MLBEDSW-8290: MLCE: Add TRANSPOSE support3.10.0.rc1

 - Added graph optimiser function to convert TRANSPOSE op
into an AvgPool op with swapped stride for height and width
 - Added TRANSPOSE supported op check
 - Added unit tests for TRANSPOSE supported op check
 - Updated SUPPORTED_OPS.md
 - Fixed problem in pass packing when optimizing the pass list.
Old problem, but now seen when moving TRANSPOSE from cpu.

Change-Id: I0a0ef420b0fb8241090c2e2434622881105cde15
Signed-off-by: Johan Alfven <johan.alfven@arm.com>

1 files changed, 18 insertions, 2 deletions

diff --git a/ethosu/vela/high_level_command_to_npu_op.py b/ethosu/vela/high_level_command_to_npu_op.py
index 5e9dffac..53df0966 100644
--- a/ethosu/vela/high_level_command_to_npu_op.py
+++ b/ethosu/vela/high_level_command_to_npu_op.py
@@ -383,6 +383,7 @@ def create_feature_map(
     op_shape4D: Shape4D,
     tile_base_offsets: List[int],
     stride_multiplier: Optional[List[int]] = None,
+    is_ofm: bool = False,
 ) -> NpuFeatureMap:
     """Creates feature map with common fields populated"""
     fm = NpuFeatureMap()
@@ -395,7 +396,16 @@ def create_feature_map(
     else:
         assert 0, "Incorrect tensor format"
 
-    strides = tens.get_strides(op_shape4D)
+    if is_ofm and tens.ops[0] is not None and tens.ops[0].original_type == Op.Transpose:
+        # op_shape4D has ifm shape, see fixup_transpose. Stride calculations needs to be
+        # based on the correct ofm shape.
+        op_shape4D_ofm_shape = Shape4D([op_shape4D.batch, op_shape4D.width, op_shape4D.height, op_shape4D.depth])
+        strides = tens.get_strides(op_shape4D_ofm_shape)
+        # Swap h and w strides which will cause the transpose to happen
+        strides[-3], strides[-2] = strides[-2], strides[-3]
+    else:
+        strides = tens.get_strides(op_shape4D)
+
     assert strides is not None
 
     if stride_multiplier and stride_multiplier != [1, 1, 1]:
@@ -513,7 +523,13 @@ def set_common_op_fields(npu_op: NpuBlockOperation, cmd: NpuStripe, arch: Archit
 
     out_block = cmd.ofm_box.get_block()
     npu_op.ofm = create_feature_map(
-        cmd.ofm_tensor, cmd.ofm_box, arch, ps.ofm_shapes[0], op.tile_base_offsets_ofm, op.ofm_stride_multiplier
+        cmd.ofm_tensor,
+        cmd.ofm_box,
+        arch,
+        ps.ofm_shapes[0],
+        op.tile_base_offsets_ofm,
+        op.ofm_stride_multiplier,
+        is_ofm=True,
     )
     npu_op.ofm.shape = NpuShape3D(height=out_block.height, width=out_block.width, depth=out_block.depth)
     npu_op.ofm.quantization = get_ofm_quantization(ps, cmd.ofm_tensor)