421e7cee80
* initially add text_generation_webui support * add env requirements install * add necessary dependencies * update for starting webui * update shared and noted to place models * update heading of part3 * meet comments * add copyright license * remove extensions * convert tutorial to windows side * add warm-up to optimize performance
138 lines
4 KiB
Python
138 lines
4 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is adapted from
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# https://github.com/oobabooga/text-generation-webui/blob/main/modules/presets.py
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import functools
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import random
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from pathlib import Path
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import yaml
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from modules import shared
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from modules.loaders import loaders_samplers
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def default_preset():
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return {
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'temperature': 1,
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'temperature_last': False,
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'dynamic_temperature': False,
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'dynamic_temperature_low': 0.1,
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'top_p': 1,
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'min_p': 0,
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'top_k': 0,
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'repetition_penalty': 1,
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'presence_penalty': 0,
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'frequency_penalty': 0,
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'repetition_penalty_range': 1024,
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'typical_p': 1,
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'tfs': 1,
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'top_a': 0,
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'epsilon_cutoff': 0,
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'eta_cutoff': 0,
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'guidance_scale': 1,
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'penalty_alpha': 0,
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'mirostat_mode': 0,
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'mirostat_tau': 5,
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'mirostat_eta': 0.1,
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'do_sample': True,
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'encoder_repetition_penalty': 1,
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'no_repeat_ngram_size': 0,
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'min_length': 0,
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'num_beams': 1,
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'length_penalty': 1,
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'early_stopping': False,
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}
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def presets_params():
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return [k for k in default_preset()]
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def load_preset(name):
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generate_params = default_preset()
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if name not in ['None', None, '']:
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with open(Path(f'presets/{name}.yaml'), 'r') as infile:
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preset = yaml.safe_load(infile)
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for k in preset:
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generate_params[k] = preset[k]
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return generate_params
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@functools.cache
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def load_preset_memoized(name):
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return load_preset(name)
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def load_preset_for_ui(name, state):
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generate_params = load_preset(name)
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state.update(generate_params)
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return state, *[generate_params[k] for k in presets_params()]
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def random_preset(state):
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params_and_values = {
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'remove_tail_tokens': {
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'top_p': [0.5, 0.8, 0.9, 0.95, 0.99],
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'min_p': [0.5, 0.2, 0.1, 0.05, 0.01],
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'top_k': [3, 5, 10, 20, 30, 40],
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'typical_p': [0.2, 0.575, 0.95],
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'tfs': [0.5, 0.8, 0.9, 0.95, 0.99],
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'top_a': [0.5, 0.2, 0.1, 0.05, 0.01],
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'epsilon_cutoff': [1, 3, 5, 7, 9],
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'eta_cutoff': [3, 6, 9, 12, 15, 18],
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},
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'flatten_distribution': {
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'temperature': [0.5, 0.7, 0.8, 1, 1.2, 1.5, 2.0],
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},
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'repetition': {
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'repetition_penalty': [1, 1.05, 1.1, 1.15, 1.20, 1.25],
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'presence_penalty': [0, 0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 2.0],
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'frequency_penalty': [0, 0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 2.0],
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},
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'other': {
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'temperature_last': [True, False],
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}
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}
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generate_params = default_preset()
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for cat in params_and_values:
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choices = list(params_and_values[cat].keys())
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if shared.args.loader is not None:
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choices = [x for x in choices if x in loaders_samplers[shared.args.loader]]
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if len(choices) > 0:
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choice = random.choice(choices)
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generate_params[choice] = random.choice(params_and_values[cat][choice])
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state.update(generate_params)
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return state, *[generate_params[k] for k in presets_params()]
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def generate_preset_yaml(state):
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defaults = default_preset()
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data = {k: state[k] for k in presets_params()}
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# Remove entries that are identical to the defaults
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for k in list(data.keys()):
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if data[k] == defaults[k]:
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del data[k]
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return yaml.dump(data, sort_keys=False)
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