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# Copyright (c) Meta Platforms, Inc. and affiliates
# Owner(s): ["oncall: distributed"]
import torch
import torch.distributed as dist
from torch.distributed._tensor import DeviceMesh, distribute_tensor, Replicate
from torch.testing._internal.common_utils import run_tests
from torch.testing._internal.distributed._tensor.common_dtensor import (
DTensorTestBase,
with_comms,
)
ITER_TIME = 10
LR = 0.001
class DistOtherOpsTest(DTensorTestBase):
@property
def world_size(self) -> int:
# hard code world size to 2
return 2
@with_comms
def test_slice(self):
device_mesh = DeviceMesh(self.device_type, list(range(self.world_size)))
shard_spec = [Replicate()]
input_list = torch.rand(ITER_TIME, 1024, 10)
grad_output_list = torch.rand(ITER_TIME, 1024, 5) * 1e-3
for i in range(ITER_TIME):
inp = input_list[i].to(self.device_type).requires_grad_()
grad_output = grad_output_list[i].to(self.device_type)
# droppath with dtensor
inp_dtensor = distribute_tensor(inp, device_mesh, shard_spec)
grad_output_dtensor = distribute_tensor(
grad_output, device_mesh, shard_spec
)
output = inp_dtensor[:, :5]
output.backward(grad_output_dtensor)
# nll with plain tensor
output_gt = inp[:, :5]
output_gt.backward(grad_output)
output_diff_abs = output.to_local() - output_gt
output_diff_rel = output_diff_abs / (torch.abs(output_gt) + 1e-8)
output_mse_abs = torch.mean(output_diff_abs * output_diff_abs).item()
output_mse_rel = torch.mean(output_diff_rel * output_diff_rel).item()
grad_diff_abs = inp_dtensor.grad.to_local() - inp.grad
grad_diff_rel = grad_diff_abs / (torch.abs(inp.grad) + 1e-8)
grad_mse_abs = torch.mean(grad_diff_abs * grad_diff_abs).item()
grad_mse_rel = torch.mean(grad_diff_rel * grad_diff_rel).item()
self.assertTrue(
output_mse_abs <= 1e-6,
f"Too large absolute mse for output, expected less equal 1e-6, got {output_mse_abs}",
)
self.assertTrue(
output_mse_rel <= 1e-6,
f"Too large relative mse for output, expected less equal 1e-6, got {output_mse_rel}",
)
self.assertTrue(
grad_mse_abs <= 1e-6,
f"Too large absolute mse for gradient, expected less equal 1e-6, got {grad_mse_abs}",
)
self.assertTrue(
grad_mse_rel <= 1e-6,
f"Too large relative mse for gradient, expected less equal 1e-6, got {grad_mse_rel}",
)
@with_comms
def test_bernoulli(self):
rank = dist.get_rank()
device_mesh = DeviceMesh(self.device_type, list(range(self.world_size)))
shard_spec = [Replicate()]
input_list = torch.rand(ITER_TIME, 1024, 10)
grad_output_list = torch.rand(ITER_TIME, 1024, 10) * 1e-3
for i in range(ITER_TIME):
inp = input_list[i].to(self.device_type).requires_grad_()
grad_output = grad_output_list[i].to(self.device_type)
# bernoulli with dtensor
inp_dtensor = distribute_tensor(inp, device_mesh, shard_spec)
grad_output_dtensor = distribute_tensor(
grad_output, device_mesh, shard_spec
)
output = torch.bernoulli(inp_dtensor)
output.backward(grad_output_dtensor)
send_output_tensor = output.to_local()
recv_output_tensor = torch.zeros_like(send_output_tensor)
send_grad_tensor = inp_dtensor.grad.to_local()
recv_grad_tensor = torch.zeros_like(send_grad_tensor)
