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-rw-r--r--SUPPORTED_OPS.md16
1 files changed, 13 insertions, 3 deletions
diff --git a/SUPPORTED_OPS.md b/SUPPORTED_OPS.md
index 81704e5..ceb0205 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.