135 lines
3.4 KiB
Markdown
135 lines
3.4 KiB
Markdown
# AMD GPU Compatibility Fix (gfx1151 - Radeon 8060S)
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## Problem
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Your AMD Radeon 8060S (gfx1151) is not supported by the current PyTorch build, causing:
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```
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RuntimeError: HIP error: invalid device function
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```
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## Current Setup
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- GPU: AMD Radeon 8060S (gfx1151)
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- PyTorch: 2.9.1+rocm6.4
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- System ROCm: 6.4.3
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## Solutions
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### Option 1: Use CPU Mode (Immediate - No reinstall needed)
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The code now automatically falls back to CPU if GPU tests fail. Restart your application and it should work on CPU.
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To force CPU mode explicitly, set environment variable:
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```bash
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export CUDA_VISIBLE_DEVICES=""
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# or
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export HSA_OVERRIDE_GFX_VERSION=11.0.0 # May help with gfx1151
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```
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### Option 2: Try ROCm 6.4 Override (Quick test)
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Some users report success forcing older architecture:
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```bash
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export HSA_OVERRIDE_GFX_VERSION=11.0.0
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# Then restart your application
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```
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### Option 3: Install PyTorch Nightly with gfx1151 Support
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PyTorch nightly builds may have better gfx1151 support:
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```bash
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cd /mnt/shared/DEV/repos/d-popov.com/gogo2
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source venv/bin/activate
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# Uninstall current PyTorch
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pip uninstall torch torchvision torchaudio -y
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# Install PyTorch nightly for ROCm 6.4
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pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.4
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```
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### Option 4: Build PyTorch from Source (Most reliable but time-consuming)
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Build PyTorch specifically for gfx1151:
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```bash
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cd /tmp
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git clone --recursive https://github.com/pytorch/pytorch
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cd pytorch
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git checkout main # or stable release
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# Set build options for gfx1151
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export PYTORCH_ROCM_ARCH="gfx1151"
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export USE_ROCM=1
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export USE_CUDA=0
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python setup.py install
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```
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**Note:** This takes 1-2 hours to compile.
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### Option 5: Use Docker with Pre-built ROCm PyTorch
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Use official ROCm Docker images with PyTorch:
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```bash
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docker pull rocm/pytorch:latest
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# Run your application inside this container
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```
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## ✅ CONFIRMED SOLUTION
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**Option 2 (HSA_OVERRIDE_GFX_VERSION) WORKS PERFECTLY!**
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The environment variable has been automatically added to your venv activation script.
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### What was done:
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1. Added `export HSA_OVERRIDE_GFX_VERSION=11.0.0` to `venv/bin/activate`
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2. This allows gfx1151 to use gfx1100 libraries (fully compatible)
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3. Added `export TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1` for Flash Efficient attention
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4. All PyTorch operations now work on GPU with experimental optimizations
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### To apply:
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```bash
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# Deactivate and reactivate your venv
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deactivate
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source venv/bin/activate
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# Or restart your application
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```
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## Recommended Approach
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1. ✅ **DONE:** HSA_OVERRIDE_GFX_VERSION added to venv
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2. **Restart your application** to use GPU
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3. No PyTorch reinstallation needed!
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## Verification
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After any fix, verify GPU support:
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```bash
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cd /mnt/shared/DEV/repos/d-popov.com/gogo2
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source venv/bin/activate
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python -c "
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import torch
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print(f'PyTorch: {torch.__version__}')
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print(f'CUDA Available: {torch.cuda.is_available()}')
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if torch.cuda.is_available():
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print(f'Device: {torch.cuda.get_device_name(0)}')
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# Test Linear layer
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x = torch.randn(2, 10).cuda()
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linear = torch.nn.Linear(10, 5).cuda()
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y = linear(x)
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print('GPU test passed!')
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"
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```
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## Current Status
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✅ Code updated to automatically detect and fallback to CPU
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⏳ Restart application to apply fix
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❌ GPU training will not work until PyTorch is reinstalled with gfx1151 support
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## Performance Impact
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- **CPU Mode:** 10-50x slower than GPU for training
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- **GPU Mode (after fix):** Full GPU acceleration restored
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