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[TRTLLM-9676][fix] Fix mamba_cache_manager when enabling cuda_graph_padding and let test cover this case #9873

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[TRTLLM-9676][fix] Fix mamba_cache_manager when enabling cuda_graph_padding and let test cover this case #9873

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08d4e36
fix mamba_cache_manager when cuda_graph_padding && test cover this case
JadoTu Dec 10, 2025
ea1c502
Merge branch 'main' into fix_mamba_cache_manager
JadoTu Dec 15, 2025
9edd777
pass pre-commit check
JadoTu Dec 24, 2025
6181cf6
Merge branch 'main' into fix_mamba_cache_manager
JadoTu Dec 29, 2025
e876a20
new strategy of low overhead
JadoTu Dec 29, 2025
b48a00d
Merge branch 'main' into fix_mamba_cache_manager
JadoTu Dec 30, 2025
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7 changes: 7 additions & 0 deletions tensorrt_llm/_torch/pyexecutor/cuda_graph_runner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
from ..speculative.mtp import SampleStateTensorsMTP
from ..utils import make_weak_ref, piecewise_cuda_graph
from .llm_request import get_draft_token_length
from .mamba_cache_manager import MambaCacheManager, MambaHybridCacheManager
from .resource_manager import (BaseResourceManager, ResourceManager,
ResourceManagerType)
from .sampler import SampleStateTensors
Expand Down Expand Up @@ -450,6 +451,12 @@ def _get_padded_batch(self, batch: ScheduledRequests,
if spec_res_mgr:
spec_res_mgr.add_dummy_requests([CUDA_GRAPH_DUMMY_REQUEST_ID])

# handle special cases of padding requests + MambaCacheManager or MambaHybridCacheManager
if isinstance(kv_cache_manager,
(MambaCacheManager, MambaHybridCacheManager)):
kv_cache_manager.reorder_state_indices_when_padding_requests(
batch_size, padding_size)

self.padding_dummy_request.py_draft_tokens = [0] * runtime_draft_len
batch.generation_requests.extend([self.padding_dummy_request] *
padding_size)
Expand Down
20 changes: 20 additions & 0 deletions tensorrt_llm/_torch/pyexecutor/mamba_cache_manager.py
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Original file line number Diff line number Diff line change
Expand Up @@ -110,6 +110,14 @@ def __init__(
device=device,
dtype=torch.int32)

# only for `reorder_state_indices_when_padding_requests`
self.request_mask = torch.ones(max_batch_size,
dtype=torch.bool,
device=device)
self.state_indices_arange = torch.arange(max_batch_size,
dtype=torch.int32,
device=device)

def _prepare_mamba_cache_blocks(self, request_ids: List[int]):
state_indices = []
for r in request_ids:
Expand All @@ -127,6 +135,18 @@ def _prepare_mamba_cache_blocks(self, request_ids: List[int]):
state_indices, dtype=torch.int32, pin_memory=True),
non_blocking=True)

# When there exists padded requests, the state indices should not be repeated.
def reorder_state_indices_when_padding_requests(self, request_size,
padding_size):
if padding_size == 0:
return

self.request_mask[:] = True
self.request_mask[self.state_indices[:request_size]] = False
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@yuantailing yuantailing Dec 24, 2025

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There are implicit CUDA synchronizations which negate the benefits of the overlap scheduler.

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@JadoTu JadoTu Dec 29, 2025

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I checked whether there is CUDA synchronizations here and there is none.
Screenshot 2025-12-29 at 15 58 04

But anyway, there is a new method in the newer patch with lower overhead.

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@yuantailing yuantailing Dec 30, 2025

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Here are synchronizations I said. The NVTX range "====A1==" is this code snippet I quoted.

image

There is no synchronization when I delete the code snippet. But my NVTX trace is generated before your new method.

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@yuantailing yuantailing Dec 30, 2025

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From your screenshot, I cannot say whether there is or is not newly introduced synchronization. Because there is launch overhead, synchronization (if exists) will return immediately and is not easy to notice.

I wanted to verify it by myself, but I faced an error, so please resolve the error first.

self.state_indices[request_size:request_size +
padding_size] = self.state_indices_arange[
self.request_mask][:padding_size]

def prepare_resources(self, scheduled_batch: ScheduledRequests):
context_ids = [
i.py_request_id for i in scheduled_batch.context_requests
Expand Down
11 changes: 7 additions & 4 deletions tests/integration/defs/accuracy/test_llm_api_pytorch.py
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Expand Up @@ -4602,10 +4602,12 @@ def test_bf16_4gpu(self, tp_size, pp_size, ep_size, cuda_graph,
model_path = f"{self.MODEL_PATH}/Qwen3-Next-80B-A3B-Instruct"
kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.6,
enable_block_reuse=False)
pytorch_config = dict(disable_overlap_scheduler=not overlap_scheduler,
cuda_graph_config=CudaGraphConfig(
max_batch_size=512, enable_padding=True)
if cuda_graph else None)
pytorch_config = dict(
disable_overlap_scheduler=not overlap_scheduler,
cuda_graph_config=CudaGraphConfig(
enable_padding=True,
batch_sizes=[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048])
if cuda_graph else None)

with LLM(
model_path,
Expand All @@ -4620,6 +4622,7 @@ def test_bf16_4gpu(self, tp_size, pp_size, ep_size, cuda_graph,
task.evaluate(llm)
mocker.patch.object(GSM8K, "MAX_OUTPUT_LEN",
self.GSM8K_MAX_OUTPUT_LEN)
mocker.patch.object(GSM8K, "NUM_SAMPLES", 1319)
task = GSM8K(self.MODEL_NAME)
task.evaluate(llm)

Expand Down