[Bugfix] Use null block (0) for padded block table entries (#35431)

Signed-off-by: SandishKumarHN <sandish@fb.com>
Signed-off-by: Matthew Bonanni <mbonanni@redhat.com>
Co-authored-by: Lucas Wilkinson <LucasWilkinson@users.noreply.github.com>
Co-authored-by: Matthew Bonanni <mbonanni@redhat.com>

csrc/mamba/mamba_ssm/selective_scan.h

@@ -21,7 +21,7 @@ struct SSMParamsBase {
     int dim_ngroups_ratio;
     bool is_variable_B;
     bool is_variable_C;
-    int64_t pad_slot_id;
+    int64_t null_block_id;
 
     bool delta_softplus;
     bool cache_enabled;

csrc/mamba/mamba_ssm/selective_scan_fwd.cu

@@ -118,9 +118,17 @@ void selective_scan_fwd_kernel(SSMParamsBase params) {
 
     const int* cache_indices = params.cache_indices_ptr == nullptr ? nullptr
         : reinterpret_cast<int *>(params.cache_indices_ptr);
-    const int cache_index = cache_indices == nullptr ? batch_id : cache_indices[batch_id]; 
-    // cache_index == params.pad_slot_id is defined as padding, so we exit early
-    if (cache_index == params.pad_slot_id){
+    int cache_index;
+    if (cache_indices == nullptr) {
+        cache_index = batch_id;
+    } else if (params.cache_enabled) {
+        const int* initial_state_idx = reinterpret_cast<const int*>(params.initial_state_idx_ptr);
+        cache_index = cache_indices[batch_id * params.cache_indices_stride + initial_state_idx[batch_id]];
+    } else {
+        cache_index = cache_indices[batch_id];
+    }
+    // Skip batch entries whose cache index maps to the null block (padding).
+    if (cache_indices != nullptr && cache_index == params.null_block_id){
         return;
     }
     input_t *u = reinterpret_cast<input_t *>(params.u_ptr) + sequence_start_index * params.u_batch_stride
@@ -527,7 +535,7 @@ void set_ssm_params_fwd(SSMParamsBase &params,
                         const std::optional<at::Tensor>& cache_indices,
                         const std::optional<at::Tensor>& has_initial_state,
                         bool varlen,
-                        int64_t pad_slot_id,
+                        int64_t null_block_id,
                         int64_t block_size,
                         const std::optional<torch::Tensor> &block_idx_first_scheduled_token,
                         const std::optional<torch::Tensor> &block_idx_last_scheduled_token,
@@ -544,7 +552,7 @@ void set_ssm_params_fwd(SSMParamsBase &params,
     params.dstate = dstate;
     params.n_groups = n_groups;
     params.dim_ngroups_ratio = dim / n_groups;
-    params.pad_slot_id = pad_slot_id;
+    params.null_block_id = null_block_id;
 
     params.delta_softplus = delta_softplus;
 
@@ -658,7 +666,7 @@ void selective_scan_fwd(const torch::Tensor &u, const torch::Tensor &delta,
                   const torch::Tensor &ssm_states,
                   // used to identify padding entries if cache_indices provided
                   // in case of padding, the kernel will return early
-                  int64_t pad_slot_id,
+                  int64_t null_block_id,
                   int64_t block_size,
                   const std::optional<torch::Tensor> &block_idx_first_scheduled_token,
                   const std::optional<torch::Tensor> &block_idx_last_scheduled_token,
@@ -805,7 +813,7 @@ void selective_scan_fwd(const torch::Tensor &u, const torch::Tensor &delta,
                        cache_indices,
                        has_initial_state,
                        varlen,
-                       pad_slot_id,
+                       null_block_id,
                        block_size,
                        block_idx_first_scheduled_token,
                        block_idx_last_scheduled_token,

csrc/ops.h

@@ -298,7 +298,7 @@ void selective_scan_fwd(
     const std::optional<torch::Tensor>& query_start_loc,
     const std::optional<torch::Tensor>& cache_indices,
     const std::optional<torch::Tensor>& has_initial_state,
-    const torch::Tensor& ssm_states, int64_t pad_slot_id, int64_t block_size,
+    const torch::Tensor& ssm_states, int64_t null_block_id, int64_t block_size,
     const std::optional<torch::Tensor>& block_idx_first_scheduled_token,
     const std::optional<torch::Tensor>& block_idx_last_scheduled_token,
     const std::optional<torch::Tensor>& initial_state_idx,

