[NemotronH] Small fix reasoning parser (#36635)

Signed-off-by: Roi Koren <roik@nvidia.com>
(cherry picked from commit e661b9ee83)

requirements/cuda.txt

@@ -11,6 +11,9 @@ torchaudio==2.10.0
 torchvision==0.25.0 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
 # FlashInfer should be updated together with the Dockerfile
 flashinfer-python==0.6.4
+# Cap nvidia-cudnn-frontend (transitive dep of flashinfer) due to
+# breaking changes in 1.19.0
+nvidia-cudnn-frontend>=1.13.0,<1.19.0
 
 # QuACK and Cutlass DSL for FA4 (cute-DSL implementation)
 nvidia-cutlass-dsl>=4.4.0.dev1

tests/reasoning/test_nemotron_v3_reasoning_parser.py

@@ -0,0 +1,172 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import TypedDict
+
+import pytest
+import regex as re
+
+from tests.reasoning.utils import run_reasoning_extraction
+from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
+from vllm.reasoning import ReasoningParser, ReasoningParserManager
+
+parser_name = "nemotron_v3"
+
+
+class ReasoningCase(TypedDict):
+    output: str
+    reasoning: str | None
+    content: str | None
+
+
+class FakeNemotronTokenizer:
+    def __init__(self):
+        self._vocab = {
+            "<think>": 1,
+            "</think>": 2,
+        }
+        self._pattern = re.compile(r"(<think>|</think>)")
+
+    def get_vocab(self) -> dict[str, int]:
+        return self._vocab
+
+    def tokenize(self, text: str) -> list[str]:
+        tokens: list[str] = []
+        for part in self._pattern.split(text):
+            if part:
+                tokens.append(part)
+        return tokens
+
+    def convert_tokens_to_string(self, tokens: list[str]) -> str:
+        return "".join(tokens)
+
+
+@pytest.fixture
+def tokenizer():
+    return FakeNemotronTokenizer()
+
+
+@pytest.mark.parametrize(
+    "streaming,param_dict",
+    [
+        pytest.param(
+            False,
+            {
+                "output": "This is a reasoning section</think>This is the rest",
+                "reasoning": "This is a reasoning section",
+                "content": "This is the rest",
+            },
+            id="without_start_token",
+        ),
+        pytest.param(
+            True,
+            {
+                "output": "This is a reasoning section</think>This is the rest",
+                "reasoning": "This is a reasoning section",
+                "content": "This is the rest",
+            },
+            id="without_start_token_streaming",
+        ),
+        pytest.param(
+            False,
+            {
+                "output": "<think>This is a reasoning section</think>This is the rest",
+                "reasoning": "This is a reasoning section",
+                "content": "This is the rest",
+            },
+            id="with_start_token",
+        ),
+        pytest.param(
+            True,
+            {
+                "output": "<think>This is a reasoning section</think>This is the rest",
+                "reasoning": "This is a reasoning section",
+                "content": "This is the rest",
+            },
+            id="with_start_token_streaming",
+        ),
+    ],
+)
+def test_nemotron_v3_reasoning(
+    tokenizer: FakeNemotronTokenizer,
+    streaming: bool,
+    param_dict: ReasoningCase,
+):
+    output = tokenizer.tokenize(param_dict["output"])
+    model_output = [tokenizer.convert_tokens_to_string([token]) for token in output]
+    parser: ReasoningParser = ReasoningParserManager.get_reasoning_parser(parser_name)(
+        tokenizer
+    )
+
+    reasoning, content = run_reasoning_extraction(
+        parser, model_output, streaming=streaming
+    )
+
+    assert reasoning == param_dict["reasoning"]
+    assert content == param_dict["content"]
+
+
+def test_nemotron_v3_without_thinking_returns_content(
+    tokenizer: FakeNemotronTokenizer,
+):
+    parser_cls = ReasoningParserManager.get_reasoning_parser(parser_name)
+    parser = parser_cls(tokenizer)
+    request = ChatCompletionRequest(
+        model="test-model",
+        messages=[],
+        chat_template_kwargs={"enable_thinking": False},
+    )
+
+    reasoning, content = run_reasoning_extraction(
+        parser,
+        ["This is plain content"],
+        request=request,
+        streaming=False,
+    )
+
+    assert reasoning is None
+    assert content == "This is plain content"
+
+
+def test_nemotron_v3_force_nonempty_content_returns_content(
+    tokenizer: FakeNemotronTokenizer,
+):
+    parser_cls = ReasoningParserManager.get_reasoning_parser(parser_name)
+    parser = parser_cls(tokenizer)
+    request = ChatCompletionRequest(
+        model="test-model",
+        messages=[],
+        chat_template_kwargs={"force_nonempty_content": True},
+    )
+
+    reasoning, content = run_reasoning_extraction(
+        parser,
+        ["<think>This is plain content"],
+        request=request,
+        streaming=False,
+    )
+
+    assert reasoning is None
+    assert content == "This is plain content"
+
+
+def test_nemotron_v3_with_thinking_keeps_truncated_reasoning(
+    tokenizer: FakeNemotronTokenizer,
+):
+    parser_cls = ReasoningParserManager.get_reasoning_parser(parser_name)
+    parser = parser_cls(tokenizer)
+    request = ChatCompletionRequest(
+        model="test-model",
+        messages=[],
+        chat_template_kwargs={"enable_thinking": True},
+    )
+
+    reasoning, content = run_reasoning_extraction(
+        parser,
+        ["This is truncated reasoning"],
+        request=request,
+        streaming=False,
+    )
+
+    assert reasoning == "This is truncated reasoning"
+    assert content is None

