78d5ce3e01
### What this PR does / why we need it?
This PR fixes linting issues in the `example/` to align with the
project's Ruff configuration.
- vLLM version: v0.13.0
- vLLM main:
bde38c11df
Signed-off-by: root <root@LAPTOP-VQKDDVMG.localdomain>
Co-authored-by: root <root@LAPTOP-VQKDDVMG.localdomain>
83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
from datasets import load_dataset
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from llmcompressor import oneshot
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from llmcompressor.modifiers.quantization import GPTQModifier
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from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
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from llmcompressor.utils import dispatch_for_generation
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Select model and load it.
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MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"
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model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto")
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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# Select calibration dataset.
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DATASET_ID = "HuggingFaceH4/ultrachat_200k"
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DATASET_SPLIT = "train_sft"
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# Select number of samples. 512 samples is a good place to start.
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# Increasing the number of samples can improve accuracy.
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NUM_CALIBRATION_SAMPLES = 512
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MAX_SEQUENCE_LENGTH = 2048
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# Load dataset and preprocess.
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ds = load_dataset(DATASET_ID, split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]")
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ds = ds.shuffle(seed=42)
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def preprocess(example):
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return {
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"text": tokenizer.apply_chat_template(
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example["messages"],
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tokenize=False,
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)
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}
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ds = ds.map(preprocess)
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# Tokenize inputs.
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def tokenize(sample):
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return tokenizer(
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sample["text"],
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padding=False,
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max_length=MAX_SEQUENCE_LENGTH,
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truncation=True,
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add_special_tokens=False,
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)
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ds = ds.map(tokenize, remove_columns=ds.column_names)
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# Configure algorithms. In this case, we:
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# * apply SmoothQuant to make the activations easier to quantize
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# * quantize the weights to int8 with GPTQ (static per channel)
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# * quantize the activations to int8 (dynamic per token)
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recipe = [
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SmoothQuantModifier(smoothing_strength=0.8),
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GPTQModifier(targets="Linear", scheme="W8A8", ignore=["lm_head"]),
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]
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# Apply algorithms and save to output_dir
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oneshot(
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model=model,
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dataset=ds,
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recipe=recipe,
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max_seq_length=MAX_SEQUENCE_LENGTH,
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num_calibration_samples=NUM_CALIBRATION_SAMPLES,
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)
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# Confirm generations of the quantized model look sane.
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print("\n\n")
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print("========== SAMPLE GENERATION ==============")
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dispatch_for_generation(model)
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input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to("npu")
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output = model.generate(input_ids, max_new_tokens=100)
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print(tokenizer.decode(output[0]))
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print("==========================================\n\n")
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# Save to disk compressed.
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SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-W8A8-Dynamic-Per-Token"
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model.save_pretrained(SAVE_DIR, save_compressed=True)
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tokenizer.save_pretrained(SAVE_DIR)
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