!14744 add is dynamic format op info

From: @jjfeing
Reviewed-by: @kisnwang,@zhoufeng54
Signed-off-by:

mindspore/ccsrc/backend/kernel_compiler/kernel.h

@@ -53,7 +53,6 @@ enum OpPattern {
   kFormatAgnosticPattern = 1,
   kBroadcastPattern = 2,
   kReducePattern = 3,
-  kDynamicFormatPattern = 4,
 };
 
 // Backend processor

mindspore/ccsrc/backend/kernel_compiler/oplib/opinfo.h

@@ -101,11 +101,11 @@ class OpInfo {
     compute_cost_ = opinfo.compute_cost_;
     kernel_name_ = opinfo.kernel_name();
     partial_flag_ = opinfo.partial_flag_;
-    dynamic_format_ = opinfo.dynamic_format_;
     dynamic_shape_ = opinfo.dynamic_shape_;
     op_pattern_ = opinfo.op_pattern();
     processor_ = opinfo.processor_;
     need_check_supported_ = opinfo.need_check_supported();
+    is_dynamic_format_ = opinfo.is_dynamic_format();
     for (const auto &attr : opinfo.attrs_ptr()) {
       attrs_ptr_.push_back(std::make_shared<OpAttr>(*attr));
     }
@@ -127,6 +127,7 @@ class OpInfo {
   bool dynamic_shape() const { return dynamic_shape_; }
   std::string processor() const { return processor_; }
   bool need_check_supported() const { return need_check_supported_; }
+  bool is_dynamic_format() const { return is_dynamic_format_; }
   std::vector<std::shared_ptr<OpAttr>> attrs_ptr() const { return attrs_ptr_; }
   std::vector<std::shared_ptr<OpIOInfo>> inputs_ptr() const { return inputs_ptr_; }
   std::vector<std::shared_ptr<OpIOInfo>> outputs_ptr() const { return outputs_ptr_; }
@@ -144,7 +145,8 @@ class OpInfo {
   void set_partial_flag(const bool partial_flag) { partial_flag_ = partial_flag; }
   void set_op_pattern(const OpPattern op_pattern) { op_pattern_ = op_pattern; }
   void set_processor(const std::string &processor) { processor_ = processor; }
-  void set_need_check_supported(const bool need_check_supported) { need_check_supported_ = need_check_supported; }
+  void set_need_check_supported(bool need_check_supported) { need_check_supported_ = need_check_supported; }
+  void set_is_dynamic_format(bool is_dynamic_format) { is_dynamic_format_ = is_dynamic_format; }
   void add_attrs_ptr(const std::shared_ptr<OpAttr> &attr) { attrs_ptr_.push_back(attr); }
   void add_inputs_ptr(const std::shared_ptr<OpIOInfo> &input) { inputs_ptr_.push_back(input); }
   void add_outputs_ptr(const std::shared_ptr<OpIOInfo> &output) { outputs_ptr_.push_back(output); }
@@ -169,9 +171,9 @@ class OpInfo {
   int compute_cost_ = 0;
   std::string kernel_name_;
   bool partial_flag_ = false;
-  bool dynamic_format_ = false;
   bool dynamic_shape_ = false;
   bool need_check_supported_ = false;
+  bool is_dynamic_format_ = false;
   OpPattern op_pattern_ = kCommonPattern;
   std::string processor_;
   std::vector<std::shared_ptr<OpAttr>> attrs_ptr_;

mindspore/ccsrc/backend/kernel_compiler/oplib/oplib.cc

@@ -34,6 +34,7 @@ constexpr auto kKernelName = "kernel_name";
 constexpr auto kPartialFlag = "partial_flag";
 constexpr auto kReshapeType = "reshape_type";
 constexpr auto kOpPattern = "op_pattern";
+constexpr auto kIsDynamicFormat = "is_dynamic_format";
 constexpr auto kDynamicFormat = "dynamicFormat";
 constexpr auto kFormatAgnostic = "formatAgnostic";
 constexpr auto kNeedCheckSupported = "need_check_supported";
@@ -102,10 +103,8 @@ bool OpLib::RegOp(const std::string &json_string, const std::string &impl_path)
 }
 
