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[Inference/NFC] Clean outdated inference tests and deprecated kernels (#5159)

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* [inference/nfc] remove outdated inference tests

* remove outdated kernel tests

* remove deprecated triton kernels

* remove imports from deprecated kernels
This commit is contained in:
Yuanheng Zhao
2023-12-05 15:12:57 +08:00
committed by FrankLeeeee
parent 56e75eeb06
commit 2bb92243d4
18 changed files with 0 additions and 2543 deletions
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66
tests/test_infer/test_kvcache_manager.py
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import os
import pytest
import torch
from packaging import version
from colossalai.inference.kv_cache import MemoryManager
from colossalai.logging import disable_existing_loggers
from colossalai.testing import rerun_if_address_is_in_use, spawn
BATCH_SIZE = 4
INPUT_LEN = 16
OUTPUT_LEN = 8
LAYER_NUM = 4
HEAD_NUM = 32
HEAD_DIM = 128
CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.5")
def create_cache_manager(rank, world_size, port, batch_size, input_len, output_len, layer_num, head_num, head_dim):
os.environ["RANK"] = str(rank)
os.environ["LOCAL_RANK"] = str(rank)
os.environ["WORLD_SIZE"] = str(world_size)
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = str(port)
disable_existing_loggers()
size = batch_size * (input_len + output_len)
kvcache_manager = MemoryManager(size, torch.float16, head_num // world_size, head_dim, layer_num, rank)
key_buffers = kvcache_manager.key_buffer
value_buffers = kvcache_manager.value_buffer
assert len(key_buffers) == len(value_buffers) == layer_num
assert key_buffers[0].shape == value_buffers[0].shape
# required size exceeds the maximum allocated size
invalid_locs = kvcache_manager.alloc_contiguous(size + 1)
assert invalid_locs is None
# for prefill stage, allocation via alloc and alloc_contiguous should be the same
total_token_prefill = batch_size * input_len
prefill_locs = kvcache_manager.alloc(total_token_prefill)
kvcache_manager.free_all()
prefill_locs_contiguous = kvcache_manager.alloc_contiguous(total_token_prefill)[0]
assert torch.equal(prefill_locs, prefill_locs_contiguous)
assert torch.sum(kvcache_manager.mem_state).item() == size - total_token_prefill
kvcache_manager.alloc_contiguous(batch_size)
assert torch.all(kvcache_manager.mem_state[: total_token_prefill + batch_size] == False)
@pytest.mark.skipif(not CUDA_SUPPORT, reason="kv-cache manager engine requires cuda version to be higher than 11.5")
@pytest.mark.dist
@rerun_if_address_is_in_use()
def test_cache_manager_dist():
spawn(
create_cache_manager,
4,
batch_size=BATCH_SIZE,
input_len=INPUT_LEN,
output_len=OUTPUT_LEN,
layer_num=LAYER_NUM,
head_num=HEAD_NUM,
head_dim=HEAD_DIM,
)
if __name__ == "__main__":
test_cache_manager_dist()