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]) @pytest.mark.parametrize("fa_version", [2, 3]) @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, fa_version: int, ) -> None: torch.set_default_device("cuda") if fa_version == 3 and (torch.cuda.get_device_capability() == (8, 6) or torch.cuda.get_device_capability() == (8, 9)): pytest.skip("Flash attention version 3 fails on 8.6 and 8.9 due to " "insufficient shared memory for some shapes") 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) kv_lens_tensor = torch.tensor(kv_lens, 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, seqused_k=kv_lens_tensor, 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) prefix_kv_lens = torch.tensor([common_prefix_len], dtype=torch.int32) suffix_kv_lens = kv_lens_tensor - 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, prefix_kv_lens=prefix_kv_lens, suffix_kv_lens=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, fa_version=fa_version, ) # Compare the results. torch.testing.assert_close(output, ref_output, atol=1e-2, rtol=1e-2)