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handler.py

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+# 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]]:
+        if "instances" not in data:
+            raise ValueError(
+                "The request body must contain a key 'instances' with a list of instances."
+            )
+
+        logger.debug(f"Received incoming request with {data=}")
+
+        predictions = []
+        for input in data["instances"]:
+            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}

requirements.txt

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+einops
+timm