[ILUVATAR_GPU] Support rnn op (#1924)

Author: tianyuzhou668

backends/iluvatar_gpu/kernels/cuda_kernels/rnn_grad_kernel.cu

@@ -0,0 +1,14 @@
+file(
+  GLOB TEST_OPS
+  RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}"
+  "test_*.py")
+string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
+
+foreach(TEST_OP ${TEST_OPS})
+  py_test_modules(${TEST_OP} MODULES ${TEST_OP})
+endforeach()
+
+if(NOT WIN32)
+  set_tests_properties(test_rnn_nets_static PROPERTIES TIMEOUT 120)
+  set_tests_properties(test_rnn_nets PROPERTIES TIMEOUT 120)
+endif()

backends/iluvatar_gpu/kernels/cuda_kernels/rnn_kernel.cu

@@ -0,0 +1,13 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

backends/iluvatar_gpu/tests/rnn/CMakeLists.txt

@@ -0,0 +1,84 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+
+
+def convert_params_for_cell(np_cell, paddle_cell):
+    state = np_cell.parameters
+    for k, v in paddle_cell.named_parameters():
+        v.set_value(state[k])
+
+
+def convert_params_for_cell_static(np_cell, paddle_cell, place):
+    state = np_cell.parameters
+    for k, v in paddle_cell.named_parameters():
+        scope = paddle.static.global_scope()
+        tensor = scope.find_var(v.name).get_tensor()
+        tensor.set(state[k], place)
+
+
+def convert_params_for_net(np_net, paddle_net):
+    for np_layer, paddle_layer in zip(np_net, paddle_net):
+        if hasattr(np_layer, "cell"):
+            convert_params_for_cell(np_layer.cell, paddle_layer.cell)
+        else:
+            convert_params_for_cell(np_layer.cell_fw, paddle_layer.cell_fw)
+            convert_params_for_cell(np_layer.cell_bw, paddle_layer.cell_bw)
+
+
+def convert_params_for_net_static(np_net, paddle_net, place):
+    for np_layer, paddle_layer in zip(np_net, paddle_net):
+        if hasattr(np_layer, "cell"):
+            convert_params_for_cell_static(np_layer.cell, paddle_layer.cell, place)
+        else:
+            convert_params_for_cell_static(
+                np_layer.cell_fw, paddle_layer.cell_fw, place
+            )
+            convert_params_for_cell_static(
+                np_layer.cell_bw, paddle_layer.cell_bw, place
+            )
+
+
+def get_params_for_cell(np_cell, num_layers, idx):
+    state = np_cell.parameters
+    weight_list = [
+        (f"{num_layers}.weight_{idx}", state["weight_ih"]),
+        (f"{num_layers}.weight_{idx + 1}", state["weight_hh"]),
+    ]
+    bias_list = [
+        (f"{num_layers}.bias_{idx}", state["bias_ih"]),
+        (f"{num_layers}.bias_{idx + 1}", state["bias_hh"]),
+    ]
+    return weight_list, bias_list
+
+
+def get_params_for_net(np_net):
+    weight_list = []
+    bias_list = []
+    for layer_idx, np_layer in enumerate(np_net):
+        if hasattr(np_layer, "cell"):
+            weight, bias = get_params_for_cell(np_layer.cell, layer_idx, 0)
+            for w, b in zip(weight, bias):
+                weight_list.append(w)
+                bias_list.append(b)
+        else:
+            for count, cell in enumerate([np_layer.cell_fw, np_layer.cell_bw]):
+                weight, bias = get_params_for_cell(cell, layer_idx, count * 2)
+                for w, b in zip(weight, bias):
+                    weight_list.append(w)
+                    bias_list.append(b)
+
+    weight_list.extend(bias_list)
+    return weight_list