Add unidirectional sequence lstm (apache#11183)

* UnidirectionalLSTM added

* fixed missing import

* fixed pylint warnings

* black formatted tflite.py

* corrections according to reviewer comments

* fixed black formatting

* just to trigger the CI again

* assertion now tests that there are exactly 24 input tensors.

* black formatted tflite.py

* added explanatory comment regarding unused imports

* removed unused import

* nothing

* nothing

* added some details in a comment about the differences in unbind regarding to the version in common.py

* improved comment on unbind

* fix of black issue

python/tvm/relay/frontend/tflite.py

@@ -33,7 +33,7 @@
 from ..backend.name_transforms import sanitize_name
 from .common import ExprTable
 from .common import infer_shape as _infer_shape
-from .common import to_int_list, shape_of
+from .common import lstm_cell, to_int_list, shape_of
 from .tflite_flexbuffer import FlexBufferDecoder
 
 __all__ = ["from_tflite"]
@@ -173,6 +173,7 @@ def __init__(self, model, subgraph, exp_tab):
             "TRANSPOSE_CONV": self.convert_transpose_conv,
             "TRANSPOSE": self.convert_transpose,
             "UNPACK": self.convert_unpack,
+            "UNIDIRECTIONAL_SEQUENCE_LSTM": self.convert_unidirectional_sequence_lstm,
             "WHERE": self.convert_select,
             "ZEROS_LIKE": self.convert_zeros_like,
         }
@@ -220,6 +221,41 @@ def check_unsupported_ops(self):
         if len(raise_msg) > 0:
             raise tvm.error.OpNotImplemented(raise_msg)
 
+    def unbind(self, data, axis=1):
+        """
+        This is a modified version compared to the one in common.py.
+        The onnx version takes a relay.Expr.Call, the tflite
+        version a TensorWrapper. Also this version by default splits
+        along axis 1 and not axis 0 as the onnx version.
+
+         Parameters
+         ----------
+         data : tvm.relay.frontend.tflite.TensorWrapper
+             Input tensor
+         axis : int
+             Axis along which tensor is split.
+         Returns
+         -------
+         result : List[relay.Expr]
+             The sequence of computed tensors
+        """
+        shape = to_int_list(self.get_tensor_shape(data))
+        if axis >= len(shape):
+            msg = "Please check input dim, it shouldn't be greater than or equal to rank."
+            raise AttributeError(msg)
+
+        selections = shape[axis]
+        shape.pop(axis)
+        timestep = 0  # Reshape to make time step as the first dim
+        shape.insert(timestep, selections)
+        res_split = _op.split(
+            _op.reshape(self.get_expr(data.tensor_idx), tuple(shape)), selections, timestep
+        )
+        ret = []
+        for i in range(selections):
+            ret.append(_op.squeeze(res_split[i], axis=[timestep]))
+        return _expr.TupleWrapper(_expr.Tuple(ret), selections)
+
     def convert_op_to_relay(self):
         """Convert TFLite ops to relay ops"""
         for op_idx in range(self.subgraph.OperatorsLength()):
@@ -2715,6 +2751,148 @@ def convert_unpack(self, op):
 
         return squeezed
 
+    def convert_unidirectional_sequence_lstm(self, op):
+        """Long Short Term Memory for TFLite implementation."""
+        if self.is_quantized(op):
+            raise tvm.error.OpNotImplemented(
+                "TFlite quantized UNIDIRECTIONALSEQUENCELSTM operator is not supported yet."
+            )
+
+        input_tensors = self.get_input_tensors(op)
+        assert len(input_tensors) == 24, "input tensors length should be == 24"
+
+        # Extract input tensor from saved model
+        input_tensor = input_tensors[0]
+
+        # Extract tensors from input tensors from saved model
+        # Input weights
+        input_input_weights = input_tensors[1]
+        input_forget_weights = input_tensors[2]
+        input_cell_weights = input_tensors[3]
+        input_output_weights = input_tensors[4]
+        # Recurrent weights
+        recurrent_input_weights = input_tensors[5]
+        recurrent_forget_weights = input_tensors[6]
+        recurrent_cell_weights = input_tensors[7]
+        recurrent_output_weights = input_tensors[8]
+        # inputs 9, 10, 11, 16, 17, 20, 21, 22, 23 are not occupied
+        # there locations are -1 in the flatbuffer
+        # Bias weights
+        input_gate_bias = input_tensors[12]
+        forget_gate_bias = input_tensors[13]
+        cell_gate_bias = input_tensors[14]
+        output_gate_bias = input_tensors[15]
+
+        # State input
+        output_state_in = input_tensors[18]
+        cell_state_in = input_tensors[19]
+
+        # Extract output tensor from saved model
+        output_tensors = self.get_output_tensors(op)
+        assert len(output_tensors) == 1, "output tensors length should be 1"
+        X_steps = self.unbind(input_tensor, axis=1)
+        weights_dict = {}
+
+        # hidden_state_weights is equivalent to output_state_in in tflite model
+        out_state_in_shape = tuple(self.get_tensor_shape(output_state_in))
+        out_state_in_dtype = self.get_tensor_type_str(output_state_in.tensor.Type())
+        out_state_in_expr = _op.zeros(out_state_in_shape, dtype=out_state_in_dtype)
+        weights_dict["hidden_state"] = _op.split(out_state_in_expr, 1)[0]
+
+        # cell_state_weights is equivalent to output_state_in tflite model
+        cell_state_in_shape = tuple(self.get_tensor_shape(cell_state_in))
+        cell_state_in_dtype = self.get_tensor_type_str(cell_state_in.tensor.Type())
+        cell_state_in_expr = _op.zeros(cell_state_in_shape, dtype=cell_state_in_dtype)
+        weights_dict["cell_state"] = _op.split(cell_state_in_expr, 1)[0]
+
+        # Process weight matrix of input: w_inp
+        # Concatenate of [input_input_weight, input_forget_weights,
+        # input_cell_weights, input_output_weights]
+        input_input_weights_default_values = self.get_tensor_value(input_input_weights)
+        input_input_weights_op = _op.split(
+            _op.const(input_input_weights_default_values.tolist()), 1
+        )
+        input_output_weights_default_values = self.get_tensor_value(input_output_weights)
+        input_output_weights_op = _op.split(
+            _op.const(input_output_weights_default_values.tolist()), 1
+        )
+        input_forget_weights_default_values = self.get_tensor_value(input_forget_weights)
+        input_forget_weights_op = _op.split(
+            _op.const(input_forget_weights_default_values.tolist()), 1
+        )
+        input_cell_weights_default_values = self.get_tensor_value(input_cell_weights)
+        input_cell_weights_op = _op.split(_op.const(input_cell_weights_default_values.tolist()), 1)
+        weights_dict["w_inp"] = _op.concatenate(
+            [
+                _op.squeeze(input_input_weights_op[0]),
+                _op.squeeze(input_forget_weights_op[0]),
+                _op.squeeze(input_cell_weights_op[0]),
+                _op.squeeze(input_output_weights_op[0]),
+            ],
+            axis=0,
+        )
+
+        # Process weight matrix of hidden state:
+        # w_hid to support lstm_cell function. Not used in tflite
+        recurrent_input_weights_values = self.get_tensor_value(recurrent_input_weights)
+        recurrent_input_weights_op = _op.split(
+            _op.const(recurrent_input_weights_values.tolist()), 1
+        )
+        recurrent_output_weights_values = self.get_tensor_value(recurrent_output_weights)
+        recurrent_output_weights_op = _op.split(
+            _op.const(recurrent_output_weights_values.tolist()), 1
+        )
+        recurrent_forget_weights_values = self.get_tensor_value(recurrent_forget_weights)
+        recurrent_forget_weights_op = _op.split(
+            _op.const(recurrent_forget_weights_values.tolist()), 1
+        )
+        recurrent_cell_weights_values = self.get_tensor_value(recurrent_cell_weights)
+        recurrent_cell_weights_op = _op.split(_op.const(recurrent_cell_weights_values.tolist()), 1)
+        weights_dict["w_hid"] = _op.concatenate(
+            [
+                recurrent_input_weights_op[0],
+                recurrent_forget_weights_op[0],
+                recurrent_cell_weights_op[0],
+                recurrent_output_weights_op[0],
+            ],
+            axis=0,
+        )
+
+        # Process weight matrix of bias: b_inp
+        input_gate_bias_values = self.get_tensor_value(input_gate_bias)
+        input_gate_bias_op = _op.split(_op.const(input_gate_bias_values.tolist()), 1)
+        output_gate_bias_values = self.get_tensor_value(output_gate_bias)
+        output_gate_bias_op = _op.split(_op.const(output_gate_bias_values.tolist()), 1)
+        forget_gate_bias_values = self.get_tensor_value(forget_gate_bias)
+        forget_gate_bias_op = _op.split(_op.const(forget_gate_bias_values.tolist()), 1)
+        cell_gate_bias_values = self.get_tensor_value(cell_gate_bias)
+        cell_gate_bias_op = _op.split(_op.const(cell_gate_bias_values.tolist()), 1)
+        weights_dict["b_inp"] = _op.concatenate(
+            [
+                input_gate_bias_op[0],
+                forget_gate_bias_op[0],
+                cell_gate_bias_op[0],
+                output_gate_bias_op[0],
+            ],
+            axis=0,
+        )
+
+        # Process weight matrix of hidden bias:
+        # b_hid (with the same shape as b_inp)
+        gate_bias_dtype = self.get_tensor_type_str(input_gate_bias.tensor.Type())
+        weights_dict["b_hid"] = _op.split(
+            _op.const(
+                np.zeros(_infer_shape(weights_dict["b_inp"]), dtype=gate_bias_dtype),
+                dtype=gate_bias_dtype,
+            ),
+            1,
+        )[0]
+
+        outputs, _, _ = lstm_cell(input_seqs=X_steps, **weights_dict)
+
+        output = _op.stack(outputs, axis=1)
+        return output
+
     def convert_batch_to_space_nd(self, op):
         """batch_to_space_nd implementation."""
 

tests/python/frontend/tflite/test_forward.py

@@ -1867,7 +1867,7 @@ def tf_function(self, x):
         model,
         export_dir,
         signatures=model.tf_function.get_concrete_function(
-            tf.TensorSpec(data.shape, tf.float32, name="input"),
+            tf.TensorSpec(data.shape, tf.float32, name="input")
         ),
     )
 
@@ -3759,8 +3759,7 @@ def test_forward_prelu():
         np.full((32, 3), 0.2, dtype="float32"),
     )
     _test_prelu(
-        np.random.uniform(-5, 5, size=(32, 3)).astype("float32"),
-        np.full((3), 0.2, dtype="float32"),
+        np.random.uniform(-5, 5, size=(32, 3)).astype("float32"), np.full((3), 0.2, dtype="float32")
     )
 
 
@@ -4693,6 +4692,36 @@ def representative_dataset():
     tvm.testing.assert_allclose(tvm_sorted_labels, tflite_sorted_labels)
 
 
+#######################################################################
+# Unidirectional Sequence LSTM
+# ---------------------
+def test_forward_unidirectional_sequence_lstm():
+    """Test the UnidirectionalSequenceLSTM TFLite"""
+    if package_version.parse(tf.VERSION) >= package_version.parse("2.1.0"):
+        tflite_model_file = download_testdata(
+            "https://github.com/SebastianBoblestETAS/nn_models/blob/ce49c5de64889493161ca4194a20e0fd5eb707e6/lstm_1_in_3_out_2_ts_4.tflite?raw=true",
+            "lstm_1_in_3_out_2_ts_4.tflite",
+        )
+        with open(tflite_model_file, "rb") as f:
+            tflite_model_buf = f.read()
+
+        data = np.array(
+            [
+                [
+                    [0.5488135, 0.71518934, 0.60276335],
+                    [0.5448832, 0.4236548, 0.6458941],
+                    [0.4375872, 0.891773, 0.96366274],
+                    [0.3834415, 0.79172504, 0.5288949],
+                ]
+            ],
+            dtype="float32",
+        )
+
+        tflite_output = run_tflite_graph(tflite_model_buf, data)
+        tvm_output = run_tvm_graph(tflite_model_buf, data, "serving_default_input_1:0")
+        tvm.testing.assert_allclose(tflite_output, tvm_output)
+
+
 #######################################################################
 # Quantized SSD Mobilenet
 # -----------------------
@@ -4930,10 +4959,11 @@ def test_prevent_tensorflow_dynamic_range():
     test_forward_leaky_relu()
     test_forward_relu_n1_to_1()
     test_forward_log_softmax()
-    test_forward_prelu()
     test_forward_fully_connected()
     test_forward_l2_normalization()
     test_forward_local_response_normalization()
+    test_forward_prelu()
+    test_forward_unidirectional_sequence_lstm()
 
     # Elemwise
     test_all_elemwise()