78 lines
1.8 KiB
Python
78 lines
1.8 KiB
Python
_base_ = ["image.py"]
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# ===== Smoke Test Config =====
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# cached_video + cached_text: 跳过 VAE / T5 / CLIP，直接用预算好的 latent
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# 仅用 1 GPU，3 个训练步，验证训练 loop 通路
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# --- Dataset ---
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dataset = dict(
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type="cached_video_text",
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data_path="/data/train-input/smoke_test/data.csv",
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cached_video=True,
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cached_text=True,
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load_original_video=True,
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memory_efficient=False,
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vmaf=False,
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)
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# --- Bucket: 256px 单帧，batch_size=1 ---
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bucket_config = {
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"_delete_": True,
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"256px": {
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1: (1.0, 1),
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},
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}
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# --- 跳过 VAE / T5 / CLIP 加载 ---
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cached_video = True
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cached_text = True
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# --- Null 向量路径（空提示的 embedding，用于 dropout） ---
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# train.py 里 cached_text 模式下 hardcode 读这两个路径
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# 我们在 /mnt/ddn/sora/tmp_load/ 放假的 null 向量
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# --- 模型：随机初始化，不加载预训练权重 ---
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model = dict(
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from_pretrained=None,
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strict_load=False,
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grad_ckpt_settings=(1, 100),
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)
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# --- 优化器 ---
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lr = 1e-5
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optim = dict(
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cls="HybridAdam",
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lr=lr,
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eps=1e-15,
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weight_decay=0.0,
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adamw_mode=True,
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)
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# --- 训练参数 ---
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epochs = 1
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ckpt_every = 0 # 不保存 checkpoint
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log_every = 1
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warmup_steps = 0
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grad_clip = 1.0
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accumulation_steps = 1
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ema_decay = None # 不用 EMA
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# --- 加速 ---
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dtype = "bf16"
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plugin = "zero2"
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plugin_config = dict(
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reduce_bucket_size_in_m=128,
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overlap_allgather=False,
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)
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pin_memory_cache_pre_alloc_numels = None # 关掉 pin memory 预分配
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# --- 其他 ---
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num_workers = 0
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prefetch_factor = None
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num_bucket_build_workers = 1
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seed = 42
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outputs = "/data/train-output/smoke_test_outputs"
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grad_checkpoint = False # 关掉 activation checkpoint，减少复杂度
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# dropout ratio 保持原样（0.31...），null vector 在 /mnt/ddn/sora/tmp_load/ 下
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