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quantize.py

@@ -1,118 +1,33 @@
-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
+from transformers import AutoProcessor, LlavaForConditionalGeneration
 
-# Load model.
-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
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.transformers import oneshot
 
+MODEL_ID = "llama-joycaption-beta-one-hf-llava"
 
-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,
+# 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.*"],
 )
 
+# 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)
+
 # 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("==========================================")
+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("==========================================")