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# Copyright (C) 2021 THL A29 Limited, a Tencent company.  All rights reserved.
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import unittest

import torch
from transformers import BertModel, BertConfig

from common import distributed_test
from patrickstar.core import PatrickStarClient, ParamType
from patrickstar.core.preprocess import PSPreProcessCtx


class TestModelInitContext(unittest.TestCase):
    def setUp(self):
        pass

    @distributed_test(world_size=[2], backend="gloo", use_fake_dist=True)
    def test_model_init(self):
        def model_provider():
            cfg = BertConfig()
            cfg.vocab_size = 10
            model = BertModel(cfg)
            return model

        compute_device = torch.device("cpu:0")
        default_chunk_size = 32 * 1024 * 1024
        client = PatrickStarClient(0, default_chunk_size)

        torch.manual_seed(0)
        with PSPreProcessCtx(client, dtype=torch.float, release_after_init=True):
            ps_model = model_provider()

        torch.manual_seed(0)
        torch_model = model_provider()

        for ps_param, torch_param in zip(
            ps_model.parameters(), torch_model.parameters()
        ):
            if ps_param.ps_attr.param_type == ParamType.TORCH_BASED:
                self.assertLess(
                    torch.max(torch_param.data - ps_param),
                    1e-4,
                    "PyTorch tensors are not consist with each other",
                )
            else:
                ps_data = client.access_data(ps_param, compute_device)
                if ps_param.ps_attr.is_local():
                    self.assertLess(
                        torch.max(torch_param.data - ps_data),
                        1e-4,
                        f"{ps_param.ps_attr.name} ps tensor and pytorch tensor are not consist with each other",
                    )
                client.release_data(ps_param)


if __name__ == "__main__":

    unittest.main()
