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, 25 insertions, 5 deletions

diff --git a/ethosu/vela/high_level_command_to_npu_op.py b/ethosu/vela/high_level_command_to_npu_op.py
index 55b44730..9526bd50 100644
--- a/ethosu/vela/high_level_command_to_npu_op.py
+++ b/ethosu/vela/high_level_command_to_npu_op.py
@@ -62,6 +62,7 @@ from .operation import NpuBlockType
 from .operation import Op
 from .operation import Operation
 from .operation import Padding
+from .operation import RoundingMode
 from .register_command_stream_generator import generate_command_stream
 from .register_command_stream_util import BASE_PTR_INDEX_MEM2MEM
 from .register_command_stream_util import to_npu_kernel
@@ -113,6 +114,14 @@ resampling_mode_inv_map = {
 }
 
 
+rounding_mode_map = {
+    RoundingMode.TFLite: NpuRoundingMode.TFL,
+    RoundingMode.ToZero: NpuRoundingMode.TRUNCATE,
+    RoundingMode.HalfUp: NpuRoundingMode.NATURAL,
+    RoundingMode.AwayZero: NpuRoundingMode.NATURAL,
+}
+
+
 def ifm_ifm2_correct_order(ifm_shape: Shape4D, ifm2_shape: Shape4D) -> bool:
 
     if ifm_shape is None:
@@ -146,7 +155,7 @@ def get_rounding_mode(op: Operation, fused_quantize: bool) -> NpuRoundingMode:
     ):
         rounding_mode = NpuRoundingMode.NATURAL
     if op.rounding_mode is not None:
-        rounding_mode = op.rounding_mode
+        rounding_mode = rounding_mode_map[op.rounding_mode]
     return rounding_mode
 
 
@@ -298,10 +307,21 @@ def use_zero_point_0(ps, tens: Tensor, is_ifm_tensor: bool) -> bool:
     """Checks if quantization should use 0 as zero point"""
     if tens.dtype == DataType.int32 and is_ifm_tensor:
         return True
-    # Force zero point to 0 for ResizeBilinear when converting to a DepthwiseConv since the reference kernel
-    # will ignore the zero point.
-    if ps.primary_op.original_type == Op.ResizeBilinear and ps.primary_op.type == Op.DepthwiseConv2DBias:
-        return True
+    if ps.primary_op.rounding_mode == RoundingMode.AwayZero:
+        if ps.primary_op.original_type == Op.ResizeBilinear and ps.primary_op.type == Op.DepthwiseConv2DBias:
+            # Force zero point to 0 for ResizeBilinear operators converted to a DepthwiseConv with rounding away from
+            # zero. This is because the reference kernel ignores the zero points.
+            return True
+        if (
+            not is_ifm_tensor
+            and ps.primary_op.original_type == Op.AvgPool
+            and ps.primary_op.attrs.get("padding", None) == Padding.EXPLICIT
+            and ps.primary_op.type == Op.DepthwiseConv2DBias
+        ):
+            # Force zero point to 0 for the OFM of AvgPool operators that have been combined with a previous PAD
+            # operator and converted to a DepthwiseConv with rounding away from zero. This is because the zero point
+            # will already have been applied in the Bias.
+            return True
     if ps.primary_op.type not in (Op.AvgPool, Op.CLZ, Op.SHL) and not ps.primary_op.type.is_resize_op():
         return False
     if ps.primary_op.type == Op.AvgPool and ps.primary_op.explicit_scaling: