MLBEDSW-2412 All constraints have been refactored
All existing constraints have now been refactored using the new
framework.
Signed-off-by: Michael McGeagh <michael.mcgeagh@arm.com>
Change-Id: Ic9ba0d7040cb9f114b959a949bfdf777f86752c7
diff --git a/ethosu/vela/test/test_supported_operators.py b/ethosu/vela/test/test_supported_operators.py
index 665ebc2..595ea59 100644
--- a/ethosu/vela/test/test_supported_operators.py
+++ b/ethosu/vela/test/test_supported_operators.py
@@ -29,67 +29,9 @@
support = SupportedOperators()
-def create_strided_slice_op(in_shape, out_shape, start_offsets, end_offsets):
- qp = QuantizationParameters()
- 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])
- op.attrs["begin_mask"] = 1
- op.attrs["end_mask"] = 9
- assert support.is_operator_supported(op)
- return op
-
-
-def test_strided_slice():
- # Tests support for StridedSlice operator
- 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)
- # begin values must not be None
- op.inputs[1].values = None
- assert not support.is_operator_supported(op)
- # Unsupported strides
- op = create_strided_slice()
- op.inputs[3].values = [1, 1, 2, 1]
- assert not support.is_operator_supported(op)
- # Wrong number of input tensors
- op = create_strided_slice()
- op.add_input_tensor(op.inputs[0].clone())
- assert not support.is_operator_supported(op)
- # Unsupported ellipsis mask
- op = create_strided_slice()
- op.attrs["ellipsis_mask"] = 1
- assert not support.is_operator_supported(op)
- # 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
+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)
@@ -99,18 +41,20 @@
assert not support.is_operator_supported(op)
-def test_constraint_tens_output_shapeless():
- # Shapeless output is not allowed at all:
+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_shapeless():
+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)
@@ -149,6 +93,7 @@
def test_constraint_tens_quant_scale():
# Quantization scale cannot be infinit
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)
@@ -219,12 +164,12 @@
assert not support.is_operator_supported(op)
-def test_constraint_weights_nonconst():
+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.attrs = {"stride_w": 1, "stride_h": 1}
weights = Tensor([64, 64, 1, 1], DataType.uint8, "weights")
- weights.quantization = QuantizationParameters()
+ weights.quantization = testutil.default_quant_params()
op.add_input_tensor(weights)
assert not support.is_operator_supported(op)
@@ -251,7 +196,7 @@
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}
bias = Tensor([1, 1, 1, 1], DataType.int64, "bias")
- bias.quant_values = np.array([0x1FF_FFFF_FFFF])
+ bias.quant_values = np.array([0x01FF_FFFF_FFFF])
op.add_input_tensor(bias)
assert not support.is_operator_supported(op)
@@ -260,3 +205,452 @@
op = testutil.create_op_with_quant_tensors(Op.Conv2D, [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():
+ op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [1, 8, 8, 8])
+ op.ofm.quantization.scale_f32 = np.float32(1e-39)
+ 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(
+ Op.DepthwiseConv2DBias, [1, 1, 1, 1], [1, 1, 1, 2], weights_shape=[1, 1, 1, 1]
+ )
+ op.attrs = {"stride_w": 1, "stride_h": 1, "depth_multiplier": 1}
+ assert support.is_operator_supported(op)
+ # Invalid. Depth multiplier doesnt equal ofm channel
+ op = testutil.create_op_with_quant_tensors(
+ Op.DepthwiseConv2DBias, [1, 1, 1, 1], [1, 1, 1, 1], weights_shape=[1, 1, 1, 1]
+ )
+ op.attrs = {"stride_w": 1, "stride_h": 1, "depth_multiplier": 2}
+ assert not support.is_operator_supported(op)
+ # Valid. Depth multiplier is equal to ofm channel
+ op = testutil.create_op_with_quant_tensors(
+ Op.DepthwiseConv2DBias, [1, 1, 1, 1], [1, 1, 1, 2], weights_shape=[1, 1, 1, 1]
+ )
+ op.attrs = {"stride_w": 1, "stride_h": 1, "depth_multiplier": 2}
+ assert support.is_operator_supported(op)
+
+
+def test_constraint_tconv_stride():
+ # Strides must be 2
+ op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 2, 2, 1], weights_shape=[1, 1, 1, 1])
+ op.attrs = {"stride_w": 1, "stride_h": 1, "padding": b"SAME"}
+ ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
+ ifm.quantization = testutil.default_quant_params()
+ op.add_input_tensor(ifm)