vllm/model_executor/layers/fused_moe/experts/trtllm_fp8_moe.py

@@ -35,12 +35,6 @@ class TrtLlmFp8Experts(mk.FusedMoEExpertsMonolithic):
     ):
         super().__init__(moe_config, quant_config)
 
-        if moe_config.moe_parallel_config.use_ep and quant_config.is_per_tensor:
-            raise NotImplementedError(
-                "EP parallelism is not supported with TRTLLM"
-                "per-tensor FP8 quantization."
-            )
-
         self.routing_method_type = moe_config.routing_method
         self.topk = moe_config.experts_per_token
         self.intermediate_size_per_partition = (
@@ -182,9 +176,6 @@ class TrtLlmFp8Experts(mk.FusedMoEExpertsMonolithic):
         assert not apply_router_weight_on_input
         assert activation == MoEActivation.SILU
 
-        if e_score_correction_bias is not None:
-            e_score_correction_bias = e_score_correction_bias.to(hidden_states.dtype)
-
         if self.routing_method_type == RoutingMethodType.DeepSeekV3:
             router_logits = router_logits.to(torch.float32)
 
@@ -240,12 +231,11 @@ class TrtLlmFp8Experts(mk.FusedMoEExpertsMonolithic):
     ) -> torch.Tensor:
         # Delay import for non-CUDA.
         import flashinfer
-        from flashinfer.fused_moe.core import ActivationType
 
         # Confirm supported activation function.
         assert activation in [MoEActivation.SILU, MoEActivation.RELU2_NO_MUL]
 
-        activation_type = ActivationType(activation_to_flashinfer_int(activation))
+        activation_type = activation_to_flashinfer_int(activation)
 
         # Confirm Llama-4 routing is proper.
         if self.routing_method_type == RoutingMethodType.Llama4:

vllm/model_executor/layers/fused_moe/experts/trtllm_nvfp4_moe.py

@@ -323,4 +323,5 @@ class TrtLlmNvFp4ExpertsMonolithic(
             routed_scaling_factor=routed_scaling_factor,
             routing_method_type=self.routing_method_type,
             do_finalize=True,
+            activation_type=activation_to_flashinfer_int(activation),
         )[0]

vllm/model_executor/layers/fused_moe/fused_batched_moe.py

@@ -912,7 +912,7 @@ class BatchedTritonExperts(mk.FusedMoEExpertsModular):
 
