422 lines
15 KiB
Python
422 lines
15 KiB
Python
"""
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Multi-modality utils
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"""
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import logging
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from abc import abstractmethod
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from typing import Callable, List, Optional, Tuple
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import torch
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from torch import nn
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from sglang.srt.managers.schedule_batch import (
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MultimodalDataItem,
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MultimodalInputs,
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global_server_args_dict,
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)
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.utils import print_warning_once
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logger = logging.getLogger(__name__)
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class MultiModalityDataPaddingPattern:
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"""
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Data tokens (like image tokens) often need special handling during padding
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to maintain model compatibility. This class provides the interface for
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implementing different padding strategies for data tokens
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"""
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@abstractmethod
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def pad_input_tokens(
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self, input_ids: List[int], mm_inputs: MultimodalInputs
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) -> List[int]:
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"""
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Pad the input ids sequence containing data tokens, and replace them with pad_values
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"""
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pass
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class MultiModalityDataPaddingPatternTokenPairs(MultiModalityDataPaddingPattern):
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"""In this pattern, data tokens should be enclosed by special token pairs (e.g. <image>...</image>, data_token_pairs)
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This strategy should be applied when data content is marked by start/end token pairs in the input sequence.
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"""
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def __init__(self, data_token_pairs: Optional[List[Tuple[int, int]]]) -> None:
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self.data_token_id_pairs = data_token_pairs
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def pad_input_tokens(
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self, input_ids: List[int], mm_inputs: MultimodalInputs
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) -> List[int]:
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"""
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This function will replace the data-tokens inbetween with pad_values accordingly
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"""
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pad_values = [item.pad_value for item in mm_inputs.mm_items]
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data_token_pairs = self.data_token_id_pairs
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mm_inputs.data_offsets = []
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if data_token_pairs is None:
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data_token_pairs = [mm_inputs.im_start_id, mm_inputs.im_end_id]
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if data_token_pairs is None:
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print_warning_once(
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"No data_token_pairs provided, RadixAttention might be influenced."
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)
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return input_ids
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start_token_ids = [s for s, _e in data_token_pairs]
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end_tokens_ids = [e for _s, e in data_token_pairs]
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padded_ids = []
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last_idx = 0
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data_idx = -1
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start_indices = [i for i, x in enumerate(input_ids) if x in start_token_ids]
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end_indices = [i for i, x in enumerate(input_ids) if x in end_tokens_ids]
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if len(start_indices) != len(end_indices):
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return input_ids
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for start_idx, end_idx in zip(start_indices, end_indices):
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padded_ids.extend(input_ids[last_idx : start_idx + 1])
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if input_ids[start_idx] in start_token_ids:
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data_idx += 1
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mm_inputs.data_offsets += [start_idx]
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if data_idx >= len(pad_values):
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data_idx = len(pad_values) - 1
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num_tokens = end_idx - start_idx - 1
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pad_value = pad_values[data_idx]
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padded_ids.extend([pad_value] * num_tokens)
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last_idx = end_idx
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padded_ids.extend(input_ids[last_idx:])
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assert len(input_ids) == len(padded_ids), "Length validation fails"
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return padded_ids
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class MultiModalityDataPaddingPatternImageTokens(MultiModalityDataPaddingPattern):
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"""In this pattern, data tokens should be represented as repetitions of a single token
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e.g. <image><image>....<image>, or <audio><audio>...<audio>
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"""
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def __init__(self, image_token_id: torch.Tensor) -> None:
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self.image_token_id = image_token_id
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def pad_input_tokens(self, input_ids: List[int], mm_inputs) -> List[int]:
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"""
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This function will replace the data-tokens in between with pad_values accordingly
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"""
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pad_values = [item.pad_value for item in mm_inputs.mm_items]
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assert len(pad_values) != 0
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input_ids_tensor = torch.tensor(input_ids)
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mask = torch.isin(input_ids_tensor, self.image_token_id)
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num_image_tokens = mask.sum().item()
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repeated_pad_values = torch.tensor(pad_values).repeat(
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num_image_tokens // len(pad_values) + 1
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)[:num_image_tokens]
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input_ids_tensor[mask] = repeated_pad_values
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return input_ids_tensor.tolist()
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def get_embedding_and_mask(
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data_embedding_func: Callable[[List[MultimodalDataItem]], torch.Tensor],
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embedding_items: List[MultimodalDataItem],
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placeholder_tensor: torch.Tensor,
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input_ids: torch.Tensor,
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):
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"""
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Get the multimodal embedding and its mask from input_ids
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"""
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# 1. Get the embedding
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embedding = data_embedding_func(embedding_items)
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# 2. Check the embedding
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if embedding.dim() == 2:
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num_mm_tokens_in_embedding = embedding.shape[0]
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else:
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num_mm_tokens_in_embedding = embedding.shape[0] * embedding.shape[1]
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# the mask of multimodal tokens from input_ids
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special_multimodal_mask = torch.isin(
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input_ids,
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placeholder_tensor,
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).unsqueeze(-1)
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num_mm_tokens_in_input_ids = special_multimodal_mask.sum().item()
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if num_mm_tokens_in_input_ids != num_mm_tokens_in_embedding:
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logger.warning(
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f"Number of tokens in multimodal embedding does not match those in the input text."
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f"Got {num_mm_tokens_in_input_ids} tokens in the text but {num_mm_tokens_in_embedding} "
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"tokens from multimodal embeddings."
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)
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if num_mm_tokens_in_input_ids < num_mm_tokens_in_embedding:
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# TODO: chunked prefill will split special tokens from input_ids into several passes, failing the embedding
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# a fix may be cache the unfinished multimodal embedding for future reuse, determine the tokens to embed with
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# extend_start_loc and extend_seq_lens
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chunked_prefill_size = global_server_args_dict["chunked_prefill_size"]
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if chunked_prefill_size != -1:
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logger.warning(
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"You may want to avoid this issue by raising `chunked_prefill_size`, or disabling chunked prefill"
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)
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# extract from the end: this is a compromise
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if embedding.dim() == 2:
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embedding = embedding[-num_mm_tokens_in_input_ids:, :]
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else:
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num_multimodal = num_mm_tokens_in_input_ids // embedding.shape[0]
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embedding = embedding[-num_multimodal:, :]
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else:
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raise RuntimeError(
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f"Insufficient multimodal embedding length: {num_mm_tokens_in_input_ids=} vs {num_mm_tokens_in_embedding=}. This is an internal error"
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)
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return embedding, special_multimodal_mask
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def embed_mm_inputs(
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mm_inputs: MultimodalInputs,
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input_ids: torch.Tensor,
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input_embedding: nn.Embedding,
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image_data_embedding_func: Callable[
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[List[MultimodalDataItem]], torch.Tensor
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] = None,
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audio_data_embedding_func: Callable[
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[List[MultimodalDataItem]], torch.Tensor
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] = None,
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placeholder_token_ids: List[int] = None,
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) -> Optional[torch.Tensor]:
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"""
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Calculate the multimodal embeddings if necessary, then scatter the result with the help of a boolean mask denoting the embed locations
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Args:
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placeholder_token_ids: denoting the token of multimodal data in input_ids.
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If none, the pad_values of multimodal items are used
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Returns:
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final embedding: Optional[torch.Tensor]
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"""
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if mm_inputs is None:
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return None
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# 1. Calculate the multimodal data which exists in input_ids, with the help of pad_values
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# we assume that multimodal data are represented with its pad_values in input_ids
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placeholder_token_ids = placeholder_token_ids or [
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item.pad_value for item in mm_inputs.mm_items
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]
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placeholder_tensor = torch.tensor(placeholder_token_ids, device=input_ids.device)
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placeholder_masks = torch.isin(input_ids, placeholder_tensor)
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appearing_pad_values = torch.unique(
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input_ids[placeholder_masks], return_counts=False
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)
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if appearing_pad_values.numel() == 0:
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# all been prefixed
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inputs_embeds = input_embedding(input_ids)
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else:
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appearing_items = [
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item
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for item in mm_inputs.mm_items
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if item.pad_value is not None and item.pad_value in appearing_pad_values
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]
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using_all_items = False
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if len(appearing_items) == 0:
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# This happens mostly when arg placeholder_token_ids is passed
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logger.warning_once(
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"No multimodal data item's pad value exist in placeholder ids. Using all items"
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)
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using_all_items = True
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appearing_items = mm_inputs.mm_items
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embeddings, masks = [], []
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# 2. Get multimodal embedding separately
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# TODO: make this more generic
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# Try get image embedding if any
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if (
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any(True for item in appearing_items if item.is_image())
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and image_data_embedding_func
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):
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items = [item for item in appearing_items if item.is_image()]
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embedding, mask = get_embedding_and_mask(
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data_embedding_func=image_data_embedding_func,
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embedding_items=items,
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placeholder_tensor=(
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placeholder_tensor
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if using_all_items
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else torch.tensor(
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[item.pad_value for item in items],
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device=input_ids.device,
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)
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),
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input_ids=input_ids,
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)
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embeddings += [embedding]
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masks += [mask]
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# Try get audio embedding if any
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if (
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any(True for item in appearing_items if item.is_audio())
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and audio_data_embedding_func
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):
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items = [item for item in appearing_items if item.is_audio()]
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embedding, mask = get_embedding_and_mask(
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data_embedding_func=audio_data_embedding_func,
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embedding_items=items,
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placeholder_tensor=(
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placeholder_tensor
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if using_all_items
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else torch.tensor(
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[item.pad_value for item in items],
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device=input_ids.device,
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)
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),
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input_ids=input_ids,
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)
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embeddings += [embedding]
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masks += [mask]
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# 3. Get input embeddings
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vocab_size = input_embedding.num_embeddings
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# Important: clamp after getting original multimodal regions
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# Clamp input ids. This is because the input_ids for the multimodal tokens are
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# filled with the hash values of the multimodal for the prefix matching in the radix attention.