csrc/torch_bindings.cpp

@@ -556,7 +556,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "Tensor? cache_indices,"
       "Tensor? has_initial_state,"
       "Tensor! ssm_states,"
-      "int pad_slot_id,"
+      "int null_block_id,"
       "int block_size,"
       "Tensor? block_idx_first_scheduled_token,"
       "Tensor? block_idx_last_scheduled_token,"

tests/kernels/mamba/test_causal_conv1d.py

@@ -12,7 +12,7 @@ from vllm.model_executor.layers.mamba.ops.causal_conv1d import (
     causal_conv1d_update,
 )
 from vllm.utils.torch_utils import set_random_seed
-from vllm.v1.attention.backends.utils import PAD_SLOT_ID
+from vllm.v1.attention.backends.utils import NULL_BLOCK_ID
 
 
 def causal_conv1d_ref(
@@ -122,7 +122,7 @@ def causal_conv1d_opcheck_fn(
     has_initial_state: torch.Tensor | None = None,
     conv_states: torch.Tensor | None = None,
     activation: str | None = "silu",
-    pad_slot_id: int = PAD_SLOT_ID,
+    null_block_id: int = NULL_BLOCK_ID,
 ):
     """
     x: (batch, dim, seqlen)
@@ -158,15 +158,16 @@ def test_causal_conv1d_update(dim, width, seqlen, has_bias, silu_activation, ity
     batch = 2
     x = torch.randn(batch, dim, seqlen, device=device, dtype=itype)
     x_ref = x.clone()
-    conv_state = torch.randn(batch, dim, width - 1, device=device, dtype=itype)
+    # +1 entry to reserve index 0 as null block
+    conv_state = torch.randn(batch + 1, dim, width - 1, device=device, dtype=itype)
 
     weight = torch.randn(dim, width, device=device, dtype=itype)
     bias = torch.randn(dim, device=device, dtype=itype) if has_bias else None
-    conv_state_ref = conv_state.detach().clone()
+    # Start indices from 1, skipping null block at index 0
+    conv_state_indices = torch.arange(1, batch + 1, dtype=torch.int32, device=device)
+    conv_state_ref = conv_state[conv_state_indices].detach().clone()
     activation = None if not silu_activation else "silu"
 
-    conv_state_indices = torch.arange(batch, dtype=torch.int32, device=device)
-
     out = causal_conv1d_update(
         x,
         conv_state,
@@ -179,7 +180,7 @@ def test_causal_conv1d_update(dim, width, seqlen, has_bias, silu_activation, ity
         x_ref, conv_state_ref, weight, bias, activation=activation
     )
 
-    assert torch.equal(conv_state, conv_state_ref)
+    assert torch.equal(conv_state[conv_state_indices], conv_state_ref)
     assert torch.allclose(out, out_ref, rtol=rtol, atol=atol)
 
 
@@ -215,7 +216,8 @@ def test_causal_conv1d_update_with_batch_gather(
 
     x_ref = x.clone()
 
-    conv_state_indices = torch.randperm(total_entries)[:batch_size].to(
+    # +1 to exclude index 0 (null block)
+    conv_state_indices = (torch.randperm(total_entries - 1)[:batch_size] + 1).to(
         dtype=torch.int32, device=device
     )
     unused_states_bool = torch.ones(total_entries, dtype=torch.bool, device=device)
@@ -223,7 +225,9 @@ def test_causal_conv1d_update_with_batch_gather(
     padded_state_indices = torch.concat(
         [
             conv_state_indices,
-            torch.as_tensor([PAD_SLOT_ID] * padding, dtype=torch.int32, device=device),
+            torch.as_tensor(
+                [NULL_BLOCK_ID] * padding, dtype=torch.int32, device=device
+            ),
         ],
         dim=0,
     )
@@ -248,7 +252,6 @@ def test_causal_conv1d_update_with_batch_gather(
         bias,
         activation=activation,
         conv_state_indices=padded_state_indices,
-        pad_slot_id=PAD_SLOT_ID,
     )
     out_ref = causal_conv1d_update_ref(
         x_ref[:batch_size], conv_state_ref, weight, bias, activation=activation
@@ -317,13 +320,19 @@ def test_causal_conv1d_varlen(
     has_initial_states = torch.randint(
         0, 2, (cumsum.shape[0] - 1,), dtype=torch.bool, device=x.device
     )
-    state_indices = torch.randperm(total_entries, dtype=torch.int32, device=x.device)[
+    # +1 to exclude index 0 (null block)
+    state_indices = (
+        torch.randperm(total_entries - 1, dtype=torch.int32, device=x.device)[
             :batch_size
         ]
+        + 1
+    )
     padded_state_indices = torch.concat(
         [
             state_indices,
-            torch.as_tensor([PAD_SLOT_ID] * padding, dtype=torch.int32, device=device),
+            torch.as_tensor(
+                [NULL_BLOCK_ID] * padding, dtype=torch.int32, device=device
+            ),
         ],
         dim=-1,
     )
@@ -336,7 +345,6 @@ def test_causal_conv1d_varlen(
         cache_indices=padded_state_indices,
         has_initial_state=has_initial_states,
         activation=activation,
-        pad_slot_id=PAD_SLOT_ID,
     )
 
