mirror of https://github.com/hpcaitech/ColossalAI
93 lines
3.6 KiB
Python
93 lines
3.6 KiB
Python
from functools import partial
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import pytest
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import torch
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import torch.multiprocessing as mp
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import colossalai
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from colossalai.gemini.chunk import ChunkManager, search_chunk_configuration
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from colossalai.gemini.gemini_mgr import GeminiManager
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from colossalai.gemini.memory_tracer.runtime_mem_tracer import RuntimeMemTracer
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from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer
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from colossalai.nn.parallel import GeminiDDP, ZeroDDP
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from colossalai.tensor import ProcessGroup
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from colossalai.testing import parameterize, rerun_if_address_is_in_use
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from colossalai.utils import free_port
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from colossalai.utils.model.colo_init_context import ColoInitContext
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from tests.components_to_test import run_fwd_bwd
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from tests.components_to_test.registry import non_distributed_component_funcs
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from tests.test_tensor.common_utils import set_seed
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# run gemini use the runtime memory tracer
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@parameterize('placement_policy', ['auto'])
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@parameterize('keep_gather', [False])
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@parameterize('model_name', ['bert', 'albert', 'gpt2'])
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@parameterize('use_grad_checkpoint', [False, True])
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def run_gemini_use_rmt(placement_policy, keep_gather, model_name: str, use_grad_checkpoint: bool = False):
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set_seed(42)
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get_components_func = non_distributed_component_funcs.get_callable(model_name)
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model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
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with ColoInitContext(device='cpu'):
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model = model_builder(use_grad_checkpoint)
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print(f'model_name {model_name}')
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runtime_mem_tracer = RuntimeMemTracer(model)
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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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input_ids, label = input_ids.cuda(), label.cuda()
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# mem tracing
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if i == 0:
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run_fwd_bwd(runtime_mem_tracer, input_ids, label, criterion, runtime_mem_tracer)
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memstats = runtime_mem_tracer.memstats()
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runtime_tracer_non_model_data = runtime_mem_tracer._memstats._non_model_data_cuda_list
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print('runtime tracer: ', runtime_tracer_non_model_data)
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print([memstats.param_used_timestep(p) for p in model.parameters()])
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model = GeminiDDP(model, device='cuda', placement_policy=placement_policy, search_range_mb=1, memstats=memstats)
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zero_optim = GeminiAdamOptimizer(model, lr=1e-3, initial_scale=1)
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pg = ProcessGroup()
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set_seed(pg.dp_local_rank())
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for i, (input_ids, label) in enumerate(train_dataloader):
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# you can only test a single fwd + bwd.
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# after bwd param is grad for Gemini, due to the chunk reuse optimization.
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# print(f'iteration {i}')
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if i > 4:
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break
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input_ids, label = input_ids.cuda(), label.cuda()
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zero_optim.zero_grad()
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set_seed(42)
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loss = run_fwd_bwd(model, input_ids, label, criterion, zero_optim)
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zero_optim.step()
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gemini_non_model_data = model.gemini_manager._mem_stats_collector._memstats.non_model_data_list('cuda')
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# print('gemini non model data:', gemini_non_model_data)
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assert len(gemini_non_model_data) == len(runtime_tracer_non_model_data), \
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f'model_name {model_name} {len(gemini_non_model_data)} vs {len(runtime_tracer_non_model_data)}'
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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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run_gemini_use_rmt()
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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_gemini_use_rmt(world_size):
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run_func = partial(run_dist, world_size=world_size, port=free_port())
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mp.spawn(run_func, nprocs=world_size)
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if __name__ == '__main__':
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test_gemini_use_rmt(1)
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