diff --git a/csrc/flash_attn_ck/mha_fwd_kvcache.cpp b/csrc/flash_attn_ck/mha_fwd_kvcache.cpp
index 2f8b64363..b769d626a 100644
--- a/csrc/flash_attn_ck/mha_fwd_kvcache.cpp
+++ b/csrc/flash_attn_ck/mha_fwd_kvcache.cpp
@@ -287,6 +287,8 @@ mha_fwd_kvcache(at::Tensor &q,                                      // batch_siz
                 bool is_rotary_interleaved, // if true, rotary combines indices 0 & 1, else indices 0 & rotary_dim / 2
                 int num_splits)
 {
+    TORCH_CHECK(false, "vllm layout does not support mha_fwd_kvcache for now");
+
     auto q_dtype = q.dtype();
     TORCH_CHECK(q_dtype == torch::kFloat16 || q_dtype == torch::kBFloat16,
                 "FlashAttention only support fp16 and bf16 data type");
diff --git a/tests/test_flash_attn_ck.py b/tests/test_flash_attn_ck.py
index b9091f0dc..e2bb1187c 100644
--- a/tests/test_flash_attn_ck.py
+++ b/tests/test_flash_attn_ck.py
@@ -1035,271 +1035,271 @@ def test_flash_attn_varlen_causal(
 
 
 # TODO - Support has_leftpad
-@pytest.mark.parametrize("dtype", [torch.float16])
-@pytest.mark.parametrize("num_splits", [1, 0])
-@pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
-@pytest.mark.parametrize("new_kv", [False, True])
-@pytest.mark.parametrize("alibi", [False, True])
-@pytest.mark.parametrize("local", [False, True])
-@pytest.mark.parametrize("causal", [False, True])
-@pytest.mark.parametrize("seqlen_new_eq_seqlen_q", [True, False])
-@pytest.mark.parametrize("rotary_interleaved", [False, True])
-@pytest.mark.parametrize("rotary_fraction", [0.0, 0.5, 1.0])
-@pytest.mark.parametrize("paged_kv_block_size", [None, 256])
-@pytest.mark.parametrize("has_leftpad", [False])
-@pytest.mark.parametrize("has_batch_idx", [False, True])
-@pytest.mark.parametrize("d", [32, 59, 64, 80, 128, 256])
-@pytest.mark.parametrize(
-    "seqlen_q,seqlen_k",
-    [
-        (1, 128),
-        (1, 339),
-        (3, 1024),
-        (64, 800),
-        (64, 256),
-        (3, 799),
-        (64, 2048),
-        (16, 20000),
-        (1, 128 * 1024),
-        (16, 128 * 1024),
-        (128, 128),
-    ],
-)
-def test_flash_attn_kvcache(
-    seqlen_q,
-    seqlen_k,
-    d,
-    has_batch_idx,
-    has_leftpad,
-    paged_kv_block_size,
-    rotary_fraction,
-    rotary_interleaved,
-    seqlen_new_eq_seqlen_q,
-    causal,
-    local,
-    alibi,
-    new_kv,
-    mha_type,
-    num_splits,
-    dtype,
-):
-    if seqlen_q > seqlen_k and new_kv:
-        pytest.skip()
-    if not new_kv and rotary_fraction > 0.0:
-        pytest.skip()
-    if has_batch_idx and paged_kv_block_size is not None:
-        pytest.skip()
-    if has_leftpad and paged_kv_block_size is not None:
-        pytest.skip()
-    device = "cuda"
-    # set seed
-    torch.random.manual_seed(0)
-    batch_size = 1
-    batch_size_cache = batch_size if not has_batch_idx else batch_size * 2
-    nheads = 6
-    # rotary_dim must be a multiple of 16, and must be <= d
-    rotary_dim = math.floor(int(rotary_fraction * d) / 16) * 16
-    nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3)
-    assert nheads % nheads_k == 0
-    window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
-    q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype)
-    seqlen_new = seqlen_q if seqlen_new_eq_seqlen_q else torch.randint(1, seqlen_q + 1, (1,)).item()
-    if new_kv:
-        k = torch.randn(batch_size, seqlen_new, nheads_k, d, device=device, dtype=dtype)
-        v = torch.randn(batch_size, seqlen_new, nheads_k, d, device=device, dtype=dtype)
-    else:
-        k, v = None, None
-    if paged_kv_block_size is None:
-        k_cache = torch.randn(batch_size_cache, seqlen_k, nheads_k, d, device=device, dtype=dtype)
-        v_cache = torch.randn(batch_size_cache, seqlen_k, nheads_k, d, device=device, dtype=dtype)
-        block_table = None
-    else:
-        (
-            k_cache,
-            v_cache,
-            block_table,
-            k_cache_paged,
-            v_cache_paged,
-            num_blocks,
-        ) = _generate_block_kvcache(
-            seqlen_k, paged_kv_block_size, batch_size, nheads_k, d, device, dtype
-        )
-    cache_seqlens = torch.randint(
-        0 if new_kv else 1,
-        # If we don't use seqlen_q in the case of causal and rotary, cos/sin won't be long enough
-        (
-            (seqlen_k - (seqlen_q if (causal or local) and rotary_dim > 1 else seqlen_new) + 1)
-            if new_kv
-            else (seqlen_k + 1)
-        ),
-        (batch_size,),
-        dtype=torch.int32,
-        device=device,
-    )
-    if has_leftpad:
-        cache_leftpad = torch.cat([torch.randint(0, cache_seqlens[i].item(), (1,), dtype=torch.int32, device=device)
-                                   if cache_seqlens[i].item() > 0 else torch.zeros(1, dtype=torch.int32, device=device)
-                                   for i in range(batch_size)])
-    else:
-        cache_leftpad = None
-    arange = rearrange(torch.arange(seqlen_k, device=device), "s -> 1 s")
-    cache_seqlens_expanded = rearrange(cache_seqlens, "b -> b 1")
-    key_padding_mask = arange < cache_seqlens_expanded + (seqlen_new if new_kv else 0)
-    if has_leftpad:
-        key_padding_mask = torch.logical_and(
-            key_padding_mask, arange >= cache_leftpad.unsqueeze(-1).expand(-1, seqlen_k)
-        )
-    if has_batch_idx:
-        cache_batch_idx = torch.randperm(batch_size_cache, dtype=torch.int32, device=device)[
-            :batch_size
-        ]
-    else:
-        cache_batch_idx = None
-    if alibi:
-        alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3
-        attn_bias = attn_bias_from_alibi_slopes(
-            alibi_slopes, seqlen_q, seqlen_k, None, key_padding_mask, causal=causal, key_leftpad=cache_leftpad
-        )
-    else:
-        alibi_slopes, attn_bias = None, None
-    # cache_seqlens = torch.tensor([64], dtype=torch.int32, device=device)
-    if rotary_dim > 0:
-        angle = (
-            torch.rand(
-                seqlen_k if paged_kv_block_size is None else num_blocks * paged_kv_block_size,
-                rotary_dim // 2,
-                device=device,
-            )
-            * 2
-            * math.pi
-        )
-        cos = torch.cos(angle).to(dtype=dtype)
-        sin = torch.sin(angle).to(dtype=dtype)
-        if causal or local:
-            q_ro = apply_rotary_emb(
-                q, cos, sin, seqlen_offsets=cache_seqlens, interleaved=rotary_interleaved
-            )
-        else:
-            q_ro = rearrange(
-                apply_rotary_emb(
-                    rearrange(q, "b s h d -> b 1 (s h) d"),
-                    cos,
-                    sin,
-                    seqlen_offsets=cache_seqlens,
-                    interleaved=rotary_interleaved,
-                ),
-                "b 1 (s h) d -> b s h d",
-                s=seqlen_q,
-            )
-        # q_ro = q
-        k_ro = apply_rotary_emb(
-            k, cos, sin, seqlen_offsets=cache_seqlens, interleaved=rotary_interleaved
-        )
-    else:
-        cos, sin = None, None
-        q_ro, k_ro = q, k
-    # k_cache[:, 64:] = -1
-    k_cache_ref = (
-        k_cache if not has_batch_idx else k_cache[cache_batch_idx.to(dtype=torch.long)]
-    ).clone()
-    v_cache_ref = (
-        v_cache if not has_batch_idx else v_cache[cache_batch_idx.to(dtype=torch.long)]
-    ).clone()
-    if new_kv:
-        update_mask = torch.logical_and(
-            cache_seqlens_expanded <= arange, arange < cache_seqlens_expanded + seqlen_new
-        )
-        k_cache_ref[update_mask] = rearrange(k_ro, "b s ... -> (b s) ...")
-        v_cache_ref[update_mask] = rearrange(v, "b s ... -> (b s) ...")
-    k_cache_rep = repeat(k_cache_ref, "b s h d -> b s (h g) d", g=nheads // nheads_k)
-    v_cache_rep = repeat(v_cache_ref, "b s h d -> b s (h g) d", g=nheads // nheads_k)
-    out = flash_attn_with_kvcache(
-        q,
-        k_cache if paged_kv_block_size is None else k_cache_paged,
-        v_cache if paged_kv_block_size is None else v_cache_paged,
-        k,
-        v,
-        rotary_cos=cos,
-        rotary_sin=sin,
-        cache_seqlens=cache_seqlens,
-        cache_batch_idx=cache_batch_idx,
-        cache_leftpad=cache_leftpad,
-        block_table=block_table,
-        causal=causal,
-        window_size=window_size,
-        rotary_interleaved=rotary_interleaved,
-        alibi_slopes=alibi_slopes,
-        num_splits=num_splits,
-    )
-    # out = flash_attn_with_kvcache(
-    #     q, k_cache, v_cache, cache_seqlens=cache_seqlens, causal=causal, window_size=window_size
-    # )
-    # out = flash_attn_with_kvcache(q, k_cache, v_cache, causal=causal, window_size=window_size)
-    # qk = torch.einsum("bqhd,bkhd->bhqk", q, k_cache_ref)
-    # m = qk.amax(-1, keepdim=True)
-    # s_tmp = torch.exp((qk - m) / math.sqrt(d))
-    # o1 = torch.einsum('bhst,bthd->bshd', s_tmp, v_cache_ref)
-    # lse_ref = torch.logsumexp(qk / math.sqrt(d), -1)
-    # probs = torch.softmax(qk, dim=-1)
-    out_ref, _ = attention_ref(
-        q_ro,
-        k_cache_rep,
-        v_cache_rep,
-        None,
-        key_padding_mask,
-        attn_bias,
-        0.0,
-        None,
-        causal=causal,
-        window_size=window_size,
-        key_leftpad=cache_leftpad,
-    )
-    out_pt, _ = attention_ref(
-        q_ro,
-        k_cache_rep,
-        v_cache_rep,
-        None,
-        key_padding_mask,
-        attn_bias,
-        0.0,
-        None,
-        causal=causal,
-        window_size=window_size,
-        upcast=False,
-        reorder_ops=True,
-        key_leftpad=cache_leftpad,
-    )
-    print(f"Output max diff: {(out - out_ref).abs().max().item()}")
-    print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
-    print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}")
-    print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}")
-
-    # Check that FlashAttention's numerical error is at most twice the numerical error
-    # of a Pytorch implementation.
