guard fa3 prefill

Author: RunningLeon

lmdeploy/pytorch/backends/cuda/attention/fa3.py

@@ -4,7 +4,7 @@
 from lmdeploy.messages import QuantPolicy
 from lmdeploy.utils import get_logger
 
-from .default import TritonAttentionImpl, TritonAttentionMetadata
+from .default import TritonAttentionImpl, TritonAttentionMetadata, _cdiv
 
 logger = get_logger('lmdeploy')
 
@@ -261,13 +261,15 @@ def _forward_prefill(
         quant_policy = attn_metadata.quant_policy
 
         # Flatten KV cache for varlen attention
+        block_size = k_cache.size(1)
+        out_size = _cdiv(kv_flatten_size, block_size) * block_size + block_size
         flatten_k, flatten_v = self.flatten_kv_cache(
             k_cache,
             v_cache,
             kv_seqlens,
             block_offsets,
             start_loc=kv_start_loc,
-            out_size=kv_flatten_size,
+            out_size=out_size,
             out_dtype=query.dtype,
             k_scales_zeros=k_scales_zeros,
             v_scales_zeros=v_scales_zeros,
@@ -293,7 +295,7 @@ def _forward_prefill(
             cu_seqlens_q=attn_metadata.cu_seqlens_q,
             cu_seqlens_k=attn_metadata.cu_seqlens_k,
             max_seqlen_q=max_q_seqlen,
-            max_seqlen_k=kv_flatten_size,
+            max_seqlen_k=attn_metadata.max_kv_seqlen,
             softmax_scale=self.scale,
             causal=self.causal,
             window_size=sliding_window,

tests/pytorch/kernel/test_fa3_attention.py

@@ -0,0 +1,91 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from lmdeploy.messages import QuantPolicy
+from lmdeploy.pytorch.backends.cuda.attention.default import TritonAttentionMetadata
+from lmdeploy.pytorch.backends.cuda.attention.fa3 import FA3Impl
+
+_BLOCK_SIZE = 16
+_PREFILL_SEQLENS = (29, 18)
+
+
+def _make_prefill_metadata(q_seqlens, block_offsets):
+    cu_seqlens = torch.nn.functional.pad(torch.cumsum(q_seqlens, dim=0, dtype=torch.int32), (1, 0))
+    return TritonAttentionMetadata(
+        is_decoding=False,
+        block_offsets=block_offsets,
+        q_start_loc=cu_seqlens[:-1],
+        q_seqlens=q_seqlens,
+        kv_start_loc=cu_seqlens[:-1],
+        kv_seqlens=q_seqlens,
+        quant_policy=QuantPolicy.NONE,
+        kv_flatten_size=int(q_seqlens.sum().item()),
+        cu_seqlens_q=cu_seqlens,
+        cu_seqlens_k=cu_seqlens.clone(),
+        max_kv_seqlen=int(q_seqlens.max().item()),
+        max_q_seqlen=int(q_seqlens.max().item()),
+    )
+
+
+def _make_recycled_block_offsets(device):
+    return torch.tensor([
+        [0, 2, 1],
+        [3, 4, 0],
+    ],
+                        dtype=torch.int32,
+                        device=device)
+
+
+def _make_prefill_seqlens(device='cpu'):
+    return torch.tensor(_PREFILL_SEQLENS, dtype=torch.int32, device=device)
+
+
+def _guarded_flatten_size(q_seqlens):
+    kv_flatten_size = int(q_seqlens.sum().item())
+    return (kv_flatten_size + _BLOCK_SIZE - 1) // _BLOCK_SIZE * _BLOCK_SIZE + _BLOCK_SIZE
+
+
+def _num_cache_blocks(block_offsets):
+    return int(block_offsets.max().item()) + 1
+
+
+def test_fa3_prefill_uses_guarded_flatten_buffer_and_max_kv_seqlen():
+    """Regression test for FA3 prefill with recycled paged KV blocks."""
+    impl = FA3Impl.__new__(FA3Impl)
+    impl.scale = 1.0
+    impl.causal = True
+    impl.sliding_window = None
+    impl.logit_softcapping = 0.0
+
+    q_seqlens = _make_prefill_seqlens()
+    block_offsets = _make_recycled_block_offsets(device='cpu')
+    metadata = _make_prefill_metadata(q_seqlens, block_offsets)
+
+    query = torch.empty((int(q_seqlens.sum().item()), 2, 8), dtype=torch.float16)
+    k_cache = torch.empty((_num_cache_blocks(block_offsets), _BLOCK_SIZE, 2, 8), dtype=torch.float16)
+    v_cache = torch.empty_like(k_cache)
+    captured = {}
+
+    def fake_flatten_kv_cache(k_cache_arg, v_cache_arg, seqlens, offsets, **kwargs):
+        captured['flatten_out_size'] = kwargs['out_size']
+        captured['flatten_start_loc'] = kwargs['start_loc']
+        return (
+            torch.empty((kwargs['out_size'], 2, 8), dtype=kwargs['out_dtype']),
+            torch.empty((kwargs['out_size'], 2, 8), dtype=kwargs['out_dtype']),
+        )
+
+    def fake_flash_attn_varlen_func(**kwargs):
+        captured['flash_max_seqlen_k'] = kwargs['max_seqlen_k']
+        captured['flash_k_size'] = kwargs['k'].size(0)
+        return torch.empty_like(kwargs['q'])
+
+    impl.flatten_kv_cache = fake_flatten_kv_cache
+    impl.flash_attn_varlen_func_v3 = fake_flash_attn_varlen_func
+
+    out = impl._forward_prefill(query, k_cache, v_cache, metadata, max_q_seqlen=int(q_seqlens.max().item()))
+
+    assert out.shape == query.shape
+    assert captured['flatten_start_loc'] is metadata.kv_start_loc
+    assert captured['flatten_out_size'] == _guarded_flatten_size(q_seqlens)
+    assert captured['flash_k_size'] == _guarded_flatten_size(q_seqlens)
+    assert captured['flash_max_seqlen_k'] == metadata.max_kv_seqlen