vela: Improve extra info in constraint checks
Keeping the constraint functions consistent with each other
Added specific tensor names in the extra info
Added operator name to the warning generated
This should help easily identify specific problematic nodes in a graph
and give a good enough explanation as to why they are placed on the CPU
Signed-off-by: Michael McGeagh <michael.mcgeagh@arm.com>
Change-Id: Ie5bbdd31e5e75fe37e3d8bb8fee1d260080bce83
diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py
index 88e1083..4e98912 100644
--- a/ethosu/vela/supported_operators.py
+++ b/ethosu/vela/supported_operators.py
@@ -122,16 +122,18 @@
self.supported_operator_restrictions.update(
{op: self.check_activation_ops for op in SupportedOperators.activation_ops}
)
- # Setup the generic constraints
+ # Setup the generic constraints. Note: the order matters
self.generic_constraints = []
self.generic_constraints.append(SupportedOperators.constraint_tens_defined_shape)
- self.generic_constraints.append(SupportedOperators.constraint_tens_shapeless)
+ self.generic_constraints.append(SupportedOperators.constraint_tens_output_shapeless)
+ self.generic_constraints.append(SupportedOperators.constraint_tens_input_shapeless)
self.generic_constraints.append(SupportedOperators.constraint_tens_shape_size)
self.generic_constraints.append(SupportedOperators.constraint_tens_dtype)
+ self.generic_constraints.append(SupportedOperators.constraint_tens_int32_ops)
self.generic_constraints.append(SupportedOperators.constraint_tens_dimension)
- self.generic_constraints.append(SupportedOperators.constraint_faf)
- self.generic_constraints.append(SupportedOperators.constraint_tens_quant_scale)
self.generic_constraints.append(SupportedOperators.constraint_tens_quant_none_check)
+ self.generic_constraints.append(SupportedOperators.constraint_tens_quant_scale)
+ self.generic_constraints.append(SupportedOperators.constraint_faf)
def is_operator_supported(self, op):
if op.type not in SupportedOperators.supported_operators:
@@ -140,7 +142,7 @@
for constraint in self.generic_constraints:
valid, extra = constraint(op)
if not valid:
- print('Warning: "{}" is not supported on the NPU. Placing on CPU instead'.format(op.type))
+ print("Warning: {} '{}' is not supported on the NPU. Placing on CPU instead".format(op.type, op.name))
print(" - {}".format(constraint.__doc__))
if extra:
print(" {}".format(extra))
@@ -154,54 +156,78 @@
"Input(s) and Output Tensors must have a defined shape"
valid = True
extra = []
- for tens in op.inputs + op.outputs:
- if tens:
- valid &= tens.has_fully_defined_shape()
- extra.append("shape={}".format(tens.shape))
- return valid, " ".join(extra)
+ tensors = [tens for tens in op.inputs + op.outputs if tens]
+ for tens in tensors:
+ if not tens.has_fully_defined_shape():
+ valid = False
+ extra.append("Tensor '{}' has shape: {}".format(tens.name, tens.shape))
+ return valid, ", ".join(extra)
- @classmethod
- @docstring_format_args([shapeless_input_ops])
- def constraint_tens_shapeless(cls, op):
- "Scalar or Broadcasting Tensors are only valid for Input Tensors, and when op type is: {}"
+ @staticmethod
+ def constraint_tens_output_shapeless(op):
+ "Scalar or Broadcasting Tensors are only valid for Input Tensors"
valid = True
extra = []
- for tens in op.inputs:
- if tens and tens.shape == []:
- valid &= op.type in cls.shapeless_input_ops
- extra.append("shape={}".format(tens.shape))
for tens in op.outputs:
if tens.shape == []:
valid = False
- extra.append("shape={}".format(tens.shape))
- return valid, " ".join(extra)
+ extra.append("Output Tensor '{}' is shapeless".format(tens.name))
+ return valid, ", ".join(extra)
+
+ @classmethod
+ @docstring_format_args([shapeless_input_ops])
+ def constraint_tens_input_shapeless(cls, op):
+ "Scalar or Broadcasting Input Tensors are only valid for op type: {}"
+ valid = True
+ extra = []
+ tensors = [tens for tens in op.inputs if tens]
+ for tens in tensors:
+ if (tens.shape == []) and (op.type not in cls.shapeless_input_ops):
+ valid = False
+ extra.append(tens.name)
+ extra = "Op '{}' has shapeless input tensor(s): {}".format(op.name, ", ".join(extra))
+ return valid, extra
@staticmethod
def constraint_tens_shape_size(op):
"Input(s) and Output Tensors must not be greater than 4D"
valid = True
extra = []
- for tens in op.inputs + op.outputs:
- if tens:
- valid &= len(tens.shape) <= 4
- extra.append("shape={}".format(tens.shape))
- return valid, " ".join(extra)
+ tensors = [tens for tens in op.inputs + op.outputs if tens]
+ for tens in tensors:
+ if len(tens.shape) > 4:
+ valid = False
+ extra.append("Tensor '{}' has shape: {}".format(tens.name, tens.shape))
+ return valid, ", ".join(extra)
@classmethod
- @docstring_format_args([supported_dtypes, supported_int32_tensor_ops])
+ @docstring_format_args([supported_dtypes])
def constraint_tens_dtype(cls, op):
- "Tensors must be of type: {}. Tensors which are int32 are only valid when op type is: {}"
+ "Tensors must be of type: {}"
valid = True
extra = []
tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
tensors = tensors if tensors else op.inputs
for tens in tensors:
- if tens.dtype == DataType.int32:
- valid &= op.type in cls.supported_int32_tensor_ops
- else:
- valid &= tens.dtype in cls.supported_dtypes
- extra.append("dtype={}".format(tens.dtype))
- return valid, " ".join(extra)
+ if tens.dtype not in cls.supported_dtypes:
+ valid = False
+ extra.append("Tensor '{}' has data type: {}".format(tens.name, tens.dtype))
+ return valid, ", ".join(extra)
+
+ @classmethod
+ @docstring_format_args([supported_int32_tensor_ops])
+ def constraint_tens_int32_ops(cls, op):
+ "Tensors which are int32 are only valid when op type is: {}"
+ valid = True
+ extra = []
+ tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
+ tensors = tensors if tensors else op.inputs
+ for tens in tensors:
+ if (tens.dtype == DataType.int32) and (op.type not in cls.supported_int32_tensor_ops):
+ valid = False
+ extra.append(tens.name)
+ extra = "Op '{}' has int32 tensor(s): {}".format(op.name, ", ".join(extra))
+ return valid, extra
@classmethod
@docstring_format_args(tens_dim_range)
@@ -213,30 +239,10 @@
tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
tensors = tensors if tensors else op.inputs
for tens in tensors:
- valid &= all(tens_min <= dim <= tens_max for dim in tens.shape)
- extra.append("shape={}".format(tens.shape))
- return valid, " ".join(extra)
-
- @classmethod
- @docstring_format_args([supported_fused_activations])
- def constraint_faf(cls, op):
- "The fused activation function (if present) must be one of type: {}"
- faf = op.activation
- valid = (faf is None) or (faf in cls.supported_fused_activations)
- extra = "fused_activation_function={}".format(faf)
- return valid, extra
-
- @staticmethod
- def constraint_tens_quant_scale(op):
- "Tensors with quantization scales must be finite"
- valid = True
- extra = []
- tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
- for tens in tensors:
- if tens.quantization is not None and tens.quantization.scale_f32 is not None:
- valid &= not np.isinf(tens.quantization.scale_f32).any()
- extra.append("quantization.scale_f32={}".format(tens.quantization.scale_f32))
- return valid, " ".join(extra)
+ if not all(tens_min <= dim <= tens_max for dim in tens.shape):
+ valid = False
+ extra.append("Tensor '{}' has shape: {}".format(tens.name, tens.shape))
+ return valid, ", ".join(extra)
@staticmethod
def constraint_tens_quant_none_check(op):
@@ -250,6 +256,27 @@
extra.append("Tensor '{}' has no quantization parameters".format(tens.name))
return valid, ", ".join(extra)
+ @staticmethod
+ def constraint_tens_quant_scale(op):
+ "Tensors with quantization scales must be finite"
+ valid = True
+ extra = []
+ tensors = [tens for tens in op.get_ifm_ifm2_weights_ofm() if tens]
+ for tens in tensors:
+ if (tens.quantization.scale_f32 is not None) and np.isinf(tens.quantization.scale_f32).any():
+ valid = False
+ extra.append("Tensor '{}' has quantization scale: {}".format(tens.name, tens.quantization.scale_f32))
+ return valid, ", ".join(extra)
+
+ @classmethod
+ @docstring_format_args([supported_fused_activations])
+ def constraint_faf(cls, op):
+ "The fused activation function (if present) must be one of type: {}"
+ faf = op.activation
+ valid = (faf is None) or (faf in cls.supported_fused_activations)
+ extra = "Op '{}' has its fused activation function as: {}".format(op.name, faf)
+ return valid, extra
+
@classmethod
def check_convolution_restrictions(cls, op):
# check stride