782 lines
29 KiB
Python
782 lines
29 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import asyncio
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from collections.abc import Mapping
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from typing import Optional, Union, cast
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from typing_extensions import assert_never
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from vllm.config import ModelConfig
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from vllm.logger import init_logger
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from vllm.lora.request import LoRARequest
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from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
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from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalEncDecInputs,
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MultiModalInputs)
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from vllm.prompt_adapter.request import PromptAdapterRequest
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from vllm.transformers_utils.tokenizer_group import BaseTokenizerGroup
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from .data import (DecoderOnlyInputs, EncoderDecoderInputs, ProcessorInputs,
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PromptType, SingletonInputs, SingletonPrompt, token_inputs)
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from .parse import is_explicit_encoder_decoder_prompt, parse_singleton_prompt
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logger = init_logger(__name__)
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class InputPreprocessor:
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def __init__(
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self,
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model_config: ModelConfig,
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tokenizer: Optional[BaseTokenizerGroup],
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mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
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) -> None:
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super().__init__()
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self.model_config = model_config
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self.tokenizer = tokenizer
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self.mm_registry = mm_registry
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def get_tokenizer_group(self) -> BaseTokenizerGroup:
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if self.tokenizer is None:
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raise ValueError("You cannot pass text prompts when "
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"`skip_tokenizer_init` is True")
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return self.tokenizer
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def get_bos_token_id(self,
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lora_request: Optional[LoRARequest] = None
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) -> Optional[int]:
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if self.tokenizer is None:
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logger.warning("Using None for BOS token id because tokenizer "
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"is not initialized")
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return None
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return self.tokenizer.get_lora_tokenizer(lora_request).bos_token_id
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def get_eos_token_id(self,
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lora_request: Optional[LoRARequest] = None
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) -> Optional[int]:
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if self.tokenizer is None:
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logger.warning("Using None for EOS token id because tokenizer "
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"is not initialized")
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return None
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return self.tokenizer.get_lora_tokenizer(lora_request).eos_token_id
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def get_decoder_start_token_id(self) -> Optional[int]:
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'''
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Obtain the decoder start token id employed by an encoder/decoder
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model. Returns None for non-encoder/decoder models or if the
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model config is unavailable.
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'''
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if not self.model_config.is_encoder_decoder:
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logger.warning_once(
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"Using None for decoder start token id because "
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"this is not an encoder/decoder model.")
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return None
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if (self.model_config is None or self.model_config.hf_config is None):
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logger.warning_once(
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"Using None for decoder start token id because "
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"model config is not available.")
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return None
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dec_start_token_id = getattr(self.model_config.hf_config,
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'decoder_start_token_id', None)
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if dec_start_token_id is None:
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logger.warning_once(
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"Falling back on <BOS> for decoder start token "
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"id because decoder start token id is not "
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"available.")
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dec_start_token_id = self.get_bos_token_id()
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return dec_start_token_id
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def _get_default_enc_dec_decoder_prompt(self) -> list[int]:
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'''
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Specifically for encoder/decoder models:
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generate a default decoder prompt for when
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the user specifies only the encoder prompt.
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Encoder/decoder models utilize the decoder
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prompt in different ways; as new models are
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added, it is intended that this function
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will be extended to produce differing
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default decoder prompts, depending on the
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model variety.
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Absent a special case, the default behavior
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of this method is to mirror the behavior of
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the HuggingFace (HF) GenerationMixin for a None
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decoder prompt, which is to employ a logit processor
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setting to force the first decoded token to be <BOS>.
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Here, this behavior is approximated by having the
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"default" decoder prompt be <BOS>.
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However, it is possible that in the future
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other models may have different or more
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complex logic for the default decoder prompt.
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This motivates having a special helper method
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for default decoder prompts.
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Returns:
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* prompt_token_ids
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'''
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bos_token_id = self.get_bos_token_id()
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assert bos_token_id is not None
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return [bos_token_id]
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def _prepare_decoder_input_ids_for_generation(
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self,
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decoder_input_ids: Optional[list[int]],
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) -> list[int]:
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"""
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Prepares `decoder_input_ids` for generation with encoder-decoder models.
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Based on
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https://github.com/huggingface/transformers/blob/
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4037a2b5b1278736e566aec12e169100275545ea/
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src/transformers/generation/utils.py
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specifically GenerationMixin._prepare_decoder_input_ids_for_generation()
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Arguments:
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* decoder_input_ids: input token ids to preprocess
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Returns:
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* Processed token list
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"""
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decoder_start_token_id = self.get_decoder_start_token_id()
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assert decoder_start_token_id is not None
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if decoder_input_ids is None:
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# no decoder prompt input ->
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# use decoder_start_token_id as decoder_input_ids
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decoder_input_ids = self._get_default_enc_dec_decoder_prompt()
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if (len(decoder_input_ids) == 0
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or decoder_input_ids[0] != decoder_start_token_id):
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decoder_input_ids = [decoder_start_token_id] + decoder_input_ids
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return decoder_input_ids
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def _apply_prompt_adapter(
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self,
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prompt_token_ids: list[int],
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prompt_adapter_request: Optional[PromptAdapterRequest],
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) -> list[int]:
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if prompt_adapter_request:
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prompt_token_ids = (
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[0] * prompt_adapter_request.prompt_adapter_num_virtual_tokens
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+ prompt_token_ids)
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return prompt_token_ids
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def _tokenize_prompt(
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self,
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prompt: str,
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lora_request: Optional[LoRARequest],
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) -> list[int]:
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"""
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Apply the model's tokenizer to a text prompt, returning the
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corresponding token IDs.
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"""
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tokenizer = self.get_tokenizer_group()
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add_special_tokens = None
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if self.model_config.hf_config.model_type == "whisper":
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# For Whisper, special tokens should be provided by the user based
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# on the task and language of their request. Also needed to avoid
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# appending an EOS token to the prompt which disrupts generation.
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add_special_tokens = False
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if (self.model_config.encoder_config is not None
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and self.model_config.encoder_config.get(
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"do_lower_case", False)):
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prompt = prompt.lower()
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return tokenizer.encode(prompt=prompt,
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lora_request=lora_request,
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add_special_tokens=add_special_tokens)
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async def _tokenize_prompt_async(
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self,
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prompt: str,
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lora_request: Optional[LoRARequest],
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) -> list[int]:
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"""Async version of :meth:`_tokenize_prompt`."""
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tokenizer = self.get_tokenizer_group()
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add_special_tokens = None
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if self.model_config.hf_config.model_type == "whisper":
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# For Whisper, special tokens should be provided by the user based
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# on the task and language of their request. Also needed to avoid
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# appending an EOS token to the prompt which disrupts generation.
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add_special_tokens = False
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return await tokenizer.encode_async(
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prompt=prompt,
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lora_request=lora_request,
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add_special_tokens=add_special_tokens)
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def _can_process_multimodal(self) -> bool:
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model_config = self.model_config
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if not model_config.is_multimodal_model:
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raise ValueError("Your model does not support multi-modal inputs")
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# Interim measure so we can handle models that have yet to be
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# updated to use the new multi-modal processor
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can_process_multimodal = self.mm_registry.has_processor(model_config)
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if not can_process_multimodal:
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from vllm.model_executor.models.registry import _VLLM_MODELS
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if not any(arch in _VLLM_MODELS
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for arch in model_config.architectures):
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logger.warning_once(
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"Your model uses the legacy input pipeline, which will be "
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"removed in an upcoming release. "
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"Please upgrade to the new multi-modal processing pipeline "
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"(https://docs.vllm.ai/en/latest/design/mm_processing.html)"
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)
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return can_process_multimodal
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def _process_multimodal(
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self,
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prompt: Union[str, list[int]],
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mm_data: MultiModalDataDict,
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mm_processor_kwargs: Optional[Mapping[str, object]],
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lora_request: Optional[LoRARequest],
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return_mm_hashes: bool = False,
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) -> MultiModalInputs:
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"""
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Apply the model's multi-modal processor to a multi-modal prompt,
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returning the corresponding token IDs and metadata.
