mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
616 lines
24 KiB
Python
616 lines
24 KiB
Python
"""Multimodal utilities for handling images and other media types in TensorRT-LLM."""
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional, Tuple, Union
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import numpy as np
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import PIL
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import torch
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from blake3 import blake3
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from torchvision.transforms import ToPILImage
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# Default hasher
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default_hasher = blake3
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@dataclass
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class MultimodalInput:
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multimodal_hashes: List[List[int]]
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"""Hash values for multimodal data items (e.g., images).
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Each element is a list of 8 integers representing the hash digest of a multimodal item.
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"""
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multimodal_positions: List[int]
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"""Starting positions of each multimodal chunk in the token sequence.
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Contains only the start position of each chunk, not all positions of multimodal tokens.
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This is different from mm_positions elsewhere which contains all positions.
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"""
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multimodal_lengths: List[int]
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"""Length (number of tokens) of each multimodal item.
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Combined with multimodal_positions, this defines the token spans for each multimodal item.
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"""
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def __post_init__(self):
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"""Validate input data structure and consistency."""
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# Validate multimodal_hashes
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if not isinstance(self.multimodal_hashes, list):
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raise TypeError("multimodal_hashes must be a list")
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# Check that hashes are lists of consistent length containing integers
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if not all(isinstance(h, list) for h in self.multimodal_hashes):
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raise TypeError("Each element in multimodal_hashes must be a list")
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# Check consistent length of hash arrays
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hash_lengths = [len(h) for h in self.multimodal_hashes]
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if min(hash_lengths) != max(hash_lengths):
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raise ValueError(
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f"All hash arrays must have the same length, got lengths: {hash_lengths}"
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)
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# Check that positions and lengths are valid
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if not all(isinstance(x, int) for x in self.multimodal_positions):
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raise TypeError("multimodal_positions must contain only integers")
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if not all(isinstance(x, int) for x in self.multimodal_lengths):
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raise TypeError("multimodal_lengths must contain only integers")
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# Check position and length arrays match in size
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if len(self.multimodal_positions) != len(self.multimodal_lengths):
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raise ValueError(
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f"Position and length arrays must match in size: "
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f"positions={len(self.multimodal_positions)}, lengths={len(self.multimodal_lengths)}"
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)
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@classmethod
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def from_components(cls, mm_hashes: List[List[int]],
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mm_positions: List[int],
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mm_lengths: List[int]) -> 'MultimodalInput':
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return cls(multimodal_hashes=mm_hashes,
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multimodal_positions=mm_positions,
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multimodal_lengths=mm_lengths)
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def to_tensor(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""Convert data to tensors"""
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return (
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# int32 to match the type in TRTLLM SizeType32
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torch.tensor(self.multimodal_hashes, dtype=torch.int32),
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torch.tensor(self.multimodal_positions, dtype=torch.int32),
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torch.tensor(self.multimodal_lengths, dtype=torch.int32))
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@dataclass
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class MultimodalRuntimeData:
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"""Runtime data for tracking multimodal token caching and reuse per request sequence.
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This class tracks which multimodal tokens are cached vs. need to be processed
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for each request sequence during KV cache reuse scenarios.
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Attributes:
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num_cached_tokens: Total number of cached tokens for this sequence
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mm_token_lengths: Length of each multimodal token chunk
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mm_token_positions: Starting positions of each multimodal token chunk
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prompt_tokens: Current iteration of prompt tokens for this sequence (optional). Need it for chunk prefill if enabled (#TODO)
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num_cached_mm_tokens: Number of multimodal tokens that are cached in this iteration (computed)
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total_mm_tokens: Total number of multimodal tokens in this sequence (computed)
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"""
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num_cached_tokens: int
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mm_token_lengths: List[int]
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mm_token_positions: List[int]
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# TODO: support chunk prefill for multimodal
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# When chunk prefill is enabled, we need to pass the prompt tokens for current chunk and mask to find the included mm tokens
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prompt_tokens: Optional[List[int]] = None
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num_cached_mm_tokens: Optional[int] = None
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total_mm_tokens: Optional[int] = None
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def __post_init__(self):
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# Validate input data
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if len(self.mm_token_positions) != len(self.mm_token_lengths):
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raise ValueError(
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f"mm_token_positions ({len(self.mm_token_positions)}) and mm_token_lengths ({len(self.mm_token_lengths)}) must have the same length"
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)
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if self.num_cached_tokens < 0:
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raise ValueError(
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f"num_cached_tokens must be non-negative, got {self.num_cached_tokens}"
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)
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if any(length <= 0 for length in self.mm_token_lengths):
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raise ValueError(
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f"All mm_token_lengths must be positive, got {self.mm_token_lengths}"
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)
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if any(pos < 0 for pos in self.mm_token_positions):
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raise ValueError(
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f"All mm_token_positions must be non-negative, got {self.mm_token_positions}"
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)
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if self.num_cached_mm_tokens is None:
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# Compute cached multimodal tokens based on positions and cached tokens
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self.num_cached_mm_tokens = 0
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for pos, length in zip(self.mm_token_positions,
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self.mm_token_lengths):
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if pos + length <= self.num_cached_tokens:
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self.num_cached_mm_tokens += length
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elif pos < self.num_cached_tokens:
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# Partial overlap - only count the cached portion
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self.num_cached_mm_tokens += self.num_cached_tokens - pos
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if self.num_cached_mm_tokens > self.num_cached_tokens:
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raise ValueError(
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f"num_cached_mm_tokens ({self.num_cached_mm_tokens}) must be less than or equal to "
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f"num_cached_tokens ({self.num_cached_tokens})")
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self.total_mm_tokens = sum(self.mm_token_lengths)
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@dataclass
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class MultimodalParams:
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"""Unified container for multimodal parameters.
