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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()
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