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"""
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# At the moment on model (PrithviGeoSpatialMAE) requires to be
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# initialized without a tokenizer while using also multi-modal
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# input.
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if not self.tokenizer:
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tokenizer = None
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else:
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tokenizer_group = self.get_tokenizer_group()
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tokenizer = tokenizer_group.get_lora_tokenizer(lora_request)
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mm_processor = self.mm_registry.create_processor(
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self.model_config, tokenizer)
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if mm_processor_kwargs is None:
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mm_processor_kwargs = {}
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return mm_processor.apply(prompt, mm_data, mm_processor_kwargs,
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return_mm_hashes)
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async def _process_multimodal_async(
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self,
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prompt: Union[str, list[int]],
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mm_data: MultiModalDataDict,
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mm_processor_kwargs: Optional[Mapping[str, object]],
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lora_request: Optional[LoRARequest],
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return_mm_hashes: bool = False,
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) -> MultiModalInputs:
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"""Async version of :meth:`_process_multimodal`."""
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# At the moment on model (PrithviGeoSpatialMAE) requires to be
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# initialized without a tokenizer while using also multi-modal
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# input.
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if not self.tokenizer:
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tokenizer = None
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else:
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tokenizer_group = self.get_tokenizer_group()
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tokenizer = await tokenizer_group.get_lora_tokenizer_async(
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lora_request)
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mm_processor = self.mm_registry.create_processor(
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self.model_config, tokenizer)
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if mm_processor_kwargs is None:
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mm_processor_kwargs = {}
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return mm_processor.apply(prompt, mm_data, mm_processor_kwargs,
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return_mm_hashes)
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def _prompt_to_llm_inputs(
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self,
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prompt: SingletonPrompt,
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lora_request: Optional[LoRARequest] = None,
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return_mm_hashes: bool = False,
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) -> SingletonInputs:
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"""
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Extract the singleton inputs from a prompt.
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Arguments:
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* prompt: single encoder or decoder input prompt
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* lora_request: this is only valid for decoder prompts
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* return_mm_hashes: whether to return multimodal hashes
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Returns:
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* :class:`SingletonInputs` instance
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"""
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parsed = parse_singleton_prompt(prompt)
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if parsed["type"] == "str":
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prompt_text = parsed["content"]
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prompt_token_ids = self._tokenize_prompt(
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prompt_text,
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lora_request=lora_request,
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)
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return token_inputs(
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prompt=prompt_text,
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prompt_token_ids=prompt_token_ids,
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)
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if parsed["type"] == "tokens":
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tokens_content = parsed["content"]
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prompt_token_ids = tokens_content["prompt_token_ids"]
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token_type_ids = tokens_content.get("token_type_ids")
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multi_modal_data = tokens_content.get("multi_modal_data")
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mm_processor_kwargs = tokens_content.get("mm_processor_kwargs")
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if multi_modal_data is not None and self._can_process_multimodal():
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return self._process_multimodal(
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prompt_token_ids,
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multi_modal_data,
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mm_processor_kwargs,
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lora_request=lora_request,
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return_mm_hashes=return_mm_hashes,
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)
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return token_inputs(
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prompt_token_ids=prompt_token_ids,
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token_type_ids=token_type_ids,
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multi_modal_data=multi_modal_data,
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mm_processor_kwargs=mm_processor_kwargs,
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)
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if parsed["type"] == "text":
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text_content = parsed["content"]
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prompt_text = text_content["prompt"]
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multi_modal_data = text_content.get("multi_modal_data")
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mm_processor_kwargs = text_content.get("mm_processor_kwargs")
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if multi_modal_data is not None and self._can_process_multimodal():
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return self._process_multimodal(
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prompt_text,
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multi_modal_data,
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mm_processor_kwargs,
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lora_request=lora_request,
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return_mm_hashes=return_mm_hashes,
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)
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prompt_token_ids = self._tokenize_prompt(
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prompt_text,
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lora_request=lora_request,
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)
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return token_inputs(
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prompt=prompt_text,
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prompt_token_ids=prompt_token_ids,
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multi_modal_data=multi_modal_data,
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mm_processor_kwargs=mm_processor_kwargs,
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)
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assert_never(parsed)
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async def _prompt_to_llm_inputs_async(
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self,
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prompt: SingletonPrompt,
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lora_request: Optional[LoRARequest] = None,
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return_mm_hashes: bool = False,
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) -> SingletonInputs:
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"""Async version of :meth:`_extract_prompt_components`."""
