mirror of
https://git.datalinker.icu/vllm-project/vllm.git
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fd95e026e0
Co-authored-by: Andrew Feldman <afeld2012@gmail.com> Co-authored-by: Nick Hill <nickhill@us.ibm.com>
473 lines
19 KiB
Python
473 lines
19 KiB
Python
import dataclasses
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from typing import Any, Dict, List, Optional, Tuple, Type, cast
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import torch
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import torch.distributed
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from vllm.attention.backends.abstract import (AttentionBackend,
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AttentionMetadata)
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from vllm.attention.selector import (_Backend, get_env_variable_attn_backend,
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get_global_forced_attn_backend,
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global_force_attn_backend)
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from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
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ModelConfig, MultiModalConfig, ParallelConfig,
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PromptAdapterConfig, SchedulerConfig)
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from vllm.inputs import INPUT_REGISTRY
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from vllm.logger import init_logger
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from vllm.model_executor import SamplingMetadata
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from vllm.sampling_params import SamplingParams
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from vllm.sequence import (IntermediateTensors, PoolerOutput, SamplerOutput,
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SequenceGroupMetadata)
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from vllm.utils import STR_NOT_IMPL_ENC_DEC_BACKEND, make_tensor_with_pad
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from vllm.worker.model_runner import (_PAD_SLOT_ID, GPUModelRunnerBase,
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ModelInputForGPUBuilder,
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ModelInputForGPUWithSamplingMetadata)
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from vllm.worker.model_runner_base import (
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_add_attn_metadata_broadcastable_dict,
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_add_sampling_metadata_broadcastable_dict)
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from vllm.worker.utils import assert_enc_dec_mr_supported_scenario
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logger = init_logger(__name__)
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@dataclasses.dataclass(frozen=True)
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class EncoderDecoderModelInput(ModelInputForGPUWithSamplingMetadata):
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"""
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Used by the EncoderDecoderModelRunner.
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"""
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encoder_input_tokens: Optional[torch.Tensor] = None
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encoder_input_positions: Optional[torch.Tensor] = None
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def as_broadcastable_tensor_dict(self) -> Dict[str, Any]:
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tensor_dict = {
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"input_tokens": self.input_tokens,
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"input_positions": self.input_positions,
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"encoder_input_tokens": self.encoder_input_tokens,
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"encoder_input_positions": self.encoder_input_positions,
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"virtual_engine": self.virtual_engine,
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"request_ids_to_seq_ids": self.request_ids_to_seq_ids,
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"finished_requests_ids": self.finished_requests_ids,
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}
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_add_attn_metadata_broadcastable_dict(tensor_dict, self.attn_metadata)
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_add_sampling_metadata_broadcastable_dict(tensor_dict,
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self.sampling_metadata)
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return tensor_dict
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@classmethod
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def from_broadcasted_tensor_dict(
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cls,
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tensor_dict: Dict[str, Any],
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attn_backend: Optional["AttentionBackend"] = None,
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) -> "EncoderDecoderModelInput":
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return cast(
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EncoderDecoderModelInput,
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super().from_broadcasted_tensor_dict(tensor_dict, attn_backend))
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class EncoderDecoderModelRunner(GPUModelRunnerBase[EncoderDecoderModelInput]):
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_model_input_cls: Type[EncoderDecoderModelInput] = (
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EncoderDecoderModelInput)
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_builder_cls: Type[ModelInputForGPUBuilder] = (ModelInputForGPUBuilder)
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def __init__(
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self,
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model_config: ModelConfig,
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parallel_config: ParallelConfig,
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scheduler_config: SchedulerConfig,
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device_config: DeviceConfig,
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cache_config: CacheConfig,
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load_config: LoadConfig,
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lora_config: Optional[LoRAConfig],
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kv_cache_dtype: Optional[str] = "auto",
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is_driver_worker: bool = False,
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prompt_adapter_config: Optional[PromptAdapterConfig] = None,
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multimodal_config: Optional[MultiModalConfig] = None,
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):
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'''
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EncoderDecoderModelRunner constructor.
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`lora_config`, `multimodal_config`, and prompt_adapter_config are
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unused (since these features are not yet supported for encoder/decoder
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models) but these arguments are present here for compatibility with
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the base-class constructor.
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'''
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self._maybe_force_supported_attention_backend()
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super().__init__(
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model_config,
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parallel_config,
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scheduler_config,
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device_config,
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cache_config,
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load_config,
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lora_config=None,
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kv_cache_dtype=kv_cache_dtype,
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is_driver_worker=is_driver_worker,
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)
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# Crash for unsupported encoder/scenarios
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assert_enc_dec_mr_supported_scenario(self)
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def _maybe_force_supported_attention_backend(self):
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'''
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Force vLLM to use the XFormers attention backend,
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which is currently the only supported option.
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'''
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def raise_backend_err():
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# The user has specified an attention backend override
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# which is invalid for encoder/decoder models
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raise NotImplementedError(STR_NOT_IMPL_ENC_DEC_BACKEND)
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maybe_env_var_forced_backend = get_env_variable_attn_backend()
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maybe_global_forced_backend = get_global_forced_attn_backend()
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is_forced_by_global = maybe_global_forced_backend is not None
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is_forced_by_env_var = maybe_env_var_forced_backend is not None
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if not (is_forced_by_global or is_forced_by_env_var):
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# The user has not already specified an attention backend
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# override
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logger.info("EncoderDecoderModelRunner requires "
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"XFormers backend; overriding backend "
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"auto-selection and forcing XFormers.")
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global_force_attn_backend(_Backend.XFORMERS)
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elif is_forced_by_global:
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# Backend override enforced by global variable takes
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# precedence over vLLM backend environment variable.
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if maybe_global_forced_backend != _Backend.XFORMERS:
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raise_backend_err()
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elif is_forced_by_env_var:
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# Backend override enforced by vLLM backend
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# environment variable
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if maybe_env_var_forced_backend != _Backend.XFORMERS:
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raise_backend_err()
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def _list_to_int32_tensor(
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self,
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_list: List[int],
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) -> torch.Tensor:
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return torch.tensor(_list, dtype=torch.int32, device=self.device)
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def _list_to_long_tensor(
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self,
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_list: List[int],
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) -> torch.Tensor:
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return torch.tensor(_list, dtype=torch.long, device=self.device)
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def _empty_int32_tensor(self) -> torch.Tensor:
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return self._list_to_int32_tensor([])
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def _empty_long_tensor(self) -> torch.Tensor:
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return self._list_to_long_tensor([])
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@torch.inference_mode()
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def execute_model(
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self,
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model_input: EncoderDecoderModelInput,
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kv_caches: List[torch.Tensor],
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intermediate_tensors: Optional[IntermediateTensors] = None,
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num_steps: int = 1,
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) -> Optional[List[PoolerOutput]]:
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if num_steps > 1:
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raise ValueError("num_steps > 1 is not supported in "
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"EncoderDecoderModelRunner")
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model_executable = self.model
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seqlen_agnostic_kwargs = {
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"finished_requests_ids": model_input.finished_requests_ids,
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"request_ids_to_seq_ids": model_input.request_ids_to_seq_ids,
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} if self.has_seqlen_agnostic else {}
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hidden_or_intermediate_states = model_executable(
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input_ids=model_input.input_tokens,
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positions=model_input.input_positions,
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encoder_input_ids=model_input.encoder_input_tokens,
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encoder_positions=model_input.encoder_input_positions,
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kv_caches=kv_caches,
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attn_metadata=model_input.attn_metadata,
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intermediate_tensors=intermediate_tensors,
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**seqlen_agnostic_kwargs)
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logits = self.model.compute_logits(hidden_or_intermediate_states,
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model_input.sampling_metadata)
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if not self.is_driver_worker:
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return []
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# Sample the next token.
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output: SamplerOutput = self.model.sample(
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logits=logits,
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sampling_metadata=model_input.sampling_metadata,
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)
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return [output]
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def make_model_input_from_broadcasted_tensor_dict(
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self, tensor_dict: Dict[str, Any]) -> EncoderDecoderModelInput:
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return EncoderDecoderModelInput.from_broadcasted_tensor_dict(
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tensor_dict,
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attn_backend=self.attn_backend,
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)
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def prepare_model_input(
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self,
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seq_group_metadata_list: List[SequenceGroupMetadata],
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virtual_engine: int = 0,
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finished_requests_ids: Optional[List[str]] = None
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) -> EncoderDecoderModelInput:
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"""Prepare the model input based on a given sequence group, including
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metadata for the sampling step.
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Since chunked prefill is not supported for encoder/decoder models,
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`input_tokens` is assumed to be either entirely prefill tokens or
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entirely decode tokens.
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"""
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model_input = self._prepare_model_input_tensors(
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seq_group_metadata_list, finished_requests_ids)
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(
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attn_metadata,
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encoder_input_tokens_tensor,
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encoder_input_positions_tensor,
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) = (self._prepare_encoder_model_input_tensors(seq_group_metadata_list,
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model_input))
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# Inject attn_metadata encoder/cross-attention fields &
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# encoder input tokens/positions into model_input.
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# Frozen dataclass fields cannot be modified, so use
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# dataclasses.replace to construct a new model input
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# instance.
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model_input = dataclasses.replace(
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model_input,
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attn_metadata=attn_metadata,
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encoder_input_tokens=encoder_input_tokens_tensor,
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encoder_input_positions=encoder_input_positions_tensor,
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)
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sampling_metadata = SamplingMetadata.prepare(seq_group_metadata_list,
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model_input.seq_lens,
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model_input.query_lens,
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self.device,
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self.pin_memory)
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is_prompt = (seq_group_metadata_list[0].is_prompt
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if seq_group_metadata_list else None)
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return dataclasses.replace(model_input,
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sampling_metadata=sampling_metadata,
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is_prompt=is_prompt,
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virtual_engine=virtual_engine)
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@torch.inference_mode()
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def profile_run(self) -> None:
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# Enable top-k sampling to reflect the accurate memory usage.
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sampling_params = SamplingParams(top_p=0.99, top_k=self.vocab_size - 1)
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max_num_batched_tokens = self.scheduler_config.max_num_batched_tokens
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max_num_seqs = self.scheduler_config.max_num_seqs
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# Profile memory usage with max_num_sequences sequences and the total
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# number of tokens equal to max_num_batched_tokens.
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seqs: List[SequenceGroupMetadata] = []
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model_config = self.model_config
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batch_size = 0
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for group_id in range(max_num_seqs):
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seq_len = (max_num_batched_tokens // max_num_seqs +
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(group_id < max_num_batched_tokens % max_num_seqs))
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batch_size += seq_len
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seq_data, _ = INPUT_REGISTRY \
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.dummy_data_for_profiling(model_config, seq_len)
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# Having more tokens is over-conservative but otherwise fine
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assert len(seq_data.prompt_token_ids) >= seq_len, (
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f"Expected at least {seq_len} dummy tokens for profiling, "
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f"but got: {len(seq_data.prompt_token_ids)}")
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seq = SequenceGroupMetadata(
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request_id=str(group_id),
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is_prompt=True,
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seq_data={group_id: seq_data},
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sampling_params=sampling_params,
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block_tables=None,
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encoder_seq_data=seq_data,
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cross_block_table=None,
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)
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seqs.append(seq)
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# Run the model with the dummy inputs.
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num_layers = self.model_config.get_num_layers(self.parallel_config)
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kv_caches = [None] * num_layers
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finished_requests_ids = [seq.request_id for seq in seqs]
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model_input = self.prepare_model_input(
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seqs, finished_requests_ids=finished_requests_ids)
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intermediate_tensors = None
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self.execute_model(model_input, kv_caches, intermediate_tensors)
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torch.cuda.synchronize()
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return
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def _prepare_encoder_model_input_tensors(
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self,
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seq_group_metadata_list: List[SequenceGroupMetadata],
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model_input: EncoderDecoderModelInput,
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) -> Tuple[AttentionMetadata, Optional[torch.Tensor],
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Optional[torch.Tensor]]:
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"""Helper method to prepare the encoder- and cross-attn-related
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model inputs based on a given sequence group. These additional inputs
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are used to augment an already-computed `EncoderDecoderModelInput`
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data structure which already has decoder-related model inputs
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populated.
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Sets the following attn_metadata fields:
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* `num_encoder_tokens`
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* `encoder_seq_lens`
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* `encoder_seq_lens_tensor`
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* `max_encoder_seq_len`
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* `cross_slot_mapping`
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* `cross_block_tables`
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Constructs a new model inputs data structure, based on
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(1) the existing fields in the `model_inputs` argument,
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and (2) the following additional fields which are
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computed (or in the case of `attn_metadata`, updated)
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by this function:
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* attn_metadata
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* encoder_input_tokens
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* encoder_input_positions
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Arguments:
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* seq_group_metadata_list: list of sequence groups for which to
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compute inputs
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* model_inputs: model inputs data structure with decoder-oriented
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fields already computed.
