!9640 redundant code

From: @zhupuxu
Reviewed-by: 
Signed-off-by:

mindspore/_checkparam.py

@@ -131,7 +131,7 @@ def check_number(arg_value, value, rel, arg_type=int, arg_name=None, prim_name=N
     """
     Check argument integer.
 
-    Usage:
+    Example:
     - number = check_int(number, 0, Rel.GE, "number", None) # number >= 0
     """
     rel_fn = Rel.get_fns(rel)

mindspore/_extends/graph_kernel/expanders/gelu.py

@@ -24,8 +24,8 @@ HALF = 0.5
 def expand_gelu(expand_info):
     """Gelu expander"""
     # cal formula are:
-    # gelu(x) = 0.5 * x * (1.0 + tanh(y))
-    # y = sqrt(2.0 / pi) * (x + 0.044715 * x * x * x)
+    # gelu(x) is 0.5 * x * (1.0 + tanh(y))
+    # y is sqrt(2.0 / pi) * (x + 0.044715 * x * x * x)
 
     # get op info.
     input_desc = expand_info['input_desc'][0]

mindspore/_extends/graph_kernel/expanders/gelu_grad.py

@@ -25,10 +25,10 @@ HALF = 0.5
 def expand_gelugrad(expand_info):
     """GeluGrad expander"""
     # cal formula are:
-    # gelu_grad(dy, x) = dy * y'
-    # y' = 0.5 * (1.0 + tanh(tanh_para)) + 0.5 * x * (1.0 - tanh(tanh_para) * tanh(para)) * mul_right
-    # tanh_para = sqrt(2.0 / pi) * (x + 0.044715 * x * x * x)
-    # mul_right = sqrt(2.0 / pi) * (1 + 3 * 0.044715 * x * x)
+    # gelu_grad(dy, x) is dy * y'
+    # y' is 0.5 * (1.0 + tanh(tanh_para)) + 0.5 * x * (1.0 - tanh(tanh_para) * tanh(para)) * mul_right
+    # tanh_para is sqrt(2.0 / pi) * (x + 0.044715 * x * x * x)
+    # mul_right is sqrt(2.0 / pi) * (1 + 3 * 0.044715 * x * x)
 
     # get op info.
     input_desc_0 = expand_info['input_desc'][0]

mindspore/_extends/graph_kernel/expanders/layernorm_grad.py

@@ -66,8 +66,6 @@ def expand_layernormgrad(expand_info):
         mean_cof = graph_builder.value(x.dtype, (1.0 / reduce_size), x.data_format)
 
         # cal dg db
-        # dg = np.sum(dy * np.power(var + epsilon, -0.5) * (x - mean), axis=tuple(param_axis), keepdims=True)
-        # db = np.sum(dy, axis=tuple(param_axis), keepdims=True)
         var_eps = graph_builder.emit('TensorAdd', [variance, eps])
         sqrt_var_eps = graph_builder.emit('Sqrt', [var_eps])
         rsqrt_var_eps = graph_builder.emit('RealDiv', [const_one, sqrt_var_eps])
@@ -78,8 +76,6 @@ def expand_layernormgrad(expand_info):
         db = graph_builder.emit('ReduceSum', [dy], attrs={'reduce_axis': param_axis, 'keep_dims': False})
 
         # cal sum_1
-        # sum1 = np.sum((-0.5) * dy * gamma * (x - mean) * np.power(var + epsilon, -1.5), axis=tuple(norm_axis),
-        #          keepdims=True)
         tmp_var_eps = graph_builder.emit('Mul', [sqrt_var_eps, var_eps])
         r_tmp_var_eps = graph_builder.emit('RealDiv', [const_one, tmp_var_eps])
         x_sub_mean_mul_r_tmp_var_eps = graph_builder.emit('Mul', [x_sub_mean, r_tmp_var_eps])
@@ -89,18 +85,13 @@ def expand_layernormgrad(expand_info):
         sum_1 = graph_builder.emit('ReduceSum', [sum_1_mul], attrs={'reduce_axis': norm_axis, 'keep_dims': True})
 
         # cal sum_2
-        # sum2 = np.sum(dy * gamma, axis=tuple(norm_axis), keepdims=True)
         sum_2 = graph_builder.emit('ReduceSum', [dy_mul_gamma], attrs={'reduce_axis': norm_axis, 'keep_dims': True})
 
         # cal sum_3
-        # sum3 = np.sum(-2.0 * (x - mean), axis=tuple(norm_axis), keepdims=True)
         sum_3_mul = graph_builder.emit('Mul', [const_neg_two, x_sub_mean])
         sum_3 = graph_builder.emit('ReduceSum', [sum_3_mul], attrs={'reduce_axis': norm_axis, 'keep_dims': True})
 
         # cal dx = dx1 + dx2 + dx3
-        # dx1 = dy * gamma * rsqrt_var_eps
-        # dx2 = sum1 * 2.0 / mean_cof * x_sub_mean
-        # dx3 = (1.0 / mean_cof) * (-1.0 * rsqrt_var_eps * sum2 + 1.0 / mean_cof * sum1 * sum3)
         dx_1 = graph_builder.emit('Mul', [dy_mul_gamma, rsqrt_var_eps])
         sum_1_mul_two = graph_builder.emit('Mul', [sum_1, const_two])
         sum_1_mul_two_tmp = graph_builder.emit('Mul', [sum_1_mul_two, mean_cof])

mindspore/_extends/graph_kernel/expanders/reduce_mean.py

@@ -38,8 +38,7 @@ def expand_reducemean(expand_info):
         x_shape = input_x.shape
         graph_scope.set_input(input_x)
 
-        # cal reduce_mean
-        # when axis = None, reduce axis are all
+        # cal reduce_mean, when axis = None, reduce axis are all
         all_shape = 1.0
         real_axis = []
         if not axis:

mindspore/_extends/graph_kernel/expanders/sqrt_grad.py

@@ -19,7 +19,7 @@ from mindspore._extends.graph_kernel.model import model_builder as builder
 def expand_sqrtgrad(expand_info):
     """SqrtGrad expander"""
     # cal formula are:
-    # sqrt_grad(x, dout) = dout / (2 * x)
+    # sqrt_grad(x, dout) is dout / (2 * x)
 
     # get op info.
     input_desc_0 = expand_info['input_desc'][0]

mindspore/_extends/graph_kernel/expanders/tanh_grad.py

@@ -21,7 +21,6 @@ ONE = 1.0
 def expand_tanhgrad(expand_info):
     """TanhGrad expander"""
 
-    # tanh_grad(y, dy) = dy * (1- y * y)
     # get op info.
     input_desc_0 = expand_info['input_desc'][0]
     input_desc_1 = expand_info['input_desc'][1]

mindspore/ccsrc/frontend/optimizer/irpass/arithmetic_simplify.cc

@@ -156,7 +156,6 @@ AnfNodePtr AdjustAllReduceMulAdd::operator()(const OptimizerPtr &, const AnfNode
     if (x_shape != z_shape) {
       // AddN requires x_ and z_ have the same shape.
       // If broadcasting TensorAdd is supported then can use this
-      // AnfNodePtr add = NewCNode({NewValueNode(prim::kPrimTensorAdd), z_, x_}, fg);
       return nullptr;
     }
     AnfNodePtr tuple = NewCNode({make_tuple_op_node, z_, x_}, fg);

mindspore/ccsrc/minddata/dataset/engine/opt/pre/node_removal_pass.cc

@@ -62,13 +62,6 @@ Status NodeRemovalPass::RemovalNodes::Visit(std::shared_ptr<RepeatNode> node, bo
 // Perform ShuffleNode removal check.
 Status NodeRemovalPass::RemovalNodes::Visit(std::shared_ptr<ShuffleNode> node, bool *modified) {
   *modified = false;
-#if 0
-  // If we are in a cache descendant tree, then this shuffle op needs to be removed
-  if (is_caching_) {
-    MS_LOG(INFO) << "Shuffle under an operation with cache is identified for removal.";
-    nodes_to_remove_.push_back(std::static_pointer_cast<DatasetNode>(node));
-  }
-#endif
   return Status::OK();
 }
 

