MLBEDSW-4853: Refactor supported operators

Refactor supported operators by breaking out model semantics
into its own class. Model semantics checked right after model
read.

Signed-off-by: Jonas Ohlsson <jonas.ohlsson@arm.com>
Change-Id: If442b189efcd91dda01af60b2b3adedfacdf2fad

2 files changed, 485 insertions, 383 deletions

diff --git a/ethosu/vela/test/test_tflite_model_semantic.py b/ethosu/vela/test/test_tflite_model_semantic.py
new file mode 100644
index 00000000..4c329844
--- /dev/null
+++ b/ethosu/vela/test/test_tflite_model_semantic.py
@@ -0,0 +1,460 @@
+# Copyright (C) 2021 Arm Limited or its affiliates. All rights reserved.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the License); you may
+# not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an AS IS BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Description:
+# Unit tests for tflite_model_semantic
+import numpy as np
+
+from ethosu.vela.data_type import DataType
+from ethosu.vela.operation import Op
+from ethosu.vela.operation import Padding
+from ethosu.vela.tensor import create_const_tensor
+from ethosu.vela.tensor import QuantizationParameters
+from ethosu.vela.tensor import Tensor
+from ethosu.vela.test import testutil
+from ethosu.vela.tflite_model_semantic import TFLiteSemantic
+
+semantic_checker = TFLiteSemantic()
+
+
+def test_constraint_tens_no_dynamic():
+    # Tensors cannot be dynamic (no shape, not a scalar)
+    op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [])
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_tens_defined_shape():
+    # Tensors cannot have None in them
+    op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, None, 8], [1, 8, 8, 8])
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_tens_output_scalar():
+    # Scalar output is not allowed at all:
+    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [1, 8, 8, 8], [])
+    op.ofm.values = 0.5
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_tens_input_scalar():
+    # Shapeless input is allowed if its of a certain type:
+    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [], [1, 8, 8, 8])
+    assert semantic_checker.is_operator_semantic_valid(op)
+    # Invalid shapeless input due to op type:
+    op = testutil.create_op_with_quant_tensors(Op.Relu, [], [1, 8, 8, 8])
+    op.ifm.values = 0.5
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_tens_shape_size():
+    # Tensors cannot be > 4D
+    op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 1, 8, 8, 8], [1, 1, 8, 8, 8], set_ifm_ofm_shapes=False)
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_tens_quant_none_check():
+    # Tensors must have quantization parameters
+    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [], [1, 8, 8, 8], ifm2_quant=None)
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_tens_quant_scale():
+    # Quantization scale cannot be infinite
+    qp = QuantizationParameters()
+    qp.zero_point = 0
+    qp.scale_f32 = np.inf
+    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [], [1, 8, 8, 8], ifm_quant=qp)
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_fc_output_2d_not_supp():
+    op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [12, 1], [3, 2, 2, 1], weights_shape=[12, 1, 1, 1])
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [12, 1, 1, 1], [1, 3, 4], weights_shape=[12, 1, 1, 1])
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [1, 1, 1, 1], [1], weights_shape=[1, 1, 1, 1])
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_fc_output_2d_is_supp():
+    op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [4, 8, 8, 4], [32, 32], weights_shape=[4, 8, 8, 4])
+    assert semantic_checker.is_operator_semantic_valid(op)
+    op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [1, 1024], [16, 64], weights_shape=[1, 1024])
+    assert semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_conv_pass():
+    # First test a simple conv passes
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 1, 1, 1], [1, 1, 1, 1], weights_shape=[1, 1, 1, 1])
+    op.attrs = {"stride_w": 1, "stride_h": 1}
+    assert semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_stride_type():
+    # Stride width and height must be integer types
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8])
+    op.attrs = {"stride_w": 1.5, "stride_h": "1"}
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_dilation_type():
+    # Dilation width and height must be integer types
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8])
+    op.attrs = {"stride_w": 1, "stride_h": 1, "dilation_w_factor": 1.5, "dilation_h_factor": "1"}
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_quant_scale_inf():
+    # Test handling IFM scale/OFM scale is infinite
+    op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [1, 8, 8, 8])
+    op.ifm.quantization.scale_f32 = np.float32(1e9)
+    op.ofm.quantization.scale_f32 = np.float32(1e-35)
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_ofm_scale_too_small():
+    # Tests handling of OFM scale < 1e-38
+    shp = [1, 10, 20, 16]
+    op = testutil.create_elemwise_op(Op.Mul, "mul", shp, shp, shp, ofm_quant=testutil.default_quant_params(),)
+    assert semantic_checker.is_operator_semantic_valid(op)
+    op.ofm.quantization.scale_f32 = 1e-43
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_matching_in_out_types():
+    # Valid
+    op = testutil.create_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8])
+    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2, "filter_height": 2, "padding": Padding.SAME}
+    assert semantic_checker.is_operator_semantic_valid(op)
+    # Invalid. datatypes for ifm and ofm must match (default uint8)
+    op.ifm.dtype = DataType.int8
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_filter_type():
+    # Filter width/height must be integers
+    op = testutil.create_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8])
+    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2.5, "filter_height": "2", "padding": Padding.SAME}
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_matching_shapes():
+    # Softmax requires the ifm and ofm shapes to match
+    op = testutil.create_op_with_quant_tensors(Op.Softmax, [1, 1, 1, 8], [1, 2, 2, 4])
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op = testutil.create_op_with_quant_tensors(Op.Softmax, [1, 1, 1, 8], [1, 1, 1, 8])
+    assert semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_beta_value_range():
+    # beta must be positive
+    op = testutil.create_op_with_quant_tensors(Op.Softmax, [1, 1, 1, 8], [1, 1, 1, 8])
