53 lines
2.0 KiB
Python
53 lines
2.0 KiB
Python
from typing import List, Dict, Tuple
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import torch
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from xformers.ops.fmha.attn_bias import BlockDiagonalCausalMask
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from cacheflow.sampling_params import SamplingParams
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class InputMetadata:
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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_logprobs: Dict[int, float], # Seq id -> cumulative logprobs.
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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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) -> None:
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self.seq_groups = seq_groups
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self.seq_logprobs = seq_logprobs
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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.attn_bias = BlockDiagonalCausalMask.from_seqlens(prompt_lens)
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self.num_prompts = len(prompt_lens)
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self.num_prompt_tokens = sum(prompt_lens)
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self.num_generation_tokens = context_lens.shape[0]
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self.num_valid_tokens = slot_mapping.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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assert context_lens.shape[0] == self.num_generation_tokens
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def __repr__(self) -> str:
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return (f'InputMetadata('
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f'num_valid_tokens={self.num_valid_tokens}, '
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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'slot_mapping={self.slot_mapping}')
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