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Merge pull request #192 from xanthousm/main

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Add text generation stream status to shared module, use for better TTS with auto-play
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oobabooga 2023-03-12 13:40:16 -03:00 • committed by GitHub
commit d8bea766d7
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3 changed files with 132 additions and 11 deletions
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1
modules/shared.py
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@ -11,6 +11,7 @@ is_RWKV = False
history = {'internal': [], 'visible': []}
character = 'None'
stop_everything = False
still_streaming = False
# UI elements (buttons, sliders, HTML, etc)
gradio = {}

12
modules/text_generation.py
View file

@ -187,6 +187,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
def generate_with_streaming(**kwargs):
return Iteratorize(generate_with_callback, kwargs, callback=None)
shared.still_streaming = True
yield formatted_outputs(original_question, shared.model_name)
with eval(f"generate_with_streaming({', '.join(generate_params)})") as generator:
for output in generator:
@ -196,13 +197,17 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
if not (shared.args.chat or shared.args.cai_chat):
reply = original_question + apply_extensions(reply[len(question):], "output")
yield formatted_outputs(reply, shared.model_name)
if output[-1] == n:
break
yield formatted_outputs(reply, shared.model_name)
shared.still_streaming = False
yield formatted_outputs(reply, shared.model_name)
# Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
else:
shared.still_streaming = True
for i in range(max_new_tokens//8+1):
clear_torch_cache()
with torch.no_grad():
@ -213,15 +218,18 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
if not (shared.args.chat or shared.args.cai_chat):
reply = original_question + apply_extensions(reply[len(question):], "output")
yield formatted_outputs(reply, shared.model_name)
if np.count_nonzero(input_ids[0] == n) < np.count_nonzero(output == n):
break
yield formatted_outputs(reply, shared.model_name)
input_ids = np.reshape(output, (1, output.shape[0]))
if shared.soft_prompt:
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
shared.still_streaming = False
yield formatted_outputs(reply, shared.model_name)
finally:
t1 = time.time()
print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(original_input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(original_input_ids[0])} tokens)")