diff options
Diffstat (limited to 'ethosu/vela/operation.py')
| -rw-r--r-- | ethosu/vela/operation.py | 45 |
1 files changed, 42 insertions, 3 deletions
diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py index 161b17fd..52f06cf0 100644 --- a/ethosu/vela/operation.py +++ b/ethosu/vela/operation.py @@ -21,6 +21,7 @@ from __future__ import annotations import copy from collections import namedtuple +from enum import auto from enum import Enum from typing import Any from typing import Dict @@ -29,7 +30,6 @@ from typing import Optional from typing import Tuple from typing import TYPE_CHECKING -from .api import NpuRoundingMode from .errors import VelaError from .ethos_u55_regs.ethos_u55_regs import resampling_mode from .numeric_util import full_shape @@ -44,6 +44,13 @@ PointXY = namedtuple("PointXY", "x y") PointXYZ = namedtuple("PointXYZ", "x y z") +class RoundingMode(Enum): + TFLite = auto() # Round like TensorFlow Lite + ToZero = auto() # Round towards zero (truncate) + HalfUp = auto() # Round to nearest with x.5 rounded up towards positive infinity (natural) + AwayZero = auto() # Round away from zero (towards infinity) + + class NpuBlockType(Enum): Default = 0 ConvolutionMxN = 1 @@ -491,7 +498,7 @@ class Operation: "rescale", "read_offsets", "read_shapes", - "rounding_mode", + "_rounding_mode", "explicit_scaling", "write_offset", "write_shape", @@ -528,7 +535,7 @@ class Operation: self.ofm_shapes: List[Shape4D] = [] self.read_offsets: List[Optional[Shape4D]] = [None, None] # offset for [ifm, ifm2] self.read_shapes: List[Optional[Shape4D]] = [None, None] # read shape for [ifm, ifm2] - self.rounding_mode: Optional[NpuRoundingMode] = None + self._rounding_mode: Optional[RoundingMode] = None # Rescale op in TOSA supplies explicit multiplier and shift values self.explicit_scaling: Optional[ExplicitScaling] = None # Write offset, for operations that only produce a part of the OFM @@ -587,6 +594,38 @@ class Operation: return self._original_type @property + def rounding_mode(self): + return self._rounding_mode + + @rounding_mode.setter + def rounding_mode(self, mode: RoundingMode): + # All rounding modes are supported by all operators with the exception of rounding away from zero (see comment + # below) + is_supported = True + if mode == RoundingMode.AwayZero: + # Rounding away from zero does not have direct hardware support and so the compiler implements it indirectly + # in different ways. The exact process depends upon the operator type and not all operators are supported. + # Basically, rounding away from zero works by adjusting the accumulated value by a "small" amount before + # rounding up with the addition of a half (natural rounding). This "small" amount should be big enough to + # cause x.5 to be rounded correctly but small enough that smaller values are not incorrectly rounded. This + # is done by slightly adjusting the scale and shift on the ofm tensor using the scale and bias tensor, + # it has no affect on global scaling (i.e. the ofm_scale register). In addition, the zero points of the + # input and/or output tensors may also require forcing to zero but the exact behaviour also depends upon the + # corresponding optimisation performed in graph_optimisation.py where the rounding mode is set + is_supported = False + if self.original_type == Op.ResizeBilinear and self.type == Op.DepthwiseConv2DBias: + is_supported = True + if self.original_type == Op.AvgPool and self.type == Op.DepthwiseConv2DBias: + is_supported = True + + if is_supported: + self._rounding_mode = mode + else: + assert ( + False + ), f"Setting rounding mode = {mode} on {self.original_type} operator '{self.name}' is not supported." + + @property def type_changed(self): return self.type != self.original_type |