Responding to the reviews

python/tvm/relay/backend/contrib/ethosu/codegen.py

@@ -46,9 +46,12 @@ def __init__(self):
     def create_op_with_lut(self, call):
         """Extract the parameters and attributes from the NPU operator and create
         a new operator with LUT.
+
+        Parameters
         ----------
         call : tvm.relay.expr.Call
             The current call node being visited.
+
         Returns
         -------
         tvm.relay.expr.Call
@@ -63,8 +66,7 @@ def create_op_with_lut(self, call):
         new_attrs["activation"] = activation
 
         # Assume that LUT is always the last argument
-        new_args = [ethosu_op.args[n] for n in range(len(ethosu_op.args) - 1)]
-        new_args.append(lut)
+        new_args = ethosu_op.args[:-1] + [lut]
         assert ethosu_op.op.name in self.lut_ops.keys()
 
         return self.lut_ops[ethosu_op.op.name](*new_args, **new_attrs)
@@ -73,10 +75,12 @@ def visit_call(self, call: tvm.relay.expr.Call) -> tvm.relay.expr.Call:
         """Recursively visit call nodes in the input graph and if an ethosu.identity
         operator with LUT is found and the preceding operator has a LUT attribute, create
         a new NPU operator.
+
         Parameters
         ----------
         call : tvm.relay.expr.Call
             The current call node being visited.
+
         Returns
         -------
         tvm.relay.expr.Call
@@ -104,24 +108,26 @@ def visit_call(self, call: tvm.relay.expr.Call) -> tvm.relay.expr.Call:
         return new_call
 
 
-@relay.transform.function_pass(opt_level=1, name="LutOptimizer")
+@relay.transform.function_pass(opt_level=1, name="LUTsOptimizer")
 class LUTsOptimizer(Pass):
-    """Register LutOptimizer as a relay pass."""
+    """Register LUTsOptimizer as a relay pass."""
 
     def transform_function(
         self, func: tvm.relay.function.Function, mod: tvm.IRModule, _
     ) -> tvm.IRModule:
         """Visit relay nodes in the given module.
+
         Parameters
         ----------
         func : tvm.relay.function.Function
-            The function to apply the layout optimization pass to.
+            The function to apply the optimization pass for multiple LUTs to.
         mod : tvm.IRModule
-            The module to apply the layout optimization pass to.
+            The module to apply the optimization pass for multiple LUTs to.
+
         Returns
         -------
         mod : tvm.IRModule
-            New module with augmented layouts.
+            New module with optimized LUTs.
         """
         assert len(mod.functions.items()) == 1, "Module can only contain one function."
         return OptimizeLUTs().visit(func)

python/tvm/relay/backend/contrib/ethosu/legalize.py

@@ -32,6 +32,7 @@
 from tvm.relay.backend.contrib.ethosu import op as ethosu_ops  # type: ignore
 from tvm.relay.backend.contrib.ethosu.errors import UnsupportedLayout  # type: ignore
 from tvm.relay.backend.contrib.ethosu import vela_api
+from tvm.relay.backend.contrib.ethosu import util
 from tvm.relay.op.contrib import ethosu as ethosu_patterns  # type: ignore
 
 
@@ -124,11 +125,6 @@ def __call__(self, *args, **kwargs):
         pass
 
 
-def round_away_zero(f):
-    r = -0.5 if (f < 0) else 0.5
-    return np.trunc(f + r)
-
-
 def find_tanh_values(ifm_scale, ifm_zp, ofm_scale, ofm_zp):
     """Method to calculate the values of the tanh lookup table"""
     lut_values = list()
@@ -138,7 +134,7 @@ def find_tanh_values(ifm_scale, ifm_zp, ofm_scale, ofm_zp):
     for x in range(qmin, qmax + 1):
         x_real = ifm_scale * (x - ifm_zp)
         out_real = math.tanh(x_real)
-        lut_result = int(round_away_zero(ofm_zp + out_real / ofm_scale))
+        lut_result = int(util.round_away_zero(ofm_zp + out_real / ofm_scale))
         lut_result = min(qmax, max(qmin, lut_result))
         lut_values.append(lut_result)
 

tests/python/contrib/test_ethosu/test_lookup_table.py

@@ -40,8 +40,6 @@ def test_tflite_lut_activations(accel_type):
     ifm_shape = (1, 55, 55, 3)
 
     def create_tflite_graph():
-        tf.config.run_functions_eagerly(True)
-
         class Model(tf.Module):
             @tf.function
             def tf_func(self, x):