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

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

eric-haibin-lin 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_r155877035
 
 

 ##########
 File path: example/numpy-ops/custom_sparse_sqr.py
 ##########
 @@ -0,0 +1,93 @@
+# 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
+
+class Sqr(mx.operator.CustomOp):
+    '''Example of how to use custom op with sparse ndarrays
+    '''
+    def forward(self, is_train, req, in_data, out_data, aux):
+        self.assign(out_data[0], req[0], mx.nd.sparse.square(in_data[0]))
+
+    def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
+        self.assign(in_grad[0], req[0], 2 * in_data[0] * out_grad[0])
+
+@mx.operator.register("sqr")
+class SqrProp(mx.operator.CustomOpProp):
+    def __init__(self):
+        super(SqrProp, self).__init__(need_top_grad=True)
+
+    def list_arguments(self):
+        return ['data']
+
+    def list_outputs(self):
+        return ['output']
+
+    def infer_shape(self, in_shape):
+        return in_shape, [in_shape[0]], []
+
+    def infer_type(self, in_type):
+        return in_type, [in_type[0]], []
+
+    def infer_storage_type(self, in_stype):
+        '''Infer storage type logic for the forward pass
+        Takes a list of storage types for inputs
+        Returns three lists lists, one for input storage types inferred,
+        second for output storage types inferred and third for aux storage
+        types inferred
+        The in_stype is the list containing storage type for inputs
+        If the input is a dense ndarray then we infer the input
+        and output to be dense. If input is csr then input and output
+        are inferred as csr.
+        '''
+        if in_stype[0] == 'default':
+            return ['default'], ['default'], []
+        return ['csr'], ['csr'], []
+
+    def infer_storage_type_backward(self, ograd_stype, in_stype, out_stype, igrad_stype, aux_stype):
+        '''Infer storage type logic for the backward pass
+        Takes storage type of output gradients(ograd_stype), inputs(in_stype),
+        outputs(out_stype) and aux(aux_stype).
+        Returns inferred storage types in the following order:
+        ograd_stype, in_stype, out_stype, igrad_stype (Storage type for input gradients)
+        and aux_stype.
+        '''
+        if in_stype[0] == 'default':
+            return ['default'], ['default'], ['default'], ['default'], []
+        return ['csr'], ['csr'], ['csr'], ['default'], []
+
+    def create_operator(self, ctx, shapes, dtypes):
+        return Sqr()
+
+x = mx.nd.array(np.random.uniform(1, 10, size=(4,10)))
+x = x.tostype('csr')
+x.attach_grad(stype='default')
+z = mx.nd.zeros_like(x)
+with mx.contrib.autograd.train_section():
+    y = mx.nd.Custom(x, op_type='sqr')
+    y.backward(out_grad=z)
+mx.nd.waitall()
 
 Review comment:
   Why adding `mx.nd.waitall()` here?

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