text-generation-webui/modules/text_generation.py

import ast
import copy
import random
import re
import time
import traceback

import numpy as np
import torch
import transformers
from transformers import LogitsProcessorList

import modules.shared as shared
from modules.callbacks import (
    Iteratorize,
    Stream,
    _StopEverythingStoppingCriteria
)
from modules.extensions import apply_extensions
from modules.html_generator import generate_4chan_html, generate_basic_html
from modules.logging_colors import logger
from modules.models import clear_torch_cache, local_rank


def generate_reply(*args, **kwargs):
    shared.generation_lock.acquire()
    try:
        for result in _generate_reply(*args, **kwargs):
            yield result
    finally:
        shared.generation_lock.release()


def get_max_prompt_length(state):
    return state['truncation_length'] - state['max_new_tokens']


def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_length=None):
    if shared.model.__class__.__name__ in ['LlamaCppModel', 'RWKVModel']:
        input_ids = shared.tokenizer.encode(str(prompt))
        input_ids = np.array(input_ids).reshape(1, len(input_ids))
        return input_ids
    else:
        input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', add_special_tokens=add_special_tokens)

        # This is a hack for making replies more creative.
        if not add_bos_token and input_ids[0][0] == shared.tokenizer.bos_token_id:
            input_ids = input_ids[:, 1:]

    # Handling truncation
    if truncation_length is not None:
        input_ids = input_ids[:, -truncation_length:]

    if shared.model.__class__.__name__ in ['LlamaCppModel', 'RWKVModel', 'ExllamaModel'] or shared.args.cpu:
        return input_ids
    elif shared.args.flexgen:
        return input_ids.numpy()
    elif shared.args.deepspeed:
        return input_ids.to(device=local_rank)
    elif torch.backends.mps.is_available():
        device = torch.device('mps')
        return input_ids.to(device)
    else:
        return input_ids.cuda()


def get_encoded_length(prompt):
    length_after_extensions = apply_extensions('tokenized_length', prompt)
    if length_after_extensions is not None:
        return length_after_extensions

    return len(encode(prompt)[0])


def decode(output_ids, skip_special_tokens=True):
    return shared.tokenizer.decode(output_ids, skip_special_tokens)


# Removes empty replies from gpt4chan outputs
def fix_gpt4chan(s):
    for i in range(10):
        s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s)
        s = re.sub("--- [0-9]*\n *\n---", "---", s)
        s = re.sub("--- [0-9]*\n\n\n---", "---", s)

    return s


# Fix the LaTeX equations in galactica
def fix_galactica(s):
    s = s.replace(r'\[', r'$')
    s = s.replace(r'\]', r'$')
    s = s.replace(r'\(', r'$')
    s = s.replace(r'\)', r'$')
    s = s.replace(r'$$', r'$')
    s = re.sub(r'\n', r'\n\n', s)
    s = re.sub(r"\n{3,}", "\n\n", s)
    return s


def get_reply_from_output_ids(output_ids, input_ids, original_question, state, is_chat=False):
    if shared.is_seq2seq:
        reply = decode(output_ids, state['skip_special_tokens'])
    else:
        new_tokens = len(output_ids) - len(input_ids[0])
        reply = decode(output_ids[-new_tokens:], state['skip_special_tokens'])
        # Prevent LlamaTokenizer from skipping a space
        if type(shared.tokenizer) in [transformers.LlamaTokenizer, transformers.LlamaTokenizerFast] and len(output_ids) > 0:
            if shared.tokenizer.convert_ids_to_tokens(int(output_ids[-new_tokens])).startswith('▁'):
                reply = ' ' + reply

    return reply


def formatted_outputs(reply, model_name):
    if any(s in model_name for s in ['gpt-4chan', 'gpt4chan']):
        reply = fix_gpt4chan(reply)
        return reply, generate_4chan_html(reply)
    else:
        return reply, generate_basic_html(reply)


def set_manual_seed(seed):
    seed = int(seed)
    if seed == -1:
        seed = random.randint(1, 2**31)

    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)

    return seed


def stop_everything_event():
    shared.stop_everything = True


def generate_reply_wrapper(question, state, stopping_strings=None):
    reply = question if not shared.is_seq2seq else ''
    yield formatted_outputs(reply, shared.model_name)

    for reply in generate_reply(question, state, stopping_strings, is_chat=False):
        if not shared.is_seq2seq:
            reply = question + reply

        yield formatted_outputs(reply, shared.model_name)


def apply_stopping_strings(reply, all_stop_strings):
    stop_found = False
    for string in all_stop_strings:
        idx = reply.find(string)
        if idx != -1:
            reply = reply[:idx]
            stop_found = True
            break

    if not stop_found:
        # If something like "\nYo" is generated just before "\nYou:"
        # is completed, trim it
        for string in all_stop_strings:
            for j in range(len(string) - 1, 0, -1):
                if reply[-j:] == string[:j]:
                    reply = reply[:-j]
                    break
            else:
                continue

            break

    return reply, stop_found


def _generate_reply(question, state, stopping_strings=None, is_chat=False):
    generate_func = apply_extensions('custom_generate_reply')
    if generate_func is None:
        if shared.model_name == 'None' or shared.model is None:
            logger.error("No model is loaded! Select one in the Model tab.")
            yield ''
            return

        if shared.model.__class__.__name__ in ['LlamaCppModel', 'RWKVModel', 'ExllamaModel']:
            generate_func = generate_reply_custom
        elif shared.args.flexgen:
            generate_func = generate_reply_flexgen
        else:
            generate_func = generate_reply_HF

    # Preparing the input
    original_question = question
    if not is_chat:
        state = apply_extensions('state', state)
        question = apply_extensions('input', question, state)

    # Finding the stopping strings
    all_stop_strings = []
    for st in (stopping_strings, ast.literal_eval(f"[{state['custom_stopping_strings']}]")):
        if type(st) is list and len(st) > 0:
            all_stop_strings += st

    if shared.args.verbose:
        print(f'\n\n{question}\n--------------------\n')

    shared.stop_everything = False
    clear_torch_cache()
    seed = set_manual_seed(state['seed'])
    last_update = -1
    reply = ''
    is_stream = state['stream']
    if len(all_stop_strings) > 0 and not state['stream']:
        state = copy.deepcopy(state)
        state['stream'] = True

    for reply in generate_func(question, original_question, seed, state, stopping_strings, is_chat=is_chat):
        reply, stop_found = apply_stopping_strings(reply, all_stop_strings)
        if is_stream:
            cur_time = time.time()
            if cur_time - last_update > 0.041666666666666664:  # Limit streaming to 24 fps
                last_update = cur_time
                yield reply

        if stop_found:
            break

    if not is_chat:
        reply = apply_extensions('output', reply, state)

    yield reply


def generate_reply_HF(question, original_question, seed, state, stopping_strings=None, is_chat=False):
    generate_params = {}
    for k in ['max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'tfs', 'top_a', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta']:
        generate_params[k] = state[k]

    for k in ['epsilon_cutoff', 'eta_cutoff']:
        if state[k] > 0:
            generate_params[k] = state[k] * 1e-4

    if state['ban_eos_token']:
        generate_params['suppress_tokens'] = [shared.tokenizer.eos_token_id]

    if shared.args.no_cache:
        generate_params.update({'use_cache': False})

    if shared.args.deepspeed:
        generate_params.update({'synced_gpus': True})

    # Encode the input
    input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state))
    output = input_ids[0]
    cuda = not any((shared.args.cpu, shared.args.deepspeed))

    # Add the encoded tokens to generate_params
    question, input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, input_ids, None)
    original_input_ids = input_ids
    generate_params.update({'inputs': input_ids})
    if inputs_embeds is not None:
        generate_params.update({'inputs_embeds': inputs_embeds})

    # Stopping criteria / eos token
    eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
    generate_params['eos_token_id'] = eos_token_ids
    generate_params['stopping_criteria'] = transformers.StoppingCriteriaList()
    generate_params['stopping_criteria'].append(_StopEverythingStoppingCriteria())

    processor = state.get('logits_processor', LogitsProcessorList([]))
    # In case folks just pass in a processor by itself.
    if type(processor) != LogitsProcessorList:
        processor = LogitsProcessorList([processor])
    apply_extensions('logits_processor', processor, input_ids)
    generate_params['logits_processor'] = processor

    t0 = time.time()
    try:
        if not is_chat and not shared.is_seq2seq:
            yield ''

        # Generate the entire reply at once.
        if not state['stream']:
            with torch.no_grad():
                output = shared.model.generate(**generate_params)[0]
                if cuda:
                    output = output.cuda()

            yield get_reply_from_output_ids(output, input_ids, original_question, state, is_chat=is_chat)

        # Stream the reply 1 token at a time.
        # This is based on the trick of using 'stopping_criteria' to create an iterator.
        else:

            def generate_with_callback(callback=None, *args, **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)

            with generate_with_streaming(**generate_params) as generator:
                for output in generator:
                    yield get_reply_from_output_ids(output, input_ids, original_question, state, is_chat=is_chat)
                    if output[-1] in eos_token_ids:
                        break

    except Exception:
        traceback.print_exc()
    finally:
        t1 = time.time()
        original_tokens = len(original_input_ids[0])
        new_tokens = len(output) - (original_tokens if not shared.is_seq2seq else 0)
        print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
        return


def generate_reply_custom(question, original_question, seed, state, stopping_strings=None, is_chat=False):
    seed = set_manual_seed(state['seed'])

    t0 = time.time()
    reply = ''
    try:
        if not is_chat:
            yield ''

        if not state['stream']:
            reply = shared.model.generate(question, state)
            yield reply
        else:
            for reply in shared.model.generate_with_streaming(question, state):
                yield reply

    except Exception:
        traceback.print_exc()
    finally:
        t1 = time.time()
        original_tokens = len(encode(original_question)[0])
        new_tokens = len(encode(original_question + reply)[0]) - original_tokens
        print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
        return


def generate_reply_flexgen(question, original_question, seed, state, stopping_strings=None, is_chat=False):
    generate_params = {}
    for k in ['max_new_tokens', 'do_sample', 'temperature']:
        generate_params[k] = state[k]

    if state['stream']:
        generate_params['max_new_tokens'] = 8

    # Encode the input
    input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state))
    output = input_ids[0]

    # Find the eos tokens
    eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
    if not state['ban_eos_token']:
        generate_params['stop'] = eos_token_ids[-1]

    # Add the encoded tokens to generate_params
    question, input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, input_ids, None)
    original_input_ids = input_ids
    generate_params.update({'inputs': input_ids})
    if inputs_embeds is not None:
        generate_params.update({'inputs_embeds': inputs_embeds})

    t0 = time.time()
    try:
        if not is_chat:
            yield ''

        # Generate the entire reply at once.
        if not state['stream']:
            with torch.no_grad():
                output = shared.model.generate(**generate_params)[0]

            yield get_reply_from_output_ids(output, input_ids, original_question, state, is_chat=is_chat)

        # Stream the output naively for FlexGen since it doesn't support 'stopping_criteria'
        else:
            for i in range(state['max_new_tokens'] // 8 + 1):
                if shared.stop_everything:
                    break

                clear_torch_cache()
                with torch.no_grad():
                    output = shared.model.generate(**generate_params)[0]

                if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)):
                    break

                yield get_reply_from_output_ids(output, original_input_ids, original_question, state)
                input_ids = np.reshape(output, (1, output.shape[0]))
                generate_params.update({'inputs': input_ids})

    except Exception:
        traceback.print_exc()
    finally:
        t1 = time.time()
        original_tokens = len(original_input_ids[0])
        new_tokens = len(output) - (original_tokens if not shared.is_seq2seq else 0)
        print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})')
        return