Phi-3-medium-128k-instruct-quantized.w8a8

Phi-3-medium-128k-instruct-quantized.w8a8

Model Overview

• Model Architecture: Phi-3
  • Input: Text
  • Output: Text
• Model Optimizations:
  • Activation quantization: INT8
  • Weight quantization: INT8
• Intended Use Cases: Intended for commercial and research use in English. Similarly to Phi-3-medium-128k-instruct, this models is intended for assistant-like chat.
• Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English.
• Release Date: 7/11/2024
• Version: 1.0
• License(s): MIT
• Model Developers: Neural Magic

Quantized version of Phi-3-medium-128k-instruct, a 14 billion-parameter open model trained using the Phi-3 datasets. It achieves an average score of 73.90 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 74.10.

Model Optimizations

This model was obtained by quantizing the weights of Phi-3-medium-128k-instruct to INT8 data type. This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x). Weight quantization also reduces disk size requirements by approximately 50%.

Only weights and activations of the linear operators within transformers blocks are quantized. Weights are quantized with a symmetric static per-channel scheme, where a fixed linear scaling factor is applied between INT8 and floating point representations for each output channel dimension. Activations are quantized with a symmetric dynamic per-token scheme, computing a linear scaling factor at runtime for each token between INT8 and floating point representations. Linear scaling factors are computed via by minimizing the mean squarred error (MSE). The SmoothQuant algorithm is used to alleviate outliers in the activations, whereas rhe GPTQ algorithm is applied for quantization. Both algorithms are implemented in the llm-compressor library. GPTQ used a 1% damping factor and 512 sequences sequences taken from Neural Magic's LLM compression calibration dataset.

Deployment

Use with vLLM

This model can be deployed efficiently using the vLLM backend, as shown in the example below (using 2 GPUs).

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "neuralmagic/Phi-3-medium-128k-instruct-quantized.w8a8"
number_gpus = 2

sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)

tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)

llm = LLM(model=model_id, trust_remote_code=True, max_model_len=8196, tensor_parallel_size=number_gpus)

outputs = llm.generate(prompts, sampling_params)

generated_text = outputs[0].outputs[0].text
print(generated_text)

vLLM aslo supports OpenAI-compatible serving. See the documentation for more details.

Creation

This model was created by using the llm-compressor library as presented in the code snipet below.

from transformers import AutoTokenizer
from datasets import Dataset
from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
from llmcompressor.modifiers.quantization import GPTQModifier
import random

model_id = "microsoft/Phi-3-medium-128k-instruct"

num_samples = 512
max_seq_len = 8192

tokenizer = AutoTokenizer.from_pretrained(model_id)

def preprocess_fn(example):
  return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}

ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
ds = ds.shuffle().select(range(num_samples))
ds = ds.map(preprocess_fn)

recipe = [
  SmoothQuantModifier(
    smoothing_strength=0.8,
    mappings=[
      [["re:.*qkv_proj"], "re:.*input_layernorm"],
      [["re:.*gate_up_proj"], "re:.*post_attention_layernorm"],
    ],
  ),
  GPTQModifier(
    sequential=True,
    targets="Linear",
    scheme="W8A8",
    ignore=["lm_head"],
    dampening_frac=0.01,
    observer="mse",
  )
]

model = SparseAutoModelForCausalLM.from_pretrained(
  model_id,
  device_map="auto",
  trust_remote_code=True,
)

oneshot(
  model=model,
  dataset=ds,
  recipe=recipe,
  max_seq_length=max_seq_len,
  num_calibration_samples=num_samples,
)

model.save_pretrained("Phi-3-medium-128k-instruct-quantized.w8a8")

Evaluation

The model was evaluated on the OpenLLM leaderboard tasks (version 1) with the lm-evaluation-harness (commit 383bbd54bc621086e05aa1b030d8d4d5635b25e6) and the vLLM engine, using the following command (using 2 GPUs):

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Phi-3-medium-128k-instruct-quantized.w8a8",dtype=auto,gpu_memory_utilization=0.4,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2 \
  --tasks openllm \
  --batch_size auto

Accuracy

Open LLM Leaderboard evaluation scores

<table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>Phi-3-medium-128k-instruct </strong> </td> <td><strong>Phi-3-medium-128k-instruct-quantized.w8a8 (this model)</strong> </td> <td><strong>Recovery</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>76.69 </td> <td>76.74 </td> <td>100.1% </td> </tr> <tr> <td>ARC Challenge (25-shot) </td> <td>69.45 </td> <td>69.37 </td> <td>99.9% </td> </tr> <tr> <td>GSM-8K (5-shot, strict-match) </td> <td>85.22 </td> <td>84.15 </td> <td>98.7% </td> </tr> <tr> <td>Hellaswag (10-shot) </td> <td>85.10 </td> <td>84.76 </td> <td>99.6% </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>73.56 </td> <td>73.80 </td> <td>100.3% </td> </tr> <tr> <td>TruthfulQA (0-shot) </td> <td>54.57 </td> <td>54.57 </td> <td>100.0% </td> </tr> <tr> <td><strong>Average</strong> </td> <td><strong>74.10</strong> </td> <td><strong>73.90</strong> </td> <td><strong>99.7%</strong> </td> </tr> </table>