     out_ref = []
@@ -345,7 +353,7 @@ def test_causal_conv1d_varlen(
     splits = [torch.split(var, seqlens[0], dim=-1) for var in (x_ref)]
     for i in range(len(seqlens[0])):
         x_s = [v[i].unsqueeze(0) for v in splits][0]
-        if padded_state_indices[i] == PAD_SLOT_ID:
+        if padded_state_indices[i] == NULL_BLOCK_ID:
             continue
         out_ref_b.append(
             causal_conv1d_ref(

tests/kernels/mamba/test_mamba_ssm.py

@@ -14,7 +14,7 @@ from vllm.model_executor.layers.mamba.ops.mamba_ssm import (
 )
 from vllm.platforms import current_platform
 from vllm.utils.torch_utils import set_random_seed
-from vllm.v1.attention.backends.utils import PAD_SLOT_ID
+from vllm.v1.attention.backends.utils import NULL_BLOCK_ID
 
 
 def selective_state_update_ref(
@@ -179,7 +179,7 @@ def selective_scan_opcheck_fn(
     cache_indices=None,
     has_initial_state=None,
     ssm_states=None,
-    pad_slot_id=PAD_SLOT_ID,
+    null_block_id=NULL_BLOCK_ID,
     block_size=2048,
     block_idx_first_scheduled_token=None,
     block_idx_last_scheduled_token=None,
@@ -229,7 +229,7 @@ def selective_scan_opcheck_fn(
             cache_indices,
             has_initial_state,
             ssm_states,
-            pad_slot_id,
+            null_block_id,
             block_size,
             block_idx_first_scheduled_token,
             block_idx_last_scheduled_token,
@@ -351,7 +351,6 @@ def test_selective_scan(
             has_initial_state=torch.ones(batch_size, device=u.device, dtype=torch.bool)
             if c > 0
             else None,
-            pad_slot_id=PAD_SLOT_ID,
             block_size=2048,
             block_idx_first_scheduled_token=None,
             block_idx_last_scheduled_token=None,
@@ -652,15 +651,21 @@ def test_selective_scan_varlen(
         prev_state_shape, device=u.device, dtype=itype, requires_grad=False
     )
     prev_state_ref = prev_state.clone()
-    state_indices = torch.randperm(total_entries, dtype=torch.int32, device=u.device)[
+    # +1 to exclude index 0 (null block)
+    state_indices = (
+        torch.randperm(total_entries - 1, dtype=torch.int32, device=u.device)[
             :batch_size
         ]
+        + 1
+    )
     unused_states_bool = torch.ones(total_entries, dtype=torch.bool, device=device)
     unused_states_bool[state_indices] = False
     padded_state_indices = torch.concat(
         [
             state_indices,
-            torch.as_tensor([PAD_SLOT_ID] * padding, dtype=torch.int32, device=device),
+            torch.as_tensor(
+                [NULL_BLOCK_ID] * padding, dtype=torch.int32, device=device
+            ),
         ],
         dim=-1,
     )
@@ -690,7 +695,7 @@ def test_selective_scan_varlen(
     ]
     for i in range(len(seqlens[0])):
         u_s, delta_s, B_s, C_s, z_s = (v[i].unsqueeze(0) for v in splits)
-        if padded_state_indices[i] == PAD_SLOT_ID:
+        if padded_state_indices[i] == NULL_BLOCK_ID:
             continue
         out_ref_s, _ = selective_scan_ref(
             u_s,
@@ -758,7 +763,8 @@ def test_selective_state_update_with_batch_indices(
     padded_batch_size = batch_size + padding
     total_entries = 10 * batch_size
     state = torch.randn(total_entries, dim, dstate, dtype=itype, device=device)
-    state_indices = torch.randperm(total_entries)[:batch_size].to(
+    # +1 to exclude index 0 (null block)
+    state_indices = (torch.randperm(total_entries - 1)[:batch_size] + 1).to(
         dtype=torch.int32, device=device
     )
     unused_states_bool = torch.ones(total_entries, dtype=torch.bool, device=device)
@@ -766,7 +772,9 @@ def test_selective_state_update_with_batch_indices(
     padded_state_indices = torch.concat(
         [
             state_indices,
-            torch.as_tensor([PAD_SLOT_ID] * padding, dtype=torch.int32, device=device),
+            torch.as_tensor(
+                [NULL_BLOCK_ID] * padding, dtype=torch.int32, device=device
+            ),
         ],
         dim=0,
     )
@@ -793,7 +801,6 @@ def test_selective_state_update_with_batch_indices(
         dt_bias=dt_bias,
         dt_softplus=True,
         state_batch_indices=padded_state_indices,
-        pad_slot_id=PAD_SLOT_ID,
         out=out,
     )
     out_ref = selective_state_update_ref(
@@ -849,7 +856,8 @@ def test_selective_state_update_with_heads_with_batch_indices(
     state = torch.randn(
         total_entries, nheads, headdim, dstate, dtype=itype, device=device
     )
-    state_indices = torch.randperm(total_entries)[:batch_size].to(
+    # +1 to exclude index 0 (null block)
+    state_indices = (torch.randperm(total_entries - 1)[:batch_size] + 1).to(
         dtype=torch.int32, device=device
     )
 
