Add backward to fully connected. (_backward_FullyConnected)

src/operator/nn/fully_connected.cc

@@ -176,6 +176,20 @@ struct FullyConnectedGrad {
   }
 };
 
+std::vector<nnvm::NodeEntry> FullyConnectedBackwardGrad(
+    const nnvm::NodePtr& n,
+    const std::vector<nnvm::NodeEntry>& ograds) {
+  std::vector<nnvm::NodeEntry> ret;
+  size_t i = 0;
+  for (const auto& x : n->inputs) {
+    std::ostringstream os;
+    os << n->attrs.name << "_backward_" << i;
+    ret.emplace_back(nnvm::NodeEntry{MakeNode("zeros_like", os.str(), {x}, nullptr, &n), 0, 0});
+    ++i;
+  }
+  return ret;
+}
+
 inline static bool FCStorageType(const nnvm::NodeAttrs& attrs,
                                  const int dev_mask,
                                  DispatchMode* dispatch_mode,
@@ -325,6 +339,7 @@ NNVM_REGISTER_OP(_backward_FullyConnected)
 .set_attr<nnvm::FInplaceOption>("FInplaceOption", [](const NodeAttrs& attrs){
   return std::vector<std::pair<int, int> >{{1, 0}};
 })
+.set_attr<nnvm::FGradient>("FGradient", FullyConnectedBackwardGrad)
 .set_attr<FInferStorageType>("FInferStorageType", BackwardFCStorageType)
 .set_attr_parser(ParamParser<FullyConnectedParam>)
 #if MXNET_USE_MKLDNN == 1

tests/python/unittest/test_gluon.py

@@ -21,7 +21,7 @@
 import mxnet as mx
 from mxnet import gluon
 from mxnet.gluon import nn
-from mxnet.test_utils import assert_almost_equal
+from mxnet.test_utils import assert_almost_equal, same
 from mxnet.ndarray.ndarray import _STORAGE_TYPE_STR_TO_ID
 from common import (setup_module, with_seed, assertRaises, teardown,
                     assert_raises_cudnn_not_satisfied)
@@ -915,6 +915,24 @@ def test_sequential_warning():
         assert len(w) == 1
 
 
+@with_seed()
+def test_dense_backward():
+    import mxnet.autograd as ag
+    import mxnet.ndarray as nd
+    x = nd.array([[1,2,3,400]])
+    net = gluon.nn.Sequential()
+    with net.name_scope():
+        net.add(gluon.nn.Dense(1, in_units=x.shape[1]))
+    net.initialize(mx.initializer.Constant(.5))
+    params = [p.data() for p in net.collect_params().values()]
+    x.attach_grad()
+    with ag.record():
+        y = net.forward(x)
+        y_grad = ag.grad(y, x, create_graph=True, retain_graph=True)[0]
+    y_grad.backward()
+    same(x.grad, nd.zeros(4))
+
+
 @with_seed()
 def test_global_norm_clip():
     stypes = ['default', 'row_sparse']