recurrent_op pass the unit test

paddle/operators/recurrent_op.cc

@@ -28,7 +28,8 @@ using Variable = framework::Variable;
 using Tensor = framework::Tensor;
 using LoDTensor = framework::LoDTensor;
 
-void RecurrentAlgorithm::InferShape(const Scope& scope) const {
+void RecurrentAlgorithm::Run(const Scope& scope,
+                             const platform::DeviceContext& dev_ctx) const {
   auto* input0 = scope.FindVar(arg_->inlinks[0]);
   PADDLE_ENFORCE_NOT_NULL(input0);
   seq_len_ = input0->GetMutable<LoDTensor>()->dims()[0];
@@ -36,38 +37,16 @@ void RecurrentAlgorithm::InferShape(const Scope& scope) const {
 
   CreateScopes(scope);
   auto& step_scopes = GetStepScopes(scope);
-  rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
-                     true /*infer_shape_mode*/);
-  InitMemories(step_scopes[0], true /*infer_shape_mode*/);
+  rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_);
+  InitMemories(step_scopes[0]);
 
   for (size_t i = 0; i < seq_len_; i++) {
     if (i > 0) {
-      rnn::LinkMemories(step_scopes, arg_->memories, i, -1,
-                        true /*infer_shape_mode*/);
-    }
-    (*stepnet_)->InferShape(*step_scopes[i]);
-  }
-  rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
-                     true /*infer_shape_mode*/);
-}
-
-void RecurrentAlgorithm::Run(const Scope& scope,
-                             const platform::DeviceContext& dev_ctx) const {
-  auto step_scopes = GetStepScopes(scope);
-  rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
-                     false /*infer_shape_mode*/);
-  InitMemories(step_scopes[0], false /*infer_shape_mode*/);
-
-  for (size_t step_id = 0; step_id < seq_len_; step_id++) {
-    // create output alias variables
-    if (step_id > 0) {
-      rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1,
-                        false /*infer_shape_mode*/);
+      rnn::LinkMemories(step_scopes, arg_->memories, i, -1);
     }
-    (*stepnet_)->Run(*step_scopes[step_id], dev_ctx);
+    (*stepnet_)->Run(*step_scopes[i], dev_ctx);
   }
-  rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
-                     false /*infer_shape_mode*/);
+  rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_);
 }
 
 void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
@@ -105,8 +84,7 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
   }
 }
 
-void RecurrentAlgorithm::InitMemories(Scope* step_scope,
-                                      bool infer_shape_mode) const {
+void RecurrentAlgorithm::InitMemories(Scope* step_scope) const {
   for (auto& attr : arg_->memories) {
     auto* pre_mem = step_scope->NewVar(attr.pre_var)->GetMutable<LoDTensor>();
     PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr,
@@ -114,13 +92,10 @@ void RecurrentAlgorithm::InitMemories(Scope* step_scope,
                    attr.boot_var);
     auto* boot_mem =
         step_scope->FindVar(attr.boot_var)->GetMutable<LoDTensor>();
-    if (infer_shape_mode) {
     pre_mem->Resize(boot_mem->dims());
     PADDLE_ENFORCE_EQ(pre_mem->dims().size(), 2);
-    } else {
     pre_mem->ShareDataWith<float>(*boot_mem);
   }
-  }
 }
 
 const rnn::ArgumentName RecurrentOp::kArgName{
@@ -169,23 +144,22 @@ class RecurrentAlgorithmProtoAndCheckerMaker
 
 void RecurrentGradientAlgorithm::Run(
     const Scope& scope, const platform::DeviceContext& dev_ctx) const {
+  seq_len_ =
+      scope.FindVar(arg_->inlinks[0])->GetMutable<LoDTensor>()->dims()[0];
   auto step_scopes = GetStepScopes(scope);
-  rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
-                     false /*infer_shape_mode*/);
+  rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_);
   for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
     if (static_cast<size_t>(step_id) != seq_len_ - 1) {
-      rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1,
-                        false /*infer_shape_mode*/);
+      rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1);
     }
     (*stepnet_)->Run(*step_scopes[step_id], dev_ctx);
   }
-  LinkBootMemoryGradients(step_scopes[0], false);
-  rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
-                     false /*infer_shape_mode*/);
+  rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_);
+  LinkBootMemoryGradients(step_scopes[0]);
 }
 
