ViperEkura
6f67ba8942
- Replace per-.cu-file static cached tensors with workspace-managed pre-allocated buffers - InferenceWorkspace now owns decode_o_part / decode_ml_part (mirrors FlashInfer's workspace pattern) - KVCache carries the buffers through the backend -> C++ kernel chain - C++ kernels accept optional pre-allocated buffers; fallback to alloc_split_partials for backward compat - Pre-allocates once at Executor init, zero allocation in the decode hot loop - Prerequisite for CUDA-graph capture (all kernel addresses are stable)
415 lines
11 KiB
Python
415 lines
11 KiB
Python
"""Unit tests for inference cache components."""
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import torch
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from astrai.inference import (
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Allocator,
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KVStorage,
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PagePool,
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RadixCache,
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ReqToTokenPool,
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page_hash,
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)
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from astrai.inference.core.workspace import InferenceWorkspace
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def _ws(pool: PagePool) -> InferenceWorkspace:
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"""Workspace sized to the pool (bind_tasks requires it)."""
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return InferenceWorkspace(
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pool.max_batch_size,
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pool.max_seq_len,
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max_q_heads=2,
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head_dim=4,
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device=pool.device,
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dtype=pool.dtype,
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)
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# ---- page_hash ----
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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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# ---- Allocator ----
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def test_allocator_alloc_free_cycle():
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alloc = Allocator(4)
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a = alloc.alloc()
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b = alloc.alloc()
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assert a != b
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alloc.free(a)
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alloc.free(b)
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c = alloc.alloc()
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assert c in (a, b)
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def test_allocator_alloc_when_full():
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alloc = Allocator(2)
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alloc.alloc()
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alloc.alloc()
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assert alloc.alloc() == -1
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def test_allocator_lru_eviction():
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alloc = Allocator(2)
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p0 = alloc.alloc()
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p1 = alloc.alloc()
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alloc.free(p0, keep_cached=True)
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alloc.free(p1, keep_cached=True)
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alloc.alloc()
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assert p0 in alloc._lru or p1 in alloc._lru
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def test_allocator_inc_ref_and_free():
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alloc = Allocator(2)
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p = alloc.alloc()
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alloc.inc_ref(p)
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assert alloc._refs[p] == 2
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alloc.free(p)
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assert alloc._refs[p] == 1
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alloc.free(p)
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assert alloc._refs[p] == 0
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# ---- RadixCache ----
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def test_prefix_cache_lookup_returns_hits():
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token_ids = list(range(256))
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prefix = RadixCache(64)
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pages = [0, 1, 2, 3]
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for i, p in enumerate(pages):
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prefix.record(p, token_ids, i)
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hits = prefix.lookup(token_ids)
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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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prefix = RadixCache(64)
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prefix.record(0, token_ids, 0)
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prefix.record(1, [99] * 64, 1)
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hits = prefix.lookup(token_ids)
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assert len(hits) == 1
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assert hits[0] == 0
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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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prefix = RadixCache(64)
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prefix.record(0, token_ids, 0)
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hits = prefix.lookup(token_ids)
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assert len(hits) == 1
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def test_prefix_cache_on_evict_clears_mappings():
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prefix = RadixCache(64)
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prefix.record(0, list(range(64)), 0)
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assert 0 in prefix._page_to_hash
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prefix.evict(0)
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assert 0 not in prefix._page_to_hash
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def test_prefix_cache_has_page():
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prefix = RadixCache(64)
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assert not prefix.has_page(0)
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prefix.record(0, list(range(64)), 0)
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assert prefix.has_page(0)
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def test_prefix_cache_does_not_reuse_page_without_parent_prefix():
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prefix = RadixCache(2)
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prefix.record(0, [1, 2, 3, 4], 0)
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prefix.record(1, [1, 2, 3, 4, 5, 6], 1)
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prefix.record(2, [9, 10, 5, 6], 0)
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prefix.record(3, [9, 10, 5, 6, 7, 8], 1)
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assert prefix.lookup([1, 2, 3, 4, 5, 6]) == [0, 1]
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assert prefix.lookup([9, 10, 5, 6, 7, 8]) == [2, 3]
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def test_prefix_cache_shares_branch_prefix():
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prefix = RadixCache(2)
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prefix.record(0, [1, 2, 3, 4], 0)
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prefix.record(1, [1, 2, 3, 4], 1)
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prefix.record(2, [1, 2, 7, 8], 1)
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assert prefix.lookup([1, 2, 3, 4]) == [0, 1]
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assert prefix.lookup([1, 2, 7, 8]) == [0, 2]
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prefix.evict(1)
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assert prefix.lookup([1, 2, 3, 4]) == [0]
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assert prefix.lookup([1, 2, 7, 8]) == [0, 2]
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def test_prefix_cache_does_not_record_partial_page():
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prefix = RadixCache(4)
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prefix.record(0, [1, 2, 3, 4, 5, 6], 0)
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prefix.record(1, [1, 2, 3, 4, 5, 6], 1)
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assert prefix.lookup([1, 2, 3, 4, 5, 6]) == [0]
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prefix.record(1, [1, 2, 3, 4, 5, 6, 7, 8], 1)
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assert prefix.lookup([1, 2, 3, 4, 5, 6, 7, 8]) == [0, 1]
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def test_page_pool_task_cacheable_ids_excludes_unmaterialized_tail():
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pool = _make_paged_pool_ps64()
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assert pool.task_cacheable_ids("missing", [1, 2], [3, 4]) == [1, 2, 3]
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# ---- ReqToTokenPool ----
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def test_req_to_token_pool_alloc_free():
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pool = ReqToTokenPool(4, 128, torch.device("cpu"))
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slots = pool.alloc(2)
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assert len(slots) == 2
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assert len(pool.free_slots) == 2
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pool.free(slots)
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assert len(pool.free_slots) == 4
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def test_req_to_token_pool_alloc_when_full():
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pool = ReqToTokenPool(2, 128, torch.device("cpu"))
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pool.alloc(2)
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assert pool.alloc(1) is None
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def test_req_to_token_pool_write():
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pool = ReqToTokenPool(4, 128, torch.device("cpu"))
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slots = pool.alloc(1)
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pool.write((slots[0], slice(0, 3)), torch.tensor([10, 20, 30]))
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assert pool.req_to_token[slots[0], 0].item() == 10
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assert pool.req_to_token[slots[0], 2].item() == 30
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# ---- KVStorage ----
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def test_kv_storage_set_and_get():
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storage = KVStorage(
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size=16,
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n_layers=2,
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n_kv_heads=4,
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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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loc = torch.tensor([[0, 1]], dtype=torch.long)
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k = torch.randn(1, 2, 4, 8)
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v = torch.randn(1, 2, 4, 8)
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storage.set_kv_buffer(0, loc, k, v)
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assert torch.allclose(storage.get_key_buffer(0)[loc], k)
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assert torch.allclose(storage.get_value_buffer(0)[loc], v)
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def test_kv_storage_buffer_shape():
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storage = KVStorage(
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size=32,
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n_layers=3,
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n_kv_heads=8,
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head_dim=16,
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device=torch.device("cpu"),
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dtype=torch.float32,
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)
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assert storage.k_buffer.shape == (3, 32, 8, 16)
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assert storage.v_buffer.shape == (3, 32, 8, 16)
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# ---- PagePool (contiguous mode) ----
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def _make_contiguous_pool(**kwargs):
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defaults = dict(
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n_layers=2,
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n_kv_heads=4,
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head_dim=8,
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max_batch_size=4,
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max_seq_len=64,
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device=torch.device("cpu"),
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dtype=torch.float32,
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)
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defaults.update(kwargs)
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return PagePool(**defaults)
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def test_page_pool_contiguous_task_alloc_free():
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pool = _make_contiguous_pool()
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assert pool.task_alloc("t1", [1, 2, 3])
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assert "t1" in pool._task_req
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pool.task_free("t1")
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assert "t1" not in pool._task_req
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def test_page_pool_contiguous_task_extend():
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pool = _make_contiguous_pool()
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pool.task_alloc("t1", [1, 2, 3])
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assert pool.task_extend("t1", 3)
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assert pool.task_extend("t1", 63)
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assert not pool.task_extend("t1", 64)
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def test_page_pool_contiguous_task_cached():
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pool = _make_contiguous_pool()
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pool.task_alloc("t1", [1, 2, 3])
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assert pool.task_cached("t1") == 0
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def test_page_pool_contiguous_bind_tasks_prefill():
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pool = _make_contiguous_pool()
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pool.task_alloc("t1", list(range(10)))
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pool.task_alloc("t2", list(range(10)))
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kv = pool.bind_tasks(["t1", "t2"], _ws(pool), start_pos=0)
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assert kv.out_cache_loc.shape == (2, 10)
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assert kv.seq_lens.tolist() == [10, 10]
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assert kv.req_pool_indices.shape == (2,)
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def test_page_pool_contiguous_bind_tasks_decode():
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pool = _make_contiguous_pool()
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pool.task_alloc("t1", list(range(10)))
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pool.task_alloc("t2", list(range(8)))
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# Simulate one decode extension so seq_lens advance to 11 and 9.
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assert pool.task_extend("t1", 10)
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assert pool.task_extend("t2", 8)
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kv = pool.bind_tasks(["t1", "t2"], _ws(pool))
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assert kv.out_cache_loc.shape == (2, 1)
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assert kv.seq_lens.tolist() == [11, 9]
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def test_page_pool_contiguous_bind_roundtrip():
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"""Write KV via bind_tasks, then gather via req_to_token indexing."""