     @staticmethod
     def _supports_no_act_and_mul() -> bool:
-        return False
+        return True
 
     @staticmethod
     def _supports_quant_scheme(

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -1944,7 +1944,7 @@ class TritonExperts(mk.FusedMoEExpertsModular):
 
     @staticmethod
     def _supports_no_act_and_mul() -> bool:
-        return False
+        return True
 
     @staticmethod
     def _supports_quant_scheme(
@@ -1983,6 +1983,9 @@ class TritonExperts(mk.FusedMoEExpertsModular):
             MoEActivation.GELU,
             MoEActivation.SWIGLUOAI,
             MoEActivation.SWIGLUSTEP,
+            MoEActivation.SILU_NO_MUL,
+            MoEActivation.GELU_NO_MUL,
+            MoEActivation.RELU2_NO_MUL,
         ]
 
     @staticmethod

vllm/reasoning/__init__.py

@@ -68,6 +68,10 @@ _REASONING_PARSERS_TO_REGISTER = {
         "mistral_reasoning_parser",
         "MistralReasoningParser",
     ),
+    "nemotron_v3": (
+        "nemotron_v3_reasoning_parser",
+        "NemotronV3ReasoningParser",
+    ),
     "olmo3": (
         "olmo3_reasoning_parser",
         "Olmo3ReasoningParser",

vllm/reasoning/nemotron_v3_reasoning_parser.py

@@ -0,0 +1,35 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionRequest,
+)
+from vllm.entrypoints.openai.responses.protocol import (
+    ResponsesRequest,
+)
+from vllm.reasoning.deepseek_r1_reasoning_parser import DeepSeekR1ReasoningParser
+
+
+class NemotronV3ReasoningParser(DeepSeekR1ReasoningParser):
+    """
+    Reasoning parser for Nemotron V3 models.
+    """
+
+    def extract_reasoning(
+        self, model_output: str, request: ChatCompletionRequest | ResponsesRequest
+    ) -> tuple[str | None, str | None]:
+        reasoning_content, final_content = super().extract_reasoning(
+            model_output, request
+        )
+        chat_template_kwargs = getattr(request, "chat_template_kwargs", None)
+
+        if (
+            chat_template_kwargs
+            and (
+                chat_template_kwargs.get("enable_thinking") is False
+                or chat_template_kwargs.get("force_nonempty_content") is True
+            )
+            and final_content is None
+        ):
+            reasoning_content, final_content = final_content, reasoning_content
+
+        return reasoning_content, final_content

vllm/utils/math_utils.py

@@ -30,3 +30,8 @@ def round_up(x: int, y: int) -> int:
 def round_down(x: int, y: int) -> int:
     """Round down x to the nearest multiple of y."""
     return (x // y) * y
+
+
+def largest_power_of_2_divisor(n: int) -> int:
+    """Return the largest power-of-2 that divides *n* (isolate lowest set bit)."""
+    return n & (-n)

vllm/v1/attention/backend.py

@@ -86,6 +86,26 @@ class AttentionBackend(ABC):
     ) -> tuple[int, ...]:
         raise NotImplementedError
 
+    @classmethod
+    def get_kv_cache_block_dim(
+        cls,
+        block_size: int,
+        num_kv_heads: int,
+        head_size: int,
+        cache_dtype_str: str = "auto",
+    ) -> int:
+        """Discover which tensor dim is the block index, since different
+        backends lay out dims differently."""
+        _S = 1234567
+        shape = cls.get_kv_cache_shape(
+            _S,
+            block_size,
+            num_kv_heads,
+            head_size,
+            cache_dtype_str=cache_dtype_str,
+        )
+        return shape.index(_S)
+
     @staticmethod
     def get_kv_cache_stride_order(
         include_num_layers_dimension: bool = False,

