MLBEDSW-3558: Put FC on CPU when OFM != 2D

This commit adds a constraint to FullyConnected
ops in supported_operators.py that puts any
such op on the CPU if tensor dimensions of the
output(s) are not 2D.

Signed-off-by: Dwight Lidman <dwight.lidman@arm.com>
Change-Id: I8c898a780b40fc4a1383c09213f0696ea6699b7d

3 files changed, 35 insertions, 2 deletions

diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py
index deae75a..f7dfec2 100644
--- a/ethosu/vela/supported_operators.py
+++ b/ethosu/vela/supported_operators.py
@@ -247,6 +247,9 @@ class SupportedOperators:
         # LeakyRelu specific checks:
         self.specific_constraints[Op.LeakyRelu].append(SupportedOperators.constraint_alpha_valid)
 
+        # FullyConnected specific checks:
+        self.specific_constraints[Op.FullyConnected].append(SupportedOperators.constraint_fc_output_2d)
+
     def is_operator_supported(self, op):
         ext_type = optype_to_builtintype(op.type)
         if op.type not in SupportedOperators.supported_operators:
@@ -409,6 +412,17 @@ class SupportedOperators:
                     extra.append(tens.name)
         return valid, "The following tensor(s) have per-axis quantization parameters: " + ", ".join(extra)
 
+    @staticmethod
+    def constraint_fc_output_2d(op):
+        "The output tensor(s) must have 2D shape"
+        valid = True
+        extra = []
+        for tens in op.outputs:
+            if len(tens.shape) != 2:
+                valid = False
+                extra.append(f"Tensor '{tens.name}' is {len(tens.shape)}D")
+        return valid, ", ".join(extra)
+
     @classmethod
     @docstring_format_args([_optype_formatter(supported_fused_activations)])
     def constraint_faf(cls, op):
diff --git a/ethosu/vela/test/test_supported_operators.py b/ethosu/vela/test/test_supported_operators.py
index 72ccad2..f132eef 100644
--- a/ethosu/vela/test/test_supported_operators.py
+++ b/ethosu/vela/test/test_supported_operators.py
@@ -122,6 +122,22 @@ def test_constraint_tens_quant_per_axis_is_supp():
     assert support.is_operator_supported(op)
 
 
+def test_constraint_fc_output_2d_not_supp():
+    op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [12, 1], [3, 2, 2, 1], weights_shape=[12, 1, 1, 1])
+    assert not support.is_operator_supported(op)
+    op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [12, 1, 1, 1], [1, 3, 4], weights_shape=[12, 1, 1, 1])
+    assert not support.is_operator_supported(op)
+    op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [1, 1, 1, 1], [1], weights_shape=[1, 1, 1, 1])
+    assert not support.is_operator_supported(op)
+
+
+def test_constraint_fc_output_2d_is_supp():
+    op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [4, 8, 8, 4], [32, 32], weights_shape=[4, 8, 8, 4])
+    assert support.is_operator_supported(op)
+    op = testutil.create_op_with_quant_tensors(Op.FullyConnected, [1, 1024], [16, 64], weights_shape=[1, 1024])
+    assert support.is_operator_supported(op)
+
+
 def test_constraint_faf():
     # Fused activation functions, if set, must be a valid op type
     op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 8, 8, 8], [1, 8, 8, 8])
diff --git a/ethosu/vela/test/testutil.py b/ethosu/vela/test/testutil.py
index ee407b6..4b2938b 100644
--- a/ethosu/vela/test/testutil.py
+++ b/ethosu/vela/test/testutil.py
@@ -20,6 +20,7 @@ import numpy as np
 from ethosu.vela import architecture_features
 from ethosu.vela.data_type import DataType
 from ethosu.vela.nn_graph import Subgraph
+from ethosu.vela.operation import Op
 from ethosu.vela.operation import Operation
 from ethosu.vela.tensor import create_const_tensor
 from ethosu.vela.tensor import QuantizationParameters
@@ -90,7 +91,8 @@ def create_op_with_quant_tensors(
         else:
             np_type = np.int32
         qp = default_quant_params()
-        qp.zero_point = np.zeros(weights_shape)
+        if op.type is not Op.FullyConnected:
+            qp.zero_point = np.zeros(weights_shape)
         weights = create_const_tensor(
             "weights", weights_shape, datatype, np.zeros(weights_shape), np_type, quantization=qp
         )
@@ -98,7 +100,8 @@ def create_op_with_quant_tensors(
     # Optional bias tensor
     if bias_shape is not None:
         qp = default_quant_params()
-        qp.zero_point = np.zeros(bias_shape)
+        if op.type is not Op.FullyConnected:
+            qp.zero_point = np.zeros(bias_shape)
         bias = create_const_tensor("bias", bias_shape, DataType.int32, np.zeros(bias_shape), np.int32, quantization=qp)
         op.add_input_tensor(bias)
     return op