Spaces:
Running
Running
File size: 4,619 Bytes
4f6613a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 |
import pyrootutils
import torch
import torch.nn.functional as F
from matplotlib import pyplot as plt
from transformers import AutoTokenizer
# register eval resolver and root
pyrootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
from torch.utils.data import DataLoader
from fish_speech.datasets.semantic import AutoAugTextDataset, TextDataCollator
from tools.llama.generate import load_model
def smooth(
scalars: list[float], weight: float
) -> list[float]: # Weight between 0 and 1
last = scalars[0] # First value in the plot (first timestep)
smoothed = list()
for point in scalars:
smoothed_val = last * weight + (1 - weight) * point # Calculate smoothed value
smoothed.append(smoothed_val) # Save it
last = smoothed_val # Anchor the last smoothed value
return smoothed
@torch.inference_mode()
def analyze_one_model(loader, config, weight, max_length):
device = "cuda" if torch.cuda.is_available() else "cpu"
model = load_model(
config,
weight,
device,
torch.bfloat16,
max_length,
compile=False,
)[0]
current_step = 0
model.eval()
semantic_loss_sum = torch.zeros(
max_length,
dtype=torch.float32,
device=device,
)
counter = torch.zeros(
max_length,
dtype=torch.long,
device=device,
)
for batch in loader:
batch = {k: v.to(device) for k, v in batch.items()}
labels = batch["labels"]
outputs = model(
inp=batch["inputs"],
key_padding_mask=batch["attention_masks"],
)
token_logits = outputs.token_logits
codebook_logits = outputs.codebook_logits
# Generate labels
base_loss = F.cross_entropy(
token_logits.reshape(-1, token_logits.size(-1)),
labels[:, 0].reshape(-1),
ignore_index=-100,
reduction="none",
)
codebook_labels = labels[:, 1 : 1 + model.config.num_codebooks].mT
semantic_loss = F.cross_entropy(
codebook_logits.reshape(-1, codebook_logits.size(-1)),
codebook_labels.reshape(-1),
ignore_index=-100,
reduction="none",
)
base_loss = base_loss.reshape(labels[:, 0].shape)
semantic_loss = semantic_loss.reshape(codebook_labels.shape)
semantic_loss_frame = semantic_loss.mean(-1)
pad_pos = codebook_labels.sum(-1) == -100 * model.config.num_codebooks
for loss_sample, pad in zip(semantic_loss_frame, pad_pos):
semantic_loss_sum[~pad] += loss_sample[~pad]
counter[~pad] += 1
current_step += 1
if current_step == 10:
break
semantic_loss = semantic_loss.cpu()
counter = counter.cpu()
xs, ys = [], []
for i, (loss, count) in enumerate(zip(semantic_loss_sum, counter)):
if count > 0:
xs.append(i)
ys.append((loss / count).item()) # for better loss visualization
smoothed_ys = smooth(ys, 0.95)
# Unload model
del model
torch.cuda.empty_cache()
return xs, ys, smoothed_ys
def main():
tokenizer = AutoTokenizer.from_pretrained("fishaudio/fish-speech-1")
max_length = 4096
ds = AutoAugTextDataset(
["data/protos/sft/云天河"],
tokenizer=tokenizer,
use_speaker=False,
interactive_prob=1.0,
max_length=max_length,
)
loader = DataLoader(
ds,
batch_size=8,
collate_fn=TextDataCollator(tokenizer, max_length=max_length),
num_workers=0,
shuffle=False,
)
plt.figure(figsize=(10, 5), dpi=200)
plt.xlabel("Frame")
plt.ylabel("Loss")
plt.yscale("log")
plt.title("Semantic Loss")
plt.grid(which="both", axis="both")
plt.xlim(0, max_length)
tests = [
(
"pertrain-medium",
"dual_ar_2_codebook_medium",
"checkpoints/text2semantic-pretrain-medium-2k-v1.pth",
),
(
"sft-medium",
"dual_ar_2_codebook_medium",
"checkpoints/text2semantic-sft-medium-v1.1-4k.pth",
),
(
"sft-large",
"dual_ar_2_codebook_large",
"checkpoints/text2semantic-sft-large-v1.1-4k.pth",
),
]
for name, config, weight in tests:
xs, _, smoothed_ys = analyze_one_model(loader, config, weight, max_length)
plt.plot(xs, smoothed_ys, label=name)
plt.legend()
plt.savefig("semantic_loss.png")
if __name__ == "__main__":
main()
|