 void OpLib::DecodeTBESpecificInfo(const nlohmann::json &obj, const std::shared_ptr<OpInfo> &op_info) {
-  const std::map<std::string, kernel::OpPattern> kOpPatternMap = {{kFormatAgnostic, kFormatAgnosticPattern},
-                                                                  {kBroadcast, kBroadcastPattern},
-                                                                  {kReduce, kReducePattern},
-                                                                  {kDynamicFormat, kDynamicFormatPattern}};
+  const std::map<std::string, kernel::OpPattern> kOpPatternMap = {
+    {kFormatAgnostic, kFormatAgnosticPattern}, {kBroadcast, kBroadcastPattern}, {kReduce, kReducePattern}};
   MS_EXCEPTION_IF_NULL(op_info);
   op_info->set_async_flag(obj.at(kAsyncFlag));
   op_info->set_binfile_name(obj.at(kBinfileName));
@@ -118,6 +117,10 @@ void OpLib::DecodeTBESpecificInfo(const nlohmann::json &obj, const std::shared_p
     op_info->set_dynamic_shape(obj.at(kDynamicShape));
   }
 
+  if (obj.find(kIsDynamicFormat) != obj.end()) {
+    op_info->set_is_dynamic_format(obj.at(kIsDynamicFormat));
+  }
+
   if (obj.find(kOpPattern) != obj.end()) {
     std::string op_pattern = obj.at(kOpPattern);
     auto find_iter = kOpPatternMap.find(op_pattern);

mindspore/ccsrc/backend/kernel_compiler/tbe/tbe_kernel_select/tbe_kernel_select.cc

@@ -63,19 +63,22 @@ void TbeKernelSelect::TbeMetadataInfoEx() {
     MS_LOG(INFO) << "Warning: node(" << cnode_ptr_->fullname_with_scope() << ") not support tbe aicore.";
     return;
   }
-  OpPattern pattern = op_info_ptr->op_pattern();
-  if (pattern == kCommonPattern) {
-    GetCommonPatternKernelInfo(*op_info_ptr);
-  } else if (pattern == kDynamicFormatPattern) {
+
+  if (op_info_ptr->is_dynamic_format()) {
     GetDynamicFormatPatternKernelInfo(*op_info_ptr);
-  } else if (pattern == kFormatAgnosticPattern) {
-    GetAgnosticPatternKernelInfo(*op_info_ptr);
-  } else if (pattern == kBroadcastPattern) {
-    GetBroadcastPatternKernelInfo(*op_info_ptr);
-  } else if (pattern == kReducePattern) {
-    GetReducePatternKernelInfo(*op_info_ptr);
   } else {
-    MS_LOG(INFO) << "Warning: op pattern is invailed.";
+    OpPattern pattern = op_info_ptr->op_pattern();
+    if (pattern == kCommonPattern) {
+      GetCommonPatternKernelInfo(*op_info_ptr);
+    } else if (pattern == kFormatAgnosticPattern) {
+      GetAgnosticPatternKernelInfo(*op_info_ptr);
+    } else if (pattern == kBroadcastPattern) {
+      GetBroadcastPatternKernelInfo(*op_info_ptr);
+    } else if (pattern == kReducePattern) {
+      GetReducePatternKernelInfo(*op_info_ptr);
+    } else {
+      MS_LOG(INFO) << "Warning: op pattern is invailed.";
+    }
   }
   // check support
   FilterInVaildKernelInfo(*op_info_ptr);

mindspore/ops/_op_impl/tbe/adam_apply_one.py

@@ -36,7 +36,7 @@ adam_apply_one_op_info = TBERegOp("AdamApplyOne") \
     .output(0, "output0", False, "required", "all") \
     .output(1, "output1", False, "required", "all") \
     .output(2, "output2", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None, DataType.None_None, DataType.None_None,
                   DataType.None_None, DataType.None_None, DataType.None_None, DataType.None_None,
                   DataType.None_None, DataType.None_None, DataType.None_None, DataType.None_None,

mindspore/ops/_op_impl/tbe/batch_matmul.py

@@ -30,7 +30,7 @@ batch_matmul_op_info = TBERegOp("BatchMatMul") \
     .input(1, "x2", False, "required", "all") \
     .input(2, "bias", False, "optional", "all") \
     .output(0, "y", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.I32_None, DataType.I32_None, DataType.I32_None, DataType.I32_None) \
     .dtype_format(DataType.F16_None, DataType.F16_None, DataType.F16_None, DataType.F16_None) \
     .dtype_format(DataType.F32_None, DataType.F32_None, DataType.F32_None, DataType.F32_None) \

mindspore/ops/_op_impl/tbe/bce_with_logits_loss.py

@@ -23,7 +23,7 @@ bce_with_logits_loss_op_info = TBERegOp("BCEWithLogitsLoss") \
     .compute_cost(10) \
     .kernel_name("sigmoid_cross_entropy_with_logits_v2") \
     .partial_flag(True) \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .attr("reduction", "optional", "str", "all", "mean") \
     .input(0, "predict", False, "required", "all") \
     .input(1, "target", False, "required", "all") \

mindspore/ops/_op_impl/tbe/bias_add.py

@@ -27,7 +27,7 @@ bias_add_grad_op_info = TBERegOp("BiasAdd") \
     .input(0, "x", False, "required", "all") \
     .input(1, "bias", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/binary_cross_entropy.py

@@ -28,7 +28,7 @@ binary_cross_entropy_op_info = TBERegOp("BinaryCrossEntropy") \
     .input(1, "y", False, "required", "all") \
     .input(2, "weight", False, "optional", "all") \
     .output(0, "output", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None, DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/bn_training_reduce.py

@@ -26,7 +26,7 @@ bn_training_reduce_op_info = TBERegOp("BNTrainingReduce") \
     .input(0, "x", False, "required", "all", reshape_type="NC") \
     .output(0, "sum", False, "required", "all") \
     .output(1, "square_sum", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.F16_None, DataType.F32_None, DataType.F32_None) \
     .dtype_format(DataType.F32_None, DataType.F32_None, DataType.F32_None) \
     .get_op_info()

mindspore/ops/_op_impl/tbe/bn_training_reduce_grad.py

@@ -32,7 +32,7 @@ bn_training_reduce_grad_op_info = TBERegOp("BNTrainingReduceGrad") \
     .input(5, "batch_mean", False, "required", "all") \
     .input(6, "batch_variance", False, "required", "all") \
     .output(0, "y", False, "required", "all", reshape_type="NC") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.F16_None, DataType.F32_None, DataType.F32_None, DataType.F32_None,
                   DataType.F32_None, DataType.F32_None, DataType.F32_None, DataType.F16_None) \
     .dtype_format(DataType.F32_None, DataType.F32_None, DataType.F32_None, DataType.F32_None,

mindspore/ops/_op_impl/tbe/bn_training_update_grad.py

@@ -30,7 +30,7 @@ bn_training_update_grad_op_info = TBERegOp("BNTrainingUpdateGrad") \
     .input(3, "batch_variance", False, "required", "all") \
     .output(0, "diff_scale", False, "required", "all") \
     .output(1, "diff_offset", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.F16_None, DataType.F16_None, DataType.F32_None, DataType.F32_None,
                   DataType.F32_None, DataType.F32_None) \
     .dtype_format(DataType.F32_None, DataType.F32_None, DataType.F32_None, DataType.F32_None,

mindspore/ops/_op_impl/tbe/bn_training_update_v2.py

@@ -32,7 +32,7 @@ bn_training_update_v2_op_info = TBERegOp("BNTrainingUpdateV2") \
     .output(0, "y", False, "required", "all", reshape_type="NC") \
     .output(1, "batch_mean", False, "required", "all") \
     .output(2, "batch_variance", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.F16_None, DataType.F32_None, DataType.F32_None,
                   DataType.F32_None, DataType.F32_None, DataType.F16_None,
                   DataType.F32_None, DataType.F32_None) \

mindspore/ops/_op_impl/tbe/concat.py

@@ -26,7 +26,7 @@ concat_op_info = TBERegOp("Concat") \
     .attr("axis", "required", "int", "all") \
     .input(0, "input_values", False, "dynamic", "all") \
     .output(0, "output_data", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/concat_ds.py

