diff --git a/README.md b/README.md index 60444401..3bfbc72f 100644 --- a/README.md +++ b/README.md @@ -176,10 +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/modules/GPTQ_loader.py b/modules/GPTQ_loader.py index bec6c66f..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 @@ -35,35 +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' - elif path_to_model.name.lower().startswith('llama-13b'): - pt_model = f'llama-13b-{shared.args.gptq_bits}bit' - elif path_to_model.name.lower().startswith('llama-30b'): - pt_model = f'llama-30b-{shared.args.gptq_bits}bit' - elif path_to_model.name.lower().startswith('llama-65b'): - pt_model = f'llama-65b-{shared.args.gptq_bits}bit' - else: - pt_model = f'{model_name}-{shared.args.gptq_bits}bit' - - # Try to find the .safetensors or .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+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 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/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 720c697e..87896faf 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -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/server.py b/server.py index f423e368..f1b95a5b 100644 --- a/server.py +++ b/server.py @@ -237,8 +237,9 @@ if 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'