| 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', |
| 'alpha_value', |
| 'rope_freq_base', |
| 'compress_pos_emb', |
| 'disable_exllama', |
| 'disable_exllamav2', |
| 'transformers_info' |
| ], |
| 'llama.cpp': [ |
| 'n_ctx', |
| 'n_gpu_layers', |
| 'tensor_split', |
| 'n_batch', |
| 'threads', |
| 'threads_batch', |
| 'no_mmap', |
| 'mlock', |
| 'no_mul_mat_q', |
| 'alpha_value', |
| 'rope_freq_base', |
| 'compress_pos_emb', |
| 'cpu', |
| 'numa', |
| 'no_offload_kqv', |
| 'tensorcores', |
| ], |
| 'llamacpp_HF': [ |
| 'n_ctx', |
| 'n_gpu_layers', |
| 'tensor_split', |
| 'n_batch', |
| 'threads', |
| 'threads_batch', |
| 'no_mmap', |
| 'mlock', |
| 'no_mul_mat_q', |
| 'alpha_value', |
| 'rope_freq_base', |
| 'compress_pos_emb', |
| 'cpu', |
| 'numa', |
| 'cfg_cache', |
| 'trust_remote_code', |
| 'no_use_fast', |
| 'logits_all', |
| 'no_offload_kqv', |
| 'tensorcores', |
| 'llamacpp_HF_info', |
| ], |
| 'ExLlamav2_HF': [ |
| 'gpu_split', |
| 'max_seq_len', |
| 'cfg_cache', |
| 'no_flash_attn', |
| 'num_experts_per_token', |
| 'cache_8bit', |
| 'alpha_value', |
| 'compress_pos_emb', |
| 'trust_remote_code', |
| 'no_use_fast', |
| ], |
| 'ExLlamav2': [ |
| 'gpu_split', |
| 'max_seq_len', |
| 'no_flash_attn', |
| 'num_experts_per_token', |
| 'cache_8bit', |
| 'alpha_value', |
| 'compress_pos_emb', |
| 'exllamav2_info', |
| ], |
| 'AutoGPTQ': [ |
| 'triton', |
| 'no_inject_fused_attention', |
| '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', |
| ], |
| 'AutoAWQ': [ |
| 'cpu_memory', |
| 'gpu_memory', |
| 'auto_devices', |
| 'max_seq_len', |
| 'no_inject_fused_attention', |
| 'trust_remote_code', |
| 'no_use_fast', |
| ], |
| 'GPTQ-for-LLaMa': [ |
| 'wbits', |
| 'groupsize', |
| 'model_type', |
| 'pre_layer', |
| 'trust_remote_code', |
| 'no_use_fast', |
| 'gptq_for_llama_info', |
| ], |
| 'ctransformers': [ |
| 'n_ctx', |
| 'n_gpu_layers', |
| 'n_batch', |
| 'threads', |
| 'model_type', |
| 'no_mmap', |
| 'mlock' |
| ], |
| 'QuIP#': [ |
| 'trust_remote_code', |
| 'no_use_fast', |
| 'no_flash_attn', |
| 'quipsharp_info', |
| ], |
| 'HQQ': [ |
| 'hqq_backend', |
| 'trust_remote_code', |
| 'no_use_fast', |
| ] |
| }) |
|
|
|
|
| def transformers_samplers(): |
| return { |
| 'temperature', |
| 'temperature_last', |
| 'dynamic_temperature', |
| 'dynatemp_low', |
| 'dynatemp_high', |
| 'dynatemp_exponent', |
| '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', |
| 'min_length', |
| 'seed', |
| 'do_sample', |
| 'penalty_alpha', |
| 'num_beams', |
| 'length_penalty', |
| 'early_stopping', |
| 'mirostat_mode', |
| 'mirostat_tau', |
| 'mirostat_eta', |
| 'grammar_file_row', |
| 'grammar_string', |
| 'guidance_scale', |
| 'negative_prompt', |
| 'ban_eos_token', |
| 'custom_token_bans', |
| 'add_bos_token', |
| 'skip_special_tokens', |
| 'auto_max_new_tokens', |
| } |
|
|
|
|
| loaders_samplers = { |
| 'Transformers': transformers_samplers(), |
| 'AutoGPTQ': transformers_samplers(), |
| 'GPTQ-for-LLaMa': transformers_samplers(), |
| 'AutoAWQ': transformers_samplers(), |
| 'QuIP#': transformers_samplers(), |
| 'HQQ': transformers_samplers(), |
| 'ExLlamav2': { |
| 'temperature', |
| 'top_p', |
| 'min_p', |
| 'top_k', |
| 'typical_p', |
| 'tfs', |
| 'repetition_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', |
| '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', |
| 'min_length', |
| 'seed', |
| 'do_sample', |
| 'mirostat_mode', |
| 'mirostat_tau', |
| 'mirostat_eta', |
| 'grammar_file_row', |
| 'grammar_string', |
| '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', |
| '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', |
| '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', |
| 'min_length', |
| 'seed', |
| 'do_sample', |
| 'mirostat_mode', |
| 'mirostat_tau', |
| 'mirostat_eta', |
| 'grammar_file_row', |
| 'grammar_string', |
| '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, 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_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] |
|
|
