VLM: Qwen2_VL Example

examples/multimodal_vision/qwen2_vl_example.py

@@ -0,0 +1,123 @@
+import base64
+from io import BytesIO
+
+from datasets import load_dataset
+from qwen_vl_utils import process_vision_info
+from transformers import AutoProcessor
+
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers import oneshot
+from llmcompressor.transformers.tracing import TraceableQwen2VLForConditionalGeneration
+from llmcompressor.transformers.utils.data_collator import qwen2_vl_data_collator
+
+# Load model.
+model_id = "Qwen/Qwen2-VL-2B-Instruct"
+model = TraceableQwen2VLForConditionalGeneration.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 = {"calibration": "test[:512]"}
+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)
+
+
+# Apply chat template and tokenize inputs.
+def preprocess_and_tokenize(example):
+    # preprocess
+    buffered = BytesIO()
+    example["image"].save(buffered, format="PNG")
+    encoded_image = base64.b64encode(buffered.getvalue())
+    encoded_image_text = encoded_image.decode("utf-8")
+    base64_qwen = f"data:image;base64,{encoded_image_text}"
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": base64_qwen},
+                {"type": "text", "text": "What does the image show?"},
+            ],
+        }
+    ]
+    text = processor.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    image_inputs, video_inputs = process_vision_info(messages)
+
+    # tokenize
+    return processor(
+        text=[text],
+        images=image_inputs,
+        videos=video_inputs,
+        padding=False,
+        max_length=MAX_SEQUENCE_LENGTH,
+        truncation=True,
+    )
+
+
+ds = ds.map(preprocess_and_tokenize, remove_columns=ds["calibration"].column_names)
+
+# Recipe
+recipe = [
+    GPTQModifier(
+        targets="Linear",
+        scheme="W4A16",
+        sequential_targets=["Qwen2VLDecoderLayer"],
+        ignore=["lm_head", "re:visual.*"],
+    ),
+]
+
+# Perform oneshot
+oneshot(
+    model=model,
+    tokenizer=model_id,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    trust_remote_code_model=True,
+    data_collator=qwen2_vl_data_collator,
+)
+
+# Confirm generations of the quantized model look sane.
+print("========== SAMPLE GENERATION ==============")
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "http://images.cocodataset.org/train2017/000000231895.jpg",
+            },
+            {"type": "text", "text": "Please describe the animal in this image\n"},
+        ],
+    }
+]
+prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[prompt],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=False,
+    max_length=MAX_SEQUENCE_LENGTH,
+    truncation=True,
+    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

@@ -5,9 +5,13 @@
 from .mllama import (
     MllamaForConditionalGeneration as TraceableMllamaForConditionalGeneration,
 )
+from .qwen2_vl import (
+    Qwen2VLForConditionalGeneration as TraceableQwen2VLForConditionalGeneration,
+)
 
 __all__ = [
     "TraceableLlavaForConditionalGeneration",
     "TraceableMllamaForConditionalGeneration",
     "TraceableMistralForCausalLM",
+    "TraceableQwen2VLForConditionalGeneration",
 ]