mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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86 lines
3.2 KiB
86 lines
3.2 KiB
import torch |
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import colossalai |
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import pytest |
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import torch.multiprocessing as mp |
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from typing import List |
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from functools import partial |
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from colossalai.gemini import ChunkManager |
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from colossalai.testing import rerun_if_address_is_in_use, parameterize |
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from colossalai.utils import free_port |
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from colossalai.core import global_context as gpc |
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from colossalai.context import ParallelMode |
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def check_has_params(params: List[torch.Tensor], has_tensors: List[bool]): |
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for p, has_tensor in zip(params, has_tensors): |
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if has_tensor: |
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assert p.storage().size() > 0 |
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assert p.device.type == 'cuda' |
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else: |
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assert p.storage().size() == 0 |
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# HAS_TENSORS[use_chunk][use_zero] |
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HAS_TENSORS = { |
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True: { |
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True: [[True, True, False], [False, False, True]], |
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False: [[True, True, True], [True, True, True]] |
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}, |
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False: { |
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True: [[True, False, True], [False, True, False]], |
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False: [[True, True, True], [True, True, True]] |
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} |
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} |
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TOTAL_MEM = {True: {True: [512, 512], False: [1024, 1024]}, False: {True: [512, 256], False: [768, 768]}} |
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@parameterize('use_chunk', [False, True]) |
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@parameterize('use_zero', [False, True]) |
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def run_chunk_zero(use_chunk, use_zero): |
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rank = gpc.get_local_rank(ParallelMode.DATA) |
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if rank == 0: |
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print(f'use_chunk={use_chunk}, use_zero={use_zero}') |
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params = [torch.rand(8, 8) for _ in range(3)] |
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chunk_size = 128 if use_chunk else None |
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chunk_manager = ChunkManager(chunk_size, enable_distributed_storage=use_zero) |
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chunk_manager.create_group('param') |
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assert chunk_manager.total_mem['cpu'] == 0 |
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assert chunk_manager.total_mem['cuda'] == 0 |
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for p in params: |
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chunk_manager.append_tensor(p, 'param') |
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check_has_params(params, HAS_TENSORS[use_chunk][use_zero][rank]) |
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assert chunk_manager.total_mem['cpu'] == 0 |
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assert chunk_manager.total_mem['cuda'] == TOTAL_MEM[use_chunk][use_zero][rank] |
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chunks = chunk_manager.get_chunks(params) |
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for chunk in chunks: |
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chunk_manager.access_chunk(chunk) |
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check_has_params(params, [True, True, True]) |
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assert chunk_manager.total_mem['cpu'] == 0 |
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assert chunk_manager.total_mem['cuda'] == TOTAL_MEM[use_chunk][False][rank] |
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for chunk in chunks: |
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chunk_manager.release_chunk(chunk) |
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check_has_params(params, HAS_TENSORS[use_chunk][use_zero][rank]) |
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assert chunk_manager.total_mem['cpu'] == 0 |
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assert chunk_manager.total_mem['cuda'] == TOTAL_MEM[use_chunk][use_zero][rank], chunk_manager.total_mem['cuda'] |
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for chunk in chunks: |
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chunk_manager.move_chunk(chunk, torch.device('cpu')) |
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assert chunk_manager.total_mem['cpu'] == TOTAL_MEM[use_chunk][use_zero][rank], chunk_manager.total_mem['cuda'] |
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assert chunk_manager.total_mem['cuda'] == 0 |
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def run_dist(rank, world_size, port): |
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colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl') |
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run_chunk_zero() |
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@pytest.mark.dist |
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@pytest.mark.parametrize('world_size', [2]) |
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@rerun_if_address_is_in_use() |
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def test_chunk_mapping(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_chunk_mapping(2)
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