554 lines
30 KiB
Python
554 lines
30 KiB
Python
import gc
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import io
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import json
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import os
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import re
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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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import gradio as gr
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import numpy as np
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import torch
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import transformers
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from PIL import Image
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from transformers import AutoConfig
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from transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer
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import modules.chat as chat
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import modules.extensions as extensions_module
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import modules.shared as shared
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from modules.extensions import extension_state
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from modules.extensions import load_extensions
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from modules.extensions import update_extensions_parameters
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from modules.html_generator import *
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from modules.prompt import generate_reply
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from modules.ui import *
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transformers.logging.set_verbosity_error()
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if (shared.args.chat or shared.args.cai_chat) and not shared.args.no_stream:
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print("Warning: chat mode currently becomes somewhat slower with text streaming on.\nConsider starting the web UI with the --no-stream option.\n")
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settings = {
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'max_new_tokens': 200,
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'max_new_tokens_min': 1,
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'max_new_tokens_max': 2000,
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'preset': 'NovelAI-Sphinx Moth',
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'name1': 'Person 1',
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'name2': 'Person 2',
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'context': 'This is a conversation between two people.',
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'prompt': 'Common sense questions and answers\n\nQuestion: \nFactual answer:',
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'prompt_gpt4chan': '-----\n--- 865467536\nInput text\n--- 865467537\n',
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'stop_at_newline': True,
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'chat_prompt_size': 2048,
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'chat_prompt_size_min': 0,
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'chat_prompt_size_max': 2048,
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'preset_pygmalion': 'Pygmalion',
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'name1_pygmalion': 'You',
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'name2_pygmalion': 'Kawaii',
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'context_pygmalion': "Kawaii's persona: Kawaii is a cheerful person who loves to make others smile. She is an optimist who loves to spread happiness and positivity wherever she goes.\n<START>",
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'stop_at_newline_pygmalion': False,
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}
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if shared.args.settings is not None and Path(shared.args.settings).exists():
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new_settings = json.loads(open(Path(shared.args.settings), 'r').read())
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for item in new_settings:
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settings[item] = new_settings[item]
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if shared.args.flexgen:
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from flexgen.flex_opt import (Policy, OptLM, TorchDevice, TorchDisk, TorchMixedDevice, CompressionConfig, Env, Task, get_opt_config)
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if shared.args.deepspeed:
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import deepspeed
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from transformers.deepspeed import HfDeepSpeedConfig, is_deepspeed_zero3_enabled
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from modules.deepspeed_parameters import generate_ds_config
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# Distributed setup
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local_rank = shared.args.local_rank if shared.args.local_rank is not None else int(os.getenv("LOCAL_RANK", "0"))
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world_size = int(os.getenv("WORLD_SIZE", "1"))
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torch.cuda.set_device(local_rank)
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deepspeed.init_distributed()
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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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if shared.args.picture and (shared.args.cai_chat or shared.args.chat):
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import modules.bot_picture as bot_picture
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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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# Default settings
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if not (shared.args.cpu or shared.args.load_in_8bit or shared.args.auto_devices or shared.args.disk or shared.args.gpu_memory is not None or shared.args.cpu_memory is not None or shared.args.deepspeed or shared.args.flexgen):
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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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model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16).cuda()
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# FlexGen
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elif shared.args.flexgen:
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gpu = TorchDevice("cuda:0")
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cpu = TorchDevice("cpu")
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disk = TorchDisk(shared.args.disk_cache_dir)
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env = Env(gpu=gpu, cpu=cpu, disk=disk, mixed=TorchMixedDevice([gpu, cpu, disk]))
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# Offloading policy
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policy = Policy(1, 1,
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shared.args.percent[0], shared.args.percent[1],
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shared.args.percent[2], shared.args.percent[3],
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shared.args.percent[4], shared.args.percent[5],
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overlap=True, sep_layer=True, pin_weight=True,
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cpu_cache_compute=False, attn_sparsity=1.0,
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compress_weight=shared.args.compress_weight,
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comp_weight_config=CompressionConfig(
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num_bits=4, group_size=64,
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group_dim=0, symmetric=False),
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compress_cache=False,
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comp_cache_config=CompressionConfig(
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num_bits=4, group_size=64,
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group_dim=2, symmetric=False))
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opt_config = get_opt_config(f"facebook/{shared.model_name}")
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model = OptLM(opt_config, env, "models", policy)
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model.init_all_weights()
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# DeepSpeed ZeRO-3
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elif shared.args.deepspeed:
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model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
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model = deepspeed.initialize(model=model, config_params=ds_config, model_parameters=None, optimizer=None, lr_scheduler=None)[0]
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model.module.eval() # Inference
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print(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}")
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# Custom
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else:
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command = "AutoModelForCausalLM.from_pretrained"
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params = ["low_cpu_mem_usage=True"]
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if not shared.args.cpu and not torch.cuda.is_available():
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print("Warning: no GPU has been detected.\nFalling back to CPU mode.\n")
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shared.args.cpu = True
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if shared.args.cpu:
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params.append("low_cpu_mem_usage=True")
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params.append("torch_dtype=torch.float32")
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else:
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params.append("device_map='auto'")
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params.append("load_in_8bit=True" if shared.args.load_in_8bit else "torch_dtype=torch.bfloat16" if shared.args.bf16 else "torch_dtype=torch.float16")
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if shared.args.gpu_memory:
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params.append(f"max_memory={{0: '{shared.args.gpu_memory or '99'}GiB', 'cpu': '{shared.args.cpu_memory or '99'}GiB'}}")
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elif not shared.args.load_in_8bit:
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total_mem = (torch.cuda.get_device_properties(0).total_memory/(1024*1024))
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suggestion = round((total_mem-1000)/1000)*1000
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if total_mem-suggestion < 800:
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suggestion -= 1000
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suggestion = int(round(suggestion/1000))
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print(f"\033[1;32;1mAuto-assiging --gpu-memory {suggestion} for your GPU to try to prevent out-of-memory errors.\nYou can manually set other values.\033[0;37;0m")
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params.append(f"max_memory={{0: '{suggestion}GiB', 'cpu': '{shared.args.cpu_memory or '99'}GiB'}}")
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if shared.args.disk:
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params.append(f"offload_folder='{shared.args.disk_cache_dir}'")
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command = f"{command}(Path(f'models/{shared.model_name}'), {', '.join(set(params))})"
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model = eval(command)
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# Loading the tokenizer
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if shared.model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')) and Path(f"models/gpt-j-6B/").exists():
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tokenizer = AutoTokenizer.from_pretrained(Path("models/gpt-j-6B/"))
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else:
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tokenizer = AutoTokenizer.from_pretrained(Path(f"models/{shared.model_name}/"))
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tokenizer.truncation_side = 'left'
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print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
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return model, tokenizer
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def load_soft_prompt(name):
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if name == 'None':
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shared.soft_prompt = False
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shared.soft_prompt_tensor = None
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else:
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with zipfile.ZipFile(Path(f'softprompts/{name}.zip')) as zf:
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zf.extract('tensor.npy')
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zf.extract('meta.json')
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j = json.loads(open('meta.json', 'r').read())
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print(f"\nLoading the softprompt \"{name}\".")
