[MXNET-798] Fix the dtype cast from non float32 in Gradient computation

src/executor/infer_graph_attr_pass.cc

@@ -254,7 +254,8 @@ nnvm::Graph InferAttr(nnvm::Graph &&ret,
         dispatch_mode = &dispatch_modes[nid];
         if (dispatch_modes[nid] == DispatchMode::kUndefined) forward_known = false;
       }
-      auto finfer = finfer_shape.get(inode.source->op(), fdefault);
+      auto finfer = (inode.source->op() == Op::Get("_zeros")) ? fdefault :
+        finfer_shape.get(inode.source->op(), fdefault);
       if (!forward_known) {
         if (finfer != nullptr) {
           // Call inference function of the operator.

tests/python/unittest/test_infer_type.py

@@ -0,0 +1,55 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+
+# pylint: skip-file
+import mxnet as mx
+import numpy as np
+from common import models
+from mxnet import autograd
+from nose.tools import *
+
+def test_infer_multiout_op():
+    data = mx.nd.arange(16, dtype=np.float64).reshape((4, 4))
+    data.attach_grad()
+
+    with autograd.record():
+        y = mx.nd.split(data, axis=0, num_outputs=2)
+    y[0].backward()
+    assert data.grad.dtype == np.float64
+
+def test_infer_multiout_op2():
+    def test_func(a):
+        q, l = mx.nd.linalg.gelqf(a)
+        return mx.nd.sum(l)
+
+    data32 = mx.nd.random.normal(shape=(2, 3), ctx=mx.cpu(), dtype=np.float32)
+    data32.attach_grad()
+    with autograd.record():
+        test32 = test_func(data32)
+        test32.backward()
+
+    data64 = mx.nd.Cast(data32, dtype=np.float64)
+    data64.attach_grad()
+    with autograd.record():
+        test64 = test_func(data64)
+        test64.backward()
+    assert_almost_equal(data64.grad.asnumpy().all(), data32.grad.asnumpy().all())
+
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()