MLBEDSW-2412 Replace generic restrictions

A new mechanism to report generic restrictions/constraints for
operators has been implemented.
Each check is its own defined function, and has a general reason for
the constraint defined as its docstring.
This allows us to query all reasons up front and report this without
having to run through real data to trigger the checks.
This is part of a larger refactoring and the specific restrictions will
be replaced by a similar mechanism.

Signed-off-by: Michael McGeagh <michael.mcgeagh@arm.com>
Change-Id: Id3fb2639f91cfac5fc5b8c14f7620de1a85972b2

1 files changed, 117 insertions, 76 deletions

diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py
index 867613cd..fbb306e8 100644
--- a/ethosu/vela/supported_operators.py
+++ b/ethosu/vela/supported_operators.py
@@ -22,6 +22,15 @@ from .data_type import DataType
 from .operation import get_slice_offsets
 
 
+# Custom decorator function to allow formatting docstrings containing "{}"
+def docstring_format_args(args):
+    def docstring(func):
+        func.__doc__ = func.__doc__.format(*args)
+        return func
+
+    return docstring
+
+
 def warn_cpu(op, msg):
     print("Warning: {} {}, placing on CPU".format(op.type, msg))
 
@@ -61,6 +70,9 @@ class SupportedOperators:
     )
     binary_elem_wise_main_ops = binary_elem_wise_min_max_ops | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops
     elem_wise_main_ops = binary_elem_wise_main_ops | unary_elem_wise_main_ops
+    supported_int32_tensor_ops = (
+        set(("Requantize", "ReduceSum", "CLZ",)) | binary_elem_wise_add_mul_sub | binary_elem_wise_shift_ops
+    )
     activation_ops = set(
         (
             "QuantizedRelu",
@@ -90,6 +102,9 @@ class SupportedOperators:
     shapeless_input_ops = set(("Split", "SplitV",)) | binary_elem_wise_main_ops
     supported_fused_activations = set(("Relu", "Relu6", "ReluN1To1", "Tanh", "Sigmoid", "LUT",))
     supported_operators = npu_pre_ops | mac_main_ops | elem_wise_main_ops | npu_post_ops | memory_only_ops
+    supported_dtypes = set((DataType.uint8, DataType.int8, DataType.int16, DataType.int32))
+    # Defined ranges for allowed values:
+    tens_dim_range = (1, 65535)
 
     def __init__(self):
         # Setup supported operator restriction checkers
@@ -121,93 +136,119 @@ class SupportedOperators:
         self.supported_operator_restrictions.update(
             {op: self.check_activation_ops for op in SupportedOperators.activation_ops}
         )
+        # Setup the generic constraints
+        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_shape_size)
+        self.generic_constraints.append(SupportedOperators.constraint_tens_dtype)
+        self.generic_constraints.append(SupportedOperators.constraint_tens_dimension)
+        self.generic_constraints.append(SupportedOperators.constraint_faf)
+        self.generic_constraints.append(SupportedOperators.constraint_tens_quant_scale)
 
     def is_operator_supported(self, op):
         if op.type not in SupportedOperators.supported_operators:
             return False
-        if not self.check_generic_restrictions(op):
-            return False
+        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(" - {}".format(constraint.__doc__))
+                if extra:
+                    print("   {}".format(extra))
+                return False
         if op.type in self.supported_operator_restrictions:
             return self.supported_operator_restrictions[op.type](op)
         return True
 
-    @classmethod
-    def check_generic_restrictions(cls, op):
-        # check fully defined shapes
-        for t in op.inputs:
-            if not t:
-                continue
-            if not t.has_fully_defined_shape():
-                print("Warning:", op.type, "has input(s) of undefined shape, placing on CPU")
-                return False
-            if t.shape == [] and op.type not in cls.shapeless_input_ops:
-                print(
-                    "Warning:",
-                    op.type,
-                    "has input(s) of shape [].",
-                    "Scalar input or broadcasting is not supported for this operator,",
-                    "placing on CPU",
-                )
-                return False
-            if len(t.shape) > 4:
-                print("Warning:", op.type, "has input(s) of unsupported shape", t.shape, "placing on CPU")
-                return False
-        for t in op.outputs:
-            if not t.has_fully_defined_shape():
-                print("Warning:", op.type, "has output(s) of undefined shape, placing on CPU")
-                return False
-            if t.shape == []:
-                print(
-                    "Warning:",
-                    op.type,
-                    "has output(s) of shape [].",
-                    "Scalar input or broadcasting is not supported for this operator,",
-                    "placing on CPU",
-                )
-                return False
-            if len(t.shape) > 4:
-                print("Warning:", op.type, "has output(s) of unsupported shape", t.shape, "placing on CPU")
-                return False
+    @staticmethod
+    def constraint_tens_defined_shape(op):
+        "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)
 
-        # check data type
-        tensors = [t for t in op.get_ifm_ifm2_weights_ofm() if t is not None]
-        if not tensors:
-            tensors = op.inputs
-        for t in tensors:
-            if not (t.dtype.type & BaseType.Int):
-                return False
-            if (
-                t.element_size() > 2
-                and op.type
-                not in set(("Requantize", "ReduceSum", "CLZ",))
-                | cls.binary_elem_wise_add_mul_sub
-                | cls.binary_elem_wise_shift_ops
-            ):
-                return False
-            # check size
-            if any(dim > 65536 for dim in t.shape):
-                return False
+    @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: {}"
+        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)
+
+    @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)
 
-        # check fused activations
-        if (
-            "fused_activation_function" in op.attrs
-            and op.attrs["fused_activation_function"] is not None
-            and op.attrs["fused_activation_function"] not in cls.supported_fused_activations
-        ):
-            return False
+    @classmethod
+    @docstring_format_args([supported_dtypes, supported_int32_tensor_ops])
+    def constraint_tens_dtype(cls, op):
+        "Tensors must be of type: {}. 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:
+                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)
 
-        # check inf values
-        for tens in op.get_ifm_ifm2_weights_ofm():
-            if (
-                (tens is not None)
-                and (tens.quantization is not None)
-                and (tens.quantization.scale_f32 is not None)
-                and (np.isinf(tens.quantization.scale_f32).any())
-            ):
-                print("Warning:", op.type, "has inf valued tensor(s), placing on CPU")
-                return False
+    @classmethod
+    @docstring_format_args(tens_dim_range)
+    def constraint_tens_dimension(cls, op):
+        "Tensor dimensions must be in the range {}-{} (inclusive)"
+        tens_min, tens_max = cls.tens_dim_range
+        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:
+            valid &= all(tens_min <= dim <= tens_max for dim in tens.shape)
+            extra.append("shape={}".format(tens.shape))
+        return valid, " ".join(extra)
 
-        return True
+    @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.attrs.get("fused_activation_function")
+        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)
 
     @classmethod
     def check_convolution_restrictions(cls, op):
@@ -525,7 +566,7 @@ class SupportedOperators:
                 return False
 
             # check shape
-            if len(ifm_tensor.shape) > 4 or ifm_tensor.shape != ofm_tensor.shape:
+            if ifm_tensor.shape != ofm_tensor.shape:
                 return False
 
         return True