Upload freezestuff.py

freezestuff.py

@@ -0,0 +1,155 @@
+    freeze_preset_selector = 5 # Change this
+    # 0: no freeze
+    # 1  freeze: phone embeddings, first text enc attention layer, pos encoder pre-processing layer
+    # 2: freeze: phone embeddings, first 2 text enc attention layers, pos encoder pre-processing & first few layers, and initial layers of decoder.
+    # 3: freeze: only phone embeddings
+    # 4: aggressive - freeze phone, all of text enc main encoder, pos encoder pre-processing, first 4 layers in pos encoder. adapts decoder, flow, and later pos encoder layers.
+    # 5: freeze phone embeddings, first 2 text enc attention layers, pos encoder pre-processing, first layers in pos encoder, and first decoder upsample block
+
+    net_g_mod = net_g.module if hasattr(net_g, 'module') else net_g
+
+    # Default all parameters to trainable, then selectively freeze
+    for param in net_g_mod.parameters():
+        param.requires_grad = True
+
+    active_freezing = False
+
+    if freeze_preset_selector == 0:
+        print("no layer freeze")
+        active_freezing = False
+    elif freeze_preset_selector == 1:
+        print("freeze: phone embeddings, first text enc attention layer, pos encoder pre-processing") 
+        active_freezing = True
+        # phone embeddings
+        for param in net_g_mod.enc_p.emb_phone.parameters():
+            param.requires_grad = False
+
+        # text enc attention layer
+        for i, layer in enumerate(net_g_mod.enc_p.encoder.attn_layers):
+            if i < 1:  # Only freeze first layer
+                for param in layer.parameters():
+                    param.requires_grad = False
+
+        # pre-processing layer of pos encoder
+        for param in net_g_mod.enc_q.pre.parameters():
+            param.requires_grad = False
+
+
+    elif freeze_preset_selector == 2:
+        print("freeze: phone, first 2 text enc attention layers, pos encoder pre-processing & first few layers, and initial layers of decoder")
+        active_freezing = True
+        # phone embeddings
+        for param in net_g_mod.enc_p.emb_phone.parameters():
+            param.requires_grad = False
+
+        # first 2 text enc attention layers
+        for i, layer in enumerate(net_g_mod.enc_p.encoder.attn_layers):
+            if i < 2: # Freeze first two layers
+                for param in layer.parameters():
+                    param.requires_grad = False
+
+        # pos encoder pre-processing and main encoder layers
+        for param in net_g_mod.enc_q.pre.parameters():
+            param.requires_grad = False
+            
+        # first few layers in PosteriorEncoder
+        wavenet_module = net_g_mod.enc_q.enc
+        num_wavenet_layers_to_freeze = 2 #layers to freeze
+
+        for i, layer in enumerate(wavenet_module.in_layers):
+            if i < num_wavenet_layers_to_freeze:
+                for param in layer.parameters():
+                    param.requires_grad = False
+
+        for i, layer in enumerate(wavenet_module.res_skip_layers):
+            if i < num_wavenet_layers_to_freeze:
+                for param in layer.parameters():
+                    param.requires_grad = False
+            
+        # 4. Freeze initial layers of the dec
+        for i, upsample_layer in enumerate(net_g_mod.dec.ups):
+            if i < 1: #  upsampling layer
+                for param in upsample_layer.parameters():
+                    param.requires_grad = False
+
+    elif freeze_preset_selector == 3:
+        print("freezing only phone embeddings")
+        active_freezing = True
+        # 1. Only freeze phone embeddings
+        for param in net_g_mod.enc_p.emb_phone.parameters():
+            param.requires_grad = False
+
+
+    elif freeze_preset_selector == 4:
+        print("freezing phone embeddings, all text enc main layers, pos encoder pre-processing, first 4 layers in pos encoder")
+        active_freezing = True
+
+        for param in net_g_mod.enc_p.emb_phone.parameters():
+            param.requires_grad = False
+
+
+        for param in net_g_mod.enc_p.encoder.parameters():
+            param.requires_grad = False
+
+
+        for param in net_g_mod.enc_q.pre.parameters():
+            param.requires_grad = False
+
+        wavenet_module_p4 = net_g_mod.enc_q.enc
+        num_wavenet_layers_to_freeze_p4 = 4
+        for i, layer in enumerate(wavenet_module_p4.in_layers):
+            if i < num_wavenet_layers_to_freeze_p4:
+                for param in layer.parameters():
+                    param.requires_grad = False
+        for i, layer in enumerate(wavenet_module_p4.res_skip_layers):
+            if i < num_wavenet_layers_to_freeze_p4:
+                for param in layer.parameters():
+                    param.requires_grad = False
+
+    elif freeze_preset_selector == 5:
+        print("freeze phone embedding, first 2 text enc attention layeer, pos encoder pre-processing, first 3 layers in pos encoder, decoder upsample block")
+        active_freezing = True
+        for param in net_g_mod.enc_p.emb_phone.parameters():
+            param.requires_grad = False
+
+        for i, layer in enumerate(net_g_mod.enc_p.encoder.attn_layers):
+            if i < 2:
+                for param in layer.parameters():
+                    param.requires_grad = False
+
+
+        for param in net_g_mod.enc_q.pre.parameters():
+            param.requires_grad = False
+
+
+        wavenet_module_p5 = net_g_mod.enc_q.enc
+        num_wavenet_layers_to_freeze_p5 = 3
+        for i, layer in enumerate(wavenet_module_p5.in_layers):
+            if i < num_wavenet_layers_to_freeze_p5:
+                for param in layer.parameters():
+                    param.requires_grad = False
+        for i, layer in enumerate(wavenet_module_p5.res_skip_layers):
+            if i < num_wavenet_layers_to_freeze_p5:
+                for param in layer.parameters():
+                    param.requires_grad = False
+
+        for i, upsample_layer in enumerate(net_g_mod.dec.ups):
+            if i < 1:
+                for param in upsample_layer.parameters():
+                    param.requires_grad = False
+
+
+    else:
+        raise ValueError(f"invalid preset")
+
+    if active_freezing:
+        total_params = 0
+        frozen_params = 0
+        for name, param in net_g_mod.named_parameters():
+            total_params += param.numel()
+            if not param.requires_grad:
+                frozen_params += param.numel()
+        print(f"Freezing applied (Preset {freeze_preset_selector}): {frozen_params:,}/{total_params:,} parameters frozen.")
+    else:
+        total_params = sum(p.numel() for p in net_g_mod.parameters())
+        print(f"no freezing applied")