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This class encapsulates all multimodal-related data that flows through the system,
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providing a clean interface for handling multimodal inputs across different models.
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Attributes:
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multimodal_input: Multimodal input data with hashing information.
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multimodal_data: Processed multimodal data containing embeddings, configurations,
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and modality-specific data organized by type.
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multimodal_runtime: Runtime data for tracking multimodal token caching and reuse
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during KV cache scenarios. Contains information about cached
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tokens, multimodal token positions, and lengths for efficient
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processing during inference.
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Structure of multimodal_data:
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{
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"mrope_config": {
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"mrope_rotary_cos_sin": torch.Tensor, # Rotary embeddings (Qwen2/2.5-VL)
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"mrope_position_deltas": torch.Tensor, # Position deltas (Qwen2/2.5-VL)
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},
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"multimodal_embedding": torch.Tensor, # Pre-computed vision embeddings
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"image": {
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"pixel_values": torch.Tensor,
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"image_height": torch.Tensor | List[int],
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"image_width": torch.Tensor | List[int],
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},
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"video": {
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"pixel_values": torch.Tensor,
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"video_height": torch.Tensor | List[int],
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"video_width": torch.Tensor | List[int],
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},
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# ... other modalities
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}
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"""
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multimodal_input: Optional[MultimodalInput] = None
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multimodal_data: Optional[Dict[str, Any]] = field(default_factory=dict)
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multimodal_runtime: Optional[MultimodalRuntimeData] = None
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def __post_init__(self):
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"""Ensure default values are properly set."""
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if self.multimodal_data is None:
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self.multimodal_data = {}
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def _is_shared_tensor_dict(self, obj: Any) -> bool:
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"""Check if an object is a shared tensor dictionary.
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Args:
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obj: Object to check
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Returns:
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True if the object is a shared tensor dictionary, False otherwise
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"""
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if not isinstance(obj, dict):
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return False
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# Check for required keys that uniquely identify a shared tensor dict
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required_keys = {'method_key'}
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if not required_keys.issubset(obj.keys()):
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return False
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# Additional validation based on method_key
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method_key = obj.get('method_key')
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# Import here to avoid circular imports
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from tensorrt_llm._torch.shared_tensor import \
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_SharedTensorRebuildMethodRegistry
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if method_key == _SharedTensorRebuildMethodRegistry.REBUILD_CUDA:
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cuda_keys = {'tensor_size', 'storage_handle', 'storage_device'}
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return cuda_keys.issubset(obj.keys())
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elif method_key == _SharedTensorRebuildMethodRegistry.REBUILD_CPU:
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cpu_keys = {'tensor_size', 'storage_handle', 'manager_handle'}
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return cpu_keys.issubset(obj.keys())
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return False
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def _apply_tensor_operation(
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self, input_data: Union[torch.Tensor, List, dict, None],
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operation: str, **kwargs) -> Union[torch.Tensor, List, dict, None]:
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"""Apply tensor operations recursively to nested data structures.
