text-generation-webui/modules/loaders.py
2023-09-25 12:19:43 -07:00

434 lines
9.6 KiB
Python

import functools
from collections import OrderedDict
import gradio as gr
from modules import shared
loaders_and_params = OrderedDict({
'Transformers': [
'cpu_memory',
'gpu_memory',
'trust_remote_code',
'load_in_8bit',
'bf16',
'cpu',
'disk',
'auto_devices',
'load_in_4bit',
'use_double_quant',
'quant_type',
'compute_dtype',
'trust_remote_code',
'use_fast',
'alpha_value',
'rope_freq_base',
'compress_pos_emb',
'disable_exllama',
'transformers_info'
],
'ExLlama_HF': [
'gpu_split',
'max_seq_len',
'alpha_value',
'rope_freq_base',
'compress_pos_emb',
'cfg_cache',
'use_fast',
'exllama_HF_info',
],
'ExLlamav2_HF': [
'gpu_split',
'max_seq_len',
'cfg_cache',
'alpha_value',
'compress_pos_emb',
'use_fast',
],
'ExLlama': [
'gpu_split',
'max_seq_len',
'alpha_value',
'rope_freq_base',
'compress_pos_emb',
'exllama_info',
],
'ExLlamav2': [
'gpu_split',
'max_seq_len',
'alpha_value',
'compress_pos_emb',
],
'AutoGPTQ': [
'triton',
'no_inject_fused_attention',
'no_inject_fused_mlp',
'no_use_cuda_fp16',
'wbits',
'groupsize',
'desc_act',
'disable_exllama',
'gpu_memory',
'cpu_memory',
'cpu',
'disk',
'auto_devices',
'trust_remote_code',
'use_fast',
'autogptq_info',
],
'GPTQ-for-LLaMa': [
'wbits',
'groupsize',
'model_type',
'pre_layer',
'use_fast',
'gptq_for_llama_info',
],
'llama.cpp': [
'n_ctx',
'n_gpu_layers',
'tensor_split',
'n_batch',
'threads',
'no_mmap',
'low_vram',
'mlock',
'mul_mat_q',
'llama_cpp_seed',
'alpha_value',
'rope_freq_base',
'compress_pos_emb',
'cpu',
],
'llamacpp_HF': [
'n_ctx',
'n_gpu_layers',
'tensor_split',
'n_batch',
'threads',
'no_mmap',
'low_vram',
'mlock',
'mul_mat_q',
'alpha_value',
'rope_freq_base',
'compress_pos_emb',
'cpu',
'cfg_cache',
'use_fast',
'llamacpp_HF_info',
],
'ctransformers': [
'n_ctx',
'n_gpu_layers',
'n_batch',
'threads',
'model_type',
'no_mmap',
'mlock'
]
})
loaders_samplers = {
'Transformers': {
'temperature',
'top_p',
'top_k',
'typical_p',
'epsilon_cutoff',
'eta_cutoff',
'tfs',
'top_a',
'repetition_penalty',
'repetition_penalty_range',
'encoder_repetition_penalty',
'no_repeat_ngram_size',
'min_length',
'seed',
'do_sample',
'penalty_alpha',
'num_beams',
'length_penalty',
'early_stopping',
'mirostat_mode',
'mirostat_tau',
'mirostat_eta',
'guidance_scale',
'negative_prompt',
'ban_eos_token',
'custom_token_bans',
'add_bos_token',
'skip_special_tokens',
'auto_max_new_tokens',
},
'ExLlama_HF': {
'temperature',
'top_p',
'top_k',
'typical_p',
'epsilon_cutoff',
'eta_cutoff',
'tfs',
'top_a',
'repetition_penalty',
'repetition_penalty_range',
'encoder_repetition_penalty',
'no_repeat_ngram_size',
'min_length',
'seed',
'do_sample',
'mirostat_mode',
'mirostat_tau',
'mirostat_eta',
'guidance_scale',
'negative_prompt',
'ban_eos_token',
'custom_token_bans',
'add_bos_token',
'skip_special_tokens',
'auto_max_new_tokens',
},
'ExLlama': {
'temperature',
'top_p',
'top_k',
'typical_p',
'repetition_penalty',
'repetition_penalty_range',
'seed',
'guidance_scale',
'negative_prompt',
'ban_eos_token',
'custom_token_bans',
'auto_max_new_tokens',
},
'ExLlamav2': {
'temperature',
'top_p',
'top_k',
'repetition_penalty',
'repetition_penalty_range',
'seed',
'ban_eos_token',
'custom_token_bans',
'auto_max_new_tokens',
},
'ExLlamav2_HF': {
'temperature',
'top_p',
'top_k',
'typical_p',
'epsilon_cutoff',
'eta_cutoff',
'tfs',
'top_a',
'repetition_penalty',
'repetition_penalty_range',
'encoder_repetition_penalty',
'no_repeat_ngram_size',
