mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-22 08:07:56 +01:00
430 lines
9.7 KiB
Python
430 lines
9.7 KiB
Python
import functools
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from collections import OrderedDict
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import gradio as gr
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from modules import shared
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loaders_and_params = OrderedDict({
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'Transformers': [
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'cpu_memory',
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'gpu_memory',
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'load_in_8bit',
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'bf16',
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'cpu',
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'disk',
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'auto_devices',
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'load_in_4bit',
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'use_double_quant',
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'quant_type',
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'compute_dtype',
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'trust_remote_code',
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'no_use_fast',
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'use_flash_attention_2',
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'alpha_value',
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'rope_freq_base',
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'compress_pos_emb',
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'disable_exllama',
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'disable_exllamav2',
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'transformers_info',
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],
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'llama.cpp': [
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'n_ctx',
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'n_gpu_layers',
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'tensor_split',
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'n_batch',
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'threads',
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'threads_batch',
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'no_mmap',
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'mlock',
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'no_mul_mat_q',
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'alpha_value',
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'rope_freq_base',
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'compress_pos_emb',
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'cpu',
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'numa',
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'no_offload_kqv',
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'row_split',
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'tensorcores',
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'streaming_llm',
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'attention_sink_size',
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],
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'llamacpp_HF': [
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'n_ctx',
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'n_gpu_layers',
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'tensor_split',
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'n_batch',
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'threads',
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'threads_batch',
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'no_mmap',
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'mlock',
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'no_mul_mat_q',
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'alpha_value',
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'rope_freq_base',
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'compress_pos_emb',
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'cpu',
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'numa',
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'cfg_cache',
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'trust_remote_code',
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'no_use_fast',
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'logits_all',
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'no_offload_kqv',
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'row_split',
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'tensorcores',
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'streaming_llm',
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'attention_sink_size',
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'llamacpp_HF_info',
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],
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'ExLlamav2_HF': [
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'gpu_split',
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'max_seq_len',
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'cfg_cache',
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'no_flash_attn',
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'num_experts_per_token',
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'cache_8bit',
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'cache_4bit',
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'autosplit',
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'alpha_value',
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'compress_pos_emb',
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'trust_remote_code',
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'no_use_fast',
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],
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'ExLlamav2': [
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'gpu_split',
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'max_seq_len',
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'no_flash_attn',
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'num_experts_per_token',
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'cache_8bit',
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'cache_4bit',
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'autosplit',
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'alpha_value',
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'compress_pos_emb',
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'exllamav2_info',
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],
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'AutoGPTQ': [
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'triton',
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'no_inject_fused_attention',
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'no_inject_fused_mlp',
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'no_use_cuda_fp16',
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'wbits',
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'groupsize',
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'desc_act',
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'disable_exllama',
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'disable_exllamav2',
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'gpu_memory',
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'cpu_memory',
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'cpu',
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'disk',
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'auto_devices',
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'trust_remote_code',
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'no_use_fast',
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'autogptq_info',
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],
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'AutoAWQ': [
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'cpu_memory',
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'gpu_memory',
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'auto_devices',
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'max_seq_len',
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'no_inject_fused_attention',
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'trust_remote_code',
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'no_use_fast',
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],
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'GPTQ-for-LLaMa': [
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'wbits',
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'groupsize',
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'model_type',
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'pre_layer',
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'trust_remote_code',
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'no_use_fast',
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'gptq_for_llama_info',
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],
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'ctransformers': [
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'n_ctx',
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'n_gpu_layers',
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'n_batch',
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'threads',
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'model_type',
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'no_mmap',
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'mlock'
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],
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'QuIP#': [
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'trust_remote_code',
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'no_use_fast',
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'no_flash_attn',
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'quipsharp_info',
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],
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'HQQ': [
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'hqq_backend',
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'trust_remote_code',
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'no_use_fast',
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]
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})
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def transformers_samplers():
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return {
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'temperature',
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'temperature_last',
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'dynamic_temperature',
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'dynatemp_low',
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'dynatemp_high',
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'dynatemp_exponent',
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'smoothing_factor',
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'smoothing_curve',
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'top_p',
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'min_p',
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'top_k',
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'typical_p',
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'epsilon_cutoff',
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'eta_cutoff',
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'tfs',
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'top_a',
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'repetition_penalty',
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'presence_penalty',
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'frequency_penalty',
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'repetition_penalty_range',
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'encoder_repetition_penalty',
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'no_repeat_ngram_size',
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'seed',
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'do_sample',
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'penalty_alpha',
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'mirostat_mode',
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'mirostat_tau',
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'mirostat_eta',
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'grammar_file_row',
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'grammar_string',
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'guidance_scale',
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'negative_prompt',
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'ban_eos_token',
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'custom_token_bans',
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'sampler_priority',
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'add_bos_token',
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'skip_special_tokens',
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'auto_max_new_tokens',
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'prompt_lookup_num_tokens'
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}
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loaders_samplers = {
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'Transformers': transformers_samplers(),
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'AutoGPTQ': transformers_samplers(),
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'GPTQ-for-LLaMa': transformers_samplers(),
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'AutoAWQ': transformers_samplers(),
