MLBEDSW-3572: Fused activations must not be int32

Added supported operator check that 32-bit fused activation functions
are not supported.

Change-Id: I01fdafeff8fdb13c71eae4f63be7e6f81b9223df
Signed-off-by: Louis Verhaard <louis.verhaard@arm.com>

2 files changed, 24 insertions, 0 deletions

diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py
index 505d4d1..8bb9c58 100644
--- a/ethosu/vela/supported_operators.py
+++ b/ethosu/vela/supported_operators.py
@@ -105,6 +105,7 @@ class SupportedOperators:
     supported_operators = npu_pre_ops | mac_main_ops | elem_wise_main_ops | pad_ops | npu_post_ops | memory_only_ops
     # Supported data types
     supported_op_dtypes = set((DataType.uint8, DataType.int8, DataType.int16, DataType.int32))
+    supported_faf_dtypes = set((DataType.uint8, DataType.int8, DataType.int16))
     supported_bias_dtypes = set((DataType.int32, DataType.int64))
     supported_pad_dtypes = set((DataType.int32, DataType.int64))
     # Defined ranges for allowed values:
@@ -135,6 +136,7 @@ class SupportedOperators:
         self.generic_constraints.append(SupportedOperators.constraint_tens_quant_scale)
         self.generic_constraints.append(SupportedOperators.constraint_tens_quant_per_axis)
         self.generic_constraints.append(SupportedOperators.constraint_faf)
+        self.generic_constraints.append(SupportedOperators.constraint_faf_type)
         self.generic_constraints.append(SupportedOperators.constraint_quant_scale_inf)
 
         # Setup specific constraints. Note: the order matters
@@ -451,6 +453,18 @@ class SupportedOperators:
             res = valid, f"Op has its fused activation function as: {faf}"
         return res
 
+    @classmethod
+    @docstring_format_args([_list_formatter(supported_faf_dtypes)])
+    def constraint_faf_type(cls, op):
+        "If a fused activation function is present, the Output tensor must be one of type: {}"
+        if op.activation is None:
+            res = True, "Op has no fused activation function"
+        else:
+            valid = op.ofm.dtype in cls.supported_faf_dtypes
+            ext_type = optype_to_builtintype(op.activation.op_type)
+            res = valid, f"Op has fused activation function {ext_type}, and Output tensor data type: {op.ofm.dtype}"
+        return res
+
     @staticmethod
     def constraint_stride_type(op):
         "Stride values for both width and height must be integer types"
diff --git a/ethosu/vela/test/test_supported_operators.py b/ethosu/vela/test/test_supported_operators.py
index 5f64dd9..3e9724d 100644
--- a/ethosu/vela/test/test_supported_operators.py
+++ b/ethosu/vela/test/test_supported_operators.py
@@ -147,6 +147,16 @@ def test_constraint_faf():
     assert not support.is_operator_supported(op)
 
 
+def test_constraint_faf_ofm_dtype():
+    # If fused activation function is present, OFM must be 8 or 16 bit
+    shp = [1, 8, 8, 8]
+    for dtype in [DataType.int8, DataType.uint8, DataType.int16, DataType.int32]:
+        op = testutil.create_elemwise_op(Op.Add, "op", shp, shp, shp, datatype=dtype)
+        op.activation = ActivationFunction(Op.Relu)
+        expected = dtype.size_in_bytes() <= 2
+        assert support.is_operator_supported(op) == expected, f"Data type: {dtype}"
+
+
 def test_constraint_conv_pass():
     # First test a simple conv passes
     op = testutil.create_op_with_quant_tensors(Op.Conv2D, [1, 1, 1, 1], [1, 1, 1, 1], weights_shape=[1, 1, 1, 1])