Revert mostly to `x=` assignment form.

hypertrain.py

@@ -29,6 +29,7 @@ prompts = ["<|endoftext|>",
            "[ Tags:",
            "***"]
 
+
 def _init_weights(module):
     if isinstance(module, nn.Linear):
         module.weight.data.normal_(mean=0.0, std=0.02)
@@ -72,13 +73,19 @@ class HyperNetworkGRU(nn.Module):
             param.data.normal_(mean=0.0,
                                std=(0.02 / math.sqrt(2 * config["n_layer"])))
 
+        self.linear_gru = nn.Sequential(
+            self.linear1,
+            self.gru)
+        self.layernorm_linear = nn.Sequential(
+            self.ln_1,
+            self.linear2)
+
     def forward(self, x):
-        return ck(self.activation,
-                  self.linear2(
-                      self.ln_1(
-                          self.gru(
-                              self.linear1(
-                                  x.float()))[0]))).bfloat16()
+        x = x.float()
+        x = self.linear_gru.forward(x)[0]
+        x = ck(self.activation,
+               self.layernorm_linear.forward(x))
+        return x.bfloat16()
 
 
 class HyperNetwork(nn.Module):
@@ -96,11 +103,12 @@ class HyperNetwork(nn.Module):
                                std=(0.02 / math.sqrt(2 * config["n_layer"])))
 
     def forward(self, x):
-        x = self.linear2(
-            ck(self.activation,
-               self.linear(x.float())))
-        return x.mul(torch.sigmoid(x)).bfloat16()
-
+        x = x.float()
+        x = self.linear(x)
+        x = ck(self.activation, x)
+        x = self.linear2(x)
+        x = x.mul(torch.sigmoid(x))
+        return x.bfloat16()
 
 class HyperNetworkSingle(nn.Module):
     def __init__(self, config):
@@ -115,14 +123,12 @@ class HyperNetworkSingle(nn.Module):
         for param in self.linear.parameters():
             param.data.normal_(mean=0.0,
                                std=(0.02 / math.sqrt(2 * config["n_layer"])))
-        # state = self.state_dict()
-        # for k in state:
-        #    state[k] = state[k] * 1 / math.sqrt(2 * config["n_layer"])
-        # self.load_state_dict(state)
 
     def forward(self, x):
-        x = self.linear(x.float())
-        return x.mul(torch.sigmoid(x)).bfloat16()
+        x = x.float()
+        x = self.linear(x)
+        x = x.mul(torch.sigmoid(x))
+        return x.bfloat16()
 
 
 tokenizer = AutoTokenizer.from_pretrained('gpt2')
@@ -183,14 +189,17 @@ def report_console(data):
     print(colored("======================================================",
                   "red"))
 
+
 def make_hypernet_saver(train_config, hypernetwork):
     def hypernet_saver(id: str):
         save_folder = Path(train_config["save_path"]) / id
         save_folder.mkdir(parents=True, exist_ok=True)
         torch.save(hypernetwork.state_dict(), save_folder / "hyper.pt")
         opt.save(save_folder / "opt")
+
     return hypernet_saver
 
+
 parser = argparse.ArgumentParser(description='Hypernetwork Finetuner')
 parser.add_argument('--run_name', type=str, help='the run name to use',
                     required=True)