[Bug / Performance] spk_model spectral clustering saturates every CPU core by default

Summary

Speaker diarization (spk_model="cam++") pins every CPU core during the clustering tail of a request. No preset_spk_num is needed -- this is the default path, and it happens even for short audio.

Environment

• FunASR 1.2.7 (also reproduced on current main)
• 64-core host, Tesla T4
• AutoModel(model=..., vad_model="fsmn-vad", punc_model="ct-punc", spk_model="cam++", device="cuda")
• 35-minute recording -> N=357 segments

Symptom

Container CPU over one request:

t=44s  cpu=100.79%    <- ASR on GPU
t=48s  cpu=5767.99%   <- 57.7 of 64 cores, GPU utilisation back to 0
t=56s  cpu=5767.21%
t=60s  cpu=1.79%      <- done

All of it lands in one ClusterBackend pass at the very end of the request.

Root cause

SpectralCluster.get_spec_embs calls a full dense scipy.linalg.eigh on the N x N affinity Laplacian, then uses almost none of the result:

• the speaker count reads only the gaps among the first max_num_spks + 1 (i.e. 16) eigenvalues;
• the embedding keeps only the first num_of_spk eigenvectors.

Two independent problems follow:

1. The full spectrum is O(N^3) work that is thrown away.
2. scipy.linalg.eigh goes through BLAS, whose default thread count is one per core. At a few hundred rows the parallel drivers spend more time synchronizing than computing.

Measured with threadpoolctl.threadpool_info(), the process had:

openblas (numpy)         num_threads=64
openblas (scipy)         num_threads=64
libgomp  (scikit-learn)  num_threads=64
libgomp  (torch)         num_threads=4      <- only torch is capped

AutoModel's ncpu -> torch.set_num_threads only reaches the torch pool, so the clustering call is left unbounded.

Instrumenting the pipeline (wall / CPU in core-seconds, N=357):

ClusterBackend.forward          6.553s / 327.04 core-s
  get_sim_mat                   0.063s /   2.61
  p_pruning                     0.148s /   5.92
  get_spec_embs  (eigh)         6.190s / 312.04   <-- 50 cores average
  cluster_embs   (k_means)      0.113s /   6.42

Microbenchmark

eigh on the same input, varying BLAS threads:

threads   n=357 wall   n=357 CPU     n=1200 wall   n=1200 CPU
1         0.030s        0.03          0.746s          0.74
4         0.027s        0.10          0.471s          1.31
32        0.037s        1.06          0.500s          9.93
64        0.973s       57.01          3.701s        205.72

Wall-clock is essentially flat from 1 to 32 threads while CPU scales linearly -- the extra threads are pure overhead, and at 64 they make it slower.

Suggested fix

Both changes are independent; together they are ~17x faster and ~950x cheaper in CPU on n=1200:

# only the leading eigenpairs are ever used
n_eig = max_num_spks + 1
if k_oracle is not None:
    n_eig = max(n_eig, int(k_oracle))
n_eig = min(n_eig, L.shape[0])
lambdas, eig_vecs = scipy.linalg.eigh(L, subset_by_index=[0, n_eig - 1])

# and cap BLAS, which defaults to one thread per core
with threadpoolctl.threadpool_limits(limits=1, user_api="blas"):
    ...

Combined, on the same hosts:

n=1200   full + 64 threads     4.278s / 237.59 core-s   (before)
         subset + 1 thread     0.246s /   0.25 core-s

End-to-end the ClusterBackend pass goes from 327 -> 8.7 core-seconds and 6.55s -> 0.99s, with byte-identical speaker labels (761 sentences, {0:322, 1:156, 2:144, 3:139}). A sweep over n=3..900 and k_oracle in {None, 2, 20, 40} reproduces the original speaker counts and eigenvector subspaces exactly, including the n < max_num_spks + 1 edge case.

A PR is linked below.

Relationship to #3514

#3514 covers the large-N + preset_spk_num path, where the fix routes to kmeans_cluster. That fix is correct but does not cover this report:

• here X.shape[0] = 357 < 2048, so ClusterBackend.forward takes if X.shape[0] < 2048: spectral_cluster(X, k) -- a different branch;
• preset_spk_num is not set at all in the repro above.

Neither path had any BLAS thread control, before or after #3514, so the core-saturation half of this is present in all versions up to and including 1.4.15 (verified by diffing cluster_backend.py across 1.2.7, 1.2.9, 1.3.0, 1.3.10, 1.3.20, 1.3.30, 1.4.7, 1.4.15).