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This is the release of the CUDA 13.3 samples, which include additions for CUDA Tile C++, and updated CCCL and Python samples.
63 lines
1.9 KiB
Markdown
63 lines
1.9 KiB
Markdown
# Sample: PyTorch Custom GPU Operator
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## Description
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This sample demonstrates how to add a custom GPU operation to PyTorch using the `cuda.core` API. It implements a simple square operation (y = x²) to show the complete workflow from CUDA kernel to PyTorch integration with autograd support.
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## Requirements
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- NVIDIA GPU with Compute Capability 7.0+
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- CUDA Toolkit 13.0+
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- Python 3.10+
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- PyTorch 2.0+
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- cuda-python >= 13.0.0
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- cuda-core >=1.0.0
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## Installation
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```bash
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cd python/3_FrameworkInterop/customPyTorchKernel
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pip install -r requirements.txt
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```
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**Windows users:** The default `torch` wheel on PyPI for Windows is CPU-only and will cause `torch.cuda.is_available()` to return `False`. Install a CUDA-enabled build from PyTorch's wheel index *before* (or after) the command above:
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```bash
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pip install torch --index-url https://download.pytorch.org/whl/cu128
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```
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Replace `cu128` with the wheel suffix matching your installed CUDA driver (e.g. `cu121`, `cu124`, `cu126`, `cu128`). The driver's CUDA version must be >= the wheel's bundled runtime.
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## How to Run
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```bash
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# Basic usage
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python customPyTorchKernel.py
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# Test with more elements
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python customPyTorchKernel.py --size 1000000
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# Use specific GPU
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CUDA_VISIBLE_DEVICES=1 python customPyTorchKernel.py
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```
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## Expected Output
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The sample runs three tests:
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1. Forward pass correctness (y = x²)
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2. Backward pass correctness (gradient computation)
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3. Multi-dimensional tensor support
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All tests should pass, confirming the custom operator works correctly with PyTorch's autograd system.
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## Key Concepts
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The sample demonstrates:
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- Writing CUDA kernels with grid-stride loops
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- Runtime kernel compilation with cuda.core
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- PyTorch autograd integration via `torch.autograd.Function`
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- Stream management using PyTorch's current stream
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- Kernel caching for performance
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The code is self-documenting with inline comments explaining each step.
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