send_op_1 = dist.P2POp(dist.isend, send_output_tensor, 1 ^ rank)
send_op_2 = dist.P2POp(dist.isend, send_grad_tensor, 1 ^ rank)
recv_op_1 = dist.P2POp(dist.irecv, recv_output_tensor, 1 ^ rank)
recv_op_2 = dist.P2POp(dist.irecv, recv_grad_tensor, 1 ^ rank)
reqs = dist.batch_isend_irecv([send_op_1, send_op_2, recv_op_1, recv_op_2])
for req in reqs:
req.wait()
output_diff_abs = send_output_tensor - recv_output_tensor
output_diff_rel = output_diff_abs / (torch.abs(recv_output_tensor) + 1e-8)
output_mse_abs = torch.mean(output_diff_abs * output_diff_abs).item()
output_mse_rel = torch.mean(output_diff_rel * output_diff_rel).item()
grad_diff_abs = send_grad_tensor - recv_grad_tensor
grad_diff_rel = grad_diff_abs / (torch.abs(recv_grad_tensor) + 1e-8)
grad_mse_abs = torch.mean(grad_diff_abs * grad_diff_abs).item()
grad_mse_rel = torch.mean(grad_diff_rel * grad_diff_rel).item()
self.assertTrue(
output_mse_abs <= 1e-6,
f"Too large absolute mse for output, expected less equal 1e-6, got {output_mse_abs}",
)
self.assertTrue(
output_mse_rel <= 1e-6,
f"Too large relative mse for output, expected less equal 1e-6, got {output_mse_rel}",
)
self.assertTrue(
grad_mse_abs <= 1e-6,
f"Too large absolute mse for gradient, expected less equal 1e-6, got {grad_mse_abs}",
)
self.assertTrue(
grad_mse_rel <= 1e-6,
f"Too large relative mse for gradient, expected less equal 1e-6, got {grad_mse_rel}",
)
@with_comms
def test_nll(self):
device_mesh = DeviceMesh(self.device_type, list(range(self.world_size)))
shard_spec = [Replicate()]
pred_list = torch.rand(ITER_TIME, 1024, 10)
target_list = torch.randint(0, 10, (ITER_TIME, 1024), dtype=torch.long)
criterion = torch.nn.CrossEntropyLoss()
for i in range(ITER_TIME):
pred = pred_list[i].to(self.device_type).requires_grad_()
target = target_list[i].to(self.device_type)
# nll with dtensor
pred_dtensor = distribute_tensor(pred, device_mesh, shard_spec)
target_dtensor = distribute_tensor(target, device_mesh, shard_spec)
loss = criterion(pred_dtensor, target_dtensor)
loss.backward()
# nll with plain tensor
loss_gt = criterion(pred, target)
loss_gt.backward()
loss_diff_abs = loss.to_local() - loss_gt
loss_diff_rel = loss_diff_abs / (torch.abs(loss_gt) + 1e-8)
loss_mse_abs = torch.mean(loss_diff_abs * loss_diff_abs).item()
loss_mse_rel = torch.mean(loss_diff_rel * loss_diff_rel).item()
grad_diff_abs = pred_dtensor.grad.to_local() - pred.grad
grad_diff_rel = grad_diff_abs / (torch.abs(pred.grad) + 1e-8)
grad_mse_abs = torch.mean(grad_diff_abs * grad_diff_abs).item()
grad_mse_rel = torch.mean(grad_diff_rel * grad_diff_rel).item()
self.assertTrue(
loss_mse_abs <= 1e-6,
f"Too large absolute mse for loss, expected less equal 1e-6, got {loss_mse_abs}",
)
self.assertTrue(
loss_mse_rel <= 1e-6,
f"Too large relative mse for loss, expected less equal 1e-6, got {loss_mse_rel}",
)
self.assertTrue(
grad_mse_abs <= 1e-6,
f"Too large absolute mse for gradient, expected less equal 1e-6, got {grad_mse_abs}",
)
self.assertTrue(
grad_mse_rel <= 1e-6,
f"Too large relative mse for gradient, expected less equal 1e-6, got {grad_mse_rel}",
)
if __name__ == "__main__":
run_tests()
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