@@ -887,7 +895,6 @@ def test_selective_state_update_with_heads_with_batch_indices(
         dt_bias=dt_bias,
         dt_softplus=True,
         state_batch_indices=state_indices,
-        pad_slot_id=PAD_SLOT_ID,
         out=out,
     )
     out_ref = selective_state_update_ref(
@@ -935,17 +942,18 @@ def test_selective_state_update_with_num_accepted_tokens(
     state = torch.randn(total_state_slots, dim, dstate, dtype=itype, device=device)
 
     state_batch_indices = torch.full(
-        (batch_size, max_seq_len), PAD_SLOT_ID, dtype=torch.int32, device=device
+        (batch_size, max_seq_len), NULL_BLOCK_ID, dtype=torch.int32, device=device
     )
+    # Start from 1 to exclude null block at index 0
     initial_state_slots = torch.randint(
-        0, 15, (batch_size,), device=device, dtype=torch.int32
+        1, 15, (batch_size,), device=device, dtype=torch.int32
     )
     for seq_idx in range(batch_size):
         token_pos = max(num_accepted_tokens[seq_idx].item() - 1, 0)
         state_batch_indices[seq_idx, token_pos] = initial_state_slots[seq_idx]
 
     dst_state_batch_indices = torch.full(
-        (batch_size, max_seq_len), PAD_SLOT_ID, dtype=torch.int32, device=device
+        (batch_size, max_seq_len), NULL_BLOCK_ID, dtype=torch.int32, device=device
     )
     slot_offset = 15
     dst_slots_map = {}
@@ -1013,7 +1021,6 @@ def test_selective_state_update_with_num_accepted_tokens(
         state_batch_indices=state_batch_indices,
         dst_state_batch_indices=dst_state_batch_indices,
         num_accepted_tokens=num_accepted_tokens,
-        pad_slot_id=PAD_SLOT_ID,
     )
 
     assert torch.allclose(out, out_ref, rtol=rtol, atol=atol)
@@ -1061,18 +1068,19 @@ def test_selective_state_update_varlen_with_num_accepted(
     state = torch.randn(total_state_slots, dim, dstate, dtype=itype, device=device)
 
     state_batch_indices = torch.full(
-        (batch_size, max_seq_len), PAD_SLOT_ID, dtype=torch.int32, device=device
+        (batch_size, max_seq_len), NULL_BLOCK_ID, dtype=torch.int32, device=device
     )
 
+    # Start from 1 to exclude null block at index 0
     initial_state_slots = torch.randint(
-        0, 15, (batch_size,), device=device, dtype=torch.int32
+        1, 15, (batch_size,), device=device, dtype=torch.int32
     )
     for seq_idx in range(batch_size):
         token_pos = max(num_accepted_tokens[seq_idx].item() - 1, 0)
         state_batch_indices[seq_idx, token_pos] = initial_state_slots[seq_idx]
 
     dst_state_batch_indices = torch.full(
-        (batch_size, max_seq_len), PAD_SLOT_ID, dtype=torch.int32, device=device
+        (batch_size, max_seq_len), NULL_BLOCK_ID, dtype=torch.int32, device=device
     )
 