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

@@ -372,12 +372,14 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
 
                 # [7, 6, 8, 0] -> [7, 7, 7, 6, 8, 8, 8, 8]
                 expanded_base = torch.repeat_interleave(
-                    seq_lens - decode_lens, decode_lens
+                    seq_lens - decode_lens, decode_lens, output_size=actual_expanded
                 )
 
                 # [0, 3, 4, 8] -> [0, 0, 0, 3, 4, 4, 4, 4]
                 expanded_starts = torch.repeat_interleave(
-                    common_attn_metadata.query_start_loc[:num_decodes], decode_lens
+                    common_attn_metadata.query_start_loc[:num_decodes],
+                    decode_lens,
+                    output_size=actual_expanded,
                 )
 
                 # [0, 1, 2, 0, 0, 1, 2, 3]
@@ -395,7 +397,9 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
                 # Give each of the flattened entries the same block table row as the
                 # original request.
                 self.expanded_block_table_buffer[:actual_expanded] = (
-                    torch.repeat_interleave(block_table, decode_lens, dim=0)
+                    torch.repeat_interleave(
+                        block_table, decode_lens, dim=0, output_size=actual_expanded
+                    )
                 )
                 if actual_expanded < num_decode_tokens:
                     self.expanded_block_table_buffer[

vllm/v1/core/kv_cache_manager.py

@@ -489,6 +489,13 @@ class KVCacheManager:
         # Only create new KVCacheBlocks for non-empty blocks
         return KVCacheBlocks(blocks) if any(blocks) else self.empty_kv_cache_blocks
 
+    def take_new_block_ids(self) -> list[int]:
+        """Drain and return new attention block IDs for zeroing."""
+        ids: list[int] = []
+        for mgr in self.coordinator.single_type_managers:
+            ids.extend(mgr.take_new_block_ids())
+        return ids
+
     def new_step_starts(self) -> None:
         """Called when a new step is started."""
         self.coordinator.new_step_starts()

vllm/v1/core/sched/output.py

@@ -233,6 +233,11 @@ class SchedulerOutput:
     # EC Cache Connector metadata
     ec_connector_metadata: ECConnectorMetadata | None = None
 
+    # Block IDs freshly allocated from the pool during this scheduling step.
+    # The worker zeros the corresponding GPU memory before the blocks are used,
+    # preventing stale NaN/data from corrupting attention or SSM computation.
+    new_block_ids_to_zero: list[int] | None = None
+
     @classmethod
     def make_empty(cls) -> "SchedulerOutput":
         return cls(

vllm/v1/core/sched/scheduler.py

@@ -48,7 +48,7 @@ from vllm.v1.core.sched.output import (
 from vllm.v1.core.sched.request_queue import SchedulingPolicy, create_request_queue
 from vllm.v1.core.sched.utils import check_stop, remove_all
 from vllm.v1.engine import EngineCoreEventType, EngineCoreOutput, EngineCoreOutputs
-from vllm.v1.kv_cache_interface import KVCacheConfig, MambaSpec
+from vllm.v1.kv_cache_interface import KVCacheConfig
 from vllm.v1.metrics.perf import ModelMetrics, PerfStats
 from vllm.v1.metrics.stats import PrefixCacheStats, SchedulerStats
 from vllm.v1.outputs import DraftTokenIds, KVConnectorOutput, ModelRunnerOutput
@@ -233,13 +233,8 @@ class Scheduler(SchedulerInterface):
         self.use_pp = self.parallel_config.pipeline_parallel_size > 1
         self.use_v2_model_runner = envs.VLLM_USE_V2_MODEL_RUNNER
 
-        def has_mamba_layers(kv_cache_config: KVCacheConfig) -> bool:
-            return any(
-                isinstance(group_spec.kv_cache_spec, MambaSpec)
-                for group_spec in kv_cache_config.kv_cache_groups
-            )
-
-        self.has_mamba_layers = has_mamba_layers(kv_cache_config)
+        self.has_mamba_layers = kv_cache_config.has_mamba_layers
+        self.needs_kv_cache_zeroing = kv_cache_config.needs_kv_cache_zeroing
         self.need_mamba_block_aligned_split = (
             self.has_mamba_layers and self.cache_config.mamba_cache_mode == "align"
         )
@@ -871,6 +866,12 @@ class Scheduler(SchedulerInterface):
         self.prev_step_scheduled_req_ids.clear()
         self.prev_step_scheduled_req_ids.update(num_scheduled_tokens.keys())
 
+        new_block_ids_to_zero = (
+            (self.kv_cache_manager.take_new_block_ids() or None)
+            if self.needs_kv_cache_zeroing
+            else None
+        )
+
         scheduler_output = SchedulerOutput(
             scheduled_new_reqs=new_reqs_data,
             scheduled_cached_reqs=cached_reqs_data,
@@ -886,6 +887,7 @@ class Scheduler(SchedulerInterface):
             # the previous and the current steps.
             finished_req_ids=self.finished_req_ids,
             free_encoder_mm_hashes=self.encoder_cache_manager.get_freed_mm_hashes(),
+            new_block_ids_to_zero=new_block_ids_to_zero,
         )
 
         # NOTE(Kuntai): this function is designed for multiple purposes:

vllm/v1/core/single_type_kv_cache_manager.py

@@ -55,6 +55,7 @@ class SingleTypeKVCacheManager(ABC):
         self.kv_cache_spec = kv_cache_spec
         self.block_pool = block_pool
         self.enable_caching = enable_caching
+        self.new_block_ids: list[int] = []
 
         # Mapping from request ID to blocks to track the blocks allocated
         # for each request, so that we can free the blocks when the request
@@ -208,6 +209,8 @@ class SingleTypeKVCacheManager(ABC):
                 cdiv(num_total_computed_tokens, self.block_size) - len(req_blocks)
             )
             req_blocks.extend(allocated_blocks)
+            if type(self.kv_cache_spec) is FullAttentionSpec:
+                self.new_block_ids.extend(b.block_id for b in allocated_blocks)
 
     def allocate_new_blocks(
         self, request_id: str, num_tokens: int, num_tokens_main_model: int
@@ -234,8 +237,16 @@ class SingleTypeKVCacheManager(ABC):
         else:
             new_blocks = self.block_pool.get_new_blocks(num_new_blocks)
             req_blocks.extend(new_blocks)
+            if type(self.kv_cache_spec) is FullAttentionSpec:
+                self.new_block_ids.extend(b.block_id for b in new_blocks)
             return new_blocks
 
+    def take_new_block_ids(self) -> list[int]:
+        """Drain and return block IDs allocated since the last call."""
+        ids = self.new_block_ids
+        self.new_block_ids = []
+        return ids
+
     def cache_blocks(self, request: Request, num_tokens: int) -> None:
         """
         Cache the blocks for the request.

vllm/v1/kv_cache_interface.py

@@ -489,3 +489,11 @@ class KVCacheConfig:
     For models with multiple types of attention, there will be multiple groups,
     see `_get_kv_cache_config_uniform_page_size` for more details.
     """
+
+    @property
+    def has_mamba_layers(self) -> bool:
+        return any(isinstance(g.kv_cache_spec, MambaSpec) for g in self.kv_cache_groups)
+
+    @property
+    def needs_kv_cache_zeroing(self) -> bool:
+        return self.has_mamba_layers

vllm/v1/worker/gpu_model_runner.py

@@ -187,6 +187,7 @@ from vllm.v1.worker.workspace import lock_workspace
 
 from .utils import (
     AttentionGroup,
+    KVBlockZeroer,
     add_kv_sharing_layers_to_kv_cache_groups,
     bind_kv_cache,
     prepare_kernel_block_sizes,
@@ -918,6 +919,26 @@ class GPUModelRunner(
                 decode_threshold=self.reorder_batch_threshold,
             )
 
