feat: add smoke test config and synthetic data generator

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

configs/diffusion/train/smoke_test.py

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+_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=False,
+    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

scripts/diffusion/gen_smoke_data.py

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+"""
+生成 smoke test 所需的合成数据（fake latents）。
+
+数据布局：
+  /data/train-input/smoke_test/
+    data.csv
+    latents/sample_{i}.pt     # video latent [16, 1, 32, 32]
+    t5/sample_{i}.pt          # T5 embedding  [256, 4096]
+    clip/sample_{i}.pt        # CLIP embedding [768]
+
+  /mnt/ddn/sora/tmp_load/
+    null_t5.pt                # null T5  [1, 256, 4096]
+    null_clip.pt              # null CLIP [1, 768]
+
+Shape 说明（256px 单帧，AE_SPATIAL_COMPRESSION=16，patch_size=2）：
+  VAE latent: [C=16, T=1, H_lat=32, W_lat=32]
+    32 = patch_size(2) * ceil(256 / AE_SPATIAL_COMPRESSION(16)) = 2 * 16
+  T5: [seq=256, dim=4096]
+  CLIP pooled: [dim=768]
+"""
+
+import os
+import csv
+import torch
+
+N_SAMPLES = 4           # 生成 4 条假样本
+BASE_DIR  = "/data/train-input/smoke_test"
+NULL_DIR  = "/mnt/ddn/sora/tmp_load"
+
+LAT_C, LAT_T, LAT_H, LAT_W = 16, 1, 32, 32   # video latent shape
+T5_SEQ,  T5_DIM  = 256, 4096                   # T5 embedding
+CLIP_DIM          = 768                         # CLIP pooled
+
+def main():
+    os.makedirs(f"{BASE_DIR}/latents", exist_ok=True)
+    os.makedirs(f"{BASE_DIR}/t5",      exist_ok=True)
+    os.makedirs(f"{BASE_DIR}/clip",    exist_ok=True)
+    os.makedirs(NULL_DIR, exist_ok=True)
+
+    rows = []
+    for i in range(N_SAMPLES):
+        lat_path  = f"{BASE_DIR}/latents/sample_{i}.pt"
+        t5_path   = f"{BASE_DIR}/t5/sample_{i}.pt"
+        clip_path = f"{BASE_DIR}/clip/sample_{i}.pt"
+
+        torch.save(torch.randn(LAT_C, LAT_T, LAT_H, LAT_W), lat_path)
+        torch.save(torch.randn(T5_SEQ, T5_DIM),              t5_path)
+        torch.save(torch.randn(CLIP_DIM),                     clip_path)
+
+        rows.append({
+            "path":           lat_path,   # 占位（cached 模式不读原始视频）
+            "text":           f"smoke test sample {i}",
+            "latents_path":   lat_path,
+            "text_t5_path":   t5_path,
+            "text_clip_path": clip_path,
+        })
+
+    csv_path = f"{BASE_DIR}/data.csv"
+    with open(csv_path, "w", newline="") as f:
+        writer = csv.DictWriter(f, fieldnames=rows[0].keys())
+        writer.writeheader()
+        writer.writerows(rows)
+    print(f"Wrote {N_SAMPLES} samples to {csv_path}")
+
+    # null vectors（空提示的 embedding，用于 cfg dropout）
+    null_t5   = torch.zeros(1, T5_SEQ, T5_DIM)
+    null_clip = torch.zeros(1, CLIP_DIM)
+    torch.save(null_t5,   f"{NULL_DIR}/null_t5.pt")
+    torch.save(null_clip, f"{NULL_DIR}/null_clip.pt")
+    print(f"Wrote null vectors to {NULL_DIR}/")
+
+    print("Done.")
+
+if __name__ == "__main__":
+    main()