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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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'tensorcores', |
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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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'tensorcores', |
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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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'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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'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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'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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'min_length', |
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'seed', |
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'do_sample', |
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'penalty_alpha', |
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'num_beams', |
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'length_penalty', |
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'early_stopping', |
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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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'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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'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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'min_length', |
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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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'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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'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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'min_length', |
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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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'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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