ColossalAI/tests/test_zero/test_gemini/test_zeroddp_state_dict.py

import pytest
import torch
from torch.testing import assert_close

import colossalai
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
from colossalai.utils import set_seed
from colossalai.zero import GeminiDDP
from colossalai.zero.gemini.chunk import search_chunk_configuration
from tests.kit.model_zoo import model_zoo

PLACEMENT_CONFIGS = [
    {"placement_policy": "static", "shard_param_frac": 0.0},  # zero2
    {"placement_policy": "static", "shard_param_frac": 1.0},  # zero3
    {"placement_policy": "static", "shard_param_frac": 0.5},  # zero3-half
    {"placement_policy": "auto"},
]


def ignore_the_first_parameter(model: torch.nn.Module):
    for name, param in model.named_parameters():
        print(f"parameter `{name}` is set ignored")
        GeminiDDP.set_params_to_ignore([param])
        return


@parameterize("placement_config", PLACEMENT_CONFIGS)
@parameterize("keep_gathered", [True, False])
@parameterize("model_name", ["transformers_gpt_lm", "transformers_bert_for_sequence_classification"])
@parameterize("master_weights", [False, True])
def exam_state_dict(placement_config, keep_gathered, model_name: str, master_weights: bool):
    set_seed(431)
    model_builder, data_gen_fn, output_transform_fn, *_ = next(iter(model_zoo.get_sub_registry(model_name).values()))

    model = model_builder()

    model_size = sum(p.numel() * p.element_size() for p in model.parameters()) / 1024**2

    torch_model = model_builder()
    for torch_p, p in zip(torch_model.parameters(), model.parameters()):
        torch_p.data.copy_(p.data)

    world_size = torch.distributed.get_world_size()
    config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
    config_dict[world_size]["chunk_size"] = 5000
    config_dict[world_size]["keep_gathered"] = keep_gathered
    model = GeminiDDP(model, config_dict, **placement_config, pin_memory=True, master_weights=master_weights)
    model.train()

    zero_dict = model.state_dict(only_rank_0=False)
    torch_dict = torch_model.state_dict()

    for key, value in torch_dict.items():
        assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
        temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
        assert_close(value, temp_zero_value, rtol=1e-3, atol=1e-5)

    # check load state dict
    model.load_state_dict(torch_dict, strict=False)
    zero_dict = model.state_dict(only_rank_0=False)

    for key, value in torch_dict.items():
        assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
        temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
        assert_close(value, temp_zero_value, rtol=1e-3, atol=1e-5)

    # check state dict shard
    accumulated_keys = set()
    # ensure number of shards > 1
    for shard, _ in model.state_dict_shard(max_shard_size=(model_size / 3), only_rank_0=False):
        for key, value in shard.items():
            assert key not in accumulated_keys, f"key `{key}` is duplicated."
            accumulated_keys.add(key)
            assert key in zero_dict, f"{key} not in ZeRO dictionary."
            assert torch.equal(value, zero_dict[key]), f"{key} not equal."


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


@pytest.mark.dist
@pytest.mark.parametrize("world_size", [1, 4])
@rerun_if_address_is_in_use()
def test_zero_ddp(world_size):
    spawn(run_dist, world_size)


if __name__ == "__main__":
    test_zero_ddp(1)