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122 lines
4.7 KiB
122 lines
4.7 KiB
from typing import Callable
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import pytest
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import torch
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import torch.distributed as dist
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.testing import assert_close
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import colossalai
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from colossalai.legacy.amp import convert_to_apex_amp
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from colossalai.nn.optimizer import HybridAdam
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from colossalai.testing import DummyDataloader, parameterize, rerun_if_address_is_in_use, spawn
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from colossalai.utils import set_seed
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from colossalai.utils.cuda import get_current_device
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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.kit.model_zoo import model_zoo, run_fwd, run_fwd_bwd
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PLACEMENT_CONFIGS = [
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{"placement_policy": "static", "shard_param_frac": 0.0}, # zero2
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{"placement_policy": "static", "shard_param_frac": 1.0}, # zero3
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{"placement_policy": "static", "shard_param_frac": 0.5}, # zero3-half
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{"placement_policy": "auto"},
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]
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def check_param(model: GeminiDDP, torch_model: torch.nn.Module):
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zero_dict = model.state_dict(only_rank_0=False)
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torch_dict = torch_model.state_dict()
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for key, value in torch_dict.items():
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# key is 'module.model.PARAMETER', so we truncate it
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key = key[7:]
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assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
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temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
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# debug_print([0], "max range: ", key, torch.max(torch.abs(value - temp_zero_value)))
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assert_close(value, temp_zero_value, rtol=1e-3, atol=4e-3)
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def multi_chunk_init(model: torch.nn.Module, placement_config: dict):
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world_size = dist.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"] = False
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model = GeminiDDP(model, config_dict, pin_memory=True, **placement_config)
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return model
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def single_chunk_init(model: torch.nn.Module, placement_config: dict):
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model = GeminiDDP(model, chunk_init_device=get_current_device(), pin_memory=True, **placement_config)
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return model
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@parameterize("placement_config", PLACEMENT_CONFIGS)
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@parameterize("model_name", ["transformers_gpt_lm"])
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@parameterize("model_init_func", [single_chunk_init, multi_chunk_init])
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def exam_inference(placement_config: dict, model_name: str, model_init_func: Callable):
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set_seed(19360226)
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model_builder, data_gen_fn, output_transform_fn, *_ = next(iter(model_zoo.get_sub_registry(model_name).values()))
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torch_model = model_builder().cuda()
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amp_config = dict(opt_level="O2", keep_batchnorm_fp32=False, loss_scale=128)
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torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3)
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torch_model, torch_optim = convert_to_apex_amp(torch_model, torch_optim, amp_config)
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torch_model = DDP(torch_model, device_ids=[dist.get_rank()])
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init_dev = get_current_device()
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model = model_builder().to(init_dev)
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for torch_p, p in zip(torch_model.parameters(), model.parameters()):
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p.data.copy_(torch_p.data)
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model = model_init_func(model, placement_config)
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optimizer = HybridAdam(model.parameters(), lr=1e-3)
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zero_optim = GeminiOptimizer(optimizer, model, initial_scale=128)
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model.eval()
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torch_model.eval()
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set_seed(dist.get_rank() * 3 + 128)
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train_dataloader = iter(DummyDataloader(data_gen_fn))
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def train_iter():
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data = next(train_dataloader)
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data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
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zero_optim.zero_grad()
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torch_optim.zero_grad()
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torch_loss = run_fwd_bwd(torch_model, data, output_transform_fn, optimizer=torch_optim)
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loss = run_fwd_bwd(model, data, output_transform_fn, optimizer=zero_optim)
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assert_close(torch_loss.float(), loss.float(), rtol=1e-5, atol=1e-5)
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zero_optim.step()
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torch_optim.step()
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check_param(model, torch_model)
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def inference_iter():
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data = next(train_dataloader)
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data = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in data.items()}
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with torch.no_grad():
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torch_loss = run_fwd(torch_model, data, output_transform_fn)
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zero_loss = run_fwd(model, data, output_transform_fn)
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assert_close(torch_loss.float(), zero_loss.float(), rtol=1e-5, atol=1e-5)
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train_iter()
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inference_iter()
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train_iter()
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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_inference()
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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_inference(world_size):
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spawn(run_dist, world_size)
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if __name__ == "__main__":
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test_inference(1)
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