Adapt ApplyCenteredRmsProp.

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

@@ -81,6 +81,7 @@ static std::map<string, string> tbe_func_adapter_map = {
   {"sparse_apply_proximal_adagrad", "sparse_apply_proximal_adagrad_d"},
   {"apply_add_sign", "apply_add_sign_d"},
   {"apply_power_sign", "apply_power_sign_d"},
+  {"apply_centered_rms_prop", "apply_centered_rms_prop_d"},
   {"transpose", "transpose_d"},
   {"fill", "fill_d"},
   {"unsorted_segment_sum", "unsorted_segment_sum_d"},

mindspore/ccsrc/transform/graph_ir/convert.cc

@@ -409,7 +409,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {string(kNameBatchToSpace), ADPT_DESC(BatchToSpaceD)},
     {string(kNameAtan2), ADPT_DESC(Atan2)},
     {string(kNameApplyRMSProp), ADPT_DESC(ApplyRMSPropD)},
-    {string(kNameApplyCenteredRMSProp), ADPT_DESC(ApplyCenteredRMSProp)},
+    {string(kNameApplyCenteredRMSProp), ADPT_DESC(ApplyCenteredRMSPropD)},
     {string(kNameL2Loss), ADPT_DESC(L2Loss)},
     {string(kNameCTCLoss), ADPT_DESC(CTCLoss)},
     {string(kNameRange), ADPT_DESC(RangeD)},

mindspore/ccsrc/transform/graph_ir/op_declare.cc

@@ -1284,12 +1284,13 @@ INPUT_ATTR_MAP(ApplyRMSPropD) = {{6, ATTR_DESC(rho, AnyTraits<float>())},
 ATTR_MAP(ApplyRMSPropD) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
 OUTPUT_MAP(ApplyRMSPropD) = {{0, OUTPUT_DESC(var)}};
 
-// ApplyCenteredRMSProp
-INPUT_MAP(ApplyCenteredRMSProp) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(mg)},       {3, INPUT_DESC(ms)},
-                                   {4, INPUT_DESC(mom)}, {5, INPUT_DESC(grad)},     {6, INPUT_DESC(lr)},
-                                   {7, INPUT_DESC(rho)}, {8, INPUT_DESC(momentum)}, {9, INPUT_DESC(epsilon)}};
-ATTR_MAP(ApplyCenteredRMSProp) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
-OUTPUT_MAP(ApplyCenteredRMSProp) = {{0, OUTPUT_DESC(var)}};
+// ApplyCenteredRMSPropD
+INPUT_MAP(ApplyCenteredRMSPropD) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(mg)},       {3, INPUT_DESC(ms)},
+                                    {4, INPUT_DESC(mom)}, {5, INPUT_DESC(grad)},     {6, INPUT_DESC(lr)},
+                                    {7, INPUT_DESC(rho)}, {8, INPUT_DESC(momentum)}, {9, INPUT_DESC(epsilon)}};
+ATTR_MAP(ApplyCenteredRMSPropD) = {{"use_locking", ATTR_DESC(use_locking, AnyTraits<bool>())}};
+OUTPUT_MAP(ApplyCenteredRMSPropD) = {
+  {0, OUTPUT_DESC(var)}, {1, OUTPUT_DESC(mg)}, {2, OUTPUT_DESC(ms)}, {3, OUTPUT_DESC(mom)}};
 
 // L2Loss
 INPUT_MAP(L2Loss) = {{1, INPUT_DESC(x)}};

mindspore/ccsrc/transform/graph_ir/op_declare.h

@@ -486,8 +486,8 @@ DECLARE_OP_USE_OUTPUT(Atan2)
 DECLARE_OP_ADAPTER(ApplyRMSPropD)
 DECLARE_OP_USE_INPUT_ATTR(ApplyRMSPropD)
 DECLARE_OP_USE_OUTPUT(ApplyRMSPropD)
-DECLARE_OP_ADAPTER(ApplyCenteredRMSProp)
-DECLARE_OP_USE_OUTPUT(ApplyCenteredRMSProp)
+DECLARE_OP_ADAPTER(ApplyCenteredRMSPropD)
+DECLARE_OP_USE_OUTPUT(ApplyCenteredRMSPropD)
 DECLARE_OP_ADAPTER(L2Loss)
 DECLARE_OP_USE_OUTPUT(L2Loss)
 DECLARE_OP_ADAPTER(CTCLoss)

mindspore/ops/_op_impl/tbe/apply_centered_rms_prop.py

@@ -13,15 +13,15 @@
 # limitations under the License.
 # ============================================================================
 