     slot_offset = 15
@@ -1138,7 +1146,6 @@ def test_selective_state_update_varlen_with_num_accepted(
         state_batch_indices=state_batch_indices,
         dst_state_batch_indices=dst_state_batch_indices,
         num_accepted_tokens=num_accepted_tokens,
-        pad_slot_id=PAD_SLOT_ID,
     )
 
     for seq_idx in range(batch_size):

vllm/_custom_ops.py

@@ -2062,7 +2062,7 @@ def selective_scan_fwd(
     cache_indices: torch.Tensor | None,
     has_initial_state: torch.Tensor | None,
     ssm_states: torch.Tensor,
-    pad_slot_id: int,
+    null_block_id: int,
     block_size: int = 1024,
     block_idx_first_scheduled_token: torch.Tensor | None = None,
     block_idx_last_scheduled_token: torch.Tensor | None = None,
@@ -2084,7 +2084,7 @@ def selective_scan_fwd(
         cache_indices,
         has_initial_state,
         ssm_states,
-        pad_slot_id,
+        null_block_id,
         block_size,
         block_idx_first_scheduled_token,
         block_idx_last_scheduled_token,

vllm/model_executor/layers/lightning_attn.py

@@ -5,6 +5,7 @@ import torch
 from einops import rearrange
 
 from vllm.triton_utils import tl, triton
+from vllm.v1.attention.backends.utils import PAD_SLOT_ID
 
 
 @triton.jit
@@ -602,6 +603,7 @@ def _linear_attn_decode_kernel(
     cache_h_stride,
     cache_d0_stride,
     cache_d1_stride,
+    pad_slot_id: tl.constexpr,
     BLOCK_SIZE: tl.constexpr,
 ):
     """
@@ -616,8 +618,8 @@ def _linear_attn_decode_kernel(
     # Load slot index for the current batch
     slot_id = tl.load(slot_idx + pid_b).to(tl.int64)
 
-    # Skip if slot_id is -1 (padding)
-    if slot_id == -1:
+    # Skip if slot_id is PAD_SLOT_ID (padding)
+    if slot_id == pad_slot_id:
         return
 
     batch_id = pid_b
@@ -727,6 +729,7 @@ def linear_decode_forward_triton(
         cache_h_stride,
         cache_d0_stride,
         cache_d1_stride,
+        pad_slot_id=PAD_SLOT_ID,
         BLOCK_SIZE=BLOCK_SIZE,
     )
 

vllm/model_executor/layers/mamba/ops/causal_conv1d.py

@@ -9,7 +9,7 @@ import numpy as np
 import torch
 
 from vllm.triton_utils import tl, triton
-from vllm.v1.attention.backends.utils import PAD_SLOT_ID
+from vllm.v1.attention.backends.utils import NULL_BLOCK_ID, PAD_SLOT_ID
 
 
 @triton.jit()
@@ -49,12 +49,13 @@ def _causal_conv1d_fwd_kernel(  # continuous batching
     stride_block_m: tl.constexpr,  # Stride block to align divided by BLOCK_M
     # others
     pad_slot_id: tl.constexpr,
+    null_block_id: tl.constexpr,
     # Meta-parameters
     HAS_BIAS: tl.constexpr,
     KERNEL_WIDTH: tl.constexpr,
     SILU_ACTIVATION: tl.constexpr,
     IS_APC_ENABLED: tl.constexpr,
-    USE_PAD_SLOT: tl.constexpr,
+    HAS_NULL_BLOCK: tl.constexpr,
     NP2_STATELEN: tl.constexpr,
     BLOCK_M: tl.constexpr,
     BLOCK_N: tl.constexpr,
@@ -133,9 +134,9 @@ def _causal_conv1d_fwd_kernel(  # continuous batching
         conv_state_indices_ptr + idx_seq * stride_cache_indices + conv_state_init_index
     ).to(tl.int64)
 