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'QuIP#': transformers_samplers(),
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'HQQ': transformers_samplers(),
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'ExLlamav2': {
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'temperature',
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'temperature_last',
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'top_p',
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'min_p',
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'top_k',
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'typical_p',
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'tfs',
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'top_a',
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'repetition_penalty',
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'presence_penalty',
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'frequency_penalty',
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'repetition_penalty_range',
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'seed',
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'mirostat_mode',
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'mirostat_tau',
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'mirostat_eta',
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'ban_eos_token',
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'add_bos_token',
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'custom_token_bans',
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'skip_special_tokens',
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'auto_max_new_tokens',
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},
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'ExLlamav2_HF': {
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'temperature',
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'temperature_last',
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'dynamic_temperature',
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'dynatemp_low',
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'dynatemp_high',
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'dynatemp_exponent',
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'smoothing_factor',
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'smoothing_curve',
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'top_p',
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'min_p',
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'top_k',
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'typical_p',
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'epsilon_cutoff',
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'eta_cutoff',
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'tfs',
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'top_a',
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'repetition_penalty',
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'presence_penalty',
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'frequency_penalty',
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'repetition_penalty_range',
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'encoder_repetition_penalty',
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'no_repeat_ngram_size',
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'seed',
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'do_sample',
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'mirostat_mode',
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'mirostat_tau',
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'mirostat_eta',
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'grammar_file_row',
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'grammar_string',
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'guidance_scale',
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'negative_prompt',
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'ban_eos_token',
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'custom_token_bans',
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'sampler_priority',
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'add_bos_token',
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'skip_special_tokens',
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'auto_max_new_tokens',
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},
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'llama.cpp': {
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'temperature',
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'top_p',
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'min_p',
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'top_k',
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'typical_p',
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'tfs',
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'repetition_penalty',
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'presence_penalty',
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'frequency_penalty',
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'seed',
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'mirostat_mode',
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'mirostat_tau',
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'mirostat_eta',
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'grammar_file_row',
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'grammar_string',
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'ban_eos_token',
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'custom_token_bans',
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},
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'llamacpp_HF': {
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'temperature',
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'temperature_last',
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'dynamic_temperature',
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'dynatemp_low',
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'dynatemp_high',
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'dynatemp_exponent',
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'smoothing_factor',
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'smoothing_curve',
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'top_p',
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'min_p',
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'top_k',
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'typical_p',
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'epsilon_cutoff',
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'eta_cutoff',
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'tfs',
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'top_a',
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'repetition_penalty',
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'presence_penalty',
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'frequency_penalty',
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'repetition_penalty_range',
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'encoder_repetition_penalty',
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'no_repeat_ngram_size',
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'seed',
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'do_sample',
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'mirostat_mode',
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'mirostat_tau',
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'mirostat_eta',
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'grammar_file_row',
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'grammar_string',
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'guidance_scale',
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'negative_prompt',
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'ban_eos_token',
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'custom_token_bans',
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'sampler_priority',
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'add_bos_token',
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'skip_special_tokens',
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'auto_max_new_tokens',
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},
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'ctransformers': {
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'temperature',
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'top_p',
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'top_k',
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'repetition_penalty',
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'repetition_penalty_range',
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},
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}
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loaders_model_types = {
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'GPTQ-for-LLaMa': [
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"None",
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"llama",
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"opt",
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"gptj"
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],
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'ctransformers': [
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"None",
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"gpt2",
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"gptj",
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"gptneox",
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"llama",
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"mpt",
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"dollyv2",
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"replit",
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"starcoder",
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"gptbigcode",
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"falcon"
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],
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}
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@functools.cache
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def list_all_samplers():
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all_samplers = set()
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for k in loaders_samplers:
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for sampler in loaders_samplers[k]:
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all_samplers.add(sampler)
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return sorted(all_samplers)
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def blacklist_samplers(loader, dynamic_temperature):
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all_samplers = list_all_samplers()
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output = []
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for sampler in all_samplers:
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if loader == 'All' or sampler in loaders_samplers[loader]:
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if sampler.startswith('dynatemp'):
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output.append(gr.update(visible=dynamic_temperature))
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else:
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output.append(gr.update(visible=True))
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else:
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output.append(gr.update(visible=False))
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return output
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def get_model_types(loader):
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if loader in loaders_model_types:
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return loaders_model_types[loader]
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return ["None"]
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def get_gpu_memory_keys():
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return [k for k in shared.gradio if k.startswith('gpu_memory')]
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@functools.cache
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def get_all_params():
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all_params = set()
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for k in loaders_and_params:
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for el in loaders_and_params[k]:
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all_params.add(el)
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if 'gpu_memory' in all_params:
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all_params.remove('gpu_memory')
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for k in get_gpu_memory_keys():
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all_params.add(k)
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return sorted(all_params)
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def make_loader_params_visible(loader):
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params = []
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all_params = get_all_params()
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if loader in loaders_and_params:
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params = loaders_and_params[loader]
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if 'gpu_memory' in params:
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params.remove('gpu_memory')
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params += get_gpu_memory_keys()
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return [gr.update(visible=True) if k in params else gr.update(visible=False) for k in all_params]
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