diff --git a/README.md b/README.md index dbc8c59..1c26773 100644 --- a/README.md +++ b/README.md @@ -140,8 +140,9 @@ 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` | Load the model with 4-bit precision. Currently only works with LLaMA.| -| `--gptq-bits GPTQ_BITS` | Load a pre-quantized model with specified precision. 2, 3, 4 and 8 (bit) are supported. Currently only works with LLaMA. | +| `--load-in-4bit` | DEPRECATED: use `--gptq-bits 4` instead. | +| `--gptq-bits GPTQ_BITS` | 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` | Model type of pre-quantized model. Currently only LLaMa and OPT are supported. | | `--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/quantized_LLaMA.py b/modules/GPTQ_loader.py similarity index 54% rename from modules/quantized_LLaMA.py rename to modules/GPTQ_loader.py index a5757c6..c272349 100644 --- a/modules/quantized_LLaMA.py +++ b/modules/GPTQ_loader.py @@ -7,28 +7,40 @@ import torch import modules.shared as shared sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa"))) -from llama import load_quant +import llama +import opt -# 4-bit LLaMA -def load_quantized_LLaMA(model_name): - if shared.args.load_in_4bit: - bits = 4 +def load_quantized(model_name): + if not shared.args.gptq_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 " + "argument") + exit() else: - bits = shared.args.gptq_bits + model_type = shared.args.gptq_model_type.lower() + + if model_type == 'llama': + load_quant = llama.load_quant + elif model_type == 'opt': + load_quant = opt.load_quant + else: + print("Unknown pre-quantized model type specified. Only 'llama' and 'opt' are supported") + exit() path_to_model = Path(f'models/{model_name}') - pt_model = '' if path_to_model.name.lower().startswith('llama-7b'): - pt_model = f'llama-7b-{bits}bit.pt' + 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-{bits}bit.pt' + 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-{bits}bit.pt' + 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-{bits}bit.pt' + pt_model = f'llama-65b-{shared.args.gptq_bits}bit.pt' else: - pt_model = f'{model_name}-{bits}bit.pt' + pt_model = f'{model_name}-{shared.args.gptq_bits}bit.pt' # Try to find the .pt both in models/ and in the subfolder pt_path = None @@ -40,7 +52,7 @@ def load_quantized_LLaMA(model_name): print(f"Could not find {pt_model}, exiting...") exit() - model = load_quant(str(path_to_model), str(pt_path), bits) + model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits) # Multiple GPUs or GPU+CPU if shared.args.gpu_memory: diff --git a/modules/models.py b/modules/models.py index 7d094ed..f4bb11f 100644 --- a/modules/models.py +++ b/modules/models.py @@ -1,6 +1,5 @@ import json import os -import sys import time import zipfile from pathlib import Path @@ -35,6 +34,7 @@ if shared.args.deepspeed: ds_config = generate_ds_config(shared.args.bf16, 1 * world_size, shared.args.nvme_offload_dir) dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration + def load_model(model_name): print(f"Loading {model_name}...") t0 = time.time() @@ -42,7 +42,7 @@ 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.load_in_4bit, shared.args.gptq_bits > 0, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]): + 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 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: @@ -87,11 +87,11 @@ def load_model(model_name): return model, tokenizer - # 4-bit LLaMA - elif shared.args.gptq_bits > 0 or shared.args.load_in_4bit: - from modules.quantized_LLaMA import load_quantized_LLaMA + # Quantized model + elif shared.args.gptq_bits > 0: + from modules.GPTQ_loader import load_quantized - model = load_quantized_LLaMA(model_name) + model = load_quantized(model_name) # Custom else: diff --git a/modules/shared.py b/modules/shared.py index 5411009..ea2eb50 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -69,8 +69,9 @@ 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='Load the model with 4-bit precision. Currently only works with LLaMA.') -parser.add_argument('--gptq-bits', type=int, default=0, help='Load a pre-quantized model with specified precision. 2, 3, 4 and 8bit are supported. Currently only works with LLaMA.') +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='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='Model type of pre-quantized model. Currently only LLaMa and OPT are supported.') 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.') @@ -95,3 +96,8 @@ parser.add_argument('--share', action='store_true', help='Create a public URL. T parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.') parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.') 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