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parsed = parse_singleton_prompt(prompt)
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if parsed["type"] == "str":
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prompt_text = parsed["content"]
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prompt_token_ids = await self._tokenize_prompt_async(
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prompt_text,
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lora_request=lora_request,
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)
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return token_inputs(
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prompt=prompt_text,
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prompt_token_ids=prompt_token_ids,
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)
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if parsed["type"] == "tokens":
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tokens_content = parsed["content"]
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prompt_token_ids = tokens_content["prompt_token_ids"]
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multi_modal_data = tokens_content.get("multi_modal_data")
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mm_processor_kwargs = tokens_content.get("mm_processor_kwargs")
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if multi_modal_data is not None and self._can_process_multimodal():
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return await self._process_multimodal_async(
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prompt_token_ids,
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multi_modal_data,
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mm_processor_kwargs,
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lora_request=lora_request,
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return_mm_hashes=return_mm_hashes,
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)
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return token_inputs(
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prompt_token_ids=prompt_token_ids,
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multi_modal_data=multi_modal_data,
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mm_processor_kwargs=mm_processor_kwargs,
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)
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if parsed["type"] == "text":
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text_content = parsed["content"]
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prompt_text = text_content["prompt"]
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multi_modal_data = text_content.get("multi_modal_data")
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mm_processor_kwargs = text_content.get("mm_processor_kwargs")
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if multi_modal_data is not None and self._can_process_multimodal():
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return await self._process_multimodal_async(
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prompt_text,
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multi_modal_data,
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mm_processor_kwargs,
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lora_request=lora_request,
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return_mm_hashes=return_mm_hashes,
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)
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prompt_token_ids = await self._tokenize_prompt_async(
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prompt_text,
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lora_request=lora_request,
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)
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return token_inputs(
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prompt=prompt_text,
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prompt_token_ids=prompt_token_ids,
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multi_modal_data=multi_modal_data,
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mm_processor_kwargs=mm_processor_kwargs,
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)
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assert_never(parsed)
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def _build_enc_dec_llm_inputs(
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self,
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encoder_inputs: SingletonInputs,
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decoder_inputs: Optional[SingletonInputs],
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) -> EncoderDecoderInputs:
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if (encoder_inputs["type"] == "token"
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or encoder_inputs["type"] == "multimodal"):
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pass
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else:
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assert_never(encoder_inputs) # type: ignore[arg-type]
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if decoder_inputs is None:
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if self.model_config.hf_config.model_type == "whisper":
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# For Whisper models, the text prompt should go to the decoder.
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# If no explicit encoder/decoder inputs, then copy the prompt
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# from the encoder to the decoder. The encoder tokens are later
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# overridden by the audio features.
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dec_token_ids = encoder_inputs["prompt_token_ids"].copy()
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else:
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dec_token_ids = self._prepare_decoder_input_ids_for_generation(
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None)
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decoder_inputs = token_inputs(dec_token_ids)
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elif (decoder_inputs["type"] == "token"
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or decoder_inputs["type"] == "multimodal"):
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dec_token_ids = self._prepare_decoder_input_ids_for_generation(
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decoder_inputs["prompt_token_ids"])
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decoder_inputs["prompt_token_ids"] = dec_token_ids
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if "multi_modal_data" in decoder_inputs:
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raise ValueError("Multi-modal decoder inputs of encoder-"
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"decoder models are not supported yet")
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else:
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assert_never(encoder_inputs) # type: ignore[arg-type]
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return EncoderDecoderInputs(
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encoder=encoder_inputs,
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decoder=decoder_inputs,
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)
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def _separate_enc_dec_inputs_from_mm_processor_outputs(
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self,
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inputs: SingletonInputs,
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decoder_inputs_to_override: Optional[SingletonInputs] = None,
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) -> tuple[SingletonInputs, SingletonInputs]:
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"""
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For encoder/decoder models only:
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Separate Encoder/Decoder inputs from a MultiModalEncDecInputs
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"""
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encoder_inputs: SingletonInputs
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decoder_inputs: SingletonInputs
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if inputs["type"] == "multimodal":
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# Multimodal data inputs
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assert ("encoder_prompt" in inputs
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and "encoder_prompt_token_ids" in inputs)
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inputs = cast(MultiModalEncDecInputs, inputs)
|
|
encoder_inputs = token_inputs(
|
|
prompt=inputs["encoder_prompt"],
|
|
prompt_token_ids=inputs["encoder_prompt_token_ids"],
|
|
)
|
|
if decoder_inputs_to_override is not None:
|
|
decoder_inputs = MultiModalInputs(
|
|
type="multimodal",
|
|
prompt=decoder_inputs_to_override.get("prompt", ""),
|
|
prompt_token_ids=decoder_inputs_to_override[
|
|
"prompt_token_ids"],
|
|
mm_kwargs=inputs["mm_kwargs"],
|
|
mm_placeholders=inputs["mm_placeholders"],
|
|
)
|
|
else:
|
|
decoder_inputs = MultiModalInputs(
|
|
type="multimodal",
|
|
prompt=inputs["prompt"],
|
|
prompt_token_ids=inputs["prompt_token_ids"],
|
|
mm_kwargs=inputs["mm_kwargs"],
|
|
mm_placeholders=inputs["mm_placeholders"],
|
|
)
|
|
elif inputs["type"] == "token":
|
|
# Text-only inputs
|
|
encoder_inputs = token_inputs(prompt="", prompt_token_ids=[])
|
|
decoder_inputs = decoder_inputs_to_override or inputs
|
|
else:
|
|
assert_never(inputs) # type: ignore[arg-type]
|
|
return encoder_inputs, decoder_inputs
|
|
|
|
def _process_encoder_decoder_prompt(
|
|
self,
|
|
prompt: PromptType,
|
|
) -> EncoderDecoderInputs:
|
|
"""
|
|
For encoder/decoder models only:
|
|
Process an input prompt into an :class:`EncoderDecoderInputs` instance.
|
|
|
|
There are two types of input prompts:
|
|
singleton prompts which carry only the
|
|
encoder prompt, and explicit encoder/decoder
|
|
prompts which carry both the encoder and the
|
|
decoder prompts as member variables.
|
|
|
|
This function handles the following scenarios:
|
|
* Singleton encoder prompt: extract encoder prompt
|
|
token ids & infer default decoder prompt token ids
|
|
* Explicit encoder/decoder prompt: extract encoder
|
|
and decoder prompt token ids
|
|
|
|
Note that for Explicit encoder/decoder prompts,
|
|
each sub-prompt (encoder or decoder prompt) can
|
|
have any possible singleton type; thus this
|
|
method relies on helper functions to obtain
|
|
token ids for the sub-prompts.
|
|
|
|
Arguments:
|
|
|
|
* prompt: an input prompt
|
|
|
|
Returns:
|
|
|
|
* :class:`EncoderDecoderInputs` instance
|
|
"""
|
|
encoder_inputs: SingletonInputs
|
|
decoder_inputs: Optional[SingletonInputs]
|
|
|
|
if is_explicit_encoder_decoder_prompt(prompt):
|
|
encoder_inputs = self._prompt_to_llm_inputs(
|
|
prompt["encoder_prompt"])
|
|
if (decoder_input := prompt["decoder_prompt"]) is None:
|
|
decoder_inputs = None
|
|
else:
|
|
decoder_inputs = self._prompt_to_llm_inputs(decoder_input)
|
|
# For multimodal model, override decoder prompt from processor
|
|
# with explicit decoder prompt.
|
|
if self.model_config.is_multimodal_model and (
|
|
self._can_process_multimodal()):
|
|
encoder_inputs, decoder_inputs = (
|
|
self._separate_enc_dec_inputs_from_mm_processor_outputs(
|
|
encoder_inputs, decoder_inputs))
|
|
else:
|
|
inputs = self._prompt_to_llm_inputs(prompt)
|
|
if self.model_config.is_multimodal_model and (
|
|
self._can_process_multimodal()):
|
|
# Encoder-Decoder Multimodal model
|
|
encoder_inputs, decoder_inputs = (
|
|
self._separate_enc_dec_inputs_from_mm_processor_outputs(
|
|
inputs))
|
|
else:
|
|
encoder_inputs = inputs
|
|
|
|
decoder_inputs = None
|
|
|
|
return self._build_enc_dec_llm_inputs(encoder_inputs, decoder_inputs)
|
|
|
|
async def _process_encoder_decoder_prompt_async(
|
|
self,
|
|
prompt: PromptType,
|
|
) -> EncoderDecoderInputs:
|
|
"""Async version of :meth:`_process_encoder_decoder_prompt`."""
|
|
encoder_inputs: SingletonInputs
|
|
decoder_inputs: Optional[SingletonInputs]
|
|
|
|
if is_explicit_encoder_decoder_prompt(prompt):
|
|
encoder_task = self._prompt_to_llm_inputs_async(
|
|
prompt["encoder_prompt"])
|
|
|
|
if (decoder_input := prompt["decoder_prompt"]) is None:
|
|
encoder_inputs = await encoder_task
|
|
decoder_inputs = None
|
|
else:
|
|
decoder_task = self._prompt_to_llm_inputs_async(decoder_input)
|
|
|
|
encoder_inputs, decoder_inputs = await asyncio.gather(
|
|
encoder_task, decoder_task)
|
|
|
|
# For multimodal model, override decoder prompt from processor
|
|
# with explicit decoder prompt.
|
|
if self.model_config.is_multimodal_model and (
|
|
self._can_process_multimodal()):
|
|
encoder_inputs, decoder_inputs = (
|
|
self._separate_enc_dec_inputs_from_mm_processor_outputs(
|
|
encoder_inputs, decoder_inputs))
|
|
else:
|
|
inputs = await self._prompt_to_llm_inputs_async(prompt)
|
|
if self.model_config.is_multimodal_model and (
|
|
self._can_process_multimodal()):
|
|
# Encoder-Decoder Multimodal model
|
|
encoder_inputs, decoder_inputs = (
|
|
self._separate_enc_dec_inputs_from_mm_processor_outputs(
|
|
inputs))
|
|
else:
|
|
encoder_inputs = inputs
|
|
|
|
decoder_inputs = None
|
|
|
|
return self._build_enc_dec_llm_inputs(encoder_inputs, decoder_inputs)
|
|
|
|
def _build_decoder_only_llm_inputs(
|
|
self,
|
|
prompt_inputs: DecoderOnlyInputs,
|
|
prompt_adapter_request: Optional[PromptAdapterRequest],
|
|
) -> DecoderOnlyInputs:
|
|
if (prompt_inputs["type"] == "token"
|
|
or prompt_inputs["type"] == "multimodal"):
|
|
prompt_inputs["prompt_token_ids"] = self._apply_prompt_adapter(
|
|
prompt_inputs["prompt_token_ids"],
|
|
prompt_adapter_request=prompt_adapter_request,
|
|
)
|
|
else:
|
|
assert_never(prompt_inputs) # type: ignore[arg-type]
|
|
|
|
return prompt_inputs
|
|
|
|
def _process_decoder_only_prompt(
|
|
self,
|
|
prompt: SingletonPrompt,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
|
|
return_mm_hashes: bool = False,
|
|
) -> DecoderOnlyInputs:
|
|
"""
|
|
For decoder-only models:
|
|
Process an input prompt into an :class:`DecoderOnlyInputs` instance.
