Comments (7)
ktaebum
commented on June 21, 2024
Also encountered the same problem with non-MoE model.
I tried to run training job of Llama 13B model on two DGX A100 nodes, but the time breakdown shows:
forward-backward ...............................: (5662.82, 5666.72)
forward-compute ................................: (2146.30, 2210.56)
backward-compute ...............................: (3431.20, 3509.58)
batch-generator ................................: (17.31, 33.45)
layernorm-grads-all-reduce .....................: (5.24, 218.94)
embedding-grads-all-reduce .....................: (0.06, 0.11)
all-grads-sync .................................: (215891.91, 225072.22)
optimizer-copy-to-main-grad ....................: (9.13, 9.19)
optimizer-unscale-and-check-inf ................: (9.69, 9.88)
optimizer-clip-main-grad .......................: (14.55, 14.77)
optimizer-count-zeros ..........................: (0.02, 0.07)
optimizer-inner-step ...........................: (31.58, 32.33)
optimizer-copy-main-to-model-params ............: (9.36, 9.57)
optimizer ......................................: (77.15, 77.37)
(I disabled all overlap-* optimizations and distributed-optimizer for more accurate time breakdown. Gradient AllReduce takes more than 200 seconds while forward-backward takes just 5.6 seconds.
The problem occurs regardless of using distributed-optimizer:
forward-backward ...............................: (6640.79, 6647.08)
forward-compute ................................: (3118.90, 3181.81)
backward-compute ...............................: (3428.96, 3512.83)
batch-generator ................................: (16.72, 34.26)
layernorm-grads-all-reduce .....................: (4.97, 11.08)
embedding-grads-all-reduce .....................: (0.06, 0.12)
all-grads-sync .................................: (77025.69, 112368.28)
params-all-gather ..............................: (77461.61, 112343.53)
optimizer-copy-to-main-grad ....................: (4.65, 4.82)
optimizer-unscale-and-check-inf ................: (5.37, 5.39)
optimizer-clip-main-grad .......................: (7.70, 7.74)
optimizer-count-zeros ..........................: (0.02, 0.03)
optimizer-inner-step ...........................: (15.89, 16.30)
optimizer-copy-main-to-model-params ............: (4.53, 4.56)
optimizer ......................................: (77502.36, 112384.28)
When I run the same job on a single node, the problem disappears.
My environment is
- Megatron commit ID:
core_v0.4.0 - NGC PyTorch container version:
23.04
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ktaebum
commented on June 21, 2024
My issue has been resolved by passing --device=/dev/infiniband in docker run argument.
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rahul003
commented on June 21, 2024
ktaebum's issue is unrelated. We only notice slowdown in some steps, and due to intra node AllGather calls which are surprisingly high for those steps
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dawson-chen
commented on June 21, 2024
I have encountered the same problem with MoE, when route type is sinkhorn and topK > 1.
From my log, I found the main comsumption is from sinkhorn function
norm_logits = sinkhorn(
logits.to(dtype=torch.float32)
)from megatron-lm.
dawson-chen
commented on June 21, 2024
When topk > 1 and route type is sinkhorn, the sinkhorn function inner code loop thousands times for some logits cases.
But I didn't found any clue on those logits, look similar with normal ones.
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yanring
commented on June 21, 2024
@Teng-xu @dawson-chen
Thanks for reporting this issue. This could be due to too many iterations in Sinkhorn on some ranks. You can try adding an early stop to Sinkhorn or using aux_loss for load balancing.
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wen020
commented on June 21, 2024
how to get Model TFLOPS/GPU?
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