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
diff --git a/ethosu/vela/tosa_supported_operators.py b/ethosu/vela/tosa_supported_operators.py
new file mode 100644
index 0000000..c87d653
--- /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)