mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-22 08:07:56 +01:00
115 lines
3.9 KiB
Python
115 lines
3.9 KiB
Python
import torch
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from numba import njit
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from modules import shared
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def process_llamacpp_cache(model, new_sequence, past_sequence):
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if len(past_sequence) == 0 or len(new_sequence) == 0:
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return past_sequence
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i1, i2, j1, j2 = find_longest_common_substring_indices(past_sequence, new_sequence)
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overlap_length = i2 - i1 + 1
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# Do StreamingLLM if i1 > 0 (ie the longest common subsequence is not a prefix)
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# and the overlap length is sufficiently long.
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if i1 > 0 and overlap_length > 0.2 * len(new_sequence):
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new_sequence = torch.tensor(new_sequence)
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past_sequence = torch.tensor(past_sequence)
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prefix_length = find_prefix_length(past_sequence[:i1], new_sequence[:j1])
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sink_length = prefix_length
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if sink_length < shared.args.attention_sink_size:
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sink_length = shared.args.attention_sink_size
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removed_length = i1 - sink_length
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matching_prefix = past_sequence[:prefix_length]
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removed_chunk = past_sequence[sink_length:i1]
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overlapping_sequence = new_sequence[j1:j2 + 1]
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added_chunk = new_sequence[j2 + 1:]
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# print(past_sequence)
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# print(new_sequence)
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print()
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print('MATCHING PREFIX=', repr(shared.tokenizer.decode(matching_prefix)))
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print('ADDED CHUNK=', repr(shared.tokenizer.decode(added_chunk)))
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print('REMOVED CHUNK=', repr(shared.tokenizer.decode(removed_chunk)))
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print()
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# Remove interval [sink_length, sink_length + removed_length) from the context
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# Subtract removed_length from model.n_tokens
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model._ctx.kv_cache_seq_rm(0, sink_length, sink_length + removed_length)
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model._ctx.kv_cache_seq_shift(0, sink_length + removed_length, -1, -removed_length)
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new_sequence = new_sequence.tolist()
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model.input_ids[:j2 + 1] = new_sequence[:j2 + 1]
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model.n_tokens = j2 + 1
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return new_sequence[:j2 + 1]
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else:
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return past_sequence
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def find_prefix_length(past_seq, seq_tensor):
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'''
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Given two torch tensors, finds the length of the longest
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common prefix between the two.
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'''
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min_length = min(past_seq.shape[0], seq_tensor.shape[0])
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indices = torch.nonzero(~torch.eq(past_seq[:min_length], seq_tensor[:min_length]))
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if len(indices) > 0:
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prefix_length = indices[0].item()
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else:
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prefix_length = min_length
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return prefix_length
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@njit
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def find_longest_common_substring_indices(list1, list2):
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'''
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Given two lists, solves the Longest Common Substring problem.
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It returns the indices where the substring starts and ends in
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s1 and s2.
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Example:
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ir, jr, ir2, jr2 = find_longest_common_substring_indices(s1, s2)
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print(s1[ir:jr + 1])
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print(s2[ir2:jr2 + 1])
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Adapted from
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https://rosettacode.org/wiki/Longest_common_substring#Python
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'''
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len_list1, len_list2 = len(list1), len(list2)
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start_index_list1, end_index_list1 = 0, -1
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start_index_list2, end_index_list2 = 0, -1
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# for index1 in tqdm(range(0, len_list1), desc="StreamingLLM prompt comparison", leave=False):
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for index1 in range(0, len_list1):
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try:
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index2 = list2.index(list1[index1])
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except:
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continue
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while index2 >= 0:
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temp_index1, temp_index2 = index1, index2
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while temp_index1 < len_list1 and temp_index2 < len_list2 and list2[temp_index2] == list1[temp_index1]:
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if temp_index1 - index1 >= end_index_list1 - start_index_list1:
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start_index_list1, end_index_list1 = index1, temp_index1
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start_index_list2, end_index_list2 = index2, temp_index2
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temp_index1 += 1
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temp_index2 += 1
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try:
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index2 = list2.index(list1[index1], index2 + 1)
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except:
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break
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return start_index_list1, end_index_list1, start_index_list2, end_index_list2
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