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_mapping.py b/ethosu/vela/tosa_mapping.py
new file mode 100644
index 0000000..82f61f7
--- /dev/null
+++ b/ethosu/vela/tosa_mapping.py
@@ -0,0 +1,325 @@
+# 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:
+# TOSA mapping functions used by reader.
+# Contains a mapping from the various TOSA enums and options structs, generated by the FlatBuffer code
+# generator, to Vela's internal format.
+from .data_type import DataType
+from .operation import Op
+from .operation import TensorIndices
+from .tosa import ArithmeticRightShiftAttribute # noqa: F401
+from .tosa import AxisAttribute # noqa: F401
+from .tosa import ClampAttribute # noqa: F401
+from .tosa import CondIfAttribute # noqa: F401
+from .tosa import Conv2dAttribute # noqa: F401
+from .tosa import ConvQuantInfo # noqa: F401
+from .tosa import MatMulQuantInfo # noqa: F401
+from .tosa import MulAttribute # noqa: F401
+from .tosa import PadQuantInfo # noqa: F401
+from .tosa import Pool2dAttribute # noqa: F401
+from .tosa import ReluNAttribute # noqa: F401
+from .tosa import RescaleAttribute # noqa: F401
+from .tosa import ReshapeAttribute # noqa: F401
+from .tosa import ResizeAttribute # noqa: F401
+from .tosa import SliceAttribute # noqa: F401
+from .tosa import TileAttribute # noqa: F401
+from .tosa import TransposeConv2dAttribute # noqa: F401
+from .tosa import UnaryQuantInfo # noqa: F401
+from .tosa import WhileLoopAttribute # noqa: F401
+from .tosa.DType import DType
+from .tosa.Op import Op as TosaOp
+
+
+datatype_map = {
+ DType.BOOL: DataType.bool,
+ DType.UINT8: DataType.uint8,
+ DType.INT4: DataType.int4,
+ DType.INT8: DataType.int8,
+ DType.INT16: DataType.int16,
+ DType.INT32: DataType.int32,
+ DType.INT48: DataType.int48,
+ DType.FLOAT: DataType.float32,
+}
+
+
+# TODO duplicate of tflite_mapping
+def underscore_to_camel_case(s):
+ return "".join(x.title() for x in s.split("_"))
+
+
+# TODO duplicate of tflite_mapping
+def identity(x):
+ return x
+
+
+class AttrSerializer:
+ def __init__(self, name, members=None):
+ self.name = name
+ self.module = globals()[self.name]
+ self.cls = getattr(self.module, self.name)
+ self.members = []
+ if members is not None:
+ for mem in members:
+ deserialize = identity
+ is_vector = False
+ if isinstance(mem, tuple):
+ if len(mem) == 2:
+ mem, is_vector = mem
+ deserialize = tuple
+ else:
+ assert 0
+ underscore_mem = mem
+ camelcase_mem = underscore_to_camel_case(mem)
+ self.members.append((underscore_mem, camelcase_mem, deserialize, is_vector))
+
+ def deserialize(self, op_data):
