[GitHub] anirudh2290 commented on a change in pull request #8738: Fix custom op - infer_storage_type_backward

[GitBox <gi...@apache.org>]

anirudh2290 commented on a change in pull request #8738: Fix custom op - infer_storage_type_backward
URL: https://github.com/apache/incubator-mxnet/pull/8738#discussion_r155967282
 
 

 ##########
 File path: tests/python/unittest/test_operator.py
 ##########
 @@ -3624,15 +3640,93 @@ def create_operator(self, ctx, shapes, dtypes):
 
     data = mx.symbol.cast(data, dtype='float64')
     op = mx.symbol.cast(op, dtype='float32')
-    x = mx.nd.array(np.random.uniform(-1, 1, size=(4, 10)))
-    aux = mx.nd.zeros_like(x)
     check_numeric_gradient(op, [x], [aux])
 
+    x = x.tostype('csr')
+    aux = mx.nd.zeros_like(x)
     x.attach_grad()
     with mx.contrib.autograd.train_section():
-        y = mx.nd.Custom(x, aux, op_type='sqr')
+        y = mx.nd.Custom(x, aux, name='sqr', op_type='sqr')
         y.backward()
+    mx.nd.waitall()
+    assert (x.grad.stype == 'csr')
+    assert (y.stype == 'csr')
+    assert (aux.stype == 'csr')
+
+
+
+    # test for backward compatibility, i.e. the correctness of default implementation of
+    # infer storage in custom operator
+    class Mult(mx.operator.CustomOp):
+        def forward(self, is_train, req, in_data, out_data, aux):
+            self.assign(out_data[0], req[0], in_data[0]*in_data[1])
+
+        def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+            self.assign(in_grad[0], req[0], in_data[1])
+            self.assign(in_grad[1], req[1], in_data[0])
+
+    @mx.operator.register("mult")
+    class MultProp(mx.operator.CustomOpProp):
+        def __init__(self):
+            super(MultProp, self).__init__(need_top_grad=True)
 
+        def list_arguments(self):
+            return ['lhs', 'rhs']
+
+        def list_outputs(self):
+            return ['output']
+
+        def infer_shape(self, in_shape):
+            return in_shape, [in_shape[0]], []
+
+        def create_operator(self, ctx, shapes, dtypes):
+            return Mult()
+
+    lhs = mx.nd.array(np.random.uniform(-1, 1, size=(4, 10)))
+    rhs = mx.nd.array(np.random.uniform(-1, 1, size=(4, 10)))
+    lhs.attach_grad()
+    rhs.attach_grad()
+    with mx.contrib.autograd.train_section():
+        y = mx.nd.Custom(lhs, rhs, name='mult', op_type='mult')
+        y.backward()
+    mx.nd.waitall()
 
 Review comment:
   Removed

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