[Streamline] Soften initializer tests in Absorb1BitMulIntoMatMul/Conv

Assertions are to restrictive, causing the program to terminate in cases
the streamlining simply encounters nodes to which the transforms are not
applicable: Just skip those nodes.

Only the two transforms currently affecting the streamlining of scaled
dot-product attention have been changed.

src/finn/transformation/streamline/absorb.py

@@ -224,16 +224,22 @@ def apply(self, model):
         graph_modified = False
         for n in graph.node:
             node_ind += 1
+            # TODO: Maybe test for join-node here and reject?
             if n.op_type == "MatMul":
                 matmul_weight_name = n.input[1]
                 W = model.get_initializer(matmul_weight_name)
                 Wdt = model.get_tensor_datatype(matmul_weight_name)
-                assert W is not None, "Initializer for matmul weights is not set."
+                # Just skip matmuls with non-existing weight initializers
+                if W is None:
+                    continue
                 consumer = model.find_consumer(n.output[0])
+                # TODO: Maybe test for join-node here and reject?
                 if consumer is not None and consumer.op_type == "Mul":
                     mul_weight_name = consumer.input[1]
                     A = model.get_initializer(mul_weight_name)
-                    assert A is not None, "Initializer for mul weights is not set."
+                    # Just skip muls with non-existing scale initializers
+                    if A is None:
+                        continue
                     is_1bit = model.get_tensor_datatype(mul_weight_name).bitwidth() == 1
                     if is_1bit:
                         Wnew = A * W
@@ -260,16 +266,22 @@ def apply(self, model):
         graph_modified = False
         for n in graph.node:
             node_ind += 1
+            # TODO: Maybe test for join-node here and reject?
             if n.op_type == "Conv":
                 conv_weight_name = n.input[1]
                 W = model.get_initializer(conv_weight_name)
                 Wdt = model.get_tensor_datatype(conv_weight_name)
-                assert W is not None, "Initializer for conv weights is not set."
+                # Just skip convs with non-existing weight initializers
+                if W is None:
+                    continue
                 consumer = model.find_consumer(n.output[0])
+                # TODO: Maybe test for join-node here and reject?
                 if consumer is not None and consumer.op_type == "Mul":
                     mul_weight_name = consumer.input[1]
                     A = model.get_initializer(mul_weight_name)
-                    assert A is not None, "Initializer for mul weights is not set."
+                    # Just skip muls with non-existing scale initializers
+                    if A is None:
+                        continue
                     is_1bit = model.get_tensor_datatype(mul_weight_name).bitwidth() == 1
                     is_scalar = np.prod(A.shape) == 1
                     actual_ndims = len(tuple(filter(lambda x: x > 1, A.shape)))