[Transform] reiterated fixes for AbsorbAdd/Mul

src/finn/transformation/streamline/absorb.py

@@ -26,8 +26,8 @@ class AbsorbAddIntoMultiThreshold(Transformation):
                     assert T is not None
                     start_name = n.input[0]
                     # we can only absorb 0d or 1d adds
-                    is_scalar = all(x == 1 for x in A.shape)
-                    is_1d = np.prod(A.shape) == A.shape[-1]
+                    is_scalar = A.ndim == 0 or all(x == 1 for x in A.shape)
+                    is_1d = A.ndim > 0 and np.prod(A.shape) == A.shape[-1]
                     if is_scalar or is_1d:
                         Tnew = T - A.reshape(-1, 1)
                         # Tnew = T - A.reshape(-1, T.shape[1])
@@ -56,8 +56,8 @@ class AbsorbMulIntoMultiThreshold(Transformation):
                 A = model.get_initializer(mul_weight_name)
                 assert A is not None
                 is_signed = (A < 0).any()
-                is_scalar = np.prod(A.shape) == 1
-                is_1d = len(A.shape) == 2 and A.shape[0] == 1
+                is_scalar = A.ndim == 0 or all(x == 1 for x in A.shape)
+                is_1d = A.ndim > 0 and np.prod(A.shape) == A.shape[-1]
                 consumer = model.find_consumer(n.output[0])
                 if consumer is not None and consumer.op_type == "MultiThreshold":
                     if not is_signed and (is_1d or is_scalar):
@@ -66,7 +66,7 @@ class AbsorbMulIntoMultiThreshold(Transformation):
                         assert T is not None
                         start_name = n.input[0]
                         # compute new thresholds and set initializer
-                        Tnew = T / A.reshape(-1, T.shape[1])
+                        Tnew = T / A.reshape(-1, 1)
                         # TODO: need to handle negative A values correctly; produce
                         # mul sign mask and merge into preceding matmul?
                         model.set_initializer(threshold_name, Tnew)