# Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: BSD-3-Clause # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # 1. Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # 3. Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """Example demonstrating how to use TVM-FFI ABI with CuTe. This example shows how to: 1. Compile a CuTe function with "--enable-tvm-ffi" option 2. Export the compiled function to a shared library 3. Load the shared library and use the compiled function to work with torch.Tensor To run this example: .. code-block:: bash python examples/cute/tvm_ffi/aot_export.py # run example to use in torch python examples/cute/tvm_ffi/aot_use_in_torch.py # run example to use in jax python examples/cute/tvm_ffi/aot_use_in_jax.py # run example to use in c++ bundle bash examples/cute/tvm_ffi/aot_use_in_cpp_bundle.sh """ from pathlib import Path import torch import os import subprocess import tvm_ffi import torch import cutlass.cute as cute from cutlass.cute.runtime import from_dlpack @cute.kernel def device_add_one(a: cute.Tensor, b: cute.Tensor): for i in range(a.shape[0]): b[i] = a[i] + 1 @cute.jit def add_one(a: cute.Tensor, b: cute.Tensor): """b = a + 1""" device_add_one(a, b).launch(grid=(1, 1, 1), block=(1, 1, 1)) def main(): # compile the kernel with "--enable-tvm-ffi" option a_torch = torch.arange(10, dtype=torch.float32, device="cuda") b_torch = torch.zeros(10, dtype=torch.float32, device="cuda") a_cute = from_dlpack(a_torch, enable_tvm_ffi=True).mark_layout_dynamic() b_cute = from_dlpack(b_torch, enable_tvm_ffi=True).mark_layout_dynamic() # compile the kernel with "--enable-tvm-ffi" option compiled_add_one = cute.compile(add_one, a_cute, b_cute, options="--enable-tvm-ffi") os.makedirs("./build", exist_ok=True) object_file_path = "./build/add_one.o" lib_path = "./build/add_one.so" compiled_add_one.export_to_c(object_file_path, function_name="add_one") shared_libs = cute.runtime.find_runtime_libraries(enable_tvm_ffi=True) # compile the object file to a shared library cmd = ["gcc", "-shared", "-o", lib_path, object_file_path, *shared_libs] print(cmd) subprocess.run(cmd, check=True) print(f"Successfully created shared library: {lib_path}") if __name__ == "__main__": main()