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@@ -58,3 +58,22 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+transfer/code/CCFM/logs/ccfm_1gpu_online_5404417.out filter=lfs diff=lfs merge=lfs -text
+transfer/code/CCFM/logs/ccfm_v2_5404735.out filter=lfs diff=lfs merge=lfs -text
+transfer/code/CCFM/logs/ccfm_v2_resume_5406605.out filter=lfs diff=lfs merge=lfs -text
+transfer/code/CCFM/result/ccfm-fusion_differential_perceiver-norman-cascaded-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-latent_weight_1.0-choose_latent_p_0.4/iteration_110000/pred.h5ad filter=lfs diff=lfs merge=lfs -text
+transfer/code/CCFM/result/ccfm-fusion_differential_perceiver-norman-cascaded-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-latent_weight_1.0-choose_latent_p_0.4/iteration_110000/real.h5ad filter=lfs diff=lfs merge=lfs -text
+transfer/code/CCFM/result/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-ema0.9999-ln-wu2000-rk4-online/eval_only/pred.h5ad filter=lfs diff=lfs merge=lfs -text
+transfer/code/CCFM/result/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-ema0.9999-ln-wu2000-rk4-online/eval_only/real.h5ad filter=lfs diff=lfs merge=lfs -text
+transfer/code/CCFM/result/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-ema0.9999-ln-wu2000-rk4-online/iteration_190000/pred.h5ad filter=lfs diff=lfs merge=lfs -text
+transfer/code/CCFM/result/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-ema0.9999-ln-wu2000-rk4-online/iteration_190000/real.h5ad filter=lfs diff=lfs merge=lfs -text
+transfer/code/CCFM/result/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-ema0.9999-ln-wu2000-rk4-online/loss_curve.csv filter=lfs diff=lfs merge=lfs -text
+transfer/code/CCFM/result_joint_generate/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-online/eval_only/pred.h5ad filter=lfs diff=lfs merge=lfs -text
+transfer/code/CCFM/result_joint_generate/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-online/eval_only/real.h5ad filter=lfs diff=lfs merge=lfs -text
+transfer/code/CCFM/test_result/ccfm-fusion_differential_perceiver-norman-cascaded-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-latent_weight_1.0-choose_latent_p_0.4/iteration_0/pred.h5ad filter=lfs diff=lfs merge=lfs -text
+transfer/code/CCFM/test_result/ccfm-fusion_differential_perceiver-norman-cascaded-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-latent_weight_1.0-choose_latent_p_0.4/iteration_0/real.h5ad filter=lfs diff=lfs merge=lfs -text
+transfer/code/ori_scDFM/data/norman.h5ad filter=lfs diff=lfs merge=lfs -text
+transfer/code/ori_scDFM/logs/ccfm_baseline_5404238.out filter=lfs diff=lfs merge=lfs -text
+transfer/code/scDFM/data/norman/go.csv filter=lfs diff=lfs merge=lfs -text
+transfer/code/scDFM/data/norman/perturb_processed.h5ad filter=lfs diff=lfs merge=lfs -text
+transfer/code/scDFM/data/norman.h5ad filter=lfs diff=lfs merge=lfs -text

transfer/code/CCFM/CCFM_Architecture.md

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+# CCFM (Cascaded Conditioned Flow Matching) 架构文档
+
+## 一、项目概述
+
+CCFM 是一个**级联流匹配**框架，融合三个模型的优势来做单细胞扰动预测：
+
+- **scDFM**：基础流匹配架构（backbone、数据加载、训练范式）
+- **LatentForcing**：级联双流思想（latent 流 + 表达流分阶段生成）
+- **scGPT**：冻结的预训练模型，提供 per-gene 上下文化特征
+
+### 核心创新
+
+借鉴 LatentForcing 的双流架构，将其从图像域迁移到单细胞域：
+
+| LatentForcing (图像) | CCFM (单细胞) |
+|---|---|
+| 像素值 | 基因表达值 |
+| DINO-v2 特征（辅助生成目标） | scGPT 上下文特征（辅助生成目标） |
+| 类别标签（条件信号） | control 表达 + perturbation_id（条件信号） |
+
+**关键区分**：scGPT 特征是从 target（扰动细胞）提取的**辅助生成目标**，不是条件信号。推理时模型从噪声生成 scGPT 特征（Stage 1），再用生成的特征引导表达值生成（Stage 2）。真正的条件信号是 control 表达和 perturbation_id，它们在推理时始终可获取。
+
+### 文件结构
+
+```
+CCFM/
+├── _bootstrap_scdfm.py                    # Bootstrap scDFM 模块，命名空间隔离
+├── config/
+│   └── config_cascaded.py                 # CascadedFlowConfig dataclass (tyro CLI)
+├── src/
+│   ├── __init__.py
+│   ├── utils.py                           # Re-exports scDFM utils
+│   ├── _scdfm_imports.py                  # scDFM 模块集中导入
+│   ├── denoiser.py                        # CascadedDenoiser (训练/推理逻辑)
+│   ├── model/
+│   │   ├── model.py                       # CascadedFlowModel 双流模型
+│   │   └── layers.py                      # LatentEmbedder, LatentDecoder
+│   └── data/
+│       ├── data.py                        # scDFM 数据加载集成
+│       └── scgpt_extractor.py             # FrozenScGPTExtractor
+├── scripts/
+│   ├── run_cascaded.py                    # 训练/评估入口
+│   └── download_scgpt.py                  # 下载 scGPT 预训练模型
+├── run.sh                                 # pjsub 模板
+└── run_topk30_neg.sh                      # 完整参数化 job 脚本
+```
+
+### 默认超参数
+
+| 参数 | 值 | 说明 |
+|---|---|---|
+| `B` | 48 | batch size |
+| `G_full` | 5000 | HVG 总数 |
+| `G` | 1000 | 训练时随机基因子集 |
+| `d_model` | 128 | 隐藏维度 |
+| `scgpt_dim` | 512 | scGPT 输出特征维度 |
+| `bottleneck_dim` | 128 | LatentEmbedder 瓶颈维度 |
+| `nhead` | 8 | 注意力头数 |
+| `nlayers` | 4 | Transformer 层数 |
+| `dh_depth` | 2 | LatentDecoder block 数 |
+| `choose_latent_p` | 0.4 | 训练 latent 流的概率 |
+| `latent_weight` | 1.0 | latent loss 权重 |
+| `gamma` | 0.5 | MMD loss 权重 |
+| `lr` | 5e-5 | 学习率 |
+| `steps` | 200000 | 训练总步数 |
+| `latent_steps` | 20 | 推理 ODE 步数（latent） |
+| `expr_steps` | 20 | 推理 ODE 步数（表达） |
+| `warmup_batches` | 200 | scGPT running stats 预热 |
+
+---
+
+## 二、训练流程 Tensor 数据流
+
+### 2.1 数据准备
+
+```
+输入:
+  source:           (B, G_full=5000)    控制组表达
+  target:           (B, G_full=5000)    扰动后表达
+  perturbation_id:  (B, 2)              扰动 token ID
+  gene_ids:         (G_full=5000,)      全部基因的 vocab ID
+
+随机采样 1000 个基因:
+  input_gene_ids = randperm(5000)[:1000]          → (1000,)
+  source_sub  = source[:, input_gene_ids]         → (48, 1000)
+  target_sub  = target[:, input_gene_ids]         → (48, 1000)
+  gene_input  = gene_ids[input_gene_ids].expand   → (48, 1000)
+```
+
+### 2.2 冻结 scGPT 提取辅助生成目标（类似 LatentForcing 的 DINO 特征提取）
+
+```
+scgpt_extractor.extract(target_sub, gene_indices=input_gene_ids):
+
+  target_sub:        (48, 1000)         输入表达
+  hvg_ids:           (1000,)            HVG → scGPT vocab ID 映射
+  valid_mask:        (1000,)            bool, 过滤在 scGPT vocab 中的基因
+  expr_valid:        (48, G_valid)      有效基因的表达值
+
+  若 G_valid+1 > max_seq_len(1200):
+    随机采样 1199 个基因
+    seq_len = 1200
+  否则:
+    seq_len = G_valid + 1
+
+  拼接 CLS token:
+    src    = [cls_id | gene_ids]        → (48, seq_len)       long
+    values = [0      | expr_valid]      → (48, seq_len)       float
+
+  scGPT frozen forward:
+    encoder_out = scgpt._encode(src, values, mask)  → (48, seq_len, 512)
+
+  去掉 CLS, scatter 回固定位置:
+    gene_features = encoder_out[:, 1:, :]           → (48, seq_len-1, 512)
+    output = zeros(48, 1000, 512)
+    output.scatter_(1, idx, gene_features)          → (48, 1000, 512)
+
+  归一化 (running mean/var, warmup 200 batches 后冻结):
+    output = (output - running_mean) / sqrt(running_var) * target_std
+
+  z_target:  (48, 1000, 512)
+```
+
+### 2.3 级联时间采样
+
+```
+t_latent = rand(48)                         → (48,)
+t_expr   = rand(48)                         → (48,)
+choose_latent_mask = rand(48) < 0.4         → (48,)  bool
+
+对每个样本二选一:
+  若 mask=True  (40%概率): t_latent 保留, t_expr=0, w_expr=0, w_latent=1  → 训练 latent 流
+  若 mask=False (60%概率): t_expr 保留, t_latent=1, w_expr=1, w_latent=0  → 训练表达流
+
+输出:
+  t_expr:    (48,)    表达流时间步
+  t_latent:  (48,)    潜变量流时间步
+  w_expr:    (48,)    表达 loss 权重 (0 或 1)
+  w_latent:  (48,)    潜变量 loss 权重 (0 或 1)
+```
+
+### 2.4 Flow Path 采样（线性插值 + 速度）
+
+```
+表达流:
+  noise_expr = randn_like(source_sub)                    → (48, 1000)
+  path_expr  = AffineProbPath.sample(t_expr, noise_expr, target_sub)
+    path_expr.x_t  = (1-t)*noise + t*target              → (48, 1000)    插值后的噪声表达
+    path_expr.dx_t = target - noise                       → (48, 1000)    目标速度 (ground truth)
+
+潜变量流:
+  noise_latent = randn_like(z_target)                     → (48, 1000, 512)
+  展平:
+    z_target_flat      = z_target.reshape(48, 512000)     → (48, 512000)
+    noise_latent_flat  = noise_latent.reshape(48, 512000) → (48, 512000)
+  path_latent_flat = AffineProbPath.sample(t_latent, noise_latent_flat, z_target_flat)
+  还原:
+    path_latent.x_t  = reshape → (48, 1000, 512)          插值后的噪声 latent
+    path_latent.dx_t = reshape → (48, 1000, 512)          目标速度
+```
+
+### 2.5 模型前向传播 (`CascadedFlowModel.forward`)
+
+```
+输入:
+  gene_input:       (48, 1000)          基因 token ID
+  source_sub:       (48, 1000)          源表达
+  path_expr.x_t:    (48, 1000)          噪声表达
+  path_latent.x_t:  (48, 1000, 512)     噪声 latent
+  t_expr:           (48,)
+  t_latent:         (48,)
+  perturbation_id:  (48, 2)
+```
+
+#### Step 5a: 表达流 Embedding
+
+```
+gene_emb   = GeneEncoder(gene_input)                        → (48, 1000, 128)
+val_emb_1  = ContinuousValueEncoder(x_t)                    → (48, 1000, 128)   encoder_1 = 噪声 target (同 scDFM)
+val_emb_2  = ContinuousValueEncoder(source_sub)             → (48, 1000, 128)   encoder_2 = control (同 scDFM)
+expr_tokens = fusion_layer(cat[val_emb_1, val_emb_2]) + gene_emb
+            = Linear(256→128) → GELU → Linear(128→128) → LN + gene_emb → (48, 1000, 128)
+```
+
+> **设计说明**（与 scDFM 对齐）：
+> - `value_encoder_1` 编码噪声 target（`x_t`），`value_encoder_2` 编码 control（`source`），与 scDFM 角色一致
+> - `gene_emb` 只在融合后加一次（去冗余），而非分别加到两个 encoder 的输出上
+> - `val_emb_2`（control 嵌入）传入 backbone 的 DiffPerceiverBlock 作为交叉注意力 KV（稳定参考基线）
+
+#### Step 5b: 潜变量流 Embedding
+
+```
+latent_tokens = LatentEmbedder(path_latent.x_t)
+              = Linear(512→128) → GELU → Linear(128→128)    → (48, 1000, 128)
+```
+
+#### Step 5c: 双流融合
+
+```
+x = expr_tokens + latent_tokens                              → (48, 1000, 128)
+```
+
+#### Step 5d: 条件向量
+
+```
+t_expr_emb   = TimestepEmbedder(t_expr)                      → (48, 128)
+t_latent_emb = TimestepEmbedder(t_latent)                     → (48, 128)
+pert_emb     = GeneEncoder(perturbation_id).mean(dim=1)       → (48, 128)
+               # perturbation_id: (48,2) → encoder → (48,2,128) → mean → (48,128)
+c = t_expr_emb + t_latent_emb + pert_emb                     → (48, 128)
+```
+
+#### Step 5e: 共享 Backbone (4 层 DiffPerceiverBlock)
+
+```
+for i in range(4):
+  # GeneadaLN: 用 gene_emb 调制 x
+  x = gene_adaLN[i](gene_emb, x)                             → (48, 1000, 128)
+
+  # Adapter: 拼接 pert_emb 后降维
+  pert_exp = pert_emb[:, None, :].expand(-1, 1000, -1)        → (48, 1000, 128)
+  x = cat[x, pert_exp]                                        → (48, 1000, 256)
+  x = adapter_layer[i](x)
+    = Linear(256→128) → LeakyReLU → Linear(128→128) → LeakyReLU
+                                                               → (48, 1000, 128)
+
+  # DiffPerceiverBlock: attention + MLP, 用 c 做 AdaLN 条件
+  x = DiffPerceiverBlock(x, val_emb_2, c)                     → (48, 1000, 128)
+```
+
+#### Step 5f: 表达 Decoder Head
+
+```
+x_with_pert = cat[x, pert_exp]                                → (48, 1000, 256)
+pred_v_expr = ExprDecoder(x_with_pert)["pred"]                → (48, 1000)
+```
+
+#### Step 5g: Latent Decoder Head
+
+```
+h = dh_proj(x)              # Linear(128→128)                 → (48, 1000, 128)
+
+for j in range(2):           # dh_depth=2
+  # AdaLN: c → SiLU → Linear(128→768) → chunk 6
+  shift_msa, scale_msa, gate_msa,
+  shift_mlp, scale_mlp, gate_mlp = adaLN_modulation(c)        各 (48, 128)
+
+  # Self-Attention with AdaLN
+  h_norm = LayerNorm(h) * (1+scale_msa[:,None,:]) + shift_msa[:,None,:]
+                                                               → (48, 1000, 128)
+  h_attn = MultiheadAttention(h_norm, h_norm, h_norm)          → (48, 1000, 128)
+  h = h + gate_msa[:,None,:] * h_attn                          → (48, 1000, 128)
+
+  # MLP with AdaLN
+  h_norm = LayerNorm(h) * (1+scale_mlp[:,None,:]) + shift_mlp[:,None,:]
+                                                               → (48, 1000, 128)
+  h_mlp  = Linear(128→512) → GELU → Linear(512→128)           → (48, 1000, 128)
+  h = h + gate_mlp[:,None,:] * h_mlp                           → (48, 1000, 128)
+
+pred_v_latent = final(h)
+              = LN → Linear(128→128) → GELU → Linear(128→512) → (48, 1000, 512)
+```
+
+### 2.6 Loss 计算
+
+```
+loss_expr   = MSE(pred_v_expr - path_expr.dx_t) * w_expr[:, None]
+            = ((48,1000) - (48,1000))^2 * (48,1)  → mean → scalar
+              只对 w_expr=1 的样本有贡献 (约60%)
+
+loss_latent = MSE(pred_v_latent - path_latent.dx_t) * w_latent[:, None, None]
+            = ((48,1000,512) - (48,1000,512))^2 * (48,1,1) → mean → scalar
+              只对 w_latent=1 的样本有贡献 (约40%)
+
+loss = loss_expr + latent_weight * loss_latent
+
+可选 MMD loss (对 w_expr>0 的样本):
+  x1_hat = x_t + pred_v_expr * (1-t_expr)    # 单步重建 → (N_expr, 1000)
+  mmd_loss = mmd2_unbiased_multi_sigma(x1_hat, target_sub)  → scalar
+  loss += gamma * mmd_loss
+```
+
+### 2.7 训练循环
+
+```
+for iteration in range(200000):
+    batch → source(48, 5000), target(48, 5000), pert_id(48, 2)
+    loss = denoiser.train_step(source, target, pert_id, gene_ids, infer_top_gene=1000)
+    optimizer.zero_grad()
+    accelerator.backward(loss)
+    optimizer.step()
+    scheduler.step()  # CosineAnnealing
+
+    if iteration % 5000 == 0:
+        save checkpoint + run evaluation
+```
+
+---
+
+## 三、推理流程 Tensor 数据流
+
+推理时**不调用 scGPT 编码器**，模型从噪声自行生成 latent（辅助语义表征）。用**全部 5000 个基因**，不做子集采样。
+条件信号（control 表达 + perturbation_id）在每个 ODE 步中持续提供。
+
+```
+输入:
+  source:           (B, 5000)       控制组表达
+  perturbation_id:  (B, 2)          扰动 ID
+  gene_ids:         (B, 5000)       基因 vocab ID
+```
+
+### 3.1 Stage 1: 生成 Latent（t_latent: 0→1, t_expr 固定=0）
+
+```
+z_noise = randn(B, 5000, 512)              初始噪声
+
+ODE 求解 (RK4, 20步, t ∈ [0,1]):
+  每一步调用 latent_vf(t, z_t):
+    t_latent = t.expand(B)                  → (B,)     当前时间步
+    t_expr   = zeros(B)                     → (B,)     固定为 0
+
+    model.forward(
+      gene_ids,                             (B, 5000)
+      source,                               (B, 5000)   源表达
+      source,                               (B, 5000)   ← 注意: x_t 用 source (因为 t_expr=0)
+      z_t,                                  (B, 5000, 512)  当前 latent 状态
+      t_expr=0,                             (B,)
+      t_latent=t,                           (B,)
+      perturbation_id,                      (B, 2)
+    )
+    → _, v_latent                           (B, 5000, 512)  latent 速度场
+
+    return v_latent
+
+  z_traj = odeint(latent_vf, z_noise, linspace(0,1,20))
+                                            → (20, B, 5000, 512)
+  z_generated = z_traj[-1]                  → (B, 5000, 512)  ★ 生成的 latent
+```
+
+### 3.2 Stage 2: 用生成的 Latent 引导表达生成（t_expr: 0→1, t_latent 固定=1）
+
+```
+expr_noise = randn_like(source)             → (B, 5000)  高斯噪声 (或 Poisson)
+
+ODE 求解 (RK4, 20步, t ∈ [0,1]):
+  每一步调用 expr_vf(t, x_t):
+    t_expr   = t.expand(B)                  → (B,)     当前时间步
+    t_latent = ones(B)                      → (B,)     固定为 1 (latent 已完成)
+
+    model.forward(
+      gene_ids,                             (B, 5000)
+      source,                               (B, 5000)
+      x_t,                                  (B, 5000)   ← 当前噪声表达
+      z_generated,                          (B, 5000, 512)  ★ Stage 1 的结果 (固定)
+      t_expr=t,                             (B,)
+      t_latent=1,                           (B,)
+      perturbation_id,                      (B, 2)
+    )
+    → v_expr, _                             (B, 5000)  表达速度场
+
+    return v_expr
+
+  x_traj = odeint(expr_vf, expr_noise, linspace(0,1,20))
+                                            → (20, B, 5000)
+  x_generated = clamp(x_traj[-1], min=0)   → (B, 5000)  ★ 最终预测表达
+```
+
+### 3.3 评估流程
+
+```
+对每个扰动条件:
+  source = 随机采样 128 个控制细胞              → (128, 5000)
+  按 batch_size 调用 generate:
+    pred = denoiser.generate(source, pert_id, gene_ids)  → (128, 5000)
+
+汇总:
+  pred_adata = AnnData(X=所有预测表达, obs=扰动标签)
+  real_adata = AnnData(X=所有真实表达, obs=扰动标签)
+  MetricsEvaluator(pred_adata, real_adata) → results.csv, agg_results.csv
+```
+
+---
+
+## ��、训练 vs 推理 对比总结
+
+| 维度 | 训练 | 推理 |
+|---|---|---|
+| 基因数 | 随机采样 G=1000 | 全量 G=5000 |
+| scGPT 特征（辅助生成目标） | 冻结提取 target 特征 `z_target` 作为 latent 流的生成目标 | **不使用**，模型从噪声自行生成 latent（Stage 1） |
+| 时间步 | 级联采样：40% 训练 latent, 60% 训练表达 | 两阶段串行：先 latent(0→1), 再表达(0→1) |
+| 流采样 | 单步：`x_t = (1-t)*noise + t*target` | ODE 积分：RK4, 20 步 |
+| Loss | MSE(velocity) + MMD | 无 |
+| 输出 | scalar loss | `(B, G_full)` 表达矩阵 |
+| 条件信号 | control + pert_id（真条件）；scGPT 特征是生成目标 | control + pert_id（每步输入）；latent 由 Stage1 生成 |
+| 时间步 | `t_expr` 和 `t_latent` 随机，互斥激活 | Stage1: `t_expr=0, t_latent∈[0,1]`; Stage2: `t_latent=1, t_expr∈[0,1]` |
+
+---
+
+## 五、模型架构总览图
+
+```
+                        ┌──────────────────────────────────────────────┐
+                        │           CascadedFlowModel                  │
+                        │                                              │
+  gene_id (B,G) ───────→│  GeneEncoder ──→ gene_emb (B,G,d)           │
+                        │       │                                      │
+  x_t (B,G) ──────────→│  ValEnc1 ──→ val_emb_1 (B,G,d)  噪声target  │
+  source (B,G) ────────→│  ValEnc2 ──→ val_emb_2 (B,G,d)  control     │
+                        │       │                                      │
+                        │  fusion_layer(cat[val_emb_1, val_emb_2])     │
+                        │       + gene_emb (融合后加一次)                │
+                        │       ↓                                      │
+                        │  expr_tokens (B,G,d)                         │
+                        │       │                                      │
+  z_t (B,G,512) ──────→│  LatentEmbedder(512→128→d)                   │
+                        │       ↓                                      │
+                        │  latent_tokens (B,G,d)                       │
+                        │       │                                      │
+                        │  x = expr_tokens + latent_tokens  (B,G,d)   │
+                        │                                              │
+  t_expr (B,) ────────→│  TimestepEmb ──┐                             │
+  t_latent (B,) ──────→│  TimestepEmb ──┼→ c = sum (B,d)             │
+  pert_id (B,2) ──────→│  PertEmb ──────┘                             │
+                        │                                              │
+                        │  ┌─ 4x DiffPerceiverBlock ──────────────┐    │
+                        │  │  gene_adaLN(gene_emb, x)             │    │
+                        │  │  cat[x, pert_emb] → adapter → x      │    │
+                        │  │  DiffPerceiverBlock(x, val_emb_2, c)  │    │
+                        │  └──────────────────────────────────────┘    │
+                        │       │                                      │
+                        │       ├──→ ExprDecoder(cat[x, pert]) ──→ pred_v_expr (B,G)
+                        │       │                                      │
+                        │       └──→ LatentDecoder(x, c)               │
+                        │              dh_proj → 2x AdaLN Block        │
+                        │              → final proj ──→ pred_v_latent (B,G,512)
+                        └──────────────────────────────────────────────┘
+```

transfer/code/CCFM/_bootstrap_scdfm.py

@@ -0,0 +1,101 @@
+"""
+Bootstrap scDFM imports by creating missing __init__.py files and loading
+its modules under a 'scdfm_src' prefix in sys.modules.
+
+This module MUST be imported before any CCFM src imports.
+"""
+
+import sys
+import os
+import types
+
+_SCDFM_ROOT = os.path.normpath(
+    os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "scDFM")
+)
+
+# Directories in scDFM that need __init__.py to be proper packages
+_DIRS_NEEDING_INIT = [
+    "src",
+    "src/models",
+    "src/models/origin",
+    "src/data_process",
+    "src/tokenizer",
+    "src/script",
+    "src/models/perturbation",
+]
+
+
+def _ensure_init_files():
+    """Create missing __init__.py files in scDFM so it can be imported as packages."""
+    created = []
+    for d in _DIRS_NEEDING_INIT:
+        init_path = os.path.join(_SCDFM_ROOT, d, "__init__.py")
+        if not os.path.exists(init_path):
+            with open(init_path, "w") as f:
+                f.write("# Auto-created by CCFM bootstrap\n")
+            created.append(init_path)
+    return created
+
+
+def bootstrap():
+    """Load scDFM's src package as 'scdfm_src' in sys.modules."""
+    if "scdfm_src" in sys.modules:
+        return  # Already bootstrapped
+
+    # Create missing __init__.py files
+    _ensure_init_files()
+
+    # Save CCFM's src modules
+    saved = {}
+    for key in list(sys.modules.keys()):
+        if key == "src" or key.startswith("src."):
+            saved[key] = sys.modules.pop(key)
+
+    # Add scDFM root to path
+    sys.path.insert(0, _SCDFM_ROOT)
+
+    try:
+        # Import scDFM modules (their relative imports work now)
+        import src as scdfm_src_pkg
+        import src.models
+        import src.models.origin
+        import src.models.origin.blocks
+        import src.models.origin.layers
+        import src.models.origin.model
+        import src.flow_matching
+        import src.flow_matching.path
+        import src.flow_matching.path.path
+        import src.flow_matching.path.path_sample
+        import src.flow_matching.path.affine
+        import src.flow_matching.path.scheduler
+        import src.flow_matching.path.scheduler.scheduler
+        # Skip src.flow_matching.ot (requires 'ot' package, not needed for CCFM)
+        import src.utils
+        import src.utils.utils
+        import src.tokenizer
+        import src.tokenizer.gene_tokenizer
+        # Skip src.data_process (has heavy deps like bs4, rdkit)
+        # We handle data loading separately in CCFM
+
+        # Re-register all under scdfm_src.* prefix
+        for key in list(sys.modules.keys()):
+            if key == "src" or key.startswith("src."):
+                new_key = "scdfm_" + key
+                sys.modules[new_key] = sys.modules[key]
+
+    finally:
+        # Remove scDFM's src.* entries
+        for key in list(sys.modules.keys()):
+            if (key == "src" or key.startswith("src.")) and not key.startswith("scdfm_"):
+                del sys.modules[key]
+
+        # Restore CCFM's src modules
+        for key, mod in saved.items():
+            sys.modules[key] = mod
+
+        # Remove scDFM from front of path
+        if _SCDFM_ROOT in sys.path:
+            sys.path.remove(_SCDFM_ROOT)
+
+
+bootstrap()

transfer/code/CCFM/cell-eval-outdir/agg_results.csv

@@ -0,0 +1,10 @@
+statistic,overlap_at_N,overlap_at_50,overlap_at_100,overlap_at_200,overlap_at_500,precision_at_N,precision_at_50,precision_at_100,precision_at_200,precision_at_500,de_spearman_sig,de_direction_match,de_spearman_lfc_sig,de_sig_genes_recall,de_nsig_counts_real,de_nsig_counts_pred,pr_auc,roc_auc,pearson_delta,mse,mae,mse_delta,mae_delta,discrimination_score_l1,discrimination_score_l2,discrimination_score_cosine,pearson_edistance,clustering_agreement
+count,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0
+null_count,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+mean,0.07395320375987084,0.06849358974358978,0.0725404899006272,0.07431250785187005,0.07395320375987084,0.12877704205127136,0.06871794871794874,0.07282051282051284,0.07500000000000001,0.06682051282051284,0.0006180401974148523,0.7319719710281621,0.7355229056966586,0.9061342423642376,137.89743589743588,977.6153846153846,0.21954205885448827,0.502651350313865,-0.08164090049789667,0.03866601647089939,0.054930761086430545,0.03866601647089939,0.054930761086430545,0.5141354372123603,0.5121630506245891,0.5115055884286653,0.12324841081988779,0.007165149729861788
+std,0.06327073571923675,0.04142623783921817,0.06430260938703716,0.06497823680683154,0.06327073571923675,0.0713575372552735,0.04124251046390996,0.06398802182105803,0.06369127842466676,0.06055657285150447,1.0983753514559728e-19,0.05548309801759892,0.06748831752084138,0.027067878739138167,72.75871981447884,2.2197129332580094,0.07449489082434584,0.025106101263114602,0.11079253285523155,0.01804318670820533,0.013893820583993645,0.01804318670820533,0.013893820583993646,0.2932360142364609,0.29232232244842243,0.30752068526485193,2.8118408997272905e-17,2.636100843494335e-18
+min,0.0,0.0,0.0,0.0,0.0,0.030706243602865915,0.0,0.0,0.005,0.008,0.0006180401974148526,0.6301369863013698,0.5466766243465273,0.8247422680412371,32.0,972.0,0.07528683946739989,0.45502409257502185,-0.25674855274153263,0.017089386967776357,0.041686743621341026,0.017089386967776357,0.041686743621341026,0.02564102564102566,0.02564102564102566,0.02564102564102566,0.12324841081988787,0.007165149729861791
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+50%,0.05454545454545454,0.06,0.0547945205479452,0.05454545454545454,0.05454545454545454,0.10827374872318693,0.06,0.05,0.055,0.048,0.0006180401974148526,0.7272727272727273,0.7283894176721877,0.9029126213592233,117.0,978.0,0.2155530179012921,0.5015608589100246,-0.10351595518920989,0.03463771528656705,0.05123648361160402,0.03463771528656705,0.05123648361160402,0.5128205128205128,0.5384615384615384,0.5128205128205128,0.12324841081988787,0.007165149729861791
+75%,0.08333333333333333,0.1,0.08,0.08333333333333333,0.08333333333333333,0.14974358974358976,0.1,0.08,0.08,0.068,0.0006180401974148526,0.7551020408163265,0.793306240068219,0.9259259259259259,158.0,979.0,0.2577585712884973,0.5154239019407559,0.007450254288251119,0.04417537517338548,0.0574020448039485,0.04417537517338548,0.0574020448039485,0.7435897435897436,0.7692307692307692,0.7948717948717949,0.12324841081988787,0.007165149729861791
+max,0.30548302872062666,0.16,0.34,0.33,0.30548302872062666,0.3719262295081967,0.16,0.34,0.33,0.3,0.0006180401974148526,0.8705882352941177,0.8543173721436267,0.9506172839506173,384.0,983.0,0.41857905891786573,0.5534962262791009,0.22806546181808088,0.11179507217860543,0.1123107911192389,0.11179507217860543,0.1123107911192389,1.0,1.0,1.0,0.12324841081988787,0.007165149729861791

transfer/code/CCFM/cell-eval-outdir/results.csv

@@ -0,0 +1,40 @@
+perturbation,overlap_at_N,overlap_at_50,overlap_at_100,overlap_at_200,overlap_at_500,precision_at_N,precision_at_50,precision_at_100,precision_at_200,precision_at_500,de_spearman_sig,de_direction_match,de_spearman_lfc_sig,de_sig_genes_recall,de_nsig_counts_real,de_nsig_counts_pred,pr_auc,roc_auc,pearson_delta,mse,mae,mse_delta,mae_delta,discrimination_score_l1,discrimination_score_l2,discrimination_score_cosine,pearson_edistance,clustering_agreement
+AHR+FEV,0.1308411214953271,0.14,0.14,0.125,0.1308411214953271,0.20245398773006135,0.14,0.14,0.125,0.1,0.0006180401974148526,0.7897196261682243,0.793306240068219,0.9252336448598131,214.0,978.0,0.289355620021763,0.5044885971796151,-0.000681255195205219,0.05580747882233428,0.0703616503153296,0.05580747882233427,0.0703616503153296,0.10256410256410253,0.15384615384615385,0.7948717948717949,0.12324841081988787,0.007165149729861791
+AHR+KLF1,0.1,0.1,0.09,0.1,0.1,0.12589559877175024,0.1,0.09,0.085,0.068,0.0006180401974148526,0.6714285714285714,0.7230217243868823,0.8785714285714286,140.0,977.0,0.2155530179012921,0.5011627906976744,-0.17882777360167185,0.03415252740506693,0.053647045372835565,0.03415252740506693,0.053647045372835565,0.3076923076923077,0.46153846153846156,0.10256410256410253,0.12324841081988787,0.007165149729861791
+BCL2L11+BAK1,0.03125,0.03125,0.03125,0.03125,0.03125,0.030706243602865915,0.04,0.05,0.045,0.03,0.0006180401974148526,0.75,0.8320806599450046,0.9375,32.0,977.0,0.07528683946739989,0.5477143595041322,0.007450254288251119,0.018631415934436527,0.042142537012775594,0.018631415934436527,0.042142537012775594,0.9487179487179487,0.9487179487179487,0.7948717948717949,0.12324841081988787,0.007165149729861791
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+CBL+PTPN12,0.04807692307692308,0.06,0.05,0.04807692307692308,0.04807692307692308,0.09397344228804903,0.06,0.05,0.05,0.03,0.0006180401974148526,0.7115384615384616,0.5466766243465273,0.8846153846153846,104.0,979.0,0.2527906962637153,0.5022428743131868,-0.18550991579880255,0.04731173214331579,0.05330420638914829,0.04731173214331579,0.05330420638914829,0.3846153846153846,0.23076923076923073,0.17948717948717952,0.12324841081988787,0.007165149729861791
+CBL+PTPN9,0.042105263157894736,0.02,0.042105263157894736,0.042105263157894736,0.042105263157894736,0.08709016393442623,0.02,0.04,0.05,0.032,0.0006180401974148526,0.7157894736842105,0.6192082645090848,0.8947368421052632,95.0,976.0,0.23347166134256753,0.5006746147135795,-0.11418825671976725,0.0345185799724352,0.048704704053449004,0.0345185799724352,0.048704704053449004,0.717948717948718,0.5897435897435898,0.46153846153846156,0.12324841081988787,0.007165149729861791
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+CDKN1C+CDKN1B,0.03636363636363636,0.04,0.03,0.03636363636363636,0.03636363636363636,0.1023541453428864,0.04,0.03,0.045,0.042,0.0006180401974148526,0.7090909090909091,0.8149440522239354,0.9090909090909091,110.0,977.0,0.15180512654084866,0.5154239019407559,-0.14115175381435643,0.029967122965036647,0.05138300370287098,0.029967122965036647,0.05138300370287098,0.5897435897435898,0.6923076923076923,0.5128205128205128,0.12324841081988787,0.007165149729861791
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transfer/code/CCFM/config/config_cascaded.py

@@ -0,0 +1,92 @@
+from dataclasses import dataclass
+import os
+
+
+@dataclass
+class CascadedFlowConfig:
+    # === Base (same as scDFM FlowConfig) ===
+    model_type: str = "cascaded"
+    batch_size: int = 48
+    ntoken: int = 512
+    d_model: int = 128
+    nhead: int = 8
+    nlayers: int = 4
+    lr: float = 5e-5
+    steps: int = 200000
+    eta_min: float = 1e-6
+    devices: str = "1"
+    test_only: bool = False
+
+    data_name: str = "norman"
+    perturbation_function: str = "crisper"
+    noise_type: str = "Gaussian"
+    poisson_alpha: float = 0.8
+    poisson_target_sum: int = -1
+
+    print_every: int = 5000
+    mode: str = "predict_y"
+    result_path: str = "./result_online"
+    fusion_method: str = "differential_perceiver"
+    infer_top_gene: int = 1000
+    n_top_genes: int = 5000
+    checkpoint_path: str = ""
+    gamma: float = 0.5
+    split_method: str = "additive"
+    use_mmd_loss: bool = True
+    fold: int = 1
+    use_negative_edge: bool = True
+    topk: int = 30
+
+    # === Cascaded / Latent specific ===
+    scgpt_dim: int = 512
+    bottleneck_dim: int = 128
+    latent_weight: float = 1.0
+    choose_latent_p: float = 0.4
+    target_std: float = 1.0
+    dh_depth: int = 2
+    warmup_batches: int = 200  # batches to collect running stats
+
+    # === Cascaded noise (LatentForcing dino_first_cascaded_noised) ===
+    noise_beta: float = 0.25  # when training expr flow, t_latent ~ U[1-beta, 1] instead of 1.0
+
+    # === EMA ===
+    ema_decay: float = 0.9999
+
+    # === Logit-normal time-step sampling ===
+    t_sample_mode: str = "logit_normal"  # "uniform" or "logit_normal"
+    t_expr_mean: float = 0.0   # expression flow logit-normal mu
+    t_expr_std: float = 1.0    # expression flow logit-normal sigma
+    t_latent_mean: float = 0.0  # latent flow logit-normal mu
+    t_latent_std: float = 1.0   # latent flow logit-normal sigma
+
+    # === LR warmup ===
+    warmup_steps: int = 2000
+
+    # === scGPT paths ===
+    scgpt_model_dir: str = "transfer/data/scGPT_pretrained"
+    scgpt_max_seq_len: int = 1200
+    scgpt_cache_path: str = ""  # Pre-extracted HDF5 path. Empty = online extraction (default)
+
+    # === Inference ===
+    latent_steps: int = 20
+    expr_steps: int = 20
+    ode_method: str = "rk4"  # "euler" or "rk4"
+
+    def __post_init__(self):
+        if self.data_name == "norman_umi_go_filtered":
+            self.n_top_genes = 5054
+        if self.data_name == "norman":
+            self.n_top_genes = 5000
+
+    def make_path(self):
+        scgpt_mode = "cached" if self.scgpt_cache_path else "online"
+        t_mode = "ln" if self.t_sample_mode == "logit_normal" else "uni"
+        exp_name = (
+            f"ccfm-{self.data_name}-f{self.fold}"
+            f"-topk{self.topk}-neg{self.use_negative_edge}"
+            f"-d{self.d_model}-lr{self.lr}"
+            f"-lw{self.latent_weight}-lp{self.choose_latent_p}"
+            f"-ema{self.ema_decay}-{t_mode}-wu{self.warmup_steps}"
+            f"-{self.ode_method}-{scgpt_mode}"
+        )
+        return os.path.join(self.result_path, exp_name)

transfer/code/CCFM/config/config_cascaded.py.bak

@@ -0,0 +1,89 @@
+from dataclasses import dataclass
+import os
+
+
+@dataclass
+class CascadedFlowConfig:
+    # === Base (same as scDFM FlowConfig) ===
+    model_type: str = "cascaded"
+    batch_size: int = 48
+    ntoken: int = 512
+    d_model: int = 128
+    nhead: int = 8
+    nlayers: int = 4
+    lr: float = 5e-5
+    steps: int = 200000
+    eta_min: float = 1e-6
+    devices: str = "1"
+    test_only: bool = False
+
+    data_name: str = "norman"
+    perturbation_function: str = "crisper"
+    noise_type: str = "Gaussian"
+    poisson_alpha: float = 0.8
+    poisson_target_sum: int = -1
+
+    print_every: int = 5000
+    mode: str = "predict_y"
+    result_path: str = "./result_online"
+    fusion_method: str = "differential_perceiver"
+    infer_top_gene: int = 1000
+    n_top_genes: int = 5000
+    checkpoint_path: str = ""
+    gamma: float = 0.5
+    split_method: str = "additive"
+    use_mmd_loss: bool = True
+    fold: int = 1
+    use_negative_edge: bool = True
+    topk: int = 30
+
+    # === Cascaded / Latent specific ===
+    scgpt_dim: int = 512
+    bottleneck_dim: int = 128
+    latent_weight: float = 1.0
+    choose_latent_p: float = 0.4
+    target_std: float = 1.0
+    dh_depth: int = 2
+    warmup_batches: int = 200  # batches to collect running stats
+
+    # === EMA ===
+    ema_decay: float = 0.9999
+
+    # === Logit-normal time-step sampling ===
+    t_sample_mode: str = "logit_normal"  # "uniform" or "logit_normal"
+    t_expr_mean: float = 0.0   # expression flow logit-normal mu
+    t_expr_std: float = 1.0    # expression flow logit-normal sigma
+    t_latent_mean: float = 0.0  # latent flow logit-normal mu
+    t_latent_std: float = 1.0   # latent flow logit-normal sigma
+
+    # === LR warmup ===
+    warmup_steps: int = 2000
+
+    # === scGPT paths ===
+    scgpt_model_dir: str = "transfer/data/scGPT_pretrained"
+    scgpt_max_seq_len: int = 1200
+    scgpt_cache_path: str = ""  # Pre-extracted HDF5 path. Empty = online extraction (default)
+
+    # === Inference ===
+    latent_steps: int = 20
+    expr_steps: int = 20
+    ode_method: str = "rk4"  # "euler" or "rk4"
+
+    def __post_init__(self):
+        if self.data_name == "norman_umi_go_filtered":
+            self.n_top_genes = 5054
+        if self.data_name == "norman":
+            self.n_top_genes = 5000
+
+    def make_path(self):
+        scgpt_mode = "cached" if self.scgpt_cache_path else "online"
+        t_mode = "ln" if self.t_sample_mode == "logit_normal" else "uni"
+        exp_name = (
+            f"ccfm-{self.data_name}-f{self.fold}"
+            f"-topk{self.topk}-neg{self.use_negative_edge}"
+            f"-d{self.d_model}-lr{self.lr}"
+            f"-lw{self.latent_weight}-lp{self.choose_latent_p}"
+            f"-ema{self.ema_decay}-{t_mode}-wu{self.warmup_steps}"
+            f"-{self.ode_method}-{scgpt_mode}"
+        )
+        return os.path.join(self.result_path, exp_name)

transfer/code/CCFM/eval_ccfm_v2.sh

@@ -0,0 +1,68 @@
+#!/bin/sh
+#PJM -L rscgrp=b-batch
+#PJM -L gpu=1
+#PJM -L elapse=3:00:00
+#PJM -N eval_ccfm_v2
+#PJM -j
+#PJM -o logs/eval_ccfm_v2_%j.out
+
+module load cuda/12.2.2
+module load cudnn/8.9.7
+module load gcc-toolset/12
+
+source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate
+cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM
+
+export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256
+
+CKPT="/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/result/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-ema0.9999-ln-wu2000-rk4-online/iteration_100000/checkpoint.pt"
+
+echo "=========================================="
+echo "Job ID:    $PJM_JOBID"
+echo "Job Name:  $PJM_JOBNAME"
+echo "Start:     $(date)"
+echo "Node:      $(hostname)"
+echo "GPU:       $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')"
+echo "Eval:      CCFM v2 (online) iteration_100000"
+echo "=========================================="
+
+python scripts/run_cascaded.py \
+    --data-name norman \
+    --d-model 128 \
+    --nhead 8 \
+    --nlayers 4 \
+    --batch-size 48 \
+    --lr 5e-5 \
+    --steps 200000 \
+    --fusion-method differential_perceiver \
+    --perturbation-function crisper \
+    --noise-type Gaussian \
+    --infer-top-gene 1000 \
+    --n-top-genes 5000 \
+    --use-mmd-loss \
+    --gamma 0.5 \
+    --split-method additive \
+    --fold 1 \
+    --scgpt-dim 512 \
+    --bottleneck-dim 128 \
+    --latent-weight 1.0 \
+    --choose-latent-p 0.4 \
+    --dh-depth 2 \
+    --print-every 10000 \
+    --topk 30 \
+    --use-negative-edge \
+    --ema-decay 0.9999 \
+    --t-sample-mode logit_normal \
+    --t-expr-mean 0.0 \
+    --t-expr-std 1.0 \
+    --t-latent-mean 0.0 \
+    --t-latent-std 1.0 \
+    --warmup-steps 2000 \
+    --ode-method rk4 \
+    --result-path ./result \
+    --checkpoint-path "$CKPT" \
+    --test-only
+
+echo "=========================================="
+echo "Finished:  $(date)"
+echo "=========================================="

transfer/code/CCFM/eval_joint_generate.sh

@@ -0,0 +1,56 @@
+#!/bin/bash
+#PJM -L rscgrp=b-batch
+#PJM -L gpu=1
+#PJM -L elapse=3:00:00
+#PJM -j
+#PJM -S
+#PJM -o /home/hp250092/ku50001222/qian/aivc/lfj/transfer/logs/ccfm_eval_joint_%j.out
+
+# Evaluate CCFM with joint-generation inference (LatentForcing style)
+# using the checkpoint from two-stage ODE training.
+
+source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate
+cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM
+
+export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256
+
+echo "=========================================="
+echo "Job ID:    $PJM_JOBID"
+echo "Start:     $(date)"
+echo "Node:      $(hostname)"
+echo "Eval mode: joint-generate (LatentForcing style)"
+echo "=========================================="
+
+CHECKPOINT="/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/result/ccfm-fusion_differential_perceiver-norman-cascaded-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-latent_weight_1.0-choose_latent_p_0.4/iteration_110000/checkpoint.pt"
+
+python scripts/run_cascaded.py \
+    --data-name norman \
+    --d-model 128 \
+    --nhead 8 \
+    --nlayers 4 \
+    --batch-size 128 \
+    --lr 5e-5 \
+    --steps 200000 \
+    --fusion-method differential_perceiver \
+    --perturbation-function crisper \
+    --noise-type Gaussian \
+    --infer-top-gene 1000 \
+    --n-top-genes 5000 \
+    --use-mmd-loss \
+    --gamma 0.5 \
+    --split-method additive \
+    --fold 1 \
+    --scgpt-dim 512 \
+    --bottleneck-dim 128 \
+    --latent-weight 1.0 \
+    --choose-latent-p 0.4 \
+    --dh-depth 2 \
+    --latent-steps 20 \
+    --expr-steps 20 \
+    --result-path ./result_joint_generate \
+    --checkpoint-path "$CHECKPOINT" \
+    --test-only
+
+echo "=========================================="
+echo "Finished:  $(date)"
+echo "=========================================="

transfer/code/CCFM/eval_joint_generate.sh.5404217.stats

@@ -0,0 +1,467 @@
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+ rsc014 (USE)              : -
+

transfer/code/CCFM/logs/ccfm_1gpu_cached_5404438.out

@@ -0,0 +1,57 @@
+==========================================
+Job ID:    5404438
+Job Name:  ccfm_1gpu_cached
+Start:     Sun Mar 15 00:10:32 JST 2026
+Node:      b0031
+GPU:       NVIDIA H100, 95830 MiB
+Run:       CCFM 1GPU cached scGPT
+==========================================
+The following values were not passed to `accelerate launch` and had defaults used instead:
+	`--num_machines` was set to a value of `1`
+	`--mixed_precision` was set to a value of `'no'`
+	`--dynamo_backend` was set to a value of `'no'`
+To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+WARNING:accelerate.utils.other:[RANK 0] Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+CascadedFlowConfig(model_type='cascaded', batch_size=48, ntoken=512, d_model=128, nhead=8, nlayers=4, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=False, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=5000, mode='predict_y', result_path='./result_scgpt', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30, scgpt_dim=512, bottleneck_dim=128, latent_weight=1.0, choose_latent_p=0.4, target_std=1.0, dh_depth=2, warmup_batches=200, scgpt_model_dir='transfer/data/scGPT_pretrained', scgpt_max_seq_len=1200, scgpt_cache_path='scgpt_cache_norman.h5', latent_steps=20, expr_steps=20)
+Converted var_names to gene symbols, sample: ['RP11-34P13.8', 'RP11-54O7.3', 'SAMD11', 'PERM1', 'HES4']
+Warning: ctrl is not in the gene names
+##### loading vocab from file #####
+Loaded 159/163 pretrained parameters
+Using pre-extracted scGPT cache: scgpt_cache_norman.h5
+  Cache shape: (91205, 5035, 512), cells: 91205
+
  0%|          | 0/200000 [00:00<?, ?it/s]Traceback (most recent call last):
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 324, in <module>
+    cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device)
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 50, in lookup
+    row_indices = [self.name_to_idx[n] for n in cell_names]
+                   ~~~~~~~~~~~~~~~~^^^
+KeyError: ('TGAGCCGCAGTAAGAT-8',)
+
  0%|          | 0/200000 [00:42<?, ?it/s]
+Traceback (most recent call last):
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/accelerate", line 8, in <module>
+    sys.exit(main())
+             ~~~~^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main
+    args.func(args)
+    ~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1405, in launch_command
+    simple_launcher(args)
+    ~~~~~~~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 993, in simple_launcher
+    raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
+subprocess.CalledProcessError: Command '['/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/python', 'scripts/run_cascaded.py', '--data-name', 'norman', '--d-model', '128', '--nhead', '8', '--nlayers', '4', '--batch-size', '48', '--lr', '5e-5', '--steps', '200000', '--fusion-method', 'differential_perceiver', '--perturbation-function', 'crisper', '--noise-type', 'Gaussian', '--infer-top-gene', '1000', '--n-top-genes', '5000', '--use-mmd-loss', '--gamma', '0.5', '--split-method', 'additive', '--fold', '1', '--scgpt-dim', '512', '--bottleneck-dim', '128', '--latent-weight', '1.0', '--choose-latent-p', '0.4', '--dh-depth', '2', '--print-every', '5000', '--topk', '30', '--use-negative-edge', '--scgpt-cache-path', 'scgpt_cache_norman.h5', '--result-path', './result_scgpt']' returned non-zero exit status 1.
+==========================================
+Finished:  Sun Mar 15 00:12:56 JST 2026
+==========================================

transfer/code/CCFM/logs/ccfm_1gpu_cached_5404526.out

@@ -0,0 +1,68 @@
+==========================================
+Job ID:    5404526
+Job Name:  ccfm_1gpu_cached
+Start:     Sun Mar 15 01:17:06 JST 2026
+Node:      b0027
+GPU:       NVIDIA H100, 95830 MiB
+Run:       CCFM 1GPU cached scGPT
+==========================================
+The following values were not passed to `accelerate launch` and had defaults used instead:
+	`--num_machines` was set to a value of `1`
+	`--mixed_precision` was set to a value of `'no'`
+	`--dynamo_backend` was set to a value of `'no'`
+To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+WARNING:accelerate.utils.other:[RANK 0] Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+CascadedFlowConfig(model_type='cascaded', batch_size=48, ntoken=512, d_model=128, nhead=8, nlayers=4, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=False, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=5000, mode='predict_y', result_path='./result_scgpt', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30, scgpt_dim=512, bottleneck_dim=128, latent_weight=1.0, choose_latent_p=0.4, target_std=1.0, dh_depth=2, warmup_batches=200, scgpt_model_dir='transfer/data/scGPT_pretrained', scgpt_max_seq_len=1200, scgpt_cache_path='scgpt_cache_norman.h5', latent_steps=20, expr_steps=20)
+Converted var_names to gene symbols, sample: ['RP11-34P13.8', 'RP11-54O7.3', 'SAMD11', 'PERM1', 'HES4']
+Warning: ctrl is not in the gene names
+##### loading vocab from file #####
+Loaded 159/163 pretrained parameters
+Using pre-extracted scGPT cache: scgpt_cache_norman.h5
+  Cache shape: (91205, 5035, 512), cells: 91205
+
  0%|          | 0/200000 [00:00<?, ?it/s]Traceback (most recent call last):
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 325, in <module>
+    cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device)
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup
+    raw = self.features[row_indices]  # (B, G_full, D) as numpy
+          ~~~~~~~~~~~~~^^^^^^^^^^^^^
+  File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+  File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__
+    selection = sel.select(self.shape, args, dataset=self)
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select
+    return selector.make_selection(args)
+           ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^
+  File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+  File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+  File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection
+  File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args
+TypeError: Indexing elements must be in increasing order
+
  0%|          | 0/200000 [00:42<?, ?it/s]
+Traceback (most recent call last):
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/accelerate", line 8, in <module>
+    sys.exit(main())
+             ~~~~^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main
+    args.func(args)
+    ~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1405, in launch_command
+    simple_launcher(args)
+    ~~~~~~~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 993, in simple_launcher
+    raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
+subprocess.CalledProcessError: Command '['/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/python', 'scripts/run_cascaded.py', '--data-name', 'norman', '--d-model', '128', '--nhead', '8', '--nlayers', '4', '--batch-size', '48', '--lr', '5e-5', '--steps', '200000', '--fusion-method', 'differential_perceiver', '--perturbation-function', 'crisper', '--noise-type', 'Gaussian', '--infer-top-gene', '1000', '--n-top-genes', '5000', '--use-mmd-loss', '--gamma', '0.5', '--split-method', 'additive', '--fold', '1', '--scgpt-dim', '512', '--bottleneck-dim', '128', '--latent-weight', '1.0', '--choose-latent-p', '0.4', '--dh-depth', '2', '--print-every', '5000', '--topk', '30', '--use-negative-edge', '--scgpt-cache-path', 'scgpt_cache_norman.h5', '--result-path', './result_scgpt']' returned non-zero exit status 1.
+==========================================
+Finished:  Sun Mar 15 01:19:22 JST 2026
+==========================================

transfer/code/CCFM/logs/ccfm_1gpu_online_5404417.out

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf48151b252a3896e8ae3132cae1e9fafbd433de265dd15f653765d9919a5ea1
+size 34640252

transfer/code/CCFM/logs/ccfm_topk30_neg_5402619.out

@@ -0,0 +1,111 @@
+==========================================
+Job ID:    5402619
+Job Name:  ccfm_topk30_neg
+Start:     Sat Mar 14 14:34:35 JST 2026
+Node:      b0018
+GPU:       NVIDIA H100, 95830 MiB
+NVIDIA H100, 95830 MiB
+NVIDIA H100, 95830 MiB
+NVIDIA H100, 95830 MiB
+Run:       CCFM (topk=30, use_negative_edge=True)
+==========================================
+The following values were not passed to `accelerate launch` and had defaults used instead:
+		More than one GPU was found, enabling multi-GPU training.
+		If this was unintended please pass in `--num_processes=1`.
+	`--num_machines` was set to a value of `1`
+	`--mixed_precision` was set to a value of `'no'`
+	`--dynamo_backend` was set to a value of `'no'`
+To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.
+[W314 14:35:31.809931854 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+[W314 14:36:13.805735826 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+[W314 14:36:13.808311201 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+[W314 14:36:13.810035709 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+[W314 14:36:13.815100046 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+WARNING:accelerate.utils.other:[RANK 0] Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+CascadedFlowConfig(model_type='cascaded', batch_size=48, ntoken=512, d_model=128, nhead=8, nlayers=4, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=False, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=10000, mode='predict_y', result_path='./result', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30, scgpt_dim=512, bottleneck_dim=128, latent_weight=1.0, choose_latent_p=0.4, target_std=1.0, dh_depth=2, warmup_batches=200, scgpt_model_dir='transfer/data/scGPT_pretrained', scgpt_max_seq_len=1200, scgpt_cache_path='', latent_steps=20, expr_steps=20)
+[rank0]: Traceback (most recent call last):
+[rank0]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 146, in <module>
+[rank0]:     os.makedirs(save_path, exist_ok=True)
+[rank0]:     ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "<frozen os>", line 227, in makedirs
+[rank0]: OSError: [Errno 36] File name too long: './result/ccfm-fusion_differential_perceiver-norman-cascaded-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-latent_weight_1.0-choose_latent_p_0.4-use_negative_edge_True-topk_30-scgpt_online'
+[rank0]:[W314 14:36:18.955909336 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+W0314 14:36:24.320000 128 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 201 closing signal SIGTERM
+W0314 14:36:24.322000 128 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 202 closing signal SIGTERM
+W0314 14:36:24.322000 128 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 203 closing signal SIGTERM
+E0314 14:36:24.687000 128 torch/distributed/elastic/multiprocessing/api.py:984] failed (exitcode: 1) local_rank: 0 (pid: 200) of binary: /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/python
+Traceback (most recent call last):
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/accelerate", line 8, in <module>
+    sys.exit(main())
+             ~~~~^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main
+    args.func(args)
+    ~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1396, in launch_command
+    multi_gpu_launcher(args)
+    ~~~~~~~~~~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1023, in multi_gpu_launcher
+    distrib_run.run(args)
+    ~~~~~~~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/run.py", line 982, in run
+    elastic_launch(
+    ~~~~~~~~~~~~~~~
+        config=config,
+        ~~~~~~~~~~~~~~
+        entrypoint=cmd,
+        ~~~~~~~~~~~~~~~
+    )(*cmd_args)
+    ~^^^^^^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 170, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 317, in launch_agent
+    raise ChildFailedError(
+    ...<2 lines>...
+    )
+torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
+============================================================
+scripts/run_cascaded.py FAILED
+------------------------------------------------------------
+Failures:
+[1]:
+  time      : 2026-03-14_14:36:24
+  host      : b0018
+  rank      : 1 (local_rank: 1)
+  exitcode  : -15 (pid: 201)
+  error_file: <N/A>
+  traceback : Signal 15 (SIGTERM) received by PID 201
+[2]:
+  time      : 2026-03-14_14:36:24
+  host      : b0018
+  rank      : 2 (local_rank: 2)
+  exitcode  : -15 (pid: 202)
+  error_file: <N/A>
+  traceback : Signal 15 (SIGTERM) received by PID 202
+[3]:
+  time      : 2026-03-14_14:36:24
+  host      : b0018
+  rank      : 3 (local_rank: 3)
+  exitcode  : -15 (pid: 203)
+  error_file: <N/A>
+  traceback : Signal 15 (SIGTERM) received by PID 203
+------------------------------------------------------------
+Root Cause (first observed failure):
+[0]:
+  time      : 2026-03-14_14:36:24
+  host      : b0018
+  rank      : 0 (local_rank: 0)
+  exitcode  : 1 (pid: 200)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+============================================================
+==========================================
+Finished:  Sat Mar 14 14:36:25 JST 2026
+==========================================

transfer/code/CCFM/logs/ccfm_v2_5404727.out

@@ -0,0 +1,214 @@
+==========================================
+Job ID:    5404727
+Job Name:  ccfm_v2
+Start:     Sun Mar 15 04:49:31 JST 2026
+Node:      b0019
+GPU:       NVIDIA H100, 95830 MiB
+NVIDIA H100, 95830 MiB
+NVIDIA H100, 95830 MiB
+NVIDIA H100, 95830 MiB
+Run:       CCFM v2 (KV fix + loss fix + EMA + RK4 + logit-normal + warmup)
+==========================================
+The following values were not passed to `accelerate launch` and had defaults used instead:
+		More than one GPU was found, enabling multi-GPU training.
+		If this was unintended please pass in `--num_processes=1`.
+	`--num_machines` was set to a value of `1`
+	`--mixed_precision` was set to a value of `'no'`
+	`--dynamo_backend` was set to a value of `'no'`
+To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.
+[W315 04:50:08.654423221 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+[W315 04:50:33.963867213 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+[W315 04:50:33.963864886 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+[W315 04:50:33.963886032 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+[W315 04:50:33.963888124 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+WARNING:accelerate.utils.other:[RANK 0] Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+CascadedFlowConfig(model_type='cascaded', batch_size=48, ntoken=512, d_model=128, nhead=8, nlayers=4, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=False, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=10000, mode='predict_y', result_path='./result', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30, scgpt_dim=512, bottleneck_dim=128, latent_weight=1.0, choose_latent_p=0.4, target_std=1.0, dh_depth=2, warmup_batches=200, ema_decay=0.9999, t_sample_mode='logit_normal', t_expr_mean=0.0, t_expr_std=1.0, t_latent_mean=0.0, t_latent_std=1.0, warmup_steps=2000, scgpt_model_dir='transfer/data/scGPT_pretrained', scgpt_max_seq_len=1200, scgpt_cache_path='', latent_steps=20, expr_steps=20, ode_method='rk4')
+Converted var_names to gene symbols, sample: ['RP11-34P13.8', 'RP11-54O7.3', 'SAMD11', 'PERM1', 'HES4']
+Warning: ctrl is not in the gene namesWarning: ctrl is not in the gene names
+
+Warning: ctrl is not in the gene names
+Warning: ctrl is not in the gene names
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+##### loading vocab from file #####
+##### loading vocab from file #####
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+##### loading vocab from file #####
+##### loading vocab from file #####
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+Loaded 159/163 pretrained parameters
+Loaded 159/163 pretrained parameters
+Loaded 159/163 pretrained parameters
+Loaded 159/163 pretrained parameters
+
  0%|          | 0/200000 [00:00<?, ?it/s]
  0%|          | 0/200000 [00:00<?, ?it/s]
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  0%|          | 0/200000 [00:00<?, ?it/s]/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/modules/transformer.py:531: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. We recommend specifying layout=torch.jagged when constructing a nested tensor, as this layout receives active development, has better operator coverage, and works with torch.compile. (Triggered internally at /pytorch/aten/src/ATen/NestedTensorImpl.cpp:178.)
+  output = torch._nested_tensor_from_mask(
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/modules/transformer.py:531: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. We recommend specifying layout=torch.jagged when constructing a nested tensor, as this layout receives active development, has better operator coverage, and works with torch.compile. (Triggered internally at /pytorch/aten/src/ATen/NestedTensorImpl.cpp:178.)
+  output = torch._nested_tensor_from_mask(
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/modules/transformer.py:531: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. We recommend specifying layout=torch.jagged when constructing a nested tensor, as this layout receives active development, has better operator coverage, and works with torch.compile. (Triggered internally at /pytorch/aten/src/ATen/NestedTensorImpl.cpp:178.)
+  output = torch._nested_tensor_from_mask(
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/modules/transformer.py:531: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. We recommend specifying layout=torch.jagged when constructing a nested tensor, as this layout receives active development, has better operator coverage, and works with torch.compile. (Triggered internally at /pytorch/aten/src/ATen/NestedTensorImpl.cpp:178.)
+  output = torch._nested_tensor_from_mask(
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/modules/linear.py:134: UserWarning: gemm_and_bias error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 128 n 48 k 256 mat1_ld 256 mat2_ld 256 result_ld 128 abType 0 cType 0 computeType 68 scaleType 0. Will attempt to recover by calling unfused cublas path. (Triggered internally at /pytorch/aten/src/ATen/cuda/CUDABlas.cpp:1765.)
+  return F.linear(input, self.weight, self.bias)
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/modules/linear.py:134: UserWarning: gemm_and_bias error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 128 n 48 k 256 mat1_ld 256 mat2_ld 256 result_ld 128 abType 0 cType 0 computeType 68 scaleType 0. Will attempt to recover by calling unfused cublas path. (Triggered internally at /pytorch/aten/src/ATen/cuda/CUDABlas.cpp:1765.)
+  return F.linear(input, self.weight, self.bias)
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/modules/linear.py:134: UserWarning: gemm_and_bias error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 128 n 48 k 256 mat1_ld 256 mat2_ld 256 result_ld 128 abType 0 cType 0 computeType 68 scaleType 0. Will attempt to recover by calling unfused cublas path. (Triggered internally at /pytorch/aten/src/ATen/cuda/CUDABlas.cpp:1765.)
+  return F.linear(input, self.weight, self.bias)
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/modules/linear.py:134: UserWarning: gemm_and_bias error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 128 n 48 k 256 mat1_ld 256 mat2_ld 256 result_ld 128 abType 0 cType 0 computeType 68 scaleType 0. Will attempt to recover by calling unfused cublas path. (Triggered internally at /pytorch/aten/src/ATen/cuda/CUDABlas.cpp:1765.)
+  return F.linear(input, self.weight, self.bias)
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/modules/linear.py:134: UserWarning: gemm_and_bias error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 128 n 48 k 128 mat1_ld 128 mat2_ld 128 result_ld 128 abType 0 cType 0 computeType 68 scaleType 0. Will attempt to recover by calling unfused cublas path. (Triggered internally at /pytorch/aten/src/ATen/cuda/CUDABlas.cpp:1765.)
+  return F.linear(input, self.weight, self.bias)
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/modules/linear.py:134: UserWarning: gemm_and_bias error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 128 n 48 k 128 mat1_ld 128 mat2_ld 128 result_ld 128 abType 0 cType 0 computeType 68 scaleType 0. Will attempt to recover by calling unfused cublas path. (Triggered internally at /pytorch/aten/src/ATen/cuda/CUDABlas.cpp:1765.)
+  return F.linear(input, self.weight, self.bias)
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/modules/linear.py:134: UserWarning: gemm_and_bias error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 128 n 48 k 128 mat1_ld 128 mat2_ld 128 result_ld 128 abType 0 cType 0 computeType 68 scaleType 0. Will attempt to recover by calling unfused cublas path. (Triggered internally at /pytorch/aten/src/ATen/cuda/CUDABlas.cpp:1765.)
+  return F.linear(input, self.weight, self.bias)
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/modules/linear.py:134: UserWarning: gemm_and_bias error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 128 n 48 k 128 mat1_ld 128 mat2_ld 128 result_ld 128 abType 0 cType 0 computeType 68 scaleType 0. Will attempt to recover by calling unfused cublas path. (Triggered internally at /pytorch/aten/src/ATen/cuda/CUDABlas.cpp:1765.)
+  return F.linear(input, self.weight, self.bias)
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/functional.py:6637: UserWarning: gemm_and_bias error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 128 n 96 k 128 mat1_ld 128 mat2_ld 128 result_ld 128 abType 0 cType 0 computeType 68 scaleType 0. Will attempt to recover by calling unfused cublas path. (Triggered internally at /pytorch/aten/src/ATen/cuda/CUDABlas.cpp:1765.)
+  attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/functional.py:6637: UserWarning: gemm_and_bias error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 128 n 96 k 128 mat1_ld 128 mat2_ld 128 result_ld 128 abType 0 cType 0 computeType 68 scaleType 0. Will attempt to recover by calling unfused cublas path. (Triggered internally at /pytorch/aten/src/ATen/cuda/CUDABlas.cpp:1765.)
+  attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/functional.py:6637: UserWarning: gemm_and_bias error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 128 n 96 k 128 mat1_ld 128 mat2_ld 128 result_ld 128 abType 0 cType 0 computeType 68 scaleType 0. Will attempt to recover by calling unfused cublas path. (Triggered internally at /pytorch/aten/src/ATen/cuda/CUDABlas.cpp:1765.)
+  attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/functional.py:6637: UserWarning: gemm_and_bias error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 128 n 96 k 128 mat1_ld 128 mat2_ld 128 result_ld 128 abType 0 cType 0 computeType 68 scaleType 0. Will attempt to recover by calling unfused cublas path. (Triggered internally at /pytorch/aten/src/ATen/cuda/CUDABlas.cpp:1765.)
+  attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
+[rank2]: Traceback (most recent call last):
+[rank2]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 372, in <module>
+[rank2]:     ema_p.lerp_(model_p.data, 1 - decay)
+[rank2]:     ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank2]: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:2 and cpu!
+[rank1]: Traceback (most recent call last):
+[rank1]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 372, in <module>
+[rank1]:     ema_p.lerp_(model_p.data, 1 - decay)
+[rank1]:     ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank1]: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:1 and cpu!
+[rank0]: Traceback (most recent call last):
+[rank0]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 372, in <module>
+[rank0]:     ema_p.lerp_(model_p.data, 1 - decay)
+[rank0]:     ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!
+[rank3]: Traceback (most recent call last):
+[rank3]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 372, in <module>
+[rank3]:     ema_p.lerp_(model_p.data, 1 - decay)
+[rank3]:     ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank3]: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:3 and cpu!
+[rank0]:[W315 04:52:01.076850828 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+
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+
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+
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+
  0%|          | 0/200000 [00:53<?, ?it/s]
+W0315 04:52:07.763000 128 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 200 closing signal SIGTERM
+W0315 04:52:07.765000 128 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 201 closing signal SIGTERM
+W0315 04:52:07.765000 128 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 202 closing signal SIGTERM
+E0315 04:52:08.130000 128 torch/distributed/elastic/multiprocessing/api.py:984] failed (exitcode: 1) local_rank: 3 (pid: 203) of binary: /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/python
+Traceback (most recent call last):
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/accelerate", line 8, in <module>
+    sys.exit(main())
+             ~~~~^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main
+    args.func(args)
+    ~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1396, in launch_command
+    multi_gpu_launcher(args)
+    ~~~~~~~~~~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1023, in multi_gpu_launcher
+    distrib_run.run(args)
+    ~~~~~~~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/run.py", line 982, in run
+    elastic_launch(
+    ~~~~~~~~~~~~~~~
+        config=config,
+        ~~~~~~~~~~~~~~
+        entrypoint=cmd,
+        ~~~~~~~~~~~~~~~
+    )(*cmd_args)
+    ~^^^^^^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 170, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 317, in launch_agent
+    raise ChildFailedError(
+    ...<2 lines>...
+    )
+torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
+============================================================
+scripts/run_cascaded.py FAILED
+------------------------------------------------------------
+Failures:
+[1]:
+  time      : 2026-03-15_04:52:08
+  host      : b0019
+  rank      : 0 (local_rank: 0)
+  exitcode  : 1 (pid: 200)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+[2]:
+  time      : 2026-03-15_04:52:08
+  host      : b0019
+  rank      : 1 (local_rank: 1)
+  exitcode  : 1 (pid: 201)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+[3]:
+  time      : 2026-03-15_04:52:08
+  host      : b0019
+  rank      : 2 (local_rank: 2)
+  exitcode  : 1 (pid: 202)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+------------------------------------------------------------
+Root Cause (first observed failure):
+[0]:
+  time      : 2026-03-15_04:52:07
+  host      : b0019
+  rank      : 3 (local_rank: 3)
+  exitcode  : 1 (pid: 203)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+============================================================
+==========================================
+Finished:  Sun Mar 15 04:52:09 JST 2026
+==========================================

transfer/code/CCFM/logs/ccfm_v2_5404735.out

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:78ae507c08bcf68993af70a80ee5dc04ea4c1b5236c845f4c2da3d8cb3356573
+size 81641352

transfer/code/CCFM/logs/ccfm_v2_cached_5404728.out

@@ -0,0 +1,236 @@
+==========================================
+Job ID:    5404728
+Job Name:  ccfm_v2_cached
+Start:     Sun Mar 15 04:52:33 JST 2026
+Node:      b0021
+GPU:       NVIDIA H100, 95830 MiB
+NVIDIA H100, 95830 MiB
+NVIDIA H100, 95830 MiB
+NVIDIA H100, 95830 MiB
+Run:       CCFM v2 cached (KV fix + loss fix + EMA + RK4 + logit-normal + warmup + scGPT cache)
+==========================================
+The following values were not passed to `accelerate launch` and had defaults used instead:
+		More than one GPU was found, enabling multi-GPU training.
+		If this was unintended please pass in `--num_processes=1`.
+	`--num_machines` was set to a value of `1`
+	`--mixed_precision` was set to a value of `'no'`
+	`--dynamo_backend` was set to a value of `'no'`
+To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.
+[W315 04:52:57.426142473 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+[W315 04:53:16.185740786 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+[W315 04:53:16.441379224 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+[W315 04:53:16.447651346 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+[W315 04:53:16.490639280 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+WARNING:accelerate.utils.other:[RANK 0] Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+CascadedFlowConfig(model_type='cascaded', batch_size=48, ntoken=512, d_model=128, nhead=8, nlayers=4, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=False, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=10000, mode='predict_y', result_path='./result', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30, scgpt_dim=512, bottleneck_dim=128, latent_weight=1.0, choose_latent_p=0.4, target_std=1.0, dh_depth=2, warmup_batches=200, ema_decay=0.9999, t_sample_mode='logit_normal', t_expr_mean=0.0, t_expr_std=1.0, t_latent_mean=0.0, t_latent_std=1.0, warmup_steps=2000, scgpt_model_dir='transfer/data/scGPT_pretrained', scgpt_max_seq_len=1200, scgpt_cache_path='scgpt_cache_norman.h5', latent_steps=20, expr_steps=20, ode_method='rk4')
+Converted var_names to gene symbols, sample: ['RP11-34P13.8', 'RP11-54O7.3', 'SAMD11', 'PERM1', 'HES4']
+Warning: ctrl is not in the gene namesWarning: ctrl is not in the gene names
+Warning: ctrl is not in the gene names
+
+Warning: ctrl is not in the gene names
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+##### loading vocab from file #####
+##### loading vocab from file #####
+##### loading vocab from file #####
+##### loading vocab from file #####
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+Loaded 159/163 pretrained parameters
+Loaded 159/163 pretrained parameters
+Loaded 159/163 pretrained parameters
+Loaded 159/163 pretrained parameters
+Using pre-extracted scGPT cache: scgpt_cache_norman.h5
+  Cache shape: (91205, 5035, 512), cells: 91205
+
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  0%|          | 0/200000 [00:00<?, ?it/s][rank3]: Traceback (most recent call last):
+[rank3]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in <module>
+[rank3]:     cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device)
+[rank3]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup
+[rank3]:     raw = self.features[row_indices]  # (B, G_full, D) as numpy
+[rank3]:           ~~~~~~~~~~~~~^^^^^^^^^^^^^
+[rank3]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank3]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank3]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__
+[rank3]:     selection = sel.select(self.shape, args, dataset=self)
+[rank3]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select
+[rank3]:     return selector.make_selection(args)
+[rank3]:            ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^
+[rank3]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank3]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank3]:   File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection
+[rank3]:   File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args
+[rank3]: TypeError: Indexing elements must be in increasing order
+[rank0]: Traceback (most recent call last):
+[rank0]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in <module>
+[rank0]:     cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device)
+[rank0]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup
+[rank0]:     raw = self.features[row_indices]  # (B, G_full, D) as numpy
+[rank0]:           ~~~~~~~~~~~~~^^^^^^^^^^^^^
+[rank0]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank0]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank0]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__
+[rank0]:     selection = sel.select(self.shape, args, dataset=self)
+[rank0]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select
+[rank0]:     return selector.make_selection(args)
+[rank0]:            ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^
+[rank0]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank0]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank0]:   File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection
+[rank0]:   File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args
+[rank0]: TypeError: Indexing elements must be in increasing order
+[rank1]: Traceback (most recent call last):
+[rank1]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in <module>
+[rank1]:     cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device)
+[rank1]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup
+[rank1]:     raw = self.features[row_indices]  # (B, G_full, D) as numpy
+[rank1]:           ~~~~~~~~~~~~~^^^^^^^^^^^^^
+[rank1]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank1]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank1]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__
+[rank1]:     selection = sel.select(self.shape, args, dataset=self)
+[rank1]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select
+[rank1]:     return selector.make_selection(args)
+[rank1]:            ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^
+[rank1]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank1]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank1]:   File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection
+[rank1]:   File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args
+[rank1]: TypeError: Indexing elements must be in increasing order
+[rank2]: Traceback (most recent call last):
+[rank2]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in <module>
+[rank2]:     cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device)
+[rank2]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup
+[rank2]:     raw = self.features[row_indices]  # (B, G_full, D) as numpy
+[rank2]:           ~~~~~~~~~~~~~^^^^^^^^^^^^^
+[rank2]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank2]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank2]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__
+[rank2]:     selection = sel.select(self.shape, args, dataset=self)
+[rank2]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select
+[rank2]:     return selector.make_selection(args)
+[rank2]:            ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^
+[rank2]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank2]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank2]:   File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection
+[rank2]:   File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args
+[rank2]: TypeError: Indexing elements must be in increasing order
+[rank0]:[W315 04:54:29.650127266 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+
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+
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+
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+
  0%|          | 0/200000 [00:42<?, ?it/s]
+W0315 04:54:34.559000 129 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 201 closing signal SIGTERM
+W0315 04:54:34.560000 129 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 202 closing signal SIGTERM
+W0315 04:54:34.561000 129 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 204 closing signal SIGTERM
+E0315 04:54:34.775000 129 torch/distributed/elastic/multiprocessing/api.py:984] failed (exitcode: 1) local_rank: 2 (pid: 203) of binary: /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/python
+Traceback (most recent call last):
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/accelerate", line 8, in <module>
+    sys.exit(main())
+             ~~~~^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main
+    args.func(args)
+    ~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1396, in launch_command
+    multi_gpu_launcher(args)
+    ~~~~~~~~~~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1023, in multi_gpu_launcher
+    distrib_run.run(args)
+    ~~~~~~~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/run.py", line 982, in run
+    elastic_launch(
+    ~~~~~~~~~~~~~~~
+        config=config,
+        ~~~~~~~~~~~~~~
+        entrypoint=cmd,
+        ~~~~~~~~~~~~~~~
+    )(*cmd_args)
+    ~^^^^^^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 170, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 317, in launch_agent
+    raise ChildFailedError(
+    ...<2 lines>...
+    )
+torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
+============================================================
+scripts/run_cascaded.py FAILED
+------------------------------------------------------------
+Failures:
+[1]:
+  time      : 2026-03-15_04:54:34
+  host      : b0021
+  rank      : 0 (local_rank: 0)
+  exitcode  : 1 (pid: 201)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+[2]:
+  time      : 2026-03-15_04:54:34
+  host      : b0021
+  rank      : 1 (local_rank: 1)
+  exitcode  : 1 (pid: 202)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+[3]:
+  time      : 2026-03-15_04:54:34
+  host      : b0021
+  rank      : 3 (local_rank: 3)
+  exitcode  : 1 (pid: 204)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+------------------------------------------------------------
+Root Cause (first observed failure):
+[0]:
+  time      : 2026-03-15_04:54:34
+  host      : b0021
+  rank      : 2 (local_rank: 2)
+  exitcode  : 1 (pid: 203)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+============================================================
+==========================================
+Finished:  Sun Mar 15 04:54:35 JST 2026
+==========================================

transfer/code/CCFM/logs/ccfm_v2_cached_5404736.out

@@ -0,0 +1,236 @@
+==========================================
+Job ID:    5404736
+Job Name:  ccfm_v2_cached
+Start:     Sun Mar 15 04:58:13 JST 2026
+Node:      b0021
+GPU:       NVIDIA H100, 95830 MiB
+NVIDIA H100, 95830 MiB
+NVIDIA H100, 95830 MiB
+NVIDIA H100, 95830 MiB
+Run:       CCFM v2 cached (KV fix + loss fix + EMA + RK4 + logit-normal + warmup + scGPT cache)
+==========================================
+The following values were not passed to `accelerate launch` and had defaults used instead:
+		More than one GPU was found, enabling multi-GPU training.
+		If this was unintended please pass in `--num_processes=1`.
+	`--num_machines` was set to a value of `1`
+	`--mixed_precision` was set to a value of `'no'`
+	`--dynamo_backend` was set to a value of `'no'`
+To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.
+[W315 04:58:44.021644370 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+[W315 04:59:02.163293234 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+[W315 04:59:02.432132628 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+[W315 04:59:02.432621699 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+[W315 04:59:02.442458875 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+WARNING:accelerate.utils.other:[RANK 0] Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+CascadedFlowConfig(model_type='cascaded', batch_size=48, ntoken=512, d_model=128, nhead=8, nlayers=4, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=False, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=10000, mode='predict_y', result_path='./result', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30, scgpt_dim=512, bottleneck_dim=128, latent_weight=1.0, choose_latent_p=0.4, target_std=1.0, dh_depth=2, warmup_batches=200, ema_decay=0.9999, t_sample_mode='logit_normal', t_expr_mean=0.0, t_expr_std=1.0, t_latent_mean=0.0, t_latent_std=1.0, warmup_steps=2000, scgpt_model_dir='transfer/data/scGPT_pretrained', scgpt_max_seq_len=1200, scgpt_cache_path='scgpt_cache_norman.h5', latent_steps=20, expr_steps=20, ode_method='rk4')
+Converted var_names to gene symbols, sample: ['RP11-34P13.8', 'RP11-54O7.3', 'SAMD11', 'PERM1', 'HES4']
+Warning: ctrl is not in the gene names
+Warning: ctrl is not in the gene namesWarning: ctrl is not in the gene names
+
+Warning: ctrl is not in the gene names
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+##### loading vocab from file #####
+##### loading vocab from file #####
+##### loading vocab from file #####
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+##### loading vocab from file #####
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+Loaded 159/163 pretrained parameters
+Loaded 159/163 pretrained parameters
+Loaded 159/163 pretrained parameters
+Using pre-extracted scGPT cache: scgpt_cache_norman.h5
+  Cache shape: (91205, 5035, 512), cells: 91205
+Loaded 159/163 pretrained parameters
+
  0%|          | 0/200000 [00:00<?, ?it/s]
  0%|          | 0/200000 [00:00<?, ?it/s]
  0%|          | 0/200000 [00:00<?, ?it/s]
  0%|          | 0/200000 [00:00<?, ?it/s][rank0]: Traceback (most recent call last):
+[rank0]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in <module>
+[rank0]:     cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device)
+[rank0]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup
+[rank0]:     raw = self.features[row_indices]  # (B, G_full, D) as numpy
+[rank0]:           ~~~~~~~~~~~~~^^^^^^^^^^^^^
+[rank0]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank0]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank0]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__
+[rank0]:     selection = sel.select(self.shape, args, dataset=self)
+[rank0]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select
+[rank0]:     return selector.make_selection(args)
+[rank0]:            ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^
+[rank0]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank0]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank0]:   File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection
+[rank0]:   File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args
+[rank0]: TypeError: Indexing elements must be in increasing order
+[rank2]: Traceback (most recent call last):
+[rank2]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in <module>
+[rank2]:     cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device)
+[rank2]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup
+[rank2]:     raw = self.features[row_indices]  # (B, G_full, D) as numpy
+[rank2]:           ~~~~~~~~~~~~~^^^^^^^^^^^^^
+[rank2]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank2]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank2]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__
+[rank2]:     selection = sel.select(self.shape, args, dataset=self)
+[rank2]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select
+[rank2]:     return selector.make_selection(args)
+[rank2]:            ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^
+[rank2]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank2]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank2]:   File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection
+[rank2]:   File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args
+[rank2]: TypeError: Indexing elements must be in increasing order
+[rank1]: Traceback (most recent call last):
+[rank1]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in <module>
+[rank1]:     cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device)
+[rank1]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup
+[rank1]:     raw = self.features[row_indices]  # (B, G_full, D) as numpy
+[rank1]:           ~~~~~~~~~~~~~^^^^^^^^^^^^^
+[rank1]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank1]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank1]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__
+[rank1]:     selection = sel.select(self.shape, args, dataset=self)
+[rank1]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select
+[rank1]:     return selector.make_selection(args)
+[rank1]:            ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^
+[rank1]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank1]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank1]:   File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection
+[rank1]:   File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args
+[rank1]: TypeError: Indexing elements must be in increasing order
+[rank3]: Traceback (most recent call last):
+[rank3]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in <module>
+[rank3]:     cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device)
+[rank3]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup
+[rank3]:     raw = self.features[row_indices]  # (B, G_full, D) as numpy
+[rank3]:           ~~~~~~~~~~~~~^^^^^^^^^^^^^
+[rank3]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank3]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank3]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__
+[rank3]:     selection = sel.select(self.shape, args, dataset=self)
+[rank3]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select
+[rank3]:     return selector.make_selection(args)
+[rank3]:            ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^
+[rank3]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank3]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank3]:   File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection
+[rank3]:   File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args
+[rank3]: TypeError: Indexing elements must be in increasing order
+[rank0]:[W315 05:00:15.940375045 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+
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+
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+
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+
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+W0315 05:00:19.854000 128 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 200 closing signal SIGTERM
+W0315 05:00:19.856000 128 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 201 closing signal SIGTERM
+W0315 05:00:19.856000 128 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 203 closing signal SIGTERM
+E0315 05:00:20.071000 128 torch/distributed/elastic/multiprocessing/api.py:984] failed (exitcode: 1) local_rank: 2 (pid: 202) of binary: /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/python
+Traceback (most recent call last):
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/accelerate", line 8, in <module>
+    sys.exit(main())
+             ~~~~^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main
+    args.func(args)
+    ~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1396, in launch_command
+    multi_gpu_launcher(args)
+    ~~~~~~~~~~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1023, in multi_gpu_launcher
+    distrib_run.run(args)
+    ~~~~~~~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/run.py", line 982, in run
+    elastic_launch(
+    ~~~~~~~~~~~~~~~
+        config=config,
+        ~~~~~~~~~~~~~~
+        entrypoint=cmd,
+        ~~~~~~~~~~~~~~~
+    )(*cmd_args)
+    ~^^^^^^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 170, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 317, in launch_agent
+    raise ChildFailedError(
+    ...<2 lines>...
+    )
+torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
+============================================================
+scripts/run_cascaded.py FAILED
+------------------------------------------------------------
+Failures:
+[1]:
+  time      : 2026-03-15_05:00:20
+  host      : b0021
+  rank      : 0 (local_rank: 0)
+  exitcode  : 1 (pid: 200)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+[2]:
+  time      : 2026-03-15_05:00:20
+  host      : b0021
+  rank      : 1 (local_rank: 1)
+  exitcode  : 1 (pid: 201)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+[3]:
+  time      : 2026-03-15_05:00:20
+  host      : b0021
+  rank      : 3 (local_rank: 3)
+  exitcode  : 1 (pid: 203)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+------------------------------------------------------------
+Root Cause (first observed failure):
+[0]:
+  time      : 2026-03-15_05:00:19
+  host      : b0021
+  rank      : 2 (local_rank: 2)
+  exitcode  : 1 (pid: 202)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+============================================================
+==========================================
+Finished:  Sun Mar 15 05:00:21 JST 2026
+==========================================

transfer/code/CCFM/logs/ccfm_v2_cached_5404737.out

@@ -0,0 +1,236 @@
+==========================================
+Job ID:    5404737
+Job Name:  ccfm_v2_cached
+Start:     Sun Mar 15 05:02:01 JST 2026
+Node:      b0021
+GPU:       NVIDIA H100, 95830 MiB
+NVIDIA H100, 95830 MiB
+NVIDIA H100, 95830 MiB
+NVIDIA H100, 95830 MiB
+Run:       CCFM v2 cached (KV fix + loss fix + EMA + RK4 + logit-normal + warmup + scGPT cache)
+==========================================
+The following values were not passed to `accelerate launch` and had defaults used instead:
+		More than one GPU was found, enabling multi-GPU training.
+		If this was unintended please pass in `--num_processes=1`.
+	`--num_machines` was set to a value of `1`
+	`--mixed_precision` was set to a value of `'no'`
+	`--dynamo_backend` was set to a value of `'no'`
+To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.
+[W315 05:02:27.190711515 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+[W315 05:02:46.969095263 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+[W315 05:02:46.996806598 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+[W315 05:02:46.020582775 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+[W315 05:02:46.024342649 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol).
+WARNING:accelerate.utils.other:[RANK 0] Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+CascadedFlowConfig(model_type='cascaded', batch_size=48, ntoken=512, d_model=128, nhead=8, nlayers=4, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=False, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=10000, mode='predict_y', result_path='./result', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30, scgpt_dim=512, bottleneck_dim=128, latent_weight=1.0, choose_latent_p=0.4, target_std=1.0, dh_depth=2, warmup_batches=200, ema_decay=0.9999, t_sample_mode='logit_normal', t_expr_mean=0.0, t_expr_std=1.0, t_latent_mean=0.0, t_latent_std=1.0, warmup_steps=2000, scgpt_model_dir='transfer/data/scGPT_pretrained', scgpt_max_seq_len=1200, scgpt_cache_path='scgpt_cache_norman.h5', latent_steps=20, expr_steps=20, ode_method='rk4')
+Converted var_names to gene symbols, sample: ['RP11-34P13.8', 'RP11-54O7.3', 'SAMD11', 'PERM1', 'HES4']
+Warning: ctrl is not in the gene namesWarning: ctrl is not in the gene namesWarning: ctrl is not in the gene names
+
+
+Warning: ctrl is not in the gene names
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+##### loading vocab from file #####
+##### loading vocab from file #####
+##### loading vocab from file #####
+##### loading vocab from file #####
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+Loaded 159/163 pretrained parameters
+Loaded 159/163 pretrained parameters
+Loaded 159/163 pretrained parameters
+Loaded 159/163 pretrained parameters
+Using pre-extracted scGPT cache: scgpt_cache_norman.h5
+  Cache shape: (91205, 5035, 512), cells: 91205
+
  0%|          | 0/200000 [00:00<?, ?it/s]
  0%|          | 0/200000 [00:00<?, ?it/s]
  0%|          | 0/200000 [00:00<?, ?it/s]
  0%|          | 0/200000 [00:00<?, ?it/s][rank0]: Traceback (most recent call last):
+[rank0]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in <module>
+[rank0]:     cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device)
+[rank0]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup
+[rank0]:     raw = self.features[sorted_indices.tolist()]  # (B, G_full, D) as numpy
+[rank0]:           ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank0]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank0]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank0]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__
+[rank0]:     selection = sel.select(self.shape, args, dataset=self)
+[rank0]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select
+[rank0]:     return selector.make_selection(args)
+[rank0]:            ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^
+[rank0]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank0]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank0]:   File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection
+[rank0]:   File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args
+[rank0]: TypeError: Indexing elements must be in increasing order
+[rank1]: Traceback (most recent call last):
+[rank1]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in <module>
+[rank1]:     cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device)
+[rank1]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup
+[rank1]:     raw = self.features[sorted_indices.tolist()]  # (B, G_full, D) as numpy
+[rank1]:           ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank1]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank1]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank1]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__
+[rank1]:     selection = sel.select(self.shape, args, dataset=self)
+[rank1]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select
+[rank1]:     return selector.make_selection(args)
+[rank1]:            ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^
+[rank1]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank1]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank1]:   File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection
+[rank1]:   File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args
+[rank1]: TypeError: Indexing elements must be in increasing order
+[rank2]: Traceback (most recent call last):
+[rank2]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in <module>
+[rank2]:     cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device)
+[rank2]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup
+[rank2]:     raw = self.features[sorted_indices.tolist()]  # (B, G_full, D) as numpy
+[rank2]:           ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank2]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank2]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank2]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__
+[rank2]:     selection = sel.select(self.shape, args, dataset=self)
+[rank2]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select
+[rank2]:     return selector.make_selection(args)
+[rank2]:            ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^
+[rank2]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank2]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank2]:   File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection
+[rank2]:   File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args
+[rank2]: TypeError: Indexing elements must be in increasing order
+[rank3]: Traceback (most recent call last):
+[rank3]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in <module>
+[rank3]:     cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device)
+[rank3]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup
+[rank3]:     raw = self.features[sorted_indices.tolist()]  # (B, G_full, D) as numpy
+[rank3]:           ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^
+[rank3]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank3]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank3]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__
+[rank3]:     selection = sel.select(self.shape, args, dataset=self)
+[rank3]:   File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select
+[rank3]:     return selector.make_selection(args)
+[rank3]:            ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^
+[rank3]:   File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
+[rank3]:   File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
+[rank3]:   File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection
+[rank3]:   File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args
+[rank3]: TypeError: Indexing elements must be in increasing order
+[rank0]:[W315 05:04:02.931689961 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
+
  0%|          | 0/200000 [00:42<?, ?it/s]
+
  0%|          | 0/200000 [00:42<?, ?it/s]
+
  0%|          | 0/200000 [00:42<?, ?it/s]
+
  0%|          | 0/200000 [00:42<?, ?it/s]
+W0315 05:04:06.726000 128 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 200 closing signal SIGTERM
+W0315 05:04:06.728000 128 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 201 closing signal SIGTERM
+W0315 05:04:06.729000 128 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 203 closing signal SIGTERM
+E0315 05:04:06.943000 128 torch/distributed/elastic/multiprocessing/api.py:984] failed (exitcode: 1) local_rank: 2 (pid: 202) of binary: /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/python
+Traceback (most recent call last):
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/accelerate", line 8, in <module>
+    sys.exit(main())
+             ~~~~^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main
+    args.func(args)
+    ~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1396, in launch_command
+    multi_gpu_launcher(args)
+    ~~~~~~~~~~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1023, in multi_gpu_launcher
+    distrib_run.run(args)
+    ~~~~~~~~~~~~~~~^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/run.py", line 982, in run
+    elastic_launch(
+    ~~~~~~~~~~~~~~~
+        config=config,
+        ~~~~~~~~~~~~~~
+        entrypoint=cmd,
+        ~~~~~~~~~~~~~~~
+    )(*cmd_args)
+    ~^^^^^^^^^^^
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 170, in __call__
+    return launch_agent(self._config, self._entrypoint, list(args))
+  File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 317, in launch_agent
+    raise ChildFailedError(
+    ...<2 lines>...
+    )
+torch.distributed.elastic.multiprocessing.errors.ChildFailedError: 
+============================================================
+scripts/run_cascaded.py FAILED
+------------------------------------------------------------
+Failures:
+[1]:
+  time      : 2026-03-15_05:04:06
+  host      : b0021
+  rank      : 0 (local_rank: 0)
+  exitcode  : 1 (pid: 200)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+[2]:
+  time      : 2026-03-15_05:04:06
+  host      : b0021
+  rank      : 1 (local_rank: 1)
+  exitcode  : 1 (pid: 201)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+[3]:
+  time      : 2026-03-15_05:04:06
+  host      : b0021
+  rank      : 3 (local_rank: 3)
+  exitcode  : 1 (pid: 203)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+------------------------------------------------------------
+Root Cause (first observed failure):
+[0]:
+  time      : 2026-03-15_05:04:06
+  host      : b0021
+  rank      : 2 (local_rank: 2)
+  exitcode  : 1 (pid: 202)
+  error_file: <N/A>
+  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
+============================================================
+==========================================
+Finished:  Sun Mar 15 05:04:07 JST 2026
+==========================================

transfer/code/CCFM/logs/ccfm_v2_resume_5406605.out

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:22687c5738583ccac9adb3790fa51dd831d4e439a1254a6f63da303c2d3f52f3
+size 119504495

