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_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_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_bias_type():
# Bias must have a certain datatype
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}
bias = Tensor([1, 8, 8, 8], DataType.uint8, "bias")
op.add_input_tensor(bias)
assert not 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_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.attrs = {"stride_w": 1, "stride_h": 1}
assert not 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_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_bias_40bit():
# Bias must not exceed 40-bit
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([0x01FF_FFFF_FFFF])
op.add_input_tensor(bias)
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_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_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 = testutil.default_quant_params()
op.add_input_tensor(weights)
assert not 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_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_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 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_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_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_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_tens_quant_per_axis_is_supp():
op = testutil.create_op_with_quant_tensors(
Op.Conv2DBias, [1, 1, 1, 3], [1, 1, 1, 3], weights_shape=[1, 1, 1, 3], bias_shape=[1, 1, 1, 3]
)
op.attrs = {"stride_w": 1, "stride_h": 1}
assert support.is_operator_supported(op)
qp = QuantizationParameters()
qp.zero_point = np.zeros((1, 3))
qp.scale_f32 = np.ones((1, 3))
op.bias.quantization = qp
assert support.is_operator_supported(op)
def test_constraint_tens_dimension():
# Tensors can only have values in the inclusive range of 1-65535
op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 0], [1, 8, 8, 65536])
assert not support.is_operator_supported(op)
def test_constraint_tens_dtype():
# Tensors can only be of type uint8, int8, int16 and int32
op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.float32)
assert not support.is_operator_supported(op)
def test_constraint_tens_int32_ops():
# For int32, only select op types are allowed:
op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [], [1, 8, 8, 8], datatype=DataType.int32)
assert support.is_operator_supported(op)
op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [1, 8, 8, 8], datatype=DataType.int32)
assert not support.is_operator_supported(op)
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.attrs = {"stride_w": 1, "stride_h": 1}
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])
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_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_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_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)