@@ -27,7 +27,7 @@ concat_ds_op_info = TBERegOp("Concat") \
     .attr("axis", "required", "int", "all") \
     .input(0, "input_values", False, "dynamic", "all") \
     .output(0, "output_data", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/confusion_transpose_d.py

@@ -28,7 +28,7 @@ confusion_transpose_d_op_info = TBERegOp("ConfusionTransposeD") \
     .attr("transpose_first", "required", "bool", "all") \
     .input(0, "x", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/conv2d.py

@@ -23,7 +23,7 @@ conv2d_op_info = TBERegOp("Conv2D") \
     .compute_cost(10) \
     .kernel_name("conv2d") \
     .partial_flag(True) \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .attr("stride", "required", "listInt", "all") \
     .attr("pad_list", "required", "listInt", "all") \
     .attr("dilation", "required", "listInt", "all") \
@@ -35,7 +35,7 @@ conv2d_op_info = TBERegOp("Conv2D") \
     .input(2, "bias", False, "optional", "all") \
     .input(3, "offset_w", False, "optional", "all") \
     .output(0, "y", True, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.F16_None, DataType.F16_None, DataType.F16_None, DataType.I8_None, DataType.F16_None) \
     .dtype_format(DataType.I8_None, DataType.I8_None, DataType.I32_None, DataType.I8_None, DataType.I32_None) \
     .get_op_info()

mindspore/ops/_op_impl/tbe/dropout_do_mask.py

@@ -27,7 +27,7 @@ drop_out_do_mask_op_info = TBERegOp("DropoutDoMask") \
     .input(1, "mask", False, "required", "all") \
     .input(2, "keep_prob", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None, DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/fused_mul_add.py

@@ -27,7 +27,7 @@ fused_mul_add_op_info = TBERegOp("FusedMulAdd") \
     .input(1, "x2", False, "required", "all") \
     .input(2, "x3", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None, DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/l2_loss.py

@@ -25,7 +25,7 @@ l2_loss_op_info = TBERegOp("L2Loss") \
     .partial_flag(True) \
     .input(0, "x", None, "required", None) \
     .output(0, "y", True, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/layer_norm.py

@@ -32,7 +32,7 @@ layer_norm_op_info = TBERegOp("LayerNorm") \
     .output(0, "y", False, "required", "all") \
     .output(1, "mean", False, "required", "all") \
     .output(2, "variance", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.F16_None, DataType.F16_None, DataType.F16_None, DataType.F16_None,
                   DataType.F16_None, DataType.F16_None) \
     .dtype_format(DataType.F32_None, DataType.F32_None, DataType.F32_None, DataType.F32_None,

mindspore/ops/_op_impl/tbe/layer_norm_beta_gamma_backprop.py

@@ -30,7 +30,7 @@ layer_norm_beta_gamma_backprop_op_info = TBERegOp("LayerNormBetaGammaBackprop")
     .input(3, "mean", False, "required", "all") \
     .output(0, "pd_gamma", False, "required", "all") \
     .output(1, "pd_beta", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.F16_None, DataType.F16_None, DataType.F16_None, DataType.F16_None,
                   DataType.F32_None, DataType.F32_None) \
     .dtype_format(DataType.F32_None, DataType.F32_None, DataType.F32_None, DataType.F32_None,

mindspore/ops/_op_impl/tbe/layer_norm_x_backprop.py

@@ -29,7 +29,7 @@ layer_norm_x_backprop_op_info = TBERegOp("LayerNormXBackprop") \
     .input(3, "mean", False, "required", "all") \
     .input(4, "gamma", False, "required", "all") \
     .output(0, "pd_x", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.F16_None, DataType.F16_None, DataType.F16_None, DataType.F16_None,
                   DataType.F16_None, DataType.F16_None) \
     .dtype_format(DataType.F32_None, DataType.F32_None, DataType.F32_None, DataType.F32_None,

mindspore/ops/_op_impl/tbe/max_pool3d.py

@@ -33,7 +33,7 @@ max_pool3d_op_info = TBERegOp("MaxPool3D") \
     .attr("format", "optional", "str", "all") \
     .input(0, "x", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/mul.py

@@ -26,7 +26,7 @@ mul_op_info = TBERegOp("Mul") \
     .input(0, "x", False, "required", "all") \
     .input(1, "y", False, "required", "all") \
     .output(0, "output", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/mul_ds.py

@@ -27,7 +27,7 @@ mul_ds_op_info = TBERegOp("Mul") \
     .input(0, "x", False, "required", "all") \
     .input(1, "y", False, "required", "all") \
     .output(0, "output", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/prelu.py

@@ -26,7 +26,7 @@ prelu_op_info = TBERegOp("PReLU") \
     .input(0, "x", False, "required", "all") \
     .input(1, "weight", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/prelu_grad.py

@@ -28,7 +28,7 @@ prelu_grad_op_info = TBERegOp("PReLUGrad") \
     .input(2, "weights", False, "required", "all") \
     .output(0, "dx", False, "required", "all") \
     .output(1, "da", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None, DataType.None_None, DataType.None_None,
                   DataType.None_None) \
     .get_op_info()

mindspore/ops/_op_impl/tbe/real_div.py

@@ -27,6 +27,7 @@ realdiv_op_info = TBERegOp("RealDiv") \
     .input(1, "y", False, "required", "all") \
     .output(0, "z", False, "required", "all") \
     .op_pattern("broadcast") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.F16_None, DataType.F16_None, DataType.F16_None) \
     .dtype_format(DataType.F32_None, DataType.F32_None, DataType.F32_None) \
     .get_op_info()

mindspore/ops/_op_impl/tbe/reciprocal.py

@@ -25,7 +25,7 @@ reciprocal_op_info = TBERegOp("Reciprocal") \
     .partial_flag(True) \
     .input(0, "x", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/reverse_v2_d.py

@@ -23,7 +23,7 @@ reverse_v2_d_op_info = TBERegOp("ReverseV2") \
     .compute_cost(10) \
     .kernel_name("reverse_v2_d") \
     .partial_flag(True) \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .attr("axis", "required", "listInt", "all") \
     .input(0, "x", False, "required", "all") \
     .output(0, "y", False, "required", "all") \

mindspore/ops/_op_impl/tbe/select.py

@@ -27,7 +27,7 @@ select_op_info = TBERegOp("Select") \
     .input(1, "x1", False, "required", "all") \
     .input(2, "x2", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None, DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/slice.py

@@ -23,7 +23,7 @@ slice_op_info = TBERegOp("Slice") \
     .compute_cost(10) \
     .kernel_name("slice_d") \
     .partial_flag(True) \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .attr("begin", "required", "listInt", "all") \
     .attr("size", "required", "listInt", "all") \
     .input(0, "x", False, "required", "all") \

mindspore/ops/_op_impl/tbe/softmax.py

@@ -26,7 +26,7 @@ softmax_op_info = TBERegOp("Softmax") \
     .attr("axis", "optional", "listInt", "all") \
     .input(0, "x", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/softmax_grad_ext.py

@@ -23,14 +23,14 @@ softmax_grad_ext_op_info = TBERegOp("SoftmaxGradExt") \
     .compute_cost(10) \
     .kernel_name("softmax_grad_ext") \
     .partial_flag(True) \
-    .dynamic_format(True) \
+    .is_dynamic_format(True) \
     .attr("axis", "required", "listInt", "all") \
     .attr("keepdims", "required", "bool", "all") \
     .input(0, "grad", False, "required", "all") \
     .input(1, "x1", False, "required", "all") \
     .input(2, "x2", False, "required", "all") \
     .output(0, "y", True, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None,
                   DataType.None_None, DataType.None_None) \
     .get_op_info()

mindspore/ops/_op_impl/tbe/split_d.py

@@ -27,7 +27,7 @@ split_d_op_info = TBERegOp("Split") \
     .attr("output_num", "required", "int", "all") \
     .input(0, "value", False, "required", "all") \
     .output(0, "output", False, "dynamic", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/splitv.py

@@ -28,7 +28,7 @@ split_v_op_info = TBERegOp("SplitV") \
     .attr("num_split", "required", "int", "all") \
     .input(0, "x", False, "required", "all") \
     .output(0, "y", False, "dynamic", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/strided_slice_d.py

@@ -33,7 +33,7 @@ strided_slice_d_op_info = TBERegOp("StridedSlice") \
     .attr("shrink_axis_mask", "required", "int", "all") \
     .input(0, "x", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/tensor_add.py

@@ -26,7 +26,7 @@ tensor_add_op_info = TBERegOp("Add") \
     .input(0, "x1", False, "required", "all") \
     .input(1, "x2", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
     .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
     .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \

mindspore/ops/_op_impl/tbe/tensor_add_ds.py

@@ -27,7 +27,7 @@ tensor_add_op_info = TBERegOp("Add") \
     .input(0, "x1", False, "required", "all") \
     .input(1, "x2", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
     .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default) \
     .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \

mindspore/ops/_op_impl/tbe/tile.py

@@ -26,7 +26,7 @@ tile_op_info = TBERegOp("Tile") \
     .attr("multiples", "optional", "listInt", "all")\
     .input(0, "x1", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/unpack.py

@@ -27,7 +27,7 @@ unstack_op_info = TBERegOp("Unstack") \
     .attr("axis", "required", "int", "all") \
     .input(0, "x", False, "required", "all") \
     .output(0, "y", False, "dynamic", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/_op_impl/tbe/unsorted_segment_sum.py

@@ -27,7 +27,7 @@ unsorted_segment_sum_op_info = TBERegOp("UnsortedSegmentSum") \
     .input(0, "x", False, "required", "all") \
     .input(1, "segment_ids", False, "required", "all") \
     .output(0, "y", False, "required", "all") \
-    .op_pattern("dynamicFormat") \
+    .is_dynamic_format(True) \
     .dtype_format(DataType.None_None, DataType.None_None, DataType.None_None) \
     .get_op_info()
 

mindspore/ops/op_info_register.py

@@ -142,7 +142,7 @@ class RegOp:
         Raises:
             TypeError: If the type of value is not list.
             ValueError: If the size of param list is not equal to the size of key list, or
-                        the size of param list is not equal to the size of funtion list.
+                        the size of param list is not equal to the size of function list.
         """
         for i in [param_list, key_list, fn_list]:
             if not isinstance(i, list):
@@ -353,9 +353,9 @@ class TBERegOp(RegOp):
         self.kernel_name_ = ''
         self.partial_flag_ = False
         self.reshape_type_ = ''
-        self.dynamic_format_ = False
         self.dynamic_shape_ = False
         self.need_check_supported_ = False
+        self.is_dynamic_format_ = False
         self.op_pattern_ = ""
 
     def async_flag(self, async_flag):
@@ -424,17 +424,6 @@ class TBERegOp(RegOp):
         self.reshape_type_ = reshape_type
         return self
 
-    def dynamic_format(self, dynamic_format):
-        """
-        Whether the operator supports dynamic selection of format and dtype or not.
-
-        Args:
-            dynamic_format (bool): Value of dynamic format. Default: false.
-        """
-        self._is_bool(dynamic_format)
-        self.dynamic_format_ = dynamic_format
-        return self
-
     def dynamic_shape(self, dynamic_shape):
         """
         Whether the operator supports dynamic shape.
@@ -457,9 +446,20 @@ class TBERegOp(RegOp):
         self.need_check_supported_ = need_check_supported
         return self
 
+    def is_dynamic_format(self, is_dynamic_format):
+        """
+        Whether the operator need calop_select_format api.
+
+        Args:
+            :param is_dynamic_format: (bool): Value of is_dynamic_format_. Default: false.
+        """
+        self._is_bool(is_dynamic_format)
+        self.is_dynamic_format_ = is_dynamic_format
+        return self
+
     def op_pattern(self, pattern=None):
         """
-        The behavior type of opeator, such as broadcast, reduce and so on.
+        The behavior type of operator, such as broadcast, reduce and so on.
 
         Args:
             pattern (str): Value of op pattern.