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[Kernel][CPU] CPU MLA (#14744)

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Signed-off-by: Thien Tran <gau.nernst@yahoo.com.sg>
This commit is contained in:
Thien Tran
2025-03-25 17:34:59 +08:00
committed by GitHub
parent 4157f563b4
commit 4f044b1d67
15 changed files with 1010 additions and 17 deletions
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69
tests/kernels/test_cache.py
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@@ -749,3 +749,72 @@ def test_gather_cache_mla(kv_lora_rank, qk_rope_head_dim, block_size,
ops.gather_cache(src_cache, dst, block_table, cu_seq_lens, batch_size)
torch.testing.assert_close(dst, expected)
@pytest.mark.parametrize("kv_lora_rank", KV_LORA_RANKS)
@pytest.mark.parametrize("qk_rope_head_dim", QK_ROPE_HEAD_DIMS)
@pytest.mark.parametrize("num_tokens", NUM_TOKENS_MLA)
@pytest.mark.parametrize("block_size", BLOCK_SIZES_MLA)
@pytest.mark.parametrize("num_blocks", NUM_BLOCKS_MLA)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.cpu_model
@pytest.mark.skipif(not current_platform.is_cpu(), reason="CPU only")
@torch.inference_mode()
def test_concat_and_cache_mla_cpu(
kv_lora_rank: int,
qk_rope_head_dim: int,
num_tokens: int,
block_size: int,
num_blocks: int,
dtype: torch.dtype,
seed: int,
) -> None:
device = "cpu"
kv_cache_dtype = "auto"
current_platform.seed_everything(seed)
torch.set_default_device(device)
total_slots = num_blocks * block_size
slot_mapping_lst = random.sample(range(total_slots), num_tokens)
slot_mapping = torch.tensor(slot_mapping_lst,
dtype=torch.long,
device=device)
kv_c = torch.randn(num_tokens, kv_lora_rank, dtype=dtype, device=device)
k_pe = torch.randn(num_tokens,
qk_rope_head_dim,
dtype=dtype,
device=device)
entry_size = kv_lora_rank + qk_rope_head_dim
scale = torch.tensor(0.1, dtype=torch.float32, device=device)
kv_cache = _create_mla_cache(num_blocks, block_size, entry_size, dtype,
kv_cache_dtype, device)
ref_temp = torch.zeros(*kv_cache.shape, dtype=dtype, device=device)
for i in range(num_tokens):
slot = slot_mapping[i].item()
block_idx = slot // block_size
block_offset = slot % block_size
ref_temp[block_idx, block_offset, :kv_lora_rank] = kv_c[i]
ref_temp[block_idx, block_offset, kv_lora_rank:] = k_pe[i]
if kv_cache_dtype == "fp8":
ref_kv_cache = torch.empty_like(ref_temp, dtype=kv_cache.dtype)
ops.convert_fp8(ref_kv_cache,
ref_temp,
scale.item(),
kv_dtype=kv_cache_dtype)
else:
ref_kv_cache = ref_temp
opcheck(
torch.ops._C_cache_ops.concat_and_cache_mla,
(kv_c, k_pe, kv_cache, slot_mapping, kv_cache_dtype, scale),
test_utils=DEFAULT_OPCHECK_TEST_UTILS,
)
ops.concat_and_cache_mla(kv_c, k_pe, kv_cache, slot_mapping,
kv_cache_dtype, scale)
torch.testing.assert_close(kv_cache, ref_kv_cache)