183 lines
6.5 KiB
Python
183 lines
6.5 KiB
Python
from typing import List, Optional, Tuple
|
|
|
|
import pytest
|
|
import torch
|
|
|
|
from vllm.platforms import current_platform
|
|
from vllm.v1.attention.backends.flash_attn import (cascade_attention,
|
|
merge_attn_states)
|
|
from vllm.vllm_flash_attn import flash_attn_varlen_func
|
|
|
|
NUM_HEADS = [(4, 4), (8, 2), (16, 2)]
|
|
HEAD_SIZES = [128, 192, 256]
|
|
BLOCK_SIZES = [16]
|
|
DTYPES = [torch.float16, torch.bfloat16]
|
|
|
|
|
|
@pytest.mark.parametrize("num_tokens", [1, 39, 16912])
|
|
@pytest.mark.parametrize("num_heads", NUM_HEADS)
|
|
@pytest.mark.parametrize("head_size", HEAD_SIZES)
|
|
@pytest.mark.parametrize("dtype", DTYPES)
|
|
@torch.inference_mode()
|
|
def test_merge_kernel(
|
|
num_tokens: int,
|
|
num_heads: Tuple[int, int],
|
|
head_size: int,
|
|
dtype: torch.dtype,
|
|
):
|
|
torch.set_default_device("cuda")
|
|
current_platform.seed_everything(0)
|
|
num_query_heads = num_heads[0]
|
|
num_kv_heads = num_heads[1]
|
|
assert num_query_heads % num_kv_heads == 0
|
|
|
|
# Prepare inputs.
|
|
prefix_output = torch.randn(num_tokens,
|
|
num_query_heads,
|
|
head_size,
|
|
dtype=dtype)
|
|
suffix_output = torch.randn(num_tokens,
|
|
num_query_heads,
|
|
head_size,
|
|
dtype=dtype)
|
|
prefix_lse = torch.randn(num_query_heads, num_tokens, dtype=torch.float32)
|
|
suffix_lse = torch.randn(num_query_heads, num_tokens, dtype=torch.float32)
|
|
|
|
# Run the kernel.
|
|
output = torch.empty(num_tokens, num_query_heads, head_size, dtype=dtype)
|
|
merge_attn_states(output, prefix_output, prefix_lse, suffix_output,
|
|
suffix_lse)
|
|
|
|
# Reference implementation.
|
|
max_lse = torch.maximum(prefix_lse, suffix_lse)
|
|
p_lse = torch.exp(prefix_lse - max_lse)
|
|
s_lse = torch.exp(suffix_lse - max_lse)
|
|
p_scale = p_lse / (p_lse + s_lse)
|
|
s_scale = s_lse / (p_lse + s_lse)
|
|
p_scale = p_scale.transpose(0, 1).unsqueeze(2)
|
|
s_scale = s_scale.transpose(0, 1).unsqueeze(2)
|
|
ref_output = p_scale * prefix_output + s_scale * suffix_output
|
|
ref_output = ref_output.to(dtype)
|
|
|
|
# Compare the results.
|
|
torch.testing.assert_close(output, ref_output, atol=1e-2, rtol=1e-2)