mindspore/core/abstract/prim_arrays.cc

@@ -326,7 +326,6 @@ AbstractBasePtr InferImplUnsortedSegmentMax(const AnalysisEnginePtr &, const Pri
     } else {
       num_segments_value = *static_cast<int32_t *>(num_segments_tensor->data_c());
     }
-    // num_segments_value = *static_cast<int64_t *>(num_segments_tensor->data_c());
   } else if (args_spec_list[2]->isa<AbstractScalar>()) {  // num_segments is Scalar
     auto num_segments = CheckArg<AbstractScalar>(op_name, args_spec_list, 2);
     if (num_segments->GetTypeTrack()->type_id() == TypeId::kNumberTypeInt64) {

mindspore/core/abstract/prim_others.cc

@@ -483,7 +483,6 @@ AbstractBasePtr InferImplCast(const AnalysisEnginePtr &, const PrimitivePtr &pri
                               const AbstractBasePtrList &args_spec_list) {
   const std::string op_name = primitive->name();
   // GPU has 2 inputs while tbe has 1 only. Skip CheckArgsSize.
-  // CheckArgsSize(op_name, args_spec_list, 1);
   auto input_x = CheckArg<AbstractTensor>(op_name, args_spec_list, 0);
   MS_EXCEPTION_IF_NULL(input_x);
   MS_EXCEPTION_IF_NULL(input_x->shape());

mindspore/lite/minddata/wrapper/album_op_android.cc

@@ -202,7 +202,6 @@ Status AlbumOp::LoadImageTensor(const std::string &image_file_path, uint32_t col
     // load empty tensor since image is not jpg
     MS_LOG(INFO) << "Bin file found" << image_file_path << ".";
     RETURN_IF_NOT_OK(Tensor::CreateFromFile(image_file_path, tensor));
-    //    row->push_back(std::move(image));
     return Status::OK();
   }
 
@@ -263,7 +262,6 @@ Status AlbumOp::LoadStringArrayTensor(const nlohmann::json &json_obj, uint32_t c
   MS_LOG(INFO) << "String array label found: " << data << ".";
   //  TensorPtr label;
   RETURN_IF_NOT_OK(Tensor::CreateFromVector(data, tensor));
-  //  row->push_back(std::move(label));
   return Status::OK();
 }
 
@@ -274,7 +272,6 @@ Status AlbumOp::LoadStringTensor(const nlohmann::json &json_obj, uint32_t col_nu
   MS_LOG(INFO) << "String label found: " << data << ".";
   TensorPtr label;
   RETURN_IF_NOT_OK(Tensor::CreateScalar<std::string>(data, tensor));
-  //  row->push_back(std::move(label));
   return Status::OK();
 }
 
@@ -303,7 +300,6 @@ Status AlbumOp::LoadIntArrayTensor(const nlohmann::json &json_obj, uint32_t col_
     RETURN_STATUS_UNEXPECTED("Invalid data, column type is neither int32 nor int64, it is " +
                              data_schema_->column(col_num).type().ToString());
   }
-  //  row->push_back(std::move(label));
   return Status::OK();
 }
 
@@ -332,7 +328,6 @@ Status AlbumOp::LoadFloatArrayTensor(const nlohmann::json &json_obj, uint32_t co
     RETURN_STATUS_UNEXPECTED("Invalid data, column type is neither float32 nor float64, it is " +
                              data_schema_->column(col_num).type().ToString());
   }
-  //  row->push_back(std::move(float_array));
   return Status::OK();
 }
 
@@ -340,7 +335,6 @@ Status AlbumOp::LoadIDTensor(const std::string &file, uint32_t col_num, TensorPt
   if (data_schema_->column(col_num).type() == DataType::DE_STRING) {
     //    TensorPtr id;
     RETURN_IF_NOT_OK(Tensor::CreateScalar<std::string>(file, tensor));
-    //    row->push_back(std::move(id));
     return Status::OK();
   }
   // hack to get the file name without extension, the 1 is to get rid of the backslash character
@@ -348,7 +342,6 @@ Status AlbumOp::LoadIDTensor(const std::string &file, uint32_t col_num, TensorPt
   //  TensorPtr id;
   RETURN_IF_NOT_OK(Tensor::CreateScalar<int64_t>(image_id, tensor));
   MS_LOG(INFO) << "File ID " << image_id << ".";
-  //  row->push_back(std::move(id));
   return Status::OK();
 }
 
@@ -356,7 +349,6 @@ Status AlbumOp::LoadEmptyTensor(uint32_t col_num, TensorPtr *tensor) {
   // hack to get the file name without extension, the 1 is to get rid of the backslash character
   //  TensorPtr empty_tensor;
   RETURN_IF_NOT_OK(Tensor::CreateEmpty(TensorShape({0}), data_schema_->column(col_num).type(), tensor));
-  //  row->push_back(std::move(empty_tensor));
   return Status::OK();
 }
 
@@ -375,7 +367,6 @@ Status AlbumOp::LoadFloatTensor(const nlohmann::json &json_obj, uint32_t col_num
     RETURN_IF_NOT_OK(Tensor::CreateScalar<float>(data, tensor));
     MS_LOG(INFO) << "float found: " << json_obj << ".";
   }
-  //  row->push_back(std::move(float_tensor));
   return Status::OK();
 }
 
@@ -391,7 +382,6 @@ Status AlbumOp::LoadIntTensor(const nlohmann::json &json_obj, uint32_t col_num,
     RETURN_IF_NOT_OK(Tensor::CreateScalar<int32_t>(data, tensor));
     MS_LOG(INFO) << "int32 found: " << json_obj << ".";
   }
-  //  row->push_back(std::move(int_tensor));
   return Status::OK();
 }
 
@@ -403,7 +393,6 @@ Status AlbumOp::LoadIntTensor(const nlohmann::json &json_obj, uint32_t col_num,
 Status AlbumOp::LoadTensorRow(row_id_type row_id, const std::string &file,
                               std::unordered_map<std::string, std::shared_ptr<Tensor>> *map_row) {
   // testing here is to just print out file path
-  //  (*row) = TensorRow(row_id, {});
   MS_LOG(INFO) << "Image row file: " << file << ".";
 
   std::ifstream file_handle(folder_path_ + file);

mindspore/lite/nnacl/fp32_grad/softmax_grad.c

@@ -57,8 +57,6 @@ void SoftmaxGrad(const float *input_ptr, const float *yt_ptr, float *output_ptr,
         *(output_ptr + outter_offset + k * N + j) += a * sum_data[j];
       }
     }
-
-    // gemm(0, 0, M, N, K, -1, sum_mul, K, sum_data, N, 1, &output_ptr[outter_offset], N);
   }
 
   for (int i = 0; i < ele_size; i++) {

mindspore/lite/src/ops/one_hot.cc

@@ -91,7 +91,6 @@ int OneHot::InferShape(std::vector<Tensor *> inputs, std::vector<Tensor *> outpu
 
   int axis = GetAxis();
   // indices, depth, on_value, off_value
-  // indices, depth, on_off_value(contain 2 values);
   if (inputs.size() != kOneHotInputNum && inputs.size() != kOneHotInputNumOpt) {
     MS_LOG(ERROR) << "OneHot got inputs num " << inputs.size() << ", should be " << kOneHotInputNum << " or "
                   << kOneHotInputNumOpt;

mindspore/lite/src/runtime/kernel/opencl/kernel/sparse_to_dense.h

@@ -43,7 +43,6 @@ class SparseToDenseOpenCLKernel : public OpenCLKernel {
   int InitOutputToDefault();
 
  private:
-  //  bool IndicesIsScalar{false};
   bool enable_fp16_{false};
   float default_{0.0f};
   float weight_scalar_{0.f};