+ attr_type = op_data.AttributeType()
+ attr = op_data.Attribute()
+ attrs = {}
+ if attr_type:
+ tosa_attrs = self.cls()
+ tosa_attrs.Init(attr.Bytes, attr.Pos)
+ for underscore_mem, camelcase_mem, deserialize, is_vector in self.members:
+ fun = camelcase_mem
+ if is_vector:
+ fun += "AsNumpy"
+
+ attr = getattr(tosa_attrs, fun)()
+ try:
+ attrs[underscore_mem] = deserialize(attr)
+ except TypeError:
+ print("Warning: {0} could not read attribute '{1}'.".format(self.name, underscore_mem))
+
+ return attrs
+
+
+class QuantSerializer:
+ def __init__(self, name, members=None):
+ self.name = name
+ self.module = globals()[self.name]
+ self.cls = getattr(self.module, self.name)
+ self.members = []
+ if members is not None:
+ for mem in members:
+ deserialize = identity
+ underscore_mem = mem
+ camelcase_mem = underscore_to_camel_case(mem)
+ self.members.append((underscore_mem, camelcase_mem, deserialize))
+
+ def deserialize(self, op_data):
+ quant_info_type = op_data.QuantInfoType()
+ quant_info = op_data.QuantInfo()
+ quant = {}
+ if quant_info_type:
+ tosa_quant = self.cls()
+ tosa_quant.Init(quant_info.Bytes, quant_info.Pos)
+ for underscore_mem, camelcase_mem, deserialize in self.members:
+ attr = getattr(tosa_quant, camelcase_mem)()
+ try:
+ quant[underscore_mem] = deserialize(attr)
+ except TypeError:
+ print("Warning: {0} could not read quant info '{1}'.".format(self.name, underscore_mem))
+
+ return quant
+
+
+is_vec = True
+pool2d_attrs = AttrSerializer("Pool2dAttribute", (("padding", is_vec), ("kernel", is_vec), ("stride", is_vec)))
+conv2d_attrs = AttrSerializer("Conv2dAttribute", (("padding", is_vec), ("stride", is_vec), ("dilation", is_vec)))
+transpose_conv2d_attrs = AttrSerializer(
+ "TransposeConv2dAttribute", (("outpad", is_vec), ("stride", is_vec), ("dilation", is_vec), ("out_shape", is_vec))
+)
+relun_attrs = AttrSerializer("ReluNAttribute", ("max_int"))
+axis_attrs = AttrSerializer("AxisAttribute", ("axis"))
+reshape_attrs = AttrSerializer("ReshapeAttribute", (("shape", is_vec),))
+slice_attrs = AttrSerializer("SliceAttribute", (("begin", is_vec), ("size", is_vec)))
+tile_attrs = AttrSerializer("TileAttribute", (("multiplies", is_vec),))
+resize_attrs = AttrSerializer(
+ "ResizeAttribute", (("output_size", is_vec), ("stride", is_vec), ("offset", is_vec), ("shift"))
+)
+clamp_attrs = AttrSerializer("ClampAttribute", (("min_int"), ("max_int")))
+rescale_attrs = AttrSerializer(
+ "RescaleAttribute",
+ ("input_zp", "output_zp", ("multiplier", is_vec), ("shift", is_vec), "scale32", "double_round", "per_channel"),
+)
+mul_attrs = AttrSerializer("MulAttribute", ("shift"))
