diff --git a/dnn-providers/hip-kernel-provider/rocke/library/kernels/common/attention_unified.py b/dnn-providers/hip-kernel-provider/rocke/library/kernels/common/attention_unified.py
index 641143464c89..faf920854f4a 100644
--- a/dnn-providers/hip-kernel-provider/rocke/library/kernels/common/attention_unified.py
+++ b/dnn-providers/hip-kernel-provider/rocke/library/kernels/common/attention_unified.py
@@ -797,18 +797,19 @@ def _cache_key(problem: UnifiedAttentionProblem, arch: str) -> Tuple:
     ``arch`` sits at index 1 like every other key family in this module, so
     ``_assert_cache_key_arch`` can check key-vs-compile agreement uniformly and
     two arches can never alias to one cached kernel.
+
+    Like the tiled keys, it holds only fields the scalar 2D build reads. The
+    dynamic shape (``total_q``, ``num_seqs``, ``max_seqlen_q``, ``max_seqlen_k``)
+    reaches the kernel as kernargs or the launch grid, never as IR, so one binary
+    serves every batch. ``test_attention_scalar_cache_key.py`` enforces this.
     """
     return (
         "scalar",
         arch,
-        problem.total_q,
-        problem.num_seqs,
         problem.num_query_heads,
         problem.num_kv_heads,
         problem.head_size,
         problem.block_size,
-        problem.max_seqlen_q,
-        problem.max_seqlen_k,
         problem.dtype,
         problem.q_dtype,
         problem.sliding_window,
@@ -5745,7 +5746,7 @@ def _get_scalar_launcher(
         kernel_name=kname,
         signature=_attn_signature(
             problem.dtype,
-            include_bt_stride=False,
+            include_bt_stride=True,
             kv_dtype=_kv_storage_dtype(problem),
         ),
         cache_key=("scalar",) + cache_key,
@@ -6364,7 +6365,7 @@ def run_unified_attention_torch(
         softcap=softcap,
         sinks=sinks,
         bt_stride=bt_stride,
-        include_bt_stride=False,
+        include_bt_stride=True,
         k_scale=k_scale,
         v_scale=v_scale,
         out_scale=out_scale,
@@ -6406,7 +6407,6 @@ def kernel_name(self) -> str:
         p = self.problem
         return kernel_name_join(
             self.name,
-            f"q{p.total_q}",
             f"h{p.num_query_heads}",
             f"kv{p.num_kv_heads}",
             f"d{p.head_size}",
@@ -6511,6 +6511,7 @@ def build_unified_attention_2d(
     _out_scale = b.param("out_scale", F32)
     softcap = b.param("softcap", F32)
     num_seqs = b.param("num_seqs", I32)
+    bt_stride = b.param("block_table_stride", I32)
 
     q_tok = b.block_id_x()
     q_head = b.block_id_y()
@@ -6567,16 +6568,8 @@ def build_unified_attention_2d(
         iv_name="kpos",
     )
     with loop as (kpos, (m_val, l_val, acc_val)):
-        block_idx, token_in_block = _magic_div_mod(b, kpos, p.block_size)
-        physical = b.global_load_i32(
-            block_tables,
-            b.add(
-                b.mul(
-                    seq_idx,
-                    b.const_i32((p.max_seqlen_k + p.block_size - 1) // p.block_size),
-                ),
-                block_idx,
-            ),
+        physical, token_in_block = _physical_block_and_token(
+            b, p, block_tables, bt_stride, seq_idx, kpos
         )
 
         # Vectorised QK dot product. Both Q[q_tok, q_head, :] and
@@ -6690,7 +6683,6 @@ def kernel_name(self) -> str:
         p = self.problem
         return kernel_name_join(
             self.name,
-            f"q{p.total_q}",
             f"h{p.num_query_heads}",
             f"kv{p.num_kv_heads}",
             f"d{p.head_size}",
@@ -6748,6 +6740,7 @@ def build_unified_attention_3d(
     scale = abi["scale"]
     _softcap = b.param("softcap", F32)
     num_seqs = b.param("num_seqs", I32)
+    bt_stride = b.param("block_table_stride", I32)
 
     q_tok = b.block_id_x()
     q_head = b.block_id_y()
@@ -6798,6 +6791,7 @@ def build_unified_attention_3d(
             query,
             key,
             block_tables,
+            bt_stride,
             seq_idx,
             q_tok,
             q_head,
@@ -6823,7 +6817,9 @@ def build_unified_attention_3d(
         alpha = b.exp2(b.fsub(m_val, new_m))
         prob = b.exp2(b.fsub(score, new_m))
         new_l = b.fadd(b.fmul(l_val, alpha), prob)
-        vv = _emit_v_load(b, p, dtype, value, block_tables, seq_idx, kv_head, kpos, dim)
+        vv = _emit_v_load(
+            b, p, dtype, value, block_tables, bt_stride, seq_idx, kv_head, kpos, dim
+        )
         new_acc = b.fadd(b.fmul(acc_val, alpha), b.fmul(prob, vv))
         b.scf_yield(new_m, new_l, new_acc)
 
