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Add support for the latest GPTQ models with group-size (#530)
**Warning: old 4-bit weights will not work anymore!** See here how to get up to date weights: https://github.com/oobabooga/text-generation-webui/wiki/LLaMA-model#step-2-get-the-pre-converted-weights
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@ -176,10 +176,10 @@ Optionally, you can use the following command-line flags:
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| `--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. |
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| `--cpu` | Use the CPU to generate text.|
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| `--load-in-8bit` | Load the model with 8-bit precision.|
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| `--load-in-4bit` | DEPRECATED: use `--gptq-bits 4` instead. |
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| `--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. |
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| `--gptq-model-type MODEL_TYPE` | GPTQ: Model type of pre-quantized model. Currently only LLaMa and OPT are supported. |
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| `--gptq-pre-layer GPTQ_PRE_LAYER` | GPTQ: The number of layers to preload. |
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| `--wbits WBITS` | GPTQ: Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. |
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| `--model_type MODEL_TYPE` | GPTQ: Model type of pre-quantized model. Currently only LLaMA and OPT are supported. |
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| `--groupsize GROUPSIZE` | GPTQ: Group size. |
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| `--pre_layer PRE_LAYER` | GPTQ: The number of layers to preload. |
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| `--bf16` | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
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| `--auto-devices` | Automatically split the model across the available GPU(s) and CPU.|
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| `--disk` | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |
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@ -14,18 +14,21 @@ import opt
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def load_quantized(model_name):
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if not shared.args.gptq_model_type:
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if not shared.args.model_type:
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# Try to determine model type from model name
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model_type = model_name.split('-')[0].lower()
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if model_type not in ('llama', 'opt'):
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print("Can't determine model type from model name. Please specify it manually using --gptq-model-type "
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if model_name.lower().startswith(('llama', 'alpaca')):
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model_type = 'llama'
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elif model_name.lower().startswith(('opt', 'galactica')):
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model_type = 'opt'
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else:
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print("Can't determine model type from model name. Please specify it manually using --model_type "
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"argument")
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exit()
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else:
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model_type = shared.args.gptq_model_type.lower()
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model_type = shared.args.model_type.lower()
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if model_type == 'llama':
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if not shared.args.gptq_pre_layer:
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if not shared.args.pre_layer:
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load_quant = llama.load_quant
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else:
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load_quant = llama_inference_offload.load_quant
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@ -35,35 +38,44 @@ def load_quantized(model_name):
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print("Unknown pre-quantized model type specified. Only 'llama' and 'opt' are supported")
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exit()
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# Now we are going to try to locate the quantized model file.
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path_to_model = Path(f'models/{model_name}')
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if path_to_model.name.lower().startswith('llama-7b'):
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pt_model = f'llama-7b-{shared.args.gptq_bits}bit'
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elif path_to_model.name.lower().startswith('llama-13b'):
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pt_model = f'llama-13b-{shared.args.gptq_bits}bit'
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elif path_to_model.name.lower().startswith('llama-30b'):
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pt_model = f'llama-30b-{shared.args.gptq_bits}bit'
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elif path_to_model.name.lower().startswith('llama-65b'):
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pt_model = f'llama-65b-{shared.args.gptq_bits}bit'
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else:
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pt_model = f'{model_name}-{shared.args.gptq_bits}bit'
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# Try to find the .safetensors or .pt both in models/ and in the subfolder
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found_pts = list(path_to_model.glob("*.pt"))
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found_safetensors = list(path_to_model.glob("*.safetensors"))
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pt_path = None
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for path in [Path(p+ext) for ext in ['.safetensors', '.pt'] for p in [f"models/{pt_model}", f"{path_to_model}/{pt_model}"]]:
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if path.exists():
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print(f"Found {path}")
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pt_path = path
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break
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if len(found_pts) == 1:
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pt_path = found_pts[0]
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elif len(found_safetensors) == 1:
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pt_path = found_safetensors[0]
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else:
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if path_to_model.name.lower().startswith('llama-7b'):
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pt_model = f'llama-7b-{shared.args.wbits}bit'
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elif path_to_model.name.lower().startswith('llama-13b'):
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pt_model = f'llama-13b-{shared.args.wbits}bit'
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elif path_to_model.name.lower().startswith('llama-30b'):
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pt_model = f'llama-30b-{shared.args.wbits}bit'
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elif path_to_model.name.lower().startswith('llama-65b'):
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pt_model = f'llama-65b-{shared.args.wbits}bit'
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else:
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pt_model = f'{model_name}-{shared.args.wbits}bit'
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# Try to find the .safetensors or .pt both in models/ and in the subfolder
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for path in [Path(p+ext) for ext in ['.safetensors', '.pt'] for p in [f"models/{pt_model}", f"{path_to_model}/{pt_model}"]]:
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if path.exists():
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print(f"Found {path}")
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pt_path = path
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break
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if not pt_path:
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print(f"Could not find {pt_model}, exiting...")
