[GitHub] ZiyueHuang commented on a change in pull request #8632: a user friendly way to use g2c in module and an example of g2c

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

ZiyueHuang commented on a change in pull request #8632: a user friendly way to use g2c in module and an example of g2c
URL: https://github.com/apache/incubator-mxnet/pull/8632#discussion_r151170577
 
 

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 File path: example/sparse/matrix_fact_parallel_model.py
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+# 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.
+
+import mxnet as mx
+
+def matrix_fact_model_parallel_net(factor_size, num_hidden, max_user, max_item):
+    # set ctx_group attribute to 'dev1' for the symbols created in this scope,
+    # the symbols will be binded to the context that 'dev1' map to in group2ctxs
+    with mx.AttrScope(ctx_group='dev1'):
+        # input
+        user = mx.symbol.Variable('user')
+        item = mx.symbol.Variable('item')
+        # user feature lookup
+        user_weight = mx.symbol.Variable('user_weight', stype='row_sparse')
+        user = mx.symbol.contrib.SparseEmbedding(data=user, weight=user_weight,
+                                                 input_dim=max_user, output_dim=factor_size)
+        # item feature lookup
+        item_weight = mx.symbol.Variable('item_weight', stype='row_sparse')
+        item = mx.symbol.contrib.SparseEmbedding(data=item, weight=item_weight,
+                                                 input_dim=max_item, output_dim=factor_size)
+        # non-linear transformation of user features
 
 Review comment:
   src/executor/graph_executor.cc:396: Check failed: device[nid] == devid (0 vs. 1) device of same output not equal to each other

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