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Remove duplicate max_new_tokens parameter

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oobabooga 2023-02-24 17:19:42 -03:00
parent 65326b545a
commit 78ad55641b
3 changed files with 21 additions and 21 deletions
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28
modules/chat.py
View File

@ -24,16 +24,16 @@ def clean_chat_message(text):
text = text.strip()
return text
def generate_chat_prompt(user_input, tokens, name1, name2, context, chat_prompt_size, impersonate=False):
def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat_prompt_size, impersonate=False):
user_input = clean_chat_message(user_input)
rows = [f"{context.strip()}\n"]
if shared.soft_prompt:
chat_prompt_size -= shared.soft_prompt_tensor.shape[1]
max_length = min(get_max_prompt_length(tokens), chat_prompt_size)
max_length = min(get_max_prompt_length(max_new_tokens), chat_prompt_size)
i = len(shared.history['internal'])-1
while i >= 0 and len(encode(''.join(rows), tokens)[0]) < max_length:
while i >= 0 and len(encode(''.join(rows), max_new_tokens)[0]) < max_length:
rows.insert(1, f"{name2}: {shared.history['internal'][i][1].strip()}\n")
if not (shared.history['internal'][i][0] == '<|BEGIN-VISIBLE-CHAT|>'):
rows.insert(1, f"{name1}: {shared.history['internal'][i][0].strip()}\n")
@ -47,7 +47,7 @@ def generate_chat_prompt(user_input, tokens, name1, name2, context, chat_prompt_
rows.append(f"{name1}:")
limit = 2
while len(rows) > limit and len(encode(''.join(rows), tokens)[0]) >= max_length:
while len(rows) > limit and len(encode(''.join(rows), max_new_tokens)[0]) >= max_length:
rows.pop(1)
prompt = ''.join(rows)
@ -95,7 +95,7 @@ def generate_chat_picture(picture, name1, name2):
def stop_everything_event():
shared.stop_everything = True
def chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture=None):
def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture=None):
shared.stop_everything = False
just_started = True
eos_token = '\n' if check else None
@ -110,10 +110,10 @@ def chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p,
if shared.args.chat:
visible_text = visible_text.replace('\n', '<br>')
text = apply_extensions(text, "input")
prompt = generate_chat_prompt(text, tokens, name1, name2, context, chat_prompt_size)
prompt = generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size)
# Generate
for reply in generate_reply(prompt, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name1}:"):
for reply in generate_reply(prompt, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name1}:"):
# Extracting the reply
reply, next_character_found, substring_found = extract_message_from_reply(prompt, reply, name2, name1, check, extensions=True)
@ -138,15 +138,15 @@ def chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p,
break
yield shared.history['visible']
def impersonate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture=None):
def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture=None):
eos_token = '\n' if check else None
if 'pygmalion' in shared.model_name.lower():
name1 = "You"
prompt = generate_chat_prompt(text, tokens, name1, name2, context, chat_prompt_size, impersonate=True)
prompt = generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size, impersonate=True)
for reply in generate_reply(prompt, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name2}:"):
for reply in generate_reply(prompt, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name2}:"):
reply, next_character_found, substring_found = extract_message_from_reply(prompt, reply, name1, name2, check, extensions=False)
if not substring_found:
yield reply
@ -154,11 +154,11 @@ def impersonate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, to
break
yield reply
def cai_chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture=None):
for _history in chatbot_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture):
def cai_chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture=None):
for _history in chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture):
yield generate_chat_html(_history, name1, name2, shared.character)
def regenerate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture=None):
def regenerate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture=None):
if shared.character != 'None' and len(shared.history['visible']) == 1:
if shared.args.cai_chat:
yield generate_chat_html(shared.history['visible'], name1, name2, shared.character)
@ -168,7 +168,7 @@ def regenerate_wrapper(text, tokens, do_sample, max_new_tokens, temperature, top
last_visible = shared.history['visible'].pop()
last_internal = shared.history['internal'].pop()
for _history in chatbot_wrapper(last_internal[0], tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture):
for _history in chatbot_wrapper(last_internal[0], max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, picture):
if shared.args.cai_chat:
shared.history['visible'][-1] = [last_visible[0], _history[-1][1]]
yield generate_chat_html(shared.history['visible'], name1, name2, shared.character)

8
modules/text_generation.py
View File

@ -72,14 +72,14 @@ def formatted_outputs(reply, model_name):
else:
return reply
def generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=None, stopping_string=None):
def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=None, stopping_string=None):
original_question = question
if not (shared.args.chat or shared.args.cai_chat):
question = apply_extensions(question, "input")
if shared.args.verbose:
print(f"\n\n{question}\n--------------------\n")
input_ids = encode(question, tokens)
input_ids = encode(question, max_new_tokens)
cuda = "" if (shared.args.cpu or shared.args.deepspeed or shared.args.flexgen) else ".cuda()"
if not shared.args.flexgen:
n = shared.tokenizer.eos_token_id if eos_token is None else shared.tokenizer.encode(eos_token, return_tensors='pt')[0][-1]
@ -126,7 +126,7 @@ def generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top
if shared.args.deepspeed:
generate_params.append("synced_gpus=True")
if shared.args.no_stream:
generate_params.append("max_new_tokens=tokens")
generate_params.append("max_new_tokens=max_new_tokens")
else:
generate_params.append("max_new_tokens=8")
@ -156,7 +156,7 @@ def generate_reply(question, tokens, do_sample, max_new_tokens, temperature, top
# Generate the reply 8 tokens at a time
else:
yield formatted_outputs(original_question, shared.model_name)
for i in tqdm(range(tokens//8+1)):
for i in tqdm(range(max_new_tokens//8+1)):
with torch.no_grad():
output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
if shared.soft_prompt:

6
server.py
View File

@ -252,7 +252,7 @@ if shared.args.chat or shared.args.cai_chat:
with gr.Tab("Extensions"):
extensions_module.create_extensions_block()
input_params = [shared.gradio[i] for i in ['textbox', 'max_new_tokens', 'do_sample', 'max_new_tokens', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'name1', 'name2', 'context', 'check', 'chat_prompt_size_slider']]
input_params = [shared.gradio[i] for i in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'name1', 'name2', 'context', 'check', 'chat_prompt_size_slider']]
if shared.args.picture:
input_params.append(shared.gradio['picture_select'])
function_call = "chat.cai_chatbot_wrapper" if shared.args.cai_chat else "chat.chatbot_wrapper"
@ -312,7 +312,7 @@ elif shared.args.notebook:
if shared.args.extensions is not None:
extensions_module.create_extensions_block()
input_params = [shared.gradio[k] for k in ('textbox', 'max_new_tokens', 'do_sample', 'max_new_tokens', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping')]
input_params = [shared.gradio[k] for k in ('textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping')]
output_params = [shared.gradio[k] for k in ["textbox", "markdown", "html"]]
gen_events.append(shared.gradio["Generate"].click(generate_reply, input_params, output_params, show_progress=shared.args.no_stream, api_name="textgen"))
gen_events.append(shared.gradio['textbox'].submit(generate_reply, input_params, output_params, show_progress=shared.args.no_stream))
@ -344,7 +344,7 @@ else:
with gr.Tab('HTML'):
shared.gradio['html'] = gr.HTML()
input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'max_new_tokens', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']]
input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']]
output_params = [shared.gradio[k] for k in ['output_textbox', 'markdown', 'html']]
gen_events.append(shared.gradio['Generate'].click(generate_reply, input_params, output_params, show_progress=shared.args.no_stream, api_name="textgen"))
gen_events.append(shared.gradio['textbox'].submit(generate_reply, input_params, output_params, show_progress=shared.args.no_stream))