|
|
|
|
Arguments:
|
|
|
|
* prompt: input prompt
|
|
* lora_request
|
|
* prompt_adapter_request
|
|
* return_mm_hashes
|
|
|
|
Returns:
|
|
|
|
* :class:`DecoderOnlyInputs` instance
|
|
"""
|
|
|
|
prompt_comps = self._prompt_to_llm_inputs(
|
|
prompt,
|
|
lora_request=lora_request,
|
|
return_mm_hashes=return_mm_hashes,
|
|
)
|
|
|
|
return self._build_decoder_only_llm_inputs(
|
|
prompt_comps,
|
|
prompt_adapter_request=prompt_adapter_request,
|
|
)
|
|
|
|
async def _process_decoder_only_prompt_async(
|
|
self,
|
|
prompt: SingletonPrompt,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
|
|
return_mm_hashes: bool = False,
|
|
) -> DecoderOnlyInputs:
|
|
"""Async version of :meth:`_process_decoder_only_prompt`."""
|
|
prompt_comps = await self._prompt_to_llm_inputs_async(
|
|
prompt,
|
|
lora_request=lora_request,
|
|
return_mm_hashes=return_mm_hashes,
|
|
)
|
|
|
|
return self._build_decoder_only_llm_inputs(
|
|
prompt_comps,
|
|
prompt_adapter_request=prompt_adapter_request,
|
|
)
|
|
|
|
def preprocess(
|
|
self,
|
|
prompt: PromptType,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
|
|
return_mm_hashes: bool = False,
|
|
) -> ProcessorInputs:
|
|
"""Preprocess the input prompt."""
|
|
if self.model_config.is_encoder_decoder:
|
|
assert not return_mm_hashes, (
|
|
"Multimodal hashes for encoder-decoder models should not be ",
|
|
"returned until they are supported on vLLM V1.")
|
|
# Encoder-decoder model requires special mapping of
|
|
# input prompts to encoder & decoder
|
|
return self._process_encoder_decoder_prompt(prompt)
|
|
|
|
if is_explicit_encoder_decoder_prompt(prompt):
|
|
raise ValueError("Cannot pass encoder-decoder prompt "
|
|
"to decoder-only models")
|
|
|
|
# Decoder-only operation
|
|
return self._process_decoder_only_prompt(
|
|
prompt,
|
|
lora_request=lora_request,
|
|
prompt_adapter_request=prompt_adapter_request,
|
|
return_mm_hashes=return_mm_hashes,
|
|
)
|
|
|
|
async def preprocess_async(
|
|
self,
|
|
prompt: PromptType,
|
|
lora_request: Optional[LoRARequest] = None,
|
|
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
|
|
return_mm_hashes: bool = False,
|
|
) -> ProcessorInputs:
|
|
"""Async version of :meth:`preprocess`."""
|
|
if self.model_config.is_encoder_decoder:
|
|
assert not return_mm_hashes, (
|
|
"Multimodal hashes for encoder-decoder models should not be ",
|
|
"returned until they are supported on vLLM V1.")
|
|
# Encoder-decoder model requires special mapping of
|
|
# input prompts to encoder & decoder
|
|
return await self._process_encoder_decoder_prompt_async(prompt)
|
|
|
|
if is_explicit_encoder_decoder_prompt(prompt):
|
|
raise ValueError("Cannot pass encoder-decoder prompt "
|
|
"to decoder-only models")
|
|
|
|
# Decoder-only operation
|
|
return await self._process_decoder_only_prompt_async(
|
|
prompt,
|
|
lora_request=lora_request,
|
|
prompt_adapter_request=prompt_adapter_request,
|
|
return_mm_hashes=return_mm_hashes,
|
|
)
|