+ assert not support.is_operator_supported(op)
+
+
+def test_constraint_tconv_same():
+ # Valid
+ op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 2, 2, 1], weights_shape=[1, 1, 1, 1])
+ op.attrs = {"stride_w": 2, "stride_h": 2, "padding": b"SAME"}
+ ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
+ ifm.quantization = testutil.default_quant_params()
+ op.add_input_tensor(ifm)
+ assert support.is_operator_supported(op)
+ # Invalid
+ op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 4, 4, 1], weights_shape=[1, 1, 1, 1])
+ op.attrs = {"stride_w": 2, "stride_h": 2, "padding": b"SAME"}
+ ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
+ ifm.quantization = testutil.default_quant_params()
+ op.add_input_tensor(ifm)
+ assert not support.is_operator_supported(op)
+
+
+def test_constraint_tconv_valid():
+ # Valid
+ op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 4, 4, 1], weights_shape=[4, 4, 1, 1])
+ op.attrs = {"stride_w": 2, "stride_h": 2, "padding": b"VALID"}
+ ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
+ ifm.quantization = testutil.default_quant_params()
+ op.add_input_tensor(ifm)
+ assert support.is_operator_supported(op)
+ # Invalid
+ op = testutil.create_op_with_quant_tensors(Op.Conv2DBackpropInput, [0], [1, 4, 4, 1], weights_shape=[2, 2, 1, 1])
+ op.attrs = {"stride_w": 2, "stride_h": 2, "padding": b"VALID"}
+ ifm = Tensor([1, 1, 1, 1], DataType.uint8, "ifm")
+ ifm.quantization = testutil.default_quant_params()
+ op.add_input_tensor(ifm)
+ 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": b"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": b"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
+ 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": 20, "filter_height": 20, "padding": b"SAME"}
+ assert not support.is_operator_supported(op)
+ # VALID padding limits are much larger
+ op.attrs["padding"] = b"VALID"
+ assert support.is_operator_supported(op)
+
+
+def test_constraint_filter_height_range_valid_pad():
+ # Avg pool restrictions are dependent on padding:
+ 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": 256, "padding": b"VALID"}
+ assert support.is_operator_supported(op)
+ # VALID padding restricts to 256 in filter height
+ op.attrs["filter_height"] = 257
+ assert not support.is_operator_supported(op)
+
+
+def test_constraint_filter_product_height_range_valid_pad():
+ # Avg pool restrictions are dependent on padding:
+ 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": 256, "filter_height": 256, "padding": b"VALID"}
+ assert support.is_operator_supported(op)
+ # VALID padding restricts filter W x H to 256x256
+ op.attrs["filter_width"] = 257
+ assert not support.is_operator_supported(op)
+
+
+def test_constraint_filter_height_range():
+ # Max pool restrictions arent dependent on padding
+ op = testutil.create_op_with_quant_tensors(Op.MaxPool, [1, 8, 8, 8], [1, 8, 8, 8])
+ op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 2, "filter_height": 256, "padding": b"SAME"}
+ assert support.is_operator_supported(op)
+ # Restricts to 256 in filter height
+ op.attrs["filter_height"] = 257
+ assert not support.is_operator_supported(op)
+ # Doesnt matter if SAME or VALID
+ op.attrs["padding"] = b"VALID"
+ assert not support.is_operator_supported(op)
+
+
+def test_constraint_filter_product_height_range():
+ # Max pool restrictions arent dependent on padding
+ op = testutil.create_op_with_quant_tensors(Op.MaxPool, [1, 8, 8, 8], [1, 8, 8, 8])
+ op.attrs = {"stride_w": 2, "stride_h": 2, "filter_width": 256, "filter_height": 256, "padding": b"SAME"}
+ assert support.is_operator_supported(op)
+ # Restricts filter W x H to 256x256
+ op.attrs["filter_width"] = 257
+ assert not support.is_operator_supported(op)
+ # Doesnt matter if SAME or VALID
+ op.attrs["padding"] = b"VALID"
+ assert not support.is_operator_supported(op)
+
+
+def test_constraint_resize():
+ # IFM W and H == 1
+ op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 1, 1, 8], [1, 8, 8, 8])
+ assert support.is_operator_supported(op)
+ # IFM == OFM
+ op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 8, 8, 8], [1, 8, 8, 8])
+ assert support.is_operator_supported(op)
+ # IFM x2 == OFM ; align_corners = False
+ op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 8, 8, 8])
+ assert support.is_operator_supported(op)
+ # IFM x2 -1 == OFM ; align_corners = True
+ op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 7, 7, 8])
+ op.attrs["align_corners"] = True
+ assert support.is_operator_supported(op)
+ # Invalid cases
+ op = testutil.create_op_with_quant_tensors(Op.ResizeBilinear, [1, 4, 4, 8], [1, 20, 20, 8])
+ assert not support.is_operator_supported(op)
+ op.attrs["align_corners"] = True
+ 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_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])
+ 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 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_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 support.is_operator_supported(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 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_tens_size_matches():
+ op = create_strided_slice()
+ op.inputs[1].values = [1, 1, 1, 1, 1, 1, 1, 1]
+ assert not support.is_operator_supported(op)
+
+
+def test_constraint_stridedslice_stride_values():
+ # Unsupported strides
+ op = create_strided_slice()
+ op.inputs[3].values = [1, 1, 2, 1]
+ 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])
+ assert not support.is_operator_supported(op)
+ op = testutil.create_elemwise_op(Op.SHL, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int32)
+ assert support.is_operator_supported(op)
+ op.ifm2.dtype = DataType.int16
+ assert not support.is_operator_supported(op)
+
+
+def test_constraint_output_int32():
+ # output must be type int32
+ op = testutil.create_elemwise_op(Op.SHL, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int32)
+ assert support.is_operator_supported(op)
+ op.ofm.dtype = DataType.int16
+ assert not support.is_operator_supported(op)
+
+
+def test_constraint_matching_quantization_parameters():
+ qp = QuantizationParameters()
+ qp.scale_f32 = np.float32(1.5)
+ qp.zero_point = 128
+ # valid - all matching (uses default quant params)
+ op = testutil.create_elemwise_op(Op.Minimum, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8])
+ assert support.is_operator_supported(op)
+ # invalid - ifm mismatch ofm
+ op.ifm.quantization = qp
+ assert not support.is_operator_supported(op)
+ # invalid - ifm2 mismatch ofm
+ op = testutil.create_elemwise_op(Op.Minimum, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8])
+ op.ifm2.quantization = qp
+ assert not support.is_operator_supported(op)
+ # invalid - both ifm and ifm2 mismatch ofm
+ op = testutil.create_elemwise_op(Op.Minimum, "op", [1, 8, 8, 8], [1, 8, 8, 8], [1, 8, 8, 8])
+ op.ifm.quantization = qp
+ op.ifm2.quantization = qp
+ assert not support.is_operator_supported(op)
+ # valid - all matching
+ op.ofm.quantization = qp
+ assert support.is_operator_supported(op)
+
+
+def test_constraint_elemwise_batch_size():
+ # BINARY CASE
+ # Batch can be >1 if dims is <=2D
+ op = testutil.create_elemwise_op(Op.Add, "op", [2, 2], [2, 2], [2, 2])
+ assert support.is_operator_supported(op)
+ # For dims >2D, batch must be 1
+ op = testutil.create_elemwise_op(Op.Add, "op", [1, 2, 2], [1, 2, 2], [1, 2, 2])
+ assert support.is_operator_supported(op)
+ # invalid case
+ op = testutil.create_elemwise_op(Op.Add, "op", [2, 2, 2], [2, 2, 2], [2, 2, 2])
+ assert not support.is_operator_supported(op)
+
+ # UNARY CASE
+ # Batch can be >1 if dims is <=2D
+ op = testutil.create_elemwise_op(Op.CLZ, "op", [2, 2], None, [2, 2], datatype=DataType.int32)
+ assert support.is_operator_supported(op)
+ # For dims >2D, batch must be 1
+ op = testutil.create_elemwise_op(Op.CLZ, "op", [1, 2, 2], None, [1, 2, 2], datatype=DataType.int32)
+ assert support.is_operator_supported(op)
+ # invalid case
+ op = testutil.create_elemwise_op(Op.CLZ, "op", [2, 2, 2], None, [2, 2, 2], datatype=DataType.int32)
+ 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", [2, 2], [4, 4], [2, 2])
+ assert support.is_operator_supported(op)
+ op = testutil.create_elemwise_op(Op.Add, "op", [4, 4], [2, 2], [2, 2])
+ 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)
+
+ # 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_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)