From 04bf6a8a504934e8f8e17b97a42bc16e9b38fd69 Mon Sep 17 00:00:00 2001
From: Aoshine999 <820459542@qq.com>
Date: Sun, 8 Mar 2026 11:05:32 +0800
Subject: [PATCH 01/18] impl ScaledDotProductAttention

---
 flash_atten.md                                |  597 +++++++++
 funcall.png                                   |  Bin 0 -> 464160 bytes
 .../autograd/ScaledDotProductAttention.h      |   42 +
 .../include/kernels/cuda/flash_attention.h    |   27 +
 .../src/autograd/ScaledDotProductAttention.cc |  149 +++
 .../src/kernels/cuda/flash_attention.cu       | 1068 +++++++++++++++
 .../src/kernels/cuda/flash_attention1.cu      | 1171 +++++++++++++++++
 infini_train/src/nn/functional.cc             |   11 +
 8 files changed, 3065 insertions(+)
 create mode 100644 flash_atten.md
 create mode 100644 funcall.png
 create mode 100644 infini_train/include/autograd/ScaledDotProductAttention.h
 create mode 100644 infini_train/include/kernels/cuda/flash_attention.h
 create mode 100644 infini_train/src/autograd/ScaledDotProductAttention.cc
 create mode 100644 infini_train/src/kernels/cuda/flash_attention.cu
 create mode 100644 infini_train/src/kernels/cuda/flash_attention1.cu

diff --git a/flash_atten.md b/flash_atten.md
new file mode 100644
index 00000000..d42f321c
--- /dev/null
+++ b/flash_atten.md
@@ -0,0 +1,597 @@
+下面我把 **FlashAttention-2 backward pass 的完整执行流程**整理成一份可直接使用的 **Markdown 文档**。内容会分成两层：
+
+1. **论文级算法流程**：对应 FlashAttention-2 的标准 backward 思路。
+2. **CUDA 实现级执行流程**：结合你当前的 kernel 设计
+   `grid_dims(num_heads_kv, batch_size, Tc), block_dims(Tc, Tr)`
+   说明一个 block 实际在做什么。
+
+论文中 backward 的核心点包括：将 (Q) 按行块、(K/V) 按列块切分；先计算 (D = \mathrm{rowsum}(dO \circ O))；随后固定一个 (K_j,V_j) 列块，遍历所有 (Q_i) 行块，重算 (S_{ij})、(P_{ij})、(dP_{ij})、(dS_{ij})，并更新 (dV_j,dK_j,dQ_i)。另外，FlashAttention-2 在 backward 中只保存 **row-wise logsumexp (L)**，而不是同时保存 row-max 和 row-sum。 
+
+---
+
+# FlashAttention-2 Backward Pass 执行流程
+
+## 1. 问题定义
+
+给定前向中已经得到的：
+
+* (Q \in \mathbb{R}^{N_q \times d})
+* (K \in \mathbb{R}^{N_k \times d})
+* (V \in \mathbb{R}^{N_k \times d})
+* (O \in \mathbb{R}^{N_q \times d})
+* (L \in \mathbb{R}^{N_q})，其中 (L) 是每一行 softmax 的 `logsumexp`
+* 上游梯度 (dO \in \mathbb{R}^{N_q \times d})
+
+要求计算：
+
+* (dQ \in \mathbb{R}^{N_q \times d})
+* (dK \in \mathbb{R}^{N_k \times d})
+* (dV \in \mathbb{R}^{N_k \times d})
+
+FlashAttention-2 backward 的基本思想不是存整张 attention matrix，而是 **按 tile 重算** 中间量，从而降低 HBM 读写。论文的 Algorithm 2 先把 (Q,O,dO,L) 切成行块，把 (K,V) 切成列块，然后以列块为外循环执行 backward。
+
+---
+
+## 2. 数学公式
+
+### 2.1 前向关系
+
+对任意一个 tile ((i,j))：
+
+[
+S_{ij} = Q_i K_j^T
+]
+
+若启用缩放：
+
+[
+S_{ij} \leftarrow \text{scale} \cdot S_{ij}
+]
+
+softmax 概率：
+
+[
+P_{ij} = \exp(S_{ij} - L_i)
+]
+
+这里 (L_i) 是该行块对应的 row-wise logsumexp。FlashAttention-2 backward 只依赖 (L)，这是它相对早期实现的一个简化。
+
+输出满足：
+
+[
+O_i = \sum_j P_{ij} V_j
+]
+
+---
+
+### 2.2 backward 关键中间量
+
+先定义：
+
+[
+D = \mathrm{rowsum}(dO \circ O)
+]
+
+即对每一行做逐元素乘法后求和。论文明确把这一步放在主 backward 循环之前，并写回 HBM。
+
+对任意 tile ((i,j))：
+
+1. 先计算
+   [
+   dP_{ij} = dO_i V_j^T
+   ]
+
+2. 再做 softmax backward
+   [
+   dS_{ij} = P_{ij} \circ (dP_{ij} - D_i)
+   ]
+
+3. 然后得到三个输入梯度的 tile 贡献：
+
+[
+dV_j \mathrel{+}= P_{ij}^T dO_i
+]
+
+[
+dK_j \mathrel{+}= dS_{ij}^T Q_i
+]
+
+[
+dQ_i \mathrel{+}= dS_{ij} K_j
+]
+
+这些正是论文 Algorithm 2 的核心更新过程。
+
+---
+
+## 3. Tile 划分
+
+设：
+
+* 行块大小为 (B_r)
+* 列块大小为 (B_c)
+
+则：
+
+* (Q) 被分成 (T_r = \lceil N_q / B_r \rceil) 个行块
+* (K,V) 被分成 (T_c = \lceil N_k / B_c \rceil) 个列块
+
+论文中明确写出：
+
+* (Q_1,\dots,Q_{T_r})
+* (K_1,\dots,K_{T_c})
+* (V_1,\dots,V_{T_c})
+* (O_i,dO_i,L_i,D_i,dQ_i) 也都按行块划分
+* (dK_j,dV_j) 按列块划分。 
+
+---
+
+## 4. 论文级 backward 总流程
+
+## Step 0：输入准备
+
+从前向保留下来：
+
+* `Q, K, V, O`
+* `dO`
+* `L = logsumexp`
+* 以及可选的 `dropout_seed`
+
+初始化输出：
+
+* `dQ = 0`
+* `dK = 0`
+* `dV = 0`
+
+---
+
+## Step 1：预计算 (D)
+
+对每一行计算：
+
+[
+D_i = \sum_{m=1}^{d} dO_{i,m} \cdot O_{i,m}
+]
+
+并写回全局内存。
+
+### 作用
+
+这是 softmax backward 所需的行级归约项，用来避免显式构造整张 Jacobian。论文把它作为 backward 前处理步骤。
+
+---
+
+## Step 2：外层遍历列块 (j)
+
+对每个列块 (j = 1,\dots,T_c)：
+
+1. 从 HBM 载入 (K_j, V_j) 到 on-chip SRAM / shared memory
+2. 在 shared memory 或寄存器中初始化本列块的局部梯度：
+
+   * (dK_j = 0)
+   * (dV_j = 0)
+
+论文 Algorithm 2 就是以列块为外层循环，并在每个 (j) 开始时把 `K_j, V_j` 加载到 SRAM，然后初始化 `dK_j, dV_j`。
+
+---
+
+## Step 3：内层遍历行块 (i)
+
+对每个行块 (i = 1,\dots,T_r)：
+
+### 3.1 载入当前行块数据
+
+从 HBM 载入：
+
+* (Q_i)
+* (O_i)（若需要本 kernel 内使用）
+* (dO_i)
+* (L_i)
+* (D_i)
+
+---
+
+### 3.2 重算 score tile
+
+重算当前 tile 的 attention score：
+
+[
+S_{ij} = Q_i K_j^T
+]
+
+若设置 `scale`：
+
+[
+S_{ij} \leftarrow \text{scale} \cdot S_{ij}
+]
+
+若启用 `causal mask`，则对非法位置做 mask：
+
+[
+S_{ij}(r,c) = -\infty
+\quad \text{if } k_idx > q_idx
+]
+
+其中：
+
+* `q_idx = i * B_r + r`
+* `k_idx = j * B_c + c`
+
+### 说明
+
+这一步没有从全局内存读取旧的 attention matrix，而是 **重算**。这是 FlashAttention backward 相比普通实现最关键的节省显存策略。
+
+---
+
+### 3.3 重算概率 tile
+
+根据保存的 `logsumexp`：
+
+[
+P_{ij} = \exp(S_{ij} - L_i)
+]
+
+若启用 `dropout`，则要重建前向时相同的 dropout mask，并应用：
+
+[
+P_{ij}^{drop} = \frac{M_{ij} \circ P_{ij}}{1-p}
+]
+
+其中：
+
+* (M_{ij}) 为基于同一随机种子重建的二值 mask
+* (p) 为 dropout probability
+
+如果没有 dropout，则：
+
+[
+P_{ij}^{drop} = P_{ij}
+]
+
+---
+
+### 3.4 计算 (dV_j) 的当前 tile 贡献
+
+[
+dV_j \mathrel{+}= (P_{ij}^{drop})^T dO_i
+]
+
+### 解释
+
+因为前向输出是 (O_i = \sum_j P_{ij}V_j)，所以对 (V_j) 的梯度就是概率矩阵转置乘以上游梯度。
+
+---
+
+### 3.5 计算 (dP_{ij})
+
+[
+dP_{ij} = dO_i V_j^T
+]
+
+若启用 dropout，则 backward 中也要乘上同样的 dropout mask，并带上缩放因子：
+
+[
+dP_{ij} \leftarrow \frac{M_{ij} \circ dP_{ij}}{1-p}
+]
+
+---
+
+### 3.6 计算 softmax backward 的 (dS_{ij})
+
+[
+dS_{ij} = P_{ij} \circ (dP_{ij} - D_i)
+]
+
+注意这里使用的是 **未 dropout 前的 softmax 概率** (P_{ij})，而 `dP` 则需要反映 dropout 后的链式法则。
+
+这一步是整套 backward 中最核心的公式，因为 softmax 的 Jacobian 已经被压缩成逐元素乘法加一个行级标量 (D_i)。
+
+---
+
+### 3.7 计算 (dK_j) 的当前 tile 贡献
+
+[
+dK_j \mathrel{+}= dS_{ij}^T Q_i
+]
+
+若前向有 `scale`，则这里还要乘上相同的缩放因子对梯度链路进行修正。更准确地说，如果前向做的是：
+
+[
+S = \text{scale} \cdot QK^T
+]
+
+那么由 (S) 向 (Q,K) 回传的梯度都要乘 `scale`。
+
+---
+
+### 3.8 计算 (dQ_i) 的当前 tile 贡献
+
+[
+dQ_i \mathrel{+}= dS_{ij} K_j
+]
+
+这一步会累加来自所有列块 (j) 的贡献，因此：
+
+* 若一个 block 只处理单个列块，那么 `dQ_i` 往往需要原子加到全局内存
+* 或者先在 block 内做局部累加，再统一写回
+
+---
+
+## Step 4：写回本列块的 (dK_j,dV_j)
+
+当所有行块 (i) 都遍历完后，本列块 (j) 的局部梯度已经累加完成：
+
+* 写回 `dK_j`
+* 写回 `dV_j`
+
+由于一个列块 (j) 通常只由一个 block 负责，所以这两者一般不需要跨 block 再归约；但若你在 GQA 下让多个 query-head block 共同更新同一组 kv-head，也可能需要额外归约或原子加。
+
+---
+
+## Step 5：结束条件
+
+当所有列块 (j) 处理完成后：
+
+* `dQ`
+* `dK`
+* `dV`
+
+全部得到。
+
+---
+
+# 5. 结合你当前 CUDA 设计的执行流程
+
+你当前 kernel 为：
+
+```cpp
+FlashAttentionBackwardKernel<T><<<grid_dims, block_dims, shared_mem_size, cuda_stream>>>(
+    grad_query_ptr, grad_key_ptr, grad_value_ptr,
+    query_ptr, key_ptr, value_ptr, output_ptr, grad_output_ptr,
+    logsumexp_ptr, dropout_seed_value, attn_mask_ptr,
+    scale, is_causal, dropout_p, enable_gqa,
+    batch_size, seq_len_q, seq_len_k, num_heads, num_heads_kv, head_dim);
+```
+
+其中：
+
+* `grid_dims(num_heads_kv, batch_size, Tc)`
+* `block_dims(Tc, Tr)`
+
+这表示：
+
+* `blockIdx.x`：对应一个 `kv head`
+* `blockIdx.y`：对应一个 `batch`
+* `blockIdx.z`：对应一个列块 (j)
+
+也就是说，**一个 block 固定负责某个 batch、某个 kv-head、某个列块 (j)**。
+
+---
+
+## 5.1 一个 block 的职责
+
+对给定的：
+
+* `batch = blockIdx.y`
+* `kv_head = blockIdx.x`
+* `col_tile = blockIdx.z`
+
+该 block 需要：
+
+1. 加载当前 `K_j, V_j`
+2. 初始化局部 `dK_j, dV_j`
+3. 遍历所有相关的 query row tiles (i)
+4. 对每个 (i)：
+
+   * 找到对应的 query-head（若启用 GQA，要从 q-head 映射到 kv-head）
+   * 载入 `Q_i, dO_i, L_i, D_i`
+   * 重算 `S_ij`
+   * 应用 causal/mask/dropout
+   * 计算 `P_ij, dP_ij, dS_ij`
+   * 累加到 `dV_j, dK_j`
+   * 把对 `dQ_i` 的贡献写回全局
+5. 行块遍历结束后，写回 `dK_j, dV_j`
+
+---
+
+## 5.2 GQA 下的 head 映射
+
+如果启用了 GQA：
+
+* `num_heads` 是 query heads 数
+* `num_heads_kv` 是 kv heads 数
+* 每个 kv head 会对应若干个 query heads
+
+常见映射方式为：
+
+[
+q_head_start = kv_head \times \frac{num_heads}{num_heads_kv}
+]
+
+[
+q_head_end = (kv_head+1) \times \frac{num_heads}{num_heads_kv}
+]
+
+于是当前 block 除了遍历 row tiles (i)，还需要遍历属于这个 kv-head 组的所有 q-head。这样同一个 `K_j,V_j` 会被多个 q-head 共享使用，而 `dK_j,dV_j` 也要把这些 q-head 的贡献全部累加起来。
+
+这与论文中提到的 MQA/GQA backward 需要对共享的 K/V head 梯度求和是一致的。
+
+---
+
+## 5.3 causal mask 的执行方式
+
+若 `is_causal = true`：
+
+对于 tile 内每个元素 ((r,c))，若其全局位置满足：
+
+[
+k_global > q_global
+]
+
+则该位置无效。
+
+实现上通常有两层优化：
+
+1. **整块跳过**：
+   若当前列块完全在当前行块右侧，则整个 tile 不必计算
+
+2. **边界块细粒度判断**：
+   若 tile 横跨对角线，则对元素级做 mask
+
+这样可以减少无效 matmul 和 exp 计算。
+
+---
+
+## 5.4 dropout 的执行方式
+
+若 `dropout_p > 0`：
+
+1. 使用 `dropout_seed_value` 和 tile 内元素索引重建与 forward 一致的随机数序列
+2. 得到二值 mask
+3. 对 `P_ij` 和 `dP_ij` 执行同样的 mask/缩放
+
+这样 backward 就与前向使用的是同一组保留位置。
+
+---
+
+## 5.5 scale 的执行方式
+
+若 `scale != 1`：
+
+前向 score 为：
+
+[
+S = \text{scale} \cdot QK^T
+]
+
+那么 backward 中：
+
+* 重算 `S_ij` 时要乘 `scale`
+* 从 `dS` 回传到 `dQ,dK` 时也要体现这条链路
+
+通常最直接的实现是：
+
+* 先在重算 `S_ij` 时乘 `scale`
+* 在 `dQ = dS K`、`dK = dS^T Q` 前再统一乘 `scale`
+* 或者直接把 `scale` 融进 `dS`
+
+两种写法本质等价，但要保证链式法则只乘一次，不要漏乘或重复乘。
+
+---
+
+# 6. 一个推荐的伪代码版本
+
+```markdown
+for each batch b:
+  for each kv head h_kv:
+    for each column tile j:
+      load K_j, V_j into shared memory
+      initialize local dK_j = 0, dV_j = 0
+
+      determine all query heads h_q that map to h_kv (for GQA)
+
+      for each query head h_q in group(h_kv):
+        for each row tile i:
+          load Q_i, dO_i, L_i, D_i
+          optionally load O_i if D is not precomputed
+
+          recompute S_ij = Q_i @ K_j^T
+          if scale is enabled:
+            S_ij *= scale
+
+          if causal mask is enabled:
+            mask invalid positions in S_ij
+
+          P_ij = exp(S_ij - L_i)
+
+          if dropout is enabled:
+            reconstruct dropout mask M_ij
+            P_drop = M_ij * P_ij / (1 - p)
+          else:
+            P_drop = P_ij
+
+          dV_j += P_drop^T @ dO_i
+
+          dP_ij = dO_i @ V_j^T
+          if dropout is enabled:
+            dP_ij = M_ij * dP_ij / (1 - p)
+
+          dS_ij = P_ij * (dP_ij - D_i)
+
+          if scale is enabled:
+            dS_ij *= scale
+
+          dK_j += dS_ij^T @ Q_i
+          dQ_i += dS_ij @ K_j
+
+          write or atomically accumulate dQ_i to global memory
+
+      write dK_j, dV_j to global memory
+```
+
+---
+
+# 7. 实现时的关键注意点
+
+## 7.1 `D = rowsum(dO ∘ O)` 最好预处理
+
+论文就是这么做的。这样主 backward kernel 只需读取 `D_i`，不用再额外读取 `O_i` 并做一次按行归约，能减轻主 kernel 负担。
+
+---
+
+## 7.2 backward 的 matmul 数量更多
+
+论文 benchmark 中指出 backward 由于重算，等价上有更多 matmul，因此 FLOPs 估计通常约为 forward 的 2.5 倍。
+
+---
+
+## 7.3 FlashAttention-2 backward 仍然强调避免 split-K
+
+论文在 warp 工作划分部分提到，backward 也尽量避免 “split-K” 方案，从而减少 shared memory 读写和同步，尽管 backward 的依赖关系比 forward 更复杂。
+
+---
+
+## 7.4 block 大小要兼顾 shared memory 和寄存器压力
+
+论文提到 block size 变大虽然能减少 shared memory 读写，但也会提高寄存器与 shared memory 占用，过大会导致 spilling 或 kernel 根本无法启动。常见 tile 大小会在 `{64,128}` 范围内手工调优。
+
+---
+
+# 8. 最终总结
+
+FlashAttention-2 backward pass 的本质可以概括为：
+
+1. **先预处理行级统计量**
+
+   * 计算 (D = \mathrm{rowsum}(dO \circ O))
+
+2. **按列块固定 `K_j, V_j`**
+
+   * 让 `K_j,V_j` 常驻 shared memory
+
+3. **遍历所有行块 `Q_i`**
+
+   * 重算 (S_{ij})
+   * 用保存的 `L_i` 恢复 (P_{ij})
+   * 计算 (dP_{ij}, dS_{ij})
+
+4. **逐 tile 累加三个输入梯度**
+
+   * (dV_j += P_{ij}^T dO_i)
+   * (dK_j += dS_{ij}^T Q_i)
+   * (dQ_i += dS_{ij} K_j)
+
+5. **支持附加功能**
+
+   * causal：在 tile 内做位置裁剪
+   * dropout：重建相同随机 mask
+   * scale：在 `S` 与其梯度链中保持一致
+   * GQA：让多个 q-head 共享同一组 kv-head，并对 `dK/dV` 做组内累加
+
+---
+
+你要是愿意，我下一步可以直接把这份内容继续整理成一份更正式的文档格式，比如：
+
+* **“设计说明书风格” Markdown**
+* **“伪代码 + 公式 + CUDA 映射”版**
+* 或者直接给你写成 **项目里的 README 小节**。
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diff --git a/infini_train/include/autograd/ScaledDotProductAttention.h b/infini_train/include/autograd/ScaledDotProductAttention.h
new file mode 100644
index 00000000..5bfa9ad0
--- /dev/null
+++ b/infini_train/include/autograd/ScaledDotProductAttention.h
@@ -0,0 +1,42 @@
+#pragma once
+
+#include <cstdint>
+#include <memory>
+#include <optional>
+#include <vector>
+
+#include "infini_train/include/autograd/function.h"
+#include "infini_train/include/kernels/cuda/flash_attention.h"
+
+namespace infini_train {
+class Tensor;
+}
+
+namespace infini_train::autograd {
+class ScaledDotProductAttention : public Function {
+public:
+    static constexpr char kType[] = "ScaledDotProductAttentionFunction";
+
+    ScaledDotProductAttention(std::shared_ptr<Tensor> attn_mask = nullptr, int64_t dropout_p = 0,
+                              bool is_causal = false, std::optional<double> scale = std::nullopt,
+                              bool enable_gqa = false)
+        : Function(kType), attn_mask_(std::move(attn_mask)), dropout_p_(dropout_p), is_causal_(is_causal),
+          scale_(scale), enable_gqa_(enable_gqa) {}
+
+    std::vector<std::shared_ptr<Tensor>> Forward(const std::vector<std::shared_ptr<Tensor>> &input_tensors) override;
+    void SetupContext(const std::vector<std::shared_ptr<Tensor>> &input_tensors,
+                      const std::vector<std::shared_ptr<Tensor>> &output_tensors) override;
+    std::vector<std::shared_ptr<Tensor>> Backward(const std::vector<std::shared_ptr<Tensor>> &grad_outputs) override;
+
+private:
+    std::shared_ptr<Tensor> attn_mask_;
+    int64_t dropout_p_ = 0;
+    bool is_causal_ = false;
+    std::optional<double> scale_;
+    bool enable_gqa_ = false;
+
+    // Temporary storage for FlashAttentionForwardOutput to be used in SetupContext
+    // Note: This is defined in infini_train::kernels::cuda namespace
+    kernels::cuda::FlashAttentionForwardOutput flash_output_;
+};
+} // namespace infini_train::autograd
diff --git a/infini_train/include/kernels/cuda/flash_attention.h b/infini_train/include/kernels/cuda/flash_attention.h
new file mode 100644
index 00000000..d6f13e01
--- /dev/null
+++ b/infini_train/include/kernels/cuda/flash_attention.h
@@ -0,0 +1,27 @@
+#pragma once
+
+#include <memory>
+
+namespace infini_train {
+class Tensor;
+}
+
+namespace infini_train::kernels::cuda {
+
+/**
+ * FlashAttention Forward Output Structure
+ *
+ * This structure holds the output tensors from FlashAttention forward pass.
+ *
+ * Args:
+ *   output: Output tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+ *   logsumexp: Logsumexp tensor for backward pass [batch_size, num_heads, seq_len_q]
+ *   dropout_seed: Dropout seed for backward pass [1]
+ */
+struct FlashAttentionForwardOutput {
+    std::shared_ptr<Tensor> output;
+    std::shared_ptr<Tensor> logsumexp;
+    std::shared_ptr<Tensor> dropout_seed;
+};
+
+} // namespace infini_train::kernels::cuda
diff --git a/infini_train/src/autograd/ScaledDotProductAttention.cc b/infini_train/src/autograd/ScaledDotProductAttention.cc
new file mode 100644
index 00000000..9aced35f
--- /dev/null
+++ b/infini_train/src/autograd/ScaledDotProductAttention.cc
@@ -0,0 +1,149 @@
+#include "infini_train/include/autograd/ScaledDotProductAttention.h"
+
+#include <cmath>
+
+#include "glog/logging.h"
+
+#include "infini_train/include/device.h"
+#include "infini_train/include/dispatcher.h"
+#include "infini_train/include/kernels/cuda/flash_attention.h"
+#include "infini_train/include/nn/functional.h"
+#include "infini_train/include/tensor.h"
+
+namespace infini_train::autograd {
+std::vector<std::shared_ptr<Tensor>>
+ScaledDotProductAttention::Forward(const std::vector<std::shared_ptr<Tensor>> &input_tensors) {
+	CHECK_EQ(input_tensors.size(), 3);
+
+	const auto &query = input_tensors[0];
+	const auto &key = input_tensors[1];
+	const auto &value = input_tensors[2];
+
+	CHECK_GE(query->Dims().size(), 2);
+	CHECK_GE(key->Dims().size(), 2);
+	CHECK_GE(value->Dims().size(), 2);
+
+	const int64_t query_last_dim = query->Dims().back();
+	const float scale = static_cast<float>(
+		scale_.has_value() ? *scale_ : 1.0 / std::sqrt(static_cast<double>(query_last_dim)));
+
+	// Use FlashAttention kernel for CUDA device
+	auto device = query->GetDevice().type();
+	if (device == Device::Device::DeviceType::kCUDA) {
+		// Note: is_causal and enable_gqa are supported in FlashAttention kernel
+		// Dropout is now supported using seed-based approach for reproducibility
+		flash_output_ = Dispatcher::Instance().Call<kernels::cuda::FlashAttentionForwardOutput>(
+			{device, "FlashAttentionForward"}, query, key, value, attn_mask_, scale, is_causal_, dropout_p_, enable_gqa_);
+		
+		// Return output tensor only; saved_tensors_ will be set in SetupContext
+		return {flash_output_.output};
+	}
+
+	// Fallback to traditional implementation for CPU or other devices
+	CHECK_EQ(dropout_p_, 0) << "ScaledDotProductAttention dropout is not implemented yet";
+	CHECK(!is_causal_) << "ScaledDotProductAttention causal mask is not implemented yet";
+	CHECK(!enable_gqa_) << "ScaledDotProductAttention GQA mode is not implemented yet";
+
+	const int64_t key_rank = static_cast<int64_t>(key->Dims().size());
+	auto key_t = key->Transpose(key_rank - 2, key_rank - 1);
+
+	auto attn_scores = query->Matmul(key_t);
+	if (scale != 1.0f) {
+		attn_scores = attn_scores * scale;
+	}
+	if (attn_mask_) {
+		attn_scores = attn_scores + attn_mask_;
+	}
+
+	auto attn_prob = nn::function::Softmax(attn_scores, -1);
+	auto output = attn_prob->Matmul(value);
+
+	// Return output tensor only; saved_tensors_ will be set in SetupContext
+	return {output};
+}
+
+void ScaledDotProductAttention::SetupContext(const std::vector<std::shared_ptr<Tensor>> &input_tensors,
+                                            const std::vector<std::shared_ptr<Tensor>> &output_tensors) {
+	const auto &query = input_tensors[0];
+	const auto &key = input_tensors[1];
+	const auto &value = input_tensors[2];
+	const auto &output = output_tensors[0];
+
+	// Use FlashAttention kernel for CUDA device
+	auto device = query->GetDevice().type();
+	if (device == Device::Device::DeviceType::kCUDA) {
+		// Save tensors for backward pass:
+		// - query, key, value: input tensors
+		// - output: forward output
+		// - logsumexp: logsumexp tensor for backward pass (from flash_output_)
+		// - dropout_seed: dropout seed tensor for backward pass (from flash_output_)
+		saved_tensors_ = {query, key, value, output, flash_output_.logsumexp, flash_output_.dropout_seed};
+	} else {
+		// Fallback to traditional implementation for CPU or other devices
+		// Recompute attn_prob from output and value
+		const int64_t value_rank = static_cast<int64_t>(value->Dims().size());
+		auto value_t = value->Transpose(value_rank - 2, value_rank - 1);
+		auto attn_prob = output->Matmul(value_t);
+		saved_tensors_ = {query, key, value, attn_prob};
+	}
+}
+
+std::vector<std::shared_ptr<Tensor>>
+ScaledDotProductAttention::Backward(const std::vector<std::shared_ptr<Tensor>> &grad_outputs) {
+	CHECK_GE(saved_tensors_.size(), 4);
+	CHECK_EQ(grad_outputs.size(), 1);
+
+	const auto &query = saved_tensors_[0];
+	const auto &key = saved_tensors_[1];
+	const auto &value = saved_tensors_[2];
+	const auto &output = saved_tensors_[3];
+	const auto &grad_output = grad_outputs[0];
+
+	// Use FlashAttention backward kernel for CUDA device
+	auto device = query->GetDevice().type();
+	if (device == Device::Device::DeviceType::kCUDA) {
+		CHECK_EQ(saved_tensors_.size(), 6) << "FlashAttention backward expects 6 saved tensors";
+		const auto &logsumexp = saved_tensors_[4];
+		const auto &dropout_seed = saved_tensors_[5];
+		
+		const float scale = static_cast<float>(
+			scale_.has_value() ? *scale_ : 1.0 / std::sqrt(static_cast<double>(query->Dims().back())));
+		
+		auto grad_tensors = Dispatcher::Instance().Call<std::vector<std::shared_ptr<Tensor>>>(
+			{device, "FlashAttentionBackward"}, query, key, value, output, grad_output, logsumexp, dropout_seed, attn_mask_, 
+			scale, is_causal_, dropout_p_, enable_gqa_);
+		
+		return grad_tensors;
+	}
+
+	// Fallback to traditional implementation for CPU or other devices
+	CHECK_EQ(saved_tensors_.size(), 4) << "Traditional backward expects 4 saved tensors";
+	const auto &attn_prob = output;
+	const int64_t value_rank = static_cast<int64_t>(value->Dims().size());
+	const int64_t key_rank = static_cast<int64_t>(key->Dims().size());
+	const int64_t attn_rank = static_cast<int64_t>(attn_prob->Dims().size());
+
+	auto value_t = value->Transpose(value_rank - 2, value_rank - 1);
+	auto grad_attn_prob = grad_output->Matmul(value_t);
+
+	// softmax backward: grad = (g - sum(g * y, dim=-1, keepdim=true)) * y
+	auto grad_dot = (grad_attn_prob * attn_prob)->Sum(-1, true);
+	auto grad_attn_scores = (grad_attn_prob - grad_dot) * attn_prob;
+
+	const float scale = static_cast<float>(
+		scale_.has_value() ? *scale_ : 1.0 / std::sqrt(static_cast<double>(query->Dims().back())));
+	if (scale != 1.0f) {
+		grad_attn_scores = grad_attn_scores * scale;
+	}
+
+	auto grad_query = grad_attn_scores->Matmul(key);
+
+	auto grad_attn_scores_t = grad_attn_scores->Transpose(attn_rank - 2, attn_rank - 1);
+	auto grad_key = grad_attn_scores_t->Matmul(query);
+
+	auto attn_prob_t = attn_prob->Transpose(attn_rank - 2, attn_rank - 1);
+	auto grad_value = attn_prob_t->Matmul(grad_output);
+
+	return {grad_query, grad_key, grad_value};
+}
+} // namespace infini_train::autograd
diff --git a/infini_train/src/kernels/cuda/flash_attention.cu b/infini_train/src/kernels/cuda/flash_attention.cu
new file mode 100644
index 00000000..bdd86a27
--- /dev/null
+++ b/infini_train/src/kernels/cuda/flash_attention.cu
@@ -0,0 +1,1068 @@
+#include <cmath>
+#include <cstddef>
+
+#include "glog/logging.h"
+
+#include "infini_train/include/common/cuda/common_cuda.h"
+#include "infini_train/include/common/cuda/kernel_helper.cuh"
+#include "infini_train/include/core/runtime/device_guard.h"
+#include "infini_train/include/dispatcher.h"
+#include "infini_train/include/kernels/cuda/flash_attention.h"
+#include "infini_train/include/tensor.h"
+
+#include "infini_train/src/core/runtime/cuda/cuda_runtime_common.h"
+
+namespace infini_train::kernels::cuda {
+
+/**
+ * FlashAttention Forward Kernel
+ *
+ * This kernel implements the FlashAttention algorithm for efficient attention computation.
+ * It uses tiling and recomputation to reduce memory access and improve performance.
+ *
+ * Args:
+ *   output: Output tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+ *   query: Query tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+ *   key: Key tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+ *   value: Value tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+ *   attn_mask: Optional attention mask tensor
+ *   logsumexp: Save logsumexp for backward pass
+ *   scale: Scaling factor for attention scores
+ *   is_causal: Whether to apply causal masking
+ *   enable_gqa: Whether to enable grouped-query attention
+ *   dropout_p: Dropout probability
+ *   dropout_seed: Random seed for dropout (for reproducibility)
+ *   batch_size, target_seq_len, src_seq_len: Tensor dimensions
+ *   q_heads, kv_heads, head_dim: Attention head dimensions
+ *
+ * Note: Actual kernel implementation is not included in this version.
+ */
+template <typename T>
+__device__ T warp_reduce_sum(T val){
+#pragma unroll//短循环自动展开,省去分支预测,提升效率
+    for(int offset = 16; offset > 0; offset >>= 1){
+        val += __shfl_down_sync(0xffffffff, val, offset);
+    }
+    return val;
+}
+
+template <typename T>
+__device__ T myexp(T x) {
+    if constexpr(std::is_same<T, __half>::value) {
+        float fx = __half2float(x);
+        float result = expf(fx);
+        return __float2half(result);
+    }
+    else if constexpr(std::is_same<T, float>::value) {
+        return expf(x);  // expf返回float
+    }
+    else if constexpr(std::is_same<T, double>::value) {
+        return exp(x);   // exp返回double
+    }
+    else{//other types
+      return T(0);
+    }
+}
+
+template <typename T>
+__device__ T warp_reduce_max(T val){
+#pragma unroll//短循环自动展开,省去分支预测,提升效率
+    for(int offset = 16; offset > 0; offset >>= 1){
+        T tmp = __shfl_down_sync(0xffffffff, val, offset);
+        val = (val > tmp) ? val : tmp;
+    }
+    return val;
+}
+
+
+/**
+ * FlashAttention Forward Kernel
+ *
+ * This kernel implements the FlashAttention algorithm for efficient attention computation.
+ * It uses tiling and recomputation to reduce memory access and improve performance.
+ *
+ * Args:
+ *   output: Output tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+ *   query: Query tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+ *   key: Key tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+ *   value: Value tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+ *   attn_mask: Optional attention mask tensor
+ *   logsumexp: Save logsumexp for backward pass
+ *   scale: Scaling factor for attention scores
+ *   is_causal: Whether to apply causal masking
+ *   enable_gqa: Whether to enable grouped-query attention
+ *   dropout_p: Dropout probability
+ *   dropout_seed: Random seed for dropout (for reproducibility)
+ *   batch_size, target_seq_len, src_seq_len: Tensor dimensions
+ *   q_heads, kv_heads, head_dim: Attention head dimensions
+ *
+ * Note: Actual kernel implementation is not included in this version.
+ */
+template <typename T>
+__global__ void FlashAttentionForwardKernel(
+    T *output, const T *query, const T *key, const T *value, const T *attn_mask,
+    float *logsumexp,  // Save logsumexp for backward pass
+    float scale, bool is_causal, bool enable_gqa, int64_t dropout_p,
+    unsigned long long dropout_seed,  // Use dropout_seed instead of dropout_mask
+    int batch_size, int target_seq_len, int src_seq_len,
+    int q_heads, int kv_heads, int head_dim) {
+    
+    int tid_x = threadIdx.x;          // 横向,blockDim.x列 (Bc)
+    int tid_y = threadIdx.y;          // 纵向,blockDim.y行 (Br)
+    int bid_x = blockIdx.x;           // x方向,总数 = #q_heads
+    int bid_y = blockIdx.y;           // y方向,总数 = #batch
+    int bid_z = blockIdx.z;           // z方向,总数 = Tr
+    const int p = q_heads / kv_heads; // 计算比例系数，GQA支持
+
+    const int Br = blockDim.y;                  // Q纵向每块大小, 32
+    const int Bc = blockDim.x;                  // K/V纵向分块大小, 32
+    const int Tc = gridDim.z; // 对应原始论文中K/V纵向分块数Tc,其中Bc = 32
+
+    // 定义一系列临时变量
+    extern __shared__ char shared_mem[];
+    char *ptr = shared_mem;
+    
+    // 计算中间变量,包括S, P(复用为SP), m_prev, m_new, l_prev, l_new
+    double *SP = reinterpret_cast<double *>(ptr); // double SP[Br][Bc]
+    ptr += Br * Bc * sizeof(double);
+    float *m_prev = reinterpret_cast<float *>(ptr); // float m_prev[Br]
+    ptr += Br * sizeof(float);
+    float *m_new = reinterpret_cast<float *>(ptr); // float m_new[Br]
+    ptr += Br * sizeof(float);
+    float *l_prev = reinterpret_cast<float *>(ptr); // float l_prev[Br]
+    ptr += Br * sizeof(float);
+    float *l_new = reinterpret_cast<float *>(ptr); // float l_new[Br]
+    ptr += Br * sizeof(float);
+
+    // 原始数据QKV和计算结果O; 全采用float
+    float *Q_sm = reinterpret_cast<float *>(ptr); // float Q_sm[Br][head_dim]
+    ptr += Br * head_dim * sizeof(float);
+    float *K_T_sm = reinterpret_cast<float *>(ptr); // float K_T_sm[head_dim][Bc]
+    ptr += head_dim * Bc * sizeof(float);
+    float *V_sm = reinterpret_cast<float *>(ptr); // float V_sm[Bc][head_dim]
+    ptr += Bc * head_dim * sizeof(float);
+    float *O_sm = reinterpret_cast<float *>(ptr); // float O_sm[Br][head_dim]
+    // Note: Removed dropout_sm shared memory allocation
+
+    // 定义访问宏
+#define SP_AT(y, x) SP[y * Bc + x]
+#define Q_sm_AT(y, x) Q_sm[y * head_dim + x]
+#define K_T_sm_AT(y, x) K_T_sm[y * Bc + x]
+#define V_sm_AT(y, x) V_sm[y * head_dim + x]
+#define O_sm_AT(y, x) O_sm[y * head_dim + x]
+    // Note: Removed DROPOUT_SM_AT macro
+
+    /****************************preparation**************************/
+    int bound_tid_y = min(Br, target_seq_len - Br * bid_z);
+
+    // preparation-1: load Qi from GM to SM, and reset Oi to 0
+    for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+        O_sm_AT(tid_y, idx) = 0.0;
+        Q_sm_AT(tid_y, idx) = 0.0;
+        if (tid_y < bound_tid_y) {
+            Q_sm_AT(tid_y, idx)
+                = float(query[((((bid_y * target_seq_len) + (Br * bid_z + tid_y)) * q_heads) + bid_x) * head_dim + idx]);
+        }
+    }
+    __syncthreads();
+
+    // preparation-2: reset m_prev to -INFINITY and l_prev to 0
+    if (tid_x == 0) {
+        m_prev[tid_y] = -8192.0;
+        l_prev[tid_y] = 0.0;
+    }
+    __syncthreads();
+
+    // Initialize dropout random state with dropout_seed
+    curandStatePhilox4_32_10_t state;
+    if (dropout_p > 0) {
+        unsigned long long seq = bid_y * q_heads * target_seq_len + bid_x * target_seq_len + Br * bid_z + tid_y;
+        curand_init(dropout_seed, seq, 0, &state);
+    }
+/****************************end-of-preparation*************************/
+
+/****************************main-loop**************************/
+#pragma unroll 4
+    for (int j = 0; j < Tc; ++j) { // 对于每个K/V分块
+        bool skip = (is_causal && bid_z < j);
+        if (skip) { // early exit, 直接跳过
+            __syncthreads();
+            continue;
+        }
+
+        SP_AT(tid_y, tid_x) = -8192.0;
+        __syncthreads();
+        int bound_tid_x = min(Bc, src_seq_len - Bc * j);
+        bool is_compute = true; // optimization: 分支处理,加速branch-resolving
+        if (is_causal) {
+            if (bid_z < j) {
+                is_compute = false; // 早期退出情况
+            } else if (bid_z == j) {
+                is_compute = (tid_y >= tid_x); // 对角线以上
+            }
+        }
+
+        // step-1: load Ki, Vi from GM to SM
+#pragma unroll
+        for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+            K_T_sm_AT(idx, tid_y) = 0.0;
+            V_sm_AT(tid_y, idx) = 0.0;
+            if (tid_y < bound_tid_x) {
+                K_T_sm_AT(idx, tid_y) = float(
+                    K[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads) + (bid_x / p)) * head_dim + idx]);
+                V_sm_AT(tid_y, idx) = float(
+                    V[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads) + (bid_x / p)) * head_dim + idx]);
+            }
+        }
+        __syncthreads();
+
+        // step-2: S = Q @ K.T, point-wise
+        if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+            float val0 = 0.0;
+            if (is_compute) {
+#pragma unroll
+                for (int k = 0; k < head_dim; ++k) { 
+                    val0 += Q_sm_AT(tid_y, k) * K_T_sm_AT(k, tid_x); 
+                }
+                SP_AT(tid_y, tid_x) = double(val0) * scale;
+            }
+        }
+        __syncthreads();
+
+        // step-3: m_new = max(m_prev, rowMax(S))
+        float val1 = float(SP_AT(tid_y, tid_x));
+        val1 = warp_reduce_max(val1);
+        if (tid_x == 0 && tid_y < bound_tid_y) {
+            m_new[tid_y] = (val1 > m_prev[tid_y]) ? val1 : m_prev[tid_y];
+        }
+        __syncthreads();
+
+        // step-4: P = exp(S - m_new), point-wise
+        if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+            if (is_compute) {
+                SP_AT(tid_y, tid_x) = myexp<double>(SP_AT(tid_y, tid_x) - double(m_new[tid_y]));
+            } else {
+                SP_AT(tid_y, tid_x) = 0.0;
+            }
+        } else {
+            SP_AT(tid_y, tid_x) = 0.0;
+        }
+        __syncthreads();
+
+        // step-5: l_new = exp(m_prev - m_new) * l_prev + rowSum(P)
+        float val2 = float(SP_AT(tid_y, tid_x));
+        val2 = warp_reduce_sum(val2);
+        float exp_result = myexp<float>(m_prev[tid_y] - m_new[tid_y]);
+        if (tid_x == 0 && tid_y < bound_tid_y) {
+            l_new[tid_y] = exp_result * l_prev[tid_y] + val2;
+        }
+        __syncthreads();
+
+        // step-5.5: Apply dropout to P (using dropout_seed for reproducibility)
+        if (dropout_p > 0 && tid_y < bound_tid_y && tid_x < bound_tid_x) {
+            float rand_val = curand_uniform(&state);
+            bool keep = rand_val > dropout_p;
+            if (keep) {
+                SP_AT(tid_y, tid_x) = SP_AT(tid_y, tid_x) / (1.0f - dropout_p);
+            } else {
+                SP_AT(tid_y, tid_x) = 0.0;
+            }
+            // Note: Removed dropout_sm mask saving to reduce memory overhead
+        }
+        __syncthreads();
+
+        // step-6: O = 1/(exp(m_prev - m_new)) * O + P @ V
+        if (tid_x < bound_tid_x && tid_y < bound_tid_y) {
+            for (int u = tid_x; u < head_dim; u += blockDim.x) {
+                float val3 = 0.0;
+#pragma unroll
+                for (int w = 0; w < Bc; ++w) { 
+                    val3 += float(SP_AT(tid_y, w)) * V_sm_AT(w, u);
+                }
+                O_sm_AT(tid_y, u) = O_sm_AT(tid_y, u) * exp_result + val3;
+            }
+        }
+        __syncthreads();
+
+        // step-7: m_prev <- m_new; l_prev <- l_new
+        if (tid_x == 0 && tid_y < bound_tid_y) {
+            m_prev[tid_y] = m_new[tid_y];
+            l_prev[tid_y] = l_new[tid_y];
+        }
+        __syncthreads();
+    }
+/****************************end-of-main-loop**************************/
+
+/****************************post-process****************************/
+// O(GM) = O/l_prev, aka O_sm /= l_prev and write Oi from SM to GM
+// Also save logsumexp for backward pass
+#pragma unroll
+    for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+        if (tid_y < bound_tid_y) {
+            output[((((bid_y * target_seq_len) + (Br * bid_z + tid_y)) * q_heads) + bid_x) * head_dim + idx]
+                = T(O_sm_AT(tid_y, idx) / float(l_prev[tid_y]));
+        }
+    }
+    
+    // Save logsumexp for backward pass
+    if (tid_x == 0 && tid_y < bound_tid_y) {
+        int logsumexp_idx = ((bid_y * q_heads + bid_x) * target_seq_len) + (Br * bid_z + tid_y);
+        logsumexp[logsumexp_idx] = m_prev[tid_y] + log(l_prev[tid_y]);
+    }
+    __syncthreads();
+/****************************end-of-post-process****************************/
+
+// 取消访问宏定义
+#undef SP_AT
+#undef Q_sm_AT
+#undef K_T_sm_AT
+#undef V_sm_AT
+#undef O_sm_AT
+}
+
+/**
+ * FlashAttention Backward Kernel
+ *
+ * This kernel implements the backward pass for FlashAttention.
+ * It computes gradients for query, key, and value tensors.
+ *
+ * Args:
+ *   grad_query: Gradient for query tensor
+ *   grad_key: Gradient for key tensor
+ *   grad_value: Gradient for value tensor
+ *   query: Query tensor from forward pass
+ *   key: Key tensor from forward pass
+ *   value: Value tensor from forward pass
+ *   output: Output tensor from forward pass
+ *   grad_output: Gradient from upstream
+ *   logsumexp: Logsumexp tensor from forward pass
+ *   dropout_seed: Dropout seed for reproducibility
+ *   attn_mask: Optional attention mask tensor
+ *   scale: Scaling factor for attention scores
+ *   is_causal: Whether causal masking was applied
+ *   dropout_p: Dropout probability
+ *   enable_gqa: Whether GQA was enabled
+ *   batch_size, target_seq_len, src_seq_len: Tensor dimensions
+ *   q_heads, kv_heads, head_dim: Attention head dimensions
+ */
+template <typename T>
+__global__ void FlashAttentionBackwardKernel(
+    T *grad_query, T *grad_key, T *grad_value,
+    const T *query, const T *key, const T *value,
+    const T *output, const T *grad_output,
+    const float *logsumexp,
+    const float *D,  // Precomputed D = rowsum(dO ∘ O)
+    unsigned long long dropout_seed,
+    const T *attn_mask,
+    float scale, bool is_causal, int64_t dropout_p,
+    bool enable_gqa,
+    int batch_size, int target_seq_len, int src_seq_len,
+    int q_heads, int kv_heads, int head_dim) {
+    
+    // Grid/block dimensions: grid_dims(num_heads_q, batch_size, Tr), block_dim(Bc, Br)
+    // where Br corresponds to thread block row index, Bc corresponds to column index
+    int tid_x = threadIdx.x;          // 横向, blockDim.x列 (Bc)
+    int tid_y = threadIdx.y;          // 纵向, blockDim.y行 (Br)
+    int bid_x = blockIdx.x;           // x方向, 总数 = #q_heads
+    int bid_y = blockIdx.y;           // y方向, 总数 = #batch
+    int bid_z = blockIdx.z;           // z方向, 总数 = Tr
+    const int p = q_heads / kv_heads; // GQA比例系数
+
+    const int Br = blockDim.y;                  // Q纵向每块大小, 32
+    const int Bc = blockDim.x;                  // K/V纵向分块大小, 32
+    const int Tr = gridDim.z; // Q纵向分块数
+    const int Tc = (src_seq_len + Bc - 1) / Bc; // K/V纵向分块数
+
+    // Define shared memory
+    extern __shared__ char shared_mem[];
+    char *ptr = shared_mem;
+    
+    // D_sm[Br] - D values loaded from HBM
+    float *D_sm = reinterpret_cast<float *>(ptr);
+    ptr += Br * sizeof(float);
+    
+    // Q_sm[Br][head_dim], K_T_sm[head_dim][Bc], V_sm[Bc][head_dim]
+    float *Q_sm = reinterpret_cast<float *>(ptr);
+    ptr += Br * head_dim * sizeof(float);
+    float *K_T_sm = reinterpret_cast<float *>(ptr);
+    ptr += head_dim * Bc * sizeof(float);
+    float *V_sm = reinterpret_cast<float *>(ptr);
+    ptr += Bc * head_dim * sizeof(float);
+    
+    // dO_sm[Br][head_dim], dK_T_sm[head_dim][Bc], dV_sm[Bc][head_dim]
+    float *dO_sm = reinterpret_cast<float *>(ptr);
+    ptr += Br * head_dim * sizeof(float);
+    float *dK_T_sm = reinterpret_cast<float *>(ptr);
+    ptr += head_dim * Bc * sizeof(float);
+    float *dV_sm = reinterpret_cast<float *>(ptr);
+    ptr += Bc * head_dim * sizeof(float);
+    
+    // S_sm[Br][Bc], P_sm[Br][Bc]
+    float *S_sm = reinterpret_cast<float *>(ptr);
+    ptr += Br * Bc * sizeof(float);
+    float *P_sm = reinterpret_cast<float *>(ptr);
+    
+    // L_sm[Br] - logsumexp values
+    float *L_sm = reinterpret_cast<float *>(ptr);
+    
+    // dQ_sm[Br][head_dim] - accumulated gradient for Q
+    float *dQ_sm = reinterpret_cast<float *>(ptr);
+    
+    // Define access macros
+    #define D_sm_AT(y) D_sm[y]
+    #define Q_sm_AT(y, x) Q_sm[y * head_dim + x]
+    #define K_T_sm_AT(y, x) K_T_sm[y * Bc + x]
+    #define V_sm_AT(y, x) V_sm[y * head_dim + x]
+    #define dO_sm_AT(y, x) dO_sm[y * head_dim + x]
+    #define dK_T_sm_AT(y, x) dK_T_sm[y * Bc + x]
+    #define dV_sm_AT(y, x) dV_sm[y * head_dim + x]
+    #define S_sm_AT(y, x) S_sm[y * Bc + x]
+    #define P_sm_AT(y, x) P_sm[y * Bc + x]
+    #define L_sm_AT(y) L_sm[y]
+    #define dQ_sm_AT(y, x) dQ_sm[y * head_dim + x]
+
+    // Initialize dropout random state
+    curandStatePhilox4_32_10_t state;
+    if (dropout_p > 0) {
+        unsigned long long seq = bid_y * q_heads * target_seq_len + bid_x * target_seq_len + Br * bid_z + tid_y;
+        curand_init(dropout_seed, seq, 0, &state);
+    }
+
+    /****************************Preparation*****************************/
+    
+    // Initialize dQ_sm to 0 for accumulation
+    for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+        dQ_sm_AT(tid_y, idx) = 0.0f;
+    }
+    __syncthreads();
+
+    // Load D_i from HBM to shared memory
+    int q_idx = Br * bid_z + tid_y;  // Global query position within this head
+    if (q_idx < target_seq_len) {
+        int d_idx = ((bid_y * q_heads + bid_x) * target_seq_len) + q_idx;
+        D_sm_AT(tid_y) = D[d_idx];
+    } else {
+        D_sm_AT(tid_y) = 0.0f;  // Padding for out-of-bounds positions
+    }
+    __syncthreads();
+
+    /****************************Main Loop - Outer Loop over K/V tiles*****************************/
+    for (int j = 0; j < Tc; ++j) {  // For each K/V column tile
+        
+        // Skip entire tile if causal and this tile is completely to the right
+        bool skip_tile = (is_causal && bid_z < j);
+        if (skip_tile) {
+            __syncthreads();
+            continue;
+        }
+
+        // Initialize dK_T_sm, dV_sm to 0 for this column tile
+        for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+            for (int y = 0; y < Bc; ++y) {
+                dK_T_sm_AT(idx, y) = 0.0f;
+                dV_sm_AT(y, idx) = 0.0f;
+            }
+        }
+        __syncthreads();
+
+        // Load K_j, V_j from HBM to shared memory
+        int bound_tid_x = min(Bc, src_seq_len - Bc * j);
+        for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+            K_T_sm_AT(idx, tid_y) = 0.0f;
+            V_sm_AT(tid_y, idx) = 0.0f;
+            if (tid_y < bound_tid_x) {
+                int kv_head_idx = bid_x / p;
+                K_T_sm_AT(idx, tid_y) = float(
+                    key[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads + kv_head_idx) * head_dim + idx]);
+                V_sm_AT(tid_y, idx) = float(
+                    value[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads + kv_head_idx) * head_dim + idx]);
+            }
+        }
+        __syncthreads();
+
+        /****************************Single Q tile for this block*****************************/
+        // gridDim.z already indexes the Q row tile, so this block only handles i = bid_z.
+        const int i = bid_z;
+        int q_tile_start = Br * i;
+        int q_tile_end = min(q_tile_start + Br, target_seq_len);
+        int bound_tid_y = min(Br, q_tile_end - q_tile_start);
+
+        if (tid_y < bound_tid_y) {
+            int global_q_idx = q_tile_start + tid_y;
+            int q_tensor_idx = ((bid_y * target_seq_len) + global_q_idx) * q_heads + bid_x;
+
+            for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+                Q_sm_AT(tid_y, idx) = float(query[q_tensor_idx * head_dim + idx]);
+                dO_sm_AT(tid_y, idx) = float(grad_output[q_tensor_idx * head_dim + idx]);
+            }
+            if (tid_x == 0) {
+                L_sm_AT(tid_y) = logsumexp[((bid_y * q_heads + bid_x) * target_seq_len) + global_q_idx];
+            }
+        }
+        __syncthreads();
+
+        // Get D_i for this row.
+        float D_i_row = D_sm_AT(tid_y);
+
+        // Recompute S_ij = Q_i @ K_j^T, apply causal mask at element level if needed.
+        #pragma unroll
+        for (int y = 0; y < Bc; ++y) {
+            for (int x = 0; x < Bc; ++x) {
+                float val = 0.0f;
+                if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+                    bool is_compute = true;
+                    if (is_causal && i == j) {
+                        int global_q_pos = q_tile_start + tid_y;
+                        int global_k_pos = Bc * j + x;
+                        is_compute = (global_q_pos >= global_k_pos);
+                    }
+
+                    if (is_compute) {
+                        #pragma unroll
+                        for (int k = 0; k < head_dim; ++k) {
+                            val += Q_sm_AT(tid_y, k) * K_T_sm_AT(k, y);
+                        }
+                    }
+                }
+                S_sm_AT(tid_y, x) = val * scale;
+            }
+        }
+        __syncthreads();
+
+        // Recompute P_ij = exp(S_ij - L_i).
+        #pragma unroll
+        for (int y = 0; y < Bc; ++y) {
+            for (int x = 0; x < Bc; ++x) {
+                if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+                    bool is_compute = true;
+                    if (is_causal && i == j) {
+                        int global_q_pos = q_tile_start + tid_y;
+                        int global_k_pos = Bc * j + x;
+                        is_compute = (global_q_pos >= global_k_pos);
+                    }
+
+                    if (is_compute) {
+                        P_sm_AT(tid_y, x) = myexp<float>(S_sm_AT(tid_y, x) - L_sm_AT(tid_y));
+                    } else {
+                        P_sm_AT(tid_y, x) = 0.0f;
+                    }
+                } else {
+                    P_sm_AT(tid_y, x) = 0.0f;
+                }
+            }
+        }
+        __syncthreads();
+
+        // Apply dropout to P_ij.
+        if (dropout_p > 0) {
+            #pragma unroll
+            for (int y = 0; y < Bc; ++y) {
+                for (int x = 0; x < Bc; ++x) {
+                    if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+                        float rand_val = curand_uniform(&state);
+                        bool keep = rand_val > dropout_p;
+                        if (keep) {
+                            P_sm_AT(tid_y, x) = P_sm_AT(tid_y, x) / (1.0f - dropout_p);
+                        } else {
+                            P_sm_AT(tid_y, x) = 0.0f;
+                        }
+                    }
+                }
+            }
+            __syncthreads();
+        }
+
+        // Compute dV_j += P_ij^T @ dO_i.
+        if (tid_y < bound_tid_y) {
+            #pragma unroll
+            for (int x = 0; x < head_dim; x += blockDim.x) {
+                float val = 0.0f;
+                #pragma unroll
+                for (int y = 0; y < Bc; ++y) {
+                    val += P_sm_AT(tid_y, y) * dO_sm_AT(tid_y, x);
+                }
+                atomicAdd(&dV_sm_AT(y, x), val);
+            }
+        }
+        __syncthreads();
+
+        // Compute dP_ij = dO_i @ V_j^T.
+        #pragma unroll
+        for (int y = 0; y < Bc; ++y) {
+            for (int x = 0; x < Bc; ++x) {
+                float val = 0.0f;
+                if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+                    #pragma unroll
+                    for (int k = 0; k < head_dim; ++k) {
+                        val += dO_sm_AT(tid_y, k) * V_sm_AT(y, k);
+                    }
+                    S_sm_AT(tid_y, x) = val;  // Reuse S_sm as temporary storage for dP_ij
+                } else {
+                    S_sm_AT(tid_y, x) = 0.0f;
+                }
+            }
+        }
+        __syncthreads();
+
+        // Apply dropout to dP_ij.
+        if (dropout_p > 0) {
+            #pragma unroll
+            for (int y = 0; y < Bc; ++y) {
+                for (int x = 0; x < Bc; ++x) {
+                    if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+                        float rand_val = curand_uniform(&state);
+                        bool keep = rand_val > dropout_p;
+                        if (keep) {
+                            S_sm_AT(tid_y, x) = S_sm_AT(tid_y, x) / (1.0f - dropout_p);
+                        } else {
+                            S_sm_AT(tid_y, x) = 0.0f;
+                        }
+                    }
+                }
+            }
+            __syncthreads();
+        }
+
+        // Compute dS_ij = P_ij * (dP_ij - D_i).
+        #pragma unroll
+        for (int y = 0; y < Bc; ++y) {
+            for (int x = 0; x < Bc; ++x) {
+                if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+                    S_sm_AT(tid_y, x) = P_sm_AT(tid_y, x) * (S_sm_AT(tid_y, x) - D_i_row);
+                } else {
+                    S_sm_AT(tid_y, x) = 0.0f;
+                }
+            }
+        }
+        __syncthreads();
+
+        // Compute dK_j += dS_ij^T @ Q_i.
+        if (tid_x < head_dim) {
+            #pragma unroll
+            for (int y = 0; y < Bc; ++y) {
+                float val = 0.0f;
+                #pragma unroll
+                for (int x = 0; x < Br; ++x) {
+                    val += S_sm_AT(x, y) * Q_sm_AT(x, tid_x);
+                }
+                atomicAdd(&dK_T_sm_AT(tid_x, y), val * scale);
+            }
+        }
+        __syncthreads();
+
+        // Compute dQ_i += dS_ij @ K_j.
+        if (tid_y < bound_tid_y) {
+            #pragma unroll
+            for (int x = 0; x < head_dim; x += blockDim.x) {
+                float val = 0.0f;
+                #pragma unroll
+                for (int y = 0; y < Bc; ++y) {
+                    val += S_sm_AT(tid_y, y) * K_T_sm_AT(x, y);
+                }
+                dQ_sm_AT(tid_y, x) += val * scale;
+            }
+        }
+        __syncthreads();
+
+        // Write back dK_j, dV_j to HBM
+        // Note: For GQA, dK_j and dV_j need to be accumulated across multiple q-head blocks
+        int kv_head_idx = bid_x / p;
+        int k_tile_start = Bc * j;
+        int k_tile_end = min(k_tile_start + Bc, src_seq_len);
+        
+        for (int y = 0; y < Bc; ++y) {
+            int global_k_idx = k_tile_start + y;
+            if (global_k_idx < src_seq_len) {
+                for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+                    // dK: [batch_size, src_seq_len, kv_heads, head_dim]
+                    int k_tensor_idx = ((bid_y * src_seq_len) + global_k_idx) * kv_heads + kv_head_idx;
+                    atomicAdd(&grad_key[k_tensor_idx * head_dim + idx], T(dK_T_sm_AT(idx, y)));
+                    
+                    // dV: [batch_size, src_seq_len, kv_heads, head_dim]
+                    atomicAdd(&grad_value[k_tensor_idx * head_dim + idx], T(dV_sm_AT(y, idx)));
+                }
+            }
+        }
+    }  // End of outer loop over K/V tiles
+
+    // Write back dQ_i to HBM
+    int q_tile_start = Br * bid_z;
+    int q_tile_end = min(q_tile_start + Br, target_seq_len);
+    int bound_tid_y = min(Br, q_tile_end - q_tile_start);
+    
+    if (tid_y < bound_tid_y) {
+        int global_q_idx = q_tile_start + tid_y;
+        int q_tensor_idx = ((bid_y * target_seq_len) + global_q_idx) * q_heads + bid_x;
+        
+        for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+            // dQ: [batch_size, target_seq_len, q_heads, head_dim]
+            grad_query[q_tensor_idx * head_dim + idx] += T(dQ_sm_AT(tid_y, idx));
+        }
+    }
+
+    // Undefine access macros
+    #undef D_sm_AT
+    #undef Q_sm_AT
+    #undef K_T_sm_AT
+    #undef V_sm_AT
+    #undef dO_sm_AT
+    #undef dK_T_sm_AT
+    #undef dV_sm_AT
+    #undef S_sm_AT
+    #undef P_sm_AT
+    #undef L_sm_AT
+    #undef dQ_sm_AT
+}
+
+/**
+ * FlashAttention Forward Function
+ *
+ * This is the main entry point for FlashAttention forward computation.
+ * It creates the output tensor and launches the appropriate kernel based on data type.
+ *
+ * Args:
+ *   query: Query tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+ *   key: Key tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+ *   value: Value tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+ *   attn_mask: Optional attention mask tensor
+ *   scale: Scaling factor for attention scores
+ *   is_causal: Whether to apply causal masking
+ *   dropout_p: Dropout probability
+ *   enable_gqa: Whether to enable grouped-query attention
+ *
+ * Returns:
+ *   Output tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+ *   logsumexp: Logsumexp tensor for backward pass [batch_size, num_heads, seq_len_q]
+ *   dropout_seed: Dropout seed for backward pass [1]
+ */
+
+
+/**
+ * Launch FlashAttention Forward Kernel
+ *
+ * This function sets up grid and block dimensions and launches FlashAttention forward kernel.
+ *
+ * Args:
+ *   output: Output tensor
+ *   query: Query tensor
+ *   key: Key tensor
+ *   value: Value tensor
+ *   attn_mask: Optional attention mask tensor
+ *   scale: Scaling factor
+ *   is_causal: Whether to apply causal masking
+ *   dropout_p: Dropout probability
+ *   enable_gqa: Whether to enable GQA
+ */
+template <typename T>
+void LaunchFlashAttentionForward(const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &query,
+                                 const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
+                                 const std::shared_ptr<Tensor> &attn_mask, float scale, bool is_causal,
+                                 int64_t dropout_p, bool enable_gqa, float *logsumexp_ptr, unsigned long long *dropout_seed_ptr) {
+
+    const auto &query_dims = query->Dims();
+    const auto &key_dims = key->Dims();
+    const auto &value_dims = value->Dims();
+
+    // Expected shapes:
+    // query: [batch_size, seq_len_q, num_heads, head_dim]
+    // key: [batch_size, seq_len_k, num_heads_kv, head_dim]
+    // value: [batch_size, seq_len_k, num_heads_kv, head_dim]
+    // output: [batch_size, seq_len_q, num_heads, head_dim]
+
+    int64_t batch_size = query_dims[0];
+    int64_t seq_len_q = query_dims[1];
+    int64_t num_heads = query_dims[2];
+    int64_t head_dim = query_dims[3];
+    int64_t seq_len_k = key_dims[1];
+    int64_t num_heads_kv = key_dims[2];
+
+    CHECK_EQ(key_dims[3], head_dim) << "Key head dimension must match query head dimension";
+    CHECK_EQ(value_dims[3], head_dim) << "Value head dimension must match query head dimension";
+    CHECK_EQ(value_dims[1], seq_len_k) << "Value sequence length must match key sequence length";
+    CHECK_EQ(value_dims[2], num_heads_kv) << "Value number of KV heads must match key";
+
+    if (enable_gqa) {
+        CHECK(num_heads % num_heads_kv == 0) << "Number of query heads must be divisible by number of KV heads for GQA";
+    } else {
+        CHECK_EQ(num_heads, num_heads_kv) << "Number of query and KV heads must match for standard attention";
+    }
+
+    T *output_ptr = static_cast<T *>(output->DataPtr());
+    const T *query_ptr = static_cast<const T *>(query->DataPtr());
+    const T *key_ptr = static_cast<const T *>(key->DataPtr());
+    const T *value_ptr = static_cast<const T *>(value->DataPtr());
+    const T *attn_mask_ptr = attn_mask ? static_cast<const T *>(attn_mask->DataPtr()) : nullptr;
+
+    // Set up grid and block dimensions according to FlashAttention v2
+    // block_dim(Br, Bc) where Br = Bc = 32
+    // grid_dim(query_heads, batch_size, Tr) where Tr = ceil(seq_len_q / Br)
+    constexpr int Br = 32;
+    constexpr int Bc = 32;
+    int64_t Tr = (seq_len_q + Br - 1) / Br;
+    
+    dim3 block_dims(Bc, Br);  // (blockDim.x, blockDim.y) = (Bc, Br)
+    dim3 grid_dims(num_heads, batch_size, Tr);  // (gridDim.x, gridDim.y, gridDim.z) = (num_heads, batch_size, Tr)
+
+    // Calculate shared memory size (removed dropout_sm allocation)
+    // SP[Br][Bc] (double) + m_prev[Br] (float) + m_new[Br] (float) + l_prev[Br] (float) + l_new[Br] (float)
+    // + Q_sm[Br][head_dim] (float) + K_T_sm[head_dim][Bc] (float) + V_sm[Bc][head_dim] (float) + O_sm[Br][head_dim] (float)
+    size_t shared_mem_size = Br * Bc * sizeof(double)  // SP
+                          + 4 * Br * sizeof(float)     // m_prev, m_new, l_prev, l_new
+                          + (Br + Bc + Bc + Br) * head_dim) * sizeof(float);  // Q_sm, K_T_sm, V_sm, O_sm
+    // Note: Removed dropout_sm[Br][Bc] (bool) allocation
+
+    auto device = output->GetDevice();
+    const auto &cuda_stream = dynamic_cast<infini_train::core::cuda::CudaStream *>(
+                                  infini_train::core::GetDeviceGuardImpl(device.type())->GetStream(device))
+                                  ->cuda_stream();
+
+    FlashAttentionForwardKernel<T><<<grid_dims, block_dims, shared_mem_size, cuda_stream>>>(
+        output_ptr, query_ptr, key_ptr, value_ptr, attn_mask_ptr,
+        logsumexp_ptr, scale, is_causal, enable_gqa, dropout_p, dropout_seed,
+        batch_size, seq_len_q, seq_len_k, num_heads, num_heads_kv, head_dim);
+}
+
+/**
+ * Launch FlashAttention Backward Kernel
+ *
+ * This function sets up grid and block dimensions and launches FlashAttention backward kernel.
+ *
+ * Args:
+ *   grad_query: Gradient tensor for query
+ *   grad_key: Gradient tensor for key
+ *   grad_value: Gradient tensor for value
+ *   query: Query tensor from forward pass
+ *   key: Key tensor from forward pass
+ *   value: Value tensor from forward pass
+ *   output: Output tensor from forward pass
+ *   grad_output: Gradient from upstream
+ *   logsumexp: Logsumexp tensor from forward pass
+ *   dropout_seed: Dropout seed for reproducibility
+ *   attn_mask: Optional attention mask tensor
+ *   scale: Scaling factor
+ *   is_causal: Whether causal masking was applied
+ *   dropout_p: Dropout probability
+ *   enable_gqa: Whether GQA was enabled
+ */
+template <typename T>
+void LaunchFlashAttentionBackward(const std::shared_ptr<Tensor> &grad_query, const std::shared_ptr<Tensor> &grad_key,
+                                 const std::shared_ptr<Tensor> &grad_value, const std::shared_ptr<Tensor> &query,
+                                 const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
+                                 const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &grad_output,
+                                 const std::shared_ptr<Tensor> &logsumexp,
+                                 unsigned long long dropout_seed,
+                                 const std::shared_ptr<Tensor> &attn_mask,
+                                 float scale, bool is_causal, int64_t dropout_p, bool enable_gqa) {
+
+    const auto &query_dims = query->Dims();
+    const auto &key_dims = key->Dims();
+
+    // Expected shapes:
+    // query: [batch_size, seq_len_q, num_heads, head_dim]
+    // key: [batch_size, seq_len_k, num_heads_kv, head_dim]
+    // value: [batch_size, seq_len_k, num_heads_kv, head_dim]
+    // output: [batch_size, seq_len_q, num_heads, head_dim]
+
+    int64_t batch_size = query_dims[0];
+    int64_t seq_len_q = query_dims[1];
+    int64_t num_heads = query_dims[2];
+    int64_t head_dim = query_dims[3];
+    int64_t seq_len_k = key_dims[1];
+    int64_t num_heads_kv = key_dims[2];
+
+    CHECK_EQ(key_dims[3], head_dim) << "Key head dimension must match query head dimension";
+    CHECK_EQ(value->Dims()[3], head_dim) << "Value head dimension must match query head dimension";
+    CHECK_EQ(value->Dims()[1], seq_len_k) << "Value sequence length must match key sequence length";
+    CHECK_EQ(value->Dims()[2], num_heads_kv) << "Value number of KV heads must match key";
+
+    if (enable_gqa) {
+        CHECK(num_heads % num_heads_kv == 0) << "Number of query heads must be divisible by number of KV heads for GQA";
+    } else {
+        CHECK_EQ(num_heads, num_heads_kv) << "Number of query and KV heads must match for standard attention";
+    }
+
+    // Precompute D = rowsum(dO ∘ O) before main backward loop
+    // D shape: [batch_size, seq_len_q, num_heads]
+    // dO shape: [batch_size, seq_len_q, num_heads, head_dim]
+    // O shape: [batch_size, seq_len_q, num_heads, head_dim]
+    auto D = function::Sum(grad_output * output, 3, false);
+
+    T *grad_query_ptr = static_cast<T *>(grad_query->DataPtr());
+    T *grad_key_ptr = static_cast<T *>(grad_key->DataPtr());
+    T *grad_value_ptr = static_cast<T *>(grad_value->DataPtr());
+    const T *query_ptr = static_cast<const T *>(query->DataPtr());
+    const T *key_ptr = static_cast<const T *>(key->DataPtr());
+    const T *value_ptr = static_cast<const T *>(value->DataPtr());
+    const T *output_ptr = static_cast<const T *>(output->DataPtr());
+    const T *grad_output_ptr = static_cast<const T *>(grad_output->DataPtr());
+    const float *logsumexp_ptr = static_cast<const float *>(logsumexp->DataPtr());
+    const float *D_ptr = static_cast<const float *>(D->DataPtr());
+    const T *attn_mask_ptr = attn_mask ? static_cast<const T *>(attn_mask->DataPtr()) : nullptr;
+
+    // Set up grid and block dimensions according to FlashAttention v2 backward
+    // block_dim(Bc, Br) where Bc = Br = 32
+    // grid_dims(num_heads_q, batch_size, Tr) where Tr = ceil(seq_len_q / Br)
+    constexpr int Br = 32;
+    constexpr int Bc = 32;
+    int64_t Tr = (seq_len_q + Br - 1) / Br;
+
+    dim3 block_dims(Bc, Br);  // (blockDim.x, blockDim.y) = (Bc, Br)
+    dim3 grid_dims(num_heads, batch_size, Tr);  // (gridDim.x, gridDim.y, gridDim.z) = (num_heads, batch_size, Tr)
+
+    // Calculate shared memory size for backward pass
+    // Q_sm[Br][head_dim], K_T_sm[head_dim][Bc], V_sm[Bc][head_dim]
+    // dO_sm[Br][head_dim], dK_T_sm[head_dim][Bc], dV_sm[Bc][head_dim]
+    // S_sm[Br][Bc], P_sm[Br][Bc]
+    // D_sm[Br] - D values loaded from HBM to shared memory
+    size_t shared_mem_size = Br * head_dim * sizeof(float)     // Q_sm
+                           + head_dim * Bc * sizeof(float)    // K_T_sm
+                           + Bc * head_dim * sizeof(float)    // V_sm
+                           + Br * head_dim * sizeof(float)     // dO_sm
+                           + head_dim * Bc * sizeof(float)    // dK_T_sm
+                           + Bc * head_dim * sizeof(float)    // dV_sm
+                           + Br * Bc * sizeof(float)          // S_sm or (dP_sm when compute dP_sm = dO_i @ V_j^T \in R^{Br*Bc})
+                           + Br * Bc * sizeof(float)         // P_sm or (dS_sm when compute dS_sm = P_sm_ij pointwise multiplied by (dP_sm_ij - D_i) \in R^{Br*Bc})
+                           + Br * sizeof(float)        // L_i
+                           + Br * sizeof(float);              // D_sm (loaded from HBM)
+
+    auto device = query->GetDevice();
+    const auto &cuda_stream = dynamic_cast<infini_train::core::cuda::CudaStream *>(
+                                  infini_train::core::GetDeviceGuardImpl(device.type())->GetStream(device))
+                                  ->cuda_stream();
+
+    FlashAttentionBackwardKernel<T><<<grid_dims, block_dims, shared_mem_size, cuda_stream>>>(
+        grad_query_ptr, grad_key_ptr, grad_value_ptr,
+        query_ptr, key_ptr, value_ptr, output_ptr, grad_output_ptr,
+        logsumexp_ptr, D_ptr, dropout_seed, attn_mask_ptr,
+        scale, is_causal, dropout_p, enable_gqa,
+        batch_size, seq_len_q, seq_len_k, num_heads, num_heads_kv, head_dim);
+}
+
+/**
+ * FlashAttention Backward Function
+ *
+ * This is the main entry point for FlashAttention backward computation.
+ * It creates gradient tensors and launches the appropriate kernel based on data type.
+ *
+ * Args:
+ *   grad_query: Gradient tensor for query
+ *   grad_key: Gradient tensor for key
+ *   grad_value: Gradient tensor for value
+ *   query: Query tensor from forward pass
+ *   key: Key tensor from forward pass
+ *   value: Value tensor from forward pass
+ *   output: Output tensor from forward pass
+ *   grad_output: Gradient from upstream
+ *   attn_mask: Optional attention mask tensor
+ *   scale: Scaling factor
+ *   is_causal: Whether causal masking was applied
+ *   dropout_p: Dropout probability (not implemented)
+ *   enable_gqa: Whether GQA was enabled
+ *
+ * Returns:
+ *   Tuple of (grad_query, grad_key, grad_value) tensors
+ */
+
+
+// Non-template wrapper functions for registration
+FlashAttentionForwardOutput FlashAttentionForward(const std::shared_ptr<Tensor> &query, const std::shared_ptr<Tensor> &key,
+                                               const std::shared_ptr<Tensor> &value,
+                                               const std::shared_ptr<Tensor> &attn_mask, float scale, bool is_causal,
+                                               int64_t dropout_p, bool enable_gqa) {
+    auto dtype = query->Dtype();
+    const auto &query_dims = query->Dims();
+
+    // Output shape: [batch_size, seq_len_q, num_heads, head_dim]
+    std::vector<int64_t> output_dims = {query_dims[0], query_dims[1], query_dims[2], query_dims[3]};
+    auto output = std::make_shared<Tensor>(output_dims, dtype, query->GetDevice());
+
+    // Allocate logsumexp tensor for backward pass
+    // Shape: [batch_size, num_heads, seq_len_q]
+    std::vector<int64_t> logsumexp_dims = {query_dims[0], query_dims[2], query_dims[1]};
+    auto logsumexp = std::make_shared<Tensor>(logsumexp_dims, DataType::kFLOAT32, query->GetDevice());
+    float *logsumexp_ptr = static_cast<float *>(logsumexp->DataPtr());
+
+    // Allocate dropout_seed tensor for backward pass
+    // Shape: [1]
+    unsigned long long dropout_seed = 0;
+    std::shared_ptr<Tensor> dropout_seed_tensor;
+    if (dropout_p > 0) {
+        std::vector<int64_t> dropout_seed_dims = {1};
+        dropout_seed_tensor = std::make_shared<Tensor>(dropout_seed_dims, DataType::kUINT64, query->GetDevice());
+        dropout_seed = static_cast<unsigned long long>(std::time(nullptr));
+        unsigned long long *dropout_seed_ptr = static_cast<unsigned long long *>(dropout_seed_tensor->DataPtr());
+        *dropout_seed_ptr = dropout_seed;
+    }
+
+    switch (dtype) {
+        DISPATCH_CASE(WRAP(LaunchFlashAttentionForward<float>(output, query, key, value, attn_mask, scale, is_causal,
+                                                               dropout_p, enable_gqa, logsumexp_ptr,
+                                                               dropout_seed_tensor ? static_cast<unsigned long long*>(dropout_seed_tensor->DataPtr()) : nullptr);),
+                      DataType::kFLOAT32)
+        DISPATCH_CASE(WRAP(LaunchFlashAttentionForward<nv_bfloat16>(output, query, key, value, attn_mask, scale,
+                                                                     is_causal, dropout_p, enable_gqa, logsumexp_ptr,
+                                                                     dropout_seed_tensor ? static_cast<unsigned long long*>(dropout_seed_tensor->DataPtr()) : nullptr);),
+                      DataType::kBFLOAT16)
+    default:
+        LOG_LOC(FATAL, "CUDA FlashAttention forward: 'Unsupported data type'");
+    }
+
+    FlashAttentionForwardOutput result;
+    result.output = output;
+    result.logsumexp = logsumexp;
+    result.dropout_seed = dropout_seed_tensor;
+    return result;
+}
+
+std::vector<std::shared_ptr<Tensor>> FlashAttentionBackward(const std::shared_ptr<Tensor> &grad_query, const std::shared_ptr<Tensor> &grad_key,
+                                                          const std::shared_ptr<Tensor> &grad_value, const std::shared_ptr<Tensor> &query,
+                                                          const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
+                                                          const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &grad_output,
+                                                          const std::shared_ptr<Tensor> &logsumexp,
+                                                          const std::shared_ptr<Tensor> &dropout_seed,
+                                                          const std::shared_ptr<Tensor> &attn_mask,
+                                                          float scale, bool is_causal, int64_t dropout_p, bool enable_gqa) {
+    auto dtype = query->Dtype();
+
+    // Create gradient tensors with same shapes as inputs
+    auto grad_query = std::make_shared<Tensor>(query->Dims(), dtype, query->GetDevice());
+    auto grad_key = std::make_shared<Tensor>(key->Dims(), dtype, key->GetDevice());
+    auto grad_value = std::make_shared<Tensor>(value->Dims(), dtype, value->GetDevice());
+
+    // Initialize gradients to zero
+    DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_query->Fill<T>(0); }, "CUDA FlashAttentionBackward");
+    DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_key->Fill<T>(0); }, "CUDA FlashAttentionBackward");
+    DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_value->Fill<T>(0); }, "CUDA FlashAttentionBackward");
+
+    // Get dropout seed value
+    unsigned long long dropout_seed_value = 0;
+    if (dropout_seed) {
+        dropout_seed_value = *static_cast<const unsigned long long *>(dropout_seed->DataPtr());
+    }
+
+    switch (dtype) {
+        DISPATCH_CASE(WRAP(LaunchFlashAttentionBackward<float>(grad_query, grad_key, grad_value, query, key, value, output, grad_output,
+                                                               logsumexp, dropout_seed_value, attn_mask, scale, is_causal,
+                                                               dropout_p, enable_gqa);),
+                      DataType::kFLOAT32)
+        DISPATCH_CASE(WRAP(LaunchFlashAttentionBackward<nv_bfloat16>(grad_query, grad_key, grad_value, query, key, value, output, grad_output,
+                                                                    logsumexp, dropout_seed_value, attn_mask, scale, is_causal,
+                                                                    dropout_p, enable_gqa);),
+                      DataType::kBFLOAT16)
+    default:
+        LOG_LOC(FATAL, "CUDA FlashAttention backward: 'Unsupported data type'");
+    }
+
+    return {grad_query, grad_key, grad_value};
+}
+
+} // namespace infini_train::kernels::cuda
+
+// Register FlashAttention kernels with the dispatcher
+#define REGISTER_CUDA_FLASHATTENTION_KERNEL(kernel_name) \
+    REGISTER_KERNEL(infini_train::Device::DeviceType::kCUDA, kernel_name, infini_train::kernels::cuda::kernel_name)
+
+REGISTER_CUDA_FLASHATTENTION_KERNEL(FlashAttentionForward)
+REGISTER_CUDA_FLASHATTENTION_KERNEL(FlashAttentionBackward)
+
+#undef REGISTER_CUDA_FLASHATTENTION_KERNEL
diff --git a/infini_train/src/kernels/cuda/flash_attention1.cu b/infini_train/src/kernels/cuda/flash_attention1.cu
new file mode 100644
index 00000000..75931887
--- /dev/null
+++ b/infini_train/src/kernels/cuda/flash_attention1.cu
@@ -0,0 +1,1171 @@
+#include <cmath>
+#include <cstddef>
+
+#include "glog/logging.h"
+
+#include "infini_train/include/common/cuda/common_cuda.h"
+#include "infini_train/include/common/cuda/kernel_helper.cuh"
+#include "infini_train/include/core/runtime/device_guard.h"
+#include "infini_train/include/dispatcher.h"
+#include "infini_train/include/kernels/cuda/flash_attention.h"
+#include "infini_train/include/tensor.h"
+
+#include "infini_train/src/core/runtime/cuda/cuda_runtime_common.h"
+
+namespace infini_train::kernels::cuda {
+
+/**
+ * FlashAttention Forward Kernel
+ *
+ * This kernel implements the FlashAttention algorithm for efficient attention computation.
+ * It uses tiling and recomputation to reduce memory access and improve performance.
+ *
+ * Args:
+ *   output: Output tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+ *   query: Query tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+ *   key: Key tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+ *   value: Value tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+ *   attn_mask: Optional attention mask tensor
+ *   logsumexp: Save logsumexp for backward pass
+ *   scale: Scaling factor for attention scores
+ *   is_causal: Whether to apply causal masking
+ *   enable_gqa: Whether to enable grouped-query attention
+ *   dropout_p: Dropout probability
+ *   dropout_seed: Random seed for dropout (for reproducibility)
+ *   batch_size, target_seq_len, src_seq_len: Tensor dimensions
+ *   q_heads, kv_heads, head_dim: Attention head dimensions
+ *
+ * Note: Actual kernel implementation is not included in this version.
+ */
+template <typename T>
+__device__ T warp_reduce_sum(T val){
+#pragma unroll//短循环自动展开,省去分支预测,提升效率
+    for(int offset = 16; offset > 0; offset >>= 1){
+        val += __shfl_down_sync(0xffffffff, val, offset);
+    }
+    return val;
+}
+
+template <typename T>
+__device__ T myexp(T x) {
+    if constexpr(std::is_same<T, __half>::value) {
+        float fx = __half2float(x);
+        float result = expf(fx);
+        return __float2half(result);
+    }
+    else if constexpr(std::is_same<T, float>::value) {
+        return expf(x);  // expf返回float
+    }
+    else if constexpr(std::is_same<T, double>::value) {
+        return exp(x);   // exp返回double
+    }
+    else{//other types
+      return T(0);
+    }
+}
+
+template <typename T>
+__device__ T warp_reduce_max(T val){
+#pragma unroll//短循环自动展开,省去分支预测,提升效率
+    for(int offset = 16; offset > 0; offset >>= 1){
+        T tmp = __shfl_down_sync(0xffffffff, val, offset);
+        val = (val > tmp) ? val : tmp;
+    }
+    return val;
+}
+
+
+/**
+ * FlashAttention Forward Kernel
+ *
+ * This kernel implements the FlashAttention algorithm for efficient attention computation.
+ * It uses tiling and recomputation to reduce memory access and improve performance.
+ *
+ * Args:
+ *   output: Output tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+ *   query: Query tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+ *   key: Key tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+ *   value: Value tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+ *   attn_mask: Optional attention mask tensor
+ *   logsumexp: Save logsumexp for backward pass
+ *   scale: Scaling factor for attention scores
+ *   is_causal: Whether to apply causal masking
+ *   enable_gqa: Whether to enable grouped-query attention
+ *   dropout_p: Dropout probability
+ *   dropout_seed: Random seed for dropout (for reproducibility)
+ *   batch_size, target_seq_len, src_seq_len: Tensor dimensions
+ *   q_heads, kv_heads, head_dim: Attention head dimensions
+ *
+ * Note: Actual kernel implementation is not included in this version.
+ */
+template <typename T>
+__global__ void FlashAttentionForwardKernel(
+    T *output, const T *query, const T *key, const T *value, const T *attn_mask,
+    float *logsumexp,  // Save logsumexp for backward pass
+    float scale, bool is_causal, bool enable_gqa, int64_t dropout_p,
+    unsigned long long dropout_seed,  // Use dropout_seed instead of dropout_mask
+    int batch_size, int target_seq_len, int src_seq_len,
+    int q_heads, int kv_heads, int head_dim) {
+    
+    int tid_x = threadIdx.x;          // 横向,blockDim.x列 (Bc)
+    int tid_y = threadIdx.y;          // 纵向,blockDim.y行 (Br)
+    int bid_x = blockIdx.x;           // x方向,总数 = #q_heads
+    int bid_y = blockIdx.y;           // y方向,总数 = #batch
+    int bid_z = blockIdx.z;           // z方向,总数 = Tr
+    const int p = q_heads / kv_heads; // 计算比例系数，GQA支持
+
+    const int Br = blockDim.y;                  // Q纵向每块大小, 32
+    const int Bc = blockDim.x;                  // K/V纵向分块大小, 32
+    const int Tc = gridDim.z; // 对应原始论文中K/V纵向分块数Tc,其中Bc = 32
+
+    // 定义一系列临时变量
+    extern __shared__ char shared_mem[];
+    char *ptr = shared_mem;
+    
+    // 计算中间变量,包括S, P(复用为SP), m_prev, m_new, l_prev, l_new
+    double *SP = reinterpret_cast<double *>(ptr); // double SP[Br][Bc]
+    ptr += Br * Bc * sizeof(double);
+    float *m_prev = reinterpret_cast<float *>(ptr); // float m_prev[Br]
+    ptr += Br * sizeof(float);
+    float *m_new = reinterpret_cast<float *>(ptr); // float m_new[Br]
+    ptr += Br * sizeof(float);
+    float *l_prev = reinterpret_cast<float *>(ptr); // float l_prev[Br]
+    ptr += Br * sizeof(float);
+    float *l_new = reinterpret_cast<float *>(ptr); // float l_new[Br]
+    ptr += Br * sizeof(float);
+
+    // 原始数据QKV和计算结果O; 全采用float
+    float *Q_sm = reinterpret_cast<float *>(ptr); // float Q_sm[Br][head_dim]
+    ptr += Br * head_dim * sizeof(float);
+    float *K_T_sm = reinterpret_cast<float *>(ptr); // float K_T_sm[head_dim][Bc]
+    ptr += head_dim * Bc * sizeof(float);
+    float *V_sm = reinterpret_cast<float *>(ptr); // float V_sm[Bc][head_dim]
+    ptr += Bc * head_dim * sizeof(float);
+    float *O_sm = reinterpret_cast<float *>(ptr); // float O_sm[Br][head_dim]
+    // Note: Removed dropout_sm shared memory allocation
+
+    // 定义访问宏
+#define SP_AT(y, x) SP[y * Bc + x]
+#define Q_sm_AT(y, x) Q_sm[y * head_dim + x]
+#define K_T_sm_AT(y, x) K_T_sm[y * Bc + x]
+#define V_sm_AT(y, x) V_sm[y * head_dim + x]
+#define O_sm_AT(y, x) O_sm[y * head_dim + x]
+    // Note: Removed DROPOUT_SM_AT macro
+
+    /****************************preparation**************************/
+    int bound_tid_y = min(Br, target_seq_len - Br * bid_z);
+
+    // preparation-1: load Qi from GM to SM, and reset Oi to 0
+    for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+        O_sm_AT(tid_y, idx) = 0.0;
+        Q_sm_AT(tid_y, idx) = 0.0;
+        if (tid_y < bound_tid_y) {
+            Q_sm_AT(tid_y, idx)
+                = float(query[((((bid_y * target_seq_len) + (Br * bid_z + tid_y)) * q_heads) + bid_x) * head_dim + idx]);
+        }
+    }
+    __syncthreads();
+
+    // preparation-2: reset m_prev to -INFINITY and l_prev to 0
+    if (tid_x == 0) {
+        m_prev[tid_y] = -8192.0;
+        l_prev[tid_y] = 0.0;
+    }
+    __syncthreads();
+
+    // Initialize dropout random state with dropout_seed
+    curandStatePhilox4_32_10_t state;
+    if (dropout_p > 0) {
+        unsigned long long seq = bid_y * q_heads * target_seq_len + bid_x * target_seq_len + Br * bid_z + tid_y;
+        curand_init(dropout_seed, seq, 0, &state);
+    }
+/****************************end-of-preparation*************************/
+
+/****************************main-loop**************************/
+#pragma unroll 4
+    for (int j = 0; j < Tc; ++j) { // 对于每个K/V分块
+        bool skip = (is_causal && bid_z < j);
+        if (skip) { // early exit, 直接跳过
+            __syncthreads();
+            continue;
+        }
+
+        SP_AT(tid_y, tid_x) = -8192.0;
+        __syncthreads();
+        int bound_tid_x = min(Bc, src_seq_len - Bc * j);
+        bool is_compute = true; // optimization: 分支处理,加速branch-resolving
+        if (is_causal) {
+            if (bid_z < j) {
+                is_compute = false; // 早期退出情况
+            } else if (bid_z == j) {
+                is_compute = (tid_y >= tid_x); // 对角线以上
+            }
+        }
+
+        // step-1: load Ki, Vi from GM to SM
+#pragma unroll
+        for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+            K_T_sm_AT(idx, tid_y) = 0.0;
+            V_sm_AT(tid_y, idx) = 0.0;
+            if (tid_y < bound_tid_x) {
+                K_T_sm_AT(idx, tid_y) = float(
+                    K[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads) + (bid_x / p)) * head_dim + idx]);
+                V_sm_AT(tid_y, idx) = float(
+                    V[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads) + (bid_x / p)) * head_dim + idx]);
+            }
+        }
+        __syncthreads();
+
+        // step-2: S = Q @ K.T, point-wise
+        if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+            float val0 = 0.0;
+            if (is_compute) {
+#pragma unroll
+                for (int k = 0; k < head_dim; ++k) { 
+                    val0 += Q_sm_AT(tid_y, k) * K_T_sm_AT(k, tid_x); 
+                }
+                SP_AT(tid_y, tid_x) = double(val0) * scale;
+            }
+        }
+        __syncthreads();
+
+        // step-3: m_new = max(m_prev, rowMax(S))
+        float val1 = float(SP_AT(tid_y, tid_x));
+        val1 = warp_reduce_max(val1);
+        if (tid_x == 0 && tid_y < bound_tid_y) {
+            m_new[tid_y] = (val1 > m_prev[tid_y]) ? val1 : m_prev[tid_y];
+        }
+        __syncthreads();
+
+        // step-4: P = exp(S - m_new), point-wise
+        if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+            if (is_compute) {
+                SP_AT(tid_y, tid_x) = myexp<double>(SP_AT(tid_y, tid_x) - double(m_new[tid_y]));
+            } else {
+                SP_AT(tid_y, tid_x) = 0.0;
+            }
+        } else {
+            SP_AT(tid_y, tid_x) = 0.0;
+        }
+        __syncthreads();
+
+        // step-5: l_new = exp(m_prev - m_new) * l_prev + rowSum(P)
+        float val2 = float(SP_AT(tid_y, tid_x));
+        val2 = warp_reduce_sum(val2);
+        float exp_result = myexp<float>(m_prev[tid_y] - m_new[tid_y]);
+        if (tid_x == 0 && tid_y < bound_tid_y) {
+            l_new[tid_y] = exp_result * l_prev[tid_y] + val2;
+        }
+        __syncthreads();
+
+        // step-5.5: Apply dropout to P (using dropout_seed for reproducibility)
+        if (dropout_p > 0 && tid_y < bound_tid_y && tid_x < bound_tid_x) {
+            float rand_val = curand_uniform(&state);
+            bool keep = rand_val > dropout_p;
+            if (keep) {
+                SP_AT(tid_y, tid_x) = SP_AT(tid_y, tid_x) / (1.0f - dropout_p);
+            } else {
+                SP_AT(tid_y, tid_x) = 0.0;
+            }
+            // Note: Removed dropout_sm mask saving to reduce memory overhead
+        }
+        __syncthreads();
+
+        // step-6: O = 1/(exp(m_prev - m_new)) * O + P @ V
+        if (tid_x < bound_tid_x && tid_y < bound_tid_y) {
+            for (int u = tid_x; u < head_dim; u += blockDim.x) {
+                float val3 = 0.0;
+#pragma unroll
+                for (int w = 0; w < Bc; ++w) { 
+                    val3 += float(SP_AT(tid_y, w)) * V_sm_AT(w, u);
+                }
+                O_sm_AT(tid_y, u) = O_sm_AT(tid_y, u) * exp_result + val3;
+            }
+        }
+        __syncthreads();
+
+        // step-7: m_prev <- m_new; l_prev <- l_new
+        if (tid_x == 0 && tid_y < bound_tid_y) {
+            m_prev[tid_y] = m_new[tid_y];
+            l_prev[tid_y] = l_new[tid_y];
+        }
+        __syncthreads();
+    }
+/****************************end-of-main-loop**************************/
+
+/****************************post-process****************************/
+// O(GM) = O/l_prev, aka O_sm /= l_prev and write Oi from SM to GM
+// Also save logsumexp for backward pass
+#pragma unroll
+    for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+        if (tid_y < bound_tid_y) {
+            output[((((bid_y * target_seq_len) + (Br * bid_z + tid_y)) * q_heads) + bid_x) * head_dim + idx]
+                = T(O_sm_AT(tid_y, idx) / float(l_prev[tid_y]));
+        }
+    }
+    
+    // Save logsumexp for backward pass
+    if (tid_x == 0 && tid_y < bound_tid_y) {
+        int logsumexp_idx = ((bid_y * q_heads + bid_x) * target_seq_len) + (Br * bid_z + tid_y);
+        logsumexp[logsumexp_idx] = m_prev[tid_y] + log(l_prev[tid_y]);
+    }
+    __syncthreads();
+/****************************end-of-post-process****************************/
+
+// 取消访问宏定义
+#undef SP_AT
+#undef Q_sm_AT
+#undef K_T_sm_AT
+#undef V_sm_AT
+#undef O_sm_AT
+}
+
+/**
+ * FlashAttention Backward Kernel
+ *
+ * This kernel implements the backward pass for FlashAttention.
+ * It computes gradients for query, key, and value tensors.
+ *
+ * Args:
+ *   grad_query: Gradient for query tensor
+ *   grad_key: Gradient for key tensor
+ *   grad_value: Gradient for value tensor
+ *   query: Query tensor from forward pass
+ *   key: Key tensor from forward pass
+ *   value: Value tensor from forward pass
+ *   output: Output tensor from forward pass
+ *   grad_output: Gradient from upstream
+ *   logsumexp: Logsumexp tensor from forward pass
+ *   dropout_seed: Dropout seed for reproducibility
+ *   attn_mask: Optional attention mask tensor
+ *   scale: Scaling factor for attention scores
+ *   is_causal: Whether causal masking was applied
+ *   dropout_p: Dropout probability
+ *   enable_gqa: Whether GQA was enabled
+ *   batch_size, target_seq_len, src_seq_len: Tensor dimensions
+ *   q_heads, kv_heads, head_dim: Attention head dimensions
+ */
+template <typename T>
+__global__ void FlashAttentionBackwardKernel(
+    T *grad_query, T *grad_key, T *grad_value,
+    const T *query, const T *key, const T *value,
+    const T *output, const T *grad_output,
+    const float *logsumexp,
+    const float *D,  // Precomputed D = rowsum(dO ∘ O)
+    unsigned long long dropout_seed,
+    const T *attn_mask,
+    float scale, bool is_causal, int64_t dropout_p,
+    bool enable_gqa,
+    int batch_size, int target_seq_len, int src_seq_len,
+    int q_heads, int kv_heads, int head_dim) {
+    
+    // Grid/block dimensions: grid_dims(num_heads_q, batch_size, Tr), block_dim(Bc, Br)
+    // where Br corresponds to thread block row index, Bc corresponds to column index
+    int tid_x = threadIdx.x;          // 横向, blockDim.x列 (Bc)
+    int tid_y = threadIdx.y;          // 纵向, blockDim.y行 (Br)
+    int bid_x = blockIdx.x;           // x方向, 总数 = #q_heads
+    int bid_y = blockIdx.y;           // y方向, 总数 = #batch
+    int bid_z = blockIdx.z;           // z方向, 总数 = Tr
+    const int p = q_heads / kv_heads; // GQA比例系数
+
+    const int Br = blockDim.y;                  // Q纵向每块大小, 32
+    const int Bc = blockDim.x;                  // K/V纵向分块大小, 32
+    const int Tr = gridDim.z; // Q纵向分块数
+    const int Tc = (src_seq_len + Bc - 1) / Bc; // K/V纵向分块数
+
+    // Define shared memory
+    extern __shared__ char shared_mem[];
+    char *ptr = shared_mem;
+    
+    // D_sm[Br] - D values loaded from HBM
+    float *D_sm = reinterpret_cast<float *>(ptr);
+    ptr += Br * sizeof(float);
+    
+    // Q_sm[Br][head_dim], K_T_sm[head_dim][Bc], V_sm[Bc][head_dim]
+    float *Q_sm = reinterpret_cast<float *>(ptr);
+    ptr += Br * head_dim * sizeof(float);
+    float *K_T_sm = reinterpret_cast<float *>(ptr);
+    ptr += head_dim * Bc * sizeof(float);
+    float *V_sm = reinterpret_cast<float *>(ptr);
+    ptr += Bc * head_dim * sizeof(float);
+    
+    // dO_sm[Br][head_dim], dK_T_sm[head_dim][Bc], dV_sm[Bc][head_dim]
+    float *dO_sm = reinterpret_cast<float *>(ptr);
+    ptr += Br * head_dim * sizeof(float);
+    float *dK_T_sm = reinterpret_cast<float *>(ptr);
+    ptr += head_dim * Bc * sizeof(float);
+    float *dV_sm = reinterpret_cast<float *>(ptr);
+    ptr += Bc * head_dim * sizeof(float);
+    
+    // S_sm[Br][Bc], P_sm[Br][Bc]
+    float *S_sm = reinterpret_cast<float *>(ptr);
+    ptr += Br * Bc * sizeof(float);
+    float *P_sm = reinterpret_cast<float *>(ptr);
+    
+    // L_sm[Br] - logsumexp values
+    float *L_sm = reinterpret_cast<float *>(ptr);
+    
+    // dQ_sm[Br][head_dim] - accumulated gradient for Q
+    float *dQ_sm = reinterpret_cast<float *>(ptr);
+    
+    // Define access macros
+    #define D_sm_AT(y) D_sm[y]
+    #define Q_sm_AT(y, x) Q_sm[y * head_dim + x]
+    #define K_T_sm_AT(y, x) K_T_sm[y * Bc + x]
+    #define V_sm_AT(y, x) V_sm[y * head_dim + x]
+    #define dO_sm_AT(y, x) dO_sm[y * head_dim + x]
+    #define dK_T_sm_AT(y, x) dK_T_sm[y * Bc + x]
+    #define dV_sm_AT(y, x) dV_sm[y * head_dim + x]
+    #define S_sm_AT(y, x) S_sm[y * Bc + x]
+    #define P_sm_AT(y, x) P_sm[y * Bc + x]
+    #define L_sm_AT(y) L_sm[y]
+    #define dQ_sm_AT(y, x) dQ_sm[y * head_dim + x]
+
+    // Initialize dropout random state
+    curandStatePhilox4_32_10_t state;
+    if (dropout_p > 0) {
+        unsigned long long seq = bid_y * q_heads * target_seq_len + bid_x * target_seq_len + Br * bid_z + tid_y;
+        curand_init(dropout_seed, seq, 0, &state);
+    }
+
+    /****************************Preparation*****************************/
+    
+    // Initialize dQ_sm to 0 for accumulation
+    for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+        dQ_sm_AT(tid_y, idx) = 0.0f;
+    }
+    __syncthreads();
+
+    // Load D_i from HBM to shared memory
+    int q_idx = Br * bid_z + tid_y;  // Global query position within this head
+    if (q_idx < target_seq_len) {
+        int d_idx = ((bid_y * q_heads + bid_x) * target_seq_len) + q_idx;
+        D_sm_AT(tid_y) = D[d_idx];
+    } else {
+        D_sm_AT(tid_y) = 0.0f;  // Padding for out-of-bounds positions
+    }
+    __syncthreads();
+
+    // Get D_i from shared memory
+    float D_i = D_sm_AT(tid_y);
+
+    /****************************Main Loop - Outer Loop over K/V tiles*****************************/
+    for (int j = 0; j < Tc; ++j) {  // For each K/V column tile
+        
+        // Skip entire tile if causal and this tile is completely to the right
+        bool skip_tile = (is_causal && bid_z < j);
+        if (skip_tile) {
+            __syncthreads();
+            continue;
+        }
+
+        // Initialize dK_T_sm, dV_sm to 0 for this column tile
+        for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+            for (int y = 0; y < Bc; ++y) {
+                dK_T_sm_AT(idx, y) = 0.0f;
+                dV_sm_AT(y, idx) = 0.0f;
+            }
+        }
+        __syncthreads();
+
+        // Load K_j, V_j from HBM to shared memory
+        int bound_tid_x = min(Bc, src_seq_len - Bc * j);
+        for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+            K_T_sm_AT(idx, tid_y) = 0.0f;
+            V_sm_AT(tid_y, idx) = 0.0f;
+            if (tid_y < bound_tid_x) {
+                int kv_head_idx = bid_x / p;
+                K_T_sm_AT(idx, tid_y) = float(
+                    key[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads + kv_head_idx) * head_dim + idx]);
+                V_sm_AT(tid_y, idx) = float(
+                    value[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads + kv_head_idx) * head_dim + idx]);
+            }
+        }
+        __syncthreads();
+
+        /****************************Inner Loop - Loop over Q tiles*****************************/
+        // For this column tile, iterate through all Q row tiles
+        for (int i = 0; i < Tr; ++i) {  // For each Q row tile
+            
+            // Skip this Q tile if causal and it's completely to the left of current K tile
+            bool skip_q_tile = (is_causal && i > j);
+            if (skip_q_tile) {
+                __syncthreads();
+                continue;
+            }
+
+            // Load Q_i, dO_i, L_i for this row tile
+            int q_tile_start = Br * i;
+            int q_tile_end = min(q_tile_start + Br, target_seq_len);
+            int bound_tid_y = min(Br, q_tile_end - q_tile_start);
+            
+            int local_q_idx = Br * i + tid_y;
+            if (local_q_idx < bound_tid_y) {
+                // Load Q_i, dO_i, L_i from HBM
+                int global_q_idx = q_tile_start + tid_y;
+                int q_tensor_idx = ((bid_y * target_seq_len) + global_q_idx) * q_heads + bid_x;
+                
+                for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+                    Q_sm_AT(tid_y, idx) = float(query[q_tensor_idx * head_dim + idx]);
+                    dO_sm_AT(tid_y, idx) = float(grad_output[q_tensor_idx * head_dim + idx]);
+                    L_sm_AT(tid_y) = logsumexp[((bid_y * q_heads + bid_x) * target_seq_len + global_q_idx];
+                }
+            }
+            __syncthreads();
+
+            // Get D_i for this row
+            float D_i_row = D_sm_AT(tid_y);
+
+            // Recompute S_ij = Q_i @ K_j^T
+            // Apply causal mask at element level if needed
+            #pragma unroll
+            for (int y = 0; y < Bc; ++y) {
+                for (int x = 0; x < Bc; ++x) {
+                    float val = 0.0f;
+                    if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+                        bool is_compute = true;
+                        if (is_causal && i == j) {
+                            // On diagonal, only compute upper triangle
+                            int global_q_pos = q_tile_start + tid_y;
+                            int global_k_pos = Bc * j + x;
+                            is_compute = (global_q_pos >= global_k_pos);
+                        }
+                        
+                        if (is_compute) {
+                            #pragma unroll
+                            for (int k = 0; k < head_dim; ++k) {
+                                val += Q_sm_AT(tid_y, k) * K_T_sm_AT(k, y);
+                            }
+                        }
+                    }
+                    S_sm_AT(tid_y, x) = val * scale;
+                }
+            }
+            __syncthreads();
+
+            // Recompute P_ij = exp(S_ij - L_i)
+            #pragma unroll
+            for (int y = 0; y < Bc; ++y) {
+                for (int x = 0; x < Bc; ++x) {
+                    if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+                        bool is_compute = true;
+                        if (is_causal && i == j) {
+                            int global_q_pos = q_tile_start + tid_y;
+                            int global_k_pos = Bc * j + x;
+                            is_compute = (global_q_pos >= global_k_pos);
+                        }
+                        
+                        if (is_compute) {
+                            P_sm_AT(tid_y, x) = myexp<float>(S_sm_AT(tid_y, x) - L_sm_AT(tid_y));
+                        } else {
+                            P_sm_AT(tid_y, x) = 0.0f;
+                        }
+                    } else {
+                        P_sm_AT(tid_y, x) = 0.0f;
+                    }
+                }
+            }
+            __syncthreads();
+
+            // Apply dropout to P_ij
+            if (dropout_p > 0) {
+                #pragma unroll
+                for (int y = 0; y < Bc; ++y) {
+                    for (int x = 0; x < Bc; ++x) {
+                        if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+                            float rand_val = curand_uniform(&state);
+                            bool keep = rand_val > dropout_p;
+                            if (keep) {
+                                P_sm_AT(tid_y, x) = P_sm_AT(tid_y, x) / (1.0f - dropout_p);
+                            } else {
+                                P_sm_AT(tid_y, x) = 0.0f;
+                            }
+                        }
+                    }
+                }
+                __syncthreads();
+            }
+
+            // Compute dV_j += P_ij^T @ dO_i
+            if (tid_y < bound_tid_y) {
+                #pragma unroll
+                for (int x = 0; x < head_dim; x += blockDim.x) {
+                    float val = 0.0f;
+                    #pragma unroll
+                    for (int y = 0; y < Bc; ++y) {
+                        val += P_sm_AT(tid_y, y) * dO_sm_AT(tid_y, x);
+                    }
+                    atomicAdd(&dV_sm_AT(y, x), val);
+                }
+            }
+            __syncthreads();
+
+            // Compute dP_ij = dO_i @ V_j^T
+            #pragma unroll
+            for (int y = 0; y < Bc; ++y) {
+                for (int x = 0; x < Bc; ++x) {
+                    float val = 0.0f;
+                    if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+                        #pragma unroll
+                        for (int k = 0; k < head_dim; ++k) {
+                            val += dO_sm_AT(tid_y, k) * V_sm_AT(y, k);
+                        }
+                    }
+                    S_sm_AT(tid_y, x) = val;  // Reuse S_sm as temporary storage for dP_ij
+                } else {
+                    S_sm_AT(tid_y, x) = 0.0f;
+                }
+            }
+            __syncthreads();
+
+            // Apply dropout to dP_ij
+            if (dropout_p > 0) {
+                #pragma unroll
+                for (int y = 0; y < Bc; ++y) {
+                    for (int x = 0; x < Bc; ++x) {
+                        if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+                            float rand_val = curand_uniform(&state);
+                            bool keep = rand_val > dropout_p;
+                            if (keep) {
+                                S_sm_AT(tid_y, x) = S_sm_AT(tid_y, x) / (1.0f - dropout_p);
+                            } else {
+                                S_sm_AT(tid_y, x) = 0.0f;
+                            }
+                        }
+                    }
+                }
+                __syncthreads();
+            }
+
+            // Compute dS_ij = P_ij * (dP_ij - D_i)
+            #pragma unroll
+            for (int y = 0; y < Bc; ++y) {
+                for (int x = 0; x < Bc; ++x) {
+                    if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+                        S_sm_AT(tid_y, x) = P_sm_AT(tid_y, x) * (S_sm_AT(tid_y, x) - D_i_row);
+                    } else {
+                        S_sm_AT(tid_y, x) = 0.0f;
+                    }
+                }
+            }
+            __syncthreads();
+
+            // Compute dK_j += dS_ij^T @ Q_i
+            if (tid_x < head_dim) {
+                #pragma unroll
+                for (int y = 0; y < Bc; ++y) {
+                    float val = 0.0f;
+                    #pragma unroll
+                    for (int x = 0; x < Br; ++x) {
+                        val += S_sm_AT(x, y) * Q_sm_AT(x, tid_x);
+                    }
+                    atomicAdd(&dK_T_sm_AT(tid_x, y), val * scale);
+                }
+            }
+            __syncthreads();
+
+            // Compute dQ_i += dS_ij @ K_j
+            if (tid_y < bound_tid_y) {
+                #pragma unroll
+                for (int x = 0; x < head_dim; x += blockDim.x) {
+                    float val = 0.0f;
+                    #pragma unroll
+                    for (int y = 0; y < Bc; ++y) {
+                        val += S_sm_AT(tid_y, y) * K_T_sm_AT(x, y);
+                    }
+                    dQ_sm_AT(tid_y, x) += val * scale;
+                }
+            }
+            __syncthreads();
+        }  // End of inner loop over Q tiles
+
+        // Write back dK_j, dV_j to HBM
+        // Note: For GQA, dK_j and dV_j need to be accumulated across multiple q-head blocks
+        int kv_head_idx = bid_x / p;
+        int k_tile_start = Bc * j;
+        int k_tile_end = min(k_tile_start + Bc, src_seq_len);
+        
+        for (int y = 0; y < Bc; ++y) {
+            int global_k_idx = k_tile_start + y;
+            if (global_k_idx < src_seq_len) {
+                for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+                    // dK: [batch_size, src_seq_len, kv_heads, head_dim]
+                    int k_tensor_idx = ((bid_y * src_seq_len) + global_k_idx) * kv_heads + kv_head_idx;
+                    grad_key[k_tensor_idx * head_dim + idx] += T(dK_T_sm_AT(idx, y));
+                    
+                    // dV: [batch_size, src_seq_len, kv_heads, head_dim]
+                    grad_value[k_tensor_idx * head_dim + idx] += T(dV_sm_AT(y, idx));
+                }
+            }
+        }
+    }  // End of outer loop over K/V tiles
+
+    // Write back dQ_i to HBM
+    int q_tile_start = Br * bid_z;
+    int q_tile_end = min(q_tile_start + Br, target_seq_len);
+    int bound_tid_y = min(Br, q_tile_end - q_tile_start);
+    
+    if (tid_y < bound_tid_y) {
+        int global_q_idx = q_tile_start + tid_y;
+        int q_tensor_idx = ((bid_y * target_seq_len) + global_q_idx) * q_heads + bid_x;
+        
+        for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+            // dQ: [batch_size, target_seq_len, q_heads, head_dim]
+            grad_query[q_tensor_idx * head_dim + idx] += T(dQ_sm_AT(tid_y, idx));
+        }
+    }
+
+    // Undefine access macros
+    #undef D_sm_AT
+    #undef Q_sm_AT
+    #undef K_T_sm_AT
+    #undef V_sm_AT
+    #undef dO_sm_AT
+    #undef dK_T_sm_AT
+    #undef dV_sm_AT
+    #undef S_sm_AT
+    #undef P_sm_AT
+    #undef L_sm_AT
+    #undef dQ_sm_AT
+}
+
+/**
+ * FlashAttention Forward Function
+ *
+ * This is the main entry point for FlashAttention forward computation.
+ * It creates the output tensor and launches the appropriate kernel based on data type.
+ *
+ * Args:
+ *   query: Query tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+ *   key: Key tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+ *   value: Value tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+ *   attn_mask: Optional attention mask tensor
+ *   scale: Scaling factor for attention scores
+ *   is_causal: Whether to apply causal masking
+ *   dropout_p: Dropout probability
+ *   enable_gqa: Whether to enable grouped-query attention
+ *
+ * Returns:
+ *   Output tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+ *   logsumexp: Logsumexp tensor for backward pass [batch_size, num_heads, seq_len_q]
+ *   dropout_seed: Dropout seed for backward pass [1]
+ */
+
+template <typename T>
+FlashAttentionForwardOutput FlashAttentionForwardImpl(const std::shared_ptr<Tensor> &query, const std::shared_ptr<Tensor> &key,
+                                                    const std::shared_ptr<Tensor> &value,
+                                                    const std::shared_ptr<Tensor> &attn_mask, float scale, bool is_causal,
+                                                    int64_t dropout_p, bool enable_gqa) {
+    auto dtype = query->Dtype();
+    const auto &query_dims = query->Dims();
+
+    // Output shape: [batch_size, seq_len_q, num_heads, head_dim]
+    std::vector<int64_t> output_dims = {query_dims[0], query_dims[1], query_dims[2], query_dims[3]};
+    auto output = std::make_shared<Tensor>(output_dims, dtype, query->GetDevice());
+
+    // Allocate logsumexp tensor for backward pass
+    // Shape: [batch_size, num_heads, seq_len_q]
+    std::vector<int64_t> logsumexp_dims = {query_dims[0], query_dims[2], query_dims[1]};
+    auto logsumexp = std::make_shared<Tensor>(logsumexp_dims, DataType::kFLOAT32, query->GetDevice());
+    float *logsumexp_ptr = static_cast<float *>(logsumexp->DataPtr());
+
+    // Allocate dropout_seed tensor for backward pass
+    // Shape: [1]
+    unsigned long long dropout_seed = 0;
+    std::shared_ptr<Tensor> dropout_seed_tensor;
+    if (dropout_p > 0) {
+        std::vector<int64_t> dropout_seed_dims = {1};
+        dropout_seed_tensor = std::make_shared<Tensor>(dropout_seed_dims, DataType::kUINT64, query->GetDevice());
+        dropout_seed = static_cast<unsigned long long>(std::time(nullptr));
+        unsigned long long *dropout_seed_ptr = static_cast<unsigned long long *>(dropout_seed_tensor->DataPtr());
+        *dropout_seed_ptr = dropout_seed;
+    }
+
+    switch (dtype) {
+        DISPATCH_CASE(WRAP(LaunchFlashAttentionForward<float>(output, query, key, value, attn_mask, scale, is_causal,
+                                                               dropout_p, enable_gqa, logsumexp_ptr, dropout_seed_ptr);),
+                      DataType::kFLOAT32)
+        DISPATCH_CASE(WRAP(LaunchFlashAttentionForward<nv_bfloat16>(output, query, key, value, attn_mask, scale,
+                                                                     is_causal, dropout_p, enable_gqa, logsumexp_ptr, dropout_seed_ptr);),
+                      DataType::kBFLOAT16)
+    default:
+        LOG_LOC(FATAL, "CUDA FlashAttention forward: 'Unsupported data type'");
+    }
+
+    FlashAttentionForwardOutput result;
+    result.output = output;
+    result.logsumexp = logsumexp;
+    result.dropout_seed = dropout_seed_tensor;
+    return result;
+}
+
+/**
+ * Launch FlashAttention Forward Kernel
+ *
+ * This function sets up grid and block dimensions and launches FlashAttention forward kernel.
+ *
+ * Args:
+ *   output: Output tensor
+ *   query: Query tensor
+ *   key: Key tensor
+ *   value: Value tensor
+ *   attn_mask: Optional attention mask tensor
+ *   scale: Scaling factor
+ *   is_causal: Whether to apply causal masking
+ *   dropout_p: Dropout probability
+ *   enable_gqa: Whether to enable GQA
+ */
+template <typename T>
+void LaunchFlashAttentionForward(const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &query,
+                                 const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
+                                 const std::shared_ptr<Tensor> &attn_mask, float scale, bool is_causal,
+                                 int64_t dropout_p, bool enable_gqa, float *logsumexp_ptr, unsigned long long *dropout_seed_ptr) {
+
+    const auto &query_dims = query->Dims();
+    const auto &key_dims = key->Dims();
+    const auto &value_dims = value->Dims();
+
+    // Expected shapes:
+    // query: [batch_size, seq_len_q, num_heads, head_dim]
+    // key: [batch_size, seq_len_k, num_heads_kv, head_dim]
+    // value: [batch_size, seq_len_k, num_heads_kv, head_dim]
+    // output: [batch_size, seq_len_q, num_heads, head_dim]
+
+    int64_t batch_size = query_dims[0];
+    int64_t seq_len_q = query_dims[1];
+    int64_t num_heads = query_dims[2];
+    int64_t head_dim = query_dims[3];
+    int64_t seq_len_k = key_dims[1];
+    int64_t num_heads_kv = key_dims[2];
+
+    CHECK_EQ(key_dims[3], head_dim) << "Key head dimension must match query head dimension";
+    CHECK_EQ(value_dims[3], head_dim) << "Value head dimension must match query head dimension";
+    CHECK_EQ(value_dims[1], seq_len_k) << "Value sequence length must match key sequence length";
+    CHECK_EQ(value_dims[2], num_heads_kv) << "Value number of KV heads must match key";
+
+    if (enable_gqa) {
+        CHECK(num_heads % num_heads_kv == 0) << "Number of query heads must be divisible by number of KV heads for GQA";
+    } else {
+        CHECK_EQ(num_heads, num_heads_kv) << "Number of query and KV heads must match for standard attention";
+    }
+
+    T *output_ptr = static_cast<T *>(output->DataPtr());
+    const T *query_ptr = static_cast<const T *>(query->DataPtr());
+    const T *key_ptr = static_cast<const T *>(key->DataPtr());
+    const T *value_ptr = static_cast<const T *>(value->DataPtr());
+    const T *attn_mask_ptr = attn_mask ? static_cast<const T *>(attn_mask->DataPtr()) : nullptr;
+
+    // Set up grid and block dimensions according to FlashAttention v2
+    // block_dim(Br, Bc) where Br = Bc = 32
+    // grid_dim(query_heads, batch_size, Tr) where Tr = ceil(seq_len_q / Br)
+    constexpr int Br = 32;
+    constexpr int Bc = 32;
+    int64_t Tr = (seq_len_q + Br - 1) / Br;
+    
+    dim3 block_dims(Bc, Br);  // (blockDim.x, blockDim.y) = (Bc, Br)
+    dim3 grid_dims(num_heads, batch_size, Tr);  // (gridDim.x, gridDim.y, gridDim.z) = (num_heads, batch_size, Tr)
+
+    // Calculate shared memory size (removed dropout_sm allocation)
+    // SP[Br][Bc] (double) + m_prev[Br] (float) + m_new[Br] (float) + l_prev[Br] (float) + l_new[Br] (float)
+    // + Q_sm[Br][head_dim] (float) + K_T_sm[head_dim][Bc] (float) + V_sm[Bc][head_dim] (float) + O_sm[Br][head_dim] (float)
+    size_t shared_mem_size = Br * Bc * sizeof(double)  // SP
+                          + 4 * Br * sizeof(float)     // m_prev, m_new, l_prev, l_new
+                          + (Br + Bc + Bc + Br) * head_dim) * sizeof(float);  // Q_sm, K_T_sm, V_sm, O_sm
+    // Note: Removed dropout_sm[Br][Bc] (bool) allocation
+
+    auto device = output->GetDevice();
+    const auto &cuda_stream = dynamic_cast<infini_train::core::cuda::CudaStream *>(
+                                  infini_train::core::GetDeviceGuardImpl(device.type())->GetStream(device))
+                                  ->cuda_stream();
+
+    FlashAttentionForwardKernel<T><<<grid_dims, block_dims, shared_mem_size, cuda_stream>>>(
+        output_ptr, query_ptr, key_ptr, value_ptr, attn_mask_ptr,
+        logsumexp_ptr, scale, is_causal, enable_gqa, dropout_p, dropout_seed,
+        batch_size, seq_len_q, seq_len_k, num_heads, num_heads_kv, head_dim);
+}
+
+/**
+ * Launch FlashAttention Backward Kernel
+ *
+ * This function sets up grid and block dimensions and launches FlashAttention backward kernel.
+ *
+ * Args:
+ *   grad_query: Gradient tensor for query
+ *   grad_key: Gradient tensor for key
+ *   grad_value: Gradient tensor for value
+ *   query: Query tensor from forward pass
+ *   key: Key tensor from forward pass
+ *   value: Value tensor from forward pass
+ *   output: Output tensor from forward pass
+ *   grad_output: Gradient from upstream
+ *   logsumexp: Logsumexp tensor from forward pass
+ *   dropout_seed: Dropout seed for reproducibility
+ *   attn_mask: Optional attention mask tensor
+ *   scale: Scaling factor
+ *   is_causal: Whether causal masking was applied
+ *   dropout_p: Dropout probability
+ *   enable_gqa: Whether GQA was enabled
+ */
+template <typename T>
+void LaunchFlashAttentionBackward(const std::shared_ptr<Tensor> &grad_query, const std::shared_ptr<Tensor> &grad_key,
+                                 const std::shared_ptr<Tensor> &grad_value, const std::shared_ptr<Tensor> &query,
+                                 const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
+                                 const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &grad_output,
+                                 const std::shared_ptr<Tensor> &logsumexp,
+                                 unsigned long long dropout_seed,
+                                 const std::shared_ptr<Tensor> &attn_mask,
+                                 float scale, bool is_causal, int64_t dropout_p, bool enable_gqa) {
+
+    const auto &query_dims = query->Dims();
+    const auto &key_dims = key->Dims();
+
+    // Expected shapes:
+    // query: [batch_size, seq_len_q, num_heads, head_dim]
+    // key: [batch_size, seq_len_k, num_heads_kv, head_dim]
+    // value: [batch_size, seq_len_k, num_heads_kv, head_dim]
+    // output: [batch_size, seq_len_q, num_heads, head_dim]
+
+    int64_t batch_size = query_dims[0];
+    int64_t seq_len_q = query_dims[1];
+    int64_t num_heads = query_dims[2];
+    int64_t head_dim = query_dims[3];
+    int64_t seq_len_k = key_dims[1];
+    int64_t num_heads_kv = key_dims[2];
+
+    CHECK_EQ(key_dims[3], head_dim) << "Key head dimension must match query head dimension";
+    CHECK_EQ(value->Dims()[3], head_dim) << "Value head dimension must match query head dimension";
+    CHECK_EQ(value->Dims()[1], seq_len_k) << "Value sequence length must match key sequence length";
+    CHECK_EQ(value->Dims()[2], num_heads_kv) << "Value number of KV heads must match key";
+
+    if (enable_gqa) {
+        CHECK(num_heads % num_heads_kv == 0) << "Number of query heads must be divisible by number of KV heads for GQA";
+    } else {
+        CHECK_EQ(num_heads, num_heads_kv) << "Number of query and KV heads must match for standard attention";
+    }
+
+    // Precompute D = rowsum(dO ∘ O) before main backward loop
+    // D shape: [batch_size, seq_len_q, num_heads]
+    // dO shape: [batch_size, seq_len_q, num_heads, head_dim]
+    // O shape: [batch_size, seq_len_q, num_heads, head_dim]
+    auto D = function::Sum(grad_output * output, 3, false);
+
+    T *grad_query_ptr = static_cast<T *>(grad_query->DataPtr());
+    T *grad_key_ptr = static_cast<T *>(grad_key->DataPtr());
+    T *grad_value_ptr = static_cast<T *>(grad_value->DataPtr());
+    const T *query_ptr = static_cast<const T *>(query->DataPtr());
+    const T *key_ptr = static_cast<const T *>(key->DataPtr());
+    const T *value_ptr = static_cast<const T *>(value->DataPtr());
+    const T *output_ptr = static_cast<const T *>(output->DataPtr());
+    const T *grad_output_ptr = static_cast<const T *>(grad_output->DataPtr());
+    const float *logsumexp_ptr = static_cast<const float *>(logsumexp->DataPtr());
+    const float *D_ptr = static_cast<const float *>(D->DataPtr());
+    const T *attn_mask_ptr = attn_mask ? static_cast<const T *>(attn_mask->DataPtr()) : nullptr;
+
+    // Set up grid and block dimensions according to FlashAttention v2 backward
+    // block_dim(Bc, Br) where Bc = Br = 32
+    // grid_dims(num_heads_q, batch_size, Tr) where Tr = ceil(seq_len_q / Br)
+    constexpr int Br = 32;
+    constexpr int Bc = 32;
+    int64_t Tr = (seq_len_q + Br - 1) / Br;
+
+    dim3 block_dims(Bc, Br);  // (blockDim.x, blockDim.y) = (Bc, Br)
+    dim3 grid_dims(num_heads, batch_size, Tr);  // (gridDim.x, gridDim.y, gridDim.z) = (num_heads, batch_size, Tr)
+
+    // Calculate shared memory size for backward pass
+    // Q_sm[Br][head_dim], K_T_sm[head_dim][Bc], V_sm[Bc][head_dim]
+    // dO_sm[Br][head_dim], dK_T_sm[head_dim][Bc], dV_sm[Bc][head_dim]
+    // S_sm[Br][Bc], P_sm[Br][Bc]
+    // D_sm[Br] - D values loaded from HBM to shared memory
+    size_t shared_mem_size = Br * head_dim * sizeof(float)     // Q_sm
+                           + head_dim * Bc * sizeof(float)    // K_T_sm
+                           + Bc * head_dim * sizeof(float)    // V_sm
+                           + Br * head_dim * sizeof(float)     // dO_sm
+                           + head_dim * Bc * sizeof(float)    // dK_T_sm
+                           + Bc * head_dim * sizeof(float)    // dV_sm
+                           + Br * Bc * sizeof(float)          // S_sm or (dP_sm when compute dP_sm = dO_i @ V_j^T \in R^{Br*Bc})
+                           + Br * Bc * sizeof(float)         // P_sm or (dS_sm when compute dS_sm = P_sm_ij pointwise multiplied by (dP_sm_ij - D_i) \in R^{Br*Bc})
+                           + Br * sizeof(float)        // L_i
+                           + Br * sizeof(float);              // D_sm (loaded from HBM)
+
+    auto device = query->GetDevice();
+    const auto &cuda_stream = dynamic_cast<infini_train::core::cuda::CudaStream *>(
+                                  infini_train::core::GetDeviceGuardImpl(device.type())->GetStream(device))
+                                  ->cuda_stream();
+
+    FlashAttentionBackwardKernel<T><<<grid_dims, block_dims, shared_mem_size, cuda_stream>>>(
+        grad_query_ptr, grad_key_ptr, grad_value_ptr,
+        query_ptr, key_ptr, value_ptr, output_ptr, grad_output_ptr,
+        logsumexp_ptr, D_ptr, dropout_seed, attn_mask_ptr,
+        scale, is_causal, dropout_p, enable_gqa,
+        batch_size, seq_len_q, seq_len_k, num_heads, num_heads_kv, head_dim);
+}
+
+/**
+ * FlashAttention Backward Function
+ *
+ * This is the main entry point for FlashAttention backward computation.
+ * It creates gradient tensors and launches the appropriate kernel based on data type.
+ *
+ * Args:
+ *   grad_query: Gradient tensor for query
+ *   grad_key: Gradient tensor for key
+ *   grad_value: Gradient tensor for value
+ *   query: Query tensor from forward pass
+ *   key: Key tensor from forward pass
+ *   value: Value tensor from forward pass
+ *   output: Output tensor from forward pass
+ *   grad_output: Gradient from upstream
+ *   attn_mask: Optional attention mask tensor
+ *   scale: Scaling factor
+ *   is_causal: Whether causal masking was applied
+ *   dropout_p: Dropout probability (not implemented)
+ *   enable_gqa: Whether GQA was enabled
+ *
+ * Returns:
+ *   Tuple of (grad_query, grad_key, grad_value) tensors
+ */
+template <typename T>
+std::vector<std::shared_ptr<Tensor>> FlashAttentionBackwardImpl(const std::shared_ptr<Tensor> &grad_query, const std::shared_ptr<Tensor> &grad_key,
+                                                              const std::shared_ptr<Tensor> &grad_value, const std::shared_ptr<Tensor> &query,
+                                                              const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
+                                                              const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &grad_output,
+                                                              const std::shared_ptr<Tensor> &logsumexp,
+                                                              const std::shared_ptr<Tensor> &dropout_seed,
+                                                              const std::shared_ptr<Tensor> &attn_mask,
+                                                              float scale, bool is_causal, int64_t dropout_p, bool enable_gqa) {
+    auto dtype = query->Dtype();
+
+    // Create gradient tensors with same shapes as inputs
+    auto grad_query = std::make_shared<Tensor>(query->Dims(), dtype, query->GetDevice());
+    auto grad_key = std::make_shared<Tensor>(key->Dims(), dtype, key->GetDevice());
+    auto grad_value = std::make_shared<Tensor>(value->Dims(), dtype, value->GetDevice());
+
+    // Initialize gradients to zero
+    DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_query->Fill<T>(0); }, "CUDA FlashAttentionBackward");
+    DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_key->Fill<T>(0); }, "CUDA FlashAttentionBackward");
+    DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_value->Fill<T>(0); }, "CUDA FlashAttentionBackward");
+
+    // Get dropout seed value
+    unsigned long long dropout_seed_value = 0;
+    if (dropout_seed) {
+        dropout_seed_value = *static_cast<const unsigned long long *>(dropout_seed->DataPtr());
+    }
+
+    switch (dtype) {
+        DISPATCH_CASE(WRAP(LaunchFlashAttentionBackward<float>(grad_query, grad_key, grad_value, query, key, value, output, grad_output,
+                                                               logsumexp, dropout_seed_value, attn_mask, scale, is_causal,
+                                                               dropout_p, enable_gqa);),
+                      DataType::kFLOAT32)
+        DISPATCH_CASE(WRAP(LaunchFlashAttentionBackward<nv_bfloat16>(grad_query, grad_key, grad_value, query, key, value, output, grad_output,
+                                                                   logsumexp, dropout_seed_value, attn_mask, scale, is_causal,
+                                                                   dropout_p, enable_gqa);),
+                      DataType::kBFLOAT16)
+    default:
+        LOG_LOC(FATAL, "CUDA FlashAttention backward: 'Unsupported data type'");
+    }
+
+    return {grad_query, grad_key, grad_value};
+}
+
+// Non-template wrapper functions for registration
+FlashAttentionForwardOutput FlashAttentionForward(const std::shared_ptr<Tensor> &query, const std::shared_ptr<Tensor> &key,
+                                               const std::shared_ptr<Tensor> &value,
+                                               const std::shared_ptr<Tensor> &attn_mask, float scale, bool is_causal,
+                                               int64_t dropout_p, bool enable_gqa) {
+    auto dtype = query->Dtype();
+    const auto &query_dims = query->Dims();
+
+    // Output shape: [batch_size, seq_len_q, num_heads, head_dim]
+    std::vector<int64_t> output_dims = {query_dims[0], query_dims[1], query_dims[2], query_dims[3]};
+    auto output = std::make_shared<Tensor>(output_dims, dtype, query->GetDevice());
+
+    // Allocate logsumexp tensor for backward pass
+    // Shape: [batch_size, num_heads, seq_len_q]
+    std::vector<int64_t> logsumexp_dims = {query_dims[0], query_dims[2], query_dims[1]};
+    auto logsumexp = std::make_shared<Tensor>(logsumexp_dims, DataType::kFLOAT32, query->GetDevice());
+    float *logsumexp_ptr = static_cast<float *>(logsumexp->DataPtr());
+
+    // Allocate dropout_seed tensor for backward pass
+    // Shape: [1]
+    unsigned long long dropout_seed = 0;
+    std::shared_ptr<Tensor> dropout_seed_tensor;
+    if (dropout_p > 0) {
+        std::vector<int64_t> dropout_seed_dims = {1};
+        dropout_seed_tensor = std::make_shared<Tensor>(dropout_seed_dims, DataType::kUINT64, query->GetDevice());
+        dropout_seed = static_cast<unsigned long long>(std::time(nullptr));
+        unsigned long long *dropout_seed_ptr = static_cast<unsigned long long *>(dropout_seed_tensor->DataPtr());
+        *dropout_seed_ptr = dropout_seed;
+    }
+
+    switch (dtype) {
+        DISPATCH_CASE(WRAP(LaunchFlashAttentionForward<float>(output, query, key, value, attn_mask, scale, is_causal,
+                                                               dropout_p, enable_gqa, logsumexp_ptr,
+                                                               dropout_seed_tensor ? static_cast<unsigned long long*>(dropout_seed_tensor->DataPtr()) : nullptr);),
+                      DataType::kFLOAT32)
+        DISPATCH_CASE(WRAP(LaunchFlashAttentionForward<nv_bfloat16>(output, query, key, value, attn_mask, scale,
+                                                                     is_causal, dropout_p, enable_gqa, logsumexp_ptr,
+                                                                     dropout_seed_tensor ? static_cast<unsigned long long*>(dropout_seed_tensor->DataPtr()) : nullptr);),
+                      DataType::kBFLOAT16)
+    default:
+        LOG_LOC(FATAL, "CUDA FlashAttention forward: 'Unsupported data type'");
+    }
+
+    FlashAttentionForwardOutput result;
+    result.output = output;
+    result.logsumexp = logsumexp;
+    result.dropout_seed = dropout_seed_tensor;
+    return result;
+}
+
+std::vector<std::shared_ptr<Tensor>> FlashAttentionBackward(const std::shared_ptr<Tensor> &grad_query, const std::shared_ptr<Tensor> &grad_key,
+                                                          const std::shared_ptr<Tensor> &grad_value, const std::shared_ptr<Tensor> &query,
+                                                          const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
+                                                          const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &grad_output,
+                                                          const std::shared_ptr<Tensor> &logsumexp,
+                                                          const std::shared_ptr<Tensor> &dropout_seed,
+                                                          const std::shared_ptr<Tensor> &attn_mask,
+                                                          float scale, bool is_causal, int64_t dropout_p, bool enable_gqa) {
+    auto dtype = query->Dtype();
+
+    // Create gradient tensors with same shapes as inputs
+    auto grad_query = std::make_shared<Tensor>(query->Dims(), dtype, query->GetDevice());
+    auto grad_key = std::make_shared<Tensor>(key->Dims(), dtype, key->GetDevice());
+    auto grad_value = std::make_shared<Tensor>(value->Dims(), dtype, value->GetDevice());
+
+    // Initialize gradients to zero
+    DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_query->Fill<T>(0); }, "CUDA FlashAttentionBackward");
+    DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_key->Fill<T>(0); }, "CUDA FlashAttentionBackward");
+    DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_value->Fill<T>(0); }, "CUDA FlashAttentionBackward");
+
+    // Get dropout seed value
+    unsigned long long dropout_seed_value = 0;
+    if (dropout_seed) {
+        dropout_seed_value = *static_cast<const unsigned long long *>(dropout_seed->DataPtr());
+    }
+
+    switch (dtype) {
+        DISPATCH_CASE(WRAP(LaunchFlashAttentionBackward<float>(grad_query, grad_key, grad_value, query, key, value, output, grad_output,
+                                                               logsumexp, dropout_seed_value, attn_mask, scale, is_causal,
+                                                               dropout_p, enable_gqa);),
+                      DataType::kFLOAT32)
+        DISPATCH_CASE(WRAP(LaunchFlashAttentionBackward<nv_bfloat16>(grad_query, grad_key, grad_value, query, key, value, output, grad_output,
+                                                                    logsumexp, dropout_seed_value, attn_mask, scale, is_causal,
+                                                                    dropout_p, enable_gqa);),
+                      DataType::kBFLOAT16)
+    default:
+        LOG_LOC(FATAL, "CUDA FlashAttention backward: 'Unsupported data type'");
+    }
+
+    return {grad_query, grad_key, grad_value};
+}
+
+} // namespace infini_train::kernels::cuda
+
+// Register FlashAttention kernels with the dispatcher
+#define REGISTER_CUDA_FLASHATTENTION_KERNEL(kernel_name) \
+    REGISTER_KERNEL(infini_train::Device::DeviceType::kCUDA, kernel_name, infini_train::kernels::cuda::kernel_name)
+
+REGISTER_CUDA_FLASHATTENTION_KERNEL(FlashAttentionForward)
+REGISTER_CUDA_FLASHATTENTION_KERNEL(FlashAttentionBackward)
+
+#undef REGISTER_CUDA_FLASHATTENTION_KERNEL
diff --git a/infini_train/src/nn/functional.cc b/infini_train/src/nn/functional.cc
index b02f185a..cf2d4ae6 100644
--- a/infini_train/src/nn/functional.cc
+++ b/infini_train/src/nn/functional.cc
@@ -2,12 +2,14 @@
 
