MLBEDSW-4838 Added basic TOSA support.

Added basic TOSA support, enabling Vela to
read and compile  a .tosa file corresponding to
CONV2D + Rescale + Clamp, and writing it to an
optimized .tflite file.

The optimized .tflite file, will in this case, hold
a commandstream where the Rescale and Clamp has been
fused into the CONV2D.

The optimized tflite file is not output from Vela.

  -Added support to read .tosa file into Vela
    internal structure.
      - Added tosa_reader.py, tosa_mapper.py and
        helper files stored under tosa/
      - Support for this limited to ~10 ops

    -Added reader_util.py for functions common
    for TOSA and TFLite

    -Added tosa_graph_optimiser.py
      -Added support to fuse Rescale into convolution
      -Modified handling for padding

    -Added support to fuse Clamp to previous op

    -Added graph_optimiser_util.py
      -Moved functions common for TOSA/TFLite graph
       optimization to this file.

    -Renamed graph_optimiser.py to tflite_graph_optmiser.py

    -Added separate tosa_supported_operators.py

    -Added supported_operator_util.py
       -For functions in common for TOSA/TFLite

Signed-off-by: Patrik Gustavsson <patrik.gustavsson@arm.com>
Change-Id: Ic3c540504ec8c5eb4771397fdc6882050ecf33ab

1 files changed, 9 insertions, 20 deletions

diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py
index dfa27199..c993da13 100644
--- a/ethosu/vela/supported_operators.py
+++ b/ethosu/vela/supported_operators.py
@@ -25,31 +25,19 @@ from .numeric_util import is_integer
 from .operation import get_slice_offsets
 from .operation import Op
 from .operation import Padding
+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
 
 
-# Custom decorator function to allow formatting docstrings containing "{}"
-def docstring_format_args(args):
-    def docstring(func):
-        func.__doc__ = func.__doc__.format(*args)
-        return func
-
-    return docstring
-
-
-def _list_formatter(arg):
-    # Order and join into a string representation
-    return ", ".join(sorted(map(str, arg)))
-
-
 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)
-    return _list_formatter(output)
+    return list_formatter(output)
 
 
 class SupportedOperators:
@@ -88,7 +76,8 @@ class SupportedOperators:
     supported_int32_tensor_ops = (
         set((Op.ReduceSum, Op.CLZ,)) | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops
     )
-    relu_ops = Op.op_set(Op.is_relu_op)
+
+    relu_ops = set((Op.Relu, Op.Relu6, Op.ReluN1To1, Op.Clip,))
     activation_ops = relu_ops | set((Op.Tanh, Op.Sigmoid, Op.Softmax, Op.HardSwish))
     npu_post_ops = (
         # activation functions
@@ -354,7 +343,7 @@ class SupportedOperators:
         return valid, ", ".join(extra)
 
     @classmethod
-    @docstring_format_args([_list_formatter(supported_op_dtypes)])
+    @docstring_format_args([list_formatter(supported_op_dtypes)])
     def constraint_tens_dtype(cls, op):
         "Tensors must be of type: {}"
         valid = True
@@ -463,7 +452,7 @@ class SupportedOperators:
         return res
 
     @classmethod
-    @docstring_format_args([_list_formatter(supported_faf_dtypes)])
+    @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:
@@ -549,7 +538,7 @@ class SupportedOperators:
         return valid, f"Tensor '{weights.name}' has the sum of weights: {limit}"
 
     @classmethod
-    @docstring_format_args([_list_formatter(supported_bias_dtypes)])
+    @docstring_format_args([list_formatter(supported_bias_dtypes)])
     def constraint_bias_type(cls, op):
         "Optional Bias tensor must be of type: {}"
         bias = op.bias
@@ -832,7 +821,7 @@ class SupportedOperators:
         return valid, f"The pad tensor has the shape: {op.inputs[1].shape}"
 
     @classmethod
-    @docstring_format_args([_list_formatter(supported_pad_dtypes)])
+    @docstring_format_args([list_formatter(supported_pad_dtypes)])
     def constraint_pad_type(cls, op):
         "Pad tensor must be of type: {}"
         pad_tensor = op.inputs[1]