for pylint 4th

example/resnet50_imagenet2012_THOR/config.py

@@ -33,5 +33,5 @@ config = ed({
     "save_checkpoint_path": "./",
     "label_smooth": 1,
     "label_smooth_factor": 0.1,
-    "frequency": 278
+    "frequency": 834
 })

example/resnet50_imagenet2012_THOR/model/thor.py

@@ -22,12 +22,6 @@ from mindspore.nn.optim.optimizer import Optimizer
 from mindspore.ops import functional as F, composite as C, operations as P
 from mindspore.parallel._utils import _get_device_num, _get_mirror_mean
 
-from cus_ops.cus_matmul_cube_dense_right import CusMatMulCubeDenseRight
-from cus_ops.cus_matmul_cube_fracz_left_cast import CusMatMulCubeFraczLeftCast
-from cus_ops.cus_matmul_cube_dense_left import CusMatMulCubeDenseLeft
-from cus_ops.cus_matmul_cube_fracz_right_mul import CusMatMulCubeFraczRightMul
-from model.grad_reducer_thor import DistributedGradReducerThor
-
 momentum_opt = C.MultitypeFuncGraph("momentum_opt")
 
 
@@ -68,10 +62,10 @@ class THOR(Optimizer):
         self.matrix_G = ParameterTuple(matrix_G)
         self.A_inv_max = ParameterTuple(A_inv_max)
         self.G_inv_max = ParameterTuple(G_inv_max)
-        self.cube_matmul_left = CusMatMulCubeFraczLeftCast()
-        self.cube_matmul_left_fc = CusMatMulCubeDenseLeft()
-        self.cube_matmul_right_fc = CusMatMulCubeDenseRight()
-        self.cube_matmul_right_mul = CusMatMulCubeFraczRightMul()
+        self.cube_matmul_left = P.CusMatMulCubeFraczLeftCast()
+        self.cube_matmul_left_fc = P.CusMatMulCubeDenseLeft()
+        self.cube_matmul_right_fc = P.CusMatMulCubeDenseRight()
+        self.cube_matmul_right_mul = P.CusMatMulCubeFraczRightMul()
         self.transpose = P.Transpose()
         self.shape = P.Shape()
         self.reshape = P.Reshape()

example/resnet50_imagenet2012_THOR/model/thor_layer.py

@@ -23,19 +23,9 @@ from mindspore.common.tensor import Tensor
 from mindspore.nn.cell import Cell
 from mindspore.nn.layer.activation import get_activation
 from mindspore.ops import operations as P
-
-from cus_ops.cus_batch_matmul import CusBatchMatMul
-from cus_ops.cus_cholesky_trsm import CusCholeskyTrsm
-from cus_ops.cus_fused_abs_max1 import CusFusedAbsMax1
-from cus_ops.cus_img2col import CusImg2Col
-from cus_ops.cus_matmul_cube import CusMatMulCube
-from cus_ops.cus_matrix_combine import CusMatrixCombine
-from cus_ops.cus_transpose02314 import CusTranspose02314
-
 import numpy as np
 C0 = 16
 
-
 def caculate_device_shape(matrix_dim, channel, is_A):
     ll = (0)
     if is_A:
@@ -153,11 +143,11 @@ class Conv2d_Thor(_Conv):
                                group=self.group
                                )
 
-        self.img2col = CusImg2Col(ksizes=ksizes, strides=strides)
-        self.cube_matmul = CusMatMulCube(transpose_a=True)
-        self.matrix_combine = CusMatrixCombine()
-        self.cholesky = CusCholeskyTrsm()
-        self.transpose02314 = CusTranspose02314()
+        self.img2col = P.CusImg2Col(ksizes=ksizes, strides=strides)
+        self.cube_matmul = P.CusMatMulCube(transpose_a=True)
+        self.matrix_combine = P.CusMatrixCombine()
+        self.cholesky = P.CusCholeskyTrsm()
+        self.transpose02314 = P.CusTranspose02314()
         self.matrix_A_dim = self.in_channels * self.kernel_size[0] * self.kernel_size[1]
         self.matrix_G_dim = self.out_channels
         self.matrix_A_device_shape, self.matrix_A_device_dim = caculate_device_shape(self.matrix_A_dim,
@@ -190,7 +180,7 @@ class Conv2d_Thor(_Conv):
         self.mul = P.Mul()
         self.cast = P.Cast()
         self.damping = Tensor(damping)
-        self.vector_matmul = CusBatchMatMul()
+        self.vector_matmul = P.CusBatchMatMul()
         self.diag_block_dim = 128
         self.channels_slice_flag = False
         if self.in_channels % C0 != 0:
@@ -221,8 +211,8 @@ class Conv2d_Thor(_Conv):
 
