update the MXNet backend. (#89)

* update mxnet.

* add get_tvmtype.

* remove undefined test.

python/dgl/backend/mxnet.py

@@ -46,8 +46,14 @@ def from_numpy(np_data):
 def pack(tensors):
     return F.concat(*tensors, dim=0)
 
-def unpack(x, indices_or_sections=1):
-    return th.split(x, indices_or_sections)
+def unpack(x, split_sizes_or_sections=1):
+    if isinstance(split_sizes_or_sections, list):
+        np_arr = x.asnumpy()
+        indices = np.cumsum(split_sizes_or_sections)
+        res = np.split(np_arr, indices[:-1])
+        return [tensor(arr, dtype=x.dtype) for arr in res]
+    else:
+        return F.split(x, split_sizes_or_sections)
 
 # TODO this doesn't exist for symbol.
 def shape(x):
@@ -66,7 +72,10 @@ def unique(x):
     return mx.nd.array(tmp, ctx=x.context, dtype=x.dtype)
 
 def gather_row(data, row_index):
-    return data[row_index,]
+    if isinstance(row_index, F.NDArray):
+        return F.take(data, row_index)
+    else:
+        return data[row_index,]
 
 scatter_row = mx.nd.contrib.index_copy
 
@@ -114,6 +123,27 @@ def get_context(x):
 def _typestr(arr_dtype):
     return arr_dtype
 
+def get_tvmtype(arr):
+    arr_dtype = arr.dtype
+    if arr_dtype == np.float16:
+        return TVMType('float16')
+    elif arr_dtype == np.float32:
+        return TVMType('float32')
+    elif arr_dtype == np.float64:
+        return TVMType('float64')
+    elif arr_dtype == np.int16:
+        return TVMType('int16')
+    elif arr_dtype == np.int32:
+        return TVMType('int32')
+    elif arr_dtype == np.int64:
+        return TVMType('int64')
+    elif arr_dtype == np.int8:
+        return TVMType('int8')
+    elif arr_dtype == np.uint8:
+        return TVMType('uint8')
+    else:
+        raise RuntimeError('Unsupported data type:', arr_dtype)
+
 def zerocopy_to_dlpack(arr):
     """Return a dlpack compatible array using zero copy."""
     return arr.to_dlpack_for_read()

tests/mxnet/test_basics.py

@@ -9,7 +9,7 @@ reduce_msg_shapes = set()
 
 def check_eq(a, b):
     assert a.shape == b.shape
-    assert mx.sum(a == b) == int(np.prod(list(a.shape)))
+    assert mx.nd.sum(a == b).asnumpy() == int(np.prod(list(a.shape)))
 
 def message_func(src, edge):
     assert len(src['h'].shape) == 2
@@ -53,16 +53,12 @@ def test_batch_setter_getter():
     assert len(g.get_n_repr()) == 0
     g.set_n_repr({'h' : mx.nd.zeros((10, D))})
     # set partial nodes
-    # TODO we need to enable the test later.
-    '''
     u = mx.nd.array([1, 3, 5], dtype='int64')
     g.set_n_repr({'h' : mx.nd.ones((3, D))}, u)
     assert _pfc(g.get_n_repr()['h']) == [0., 1., 0., 1., 0., 1., 0., 0., 0., 0.]
     # get partial nodes
     u = mx.nd.array([1, 2, 3], dtype='int64')
-    print(g.get_n_repr(u)['h'])
     assert _pfc(g.get_n_repr(u)['h']) == [1., 0., 1.]
-    '''
 
     '''
     s, d, eid
@@ -127,9 +123,11 @@ def test_batch_setter_autograd():
     with mx.autograd.record():
         g = generate_graph(grad=True)
         h1 = g.get_n_repr()['h']
+        h1.attach_grad()
         # partial set
         v = mx.nd.array([1, 2, 8], dtype='int64')
         hh = mx.nd.zeros((len(v), D))
+        hh.attach_grad()
         g.set_n_repr({'h' : hh}, v)
         h2 = g.get_n_repr()['h']
     h2.backward(mx.nd.ones((10, D)) * 2)
@@ -252,8 +250,7 @@ def test_pull_0deg():
 
 if __name__ == '__main__':
     test_batch_setter_getter()
-    # TODO we need to enable it after index_copy is implemented.
-    #test_batch_setter_autograd()
+    test_batch_setter_autograd()
     test_batch_send()
     test_batch_recv()
     test_update_routines()

tests/pytorch/test_basics.py

@@ -355,5 +355,4 @@ if __name__ == '__main__':
     test_update_routines()
     test_reduce_0deg()
     test_pull_0deg()
-    test_send_twice()
     test_send_multigraph()