+    op.attrs["beta"] = -1.0
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op.attrs["beta"] = 0.0
+    assert semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_splitv_inferred():
+    # SplitV requires a maximum of one inferred shape (-1)
+    qp = testutil.default_quant_params()
+    op = testutil.create_op_with_quant_tensors(Op.SplitV, [1, 1, 1, 8], [1, 1, 1, 8])
+    sizes = create_const_tensor("sizes", [1, 1, 1, 4], DataType.int16, [[[[0, -1, 2, -1]]]], np.int16, quantization=qp)
+    op.add_input_tensor(sizes)
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op = testutil.create_op_with_quant_tensors(Op.SplitV, [1, 1, 1, 8], [1, 1, 1, 8])
+    sizes = create_const_tensor("sizes", [1, 1, 1, 4], DataType.int16, [[[[0, 1, 2, -1]]]], np.int16, quantization=qp)
+    op.add_input_tensor(sizes)
+    assert semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_concat_pass():
+    # A working concat
+    op = testutil.create_op_with_quant_tensors(Op.ConcatTFLite, [1, 1, 1, 4], [1, 1, 1, 8])
+    ifm2 = Tensor([1, 1, 1, 4], DataType.uint8, "in2")
+    ifm2.quantization = testutil.default_quant_params()
+    op.add_input_tensor(ifm2)
+    op.attrs["axis"] = 3
+    assert semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_axis_exists():
+    # Missing axis attribute
+    op = testutil.create_op_with_quant_tensors(Op.ConcatTFLite, [1, 1, 1, 4], [1, 1, 1, 8])
+    ifm2 = Tensor([1, 1, 1, 4], DataType.uint8, "in2")
+    ifm2.quantization = testutil.default_quant_params()
+    op.add_input_tensor(ifm2)
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_axis_valid():
+    # Invalid axis attribute
+    op = testutil.create_op_with_quant_tensors(Op.ConcatTFLite, [1, 1, 1, 4], [1, 1, 1, 8])
+    ifm2 = Tensor([1, 1, 1, 4], DataType.uint8, "in2")
+    ifm2.quantization = testutil.default_quant_params()
+    op.add_input_tensor(ifm2)
+    op.attrs["axis"] = 7
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_matching_dimensionality():
+    # Mismatching dimensionality: 4D+2D=4D
+    op = testutil.create_op_with_quant_tensors(Op.ConcatTFLite, [1, 1, 1, 4], [1, 1, 1, 8])
+    ifm2 = Tensor([1, 4], DataType.uint8, "in2")
+    ifm2.quantization = testutil.default_quant_params()
+    op.add_input_tensor(ifm2)
+    op.attrs["axis"] = 3
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_valid_dimensions():
+    # Mismatching dimension value:
+    # ifm2 has w and h as 2, which is not the axis to concat and doesnt match ifm1 or ofm
+    op = testutil.create_op_with_quant_tensors(Op.ConcatTFLite, [1, 1, 1, 4], [1, 1, 1, 8])
+    ifm2 = Tensor([1, 2, 2, 4], DataType.uint8, "in2")
+    ifm2.quantization = testutil.default_quant_params()
+    op.add_input_tensor(ifm2)
+    op.attrs["axis"] = 3
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def create_strided_slice_op(in_shape, out_shape, start_offsets, end_offsets):
+    qp = testutil.default_quant_params()
+    in0 = Tensor(in_shape, DataType.uint8, "in")
+    in0.quantization = qp
+    in1 = create_const_tensor("begin", [len(start_offsets)], DataType.uint8, start_offsets, quantization=qp)
+    in2 = create_const_tensor("end", [len(end_offsets)], DataType.uint8, end_offsets, quantization=qp)
+    in3 = create_const_tensor("strides", [len(end_offsets)], DataType.uint8, len(end_offsets) * [1], quantization=qp)
+    out = Tensor(out_shape, DataType.uint8, "out")
+    out.quantization = qp
+    attrs = {"ellipsis_mask": 0, "new_axis_mask": 0, "shrink_axis_mask": 0, "begin_mask": 0, "end_mask": 0}
+    return testutil.create_op(Op.StridedSlice, [in0, in1, in2, in3], out, attrs=attrs)
+
+
+def create_pad_op(
+    in_shape, out_shape, padding, in_dtype=DataType.int8, out_dtype=DataType.int8, pad_dtype=DataType.int32,
+):
+    qp = testutil.default_quant_params()
+    in0 = Tensor(in_shape, in_dtype, "in")
+    in0.quantization = qp
+    pad_tensor = create_const_tensor(name="pad", shape=list(np.shape(padding)), values=padding, dtype=pad_dtype)
+    out = Tensor(out_shape, out_dtype, "out")
+    out.quantization = qp.clone()
+    op = testutil.create_op(Op.Pad, [in0, pad_tensor], out)
+    return op
+
+
+def test_constraint_pad_input_count():
+    # Incorrect number of input tensors (2)
+    op = create_pad_op(in_shape=[1, 1, 1, 1], out_shape=[1, 3, 3, 1], padding=[[0, 0], [1, 1], [1, 1], [0, 0]],)
+    assert semantic_checker.is_operator_semantic_valid(op)
+    op.add_input_tensor(op.inputs[0].clone())
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def create_strided_slice():
+    # Creates a valid strided slice operator with some valid inputs/outputs
+    op = create_strided_slice_op([1, 10, 10, 10], [1, 5, 5, 10], [127, 2, 2, 0], [0, 7, -3, 0])
+    op.attrs["begin_mask"] = 1
+    op.attrs["end_mask"] = 9
+    assert semantic_checker.is_operator_semantic_valid(op)
+    return op
+
+
+def test_constraint_stridedslice_input_count():
+    # Wrong number of input tensors
+    op = create_strided_slice()
+    op.add_input_tensor(op.inputs[0].clone())
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_stridedslice_inputs_const():
+    # begin, end, stride values must not be None
+    op = create_strided_slice()
+    op.inputs[1].values = None
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op = create_strided_slice()
+    op.inputs[2].values = None
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op = create_strided_slice()
+    op.inputs[3].values = None
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_ellipsis_mask():
+    # Unsemantic_checkered ellipsis mask
+    op = create_strided_slice()
+    op.attrs["ellipsis_mask"] = 1
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_axis_masks():
+    op = create_strided_slice()
+    # Setting one of new_axis_mask/shrink_axis_mask to non-zero is ok
+    op.attrs["new_axis_mask"] = 2
+    assert semantic_checker.is_operator_semantic_valid(op)
+    op = create_strided_slice()
+    op.attrs["shrink_axis_mask"] = 3
+    assert semantic_checker.is_operator_semantic_valid(op)
+    # But setting both to non-zero is not semantic_checkered
+    op.attrs["new_axis_mask"] = 2
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_slice_ranges():
+    # Examples where end offset <= begin offset
+    op = create_strided_slice()
+    op.inputs[1].values = [0, 7, 2, 0]
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op = create_strided_slice()
+    op.inputs[2].values = [0, 7, 2, 0]
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op = create_strided_slice()
+    op.attrs["begin_mask"] = 0
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op = create_strided_slice()