-    if USE_PAD_SLOT:  # noqa
-        if conv_states_input_coord == pad_slot_id:
-            # not processing as this is not the actual sequence
+    if HAS_NULL_BLOCK:  # noqa
+        if conv_states_input_coord == null_block_id:
+            # not processing as this is a null block (padding)
             return
     conv_states_base = (
         conv_states_ptr
@@ -475,6 +476,7 @@ def causal_conv1d_fn(
     has_initial_state: torch.Tensor | None = None,
     activation: str | None = "silu",
     pad_slot_id: int = PAD_SLOT_ID,
+    null_block_id: int = NULL_BLOCK_ID,
     block_idx_first_scheduled_token: torch.Tensor | None = None,
     block_idx_last_scheduled_token: torch.Tensor | None = None,
     initial_state_idx: torch.Tensor | None = None,
@@ -730,12 +732,13 @@ def causal_conv1d_fn(
         block_size_to_align // BLOCK_M,
         # others
         pad_slot_id,
+        null_block_id,
         # META
         HAS_BIAS=bias is not None,
         KERNEL_WIDTH=width,
         SILU_ACTIVATION=activation in ["silu", "swish"],
         IS_APC_ENABLED=block_idx_last_scheduled_token is not None,
-        USE_PAD_SLOT=pad_slot_id is not None,
+        HAS_NULL_BLOCK=null_block_id is not None,
         NP2_STATELEN=np2_statelen,
         # launch_cooperative_grid=True
         BLOCK_M=BLOCK_M,
@@ -778,7 +781,7 @@ def _causal_conv1d_update_kernel(
     stride_o_dim: tl.constexpr,
     stride_o_token: tl.constexpr,
     # others
-    pad_slot_id: tl.constexpr,
+    null_block_id: tl.constexpr,
     # Meta-parameters
     HAS_BIAS: tl.constexpr,
     KERNEL_WIDTH: tl.constexpr,
@@ -787,7 +790,7 @@ def _causal_conv1d_update_kernel(
     IS_APC_ENABLED: tl.constexpr,
     IS_SPEC_DECODING: tl.constexpr,
     NP2_STATELEN: tl.constexpr,
-    USE_PAD_SLOT: tl.constexpr,
+    HAS_NULL_BLOCK: tl.constexpr,
     BLOCK_N: tl.constexpr,
 ):
     # ruff: noqa: E501
@@ -811,8 +814,8 @@ def _causal_conv1d_update_kernel(
         conv_state_indices_ptr + idx_seq * stride_state_indices + conv_state_init
     ).to(tl.int64)
 
-    if USE_PAD_SLOT:  # noqa
-        if conv_states_input_coord == pad_slot_id:
+    if HAS_NULL_BLOCK:  # noqa
+        if conv_states_input_coord == null_block_id:
             # not processing as this is not the actual sequence
             return
 
@@ -1076,7 +1079,7 @@ def causal_conv1d_update(
     num_accepted_tokens: torch.Tensor | None = None,
     query_start_loc: torch.Tensor | None = None,
     max_query_len: int = -1,
-    pad_slot_id: int = PAD_SLOT_ID,
+    null_block_id: int = NULL_BLOCK_ID,
     block_idx_last_scheduled_token: torch.Tensor | None = None,
     initial_state_idx: torch.Tensor | None = None,
     validate_data=False,
@@ -1111,16 +1114,16 @@ def causal_conv1d_update(
     max_query_len: int
         If query_start_loc is not None, this indicates the maximum query
         length in the batch.
-    pad_slot_id: int
-            if conv_state_indices is passed, lets the kernel identify padded
-            entries that will not be processed,
-            for example: conv_state_indices = [pad_slot_id, 1 ,20 ,pad_slot_id]
+    null_block_id: int
+            Block ID used to identify padded entries in
+            conv_state_indices. Block 0 is the null block.
+            for example: conv_state_indices = [null_block_id, 1, 20, null_block_id]
             in this case, the kernel will not process entries at
             indices 0 and 3
     out: (batch, dim) or (batch, dim, seqlen) or (num_tokens, dim), same shape as `x`
     """
     if validate_data:
-        assert pad_slot_id is not None
+        assert null_block_id is not None
         assert x.stride(1) == 1
     if isinstance(activation, bool):
         activation = "silu" if activation is True else None
@@ -1225,7 +1228,7 @@ def causal_conv1d_update(
         stride_o_dim,
         stride_o_token,
         # others
-        pad_slot_id,
+        null_block_id,
         # META
         HAS_BIAS=bias is not None,
         KERNEL_WIDTH=width,
@@ -1234,7 +1237,7 @@ def causal_conv1d_update(
         IS_APC_ENABLED=block_idx_last_scheduled_token is not None,
         IS_SPEC_DECODING=num_accepted_tokens is not None,
         NP2_STATELEN=np2_statelen,
-        USE_PAD_SLOT=pad_slot_id is not None,
+        HAS_NULL_BLOCK=null_block_id is not None,
         BLOCK_N=256,
     )
     if unsqueeze:

vllm/model_executor/layers/mamba/ops/mamba_ssm.py

@@ -10,7 +10,7 @@ from packaging import version
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.mamba.ops.triton_helpers import fast_exp
 from vllm.triton_utils import HAS_TRITON, tl, triton
-from vllm.v1.attention.backends.utils import PAD_SLOT_ID
+from vllm.v1.attention.backends.utils import NULL_BLOCK_ID
 
