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I reviewed the Megatron-LM documentation related to pipeline parallelism. Is the following understanding correct?
Assume I have 4 GPUs, num_layers=8, and pipeline_parallel_size=2. Then each GPU will be assigned consecutive layers:
GPU0: layers [0,1]
GPU1: layers [2,3]
GPU2: layers [4,5]
GPU3: layers [6,7]
If I enable virtual_pipeline_size=2 (interleaved), the paper says each physical pipeline stage will be assigned non-consecutive layers:
GPU0: layers [0,4]
GPU1: layers [1,5]
GPU2: layers [2,6]
GPU3: layers [3,7]
However, when I trained two different tasks (one with vpp_size=1, the other with vpp_size=2), and checked the final checkpoint weights, I found that GPU0 still held layer 0 and 1, GPU1 still had layer 2 and 3, only they were distributed into different model chunks(model0, model1).
This seems inconsistent with what the paper described — why is that?
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I reviewed the Megatron-LM documentation related to pipeline parallelism. Is the following understanding correct?
Assume I have 4 GPUs, num_layers=8, and pipeline_parallel_size=2. Then each GPU will be assigned consecutive layers:
GPU0: layers [0,1]
GPU1: layers [2,3]
GPU2: layers [4,5]
GPU3: layers [6,7]
If I enable virtual_pipeline_size=2 (interleaved), the paper says each physical pipeline stage will be assigned non-consecutive layers:
GPU0: layers [0,4]
GPU1: layers [1,5]
GPU2: layers [2,6]
GPU3: layers [3,7]
However, when I trained two different tasks (one with vpp_size=1, the other with vpp_size=2), and checked the final checkpoint weights, I found that GPU0 still held layer 0 and 1, GPU1 still had layer 2 and 3, only they were distributed into different model chunks(model0, model1).
This seems inconsistent with what the paper described — why is that?
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