transfer/code/CCFM/logs/eval_ccfm_v2_5406214.out

@@ -0,0 +1,437 @@
+==========================================
+Job ID:    5406214
+Job Name:  eval_ccfm_v2
+Start:     Mon Mar 16 00:48:18 JST 2026
+Node:      b0031
+GPU:       NVIDIA H100, 95830 MiB
+Eval:      CCFM v2 (online) iteration_100000
+==========================================
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+WARNING:accelerate.utils.other:[RANK 0] Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1)
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+CascadedFlowConfig(model_type='cascaded', batch_size=48, ntoken=512, d_model=128, nhead=8, nlayers=4, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=True, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=10000, mode='predict_y', result_path='./result', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/result/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-ema0.9999-ln-wu2000-rk4-online/iteration_100000/checkpoint.pt', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30, scgpt_dim=512, bottleneck_dim=128, latent_weight=1.0, choose_latent_p=0.4, target_std=1.0, dh_depth=2, warmup_batches=200, ema_decay=0.9999, t_sample_mode='logit_normal', t_expr_mean=0.0, t_expr_std=1.0, t_latent_mean=0.0, t_latent_std=1.0, warmup_steps=2000, scgpt_model_dir='transfer/data/scGPT_pretrained', scgpt_max_seq_len=1200, scgpt_cache_path='', latent_steps=20, expr_steps=20, ode_method='rk4')
+Converted var_names to gene symbols, sample: ['RP11-34P13.8', 'RP11-54O7.3', 'SAMD11', 'PERM1', 'HES4']
+Warning: ctrl is not in the gene names
+##### loading vocab from file #####
+Loaded 159/163 pretrained parameters
+loading /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/result/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-ema0.9999-ln-wu2000-rk4-online/iteration_100000/checkpoint.pt checkpoint, iteration: 100000, eval_score: None
+Test-only mode. Saving results to ./result/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-ema0.9999-ln-wu2000-rk4-online/eval_only
+perturbation_name_list: 39
+
+  return F.linear(input, self.weight, self.bias)
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/modules/linear.py:134: UserWarning: gemm_and_bias error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 128 n 48 k 128 mat1_ld 128 mat2_ld 128 result_ld 128 abType 0 cType 0 computeType 68 scaleType 0. Will attempt to recover by calling unfused cublas path. (Triggered internally at /pytorch/aten/src/ATen/cuda/CUDABlas.cpp:1765.)
+  return F.linear(input, self.weight, self.bias)
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/functional.py:6637: UserWarning: gemm_and_bias error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 128 n 96 k 128 mat1_ld 128 mat2_ld 128 result_ld 128 abType 0 cType 0 computeType 68 scaleType 0. Will attempt to recover by calling unfused cublas path. (Triggered internally at /pytorch/aten/src/ATen/cuda/CUDABlas.cpp:1765.)
+  attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
+
+  return F.linear(input, self.weight, self.bias)
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/modules/linear.py:134: UserWarning: gemm_and_bias error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 128 n 32 k 128 mat1_ld 128 mat2_ld 128 result_ld 128 abType 0 cType 0 computeType 68 scaleType 0. Will attempt to recover by calling unfused cublas path. (Triggered internally at /pytorch/aten/src/ATen/cuda/CUDABlas.cpp:1765.)
+  return F.linear(input, self.weight, self.bias)
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/nn/functional.py:6637: UserWarning: gemm_and_bias error: CUBLAS_STATUS_NOT_INITIALIZED when calling cublasLtMatmul with transpose_mat1 1 transpose_mat2 0 m 128 n 64 k 128 mat1_ld 128 mat2_ld 128 result_ld 128 abType 0 cType 0 computeType 68 scaleType 0. Will attempt to recover by calling unfused cublas path. (Triggered internally at /pytorch/aten/src/ATen/cuda/CUDABlas.cpp:1765.)
+  attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+/home/hp250092/ku50001222/miniconda3/lib/python3.13/functools.py:934: ImplicitModificationWarning: Transforming to str index.
+  return dispatch(args[0].__class__)(*args, **kw)
+/home/hp250092/ku50001222/miniconda3/lib/python3.13/functools.py:934: ImplicitModificationWarning: Transforming to str index.
+  return dispatch(args[0].__class__)(*args, **kw)
+WARNING:cell_eval._evaluator:Output directory ./cell-eval-outdir already exists, potential overwrite occurring
+INFO:cell_eval.utils:Data appears to be log1p normalized (decimals detected, range [0.00, 8.82])
+INFO:cell_eval._evaluator:Input is found to be log-normalized already - skipping transformation.
+INFO:cell_eval.utils:Data appears to be log1p normalized (decimals detected, range [0.00, 9.75])
+INFO:cell_eval._evaluator:Input is found to be log-normalized already - skipping transformation.
+INFO:cell_eval._evaluator:Computing DE for real data
+INFO:cell_eval._evaluator:Using the following pdex kwargs: {'reference': 'control', 'groupby_key': 'perturbation', 'num_workers': 32, 'batch_size': 100, 'metric': 'wilcoxon', 'is_log1p': True, 'as_polars': True}
+INFO:pdex._single_cell:Log1p status: True
+INFO:pdex._single_cell:Precomputing masks for each target gene
+
+INFO:pdex._single_cell:Precomputing variable indices for each feature
+
+INFO:pdex._single_cell:Creating shared memory matrix for parallel computing
+INFO:pdex._single_cell:Creating generator of all combinations: N=40000
+INFO:pdex._single_cell:Creating generator of all batches: N=401
+INFO:pdex._single_cell:Initializing parallel processing pool with 32 workers
+INFO:pdex._single_cell:Processing batches
+
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scipy/stats/_mannwhitneyu.py:152: RuntimeWarning: invalid value encountered in sqrt
+  s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+
+INFO:pdex._single_cell:Flattening results
+INFO:pdex._single_cell:Closing shared memory pool
+INFO:cell_eval._evaluator:Writing real DE results to: real_de.csv
+INFO:cell_eval._evaluator:Computing DE for pred data
+INFO:cell_eval._evaluator:Using the following pdex kwargs: {'reference': 'control', 'groupby_key': 'perturbation', 'num_workers': 32, 'batch_size': 100, 'metric': 'wilcoxon', 'is_log1p': True, 'as_polars': True}
+INFO:pdex._single_cell:Log1p status: True
+INFO:pdex._single_cell:Precomputing masks for each target gene
+
+INFO:pdex._single_cell:Precomputing variable indices for each feature
+
+INFO:pdex._single_cell:Creating shared memory matrix for parallel computing
+INFO:pdex._single_cell:Creating generator of all combinations: N=40000
+INFO:pdex._single_cell:Creating generator of all batches: N=401
+INFO:pdex._single_cell:Initializing parallel processing pool with 32 workers
+INFO:pdex._single_cell:Processing batches
+
+INFO:pdex._single_cell:Flattening results
+INFO:pdex._single_cell:Closing shared memory pool
+INFO:cell_eval._evaluator:Writing pred DE results to: pred_de.csv
+INFO:cell_eval._types._de:Checking DE data integrity... (real)
+INFO:cell_eval._types._de:DE data integrity check complete. (real)
+INFO:cell_eval._types._de:Checking DE data integrity... (pred)
+INFO:cell_eval._types._de:DE data integrity check complete. (pred)
+INFO:cell_eval._pipeline._runner:Computing metric 'overlap_at_N'
+INFO:cell_eval._pipeline._runner:Computing metric 'overlap_at_50'
+INFO:cell_eval._pipeline._runner:Computing metric 'overlap_at_100'
+INFO:cell_eval._pipeline._runner:Computing metric 'overlap_at_200'
+INFO:cell_eval._pipeline._runner:Computing metric 'overlap_at_500'
+INFO:cell_eval._pipeline._runner:Computing metric 'precision_at_N'
+INFO:cell_eval._pipeline._runner:Computing metric 'precision_at_50'
+INFO:cell_eval._pipeline._runner:Computing metric 'precision_at_100'
+INFO:cell_eval._pipeline._runner:Computing metric 'precision_at_200'
+INFO:cell_eval._pipeline._runner:Computing metric 'precision_at_500'
+INFO:cell_eval._pipeline._runner:Computing metric 'de_spearman_sig'
+INFO:cell_eval._pipeline._runner:Computing metric 'de_direction_match'
+INFO:cell_eval._pipeline._runner:Computing metric 'de_spearman_lfc_sig'
+INFO:cell_eval._pipeline._runner:Computing metric 'de_sig_genes_recall'
+INFO:cell_eval._pipeline._runner:Computing metric 'de_nsig_counts'
+INFO:cell_eval._pipeline._runner:Computing metric 'pr_auc'
+INFO:cell_eval._pipeline._runner:Computing metric 'roc_auc'
+INFO:cell_eval._pipeline._runner:Computing metric 'pearson_delta'
+INFO:cell_eval._types._anndata:Building pseudobulk embeddings for real anndata on: .X
+INFO:cell_eval._types._anndata:Building pseudobulk embeddings for predicted anndata on: .X
+
+INFO:cell_eval._pipeline._runner:Computing metric 'mse'
+
+INFO:cell_eval._pipeline._runner:Computing metric 'mae'
+
+INFO:cell_eval._pipeline._runner:Computing metric 'mse_delta'
+
+INFO:cell_eval._pipeline._runner:Computing metric 'mae_delta'
+
+INFO:cell_eval._pipeline._runner:Computing metric 'discrimination_score_l1'
+
+
+INFO:cell_eval._pipeline._runner:Computing metric 'discrimination_score_l2'
+
+
+INFO:cell_eval._pipeline._runner:Computing metric 'discrimination_score_cosine'
+
+
+INFO:cell_eval._pipeline._runner:Computing metric 'pearson_edistance'
+INFO:cell_eval.metrics._anndata:Precomputing sigma for control data (real)
+INFO:cell_eval.metrics._anndata:Precomputing sigma for control data (pred)
+
+INFO:cell_eval._pipeline._runner:Computing metric 'clustering_agreement'
+INFO:cell_eval._evaluator:Writing perturbation level metrics to ./cell-eval-outdir/results.csv
+INFO:cell_eval._evaluator:Writing aggregate metrics to ./cell-eval-outdir/agg_results.csv
+... storing 'perturbation' as categorical
+... storing 'perturbation' as categorical
+Current evaluation score: 39.0000
+Final evaluation score: 39.0000
+==========================================
+Finished:  Mon Mar 16 01:24:00 JST 2026
+==========================================

transfer/code/CCFM/logs/preextract_5402629.out

@@ -0,0 +1 @@
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/.d0005402629: line 13: /home/pj24002027/ku50002536/Takoai/lfj/stack_env/bin/activate: Permission denied

transfer/code/CCFM/logs/preextract_5402631.out

@@ -0,0 +1,36 @@
+==========================================
+Job ID:    5402631
+Job Name:  preextract_scgpt
+Start:     Fri Mar 13 23:46:15 JST 2026
+Node:      b0034
+GPU:       NVIDIA H100, 95830 MiB
+==========================================
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed
+  warnings.warn("flash_attn is not installed")
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.
+  self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control')
+/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.
+  adata.uns["hvg"] = {"flavor": flavor}
+/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors.
+  warnings.warn(
+Device: cuda
+Converted var_names to gene symbols, sample: ['RP11-34P13.8', 'RP11-54O7.3', 'SAMD11', 'PERM1', 'HES4']
+Warning: ctrl is not in the gene names
+Cells: 91205, Genes: 5035
+HVG gene names sample: ['RP11-34P13.8', 'RP11-54O7.3', 'SAMD11', 'PERM1', 'HES4']
+Valid genes in scGPT vocab: 4537/5035, max_seq_len=4539
+Loaded 159/163 pretrained parameters
+scGPT d_model: 512
+Output: /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scgpt_cache_norman.h5
+Features shape: (91205, 5035, 512) float16
+
+  output = torch._nested_tensor_from_mask(
+
+Global mean range: [-12.3974, 11.9278]
+Global var range: [0.0192, 1.8779]
+Done! Saved to /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scgpt_cache_norman.h5
+  Features: (91205, 5035, 512) float16
+  Valid features counted: 413797085
+==========================================
+Finished:  Sat Mar 14 00:45:12 JST 2026
+==========================================