 TRITON3 = HAS_TRITON and (version.parse(triton.__version__) >= version.parse("3.0.0"))
 
@@ -75,7 +75,7 @@ def _selective_scan_update_kernel(
     out_ptr,
     state_batch_indices_ptr,
     dst_state_batch_indices_ptr,
-    pad_slot_id,
+    null_block_id,
     num_accepted_tokens_ptr,
     cu_seqlens_ptr,
     # Matrix dimensions
@@ -203,7 +203,7 @@ def _selective_scan_update_kernel(
 
     mask = (offs_m[:, None] < dim) & (offs_n[None, :] < dstate)
     if HAS_STATE_BATCH_INDICES:
-        mask &= state_batch_idx != pad_slot_id
+        mask &= state_batch_idx != null_block_id
     state = tl.load(state_ptrs, mask=mask, other=0.0).to(tl.float32)
 
     if HAS_DT_BIAS:
@@ -257,7 +257,7 @@ def _selective_scan_update_kernel(
         if IS_SPEC_DECODING:
             dst_idx_ptr = dst_state_batch_indices_ptr + i_t * stride_dst_state_indices_T
             token_dst_idx = tl.load(dst_idx_ptr).to(tl.int64)
-            if token_dst_idx != pad_slot_id:
+            if token_dst_idx != null_block_id:
                 token_dst_ptrs = (
                     state_ptr_base
                     + token_dst_idx * stride_state_batch
@@ -329,7 +329,7 @@ def selective_state_update(
     dt_softplus=False,
     state_batch_indices=None,
     dst_state_batch_indices=None,
-    pad_slot_id=PAD_SLOT_ID,
+    null_block_id=NULL_BLOCK_ID,
     out=None,
     num_accepted_tokens=None,
     cu_seqlens=None,
@@ -348,12 +348,12 @@ def selective_state_update(
         D: (dim,) or (nheads, dim)
         z: (batch, dim) or (batch, nheads, dim)
         dt_bias: (dim,) or (nheads, dim)
-        pad_slot_id: int
-            if cache_indices is passed, lets the kernel identify padded
-            entries that will not be processed,
-            for example: cache_indices = [pad_slot_id, 1, 20, pad_slot_id]
-            in this case, the kernel will not process entries at
-            indices 0 and 3
+        null_block_id: int
+            if state_batch_indices is passed, lets the kernel identify
+            padded entries that will not be processed,
+            for example: state_batch_indices = [null_block_id, 1, 20,
+            null_block_id] in this case, the kernel will not process
+            entries at indices 0 and 3
         out: Preallocated ssm output tensor. Assume same shape as x.
              In-place updated.
         num_accepted_tokens: (batch,)
@@ -488,7 +488,7 @@ def selective_state_update(
             out,
             state_batch_indices,
             dst_state_batch_indices,
-            pad_slot_id,
+            null_block_id,
             num_accepted_tokens,
             cu_seqlens,
             N,
@@ -550,7 +550,7 @@ def selective_scan_fn(
     query_start_loc=None,
     cache_indices=None,
     has_initial_state=None,
-    pad_slot_id=PAD_SLOT_ID,
+    null_block_id=NULL_BLOCK_ID,
     block_size=1024,
     block_idx_first_scheduled_token=None,
     block_idx_last_scheduled_token=None,
@@ -588,10 +588,10 @@ def selective_scan_fn(
         indicate if the ssm_state at the corresponding index should be
         used as initial state. Not providing argument assumes
         there's no initial state
-    pad_slot_id: int
+    null_block_id: int
         if cache_indices is passed, lets the kernel identify padding entries
         that will not be processed,
-        for example: cache_indices = [pad_slot_id, 1 ,20 ,pad_slot_id]
+        for example: cache_indices = [null_block_id, 1 ,20 ,null_block_id]
         in this case, the kernel will not process entries at indices 0 and 3
     block_size: int
         The block size to align the cached states to
@@ -643,7 +643,7 @@ def selective_scan_fn(
         cache_indices,
         has_initial_state,
         ssm_states,
-        pad_slot_id,
+        null_block_id,
         block_size,
         block_idx_first_scheduled_token,
         block_idx_last_scheduled_token,

