[GitHub] ZiyueHuang commented on a change in pull request #8632: [WIP] a user friendly way to use g2c in module and an example of g2c
ZiyueHuang commented on a change in pull request #8632: [WIP] a user friendly
way to use g2c in module and an example of g2c
URL: https://github.com/apache/incubator-mxnet/pull/8632#discussion_r151738034
##
File path: example/sparse/matrix_fact_parallel_model.py
##
@@ -0,0 +1,52 @@
+# 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:
@eric-haibin-lin @reminisce I added some codes in `graph_executor.cc` for
debug,
`python matrix_factorization_model_parallel.py`
```
[00:53:08] src/executor/graph_executor.cc:365: args context
[00:53:08] src/executor/graph_executor.cc:384: nid: 0 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 1 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 3 ctx.dev_id 1
[00:53:08] src/executor/graph_executor.cc:384: nid: 4 ctx.dev_id 1
[00:53:08] src/executor/graph_executor.cc:384: nid: 6 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 7 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 12 ctx.dev_id 1
[00:53:08] src/executor/graph_executor.cc:386: =
[00:53:08] src/executor/graph_executor.cc:387: 1 num_forward_outputs
[00:53:08] src/executor/graph_executor.cc:388: 5 g.outputs.size()
[00:53:08] src/executor/graph_executor.cc:389: 7 arg_grad_ctxes.size()
[00:53:08] src/executor/graph_executor.cc:393: arg grads contexts
[00:53:08] src/executor/graph_executor.cc:397: nid 19 ctx 0
[00:53:08] src/executor/graph_executor.cc:397: nid 18 ctx 0
[00:53:08] src/executor/graph_executor.cc:397: nid 18 ctx 1
[00:53:08] src/executor/graph_executor.cc:397: nid 20 ctx 1
[00:53:08] src/executor/graph_executor.cc:399: =
[00:53:08] src/executor/graph_executor.cc:409: fail nid 18 ctx 1
[00:53:08] src/executor/graph_executor.cc:423: node 0 var user
[00:53:08] src/executor/graph_executor.cc:423: node 1 var user_weight
[00:53:08] src/executor/graph_executor.cc:425: node 2
_contrib_SparseEmbedding
[00:53:08] src/executor/graph_executor.cc:428: input 0 (entry
id)
[00:53:08] src/executor/graph_executor.cc:428: input 1 (entry
id)
[00:53:08] src/executor/graph_executor.cc:432: output 2 (entry
id)
[00:53:08] src/executor/graph_executor.cc:423: node 3 var ufcweight
[00:53:08] src/executor/graph_executor.cc:423: node 4 var ufcbias
[00:53:08] src/executor/graph_executor.cc:425: node 5 FullyConnected
[00:53:08] src/executor/graph_executor.cc:428: input 2 (entry
id)
[00:53:08] src/executor/graph_executor.cc:428: input 3 (entry
id)
[00:53:08] src/executor/graph_executor.cc:428: input 4 (entry
id)
[00:53:08] src/executor/graph_executor.cc:432: output 5 (entry
id)
[00:53:08] src/executor/graph_executor.cc:423: node 6 var item
[00:53:08] src/executor/graph_executor.cc:423: node 7 var item_weight
[00:53:08] src/executor/graph_executor.cc:425: node 8
_contrib_SparseEmbedding
[00:53:08] src/executor/graph_executor.cc:428: input 6 (entry
id)
[00:53:08]
[GitHub] ZiyueHuang commented on a change in pull request #8632: [WIP] a user friendly way to use g2c in module and an example of g2c
ZiyueHuang commented on a change in pull request #8632: [WIP] a user friendly
way to use g2c in module and an example of g2c
URL: https://github.com/apache/incubator-mxnet/pull/8632#discussion_r151738034
##
File path: example/sparse/matrix_fact_parallel_model.py
##
@@ -0,0 +1,52 @@
+# 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:
@eric-haibin-lin I added some codes in `graph_executor.cc` for debug,
`python matrix_factorization_model_parallel.py`
```
[00:53:08] src/executor/graph_executor.cc:365: args context
[00:53:08] src/executor/graph_executor.cc:384: nid: 0 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 1 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 3 ctx.dev_id 1
[00:53:08] src/executor/graph_executor.cc:384: nid: 4 ctx.dev_id 1
[00:53:08] src/executor/graph_executor.cc:384: nid: 6 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 7 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 12 ctx.dev_id 1
[00:53:08] src/executor/graph_executor.cc:386: =
[00:53:08] src/executor/graph_executor.cc:387: 1 num_forward_outputs
[00:53:08] src/executor/graph_executor.cc:388: 5 g.outputs.size()
[00:53:08] src/executor/graph_executor.cc:389: 7 arg_grad_ctxes.size()
[00:53:08] src/executor/graph_executor.cc:393: arg grads contexts
[00:53:08] src/executor/graph_executor.cc:397: nid 19 ctx 0
[00:53:08] src/executor/graph_executor.cc:397: nid 18 ctx 0
[00:53:08] src/executor/graph_executor.cc:397: nid 18 ctx 1
[00:53:08] src/executor/graph_executor.cc:397: nid 20 ctx 1
[00:53:08] src/executor/graph_executor.cc:399: =
[00:53:08] src/executor/graph_executor.cc:409: fail nid 18 ctx 1
[00:53:08] src/executor/graph_executor.cc:423: node 0 var user
[00:53:08] src/executor/graph_executor.cc:423: node 1 var user_weight
[00:53:08] src/executor/graph_executor.cc:425: node 2
_contrib_SparseEmbedding
[00:53:08] src/executor/graph_executor.cc:428: input 0 (entry
id)
[00:53:08] src/executor/graph_executor.cc:428: input 1 (entry
id)
[00:53:08] src/executor/graph_executor.cc:432: output 2 (entry
id)
[00:53:08] src/executor/graph_executor.cc:423: node 3 var ufcweight
[00:53:08] src/executor/graph_executor.cc:423: node 4 var ufcbias
[00:53:08] src/executor/graph_executor.cc:425: node 5 FullyConnected
[00:53:08] src/executor/graph_executor.cc:428: input 2 (entry
id)
[00:53:08] src/executor/graph_executor.cc:428: input 3 (entry
id)
[00:53:08] src/executor/graph_executor.cc:428: input 4 (entry
id)
[00:53:08] src/executor/graph_executor.cc:432: output 5 (entry
id)
[00:53:08] src/executor/graph_executor.cc:423: node 6 var item
[00:53:08] src/executor/graph_executor.cc:423: node 7 var item_weight
[00:53:08] src/executor/graph_executor.cc:425: node 8
_contrib_SparseEmbedding
[00:53:08] src/executor/graph_executor.cc:428: input 6 (entry
id)
[00:53:08]
[GitHub] ZiyueHuang commented on a change in pull request #8632: [WIP] a user friendly way to use g2c in module and an example of g2c
ZiyueHuang commented on a change in pull request #8632: [WIP] a user friendly
way to use g2c in module and an example of g2c
URL: https://github.com/apache/incubator-mxnet/pull/8632#discussion_r151738034
##
File path: example/sparse/matrix_fact_parallel_model.py
##
@@ -0,0 +1,52 @@
+# 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:
@eric-haibin-lin I added some codes in `graph_executor.cc` for debug,
`python matrix_factorization_model_parallel.py`
```
[00:53:08] src/executor/graph_executor.cc:365: args context
[00:53:08] src/executor/graph_executor.cc:384: nid: 0 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 1 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 3 ctx.dev_id 1
[00:53:08] src/executor/graph_executor.cc:384: nid: 4 ctx.dev_id 1
[00:53:08] src/executor/graph_executor.cc:384: nid: 6 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 7 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 12 ctx.dev_id 1
[00:53:08] src/executor/graph_executor.cc:386: =
[00:53:08] src/executor/graph_executor.cc:387: 1 num_forward_outputs
[00:53:08] src/executor/graph_executor.cc:388: 5 g.outputs.size()
[00:53:08] src/executor/graph_executor.cc:389: 7 arg_grad_ctxes.size()
[00:53:08] src/executor/graph_executor.cc:393: arg grads contexts
[00:53:08] src/executor/graph_executor.cc:397: nid 19 ctx 0
[00:53:08] src/executor/graph_executor.cc:397: nid 18 ctx 0
[00:53:08] src/executor/graph_executor.cc:397: nid 18 ctx 1
[00:53:08] src/executor/graph_executor.cc:397: nid 20 ctx 1
[00:53:08] src/executor/graph_executor.cc:399: =
[00:53:08] src/executor/graph_executor.cc:409: fail nid 18 ctx 1
[00:53:08] src/executor/graph_executor.cc:423: node 0 var user
[00:53:08] src/executor/graph_executor.cc:423: node 1 var user_weight
[00:53:08] src/executor/graph_executor.cc:425: node 2
_contrib_SparseEmbedding
[00:53:08] src/executor/graph_executor.cc:428: input 0 (entry
id)
[00:53:08] src/executor/graph_executor.cc:428: input 1 (entry
id)
[00:53:08] src/executor/graph_executor.cc:432: output 2 (entry
id)
[00:53:08] src/executor/graph_executor.cc:423: node 3 var ufcweight
[00:53:08] src/executor/graph_executor.cc:423: node 4 var ufcbias
[00:53:08] src/executor/graph_executor.cc:425: node 5 FullyConnected
[00:53:08] src/executor/graph_executor.cc:428: input 2 (entry
id)
[00:53:08] src/executor/graph_executor.cc:428: input 3 (entry
id)
[00:53:08] src/executor/graph_executor.cc:428: input 4 (entry
id)
[00:53:08] src/executor/graph_executor.cc:432: output 5 (entry
id)
[00:53:08] src/executor/graph_executor.cc:423: node 6 var item
[00:53:08] src/executor/graph_executor.cc:423: node 7 var item_weight
[00:53:08] src/executor/graph_executor.cc:425: node 8
_contrib_SparseEmbedding
[00:53:08] src/executor/graph_executor.cc:428: input 6 (entry
id)
[00:53:08]
[GitHub] ZiyueHuang commented on a change in pull request #8632: [WIP] a user friendly way to use g2c in module and an example of g2c
ZiyueHuang commented on a change in pull request #8632: [WIP] a user friendly
way to use g2c in module and an example of g2c
URL: https://github.com/apache/incubator-mxnet/pull/8632#discussion_r151738034
##
File path: example/sparse/matrix_fact_parallel_model.py
##
@@ -0,0 +1,52 @@
+# 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:
@eric-haibin-lin I added some codes in `graph_executor.cc` for debug,
`python matrix_factorization_model_parallel.py`
```
[00:53:08] src/executor/graph_executor.cc:365: args context
[00:53:08] src/executor/graph_executor.cc:384: nid: 0 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 1 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 3 ctx.dev_id 1
[00:53:08] src/executor/graph_executor.cc:384: nid: 4 ctx.dev_id 1
[00:53:08] src/executor/graph_executor.cc:384: nid: 6 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 7 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 12 ctx.dev_id 1
[00:53:08] src/executor/graph_executor.cc:386: =
[00:53:08] src/executor/graph_executor.cc:387: 1 num_forward_outputs
[00:53:08] src/executor/graph_executor.cc:388: 5 g.outputs.size()
[00:53:08] src/executor/graph_executor.cc:389: 7 arg_grad_ctxes.size()
[00:53:08] src/executor/graph_executor.cc:393: arg grads contexts
[00:53:08] src/executor/graph_executor.cc:397: nid 19 ctx 0
[00:53:08] src/executor/graph_executor.cc:397: nid 18 ctx 0
[00:53:08] src/executor/graph_executor.cc:397: nid 18 ctx 1
[00:53:08] src/executor/graph_executor.cc:397: nid 20 ctx 1
[00:53:08] src/executor/graph_executor.cc:399: =
[00:53:08] src/executor/graph_executor.cc:409: fail nid 18 ctx 1
[00:53:08] src/executor/graph_executor.cc:423: node 0 var user
[00:53:08] src/executor/graph_executor.cc:423: node 1 var user_weight
[00:53:08] src/executor/graph_executor.cc:425: node 2
_contrib_SparseEmbedding
[00:53:08] src/executor/graph_executor.cc:428: input 0 (entry
id)
[00:53:08] src/executor/graph_executor.cc:428: input 1 (entry
id)
[00:53:08] src/executor/graph_executor.cc:432: output 2 (entry
id)
[00:53:08] src/executor/graph_executor.cc:423: node 3 var ufcweight
[00:53:08] src/executor/graph_executor.cc:423: node 4 var ufcbias
[00:53:08] src/executor/graph_executor.cc:425: node 5 FullyConnected
[00:53:08] src/executor/graph_executor.cc:428: input 2 (entry
id)
[00:53:08] src/executor/graph_executor.cc:428: input 3 (entry
id)
[00:53:08] src/executor/graph_executor.cc:428: input 4 (entry
id)
[00:53:08] src/executor/graph_executor.cc:432: output 5 (entry
id)
[00:53:08] src/executor/graph_executor.cc:423: node 6 var item
[00:53:08] src/executor/graph_executor.cc:423: node 7 var item_weight
[00:53:08] src/executor/graph_executor.cc:425: node 8
_contrib_SparseEmbedding
[00:53:08] src/executor/graph_executor.cc:428: input 6 (entry
id)
[00:53:08]
[GitHub] ZiyueHuang commented on a change in pull request #8632: [WIP] a user friendly way to use g2c in module and an example of g2c
ZiyueHuang commented on a change in pull request #8632: [WIP] a user friendly
way to use g2c in module and an example of g2c
URL: https://github.com/apache/incubator-mxnet/pull/8632#discussion_r151738034
##
File path: example/sparse/matrix_fact_parallel_model.py
##
@@ -0,0 +1,52 @@
+# 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:
@eric-haibin-lin I added some codes in `graph_executor.cc` for debug,
`python matrix_factorization_model_parallel.py`
```
[00:53:08] src/executor/graph_executor.cc:365: args context
[00:53:08] src/executor/graph_executor.cc:384: nid: 0 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 1 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 3 ctx.dev_id 1
[00:53:08] src/executor/graph_executor.cc:384: nid: 4 ctx.dev_id 1
[00:53:08] src/executor/graph_executor.cc:384: nid: 6 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 7 ctx.dev_id 0
[00:53:08] src/executor/graph_executor.cc:384: nid: 12 ctx.dev_id 1
[00:53:08] src/executor/graph_executor.cc:386: =
[00:53:08] src/executor/graph_executor.cc:387: 1 num_forward_outputs
[00:53:08] src/executor/graph_executor.cc:388: 5 g.outputs.size()
[00:53:08] src/executor/graph_executor.cc:389: 7 arg_grad_ctxes.size()
[00:53:08] src/executor/graph_executor.cc:393: arg grads contexts
[00:53:08] src/executor/graph_executor.cc:397: nid 19 ctx 0
[00:53:08] src/executor/graph_executor.cc:397: nid 18 ctx 0
[00:53:08] src/executor/graph_executor.cc:397: nid 18 ctx 1
[00:53:08] src/executor/graph_executor.cc:397: nid 20 ctx 1
[00:53:08] src/executor/graph_executor.cc:399: =
[00:53:08] src/executor/graph_executor.cc:409: fail nid 18 ctx 1
[00:53:08] src/executor/graph_executor.cc:423: node 0 var user
[00:53:08] src/executor/graph_executor.cc:423: node 1 var user_weight
[00:53:08] src/executor/graph_executor.cc:425: node 2
_contrib_SparseEmbedding
[00:53:08] src/executor/graph_executor.cc:428: input 0 (entry
id)
[00:53:08] src/executor/graph_executor.cc:428: input 1 (entry
id)
[00:53:08] src/executor/graph_executor.cc:432: output 2 (entry
id)
[00:53:08] src/executor/graph_executor.cc:423: node 3 var ufcweight
[00:53:08] src/executor/graph_executor.cc:423: node 4 var ufcbias
[00:53:08] src/executor/graph_executor.cc:425: node 5 FullyConnected
[00:53:08] src/executor/graph_executor.cc:428: input 2 (entry
id)
[00:53:08] src/executor/graph_executor.cc:428: input 3 (entry
id)
[00:53:08] src/executor/graph_executor.cc:428: input 4 (entry
id)
[00:53:08] src/executor/graph_executor.cc:432: output 5 (entry
id)
[00:53:08] src/executor/graph_executor.cc:423: node 6 var item
[00:53:08] src/executor/graph_executor.cc:423: node 7 var item_weight
[00:53:08] src/executor/graph_executor.cc:425: node 8
_contrib_SparseEmbedding
[00:53:08] src/executor/graph_executor.cc:428: input 6 (entry
id)
[00:53:08] src/executor/graph_executor.cc:428:
