294 lines
7.6 KiB
Python
294 lines
7.6 KiB
Python
"""Unit tests for inference cache components."""
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import torch
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from astrai.inference.cache import (
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PagedCache,
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PagePool,
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PrefixCache,
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TaskTable,
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page_hash,
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)
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def test_page_hash_full_page():
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token_ids = list(range(256))
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h = page_hash(token_ids, 0, 64)
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assert isinstance(h, int)
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assert h >= 0
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def test_page_hash_different_page_differs():
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token_ids = list(range(256))
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assert page_hash(token_ids, 0, 64) != page_hash(token_ids, 1, 64)
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def test_page_pool_alloc_free_cycle():
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pool = PagePool(n_pages=4)
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a = pool.alloc()
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b = pool.alloc()
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assert a != b
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pool.free(a)
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pool.free(b)
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c = pool.alloc()
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assert c in (a, b)
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def test_page_pool_alloc_when_full():
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pool = PagePool(n_pages=2)
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pool.alloc()
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pool.alloc()
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assert pool.alloc() == -1
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def test_page_pool_lru_eviction():
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evicted = []
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def on_evict(idx):
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evicted.append(idx)
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pool = PagePool(n_pages=2, on_evict=on_evict)
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p0 = pool.alloc()
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p1 = pool.alloc()
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pool.free(p0, keep_cached=True)
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pool.free(p1, keep_cached=True)
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pool.alloc()
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assert len(evicted) == 1
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assert evicted[0] == p0
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def test_page_pool_inc_ref_and_free():
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pool = PagePool(n_pages=2)
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p = pool.alloc()
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pool.inc_ref(p)
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assert pool._refs[p] == 2
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pool.free(p)
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assert pool._refs[p] == 1
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pool.free(p)
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assert pool._refs[p] == 0
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def test_page_pool_touch_moves_to_end():
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pool = PagePool(n_pages=4)
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p0 = pool.alloc()
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p1 = pool.alloc()
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p2 = pool.alloc()
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pool.free(p0, keep_cached=True)
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pool.free(p1, keep_cached=True)
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pool.free(p2, keep_cached=True)
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assert next(iter(pool._lru)) == p0
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pool.touch(p0)
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assert next(reversed(pool._lru)) == p0
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def test_page_pool_remove_from_lru():
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pool = PagePool(n_pages=4)
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p0 = pool.alloc()
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pool.free(p0, keep_cached=True)
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assert p0 in pool._lru
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pool.remove_from_lru(p0)
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assert p0 not in pool._lru
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def test_page_pool_keep_cached_realloc():
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"""Free mask has priority over LRU; cached page returned only when no free pages."""
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pool = PagePool(n_pages=3)
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p0 = pool.alloc()
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p1 = pool.alloc()
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p2 = pool.alloc()
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pool.free(p0, keep_cached=True)
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pool.free(p1, keep_cached=True)
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pool.free(p2, keep_cached=True)
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assert pool.alloc() == p0
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def _record_then_cache(pool, prefix, page, token_ids, logical_idx):
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"""Simulate the real lifecycle: record → ref stays >0, then free cached returns to LRU."""
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prefix.record(page, token_ids, logical_idx, pool)
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pool.free(page, keep_cached=True)
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def test_prefix_cache_lookup_returns_hits():
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token_ids = list(range(256))
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pool = PagePool(n_pages=16)
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prefix = PrefixCache(page_size=64)
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pages = [pool.alloc() for _ in range(4)]
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for i, p in enumerate(pages):
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_record_then_cache(pool, prefix, p, token_ids, i)
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hits = prefix.lookup(token_ids, pool)
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assert hits == pages
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def test_prefix_cache_lookup_stops_at_first_miss():
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token_ids = list(range(256))
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pool = PagePool(n_pages=16)
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prefix = PrefixCache(page_size=64)
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p0 = pool.alloc()
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_record_then_cache(pool, prefix, p0, token_ids, 0)
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p1 = pool.alloc()
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_record_then_cache(pool, prefix, p1, [99] * 64, 1)
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hits = prefix.lookup(token_ids, pool)
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assert len(hits) == 1
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assert hits[0] == p0
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def test_prefix_cache_ignores_partial_last_page():
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token_ids = list(range(100))
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pool = PagePool(n_pages=16)
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prefix = PrefixCache(page_size=64)
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p = pool.alloc()
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_record_then_cache(pool, prefix, p, token_ids, 0)
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hits = prefix.lookup(token_ids, pool)
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assert len(hits) == 1
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def test_prefix_cache_on_evict_clears_mappings():
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pool = PagePool(n_pages=4)
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prefix = PrefixCache(page_size=64)
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p = pool.alloc()
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_record_then_cache(pool, prefix, p, list(range(64)), 0)
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assert prefix.has_page(p)
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prefix.on_evict(p)
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assert not prefix.has_page(p)
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def test_prefix_cache_has_page():
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pool = PagePool(n_pages=4)
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prefix = PrefixCache(page_size=64)
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p = pool.alloc()
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assert not prefix.has_page(p)
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_record_then_cache(pool, prefix, p, list(range(64)), 0)
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assert prefix.has_page(p)
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def test_task_table_set_get():
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pool = PagePool(n_pages=8)
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table = TaskTable(pool, page_size=64)
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table.set("task1", [0, 1, 2], 128)
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assert table.get("task1") == [0, 1, 2]
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assert table.get_cached("task1") == 128
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def test_task_table_get_missing():
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pool = PagePool(n_pages=8)
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table = TaskTable(pool, page_size=64)
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assert table.get("nonexistent") == []
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assert table.get_cached("nonexistent") == 0
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def test_task_table_pop():
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pool = PagePool(n_pages=8)
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table = TaskTable(pool, page_size=64)
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table.set("task1", [0, 1], 64)
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pages, cached = table.pop("task1")
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assert pages == [0, 1]
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assert cached == 64
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assert table.get("task1") == []
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def test_task_table_extend_allocates_pages():
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pool = PagePool(n_pages=8)
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table = TaskTable(pool, page_size=64)
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table.set("task1", [], 0)
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ok = table.extend("task1", 200)
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assert ok
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assert len(table.get("task1")) == 4
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def test_task_table_extend_fails_when_pool_full():
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pool = PagePool(n_pages=2)
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table = TaskTable(pool, page_size=64)
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table.set("task1", [pool.alloc(), pool.alloc()], 0)
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ok = table.extend("task1", 300)
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assert not ok
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def test_task_table_table_tensor():
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pool = PagePool(n_pages=16)
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table = TaskTable(pool, page_size=64)
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table.set("a", [0, 1], 0)
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table.set("b", [2, 3, 4], 0)
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t = table.table_tensor(["a", "b"], torch.device("cpu"))
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assert t.shape == (2, 3)
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assert t[0].tolist() == [0, 1, -1]
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assert t[1].tolist() == [2, 3, 4]
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def test_task_table_table_tensor_empty_input():
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pool = PagePool(n_pages=4)
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table = TaskTable(pool, page_size=64)
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t = table.table_tensor([], torch.device("cpu"))
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assert t.numel() == 0
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def test_paged_cache_write_gather_single_page():
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cache = PagedCache(
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n_layers=2,
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n_pages=8,
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page_size=4,
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n_kv_heads=2,
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head_dim=8,
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device=torch.device("cpu"),
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dtype=torch.float32,
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)
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page_table = torch.tensor([[0]], dtype=torch.long)
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k = torch.randn(1, 2, 2, 8)
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v = torch.randn(1, 2, 2, 8)
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cache.write(0, page_table, 0, k, v)
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gk, gv = cache.gather(0, page_table, 2)
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assert torch.allclose(gk, k)
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def test_paged_cache_write_cross_page():
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cache = PagedCache(
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n_layers=1,
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n_pages=8,
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page_size=4,
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n_kv_heads=2,
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head_dim=8,
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device=torch.device("cpu"),
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dtype=torch.float32,
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)
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page_table = torch.tensor([[0, 1]], dtype=torch.long)
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k = torch.randn(1, 8, 2, 8)
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v = torch.randn(1, 8, 2, 8)
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cache.write(0, page_table, 0, k, v)
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gk, gv = cache.gather(0, page_table, 8)
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assert torch.allclose(gk, k)
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def test_paged_cache_gather_truncates_to_total_len():
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cache = PagedCache(
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n_layers=1,
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n_pages=8,
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page_size=4,
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n_kv_heads=2,
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head_dim=8,
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device=torch.device("cpu"),
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dtype=torch.float32,
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)
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page_table = torch.tensor([[0, 1]], dtype=torch.long)
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k = torch.randn(1, 6, 2, 8)
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v = torch.randn(1, 6, 2, 8)
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cache.write(0, page_table, 0, k, v)
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gk, gv = cache.gather(0, page_table, 5)
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assert gk.shape == (1, 5, 2, 8)
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def test_paged_cache_gather_clamps_negative_padding():
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cache = PagedCache(
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n_layers=1,
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n_pages=8,
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page_size=4,
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n_kv_heads=2,
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head_dim=8,
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device=torch.device("cpu"),
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dtype=torch.float32,
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)
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page_table = torch.tensor([[0, -1]], dtype=torch.long)
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gk, gv = cache.gather(0, page_table, 4)
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assert gk.shape == (1, 4, 2, 8)
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