Follow-up to the review on #854.
DDP re-broadcasts every registered buffer from rank 0 on each forward. The only
buffers here are freqs_cos / freqs_sin, which each rank computes deterministically
from config and never mutates from data, so the broadcast moves identical bytes
every step for nothing.
Verified with a two-rank CPU/Gloo run: with broadcast_buffers=False the
gradients stay identical across ranks and so do the buffers.
Also document the critic's tied-embedding assumption. CriticModel.forward never
calls lm_head, which is only safe under DDP because tie_word_embeddings (default
True) makes lm_head.weight the same Parameter as embed_tokens.weight. Untied, it
becomes a genuinely unused parameter and DDP raises.
train_grpo.py, train_ppo.py and train_agent.py wrap the policy in
DistributedDataParallel but run the loss-bearing forward through
.module, bypassing the wrapper. DDP arms its gradient all-reduce hooks
inside DistributedDataParallel.forward(), so calling .module directly
means backward() performs no all-reduce: every rank steps on purely
local gradients and the replicas silently diverge. Only rank 0 is
saved, so multi-GPU RL training quietly degrades to single-GPU
training on 1/N of the data with no error.
The supervised trainers are unaffected; they already call the wrapped
module. no_grad uses of .module (rollout generation, checkpoint
saving) are left untouched, where unwrapping is correct.
Single-GPU behaviour is unchanged: with no process group, model is the
bare module and these calls are identical to before.
On macOS there's no CUDA, so these trainer scripts fall back to CPU. Detect MPS in the --device default and switch mixed precision to the device-agnostic torch.autocast / torch.amp.GradScaler so it works there.
CUDA and CPU paths are unchanged.
Jiyang
jingyaogong
jingyaogong
basil-k-aji-dev
TOPSAIL\78190
TKiteRunner
jingyaogong
readlnh