 void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
-    Scope* step_scope, bool infer_shape_mode) const {
+    Scope* step_scope) const {
   for (auto& attr : arg_->memories) {
     PADDLE_ENFORCE(step_scope->FindVar(attr.var) != nullptr,
                    "memory variable [%s] does not exists", attr.var);
@@ -194,30 +168,9 @@ void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
     auto* mem_grad = step_scope->NewVar(attr.var)->GetMutable<LoDTensor>();
     auto* boot_mem_grad =
         step_scope->NewVar(attr.boot_var)->GetMutable<LoDTensor>();
-    if (infer_shape_mode) {
     boot_mem_grad->Resize(mem_grad->dims());
-    } else {
     boot_mem_grad->ShareDataWith<float>(*mem_grad);
   }
-  }
-}
-
-void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const {
-  seq_len_ =
-      scope.FindVar(arg_->inlinks[0])->GetMutable<LoDTensor>()->dims()[0];
-  auto step_scopes = GetStepScopes(scope);
-  rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
-                     true /*infer_shape_mode*/);
-  for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) {
-    if (static_cast<size_t>(step_id) != seq_len_ - 1) {
-      rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1,
-                        true /*infer_shape_mode*/);
-    }
-    (*stepnet_)->InferShape(*step_scopes[step_id]);
-  }
-  rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_,
-                     true /*infer_shape_mode*/);
-  LinkBootMemoryGradients(step_scopes[0], true /*infer_shape_mode*/);
 }
 
 RecurrentGradientOp::RecurrentGradientOp(

paddle/operators/recurrent_op.h

@@ -41,11 +41,6 @@ class RecurrentAlgorithm {
     stepnet_ = stepnet;
   }
 
-  /**
-   * InferShape must be called before Run.
-   */
-  void InferShape(const framework::Scope& scope) const;
-
  protected:
   /*
    * The step scopes will be stored in the father scope as a variable.
@@ -61,7 +56,7 @@ class RecurrentAlgorithm {
                 ->GetMutable<std::vector<framework::Scope*>>();
   }
 
-  void InitMemories(framework::Scope* step_scopes, bool infer_shape_mode) const;
+  void InitMemories(framework::Scope* step_scopes) const;
 
  private:
   std::unique_ptr<framework::OperatorBase>* stepnet_;
@@ -91,13 +86,7 @@ class RecurrentGradientAlgorithm {
   void Run(const framework::Scope& scope,
            const platform::DeviceContext& dev_ctx) const;
 
-  void LinkBootMemoryGradients(framework::Scope* step_scopes,
-                               bool infer_shape_mode) const;
-
-  /**
- * InferShape must be called before Run.
- */
-  void InferShape(const framework::Scope& scope) const;
+  void LinkBootMemoryGradients(framework::Scope* step_scopes) const;
 
  protected:
   inline const std::vector<framework::Scope*>& GetStepScopes(
@@ -136,10 +125,6 @@ class RecurrentOp : public framework::OperatorBase {
 
   const OperatorBase& stepnet() const { return *stepnet_; }
 
-  void InferShape(const framework::Scope& scope) const {
-    alg_.InferShape(scope);
-  }
-
   static const rnn::ArgumentName kArgName;
 
  private:
@@ -162,10 +147,6 @@ class RecurrentGradientOp : public framework::OperatorBase {
     PADDLE_THROW("Not Implemented");
   }
 
-  void InferShape(const framework::Scope& scope) const {
-    alg_.InferShape(scope);
-  }
-
   void Run(const framework::Scope& scope,
            const platform::DeviceContext& dev_ctx) const override {
     alg_.Run(scope, dev_ctx);

paddle/operators/rnn/recurrent_op_utils.cc

@@ -25,7 +25,7 @@ using LoDTensor = framework::LoDTensor;
 
 void SegmentInputs(const std::vector<Scope*>& step_scopes,
                    const std::vector<std::string>& inlinks,
-                   const size_t seq_len, bool infer_shape_mode) {
+                   const size_t seq_len) {
   PADDLE_ENFORCE(!inlinks.empty(), "no in links are provided.");
   for (size_t i = 0; i < inlinks.size(); ++i) {
     // global inputs
@@ -41,11 +41,9 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
     for (size_t j = 0; j < seq_len; j++) {
       Tensor* step_input =
           step_scopes[j]->NewVar(inlinks[i])->GetMutable<Tensor>();
-      if (!infer_shape_mode) {
       // The input of operators of each step is Tensor here.
       // Maybe need to modify Slice function.
       *step_input = input->Slice<float>(j, j + 1);
-      }
       step_input->Resize(step_dims);
     }
   }
@@ -53,14 +51,13 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
 
 void ConcatOutputs(const std::vector<Scope*>& step_scopes,
                    const std::vector<std::string>& outlinks,
-                   const size_t seq_len, bool infer_shape_mode) {
+                   const size_t seq_len) {
   for (size_t i = 0; i < outlinks.size(); i++) {
     auto output_var = step_scopes[0]->parent().FindVar(outlinks[i]);
     PADDLE_ENFORCE_NOT_NULL(output_var, "output link [%s] is not in scope.",
                             outlinks[i]);
     LoDTensor* output = output_var->GetMutable<LoDTensor>();
 
-    if (infer_shape_mode) {
     auto step_scope_var = step_scopes[0]->FindVar(outlinks[i]);
     PADDLE_ENFORCE_NOT_NULL(step_scope_var, "%s not in scope", outlinks[i]);
     f::DDim step_dims =
@@ -68,7 +65,6 @@ void ConcatOutputs(const std::vector<Scope*>& step_scopes,
     std::vector<int64_t> dims_vec = vectorize(step_dims);
     dims_vec.insert(dims_vec.begin(), seq_len);
     output->Resize(f::make_ddim(dims_vec));
-    } else {
     output->mutable_data<float>(platform::CPUPlace());
     for (size_t j = 0; j < seq_len; j++) {
       LoDTensor* step_output =
@@ -79,13 +75,11 @@ void ConcatOutputs(const std::vector<Scope*>& step_scopes,
           .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) {
+                  const size_t step_id, const int offset) {
   PADDLE_ENFORCE_LT(step_id, scopes.size(),
                     "step [%d] is out of range of step scopes' size [%d]",
                     step_id, scopes.size());
@@ -100,12 +94,9 @@ void LinkMemories(const std::vector<Scope*>& scopes,
   for (auto& attr : memories) {
     auto mem = scope->FindVar(attr.pre_var)->GetMutable<LoDTensor>();
     auto linked_mem = linked_scope->FindVar(attr.var)->GetMutable<LoDTensor>();
-    if (infer_shape_mode) {
     mem->Resize(linked_mem->dims());
-    } else {
     mem->ShareDataWith<float>(*linked_mem);
   }
-  }
 }
 
 void InitArgument(const ArgumentName& name, Argument* arg,

paddle/operators/rnn/recurrent_op_utils.h

@@ -64,18 +64,18 @@ struct ArgumentName {
  */
 void SegmentInputs(const std::vector<Scope*>& step_scopes,
                    const std::vector<std::string>& inlinks,
-                   const size_t seq_len, bool infer_shape_mode);
+                   const size_t seq_len);
 
 /**
  * Process outputs of step nets and merge to variables.
  */
 void ConcatOutputs(const std::vector<Scope*>& step_scopes,
                    const std::vector<std::string>& outlinks,
-                   const size_t seq_len, bool infer_shape_mode);
+                   const size_t seq_len);
 
 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);
+                  const int offset);
 
 void InitArgument(const ArgumentName& name, Argument* arg,
                   const framework::OperatorBase& op, bool is_grad = false);

paddle/operators/sum_op.cc

@@ -22,14 +22,15 @@ class SumOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(framework::InferShapeContextBase* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInputs("X"), "Inputs(X) should not be null");
     auto x_dims = ctx->GetInputsDim("X");
-    PADDLE_ENFORCE(!x_dims.empty(), "Input(X) of SumOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of SumOp should not be null.");
 
