Add Idefics3/SmolVLM quant support via traceable class (#1095)

SUMMARY:
Adding a traceable Idefics3 class following the new
[guide](https://github.com/vllm-project/llm-compressor/blob/main/src/llmcompressor/transformers/tracing/GUIDE.md)
to allow W4A16 quants of Idefics3 and SmolVLM (which share the same
architecture). Idefics3 seems to require a max_sequence_length of 4096
and I copied the example from the Phi 3 Vision example as the dataset
loading approach from the Llava example led to OOM on 64 GB RAM.

TEST PLAN:
Tested on A100 with Idefics3 @512 samples and on a 4060 Ti with SmolVLM
@128 samples.

---------

Co-authored-by: Kyle Sayers <[email protected]>

Author: leon-seidel

examples/multimodal_vision/idefics3_example.py

@@ -0,0 +1,117 @@
+import requests
+import torch
+from datasets import load_dataset
+from PIL import Image
+from transformers import AutoProcessor
+
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers import oneshot
+from llmcompressor.transformers.tracing import TraceableIdefics3ForConditionalGeneration
+
+# Load model.
+model_id = "HuggingFaceM4/Idefics3-8B-Llama3"  # or "HuggingFaceTB/SmolVLM-Instruct"
+model = TraceableIdefics3ForConditionalGeneration.from_pretrained(
+    model_id, device_map="auto", torch_dtype="auto"
+)
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+
+# Oneshot arguments
+DATASET_ID = "lmms-lab/flickr30k"
+DATASET_SPLIT = "test[:512]"
+NUM_CALIBRATION_SAMPLES = 512
+MAX_SEQUENCE_LENGTH = 4096  # Seems to be required here
+
+
+# Define a oneshot data collator for multimodal inputs.
+def data_collator(batch):
+    assert len(batch) == 1
+    return {key: torch.tensor(value) for key, value in batch[0].items()}
+
+
+# Recipe
+recipe = [
+    GPTQModifier(
+        targets="Linear",
+        scheme="W4A16",
+        sequential_targets=["LlamaDecoderLayer"],
+        ignore=["re:.*lm_head", "re:model.vision_model.*", "re:model.connector.*"],
+    ),
+]
+
+# Load dataset and preprocess.
+ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
+ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))
+
+
+# Apply chat template
+def preprocess(example):
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "What does the image show?"},
+                {"type": "image"},
+            ],
+        }
+    ]
+    return {
+        "text": processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+        ),
+        "images": example["image"],
+    }
+
+
+ds = ds.map(preprocess)
+
+
+# Tokenize inputs.
+def tokenize(sample):
+    return processor(
+        text=sample["text"],
+        images=sample["images"],
+        padding=False,
+        max_length=MAX_SEQUENCE_LENGTH,
+        truncation=True,
+    )
+
+
+# avoid errors with writer_batch_size
+ds = ds.map(tokenize, writer_batch_size=1, remove_columns=ds.column_names)
+
+# Perform oneshot
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    trust_remote_code_model=True,
+    data_collator=data_collator,
+)
+
+# Confirm generations of the quantized model look sane.
+print("========== SAMPLE GENERATION ==============")
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "Please describe the animal in this image\n"},
+            {"type": "image"},
+        ],
+    },
+]
+prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+image_url = "http://images.cocodataset.org/train2017/000000231895.jpg"
+raw_image = Image.open(requests.get(image_url, stream=True).raw)
+
+inputs = processor(images=raw_image, text=prompt, return_tensors="pt").to("cuda")
+output = model.generate(**inputs, max_new_tokens=100)
+print(processor.decode(output[0], skip_special_tokens=True))
+print("==========================================")
+
+# Save to disk compressed.
+SAVE_DIR = model_id.split("/")[1] + "-W4A16-G128"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+processor.save_pretrained(SAVE_DIR)

src/llmcompressor/transformers/tracing/__init__.py

@@ -7,9 +7,13 @@
 from .qwen2_vl import (
     Qwen2VLForConditionalGeneration as TraceableQwen2VLForConditionalGeneration,
 )
+from .idefics3 import (
+    Idefics3ForConditionalGeneration as TraceableIdefics3ForConditionalGeneration
+)
 
 __all__ = [
     "TraceableLlavaForConditionalGeneration",
     "TraceableMllamaForConditionalGeneration",
     "TraceableQwen2VLForConditionalGeneration",
+    "TraceableIdefics3ForConditionalGeneration"
 ]