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This method handles three types of operations:
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- "to_handle": Convert tensors to shared tensor dictionaries
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- "to_tensor": Convert shared tensor dictionaries back to tensors
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- "to_device": Move tensors to specified device
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Args:
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input_data: Input data structure (tensor, list, dict, or None)
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operation: Operation to apply
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**kwargs: Additional arguments for the operation
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Returns:
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Transformed data structure
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"""
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# Handle None case
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if input_data is None:
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return None
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# Handle list case - recursively process each element
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if isinstance(input_data, list):
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return [
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self._apply_tensor_operation(item, operation, **kwargs)
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for item in input_data
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]
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# Handle dictionary case
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if isinstance(input_data, dict):
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if operation == "to_tensor" and self._is_shared_tensor_dict(
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input_data):
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# Convert shared tensor dict back to tensor
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try:
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# Import here to avoid circular imports
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from tensorrt_llm._torch.shared_tensor import \
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SharedTensorContainer
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return SharedTensorContainer.from_dict(
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input_data).get_local_view()
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except Exception as e:
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raise RuntimeError(
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f"Failed to restore tensor from shared tensor dict: {e}"
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)
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else:
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# Regular dictionary - recursively process values
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return {
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key: self._apply_tensor_operation(value, operation,
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**kwargs)
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for key, value in input_data.items()
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}
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# Handle tensor case
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if isinstance(input_data, torch.Tensor):
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if operation == "to_handle":
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try:
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# Import here to avoid circular imports
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from tensorrt_llm._torch.shared_tensor import \
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SharedTensorContainer
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return SharedTensorContainer.from_tensor(
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input_data).dump_to_dict()
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except Exception as e:
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raise RuntimeError(
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f"Failed to convert tensor to shared tensor: {e}")
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elif operation == "to_device":
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device = kwargs.get('device')
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if device is None:
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raise ValueError(
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"Device must be specified for 'to_device' operation")
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pin_memory = kwargs.get('pin_memory', False)
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try:
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if pin_memory and input_data.device.type == 'cpu':
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return input_data.pin_memory().to(device,
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non_blocking=True)
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else:
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return input_data.to(device, non_blocking=True)
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except Exception as e:
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raise RuntimeError(
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f"Failed to move tensor to device {device}: {e}")
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# For any other type, return as-is
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return input_data
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def to_handle(self, element: str) -> None:
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"""Move specified multimodal data element to shared tensor.
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Args:
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element: Element to move (only "multimodal_data" is supported)
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Raises:
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ValueError: If element is not "multimodal_data"
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RuntimeError: If tensor conversion fails
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"""
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if element != "multimodal_data":
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raise ValueError(
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f"Unsupported element '{element}'. Only 'multimodal_data' is supported."
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)
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data = getattr(self, element)
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if data is None:
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return # Nothing to convert
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transformed_data = self._apply_tensor_operation(data, "to_handle")
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setattr(self, element, transformed_data)
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def to_tensor(self, element: str) -> None:
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"""Move specified multimodal data element from shared tensor.
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Args:
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element: Element to restore (only "multimodal_data" is supported)
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Raises:
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ValueError: If element is not "multimodal_data"
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RuntimeError: If tensor restoration fails
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"""
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if element != "multimodal_data":
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raise ValueError(
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f"Unsupported element '{element}'. Only 'multimodal_data' is supported."
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)
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data = getattr(self, element)
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if data is None:
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return # Nothing to restore
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restored_data = self._apply_tensor_operation(data, "to_tensor")
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setattr(self, element, restored_data)
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def to_device(self,
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element: str,
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device: str,
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pin_memory: bool = False) -> None:
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"""Move specified multimodal data element to target device.
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Args:
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element: Element to move (only "multimodal_data" is supported)
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device: Target device (e.g., "cuda", "cpu")
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pin_memory: Whether to pin memory for faster transfers
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Raises:
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ValueError: If element is not "multimodal_data" or device is invalid
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RuntimeError: If device transfer fails
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"""
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if element != "multimodal_data":
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raise ValueError(
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f"Unsupported element '{element}'. Only 'multimodal_data' is supported."
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)
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data = getattr(self, element)
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if data is None:
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return # Nothing to move
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transformed_data = self._apply_tensor_operation(data,
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"to_device",
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device=device,
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pin_memory=pin_memory)
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setattr(self, element, transformed_data)
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def strip_for_generation(self) -> None:
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"""Strip multimodal data for generation processing.
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Keeps only mrope_config and removes all other multimodal data
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(embeddings, images, etc.) as they're not needed during generation.