vllm/v1/attention/backends/gdn_attn.py

@@ -14,7 +14,7 @@ from vllm.v1.attention.backend import (
     CommonAttentionMetadata,
 )
 from vllm.v1.attention.backends.utils import (
-    PAD_SLOT_ID,
+    NULL_BLOCK_ID,
     compute_causal_conv1d_metadata,
     mamba_get_block_table_tensor,
     split_decodes_and_prefills,
@@ -341,7 +341,7 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
                 spec_state_indices_tensor, non_blocking=True
             )
             spec_state_indices_tensor = self.spec_state_indices_tensor[:batch_size]
-            spec_state_indices_tensor[num_spec_decodes:].fill_(PAD_SLOT_ID)
+            spec_state_indices_tensor[num_spec_decodes:].fill_(NULL_BLOCK_ID)
 
             self.spec_sequence_masks[:num_spec_decodes].copy_(
                 spec_sequence_masks[:num_spec_decodes], non_blocking=True
@@ -387,7 +387,7 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
             non_spec_state_indices_tensor = self.non_spec_state_indices_tensor[
                 :batch_size
             ]
-            non_spec_state_indices_tensor[num_decodes:].fill_(PAD_SLOT_ID)
+            non_spec_state_indices_tensor[num_decodes:].fill_(NULL_BLOCK_ID)
 
             self.non_spec_query_start_loc[: num_decodes + 1].copy_(
                 non_spec_query_start_loc, non_blocking=True

vllm/v1/attention/backends/mamba_attn.py

@@ -15,7 +15,7 @@ from vllm.v1.attention.backend import (
     CommonAttentionMetadata,
 )
 from vllm.v1.attention.backends.utils import (
-    PAD_SLOT_ID,
+    NULL_BLOCK_ID,
     compute_causal_conv1d_metadata,
     mamba_get_block_table_tensor,
     split_decodes_and_prefills,
@@ -504,7 +504,7 @@ class BaseMambaAttentionMetadataBuilder(AttentionMetadataBuilder[M], abc.ABC):
                 state_indices_tensor_d, non_blocking=True
             )
             state_indices_tensor_d = self.state_indices_tensor_d[:padded_bs]
-            state_indices_tensor_d[metadata.num_decodes :] = PAD_SLOT_ID
+            state_indices_tensor_d[metadata.num_decodes :] = NULL_BLOCK_ID
 
             if self.use_spec_decode and num_accepted_tokens is not None:
                 assert query_start_loc_d is not None

vllm/v1/attention/backends/mla/indexer.py

@@ -366,12 +366,6 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
             seq_lens = common_attn_metadata.seq_lens[:num_decodes]
             block_table = common_attn_metadata.block_table_tensor[:num_decodes, ...]
 
-            # Padded CUDA graph requests have block_table entries of -1.
-            # Clamp to 0 to prevent OOB access in the DeepGEMM kernel.
-            # This is safe because padded requests have seq_lens=0, so the
-            # kernel produces no meaningful output for those rows.
-            block_table.clamp_(min=0)
-
             max_decode_len = int(decode_lens_cpu.max().item())
             next_n = 1 + self.num_speculative_tokens
             use_native = not self.use_flattening and max_decode_len == next_n

vllm/v1/attention/backends/utils.py

@@ -42,6 +42,7 @@ KVCacheLayoutType = Literal["NHD", "HND"]
 _KV_CACHE_LAYOUT_OVERRIDE: KVCacheLayoutType | None = None
 
 PAD_SLOT_ID = -1
+NULL_BLOCK_ID = 0
 
 
 def is_valid_kv_cache_layout(value: str) -> bool:

vllm/v1/worker/gpu_model_runner.py

@@ -122,6 +122,7 @@ from vllm.v1.attention.backend import (
 from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
 from vllm.v1.attention.backends.mamba2_attn import Mamba2AttentionMetadataBuilder
 from vllm.v1.attention.backends.utils import (
+    NULL_BLOCK_ID,
     create_fast_prefill_custom_backend,
     get_dcp_local_seq_lens,
     reorder_batch_to_split_decodes_and_prefills,
@@ -2135,9 +2136,9 @@ class GPUModelRunner(
                 blk_table = self.input_batch.block_table[kv_cache_gid]
                 blk_table_tensor = blk_table.get_device_tensor(num_reqs_padded)
 
-            # Fill unused with -1. Needed for reshape_and_cache in full cuda
-            # graph mode. `blk_table_tensor` -1 to match mamba PAD_SLOT_ID
-            blk_table_tensor[num_reqs:num_reqs_padded].fill_(-1)
+            # Fill unused block table entries with NULL_BLOCK_ID (null block)
+            # for CUDAGraph padding. Block 0 is reserved for padding.
+            blk_table_tensor[num_reqs:num_reqs_padded].fill_(NULL_BLOCK_ID)
             return blk_table_tensor
 
         assert slot_mappings is not None