Refactor chat functions (#2003)
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oobabooga
GitHub
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4e9da22c58
commit
638c6a65a2
8 changed files with 138 additions and 157 deletions
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@ -101,10 +101,10 @@ def fix_galactica(s):
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return s
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def get_reply_from_output_ids(output_ids, input_ids, original_question, state):
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def get_reply_from_output_ids(output_ids, input_ids, original_question, state, is_chat=False):
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if shared.model_type == 'HF_seq2seq':
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reply = decode(output_ids, state['skip_special_tokens'])
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if not shared.is_chat():
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if not is_chat:
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reply = apply_extensions('output', reply)
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else:
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new_tokens = len(output_ids) - len(input_ids[0])
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@ -114,24 +114,21 @@ def get_reply_from_output_ids(output_ids, input_ids, original_question, state):
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if len(original_question) > 0 and original_question[-1] not in [' ', '\n']:
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reply = ' ' + reply
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if not shared.is_chat():
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if not is_chat:
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reply = original_question + apply_extensions('output', reply)
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return reply
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def formatted_outputs(reply, model_name):
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if not shared.is_chat():
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if shared.model_type == 'galactica':
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reply = fix_galactica(reply)
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return reply, reply, generate_basic_html(reply)
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elif shared.model_type == 'gpt4chan':
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reply = fix_gpt4chan(reply)
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return reply, 'Only applicable for GALACTICA models.', generate_4chan_html(reply)
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else:
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return reply, 'Only applicable for GALACTICA models.', generate_basic_html(reply)
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if shared.model_type == 'galactica':
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reply = fix_galactica(reply)
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return reply, reply, generate_basic_html(reply)
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elif shared.model_type == 'gpt4chan':
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reply = fix_gpt4chan(reply)
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return reply, 'Only applicable for GALACTICA models.', generate_4chan_html(reply)
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else:
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return reply
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return reply, 'Only applicable for GALACTICA models.', generate_basic_html(reply)
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def set_manual_seed(seed):
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@ -150,13 +147,18 @@ 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=None):
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def generate_reply_wrapper(question, state, eos_token=None, stopping_strings=None):
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for reply in generate_reply(question, state, eos_token, stopping_strings, is_chat=False):
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yield formatted_outputs(reply, shared.model_name)
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def generate_reply(question, state, eos_token=None, stopping_strings=None, is_chat=False):
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state = apply_extensions('state', state)
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generate_func = apply_extensions('custom_generate_reply')
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if generate_func is None:
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if shared.model_name == 'None' or shared.model is None:
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logging.error("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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yield question
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return
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if shared.model_type in ['rwkv', 'llamacpp']:
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@ -168,7 +170,7 @@ def generate_reply(question, state, eos_token=None, stopping_strings=None):
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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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if not is_chat:
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question = apply_extensions('input', question)
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if shared.args.verbose:
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@ -177,11 +179,11 @@ def generate_reply(question, state, eos_token=None, stopping_strings=None):
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shared.stop_everything = False
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clear_torch_cache()
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seed = set_manual_seed(state['seed'])
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for reply in generate_func(question, original_question, seed, state, eos_token, stopping_strings):
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yield formatted_outputs(reply, shared.model_name)
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for reply in generate_func(question, original_question, seed, state, eos_token, stopping_strings, is_chat=is_chat):
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yield reply
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def generate_reply_HF(question, original_question, seed, state, eos_token=None, stopping_strings=None):
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def generate_reply_HF(question, original_question, seed, state, eos_token=None, stopping_strings=None, is_chat=False):
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generate_params = {}
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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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@ -233,7 +235,7 @@ def generate_reply_HF(question, original_question, seed, state, eos_token=None,
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t0 = time.time()
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try:
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if not shared.is_chat() and shared.model_type != 'HF_seq2seq':
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if not is_chat and shared.model_type != 'HF_seq2seq':
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yield original_question
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# Generate the entire reply at once.
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@ -246,7 +248,7 @@ def generate_reply_HF(question, original_question, seed, state, eos_token=None,
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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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yield get_reply_from_output_ids(output, input_ids, original_question, state)
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yield get_reply_from_output_ids(output, input_ids, original_question, state, is_chat=is_chat)
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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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@ -266,7 +268,7 @@ def generate_reply_HF(question, original_question, seed, state, eos_token=None,
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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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yield get_reply_from_output_ids(output, input_ids, original_question, state)
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yield get_reply_from_output_ids(output, input_ids, original_question, state, is_chat=is_chat)
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if output[-1] in eos_token_ids:
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break
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@ -280,7 +282,7 @@ def generate_reply_HF(question, original_question, seed, state, eos_token=None,
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return
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def generate_reply_custom(question, original_question, seed, state, eos_token=None, stopping_strings=None):
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def generate_reply_custom(question, original_question, seed, state, eos_token=None, stopping_strings=None, is_chat=False):
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seed = set_manual_seed(state['seed'])
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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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@ -288,13 +290,13 @@ def generate_reply_custom(question, original_question, seed, state, eos_token=No
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t0 = time.time()
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try:
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if not shared.is_chat():
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if not is_chat:
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yield question
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if not state['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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if not is_chat:
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reply = original_question + apply_extensions('output', reply)
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yield reply
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@ -302,7 +304,7 @@ def generate_reply_custom(question, original_question, seed, state, eos_token=No
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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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if not is_chat:
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reply = original_question + apply_extensions('output', reply)
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yield reply
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@ -317,7 +319,7 @@ def generate_reply_custom(question, original_question, seed, state, eos_token=No
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return
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def generate_reply_flexgen(question, original_question, seed, state, eos_token=None, stopping_strings=None):
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def generate_reply_flexgen(question, original_question, seed, state, eos_token=None, stopping_strings=None, is_chat=False):
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generate_params = {}
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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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@ -346,7 +348,7 @@ def generate_reply_flexgen(question, original_question, seed, state, eos_token=N
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t0 = time.time()
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try:
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if not shared.is_chat():
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if not is_chat:
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yield question
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# Generate the entire reply at once.
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@ -354,7 +356,7 @@ def generate_reply_flexgen(question, original_question, seed, state, eos_token=N
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with torch.no_grad():
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output = shared.model.generate(**generate_params)[0]
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yield get_reply_from_output_ids(output, input_ids, original_question, state)
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yield get_reply_from_output_ids(output, input_ids, original_question, state, is_chat=is_chat)
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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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