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Add -gptq-preload for 4-bit offloading (#460)

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This works in a 4GB card now:

```
python server.py --model llama-7b-hf --gptq-bits 4 --gptq-pre-layer 20
```
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oobabooga 2023-03-20 16:30:56 -03:00 committed by GitHub
parent 9a3bed50c3
commit 7618f3fe8c
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2 changed files with 27 additions and 20 deletions
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12
modules/GPTQ_loader.py
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@ -9,6 +9,7 @@ import modules.shared as shared
sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa")))
import llama
import llama_inference_offload
import opt
@ -24,7 +25,10 @@ def load_quantized(model_name):
model_type = shared.args.gptq_model_type.lower()
if model_type == 'llama':
if not shared.args.gptq_pre_layer:
load_quant = llama.load_quant
else:
load_quant = llama_inference_offload.load_quant
elif model_type == 'opt':
load_quant = opt.load_quant
else:
@ -53,9 +57,13 @@ def load_quantized(model_name):
print(f"Could not find {pt_model}, exiting...")
exit()
# Using 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)
else:
model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits)
# Multiple GPUs or GPU+CPU
# Using accelerate offload (doesn't work properly)
if shared.args.gpu_memory:
memory_map = list(map(lambda x : x.strip(), shared.args.gpu_memory))
max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB'
@ -68,8 +76,6 @@ def load_quantized(model_name):
print("Using the following device map for the 4-bit model:", device_map)
# https://huggingface.co/docs/accelerate/package_reference/big_modeling#accelerate.dispatch_model
model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True)
# Single GPU
elif not shared.args.cpu:
model = model.to(torch.device('cuda:0'))

5
modules/shared.py
View File

@ -79,8 +79,9 @@ parser.add_argument('--cai-chat', action='store_true', help='Launch the web UI i
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='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('--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('--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.')