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pool = _make_contiguous_pool(n_layers=1, n_kv_heads=2, head_dim=4)
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pool.task_alloc("t1", list(range(4)))
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kv = pool.bind_tasks(["t1"], _ws(pool), start_pos=0)
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k = torch.randn(1, 4, 2, 4)
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v = torch.randn(1, 4, 2, 4)
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kv.k_buffer[0, kv.out_cache_loc] = k
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kv.v_buffer[0, kv.out_cache_loc] = v
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indices = kv.req_to_token[kv.req_pool_indices, :4]
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gathered_k = kv.k_buffer[0, indices]
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gathered_v = kv.v_buffer[0, indices]
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assert torch.allclose(gathered_k, k)
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assert torch.allclose(gathered_v, v)
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# ---- PagePool (paged mode, page_size=1) ----
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def _make_paged_pool(**kwargs):
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defaults = dict(
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n_layers=1,
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n_kv_heads=2,
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head_dim=4,
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max_batch_size=4,
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max_seq_len=64,
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device=torch.device("cpu"),
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dtype=torch.float32,
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page_size=1,
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n_tokens=128,
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)
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defaults.update(kwargs)
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return PagePool(**defaults)
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def test_page_pool_paged_task_alloc():
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pool = _make_paged_pool()
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assert pool.task_alloc("t1", list(range(10)))
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req_idx = pool._task_req["t1"]
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slots = pool._task_slots["t1"]
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assert len(slots) == 10
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assert pool._req_pool.req_to_token[req_idx, 0].item() == slots[0]
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def test_page_pool_paged_task_extend():
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pool = _make_paged_pool()
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pool.task_alloc("t1", list(range(4)))
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assert pool.task_extend("t1", 4)
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req_idx = pool._task_req["t1"]
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slot = pool._req_pool.req_to_token[req_idx, 4].item()
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assert slot >= 0
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def test_page_pool_paged_task_free_releases_slots():
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pool = _make_paged_pool(n_tokens=16)
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pool.task_alloc("t1", list(range(8)))
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pool.task_free("t1")
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assert "t1" not in pool._task_req
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assert len(pool._req_pool.free_slots) == 4
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def test_page_pool_paged_bind_roundtrip():
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pool = _make_paged_pool(n_layers=1, n_kv_heads=2, head_dim=4)
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pool.task_alloc("t1", list(range(4)))
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kv = pool.bind_tasks(["t1"], _ws(pool), start_pos=0)
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k = torch.randn(1, 4, 2, 4)
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v = torch.randn(1, 4, 2, 4)
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kv.k_buffer[0, kv.out_cache_loc] = k
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kv.v_buffer[0, kv.out_cache_loc] = v
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indices = kv.req_to_token[kv.req_pool_indices, :4]
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gathered_k = kv.k_buffer[0, indices]
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assert torch.allclose(gathered_k, k)
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# ---- PagePool (paged mode, page_size>1) ----
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def _make_paged_pool_ps64(**kwargs):
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defaults = dict(
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n_layers=1,
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n_kv_heads=2,
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head_dim=4,
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max_batch_size=4,
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max_seq_len=256,
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device=torch.device("cpu"),
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dtype=torch.float32,
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page_size=64,
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n_tokens=512,
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)
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defaults.update(kwargs)
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return PagePool(**defaults)
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def test_page_pool_paged_ps64_task_alloc():
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pool = _make_paged_pool_ps64()
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prompt = list(range(200))
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assert pool.task_alloc("t1", prompt)
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assert pool.task_cached("t1") == 0
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n_pages = (200 + 63) // 64
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assert len(pool._task_pages["t1"]) == n_pages
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def test_page_pool_paged_ps64_task_extend_crosses_page():
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pool = _make_paged_pool_ps64()
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pool.task_alloc("t1", list(range(64)))
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assert pool.task_extend("t1", 64)
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assert len(pool._task_pages["t1"]) >= 2
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def test_page_pool_paged_ps64_bind_roundtrip():
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pool = _make_paged_pool_ps64(n_layers=1, n_kv_heads=2, head_dim=4)
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prompt = list(range(128))
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pool.task_alloc("t1", prompt)
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kv = pool.bind_tasks(["t1"], _ws(pool), start_pos=0)
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k = torch.randn(1, 128, 2, 4)
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v = torch.randn(1, 128, 2, 4)
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kv.k_buffer[0, kv.out_cache_loc] = k
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kv.v_buffer[0, kv.out_cache_loc] = v
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indices = kv.req_to_token[kv.req_pool_indices, :128]
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gathered_k = kv.k_buffer[0, indices]
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assert torch.allclose(gathered_k, k)
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