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print("Could not find the quantized model in .pt or .safetensors format, exiting...")
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exit()
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# qwopqwop200's offload
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if shared.args.gptq_pre_layer:
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model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits, shared.args.gptq_pre_layer)
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if shared.args.pre_layer:
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model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, shared.args.pre_layer)
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else:
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model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits)
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model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize)
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# accelerate offload (doesn't work properly)
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if shared.args.gpu_memory:
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@ -44,7 +44,7 @@ def load_model(model_name):
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shared.is_RWKV = model_name.lower().startswith('rwkv-')
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# Default settings
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if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.gptq_bits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
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if 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]):
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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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@ -95,7 +95,7 @@ def load_model(model_name):
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return model, tokenizer
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# Quantized model
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elif shared.args.gptq_bits > 0:
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elif shared.args.wbits > 0:
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from modules.GPTQ_loader import load_quantized
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model = load_quantized(model_name)
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@ -52,7 +52,8 @@ settings = {
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'default': 'Common sense questions and answers\n\nQuestion: \nFactual answer:',
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'^(gpt4chan|gpt-4chan|4chan)': '-----\n--- 865467536\nInput text\n--- 865467537\n',
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'(rosey|chip|joi)_.*_instruct.*': 'User: \n',
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'oasst-*': '<|prompter|>Write a story about future of AI development<|endoftext|><|assistant|>'
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'oasst-*': '<|prompter|>Write a story about future of AI development<|endoftext|><|assistant|>',
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'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",
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},
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'lora_prompts': {
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'default': 'Common sense questions and answers\n\nQuestion: \nFactual answer:',
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@ -78,10 +79,15 @@ parser.add_argument('--chat', action='store_true', help='Launch the web UI in ch
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parser.add_argument('--cai-chat', action='store_true', help='Launch the web UI in chat mode with a style similar to Character.AI\'s. If the file img_bot.png or img_bot.jpg exists in the same folder as server.py, this image will be used as the bot\'s profile picture. Similarly, img_me.png or img_me.jpg will be used as your profile picture.')
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parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.')
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parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.')
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parser.add_argument('--load-in-4bit', action='store_true', help='DEPRECATED: use --gptq-bits 4 instead.')
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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.')
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parser.add_argument('--gptq-model-type', type=str, help='GPTQ: Model type of pre-quantized model. Currently only LLaMa and OPT are supported.')
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parser.add_argument('--gptq-pre-layer', type=int, default=0, help='GPTQ: The number of layers to preload.')
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parser.add_argument('--gptq-bits', type=int, default=0, help='DEPRECATED: use --wbits instead.')
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parser.add_argument('--gptq-model-type', type=str, help='DEPRECATED: use --model_type instead.')
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parser.add_argument('--gptq-pre-layer', type=int, default=0, help='DEPRECATED: use --pre_layer instead.')
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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.')
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parser.add_argument('--model_type', type=str, help='GPTQ: Model type of pre-quantized model. Currently only LLaMA and OPT are supported.')
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parser.add_argument('--groupsize', type=int, default=-1, help='GPTQ: Group size.')
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parser.add_argument('--pre_layer', type=int, default=0, help='GPTQ: The number of layers to preload.')
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parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
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parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
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parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.')
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@ -109,6 +115,8 @@ parser.add_argument('--verbose', action='store_true', help='Print the prompts to
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args = parser.parse_args()
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# Provisional, this will be deleted later
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if args.load_in_4bit:
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print("Warning: --load-in-4bit is deprecated and will be removed. Use --gptq-bits 4 instead.\n")
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args.gptq_bits = 4
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deprecated_dict = {'gptq_bits': ['wbits', 0], 'gptq_model_type': ['model_type', None], 'gptq_pre_layer': ['prelayer', 0]}
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for k in deprecated_dict:
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if eval(f"args.{k}") != deprecated_dict[k][1]:
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print(f"Warning: --{k} is deprecated and will be removed. Use --{deprecated_dict[k][0]} instead.")
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exec(f"args.{deprecated_dict[k][0]} = args.{k}")
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@ -237,8 +237,9 @@ if shared.args.lora:
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# Default UI settings
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default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')]
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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')]
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if default_text == '':
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if shared.lora_name != "None":
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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')]
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else:
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default_text = shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')]
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title ='Text generation web UI'
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description = '\n\n# Text generation lab\nGenerate text using Large Language Models.\n'
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