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* Add dataclass example: passing pointers via frozen dataclass Demonstrates passing pointers from tensor arguments in @cute.jit to @cute.kernel using @dataclass(frozen=True). Shows the pattern of extracting pointers with tensor.iterator, bundling into a dataclass, and reconstructing tensors in the kernel. Uses fake tensors for compilation and TVM-FFI for runtime dispatch. Co-authored-by: Cursor <cursoragent@cursor.com> * Add dataclass example: passing tensors via frozen dataclass Demonstrates passing tensors from @cute.jit to @cute.kernel using @dataclass(frozen=True). Shows the pattern of bundling tensors into a dataclass with static configuration. Uses fake tensors for compilation and TVM-FFI for runtime dispatch. Includes reference check against PyTorch implementation. Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: Cursor <cursoragent@cursor.com>
99 lines
2.5 KiB
Python
99 lines
2.5 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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"""
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Dataclass Example: Passing Tensors via Frozen Dataclass
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=======================================================
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Demonstrates passing tensors from `@cute.jit` to `@cute.kernel`
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using `@dataclass(frozen=True)`.
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Uses fake tensors for compilation and TVM-FFI for efficient runtime dispatch.
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To run:
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.. code-block:: bash
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python examples/python/CuTeDSL/cute/dataclass_immutable.py
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"""
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from dataclasses import dataclass
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import cutlass
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import cutlass.cute as cute
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from cutlass import Int32
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@dataclass(frozen=True)
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class CopyParams:
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"""
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Dataclass with one static attribute and two tensors.
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Attributes:
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num_threads: Threads per block (static - used for loop unrolling)
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src: Source tensor (dynamic)
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dst: Destination tensor (dynamic)
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"""
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num_threads: int # static
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src: cute.Tensor # dynamic
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dst: cute.Tensor # dynamic
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@cute.jit
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def memcpy_jit(mSrc: cute.Tensor, mDst: cute.Tensor, num_threads: cutlass.Constexpr[int]):
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"""Host function - passes tensors via dataclass."""
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params = CopyParams(
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num_threads=num_threads,
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src=mSrc,
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dst=mDst,
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)
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N = mSrc.shape[0]
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num_blocks = cute.ceil_div(N, num_threads)
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memcpy_kernel(params, N).launch(
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grid=[num_blocks, 1, 1],
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block=[num_threads, 1, 1],
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)
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@cute.kernel
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def memcpy_kernel(params: CopyParams, N: Int32):
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"""Device kernel - receives tensors via CopyParams dataclass."""
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tidx, _, _ = cute.arch.thread_idx()
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bidx, _, _ = cute.arch.block_idx()
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idx = bidx * params.num_threads + tidx
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if idx < N:
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params.dst[idx] = params.src[idx]
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if __name__ == "__main__":
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import torch
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from cutlass.cute.runtime import make_fake_compact_tensor
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n = cute.sym_int64()
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# 1. Create fake tensors for compilation (no GPU memory allocated)
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fake_src = make_fake_compact_tensor(cutlass.Float32, (n,), stride_order=(0,))
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fake_dst = make_fake_compact_tensor(cutlass.Float32, (n,), stride_order=(0,))
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# 2. Compile with --enable-tvm-ffi
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compiled_fn = cute.compile(
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memcpy_jit, fake_src, fake_dst, num_threads=128, options="--enable-tvm-ffi"
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)
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# 3. Create real torch tensors
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src = torch.randn(1024, device="cuda", dtype=torch.float32)
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dst = torch.zeros_like(src)
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# 4. Run compiled kernel
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compiled_fn(src, dst)
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# 5. Verify dst == src
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torch.cuda.synchronize()
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torch.testing.assert_close(dst, src)
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print("PASS")
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