Verify each finding against the current code and only fix it if needed.

Inline comments:
In `@cpp/tensorrt_llm/kernels/mamba2MTPSSMCache/mamba2MTPSSMCache.cu`:
- Around line 56-65: Add a precondition check in invokeMamba2MTPSSMCacheUpdate
to validate params.ngroups and the head/group ratio before dispatch: ensure
params.ngroups > 0 and params.nheads % params.ngroups == 0 (so
heads_groups_ratio = nheads/ngroups is integral and non-zero) to prevent
division-by-zero and incorrect grouping used by mamba2MTPSSMCacheKernel.cuh; use
the existing TLLM_CHECK_WITH_INFO pattern (same as the head_dim check) to fail
fast with a clear message referencing ngroups and nheads before calling
MTP_DISPATCH_VEC_SIZE and launchMamba2MTPSSMCacheKernel.

In `@cpp/tensorrt_llm/kernels/mamba2MTPSSMCache/mamba2MTPSSMCache.h`:
- Around line 35-79: Update the Mamba2MTPSSMCacheParams struct documentation to
reflect the actual tensor layouts used by mamba2MTPSSMCacheKernel: change dt, A,
D, and dt_bias comments to indicate they are indexed only by head (no
head_dim/ssm_dim axes) and correct their shapes accordingly (e.g., dt: [bs,
cache_steps, nheads], A: [nheads], D/dt_bias: [nheads] or similarly 1D per head
as used in kernel), and convert all public comments to Doxygen C++ style (use
//! for single-line comments and //!< for member annotations) including the
prototype for invokeMamba2MTPSSMCacheUpdate; reference struct name
Mamba2MTPSSMCacheParams and the kernel usage locations
(mamba2MTPSSMCacheKernel.cuh lines mentioned) so callers provision tensors with
the right layouts.

In `@cpp/tensorrt_llm/kernels/mamba2MTPSSMCache/mamba2MTPSSMCacheKernel.cuh`:
- Around line 372-383: The kernel is restoring parent state from
retrieve_parent_token even when parent_step_idx == t or > t (future/unwritten
entries); update the guard in the RETRIEVE_PARENT_TOKEN block so you only load
when parent_step_idx >= 0 && parent_step_idx < t (not < cache_steps), e.g. in
the block around retrieve_parent_token[bs_id * cache_steps + t] before calling
mtp_load_vec_to_float for state_4_a/state_4_b with inter_base_a/inter_base_b and
stride_nheads_hdim_ssm_dim ensure the check uses t as the upper bound to only
restore from already-materialized steps.
- Around line 113-116: The current mtp_softplus uses __logf(1 +
__expf(dt_value)) which overflows for large dt_value (causing
xdt_val_a/xdt_val_b to become inf); replace it with a numerically stable branch:
for dt_value > 0 return dt_value + __logf(1.f + __expf(-dt_value)), otherwise
return __logf(1.f + __expf(dt_value)) so exponentials are computed on non-large
positive inputs and downstream state/output corruption is avoided; update the
mtp_softplus device function accordingly (it’s the function to change).

In `@cpp/tensorrt_llm/thop/mamba2MTPSSMCacheOp.cpp`:
- Around line 78-82: B.size(2) can be zero, causing undefined host behavior when
evaluating nheads % ngroups; add an explicit check that rejects ngroups == 0
before performing the modulo. Concretely, after computing int const ngroups =
B.size(2); add a TORCH_CHECK(ngroups > 0, "ngroups must be > 0") (or similar)
and then keep the existing TORCH_CHECK(nheads % ngroups == 0, "unsupported pair
of nheads and ngroups") so the modulo is only executed on a non-zero ngroups.
- Around line 50-63: Add a host-side validation that when ssm_batch_indices is
not provided (i.e., kernel will use direct bs_id indexing) the first dimension
of ssm is at least the batch size of x: check ssm.size(0) >= x.size(0) and raise
a TORCH_CHECK with a clear message if not; locate this guard near the existing
tensor shape/device checks around ssm, x, and intermediate_states in
mamba2MTPSSMCacheOp.cpp so it runs before launching the kernel and prevents
out-of-bounds access when ssm_batch_indices is null.
- Around line 209-210: The code calls at::cuda::getCurrentCUDAStream() without
setting the device, which can pick the thread-local device instead of the device
associated with params; fix by including <c10/cuda/CUDAGuard.h> and creating a
c10::cuda::CUDAGuard guard(params.device()) (or the appropriate device accessor
from params) immediately before calling at::cuda::getCurrentCUDAStream(), then
get the stream and call tk::invokeMamba2MTPSSMCacheUpdate(params, stream) so the
kernel is launched on the correct device.