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Fix ci 2 #31

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Fix ci 2 #31

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13d97b0
add smth special maybe ?
samsja Sep 24, 2024
b3e2dd8
fix it
samsja Sep 24, 2024
1e02972
fix it
samsja Sep 24, 2024
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2 changes: 1 addition & 1 deletion open_diloco/simulate_multi_node.sh
100644 → 100755
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Original file line number Diff line number Diff line change
Expand Up @@ -57,7 +57,7 @@ mkdir -p logs
for i in $(seq 0 $(($N - 1 )))
do
> logs/log$i
CUDA_VISIBLE_DEVICES=$(get_cuda_devices $NUM_GPU $i) uv run torchrun --nproc_per_node=$NUM_GPU --node-rank $i --rdzv-endpoint localhost:9999 --nnodes=$N $@ > logs/log$i 2>&1 &
CUDA_VISIBLE_DEVICES=$(get_cuda_devices $NUM_GPU $i) torchrun --nproc_per_node=$NUM_GPU --node-rank $i --rdzv-endpoint localhost:9999 --nnodes=$N $@ > logs/log$i 2>&1 &
child_pids+=($!)
done

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20 changes: 12 additions & 8 deletions open_diloco/train_pure_fsdp.py
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Expand Up @@ -167,7 +167,7 @@

cpu_model = get_offloaded_param(
model
) # todo: in case of sharded grap op we need to offload the cpu model only once per nodes

Check failure on line 170 in open_diloco/train_pure_fsdp.py

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grap ==> grep, grape
outer_optimizer = torch.optim.SGD(cpu_model, lr=config.diloco.outer_lr, momentum=0.9, nesterov=True)

# for param in outer_optimizer.param_groups[0]["params"]:
Expand Down Expand Up @@ -240,14 +240,18 @@
for param_offloaded, param in zip(cpu_model, model.parameters()):
# todo check how to handle the SHARD_GRAD_OP strategy where the weight are replicated across the local devices
param_offloaded.grad = param_offloaded.data - param.data.to(param_offloaded.device)

if param_offloaded.grad.device == torch.device("cpu"):
# gloo does not support AVG
param_offloaded.grad = param_offloaded.grad / global_pg.size()
dist.all_reduce(param_offloaded.grad, op=dist.ReduceOp.SUM, group=global_pg)
else:
dist.all_reduce(param_offloaded.grad, op=dist.ReduceOp.AVG, group=global_pg)


mask = torch.rand_like(param_offloaded.grad) > 0.95

data_to_send = param_offloaded.grad * mask
data_to_send_pre_reduce = data_to_send.clone()

# gloo does not support AVG
data_to_send = data_to_send / global_pg.size()
dist.all_reduce(data_to_send, op=dist.ReduceOp.SUM, group=global_pg)

param_offloaded.grad += data_to_send - data_to_send_pre_reduce # removing the

outer_optimizer.step()
outer_optimizer.zero_grad()

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