-    if new_kv:
-        if paged_kv_block_size is None:
-            k_cache_select = (
-                k_cache if not has_batch_idx else k_cache[cache_batch_idx.to(dtype=torch.long)]
-            )
-            v_cache_select = (
-                v_cache if not has_batch_idx else v_cache[cache_batch_idx.to(dtype=torch.long)]
-            )
-        else:
-            k_cache_select = rearrange(
-                k_cache_paged[block_table.to(dtype=torch.long).flatten()],
-                "(b nblocks) block_size ... -> b (nblocks block_size) ...",
-                b=batch_size,
-            )[:, :seqlen_k]
-            v_cache_select = rearrange(
-                v_cache_paged[block_table.to(dtype=torch.long).flatten()],
-                "(b nblocks) block_size ... -> b (nblocks block_size) ...",
-                b=batch_size,
-            )[:, :seqlen_k]
-        assert torch.allclose(k_cache_select, k_cache_ref, rtol=1e-3, atol=1e-3)
-        assert torch.equal(v_cache_select, v_cache_ref)
-    # mult = 3 if f16, bf16 need 4
-    mult = 4 if not alibi else 5
-    assert (out - out_ref).abs().max().item() <= mult * (out_pt - out_ref).abs().max().item() + 1e-5
+# @pytest.mark.parametrize("dtype", [torch.float16])
+# @pytest.mark.parametrize("num_splits", [1, 0])
+# @pytest.mark.parametrize("mha_type", ["mha", "mqa", "gqa"])
+# @pytest.mark.parametrize("new_kv", [False, True])
+# @pytest.mark.parametrize("alibi", [False, True])
+# @pytest.mark.parametrize("local", [False, True])
+# @pytest.mark.parametrize("causal", [False, True])
+# @pytest.mark.parametrize("seqlen_new_eq_seqlen_q", [True, False])
+# @pytest.mark.parametrize("rotary_interleaved", [False, True])
+# @pytest.mark.parametrize("rotary_fraction", [0.0, 0.5, 1.0])
+# @pytest.mark.parametrize("paged_kv_block_size", [None, 256])
+# @pytest.mark.parametrize("has_leftpad", [False])
+# @pytest.mark.parametrize("has_batch_idx", [False, True])
+# @pytest.mark.parametrize("d", [32, 59, 64, 80, 128, 256])
+# @pytest.mark.parametrize(
+#     "seqlen_q,seqlen_k",
+#     [
+#         (1, 128),
+#         (1, 339),
+#         (3, 1024),
+#         (64, 800),
+#         (64, 256),
+#         (3, 799),
+#         (64, 2048),
+#         (16, 20000),
+#         (1, 128 * 1024),
+#         (16, 128 * 1024),
+#         (128, 128),
+#     ],
+# )
+# def test_flash_attn_kvcache(
+#     seqlen_q,
+#     seqlen_k,
+#     d,
+#     has_batch_idx,
+#     has_leftpad,
+#     paged_kv_block_size,
+#     rotary_fraction,
+#     rotary_interleaved,
+#     seqlen_new_eq_seqlen_q,
+#     causal,
+#     local,
+#     alibi,
+#     new_kv,
+#     mha_type,
+#     num_splits,
+#     dtype,
+# ):
+#     if seqlen_q > seqlen_k and new_kv:
+#         pytest.skip()
+#     if not new_kv and rotary_fraction > 0.0:
+#         pytest.skip()
+#     if has_batch_idx and paged_kv_block_size is not None:
+#         pytest.skip()
+#     if has_leftpad and paged_kv_block_size is not None:
+#         pytest.skip()
+#     device = "cuda"
+#     # set seed
+#     torch.random.manual_seed(0)
+#     batch_size = 1
+#     batch_size_cache = batch_size if not has_batch_idx else batch_size * 2
+#     nheads = 6
+#     # rotary_dim must be a multiple of 16, and must be <= d
+#     rotary_dim = math.floor(int(rotary_fraction * d) / 16) * 16
+#     nheads_k = nheads if mha_type == "mha" else (1 if mha_type == "mqa" else 3)
+#     assert nheads % nheads_k == 0
+#     window_size = (-1, -1) if not local else torch.randint(0, seqlen_k, (2,))
+#     q = torch.randn(batch_size, seqlen_q, nheads, d, device=device, dtype=dtype)
+#     seqlen_new = seqlen_q if seqlen_new_eq_seqlen_q else torch.randint(1, seqlen_q + 1, (1,)).item()
+#     if new_kv:
+#         k = torch.randn(batch_size, seqlen_new, nheads_k, d, device=device, dtype=dtype)
+#         v = torch.randn(batch_size, seqlen_new, nheads_k, d, device=device, dtype=dtype)
+#     else:
+#         k, v = None, None
+#     if paged_kv_block_size is None:
+#         k_cache = torch.randn(batch_size_cache, seqlen_k, nheads_k, d, device=device, dtype=dtype)
+#         v_cache = torch.randn(batch_size_cache, seqlen_k, nheads_k, d, device=device, dtype=dtype)
+#         block_table = None
+#     else:
+#         (
+#             k_cache,
+#             v_cache,
+#             block_table,
+#             k_cache_paged,
+#             v_cache_paged,
+#             num_blocks,
+#         ) = _generate_block_kvcache(
+#             seqlen_k, paged_kv_block_size, batch_size, nheads_k, d, device, dtype
+#         )
+#     cache_seqlens = torch.randint(
+#         0 if new_kv else 1,
+#         # If we don't use seqlen_q in the case of causal and rotary, cos/sin won't be long enough
+#         (
+#             (seqlen_k - (seqlen_q if (causal or local) and rotary_dim > 1 else seqlen_new) + 1)
+#             if new_kv
+#             else (seqlen_k + 1)
+#         ),
+#         (batch_size,),
+#         dtype=torch.int32,
+#         device=device,
+#     )
+#     if has_leftpad:
+#         cache_leftpad = torch.cat([torch.randint(0, cache_seqlens[i].item(), (1,), dtype=torch.int32, device=device)
+#                                    if cache_seqlens[i].item() > 0 else torch.zeros(1, dtype=torch.int32, device=device)
+#                                    for i in range(batch_size)])
+#     else:
+#         cache_leftpad = None
+#     arange = rearrange(torch.arange(seqlen_k, device=device), "s -> 1 s")
+#     cache_seqlens_expanded = rearrange(cache_seqlens, "b -> b 1")
+#     key_padding_mask = arange < cache_seqlens_expanded + (seqlen_new if new_kv else 0)
+#     if has_leftpad:
+#         key_padding_mask = torch.logical_and(
+#             key_padding_mask, arange >= cache_leftpad.unsqueeze(-1).expand(-1, seqlen_k)
+#         )
+#     if has_batch_idx:
+#         cache_batch_idx = torch.randperm(batch_size_cache, dtype=torch.int32, device=device)[
+#             :batch_size
+#         ]
+#     else:
+#         cache_batch_idx = None
+#     if alibi:
+#         alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3
+#         attn_bias = attn_bias_from_alibi_slopes(
+#             alibi_slopes, seqlen_q, seqlen_k, None, key_padding_mask, causal=causal, key_leftpad=cache_leftpad
+#         )
+#     else:
+#         alibi_slopes, attn_bias = None, None
+#     # cache_seqlens = torch.tensor([64], dtype=torch.int32, device=device)
+#     if rotary_dim > 0:
+#         angle = (
+#             torch.rand(
+#                 seqlen_k if paged_kv_block_size is None else num_blocks * paged_kv_block_size,
+#                 rotary_dim // 2,
+#                 device=device,
+#             )
+#             * 2
+#             * math.pi
+#         )
+#         cos = torch.cos(angle).to(dtype=dtype)
+#         sin = torch.sin(angle).to(dtype=dtype)
+#         if causal or local:
+#             q_ro = apply_rotary_emb(
+#                 q, cos, sin, seqlen_offsets=cache_seqlens, interleaved=rotary_interleaved
+#             )
+#         else:
+#             q_ro = rearrange(
+#                 apply_rotary_emb(
+#                     rearrange(q, "b s h d -> b 1 (s h) d"),
+#                     cos,
+#                     sin,
+#                     seqlen_offsets=cache_seqlens,
+#                     interleaved=rotary_interleaved,
+#                 ),
+#                 "b 1 (s h) d -> b s h d",
+#                 s=seqlen_q,
+#             )
+#         # q_ro = q
+#         k_ro = apply_rotary_emb(
+#             k, cos, sin, seqlen_offsets=cache_seqlens, interleaved=rotary_interleaved
+#         )
+#     else:
+#         cos, sin = None, None
+#         q_ro, k_ro = q, k
+#     # k_cache[:, 64:] = -1
+#     k_cache_ref = (
+#         k_cache if not has_batch_idx else k_cache[cache_batch_idx.to(dtype=torch.long)]
+#     ).clone()
+#     v_cache_ref = (
+#         v_cache if not has_batch_idx else v_cache[cache_batch_idx.to(dtype=torch.long)]
+#     ).clone()
+#     if new_kv:
+#         update_mask = torch.logical_and(
+#             cache_seqlens_expanded <= arange, arange < cache_seqlens_expanded + seqlen_new
+#         )
+#         k_cache_ref[update_mask] = rearrange(k_ro, "b s ... -> (b s) ...")
+#         v_cache_ref[update_mask] = rearrange(v, "b s ... -> (b s) ...")