'min_length',
'seed',
'do_sample',
'mirostat_mode',
'mirostat_tau',
'mirostat_eta',
'guidance_scale',
'negative_prompt',
'ban_eos_token',
'custom_token_bans',
'add_bos_token',
'skip_special_tokens',
'auto_max_new_tokens',
},
'AutoGPTQ': {
'temperature',
'top_p',
'top_k',
'typical_p',
'epsilon_cutoff',
'eta_cutoff',
'tfs',
'top_a',
'repetition_penalty',
'repetition_penalty_range',
'encoder_repetition_penalty',
'no_repeat_ngram_size',
'min_length',
'seed',
'do_sample',
'penalty_alpha',
'num_beams',
'length_penalty',
'early_stopping',
'mirostat_mode',
'mirostat_tau',
'mirostat_eta',
'guidance_scale',
'negative_prompt',
'ban_eos_token',
'custom_token_bans',
'add_bos_token',
'skip_special_tokens',
'auto_max_new_tokens',
},
'GPTQ-for-LLaMa': {
'temperature',
'top_p',
'top_k',
'typical_p',
'epsilon_cutoff',
'eta_cutoff',
'tfs',
'top_a',
'repetition_penalty',
'repetition_penalty_range',
'encoder_repetition_penalty',
'no_repeat_ngram_size',
'min_length',
'seed',
'do_sample',
'penalty_alpha',
'num_beams',
'length_penalty',
'early_stopping',
'mirostat_mode',
'mirostat_tau',
'mirostat_eta',
'guidance_scale',
'negative_prompt',
'ban_eos_token',
'custom_token_bans',
'add_bos_token',
'skip_special_tokens',
'auto_max_new_tokens',
},
'llama.cpp': {
'temperature',
'top_p',
'top_k',
'tfs',
'repetition_penalty',
'mirostat_mode',
'mirostat_tau',
'mirostat_eta',
'grammar_file_row',
'grammar_string',
'ban_eos_token',
'custom_token_bans',
},
'llamacpp_HF': {
'temperature',
'top_p',
'top_k',
'typical_p',
'epsilon_cutoff',
'eta_cutoff',
'tfs',
'top_a',
'repetition_penalty',
'repetition_penalty_range',
'encoder_repetition_penalty',
'no_repeat_ngram_size',
'min_length',
'seed',
'do_sample',
'mirostat_mode',
'mirostat_tau',
'mirostat_eta',
'guidance_scale',
'negative_prompt',
'ban_eos_token',
'custom_token_bans',
'add_bos_token',
'skip_special_tokens',
'auto_max_new_tokens',
},
'ctransformers': {
'temperature',
'top_p',
'top_k',
'repetition_penalty',
'repetition_penalty_range',
}
}
loaders_model_types = {
'GPTQ-for-LLaMa': [
"None",
"llama",
"opt",
"gptj"
],
'ctransformers': [
"None",
"gpt2",
"gptj",
"gptneox",
"llama",
"mpt",
"dollyv2",
"replit",
"starcoder",
"gptbigcode",
"falcon"
],
}
@functools.cache
def list_all_samplers():
all_samplers = set()
for k in loaders_samplers:
for sampler in loaders_samplers[k]:
all_samplers.add(sampler)
return sorted(all_samplers)
def blacklist_samplers(loader):
all_samplers = list_all_samplers()
if loader == 'All':
return [gr.update(visible=True) for sampler in all_samplers]
else:
return [gr.update(visible=True) if sampler in loaders_samplers[loader] else gr.update(visible=False) for sampler in all_samplers]
def get_model_types(loader):
if loader in loaders_model_types:
return loaders_model_types[loader]
return ["None"]
def get_gpu_memory_keys():
return [k for k in shared.gradio if k.startswith('gpu_memory')]
@functools.cache
def get_all_params():
all_params = set()
for k in loaders_and_params:
for el in loaders_and_params[k]:
all_params.add(el)
if 'gpu_memory' in all_params:
all_params.remove('gpu_memory')
for k in get_gpu_memory_keys():
all_params.add(k)
return sorted(all_params)
def make_loader_params_visible(loader):
params = []
all_params = get_all_params()
if loader in loaders_and_params:
params = loaders_and_params[loader]
if 'gpu_memory' in params:
params.remove('gpu_memory')
params += get_gpu_memory_keys()
return [gr.update(visible=True) if k in params else gr.update(visible=False) for k in all_params]