@@ -6860,7 +6856,6 @@ def kernel_name(self) -> str:
         p = self.problem
         return kernel_name_join(
             self.name,
-            f"q{p.total_q}",
             f"h{p.num_query_heads}",
             f"d{p.head_size}",
             f"seg{self.num_segments}",
@@ -7099,6 +7094,7 @@ def _emit_qk_score(
     query: Value,
     key: Value,
     block_tables: Value,
+    bt_stride: Value,
     seq_idx: Value,
     q_tok: Value,
     q_head: Value,
@@ -7127,7 +7123,7 @@ def _emit_qk_score(
     """
     score = b.const_f32(0.0)
     physical, token_in_block = _physical_block_and_token(
-        b, p, block_tables, seq_idx, kpos
+        b, p, block_tables, bt_stride, seq_idx, kpos
     )
     q_desc = _q_descriptor(p)
     kv_desc = _paged_kv_descriptor(p)
@@ -7178,13 +7174,14 @@ def _emit_v_load(
     dtype: Type,
     value: Value,
     block_tables: Value,
+    bt_stride: Value,
     seq_idx: Value,
     kv_head: Value,
     kpos: Value,
     dim: Value,
 ) -> Value:
     physical, token_in_block = _physical_block_and_token(
-        b, p, block_tables, seq_idx, kpos
+        b, p, block_tables, bt_stride, seq_idx, kpos
     )
     v_off = _paged_kv_descriptor(p).offset(
         b,
@@ -7200,12 +7197,17 @@ def _physical_block_and_token(
     b: IRBuilder,
     p: UnifiedAttentionProblem,
     block_tables: Value,
+    bt_stride: Value,
     seq_idx: Value,
     kpos: Value,
 ) -> Tuple[Value, Value]:
+    """Look up ``kpos``'s physical KV block in row ``seq_idx`` of the block table.
+
+    ``bt_stride`` is the table's row stride kernarg, not a value derived from
+    ``max_seqlen_k``: callers may allocate rows wider than the longest sequence.
+    """
     block_idx, token_in_block = _magic_div_mod(b, kpos, p.block_size)
-    max_blocks = (p.max_seqlen_k + p.block_size - 1) // p.block_size
     physical = b.global_load_i32(
-        block_tables, b.add(b.mul(seq_idx, b.const_i32(max_blocks)), block_idx)
+        block_tables, b.add(b.mul(seq_idx, bt_stride), block_idx)
     )
     return physical, token_in_block
diff --git a/dnn-providers/hip-kernel-provider/rocke/library/tests/test_attention_scalar_block_table_numeric.py b/dnn-providers/hip-kernel-provider/rocke/library/tests/test_attention_scalar_block_table_numeric.py
new file mode 100644
index 000000000000..cdf3703a6c30
--- /dev/null
+++ b/dnn-providers/hip-kernel-provider/rocke/library/tests/test_attention_scalar_block_table_numeric.py
@@ -0,0 +1,185 @@
+# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
+# SPDX-License-Identifier: MIT
+
+"""Scalar fallback launches with block-table rows wider than the batch needs.
+
+Callers usually size block-table rows for the longest sequence they admit, not
+the longest one in the current batch. The scalar kernels index rows by the
+table's real row stride (the ``block_table_stride`` kernarg), so every sequence
+after the first must still find its own blocks. Unused table slots point at a
+poison block, so a wrong row stride corrupts the output instead of passing by
+luck.
+
+The launch goes through ``run_unified_attention_torch(backend="scalar")`` with
+``rocke.runtime`` device buffers instead of torch tensors, so the test runs in
+GPU lanes without torch. Reference attention is computed independently in FP32
+with numpy from the logical K/V, after rounding inputs to fp16. Skips without a
+GPU, and on devices whose target id the attention builders do not know.
+"""
+
+import ctypes
+import itertools
+import math
+
+import numpy as np
+import pytest
+
+from kernels.common import attention_unified as au
+from rocke.core.arch.target import known_arches
+from rocke.runtime.hip_module import get_device_arch
+
+_HQ, _HKV, _D, _BS = 8, 2, 64, 16
+_ATOL = 1e-2
+
+# The launch compiles for the device's own target id, which the builders must
+# know (gfx11 devices are only known through their generic target).
+_ARCH = get_device_arch()
+pytestmark = [
+    pytest.mark.gpu,
+    pytest.mark.skipif(
+        _ARCH not in known_arches(),
+        reason=f"needs a GPU with a known attention target, found {_ARCH}",
+    ),
+]
+
+
+@pytest.fixture(scope="module")
+def rt():
+    from rocke.runtime.hip_module import Runtime
+
+    return Runtime()
+
+
+def _u8(array):
+    return (ctypes.c_uint8 * array.nbytes).from_buffer(array)
+
+
+class _DeviceArray:
+    """A device copy of a numpy array, exposing the tensor attributes
+    ``run_unified_attention_torch`` reads. ``shape`` may be narrower than the
+    host array along the last axis, which models a column view of it."""
+
+    device = None
+
+    def __init__(self, rt, host, *, shape=None):
+        from rocke.runtime.launcher import DeviceMem
+
+        self._rt = rt
+        self._host = np.ascontiguousarray(host)
+        self._mem = DeviceMem(self._host.nbytes)
+        rt.memcpy_h2d(self._mem.ptr(), _u8(self._host), self._host.nbytes)
+        self.shape = self._host.shape if shape is None else shape
+
+    def stride(self, dim):
+        return self._host.strides[dim] // self._host.itemsize
+
+    def data_ptr(self):
+        return self._mem.ptr()
+
+    def read(self):
+        out = np.empty_like(self._host)
+        self._rt.memcpy_d2h(_u8(out), self._mem.ptr(), out.nbytes)
+        return out
+
+
+def _case(q_lens, kv_lens, row_width, *, seed):
+    """A paged case whose block-table rows hold ``row_width`` slots."""
+    rng = np.random.default_rng(seed)
+    needed = [math.ceil(n / _BS) for n in kv_lens]
+    num_blocks = sum(needed) + 1
+    poison = num_blocks - 1
+    order = iter(rng.permutation(num_blocks - 1))
+    table = np.full((len(kv_lens), row_width), poison, dtype=np.int32)
+    for s, n in enumerate(needed):
+        table[s, :n] = [next(order) for _ in range(n)]
+
+    def randn(*shape):
+        return rng.standard_normal(shape).astype(np.float16)
+
+    q = randn(sum(q_lens), _HQ, _D)
+    k = randn(num_blocks, _BS, _HKV, _D)
+    v = randn(num_blocks, _BS, _HKV, _D)
+    k[poison] = 64.0
+    v[poison] = 64.0
+    cu = np.array([0, *itertools.accumulate(q_lens)], dtype=np.int32)
+    problem = au.UnifiedAttentionProblem(
+        total_q=sum(q_lens),
+        num_seqs=len(q_lens),
+        num_query_heads=_HQ,
+        num_kv_heads=_HKV,
+        head_size=_D,
+        block_size=_BS,
+        max_seqlen_q=max(q_lens),
+        max_seqlen_k=max(kv_lens),
+        dtype="fp16",
+    )
+    return problem, q, k, v, cu, table
+
+
+def _reference(q, k, v, cu, table, q_lens, kv_lens):
+    out = np.zeros(q.shape, dtype=np.float32)
+    ratio = _HQ // _HKV
+    for s, (q_len, kv_len) in enumerate(zip(q_lens, kv_lens)):
+        blocks = table[s, : math.ceil(kv_len / _BS)]
+        keys = k[blocks].reshape(-1, _HKV, _D)[:kv_len].astype(np.float32)
+        values = v[blocks].reshape(-1, _HKV, _D)[:kv_len].astype(np.float32)
+        context = kv_len - q_len
+        for i in range(q_len):
+            tok = int(cu[s]) + i
+            visible = context + i + 1
+            for h in range(_HQ):
+                scores = keys[:visible, h // ratio] @ q[tok, h].astype(np.float32)
+                scores /= math.sqrt(_D)
+                probs = np.exp(scores - scores.max())
+                out[tok, h] = (probs / probs.sum()) @ values[:visible, h // ratio]
+    return out
+
+
+def _launch(rt, problem, q, k, v, cu, kv_lens, block_table):
+    out = _DeviceArray(rt, np.zeros_like(q))
+    au.run_unified_attention_torch(
+        problem=problem,
+        q=_DeviceArray(rt, q),
+        k=_DeviceArray(rt, k),
+        v=_DeviceArray(rt, v),
+        out=out,
+        cu_seqlens_q=_DeviceArray(rt, cu),
+        seqused_k=_DeviceArray(rt, np.array(kv_lens, dtype=np.int32)),
+        softmax_scale=1.0 / math.sqrt(_D),
+        block_table=block_table,
+        softcap=0.0,
+        backend="scalar",
+    )
+    return out.read().astype(np.float32)
+
+
+@pytest.mark.parametrize("view_of_wider", (False, True), ids=("wide", "view"))
+def test_wide_block_table_rows(rt, view_of_wider):
+    q_lens, kv_lens = [5, 1, 9], [37, 20, 61]
+    row_width = math.ceil(max(kv_lens) / _BS) + 6
+    alloc_width = row_width + 5 if view_of_wider else row_width
+    problem, q, k, v, cu, table = _case(q_lens, kv_lens, alloc_width, seed=0)
+    dev = _DeviceArray(rt, table, shape=(len(q_lens), row_width))
+    assert dev.stride(0) > math.ceil(problem.max_seqlen_k / _BS)
+    got = _launch(rt, problem, q, k, v, cu, kv_lens, dev)
+    want = _reference(q, k, v, cu, table, q_lens, kv_lens)
+    max_abs = float(np.abs(got - want).max())
+    assert max_abs <= _ATOL, f"max_abs {max_abs:.3e} vs fp32 reference"
