mirror of https://github.com/hpcaitech/ColossalAI
62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
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import os
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from packaging import version
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import pytest
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import torch
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from colossalai.inference.tensor_parallel import MemoryManager
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from colossalai.logging import disable_existing_loggers
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from colossalai.testing import rerun_if_address_is_in_use, spawn
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BATCH_SIZE = 4
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INPUT_LEN = 16
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OUTPUT_LEN = 8
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LAYER_NUM = 4
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HEAD_NUM = 32
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HEAD_DIM = 128
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CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse('11.5')
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def create_cache_manager(rank, world_size, port, batch_size, input_len, output_len, layer_num, head_num, head_dim):
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os.environ['RANK'] = str(rank)
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os.environ['LOCAL_RANK'] = str(rank)
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os.environ['WORLD_SIZE'] = str(world_size)
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os.environ['MASTER_ADDR'] = 'localhost'
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os.environ['MASTER_PORT'] = str(port)
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disable_existing_loggers()
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size = batch_size * (input_len + output_len)
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kvcache_manager = MemoryManager(size, torch.float16, head_num // world_size, head_dim, layer_num, rank)
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key_buffers = kvcache_manager.key_buffer
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value_buffers = kvcache_manager.value_buffer
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assert len(key_buffers) == len(value_buffers) == layer_num
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assert key_buffers[0].shape == value_buffers[0].shape
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# required size exceeds the maximum allocated size
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invalid_locs = kvcache_manager.alloc_contiguous(size + 1)
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assert invalid_locs is None
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# for prefill stage, allocation via alloc and alloc_contiguous should be the same
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total_token_prefill = batch_size * input_len
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prefill_locs = kvcache_manager.alloc(total_token_prefill)
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kvcache_manager.free_all()
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prefill_locs_contiguous = kvcache_manager.alloc_contiguous(total_token_prefill)[0]
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assert torch.equal(prefill_locs, prefill_locs_contiguous)
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assert torch.sum(kvcache_manager.mem_state).item() == size - total_token_prefill
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kvcache_manager.alloc_contiguous(batch_size)
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assert torch.all(kvcache_manager.mem_state[:total_token_prefill + batch_size] == False)
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@pytest.mark.skipif(not CUDA_SUPPORT, reason="kv-cache manager engine requires cuda version to be higher than 11.5")
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@pytest.mark.dist
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@rerun_if_address_is_in_use()
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def test_cache_manager_dist():
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spawn(create_cache_manager,
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4,
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batch_size=BATCH_SIZE,
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input_len=INPUT_LEN,
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output_len=OUTPUT_LEN,
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layer_num=LAYER_NUM,
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head_num=HEAD_NUM,
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head_dim=HEAD_DIM)
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if __name__ == '__main__':
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test_cache_manager_dist()
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