fixing pylint for operators

mindspore/ops/_grad/grad_nn_ops.py

@@ -678,16 +678,13 @@ def get_bprop_top_kv2(self):
 
     def bprop(input_x, k, out, dout):
 
-        # (n1, n2, ...., n_p), in_lastdim = n_p
         in_shape = shape_op(input_x)
         in_lastdim = in_shape[-1]
 
-        # (n_1, ... n_(p-1), k), ind_lastdim = k
         indices = out[1]
         ind_shape = shape_op(indices)
         ind_lastdim = ind_shape[-1]
 
-        # (n_1*n_2..*n_(p-1), k),  outerdim = n_1*n_2..*n_(p-1)
         ind_2d = reshape_op(indices, (-1, ind_lastdim))
         outerdim = shape_op(ind_2d)[0]
 

mindspore/ops/_op_impl/_custom_op/correction_mul.py

@@ -71,7 +71,6 @@ def correction_mul(x, batch_std, running_std, y, channel, kernel_name="correctio
     if not inp_dtype in check_list:
         raise RuntimeError("Dtype of input only support float16, float32")
 
-    # shape = util.shape_refine(shape)
     x_t = tvm.placeholder(shape, name="x", dtype=inp_dtype)
     shape_c = [1] * len(shape)
     shape_c[channel] = batch_std.get("ori_shape")[0]

mindspore/ops/_op_impl/_custom_op/fake_quant_perchannel.py

@@ -60,7 +60,6 @@ def fake_quant_perchannel_compute(x, min_val, max_val, y, quant_min, quant_max,
     quant_min = te.lang.cce.broadcast(quant_min, minmax_shape, x.dtype)
     quant_max = te.lang.cce.broadcast(quant_max, minmax_shape, x.dtype)
 
-    # CalNudge(NudgeMinMax)
     scale = te.lang.cce.vdiv(te.lang.cce.vsub(
         max_val, min_val), te.lang.cce.vsub(quant_max, quant_min))
     zp_from_min = te.lang.cce.vsub(quant_min, te.lang.cce.vdiv(min_val, scale))

mindspore/ops/_op_impl/_custom_op/fake_quant_perchannel_grad.py

@@ -87,7 +87,6 @@ def fake_quant_perchannel_grad_compute(dout, x, min_val, max_val, quant_min, qua
     quant_min = te.lang.cce.broadcast(quant_min, minmax_shape, x.dtype)
     quant_max = te.lang.cce.broadcast(quant_max, minmax_shape, x.dtype)
 
-    # CalNudge(NudgeMinMax)
     scale = te.lang.cce.vdiv(te.lang.cce.vsub(
         max_val, min_val), te.lang.cce.vsub(quant_max, quant_min))
     zp_from_min = te.lang.cce.vsub(quant_min, te.lang.cce.vdiv(min_val, scale))

mindspore/ops/_op_impl/_custom_op/fake_quant_perlayer.py

@@ -61,7 +61,6 @@ def fake_quant_per_layer_compute(x, min_val, max_val, y, quant_min, quant_max, s
         max_val = te.lang.cce.vmax(te.lang.cce.vmuls(min_val, -1.), max_val)
         min_val = te.lang.cce.vmuls(max_val, -1.)
 
-    # CalNudge(NudgeMinMax)
     scale = te.lang.cce.vdiv(te.lang.cce.vsub(
         max_val, min_val), te.lang.cce.vsub(quant_max, quant_min))
     zp_from_min = te.lang.cce.vsub(quant_min, te.lang.cce.vdiv(min_val, scale))

mindspore/ops/_op_impl/_custom_op/fake_quant_perlayer_grad.py

@@ -92,7 +92,6 @@ def fake_quant_per_layer_grad_compute(dout, x, min_val, max_val, quant_min, quan
         max_val = te.lang.cce.vmax(te.lang.cce.vmuls(min_val, -1.), max_val)
         min_val = te.lang.cce.vmuls(max_val, -1.)
 
