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MohamedAAK
/
test

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Args:
model: HuggingFace model with automatic device mapping
input_ids: Input token ids
past_key_values: KV Cache for knowledge
max_new_tokens: Maximum new tokens to generate
"""
embed_device = model.model.embed_tokens.weight.device
origin_ids = input_ids
input_ids = input_ids.to(embed_device)
output_ids = input_ids.clone()
next_token = input_ids
with torch.no_grad():
for _ in range(max_new_tokens):
outputs = model(
input_ids=next_token,
past_key_values=past_key_values,
use_cache=True
)
next_token_logits = outputs.logits[:, -1, :]
next_token = next_token_logits.argmax(dim=-1).unsqueeze(-1)
next_token = next_token.to(embed_device)
past_key_values = outputs.past_key_values
output_ids = torch.cat([output_ids, next_token], dim=1)
if next_token.item() in model.config.eos_token_id:
break
return output_ids[:, origin_ids.shape[-1]:]
"""KV Cache test"""
# Allowlist the DynamicCache class
torch.serialization.add_safe_globals([DynamicCache])
torch.serialization.add_safe_globals([set])
def preprocess_knowledge(
model,
tokenizer,
prompt: str,
) -> DynamicCache:
"""
Prepare knowledge kv cache for CAG.
Args:
model: HuggingFace model with automatic device mapping
tokenizer: HuggingFace tokenizer
prompt: The knowledge to preprocess, which is basically a prompt
Returns:
DynamicCache: KV Cache
"""
embed_device = model.model.embed_tokens.weight.device
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(embed_device)
past_key_values = DynamicCache()
with torch.no_grad():
outputs = model(
input_ids=input_ids,
past_key_values=past_key_values,
use_cache=True,
output_attentions=False,
output_hidden_states=False
)
return outputs.past_key_values
def write_kv_cache(kv: DynamicCache, path: str):
"""
Write the KV Cache to a file.
"""
torch.save(kv, path)
def clean_up(kv: DynamicCache, origin_len: int):
"""
Truncate the KV Cache to the original length.
"""
for i in range(len(kv.key_cache)):
kv.key_cache[i] = kv.key_cache[i][:, :, :origin_len, :]
kv.value_cache[i] = kv.value_cache[i][:, :, :origin_len, :]
def read_kv_cache(path: str) -> DynamicCache:
"""
Read the KV Cache from a file.
"""
kv = torch.load(path, weights_only=True)
return kv
"""Sentence-BERT for evaluate semantic similarity"""
bert_model = SentenceTransformer('all-MiniLM-L6-v2') # Use a lightweight sentence-transformer
def get_bert_similarity(response, ground_truth):
# Encode the query and text
query_embedding = bert_model.encode(response, convert_to_tensor=True)