MLBEDSW-3932: Remove Squeeze from supported operators

- Squeeze is no longer listed as supported operator
- Added missing doc-string for a Pad constraint

Change-Id: Ifd5e493acb0eb28bc4f104df74b3491589db8c29
Signed-off-by: Louis Verhaard <louis.verhaard@arm.com>

2 files changed, 27 insertions, 21 deletions

diff --git a/SUPPORTED_OPS.md b/SUPPORTED_OPS.md
index e1de48c..dfa24d0 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: `2.0.0`
+Vela version: `2.0.2.dev49+gda756aa`
 
 This file complies with
 [**Gitiles Markdown syntax**](https://github.com/google/gitiles/blob/master/Documentation/markdown.md)
@@ -22,6 +22,7 @@ Please check the supported operator list for your chosen runtime for further inf
 | CONV_2D | [Generic](#generic-constraints), [Specific](#conv_2d-constraints) |
 | DEPTHWISE_CONV_2D | [Generic](#generic-constraints), [Specific](#depthwise_conv_2d-constraints) |
 | FULLY_CONNECTED | [Generic](#generic-constraints), [Specific](#fully_connected-constraints) |
+| HARD_SWISH | [Generic](#generic-constraints), [Specific](#hard_swish-constraints) |
 | LEAKY_RELU | [Generic](#generic-constraints), [Specific](#leaky_relu-constraints) |
 | LOGISTIC | [Generic](#generic-constraints) |
 | MAXIMUM | [Generic](#generic-constraints), [Specific](#maximum-constraints) |
@@ -29,17 +30,17 @@ Please check the supported operator list for your chosen runtime for further inf
 | MINIMUM | [Generic](#generic-constraints), [Specific](#minimum-constraints) |
 | MUL | [Generic](#generic-constraints), [Specific](#mul-constraints) |
 | PACK | [Generic](#generic-constraints) |
+| PAD | [Generic](#generic-constraints), [Specific](#pad-constraints) |
 | QUANTIZE | [Generic](#generic-constraints) |
-| RELU | [Generic](#generic-constraints), [Specific](#relu-constraints) |
-| RELU6 | [Generic](#generic-constraints), [Specific](#relu6-constraints) |
-| RELU_N1_TO_1 | [Generic](#generic-constraints), [Specific](#relu_n1_to_1-constraints) |
+| RELU | [Generic](#generic-constraints) |
+| RELU6 | [Generic](#generic-constraints) |
+| RELU_N1_TO_1 | [Generic](#generic-constraints) |
 | RESHAPE | [Generic](#generic-constraints) |
 | RESIZE_BILINEAR | [Generic](#generic-constraints), [Specific](#resize_bilinear-constraints) |
 | SLICE | [Generic](#generic-constraints) |
 | SOFTMAX | [Generic](#generic-constraints), [Specific](#softmax-constraints) |
 | SPLIT | [Generic](#generic-constraints) |
 | SPLIT_V | [Generic](#generic-constraints), [Specific](#split_v-constraints) |
-| SQUEEZE | [Generic](#generic-constraints) |
 | STRIDED_SLICE | [Generic](#generic-constraints), [Specific](#strided_slice-constraints) |
 | SUB | [Generic](#generic-constraints), [Specific](#sub-constraints) |
 | TANH | [Generic](#generic-constraints) |
@@ -62,6 +63,7 @@ This is a list of constraints that all NPU operators must satisfy in order to be
 - Input(s), Output and Weight tensors with quantization scales must be finite
 - Per-axis quantization is only supported for the following op types: CONV_2D, DEPTHWISE_CONV_2D, TRANSPOSE_CONV
 - The fused activation function (if present) must be one of type: LOGISTIC, RELU, RELU6, RELU_N1_TO_1, TANH
+- Input and Output tensors must have quantization scales that fit within float32 precision
 
 ## ABS Constraints
 
@@ -149,6 +151,13 @@ This is a list of constraints that the FULLY_CONNECTED operator must satisfy in
 - Optional Bias tensor values must fit within 40-bits
 - The output tensor(s) must have 2D shape
 
+## HARD_SWISH Constraints
+
+This is a list of constraints that the HARD_SWISH operator must satisfy in order to be scheduled on the NPU.
+
+- IFM must be int8 or uint8
+- IFM and OFM data types must match
+
 ## LEAKY_RELU Constraints
 
 This is a list of constraints that the LEAKY_RELU operator must satisfy in order to be scheduled on the NPU.
@@ -201,23 +210,19 @@ This is a list of constraints that the MUL 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
 
-## RELU Constraints
-
-This is a list of constraints that the RELU operator must satisfy in order to be scheduled on the NPU.
-
-- The IFM quantization scale divided by the OFM quantization scale must not be infinite
+## PAD Constraints
 
-## RELU6 Constraints
+This is a list of constraints that the PAD operator must satisfy in order to be scheduled on the NPU.
 
-This is a list of constraints that the RELU6 operator must satisfy in order to be scheduled on the NPU.
-
-- The IFM quantization scale divided by the OFM quantization scale must not be infinite
-
-## RELU_N1_TO_1 Constraints
-
-This is a list of constraints that the RELU_N1_TO_1 operator must satisfy in order to be scheduled on the NPU.
-
-- The IFM quantization scale divided by the OFM quantization scale must not be infinite
+- IFM and OFM data types must match
+- Both Input quantization parameters must match OFM quantization parameters
+- Number of input tensors must be exactly 2
+- The padding tensor must have the shape [4,2]
+- The pad tensor can only pad width and height
+- Pad tensor must be of type: int32, int64
+- The padding tensor must be constant
+- Must be followed by one of the following operator types: CONV_2D, DEPTHWISE_CONV_2D
+- Padding must be at most kernel size divided by 2
 
 ## RESIZE_BILINEAR Constraints
 
diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py
index 8bb9c58..8446ec2 100644
--- a/ethosu/vela/supported_operators.py
+++ b/ethosu/vela/supported_operators.py
@@ -98,7 +98,7 @@ class SupportedOperators:
     )
     split_ops = set((Op.Split, Op.SplitV, Op.StridedSlice, Op.Slice, Op.UnpackReshaped, Op.Unpack,))
     concat_ops = set((Op.Concat, Op.ConcatTFLite, Op.PackReshaped, Op.Pack,))
-    memory_only_ops = set((Op.Squeeze, Op.Reshape, Op.QuantizedReshape,)) | concat_ops | split_ops
+    memory_only_ops = set((Op.Reshape, Op.QuantizedReshape,)) | concat_ops | split_ops
     shapeless_input_ops = binary_elem_wise_main_ops | set((Op.Split, Op.SplitV,))
     per_axis_quant_ops = convolution_like_ops  # per-axis/channel quantization only currently supported for conv ops
     supported_fused_activations = relu_ops | set((Op.Tanh, Op.Sigmoid, Op.LUT,))
@@ -839,6 +839,7 @@ class SupportedOperators:
 
     @staticmethod
     def constraint_pad_constant(op):
+        "The padding tensor must be constant"
         pad_tensor = op.inputs[1].values
         valid = pad_tensor is not None
         return valid, f"Op has non-constant padding tensor: {op.inputs[1].values}"