+    op.attrs["end_mask"] = 0
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_matching_inputs_types():
+    # input data types must match (default is uint8)
+    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8])
+    op.ifm2.dtype = DataType.int8
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_matching_signed():
+    # signed inputs require output to also be signed
+    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int8)
+    op.ofm.dtype = DataType.uint8
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_unsigned_valid():
+    # unsigned inputs require output to be either:
+    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8])
+    # the same (default uint8)
+    assert semantic_checker.is_operator_semantic_valid(op)
+    op.ofm.dtype = DataType.int8
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op.ofm.dtype = DataType.int16
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    # or int32
+    op.ofm.dtype = DataType.int32
+    assert semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_matching_either_shapes():
+    # BINARY CASE
+    # At least one ifm shape must match ofm's shape
+    op = testutil.create_elemwise_op(Op.Add, "op", [1, 4], [4, 4], [4, 4])
+    assert semantic_checker.is_operator_semantic_valid(op)
+    op = testutil.create_elemwise_op(Op.Add, "op", [4, 4], [1, 4], [4, 4])
+    assert semantic_checker.is_operator_semantic_valid(op)
+    op = testutil.create_elemwise_op(Op.Add, "op", [4, 4], [4, 4], [2, 2])
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op = testutil.create_elemwise_op(Op.Add, "op", [1, 4, 1, 16], [1, 1, 4, 1], [1, 4, 4, 16])
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op = testutil.create_elemwise_op(Op.Add, "op", [1, 1, 4, 1], [1, 4, 1, 16], [1, 4, 4, 16])
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+    # UNARY CASE
+    # No second input so this is treated the same as requiring ifm shape to match ofm shape
+    op = testutil.create_elemwise_op(Op.CLZ, "op", [2, 2], None, [2, 2], datatype=DataType.int32)
+    assert semantic_checker.is_operator_semantic_valid(op)
+    op = testutil.create_elemwise_op(Op.CLZ, "op", [4, 4], None, [2, 2], datatype=DataType.int32)
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_alpha_valid():
+    # Alpha cannot be negative
+    op = testutil.create_elemwise_op(Op.LeakyRelu, "op", [2, 2], None, [2, 2])
+    op.attrs["alpha"] = 0
+    assert semantic_checker.is_operator_semantic_valid(op)
+    op.attrs["alpha"] = -1
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_hardswish_dtype():
+    # HardSwish operator dtype should be int8 or uint8, and input dtype must match output
+    # UINT8
+    op = testutil.create_op_with_quant_tensors(Op.HardSwish, [1, 8, 8, 8], [1, 8, 8, 8])
+    assert semantic_checker.is_operator_semantic_valid(op)
+    # INT8
+    op = testutil.create_op_with_quant_tensors(Op.HardSwish, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int8)
+    assert semantic_checker.is_operator_semantic_valid(op)
+
+    # Invalid
+    op = testutil.create_op_with_quant_tensors(Op.HardSwish, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int16)
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op = testutil.create_op_with_quant_tensors(Op.HardSwish, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.uint16)
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op = testutil.create_op_with_quant_tensors(Op.HardSwish, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int32)
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+    in_tens = Tensor([1, 8, 8, 8], DataType.int8, "in")
+    out_tens = Tensor([1, 8, 8, 8], DataType.uint8, "out")
+    op = testutil.create_op(Op.HardSwish, [in_tens], out_tens)
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_constraint_keep_dims_ifm_ofm():
+    op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [4, 8, 8, 4], [32, 32], weights_shape=[4, 8, 8, 4])
+    op.attrs["keep_num_dims"] = True
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op.attrs["keep_num_dims"] = False
+    assert semantic_checker.is_operator_semantic_valid(op)
+
+
+def create_mean(input_shape, output_shape, axis, datatype, attrs):
+    ifm = Tensor(input_shape, datatype, "in")
+    ifm.quantization = testutil.default_quant_params()
+    ofm = Tensor(output_shape, datatype, "out")
+    ofm.quantization = testutil.default_quant_params()
+    if type(axis) is list:
+        indices = create_const_tensor("indices", [len(axis)], DataType.int32, axis, np.uint8)
+    elif type(axis) is int:
+        indices = create_const_tensor("indices", [], DataType.int32, axis, np.uint8)
+    op = testutil.create_op(Op.Mean, [ifm, indices], ofm, attrs)
+    return op
+
+
+def test_mean_dtype():
+    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [1, 2], DataType.int8, {"keep_dims": True})
+    assert semantic_checker.is_operator_semantic_valid(op)
+    op.ifm.dtype = DataType.int16
+    op.ofm.dtype = DataType.int16
+    assert not semantic_checker.is_operator_semantic_valid(op)
+
+
+def test_mean_axis():
+    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], 0, DataType.int8, {"keep_dims": True})
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [3], DataType.int8, {"keep_dims": True})
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [1, 3], DataType.int8, {"keep_dims": True})
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [0, 1], DataType.int8, {"keep_dims": True})
+    assert not semantic_checker.is_operator_semantic_valid(op)
+    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [1, 2], DataType.int8, {"keep_dims": True})
+    assert semantic_checker.is_operator_semantic_valid(op)
+    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [1], DataType.int8, {"keep_dims": True})
+    assert semantic_checker.is_operator_semantic_valid(op)
+    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], 2, DataType.int8, {"keep_dims": True})
+    assert semantic_checker.is_operator_semantic_valid(op)
+    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [2, 1], DataType.int8, {"keep_dims": True})
+    assert semantic_checker.is_operator_semantic_valid(op)
diff --git a/ethosu/vela/test/test_supported_operators.py b/ethosu/vela/test/test_tflite_supported_operators.py
index 38308154..af5dc174 100644
--- a/ethosu/vela/test/test_supported_operators.py
+++ b/ethosu/vela/test/test_tflite_supported_operators.py
@@ -15,55 +15,20 @@
 # limitations under the License.
 #
 # Description:
-# Unit tests for support_operators
+# Unit tests for tflite support_operators
 import numpy as np
 
 from ethosu.vela.data_type import DataType
 from ethosu.vela.operation import ActivationFunction
 from ethosu.vela.operation import Op
 from ethosu.vela.operation import Padding
-from ethosu.vela.supported_operators import SupportedOperators
 from ethosu.vela.tensor import create_const_tensor
 from ethosu.vela.tensor import QuantizationParameters
 from ethosu.vela.tensor import Tensor
 from ethosu.vela.test import testutil
+from ethosu.vela.tflite_supported_operators import TFLiteSupportedOperators
 
-support = SupportedOperators()
-
-
-def test_constraint_tens_no_dynamic():
-    # Tensors cannot be dynamic (no shape, not a scalar)
-    op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [])
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_tens_defined_shape():
-    # Tensors cannot have None in them
-    op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, None, 8], [1, 8, 8, 8])
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_tens_output_scalar():
-    # Scalar output is not allowed at all:
-    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [1, 8, 8, 8], [])
-    op.ofm.values = 0.5
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_tens_input_scalar():
-    # Shapeless input is allowed if its of a certain type:
-    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [], [1, 8, 8, 8])
-    assert support.is_operator_supported(op)
-    # Invalid shapeless input due to op type:
-    op = testutil.create_op_with_quant_tensors(Op.Relu, [], [1, 8, 8, 8])
-    op.ifm.values = 0.5
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_tens_shape_size():
-    # Tensors cannot be > 4D
-    op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 1, 8, 8, 8], [1, 1, 8, 8, 8], set_ifm_ofm_shapes=False)
-    assert not support.is_operator_supported(op)
+support = TFLiteSupportedOperators()
 
 
 def test_constraint_tens_dtype():
@@ -86,21 +51,6 @@ def test_constraint_tens_dimension():
     assert not support.is_operator_supported(op)
 
 
-def test_constraint_tens_quant_none_check():
-    # Tensors must have quantization parameters
-    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [], [1, 8, 8, 8], ifm2_quant=None)
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_tens_quant_scale():
-    # Quantization scale cannot be infinite
-    qp = QuantizationParameters()
-    qp.zero_point = 0
-    qp.scale_f32 = np.inf
-    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [], [1, 8, 8, 8], ifm_quant=qp)
-    assert not support.is_operator_supported(op)
-
-
 def test_constraint_tens_quant_per_axis_not_supp():
     # Quantization scale cannot be array-valued for elemwise ops
     qp = QuantizationParameters()
@@ -123,15 +73,6 @@ def test_constraint_tens_quant_per_axis_is_supp():
     assert support.is_operator_supported(op)
 
 
-def test_constraint_fc_output_2d_not_supp():
-    op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [12, 1], [3, 2, 2, 1], weights_shape=[12, 1, 1, 1])
-    assert not support.is_operator_supported(op)
-    op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [12, 1, 1, 1], [1, 3, 4], weights_shape=[12, 1, 1, 1])
-    assert not support.is_operator_supported(op)
-    op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [1, 1, 1, 1], [1], weights_shape=[1, 1, 1, 1])
-    assert not support.is_operator_supported(op)
-
-
 def test_constraint_fc_output_2d_is_supp():
     op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [4, 8, 8, 4], [32, 32], weights_shape=[4, 8, 8, 4])
     assert support.is_operator_supported(op)
@@ -158,49 +99,35 @@ def test_constraint_faf_ofm_dtype():
 
 def test_constraint_conv_pass():
     # First test a simple conv passes
-    op = testutil.create_op_with_quant_tensors(Op.Conv2D, [1, 1, 1, 1], [1, 1, 1, 1], weights_shape=[1, 1, 1, 1])
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 1, 1, 1], [1, 1, 1, 1], weights_shape=[1, 1, 1, 1])
     op.attrs = {"stride_w": 1, "stride_h": 1}
     assert support.is_operator_supported(op)
 
 
-def test_constraint_stride_type():
-    # Stride width and height must be integer types
-    op = testutil.create_op_with_quant_tensors(Op.Conv2D, [1, 8, 8, 8], [1, 8, 8, 8])
-    op.attrs = {"stride_w": 1.5, "stride_h": "1"}
-    assert not support.is_operator_supported(op)
-
-
 def test_constraint_stride_range():
     # Stride width and height must lie within a certain range
-    op = testutil.create_op_with_quant_tensors(Op.Conv2D, [1, 8, 8, 8], [1, 8, 8, 8])
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8])
     op.attrs = {"stride_w": 0, "stride_h": 20}
     assert not support.is_operator_supported(op)
 
 
-def test_constraint_dilation_type():
-    # Dilation width and height must be integer types
-    op = testutil.create_op_with_quant_tensors(Op.Conv2D, [1, 8, 8, 8], [1, 8, 8, 8])
-    op.attrs = {"stride_w": 1, "stride_h": 1, "dilation_w_factor": 1.5, "dilation_h_factor": "1"}
-    assert not support.is_operator_supported(op)
-
-
 def test_constraint_dilation_range():
     # Dilation width and height must lie within a certain range
-    op = testutil.create_op_with_quant_tensors(Op.Conv2D, [1, 8, 8, 8], [1, 8, 8, 8])
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8])
     op.attrs = {"stride_w": 1, "stride_h": 1, "dilation_w_factor": 0, "dilation_h_factor": 20}
     assert not support.is_operator_supported(op)
 
 
 def test_constraint_dilated_height_range():
     # Dilated kernel height must lie within a certain range
-    op = testutil.create_op_with_quant_tensors(Op.Conv2D, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[65, 64, 1, 1])
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[65, 64, 1, 1])
     op.attrs = {"stride_w": 1, "stride_h": 1}
     assert not support.is_operator_supported(op)
 
 
 def test_constraint_dilated_product_range():
     # Dilated kernel width x height must lie within a certain range
-    op = testutil.create_op_with_quant_tensors(Op.Conv2D, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[64, 65, 1, 1])
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[64, 65, 1, 1])
     op.attrs = {"stride_w": 1, "stride_h": 1}
     assert not support.is_operator_supported(op)
 
@@ -208,7 +135,7 @@ def test_constraint_dilated_product_range():
 def test_constraint_weights_type():
     # Weight tensor must be 8-bit
     op = testutil.create_op_with_quant_tensors(
-        Op.Conv2D, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[1, 1, 1, 1], datatype=DataType.int16
+        Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[1, 1, 1, 1], datatype=DataType.int16
     )
     op.attrs = {"stride_w": 1, "stride_h": 1}
     assert not support.is_operator_supported(op)
@@ -216,7 +143,7 @@ def test_constraint_weights_type():
 
 def test_constraint_weights_const():
     # Weight tensor cannot be non-const tensors
-    op = testutil.create_op_with_quant_tensors(Op.Conv2D, [1, 8, 8, 8], [1, 8, 8, 8])
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8])
     op.attrs = {"stride_w": 1, "stride_h": 1}
     weights = Tensor([64, 64, 1, 1], DataType.uint8, "weights")
     weights.quantization = testutil.default_quant_params()
@@ -226,7 +153,7 @@ def test_constraint_weights_const():
 
 def test_constraint_weights_limit():
     # Sum of weights has a limit
-    op = testutil.create_op_with_quant_tensors(Op.Conv2D, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[1, 1, 1, 1])
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [1, 8, 8, 8], [1, 8, 8, 8], weights_shape=[1, 1, 1, 1])
     op.attrs = {"stride_w": 1, "stride_h": 1}
     op.weights.quantization.zero_point = np.array([[[[(127 * 65536) + 1]]]])
     assert not support.is_operator_supported(op)
@@ -252,28 +179,11 @@ def test_constraint_bias_40bit():
 
 
 def test_constraint_batch_size():
-    op = testutil.create_op_with_quant_tensors(Op.Conv2D, [2, 8, 8, 8], [1, 8, 8, 8], weights_shape=[1, 1, 1, 1])
+    op = testutil.create_op_with_quant_tensors(Op.Conv2DBias, [2, 8, 8, 8], [1, 8, 8, 8], weights_shape=[1, 1, 1, 1])
     op.attrs = {"stride_w": 1, "stride_h": 1}
     assert not support.is_operator_supported(op)
 
 
-def test_constraint_quant_scale_inf():
-    # Test handling IFM scale/OFM scale is infinite
-    op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [1, 8, 8, 8])
-    op.ifm.quantization.scale_f32 = np.float32(1e9)
-    op.ofm.quantization.scale_f32 = np.float32(1e-35)
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_ofm_scale_too_small():
-    # Tests handling of OFM scale < 1e-38
-    shp = [1, 10, 20, 16]
-    op = testutil.create_elemwise_op(Op.Mul, "mul", shp, shp, shp, ofm_quant=testutil.default_quant_params(),)
-    assert support.is_operator_supported(op)
-    op.ofm.quantization.scale_f32 = 1e-43
-    assert not support.is_operator_supported(op)
-
-
 def test_constraint_depth_multiplier():
     # Valid. Depth multiplier is 1 so no further constraints
     op = testutil.create_op_with_quant_tensors(
@@ -339,23 +249,6 @@ def test_constraint_tconv_valid():
     assert not support.is_operator_supported(op)
 
 
-def test_constraint_matching_in_out_types():
-    # Valid
-    op = testutil.create_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8])
-    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2, "filter_height": 2, "padding": Padding.SAME}
-    assert support.is_operator_supported(op)
-    # Invalid. datatypes for ifm and ofm must match (default uint8)
-    op.ifm.dtype = DataType.int8
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_filter_type():
-    # Filter width/height must be integers
-    op = testutil.create_op_with_quant_tensors(Op.AvgPool, [1, 8, 8, 8], [1, 8, 8, 8])
-    op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2.5, "filter_height": "2", "padding": Padding.SAME}
-    assert not support.is_operator_supported(op)
-
-
 def test_constraint_filter_range():
     # Avg pool restrictions are dependent on padding:
     # SAME padding restricts both W and H to max 8
@@ -434,36 +327,6 @@ def test_constraint_resize():
     assert not support.is_operator_supported(op)
 
 
-def test_constraint_matching_shapes():
-    # Softmax requires the ifm and ofm shapes to match
-    op = testutil.create_op_with_quant_tensors(Op.Softmax, [1, 1, 1, 8], [1, 2, 2, 4])
-    assert not support.is_operator_supported(op)
-    op = testutil.create_op_with_quant_tensors(Op.Softmax, [1, 1, 1, 8], [1, 1, 1, 8])
-    assert support.is_operator_supported(op)
-
-
-def test_constraint_beta_value_range():
-    # beta must be positive
-    op = testutil.create_op_with_quant_tensors(Op.Softmax, [1, 1, 1, 8], [1, 1, 1, 8])
-    op.attrs["beta"] = -1.0
-    assert not support.is_operator_supported(op)
-    op.attrs["beta"] = 0.0
-    assert support.is_operator_supported(op)
-
-
-def test_constraint_splitv_inferred():
-    # SplitV requires a maximum of one inferred shape (-1)
-    qp = testutil.default_quant_params()
-    op = testutil.create_op_with_quant_tensors(Op.SplitV, [1, 1, 1, 8], [1, 1, 1, 8])
-    sizes = create_const_tensor("sizes", [1, 1, 1, 4], DataType.int16, [[[[0, -1, 2, -1]]]], np.int16, quantization=qp)
-    op.add_input_tensor(sizes)
-    assert not support.is_operator_supported(op)
-    op = testutil.create_op_with_quant_tensors(Op.SplitV, [1, 1, 1, 8], [1, 1, 1, 8])
-    sizes = create_const_tensor("sizes", [1, 1, 1, 4], DataType.int16, [[[[0, 1, 2, -1]]]], np.int16, quantization=qp)
-    op.add_input_tensor(sizes)
-    assert support.is_operator_supported(op)
-
-
 def test_constraint_concat_pass():
     # A working concat
     op = testutil.create_op_with_quant_tensors(Op.Concat, [1, 1, 1, 4], [1, 1, 1, 8])
@@ -474,59 +337,6 @@ def test_constraint_concat_pass():
     assert support.is_operator_supported(op)
 
 
-def test_constraint_axis_exists():
-    # Missing axis attribute
-    op = testutil.create_op_with_quant_tensors(Op.Concat, [1, 1, 1, 4], [1, 1, 1, 8])
-    ifm2 = Tensor([1, 1, 1, 4], DataType.uint8, "in2")
-    ifm2.quantization = testutil.default_quant_params()
-    op.add_input_tensor(ifm2)
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_axis_valid():
-    # Invalid axis attribute
-    op = testutil.create_op_with_quant_tensors(Op.Concat, [1, 1, 1, 4], [1, 1, 1, 8])
-    ifm2 = Tensor([1, 1, 1, 4], DataType.uint8, "in2")
-    ifm2.quantization = testutil.default_quant_params()
-    op.add_input_tensor(ifm2)
-    op.attrs["axis"] = 7
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_matching_dimensionality():
-    # Mismatching dimensionality: 4D+2D=4D
-    op = testutil.create_op_with_quant_tensors(Op.Concat, [1, 1, 1, 4], [1, 1, 1, 8])
-    ifm2 = Tensor([1, 4], DataType.uint8, "in2")
-    ifm2.quantization = testutil.default_quant_params()
-    op.add_input_tensor(ifm2)
-    op.attrs["axis"] = 3
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_valid_dimensions():
-    # Mismatching dimension value:
-    # ifm2 has w and h as 2, which is not the axis to concat and doesnt match ifm1 or ofm
-    op = testutil.create_op_with_quant_tensors(Op.Concat, [1, 1, 1, 4], [1, 1, 1, 8])
-    ifm2 = Tensor([1, 2, 2, 4], DataType.uint8, "in2")
-    ifm2.quantization = testutil.default_quant_params()
-    op.add_input_tensor(ifm2)
-    op.attrs["axis"] = 3
-    assert not support.is_operator_supported(op)
-
-
-def create_strided_slice_op(in_shape, out_shape, start_offsets, end_offsets):
-    qp = testutil.default_quant_params()
-    in0 = Tensor(in_shape, DataType.uint8, "in")
-    in0.quantization = qp
-    in1 = create_const_tensor("begin", [len(start_offsets)], DataType.uint8, start_offsets, quantization=qp)
-    in2 = create_const_tensor("end", [len(end_offsets)], DataType.uint8, end_offsets, quantization=qp)
-    in3 = create_const_tensor("strides", [len(end_offsets)], DataType.uint8, len(end_offsets) * [1], quantization=qp)
-    out = Tensor(out_shape, DataType.uint8, "out")
-    out.quantization = qp
-    attrs = {"ellipsis_mask": 0, "new_axis_mask": 0, "shrink_axis_mask": 0, "begin_mask": 0, "end_mask": 0}
-    return testutil.create_op(Op.StridedSlice, [in0, in1, in2, in3], out, attrs=attrs)
-
-
 def create_pad_op(
     in_shape, out_shape, padding, in_dtype=DataType.int8, out_dtype=DataType.int8, pad_dtype=DataType.int32,
 ):
@@ -540,14 +350,6 @@ def create_pad_op(
     return op
 
 
-def test_constraint_pad_input_count():
-    # Incorrect number of input tensors (2)
-    op = create_pad_op(in_shape=[1, 1, 1, 1], out_shape=[1, 3, 3, 1], padding=[[0, 0], [1, 1], [1, 1], [0, 0]],)
-    assert support.is_operator_supported(op)
-    op.add_input_tensor(op.inputs[0].clone())
-    assert not support.is_operator_supported(op)
-
-
 def test_constraint_padded_dimensions():
     # Incorrect padding dimensions, can only pad width and height
     op = create_pad_op(in_shape=[1, 1, 1, 1], out_shape=[1, 3, 3, 1], padding=[[1, 1], [1, 1], [1, 1], [0, 0]],)
@@ -582,6 +384,19 @@ def test_constraint_pad_dtype():
     assert not support.is_operator_supported(op)
 
 
+def create_strided_slice_op(in_shape, out_shape, start_offsets, end_offsets):
+    qp = testutil.default_quant_params()
+    in0 = Tensor(in_shape, DataType.uint8, "in")
+    in0.quantization = qp
+    in1 = create_const_tensor("begin", [len(start_offsets)], DataType.uint8, start_offsets, quantization=qp)
+    in2 = create_const_tensor("end", [len(end_offsets)], DataType.uint8, end_offsets, quantization=qp)
+    in3 = create_const_tensor("strides", [len(end_offsets)], DataType.uint8, len(end_offsets) * [1], quantization=qp)
+    out = Tensor(out_shape, DataType.uint8, "out")
+    out.quantization = qp
+    attrs = {"ellipsis_mask": 0, "new_axis_mask": 0, "shrink_axis_mask": 0, "begin_mask": 0, "end_mask": 0}
+    return testutil.create_op(Op.StridedSlice, [in0, in1, in2, in3], out, attrs=attrs)
+
+
 def create_strided_slice():
     # Creates a valid strided slice operator with some valid inputs/outputs
     op = create_strided_slice_op([1, 10, 10, 10], [1, 5, 5, 10], [127, 2, 2, 0], [0, 7, -3, 0])
@@ -591,26 +406,6 @@ def create_strided_slice():
     return op
 
 
-def test_constraint_stridedslice_input_count():
-    # Wrong number of input tensors
-    op = create_strided_slice()
-    op.add_input_tensor(op.inputs[0].clone())
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_stridedslice_inputs_const():
-    # begin, end, stride values must not be None
-    op = create_strided_slice()
-    op.inputs[1].values = None
-    assert not support.is_operator_supported(op)
-    op = create_strided_slice()
-    op.inputs[2].values = None
-    assert not support.is_operator_supported(op)
-    op = create_strided_slice()
-    op.inputs[3].values = None
-    assert not support.is_operator_supported(op)
-
-
 def test_constraint_stridedslice_stride_values():
     # Unsupported strides
     op = create_strided_slice()
@@ -618,70 +413,6 @@ def test_constraint_stridedslice_stride_values():
     assert not support.is_operator_supported(op)
 
 
-def test_constraint_ellipsis_mask():
-    # Unsupported ellipsis mask
-    op = create_strided_slice()
-    op.attrs["ellipsis_mask"] = 1
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_axis_masks():
-    op = create_strided_slice()
-    # Setting one of new_axis_mask/shrink_axis_mask to non-zero is ok
-    op.attrs["new_axis_mask"] = 2
-    assert support.is_operator_supported(op)
-    op = create_strided_slice()
-    op.attrs["shrink_axis_mask"] = 3
-    assert support.is_operator_supported(op)
-    # But setting both to non-zero is not supported
-    op.attrs["new_axis_mask"] = 2
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_slice_ranges():
-    # Examples where end offset <= begin offset
-    op = create_strided_slice()
-    op.inputs[1].values = [0, 7, 2, 0]
-    assert not support.is_operator_supported(op)
-    op = create_strided_slice()
-    op.inputs[2].values = [0, 7, 2, 0]
-    assert not support.is_operator_supported(op)
-    op = create_strided_slice()
-    op.attrs["begin_mask"] = 0
-    assert not support.is_operator_supported(op)
-    op = create_strided_slice()
-    op.attrs["end_mask"] = 0
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_matching_inputs_types():
-    # input data types must match (default is uint8)
-    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8])
-    op.ifm2.dtype = DataType.int8
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_matching_signed():
-    # signed inputs require output to also be signed
-    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int8)
-    op.ofm.dtype = DataType.uint8
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_unsigned_valid():
-    # unsigned inputs require output to be either:
-    op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8])
-    # the same (default uint8)
-    assert support.is_operator_supported(op)
-    op.ofm.dtype = DataType.int8
-    assert not support.is_operator_supported(op)
-    op.ofm.dtype = DataType.int16
-    assert not support.is_operator_supported(op)
-    # or int32
-    op.ofm.dtype = DataType.int32
-    assert support.is_operator_supported(op)
-
-
 def test_constraint_inputs_int32():
     # both inputs must be type int32
     op = testutil.create_elemwise_op(Op.SHL, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8])
@@ -750,28 +481,6 @@ def test_constraint_elemwise_batch_size():
     assert not support.is_operator_supported(op)
 
 
-def test_constraint_matching_either_shapes():
-    # BINARY CASE
-    # At least one ifm shape must match ofm's shape
-    op = testutil.create_elemwise_op(Op.Add, "op", [1, 4], [4, 4], [4, 4])
-    assert support.is_operator_supported(op)
-    op = testutil.create_elemwise_op(Op.Add, "op", [4, 4], [1, 4], [4, 4])
-    assert support.is_operator_supported(op)
-    op = testutil.create_elemwise_op(Op.Add, "op", [4, 4], [4, 4], [2, 2])
-    assert not support.is_operator_supported(op)
-    op = testutil.create_elemwise_op(Op.Add, "op", [1, 4, 1, 16], [1, 1, 4, 1], [1, 4, 4, 16])
-    assert not support.is_operator_supported(op)
-    op = testutil.create_elemwise_op(Op.Add, "op", [1, 1, 4, 1], [1, 4, 1, 16], [1, 4, 4, 16])
-    assert not support.is_operator_supported(op)
-
-    # UNARY CASE
-    # No second input so this is treated the same as requiring ifm shape to match ofm shape
-    op = testutil.create_elemwise_op(Op.CLZ, "op", [2, 2], None, [2, 2], datatype=DataType.int32)
-    assert support.is_operator_supported(op)
-    op = testutil.create_elemwise_op(Op.CLZ, "op", [4, 4], None, [2, 2], datatype=DataType.int32)
-    assert not support.is_operator_supported(op)
-
-
 def test_constraint_broadcast_shapes():
     # BINARY CASE
     # Only allow broadcast to 1 dim, for 1 rank index
@@ -800,46 +509,6 @@ def test_constraint_broadcast_shapes():
     assert not support.is_operator_supported(op)
 
 
-def test_constraint_alpha_valid():
-    # Alpha cannot be negative
-    op = testutil.create_elemwise_op(Op.LeakyRelu, "op", [2, 2], None, [2, 2])
-    op.attrs["alpha"] = 0
-    assert support.is_operator_supported(op)
-    op.attrs["alpha"] = -1
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_hardswish_dtype():
-    # HardSwish operator dtype should be int8 or uint8, and input dtype must match output
-    # UINT8
-    op = testutil.create_op_with_quant_tensors(Op.HardSwish, [1, 8, 8, 8], [1, 8, 8, 8])
-    assert support.is_operator_supported(op)
-    # INT8
-    op = testutil.create_op_with_quant_tensors(Op.HardSwish, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int8)
-    assert support.is_operator_supported(op)
-
-    # Invalid
-    op = testutil.create_op_with_quant_tensors(Op.HardSwish, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int16)
-    assert not support.is_operator_supported(op)
-    op = testutil.create_op_with_quant_tensors(Op.HardSwish, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.uint16)
-    assert not support.is_operator_supported(op)
-    op = testutil.create_op_with_quant_tensors(Op.HardSwish, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int32)
-    assert not support.is_operator_supported(op)
-
-    in_tens = Tensor([1, 8, 8, 8], DataType.int8, "in")
-    out_tens = Tensor([1, 8, 8, 8], DataType.uint8, "out")
-    op = testutil.create_op(Op.HardSwish, [in_tens], out_tens)
-    assert not support.is_operator_supported(op)
-
-
-def test_constraint_keep_dims_ifm_ofm():
-    op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [4, 8, 8, 4], [32, 32], weights_shape=[4, 8, 8, 4])
-    op.attrs["keep_num_dims"] = True
-    assert not support.is_operator_supported(op)
-    op.attrs["keep_num_dims"] = False
-    assert support.is_operator_supported(op)
-
-
 def create_mean(input_shape, output_shape, axis, datatype, attrs):
     ifm = Tensor(input_shape, datatype, "in")
     ifm.quantization = testutil.default_quant_params()
@@ -853,33 +522,6 @@ def create_mean(input_shape, output_shape, axis, datatype, attrs):
     return op
 
 
-def test_mean_dtype():
-    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [1, 2], DataType.int8, {"keep_dims": True})
-    assert support.is_operator_supported(op)
-    op.ifm.dtype = DataType.int16
-    op.ofm.dtype = DataType.int16
-    assert not support.is_operator_supported(op)
-
-
-def test_mean_axis():
-    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], 0, DataType.int8, {"keep_dims": True})
-    assert not support.is_operator_supported(op)
-    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [3], DataType.int8, {"keep_dims": True})
-    assert not support.is_operator_supported(op)
-    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [1, 3], DataType.int8, {"keep_dims": True})
-    assert not support.is_operator_supported(op)
-    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [0, 1], DataType.int8, {"keep_dims": True})
-    assert not support.is_operator_supported(op)
-    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [1, 2], DataType.int8, {"keep_dims": True})
-    assert support.is_operator_supported(op)
-    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [1], DataType.int8, {"keep_dims": True})
-    assert support.is_operator_supported(op)
-    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], 2, DataType.int8, {"keep_dims": True})
-    assert support.is_operator_supported(op)
-    op = create_mean([1, 6, 6, 16], [1, 1, 1, 16], [2, 1], DataType.int8, {"keep_dims": True})
-    assert support.is_operator_supported(op)
-
-
 def test_mean_hw_product():
     op = create_mean([1, 64, 64, 16], [1, 16], [1, 2], DataType.uint8, {})
     assert support.is_operator_supported(op)