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Two new options: truncation length and ban eos token

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oobabooga 2023-04-11 18:46:06 -03:00 • committed by GitHub
parent 749c08a4ff
commit cacbcda208
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6 changed files with 62 additions and 48 deletions
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modules/text_generation.py
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@ -15,20 +15,20 @@ from modules.html_generator import generate_4chan_html, generate_basic_html
from modules.models import clear_torch_cache, local_rank
def get_max_prompt_length(tokens):
max_length = 2048 - tokens
def get_max_prompt_length(state):
max_length = state['truncation_length'] - state['max_new_tokens']
if shared.soft_prompt:
max_length -= shared.soft_prompt_tensor.shape[1]
return max_length
def encode(prompt, tokens_to_generate=0, add_special_tokens=True, add_bos_token=True):
def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_length=None):
if any((shared.is_RWKV, shared.is_llamacpp)):
input_ids = shared.tokenizer.encode(str(prompt))
input_ids = np.array(input_ids).reshape(1, len(input_ids))
return input_ids
else:
input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', truncation=True, max_length=get_max_prompt_length(tokens_to_generate), add_special_tokens=add_special_tokens)
input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', add_special_tokens=add_special_tokens)
# This is a hack for making replies more creative.
if not add_bos_token and input_ids[0][0] == shared.tokenizer.bos_token_id:
@ -39,17 +39,21 @@ def encode(prompt, tokens_to_generate=0, add_special_tokens=True, add_bos_token=
if type(shared.tokenizer) is transformers.LlamaTokenizer and input_ids[0][0] == 29871:
input_ids = input_ids[:, 1:]
if shared.args.cpu:
return input_ids
elif shared.args.flexgen:
return input_ids.numpy()
elif shared.args.deepspeed:
return input_ids.to(device=local_rank)
elif torch.has_mps:
device = torch.device('mps')
return input_ids.to(device)
else:
return input_ids.cuda()
# Handling truncation
if truncation_length is not None:
input_ids = input_ids[:, -truncation_length:]
if any((shared.is_RWKV, shared.is_llamacpp, shared.args.cpu)):
return input_ids
elif shared.args.flexgen:
return input_ids.numpy()
elif shared.args.deepspeed:
return input_ids.to(device=local_rank)
elif torch.has_mps:
device = torch.device('mps')
return input_ids.to(device)
else:
return input_ids.cuda()
def decode(output_ids):
@ -129,12 +133,14 @@ def generate_reply(question, state, eos_token=None, stopping_strings=[]):
original_question = question
if not shared.is_chat():
question = apply_extensions(question, 'input')
if shared.args.verbose:
print(f'\n\n{question}\n--------------------\n')
# These models are not part of Hugging Face, so we handle them
# separately and terminate the function call earlier
if any((shared.is_RWKV, shared.is_llamacpp)):
if shared.args.verbose:
print(f'\n\n{question}\n--------------------\n')
for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
generate_params[k] = state[k]
generate_params['token_count'] = state['max_new_tokens']
@ -166,10 +172,13 @@ def generate_reply(question, state, eos_token=None, stopping_strings=[]):
print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
return
input_ids = encode(question, state['max_new_tokens'], add_bos_token=state['add_bos_token'])
input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state))
original_input_ids = input_ids
output = input_ids[0]
if shared.args.verbose:
print(f'\n\n{decode(input_ids[0])}\n--------------------\n')
cuda = not any((shared.args.cpu, shared.args.deepspeed, shared.args.flexgen))
eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
if eos_token is not None:
@ -179,7 +188,7 @@ def generate_reply(question, state, eos_token=None, stopping_strings=[]):
stopping_criteria_list = transformers.StoppingCriteriaList()
for st in [stopping_strings, state['custom_stopping_strings']]:
if type(st) is list and len(st) > 0:
sentinel_token_ids = [encode(string, 0, add_special_tokens=False) for string in st]
sentinel_token_ids = [encode(string, add_special_tokens=False) for string in st]
stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=sentinel_token_ids, starting_idx=len(input_ids[0])))
break
@ -188,6 +197,8 @@ def generate_reply(question, state, eos_token=None, stopping_strings=[]):
generate_params[k] = state[k]
generate_params['eos_token_id'] = eos_token_ids
generate_params['stopping_criteria'] = stopping_criteria_list
if state['ban_eos_token']:
generate_params['suppress_tokens'] = [shared.tokenizer.eos_token_id]
else:
for k in ['max_new_tokens', 'do_sample', 'temperature']:
generate_params[k] = state[k]