move namespace rnn to a directory (#3261)

* move namespace rnn to a directory

paddle/operators/CMakeLists.txt

@@ -63,5 +63,6 @@ op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
 op_library(fc_op
     SRCS fc_op.cc
     DEPS mul_op rowwise_add_op sigmoid_op softmax_op net_op)
-op_library(recurrent_op SRCS recurrent_op.cc DEPS op_desc tensor op_registry operator net_op)
+op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
+    DEPS op_desc tensor op_registry operator net_op)
 cc_test(recurrent_op_test SRCS recurrent_op_test.cc DEPS recurrent_op gtest mul_op add_op)

paddle/operators/recurrent_op.cc

@@ -25,141 +25,6 @@
 namespace paddle {
 namespace operators {
 
-namespace rnn {
-
-void SegmentInputs(const std::vector<Scope*>& step_scopes,
-                   const std::vector<Link>& inlinks, const size_t seq_len,
-                   bool infer_shape_mode) {
-  PADDLE_ENFORCE(!inlinks.empty(), "no in links are provided.");
-  for (size_t i = 0; i < inlinks.size(); ++i) {
-    auto input_var = step_scopes[0]->FindVar(inlinks[i].external);
-    PADDLE_ENFORCE(input_var != nullptr, "input link [%s] is not in scope.",
-                   inlinks[i].external);
-    Tensor* input = input_var->GetMutable<Tensor>();
-    framework::DDim dims = input->dims();
-    PADDLE_ENFORCE(static_cast<size_t>(dims[0]) == seq_len,
-                   "all the inlinks must have same length");
-    framework::DDim step_dims = slice_ddim(dims, 1, dims.size());
-    for (size_t j = 0; j < seq_len; j++) {
-      Tensor* step_input =
-          step_scopes[j]->NewVar(inlinks[i].internal)->GetMutable<Tensor>();
-      if (!infer_shape_mode) {
-        *step_input = input->Slice<float>(j, j + 1);
-      }
-      step_input->Resize(step_dims);
-    }
-  }
-}
-
-void ConcatOutputs(const std::vector<Scope*>& step_scopes,
-                   const std::vector<Link>& outlinks, const size_t seq_len,
-                   bool infer_shape_mode) {
-  for (size_t i = 0; i < outlinks.size(); i++) {
-    auto output_var = step_scopes[0]->FindVar(outlinks[i].external);
-    PADDLE_ENFORCE(output_var != nullptr, "output link [%s] is not in scope.",
-                   outlinks[i].external);
-    Tensor* output = output_var->GetMutable<Tensor>();
-    if (infer_shape_mode) {
-      framework::DDim step_dims = step_scopes[0]
-                                      ->FindVar(outlinks[i].internal)
-                                      ->GetMutable<Tensor>()
-                                      ->dims();
-      std::vector<int> dims_vec = vectorize(step_dims);
-      dims_vec.insert(dims_vec.begin(), seq_len);
-      output->Resize(framework::make_ddim(dims_vec));
-    } else {
-      output->mutable_data<float>(platform::CPUPlace());
-      for (size_t j = 0; j < seq_len; j++) {
-        Tensor* step_output =
-            step_scopes[j]->FindVar(outlinks[i].internal)->GetMutable<Tensor>();
-        // TODO(luotao02) data type and platform::DeviceContext() should set
-        // correctly
-        (output->Slice<float>(j, j + 1))
-            .CopyFrom<float>(*step_output, platform::CPUPlace());
-      }
-    }
-  }
-}
-
-void LinkMemories(const std::vector<Scope*>& scopes,
-                  const std::vector<rnn::MemoryAttr>& memories,
-                  const size_t step_id, const int offset,
-                  bool infer_shape_mode) {