-"""ApplyCenteredRMSProp op"""
+"""ApplyCenteredRMSPropD op"""
 from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
 
 apply_centered_rms_prop_op_info = TBERegOp("ApplyCenteredRMSProp") \
     .fusion_type("OPAQUE") \
     .async_flag(False) \
-    .binfile_name("apply_centered_rms_prop.so") \
+    .binfile_name("apply_centered_rms_prop_d.so") \
     .compute_cost(10) \
-    .kernel_name("apply_centered_rms_prop") \
+    .kernel_name("apply_centered_rms_prop_d") \
     .partial_flag(True) \
     .input(0, "var", False, "required", "all") \
     .input(1, "mg", False, "required", "all") \
@@ -33,34 +33,45 @@ apply_centered_rms_prop_op_info = TBERegOp("ApplyCenteredRMSProp") \
     .input(7, "epsilon", False, "required", "all") \
     .input(8, "grad", False, "required", "all") \
     .output(0, "var", False, "required", "all") \
+    .output(1, "mg", False, "required", "all") \
+    .output(2, "ms", False, "required", "all") \
+    .output(3, "mom", False, "required", "all") \
     .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
                   DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
-                  DataType.F16_5HD, DataType.F16_5HD) \
+                  DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
+                  DataType.F16_5HD) \
     .dtype_format(DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ,
                   DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
-                  DataType.F16_FracZ, DataType.F16_FracZ) \
+                  DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ, DataType.F16_FracZ,
+                  DataType.F16_FracZ) \
     .dtype_format(DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0,
                   DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
-                  DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0) \
+                  DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0, DataType.F16_C1HWNCoC0,
+                  DataType.F16_C1HWNCoC0) \
     .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
                   DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
-                  DataType.F16_Default, DataType.F16_Default) \
+                  DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
+                  DataType.F16_Default) \
     .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
                   DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
-                  DataType.F32_5HD, DataType.F32_5HD) \
+                  DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD) \
     .dtype_format(DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ,
                   DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
-                  DataType.F32_FracZ, DataType.F32_FracZ) \
+                  DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ, DataType.F32_FracZ,
+                  DataType.F32_FracZ) \
     .dtype_format(DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0,
                   DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
-                  DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0) \
+                  DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0, DataType.F32_C1HWNCoC0,
+                  DataType.F32_C1HWNCoC0) \
     .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
                   DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
-                  DataType.F32_Default, DataType.F32_Default) \
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default) \
     .get_op_info()
 
 
 @op_info_register(apply_centered_rms_prop_op_info)
 def _apply_centered_rms_prop_tbe():
-    """ApplyCenteredRMSProp TBE register"""
+    """ApplyCenteredRMSPropD TBE register"""
     return

mindspore/ops/operations/nn_ops.py

@@ -1962,6 +1962,7 @@ class ApplyCenteredRMSProp(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self, use_locking=False):
         self.use_locking = validator.check_value_type("use_locking", use_locking, [bool], self.name)
+        self.is_ascend = context.get_context("device_target") == "Ascend"
 
     def infer_shape(self, var_shape, mean_gradient_shape, mean_square_shape, moment_shape, grad_shape,
                     learning_rate_shape, decay_shape, momentum_shape, epsilon_shape):
@@ -1969,6 +1970,8 @@ class ApplyCenteredRMSProp(PrimitiveWithInfer):
         validator.check("var_shape", var_shape, "mean_square_shape", mean_square_shape, Rel.EQ, self.name)
         validator.check("var_shape", var_shape, "moment_shape", moment_shape, Rel.EQ, self.name)
         validator.check("var_shape", var_shape, "grad_shape", grad_shape, Rel.EQ, self.name)
+        if self.is_ascend:
+            return var_shape, mean_gradient_shape, mean_square_shape, moment_shape
         return var_shape
 
     def infer_dtype(self, var_dtype, mean_gradient_dtype, mean_square_dtype, moment_dtype, grad_dtype,
@@ -1982,6 +1985,8 @@ class ApplyCenteredRMSProp(PrimitiveWithInfer):
         validator.check_type_same(args_rho, valid_types, self.name)
         args_lr = {"learning_rate": learning_rate_dtype, "rho": rho_dtype}
         validator.check_scalar_or_tensor_type_same(args_lr, valid_types, self.name, allow_mix=True)
+        if self.is_ascend:
+            return var_dtype, mean_gradient_dtype, mean_square_dtype, moment_dtype
         return var_dtype