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 <michael.mcgeagh@arm.com>
Change-Id: If81177deca2a3b57c9dd9a3a08868cbc9cef0c23
diff --git a/ethosu/vela/test/test_supported_operators.py b/ethosu/vela/test/test_supported_operators.py
index 6e640b5..665ebc2 100644
--- a/ethosu/vela/test/test_supported_operators.py
+++ b/ethosu/vela/test/test_supported_operators.py
@@ -95,70 +95,54 @@
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 @@
# 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)