-  PADDLE_ENFORCE(step_id < scopes.size(),
-                 "step [%d] is out of range of step scopes' size [%d]", step_id,
-                 scopes.size());
-  PADDLE_ENFORCE(static_cast<int>(step_id) + offset >= 0,
-                 "offset [%d] must be large than -[%d]", offset, step_id);
-  PADDLE_ENFORCE(step_id + offset < scopes.size(),
-                 "offset [%d] is out of range, it must be less than (%d - %d)",
-                 offset, scopes.size(), step_id);
-  auto scope = scopes[step_id];
-  auto linked_scope = scopes[step_id + offset];
-  for (auto& attr : memories) {
-    auto mem = scope->FindVar(attr.pre_var)->GetMutable<Tensor>();
-    auto linked_mem = linked_scope->FindVar(attr.var)->GetMutable<Tensor>();
-    if (infer_shape_mode) {
-      mem->Resize(linked_mem->dims());
-    } else {
-      mem->ShareDataWith<float>(*linked_mem);
-    }
-  }
-}
-
-void InitArgument(const ArgumentName& name, Argument* arg,
-                  const OperatorBase& op) {
-  arg->step_net = op.Input(name.step_net);
-  arg->step_scopes = op.Output(name.step_scopes);
-
-  auto inlinks = op.Inputs(name.inlinks);
-  auto inlink_alias = op.GetAttr<std::vector<std::string>>(name.inlink_alias);
-  PADDLE_ENFORCE(inlinks.size() == inlink_alias.size(),
-                 "the size of inlinks and inlink_alias don't match:%d,%d",
-                 inlinks.size(), inlink_alias.size());
-  for (size_t i = 0; i < inlinks.size(); ++i) {
-    rnn::Link link;
-    link.external = inlinks[i];
-    link.internal = inlink_alias[i];
-    (arg->inlinks).push_back(link);
-  }
-
-  auto outlinks = op.Outputs(name.outlinks);
-  auto outlink_alias = op.GetAttr<std::vector<std::string>>(name.outlink_alias);
-  PADDLE_ENFORCE(outlinks.size() == outlink_alias.size(),
-                 "the size of outlinks and outlink_alias don't match:%d,%d",
-                 outlinks.size(), outlink_alias.size());
-  for (size_t i = 0; i < outlinks.size(); ++i) {
-    rnn::Link link;
-    link.external = outlinks[i];
-    link.internal = outlink_alias[i];
-    (arg->outlinks).push_back(link);
-  }
-
-  auto boot_memories = op.Inputs(name.boot_memories);
-
-  // attributes
-  auto memories = op.GetAttr<std::vector<std::string>>(name.memories);
-  auto pre_memories = op.GetAttr<std::vector<std::string>>(name.pre_memories);
-
-  PADDLE_ENFORCE(memories.size() == boot_memories.size(),
-                 "the size of memories, boot_memories don't match:%d,%d",
-                 memories.size(), boot_memories.size());
-  PADDLE_ENFORCE(pre_memories.size() == boot_memories.size(),
-                 "the size of pre_memories, boot_memories don't match:%d,%d",
-                 pre_memories.size(), boot_memories.size());
-  PADDLE_ENFORCE(memories.size() > 0, "more than 1 memories should be set");
-
-  for (size_t i = 0; i < memories.size(); ++i) {
-    rnn::MemoryAttr mem_attr;
-    mem_attr.var = memories[i];
-    mem_attr.pre_var = pre_memories[i];
-    mem_attr.boot_var = boot_memories[i];
-    (arg->memories).push_back(mem_attr);
-  }
-}
-
-}  // namespace rnn
-
 void RecurrentAlgorithm::InferShape(const Scope& scope) const {
   seq_len_ = scope.FindVar((arg_->inlinks[0]).external)
                  ->GetMutable<Tensor>()

paddle/operators/recurrent_op.h

@@ -15,78 +15,11 @@
 #pragma once
 
 #include "paddle/framework/operator.h"
+#include "paddle/operators/rnn/recurrent_op_utils.h"
 
 namespace paddle {
 namespace operators {
 
-namespace rnn {
-
-/**
- * Memory of a RNN (same as the role of `Momory` in PaddlePaddle).
- *
- * Memory attributes cached by this op, dims will be infered from
- * boot memories in father scope. Other attributes are copied from Op's proto
- * attributes.
- */
-struct MemoryAttr {
-  // name of current state variable
-  std::string var;
-  // name of previous step's state variable
-  std::string pre_var;
-  // name of the variables to init this memory (same role of `boot_layer` in
-  // PaddlePaddle), which is store in father's scope.
-  std::string boot_var;
-};
-
-struct Link {
-  // input or output links name.
-  std::string internal;
-  // alias to avoid duplicate keys in scopes.
-  std::string external;
-};
-
-struct Argument {
-  std::string step_net;
-  std::string step_scopes;
-  std::vector<Link> inlinks;
-  std::vector<Link> outlinks;
-  std::vector<rnn::MemoryAttr> memories;
-};
-
-struct ArgumentName {
-  std::string step_net;
-  std::string step_scopes;
-  std::string inlinks;
-  std::string outlinks;
-  std::string inlink_alias;   // the alias of inlinks in step net.
-  std::string outlink_alias;  // the alias of outlinks in step net.
-  std::string memories;       // the memory name
-  std::string pre_memories;   // the previous memory name
-  std::string boot_memories;  // the boot memory name
-};
-
-/**
- * Prepare inputs for each step net.
- */
-void SegmentInputs(const std::vector<framework::Scope*>& step_scopes,
-                   const std::vector<Link>& inlinks, const size_t seq_len,
-                   bool infer_shape_mode);
-
-/**
- * Process outputs of step nets and merge to variables.
- */
-void ConcatOutputs(const std::vector<framework::Scope*>& step_scopes,
-                   const std::vector<Link>& outlinks, const size_t seq_len,
-                   bool infer_shape_mode);
-
-void LinkMemories(const std::vector<framework::Scope*>& step_scopes,
-                  const std::vector<MemoryAttr>& memories, const size_t step_id,
-                  const int offset, bool infer_shape_mode);
-
-void InitArgument(const ArgumentName& name, Argument* arg);
-
-};  // namespace rnn
-
 // The sequence format in RecurrentOp is Tensor<seq_len, batch_size, dim> now.
 // TODO(Yan Chunwei):
 // 1. No-padding computing for sequences with indifinite length in one batch.

paddle/operators/recurrent_op_test.cc

@@ -391,3 +391,4 @@ TEST(RecurrentOp, LinkMemories) {
 
 USE_OP(add_two);
 USE_OP(mul);
+USE_OP_WITHOUT_KERNEL(recurrent_op);

paddle/operators/rnn/recurrent_op_utils.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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. */
+
+#include "paddle/operators/rnn/recurrent_op_utils.h"
+
+namespace paddle {
+namespace operators {
+namespace rnn {
+
+namespace fmw = paddle::framework;
+
+void SegmentInputs(const std::vector<Scope*>& step_scopes,
+                   const std::vector<Link>& inlinks, const size_t seq_len,
+                   bool infer_shape_mode) {
+  PADDLE_ENFORCE(!inlinks.empty(), "no in links are provided.");
+  for (size_t i = 0; i < inlinks.size(); ++i) {
+    auto input_var = step_scopes[0]->FindVar(inlinks[i].external);
+    PADDLE_ENFORCE(input_var != nullptr, "input link [%s] is not in scope.",
+                   inlinks[i].external);
+
+    Tensor* input = input_var->GetMutable<Tensor>();
+    fmw::DDim dims = input->dims();
+    PADDLE_ENFORCE(static_cast<size_t>(dims[0]) == seq_len,
+                   "all the inlinks must have same length");
+    fmw::DDim step_dims = slice_ddim(dims, 1, dims.size());
+    for (size_t j = 0; j < seq_len; j++) {
+      Tensor* step_input =
+          step_scopes[j]->NewVar(inlinks[i].internal)->GetMutable<Tensor>();
+      if (!infer_shape_mode) {
+        *step_input = input->Slice<float>(j, j + 1);
+      }
+      step_input->Resize(step_dims);
+    }
+  }
+}
+
+void ConcatOutputs(const std::vector<Scope*>& step_scopes,
+                   const std::vector<Link>& outlinks, const size_t seq_len,
+                   bool infer_shape_mode) {
+  for (size_t i = 0; i < outlinks.size(); i++) {
+    auto output_var = step_scopes[0]->FindVar(outlinks[i].external);
+    PADDLE_ENFORCE(output_var != nullptr, "output link [%s] is not in scope.",
+                   outlinks[i].external);
+    Tensor* output = output_var->GetMutable<Tensor>();
+    if (infer_shape_mode) {
+      fmw::DDim step_dims = step_scopes[0]
+                                ->FindVar(outlinks[i].internal)
+                                ->GetMutable<Tensor>()
+                                ->dims();
+      std::vector<int> dims_vec = vectorize(step_dims);
+      dims_vec.insert(dims_vec.begin(), seq_len);
+      output->Resize(fmw::make_ddim(dims_vec));
+    } else {
+      output->mutable_data<float>(platform::CPUPlace());
+      for (size_t j = 0; j < seq_len; j++) {
+        Tensor* step_output =
+            step_scopes[j]->FindVar(outlinks[i].internal)->GetMutable<Tensor>();
+        // TODO(luotao02) data type and platform::DeviceContext() should set
+        // correctly
+        (output->Slice<float>(j, j + 1))
+            .CopyFrom<float>(*step_output, platform::CPUPlace());
+      }
+    }
+  }
+}
+
+void LinkMemories(const std::vector<Scope*>& scopes,
+                  const std::vector<rnn::MemoryAttr>& memories,
+                  const size_t step_id, const int offset,
+                  bool infer_shape_mode) {
+  PADDLE_ENFORCE(step_id < scopes.size(),
+                 "step [%d] is out of range of step scopes' size [%d]", step_id,
+                 scopes.size());
+  PADDLE_ENFORCE(static_cast<int>(step_id) + offset >= 0,
+                 "offset [%d] must be large than -[%d]", offset, step_id);
+  PADDLE_ENFORCE(step_id + offset < scopes.size(),
+                 "offset [%d] is out of range, it must be less than (%d - %d)",
+                 offset, scopes.size(), step_id);
+  auto scope = scopes[step_id];
+  auto linked_scope = scopes[step_id + offset];
+  for (auto& attr : memories) {
+    auto mem = scope->FindVar(attr.pre_var)->GetMutable<Tensor>();
+    auto linked_mem = linked_scope->FindVar(attr.var)->GetMutable<Tensor>();
+    if (infer_shape_mode) {
+      mem->Resize(linked_mem->dims());
+    } else {
+      mem->ShareDataWith<float>(*linked_mem);
+    }
+  }
+}
+
+void InitArgument(const ArgumentName& name, Argument* arg,
+                  const OperatorBase& op) {
+  arg->step_net = op.Input(name.step_net);
+  arg->step_scopes = op.Output(name.step_scopes);
+
+  auto inlinks = op.Inputs(name.inlinks);
+  auto inlink_alias = op.GetAttr<std::vector<std::string>>(name.inlink_alias);
+  PADDLE_ENFORCE(inlinks.size() == inlink_alias.size(),
+                 "the size of inlinks and inlink_alias don't match:%d,%d",
+                 inlinks.size(), inlink_alias.size());
+  for (size_t i = 0; i < inlinks.size(); ++i) {
+    rnn::Link link;
+    link.external = inlinks[i];
+    link.internal = inlink_alias[i];
+    (arg->inlinks).push_back(link);
+  }
+
+  auto outlinks = op.Outputs(name.outlinks);
+  auto outlink_alias = op.GetAttr<std::vector<std::string>>(name.outlink_alias);
+  PADDLE_ENFORCE(outlinks.size() == outlink_alias.size(),
+                 "the size of outlinks and outlink_alias don't match:%d,%d",
+                 outlinks.size(), outlink_alias.size());
+  for (size_t i = 0; i < outlinks.size(); ++i) {
+    rnn::Link link;
+    link.external = outlinks[i];
+    link.internal = outlink_alias[i];
+    (arg->outlinks).push_back(link);
+  }
+
+  auto boot_memories = op.Inputs(name.boot_memories);
+
+  // attributes
+  auto memories = op.GetAttr<std::vector<std::string>>(name.memories);
+  auto pre_memories = op.GetAttr<std::vector<std::string>>(name.pre_memories);
+
+  PADDLE_ENFORCE(memories.size() == boot_memories.size(),
+                 "the size of memories, boot_memories don't match:%d,%d",
+                 memories.size(), boot_memories.size());
+  PADDLE_ENFORCE(pre_memories.size() == boot_memories.size(),
+                 "the size of pre_memories, boot_memories don't match:%d,%d",
+                 pre_memories.size(), boot_memories.size());
+  PADDLE_ENFORCE(memories.size() > 0, "more than 1 memories should be set");
+
+  for (size_t i = 0; i < memories.size(); ++i) {
+    rnn::MemoryAttr mem_attr;
+    mem_attr.var = memories[i];
+    mem_attr.pre_var = pre_memories[i];
+    mem_attr.boot_var = boot_memories[i];
+    (arg->memories).push_back(mem_attr);
+  }
+}
+
+}  // namespace rnn
+}  // namespace operators
+}  // namespace paddle

