AI/text-generation-webui
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oobabooga 2023-04-07 11:14:32 -03:00
parent a453d4e9c4
commit 6762e62a40
1 changed files with 25 additions and 26 deletions
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51
modules/text_generation.py
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@ -127,22 +127,22 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
original_question = question original_question = question
if not shared.is_chat(): if not shared.is_chat():
question = apply_extensions(question, "input") question = apply_extensions(question, 'input')
if shared.args.verbose: if shared.args.verbose:
print(f"\n\n{question}\n--------------------\n") print(f'\n\n{question}\n--------------------\n')
# These models are not part of Hugging Face, so we handle them # These models are not part of Hugging Face, so we handle them
# separately and terminate the function call earlier # separately and terminate the function call earlier
if any((shared.is_RWKV, shared.is_llamacpp)): if any((shared.is_RWKV, shared.is_llamacpp)):
for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']: for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
generate_params[k] = generate_state[k] generate_params[k] = generate_state[k]
generate_params["token_count"] = generate_state["max_new_tokens"] generate_params['token_count'] = generate_state['max_new_tokens']
try: try:
if shared.args.no_stream: if shared.args.no_stream:
reply = shared.model.generate(context=question, **generate_params) reply = shared.model.generate(context=question, **generate_params)
output = original_question + reply output = original_question + reply
if not shared.is_chat(): if not shared.is_chat():
reply = original_question + apply_extensions(reply, "output") reply = original_question + apply_extensions(reply, 'output')
yield formatted_outputs(reply, shared.model_name) yield formatted_outputs(reply, shared.model_name)
else: else:
if not shared.is_chat(): if not shared.is_chat():
@ -153,7 +153,7 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
for reply in shared.model.generate_with_streaming(context=question, **generate_params): for reply in shared.model.generate_with_streaming(context=question, **generate_params):
output = original_question + reply output = original_question + reply
if not shared.is_chat(): if not shared.is_chat():
reply = original_question + apply_extensions(reply, "output") reply = original_question + apply_extensions(reply, 'output')
yield formatted_outputs(reply, shared.model_name) yield formatted_outputs(reply, shared.model_name)
except Exception: except Exception:
@ -162,7 +162,7 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
t1 = time.time() t1 = time.time()
original_tokens = len(encode(original_question)[0]) original_tokens = len(encode(original_question)[0])
new_tokens = len(encode(output)[0]) - original_tokens new_tokens = len(encode(output)[0]) - original_tokens
print(f"Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens})") print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens})')
return return
input_ids = encode(question, generate_state['max_new_tokens']) input_ids = encode(question, generate_state['max_new_tokens'])
@ -178,31 +178,30 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
t = [encode(string, 0, add_special_tokens=False) for string in stopping_strings] t = [encode(string, 0, add_special_tokens=False) for string in stopping_strings]
stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0]))) stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0])))
generate_params["max_new_tokens"] = generate_state['max_new_tokens']
if not shared.args.flexgen: if not shared.args.flexgen:
for k in ["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"]: 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] = generate_state[k]
generate_params["eos_token_id"] = eos_token_ids generate_params['eos_token_id'] = eos_token_ids
generate_params["stopping_criteria"] = stopping_criteria_list generate_params['stopping_criteria'] = stopping_criteria_list
if shared.args.no_stream: if shared.args.no_stream:
generate_params["min_length"] = 0 generate_params['min_length'] = 0
else: else:
for k in ["do_sample", "temperature"]: for k in ['max_new_tokens', 'do_sample', 'temperature']:
generate_params[k] = generate_state[k] generate_params[k] = generate_state[k]
generate_params["stop"] = generate_state["eos_token_ids"][-1] generate_params['stop'] = generate_state['eos_token_ids'][-1]
if not shared.args.no_stream: if not shared.args.no_stream:
generate_params["max_new_tokens"] = 8 generate_params['max_new_tokens'] = 8
if shared.args.no_cache: if shared.args.no_cache:
generate_params.update({"use_cache": False}) generate_params.update({'use_cache': False})
if shared.args.deepspeed: if shared.args.deepspeed:
generate_params.update({"synced_gpus": True}) generate_params.update({'synced_gpus': True})
if shared.soft_prompt: if shared.soft_prompt:
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids) inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
generate_params.update({"inputs_embeds": inputs_embeds}) generate_params.update({'inputs_embeds': inputs_embeds})
generate_params.update({"inputs": filler_input_ids}) generate_params.update({'inputs': filler_input_ids})
else: else:
generate_params.update({"inputs": input_ids}) generate_params.update({'inputs': input_ids})
try: try:
# Generate the entire reply at once. # Generate the entire reply at once.
@ -217,7 +216,7 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
new_tokens = len(output) - len(input_ids[0]) new_tokens = len(output) - len(input_ids[0])
reply = decode(output[-new_tokens:]) reply = decode(output[-new_tokens:])
if not shared.is_chat(): if not shared.is_chat():
reply = original_question + apply_extensions(reply, "output") reply = original_question + apply_extensions(reply, 'output')
yield formatted_outputs(reply, shared.model_name) yield formatted_outputs(reply, shared.model_name)
@ -244,7 +243,7 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
new_tokens = len(output) - len(input_ids[0]) new_tokens = len(output) - len(input_ids[0])
reply = decode(output[-new_tokens:]) reply = decode(output[-new_tokens:])
if not shared.is_chat(): if not shared.is_chat():
reply = original_question + apply_extensions(reply, "output") reply = original_question + apply_extensions(reply, 'output')
if output[-1] in eos_token_ids: if output[-1] in eos_token_ids:
break break
@ -262,7 +261,7 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
new_tokens = len(output) - len(original_input_ids[0]) new_tokens = len(output) - len(original_input_ids[0])
reply = decode(output[-new_tokens:]) reply = decode(output[-new_tokens:])
if not shared.is_chat(): if not shared.is_chat():
reply = original_question + apply_extensions(reply, "output") reply = original_question + apply_extensions(reply, 'output')
if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)): if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)):
break break
@ -271,10 +270,10 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
input_ids = np.reshape(output, (1, output.shape[0])) input_ids = np.reshape(output, (1, output.shape[0]))
if shared.soft_prompt: if shared.soft_prompt:
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids) inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
generate_params.update({"inputs_embeds": inputs_embeds}) generate_params.update({'inputs_embeds': inputs_embeds})
generate_params.update({"inputs": filler_input_ids}) generate_params.update({'inputs': filler_input_ids})
else: else:
generate_params.update({"inputs": input_ids}) generate_params.update({'inputs': input_ids})
yield formatted_outputs(reply, shared.model_name) yield formatted_outputs(reply, shared.model_name)
@ -284,5 +283,5 @@ def generate_reply(question, generate_state, eos_token=None, stopping_strings=[]
t1 = time.time() t1 = time.time()
original_tokens = len(original_input_ids[0]) original_tokens = len(original_input_ids[0])
new_tokens = len(output) - original_tokens new_tokens = len(output) - original_tokens
print(f"Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens})") print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens})')
return return