mindspore/lite/tools/net_train/net_train.cc

@@ -337,7 +337,6 @@ int NetTrain::RunExportedNet() {
   }
 
   context->thread_num_ = flags_->num_threads_;
-  // context->enable_float16_ = flags_->enable_fp16_;
   session_ = session::TrainSession::CreateSession(flags_->export_file_.c_str(), context.get());
   if (session_ == nullptr) {
     MS_LOG(ERROR) << "CreateSession failed while running ", model_name.c_str();
@@ -406,7 +405,6 @@ int NetTrain::RunNetTrain() {
     context->device_list_[0].device_info_.cpu_device_info_.cpu_bind_mode_ = NO_BIND;
   }
   context->thread_num_ = flags_->num_threads_;
-  // context->enable_float16_ = flags_->enable_fp16_;
   session_ = session::TrainSession::CreateSession(flags_->model_file_.c_str(), context.get());
   if (session_ == nullptr) {
     MS_LOG(ERROR) << "CreateSession failed while running ", model_name.c_str();

mindspore/nn/graph_kernels/graph_kernels.py

@@ -901,7 +901,8 @@ class Gelu(GraphKernel):
 
         def _math_four_compute(data_x):
             """
-            return: math_four equal 2*(np(sqrt(2 / np.pi)*(x + 0.044715*tf.pow(x, 3)))
+            Return:
+                math_four equal 2*(np(sqrt(2 / np.pi)*(x + 0.044715*tf.pow(x, 3)))
             """
             datax_pow = data_x * data_x * data_x
             datax_muls_c = self.mul(datax_pow, self.CSVALUE)

mindspore/nn/loss/loss.py

@@ -431,8 +431,8 @@ class SampledSoftmaxLoss(_Loss):
 
         # Sample the negative labels.
         #   sampled shape: [num_sampled] tensor
-        #   true_expected_count shape = [batch_size, 1] tensor
-        #   sampled_expected_count shape = [num_sampled] tensor
+        #   true_expected_count shape is [batch_size, 1] tensor
+        #   sampled_expected_count shape is [num_sampled] tensor
         if sampled_values is None:
             sampled_values = self.sampler(labels)
 

mindspore/nn/probability/distribution/categorical.py

@@ -308,7 +308,6 @@ class Categorical(Distribution):
 
         broadcast_shape_tensor = logits * value
         broadcast_shape = self.shape(broadcast_shape_tensor)
-        # broadcast_shape (N, C)
         num_classes = broadcast_shape[-1]
         label_shape = broadcast_shape[:-1]
 
@@ -373,7 +372,6 @@ class Categorical(Distribution):
 
         broadcast_shape_tensor = probs * value
         broadcast_shape = self.shape(broadcast_shape_tensor)
-        # broadcast_shape (N, C)
         num_classes = broadcast_shape[-1]
         label_shape = broadcast_shape[:-1]
 

mindspore/ops/composite/multitype_ops/_compile_utils.py

@@ -276,7 +276,6 @@ def tensor_expand_dims(data, tuple_index):
 
 def tensor_index_by_tuple(data, tuple_index):
     """Tensor getitem by tuple of various types with None"""
-    # data, tuple_index_without_none = tensor_expand_dims(data, tuple_index)
     tuple_index_without_none = tuple_index
     if len(tuple_index) == 1:
         return data[tuple_index_without_none[0]]

mindspore/ops/operations/math_ops.py

@@ -743,9 +743,7 @@ class MatMul(PrimitiveWithInfer):
         # validate whether last two dims satisfying matrix multiply
         x1_last = x1[-2:]
         x2_last = x2[-2:]
-        # x1_col = x1_last[1] if (not transpose_a) else x1_last[0]
         x1_col = x1_last[not self.transpose_a]
-        # x2_row = x2_last[0] if (not transpose_b) else x2_last[1]
         x2_row = x2_last[self.transpose_b]
         if x1_col != x2_row:
             raise ValueError(f'For \'{cls_name}\' evaluator shapes of inputs can not do this operator,'