test_reclayer_optimize_out_cum_concat_gen_self_att

Author: albertz

tests/test_TFNetworkRecLayer.py

@@ -3343,8 +3343,7 @@ def check_reclayer_optimize_out(subnet_layer_dict, other_subnet_layers=None, sha
     rec_layer_dict["unit"].update(other_subnet_layers)
   config = Config({
     "debug_print_layer_output_template": True,
-    "num_inputs": n_in,
-    "num_outputs": n_out
+    "extern_data": {"data": {"dim": n_in}},
   })
   from returnn.tf.layers.rec import _SubnetworkRecCell
   with make_scope() as session:
@@ -3421,6 +3420,38 @@ def test_reclayer_optimize_out_selfatt_left():
     "class": "self_attention", "attention_left_only": True, "num_heads": 2, "total_key_dim": 6, "n_out": 18})
 
 
+def test_reclayer_optimize_out_cum_concat_gen_self_att():
+  new_dim = DimensionTag(kind=DimensionTag.Types.Spatial, description="cum_concat_new_dim")
+  n_key = 5
+  n_value = 7
+  check_reclayer_optimize_out(
+    {"class": "linear", "from": "att", "activation": None},
+    {
+      # This is very much the vanilla self attention,
+      # implemented via the new generic way.
+      # See https://github.com/rwth-i6/returnn/issues/391 for a long discussion.
+      # Commented shapes are always for the layers inside the loop (not optimized).
+      "qkv": {"class": "linear", "from": "data:source", "activation": None, "n_out": n_key * 2 + n_value},  # [B,2*K+V]
+      "qkv_split": {"class": "split", "from": "qkv", "size_splits": [n_key, n_key, n_value]},
+      "q": {"class": "copy", "from": "qkv_split/0"},  # [B,K]
+      "k": {"class": "copy", "from": "qkv_split/1"},  # [B,K]
+      "v": {"class": "copy", "from": "qkv_split/2"},  # [B,V]
+      # cum_concat here. Note that the optimized-out shape is not as you might expect [T,max(t),B,K],
+      # but instead using the optimized format, with extended dyn size on the special dim tag.
+      "k_accum": {"class": "cum_concat", "new_dim": new_dim, "from": "k"},  # [t,B,K]
+      "v_accum": {"class": "cum_concat", "new_dim": new_dim, "from": "v"},  # [t,B,V]
+      "energy": {
+        "class": "dot", "from": ["q", "k_accum"],
+        "red1": "static:-1", "red2": "static:-1",
+        "var1": None, "var2": new_dim},  # [B,t]
+      "att_weights": {"class": "softmax_over_spatial", "from": "energy", "axis": new_dim},  # [B,t]
+      "att": {
+        "class": "dot", "from": ["att_weights", "v_accum"],
+        "red1": new_dim, "red2": new_dim,
+        "var1": None, "var2": "static:-1"},  # [B,V]
+    })
+
+
 def test_reclayer_optimize_out_dot():
   # Used for multi-head dot-attention.
   AttNumHeads = 4