Merge branch 'main' into HideLord-main
This commit is contained in:
oobabooga
commit
693b53d957
17 changed files with 317 additions and 151 deletions
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@ -7,28 +7,40 @@ import torch
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import modules.shared as shared
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sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa")))
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from llama import load_quant
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import llama
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import opt
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# 4-bit LLaMA
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def load_quantized_LLaMA(model_name):
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if shared.args.load_in_4bit:
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bits = 4
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def load_quantized(model_name):
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if not shared.args.gptq_model_type:
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# Try to determine model type from model name
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model_type = model_name.split('-')[0].lower()
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if model_type not in ('llama', 'opt'):
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print("Can't determine model type from model name. Please specify it manually using --gptq-model-type "
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"argument")
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exit()
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else:
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bits = shared.args.gptq_bits
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model_type = shared.args.gptq_model_type.lower()
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if model_type == 'llama':
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load_quant = llama.load_quant
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elif model_type == 'opt':
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load_quant = opt.load_quant
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else:
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print("Unknown pre-quantized model type specified. Only 'llama' and 'opt' are supported")
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exit()
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path_to_model = Path(f'models/{model_name}')
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pt_model = ''
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if path_to_model.name.lower().startswith('llama-7b'):
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pt_model = f'llama-7b-{bits}bit.pt'
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pt_model = f'llama-7b-{shared.args.gptq_bits}bit.pt'
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elif path_to_model.name.lower().startswith('llama-13b'):
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pt_model = f'llama-13b-{bits}bit.pt'
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pt_model = f'llama-13b-{shared.args.gptq_bits}bit.pt'
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elif path_to_model.name.lower().startswith('llama-30b'):
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pt_model = f'llama-30b-{bits}bit.pt'
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pt_model = f'llama-30b-{shared.args.gptq_bits}bit.pt'
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elif path_to_model.name.lower().startswith('llama-65b'):
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pt_model = f'llama-65b-{bits}bit.pt'
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pt_model = f'llama-65b-{shared.args.gptq_bits}bit.pt'
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else:
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pt_model = f'{model_name}-{bits}bit.pt'
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pt_model = f'{model_name}-{shared.args.gptq_bits}bit.pt'
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# Try to find the .pt both in models/ and in the subfolder
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pt_path = None
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@ -40,7 +52,7 @@ def load_quantized_LLaMA(model_name):
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print(f"Could not find {pt_model}, exiting...")
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exit()
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model = load_quant(str(path_to_model), str(pt_path), bits)
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model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits)
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# Multiple GPUs or GPU+CPU
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if shared.args.gpu_memory:
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@ -97,7 +97,7 @@ def extract_message_from_reply(question, reply, name1, name2, check, impersonate
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def stop_everything_event():
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shared.stop_everything = True
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def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1, regenerate=False):
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def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1, regenerate=False):
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shared.stop_everything = False
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just_started = True
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eos_token = '\n' if check else None
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@ -126,13 +126,14 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
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else:
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prompt = custom_generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size)
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# Yield *Is typing...*
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if not regenerate:
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yield shared.history['visible']+[[visible_text, '*Is typing...*']]
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yield shared.history['visible']+[[visible_text, shared.processing_message]]
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# Generate
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reply = ''
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for i in range(chat_generation_attempts):
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for reply in generate_reply(f"{prompt}{' ' if len(reply) > 0 else ''}{reply}", max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name1}:"):
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for reply in generate_reply(f"{prompt}{' ' if len(reply) > 0 else ''}{reply}", max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name1}:"):
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# Extracting the reply
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reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check)
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@ -159,7 +160,7 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
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yield shared.history['visible']
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def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
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def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
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eos_token = '\n' if check else None
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if 'pygmalion' in shared.model_name.lower():
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@ -168,28 +169,29 @@ def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typ
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prompt = generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size, impersonate=True)
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reply = ''
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yield '*Is typing...*'
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# Yield *Is typing...*
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yield shared.processing_message
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for i in range(chat_generation_attempts):
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for reply in generate_reply(prompt+reply, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name2}:"):
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for reply in generate_reply(prompt+reply, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name2}:"):
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reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check, impersonate=True)
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yield reply
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if next_character_found:
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break
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yield reply
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def cai_chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
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for _history in chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts):
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def cai_chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
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for _history in chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts):
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yield generate_chat_html(_history, name1, name2, shared.character)
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def regenerate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
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def regenerate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
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if (shared.character != 'None' and len(shared.history['visible']) == 1) or len(shared.history['internal']) == 0:
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yield generate_chat_output(shared.history['visible'], name1, name2, shared.character)
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else:
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last_visible = shared.history['visible'].pop()
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last_internal = shared.history['internal'].pop()
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yield generate_chat_output(shared.history['visible']+[[last_visible[0], '*Is typing...*']], name1, name2, shared.character)
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for _history in chatbot_wrapper(last_internal[0], max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts, regenerate=True):
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# Yield '*Is typing...*'
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yield generate_chat_output(shared.history['visible']+[[last_visible[0], shared.processing_message]], name1, name2, shared.character)
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for _history in chatbot_wrapper(last_internal[0], max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts, regenerate=True):
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if shared.args.cai_chat:
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shared.history['visible'][-1] = [last_visible[0], _history[-1][1]]
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else:
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@ -1,6 +1,5 @@
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import json
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import os
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import sys
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import time
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import zipfile
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from pathlib import Path
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@ -35,6 +34,7 @@ if shared.args.deepspeed:
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ds_config = generate_ds_config(shared.args.bf16, 1 * world_size, shared.args.nvme_offload_dir)
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dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration
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def load_model(model_name):
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print(f"Loading {model_name}...")