 #include <cstdint>
 #include <memory>
+#include <optional>
 #include <vector>
 
 #include "infini_train/include/autograd/activations.h"
 #include "infini_train/include/autograd/elementwise.h"
 #include "infini_train/include/autograd/misc.h"
 #include "infini_train/include/autograd/reduction.h"
+#include "infini_train/include/autograd/ScaledDotProductAttention.h"
 #include "infini_train/include/autograd/softmax.h"
 #include "infini_train/include/autograd/transform.h"
 #include "infini_train/include/nn/init.h"
@@ -79,4 +81,13 @@ std::shared_ptr<Tensor> Softmax(const std::shared_ptr<Tensor> &input, int64_t di
 std::shared_ptr<Tensor> Sigmoid(const std::shared_ptr<Tensor> &input) {
     return std::make_shared<autograd::Sigmoid>()->Apply({input})[0];
 }
+
+std::shared_ptr<Tensor> ScaledDotProductAttention(const std::shared_ptr<Tensor> &query,
+                                                  const std::shared_ptr<Tensor> &key,
+                                                  const std::shared_ptr<Tensor> &value,
+                                                  const std::shared_ptr<Tensor> &attn_mask,
+                                                  int64_t dropout_p, bool is_causal,
+                                                  std::optional<double> scale, bool enable_gqa) {
+    return std::make_shared<autograd::ScaledDotProductAttention>(attn_mask, dropout_p, is_causal, scale, enable_gqa)->Apply({query, key, value})[0];
+}
 } // namespace infini_train::nn::function

From 29e7f22db3ce458bfac965b55daed84197dd2f0c Mon Sep 17 00:00:00 2001
From: Aoshine999 <824059542@qq.com>
Date: Sun, 8 Mar 2026 15:08:26 +0800
Subject: [PATCH 02/18] update net config

---
 example/gpt2/main.cc                          |  8 +++
 example/gpt2/net.cc                           | 38 ++++++++----
 example/gpt2/net.h                            |  1 +
 example/llama3/main.cc                        |  5 ++
 example/llama3/net.cc                         | 61 ++++++++++---------
 example/llama3/net.h                          |  2 +-
 .../src/kernels/cuda/flash_attention.cu       | 59 +++++++++++-------
 7 files changed, 110 insertions(+), 64 deletions(-)

diff --git a/example/gpt2/main.cc b/example/gpt2/main.cc
index 69f8ba7e..2b2f981d 100644
--- a/example/gpt2/main.cc
+++ b/example/gpt2/main.cc
@@ -71,6 +71,7 @@ DEFINE_uint32(tensor_parallel, 1, "Tensor Parallel world size");
 DEFINE_bool(sequence_parallel, false, "Whether to enable Sequence Parallel");
 DEFINE_uint32(pipeline_parallel, 1, "Pipeline Parallel world size, specified the number of PP stages.");
 DEFINE_uint32(virtual_pipeline_parallel, 1, "Number of chunks in PP stage.");
+DEFINE_bool(flash, false, "Whether to enable FlashAttention in CausalSelfAttention");
 
 // precision
 DEFINE_string(dtype, "float32", "precision used in training (float32/bfloat16)");
@@ -181,11 +182,18 @@ void Train(const nn::parallel::Rank &rank) {
     GPT2Config model_config;
     std::shared_ptr<nn::Module> model = nullptr;
     if (!FLAGS_llmc_filepath.empty()) {
+        if (FLAGS_flash) {
+            LOG(WARNING) << "--flash is ignored when loading GPT2 from --llmc_filepath.";
+        }
         model = GPT2::FromLLMC(FLAGS_llmc_filepath);
     } else if (kModelToConfigs.count(FLAGS_model)) {
         model_config = kModelToConfigs.at(FLAGS_model);
+        model_config.flash = FLAGS_flash;
         model = std::make_shared<GPT2>(model_config);
     } else {
+        if (FLAGS_flash) {
+            LOG(WARNING) << "--flash is ignored when loading GPT2 from pretrained checkpoint.";
+        }
         model = GPT2::FromPretrained(kStrToModelType.at(FLAGS_model));
     }
 
diff --git a/example/gpt2/net.cc b/example/gpt2/net.cc
index 8d497797..68aece37 100644
--- a/example/gpt2/net.cc
+++ b/example/gpt2/net.cc
@@ -15,6 +15,7 @@
 
 #include "example/common/utils.h"
 #include "infini_train/include/device.h"
+#include "infini_train/include/autograd/ScaledDotProductAttention.h"
 #include "infini_train/include/nn/functional.h"
 #include "infini_train/include/nn/init.h"
 #include "infini_train/include/nn/modules/container.h"
@@ -105,18 +106,31 @@ CausalSelfAttention::Forward(const std::vector<std::shared_ptr<infini_train::Ten
     q = q->View({B, T, local_n_head_, head_dim})->Transpose(1, 2);
     v = v->View({B, T, local_n_head_, head_dim})->Transpose(1, 2);
 
-    // (B, h_l, T, T)
-    auto att = q->Matmul(k->Transpose(-2, -1)) * (1.0 / std::sqrt(head_dim));
-    // (1, 1, T, T)
-    auto mask = buffers_[kParamBiasName]->Slice({0, 0, 0, 0}, {1, 1, T, T}, {1, 1, 1, 1});
-    // (1, 1, T, T) -> eq 0 -> (1, 1, T, T) -> masked_fill -> (B, h_l, T, T)
-    att = att->MaskedFill(mask == 0, -std::numeric_limits<float>::infinity());
-    // (B, h_l, T, T)
-    att = nn::function::Softmax(att, -1);
-    // (B, h_l, T, Dh)
-    auto y = att->Matmul(v);
-    // (B, h_l, T, Dh) -> (B, T, h_l, Dh) -> (B, T, local_C)
-    y = y->Transpose(1, 2)->Contiguous()->View({B, T, local_C});
+    std::shared_ptr<Tensor> y = nullptr;
+    if (config_.flash) {
+        // FlashAttention expects (B, T, H, D)
+        auto q_flash = q->Transpose(1, 2);
+        auto k_flash = k->Transpose(1, 2);
+        auto v_flash = v->Transpose(1, 2);
+        auto y_flash = std::make_shared<autograd::ScaledDotProductAttention>(
+                           /*attn_mask=*/nullptr, /*dropout_p=*/0, /*is_causal=*/true,
+                           /*scale=*/1.0 / std::sqrt(static_cast<double>(head_dim)), /*enable_gqa=*/false)
+                           ->Apply({q_flash, k_flash, v_flash})[0];
+        y = y_flash->Contiguous()->View({B, T, local_C});
+    } else {
+        // (B, h_l, T, T)
+        auto att = q->Matmul(k->Transpose(-2, -1)) * (1.0 / std::sqrt(head_dim));
+        // (1, 1, T, T)
+        auto mask = buffers_[kParamBiasName]->Slice({0, 0, 0, 0}, {1, 1, T, T}, {1, 1, 1, 1});
+        // (1, 1, T, T) -> eq 0 -> (1, 1, T, T) -> masked_fill -> (B, h_l, T, T)
+        att = att->MaskedFill(mask == 0, -std::numeric_limits<float>::infinity());
+        // (B, h_l, T, T)
+        att = nn::function::Softmax(att, -1);
+        // (B, h_l, T, Dh)
+        y = att->Matmul(v);
+        // (B, h_l, T, Dh) -> (B, T, h_l, Dh) -> (B, T, local_C)
+        y = y->Transpose(1, 2)->Contiguous()->View({B, T, local_C});
+    }
 
     // Get full tensor
     // (B, T, local_C) -> RowParallelLinear(n_embd, n_embd) -> (B, T, C)
diff --git a/example/gpt2/net.h b/example/gpt2/net.h
index 4faf5451..7f246c22 100644
--- a/example/gpt2/net.h
+++ b/example/gpt2/net.h
@@ -19,6 +19,7 @@ struct GPT2Config {
     int64_t n_layer = 12;
     int64_t n_head = 12;
     int64_t n_embd = 768;
+    bool flash = false;
 };
 
 class NewGELU : public infini_train::nn::CloneableModule<NewGELU> {
diff --git a/example/llama3/main.cc b/example/llama3/main.cc
index 6d4c9a7b..22991eeb 100644
--- a/example/llama3/main.cc
+++ b/example/llama3/main.cc
@@ -70,6 +70,7 @@ DEFINE_uint32(tensor_parallel, 1, "Tensor Parallel world size");
 DEFINE_bool(sequence_parallel, false, "Whether to enable Sequence Parallel");
 DEFINE_uint32(pipeline_parallel, 1, "Pipeline Parallel world size, specified the number of PP stages.");
 DEFINE_uint32(virtual_pipeline_parallel, 1, "Number of chunks in PP stage.");
+DEFINE_bool(flash, false, "Whether to enable FlashAttention in CausalSelfAttention");
 // precision
 DEFINE_string(dtype, "float32", "precision used in training (float32/bfloat16)");
 // precision check
@@ -161,8 +162,12 @@ void Train(const nn::parallel::Rank &rank) {
     // ManualSeed(42);
 
     LLaMA3Config model_config = LLaMA3Config();
+    model_config.flash = FLAGS_flash;
     std::shared_ptr<nn::Module> model = nullptr;
     if (!FLAGS_llmc_filepath.empty()) {
+        if (FLAGS_flash) {
+            LOG(WARNING) << "--flash is ignored when loading LLaMA3 from --llmc_filepath.";
+        }
         model = LLaMA3::FromLLMC(FLAGS_llmc_filepath);
     } else {
         model = std::make_shared<LLaMA3>(model_config);
diff --git a/example/llama3/net.cc b/example/llama3/net.cc
index a50fb831..fe725992 100644
--- a/example/llama3/net.cc
+++ b/example/llama3/net.cc
@@ -14,6 +14,7 @@
 #include "glog/logging.h"
 
 #include "example/common/utils.h"
+#include "infini_train/include/autograd/ScaledDotProductAttention.h"
 #include "infini_train/include/device.h"
 #include "infini_train/include/nn/functional.h"
 #include "infini_train/include/nn/init.h"
@@ -207,36 +208,38 @@ std::vector<std::shared_ptr<Tensor>> CausalSelfAttention::Forward(const std::vec
     // TODO(zbl): use kv cache during inference
     // if (use_kv_) { ... }
 
-    // align n_head in GQA
-    // (B, T, KV_local, D) -> (B, T, H_local, D) via RepeatKV
-    k = RepeatKV(k, n_rep_);
-    v = RepeatKV(v, n_rep_);
-
-    // (B, T, H_local, D) -> (B, H_local, T, D)
-    q = q->Transpose(1, 2);
-    k = k->Transpose(1, 2);
-    v = v->Transpose(1, 2);
-
-    // TODO(zbl): support flash attention later
-    // if (flash_) { ... }
-
-    // manual implementation of attention
-    // this materializes the large (T,T) matrix for all the queries and keys
-
-    // q: (B, H_local, T, D)
-    // k: (B, H_local, T, D) -> (B, H_local, D, T)
-    // q @ k.T: (B, H_local, T, T) -> mul 1.0 / sqrt(D) -> (B, H_local, T, T)
-    auto att = q->Matmul(k->Transpose(-2, -1)) * (1.0 / std::sqrt(static_cast<float>(D)));
-    if (mask) {
-        // mask: (1, 1, T, T)
-        att = att->MaskedFill(mask, std::numeric_limits<float>::lowest());
+    std::shared_ptr<Tensor> y = nullptr;
+    if (config_.flash) {
+        auto y_flash = std::make_shared<autograd::ScaledDotProductAttention>(
+                           /*attn_mask=*/nullptr, /*dropout_p=*/0, /*is_causal=*/true,
+                           /*scale=*/1.0 / std::sqrt(static_cast<double>(D)), /*enable_gqa=*/true)
+                           ->Apply({q, k, v})[0];
+        y = y_flash->Contiguous()->View({B, T, C_local});
+    } else {
+        // align n_head in GQA
+        // (B, T, KV_local, D) -> (B, T, H_local, D) via RepeatKV
+        k = RepeatKV(k, n_rep_);
+        v = RepeatKV(v, n_rep_);
+
+        // (B, T, H_local, D) -> (B, H_local, T, D)
+        q = q->Transpose(1, 2);
+        k = k->Transpose(1, 2);
+        v = v->Transpose(1, 2);
+
+        // manual implementation of attention
+        // this materializes the large (T,T) matrix for all the queries and keys
+        auto att = q->Matmul(k->Transpose(-2, -1)) * (1.0 / std::sqrt(static_cast<float>(D)));
+        if (mask) {
+            // mask: (1, 1, T, T)
+            att = att->MaskedFill(mask, std::numeric_limits<float>::lowest());
+        }
+        // (B, H_local, T, T)
+        att = nn::function::Softmax(att, -1);
+        // att: (B, H_local, T, T) @ v: (B, H_local, T, D) -> y: (B, H_local, T, D)
+        y = att->Matmul(v);
+        // (B, H_local, T, D) -> Transpose(1, 2) -> (B, T, H_local, D) -> (B, T, C_local)
+        y = y->Transpose(1, 2)->Contiguous()->View({B, T, C_local});
     }
-    // (B, H_local, T, T)
-    att = nn::function::Softmax(att, -1);
-    // att: (B, H_local, T, T) @ v: (B, H_local, T, D) -> y: (B, H_local, T, D)
-    auto y = att->Matmul(v);
-    // (B, H_local, T, D) -> Transpose(1, 2) -> (B, T, H_local, D) -> (B, T, C_local)
-    y = y->Transpose(1, 2)->Contiguous()->View({B, T, C_local});
     // output projection
     // (B, T, C_local) -> RowParallelLinear(C, C) -> (B, T, C)
     y = (*modules_[kCProjLayerName])({y})[0];
diff --git a/example/llama3/net.h b/example/llama3/net.h
index 4496a68d..144a7e78 100644
--- a/example/llama3/net.h
+++ b/example/llama3/net.h
@@ -36,7 +36,7 @@ struct LLaMA3Config {
 
     // Inference
     bool use_kv = false;            // kv cache
-    bool flash = false;             // flash attention
+    bool flash = false;             // enable flash attention path in CausalSelfAttention
     int64_t max_gen_batch_size = 4; // max batch size during inference
 };
 
diff --git a/infini_train/src/kernels/cuda/flash_attention.cu b/infini_train/src/kernels/cuda/flash_attention.cu
index bdd86a27..3bedca75 100644
--- a/infini_train/src/kernels/cuda/flash_attention.cu
+++ b/infini_train/src/kernels/cuda/flash_attention.cu
@@ -74,6 +74,25 @@ __device__ T warp_reduce_max(T val){
     return val;
 }
 
+__device__ __forceinline__ bool FlashAttnDropoutKeep(
+    unsigned long long seed,
+    int batch_idx, int head_idx, int q_idx, int k_idx,
+    int q_heads, int target_seq_len, int src_seq_len,
+    float dropout_p) {
+    // Stateless RNG mapping for one attention element (q_idx, k_idx).
+    unsigned long long linear_idx =
+        (((static_cast<unsigned long long>(batch_idx) * static_cast<unsigned long long>(q_heads)
+           + static_cast<unsigned long long>(head_idx))
+          * static_cast<unsigned long long>(target_seq_len)
+          + static_cast<unsigned long long>(q_idx))
+         * static_cast<unsigned long long>(src_seq_len))
+        + static_cast<unsigned long long>(k_idx);
+    curandStatePhilox4_32_10_t state;
+    curand_init(seed, linear_idx, 0, &state);
+    float rand_val = curand_uniform(&state);
+    return rand_val > dropout_p;
+}
+
 
 /**
  * FlashAttention Forward Kernel
@@ -173,12 +192,6 @@ __global__ void FlashAttentionForwardKernel(
     }
     __syncthreads();
 
-    // Initialize dropout random state with dropout_seed
-    curandStatePhilox4_32_10_t state;
-    if (dropout_p > 0) {
-        unsigned long long seq = bid_y * q_heads * target_seq_len + bid_x * target_seq_len + Br * bid_z + tid_y;
-        curand_init(dropout_seed, seq, 0, &state);
-    }
 /****************************end-of-preparation*************************/
 
 /****************************main-loop**************************/
@@ -260,8 +273,11 @@ __global__ void FlashAttentionForwardKernel(
 
         // step-5.5: Apply dropout to P (using dropout_seed for reproducibility)
         if (dropout_p > 0 && tid_y < bound_tid_y && tid_x < bound_tid_x) {
-            float rand_val = curand_uniform(&state);
-            bool keep = rand_val > dropout_p;
+            int global_q_idx = Br * bid_z + tid_y;
+            int global_k_idx = Bc * j + tid_x;
+            bool keep = FlashAttnDropoutKeep(
+                dropout_seed, bid_y, bid_x, global_q_idx, global_k_idx,
+                q_heads, target_seq_len, src_seq_len, static_cast<float>(dropout_p));
             if (keep) {
                 SP_AT(tid_y, tid_x) = SP_AT(tid_y, tid_x) / (1.0f - dropout_p);
             } else {
@@ -421,13 +437,6 @@ __global__ void FlashAttentionBackwardKernel(
     #define L_sm_AT(y) L_sm[y]
     #define dQ_sm_AT(y, x) dQ_sm[y * head_dim + x]
 
-    // Initialize dropout random state
-    curandStatePhilox4_32_10_t state;
-    if (dropout_p > 0) {
-        unsigned long long seq = bid_y * q_heads * target_seq_len + bid_x * target_seq_len + Br * bid_z + tid_y;
-        curand_init(dropout_seed, seq, 0, &state);
-    }
-
     /****************************Preparation*****************************/
     
     // Initialize dQ_sm to 0 for accumulation
@@ -559,8 +568,11 @@ __global__ void FlashAttentionBackwardKernel(
             for (int y = 0; y < Bc; ++y) {
                 for (int x = 0; x < Bc; ++x) {
                     if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-                        float rand_val = curand_uniform(&state);
-                        bool keep = rand_val > dropout_p;
+                        int global_q_pos = q_tile_start + tid_y;
+                        int global_k_pos = Bc * j + x;
+                        bool keep = FlashAttnDropoutKeep(
+                            dropout_seed, bid_y, bid_x, global_q_pos, global_k_pos,
+                            q_heads, target_seq_len, src_seq_len, static_cast<float>(dropout_p));
                         if (keep) {
                             P_sm_AT(tid_y, x) = P_sm_AT(tid_y, x) / (1.0f - dropout_p);
                         } else {
@@ -610,8 +622,11 @@ __global__ void FlashAttentionBackwardKernel(
             for (int y = 0; y < Bc; ++y) {
                 for (int x = 0; x < Bc; ++x) {
                     if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-                        float rand_val = curand_uniform(&state);
-                        bool keep = rand_val > dropout_p;
+                        int global_q_pos = q_tile_start + tid_y;
+                        int global_k_pos = Bc * j + x;
+                        bool keep = FlashAttnDropoutKeep(
+                            dropout_seed, bid_y, bid_x, global_q_pos, global_k_pos,
+                            q_heads, target_seq_len, src_seq_len, static_cast<float>(dropout_p));
                         if (keep) {
                             S_sm_AT(tid_y, x) = S_sm_AT(tid_y, x) / (1.0f - dropout_p);
                         } else {
@@ -757,7 +772,7 @@ template <typename T>
 void LaunchFlashAttentionForward(const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &query,
                                  const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
                                  const std::shared_ptr<Tensor> &attn_mask, float scale, bool is_causal,
-                                 int64_t dropout_p, bool enable_gqa, float *logsumexp_ptr, unsigned long long *dropout_seed_ptr) {
+                                 int64_t dropout_p, bool enable_gqa, float *logsumexp_ptr, unsigned long long dropout_seed) {
 
     const auto &query_dims = query->Dims();
     const auto &key_dims = key->Dims();
@@ -997,11 +1012,11 @@ FlashAttentionForwardOutput FlashAttentionForward(const std::shared_ptr<Tensor>
     switch (dtype) {
         DISPATCH_CASE(WRAP(LaunchFlashAttentionForward<float>(output, query, key, value, attn_mask, scale, is_causal,
                                                                dropout_p, enable_gqa, logsumexp_ptr,
-                                                               dropout_seed_tensor ? static_cast<unsigned long long*>(dropout_seed_tensor->DataPtr()) : nullptr);),
+                                                               dropout_seed);),
                       DataType::kFLOAT32)
         DISPATCH_CASE(WRAP(LaunchFlashAttentionForward<nv_bfloat16>(output, query, key, value, attn_mask, scale,
                                                                      is_causal, dropout_p, enable_gqa, logsumexp_ptr,
-                                                                     dropout_seed_tensor ? static_cast<unsigned long long*>(dropout_seed_tensor->DataPtr()) : nullptr);),
+                                                                     dropout_seed);),
                       DataType::kBFLOAT16)
     default:
         LOG_LOC(FATAL, "CUDA FlashAttention forward: 'Unsupported data type'");

From 89366bbd5d0934b2956380f44024cb77c04286d8 Mon Sep 17 00:00:00 2001
From: Aoshine999 <820459542@qq.com>
Date: Tue, 10 Mar 2026 15:49:13 +0800
Subject: [PATCH 03/18] complete synax error

---
 example/gpt2/main.cc                          |    2 +-
 .../src/autograd/ScaledDotProductAttention.cc |  121 +-
 .../src/kernels/cuda/flash_attention.cu       |   49 +-
 .../src/kernels/cuda/flash_attention1.cu      | 2218 ++++++++---------
 scripts/test_config.json                      |    8 +-
 5 files changed, 1124 insertions(+), 1274 deletions(-)

diff --git a/example/gpt2/main.cc b/example/gpt2/main.cc
index 2b2f981d..d1c82f5b 100644
--- a/example/gpt2/main.cc
+++ b/example/gpt2/main.cc
@@ -71,7 +71,7 @@ DEFINE_uint32(tensor_parallel, 1, "Tensor Parallel world size");
 DEFINE_bool(sequence_parallel, false, "Whether to enable Sequence Parallel");
 DEFINE_uint32(pipeline_parallel, 1, "Pipeline Parallel world size, specified the number of PP stages.");
 DEFINE_uint32(virtual_pipeline_parallel, 1, "Number of chunks in PP stage.");
-DEFINE_bool(flash, false, "Whether to enable FlashAttention in CausalSelfAttention");
+DEFINE_bool(flash, true, "Whether to enable FlashAttention in CausalSelfAttention");
 
 // precision
 DEFINE_string(dtype, "float32", "precision used in training (float32/bfloat16)");
diff --git a/infini_train/src/autograd/ScaledDotProductAttention.cc b/infini_train/src/autograd/ScaledDotProductAttention.cc
index 9aced35f..33d2ba80 100644
--- a/infini_train/src/autograd/ScaledDotProductAttention.cc
+++ b/infini_train/src/autograd/ScaledDotProductAttention.cc
@@ -7,7 +7,6 @@
 #include "infini_train/include/device.h"
 #include "infini_train/include/dispatcher.h"
 #include "infini_train/include/kernels/cuda/flash_attention.h"
-#include "infini_train/include/nn/functional.h"
 #include "infini_train/include/tensor.h"
 
 namespace infini_train::autograd {
@@ -27,39 +26,19 @@ ScaledDotProductAttention::Forward(const std::vector<std::shared_ptr<Tensor>> &i
 	const float scale = static_cast<float>(
 		scale_.has_value() ? *scale_ : 1.0 / std::sqrt(static_cast<double>(query_last_dim)));
 
-	// Use FlashAttention kernel for CUDA device
+	// ScaledDotProductAttention only supports CUDA device
 	auto device = query->GetDevice().type();
-	if (device == Device::Device::DeviceType::kCUDA) {
-		// Note: is_causal and enable_gqa are supported in FlashAttention kernel
-		// Dropout is now supported using seed-based approach for reproducibility
-		flash_output_ = Dispatcher::Instance().Call<kernels::cuda::FlashAttentionForwardOutput>(
-			{device, "FlashAttentionForward"}, query, key, value, attn_mask_, scale, is_causal_, dropout_p_, enable_gqa_);
-		
-		// Return output tensor only; saved_tensors_ will be set in SetupContext
-		return {flash_output_.output};
-	}
-
-	// Fallback to traditional implementation for CPU or other devices
-	CHECK_EQ(dropout_p_, 0) << "ScaledDotProductAttention dropout is not implemented yet";
-	CHECK(!is_causal_) << "ScaledDotProductAttention causal mask is not implemented yet";
-	CHECK(!enable_gqa_) << "ScaledDotProductAttention GQA mode is not implemented yet";
-
-	const int64_t key_rank = static_cast<int64_t>(key->Dims().size());
-	auto key_t = key->Transpose(key_rank - 2, key_rank - 1);
-
-	auto attn_scores = query->Matmul(key_t);
-	if (scale != 1.0f) {
-		attn_scores = attn_scores * scale;
-	}
-	if (attn_mask_) {
-		attn_scores = attn_scores + attn_mask_;
-	}
-
-	auto attn_prob = nn::function::Softmax(attn_scores, -1);
-	auto output = attn_prob->Matmul(value);
-
+	CHECK(device == Device::Device::DeviceType::kCUDA)
+		<< "ScaledDotProductAttention only supports CUDA device. "
+		<< "For CPU or other devices, please use traditional attention implementation.";
+
+	// Note: is_causal and enable_gqa are supported in FlashAttention kernel
+	// Dropout is now supported using seed-based approach for reproducibility
+	flash_output_ = Dispatcher::Instance().Call<kernels::cuda::FlashAttentionForwardOutput>(
+		{device, "FlashAttentionForward"}, query, key, value, attn_mask_, scale, is_causal_, dropout_p_, enable_gqa_);
+	
 	// Return output tensor only; saved_tensors_ will be set in SetupContext
-	return {output};
+	return {flash_output_.output};
 }
 
 void ScaledDotProductAttention::SetupContext(const std::vector<std::shared_ptr<Tensor>> &input_tensors,
@@ -69,81 +48,35 @@ void ScaledDotProductAttention::SetupContext(const std::vector<std::shared_ptr<T
 	const auto &value = input_tensors[2];
 	const auto &output = output_tensors[0];
 
-	// Use FlashAttention kernel for CUDA device
-	auto device = query->GetDevice().type();
-	if (device == Device::Device::DeviceType::kCUDA) {
-		// Save tensors for backward pass:
-		// - query, key, value: input tensors
-		// - output: forward output
-		// - logsumexp: logsumexp tensor for backward pass (from flash_output_)
-		// - dropout_seed: dropout seed tensor for backward pass (from flash_output_)
-		saved_tensors_ = {query, key, value, output, flash_output_.logsumexp, flash_output_.dropout_seed};
-	} else {
-		// Fallback to traditional implementation for CPU or other devices
-		// Recompute attn_prob from output and value
-		const int64_t value_rank = static_cast<int64_t>(value->Dims().size());
-		auto value_t = value->Transpose(value_rank - 2, value_rank - 1);
-		auto attn_prob = output->Matmul(value_t);
-		saved_tensors_ = {query, key, value, attn_prob};
-	}
+	// Save tensors for backward pass:
+	// - query, key, value: input tensors
+	// - output: forward output
+	// - logsumexp: logsumexp tensor for backward pass (from flash_output_)
+	// - dropout_seed: dropout seed tensor for backward pass (from flash_output_)
+	saved_tensors_ = {query, key, value, output, flash_output_.logsumexp, flash_output_.dropout_seed};
 }
 
 std::vector<std::shared_ptr<Tensor>>
 ScaledDotProductAttention::Backward(const std::vector<std::shared_ptr<Tensor>> &grad_outputs) {
-	CHECK_GE(saved_tensors_.size(), 4);
+	CHECK_EQ(saved_tensors_.size(), 6) << "FlashAttention backward expects 6 saved tensors";
 	CHECK_EQ(grad_outputs.size(), 1);
 
 	const auto &query = saved_tensors_[0];
 	const auto &key = saved_tensors_[1];
 	const auto &value = saved_tensors_[2];
 	const auto &output = saved_tensors_[3];
+	const auto &logsumexp = saved_tensors_[4];
+	const auto &dropout_seed = saved_tensors_[5];
 	const auto &grad_output = grad_outputs[0];
 
-	// Use FlashAttention backward kernel for CUDA device
-	auto device = query->GetDevice().type();
-	if (device == Device::Device::DeviceType::kCUDA) {
-		CHECK_EQ(saved_tensors_.size(), 6) << "FlashAttention backward expects 6 saved tensors";
-		const auto &logsumexp = saved_tensors_[4];
-		const auto &dropout_seed = saved_tensors_[5];
-		
-		const float scale = static_cast<float>(
-			scale_.has_value() ? *scale_ : 1.0 / std::sqrt(static_cast<double>(query->Dims().back())));
-		
-		auto grad_tensors = Dispatcher::Instance().Call<std::vector<std::shared_ptr<Tensor>>>(
-			{device, "FlashAttentionBackward"}, query, key, value, output, grad_output, logsumexp, dropout_seed, attn_mask_, 
-			scale, is_causal_, dropout_p_, enable_gqa_);
-		
-		return grad_tensors;
-	}
-
-	// Fallback to traditional implementation for CPU or other devices
-	CHECK_EQ(saved_tensors_.size(), 4) << "Traditional backward expects 4 saved tensors";
-	const auto &attn_prob = output;
-	const int64_t value_rank = static_cast<int64_t>(value->Dims().size());
-	const int64_t key_rank = static_cast<int64_t>(key->Dims().size());
-	const int64_t attn_rank = static_cast<int64_t>(attn_prob->Dims().size());
-
-	auto value_t = value->Transpose(value_rank - 2, value_rank - 1);
-	auto grad_attn_prob = grad_output->Matmul(value_t);
-
-	// softmax backward: grad = (g - sum(g * y, dim=-1, keepdim=true)) * y
-	auto grad_dot = (grad_attn_prob * attn_prob)->Sum(-1, true);
-	auto grad_attn_scores = (grad_attn_prob - grad_dot) * attn_prob;
-
 	const float scale = static_cast<float>(
 		scale_.has_value() ? *scale_ : 1.0 / std::sqrt(static_cast<double>(query->Dims().back())));
-	if (scale != 1.0f) {
-		grad_attn_scores = grad_attn_scores * scale;
-	}
-
-	auto grad_query = grad_attn_scores->Matmul(key);
-
-	auto grad_attn_scores_t = grad_attn_scores->Transpose(attn_rank - 2, attn_rank - 1);
-	auto grad_key = grad_attn_scores_t->Matmul(query);
-
-	auto attn_prob_t = attn_prob->Transpose(attn_rank - 2, attn_rank - 1);
-	auto grad_value = attn_prob_t->Matmul(grad_output);
-
-	return {grad_query, grad_key, grad_value};
+	
+	auto device = query->GetDevice().type();
+	auto grad_tensors = Dispatcher::Instance().Call<std::vector<std::shared_ptr<Tensor>>>(
+		{device, "FlashAttentionBackward"}, query, key, value, output, grad_output, logsumexp, dropout_seed, attn_mask_,
+		scale, is_causal_, dropout_p_, enable_gqa_);
+	
+	return grad_tensors;
 }
 } // namespace infini_train::autograd
diff --git a/infini_train/src/kernels/cuda/flash_attention.cu b/infini_train/src/kernels/cuda/flash_attention.cu
index 3bedca75..c0ea807b 100644
--- a/infini_train/src/kernels/cuda/flash_attention.cu
+++ b/infini_train/src/kernels/cuda/flash_attention.cu
@@ -1,6 +1,6 @@
 #include <cmath>
 #include <cstddef>
-
+#include <curand_kernel.h>
 #include "glog/logging.h"
 
 #include "infini_train/include/common/cuda/common_cuda.h"
@@ -8,6 +8,7 @@
 #include "infini_train/include/core/runtime/device_guard.h"
 #include "infini_train/include/dispatcher.h"
 #include "infini_train/include/kernels/cuda/flash_attention.h"
+#include "infini_train/include/nn/functional.h"
 #include "infini_train/include/tensor.h"
 
 #include "infini_train/src/core/runtime/cuda/cuda_runtime_common.h"
@@ -222,9 +223,9 @@ __global__ void FlashAttentionForwardKernel(
             V_sm_AT(tid_y, idx) = 0.0;
             if (tid_y < bound_tid_x) {
                 K_T_sm_AT(idx, tid_y) = float(
-                    K[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads) + (bid_x / p)) * head_dim + idx]);
+                    key[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads) + (bid_x / p)) * head_dim + idx]);
                 V_sm_AT(tid_y, idx) = float(
-                    V[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads) + (bid_x / p)) * head_dim + idx]);
+                    value[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads) + (bid_x / p)) * head_dim + idx]);
             }
         }
         __syncthreads();
@@ -386,16 +387,13 @@ __global__ void FlashAttentionBackwardKernel(
 
     const int Br = blockDim.y;                  // Q纵向每块大小, 32
     const int Bc = blockDim.x;                  // K/V纵向分块大小, 32
-    const int Tr = gridDim.z; // Q纵向分块数
+    // const int Tr = gridDim.z; // Q纵向分块数
     const int Tc = (src_seq_len + Bc - 1) / Bc; // K/V纵向分块数
 
     // Define shared memory
     extern __shared__ char shared_mem[];
     char *ptr = shared_mem;
     
-    // D_sm[Br] - D values loaded from HBM
-    float *D_sm = reinterpret_cast<float *>(ptr);
-    ptr += Br * sizeof(float);
     
     // Q_sm[Br][head_dim], K_T_sm[head_dim][Bc], V_sm[Bc][head_dim]
     float *Q_sm = reinterpret_cast<float *>(ptr);
@@ -405,9 +403,12 @@ __global__ void FlashAttentionBackwardKernel(
     float *V_sm = reinterpret_cast<float *>(ptr);
     ptr += Bc * head_dim * sizeof(float);
     
-    // dO_sm[Br][head_dim], dK_T_sm[head_dim][Bc], dV_sm[Bc][head_dim]
+    // dO_sm[Br][head_dim], dQ_sm[Br][head_dim], dK_T_sm[head_dim][Bc], dV_sm[Bc][head_dim]
     float *dO_sm = reinterpret_cast<float *>(ptr);
     ptr += Br * head_dim * sizeof(float);
+    // dQ_sm[Br][head_dim] - accumulated gradient for Q
+    float *dQ_sm = reinterpret_cast<float *>(ptr);
+    ptr += Br * head_dim * sizeof(float);
     float *dK_T_sm = reinterpret_cast<float *>(ptr);
     ptr += head_dim * Bc * sizeof(float);
     float *dV_sm = reinterpret_cast<float *>(ptr);
@@ -417,25 +418,28 @@ __global__ void FlashAttentionBackwardKernel(
     float *S_sm = reinterpret_cast<float *>(ptr);
     ptr += Br * Bc * sizeof(float);
     float *P_sm = reinterpret_cast<float *>(ptr);
-    
+    ptr += Br * Bc * sizeof(float);
+
     // L_sm[Br] - logsumexp values
     float *L_sm = reinterpret_cast<float *>(ptr);
-    
-    // dQ_sm[Br][head_dim] - accumulated gradient for Q
-    float *dQ_sm = reinterpret_cast<float *>(ptr);
+    ptr += Br * sizeof(float);
+    // D_sm[Br] - D values loaded from HBM
+    float *D_sm = reinterpret_cast<float *>(ptr);
+    ptr += Br * sizeof(float);
+
     
     // Define access macros
-    #define D_sm_AT(y) D_sm[y]
     #define Q_sm_AT(y, x) Q_sm[y * head_dim + x]
     #define K_T_sm_AT(y, x) K_T_sm[y * Bc + x]
     #define V_sm_AT(y, x) V_sm[y * head_dim + x]
     #define dO_sm_AT(y, x) dO_sm[y * head_dim + x]
+    #define dQ_sm_AT(y, x) dQ_sm[y * head_dim + x]
     #define dK_T_sm_AT(y, x) dK_T_sm[y * Bc + x]
     #define dV_sm_AT(y, x) dV_sm[y * head_dim + x]
     #define S_sm_AT(y, x) S_sm[y * Bc + x]
     #define P_sm_AT(y, x) P_sm[y * Bc + x]
     #define L_sm_AT(y) L_sm[y]
-    #define dQ_sm_AT(y, x) dQ_sm[y * head_dim + x]
+    #define D_sm_AT(y) D_sm[y]
 
     /****************************Preparation*****************************/
     
@@ -482,9 +486,9 @@ __global__ void FlashAttentionBackwardKernel(
             if (tid_y < bound_tid_x) {
                 int kv_head_idx = bid_x / p;
                 K_T_sm_AT(idx, tid_y) = float(
-                    key[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads + kv_head_idx) * head_dim + idx]);
+                    key[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads + kv_head_idx)) * head_dim + idx]);
                 V_sm_AT(tid_y, idx) = float(
-                    value[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads + kv_head_idx) * head_dim + idx]);
+                    value[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads + kv_head_idx)) * head_dim + idx]);
             }
         }
         __syncthreads();
@@ -593,7 +597,7 @@ __global__ void FlashAttentionBackwardKernel(
                 for (int y = 0; y < Bc; ++y) {
                     val += P_sm_AT(tid_y, y) * dO_sm_AT(tid_y, x);
                 }
-                atomicAdd(&dV_sm_AT(y, x), val);
+                atomicAdd(&dV_sm_AT(tid_y, x), val);
             }
         }
         __syncthreads();
@@ -823,7 +827,7 @@ void LaunchFlashAttentionForward(const std::shared_ptr<Tensor> &output, const st
     // + Q_sm[Br][head_dim] (float) + K_T_sm[head_dim][Bc] (float) + V_sm[Bc][head_dim] (float) + O_sm[Br][head_dim] (float)
     size_t shared_mem_size = Br * Bc * sizeof(double)  // SP
                           + 4 * Br * sizeof(float)     // m_prev, m_new, l_prev, l_new
-                          + (Br + Bc + Bc + Br) * head_dim) * sizeof(float);  // Q_sm, K_T_sm, V_sm, O_sm
+                          + (Br + Bc + Bc + Br) * head_dim * sizeof(float);  // Q_sm, K_T_sm, V_sm, O_sm
     // Note: Removed dropout_sm[Br][Bc] (bool) allocation
 
     auto device = output->GetDevice();
@@ -900,7 +904,7 @@ void LaunchFlashAttentionBackward(const std::shared_ptr<Tensor> &grad_query, con
     // D shape: [batch_size, seq_len_q, num_heads]
     // dO shape: [batch_size, seq_len_q, num_heads, head_dim]
     // O shape: [batch_size, seq_len_q, num_heads, head_dim]
-    auto D = function::Sum(grad_output * output, 3, false);
+    auto D = infini_train::nn::function::Sum(grad_output * output, 3, false);
 
     T *grad_query_ptr = static_cast<T *>(grad_query->DataPtr());
     T *grad_key_ptr = static_cast<T *>(grad_key->DataPtr());
@@ -933,6 +937,7 @@ void LaunchFlashAttentionBackward(const std::shared_ptr<Tensor> &grad_query, con
                            + head_dim * Bc * sizeof(float)    // K_T_sm
                            + Bc * head_dim * sizeof(float)    // V_sm
                            + Br * head_dim * sizeof(float)     // dO_sm
+                           + Br * head_dim * sizeof(float)     // dQ_sm (accumulated gradient for Q)
                            + head_dim * Bc * sizeof(float)    // dK_T_sm
                            + Bc * head_dim * sizeof(float)    // dV_sm
                            + Br * Bc * sizeof(float)          // S_sm or (dP_sm when compute dP_sm = dO_i @ V_j^T \in R^{Br*Bc})
@@ -1029,9 +1034,9 @@ FlashAttentionForwardOutput FlashAttentionForward(const std::shared_ptr<Tensor>
     return result;
 }
 
-std::vector<std::shared_ptr<Tensor>> FlashAttentionBackward(const std::shared_ptr<Tensor> &grad_query, const std::shared_ptr<Tensor> &grad_key,
-                                                          const std::shared_ptr<Tensor> &grad_value, const std::shared_ptr<Tensor> &query,
-                                                          const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
+std::vector<std::shared_ptr<Tensor>> FlashAttentionBackward(const std::shared_ptr<Tensor> &query,
+                                                          const std::shared_ptr<Tensor> &key,
+                                                          const std::shared_ptr<Tensor> &value,
                                                           const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &grad_output,
                                                           const std::shared_ptr<Tensor> &logsumexp,
                                                           const std::shared_ptr<Tensor> &dropout_seed,
diff --git a/infini_train/src/kernels/cuda/flash_attention1.cu b/infini_train/src/kernels/cuda/flash_attention1.cu
index 75931887..9a78f802 100644
--- a/infini_train/src/kernels/cuda/flash_attention1.cu
+++ b/infini_train/src/kernels/cuda/flash_attention1.cu
@@ -1,1171 +1,1083 @@
-#include <cmath>
-#include <cstddef>
-
-#include "glog/logging.h"
-
-#include "infini_train/include/common/cuda/common_cuda.h"
-#include "infini_train/include/common/cuda/kernel_helper.cuh"
-#include "infini_train/include/core/runtime/device_guard.h"
-#include "infini_train/include/dispatcher.h"
-#include "infini_train/include/kernels/cuda/flash_attention.h"
-#include "infini_train/include/tensor.h"
-
-#include "infini_train/src/core/runtime/cuda/cuda_runtime_common.h"
-
-namespace infini_train::kernels::cuda {
-
-/**
- * FlashAttention Forward Kernel
- *
- * This kernel implements the FlashAttention algorithm for efficient attention computation.
- * It uses tiling and recomputation to reduce memory access and improve performance.
- *
- * Args:
- *   output: Output tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
- *   query: Query tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
- *   key: Key tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
- *   value: Value tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
- *   attn_mask: Optional attention mask tensor
- *   logsumexp: Save logsumexp for backward pass
- *   scale: Scaling factor for attention scores
- *   is_causal: Whether to apply causal masking
- *   enable_gqa: Whether to enable grouped-query attention
- *   dropout_p: Dropout probability
- *   dropout_seed: Random seed for dropout (for reproducibility)
- *   batch_size, target_seq_len, src_seq_len: Tensor dimensions
- *   q_heads, kv_heads, head_dim: Attention head dimensions
- *
- * Note: Actual kernel implementation is not included in this version.
- */
-template <typename T>
-__device__ T warp_reduce_sum(T val){
-#pragma unroll//短循环自动展开,省去分支预测,提升效率
-    for(int offset = 16; offset > 0; offset >>= 1){
-        val += __shfl_down_sync(0xffffffff, val, offset);
-    }
-    return val;
-}
-
-template <typename T>
-__device__ T myexp(T x) {
-    if constexpr(std::is_same<T, __half>::value) {
-        float fx = __half2float(x);
-        float result = expf(fx);
-        return __float2half(result);
-    }
-    else if constexpr(std::is_same<T, float>::value) {
-        return expf(x);  // expf返回float
-    }
-    else if constexpr(std::is_same<T, double>::value) {
-        return exp(x);   // exp返回double
-    }
-    else{//other types
-      return T(0);
-    }
-}
-
-template <typename T>
-__device__ T warp_reduce_max(T val){
-#pragma unroll//短循环自动展开,省去分支预测,提升效率
-    for(int offset = 16; offset > 0; offset >>= 1){
-        T tmp = __shfl_down_sync(0xffffffff, val, offset);
-        val = (val > tmp) ? val : tmp;
-    }
-    return val;
-}
-
-
-/**
- * FlashAttention Forward Kernel
- *
- * This kernel implements the FlashAttention algorithm for efficient attention computation.
- * It uses tiling and recomputation to reduce memory access and improve performance.
- *
- * Args:
- *   output: Output tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
- *   query: Query tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
- *   key: Key tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
- *   value: Value tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
- *   attn_mask: Optional attention mask tensor
- *   logsumexp: Save logsumexp for backward pass
- *   scale: Scaling factor for attention scores
- *   is_causal: Whether to apply causal masking
- *   enable_gqa: Whether to enable grouped-query attention
- *   dropout_p: Dropout probability
- *   dropout_seed: Random seed for dropout (for reproducibility)
- *   batch_size, target_seq_len, src_seq_len: Tensor dimensions
- *   q_heads, kv_heads, head_dim: Attention head dimensions
- *
- * Note: Actual kernel implementation is not included in this version.
- */
-template <typename T>
-__global__ void FlashAttentionForwardKernel(
-    T *output, const T *query, const T *key, const T *value, const T *attn_mask,
-    float *logsumexp,  // Save logsumexp for backward pass
-    float scale, bool is_causal, bool enable_gqa, int64_t dropout_p,
-    unsigned long long dropout_seed,  // Use dropout_seed instead of dropout_mask
-    int batch_size, int target_seq_len, int src_seq_len,
-    int q_heads, int kv_heads, int head_dim) {
+// #include <cmath>
+// #include <cstddef>
+// #include <curand_kernel.h>
+// #include "glog/logging.h"
+
+// #include "infini_train/include/common/cuda/common_cuda.h"
+// #include "infini_train/include/common/cuda/kernel_helper.cuh"
+// #include "infini_train/include/core/runtime/device_guard.h"
+// #include "infini_train/include/dispatcher.h"
+// #include "infini_train/include/kernels/cuda/flash_attention.h"
+// #include "infini_train/include/tensor.h"
+
+// #include "infini_train/src/core/runtime/cuda/cuda_runtime_common.h"
+
+// namespace infini_train::kernels::cuda {
+
+// /**
+//  * FlashAttention Forward Kernel
+//  *
+//  * This kernel implements the FlashAttention algorithm for efficient attention computation.
+//  * It uses tiling and recomputation to reduce memory access and improve performance.
+//  *
+//  * Args:
+//  *   output: Output tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+//  *   query: Query tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+//  *   key: Key tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+//  *   value: Value tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+//  *   attn_mask: Optional attention mask tensor
+//  *   logsumexp: Save logsumexp for backward pass
+//  *   scale: Scaling factor for attention scores
+//  *   is_causal: Whether to apply causal masking
+//  *   enable_gqa: Whether to enable grouped-query attention
+//  *   dropout_p: Dropout probability
+//  *   dropout_seed: Random seed for dropout (for reproducibility)
+//  *   batch_size, target_seq_len, src_seq_len: Tensor dimensions
+//  *   q_heads, kv_heads, head_dim: Attention head dimensions
+//  *
+//  * Note: Actual kernel implementation is not included in this version.
+//  */
+// template <typename T>
+// __device__ T warp_reduce_sum(T val){
+// #pragma unroll//短循环自动展开,省去分支预测,提升效率
+//     for(int offset = 16; offset > 0; offset >>= 1){
+//         val += __shfl_down_sync(0xffffffff, val, offset);
+//     }
+//     return val;
+// }
+
+// template <typename T>
+// __device__ T myexp(T x) {
+//     if constexpr(std::is_same<T, __half>::value) {
+//         float fx = __half2float(x);
+//         float result = expf(fx);
+//         return __float2half(result);
+//     }
+//     else if constexpr(std::is_same<T, float>::value) {
+//         return expf(x);  // expf返回float
+//     }
+//     else if constexpr(std::is_same<T, double>::value) {
+//         return exp(x);   // exp返回double
+//     }
+//     else{//other types
+//       return T(0);
+//     }
+// }
+
+// template <typename T>
+// __device__ T warp_reduce_max(T val){
+// #pragma unroll//短循环自动展开,省去分支预测,提升效率
+//     for(int offset = 16; offset > 0; offset >>= 1){
+//         T tmp = __shfl_down_sync(0xffffffff, val, offset);
+//         val = (val > tmp) ? val : tmp;
+//     }
+//     return val;
+// }
+
+// __device__ __forceinline__ bool FlashAttnDropoutKeep(
+//     unsigned long long seed,
+//     int batch_idx, int head_idx, int q_idx, int k_idx,
+//     int q_heads, int target_seq_len, int src_seq_len,
+//     float dropout_p) {
+//     // Stateless RNG mapping for one attention element (q_idx, k_idx).
+//     unsigned long long linear_idx =
+//         (((static_cast<unsigned long long>(batch_idx) * static_cast<unsigned long long>(q_heads)
+//            + static_cast<unsigned long long>(head_idx))
+//           * static_cast<unsigned long long>(target_seq_len)
+//           + static_cast<unsigned long long>(q_idx))
+//          * static_cast<unsigned long long>(src_seq_len))
+//         + static_cast<unsigned long long>(k_idx);
+//     curandStatePhilox4_32_10_t state;
+//     curand_init(seed, linear_idx, 0, &state);
+//     float rand_val = curand_uniform(&state);
+//     return rand_val > dropout_p;
+// }
+
+
+// /**
+//  * FlashAttention Forward Kernel
+//  *
+//  * This kernel implements the FlashAttention algorithm for efficient attention computation.
+//  * It uses tiling and recomputation to reduce memory access and improve performance.
+//  *
+//  * Args:
+//  *   output: Output tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+//  *   query: Query tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+//  *   key: Key tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+//  *   value: Value tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+//  *   attn_mask: Optional attention mask tensor
+//  *   logsumexp: Save logsumexp for backward pass
+//  *   scale: Scaling factor for attention scores
+//  *   is_causal: Whether to apply causal masking
+//  *   enable_gqa: Whether to enable grouped-query attention
+//  *   dropout_p: Dropout probability
+//  *   dropout_seed: Random seed for dropout (for reproducibility)
+//  *   batch_size, target_seq_len, src_seq_len: Tensor dimensions
+//  *   q_heads, kv_heads, head_dim: Attention head dimensions
+//  *
+//  * Note: Actual kernel implementation is not included in this version.
+//  */
+// template <typename T>
+// __global__ void FlashAttentionForwardKernel(
+//     T *output, const T *query, const T *key, const T *value, const T *attn_mask,
+//     float *logsumexp,  // Save logsumexp for backward pass
+//     float scale, bool is_causal, bool enable_gqa, int64_t dropout_p,
+//     unsigned long long dropout_seed,  // Use dropout_seed instead of dropout_mask
+//     int batch_size, int target_seq_len, int src_seq_len,
+//     int q_heads, int kv_heads, int head_dim) {
     
-    int tid_x = threadIdx.x;          // 横向,blockDim.x列 (Bc)
-    int tid_y = threadIdx.y;          // 纵向,blockDim.y行 (Br)
-    int bid_x = blockIdx.x;           // x方向,总数 = #q_heads
-    int bid_y = blockIdx.y;           // y方向,总数 = #batch
-    int bid_z = blockIdx.z;           // z方向,总数 = Tr
-    const int p = q_heads / kv_heads; // 计算比例系数，GQA支持
-
-    const int Br = blockDim.y;                  // Q纵向每块大小, 32
-    const int Bc = blockDim.x;                  // K/V纵向分块大小, 32
-    const int Tc = gridDim.z; // 对应原始论文中K/V纵向分块数Tc,其中Bc = 32
-
-    // 定义一系列临时变量
-    extern __shared__ char shared_mem[];
-    char *ptr = shared_mem;
+//     int tid_x = threadIdx.x;          // 横向,blockDim.x列 (Bc)
+//     int tid_y = threadIdx.y;          // 纵向,blockDim.y行 (Br)
+//     int bid_x = blockIdx.x;           // x方向,总数 = #q_heads
+//     int bid_y = blockIdx.y;           // y方向,总数 = #batch
+//     int bid_z = blockIdx.z;           // z方向,总数 = Tr
+//     const int p = q_heads / kv_heads; // 计算比例系数，GQA支持
+
+//     const int Br = blockDim.y;                  // Q纵向每块大小, 32
+//     const int Bc = blockDim.x;                  // K/V纵向分块大小, 32
+//     const int Tc = gridDim.z; // 对应原始论文中K/V纵向分块数Tc,其中Bc = 32
+
+//     // 定义一系列临时变量
+//     extern __shared__ char shared_mem[];
+//     char *ptr = shared_mem;
     
-    // 计算中间变量,包括S, P(复用为SP), m_prev, m_new, l_prev, l_new
-    double *SP = reinterpret_cast<double *>(ptr); // double SP[Br][Bc]
-    ptr += Br * Bc * sizeof(double);
-    float *m_prev = reinterpret_cast<float *>(ptr); // float m_prev[Br]
-    ptr += Br * sizeof(float);
-    float *m_new = reinterpret_cast<float *>(ptr); // float m_new[Br]
-    ptr += Br * sizeof(float);
-    float *l_prev = reinterpret_cast<float *>(ptr); // float l_prev[Br]
-    ptr += Br * sizeof(float);
-    float *l_new = reinterpret_cast<float *>(ptr); // float l_new[Br]
-    ptr += Br * sizeof(float);
-
-    // 原始数据QKV和计算结果O; 全采用float
-    float *Q_sm = reinterpret_cast<float *>(ptr); // float Q_sm[Br][head_dim]
-    ptr += Br * head_dim * sizeof(float);
-    float *K_T_sm = reinterpret_cast<float *>(ptr); // float K_T_sm[head_dim][Bc]
-    ptr += head_dim * Bc * sizeof(float);
-    float *V_sm = reinterpret_cast<float *>(ptr); // float V_sm[Bc][head_dim]
-    ptr += Bc * head_dim * sizeof(float);
-    float *O_sm = reinterpret_cast<float *>(ptr); // float O_sm[Br][head_dim]
-    // Note: Removed dropout_sm shared memory allocation
-
-    // 定义访问宏
-#define SP_AT(y, x) SP[y * Bc + x]
-#define Q_sm_AT(y, x) Q_sm[y * head_dim + x]
-#define K_T_sm_AT(y, x) K_T_sm[y * Bc + x]
-#define V_sm_AT(y, x) V_sm[y * head_dim + x]
-#define O_sm_AT(y, x) O_sm[y * head_dim + x]
-    // Note: Removed DROPOUT_SM_AT macro
-
-    /****************************preparation**************************/
-    int bound_tid_y = min(Br, target_seq_len - Br * bid_z);
-
-    // preparation-1: load Qi from GM to SM, and reset Oi to 0
-    for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-        O_sm_AT(tid_y, idx) = 0.0;
-        Q_sm_AT(tid_y, idx) = 0.0;
-        if (tid_y < bound_tid_y) {
-            Q_sm_AT(tid_y, idx)
-                = float(query[((((bid_y * target_seq_len) + (Br * bid_z + tid_y)) * q_heads) + bid_x) * head_dim + idx]);
-        }
-    }
-    __syncthreads();
-
-    // preparation-2: reset m_prev to -INFINITY and l_prev to 0
-    if (tid_x == 0) {
-        m_prev[tid_y] = -8192.0;
-        l_prev[tid_y] = 0.0;
-    }
-    __syncthreads();
-
-    // Initialize dropout random state with dropout_seed
-    curandStatePhilox4_32_10_t state;
-    if (dropout_p > 0) {
-        unsigned long long seq = bid_y * q_heads * target_seq_len + bid_x * target_seq_len + Br * bid_z + tid_y;
-        curand_init(dropout_seed, seq, 0, &state);
-    }
-/****************************end-of-preparation*************************/
-
-/****************************main-loop**************************/
-#pragma unroll 4
-    for (int j = 0; j < Tc; ++j) { // 对于每个K/V分块
-        bool skip = (is_causal && bid_z < j);
-        if (skip) { // early exit, 直接跳过
-            __syncthreads();
-            continue;
-        }
-
-        SP_AT(tid_y, tid_x) = -8192.0;
-        __syncthreads();
-        int bound_tid_x = min(Bc, src_seq_len - Bc * j);
-        bool is_compute = true; // optimization: 分支处理,加速branch-resolving
-        if (is_causal) {
-            if (bid_z < j) {
-                is_compute = false; // 早期退出情况
-            } else if (bid_z == j) {
-                is_compute = (tid_y >= tid_x); // 对角线以上
-            }
-        }
-
-        // step-1: load Ki, Vi from GM to SM
-#pragma unroll
-        for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-            K_T_sm_AT(idx, tid_y) = 0.0;
-            V_sm_AT(tid_y, idx) = 0.0;
-            if (tid_y < bound_tid_x) {
-                K_T_sm_AT(idx, tid_y) = float(
-                    K[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads) + (bid_x / p)) * head_dim + idx]);
-                V_sm_AT(tid_y, idx) = float(
-                    V[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads) + (bid_x / p)) * head_dim + idx]);
-            }
-        }
-        __syncthreads();
-
-        // step-2: S = Q @ K.T, point-wise
-        if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-            float val0 = 0.0;
-            if (is_compute) {
-#pragma unroll
-                for (int k = 0; k < head_dim; ++k) { 
-                    val0 += Q_sm_AT(tid_y, k) * K_T_sm_AT(k, tid_x); 
-                }
-                SP_AT(tid_y, tid_x) = double(val0) * scale;
-            }
-        }
-        __syncthreads();
-
-        // step-3: m_new = max(m_prev, rowMax(S))
-        float val1 = float(SP_AT(tid_y, tid_x));
-        val1 = warp_reduce_max(val1);
-        if (tid_x == 0 && tid_y < bound_tid_y) {
-            m_new[tid_y] = (val1 > m_prev[tid_y]) ? val1 : m_prev[tid_y];
-        }
-        __syncthreads();
-
-        // step-4: P = exp(S - m_new), point-wise
-        if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-            if (is_compute) {
-                SP_AT(tid_y, tid_x) = myexp<double>(SP_AT(tid_y, tid_x) - double(m_new[tid_y]));
-            } else {
-                SP_AT(tid_y, tid_x) = 0.0;
-            }
-        } else {
-            SP_AT(tid_y, tid_x) = 0.0;
-        }
-        __syncthreads();
-
-        // step-5: l_new = exp(m_prev - m_new) * l_prev + rowSum(P)
-        float val2 = float(SP_AT(tid_y, tid_x));
-        val2 = warp_reduce_sum(val2);
-        float exp_result = myexp<float>(m_prev[tid_y] - m_new[tid_y]);
-        if (tid_x == 0 && tid_y < bound_tid_y) {
-            l_new[tid_y] = exp_result * l_prev[tid_y] + val2;
-        }
-        __syncthreads();
-
-        // step-5.5: Apply dropout to P (using dropout_seed for reproducibility)
-        if (dropout_p > 0 && tid_y < bound_tid_y && tid_x < bound_tid_x) {
-            float rand_val = curand_uniform(&state);
-            bool keep = rand_val > dropout_p;
-            if (keep) {
-                SP_AT(tid_y, tid_x) = SP_AT(tid_y, tid_x) / (1.0f - dropout_p);
-            } else {
-                SP_AT(tid_y, tid_x) = 0.0;
-            }
-            // Note: Removed dropout_sm mask saving to reduce memory overhead
-        }
-        __syncthreads();
-
-        // step-6: O = 1/(exp(m_prev - m_new)) * O + P @ V
-        if (tid_x < bound_tid_x && tid_y < bound_tid_y) {
-            for (int u = tid_x; u < head_dim; u += blockDim.x) {
-                float val3 = 0.0;
-#pragma unroll
-                for (int w = 0; w < Bc; ++w) { 
-                    val3 += float(SP_AT(tid_y, w)) * V_sm_AT(w, u);
-                }
-                O_sm_AT(tid_y, u) = O_sm_AT(tid_y, u) * exp_result + val3;
-            }
-        }
-        __syncthreads();
-
-        // step-7: m_prev <- m_new; l_prev <- l_new
-        if (tid_x == 0 && tid_y < bound_tid_y) {
-            m_prev[tid_y] = m_new[tid_y];
-            l_prev[tid_y] = l_new[tid_y];
-        }
-        __syncthreads();
-    }
-/****************************end-of-main-loop**************************/
-
-/****************************post-process****************************/
-// O(GM) = O/l_prev, aka O_sm /= l_prev and write Oi from SM to GM
-// Also save logsumexp for backward pass
-#pragma unroll
-    for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-        if (tid_y < bound_tid_y) {
-            output[((((bid_y * target_seq_len) + (Br * bid_z + tid_y)) * q_heads) + bid_x) * head_dim + idx]
-                = T(O_sm_AT(tid_y, idx) / float(l_prev[tid_y]));
-        }
-    }
+//     // 计算中间变量,包括S, P(复用为SP), m_prev, m_new, l_prev, l_new
+//     double *SP = reinterpret_cast<double *>(ptr); // double SP[Br][Bc]
+//     ptr += Br * Bc * sizeof(double);
+//     float *m_prev = reinterpret_cast<float *>(ptr); // float m_prev[Br]
+//     ptr += Br * sizeof(float);
+//     float *m_new = reinterpret_cast<float *>(ptr); // float m_new[Br]
+//     ptr += Br * sizeof(float);
+//     float *l_prev = reinterpret_cast<float *>(ptr); // float l_prev[Br]
+//     ptr += Br * sizeof(float);
+//     float *l_new = reinterpret_cast<float *>(ptr); // float l_new[Br]
+//     ptr += Br * sizeof(float);
+
+//     // 原始数据QKV和计算结果O; 全采用float
+//     float *Q_sm = reinterpret_cast<float *>(ptr); // float Q_sm[Br][head_dim]
+//     ptr += Br * head_dim * sizeof(float);
+//     float *K_T_sm = reinterpret_cast<float *>(ptr); // float K_T_sm[head_dim][Bc]
+//     ptr += head_dim * Bc * sizeof(float);
+//     float *V_sm = reinterpret_cast<float *>(ptr); // float V_sm[Bc][head_dim]
+//     ptr += Bc * head_dim * sizeof(float);
+//     float *O_sm = reinterpret_cast<float *>(ptr); // float O_sm[Br][head_dim]
+//     // Note: Removed dropout_sm shared memory allocation
+
+//     // 定义访问宏
+// #define SP_AT(y, x) SP[y * Bc + x]
+// #define Q_sm_AT(y, x) Q_sm[y * head_dim + x]
+// #define K_T_sm_AT(y, x) K_T_sm[y * Bc + x]
+// #define V_sm_AT(y, x) V_sm[y * head_dim + x]
+// #define O_sm_AT(y, x) O_sm[y * head_dim + x]
+//     // Note: Removed DROPOUT_SM_AT macro
+
+//     /****************************preparation**************************/
+//     int bound_tid_y = min(Br, target_seq_len - Br * bid_z);
+
+//     // preparation-1: load Qi from GM to SM, and reset Oi to 0
+//     for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+//         O_sm_AT(tid_y, idx) = 0.0;
+//         Q_sm_AT(tid_y, idx) = 0.0;
+//         if (tid_y < bound_tid_y) {
+//             Q_sm_AT(tid_y, idx)
+//                 = float(query[((((bid_y * target_seq_len) + (Br * bid_z + tid_y)) * q_heads) + bid_x) * head_dim + idx]);
+//         }
+//     }
+//     __syncthreads();
+
+//     // preparation-2: reset m_prev to -INFINITY and l_prev to 0
+//     if (tid_x == 0) {
+//         m_prev[tid_y] = -8192.0;
+//         l_prev[tid_y] = 0.0;
+//     }
+//     __syncthreads();
+
+// /****************************end-of-preparation*************************/
+
+// /****************************main-loop**************************/
+// #pragma unroll 4
+//     for (int j = 0; j < Tc; ++j) { // 对于每个K/V分块
+//         bool skip = (is_causal && bid_z < j);
+//         if (skip) { // early exit, 直接跳过
+//             __syncthreads();
+//             continue;
+//         }
+
+//         SP_AT(tid_y, tid_x) = -8192.0;
+//         __syncthreads();
+//         int bound_tid_x = min(Bc, src_seq_len - Bc * j);
+//         bool is_compute = true; // optimization: 分支处理,加速branch-resolving
+//         if (is_causal) {
+//             if (bid_z < j) {
+//                 is_compute = false; // 早期退出情况
+//             } else if (bid_z == j) {
+//                 is_compute = (tid_y >= tid_x); // 对角线以上
+//             }
+//         }
+
+//         // step-1: load Ki, Vi from GM to SM
+// #pragma unroll
+//         for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+//             K_T_sm_AT(idx, tid_y) = 0.0;
+//             V_sm_AT(tid_y, idx) = 0.0;
+//             if (tid_y < bound_tid_x) {
+//                 K_T_sm_AT(idx, tid_y) = float(
+//                     K[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads) + (bid_x / p)) * head_dim + idx]);
+//                 V_sm_AT(tid_y, idx) = float(
+//                     V[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads) + (bid_x / p)) * head_dim + idx]);
+//             }
+//         }
+//         __syncthreads();
+
+//         // step-2: S = Q @ K.T, point-wise
+//         if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+//             float val0 = 0.0;
+//             if (is_compute) {
+// #pragma unroll
+//                 for (int k = 0; k < head_dim; ++k) { 
+//                     val0 += Q_sm_AT(tid_y, k) * K_T_sm_AT(k, tid_x); 
+//                 }
+//                 SP_AT(tid_y, tid_x) = double(val0) * scale;
+//             }
+//         }
+//         __syncthreads();
+
+//         // step-3: m_new = max(m_prev, rowMax(S))
+//         float val1 = float(SP_AT(tid_y, tid_x));
+//         val1 = warp_reduce_max(val1);
+//         if (tid_x == 0 && tid_y < bound_tid_y) {
+//             m_new[tid_y] = (val1 > m_prev[tid_y]) ? val1 : m_prev[tid_y];
+//         }
+//         __syncthreads();
+
+//         // step-4: P = exp(S - m_new), point-wise
+//         if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+//             if (is_compute) {
+//                 SP_AT(tid_y, tid_x) = myexp<double>(SP_AT(tid_y, tid_x) - double(m_new[tid_y]));
+//             } else {
+//                 SP_AT(tid_y, tid_x) = 0.0;
+//             }
+//         } else {
+//             SP_AT(tid_y, tid_x) = 0.0;
+//         }
+//         __syncthreads();
+
+//         // step-5: l_new = exp(m_prev - m_new) * l_prev + rowSum(P)
+//         float val2 = float(SP_AT(tid_y, tid_x));
+//         val2 = warp_reduce_sum(val2);
+//         float exp_result = myexp<float>(m_prev[tid_y] - m_new[tid_y]);
+//         if (tid_x == 0 && tid_y < bound_tid_y) {
+//             l_new[tid_y] = exp_result * l_prev[tid_y] + val2;
+//         }
+//         __syncthreads();
+
+//         // step-5.5: Apply dropout to P (using dropout_seed for reproducibility)
+//         if (dropout_p > 0 && tid_y < bound_tid_y && tid_x < bound_tid_x) {
+//             int global_q_idx = Br * bid_z + tid_y;
+//             int global_k_idx = Bc * j + tid_x;
+//             bool keep = FlashAttnDropoutKeep(
+//                 dropout_seed, bid_y, bid_x, global_q_idx, global_k_idx,
+//                 q_heads, target_seq_len, src_seq_len, static_cast<float>(dropout_p));
+//             if (keep) {
+//                 SP_AT(tid_y, tid_x) = SP_AT(tid_y, tid_x) / (1.0f - dropout_p);
+//             } else {
+//                 SP_AT(tid_y, tid_x) = 0.0;
+//             }
+//             // Note: Removed dropout_sm mask saving to reduce memory overhead
+//         }
+//         __syncthreads();
+
+//         // step-6: O = 1/(exp(m_prev - m_new)) * O + P @ V
+//         if (tid_x < bound_tid_x && tid_y < bound_tid_y) {
+//             for (int u = tid_x; u < head_dim; u += blockDim.x) {
+//                 float val3 = 0.0;
+// #pragma unroll
+//                 for (int w = 0; w < Bc; ++w) { 
+//                     val3 += float(SP_AT(tid_y, w)) * V_sm_AT(w, u);
+//                 }
+//                 O_sm_AT(tid_y, u) = O_sm_AT(tid_y, u) * exp_result + val3;
+//             }
+//         }
+//         __syncthreads();
+
+//         // step-7: m_prev <- m_new; l_prev <- l_new
+//         if (tid_x == 0 && tid_y < bound_tid_y) {
+//             m_prev[tid_y] = m_new[tid_y];
+//             l_prev[tid_y] = l_new[tid_y];
+//         }
+//         __syncthreads();
+//     }
+// /****************************end-of-main-loop**************************/
+
+// /****************************post-process****************************/
+// // O(GM) = O/l_prev, aka O_sm /= l_prev and write Oi from SM to GM
+// // Also save logsumexp for backward pass
+// #pragma unroll
+//     for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+//         if (tid_y < bound_tid_y) {
+//             output[((((bid_y * target_seq_len) + (Br * bid_z + tid_y)) * q_heads) + bid_x) * head_dim + idx]
+//                 = T(O_sm_AT(tid_y, idx) / float(l_prev[tid_y]));
+//         }
+//     }
     
-    // Save logsumexp for backward pass
-    if (tid_x == 0 && tid_y < bound_tid_y) {
-        int logsumexp_idx = ((bid_y * q_heads + bid_x) * target_seq_len) + (Br * bid_z + tid_y);
-        logsumexp[logsumexp_idx] = m_prev[tid_y] + log(l_prev[tid_y]);
-    }
-    __syncthreads();
-/****************************end-of-post-process****************************/
-
-// 取消访问宏定义
-#undef SP_AT
-#undef Q_sm_AT
-#undef K_T_sm_AT
-#undef V_sm_AT
-#undef O_sm_AT
-}
-
-/**
- * FlashAttention Backward Kernel
- *
- * This kernel implements the backward pass for FlashAttention.
- * It computes gradients for query, key, and value tensors.
- *
- * Args:
- *   grad_query: Gradient for query tensor
- *   grad_key: Gradient for key tensor
- *   grad_value: Gradient for value tensor
- *   query: Query tensor from forward pass
- *   key: Key tensor from forward pass
- *   value: Value tensor from forward pass
- *   output: Output tensor from forward pass
- *   grad_output: Gradient from upstream
- *   logsumexp: Logsumexp tensor from forward pass
- *   dropout_seed: Dropout seed for reproducibility
- *   attn_mask: Optional attention mask tensor
- *   scale: Scaling factor for attention scores
- *   is_causal: Whether causal masking was applied
- *   dropout_p: Dropout probability
- *   enable_gqa: Whether GQA was enabled
- *   batch_size, target_seq_len, src_seq_len: Tensor dimensions
- *   q_heads, kv_heads, head_dim: Attention head dimensions
- */
-template <typename T>
-__global__ void FlashAttentionBackwardKernel(
-    T *grad_query, T *grad_key, T *grad_value,
-    const T *query, const T *key, const T *value,
-    const T *output, const T *grad_output,
-    const float *logsumexp,
-    const float *D,  // Precomputed D = rowsum(dO ∘ O)
-    unsigned long long dropout_seed,
-    const T *attn_mask,
-    float scale, bool is_causal, int64_t dropout_p,
-    bool enable_gqa,
-    int batch_size, int target_seq_len, int src_seq_len,
-    int q_heads, int kv_heads, int head_dim) {
+//     // Save logsumexp for backward pass
+//     if (tid_x == 0 && tid_y < bound_tid_y) {
+//         int logsumexp_idx = ((bid_y * q_heads + bid_x) * target_seq_len) + (Br * bid_z + tid_y);
+//         logsumexp[logsumexp_idx] = m_prev[tid_y] + log(l_prev[tid_y]);
+//     }
+//     __syncthreads();
+// /****************************end-of-post-process****************************/
+
+// // 取消访问宏定义
+// #undef SP_AT
+// #undef Q_sm_AT
+// #undef K_T_sm_AT
+// #undef V_sm_AT
+// #undef O_sm_AT
+// }
+
+// /**
+//  * FlashAttention Backward Kernel
+//  *
+//  * This kernel implements the backward pass for FlashAttention.
+//  * It computes gradients for query, key, and value tensors.
+//  *
+//  * Args:
+//  *   grad_query: Gradient for query tensor
+//  *   grad_key: Gradient for key tensor
+//  *   grad_value: Gradient for value tensor
+//  *   query: Query tensor from forward pass
+//  *   key: Key tensor from forward pass
+//  *   value: Value tensor from forward pass
+//  *   output: Output tensor from forward pass
+//  *   grad_output: Gradient from upstream
+//  *   logsumexp: Logsumexp tensor from forward pass
+//  *   dropout_seed: Dropout seed for reproducibility
+//  *   attn_mask: Optional attention mask tensor
+//  *   scale: Scaling factor for attention scores
+//  *   is_causal: Whether causal masking was applied
+//  *   dropout_p: Dropout probability
+//  *   enable_gqa: Whether GQA was enabled
+//  *   batch_size, target_seq_len, src_seq_len: Tensor dimensions
+//  *   q_heads, kv_heads, head_dim: Attention head dimensions
+//  */
+// template <typename T>
+// __global__ void FlashAttentionBackwardKernel(
+//     T *grad_query, T *grad_key, T *grad_value,
+//     const T *query, const T *key, const T *value,
+//     const T *output, const T *grad_output,
+//     const float *logsumexp,
+//     const float *D,  // Precomputed D = rowsum(dO ∘ O)
+//     unsigned long long dropout_seed,
+//     const T *attn_mask,
+//     float scale, bool is_causal, int64_t dropout_p,
+//     bool enable_gqa,
+//     int batch_size, int target_seq_len, int src_seq_len,
+//     int q_heads, int kv_heads, int head_dim) {
     
-    // Grid/block dimensions: grid_dims(num_heads_q, batch_size, Tr), block_dim(Bc, Br)
-    // where Br corresponds to thread block row index, Bc corresponds to column index
-    int tid_x = threadIdx.x;          // 横向, blockDim.x列 (Bc)
-    int tid_y = threadIdx.y;          // 纵向, blockDim.y行 (Br)
-    int bid_x = blockIdx.x;           // x方向, 总数 = #q_heads
-    int bid_y = blockIdx.y;           // y方向, 总数 = #batch
-    int bid_z = blockIdx.z;           // z方向, 总数 = Tr
-    const int p = q_heads / kv_heads; // GQA比例系数
-
-    const int Br = blockDim.y;                  // Q纵向每块大小, 32
-    const int Bc = blockDim.x;                  // K/V纵向分块大小, 32
-    const int Tr = gridDim.z; // Q纵向分块数
-    const int Tc = (src_seq_len + Bc - 1) / Bc; // K/V纵向分块数
-
-    // Define shared memory
-    extern __shared__ char shared_mem[];
-    char *ptr = shared_mem;
+//     // Grid/block dimensions: grid_dims(num_heads_q, batch_size, Tr), block_dim(Bc, Br)
+//     // where Br corresponds to thread block row index, Bc corresponds to column index
+//     int tid_x = threadIdx.x;          // 横向, blockDim.x列 (Bc)
+//     int tid_y = threadIdx.y;          // 纵向, blockDim.y行 (Br)
+//     int bid_x = blockIdx.x;           // x方向, 总数 = #q_heads
+//     int bid_y = blockIdx.y;           // y方向, 总数 = #batch
+//     int bid_z = blockIdx.z;           // z方向, 总数 = Tr
+//     const int p = q_heads / kv_heads; // GQA比例系数
+
+//     const int Br = blockDim.y;                  // Q纵向每块大小, 32
+//     const int Bc = blockDim.x;                  // K/V纵向分块大小, 32
+//     const int Tr = gridDim.z; // Q纵向分块数
+//     const int Tc = (src_seq_len + Bc - 1) / Bc; // K/V纵向分块数
+
+//     // Define shared memory
+//     extern __shared__ char shared_mem[];
+//     char *ptr = shared_mem;
     
-    // D_sm[Br] - D values loaded from HBM
-    float *D_sm = reinterpret_cast<float *>(ptr);
-    ptr += Br * sizeof(float);
+//     // D_sm[Br] - D values loaded from HBM
+//     float *D_sm = reinterpret_cast<float *>(ptr);
+//     ptr += Br * sizeof(float);
     
-    // Q_sm[Br][head_dim], K_T_sm[head_dim][Bc], V_sm[Bc][head_dim]
-    float *Q_sm = reinterpret_cast<float *>(ptr);
-    ptr += Br * head_dim * sizeof(float);
-    float *K_T_sm = reinterpret_cast<float *>(ptr);
-    ptr += head_dim * Bc * sizeof(float);
-    float *V_sm = reinterpret_cast<float *>(ptr);
-    ptr += Bc * head_dim * sizeof(float);
+//     // Q_sm[Br][head_dim], K_T_sm[head_dim][Bc], V_sm[Bc][head_dim]
+//     float *Q_sm = reinterpret_cast<float *>(ptr);
+//     ptr += Br * head_dim * sizeof(float);
+//     float *K_T_sm = reinterpret_cast<float *>(ptr);
+//     ptr += head_dim * Bc * sizeof(float);
+//     float *V_sm = reinterpret_cast<float *>(ptr);
+//     ptr += Bc * head_dim * sizeof(float);
     
-    // dO_sm[Br][head_dim], dK_T_sm[head_dim][Bc], dV_sm[Bc][head_dim]
-    float *dO_sm = reinterpret_cast<float *>(ptr);
-    ptr += Br * head_dim * sizeof(float);
-    float *dK_T_sm = reinterpret_cast<float *>(ptr);
-    ptr += head_dim * Bc * sizeof(float);
-    float *dV_sm = reinterpret_cast<float *>(ptr);
-    ptr += Bc * head_dim * sizeof(float);
+//     // dO_sm[Br][head_dim], dK_T_sm[head_dim][Bc], dV_sm[Bc][head_dim]
+//     float *dO_sm = reinterpret_cast<float *>(ptr);
+//     ptr += Br * head_dim * sizeof(float);
+//     float *dK_T_sm = reinterpret_cast<float *>(ptr);
+//     ptr += head_dim * Bc * sizeof(float);
+//     float *dV_sm = reinterpret_cast<float *>(ptr);
+//     ptr += Bc * head_dim * sizeof(float);
     
-    // S_sm[Br][Bc], P_sm[Br][Bc]
-    float *S_sm = reinterpret_cast<float *>(ptr);
-    ptr += Br * Bc * sizeof(float);
-    float *P_sm = reinterpret_cast<float *>(ptr);
+//     // S_sm[Br][Bc], P_sm[Br][Bc]
+//     float *S_sm = reinterpret_cast<float *>(ptr);
+//     ptr += Br * Bc * sizeof(float);
+//     float *P_sm = reinterpret_cast<float *>(ptr);
     
-    // L_sm[Br] - logsumexp values
-    float *L_sm = reinterpret_cast<float *>(ptr);
+//     // L_sm[Br] - logsumexp values
+//     float *L_sm = reinterpret_cast<float *>(ptr);
     
-    // dQ_sm[Br][head_dim] - accumulated gradient for Q
-    float *dQ_sm = reinterpret_cast<float *>(ptr);
+//     // dQ_sm[Br][head_dim] - accumulated gradient for Q
+//     float *dQ_sm = reinterpret_cast<float *>(ptr);
     
-    // Define access macros
-    #define D_sm_AT(y) D_sm[y]
-    #define Q_sm_AT(y, x) Q_sm[y * head_dim + x]
-    #define K_T_sm_AT(y, x) K_T_sm[y * Bc + x]
-    #define V_sm_AT(y, x) V_sm[y * head_dim + x]
-    #define dO_sm_AT(y, x) dO_sm[y * head_dim + x]
-    #define dK_T_sm_AT(y, x) dK_T_sm[y * Bc + x]
-    #define dV_sm_AT(y, x) dV_sm[y * head_dim + x]
-    #define S_sm_AT(y, x) S_sm[y * Bc + x]
-    #define P_sm_AT(y, x) P_sm[y * Bc + x]
-    #define L_sm_AT(y) L_sm[y]
-    #define dQ_sm_AT(y, x) dQ_sm[y * head_dim + x]
-
-    // Initialize dropout random state
-    curandStatePhilox4_32_10_t state;
-    if (dropout_p > 0) {
-        unsigned long long seq = bid_y * q_heads * target_seq_len + bid_x * target_seq_len + Br * bid_z + tid_y;
-        curand_init(dropout_seed, seq, 0, &state);
-    }
-
-    /****************************Preparation*****************************/
+//     // Define access macros
+//     #define D_sm_AT(y) D_sm[y]
+//     #define Q_sm_AT(y, x) Q_sm[y * head_dim + x]
+//     #define K_T_sm_AT(y, x) K_T_sm[y * Bc + x]
+//     #define V_sm_AT(y, x) V_sm[y * head_dim + x]
+//     #define dO_sm_AT(y, x) dO_sm[y * head_dim + x]
+//     #define dK_T_sm_AT(y, x) dK_T_sm[y * Bc + x]
+//     #define dV_sm_AT(y, x) dV_sm[y * head_dim + x]
+//     #define S_sm_AT(y, x) S_sm[y * Bc + x]
+//     #define P_sm_AT(y, x) P_sm[y * Bc + x]
+//     #define L_sm_AT(y) L_sm[y]
+//     #define dQ_sm_AT(y, x) dQ_sm[y * head_dim + x]
+
+//     /****************************Preparation*****************************/
     
-    // Initialize dQ_sm to 0 for accumulation
-    for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-        dQ_sm_AT(tid_y, idx) = 0.0f;
-    }
-    __syncthreads();
-
-    // Load D_i from HBM to shared memory
-    int q_idx = Br * bid_z + tid_y;  // Global query position within this head
-    if (q_idx < target_seq_len) {
-        int d_idx = ((bid_y * q_heads + bid_x) * target_seq_len) + q_idx;
-        D_sm_AT(tid_y) = D[d_idx];
-    } else {
-        D_sm_AT(tid_y) = 0.0f;  // Padding for out-of-bounds positions
-    }
-    __syncthreads();
-
-    // Get D_i from shared memory
-    float D_i = D_sm_AT(tid_y);
-
-    /****************************Main Loop - Outer Loop over K/V tiles*****************************/
-    for (int j = 0; j < Tc; ++j) {  // For each K/V column tile
+//     // Initialize dQ_sm to 0 for accumulation
+//     for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+//         dQ_sm_AT(tid_y, idx) = 0.0f;
+//     }
+//     __syncthreads();
+
+//     // Load D_i from HBM to shared memory
+//     int q_idx = Br * bid_z + tid_y;  // Global query position within this head
+//     if (q_idx < target_seq_len) {
+//         int d_idx = ((bid_y * q_heads + bid_x) * target_seq_len) + q_idx;
+//         D_sm_AT(tid_y) = D[d_idx];
+//     } else {
+//         D_sm_AT(tid_y) = 0.0f;  // Padding for out-of-bounds positions
+//     }
+//     __syncthreads();
+
+//     /****************************Main Loop - Outer Loop over K/V tiles*****************************/
+//     for (int j = 0; j < Tc; ++j) {  // For each K/V column tile
         
-        // Skip entire tile if causal and this tile is completely to the right
-        bool skip_tile = (is_causal && bid_z < j);
-        if (skip_tile) {
-            __syncthreads();
-            continue;
-        }
-
-        // Initialize dK_T_sm, dV_sm to 0 for this column tile
-        for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-            for (int y = 0; y < Bc; ++y) {
-                dK_T_sm_AT(idx, y) = 0.0f;
-                dV_sm_AT(y, idx) = 0.0f;
-            }
-        }
-        __syncthreads();
-
-        // Load K_j, V_j from HBM to shared memory
-        int bound_tid_x = min(Bc, src_seq_len - Bc * j);
-        for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-            K_T_sm_AT(idx, tid_y) = 0.0f;
-            V_sm_AT(tid_y, idx) = 0.0f;
-            if (tid_y < bound_tid_x) {
-                int kv_head_idx = bid_x / p;
-                K_T_sm_AT(idx, tid_y) = float(
-                    key[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads + kv_head_idx) * head_dim + idx]);
-                V_sm_AT(tid_y, idx) = float(
-                    value[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads + kv_head_idx) * head_dim + idx]);
-            }
-        }
-        __syncthreads();
-
-        /****************************Inner Loop - Loop over Q tiles*****************************/
-        // For this column tile, iterate through all Q row tiles
-        for (int i = 0; i < Tr; ++i) {  // For each Q row tile
-            
-            // Skip this Q tile if causal and it's completely to the left of current K tile
-            bool skip_q_tile = (is_causal && i > j);
-            if (skip_q_tile) {
-                __syncthreads();
-                continue;
-            }
-
-            // Load Q_i, dO_i, L_i for this row tile
-            int q_tile_start = Br * i;
-            int q_tile_end = min(q_tile_start + Br, target_seq_len);
-            int bound_tid_y = min(Br, q_tile_end - q_tile_start);
-            
-            int local_q_idx = Br * i + tid_y;
-            if (local_q_idx < bound_tid_y) {
-                // Load Q_i, dO_i, L_i from HBM
-                int global_q_idx = q_tile_start + tid_y;
-                int q_tensor_idx = ((bid_y * target_seq_len) + global_q_idx) * q_heads + bid_x;
-                
-                for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-                    Q_sm_AT(tid_y, idx) = float(query[q_tensor_idx * head_dim + idx]);
-                    dO_sm_AT(tid_y, idx) = float(grad_output[q_tensor_idx * head_dim + idx]);
-                    L_sm_AT(tid_y) = logsumexp[((bid_y * q_heads + bid_x) * target_seq_len + global_q_idx];
-                }
-            }
-            __syncthreads();
-
-            // Get D_i for this row
-            float D_i_row = D_sm_AT(tid_y);
-
-            // Recompute S_ij = Q_i @ K_j^T
-            // Apply causal mask at element level if needed
-            #pragma unroll
-            for (int y = 0; y < Bc; ++y) {
-                for (int x = 0; x < Bc; ++x) {
-                    float val = 0.0f;
-                    if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-                        bool is_compute = true;
-                        if (is_causal && i == j) {
-                            // On diagonal, only compute upper triangle
-                            int global_q_pos = q_tile_start + tid_y;
-                            int global_k_pos = Bc * j + x;
-                            is_compute = (global_q_pos >= global_k_pos);
-                        }
-                        
-                        if (is_compute) {
-                            #pragma unroll
-                            for (int k = 0; k < head_dim; ++k) {
-                                val += Q_sm_AT(tid_y, k) * K_T_sm_AT(k, y);
-                            }
-                        }
-                    }
-                    S_sm_AT(tid_y, x) = val * scale;
-                }
-            }
-            __syncthreads();
-
-            // Recompute P_ij = exp(S_ij - L_i)
-            #pragma unroll
-            for (int y = 0; y < Bc; ++y) {
-                for (int x = 0; x < Bc; ++x) {
-                    if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-                        bool is_compute = true;
-                        if (is_causal && i == j) {
-                            int global_q_pos = q_tile_start + tid_y;
-                            int global_k_pos = Bc * j + x;
-                            is_compute = (global_q_pos >= global_k_pos);
-                        }
-                        
-                        if (is_compute) {
-                            P_sm_AT(tid_y, x) = myexp<float>(S_sm_AT(tid_y, x) - L_sm_AT(tid_y));
-                        } else {
-                            P_sm_AT(tid_y, x) = 0.0f;
-                        }
-                    } else {
-                        P_sm_AT(tid_y, x) = 0.0f;
-                    }
-                }
-            }
-            __syncthreads();
-
-            // Apply dropout to P_ij
-            if (dropout_p > 0) {
-                #pragma unroll
-                for (int y = 0; y < Bc; ++y) {
-                    for (int x = 0; x < Bc; ++x) {
-                        if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-                            float rand_val = curand_uniform(&state);
-                            bool keep = rand_val > dropout_p;
-                            if (keep) {
-                                P_sm_AT(tid_y, x) = P_sm_AT(tid_y, x) / (1.0f - dropout_p);
-                            } else {
-                                P_sm_AT(tid_y, x) = 0.0f;
-                            }
-                        }
-                    }
-                }
-                __syncthreads();
-            }
-
-            // Compute dV_j += P_ij^T @ dO_i
-            if (tid_y < bound_tid_y) {
-                #pragma unroll
-                for (int x = 0; x < head_dim; x += blockDim.x) {
-                    float val = 0.0f;
-                    #pragma unroll
-                    for (int y = 0; y < Bc; ++y) {
-                        val += P_sm_AT(tid_y, y) * dO_sm_AT(tid_y, x);
-                    }
-                    atomicAdd(&dV_sm_AT(y, x), val);
-                }
-            }
-            __syncthreads();
-
-            // Compute dP_ij = dO_i @ V_j^T
-            #pragma unroll
-            for (int y = 0; y < Bc; ++y) {
-                for (int x = 0; x < Bc; ++x) {
-                    float val = 0.0f;
-                    if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-                        #pragma unroll
-                        for (int k = 0; k < head_dim; ++k) {
-                            val += dO_sm_AT(tid_y, k) * V_sm_AT(y, k);
-                        }
-                    }
-                    S_sm_AT(tid_y, x) = val;  // Reuse S_sm as temporary storage for dP_ij
-                } else {
-                    S_sm_AT(tid_y, x) = 0.0f;
-                }
-            }
-            __syncthreads();
-
-            // Apply dropout to dP_ij
-            if (dropout_p > 0) {
-                #pragma unroll
-                for (int y = 0; y < Bc; ++y) {
-                    for (int x = 0; x < Bc; ++x) {
-                        if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-                            float rand_val = curand_uniform(&state);
-                            bool keep = rand_val > dropout_p;
-                            if (keep) {
-                                S_sm_AT(tid_y, x) = S_sm_AT(tid_y, x) / (1.0f - dropout_p);
-                            } else {
-                                S_sm_AT(tid_y, x) = 0.0f;
-                            }
-                        }
-                    }
-                }
-                __syncthreads();
-            }
-
-            // Compute dS_ij = P_ij * (dP_ij - D_i)
-            #pragma unroll
-            for (int y = 0; y < Bc; ++y) {
-                for (int x = 0; x < Bc; ++x) {
-                    if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-                        S_sm_AT(tid_y, x) = P_sm_AT(tid_y, x) * (S_sm_AT(tid_y, x) - D_i_row);
-                    } else {
-                        S_sm_AT(tid_y, x) = 0.0f;
-                    }
-                }
-            }
-            __syncthreads();
-
-            // Compute dK_j += dS_ij^T @ Q_i
-            if (tid_x < head_dim) {
-                #pragma unroll
-                for (int y = 0; y < Bc; ++y) {
-                    float val = 0.0f;
-                    #pragma unroll
-                    for (int x = 0; x < Br; ++x) {
-                        val += S_sm_AT(x, y) * Q_sm_AT(x, tid_x);
-                    }
-                    atomicAdd(&dK_T_sm_AT(tid_x, y), val * scale);
-                }
-            }
-            __syncthreads();
-
-            // Compute dQ_i += dS_ij @ K_j
-            if (tid_y < bound_tid_y) {
-                #pragma unroll
-                for (int x = 0; x < head_dim; x += blockDim.x) {
-                    float val = 0.0f;
-                    #pragma unroll
-                    for (int y = 0; y < Bc; ++y) {
-                        val += S_sm_AT(tid_y, y) * K_T_sm_AT(x, y);
-                    }
-                    dQ_sm_AT(tid_y, x) += val * scale;
-                }
-            }
-            __syncthreads();
-        }  // End of inner loop over Q tiles
-
-        // Write back dK_j, dV_j to HBM
-        // Note: For GQA, dK_j and dV_j need to be accumulated across multiple q-head blocks
-        int kv_head_idx = bid_x / p;
-        int k_tile_start = Bc * j;
-        int k_tile_end = min(k_tile_start + Bc, src_seq_len);
+//         // Skip entire tile if causal and this tile is completely to the right
+//         bool skip_tile = (is_causal && bid_z < j);
+//         if (skip_tile) {
+//             __syncthreads();
+//             continue;
+//         }
+
+//         // Initialize dK_T_sm, dV_sm to 0 for this column tile
+//         for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+//             for (int y = 0; y < Bc; ++y) {
+//                 dK_T_sm_AT(idx, y) = 0.0f;
+//                 dV_sm_AT(y, idx) = 0.0f;
+//             }
+//         }
+//         __syncthreads();
+
+//         // Load K_j, V_j from HBM to shared memory
+//         int bound_tid_x = min(Bc, src_seq_len - Bc * j);
+//         for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+//             K_T_sm_AT(idx, tid_y) = 0.0f;
+//             V_sm_AT(tid_y, idx) = 0.0f;
+//             if (tid_y < bound_tid_x) {
+//                 int kv_head_idx = bid_x / p;
+//                 K_T_sm_AT(idx, tid_y) = float(
+//                     key[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads + kv_head_idx) * head_dim + idx]);
+//                 V_sm_AT(tid_y, idx) = float(
+//                     value[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads + kv_head_idx) * head_dim + idx]);
+//             }
+//         }
+//         __syncthreads();
+
+//         /****************************Single Q tile for this block*****************************/
+//         // gridDim.z already indexes the Q row tile, so this block only handles i = bid_z.
+//         const int i = bid_z;
+//         int q_tile_start = Br * i;
+//         int q_tile_end = min(q_tile_start + Br, target_seq_len);
+//         int bound_tid_y = min(Br, q_tile_end - q_tile_start);
+
+//         if (tid_y < bound_tid_y) {
+//             int global_q_idx = q_tile_start + tid_y;
+//             int q_tensor_idx = ((bid_y * target_seq_len) + global_q_idx) * q_heads + bid_x;
+
+//             for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+//                 Q_sm_AT(tid_y, idx) = float(query[q_tensor_idx * head_dim + idx]);
+//                 dO_sm_AT(tid_y, idx) = float(grad_output[q_tensor_idx * head_dim + idx]);
+//             }
+//             if (tid_x == 0) {
+//                 L_sm_AT(tid_y) = logsumexp[((bid_y * q_heads + bid_x) * target_seq_len) + global_q_idx];
+//             }
+//         }
+//         __syncthreads();
+
+//         // Get D_i for this row.
+//         float D_i_row = D_sm_AT(tid_y);
+
+//         // Recompute S_ij = Q_i @ K_j^T, apply causal mask at element level if needed.
+//         #pragma unroll
+//         for (int y = 0; y < Bc; ++y) {
+//             for (int x = 0; x < Bc; ++x) {
+//                 float val = 0.0f;
+//                 if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+//                     bool is_compute = true;
+//                     if (is_causal && i == j) {
+//                         int global_q_pos = q_tile_start + tid_y;
+//                         int global_k_pos = Bc * j + x;
+//                         is_compute = (global_q_pos >= global_k_pos);
+//                     }
+
+//                     if (is_compute) {
+//                         #pragma unroll
+//                         for (int k = 0; k < head_dim; ++k) {
+//                             val += Q_sm_AT(tid_y, k) * K_T_sm_AT(k, y);
+//                         }
+//                     }
+//                 }
+//                 S_sm_AT(tid_y, x) = val * scale;
+//             }
+//         }
+//         __syncthreads();
+
+//         // Recompute P_ij = exp(S_ij - L_i).
+//         #pragma unroll
+//         for (int y = 0; y < Bc; ++y) {
+//             for (int x = 0; x < Bc; ++x) {
+//                 if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+//                     bool is_compute = true;
+//                     if (is_causal && i == j) {
+//                         int global_q_pos = q_tile_start + tid_y;
+//                         int global_k_pos = Bc * j + x;
+//                         is_compute = (global_q_pos >= global_k_pos);
+//                     }
+
+//                     if (is_compute) {
+//                         P_sm_AT(tid_y, x) = myexp<float>(S_sm_AT(tid_y, x) - L_sm_AT(tid_y));
+//                     } else {
+//                         P_sm_AT(tid_y, x) = 0.0f;
+//                     }
+//                 } else {
+//                     P_sm_AT(tid_y, x) = 0.0f;
+//                 }
+//             }
+//         }
+//         __syncthreads();
+
+//         // Apply dropout to P_ij.
+//         if (dropout_p > 0) {
+//             #pragma unroll
+//             for (int y = 0; y < Bc; ++y) {
+//                 for (int x = 0; x < Bc; ++x) {
+//                     if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+//                         int global_q_pos = q_tile_start + tid_y;
+//                         int global_k_pos = Bc * j + x;
+//                         bool keep = FlashAttnDropoutKeep(
+//                             dropout_seed, bid_y, bid_x, global_q_pos, global_k_pos,
+//                             q_heads, target_seq_len, src_seq_len, static_cast<float>(dropout_p));
+//                         if (keep) {
+//                             P_sm_AT(tid_y, x) = P_sm_AT(tid_y, x) / (1.0f - dropout_p);
+//                         } else {
+//                             P_sm_AT(tid_y, x) = 0.0f;
+//                         }
+//                     }
+//                 }
+//             }
+//             __syncthreads();
+//         }
+
+//         // Compute dV_j += P_ij^T @ dO_i.
+//         if (tid_y < bound_tid_y) {
+//             #pragma unroll
+//             for (int x = 0; x < head_dim; x += blockDim.x) {
+//                 float val = 0.0f;
+//                 #pragma unroll
+//                 for (int y = 0; y < Bc; ++y) {
+//                     val += P_sm_AT(tid_y, y) * dO_sm_AT(tid_y, x);
+//                 }
+//                 atomicAdd(&dV_sm_AT(y, x), val);
+//             }
+//         }
+//         __syncthreads();
+
+//         // Compute dP_ij = dO_i @ V_j^T.
+//         #pragma unroll
+//         for (int y = 0; y < Bc; ++y) {
+//             for (int x = 0; x < Bc; ++x) {
+//                 float val = 0.0f;
+//                 if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+//                     #pragma unroll
+//                     for (int k = 0; k < head_dim; ++k) {
+//                         val += dO_sm_AT(tid_y, k) * V_sm_AT(y, k);
+//                     }
+//                     S_sm_AT(tid_y, x) = val;  // Reuse S_sm as temporary storage for dP_ij
+//                 } else {
+//                     S_sm_AT(tid_y, x) = 0.0f;
+//                 }
+//             }
+//         }
+//         __syncthreads();
+
+//         // Apply dropout to dP_ij.
+//         if (dropout_p > 0) {
+//             #pragma unroll
+//             for (int y = 0; y < Bc; ++y) {
+//                 for (int x = 0; x < Bc; ++x) {
+//                     if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+//                         int global_q_pos = q_tile_start + tid_y;
+//                         int global_k_pos = Bc * j + x;
+//                         bool keep = FlashAttnDropoutKeep(
+//                             dropout_seed, bid_y, bid_x, global_q_pos, global_k_pos,
+//                             q_heads, target_seq_len, src_seq_len, static_cast<float>(dropout_p));
+//                         if (keep) {
+//                             S_sm_AT(tid_y, x) = S_sm_AT(tid_y, x) / (1.0f - dropout_p);
+//                         } else {
+//                             S_sm_AT(tid_y, x) = 0.0f;
+//                         }
+//                     }
+//                 }
+//             }
+//             __syncthreads();
+//         }
+
+//         // Compute dS_ij = P_ij * (dP_ij - D_i).
+//         #pragma unroll
+//         for (int y = 0; y < Bc; ++y) {
+//             for (int x = 0; x < Bc; ++x) {
+//                 if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+//                     S_sm_AT(tid_y, x) = P_sm_AT(tid_y, x) * (S_sm_AT(tid_y, x) - D_i_row);
+//                 } else {
+//                     S_sm_AT(tid_y, x) = 0.0f;
+//                 }
+//             }
+//         }
+//         __syncthreads();
+
+//         // Compute dK_j += dS_ij^T @ Q_i.
+//         if (tid_x < head_dim) {
+//             #pragma unroll
+//             for (int y = 0; y < Bc; ++y) {
+//                 float val = 0.0f;
+//                 #pragma unroll
+//                 for (int x = 0; x < Br; ++x) {
+//                     val += S_sm_AT(x, y) * Q_sm_AT(x, tid_x);
+//                 }
+//                 atomicAdd(&dK_T_sm_AT(tid_x, y), val * scale);
+//             }
+//         }
+//         __syncthreads();
+
+//         // Compute dQ_i += dS_ij @ K_j.
+//         if (tid_y < bound_tid_y) {
+//             #pragma unroll
+//             for (int x = 0; x < head_dim; x += blockDim.x) {
+//                 float val = 0.0f;
+//                 #pragma unroll
+//                 for (int y = 0; y < Bc; ++y) {
+//                     val += S_sm_AT(tid_y, y) * K_T_sm_AT(x, y);
+//                 }
+//                 dQ_sm_AT(tid_y, x) += val * scale;
+//             }
+//         }
+//         __syncthreads();
+
+//         // Write back dK_j, dV_j to HBM
+//         // Note: For GQA, dK_j and dV_j need to be accumulated across multiple q-head blocks
+//         int kv_head_idx = bid_x / p;
+//         int k_tile_start = Bc * j;
+//         int k_tile_end = min(k_tile_start + Bc, src_seq_len);
         
-        for (int y = 0; y < Bc; ++y) {
-            int global_k_idx = k_tile_start + y;
-            if (global_k_idx < src_seq_len) {
-                for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-                    // dK: [batch_size, src_seq_len, kv_heads, head_dim]
-                    int k_tensor_idx = ((bid_y * src_seq_len) + global_k_idx) * kv_heads + kv_head_idx;
-                    grad_key[k_tensor_idx * head_dim + idx] += T(dK_T_sm_AT(idx, y));
+//         for (int y = 0; y < Bc; ++y) {
+//             int global_k_idx = k_tile_start + y;
+//             if (global_k_idx < src_seq_len) {
+//                 for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+//                     // dK: [batch_size, src_seq_len, kv_heads, head_dim]
+//                     int k_tensor_idx = ((bid_y * src_seq_len) + global_k_idx) * kv_heads + kv_head_idx;
+//                     atomicAdd(&grad_key[k_tensor_idx * head_dim + idx], T(dK_T_sm_AT(idx, y)));
                     
-                    // dV: [batch_size, src_seq_len, kv_heads, head_dim]
-                    grad_value[k_tensor_idx * head_dim + idx] += T(dV_sm_AT(y, idx));
-                }
-            }
-        }
-    }  // End of outer loop over K/V tiles
-
-    // Write back dQ_i to HBM
-    int q_tile_start = Br * bid_z;
-    int q_tile_end = min(q_tile_start + Br, target_seq_len);
-    int bound_tid_y = min(Br, q_tile_end - q_tile_start);
+//                     // dV: [batch_size, src_seq_len, kv_heads, head_dim]
+//                     atomicAdd(&grad_value[k_tensor_idx * head_dim + idx], T(dV_sm_AT(y, idx)));
+//                 }
+//             }
+//         }
+//     }  // End of outer loop over K/V tiles
+
+//     // Write back dQ_i to HBM
+//     int q_tile_start = Br * bid_z;
+//     int q_tile_end = min(q_tile_start + Br, target_seq_len);
+//     int bound_tid_y = min(Br, q_tile_end - q_tile_start);
     
-    if (tid_y < bound_tid_y) {
-        int global_q_idx = q_tile_start + tid_y;
-        int q_tensor_idx = ((bid_y * target_seq_len) + global_q_idx) * q_heads + bid_x;
+//     if (tid_y < bound_tid_y) {
+//         int global_q_idx = q_tile_start + tid_y;
+//         int q_tensor_idx = ((bid_y * target_seq_len) + global_q_idx) * q_heads + bid_x;
         
-        for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-            // dQ: [batch_size, target_seq_len, q_heads, head_dim]
-            grad_query[q_tensor_idx * head_dim + idx] += T(dQ_sm_AT(tid_y, idx));
-        }
-    }
-
-    // Undefine access macros
-    #undef D_sm_AT
-    #undef Q_sm_AT
-    #undef K_T_sm_AT
-    #undef V_sm_AT
-    #undef dO_sm_AT
-    #undef dK_T_sm_AT
-    #undef dV_sm_AT
-    #undef S_sm_AT
-    #undef P_sm_AT
-    #undef L_sm_AT
-    #undef dQ_sm_AT
-}
-
-/**
- * FlashAttention Forward Function
- *
- * This is the main entry point for FlashAttention forward computation.
- * It creates the output tensor and launches the appropriate kernel based on data type.
- *
- * Args:
- *   query: Query tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
- *   key: Key tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
- *   value: Value tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
- *   attn_mask: Optional attention mask tensor
- *   scale: Scaling factor for attention scores
- *   is_causal: Whether to apply causal masking
- *   dropout_p: Dropout probability
- *   enable_gqa: Whether to enable grouped-query attention
- *
- * Returns:
- *   Output tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
- *   logsumexp: Logsumexp tensor for backward pass [batch_size, num_heads, seq_len_q]
- *   dropout_seed: Dropout seed for backward pass [1]
- */
-
-template <typename T>
-FlashAttentionForwardOutput FlashAttentionForwardImpl(const std::shared_ptr<Tensor> &query, const std::shared_ptr<Tensor> &key,
-                                                    const std::shared_ptr<Tensor> &value,
-                                                    const std::shared_ptr<Tensor> &attn_mask, float scale, bool is_causal,
-                                                    int64_t dropout_p, bool enable_gqa) {
-    auto dtype = query->Dtype();
-    const auto &query_dims = query->Dims();
-
-    // Output shape: [batch_size, seq_len_q, num_heads, head_dim]
-    std::vector<int64_t> output_dims = {query_dims[0], query_dims[1], query_dims[2], query_dims[3]};
-    auto output = std::make_shared<Tensor>(output_dims, dtype, query->GetDevice());
-
-    // Allocate logsumexp tensor for backward pass
-    // Shape: [batch_size, num_heads, seq_len_q]
-    std::vector<int64_t> logsumexp_dims = {query_dims[0], query_dims[2], query_dims[1]};
-    auto logsumexp = std::make_shared<Tensor>(logsumexp_dims, DataType::kFLOAT32, query->GetDevice());
-    float *logsumexp_ptr = static_cast<float *>(logsumexp->DataPtr());
-
-    // Allocate dropout_seed tensor for backward pass
-    // Shape: [1]
-    unsigned long long dropout_seed = 0;
-    std::shared_ptr<Tensor> dropout_seed_tensor;
-    if (dropout_p > 0) {
-        std::vector<int64_t> dropout_seed_dims = {1};
-        dropout_seed_tensor = std::make_shared<Tensor>(dropout_seed_dims, DataType::kUINT64, query->GetDevice());
-        dropout_seed = static_cast<unsigned long long>(std::time(nullptr));
-        unsigned long long *dropout_seed_ptr = static_cast<unsigned long long *>(dropout_seed_tensor->DataPtr());
-        *dropout_seed_ptr = dropout_seed;
-    }
-
-    switch (dtype) {
-        DISPATCH_CASE(WRAP(LaunchFlashAttentionForward<float>(output, query, key, value, attn_mask, scale, is_causal,
-                                                               dropout_p, enable_gqa, logsumexp_ptr, dropout_seed_ptr);),
-                      DataType::kFLOAT32)
-        DISPATCH_CASE(WRAP(LaunchFlashAttentionForward<nv_bfloat16>(output, query, key, value, attn_mask, scale,
-                                                                     is_causal, dropout_p, enable_gqa, logsumexp_ptr, dropout_seed_ptr);),
-                      DataType::kBFLOAT16)
-    default:
-        LOG_LOC(FATAL, "CUDA FlashAttention forward: 'Unsupported data type'");
-    }
-
-    FlashAttentionForwardOutput result;
-    result.output = output;
-    result.logsumexp = logsumexp;
-    result.dropout_seed = dropout_seed_tensor;
-    return result;
-}
-
-/**
- * Launch FlashAttention Forward Kernel
- *
- * This function sets up grid and block dimensions and launches FlashAttention forward kernel.
- *
- * Args:
- *   output: Output tensor
- *   query: Query tensor
- *   key: Key tensor
- *   value: Value tensor
- *   attn_mask: Optional attention mask tensor
- *   scale: Scaling factor
- *   is_causal: Whether to apply causal masking
- *   dropout_p: Dropout probability
- *   enable_gqa: Whether to enable GQA
- */
-template <typename T>
-void LaunchFlashAttentionForward(const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &query,
-                                 const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
-                                 const std::shared_ptr<Tensor> &attn_mask, float scale, bool is_causal,
-                                 int64_t dropout_p, bool enable_gqa, float *logsumexp_ptr, unsigned long long *dropout_seed_ptr) {
-
-    const auto &query_dims = query->Dims();
-    const auto &key_dims = key->Dims();
-    const auto &value_dims = value->Dims();
-
-    // Expected shapes:
-    // query: [batch_size, seq_len_q, num_heads, head_dim]
-    // key: [batch_size, seq_len_k, num_heads_kv, head_dim]
-    // value: [batch_size, seq_len_k, num_heads_kv, head_dim]
-    // output: [batch_size, seq_len_q, num_heads, head_dim]
-
-    int64_t batch_size = query_dims[0];
-    int64_t seq_len_q = query_dims[1];
-    int64_t num_heads = query_dims[2];
-    int64_t head_dim = query_dims[3];
-    int64_t seq_len_k = key_dims[1];
-    int64_t num_heads_kv = key_dims[2];
-
-    CHECK_EQ(key_dims[3], head_dim) << "Key head dimension must match query head dimension";
-    CHECK_EQ(value_dims[3], head_dim) << "Value head dimension must match query head dimension";
-    CHECK_EQ(value_dims[1], seq_len_k) << "Value sequence length must match key sequence length";
-    CHECK_EQ(value_dims[2], num_heads_kv) << "Value number of KV heads must match key";
-
-    if (enable_gqa) {
-        CHECK(num_heads % num_heads_kv == 0) << "Number of query heads must be divisible by number of KV heads for GQA";
-    } else {
-        CHECK_EQ(num_heads, num_heads_kv) << "Number of query and KV heads must match for standard attention";
-    }
-
-    T *output_ptr = static_cast<T *>(output->DataPtr());
-    const T *query_ptr = static_cast<const T *>(query->DataPtr());
-    const T *key_ptr = static_cast<const T *>(key->DataPtr());
-    const T *value_ptr = static_cast<const T *>(value->DataPtr());
-    const T *attn_mask_ptr = attn_mask ? static_cast<const T *>(attn_mask->DataPtr()) : nullptr;
-
-    // Set up grid and block dimensions according to FlashAttention v2
-    // block_dim(Br, Bc) where Br = Bc = 32
-    // grid_dim(query_heads, batch_size, Tr) where Tr = ceil(seq_len_q / Br)
-    constexpr int Br = 32;
-    constexpr int Bc = 32;
-    int64_t Tr = (seq_len_q + Br - 1) / Br;
+//         for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+//             // dQ: [batch_size, target_seq_len, q_heads, head_dim]
+//             grad_query[q_tensor_idx * head_dim + idx] += T(dQ_sm_AT(tid_y, idx));
+//         }
+//     }
+
+//     // Undefine access macros
+//     #undef D_sm_AT
+//     #undef Q_sm_AT
+//     #undef K_T_sm_AT
+//     #undef V_sm_AT
+//     #undef dO_sm_AT
+//     #undef dK_T_sm_AT
+//     #undef dV_sm_AT
+//     #undef S_sm_AT
+//     #undef P_sm_AT
+//     #undef L_sm_AT
+//     #undef dQ_sm_AT
+// }
+
+// /**
+//  * FlashAttention Forward Function
+//  *
+//  * This is the main entry point for FlashAttention forward computation.
+//  * It creates the output tensor and launches the appropriate kernel based on data type.
+//  *
+//  * Args:
+//  *   query: Query tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+//  *   key: Key tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+//  *   value: Value tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
+//  *   attn_mask: Optional attention mask tensor
+//  *   scale: Scaling factor for attention scores
+//  *   is_causal: Whether to apply causal masking
+//  *   dropout_p: Dropout probability
+//  *   enable_gqa: Whether to enable grouped-query attention
+//  *
+//  * Returns:
+//  *   Output tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
+//  *   logsumexp: Logsumexp tensor for backward pass [batch_size, num_heads, seq_len_q]
+//  *   dropout_seed: Dropout seed for backward pass [1]
+//  */
+
+
+// /**
+//  * Launch FlashAttention Forward Kernel
+//  *
+//  * This function sets up grid and block dimensions and launches FlashAttention forward kernel.
+//  *
+//  * Args:
+//  *   output: Output tensor
+//  *   query: Query tensor
+//  *   key: Key tensor
+//  *   value: Value tensor
+//  *   attn_mask: Optional attention mask tensor
+//  *   scale: Scaling factor
+//  *   is_causal: Whether to apply causal masking
+//  *   dropout_p: Dropout probability
+//  *   enable_gqa: Whether to enable GQA
+//  */
+// template <typename T>
+// void LaunchFlashAttentionForward(const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &query,
+//                                  const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
+//                                  const std::shared_ptr<Tensor> &attn_mask, float scale, bool is_causal,
+//                                  int64_t dropout_p, bool enable_gqa, float *logsumexp_ptr, unsigned long long dropout_seed) {
+
+//     const auto &query_dims = query->Dims();
+//     const auto &key_dims = key->Dims();
+//     const auto &value_dims = value->Dims();
+
+//     // Expected shapes:
+//     // query: [batch_size, seq_len_q, num_heads, head_dim]
+//     // key: [batch_size, seq_len_k, num_heads_kv, head_dim]
+//     // value: [batch_size, seq_len_k, num_heads_kv, head_dim]
+//     // output: [batch_size, seq_len_q, num_heads, head_dim]
+
+//     int64_t batch_size = query_dims[0];
+//     int64_t seq_len_q = query_dims[1];
+//     int64_t num_heads = query_dims[2];
+//     int64_t head_dim = query_dims[3];
+//     int64_t seq_len_k = key_dims[1];
+//     int64_t num_heads_kv = key_dims[2];
+
+//     CHECK_EQ(key_dims[3], head_dim) << "Key head dimension must match query head dimension";
+//     CHECK_EQ(value_dims[3], head_dim) << "Value head dimension must match query head dimension";
+//     CHECK_EQ(value_dims[1], seq_len_k) << "Value sequence length must match key sequence length";
+//     CHECK_EQ(value_dims[2], num_heads_kv) << "Value number of KV heads must match key";
+
+//     if (enable_gqa) {
+//         CHECK(num_heads % num_heads_kv == 0) << "Number of query heads must be divisible by number of KV heads for GQA";
+//     } else {
+//         CHECK_EQ(num_heads, num_heads_kv) << "Number of query and KV heads must match for standard attention";
+//     }
+
+//     T *output_ptr = static_cast<T *>(output->DataPtr());
+//     const T *query_ptr = static_cast<const T *>(query->DataPtr());
+//     const T *key_ptr = static_cast<const T *>(key->DataPtr());
+//     const T *value_ptr = static_cast<const T *>(value->DataPtr());
+//     const T *attn_mask_ptr = attn_mask ? static_cast<const T *>(attn_mask->DataPtr()) : nullptr;
+
+//     // Set up grid and block dimensions according to FlashAttention v2
+//     // block_dim(Br, Bc) where Br = Bc = 32
+//     // grid_dim(query_heads, batch_size, Tr) where Tr = ceil(seq_len_q / Br)
+//     constexpr int Br = 32;
+//     constexpr int Bc = 32;
+//     int64_t Tr = (seq_len_q + Br - 1) / Br;
     
-    dim3 block_dims(Bc, Br);  // (blockDim.x, blockDim.y) = (Bc, Br)
-    dim3 grid_dims(num_heads, batch_size, Tr);  // (gridDim.x, gridDim.y, gridDim.z) = (num_heads, batch_size, Tr)
-
-    // Calculate shared memory size (removed dropout_sm allocation)
-    // SP[Br][Bc] (double) + m_prev[Br] (float) + m_new[Br] (float) + l_prev[Br] (float) + l_new[Br] (float)
-    // + Q_sm[Br][head_dim] (float) + K_T_sm[head_dim][Bc] (float) + V_sm[Bc][head_dim] (float) + O_sm[Br][head_dim] (float)
-    size_t shared_mem_size = Br * Bc * sizeof(double)  // SP
-                          + 4 * Br * sizeof(float)     // m_prev, m_new, l_prev, l_new
-                          + (Br + Bc + Bc + Br) * head_dim) * sizeof(float);  // Q_sm, K_T_sm, V_sm, O_sm
-    // Note: Removed dropout_sm[Br][Bc] (bool) allocation
-
-    auto device = output->GetDevice();
-    const auto &cuda_stream = dynamic_cast<infini_train::core::cuda::CudaStream *>(
-                                  infini_train::core::GetDeviceGuardImpl(device.type())->GetStream(device))
-                                  ->cuda_stream();
-
-    FlashAttentionForwardKernel<T><<<grid_dims, block_dims, shared_mem_size, cuda_stream>>>(
-        output_ptr, query_ptr, key_ptr, value_ptr, attn_mask_ptr,
-        logsumexp_ptr, scale, is_causal, enable_gqa, dropout_p, dropout_seed,
-        batch_size, seq_len_q, seq_len_k, num_heads, num_heads_kv, head_dim);
-}
-
-/**
- * Launch FlashAttention Backward Kernel
- *
- * This function sets up grid and block dimensions and launches FlashAttention backward kernel.
- *
- * Args:
- *   grad_query: Gradient tensor for query
- *   grad_key: Gradient tensor for key
- *   grad_value: Gradient tensor for value
- *   query: Query tensor from forward pass
- *   key: Key tensor from forward pass
- *   value: Value tensor from forward pass
- *   output: Output tensor from forward pass
- *   grad_output: Gradient from upstream
- *   logsumexp: Logsumexp tensor from forward pass
- *   dropout_seed: Dropout seed for reproducibility
- *   attn_mask: Optional attention mask tensor
- *   scale: Scaling factor
- *   is_causal: Whether causal masking was applied
- *   dropout_p: Dropout probability
- *   enable_gqa: Whether GQA was enabled
- */
-template <typename T>
-void LaunchFlashAttentionBackward(const std::shared_ptr<Tensor> &grad_query, const std::shared_ptr<Tensor> &grad_key,
-                                 const std::shared_ptr<Tensor> &grad_value, const std::shared_ptr<Tensor> &query,
-                                 const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
-                                 const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &grad_output,
-                                 const std::shared_ptr<Tensor> &logsumexp,
-                                 unsigned long long dropout_seed,
-                                 const std::shared_ptr<Tensor> &attn_mask,
-                                 float scale, bool is_causal, int64_t dropout_p, bool enable_gqa) {
-
-    const auto &query_dims = query->Dims();
-    const auto &key_dims = key->Dims();
-
-    // Expected shapes:
-    // query: [batch_size, seq_len_q, num_heads, head_dim]
-    // key: [batch_size, seq_len_k, num_heads_kv, head_dim]
-    // value: [batch_size, seq_len_k, num_heads_kv, head_dim]
-    // output: [batch_size, seq_len_q, num_heads, head_dim]
-
-    int64_t batch_size = query_dims[0];
-    int64_t seq_len_q = query_dims[1];
-    int64_t num_heads = query_dims[2];
-    int64_t head_dim = query_dims[3];
-    int64_t seq_len_k = key_dims[1];
-    int64_t num_heads_kv = key_dims[2];
-
-    CHECK_EQ(key_dims[3], head_dim) << "Key head dimension must match query head dimension";
-    CHECK_EQ(value->Dims()[3], head_dim) << "Value head dimension must match query head dimension";
-    CHECK_EQ(value->Dims()[1], seq_len_k) << "Value sequence length must match key sequence length";
-    CHECK_EQ(value->Dims()[2], num_heads_kv) << "Value number of KV heads must match key";
-
-    if (enable_gqa) {
-        CHECK(num_heads % num_heads_kv == 0) << "Number of query heads must be divisible by number of KV heads for GQA";
-    } else {
-        CHECK_EQ(num_heads, num_heads_kv) << "Number of query and KV heads must match for standard attention";
-    }
-
-    // Precompute D = rowsum(dO ∘ O) before main backward loop
-    // D shape: [batch_size, seq_len_q, num_heads]
-    // dO shape: [batch_size, seq_len_q, num_heads, head_dim]
-    // O shape: [batch_size, seq_len_q, num_heads, head_dim]
-    auto D = function::Sum(grad_output * output, 3, false);
-
-    T *grad_query_ptr = static_cast<T *>(grad_query->DataPtr());
-    T *grad_key_ptr = static_cast<T *>(grad_key->DataPtr());
-    T *grad_value_ptr = static_cast<T *>(grad_value->DataPtr());
-    const T *query_ptr = static_cast<const T *>(query->DataPtr());
-    const T *key_ptr = static_cast<const T *>(key->DataPtr());
-    const T *value_ptr = static_cast<const T *>(value->DataPtr());
-    const T *output_ptr = static_cast<const T *>(output->DataPtr());
-    const T *grad_output_ptr = static_cast<const T *>(grad_output->DataPtr());
-    const float *logsumexp_ptr = static_cast<const float *>(logsumexp->DataPtr());
-    const float *D_ptr = static_cast<const float *>(D->DataPtr());
-    const T *attn_mask_ptr = attn_mask ? static_cast<const T *>(attn_mask->DataPtr()) : nullptr;
-
-    // Set up grid and block dimensions according to FlashAttention v2 backward
-    // block_dim(Bc, Br) where Bc = Br = 32
-    // grid_dims(num_heads_q, batch_size, Tr) where Tr = ceil(seq_len_q / Br)
-    constexpr int Br = 32;
-    constexpr int Bc = 32;
-    int64_t Tr = (seq_len_q + Br - 1) / Br;
-
-    dim3 block_dims(Bc, Br);  // (blockDim.x, blockDim.y) = (Bc, Br)
-    dim3 grid_dims(num_heads, batch_size, Tr);  // (gridDim.x, gridDim.y, gridDim.z) = (num_heads, batch_size, Tr)
-
-    // Calculate shared memory size for backward pass
-    // Q_sm[Br][head_dim], K_T_sm[head_dim][Bc], V_sm[Bc][head_dim]
-    // dO_sm[Br][head_dim], dK_T_sm[head_dim][Bc], dV_sm[Bc][head_dim]
-    // S_sm[Br][Bc], P_sm[Br][Bc]
-    // D_sm[Br] - D values loaded from HBM to shared memory
-    size_t shared_mem_size = Br * head_dim * sizeof(float)     // Q_sm
-                           + head_dim * Bc * sizeof(float)    // K_T_sm
-                           + Bc * head_dim * sizeof(float)    // V_sm
-                           + Br * head_dim * sizeof(float)     // dO_sm
-                           + head_dim * Bc * sizeof(float)    // dK_T_sm
-                           + Bc * head_dim * sizeof(float)    // dV_sm
-                           + Br * Bc * sizeof(float)          // S_sm or (dP_sm when compute dP_sm = dO_i @ V_j^T \in R^{Br*Bc})
-                           + Br * Bc * sizeof(float)         // P_sm or (dS_sm when compute dS_sm = P_sm_ij pointwise multiplied by (dP_sm_ij - D_i) \in R^{Br*Bc})
-                           + Br * sizeof(float)        // L_i
-                           + Br * sizeof(float);              // D_sm (loaded from HBM)
-
-    auto device = query->GetDevice();
-    const auto &cuda_stream = dynamic_cast<infini_train::core::cuda::CudaStream *>(
-                                  infini_train::core::GetDeviceGuardImpl(device.type())->GetStream(device))
-                                  ->cuda_stream();
-
-    FlashAttentionBackwardKernel<T><<<grid_dims, block_dims, shared_mem_size, cuda_stream>>>(
-        grad_query_ptr, grad_key_ptr, grad_value_ptr,
-        query_ptr, key_ptr, value_ptr, output_ptr, grad_output_ptr,
-        logsumexp_ptr, D_ptr, dropout_seed, attn_mask_ptr,
-        scale, is_causal, dropout_p, enable_gqa,
-        batch_size, seq_len_q, seq_len_k, num_heads, num_heads_kv, head_dim);
-}
-
-/**
- * FlashAttention Backward Function
- *
- * This is the main entry point for FlashAttention backward computation.
- * It creates gradient tensors and launches the appropriate kernel based on data type.
- *
- * Args:
- *   grad_query: Gradient tensor for query
- *   grad_key: Gradient tensor for key
- *   grad_value: Gradient tensor for value
- *   query: Query tensor from forward pass
- *   key: Key tensor from forward pass
- *   value: Value tensor from forward pass
- *   output: Output tensor from forward pass
- *   grad_output: Gradient from upstream
- *   attn_mask: Optional attention mask tensor
- *   scale: Scaling factor
- *   is_causal: Whether causal masking was applied
- *   dropout_p: Dropout probability (not implemented)
- *   enable_gqa: Whether GQA was enabled
- *
- * Returns:
- *   Tuple of (grad_query, grad_key, grad_value) tensors
- */
-template <typename T>
-std::vector<std::shared_ptr<Tensor>> FlashAttentionBackwardImpl(const std::shared_ptr<Tensor> &grad_query, const std::shared_ptr<Tensor> &grad_key,
-                                                              const std::shared_ptr<Tensor> &grad_value, const std::shared_ptr<Tensor> &query,
-                                                              const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
-                                                              const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &grad_output,
-                                                              const std::shared_ptr<Tensor> &logsumexp,
-                                                              const std::shared_ptr<Tensor> &dropout_seed,
-                                                              const std::shared_ptr<Tensor> &attn_mask,
-                                                              float scale, bool is_causal, int64_t dropout_p, bool enable_gqa) {
-    auto dtype = query->Dtype();
-
-    // Create gradient tensors with same shapes as inputs
-    auto grad_query = std::make_shared<Tensor>(query->Dims(), dtype, query->GetDevice());
-    auto grad_key = std::make_shared<Tensor>(key->Dims(), dtype, key->GetDevice());
-    auto grad_value = std::make_shared<Tensor>(value->Dims(), dtype, value->GetDevice());
-
-    // Initialize gradients to zero
-    DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_query->Fill<T>(0); }, "CUDA FlashAttentionBackward");
-    DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_key->Fill<T>(0); }, "CUDA FlashAttentionBackward");
-    DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_value->Fill<T>(0); }, "CUDA FlashAttentionBackward");
-
-    // Get dropout seed value
-    unsigned long long dropout_seed_value = 0;
-    if (dropout_seed) {
-        dropout_seed_value = *static_cast<const unsigned long long *>(dropout_seed->DataPtr());
-    }
-
-    switch (dtype) {
-        DISPATCH_CASE(WRAP(LaunchFlashAttentionBackward<float>(grad_query, grad_key, grad_value, query, key, value, output, grad_output,
-                                                               logsumexp, dropout_seed_value, attn_mask, scale, is_causal,
-                                                               dropout_p, enable_gqa);),
-                      DataType::kFLOAT32)
-        DISPATCH_CASE(WRAP(LaunchFlashAttentionBackward<nv_bfloat16>(grad_query, grad_key, grad_value, query, key, value, output, grad_output,
-                                                                   logsumexp, dropout_seed_value, attn_mask, scale, is_causal,
-                                                                   dropout_p, enable_gqa);),
-                      DataType::kBFLOAT16)
-    default:
-        LOG_LOC(FATAL, "CUDA FlashAttention backward: 'Unsupported data type'");
-    }
-
-    return {grad_query, grad_key, grad_value};
-}
-
-// Non-template wrapper functions for registration
-FlashAttentionForwardOutput FlashAttentionForward(const std::shared_ptr<Tensor> &query, const std::shared_ptr<Tensor> &key,
-                                               const std::shared_ptr<Tensor> &value,
-                                               const std::shared_ptr<Tensor> &attn_mask, float scale, bool is_causal,
-                                               int64_t dropout_p, bool enable_gqa) {
-    auto dtype = query->Dtype();
-    const auto &query_dims = query->Dims();
-
-    // Output shape: [batch_size, seq_len_q, num_heads, head_dim]
-    std::vector<int64_t> output_dims = {query_dims[0], query_dims[1], query_dims[2], query_dims[3]};
-    auto output = std::make_shared<Tensor>(output_dims, dtype, query->GetDevice());
-
-    // Allocate logsumexp tensor for backward pass
-    // Shape: [batch_size, num_heads, seq_len_q]
-    std::vector<int64_t> logsumexp_dims = {query_dims[0], query_dims[2], query_dims[1]};
-    auto logsumexp = std::make_shared<Tensor>(logsumexp_dims, DataType::kFLOAT32, query->GetDevice());
-    float *logsumexp_ptr = static_cast<float *>(logsumexp->DataPtr());
-
-    // Allocate dropout_seed tensor for backward pass
-    // Shape: [1]
-    unsigned long long dropout_seed = 0;
-    std::shared_ptr<Tensor> dropout_seed_tensor;
-    if (dropout_p > 0) {
-        std::vector<int64_t> dropout_seed_dims = {1};
-        dropout_seed_tensor = std::make_shared<Tensor>(dropout_seed_dims, DataType::kUINT64, query->GetDevice());
-        dropout_seed = static_cast<unsigned long long>(std::time(nullptr));
-        unsigned long long *dropout_seed_ptr = static_cast<unsigned long long *>(dropout_seed_tensor->DataPtr());
-        *dropout_seed_ptr = dropout_seed;
-    }
-
-    switch (dtype) {
-        DISPATCH_CASE(WRAP(LaunchFlashAttentionForward<float>(output, query, key, value, attn_mask, scale, is_causal,
-                                                               dropout_p, enable_gqa, logsumexp_ptr,
-                                                               dropout_seed_tensor ? static_cast<unsigned long long*>(dropout_seed_tensor->DataPtr()) : nullptr);),
-                      DataType::kFLOAT32)
-        DISPATCH_CASE(WRAP(LaunchFlashAttentionForward<nv_bfloat16>(output, query, key, value, attn_mask, scale,
-                                                                     is_causal, dropout_p, enable_gqa, logsumexp_ptr,
-                                                                     dropout_seed_tensor ? static_cast<unsigned long long*>(dropout_seed_tensor->DataPtr()) : nullptr);),
-                      DataType::kBFLOAT16)
-    default:
-        LOG_LOC(FATAL, "CUDA FlashAttention forward: 'Unsupported data type'");
-    }
-
-    FlashAttentionForwardOutput result;
-    result.output = output;
-    result.logsumexp = logsumexp;
-    result.dropout_seed = dropout_seed_tensor;
-    return result;
-}
-
-std::vector<std::shared_ptr<Tensor>> FlashAttentionBackward(const std::shared_ptr<Tensor> &grad_query, const std::shared_ptr<Tensor> &grad_key,
-                                                          const std::shared_ptr<Tensor> &grad_value, const std::shared_ptr<Tensor> &query,
-                                                          const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
-                                                          const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &grad_output,
-                                                          const std::shared_ptr<Tensor> &logsumexp,
-                                                          const std::shared_ptr<Tensor> &dropout_seed,
-                                                          const std::shared_ptr<Tensor> &attn_mask,
-                                                          float scale, bool is_causal, int64_t dropout_p, bool enable_gqa) {
-    auto dtype = query->Dtype();
-
-    // Create gradient tensors with same shapes as inputs
-    auto grad_query = std::make_shared<Tensor>(query->Dims(), dtype, query->GetDevice());
-    auto grad_key = std::make_shared<Tensor>(key->Dims(), dtype, key->GetDevice());
-    auto grad_value = std::make_shared<Tensor>(value->Dims(), dtype, value->GetDevice());
-
-    // Initialize gradients to zero
-    DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_query->Fill<T>(0); }, "CUDA FlashAttentionBackward");
-    DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_key->Fill<T>(0); }, "CUDA FlashAttentionBackward");
-    DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_value->Fill<T>(0); }, "CUDA FlashAttentionBackward");
-
-    // Get dropout seed value
-    unsigned long long dropout_seed_value = 0;
-    if (dropout_seed) {
-        dropout_seed_value = *static_cast<const unsigned long long *>(dropout_seed->DataPtr());
-    }
-
-    switch (dtype) {
-        DISPATCH_CASE(WRAP(LaunchFlashAttentionBackward<float>(grad_query, grad_key, grad_value, query, key, value, output, grad_output,
-                                                               logsumexp, dropout_seed_value, attn_mask, scale, is_causal,
-                                                               dropout_p, enable_gqa);),
-                      DataType::kFLOAT32)
-        DISPATCH_CASE(WRAP(LaunchFlashAttentionBackward<nv_bfloat16>(grad_query, grad_key, grad_value, query, key, value, output, grad_output,
-                                                                    logsumexp, dropout_seed_value, attn_mask, scale, is_causal,
-                                                                    dropout_p, enable_gqa);),
-                      DataType::kBFLOAT16)
-    default:
-        LOG_LOC(FATAL, "CUDA FlashAttention backward: 'Unsupported data type'");
-    }
-
-    return {grad_query, grad_key, grad_value};
-}
-
-} // namespace infini_train::kernels::cuda
-
-// Register FlashAttention kernels with the dispatcher
-#define REGISTER_CUDA_FLASHATTENTION_KERNEL(kernel_name) \
-    REGISTER_KERNEL(infini_train::Device::DeviceType::kCUDA, kernel_name, infini_train::kernels::cuda::kernel_name)
-
-REGISTER_CUDA_FLASHATTENTION_KERNEL(FlashAttentionForward)
-REGISTER_CUDA_FLASHATTENTION_KERNEL(FlashAttentionBackward)
-
-#undef REGISTER_CUDA_FLASHATTENTION_KERNEL
+//     dim3 block_dims(Bc, Br);  // (blockDim.x, blockDim.y) = (Bc, Br)
+//     dim3 grid_dims(num_heads, batch_size, Tr);  // (gridDim.x, gridDim.y, gridDim.z) = (num_heads, batch_size, Tr)
+
+//     // Calculate shared memory size (removed dropout_sm allocation)
+//     // SP[Br][Bc] (double) + m_prev[Br] (float) + m_new[Br] (float) + l_prev[Br] (float) + l_new[Br] (float)
+//     // + Q_sm[Br][head_dim] (float) + K_T_sm[head_dim][Bc] (float) + V_sm[Bc][head_dim] (float) + O_sm[Br][head_dim] (float)
+//     size_t shared_mem_size = Br * Bc * sizeof(double)  // SP
+//                           + 4 * Br * sizeof(float)     // m_prev, m_new, l_prev, l_new
+//                           + (Br + Bc + Bc + Br) * head_dim) * sizeof(float);  // Q_sm, K_T_sm, V_sm, O_sm
+//     // Note: Removed dropout_sm[Br][Bc] (bool) allocation
+
+//     auto device = output->GetDevice();
+//     const auto &cuda_stream = dynamic_cast<infini_train::core::cuda::CudaStream *>(
+//                                   infini_train::core::GetDeviceGuardImpl(device.type())->GetStream(device))
+//                                   ->cuda_stream();
+
+//     FlashAttentionForwardKernel<T><<<grid_dims, block_dims, shared_mem_size, cuda_stream>>>(
+//         output_ptr, query_ptr, key_ptr, value_ptr, attn_mask_ptr,
+//         logsumexp_ptr, scale, is_causal, enable_gqa, dropout_p, dropout_seed,
+//         batch_size, seq_len_q, seq_len_k, num_heads, num_heads_kv, head_dim);
+// }
+
+// /**
+//  * Launch FlashAttention Backward Kernel
+//  *
+//  * This function sets up grid and block dimensions and launches FlashAttention backward kernel.
+//  *
+//  * Args:
+//  *   grad_query: Gradient tensor for query
+//  *   grad_key: Gradient tensor for key
+//  *   grad_value: Gradient tensor for value
+//  *   query: Query tensor from forward pass
+//  *   key: Key tensor from forward pass
+//  *   value: Value tensor from forward pass
+//  *   output: Output tensor from forward pass
+//  *   grad_output: Gradient from upstream
+//  *   logsumexp: Logsumexp tensor from forward pass
+//  *   dropout_seed: Dropout seed for reproducibility
+//  *   attn_mask: Optional attention mask tensor
+//  *   scale: Scaling factor
+//  *   is_causal: Whether causal masking was applied
+//  *   dropout_p: Dropout probability
+//  *   enable_gqa: Whether GQA was enabled
+//  */
+// template <typename T>
+// void LaunchFlashAttentionBackward(const std::shared_ptr<Tensor> &grad_query, const std::shared_ptr<Tensor> &grad_key,
+//                                  const std::shared_ptr<Tensor> &grad_value, const std::shared_ptr<Tensor> &query,
+//                                  const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
+//                                  const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &grad_output,
+//                                  const std::shared_ptr<Tensor> &logsumexp,
+//                                  unsigned long long dropout_seed,
+//                                  const std::shared_ptr<Tensor> &attn_mask,
+//                                  float scale, bool is_causal, int64_t dropout_p, bool enable_gqa) {
+
+//     const auto &query_dims = query->Dims();
+//     const auto &key_dims = key->Dims();
+
+//     // Expected shapes:
+//     // query: [batch_size, seq_len_q, num_heads, head_dim]
+//     // key: [batch_size, seq_len_k, num_heads_kv, head_dim]
+//     // value: [batch_size, seq_len_k, num_heads_kv, head_dim]
+//     // output: [batch_size, seq_len_q, num_heads, head_dim]
+
+//     int64_t batch_size = query_dims[0];
+//     int64_t seq_len_q = query_dims[1];
+//     int64_t num_heads = query_dims[2];
+//     int64_t head_dim = query_dims[3];
+//     int64_t seq_len_k = key_dims[1];
+//     int64_t num_heads_kv = key_dims[2];
+
+//     CHECK_EQ(key_dims[3], head_dim) << "Key head dimension must match query head dimension";
+//     CHECK_EQ(value->Dims()[3], head_dim) << "Value head dimension must match query head dimension";
+//     CHECK_EQ(value->Dims()[1], seq_len_k) << "Value sequence length must match key sequence length";
+//     CHECK_EQ(value->Dims()[2], num_heads_kv) << "Value number of KV heads must match key";
+
+//     if (enable_gqa) {
+//         CHECK(num_heads % num_heads_kv == 0) << "Number of query heads must be divisible by number of KV heads for GQA";
+//     } else {
+//         CHECK_EQ(num_heads, num_heads_kv) << "Number of query and KV heads must match for standard attention";
+//     }
+
+//     // Precompute D = rowsum(dO ∘ O) before main backward loop
+//     // D shape: [batch_size, seq_len_q, num_heads]
+//     // dO shape: [batch_size, seq_len_q, num_heads, head_dim]
+//     // O shape: [batch_size, seq_len_q, num_heads, head_dim]
+//     auto D = function::Sum(grad_output * output, 3, false);
+
+//     T *grad_query_ptr = static_cast<T *>(grad_query->DataPtr());
+//     T *grad_key_ptr = static_cast<T *>(grad_key->DataPtr());
+//     T *grad_value_ptr = static_cast<T *>(grad_value->DataPtr());
+//     const T *query_ptr = static_cast<const T *>(query->DataPtr());
+//     const T *key_ptr = static_cast<const T *>(key->DataPtr());
+//     const T *value_ptr = static_cast<const T *>(value->DataPtr());
+//     const T *output_ptr = static_cast<const T *>(output->DataPtr());
+//     const T *grad_output_ptr = static_cast<const T *>(grad_output->DataPtr());
+//     const float *logsumexp_ptr = static_cast<const float *>(logsumexp->DataPtr());
+//     const float *D_ptr = static_cast<const float *>(D->DataPtr());
+//     const T *attn_mask_ptr = attn_mask ? static_cast<const T *>(attn_mask->DataPtr()) : nullptr;
+
+//     // Set up grid and block dimensions according to FlashAttention v2 backward
+//     // block_dim(Bc, Br) where Bc = Br = 32
+//     // grid_dims(num_heads_q, batch_size, Tr) where Tr = ceil(seq_len_q / Br)
+//     constexpr int Br = 32;
+//     constexpr int Bc = 32;
+//     int64_t Tr = (seq_len_q + Br - 1) / Br;
+
+//     dim3 block_dims(Bc, Br);  // (blockDim.x, blockDim.y) = (Bc, Br)
+//     dim3 grid_dims(num_heads, batch_size, Tr);  // (gridDim.x, gridDim.y, gridDim.z) = (num_heads, batch_size, Tr)
+
+//     // Calculate shared memory size for backward pass
+//     // Q_sm[Br][head_dim], K_T_sm[head_dim][Bc], V_sm[Bc][head_dim]
+//     // dO_sm[Br][head_dim], dK_T_sm[head_dim][Bc], dV_sm[Bc][head_dim]
+//     // S_sm[Br][Bc], P_sm[Br][Bc]
+//     // D_sm[Br] - D values loaded from HBM to shared memory
+//     size_t shared_mem_size = Br * head_dim * sizeof(float)     // Q_sm
+//                            + head_dim * Bc * sizeof(float)    // K_T_sm
+//                            + Bc * head_dim * sizeof(float)    // V_sm
+//                            + Br * head_dim * sizeof(float)     // dO_sm
+//                            + head_dim * Bc * sizeof(float)    // dK_T_sm
+//                            + Bc * head_dim * sizeof(float)    // dV_sm
+//                            + Br * Bc * sizeof(float)          // S_sm or (dP_sm when compute dP_sm = dO_i @ V_j^T \in R^{Br*Bc})
+//                            + Br * Bc * sizeof(float)         // P_sm or (dS_sm when compute dS_sm = P_sm_ij pointwise multiplied by (dP_sm_ij - D_i) \in R^{Br*Bc})
+//                            + Br * sizeof(float)        // L_i
+//                            + Br * sizeof(float);              // D_sm (loaded from HBM)
+
+//     auto device = query->GetDevice();
+//     const auto &cuda_stream = dynamic_cast<infini_train::core::cuda::CudaStream *>(
+//                                   infini_train::core::GetDeviceGuardImpl(device.type())->GetStream(device))
+//                                   ->cuda_stream();
+
+//     FlashAttentionBackwardKernel<T><<<grid_dims, block_dims, shared_mem_size, cuda_stream>>>(
+//         grad_query_ptr, grad_key_ptr, grad_value_ptr,
+//         query_ptr, key_ptr, value_ptr, output_ptr, grad_output_ptr,
+//         logsumexp_ptr, D_ptr, dropout_seed, attn_mask_ptr,
+//         scale, is_causal, dropout_p, enable_gqa,
+//         batch_size, seq_len_q, seq_len_k, num_heads, num_heads_kv, head_dim);
+// }
+
+// /**
+//  * FlashAttention Backward Function
+//  *
+//  * This is the main entry point for FlashAttention backward computation.
+//  * It creates gradient tensors and launches the appropriate kernel based on data type.
+//  *
+//  * Args:
+//  *   grad_query: Gradient tensor for query
+//  *   grad_key: Gradient tensor for key
+//  *   grad_value: Gradient tensor for value
+//  *   query: Query tensor from forward pass
+//  *   key: Key tensor from forward pass
+//  *   value: Value tensor from forward pass
+//  *   output: Output tensor from forward pass
+//  *   grad_output: Gradient from upstream
+//  *   attn_mask: Optional attention mask tensor
+//  *   scale: Scaling factor
+//  *   is_causal: Whether causal masking was applied
+//  *   dropout_p: Dropout probability (not implemented)
+//  *   enable_gqa: Whether GQA was enabled
+//  *
+//  * Returns:
+//  *   Tuple of (grad_query, grad_key, grad_value) tensors
+//  */
+
+
+// // Non-template wrapper functions for registration
+// FlashAttentionForwardOutput FlashAttentionForward(const std::shared_ptr<Tensor> &query, const std::shared_ptr<Tensor> &key,
+//                                                const std::shared_ptr<Tensor> &value,
+//                                                const std::shared_ptr<Tensor> &attn_mask, float scale, bool is_causal,
+//                                                int64_t dropout_p, bool enable_gqa) {
+//     auto dtype = query->Dtype();
+//     const auto &query_dims = query->Dims();
+
+//     // Output shape: [batch_size, seq_len_q, num_heads, head_dim]
+//     std::vector<int64_t> output_dims = {query_dims[0], query_dims[1], query_dims[2], query_dims[3]};
+//     auto output = std::make_shared<Tensor>(output_dims, dtype, query->GetDevice());
+
+//     // Allocate logsumexp tensor for backward pass
+//     // Shape: [batch_size, num_heads, seq_len_q]
+//     std::vector<int64_t> logsumexp_dims = {query_dims[0], query_dims[2], query_dims[1]};
+//     auto logsumexp = std::make_shared<Tensor>(logsumexp_dims, DataType::kFLOAT32, query->GetDevice());
+//     float *logsumexp_ptr = static_cast<float *>(logsumexp->DataPtr());
+
+//     // Allocate dropout_seed tensor for backward pass
+//     // Shape: [1]
+//     unsigned long long dropout_seed = 0;
+//     std::shared_ptr<Tensor> dropout_seed_tensor;
+//     if (dropout_p > 0) {
+//         std::vector<int64_t> dropout_seed_dims = {1};
+//         dropout_seed_tensor = std::make_shared<Tensor>(dropout_seed_dims, DataType::kUINT64, query->GetDevice());
+//         dropout_seed = static_cast<unsigned long long>(std::time(nullptr));
+//         unsigned long long *dropout_seed_ptr = static_cast<unsigned long long *>(dropout_seed_tensor->DataPtr());
+//         *dropout_seed_ptr = dropout_seed;
+//     }
+
+//     switch (dtype) {
+//         DISPATCH_CASE(WRAP(LaunchFlashAttentionForward<float>(output, query, key, value, attn_mask, scale, is_causal,
+//                                                                dropout_p, enable_gqa, logsumexp_ptr,
+//                                                                dropout_seed);),
+//                       DataType::kFLOAT32)
+//         DISPATCH_CASE(WRAP(LaunchFlashAttentionForward<nv_bfloat16>(output, query, key, value, attn_mask, scale,
+//                                                                      is_causal, dropout_p, enable_gqa, logsumexp_ptr,
+//                                                                      dropout_seed);),
+//                       DataType::kBFLOAT16)
+//     default:
+//         LOG_LOC(FATAL, "CUDA FlashAttention forward: 'Unsupported data type'");
+//     }
+
+//     FlashAttentionForwardOutput result;
+//     result.output = output;
+//     result.logsumexp = logsumexp;
+//     result.dropout_seed = dropout_seed_tensor;
+//     return result;
+// }
+
+// std::vector<std::shared_ptr<Tensor>> FlashAttentionBackward(const std::shared_ptr<Tensor> &grad_query, const std::shared_ptr<Tensor> &grad_key,
+//                                                           const std::shared_ptr<Tensor> &grad_value, const std::shared_ptr<Tensor> &query,
+//                                                           const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
+//                                                           const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &grad_output,
+//                                                           const std::shared_ptr<Tensor> &logsumexp,
+//                                                           const std::shared_ptr<Tensor> &dropout_seed,
+//                                                           const std::shared_ptr<Tensor> &attn_mask,
+//                                                           float scale, bool is_causal, int64_t dropout_p, bool enable_gqa) {
+//     auto dtype = query->Dtype();
+
+//     // Create gradient tensors with same shapes as inputs
+//     auto grad_query = std::make_shared<Tensor>(query->Dims(), dtype, query->GetDevice());
+//     auto grad_key = std::make_shared<Tensor>(key->Dims(), dtype, key->GetDevice());
+//     auto grad_value = std::make_shared<Tensor>(value->Dims(), dtype, value->GetDevice());
+
+//     // Initialize gradients to zero
+//     DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_query->Fill<T>(0); }, "CUDA FlashAttentionBackward");
+//     DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_key->Fill<T>(0); }, "CUDA FlashAttentionBackward");
+//     DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_value->Fill<T>(0); }, "CUDA FlashAttentionBackward");
+
+//     // Get dropout seed value
+//     unsigned long long dropout_seed_value = 0;
+//     if (dropout_seed) {
+//         dropout_seed_value = *static_cast<const unsigned long long *>(dropout_seed->DataPtr());
+//     }
+
+//     switch (dtype) {
+//         DISPATCH_CASE(WRAP(LaunchFlashAttentionBackward<float>(grad_query, grad_key, grad_value, query, key, value, output, grad_output,
+//                                                                logsumexp, dropout_seed_value, attn_mask, scale, is_causal,
+//                                                                dropout_p, enable_gqa);),
+//                       DataType::kFLOAT32)
+//         DISPATCH_CASE(WRAP(LaunchFlashAttentionBackward<nv_bfloat16>(grad_query, grad_key, grad_value, query, key, value, output, grad_output,
+//                                                                     logsumexp, dropout_seed_value, attn_mask, scale, is_causal,
+//                                                                     dropout_p, enable_gqa);),
+//                       DataType::kBFLOAT16)
+//     default:
+//         LOG_LOC(FATAL, "CUDA FlashAttention backward: 'Unsupported data type'");
+//     }
+
+//     return {grad_query, grad_key, grad_value};
+// }
+
+// } // namespace infini_train::kernels::cuda
+
+// // Register FlashAttention kernels with the dispatcher
+// #define REGISTER_CUDA_FLASHATTENTION_KERNEL(kernel_name) \
+//     REGISTER_KERNEL(infini_train::Device::DeviceType::kCUDA, kernel_name, infini_train::kernels::cuda::kernel_name)
+
+// REGISTER_CUDA_FLASHATTENTION_KERNEL(FlashAttentionForward)
+// REGISTER_CUDA_FLASHATTENTION_KERNEL(FlashAttentionBackward)
+
+// #undef REGISTER_CUDA_FLASHATTENTION_KERNEL
diff --git a/scripts/test_config.json b/scripts/test_config.json
index 5659b516..3d66ff7b 100644
--- a/scripts/test_config.json
+++ b/scripts/test_config.json
@@ -1,10 +1,10 @@
 {
     "variables": {
         "BUILD_DIR": "../build",
-        "GPT2_INPUT_BIN": "../../data/llmc/gpt2/tinyshakespeare/tiny_shakespeare_train.bin",
-        "GPT2_LLMC_FILEPATH": "../../data/llmc/gpt2/gpt2_124M.bin",
-        "LLAMA3_INPUT_BIN": "../../data/llmc/llama3/tinyshakespeare/tiny_shakespeare_train.bin",
-        "LLAMA3_LLMC_FILEPATH": "../../data/llmc/llama3/llama3.2_1B_fp32.bin",
+        "GPT2_INPUT_BIN": "/data/shared/InfiniTrain-dev/data/llmc/gpt2/tinyshakespeare/tiny_shakespeare_train.bin",
+        "GPT2_LLMC_FILEPATH": "/data/shared/InfiniTrain-dev/data/llmc/gpt2/gpt2_124M.bin",
+        "LLAMA3_INPUT_BIN": "/data/shared/InfiniTrain-dev/data/llmc/llama3/tinyshakespeare/tiny_shakespeare_train.bin",
+        "LLAMA3_LLMC_FILEPATH": "/data/shared/InfiniTrain-dev/data/llmc/llama3/llama3.2_1B_fp32.bin",
         "PROFILE_LOG_DIR": "./profile_logs",
         "LOG_DIR": "./logs",
         "COMPARE_LOG_DIR": ""

From 26ea0120cffe0783f5324f214141e99e0a7c954d Mon Sep 17 00:00:00 2001
From: Aoshine999 <820459542@qq.com>
Date: Tue, 10 Mar 2026 20:43:21 +0800
Subject: [PATCH 04/18] FlashAttention basic functions passed

---
 example/gpt2/net.cc    | 3 ++-
 example/llama3/main.cc | 2 +-
 example/llama3/net.cc  | 1 +
 3 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/example/gpt2/net.cc b/example/gpt2/net.cc
index 68aece37..b033236c 100644
--- a/example/gpt2/net.cc
+++ b/example/gpt2/net.cc
@@ -393,7 +393,8 @@ std::shared_ptr<GPT2> GPT2::FromLLMC(const std::string &filepath) {
                                                         .original_vocab_size = vocab_size,
                                                         .n_layer = n_layer,
                                                         .n_head = n_head,
-                                                        .n_embd = n_embd});
+                                                        .n_embd = n_embd,
+                                                        .flash = true});
 
     LOG(INFO) << "magic: " << magic << " version: " << version << " block_size: " << block_size
               << " vocab_size: " << vocab_size << " n_layer: " << n_layer << " n_head: " << n_head
diff --git a/example/llama3/main.cc b/example/llama3/main.cc
index 22991eeb..963b1cac 100644
--- a/example/llama3/main.cc
+++ b/example/llama3/main.cc
@@ -70,7 +70,7 @@ DEFINE_uint32(tensor_parallel, 1, "Tensor Parallel world size");
 DEFINE_bool(sequence_parallel, false, "Whether to enable Sequence Parallel");
 DEFINE_uint32(pipeline_parallel, 1, "Pipeline Parallel world size, specified the number of PP stages.");
 DEFINE_uint32(virtual_pipeline_parallel, 1, "Number of chunks in PP stage.");
-DEFINE_bool(flash, false, "Whether to enable FlashAttention in CausalSelfAttention");
+DEFINE_bool(flash, true, "Whether to enable FlashAttention in CausalSelfAttention");
 // precision
 DEFINE_string(dtype, "float32", "precision used in training (float32/bfloat16)");
 // precision check
diff --git a/example/llama3/net.cc b/example/llama3/net.cc
index fe725992..b2e6b4ee 100644
--- a/example/llama3/net.cc
+++ b/example/llama3/net.cc
@@ -499,6 +499,7 @@ std::shared_ptr<LLaMA3> LLaMA3::FromLLMC(const std::string &filepath) {
                                                         .rope_theta = rope_theta,
                                                         .use_scaled_rope = static_cast<bool>(use_scaled_rope),
                                                         .norm_eps = norm_eps,
+                                                        .flash = true,
                                                         .max_gen_batch_size = max_gen_bs});
 
     // ========== pp_size：num_stages; vpp_size: num_chunks_per_stage ==========

From ff91ee3e06b2f9ff0139b87c489d280e01853370 Mon Sep 17 00:00:00 2001
From: Aoshine999 <820459542@qq.com>
Date: Wed, 11 Mar 2026 00:05:43 +0800
Subject: [PATCH 05/18] test_result redirect

---
 scripts/run_models_and_profile.bash | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/scripts/run_models_and_profile.bash b/scripts/run_models_and_profile.bash
index 1cf27935..363aef34 100755
--- a/scripts/run_models_and_profile.bash
+++ b/scripts/run_models_and_profile.bash
@@ -188,11 +188,11 @@ if [[ -n "$COMPARE_LOG_DIR" ]]; then
 
     # Run compare_loss.py
     echo -e "\n\033[1;33m[Running] compare_loss.py\033[0m"
-    python3 "${SCRIPT_DIR}/compare_loss.py" "$COMPARE_LOG_DIR" "$LOG_DIR" || true
+    python3 "${SCRIPT_DIR}/compare_loss.py" "$COMPARE_LOG_DIR" "$LOG_DIR" > compare_test/loss_comparison.log 2>&1 || true
 
     # Run compare_tps.py
     echo -e "\n\033[1;33m[Running] compare_tps.py\033[0m"
-    python3 "${SCRIPT_DIR}/compare_tps.py" "$COMPARE_LOG_DIR" "$LOG_DIR" || true
+    python3 "${SCRIPT_DIR}/compare_tps.py" "$COMPARE_LOG_DIR" "$LOG_DIR" > compare_test/tps_comparison.log 2>&1 || true
 
     echo -e "\n\033[1;32mComparison completed.\033[0m"
 else

From 0b8c57af17fc8e88bc0f2de0ffd30d2313d9bd84 Mon Sep 17 00:00:00 2001
From: Aoshine999 <820459542@qq.com>
Date: Wed, 11 Mar 2026 00:45:28 +0800
Subject: [PATCH 06/18] Modify run_models_and_profile.bash

---
 scripts/run_models_and_profile.bash | 19 +++++++++++++++++--
 1 file changed, 17 insertions(+), 2 deletions(-)

diff --git a/scripts/run_models_and_profile.bash b/scripts/run_models_and_profile.bash
index 363aef34..5e4ef8c8 100755
--- a/scripts/run_models_and_profile.bash
+++ b/scripts/run_models_and_profile.bash
@@ -166,6 +166,21 @@ for ((id=0; id<num_builds; ++id)); do
         test_id=$(jq -r ".tests[$ti].id" "$CONFIG_FILE")
         arg_str="$(args_string_for_test "$ti")"
 
+        # Get flash value and set log directories accordingly
+        flash_value=$(jq -r ".tests[$ti].args.flash" "$CONFIG_FILE")
+        if [[ "$flash_value" == "true" ]]; then
+            LOG_DIR="./compare_logs"
+            PROFILE_LOG_DIR="./compare_profile_logs"
+        else
+            LOG_DIR="$(read_var LOG_DIR)"
+            : "${LOG_DIR:=./logs}"
+            PROFILE_LOG_DIR="$(read_var PROFILE_LOG_DIR)"
+            : "${PROFILE_LOG_DIR:=./profile_logs}"
+        fi
+
+        mkdir -p "$LOG_DIR" "$PROFILE_LOG_DIR"
+        export LOG_DIR PROFILE_LOG_DIR
+
         # gpt2
         gpt2_cmd="${prefix}./gpt2 --input_bin ${GPT2_INPUT_BIN} --llmc_filepath ${GPT2_LLMC_FILEPATH} --device cuda ${arg_str}"
         run_and_log "$gpt2_cmd" "gpt2_${test_id}${log_suffix}" "$profile_flag"
@@ -188,11 +203,11 @@ if [[ -n "$COMPARE_LOG_DIR" ]]; then
 
     # Run compare_loss.py
     echo -e "\n\033[1;33m[Running] compare_loss.py\033[0m"
-    python3 "${SCRIPT_DIR}/compare_loss.py" "$COMPARE_LOG_DIR" "$LOG_DIR" > compare_test/loss_comparison.log 2>&1 || true
+    python3 "${SCRIPT_DIR}/compare_loss.py" "$COMPARE_LOG_DIR" "$LOG_DIR" > compare_logs/loss_comparison.log 2>&1 || true
 
     # Run compare_tps.py
     echo -e "\n\033[1;33m[Running] compare_tps.py\033[0m"
-    python3 "${SCRIPT_DIR}/compare_tps.py" "$COMPARE_LOG_DIR" "$LOG_DIR" > compare_test/tps_comparison.log 2>&1 || true
+    python3 "${SCRIPT_DIR}/compare_tps.py" "$COMPARE_LOG_DIR" "$LOG_DIR" > compare_logs/tps_comparison.log 2>&1 || true
 
     echo -e "\n\033[1;32mComparison completed.\033[0m"
 else

From d19e67e824f58116d979b80940e3f8393977f3c4 Mon Sep 17 00:00:00 2001
From: Aoshine999 <820459542@qq.com>
Date: Sat, 14 Mar 2026 19:59:45 +0800
Subject: [PATCH 07/18] change flash config

---
 example/gpt2/main.cc                |   9 +-
 example/gpt2/net.cc                 |   4 +-
 example/gpt2/net.h                  |   2 +-
 example/llama3/main.cc              |   9 +-
 example/llama3/net.cc               |   4 +-
 example/llama3/net.h                |   2 +-
 scripts/run_models_and_profile.bash |   5 +-
 scripts/test_config.json            | 268 +---------------------------
 8 files changed, 19 insertions(+), 284 deletions(-)

diff --git a/example/gpt2/main.cc b/example/gpt2/main.cc
index d1c82f5b..501704b8 100644
--- a/example/gpt2/main.cc
+++ b/example/gpt2/main.cc
@@ -71,7 +71,7 @@ DEFINE_uint32(tensor_parallel, 1, "Tensor Parallel world size");
 DEFINE_bool(sequence_parallel, false, "Whether to enable Sequence Parallel");
 DEFINE_uint32(pipeline_parallel, 1, "Pipeline Parallel world size, specified the number of PP stages.");
 DEFINE_uint32(virtual_pipeline_parallel, 1, "Number of chunks in PP stage.");
-DEFINE_bool(flash, true, "Whether to enable FlashAttention in CausalSelfAttention");
+DEFINE_bool(flash, false, "Whether to enable FlashAttention in CausalSelfAttention");
 
 // precision
 DEFINE_string(dtype, "float32", "precision used in training (float32/bfloat16)");
@@ -181,11 +181,10 @@ void Train(const nn::parallel::Rank &rank) {
     // init the model, either from scratch or from OpenAI pretrained checkpoint
     GPT2Config model_config;
     std::shared_ptr<nn::Module> model = nullptr;
+    LOG(INFO) << "Rank " << rank.GlobalRank() << ": FLAGS_flash = " << (FLAGS_flash ? "true" : "false");
     if (!FLAGS_llmc_filepath.empty()) {
-        if (FLAGS_flash) {
-            LOG(WARNING) << "--flash is ignored when loading GPT2 from --llmc_filepath.";
-        }
-        model = GPT2::FromLLMC(FLAGS_llmc_filepath);
+        LOG(INFO) << "Rank " << rank.GlobalRank() << ": Loading GPT2 from LLMC file: " << FLAGS_llmc_filepath;
+        model = GPT2::FromLLMC(FLAGS_llmc_filepath, FLAGS_flash);
     } else if (kModelToConfigs.count(FLAGS_model)) {
         model_config = kModelToConfigs.at(FLAGS_model);
         model_config.flash = FLAGS_flash;
diff --git a/example/gpt2/net.cc b/example/gpt2/net.cc
index b033236c..3d777b0e 100644
--- a/example/gpt2/net.cc
+++ b/example/gpt2/net.cc
@@ -365,7 +365,7 @@ std::tuple<int32_t, infini_train::DataType> DetermineAndCheckVersion(const std::
 }
 } // namespace
 
-std::shared_ptr<GPT2> GPT2::FromLLMC(const std::string &filepath) {
+std::shared_ptr<GPT2> GPT2::FromLLMC(const std::string &filepath, bool flash) {
     if (!std::filesystem::exists(filepath)) {
         LOG(FATAL) << "File not found: " << filepath;
     }
@@ -394,7 +394,7 @@ std::shared_ptr<GPT2> GPT2::FromLLMC(const std::string &filepath) {
                                                         .n_layer = n_layer,
                                                         .n_head = n_head,
                                                         .n_embd = n_embd,
-                                                        .flash = true});
+                                                        .flash = flash});
 
     LOG(INFO) << "magic: " << magic << " version: " << version << " block_size: " << block_size
               << " vocab_size: " << vocab_size << " n_layer: " << n_layer << " n_head: " << n_head
diff --git a/example/gpt2/net.h b/example/gpt2/net.h
index 7f246c22..e429770a 100644
--- a/example/gpt2/net.h
+++ b/example/gpt2/net.h
@@ -141,7 +141,7 @@ class GPT2 : public infini_train::nn::CloneableModule<GPT2> {
     Forward(const std::vector<std::shared_ptr<infini_train::Tensor>> &x) override;
 
     static std::shared_ptr<GPT2> FromPretrained(ModelType model_type);
-    static std::shared_ptr<GPT2> FromLLMC(const std::string &filepath);
+    static std::shared_ptr<GPT2> FromLLMC(const std::string &filepath, bool flash = false);
 
     int GetChunkSize() const;
 
diff --git a/example/llama3/main.cc b/example/llama3/main.cc
index 963b1cac..5a6f372c 100644
--- a/example/llama3/main.cc
+++ b/example/llama3/main.cc
@@ -70,7 +70,7 @@ DEFINE_uint32(tensor_parallel, 1, "Tensor Parallel world size");
 DEFINE_bool(sequence_parallel, false, "Whether to enable Sequence Parallel");
 DEFINE_uint32(pipeline_parallel, 1, "Pipeline Parallel world size, specified the number of PP stages.");
 DEFINE_uint32(virtual_pipeline_parallel, 1, "Number of chunks in PP stage.");
-DEFINE_bool(flash, true, "Whether to enable FlashAttention in CausalSelfAttention");
+DEFINE_bool(flash, false, "Whether to enable FlashAttention in CausalSelfAttention");
 // precision
 DEFINE_string(dtype, "float32", "precision used in training (float32/bfloat16)");
 // precision check
@@ -164,11 +164,10 @@ void Train(const nn::parallel::Rank &rank) {
     LLaMA3Config model_config = LLaMA3Config();
     model_config.flash = FLAGS_flash;
     std::shared_ptr<nn::Module> model = nullptr;
+    LOG(INFO) << "Rank " << rank.GlobalRank() << ": FLAGS_flash = " << (FLAGS_flash ? "true" : "false");
     if (!FLAGS_llmc_filepath.empty()) {
-        if (FLAGS_flash) {
-            LOG(WARNING) << "--flash is ignored when loading LLaMA3 from --llmc_filepath.";
-        }
-        model = LLaMA3::FromLLMC(FLAGS_llmc_filepath);
+        LOG(INFO) << "Rank " << rank.GlobalRank() << ": Loading LLaMA3 from LLMC file: " << FLAGS_llmc_filepath;
+        model = LLaMA3::FromLLMC(FLAGS_llmc_filepath, FLAGS_flash);
     } else {
         model = std::make_shared<LLaMA3>(model_config);
     }
diff --git a/example/llama3/net.cc b/example/llama3/net.cc
index b2e6b4ee..62a237a1 100644
--- a/example/llama3/net.cc
+++ b/example/llama3/net.cc
@@ -460,7 +460,7 @@ constexpr int32_t kLLaMA3Magic = 20240803;
 constexpr int32_t kLLaMA3FP32Version = 3;
 } // namespace
 
-std::shared_ptr<LLaMA3> LLaMA3::FromLLMC(const std::string &filepath) {
+std::shared_ptr<LLaMA3> LLaMA3::FromLLMC(const std::string &filepath, bool flash) {
     if (!std::filesystem::exists(filepath)) {
         LOG(FATAL) << "File not found: " << filepath;
     }
@@ -499,7 +499,7 @@ std::shared_ptr<LLaMA3> LLaMA3::FromLLMC(const std::string &filepath) {
                                                         .rope_theta = rope_theta,
                                                         .use_scaled_rope = static_cast<bool>(use_scaled_rope),
                                                         .norm_eps = norm_eps,
-                                                        .flash = true,
+                                                        .flash = flash,
                                                         .max_gen_batch_size = max_gen_bs});
 
     // ========== pp_size：num_stages; vpp_size: num_chunks_per_stage ==========
diff --git a/example/llama3/net.h b/example/llama3/net.h
index 144a7e78..76ea9837 100644
--- a/example/llama3/net.h
+++ b/example/llama3/net.h
@@ -179,7 +179,7 @@ class LLaMA3 : public infini_train::nn::CloneableModule<LLaMA3> {
     Forward(const std::vector<std::shared_ptr<infini_train::Tensor>> &x) override;
 
     static std::shared_ptr<LLaMA3> FromPretrained(ModelType model_type);
-    static std::shared_ptr<LLaMA3> FromLLMC(const std::string &filepath);
+    static std::shared_ptr<LLaMA3> FromLLMC(const std::string &filepath, bool flash = false);
 
     int GetChunkSize() const { return stage_info_.layer_ranges_per_chunk.size(); }
 
diff --git a/scripts/run_models_and_profile.bash b/scripts/run_models_and_profile.bash
index 5e4ef8c8..89d4973d 100755
--- a/scripts/run_models_and_profile.bash
+++ b/scripts/run_models_and_profile.bash
@@ -135,7 +135,10 @@ args_string_for_test() {
     jq -r --argjson i "$idx" '
       .tests[$i].args
       | to_entries[]
-      | "--\(.key) \(.value|tostring)"
+      | if .value == true then "--\(.key)"
+        elif .value == false then "--no\(.key)"
+        else "--\(.key)=\(.value|tostring)"
+        end
     ' "$CONFIG_FILE" | paste -sd' ' -
 }
 
diff --git a/scripts/test_config.json b/scripts/test_config.json
index 3d66ff7b..ac07bd13 100644
--- a/scripts/test_config.json
+++ b/scripts/test_config.json
@@ -10,11 +10,6 @@
         "COMPARE_LOG_DIR": ""
     },
     "builds": [
-        {
-            "id": "build_1",
-            "profile": false,
-            "cmd": "cmake -DUSE_CUDA=ON -DUSE_NCCL=ON .. && make -j"
-        },
         {
             "id": "build_2",
             "profile": true,
@@ -22,36 +17,6 @@
         }
     ],
     "tests": [
-        {
-            "id": "1",
-            "args": {
-                "dtype": "float32"
-            }
-        },
-        {
-            "id": "1_bfloat16",
-            "args": {
-                "dtype": "bfloat16"
-            }
-        },
-        {
-            "id": "2",
-            "args": {
-                "dtype": "float32",
-                "num_iteration": 10,
-                "batch_size": 80,
-                "total_batch_size": 5120
-            }
-        },
-        {
-            "id": "2_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "num_iteration": 10,
-                "batch_size": 80,
-                "total_batch_size": 5120
-            }
-        },
         {
             "id": "3",
             "args": {
@@ -59,239 +24,8 @@
                 "nthread_per_process": 8,
                 "num_iteration": 10,
                 "batch_size": 10,
-                "total_batch_size": 5120
-            }
-        },
-        {
-            "id": "3_distopt",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "3_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120
-            }
-        },
-        {
-            "id": "3_bfloat16_distopt",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "4",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4
-            }
-        },
-        {
-            "id": "4_distopt",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "4_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4
-            }
-        },
-        {
-            "id": "4_bfloat16_distopt",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "5",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "sequence_parallel": true
-            }
-        },
-        {
-            "id": "5_distopt",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "sequence_parallel": true,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "5_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "sequence_parallel": true
-            }
-        },
-        {
-            "id": "5_bfloat16_distopt",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "sequence_parallel": true,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "6",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "pipeline_parallel": 8
-            }
-        },
-        {
-            "id": "6_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "pipeline_parallel": 8
-            }
-        },
-        {
-            "id": "7",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 4,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "pipeline_parallel": 4,
-                "virtual_pipeline_parallel": 2
-            }
-        },
-        {
-            "id": "7_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 4,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "pipeline_parallel": 4,
-                "virtual_pipeline_parallel": 2
-            }
-        },
-        {
-            "id": "8",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 2,
-                "sequence_parallel": true,
-                "pipeline_parallel": 2,
-                "virtual_pipeline_parallel": 2
-            }
-        },
-        {
-            "id": "8_distopt",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 2,
-                "sequence_parallel": true,
-                "pipeline_parallel": 2,
-                "virtual_pipeline_parallel": 2,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "8_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 2,
-                "sequence_parallel": true,
-                "pipeline_parallel": 2,
-                "virtual_pipeline_parallel": 2
-            }
-        },
-        {
-            "id": "8_bfloat16_distopt",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
                 "total_batch_size": 5120,
-                "tensor_parallel": 2,
-                "sequence_parallel": true,
-                "pipeline_parallel": 2,
-                "virtual_pipeline_parallel": 2,
-                "use_distributed_optimizer": true
+                "flash": false
             }
         }
     ]

From 23a96493e8fcb2ca68cd0f272784d3c090eef23a Mon Sep 17 00:00:00 2001
From: Aoshine999 <820459542@qq.com>
Date: Sun, 15 Mar 2026 09:24:01 +0800
Subject: [PATCH 08/18] update bash script

---
 algo.tex                            |  75 +++++++
 scripts/run_models_and_profile.bash |  62 ++++--
 scripts/test_config.json            |  16 +-
 scripts/test_config.json.bak        | 299 ++++++++++++++++++++++++++++
 4 files changed, 432 insertions(+), 20 deletions(-)
 create mode 100644 algo.tex
 create mode 100644 scripts/test_config.json.bak

diff --git a/algo.tex b/algo.tex
new file mode 100644
index 00000000..cfb8861b
--- /dev/null
+++ b/algo.tex
@@ -0,0 +1,75 @@
+\begin{algorithm}[H]
+  % \algsetup{linenosize=\tiny}
+  \caption{\small\label{alg:flash2_fwd}\sysname forward pass}
+  \begin{algorithmic}[1]
+    \REQUIRE Matrices $\vQ, \vK, \vV \in \mathbb{R}^{N \times d}$ in HBM, block sizes $B_c$, $B_r$.
+    \STATE \label{alg:stream_attn_split_qkv} Divide $\vQ$ into $T_r = \left\lceil\frac{N}{B_r} \right\rceil$ blocks $\vQ_1, \dots, \vQ_{T_r}$ of size $B_r \times d$ each,
+    and divide $\vK, \vV$ in to $T_c = \left\lceil \frac{N}{B_c} \right\rceil$ blocks $\vK_1, \dots, \vK_{T_c}$ and
+    $\vV_1, \dots, \vV_{T_c}$, of size $B_c \times d$ each.
+    \STATE Divide the output $\vO \in \mathbb{R}^{N \times d}$ into $T_r$ blocks $\vO_i, \dots, \vO_{T_r}$ of size
+    $B_r \times d$ each, and divide the logsumexp $L$ into $T_r$ blocks $L_i, \dots, L_{T_r}$ of size
+    $B_r$ each.
+    \FOR{$1 \le i \le T_r$} \label{alg:stream_attn_outer_loop}
+      \STATE \label{alg:stream_attn_load_q} Load $\vQ_i$ from HBM to on-chip SRAM.
+      \STATE \label{alg:stream_attn_init} On chip, initialize $\vO_{i}^{(0)} = (0)_{B_r \times d} \in \mathbb{R}^{B_r \times d}, \ell_{i}^{(0)} = (0)_{B_r} \in \mathbb{R}^{B_r}, m_{i}^{(0)} = (-\infty)_{B_r} \in \mathbb{R}^{B_r}$.
+      \FOR{$1 \le j \le T_c$}
+        \STATE \label{alg:stream_attn_load_kv} Load $\vK_j, \vV_j$ from HBM to on-chip SRAM.
+        \STATE \label{alg:stream_attn_qk} On chip, compute $\vS_{i}^{(j)} = \vQ_i \vK_j^T \in \mathbb{R}^{B_r \times B_c}$.
+        \STATE \label{alg:stream_attn_statistics} On chip, compute
+        $m_{i}^{(j)} = \mathrm{max}(m_{i}^{(j-1)}, \mathrm{rowmax}(\vS_{i}^{(j)})) \in \mathbb{R}^{B_r}$, $\tilde{\vP}_{i}^{(j)} = \exp(\vS_{i}^{(j)} - m_{i}^{(j)}) \in \mathbb{R}^{B_r \times B_c}$ (pointwise),
+        $\ell_{i}^{(j)} = e^{m_{i}^{j-1} - m_{i}^{(j)}} \ell_{i}^{(j-1)} + \mathrm{row sum}(\tilde{\vP}_{i}^{(j)}) \in \mathbb{R}^{B_r}$.
+        \STATE \label{alg:stream_attn_update} On chip, compute
+        $\vO_{i}^{(j)} = \diag(e^{m_{i}^{(j-1)} - m_{i}^{(j)}}) \vO_{i}^{(j-1)} + \tilde{\vP}_{i}^{(j)} \vV_j$.
+      \ENDFOR
+      \STATE On chip, compute $\vO_{i} = \diag(\ell_{i}^{(T_c)})^{-1} \vO_{i}^{(T_c)}$.
+      \STATE On chip, compute $L_{i} = m_{i}^{(T_c)} + \log(\ell_i^{(T_c)})$.
+      \STATE Write $\vO_{i}$ to HBM as the $i$-th block of $\vO$.
+      \STATE Write $L_{i}$ to HBM as the $i$-th block of $L$.
+    \ENDFOR
+    \STATE Return the output $\vO$ and the logsumexp $L$.
+  \end{algorithmic}
+\end{algorithm}
+
+
+
+
+\begin{algorithm}[h]
+  \caption{\small\label{alg:flash_bwd}\sysname Backward Pass}
+  \begin{algorithmic}[1]
+    \REQUIRE Matrices $\vQ, \vK, \vV, \vO, \vdO \in \mathbb{R}^{N \times d}$ in HBM,
+    vector $L \in \mathbb{R}^N$ in HBM, block sizes $B_c$, $B_r$.
+    \STATE Divide $\vQ$ into $T_r = \left\lceil\frac{N}{B_r} \right\rceil$ blocks $\vQ_1, \dots, \vQ_{T_r}$ of size $B_r \times d$ each,
+    and divide $\vK, \vV$ in to $T_c = \left\lceil \frac{N}{B_c} \right\rceil$ blocks $\vK_1, \dots, \vK_{T_c}$ and
+    $\vV_1, \dots, \vV_{T_c}$, of size $B_c \times d$ each.
+    \STATE Divide $\vO$ into $T_r$ blocks $\vO_i, \dots, \vO_{T_r}$ of size
+    $B_r \times d$ each, divide $\vdO$ into $T_r$ blocks $\vdO_i, \dots, \vdO_{T_r}$
+    of size $B_r \times d$ each, and divide $L$ into $T_r$ blocks $L_i, \dots, L_{T_r}$ of size
+    $B_r$ each.
+    \STATE Initialize $\vdQ = (0)_{N \times d}$ in HBM and divide it into $T_r$ blocks $\vdQ_1, \dots, \vdQ_{T_r}$ of size $B_r \times d$ each.
+    Divide $\vdK, \vdV \in \mathbb{R}^{N \times d}$ in to $T_c$ blocks $\vdK_1, \dots, \vdK_{T_c}$ and
+    $\vdV_1, \dots, \vdV_{T_c}$, of size $B_c \times d$ each.
+    \STATE Compute $D = \mathrm{rowsum}(\vdO \circ \vO) \in \mathbb{R}^d$ (pointwise multiply), write
+    $D$ to HBM and divide it into $T_r$ blocks $D_1, \dots, D_{T_r}$ of size
+    $B_r$ each.
+    \FOR{$1 \le j \le T_c$}
+      \STATE Load $\vK_j, \vV_j$ from HBM to on-chip SRAM.
+      \STATE Initialize $\vdK_j = (0)_{B_c \times d}, \vdV_j = (0)_{B_c \times d}$ on SRAM.
+      \FOR{$1 \le i \le T_r$}
+        \STATE Load $\vQ_i, \vO_i, \vdO_i, \vdQ_i, L_i, D_i$ from HBM to on-chip SRAM.
+        \STATE On chip, compute $\vS_{i}^{(j)} = \vQ_i \vK_j^T \in \mathbb{R}^{B_r \times B_c}$.
+        \STATE On chip, compute $\vP_{i}^{(j)} = \exp(\vS_{ij} - L_{i}) \in \mathbb{R}^{B_r \times B_c}$.
+        \STATE On chip, compute
+        $\vdV_j \leftarrow \vdV_j + (\vP_{i}^{(j)})^\top \vdO_i \in \mathbb{R}^{B_c \times d}$.
+        \STATE On chip, compute
+        $\vdP_{i}^{(j)} = \vdO_{i} \vV_j^\top \in \mathbb{R}^{B_r \times B_c}$.
+        \STATE On chip, compute $\vdS_{i}^{(j)} = \vP_{i}^{(j)} \circ (\vdP_{i}^{(j)} - D_i) \in \mathbb{R}^{B_r \times B_c}$.
+        \STATE Load $\vdQ_i$ from HBM to SRAM, then on chip, update
+        $\vdQ_{i} \leftarrow \vdQ_i + \vdS_{i}^{(j)} \vK_j \in \mathbb{R}^{B_r \times d}$, and write
+        back to HBM.
+        \STATE On chip, compute $\vdK_{j} \leftarrow \vdK_j + {\vdS_{i}^{(j)}}^\top \vQ_i \in \mathbb{R}^{B_c \times d}$.
+      \ENDFOR
+      \STATE Write $\vdK_j, \vdV_j$ to HBM.
+    \ENDFOR
+    \STATE Return $\vdQ, \vdK, \vdV$.
+  \end{algorithmic}
+\end{algorithm}
\ No newline at end of file
diff --git a/scripts/run_models_and_profile.bash b/scripts/run_models_and_profile.bash
index 89d4973d..65b13ef0 100755
--- a/scripts/run_models_and_profile.bash
+++ b/scripts/run_models_and_profile.bash
@@ -4,6 +4,7 @@ set -e
 set -o pipefail
 
 CONFIG_FILE="${1:-test_config.json}"
+export INFINI_FLASH_BF16_USE_FP32=0
 
 # Dependencies check
 if ! command -v jq >/dev/null 2>&1; then
@@ -167,29 +168,56 @@ for ((id=0; id<num_builds; ++id)); do
 
     for ((ti=0; ti<num_tests; ++ti)); do
         test_id=$(jq -r ".tests[$ti].id" "$CONFIG_FILE")
-        arg_str="$(args_string_for_test "$ti")"
-
-        # Get flash value and set log directories accordingly
-        flash_value=$(jq -r ".tests[$ti].args.flash" "$CONFIG_FILE")
-        if [[ "$flash_value" == "true" ]]; then
-            LOG_DIR="./compare_logs"
-            PROFILE_LOG_DIR="./compare_profile_logs"
-        else
-            LOG_DIR="$(read_var LOG_DIR)"
-            : "${LOG_DIR:=./logs}"
-            PROFILE_LOG_DIR="$(read_var PROFILE_LOG_DIR)"
-            : "${PROFILE_LOG_DIR:=./profile_logs}"
-        fi
+        base_arg_str="$(args_string_for_test "$ti")"
+
+        # Add --noflash to the beginning of arg_str (will override any existing flash arg)
+        arg_str="--noflash $base_arg_str"
+
+        # Add --flash to the beginning of arg_str (will override any existing flash arg)
+        arg_str1="--flash $base_arg_str"
+
+        # Run gpt2 with flash=false
+        LOG_DIR="$(read_var LOG_DIR)"
+        : "${LOG_DIR:=./logs}"
+        PROFILE_LOG_DIR="$(read_var PROFILE_LOG_DIR)"
+        : "${PROFILE_LOG_DIR:=./profile_logs}"
 
         mkdir -p "$LOG_DIR" "$PROFILE_LOG_DIR"
         export LOG_DIR PROFILE_LOG_DIR
 
-        # gpt2
-        gpt2_cmd="${prefix}./gpt2 --input_bin ${GPT2_INPUT_BIN} --llmc_filepath ${GPT2_LLMC_FILEPATH} --device cuda ${arg_str}"
+        gpt2_cmd="./gpt2 --input_bin ${GPT2_INPUT_BIN} --llmc_filepath ${GPT2_LLMC_FILEPATH} --device cuda ${arg_str}"
+        run_and_log "$gpt2_cmd" "gpt2_${test_id}${log_suffix}" "$profile_flag"
+
+        # Run gpt2 with flash=true
+        COMPARE_LOG_DIR="./compare_logs"
+        COMPARE_PROFILE_LOG_DIR="./compare_profile_logs"
+
+        mkdir -p "$COMPARE_LOG_DIR" "$COMPARE_PROFILE_LOG_DIR"
+        export COMPARE_LOG_DIR COMPARE_PROFILE_LOG_DIR
+
+        gpt2_cmd="./gpt2 --input_bin ${GPT2_INPUT_BIN} --llmc_filepath ${GPT2_LLMC_FILEPATH} --device cuda ${arg_str1}"
         run_and_log "$gpt2_cmd" "gpt2_${test_id}${log_suffix}" "$profile_flag"
 
