Add a third template parameter `N` (slot index, default 0) to
`ActiveImplementationsImpl`. Slot 0 holds the base/device-native
indices, and higher slots let add-on backends register extra indices
without conflicting with existing specializations. `ActiveImplementations`
flattens slots 0-3 via `Flatten`.

Add `WITH_TORCH` option that finds PyTorch via pip, links against
libtorch, and compiles sources under `src/torch/`. Pass `--with-torch`
to `generate_wrappers.py` so it scans `src/torch/` for operator
specializations.

Add `ToAtenDtype()` and `ToAtenTensor<kDev>()` in `src/torch/tensor_.h`
for zero-copy conversion from `infini::ops::Tensor` to `at::Tensor`
via `at::from_blob()`.

Register torch `Add` via `ActiveImplementationsImpl` slot 1 for all
devices. The implementation uses `at::add_out()` through ATen's
device-generic dispatch.

Register torch `Gemm` via `ActiveImplementationsImpl` slot 1 for all
devices. The implementation uses `at::addmm_out()` / `at::baddbmm_out()`
through ATen's device-generic dispatch.

Auto-detect PyTorch by attempting `import torch`. When found,
`WITH_TORCH` is enabled automatically.

`find_package(Torch)` pulls in Caffe2's cmake config, which calls
`enable_language(CUDA)` and breaks on platforms with non-standard
CUDA toolchains (e.g. Iluvatar). Query include and library paths
directly via `torch.utils.cpp_extension` instead.

…(CUDA)`

CMake 4.3+ requires `CMAKE_CUDA_ARCHITECTURES` to be set before
`enable_language(CUDA)` when using non-standard CUDA compilers like
Iluvatar's `clang++`. Without it, CMake fails to detect a default
architecture.

When `pybind11` is installed via pip but not in a standard CMake search
path, `find_package(pybind11 CONFIG)` fails. Query `python -m pybind11
--cmakedir` as a fallback to locate the package.

- Iluvatar: set `CMAKE_CUDA_ARCHITECTURES` to `OFF` instead of
  `ivcore20` (CMake 4.3 rejects non-integer architecture names; the
  architecture is already passed via `CMAKE_CUDA_FLAGS`).

- MetaX/Moore: split torch operator headers into declaration-only `.h`
  files and `.cc` implementation files with explicit instantiations.
  Compile the `.cc` files with the system `g++` instead of the vendor
  compiler (`mxcc`/`mcc`), which cannot parse vendor-forked torch
  headers in C++ extension mode.

- Cambricon: guard `UInt16`/`UInt32`/`UInt64` scalar types in
  `ToAtenDtype()` with a `TORCH_VERSION` check (these types require
  PyTorch 2.4+; Cambricon ships torch 2.1).

- Wrapper generator: scan only `.h` files to avoid including `.cc`
  explicit-instantiation files in the generated `ops.cc`.

- Iluvatar: move `-x ivcore` from CMAKE_CUDA_FLAGS to compile-only
  options so it doesn't get passed during linking (which caused
  clang++ to re-parse .o files as source code)
- MetaX: add `-DUSE_MACA=1` to g++ flags for torch source
  compilation (MetaX torch fork headers require this define)
- Cambricon: query `torch.compiled_with_cxx11_abi()` and set
  `_GLIBCXX_USE_CXX11_ABI` globally to match torch's ABI setting
  (fixes undefined reference to `c10::Device::Device(std::string)`)

Use backtick-fenced Markdown syntax for identifiers in comments
and error messages, and ensure comments are complete sentences.

…or_.h`

Use `constexpr int kTorchVersion` and `if constexpr` instead of `#if`
macros for PyTorch version checks. Extract unsigned dtype handling into
`detail::ToAtenUnsignedDataType`.

`c10::ScalarType::UInt16` is a non-dependent name resolved at template
definition time.  Introduce `DependentScalarType<kVersion>::type` so
the enum member access becomes dependent and is properly discarded by
`if constexpr` on older PyTorch versions.

ATen `addmm`/`baddbmm` does not support `float16`/`bfloat16` on CPU.