diff --git a/tests/kernel/tensor/flash_attn.py b/tests/kernel/tensor/flash_attn.py
index e7fb8300..9c934adb 100644
--- a/tests/kernel/tensor/flash_attn.py
+++ b/tests/kernel/tensor/flash_attn.py
@@ -94,7 +94,11 @@ if __name__ == "__main__":
     def exp2(x):
         return (x**2) / 2.0 + x + 1.0
 
-    col_to_save = 64
+    full_S = A_array @ B_array
+    full_S_T = full_S.transpose([1, 0])
+    full_S.astype('float32').tofile("full_S.bin")
+
+    col_to_save = 128
 
     for col in range(0, seqlen, Bc):
         print(f"tile iteration {col}~{col + Bc} ======================================")
@@ -137,8 +141,6 @@ if __name__ == "__main__":
             rowsum.astype('float32').tofile("rowsum.bin")
 
         x = rowmax_prev - rowmax
-        print("haha")
-        print(exp2(x))
         O = O / (exp2(x)[:, np.newaxis])
         if col == col_to_save:
             print('O_before_PV:')
diff --git a/tests/kernel/tensor/generate_matrix.py b/tests/kernel/tensor/generate_matrix.py
index 7d49e193..8626ba43 100644
--- a/tests/kernel/tensor/generate_matrix.py
+++ b/tests/kernel/tensor/generate_matrix.py
@@ -46,11 +46,12 @@ def pack_fp16_by_row(array):
 if __name__ == "__main__":
     M, N, K = parse_mnk()
 
-    rand = True
+    rand = False
     if not rand:
         A_array = np.arange(M * K).reshape([M, K])
         B_array = np.arange(K * N).reshape([K, N])
-        C_array = np.arange(M * N).reshape([M, N])
+        # C_array = np.arange(M * N).reshape([M, N])
+        C_array = np.zeros([M, N])
     else:
         np.random.seed(0)
         A_array = np.random.rand(M, K) - 0.5
@@ -76,11 +77,6 @@ if __name__ == "__main__":
 
     np.savez("abc", A_array=A_array, B_array=B_array, C_array=C_array)
 
-    D_expected = A_array @ B_array
-    D_expected.astype('float32').tofile("d_expected.bin")
-    print('D_expected:')
-    print(D_expected)
-
     fp16 = False
     if fp16:
         A_packed = pack_fp16_by_row(A_array)
@@ -105,29 +101,8 @@ if __name__ == "__main__":
         print('B:')
         print(B_array)
 
-    # generate rowmax result in online softmax
-    row_max = np.max(D_expected, axis=1)
-    row_max.astype('float32').tofile("rowmax.bin")
+    D_expected = A_array @ B_array
+    D_expected.astype('float32').tofile("d_expected.bin")
+    print('D_expected:')
+    print(D_expected)
 
-    # subtrace row_max from each row by broadcasting
-    # (placeholder for exp)
-    x = D_expected - row_max[:, np.newaxis]
-    P = (x**2) / 2.0 + x + 1.0
-    # for i in range(3, 4):
-    #     P += (x**i) / np.math.factorial(i)
-    # P = np.exp(exp)
-    P.astype('float32').tofile("P_expected.bin")
-    # print('P error:')
-    # print(P / np.exp(x))
-    print('P_expected:')
-    print(P)
-
-    row_sum = np.sum(P, axis=1)
-    row_sum.astype('float32').tofile("rowsum.bin")
-
-    V = C_array
-    # O = P.transpose([1, 0]) @ V
-    O = P @ V
-    O.astype('float32').tofile("O_expected.bin")
-    print('O_expected:')
-    print(O)