for impl of 2nd-order and format

mindspore/ops/_op_impl/_custom_op/__init__.py

@@ -24,4 +24,4 @@ from .matmul_cube_fracz_left_cast_impl import CusMatMulCubeFraczLeftCast
 from .matmul_cube_fracz_right_mul_impl import CusMatMulCubeFraczRightMul
 from .matmul_cube_impl import CusMatMulCube
 from .matrix_combine_impl import CusMatrixCombine
-from .transpose_02314_impl import CusTranspose02314
+from .transpose02314_impl import CusTranspose02314

mindspore/ops/_op_impl/_custom_op/cholesky_trsm_impl.py

@@ -101,4 +101,4 @@ def CusCholeskyTrsm(input_x,output, kernel_name):
         tik_instance.data_move(res[block_index,0,0], temp_ub, 0, 1, 8 * vector_repeat_times * split_dim,0,0)
 
     tik_instance.BuildCCE(kernel_name=kernel_name, inputs=[input_x], outputs=[res])
-        return tik_instance
+    return tik_instance

mindspore/ops/_op_impl/_custom_op/fused_abs_max1_impl.py

@@ -42,7 +42,8 @@ def CusFusedAbsMax1(input_x, output, origin_shape = None, kernel_name="fused_abs
 
 
     if len(input_x_shape) > 2:
-        if (input_x_shape[0] == 1 and input_x_shape[1] == 128 and input_x_shape[2] == 128)  or (input_x_shape[0] == 4 and input_x_shape[1] == 16) or (input_x_shape[0] == 16 and input_x_shape[1] == 4):             input_x = tik_instance.Tensor("float32", input_x_shape, name="input_x", scope=tik.scope_gm)
+        if (input_x_shape[0] == 1 and input_x_shape[1] == 128 and input_x_shape[2] == 128)  or (input_x_shape[0] == 4 and input_x_shape[1] == 16) or (input_x_shape[0] == 16 and input_x_shape[1] == 4):
+            input_x = tik_instance.Tensor("float32", input_x_shape, name="input_x", scope=tik.scope_gm)
             res = tik_instance.Tensor("float32", output_shape, name="res", scope=tik.scope_gm)
             total_elements = 1
             for val in input_x_shape:
@@ -131,7 +132,8 @@ def CusFusedAbsMax1(input_x, output, origin_shape = None, kernel_name="fused_abs
                     tik_instance.vmax(64, broadcast_0_local_UB, broadcast_0_local_UB, broadcast_0_local_UB[128], 2, 1, 1, 1, 8, 8, 8)
                     tik_instance.vmax(64, broadcast_0_local_UB, broadcast_0_local_UB, broadcast_0_local_UB[64], 1, 1, 1, 1, 8, 8, 8)
                     tik_instance.data_move(res[block_index, 0], broadcast_0_local_UB, 0, 1, 8, 0, 0)
-        elif (input_x_shape[0] == 4 and input_x_shape[1] == 128 and input_x_shape[2] == 128) or (input_x_shape[0] == 8 and input_x_shape[1] == 32) or (input_x_shape[0] == 32 and input_x_shape[1] == 8):            input_x = tik_instance.Tensor("float32", input_x_shape, name="input_x", scope=tik.scope_gm)
+        elif (input_x_shape[0] == 4 and input_x_shape[1] == 128 and input_x_shape[2] == 128) or (input_x_shape[0] == 8 and input_x_shape[1] == 32) or (input_x_shape[0] == 32 and input_x_shape[1] == 8):
+            input_x = tik_instance.Tensor("float32", input_x_shape, name="input_x", scope=tik.scope_gm)
             res = tik_instance.Tensor("float32", output_shape, name="res", scope=tik.scope_gm)
             total_elements = 1
             for val in input_x_shape:
@@ -608,6 +610,7 @@ def CusFusedAbsMax1(input_x, output, origin_shape = None, kernel_name="fused_abs
             res = tik_instance.Tensor("float32", output_shape, name="res", scope=tik.scope_gm)
             total_elements = 1
             for val in input_x_shape:
+                total_elements *= val
             blocks = 32
             each_block_element = total_elements // blocks
             with tik_instance.for_range(0,blocks,block_num=blocks) as block_index:

mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_right_impl.py

@@ -54,7 +54,7 @@ def CusMatMulCubeDenseRight(input_x1, input_x2, input_x3, bias=None, output_y={}
     shape_b_input = input_x2.get("shape")
     matrix_max_input = input_x3.get("shape")
     input_shape = (tuple(shape_a_input), tuple(shape_b_input), tuple(matrix_max_input))
-if input_shape not in support_shape:
+    if input_shape not in support_shape:
         raise RuntimeError("input_shape %s is not supported" % str(input_shape))
  
     if shape_a_temp[0] == 128 and shape_a_temp[1] == 63 and shape_b_temp[0] == 128 and shape_b_temp[1] == 128: