test_meta_mode.py

import pytest
import torch
import torch.distributed as dist
import torchvision.models as tm
try:
    from colossalai._analyzer._subclasses import MetaTensor, MetaTensorMode
except:
    pass
from .zoo import tm_models, tmm_models


def compare_all(tensor: torch.Tensor, meta_tensor: torch.Tensor):
    assert tensor.shape == meta_tensor.shape, f'the shape of tensor ({tensor.shape}) and meta tensor ({meta_tensor.shape}) does not match.'
    assert tensor.dtype == meta_tensor.dtype, f'the dtype of tensor ({tensor.dtype}) and meta tensor ({meta_tensor.dtype}) does not match.'
    assert tensor.stride() == meta_tensor.stride(
    ), f'the stride of tensor ({tensor.stride()}) and meta tensor ({meta_tensor.stride()}) does not match.'


def run_and_compare(model):
    x = torch.rand(2, 3, 224, 224, requires_grad=True)
    x_out = model(x)
    with MetaTensorMode():
        meta_x = torch.rand(2, 3, 224, 224, requires_grad=True)
        meta_out = model(meta_x)
    compare_all(x_out, meta_out)
    x_out.sum().backward()
    meta_out.sum().backward()
    compare_all(x.grad, meta_x.grad)


@pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12')
@pytest.mark.parametrize('m', tm_models + tmm_models)
def test_meta_mode_shape(m):
    run_and_compare(m())


if __name__ == '__main__':
    test_meta_mode_shape(tm.resnet18)