|
|
|
|
|
|
CASES = [
|
|
# Case 1. A general case.
|
|
([(129, 871), (18, 280), (37, 988), (1023, 2304), (1, 257)], 256),
|
|
# Case 2. Flash-decoding case.
|
|
([(1, 1023), (1, 879), (1, 778), (1, 1777)] * 100, 512),
|
|
]
|
|
|
|
|
|
@pytest.mark.parametrize("seq_lens_and_common_prefix", CASES)
|
|
@pytest.mark.parametrize("num_heads", NUM_HEADS)
|
|
@pytest.mark.parametrize("head_size", HEAD_SIZES)
|
|
@pytest.mark.parametrize("dtype", DTYPES)
|
|
@pytest.mark.parametrize("block_size", BLOCK_SIZES)
|
|
@pytest.mark.parametrize("soft_cap", [None, 50])
|
|
@pytest.mark.parametrize("num_blocks", [2048])
|
|
@torch.inference_mode()
|
|
def test_cascade(
|
|
seq_lens_and_common_prefix: Tuple[List[Tuple[int, int]], int],
|
|
num_heads: Tuple[int, int],
|
|
head_size: int,
|
|
dtype: torch.dtype,
|
|
block_size: int,
|
|
soft_cap: Optional[float],
|
|
num_blocks: int,
|
|
) -> None:
|
|
torch.set_default_device("cuda")
|
|
current_platform.seed_everything(0)
|
|
|
|
window_size = (-1, -1)
|
|
scale = head_size**-0.5
|
|
num_query_heads = num_heads[0]
|
|
num_kv_heads = num_heads[1]
|
|
assert num_query_heads % num_kv_heads == 0
|
|
key_cache = torch.randn(num_blocks,
|
|
block_size,
|
|
num_kv_heads,
|
|
head_size,
|
|
dtype=dtype)
|
|
value_cache = torch.randn_like(key_cache)
|
|
|
|
seq_lens, common_prefix_len = seq_lens_and_common_prefix
|
|
num_seqs = len(seq_lens)
|
|
query_lens = [x[0] for x in seq_lens]
|
|
kv_lens = [x[1] for x in seq_lens]
|
|
max_query_len = max(query_lens)
|
|
max_kv_len = max(kv_lens)
|
|
|
|
total_num_query_tokens = sum(query_lens)
|
|
query = torch.randn(total_num_query_tokens,
|
|
num_query_heads,
|
|
head_size,
|
|
dtype=dtype)
|
|
cu_query_lens = torch.tensor([0] + query_lens,
|
|
dtype=torch.int32).cumsum(dim=0,
|
|
dtype=torch.int32)
|
|
cu_kv_lens = torch.tensor([0] + kv_lens,
|
|
dtype=torch.int32).cumsum(dim=0,
|
|
dtype=torch.int32)
|
|
max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
|
|
block_tables = torch.randint(0,
|
|
num_blocks,
|
|
(num_seqs, max_num_blocks_per_seq),
|
|
dtype=torch.int32)
|
|
|
|
assert common_prefix_len > 0
|
|
assert common_prefix_len % block_size == 0
|
|
num_common_kv_blocks = common_prefix_len // block_size
|
|
# Make sure the first `num_common_kv_blocks` blocks are the same.
|
|
block_tables[:, :num_common_kv_blocks] = \
|
|
block_tables[0, :num_common_kv_blocks]
|
|
|
|
# Run the regular attention.
|
|
ref_output = flash_attn_varlen_func(
|
|
q=query,
|
|
k=key_cache,
|
|
v=value_cache,
|
|
cu_seqlens_q=cu_query_lens,
|
|
cu_seqlens_k=cu_kv_lens,
|
|
max_seqlen_q=max_query_len,
|
|
max_seqlen_k=max_kv_len,
|
|
softmax_scale=scale,
|
|
causal=True,
|
|
window_size=window_size,
|
|
block_table=block_tables,
|
|
softcap=soft_cap if soft_cap is not None else 0,
|
|
)
|
|
|
|
# Run cascade attention.
|
|
assert all(common_prefix_len < kv_len for kv_len in kv_lens)
|
|
cu_prefix_query_lens = torch.tensor([0, total_num_query_tokens],
|
|
dtype=torch.int32)
|
|
cu_prefix_kv_lens = torch.tensor([0, common_prefix_len], dtype=torch.int32)
|
|
cu_suffix_kv_lens = (
|
|
cu_kv_lens -
|
|
torch.arange(num_seqs + 1, dtype=torch.int32) * common_prefix_len)
|
|
output = torch.empty_like(query)
|
|
cascade_attention(
|
|
output=output,
|
|
query=query,
|
|
key_cache=key_cache,
|
|
value_cache=value_cache,
|
|
cu_query_lens=cu_query_lens,
|
|
max_query_len=max_query_len,
|
|
cu_prefix_query_lens=cu_prefix_query_lens,
|
|
cu_prefix_kv_lens=cu_prefix_kv_lens,
|
|
cu_suffix_kv_lens=cu_suffix_kv_lens,
|
|
max_kv_len=max_kv_len,
|
|
softmax_scale=scale,
|
|
alibi_slopes=None,
|
|
sliding_window=window_size,
|
|
logits_soft_cap=soft_cap if soft_cap is not None else 0,
|
|
block_table=block_tables,
|
|
common_prefix_len=common_prefix_len,
|
|
)
|
|
|
|
# Compare the results.
|
|
torch.testing.assert_close(output, ref_output, atol=1e-2, rtol=1e-2)
|