transfer/code/CCFM/plan.md

@@ -0,0 +1,490 @@
+# Cascaded Conditioned Flow Matching (CCFM) — 实现计划
+
+## Context
+
+**目标**: 构建一个级联条件流匹配模型，用于单细胞扰动预测。以 scDFM 为底盘，引入 LatentForcing 的级联引导思想，使用 scGPT 提取的 **per-cell-per-gene contextualized features** 作为 latent 引导信号。
+
+**核心概念映射**:
+| LatentForcing (图像) | CCFM (单细胞) |
+|---|---|
+| Pixel patches → tokens | Gene expression → tokens |
+| DINO-v2 feature patches → tokens | scGPT per-gene features → tokens |
+| `pixel_embed + dino_embed` (element-wise add) | `expr_embed + scgpt_embed` (element-wise add) |
+| `c = t_pixel + t_dino + y_emb` (AdaLN) | `c = t_expr + t_latent + pert_emb` (AdaLN) |
+| Frozen DINO-v2 on-the-fly | Frozen scGPT on-the-fly |
+| Separate pixel/dino decoder heads | Separate expr/latent decoder heads |
+
+**数据集**: Norman (CRISPR) + ComboSciPlex (drug combination)
+
+---
+
+## 0. 项目结构
+
+**工作目录**: `transfer/code/CCFM/`
+
+```
+transfer/code/CCFM/
+├── plan.md                          # 本计划文件的副本
+├── config/
+│   └── config_cascaded.py           # CascadedFlowConfig 配置
+├── src/
+│   ├── model/
+│   │   ├── __init__.py
+│   │   ├── model.py                 # CascadedFlowModel 主干网络
+│   │   └── layers.py                # 新增层: LatentEmbedder, LatentDecoder
+│   ├── data/
+│   │   ├── __init__.py
+│   │   ├── data.py                  # 数据加载 (基于 scDFM, 新增 latent 支持)
+│   │   └── scgpt_extractor.py       # 冻结 scGPT 特征提取器封装
+│   ├── denoiser.py                  # CascadedDenoiser (时间采样、损失、生成)
+│   └── utils.py                     # 工具函数
+├── scripts/
+│   ├── run_cascaded.py              # 训练/推理入口
+│   └── download_scgpt.py            # 下载 scGPT 预训练模型
+└── run.sh                           # pjsub 提交脚本模板
+```
+
+所有新代码写在 `transfer/code/CCFM/` 下，通过 import 引用 scDFM 和 scGPT 的现有模块。
+
+---
+
+## 1. 数据准备
+
+### 1.1 下载 scGPT 预训练模型
+
+- 下载 `whole-human` 模型到 `transfer/data/scGPT_pretrained/`
+- 包含: `best_model.pt`, `vocab.json`, `args.json`
+- 脚本: `scripts/download_scgpt.py`
+
+### 1.2 数据加载 (`src/data/data.py`)
+
+复用 scDFM 的 `Data` 类进行数据加载和 train/test 划分。修改 `PerturbationDataset` 以返回 **raw expression values** (不仅是 gene indices)，供 scGPT on-the-fly 提取使用。
+
+关键改动:
+- 在 `__init__` 中预计算 scDFM gene names → scGPT vocab ID 的映射表
+- `__getitem__` 返回: `src_cell_data`, `tgt_cell_data`, `condition_id`, `src_cell_raw` (供 scGPT), `tgt_cell_raw` (供 scGPT)
+
+### 1.3 scGPT 特征提取器 (`src/data/scgpt_extractor.py`)
+
+**冻结 scGPT 作为 on-the-fly 特征提取器** (类似 LatentForcing 中冻结 DINO-v2):
+
+```python
+class FrozenScGPTExtractor(nn.Module):
+    """
+    封装冻结的 scGPT 模型，用于在训练时 on-the-fly 提取 per-gene features。
+    类似 LatentForcing 的 dinov2_hf.py 中的 RAE 类。
+    """
+    def __init__(self, model_dir, n_hvg_genes, hvg_gene_names, device):
+        # 1. 加载预训练 scGPT (TransformerModel)
+        # 2. 冻结所有参数 (requires_grad=False)
+        # 3. 建立 hvg_gene_names → scGPT vocab ID 映射
+        # 4. 预计算全局归一化统计量 (running mean/var)
+
+    @torch.no_grad()
+    def extract(self, expression_values, gene_indices=None):
+        """
+        输入: expression_values (B, G) — G=infer_top_gene 的表达值
+        输出: per_gene_features (B, G, scgpt_d_model) — contextualized 特征
+
+        流程:
+        1. 筛选非零基因，构建 scGPT 输入 (token IDs + values)
+        2. 运行冻结 scGPT._encode() → (B, seq_len, d_model)
+        3. 将输出 scatter 回固定的 G 个基因位置
+        4. 未覆盖的位置用零向量填充
+        5. 全局归一化 + 方差匹配
+        """
+```
+
+**关键技术细节**:
+- scGPT 的 `_encode()` 返回 `(batch, seq_len, d_model=512)`，其中 seq_len 是变长的（仅包含非零表达的基因 + `<cls>` token）
+- 需要用 gene ID 映射将变长输出 **scatter** 回固定的 G 个基因位置
+- 基因对齐: scDFM 使用 gene symbol (如 "MAPK1")，scGPT 使用自己的 vocab。在 `__init__` 中建立 `hvg_name → scGPT_vocab_id` 的映射
+- 不在 scGPT vocab 中的基因: 用零向量替代
+
+### 1.4 全局归一化与方差匹配 (CRITICAL)
+
+在 `FrozenScGPTExtractor` 内部实现:
+
+```python
+# 方案: 使用 running statistics (类似 BatchNorm 的 running_mean/var)
+# 在前 N 个 batch 中收集统计量，之后固定
+
+# Step 1: 标准化到零均值单位方差
+z_normalized = (z_raw - running_mean) / (running_std + 1e-6)
+
+# Step 2: 方差匹配 — 缩放到与表达嵌入相近的量级
+z_matched = z_normalized * target_std  # target_std 默认 1.0
+```
+
+这对应 LatentForcing 中 `dinov2_hf.py` 的归一化:
+```python
+z = (z - latent_mean) / sqrt(latent_var + eps) * match_pixel_norm
+```
+
+---
+
+## 2. 网络架构
+
+### 2.1 核心设计 — 严格参照 LatentForcing CoT (`model_cot.py`)
+
+**LatentForcing 的做法 (model_cot.py:398-446)**:
+1. **双流嵌入 element-wise 相加** (line 414):
+   ```python
+   x = pixel_embedder(x_pixel) + dino_embedder(x_dino)
+   ```
+2. **条件向量求和通过 AdaLN** (line 409):
+   ```python
+   c = t_emb_pixel + t_emb_dino + y_emb
+   ```
+3. **共享 Transformer backbone**: 所有 block 处理融合后的 tokens
+4. **分离解码头** (line 429-441): `dh_blocks_pixel` + `dh_blocks_dino` 分别解码
+
+**CCFM 的对应实现**:
+1. `x = expr_embedder(x_t_expr, gene_emb) + latent_embedder(z_t_latent)`
+2. `c = t_expr_emb + t_latent_emb + pert_emb`
+3. 复用 scDFM 的 DiffPerceiverBlock / PerceiverBlock
+4. ExprDecoder (复用) + LatentDecoder (新增)
+
+### 2.2 CascadedFlowModel 架构 (`src/model/model.py`)
+
+```
+输入:
+  - gene_id:          (B, G)       基因 token IDs
+  - cell_1 (source):  (B, G)       control 细胞表达值
+  - x_t:              (B, G)       加噪后的扰动细胞表达值 (expression flow)
+  - z_t:              (B, G, 512)  加噪后的 scGPT per-gene 特征 (latent flow)
+  - t_expr:           (B,)         表达流的时间步
+  - t_latent:         (B,)         潜变量流的时间步
+  - perturbation_id:  (B, 2)       扰动 token IDs
+
+数据流 (参照 model_cot.py 的 forward):
+  ┌──────────────────────────────────────────────────────┐
+  │ 1. 表达流嵌入 (复用 scDFM 的 origin 层)               │
+  │    gene_emb = GeneEncoder(gene_id)         (B,G,d)   │
+  │    val_emb_1 = ValueEncoder_1(cell_1) + gene_emb     │
+  │    val_emb_2 = ValueEncoder_2(x_t) + gene_emb        │
+  │    expr_tokens = fusion_layer(cat(val_1, val_2))      │
+  │    → (B, G, d_model)                                  │
+  ├──────────────────────────────────────────────────────┤
+  │ 2. Latent 流嵌入 (新增, 对应 dino_embedder)           │
+  │    latent_tokens = LatentEmbedder(z_t)                │
+  │    → (B, G, d_model)     [bottleneck: 512 → d_model]  │
+  ├──────────────────────────────────────────────────────┤
+  │ 3. Element-wise 相加 (对应 model_cot.py line 414)     │
+  │    x = expr_tokens + latent_tokens                    │
+  │    → (B, G, d_model)                                  │
+  ├──────────────────────────────────────────────────────┤
+  │ 4. 条件向量 (对应 model_cot.py line 409)              │
+  │    c = t_expr_emb + t_latent_emb + pert_emb          │
+  │    → (B, d_model)                                     │
+  ├──────────────────────────────────────────────────────┤
+  │ 5. 共享 Backbone (复用 scDFM blocks)                  │
+  │    for i, block in enumerate(blocks):                 │
+  │      x = gene_adaLN[i](gene_emb, x)                  │
+  │      x = cat([x, pert_emb.expand], dim=-1)            │
+  │      x = adapter_layer[i](x)                          │
+  │      x = block(x, val_emb_2, c)                       │
+  │    → (B, G, d_model)                                  │
+  ├──────────────────────────────────────────────────────┤
+  │ 6a. 表达解码头 (复用 ExprDecoder)                      │
+  │    → pred_v_expr: (B, G)                              │
+  ├──────────────────────────────────────────────────────┤
+  │ 6b. Latent 解码头 (新增, 对应 dh_blocks_dino)          │
+  │    latent_feats = dh_latent_proj(x)                   │
+  │    for block in dh_blocks_latent:                     │
+  │      latent_feats = block(latent_feats, c)            │
+  │    pred_v_latent = latent_final_layer(latent_feats)   │
+  │    → (B, G, 512)                                      │
+  └──────────────────────────────────────────────────────┘
+
+输出: (pred_v_expr, pred_v_latent)
+```
+
+### 2.3 新增模块 (`src/model/layers.py`)
+
+```python
+class LatentEmbedder(nn.Module):
+    """
+    对应 LatentForcing 的 dino_embedder (BottleneckPatchEmbed)。
+    将 per-gene scGPT 特征 (B, G, 512) 投影到 (B, G, d_model)。
+    """
+    def __init__(self, scgpt_dim=512, bottleneck_dim=128, d_model=512):
+        self.proj = nn.Sequential(
+            nn.Linear(scgpt_dim, bottleneck_dim),
+            nn.GELU(),
+            nn.Linear(bottleneck_dim, d_model),
+        )
+
+    def forward(self, z):  # z: (B, G, scgpt_dim)
+        return self.proj(z)  # (B, G, d_model)
+
+class LatentDecoder(nn.Module):
+    """
+    对应 LatentForcing 的 final_layer_dino + dh_blocks_dino。
+    从 backbone 输出 (B, G, d_model) 解码回 (B, G, scgpt_dim)。
+    """
+    def __init__(self, d_model=512, scgpt_dim=512, dh_depth=2):
+        # 可选: 额外的 decoder head blocks
+        if dh_depth > 0:
+            self.dh_blocks = nn.ModuleList([...])
+        self.final = nn.Sequential(
+            nn.LayerNorm(d_model),
+            nn.Linear(d_model, d_model),
+            nn.GELU(),
+            nn.Linear(d_model, scgpt_dim),
+        )
+```
+
+### 2.4 复用的现有模块
+
+| 模块 | 来源 | 文件路径 |
+|------|------|---------|
+| `TimestepEmbedder` | scDFM | `scDFM/src/models/origin/layers.py:157` |
+| `ContinuousValueEncoder` | scDFM | `scDFM/src/models/origin/layers.py:26` |
+| `GeneEncoder` | scDFM | `scDFM/src/models/origin/layers.py:55` |
+| `BatchLabelEncoder` | scDFM | `scDFM/src/models/origin/layers.py:98` |
+| `ExprDecoder` | scDFM | `scDFM/src/models/origin/layers.py:116` |
+| `GeneadaLN` | scDFM | `scDFM/src/models/origin/layers.py:10` |
+| `DiffPerceiverBlock` / `PerceiverBlock` | scDFM | `scDFM/src/models/origin/model.py:55,92` |
+| `modulate` | scDFM | `scDFM/src/models/origin/blocks.py` |
+| `AffineProbPath` + `CondOTScheduler` | scDFM | `scDFM/src/flow_matching/path/` |
+| `make_lognorm_poisson_noise` | scDFM | `scDFM/src/utils/utils.py` |
+| `TransformerModel` | scGPT | `scGPT/scgpt/model/model.py` |
+| `GeneVocab` | scGPT | `scGPT/scgpt/tokenizer/gene_tokenizer.py` |
+| `load_pretrained` | scGPT | `scGPT/scgpt/utils/util.py` |
+| `DataCollator` | scGPT | `scGPT/scgpt/data_collator.py` |
+
+---
+
+## 3. 训练逻辑
+
+### 3.1 CascadedDenoiser (`src/denoiser.py`)
+
+封装 `CascadedFlowModel` + `FrozenScGPTExtractor`。
+
+### 3.2 级联时间步采样 — 参照 `denoiser_cot.py:121-132`
+
+```python
+def sample_t(self, n, device):
+    """
+    dino_first_cascaded 模式 (denoiser_cot.py line 121-132):
+    - choose_latent_mask=True → 训练 latent 流
+      t_latent 随机, t_expr=0, loss_weight_expr=0
+    - choose_latent_mask=False → 训练 expression 流
+      t_expr 随机, t_latent=1 (clean latent), loss_weight_latent=0
+    """
+    t_latent = torch.rand(n, device=device)
+    t_expr = torch.rand(n, device=device)
+    choose_latent_mask = torch.rand(n, device=device) < self.choose_latent_p
+
+    t_latent = torch.where(choose_latent_mask, t_latent, torch.ones_like(t_latent))
+    t_expr = torch.where(choose_latent_mask, torch.zeros_like(t_expr), t_expr)
+
+    w_expr = (~choose_latent_mask).float()
+    w_latent = choose_latent_mask.float()
+    return t_expr, t_latent, w_expr, w_latent
+```
+
+### 3.3 训练步骤
+
+```python
+def train_step(self, source, target, perturbation_id, gene_ids):
+    B = source.shape[0]
+
+    # 1. On-the-fly 提取 scGPT per-gene 特征 (冻结)
+    z_target = self.scgpt_extractor.extract(target)  # (B, G, 512)
+
+    # 2. 时间采样
+    t_expr, t_latent, w_expr, w_latent = self.sample_t(B, device)
+
+    # 3. Expression path (同 scDFM)
+    noise_expr = make_noise(source, noise_type)
+    path_expr = affine_path.sample(t=t_expr, x_0=noise_expr, x_1=target)
+
+    # 4. Latent path
+    noise_latent = torch.randn_like(z_target)
+    path_latent = affine_path.sample(
+        t=t_latent.unsqueeze(-1).unsqueeze(-1),  # broadcast to (B,1,1)
+        x_0=noise_latent, x_1=z_target
+    )
+
+    # 5. Model forward
+    pred_v_expr, pred_v_latent = model(
+        gene_ids, source, path_expr.x_t, path_latent.x_t,
+        t_expr, t_latent, perturbation_id
+    )
+
+    # 6. Losses
+    loss_expr = ((pred_v_expr - path_expr.dx_t)**2
+                 * w_expr[:, None]).mean()
+    loss_latent = ((pred_v_latent - path_latent.dx_t)**2
+                   * w_latent[:, None, None]).mean()
+    loss = loss_expr + latent_weight * loss_latent
+
+    # Optional MMD (同 scDFM)
+    if use_mmd_loss:
+        x1_hat = path_expr.x_t + pred_v_expr * (1 - t_expr).unsqueeze(-1)
+        loss += gamma * mmd_loss(x1_hat, target)
+
+    return loss
+```
+
+### 3.4 训练入口 (`scripts/run_cascaded.py`)
+
+基于 scDFM 的 `run.py` 改造:
+- 初始化 `FrozenScGPTExtractor` + `CascadedFlowModel`
+- 封装进 `CascadedDenoiser`
+- 训练循环: 使用 `denoiser.train_step()` 替代 `train_step()`
+- 评估: 使用 `denoiser.generate()` + `MetricsEvaluator`
+- 其余（optimizer, scheduler, checkpoint）保持不变
+
+---
+
+## 4. 推理逻辑 — 两阶段级联生成
+
+参照 LatentForcing `denoiser_cot.py:224-247`:
+
+```python
+@torch.no_grad()
+def generate(self, source, perturbation_id, gene_ids,
+             latent_steps=20, expr_steps=20):
+    B = source.shape[0]
+    G = source.shape[1]
+
+    # ═══ Stage 1: 生成 Latent (per-gene scGPT features) ═══
+    # t_latent: 0→1, t_expr=0 (表达流不参与)
+    z_noise = torch.randn(B, G, scgpt_dim, device=device)
+
+    def latent_vf(t, z_t):
+        t_latent = t.expand(B)
+        t_expr = torch.zeros(B, device=device)
+        _, v_latent = model(gene_ids, source, source, z_t,
+                            t_expr, t_latent, perturbation_id)
+        return v_latent
+
+    z_generated = torchdiffeq.odeint(
+        latent_vf, z_noise,
+        torch.linspace(0, 1, latent_steps, device=device),
+        method='rk4'
+    )[-1]  # (B, G, 512)
+
+    # ═══ Stage 2: 用生成的 Latent 引导生成表达谱 ═══
+    # t_expr: 0→1, t_latent=1 (latent "clean")
+    expr_noise = make_noise(source, noise_type)
+
+    def expr_vf(t, x_t):
+        t_expr = t.expand(B)
+        t_latent = torch.ones(B, device=device)
+        v_expr, _ = model(gene_ids, source, x_t, z_generated,
+                          t_expr, t_latent, perturbation_id)
+        return v_expr
+
+    x_generated = torchdiffeq.odeint(
+        expr_vf, expr_noise,
+        torch.linspace(0, 1, expr_steps, device=device),
+        method='rk4'
+    )[-1]  # (B, G)
+
+    return torch.clamp(x_generated, min=0)
+```
+
+**关键设计** (与 LatentForcing 的 `dino_first_cascaded` 推理完全对应):
+- Stage 1: `t_expr=0` → 表达流为纯噪声，模型聚焦 latent 预测
+- Stage 2: `t_latent=1` → latent 已解析，`z_generated` 通过 `LatentEmbedder` 投影后与表达 tokens 相加，引导表达生成
+- 评估: 复用 scDFM 的 `test()` 函数结构 + `cell-eval MetricsEvaluator`
+
+---
+
+## 5. 配置 (`config/config_cascaded.py`)
+
+```python
+@dataclass
+class CascadedFlowConfig:
+    # === 基础 (同 scDFM FlowConfig) ===
+    model_type: str = 'cascaded'
+    batch_size: int = 48
+    d_model: int = 128
+    nhead: int = 8
+    nlayers: int = 4
+    lr: float = 5e-5
+    steps: int = 200000
+    eta_min: float = 1e-6
+    data_name: str = 'norman'
+    fusion_method: str = 'differential_perceiver'
+    perturbation_function: str = 'crisper'
+    noise_type: str = 'Gaussian'
+    infer_top_gene: int = 1000
+    n_top_genes: int = 5000
+    print_every: int = 5000
+    gamma: float = 0.5
+    use_mmd_loss: bool = True
+    split_method: str = 'additive'
+    fold: int = 1
+
+    # === 级联/Latent 特有 ===
+    scgpt_dim: int = 512              # scGPT embedding 维度
+    bottleneck_dim: int = 128         # LatentEmbedder 瓶颈维度
+    latent_weight: float = 1.0        # latent 流损失权重
+    choose_latent_p: float = 0.4      # 训练 latent 流的概率
+    target_std: float = 1.0           # 方差匹配目标标准差
+    dh_depth: int = 2                 # latent decoder head 层数
+
+    # === scGPT 路径 ===
+    scgpt_model_dir: str = 'transfer/data/scGPT_pretrained'
+
+    # === 推理 ===
+    latent_steps: int = 20
+    expr_steps: int = 20
+```
+
+---
+
+## 6. 实现顺序
+
+### Step 1: 项目初始化 + 下载 scGPT
+- 创建 `transfer/code/CCFM/` 目录结构
+- 下载 scGPT `whole-human` 预训练模型
+- 验证: 加载 scGPT 模型成功
+
+### Step 2: FrozenScGPTExtractor
+- 实现 `src/data/scgpt_extractor.py`
+- 处理 scDFM ↔ scGPT 的基因名对齐
+- 实现 per-gene feature scatter 和归一化
+- 验证: 输入 (48, 1000) expression → 输出 (48, 1000, 512) features
+
+### Step 3: 数据加载
+- 实现 `src/data/data.py`，复用 scDFM 的 Data 类
+- 验证: DataLoader 正确返回 expression + perturbation_id
+
+### Step 4: CascadedFlowModel
+- 实现 `src/model/model.py` 和 `src/model/layers.py`
+- 验证: dummy forward pass → (pred_v_expr, pred_v_latent) 形状正确
+
+### Step 5: CascadedDenoiser
+- 实现 `src/denoiser.py`
+- 验证: `train_step()` 返回 scalar loss, `generate()` 返回正确形状
+
+### Step 6: 训练入口 + 配置
+- 实现 `scripts/run_cascaded.py` + `config/config_cascaded.py`
+- 编写 `run.sh` (pjsub 脚本)
+- 验证: 完整训练循环运行，loss 下降
+
+### Step 7: 端到端验证
+- Norman 数据集上训练（先小 step 数）
+- 检查 loss_expr 和 loss_latent 分别下降
+- 推理并通过 cell-eval 评估
+- 与 baseline scDFM 对比
+
+---
+
+## 7. 关键技术决策
+
+| 决策点 | 选择 | 理由 |
+|--------|------|------|
+| Latent 注入方式 | Element-wise add (token-level) | 严格对标 LatentForcing `model_cot.py:414` |
+| 条件注入 | AdaLN (c = sum of all embeddings) | 对标 `model_cot.py:409` |
+| scGPT 特征提取 | On-the-fly (冻结) | 存储不可行 (~1TB); 对标 LatentForcing 冻结 DINO-v2 |
+| 时间步采样 | dino_first_cascaded | 对标 `denoiser_cot.py:121-132` |
+| 方差匹配 | 全局 running statistics + scaling | 对标 `dinov2_hf.py` 的归一化 |
+| Backbone | 复用 scDFM 的 DiffPerceiver/Perceiver | 保持底盘一致 |
+| 解码头 | 分离: ExprDecoder + LatentDecoder | 对标 `model_cot.py:429-441` 的分离头 |

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