From 1f951fc47abd52db0ac048802dab0c95b149d7b8 Mon Sep 17 00:00:00 2001 From: Michael McGeagh Date: Wed, 14 Oct 2020 09:30:02 +0100 Subject: MLBEDSW-2412 Refactor constraints for conv ops Using a new system to report constraints, replaced existing functionality for checking conv-like ops. This new system will allow reporting of all constraints regardless of any input network. Signed-off-by: Michael McGeagh Change-Id: If81177deca2a3b57c9dd9a3a08868cbc9cef0c23 --- ethosu/vela/test/test_supported_operators.py | 143 +++++++++++++++++++++------ 1 file changed, 113 insertions(+), 30 deletions(-) (limited to 'ethosu/vela/test/test_supported_operators.py') diff --git a/ethosu/vela/test/test_supported_operators.py b/ethosu/vela/test/test_supported_operators.py index 6e640b51..665ebc2c 100644 --- a/ethosu/vela/test/test_supported_operators.py +++ b/ethosu/vela/test/test_supported_operators.py @@ -95,70 +95,54 @@ def test_strided_slice(): def test_constraint_tens_defined_shape(): # Tensors cannot have None in them - inp = Tensor([1, 8, None, 8], DataType.uint8, "in") - out = Tensor([1, 8, 8, 8], DataType.uint8, "out") - op = testutil.create_op(Op.Relu, [inp], out) + 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_shapeless(): # Shapeless output is not allowed at all: - op = testutil.create_elemwise_op(Op.Mul, "scalar_mul", [1, 8, 8, 8], [1, 8, 8, 8], []) + op = testutil.create_elemwise_op(Op.Mul, "op", [1, 8, 8, 8], [1, 8, 8, 8], []) assert not support.is_operator_supported(op) def test_constraint_tens_input_shapeless(): # Shapeless input is allowed if its of a certain type: - op = testutil.create_elemwise_op(Op.Mul, "scalar_mul", [1, 8, 8, 8], [], [1, 8, 8, 8]) + 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: - inp = Tensor([], DataType.uint8, "in") - out = Tensor([1, 8, 8, 8], DataType.uint8, "out") - op = testutil.create_op(Op.Relu, [inp], out) + op = testutil.create_op_with_quant_tensors(Op.Relu, [], [1, 8, 8, 8]) assert not support.is_operator_supported(op) def test_constraint_tens_shape_size(): # Tensors cannot be > 4D - inp = Tensor([1, 1, 8, 8, 8], DataType.uint8, "in") - out = Tensor([1, 1, 8, 8, 8], DataType.uint8, "out") - op = testutil.create_op(Op.Relu, [inp], out) + 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_tens_dtype(): # Tensors can only be of type uint8, int8, int16 and int32 - inp = Tensor([1, 8, 8, 8], DataType.float32, "in") - out = Tensor([1, 8, 8, 8], DataType.float32, "out") - op = testutil.create_op(Op.Relu, [inp], out) + 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, "scalar_mul", [1, 8, 8, 8], [], [1, 8, 8, 8], DataType.int32) + 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) - inp = Tensor([1, 8, 8, 8], DataType.int32, "in") - out = Tensor([1, 8, 8, 8], DataType.int32, "out") - op = testutil.create_op(Op.Relu, [inp], out) + 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_tens_dimension(): # Tensors can only have values in the inclusive range of 1-65535 - inp = Tensor([1, 8, 8, 0], DataType.uint8, "in") - out = Tensor([1, 8, 8, 0], DataType.uint8, "out") - op = testutil.create_op(Op.Relu, [inp], out) - assert not support.is_operator_supported(op) - inp = Tensor([1, 8, 8, 65536], DataType.uint8, "in") - out = Tensor([1, 8, 8, 65536], DataType.uint8, "out") - op = testutil.create_op(Op.Relu, [inp], out) + 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_quant_none_check(): # Tensors must have quantization parameters - op = testutil.create_elemwise_op(Op.Mul, "scalar_mul", [1, 8, 8, 8], [], [1, 8, 8, 8], ifm2_quant=None) + 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) @@ -166,14 +150,113 @@ def test_constraint_tens_quant_scale(): # Quantization scale cannot be infinit qp = QuantizationParameters() qp.scale_f32 = np.inf - op = testutil.create_elemwise_op(Op.Mul, "scalar_mul", [1, 8, 8, 8], [], [1, 8, 8, 8], ifm_quant=qp) + 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_faf(): # Fused activation functions, if set, must be a valid op type - inp = Tensor([1, 8, 8, 8], DataType.uint8, "in") - out = Tensor([1, 8, 8, 8], DataType.uint8, "out") - op = testutil.create_op(Op.Relu, [inp], out) + op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [1, 8, 8, 8]) op.activation = Op.Conv2D assert not support.is_operator_supported(op) + + +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.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.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.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.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.attrs = {"stride_w": 1, "stride_h": 1} + 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_weights_nonconst(): + # 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() + op.add_input_tensor(weights) + assert not support.is_operator_supported(op) + + +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.attrs = {"stride_w": 1, "stride_h": 1} + op.weights.quantization.zero_point = np.array([[[[(127 * 65536) + 1]]]]) + assert not 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_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([0x1FF_FFFF_FFFF]) + op.add_input_tensor(bias) + 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) -- cgit v1.2.1