MLBEDSW-3403 Generate supported op report

A new CLI has been added that allows the generation of a report
containing a summary table of all TFLite ops that can be placed on the
NPU, and what the constraints are for that operator to be successfully
scheduled on the NPU.
This option will generate a new file, SUPPORTED_OPS.md containing this
information, in the current working directory.

Signed-off-by: Michael McGeagh <michael.mcgeagh@arm.com>
Change-Id: I6a7e2a49f251b76b2ea1168fff78e00da1910b25

1 files changed, 17 insertions, 3 deletions

diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py
index 46f7a5d3..ccf61042 100644
--- a/ethosu/vela/supported_operators.py
+++ b/ethosu/vela/supported_operators.py
@@ -25,6 +25,7 @@ from .numeric_util import is_integer
 from .operation import get_slice_offsets
 from .operation import Op
 from .tensor import check_quantized_tens_scaling_equal
+from .tflite_mapping import BUILTIN_OPERATOR_UNKNOWN
 from .tflite_mapping import optype_to_builtintype
 
 
@@ -37,6 +38,15 @@ def docstring_format_args(args):
     return docstring
 
 
+def _optype_formatter(op_list):
+    # Convert internal op types to external names
+    output = map(optype_to_builtintype, op_list)
+    # Remove UNKNOWNs
+    output = (x for x in output if x is not BUILTIN_OPERATOR_UNKNOWN)
+    # Order alphabetically
+    return sorted(output)
+
+
 class SupportedOperators:
     # Categorised lists of supported operators
     npu_pre_ops = set((Op.SplitSliceRead,))
@@ -99,6 +109,10 @@ class SupportedOperators:
     filter_range = (1, 8)
     filter_height_range = (1, 256)
     filter_product_range = (1, 256 * 256)
+    # Ordered, external names of op types for the constraint reasons
+    docstring_shapeless_input_ops = _optype_formatter(shapeless_input_ops)
+    docstring_supported_int32_tensor_ops = _optype_formatter(supported_int32_tensor_ops)
+    docstring_supported_fused_activations = _optype_formatter(supported_fused_activations)
 
     def __init__(self):
         # Setup the generic constraints. Note: the order matters
@@ -279,7 +293,7 @@ class SupportedOperators:
         return valid, f"Output Tensor '{ofm.name}' is scalar"
 
     @classmethod
-    @docstring_format_args([shapeless_input_ops])
+    @docstring_format_args([docstring_shapeless_input_ops])
     def constraint_tens_input_scalar(cls, op):
         "Scalar Input tensors are only valid for op type: {}"
         valid = True
@@ -320,7 +334,7 @@ class SupportedOperators:
         return valid, ", ".join(extra)
 
     @classmethod
-    @docstring_format_args([supported_int32_tensor_ops])
+    @docstring_format_args([docstring_supported_int32_tensor_ops])
     def constraint_tens_int32_ops(cls, op):
         "Tensors which are int32 are only valid when op type is: {}"
         valid = True
@@ -377,7 +391,7 @@ class SupportedOperators:
         return valid, ", ".join(extra)
 
     @classmethod
-    @docstring_format_args([supported_fused_activations])
+    @docstring_format_args([docstring_supported_fused_activations])
     def constraint_faf(cls, op):
         "The fused activation function (if present) must be one of type: {}"
         if op.activation is None: