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84 lines
2.9 KiB
84 lines
2.9 KiB
import pytest
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import torch
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import torch.distributed as dist
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import colossalai
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
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from colossalai.utils import set_seed
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from colossalai.zero import GeminiDDP, GeminiOptimizer
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from colossalai.zero.gemini.chunk import search_chunk_configuration
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from tests.components_to_test.registry import non_distributed_component_funcs
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PLACEMENT_CONFIGS = [
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{"placement_policy": "static", "shard_param_frac": 0.0, "offload_optim_frac": 0.0}, # zero2
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{"placement_policy": "static", "shard_param_frac": 0.0, "offload_optim_frac": 1.0}, # zero2-offload
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{"placement_policy": "static", "shard_param_frac": 0.0, "offload_optim_frac": 0.5}, # zero2-offload-half
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{"placement_policy": "auto"},
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]
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@parameterize("placement_config", PLACEMENT_CONFIGS)
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@parameterize("keep_gathered", [True, False])
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def exam_zero_optim_state_dict(placement_config, keep_gathered):
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set_seed(431)
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get_components_func = non_distributed_component_funcs.get_callable("gpt2")
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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model = model_builder()
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set_seed(451)
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world_size = torch.distributed.get_world_size()
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config_dict, *_ = search_chunk_configuration(model, search_range_m=1, search_interval=100)
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config_dict[world_size]["chunk_size"] = 5000
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config_dict[world_size]["keep_gathered"] = keep_gathered
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model = GeminiDDP(model, config_dict, **placement_config, pin_memory=True)
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optimizer = HybridAdam(model.parameters())
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optim = GeminiOptimizer(optimizer, model, initial_scale=32) # initialize the link between chunk16 and chunk32
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set_seed(dist.get_rank() * 3 + 128)
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model.train()
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for i, (input_ids, label) in enumerate(train_dataloader):
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if i > 0:
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break
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optim.zero_grad()
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logits = model(input_ids)
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logits = logits.float()
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loss = criterion(logits, input_ids)
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optim.backward(loss)
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optim.step()
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optim_state_dict = optim.state_dict()
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optim.load_state_dict(optim_state_dict)
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new_state = optim.state_dict()["state"]
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org_state = optim_state_dict["state"]
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for k, v in org_state.items():
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w = new_state[k]
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for n, m in v.items():
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if isinstance(m, torch.Tensor):
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o = w[n]
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assert torch.equal(m, o)
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else:
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assert m == w[n]
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def run_dist(rank, world_size, port):
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config = {}
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colossalai.launch(config=config, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
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exam_zero_optim_state_dict()
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@pytest.mark.dist
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@pytest.mark.parametrize("world_size", [1, 4])
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@rerun_if_address_is_in_use()
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def test_zero_optim(world_size):
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spawn(run_dist, world_size)
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if __name__ == "__main__":
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test_zero_optim(1)
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