+
+
+def test_one_binary_serves_different_batches(rt):
+    """Shapes that differ only in batch geometry share a key; the second
+    launch must reuse the first binary and still match the reference."""
+    first = ([4, 2], [30, 17], 4)
+    second = ([1, 7, 3, 2], [90, 64, 15, 33], 12)
+    launchers = None
+    for i, (q_lens, kv_lens, width) in enumerate((first, second)):
+        problem, q, k, v, cu, table = _case(q_lens, kv_lens, width, seed=i)
+        got = _launch(rt, problem, q, k, v, cu, kv_lens, _DeviceArray(rt, table))
+        if launchers is None:
+            launchers = len(au._SCALAR_LAUNCHERS)
+        else:
+            assert len(au._SCALAR_LAUNCHERS) == launchers
+        want = _reference(q, k, v, cu, table, q_lens, kv_lens)
+        max_abs = float(np.abs(got - want).max())
+        assert max_abs <= _ATOL, f"case {i}: max_abs {max_abs:.3e}"
diff --git a/dnn-providers/hip-kernel-provider/rocke/library/tests/test_attention_scalar_cache_key.py b/dnn-providers/hip-kernel-provider/rocke/library/tests/test_attention_scalar_cache_key.py
new file mode 100644
index 000000000000..819cc9217006
--- /dev/null
+++ b/dnn-providers/hip-kernel-provider/rocke/library/tests/test_attention_scalar_cache_key.py
@@ -0,0 +1,185 @@
+# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
+# SPDX-License-Identifier: MIT
+
+"""Cache-key contract for the scalar unified-attention fallback.
+
+The scalar 2D / 3D / reduce kernels read the dynamic shape (``total_q``,
+``num_seqs``, ``max_seqlen_q``, ``max_seqlen_k``) only through kernargs and the
+launch grid. The block-table row stride is the ``block_table_stride`` kernarg,
+so a caller's table may be wider than ``ceil(max_seqlen_k / block_size)``.
+``_cache_key`` therefore omits the dynamic shape, and one binary serves every
+batch.
+
+Layers:
+  * Shapes that differ only in the dynamic shape share a key and lower to
+    byte-identical IR at every LLVM flavor.
+  * Every static field the build reads splits the key.
+  * The launch signature matches the kernel's params, including the stride.
+  * ``run_unified_attention_torch`` packs the table's real row stride.
+
+No GPU required. ``test_attention_scalar_block_table_numeric.py`` runs the
+launch path on hardware.
+"""
+
+from __future__ import annotations
+
+import dataclasses
+import re
+
+import pytest
+
+from kernels.common import attention_unified as au
+from rocke.core.lower_llvm import LLVM_FLAVORS, _lower_kernel_to_llvm_python
+
+P = au.UnifiedAttentionProblem
+
+_BASE = P(
+    total_q=64,
+    num_seqs=4,
+    num_query_heads=8,
+    num_kv_heads=2,
+    head_size=128,
+    block_size=16,
+    max_seqlen_q=16,
+    max_seqlen_k=256,
+    dtype="bf16",
+)
+
+# Each entry moves only the dynamic shape. max_seqlen_k=1000 changes the block
+# count, which used to be compiled into the block-table row stride.
+_DYNAMIC_VARIANTS = (
+    dict(total_q=200),
+    dict(num_seqs=1, total_q=16),
+    dict(num_seqs=37, total_q=37, max_seqlen_q=1),
+    dict(max_seqlen_q=50),
+    dict(max_seqlen_k=250),
+    dict(max_seqlen_k=1000),
+    dict(total_q=1, num_seqs=1, max_seqlen_q=1, max_seqlen_k=65536),
+)
+
+# Each entry moves one static field the scalar build reads.
+_STATIC_VARIANTS = (
+    dict(num_query_heads=16),
+    dict(num_kv_heads=8),
+    dict(head_size=64),
+    dict(block_size=32),
+    dict(dtype="fp16"),
+    dict(sliding_window=128),
+    dict(use_sinks=True),
+    dict(softcap=30.0),
+    dict(use_fp8=True),
+    dict(causal_top_left=True),
+    dict(right_bound=8),
+)
+
+
+def _lower_all(problem: P, flavor: str) -> tuple[str, str, str]:
+    kernels = (
+        au.build_unified_attention_2d(au.UnifiedAttention2DSpec(problem)),
+        au.build_unified_attention_3d(au.UnifiedAttention3DSpec(problem)),
+        au.build_unified_attention_reduce(au.UnifiedAttentionReduceSpec(problem, 8)),
+    )
+    return tuple(
+        _lower_kernel_to_llvm_python(k, arch="gfx950", llvm_flavor=flavor)
+        for k in kernels
+    )
+
+
+@pytest.mark.parametrize("change", _DYNAMIC_VARIANTS, ids=repr)
+def test_dynamic_shape_shares_key(change):
+    other = dataclasses.replace(_BASE, **change)
+    assert au._cache_key(other, "gfx950") == au._cache_key(_BASE, "gfx950")
+
+
+@pytest.mark.parametrize("flavor", LLVM_FLAVORS)
+def test_dynamic_shape_lowers_identical_ir(flavor):
+    """Same key means same binary: every variant must lower byte-identically
+    for the 2D, 3D and reduce scalar kernels, kernel name included."""
+    base = _lower_all(_BASE, flavor)
+    for change in _DYNAMIC_VARIANTS:
+        other = _lower_all(dataclasses.replace(_BASE, **change), flavor)
+        for kind, a, b in zip(("2d", "3d", "reduce"), base, other):
+            assert a == b, f"{kind} IR differs for {change} at {flavor}"
+
+
+@pytest.mark.parametrize("change", _STATIC_VARIANTS, ids=repr)
+def test_static_field_splits_key_and_ir(change):
+    other = dataclasses.replace(_BASE, **change)
+    assert au._cache_key(other, "gfx950") != au._cache_key(_BASE, "gfx950")
+    a = _lower_all(_BASE, "llvm20")[0]
+    b = _lower_all(other, "llvm20")[0]
+    assert a != b, f"{change} splits the key but not the 2D IR"
+
+
+def test_arch_splits_key():
+    assert au._cache_key(_BASE, "gfx942") != au._cache_key(_BASE, "gfx950")
+
+
+def test_block_table_stride_is_a_kernarg():
+    """The row stride must come from the launch, not ``max_seqlen_k``."""
+    ll = _lower_all(_BASE, "llvm20")
+    for kind, text in zip(("2d", "3d"), ll[:2]):
+        assert "i32 %block_table_stride)" in text, kind
+        assert re.search(r"mul nsw i32 %\w+, %block_table_stride$", text, re.M), kind
+
+
+def test_launch_signature_matches_kernel_params():
+    """``_get_scalar_launcher`` packs kernargs by this signature; a name or
+    order mismatch with the kernel's params silently shifts every argument."""
+    kernel = au.build_unified_attention_2d(au.UnifiedAttention2DSpec(_BASE))
+    signature = au._attn_signature(
+        _BASE.dtype, include_bt_stride=True, kv_dtype=au._kv_storage_dtype(_BASE)
+    )
+    assert [item["name"] for item in signature] == [p.name for p in kernel.params]
+
+
+class _FakeArray:
+    """The tensor attributes ``run_unified_attention_torch`` reads on the
+    scalar path; nothing is launched, so the pointer is never dereferenced."""
+
+    device = None
+
+    def __init__(self, shape, *, row_stride=None):
+        self.shape = shape
+        self._row_stride = shape[-1] if row_stride is None else row_stride
+
+    def stride(self, dim):
+        assert dim == 0
+        return self._row_stride
+
+    def data_ptr(self):
+        return 0x1000
+
+
+@pytest.mark.parametrize("row_stride", (16, 24), ids=("exact", "wider"))
+def test_scalar_launch_packs_the_table_row_stride(monkeypatch, row_stride):
+    """The scalar launch packs ``block_table.stride(0)``, not a stride derived
+    from ``max_seqlen_k``. 24 models a column view of a wider table: its rows
+    hold 16 slots, but the next row starts 24 slots on."""
+    launches = []
+    monkeypatch.setattr(au, "_RESOLVED_ATTENTION_ARCH", "gfx950")
+    monkeypatch.setattr(
+        au,
+        "_get_scalar_launcher",
+        lambda problem, key, *, arch: lambda vals, *, config: launches.append(
+            (vals, config)
+        ),
+    )
+    p = _BASE
+    blocks_per_seq = -(-p.max_seqlen_k // p.block_size)
+    au.run_unified_attention_torch(
+        problem=p,
+        q=_FakeArray((p.total_q, p.num_query_heads, p.head_size)),
+        k=_FakeArray((64, p.block_size, p.num_kv_heads, p.head_size)),
+        v=_FakeArray((64, p.block_size, p.num_kv_heads, p.head_size)),
+        out=_FakeArray((p.total_q, p.num_query_heads, p.head_size)),
+        cu_seqlens_q=_FakeArray((p.num_seqs + 1,)),
+        seqused_k=_FakeArray((p.num_seqs,)),
+        softmax_scale=0.125,
+        block_table=_FakeArray((p.num_seqs, blocks_per_seq), row_stride=row_stride),
+        softcap=0.0,
+        backend="scalar",
+    )
+    [(vals, config)] = launches
+    assert vals["block_table_stride"] == row_stride
+    assert config.grid == (p.total_q, p.num_query_heads, p.head_size)
diff --git a/dnn-providers/hip-kernel-provider/rocke/platform/CMakeLists.txt b/dnn-providers/hip-kernel-provider/rocke/platform/CMakeLists.txt
index 89da12902774..28daeecee08a 100644
--- a/dnn-providers/hip-kernel-provider/rocke/platform/CMakeLists.txt
+++ b/dnn-providers/hip-kernel-provider/rocke/platform/CMakeLists.txt
@@ -624,13 +624,15 @@ if(ROCKE_INSTALL_TESTS)
             "  TIMEOUT 180)\n")
 
