_base_ = ["image.py"]

# ===== Smoke Test Config =====
# cached_video + cached_text: 跳过 VAE / T5 / CLIP，直接用预算好的 latent
# 仅用 1 GPU，3 个训练步，验证训练 loop 通路

# --- Dataset ---
dataset = dict(
    type="cached_video_text",
    data_path="/data/train-input/smoke_test/data.csv",
    cached_video=True,
    cached_text=True,
    load_original_video=True,
    memory_efficient=False,
    vmaf=False,
)

# --- Bucket: 256px 单帧，batch_size=1 ---
bucket_config = {
    "_delete_": True,
    "256px": {
        1: (1.0, 1),
    },
}

# --- 跳过 VAE / T5 / CLIP 加载 ---
cached_video = True
cached_text = True

# --- Null 向量路径（空提示的 embedding，用于 dropout） ---
# train.py 里 cached_text 模式下 hardcode 读这两个路径
# 我们在 /mnt/ddn/sora/tmp_load/ 放假的 null 向量

# --- 模型：随机初始化，不加载预训练权重 ---
model = dict(
    from_pretrained=None,
    strict_load=False,
    grad_ckpt_settings=(1, 100),
)

# --- 优化器 ---
lr = 1e-5
optim = dict(
    cls="HybridAdam",
    lr=lr,
    eps=1e-15,
    weight_decay=0.0,
    adamw_mode=True,
)

# --- 训练参数 ---
epochs = 1
ckpt_every = 0      # 不保存 checkpoint
log_every = 1
warmup_steps = 0
grad_clip = 1.0
accumulation_steps = 1
ema_decay = None    # 不用 EMA

# --- 加速 ---
dtype = "bf16"
plugin = "zero2"
plugin_config = dict(
    reduce_bucket_size_in_m=128,
    overlap_allgather=False,
)
pin_memory_cache_pre_alloc_numels = None  # 关掉 pin memory 预分配

# --- 其他 ---
num_workers = 0
prefetch_factor = None
num_bucket_build_workers = 1
seed = 42
outputs = "/data/train-output/smoke_test_outputs"
grad_checkpoint = False  # 关掉 activation checkpoint，减少复杂度

# dropout ratio 保持原样（0.31...），null vector 在 /mnt/ddn/sora/tmp_load/ 下