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f9dff7a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | import pytest
import torch
from lightning import Fabric
from litgpt.utils import _RunIf
@_RunIf(min_cuda_gpus=2, standalone=True)
@pytest.mark.parametrize("strategy", ["ddp", "fsdp"])
def test_no_backward_sync(strategy):
fabric = Fabric(devices=2, accelerator="cuda", strategy=strategy)
fabric.launch()
# account for sharding in the case of FSDP
out_features = 1 if "ddp" in strategy else fabric.world_size
model = torch.nn.Linear(1, out_features, bias=False, device=fabric.device)
x = torch.randn(1, 1, device=fabric.device)
model = fabric.setup(model)
# 6 iters, 3 grad accumulation iters
for i, enabled in enumerate((True, True, False, True, True, False), 1):
x = torch.tensor([i * (fabric.local_rank + 1)], device=fabric.device, dtype=torch.float32)
with fabric.no_backward_sync(model, enabled):
y = model(x)
fabric.backward(y.sum())
if not enabled:
# Math for the first 3 iters
#
# DistributedDataParallel
# (1*1+2*1+3*1 + 1*2+2*2+3*2) / 2 = 9
# ^^^^^^^^^^^ ^^^^^^^^^^^ ^^^
# rank0 rank1 allreduce
#
# thunder.distributed.ddp
# ((1*1+2*1) + (1*2+2*2)) / 2 + (3*1 + 3*2) / 2 = 9
# ^^^^^^^ ^^^^^^^ ^^^ ^^^ ^^^ ^^^
# rank0 rank1 allreduce1 rank0 rank1 allreduce2
assert model.weight.grad.shape.numel() == 1, model.weight.grad.shape
assert model.weight.grad.item() == (9.0 if i == 3 else 22.5)
model.weight.grad = None
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