     # Library attention CPU tests (no GPU required): gfx950 LDS budget model,
-    # dense block-order decodes, spec-wrapper parity gate, and the 3D decode split
-    # rule and K/V cache policy.
+    # dense block-order decodes, spec-wrapper parity gate, the 3D decode split rule
+    # and K/V cache policy, and the scalar fallback's cache key and block-table
+    # stride kernarg.
     # File-list style (same pattern as conv_pytest) so PYTHONPATH resolves rocke/
     # correctly from the installed tree.
     set(_ROCKE_LIBRARY_ATTENTION_CPU_TEST_RELPATHS
         "tests/library/tests/test_attention_dense_decode.py"
         "tests/library/tests/test_attention_dense_gfx950_lds.py"
+        "tests/library/tests/test_attention_scalar_cache_key.py"
         "tests/library/tests/test_attention_spec_predicate_parity.py"
         "tests/library/tests/test_attention_tiled_3d_decode_split.py"
         "tests/library/tests/test_attention_tiled_3d_kv_cache_policy.py")
@@ -829,6 +831,7 @@ if(ROCKE_INSTALL_TESTS)
         "tests/library/tests/test_conv_dgrad_fused_weights.py"
         "tests/library/tests/test_direct_mfma_dgrad_correctness.py"
         "tests/library/tests/test_attention_dense_gfx942_numeric.py"
+        "tests/library/tests/test_attention_scalar_block_table_numeric.py"
         "tests/library/tests/test_attention_unified_lse_numeric.py"
         "tests/library/tests/test_attn_bf16_d128_ring.py"
         "tests/library/tests/test_dtype_dispatch.py"
diff --git a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/include/rocke/helper_helper_rocke.instances.common.attention_unified.h b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/include/rocke/helper_helper_rocke.instances.common.attention_unified.h
index b795a85c52e0..d9bcb2a32967 100644
--- a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/include/rocke/helper_helper_rocke.instances.common.attention_unified.h
+++ b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/include/rocke/helper_helper_rocke.instances.common.attention_unified.h
@@ -13,7 +13,7 @@
  *   build_unified_attention_2d               rocke_build_unified_attention_2d_scalar
  *   UnifiedAttention2DSpec.kernel_name       rocke_unified_attention_2d_scalar_kernel_name
  *   (block_id grid of build_..._2d)          rocke_unified_attention_2d_scalar_grid
- *   _attn_signature(include_bt_stride=False) rocke_unified_attention_2d_scalar_signature
+ *   _attn_signature(include_bt_stride=True)  rocke_unified_attention_2d_scalar_signature
  *   build_unified_attention_3d               rocke_build_unified_attention_3d_scalar
  *   UnifiedAttention3DSpec.kernel_name       rocke_unified_attention_3d_scalar_kernel_name
  *   (block_id grid of build_..._3d)          rocke_unified_attention_3d_scalar_grid
@@ -123,7 +123,7 @@ rocke_kernel_def_t* rocke_build_unified_attention_2d_scalar(
  * extents = (total_q, num_query_heads, head_size). Writes out[0..2]. */
 void rocke_unified_attention_2d_scalar_grid(const rocke_unified_attention_problem_t* p, int out[3]);
 
-/* _attn_signature(dtype, include_bt_stride=False): the 2D scalar kernel ABI.
+/* _attn_signature(dtype, include_bt_stride=True): the 2D scalar kernel ABI.
  * On ROCKE_OK *out_items / *out_count hold the arena-owned array. */
 rocke_status_t
     rocke_unified_attention_2d_scalar_signature(const rocke_unified_attention_problem_t* p,
diff --git a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/include/rocke/helper_helper_rocke.instances.common.attention_unified_selectors.h b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/include/rocke/helper_helper_rocke.instances.common.attention_unified_selectors.h
index d59fd5bb26e8..39843a3938fb 100644
--- a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/include/rocke/helper_helper_rocke.instances.common.attention_unified_selectors.h
+++ b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/include/rocke/helper_helper_rocke.instances.common.attention_unified_selectors.h
@@ -267,22 +267,24 @@ bool rocke_unified_attn_segm_descriptors(rocke_ir_builder_t* b,
 
 /* ------------------------------------------------------- IR emit helpers */
 
-/* Python: _physical_block_and_token(b, p, block_tables, seq_idx, kpos)
+/* Python: _physical_block_and_token(b, p, block_tables, bt_stride, seq_idx, kpos)
  *   -> (physical, token_in_block).
  * (block_idx, token_in_block) = magic_div_mod(kpos, block_size);
- * max_blocks = ceil(max_seqlen_k / block_size);
- * physical = global_load_i32(block_tables, seq_idx*max_blocks + block_idx).
+ * physical = global_load_i32(block_tables, seq_idx*bt_stride + block_idx).
+ * bt_stride is the block_table_stride kernarg (the table's row stride).
  * Writes *out_physical and *out_token_in_block; returns true on success. */
 bool rocke_unified_attn_physical_block_and_token(rocke_ir_builder_t* b,
                                                  const rocke_unified_attn_problem_t* p,
                                                  rocke_value_t* block_tables,
+                                                 rocke_value_t* bt_stride,
                                                  rocke_value_t* seq_idx,
                                                  rocke_value_t* kpos,
                                                  rocke_value_t** out_physical,
                                                  rocke_value_t** out_token_in_block);
 
-/* Python: _emit_qk_score(b, p, dtype, query, key, block_tables, seq_idx, q_tok,
- *                        q_head, kv_head, kpos, scale, rcp_ln2) -> Value.
+/* Python: _emit_qk_score(b, p, dtype, query, key, block_tables, bt_stride,
+ *                        seq_idx, q_tok, q_head, kv_head, kpos, scale,
+ *                        rcp_ln2) -> Value.
  * Per-(query_token, query_head) QK dot product for the scalar kernels (vec8 main
  * fold + scalar tail), scaled by scale*rcp_ln2. `dtype` is the IR element type
  * (the Python passes a Type). */
@@ -292,6 +294,7 @@ rocke_value_t* rocke_unified_attn_emit_qk_score(rocke_ir_builder_t* b,
                                                 rocke_value_t* query,
                                                 rocke_value_t* key,
                                                 rocke_value_t* block_tables,
+                                                rocke_value_t* bt_stride,
                                                 rocke_value_t* seq_idx,
                                                 rocke_value_t* q_tok,
                                                 rocke_value_t* q_head,
@@ -300,14 +303,15 @@ rocke_value_t* rocke_unified_attn_emit_qk_score(rocke_ir_builder_t* b,
                                                 rocke_value_t* scale,
                                                 rocke_value_t* rcp_ln2);
 
-/* Python: _emit_v_load(b, p, dtype, value, block_tables, seq_idx, kv_head, kpos,
- *                      dim) -> Value.
+/* Python: _emit_v_load(b, p, dtype, value, block_tables, bt_stride, seq_idx,
+ *                      kv_head, kpos, dim) -> Value.
  * Loads V[physical, token_in_block, kv_head, dim] and casts to f32. */
 rocke_value_t* rocke_unified_attn_emit_v_load(rocke_ir_builder_t* b,
                                               const rocke_unified_attn_problem_t* p,
                                               const rocke_type_t* dtype,
                                               rocke_value_t* value,
                                               rocke_value_t* block_tables,
+                                              rocke_value_t* bt_stride,
                                               rocke_value_t* seq_idx,
                                               rocke_value_t* kv_head,
                                               rocke_value_t* kpos,
diff --git a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/include/rocke/instance_attention_unified.h b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/include/rocke/instance_attention_unified.h
index 206ec293390d..44eb78e40ed7 100644
--- a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/include/rocke/instance_attention_unified.h
+++ b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/include/rocke/instance_attention_unified.h
@@ -36,7 +36,7 @@
  *   build_unified_attention_reduce(spec)         rocke_build_unified_attention_reduce_scalar
  *   *.kernel_name()                              rocke_unified_attention_*_kernel_name
  *   (block_id grid extents)                      rocke_unified_attention_*_grid
- *   _attn_signature(include_bt_stride=False)     rocke_unified_attention_2d_scalar_signature
+ *   _attn_signature(include_bt_stride=True)      rocke_unified_attention_2d_scalar_signature
  *   supports_native_unified_attention(problem)   rocke_unified_attention_supports_scalar
  *   (build -> lower .ll convenience, NEW)        rocke_unified_attention_*_lower_to_llvm
  *
diff --git a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/include/rocke/instance_attention_unified_internal.h b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/include/rocke/instance_attention_unified_internal.h
index 67d0128605c5..13f7aaf61705 100644
--- a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/include/rocke/instance_attention_unified_internal.h
+++ b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/include/rocke/instance_attention_unified_internal.h
@@ -108,7 +108,6 @@ typedef struct rocke_attn_unified_build_ctx
     const rocke_type_t* dtype; /* spec.dtype_ir (F16 / BF16)    */
     int num_segments; /* 3D/reduce; 0 for 2D           */
     int num_queries_per_kv; /* p->num_query_heads / kv       */
-    int max_blocks; /* ceil(max_seqlen_k/block_size) */
 
     /* ---------- kernel result + extra outputs ---------- */
     rocke_kernel_def_t* kernel; /* == b->kernel; returned by the driver          */
@@ -139,11 +138,12 @@ typedef struct rocke_attn_unified_build_ctx
     rocke_value_t* abi_v_scale; /* "v_scale"              (F32)                */
 
     /* ---------- kernel-specific tail params (after the ABI prefix) ----------
-     * 2D appends out_scale, softcap, num_seqs. 3D appends softcap (unused) +
-     * num_seqs. reduce appends none. */
+     * 2D appends out_scale, softcap, num_seqs, block_table_stride. 3D appends
+     * softcap (unused), num_seqs, block_table_stride. reduce appends none. */
     rocke_value_t* out_scale; /* 2D "out_scale" (F32; unused by body)        */
     rocke_value_t* softcap; /* 2D/3D "softcap" (F32; 3D unused)            */
     rocke_value_t* num_seqs; /* 2D/3D "num_seqs" (I32)                       */
+    rocke_value_t* block_table_stride; /* 2D/3D "block_table_stride" (I32)  */
 
     /* ---------- grid ids + thread predicate (prologue) ---------- */
     rocke_value_t* q_tok; /* block_id_x()                                 */
@@ -209,9 +209,8 @@ typedef struct rocke_attn_unified_build_ctx
  * ============================================================ */
 
 /* Zero-init the ctx and copy in the problem/spec slice (b, kind, p, sel_p,
- * dtype, num_segments) + derive num_queries_per_kv / max_blocks. On a non-
- * divisible head ratio or unsupported dtype, sets b's sticky error and returns
- * false. */
+ * dtype, num_segments) + derive num_queries_per_kv. On a non-divisible head
+ * ratio or unsupported dtype, sets b's sticky error and returns false. */
 bool rocke_attn_unified_ctx_init(rocke_attn_unified_build_ctx_t* ctx,
                                  rocke_ir_builder_t* b,
                                  rocke_attn_unified_kind_t kind,
diff --git a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_2d.cpp b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_2d.cpp
index 4d5ac35da835..6389cbb01ad2 100644
--- a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_2d.cpp
+++ b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_2d.cpp
@@ -115,20 +115,18 @@ void rocke_attn_unified_emit_2d_softmax_loop(rocke_attn_unified_build_ctx_t* ctx
         rocke_value_t* l_val = loop.iter_vars[1];
         rocke_value_t* acc_val = loop.iter_vars[2];
 
-        /* block_idx, token_in_block = _magic_div_mod(b, kpos, p.block_size) */
-        rocke_value_t* block_idx = NULL;
+        /* physical, token_in_block = _physical_block_and_token(
+         *     b, p, block_tables, bt_stride, seq_idx, kpos) */
+        rocke_value_t* physical = NULL;
         rocke_value_t* token_in_block = NULL;
-        (void)rocke_unified_attn_magic_div_mod(b, kpos, p->block_size, &block_idx, &token_in_block);
-
-        /* physical = global_load_i32(block_tables,
-         *     seq_idx * ceil(max_seqlen_k/block_size) + block_idx) */
-        int max_blocks_2d = (p->max_seqlen_k + p->block_size - 1) / p->block_size;
-        rocke_value_t* physical = rocke_b_global_load_i32(
-            b,
-            ctx->abi_block_tables,
-            rocke_b_add(
-                b, rocke_b_mul(b, ctx->seq_idx, rocke_b_const_i32(b, max_blocks_2d)), block_idx),
-            /*align=*/4);
+        (void)rocke_unified_attn_physical_block_and_token(b,
+                                                          &ctx->sel_p,
+                                                          ctx->abi_block_tables,
+                                                          ctx->block_table_stride,
+                                                          ctx->seq_idx,
+                                                          kpos,
+                                                          &physical,
+                                                          &token_in_block);
 
         /* Vectorised QK dot product (vec8 main fold + scalar tail). */
         rocke_value_t* score = ctx->zero_f;
diff --git a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_3d_reduce.cpp b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_3d_reduce.cpp
index df051acff079..569c16653ef9 100644
--- a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_3d_reduce.cpp
+++ b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_3d_reduce.cpp
@@ -152,6 +152,7 @@ void rocke_attn_unified_emit_3d_segment_loop(rocke_attn_unified_build_ctx_t* ctx
                                              ctx->abi_query,
                                              ctx->abi_key,
                                              ctx->abi_block_tables,
+                                             ctx->block_table_stride,
                                              ctx->seq_idx,
                                              ctx->q_tok,
                                              ctx->q_head,
@@ -174,6 +175,7 @@ void rocke_attn_unified_emit_3d_segment_loop(rocke_attn_unified_build_ctx_t* ctx
                                         ctx->dtype,
                                         ctx->abi_value,
                                         ctx->abi_block_tables,
+                                        ctx->block_table_stride,
                                         ctx->seq_idx,
                                         ctx->kv_head,
                                         kpos,
diff --git a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_entry.cpp b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_entry.cpp
index fcbdc10959d4..1a68425e5cbb 100644
--- a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_entry.cpp
+++ b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_entry.cpp
@@ -17,7 +17,7 @@
  *   - the three launch grid fns
  *       rocke_unified_attention_{2d,3d,reduce}_scalar_grid
  *   - the 2D signature
- *       rocke_unified_attention_2d_scalar_signature  (_attn_signature, no bt_stride)
+ *       rocke_unified_attention_2d_scalar_signature  (_attn_signature, with bt_stride)
  *   - the three build drivers, each ctx_init -> phase functions in Python
  *     execution order (see instance_attention_unified_internal.h)
  *       rocke_build_unified_attention_{2d,3d,reduce}_scalar
@@ -188,25 +188,24 @@ bool rocke_unified_attention_supports_scalar(const rocke_unified_attention_probl
 rocke_status_t rocke_unified_attention_2d_scalar_kernel_name(
     const rocke_unified_attention_problem_t* p, const char* name, char* out, size_t out_cap)
 {
-    char qbuf[32], hbuf[32], kvbuf[32], dbuf[32], bbuf[32];
+    char hbuf[32], kvbuf[32], dbuf[32], bbuf[32];
     const char* prefix;
     if(p == NULL || out == NULL)
         return ROCKE_ERR_VALUE;
     prefix = name ? name : "rocke_unified_attention_2d_scalar";
 
-    snprintf(qbuf, sizeof(qbuf), "q%d", p->total_q);
     snprintf(hbuf, sizeof(hbuf), "h%d", p->num_query_heads);
     snprintf(kvbuf, sizeof(kvbuf), "kv%d", p->num_kv_heads);
     snprintf(dbuf, sizeof(dbuf), "d%d", p->head_size);
     snprintf(bbuf, sizeof(bbuf), "b%d", p->block_size);
 
     {
-        const char* parts[6] = {qbuf, hbuf, kvbuf, dbuf, bbuf, p->dtype ? p->dtype : ""};
+        const char* parts[5] = {hbuf, kvbuf, dbuf, bbuf, p->dtype ? p->dtype : ""};
         /* flags={"sink":use_sinks,"sw":sliding_window>0,"softcap":softcap>0} */
         const char* flag_names[3] = {"sink", "sw", "softcap"};
         int flag_on[3]
             = {p->use_sinks ? 1 : 0, p->sliding_window > 0 ? 1 : 0, p->softcap > 0.0 ? 1 : 0};
-        return rocke_kernel_name_join(prefix, parts, 6, flag_names, flag_on, 3, out, out_cap, NULL);
+        return rocke_kernel_name_join(prefix, parts, 5, flag_names, flag_on, 3, out, out_cap, NULL);
     }
 }
 
@@ -217,13 +216,12 @@ rocke_status_t
                                                   char* out,
                                                   size_t out_cap)
 {
-    char qbuf[32], hbuf[32], kvbuf[32], dbuf[32], bbuf[32], segbuf[32];
+    char hbuf[32], kvbuf[32], dbuf[32], bbuf[32], segbuf[32];
     const char* prefix;
     if(p == NULL || out == NULL)
         return ROCKE_ERR_VALUE;
     prefix = name ? name : "rocke_unified_attention_3d_scalar";
 
-    snprintf(qbuf, sizeof(qbuf), "q%d", p->total_q);
     snprintf(hbuf, sizeof(hbuf), "h%d", p->num_query_heads);
     snprintf(kvbuf, sizeof(kvbuf), "kv%d", p->num_kv_heads);
     snprintf(dbuf, sizeof(dbuf), "d%d", p->head_size);
@@ -231,8 +229,8 @@ rocke_status_t
     snprintf(segbuf, sizeof(segbuf), "seg%d", num_segments);
 
     {
-        const char* parts[7] = {qbuf, hbuf, kvbuf, dbuf, bbuf, segbuf, p->dtype ? p->dtype : ""};
-        return rocke_kernel_name_join(prefix, parts, 7, NULL, NULL, 0, out, out_cap, NULL);
+        const char* parts[6] = {hbuf, kvbuf, dbuf, bbuf, segbuf, p->dtype ? p->dtype : ""};
+        return rocke_kernel_name_join(prefix, parts, 6, NULL, NULL, 0, out, out_cap, NULL);
     }
 }
 
