Rnn forward logic test (#3291)

* finish forward debug

paddle/framework/operator.h

@@ -174,7 +174,11 @@ class OperatorContext {
   template <typename T>
   T* Output(const size_t index) const {
     auto var = OutputVar(index);
-    PADDLE_ENFORCE(var != nullptr, "Output(%d) should not be nullptr", index);
+    PADDLE_ENFORCE(
+        var != nullptr,
+        "Output(%d) not be nullptr, which means variable [%s] does not "
+        "exist in scope",
+        index, op_.outputs_[index]);
     return var->GetMutable<T>();
   }
 

paddle/operators/add_op.cc

@@ -20,8 +20,8 @@ namespace operators {
 class AddOp : public OperatorWithKernel {
  protected:
   void InferShape(const InferShapeContext &ctx) const override {
-    PADDLE_ENFORCE(ctx.InputSize() == 2, "Input size of AddOp must be two");
-    PADDLE_ENFORCE(ctx.OutputSize() == 1, "Output size of AddOp must be one");
+    PADDLE_ENFORCE_EQ(ctx.InputSize(), 2);
+    PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1);
     PADDLE_ENFORCE(ctx.InputVar(0) != nullptr && ctx.InputVar(1) != nullptr,
                    "Inputs of AddOp must all be set");
     PADDLE_ENFORCE(ctx.OutputVar(0) != nullptr,

paddle/operators/mul_op.cc

@@ -23,12 +23,16 @@ class MulOp : public OperatorWithKernel {
     PADDLE_ENFORCE(ctx.InputSize() == 2, "The mul op must take two inputs");
     auto dim0 = ctx.Input<Tensor>(0)->dims();
     auto dim1 = ctx.Input<Tensor>(1)->dims();
-    PADDLE_ENFORCE(dim0.size() == 2 && dim1.size() == 2,
-                   "The input of mul op must be matrix");
-    PADDLE_ENFORCE(
-        dim0[1] == dim1[0],
+    PADDLE_ENFORCE_EQ(dim0.size(), 2,
+                      "input X(%s) should be a tensor with 2 dims, a matrix",
+                      ctx.op_.Input("X"));
+    PADDLE_ENFORCE_EQ(dim1.size(), 2,
+                      "input Y(%s) should be a tensor with 2 dims, a matrix",
+                      ctx.op_.Input("Y"));
+    PADDLE_ENFORCE_EQ(
+        dim0[1], dim1[0],
         "First matrix's width must be equal with second matrix's height.");
-    PADDLE_ENFORCE(ctx.OutputSize() == 1, "The mul op must take one output");
+    PADDLE_ENFORCE_EQ(ctx.OutputSize(), 1, "The mul op takes only one output");
     ctx.Output<Tensor>(0)->Resize({dim0[0], dim1[1]});
   }
 };

paddle/operators/recurrent_op.cc

@@ -36,6 +36,7 @@ void RecurrentAlgorithm::InferShape(const Scope& scope) const {
   InitMemories(step_scopes[0], true /*infer_shape_mode*/);
   Variable* net = scope.FindVar(arg_->step_net);
   PADDLE_ENFORCE(net != nullptr, "failed to get step net");
+
   for (size_t i = 0; i < seq_len_; i++) {
     if (i > 0) {
       rnn::LinkMemories(step_scopes, arg_->memories, i, -1,
@@ -56,6 +57,7 @@ void RecurrentAlgorithm::Run(const Scope& scope,
   Variable* net = scope.FindVar(arg_->step_net);
 
   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*/);
@@ -67,22 +69,31 @@ void RecurrentAlgorithm::Run(const Scope& scope,
 }
 
 void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
-  // TODO(xxx) Only two scopes are needed for inference, this case will be
+  // TODO(superjom) Only two scopes are needed for inference, this case will be
   // supported later.
-  auto step_scopes =
-      scope.FindVar(arg_->step_scopes)->GetMutable<std::vector<Scope*>>();
+  auto step_scopes_var = scope.FindVar(arg_->step_scopes);
+  PADDLE_ENFORCE(step_scopes_var != nullptr, "");
+  auto step_scopes = step_scopes_var->GetMutable<std::vector<Scope*>>();
+
+  // Now all variables in scope must be created outside of op.
+  auto net_var = scope.FindVar(arg_->step_net);
+  PADDLE_ENFORCE(net_var != nullptr, "no stepnet called %s in scope",
+                 arg_->step_net);
+  auto net_op = net_var->GetMutable<NetOp>();
+  PADDLE_ENFORCE(!net_op->outputs_.empty(), "net_op has no outputs");
 
   if (seq_len_ > step_scopes->size()) {
     for (size_t i = step_scopes->size(); i < seq_len_; ++i) {
       auto& step_scope = scope.NewScope();
 
