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