209 lines
7.2 KiB
Python
209 lines
7.2 KiB
Python
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from typing import List, Optional, Tuple
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import pytest
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import torch
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from vllm_flash_attn import flash_attn_varlen_func, flash_attn_with_kvcache
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NUM_HEADS = [(16, 16), (32, 8), (64, 8)]
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HEAD_SIZES = [128, 256]
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BLOCK_SIZES = [16, 32]
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DTYPES = [torch.float16, torch.bfloat16]
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NUM_BLOCKS = 32768 # Large enough to test overflow in index calculation.
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def ref_paged_attn(
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query: torch.Tensor,
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key_cache: torch.Tensor,
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value_cache: torch.Tensor,
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query_lens: List[int],
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kv_lens: List[int],
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block_tables: torch.Tensor,
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scale: float,
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sliding_window: Optional[int] = None,
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) -> torch.Tensor:
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num_seqs = len(query_lens)
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block_tables = block_tables.cpu().numpy()
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_, block_size, num_kv_heads, head_size = key_cache.shape
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outputs = []
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start_idx = 0
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for i in range(num_seqs):
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query_len = query_lens[i]
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kv_len = kv_lens[i]
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q = query[start_idx:start_idx + query_len]
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q *= scale
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num_kv_blocks = (kv_len + block_size - 1) // block_size
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block_indices = block_tables[i, :num_kv_blocks]
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k = key_cache[block_indices].view(-1, num_kv_heads, head_size)
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k = k[:kv_len]
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v = value_cache[block_indices].view(-1, num_kv_heads, head_size)
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v = v[:kv_len]
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if q.shape[1] != k.shape[1]:
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k = torch.repeat_interleave(k, q.shape[1] // k.shape[1], dim=1)
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v = torch.repeat_interleave(v, q.shape[1] // v.shape[1], dim=1)
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attn = torch.einsum("qhd,khd->hqk", q, k).float()
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empty_mask = torch.ones(query_len, kv_len)
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mask = torch.triu(empty_mask, diagonal=kv_len - query_len + 1).bool()
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if sliding_window is not None:
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sliding_window_mask = torch.triu(empty_mask,
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diagonal=kv_len -
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(query_len + sliding_window) +
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1).bool().logical_not()
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mask |= sliding_window_mask
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attn.masked_fill_(mask, float("-inf"))
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attn = torch.softmax(attn, dim=-1).to(v.dtype)
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out = torch.einsum("hqk,khd->qhd", attn, v)
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outputs.append(out)
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start_idx += query_len
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return torch.cat(outputs, dim=0)
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@pytest.mark.parametrize("kv_lens", [[1328, 18, 463], [1, 54, 293, 70]])
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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("dtype", DTYPES)
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@torch.inference_mode
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def test_flash_attn_with_paged_kv(
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kv_lens: List[Tuple[int, int]],
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num_heads: Tuple[int, int],
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head_size: int,
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dtype: torch.dtype,
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block_size: int,
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) -> None:
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torch.set_default_device("cuda")
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torch.cuda.manual_seed_all(0)
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num_seqs = len(kv_lens)
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num_query_heads = num_heads[0]
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num_kv_heads = num_heads[1]
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assert num_query_heads % num_kv_heads == 0
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max_kv_len = max(kv_lens)
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scale = head_size**-0.5
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query = torch.randn(num_seqs, num_query_heads, head_size, dtype=dtype)
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key_cache = torch.randn(NUM_BLOCKS,
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block_size,
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num_kv_heads,
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head_size,
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dtype=dtype)
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value_cache = torch.randn_like(key_cache)
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kv_lens_tensor = torch.tensor(kv_lens, dtype=torch.int32)
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max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
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block_tables = torch.randint(0,
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NUM_BLOCKS,
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(num_seqs, max_num_blocks_per_seq),
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dtype=torch.int32)
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output = flash_attn_with_kvcache(
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q=query.unsqueeze(1),
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k_cache=key_cache,
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v_cache=value_cache,
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softmax_scale=scale,
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causal=True,
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block_table=block_tables,
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cache_seqlens=kv_lens_tensor,
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).squeeze(1)
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ref_output = ref_paged_attn(
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query=query,
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key_cache=key_cache,
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value_cache=value_cache,
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query_lens=[1] * num_seqs,
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kv_lens=kv_lens,
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block_tables=block_tables,
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scale=scale,
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)
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assert torch.allclose(output, ref_output, atol=1e-2, rtol=1e-2), \
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f"{torch.max(torch.abs(output - ref_output))}"
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@pytest.mark.parametrize("seq_lens", [[(1, 1328), (5, 18), (129, 463)]])
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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("sliding_window", [None])
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@pytest.mark.parametrize("dtype", DTYPES)
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@torch.inference_mode
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def test_varlen_with_paged_kv(
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seq_lens: List[Tuple[int, int]],
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num_heads: Tuple[int, int],
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head_size: int,
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sliding_window: Optional[int],
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dtype: torch.dtype,
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block_size: int,
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) -> None:
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torch.set_default_device("cuda")
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torch.cuda.manual_seed_all(0)
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num_seqs = len(seq_lens)
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query_lens = [x[0] for x in seq_lens]
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kv_lens = [x[1] for x in seq_lens]
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num_query_heads = num_heads[0]
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num_kv_heads = num_heads[1]
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assert num_query_heads % num_kv_heads == 0
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max_query_len = max(query_lens)
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max_kv_len = max(kv_lens)
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window_size = ((sliding_window,
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sliding_window) if sliding_window is not None else
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(-1, -1))
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scale = head_size**-0.5
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query = torch.randn(sum(query_lens),
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num_query_heads,
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head_size,
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dtype=dtype)
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key_cache = torch.randn(NUM_BLOCKS,
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block_size,
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num_kv_heads,
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head_size,
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dtype=dtype)
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value_cache = torch.randn_like(key_cache)
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# Normalize the scale of the key and value caches to mitigate
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# numerical instability.
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key_cache /= head_size**0.5
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value_cache /= head_size**0.5
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cu_query_lens = torch.tensor([0] + query_lens,
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dtype=torch.int32).cumsum(dim=0,
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dtype=torch.int32)
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cu_kv_lens = torch.tensor([0] + kv_lens,
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dtype=torch.int32).cumsum(dim=0,
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dtype=torch.int32)
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max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
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block_tables = torch.randint(0,
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NUM_BLOCKS,
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(num_seqs, max_num_blocks_per_seq),
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dtype=torch.int32)
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output = flash_attn_varlen_func(
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q=query,
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k=key_cache,
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v=value_cache,
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cu_seqlens_q=cu_query_lens,
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cu_seqlens_k=cu_kv_lens,
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max_seqlen_q=max_query_len,
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max_seqlen_k=max_kv_len,
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softmax_scale=scale,
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causal=True,
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window_size=window_size,
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block_table=block_tables,
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)
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ref_output = ref_paged_attn(
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query=query,
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key_cache=key_cache,
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value_cache=value_cache,
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query_lens=query_lens,
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kv_lens=kv_lens,
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block_tables=block_tables,
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scale=scale,
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sliding_window=sliding_window,
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)
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assert torch.allclose(output, ref_output, atol=1e-2, rtol=1e-2), \
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f"{torch.max(torch.abs(output - ref_output))}"
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