@@ -243,20 +241,19 @@ rocke_status_t
                                                       char* out,
                                                       size_t out_cap)
 {
-    char qbuf[32], hbuf[32], dbuf[32], segbuf[32];
+    char hbuf[32], dbuf[32], segbuf[32];
     const char* prefix;
     if(p == NULL || out == NULL)
         return ROCKE_ERR_VALUE;
     prefix = name ? name : "rocke_unified_attention_reduce_scalar";
 
-    snprintf(qbuf, sizeof(qbuf), "q%d", p->total_q);
     snprintf(hbuf, sizeof(hbuf), "h%d", p->num_query_heads);
     snprintf(dbuf, sizeof(dbuf), "d%d", p->head_size);
     snprintf(segbuf, sizeof(segbuf), "seg%d", num_segments);
 
     {
-        const char* parts[5] = {qbuf, hbuf, dbuf, segbuf, p->dtype ? p->dtype : ""};
-        return rocke_kernel_name_join(prefix, parts, 5, NULL, NULL, 0, out, out_cap, NULL);
+        const char* parts[4] = {hbuf, dbuf, segbuf, p->dtype ? p->dtype : ""};
+        return rocke_kernel_name_join(prefix, parts, 4, NULL, NULL, 0, out, out_cap, NULL);
     }
 }
 
@@ -295,7 +292,7 @@ void rocke_unified_attention_reduce_scalar_grid(const rocke_unified_attention_pr
 }
 
 /* ===================================================================== *
- *  2D signature -- _attn_signature(dtype, include_bt_stride=False)
+ *  2D signature -- _attn_signature(dtype, include_bt_stride=True)
  * ===================================================================== */
 
 rocke_status_t
@@ -331,7 +328,8 @@ rocke_status_t
     rocke_signature_builder_scalar(&sb, "out_scale", "f32");
     rocke_signature_builder_scalar(&sb, "softcap", "f32");
     rocke_signature_builder_scalar(&sb, "num_seqs", "i32");
-    /* include_bt_stride=False, include_qq_bias_stride=False => no tail scalars */
+    rocke_signature_builder_scalar(&sb, "block_table_stride", "i32");
+    /* include_qq_bias_stride=False => no qq_bias_stride_0 tail scalar */
 
     return rocke_signature_builder_build(&sb, out_items, out_count);
 }
@@ -341,9 +339,9 @@ rocke_status_t
  *
  *  ctx_init -> declare_scalar_params (+ 2D tail params) -> emit_find_seq_idx ->
  *  emit_prologue -> emit_2d_softmax_loop -> emit_2d_epilogue -> ctx.kernel.
- *  The 2D tail params (out_scale, softcap, num_seqs) follow the shared ABI
- *  prefix; declare_scalar_params declares only the prefix, so the tail is
- *  appended here -- matching the Python build body's param order.
+ *  The 2D tail params (out_scale, softcap, num_seqs, block_table_stride) follow
+ *  the shared ABI prefix; declare_scalar_params declares only the prefix, so
+ *  the tail is appended here, matching the Python build body's param order.
  * ===================================================================== */
 rocke_kernel_def_t* rocke_build_unified_attention_2d_scalar(
     rocke_ir_builder_t* b, const rocke_unified_attention_problem_t* p, const char* name)
@@ -382,10 +380,11 @@ rocke_kernel_def_t* rocke_build_unified_attention_2d_scalar(
         /* abi = _declare_scalar_attn_params(b, dtype) */
         rocke_attn_unified_declare_scalar_params(&ctx);
 
-        /* 2D tail params: out_scale, softcap, num_seqs. */
+        /* 2D tail params: out_scale, softcap, num_seqs, block_table_stride. */
         ctx.out_scale = rocke_b_param(b, "out_scale", rocke_f32(), NULL);
         ctx.softcap = rocke_b_param(b, "softcap", rocke_f32(), NULL);
         ctx.num_seqs = rocke_b_param(b, "num_seqs", rocke_i32(), NULL);
+        ctx.block_table_stride = rocke_b_param(b, "block_table_stride", rocke_i32(), NULL);
 
         /* prologue: grid ids + seq-idx scan + per-seq geometry + SSA constants.
          * The seq-idx scan reads q_tok, which emit_prologue computes first; the
@@ -450,9 +449,10 @@ rocke_kernel_def_t*
     /* abi = _declare_scalar_attn_params(b, dtype) */
     rocke_attn_unified_declare_scalar_params(&ctx);
 
-    /* 3D tail params: softcap (unused by body) + num_seqs. */
+    /* 3D tail params: softcap (unused by body), num_seqs, block_table_stride. */
     ctx.softcap = rocke_b_param(b, "softcap", rocke_f32(), NULL);
     ctx.num_seqs = rocke_b_param(b, "num_seqs", rocke_i32(), NULL);
+    ctx.block_table_stride = rocke_b_param(b, "block_table_stride", rocke_i32(), NULL);
 
     /* emit_prologue runs the seq-idx scan internally after computing q_tok;
      * do not call emit_find_seq_idx here (q_tok would still be NULL). */
diff --git a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_prologue.cpp b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_prologue.cpp
index 564a490e4558..c6f6ad3f9796 100644
--- a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_prologue.cpp
+++ b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_prologue.cpp
@@ -40,8 +40,7 @@
  *      if p.dtype not in ("fp16", "bf16"): raise ValueError(...)   (2D only)
  *      dtype = spec.dtype_ir
  *  plus deriving num_queries_per_kv (= p.num_query_heads // num_kv_heads, which
- *  raises on a non-divisible ratio) and max_blocks
- *  (= ceil(max_seqlen_k / block_size)).
+ *  raises on a non-divisible ratio).
  *
  *  Zero-inits the ctx, copies the problem/spec slice into it, mirrors `p` into
  *  the selector-helper problem shape (ctx->sel_p) so the ported emit/descriptor
@@ -122,18 +121,6 @@ bool rocke_attn_unified_ctx_init(rocke_attn_unified_build_ctx_t* ctx,
         return false;
     }
 
-    /* max_blocks = ceil(max_seqlen_k / block_size). Python folds this inline as
-     * (p.max_seqlen_k + p.block_size - 1) // p.block_size when computing the
-     * block_tables row stride. */
-    if(p->block_size > 0)
-    {
-        ctx->max_blocks = (p->max_seqlen_k + p->block_size - 1) / p->block_size;
-    }
-    else
-    {
-        ctx->max_blocks = 0;
-    }
-
     return true;
 }
 
diff --git a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_selectors.cpp b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_selectors.cpp
index c015d734ad53..b838a8afc6f7 100644
--- a/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_selectors.cpp
+++ b/dnn-providers/hip-kernel-provider/rocke/platform/cpp/instances/common/attention_unified_selectors.cpp
@@ -1306,6 +1306,7 @@ bool rocke_unified_attn_segm_descriptors(rocke_ir_builder_t* b,
 bool rocke_unified_attn_physical_block_and_token(rocke_ir_builder_t* b,
                                                  const rocke_unified_attn_problem_t* p,
                                                  rocke_value_t* block_tables,
+                                                 rocke_value_t* bt_stride,
                                                  rocke_value_t* seq_idx,
                                                  rocke_value_t* kpos,
                                                  rocke_value_t** out_physical,
@@ -1313,19 +1314,17 @@ bool rocke_unified_attn_physical_block_and_token(rocke_ir_builder_t* b,
 {
     rocke_value_t* block_idx = NULL;
     rocke_value_t* token_in_block = NULL;
-    int max_blocks;
     rocke_value_t* physical;
 
     if(!rocke_unified_attn_magic_div_mod(b, kpos, p->block_size, &block_idx, &token_in_block))
     {
         return false;
     }
-    max_blocks = (p->max_seqlen_k + p->block_size - 1) / p->block_size;
-    physical = rocke_b_global_load_i32(
-        b,
-        block_tables,
-        rocke_b_add(b, rocke_b_mul(b, seq_idx, rocke_b_const_i32(b, max_blocks)), block_idx),
-        0 /* align default */);
+    physical
+        = rocke_b_global_load_i32(b,
+                                  block_tables,
+                                  rocke_b_add(b, rocke_b_mul(b, seq_idx, bt_stride), block_idx),
+                                  0 /* align default */);
 
     if(out_physical != NULL)
     {
@@ -1344,6 +1343,7 @@ rocke_value_t* rocke_unified_attn_emit_qk_score(rocke_ir_builder_t* b,
                                                 rocke_value_t* query,
                                                 rocke_value_t* key,
                                                 rocke_value_t* block_tables,
+                                                rocke_value_t* bt_stride,
                                                 rocke_value_t* seq_idx,
                                                 rocke_value_t* q_tok,
                                                 rocke_value_t* q_head,
@@ -1365,7 +1365,7 @@ rocke_value_t* rocke_unified_attn_emit_qk_score(rocke_ir_builder_t* b,
     int d;
 
