mmarch/text-generation-webui
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Refactor the UI

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A single dictionary called 'interface_state' is now passed as input to all functions. The values are updated only when necessary.

The goal is to make it easier to add new elements to the UI.
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oobabooga 2023-04-11 11:46:30 -03:00 • committed by GitHub
parent 64f5c90ee7
commit 0f212093a3
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3 changed files with 136 additions and 100 deletions
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20
modules/text_generation.py
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@ -69,6 +69,7 @@ def generate_softprompt_input_tensors(input_ids):
# filler_input_ids += shared.model.config.bos_token_id # setting dummy input_ids to bos tokens
return inputs_embeds, filler_input_ids
# Removes empty replies from gpt4chan outputs
def fix_gpt4chan(s):
for i in range(10):
@ -77,6 +78,7 @@ def fix_gpt4chan(s):
s = re.sub("--- [0-9]*\n\n\n---", "---", s)
return s
# Fix the LaTeX equations in galactica
def fix_galactica(s):
s = s.replace(r'\[', r'$')
@ -117,9 +119,9 @@ def stop_everything_event():
shared.stop_everything = True
def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]):
def generate_reply(question, state, eos_token=None, stopping_strings=[]):
clear_torch_cache()
seed = set_manual_seed(generate_state['seed'])
seed = set_manual_seed(state['seed'])
shared.stop_everything = False
generate_params = {}
t0 = time.time()
@ -134,8 +136,8 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
# separately and terminate the function call earlier
if any((shared.is_RWKV, shared.is_llamacpp)):
for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
generate_params[k] = generate_state[k]
generate_params['token_count'] = generate_state['max_new_tokens']
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)
@ -164,7 +166,7 @@ def generate_reply(question, generate_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, generate_state['max_new_tokens'], add_bos_token=generate_state['add_bos_token'])
input_ids = encode(question, state['max_new_tokens'], add_bos_token=state['add_bos_token'])
original_input_ids = input_ids
output = input_ids[0]
@ -179,13 +181,13 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
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] = generate_state[k]
generate_params[k] = state[k]
generate_params['eos_token_id'] = eos_token_ids
generate_params['stopping_criteria'] = stopping_criteria_list
else:
for k in ['max_new_tokens', 'do_sample', 'temperature']:
generate_params[k] = generate_state[k]
generate_params['stop'] = generate_state['eos_token_ids'][-1]
generate_params[k] = state[k]
generate_params['stop'] = state['eos_token_ids'][-1]
if not shared.args.no_stream:
generate_params['max_new_tokens'] = 8
@ -248,7 +250,7 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
# Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
else:
for i in range(generate_state['max_new_tokens'] // 8 + 1):
for i in range(state['max_new_tokens'] // 8 + 1):
clear_torch_cache()
with torch.no_grad():
output = shared.model.generate(**generate_params)[0]