqs=num_seqs, num_queries=num_queries, block_size=self.block_size, input_tokens=model_input.input_tokens, sampled_token_ids=sampled_token_ids, input_positions=model_input.input_positions, seq_lens=attn_metadata.seq_lens_tensor, slot_mapping=attn_metadata.slot_mapping, block_tables=attn_metadata.block_tables) # Update sampling_metadata sampling_metadata = model_input.sampling_metadata self._update_sampling_metadata(sampling_metadata, num_seqs, num_queries) # Create new input new_model_input = self._model_input_cls( input_tokens=model_input.input_tokens, input_positions=model_input.input_positions, attn_metadata=attn_metadata, seq_lens=attn_metadata.seq_lens, query_lens=model_input.query_lens, lora_mapping=model_input.lora_mapping, lora_requests=model_input.lora_requests, multi_modal_kwargs=model_input.multi_modal_kwargs, sampling_metadata=model_input.sampling_metadata, is_prompt=False, ) # Ensure we skip CPU samples assert new_model_input.sampling_metadata.skip_sampler_cpu_output is True # We can reuse sampling tensors since every decode iteration is the same new_model_input.sampling_metadata.reuse_sampling_tensors = True if debug_advance_input: logger.debug("NEW INPUT: ") logger.debug(" input_tokens = %s", new_model_input.input_tokens) logger.debug(" input_positions = %s", new_model_input.input_positions) logger.debug(" seq_lens = %d", new_model_input.seq_lens) logger.debug(" query_lens = %d", new_model_input.query_lens) logger.debug(" attn_metadata:") logger.debug(" seq_lens_tensor: %s", attn_metadata.seq_lens_tensor) logger.debug(" slot_mapping: %s", attn_metadata.slot_mapping) logger.debug(" block_tables: %s", attn_metadata.block_tables) return new_model_input def supports_gpu_multi_step(self, execute_model_req: ExecuteModelRequest): """Determines if draft_model_runner GPU multi-step can be used. Currently required conditions are: 1. Only decodes 2. Only flash-attn 3. No LORA 4. No prompt_adapter_config """ if not allow_gpu_advance_step: return False # We allow multi-step GPU only in decode mode for seq_group in execute_model_req.seq_group_metadata_list: if seq_group.is_prompt: return False # TODO: Add support for other attn backends if self.attn_backend.get_name() != "flash-attn": return False # TODO: Add support for LORA if self.lora_config: return False # TODO: Add soft-tuning prompt adapter support if self.prompt_adapter_config: return False return True @torch.inference_mode() def execute_model( self, model_input: ModelInputForGPUWithSamplingMetadata, kv_caches: List[torch.Tensor], previous_hidden_states: Optional[torch.Tensor] = None, intermediate_tensors: Optional[IntermediateTensors] = None, num_steps: int = 1, ) -> Optional[List[SamplerOutput]]: """Executes num_steps forward passes with advacement of input tensors on the GPU. Look at supports_gpu_multi_step(..) for pre-conditions. Optimizations used: 1. Input tensors are updated on the GPU directly 2. Skips GPU=>CPU serialization of sampler outputs (we don't need them since we do batch expansion later that uses GPU outputs) 3. Reuses sampling tensors (since we run only decodes and they have a repeating sampling logic) """ # When num_steps == 1, we execute the fallback here for the GPU # advance_step, which runs prepare_inputs on CPU and for each spec # iteration invokes this function only once # (Look at multi-step-worker code) is_fallback = num_steps == 1 if not is_fallback: # Since we do not broadcast data inside execute_model anymore, # we need to figure out the best way to support TP > 1 in this # case, because we will at least need to broadcast the sampled # tokens to all workers. if not self.is_driver_worker: raise ValueError("TP1DraftModelRunner only supports TP=1.") # Sanity if self.lora_config is not None: raise ValueError("TP1DraftModelRunner has no support for LORA") if self.prompt_adapter_config is not None: raise ValueError("TP1DraftModelRunner has no support for " "prompt_adapter_config") if model_input.multi_modal_kwargs: raise ValueError( "TP1DraftModelRunner has no support for multi_modal_kwargs" ) else: if self.lora_config: assert model_input.lora_requests is not None assert model_input.lora_mapping is not None self.set_active_loras(model_input.lora_requests, model_input.lora_mapping) if self.prompt_adapter_config: assert model_input.prompt_adapter_requests is not None assert model_input.prompt_adapter_mapping is not None self.set_active_prompt_adapters( model_input.prompt_adapter_requests, model_input.prompt_adapter_mapping) self.attn_state.begin_forward(model_input) # Detect exec mode assert model_input.attn_metadata is not None use_cuda_graph = False if model_input.attn_metadata.num_prefills > 0: # In this case, execute_model(..) was called directly if num_steps > 1: raise ValueError( "execute_model(..) of draft_model_runner can be called " "directly only with a single-step prefill") else: # We can skip CPU samples for spec token generation. # (We do allow CPU samples for num_steps == 1 to support the # fallback case, where supports_gpu_multi_step(..) does not pass) model_input.sampling_metadata.skip_sampler_cpu_output = ( not is_fallback) # Attn attr defines if we use cuda graphs use_cuda_graph = model_input.attn_metadata.use_cuda_graph # Get model if use_cuda_graph: graph_batch_size = model_input.input_tokens.shape[0] model_executable = (self.graph_runners[model_input.virtual_engine] [graph_batch_size]) if previous_hidden_states is not None: hidden_states = torch.cat([ previous_hidden_states, torch.empty([ graph_batch_size - previous_hidden_states.shape[0], *previous_hidden_states.shape[1:] ], dtype=previous_hidden_states.dtype, device=previous_hidden_states.device) ]) else: hidden_states = None else: model_executable = self.model hidden_states = previous_hidden_states outputs: List[SamplerOutput] = [] for step in range(num_steps): multi_modal_kwargs = model_input.multi_modal_kwargs or {} kwargs = {"previous_hidden_states": hidden_states} \ if previous_hidden_states is not None else {} # Run model hidden_states = model_executable( input_ids=model_input.input_tokens, positions=model_input.input_positions, kv_caches=kv_caches, attn_metadata=model_input.attn_metadata, intermediate_tensors=intermediate_tensors, **MultiModalInputs.as_kwargs(multi_modal_kwargs, device=self.device), **kwargs, ) # Compute the logits. logits = self.model.compute_logits(hidden_states, model_input.sampling_metadata) # Sample the next token. outputs.append( self.model.sample( logits=logits, sampling_metadata=model_input.sampling_metadata, )) # Prepare inputs for the next step if step != num_steps - 1: model_input = self._gpu_advance_step(model_input, outputs[-1]) return outputs