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# There values are useless because their embeddings will be replaced by vision embeddings anyway.
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input_ids.clamp_(min=0, max=vocab_size - 1)
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inputs_embeds = input_embedding(input_ids)
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# 4. scatter embeddings into input embedding
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for embedding, mask in zip(embeddings, masks):
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mask = mask.expand_as(inputs_embeds).to(inputs_embeds.device)
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inputs_embeds = inputs_embeds.masked_scatter(
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mask,
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embedding.to(inputs_embeds.device, inputs_embeds.dtype),
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)
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return inputs_embeds
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def general_mm_embed_routine(
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input_ids: torch.Tensor,
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forward_batch: ForwardBatch,
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language_model: nn.Module,
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image_data_embedding_func: Callable[
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[List[MultimodalDataItem]], torch.Tensor
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] = None,
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audio_data_embedding_func: Callable[
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[List[MultimodalDataItem]], torch.Tensor
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] = None,
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placeholder_token_ids: List[int] = None,
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**kwargs,
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) -> torch.Tensor:
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"""
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A general wrapper function to get final input embeds from multimodal models with a language model as causal model
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Args:
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placeholder_token_ids (List[int]): the ids of mm data placeholder tokens
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image_data_embedding_func : the function returning the image embedding
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audio_data_embedding_func : the function returning the image embedding
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Returns:
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inputs_embedding
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forwarded hidden states
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"""
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assert hasattr(language_model, "get_input_embeddings")
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embed_tokens = language_model.get_input_embeddings()
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if (
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not forward_batch.forward_mode.is_decode()
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and forward_batch.contains_mm_inputs()
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):
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mm_input = forward_batch.merge_mm_inputs()
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inputs_embeds = embed_mm_inputs(
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mm_inputs=mm_input,
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input_ids=input_ids,
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input_embedding=embed_tokens,
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image_data_embedding_func=image_data_embedding_func,
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audio_data_embedding_func=audio_data_embedding_func,
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placeholder_token_ids=placeholder_token_ids,
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)
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# once used, mm_inputs is useless
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# just being defensive here
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forward_batch.mm_inputs = None
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else:
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inputs_embeds = embed_tokens(input_ids)
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hidden_states = language_model(
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input_ids=None,
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forward_batch=forward_batch,
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input_embeds=inputs_embeds,
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**kwargs,
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)
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return hidden_states
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def get_multimodal_data_bounds(
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input_ids: torch.Tensor, pad_values: List[int], token_pairs: List[Tuple[int, int]]
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) -> torch.Tensor:
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"""
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Returns a tensor indicating the bounds of multimodal data (images, video, audio, etc.)
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Returns:
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[bounds_count, 2]
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"""
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# All the multimodal data in the batch should share the same special bound token ids.
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start_tokens = [s for s, _e in token_pairs]
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end_tokens = [e for _s, e in token_pairs]
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assert all(isinstance(t, int) for t in start_tokens)
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assert all(isinstance(t, int) for t in end_tokens)
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start_cond = torch.isin(
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input_ids, torch.tensor(start_tokens, device=input_ids.device)
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)
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end_cond = torch.isin(input_ids, torch.tensor(end_tokens, device=input_ids.device))
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(data_start_tokens,) = torch.where(start_cond)
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(data_end_tokens,) = torch.where(end_cond)
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# the im_start_id sometimes can be cached as prefix, but it is needed for the embedding of the multimodal data
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if len(data_start_tokens) != len(data_end_tokens):
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if (
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len(data_start_tokens) + 1 == len(data_end_tokens)
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and input_ids[0] in pad_values
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and data_end_tokens[0] < data_start_tokens[0]
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):
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data_start_tokens = torch.cat(
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[
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torch.tensor([0], device=data_start_tokens.device),
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data_start_tokens,
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]
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)
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valid_mm_data_nums = min(len(data_start_tokens), len(data_end_tokens))
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if valid_mm_data_nums == 0:
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return torch.zeros((0, 2), device=input_ids.device)
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# Filter out pairs where start_token >= end_token
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valid_pairs = []
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for i in range(valid_mm_data_nums):
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start_token = data_start_tokens[i]
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end_token = data_end_tokens[i]
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if start_token < end_token:
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valid_pairs.append((start_token + 1, end_token - 1))
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if not valid_pairs:
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return torch.zeros((0, 2), device=input_ids.device)
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# Convert valid pairs to tensor
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valid_pairs_tensor = torch.tensor(valid_pairs, device=input_ids.device)
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return valid_pairs_tensor
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