[GitHub] [tvm] apivovarov commented on a change in pull request #9303: [Keras] Support return_sequences in LSTM

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

apivovarov commented on a change in pull request #9303:
URL: https://github.com/apache/tvm/pull/9303#discussion_r731380430



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File path: tests/python/frontend/keras/test_forward.py
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@@ -417,12 +417,20 @@ def test_forward_reuse_layers(self, keras):
         keras_model = keras.models.Model(data, z)
         verify_keras_frontend(keras_model)
 
+    def test_forward_lstm(self, keras):
+        data = keras.layers.Input(shape=(10, 32))
+        rnn_funcs = [
+            keras.layers.LSTM(16),
+            keras.layers.LSTM(16, return_sequences=True),
+        ]
+        for rnn_func in rnn_funcs:
+            x = rnn_func(data)
+            keras_model = keras.models.Model(data, x)
+            verify_keras_frontend(keras_model, need_transpose=False)
+
     def test_forward_rnn(self, keras):
         data = keras.layers.Input(shape=(1, 32))
         rnn_funcs = [
-            keras.layers.LSTM(

Review comment:
       ok, I returned LSTM test back to test_forward_rnn.  Lets also test both LSTMs (with and without return_sequence flag) in `test_forward_lstm`. New test `test_forward_lstm` uses more realistic input with shape `(10, 32)` instead of `(1, 32)`. Using one word for LSTM input does not test the Recursion logic inside LSTM impl.




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