ColossalAI/tests/test_shardformer/test_layer/test_dist_crossentropy.py

import pytest
import torch
import torch.nn.functional as F

import colossalai
from colossalai.logging import disable_existing_loggers
from colossalai.shardformer.layer import cross_entropy_1d
from colossalai.testing import rerun_if_address_is_in_use, spawn

CONFIG = dict(
    parallel=dict(data=1, pipeline=1, tensor=dict(size=2, mode="1d")),
)


def check_dist_crossentropy(rank, world_size, port, ignore_index):
    disable_existing_loggers()
    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, port=port, host="localhost", backend="nccl")

    # prepare data
    pred = torch.randn(2, 4, 8, requires_grad=True).cuda()
    labels = torch.randint(8, (2, 4)).cuda()
    # set some label to -100 to test the ignore index
    labels[0, -1] = ignore_index

    org_pred = pred.view(-1, 8)
    org_labels = labels.view(-1)
    org_loss = F.cross_entropy(org_pred, org_labels)
    pred.retain_grad()
    org_loss.backward()

    dist_pred = pred.clone().chunk(world_size, -1)[rank].detach()
    dist_pred.requires_grad = True
    dist_loss = cross_entropy_1d(dist_pred, labels, ignore_index=ignore_index)
    dist_pred.retain_grad()
    dist_loss.backward()

    assert torch.allclose(
        org_loss, dist_loss, atol=1e-5
    ), f"dist cross entropy loss is not equal to orgin loss\n{org_loss}\n{dist_loss}"


    target_grad = torch.chunk(pred.grad, world_size, dim=-1)[rank]
    assert torch.allclose(target_grad, dist_pred.grad), f"dist grad is not equal to orgin grad\n{target_grad}\n{dist_pred.grad}"


@pytest.mark.dist
@rerun_if_address_is_in_use()
def test_dist_crossentropy():
    ignore_index = -100
    spawn(check_dist_crossentropy, 2, ignore_index=ignore_index)


if __name__ == "__main__":
    test_dist_crossentropy()