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https://github.com/oobabooga/text-generation-webui.git
synced 2024-12-23 21:18:00 +01:00
Various fixes in chat mode
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oobabooga
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0bd5430988
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b0e8cb8c88
@ -115,14 +115,18 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
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visible_text = visible_text.replace('\n', '<br>')
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text = apply_extensions(text, "input")
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if custom_generate_chat_prompt is None:
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prompt = generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size)
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else:
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prompt = custom_generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size)
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# Generate
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reply = ''
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for i in range(chat_generation_attempts):
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# The prompt needs to be generated here because, as the reply
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# grows, it may become necessary to remove more old messages to
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# fit into the 2048 tokens window.
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if custom_generate_chat_prompt is None:
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prompt = generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size-len(encode(' '+reply)[0]))
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else:
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prompt = custom_generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size-len(encode(' '+reply)[0]))
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for reply in generate_reply(f"{prompt}{' ' if len(reply) > 0 else ''}{reply}", 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}:"):
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# Extracting the reply
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@ -156,10 +160,10 @@ def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typ
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if 'pygmalion' in shared.model_name.lower():
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name1 = "You"
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prompt = generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size, impersonate=True)
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reply = ''
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for i in range(chat_generation_attempts):
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prompt = generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size-len(encode(' '+reply)[0]), impersonate=True)
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for reply in generate_reply(prompt+reply, 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}:"):
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reply, next_character_found, substring_found = extract_message_from_reply(prompt, reply, name1, name2, check, impersonate=True)
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if not substring_found:
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@ -159,35 +159,53 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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else:
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generate_params.insert(0, "inputs=input_ids")
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# Generate the entire reply at once.
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if shared.args.no_stream:
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with torch.no_grad():
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output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
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if shared.soft_prompt:
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output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
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reply = decode(output)
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if not (shared.args.chat or shared.args.cai_chat):
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reply = original_question + apply_extensions(reply[len(question):], "output")
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yield formatted_outputs(reply, shared.model_name)
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# Stream the reply 1 token at a time.
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# This is based on the trick of using 'stopping_criteria' to create an iterator.
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elif not shared.args.flexgen:
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def generate_with_callback(callback=None, **kwargs):
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kwargs['stopping_criteria'].append(Stream(callback_func=callback))
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clear_torch_cache()
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try:
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# Generate the entire reply at once.
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if shared.args.no_stream:
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with torch.no_grad():
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shared.model.generate(**kwargs)
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output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
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if shared.soft_prompt:
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output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
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def generate_with_streaming(**kwargs):
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return Iteratorize(generate_with_callback, kwargs, callback=None)
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reply = decode(output)
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if not (shared.args.chat or shared.args.cai_chat):
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reply = original_question + apply_extensions(reply[len(question):], "output")
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yield formatted_outputs(original_question, shared.model_name)
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with eval(f"generate_with_streaming({', '.join(generate_params)})") as generator:
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for output in generator:
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yield formatted_outputs(reply, shared.model_name)
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# Stream the reply 1 token at a time.
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# This is based on the trick of using 'stopping_criteria' to create an iterator.
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elif not shared.args.flexgen:
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def generate_with_callback(callback=None, **kwargs):
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kwargs['stopping_criteria'].append(Stream(callback_func=callback))
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clear_torch_cache()
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with torch.no_grad():
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shared.model.generate(**kwargs)
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def generate_with_streaming(**kwargs):
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return Iteratorize(generate_with_callback, kwargs, callback=None)
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yield formatted_outputs(original_question, shared.model_name)
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with eval(f"generate_with_streaming({', '.join(generate_params)})") as generator:
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for output in generator:
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if shared.soft_prompt:
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output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
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reply = decode(output)
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if not (shared.args.chat or shared.args.cai_chat):
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reply = original_question + apply_extensions(reply[len(question):], "output")
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yield formatted_outputs(reply, shared.model_name)
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if output[-1] == n:
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break
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# Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
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else:
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for i in range(max_new_tokens//8+1):
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clear_torch_cache()
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with torch.no_grad():
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output = eval(f"shared.model.generate({', '.join(generate_params)})")[0]
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if shared.soft_prompt:
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output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
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reply = decode(output)
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@ -196,30 +214,14 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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reply = original_question + apply_extensions(reply[len(question):], "output")
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yield formatted_outputs(reply, shared.model_name)
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if output[-1] == n:
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if np.count_nonzero(input_ids[0] == n) < np.count_nonzero(output == n):
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break
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# Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
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else:
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for i in range(max_new_tokens//8+1):
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clear_torch_cache()
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with torch.no_grad():
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output = eval(f"shared.model.generate({', '.join(generate_params)})")[0]
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if shared.soft_prompt:
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output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
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reply = decode(output)
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input_ids = np.reshape(output, (1, output.shape[0]))
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if shared.soft_prompt:
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inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
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if not (shared.args.chat or shared.args.cai_chat):
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reply = original_question + apply_extensions(reply[len(question):], "output")
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yield formatted_outputs(reply, shared.model_name)
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if np.count_nonzero(input_ids[0] == n) < np.count_nonzero(output == n):
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break
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input_ids = np.reshape(output, (1, output.shape[0]))
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if shared.soft_prompt:
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inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
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t1 = time.time()
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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)")
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return
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finally:
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t1 = time.time()
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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)")
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return
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