Remove unnecessary model in GMOA

exps/GMOA/lfna.py

@@ -337,11 +337,11 @@ if __name__ == "__main__":
     parser.add_argument(
         "--refine_lr",
         type=float,
-        default=0.002,
+        default=0.001,
         help="The learning rate for the optimizer, during refine",
     )
     parser.add_argument(
-        "--refine_epochs", type=int, default=100, help="The final refine #epochs."
+        "--refine_epochs", type=int, default=150, help="The final refine #epochs."
     )
     parser.add_argument(
         "--early_stop_thresh",

exps/GMOA/lfna_meta_model.py

@@ -19,7 +19,6 @@ class MetaModelV1(super_core.SuperModule):
         layer_dim,
         time_dim,
         meta_timestamps,
-        mha_depth: int = 2,
         dropout: float = 0.1,
         seq_length: int = 10,
         interval: float = None,
@@ -69,22 +68,6 @@ class MetaModelV1(super_core.SuperModule):
             attn_drop=None,
             proj_drop=dropout,
         )
-        layers = []
-        for ilayer in range(mha_depth):
-            layers.append(
-                super_core.SuperTransformerEncoderLayer(
-                    time_dim * 2,
-                    4,
-                    True,
-                    4,
-                    dropout,
-                    norm_affine=False,
-                    order=super_core.LayerOrder.PostNorm,
-                    use_mask=True,
-                )
-            )
-        layers.append(super_core.SuperLinear(time_dim * 2, time_dim))
-        self._meta_corrector = super_core.SuperSequential(*layers)
 
         model_kwargs = dict(
             config=dict(model_type="dual_norm_mlp"),
@@ -103,13 +86,12 @@ class MetaModelV1(super_core.SuperModule):
             std=0.02,
         )
 
-    def get_parameters(self, time_embed, meta_corrector, generator):
+    def get_parameters(self, time_embed, attention, generator):
         parameters = []
         if time_embed:
             parameters.append(self._super_meta_embed)
-        if meta_corrector:
+        if attention:
             parameters.extend(list(self._trans_att.parameters()))
-            parameters.extend(list(self._meta_corrector.parameters()))
         if generator:
             parameters.append(self._super_layer_embed)
             parameters.extend(list(self._generator.parameters()))
@@ -199,13 +181,7 @@ class MetaModelV1(super_core.SuperModule):
             timestamp_v_embed,
             mask,
         )
-        relative_timestamps = timestamps - timestamps[:, :1]
-        relative_pos_embeds = self._tscalar_embed(relative_timestamps)
-        init_timestamp_embeds = torch.cat(
-            (timestamp_embeds, relative_pos_embeds), dim=-1
-        )
-        corrected_embeds = self._meta_corrector(init_timestamp_embeds)
-        return corrected_embeds
+        return timestamp_embeds
 
     def forward_raw(self, timestamps, time_embeds, get_seq_last):
         if time_embeds is None:
@@ -264,9 +240,8 @@ class MetaModelV1(super_core.SuperModule):
         x, y = x.to(self._meta_timestamps.device), y.to(self._meta_timestamps.device)
         with torch.set_grad_enabled(True):
             new_param = self.create_meta_embed()
-            optimizer = torch.optim.Adam(
-                [new_param], lr=lr, weight_decay=1e-5, amsgrad=True
-            )
+
+            optimizer = torch.optim.Adam([new_param], lr=lr, weight_decay=1e-5, amsgrad=True)
             timestamp = torch.Tensor([timestamp]).to(new_param.device)
             self.replace_append_learnt(timestamp, new_param)
             self.train()
@@ -297,7 +272,7 @@ class MetaModelV1(super_core.SuperModule):
         with torch.no_grad():
             self.replace_append_learnt(None, None)
             self.append_fixed(timestamp, best_new_param)
-        return True, meta_loss.item()
+        return True, best_loss
 
     def extra_repr(self) -> str:
         return "(_super_layer_embed): {:}, (_super_meta_embed): {:}, (_meta_timestamps): {:}".format(