+#     k_cache_rep = repeat(k_cache_ref, "b s h d -> b s (h g) d", g=nheads // nheads_k)
+#     v_cache_rep = repeat(v_cache_ref, "b s h d -> b s (h g) d", g=nheads // nheads_k)
+#     out = flash_attn_with_kvcache(
+#         q,
+#         k_cache if paged_kv_block_size is None else k_cache_paged,
+#         v_cache if paged_kv_block_size is None else v_cache_paged,
+#         k,
+#         v,
+#         rotary_cos=cos,
+#         rotary_sin=sin,
+#         cache_seqlens=cache_seqlens,
+#         cache_batch_idx=cache_batch_idx,
+#         cache_leftpad=cache_leftpad,
+#         block_table=block_table,
+#         causal=causal,
+#         window_size=window_size,
+#         rotary_interleaved=rotary_interleaved,
+#         alibi_slopes=alibi_slopes,
+#         num_splits=num_splits,
+#     )
+#     # out = flash_attn_with_kvcache(
+#     #     q, k_cache, v_cache, cache_seqlens=cache_seqlens, causal=causal, window_size=window_size
+#     # )
+#     # out = flash_attn_with_kvcache(q, k_cache, v_cache, causal=causal, window_size=window_size)
+#     # qk = torch.einsum("bqhd,bkhd->bhqk", q, k_cache_ref)
+#     # m = qk.amax(-1, keepdim=True)
+#     # s_tmp = torch.exp((qk - m) / math.sqrt(d))
+#     # o1 = torch.einsum('bhst,bthd->bshd', s_tmp, v_cache_ref)
+#     # lse_ref = torch.logsumexp(qk / math.sqrt(d), -1)
+#     # probs = torch.softmax(qk, dim=-1)
+#     out_ref, _ = attention_ref(
+#         q_ro,
+#         k_cache_rep,
+#         v_cache_rep,
+#         None,
+#         key_padding_mask,
+#         attn_bias,
+#         0.0,
+#         None,
+#         causal=causal,
+#         window_size=window_size,
+#         key_leftpad=cache_leftpad,
+#     )
+#     out_pt, _ = attention_ref(
+#         q_ro,
+#         k_cache_rep,
+#         v_cache_rep,
+#         None,
+#         key_padding_mask,
+#         attn_bias,
+#         0.0,
+#         None,
+#         causal=causal,
+#         window_size=window_size,
+#         upcast=False,
+#         reorder_ops=True,
+#         key_leftpad=cache_leftpad,
+#     )
+#     print(f"Output max diff: {(out - out_ref).abs().max().item()}")
+#     print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
+#     print(f"Pytorch max diff: {(out_pt - out_ref).abs().max().item()}")
+#     print(f"Pytorch mean diff: {(out_pt - out_ref).abs().mean().item()}")
+
+#     # Check that FlashAttention's numerical error is at most twice the numerical error
+#     # of a Pytorch implementation.
+#     if new_kv:
+#         if paged_kv_block_size is None:
+#             k_cache_select = (
+#                 k_cache if not has_batch_idx else k_cache[cache_batch_idx.to(dtype=torch.long)]
+#             )
+#             v_cache_select = (
+#                 v_cache if not has_batch_idx else v_cache[cache_batch_idx.to(dtype=torch.long)]
+#             )
+#         else:
+#             k_cache_select = rearrange(
+#                 k_cache_paged[block_table.to(dtype=torch.long).flatten()],
+#                 "(b nblocks) block_size ... -> b (nblocks block_size) ...",
+#                 b=batch_size,
+#             )[:, :seqlen_k]
+#             v_cache_select = rearrange(
+#                 v_cache_paged[block_table.to(dtype=torch.long).flatten()],
+#                 "(b nblocks) block_size ... -> b (nblocks block_size) ...",
+#                 b=batch_size,
+#             )[:, :seqlen_k]
+#         assert torch.allclose(k_cache_select, k_cache_ref, rtol=1e-3, atol=1e-3)
+#         assert torch.equal(v_cache_select, v_cache_ref)
+#     # mult = 3 if f16, bf16 need 4
+#     mult = 4 if not alibi else 5
+#     assert (out - out_ref).abs().max().item() <= mult * (out_pt - out_ref).abs().max().item() + 1e-5