[GitHub] ArmageddonKnight commented on a change in pull request #11364: [MXNET-490] Added OpenLSTMRNN together with benchmarks and Tensorboard callback routines.

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ArmageddonKnight commented on a change in pull request #11364: [MXNET-490] Added OpenLSTMRNN together with benchmarks and Tensorboard callback routines.
URL: https://github.com/apache/incubator-mxnet/pull/11364#discussion_r229943448
 
 

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 File path: example/rnn-backends/word_lm/model.py
 ##########
 @@ -0,0 +1,152 @@
+# 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.
+
+"""
+Module: model
+
+Description: This file creates the computation graph for training purposes.
+"""
+
+import mxnet as mx
+
+
+def rnn(bptt, vocab_size, num_embed, nhid,
+        num_layers, dropout, batch_size, tied, backend):
+    """
+    Creates the computation graph for training.
+    """
+    # ==================================================================================================================
+    # Encoder
+    # ==================================================================================================================
+
+    data = mx.sym.Variable('data')
+    weight = mx.sym.Variable("encoder_weight", init=mx.init.Uniform(0.1))
+    embed = mx.sym.Embedding(data=data, weight=weight, input_dim=vocab_size,
+                             output_dim=num_embed, name='embed')
+
+    outputs = mx.sym.Dropout(embed, p=dropout)
+
+    # ==================================================================================================================
+    # RNN
+    # ==================================================================================================================
+
+    # stacked rnn layers
+
+    # Given below is the original source code, which we do argue makes bad use of CuDNN-RNN
+    # as there is no need to slice each layer apart, please refer to the open issue on github @
+    #     https://github.com/apache/incubator-mxnet/issues/10304
+    # The throughput measurements reported by MXNet speedometer showed that
+    # there is around 10~20% increase in throughput after all the layers have been fused.
+    # The implementation also messed up with the order of cell and hidden state,
+    # because according to the state order specified in rnn_cell.py (+L682),
+    # cell state should appear AFTER hidden state in FusedRNNCell. However, this has zero effect
+    # on the final training results because they are both zero-initialized.
+
+    states = []
+    state_names = []
+
+    # for i in range(num_layers):
+    #     prefix = 'lstm_l%d_' % i
 
 Review comment:
   @Roshrini I am terribly sorry for the late response. I do not this it is appropriate for me to remove this part of the comments because they are used to show the discrepencies between the original word language-modeling benchmark and the benchmark that I am using (i.e. it is deliberately left there for **comparison** purpose). 

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