import torch
import torch.distributed as dist
import torch.nn as nn
from torch.testing import assert_close

import colossalai
from colossalai.shardformer.layer import VocabParallelEmbedding1D
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn


def check_vocab_embedding_1d():
    embedding = nn.Embedding(128, 32).to('cuda')
    dist_embedding_1d = VocabParallelEmbedding1D.from_native_module(embedding, process_group=None)

    assert dist_embedding_1d.weight.shape == torch.Size([64, 32])
    assert dist_embedding_1d.num_embeddings == 64
    assert dist_embedding_1d.embedding_dim == 32

    # ensure state dict is reversibly loadable
    embedding.load_state_dict(dist_embedding_1d.state_dict())
    dist_embedding_1d.load_state_dict(embedding.state_dict())

    # check embedding correctness
    x = torch.randint(0, 128, (4, 32)).to('cuda')
    org_out = embedding(x)
    dist_out = dist_embedding_1d(x)
    assert_close(org_out, dist_out)

    # check backward correctness
    org_out.sum().backward()
    dist_out.sum().backward()

    rank = dist.get_rank()
    target_grad = torch.chunk(embedding.weight.grad, 2, dim=0)[rank]
    assert_close(target_grad, dist_embedding_1d.weight.grad)


def run_dist(rank, world_size, port):
    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
    check_vocab_embedding_1d()


@rerun_if_address_is_in_use()
def test_vocab_embedding():
    spawn(run_dist, nprocs=2)


if __name__ == '__main__':
    test_vocab_embedding()
