Add handling of HardSigmoid recurrent activation for Keras LSTM

tests/test_lstm.py

@@ -751,6 +751,28 @@ def func(x):
             return tf.identity(y[0], name="output"), tf.identity(y[1], name="output1")
         self.run_test_case(func, {"input:0": x_val}, [], ["output:0", "output1:0"], rtol=1e-05, atol=1e-06)
 
+    @check_tf_min_version("2.0")
+    @skip_tf_versions("2.1", "Bug in TF 2.1")
+    def test_keras_lstm_recurrent_activation_is_hard_sigmoid(self):
+        in_shape = [10, 3]
+        x_val = np.random.uniform(size=[2, 10, 3]).astype(np.float32)
+
+        model_in = tf.keras.layers.Input(tuple(in_shape), batch_size=2)
+        x = tf.keras.layers.LSTM(
+            units=5,
+            return_sequences=True,
+            return_state=True,
+            kernel_initializer=tf.random_uniform_initializer(0.0, 1.0, seed=42),
+            recurrent_initializer=tf.random_uniform_initializer(0.0, 1.0, seed=44),
+            bias_initializer=tf.random_uniform_initializer(0.0, 1.0, seed=43),
+            recurrent_activation="hard_sigmoid"
+        )(model_in)
+        model = tf.keras.models.Model(inputs=model_in, outputs=x)
+
+        def func(x):
+            y = model(x)
+            return tf.identity(y[0], name="output"), tf.identity(y[1], name="output1")
+        self.run_test_case(func, {"input:0": x_val}, [], ["output:0", "output1:0"], rtol=1e-05, atol=1e-06)
 
 if __name__ == '__main__':
     unittest_main()

tf2onnx/rewriter/lstm_tf2_rewriter.py

@@ -16,29 +16,52 @@
 
 # pylint: disable=invalid-name,unused-argument,missing-docstring, unused-variable
 
+def _make_lstm_pattern_from_params(params):
+    return make_lstm_pattern(enter_or_id="Identity") if not params.get("from_keras", False) \
+        else make_lstm_pattern(
+            from_keras=True,
+            use_bias=params.get("use_bias", False),
+            activation=params.get("activation", ""),
+            recurrent_activation=params.get("recurrent_activation", "")
+        )
 
 def rewriter_lstm_tf2(g, ops):
-
-    pattern1 = make_lstm_pattern(enter_or_id="Identity")          # TF LSTM
-    pattern2 = make_lstm_pattern(from_keras=True, use_bias=False) # keras LSTM
-    pattern3 = make_lstm_pattern(from_keras=True, use_bias=True)  # keras LSTM with bias
-
-    for pattern in [pattern1, pattern2, pattern3]:
+    lstm_params_variations = [
+        # default activations
+        {"enter_or_id": "Identity"},                                                  # TF LSTM
+        {"from_keras": True, "use_bias": False},                                        # keras LSTM
+        {"from_keras": True, "use_bias": True},                                         # keras LSTM with bias
+        # hard sigmoid as recurrent activation
+        {"from_keras": True, "use_bias": False, "recurrent_activation": "hard_sigmoid"}, # keras LSTM
+        {"from_keras": True, "use_bias": True, "recurrent_activation": "hard_sigmoid"}   # keras LSTM with bias
+        # Note: add other LSTM variations as needed
+    ]
+    for params in lstm_params_variations:
+        pattern = _make_lstm_pattern_from_params(params)
         matcher = GraphMatcher(pattern, allow_reorder=False)
         match_results = list(matcher.match_ops(ops))
 
         for match_result in match_results:
-            from_keras = pattern != pattern1
+            is_ft_hard_sigmoid = params.get("recurrent_activation", "") == "hard_sigmoid"
+            recurrent_activation_f = "HardSigmoid" if is_ft_hard_sigmoid else  \
+                match_result.get_op("ft").type
+            activation_g = match_result.get_op("gt").type
+            activation_h = match_result.get_op("ct'").type
+
+            default_activations = ["Relu", "Sigmoid", "Tanh"]
+            if ((activation_g not in default_activations) or
+                    (activation_h not in default_activations) or
+                    (not is_ft_hard_sigmoid and recurrent_activation_f not in default_activations)):
+                continue
+
             activations_fgh = [
-                match_result.get_op("ft").type,
-                match_result.get_op("gt").type,
-                match_result.get_op("ct'").type
+                recurrent_activation_f,
+                activation_g,
+                activation_h
             ]
-            supported_activations = ['Relu', 'Sigmoid', 'Tanh']
-            if any(f not in supported_activations for f in activations_fgh):
-                continue
 
             # extract input x_t
+            from_keras = params.get("from_keras", False)
             if from_keras:
                 get_item = match_result.get_op("xt")
             else:
@@ -134,7 +157,7 @@ def has_tensor_list_consumer(n):
 
                 # Wb and Rb are concatenated
                 b_idx = None
-                if pattern is pattern3:
+                if from_keras and params.get("use_bias", False):
                     bias_add = match_result.get_op("bias_add")
                     if bias_add is not None and bias_add.data_format != "NHWC":
                         continue

tf2onnx/rewriter/rnn_utils.py

@@ -30,6 +30,25 @@ class REWRITER_RESULT(Enum):
 
 # TensorFlow LSTMCell/BasicLSTMCell and Keras LSTM computation graph matching
 
+def insert_activation(activation, name="", inputs=None):
+    inputs = inputs if inputs else [] # to avoid empty list as default arg
+    if activation == "hard_sigmoid":
+        return OpTypePattern("Maximum", inputs=[
+            OpTypePattern("Minimum", inputs=[
+                OpTypePattern("Add|AddV2", inputs=[
+                    OpTypePattern("Mul", inputs=[
+                        *inputs,
+                        OpTypePattern("*") # mul(x, 0.2)
+                    ]), OpTypePattern("*") # add(x, 0.5)
+                ]), OpTypePattern("*") # minimum(x, 1)
+            ]), OpTypePattern("*") # maximum(x, 0)
+        ])
+    # Additional activation pattern can be added when needed:
+    # https://www.tensorflow.org/api_docs/python/tf/keras/activations
+    # otherwise, use default activations
+    return OpTypePattern("Tanh|Relu|Sigmoid", name=name, inputs=inputs)
+
+
 def make_lstm_xc_pattern(enter_or_id="Enter", from_keras=False, use_bias=False):
     if from_keras:
         lstm_xh_pattern = OpTypePattern("Add|AddV2", allow_reorder=False, inputs=[
@@ -63,7 +82,8 @@ def make_lstm_xc_pattern(enter_or_id="Enter", from_keras=False, use_bias=False):
     ])
 
 
-def make_lstm_pattern(enter_or_id="Enter", from_keras=False, use_bias=False):
+def make_lstm_pattern(enter_or_id="Enter", from_keras=False, use_bias=False,
+                      activation="", recurrent_activation=""):
     # split (Xt*(W[ifco]^T) + Ht-1*(R[ifco]^T)) on 'Const' axis
     lstm_xc_pattern = OpTypePattern('Split', inputs=[
         OpTypePattern("Const"),
@@ -77,23 +97,21 @@ def make_lstm_pattern(enter_or_id="Enter", from_keras=False, use_bias=False):
             OpTypePattern("*", name="ft_bias"),
         ])
 
-    activation = "Tanh|Relu|Sigmoid"
-    recurrent_activation = "Tanh|Relu|Sigmoid"
-
-    return OpTypePattern("Mul", name='ht', inputs=[
-        OpTypePattern(recurrent_activation, name="ot", inputs=[lstm_xc_pattern]),
-        OpTypePattern(activation, name="ct'", inputs=[
-            OpTypePattern("Add|AddV2", name="ct", inputs=[
-                OpTypePattern("Mul", name="ct_identity_consumer", inputs=[
-                    OpTypePattern(recurrent_activation, name="ft", inputs=[lstm_fb_pattern]),
-                    OpTypePattern("*", name="c"),
-                ]),
-                OpTypePattern("Mul", inputs=[
-                    OpTypePattern(recurrent_activation, name="it", inputs=[lstm_xc_pattern]),
-                    OpTypePattern(activation, name="gt", inputs=[lstm_xc_pattern]),
-                ]),
-            ]),
+    # cell state
+    lstm_ct_pattern = OpTypePattern("Add|AddV2", name="ct", inputs=[
+        OpTypePattern("Mul", name="ct_identity_consumer", inputs=[
+            insert_activation(recurrent_activation, name="ft", inputs=[lstm_fb_pattern]),
+            OpTypePattern("*", name="c"),
         ]),
+        OpTypePattern("Mul", inputs=[
+            insert_activation(recurrent_activation, name="it", inputs=[lstm_xc_pattern]),
+            insert_activation(activation, name="gt", inputs=[lstm_xc_pattern]),
+        ]),
+    ])
+
+    return OpTypePattern("Mul", name="ht", inputs=[
+        insert_activation(recurrent_activation, name="ot", inputs=[lstm_xc_pattern]),
+        insert_activation(activation, name="ct'", inputs=[lstm_ct_pattern]),
     ])
 
 lstmcell_pattern = make_lstm_pattern()