-      // Now all variables in scope must be created outside of op.
-      auto net_op = scope.FindVar(arg_->step_net)->GetMutable<NetOp>();
+      // create step net's temp inputs
       for (auto& input : net_op->inputs_) {
         // the weight are located in parent scope
-        if (!step_scope.FindVar(input)) step_scope.NewVar(input);
+        if (!step_scope.FindVar(input))
+          step_scope.NewVar(input)->GetMutable<Tensor>();
       }
-      for (auto& output : net_op->outputs_) {
+      // create stepnet's outputs
+      for (const auto& output : net_op->outputs_) {
         step_scope.NewVar(output);
       }
       step_scopes->emplace_back(&step_scope);
@@ -100,6 +111,7 @@ void RecurrentAlgorithm::InitMemories(Scope* step_scope,
     Tensor* boot_mem = step_scope->FindVar(attr.boot_var)->GetMutable<Tensor>();
     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);
     }

paddle/operators/rnn/recurrent_op_utils.cc

@@ -53,11 +53,13 @@ void ConcatOutputs(const std::vector<Scope*>& step_scopes,
     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();
+      auto step_scope_var = step_scopes[0]->FindVar(outlinks[i].internal);
+      PADDLE_ENFORCE(step_scope_var != nullptr, "%s not in scope",
+                     outlinks[i].internal);
+      fmw::DDim step_dims =
+          step_scope_var->template 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));
@@ -79,14 +81,15 @@ 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,
+  PADDLE_ENFORCE_LT(step_id, scopes.size(),
+                    "step [%d] is out of range of step scopes' size [%d]",
+                    step_id, scopes.size());
+  PADDLE_ENFORCE_GE(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);
+  PADDLE_ENFORCE_LT(
+      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) {

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

+import logging
 import paddle.v2.framework.core as core
 import unittest
 import numpy as np
@@ -7,10 +8,9 @@ ops = creation.op_creations
 
 
 def create_tensor(scope, name, shape):
-    tensor = scope.create_var(name).get_tensor()
+    tensor = scope.new_var(name).get_tensor()
     tensor.set_dims(shape)
-    tensor.alloc_float()
-    tensor.set(np.random.random(shape))
+    tensor.set(np.random.random(shape), core.CPUPlace())
     return tensor
 
 
@@ -31,40 +31,36 @@ class TestRNN(unittest.TestCase):
         - h
     '''
 
-    def init(self):
     input_dim = 30
     batch_size = 50
     weight_dim = 15
+    sent_len = 11
 
-        self.scope = core.Scope(None)
-
-        # create vars
-        create_tensor(self.scope, "x", [batch_size, input_dim])
-        create_tensor(self.scope, "W", [input_dim, weight_dim])
-        create_tensor(self.scope, "U", [weight_dim, weight_dim])
-        create_tensor(self.scope, "h_boot", [batch_size, weight_dim])
-
-        x_alias = "x@alias"
-        y_alias = "y@alias"
-        memory = "h@alias"
-        prememory = "h@pre"
-        output = "rnn_out"
-        output_alias = "rnn_out@alias"
-
-        # create step net
-        stepnet_var = self.scope.create_var("stepnet")
-        stepnet = stepnet_var.get_net()
-        # stepnet = core.Net.create()
-        x_fc_op = ops.fc(X=x_alias, W="W", Y="Wx")
-        h_fc_op = ops.fc(X=prememory, W="U", Y="Uh")
-        sum_op = ops.add_two(X="Wx", Y="Uh", Out="sum")
-        sig_op = ops.sigmoid(X="sum", Y=memory)
-        stepnet.add_op(x_fc_op)
-        stepnet.add_op(h_fc_op)
-        stepnet.add_op(sum_op)
-        stepnet.add_op(sig_op)
-        stepnet.complete_add_op(True)
+    def init(self):
 
+        self.scope = core.Scope()
+
+        self.create_global_variables()
+        self.create_step_net()
+        rnn_op = self.create_rnn_op()
+        ctx = core.DeviceContext.create(core.CPUPlace())
+        print 'infer_shape'
+        rnn_op.infer_shape(self.scope)
+
+        rnn_op.run(self.scope, ctx)
+
+    def create_global_variables(self):
+        # create inlink
+        create_tensor(self.scope, "x",
+                      [self.sent_len, self.batch_size, self.input_dim])
+        create_tensor(self.scope, "W", [self.input_dim, self.input_dim])
+        create_tensor(self.scope, "U", [self.input_dim, self.input_dim])
+        create_tensor(self.scope, "h_boot", [self.batch_size, self.input_dim])
+        self.scope.new_var("step_scopes")
+        self.scope.new_var("h@alias")
+        self.scope.new_var("h")
+
+    def create_rnn_op(self):
         # create RNNOp
         rnnop = ops.recurrent_op(
             # inputs
@@ -72,17 +68,27 @@ class TestRNN(unittest.TestCase):
             boot_memories=["h_boot"],
             step_net="stepnet",
             # outputs
-            outlinks=[output],
+            outlinks=["h"],
             step_scopes="step_scopes",
             # attributes
             inlink_alias=["x@alias"],
-            outlink_alias=[output_alias],
-            pre_memories=[prememory],
-            memories=[memory])
+            outlink_alias=["h@alias"],
+            pre_memories=["h@pre"],
+            memories=["h@alias"])
+        return rnnop
 
-        ctx = core.DeviceContext.cpu_context()
-        rnnop.infer_shape(self.scope)
-        rnnop.run(self.scope, ctx)
+    def create_step_net(self):
+        var = self.scope.new_var("stepnet")
+        stepnet = var.get_net()
+
+        x_fc_op = ops.fc(X="x@alias", W="W", Y="Wx")
+        h_fc_op = ops.fc(X="h@pre", W="U", Y="Uh")
+        sum_op = ops.add_two(X="Wx", Y="Uh", Out="sum")
+        sig_op = ops.sigmoid(X="sum", Y="h@alias")
+
+        for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
+            stepnet.add_op(op)
+        stepnet.complete_add_op(True)
 
     def test_recurrent(self):
         self.init()