-    auto in_dim = x_dims[0];
     size_t N = x_dims.size();
     PADDLE_ENFORCE_GT(N, 1, "Input tensors count should > 1.");
+
+    auto in_dim = x_dims[0];
     for (size_t i = 1; i < N; i++) {
       auto dim = x_dims[i];
       PADDLE_ENFORCE(in_dim == dim, "Input tensors must have same shape");

python/paddle/v2/framework/tests/test_recurrent_op.py

@@ -16,14 +16,17 @@ class PySimpleRNN(object):
     '''
 
     def __init__(self, input_dim=30, batch_size=50, weight_dim=15, sent_len=11):
-        self.x = np.random.normal(size=(sent_len, batch_size, input_dim))
-        self.W = np.random.normal(size=(input_dim, input_dim))
-        self.U = np.random.normal(size=(input_dim, input_dim))
-        self.h_boot = np.random.normal(size=(batch_size, input_dim))
+        self.x = np.random.normal(size=(sent_len, batch_size,
+                                        input_dim)).astype("float32")
+        self.W = np.random.normal(size=(input_dim, input_dim)).astype("float32")
+        self.U = np.random.normal(size=(input_dim, input_dim)).astype("float32")
+        self.h_boot = np.random.normal(size=(batch_size,
+                                             input_dim)).astype("float32")
 
         # memories
         self.mems = [
-            np.zeros(shape=(batch_size, input_dim)) for i in range(sent_len)
+            np.zeros(shape=(batch_size, input_dim)).astype("float32")
+            for i in range(sent_len)
         ]
 
     def forward(self):
@@ -36,7 +39,7 @@ class PySimpleRNN(object):
         return [self.x[i] for i in range(self.x.shape[0])]
 
     def concat_outputs(self):
-        return np.array(self.mems)
+        return np.array(self.mems).astype("float32")
 
     def step(self, step_id, x):
         '''
@@ -47,8 +50,8 @@ class PySimpleRNN(object):
             pre_mem = self.mems[step_id - 1]
         else:
             pre_mem = self.h_boot
-        xW = np.matmul(x, self.W)
-        hU = np.matmul(pre_mem, self.U)
+        xW = np.matmul(x, self.W).astype("float32")
+        hU = np.matmul(pre_mem, self.U).astype("float32")
 
         sum = xW + hU
         self.mems[step_id] = py_sigmoid(sum)
@@ -102,7 +105,8 @@ class RecurrentOpTest(unittest.TestCase):
         self.create_step_net()
         ctx = core.DeviceContext.create(core.CPUPlace())
         self.rnnop.run(self.scope, ctx)
-        return np.array(self.scope.find_var("h@mem").get_tensor())
+        return np.array(self.scope.find_var("h@mem").get_tensor()).astype(
+            "float32")
 
     def create_global_variables(self):
         # create inlink
@@ -142,7 +146,7 @@ class RecurrentOpTest(unittest.TestCase):
         stepnet = core.Net.create()
         x_fc_op = Operator("mul", X="x", Y="W", Out="Wx")
         h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh")
-        sum_op = Operator("add", X="Wx", Y="Uh", Out="sum")
+        sum_op = Operator("sum", X=["Wx", "Uh"], Out="sum")
         sig_op = Operator("sigmoid", X="sum", Y="h@mem")
 
         for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
@@ -179,7 +183,7 @@ class RecurrentGradientOpTest(unittest.TestCase):
         stepnet = core.Net.create()
         x_fc_op = Operator("mul", X="x@alias", Y="W", Out="Wx")
         h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh")
-        sum_op = Operator("add", X="Wx", Y="Uh", Out="sum")
+        sum_op = Operator("sum", X=["Wx", "Uh"], Out="sum")
         sig_op = Operator("sigmoid", X="sum", Y="h@alias")
 
         for op in [x_fc_op, h_fc_op, sum_op, sig_op]: