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https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-22 16:17:57 +01:00
Refactor text-generation.py a bit
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oobabooga
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@ -113,9 +113,11 @@ def set_manual_seed(seed):
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seed = int(seed)
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if seed == -1:
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seed = random.randint(1, 2**31)
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torch.manual_seed(seed)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(seed)
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return seed
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@ -123,8 +125,41 @@ def stop_everything_event():
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shared.stop_everything = True
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def generate_reply(question, state, eos_token=None, stopping_strings=[]):
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def get_generate_params(state):
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generate_params = {}
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# Models that are not on transformers
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if shared.model_type in ['rwkv', 'llamacpp']:
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generate_params['token_count'] = state['max_new_tokens']
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for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
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generate_params[k] = state[k]
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else:
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# FlexGen
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if shared.args.flexgen:
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for k in ['max_new_tokens', 'do_sample', 'temperature']:
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generate_params[k] = state[k]
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if not shared.args.no_stream:
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generate_params['max_new_tokens'] = 8
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# transformers
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else:
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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']:
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generate_params[k] = state[k]
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if state['ban_eos_token']:
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generate_params['suppress_tokens'] = [shared.tokenizer.eos_token_id]
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if shared.args.no_cache:
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generate_params.update({'use_cache': False})
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if shared.args.deepspeed:
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generate_params.update({'synced_gpus': True})
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return generate_params
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def generate_reply(question, state, eos_token=None, stopping_strings=[]):
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if shared.model_name == 'None' or shared.model is None:
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print("No model is loaded! Select one in the Model tab.")
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yield formatted_outputs(question, shared.model_name)
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@ -133,40 +168,37 @@ def generate_reply(question, state, eos_token=None, stopping_strings=[]):
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clear_torch_cache()
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seed = set_manual_seed(state['seed'])
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shared.stop_everything = False
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generate_params = {}
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generate_params = get_generate_params(state)
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t0 = time.time()
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# Preparing the input
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original_question = question
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if not shared.is_chat():
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question = apply_extensions('input', question)
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# These models are not part of Hugging Face, so we handle them
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# separately and terminate the function call earlier
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# If the model is not on transformers, handle it separately and end this
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# function call earlier.
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if shared.model_type in ['rwkv', 'llamacpp']:
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if shared.args.verbose:
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print(f'\n\n{question}\n--------------------\n')
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for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']:
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generate_params[k] = state[k]
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generate_params['token_count'] = state['max_new_tokens']
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try:
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if shared.args.no_stream:
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reply = shared.model.generate(context=question, **generate_params)
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output = original_question + reply
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if not shared.is_chat():
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reply = original_question + apply_extensions('output', reply)
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yield formatted_outputs(reply, shared.model_name)
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else:
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if not shared.is_chat():
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yield formatted_outputs(question, shared.model_name)
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# RWKV has proper streaming, which is very nice.
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# No need to generate 8 tokens at a time.
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for reply in shared.model.generate_with_streaming(context=question, **generate_params):
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output = original_question + reply
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if not shared.is_chat():
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reply = original_question + apply_extensions('output', reply)
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yield formatted_outputs(reply, shared.model_name)
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except Exception:
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@ -178,18 +210,19 @@ def generate_reply(question, state, eos_token=None, stopping_strings=[]):
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print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
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return
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# Encode the input
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input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state))
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output = input_ids[0]
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cuda = not any((shared.args.cpu, shared.args.deepspeed, shared.args.flexgen))
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if shared.args.verbose:
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print(f'\n\n{decode(input_ids[0], state["skip_special_tokens"])}\n--------------------\n')
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cuda = not any((shared.args.cpu, shared.args.deepspeed, shared.args.flexgen))
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# Find the eos tokens
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eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
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if eos_token is not None:
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eos_token_ids.append(int(encode(eos_token)[0][-1]))
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# Handling the stopping strings
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# Create the StoppingCriteriaList with the stopping strings
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stopping_criteria_list = transformers.StoppingCriteriaList()
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for st in (stopping_strings, ast.literal_eval(f"[{state['custom_stopping_strings']}]")):
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if type(st) is list and len(st) > 0:
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@ -197,24 +230,14 @@ def generate_reply(question, state, eos_token=None, stopping_strings=[]):
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stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=sentinel_token_ids, starting_idx=len(input_ids[0])))
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break
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if not shared.args.flexgen:
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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']:
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generate_params[k] = state[k]
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# Update generate_params with the eos token and the stopping strings
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if shared.args.flexgen:
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generate_params['stop'] = eos_token_ids[-1]
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else:
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generate_params['eos_token_id'] = eos_token_ids
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generate_params['stopping_criteria'] = stopping_criteria_list
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if state['ban_eos_token']:
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generate_params['suppress_tokens'] = [shared.tokenizer.eos_token_id]
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else:
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for k in ['max_new_tokens', 'do_sample', 'temperature']:
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generate_params[k] = state[k]
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generate_params['stop'] = eos_token_ids[-1]
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if not shared.args.no_stream:
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generate_params['max_new_tokens'] = 8
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if shared.args.no_cache:
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generate_params.update({'use_cache': False})
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if shared.args.deepspeed:
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generate_params.update({'synced_gpus': True})
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# Add the encoded tokens to generate_params
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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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question, filler_input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, filler_input_ids, inputs_embeds)
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