         self.dampingA = Tensor(np.identity(dampingA_dim), mstype.float32)
         self.dampingG = Tensor(np.identity(dampingG_dim), mstype.float32)
-        self.fused_abs_max1 = CusFusedAbsMax1([self.matrix_A_dim, self.matrix_A_dim])
-        self.fused_abs_max2 = CusFusedAbsMax1()
+        self.fused_abs_max1 = P.CusFusedAbsMax1([self.matrix_A_dim, self.matrix_A_dim])
+        self.fused_abs_max2 = P.CusFusedAbsMax1()
         self.log = P.Log()
         self.exp = P.Exp()
         self.sqrt = P.Sqrt()
@@ -375,9 +365,9 @@ class Dense_Thor(Cell):
         self.fake_G = Tensor(np.zeros([63, 63, 16, 16]).astype(np.float16))
 
         self.matmul = P.MatMul(transpose_b=True)
-        self.cube_matmul = CusMatMulCube(transpose_a=True)
-        self.matrix_combine = CusMatrixCombine()
-        self.cholesky = CusCholeskyTrsm()
+        self.cube_matmul = P.CusMatMulCube(transpose_a=True)
+        self.matrix_combine = P.CusMatrixCombine()
+        self.cholesky = P.CusCholeskyTrsm()
         self.shape = P.Shape()
         self.reshape = P.Reshape()
         self.transpose = P.Transpose()
@@ -386,7 +376,7 @@ class Dense_Thor(Cell):
         self.cast = P.Cast()
         self.damping = Tensor(damping)
         self.loss_scale = Tensor(1 / loss_scale, mstype.float16)
-        self.vector_matmul = CusBatchMatMul()
+        self.vector_matmul = P.CusBatchMatMul()
         self.pad = P.Pad(((0, 24), (0, 24)))
         self.pad1 = P.Pad(((0, 8), (0, 8)))
         self.slice = P.Slice()
@@ -396,8 +386,8 @@ class Dense_Thor(Cell):
         self.axis = 0
         self.A_inv_max = Parameter(initializer(0, [1], mstype.float32), name="A_inv_max", requires_grad=False)
         self.G_inv_max = Parameter(initializer(0, [1], mstype.float32), name="G_inv_max", requires_grad=False)
-        self.fused_abs_max1 = CusFusedAbsMax1([1000, 1000])
-        self.fused_abs_max2 = CusFusedAbsMax1()
+        self.fused_abs_max1 = P.CusFusedAbsMax1([1000, 1000])
+        self.fused_abs_max2 = P.CusFusedAbsMax1()
         self.log = P.Log()
         self.exp = P.Exp()
         self.dampingA = Tensor(np.identity(2048), mstype.float32)

mindspore/ops/_op_impl/_custom_op/batch_matmul_impl.py

@@ -33,6 +33,7 @@ cus_batchmatmul_op_info = TBERegOp("CusBatchMatMul") \
 
 
 def _get_flattern_shape(shape):
+    """_get_flattern_shape"""
     flattern_shape = 1
     for dim in shape:
         flattern_shape *= dim
@@ -40,6 +41,7 @@ def _get_flattern_shape(shape):
 
 
 def _inner_matmul_new(tik_instance, dtype, input1, input1_index, input2, input2_index, res, res_index):
+    """_inner_matmul_new"""
     input_1_local_UB = tik_instance.Tensor(dtype, [128], name="input_1_local_UB", scope=tik.scope_ubuf)
     t_1_0_local_UB = tik_instance.Tensor(dtype, [64 * 128], name="t_1_0_local_UB", scope=tik.scope_ubuf)
     tik_instance.data_move(input_1_local_UB, input1[input1_index], 0, 1, 16, 0, 0)
@@ -71,6 +73,7 @@ def _inner_matmul_new(tik_instance, dtype, input1, input1_index, input2, input2_
 
