Fairscale pipeline - #35
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mrzzd
commented
Jul 8, 2021
| WorkerModule("worker3", pipeline_on_devices(1, 4, n_encoders=args.nlayers // 3), MLMTaskSharder, ntokens, args.emsize, args.nhead, args.nhid, args.dropout), | ||
| WorkerModule("worker4", pipeline_on_devices(6, 3, n_encoders=args.nlayers // 6, n_encoders_on_first_gpu=args.nlayers // 6, include_head=True), MLMTaskSharder, ntokens, args.emsize, args.nhead, args.nhid, args.dropout), | ||
| ) | ||
| layers = [RemoteModule("w0/cuda:0", MLMTaskEmbedding, (ntokens, args.emsize))] |
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Instead of RemoteModule("w0/cuda:0", MLMTaskEmbedding, (ntokens, args.emsize))
Do:
MLMTaskEmbedding(ntokens, args.emsize).to('cuda:0')
for old implementation
mrzzd
commented
Jul 8, 2021
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| graph = make_graph(org_model) | ||
| #for node in graph.nodes: print(node.module.on, node.get_name()) | ||
| model = DistributedPipeline(graph, chunks=8) |
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instead of creating graph and model this way, just do: model=Pipe(org_model)
for old implementation
wayi1
reviewed
Jul 8, 2021
| this_encoders = n_encoders // (7-i) | ||
| layers.append(RemoteModule(device, MLMTaskEncoder, (args.emsize, args.nhead, args.nhid, this_encoders, args.dropout))) | ||
| n_encoders -= this_encoders | ||
| layers.append(RemoteModule("w7/cuda:7", MLMTaskHead, (ntokens, args.emsize))) |
wayi1
reviewed
Jul 8, 2021
| assert args.world_size % args.num_workers == 0 | ||
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| #run_worker(args.rank, args.world_size, args) | ||
| mp.spawn(run_worker, args=(args,), nprocs=args.world_size // args.num_workers) |
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Don't use mp.spawn -- all the ranks will be 0.
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