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t0 = time.time()
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@ -42,7 +42,7 @@ def load_model(model_name):
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shared.is_RWKV = model_name.lower().startswith('rwkv-')
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# Default settings
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if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.load_in_4bit, shared.args.gptq_bits > 0, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
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if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.gptq_bits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
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if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
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model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True)
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else:
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@ -87,11 +87,11 @@ def load_model(model_name):
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return model, tokenizer
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# 4-bit LLaMA
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elif shared.args.gptq_bits > 0 or shared.args.load_in_4bit:
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from modules.quantized_LLaMA import load_quantized_LLaMA
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# Quantized model
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elif shared.args.gptq_bits > 0:
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from modules.GPTQ_loader import load_quantized
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model = load_quantized_LLaMA(model_name)
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model = load_quantized(model_name)
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# Custom
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else:
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@ -11,6 +11,7 @@ is_RWKV = False
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history = {'internal': [], 'visible': []}
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character = 'None'
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stop_everything = False
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processing_message = '*Is typing...*'
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# UI elements (buttons, sliders, HTML, etc)
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gradio = {}
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@ -68,8 +69,9 @@ parser.add_argument('--chat', action='store_true', help='Launch the web UI in ch
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parser.add_argument('--cai-chat', action='store_true', help='Launch the web UI in chat mode with a style similar to Character.AI\'s. If the file img_bot.png or img_bot.jpg exists in the same folder as server.py, this image will be used as the bot\'s profile picture. Similarly, img_me.png or img_me.jpg will be used as your profile picture.')
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parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.')
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parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.')
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parser.add_argument('--load-in-4bit', action='store_true', help='Load the model with 4-bit precision. Currently only works with LLaMA.')
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parser.add_argument('--gptq-bits', type=int, default=0, help='Load a pre-quantized model with specified precision. 2, 3, 4 and 8bit are supported. Currently only works with LLaMA.')
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parser.add_argument('--load-in-4bit', action='store_true', help='DEPRECATED: use --gptq-bits 4 instead.')
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parser.add_argument('--gptq-bits', type=int, default=0, help='Load a pre-quantized model with specified precision. 2, 3, 4 and 8bit are supported. Currently only works with LLaMA and OPT.')
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parser.add_argument('--gptq-model-type', type=str, help='Model type of pre-quantized model. Currently only LLaMa and OPT are supported.')
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parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
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parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
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parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.')
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@ -94,3 +96,8 @@ parser.add_argument('--share', action='store_true', help='Create a public URL. T
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parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.')
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parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
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args = parser.parse_args()
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# Provisional, this will be deleted later
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if args.load_in_4bit:
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print("Warning: --load-in-4bit is deprecated and will be removed. Use --gptq-bits 4 instead.\n")
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args.gptq_bits = 4
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@ -89,7 +89,7 @@ def clear_torch_cache():
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if not shared.args.cpu:
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torch.cuda.empty_cache()
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def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=None, stopping_string=None):
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def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=None, stopping_string=None):
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clear_torch_cache()
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t0 = time.time()
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@ -122,7 +122,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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input_ids = encode(question, max_new_tokens)
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original_input_ids = input_ids
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output = input_ids[0]
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cuda = "" if (shared.args.cpu or shared.args.deepspeed or shared.args.flexgen) else ".cuda()"
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cuda = not any((shared.args.cpu, shared.args.deepspeed, shared.args.flexgen))
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eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else []
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if eos_token is not None:
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eos_token_ids.append(int(encode(eos_token)[0][-1]))
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@ -132,45 +132,49 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
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t = encode(stopping_string, 0, add_special_tokens=False)
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stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0])))
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generate_params = {}
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if not shared.args.flexgen:
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generate_params = [
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f"max_new_tokens=max_new_tokens",
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f"eos_token_id={eos_token_ids}",
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f"stopping_criteria=stopping_criteria_list",
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f"do_sample={do_sample}",
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f"temperature={temperature}",
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f"top_p={top_p}",
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f"typical_p={typical_p}",
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f"repetition_penalty={repetition_penalty}",
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f"top_k={top_k}",
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f"min_length={min_length if shared.args.no_stream else 0}",
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f"no_repeat_ngram_size={no_repeat_ngram_size}",
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f"num_beams={num_beams}",
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f"penalty_alpha={penalty_alpha}",
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f"length_penalty={length_penalty}",
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f"early_stopping={early_stopping}",
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]
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generate_params.update({
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"max_new_tokens": max_new_tokens,
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"eos_token_id": eos_token_ids,
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"stopping_criteria": stopping_criteria_list,
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"do_sample": do_sample,
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"temperature": temperature,
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"top_p": top_p,
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"typical_p": typical_p,
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"repetition_penalty": repetition_penalty,
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"encoder_repetition_penalty": encoder_repetition_penalty,
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"top_k": top_k,
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"min_length": min_length if shared.args.no_stream else 0,
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"no_repeat_ngram_size": no_repeat_ngram_size,
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"num_beams": num_beams,
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"penalty_alpha": penalty_alpha,
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"length_penalty": length_penalty,
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"early_stopping": early_stopping,
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})
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else:
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generate_params = [
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f"max_new_tokens={max_new_tokens if shared.args.no_stream else 8}",
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f"do_sample={do_sample}",
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f"temperature={temperature}",
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f"stop={eos_token_ids[-1]}",
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]
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generate_params.update({
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"max_new_tokens": max_new_tokens if shared.args.no_stream else 8,
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"do_sample": do_sample,
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"temperature": temperature,
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"stop": eos_token_ids[-1],
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})
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if shared.args.deepspeed:
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generate_params.append("synced_gpus=True")
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generate_params.update({"synced_gpus": True})
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if shared.soft_prompt:
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inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids)
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generate_params.insert(0, "inputs_embeds=inputs_embeds")
|
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generate_params.insert(0, "inputs=filler_input_ids")
|
||||
generate_params.update({"inputs_embeds": inputs_embeds})
|
||||
generate_params.update({"inputs": filler_input_ids})
|
||||
else:
|
||||
generate_params.insert(0, "inputs=input_ids")
|
||||
generate_params.update({"inputs": input_ids})
|
||||
|
||||
try:
|
||||
# Generate the entire reply at once.
|
||||
if shared.args.no_stream:
|
||||
with torch.no_grad():
|
||||
output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0]
|
||||
output = shared.model.generate(**generate_params)[0]
|
||||
if cuda:
|
||||
output = output.cuda()
|
||||
if shared.soft_prompt:
|
||||
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
|
||||
|
||||
|
|
@ -194,7 +198,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
|
|||
return Iteratorize(generate_with_callback, kwargs, callback=None)
|
||||
|
||||
yield formatted_outputs(original_question, shared.model_name)
|
||||
with eval(f"generate_with_streaming({', '.join(generate_params)})") as generator:
|
||||
with generate_with_streaming(**generate_params) as generator:
|
||||
for output in generator:
|
||||
if shared.soft_prompt:
|
||||
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
|
||||
|
|
@ -214,7 +218,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
|
|||
for i in range(max_new_tokens//8+1):
|
||||
clear_torch_cache()
|
||||
with torch.no_grad():
|
||||
output = eval(f"shared.model.generate({', '.join(generate_params)})")[0]
|
||||
output = shared.model.generate(**generate_params)[0]
|
||||
if shared.soft_prompt:
|
||||
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
|
||||
reply = decode(output)
|
||||
|
|
|
|||
|
|
@ -38,6 +38,9 @@ svg {
|
|||
ol li p, ul li p {
|
||||
display: inline-block;
|
||||
}
|
||||
#main, #settings, #extensions, #chat-settings {
|
||||
border: 0;
|
||||
}
|
||||
"""
|
||||
|
||||
chat_css = """
|
||||
|
|
@ -64,6 +67,12 @@ div.svelte-362y77>*, div.svelte-362y77>.form>* {
|
|||
}
|
||||
"""
|
||||
|
||||
page_js = """
|
||||
document.getElementById("main").parentNode.childNodes[0].style = "border: none; background-color: #8080802b; margin-bottom: 40px"
|
||||
document.getElementById("main").parentNode.style = "padding: 0; margin: 0"
|
||||
document.getElementById("main").parentNode.parentNode.parentNode.style = "padding: 0"
|
||||
"""
|
||||
|
||||
class ToolButton(gr.Button, gr.components.FormComponent):
|
||||
"""Small button with single emoji as text, fits inside gradio forms"""
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue