MLBEDSW-5102 Update removal of memory only operators

Memory only operators such as Reshape, Squeeze and ExpandDims are
removed in the graph optimiser step.

- Added semantic check that memory only operators have same
  quantisation parameters on ifm/ofm.
- Added support for the ExpandDims operator.
- Addition and cleanup of related unit tests.
- Removed TOSA from the generated SUPPORTED_OPS.md documentation.

Signed-off-by: Jonas Ohlsson <jonas.ohlsson@arm.com>
Change-Id: If848d8afc58c18806e10997ed94e4dae83f30879

1 files changed, 14 insertions, 1 deletions

diff --git a/ethosu/vela/tflite_model_semantic.py b/ethosu/vela/tflite_model_semantic.py
index c8b373a3..6e2467bb 100644
--- a/ethosu/vela/tflite_model_semantic.py
+++ b/ethosu/vela/tflite_model_semantic.py
@@ -26,6 +26,7 @@ from .operation import get_slice_offsets
 from .operation import Op
 from .supported_operators_util import docstring_format_args
 from .supported_operators_util import list_formatter
+from .tensor import check_quantized_tens_scaling_equal
 from .tflite_mapping import BUILTIN_OPERATOR_UNKNOWN
 from .tflite_mapping import optype_to_builtintype
 
@@ -53,7 +54,8 @@ class TFLiteSemantic:
     binary_elem_wise_add_mul_sub = set((Op.Add, Op.Mul, Op.Sub,))
     binary_elem_wise_main_ops = binary_elem_wise_min_max_ops | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops
     elem_wise_main_ops = binary_elem_wise_main_ops | unary_elem_wise_main_ops
-    shapeless_input_ops = binary_elem_wise_main_ops | set((Op.Split, Op.SplitV, Op.Mean))
+    shapeless_input_ops = binary_elem_wise_main_ops | set((Op.Split, Op.SplitV, Op.Mean, Op.ExpandDims))
+    reshape_ops = set((Op.Reshape, Op.QuantizedReshape, Op.Squeeze, Op.ExpandDims,))
 
     def __init__(self):
         # Setup the generic constraints. Note: the order matters
@@ -110,6 +112,10 @@ class TFLiteSemantic:
             self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_signed)
             self.specific_constraints[op_type].append(TFLiteSemantic.constraint_unsigned_valid)
 
+        # Ops reshaping dimensions: Reshape, Squeeze and ExpandDims
+        for op_type in TFLiteSemantic.reshape_ops:
+            self.specific_constraints[op_type].append(TFLiteSemantic.constraint_matching_in_out_quant)
+
         # Softmax specific checks:
         self.specific_constraints[Op.Softmax].append(TFLiteSemantic.constraint_matching_shapes)
         self.specific_constraints[Op.Softmax].append(TFLiteSemantic.constraint_matching_in_out_types)
@@ -518,6 +524,13 @@ class TFLiteSemantic:
             valid = axis in (1, 2, [1], [2], [1, 2], [2, 1])
         return valid, f"Axis is {axis}"
 
+    @staticmethod
+    def constraint_matching_in_out_quant(op):
+        "Input and output quantisation must match."
+        if not check_quantized_tens_scaling_equal(op.ifm, op.ofm):
+            return False, "IFM and OFM quantisation parameters are not equal."
+        return True, "IFM and OFM quantisation parameters matches."
+
 
 def tflite_semantic_checker(nng):
     semantic_checker = TFLiteSemantic()