Llama 3 70B Instruct β OmniQuant
Based on Llama 3 70B Instruct.
Quantized with OmniQuant.
Evaluation
PPL (β)
wiki | |
---|---|
FP | 5,33 |
Quantized | 5,90 |
Accuracy on English Benchmarks, % (β)
piqa | arc_easy | arc_challenge | boolq | hellaswag | winogrande | mmlu_humanities | mmlu_social_sciences | mmlu_stem | mmlu_other | |
---|---|---|---|---|---|---|---|---|---|---|
FP | 81,5 | 86,2 | 61,9 | 87,4 | 63,7 | 75,8 | 78,7 | 84,4 | 71,1 | 80,2 |
Quantized | 80,7 | 85,8 | 61,4 | 87,0 | 62,7 | 73,0 | 75,5 | 81,0 | 68,6 | 77,9 |
Accuracy on Russian Benchmarks, % (β)
danetqa | terra | rwsd | muserc | rucos | lidirus | parus | rcb | russe | rucola | |
---|---|---|---|---|---|---|---|---|---|---|
FP | 88,9 | 88,6 | 75,5 | 81,8 | 82,4 | 70,7 | 77,0 | 35,0 | 63,1 | 34,7 |
Quantized | 86,6 | 81,8 | 71,6 | 75,6 | 69,5 | 60,3 | 64,0 | 26,8 | 63,1 | 32,5 |
Summary
Avg acc diff on Eng, % (β) | Avg acc diff on Rus, % (β) | Occupied disk space, % (β) | |
---|---|---|---|
FP | 0 | 0 | 100 |
Quantized | -1,7 | -6,6 | 28,2 |
Examples
Imports and Model Loading
Expand
import gc
import auto_gptq.nn_modules.qlinear.qlinear_cuda as qlinear_cuda
import auto_gptq.nn_modules.qlinear.qlinear_triton as qlinear_triton
import torch
from accelerate import (
init_empty_weights,
infer_auto_device_map,
load_checkpoint_in_model,
)
from tqdm import tqdm
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
pipeline,
)
def get_named_linears(model):
return {
name: module for name, module in model.named_modules()
if isinstance(module, torch.nn.Linear)
}
def set_module(model, name, module):
parent = model
levels = name.split('.')
for i in range(len(levels) - 1):
cur_name = levels[i]
if cur_name.isdigit():
parent = parent[int(cur_name)]
else:
parent = getattr(parent, cur_name)
setattr(parent, levels[-1], module)
def load_model(model_path):
# Based on: https://github.com/OpenGVLab/OmniQuant/blob/main/runing_quantized_mixtral_7bx8.ipynb
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
if not hasattr(config, 'quantization_config'):
raise AttributeError(
f'No quantization info found in model config "{model_path}"'
f' (`quantization_config` section is missing).'
)
wbits = config.quantization_config['bits']
group_size = config.quantization_config['group_size']
# We are going to init an ordinary model and then manually replace all Linears with QuantLinears
del config.quantization_config
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config=config, torch_dtype=torch.float16, trust_remote_code=True)
layers = model.model.layers
for i in tqdm(range(len(layers))):
layer = layers[i]
named_linears = get_named_linears(layer)
for name, module in named_linears.items():
params = (
wbits, group_size,
module.in_features, module.out_features,
module.bias is not None
)
if wbits in [2, 4]:
q_linear = qlinear_triton.QuantLinear(*params)
elif wbits == 3:
q_linear = qlinear_cuda.QuantLinear(*params)
else:
raise NotImplementedError("Only 2, 3 and 4 bits are supported.")
q_linear.to(next(layer.parameters()).device)
set_module(layer, name, q_linear)
torch.cuda.empty_cache()
gc.collect()
model.tie_weights()
device_map = infer_auto_device_map(model)
print("Loading pre-computed quantized weights...")
load_checkpoint_in_model(
model, checkpoint=model_path,
device_map=device_map, offload_state_dict=True,
)
print("Model loaded successfully!")
return model
Inference
model_path = "compressa-ai/Llama-3-70B-Instruct-OmniQuant"
model = load_model(model_path).cuda()
tokenizer = AutoTokenizer.from_pretrained(
model_path, use_fast=False, trust_remote_code=True
)
# Llama 3 "specifics"
# https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/discussions/4
terminators = [
tokenizer.convert_tokens_to_ids("<|end_of_text|>"),
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
system_message = "You are a friendly chatbot who responds as if you are the Sandy Cheeks squirrel from the SpongeBob SquarePants cartoon."
user_message = "Do squirrels communicate with birds?"
messages = [
{"role": "system", "content": system_message},
{"role": "user", "content": user_message},
]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(prompt, return_tensors="pt")
inputs = {k: v.cuda() for k, v in inputs.items()}
outputs = model.generate(
**inputs, max_new_tokens=512,
do_sample=True, temperature=0.7, top_p=0.95,
eos_token_id=terminators,
)
response = tokenizer.decode(outputs[0])
continuation = response.removeprefix(prompt).removesuffix(tokenizer.eos_token)
print(f'Prompt:\n{prompt}')
print(f'Continuation:\n{continuation}\n')
Inference Using Pipeline
pipe = pipeline(
"text-generation",
model=model, tokenizer=tokenizer,
eos_token_id=terminators,
max_new_tokens=512, do_sample=True,
temperature=0.7, top_p=0.95,
device=0,
)
prompt = pipe.tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
outputs = pipe(prompt)
response = outputs[0]["generated_text"]
continuation = response.removeprefix(prompt)
print(f'Prompt:\n{prompt}')
print(f'Continuation:\n{continuation}\n')
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