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"""
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if not self.multimodal_data:
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return
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# Extract mrope_config before clearing
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mrope_config = None
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if 'mrope_config' in self.multimodal_data:
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mrope_config = self.multimodal_data['mrope_config']
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# Clear all data and restore only mrope_config if it exists
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self.multimodal_data = {}
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if mrope_config is not None:
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self.multimodal_data['mrope_config'] = mrope_config
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def has_content(self) -> bool:
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"""Check if this object contains any multimodal data."""
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return bool(self.multimodal_input or self.multimodal_data)
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# adopt from vllm : https://github.com/vllm-project/vllm/blob/main/vllm/vllm/multimodal/hash.py
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def serialize_item(obj: object) -> bytes:
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# Simple cases
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if isinstance(obj, str):
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return obj.encode("utf-8")
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if isinstance(obj, bytes):
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return obj
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if isinstance(obj, (int, float)):
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return np.array(obj).tobytes()
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if isinstance(obj, PIL.Image.Image):
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return np.array(obj.convert("RGBA")).tobytes()
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if isinstance(obj, torch.Tensor):
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return obj.numpy().tobytes()
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if isinstance(obj, np.ndarray):
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return obj.tobytes()
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raise ValueError(f"Unsupported object type: {type(obj)}")
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def apply_mm_hashes(mm_data: Dict[str, Any],
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hash_lib=default_hasher) -> Dict[str, List[str]]:
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"""Apply hashing to multimodal data items."""
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def _hash_image(image):
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# only support single modality w/ PIL.Image.Image for now
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# TODO: possible hash collision w/ this simplified version (vllm/PR/17378)
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hasher = hash_lib()
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if isinstance(image, torch.Tensor):
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# TODO: Device tensor hashing is an open issue. Limited hashing to CPU for now.
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image = image.cpu()
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hasher.update(serialize_item(image))
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return hasher.hexdigest()
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mm_items = {
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modality: items if isinstance(items, list) else [items]
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for modality, items in mm_data.items()
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}
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# TODO: need to hash both modality and item to distinguish modality (vllm/PR)
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mm_hashes = {
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modality: [_hash_image(item) for item in items]
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for modality, items in mm_items.items()
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}
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return mm_hashes
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def hexdigest_to_int32(hex_digest: str) -> List[int]:
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"""Convert a 256-bit hexadecimal digest to 8 int32 values."""
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if len(hex_digest) != 64:
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raise ValueError(
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f"Expected 64 character hexadecimal string, got {len(hex_digest)}")
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result = []
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for i in range(0, 64, 8):
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hex_chunk = hex_digest[i:i + 8]
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value = int(hex_chunk, 16)
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if value > 0x7FFFFFFF: # Check if the highest bit is set (value > 2^31-1)
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value = value - 0x100000000 # Convert to signed by subtracting 2^32
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result.append(value)
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return result
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def find_mm_token_lengths(mm_data: Dict[str, Any],
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input_processor: Any) -> List[int]:
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"""Get multimodal token lengths from multimodal data items. """
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mm_items = {
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modality: items if isinstance(items, list) else [items]
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for modality, items in mm_data.items()
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}
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num_mm_tokens = {}
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for modality, items in mm_items.items():
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if modality != "image":
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#TODO: support other modalities
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raise ValueError(
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f"Unsupported modality: {modality}. Only 'image' modality is currently supported for hashing."
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)
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if not hasattr(input_processor, "get_num_tokens_per_image"):
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#TODO: backward compatibility for models that don't yet have get_num_tokens_per_image implemented
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#TODO: only support qwen2_vl for now
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raise AttributeError(
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f"Input processor {type(input_processor).__name__} does not have 'get_num_tokens_per_image' method required for multimodal hashing."
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)
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modality_token_lengths = []
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for item in items:
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if isinstance(item, torch.Tensor):
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item = ToPILImage()(item)
|
|
num_tokens = input_processor.get_num_tokens_per_image(
|
|
image_width=item.width,
|
|
image_height=item.height,
|
|
)
|
|
modality_token_lengths.append(num_tokens)
|
|
|
|
num_mm_tokens[modality] = modality_token_lengths
|
|
|
|
return num_mm_tokens['image'] # flatten all mm instances to a single list
|
|
|
|
|
|
def find_mm_token_positions(input_ids: Union[torch.Tensor, List[int],
|
|
np.ndarray],
|
|
num_mm_tokens: List[int],
|
|
vocab_size: int,
|
|
mm_token_ids: torch.Tensor = None) -> List[int]:
|
|
"""Get multimodal token positions using IDs > vocab_size and known lengths.
|
|
|
|
This function finds multimodal tokens (with IDs > vocab_size) and uses the
|
|
provided lengths in num_mm_tokens to identify where each chunk starts.
|
|
This works even when there are no gaps between different image sequences
|
|
(e.g., when all images use the same token IDs).
|
|
|
|
Args:
|
|
input_ids: Token sequence (tensor, list, or numpy array)
|
|
num_mm_tokens: List of lengths for each multimodal token chunk
|
|
vocab_size: Size of the model's vocabulary
|
|
mm_token_ids (optional): possible token ids for multimodal tokens
|
|
|
|
Returns:
|
|
List of starting positions for each multimodal token chunk
|
|
"""
|
|
# Convert input_ids to tensor if needed
|
|
if not isinstance(input_ids, torch.Tensor):
|
|
if isinstance(input_ids, list):
|
|
input_ids = torch.tensor(input_ids)
|
|
elif isinstance(input_ids, np.ndarray):
|
|
input_ids = torch.from_numpy(input_ids)
|
|
|
|
# Create mask for multimodal tokens
|
|
if mm_token_ids is None:
|
|
mm_mask = input_ids >= vocab_size
|
|
else:
|
|
mm_mask = torch.isin(input_ids, mm_token_ids)
|
|
|
|
# If no multimodal tokens found, return empty list
|
|
if not torch.any(mm_mask):
|
|
return []
|
|
|
|
# Get positions of all multimodal tokens
|
|
mm_positions = torch.where(mm_mask)[0].tolist()
|
|
assert len(mm_positions) == sum(
|
|
num_mm_tokens
|
|
), f"Number of multimodal tokens does not match sum of all lengths"
|
|
|
|
# Use num_mm_tokens to find the starting position of each chunk
|
|
start_positions = []
|
|
current_position = 0
|
|
|
|
# Process each expected length
|
|
for length in num_mm_tokens:
|
|
if current_position < len(mm_positions):
|
|
# Add the starting position of this chunk
|
|
start_positions.append(mm_positions[current_position])
|
|
# Move to the next chunk
|
|
current_position += length
|
|
|
|
return start_positions
|
|
|
|
|
|
def validate_mm_inputs(prompt_token_ids: Union[torch.Tensor, List[int],
|
|
np.ndarray],
|
|
mm_hashes: List[List[int]], start_positions: List[int],
|
|
num_mm_tokens: List[int]) -> None:
|
|
"""Validates multimodal inputs for consistency and correctness."""
|
|
# Validate number of hashes matches number of chunks
|
|
if len(mm_hashes) != len(num_mm_tokens):
|
|
raise AssertionError(
|
|
f"Number of hashes ({len(mm_hashes)}) does not match "
|
|
f"number of multimodal chunks ({len(num_mm_tokens)})")
|
|
|
|
# Validate number of start positions matches number of chunks
|
|
if len(start_positions) != len(num_mm_tokens):
|
|
raise AssertionError(
|
|
f"Number of start positions ({len(start_positions)}) does not match "
|
|
f"number of multimodal chunks ({len(num_mm_tokens)})")
|
|
# Validate each chunk's position and length
|
|
prompt_len = len(prompt_token_ids)
|
|
# Verify start_positions are sorted
|
|
if not all(start_positions[i] < start_positions[i + 1]
|
|
for i in range(len(start_positions) - 1)):
|
|
raise AssertionError(
|
|
"start_positions must be sorted in ascending order")
|
|
for chunk_idx, (start_pos,
|
|
chunk_len) in enumerate(zip(start_positions,
|
|
num_mm_tokens)):
|
|
if start_pos < 0:
|
|
raise AssertionError(
|
|
f"Invalid negative start position {start_pos} for chunk {chunk_idx}"
|
|
)
|
|
|
|
if start_pos + chunk_len > prompt_len:
|
|
raise AssertionError(
|
|
f"Multimodal chunk {chunk_idx} at position {start_pos} with length {chunk_len} "
|
|
f"exceeds input sequence length {prompt_len}")
|
|
|
|
# Check for overlap with next chunk
|
|
if chunk_idx < len(start_positions) - 1:
|
|
next_start = start_positions[chunk_idx + 1]
|
|
if start_pos + chunk_len > next_start:
|
|
raise AssertionError(
|
|
f"Multimodal chunk {chunk_idx} at position {start_pos} with length {chunk_len} "
|
|
f"overlaps with chunk {chunk_idx + 1} at position {next_start}"
|
|
)
|