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| author | Tim Hall <tim.hall@arm.com> | 2023-05-16 22:39:14 +0100 |
|---|---|---|
| committer | tim.hall <tim.hall@arm.com> | 2023-05-17 11:05:57 +0000 |
| commit | 5ff4cd12898f44228288a7969b52dff97be30cb2 (patch) | |
| tree | 1c8068c02254d5479706e41379bbd1f8c7b33205 /ethosu/vela/tosa_graph_optimiser.py | |
| parent | 0426fe9de82e0f6b339edbd2bec78a5d322fb05f (diff) | |
| download | ethos-u-vela-5ff4cd12898f44228288a7969b52dff97be30cb2.tar.gz | |
MLBEDSW-7223: Fusing Pad and AvgPool causes diff
- Fixed an issue with the fusing of PAD and AVERAGE_POOL_2D whereby
the rounding away from zero didn't work because it requires the zero
point to be at zero but the input padding required it to be set to the
desired zero point. This affected both int8 and int16. The solution
was to remove it by using the bias prior to the scaling
- Refactored the rounding away from zero mode
Change-Id: I8f2df69df06d2a9722315c346646e5a901cb2c3b
Signed-off-by: Tim Hall <tim.hall@arm.com>
Diffstat (limited to 'ethosu/vela/tosa_graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/tosa_graph_optimiser.py | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/ethosu/vela/tosa_graph_optimiser.py b/ethosu/vela/tosa_graph_optimiser.py index b3474147..df6b5759 100644 --- a/ethosu/vela/tosa_graph_optimiser.py +++ b/ethosu/vela/tosa_graph_optimiser.py @@ -19,7 +19,6 @@ import numpy as np from . import rewrite_graph -from .api import NpuRoundingMode from .data_type import DataType from .debug_database import DebugDatabase from .graph_optimiser_util import bypass_memory_only_ops @@ -32,6 +31,7 @@ from .graph_optimiser_util import set_tensor_equivalence from .lut import convert_to_lut from .operation import ExplicitScaling from .operation import Op +from .operation import RoundingMode from .operation_util import create_add_nop from .operation_util import create_avgpool_nop from .operation_util import create_pad_nop @@ -115,7 +115,7 @@ def calc_scaling_avgpool(op, arch, nng): multiplier.append(numerator // kernel_wh) shift.append(30 + k) - op.rounding_mode = NpuRoundingMode.NATURAL + op.rounding_mode = RoundingMode.HalfUp op.explicit_scaling = ExplicitScaling(False, shift, multiplier) return op @@ -399,9 +399,9 @@ def rewrite_rescale(op, arch, nng): explicit_scaling = ExplicitScaling(per_channel, shift, multiplier) if double_round and scale32: - rounding_mode = NpuRoundingMode.TFL + rounding_mode = RoundingMode.TFLite else: - rounding_mode = NpuRoundingMode.NATURAL + rounding_mode = RoundingMode.HalfUp if prev_op.type.is_depthwise_conv2d_op() or prev_op.type.is_conv2d_op() or prev_op.type == Op.FullyConnected: assert len(multiplier) == len(shift) == len(prev_op.bias.values) |