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quantize.py

@@ -1,33 +1,118 @@
-from transformers import AutoProcessor, LlavaForConditionalGeneration
-
-from llmcompressor.modifiers.quantization import QuantizationModifier
-from llmcompressor.transformers import oneshot
-
-MODEL_ID = "llama-joycaption-beta-one-hf-llava"
+import re
+import requests
+import torch
+from PIL import Image
+from transformers import AutoProcessor
+from datasets import load_dataset
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers.tracing import TraceableLlavaForConditionalGeneration
 
 # Load model.
-model_class = LlavaForConditionalGeneration
-model = model_class.from_pretrained(MODEL_ID, device_map="auto", torch_dtype="auto")
-processor = AutoProcessor.from_pretrained(MODEL_ID)
-
-# Configure the quantization algorithm and scheme.
-# In this case, we:
-#   * quantize the weights to fp8 with per channel via ptq
-#   * quantize the activations to fp8 with dynamic per token
-recipe = QuantizationModifier(
-    targets="Linear",
-    scheme="FP8_DYNAMIC",
-    ignore=["re:.*lm_head", "re:multi_modal_projector.*", "re:vision_tower.*"],
+model_id = "llama-joycaption-beta-one-hf-llava"
+model = TraceableLlavaForConditionalGeneration.from_pretrained(
+    model_id, device_map="auto", torch_dtype="bfloat16"
 )
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+
+# Oneshot arguments
+DATASET_ID = "lmms-lab/flickr30k"
+DATASET_SPLIT = "test"
+NUM_CALIBRATION_SAMPLES = 512
+MAX_SEQUENCE_LENGTH = 2048
+
+PROMPT = "Write a long descriptive caption for this image in a formal tone."
+
+ds = load_dataset(DATASET_ID, split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]")
+ds = ds.shuffle(seed=42)
+
+
+def preprocess_function(example):
+    # Build the conversation
+    convo = [
+        {
+            "role": "system",
+            "content": "You are a helpful image captioner.",
+        },
+        {
+            "role": "user",
+            "content": PROMPT,
+        },
+        {"role": "assistant", "content": " ".join(example["caption"])},
+    ]
+
+    # Format the conversation
+    # WARNING: HF's handling of chat's on Llava models is very fragile.  This specific combination of processor.apply_chat_template(), and processor() works
+    # but if using other combinations always inspect the final input_ids to ensure they are correct.  Often times you will end up with multiple <bos> tokens
+    # if not careful, which can make the model perform poorly.
+    convo_string = processor.apply_chat_template(
+        convo, tokenize=False, add_generation_prompt=True
+    )
+    assert isinstance(convo_string, str)
+
+    # Process the inputs
+    inputs = processor(
+        text=[convo_string], images=[example["image"]], return_tensors="pt"
+    ).to("cuda")
+    inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16)
+    return inputs
 
-# Apply quantization and save to disk in compressed-tensors format.
-SAVE_DIR = MODEL_ID + "-FP8-Dynamic"
-oneshot(model=model, recipe=recipe, output_dir=SAVE_DIR)
-processor.save_pretrained(SAVE_DIR)
+
+ds = ds.map(preprocess_function)
+
+
+# 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()}
+    return {
+        "input_ids": torch.LongTensor(batch[0]["input_ids"]),
+        "attention_mask": torch.tensor(batch[0]["attention_mask"]),
+        "pixel_values": torch.tensor(batch[0]["pixel_values"]),
+    }
+
+
+# Recipe
+recipe = [
+    GPTQModifier(
+        targets="Linear",
+        scheme="W8A8",
+        sequential_targets=["LlamaDecoderLayer"],
+        ignore=["re:.*lm_head", "re:vision_tower.*", "re:multi_modal_projector.*"],
+    ),
+]
+SAVE_DIR = model_id + "-W8A8"
+
+# Perform oneshot
+oneshot(
+    model=model,
+    tokenizer=model_id,
+    dataset=ds,
+    splits={"calibration": f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]"},
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    trust_remote_code_model=True,
+    data_collator=data_collator,
+    output_dir=SAVE_DIR,
+)
 
 # Confirm generations of the quantized model look sane.
 print("========== SAMPLE GENERATION ==============")
-input_ids = processor(text="Hello my name is", return_tensors="pt").input_ids.to("cuda")
-output = model.generate(input_ids, max_new_tokens=20)
-print(processor.decode(output[0]))
-print("==========================================")
+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("==========================================")