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Various fixes in chat mode

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oobabooga 2023-03-12 02:31:45 -03:00
parent 0bd5430988
commit b0e8cb8c88
2 changed files with 62 additions and 56 deletions
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102
modules/text_generation.py
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@ -159,35 +159,53 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
else:
generate_params.insert(0, "inputs=input_ids")
# Generate the entire reply at once.
if shared.args.no_stream:
with torch.no_grad():
output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
reply = decode(output)
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)
# Stream the reply 1 token at a time.
# This is based on the trick of using 'stopping_criteria' to create an iterator.
elif not shared.args.flexgen:
def generate_with_callback(callback=None, **kwargs):
kwargs['stopping_criteria'].append(Stream(callback_func=callback))
clear_torch_cache()
try:
# Generate the entire reply at once.
if shared.args.no_stream:
with torch.no_grad():
shared.model.generate(**kwargs)
output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
def generate_with_streaming(**kwargs):
return Iteratorize(generate_with_callback, kwargs, callback=None)
reply = decode(output)
if not (shared.args.chat or shared.args.cai_chat):
reply = original_question + apply_extensions(reply[len(question):], "output")
yield formatted_outputs(original_question, shared.model_name)
with eval(f"generate_with_streaming({', '.join(generate_params)})") as generator:
for output in generator:
yield formatted_outputs(reply, shared.model_name)
# Stream the reply 1 token at a time.
# This is based on the trick of using 'stopping_criteria' to create an iterator.
elif not shared.args.flexgen:
def generate_with_callback(callback=None, **kwargs):
kwargs['stopping_criteria'].append(Stream(callback_func=callback))
clear_torch_cache()
with torch.no_grad():
shared.model.generate(**kwargs)
def generate_with_streaming(**kwargs):
return Iteratorize(generate_with_callback, kwargs, callback=None)
yield formatted_outputs(original_question, shared.model_name)
with eval(f"generate_with_streaming({', '.join(generate_params)})") as generator:
for output in generator:
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
reply = decode(output)
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
# Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
else:
for i in range(max_new_tokens//8+1):
clear_torch_cache()
with torch.no_grad():
output = eval(f"shared.model.generate({', '.join(generate_params)})")[0]
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
reply = decode(output)
@ -196,30 +214,14 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
reply = original_question + apply_extensions(reply[len(question):], "output")
yield formatted_outputs(reply, shared.model_name)
if output[-1] == n:
if np.count_nonzero(input_ids[0] == n) < np.count_nonzero(output == n):
break
# Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
else:
for i in range(max_new_tokens//8+1):
clear_torch_cache()
with torch.no_grad():
output = eval(f"shared.model.generate({', '.join(generate_params)})")[0]
if shared.soft_prompt:
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
reply = decode(output)
input_ids = np.reshape(output, (1, output.shape[0]))
if shared.soft_prompt:
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
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
input_ids = np.reshape(output, (1, output.shape[0]))
if shared.soft_prompt:
inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
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)")
return
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)")
return