 
 def _inner_matmul_new_1_64_32_64(tik_instance, dtype, input1, input1_index, input2, input2_index, res, res_index):
+    """_inner_matmul_new_1_64_32_64"""
     input_1_local_UB = tik_instance.Tensor(dtype, [64], name="input_1_local_UB", scope=tik.scope_ubuf)
     tik_instance.data_move(input_1_local_UB, input1[input1_index], 0, 1, 8, 0, 0)
     with tik_instance.for_range(0, 2, thread_num=2) as thread_idx2:
@@ -90,6 +93,7 @@ def _inner_matmul_new_1_64_32_64(tik_instance, dtype, input1, input1_index, inpu
 
 @op_info_register(cus_batchmatmul_op_info)
 def CusBatchMatMul(input_x1, input_x2, output, transpose_a=False, transpose_b=True, kernel_name="batchmatmul"):
+    """CusBatchMatMul"""
     if util.get_product_version() == util.VERSION_MINI:
         tik_instance = tik.Tik(tik.Dprofile("v100", "mini"))
     else:
@@ -116,7 +120,6 @@ def CusBatchMatMul(input_x1, input_x2, output, transpose_a=False, transpose_b=Tr
 
     # if not transpose_a and transpose_b:
     batch, m, k = x1_shape
-    _, n, _ = x2_shape
 
     input1_shape = _get_flattern_shape(x1_shape)
     input1 = tik_instance.Tensor(dtype, input1_shape, name="input1", scope=tik.scope_gm)

mindspore/ops/_op_impl/_custom_op/cholesky_trsm_impl.py

@@ -32,6 +32,7 @@ cus_cholesky_trsm_op_info = TBERegOp("CusCholeskyTrsm") \
 
 @op_info_register(cus_cholesky_trsm_op_info)
 def CusCholeskyTrsm(input_x, output, kernel_name):
+    """CusCholeskyTrsm"""
     input_x_shape = input_x.get("shape")
     output_shape = output.get("shape")
     split_dim = 128

mindspore/ops/_op_impl/_custom_op/fused_abs_max1_impl.py

@@ -33,6 +33,7 @@ cus_fused_abs_max1_op_info = TBERegOp("CusFusedAbsMax1") \
 
 @op_info_register(cus_fused_abs_max1_op_info)
 def CusFusedAbsMax1(input_x, output, origin_shape=None, kernel_name="fused_abs_max1"):
+    """CusFusedAbsMax1"""
     input_x_shape = input_x.get("shape")
     output_shape = output.get("shape")
 
@@ -203,9 +204,9 @@ def CusFusedAbsMax1(input_x, output, origin_shape=None, kernel_name="fused_abs_m
                 tik_instance.data_move(res[block_index, 0], broadcast_0_local_UB, 0, 1, 8, 0, 0)
         elif (input_x_shape[0] == 8 and input_x_shape[1] == 128 and input_x_shape[2] == 128) or (
                 input_x_shape[0] == 32 and input_x_shape[1] == 16) or (
-                input_x_shape[0] == 16 and input_x_shape[1] == 32):
+                    input_x_shape[0] == 16 and input_x_shape[1] == 32):
             if (input_x_shape[0] == 8 and input_x_shape[1] == 128 and input_x_shape[2] == 128) and origin_shape[
-                0] == 1000:
+                    0] == 1000:
                 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)
                 blocks = 32
@@ -257,7 +258,7 @@ def CusFusedAbsMax1(input_x, output, origin_shape=None, kernel_name="fused_abs_m
                     tik_instance.data_move(res[block_index, 0], broadcast_0_local_UB, 0, 1, 8, 0, 0)
 
             elif (input_x_shape[0] == 8 and input_x_shape[1] == 128 and input_x_shape[2] == 128) and origin_shape[
-                0] == 1001:
+                    0] == 1001:
                 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)
                 blocks = 32
@@ -350,7 +351,7 @@ def CusFusedAbsMax1(input_x, output, origin_shape=None, kernel_name="fused_abs_m
                     tik_instance.data_move(res[block_index, 0], broadcast_0_local_UB, 0, 1, 8, 0, 0)
         elif (input_x_shape[0] == 16 and input_x_shape[1] == 128 and input_x_shape[2] == 128) or (
                 input_x_shape[0] == 16 and input_x_shape[1] == 64) or (
-                input_x_shape[0] == 64 and input_x_shape[1] == 16):
+                    input_x_shape[0] == 64 and input_x_shape[1] == 16):
             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

mindspore/ops/_op_impl/_custom_op/img2col_impl.py

@@ -36,6 +36,7 @@ cus_img2col_info = TBERegOp("CusImg2Col") \
 
 @op_info_register(cus_img2col_info)
 def CusImg2Col(input_x, output, ksizes, strides, dilates, mode, kernel_name="img2col"):
+    """CusImg2Col"""
     input_x_shape = input_x.get("shape")
     input_x_dtype = input_x.get("dtype")
     N, C1, H, W, C0 = input_x_shape
@@ -64,7 +65,7 @@ def CusImg2Col(input_x, output, ksizes, strides, dilates, mode, kernel_name="img
                        ((32, 8, 28, 28, 16), 'float16', (1, 1), (1, 1)),
                        ((32, 32, 28, 28, 16), 'float16', (1, 1), (1, 1)),
                        ((32, 16, 14, 14, 16), 'float16', (1, 1), (1, 1)),
-                       ((32, 16, 56, 56, 16), 'float16', (1, 1), (1, 1)), ]
+                       ((32, 16, 56, 56, 16), 'float16', (1, 1), (1, 1)),]
 
     if input_shape not in supported_shape:
         raise RuntimeError("input_shape %s is not supported" % str(input_shape))

mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_left_impl.py

@@ -17,10 +17,9 @@ limitations under the License.
 matmul
 """
 from __future__ import absolute_import
-
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 import te.lang.cce
 import te.platform.cce_params as cce
-from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 from te import tik
 from te import tvm
 from topi import generic
@@ -128,7 +127,7 @@ def _shape_check(shape_a, shape_b, shape_bias, src_dtype, trans_a, trans_b):
     if n_shape % cce.BLOCK_IN != 0 and n_shape != 1:
         raise RuntimeError("input shape N should be 1 or multiple of %d" % cce.BLOCK_IN)
 
-    if len(shape_bias):
+    if len(shape_bias) != 0:
         if len(shape_bias) == 1:
             if is_gevm or is_gemv:
                 if shape_bias[0] != m_shape * n_shape:
@@ -145,16 +144,19 @@ def _shape_check(shape_a, shape_b, shape_bias, src_dtype, trans_a, trans_b):
 
 def _get_bias(shape_bias):
     bias_length = shape_bias[0]
+    shb = []
     if bias_length % 16 == 0:
-        return shape_bias
+        shb = shape_bias
     else:
         bias_length = (bias_length // 16) * 16 + 16
         shape_bias = []
         shape_bias.append(bias_length)
-        return shape_bias
+        shb = shape_bias
+    return shb
 
 
 def _get_input_shape(shape_x):
+    """_get_input_shape"""
     dim_a = shape_x[0]
     dim_b = shape_x[1]
     res = []
@@ -173,6 +175,7 @@ def _get_input_shape(shape_x):
 
 
 def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, trans_b=False, kernel_name="matmulcube"):
+    """check_supported"""
     shape_a = input_x1.get("shape")
     shape_b = input_x2.get("shape")
     print("shape_a: ", shape_a)
@@ -183,8 +186,6 @@ def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, t
     util.check_shape_rule(shape_b)
     util.check_shape_size(shape_a, SHAPE_SIZE_LIMIT)
     util.check_shape_size(shape_b, SHAPE_SIZE_LIMIT)
-    if bias is not None and bool(bias):
-        shape_bias = bias.get("shape")
     try:
         trans_a_f = bool(1 - trans_a)
         if src_dtype == "float32" or src_dtype == "int32":
@@ -250,7 +251,7 @@ def CusMatMulCubeDenseLeft(input_x1, input_x2, bias=None, output_y={}, trans_a=F
     """
     calculating  matrix multiplication with bias, C = A*B + bias, support input
     data with fractal format.
- 
+
     Parameters:
     shape_a: list or tuple
             Shape of the first tensor a with rank > 1
@@ -269,7 +270,7 @@ def CusMatMulCubeDenseLeft(input_x1, input_x2, bias=None, output_y={}, trans_a=F
             If True, the input data format of a and b must be fractal format
     shape_bias: list or tuple
             Shape of bias, only support the input data format with ND
- 
+
     Returns
     -------
     None

mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_right_impl.py

@@ -2,19 +2,19 @@
 # -*- coding:utf-8 -*-
 """
 copyright 2020 Huawei Technologies Co., Ltd
- 
+
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
- 
+
 http://www.apache.org/licenses/LICENSE-2.0
- 
+
 Unless required by applicable law or agreed to in writing, software
 distributed under the License == distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
- 
+
 matmul
 """
 from __future__ import absolute_import
@@ -43,11 +43,12 @@ matmul_cube_dense_right_op_info = TBERegOp("CusMatMulCubeDenseRight") \
 @op_info_register(matmul_cube_dense_right_op_info)
 def CusMatMulCubeDenseRight(input_x1, input_x2, input_x3, bias=None, output_y={}, trans_a=False, trans_b=False,
                             kernel_name="matmulcube"):
+    """CusMatMulCubeDenseRight"""
     shape_a_temp = (128, 63, 16, 16)
     shape_b_temp = (128, 128, 16, 16)
     shape_output = output_y.get("shape")
     matrix_max_shape = (1,)
-    support_shape = [(shape_a_temp, shape_b_temp, matrix_max_shape), ]
+    support_shape = [(shape_a_temp, shape_b_temp, matrix_max_shape),]
     shape_a_input = input_x1.get("shape")
     shape_b_input = input_x2.get("shape")
     matrix_max_input = input_x3.get("shape")
@@ -62,7 +63,7 @@ def CusMatMulCubeDenseRight(input_x1, input_x2, input_x3, bias=None, output_y={}
             tik_instance = tik.Tik(tik.Dprofile("v100", "cloud"))
         input_x1 = tik_instance.Tensor("float16", shape_a_temp, name="left_matrix", scope=tik.scope_gm)
         input_x2 = tik_instance.Tensor("float16", shape_b_temp, name="right_matrix", scope=tik.scope_gm)
-        input_x3 = tik_instance.Tensor("float32", [1, ], name="matrix_max", scope=tik.scope_gm)
+        input_x3 = tik_instance.Tensor("float32", [1,], name="matrix_max", scope=tik.scope_gm)
         resMatmul = tik_instance.Tensor("float32", shape_output, name="output", scope=tik.scope_gm)
         with tik_instance.for_range(0, 32, block_num=32) as block_index:
             core_m_idx = block_index // 16

mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_left_cast_impl.py

@@ -17,9 +17,8 @@ limitations under the License.
 matmul
 """
 from __future__ import absolute_import
-
-import te.platform.cce_params as cce
 from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+import te.platform.cce_params as cce
 from te import tik
 from topi.cce import util
 
@@ -141,6 +140,7 @@ src_dtype: str
 
 
 def _get_bias(shape_bias):
+    """_get_bias"""
     bias_length = shape_bias[0]
     if bias_length % 16 == 0:
         return shape_bias
@@ -152,6 +152,7 @@ def _get_bias(shape_bias):
 
 
 def _get_input_shape(shape_x):
+    """_get_input_shape"""
     dim_a = shape_x[0]
     dim_b = shape_x[1]
     res = []
@@ -170,6 +171,7 @@ def _get_input_shape(shape_x):
 
 
 def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, trans_b=False, kernel_name="matmulcube"):
+    """check_supported"""
     shape_a = input_x1.get("shape")
     shape_b = input_x2.get("shape")
     print("shape_a: ", shape_a)
@@ -180,8 +182,6 @@ def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, t
     util.check_shape_rule(shape_b)
     util.check_shape_size(shape_a, SHAPE_SIZE_LIMIT)
     util.check_shape_size(shape_b, SHAPE_SIZE_LIMIT)
-    if bias is not None and bool(bias):
-        shape_bias = bias.get("shape")
     try:
         trans_a_f = bool(1 - trans_a)
         if src_dtype == "float32" or src_dtype == "int32":
@@ -265,7 +265,7 @@ def CusMatMulCubeFraczLeftCast(input_x1, input_x2, bias=None, output_y={}, trans
             If True, the input data format of a and b must be fractal format
     shape_bias: list or tuple
             Shape of bias, only support the input data format with ND
- 
+
     Returns
     -------
     None
@@ -381,6 +381,7 @@ def CusMatMulCubeFraczLeftCast(input_x1, input_x2, bias=None, output_y={}, trans
 
 
 def get_cus_tile_info(input_x1, input_x2, diag_size):
+    """get_cus_tile_info"""
     tile_map = {
         ((32, 32, 16, 16), (128, 32, 16, 16)): (8, 8, 16),
         ((8, 8, 16, 16), (72, 8, 16, 16)): (8, 8, 4),
@@ -415,8 +416,9 @@ def get_cus_tile_info(input_x1, input_x2, diag_size):
 
 def cus_cube_matmul_cast(tik_instance, input_x1, trans_a, input_x2, trans_b,
                          res, mo_tile, ko_tile, no_tile, diag_opt=False, diag_size=128):
-    ko, mo, mi, ki = input_x1.shape
-    no, ko, ki, ni = input_x2.shape
+    """cus_cube_matmul_cast"""
+    ko, mo, _, _ = input_x1.shape
+    no, ko, ki, _ = input_x2.shape
     c0 = input_x1.shape[-1]
     diag_outer = diag_size // c0
     maxblocknum = 32

mindspore/ops/_op_impl/_custom_op/matmul_cube_fracz_right_mul_impl.py

@@ -47,6 +47,7 @@ cus_matmul_cube_fracz_right_mul_op_info = TBERegOp("CusMatMulCubeFraczRightMul")
 @op_info_register(cus_matmul_cube_fracz_right_mul_op_info)
 def CusMatMulCubeFraczRightMul(input_x1, input_x2, input_x3, bias=None, output_y={}, trans_a=False, trans_b=False,
                                kernel_name="matmulcube"):
+    """CusMatMulCubeFraczRightMul"""
     if util.get_product_version() == util.VERSION_MINI:
         tik_instance = tik.Tik(tik.Dprofile("v100", "mini"))
     else:
@@ -80,7 +81,7 @@ def CusMatMulCubeFraczRightMul(input_x1, input_x2, input_x3, bias=None, output_y
                  ((64, 32, 16, 16), 'float16', (64, 64, 16, 16), 'float16', (1,), 'float32'),
                  ((16, 64, 16, 16), 'float16', (16, 16, 16, 16), 'float16', (1,), 'float32')]
     input_shape = (
-    tuple(input_x1_shape), input_x1_dtype, tuple(input_x2_shape), input_x2_dtype, tuple(input_x3_shape), input_x3_dtype)
+        tuple(input_x1_shape), input_x1_dtype, tuple(input_x2_shape), input_x2_dtype, tuple(input_x3_shape), input_x3_dtype)
     if input_shape not in Supported:
         raise RuntimeError("input_shape %s is not supported" % str(input_shape))
 
@@ -95,15 +96,17 @@ def CusMatMulCubeFraczRightMul(input_x1, input_x2, input_x3, bias=None, output_y
 
 def cus_cube_matmul_right_mul(tik_instance, input_x1, input_x2, input_x3,
                               res):
+    """cus_cube_matmul_right_mul"""
     diag_size = 128
-    ko, mo, mi, ki = input_x1.shape
-    no, ko, ki, ni = input_x2.shape
+    ko, mo, _, _ = input_x1.shape
+    no, ko, ki, _ = input_x2.shape
     c0 = input_x1.shape[-1]
     diag_outer = diag_size // c0
     if [input_x1.shape[-1], input_x1.shape[-2], input_x2.shape[-1], input_x2.shape[-2]] != [c0, c0, c0, c0]:
         raise ValueError("shape of input_x1 or input_x2 is not supported!")
 
     def get_cus_tile_info(input_x1, input_x2, input_x3):
+        """get_cus_tile_info"""
         input_shape = (tuple(input_x1.shape), input_x1.dtype, tuple(input_x2.shape), input_x2.dtype,
                        tuple(input_x3.shape), input_x3.dtype)
         tile_map = {

mindspore/ops/_op_impl/_custom_op/matmul_cube_impl.py

@@ -18,11 +18,10 @@ limitations under the License.
 matmul
 """
 from __future__ import absolute_import
-
-import te.lang.cce
-import te.platform.cce_params as cce
 from impl.matmul_vector import matmul_vector_cce
 from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+import te.lang.cce
+import te.platform.cce_params as cce
 from te import tvm
 from topi import generic
 from topi.cce import util
@@ -146,6 +145,7 @@ def _shape_check(shape_a, shape_b, shape_bias, src_dtype, trans_a, trans_b):
 
 
 def _get_bias(shape_bias):
+    """_get_bias"""
     bias_length = shape_bias[0]
     if bias_length % 16 == 0:
         return shape_bias
@@ -157,6 +157,7 @@ def _get_bias(shape_bias):
 
 
 def _get_input_shape(shape_x):
+    """_get_input_shape"""
     dim_a = shape_x[0]
     dim_b = shape_x[1]
     res = []
@@ -175,6 +176,7 @@ def _get_input_shape(shape_x):
 
 
 def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, trans_b=False, kernel_name="matmulcube"):
+    """check_supported"""
     shape_a = input_x1.get("shape")
     shape_b = input_x2.get("shape")
     print("shape_a: ", shape_a)
@@ -185,8 +187,6 @@ def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, t
     util.check_shape_rule(shape_b)
     util.check_shape_size(shape_a, SHAPE_SIZE_LIMIT)
     util.check_shape_size(shape_b, SHAPE_SIZE_LIMIT)
-    if bias is not None and bool(bias):
-        shape_bias = bias.get("shape")
     try:
         trans_a_f = bool(1 - trans_a)
         if src_dtype == "float32" or src_dtype == "int32":
@@ -250,7 +250,7 @@ def CusMatMulCube(input_x1, input_x2, bias=None, output_y={}, trans_a=False, tra
     """
     calculating  matrix multiplication with bias, C = A*B + bias, support input
     data with fractal format.
- 
+
     Parameters:
     shape_a: list or tuple
             Shape of the first tensor a with rank > 1
@@ -269,7 +269,7 @@ def CusMatMulCube(input_x1, input_x2, bias=None, output_y={}, trans_a=False, tra
             If True, the input data format of a and b must be fractal format
     shape_bias: list or tuple
             Shape of bias, only support the input data format with ND
- 
+
     Returns
     -------
     None

mindspore/ops/_op_impl/_custom_op/transpose02314_impl.py

@@ -32,6 +32,7 @@ cus_transpose02314_op_info = TBERegOp("CusTranspose02314") \
 
 @op_info_register(cus_transpose02314_op_info)
 def CusTranspose02314(input_x, output, kernel_name="transpose021354"):
+    """CusTranspose02314"""
     input_x_shape = input_x.get("shape")
     output_shape = output.get("shape")
     perm = (0, 2, 3, 1, 4)
@@ -263,7 +264,7 @@ def CusTranspose02314(input_x, output, kernel_name="transpose021354"):
 
         with tik_instance.for_range(0, 32, block_num=32) as block_idx:
             with tik_instance.for_range(0, 6, thread_num=2) as cc1:
-                _inner_ + compute(cc1)
+                _inner_compute(cc1)
             _inner_compute(6)
     elif tuple(input_x_shape) == (32, 64, 14, 14, 16) and tuple(perm) == (0, 2, 3, 1, 4) and dtype == "float16":
         def _inner_compute(split_index, block_idx):

mindspore/ops/operations/thor_ops.py

@@ -91,7 +91,7 @@ class CusFusedAbsMax1(PrimitiveWithInfer):
     def infer_shape(self, data1_shape):
         ll = []
         if len(data1_shape) == 2:
-            ll = [1, ]
+            ll = [1,]
         else:
             ll = [32, 64]
         return ll