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, 13 insertions, 3 deletions

diff --git a/SUPPORTED_OPS.md b/SUPPORTED_OPS.md
index 81704e53..ceb02051 100644
--- a/SUPPORTED_OPS.md
+++ b/SUPPORTED_OPS.md
@@ -19,7 +19,7 @@ limitations under the License.
 # Supported Ops
 
 This file was automatically generated by Vela using the `--supported-ops-report` parameter.  
-Vela version: `3.9.1.dev16+gd230ce9.d20231030`
+Vela version: `3.9.1.dev21+gb724cdb.d20231107`
 
 This file complies with
 [**Gitiles Markdown syntax**](https://gerrit.googlesource.com/gitiles/+/HEAD/Documentation/markdown.md)
@@ -75,6 +75,7 @@ Please check the supported operator list for your chosen runtime for further inf
 | STRIDED_SLICE | [Generic](#tflite-generic-constraints), [Specific](#tflite-strided_slice-constraints) |
 | SUB | [Generic](#tflite-generic-constraints), [Specific](#tflite-sub-constraints) |
 | TANH | [Generic](#tflite-generic-constraints) |
+| TRANSPOSE | [Generic](#tflite-generic-constraints), [Specific](#tflite-transpose-constraints) |
 | TRANSPOSE_CONV | [Generic](#tflite-generic-constraints), [Specific](#tflite-transpose_conv-constraints) |
 | UNIDIRECTIONAL_SEQUENCE_LSTM | [Generic](#tflite-generic-constraints), [Specific](#tflite-unidirectional_sequence_lstm-constraints) |
 | UNPACK | [Generic](#tflite-generic-constraints) |
@@ -90,12 +91,12 @@ This is a list of constraints most NPU operators must satisfy in order to be sch
 - Output tensors cannot be scalar - [QUANTIZE]
 - Scalar Input tensors are only valid for op type: ADD, ARG_MAX, EXPAND_DIMS, MAXIMUM, MEAN, MINIMUM, MUL, QUANTIZE, SPLIT, SPLIT_V, SUB
 - Input(s) and Output tensors must not be greater than 4D
-- Input(s), Output and Weight tensors must have quantization parameters - [ARG_MAX, SHAPE]
+- Input(s), Output and Weight tensors must have quantization parameters - [ARG_MAX, SHAPE, TRANSPOSE]
 - Input(s), Output and Weight tensors with quantization scales must be finite
 - Input and Output tensors must have quantization scales that fit within float32 precision
 - Constant tensors should not have NoneType-values
 - Tensors must be of type: int16, int32, int8, uint8 - [ARG_MAX]
-- Tensors which are int32 are only valid when op type is: ADD, ARG_MAX, MUL, SHAPE, SUB
+- Tensors which are int32 are only valid when op type is: ADD, ARG_MAX, MUL, SHAPE, SUB, TRANSPOSE
 - Tensor dimensions must be in the range [1, 65535]
 - Per-axis quantization is only supported for the following op types: CONV_2D, DEPTHWISE_CONV_2D, TRANSPOSE_CONV
 - IFM Tensor batch size must be 1 - [FULLY_CONNECTED, RESHAPE, SHAPE, SLICE, SOFTMAX, SPLIT, SPLIT_V, SQUEEZE, STRIDED_SLICE, UNPACK]
@@ -405,6 +406,15 @@ This is a list of constraints that the SUB operator must satisfy in order to be
 - For IFM that are unsigned, OFM must either be the same type or int32
 - Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2
 
+### TFLite TRANSPOSE Constraints
+
+This is a list of constraints that the TRANSPOSE operator must satisfy in order to be scheduled on the NPU.
+
+- The following shape/permutations are supported for transpose:  
+        When ifm rank is 2: WxC -> CxW  
+        When ifm rank is 3: HxWxC -> WxHxC, 1xWxC -> 1xCxW, Hx1xC -> Cx1xH  
+        When ifm rank is 4: 1xHxWxC -> 1xWxHxC, 1x1xWxC -> 1x1xCxW, 1xHx1xC -> 1xCx1xW
+
 ### TFLite TRANSPOSE_CONV Constraints
 
 This is a list of constraints that the TRANSPOSE_CONV operator must satisfy in order to be scheduled on the NPU.