MLBEDSW-4075 PACK axis 0 + tanh fails with output diff

The test failed since the tanh had batch size > 1.
Added checks for batch size for all supported operators.

Signed-off-by: Fredrik Svedberg <fredrik.svedberg@arm.com>
Change-Id: I3570352740c40eb96bd9db965dfa3c91c81ff2ad

1 files changed, 11 insertions, 23 deletions

diff --git a/SUPPORTED_OPS.md b/SUPPORTED_OPS.md
index 3ccf3ab5..9488a78b 100644
--- a/SUPPORTED_OPS.md
+++ b/SUPPORTED_OPS.md
@@ -1,7 +1,7 @@
 # Supported Ops
 
 This file was automatically generated by Vela using the `--supported-ops-report` parameter.  
-Vela version: `3.6.0rc1.dev11+gac5e33e`
+Vela version: `3.5.1.dev10+gf616c9d6.d20220915`
 
 This file complies with
 [**Gitiles Markdown syntax**](https://github.com/google/gitiles/blob/master/Documentation/markdown.md)
@@ -61,19 +61,20 @@ Please check the supported operator list for your chosen runtime for further inf
 This is a list of constraints most NPU operators must satisfy in order to be scheduled on the NPU.
 (Operators excluded from certain constraints are shown in brackets [ ] )
 
-- Input(s) and Output tensors must not be dynamic - [Quantize]
-- Input(s) and Output tensors must have a defined shape 
-- Output tensors cannot be scalar - [Quantize]
-- Scalar Input tensors are only valid for op type: ADD, 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 - [Shape]
-- 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 
+- Input(s) and Output tensors must not be dynamic - [QUANTIZE]
+- Input(s) and Output tensors must have a defined shape
+- Output tensors cannot be scalar - [QUANTIZE]
+- Scalar Input tensors are only valid for op type: ADD, 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 - [SHAPE]
+- 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
 - Tensors which are int32 are only valid when op type is: ADD, MUL, SHAPE, SUB
 - 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]
 - The fused activation function (if present) must be one of type: LOGISTIC, RELU, RELU6, RELU_N1_TO_1, TANH
 - If a fused activation function is present, the Output tensor must be one of type: int16, int8, uint8
 
@@ -83,7 +84,6 @@ This is a list of constraints that the ABS operator must satisfy in order to be
 
 - At least one Input's shape must match the OFM's shape
 - IFM and OFM data types must match
-- Batch size must be 1 for Input tensors with more than 2 dimensions
 
 ### TFLite ADD Constraints
 
@@ -93,7 +93,6 @@ This is a list of constraints that the ADD operator must satisfy in order to be
 - Both Input data types must match
 - For IFM that are signed, OFM must also be signed
 - For IFM that are unsigned, OFM must either be the same type or int32
-- Batch size must be 1 for Input tensors with more than 2 dimensions
 - Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2
 
 ### TFLite AVERAGE_POOL_2D Constraints
@@ -103,7 +102,6 @@ This is a list of constraints that the AVERAGE_POOL_2D operator must satisfy in
 - Stride values for both width and height must be integer types
 - IFM and OFM data types must match
 - Kernel filter values for both width and height must be integer types
-- IFM Tensor batch size must be 1
 - Stride values for both width and height must be in the range [1, 3]
 - Kernel filter values for both width and height must be in the range [1, 8]
 - VALID padding: Kernel filter height must be in the range [1, 256]
@@ -134,7 +132,6 @@ This is a list of constraints that the CONV_2D operator must satisfy in order to
 - The sum of the weights cannot exceed 8323072
 - Optional Bias tensor must be of type: int32, int64
 - Optional Bias tensor values must fit within 40-bits
-- IFM Tensor batch size must be 1
 
 ### TFLite DEPTHWISE_CONV_2D Constraints
 
@@ -151,7 +148,6 @@ This is a list of constraints that the DEPTHWISE_CONV_2D operator must satisfy i
 - The sum of the weights cannot exceed 8323072
 - Optional Bias tensor must be of type: int32, int64
 - Optional Bias tensor values must fit within 40-bits
-- IFM Tensor batch size must be 1
 - For depth multipliers > 1, IFM channels must be 1 and OFM channels must be equal to the depth multiplier
 
 ### TFLite EXPAND_DIMS Constraints
@@ -184,7 +180,6 @@ This is a list of constraints that the LEAKY_RELU operator must satisfy in order
 
 - At least one Input's shape must match the OFM's shape
 - IFM and OFM data types must match
-- Batch size must be 1 for Input tensors with more than 2 dimensions
 
 ### TFLite MAXIMUM Constraints
 
@@ -192,7 +187,6 @@ This is a list of constraints that the MAXIMUM operator must satisfy in order to
 
 - At least one Input's shape must match the OFM's shape
 - IFM and OFM data types must match
-- Batch size must be 1 for Input tensors with more than 2 dimensions
 - Both Input quantization parameters must match OFM quantization parameters
 - Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2
 
@@ -203,7 +197,6 @@ This is a list of constraints that the MAX_POOL_2D operator must satisfy in orde
 - Stride values for both width and height must be integer types
 - IFM and OFM data types must match
 - Kernel filter values for both width and height must be integer types
-- IFM Tensor batch size must be 1
 - Stride values for both width and height must be in the range [1, 3]
 - Kernel filter height must be in the range [1, 256]
 - Product of kernel filter width and height must be in the range [1, 65536]
@@ -215,7 +208,6 @@ This is a list of constraints that the MEAN operator must satisfy in order to be
 - IFM must be int8 or uint8
 - Input tensor must be at least 2D
 - Axis indices must correspond to height and width axes
-- IFM Tensor batch size must be 1
 - Product of height and width must be no greater than 65536
 - Product of height and width must be no greater than 4096 when:  
         IFM and OFM have different scale or zero point; or  
@@ -236,7 +228,6 @@ This is a list of constraints that the MINIMUM operator must satisfy in order to
 
 - At least one Input's shape must match the OFM's shape
 - IFM and OFM data types must match
-- Batch size must be 1 for Input tensors with more than 2 dimensions
 - Both Input quantization parameters must match OFM quantization parameters
 - Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2
 
@@ -248,7 +239,6 @@ This is a list of constraints that the MUL operator must satisfy in order to be
 - Both Input data types must match
 - For IFM that are signed, OFM must also be signed
 - For IFM that are unsigned, OFM must either be the same type or int32
-- Batch size must be 1 for Input tensors with more than 2 dimensions
 - Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2
 
 ### TFLite PAD Constraints
@@ -335,7 +325,6 @@ This is a list of constraints that the SUB operator must satisfy in order to be
 - Both Input data types must match
 - For IFM that are signed, OFM must also be signed
 - For IFM that are unsigned, OFM must either be the same type or int32
-- Batch size must be 1 for Input tensors with more than 2 dimensions
 - Broadcasting is only allowed for rank indices with dimension 1, from either IFM1 or IFM2
 
 ### TFLite TRANSPOSE_CONV Constraints
@@ -353,7 +342,6 @@ This is a list of constraints that the TRANSPOSE_CONV operator must satisfy in o
 - The sum of the weights cannot exceed 8323072
 - Optional Bias tensor must be of type: int32, int64
 - Optional Bias tensor values must fit within 40-bits
-- IFM Tensor batch size must be 1
 - Stride values for both width and height must be 2
 - SAME padding: OFM dimensions must equal IFM dimensions multiplied by stride
 - VALID padding: OFM dimensions must equal IFM dimensions multiplied by stride,