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Add RoPE scaling support for transformers (including dynamic NTK)

Browse Source 
https://github.com/huggingface/transformers/pull/24653
This commit is contained in:
oobabooga 2023-08-08 21:24:28 -07:00
parent f4caaf337a
commit d8fb506aff
5 changed files with 16 additions and 9 deletions
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4
README.md
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@ -299,12 +299,12 @@ Optionally, you can use the following command-line flags:
| `--rwkv-strategy RWKV_STRATEGY` | RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8". |
| `--rwkv-cuda-on` | RWKV: Compile the CUDA kernel for better performance. |
#### RoPE (for llama.cpp and ExLlama only)
#### RoPE (for llama.cpp, ExLlama, and transformers)
| Flag | Description |
|------------------|-------------|
|`--compress_pos_emb COMPRESS_POS_EMB` | Positional embeddings compression factor. Should typically be set to max_seq_len / 2048. |
|`--alpha_value ALPHA_VALUE` | Positional embeddings alpha factor for NTK RoPE scaling. Scaling is not identical to embedding compression. Use either this or compress_pos_emb, not both. |
|`--alpha_value ALPHA_VALUE` | Positional embeddings alpha factor for NTK RoPE scaling. Use either this or compress_pos_emb, not both. |
#### Gradio

10
modules/loaders.py
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@ -39,8 +39,8 @@ loaders_and_params = {
'low_vram',
'mlock',
'llama_cpp_seed',
'compress_pos_emb',
'alpha_value',
'compress_pos_emb',
'cpu',
],
'llamacpp_HF': [
@ -54,8 +54,8 @@ loaders_and_params = {
'low_vram',
'mlock',
'llama_cpp_seed',
'compress_pos_emb',
'alpha_value',
'compress_pos_emb',
'cpu',
'llamacpp_HF_info',
],
@ -73,20 +73,22 @@ loaders_and_params = {
'quant_type',
'compute_dtype',
'trust_remote_code',
'alpha_value',
'compress_pos_emb',
'transformers_info'
],
'ExLlama': [
'gpu_split',
'max_seq_len',
'compress_pos_emb',
'alpha_value',
'compress_pos_emb',
'exllama_info',
],
'ExLlama_HF': [
'gpu_split',
'max_seq_len',
'compress_pos_emb',
'alpha_value',
'compress_pos_emb',
'exllama_HF_info',
]
}

7
modules/models.py
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@ -144,7 +144,7 @@ def huggingface_loader(model_name):
LoaderClass = AutoModelForCausalLM
# Load the model in simple 16-bit mode by default
if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.load_in_4bit, shared.args.auto_devices, shared.args.disk, shared.args.deepspeed, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None]):
if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.load_in_4bit, shared.args.auto_devices, shared.args.disk, shared.args.deepspeed, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.compress_pos_emb > 1, shared.args.alpha_value > 1]):
model = LoaderClass.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16, trust_remote_code=shared.args.trust_remote_code)
if torch.backends.mps.is_available():
device = torch.device('mps')
@ -215,6 +215,11 @@ def huggingface_loader(model_name):
no_split_module_classes=model._no_split_modules
)
if shared.args.compress_pos_emb > 1:
params['rope_scaling'] = {'type': 'linear', 'factor': shared.args.compress_pos_emb}
elif shared.args.alpha_value > 1:
params['rope_scaling'] = {'type': 'dynamic', 'factor': shared.args.alpha_value}
model = LoaderClass.from_pretrained(checkpoint, **params)
return model

2
modules/shared.py
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@ -164,7 +164,7 @@ parser.add_argument('--rwkv-cuda-on', action='store_true', help='RWKV: Compile t
# RoPE
parser.add_argument('--compress_pos_emb', type=int, default=1, help="Positional embeddings compression factor. Should typically be set to max_seq_len / 2048.")
parser.add_argument('--alpha_value', type=int, default=1, help="Positional embeddings alpha factor for NTK RoPE scaling. Scaling is not identical to embedding compression. Use either this or compress_pos_emb, not both.")
parser.add_argument('--alpha_value', type=int, default=1, help="Positional embeddings alpha factor for NTK RoPE scaling. Use either this or compress_pos_emb, not both.")
# Gradio
parser.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.')

2
modules/ui_model_menu.py
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@ -89,8 +89,8 @@ def create_ui():
shared.gradio['autogptq_info'] = gr.Markdown('* ExLlama_HF is recommended over AutoGPTQ for models derived from LLaMA.')
shared.gradio['gpu_split'] = gr.Textbox(label='gpu-split', info='Comma-separated list of VRAM (in GB) to use per GPU. Example: 20,7,7')
shared.gradio['max_seq_len'] = gr.Slider(label='max_seq_len', minimum=0, maximum=16384, step=256, info='Maximum sequence length.', value=shared.args.max_seq_len)
shared.gradio['alpha_value'] = gr.Slider(label='alpha_value', minimum=1, maximum=8, step=0.1, info='Positional embeddings alpha factor for NTK RoPE scaling. Use either this or compress_pos_emb, not both.', value=shared.args.alpha_value)
shared.gradio['compress_pos_emb'] = gr.Slider(label='compress_pos_emb', minimum=1, maximum=8, step=1, info='Positional embeddings compression factor. Should typically be set to max_seq_len / 2048.', value=shared.args.compress_pos_emb)
shared.gradio['alpha_value'] = gr.Slider(label='alpha_value', minimum=1, maximum=8, step=0.1, info='Positional embeddings alpha factor for NTK RoPE scaling. Scaling is not identical to embedding compression. Use either this or compress_pos_emb, not both.', value=shared.args.alpha_value)
with gr.Column():
shared.gradio['triton'] = gr.Checkbox(label="triton", value=shared.args.triton)