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Cache: /tmp/work/teacher_cache_v3/shard_*.pt (precomputed in parallel on 2x T4).
Recipe: same losses/adapter as v2; 4000 AdamW steps (batch 8 -> ~32k samples seen).
Held-out quality gate stays on RAVDESS actors 19-24 (never in any training pool).
"""
import glob
import math
import os
import random
import sys
import time
import warnings
warnings.filterwarnings("ignore")
import logging
logging.disable(logging.CRITICAL)
import numpy as np
import torch
import torch.nn.functional as F
sys.path.insert(0, "/tmp/work/s2st_emotion")
from surgical_emotion import SurgicalConfig, SurgicalEmotionEncoder
FUNASR_DIR = "/tmp/work/emotion2vec_plus_base"
CACHE_DIR = "/tmp/work/teacher_cache_v3"
OUT_DIR = "/tmp/work/surgical_out"
BATCH = 8
LR = 3e-4
STEPS = 4000
WARMUP = 200
SEED = 42
VAL_FRAC = 0.05
FP16 = True
DEV = "cuda:0"
def load_cache():
shards = sorted(glob.glob(os.path.join(CACHE_DIR, "shard_*.pt")))
assert shards, "no teacher shards found"
cache = []
for s in shards:
cache.extend(torch.load(s, map_location="cpu", weights_only=False))
return cache
def make_batch(pool):
items = random.sample(pool, min(BATCH, len(pool)))
T = max(it["states"].size(0) for it in items)
B = len(items)
states = torch.zeros(B, T, 768)
probs = torch.zeros(B, T, 9)
inten = torch.zeros(B, T, 1)
lengths = torch.zeros(B, dtype=torch.long)
for j, it in enumerate(items):
t = it["states"].size(0)
states[j, :t] = it["states"].float()
probs[j, :t] = it["probs"].float()
inten[j, :t] = it["inten"].float()
lengths[j] = t
return states.to(DEV), probs.to(DEV), inten.to(DEV), lengths.to(DEV)
def compute_loss(enc, batch):
states, tprobs, tinten, lengths = batch
out = enc.adapter(states, lengths)
embs, flen = out["frame_embs"], out["frame_lengths"]
T = states.size(1)
T_out = embs.size(1)
up = F.interpolate(embs.transpose(1, 2), size=T, mode="nearest").transpose(1, 2)
m = (torch.arange(T, device=states.device)[None, :] < lengths[:, None]).unsqueeze(-1)
cos = F.cosine_similarity(up, states, dim=-1).unsqueeze(-1)
l_frame = ((1 - cos) * m).sum() / m.sum().clamp(min=1)
l_mse = F.mse_loss(F.layer_norm(up, (768,)), F.layer_norm(states, (768,)), reduction="none")
l_mse = (l_mse * m).sum() / m.sum().clamp(min=1)
om = (torch.arange(T_out, device=states.device)[None, :] < flen[:, None]).unsqueeze(-1)
utt_s = (embs * om).sum(1) / flen.unsqueeze(-1).float()
utt_probs_s = torch.softmax(enc.adapter.emotion_head(utt_s.float()), dim=-1)
utt_t = (states * m).sum(1) / lengths.unsqueeze(-1).float()
l_utt_cos = (1 - F.cosine_similarity(utt_s, utt_t, dim=-1)).mean()
utt_probs_t = torch.softmax(enc.teacher.proj(utt_t), dim=-1)
l_utt_kl = F.kl_div(torch.log(utt_probs_s.clamp(min=1e-8)), utt_probs_t, reduction="batchmean")
tprobs_g = F.interpolate(tprobs.transpose(1, 2), size=T_out, mode="area").transpose(1, 2)
tprobs_g = tprobs_g / tprobs_g.sum(-1, keepdim=True).clamp(min=1e-8)
l_frm = F.kl_div(torch.log(out["frame_probs"].clamp(min=1e-8)), tprobs_g,
reduction="none").sum(-1)
l_frm = (l_frm * om.squeeze(-1)).sum() / om.sum().clamp(min=1)
tinten_g = F.interpolate(tinten.transpose(1, 2), size=T_out, mode="area").transpose(1, 2)
l_int = (((out["frame_intensity"] - tinten_g) ** 2) * om).sum() / om.sum().clamp(min=1)
loss = (1.0 * l_frame + 0.5 * l_mse + 0.5 * l_utt_cos
+ 1.0 * l_utt_kl + 1.0 * l_frm + 0.5 * l_int)
return loss, {
"frame": l_frame.item(), "mse": l_mse.item(), "utt_cos": l_utt_cos.item(),
"utt_kl": l_utt_kl.item(), "frm_kl": l_frm.item(), "inten": l_int.item(),
}
def main():
random.seed(SEED); np.random.seed(SEED); torch.manual_seed(SEED)
os.makedirs(OUT_DIR, exist_ok=True)
log = open(os.path.join(OUT_DIR, "train_v3.log"), "a", buffering=1)
def P(*a):
print(*a, flush=True); print(*a, file=log)
P(f"=== distill v3 start {time.strftime('%H:%M:%S')} device={DEV} ===")
enc = SurgicalEmotionEncoder.from_pretrained(FUNASR_DIR, device=DEV)
adapter = enc.adapter
opt = torch.optim.AdamW(adapter.parameters(), lr=LR, weight_decay=0.01)
sched = torch.optim.lr_scheduler.LambdaLR(
opt, lambda s: min((s + 1) / WARMUP, 0.5 * (1 + math.cos(math.pi * s / STEPS))))
scaler = torch.amp.GradScaler("cuda", enabled=FP16)
cache = load_cache()
P(f"teacher cache: {len(cache)} utts from {len(glob.glob(CACHE_DIR + '/shard_*.pt'))} shards")
random.shuffle(cache)
n_val = max(16, int(len(cache) * VAL_FRAC))
val, train = cache[:n_val], cache[n_val:]
P(f"train={len(train)} val={len(val)}")
best_val = float("inf")
for step in range(STEPS):
adapter.train()
with torch.autocast("cuda", dtype=torch.float16, enabled=FP16):
loss, parts = compute_loss(enc, make_batch(train))
opt.zero_grad(set_to_none=True)
scaler.scale(loss).backward()
scaler.unscale_(opt)
torch.nn.utils.clip_grad_norm_(adapter.parameters(), 1.0)
scaler.step(opt); scaler.update(); sched.step()
if step % 100 == 0:
P(f"step {step:4d} loss {loss.item():.4f} " +
" ".join(f"{k}={v:.3f}" for k, v in parts.items()))
if step % 400 == 0 or step == STEPS - 1:
adapter.eval()
vlosses = []
with torch.no_grad(), torch.autocast("cuda", dtype=torch.float16, enabled=FP16):
for _ in range(4):
_, vp = compute_loss(enc, make_batch(val))
vlosses.append(sum(vp.values()))
vloss = float(np.mean(vlosses))
P(f" >> val step {step}: {vloss:.4f}")
if vloss < best_val:
best_val = vloss
enc.save_surgical(os.path.join(OUT_DIR, "surgical_emotion_module.pt"))
P(" >> saved best")
enc.save_surgical(os.path.join(OUT_DIR, "surgical_emotion_module_last.pt"))
P(f"=== done, best_val={best_val:.4f} ===")
if __name__ == "__main__":
main()
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