Add base classes: Cast, Cat, Linear, Matmul (replaces MatMul), Mul,
PagedAttention, SiluAndMul.

Rename AddRmsNorm params to match CANN convention (x1/x2/gamma/y_out/x_out).
Remove verbose doc comments from FlashAttention, ReshapeAndCache,
RotaryEmbedding base classes (implementation details belong in kernels).

Add ACLNN-based implementations for: Add, Cast, Cat, CausalSoftmax,
FlashAttention, Linear, Matmul, Mul, RmsNorm, RotaryEmbedding,
ReshapeAndCache (+ v2), Swiglu, SiluAndMul.

All kernels use AclTensorCache for descriptor reuse and
WorkspacePool for device memory management. Executor instances
are cached with aclSetAclOpExecutorRepeatable for repeat dispatch.

Add alternative implementations with registries:
- AddRmsNorm: decomposed (0), fused aclnnAddRmsNorm (1), custom AscendC (2)
- RmsNorm: ACLNN (0), custom AscendC (1)
- RotaryEmbedding: ACLNN (0), ATB Rope (1)
- ReshapeAndCache: ACLNN (0), ScatterPaKvCache (1), ATB (2)
- Swiglu: decomposed (0), fused aclnnSwiGlu (1)
- SiluAndMul: fused aclnnSwiGlu (0), registry (1)
- PagedAttention: ATB (0)

Standalone AscendC kernel project with CMake build system.
Includes op_host tiling, op_kernel device code, precision tests,
and msprof benchmarks for both operators.

Add new tests: Cast, Cat, E2E Layer, FlashAttention, Linear, Matmul,
Mul, PagedAttention, ReshapeAndCache, RotaryEmbedding, SiluAndMul.
Update existing tests with NPU stream handling and Ascend-specific
parametrization.

- C1: auto-format all C++ files with clang-format (25 files)
- C4: lowercase assert messages, remove trailing periods (10 messages)
- G4: backtick-fence identifiers in comments (causal_softmax)
- P5: add blank lines before return statements (generate_wrappers.py)

- C4: lowercase assert message starts (workspace_pool_, rms_norm, rotary_embedding)
- C4: remove trailing period from workspace_pool_ assert
- C9: add blank line between SlotKey struct members
- G4: backtick-fence identifiers in comments across 12 files
- G4: backtick-fence identifiers in assert messages (flash_attention, rotary_embedding)
- P1: remove duplicate `import re` in generate_wrappers.py
- P4: add blank lines around control flow in test_flash_attention.py

- C4: lowercase "rope" in ATB assert messages
- G4: backtick-fence `VariantPack`, `rotaryCoeff`, `sparseMode`, `hostData`
- G4: backtick-fence identifiers in Python test comments
- P4: add blank line before `if` in test_rms_norm_precision.py

… loading

- Delete `test_rms_norm_precision.py` (duplicate of `tests/test_rms_norm.py`)
- Delete `run_rms_norm_precision_report.py` (another copy with hardcoded path)
- Unify `test_add_rms_norm.py` to use `import ascend_kernel` instead of
  ctypes manual loading

New operators and features:
- ApplyRotaryPosEmb: pre-gathered cos/sin operator with ATB backend
- TopkToppSampling: ATB-based fused sampling operator
- SiluAndMul: standalone operator backed by aclnnSwiGlu
- ATB PagedAttention: graph-safe decode attention

Enhancements:
- WorkspacePool: multi-slot support and capture-mode assertion
- Migrate temp buffers to WorkspacePool slots (Swiglu, CausalSoftmax,
  RmsNorm, AddRmsNorm)
- RotaryEmbedding: accept 2D [T, N*D] input, fix ATB cos/sin gathering
- ReshapeAndCache: handle int64 slot_mapping in ATB kernel
- Swiglu: add fused aclnnSwiGlu implementation (index=1)
- Parametrize rms_norm and reshape_and_cache tests by implementation_index

… data

The operator cache keys ignore data pointers (compare only shape/dtype/
device/strides).  When RotaryEmbedding was cached from one test and
reused by another with a different cos_sin_cache tensor (same shape,
different random data), the IndexSelect gathered from the old tables,
producing garbage output.

Track the cos_sin_cache data pointer and re-upload the expanded cos/sin
tables when it changes.  In production this is a single pointer
comparison per call (no-op); the cos_sin_cache weight tensor has a
stable address.

Fixes 6 rotary_embedding_2d test failures (head_size=64, fp16, both
CANN and ATB paths) that only reproduced when test_apply_rotary_pos_emb
ran first.

Replace per-operator stale-cache workaround with Operator::clear_cache()
generation counter.  pytest autouse fixture clears caches between test
modules.  Skip aclnnScatterPaKvCache (impl_index=1) on 910B hardware.

Synced from feat/ascend-operators commits c68633f, 57f96bf.

ATB Rope with rotaryCoeff=2 supports bf16 on 910B. Remove the
fp16-only skip guard — all 6 previously skipped bf16 test cases pass.

Extend PagedAttention base class and ATB kernel with optional
seq_lens_host / block_table_host params that skip aclrtMemcpy
D2H copies when caller provides CPU-pinned host tensors.

Add unit tests for host-tensor PA and FA paged decode with CPU
cu_seqlens_kv.

`aclDestroyAclOpExecutor` internally frees `aclTensor` descriptors it holds.
Add `AclTensorCache::release()` and `destroy()` methods, guard all destructors
with `isAclRuntimeAlive()`, and remove redundant `aclDestroyTensor` calls for
executor-owned tensors. Verified: CANN reference-counts tensors, so
destroy-tensor-then-destroy-executor order is safe.

…STOM_KERNELS

…, narrow PA skip probe