import dataclasses from typing import Any, Dict, List, Optional, Tuple, Type, cast import torch import torch.distributed from vllm.attention.backends.abstract import (AttentionBackend, AttentionMetadata) from vllm.attention.backends.utils import PAD_SLOT_ID from vllm.attention.selector import (_Backend, get_env_variable_attn_backend, get_global_forced_attn_backend, global_force_attn_backend) from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig, ModelConfig, ObservabilityConfig, ParallelConfig, PromptAdapterConfig, SchedulerConfig) from vllm.inputs import INPUT_REGISTRY, InputRegistry from vllm.logger import init_logger from vllm.model_executor import SamplingMetadata from vllm.model_executor.layers.sampler import SamplerOutput from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry from vllm.sampling_params import SamplingParams from vllm.sequence import (IntermediateTensors, PoolerOutput, SequenceGroupMetadata) from vllm.utils import STR_NOT_IMPL_ENC_DEC_BACKEND, make_tensor_with_pad from vllm.worker.model_runner import (GPUModelRunnerBase, ModelInputForGPUBuilder, ModelInputForGPUWithSamplingMetadata) from vllm.worker.model_runner_base import ( _add_attn_metadata_broadcastable_dict, _add_sampling_metadata_broadcastable_dict) from vllm.worker.utils import assert_enc_dec_mr_supported_scenario logger = init_logger(__name__) @dataclasses.dataclass(frozen=True) class EncoderDecoderModelInput(ModelInputForGPUWithSamplingMetadata): """ Used by the EncoderDecoderModelRunner. """ encoder_input_tokens: Optional[torch.Tensor] = None encoder_input_positions: Optional[torch.Tensor] = None def as_broadcastable_tensor_dict(self) -> Dict[str, Any]: tensor_dict = { "input_tokens": self.input_tokens, "input_positions": self.input_positions, "encoder_input_tokens": self.encoder_input_tokens, "encoder_input_positions": self.encoder_input_positions, "virtual_engine": self.virtual_engine, "request_ids_to_seq_ids": self.request_ids_to_seq_ids, "finished_requests_ids": self.finished_requests_ids, } _add_attn_metadata_broadcastable_dict(tensor_dict, self.attn_metadata) _add_sampling_metadata_broadcastable_dict(tensor_dict, self.sampling_metadata) return tensor_dict @classmethod def from_broadcasted_tensor_dict( cls, tensor_dict: Dict[str, Any], attn_backend: Optional["AttentionBackend"] = None, ) -> "EncoderDecoderModelInput": return cast( EncoderDecoderModelInput, super().from_broadcasted_tensor_dict(tensor_dict, attn_backend)) class EncoderDecoderModelRunner(GPUModelRunnerBase[EncoderDecoderModelInput]): _model_input_cls: Type[EncoderDecoderModelInput] = ( EncoderDecoderModelInput) _builder_cls: Type[ModelInputForGPUBuilder] = (ModelInputForGPUBuilder) 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, observability_config: Optional[ObservabilityConfig] = None, input_registry: InputRegistry = INPUT_REGISTRY, mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY, ): ''' EncoderDecoderModelRunner constructor. `lora_config` and `prompt_adapter_config` are unused (since these features are not yet supported for encoder/decoder models) but these arguments are present here for compatibility with the base-class constructor. ''' self._maybe_force_supported_attention_backend() super().__init__( model_config, parallel_config, scheduler_config, device_config, cache_config, load_config, lora_config=None, kv_cache_dtype=kv_cache_dtype, is_driver_worker=is_driver_worker, ) # Crash for unsupported encoder/scenarios assert_enc_dec_mr_supported_scenario(self) def _maybe_force_supported_attention_backend(self): ''' Force vLLM to use the XFormers attention backend, which is currently the only supported option. ''' def raise_backend_err(): # The user has specified an attention backend override # which is invalid for encoder/decoder models raise NotImplementedError(STR_NOT_IMPL_ENC_DEC_BACKEND) maybe_env_var_forced_backend = get_env_variable_attn_backend() maybe_global_forced_backend = get_global_forced_attn_backend() is_forced_by_global = maybe_global_forced_backend is not None is_forced_by_env_var = maybe_env_var_forced_backend is not None if not (is_forced_by_global or is_forced_by_env_var): # The user has not already specified an attention backend # override logger.info("EncoderDecoderModelRunner requires " "XFormers backend; overriding backend " "auto-selection and forcing XFormers.") global_force_attn_backend(_Backend.XFORMERS) elif is_forced_by_global: # Backend override enforced by global variable takes # precedence over vLLM backend environment variable. if maybe_global_forced_backend != _Backend.XFORMERS: raise_backend_err() elif is_forced_by_env_var: # Backend override enforced by vLLM backend # environment variable if maybe_env_var_forced_backend != _Backend.XFORMERS: raise_backend_err() def _list_to_int32_tensor( self, _list: List[int], ) -> torch.Tensor: return torch.tensor(_list, dtype=torch.int32, device=self.device) def _list_to_long_tensor( self, _list: List[int], ) -> torch.Tensor: return torch.tensor(_list, dtype=torch.long, device=self.device) def _empty_int32_tensor(self) -> torch.Tensor: return self._list_to_int32_tensor([]) def _empty_long_tensor(self) -> torch.Tensor: return self._list_to_long_tensor([]) @torch.inference_mode() def execute_model( self, model_input: EncoderDecoderModelInput, kv_caches: List[torch.Tensor], intermediate_tensors: Optional[IntermediateTensors] = None, num_steps: int = 1, ) -> Optional[List[PoolerOutput]]: if num_steps > 1: raise ValueError("num_steps > 1 is not supported in " "EncoderDecoderModelRunner") model_executable = self.model seqlen_agnostic_kwargs = { "finished_requests_ids": model_input.finished_requests_ids, "request_ids_to_seq_ids": model_input.request_ids_to_seq_ids, } if self.has_seqlen_agnostic else {} hidden_or_intermediate_states = model_executable( input_ids=model_input.input_tokens, positions=model_input.input_positions, encoder_input_ids=model_input.encoder_input_tokens, encoder_positions=model_input.encoder_input_positions, kv_caches=kv_caches, attn_metadata=model_input.attn_metadata, intermediate_tensors=intermediate_tensors, **seqlen_agnostic_kwargs) logits = self.model.compute_logits(hidden_or_intermediate_states, model_input.sampling_metadata) if not self.is_driver_worker: return [] # Sample the next token. output: SamplerOutput = self.model.sample( logits=logits, sampling_metadata=model_input.sampling_metadata, ) return [output] def make_model_input_from_broadcasted_tensor_dict( self, tensor_dict: Dict[str, Any]) -> EncoderDecoderModelInput: return EncoderDecoderModelInput.from_broadcasted_tensor_dict( tensor_dict, attn_backend=self.attn_backend, ) def prepare_model_input( self, seq_group_metadata_list: List[SequenceGroupMetadata], virtual_engine: int = 0, finished_requests_ids: Optional[List[str]] = None ) -> EncoderDecoderModelInput: """Prepare the model input based on a given sequence group, including metadata for the sampling step. Since chunked prefill is not supported for encoder/decoder models, `input_tokens` is assumed to be either entirely prefill tokens or entirely decode tokens. """ model_input = self._prepare_model_input_tensors( seq_group_metadata_list, finished_requests_ids) ( attn_metadata, encoder_input_tokens_tensor, encoder_input_positions_tensor, ) = (self._prepare_encoder_model_input_tensors(seq_group_metadata_list, model_input)) # Inject attn_metadata encoder/cross-attention fields & # encoder input tokens/positions into model_input. # Frozen dataclass fields cannot be modified, so use # dataclasses.replace to construct a new model input # instance. model_input = dataclasses.replace( model_input, attn_metadata=attn_metadata, encoder_input_tokens=encoder_input_tokens_tensor, encoder_input_positions=encoder_input_positions_tensor, ) sampling_metadata = SamplingMetadata.prepare(seq_group_metadata_list, model_input.seq_lens, model_input.query_lens, self.device, self.pin_memory) is_prompt = (seq_group_metadata_list[0].is_prompt if seq_group_metadata_list else None) return dataclasses.replace(model_input, sampling_metadata=sampling_metadata, is_prompt=is_prompt, virtual_engine=virtual_engine) @torch.inference_mode() def profile_run(self) -> None: # Enable top-k sampling to reflect the accurate memory usage. sampling_params = SamplingParams(top_p=0.99, top_k=self.vocab_size - 1) max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens max_num_seqs = self.scheduler_config.max_num_seqs # Profile memory usage with max_num_sequences sequences and the total # number of tokens equal to max_num_batched_tokens. seqs: List[SequenceGroupMetadata] = [] max_mm_tokens = self.mm_registry.get_max_multimodal_tokens( self.model_config) if max_mm_tokens > 0: raise NotImplementedError( "Multi-modal encoder-decoder models are not supported yet") batch_size = 0 for group_id in range(max_num_seqs): seq_len = (max_num_batched_tokens // max_num_seqs + (group_id < max_num_batched_tokens % max_num_seqs)) batch_size += seq_len seq_data, _ = self.input_registry \ .dummy_data_for_profiling(self.model_config, seq_len, self.mm_registry) # Having more tokens is over-conservative but otherwise fine assert len(seq_data.prompt_token_ids) >= seq_len, ( f"Expected at least {seq_len} dummy tokens for profiling, " f"but got: {len(seq_data.prompt_token_ids)}") seq = SequenceGroupMetadata( request_id=str(group_id), is_prompt=True, seq_data={group_id: seq_data}, sampling_params=sampling_params, block_tables=None, encoder_seq_data=seq_data, cross_block_table=None, ) seqs.append(seq) # Run the model with the dummy inputs. num_layers = self.model_config.get_num_layers(self.parallel_config) kv_caches = [None] * num_layers finished_requests_ids = [seq.request_id for seq in seqs] model_input = self.prepare_model_input( seqs, finished_requests_ids=finished_requests_ids) intermediate_tensors = None self.execute_model(model_input, kv_caches, intermediate_tensors) torch.cuda.synchronize() return def _prepare_encoder_model_input_tensors( self, seq_group_metadata_list: List[SequenceGroupMetadata], model_input: EncoderDecoderModelInput, ) -> Tuple[AttentionMetadata, Optional[torch.Tensor], Optional[torch.Tensor]]: """Helper method to prepare the encoder- and cross-attn-related model inputs based on a given sequence group. These additional inputs are used to augment an already-computed `EncoderDecoderModelInput` data structure which already has decoder-related model inputs populated. Sets the following attn_metadata fields: * `num_encoder_tokens` * `encoder_seq_lens` * `encoder_seq_lens_tensor` * `max_encoder_seq_len` * `cross_slot_mapping` * `cross_block_tables` Constructs a new model inputs data structure, based on (1) the existing fields in the `model_inputs` argument, and (2) the following additional fields which are computed (or in the case of `attn_metadata`, updated) by this function: * attn_metadata * encoder_input_tokens * encoder_input_positions Arguments: * seq_group_metadata_list: list of sequence groups for which to compute inputs * model_inputs: model inputs data structure with decoder-oriented fields already computed. Return: * Updated model inputs data structure """ if len(seq_group_metadata_list) == 0: return (model_input.attn_metadata, None, None) # Since we are not supporting chunked prefill either the entire # batch is prefill or it is decode is_prompt = seq_group_metadata_list[0].is_prompt # Build encoder inputs encoder_seq_lens: List[int] = [] if is_prompt: # Prefill phase. cross_block_tables = self._empty_int32_tensor().view( len(seq_group_metadata_list), -1) # Extract input tokens/positions, cross-attention slot-mapping, # & seq len from each sequence group metadata ( encoder_input_tokens, encoder_input_positions, cross_slot_mapping, ) = ( [], [], [], ) for seq_group_metadata in seq_group_metadata_list: # Build seq lens seq_len = seq_group_metadata.encoder_seq_data.get_len() token_ids = seq_group_metadata.encoder_seq_data.get_token_ids() encoder_seq_lens.append(seq_len) # Build slot mapping is_profile_run = (seq_group_metadata.block_tables is None) if is_profile_run: # During memory profiling, the block tables are not # initialized yet. In this case, we just use a dummy # slot mapping. # In embeddings, the block tables are {seq_id: None}. cross_slot_mapping.extend([PAD_SLOT_ID] * seq_len) else: for i in range(0, seq_len): block_number = seq_group_metadata.cross_block_table[ i // self.block_size] block_offset = i % self.block_size slot = block_number * self.block_size + block_offset cross_slot_mapping.append(slot) # Build encoder input tokens encoder_input_tokens.extend(token_ids) encoder_input_positions.extend(list(range(0, seq_len))) # Convert tokens/positions & cross-attention # slot-mapping to encoder input tensors encoder_input_tokens_tensor = self._list_to_long_tensor( encoder_input_tokens) encoder_input_positions_tensor = self._list_to_long_tensor( encoder_input_positions) cross_slot_mapping_tensor = self._list_to_long_tensor( cross_slot_mapping) else: # Decode phase. encoder_input_tokens_tensor = self._empty_long_tensor() encoder_input_positions_tensor = self._empty_long_tensor() cross_slot_mapping_tensor = self._empty_long_tensor() # Extract cross-attention block tables & # seq len from each sequence group metadata. # Cross-attention block tables are empty # during vLLM memory profiling. cross_block_tables = [] for seq_group_metadata in seq_group_metadata_list: encoder_seq_lens.append( seq_group_metadata.encoder_seq_data.get_len()) cross_block_table = seq_group_metadata.cross_block_table cross_block_tables.append([] if ( cross_block_table is None) else cross_block_table) # Convert cross-attention block tables to encoder input tensor cross_block_tables = make_tensor_with_pad( cross_block_tables, max_len=max( len(block_table) for block_table in cross_block_tables), pad=0, dtype=torch.int32, device=self.device, ) # Compute encoder sequence lengths & encoder # sequence starting offset tensors max_encoder_seq_len = max(encoder_seq_lens, default=0) encoder_seq_lens_tensor = self._list_to_int32_tensor(encoder_seq_lens) encoder_seq_start_loc = torch.zeros(encoder_seq_lens_tensor.shape[0] + 1, dtype=torch.int32, device=self.device) torch.cumsum(encoder_seq_lens_tensor, dim=0, dtype=encoder_seq_start_loc.dtype, out=encoder_seq_start_loc[1:]) # Update attention metadata with encoder-oriented attributes attn_metadata = model_input.attn_metadata assert attn_metadata is not None ( attn_metadata.num_encoder_tokens, attn_metadata.encoder_seq_lens, attn_metadata.encoder_seq_lens_tensor, attn_metadata.max_encoder_seq_len, attn_metadata.cross_slot_mapping, attn_metadata.cross_block_tables, ) = ( sum(encoder_seq_lens), encoder_seq_lens, encoder_seq_lens_tensor, max_encoder_seq_len, cross_slot_mapping_tensor, cross_block_tables, ) return (attn_metadata, encoder_input_tokens_tensor, encoder_input_positions_tensor)