92 lines
3.7 KiB
Python
92 lines
3.7 KiB
Python
from typing import Dict, List, Optional, Tuple
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import torch
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from xformers.ops import AttentionBias
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from vllm.sampling_params import SamplingParams, SamplingType
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from vllm.sequence import SequenceData
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class InputMetadata:
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"""Metadata for input sequences. Used for PagedAttention.
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Args:
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seq_groups: List of (seq_ids, sampling_params).
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seq_data: Seq_id -> SequenceData.
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prompt_lens: Lengths of prompts.
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slot_mapping: The address to write the new KV to of each token.
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context_lens: the length of attention context for each generation token.
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max_context_len: The maximum context length.
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block_tables: The block tables. (Seq id -> list of physical block)
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"""
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def __init__(
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self,
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seq_groups: List[Tuple[List[int], SamplingParams]],
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seq_data: Dict[int, SequenceData],
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prompt_lens: List[int],
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slot_mapping: torch.Tensor,
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context_lens: torch.Tensor,
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max_context_len: int,
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block_tables: torch.Tensor,
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selected_token_indices: torch.Tensor,
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categorized_sample_indices: Dict[SamplingType, torch.Tensor],
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sliding_window: Optional[int] = None,
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) -> None:
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self.seq_groups = seq_groups
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self.seq_data = seq_data
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self.prompt_lens = prompt_lens
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self.slot_mapping = slot_mapping
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self.context_lens = context_lens
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self.max_context_len = max_context_len
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self.block_tables = block_tables
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self.selected_token_indices = selected_token_indices
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self.categorized_sample_indices = categorized_sample_indices
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self.max_prompt_len = max(prompt_lens) if prompt_lens else 0
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self.to_cache = None
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if sliding_window is not None:
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# We need to keep the positions of sliding windows within
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# the key / value tables, this is helpful to know which
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# elements we need to cache.
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to_cache, start_idx = [], 0
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for prompt_len in self.prompt_lens:
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to_cache.extend(
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range(
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start_idx + max(0, prompt_len - sliding_window),
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start_idx + prompt_len,
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))
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start_idx += self.max_prompt_len
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to_cache.extend(range(start_idx, slot_mapping.shape[0]))
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self.to_cache = torch.tensor(to_cache,
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dtype=torch.int32,
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device=self.slot_mapping.device)
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self.num_prompts = len(prompt_lens)
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self.num_prompt_tokens = self.num_prompts * self.max_prompt_len
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self.num_generation_tokens = context_lens.shape[0]
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if block_tables.numel() > 0:
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self.max_num_blocks_per_seq = block_tables.shape[1]
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else:
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self.max_num_blocks_per_seq = 0
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assert block_tables.shape[0] == self.num_generation_tokens
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# Set during the execution of the first attention op.
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self.attn_bias: Optional[AttentionBias] = None
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def __repr__(self) -> str:
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# Print only useful metadata.
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return (
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f'InputMetadata('
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f'num_prompt_tokens={self.num_prompt_tokens}, '
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f'num_prompts={self.num_prompts}, '
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f'prompt_lens={self.prompt_lens}, '
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f'num_generation_tokens={self.num_generation_tokens}, '
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f'context_lens={self.context_lens}, '
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f'max_context_len={self.max_context_len}), '
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f'max_num_blocks_per_seq={self.max_num_blocks_per_seq}, '
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f'block_tables={self.block_tables}, '
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f'selected_token_indices={self.selected_token_indices}, '
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f'categorized_sample_indices={self.categorized_sample_indices}, '
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f'slot_mapping={self.slot_mapping})')
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