class Embedding(mx.gluon.HybridBlock):
    def __init__(self, num_tokens, embedding_size):
        super().__init__()
        self.num_tokens = num_tokens

        with self.name_scope():
            self.embedding = mx.gluon.nn.Embedding(
                num_tokens, embedding_size, sparse_grad=True)

    def hybrid_forward(self, F, words):
        emb = self.embedding(words)
        return emb + F.ones_like(emb)


ctx = mx.cpu()
embedding = Embedding(1000, 300)
embedding.initialize(ctx=ctx)
embedding.hybridize()

loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss()
with mx.autograd.record():
    emb_in = embedding(mx.nd.arange(10, ctx=ctx))
    emb_in_2 = embedding(mx.nd.arange(10, ctx=ctx))
    loss = emb_in.sum() + emb_in_2.sum()
loss.backward()

print(embedding.embedding.weight.grad().data)

 Storage type fallback detected:
operator = add_n
input storage types = [default, default, ]
output storage types = [row_sparse, ]
params = {"num_args" : 2, }
context.dev_mask = cpu
The operator with default storage type will be dispatched for execution. You're seeing this warning message because the operator above is unable to process the given ndarrays with specified storage types, context and parameter. Temporary dense ndarrays are generated in order to execute the operator. This does not affect the correctness of the programme. You can set environment variable MXNET_STORAGE_FALLBACK_LOG_VERBOSE to 0 to suppress this warning.
Traceback (most recent call last):
  File "sparse_bug.py", line 30, in <module>
    print(embedding.embedding.weight.grad().data)
  File "/Users/lllausen/anaconda3/lib/python3.6/site-packages/mxnet/ndarray/sparse.py", line 728, in data
    return self._data()
  File "/Users/lllausen/anaconda3/lib/python3.6/site-packages/mxnet/ndarray/sparse.py", line 266, in _data
    self.wait_to_read()
  File "/Users/lllausen/anaconda3/lib/python3.6/site-packages/mxnet/ndarray/ndarray.py", line 1720, in wait_to_read
    check_call(_LIB.MXNDArrayWaitToRead(self.handle))
  File "/Users/lllausen/anaconda3/lib/python3.6/site-packages/mxnet/base.py", line 210, in check_call
    raise MXNetError(py_str(_LIB.MXGetLastError()))
mxnet.base.MXNetError: [19:37:39] src/operator/contrib/../operator_common.h:493: Not implemented: operator = _backward_Embedding
input storage types = [default, default, ]
output storage types = [default, default, ]
params = {"input_dim" : 1000, "output_dim" : 300, "dtype" : float32, "sparse_grad" : True, }
context.dev_mask = cpu