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Refactor text-generation.py a bit

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oobabooga 2023-04-24 19:24:12 -03:00 committed by GitHub
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commit 1a0c12c6f2
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1 changed files with 51 additions and 28 deletions
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modules/text_generation.py
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@ -113,9 +113,11 @@ def set_manual_seed(seed):
seed = int(seed)
if seed == -1:
seed = random.randint(1, 2**31)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
return seed
@ -123,8 +125,41 @@ def stop_everything_event():
shared.stop_everything = True
def generate_reply(question, state, eos_token=None, stopping_strings=[]):
def get_generate_params(state):
generate_params = {}
# Models that are not on transformers
if shared.model_type in ['rwkv', 'llamacpp']:
generate_params['token_count'] = state['max_new_tokens']
for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
generate_params[k] = state[k]
else:
# FlexGen
if shared.args.flexgen:
for k in ['max_new_tokens', 'do_sample', 'temperature']:
generate_params[k] = state[k]
if not shared.args.no_stream:
generate_params['max_new_tokens'] = 8
# transformers
else:
for k in ['max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']:
generate_params[k] = state[k]
if state['ban_eos_token']:
generate_params['suppress_tokens'] = [shared.tokenizer.eos_token_id]
if shared.args.no_cache:
generate_params.update({'use_cache': False})
if shared.args.deepspeed:
generate_params.update({'synced_gpus': True})
return generate_params
def generate_reply(question, state, eos_token=None, stopping_strings=[]):
if shared.model_name == 'None' or shared.model is None:
print("No model is loaded! Select one in the Model tab.")
yield formatted_outputs(question, shared.model_name)
@ -133,40 +168,37 @@ def generate_reply(question, state, eos_token=None, stopping_strings=[]):
clear_torch_cache()
seed = set_manual_seed(state['seed'])
shared.stop_everything = False
generate_params = {}
generate_params = get_generate_params(state)
t0 = time.time()
# Preparing the input
original_question = question
if not shared.is_chat():
question = apply_extensions('input', question)
# These models are not part of Hugging Face, so we handle them
# separately and terminate the function call earlier
# If the model is not on transformers, handle it separately and end this
# function call earlier.
if shared.model_type in ['rwkv', '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']
try:
if shared.args.no_stream:
reply = shared.model.generate(context=question, **generate_params)
output = original_question + reply
if not shared.is_chat():
reply = original_question + apply_extensions('output', reply)
yield formatted_outputs(reply, shared.model_name)
else:
if not shared.is_chat():
yield formatted_outputs(question, shared.model_name)
# RWKV has proper streaming, which is very nice.
# No need to generate 8 tokens at a time.
for reply in shared.model.generate_with_streaming(context=question, **generate_params):
output = original_question + reply
if not shared.is_chat():
reply = original_question + apply_extensions('output', reply)
yield formatted_outputs(reply, shared.model_name)
except Exception:
@ -178,18 +210,19 @@ 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
# Encode the input
input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state))
output = input_ids[0]
cuda = not any((shared.args.cpu, shared.args.deepspeed, shared.args.flexgen))
if shared.args.verbose:
print(f'\n\n{decode(input_ids[0], state["skip_special_tokens"])}\n--------------------\n')
cuda = not any((shared.args.cpu, shared.args.deepspeed, shared.args.flexgen))
# Find the eos tokens
eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
if eos_token is not None:
eos_token_ids.append(int(encode(eos_token)[0][-1]))
# Handling the stopping strings
# Create the StoppingCriteriaList with the stopping strings
stopping_criteria_list = transformers.StoppingCriteriaList()
for st in (stopping_strings, ast.literal_eval(f"[{state['custom_stopping_strings']}]")):
if type(st) is list and len(st) > 0:
@ -197,24 +230,14 @@ def generate_reply(question, state, eos_token=None, stopping_strings=[]):
stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=sentinel_token_ids, starting_idx=len(input_ids[0])))
break
if not shared.args.flexgen:
for k in ['max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']:
generate_params[k] = state[k]
# Update generate_params with the eos token and the stopping strings
if shared.args.flexgen:
generate_params['stop'] = eos_token_ids[-1]
else:
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]
generate_params['stop'] = eos_token_ids[-1]
if not shared.args.no_stream:
generate_params['max_new_tokens'] = 8
if shared.args.no_cache:
generate_params.update({'use_cache': False})
if shared.args.deepspeed:
generate_params.update({'synced_gpus': True})
# Add the encoded tokens to generate_params
if shared.soft_prompt:
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
question, filler_input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, filler_input_ids, inputs_embeds)