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>

1 files changed, 3 insertions, 2 deletions

diff --git a/ethosu/vela/weight_compressor.py b/ethosu/vela/weight_compressor.py
index e4779bf5..50ae26c0 100644
--- a/ethosu/vela/weight_compressor.py
+++ b/ethosu/vela/weight_compressor.py
@@ -32,6 +32,7 @@ from .errors import UnsupportedFeatureError
 from .numeric_util import round_up
 from .operation import NpuBlockType
 from .operation import Op
+from .operation import RoundingMode
 from .scaling import quantise_scale
 from .scaling import reduced_quantise_scale
 from .tensor import QuantizationParameters
@@ -303,8 +304,8 @@ def _prepare_scale_and_bias(arch, tens, explicit_scaling):
         else:
             quantised_scales = [quantise_scale(scale) for scale in scales]
 
-    # Check the output quantisation to see if the scale value needs increasing to the next one
-    if _get_output_quantization(first_consumer_op).next_after:
+    # Rounding away from zero requires the "next after" floating point value to be set on the output quantisation
+    if first_consumer_op.rounding_mode == RoundingMode.AwayZero:
         for i, quant_scale in enumerate(quantised_scales):
             q_scale, q_shift = quant_scale
             quantised_scales[i] = (q_scale + 1, q_shift)