diff --git a/.gitignore b/.gitignore index 3cfbbb22..36852916 100644 --- a/.gitignore +++ b/.gitignore @@ -1,26 +1,21 @@ -cache/* -characters/* -extensions/silero_tts/outputs/* -extensions/elevenlabs_tts/outputs/* -extensions/sd_api_pictures/outputs/* -logs/* -loras/* -models/* -softprompts/* -torch-dumps/* +cache +characters +training/datasets +extensions/silero_tts/outputs +extensions/elevenlabs_tts/outputs +extensions/sd_api_pictures/outputs +logs +loras +models +softprompts +torch-dumps *pycache* */*pycache* */*/pycache* venv/ .venv/ +repositories settings.json img_bot* img_me* - -!characters/Example.json -!characters/Example.png -!loras/place-your-loras-here.txt -!models/place-your-models-here.txt -!softprompts/place-your-softprompts-here.txt -!torch-dumps/place-your-pt-models-here.txt diff --git a/README.md b/README.md index cb070445..3bfbc72f 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,7 @@ Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github. * [FlexGen offload](https://github.com/oobabooga/text-generation-webui/wiki/FlexGen). * [DeepSpeed ZeRO-3 offload](https://github.com/oobabooga/text-generation-webui/wiki/DeepSpeed). * Get responses via API, [with](https://github.com/oobabooga/text-generation-webui/blob/main/api-example-streaming.py) or [without](https://github.com/oobabooga/text-generation-webui/blob/main/api-example.py) streaming. -* [LLaMA model, including 4-bit mode](https://github.com/oobabooga/text-generation-webui/wiki/LLaMA-model). +* [LLaMA model, including 4-bit GPTQ support](https://github.com/oobabooga/text-generation-webui/wiki/LLaMA-model). * [RWKV model](https://github.com/oobabooga/text-generation-webui/wiki/RWKV-model). * [Supports LoRAs](https://github.com/oobabooga/text-generation-webui/wiki/Using-LoRAs). * Supports softprompts. @@ -84,10 +84,6 @@ pip install -r requirements.txt > > For bitsandbytes and `--load-in-8bit` to work on Linux/WSL, this dirty fix is currently necessary: https://github.com/oobabooga/text-generation-webui/issues/400#issuecomment-1474876859 -### Alternative: native Windows installation - -As an alternative to the recommended WSL method, you can install the web UI natively on Windows using this guide. It will be a lot harder and the performance may be slower: [Installation instructions for human beings](https://github.com/oobabooga/text-generation-webui/wiki/Installation-instructions-for-human-beings). - ### Alternative: one-click installers [oobabooga-windows.zip](https://github.com/oobabooga/one-click-installers/archive/refs/heads/oobabooga-windows.zip) @@ -101,7 +97,13 @@ Just download the zip above, extract it, and double click on "install". The web Source codes: https://github.com/oobabooga/one-click-installers -This method lags behind the newest developments and does not support 8-bit mode on Windows without additional set up: https://github.com/oobabooga/text-generation-webui/issues/147#issuecomment-1456040134, https://github.com/oobabooga/text-generation-webui/issues/20#issuecomment-1411650652 +> **Note** +> +> To get 8-bit and 4-bit models working in your 1-click Windows installation, you can use the [one-click-bandaid](https://github.com/ClayShoaf/oobabooga-one-click-bandaid). + +### Alternative: native Windows installation + +As an alternative to the recommended WSL method, you can install the web UI natively on Windows using this guide. It will be a lot harder and the performance may be slower: [Installation instructions for human beings](https://github.com/oobabooga/text-generation-webui/wiki/Installation-instructions-for-human-beings). ### Alternative: Docker @@ -174,10 +176,10 @@ Optionally, you can use the following command-line flags: | `--cai-chat` | 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. | | `--cpu` | Use the CPU to generate text.| | `--load-in-8bit` | Load the model with 8-bit precision.| -| `--load-in-4bit` | DEPRECATED: use `--gptq-bits 4` instead. | -| `--gptq-bits GPTQ_BITS` | GPTQ: Load a pre-quantized model with specified precision. 2, 3, 4 and 8 (bit) are supported. Currently only works with LLaMA and OPT. | -| `--gptq-model-type MODEL_TYPE` | GPTQ: Model type of pre-quantized model. Currently only LLaMa and OPT are supported. | -| `--gptq-pre-layer GPTQ_PRE_LAYER` | GPTQ: The number of layers to preload. | +| `--wbits WBITS` | GPTQ: Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. | +| `--model_type MODEL_TYPE` | GPTQ: Model type of pre-quantized model. Currently only LLaMA and OPT are supported. | +| `--groupsize GROUPSIZE` | GPTQ: Group size. | +| `--pre_layer PRE_LAYER` | GPTQ: The number of layers to preload. | | `--bf16` | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. | | `--auto-devices` | Automatically split the model across the available GPU(s) and CPU.| | `--disk` | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. | diff --git a/css/chat.css b/css/chat.css index 8d9d88a6..1e703530 100644 --- a/css/chat.css +++ b/css/chat.css @@ -23,3 +23,9 @@ div.svelte-362y77>*, div.svelte-362y77>.form>* { .pending.svelte-1ed2p3z { opacity: 1; } + +#extensions { + padding: 0; + padding: 0; +} + diff --git a/css/main.css b/css/main.css index 09f3b6a8..97879f01 100644 --- a/css/main.css +++ b/css/main.css @@ -54,3 +54,13 @@ ol li p, ul li p { .gradio-container-3-18-0 .prose * h1, h2, h3, h4 { color: white; } + +.gradio-container { + max-width: 100% !important; + padding-top: 0 !important; +} + +#extensions { + padding: 15px; + padding: 15px; +} diff --git a/css/main.js b/css/main.js index 9db3fe8b..029ecb62 100644 --- a/css/main.js +++ b/css/main.js @@ -11,7 +11,7 @@ let extensions = document.getElementById('extensions'); main_parent.addEventListener('click', function(e) { // Check if the main element is visible if (main.offsetHeight > 0 && main.offsetWidth > 0) { - extensions.style.display = 'block'; + extensions.style.display = 'flex'; } else { extensions.style.display = 'none'; } diff --git a/download-model.py b/download-model.py index 7c2965f6..25386e5f 100644 --- a/download-model.py +++ b/download-model.py @@ -116,10 +116,11 @@ def get_download_links_from_huggingface(model, branch): is_pytorch = re.match("(pytorch|adapter)_model.*\.bin", fname) is_safetensors = re.match("model.*\.safetensors", fname) + is_pt = re.match(".*\.pt", fname) is_tokenizer = re.match("tokenizer.*\.model", fname) - is_text = re.match(".*\.(txt|json|py)", fname) or is_tokenizer + is_text = re.match(".*\.(txt|json|py|md)", fname) or is_tokenizer - if any((is_pytorch, is_safetensors, is_text, is_tokenizer)): + if any((is_pytorch, is_safetensors, is_pt, is_tokenizer, is_text)): if is_text: links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}") classifications.append('text') @@ -132,7 +133,8 @@ def get_download_links_from_huggingface(model, branch): elif is_pytorch: has_pytorch = True classifications.append('pytorch') - + elif is_pt: + classifications.append('pt') cursor = base64.b64encode(f'{{"file_name":"{dict[-1]["path"]}"}}'.encode()) + b':50' cursor = base64.b64encode(cursor) diff --git a/extensions/api/script.py b/extensions/api/script.py index 1774c345..bd7c1900 100644 --- a/extensions/api/script.py +++ b/extensions/api/script.py @@ -1,8 +1,9 @@ +import json from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer from threading import Thread + from modules import shared -from modules.text_generation import generate_reply, encode -import json +from modules.text_generation import encode, generate_reply params = { 'port': 5000, @@ -87,5 +88,5 @@ def run_server(): print(f'Starting KoboldAI compatible api at http://{server_addr[0]}:{server_addr[1]}/api') server.serve_forever() -def ui(): +def setup(): Thread(target=run_server, daemon=True).start() diff --git a/modules/GPTQ_loader.py b/modules/GPTQ_loader.py index 32a5458f..afb5695f 100644 --- a/modules/GPTQ_loader.py +++ b/modules/GPTQ_loader.py @@ -14,18 +14,21 @@ import opt def load_quantized(model_name): - if not shared.args.gptq_model_type: + if not shared.args.model_type: # Try to determine model type from model name - model_type = model_name.split('-')[0].lower() - if model_type not in ('llama', 'opt'): - print("Can't determine model type from model name. Please specify it manually using --gptq-model-type " + if model_name.lower().startswith(('llama', 'alpaca')): + model_type = 'llama' + elif model_name.lower().startswith(('opt', 'galactica')): + model_type = 'opt' + else: + print("Can't determine model type from model name. Please specify it manually using --model_type " "argument") exit() else: - model_type = shared.args.gptq_model_type.lower() + model_type = shared.args.model_type.lower() if model_type == 'llama': - if not shared.args.gptq_pre_layer: + if not shared.args.pre_layer: load_quant = llama.load_quant else: load_quant = llama_inference_offload.load_quant @@ -35,33 +38,44 @@ def load_quantized(model_name): print("Unknown pre-quantized model type specified. Only 'llama' and 'opt' are supported") exit() + # Now we are going to try to locate the quantized model file. path_to_model = Path(f'models/{model_name}') - if path_to_model.name.lower().startswith('llama-7b'): - pt_model = f'llama-7b-{shared.args.gptq_bits}bit.pt' - elif path_to_model.name.lower().startswith('llama-13b'): - pt_model = f'llama-13b-{shared.args.gptq_bits}bit.pt' - elif path_to_model.name.lower().startswith('llama-30b'): - pt_model = f'llama-30b-{shared.args.gptq_bits}bit.pt' - elif path_to_model.name.lower().startswith('llama-65b'): - pt_model = f'llama-65b-{shared.args.gptq_bits}bit.pt' - else: - pt_model = f'{model_name}-{shared.args.gptq_bits}bit.pt' - - # Try to find the .pt both in models/ and in the subfolder + found_pts = list(path_to_model.glob("*.pt")) + found_safetensors = list(path_to_model.glob("*.safetensors")) pt_path = None - for path in [Path(p) for p in [f"models/{pt_model}", f"{path_to_model}/{pt_model}"]]: - if path.exists(): - pt_path = path + + if len(found_pts) == 1: + pt_path = found_pts[0] + elif len(found_safetensors) == 1: + pt_path = found_safetensors[0] + else: + if path_to_model.name.lower().startswith('llama-7b'): + pt_model = f'llama-7b-{shared.args.wbits}bit' + elif path_to_model.name.lower().startswith('llama-13b'): + pt_model = f'llama-13b-{shared.args.wbits}bit' + elif path_to_model.name.lower().startswith('llama-30b'): + pt_model = f'llama-30b-{shared.args.wbits}bit' + elif path_to_model.name.lower().startswith('llama-65b'): + pt_model = f'llama-65b-{shared.args.wbits}bit' + else: + pt_model = f'{model_name}-{shared.args.wbits}bit' + + # Try to find the .safetensors or .pt both in models/ and in the subfolder + for path in [Path(p+ext) for ext in ['.safetensors', '.pt'] for p in [f"models/{pt_model}", f"{path_to_model}/{pt_model}"]]: + if path.exists(): + print(f"Found {path}") + pt_path = path + break if not pt_path: - print(f"Could not find {pt_model}, exiting...") + print("Could not find the quantized model in .pt or .safetensors format, exiting...") exit() # qwopqwop200's offload - if shared.args.gptq_pre_layer: - model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits, shared.args.gptq_pre_layer) + if shared.args.pre_layer: + model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, shared.args.pre_layer) else: - model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits) + model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize) # accelerate offload (doesn't work properly) if shared.args.gpu_memory: diff --git a/modules/LoRA.py b/modules/LoRA.py index 6915e157..283fcf4c 100644 --- a/modules/LoRA.py +++ b/modules/LoRA.py @@ -1,22 +1,43 @@ from pathlib import Path +import torch + import modules.shared as shared from modules.models import load_model +from modules.text_generation import clear_torch_cache +def reload_model(): + shared.model = shared.tokenizer = None + clear_torch_cache() + shared.model, shared.tokenizer = load_model(shared.model_name) + def add_lora_to_model(lora_name): from peft import PeftModel - # Is there a more efficient way of returning to the base model? - if lora_name == "None": - print("Reloading the model to remove the LoRA...") - shared.model, shared.tokenizer = load_model(shared.model_name) - else: - # Why doesn't this work in 16-bit mode? - print(f"Adding the LoRA {lora_name} to the model...") + # If a LoRA had been previously loaded, or if we want + # to unload a LoRA, reload the model + if shared.lora_name != "None" or lora_name == "None": + reload_model() + shared.lora_name = lora_name + if lora_name != "None": + print(f"Adding the LoRA {lora_name} to the model...") params = {} - params['device_map'] = {'': 0} - #params['dtype'] = shared.model.dtype + if not shared.args.cpu: + params['dtype'] = shared.model.dtype + if hasattr(shared.model, "hf_device_map"): + params['device_map'] = {"base_model.model."+k: v for k, v in shared.model.hf_device_map.items()} + elif shared.args.load_in_8bit: + params['device_map'] = {'': 0} + shared.model = PeftModel.from_pretrained(shared.model, Path(f"loras/{lora_name}"), **params) + if not shared.args.load_in_8bit and not shared.args.cpu: + shared.model.half() + if not hasattr(shared.model, "hf_device_map"): + if torch.has_mps: + device = torch.device('mps') + shared.model = shared.model.to(device) + else: + shared.model = shared.model.cuda() diff --git a/modules/RWKV.py b/modules/RWKV.py index 5cf8937a..8c7ea2b9 100644 --- a/modules/RWKV.py +++ b/modules/RWKV.py @@ -45,11 +45,11 @@ class RWKVModel: token_stop = token_stop ) - return context+self.pipeline.generate(context, token_count=token_count, args=args, callback=callback) + return self.pipeline.generate(context, token_count=token_count, args=args, callback=callback) def generate_with_streaming(self, **kwargs): with Iteratorize(self.generate, kwargs, callback=None) as generator: - reply = kwargs['context'] + reply = '' for token in generator: reply += token yield reply diff --git a/modules/callbacks.py b/modules/callbacks.py index 12a90cc3..8d30d615 100644 --- a/modules/callbacks.py +++ b/modules/callbacks.py @@ -11,24 +11,22 @@ import modules.shared as shared # Copied from https://github.com/PygmalionAI/gradio-ui/ class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria): - def __init__(self, sentinel_token_ids: torch.LongTensor, - starting_idx: int): + def __init__(self, sentinel_token_ids: list[torch.LongTensor], starting_idx: int): transformers.StoppingCriteria.__init__(self) self.sentinel_token_ids = sentinel_token_ids self.starting_idx = starting_idx - def __call__(self, input_ids: torch.LongTensor, - _scores: torch.FloatTensor) -> bool: + def __call__(self, input_ids: torch.LongTensor, _scores: torch.FloatTensor) -> bool: for sample in input_ids: trimmed_sample = sample[self.starting_idx:] - # Can't unfold, output is still too tiny. Skip. - if trimmed_sample.shape[-1] < self.sentinel_token_ids.shape[-1]: - continue - for window in trimmed_sample.unfold( - 0, self.sentinel_token_ids.shape[-1], 1): - if torch.all(torch.eq(self.sentinel_token_ids, window)): - return True + for i in range(len(self.sentinel_token_ids)): + # Can't unfold, output is still too tiny. Skip. + if trimmed_sample.shape[-1] < self.sentinel_token_ids[i].shape[-1]: + continue + for window in trimmed_sample.unfold(0, self.sentinel_token_ids[i].shape[-1], 1): + if torch.all(torch.eq(self.sentinel_token_ids[i][0], window)): + return True return False class Stream(transformers.StoppingCriteria): diff --git a/modules/chat.py b/modules/chat.py index 78fc4ab5..1a43cf3d 100644 --- a/modules/chat.py +++ b/modules/chat.py @@ -33,12 +33,14 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat i = len(shared.history['internal'])-1 while i >= 0 and len(encode(''.join(rows), max_new_tokens)[0]) < max_length: rows.insert(1, f"{name2}: {shared.history['internal'][i][1].strip()}\n") - if not (shared.history['internal'][i][0] == '<|BEGIN-VISIBLE-CHAT|>'): - rows.insert(1, f"{name1}: {shared.history['internal'][i][0].strip()}\n") + prev_user_input = shared.history['internal'][i][0] + if len(prev_user_input) > 0 and prev_user_input != '<|BEGIN-VISIBLE-CHAT|>': + rows.insert(1, f"{name1}: {prev_user_input.strip()}\n") i -= 1 if not impersonate: - rows.append(f"{name1}: {user_input}\n") + if len(user_input) > 0: + rows.append(f"{name1}: {user_input}\n") rows.append(apply_extensions(f"{name2}:", "bot_prefix")) limit = 3 else: @@ -51,41 +53,31 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat prompt = ''.join(rows) return prompt -def extract_message_from_reply(question, reply, name1, name2, check, impersonate=False): +def extract_message_from_reply(reply, name1, name2, check): next_character_found = False - asker = name1 if not impersonate else name2 - replier = name2 if not impersonate else name1 - - previous_idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", question)] - idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", reply)] - idx = idx[max(len(previous_idx)-1, 0)] - - if not impersonate: - reply = reply[idx + 1 + len(apply_extensions(f"{replier}:", "bot_prefix")):] - else: - reply = reply[idx + 1 + len(f"{replier}:"):] - if check: lines = reply.split('\n') reply = lines[0].strip() if len(lines) > 1: next_character_found = True else: - idx = reply.find(f"\n{asker}:") - if idx != -1: - reply = reply[:idx] - next_character_found = True - reply = fix_newlines(reply) + for string in [f"\n{name1}:", f"\n{name2}:"]: + idx = reply.find(string) + if idx != -1: + reply = reply[:idx] + next_character_found = True # If something like "\nYo" is generated just before "\nYou:" # is completed, trim it - next_turn = f"\n{asker}:" - for j in range(len(next_turn)-1, 0, -1): - if reply[-j:] == next_turn[:j]: - reply = reply[:-j] - break + if not next_character_found: + for string in [f"\n{name1}:", f"\n{name2}:"]: + for j in range(len(string)-1, 0, -1): + if reply[-j:] == string[:j]: + reply = reply[:-j] + break + reply = fix_newlines(reply) return reply, next_character_found def stop_everything_event(): @@ -125,12 +117,13 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical yield shared.history['visible']+[[visible_text, shared.processing_message]] # Generate - reply = '' + cumulative_reply = '' for i in range(chat_generation_attempts): - 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, seed, eos_token=eos_token, stopping_string=f"\n{name1}:"): + for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_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, seed, eos_token=eos_token, stopping_strings=[f"\n{name1}:", f"\n{name2}:"]): + reply = cumulative_reply + reply # Extracting the reply - reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check) + reply, next_character_found = extract_message_from_reply(reply, name1, name2, check) visible_reply = re.sub("(||{{user}})", name1_original, reply) visible_reply = apply_extensions(visible_reply, "output") if shared.args.chat: @@ -152,6 +145,8 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical if next_character_found: break + cumulative_reply = reply + yield shared.history['visible'] 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, seed, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1): @@ -162,16 +157,21 @@ def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typ prompt = generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size, impersonate=True) - reply = '' # Yield *Is typing...* yield shared.processing_message + + cumulative_reply = '' for i in range(chat_generation_attempts): - 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, seed, eos_token=eos_token, stopping_string=f"\n{name2}:"): - reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check, impersonate=True) + for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_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, seed, eos_token=eos_token, stopping_strings=[f"\n{name1}:", f"\n{name2}:"]): + reply = cumulative_reply + reply + reply, next_character_found = extract_message_from_reply(reply, name1, name2, check) yield reply if next_character_found: break - yield reply + + cumulative_reply = reply + + yield reply 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, seed, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1): 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, seed, name1, name2, context, check, chat_prompt_size, chat_generation_attempts): diff --git a/modules/extensions.py b/modules/extensions.py index dbc93840..c3cf4de4 100644 --- a/modules/extensions.py +++ b/modules/extensions.py @@ -7,6 +7,7 @@ import modules.shared as shared state = {} available_extensions = [] +setup_called = False def load_extensions(): global state @@ -39,6 +40,8 @@ def apply_extensions(text, typ): return text def create_extensions_block(): + global setup_called + # Updating the default values for extension, name in iterator(): if hasattr(extension, 'params'): @@ -47,10 +50,21 @@ def create_extensions_block(): if _id in shared.settings: extension.params[param] = shared.settings[_id] + should_display_ui = False + + # Running setup function + if not setup_called: + for extension, name in iterator(): + if hasattr(extension, "setup"): + extension.setup() + if hasattr(extension, "ui"): + should_display_ui = True + setup_called = True + # Creating the extension ui elements - if len(state) > 0: - with gr.Box(elem_id="extensions"): - gr.Markdown("Extensions") + if should_display_ui: + with gr.Column(elem_id="extensions"): for extension, name in iterator(): + gr.Markdown(f"\n### {name}") if hasattr(extension, "ui"): extension.ui() diff --git a/modules/html_generator.py b/modules/html_generator.py index 940d5486..ff18c913 100644 --- a/modules/html_generator.py +++ b/modules/html_generator.py @@ -119,13 +119,13 @@ def load_html_image(paths): def generate_chat_html(history, name1, name2, character): output = f'
' - + img_bot = load_html_image([f"characters/{character}.{ext}" for ext in ['png', 'jpg', 'jpeg']] + ["img_bot.png","img_bot.jpg","img_bot.jpeg"]) img_me = load_html_image(["img_me.png", "img_me.jpg", "img_me.jpeg"]) for i,_row in enumerate(history[::-1]): row = [convert_to_markdown(entry) for entry in _row] - + output += f"""
@@ -142,22 +142,24 @@ def generate_chat_html(history, name1, name2, character):
""" - if not (i == len(history)-1 and len(row[0]) == 0): - output += f""" -
-
- {img_me} -
-
-
- {name1} -
-
- {row[0]} -
-
+ if len(row[0]) == 0: # don't display empty user messages + continue + + output += f""" +
+
+ {img_me} +
+
+
+ {name1}
- """ +
+ {row[0]} +
+
+
+ """ output += "
" return output diff --git a/modules/models.py b/modules/models.py index ccb97da3..c9f03588 100644 --- a/modules/models.py +++ b/modules/models.py @@ -44,7 +44,7 @@ def load_model(model_name): shared.is_RWKV = model_name.lower().startswith('rwkv-') # Default settings - 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]): + if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.wbits, 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]): if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')): model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True) else: @@ -95,7 +95,7 @@ def load_model(model_name): return model, tokenizer # Quantized model - elif shared.args.gptq_bits > 0: + elif shared.args.wbits > 0: from modules.GPTQ_loader import load_quantized model = load_quantized(model_name) diff --git a/modules/shared.py b/modules/shared.py index 8d591f4f..87896faf 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -27,9 +27,9 @@ settings = { 'max_new_tokens': 200, 'max_new_tokens_min': 1, 'max_new_tokens_max': 2000, - 'name1': 'Person 1', - 'name2': 'Person 2', - 'context': 'This is a conversation between two people.', + 'name1': 'You', + 'name2': 'Assistant', + 'context': 'This is a conversation with your Assistant. The Assistant is very helpful and is eager to chat with you and answer your questions.', 'stop_at_newline': False, 'chat_prompt_size': 2048, 'chat_prompt_size_min': 0, @@ -52,7 +52,8 @@ settings = { 'default': 'Common sense questions and answers\n\nQuestion: \nFactual answer:', '^(gpt4chan|gpt-4chan|4chan)': '-----\n--- 865467536\nInput text\n--- 865467537\n', '(rosey|chip|joi)_.*_instruct.*': 'User: \n', - 'oasst-*': '<|prompter|>Write a story about future of AI development<|endoftext|><|assistant|>' + 'oasst-*': '<|prompter|>Write a story about future of AI development<|endoftext|><|assistant|>', + 'alpaca-*': "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n### Instruction:\nWrite a poem about the transformers Python library. \nMention the word \"large language models\" in that poem.\n### Response:\n", }, 'lora_prompts': { 'default': 'Common sense questions and answers\n\nQuestion: \nFactual answer:', @@ -78,10 +79,15 @@ parser.add_argument('--chat', action='store_true', help='Launch the web UI in ch 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.') parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.') parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.') -parser.add_argument('--load-in-4bit', action='store_true', help='DEPRECATED: use --gptq-bits 4 instead.') -parser.add_argument('--gptq-bits', type=int, default=0, help='GPTQ: Load a pre-quantized model with specified precision. 2, 3, 4 and 8bit are supported. Currently only works with LLaMA and OPT.') -parser.add_argument('--gptq-model-type', type=str, help='GPTQ: Model type of pre-quantized model. Currently only LLaMa and OPT are supported.') -parser.add_argument('--gptq-pre-layer', type=int, default=0, help='GPTQ: The number of layers to preload.') + +parser.add_argument('--gptq-bits', type=int, default=0, help='DEPRECATED: use --wbits instead.') +parser.add_argument('--gptq-model-type', type=str, help='DEPRECATED: use --model_type instead.') +parser.add_argument('--gptq-pre-layer', type=int, default=0, help='DEPRECATED: use --pre_layer instead.') +parser.add_argument('--wbits', type=int, default=0, help='GPTQ: Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.') +parser.add_argument('--model_type', type=str, help='GPTQ: Model type of pre-quantized model. Currently only LLaMA and OPT are supported.') +parser.add_argument('--groupsize', type=int, default=-1, help='GPTQ: Group size.') +parser.add_argument('--pre_layer', type=int, default=0, help='GPTQ: The number of layers to preload.') + parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.') parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.') 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.') @@ -109,6 +115,8 @@ parser.add_argument('--verbose', action='store_true', help='Print the prompts to args = parser.parse_args() # Provisional, this will be deleted later -if args.load_in_4bit: - print("Warning: --load-in-4bit is deprecated and will be removed. Use --gptq-bits 4 instead.\n") - args.gptq_bits = 4 +deprecated_dict = {'gptq_bits': ['wbits', 0], 'gptq_model_type': ['model_type', None], 'gptq_pre_layer': ['prelayer', 0]} +for k in deprecated_dict: + if eval(f"args.{k}") != deprecated_dict[k][1]: + print(f"Warning: --{k} is deprecated and will be removed. Use --{deprecated_dict[k][0]} instead.") + exec(f"args.{deprecated_dict[k][0]} = args.{k}") diff --git a/modules/text_generation.py b/modules/text_generation.py index e738cb21..9b2c233d 100644 --- a/modules/text_generation.py +++ b/modules/text_generation.py @@ -99,25 +99,37 @@ def set_manual_seed(seed): if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) -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, seed, eos_token=None, stopping_string=None): +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, seed, eos_token=None, stopping_strings=[]): clear_torch_cache() set_manual_seed(seed) t0 = time.time() + original_question = question + if not (shared.args.chat or shared.args.cai_chat): + question = apply_extensions(question, "input") + if shared.args.verbose: + print(f"\n\n{question}\n--------------------\n") + # These models are not part of Hugging Face, so we handle them # separately and terminate the function call earlier if shared.is_RWKV: try: if shared.args.no_stream: reply = shared.model.generate(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k) + if not (shared.args.chat or shared.args.cai_chat): + reply = original_question + apply_extensions(reply, "output") yield formatted_outputs(reply, shared.model_name) else: if not (shared.args.chat or shared.args.cai_chat): yield formatted_outputs(question, shared.model_name) + # RWKV has proper streaming, which is very nice. # No need to generate 8 tokens at a time. for reply in shared.model.generate_with_streaming(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k): + if not (shared.args.chat or shared.args.cai_chat): + reply = original_question + apply_extensions(reply, "output") yield formatted_outputs(reply, shared.model_name) + except Exception: traceback.print_exc() finally: @@ -127,12 +139,6 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(input_ids[0])} tokens)") return - original_question = question - if not (shared.args.chat or shared.args.cai_chat): - question = apply_extensions(question, "input") - if shared.args.verbose: - print(f"\n\n{question}\n--------------------\n") - input_ids = encode(question, max_new_tokens) original_input_ids = input_ids output = input_ids[0] @@ -142,9 +148,8 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi if eos_token is not None: eos_token_ids.append(int(encode(eos_token)[0][-1])) stopping_criteria_list = transformers.StoppingCriteriaList() - if stopping_string is not None: - # Copied from https://github.com/PygmalionAI/gradio-ui/blob/master/src/model.py - t = encode(stopping_string, 0, add_special_tokens=False) + if type(stopping_strings) is list and len(stopping_strings) > 0: + t = [encode(string, 0, add_special_tokens=False) for string in stopping_strings] stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0]))) generate_params = {} @@ -195,12 +200,10 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi if shared.soft_prompt: output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:])) + new_tokens = len(output) - len(input_ids[0]) + reply = decode(output[-new_tokens:]) if not (shared.args.chat or shared.args.cai_chat): - new_tokens = len(output) - len(input_ids[0]) - reply = decode(output[-new_tokens:]) reply = original_question + apply_extensions(reply, "output") - else: - reply = decode(output) yield formatted_outputs(reply, shared.model_name) @@ -223,12 +226,11 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi for output in generator: if shared.soft_prompt: output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:])) + + new_tokens = len(output) - len(input_ids[0]) + reply = decode(output[-new_tokens:]) if not (shared.args.chat or shared.args.cai_chat): - new_tokens = len(output) - len(input_ids[0]) - reply = decode(output[-new_tokens:]) reply = original_question + apply_extensions(reply, "output") - else: - reply = decode(output) if output[-1] in eos_token_ids: break @@ -244,12 +246,11 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi output = shared.model.generate(**generate_params)[0] if shared.soft_prompt: output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:])) + + new_tokens = len(output) - len(original_input_ids[0]) + reply = decode(output[-new_tokens:]) if not (shared.args.chat or shared.args.cai_chat): - new_tokens = len(output) - len(original_input_ids[0]) - reply = decode(output[-new_tokens:]) reply = original_question + apply_extensions(reply, "output") - else: - reply = decode(output) if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)): break @@ -269,5 +270,5 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi traceback.print_exc() finally: t1 = time.time() - print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(original_input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(original_input_ids[0])} tokens)") + print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(original_input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(original_input_ids[0])} tokens, context {len(original_input_ids[0])})") return diff --git a/requirements.txt b/requirements.txt index e5b3de69..c84f2948 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ accelerate==0.17.1 bitsandbytes==0.37.1 flexgen==0.1.7 -gradio==3.18.0 +gradio==3.23.0 markdown numpy peft==0.2.0 diff --git a/server.py b/server.py index 4c3497c9..3e31377c 100644 --- a/server.py +++ b/server.py @@ -1,4 +1,3 @@ -import gc import io import json import re @@ -8,7 +7,6 @@ import zipfile from pathlib import Path