     if(!rocke_unified_attn_physical_block_and_token(
-           b, p, block_tables, seq_idx, kpos, &physical, &token_in_block))
+           b, p, block_tables, bt_stride, seq_idx, kpos, &physical, &token_in_block))
     {
         return NULL;
     }
@@ -1444,6 +1444,7 @@ rocke_value_t* rocke_unified_attn_emit_v_load(rocke_ir_builder_t* b,
                                               const rocke_type_t* dtype,
                                               rocke_value_t* value,
                                               rocke_value_t* block_tables,
+                                              rocke_value_t* bt_stride,
                                               rocke_value_t* seq_idx,
                                               rocke_value_t* kv_head,
                                               rocke_value_t* kpos,
@@ -1455,7 +1456,7 @@ rocke_value_t* rocke_unified_attn_emit_v_load(rocke_ir_builder_t* b,
     rocke_value_t* v_off;
 
     if(!rocke_unified_attn_physical_block_and_token(
-           b, p, block_tables, seq_idx, kpos, &physical, &token_in_block))
+           b, p, block_tables, bt_stride, seq_idx, kpos, &physical, &token_in_block))
     {
         return NULL;
     }
diff --git a/dnn-providers/hip-kernel-provider/rocke/platform/tests/golden/rocke_representative_ir_sha256.json b/dnn-providers/hip-kernel-provider/rocke/platform/tests/golden/rocke_representative_ir_sha256.json
index b3afb40ec9e3..a83c506a92a0 100644
--- a/dnn-providers/hip-kernel-provider/rocke/platform/tests/golden/rocke_representative_ir_sha256.json
+++ b/dnn-providers/hip-kernel-provider/rocke/platform/tests/golden/rocke_representative_ir_sha256.json
@@ -260,58 +260,58 @@
         },
         "attention/gfx950/scalar2d_fp16_d128_no_mask": {
           "arch": "gfx950",
-          "bytes": 46260,
+          "bytes": 46297,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_2d_scalar_q64_h4_kv2_d128_b16_fp16_rbu",
-          "sha256": "534453293b4d4318daf0a9fad16ddf298a7ffaf8dbbfeb9cc901f269d46f4765",
+          "kernel_name": "rocke_unified_attention_2d_scalar_h4_kv2_d128_b16_fp16_rbu",
+          "sha256": "3f9a03e5ce27d736bfd18e922729a750d674374d64efba79c997e91f3057a323",
           "status": "ok"
         },
         "attention/gfx950/scalar2d_fp16_d128_q40_kv8": {
           "arch": "gfx950",
-          "bytes": 46293,
+          "bytes": 46330,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_2d_scalar_q64_h40_kv8_d128_b16_fp16",
-          "sha256": "f542181c3d28ec098300697fe0c61323706a6ce2f788b3728bce99fad908acd9",
+          "kernel_name": "rocke_unified_attention_2d_scalar_h40_kv8_d128_b16_fp16",
+          "sha256": "6bb8ac9d58bce9f3aacc6dafbebeb93b9312d860cdbfbe99c0f181bb5f6fb8e6",
           "status": "ok"
         },
         "attention/gfx950/scalar2d_fp16_d128_sw16_rb8": {
           "arch": "gfx950",
-          "bytes": 46594,
+          "bytes": 46631,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_2d_scalar_q64_h4_kv2_d128_b16_fp16_sw_rb8",
-          "sha256": "d81575919d468f5c88b915fac219132c9808b5fa2ccdab6031f7d868c0489c03",
+          "kernel_name": "rocke_unified_attention_2d_scalar_h4_kv2_d128_b16_fp16_sw_rb8",
+          "sha256": "1b0fd719499c69cbb6672949b471bf52ff9f5f14fc6402ef3e97a485643c8111",
           "status": "ok"
         },
         "attention/gfx950/scalar2d_fp16_d128_top_left": {
           "arch": "gfx950",
-          "bytes": 46364,
+          "bytes": 46401,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_2d_scalar_q64_h4_kv2_d128_b16_fp16_tl",
-          "sha256": "e8104b7de52d1e84b12f28a0e836dcee13477021397d3b6dbea935c1a296a335",
+          "kernel_name": "rocke_unified_attention_2d_scalar_h4_kv2_d128_b16_fp16_tl",
+          "sha256": "4e9da57dfd9249086c6aeeeb9baefd731f50941b94a029ab26967ea4eea364ae",
           "status": "ok"
         },
         "attention/gfx950/scalar3d_fp16_d128_q40_kv8": {
           "arch": "gfx950",
-          "bytes": 48783,
+          "bytes": 48834,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_3d_scalar_q64_h40_kv8_d128_b16_seg8_fp16",
-          "sha256": "9804fc433bba54dffb78e7f36f8f303d185a9401dc93b05d1da75e9b304077d1",
+          "kernel_name": "rocke_unified_attention_3d_scalar_h40_kv8_d128_b16_seg8_fp16",
+          "sha256": "41dcdeed861eb19399f523ec4715c28d48eb8fbc1622c0e4bd1e58e9f51135aa",
           "status": "ok"
         },
         "attention/gfx950/scalar3d_fp16_d128_sw16_rb8": {
           "arch": "gfx950",
-          "bytes": 49095,
+          "bytes": 49146,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_3d_scalar_q64_h4_kv2_d128_b16_seg8_fp16_rb8_sw",
-          "sha256": "5d8bf24697392fca54352be7717cfeac566b5d6dd1ed8df23f7b652cdf8a7357",
+          "kernel_name": "rocke_unified_attention_3d_scalar_h4_kv2_d128_b16_seg8_fp16_rb8_sw",
+          "sha256": "7e27fcad07b5406d73e4ca69e37cecd70fb4694e2ba7d0b0c421a705e50d9575",
           "status": "ok"
         },
         "attention/gfx950/scalar3d_fp16_d128_top_left": {
           "arch": "gfx950",
-          "bytes": 48855,
+          "bytes": 48908,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_3d_scalar_q64_h4_kv2_d128_b16_seg8_fp16_tl",
-          "sha256": "49a72d16ffa227df568072c50f77647261cb8b8023669fcec8321c76fd9a6429",
+          "kernel_name": "rocke_unified_attention_3d_scalar_h4_kv2_d128_b16_seg8_fp16_tl",
+          "sha256": "5427e7f6a7e14f59bd67d240872d1ca8d05b7818b3443edda3b57c9949874cab",
           "status": "ok"
         },
         "attention_d128_swa/gfx942/4warp_gqa_fold": {
@@ -2649,58 +2649,58 @@
         },
         "attention/gfx950/scalar2d_fp16_d128_no_mask": {
           "arch": "gfx950",
-          "bytes": 46267,
+          "bytes": 46304,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_2d_scalar_q64_h4_kv2_d128_b16_fp16_rbu",
-          "sha256": "51fe41aaaa3623be88d35d880007c86f723d1821ae0d32a511a3bbe530c682c4",
+          "kernel_name": "rocke_unified_attention_2d_scalar_h4_kv2_d128_b16_fp16_rbu",
+          "sha256": "7bfb21eb2170edf8d4256e5cba6726a66fbcafa8915278bd59ad710de5b3e33e",
           "status": "ok"
         },
         "attention/gfx950/scalar2d_fp16_d128_q40_kv8": {
           "arch": "gfx950",
-          "bytes": 46300,
+          "bytes": 46337,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_2d_scalar_q64_h40_kv8_d128_b16_fp16",
-          "sha256": "946bac7a0332cf806cbc23efe6128d7e8a147a7de5542ce27bd3d239af24d4e9",
+          "kernel_name": "rocke_unified_attention_2d_scalar_h40_kv8_d128_b16_fp16",
+          "sha256": "e44040c1a0ca888c9265a7179849e9817bb4c86453d1ac4f17ffd6d878f6e00c",
           "status": "ok"
         },
         "attention/gfx950/scalar2d_fp16_d128_sw16_rb8": {
           "arch": "gfx950",
-          "bytes": 46601,
+          "bytes": 46638,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_2d_scalar_q64_h4_kv2_d128_b16_fp16_sw_rb8",
-          "sha256": "1acd98d1834d52cec79c2a70bd09572d96d168423fdc6a11a1deac4e95901916",
+          "kernel_name": "rocke_unified_attention_2d_scalar_h4_kv2_d128_b16_fp16_sw_rb8",
+          "sha256": "9d2cd3e99bbb6496c4b592fe0b207d57d8010a5519492c22be25f7103d035187",
           "status": "ok"
         },
         "attention/gfx950/scalar2d_fp16_d128_top_left": {
           "arch": "gfx950",
-          "bytes": 46371,
+          "bytes": 46408,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_2d_scalar_q64_h4_kv2_d128_b16_fp16_tl",
-          "sha256": "56f3f2d5411ee663955e06e799c0be8770248af65df1d5dc7fede2ea8ff048be",
+          "kernel_name": "rocke_unified_attention_2d_scalar_h4_kv2_d128_b16_fp16_tl",
+          "sha256": "4ee7f6b39f7d0f87e5d06dff0041f97c72a0179398047310bc29eeb70b734a73",
           "status": "ok"
         },
         "attention/gfx950/scalar3d_fp16_d128_q40_kv8": {
           "arch": "gfx950",
-          "bytes": 48790,
+          "bytes": 48841,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_3d_scalar_q64_h40_kv8_d128_b16_seg8_fp16",
-          "sha256": "188f9394d8d01d0f00aea78d2172ea2836bac7612e7f6a338d6ccd04f73a0151",
+          "kernel_name": "rocke_unified_attention_3d_scalar_h40_kv8_d128_b16_seg8_fp16",
+          "sha256": "1d905ab5c0eba1cb417cfa4cf3b6c43dfb0316a4fa9c7e23aab2a2dfbff2089d",
           "status": "ok"
         },
         "attention/gfx950/scalar3d_fp16_d128_sw16_rb8": {
           "arch": "gfx950",
-          "bytes": 49102,
+          "bytes": 49153,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_3d_scalar_q64_h4_kv2_d128_b16_seg8_fp16_rb8_sw",
-          "sha256": "1e75fb5a2c80f393ec5687d943261ba4ee4afa9304e2fb6f143a1e1accc3fd07",
+          "kernel_name": "rocke_unified_attention_3d_scalar_h4_kv2_d128_b16_seg8_fp16_rb8_sw",
+          "sha256": "dec69e6c8561b0b8143243e254531dd0224320d1a63123b2f54208c001d66415",
           "status": "ok"
         },
         "attention/gfx950/scalar3d_fp16_d128_top_left": {
           "arch": "gfx950",
-          "bytes": 48862,
+          "bytes": 48915,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_3d_scalar_q64_h4_kv2_d128_b16_seg8_fp16_tl",
-          "sha256": "99353476cc9677d60f117ba680c708c996663844c0bb4b51f2a16308948a65ba",
+          "kernel_name": "rocke_unified_attention_3d_scalar_h4_kv2_d128_b16_seg8_fp16_tl",
+          "sha256": "a3ec8c6fbc92410df68aeb62edc5589c354f898242d2e3334c55b9b2df8ab7f3",
           "status": "ok"
         },
         "attention_d128_swa/gfx942/4warp_gqa_fold": {
@@ -5038,58 +5038,58 @@
         },
         "attention/gfx950/scalar2d_fp16_d128_no_mask": {
           "arch": "gfx950",
-          "bytes": 46331,
+          "bytes": 46368,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_2d_scalar_q64_h4_kv2_d128_b16_fp16_rbu",
-          "sha256": "1439195339b0bf240984da6a0e706798211e6266b0effecd21c730e056221d71",
+          "kernel_name": "rocke_unified_attention_2d_scalar_h4_kv2_d128_b16_fp16_rbu",
+          "sha256": "75a15fc5580b38822ceb1c20b63d62f4e807e8e7e2bfff87c0268a19f55e7b4d",
           "status": "ok"
         },
         "attention/gfx950/scalar2d_fp16_d128_q40_kv8": {
           "arch": "gfx950",
-          "bytes": 46364,
+          "bytes": 46401,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_2d_scalar_q64_h40_kv8_d128_b16_fp16",
-          "sha256": "7a80904e488e71aa45b5a983b3ca207e411f4797b21e8b68bc1a5ef0e5a2772f",
+          "kernel_name": "rocke_unified_attention_2d_scalar_h40_kv8_d128_b16_fp16",
+          "sha256": "e58b5d9f581c300df4066d6baa7bfd0b3a6180c50a18f4fe34baaff875e79dc3",
           "status": "ok"
         },
         "attention/gfx950/scalar2d_fp16_d128_sw16_rb8": {
           "arch": "gfx950",
-          "bytes": 46665,
+          "bytes": 46702,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_2d_scalar_q64_h4_kv2_d128_b16_fp16_sw_rb8",
-          "sha256": "06b90e3c577d968c02ac6171c6f0b7bda0d798b36c2059818111baa0b8994965",
+          "kernel_name": "rocke_unified_attention_2d_scalar_h4_kv2_d128_b16_fp16_sw_rb8",
+          "sha256": "3507ed5f852025708ebc67b95e6831bca37d2937c336e030c05793a17755e8d7",
           "status": "ok"
         },
         "attention/gfx950/scalar2d_fp16_d128_top_left": {
           "arch": "gfx950",
-          "bytes": 46435,
+          "bytes": 46472,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_2d_scalar_q64_h4_kv2_d128_b16_fp16_tl",
-          "sha256": "076fcd4b4e1f618f5c75534cba8333098b03497e5393d157543433476bb3d158",
+          "kernel_name": "rocke_unified_attention_2d_scalar_h4_kv2_d128_b16_fp16_tl",
+          "sha256": "af239152736fb1333cf0b27337db79f7389af7b0eace59060696a629a3a1f75d",
           "status": "ok"
         },
         "attention/gfx950/scalar3d_fp16_d128_q40_kv8": {
           "arch": "gfx950",
-          "bytes": 48854,
+          "bytes": 48905,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_3d_scalar_q64_h40_kv8_d128_b16_seg8_fp16",
-          "sha256": "f2041465d5d9e8128b2ab20a34dc1baaaad76b9b818b6c69c3f64c590b1e0a37",
+          "kernel_name": "rocke_unified_attention_3d_scalar_h40_kv8_d128_b16_seg8_fp16",
+          "sha256": "888055e6c6a30f6d67cd73d46d375e28e1ab48eae8d3c71a1106e6a971d8de89",
           "status": "ok"
         },
         "attention/gfx950/scalar3d_fp16_d128_sw16_rb8": {
           "arch": "gfx950",
-          "bytes": 49166,
+          "bytes": 49217,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_3d_scalar_q64_h4_kv2_d128_b16_seg8_fp16_rb8_sw",
-          "sha256": "0b5f59549c2dba463219d00aea1c016c547479fe1c5a3ea51daccdf3c8973ee1",
+          "kernel_name": "rocke_unified_attention_3d_scalar_h4_kv2_d128_b16_seg8_fp16_rb8_sw",
+          "sha256": "68805f1e75cd1e88e20e82aeb8727e8fb12c873041d76b3f4c10423a79716cc4",
           "status": "ok"
         },
         "attention/gfx950/scalar3d_fp16_d128_top_left": {
           "arch": "gfx950",
-          "bytes": 48926,
+          "bytes": 48979,
           "family": "attention",
-          "kernel_name": "rocke_unified_attention_3d_scalar_q64_h4_kv2_d128_b16_seg8_fp16_tl",
-          "sha256": "1b55556afefa9291e98a2a07cea73a144f8f9be96fb1790e0d3d3ab2c690d7ad",
+          "kernel_name": "rocke_unified_attention_3d_scalar_h4_kv2_d128_b16_seg8_fp16_tl",
+          "sha256": "6f3dc2facda457fc1f91e1c50f6edd2ecdd08f03b7a767c85be21a66eb132589",
           "status": "ok"
         },
         "attention_d128_swa/gfx942/4warp_gqa_fold": {
diff --git a/dnn-providers/hip-kernel-provider/rocke/platform/tests/instances/differential/golden/llvm_gfx_all.json b/dnn-providers/hip-kernel-provider/rocke/platform/tests/instances/differential/golden/llvm_gfx_all.json
index 168a95b7ce71..7215500602ed 100644
--- a/dnn-providers/hip-kernel-provider/rocke/platform/tests/instances/differential/golden/llvm_gfx_all.json
+++ b/dnn-providers/hip-kernel-provider/rocke/platform/tests/instances/differential/golden/llvm_gfx_all.json
@@ -33,46 +33,46 @@
       "9": "b932c7ec5c473d26f9c5d3364d503eba33b0d916d517114842dff4352274241e"
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@@ -1766,46 +1766,46 @@
         "9": "7866c9eb6d414e0edf3678a477c387e2742057eed6a96616544f29057d609d0a"
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@@ -3411,46 +3411,46 @@
         "9": "43cbf9fc03696e3fcf0a132e31a769d92cd48267eaea3526e2cc7c46c35225da"
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       "attention_unified": {
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@@ -5056,46 +5056,46 @@
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