mirror of https://github.com/vllm-project/vllm
Add swap_blocks unit tests (#2616)
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@ -3,8 +3,11 @@ import random
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import pytest
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import torch
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from typing import Tuple
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from vllm._C import cache_ops
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COPYING_DIRECTION = [('cuda', 'cpu'), ('cuda', 'cuda'), ('cpu', 'cuda')]
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DTYPES = [torch.half, torch.bfloat16, torch.float]
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NUM_TOKENS = [42] # Arbitrary values for testing
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NUM_LAYERS = [1] # Arbitrary values for testing
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@ -153,3 +156,68 @@ def test_reshape_and_cache(
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assert torch.allclose(key_cache, cloned_key_cache)
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assert torch.allclose(value_cache, cloned_value_cache)
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@pytest.mark.parametrize("direction", COPYING_DIRECTION)
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@pytest.mark.parametrize("num_mappings", NUM_MAPPINGS)
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@pytest.mark.parametrize("num_heads", NUM_HEADS)
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@pytest.mark.parametrize("head_size", HEAD_SIZES)
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@pytest.mark.parametrize("block_size", BLOCK_SIZES)
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@pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
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@pytest.mark.parametrize("dtype", DTYPES)
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@pytest.mark.parametrize("seed", SEEDS)
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@pytest.mark.parametrize("device", DEVICES)
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@torch.inference_mode()
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def test_swap_blocks(
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kv_cache_factory,
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direction: Tuple[str, str],
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num_mappings: int,
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num_heads: int,
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head_size: int,
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block_size: int,
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num_blocks: int,
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dtype: torch.dtype,
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seed: int,
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device: int,
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) -> None:
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random.seed(seed)
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torch.random.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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src_device = f"{direction[0]}:{device}" if direction[
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0] == "cuda" else direction[0]
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dst_device = f"{direction[1]}:{device}" if direction[
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1] == "cuda" else direction[1]
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src_blocks = random.sample(range(num_blocks), num_mappings)
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# For the same device, mapping must not overlap
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if src_device == dst_device:
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remaining_blocks = list(set(range(num_blocks)) - set(src_blocks))
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dst_blocks = random.sample(remaining_blocks, num_mappings)
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else:
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dst_blocks = random.sample(range(num_blocks), num_mappings)
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block_mapping = dict(zip(src_blocks, dst_blocks))
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# Create the KV caches on the first device.
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src_key_caches, src_value_caches = kv_cache_factory(
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num_blocks, block_size, 1, num_heads, head_size, dtype, seed,
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src_device)
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# Create the KV caches on the second device.
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dist_key_caches, dist_value_caches = kv_cache_factory(
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num_blocks, block_size, 1, num_heads, head_size, dtype, seed,
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dst_device)
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src_key_caches_clone = src_key_caches[0].clone()
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src_value_caches_clone = src_value_caches[0].clone()
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# Call the swap_blocks kernel.
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cache_ops.swap_blocks(src_key_caches[0], dist_key_caches[0], block_mapping)
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cache_ops.swap_blocks(src_value_caches[0], dist_value_caches[0],
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block_mapping)
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for src, dst in block_mapping.items():
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assert torch.allclose(src_key_caches_clone[src].cpu(),
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dist_key_caches[0][dst].cpu())
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assert torch.allclose(src_value_caches_clone[src].cpu(),
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dist_value_caches[0][dst].cpu())
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