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94 lines
3.7 KiB
Python
94 lines
3.7 KiB
Python
# Copyright (c) 2025 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: BSD-3-Clause
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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# 2. Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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# 3. Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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"""Example demonstrating how to use TVM-FFI ABI with CuTe.
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This example shows how to:
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1. Compile a CuTe function with "--enable-tvm-ffi" option
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2. Export the compiled function to a shared library
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3. Load the shared library and use the compiled function to work with torch.Tensor
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To run this example:
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.. code-block:: bash
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python cutlass_ir/compiler/python/examples/cute/tvm_ffi/aot_export.py
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# run example to use in torch
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python cutlass_ir/compiler/python/examples/cute/tvm_ffi/aot_use_in_torch.py
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# run example to use in jax
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python cutlass_ir/compiler/python/examples/cute/tvm_ffi/aot_use_in_jax.py
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# run example to use in c++ bundle
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bash cutlass_ir/compiler/python/examples/cute/tvm_ffi/aot_use_in_cpp_bundle.sh
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"""
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from pathlib import Path
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import os
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import subprocess
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import tvm_ffi
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import cutlass.cute as cute
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from cutlass.cute.runtime import from_dlpack
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@cute.kernel
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def device_add_one(a: cute.Tensor, b: cute.Tensor):
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for i in range(a.shape[0]):
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b[i] = a[i] + 1
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@cute.jit
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def add_one(a: cute.Tensor, b: cute.Tensor):
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"""b = a + 1"""
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device_add_one(a, b).launch(grid=(1, 1, 1), block=(1, 1, 1))
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def main():
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import torch
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# compile the kernel with "--enable-tvm-ffi" option
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a_torch = torch.arange(10, dtype=torch.float32, device="cuda")
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b_torch = torch.zeros(10, dtype=torch.float32, device="cuda")
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a_cute = from_dlpack(a_torch, enable_tvm_ffi=True).mark_layout_dynamic()
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b_cute = from_dlpack(b_torch, enable_tvm_ffi=True).mark_layout_dynamic()
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# compile the kernel with "--enable-tvm-ffi" option
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compiled_add_one = cute.compile(add_one, a_cute, b_cute, options="--enable-tvm-ffi")
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os.makedirs("./build", exist_ok=True)
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object_file_path = "./build/add_one.o"
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lib_path = "./build/add_one.so"
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compiled_add_one.export_to_c(object_file_path, function_name="add_one")
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shared_libs = cute.runtime.find_runtime_libraries(enable_tvm_ffi=True)
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# compile the object file to a shared library
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cmd = ["gcc", "-shared", "-o", lib_path, object_file_path, *shared_libs]
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print(cmd)
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subprocess.run(cmd, check=True)
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print(f"Successfully created shared library: {lib_path}")
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if __name__ == "__main__":
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main()
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