Upload utils_.py

utils_.py

@@ -0,0 +1,161 @@
+import os
+import math
+import numpy as np
+import torch
+import torch.distributed as dist
+import torchvision.transforms as T
+from decord import VideoReader, cpu
+from PIL import Image
+from transformers import AutoConfig
+from torchvision.transforms.functional import InterpolationMode
+
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+def load_image(image_file, input_size=448, max_num=12, upscale=False):
+    image = Image.open(image_file).convert('RGB')
+    if upscale:
+        image = image.resize((image.width * 2, image.height * 2), Image.BILINEAR)
+    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 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
+
+def get_rank_and_world_size():
+    rank = int(os.environ.get('RANK', 0))
+    world_size = int(os.environ.get('WORLD_SIZE', 1))
+    return rank, world_size
+
+def get_local_rank_and_local_world_size():
+    if not dist.is_available():
+        return 0, 1
+    if not dist.is_initialized():
+        return 0, 1
+
+    if 'SLURM_LOCALID' in os.environ:
+        local_rank = int(os.environ['SLURM_LOCALID'])
+        local_world_size = int(os.environ['SLURM_NTASKS_PER_NODE'])
+        return local_rank, local_world_size
+
+    if 'LOCAL_RANK' in os.environ and 'LOCAL_WORLD_SIZE' in os.environ:
+        return int(os.environ['LOCAL_RANK']), int(os.environ['LOCAL_WORLD_SIZE'])
+
+    raise NotImplementedError(
+        "Fail to get local_rank and local_world_size! "
+        "Please ensure that you set the environment variable "
+        "`LOCAL_RANK` and `LOCAL_WORLD_SIZE`"
+    )
+
+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=6, 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 split_model(model_path):
+    num_gpus_per_node = torch.cuda.device_count()
+    rank, world_size = get_rank_and_world_size()
+    try:
+        local_rank, local_world_size = get_local_rank_and_local_world_size()
+    except:
+        local_rank = rank
+
+    if 'GPUS_PER_PROCESS' in os.environ:
+        gpus_per_process = int(os.environ['GPUS_PER_PROCESS'])
+    else:
+        gpus_per_process = 8  # default to use 8 GPUs for one model
+    gpus_per_process = min(gpus_per_process, num_gpus_per_node // local_world_size)
+    start_gpu = local_rank * gpus_per_process
+    end_gpu = start_gpu + gpus_per_process
+
+    assert end_gpu <= num_gpus_per_node, f"Process {local_rank} tries to access GPU {end_gpu}, " \
+                                         f"but only {num_gpus_per_node} GPUs are available per node."
+
+    visible_devices = list(range(start_gpu, end_gpu))
+
+    device_map = {}
+    config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
+
+    num_gpus_for_vit = 0.5
+    num_layers = config.llm_config.num_hidden_layers
+    num_layers_per_gpu = math.ceil(num_layers / (len(visible_devices) - num_gpus_for_vit))
+    num_layers_per_gpu = [num_layers_per_gpu] * len(visible_devices)
+    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}'] = visible_devices[i]
+            layer_cnt += 1
+    device_map['vision_model'] = visible_devices[0]
+    device_map['mlp1'] = visible_devices[0]
+    device_map['language_model.model.tok_embeddings'] = visible_devices[0]
+    device_map['language_model.model.embed_tokens'] = visible_devices[0]
+    device_map['language_model.output'] = visible_devices[0]
+    device_map['language_model.model.norm'] = visible_devices[0]
+    device_map['language_model.model.rotary_emb'] = visible_devices[0]
+    device_map['language_model.lm_head'] = visible_devices[0]
+    device_map[f'language_model.model.layers.{num_layers - 1}'] = visible_devices[0]
+
+    return device_map, visible_devices
+