Llama-2-7b-NuGPTQ

Non-uniform GPTQ (NuGPTQ) combines GPTQ, SqueezeLLM and output scaling for a competitive whole-tensor (no grouping) LLM compression method.

Results for Llama-2-7b-hf:

<details> <summary>perplexity reproduction steps</summary>

git clone https://github.com/EleutherAI/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .
pip install optimum

huggingface-cli login

# Set batch size based on your GPU.
lm_eval --model hf \
    --model_args pretrained=meta-llama/Llama-2-7b-hf,dtype="float16" \
    --tasks wikitext \
    --batch_size 1

# hf (pretrained=meta-llama/Llama-2-7b-hf,dtype=float16), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
# | Tasks  |Version|Filter|n-shot|    Metric     |Value |   |Stderr|
# |--------|------:|------|-----:|---------------|-----:|---|------|
# |wikitext|      2|none  |     0|word_perplexity|8.7071|±  |N/A   |
# |        |       |none  |     0|byte_perplexity|1.4989|±  |N/A   |
# |        |       |none  |     0|bits_per_byte  |0.5839|±  |N/A   |

lm_eval --model hf \
    --model_args pretrained=smpanaro/Llama-2-7b-NuGPTQ,dtype="float16",use_safetensors=True,trust_remote_code=True \
    --tasks wikitext \
    --batch_size 1

# hf (pretrained=smpanaro/llama-2-7b-nugptq,dtype=float16,use_safetensors=True,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
# | Tasks  |Version|Filter|n-shot|    Metric     |Value |   |Stderr|
# |--------|------:|------|-----:|---------------|-----:|---|------|
# |wikitext|      2|none  |     0|word_perplexity|9.2754|±  |N/A   |
# |        |       |none  |     0|byte_perplexity|1.5167|±  |N/A   |
# |        |       |none  |     0|bits_per_byte  |0.6009|±  |N/A   |

pip install auto-gptq
lm_eval --model hf \
    --model_args pretrained=TheBloke/Llama-2-7B-GPTQ,dtype="float16",revision=gptq-4bit-128g-actorder_True \
    --tasks wikitext \
    --batch_size 1

# hf (pretrained=TheBloke/Llama-2-7B-GPTQ,dtype=float16,revision=gptq-4bit-128g-actorder_True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
# | Tasks  |Version|Filter|n-shot|    Metric     |Value |   |Stderr|
# |--------|------:|------|-----:|---------------|-----:|---|------|
# |wikitext|      2|none  |     0|word_perplexity|9.4686|±  |N/A   |
# |        |       |none  |     0|byte_perplexity|1.5225|±  |N/A   |
# |        |       |none  |     0|bits_per_byte  |0.6065|±  |N/A   |

lm_eval --model hf \
    --model_args pretrained=TheBloke/Llama-2-7B-GPTQ,dtype="float16",revision=gptq-4bit-32g-actorder_True \
    --tasks wikitext \
    --batch_size 1

# hf (pretrained=TheBloke/Llama-2-7B-GPTQ,dtype=float16,revision=gptq-4bit-32g-actorder_True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
# | Tasks  |Version|Filter|n-shot|    Metric     |Value |   |Stderr|
# |--------|------:|------|-----:|---------------|-----:|---|------|
# |wikitext|      2|none  |     0|word_perplexity|9.3801|±  |N/A   |
# |        |       |none  |     0|byte_perplexity|1.5199|±  |N/A   |
# |        |       |none  |     0|bits_per_byte  |0.6040|±  |N/A   |

pip install autoawq
lm_eval --model hf \
    --model_args pretrained=TheBloke/Llama-2-7B-AWQ,dtype="float16" \
    --tasks wikitext \
    --batch_size 1

# hf (pretrained=thebloke/llama-2-7b-awq,dtype=float16), gen_kwargs: (none), limit: none, num_fewshot: none, batch_size: 1
# | Tasks  |Version|Filter|n-shot|    Metric     |Value |   |Stderr|
# |--------|------:|------|-----:|---------------|-----:|---|------|
# |wikitext|      2|none  |     0|word_perplexity|8.9760|±  |N/A   |
# |        |       |none  |     0|byte_perplexity|1.5074|±  |N/A   |
# |        |       |none  |     0|bits_per_byte  |0.5921|±  |N/A   |

</details>

The model is fake quantized which means each weight has <= 16 (2⁴) unique values, but they are stored in float16. The uniqueness can be checked as follows:

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("smpanaro/Llama-2-7b-NuGPTQ", trust_remote_code=True)
linear_layers = ["k_proj", "q_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
count = 0
for key, tensor in model.state_dict().items():
    if "weight" not in key:
        continue
    if any([l in key for l in linear_layers]):
        assert tensor.unique().shape[0] <= 16, f"{key} has more than 16 unique values"
        print("✓", end="", flush=True)
        count += 1

print()
# 32 model layers * 7 linear layers
print(f"{count} out of 224 linear layers have 16 unique values.")