handler.py

# Adapted from https://huggingface.co/nvidia/NVLM-D-72B#inference
import math
from typing import Any, Dict, List

import torch
import torchvision.transforms as T
from torchvision.transforms.functional import InterpolationMode

import requests
from io import BytesIO
from PIL import Image

from transformers import AutoTokenizer, AutoModel

from huggingface_inference_toolkit.logging import logger


def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
    best_ratio_diff = float("inf")
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio


def dynamic_preprocess(
    image, min_num=1, max_num=12, image_size=448, use_thumbnail=False
):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    # calculate the existing image aspect ratio
    target_ratios = set(
        (i, j)
        for n in range(min_num, max_num + 1)
        for i in range(1, n + 1)
        for j in range(1, n + 1)
        if i * j <= max_num and i * j >= min_num
    )
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    # find the closest aspect ratio to the target
    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio,
        target_ratios,
        orig_width,
        orig_height,
        image_size,
    )

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size,
        )
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images


def load_image(image_url, input_size=448, max_num=12):
    response = requests.get(image_url)
    image = Image.open(BytesIO(response.content)).convert("RGB")
    transform = build_transform(input_size=input_size)
    images = dynamic_preprocess(
        image, image_size=input_size, use_thumbnail=True, max_num=max_num
    )
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values


def split_model():
    device_map = {}
    world_size = torch.cuda.device_count()
    num_layers = 80
    # Since the first GPU will be used for ViT, treat it as half a GPU.
    num_layers_per_gpu = math.ceil(num_layers / (world_size - 0.5))
    num_layers_per_gpu = [num_layers_per_gpu] * world_size
    num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5)
    layer_cnt = 0
    for i, num_layer in enumerate(num_layers_per_gpu):
        for j in range(num_layer):
            device_map[f"language_model.model.layers.{layer_cnt}"] = i
            layer_cnt += 1
    device_map["vision_model"] = 0
    device_map["mlp1"] = 0
    device_map["language_model.model.tok_embeddings"] = 0
    device_map["language_model.model.embed_tokens"] = 0
    device_map["language_model.output"] = 0
    device_map["language_model.model.norm"] = 0
    device_map["language_model.lm_head"] = 0
    device_map[f"language_model.model.layers.{num_layers - 1}"] = 0

    return device_map


IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)


def build_transform(input_size):
    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
    transform = T.Compose(
        [
            T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img),
            T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
            T.ToTensor(),
            T.Normalize(mean=MEAN, std=STD),
        ]
    )
    return transform


class EndpointHandler:
    def __init__(self, model_dir: str, **kwargs: Any) -> None:
        self.model = AutoModel.from_pretrained(
            model_dir,
            torch_dtype=torch.bfloat16,
            low_cpu_mem_usage=True,
            use_flash_attn=False,
            trust_remote_code=True,
            device_map=split_model(),
        ).eval()

        self.tokenizer = AutoTokenizer.from_pretrained(
            model_dir, trust_remote_code=True, use_fast=False
        )

    def __call__(self, data: Dict[str, Any]) -> Dict[str, List[Any]]:
        logger.info(f"Received incoming request with {data=}")
        
        if "instances" in data:
            logger.warning("Using `instances` instead of `inputs` is deprecated.")
            data["inputs"] = data.pop("instances")

        if "inputs" not in data:
            raise ValueError(
                "The request body must contain a key 'inputs' with a list of inputs."
            )

        if not isinstance(data["inputs"], list):
            raise ValueError(
                "The request inputs must be a list of dictionaries with either the key"
                " 'prompt' or 'prompt' + 'image_url', and optionally including the key"
                " 'generation_config'."
            )

        if not all(isinstance(input, dict) and "prompt" in input.keys() for input in data["inputs"]):
            raise ValueError(
                "The request inputs must be a list of dictionaries with either the key"
                " 'prompt' or 'prompt' + 'image_url', and optionally including the key"
                " 'generation_config'."
            )

        predictions = []
        for input in data["inputs"]:
            if "prompt" not in input:
                raise ValueError(
                    "The request input body must contain at least the key 'prompt' with the prompt to use."
                )

            generation_config = input.get("generation_config", dict(max_new_tokens=1024, do_sample=False))

            if "image_url" not in input:
                # pure-text conversation
                response, history = self.model.chat(
                    self.tokenizer,
                    None,
                    input["prompt"],
                    generation_config,
                    history=None,
                    return_history=True,
                )
            else:
                # single-image single-round conversation
                pixel_values = load_image(input["image_url"], max_num=6).to(
                    torch.bfloat16
                )
                response = self.model.chat(
                    self.tokenizer,
                    pixel_values,
                    f"<image>\n{input['prompt']}",
                    generation_config,
                )

            predictions.append(response)
        return {"predictions": predictions}