b1d6e2c9b3
* v4.3 update. * Update the cute_dsl_api changelog's doc link * Update version to 4.3.0 * Update the example link * Update doc to encourage user to install DSL from requirements.txt --------- Co-authored-by: Larry Wu <larwu@nvidia.com>
78 lines
3.0 KiB
Python
78 lines
3.0 KiB
Python
# Copyright (c) 2025 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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import torch
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import cutlass.cute as cute
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from cutlass.cute.runtime import from_dlpack
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"""Example demonstrating how to use CuTe with PyTorch's FakeTensor mode.
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This example shows how to:
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1. Use PyTorch's FakeTensor mode to compile a CuTe function without real data
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2. Execute the compiled function on real data later
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FakeTensor mode allows compiling code without allocating real memory, which is useful
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for ahead-of-time compilation scenarios. The compiled function can then be executed
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on real tensors that match the expected shapes and dtypes.
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Primary goals of this example are to demonstrate: How to use PyTorch's FakeTensor mode with CuTe
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to enable ahead-of-time compilation without real data allocation.
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The example:
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1. Creates a fake tensor in PyTorch using FakeTensor mode
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2. Compiles a CuTe function using the fake tensor without allocating real memory
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3. Creates a real tensor with matching shape and dtype
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4. Executes the compiled function on the real tensor
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To run this example:
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.. code-block:: bash
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python examples/cute/torch_fake_tensor.py
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"""
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@cute.jit
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def print_tensor(t: cute.Tensor):
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cute.print_tensor(t)
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if __name__ == "__main__":
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from torch._subclasses.fake_tensor import FakeTensorMode
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shape = (3, 4)
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with FakeTensorMode():
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fake_tensor = torch.zeros(shape, dtype=torch.float32)
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compiled_fn = cute.compile(print_tensor, from_dlpack(fake_tensor))
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real_tensor = torch.randn(shape, dtype=torch.float32)
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compiled_fn(from_dlpack(real_tensor))
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