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Use state as function param

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This commit is contained in:
oobabooga
2023-04-05 17:22:05 -03:00
parent 19b516b11b
commit 613996dd01
3 changed files with 71 additions and 57 deletions
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76
modules/text_generation.py
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@@ -102,10 +102,13 @@ def set_manual_seed(seed):
def stop_everything_event():
shared.stop_everything = True
def generate_reply(question, 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, seed, eos_token=None, stopping_strings=[]):
def generate_reply(question, max_new_tokens, generation_params, seed, eos_token=None, stopping_strings=[]):
print(generation_params)
print('---------------')
clear_torch_cache()
set_manual_seed(seed)
shared.stop_everything = False
updated_params = {}
t0 = time.time()
original_question = question
@@ -117,9 +120,14 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
# 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)):
for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
updated_params[k] = generation_params[k]
updated_params["token_count"] = generation_params["max_new_tokens"]
try:
if shared.args.no_stream:
reply = shared.model.generate(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty)
reply = shared.model.generate(context=question, **updated_params)
output = original_question+reply
if not shared.is_chat():
reply = original_question + apply_extensions(reply, "output")
@@ -130,7 +138,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
# 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, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty):
for reply in shared.model.generate_with_streaming(context=question, **updated_params):
output = original_question+reply
if not shared.is_chat():
reply = original_question + apply_extensions(reply, "output")
@@ -158,49 +166,39 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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])))
generate_params = {}
updated_params["max_new_tokens"] = max_new_tokens
if not shared.args.flexgen:
generate_params.update({
"max_new_tokens": max_new_tokens,
"eos_token_id": eos_token_ids,
"stopping_criteria": stopping_criteria_list,
"do_sample": do_sample,
"temperature": temperature,
"top_p": top_p,
"typical_p": typical_p,
"repetition_penalty": repetition_penalty,
"encoder_repetition_penalty": encoder_repetition_penalty,
"top_k": top_k,
"min_length": min_length if shared.args.no_stream else 0,
"no_repeat_ngram_size": no_repeat_ngram_size,
"num_beams": num_beams,
"penalty_alpha": penalty_alpha,
"length_penalty": length_penalty,
"early_stopping": early_stopping,
})
updated_params["eos_token_id"] = eos_token_ids
updated_params["stopping_criteria"] = stopping_criteria_list
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"]:
updated_params[k] = generation_params[k]
if shared.args.no_stream:
updated_params["min_length"] = 0
else:
generate_params.update({
"max_new_tokens": max_new_tokens if shared.args.no_stream else 8,
"do_sample": do_sample,
"temperature": temperature,
"stop": eos_token_ids[-1],
})
for k in ["do_sample", "temperature"]:
updated_params[k] = generation_params[k]
updated_params["stop"] = generation_params["eos_token_ids"][-1]
if not shared.args.no_stream:
updated_params["max_new_tokens"] = 8
print(updated_params)
if shared.args.no_cache:
generate_params.update({"use_cache": False})
updated_params.update({"use_cache": False})
if shared.args.deepspeed:
generate_params.update({"synced_gpus": True})
updated_params.update({"synced_gpus": True})
if shared.soft_prompt:
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
generate_params.update({"inputs_embeds": inputs_embeds})
generate_params.update({"inputs": filler_input_ids})
updated_params.update({"inputs_embeds": inputs_embeds})
updated_params.update({"inputs": filler_input_ids})
else:
generate_params.update({"inputs": input_ids})
updated_params.update({"inputs": input_ids})
try:
# Generate the entire reply at once.
if shared.args.no_stream:
with torch.no_grad():
output = shared.model.generate(**generate_params)[0]
output = shared.model.generate(**updated_params)[0]
if cuda:
output = output.cuda()
if shared.soft_prompt:
@@ -228,7 +226,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
if not shared.is_chat():
yield formatted_outputs(original_question, shared.model_name)
with generate_with_streaming(**generate_params) as generator:
with generate_with_streaming(**updated_params) as generator:
for output in generator:
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
@@ -247,7 +245,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
for i in range(max_new_tokens//8+1):
clear_torch_cache()
with torch.no_grad():
output = shared.model.generate(**generate_params)[0]
output = shared.model.generate(**updated_params)[0]
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
@@ -263,10 +261,10 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
input_ids = np.reshape(output, (1, output.shape[0]))
if shared.soft_prompt:
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
generate_params.update({"inputs_embeds": inputs_embeds})
generate_params.update({"inputs": filler_input_ids})
updated_params.update({"inputs_embeds": inputs_embeds})
updated_params.update({"inputs": filler_input_ids})
else:
generate_params.update({"inputs": input_ids})
updated_params.update({"inputs": input_ids})
yield formatted_outputs(reply, shared.model_name)