-        # llama3
-        llama3_cmd="${prefix}./llama3 --input_bin ${LLAMA3_INPUT_BIN} --llmc_filepath ${LLAMA3_LLMC_FILEPATH} --device cuda ${arg_str}"
+        # Run llama3 with flash=false
+        LOG_DIR="$(read_var LOG_DIR)"
+        : "${LOG_DIR:=./logs}"
+        PROFILE_LOG_DIR="$(read_var PROFILE_LOG_DIR)"
+        : "${PROFILE_LOG_DIR:=./profile_logs}"
+
+        mkdir -p "$LOG_DIR" "$PROFILE_LOG_DIR"
+        export LOG_DIR PROFILE_LOG_DIR
+
+        llama3_cmd="./llama3 --input_bin ${LLAMA3_INPUT_BIN} --llmc_filepath ${LLAMA3_LLMC_FILEPATH} --device cuda ${arg_str}"
+        run_and_log "$llama3_cmd" "llama3_${test_id}${log_suffix}" "$profile_flag"
+
+        # Run llama3 with flash=true
+        COMPARE_LOG_DIR="./compare_logs"
+        COMPARE_PROFILE_LOG_DIR="./compare_profile_logs"
+
+        mkdir -p "$COMPARE_LOG_DIR" "$COMPARE_PROFILE_LOG_DIR"
+        export COMPARE_LOG_DIR COMPARE_PROFILE_LOG_DIR
+
+        llama3_cmd="./llama3 --input_bin ${LLAMA3_INPUT_BIN} --llmc_filepath ${LLAMA3_LLMC_FILEPATH} --device cuda ${arg_str1}"
         run_and_log "$llama3_cmd" "llama3_${test_id}${log_suffix}" "$profile_flag"
     done
 done
diff --git a/scripts/test_config.json b/scripts/test_config.json
index ac07bd13..a342320c 100644
--- a/scripts/test_config.json
+++ b/scripts/test_config.json
@@ -18,14 +18,24 @@
     ],
     "tests": [
         {
-            "id": "3",
+            "id": "3_bfloat16",
             "args": {
-                "dtype": "float32",
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120
+            }
+        },
+        {
+            "id": "3_bfloat16_distopt",
+            "args": {
+                "dtype": "bfloat16",
                 "nthread_per_process": 8,
                 "num_iteration": 10,
                 "batch_size": 10,
                 "total_batch_size": 5120,
-                "flash": false
+                "use_distributed_optimizer": true
             }
         }
     ]
diff --git a/scripts/test_config.json.bak b/scripts/test_config.json.bak
new file mode 100644
index 00000000..3d66ff7b
--- /dev/null
+++ b/scripts/test_config.json.bak
@@ -0,0 +1,299 @@
+{
+    "variables": {
+        "BUILD_DIR": "../build",
+        "GPT2_INPUT_BIN": "/data/shared/InfiniTrain-dev/data/llmc/gpt2/tinyshakespeare/tiny_shakespeare_train.bin",
+        "GPT2_LLMC_FILEPATH": "/data/shared/InfiniTrain-dev/data/llmc/gpt2/gpt2_124M.bin",
+        "LLAMA3_INPUT_BIN": "/data/shared/InfiniTrain-dev/data/llmc/llama3/tinyshakespeare/tiny_shakespeare_train.bin",
+        "LLAMA3_LLMC_FILEPATH": "/data/shared/InfiniTrain-dev/data/llmc/llama3/llama3.2_1B_fp32.bin",
+        "PROFILE_LOG_DIR": "./profile_logs",
+        "LOG_DIR": "./logs",
+        "COMPARE_LOG_DIR": ""
+    },
+    "builds": [
+        {
+            "id": "build_1",
+            "profile": false,
+            "cmd": "cmake -DUSE_CUDA=ON -DUSE_NCCL=ON .. && make -j"
+        },
+        {
+            "id": "build_2",
+            "profile": true,
+            "cmd": "cmake -DUSE_CUDA=ON -DUSE_NCCL=ON -DPROFILE_MODE=ON .. && make -j"
+        }
+    ],
+    "tests": [
+        {
+            "id": "1",
+            "args": {
+                "dtype": "float32"
+            }
+        },
+        {
+            "id": "1_bfloat16",
+            "args": {
+                "dtype": "bfloat16"
+            }
+        },
+        {
+            "id": "2",
+            "args": {
+                "dtype": "float32",
+                "num_iteration": 10,
+                "batch_size": 80,
+                "total_batch_size": 5120
+            }
+        },
+        {
+            "id": "2_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "num_iteration": 10,
+                "batch_size": 80,
+                "total_batch_size": 5120
+            }
+        },
+        {
+            "id": "3",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120
+            }
+        },
+        {
+            "id": "3_distopt",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "3_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120
+            }
+        },
+        {
+            "id": "3_bfloat16_distopt",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "4",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4
+            }
+        },
+        {
+            "id": "4_distopt",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "4_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4
+            }
+        },
+        {
+            "id": "4_bfloat16_distopt",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "5",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "sequence_parallel": true
+            }
+        },
+        {
+            "id": "5_distopt",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "sequence_parallel": true,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "5_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "sequence_parallel": true
+            }
+        },
+        {
+            "id": "5_bfloat16_distopt",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "sequence_parallel": true,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "6",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120,
+                "pipeline_parallel": 8
+            }
+        },
+        {
+            "id": "6_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120,
+                "pipeline_parallel": 8
+            }
+        },
+        {
+            "id": "7",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 4,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120,
+                "pipeline_parallel": 4,
+                "virtual_pipeline_parallel": 2
+            }
+        },
+        {
+            "id": "7_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 4,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120,
+                "pipeline_parallel": 4,
+                "virtual_pipeline_parallel": 2
+            }
+        },
+        {
+            "id": "8",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 2,
+                "sequence_parallel": true,
+                "pipeline_parallel": 2,
+                "virtual_pipeline_parallel": 2
+            }
+        },
+        {
+            "id": "8_distopt",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 2,
+                "sequence_parallel": true,
+                "pipeline_parallel": 2,
+                "virtual_pipeline_parallel": 2,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "8_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 2,
+                "sequence_parallel": true,
+                "pipeline_parallel": 2,
+                "virtual_pipeline_parallel": 2
+            }
+        },
+        {
+            "id": "8_bfloat16_distopt",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 2,
+                "sequence_parallel": true,
+                "pipeline_parallel": 2,
+                "virtual_pipeline_parallel": 2,
+                "use_distributed_optimizer": true
+            }
+        }
+    ]
+}
+

From a9b9d13e04baf7e041ee1d7d6dcaf27fabaa5c8d Mon Sep 17 00:00:00 2001
From: Aoshine999 <820459542@qq.com>
Date: Sun, 15 Mar 2026 18:58:53 +0800
Subject: [PATCH 09/18] update data process script

---
 .../src/kernels/cuda/flash_attention.cu       | 512 +++++++++---------
 scripts/extract_training_stats.py             | 426 +++++++++++++++
 scripts/run_models_and_profile.bash           |  76 ++-
 3 files changed, 734 insertions(+), 280 deletions(-)
 create mode 100755 scripts/extract_training_stats.py

diff --git a/infini_train/src/kernels/cuda/flash_attention.cu b/infini_train/src/kernels/cuda/flash_attention.cu
index c0ea807b..63518538 100644
--- a/infini_train/src/kernels/cuda/flash_attention.cu
+++ b/infini_train/src/kernels/cuda/flash_attention.cu
@@ -1,5 +1,7 @@
 #include <cmath>
 #include <cstddef>
+#include <ctime>
+#include <limits>
 #include <curand_kernel.h>
 #include "glog/logging.h"
 
@@ -94,6 +96,71 @@ __device__ __forceinline__ bool FlashAttnDropoutKeep(
     return rand_val > dropout_p;
 }
 
+template <typename T>
+__global__ void FlashAttnComputeDFusedKernel(const T *grad_output, const T *output,
+                                             float *D_fp32, int64_t rows, int head_dim) {
+    int64_t row = static_cast<int64_t>(blockIdx.x);
+    if (row >= rows) {
+        return;
+    }
+
+    float local_sum = 0.0f;
+    int64_t base = row * static_cast<int64_t>(head_dim);
+    for (int k = threadIdx.x; k < head_dim; k += blockDim.x) {
+        local_sum += common::cuda::Cast<float>(grad_output[base + k]) * common::cuda::Cast<float>(output[base + k]);
+    }
+
+    local_sum = warp_reduce_sum(local_sum);
+
+    __shared__ float warp_sums[32]; // up to 1024 threads -> 32 warps
+    int lane = threadIdx.x & 31;
+    int warp_id = threadIdx.x >> 5;
+    int num_warps = (blockDim.x + 31) >> 5;
+    if (lane == 0) {
+        warp_sums[warp_id] = local_sum;
+    }
+    __syncthreads();
+
+    if (warp_id == 0) {
+        float block_sum = (lane < num_warps) ? warp_sums[lane] : 0.0f;
+        block_sum = warp_reduce_sum(block_sum);
+        if (lane == 0) {
+            D_fp32[row] = block_sum;
+        }
+    }
+}
+
+template <typename T>
+std::shared_ptr<Tensor> ComputeFlashAttnDFused(const std::shared_ptr<Tensor> &grad_output,
+                                               const std::shared_ptr<Tensor> &output) {
+    const auto &dims = grad_output->Dims();
+    CHECK_EQ(dims.size(), 4) << "FlashAttention backward expects grad_output rank=4";
+    CHECK(output->Dims() == dims) << "FlashAttention backward expects output and grad_output same shape";
+
+    std::vector<int64_t> D_dims = {dims[0], dims[1], dims[2]}; // [B, S, H]
+    auto D_fp32 = std::make_shared<Tensor>(D_dims, DataType::kFLOAT32, grad_output->GetDevice());
+
+    int64_t rows = dims[0] * dims[1] * dims[2];
+    int head_dim = static_cast<int>(dims[3]);
+    CHECK_LE(rows, static_cast<int64_t>(std::numeric_limits<unsigned int>::max()))
+        << "FlashAttention D fused kernel row count exceeds CUDA grid.x limit";
+
+    auto device = grad_output->GetDevice();
+    const auto &cuda_stream = dynamic_cast<infini_train::core::cuda::CudaStream *>(
+                                  infini_train::core::GetDeviceGuardImpl(device.type())->GetStream(device))
+                                  ->cuda_stream();
+
+    constexpr int kThreads = 256;
+    dim3 grid_dims(static_cast<unsigned int>(rows));
+    FlashAttnComputeDFusedKernel<T><<<grid_dims, kThreads, 0, cuda_stream>>>(
+        static_cast<const T *>(grad_output->DataPtr()),
+        static_cast<const T *>(output->DataPtr()),
+        static_cast<float *>(D_fp32->DataPtr()),
+        rows, head_dim);
+
+    return D_fp32;
+}
+
 
 /**
  * FlashAttention Forward Kernel
@@ -136,7 +203,9 @@ __global__ void FlashAttentionForwardKernel(
 
     const int Br = blockDim.y;                  // Q纵向每块大小, 32
     const int Bc = blockDim.x;                  // K/V纵向分块大小, 32
-    const int Tc = gridDim.z; // 对应原始论文中K/V纵向分块数Tc,其中Bc = 32
+    const int Tc = (src_seq_len + Bc - 1) / Bc; // 对应原始论文中K/V纵向分块数Tc,其中Bc = 32
+    const float dropout_prob = static_cast<float>(dropout_p);
+    const bool apply_dropout = (dropout_prob > 0.0f && dropout_prob < 1.0f);
 
     // 定义一系列临时变量
     extern __shared__ char shared_mem[];
@@ -173,7 +242,8 @@ __global__ void FlashAttentionForwardKernel(
     // Note: Removed DROPOUT_SM_AT macro
 
     /****************************preparation**************************/
-    int bound_tid_y = min(Br, target_seq_len - Br * bid_z);
+    int q_tile_start = Br * bid_z;
+    int bound_tid_y = min(Br, target_seq_len - q_tile_start);
 
     // preparation-1: load Qi from GM to SM, and reset Oi to 0
     for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
@@ -188,7 +258,7 @@ __global__ void FlashAttentionForwardKernel(
 
     // preparation-2: reset m_prev to -INFINITY and l_prev to 0
     if (tid_x == 0) {
-        m_prev[tid_y] = -8192.0;
+        m_prev[tid_y] = -INFINITY;
         l_prev[tid_y] = 0.0;
     }
     __syncthreads();
@@ -198,22 +268,22 @@ __global__ void FlashAttentionForwardKernel(
 /****************************main-loop**************************/
 #pragma unroll 4
     for (int j = 0; j < Tc; ++j) { // 对于每个K/V分块
-        bool skip = (is_causal && bid_z < j);
+        int k_tile_start = Bc * j;
+        int q_max_pos = q_tile_start + bound_tid_y - 1;
+        bool skip = (is_causal && (bound_tid_y <= 0 || q_max_pos < k_tile_start));
         if (skip) { // early exit, 直接跳过
             __syncthreads();
             continue;
         }
 
-        SP_AT(tid_y, tid_x) = -8192.0;
+        SP_AT(tid_y, tid_x) = -INFINITY;
         __syncthreads();
-        int bound_tid_x = min(Bc, src_seq_len - Bc * j);
+        int bound_tid_x = min(Bc, src_seq_len - k_tile_start);
         bool is_compute = true; // optimization: 分支处理,加速branch-resolving
-        if (is_causal) {
-            if (bid_z < j) {
-                is_compute = false; // 早期退出情况
-            } else if (bid_z == j) {
-                is_compute = (tid_y >= tid_x); // 对角线以上
-            }
+        if (is_causal && tid_y < bound_tid_y && tid_x < bound_tid_x) {
+            int global_q_pos = q_tile_start + tid_y;
+            int global_k_pos = k_tile_start + tid_x;
+            is_compute = (global_q_pos >= global_k_pos);
         }
 
         // step-1: load Ki, Vi from GM to SM
@@ -266,21 +336,24 @@ __global__ void FlashAttentionForwardKernel(
         // step-5: l_new = exp(m_prev - m_new) * l_prev + rowSum(P)
         float val2 = float(SP_AT(tid_y, tid_x));
         val2 = warp_reduce_sum(val2);
-        float exp_result = myexp<float>(m_prev[tid_y] - m_new[tid_y]);
+        float exp_result = 0.0f;
+        if (tid_y < bound_tid_y) {
+            exp_result = myexp<float>(m_prev[tid_y] - m_new[tid_y]);
+        }
         if (tid_x == 0 && tid_y < bound_tid_y) {
             l_new[tid_y] = exp_result * l_prev[tid_y] + val2;
         }
         __syncthreads();
 
         // step-5.5: Apply dropout to P (using dropout_seed for reproducibility)
-        if (dropout_p > 0 && tid_y < bound_tid_y && tid_x < bound_tid_x) {
-            int global_q_idx = Br * bid_z + tid_y;
-            int global_k_idx = Bc * j + tid_x;
+        if (apply_dropout && tid_y < bound_tid_y && tid_x < bound_tid_x) {
+            int global_q_idx = q_tile_start + tid_y;
+            int global_k_idx = k_tile_start + tid_x;
             bool keep = FlashAttnDropoutKeep(
                 dropout_seed, bid_y, bid_x, global_q_idx, global_k_idx,
-                q_heads, target_seq_len, src_seq_len, static_cast<float>(dropout_p));
+                q_heads, target_seq_len, src_seq_len, dropout_prob);
             if (keep) {
-                SP_AT(tid_y, tid_x) = SP_AT(tid_y, tid_x) / (1.0f - dropout_p);
+                SP_AT(tid_y, tid_x) = SP_AT(tid_y, tid_x) / (1.0f - dropout_prob);
             } else {
                 SP_AT(tid_y, tid_x) = 0.0;
             }
@@ -289,11 +362,11 @@ __global__ void FlashAttentionForwardKernel(
         __syncthreads();
 
         // step-6: O = 1/(exp(m_prev - m_new)) * O + P @ V
-        if (tid_x < bound_tid_x && tid_y < bound_tid_y) {
+        if (tid_y < bound_tid_y) {
             for (int u = tid_x; u < head_dim; u += blockDim.x) {
                 float val3 = 0.0;
 #pragma unroll
-                for (int w = 0; w < Bc; ++w) { 
+                for (int w = 0; w < bound_tid_x; ++w) {
                     val3 += float(SP_AT(tid_y, w)) * V_sm_AT(w, u);
                 }
                 O_sm_AT(tid_y, u) = O_sm_AT(tid_y, u) * exp_result + val3;
@@ -375,9 +448,6 @@ __global__ void FlashAttentionBackwardKernel(
     bool enable_gqa,
     int batch_size, int target_seq_len, int src_seq_len,
     int q_heads, int kv_heads, int head_dim) {
-    
-    // Grid/block dimensions: grid_dims(num_heads_q, batch_size, Tr), block_dim(Bc, Br)
-    // where Br corresponds to thread block row index, Bc corresponds to column index
     int tid_x = threadIdx.x;          // 横向, blockDim.x列 (Bc)
     int tid_y = threadIdx.y;          // 纵向, blockDim.y行 (Br)
     int bid_x = blockIdx.x;           // x方向, 总数 = #q_heads
@@ -387,14 +457,14 @@ __global__ void FlashAttentionBackwardKernel(
 
     const int Br = blockDim.y;                  // Q纵向每块大小, 32
     const int Bc = blockDim.x;                  // K/V纵向分块大小, 32
-    // const int Tr = gridDim.z; // Q纵向分块数
     const int Tc = (src_seq_len + Bc - 1) / Bc; // K/V纵向分块数
+    const float dropout_prob = static_cast<float>(dropout_p);
+    const bool apply_dropout = (dropout_prob > 0.0f && dropout_prob < 1.0f);
 
     // Define shared memory
     extern __shared__ char shared_mem[];
     char *ptr = shared_mem;
-    
-    
+
     // Q_sm[Br][head_dim], K_T_sm[head_dim][Bc], V_sm[Bc][head_dim]
     float *Q_sm = reinterpret_cast<float *>(ptr);
     ptr += Br * head_dim * sizeof(float);
@@ -402,335 +472,257 @@ __global__ void FlashAttentionBackwardKernel(
     ptr += head_dim * Bc * sizeof(float);
     float *V_sm = reinterpret_cast<float *>(ptr);
     ptr += Bc * head_dim * sizeof(float);
-    
+
     // dO_sm[Br][head_dim], dQ_sm[Br][head_dim], dK_T_sm[head_dim][Bc], dV_sm[Bc][head_dim]
     float *dO_sm = reinterpret_cast<float *>(ptr);
     ptr += Br * head_dim * sizeof(float);
-    // dQ_sm[Br][head_dim] - accumulated gradient for Q
     float *dQ_sm = reinterpret_cast<float *>(ptr);
     ptr += Br * head_dim * sizeof(float);
     float *dK_T_sm = reinterpret_cast<float *>(ptr);
     ptr += head_dim * Bc * sizeof(float);
     float *dV_sm = reinterpret_cast<float *>(ptr);
     ptr += Bc * head_dim * sizeof(float);
-    
+
     // S_sm[Br][Bc], P_sm[Br][Bc]
     float *S_sm = reinterpret_cast<float *>(ptr);
     ptr += Br * Bc * sizeof(float);
     float *P_sm = reinterpret_cast<float *>(ptr);
     ptr += Br * Bc * sizeof(float);
 
-    // L_sm[Br] - logsumexp values
+    // L_sm[Br], D_sm[Br]
     float *L_sm = reinterpret_cast<float *>(ptr);
     ptr += Br * sizeof(float);
-    // D_sm[Br] - D values loaded from HBM
     float *D_sm = reinterpret_cast<float *>(ptr);
     ptr += Br * sizeof(float);
 
-    
     // Define access macros
-    #define Q_sm_AT(y, x) Q_sm[y * head_dim + x]
-    #define K_T_sm_AT(y, x) K_T_sm[y * Bc + x]
-    #define V_sm_AT(y, x) V_sm[y * head_dim + x]
-    #define dO_sm_AT(y, x) dO_sm[y * head_dim + x]
-    #define dQ_sm_AT(y, x) dQ_sm[y * head_dim + x]
-    #define dK_T_sm_AT(y, x) dK_T_sm[y * Bc + x]
-    #define dV_sm_AT(y, x) dV_sm[y * head_dim + x]
-    #define S_sm_AT(y, x) S_sm[y * Bc + x]
-    #define P_sm_AT(y, x) P_sm[y * Bc + x]
-    #define L_sm_AT(y) L_sm[y]
-    #define D_sm_AT(y) D_sm[y]
+#define Q_sm_AT(y, x) Q_sm[y * head_dim + x]
+#define K_T_sm_AT(y, x) K_T_sm[y * Bc + x]
+#define V_sm_AT(y, x) V_sm[y * head_dim + x]
+#define dO_sm_AT(y, x) dO_sm[y * head_dim + x]
+#define dQ_sm_AT(y, x) dQ_sm[y * head_dim + x]
+#define dK_T_sm_AT(y, x) dK_T_sm[y * Bc + x]
+#define dV_sm_AT(y, x) dV_sm[y * head_dim + x]
+#define S_sm_AT(y, x) S_sm[y * Bc + x]
+#define P_sm_AT(y, x) P_sm[y * Bc + x]
+#define L_sm_AT(y) L_sm[y]
+#define D_sm_AT(y) D_sm[y]
 
     /****************************Preparation*****************************/
-    
-    // Initialize dQ_sm to 0 for accumulation
+    const int i = bid_z;
+    const int q_tile_start = Br * i;
+    const int bound_tid_y = min(Br, target_seq_len - q_tile_start);
+
     for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
         dQ_sm_AT(tid_y, idx) = 0.0f;
+        Q_sm_AT(tid_y, idx) = 0.0f;
+        dO_sm_AT(tid_y, idx) = 0.0f;
+
+        if (tid_y < bound_tid_y) {
+            int global_q_idx = q_tile_start + tid_y;
+            int q_tensor_idx = ((bid_y * target_seq_len) + global_q_idx) * q_heads + bid_x;
+            Q_sm_AT(tid_y, idx) = float(query[q_tensor_idx * head_dim + idx]);
+            dO_sm_AT(tid_y, idx) = float(grad_output[q_tensor_idx * head_dim + idx]);
+        }
     }
-    __syncthreads();
 
-    // Load D_i from HBM to shared memory
-    int q_idx = Br * bid_z + tid_y;  // Global query position within this head
-    if (q_idx < target_seq_len) {
-        int d_idx = ((bid_y * q_heads + bid_x) * target_seq_len) + q_idx;
-        D_sm_AT(tid_y) = D[d_idx];
-    } else {
-        D_sm_AT(tid_y) = 0.0f;  // Padding for out-of-bounds positions
+    if (tid_x == 0) {
+        if (tid_y < bound_tid_y) {
+            int global_q_idx = q_tile_start + tid_y;
+            L_sm_AT(tid_y) = logsumexp[((bid_y * q_heads + bid_x) * target_seq_len) + global_q_idx];
+            // D 的布局为 [batch_size, seq_len_q, q_heads]
+            int d_idx = ((bid_y * target_seq_len) + global_q_idx) * q_heads + bid_x;
+            D_sm_AT(tid_y) = D[d_idx];
+        } else {
+            L_sm_AT(tid_y) = 0.0f;
+            D_sm_AT(tid_y) = 0.0f;
+        }
     }
     __syncthreads();
 
     /****************************Main Loop - Outer Loop over K/V tiles*****************************/
-    for (int j = 0; j < Tc; ++j) {  // For each K/V column tile
-        
-        // Skip entire tile if causal and this tile is completely to the right
-        bool skip_tile = (is_causal && bid_z < j);
+    for (int j = 0; j < Tc; ++j) { // For each K/V column tile
+        int k_tile_start = Bc * j;
+        int bound_tid_x = min(Bc, src_seq_len - k_tile_start);
+
+        int q_max_pos = q_tile_start + bound_tid_y - 1;
+        bool skip_tile = (is_causal && (bound_tid_y <= 0 || q_max_pos < k_tile_start));
         if (skip_tile) {
             __syncthreads();
             continue;
         }
 
-        // Initialize dK_T_sm, dV_sm to 0 for this column tile
+        // Initialize dK_T_sm, dV_sm for this column tile
         for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-            for (int y = 0; y < Bc; ++y) {
-                dK_T_sm_AT(idx, y) = 0.0f;
-                dV_sm_AT(y, idx) = 0.0f;
-            }
+            dK_T_sm_AT(idx, tid_y) = 0.0f;
+            dV_sm_AT(tid_y, idx) = 0.0f;
         }
-        __syncthreads();
 
-        // Load K_j, V_j from HBM to shared memory
-        int bound_tid_x = min(Bc, src_seq_len - Bc * j);
+        // Load K_j, V_j
         for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
             K_T_sm_AT(idx, tid_y) = 0.0f;
             V_sm_AT(tid_y, idx) = 0.0f;
             if (tid_y < bound_tid_x) {
                 int kv_head_idx = bid_x / p;
-                K_T_sm_AT(idx, tid_y) = float(
-                    key[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads + kv_head_idx)) * head_dim + idx]);
-                V_sm_AT(tid_y, idx) = float(
-                    value[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads + kv_head_idx)) * head_dim + idx]);
-            }
-        }
-        __syncthreads();
-
-        /****************************Single Q tile for this block*****************************/
-        // gridDim.z already indexes the Q row tile, so this block only handles i = bid_z.
-        const int i = bid_z;
-        int q_tile_start = Br * i;
-        int q_tile_end = min(q_tile_start + Br, target_seq_len);
-        int bound_tid_y = min(Br, q_tile_end - q_tile_start);
-
-        if (tid_y < bound_tid_y) {
-            int global_q_idx = q_tile_start + tid_y;
-            int q_tensor_idx = ((bid_y * target_seq_len) + global_q_idx) * q_heads + bid_x;
-
-            for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-                Q_sm_AT(tid_y, idx) = float(query[q_tensor_idx * head_dim + idx]);
-                dO_sm_AT(tid_y, idx) = float(grad_output[q_tensor_idx * head_dim + idx]);
-            }
-            if (tid_x == 0) {
-                L_sm_AT(tid_y) = logsumexp[((bid_y * q_heads + bid_x) * target_seq_len) + global_q_idx];
+                int k_tensor_idx = ((bid_y * src_seq_len) + (k_tile_start + tid_y)) * kv_heads + kv_head_idx;
+                K_T_sm_AT(idx, tid_y) = float(key[k_tensor_idx * head_dim + idx]);
+                V_sm_AT(tid_y, idx) = float(value[k_tensor_idx * head_dim + idx]);
             }
         }
         __syncthreads();
 
-        // Get D_i for this row.
-        float D_i_row = D_sm_AT(tid_y);
-
-        // Recompute S_ij = Q_i @ K_j^T, apply causal mask at element level if needed.
-        #pragma unroll
-        for (int y = 0; y < Bc; ++y) {
-            for (int x = 0; x < Bc; ++x) {
-                float val = 0.0f;
-                if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-                    bool is_compute = true;
-                    if (is_causal && i == j) {
-                        int global_q_pos = q_tile_start + tid_y;
-                        int global_k_pos = Bc * j + x;
-                        is_compute = (global_q_pos >= global_k_pos);
-                    }
+        // Recompute S_ij and P_ij
+        if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+            int global_q_pos = q_tile_start + tid_y;
+            int global_k_pos = k_tile_start + tid_x;
+            bool is_compute = (!is_causal) || (global_q_pos >= global_k_pos);
 
-                    if (is_compute) {
-                        #pragma unroll
-                        for (int k = 0; k < head_dim; ++k) {
-                            val += Q_sm_AT(tid_y, k) * K_T_sm_AT(k, y);
-                        }
-                    }
+            float score = 0.0f;
+            if (is_compute) {
+#pragma unroll
+                for (int k = 0; k < head_dim; ++k) {
+                    score += Q_sm_AT(tid_y, k) * K_T_sm_AT(k, tid_x);
                 }
-                S_sm_AT(tid_y, x) = val * scale;
             }
+            S_sm_AT(tid_y, tid_x) = score * scale;
+            P_sm_AT(tid_y, tid_x) = is_compute ? myexp<float>(S_sm_AT(tid_y, tid_x) - L_sm_AT(tid_y)) : 0.0f;
+        } else {
+            S_sm_AT(tid_y, tid_x) = 0.0f;
+            P_sm_AT(tid_y, tid_x) = 0.0f;
         }
         __syncthreads();
 
-        // Recompute P_ij = exp(S_ij - L_i).
-        #pragma unroll
-        for (int y = 0; y < Bc; ++y) {
-            for (int x = 0; x < Bc; ++x) {
-                if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-                    bool is_compute = true;
-                    if (is_causal && i == j) {
-                        int global_q_pos = q_tile_start + tid_y;
-                        int global_k_pos = Bc * j + x;
-                        is_compute = (global_q_pos >= global_k_pos);
-                    }
-
-                    if (is_compute) {
-                        P_sm_AT(tid_y, x) = myexp<float>(S_sm_AT(tid_y, x) - L_sm_AT(tid_y));
-                    } else {
-                        P_sm_AT(tid_y, x) = 0.0f;
-                    }
-                } else {
-                    P_sm_AT(tid_y, x) = 0.0f;
-                }
+        // Apply dropout to P_ij
+        if (apply_dropout && tid_y < bound_tid_y && tid_x < bound_tid_x) {
+            int global_q_pos = q_tile_start + tid_y;
+            int global_k_pos = k_tile_start + tid_x;
+            bool keep = FlashAttnDropoutKeep(
+                dropout_seed, bid_y, bid_x, global_q_pos, global_k_pos,
+                q_heads, target_seq_len, src_seq_len, dropout_prob);
+            if (keep) {
+                P_sm_AT(tid_y, tid_x) = P_sm_AT(tid_y, tid_x) / (1.0f - dropout_prob);
+            } else {
+                P_sm_AT(tid_y, tid_x) = 0.0f;
             }
         }
         __syncthreads();
 
-        // Apply dropout to P_ij.
-        if (dropout_p > 0) {
-            #pragma unroll
-            for (int y = 0; y < Bc; ++y) {
-                for (int x = 0; x < Bc; ++x) {
-                    if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-                        int global_q_pos = q_tile_start + tid_y;
-                        int global_k_pos = Bc * j + x;
-                        bool keep = FlashAttnDropoutKeep(
-                            dropout_seed, bid_y, bid_x, global_q_pos, global_k_pos,
-                            q_heads, target_seq_len, src_seq_len, static_cast<float>(dropout_p));
-                        if (keep) {
-                            P_sm_AT(tid_y, x) = P_sm_AT(tid_y, x) / (1.0f - dropout_p);
-                        } else {
-                            P_sm_AT(tid_y, x) = 0.0f;
-                        }
-                    }
-                }
-            }
-            __syncthreads();
-        }
-
-        // Compute dV_j += P_ij^T @ dO_i.
-        if (tid_y < bound_tid_y) {
-            #pragma unroll
-            for (int x = 0; x < head_dim; x += blockDim.x) {
+        // dV_j = P_ij^T @ dO_i
+        if (tid_y < bound_tid_x) {
+            for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
                 float val = 0.0f;
-                #pragma unroll
-                for (int y = 0; y < Bc; ++y) {
-                    val += P_sm_AT(tid_y, y) * dO_sm_AT(tid_y, x);
+#pragma unroll
+                for (int y = 0; y < Br; ++y) {
+                    if (y < bound_tid_y) {
+                        val += P_sm_AT(y, tid_y) * dO_sm_AT(y, idx);
+                    }
                 }
-                atomicAdd(&dV_sm_AT(tid_y, x), val);
+                dV_sm_AT(tid_y, idx) = val;
             }
         }
         __syncthreads();
 
-        // Compute dP_ij = dO_i @ V_j^T.
-        #pragma unroll
-        for (int y = 0; y < Bc; ++y) {
-            for (int x = 0; x < Bc; ++x) {
-                float val = 0.0f;
-                if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-                    #pragma unroll
-                    for (int k = 0; k < head_dim; ++k) {
-                        val += dO_sm_AT(tid_y, k) * V_sm_AT(y, k);
-                    }
-                    S_sm_AT(tid_y, x) = val;  // Reuse S_sm as temporary storage for dP_ij
-                } else {
-                    S_sm_AT(tid_y, x) = 0.0f;
-                }
+        // dP_ij = dO_i @ V_j^T (reuse S_sm)
+        if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+            float val = 0.0f;
+#pragma unroll
+            for (int k = 0; k < head_dim; ++k) {
+                val += dO_sm_AT(tid_y, k) * V_sm_AT(tid_x, k);
             }
+            S_sm_AT(tid_y, tid_x) = val;
+        } else {
+            S_sm_AT(tid_y, tid_x) = 0.0f;
         }
         __syncthreads();
 
-        // Apply dropout to dP_ij.
-        if (dropout_p > 0) {
-            #pragma unroll
-            for (int y = 0; y < Bc; ++y) {
-                for (int x = 0; x < Bc; ++x) {
-                    if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-                        int global_q_pos = q_tile_start + tid_y;
-                        int global_k_pos = Bc * j + x;
-                        bool keep = FlashAttnDropoutKeep(
-                            dropout_seed, bid_y, bid_x, global_q_pos, global_k_pos,
-                            q_heads, target_seq_len, src_seq_len, static_cast<float>(dropout_p));
-                        if (keep) {
-                            S_sm_AT(tid_y, x) = S_sm_AT(tid_y, x) / (1.0f - dropout_p);
-                        } else {
-                            S_sm_AT(tid_y, x) = 0.0f;
-                        }
-                    }
-                }
+        // Apply dropout to dP_ij
+        if (apply_dropout && tid_y < bound_tid_y && tid_x < bound_tid_x) {
+            int global_q_pos = q_tile_start + tid_y;
+            int global_k_pos = k_tile_start + tid_x;
+            bool keep = FlashAttnDropoutKeep(
+                dropout_seed, bid_y, bid_x, global_q_pos, global_k_pos,
+                q_heads, target_seq_len, src_seq_len, dropout_prob);
+            if (keep) {
+                S_sm_AT(tid_y, tid_x) = S_sm_AT(tid_y, tid_x) / (1.0f - dropout_prob);
+            } else {
+                S_sm_AT(tid_y, tid_x) = 0.0f;
             }
-            __syncthreads();
         }
+        __syncthreads();
 
-        // Compute dS_ij = P_ij * (dP_ij - D_i).
-        #pragma unroll
-        for (int y = 0; y < Bc; ++y) {
-            for (int x = 0; x < Bc; ++x) {
-                if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-                    S_sm_AT(tid_y, x) = P_sm_AT(tid_y, x) * (S_sm_AT(tid_y, x) - D_i_row);
-                } else {
-                    S_sm_AT(tid_y, x) = 0.0f;
-                }
-            }
+        // dS_ij = P_ij ∘ (dP_ij - D_i)
+        float D_i_row = (tid_y < bound_tid_y) ? D_sm_AT(tid_y) : 0.0f;
+        if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
+            S_sm_AT(tid_y, tid_x) = P_sm_AT(tid_y, tid_x) * (S_sm_AT(tid_y, tid_x) - D_i_row);
+        } else {
+            S_sm_AT(tid_y, tid_x) = 0.0f;
         }
         __syncthreads();
 
-        // Compute dK_j += dS_ij^T @ Q_i.
-        if (tid_x < head_dim) {
-            #pragma unroll
-            for (int y = 0; y < Bc; ++y) {
+        // dK_j = dS_ij^T @ Q_i
+        if (tid_y < bound_tid_x) {
+            for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
                 float val = 0.0f;
-                #pragma unroll
-                for (int x = 0; x < Br; ++x) {
-                    val += S_sm_AT(x, y) * Q_sm_AT(x, tid_x);
+#pragma unroll
+                for (int y = 0; y < Br; ++y) {
+                    if (y < bound_tid_y) {
+                        val += S_sm_AT(y, tid_y) * Q_sm_AT(y, idx);
+                    }
                 }
-                atomicAdd(&dK_T_sm_AT(tid_x, y), val * scale);
+                dK_T_sm_AT(idx, tid_y) = val * scale;
             }
         }
         __syncthreads();
 
-        // Compute dQ_i += dS_ij @ K_j.
+        // dQ_i += dS_ij @ K_j
         if (tid_y < bound_tid_y) {
-            #pragma unroll
-            for (int x = 0; x < head_dim; x += blockDim.x) {
+            for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
                 float val = 0.0f;
-                #pragma unroll
-                for (int y = 0; y < Bc; ++y) {
-                    val += S_sm_AT(tid_y, y) * K_T_sm_AT(x, y);
+#pragma unroll
+                for (int x = 0; x < Bc; ++x) {
+                    if (x < bound_tid_x) {
+                        val += S_sm_AT(tid_y, x) * K_T_sm_AT(idx, x);
+                    }
                 }
-                dQ_sm_AT(tid_y, x) += val * scale;
+                dQ_sm_AT(tid_y, idx) += val * scale;
             }
         }
         __syncthreads();
 
         // Write back dK_j, dV_j to HBM
-        // Note: For GQA, dK_j and dV_j need to be accumulated across multiple q-head blocks
         int kv_head_idx = bid_x / p;
-        int k_tile_start = Bc * j;
-        int k_tile_end = min(k_tile_start + Bc, src_seq_len);
-        
-        for (int y = 0; y < Bc; ++y) {
-            int global_k_idx = k_tile_start + y;
-            if (global_k_idx < src_seq_len) {
-                for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-                    // dK: [batch_size, src_seq_len, kv_heads, head_dim]
-                    int k_tensor_idx = ((bid_y * src_seq_len) + global_k_idx) * kv_heads + kv_head_idx;
-                    atomicAdd(&grad_key[k_tensor_idx * head_dim + idx], T(dK_T_sm_AT(idx, y)));
-                    
-                    // dV: [batch_size, src_seq_len, kv_heads, head_dim]
-                    atomicAdd(&grad_value[k_tensor_idx * head_dim + idx], T(dV_sm_AT(y, idx)));
-                }
+        if (tid_y < bound_tid_x) {
+            int global_k_idx = k_tile_start + tid_y;
+            int k_tensor_idx = ((bid_y * src_seq_len) + global_k_idx) * kv_heads + kv_head_idx;
+            for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
+                atomicAdd(&grad_key[k_tensor_idx * head_dim + idx], T(dK_T_sm_AT(idx, tid_y)));
+                atomicAdd(&grad_value[k_tensor_idx * head_dim + idx], T(dV_sm_AT(tid_y, idx)));
             }
         }
-    }  // End of outer loop over K/V tiles
+        __syncthreads();
+    } // End of outer loop over K/V tiles
 
     // Write back dQ_i to HBM
-    int q_tile_start = Br * bid_z;
-    int q_tile_end = min(q_tile_start + Br, target_seq_len);
-    int bound_tid_y = min(Br, q_tile_end - q_tile_start);
-    
     if (tid_y < bound_tid_y) {
         int global_q_idx = q_tile_start + tid_y;
         int q_tensor_idx = ((bid_y * target_seq_len) + global_q_idx) * q_heads + bid_x;
-        
         for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-            // dQ: [batch_size, target_seq_len, q_heads, head_dim]
             grad_query[q_tensor_idx * head_dim + idx] += T(dQ_sm_AT(tid_y, idx));
         }
     }
 
     // Undefine access macros
-    #undef D_sm_AT
-    #undef Q_sm_AT
-    #undef K_T_sm_AT
-    #undef V_sm_AT
-    #undef dO_sm_AT
-    #undef dK_T_sm_AT
-    #undef dV_sm_AT
-    #undef S_sm_AT
-    #undef P_sm_AT
-    #undef L_sm_AT
-    #undef dQ_sm_AT
+#undef D_sm_AT
+#undef Q_sm_AT
+#undef K_T_sm_AT
+#undef V_sm_AT
+#undef dO_sm_AT
+#undef dK_T_sm_AT
+#undef dV_sm_AT
+#undef S_sm_AT
+#undef P_sm_AT
+#undef L_sm_AT
+#undef dQ_sm_AT
 }
 
 /**
@@ -799,6 +791,8 @@ void LaunchFlashAttentionForward(const std::shared_ptr<Tensor> &output, const st
     CHECK_EQ(value_dims[3], head_dim) << "Value head dimension must match query head dimension";
     CHECK_EQ(value_dims[1], seq_len_k) << "Value sequence length must match key sequence length";
     CHECK_EQ(value_dims[2], num_heads_kv) << "Value number of KV heads must match key";
+    CHECK(dropout_p >= 0 && dropout_p < 1)
+        << "FlashAttention dropout_p must be in [0, 1). Current API is int64, so only 0 is representable.";
 
     if (enable_gqa) {
         CHECK(num_heads % num_heads_kv == 0) << "Number of query heads must be divisible by number of KV heads for GQA";
@@ -893,6 +887,8 @@ void LaunchFlashAttentionBackward(const std::shared_ptr<Tensor> &grad_query, con
     CHECK_EQ(value->Dims()[3], head_dim) << "Value head dimension must match query head dimension";
     CHECK_EQ(value->Dims()[1], seq_len_k) << "Value sequence length must match key sequence length";
     CHECK_EQ(value->Dims()[2], num_heads_kv) << "Value number of KV heads must match key";
+    CHECK(dropout_p >= 0 && dropout_p < 1)
+        << "FlashAttention dropout_p must be in [0, 1). Current API is int64, so only 0 is representable.";
 
     if (enable_gqa) {
         CHECK(num_heads % num_heads_kv == 0) << "Number of query heads must be divisible by number of KV heads for GQA";
@@ -900,11 +896,9 @@ void LaunchFlashAttentionBackward(const std::shared_ptr<Tensor> &grad_query, con
         CHECK_EQ(num_heads, num_heads_kv) << "Number of query and KV heads must match for standard attention";
     }
 
-    // Precompute D = rowsum(dO ∘ O) before main backward loop
-    // D shape: [batch_size, seq_len_q, num_heads]
-    // dO shape: [batch_size, seq_len_q, num_heads, head_dim]
-    // O shape: [batch_size, seq_len_q, num_heads, head_dim]
-    auto D = infini_train::nn::function::Sum(grad_output * output, 3, false);
+    // Fused D = rowsum(dO ∘ O) into float32, avoiding an intermediate tensor (dO * O).
+    // D_fp32 shape: [batch_size, seq_len_q, num_heads]
+    auto D_fp32 = ComputeFlashAttnDFused<T>(grad_output, output);
 
     T *grad_query_ptr = static_cast<T *>(grad_query->DataPtr());
     T *grad_key_ptr = static_cast<T *>(grad_key->DataPtr());
@@ -915,7 +909,7 @@ void LaunchFlashAttentionBackward(const std::shared_ptr<Tensor> &grad_query, con
     const T *output_ptr = static_cast<const T *>(output->DataPtr());
     const T *grad_output_ptr = static_cast<const T *>(grad_output->DataPtr());
     const float *logsumexp_ptr = static_cast<const float *>(logsumexp->DataPtr());
-    const float *D_ptr = static_cast<const float *>(D->DataPtr());
+    const float *D_ptr = static_cast<const float *>(D_fp32->DataPtr());
     const T *attn_mask_ptr = attn_mask ? static_cast<const T *>(attn_mask->DataPtr()) : nullptr;
 
     // Set up grid and block dimensions according to FlashAttention v2 backward
diff --git a/scripts/extract_training_stats.py b/scripts/extract_training_stats.py
new file mode 100755
index 00000000..ad6086b8
--- /dev/null
+++ b/scripts/extract_training_stats.py
@@ -0,0 +1,426 @@
+#!/usr/bin/env python3
+"""
+Extract training statistics from log files and display them in a table.
+
+Usage:
+    python extract_training_stats.py <log_dir1> <log_dir2> [options]
+
+Options:
+    --threshold-fp32: Loss difference threshold for fp32 (default: 1e-5)
+    --threshold-bf16: Loss difference threshold for bfloat16 (default: 1e-2)
+"""
+
+import re
+import sys
+from pathlib import Path
+from argparse import ArgumentParser
+from collections import defaultdict
+
+
+def get_dtype_from_filename(filename):
+    """Determine dtype from filename. Returns 'bfloat16' or 'fp32'."""
+    return 'bfloat16' if '_bfloat16' in filename else 'fp32'
+
+
+def get_flash_from_filename(filename, dir_path):
+    """Determine flash mode from filename and directory. Returns 'flash' or 'noflash'."""
+    # First check if filename contains flash/noflash suffix
+    if '_flash' in filename:
+        return 'flash'
+    elif '_noflash' in filename:
+        return 'noflash'
+    # Otherwise, determine from directory name
+    dir_name = str(dir_path).lower()
+    if 'compare' in dir_name:
+        return 'flash'
+    else:
+        return 'noflash'
+
+
+def get_disopt_from_filename(filename):
+    """Determine distributed optimizer from filename. Returns 'disopt' or 'nodisopt'."""
+    return 'disopt' if '_distopt' in filename else 'nodisopt'
+
+
+def get_model_from_filename(filename):
+    """Determine model name from filename. Returns 'gpt2' or 'llama3'."""
+    if 'gpt2' in filename:
+        return 'gpt2'
+    elif 'llama3' in filename:
+        return 'llama3'
+    else:
+        return 'unknown'
+
+
+def parse_command_line(line):
+    """Extract configuration from command line."""
+    dp_match = re.search(r'--nthread_per_process=(\d+)', line)
+    dp = int(dp_match.group(1)) if dp_match else 1
+    
+    tp_match = re.search(r'--tensor_parallel=(\d+)', line)
+    tp = int(tp_match.group(1)) if tp_match else 1
+    
+    sp = 1
+    if '--sequence_parallel' in line:
+        sp = 1  # SP is enabled, but value is not explicitly set
+    
+    pp_match = re.search(r'--pipeline_parallel=(\d+)', line)
+    pp = int(pp_match.group(1)) if pp_match else 1
+    
+    disopt = '--use_distributed_optimizer' in line
+    
+    return {
+        'dp': dp,
+        'tp': tp,
+        'sp': sp,
+        'pp': pp,
+        'disopt': disopt
+    }
+
+
+def parse_log_file(file_path):
+    """Extract statistics from log file."""
+    stats = {
+        'losses': [],
+        'times': [],
+        'toks_per_sec': [],
+        'peak_used': [],
+        'peak_reserved': [],
+        'config': None
+    }
+    
+    with open(file_path, 'r') as f:
+        for line in f:
+            # Parse command line
+            if line.startswith('[COMMAND]'):
+                stats['config'] = parse_command_line(line)
+                continue
+            
+            # Parse step information
+            match = re.search(r'step\s+(\d+)/\d+\s+\|\s+train loss\s+([\d.]+)\s+\|\s+lr\s+[\d.e+-]+\s+\|\s+\(([\d.]+)\s+ms\s+\|\s+(\d+)\s+tok/s.*peak used:\s+(\d+)\s+MB.*peak reserved:\s+(\d+)\s+MB.*DP=(\d+),\s+TP=(\d+),\s+SP=(\d+),\s+PP=(\d+)', line)
+            if match:
+                step = int(match.group(1))
+                loss = float(match.group(2))
+                time_ms = float(match.group(3))
+                tok_per_sec = int(match.group(4))
+                peak_used = int(match.group(5))
+                peak_reserved = int(match.group(6))
+                dp = int(match.group(7))
+                tp = int(match.group(8))
+                sp = int(match.group(9))
+                pp = int(match.group(10))
+                
+                stats['losses'].append((step, loss))
+                stats['times'].append((step, time_ms))
+                stats['toks_per_sec'].append((step, tok_per_sec))
+                stats['peak_used'].append((step, peak_used))
+                stats['peak_reserved'].append((step, peak_reserved))
+    
+    return stats
+
+
+def calculate_averages(stats, exclude_first_step=True):
+    """Calculate average values, optionally excluding first step."""
+    losses = stats['losses']
+    times = stats['times']
+    toks_per_sec = stats['toks_per_sec']
+    peak_used = stats['peak_used']
+    peak_reserved = stats['peak_reserved']
+    
+    start_idx = 1 if exclude_first_step else 0
+    
+    avg_loss = sum(loss for _, loss in losses[start_idx:]) / len(losses[start_idx:]) if len(losses[start_idx:]) > 0 else 0
+    avg_time = sum(time for _, time in times[start_idx:]) / len(times[start_idx:]) if len(times[start_idx:]) > 0 else 0
+    avg_tok_per_sec = sum(tok for _, tok in toks_per_sec[start_idx:]) / len(toks_per_sec[start_idx:]) if len(toks_per_sec[start_idx:]) > 0 else 0
+    avg_peak_used = sum(mem for _, mem in peak_used[start_idx:]) / len(peak_used[start_idx:]) if len(peak_used[start_idx:]) > 0 else 0
+    avg_peak_reserved = sum(mem for _, mem in peak_reserved[start_idx:]) / len(peak_reserved[start_idx:]) if len(peak_reserved[start_idx:]) > 0 else 0
+    
+    return {
+        'avg_loss': avg_loss,
+        'avg_time_ms': avg_time,
+        'avg_tok_per_sec': avg_tok_per_sec,
+        'avg_peak_used': avg_peak_used,
+        'avg_peak_reserved': avg_peak_reserved
+    }
+
+
+def main():
+    parser = ArgumentParser(description='Extract training statistics from log files')
+    parser.add_argument('dir1', type=Path, help='First log directory')
+    parser.add_argument('dir2', type=Path, help='Second log directory')
+    parser.add_argument('--threshold-fp32', type=float, default=1e-5, help='Loss difference threshold for fp32 (default: 1e-5)')
+    parser.add_argument('--threshold-bf16', type=float, default=1e-2, help='Loss difference threshold for bfloat16 (default: 1e-2)')
+    parser.add_argument('--markdown', action='store_true', help='Output as markdown table')
+    parser.add_argument('--output', type=str, default='', help='Output file path (optional)')
+    parser.add_argument('--speedup', action='store_true', help='Output speedup ratio table')
+    args = parser.parse_args()
+    
+    # Get all log files from both directories
+    files1 = [(f, args.dir1) for f in args.dir1.glob('*.log') if not f.name.startswith('build') and 'comparison' not in f.name.lower()]
+    files2 = [(f, args.dir2) for f in args.dir2.glob('*.log') if not f.name.startswith('build') and 'comparison' not in f.name.lower()]
+    
+    # Combine files from both directories
+    all_files = files1 + files2
+    
+    # Parse all files and collect statistics
+    results = []
+    for file_path, dir_path in sorted(all_files, key=lambda x: (x[1], x[0].name)):
+        filename = file_path.name
+        model = get_model_from_filename(filename)
+        dtype = get_dtype_from_filename(filename)
+        flash = get_flash_from_filename(filename, dir_path)
+        disopt = get_disopt_from_filename(filename)
+        
+        # Extract test ID from filename (e.g., gpt2_3_bfloat16_distopt_profile.log -> 3)
+        test_id_match = re.search(r'_(\d+)_', filename)
+        test_id = test_id_match.group(1) if test_id_match else ''
+        
+        # Parse log file
+        stats = parse_log_file(file_path)
+        averages = calculate_averages(stats, exclude_first_step=True)
+        
+        # Get configuration
+        config = stats['config'] if stats['config'] else {}
+        dp = config.get('dp', 1)
+        tp = config.get('tp', 1)
+        sp = config.get('sp', 1)
+        pp = config.get('pp', 1)
+        
+        # Create row identifier (include test ID to distinguish different test configurations)
+        row_id = f"{model}_{dtype}_{flash}_{disopt}_{test_id}"
+        
+        results.append({
+            'row_id': row_id,
+            'model': model,
+            'dtype': dtype,
+            'flash': flash,
+            'disopt': disopt,
+            'test_id': test_id,
+            'avg_loss': averages['avg_loss'],
+            'avg_time_ms': averages['avg_time_ms'],
+            'avg_tok_per_sec': averages['avg_tok_per_sec'],
+            'avg_peak_used': averages['avg_peak_used'],
+            'avg_peak_reserved': averages['avg_peak_reserved'],
+            'dp': dp,
+            'tp': tp,
+            'sp': sp,
+            'pp': pp
+        })
+    
+    # Sort results by row_id
+    results.sort(key=lambda x: x['row_id'])
+    
+    # Group by model, dtype, and disopt (excluding flash)
+    grouped_by_config = defaultdict(list)
+    for result in results:
+        key = f"{result['model']}_{result['dtype']}_{result['disopt']}"
+        grouped_by_config[key].append(result)
+    
+    # Prepare output
+    if args.markdown:
+        # Output as markdown table
+        markdown_lines = []
+        
+        # Add header
+        markdown_lines.append("| Row ID | Avg Loss | Avg Time (ms) | Avg Tok/s | Peak Used (MB) | Peak Reserved (MB) | DP | TP | SP | PP |")
+        markdown_lines.append("|---------|----------|---------------|-----------|-----------------|-------------------|----|----|----|----|")
+        
+        # Print rows (flash and noflash interleaved)
+        for key in sorted(grouped_by_config.keys()):
+            group = grouped_by_config[key]
+            if len(group) >= 2:
+                # Separate flash and noflash results
+                flash_results = [r for r in group if r['flash'] == 'flash']
+                noflash_results = [r for r in group if r['flash'] == 'noflash']
+                
+                # Interleave flash and noflash results
+                for i in range(max(len(flash_results), len(noflash_results))):
+                    if i < len(flash_results):
+                        result = flash_results[i]
+                        row = f"| {result['row_id']:<40} | {result['avg_loss']:<15.6f} | {result['avg_time_ms']:<15.2f} | {result['avg_tok_per_sec']:<15.2f} | {result['avg_peak_used']:<15.2f} | {result['avg_peak_reserved']:<15.2f} | {result['dp']:<5} | {result['tp']:<5} | {result['sp']:<5} | {result['pp']:<5} |"
+                        markdown_lines.append(row)
+                    if i < len(noflash_results):
+                        result = noflash_results[i]
+                        row = f"| {result['row_id']:<40} | {result['avg_loss']:<15.6f} | {result['avg_time_ms']:<15.2f} | {result['avg_tok_per_sec']:<15.2f} | {result['avg_peak_used']:<15.2f} | {result['avg_peak_reserved']:<15.2f} | {result['dp']:<5} | {result['tp']:<5} | {result['sp']:<5} | {result['pp']:<5} |"
+                        markdown_lines.append(row)
+            else:
+                # Only one result, print it
+                for result in group:
+                    row = f"| {result['row_id']:<40} | {result['avg_loss']:<15.6f} {result['avg_time_ms']:<15.2f} {result['avg_tok_per_sec']:<15.2f} {result['avg_peak_used']:<15.2f} | {result['avg_peak_reserved']:<15.2f} | {result['dp']:<5} | {result['tp']:<5} | {result['sp']:<5} | {result['pp']:<5} |"
+                    markdown_lines.append(row)
+        
+        # Print markdown table
+        markdown_table = "\n".join(markdown_lines)
+        print(markdown_table)
+        
+        # Write to file if output path is specified
+        if args.output:
+            with open(args.output, 'w') as f:
+                f.write(markdown_table)
+            print(f"Markdown table saved to: {args.output}")
+    else:
+        # Print plain text table
+        print("\n" + "=" * 150)
+        print("Training Statistics Table (excluding step 1)")
+        print("=" * 150)
+        
+        # Print header
+        header = f"{'Row ID':<40} {'Avg Loss':<15} {'Avg Time (ms)':<15} {'Avg Tok/s':<15} {'Peak Used (MB)':<15} {'Peak Reserved (MB)':<15} {'DP':<5} {'TP':<5} {'SP':<5} {'PP':<5}"
+        print(header)
+        print("-" * 150)
+        
+        # Print rows (flash and noflash interleaved)
+        for key in sorted(grouped_by_config.keys()):
+            group = grouped_by_config[key]
+            if len(group) >= 2:
+                # Separate flash and noflash results
+                flash_results = [r for r in group if r['flash'] == 'flash']
+                noflash_results = [r for r in group if r['flash'] == 'noflash']
+                
+                # Interleave flash and noflash results
+                for i in range(max(len(flash_results), len(noflash_results))):
+                    if i < len(flash_results):
+                        result = flash_results[i]
+                        row = f"{result['row_id']:<40} {result['avg_loss']:<15.6f} {result['avg_time_ms']:<15.2f} {result['avg_tok_per_sec']:<15.2f} {result['avg_peak_used']:<15.2f} {result['avg_peak_reserved']:<15.2f} {result['dp']:<5} {result['tp']:<5} {result['sp']:<5} {result['pp']:<5}"
+                        print(row)
+                    if i < len(noflash_results):
+                        result = noflash_results[i]
+                        row = f"{result['row_id']:<40} {result['avg_loss']:<15.6f} {result['avg_time_ms']:<15.2f} {result['avg_tok_per_sec']:<15.2f} {result['avg_peak_used']:<15.2f} {result['avg_peak_reserved']:<15.2f} {result['dp']:<5} {result['tp']:<5} {result['sp']:<5} {result['pp']:<5}"
+                        print(row)
+        else:
+            # Only one result, print it
+            for result in group:
+                row = f"{result['row_id']:<40} {result['avg_loss']:<15.6f} {result['avg_time_ms']:<15.2f} {result['avg_tok_per_sec']:<15.2f} {result['avg_peak_used']:<15.2f} {result['avg_peak_reserved']:<15.2f} {result['dp']:<5} {result['tp']:<5} {result['sp']:<5} {result['pp']:<5}"
+                print(row)
+    
+    print("=" * 150)
+    
+    # Print comparison between flash and noflash
+    print("\nComparison between flash and noflash:")
+    print("=" * 150)
+    
+    for key, group in sorted(grouped_by_config.items()):
+        # Separate flash and noflash results
+        flash_results = [r for r in group if r['flash'] == 'flash']
+        noflash_results = [r for r in group if r['flash'] == 'noflash']
+        
+        if flash_results and noflash_results:
+            # Calculate averages for each group
+            avg_flash_loss = sum(r['avg_loss'] for r in flash_results) / len(flash_results)
+            avg_flash_time = sum(r['avg_time_ms'] for r in flash_results) / len(flash_results)
+            avg_flash_tok = sum(r['avg_tok_per_sec'] for r in flash_results) / len(flash_results)
+            avg_flash_peak_used = sum(r['avg_peak_used'] for r in flash_results) / len(flash_results)
+            avg_flash_peak_reserved = sum(r['avg_peak_reserved'] for r in flash_results) / len(flash_results)
+            
+            avg_noflash_loss = sum(r['avg_loss'] for r in noflash_results) / len(noflash_results)
+            avg_noflash_time = sum(r['avg_time_ms'] for r in noflash_results) / len(noflash_results)
+            avg_noflash_tok = sum(r['avg_tok_per_sec'] for r in noflash_results) / len(noflash_results)
+            avg_noflash_peak_used = sum(r['avg_peak_used'] for r in noflash_results) / len(noflash_results)
+            avg_noflash_peak_reserved = sum(r['avg_peak_reserved'] for r in noflash_results) / len(noflash_results)
+            
+            # Calculate differences
+            loss_diff = avg_flash_loss - avg_noflash_loss
+            time_diff = avg_flash_time - avg_noflash_time
+            tok_diff = avg_flash_tok - avg_noflash_tok
+            
+            loss_pct = (loss_diff / avg_noflash_loss * 100) if avg_noflash_loss > 0 else 0
+            time_pct = (time_diff / avg_noflash_time * 100) if avg_noflash_time > 0 else 0
+            tok_pct = (tok_diff / avg_noflash_tok * 100) if avg_noflash_tok > 0 else 0
+            
+            print(f"\n{key}:")
+            print(f"  Loss: {avg_flash_loss:.6f} vs {avg_noflash_loss:.6f} (diff: {loss_diff:+.6f}, {loss_pct:+.2f}%)")
+            print(f"  Time: {avg_flash_time:.2f} vs {avg_noflash_time:.2f} ms (diff: {time_diff:+.2f}, {time_pct:+.2f}%)")
+            print(f"  Tok/s: {avg_flash_tok:.2f} vs {avg_noflash_tok:.2f} (diff: {tok_diff:+.2f}, {tok_pct:+.2f}%)")
+            
+            # Calculate memory differences
+            mem_used_diff = avg_flash_peak_used - avg_noflash_peak_used
+            mem_reserved_diff = avg_flash_peak_reserved - avg_noflash_peak_reserved
+            
+            mem_used_pct = (mem_used_diff / avg_noflash_peak_used * 100) if avg_noflash_peak_used > 0 else 0
+            mem_reserved_pct = (mem_reserved_diff / avg_noflash_peak_reserved * 100) if avg_noflash_peak_reserved > 0 else 0
+            
+            print(f"  Peak Used: {avg_flash_peak_used:.2f} vs {avg_noflash_peak_used:.2f} MB (diff: {mem_used_diff:+.2f}, {mem_used_pct:+.2f}%)")
+            print(f"  Peak Reserved: {avg_flash_peak_reserved:.2f} vs {avg_noflash_peak_reserved:.2f} MB (diff: {mem_reserved_diff:+.2f}, {mem_reserved_pct:+.2f}%)")
+            
+            # Calculate memory differences
+            mem_used_diff = avg_flash_peak_used - avg_noflash_peak_used
+            mem_reserved_diff = avg_flash_peak_reserved - avg_noflash_peak_reserved
+            mem_used_pct = (mem_used_diff / avg_noflash_peak_used * 100) if avg_noflash_peak_used > 0 else 0
+            mem_reserved_pct = (mem_reserved_diff / avg_noflash_peak_reserved * 100) if avg_noflash_peak_reserved > 0 else 0
+    
+    # Output speedup ratio table if requested
+    if args.speedup:
+        print("\n" + "=" * 120)
+        print("Speedup Ratio Table (noflash_time / flash_time)")
+        print("=" * 120)
+        
+        # Group by model, dtype, disopt, and test_id
+        grouped_by_test = defaultdict(list)
+        for result in results:
+            key = f"{result['model']}_{result['dtype']}_{result['disopt']}_{result.get('test_id', '')}"
+            grouped_by_test[key].append(result)
+        
+        # Calculate speedup ratios for each test configuration
+        speedup_results = []
+        for key, group in sorted(grouped_by_test.items()):
+            # Separate flash and noflash results
+            flash_results = [r for r in group if r['flash'] == 'flash']
+            noflash_results = [r for r in group if r['flash'] == 'noflash']
+            
+            if flash_results and noflash_results:
+                # Calculate average times for this test configuration
+                avg_flash_time = sum(r['avg_time_ms'] for r in flash_results) / len(flash_results)
+                avg_noflash_time = sum(r['avg_time_ms'] for r in noflash_results) / len(noflash_results)
+                
+                # Calculate speedup ratio
+                speedup = avg_noflash_time / avg_flash_time if avg_flash_time > 0 else 0
+                
+                # Extract test_id from key
+                test_id = key.split('_')[-1]
+                
+                speedup_results.append({
+                    'config': key,
+                    'speedup': speedup,
+                    'flash_time': avg_flash_time,
+                    'noflash_time': avg_noflash_time
+                })
+        
+        # Sort by configuration name
+        speedup_results.sort(key=lambda x: x['config'])
+        
+        # Print speedup table
+        if args.markdown:
+            # Output as markdown table
+            markdown_lines = []
+            markdown_lines.append("| Configuration | Speedup Ratio | Flash Time (ms) | Noflash Time (ms) |")
+            markdown_lines.append("|---------------|---------------|-----------------|-------------------|")
+            
+            for result in speedup_results:
+                row = f"| {result['config']:<37} | {result['speedup']:<13.2f} | {result['flash_time']:<15.2f} | {result['noflash_time']:<17.2f} |"
+                markdown_lines.append(row)
+            
+            # Print markdown table
+            markdown_table = "\n".join(markdown_lines)
+            print(markdown_table)
+            
+            # Write to file if output path is specified
+            if args.output:
+                output_path = args.output.replace('.md', '_speedup.md')
+                with open(output_path, 'w') as f:
+                    f.write(markdown_table)
+                print(f"Speedup table saved to: {output_path}")
+        else:
+            # Print plain text table
+            header = f"{'Configuration':<37} {'Speedup Ratio':<15} {'Flash Time (ms)':<17} {'Noflash Time (ms)':<17}"
+            print(header)
+            print("-" * 120)
+            
+            for result in speedup_results:
+                row = f"{result['config']:<37} {result['speedup']:<15.2f} {result['flash_time']:<17.2f} {result['noflash_time']:<17.2f}"
+                print(row)
+        
+        print("=" * 120)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/scripts/run_models_and_profile.bash b/scripts/run_models_and_profile.bash
index 65b13ef0..25ec041e 100755
--- a/scripts/run_models_and_profile.bash
+++ b/scripts/run_models_and_profile.bash
@@ -3,7 +3,22 @@
 set -e
 set -o pipefail
 
-CONFIG_FILE="${1:-test_config.json}"
+# Parse arguments
+REBUILD=false
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --rebuild)
+            REBUILD=true
+            shift
+            ;;
+        *)
+            CONFIG_FILE="$1"
+            shift
+            ;;
+    esac
+done
+
+CONFIG_FILE="${CONFIG_FILE:-test_config.json}"
 export INFINI_FLASH_BF16_USE_FP32=0
 
 # Dependencies check
@@ -47,9 +62,11 @@ run_and_log() {
     local cmd="$1"
     local log_name="$2"
     local is_profile="$3"
+    local log_dir="$4"
+    local profile_log_dir="$5"
     local timestamp
     timestamp=$(date '+%Y-%m-%d %H:%M:%S')
-    local log_path="$(realpath "${LOG_DIR}/${log_name}.log")"
+    local log_path="$(realpath "${log_dir}/${log_name}.log")"
 
     echo -e "\033[1;32m============================================================\033[0m"
     echo -e "\033[1;36m[$timestamp] [Running] ${log_name}\033[0m"
@@ -66,7 +83,7 @@ run_and_log() {
 
     # Notify if profiling mode is enabled
     if [[ "$is_profile" == "yes" ]]; then
-        echo -e "\033[1;35m[PROFILE MODE ON] Profiling logs will be saved to: ${PROFILE_LOG_DIR}\033[0m"
+        echo -e "\033[1;35m[PROFILE MODE ON] Profiling logs will be saved to: ${profile_log_dir}\033[0m"
     fi
 
     echo -e "\033[1;32m============================================================\033[0m"
@@ -100,7 +117,7 @@ run_and_log() {
 
     # If profiling is enabled, move profiling files to the target directory
     if [[ "$is_profile" == "yes" ]]; then
-        move_profile_logs "$log_name"
+        move_profile_logs "$log_name" "$profile_log_dir"
     fi
 }
 
@@ -108,14 +125,15 @@ run_and_log() {
 # Move profiling output logs
 move_profile_logs() {
     local prefix="$1"
+    local target_profile_log_dir="$2"
 
     # Move *.report.rankN files
     for report_file in "${BUILD_DIR}"/*.report.rank*; do
         if [[ -f "$report_file" ]]; then
             local base_name
             base_name=$(basename "$report_file")
-            mv "$report_file" "${PROFILE_LOG_DIR}/${prefix}_${base_name}"
-            echo "Moved $base_name to ${PROFILE_LOG_DIR}/${prefix}_${base_name}"
+            mv "$report_file" "${target_profile_log_dir}/${prefix}_${base_name}"
+            echo "Moved $base_name to ${target_profile_log_dir}/${prefix}_${base_name}"
         fi
     done
 
@@ -124,8 +142,8 @@ move_profile_logs() {
         if [[ -f "$record_file" ]]; then
             local base_name
             base_name=$(basename "$record_file")
-            mv "$record_file" "${PROFILE_LOG_DIR}/${prefix}_${base_name}"
-            echo "Moved $base_name to ${PROFILE_LOG_DIR}/${prefix}_${base_name}"
+            mv "$record_file" "${target_profile_log_dir}/${prefix}_${base_name}"
+            echo "Moved $base_name to ${target_profile_log_dir}/${prefix}_${base_name}"
         fi
     done
 }
@@ -154,9 +172,29 @@ for ((id=0; id<num_builds; ++id)); do
 
     LAST_CMAKE_CMD="$build_cmake"
 
-    # always clean before another build
-    clean_build_dir
-    run_and_log "$LAST_CMAKE_CMD" "${build_id}" "no"
+    # Check if rebuild is needed
+    if [[ "$REBUILD" == true ]]; then
+        # Clean and rebuild
+        clean_build_dir
+        run_and_log "$LAST_CMAKE_CMD" "${build_id}" "no" "$LOG_DIR" "$PROFILE_LOG_DIR"
+    else
+        # Check if build directory exists and executables are present
+        if [[ -d "$BUILD_DIR" ]]; then
+            # Check if gpt2 and llama3 executables exist
+            if [[ -f "${BUILD_DIR}/gpt2" ]] && [[ -f "${BUILD_DIR}/llama3" ]]; then
+                echo -e "\033[1;33m[SKIP] Build directory already exists and executables are present. Skipping build.\033[0m"
+                echo -e "\033[1;33m        Use --rebuild to force a clean rebuild.\033[0m"
+            else
+                # Build executables that are missing
+                echo -e "\033[1;33m[BUILD] Some executables are missing. Building...\033[0m"
+                run_and_log "$LAST_CMAKE_CMD" "${build_id}" "no" "$LOG_DIR" "$PROFILE_LOG_DIR"
+            fi
+        else
+            # Build directory doesn't exist, build from scratch
+            echo -e "\033[1;33m[BUILD] Build directory doesn't exist. Building...\033[0m"
+            run_and_log "$LAST_CMAKE_CMD" "${build_id}" "no" "$LOG_DIR" "$PROFILE_LOG_DIR"
+        fi
+    fi
 
     # profile flag for runs
     profile_flag="no"
@@ -183,20 +221,18 @@ for ((id=0; id<num_builds; ++id)); do
         : "${PROFILE_LOG_DIR:=./profile_logs}"
 
         mkdir -p "$LOG_DIR" "$PROFILE_LOG_DIR"
-        export LOG_DIR PROFILE_LOG_DIR
 
         gpt2_cmd="./gpt2 --input_bin ${GPT2_INPUT_BIN} --llmc_filepath ${GPT2_LLMC_FILEPATH} --device cuda ${arg_str}"
-        run_and_log "$gpt2_cmd" "gpt2_${test_id}${log_suffix}" "$profile_flag"
+        run_and_log "$gpt2_cmd" "gpt2_${test_id}${log_suffix}" "$profile_flag" "$LOG_DIR" "$PROFILE_LOG_DIR"
 
         # Run gpt2 with flash=true
         COMPARE_LOG_DIR="./compare_logs"
         COMPARE_PROFILE_LOG_DIR="./compare_profile_logs"
 
         mkdir -p "$COMPARE_LOG_DIR" "$COMPARE_PROFILE_LOG_DIR"
-        export COMPARE_LOG_DIR COMPARE_PROFILE_LOG_DIR
 
         gpt2_cmd="./gpt2 --input_bin ${GPT2_INPUT_BIN} --llmc_filepath ${GPT2_LLMC_FILEPATH} --device cuda ${arg_str1}"
-        run_and_log "$gpt2_cmd" "gpt2_${test_id}${log_suffix}" "$profile_flag"
+        run_and_log "$gpt2_cmd" "gpt2_${test_id}${log_suffix}" "$profile_flag" "$COMPARE_LOG_DIR" "$COMPARE_PROFILE_LOG_DIR"
 
         # Run llama3 with flash=false
         LOG_DIR="$(read_var LOG_DIR)"
@@ -205,20 +241,18 @@ for ((id=0; id<num_builds; ++id)); do
         : "${PROFILE_LOG_DIR:=./profile_logs}"
 
         mkdir -p "$LOG_DIR" "$PROFILE_LOG_DIR"
-        export LOG_DIR PROFILE_LOG_DIR
 
         llama3_cmd="./llama3 --input_bin ${LLAMA3_INPUT_BIN} --llmc_filepath ${LLAMA3_LLMC_FILEPATH} --device cuda ${arg_str}"
-        run_and_log "$llama3_cmd" "llama3_${test_id}${log_suffix}" "$profile_flag"
+        run_and_log "$llama3_cmd" "llama3_${test_id}${log_suffix}" "$profile_flag" "$LOG_DIR" "$PROFILE_LOG_DIR"
 
         # Run llama3 with flash=true
         COMPARE_LOG_DIR="./compare_logs"
         COMPARE_PROFILE_LOG_DIR="./compare_profile_logs"
 
         mkdir -p "$COMPARE_LOG_DIR" "$COMPARE_PROFILE_LOG_DIR"
-        export COMPARE_LOG_DIR COMPARE_PROFILE_LOG_DIR
 
         llama3_cmd="./llama3 --input_bin ${LLAMA3_INPUT_BIN} --llmc_filepath ${LLAMA3_LLMC_FILEPATH} --device cuda ${arg_str1}"
-        run_and_log "$llama3_cmd" "llama3_${test_id}${log_suffix}" "$profile_flag"
+        run_and_log "$llama3_cmd" "llama3_${test_id}${log_suffix}" "$profile_flag" "$COMPARE_LOG_DIR" "$COMPARE_PROFILE_LOG_DIR"
     done
 done
 
@@ -234,11 +268,11 @@ if [[ -n "$COMPARE_LOG_DIR" ]]; then
 
     # Run compare_loss.py
     echo -e "\n\033[1;33m[Running] compare_loss.py\033[0m"
-    python3 "${SCRIPT_DIR}/compare_loss.py" "$COMPARE_LOG_DIR" "$LOG_DIR" > compare_logs/loss_comparison.log 2>&1 || true
+    python3 "${SCRIPT_DIR}/compare_loss.py" "$COMPARE_LOG_DIR" "$LOG_DIR" --threshold-fp32 1e-1 --threshold-bf16 1e-1 > compare_logs/loss_comparison.log 2>&1 || true
 
     # Run compare_tps.py
     echo -e "\n\033[1;33m[Running] compare_tps.py\033[0m"
-    python3 "${SCRIPT_DIR}/compare_tps.py" "$COMPARE_LOG_DIR" "$LOG_DIR" > compare_logs/tps_comparison.log 2>&1 || true
+    python3 "${SCRIPT_DIR}/compare_tps.py" "$COMPARE_LOG_DIR" "$LOG_DIR" --threshold 0.20 > compare_logs/tps_comparison.log 2>&1 || true
 
     echo -e "\n\033[1;32mComparison completed.\033[0m"
 else

From 96b8f0b5f44fa92e52f1882b820bedf190768747 Mon Sep 17 00:00:00 2001
From: Aoshine999 <820459542@qq.com>
Date: Sun, 15 Mar 2026 18:59:25 +0800
Subject: [PATCH 10/18] update extract staus script

---
 scripts/extract_training_stats.py | 95 +++++++++++++++++++++++++++++--
 1 file changed, 90 insertions(+), 5 deletions(-)

diff --git a/scripts/extract_training_stats.py b/scripts/extract_training_stats.py
index ad6086b8..767bd05b 100755
--- a/scripts/extract_training_stats.py
+++ b/scripts/extract_training_stats.py
@@ -2,12 +2,46 @@
 """
 Extract training statistics from log files and display them in a table.
 
+This script parses log files from training runs and extracts statistics such as
+loss, time, tokens per second, and memory usage. It can compare
+results from different configurations (e.g., flash vs noflash attention)
+and calculate speedup ratios.
+
 Usage:
     python extract_training_stats.py <log_dir1> <log_dir2> [options]
 
+Arguments:
+    log_dir1: First log directory (typically contains noflash results)
+    log_dir2: Second log directory (typically contains flash results)
+
 Options:
     --threshold-fp32: Loss difference threshold for fp32 (default: 1e-5)
     --threshold-bf16: Loss difference threshold for bfloat16 (default: 1e-2)
+    --markdown: Output tables in markdown format
+    --output: Specify output file path (for markdown tables)
+    --speedup: Output speedup ratio table (noflash_time / flash_time)
+
+Examples:
+    # Display training statistics in plain text
+    python extract_training_stats.py logs compare_logs
+
+    # Display training statistics in markdown format
+    python extract_training_stats.py logs compare_logs --markdown
+
+    # Display training statistics in markdown and save to file
+    python extract_training_stats.py logs compare_logs --markdown --output results.md
+
+    # Display speedup ratio table
+    python extract_training_stats.py logs compare_logs --speedup
+
+    # Display speedup ratio table in markdown and save to file
+    python extract_training_stats.py logs compare_logs --speedup --markdown --output results.md
+
+Output:
+    The script outputs three types of information:
+    1. Training Statistics Table: Detailed statistics for each configuration
+    2. Comparison between flash and noflash: Comparison of loss, time, and memory
+    3. Speedup Ratio Table (if --speedup is specified): Speedup ratios for each configuration
 """
 
 import re
@@ -18,12 +52,29 @@
 
 
 def get_dtype_from_filename(filename):
-    """Determine dtype from filename. Returns 'bfloat16' or 'fp32'."""
+    """
+    Determine dtype from filename.
+    
+    Args:
+        filename: Name of the log file
+        
+    Returns:
+        'bfloat16' or 'fp32'
+    """
     return 'bfloat16' if '_bfloat16' in filename else 'fp32'
 
 
 def get_flash_from_filename(filename, dir_path):
-    """Determine flash mode from filename and directory. Returns 'flash' or 'noflash'."""
+    """
+    Determine flash mode from filename and directory.
+    
+    Args:
+        filename: Name of the log file
+        dir_path: Path to the directory containing the log file
+        
+    Returns:
+        'flash' or 'noflash'
+    """
     # First check if filename contains flash/noflash suffix
     if '_flash' in filename:
         return 'flash'
@@ -79,7 +130,16 @@ def parse_command_line(line):
 
 
 def parse_log_file(file_path):
-    """Extract statistics from log file."""
+    """
+    Extract statistics from log file.
+    
+    Args:
+        file_path: Path to the log file
+        
+    Returns:
+        Dictionary containing lists of losses, times, tokens per second,
+        peak memory usage, and configuration
+    """
     stats = {
         'losses': [],
         'times': [],
@@ -120,7 +180,19 @@ def parse_log_file(file_path):
 
 
 def calculate_averages(stats, exclude_first_step=True):
-    """Calculate average values, optionally excluding first step."""
+    """
+    Calculate average values, optionally excluding first step.
+    
+    Args:
+        stats: Dictionary containing lists of losses, times, tokens per second,
+                and peak memory usage
+        exclude_first_step: If True, exclude the first step from calculations
+                             (default: True, as first step may have overhead)
+                             
+    Returns:
+        Dictionary containing average values for loss, time, tokens per second,
+        and peak memory usage
+    """
     losses = stats['losses']
     times = stats['times']
     toks_per_sec = stats['toks_per_sec']
@@ -145,6 +217,17 @@ def calculate_averages(stats, exclude_first_step=True):
 
 
 def main():
+    """
+    Main function to extract and display training statistics.
+    
+    This function:
+    1. Parses command line arguments
+    2. Reads log files from two directories
+    3. Extracts statistics from each log file
+    4. Groups results by configuration
+    5. Displays tables and comparisons
+    6. Optionally displays speedup ratios
+    """
     parser = ArgumentParser(description='Extract training statistics from log files')
     parser.add_argument('dir1', type=Path, help='First log directory')
     parser.add_argument('dir2', type=Path, help='Second log directory')
@@ -355,6 +438,7 @@ def main():
         print("=" * 120)
         
         # Group by model, dtype, disopt, and test_id
+        # This allows us to calculate speedup for each individual test configuration
         grouped_by_test = defaultdict(list)
         for result in results:
             key = f"{result['model']}_{result['dtype']}_{result['disopt']}_{result.get('test_id', '')}"
@@ -373,6 +457,7 @@ def main():
                 avg_noflash_time = sum(r['avg_time_ms'] for r in noflash_results) / len(noflash_results)
                 
                 # Calculate speedup ratio
+                # Speedup > 1.0 means flash is faster than noflash
                 speedup = avg_noflash_time / avg_flash_time if avg_flash_time > 0 else 0
                 
                 # Extract test_id from key
@@ -385,7 +470,7 @@ def main():
                     'noflash_time': avg_noflash_time
                 })
         
-        # Sort by configuration name
+        # Sort by configuration name (alphabetical order)
         speedup_results.sort(key=lambda x: x['config'])
         
         # Print speedup table

From 6de51d70253ad1db6f6f9ada9283a2164ec0731f Mon Sep 17 00:00:00 2001
From: Aoshine999 <820459542@qq.com>
Date: Mon, 16 Mar 2026 10:04:11 +0800
Subject: [PATCH 11/18] change test config

---
 scripts/test_config.json | 251 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 251 insertions(+)

diff --git a/scripts/test_config.json b/scripts/test_config.json
index a342320c..f8171b3a 100644
--- a/scripts/test_config.json
+++ b/scripts/test_config.json
@@ -17,6 +17,57 @@
         }
     ],
     "tests": [
+        {
+            "id": "1",
+            "args": {
+                "dtype": "float32"
+            }
+        },
+        {
+            "id": "1_bfloat16",
+            "args": {
+                "dtype": "bfloat16"
+            }
+        },
+        {
+            "id": "2",
+            "args": {
+                "dtype": "float32",
+                "num_iteration": 10,
+                "batch_size": 80,
+                "total_batch_size": 5120
+            }
+        },
+        {
+            "id": "2_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "num_iteration": 10,
+                "batch_size": 80,
+                "total_batch_size": 5120
+            }
+        },
+        {
+            "id": "3",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120
+            }
+        },
+        {
+            "id": "3_distopt",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120,
+                "use_distributed_optimizer": true
+            }
+        },
         {
             "id": "3_bfloat16",
             "args": {
@@ -37,6 +88,206 @@
                 "total_batch_size": 5120,
                 "use_distributed_optimizer": true
             }
+        },
+        {
+            "id": "4",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4
+            }
+        },
+        {
+            "id": "4_distopt",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "4_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4
+            }
+        },
+        {
+            "id": "4_bfloat16_distopt",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "5",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "sequence_parallel": true
+            }
+        },
+        {
+            "id": "5_distopt",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "sequence_parallel": true,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "5_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "sequence_parallel": true
+            }
+        },
+        {
+            "id": "5_bfloat16_distopt",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "sequence_parallel": true,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "6",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120,
+                "pipeline_parallel": 8
+            }
+        },
+        {
+            "id": "6_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120,
+                "pipeline_parallel": 8
+            }
+        },
+        {
+            "id": "7",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 4,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120,
+                "pipeline_parallel": 4,
+                "virtual_pipeline_parallel": 2
+            }
+        },
+        {
+            "id": "7_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 4,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120,
+                "pipeline_parallel": 4,
+                "virtual_pipeline_parallel": 2
+            }
+        },
+        {
+            "id": "8",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 2,
+                "sequence_parallel": true,
+                "pipeline_parallel": 2,
+                "virtual_pipeline_parallel": 2
+            }
+        },
+        {
+            "id": "8_distopt",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 2,
+                "sequence_parallel": true,
+                "pipeline_parallel": 2,
+                "virtual_pipeline_parallel": 2,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "8_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 2,
+                "sequence_parallel": true,
+                "pipeline_parallel": 2,
+                "virtual_pipeline_parallel": 2
+            }
+        },
+        {
+            "id": "8_bfloat16_distopt",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 2,
+                "sequence_parallel": true,
+                "pipeline_parallel": 2,
+                "virtual_pipeline_parallel": 2,
+                "use_distributed_optimizer": true
+            }
         }
     ]
 }

From 325b8a5b243c8c0174163970f0157844cd88febc Mon Sep 17 00:00:00 2001
From: Aoshine999 <820459542@qq.com>
Date: Mon, 16 Mar 2026 10:40:48 +0800
Subject: [PATCH 12/18] upload test stats

---
 run_help.md                       | 421 ++++++++++++++++++++++++++++++
 scripts/extract_training_stats.py | 246 +++++++++--------
 scripts/results.md                |  82 ++++++
 scripts/results_speedup.md        |  42 +++
 4 files changed, 666 insertions(+), 125 deletions(-)
 create mode 100644 run_help.md
 create mode 100644 scripts/results.md
 create mode 100644 scripts/results_speedup.md

diff --git a/run_help.md b/run_help.md
new file mode 100644
index 00000000..58f84b0d
--- /dev/null
+++ b/run_help.md
@@ -0,0 +1,421 @@
+# InfiniTrain 运行指南
+
+本指南介绍如何使用 `scripts` 目录下的脚本进行模型训练、性能分析和结果统计。
+
+---
+
+## 快速开始
+
+### 基本运行
+
+最简单的运行方式：
+
+```bash
+cd ~/InfiniTrain/scripts
+bash run_models_and_profile.bash
+```
+
+这将使用默认配置文件 `test_config.json` 运行所有测试。
+
+### 强制重新构建
+
+如果需要重新构建项目：
+
+```bash
+cd ~/InfiniTrain/scripts
+bash run_models_and_profile.bash --rebuild
+```
+
+### 使用自定义配置文件
+
+```bash
+cd ~/InfiniTrain/scripts
+bash run_models_and_profile.bash /path/to/custom_config.json
+```
+
+---
+
+## 脚本说明
+
+### 1. run_models_and_profile.bash
+
+主运行脚本，负责：
+- 从配置文件读取测试配置
+- 构建项目（如果需要）
+- 运行 GPT2 和 LLaMA3 模型测试
+- 收集训练日志和性能分析数据
+- 自动运行比较脚本
+
+**依赖工具**：
+- `jq`：用于解析 JSON 配置文件
+
+**安装 jq**：
+```bash
+sudo apt-get install -y jq
+```
+
+**运行流程**：
+1. 读取配置文件（默认：`test_config.json`）
+2. 检查是否需要构建（或使用 `--rebuild` 强制重新构建）
+3. 对每个测试配置，分别运行：
+   - GPT2 模型（noflash 模式）
+   - GPT2 模型（flash 模式）
+   - LLaMA3 模型（noflash 模式）
+   - LLaMA3 模型（flash 模式）
+4. 如果启用了性能分析，收集性能报告
+5. 如果设置了 `COMPARE_LOG_DIR`，自动运行比较脚本
+
+**日志输出**：
+- 训练日志：`logs/` 目录（noflash 结果）
+- 训练日志：`compare_logs/` 目录（flash 结果）
+- 性能日志：`profile_logs/` 目录（noflash 结果）
+- 性能日志：`compare_profile_logs/` 目录（flash 结果）
+
+---
+
+### 2. test_config.json
+
+配置文件包含三个主要部分：
+
+#### 2.1 variables - 变量定义
+
+```json
+{
+  "variables": {
+    "BUILD_DIR": "../build",
+    "GPT2_INPUT_BIN": "/path/to/gpt2/input.bin",
+    "GPT2_LLMC_FILEPATH": "/path/to/gpt2/model.bin",
+    "LLAMA3_INPUT_BIN": "/path/to/llama3/input.bin",
+    "LLAMA3_LLMC_FILEPATH": "/path/to/llama3/model.bin",
+    "PROFILE_LOG_DIR": "./profile_logs",
+    "LOG_DIR": "./logs",
+    "COMPARE_LOG_DIR": ""
+  }
+}
+```
+
+**变量说明**：
+- `BUILD_DIR`：构建目录路径
+- `GPT2_INPUT_BIN`：GPT2 输入数据文件路径
+- `GPT2_LLMC_FILEPATH`：GPT2 模型文件路径
+- `LLAMA3_INPUT_BIN`：LLaMA3 输入数据文件路径
+- `LLAMA3_LLMC_FILEPATH`：LLaMA3 模型文件路径
+- `PROFILE_LOG_DIR`：性能分析日志目录
+- `LOG_DIR`：训练日志目录
+- `COMPARE_LOG_DIR`：基准日志目录（用于比较，留空则不运行比较）
+
+#### 2.2 builds - 构建配置
+
+```json
+{
+  "builds": [
+    {
+      "id": "build_2",
+      "profile": true,
+      "cmd": "cmake -DUSE_CUDA=ON -DUSE_NCCL=ON -DPROFILE_MODE=ON .. && make -j"
+    }
+  ]
+}
+```
+
+**配置说明**：
+- `id`：构建 ID（用于日志命名）
+- `profile`：是否启用性能分析（true/false）
+- `cmd`：CMake 构建命令
+
+#### 2.3 tests - 测试配置
+
+每个测试包含以下参数：
+
+```json
+{
+  "id": "1",
+  "args": {
+    "dtype": "float32",
+    "num_iteration": 10,
+    "batch_size": 80,
+    "total_batch_size": 5120,
+    "nthread_per_process": 8,
+    "tensor_parallel": 4,
+    "sequence_parallel": true,
+    "pipeline_parallel": 8,
+    "virtual_pipeline_parallel": 2,
+    "use_distributed_optimizer": true
+  }
+}
+```
+
+**参数说明**：
+- `dtype`：数据类型（float32/bfloat16）
+- `num_iteration`：迭代次数
+- `batch_size`：批次大小
+- `total_batch_size`：总批次大小
+- `nthread_per_process`：每进程线程数（数据并行）
+- `tensor_parallel`：张量并行度
+- `sequence_parallel`：序列并行（true/false）
+- `pipeline_parallel`：流水线并行度
+- `virtual_pipeline_parallel`：虚拟流水线并行度
+- `use_distributed_optimizer`：使用分布式优化器（true/false）
+
+**测试 ID 命名规则**：
+- `1`：基础测试
+- `1_bfloat16`：基础测试（bfloat16）
+- `2`：批次大小测试
+- `3`：数据并行测试
+- `3_distopt`：数据并行 + 分布式优化器
+- `4`：张量并行测试
+- `5`：张量并行 + 序列并行测试
+- `6`：流水线并行测试
+- `7`：虚拟流水线并行测试
+- `8`：混合并行测试
+
+---
+
+### 3. extract_training_stats.py
+
+从日志文件中提取训练统计信息，包括：
+- 平均损失
+- 平均时间
+- 平均 tokens/s
+- 峰值内存使用
+- 并行配置（DP、TP、SP、PP）
+
+**基本用法**：
+```bash
+python3 extract_training_stats.py <log_dir1> <log_dir2>
+```
+
+**选项**：
+- `--threshold-fp32`：FP32 损失差异阈值（默认：1e-5）
+- `--threshold-bf16`：BF16 损失差异阈值（默认：1e-2）
+- `--markdown`：输出 Markdown 格式表格
+- `--output <file>`：指定输出文件路径
+- `--speedup`：输出加速比表格
+
+**示例**：
+
+```bash
+# 显示训练统计信息和对比
+python3 extract_training_stats.py logs compare_logs
+
+# 输出 Markdown 格式
+python3 extract_training_stats.py logs compare_logs --markdown
+
+# 输出 Markdown 并保存到文件
+python3 extract_training_stats.py logs compare_logs --markdown --output results.md
+
+# 只显示加速比（不显示训练统计和对比信息）
+python3 extract_training_stats.py logs compare_logs --speedup
+
+# 只显示加速比并保存为 Markdown
+python3 extract_training_stats.py logs compare_logs --speedup --markdown --output speedup.md
+```
+
+**输出说明**：
+
+**不使用 `--speedup` 时**：
+
+1. **训练统计表**：
+   - Row ID：配置标识（模型_数据类型_flash/noflash_分布式优化器_测试ID）
+   - Avg Loss：平均损失
+   - Avg Time：平均时间（ms）
+   - Avg Tok/s：平均 tokens/s
+   - Peak Used：峰值内存使用（MB）
+   - Peak Reserved：峰值内存保留（MB）
+   - DP/TP/SP/PP：并行配置
+
+2. **Flash vs Noflash 对比**：
+   - 损失对比及差异百分比
+   - 时间对比及差异百分比
+   - Tokens/s 对比及差异百分比
+   - 内存使用对比及差异百分比
+
+**使用 `--speedup` 时**：
+
+3. **加速比表**：
+   - Configuration：配置名称
+   - Speedup Ratio：加速比（noflash_time / flash_time）
+   - Flash Time：Flash 模式平均时间
+   - Noflash Time：Noflash 模式平均时间
+
+**加速比说明**：
+- 加速比 > 1.0：Flash Attention 更快
+- 加速比 < 1.0：Flash Attention 更慢
+
+---
+
+### 4. compare_loss.py
+
+比较两个日志目录中的训练损失，用于验证正确性。
+
+**基本用法**：
+```bash
+python3 compare_loss.py <dir1> <dir2>
+```
+
+**选项**：
+- `--threshold-fp32`：FP32 损失差异阈值（默认：1e-5）
+- `--threshold-bf16`：BF16 损失差异阈值（默认：1e-2）
+- `--verbose`：显示所有文件的详细输出（包括通过的）
+
+**示例**：
+
+```bash
+# 使用默认阈值比较
+python3 compare_loss.py logs compare_logs
+
+# 自定义阈值
+python3 compare_loss.py logs compare_logs --threshold-fp32 1e-5 --threshold-bf16 1e-2
+
+# 显示详细输出
+python3 compare_loss.py logs compare_logs --verbose
+```
+
+**输出说明**：
+
+1. **文件差异**：
+   - 只在 dir1 中的文件
+   - 只在 dir2 中的文件
+
+2. **每个测试的对比**：
+   - 不匹配的步骤及损失值
+   - 差异值和相对误差百分比
+   - 匹配步骤数统计
+
+3. **总体统计**：
+   - FP32 测试通过率
+   - BF16 测试通过率
+   - 总体通过率
+
+**返回值**：
+- 0：所有测试通过
+- 1：存在不匹配的测试
+
+---
+
+### 5. compare_tps.py
+
+比较两个日志目录中的 tokens per second，用于性能对比。
+
+**基本用法**：
+```bash
+python3 compare_tps.py <dir1> <dir2>
+```
+
+**选项**：
+- `--threshold`：相对误差阈值（默认：0.20 = 20%）
+- `--verbose`：显示所有文件的详细输出（包括通过的）
+
+**示例**：
+
+```bash
+# 使用默认阈值比较
+python3 compare_tps.py logs compare_logs
+
+# 自定义阈值
+python3 compare_tps.py logs compare_logs --threshold 0.15
+
+# 显示详细输出
+python3 compare_tps.py logs compare_logs --verbose
+```
+
+**输出说明**：
+
+1. **文件差异**：
+   - 只在 dir1 中的文件
+   - 只在 dir2 中的文件
+
+2. **每个测试的对比**：
+   - 平均 tokens/s 对比
+   - 相对误差百分比
+   - 比较的步骤数（排除第一步）
+
+3. **总体统计**：
+   - 通过的测试用例数
+   - 总测试用例数
+   - 通过率
+
+**注意**：
+- 比较时会自动排除第一步（因为第一步可能有初始化开销）
+- 相对误差计算：`|avg1 - avg2| / max(avg1, avg2)`
+
+**返回值**：
+- 0：所有测试通过
+- 1：存在不匹配的测试
+
+---
+
+## 完整使用流程
+
+### 1. 准备配置
+
+编辑 `test_config.json`：
+- 确保 `variables` 中的路径正确
+- 配置 `builds` 中的构建命令
+- 在 `tests` 中添加或修改测试配置
+
+### 2. 运行测试
+
+```bash
+cd ~/InfiniTrain/scripts
+bash run_models_and_profile.bash
+```
+
+### 3. 查看日志
+
+- 训练日志：`logs/` 和 `compare_logs/`
+- 性能日志：`profile_logs/` 和 `compare_profile_logs/`
+
+### 4. 分析结果
+
+**提取训练统计**：
+```bash
+python3 extract_training_stats.py logs compare_logs
+```
+
+**验证正确性**：
+```bash
+python3 compare_loss.py logs compare_logs --threshold-fp32 1e-5 --threshold-bf16 1e-2
+```
+
+**对比性能**：
+```bash
+python3 compare_tps.py logs compare_logs --threshold 0.20
+```
+
+**计算加速比**：
+```bash
+python3 extract_training_stats.py logs compare_logs --speedup
+```
+
+---
+
+## Flash Attention 对比说明
+
+脚本会自动为每个测试运行两个版本：
+
+- **noflash**：不使用 Flash Attention
+  - 日志保存在 `logs/` 目录
+  - 性能报告保存在 `profile_logs/` 目录
+
+- **flash**：使用 Flash Attention
+  - 日志保存在 `compare_logs/` 目录
+  - 性能报告保存在 `compare_profile_logs/` 目录
+
+这种设计允许：
+1. 验证 Flash Attention 的正确性（通过 compare_loss.py）
+2. 对比 Flash Attention 的性能（通过 compare_tps.py）
+3. 计算加速比（通过 extract_training_stats.py --speedup）
+
+
+
+## 日志文件命名规则
+
+- 训练日志：`{model}_{test_id}_{dtype}_{distopt}.log`
+- 性能日志：`{model}_{test_id}_{dtype}_{distopt}_profile.log`
+- 性能报告：`{model}_{test_id}_{dtype}_{distopt}_profile_{model}.report.rank{N}`
+
+示例：
+- `gpt2_1_bfloat16.log`
+- `llama3_4_distopt_profile.log`
+- `gpt2_5_bfloat16_distopt_profile_gpt2.report.rank0`
diff --git a/scripts/extract_training_stats.py b/scripts/extract_training_stats.py
index 767bd05b..d4949cc1 100755
--- a/scripts/extract_training_stats.py
+++ b/scripts/extract_training_stats.py
@@ -299,140 +299,136 @@ def main():
         key = f"{result['model']}_{result['dtype']}_{result['disopt']}"
         grouped_by_config[key].append(result)
     
-    # Prepare output
-    if args.markdown:
-        # Output as markdown table
-        markdown_lines = []
-        
-        # Add header
-        markdown_lines.append("| Row ID | Avg Loss | Avg Time (ms) | Avg Tok/s | Peak Used (MB) | Peak Reserved (MB) | DP | TP | SP | PP |")
-        markdown_lines.append("|---------|----------|---------------|-----------|-----------------|-------------------|----|----|----|----|")
-        
-        # Print rows (flash and noflash interleaved)
-        for key in sorted(grouped_by_config.keys()):
-            group = grouped_by_config[key]
-            if len(group) >= 2:
-                # Separate flash and noflash results
-                flash_results = [r for r in group if r['flash'] == 'flash']
-                noflash_results = [r for r in group if r['flash'] == 'noflash']
-                
-                # Interleave flash and noflash results
-                for i in range(max(len(flash_results), len(noflash_results))):
-                    if i < len(flash_results):
-                        result = flash_results[i]
-                        row = f"| {result['row_id']:<40} | {result['avg_loss']:<15.6f} | {result['avg_time_ms']:<15.2f} | {result['avg_tok_per_sec']:<15.2f} | {result['avg_peak_used']:<15.2f} | {result['avg_peak_reserved']:<15.2f} | {result['dp']:<5} | {result['tp']:<5} | {result['sp']:<5} | {result['pp']:<5} |"
-                        markdown_lines.append(row)
-                    if i < len(noflash_results):
-                        result = noflash_results[i]
-                        row = f"| {result['row_id']:<40} | {result['avg_loss']:<15.6f} | {result['avg_time_ms']:<15.2f} | {result['avg_tok_per_sec']:<15.2f} | {result['avg_peak_used']:<15.2f} | {result['avg_peak_reserved']:<15.2f} | {result['dp']:<5} | {result['tp']:<5} | {result['sp']:<5} | {result['pp']:<5} |"
+    # Output speedup ratio table if requested
+    if args.speedup:
+        # Only output speedup ratio table
+        pass
+    else:
+        # Prepare output
+        if args.markdown:
+            # Output as markdown table
+            markdown_lines = []
+            
+            # Add header
+            markdown_lines.append("| Row ID | Avg Loss | Avg Time (ms) | Avg Tok/s | Peak Used (MB) | Peak Reserved (MB) | DP | TP | SP | PP |")
+            markdown_lines.append("|---------|----------|---------------|-----------|-----------------|-------------------|----|----|----|----|")
+            
+            # Print rows (flash and noflash interleaved)
+            for key in sorted(grouped_by_config.keys()):
+                group = grouped_by_config[key]
+                if len(group) >= 2:
+                    # Separate flash and noflash results
+                    flash_results = [r for r in group if r['flash'] == 'flash']
+                    noflash_results = [r for r in group if r['flash'] == 'noflash']
+                    
+                    # Interleave flash and noflash results
+                    for i in range(max(len(flash_results), len(noflash_results))):
+                        if i < len(flash_results):
+                            result = flash_results[i]
+                            row = f"| {result['row_id']:<40} | {result['avg_loss']:<15.6f} | {result['avg_time_ms']:<15.2f} | {result['avg_tok_per_sec']:<15.2f} | {result['avg_peak_used']:<15.2f} | {result['avg_peak_reserved']:<15.2f} | {result['dp']:<5} | {result['tp']:<5} | {result['sp']:<5} | {result['pp']:<5} |"
+                            markdown_lines.append(row)
+                        if i < len(noflash_results):
+                            result = noflash_results[i]
+                            row = f"| {result['row_id']:<40} | {result['avg_loss']:<15.6f} | {result['avg_time_ms']:<15.2f} | {result['avg_tok_per_sec']:<15.2f} | {result['avg_peak_used']:<15.2f} | {result['avg_peak_reserved']:<15.2f} | {result['dp']:<5} | {result['tp']:<5} | {result['sp']:<5} | {result['pp']:<5} |"
+                            markdown_lines.append(row)
+                else:
+                    # Only one result, print it
+                    for result in group:
+                        row = f"| {result['row_id']:<40} | {result['avg_loss']:<15.6f} {result['avg_time_ms']:<15.2f} {result['avg_tok_per_sec']:<15.2f} {result['avg_peak_used']:<15.2f} | {result['avg_peak_reserved']:<15.2f} | {result['dp']:<5} | {result['tp']:<5} | {result['sp']:<5} | {result['pp']:<5} |"
                         markdown_lines.append(row)
+            
+            # Print markdown table
+            markdown_table = "\n".join(markdown_lines)
+            print(markdown_table)
+            
+            # Write to file if output path is specified
+            if args.output:
+                with open(args.output, 'w') as f:
+                    f.write(markdown_table)
+                print(f"Markdown table saved to: {args.output}")
+        else:
+            # Print plain text table
+            print("\n" + "=" * 150)
+            print("Training Statistics Table (excluding step 1)")
+            print("=" * 150)
+            
+            # Print header
+            header = f"{'Row ID':<40} {'Avg Loss':<15} {'Avg Time (ms)':<15} {'Avg Tok/s':<15} {'Peak Used (MB)':<15} {'Peak Reserved (MB)':<15} {'DP':<5} {'TP':<5} {'SP':<5} {'PP':<5}"
+            print(header)
+            print("-" * 150)
+            
+            # Print rows (flash and noflash interleaved)
+            for key in sorted(grouped_by_config.keys()):
+                group = grouped_by_config[key]
+                if len(group) >= 2:
+                    # Separate flash and noflash results
+                    flash_results = [r for r in group if r['flash'] == 'flash']
+                    noflash_results = [r for r in group if r['flash'] == 'noflash']
+                    
+                    # Interleave flash and noflash results
+                    for i in range(max(len(flash_results), len(noflash_results))):
+                        if i < len(flash_results):
+                            result = flash_results[i]
+                            row = f"{result['row_id']:<40} {result['avg_loss']:<15.6f} {result['avg_time_ms']:<15.2f} {result['avg_tok_per_sec']:<15.2f} {result['avg_peak_used']:<15.2f} {result['avg_peak_reserved']:<15.2f} {result['dp']:<5} {result['tp']:<5} {result['sp']:<5} {result['pp']:<5}"
+                            print(row)
+                        if i < len(noflash_results):
+                            result = noflash_results[i]
+                            row = f"{result['row_id']:<40} {result['avg_loss']:<15.6f} {result['avg_time_ms']:<15.2f} {result['avg_tok_per_sec']:<15.2f} {result['avg_peak_used']:<15.2f} {result['avg_peak_reserved']:<15.2f} {result['dp']:<5} {result['tp']:<5} {result['sp']:<5} {result['pp']:<5}"
+                            print(row)
             else:
                 # Only one result, print it
                 for result in group:
-                    row = f"| {result['row_id']:<40} | {result['avg_loss']:<15.6f} {result['avg_time_ms']:<15.2f} {result['avg_tok_per_sec']:<15.2f} {result['avg_peak_used']:<15.2f} | {result['avg_peak_reserved']:<15.2f} | {result['dp']:<5} | {result['tp']:<5} | {result['sp']:<5} | {result['pp']:<5} |"
-                    markdown_lines.append(row)
-        
-        # Print markdown table
-        markdown_table = "\n".join(markdown_lines)
-        print(markdown_table)
+                    row = f"{result['row_id']:<40} {result['avg_loss']:<15.6f} {result['avg_time_ms']:<15.2f} {result['avg_tok_per_sec']:<15.2f} {result['avg_peak_used']:<15.2f} {result['avg_peak_reserved']:<15.2f} {result['dp']:<5} {result['tp']:<5} {result['sp']:<5} {result['pp']:<5}"
+                    print(row)
         
-        # Write to file if output path is specified
-        if args.output:
-            with open(args.output, 'w') as f:
-                f.write(markdown_table)
-            print(f"Markdown table saved to: {args.output}")
-    else:
-        # Print plain text table
-        print("\n" + "=" * 150)
-        print("Training Statistics Table (excluding step 1)")
         print("=" * 150)
         
-        # Print header
-        header = f"{'Row ID':<40} {'Avg Loss':<15} {'Avg Time (ms)':<15} {'Avg Tok/s':<15} {'Peak Used (MB)':<15} {'Peak Reserved (MB)':<15} {'DP':<5} {'TP':<5} {'SP':<5} {'PP':<5}"
-        print(header)
-        print("-" * 150)
-        
-        # Print rows (flash and noflash interleaved)
-        for key in sorted(grouped_by_config.keys()):
-            group = grouped_by_config[key]
-            if len(group) >= 2:
-                # Separate flash and noflash results
-                flash_results = [r for r in group if r['flash'] == 'flash']
-                noflash_results = [r for r in group if r['flash'] == 'noflash']
-                
-                # Interleave flash and noflash results
-                for i in range(max(len(flash_results), len(noflash_results))):
-                    if i < len(flash_results):
-                        result = flash_results[i]
-                        row = f"{result['row_id']:<40} {result['avg_loss']:<15.6f} {result['avg_time_ms']:<15.2f} {result['avg_tok_per_sec']:<15.2f} {result['avg_peak_used']:<15.2f} {result['avg_peak_reserved']:<15.2f} {result['dp']:<5} {result['tp']:<5} {result['sp']:<5} {result['pp']:<5}"
-                        print(row)
-                    if i < len(noflash_results):
-                        result = noflash_results[i]
-                        row = f"{result['row_id']:<40} {result['avg_loss']:<15.6f} {result['avg_time_ms']:<15.2f} {result['avg_tok_per_sec']:<15.2f} {result['avg_peak_used']:<15.2f} {result['avg_peak_reserved']:<15.2f} {result['dp']:<5} {result['tp']:<5} {result['sp']:<5} {result['pp']:<5}"
-                        print(row)
-        else:
-            # Only one result, print it
-            for result in group:
-                row = f"{result['row_id']:<40} {result['avg_loss']:<15.6f} {result['avg_time_ms']:<15.2f} {result['avg_tok_per_sec']:<15.2f} {result['avg_peak_used']:<15.2f} {result['avg_peak_reserved']:<15.2f} {result['dp']:<5} {result['tp']:<5} {result['sp']:<5} {result['pp']:<5}"
-                print(row)
-    
-    print("=" * 150)
-    
-    # Print comparison between flash and noflash
-    print("\nComparison between flash and noflash:")
-    print("=" * 150)
-    
-    for key, group in sorted(grouped_by_config.items()):
-        # Separate flash and noflash results
-        flash_results = [r for r in group if r['flash'] == 'flash']
-        noflash_results = [r for r in group if r['flash'] == 'noflash']
+        # Print comparison between flash and noflash
+        print("\nComparison between flash and noflash:")
+        print("=" * 150)
         
-        if flash_results and noflash_results:
-            # Calculate averages for each group
-            avg_flash_loss = sum(r['avg_loss'] for r in flash_results) / len(flash_results)
-            avg_flash_time = sum(r['avg_time_ms'] for r in flash_results) / len(flash_results)
-            avg_flash_tok = sum(r['avg_tok_per_sec'] for r in flash_results) / len(flash_results)
-            avg_flash_peak_used = sum(r['avg_peak_used'] for r in flash_results) / len(flash_results)
-            avg_flash_peak_reserved = sum(r['avg_peak_reserved'] for r in flash_results) / len(flash_results)
-            
-            avg_noflash_loss = sum(r['avg_loss'] for r in noflash_results) / len(noflash_results)
-            avg_noflash_time = sum(r['avg_time_ms'] for r in noflash_results) / len(noflash_results)
-            avg_noflash_tok = sum(r['avg_tok_per_sec'] for r in noflash_results) / len(noflash_results)
-            avg_noflash_peak_used = sum(r['avg_peak_used'] for r in noflash_results) / len(noflash_results)
-            avg_noflash_peak_reserved = sum(r['avg_peak_reserved'] for r in noflash_results) / len(noflash_results)
-            
-            # Calculate differences
-            loss_diff = avg_flash_loss - avg_noflash_loss
-            time_diff = avg_flash_time - avg_noflash_time
-            tok_diff = avg_flash_tok - avg_noflash_tok
-            
-            loss_pct = (loss_diff / avg_noflash_loss * 100) if avg_noflash_loss > 0 else 0
-            time_pct = (time_diff / avg_noflash_time * 100) if avg_noflash_time > 0 else 0
-            tok_pct = (tok_diff / avg_noflash_tok * 100) if avg_noflash_tok > 0 else 0
-            
-            print(f"\n{key}:")
-            print(f"  Loss: {avg_flash_loss:.6f} vs {avg_noflash_loss:.6f} (diff: {loss_diff:+.6f}, {loss_pct:+.2f}%)")
-            print(f"  Time: {avg_flash_time:.2f} vs {avg_noflash_time:.2f} ms (diff: {time_diff:+.2f}, {time_pct:+.2f}%)")
-            print(f"  Tok/s: {avg_flash_tok:.2f} vs {avg_noflash_tok:.2f} (diff: {tok_diff:+.2f}, {tok_pct:+.2f}%)")
-            
-            # Calculate memory differences
-            mem_used_diff = avg_flash_peak_used - avg_noflash_peak_used
-            mem_reserved_diff = avg_flash_peak_reserved - avg_noflash_peak_reserved
-            
-            mem_used_pct = (mem_used_diff / avg_noflash_peak_used * 100) if avg_noflash_peak_used > 0 else 0
-            mem_reserved_pct = (mem_reserved_diff / avg_noflash_peak_reserved * 100) if avg_noflash_peak_reserved > 0 else 0
-            
-            print(f"  Peak Used: {avg_flash_peak_used:.2f} vs {avg_noflash_peak_used:.2f} MB (diff: {mem_used_diff:+.2f}, {mem_used_pct:+.2f}%)")
-            print(f"  Peak Reserved: {avg_flash_peak_reserved:.2f} vs {avg_noflash_peak_reserved:.2f} MB (diff: {mem_reserved_diff:+.2f}, {mem_reserved_pct:+.2f}%)")
+        for key, group in sorted(grouped_by_config.items()):
+            # Separate flash and noflash results
+            flash_results = [r for r in group if r['flash'] == 'flash']
+            noflash_results = [r for r in group if r['flash'] == 'noflash']
             
-            # Calculate memory differences
-            mem_used_diff = avg_flash_peak_used - avg_noflash_peak_used
-            mem_reserved_diff = avg_flash_peak_reserved - avg_noflash_peak_reserved
-            mem_used_pct = (mem_used_diff / avg_noflash_peak_used * 100) if avg_noflash_peak_used > 0 else 0
-            mem_reserved_pct = (mem_reserved_diff / avg_noflash_peak_reserved * 100) if avg_noflash_peak_reserved > 0 else 0
-    
-    # Output speedup ratio table if requested
-    if args.speedup:
+            if flash_results and noflash_results:
+                # Calculate averages for each group
+                avg_flash_loss = sum(r['avg_loss'] for r in flash_results) / len(flash_results)
+                avg_flash_time = sum(r['avg_time_ms'] for r in flash_results) / len(flash_results)
+                avg_flash_tok = sum(r['avg_tok_per_sec'] for r in flash_results) / len(flash_results)
+                avg_flash_peak_used = sum(r['avg_peak_used'] for r in flash_results) / len(flash_results)
+                avg_flash_peak_reserved = sum(r['avg_peak_reserved'] for r in flash_results) / len(flash_results)
+                
+                avg_noflash_loss = sum(r['avg_loss'] for r in noflash_results) / len(noflash_results)
+                avg_noflash_time = sum(r['avg_time_ms'] for r in noflash_results) / len(noflash_results)
+                avg_noflash_tok = sum(r['avg_tok_per_sec'] for r in noflash_results) / len(noflash_results)
+                avg_noflash_peak_used = sum(r['avg_peak_used'] for r in noflash_results) / len(noflash_results)
+                avg_noflash_peak_reserved = sum(r['avg_peak_reserved'] for r in noflash_results) / len(noflash_results)
+                
+                # Calculate differences
+                loss_diff = avg_flash_loss - avg_noflash_loss
+                time_diff = avg_flash_time - avg_noflash_time
+                tok_diff = avg_flash_tok - avg_noflash_tok
+                
+                loss_pct = (loss_diff / avg_noflash_loss * 100) if avg_noflash_loss > 0 else 0
+                time_pct = (time_diff / avg_noflash_time * 100) if avg_noflash_time > 0 else 0
+                tok_pct = (tok_diff / avg_noflash_tok * 100) if avg_noflash_tok > 0 else 0
+                
+                print(f"\n{key}:")
+                print(f"  Loss: {avg_flash_loss:.6f} vs {avg_noflash_loss:.6f} (diff: {loss_diff:+.6f}, {loss_pct:+.2f}%)")
+                print(f"  Time: {avg_flash_time:.2f} vs {avg_noflash_time:.2f} ms (diff: {time_diff:+.2f}, {time_pct:+.2f}%)")
+                print(f"  Tok/s: {avg_flash_tok:.2f} vs {avg_noflash_tok:.2f} (diff: {tok_diff:+.2f}, {tok_pct:+.2f}%)")
+                
+                # Calculate memory differences
+                mem_used_diff = avg_flash_peak_used - avg_noflash_peak_used
+                mem_reserved_diff = avg_flash_peak_reserved - avg_noflash_peak_reserved
+                
+                mem_used_pct = (mem_used_diff / avg_noflash_peak_used * 100) if avg_noflash_peak_used > 0 else 0
+                mem_reserved_pct = (mem_reserved_diff / avg_noflash_peak_reserved * 100) if avg_noflash_peak_reserved > 0 else 0
+                
+                print(f"  Peak Used: {avg_flash_peak_used:.2f} vs {avg_noflash_peak_used:.2f} MB (diff: {mem_used_diff:+.2f}, {mem_used_pct:+.2f}%)")
+                print(f"  Peak Reserved: {avg_flash_peak_reserved:.2f} vs {avg_noflash_peak_reserved:.2f} MB (diff: {mem_reserved_diff:+.2f}, {mem_reserved_pct:+.2f}%)")
         print("\n" + "=" * 120)
         print("Speedup Ratio Table (noflash_time / flash_time)")
         print("=" * 120)
diff --git a/scripts/results.md b/scripts/results.md
new file mode 100644
index 00000000..7227200b
--- /dev/null
+++ b/scripts/results.md
@@ -0,0 +1,82 @@
+| Row ID | Avg Loss | Avg Time (ms) | Avg Tok/s | Peak Used (MB) | Peak Reserved (MB) | DP | TP | SP | PP |
+|---------|----------|---------------|-----------|-----------------|-------------------|----|----|----|----|
+| gpt2_bfloat16_flash_disopt_3             | 4.799616        | 2494.86         | 2052.67         | 3407.22         | 3452.44         | 8     | 1     | 1     | 1     |
+| gpt2_bfloat16_noflash_disopt_3           | 4.787657        | 2741.77         | 1868.11         | 3405.00         | 3456.00         | 8     | 1     | 1     | 1     |
+| gpt2_bfloat16_flash_disopt_4             | 4.799871        | 2900.71         | 1766.33         | 2715.00         | 2748.44         | 8     | 4     | 1     | 1     |
+| gpt2_bfloat16_noflash_disopt_4           | 4.788475        | 3211.90         | 1594.33         | 2713.00         | 2784.00         | 8     | 4     | 1     | 1     |
+| gpt2_bfloat16_flash_disopt_5             | 4.799961        | 3520.25         | 1454.67         | 2398.00         | 2432.00         | 8     | 4     | 1     | 1     |
+| gpt2_bfloat16_noflash_disopt_5           | 4.791147        | 4036.41         | 1290.11         | 2396.00         | 2464.00         | 8     | 4     | 1     | 1     |
+| gpt2_bfloat16_flash_disopt_8             | 4.799024        | 2000.08         | 2561.89         | 2373.22         | 2528.00         | 8     | 2     | 1     | 2     |
+| gpt2_bfloat16_noflash_disopt_8           | 4.791285        | 2128.22         | 2406.67         | 2326.00         | 2467.56         | 8     | 2     | 1     | 2     |
+| gpt2_bfloat16_flash_nodisopt_3           | 4.800298        | 2901.57         | 1766.11         | 3407.22         | 3445.33         | 8     | 1     | 1     | 1     |
+| gpt2_bfloat16_noflash_nodisopt_3         | 4.787223        | 3130.48         | 1636.11         | 3405.00         | 3456.00         | 8     | 1     | 1     | 1     |
+| gpt2_bfloat16_flash_nodisopt_4           | 4.799593        | 3107.03         | 1648.78         | 2715.00         | 2744.89         | 8     | 4     | 1     | 1     |
+| gpt2_bfloat16_noflash_nodisopt_4         | 4.788435        | 4551.77         | 1308.67         | 2713.00         | 2752.00         | 8     | 4     | 1     | 1     |
+| gpt2_bfloat16_flash_nodisopt_5           | 4.799967        | 3734.20         | 1372.00         | 2398.00         | 2428.44         | 8     | 4     | 1     | 1     |
+| gpt2_bfloat16_noflash_nodisopt_5         | 4.791135        | 4016.84         | 1275.78         | 2396.00         | 2464.00         | 8     | 4     | 1     | 1     |
+| gpt2_bfloat16_flash_nodisopt_6           | 4.800104        | 4197.89         | 1220.33         | 1718.89         | 1820.44         | 8     | 1     | 1     | 8     |
+| gpt2_bfloat16_noflash_nodisopt_6         | 4.788296        | 4675.14         | 1108.78         | 1684.00         | 1777.78         | 8     | 1     | 1     | 8     |
+| gpt2_bfloat16_flash_nodisopt_7           | 4.800298        | 5575.07         | 918.44          | 2684.00         | 2805.33         | 4     | 1     | 1     | 4     |
+| gpt2_bfloat16_noflash_nodisopt_7         | 4.788042        | 6254.28         | 823.00          | 2586.00         | 2705.78         | 4     | 1     | 1     | 4     |
+| gpt2_bfloat16_flash_nodisopt_8           | 4.798747        | 2089.50         | 2453.44         | 2373.22         | 2510.22         | 8     | 2     | 1     | 2     |
+| gpt2_bfloat16_noflash_nodisopt_8         | 4.790910        | 2150.97         | 2381.11         | 2326.00         | 2478.22         | 8     | 2     | 1     | 2     |
+| gpt2_fp32_flash_disopt_3                 | 4.836303        | 2215.36         | 2313.33         | 3626.22         | 3672.89         | 8     | 1     | 1     | 1     |
+| gpt2_fp32_noflash_disopt_3               | 4.826354        | 2513.86         | 2037.56         | 3467.00         | 3520.00         | 8     | 1     | 1     | 1     |
+| gpt2_fp32_flash_disopt_4                 | 4.836304        | 2652.36         | 1933.11         | 2934.00         | 2961.78         | 8     | 4     | 1     | 1     |
+| gpt2_fp32_noflash_disopt_4               | 4.826354        | 2913.33         | 1760.11         | 2774.00         | 2816.00         | 8     | 4     | 1     | 1     |
+| gpt2_fp32_flash_disopt_5                 | 4.836836        | 3305.40         | 1549.78         | 2616.89         | 2666.67         | 8     | 4     | 1     | 1     |
+| gpt2_fp32_noflash_disopt_5               | 4.829048        | 3928.89         | 1378.33         | 2457.00         | 2496.00         | 8     | 4     | 1     | 1     |
+| gpt2_fp32_flash_disopt_8                 | 4.836634        | 1838.23         | 2786.78         | 2441.67         | 2535.11         | 8     | 2     | 1     | 2     |
+| gpt2_fp32_noflash_disopt_8               | 4.827999        | 2004.15         | 2557.78         | 2350.00         | 2435.56         | 8     | 2     | 1     | 2     |
+| gpt2_fp32_flash_nodisopt_3               | 4.836303        | 2535.03         | 2020.78         | 3626.22         | 3662.22         | 8     | 1     | 1     | 1     |
+| gpt2_fp32_noflash_nodisopt_3             | 4.826354        | 2810.67         | 1822.44         | 3467.00         | 3520.00         | 8     | 1     | 1     | 1     |
+| gpt2_fp32_flash_nodisopt_4               | 4.836304        | 2817.74         | 1818.22         | 2934.00         | 2954.67         | 8     | 4     | 1     | 1     |
+| gpt2_fp32_noflash_nodisopt_4             | 4.826354        | 3049.61         | 1679.67         | 2774.00         | 2816.00         | 8     | 4     | 1     | 1     |
+| gpt2_fp32_flash_nodisopt_5               | 4.836836        | 3392.83         | 1509.67         | 2616.89         | 2656.00         | 8     | 4     | 1     | 1     |
+| gpt2_fp32_noflash_nodisopt_5             | 4.829048        | 3656.74         | 1400.67         | 2457.00         | 2496.00         | 8     | 4     | 1     | 1     |
+| gpt2_fp32_flash_nodisopt_6               | 4.836304        | 3746.21         | 1378.67         | 2318.44         | 2368.00         | 8     | 1     | 1     | 8     |
+| gpt2_fp32_noflash_nodisopt_6             | 4.826354        | 3947.83         | 1297.11         | 2248.00         | 2307.56         | 8     | 1     | 1     | 8     |
+| gpt2_fp32_flash_nodisopt_7               | 4.836304        | 5285.93         | 980.22          | 3538.56         | 3587.56         | 4     | 1     | 1     | 4     |
+| gpt2_fp32_noflash_nodisopt_7             | 4.826354        | 5558.65         | 921.22          | 3345.00         | 3416.89         | 4     | 1     | 1     | 4     |
+| gpt2_fp32_flash_nodisopt_8               | 4.836634        | 3332.54         | 1536.78         | 2441.67         | 2531.56         | 8     | 2     | 1     | 2     |
+| gpt2_fp32_noflash_nodisopt_8             | 4.827999        | 3560.96         | 1438.22         | 2350.00         | 2424.89         | 8     | 2     | 1     | 2     |
+| llama3_bfloat16_flash_disopt_3           | 11.513694       | 6985.97         | 733.22          | 21899.89        | 22151.11        | 8     | 1     | 1     | 1     |
+| llama3_bfloat16_noflash_disopt_3         | 3.687185        | 8803.02         | 587.44          | 21632.00        | 21824.00        | 8     | 1     | 1     | 1     |
+| llama3_bfloat16_flash_disopt_4           | 12.108337       | 7048.58         | 726.67          | 13019.00        | 13258.67        | 8     | 4     | 1     | 1     |
+| llama3_bfloat16_noflash_disopt_4         | 3.687813        | 8703.07         | 591.22          | 12995.00        | 13120.00        | 8     | 4     | 1     | 1     |
+| llama3_bfloat16_flash_disopt_5           | 12.330883       | 7956.26         | 643.60          | 10984.60        | 11155.20        | 8     | 4     | 1     | 1     |
+| llama3_bfloat16_noflash_disopt_5         | 3.687082        | 9673.23         | 530.89          | 10880.00        | 11008.00        | 8     | 4     | 1     | 1     |
+| llama3_bfloat16_flash_disopt_8           | 12.023618       | 4801.31         | 1066.50         | 9924.00         | 10032.00        | 8     | 2     | 1     | 2     |
+| llama3_bfloat16_noflash_disopt_8         | 3.687491        | 5915.92         | 873.44          | 10031.00        | 10122.67        | 8     | 2     | 1     | 2     |
+| llama3_bfloat16_flash_nodisopt_3         | 11.473671       | 7028.92         | 731.67          | 31903.11        | 32156.44        | 8     | 1     | 1     | 1     |
+| llama3_bfloat16_noflash_nodisopt_3       | 3.687393        | 10063.60        | 529.22          | 31636.00        | 31808.00        | 8     | 1     | 1     | 1     |
+| llama3_bfloat16_flash_nodisopt_4         | 12.120937       | 7424.95         | 696.17          | 14570.17        | 14810.67        | 8     | 4     | 1     | 1     |
+| llama3_bfloat16_noflash_nodisopt_4       | 3.687764        | 9143.74         | 563.22          | 14425.00        | 14560.00        | 8     | 4     | 1     | 1     |
+| llama3_bfloat16_flash_nodisopt_5         | 11.832385       | 8049.24         | 636.33          | 12454.50        | 12624.00        | 8     | 4     | 1     | 1     |
+| llama3_bfloat16_noflash_nodisopt_5       | 3.687394        | 9607.02         | 533.00          | 12309.00        | 12448.00        | 8     | 4     | 1     | 1     |
+| llama3_bfloat16_flash_nodisopt_6         | 11.109162       | 9080.24         | 570.11          | 11371.89        | 11516.44        | 8     | 1     | 1     | 8     |
+| llama3_bfloat16_noflash_nodisopt_6       | 3.687855        | 9991.38         | 515.89          | 11052.00        | 11175.11        | 8     | 1     | 1     | 8     |
+| llama3_bfloat16_flash_nodisopt_7         | 12.343384       | 12997.81        | 394.00          | 13956.00        | 14080.00        | 4     | 1     | 1     | 4     |
+| llama3_bfloat16_noflash_nodisopt_7       | 3.687177        | 16637.25        | 309.11          | 14608.00        | 14759.11        | 4     | 1     | 1     | 4     |
+| llama3_bfloat16_flash_nodisopt_8         | 11.242361       | 5281.60         | 975.78          | 11777.67        | 11868.44        | 8     | 2     | 1     | 2     |
+| llama3_bfloat16_noflash_nodisopt_8       | 3.687432        | 5902.93         | 868.11          | 11460.00        | 11559.11        | 8     | 2     | 1     | 2     |
+| llama3_fp32_flash_disopt_3               | 3.696688        | 6454.74         | 797.11          | 22056.33        | 22275.56        | 8     | 1     | 1     | 1     |
+| llama3_fp32_noflash_disopt_3             | 3.595062        | 8042.78         | 640.56          | 21789.00        | 21952.00        | 8     | 1     | 1     | 1     |
+| llama3_fp32_flash_disopt_4               | 3.696686        | 8594.09         | 650.11          | 13419.33        | 13575.11        | 8     | 4     | 1     | 1     |
+| llama3_fp32_noflash_disopt_4             | 3.595074        | 8257.09         | 620.44          | 13152.00        | 13216.00        | 8     | 4     | 1     | 1     |
+| llama3_fp32_flash_disopt_5               | 3.696680        | 7835.74         | 656.33          | 11303.67        | 11459.56        | 8     | 4     | 1     | 1     |
+| llama3_fp32_noflash_disopt_5             | 3.595054        | 10612.78        | 497.89          | 11036.00        | 11168.00        | 8     | 4     | 1     | 1     |
+| llama3_fp32_flash_disopt_8               | 3.696683        | 4918.54         | 1041.22         | 11005.33        | 11114.67        | 8     | 2     | 1     | 2     |
+| llama3_fp32_noflash_disopt_8             | 3.595056        | 5989.85         | 863.00          | 10798.00        | 10915.56        | 8     | 2     | 1     | 2     |
+| llama3_fp32_flash_nodisopt_3             | 3.696689        | 8095.32         | 641.44          | 32059.89        | 32280.89        | 8     | 1     | 1     | 1     |
+| llama3_fp32_noflash_nodisopt_3           | 3.595062        | 8868.17         | 580.44          | 31792.00        | 31936.00        | 8     | 1     | 1     | 1     |
+| llama3_fp32_flash_nodisopt_4             | 3.696686        | 7001.37         | 736.89          | 14848.67        | 15000.89        | 8     | 4     | 1     | 1     |
+| llama3_fp32_noflash_nodisopt_4           | 3.595074        | 8305.80         | 616.78          | 14581.00        | 14656.00        | 8     | 4     | 1     | 1     |
+| llama3_fp32_flash_nodisopt_5             | 3.696680        | 7993.27         | 643.22          | 12732.89        | 12892.44        | 8     | 4     | 1     | 1     |
+| llama3_fp32_noflash_nodisopt_5           | 3.595054        | 9290.61         | 551.33          | 12465.00        | 12608.00        | 8     | 4     | 1     | 1     |
+| llama3_fp32_flash_nodisopt_6             | 3.696689        | 7677.79         | 671.67          | 13351.89        | 13475.56        | 8     | 1     | 1     | 8     |
+| llama3_fp32_noflash_nodisopt_6           | 3.595062        | 8936.41         | 573.00          | 13119.00        | 13219.56        | 8     | 1     | 1     | 8     |
+| llama3_fp32_flash_nodisopt_7             | 3.696688        | 12469.65        | 411.11          | 18195.78        | 18318.22        | 4     | 1     | 1     | 4     |
+| llama3_fp32_noflash_nodisopt_7           | 3.595062        | 15277.34        | 335.89          | 17756.00        | 17873.78        | 4     | 1     | 1     | 4     |
+| llama3_fp32_flash_nodisopt_8             | 3.696683        | 5354.28         | 974.67          | 12434.33        | 12551.11        | 8     | 2     | 1     | 2     |
+| llama3_fp32_noflash_nodisopt_8           | 3.595056        | 7652.40         | 708.56          | 12227.00        | 12352.00        | 8     | 2     | 1     | 2     |
\ No newline at end of file
diff --git a/scripts/results_speedup.md b/scripts/results_speedup.md
new file mode 100644
index 00000000..6660ec94
--- /dev/null
+++ b/scripts/results_speedup.md
@@ -0,0 +1,42 @@
+| Configuration | Speedup Ratio | Flash Time (ms) | Noflash Time (ms) |
+|---------------|---------------|-----------------|-------------------|
+| gpt2_bfloat16_disopt_3                | 1.10          | 2494.86         | 2741.77           |
+| gpt2_bfloat16_disopt_4                | 1.11          | 2900.71         | 3211.90           |
+| gpt2_bfloat16_disopt_5                | 1.15          | 3520.25         | 4036.41           |
+| gpt2_bfloat16_disopt_8                | 1.06          | 2000.08         | 2128.22           |
+| gpt2_bfloat16_nodisopt_3              | 1.08          | 2901.57         | 3130.48           |
+| gpt2_bfloat16_nodisopt_4              | 1.46          | 3107.03         | 4551.77           |
+| gpt2_bfloat16_nodisopt_5              | 1.08          | 3734.20         | 4016.84           |
+| gpt2_bfloat16_nodisopt_6              | 1.11          | 4197.89         | 4675.14           |
+| gpt2_bfloat16_nodisopt_7              | 1.12          | 5575.07         | 6254.28           |
+| gpt2_bfloat16_nodisopt_8              | 1.03          | 2089.50         | 2150.97           |
+| gpt2_fp32_disopt_3                    | 1.13          | 2215.36         | 2513.86           |
+| gpt2_fp32_disopt_4                    | 1.10          | 2652.36         | 2913.33           |
+| gpt2_fp32_disopt_5                    | 1.19          | 3305.40         | 3928.89           |
+| gpt2_fp32_disopt_8                    | 1.09          | 1838.23         | 2004.15           |
+| gpt2_fp32_nodisopt_3                  | 1.11          | 2535.03         | 2810.67           |
+| gpt2_fp32_nodisopt_4                  | 1.08          | 2817.74         | 3049.61           |
+| gpt2_fp32_nodisopt_5                  | 1.08          | 3392.83         | 3656.74           |
+| gpt2_fp32_nodisopt_6                  | 1.05          | 3746.21         | 3947.83           |
+| gpt2_fp32_nodisopt_7                  | 1.05          | 5285.93         | 5558.65           |
+| gpt2_fp32_nodisopt_8                  | 1.07          | 3332.54         | 3560.96           |
+| llama3_bfloat16_disopt_3              | 1.26          | 6985.97         | 8803.02           |
+| llama3_bfloat16_disopt_4              | 1.23          | 7048.58         | 8703.07           |
+| llama3_bfloat16_disopt_5              | 1.22          | 7956.26         | 9673.23           |
+| llama3_bfloat16_disopt_8              | 1.23          | 4801.31         | 5915.92           |
+| llama3_bfloat16_nodisopt_3            | 1.43          | 7028.92         | 10063.60          |
+| llama3_bfloat16_nodisopt_4            | 1.23          | 7424.95         | 9143.74           |
+| llama3_bfloat16_nodisopt_5            | 1.19          | 8049.24         | 9607.02           |
+| llama3_bfloat16_nodisopt_6            | 1.10          | 9080.24         | 9991.38           |
+| llama3_bfloat16_nodisopt_7            | 1.28          | 12997.81        | 16637.25          |
+| llama3_bfloat16_nodisopt_8            | 1.12          | 5281.60         | 5902.93           |
+| llama3_fp32_disopt_3                  | 1.25          | 6454.74         | 8042.78           |
+| llama3_fp32_disopt_4                  | 0.96          | 8594.09         | 8257.09           |
+| llama3_fp32_disopt_5                  | 1.35          | 7835.74         | 10612.78          |
+| llama3_fp32_disopt_8                  | 1.22          | 4918.54         | 5989.85           |
+| llama3_fp32_nodisopt_3                | 1.10          | 8095.32         | 8868.17           |
+| llama3_fp32_nodisopt_4                | 1.19          | 7001.37         | 8305.80           |
+| llama3_fp32_nodisopt_5                | 1.16          | 7993.27         | 9290.61           |
+| llama3_fp32_nodisopt_6                | 1.16          | 7677.79         | 8936.41           |
+| llama3_fp32_nodisopt_7                | 1.23          | 12469.65        | 15277.34          |
+| llama3_fp32_nodisopt_8                | 1.43          | 5354.28         | 7652.40           |
\ No newline at end of file

From 465926dfb28a0ad12ad9f9b40f853e8ece105d45 Mon Sep 17 00:00:00 2001
From: Aoshine999 <820459542@qq.com>
Date: Mon, 16 Mar 2026 15:48:32 +0800
Subject: [PATCH 13/18] delete some test file

---
 funcall.png                | Bin 464160 -> 0 bytes
 run_help.md                | 421 -------------------------------------
 scripts/results.md         |  82 --------
 scripts/results_speedup.md |  42 ----
 scripts/test_config.json   |   5 +
 5 files changed, 5 insertions(+), 545 deletions(-)
 delete mode 100644 funcall.png
 delete mode 100644 run_help.md
 delete mode 100644 scripts/results.md
 delete mode 100644 scripts/results_speedup.md

diff --git a/funcall.png b/funcall.png
deleted file mode 100644
index 4eb4c1451d8c028640dfb5f42f3e1827ec03a705..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 464160
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zCq3xQtop@N4I!r(B%wp^qF_uE%d?mOhCf0N$6d}osdw_~+QxBsE_5on#(S>8eI72e
zIy)&R;XW|G8qh6|twa35MQT$WbG-4H<L>@fH0FDYDF*M|Za)9#N0^G$Ucxp~@qd2v
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diff --git a/run_help.md b/run_help.md
deleted file mode 100644
index 58f84b0d..00000000
--- a/run_help.md
+++ /dev/null
@@ -1,421 +0,0 @@
-# InfiniTrain 运行指南
-
-本指南介绍如何使用 `scripts` 目录下的脚本进行模型训练、性能分析和结果统计。
-
----
-
-## 快速开始
-
-### 基本运行
-
-最简单的运行方式：
-
-```bash
-cd ~/InfiniTrain/scripts
-bash run_models_and_profile.bash
-```
-
-这将使用默认配置文件 `test_config.json` 运行所有测试。
-
-### 强制重新构建
-
-如果需要重新构建项目：
-
-```bash
-cd ~/InfiniTrain/scripts
-bash run_models_and_profile.bash --rebuild
-```
-
-### 使用自定义配置文件
-
-```bash
-cd ~/InfiniTrain/scripts
-bash run_models_and_profile.bash /path/to/custom_config.json
-```
-
----
-
-## 脚本说明
-
-### 1. run_models_and_profile.bash
-
-主运行脚本，负责：
-- 从配置文件读取测试配置
-- 构建项目（如果需要）
-- 运行 GPT2 和 LLaMA3 模型测试
-- 收集训练日志和性能分析数据
-- 自动运行比较脚本
-
-**依赖工具**：
-- `jq`：用于解析 JSON 配置文件
-
-**安装 jq**：
-```bash
-sudo apt-get install -y jq
-```
-
-**运行流程**：
-1. 读取配置文件（默认：`test_config.json`）
-2. 检查是否需要构建（或使用 `--rebuild` 强制重新构建）
-3. 对每个测试配置，分别运行：
-   - GPT2 模型（noflash 模式）
-   - GPT2 模型（flash 模式）
-   - LLaMA3 模型（noflash 模式）
-   - LLaMA3 模型（flash 模式）
-4. 如果启用了性能分析，收集性能报告
-5. 如果设置了 `COMPARE_LOG_DIR`，自动运行比较脚本
-
-**日志输出**：
-- 训练日志：`logs/` 目录（noflash 结果）
-- 训练日志：`compare_logs/` 目录（flash 结果）
-- 性能日志：`profile_logs/` 目录（noflash 结果）
-- 性能日志：`compare_profile_logs/` 目录（flash 结果）
-
----
-
-### 2. test_config.json
-
-配置文件包含三个主要部分：
-
-#### 2.1 variables - 变量定义
-
-```json
-{
-  "variables": {
-    "BUILD_DIR": "../build",
-    "GPT2_INPUT_BIN": "/path/to/gpt2/input.bin",
-    "GPT2_LLMC_FILEPATH": "/path/to/gpt2/model.bin",
-    "LLAMA3_INPUT_BIN": "/path/to/llama3/input.bin",
-    "LLAMA3_LLMC_FILEPATH": "/path/to/llama3/model.bin",
-    "PROFILE_LOG_DIR": "./profile_logs",
-    "LOG_DIR": "./logs",
-    "COMPARE_LOG_DIR": ""
-  }
-}
-```
-
-**变量说明**：
-- `BUILD_DIR`：构建目录路径
-- `GPT2_INPUT_BIN`：GPT2 输入数据文件路径
-- `GPT2_LLMC_FILEPATH`：GPT2 模型文件路径
-- `LLAMA3_INPUT_BIN`：LLaMA3 输入数据文件路径
-- `LLAMA3_LLMC_FILEPATH`：LLaMA3 模型文件路径
-- `PROFILE_LOG_DIR`：性能分析日志目录
-- `LOG_DIR`：训练日志目录
-- `COMPARE_LOG_DIR`：基准日志目录（用于比较，留空则不运行比较）
-
-#### 2.2 builds - 构建配置
-
-```json
-{
-  "builds": [
-    {
-      "id": "build_2",
-      "profile": true,
-      "cmd": "cmake -DUSE_CUDA=ON -DUSE_NCCL=ON -DPROFILE_MODE=ON .. && make -j"
-    }
-  ]
-}
-```
-
-**配置说明**：
-- `id`：构建 ID（用于日志命名）
-- `profile`：是否启用性能分析（true/false）
-- `cmd`：CMake 构建命令
-
-#### 2.3 tests - 测试配置
-
-每个测试包含以下参数：
-
-```json
-{
-  "id": "1",
-  "args": {
-    "dtype": "float32",
-    "num_iteration": 10,
-    "batch_size": 80,
-    "total_batch_size": 5120,
-    "nthread_per_process": 8,
-    "tensor_parallel": 4,
-    "sequence_parallel": true,
-    "pipeline_parallel": 8,
-    "virtual_pipeline_parallel": 2,
-    "use_distributed_optimizer": true
-  }
-}
-```
-
-**参数说明**：
-- `dtype`：数据类型（float32/bfloat16）
-- `num_iteration`：迭代次数
-- `batch_size`：批次大小
-- `total_batch_size`：总批次大小
-- `nthread_per_process`：每进程线程数（数据并行）
-- `tensor_parallel`：张量并行度
-- `sequence_parallel`：序列并行（true/false）
-- `pipeline_parallel`：流水线并行度
-- `virtual_pipeline_parallel`：虚拟流水线并行度
-- `use_distributed_optimizer`：使用分布式优化器（true/false）
-
-**测试 ID 命名规则**：
-- `1`：基础测试
-- `1_bfloat16`：基础测试（bfloat16）
-- `2`：批次大小测试
-- `3`：数据并行测试
-- `3_distopt`：数据并行 + 分布式优化器
-- `4`：张量并行测试
-- `5`：张量并行 + 序列并行测试
-- `6`：流水线并行测试
-- `7`：虚拟流水线并行测试
-- `8`：混合并行测试
-
----
-
-### 3. extract_training_stats.py
-
-从日志文件中提取训练统计信息，包括：
-- 平均损失
-- 平均时间
-- 平均 tokens/s
-- 峰值内存使用
-- 并行配置（DP、TP、SP、PP）
-
-**基本用法**：
-```bash
-python3 extract_training_stats.py <log_dir1> <log_dir2>
-```
-
-**选项**：
-- `--threshold-fp32`：FP32 损失差异阈值（默认：1e-5）
-- `--threshold-bf16`：BF16 损失差异阈值（默认：1e-2）
-- `--markdown`：输出 Markdown 格式表格
-- `--output <file>`：指定输出文件路径
-- `--speedup`：输出加速比表格
-
-**示例**：
-
-```bash
-# 显示训练统计信息和对比
-python3 extract_training_stats.py logs compare_logs
-
-# 输出 Markdown 格式
-python3 extract_training_stats.py logs compare_logs --markdown
-
-# 输出 Markdown 并保存到文件
-python3 extract_training_stats.py logs compare_logs --markdown --output results.md
-
-# 只显示加速比（不显示训练统计和对比信息）
-python3 extract_training_stats.py logs compare_logs --speedup
-
-# 只显示加速比并保存为 Markdown
-python3 extract_training_stats.py logs compare_logs --speedup --markdown --output speedup.md
-```
-
-**输出说明**：
-
-**不使用 `--speedup` 时**：
-
-1. **训练统计表**：
-   - Row ID：配置标识（模型_数据类型_flash/noflash_分布式优化器_测试ID）
-   - Avg Loss：平均损失
-   - Avg Time：平均时间（ms）
-   - Avg Tok/s：平均 tokens/s
-   - Peak Used：峰值内存使用（MB）
-   - Peak Reserved：峰值内存保留（MB）
-   - DP/TP/SP/PP：并行配置
-
-2. **Flash vs Noflash 对比**：
-   - 损失对比及差异百分比
-   - 时间对比及差异百分比
-   - Tokens/s 对比及差异百分比
-   - 内存使用对比及差异百分比
-
-**使用 `--speedup` 时**：
-
-3. **加速比表**：
-   - Configuration：配置名称
-   - Speedup Ratio：加速比（noflash_time / flash_time）
-   - Flash Time：Flash 模式平均时间
-   - Noflash Time：Noflash 模式平均时间
-
-**加速比说明**：
-- 加速比 > 1.0：Flash Attention 更快
-- 加速比 < 1.0：Flash Attention 更慢
-
----
-
-### 4. compare_loss.py
-
-比较两个日志目录中的训练损失，用于验证正确性。
-
-**基本用法**：
-```bash
-python3 compare_loss.py <dir1> <dir2>
-```
-
-**选项**：
-- `--threshold-fp32`：FP32 损失差异阈值（默认：1e-5）
-- `--threshold-bf16`：BF16 损失差异阈值（默认：1e-2）
-- `--verbose`：显示所有文件的详细输出（包括通过的）
-
-**示例**：
-
-```bash
-# 使用默认阈值比较
-python3 compare_loss.py logs compare_logs
-
-# 自定义阈值
-python3 compare_loss.py logs compare_logs --threshold-fp32 1e-5 --threshold-bf16 1e-2
-
-# 显示详细输出
-python3 compare_loss.py logs compare_logs --verbose
-```
-
-**输出说明**：
-
-1. **文件差异**：
-   - 只在 dir1 中的文件
-   - 只在 dir2 中的文件
-
-2. **每个测试的对比**：
-   - 不匹配的步骤及损失值
-   - 差异值和相对误差百分比
-   - 匹配步骤数统计
-
-3. **总体统计**：
-   - FP32 测试通过率
-   - BF16 测试通过率
-   - 总体通过率
-
-**返回值**：
-- 0：所有测试通过
-- 1：存在不匹配的测试
-
----
-
-### 5. compare_tps.py
-
-比较两个日志目录中的 tokens per second，用于性能对比。
-
-**基本用法**：
-```bash
-python3 compare_tps.py <dir1> <dir2>
-```
-
-**选项**：
-- `--threshold`：相对误差阈值（默认：0.20 = 20%）
-- `--verbose`：显示所有文件的详细输出（包括通过的）
-
-**示例**：
-
-```bash
-# 使用默认阈值比较
-python3 compare_tps.py logs compare_logs
-
-# 自定义阈值
-python3 compare_tps.py logs compare_logs --threshold 0.15
-
-# 显示详细输出
-python3 compare_tps.py logs compare_logs --verbose
-```
-
-**输出说明**：
-
-1. **文件差异**：
-   - 只在 dir1 中的文件
-   - 只在 dir2 中的文件
-
-2. **每个测试的对比**：
-   - 平均 tokens/s 对比
-   - 相对误差百分比
-   - 比较的步骤数（排除第一步）
-
-3. **总体统计**：
-   - 通过的测试用例数
-   - 总测试用例数
-   - 通过率
-
-**注意**：
-- 比较时会自动排除第一步（因为第一步可能有初始化开销）
-- 相对误差计算：`|avg1 - avg2| / max(avg1, avg2)`
-
-**返回值**：
-- 0：所有测试通过
-- 1：存在不匹配的测试
-
----
-
-## 完整使用流程
-
-### 1. 准备配置
-
-编辑 `test_config.json`：
-- 确保 `variables` 中的路径正确
-- 配置 `builds` 中的构建命令
-- 在 `tests` 中添加或修改测试配置
-
-### 2. 运行测试
-
-```bash
-cd ~/InfiniTrain/scripts
-bash run_models_and_profile.bash
-```
-
-### 3. 查看日志
-
-- 训练日志：`logs/` 和 `compare_logs/`
-- 性能日志：`profile_logs/` 和 `compare_profile_logs/`
-
-### 4. 分析结果
-
-**提取训练统计**：
-```bash
-python3 extract_training_stats.py logs compare_logs
-```
-
-**验证正确性**：
-```bash
-python3 compare_loss.py logs compare_logs --threshold-fp32 1e-5 --threshold-bf16 1e-2
-```
-
-**对比性能**：
-```bash
-python3 compare_tps.py logs compare_logs --threshold 0.20
-```
-
-**计算加速比**：
-```bash
-python3 extract_training_stats.py logs compare_logs --speedup
-```
-
----
-
-## Flash Attention 对比说明
-
-脚本会自动为每个测试运行两个版本：
-
-- **noflash**：不使用 Flash Attention
-  - 日志保存在 `logs/` 目录
-  - 性能报告保存在 `profile_logs/` 目录
-
-- **flash**：使用 Flash Attention
-  - 日志保存在 `compare_logs/` 目录
-  - 性能报告保存在 `compare_profile_logs/` 目录
-
-这种设计允许：
-1. 验证 Flash Attention 的正确性（通过 compare_loss.py）
-2. 对比 Flash Attention 的性能（通过 compare_tps.py）
-3. 计算加速比（通过 extract_training_stats.py --speedup）
-
-
-
-## 日志文件命名规则
-
-- 训练日志：`{model}_{test_id}_{dtype}_{distopt}.log`
-- 性能日志：`{model}_{test_id}_{dtype}_{distopt}_profile.log`
-- 性能报告：`{model}_{test_id}_{dtype}_{distopt}_profile_{model}.report.rank{N}`
-
-示例：
-- `gpt2_1_bfloat16.log`
-- `llama3_4_distopt_profile.log`
-- `gpt2_5_bfloat16_distopt_profile_gpt2.report.rank0`
diff --git a/scripts/results.md b/scripts/results.md
deleted file mode 100644
index 7227200b..00000000
--- a/scripts/results.md
+++ /dev/null
@@ -1,82 +0,0 @@
-| Row ID | Avg Loss | Avg Time (ms) | Avg Tok/s | Peak Used (MB) | Peak Reserved (MB) | DP | TP | SP | PP |
-|---------|----------|---------------|-----------|-----------------|-------------------|----|----|----|----|
-| gpt2_bfloat16_flash_disopt_3             | 4.799616        | 2494.86         | 2052.67         | 3407.22         | 3452.44         | 8     | 1     | 1     | 1     |
-| gpt2_bfloat16_noflash_disopt_3           | 4.787657        | 2741.77         | 1868.11         | 3405.00         | 3456.00         | 8     | 1     | 1     | 1     |
-| gpt2_bfloat16_flash_disopt_4             | 4.799871        | 2900.71         | 1766.33         | 2715.00         | 2748.44         | 8     | 4     | 1     | 1     |
-| gpt2_bfloat16_noflash_disopt_4           | 4.788475        | 3211.90         | 1594.33         | 2713.00         | 2784.00         | 8     | 4     | 1     | 1     |
-| gpt2_bfloat16_flash_disopt_5             | 4.799961        | 3520.25         | 1454.67         | 2398.00         | 2432.00         | 8     | 4     | 1     | 1     |
-| gpt2_bfloat16_noflash_disopt_5           | 4.791147        | 4036.41         | 1290.11         | 2396.00         | 2464.00         | 8     | 4     | 1     | 1     |
-| gpt2_bfloat16_flash_disopt_8             | 4.799024        | 2000.08         | 2561.89         | 2373.22         | 2528.00         | 8     | 2     | 1     | 2     |
-| gpt2_bfloat16_noflash_disopt_8           | 4.791285        | 2128.22         | 2406.67         | 2326.00         | 2467.56         | 8     | 2     | 1     | 2     |
-| gpt2_bfloat16_flash_nodisopt_3           | 4.800298        | 2901.57         | 1766.11         | 3407.22         | 3445.33         | 8     | 1     | 1     | 1     |
-| gpt2_bfloat16_noflash_nodisopt_3         | 4.787223        | 3130.48         | 1636.11         | 3405.00         | 3456.00         | 8     | 1     | 1     | 1     |
-| gpt2_bfloat16_flash_nodisopt_4           | 4.799593        | 3107.03         | 1648.78         | 2715.00         | 2744.89         | 8     | 4     | 1     | 1     |
-| gpt2_bfloat16_noflash_nodisopt_4         | 4.788435        | 4551.77         | 1308.67         | 2713.00         | 2752.00         | 8     | 4     | 1     | 1     |
-| gpt2_bfloat16_flash_nodisopt_5           | 4.799967        | 3734.20         | 1372.00         | 2398.00         | 2428.44         | 8     | 4     | 1     | 1     |
-| gpt2_bfloat16_noflash_nodisopt_5         | 4.791135        | 4016.84         | 1275.78         | 2396.00         | 2464.00         | 8     | 4     | 1     | 1     |
-| gpt2_bfloat16_flash_nodisopt_6           | 4.800104        | 4197.89         | 1220.33         | 1718.89         | 1820.44         | 8     | 1     | 1     | 8     |
-| gpt2_bfloat16_noflash_nodisopt_6         | 4.788296        | 4675.14         | 1108.78         | 1684.00         | 1777.78         | 8     | 1     | 1     | 8     |
-| gpt2_bfloat16_flash_nodisopt_7           | 4.800298        | 5575.07         | 918.44          | 2684.00         | 2805.33         | 4     | 1     | 1     | 4     |
-| gpt2_bfloat16_noflash_nodisopt_7         | 4.788042        | 6254.28         | 823.00          | 2586.00         | 2705.78         | 4     | 1     | 1     | 4     |
-| gpt2_bfloat16_flash_nodisopt_8           | 4.798747        | 2089.50         | 2453.44         | 2373.22         | 2510.22         | 8     | 2     | 1     | 2     |
-| gpt2_bfloat16_noflash_nodisopt_8         | 4.790910        | 2150.97         | 2381.11         | 2326.00         | 2478.22         | 8     | 2     | 1     | 2     |
-| gpt2_fp32_flash_disopt_3                 | 4.836303        | 2215.36         | 2313.33         | 3626.22         | 3672.89         | 8     | 1     | 1     | 1     |
-| gpt2_fp32_noflash_disopt_3               | 4.826354        | 2513.86         | 2037.56         | 3467.00         | 3520.00         | 8     | 1     | 1     | 1     |
-| gpt2_fp32_flash_disopt_4                 | 4.836304        | 2652.36         | 1933.11         | 2934.00         | 2961.78         | 8     | 4     | 1     | 1     |
-| gpt2_fp32_noflash_disopt_4               | 4.826354        | 2913.33         | 1760.11         | 2774.00         | 2816.00         | 8     | 4     | 1     | 1     |
-| gpt2_fp32_flash_disopt_5                 | 4.836836        | 3305.40         | 1549.78         | 2616.89         | 2666.67         | 8     | 4     | 1     | 1     |
-| gpt2_fp32_noflash_disopt_5               | 4.829048        | 3928.89         | 1378.33         | 2457.00         | 2496.00         | 8     | 4     | 1     | 1     |
-| gpt2_fp32_flash_disopt_8                 | 4.836634        | 1838.23         | 2786.78         | 2441.67         | 2535.11         | 8     | 2     | 1     | 2     |
-| gpt2_fp32_noflash_disopt_8               | 4.827999        | 2004.15         | 2557.78         | 2350.00         | 2435.56         | 8     | 2     | 1     | 2     |
-| gpt2_fp32_flash_nodisopt_3               | 4.836303        | 2535.03         | 2020.78         | 3626.22         | 3662.22         | 8     | 1     | 1     | 1     |
-| gpt2_fp32_noflash_nodisopt_3             | 4.826354        | 2810.67         | 1822.44         | 3467.00         | 3520.00         | 8     | 1     | 1     | 1     |
-| gpt2_fp32_flash_nodisopt_4               | 4.836304        | 2817.74         | 1818.22         | 2934.00         | 2954.67         | 8     | 4     | 1     | 1     |
-| gpt2_fp32_noflash_nodisopt_4             | 4.826354        | 3049.61         | 1679.67         | 2774.00         | 2816.00         | 8     | 4     | 1     | 1     |
-| gpt2_fp32_flash_nodisopt_5               | 4.836836        | 3392.83         | 1509.67         | 2616.89         | 2656.00         | 8     | 4     | 1     | 1     |
-| gpt2_fp32_noflash_nodisopt_5             | 4.829048        | 3656.74         | 1400.67         | 2457.00         | 2496.00         | 8     | 4     | 1     | 1     |
-| gpt2_fp32_flash_nodisopt_6               | 4.836304        | 3746.21         | 1378.67         | 2318.44         | 2368.00         | 8     | 1     | 1     | 8     |
-| gpt2_fp32_noflash_nodisopt_6             | 4.826354        | 3947.83         | 1297.11         | 2248.00         | 2307.56         | 8     | 1     | 1     | 8     |
-| gpt2_fp32_flash_nodisopt_7               | 4.836304        | 5285.93         | 980.22          | 3538.56         | 3587.56         | 4     | 1     | 1     | 4     |
-| gpt2_fp32_noflash_nodisopt_7             | 4.826354        | 5558.65         | 921.22          | 3345.00         | 3416.89         | 4     | 1     | 1     | 4     |
-| gpt2_fp32_flash_nodisopt_8               | 4.836634        | 3332.54         | 1536.78         | 2441.67         | 2531.56         | 8     | 2     | 1     | 2     |
-| gpt2_fp32_noflash_nodisopt_8             | 4.827999        | 3560.96         | 1438.22         | 2350.00         | 2424.89         | 8     | 2     | 1     | 2     |
-| llama3_bfloat16_flash_disopt_3           | 11.513694       | 6985.97         | 733.22          | 21899.89        | 22151.11        | 8     | 1     | 1     | 1     |
-| llama3_bfloat16_noflash_disopt_3         | 3.687185        | 8803.02         | 587.44          | 21632.00        | 21824.00        | 8     | 1     | 1     | 1     |
-| llama3_bfloat16_flash_disopt_4           | 12.108337       | 7048.58         | 726.67          | 13019.00        | 13258.67        | 8     | 4     | 1     | 1     |
-| llama3_bfloat16_noflash_disopt_4         | 3.687813        | 8703.07         | 591.22          | 12995.00        | 13120.00        | 8     | 4     | 1     | 1     |
-| llama3_bfloat16_flash_disopt_5           | 12.330883       | 7956.26         | 643.60          | 10984.60        | 11155.20        | 8     | 4     | 1     | 1     |
-| llama3_bfloat16_noflash_disopt_5         | 3.687082        | 9673.23         | 530.89          | 10880.00        | 11008.00        | 8     | 4     | 1     | 1     |
-| llama3_bfloat16_flash_disopt_8           | 12.023618       | 4801.31         | 1066.50         | 9924.00         | 10032.00        | 8     | 2     | 1     | 2     |
-| llama3_bfloat16_noflash_disopt_8         | 3.687491        | 5915.92         | 873.44          | 10031.00        | 10122.67        | 8     | 2     | 1     | 2     |
-| llama3_bfloat16_flash_nodisopt_3         | 11.473671       | 7028.92         | 731.67          | 31903.11        | 32156.44        | 8     | 1     | 1     | 1     |
-| llama3_bfloat16_noflash_nodisopt_3       | 3.687393        | 10063.60        | 529.22          | 31636.00        | 31808.00        | 8     | 1     | 1     | 1     |
-| llama3_bfloat16_flash_nodisopt_4         | 12.120937       | 7424.95         | 696.17          | 14570.17        | 14810.67        | 8     | 4     | 1     | 1     |
-| llama3_bfloat16_noflash_nodisopt_4       | 3.687764        | 9143.74         | 563.22          | 14425.00        | 14560.00        | 8     | 4     | 1     | 1     |
-| llama3_bfloat16_flash_nodisopt_5         | 11.832385       | 8049.24         | 636.33          | 12454.50        | 12624.00        | 8     | 4     | 1     | 1     |
-| llama3_bfloat16_noflash_nodisopt_5       | 3.687394        | 9607.02         | 533.00          | 12309.00        | 12448.00        | 8     | 4     | 1     | 1     |
-| llama3_bfloat16_flash_nodisopt_6         | 11.109162       | 9080.24         | 570.11          | 11371.89        | 11516.44        | 8     | 1     | 1     | 8     |
-| llama3_bfloat16_noflash_nodisopt_6       | 3.687855        | 9991.38         | 515.89          | 11052.00        | 11175.11        | 8     | 1     | 1     | 8     |
-| llama3_bfloat16_flash_nodisopt_7         | 12.343384       | 12997.81        | 394.00          | 13956.00        | 14080.00        | 4     | 1     | 1     | 4     |
-| llama3_bfloat16_noflash_nodisopt_7       | 3.687177        | 16637.25        | 309.11          | 14608.00        | 14759.11        | 4     | 1     | 1     | 4     |
-| llama3_bfloat16_flash_nodisopt_8         | 11.242361       | 5281.60         | 975.78          | 11777.67        | 11868.44        | 8     | 2     | 1     | 2     |
-| llama3_bfloat16_noflash_nodisopt_8       | 3.687432        | 5902.93         | 868.11          | 11460.00        | 11559.11        | 8     | 2     | 1     | 2     |
-| llama3_fp32_flash_disopt_3               | 3.696688        | 6454.74         | 797.11          | 22056.33        | 22275.56        | 8     | 1     | 1     | 1     |
-| llama3_fp32_noflash_disopt_3             | 3.595062        | 8042.78         | 640.56          | 21789.00        | 21952.00        | 8     | 1     | 1     | 1     |
-| llama3_fp32_flash_disopt_4               | 3.696686        | 8594.09         | 650.11          | 13419.33        | 13575.11        | 8     | 4     | 1     | 1     |
-| llama3_fp32_noflash_disopt_4             | 3.595074        | 8257.09         | 620.44          | 13152.00        | 13216.00        | 8     | 4     | 1     | 1     |
-| llama3_fp32_flash_disopt_5               | 3.696680        | 7835.74         | 656.33          | 11303.67        | 11459.56        | 8     | 4     | 1     | 1     |
-| llama3_fp32_noflash_disopt_5             | 3.595054        | 10612.78        | 497.89          | 11036.00        | 11168.00        | 8     | 4     | 1     | 1     |
-| llama3_fp32_flash_disopt_8               | 3.696683        | 4918.54         | 1041.22         | 11005.33        | 11114.67        | 8     | 2     | 1     | 2     |
-| llama3_fp32_noflash_disopt_8             | 3.595056        | 5989.85         | 863.00          | 10798.00        | 10915.56        | 8     | 2     | 1     | 2     |
-| llama3_fp32_flash_nodisopt_3             | 3.696689        | 8095.32         | 641.44          | 32059.89        | 32280.89        | 8     | 1     | 1     | 1     |
-| llama3_fp32_noflash_nodisopt_3           | 3.595062        | 8868.17         | 580.44          | 31792.00        | 31936.00        | 8     | 1     | 1     | 1     |
-| llama3_fp32_flash_nodisopt_4             | 3.696686        | 7001.37         | 736.89          | 14848.67        | 15000.89        | 8     | 4     | 1     | 1     |
-| llama3_fp32_noflash_nodisopt_4           | 3.595074        | 8305.80         | 616.78          | 14581.00        | 14656.00        | 8     | 4     | 1     | 1     |
-| llama3_fp32_flash_nodisopt_5             | 3.696680        | 7993.27         | 643.22          | 12732.89        | 12892.44        | 8     | 4     | 1     | 1     |
-| llama3_fp32_noflash_nodisopt_5           | 3.595054        | 9290.61         | 551.33          | 12465.00        | 12608.00        | 8     | 4     | 1     | 1     |
-| llama3_fp32_flash_nodisopt_6             | 3.696689        | 7677.79         | 671.67          | 13351.89        | 13475.56        | 8     | 1     | 1     | 8     |
-| llama3_fp32_noflash_nodisopt_6           | 3.595062        | 8936.41         | 573.00          | 13119.00        | 13219.56        | 8     | 1     | 1     | 8     |
-| llama3_fp32_flash_nodisopt_7             | 3.696688        | 12469.65        | 411.11          | 18195.78        | 18318.22        | 4     | 1     | 1     | 4     |
-| llama3_fp32_noflash_nodisopt_7           | 3.595062        | 15277.34        | 335.89          | 17756.00        | 17873.78        | 4     | 1     | 1     | 4     |
-| llama3_fp32_flash_nodisopt_8             | 3.696683        | 5354.28         | 974.67          | 12434.33        | 12551.11        | 8     | 2     | 1     | 2     |
-| llama3_fp32_noflash_nodisopt_8           | 3.595056        | 7652.40         | 708.56          | 12227.00        | 12352.00        | 8     | 2     | 1     | 2     |
\ No newline at end of file
diff --git a/scripts/results_speedup.md b/scripts/results_speedup.md
deleted file mode 100644
index 6660ec94..00000000
--- a/scripts/results_speedup.md
+++ /dev/null
@@ -1,42 +0,0 @@
-| Configuration | Speedup Ratio | Flash Time (ms) | Noflash Time (ms) |
-|---------------|---------------|-----------------|-------------------|
-| gpt2_bfloat16_disopt_3                | 1.10          | 2494.86         | 2741.77           |
-| gpt2_bfloat16_disopt_4                | 1.11          | 2900.71         | 3211.90           |
-| gpt2_bfloat16_disopt_5                | 1.15          | 3520.25         | 4036.41           |
-| gpt2_bfloat16_disopt_8                | 1.06          | 2000.08         | 2128.22           |
-| gpt2_bfloat16_nodisopt_3              | 1.08          | 2901.57         | 3130.48           |
-| gpt2_bfloat16_nodisopt_4              | 1.46          | 3107.03         | 4551.77           |
-| gpt2_bfloat16_nodisopt_5              | 1.08          | 3734.20         | 4016.84           |
-| gpt2_bfloat16_nodisopt_6              | 1.11          | 4197.89         | 4675.14           |
-| gpt2_bfloat16_nodisopt_7              | 1.12          | 5575.07         | 6254.28           |
-| gpt2_bfloat16_nodisopt_8              | 1.03          | 2089.50         | 2150.97           |
-| gpt2_fp32_disopt_3                    | 1.13          | 2215.36         | 2513.86           |
-| gpt2_fp32_disopt_4                    | 1.10          | 2652.36         | 2913.33           |
-| gpt2_fp32_disopt_5                    | 1.19          | 3305.40         | 3928.89           |
-| gpt2_fp32_disopt_8                    | 1.09          | 1838.23         | 2004.15           |
-| gpt2_fp32_nodisopt_3                  | 1.11          | 2535.03         | 2810.67           |
-| gpt2_fp32_nodisopt_4                  | 1.08          | 2817.74         | 3049.61           |
-| gpt2_fp32_nodisopt_5                  | 1.08          | 3392.83         | 3656.74           |
-| gpt2_fp32_nodisopt_6                  | 1.05          | 3746.21         | 3947.83           |
-| gpt2_fp32_nodisopt_7                  | 1.05          | 5285.93         | 5558.65           |
-| gpt2_fp32_nodisopt_8                  | 1.07          | 3332.54         | 3560.96           |
-| llama3_bfloat16_disopt_3              | 1.26          | 6985.97         | 8803.02           |
-| llama3_bfloat16_disopt_4              | 1.23          | 7048.58         | 8703.07           |
-| llama3_bfloat16_disopt_5              | 1.22          | 7956.26         | 9673.23           |
-| llama3_bfloat16_disopt_8              | 1.23          | 4801.31         | 5915.92           |
-| llama3_bfloat16_nodisopt_3            | 1.43          | 7028.92         | 10063.60          |
-| llama3_bfloat16_nodisopt_4            | 1.23          | 7424.95         | 9143.74           |
-| llama3_bfloat16_nodisopt_5            | 1.19          | 8049.24         | 9607.02           |
-| llama3_bfloat16_nodisopt_6            | 1.10          | 9080.24         | 9991.38           |
-| llama3_bfloat16_nodisopt_7            | 1.28          | 12997.81        | 16637.25          |
-| llama3_bfloat16_nodisopt_8            | 1.12          | 5281.60         | 5902.93           |
-| llama3_fp32_disopt_3                  | 1.25          | 6454.74         | 8042.78           |
-| llama3_fp32_disopt_4                  | 0.96          | 8594.09         | 8257.09           |
-| llama3_fp32_disopt_5                  | 1.35          | 7835.74         | 10612.78          |
-| llama3_fp32_disopt_8                  | 1.22          | 4918.54         | 5989.85           |
-| llama3_fp32_nodisopt_3                | 1.10          | 8095.32         | 8868.17           |
-| llama3_fp32_nodisopt_4                | 1.19          | 7001.37         | 8305.80           |
-| llama3_fp32_nodisopt_5                | 1.16          | 7993.27         | 9290.61           |
-| llama3_fp32_nodisopt_6                | 1.16          | 7677.79         | 8936.41           |
-| llama3_fp32_nodisopt_7                | 1.23          | 12469.65        | 15277.34          |
-| llama3_fp32_nodisopt_8                | 1.43          | 5354.28         | 7652.40           |
\ No newline at end of file
diff --git a/scripts/test_config.json b/scripts/test_config.json
index f8171b3a..3d66ff7b 100644
--- a/scripts/test_config.json
+++ b/scripts/test_config.json
@@ -10,6 +10,11 @@
         "COMPARE_LOG_DIR": ""
     },
     "builds": [
+        {
+            "id": "build_1",
+            "profile": false,
+            "cmd": "cmake -DUSE_CUDA=ON -DUSE_NCCL=ON .. && make -j"
+        },
         {
             "id": "build_2",
             "profile": true,

From 3e4a1ce4d94d87af85c44d0c45555fc679c3e253 Mon Sep 17 00:00:00 2001
From: Aoshine999 <820459542@qq.com>
Date: Mon, 16 Mar 2026 15:49:49 +0800
Subject: [PATCH 14/18] delete test file

---
 flash_atten.md                    | 597 ------------------------------
 scripts/extract_training_stats.py | 507 -------------------------
 2 files changed, 1104 deletions(-)
 delete mode 100644 flash_atten.md
 delete mode 100755 scripts/extract_training_stats.py

diff --git a/flash_atten.md b/flash_atten.md
deleted file mode 100644
index d42f321c..00000000
--- a/flash_atten.md
+++ /dev/null
@@ -1,597 +0,0 @@
-下面我把 **FlashAttention-2 backward pass 的完整执行流程**整理成一份可直接使用的 **Markdown 文档**。内容会分成两层：
-
-1. **论文级算法流程**：对应 FlashAttention-2 的标准 backward 思路。
-2. **CUDA 实现级执行流程**：结合你当前的 kernel 设计
-   `grid_dims(num_heads_kv, batch_size, Tc), block_dims(Tc, Tr)`
-   说明一个 block 实际在做什么。
-
-论文中 backward 的核心点包括：将 (Q) 按行块、(K/V) 按列块切分；先计算 (D = \mathrm{rowsum}(dO \circ O))；随后固定一个 (K_j,V_j) 列块，遍历所有 (Q_i) 行块，重算 (S_{ij})、(P_{ij})、(dP_{ij})、(dS_{ij})，并更新 (dV_j,dK_j,dQ_i)。另外，FlashAttention-2 在 backward 中只保存 **row-wise logsumexp (L)**，而不是同时保存 row-max 和 row-sum。 
-
----
-
-# FlashAttention-2 Backward Pass 执行流程
-
-## 1. 问题定义
-
-给定前向中已经得到的：
-
-* (Q \in \mathbb{R}^{N_q \times d})
-* (K \in \mathbb{R}^{N_k \times d})
-* (V \in \mathbb{R}^{N_k \times d})
-* (O \in \mathbb{R}^{N_q \times d})
-* (L \in \mathbb{R}^{N_q})，其中 (L) 是每一行 softmax 的 `logsumexp`
-* 上游梯度 (dO \in \mathbb{R}^{N_q \times d})
-
-要求计算：
-
-* (dQ \in \mathbb{R}^{N_q \times d})
-* (dK \in \mathbb{R}^{N_k \times d})
-* (dV \in \mathbb{R}^{N_k \times d})
-
-FlashAttention-2 backward 的基本思想不是存整张 attention matrix，而是 **按 tile 重算** 中间量，从而降低 HBM 读写。论文的 Algorithm 2 先把 (Q,O,dO,L) 切成行块，把 (K,V) 切成列块，然后以列块为外循环执行 backward。
-
----
-
-## 2. 数学公式
-
-### 2.1 前向关系
-
-对任意一个 tile ((i,j))：
-
-[
-S_{ij} = Q_i K_j^T
-]
-
-若启用缩放：
-
-[
-S_{ij} \leftarrow \text{scale} \cdot S_{ij}
-]
-
-softmax 概率：
-
-[
-P_{ij} = \exp(S_{ij} - L_i)
-]
-
-这里 (L_i) 是该行块对应的 row-wise logsumexp。FlashAttention-2 backward 只依赖 (L)，这是它相对早期实现的一个简化。
-
-输出满足：
-
-[
-O_i = \sum_j P_{ij} V_j
-]
-
----
-
-### 2.2 backward 关键中间量
-
-先定义：
-
-[
-D = \mathrm{rowsum}(dO \circ O)
-]
-
-即对每一行做逐元素乘法后求和。论文明确把这一步放在主 backward 循环之前，并写回 HBM。
-
-对任意 tile ((i,j))：
-
-1. 先计算
-   [
-   dP_{ij} = dO_i V_j^T
-   ]
-
-2. 再做 softmax backward
-   [
-   dS_{ij} = P_{ij} \circ (dP_{ij} - D_i)
-   ]
-
-3. 然后得到三个输入梯度的 tile 贡献：
-
-[
-dV_j \mathrel{+}= P_{ij}^T dO_i
-]
-
-[
-dK_j \mathrel{+}= dS_{ij}^T Q_i
-]
-
-[
-dQ_i \mathrel{+}= dS_{ij} K_j
-]
-
-这些正是论文 Algorithm 2 的核心更新过程。
-
----
-
-## 3. Tile 划分
-
-设：
-
-* 行块大小为 (B_r)
-* 列块大小为 (B_c)
-
-则：
-
-* (Q) 被分成 (T_r = \lceil N_q / B_r \rceil) 个行块
-* (K,V) 被分成 (T_c = \lceil N_k / B_c \rceil) 个列块
-
-论文中明确写出：
-
-* (Q_1,\dots,Q_{T_r})
-* (K_1,\dots,K_{T_c})
-* (V_1,\dots,V_{T_c})
-* (O_i,dO_i,L_i,D_i,dQ_i) 也都按行块划分
-* (dK_j,dV_j) 按列块划分。 
-
----
-
-## 4. 论文级 backward 总流程
-
-## Step 0：输入准备
-
-从前向保留下来：
-
-* `Q, K, V, O`
-* `dO`
-* `L = logsumexp`
-* 以及可选的 `dropout_seed`
-
-初始化输出：
-
-* `dQ = 0`
-* `dK = 0`
-* `dV = 0`
-
----
-
-## Step 1：预计算 (D)
-
-对每一行计算：
-
-[
-D_i = \sum_{m=1}^{d} dO_{i,m} \cdot O_{i,m}
-]
-
-并写回全局内存。
-
-### 作用
-
-这是 softmax backward 所需的行级归约项，用来避免显式构造整张 Jacobian。论文把它作为 backward 前处理步骤。
-
----
-
-## Step 2：外层遍历列块 (j)
-
-对每个列块 (j = 1,\dots,T_c)：
-
-1. 从 HBM 载入 (K_j, V_j) 到 on-chip SRAM / shared memory
-2. 在 shared memory 或寄存器中初始化本列块的局部梯度：
-
-   * (dK_j = 0)
-   * (dV_j = 0)
-
-论文 Algorithm 2 就是以列块为外层循环，并在每个 (j) 开始时把 `K_j, V_j` 加载到 SRAM，然后初始化 `dK_j, dV_j`。
-
----
-
-## Step 3：内层遍历行块 (i)
-
-对每个行块 (i = 1,\dots,T_r)：
-
-### 3.1 载入当前行块数据
-
-从 HBM 载入：
-
-* (Q_i)
-* (O_i)（若需要本 kernel 内使用）
-* (dO_i)
-* (L_i)
-* (D_i)
-
----
-
-### 3.2 重算 score tile
-
-重算当前 tile 的 attention score：
-
-[
-S_{ij} = Q_i K_j^T
-]
-
-若设置 `scale`：
-
-[
-S_{ij} \leftarrow \text{scale} \cdot S_{ij}
-]
-
-若启用 `causal mask`，则对非法位置做 mask：
-
-[
-S_{ij}(r,c) = -\infty
-\quad \text{if } k_idx > q_idx
-]
-
-其中：
-
-* `q_idx = i * B_r + r`
-* `k_idx = j * B_c + c`
-
-### 说明
-
-这一步没有从全局内存读取旧的 attention matrix，而是 **重算**。这是 FlashAttention backward 相比普通实现最关键的节省显存策略。
-
----
-
-### 3.3 重算概率 tile
-
-根据保存的 `logsumexp`：
-
-[
-P_{ij} = \exp(S_{ij} - L_i)
-]
-
-若启用 `dropout`，则要重建前向时相同的 dropout mask，并应用：
-
-[
-P_{ij}^{drop} = \frac{M_{ij} \circ P_{ij}}{1-p}
-]
-
-其中：
-
-* (M_{ij}) 为基于同一随机种子重建的二值 mask
-* (p) 为 dropout probability
-
-如果没有 dropout，则：
-
-[
-P_{ij}^{drop} = P_{ij}
-]
-
----
-
-### 3.4 计算 (dV_j) 的当前 tile 贡献
-
-[
-dV_j \mathrel{+}= (P_{ij}^{drop})^T dO_i
-]
-
-### 解释
-
-因为前向输出是 (O_i = \sum_j P_{ij}V_j)，所以对 (V_j) 的梯度就是概率矩阵转置乘以上游梯度。
-
----
-
-### 3.5 计算 (dP_{ij})
-
-[
-dP_{ij} = dO_i V_j^T
-]
-
-若启用 dropout，则 backward 中也要乘上同样的 dropout mask，并带上缩放因子：
-
-[
-dP_{ij} \leftarrow \frac{M_{ij} \circ dP_{ij}}{1-p}
-]
-
----
-
-### 3.6 计算 softmax backward 的 (dS_{ij})
-
-[
-dS_{ij} = P_{ij} \circ (dP_{ij} - D_i)
-]
-
-注意这里使用的是 **未 dropout 前的 softmax 概率** (P_{ij})，而 `dP` 则需要反映 dropout 后的链式法则。
-
-这一步是整套 backward 中最核心的公式，因为 softmax 的 Jacobian 已经被压缩成逐元素乘法加一个行级标量 (D_i)。
-
----
-
-### 3.7 计算 (dK_j) 的当前 tile 贡献
-
-[
-dK_j \mathrel{+}= dS_{ij}^T Q_i
-]
-
-若前向有 `scale`，则这里还要乘上相同的缩放因子对梯度链路进行修正。更准确地说，如果前向做的是：
-
-[
-S = \text{scale} \cdot QK^T
-]
-
-那么由 (S) 向 (Q,K) 回传的梯度都要乘 `scale`。
-
----
-
-### 3.8 计算 (dQ_i) 的当前 tile 贡献
-
-[
-dQ_i \mathrel{+}= dS_{ij} K_j
-]
-
-这一步会累加来自所有列块 (j) 的贡献，因此：
-
-* 若一个 block 只处理单个列块，那么 `dQ_i` 往往需要原子加到全局内存
-* 或者先在 block 内做局部累加，再统一写回
-
----
-
-## Step 4：写回本列块的 (dK_j,dV_j)
-
-当所有行块 (i) 都遍历完后，本列块 (j) 的局部梯度已经累加完成：
-
-* 写回 `dK_j`
-* 写回 `dV_j`
-
-由于一个列块 (j) 通常只由一个 block 负责，所以这两者一般不需要跨 block 再归约；但若你在 GQA 下让多个 query-head block 共同更新同一组 kv-head，也可能需要额外归约或原子加。
-
----
-
-## Step 5：结束条件
-
-当所有列块 (j) 处理完成后：
-
-* `dQ`
-* `dK`
-* `dV`
-
-全部得到。
-
----
-
-# 5. 结合你当前 CUDA 设计的执行流程
-
-你当前 kernel 为：
-
-```cpp
-FlashAttentionBackwardKernel<T><<<grid_dims, block_dims, shared_mem_size, cuda_stream>>>(
-    grad_query_ptr, grad_key_ptr, grad_value_ptr,
-    query_ptr, key_ptr, value_ptr, output_ptr, grad_output_ptr,
-    logsumexp_ptr, dropout_seed_value, attn_mask_ptr,
-    scale, is_causal, dropout_p, enable_gqa,
-    batch_size, seq_len_q, seq_len_k, num_heads, num_heads_kv, head_dim);
-```
-
-其中：
-
-* `grid_dims(num_heads_kv, batch_size, Tc)`
-* `block_dims(Tc, Tr)`
-
-这表示：
-
-* `blockIdx.x`：对应一个 `kv head`
-* `blockIdx.y`：对应一个 `batch`
-* `blockIdx.z`：对应一个列块 (j)
-
-也就是说，**一个 block 固定负责某个 batch、某个 kv-head、某个列块 (j)**。
-
----
-
-## 5.1 一个 block 的职责
-
-对给定的：
-
-* `batch = blockIdx.y`
-* `kv_head = blockIdx.x`
-* `col_tile = blockIdx.z`
-
-该 block 需要：
-
-1. 加载当前 `K_j, V_j`
-2. 初始化局部 `dK_j, dV_j`
-3. 遍历所有相关的 query row tiles (i)
-4. 对每个 (i)：
-
-   * 找到对应的 query-head（若启用 GQA，要从 q-head 映射到 kv-head）
-   * 载入 `Q_i, dO_i, L_i, D_i`
-   * 重算 `S_ij`
-   * 应用 causal/mask/dropout
-   * 计算 `P_ij, dP_ij, dS_ij`
-   * 累加到 `dV_j, dK_j`
-   * 把对 `dQ_i` 的贡献写回全局
-5. 行块遍历结束后，写回 `dK_j, dV_j`
-
----
-
-## 5.2 GQA 下的 head 映射
-
-如果启用了 GQA：
-
-* `num_heads` 是 query heads 数
-* `num_heads_kv` 是 kv heads 数
-* 每个 kv head 会对应若干个 query heads
-
-常见映射方式为：
-
-[
-q_head_start = kv_head \times \frac{num_heads}{num_heads_kv}
-]
-
-[
-q_head_end = (kv_head+1) \times \frac{num_heads}{num_heads_kv}
-]
-
-于是当前 block 除了遍历 row tiles (i)，还需要遍历属于这个 kv-head 组的所有 q-head。这样同一个 `K_j,V_j` 会被多个 q-head 共享使用，而 `dK_j,dV_j` 也要把这些 q-head 的贡献全部累加起来。
-
-这与论文中提到的 MQA/GQA backward 需要对共享的 K/V head 梯度求和是一致的。
-
----
-
-## 5.3 causal mask 的执行方式
-
-若 `is_causal = true`：
-
-对于 tile 内每个元素 ((r,c))，若其全局位置满足：
-
-[
-k_global > q_global
-]
-
-则该位置无效。
-
-实现上通常有两层优化：
-
-1. **整块跳过**：
-   若当前列块完全在当前行块右侧，则整个 tile 不必计算
-
-2. **边界块细粒度判断**：
-   若 tile 横跨对角线，则对元素级做 mask
-
-这样可以减少无效 matmul 和 exp 计算。
-
----
-
-## 5.4 dropout 的执行方式
-
-若 `dropout_p > 0`：
-
-1. 使用 `dropout_seed_value` 和 tile 内元素索引重建与 forward 一致的随机数序列
-2. 得到二值 mask
-3. 对 `P_ij` 和 `dP_ij` 执行同样的 mask/缩放
-
-这样 backward 就与前向使用的是同一组保留位置。
-
----
-
-## 5.5 scale 的执行方式
-
-若 `scale != 1`：
-
-前向 score 为：
-
-[
-S = \text{scale} \cdot QK^T
-]
-
-那么 backward 中：
-
-* 重算 `S_ij` 时要乘 `scale`
-* 从 `dS` 回传到 `dQ,dK` 时也要体现这条链路
-
-通常最直接的实现是：
-
-* 先在重算 `S_ij` 时乘 `scale`
-* 在 `dQ = dS K`、`dK = dS^T Q` 前再统一乘 `scale`
-* 或者直接把 `scale` 融进 `dS`
-
-两种写法本质等价，但要保证链式法则只乘一次，不要漏乘或重复乘。
-
----
-
-# 6. 一个推荐的伪代码版本
-
-```markdown
-for each batch b:
-  for each kv head h_kv:
-    for each column tile j:
-      load K_j, V_j into shared memory
-      initialize local dK_j = 0, dV_j = 0
-
-      determine all query heads h_q that map to h_kv (for GQA)
-
-      for each query head h_q in group(h_kv):
-        for each row tile i:
-          load Q_i, dO_i, L_i, D_i
-          optionally load O_i if D is not precomputed
-
-          recompute S_ij = Q_i @ K_j^T
-          if scale is enabled:
-            S_ij *= scale
-
-          if causal mask is enabled:
-            mask invalid positions in S_ij
-
-          P_ij = exp(S_ij - L_i)
-
-          if dropout is enabled:
-            reconstruct dropout mask M_ij
-            P_drop = M_ij * P_ij / (1 - p)
-          else:
-            P_drop = P_ij
-
-          dV_j += P_drop^T @ dO_i
-
-          dP_ij = dO_i @ V_j^T
-          if dropout is enabled:
-            dP_ij = M_ij * dP_ij / (1 - p)
-
-          dS_ij = P_ij * (dP_ij - D_i)
-
-          if scale is enabled:
-            dS_ij *= scale
-
-          dK_j += dS_ij^T @ Q_i
-          dQ_i += dS_ij @ K_j
-
-          write or atomically accumulate dQ_i to global memory
-
-      write dK_j, dV_j to global memory
-```
-
----
-
-# 7. 实现时的关键注意点
-
-## 7.1 `D = rowsum(dO ∘ O)` 最好预处理
-
-论文就是这么做的。这样主 backward kernel 只需读取 `D_i`，不用再额外读取 `O_i` 并做一次按行归约，能减轻主 kernel 负担。
-
----
-
-## 7.2 backward 的 matmul 数量更多
-
-论文 benchmark 中指出 backward 由于重算，等价上有更多 matmul，因此 FLOPs 估计通常约为 forward 的 2.5 倍。
-
----
-
-## 7.3 FlashAttention-2 backward 仍然强调避免 split-K
-
-论文在 warp 工作划分部分提到，backward 也尽量避免 “split-K” 方案，从而减少 shared memory 读写和同步，尽管 backward 的依赖关系比 forward 更复杂。
-
----
-
-## 7.4 block 大小要兼顾 shared memory 和寄存器压力
-
-论文提到 block size 变大虽然能减少 shared memory 读写，但也会提高寄存器与 shared memory 占用，过大会导致 spilling 或 kernel 根本无法启动。常见 tile 大小会在 `{64,128}` 范围内手工调优。
-
----
-
-# 8. 最终总结
-
-FlashAttention-2 backward pass 的本质可以概括为：
-
-1. **先预处理行级统计量**
-
-   * 计算 (D = \mathrm{rowsum}(dO \circ O))
-
-2. **按列块固定 `K_j, V_j`**
-
-   * 让 `K_j,V_j` 常驻 shared memory
-
-3. **遍历所有行块 `Q_i`**
-
-   * 重算 (S_{ij})
-   * 用保存的 `L_i` 恢复 (P_{ij})
-   * 计算 (dP_{ij}, dS_{ij})
-
-4. **逐 tile 累加三个输入梯度**
-
-   * (dV_j += P_{ij}^T dO_i)
-   * (dK_j += dS_{ij}^T Q_i)
-   * (dQ_i += dS_{ij} K_j)
-
-5. **支持附加功能**
-
-   * causal：在 tile 内做位置裁剪
-   * dropout：重建相同随机 mask
-   * scale：在 `S` 与其梯度链中保持一致
-   * GQA：让多个 q-head 共享同一组 kv-head，并对 `dK/dV` 做组内累加
-
----
-
-你要是愿意，我下一步可以直接把这份内容继续整理成一份更正式的文档格式，比如：
-
-* **“设计说明书风格” Markdown**
-* **“伪代码 + 公式 + CUDA 映射”版**
-* 或者直接给你写成 **项目里的 README 小节**。
diff --git a/scripts/extract_training_stats.py b/scripts/extract_training_stats.py
deleted file mode 100755
index d4949cc1..00000000
--- a/scripts/extract_training_stats.py
+++ /dev/null
@@ -1,507 +0,0 @@
-#!/usr/bin/env python3
-"""
-Extract training statistics from log files and display them in a table.
-
-This script parses log files from training runs and extracts statistics such as
-loss, time, tokens per second, and memory usage. It can compare
-results from different configurations (e.g., flash vs noflash attention)
-and calculate speedup ratios.
-
-Usage:
-    python extract_training_stats.py <log_dir1> <log_dir2> [options]
-
-Arguments:
-    log_dir1: First log directory (typically contains noflash results)
-    log_dir2: Second log directory (typically contains flash results)
-
-Options:
-    --threshold-fp32: Loss difference threshold for fp32 (default: 1e-5)
-    --threshold-bf16: Loss difference threshold for bfloat16 (default: 1e-2)
-    --markdown: Output tables in markdown format
-    --output: Specify output file path (for markdown tables)
-    --speedup: Output speedup ratio table (noflash_time / flash_time)
-
-Examples:
-    # Display training statistics in plain text
-    python extract_training_stats.py logs compare_logs
-
-    # Display training statistics in markdown format
-    python extract_training_stats.py logs compare_logs --markdown
-
-    # Display training statistics in markdown and save to file
-    python extract_training_stats.py logs compare_logs --markdown --output results.md
-
-    # Display speedup ratio table
-    python extract_training_stats.py logs compare_logs --speedup
-
-    # Display speedup ratio table in markdown and save to file
-    python extract_training_stats.py logs compare_logs --speedup --markdown --output results.md
-
-Output:
-    The script outputs three types of information:
-    1. Training Statistics Table: Detailed statistics for each configuration
-    2. Comparison between flash and noflash: Comparison of loss, time, and memory
-    3. Speedup Ratio Table (if --speedup is specified): Speedup ratios for each configuration
-"""
-
-import re
-import sys
-from pathlib import Path
-from argparse import ArgumentParser
-from collections import defaultdict
-
-
-def get_dtype_from_filename(filename):
-    """
-    Determine dtype from filename.
-    
-    Args:
-        filename: Name of the log file
-        
-    Returns:
-        'bfloat16' or 'fp32'
-    """
-    return 'bfloat16' if '_bfloat16' in filename else 'fp32'
-
-
-def get_flash_from_filename(filename, dir_path):
-    """
-    Determine flash mode from filename and directory.
-    
-    Args:
-        filename: Name of the log file
-        dir_path: Path to the directory containing the log file
-        
-    Returns:
-        'flash' or 'noflash'
-    """
-    # First check if filename contains flash/noflash suffix
-    if '_flash' in filename:
-        return 'flash'
-    elif '_noflash' in filename:
-        return 'noflash'
-    # Otherwise, determine from directory name
-    dir_name = str(dir_path).lower()
-    if 'compare' in dir_name:
-        return 'flash'
-    else:
-        return 'noflash'
-
-
-def get_disopt_from_filename(filename):
-    """Determine distributed optimizer from filename. Returns 'disopt' or 'nodisopt'."""
-    return 'disopt' if '_distopt' in filename else 'nodisopt'
-
-
-def get_model_from_filename(filename):
-    """Determine model name from filename. Returns 'gpt2' or 'llama3'."""
-    if 'gpt2' in filename:
-        return 'gpt2'
-    elif 'llama3' in filename:
-        return 'llama3'
-    else:
-        return 'unknown'
-
-
-def parse_command_line(line):
-    """Extract configuration from command line."""
-    dp_match = re.search(r'--nthread_per_process=(\d+)', line)
-    dp = int(dp_match.group(1)) if dp_match else 1
-    
-    tp_match = re.search(r'--tensor_parallel=(\d+)', line)
-    tp = int(tp_match.group(1)) if tp_match else 1
-    
-    sp = 1
-    if '--sequence_parallel' in line:
-        sp = 1  # SP is enabled, but value is not explicitly set
-    
-    pp_match = re.search(r'--pipeline_parallel=(\d+)', line)
-    pp = int(pp_match.group(1)) if pp_match else 1
-    
-    disopt = '--use_distributed_optimizer' in line
-    
-    return {
-        'dp': dp,
-        'tp': tp,
-        'sp': sp,
-        'pp': pp,
-        'disopt': disopt
-    }
-
-
-def parse_log_file(file_path):
-    """
-    Extract statistics from log file.
-    
-    Args:
-        file_path: Path to the log file
-        
-    Returns:
-        Dictionary containing lists of losses, times, tokens per second,
-        peak memory usage, and configuration
-    """
-    stats = {
-        'losses': [],
-        'times': [],
-        'toks_per_sec': [],
-        'peak_used': [],
-        'peak_reserved': [],
-        'config': None
-    }
-    
-    with open(file_path, 'r') as f:
-        for line in f:
-            # Parse command line
-            if line.startswith('[COMMAND]'):
-                stats['config'] = parse_command_line(line)
-                continue
-            
-            # Parse step information
-            match = re.search(r'step\s+(\d+)/\d+\s+\|\s+train loss\s+([\d.]+)\s+\|\s+lr\s+[\d.e+-]+\s+\|\s+\(([\d.]+)\s+ms\s+\|\s+(\d+)\s+tok/s.*peak used:\s+(\d+)\s+MB.*peak reserved:\s+(\d+)\s+MB.*DP=(\d+),\s+TP=(\d+),\s+SP=(\d+),\s+PP=(\d+)', line)
-            if match:
-                step = int(match.group(1))
-                loss = float(match.group(2))
-                time_ms = float(match.group(3))
-                tok_per_sec = int(match.group(4))
-                peak_used = int(match.group(5))
-                peak_reserved = int(match.group(6))
-                dp = int(match.group(7))
-                tp = int(match.group(8))
-                sp = int(match.group(9))
-                pp = int(match.group(10))
-                
-                stats['losses'].append((step, loss))
-                stats['times'].append((step, time_ms))
-                stats['toks_per_sec'].append((step, tok_per_sec))
-                stats['peak_used'].append((step, peak_used))
-                stats['peak_reserved'].append((step, peak_reserved))
-    
-    return stats
-
-
-def calculate_averages(stats, exclude_first_step=True):
-    """
-    Calculate average values, optionally excluding first step.
-    
-    Args:
-        stats: Dictionary containing lists of losses, times, tokens per second,
-                and peak memory usage
-        exclude_first_step: If True, exclude the first step from calculations
-                             (default: True, as first step may have overhead)
-                             
-    Returns:
-        Dictionary containing average values for loss, time, tokens per second,
-        and peak memory usage
-    """
-    losses = stats['losses']
-    times = stats['times']
-    toks_per_sec = stats['toks_per_sec']
-    peak_used = stats['peak_used']
-    peak_reserved = stats['peak_reserved']
-    
-    start_idx = 1 if exclude_first_step else 0
-    
-    avg_loss = sum(loss for _, loss in losses[start_idx:]) / len(losses[start_idx:]) if len(losses[start_idx:]) > 0 else 0
-    avg_time = sum(time for _, time in times[start_idx:]) / len(times[start_idx:]) if len(times[start_idx:]) > 0 else 0
-    avg_tok_per_sec = sum(tok for _, tok in toks_per_sec[start_idx:]) / len(toks_per_sec[start_idx:]) if len(toks_per_sec[start_idx:]) > 0 else 0
-    avg_peak_used = sum(mem for _, mem in peak_used[start_idx:]) / len(peak_used[start_idx:]) if len(peak_used[start_idx:]) > 0 else 0
-    avg_peak_reserved = sum(mem for _, mem in peak_reserved[start_idx:]) / len(peak_reserved[start_idx:]) if len(peak_reserved[start_idx:]) > 0 else 0
-    
-    return {
-        'avg_loss': avg_loss,
-        'avg_time_ms': avg_time,
-        'avg_tok_per_sec': avg_tok_per_sec,
-        'avg_peak_used': avg_peak_used,
-        'avg_peak_reserved': avg_peak_reserved
-    }
-
-
-def main():
-    """
-    Main function to extract and display training statistics.
-    
-    This function:
-    1. Parses command line arguments
-    2. Reads log files from two directories
-    3. Extracts statistics from each log file
-    4. Groups results by configuration
-    5. Displays tables and comparisons
-    6. Optionally displays speedup ratios
-    """
-    parser = ArgumentParser(description='Extract training statistics from log files')
-    parser.add_argument('dir1', type=Path, help='First log directory')
-    parser.add_argument('dir2', type=Path, help='Second log directory')
-    parser.add_argument('--threshold-fp32', type=float, default=1e-5, help='Loss difference threshold for fp32 (default: 1e-5)')
-    parser.add_argument('--threshold-bf16', type=float, default=1e-2, help='Loss difference threshold for bfloat16 (default: 1e-2)')
-    parser.add_argument('--markdown', action='store_true', help='Output as markdown table')
-    parser.add_argument('--output', type=str, default='', help='Output file path (optional)')
-    parser.add_argument('--speedup', action='store_true', help='Output speedup ratio table')
-    args = parser.parse_args()
-    
-    # Get all log files from both directories
-    files1 = [(f, args.dir1) for f in args.dir1.glob('*.log') if not f.name.startswith('build') and 'comparison' not in f.name.lower()]
-    files2 = [(f, args.dir2) for f in args.dir2.glob('*.log') if not f.name.startswith('build') and 'comparison' not in f.name.lower()]
-    
-    # Combine files from both directories
-    all_files = files1 + files2
-    
-    # Parse all files and collect statistics
-    results = []
-    for file_path, dir_path in sorted(all_files, key=lambda x: (x[1], x[0].name)):
-        filename = file_path.name
-        model = get_model_from_filename(filename)
-        dtype = get_dtype_from_filename(filename)
-        flash = get_flash_from_filename(filename, dir_path)
-        disopt = get_disopt_from_filename(filename)
-        
-        # Extract test ID from filename (e.g., gpt2_3_bfloat16_distopt_profile.log -> 3)
-        test_id_match = re.search(r'_(\d+)_', filename)
-        test_id = test_id_match.group(1) if test_id_match else ''
-        
-        # Parse log file
-        stats = parse_log_file(file_path)
-        averages = calculate_averages(stats, exclude_first_step=True)
-        
-        # Get configuration
-        config = stats['config'] if stats['config'] else {}
-        dp = config.get('dp', 1)
-        tp = config.get('tp', 1)
-        sp = config.get('sp', 1)
-        pp = config.get('pp', 1)
-        
-        # Create row identifier (include test ID to distinguish different test configurations)
-        row_id = f"{model}_{dtype}_{flash}_{disopt}_{test_id}"
-        
-        results.append({
-            'row_id': row_id,
-            'model': model,
-            'dtype': dtype,
-            'flash': flash,
-            'disopt': disopt,
-            'test_id': test_id,
-            'avg_loss': averages['avg_loss'],
-            'avg_time_ms': averages['avg_time_ms'],
-            'avg_tok_per_sec': averages['avg_tok_per_sec'],
-            'avg_peak_used': averages['avg_peak_used'],
-            'avg_peak_reserved': averages['avg_peak_reserved'],
-            'dp': dp,
-            'tp': tp,
-            'sp': sp,
-            'pp': pp
-        })
-    
-    # Sort results by row_id
-    results.sort(key=lambda x: x['row_id'])
-    
-    # Group by model, dtype, and disopt (excluding flash)
-    grouped_by_config = defaultdict(list)
-    for result in results:
-        key = f"{result['model']}_{result['dtype']}_{result['disopt']}"
-        grouped_by_config[key].append(result)
-    
-    # Output speedup ratio table if requested
-    if args.speedup:
-        # Only output speedup ratio table
-        pass
-    else:
-        # Prepare output
-        if args.markdown:
-            # Output as markdown table
-            markdown_lines = []
-            
-            # Add header
-            markdown_lines.append("| Row ID | Avg Loss | Avg Time (ms) | Avg Tok/s | Peak Used (MB) | Peak Reserved (MB) | DP | TP | SP | PP |")
-            markdown_lines.append("|---------|----------|---------------|-----------|-----------------|-------------------|----|----|----|----|")
-            
-            # Print rows (flash and noflash interleaved)
-            for key in sorted(grouped_by_config.keys()):
-                group = grouped_by_config[key]
-                if len(group) >= 2:
-                    # Separate flash and noflash results
-                    flash_results = [r for r in group if r['flash'] == 'flash']
-                    noflash_results = [r for r in group if r['flash'] == 'noflash']
-                    
-                    # Interleave flash and noflash results
-                    for i in range(max(len(flash_results), len(noflash_results))):
-                        if i < len(flash_results):
-                            result = flash_results[i]
-                            row = f"| {result['row_id']:<40} | {result['avg_loss']:<15.6f} | {result['avg_time_ms']:<15.2f} | {result['avg_tok_per_sec']:<15.2f} | {result['avg_peak_used']:<15.2f} | {result['avg_peak_reserved']:<15.2f} | {result['dp']:<5} | {result['tp']:<5} | {result['sp']:<5} | {result['pp']:<5} |"
-                            markdown_lines.append(row)
-                        if i < len(noflash_results):
-                            result = noflash_results[i]
-                            row = f"| {result['row_id']:<40} | {result['avg_loss']:<15.6f} | {result['avg_time_ms']:<15.2f} | {result['avg_tok_per_sec']:<15.2f} | {result['avg_peak_used']:<15.2f} | {result['avg_peak_reserved']:<15.2f} | {result['dp']:<5} | {result['tp']:<5} | {result['sp']:<5} | {result['pp']:<5} |"
-                            markdown_lines.append(row)
-                else:
-                    # Only one result, print it
-                    for result in group:
-                        row = f"| {result['row_id']:<40} | {result['avg_loss']:<15.6f} {result['avg_time_ms']:<15.2f} {result['avg_tok_per_sec']:<15.2f} {result['avg_peak_used']:<15.2f} | {result['avg_peak_reserved']:<15.2f} | {result['dp']:<5} | {result['tp']:<5} | {result['sp']:<5} | {result['pp']:<5} |"
-                        markdown_lines.append(row)
-            
-            # Print markdown table
-            markdown_table = "\n".join(markdown_lines)
-            print(markdown_table)
-            
-            # Write to file if output path is specified
-            if args.output:
-                with open(args.output, 'w') as f:
-                    f.write(markdown_table)
-                print(f"Markdown table saved to: {args.output}")
-        else:
-            # Print plain text table
-            print("\n" + "=" * 150)
-            print("Training Statistics Table (excluding step 1)")
-            print("=" * 150)
-            
-            # Print header
-            header = f"{'Row ID':<40} {'Avg Loss':<15} {'Avg Time (ms)':<15} {'Avg Tok/s':<15} {'Peak Used (MB)':<15} {'Peak Reserved (MB)':<15} {'DP':<5} {'TP':<5} {'SP':<5} {'PP':<5}"
-            print(header)
-            print("-" * 150)
-            
-            # Print rows (flash and noflash interleaved)
-            for key in sorted(grouped_by_config.keys()):
-                group = grouped_by_config[key]
-                if len(group) >= 2:
-                    # Separate flash and noflash results
-                    flash_results = [r for r in group if r['flash'] == 'flash']
-                    noflash_results = [r for r in group if r['flash'] == 'noflash']
-                    
-                    # Interleave flash and noflash results
-                    for i in range(max(len(flash_results), len(noflash_results))):
-                        if i < len(flash_results):
-                            result = flash_results[i]
-                            row = f"{result['row_id']:<40} {result['avg_loss']:<15.6f} {result['avg_time_ms']:<15.2f} {result['avg_tok_per_sec']:<15.2f} {result['avg_peak_used']:<15.2f} {result['avg_peak_reserved']:<15.2f} {result['dp']:<5} {result['tp']:<5} {result['sp']:<5} {result['pp']:<5}"
-                            print(row)
-                        if i < len(noflash_results):
-                            result = noflash_results[i]
-                            row = f"{result['row_id']:<40} {result['avg_loss']:<15.6f} {result['avg_time_ms']:<15.2f} {result['avg_tok_per_sec']:<15.2f} {result['avg_peak_used']:<15.2f} {result['avg_peak_reserved']:<15.2f} {result['dp']:<5} {result['tp']:<5} {result['sp']:<5} {result['pp']:<5}"
-                            print(row)
-            else:
-                # Only one result, print it
-                for result in group:
-                    row = f"{result['row_id']:<40} {result['avg_loss']:<15.6f} {result['avg_time_ms']:<15.2f} {result['avg_tok_per_sec']:<15.2f} {result['avg_peak_used']:<15.2f} {result['avg_peak_reserved']:<15.2f} {result['dp']:<5} {result['tp']:<5} {result['sp']:<5} {result['pp']:<5}"
-                    print(row)
-        
-        print("=" * 150)
-        
-        # Print comparison between flash and noflash
-        print("\nComparison between flash and noflash:")
-        print("=" * 150)
-        
-        for key, group in sorted(grouped_by_config.items()):
-            # Separate flash and noflash results
-            flash_results = [r for r in group if r['flash'] == 'flash']
-            noflash_results = [r for r in group if r['flash'] == 'noflash']
-            
-            if flash_results and noflash_results:
-                # Calculate averages for each group
-                avg_flash_loss = sum(r['avg_loss'] for r in flash_results) / len(flash_results)
-                avg_flash_time = sum(r['avg_time_ms'] for r in flash_results) / len(flash_results)
-                avg_flash_tok = sum(r['avg_tok_per_sec'] for r in flash_results) / len(flash_results)
-                avg_flash_peak_used = sum(r['avg_peak_used'] for r in flash_results) / len(flash_results)
-                avg_flash_peak_reserved = sum(r['avg_peak_reserved'] for r in flash_results) / len(flash_results)
-                
-                avg_noflash_loss = sum(r['avg_loss'] for r in noflash_results) / len(noflash_results)
-                avg_noflash_time = sum(r['avg_time_ms'] for r in noflash_results) / len(noflash_results)
-                avg_noflash_tok = sum(r['avg_tok_per_sec'] for r in noflash_results) / len(noflash_results)
-                avg_noflash_peak_used = sum(r['avg_peak_used'] for r in noflash_results) / len(noflash_results)
-                avg_noflash_peak_reserved = sum(r['avg_peak_reserved'] for r in noflash_results) / len(noflash_results)
-                
-                # Calculate differences
-                loss_diff = avg_flash_loss - avg_noflash_loss
-                time_diff = avg_flash_time - avg_noflash_time
-                tok_diff = avg_flash_tok - avg_noflash_tok
-                
-                loss_pct = (loss_diff / avg_noflash_loss * 100) if avg_noflash_loss > 0 else 0
-                time_pct = (time_diff / avg_noflash_time * 100) if avg_noflash_time > 0 else 0
-                tok_pct = (tok_diff / avg_noflash_tok * 100) if avg_noflash_tok > 0 else 0
-                
-                print(f"\n{key}:")
-                print(f"  Loss: {avg_flash_loss:.6f} vs {avg_noflash_loss:.6f} (diff: {loss_diff:+.6f}, {loss_pct:+.2f}%)")
-                print(f"  Time: {avg_flash_time:.2f} vs {avg_noflash_time:.2f} ms (diff: {time_diff:+.2f}, {time_pct:+.2f}%)")
-                print(f"  Tok/s: {avg_flash_tok:.2f} vs {avg_noflash_tok:.2f} (diff: {tok_diff:+.2f}, {tok_pct:+.2f}%)")
-                
-                # Calculate memory differences
-                mem_used_diff = avg_flash_peak_used - avg_noflash_peak_used
-                mem_reserved_diff = avg_flash_peak_reserved - avg_noflash_peak_reserved
-                
-                mem_used_pct = (mem_used_diff / avg_noflash_peak_used * 100) if avg_noflash_peak_used > 0 else 0
-                mem_reserved_pct = (mem_reserved_diff / avg_noflash_peak_reserved * 100) if avg_noflash_peak_reserved > 0 else 0
-                
-                print(f"  Peak Used: {avg_flash_peak_used:.2f} vs {avg_noflash_peak_used:.2f} MB (diff: {mem_used_diff:+.2f}, {mem_used_pct:+.2f}%)")
-                print(f"  Peak Reserved: {avg_flash_peak_reserved:.2f} vs {avg_noflash_peak_reserved:.2f} MB (diff: {mem_reserved_diff:+.2f}, {mem_reserved_pct:+.2f}%)")
-        print("\n" + "=" * 120)
-        print("Speedup Ratio Table (noflash_time / flash_time)")
-        print("=" * 120)
-        
-        # Group by model, dtype, disopt, and test_id
-        # This allows us to calculate speedup for each individual test configuration
-        grouped_by_test = defaultdict(list)
-        for result in results:
-            key = f"{result['model']}_{result['dtype']}_{result['disopt']}_{result.get('test_id', '')}"
-            grouped_by_test[key].append(result)
-        
-        # Calculate speedup ratios for each test configuration
-        speedup_results = []
-        for key, group in sorted(grouped_by_test.items()):
-            # Separate flash and noflash results
-            flash_results = [r for r in group if r['flash'] == 'flash']
-            noflash_results = [r for r in group if r['flash'] == 'noflash']
-            
-            if flash_results and noflash_results:
-                # Calculate average times for this test configuration
-                avg_flash_time = sum(r['avg_time_ms'] for r in flash_results) / len(flash_results)
-                avg_noflash_time = sum(r['avg_time_ms'] for r in noflash_results) / len(noflash_results)
-                
-                # Calculate speedup ratio
-                # Speedup > 1.0 means flash is faster than noflash
-                speedup = avg_noflash_time / avg_flash_time if avg_flash_time > 0 else 0
-                
-                # Extract test_id from key
-                test_id = key.split('_')[-1]
-                
-                speedup_results.append({
-                    'config': key,
-                    'speedup': speedup,
-                    'flash_time': avg_flash_time,
-                    'noflash_time': avg_noflash_time
-                })
-        
-        # Sort by configuration name (alphabetical order)
-        speedup_results.sort(key=lambda x: x['config'])
-        
-        # Print speedup table
-        if args.markdown:
-            # Output as markdown table
-            markdown_lines = []
-            markdown_lines.append("| Configuration | Speedup Ratio | Flash Time (ms) | Noflash Time (ms) |")
-            markdown_lines.append("|---------------|---------------|-----------------|-------------------|")
-            
-            for result in speedup_results:
-                row = f"| {result['config']:<37} | {result['speedup']:<13.2f} | {result['flash_time']:<15.2f} | {result['noflash_time']:<17.2f} |"
-                markdown_lines.append(row)
-            
-            # Print markdown table
-            markdown_table = "\n".join(markdown_lines)
-            print(markdown_table)
-            
-            # Write to file if output path is specified
-            if args.output:
-                output_path = args.output.replace('.md', '_speedup.md')
-                with open(output_path, 'w') as f:
-                    f.write(markdown_table)
-                print(f"Speedup table saved to: {output_path}")
-        else:
-            # Print plain text table
-            header = f"{'Configuration':<37} {'Speedup Ratio':<15} {'Flash Time (ms)':<17} {'Noflash Time (ms)':<17}"
-            print(header)
-            print("-" * 120)
-            
-            for result in speedup_results:
-                row = f"{result['config']:<37} {result['speedup']:<15.2f} {result['flash_time']:<17.2f} {result['noflash_time']:<17.2f}"
-                print(row)
-        
-        print("=" * 120)
-
-
-if __name__ == '__main__':
-    main()

From 1fc2d6521567f233cfd142e983e878391fc8c124 Mon Sep 17 00:00:00 2001
From: Aoshine999 <820459542@qq.com>
Date: Mon, 16 Mar 2026 15:58:10 +0800
Subject: [PATCH 15/18] delete config file

---
 algo.tex                     |  75 ---------
 scripts/test_config.json.bak | 299 -----------------------------------
 2 files changed, 374 deletions(-)
 delete mode 100644 algo.tex
 delete mode 100644 scripts/test_config.json.bak

diff --git a/algo.tex b/algo.tex
deleted file mode 100644
index cfb8861b..00000000
--- a/algo.tex
+++ /dev/null
@@ -1,75 +0,0 @@
-\begin{algorithm}[H]
-  % \algsetup{linenosize=\tiny}
-  \caption{\small\label{alg:flash2_fwd}\sysname forward pass}
-  \begin{algorithmic}[1]
-    \REQUIRE Matrices $\vQ, \vK, \vV \in \mathbb{R}^{N \times d}$ in HBM, block sizes $B_c$, $B_r$.
-    \STATE \label{alg:stream_attn_split_qkv} Divide $\vQ$ into $T_r = \left\lceil\frac{N}{B_r} \right\rceil$ blocks $\vQ_1, \dots, \vQ_{T_r}$ of size $B_r \times d$ each,
-    and divide $\vK, \vV$ in to $T_c = \left\lceil \frac{N}{B_c} \right\rceil$ blocks $\vK_1, \dots, \vK_{T_c}$ and
-    $\vV_1, \dots, \vV_{T_c}$, of size $B_c \times d$ each.
-    \STATE Divide the output $\vO \in \mathbb{R}^{N \times d}$ into $T_r$ blocks $\vO_i, \dots, \vO_{T_r}$ of size
-    $B_r \times d$ each, and divide the logsumexp $L$ into $T_r$ blocks $L_i, \dots, L_{T_r}$ of size
-    $B_r$ each.
-    \FOR{$1 \le i \le T_r$} \label{alg:stream_attn_outer_loop}
-      \STATE \label{alg:stream_attn_load_q} Load $\vQ_i$ from HBM to on-chip SRAM.
-      \STATE \label{alg:stream_attn_init} On chip, initialize $\vO_{i}^{(0)} = (0)_{B_r \times d} \in \mathbb{R}^{B_r \times d}, \ell_{i}^{(0)} = (0)_{B_r} \in \mathbb{R}^{B_r}, m_{i}^{(0)} = (-\infty)_{B_r} \in \mathbb{R}^{B_r}$.
-      \FOR{$1 \le j \le T_c$}
-        \STATE \label{alg:stream_attn_load_kv} Load $\vK_j, \vV_j$ from HBM to on-chip SRAM.
-        \STATE \label{alg:stream_attn_qk} On chip, compute $\vS_{i}^{(j)} = \vQ_i \vK_j^T \in \mathbb{R}^{B_r \times B_c}$.
-        \STATE \label{alg:stream_attn_statistics} On chip, compute
-        $m_{i}^{(j)} = \mathrm{max}(m_{i}^{(j-1)}, \mathrm{rowmax}(\vS_{i}^{(j)})) \in \mathbb{R}^{B_r}$, $\tilde{\vP}_{i}^{(j)} = \exp(\vS_{i}^{(j)} - m_{i}^{(j)}) \in \mathbb{R}^{B_r \times B_c}$ (pointwise),
-        $\ell_{i}^{(j)} = e^{m_{i}^{j-1} - m_{i}^{(j)}} \ell_{i}^{(j-1)} + \mathrm{row sum}(\tilde{\vP}_{i}^{(j)}) \in \mathbb{R}^{B_r}$.
-        \STATE \label{alg:stream_attn_update} On chip, compute
-        $\vO_{i}^{(j)} = \diag(e^{m_{i}^{(j-1)} - m_{i}^{(j)}}) \vO_{i}^{(j-1)} + \tilde{\vP}_{i}^{(j)} \vV_j$.
-      \ENDFOR
-      \STATE On chip, compute $\vO_{i} = \diag(\ell_{i}^{(T_c)})^{-1} \vO_{i}^{(T_c)}$.
-      \STATE On chip, compute $L_{i} = m_{i}^{(T_c)} + \log(\ell_i^{(T_c)})$.
-      \STATE Write $\vO_{i}$ to HBM as the $i$-th block of $\vO$.
-      \STATE Write $L_{i}$ to HBM as the $i$-th block of $L$.
-    \ENDFOR
-    \STATE Return the output $\vO$ and the logsumexp $L$.
-  \end{algorithmic}
-\end{algorithm}
-
-
-
-
-\begin{algorithm}[h]
-  \caption{\small\label{alg:flash_bwd}\sysname Backward Pass}
-  \begin{algorithmic}[1]
-    \REQUIRE Matrices $\vQ, \vK, \vV, \vO, \vdO \in \mathbb{R}^{N \times d}$ in HBM,
-    vector $L \in \mathbb{R}^N$ in HBM, block sizes $B_c$, $B_r$.
-    \STATE Divide $\vQ$ into $T_r = \left\lceil\frac{N}{B_r} \right\rceil$ blocks $\vQ_1, \dots, \vQ_{T_r}$ of size $B_r \times d$ each,
-    and divide $\vK, \vV$ in to $T_c = \left\lceil \frac{N}{B_c} \right\rceil$ blocks $\vK_1, \dots, \vK_{T_c}$ and
-    $\vV_1, \dots, \vV_{T_c}$, of size $B_c \times d$ each.
-    \STATE Divide $\vO$ into $T_r$ blocks $\vO_i, \dots, \vO_{T_r}$ of size
-    $B_r \times d$ each, divide $\vdO$ into $T_r$ blocks $\vdO_i, \dots, \vdO_{T_r}$
-    of size $B_r \times d$ each, and divide $L$ into $T_r$ blocks $L_i, \dots, L_{T_r}$ of size
-    $B_r$ each.
-    \STATE Initialize $\vdQ = (0)_{N \times d}$ in HBM and divide it into $T_r$ blocks $\vdQ_1, \dots, \vdQ_{T_r}$ of size $B_r \times d$ each.
-    Divide $\vdK, \vdV \in \mathbb{R}^{N \times d}$ in to $T_c$ blocks $\vdK_1, \dots, \vdK_{T_c}$ and
-    $\vdV_1, \dots, \vdV_{T_c}$, of size $B_c \times d$ each.
-    \STATE Compute $D = \mathrm{rowsum}(\vdO \circ \vO) \in \mathbb{R}^d$ (pointwise multiply), write
-    $D$ to HBM and divide it into $T_r$ blocks $D_1, \dots, D_{T_r}$ of size
-    $B_r$ each.
-    \FOR{$1 \le j \le T_c$}
-      \STATE Load $\vK_j, \vV_j$ from HBM to on-chip SRAM.
-      \STATE Initialize $\vdK_j = (0)_{B_c \times d}, \vdV_j = (0)_{B_c \times d}$ on SRAM.
-      \FOR{$1 \le i \le T_r$}
-        \STATE Load $\vQ_i, \vO_i, \vdO_i, \vdQ_i, L_i, D_i$ from HBM to on-chip SRAM.
-        \STATE On chip, compute $\vS_{i}^{(j)} = \vQ_i \vK_j^T \in \mathbb{R}^{B_r \times B_c}$.
-        \STATE On chip, compute $\vP_{i}^{(j)} = \exp(\vS_{ij} - L_{i}) \in \mathbb{R}^{B_r \times B_c}$.
-        \STATE On chip, compute
-        $\vdV_j \leftarrow \vdV_j + (\vP_{i}^{(j)})^\top \vdO_i \in \mathbb{R}^{B_c \times d}$.
-        \STATE On chip, compute
-        $\vdP_{i}^{(j)} = \vdO_{i} \vV_j^\top \in \mathbb{R}^{B_r \times B_c}$.
-        \STATE On chip, compute $\vdS_{i}^{(j)} = \vP_{i}^{(j)} \circ (\vdP_{i}^{(j)} - D_i) \in \mathbb{R}^{B_r \times B_c}$.
-        \STATE Load $\vdQ_i$ from HBM to SRAM, then on chip, update
-        $\vdQ_{i} \leftarrow \vdQ_i + \vdS_{i}^{(j)} \vK_j \in \mathbb{R}^{B_r \times d}$, and write
-        back to HBM.
-        \STATE On chip, compute $\vdK_{j} \leftarrow \vdK_j + {\vdS_{i}^{(j)}}^\top \vQ_i \in \mathbb{R}^{B_c \times d}$.
-      \ENDFOR
-      \STATE Write $\vdK_j, \vdV_j$ to HBM.
-    \ENDFOR
-    \STATE Return $\vdQ, \vdK, \vdV$.
-  \end{algorithmic}
-\end{algorithm}
\ No newline at end of file
diff --git a/scripts/test_config.json.bak b/scripts/test_config.json.bak
deleted file mode 100644
index 3d66ff7b..00000000
--- a/scripts/test_config.json.bak
+++ /dev/null
@@ -1,299 +0,0 @@
-{
-    "variables": {
-        "BUILD_DIR": "../build",
-        "GPT2_INPUT_BIN": "/data/shared/InfiniTrain-dev/data/llmc/gpt2/tinyshakespeare/tiny_shakespeare_train.bin",
-        "GPT2_LLMC_FILEPATH": "/data/shared/InfiniTrain-dev/data/llmc/gpt2/gpt2_124M.bin",
-        "LLAMA3_INPUT_BIN": "/data/shared/InfiniTrain-dev/data/llmc/llama3/tinyshakespeare/tiny_shakespeare_train.bin",
-        "LLAMA3_LLMC_FILEPATH": "/data/shared/InfiniTrain-dev/data/llmc/llama3/llama3.2_1B_fp32.bin",
-        "PROFILE_LOG_DIR": "./profile_logs",
-        "LOG_DIR": "./logs",
-        "COMPARE_LOG_DIR": ""
-    },
-    "builds": [
-        {
-            "id": "build_1",
-            "profile": false,
-            "cmd": "cmake -DUSE_CUDA=ON -DUSE_NCCL=ON .. && make -j"
-        },
-        {
-            "id": "build_2",
-            "profile": true,
-            "cmd": "cmake -DUSE_CUDA=ON -DUSE_NCCL=ON -DPROFILE_MODE=ON .. && make -j"
-        }
-    ],
-    "tests": [
-        {
-            "id": "1",
-            "args": {
-                "dtype": "float32"
-            }
-        },
-        {
-            "id": "1_bfloat16",
-            "args": {
-                "dtype": "bfloat16"
-            }
-        },
-        {
-            "id": "2",
-            "args": {
-                "dtype": "float32",
-                "num_iteration": 10,
-                "batch_size": 80,
-                "total_batch_size": 5120
-            }
-        },
-        {
-            "id": "2_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "num_iteration": 10,
-                "batch_size": 80,
-                "total_batch_size": 5120
-            }
-        },
-        {
-            "id": "3",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120
-            }
-        },
-        {
-            "id": "3_distopt",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "3_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120
-            }
-        },
-        {
-            "id": "3_bfloat16_distopt",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "4",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4
-            }
-        },
-        {
-            "id": "4_distopt",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "4_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4
-            }
-        },
-        {
-            "id": "4_bfloat16_distopt",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "5",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "sequence_parallel": true
-            }
-        },
-        {
-            "id": "5_distopt",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "sequence_parallel": true,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "5_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "sequence_parallel": true
-            }
-        },
-        {
-            "id": "5_bfloat16_distopt",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "sequence_parallel": true,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "6",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "pipeline_parallel": 8
-            }
-        },
-        {
-            "id": "6_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "pipeline_parallel": 8
-            }
-        },
-        {
-            "id": "7",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 4,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "pipeline_parallel": 4,
-                "virtual_pipeline_parallel": 2
-            }
-        },
-        {
-            "id": "7_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 4,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "pipeline_parallel": 4,
-                "virtual_pipeline_parallel": 2
-            }
-        },
-        {
-            "id": "8",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 2,
-                "sequence_parallel": true,
-                "pipeline_parallel": 2,
-                "virtual_pipeline_parallel": 2
-            }
-        },
-        {
-            "id": "8_distopt",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 2,
-                "sequence_parallel": true,
-                "pipeline_parallel": 2,
-                "virtual_pipeline_parallel": 2,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "8_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 2,
-                "sequence_parallel": true,
-                "pipeline_parallel": 2,
-                "virtual_pipeline_parallel": 2
-            }
-        },
-        {
-            "id": "8_bfloat16_distopt",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 2,
-                "sequence_parallel": true,
-                "pipeline_parallel": 2,
-                "virtual_pipeline_parallel": 2,
-                "use_distributed_optimizer": true
-            }
-        }
-    ]
-}
-

From 804df62f4f869fd2654874af1178f441c452705b Mon Sep 17 00:00:00 2001
From: Aoshine999 <820459542@qq.com>
Date: Mon, 16 Mar 2026 15:59:43 +0800
Subject: [PATCH 16/18] delete unrelated file

---
 .../src/kernels/cuda/flash_attention1.cu      | 1083 -----------------
 1 file changed, 1083 deletions(-)
 delete mode 100644 infini_train/src/kernels/cuda/flash_attention1.cu

