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# Copyright (c) Meta Platforms, Inc. and affiliates
# Owner(s): ["oncall: distributed"]
import copy
import torch
import torch.nn as nn
from torch.distributed._tensor import (
DeviceMesh,
distribute_module,
distribute_tensor,
Replicate,
Shard,
)
from torch.testing._internal.common_utils import run_tests
from torch.testing._internal.distributed._tensor.common_dtensor import (
DTensorTestBase,
skip_if_lt_x_gpu,
with_comms,
)
ITER_TIME = 10
LR = 0.001
def _conv_fn(
name: str,
module: nn.Module,
device_mesh: DeviceMesh,
) -> None:
for name, param in module.named_parameters():
dist_spec = [Replicate()]
dist_param = torch.nn.Parameter(
distribute_tensor(param, device_mesh, dist_spec)
)
name = "_".join(name.split("."))
module.register_parameter(name, dist_param)
class DistConvolutionOpsTest(DTensorTestBase):
@property
def world_size(self) -> int:
# hard code world size to 2
return 2
@with_comms
def test_downsampling_convolution(self):
device_mesh = DeviceMesh(self.device_type, list(range(self.world_size)))
shard_spec = [Shard(3)]
input_list = torch.rand(ITER_TIME, 7, 3, 512, 1024)
grad_output_list = torch.rand(ITER_TIME, 7, 256, 128, 256) * 1e-3
model = nn.Conv2d(3, 256, kernel_size=4, stride=4, padding=0).to(
self.device_type
)
nn.init.ones_(model.weight)
nn.init.zeros_(model.bias)
model_gt = copy.deepcopy(model).to(self.device_type)
# training with dtensor
model = distribute_module(
model, device_mesh, _conv_fn, input_fn=None, output_fn=None
)
optimizer = torch.optim.SGD(model.parameters(), lr=LR)
for i in range(ITER_TIME):
optimizer.zero_grad()
inp = input_list[i].to(self.device_type).requires_grad_()
inp_dtensor = distribute_tensor(inp, device_mesh, shard_spec)
output = model(inp_dtensor)
grad_output = grad_output_list[i].to(self.device_type)
grad_output_dtensor = distribute_tensor(
grad_output, device_mesh, shard_spec
)
output.backward(grad_output_dtensor)
optimizer.step()
# training with plain tensor
optimizer_gt = torch.optim.SGD(model_gt.parameters(), lr=LR)
for i in range(ITER_TIME):
optimizer_gt.zero_grad()
inp = input_list[i].to(self.device_type).requires_grad_()
output = model_gt(inp)
grad_output = grad_output_list[i].to(self.device_type)
output.backward(grad_output)
optimizer_gt.step()
weight_diff_abs = model.weight.to_local() - model_gt.weight
bias_diff_abs = model.bias.to_local() - model_gt.bias
weight_diff_rel = weight_diff_abs / (torch.abs(model_gt.weight) + 1e-8)
bias_diff_rel = bias_diff_abs / (torch.abs(model_gt.bias) + 1e-8)
weight_mse_abs = torch.mean(weight_diff_abs * weight_diff_abs).item()
bias_mse_abs = torch.mean(bias_diff_abs * bias_diff_abs).item()
weight_mse_rel = torch.mean(weight_diff_rel * weight_diff_rel).item()
bias_mse_rel = torch.mean(bias_diff_rel * bias_diff_rel).item()
self.assertTrue(
weight_mse_abs <= 1e-6,
f"Too large absolute mse for weight tensor, expected less equal 1e-6, got {weight_mse_abs}",
)
self.assertTrue(
bias_mse_abs <= 1e-6,
f"Too large absolute mse for bias tensor, expected less equal 1e-6, got {bias_mse_abs}",
)
self.assertTrue(
weight_mse_rel <= 1e-6,
f"Too large relative mse for weight tensor, expected less equal 1e-6, got {weight_mse_rel}",
)
self.assertTrue(
bias_mse_rel <= 1e-6,
f"Too large relative mse for bias tensor, expected less equal 1e-6, got {bias_mse_rel}",
)
# TODO: test_depthwise_convolution is broken in CI with gloo backend.
# Temporarily disable it to unblock CI.
@with_comms
@skip_if_lt_x_gpu(2)
def test_depthwise_convolution(self):
device_mesh = DeviceMesh(self.device_type, list(range(self.world_size)))
shard_spec = [Shard(3)]
input_list = torch.rand(ITER_TIME, 7, 256, 128, 256)
grad_output_list = torch.rand(ITER_TIME, 7, 256, 128, 256) * 1e-3
model = nn.Conv2d(256, 256, kernel_size=7, padding=3, groups=256).to(
self.device_type
)
nn.init.ones_(model.weight)
nn.init.zeros_(model.bias)
model_gt = copy.deepcopy(model).to(self.device_type)
# training with dtensor
model = distribute_module(
model, device_mesh, _conv_fn, input_fn=None, output_fn=None
)
optimizer = torch.optim.SGD(model.parameters(), lr=LR)
for i in range(ITER_TIME):
optimizer.zero_grad()
inp = input_list[i].to(self.device_type).requires_grad_()
inp_dtensor = distribute_tensor(inp, device_mesh, shard_spec)
output = model(inp_dtensor)
grad_output = grad_output_list[i].to(self.device_type)
grad_output_dtensor = distribute_tensor(
grad_output, device_mesh, shard_spec
)
output.backward(grad_output_dtensor)
optimizer.step()
# training with plain tensor
optimizer_gt = torch.optim.SGD(model_gt.parameters(), lr=LR)
for i in range(ITER_TIME):
optimizer_gt.zero_grad()
inp = input_list[i].to(self.device_type).requires_grad_()
output = model_gt(inp)
grad_output = grad_output_list[i].to(self.device_type)
output.backward(grad_output)
optimizer_gt.step()
weight_diff_abs = model.weight.to_local() - model_gt.weight
bias_diff_abs = model.bias.to_local() - model_gt.bias
weight_diff_rel = weight_diff_abs / (torch.abs(model_gt.weight) + 1e-8)
bias_diff_rel = bias_diff_abs / (torch.abs(model_gt.bias) + 1e-8)
weight_mse_abs = torch.mean(weight_diff_abs * weight_diff_abs).item()
bias_mse_abs = torch.mean(bias_diff_abs * bias_diff_abs).item()
weight_mse_rel = torch.mean(weight_diff_rel * weight_diff_rel).item()
bias_mse_rel = torch.mean(bias_diff_rel * bias_diff_rel).item()
self.assertTrue(
weight_mse_abs <= 1e-6,
f"Too large absolute mse for weight tensor, expected less equal 1e-6, got {weight_mse_abs}",
)
self.assertTrue(
bias_mse_abs <= 1e-6,
f"Too large absolute mse for bias tensor, expected less equal 1e-6, got {bias_mse_abs}",
)
self.assertTrue(
weight_mse_rel <= 1e-6,
f"Too large relative mse for weight tensor, expected less equal 1e-6, got {weight_mse_rel}",
)
self.assertTrue(
bias_mse_rel <= 1e-6,
f"Too large relative mse for bias tensor, expected less equal 1e-6, got {bias_mse_rel}",
)
if __name__ == "__main__":
run_tests()
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