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Return:
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* Updated model inputs data structure
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"""
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if len(seq_group_metadata_list) == 0:
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return (model_input.attn_metadata, None, None)
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# Since we are not supporting chunked prefill either the entire
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# batch is prefill or it is decode
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is_prompt = seq_group_metadata_list[0].is_prompt
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# Build encoder inputs
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encoder_seq_lens: List[int] = []
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if is_prompt:
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# Prefill phase.
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cross_block_tables = self._empty_int32_tensor().view(
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len(seq_group_metadata_list), -1)
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# Extract input tokens/positions, cross-attention slot-mapping,
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# & seq len from each sequence group metadata
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(
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encoder_input_tokens,
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encoder_input_positions,
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cross_slot_mapping,
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) = (
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[],
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[],
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[],
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)
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for seq_group_metadata in seq_group_metadata_list:
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# Build seq lens
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seq_len = seq_group_metadata.encoder_seq_data.get_len()
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token_ids = seq_group_metadata.encoder_seq_data.get_token_ids()
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encoder_seq_lens.append(seq_len)
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# Build slot mapping
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is_profile_run = (seq_group_metadata.block_tables is None)
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if is_profile_run:
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# During memory profiling, the block tables are not
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# initialized yet. In this case, we just use a dummy
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# slot mapping.
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# In embeddings, the block tables are {seq_id: None}.
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cross_slot_mapping.extend([_PAD_SLOT_ID] * seq_len)
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else:
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for i in range(0, seq_len):
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block_number = seq_group_metadata.cross_block_table[
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i // self.block_size]
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block_offset = i % self.block_size
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slot = block_number * self.block_size + block_offset
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cross_slot_mapping.append(slot)
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# Build encoder input tokens
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encoder_input_tokens.extend(token_ids)
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encoder_input_positions.extend(list(range(0, seq_len)))
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# Convert tokens/positions & cross-attention
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# slot-mapping to encoder input tensors
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encoder_input_tokens_tensor = self._list_to_long_tensor(
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encoder_input_tokens)
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encoder_input_positions_tensor = self._list_to_long_tensor(
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encoder_input_positions)
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cross_slot_mapping_tensor = self._list_to_long_tensor(
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cross_slot_mapping)
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else:
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# Decode phase.
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encoder_input_tokens_tensor = self._empty_long_tensor()
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encoder_input_positions_tensor = self._empty_long_tensor()
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cross_slot_mapping_tensor = self._empty_long_tensor()
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# Extract cross-attention block tables &
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# seq len from each sequence group metadata.
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# Cross-attention block tables are empty
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# during vLLM memory profiling.
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cross_block_tables = []
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for seq_group_metadata in seq_group_metadata_list:
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encoder_seq_lens.append(
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seq_group_metadata.encoder_seq_data.get_len())
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cross_block_table = seq_group_metadata.cross_block_table
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cross_block_tables.append([] if (
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cross_block_table is None) else cross_block_table)
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# Convert cross-attention block tables to encoder input tensor
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cross_block_tables = make_tensor_with_pad(
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cross_block_tables,
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max_len=max(
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len(block_table) for block_table in cross_block_tables),
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pad=0,
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dtype=torch.int32,
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device=self.device,
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)
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# Compute encoder sequence lengths & encoder
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# sequence starting offset tensors
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max_encoder_seq_len = max(encoder_seq_lens, default=0)
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encoder_seq_lens_tensor = self._list_to_int32_tensor(encoder_seq_lens)
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encoder_seq_start_loc = torch.zeros(encoder_seq_lens_tensor.shape[0] +
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1,
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dtype=torch.int32,
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device=self.device)
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torch.cumsum(encoder_seq_lens_tensor,
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dim=0,
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dtype=encoder_seq_start_loc.dtype,
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out=encoder_seq_start_loc[1:])
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# Update attention metadata with encoder-oriented attributes
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attn_metadata = model_input.attn_metadata
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assert attn_metadata is not None
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(
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attn_metadata.num_encoder_tokens,
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attn_metadata.encoder_seq_lens,
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attn_metadata.encoder_seq_lens_tensor,
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attn_metadata.max_encoder_seq_len,
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attn_metadata.cross_slot_mapping,
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attn_metadata.cross_block_tables,
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) = (
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sum(encoder_seq_lens),
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encoder_seq_lens,
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encoder_seq_lens_tensor,
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max_encoder_seq_len,
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cross_slot_mapping_tensor,
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cross_block_tables,
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)
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return (attn_metadata, encoder_input_tokens_tensor,
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encoder_input_positions_tensor)
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