vllm/model_executor/input_metadata.py

from typing import Optional

import torch


class InputMetadata:
    """Metadata for input sequences. Used in PagedAttention.

    Args:
        prompt_lens: Lengths of prompts.
        slot_mapping: The address to write the new KV to of each token.
        max_context_len: The maximum context length.
        context_lens: the length of attention context for each sequence.
        block_tables: The block tables. (Seq id -> list of physical block)
        kv_cache_dtype: Data type to store kv cache.
    """

    def __init__(
        self,
        is_prompt: bool,
        slot_mapping: torch.Tensor,
        prompt_lens: Optional[torch.Tensor],
        max_seq_len: Optional[int],
        start_loc: Optional[torch.Tensor],
        max_context_len: Optional[int],
        context_lens: Optional[torch.Tensor],
        block_tables: Optional[torch.Tensor],
        use_cuda_graph: bool,
        kv_cache_dtype: str,
    ) -> None:
        self.is_prompt = is_prompt
        self.prompt_lens = prompt_lens
        self.max_seq_len = max_seq_len
        self.start_loc = start_loc
        self.max_context_len = max_context_len
        self.slot_mapping = slot_mapping
        self.context_lens = context_lens
        self.block_tables = block_tables
        self.use_cuda_graph = use_cuda_graph
        self.kv_cache_dtype = kv_cache_dtype

        # Set during the execution of the first attention op.
        # FIXME(woosuk): This is a hack.
        self.attn_bias = None

    def __repr__(self) -> str:
        return ("InputMetadata("
                f"is_prompt={self.is_prompt}, "
                f"max_context_len={self.max_context_len}, "
                f"slot_mapping={self.slot_mapping}, "
                f"context_lens={self.context_lens}, "
                f"block_tables={self.block_tables}, "
                f"use_cuda_graph={self.use_cuda_graph}, "
                f"kv_cache_dtype={self.kv_cache_dtype})")