+ars_attrs = AttrSerializer("ArithmeticRightShiftAttribute", ("round",))
+condif_attrs = AttrSerializer("CondIfAttribute", (("then_branch"), ("else_branch"))) # TODO these are references
+while_attrs = AttrSerializer("WhileLoopAttribute", (("cond_branch"), ("body_branch"))) # TODO these are references
+
+unary_quant_info = QuantSerializer("UnaryQuantInfo", ("input_zp", "output_zp"))
+conv_quant_info = QuantSerializer("ConvQuantInfo", ("input_zp", "weight_zp"))
+matmul_quant_info = QuantSerializer("MatMulQuantInfo", ("a_zp", "b_zp"))
+pad_quant_info = QuantSerializer("PadQuantInfo", ("input_zp"))
+
+unsupported_tosa_operators = {
+ TosaOp.UNKNOWN,
+ TosaOp.ARGMAX,
+ TosaOp.CONV3D,
+ TosaOp.MATMUL,
+ TosaOp.TRANSPOSE_CONV2D,
+ TosaOp.SIGMOID,
+ TosaOp.TANH,
+ TosaOp.BITWISE_AND,
+ TosaOp.BITWISE_OR,
+ TosaOp.BITWISE_XOR,
+ TosaOp.DIV,
+ TosaOp.LOGICAL_AND,
+ TosaOp.LOGICAL_LEFT_SHIFT,
+ TosaOp.LOGICAL_RIGHT_SHIFT,
+ TosaOp.LOGICAL_OR,
+ TosaOp.LOGICAL_XOR,
+ TosaOp.MAXIMUM,
+ TosaOp.MINIMUM,
+ TosaOp.MUL,
+ TosaOp.POW,
+ TosaOp.TABLE,
+ TosaOp.ABS,
+ TosaOp.BITWISE_NOT,
+ TosaOp.CEIL,
+ TosaOp.CLZ,
+ TosaOp.EXP,
+ TosaOp.FLOOR,
+ TosaOp.LOG,
+ TosaOp.LOGICAL_NOT,
+ TosaOp.NEGATE,
+ TosaOp.RECIPROCAL,
+ TosaOp.RSQRT,
+ TosaOp.SELECT,
+ TosaOp.EQUAL,
+ TosaOp.GREATER,
+ TosaOp.GREATER_EQUAL,
+ TosaOp.REDUCE_ANY,
+ TosaOp.REDUCE_ALL,
+ TosaOp.REDUCE_MAX,
+ TosaOp.REDUCE_MIN,
+ TosaOp.REDUCE_PRODUCT,
+ TosaOp.REDUCE_SUM,
+ TosaOp.CONCAT,
+ TosaOp.PAD,
+ TosaOp.RESHAPE,
+ TosaOp.REVERSE,
+ TosaOp.SLICE,
+ TosaOp.TILE,
+ TosaOp.TRANSPOSE,
+ TosaOp.GATHER,
+ TosaOp.SCATTER,
+ TosaOp.RESIZE,
+ TosaOp.CAST,
+ TosaOp.IDENTITY,
+ TosaOp.CUSTOM,
+ TosaOp.COND_IF,
+ TosaOp.WHILE_LOOP,
+}
+
+
+TOSA_NO_INDICES = TensorIndices([], [], [])
+TOSA_IFM_INDICES = TensorIndices([0], [], [])
+# TOSA_IFM_WEIGHTS_INDICES = TensorIndices([0], [1], [])
+TOSA_IFM_WEIGHTS_BIAS_INDICES = TensorIndices([0], [1], [2])
+TOSA_IFM_IFM2_INDICES = TensorIndices([0, 1], [], [])
+# TOSA_CONV2D_BACKPROP_INDICES = TensorIndices([2], [1], [3])
+# TOSA_TRANSPOSE_CONV_INDICES = TensorIndices([0], [1], [3])
+# TOSA_CONCAT_INDICES = TensorIndices([1, 2], [], [])
+# TOSA_SPLIT_IFM_INDICES = TensorIndices([1], [], [])
+# TOSA_BLOCK_LSTM_INDICES = TensorIndices([3], [4], [])
+
+
+tosa_operator_map = {
+ # TosaOp.UNKNOWN: (),
+ # TODO TosaOp.ARGMAX: (Op.ArgMax, axis_attrs, None),
+ TosaOp.AVG_POOL2D: (Op.AvgPool, pool2d_attrs, unary_quant_info, TOSA_IFM_INDICES),
+ TosaOp.CONV2D: (Op.Conv2DBias, conv2d_attrs, conv_quant_info, TOSA_IFM_WEIGHTS_BIAS_INDICES),
+ # TODO TosaOp.CONV3D:
+ TosaOp.DEPTHWISE_CONV2D: (Op.DepthwiseConv2DBias, conv2d_attrs, conv_quant_info, TOSA_IFM_WEIGHTS_BIAS_INDICES),
+ TosaOp.FULLY_CONNECTED: (Op.FullyConnected, None, conv_quant_info, TOSA_IFM_WEIGHTS_BIAS_INDICES),
+ # TODO TosaOp.MATMUL:
+ TosaOp.MAX_POOL2D: (Op.MaxPool, pool2d_attrs, None, TOSA_IFM_INDICES),
+ # TODO TosaOp.TRANSPOSE_CONV2D: (Op.Conv2DBackpropInput, transpose_conv2d_attrs, conv_quant_info)
+ TosaOp.CLAMP: (Op.Clip, clamp_attrs, None, TOSA_IFM_INDICES),
+ TosaOp.RELUN: (Op.ReluN, relun_attrs, None, TOSA_IFM_INDICES),
+ # TODO TosaOp.SIGMOID
+ # TODO TosaOp.TANH
+ TosaOp.ADD: (Op.Add, None, None, TOSA_IFM_IFM2_INDICES),
+ TosaOp.ARITHMETIC_RIGHT_SHIFT: (Op.SHR, ars_attrs, None, TOSA_IFM_IFM2_INDICES),
+ # TODO TosaOp.BITWISE_AND
+ # TODO TosaOp.BITWISE_OR
+ # TODO TosaOp.BITWISE_XOR
+ # TODO TosaOp.DIV
+ # TODO TosaOp.LOGICAL_AND
+ # TODO TosaOp.LOGICAL_LEFT_SHIFT
+ # TODO TosaOp.LOGICAL_RIGHT_SHIFT
+ # TODO TosaOp.LOGICAL_OR
+ # TODO TosaOp.LOGICAL_XOR
+ # TODO TosaOp.MAXIMUM
+ # TODO TosaOp.MINIMUM
+ # TODO TosaOp.MUL
+ # TODO TosaOp.POW
+ TosaOp.SUB: (Op.Sub, None, None, TOSA_IFM_IFM2_INDICES),
+ # TODO TosaOp.TABLE
+ # TODO TosaOp.ABS
+ # TODO TosaOp.BITWISE_NOT
+ # TODO TosaOp.CEIL
+ # TODO TosaOp.CLZ
+ # TODO TosaOp.EXP
+ # TODO TosaOp.FLOOR
+ # TODO TosaOp.LOG
+ # TODO TosaOp.LOGICAL_NOT
+ # TODO TosaOp.NEGATE
+ # TODO TosaOp.RECIPROCAL
+ # TODO TosaOp.RSQRT
+ # TODO TosaOp.SELECT
+ # TODO TosaOp.EQUAL
+ # TODO TosaOp.GREATER
+ # TODO TosaOp.GREATER_EQUAL
+ # TODO TosaOp.REDUCE_ANY
+ # TODO TosaOp.REDUCE_ALL
+ # TODO TosaOp.REDUCE_MAX
+ # TODO TosaOp.REDUCE_MIN
+ # TODO TosaOp.REDUCE_PRODUCT
+ # TODO TosaOp.REDUCE_SUM
+ # TODO TosaOp.CONCAT
+ # TODO TosaOp.PAD
+ # TODO TosaOp.RESHAPE
+ # TODO TosaOp.REVERSE
+ # TODO TosaOp.SLICE
+ # TODO TosaOp.TILE
+ # TODO TosaOp.TRANSPOSE
+ # TODO TosaOp.GATHER
+ # TODO TosaOp.SCATTER
+ # TODO TosaOp.RESIZE
+ # TODO TosaOp.CAST
+ TosaOp.RESCALE: (Op.Rescale, rescale_attrs, None, TOSA_IFM_INDICES),
+ TosaOp.CONST: (Op.Const, None, None, TOSA_NO_INDICES),
+ # TODO TosaOp.IDENTITY
+ # TODO TosaOp.CUSTOM
+ # TODO TosaOp.COND_IF
+ # TODO TosaOp.WHILE_LOOP
+}
+
+tosa_operator_inv_map = {v[0]: (k, v[1]) for k, v in tosa_operator_map.items()}
+
+
+def tosa_type_name(builtin):
+ return next(k for k, v in vars(TosaOp).items() if v == builtin)
+
+
+# TODO will return UNKNOWN for the once that have not yet been defined in tosa_operator_map
+def optype_to_tosa_op_type(op_type):
+ if op_type in tosa_operator_inv_map:
+ return tosa_type_name(tosa_operator_inv_map[op_type][0])
+ else:
+ return TosaOp.UNKNOWN