Remove TraceableMistralForCausalLM

[kylesayrs]

Purpose

• Remove changes to MistralForCausalLM which was thought to be needed for Pixtral, but is not

Changes

• Remove TraceableMistralForCausalLM
• Remove TraceableMistralForCausalLM's use in LlavaForConditionalGeneration
• Remove some unneeded imports in src/llmcompressor/transformers/tracing/llava.py

Testing

• Ran examples/multimodal_vision/llava_example.py to completion
• Ran examples/multimodal_vision/pixtral_example.py to completion
• Ran mixtral_example.py to completion
• grep -r 'TraceableMistralForCausalLM' src/ examples/ tests/
• grep -r 'TraceableLlavaForConditionalGeneration' src/ examples/ tests/

mixtral_example.py

from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer

from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers import oneshot
from llmcompressor.transformers.compression.helpers import calculate_offload_device_map

# Select model and load it.
MODEL_ID = "mistralai/Mixtral-8x7B-Instruct-v0.1"
NUM_GPUS = 1

device_map = calculate_offload_device_map(
    MODEL_ID, reserve_for_hessians=True, num_gpus=NUM_GPUS, torch_dtype="auto"
)

model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map=device_map,
    torch_dtype="auto",
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

# 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": tokenizer.apply_chat_template(
            example["messages"],
            tokenize=False,
        )
    }


ds = ds.map(preprocess)


# Tokenize inputs.
def tokenize(sample):
    return tokenizer(
        sample["text"],
        padding=False,
        max_length=MAX_SEQUENCE_LENGTH,
        truncation=True,
        add_special_tokens=False,
    )


ds = ds.map(tokenize, remove_columns=ds.column_names)

# Configure the quantization algorithm to run.
#   * quantize the weights to 4 bit with GPTQ with a group size 128
recipe = GPTQModifier(targets="Linear", scheme="W4A16", ignore=["lm_head"])

# Apply algorithms.
oneshot(
    model=model,
    dataset=ds,
    recipe=recipe,
    max_seq_length=MAX_SEQUENCE_LENGTH,
    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
)

# Confirm generations of the quantized model look sane.
print("\n\n")
print("========== SAMPLE GENERATION ==============")
input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to("cuda")
output = model.generate(input_ids, max_new_tokens=100)
print(tokenizer.decode(output[0]))
print("==========================================\n\n")

# Save to disk compressed.
SAVE_DIR = MODEL_ID.split("/")[1] + "-W4A16-G128"
model.save_pretrained(SAVE_DIR, save_compressed=True)
tokenizer.save_pretrained(SAVE_DIR)

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