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, 85 insertions, 0 deletions

diff --git a/ethosu/vela/tosa_supported_operators.py b/ethosu/vela/tosa_supported_operators.py
new file mode 100644
index 00000000..c87d653a
--- /dev/null
+++ b/ethosu/vela/tosa_supported_operators.py
@@ -0,0 +1,85 @@
+# Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the License); you may
+# not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an AS IS BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# Description:
+# The TosaSupportedOperators class which is a collection of all supported operators and parameter checks.
+from collections import defaultdict
+
+from .data_type import DataType
+from .operation import Op
+from .supported_operators_util import docstring_format_args
+from .supported_operators_util import list_formatter
+from .tosa_mapping import optype_to_tosa_op_type
+
+
+class TosaSupportedOperators:
+    # TODO currently sparsely populated
+    # Categorised lists of supported operators
+    convolution_ops = set((Op.Conv2DBias,))
+    convolution_like_ops = convolution_ops
+    mac_main_ops = convolution_like_ops
+
+    type_conversion_ops = set((Op.Rescale,))
+    relu_ops = set((Op.Clip, Op.ReluN,))
+    activation_ops = relu_ops
+
+    npu_post_ops = activation_ops
+    supported_operators = mac_main_ops | type_conversion_ops | npu_post_ops
+
+    # Supported data types
+    # TODO will differ compared to TensorFlow Lite, currently set to the same
+    supported_op_dtypes = set((DataType.uint8, DataType.int8, DataType.int16, DataType.int32))
+
+    def __init__(self):
+        # Setup the generic constraints. Note: the order matters
+        self.generic_constraints = []
+        self.generic_constraints.append(TosaSupportedOperators.constraint_tens_dtype)
+
+        # Setup specific constraints. Note: the order matters
+        self.specific_constraints = defaultdict(list)
+
+    def is_operator_supported(self, op):
+        ext_type = optype_to_tosa_op_type(op.type)
+        if op.type not in TosaSupportedOperators.supported_operators:
+            if op.type not in (Op.Placeholder, Op.SubgraphInput, Op.Const):
+                print(f"Info: {ext_type} '{op.name}' is not a NPU op")
+            return False
+
+        for constraint in self.generic_constraints + self.specific_constraints[op.type]:
+            valid, extra = constraint(op)
+            if not valid:
+                print(f"Warning: {ext_type} '{op.name}' is not supported on the NPU")
+                print(f" - {constraint.__doc__}")
+                if extra:
+                    print(f"   {extra}")
+                return False
+
+        return True
+
+    # TODO this function is the same for TensorFlow Lite, but input might differ
+    @classmethod
+    @docstring_format_args([list_formatter(supported_op_dtypes)])
+    def constraint_tens_dtype(cls, op):
+        "Tensors must be of type: {}"
+        valid = True
+        extra = []
+        tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
+        if not tensors:
+            tensors = [tens for tens in op.inputs if tens]
+        for tens in tensors:
+            if tens.dtype not in cls.supported_op_dtypes:
+                valid = False
+                extra.append(f"Tensor '{tens.name}' has data type: {tens.dtype}")
+        return valid, ", ".join(extra)