import functools from collections import OrderedDict import gradio as gr from modules import shared loaders_and_params = OrderedDict({ 'Transformers': [ 'cpu_memory', 'gpu_memory', 'load_in_8bit', 'bf16', 'cpu', 'disk', 'auto_devices', 'load_in_4bit', 'use_double_quant', 'quant_type', 'compute_dtype', 'trust_remote_code', 'no_use_fast', 'use_flash_attention_2', 'use_eager_attention', 'alpha_value', 'compress_pos_emb', 'disable_exllama', 'disable_exllamav2', 'transformers_info', ], 'llama.cpp': [ 'n_ctx', 'n_gpu_layers', 'cache_8bit', 'cache_4bit', 'tensor_split', 'n_batch', 'threads', 'threads_batch', 'no_mmap', 'mlock', 'no_mul_mat_q', 'rope_freq_base', 'compress_pos_emb', 'cpu', 'numa', 'no_offload_kqv', 'row_split', 'tensorcores', 'flash_attn', 'streaming_llm', 'attention_sink_size', ], 'llamacpp_HF': [ 'n_ctx', 'n_gpu_layers', 'cache_8bit', 'cache_4bit', 'tensor_split', 'n_batch', 'threads', 'threads_batch', 'no_mmap', 'mlock', 'no_mul_mat_q', 'rope_freq_base', 'compress_pos_emb', 'cpu', 'numa', 'cfg_cache', 'trust_remote_code', 'no_use_fast', 'logits_all', 'no_offload_kqv', 'row_split', 'tensorcores', 'flash_attn', 'streaming_llm', 'attention_sink_size', 'llamacpp_HF_info', ], 'ExLlamav2_HF': [ 'gpu_split', 'max_seq_len', 'cfg_cache', 'no_flash_attn', 'no_xformers', 'no_sdpa', 'num_experts_per_token', 'cache_8bit', 'cache_4bit', 'autosplit', 'alpha_value', 'compress_pos_emb', 'trust_remote_code', 'no_use_fast', ], 'ExLlamav2': [ 'gpu_split', 'max_seq_len', 'no_flash_attn', 'no_xformers', 'no_sdpa', 'num_experts_per_token', 'cache_8bit', 'cache_4bit', 'autosplit', 'alpha_value', 'compress_pos_emb', 'exllamav2_info', ], 'AutoGPTQ': [ 'triton', 'no_inject_fused_mlp', 'no_use_cuda_fp16', 'wbits', 'groupsize', 'desc_act', 'disable_exllama', 'disable_exllamav2', 'gpu_memory', 'cpu_memory', 'cpu', 'disk', 'auto_devices', 'trust_remote_code', 'no_use_fast', 'autogptq_info', ], 'HQQ': [ 'hqq_backend', 'trust_remote_code', 'no_use_fast', ], 'TensorRT-LLM': [ 'max_seq_len', 'cpp_runner', 'tensorrt_llm_info', ] }) def transformers_samplers(): return { 'temperature', 'temperature_last', 'dynamic_temperature', 'dynatemp_low', 'dynatemp_high', 'dynatemp_exponent', 'smoothing_factor', 'smoothing_curve', 'top_p', 'min_p', 'top_k', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'tfs', 'top_a', 'repetition_penalty', 'presence_penalty', 'frequency_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'no_repeat_ngram_size', 'dry_multiplier', 'dry_base', 'dry_allowed_length', 'dry_sequence_breakers', 'seed', 'do_sample', 'penalty_alpha', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'grammar_file_row', 'grammar_string', 'guidance_scale', 'negative_prompt', 'ban_eos_token', 'custom_token_bans', 'sampler_priority', 'add_bos_token', 'skip_special_tokens', 'auto_max_new_tokens', 'prompt_lookup_num_tokens' } loaders_samplers = { 'Transformers': transformers_samplers(), 'AutoGPTQ': transformers_samplers(), 'HQQ': transformers_samplers(), 'ExLlamav2': { 'temperature', 'temperature_last', 'top_p', 'min_p', 'top_k', 'typical_p', 'tfs', 'top_a', 'repetition_penalty', 'presence_penalty', 'frequency_penalty', 'repetition_penalty_range', 'seed', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'ban_eos_token', 'add_bos_token', 'custom_token_bans', 'skip_special_tokens', 'auto_max_new_tokens', }, 'ExLlamav2_HF': { 'temperature', 'temperature_last', 'dynamic_temperature', 'dynatemp_low', 'dynatemp_high', 'dynatemp_exponent', 'smoothing_factor', 'smoothing_curve', 'top_p', 'min_p', 'top_k', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'tfs', 'top_a', 'repetition_penalty', 'presence_penalty', 'frequency_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'no_repeat_ngram_size', 'dry_multiplier', 'dry_base', 'dry_allowed_length', 'dry_sequence_breakers', 'seed', 'do_sample', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'grammar_file_row', 'grammar_string', 'guidance_scale', 'negative_prompt', 'ban_eos_token', 'custom_token_bans', 'sampler_priority', 'add_bos_token', 'skip_special_tokens', 'auto_max_new_tokens', }, 'llama.cpp': { 'temperature', 'top_p', 'min_p', 'top_k', 'typical_p', 'tfs', 'repetition_penalty', 'presence_penalty', 'frequency_penalty', 'seed', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'grammar_file_row', 'grammar_string', 'ban_eos_token', 'custom_token_bans', }, 'llamacpp_HF': { 'temperature', 'temperature_last', 'dynamic_temperature', 'dynatemp_low', 'dynatemp_high', 'dynatemp_exponent', 'smoothing_factor', 'smoothing_curve', 'top_p', 'min_p', 'top_k', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'tfs', 'top_a', 'repetition_penalty', 'presence_penalty', 'frequency_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'no_repeat_ngram_size', 'dry_multiplier', 'dry_base', 'dry_allowed_length', 'dry_sequence_breakers', 'seed', 'do_sample', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'grammar_file_row', 'grammar_string', 'guidance_scale', 'negative_prompt', 'ban_eos_token', 'custom_token_bans', 'sampler_priority', 'add_bos_token', 'skip_special_tokens', 'auto_max_new_tokens', }, 'TensorRT-LLM': { 'temperature', 'top_p', 'top_k', 'repetition_penalty', 'presence_penalty', 'frequency_penalty', 'ban_eos_token', 'auto_max_new_tokens', } } @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, dynamic_temperature): all_samplers = list_all_samplers() output = [] for sampler in all_samplers: if loader == 'All' or sampler in loaders_samplers[loader]: if sampler.startswith('dynatemp'): output.append(gr.update(visible=dynamic_temperature)) else: output.append(gr.update(visible=True)) else: output.append(gr.update(visible=False)) return output 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]