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for field in j:
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if field != 'name':
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if type(j[field]) is list:
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print(f"{field}: {', '.join(j[field])}")
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else:
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print(f"{field}: {j[field]}")
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print()
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tensor = np.load('tensor.npy')
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Path('tensor.npy').unlink()
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Path('meta.json').unlink()
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tensor = torch.Tensor(tensor).to(device=shared.model.device, dtype=shared.model.dtype)
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tensor = torch.reshape(tensor, (1, tensor.shape[0], tensor.shape[1]))
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shared.soft_prompt = True
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shared.soft_prompt_tensor = tensor
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return name
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def upload_soft_prompt(file):
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with zipfile.ZipFile(io.BytesIO(file)) as zf:
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zf.extract('meta.json')
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j = json.loads(open('meta.json', 'r').read())
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name = j['name']
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Path('meta.json').unlink()
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with open(Path(f'softprompts/{name}.zip'), 'wb') as f:
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f.write(file)
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return name
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def load_model_wrapper(selected_model):
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if selected_model != shared.model_name:
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shared.model_name = selected_model
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model = shared.tokenizer = None
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if not shared.args.cpu:
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gc.collect()
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torch.cuda.empty_cache()
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shared.model, shared.tokenizer = load_model(shared.model_name)
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return selected_model
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def load_preset_values(preset_menu, return_dict=False):
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generate_params = {
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'do_sample': True,
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'temperature': 1,
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'top_p': 1,
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'typical_p': 1,
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'repetition_penalty': 1,
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'top_k': 50,
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'num_beams': 1,
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'penalty_alpha': 0,
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'min_length': 0,
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'length_penalty': 1,
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'no_repeat_ngram_size': 0,
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'early_stopping': False,
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}
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with open(Path(f'presets/{preset_menu}.txt'), 'r') as infile:
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preset = infile.read()
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for i in preset.splitlines():
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i = i.rstrip(',').strip().split('=')
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if len(i) == 2 and i[0].strip() != 'tokens':
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generate_params[i[0].strip()] = eval(i[1].strip())
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generate_params['temperature'] = min(1.99, generate_params['temperature'])
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if return_dict:
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return generate_params
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else:
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return generate_params['do_sample'], generate_params['temperature'], generate_params['top_p'], generate_params['typical_p'], generate_params['repetition_penalty'], generate_params['top_k'], generate_params['min_length'], generate_params['no_repeat_ngram_size'], generate_params['num_beams'], generate_params['penalty_alpha'], generate_params['length_penalty'], generate_params['early_stopping']
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def get_available_models():
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return sorted([item.name for item in list(Path('models/').glob('*')) if not item.name.endswith(('.txt', '-np'))], key=str.lower)
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def get_available_presets():
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return sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('presets').glob('*.txt'))), key=str.lower)
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def get_available_characters():
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return ["None"] + sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('characters').glob('*.json'))), key=str.lower)
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def get_available_extensions():
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return sorted(set(map(lambda x : x.parts[1], Path('extensions').glob('*/script.py'))), key=str.lower)
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def get_available_softprompts():
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return ["None"] + sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('softprompts').glob('*.zip'))), key=str.lower)
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def create_extensions_block():
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extensions_ui_elements = []
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default_values = []
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if not (shared.args.chat or shared.args.cai_chat):
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gr.Markdown('## Extensions parameters')
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for ext in sorted(extension_state, key=lambda x : extension_state[x][1]):
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if extension_state[ext][0] == True:
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params = extensions_module.get_params(ext)
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for param in params:
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_id = f"{ext}-{param}"
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default_value = settings[_id] if _id in settings else params[param]
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default_values.append(default_value)
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if type(params[param]) == str:
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extensions_ui_elements.append(gr.Textbox(value=default_value, label=f"{ext}-{param}"))
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elif type(params[param]) in [int, float]:
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extensions_ui_elements.append(gr.Number(value=default_value, label=f"{ext}-{param}"))
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elif type(params[param]) == bool:
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extensions_ui_elements.append(gr.Checkbox(value=default_value, label=f"{ext}-{param}"))
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update_extensions_parameters(*default_values)
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btn_extensions = gr.Button("Apply")
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btn_extensions.click(update_extensions_parameters, [*extensions_ui_elements], [])
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def create_settings_menus():
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generate_params = load_preset_values(settings[f'preset{suffix}'] if not shared.args.flexgen else 'Naive', return_dict=True)
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with gr.Row():
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with gr.Column():
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with gr.Row():
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model_menu = gr.Dropdown(choices=available_models, value=shared.model_name, label='Model')
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create_refresh_button(model_menu, lambda : None, lambda : {"choices": get_available_models()}, "refresh-button")
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with gr.Column():
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with gr.Row():
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preset_menu = gr.Dropdown(choices=available_presets, value=settings[f'preset{suffix}'] if not shared.args.flexgen else 'Naive', label='Generation parameters preset')
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create_refresh_button(preset_menu, lambda : None, lambda : {"choices": get_available_presets()}, "refresh-button")
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with gr.Accordion("Custom generation parameters", open=False, elem_id="accordion"):
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with gr.Row():
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do_sample = gr.Checkbox(value=generate_params['do_sample'], label="do_sample")
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temperature = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label="temperature")
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with gr.Row():
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top_k = gr.Slider(0,200,value=generate_params['top_k'],step=1,label="top_k")
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top_p = gr.Slider(0.0,1.0,value=generate_params['top_p'],step=0.01,label="top_p")
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with gr.Row():
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repetition_penalty = gr.Slider(1.0,4.99,value=generate_params['repetition_penalty'],step=0.01,label="repetition_penalty")
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no_repeat_ngram_size = gr.Slider(0, 20, step=1, value=generate_params["no_repeat_ngram_size"], label="no_repeat_ngram_size")
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with gr.Row():
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typical_p = gr.Slider(0.0,1.0,value=generate_params['typical_p'],step=0.01,label="typical_p")
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min_length = gr.Slider(0, 2000, step=1, value=generate_params["min_length"] if shared.args.no_stream else 0, label="min_length", interactive=shared.args.no_stream)
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gr.Markdown("Contrastive search:")
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penalty_alpha = gr.Slider(0, 5, value=generate_params["penalty_alpha"], label="penalty_alpha")
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gr.Markdown("Beam search (uses a lot of VRAM):")
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with gr.Row():
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num_beams = gr.Slider(1, 20, step=1, value=generate_params["num_beams"], label="num_beams")
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length_penalty = gr.Slider(-5, 5, value=generate_params["length_penalty"], label="length_penalty")
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early_stopping = gr.Checkbox(value=generate_params["early_stopping"], label="early_stopping")
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with gr.Accordion("Soft prompt", open=False, elem_id="accordion"):
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with gr.Row():
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softprompts_menu = gr.Dropdown(choices=available_softprompts, value="None", label='Soft prompt')
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create_refresh_button(softprompts_menu, lambda : None, lambda : {"choices": get_available_softprompts()}, "refresh-button")
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gr.Markdown('Upload a soft prompt (.zip format):')
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with gr.Row():
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upload_softprompt = gr.File(type='binary', file_types=[".zip"])
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model_menu.change(load_model_wrapper, [model_menu], [model_menu], show_progress=True)
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preset_menu.change(load_preset_values, [preset_menu], [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])
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softprompts_menu.change(load_soft_prompt, [softprompts_menu], [softprompts_menu], show_progress=True)
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upload_softprompt.upload(upload_soft_prompt, [upload_softprompt], [softprompts_menu])
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return preset_menu, 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
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# Global variables
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available_models = get_available_models()
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available_presets = get_available_presets()
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available_characters = get_available_characters()
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extensions_module.available_extensions = get_available_extensions()
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available_softprompts = get_available_softprompts()
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if shared.args.extensions is not None:
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load_extensions()
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# Choosing the default model
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if shared.args.model is not None:
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shared.model_name = shared.args.model
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else:
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if len(available_models) == 0:
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print("No models are available! Please download at least one.")
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sys.exit(0)
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elif len(available_models) == 1:
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i = 0
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else:
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print("The following models are available:\n")
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for i,model in enumerate(available_models):
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print(f"{i+1}. {model}")
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print(f"\nWhich one do you want to load? 1-{len(available_models)}\n")
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i = int(input())-1
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print()
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shared.model_name = available_models[i]
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shared.model, shared.tokenizer = load_model(shared.model_name)
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loaded_preset = None
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# UI settings
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if shared.model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')):
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default_text = settings['prompt_gpt4chan']
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elif re.match('(rosey|chip|joi)_.*_instruct.*', shared.model_name.lower()) is not None:
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default_text = 'User: \n'
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else:
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default_text = settings['prompt']
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description = f"\n\n# Text generation lab\nGenerate text using Large Language Models.\n"
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suffix = '_pygmalion' if 'pygmalion' in shared.model_name.lower() else ''
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buttons = {}
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gen_events = []
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if shared.args.chat or shared.args.cai_chat:
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if Path(f'logs/persistent.json').exists():
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chat.load_history(open(Path(f'logs/persistent.json'), 'rb').read(), settings[f'name1{suffix}'], settings[f'name2{suffix}'])
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with gr.Blocks(css=css+chat_css, analytics_enabled=False) as interface:
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if shared.args.cai_chat:
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display = gr.HTML(value=generate_chat_html(chat.history['visible'], settings[f'name1{suffix}'], settings[f'name2{suffix}'], chat.character))
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else:
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display = gr.Chatbot(value=chat.history['visible'])
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textbox = gr.Textbox(label='Input')
|
|
with gr.Row():
|
|
buttons["Stop"] = gr.Button("Stop")
|
|
buttons["Generate"] = gr.Button("Generate")
|
|
buttons["Regenerate"] = gr.Button("Regenerate")
|
|
with gr.Row():
|
|
buttons["Impersonate"] = gr.Button("Impersonate")
|
|
buttons["Remove last"] = gr.Button("Remove last")
|
|
buttons["Clear history"] = gr.Button("Clear history")
|
|
with gr.Row():
|
|
buttons["Send last reply to input"] = gr.Button("Send last reply to input")
|
|
buttons["Replace last reply"] = gr.Button("Replace last reply")
|
|
if shared.args.picture:
|
|
with gr.Row():
|
|
picture_select = gr.Image(label="Send a picture", type='pil')
|
|
|
|
with gr.Tab("Chat settings"):
|
|
name1 = gr.Textbox(value=settings[f'name1{suffix}'], lines=1, label='Your name')
|
|
name2 = gr.Textbox(value=settings[f'name2{suffix}'], lines=1, label='Bot\'s name')
|
|
context = gr.Textbox(value=settings[f'context{suffix}'], lines=2, label='Context')
|
|
with gr.Row():
|
|
character_menu = gr.Dropdown(choices=available_characters, value="None", label='Character')
|
|
create_refresh_button(character_menu, lambda : None, lambda : {"choices": get_available_characters()}, "refresh-button")
|
|
|
|
with gr.Row():
|
|
check = gr.Checkbox(value=settings[f'stop_at_newline{suffix}'], label='Stop generating at new line character?')
|
|
with gr.Row():
|
|
with gr.Tab('Chat history'):
|
|
with gr.Row():
|
|
with gr.Column():
|
|
gr.Markdown('Upload')
|
|
upload_chat_history = gr.File(type='binary', file_types=[".json", ".txt"])
|
|
with gr.Column():
|
|
gr.Markdown('Download')
|
|
download = gr.File()
|
|
buttons["Download"] = gr.Button(value="Click me")
|
|
with gr.Tab('Upload character'):
|
|
with gr.Row():
|
|
with gr.Column():
|
|
gr.Markdown('1. Select the JSON file')
|
|
upload_char = gr.File(type='binary', file_types=[".json"])
|
|
with gr.Column():
|
|
gr.Markdown('2. Select your character\'s profile picture (optional)')
|
|
upload_img = gr.File(type='binary', file_types=["image"])
|
|
buttons["Upload character"] = gr.Button(value="Submit")
|
|
with gr.Tab('Upload your profile picture'):
|
|
upload_img_me = gr.File(type='binary', file_types=["image"])
|
|
with gr.Tab('Upload TavernAI Character Card'):
|
|
upload_img_tavern = gr.File(type='binary', file_types=["image"])
|
|
|
|
with gr.Tab("Generation settings"):
|
|
with gr.Row():
|
|
with gr.Column():
|
|
max_new_tokens = gr.Slider(minimum=settings['max_new_tokens_min'], maximum=settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=settings['max_new_tokens'])
|
|
with gr.Column():
|
|
chat_prompt_size_slider = gr.Slider(minimum=settings['chat_prompt_size_min'], maximum=settings['chat_prompt_size_max'], step=1, label='Maximum prompt size in tokens', value=settings['chat_prompt_size'])
|
|
|
|
preset_menu, 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 = create_settings_menus()
|
|
|
|
if shared.args.extensions is not None:
|
|
with gr.Tab("Extensions"):
|
|
create_extensions_block()
|
|
|
|
input_params = [textbox, max_new_tokens, do_sample, max_new_tokens, 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_slider]
|
|
if shared.args.picture:
|
|
input_params.append(picture_select)
|
|
function_call = "chat.cai_chatbot_wrapper" if shared.args.cai_chat else "chat.chatbot_wrapper"
|
|
|
|
gen_events.append(buttons["Generate"].click(eval(function_call), input_params, display, show_progress=shared.args.no_stream, api_name="textgen"))
|
|
gen_events.append(textbox.submit(eval(function_call), input_params, display, show_progress=shared.args.no_stream))
|
|
if shared.args.picture:
|
|
picture_select.upload(eval(function_call), input_params, display, show_progress=shared.args.no_stream)
|
|
gen_events.append(buttons["Regenerate"].click(chat.regenerate_wrapper, input_params, display, show_progress=shared.args.no_stream))
|
|
gen_events.append(buttons["Impersonate"].click(chat.impersonate_wrapper, input_params, textbox, show_progress=shared.args.no_stream))
|
|
buttons["Stop"].click(chat.stop_everything_event, [], [], cancels=gen_events)
|
|
|
|
buttons["Send last reply to input"].click(chat.send_last_reply_to_input, [], textbox, show_progress=shared.args.no_stream)
|
|
buttons["Replace last reply"].click(chat.replace_last_reply, [textbox, name1, name2], display, show_progress=shared.args.no_stream)
|
|
buttons["Clear history"].click(chat.clear_chat_log, [character_menu, name1, name2], display)
|
|
buttons["Remove last"].click(chat.remove_last_message, [name1, name2], [display, textbox], show_progress=False)
|
|
buttons["Download"].click(chat.save_history, inputs=[], outputs=[download])
|
|
buttons["Upload character"].click(chat.upload_character, [upload_char, upload_img], [character_menu])
|
|
|
|
# Clearing stuff and saving the history
|
|
for i in ["Generate", "Regenerate", "Replace last reply"]:
|
|
buttons[i].click(lambda x: "", textbox, textbox, show_progress=False)
|
|
buttons[i].click(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
|
|
buttons["Clear history"].click(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
|
|
textbox.submit(lambda x: "", textbox, textbox, show_progress=False)
|
|
textbox.submit(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
|
|
|
|
character_menu.change(chat.load_character, [character_menu, name1, name2], [name2, context, display])
|
|
upload_chat_history.upload(chat.load_history, [upload_chat_history, name1, name2], [])
|
|
upload_img_tavern.upload(chat.upload_tavern_character, [upload_img_tavern, name1, name2], [character_menu])
|
|
upload_img_me.upload(chat.upload_your_profile_picture, [upload_img_me], [])
|
|
if shared.args.picture:
|
|
picture_select.upload(lambda : None, [], [picture_select], show_progress=False)
|
|
if shared.args.cai_chat:
|
|
upload_chat_history.upload(chat.redraw_html, [name1, name2], [display])
|
|
upload_img_me.upload(chat.redraw_html, [name1, name2], [display])
|
|
else:
|
|
upload_chat_history.upload(lambda : chat.history['visible'], [], [display])
|
|
upload_img_me.upload(lambda : chat.history['visible'], [], [display])
|
|
|
|
elif shared.args.notebook:
|
|
with gr.Blocks(css=css, analytics_enabled=False) as interface:
|
|
gr.Markdown(description)
|
|
with gr.Tab('Raw'):
|
|
textbox = gr.Textbox(value=default_text, lines=23)
|
|
with gr.Tab('Markdown'):
|
|
markdown = gr.Markdown()
|
|
with gr.Tab('HTML'):
|
|
html = gr.HTML()
|
|
|
|
buttons["Generate"] = gr.Button("Generate")
|
|
buttons["Stop"] = gr.Button("Stop")
|
|
|
|
max_new_tokens = gr.Slider(minimum=settings['max_new_tokens_min'], maximum=settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=settings['max_new_tokens'])
|
|
|
|
preset_menu, 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 = create_settings_menus()
|
|
|
|
if shared.args.extensions is not None:
|
|
create_extensions_block()
|
|
|
|
gen_events.append(buttons["Generate"].click(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping], [textbox, markdown, html], show_progress=shared.args.no_stream, api_name="textgen"))
|
|
gen_events.append(textbox.submit(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping], [textbox, markdown, html], show_progress=shared.args.no_stream))
|
|
buttons["Stop"].click(None, None, None, cancels=gen_events)
|
|
|
|
else:
|
|
with gr.Blocks(css=css, analytics_enabled=False) as interface:
|
|
gr.Markdown(description)
|
|
with gr.Row():
|
|
with gr.Column():
|
|
textbox = gr.Textbox(value=default_text, lines=15, label='Input')
|
|
max_new_tokens = gr.Slider(minimum=settings['max_new_tokens_min'], maximum=settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=settings['max_new_tokens'])
|
|
buttons["Generate"] = gr.Button("Generate")
|
|
with gr.Row():
|
|
with gr.Column():
|
|
buttons["Continue"] = gr.Button("Continue")
|
|
with gr.Column():
|
|
buttons["Stop"] = gr.Button("Stop")
|
|
|
|
preset_menu, 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 = create_settings_menus()
|
|
if shared.args.extensions is not None:
|
|
create_extensions_block()
|
|
|
|
with gr.Column():
|
|
with gr.Tab('Raw'):
|
|
output_textbox = gr.Textbox(lines=15, label='Output')
|
|
with gr.Tab('Markdown'):
|
|
markdown = gr.Markdown()
|
|
with gr.Tab('HTML'):
|
|
html = gr.HTML()
|
|
|
|
gen_events.append(buttons["Generate"].click(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=shared.args.no_stream, api_name="textgen"))
|
|
gen_events.append(textbox.submit(generate_reply, [textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=shared.args.no_stream))
|
|
gen_events.append(buttons["Continue"].click(generate_reply, [output_textbox, max_new_tokens, do_sample, max_new_tokens, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping], [output_textbox, markdown, html], show_progress=shared.args.no_stream))
|
|
buttons["Stop"].click(None, None, None, cancels=gen_events)
|
|
|
|
interface.queue()
|
|
if shared.args.listen:
|
|
interface.launch(prevent_thread_lock=True, share=shared.args.share, server_name="0.0.0.0", server_port=shared.args.listen_port)
|
|
else:
|
|
interface.launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port)
|
|
|
|
# I think that I will need this later
|
|
while True:
|
|
time.sleep(0.5)
|