-    # CalNudge(NudgeMinMax)
     scale = te.lang.cce.vdiv(te.lang.cce.vsub(
         max_val, min_val), te.lang.cce.vsub(quant_max, quant_min))
     zp_from_min = te.lang.cce.vsub(quant_min, te.lang.cce.vdiv(min_val, scale))

mindspore/ops/composite/multitype_ops/_constexpr_utils.py

@@ -91,20 +91,16 @@ def check_tensor_setitem_index(index, element_type=None):
     """Checks tuple index type of tensor assignment."""
     if index is None:
         raise IndexError("Tensor's index cannot be None.")
-    # eg. Tensor[Slice] = u
     if isinstance(index, slice):
         return True
-    # eg. Tensor[tuple] = u
     if isinstance(index, tuple):
         if not index:
             raise IndexError("Tensor's index cannot be empty.")
-        # eg. Tensor[tuple(Slice,...)] = u
         for item in index:
             if not isinstance(item, (slice, type(...), int)):
                 raise IndexError(
                     "Index of type '{}' is not supported yet.".format(type(item)))
         return True
-    # eg. Tensor[Tensor[dtype=bool]] = u
     if isinstance(index, mstype.tensor_type):
         if element_type is None or element_type != mstype.bool_:
             raise TypeError(

mindspore/ops/composite/multitype_ops/setitem_impl.py

@@ -305,23 +305,19 @@ def _tensor_setitem_with_slice_v1(data, input_slice, value):
 
 @setitem.register("Tensor", "Number", "Number")
 def _tensor_setitem_with_int_v1(data, index, value):
-    """Syntax: A[1] = 3"""
     return compile_utils.tensor_setitem_by_number_with_number(data, index, value)
 
 
 @setitem.register("Tensor", "Number", "Tensor")
 def _tensor_setitem_with_int_v2(data, index, value):
-    """Syntax: A[1] = Tensor"""
     return compile_utils.tensor_setitem_by_number_with_tensor(data, index, value)
 
 
 @setitem.register("Tensor", "Ellipsis", "Number")
 def _tensor_setitem_with_ellipsis_v1(data, index, value):
-    """Syntax: A[...] = number."""
     return compile_utils.tensor_setitem_by_ellipsis_with_number(data, index, value)
 
 
 @setitem.register("Tensor", "Ellipsis", "Tensor")
 def _tensor_setitem_with_ellipsis_v2(data, index, value):
-    """Syntax: A[...] = Tensor."""
     return compile_utils.tensor_setitem_by_ellipsis_with_tensor(data, index, value)

mindspore/ops/operations/_thor_ops.py

@@ -320,8 +320,6 @@ class CusMatMulCube(PrimitiveWithInfer):
         from mindspore.ops._op_impl._custom_op.matmul_cube_impl import CusMatMulCube
 
     def infer_shape(self, data1_shape, data2_shape):
-        # shape = [1, data1_shape[1], data2_shape[2], 16, 16]
-        # return shape
         if self.transpose_a:
             k1, m = data1_shape
         else:

mindspore/ops/operations/array_ops.py

@@ -2068,7 +2068,6 @@ def _compute_slicing_length(begin, end, stride, x_shape, i):
             if 0 <= begin < x_dim:
                 begin += -x_dim
             if begin >= x_dim:
-                # When slicing backward, if begin >= x_dim, set begin = -1, which means start from the last element.
                 begin = -1
             if 0 <= end < x_dim:
                 end += -x_dim

mindspore/ops/operations/nn_ops.py

@@ -745,7 +745,6 @@ class BNTrainingUpdate(PrimitiveWithInfer):
     def __init__(self, isRef=True, epsilon=1e-5, factor=0.1):
         self.init_prim_io_names(inputs=['x', 'sum', 'square_sum', 'scale', 'b', 'mean', 'variance'],
                                 outputs=['y', 'running_mean', 'running_variance', 'save_mean', 'save_inv_variance'])
-        #self.isRef = validator.check_integer('isRef', isRef, [0, 1], Rel.IN)
         self.epsilon = validator.check_number_range('epsilon', epsilon, 0, 1, Rel.INC_RIGHT, 'BNTrainingUpdate')
         self.factor = validator.check_number_range('factor', factor, 0, 1, Rel.INC_BOTH, 'BNTrainingUpdate')