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import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist
from dualpipe import DualPipe, DualPipeTrain, set_p2p_tensor_shapes, set_p2p_tensor_dtype
from dualpipe.log import bcolors
from dualpipe.utils import WeightGradStore, run_backward
class LinearFunc(torch.autograd.Function):
@staticmethod
def forward(ctx, input, weight):
ctx.save_for_backward(input, weight)
output = F.linear(input, weight)
return output
@staticmethod
def backward(ctx, grad_output):
input, weight = ctx.saved_tensors
if weight.grad is None:
weight.grad = torch.zeros_like(weight)
def grad_weight_fn():
weight.grad += grad_output.flatten(0, -2).T @ input.flatten(0, -2)
if WeightGradStore.enabled:
WeightGradStore.put(grad_weight_fn)
else:
grad_weight_fn()
grad_input = grad_output @ weight
return grad_input, None
class MyLinear(nn.Linear):
def forward(self, input: torch.Tensor) -> torch.Tensor:
return LinearFunc.apply(input, self.weight)
class PipelineStage(nn.Module):
def __init__(self, hidden_size: int) -> None:
super().__init__()
self.linear1 = MyLinear(hidden_size, hidden_size * 4, bias=False)
self.linear2 = MyLinear(hidden_size * 4, hidden_size, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.linear1(x)
x = F.gelu(x)
x = self.linear2(x)
return x
@classmethod
def overlapped_forward_backward(
cls,
module0: "PipelineStage",
inputs0: List[torch.Tensor],
criterion0: Optional[Callable],
labels0: Optional[List[torch.Tensor]],
module1: "PipelineStage",
loss1: Optional[torch.Tensor],
outputs1: Optional[List[torch.Tensor]],
output_grads1: Optional[List[torch.Tensor]],
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""
You should implement custom forward-backward overlap strategy.
The code below is just an example.
"""
outputs0 = module0(*inputs0)
outputs0 = [outputs0] if isinstance(outputs0, torch.Tensor) else outputs0
if criterion0 is not None:
loss0 = criterion0(*outputs0, *labels0)
else:
loss0 = None
if loss1 is not None:
loss1.backward()
loss1.detach_()
else:
run_backward(outputs1, output_grads1)
return outputs0, loss0
def criterion(output: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
return F.mse_loss(output, target).clone()
def ref_step(x, l, model, chunks):
ys, losses = [], []
for micro_x, micro_l in zip(x.chunk(chunks), l.chunk(chunks)):
micro_y = model(micro_x)
loss = criterion(micro_y, micro_l)
loss.backward()
ys.append(micro_y)
losses.append(loss)
y = torch.cat(ys, 0)
loss = torch.stack(losses)
return loss, y
def cal_diff(x: torch.Tensor, y: torch.Tensor) -> float:
x, y = x.double(), y.double()
cos_diff = 1 - 2 * (x * y).sum().item() / (x * x + y * y).sum().item()
print(bcolors.WARNING + f"cos_diff={cos_diff}")
return cos_diff
def main(rank, pp_size):
is_first_rank = rank == 0
is_last_rank = rank == pp_size - 1
dist.init_process_group(backend='nccl', init_method="env://", world_size=pp_size, rank=rank)
torch.cuda.set_device(rank)
torch.set_default_device(f"cuda:{rank}")
torch.manual_seed(233)
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
num_chunks = 20
micro_batch_size = 3
seq_len = 256
hidden_size = 512
if is_first_rank:
print(f"{pp_size=}, {num_chunks=}, {seq_len=}, {hidden_size=}", flush=True)
set_p2p_tensor_shapes([(micro_batch_size, seq_len, hidden_size)])
set_p2p_tensor_dtype(torch.float32)
# Create a model and partition it for each process
full_modules = nn.Sequential(*[PipelineStage(hidden_size) for _ in range(pp_size)])
# Full inputs
full_x = torch.randn(num_chunks * micro_batch_size, seq_len, hidden_size)
full_l = torch.randn(num_chunks * micro_batch_size, seq_len, hidden_size)
# Reference step
loss_ref, output_ref = ref_step(full_x, full_l, full_modules, num_chunks)
# DualPipe
local_full_modules = nn.Sequential(full_modules[rank], full_modules[pp_size - 1 - rank])
local_modules = nn.Sequential(PipelineStage(hidden_size), PipelineStage(hidden_size))
local_modules[0].load_state_dict(local_full_modules[0].state_dict())
local_modules[1].load_state_dict(local_full_modules[1].state_dict())
dualpipe_model = DualPipe(local_modules)
# DualPipe inputs
if is_first_rank:
x = full_x.chunk(2)[0]
l = full_l.chunk(2)[1]
elif is_last_rank:
x = full_x.chunk(2)[1]
l = full_l.chunk(2)[0]
else:
x = None
l = None
# Training step
loss, outputs = dualpipe_model.step(x, num_chunks=num_chunks, criterion=criterion, labels=(l,), return_outputs=False)
# Check loss
if is_first_rank:
assert torch.equal(loss, loss_ref.chunk(2)[1])
elif is_last_rank:
assert torch.equal(loss, loss_ref.chunk(2)[0])
else:
assert loss is None
assert outputs is None
# Check grads
for (p0, p1) in zip(local_modules[0].parameters(), local_modules[1].parameters()):
p0all = torch.empty(pp_size, *p0.shape)
p1all = torch.empty(pp_size, *p1.shape)
dist.all_gather_into_tensor(p0all, p0.grad)
dist.all_gather_into_tensor(p1all, p1.grad)
p0.grad += p1all[pp_size - 1 - rank]
p1.grad += p0all[pp_size - 1 - rank]
for ((n, p), p_ref) in zip(local_modules.named_parameters(), local_full_modules.parameters()):
assert cal_diff(p.grad, p_ref.grad) < 1e-13
dualpipe_model.zero_grad()
# Inference step
with torch.no_grad():
loss, outputs = dualpipe_model.step(x, num_chunks=num_chunks, criterion=criterion, labels=(l,), return_outputs=True)
# Check loss and outputs
if is_first_rank:
assert torch.equal(loss, loss_ref.chunk(2)[1])
assert torch.equal(outputs, output_ref.chunk(2)[1])
elif is_last_rank:
assert torch.equal(loss, loss_ref.chunk(2)[0])
assert torch.equal(outputs, output_ref.chunk(2)[0])
else:
assert loss is None
assert outputs is None
def test_dualpipe(ngpus):
torch.multiprocessing.spawn(main, args=(ngpus, ), nprocs=ngpus, daemon=True)
if __name__ == "__main__":
num_gpus = torch.cuda.device_count() // 2 * 2
for ngpus in range(num_gpus, 0, -2):
test_dualpipe(ngpus)
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