File size: 3,495 Bytes
4b84c3e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
---
base_model:
- microsoft/Phi-3.5-vision-instruct
---
## Eval
```
vllm serve nm-testing/Phi-3.5-vision-instruct-W8A8-Dynamic-Per-Token --trust-remote-code --max-model-len 100000
```
```
python -m eval.run eval_vllm --model_name nm-testing/Phi-3.5-vision-instruct-W8A8-Dynamic-Per-Token --url http://0.0.0.0:8000 --output_dir output/ --eval_name "chartqa"
...
================================================================================
Metrics:
{
"explicit_prompt_relaxed_correctness": 0.6472,
"anywhere_in_answer_relaxed_correctness": 0.6616
}
================================================================================
```
## Creation
```python
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from llmcompressor.modifiers.quantization import GPTQModifier
# from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
from llmcompressor.transformers import oneshot, wrap_hf_model_class
# Select model and load it.
MODEL_ID = "microsoft/Phi-3.5-vision-instruct"
model_class = wrap_hf_model_class(AutoModelForCausalLM)
model = model_class.from_pretrained(
MODEL_ID,
device_map="auto",
torch_dtype="auto",
trust_remote_code=True,
_attn_implementation="eager",
)
processor = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
# Select calibration dataset.
DATASET_ID = "HuggingFaceH4/ultrachat_200k"
DATASET_SPLIT = "train_sft"
# Select number of samples. 512 samples is a good place to start.
# Increasing the number of samples can improve accuracy.
NUM_CALIBRATION_SAMPLES = 512
MAX_SEQUENCE_LENGTH = 2048
# Load dataset and preprocess.
ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))
def preprocess(example):
return {
"text": processor.apply_chat_template(
example["messages"],
tokenize=False,
)
}
ds = ds.map(preprocess)
# Tokenize inputs.
def tokenize(sample):
return processor(
sample["text"],
padding=False,
max_length=MAX_SEQUENCE_LENGTH,
truncation=True,
add_special_tokens=False,
)
ds = ds.map(tokenize, remove_columns=ds.column_names)
print(ds)
# Configure algorithms. In this case, we:
# * apply SmoothQuant to make the activations easier to quantize
# * quantize the weights to int8 with GPTQ (static per channel)
# * quantize the activations to int8 (dynamic per token)
# Note: set sequential_update: true in the recipe to reduce memory
ignore=["re:.*lm_head", "re:model.vision_embed_tokens.*"]
recipe = [
# SmoothQuantModifier(smoothing_strength=0.8, ignore=ignore),
GPTQModifier(targets="Linear", scheme="W8A8", ignore=ignore),
]
# Apply algorithms.
oneshot(
model=model,
dataset=ds,
recipe=recipe,
max_seq_length=MAX_SEQUENCE_LENGTH,
num_calibration_samples=NUM_CALIBRATION_SAMPLES,
trust_remote_code_model=True,
)
# Confirm generations of the quantized model look sane.
print("\n\n")
print("========== SAMPLE GENERATION ==============")
input_ids = processor("Hello my name is", return_tensors="pt").input_ids.to("cuda")
output = model.generate(input_ids, max_new_tokens=100)
print(processor.decode(output[0]))
print("==========================================\n\n")
# Save to disk compressed.
SAVE_DIR = MODEL_ID.split("/")[1] + "-W8A8-Dynamic-Per-Token"
model.save_pretrained(SAVE_DIR, save_compressed=True)
processor.save_pretrained(SAVE_DIR)
``` |