Add DynamicRNN for old backend.

mindspore/ccsrc/transform/graph_ir/op_adapter_map.h

@@ -183,6 +183,8 @@ constexpr const char kNameBasicLSTMCell[] = "BasicLSTMCell";
 constexpr const char kNameBasicLSTMCellInputGrad[] = "BasicLSTMCellInputGrad";
 constexpr const char kNameBasicLSTMCellWeightGrad[] = "BasicLSTMCellWeightGrad";
 constexpr const char kNameBasicLSTMCellCStateGrad[] = "BasicLSTMCellCStateGrad";
+constexpr const char kNameDynamicRNN[] = "DynamicRNN";
+constexpr const char kNameDynamicRNNGrad[] = "DynamicRNNGrad";
 constexpr const char kNameL2Loss[] = "L2Loss";
 constexpr const char kNameCTCLoss[] = "CTCLoss";
 constexpr const char kNameRange[] = "Range";

mindspore/ccsrc/transform/graph_ir/op_declare/rnn_declare.cc

@@ -48,4 +48,48 @@ ATTR_MAP(BasicLSTMCellCStateGrad) = {{"forget_bias", ATTR_DESC(forget_bias, AnyT
                                      {"activation", ATTR_DESC(activation, AnyTraits<std::string>())}};
 OUTPUT_MAP(BasicLSTMCellCStateGrad) = {{0, OUTPUT_DESC(dgate)}, {1, OUTPUT_DESC(dct_1)}};
 REG_ADPT_DESC(BasicLSTMCellCStateGrad, kNameBasicLSTMCellCStateGrad, ADPT_DESC(BasicLSTMCellCStateGrad))
+
+// DynamicRNN
+INPUT_MAP(DynamicRNN) = {{1, INPUT_DESC(x)},          {2, INPUT_DESC(w)},      {3, INPUT_DESC(b)},
+                         {4, INPUT_DESC(seq_length)}, {5, INPUT_DESC(init_h)}, {6, INPUT_DESC(init_c)},
+                         {7, INPUT_DESC(wci)},        {8, INPUT_DESC(wcf)},    {9, INPUT_DESC(wco)},
+                         {10, INPUT_DESC(mask)}};
+ATTR_MAP(DynamicRNN) = {{"cell_type", ATTR_DESC(cell_type, AnyTraits<std::string>())},
+                        {"direction", ATTR_DESC(direction, AnyTraits<std::string>())},
+                        {"cell_depth", ATTR_DESC(cell_depth, AnyTraits<int64_t>())},
+                        {"use_peephole", ATTR_DESC(use_peephole, AnyTraits<bool>())},
+                        {"keep_prob", ATTR_DESC(keep_prob, AnyTraits<float>())},
+                        {"cell_clip", ATTR_DESC(cell_clip, AnyTraits<float>())},
+                        {"num_proj", ATTR_DESC(num_proj, AnyTraits<int64_t>())},
+                        {"time_major", ATTR_DESC(time_major, AnyTraits<bool>())},
+                        {"ivation", ATTR_DESC(activation, AnyTraits<std::string>())},
+                        {"forget_bias", ATTR_DESC(forget_bias, AnyTraits<float>())},
+                        {"is_training", ATTR_DESC(is_training, AnyTraits<bool>())}};
+OUTPUT_MAP(DynamicRNN) = {{0, OUTPUT_DESC(y)}, {1, OUTPUT_DESC(output_h)}, {2, OUTPUT_DESC(output_c)},
+                          {3, OUTPUT_DESC(i)}, {4, OUTPUT_DESC(j)},        {5, OUTPUT_DESC(f)},
+                          {6, OUTPUT_DESC(o)}, {7, OUTPUT_DESC(tanhc)}};
+REG_ADPT_DESC(DynamicRNN, kNameDynamicRNN, ADPT_DESC(DynamicRNN))
+
+// DynamicRNNGrad
+INPUT_MAP(DynamicRNNGrad) = {
+  {1, INPUT_DESC(x)},      {2, INPUT_DESC(w)},      {3, INPUT_DESC(b)},   {4, INPUT_DESC(y)},
+  {5, INPUT_DESC(init_h)}, {6, INPUT_DESC(init_c)}, {7, INPUT_DESC(h)},   {8, INPUT_DESC(c)},
+  {9, INPUT_DESC(dy)},     {10, INPUT_DESC(dh)},    {11, INPUT_DESC(dc)}, {12, INPUT_DESC(i)},
+  {13, INPUT_DESC(j)},     {14, INPUT_DESC(f)},     {15, INPUT_DESC(o)},  {16, INPUT_DESC(tanhct)}};
+
+ATTR_MAP(DynamicRNNGrad) = {{"cell_type", ATTR_DESC(cell_type, AnyTraits<std::string>())},
+                            {"direction", ATTR_DESC(direction, AnyTraits<std::string>())},
+                            {"cell_depth", ATTR_DESC(cell_depth, AnyTraits<int64_t>())},
+                            {"use_peephole", ATTR_DESC(use_peephole, AnyTraits<bool>())},
+                            {"keep_prob", ATTR_DESC(keep_prob, AnyTraits<float>())},
+                            {"cell_clip", ATTR_DESC(cell_clip, AnyTraits<float>())},
+                            {"num_proj", ATTR_DESC(num_proj, AnyTraits<int64_t>())},
+                            {"time_major", ATTR_DESC(time_major, AnyTraits<bool>())},
+                            {"forget_bias", ATTR_DESC(forget_bias, AnyTraits<float>())}};
+OUTPUT_MAP(DynamicRNNGrad) = {{0, OUTPUT_DESC(dw)},
+                              {1, OUTPUT_DESC(db)},
+                              {2, OUTPUT_DESC(dx)},
+                              {3, OUTPUT_DESC(dh_prev)},
+                              {4, OUTPUT_DESC(dc_prev)}};
+REG_ADPT_DESC(DynamicRNNGrad, kNameDynamicRNNGrad, ADPT_DESC(DynamicRNNGrad))
 }  // namespace mindspore::transform

mindspore/ccsrc/transform/graph_ir/op_declare/rnn_declare.h

@@ -19,8 +19,8 @@
 
 #include <string>
 #include <unordered_map>
-#include "transform/graph_ir/op_declare/op_declare_macro.h"
 #include "ops/rnn.h"
+#include "transform/graph_ir/op_declare/op_declare_macro.h"
 
 namespace mindspore::transform {
 DECLARE_OP_ADAPTER(BasicLSTMCell)
@@ -34,5 +34,11 @@ DECLARE_OP_USE_OUTPUT(BasicLSTMCellWeightGrad)
 
 DECLARE_OP_ADAPTER(BasicLSTMCellCStateGrad)
 DECLARE_OP_USE_OUTPUT(BasicLSTMCellCStateGrad)
+
+DECLARE_OP_ADAPTER(DynamicRNN)
+DECLARE_OP_USE_OUTPUT(DynamicRNN)
+
+DECLARE_OP_ADAPTER(DynamicRNNGrad)
+DECLARE_OP_USE_OUTPUT(DynamicRNNGrad)
 }  // namespace mindspore::transform
 #endif  // MINDSPORE_CCSRC_TRANSFORM_GRAPH_IR_OP_DECLARE_RNN_DECLARE_H_

mindspore/ops/operations/_grad_ops.py

@@ -1062,7 +1062,7 @@ class DynamicRNNGrad(PrimitiveWithInfer):
                  keep_prob=-1.0,
                  cell_clip=-1.0,
                  num_proj=0,
-                 time_major=False,
+                 time_major=True,
                  forget_bias=0.0):
         self.forget_bias = validator.check_value_type("forget_bias", forget_bias, [float], self.name)
         self.add_prim_attr("io_format", "ND")