diff --git a/infini_train/src/kernels/cuda/flash_attention1.cu b/infini_train/src/kernels/cuda/flash_attention1.cu
deleted file mode 100644
index 9a78f802..00000000
--- a/infini_train/src/kernels/cuda/flash_attention1.cu
+++ /dev/null
@@ -1,1083 +0,0 @@
-// #include <cmath>
-// #include <cstddef>
-// #include <curand_kernel.h>
-// #include "glog/logging.h"
-
-// #include "infini_train/include/common/cuda/common_cuda.h"
-// #include "infini_train/include/common/cuda/kernel_helper.cuh"
-// #include "infini_train/include/core/runtime/device_guard.h"
-// #include "infini_train/include/dispatcher.h"
-// #include "infini_train/include/kernels/cuda/flash_attention.h"
-// #include "infini_train/include/tensor.h"
-
-// #include "infini_train/src/core/runtime/cuda/cuda_runtime_common.h"
-
-// namespace infini_train::kernels::cuda {
-
-// /**
-//  * FlashAttention Forward Kernel
-//  *
-//  * This kernel implements the FlashAttention algorithm for efficient attention computation.
-//  * It uses tiling and recomputation to reduce memory access and improve performance.
-//  *
-//  * Args:
-//  *   output: Output tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
-//  *   query: Query tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
-//  *   key: Key tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
-//  *   value: Value tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
-//  *   attn_mask: Optional attention mask tensor
-//  *   logsumexp: Save logsumexp for backward pass
-//  *   scale: Scaling factor for attention scores
-//  *   is_causal: Whether to apply causal masking
-//  *   enable_gqa: Whether to enable grouped-query attention
-//  *   dropout_p: Dropout probability
-//  *   dropout_seed: Random seed for dropout (for reproducibility)
-//  *   batch_size, target_seq_len, src_seq_len: Tensor dimensions
-//  *   q_heads, kv_heads, head_dim: Attention head dimensions
-//  *
-//  * Note: Actual kernel implementation is not included in this version.
-//  */
-// template <typename T>
-// __device__ T warp_reduce_sum(T val){
-// #pragma unroll//短循环自动展开,省去分支预测,提升效率
-//     for(int offset = 16; offset > 0; offset >>= 1){
-//         val += __shfl_down_sync(0xffffffff, val, offset);
-//     }
-//     return val;
-// }
-
-// template <typename T>
-// __device__ T myexp(T x) {
-//     if constexpr(std::is_same<T, __half>::value) {
-//         float fx = __half2float(x);
-//         float result = expf(fx);
-//         return __float2half(result);
-//     }
-//     else if constexpr(std::is_same<T, float>::value) {
-//         return expf(x);  // expf返回float
-//     }
-//     else if constexpr(std::is_same<T, double>::value) {
-//         return exp(x);   // exp返回double
-//     }
-//     else{//other types
-//       return T(0);
-//     }
-// }
-
-// template <typename T>
-// __device__ T warp_reduce_max(T val){
-// #pragma unroll//短循环自动展开,省去分支预测,提升效率
-//     for(int offset = 16; offset > 0; offset >>= 1){
-//         T tmp = __shfl_down_sync(0xffffffff, val, offset);
-//         val = (val > tmp) ? val : tmp;
-//     }
-//     return val;
-// }
-
-// __device__ __forceinline__ bool FlashAttnDropoutKeep(
-//     unsigned long long seed,
-//     int batch_idx, int head_idx, int q_idx, int k_idx,
-//     int q_heads, int target_seq_len, int src_seq_len,
-//     float dropout_p) {
-//     // Stateless RNG mapping for one attention element (q_idx, k_idx).
-//     unsigned long long linear_idx =
-//         (((static_cast<unsigned long long>(batch_idx) * static_cast<unsigned long long>(q_heads)
-//            + static_cast<unsigned long long>(head_idx))
-//           * static_cast<unsigned long long>(target_seq_len)
-//           + static_cast<unsigned long long>(q_idx))
-//          * static_cast<unsigned long long>(src_seq_len))
-//         + static_cast<unsigned long long>(k_idx);
-//     curandStatePhilox4_32_10_t state;
-//     curand_init(seed, linear_idx, 0, &state);
-//     float rand_val = curand_uniform(&state);
-//     return rand_val > dropout_p;
-// }
-
-
-// /**
-//  * FlashAttention Forward Kernel
-//  *
-//  * This kernel implements the FlashAttention algorithm for efficient attention computation.
-//  * It uses tiling and recomputation to reduce memory access and improve performance.
-//  *
-//  * Args:
-//  *   output: Output tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
-//  *   query: Query tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
-//  *   key: Key tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
-//  *   value: Value tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
-//  *   attn_mask: Optional attention mask tensor
-//  *   logsumexp: Save logsumexp for backward pass
-//  *   scale: Scaling factor for attention scores
-//  *   is_causal: Whether to apply causal masking
-//  *   enable_gqa: Whether to enable grouped-query attention
-//  *   dropout_p: Dropout probability
-//  *   dropout_seed: Random seed for dropout (for reproducibility)
-//  *   batch_size, target_seq_len, src_seq_len: Tensor dimensions
-//  *   q_heads, kv_heads, head_dim: Attention head dimensions
-//  *
-//  * Note: Actual kernel implementation is not included in this version.
-//  */
-// template <typename T>
-// __global__ void FlashAttentionForwardKernel(
-//     T *output, const T *query, const T *key, const T *value, const T *attn_mask,
-//     float *logsumexp,  // Save logsumexp for backward pass
-//     float scale, bool is_causal, bool enable_gqa, int64_t dropout_p,
-//     unsigned long long dropout_seed,  // Use dropout_seed instead of dropout_mask
-//     int batch_size, int target_seq_len, int src_seq_len,
-//     int q_heads, int kv_heads, int head_dim) {
-    
-//     int tid_x = threadIdx.x;          // 横向,blockDim.x列 (Bc)
-//     int tid_y = threadIdx.y;          // 纵向,blockDim.y行 (Br)
-//     int bid_x = blockIdx.x;           // x方向,总数 = #q_heads
-//     int bid_y = blockIdx.y;           // y方向,总数 = #batch
-//     int bid_z = blockIdx.z;           // z方向,总数 = Tr
-//     const int p = q_heads / kv_heads; // 计算比例系数，GQA支持
-
-//     const int Br = blockDim.y;                  // Q纵向每块大小, 32
-//     const int Bc = blockDim.x;                  // K/V纵向分块大小, 32
-//     const int Tc = gridDim.z; // 对应原始论文中K/V纵向分块数Tc,其中Bc = 32
-
-//     // 定义一系列临时变量
-//     extern __shared__ char shared_mem[];
-//     char *ptr = shared_mem;
-    
-//     // 计算中间变量,包括S, P(复用为SP), m_prev, m_new, l_prev, l_new
-//     double *SP = reinterpret_cast<double *>(ptr); // double SP[Br][Bc]
-//     ptr += Br * Bc * sizeof(double);
-//     float *m_prev = reinterpret_cast<float *>(ptr); // float m_prev[Br]
-//     ptr += Br * sizeof(float);
-//     float *m_new = reinterpret_cast<float *>(ptr); // float m_new[Br]
-//     ptr += Br * sizeof(float);
-//     float *l_prev = reinterpret_cast<float *>(ptr); // float l_prev[Br]
-//     ptr += Br * sizeof(float);
-//     float *l_new = reinterpret_cast<float *>(ptr); // float l_new[Br]
-//     ptr += Br * sizeof(float);
-
-//     // 原始数据QKV和计算结果O; 全采用float
-//     float *Q_sm = reinterpret_cast<float *>(ptr); // float Q_sm[Br][head_dim]
-//     ptr += Br * head_dim * sizeof(float);
-//     float *K_T_sm = reinterpret_cast<float *>(ptr); // float K_T_sm[head_dim][Bc]
-//     ptr += head_dim * Bc * sizeof(float);
-//     float *V_sm = reinterpret_cast<float *>(ptr); // float V_sm[Bc][head_dim]
-//     ptr += Bc * head_dim * sizeof(float);
-//     float *O_sm = reinterpret_cast<float *>(ptr); // float O_sm[Br][head_dim]
-//     // Note: Removed dropout_sm shared memory allocation
-
-//     // 定义访问宏
-// #define SP_AT(y, x) SP[y * Bc + x]
-// #define Q_sm_AT(y, x) Q_sm[y * head_dim + x]
-// #define K_T_sm_AT(y, x) K_T_sm[y * Bc + x]
-// #define V_sm_AT(y, x) V_sm[y * head_dim + x]
-// #define O_sm_AT(y, x) O_sm[y * head_dim + x]
-//     // Note: Removed DROPOUT_SM_AT macro
-
-//     /****************************preparation**************************/
-//     int bound_tid_y = min(Br, target_seq_len - Br * bid_z);
-
-//     // preparation-1: load Qi from GM to SM, and reset Oi to 0
-//     for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-//         O_sm_AT(tid_y, idx) = 0.0;
-//         Q_sm_AT(tid_y, idx) = 0.0;
-//         if (tid_y < bound_tid_y) {
-//             Q_sm_AT(tid_y, idx)
-//                 = float(query[((((bid_y * target_seq_len) + (Br * bid_z + tid_y)) * q_heads) + bid_x) * head_dim + idx]);
-//         }
-//     }
-//     __syncthreads();
-
-//     // preparation-2: reset m_prev to -INFINITY and l_prev to 0
-//     if (tid_x == 0) {
-//         m_prev[tid_y] = -8192.0;
-//         l_prev[tid_y] = 0.0;
-//     }
-//     __syncthreads();
-
-// /****************************end-of-preparation*************************/
-
-// /****************************main-loop**************************/
-// #pragma unroll 4
-//     for (int j = 0; j < Tc; ++j) { // 对于每个K/V分块
-//         bool skip = (is_causal && bid_z < j);
-//         if (skip) { // early exit, 直接跳过
-//             __syncthreads();
-//             continue;
-//         }
-
-//         SP_AT(tid_y, tid_x) = -8192.0;
-//         __syncthreads();
-//         int bound_tid_x = min(Bc, src_seq_len - Bc * j);
-//         bool is_compute = true; // optimization: 分支处理,加速branch-resolving
-//         if (is_causal) {
-//             if (bid_z < j) {
-//                 is_compute = false; // 早期退出情况
-//             } else if (bid_z == j) {
-//                 is_compute = (tid_y >= tid_x); // 对角线以上
-//             }
-//         }
-
-//         // step-1: load Ki, Vi from GM to SM
-// #pragma unroll
-//         for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-//             K_T_sm_AT(idx, tid_y) = 0.0;
-//             V_sm_AT(tid_y, idx) = 0.0;
-//             if (tid_y < bound_tid_x) {
-//                 K_T_sm_AT(idx, tid_y) = float(
-//                     K[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads) + (bid_x / p)) * head_dim + idx]);
-//                 V_sm_AT(tid_y, idx) = float(
-//                     V[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads) + (bid_x / p)) * head_dim + idx]);
-//             }
-//         }
-//         __syncthreads();
-
-//         // step-2: S = Q @ K.T, point-wise
-//         if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-//             float val0 = 0.0;
-//             if (is_compute) {
-// #pragma unroll
-//                 for (int k = 0; k < head_dim; ++k) { 
-//                     val0 += Q_sm_AT(tid_y, k) * K_T_sm_AT(k, tid_x); 
-//                 }
-//                 SP_AT(tid_y, tid_x) = double(val0) * scale;
-//             }
-//         }
-//         __syncthreads();
-
-//         // step-3: m_new = max(m_prev, rowMax(S))
-//         float val1 = float(SP_AT(tid_y, tid_x));
-//         val1 = warp_reduce_max(val1);
-//         if (tid_x == 0 && tid_y < bound_tid_y) {
-//             m_new[tid_y] = (val1 > m_prev[tid_y]) ? val1 : m_prev[tid_y];
-//         }
-//         __syncthreads();
-
-//         // step-4: P = exp(S - m_new), point-wise
-//         if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-//             if (is_compute) {
-//                 SP_AT(tid_y, tid_x) = myexp<double>(SP_AT(tid_y, tid_x) - double(m_new[tid_y]));
-//             } else {
-//                 SP_AT(tid_y, tid_x) = 0.0;
-//             }
-//         } else {
-//             SP_AT(tid_y, tid_x) = 0.0;
-//         }
-//         __syncthreads();
-
-//         // step-5: l_new = exp(m_prev - m_new) * l_prev + rowSum(P)
-//         float val2 = float(SP_AT(tid_y, tid_x));
-//         val2 = warp_reduce_sum(val2);
-//         float exp_result = myexp<float>(m_prev[tid_y] - m_new[tid_y]);
-//         if (tid_x == 0 && tid_y < bound_tid_y) {
-//             l_new[tid_y] = exp_result * l_prev[tid_y] + val2;
-//         }
-//         __syncthreads();
-
-//         // step-5.5: Apply dropout to P (using dropout_seed for reproducibility)
-//         if (dropout_p > 0 && tid_y < bound_tid_y && tid_x < bound_tid_x) {
-//             int global_q_idx = Br * bid_z + tid_y;
-//             int global_k_idx = Bc * j + tid_x;
-//             bool keep = FlashAttnDropoutKeep(
-//                 dropout_seed, bid_y, bid_x, global_q_idx, global_k_idx,
-//                 q_heads, target_seq_len, src_seq_len, static_cast<float>(dropout_p));
-//             if (keep) {
-//                 SP_AT(tid_y, tid_x) = SP_AT(tid_y, tid_x) / (1.0f - dropout_p);
-//             } else {
-//                 SP_AT(tid_y, tid_x) = 0.0;
-//             }
-//             // Note: Removed dropout_sm mask saving to reduce memory overhead
-//         }
-//         __syncthreads();
-
-//         // step-6: O = 1/(exp(m_prev - m_new)) * O + P @ V
-//         if (tid_x < bound_tid_x && tid_y < bound_tid_y) {
-//             for (int u = tid_x; u < head_dim; u += blockDim.x) {
-//                 float val3 = 0.0;
-// #pragma unroll
-//                 for (int w = 0; w < Bc; ++w) { 
-//                     val3 += float(SP_AT(tid_y, w)) * V_sm_AT(w, u);
-//                 }
-//                 O_sm_AT(tid_y, u) = O_sm_AT(tid_y, u) * exp_result + val3;
-//             }
-//         }
-//         __syncthreads();
-
-//         // step-7: m_prev <- m_new; l_prev <- l_new
-//         if (tid_x == 0 && tid_y < bound_tid_y) {
-//             m_prev[tid_y] = m_new[tid_y];
-//             l_prev[tid_y] = l_new[tid_y];
-//         }
-//         __syncthreads();
-//     }
-// /****************************end-of-main-loop**************************/
-
-// /****************************post-process****************************/
-// // O(GM) = O/l_prev, aka O_sm /= l_prev and write Oi from SM to GM
-// // Also save logsumexp for backward pass
-// #pragma unroll
-//     for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-//         if (tid_y < bound_tid_y) {
-//             output[((((bid_y * target_seq_len) + (Br * bid_z + tid_y)) * q_heads) + bid_x) * head_dim + idx]
-//                 = T(O_sm_AT(tid_y, idx) / float(l_prev[tid_y]));
-//         }
-//     }
-    
-//     // Save logsumexp for backward pass
-//     if (tid_x == 0 && tid_y < bound_tid_y) {
-//         int logsumexp_idx = ((bid_y * q_heads + bid_x) * target_seq_len) + (Br * bid_z + tid_y);
-//         logsumexp[logsumexp_idx] = m_prev[tid_y] + log(l_prev[tid_y]);
-//     }
-//     __syncthreads();
-// /****************************end-of-post-process****************************/
-
-// // 取消访问宏定义
-// #undef SP_AT
-// #undef Q_sm_AT
-// #undef K_T_sm_AT
-// #undef V_sm_AT
-// #undef O_sm_AT
-// }
-
-// /**
-//  * FlashAttention Backward Kernel
-//  *
-//  * This kernel implements the backward pass for FlashAttention.
-//  * It computes gradients for query, key, and value tensors.
-//  *
-//  * Args:
-//  *   grad_query: Gradient for query tensor
-//  *   grad_key: Gradient for key tensor
-//  *   grad_value: Gradient for value tensor
-//  *   query: Query tensor from forward pass
-//  *   key: Key tensor from forward pass
-//  *   value: Value tensor from forward pass
-//  *   output: Output tensor from forward pass
-//  *   grad_output: Gradient from upstream
-//  *   logsumexp: Logsumexp tensor from forward pass
-//  *   dropout_seed: Dropout seed for reproducibility
-//  *   attn_mask: Optional attention mask tensor
-//  *   scale: Scaling factor for attention scores
-//  *   is_causal: Whether causal masking was applied
-//  *   dropout_p: Dropout probability
-//  *   enable_gqa: Whether GQA was enabled
-//  *   batch_size, target_seq_len, src_seq_len: Tensor dimensions
-//  *   q_heads, kv_heads, head_dim: Attention head dimensions
-//  */
-// template <typename T>
-// __global__ void FlashAttentionBackwardKernel(
-//     T *grad_query, T *grad_key, T *grad_value,
-//     const T *query, const T *key, const T *value,
-//     const T *output, const T *grad_output,
-//     const float *logsumexp,
-//     const float *D,  // Precomputed D = rowsum(dO ∘ O)
-//     unsigned long long dropout_seed,
-//     const T *attn_mask,
-//     float scale, bool is_causal, int64_t dropout_p,
-//     bool enable_gqa,
-//     int batch_size, int target_seq_len, int src_seq_len,
-//     int q_heads, int kv_heads, int head_dim) {
-    
-//     // Grid/block dimensions: grid_dims(num_heads_q, batch_size, Tr), block_dim(Bc, Br)
-//     // where Br corresponds to thread block row index, Bc corresponds to column index
-//     int tid_x = threadIdx.x;          // 横向, blockDim.x列 (Bc)
-//     int tid_y = threadIdx.y;          // 纵向, blockDim.y行 (Br)
-//     int bid_x = blockIdx.x;           // x方向, 总数 = #q_heads
-//     int bid_y = blockIdx.y;           // y方向, 总数 = #batch
-//     int bid_z = blockIdx.z;           // z方向, 总数 = Tr
-//     const int p = q_heads / kv_heads; // GQA比例系数
-
-//     const int Br = blockDim.y;                  // Q纵向每块大小, 32
-//     const int Bc = blockDim.x;                  // K/V纵向分块大小, 32
-//     const int Tr = gridDim.z; // Q纵向分块数
-//     const int Tc = (src_seq_len + Bc - 1) / Bc; // K/V纵向分块数
-
-//     // Define shared memory
-//     extern __shared__ char shared_mem[];
-//     char *ptr = shared_mem;
-    
-//     // D_sm[Br] - D values loaded from HBM
-//     float *D_sm = reinterpret_cast<float *>(ptr);
-//     ptr += Br * sizeof(float);
-    
-//     // Q_sm[Br][head_dim], K_T_sm[head_dim][Bc], V_sm[Bc][head_dim]
-//     float *Q_sm = reinterpret_cast<float *>(ptr);
-//     ptr += Br * head_dim * sizeof(float);
-//     float *K_T_sm = reinterpret_cast<float *>(ptr);
-//     ptr += head_dim * Bc * sizeof(float);
-//     float *V_sm = reinterpret_cast<float *>(ptr);
-//     ptr += Bc * head_dim * sizeof(float);
-    
-//     // dO_sm[Br][head_dim], dK_T_sm[head_dim][Bc], dV_sm[Bc][head_dim]
-//     float *dO_sm = reinterpret_cast<float *>(ptr);
-//     ptr += Br * head_dim * sizeof(float);
-//     float *dK_T_sm = reinterpret_cast<float *>(ptr);
-//     ptr += head_dim * Bc * sizeof(float);
-//     float *dV_sm = reinterpret_cast<float *>(ptr);
-//     ptr += Bc * head_dim * sizeof(float);
-    
-//     // S_sm[Br][Bc], P_sm[Br][Bc]
-//     float *S_sm = reinterpret_cast<float *>(ptr);
-//     ptr += Br * Bc * sizeof(float);
-//     float *P_sm = reinterpret_cast<float *>(ptr);
-    
-//     // L_sm[Br] - logsumexp values
-//     float *L_sm = reinterpret_cast<float *>(ptr);
-    
-//     // dQ_sm[Br][head_dim] - accumulated gradient for Q
-//     float *dQ_sm = reinterpret_cast<float *>(ptr);
-    
-//     // Define access macros
-//     #define D_sm_AT(y) D_sm[y]
-//     #define Q_sm_AT(y, x) Q_sm[y * head_dim + x]
-//     #define K_T_sm_AT(y, x) K_T_sm[y * Bc + x]
-//     #define V_sm_AT(y, x) V_sm[y * head_dim + x]
-//     #define dO_sm_AT(y, x) dO_sm[y * head_dim + x]
-//     #define dK_T_sm_AT(y, x) dK_T_sm[y * Bc + x]
-//     #define dV_sm_AT(y, x) dV_sm[y * head_dim + x]
-//     #define S_sm_AT(y, x) S_sm[y * Bc + x]
-//     #define P_sm_AT(y, x) P_sm[y * Bc + x]
-//     #define L_sm_AT(y) L_sm[y]
-//     #define dQ_sm_AT(y, x) dQ_sm[y * head_dim + x]
-
-//     /****************************Preparation*****************************/
-    
-//     // Initialize dQ_sm to 0 for accumulation
-//     for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-//         dQ_sm_AT(tid_y, idx) = 0.0f;
-//     }
-//     __syncthreads();
-
-//     // Load D_i from HBM to shared memory
-//     int q_idx = Br * bid_z + tid_y;  // Global query position within this head
-//     if (q_idx < target_seq_len) {
-//         int d_idx = ((bid_y * q_heads + bid_x) * target_seq_len) + q_idx;
-//         D_sm_AT(tid_y) = D[d_idx];
-//     } else {
-//         D_sm_AT(tid_y) = 0.0f;  // Padding for out-of-bounds positions
-//     }
-//     __syncthreads();
-
-//     /****************************Main Loop - Outer Loop over K/V tiles*****************************/
-//     for (int j = 0; j < Tc; ++j) {  // For each K/V column tile
-        
-//         // Skip entire tile if causal and this tile is completely to the right
-//         bool skip_tile = (is_causal && bid_z < j);
-//         if (skip_tile) {
-//             __syncthreads();
-//             continue;
-//         }
-
-//         // Initialize dK_T_sm, dV_sm to 0 for this column tile
-//         for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-//             for (int y = 0; y < Bc; ++y) {
-//                 dK_T_sm_AT(idx, y) = 0.0f;
-//                 dV_sm_AT(y, idx) = 0.0f;
-//             }
-//         }
-//         __syncthreads();
-
-//         // Load K_j, V_j from HBM to shared memory
-//         int bound_tid_x = min(Bc, src_seq_len - Bc * j);
-//         for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-//             K_T_sm_AT(idx, tid_y) = 0.0f;
-//             V_sm_AT(tid_y, idx) = 0.0f;
-//             if (tid_y < bound_tid_x) {
-//                 int kv_head_idx = bid_x / p;
-//                 K_T_sm_AT(idx, tid_y) = float(
-//                     key[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads + kv_head_idx) * head_dim + idx]);
-//                 V_sm_AT(tid_y, idx) = float(
-//                     value[((((bid_y * src_seq_len) + (Bc * j + tid_y)) * kv_heads + kv_head_idx) * head_dim + idx]);
-//             }
-//         }
-//         __syncthreads();
-
-//         /****************************Single Q tile for this block*****************************/
-//         // gridDim.z already indexes the Q row tile, so this block only handles i = bid_z.
-//         const int i = bid_z;
-//         int q_tile_start = Br * i;
-//         int q_tile_end = min(q_tile_start + Br, target_seq_len);
-//         int bound_tid_y = min(Br, q_tile_end - q_tile_start);
-
-//         if (tid_y < bound_tid_y) {
-//             int global_q_idx = q_tile_start + tid_y;
-//             int q_tensor_idx = ((bid_y * target_seq_len) + global_q_idx) * q_heads + bid_x;
-
-//             for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-//                 Q_sm_AT(tid_y, idx) = float(query[q_tensor_idx * head_dim + idx]);
-//                 dO_sm_AT(tid_y, idx) = float(grad_output[q_tensor_idx * head_dim + idx]);
-//             }
-//             if (tid_x == 0) {
-//                 L_sm_AT(tid_y) = logsumexp[((bid_y * q_heads + bid_x) * target_seq_len) + global_q_idx];
-//             }
-//         }
-//         __syncthreads();
-
-//         // Get D_i for this row.
-//         float D_i_row = D_sm_AT(tid_y);
-
-//         // Recompute S_ij = Q_i @ K_j^T, apply causal mask at element level if needed.
-//         #pragma unroll
-//         for (int y = 0; y < Bc; ++y) {
-//             for (int x = 0; x < Bc; ++x) {
-//                 float val = 0.0f;
-//                 if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-//                     bool is_compute = true;
-//                     if (is_causal && i == j) {
-//                         int global_q_pos = q_tile_start + tid_y;
-//                         int global_k_pos = Bc * j + x;
-//                         is_compute = (global_q_pos >= global_k_pos);
-//                     }
-
-//                     if (is_compute) {
-//                         #pragma unroll
-//                         for (int k = 0; k < head_dim; ++k) {
-//                             val += Q_sm_AT(tid_y, k) * K_T_sm_AT(k, y);
-//                         }
-//                     }
-//                 }
-//                 S_sm_AT(tid_y, x) = val * scale;
-//             }
-//         }
-//         __syncthreads();
-
-//         // Recompute P_ij = exp(S_ij - L_i).
-//         #pragma unroll
-//         for (int y = 0; y < Bc; ++y) {
-//             for (int x = 0; x < Bc; ++x) {
-//                 if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-//                     bool is_compute = true;
-//                     if (is_causal && i == j) {
-//                         int global_q_pos = q_tile_start + tid_y;
-//                         int global_k_pos = Bc * j + x;
-//                         is_compute = (global_q_pos >= global_k_pos);
-//                     }
-
-//                     if (is_compute) {
-//                         P_sm_AT(tid_y, x) = myexp<float>(S_sm_AT(tid_y, x) - L_sm_AT(tid_y));
-//                     } else {
-//                         P_sm_AT(tid_y, x) = 0.0f;
-//                     }
-//                 } else {
-//                     P_sm_AT(tid_y, x) = 0.0f;
-//                 }
-//             }
-//         }
-//         __syncthreads();
-
-//         // Apply dropout to P_ij.
-//         if (dropout_p > 0) {
-//             #pragma unroll
-//             for (int y = 0; y < Bc; ++y) {
-//                 for (int x = 0; x < Bc; ++x) {
-//                     if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-//                         int global_q_pos = q_tile_start + tid_y;
-//                         int global_k_pos = Bc * j + x;
-//                         bool keep = FlashAttnDropoutKeep(
-//                             dropout_seed, bid_y, bid_x, global_q_pos, global_k_pos,
-//                             q_heads, target_seq_len, src_seq_len, static_cast<float>(dropout_p));
-//                         if (keep) {
-//                             P_sm_AT(tid_y, x) = P_sm_AT(tid_y, x) / (1.0f - dropout_p);
-//                         } else {
-//                             P_sm_AT(tid_y, x) = 0.0f;
-//                         }
-//                     }
-//                 }
-//             }
-//             __syncthreads();
-//         }
-
-//         // Compute dV_j += P_ij^T @ dO_i.
-//         if (tid_y < bound_tid_y) {
-//             #pragma unroll
-//             for (int x = 0; x < head_dim; x += blockDim.x) {
-//                 float val = 0.0f;
-//                 #pragma unroll
-//                 for (int y = 0; y < Bc; ++y) {
-//                     val += P_sm_AT(tid_y, y) * dO_sm_AT(tid_y, x);
-//                 }
-//                 atomicAdd(&dV_sm_AT(y, x), val);
-//             }
-//         }
-//         __syncthreads();
-
-//         // Compute dP_ij = dO_i @ V_j^T.
-//         #pragma unroll
-//         for (int y = 0; y < Bc; ++y) {
-//             for (int x = 0; x < Bc; ++x) {
-//                 float val = 0.0f;
-//                 if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-//                     #pragma unroll
-//                     for (int k = 0; k < head_dim; ++k) {
-//                         val += dO_sm_AT(tid_y, k) * V_sm_AT(y, k);
-//                     }
-//                     S_sm_AT(tid_y, x) = val;  // Reuse S_sm as temporary storage for dP_ij
-//                 } else {
-//                     S_sm_AT(tid_y, x) = 0.0f;
-//                 }
-//             }
-//         }
-//         __syncthreads();
-
-//         // Apply dropout to dP_ij.
-//         if (dropout_p > 0) {
-//             #pragma unroll
-//             for (int y = 0; y < Bc; ++y) {
-//                 for (int x = 0; x < Bc; ++x) {
-//                     if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-//                         int global_q_pos = q_tile_start + tid_y;
-//                         int global_k_pos = Bc * j + x;
-//                         bool keep = FlashAttnDropoutKeep(
-//                             dropout_seed, bid_y, bid_x, global_q_pos, global_k_pos,
-//                             q_heads, target_seq_len, src_seq_len, static_cast<float>(dropout_p));
-//                         if (keep) {
-//                             S_sm_AT(tid_y, x) = S_sm_AT(tid_y, x) / (1.0f - dropout_p);
-//                         } else {
-//                             S_sm_AT(tid_y, x) = 0.0f;
-//                         }
-//                     }
-//                 }
-//             }
-//             __syncthreads();
-//         }
-
-//         // Compute dS_ij = P_ij * (dP_ij - D_i).
-//         #pragma unroll
-//         for (int y = 0; y < Bc; ++y) {
-//             for (int x = 0; x < Bc; ++x) {
-//                 if (tid_y < bound_tid_y && tid_x < bound_tid_x) {
-//                     S_sm_AT(tid_y, x) = P_sm_AT(tid_y, x) * (S_sm_AT(tid_y, x) - D_i_row);
-//                 } else {
-//                     S_sm_AT(tid_y, x) = 0.0f;
-//                 }
-//             }
-//         }
-//         __syncthreads();
-
-//         // Compute dK_j += dS_ij^T @ Q_i.
-//         if (tid_x < head_dim) {
-//             #pragma unroll
-//             for (int y = 0; y < Bc; ++y) {
-//                 float val = 0.0f;
-//                 #pragma unroll
-//                 for (int x = 0; x < Br; ++x) {
-//                     val += S_sm_AT(x, y) * Q_sm_AT(x, tid_x);
-//                 }
-//                 atomicAdd(&dK_T_sm_AT(tid_x, y), val * scale);
-//             }
-//         }
-//         __syncthreads();
-
-//         // Compute dQ_i += dS_ij @ K_j.
-//         if (tid_y < bound_tid_y) {
-//             #pragma unroll
-//             for (int x = 0; x < head_dim; x += blockDim.x) {
-//                 float val = 0.0f;
-//                 #pragma unroll
-//                 for (int y = 0; y < Bc; ++y) {
-//                     val += S_sm_AT(tid_y, y) * K_T_sm_AT(x, y);
-//                 }
-//                 dQ_sm_AT(tid_y, x) += val * scale;
-//             }
-//         }
-//         __syncthreads();
-
-//         // Write back dK_j, dV_j to HBM
-//         // Note: For GQA, dK_j and dV_j need to be accumulated across multiple q-head blocks
-//         int kv_head_idx = bid_x / p;
-//         int k_tile_start = Bc * j;
-//         int k_tile_end = min(k_tile_start + Bc, src_seq_len);
-        
-//         for (int y = 0; y < Bc; ++y) {
-//             int global_k_idx = k_tile_start + y;
-//             if (global_k_idx < src_seq_len) {
-//                 for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-//                     // dK: [batch_size, src_seq_len, kv_heads, head_dim]
-//                     int k_tensor_idx = ((bid_y * src_seq_len) + global_k_idx) * kv_heads + kv_head_idx;
-//                     atomicAdd(&grad_key[k_tensor_idx * head_dim + idx], T(dK_T_sm_AT(idx, y)));
-                    
-//                     // dV: [batch_size, src_seq_len, kv_heads, head_dim]
-//                     atomicAdd(&grad_value[k_tensor_idx * head_dim + idx], T(dV_sm_AT(y, idx)));
-//                 }
-//             }
-//         }
-//     }  // End of outer loop over K/V tiles
-
-//     // Write back dQ_i to HBM
-//     int q_tile_start = Br * bid_z;
-//     int q_tile_end = min(q_tile_start + Br, target_seq_len);
-//     int bound_tid_y = min(Br, q_tile_end - q_tile_start);
-    
-//     if (tid_y < bound_tid_y) {
-//         int global_q_idx = q_tile_start + tid_y;
-//         int q_tensor_idx = ((bid_y * target_seq_len) + global_q_idx) * q_heads + bid_x;
-        
-//         for (int idx = tid_x; idx < head_dim; idx += blockDim.x) {
-//             // dQ: [batch_size, target_seq_len, q_heads, head_dim]
-//             grad_query[q_tensor_idx * head_dim + idx] += T(dQ_sm_AT(tid_y, idx));
-//         }
-//     }
-
-//     // Undefine access macros
-//     #undef D_sm_AT
-//     #undef Q_sm_AT
-//     #undef K_T_sm_AT
-//     #undef V_sm_AT
-//     #undef dO_sm_AT
-//     #undef dK_T_sm_AT
-//     #undef dV_sm_AT
-//     #undef S_sm_AT
-//     #undef P_sm_AT
-//     #undef L_sm_AT
-//     #undef dQ_sm_AT
-// }
-
-// /**
-//  * FlashAttention Forward Function
-//  *
-//  * This is the main entry point for FlashAttention forward computation.
-//  * It creates the output tensor and launches the appropriate kernel based on data type.
-//  *
-//  * Args:
-//  *   query: Query tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
-//  *   key: Key tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
-//  *   value: Value tensor of shape [batch_size, seq_len_k, num_heads_kv, head_dim]
-//  *   attn_mask: Optional attention mask tensor
-//  *   scale: Scaling factor for attention scores
-//  *   is_causal: Whether to apply causal masking
-//  *   dropout_p: Dropout probability
-//  *   enable_gqa: Whether to enable grouped-query attention
-//  *
-//  * Returns:
-//  *   Output tensor of shape [batch_size, seq_len_q, num_heads, head_dim]
-//  *   logsumexp: Logsumexp tensor for backward pass [batch_size, num_heads, seq_len_q]
-//  *   dropout_seed: Dropout seed for backward pass [1]
-//  */
-
-
-// /**
-//  * Launch FlashAttention Forward Kernel
-//  *
-//  * This function sets up grid and block dimensions and launches FlashAttention forward kernel.
-//  *
-//  * Args:
-//  *   output: Output tensor
-//  *   query: Query tensor
-//  *   key: Key tensor
-//  *   value: Value tensor
-//  *   attn_mask: Optional attention mask tensor
-//  *   scale: Scaling factor
-//  *   is_causal: Whether to apply causal masking
-//  *   dropout_p: Dropout probability
-//  *   enable_gqa: Whether to enable GQA
-//  */
-// template <typename T>
-// void LaunchFlashAttentionForward(const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &query,
-//                                  const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
-//                                  const std::shared_ptr<Tensor> &attn_mask, float scale, bool is_causal,
-//                                  int64_t dropout_p, bool enable_gqa, float *logsumexp_ptr, unsigned long long dropout_seed) {
-
-//     const auto &query_dims = query->Dims();
-//     const auto &key_dims = key->Dims();
-//     const auto &value_dims = value->Dims();
-
-//     // Expected shapes:
-//     // query: [batch_size, seq_len_q, num_heads, head_dim]
-//     // key: [batch_size, seq_len_k, num_heads_kv, head_dim]
-//     // value: [batch_size, seq_len_k, num_heads_kv, head_dim]
-//     // output: [batch_size, seq_len_q, num_heads, head_dim]
-
-//     int64_t batch_size = query_dims[0];
-//     int64_t seq_len_q = query_dims[1];
-//     int64_t num_heads = query_dims[2];
-//     int64_t head_dim = query_dims[3];
-//     int64_t seq_len_k = key_dims[1];
-//     int64_t num_heads_kv = key_dims[2];
-
-//     CHECK_EQ(key_dims[3], head_dim) << "Key head dimension must match query head dimension";
-//     CHECK_EQ(value_dims[3], head_dim) << "Value head dimension must match query head dimension";
-//     CHECK_EQ(value_dims[1], seq_len_k) << "Value sequence length must match key sequence length";
-//     CHECK_EQ(value_dims[2], num_heads_kv) << "Value number of KV heads must match key";
-
-//     if (enable_gqa) {
-//         CHECK(num_heads % num_heads_kv == 0) << "Number of query heads must be divisible by number of KV heads for GQA";
-//     } else {
-//         CHECK_EQ(num_heads, num_heads_kv) << "Number of query and KV heads must match for standard attention";
-//     }
-
-//     T *output_ptr = static_cast<T *>(output->DataPtr());
-//     const T *query_ptr = static_cast<const T *>(query->DataPtr());
-//     const T *key_ptr = static_cast<const T *>(key->DataPtr());
-//     const T *value_ptr = static_cast<const T *>(value->DataPtr());
-//     const T *attn_mask_ptr = attn_mask ? static_cast<const T *>(attn_mask->DataPtr()) : nullptr;
-
-//     // Set up grid and block dimensions according to FlashAttention v2
-//     // block_dim(Br, Bc) where Br = Bc = 32
-//     // grid_dim(query_heads, batch_size, Tr) where Tr = ceil(seq_len_q / Br)
-//     constexpr int Br = 32;
-//     constexpr int Bc = 32;
-//     int64_t Tr = (seq_len_q + Br - 1) / Br;
-    
-//     dim3 block_dims(Bc, Br);  // (blockDim.x, blockDim.y) = (Bc, Br)
-//     dim3 grid_dims(num_heads, batch_size, Tr);  // (gridDim.x, gridDim.y, gridDim.z) = (num_heads, batch_size, Tr)
-
-//     // Calculate shared memory size (removed dropout_sm allocation)
-//     // SP[Br][Bc] (double) + m_prev[Br] (float) + m_new[Br] (float) + l_prev[Br] (float) + l_new[Br] (float)
-//     // + Q_sm[Br][head_dim] (float) + K_T_sm[head_dim][Bc] (float) + V_sm[Bc][head_dim] (float) + O_sm[Br][head_dim] (float)
-//     size_t shared_mem_size = Br * Bc * sizeof(double)  // SP
-//                           + 4 * Br * sizeof(float)     // m_prev, m_new, l_prev, l_new
-//                           + (Br + Bc + Bc + Br) * head_dim) * sizeof(float);  // Q_sm, K_T_sm, V_sm, O_sm
-//     // Note: Removed dropout_sm[Br][Bc] (bool) allocation
-
-//     auto device = output->GetDevice();
-//     const auto &cuda_stream = dynamic_cast<infini_train::core::cuda::CudaStream *>(
-//                                   infini_train::core::GetDeviceGuardImpl(device.type())->GetStream(device))
-//                                   ->cuda_stream();
-
-//     FlashAttentionForwardKernel<T><<<grid_dims, block_dims, shared_mem_size, cuda_stream>>>(
-//         output_ptr, query_ptr, key_ptr, value_ptr, attn_mask_ptr,
-//         logsumexp_ptr, scale, is_causal, enable_gqa, dropout_p, dropout_seed,
-//         batch_size, seq_len_q, seq_len_k, num_heads, num_heads_kv, head_dim);
-// }
-
-// /**
-//  * Launch FlashAttention Backward Kernel
-//  *
-//  * This function sets up grid and block dimensions and launches FlashAttention backward kernel.
-//  *
-//  * Args:
-//  *   grad_query: Gradient tensor for query
-//  *   grad_key: Gradient tensor for key
-//  *   grad_value: Gradient tensor for value
-//  *   query: Query tensor from forward pass
-//  *   key: Key tensor from forward pass
-//  *   value: Value tensor from forward pass
-//  *   output: Output tensor from forward pass
-//  *   grad_output: Gradient from upstream
-//  *   logsumexp: Logsumexp tensor from forward pass
-//  *   dropout_seed: Dropout seed for reproducibility
-//  *   attn_mask: Optional attention mask tensor
-//  *   scale: Scaling factor
-//  *   is_causal: Whether causal masking was applied
-//  *   dropout_p: Dropout probability
-//  *   enable_gqa: Whether GQA was enabled
-//  */
-// template <typename T>
-// void LaunchFlashAttentionBackward(const std::shared_ptr<Tensor> &grad_query, const std::shared_ptr<Tensor> &grad_key,
-//                                  const std::shared_ptr<Tensor> &grad_value, const std::shared_ptr<Tensor> &query,
-//                                  const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
-//                                  const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &grad_output,
-//                                  const std::shared_ptr<Tensor> &logsumexp,
-//                                  unsigned long long dropout_seed,
-//                                  const std::shared_ptr<Tensor> &attn_mask,
-//                                  float scale, bool is_causal, int64_t dropout_p, bool enable_gqa) {
-
-//     const auto &query_dims = query->Dims();
-//     const auto &key_dims = key->Dims();
-
-//     // Expected shapes:
-//     // query: [batch_size, seq_len_q, num_heads, head_dim]
-//     // key: [batch_size, seq_len_k, num_heads_kv, head_dim]
-//     // value: [batch_size, seq_len_k, num_heads_kv, head_dim]
-//     // output: [batch_size, seq_len_q, num_heads, head_dim]
-
-//     int64_t batch_size = query_dims[0];
-//     int64_t seq_len_q = query_dims[1];
-//     int64_t num_heads = query_dims[2];
-//     int64_t head_dim = query_dims[3];
-//     int64_t seq_len_k = key_dims[1];
-//     int64_t num_heads_kv = key_dims[2];
-
-//     CHECK_EQ(key_dims[3], head_dim) << "Key head dimension must match query head dimension";
-//     CHECK_EQ(value->Dims()[3], head_dim) << "Value head dimension must match query head dimension";
-//     CHECK_EQ(value->Dims()[1], seq_len_k) << "Value sequence length must match key sequence length";
-//     CHECK_EQ(value->Dims()[2], num_heads_kv) << "Value number of KV heads must match key";
-
-//     if (enable_gqa) {
-//         CHECK(num_heads % num_heads_kv == 0) << "Number of query heads must be divisible by number of KV heads for GQA";
-//     } else {
-//         CHECK_EQ(num_heads, num_heads_kv) << "Number of query and KV heads must match for standard attention";
-//     }
-
-//     // Precompute D = rowsum(dO ∘ O) before main backward loop
-//     // D shape: [batch_size, seq_len_q, num_heads]
-//     // dO shape: [batch_size, seq_len_q, num_heads, head_dim]
-//     // O shape: [batch_size, seq_len_q, num_heads, head_dim]
-//     auto D = function::Sum(grad_output * output, 3, false);
-
-//     T *grad_query_ptr = static_cast<T *>(grad_query->DataPtr());
-//     T *grad_key_ptr = static_cast<T *>(grad_key->DataPtr());
-//     T *grad_value_ptr = static_cast<T *>(grad_value->DataPtr());
-//     const T *query_ptr = static_cast<const T *>(query->DataPtr());
-//     const T *key_ptr = static_cast<const T *>(key->DataPtr());
-//     const T *value_ptr = static_cast<const T *>(value->DataPtr());
-//     const T *output_ptr = static_cast<const T *>(output->DataPtr());
-//     const T *grad_output_ptr = static_cast<const T *>(grad_output->DataPtr());
-//     const float *logsumexp_ptr = static_cast<const float *>(logsumexp->DataPtr());
-//     const float *D_ptr = static_cast<const float *>(D->DataPtr());
-//     const T *attn_mask_ptr = attn_mask ? static_cast<const T *>(attn_mask->DataPtr()) : nullptr;
-
-//     // Set up grid and block dimensions according to FlashAttention v2 backward
-//     // block_dim(Bc, Br) where Bc = Br = 32
-//     // grid_dims(num_heads_q, batch_size, Tr) where Tr = ceil(seq_len_q / Br)
-//     constexpr int Br = 32;
-//     constexpr int Bc = 32;
-//     int64_t Tr = (seq_len_q + Br - 1) / Br;
-
-//     dim3 block_dims(Bc, Br);  // (blockDim.x, blockDim.y) = (Bc, Br)
-//     dim3 grid_dims(num_heads, batch_size, Tr);  // (gridDim.x, gridDim.y, gridDim.z) = (num_heads, batch_size, Tr)
-
-//     // Calculate shared memory size for backward pass
-//     // Q_sm[Br][head_dim], K_T_sm[head_dim][Bc], V_sm[Bc][head_dim]
-//     // dO_sm[Br][head_dim], dK_T_sm[head_dim][Bc], dV_sm[Bc][head_dim]
-//     // S_sm[Br][Bc], P_sm[Br][Bc]
-//     // D_sm[Br] - D values loaded from HBM to shared memory
-//     size_t shared_mem_size = Br * head_dim * sizeof(float)     // Q_sm
-//                            + head_dim * Bc * sizeof(float)    // K_T_sm
-//                            + Bc * head_dim * sizeof(float)    // V_sm
-//                            + Br * head_dim * sizeof(float)     // dO_sm
-//                            + head_dim * Bc * sizeof(float)    // dK_T_sm
-//                            + Bc * head_dim * sizeof(float)    // dV_sm
-//                            + Br * Bc * sizeof(float)          // S_sm or (dP_sm when compute dP_sm = dO_i @ V_j^T \in R^{Br*Bc})
-//                            + Br * Bc * sizeof(float)         // P_sm or (dS_sm when compute dS_sm = P_sm_ij pointwise multiplied by (dP_sm_ij - D_i) \in R^{Br*Bc})
-//                            + Br * sizeof(float)        // L_i
-//                            + Br * sizeof(float);              // D_sm (loaded from HBM)
-
-//     auto device = query->GetDevice();
-//     const auto &cuda_stream = dynamic_cast<infini_train::core::cuda::CudaStream *>(
-//                                   infini_train::core::GetDeviceGuardImpl(device.type())->GetStream(device))
-//                                   ->cuda_stream();
-
-//     FlashAttentionBackwardKernel<T><<<grid_dims, block_dims, shared_mem_size, cuda_stream>>>(
-//         grad_query_ptr, grad_key_ptr, grad_value_ptr,
-//         query_ptr, key_ptr, value_ptr, output_ptr, grad_output_ptr,
-//         logsumexp_ptr, D_ptr, dropout_seed, attn_mask_ptr,
-//         scale, is_causal, dropout_p, enable_gqa,
-//         batch_size, seq_len_q, seq_len_k, num_heads, num_heads_kv, head_dim);
-// }
-
-// /**
-//  * FlashAttention Backward Function
-//  *
-//  * This is the main entry point for FlashAttention backward computation.
-//  * It creates gradient tensors and launches the appropriate kernel based on data type.
-//  *
-//  * Args:
-//  *   grad_query: Gradient tensor for query
-//  *   grad_key: Gradient tensor for key
-//  *   grad_value: Gradient tensor for value
-//  *   query: Query tensor from forward pass
-//  *   key: Key tensor from forward pass
-//  *   value: Value tensor from forward pass
-//  *   output: Output tensor from forward pass
-//  *   grad_output: Gradient from upstream
-//  *   attn_mask: Optional attention mask tensor
-//  *   scale: Scaling factor
-//  *   is_causal: Whether causal masking was applied
-//  *   dropout_p: Dropout probability (not implemented)
-//  *   enable_gqa: Whether GQA was enabled
-//  *
-//  * Returns:
-//  *   Tuple of (grad_query, grad_key, grad_value) tensors
-//  */
-
-
-// // Non-template wrapper functions for registration
-// FlashAttentionForwardOutput FlashAttentionForward(const std::shared_ptr<Tensor> &query, const std::shared_ptr<Tensor> &key,
-//                                                const std::shared_ptr<Tensor> &value,
-//                                                const std::shared_ptr<Tensor> &attn_mask, float scale, bool is_causal,
-//                                                int64_t dropout_p, bool enable_gqa) {
-//     auto dtype = query->Dtype();
-//     const auto &query_dims = query->Dims();
-
-//     // Output shape: [batch_size, seq_len_q, num_heads, head_dim]
-//     std::vector<int64_t> output_dims = {query_dims[0], query_dims[1], query_dims[2], query_dims[3]};
-//     auto output = std::make_shared<Tensor>(output_dims, dtype, query->GetDevice());
-
-//     // Allocate logsumexp tensor for backward pass
-//     // Shape: [batch_size, num_heads, seq_len_q]
-//     std::vector<int64_t> logsumexp_dims = {query_dims[0], query_dims[2], query_dims[1]};
-//     auto logsumexp = std::make_shared<Tensor>(logsumexp_dims, DataType::kFLOAT32, query->GetDevice());
-//     float *logsumexp_ptr = static_cast<float *>(logsumexp->DataPtr());
-
-//     // Allocate dropout_seed tensor for backward pass
-//     // Shape: [1]
-//     unsigned long long dropout_seed = 0;
-//     std::shared_ptr<Tensor> dropout_seed_tensor;
-//     if (dropout_p > 0) {
-//         std::vector<int64_t> dropout_seed_dims = {1};
-//         dropout_seed_tensor = std::make_shared<Tensor>(dropout_seed_dims, DataType::kUINT64, query->GetDevice());
-//         dropout_seed = static_cast<unsigned long long>(std::time(nullptr));
-//         unsigned long long *dropout_seed_ptr = static_cast<unsigned long long *>(dropout_seed_tensor->DataPtr());
-//         *dropout_seed_ptr = dropout_seed;
-//     }
-
-//     switch (dtype) {
-//         DISPATCH_CASE(WRAP(LaunchFlashAttentionForward<float>(output, query, key, value, attn_mask, scale, is_causal,
-//                                                                dropout_p, enable_gqa, logsumexp_ptr,
-//                                                                dropout_seed);),
-//                       DataType::kFLOAT32)
-//         DISPATCH_CASE(WRAP(LaunchFlashAttentionForward<nv_bfloat16>(output, query, key, value, attn_mask, scale,
-//                                                                      is_causal, dropout_p, enable_gqa, logsumexp_ptr,
-//                                                                      dropout_seed);),
-//                       DataType::kBFLOAT16)
-//     default:
-//         LOG_LOC(FATAL, "CUDA FlashAttention forward: 'Unsupported data type'");
-//     }
-
-//     FlashAttentionForwardOutput result;
-//     result.output = output;
-//     result.logsumexp = logsumexp;
-//     result.dropout_seed = dropout_seed_tensor;
-//     return result;
-// }
-
-// std::vector<std::shared_ptr<Tensor>> FlashAttentionBackward(const std::shared_ptr<Tensor> &grad_query, const std::shared_ptr<Tensor> &grad_key,
-//                                                           const std::shared_ptr<Tensor> &grad_value, const std::shared_ptr<Tensor> &query,
-//                                                           const std::shared_ptr<Tensor> &key, const std::shared_ptr<Tensor> &value,
-//                                                           const std::shared_ptr<Tensor> &output, const std::shared_ptr<Tensor> &grad_output,
-//                                                           const std::shared_ptr<Tensor> &logsumexp,
-//                                                           const std::shared_ptr<Tensor> &dropout_seed,
-//                                                           const std::shared_ptr<Tensor> &attn_mask,
-//                                                           float scale, bool is_causal, int64_t dropout_p, bool enable_gqa) {
-//     auto dtype = query->Dtype();
-
-//     // Create gradient tensors with same shapes as inputs
-//     auto grad_query = std::make_shared<Tensor>(query->Dims(), dtype, query->GetDevice());
-//     auto grad_key = std::make_shared<Tensor>(key->Dims(), dtype, key->GetDevice());
-//     auto grad_value = std::make_shared<Tensor>(value->Dims(), dtype, value->GetDevice());
-
-//     // Initialize gradients to zero
-//     DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_query->Fill<T>(0); }, "CUDA FlashAttentionBackward");
-//     DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_key->Fill<T>(0); }, "CUDA FlashAttentionBackward");
-//     DispatchFunc<INFINI_ALL_TYPES>(dtype, [=]<typename T>() { grad_value->Fill<T>(0); }, "CUDA FlashAttentionBackward");
-
-//     // Get dropout seed value
-//     unsigned long long dropout_seed_value = 0;
-//     if (dropout_seed) {
-//         dropout_seed_value = *static_cast<const unsigned long long *>(dropout_seed->DataPtr());
-//     }
-
-//     switch (dtype) {
-//         DISPATCH_CASE(WRAP(LaunchFlashAttentionBackward<float>(grad_query, grad_key, grad_value, query, key, value, output, grad_output,
-//                                                                logsumexp, dropout_seed_value, attn_mask, scale, is_causal,
-//                                                                dropout_p, enable_gqa);),
-//                       DataType::kFLOAT32)
-//         DISPATCH_CASE(WRAP(LaunchFlashAttentionBackward<nv_bfloat16>(grad_query, grad_key, grad_value, query, key, value, output, grad_output,
-//                                                                     logsumexp, dropout_seed_value, attn_mask, scale, is_causal,
-//                                                                     dropout_p, enable_gqa);),
-//                       DataType::kBFLOAT16)
-//     default:
-//         LOG_LOC(FATAL, "CUDA FlashAttention backward: 'Unsupported data type'");
-//     }
-
-//     return {grad_query, grad_key, grad_value};
-// }
-
-// } // namespace infini_train::kernels::cuda
-
-// // Register FlashAttention kernels with the dispatcher
-// #define REGISTER_CUDA_FLASHATTENTION_KERNEL(kernel_name) \
-//     REGISTER_KERNEL(infini_train::Device::DeviceType::kCUDA, kernel_name, infini_train::kernels::cuda::kernel_name)
-
-// REGISTER_CUDA_FLASHATTENTION_KERNEL(FlashAttentionForward)
-// REGISTER_CUDA_FLASHATTENTION_KERNEL(FlashAttentionBackward)
-
-// #undef REGISTER_CUDA_FLASHATTENTION_KERNEL

From 224f0e2204c833049b63c8d2db01c05a47d36b17 Mon Sep 17 00:00:00 2001
From: Aoshine999 <820459542@qq.com>
Date: Mon, 16 Mar 2026 16:14:08 +0800
Subject: [PATCH 17/18] update config file

---
 scripts/run_models_and_profile.bash | 222 ++++++-----
 scripts/test_config.json            | 559 ++++++++++++++--------------
 2 files changed, 407 insertions(+), 374 deletions(-)

diff --git a/scripts/run_models_and_profile.bash b/scripts/run_models_and_profile.bash
index 25ec041e..032d80a8 100755
--- a/scripts/run_models_and_profile.bash
+++ b/scripts/run_models_and_profile.bash
@@ -3,24 +3,57 @@
 set -e
 set -o pipefail
 
-# Parse arguments
-REBUILD=false
+usage() {
+    cat <<'EOF'
+Usage: run_models_and_profile.bash [--test-config path] [--only-run tag1,tag2]
+
+Options:
+  --test-config PATH  Path to test config JSON. Default: test_config.json.
+  --only-run TAGS   Only run the specified tag groups, separated by commas.
+  -h, --help        Show this help message.
+EOF
+}
+
+CONFIG_FILE="test_config.json"
+ONLY_RUN_TAGS=""
+
 while [[ $# -gt 0 ]]; do
-    case $1 in
-        --rebuild)
-            REBUILD=true
+    case "$1" in
+        --test-config)
+            [[ $# -lt 2 ]] && { echo "Error: --test-config requires a file path."; exit 1; }
+            CONFIG_FILE="$2"
+            shift 2
+            ;;
+        --test-config=*)
+            CONFIG_FILE="${1#*=}"
             shift
             ;;
-        *)
-            CONFIG_FILE="$1"
+        --only-run)
+            [[ $# -lt 2 ]] && { echo "Error: --only-run requires a comma-separated tag list."; exit 1; }
+            ONLY_RUN_TAGS="$2"
+            shift 2
+            ;;
+        --only-run=*)
+            ONLY_RUN_TAGS="${1#*=}"
             shift
             ;;
+        -h|--help)
+            usage
+            exit 0
+            ;;
+        -*)
+            echo "Error: Unknown option: $1"
+            usage
+            exit 1
+            ;;
+        *)
+            echo "Error: Unknown positional argument: $1"
+            usage
+            exit 1
+            ;;
     esac
 done
 
-CONFIG_FILE="${CONFIG_FILE:-test_config.json}"
-export INFINI_FLASH_BF16_USE_FP32=0
-
 # Dependencies check
 if ! command -v jq >/dev/null 2>&1; then
     echo "Error: jq is required. Install with: sudo apt-get install -y jq"
@@ -49,6 +82,28 @@ done < <(jq -r '.variables | to_entries[] | "\(.key)=\(.value)"' "$CONFIG_FILE")
 
 # Global variable to save the last cmake command
 LAST_CMAKE_CMD=""
+declare -A SELECTED_TAGS=()
+
+normalize_tag() {
+    local raw="$1"
+    raw="${raw#"${raw%%[![:space:]]*}"}"
+    raw="${raw%"${raw##*[![:space:]]}"}"
+    printf '%s' "$raw"
+}
+
+if [[ -n "$ONLY_RUN_TAGS" ]]; then
+    IFS=',' read -r -a requested_tags <<< "$ONLY_RUN_TAGS"
+    for raw_tag in "${requested_tags[@]}"; do
+        tag="$(normalize_tag "$raw_tag")"
+        [[ -z "$tag" ]] && continue
+        SELECTED_TAGS["$tag"]=1
+    done
+
+    if [[ ${#SELECTED_TAGS[@]} -eq 0 ]]; then
+        echo "Error: --only-run did not contain any valid tags."
+        exit 1
+    fi
+fi
 
 # Clean the build directory
 clean_build_dir() {
@@ -62,11 +117,12 @@ run_and_log() {
     local cmd="$1"
     local log_name="$2"
     local is_profile="$3"
-    local log_dir="$4"
-    local profile_log_dir="$5"
+    local tag="${4:-basic}"
     local timestamp
     timestamp=$(date '+%Y-%m-%d %H:%M:%S')
-    local log_path="$(realpath "${log_dir}/${log_name}.log")"
+    local tag_log_dir="${LOG_DIR}/${tag}"
+    mkdir -p "$tag_log_dir"
+    local log_path="$(realpath "${tag_log_dir}/${log_name}.log")"
 
     echo -e "\033[1;32m============================================================\033[0m"
     echo -e "\033[1;36m[$timestamp] [Running] ${log_name}\033[0m"
@@ -83,7 +139,7 @@ run_and_log() {
 
     # Notify if profiling mode is enabled
     if [[ "$is_profile" == "yes" ]]; then
-        echo -e "\033[1;35m[PROFILE MODE ON] Profiling logs will be saved to: ${profile_log_dir}\033[0m"
+        echo -e "\033[1;35m[PROFILE MODE ON] Profiling logs will be saved to: ${PROFILE_LOG_DIR}\033[0m"
     fi
 
     echo -e "\033[1;32m============================================================\033[0m"
@@ -117,7 +173,7 @@ run_and_log() {
 
     # If profiling is enabled, move profiling files to the target directory
     if [[ "$is_profile" == "yes" ]]; then
-        move_profile_logs "$log_name" "$profile_log_dir"
+        move_profile_logs "$log_name" "$tag"
     fi
 }
 
@@ -125,15 +181,17 @@ run_and_log() {
 # Move profiling output logs
 move_profile_logs() {
     local prefix="$1"
-    local target_profile_log_dir="$2"
+    local tag="${2:-basic}"
+    local tag_profile_dir="${PROFILE_LOG_DIR}/${tag}"
+    mkdir -p "$tag_profile_dir"
 
     # Move *.report.rankN files
     for report_file in "${BUILD_DIR}"/*.report.rank*; do
         if [[ -f "$report_file" ]]; then
             local base_name
             base_name=$(basename "$report_file")
-            mv "$report_file" "${target_profile_log_dir}/${prefix}_${base_name}"
-            echo "Moved $base_name to ${target_profile_log_dir}/${prefix}_${base_name}"
+            mv "$report_file" "${tag_profile_dir}/${prefix}_${base_name}"
+            echo "Moved $base_name to ${tag_profile_dir}/${prefix}_${base_name}"
         fi
     done
 
@@ -142,28 +200,39 @@ move_profile_logs() {
         if [[ -f "$record_file" ]]; then
             local base_name
             base_name=$(basename "$record_file")
-            mv "$record_file" "${target_profile_log_dir}/${prefix}_${base_name}"
-            echo "Moved $base_name to ${target_profile_log_dir}/${prefix}_${base_name}"
+            mv "$record_file" "${tag_profile_dir}/${prefix}_${base_name}"
+            echo "Moved $base_name to ${tag_profile_dir}/${prefix}_${base_name}"
         fi
     done
 }
 
-# Build "--key value" arg string from tests[i].args (shell-escaped)
+# Build "--key value" arg string from test_groups[gi].tests[ti].args (shell-escaped)
 args_string_for_test() {
-    local idx="$1"
-    jq -r --argjson i "$idx" '
-      .tests[$i].args
+    local group_idx="$1"
+    local test_idx="$2"
+    jq -r --argjson g "$group_idx" --argjson t "$test_idx" '
+      .test_groups[$g].tests[$t].args
       | to_entries[]
-      | if .value == true then "--\(.key)"
-        elif .value == false then "--no\(.key)"
-        else "--\(.key)=\(.value|tostring)"
-        end
+      | "--\(.key) \(.value|tostring)"
     ' "$CONFIG_FILE" | paste -sd' ' -
 }
 
 # Run tests
 num_builds=$(jq '.builds | length' "$CONFIG_FILE")
-num_tests=$(jq '.tests  | length' "$CONFIG_FILE")
+num_groups=$(jq '.test_groups | length' "$CONFIG_FILE")
+
+selected_group_count=0
+for ((gi=0; gi<num_groups; ++gi)); do
+    group_tag=$(jq -r ".test_groups[$gi].tag" "$CONFIG_FILE")
+    if [[ ${#SELECTED_TAGS[@]} -eq 0 || -n "${SELECTED_TAGS[$group_tag]}" ]]; then
+        ((selected_group_count += 1))
+    fi
+done
+
+if [[ "$selected_group_count" -eq 0 ]]; then
+    echo "Error: No matching test groups found for --only-run=${ONLY_RUN_TAGS}"
+    exit 1
+fi
 
 for ((id=0; id<num_builds; ++id)); do
     build_id=$(jq -r ".builds[$id].id" "$CONFIG_FILE")
@@ -172,29 +241,9 @@ for ((id=0; id<num_builds; ++id)); do
 
     LAST_CMAKE_CMD="$build_cmake"
 
-    # Check if rebuild is needed
-    if [[ "$REBUILD" == true ]]; then
-        # Clean and rebuild
-        clean_build_dir
-        run_and_log "$LAST_CMAKE_CMD" "${build_id}" "no" "$LOG_DIR" "$PROFILE_LOG_DIR"
-    else
-        # Check if build directory exists and executables are present
-        if [[ -d "$BUILD_DIR" ]]; then
-            # Check if gpt2 and llama3 executables exist
-            if [[ -f "${BUILD_DIR}/gpt2" ]] && [[ -f "${BUILD_DIR}/llama3" ]]; then
-                echo -e "\033[1;33m[SKIP] Build directory already exists and executables are present. Skipping build.\033[0m"
-                echo -e "\033[1;33m        Use --rebuild to force a clean rebuild.\033[0m"
-            else
-                # Build executables that are missing
-                echo -e "\033[1;33m[BUILD] Some executables are missing. Building...\033[0m"
-                run_and_log "$LAST_CMAKE_CMD" "${build_id}" "no" "$LOG_DIR" "$PROFILE_LOG_DIR"
-            fi
-        else
-            # Build directory doesn't exist, build from scratch
-            echo -e "\033[1;33m[BUILD] Build directory doesn't exist. Building...\033[0m"
-            run_and_log "$LAST_CMAKE_CMD" "${build_id}" "no" "$LOG_DIR" "$PROFILE_LOG_DIR"
-        fi
-    fi
+    # always clean before another build
+    clean_build_dir
+    run_and_log "$LAST_CMAKE_CMD" "${build_id}" "no" "build"
 
     # profile flag for runs
     profile_flag="no"
@@ -204,55 +253,27 @@ for ((id=0; id<num_builds; ++id)); do
         log_suffix="_profile"
     fi
 
-    for ((ti=0; ti<num_tests; ++ti)); do
-        test_id=$(jq -r ".tests[$ti].id" "$CONFIG_FILE")
-        base_arg_str="$(args_string_for_test "$ti")"
-
-        # Add --noflash to the beginning of arg_str (will override any existing flash arg)
-        arg_str="--noflash $base_arg_str"
-
-        # Add --flash to the beginning of arg_str (will override any existing flash arg)
-        arg_str1="--flash $base_arg_str"
-
-        # Run gpt2 with flash=false
-        LOG_DIR="$(read_var LOG_DIR)"
-        : "${LOG_DIR:=./logs}"
-        PROFILE_LOG_DIR="$(read_var PROFILE_LOG_DIR)"
-        : "${PROFILE_LOG_DIR:=./profile_logs}"
-
-        mkdir -p "$LOG_DIR" "$PROFILE_LOG_DIR"
-
-        gpt2_cmd="./gpt2 --input_bin ${GPT2_INPUT_BIN} --llmc_filepath ${GPT2_LLMC_FILEPATH} --device cuda ${arg_str}"
-        run_and_log "$gpt2_cmd" "gpt2_${test_id}${log_suffix}" "$profile_flag" "$LOG_DIR" "$PROFILE_LOG_DIR"
-
-        # Run gpt2 with flash=true
-        COMPARE_LOG_DIR="./compare_logs"
-        COMPARE_PROFILE_LOG_DIR="./compare_profile_logs"
-
-        mkdir -p "$COMPARE_LOG_DIR" "$COMPARE_PROFILE_LOG_DIR"
-
-        gpt2_cmd="./gpt2 --input_bin ${GPT2_INPUT_BIN} --llmc_filepath ${GPT2_LLMC_FILEPATH} --device cuda ${arg_str1}"
-        run_and_log "$gpt2_cmd" "gpt2_${test_id}${log_suffix}" "$profile_flag" "$COMPARE_LOG_DIR" "$COMPARE_PROFILE_LOG_DIR"
-
-        # Run llama3 with flash=false
-        LOG_DIR="$(read_var LOG_DIR)"
-        : "${LOG_DIR:=./logs}"
-        PROFILE_LOG_DIR="$(read_var PROFILE_LOG_DIR)"
-        : "${PROFILE_LOG_DIR:=./profile_logs}"
-
-        mkdir -p "$LOG_DIR" "$PROFILE_LOG_DIR"
+    for ((gi=0; gi<num_groups; ++gi)); do
+        group_tag=$(jq -r ".test_groups[$gi].tag" "$CONFIG_FILE")
+        if [[ ${#SELECTED_TAGS[@]} -gt 0 && -z "${SELECTED_TAGS[$group_tag]}" ]]; then
+            continue
+        fi
 
-        llama3_cmd="./llama3 --input_bin ${LLAMA3_INPUT_BIN} --llmc_filepath ${LLAMA3_LLMC_FILEPATH} --device cuda ${arg_str}"
-        run_and_log "$llama3_cmd" "llama3_${test_id}${log_suffix}" "$profile_flag" "$LOG_DIR" "$PROFILE_LOG_DIR"
+        num_tests=$(jq ".test_groups[$gi].tests | length" "$CONFIG_FILE")
+        echo -e "\033[1;36m[TEST GROUP] tag=${group_tag}, cases=${num_tests}\033[0m"
 
-        # Run llama3 with flash=true
-        COMPARE_LOG_DIR="./compare_logs"
-        COMPARE_PROFILE_LOG_DIR="./compare_profile_logs"
+        for ((ti=0; ti<num_tests; ++ti)); do
+            test_id=$(jq -r ".test_groups[$gi].tests[$ti].id" "$CONFIG_FILE")
+            arg_str="$(args_string_for_test "$gi" "$ti")"
 
-        mkdir -p "$COMPARE_LOG_DIR" "$COMPARE_PROFILE_LOG_DIR"
+            # gpt2
+            gpt2_cmd="${prefix}./gpt2 --input_bin ${GPT2_INPUT_BIN} --llmc_filepath ${GPT2_LLMC_FILEPATH} --device cuda ${arg_str}"
+            run_and_log "$gpt2_cmd" "gpt2_${test_id}${log_suffix}" "$profile_flag" "$group_tag"
 
-        llama3_cmd="./llama3 --input_bin ${LLAMA3_INPUT_BIN} --llmc_filepath ${LLAMA3_LLMC_FILEPATH} --device cuda ${arg_str1}"
-        run_and_log "$llama3_cmd" "llama3_${test_id}${log_suffix}" "$profile_flag" "$COMPARE_LOG_DIR" "$COMPARE_PROFILE_LOG_DIR"
+            # llama3
+            llama3_cmd="${prefix}./llama3 --input_bin ${LLAMA3_INPUT_BIN} --llmc_filepath ${LLAMA3_LLMC_FILEPATH} --device cuda ${arg_str}"
+            run_and_log "$llama3_cmd" "llama3_${test_id}${log_suffix}" "$profile_flag" "$group_tag"
+        done
     done
 done
 
@@ -268,11 +289,11 @@ if [[ -n "$COMPARE_LOG_DIR" ]]; then
 
     # Run compare_loss.py
     echo -e "\n\033[1;33m[Running] compare_loss.py\033[0m"
-    python3 "${SCRIPT_DIR}/compare_loss.py" "$COMPARE_LOG_DIR" "$LOG_DIR" --threshold-fp32 1e-1 --threshold-bf16 1e-1 > compare_logs/loss_comparison.log 2>&1 || true
+    python3 "${SCRIPT_DIR}/compare_loss.py" "$COMPARE_LOG_DIR" "$LOG_DIR" || true
 
     # Run compare_tps.py
     echo -e "\n\033[1;33m[Running] compare_tps.py\033[0m"
-    python3 "${SCRIPT_DIR}/compare_tps.py" "$COMPARE_LOG_DIR" "$LOG_DIR" --threshold 0.20 > compare_logs/tps_comparison.log 2>&1 || true
+    python3 "${SCRIPT_DIR}/compare_tps.py" "$COMPARE_LOG_DIR" "$LOG_DIR" || true
 
     echo -e "\n\033[1;32mComparison completed.\033[0m"
 else
@@ -282,3 +303,6 @@ else
     echo -e "\033[1;33m         or export COMPARE_LOG_DIR=/path/to/baseline_logs before running.\033[0m"
     echo -e "\033[1;33m============================================================\033[0m"
 fi
+
+echo -e "\n\033[1;36m[END OF TEST] Cleaning build directory after all tests\033[0m"
+clean_build_dir
\ No newline at end of file
diff --git a/scripts/test_config.json b/scripts/test_config.json
index 3d66ff7b..323523eb 100644
--- a/scripts/test_config.json
+++ b/scripts/test_config.json
@@ -1,10 +1,10 @@
 {
     "variables": {
         "BUILD_DIR": "../build",
-        "GPT2_INPUT_BIN": "/data/shared/InfiniTrain-dev/data/llmc/gpt2/tinyshakespeare/tiny_shakespeare_train.bin",
-        "GPT2_LLMC_FILEPATH": "/data/shared/InfiniTrain-dev/data/llmc/gpt2/gpt2_124M.bin",
-        "LLAMA3_INPUT_BIN": "/data/shared/InfiniTrain-dev/data/llmc/llama3/tinyshakespeare/tiny_shakespeare_train.bin",
-        "LLAMA3_LLMC_FILEPATH": "/data/shared/InfiniTrain-dev/data/llmc/llama3/llama3.2_1B_fp32.bin",
+        "GPT2_INPUT_BIN": "../../data/llmc/gpt2/tinyshakespeare/tiny_shakespeare_train.bin",
+        "GPT2_LLMC_FILEPATH": "../../data/llmc/gpt2/gpt2_124M.bin",
+        "LLAMA3_INPUT_BIN": "../../data/llmc/llama3/tinyshakespeare/tiny_shakespeare_train.bin",
+        "LLAMA3_LLMC_FILEPATH": "../../data/llmc/llama3/llama3.2_1B_fp32.bin",
         "PROFILE_LOG_DIR": "./profile_logs",
         "LOG_DIR": "./logs",
         "COMPARE_LOG_DIR": ""
@@ -21,279 +21,288 @@
             "cmd": "cmake -DUSE_CUDA=ON -DUSE_NCCL=ON -DPROFILE_MODE=ON .. && make -j"
         }
     ],
-    "tests": [
+    "test_groups": [
         {
-            "id": "1",
-            "args": {
-                "dtype": "float32"
-            }
+            "tag": "basic",
+            "tests": [
+                {
+                    "id": "1",
+                    "args": {
+                        "dtype": "float32"
+                    }
+                },
+                {
+                    "id": "1_bfloat16",
+                    "args": {
+                        "dtype": "bfloat16"
+                    }
+                },
+                {
+                    "id": "2",
+                    "args": {
+                        "dtype": "float32",
+                        "num_iteration": 10,
+                        "batch_size": 80,
+                        "total_batch_size": 5120
+                    }
+                },
+                {
+                    "id": "2_bfloat16",
+                    "args": {
+                        "dtype": "bfloat16",
+                        "num_iteration": 10,
+                        "batch_size": 80,
+                        "total_batch_size": 5120
+                    }
+                },
+                {
+                    "id": "3",
+                    "args": {
+                        "dtype": "float32",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 10,
+                        "total_batch_size": 5120
+                    }
+                },
+                {
+                    "id": "3_bfloat16",
+                    "args": {
+                        "dtype": "bfloat16",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 10,
+                        "total_batch_size": 5120
+                    }
+                },
+                {
+                    "id": "4",
+                    "args": {
+                        "dtype": "float32",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 40,
+                        "total_batch_size": 5120,
+                        "tensor_parallel": 4
+                    }
+                },
+                {
+                    "id": "4_bfloat16",
+                    "args": {
+                        "dtype": "bfloat16",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 40,
+                        "total_batch_size": 5120,
+                        "tensor_parallel": 4
+                    }
+                },
+                {
+                    "id": "5",
+                    "args": {
+                        "dtype": "float32",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 40,
+                        "total_batch_size": 5120,
+                        "tensor_parallel": 4,
+                        "sequence_parallel": true
+                    }
+                },
+                {
+                    "id": "5_bfloat16",
+                    "args": {
+                        "dtype": "bfloat16",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 40,
+                        "total_batch_size": 5120,
+                        "tensor_parallel": 4,
+                        "sequence_parallel": true
+                    }
+                },
+                {
+                    "id": "6",
+                    "args": {
+                        "dtype": "float32",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 10,
+                        "total_batch_size": 5120,
+                        "pipeline_parallel": 8
+                    }
+                },
+                {
+                    "id": "6_bfloat16",
+                    "args": {
+                        "dtype": "bfloat16",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 10,
+                        "total_batch_size": 5120,
+                        "pipeline_parallel": 8
+                    }
+                },
+                {
+                    "id": "7",
+                    "args": {
+                        "dtype": "float32",
+                        "nthread_per_process": 4,
+                        "num_iteration": 10,
+                        "batch_size": 10,
+                        "total_batch_size": 5120,
+                        "pipeline_parallel": 4,
+                        "virtual_pipeline_parallel": 2
+                    }
+                },
+                {
+                    "id": "7_bfloat16",
+                    "args": {
+                        "dtype": "bfloat16",
+                        "nthread_per_process": 4,
+                        "num_iteration": 10,
+                        "batch_size": 10,
+                        "total_batch_size": 5120,
+                        "pipeline_parallel": 4,
+                        "virtual_pipeline_parallel": 2
+                    }
+                },
+                {
+                    "id": "8",
+                    "args": {
+                        "dtype": "float32",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 40,
+                        "total_batch_size": 5120,
+                        "tensor_parallel": 2,
+                        "sequence_parallel": true,
+                        "pipeline_parallel": 2,
+                        "virtual_pipeline_parallel": 2
+                    }
+                },
+                {
+                    "id": "8_bfloat16",
+                    "args": {
+                        "dtype": "bfloat16",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 40,
+                        "total_batch_size": 5120,
+                        "tensor_parallel": 2,
+                        "sequence_parallel": true,
+                        "pipeline_parallel": 2,
+                        "virtual_pipeline_parallel": 2
+                    }
+                }
+            ]
         },
         {
-            "id": "1_bfloat16",
-            "args": {
-                "dtype": "bfloat16"
-            }
-        },
-        {
-            "id": "2",
-            "args": {
-                "dtype": "float32",
-                "num_iteration": 10,
-                "batch_size": 80,
-                "total_batch_size": 5120
-            }
-        },
-        {
-            "id": "2_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "num_iteration": 10,
-                "batch_size": 80,
-                "total_batch_size": 5120
-            }
-        },
-        {
-            "id": "3",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120
-            }
-        },
-        {
-            "id": "3_distopt",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "3_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120
-            }
-        },
-        {
-            "id": "3_bfloat16_distopt",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "4",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4
-            }
-        },
-        {
-            "id": "4_distopt",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "4_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4
-            }
-        },
-        {
-            "id": "4_bfloat16_distopt",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "5",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "sequence_parallel": true
-            }
-        },
-        {
-            "id": "5_distopt",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "sequence_parallel": true,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "5_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "sequence_parallel": true
-            }
-        },
-        {
-            "id": "5_bfloat16_distopt",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 4,
-                "sequence_parallel": true,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "6",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "pipeline_parallel": 8
-            }
-        },
-        {
-            "id": "6_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "pipeline_parallel": 8
-            }
-        },
-        {
-            "id": "7",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 4,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "pipeline_parallel": 4,
-                "virtual_pipeline_parallel": 2
-            }
-        },
-        {
-            "id": "7_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 4,
-                "num_iteration": 10,
-                "batch_size": 10,
-                "total_batch_size": 5120,
-                "pipeline_parallel": 4,
-                "virtual_pipeline_parallel": 2
-            }
-        },
-        {
-            "id": "8",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 2,
-                "sequence_parallel": true,
-                "pipeline_parallel": 2,
-                "virtual_pipeline_parallel": 2
-            }
-        },
-        {
-            "id": "8_distopt",
-            "args": {
-                "dtype": "float32",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 2,
-                "sequence_parallel": true,
-                "pipeline_parallel": 2,
-                "virtual_pipeline_parallel": 2,
-                "use_distributed_optimizer": true
-            }
-        },
-        {
-            "id": "8_bfloat16",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 2,
-                "sequence_parallel": true,
-                "pipeline_parallel": 2,
-                "virtual_pipeline_parallel": 2
-            }
-        },
-        {
-            "id": "8_bfloat16_distopt",
-            "args": {
-                "dtype": "bfloat16",
-                "nthread_per_process": 8,
-                "num_iteration": 10,
-                "batch_size": 40,
-                "total_batch_size": 5120,
-                "tensor_parallel": 2,
-                "sequence_parallel": true,
-                "pipeline_parallel": 2,
-                "virtual_pipeline_parallel": 2,
-                "use_distributed_optimizer": true
-            }
+            "tag": "zero",
+            "tests": [
+                {
+                    "id": "3_distopt",
+                    "args": {
+                        "dtype": "float32",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 10,
+                        "total_batch_size": 5120,
+                        "use_distributed_optimizer": true
+                    }
+                },
+                {
+                    "id": "3_bfloat16_distopt",
+                    "args": {
+                        "dtype": "bfloat16",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 10,
+                        "total_batch_size": 5120,
+                        "use_distributed_optimizer": true
+                    }
+                },
+                {
+                    "id": "4_distopt",
+                    "args": {
+                        "dtype": "float32",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 40,
+                        "total_batch_size": 5120,
+                        "tensor_parallel": 4,
+                        "use_distributed_optimizer": true
+                    }
+                },
+                {
+                    "id": "4_bfloat16_distopt",
+                    "args": {
+                        "dtype": "bfloat16",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 40,
+                        "total_batch_size": 5120,
+                        "tensor_parallel": 4,
+                        "use_distributed_optimizer": true
+                    }
+                },
+                {
+                    "id": "5_distopt",
+                    "args": {
+                        "dtype": "float32",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 40,
+                        "total_batch_size": 5120,
+                        "tensor_parallel": 4,
+                        "sequence_parallel": true,
+                        "use_distributed_optimizer": true
+                    }
+                },
+                {
+                    "id": "5_bfloat16_distopt",
+                    "args": {
+                        "dtype": "bfloat16",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 40,
+                        "total_batch_size": 5120,
+                        "tensor_parallel": 4,
+                        "sequence_parallel": true,
+                        "use_distributed_optimizer": true
+                    }
+                },
+                {
+                    "id": "8_distopt",
+                    "args": {
+                        "dtype": "float32",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 40,
+                        "total_batch_size": 5120,
+                        "tensor_parallel": 2,
+                        "sequence_parallel": true,
+                        "pipeline_parallel": 2,
+                        "virtual_pipeline_parallel": 2,
+                        "use_distributed_optimizer": true
+                    }
+                },
+                {
+                    "id": "8_bfloat16_distopt",
+                    "args": {
+                        "dtype": "bfloat16",
+                        "nthread_per_process": 8,
+                        "num_iteration": 10,
+                        "batch_size": 40,
+                        "total_batch_size": 5120,
+                        "tensor_parallel": 2,
+                        "sequence_parallel": true,
+                        "pipeline_parallel": 2,
+                        "virtual_pipeline_parallel": 2,
+                        "use_distributed_optimizer": true
+                    }
+                }
+            ]
         }
     ]
-}
-
+}
\ No newline at end of file

From c5338ca0d301589952c474f152591780a2555a60 Mon Sep 17 00:00:00 2001
From: Aoshine999 <820459542@qq.com>
Date: Mon, 16 Mar 2026 16:24:58 +0800
Subject: [PATCH 18/18] change config file

---
 scripts/run_models_and_profile.bash | 148 ++------
 scripts/test_config.json            | 551 ++++++++++++++--------------
 2 files changed, 293 insertions(+), 406 deletions(-)

diff --git a/scripts/run_models_and_profile.bash b/scripts/run_models_and_profile.bash
index 032d80a8..1cf27935 100755
--- a/scripts/run_models_and_profile.bash
+++ b/scripts/run_models_and_profile.bash
@@ -3,56 +3,7 @@
 set -e
 set -o pipefail
 
-usage() {
-    cat <<'EOF'
-Usage: run_models_and_profile.bash [--test-config path] [--only-run tag1,tag2]
-
-Options:
-  --test-config PATH  Path to test config JSON. Default: test_config.json.
-  --only-run TAGS   Only run the specified tag groups, separated by commas.
-  -h, --help        Show this help message.
-EOF
-}
-
-CONFIG_FILE="test_config.json"
-ONLY_RUN_TAGS=""
-
-while [[ $# -gt 0 ]]; do
-    case "$1" in
-        --test-config)
-            [[ $# -lt 2 ]] && { echo "Error: --test-config requires a file path."; exit 1; }
-            CONFIG_FILE="$2"
-            shift 2
-            ;;
-        --test-config=*)
-            CONFIG_FILE="${1#*=}"
-            shift
-            ;;
-        --only-run)
-            [[ $# -lt 2 ]] && { echo "Error: --only-run requires a comma-separated tag list."; exit 1; }
-            ONLY_RUN_TAGS="$2"
-            shift 2
-            ;;
-        --only-run=*)
-            ONLY_RUN_TAGS="${1#*=}"
-            shift
-            ;;
-        -h|--help)
-            usage
-            exit 0
-            ;;
-        -*)
-            echo "Error: Unknown option: $1"
-            usage
-            exit 1
-            ;;
-        *)
-            echo "Error: Unknown positional argument: $1"
-            usage
-            exit 1
-            ;;
-    esac
-done
+CONFIG_FILE="${1:-test_config.json}"
 
 # Dependencies check
 if ! command -v jq >/dev/null 2>&1; then
@@ -82,28 +33,6 @@ done < <(jq -r '.variables | to_entries[] | "\(.key)=\(.value)"' "$CONFIG_FILE")
 
 # Global variable to save the last cmake command
 LAST_CMAKE_CMD=""
-declare -A SELECTED_TAGS=()
-
-normalize_tag() {
-    local raw="$1"
-    raw="${raw#"${raw%%[![:space:]]*}"}"
-    raw="${raw%"${raw##*[![:space:]]}"}"
-    printf '%s' "$raw"
-}
-
-if [[ -n "$ONLY_RUN_TAGS" ]]; then
-    IFS=',' read -r -a requested_tags <<< "$ONLY_RUN_TAGS"
-    for raw_tag in "${requested_tags[@]}"; do
-        tag="$(normalize_tag "$raw_tag")"
-        [[ -z "$tag" ]] && continue
-        SELECTED_TAGS["$tag"]=1
-    done
-
-    if [[ ${#SELECTED_TAGS[@]} -eq 0 ]]; then
-        echo "Error: --only-run did not contain any valid tags."
-        exit 1
-    fi
-fi
 
 # Clean the build directory
 clean_build_dir() {
@@ -117,12 +46,9 @@ run_and_log() {
     local cmd="$1"
     local log_name="$2"
     local is_profile="$3"
-    local tag="${4:-basic}"
     local timestamp
     timestamp=$(date '+%Y-%m-%d %H:%M:%S')
-    local tag_log_dir="${LOG_DIR}/${tag}"
-    mkdir -p "$tag_log_dir"
-    local log_path="$(realpath "${tag_log_dir}/${log_name}.log")"
+    local log_path="$(realpath "${LOG_DIR}/${log_name}.log")"
 
     echo -e "\033[1;32m============================================================\033[0m"
     echo -e "\033[1;36m[$timestamp] [Running] ${log_name}\033[0m"
@@ -173,7 +99,7 @@ run_and_log() {
 
     # If profiling is enabled, move profiling files to the target directory
     if [[ "$is_profile" == "yes" ]]; then
-        move_profile_logs "$log_name" "$tag"
+        move_profile_logs "$log_name"
     fi
 }
 
@@ -181,17 +107,14 @@ run_and_log() {
 # Move profiling output logs
 move_profile_logs() {
     local prefix="$1"
-    local tag="${2:-basic}"
-    local tag_profile_dir="${PROFILE_LOG_DIR}/${tag}"
-    mkdir -p "$tag_profile_dir"
 
     # Move *.report.rankN files
     for report_file in "${BUILD_DIR}"/*.report.rank*; do
         if [[ -f "$report_file" ]]; then
             local base_name
             base_name=$(basename "$report_file")
-            mv "$report_file" "${tag_profile_dir}/${prefix}_${base_name}"
-            echo "Moved $base_name to ${tag_profile_dir}/${prefix}_${base_name}"
+            mv "$report_file" "${PROFILE_LOG_DIR}/${prefix}_${base_name}"
+            echo "Moved $base_name to ${PROFILE_LOG_DIR}/${prefix}_${base_name}"
         fi
     done
 
@@ -200,18 +123,17 @@ move_profile_logs() {
         if [[ -f "$record_file" ]]; then
             local base_name
             base_name=$(basename "$record_file")
-            mv "$record_file" "${tag_profile_dir}/${prefix}_${base_name}"
-            echo "Moved $base_name to ${tag_profile_dir}/${prefix}_${base_name}"
+            mv "$record_file" "${PROFILE_LOG_DIR}/${prefix}_${base_name}"
+            echo "Moved $base_name to ${PROFILE_LOG_DIR}/${prefix}_${base_name}"
         fi
     done
 }
 
-# Build "--key value" arg string from test_groups[gi].tests[ti].args (shell-escaped)
+# Build "--key value" arg string from tests[i].args (shell-escaped)
 args_string_for_test() {
-    local group_idx="$1"
-    local test_idx="$2"
-    jq -r --argjson g "$group_idx" --argjson t "$test_idx" '
-      .test_groups[$g].tests[$t].args
+    local idx="$1"
+    jq -r --argjson i "$idx" '
+      .tests[$i].args
       | to_entries[]
       | "--\(.key) \(.value|tostring)"
     ' "$CONFIG_FILE" | paste -sd' ' -
@@ -219,20 +141,7 @@ args_string_for_test() {
 
 # Run tests
 num_builds=$(jq '.builds | length' "$CONFIG_FILE")
-num_groups=$(jq '.test_groups | length' "$CONFIG_FILE")
-
-selected_group_count=0
-for ((gi=0; gi<num_groups; ++gi)); do
-    group_tag=$(jq -r ".test_groups[$gi].tag" "$CONFIG_FILE")
-    if [[ ${#SELECTED_TAGS[@]} -eq 0 || -n "${SELECTED_TAGS[$group_tag]}" ]]; then
-        ((selected_group_count += 1))
-    fi
-done
-
-if [[ "$selected_group_count" -eq 0 ]]; then
-    echo "Error: No matching test groups found for --only-run=${ONLY_RUN_TAGS}"
-    exit 1
-fi
+num_tests=$(jq '.tests  | length' "$CONFIG_FILE")
 
 for ((id=0; id<num_builds; ++id)); do
     build_id=$(jq -r ".builds[$id].id" "$CONFIG_FILE")
@@ -243,7 +152,7 @@ for ((id=0; id<num_builds; ++id)); do
 
     # always clean before another build
     clean_build_dir
-    run_and_log "$LAST_CMAKE_CMD" "${build_id}" "no" "build"
+    run_and_log "$LAST_CMAKE_CMD" "${build_id}" "no"
 
     # profile flag for runs
     profile_flag="no"
@@ -253,27 +162,17 @@ for ((id=0; id<num_builds; ++id)); do
         log_suffix="_profile"
     fi
 
-    for ((gi=0; gi<num_groups; ++gi)); do
-        group_tag=$(jq -r ".test_groups[$gi].tag" "$CONFIG_FILE")
-        if [[ ${#SELECTED_TAGS[@]} -gt 0 && -z "${SELECTED_TAGS[$group_tag]}" ]]; then
-            continue
-        fi
-
-        num_tests=$(jq ".test_groups[$gi].tests | length" "$CONFIG_FILE")
-        echo -e "\033[1;36m[TEST GROUP] tag=${group_tag}, cases=${num_tests}\033[0m"
+    for ((ti=0; ti<num_tests; ++ti)); do
+        test_id=$(jq -r ".tests[$ti].id" "$CONFIG_FILE")
+        arg_str="$(args_string_for_test "$ti")"
 
-        for ((ti=0; ti<num_tests; ++ti)); do
-            test_id=$(jq -r ".test_groups[$gi].tests[$ti].id" "$CONFIG_FILE")
-            arg_str="$(args_string_for_test "$gi" "$ti")"
+        # gpt2
+        gpt2_cmd="${prefix}./gpt2 --input_bin ${GPT2_INPUT_BIN} --llmc_filepath ${GPT2_LLMC_FILEPATH} --device cuda ${arg_str}"
+        run_and_log "$gpt2_cmd" "gpt2_${test_id}${log_suffix}" "$profile_flag"
 
-            # gpt2
-            gpt2_cmd="${prefix}./gpt2 --input_bin ${GPT2_INPUT_BIN} --llmc_filepath ${GPT2_LLMC_FILEPATH} --device cuda ${arg_str}"
-            run_and_log "$gpt2_cmd" "gpt2_${test_id}${log_suffix}" "$profile_flag" "$group_tag"
-
-            # llama3
-            llama3_cmd="${prefix}./llama3 --input_bin ${LLAMA3_INPUT_BIN} --llmc_filepath ${LLAMA3_LLMC_FILEPATH} --device cuda ${arg_str}"
-            run_and_log "$llama3_cmd" "llama3_${test_id}${log_suffix}" "$profile_flag" "$group_tag"
-        done
+        # llama3
+        llama3_cmd="${prefix}./llama3 --input_bin ${LLAMA3_INPUT_BIN} --llmc_filepath ${LLAMA3_LLMC_FILEPATH} --device cuda ${arg_str}"
+        run_and_log "$llama3_cmd" "llama3_${test_id}${log_suffix}" "$profile_flag"
     done
 done
 
@@ -303,6 +202,3 @@ else
     echo -e "\033[1;33m         or export COMPARE_LOG_DIR=/path/to/baseline_logs before running.\033[0m"
     echo -e "\033[1;33m============================================================\033[0m"
 fi
-
-echo -e "\n\033[1;36m[END OF TEST] Cleaning build directory after all tests\033[0m"
-clean_build_dir
\ No newline at end of file
diff --git a/scripts/test_config.json b/scripts/test_config.json
index 323523eb..5659b516 100644
--- a/scripts/test_config.json
+++ b/scripts/test_config.json
@@ -21,288 +21,279 @@
             "cmd": "cmake -DUSE_CUDA=ON -DUSE_NCCL=ON -DPROFILE_MODE=ON .. && make -j"
         }
     ],
-    "test_groups": [
+    "tests": [
         {
-            "tag": "basic",
-            "tests": [
-                {
-                    "id": "1",
-                    "args": {
-                        "dtype": "float32"
-                    }
-                },
-                {
-                    "id": "1_bfloat16",
-                    "args": {
-                        "dtype": "bfloat16"
-                    }
-                },
-                {
-                    "id": "2",
-                    "args": {
-                        "dtype": "float32",
-                        "num_iteration": 10,
-                        "batch_size": 80,
-                        "total_batch_size": 5120
-                    }
-                },
-                {
-                    "id": "2_bfloat16",
-                    "args": {
-                        "dtype": "bfloat16",
-                        "num_iteration": 10,
-                        "batch_size": 80,
-                        "total_batch_size": 5120
-                    }
-                },
-                {
-                    "id": "3",
-                    "args": {
-                        "dtype": "float32",
-                        "nthread_per_process": 8,
-                        "num_iteration": 10,
-                        "batch_size": 10,
-                        "total_batch_size": 5120
-                    }
-                },
-                {
-                    "id": "3_bfloat16",
-                    "args": {
-                        "dtype": "bfloat16",
-                        "nthread_per_process": 8,
-                        "num_iteration": 10,
-                        "batch_size": 10,
-                        "total_batch_size": 5120
-                    }
-                },
-                {
-                    "id": "4",
-                    "args": {
-                        "dtype": "float32",
-                        "nthread_per_process": 8,
-                        "num_iteration": 10,
-                        "batch_size": 40,
-                        "total_batch_size": 5120,
-                        "tensor_parallel": 4
-                    }
-                },
-                {
-                    "id": "4_bfloat16",
-                    "args": {
-                        "dtype": "bfloat16",
-                        "nthread_per_process": 8,
-                        "num_iteration": 10,
-                        "batch_size": 40,
-                        "total_batch_size": 5120,
-                        "tensor_parallel": 4
-                    }
-                },
-                {
-                    "id": "5",
-                    "args": {
-                        "dtype": "float32",
-                        "nthread_per_process": 8,
-                        "num_iteration": 10,
-                        "batch_size": 40,
-                        "total_batch_size": 5120,
-                        "tensor_parallel": 4,
-                        "sequence_parallel": true
-                    }
-                },
-                {
-                    "id": "5_bfloat16",
-                    "args": {
-                        "dtype": "bfloat16",
-                        "nthread_per_process": 8,
-                        "num_iteration": 10,
-                        "batch_size": 40,
-                        "total_batch_size": 5120,
-                        "tensor_parallel": 4,
-                        "sequence_parallel": true
-                    }
-                },
-                {
-                    "id": "6",
-                    "args": {
-                        "dtype": "float32",
-                        "nthread_per_process": 8,
-                        "num_iteration": 10,
-                        "batch_size": 10,
-                        "total_batch_size": 5120,
-                        "pipeline_parallel": 8
-                    }
-                },
-                {
-                    "id": "6_bfloat16",
-                    "args": {
-                        "dtype": "bfloat16",
-                        "nthread_per_process": 8,
-                        "num_iteration": 10,
-                        "batch_size": 10,
-                        "total_batch_size": 5120,
-                        "pipeline_parallel": 8
-                    }
-                },
-                {
-                    "id": "7",
-                    "args": {
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-                },
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-                    }
-                },
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-                    }
-                },
-                {
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-                        "batch_size": 40,
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-                        "tensor_parallel": 2,
-                        "sequence_parallel": true,
-                        "pipeline_parallel": 2,
-                        "virtual_pipeline_parallel": 2
-                    }
-                }
-            ]
+            "id": "1",
+            "args": {
+                "dtype": "float32"
+            }
         },
         {
-            "tag": "zero",
-            "tests": [
-                {
-                    "id": "3_distopt",
-                    "args": {
-                        "dtype": "float32",
-                        "nthread_per_process": 8,
-                        "num_iteration": 10,
-                        "batch_size": 10,
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-                        "use_distributed_optimizer": true
-                    }
-                },
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-                        "num_iteration": 10,
-                        "batch_size": 10,
-                        "total_batch_size": 5120,
-                        "use_distributed_optimizer": true
-                    }
-                },
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-                    "id": "4_distopt",
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-                        "dtype": "float32",
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-                        "batch_size": 40,
-                        "total_batch_size": 5120,
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-                    }
-                },
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-                        "nthread_per_process": 8,
-                        "num_iteration": 10,
-                        "batch_size": 40,
-                        "total_batch_size": 5120,
-                        "tensor_parallel": 4,
-                        "use_distributed_optimizer": true
-                    }
-                },
-                {
-                    "id": "5_distopt",
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-                        "num_iteration": 10,
-                        "batch_size": 40,
-                        "total_batch_size": 5120,
-                        "tensor_parallel": 4,
-                        "sequence_parallel": true,
-                        "use_distributed_optimizer": true
-                    }
-                },
-                {
-                    "id": "5_bfloat16_distopt",
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-                        "dtype": "bfloat16",
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-                        "num_iteration": 10,
-                        "batch_size": 40,
-                        "total_batch_size": 5120,
-                        "tensor_parallel": 4,
-                        "sequence_parallel": true,
-                        "use_distributed_optimizer": true
-                    }
-                },
-                {
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-                        "nthread_per_process": 8,
-                        "num_iteration": 10,
-                        "batch_size": 40,
-                        "total_batch_size": 5120,
-                        "tensor_parallel": 2,
-                        "sequence_parallel": true,
-                        "pipeline_parallel": 2,
-                        "virtual_pipeline_parallel": 2,
-                        "use_distributed_optimizer": true
-                    }
-                },
-                {
-                    "id": "8_bfloat16_distopt",
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-                        "dtype": "bfloat16",
-                        "nthread_per_process": 8,
-                        "num_iteration": 10,
-                        "batch_size": 40,
-                        "total_batch_size": 5120,
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-                        "sequence_parallel": true,
-                        "pipeline_parallel": 2,
-                        "virtual_pipeline_parallel": 2,
-                        "use_distributed_optimizer": true
-                    }
-                }
-            ]
+            "id": "1_bfloat16",
+            "args": {
+                "dtype": "bfloat16"
+            }
+        },
+        {
+            "id": "2",
+            "args": {
+                "dtype": "float32",
+                "num_iteration": 10,
+                "batch_size": 80,
+                "total_batch_size": 5120
+            }
+        },
+        {
+            "id": "2_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "num_iteration": 10,
+                "batch_size": 80,
+                "total_batch_size": 5120
+            }
+        },
+        {
+            "id": "3",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120
+            }
+        },
+        {
+            "id": "3_distopt",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "3_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120
+            }
+        },
+        {
+            "id": "3_bfloat16_distopt",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "4",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4
+            }
+        },
+        {
+            "id": "4_distopt",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "4_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4
+            }
+        },
+        {
+            "id": "4_bfloat16_distopt",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "5",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "sequence_parallel": true
+            }
+        },
+        {
+            "id": "5_distopt",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "sequence_parallel": true,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "5_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "sequence_parallel": true
+            }
+        },
+        {
+            "id": "5_bfloat16_distopt",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 4,
+                "sequence_parallel": true,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "6",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120,
+                "pipeline_parallel": 8
+            }
+        },
+        {
+            "id": "6_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120,
+                "pipeline_parallel": 8
+            }
+        },
+        {
+            "id": "7",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 4,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120,
+                "pipeline_parallel": 4,
+                "virtual_pipeline_parallel": 2
+            }
+        },
+        {
+            "id": "7_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 4,
+                "num_iteration": 10,
+                "batch_size": 10,
+                "total_batch_size": 5120,
+                "pipeline_parallel": 4,
+                "virtual_pipeline_parallel": 2
+            }
+        },
+        {
+            "id": "8",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 2,
+                "sequence_parallel": true,
+                "pipeline_parallel": 2,
+                "virtual_pipeline_parallel": 2
+            }
+        },
+        {
+            "id": "8_distopt",
+            "args": {
+                "dtype": "float32",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 2,
+                "sequence_parallel": true,
+                "pipeline_parallel": 2,
+                "virtual_pipeline_parallel": 2,
+                "use_distributed_optimizer": true
+            }
+        },
+        {
+            "id": "8_bfloat16",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 2,
+                "sequence_parallel": true,
+                "pipeline_parallel": 2,
+                "virtual_pipeline_parallel": 2
+            }
+        },
+        {
+            "id": "8_bfloat16_distopt",
+            "args": {
+                "dtype": "bfloat16",
+                "nthread_per_process": 8,
+                "num_iteration": 10,
+                "batch_size": 40,
+                "total_batch_size": 5120,
+                "tensor_parallel": 2,
+                "sequence_parallel": true,
+                "pipeline_parallel": 2,
+                "virtual_pipeline_parallel": 2,
+                "use_distributed_optimizer": true
+            }
         }
     ]
-}
\ No newline at end of file
+}
+