File size: 6,679 Bytes
fa2e79e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 |
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") |