import gradio as gr -import torch import modules.chat as chat import modules.extensions as extensions_module @@ -17,7 +15,7 @@ import modules.ui as ui from modules.html_generator import generate_chat_html from modules.LoRA import add_lora_to_model from modules.models import load_model, load_soft_prompt -from modules.text_generation import generate_reply +from modules.text_generation import clear_torch_cache, generate_reply # Loading custom settings settings_file = None @@ -56,9 +54,7 @@ def load_model_wrapper(selected_model): if selected_model != shared.model_name: shared.model_name = selected_model shared.model = shared.tokenizer = None - if not shared.args.cpu: - gc.collect() - torch.cuda.empty_cache() + clear_torch_cache() shared.model, shared.tokenizer = load_model(shared.model_name) return selected_model @@ -75,13 +71,8 @@ def unload_model(): print("Model weights unloaded.") def load_lora_wrapper(selected_lora): - shared.lora_name = selected_lora - default_text = shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')] - - if not shared.args.cpu: - gc.collect() - torch.cuda.empty_cache() add_lora_to_model(selected_lora) + default_text = shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')] return selected_lora, default_text @@ -258,14 +249,13 @@ else: shared.model_name = available_models[i] shared.model, shared.tokenizer = load_model(shared.model_name) if shared.args.lora: - print(shared.args.lora) - shared.lora_name = shared.args.lora - add_lora_to_model(shared.lora_name) + add_lora_to_model(shared.args.lora) # Default UI settings default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')] -default_text = shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')] -if default_text == '': +if shared.lora_name != "None": + default_text = shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')] +else: default_text = shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')] title ='Text generation web UI' description = '\n\n# Text generation lab\nGenerate text using Large Language Models.\n' @@ -354,7 +344,7 @@ def create_interface(): gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)) gen_events.append(shared.gradio['Regenerate'].click(chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)) gen_events.append(shared.gradio['Impersonate'].click(chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream)) - shared.gradio['Stop'].click(chat.stop_everything_event, [], [], cancels=gen_events) + shared.gradio['Stop'].click(chat.stop_everything_event, [], [], cancels=gen_events, queue=False) shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, [], shared.gradio['textbox'], show_progress=shared.args.no_stream) shared.gradio['Replace last reply'].click(chat.replace_last_reply, [shared.gradio['textbox'], shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display'], show_progress=shared.args.no_stream) @@ -395,19 +385,22 @@ def create_interface(): elif shared.args.notebook: with gr.Tab("Text generation", elem_id="main"): - with gr.Tab('Raw'): - shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=25) - with gr.Tab('Markdown'): - shared.gradio['markdown'] = gr.Markdown() - with gr.Tab('HTML'): - shared.gradio['html'] = gr.HTML() - with gr.Row(): - shared.gradio['Stop'] = gr.Button('Stop') - shared.gradio['Generate'] = gr.Button('Generate') - shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens']) + with gr.Column(scale=4): + with gr.Tab('Raw'): + shared.gradio['textbox'] = gr.Textbox(value=default_text, elem_id="textbox", lines=25) + with gr.Tab('Markdown'): + shared.gradio['markdown'] = gr.Markdown() + with gr.Tab('HTML'): + shared.gradio['html'] = gr.HTML() - create_model_and_preset_menus() + with gr.Row(): + shared.gradio['Stop'] = gr.Button('Stop') + shared.gradio['Generate'] = gr.Button('Generate') + with gr.Column(scale=1): + shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens']) + + create_model_and_preset_menus() with gr.Tab("Parameters", elem_id="parameters"): create_settings_menus(default_preset) diff --git a/settings-template.json b/settings-template.json index 7a7de7af..79fd5023 100644 --- a/settings-template.json +++ b/settings-template.json @@ -2,9 +2,9 @@ "max_new_tokens": 200, "max_new_tokens_min": 1, "max_new_tokens_max": 2000, - "name1": "Person 1", - "name2": "Person 2", - "context": "This is a conversation between two people.", + "name1": "You", + "name2": "Assistant", + "context": "This is a conversation with your Assistant. The Assistant is very helpful and is eager to chat with you and answer your questions.", "stop_at_newline": false, "chat_prompt_size": 2048, "chat_prompt_size_min": 0,