commit 0ac0804e76e098695ee2b8a9e24e2f0a1efc324f
author Fredrik Svedberg <fredrik.svedberg@arm.com> Tue Apr 11 22:35:04 2023 +0200
committer Fredrik Svedberg <fredrik.svedberg@arm.com> Mon Apr 17 14:16:44 2023 +0200
tree 9ccb766221987a415244079ed6c596a47d693b20
parent c1ad80b3a581dd39b39a112d6c2026f6560207a4

MLBEDSW-7196 Add LSTM support

Added int8 and int16 UNIDIRECTIONAL_SEQUENCE_LSTM support.
The implementation does not include support for:
* CIFG
* Peephole
* Projection
* Normalisation

This change also:
* Removed unused Op.BlockLSTM operation type.
* Removed the only one consumer limitation on putting the SplitSliceRead
  on the tensor consumer(s), if all consumers fullfills the requirements
* Added Op.VariableTensorWrite as a Operation.memory_function to make
  sure writes to variable tensors:
  * Always use linear mode
  * Are not moved to fast scratch
  * Are not fused with other elementwise operation tensor ranges

Change-Id: Ief831738924ac3d1f2ba6d41f10bd6dc969911f3
Signed-off-by: Fredrik Svedberg <fredrik.svedberg@arm.com>

ethosu/vela/tflite_model_semantic.py

diff --git a/ethosu/vela/tflite_model_semantic.py b/ethosu/vela/tflite_model_semantic.py
index 5661f36..6ba7b83 100644
--- a/ethosu/vela/tflite_model_semantic.py
+++ b/ethosu/vela/tflite_model_semantic.py
@@ -193,6 +193,14 @@
         self.specific_constraints[Op.ArgMax].append(TFLiteSemantic.constraint_input_8bit)
         self.specific_constraints[Op.ArgMax].append(TFLiteSemantic.constraint_argmax_output)
 
+        # UnidirectionalSequenceLstm specific checks:
+        self.specific_constraints[Op.UnidirectionalSequenceLstm].append(TFLiteSemantic.constraint_input_signed)
+        self.specific_constraints[Op.UnidirectionalSequenceLstm].append(TFLiteSemantic.constraint_matching_in_out_types)
+        self.specific_constraints[Op.UnidirectionalSequenceLstm].append(TFLiteSemantic.constraint_lstm_dimensions)
+        self.specific_constraints[Op.UnidirectionalSequenceLstm].append(TFLiteSemantic.constraint_lstm_inputs)
+        self.specific_constraints[Op.UnidirectionalSequenceLstm].append(TFLiteSemantic.constraint_lstm_intermediates)
+        self.specific_constraints[Op.UnidirectionalSequenceLstm].append(TFLiteSemantic.constraint_lstm_variables)
+
     def is_operator_semantic_valid(self, op):
         ext_type = optype_to_builtintype(op.type)
 
@@ -628,6 +636,13 @@
         return valid, f"Op has ifm_dtype={ifm_dtype} and ofm_dtype={ofm_dtype}"
 
     @staticmethod
+    def constraint_input_signed(op):
+        "IFM must be int8 or int16"
+        ifm_dtype = op.ifm.dtype
+        valid = (ifm_dtype == DataType.int8) or (ifm_dtype == DataType.int16)
+        return valid, f"Op has ifm_dtype={ifm_dtype}"
+
+    @staticmethod
     def constraint_input_8bit(op):
         "IFM must be int8 or uint8"
         ifm_dtype = op.ifm.dtype
@@ -689,6 +704,36 @@
             return False, f"IFM {op.ifm.shape} and OFM {op.ofm.shape} number of elements are not equal."
         return True, "IFM and OFM number of elements are equal."
 
+    @staticmethod
+    def constraint_lstm_dimensions(op):
+        "IFM and OFM must have 3D shape"
+        valid = len(op.ifm.shape) == len(op.ofm.shape) == 3
+        return valid, f"Op has ifm shape {op.ifm.shape} and ofm shape {op.ofm.shape}"
+
+    @staticmethod
+    def constraint_lstm_inputs(op):
+        "Must have 24 input tensors"
+        n_inputs = len(op.inputs)
+        return n_inputs == 24, f"Op has {n_inputs} inputs"
+
+    @staticmethod
+    def constraint_lstm_intermediates(op):
+        "Must have 5 intermediate tensors"
+        n_intermediates = len(op.intermediates)
+        return n_intermediates == 5, f"Op has {n_intermediates} intermediates"
+
+    @staticmethod
+    def constraint_lstm_variables(op):
+        "State tensors must be variable"
+        valid = True
+        extra = []
+        for tens in op.inputs[18:20]:
+            if not tens.is_variable:
+                valid = False
+                extra.append(tens.name)
+        extra = ", ".join(extra)
+        return valid, f"Op has non-variable state tensor(s): {extra}"
+
 
 def tflite_semantic_checker(nng):
     semantic_checker = TFLiteSemantic()