text-generation-webui/modules/cache_utils.py
2024-03-08 21:48:28 -08:00

115 lines
3.9 KiB
Python

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