+    def _init_kv_zero_meta(self) -> None:
+        """One-time precomputation for _zero_block_ids.
+
+        Delegates to KVBlockZeroer.init_meta with the runner's state.
+        Called from gpu_worker.py outside the CuMem pool context.
+        """
+        self._kv_block_zeroer = KVBlockZeroer(self.device, self.pin_memory)
+        self._kv_block_zeroer.init_meta(
+            attn_groups_iter=self._kv_cache_spec_attn_group_iterator(),
+            kernel_block_sizes=self._kernel_block_sizes,
+            cache_dtype=self.cache_config.cache_dtype,
+            runner_only_attn_layers=self.runner_only_attn_layers,
+            static_forward_context=(self.compilation_config.static_forward_context),
+        )
+
+    def _zero_block_ids(self, block_ids: list[int]) -> None:
+        """Zero the KV cache memory for the given block IDs."""
+        if hasattr(self, "_kv_block_zeroer"):
+            self._kv_block_zeroer.zero_block_ids(block_ids)
+
     # Note: used for model runner override.
     def _init_device_properties(self) -> None:
         """Initialize attributes from torch.cuda.get_device_properties"""
@@ -951,6 +972,11 @@ class GPUModelRunner(
         for req_id in scheduler_output.finished_req_ids:
             self.input_batch.remove_request(req_id)
 
+        # Zero GPU memory for freshly allocated cache blocks to prevent
+        # stale NaN/data from corrupting attention or SSM computation.
+        if scheduler_output.new_block_ids_to_zero:
+            self._zero_block_ids(scheduler_output.new_block_ids_to_zero)
+
         # Free the cached encoder outputs.
         for mm_hash in scheduler_output.free_encoder_mm_hashes:
             self.encoder_cache.pop(mm_hash, None)
@@ -6066,6 +6092,7 @@ class GPUModelRunner(
         kernel_block_sizes = prepare_kernel_block_sizes(
             kv_cache_config, self.attn_groups
         )
+        self._kernel_block_sizes = kernel_block_sizes
 
         # create metadata builders
         self.initialize_metadata_builders(kv_cache_config, kernel_block_sizes)

vllm/v1/worker/gpu_worker.py

@@ -480,6 +480,14 @@ class Worker(WorkerBase):
         else:
             self.model_runner.initialize_kv_cache(kv_cache_config)
 
+        # Build KV-zero metadata outside the CuMem pool so the bookkeeping
+        # GPU tensors (seg_addrs, block-id buffers) use the standard PyTorch
+        # allocator and are not discarded during sleep/wake cycles.
+        if kv_cache_config.needs_kv_cache_zeroing and hasattr(
+            self.model_runner, "_init_kv_zero_meta"
+        ):
+            self.model_runner._init_kv_zero_meta()
+
     @instrument(span_name="Warmup (GPU)")
     def compile_or_warm_up_model(self) -> float:
         warmup_sizes = []

vllm/v1/worker/utils.py

@@ -2,7 +2,10 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import math
 from collections import defaultdict
+from collections.abc import Iterable
 from dataclasses import dataclass, field
+from itertools import product as iprod
+from typing import Any
 
 import torch
 
@@ -12,6 +15,8 @@ from vllm.model_executor.layers.attention import Attention
 from vllm.model_executor.models.interfaces import MultiModalEmbeddings
 from vllm.model_executor.models.utils import extract_layer_index
 from vllm.platforms import current_platform
+from vllm.triton_utils import tl, triton
+from vllm.utils.math_utils import largest_power_of_2_divisor
 from vllm.utils.mem_utils import MemorySnapshot, format_gib
 from vllm.v1.attention.backend import (
     AttentionBackend,
@@ -21,6 +26,7 @@ from vllm.v1.attention.backend import (
 from vllm.v1.kv_cache_interface import (
     AttentionSpec,
     EncoderOnlyAttentionSpec,
+    FullAttentionSpec,
     KVCacheConfig,
     KVCacheGroupSpec,
     KVCacheSpec,
@@ -31,6 +37,186 @@ from vllm.v1.kv_cache_interface import (
 logger = init_logger(__name__)
 
 
+@triton.jit
+def _zero_kv_blocks_kernel(
+    seg_addrs_ptr,
+    block_ids_ptr,
+    n_blocks,
+    N_SEGS: tl.constexpr,
+    PAGE_SIZE_EL: tl.constexpr,
+    BLOCK_SIZE: tl.constexpr,
+):
+    """Zero KV cache blocks across all segments in a single launch.
+
+    Each segment is a contiguous region of one block's data.  For backends
+    where blocks are outermost (block_dim=0) there is one segment per
+    buffer.  For backends where K/V is outermost (block_dim=1) there are
+    two segments per buffer (one for K, one for V).
+
+    seg_addrs_ptr holds absolute byte addresses (int64) for each segment,
+    allowing segments to live in different CUDA allocations.
+
+    Programs are mapped as (block_index, seg_index, chunk_index).
+    """
+    pid = tl.program_id(0)
+    chunks = PAGE_SIZE_EL // BLOCK_SIZE
+    work_per_block = N_SEGS * chunks
+    block_index = pid // work_per_block
+    if block_index >= n_blocks:
+        return
+    remainder = pid % work_per_block
+    seg_index = remainder // chunks
+    chunk_index = remainder % chunks
+    block_id = tl.load(block_ids_ptr + block_index)
+    seg_addr = tl.load(seg_addrs_ptr + seg_index)
+    ptr = tl.cast(seg_addr, tl.pointer_type(tl.int32))
+    offset = (
+        block_id.to(tl.int64) * PAGE_SIZE_EL + chunk_index.to(tl.int64) * BLOCK_SIZE
+    )
+    cols = tl.arange(0, BLOCK_SIZE).to(tl.int64)
+    tl.store(ptr + offset + cols, tl.zeros([BLOCK_SIZE], dtype=tl.int32))
+
+
+class KVBlockZeroer:
+    """Manages efficient zeroing of KV cache blocks via a Triton kernel.
+
+    Call :meth:`init_meta` once after KV caches are allocated to precompute
+    segment addresses, then call :meth:`zero_block_ids` each step to zero
+    newly-allocated blocks.
+    """
+
+    def __init__(self, device: torch.device, pin_memory: bool):
+        self.device = device
+        self.pin_memory = pin_memory
+        self._meta: tuple[torch.Tensor, int, int, int] | None = None
+        self._id_cap: int = 0
+        self._ids_pinned: torch.Tensor | None = None
+        self._ids_gpu: torch.Tensor | None = None
+
+    def init_meta(
+        self,
+        attn_groups_iter: Iterable["AttentionGroup"],
+        kernel_block_sizes: list[int],
+        cache_dtype: str,
+        runner_only_attn_layers: set[str],
+        static_forward_context: dict[str, Any],
+    ) -> None:
+        """One-time precomputation for zero_block_ids.
+
+        Builds absolute-address table for the Triton zeroing kernel.
+        Each entry is the absolute byte address of a segment start on the
+        GPU, so segments in different CUDA allocations work correctly.
+
+        Block IDs from the scheduler reference logical blocks whose size
+        may differ from the kernel block size (virtual block splitting).
+        PAGE_SIZE_EL accounts for this ratio so that
+        ``block_id * PAGE_SIZE_EL`` lands at the correct offset.
+
+        Only AttentionSpec layers are processed; Mamba layers are skipped.
+        """
+        seen_ptrs: set[int] = set()
+        seg_addrs: list[int] = []
+        page_size_el: int | None = None
+
+        for group in attn_groups_iter:
+            spec = group.kv_cache_spec
+            if type(spec) is not FullAttentionSpec:
+                continue
+            if group.kv_cache_group_id >= len(kernel_block_sizes):
+                continue
+            kernel_bs = kernel_block_sizes[group.kv_cache_group_id]
+            ratio = spec.block_size // kernel_bs
+            block_dim = group.backend.get_kv_cache_block_dim(
+                kernel_bs,
+                spec.num_kv_heads,
+                spec.head_size,
+                cache_dtype_str=cache_dtype,
+            )
+
+            for layer_name in group.layer_names:
+                if layer_name in runner_only_attn_layers:
+                    continue
+                kv = static_forward_context[layer_name].kv_cache[0]
+                if isinstance(kv, list):
+                    continue
+                dp = kv.data_ptr()
+                if dp in seen_ptrs:
+                    continue
+                seen_ptrs.add(dp)
+
+                el = kv.element_size()
+                cur_bytes = kv.stride(block_dim) * el
+                assert cur_bytes % 4 == 0
+                kernel_block_el = cur_bytes // 4
+                cur_page_el = kernel_block_el * ratio
+                if page_size_el is None:
+                    page_size_el = cur_page_el
+                else:
+                    assert page_size_el == cur_page_el, (
+                        f"Non-uniform page sizes: {page_size_el} vs {cur_page_el}"
+                    )
+
+                block_stride_bytes = cur_bytes
+                outer_dims = [
+                    d
+                    for d in range(block_dim)
+                    if kv.stride(d) * el > block_stride_bytes
+                ]
+                outer_strides = [kv.stride(d) * el for d in outer_dims]
+                for outer in iprod(*(range(kv.shape[d]) for d in outer_dims)):
+                    off_bytes = sum(i * s for i, s in zip(outer, outer_strides))
+                    seg_addrs.append(dp + off_bytes)
+
+        if not seg_addrs or page_size_el is None:
+            self._meta = None
+            return
+
+        blk_size = min(largest_power_of_2_divisor(page_size_el), 1024)
+        self._id_cap = 8192
+        self._ids_pinned = torch.empty(
+            self._id_cap,
+            dtype=torch.int64,
+            pin_memory=self.pin_memory,
+        )
+        self._ids_gpu = torch.empty(self._id_cap, dtype=torch.int64, device=self.device)
+        self._meta = (
+            torch.tensor(seg_addrs, dtype=torch.int64, device=self.device),
+            page_size_el,
+            blk_size,
+            len(seg_addrs),
+        )
+
+    def zero_block_ids(self, block_ids: list[int]) -> None:
+        """Zero the KV cache memory for the given block IDs."""
+        if not block_ids or self._meta is None:
+            return
+        seg_addrs, page_size_el, blk_size, n_segs = self._meta
+        n_blocks = len(block_ids)
+        if n_blocks > self._id_cap:
+            self._id_cap = n_blocks * 2
+            self._ids_pinned = torch.empty(
+                self._id_cap,
+                dtype=torch.int64,
+                pin_memory=self.pin_memory,
+            )
+            self._ids_gpu = torch.empty(
+                self._id_cap, dtype=torch.int64, device=self.device
+            )
+        assert self._ids_pinned is not None and self._ids_gpu is not None
+        self._ids_pinned[:n_blocks].numpy()[:] = block_ids
+        idx = self._ids_gpu[:n_blocks]
+        idx.copy_(self._ids_pinned[:n_blocks], non_blocking=True)
+        grid = (n_blocks * n_segs * (page_size_el // blk_size),)
+        _zero_kv_blocks_kernel[grid](
+            seg_addrs,
+            idx,
+            n_blocks,
+            N_SEGS=n_segs,
+            PAGE_SIZE_EL=page_size_el,
+            BLOCK_SIZE=blk_size,
+        )
+
+
 @dataclass
 class AttentionGroup:
     backend: type[AttentionBackend]