paddle/operators/rnn/recurrent_op_utils.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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. */
+
+#pragma once
+
+#include <string>
+
+#include "paddle/framework/operator.h"
+#include "paddle/operators/type_alias.h"
+
+namespace paddle {
+namespace operators {
+namespace rnn {
+
+/**
+ * Memory of a RNN (same as the role of `Momory` in PaddlePaddle).
+ *
+ * Memory attributes cached by this op, dims will be infered from
+ * boot memories in father scope. Other attributes are copied from Op's proto
+ * attributes.
+ */
+struct MemoryAttr {
+  // name of current state variable
+  std::string var;
+  // name of previous step's state variable
+  std::string pre_var;
+  // name of the variables to init this memory (same role of `boot_layer` in
+  // PaddlePaddle), which is store in father's scope.
+  std::string boot_var;
+};
+
+struct Link {
+  // input or output links name.
+  std::string internal;
+  // alias to avoid duplicate keys in scopes.
+  std::string external;
+};
+
+struct Argument {
+  std::string step_net;
+  std::string step_scopes;
+  std::vector<Link> inlinks;
+  std::vector<Link> outlinks;
+  std::vector<rnn::MemoryAttr> memories;
+};
+
+struct ArgumentName {
+  std::string step_net;
+  std::string step_scopes;
+  std::string inlinks;
+  std::string outlinks;
+  std::string inlink_alias;   // the alias of inlinks in step net.
+  std::string outlink_alias;  // the alias of outlinks in step net.
+  std::string memories;       // the memory name
+  std::string pre_memories;   // the previous memory name
+  std::string boot_memories;  // the boot memory name
+};
+
+/**
+ * Prepare inputs for each step net.
+ */
+void SegmentInputs(const std::vector<Scope*>& step_scopes,
+                   const std::vector<Link>& inlinks, const size_t seq_len,
+                   bool infer_shape_mode);
+
+/**
+ * Process outputs of step nets and merge to variables.
+ */
+void ConcatOutputs(const std::vector<Scope*>& step_scopes,
+                   const std::vector<Link>& outlinks, const size_t seq_len,
+                   bool infer_shape_mode);
+
+void LinkMemories(const std::vector<Scope*>& step_scopes,
+                  const std::vector<MemoryAttr>& memories, const size_t step_id,
+                  const int offset, bool infer_shape_mode);
+
+void InitArgument(const ArgumentName& name, Argument* arg,
+                  const OperatorBase& op);
+
+}  // namespace rnn
+}  // namespace operators
+}  // namespace paddle