sapientinc
import adam_atan2_backend ModuleNotFoundError: No module named 'adam_atan2_backend'
OMP_NUM_THREADS=8 python pretrain.py
data_path=data/sudoku-extreme-1k-aug-1000
epochs=20000
eval_interval=2000
global_batch_size=384
lr=7e-5
puzzle_emb_lr=7e-5
weight_decay=1.0
puzzle_emb_we> ight_decay=1.0> > > > > > >
Traceback (most recent call last):
File "/tf/HRM/pretrain.py", line 19, in
Need someone to fix this .Thank you in advanced.
Found a workaround by using adam_atan2_pytorch. Just have to initialize the learning rate to a small value > 0 in the pretrain code or else it will return an assertion error.
Just switching imports doesn't work - you'd have to modify the code. The reason we're seeing this error, I believe, is that simply adding adam_atan2 from PyPI via PIP is not resulting in the building of the CUDA module adam_atan2_backend, which is missing because it needs to be compiled.
It would be useful if the Sapient folks could clarify this step in the installation process.
Thanks @kroggen - doing the following worked for me, as you suggested. Appreciate the help! ``sed -i 's/adam-atan2/adam-atan2-pytorch/g' requirements.txt
pip install -r requirements.txt
sed -i 's/adam_atan2/adam_atan2_pytorch/g' pretrain.py
sed -i 's/AdamATan2/AdamAtan2/g' pretrain.py
sed -i 's/lr=0,/lr=0.0001,/g' pretrain.py``
Fix for "No module named 'adam_atan2_backend'" Error
Problem
When importing adam_atan2, you may encounter this error:
ModuleNotFoundError: No module named 'adam_atan2_backend'
Root Cause
This error occurs because the C++ CUDA backend extension wasn't properly compiled during installation. The adam_atan2_backend module contains the optimized CUDA kernels that are essential for the optimizer to function.
Solution
Prerequisites
Ensure you have the necessary build tools installed:
- GCC/G++ compiler
- CUDA toolkit (matching your PyTorch CUDA version)
- Python development headers
Step-by-Step Fix
-
Clean uninstall the existing package:
pip uninstall adam-atan2 -y -
Upgrade build dependencies:
pip install --upgrade pip setuptools wheel -
Reinstall with fresh compilation:
pip install --no-cache-dir --verbose adam-atan2
What to Look For
During installation, you should see output indicating successful CUDA compilation:
building 'adam_atan2_backend' extension
/usr/bin/nvcc ... -c csrc/adam_atan2.cu -o build/temp.../adam_atan2.o ...
/usr/bin/nvcc ... -c csrc/ops.cu -o build/temp.../ops.o ...
And the final shared library being created:
adam_atan2_backend.cpython-xxx-x86_64-linux-gnu.so
Verification
Test that the fix worked:
import adam_atan2
import torch
# Create test parameter
param = torch.randn(10, requires_grad=True)
# Create optimizer - this should work without errors
optimizer = adam_atan2.AdamATan2([param], lr=0.001)
print("✅ AdamATan2 optimizer created successfully!")
Common Issues
Issue: ninja not found warning
- Solution: This is just a warning. The build will fall back to distutils and still work correctly.
Issue: CUDA version mismatch warning
- Solution: Minor version mismatches are usually fine. Ensure your CUDA toolkit is compatible with your PyTorch installation.
Issue: Missing build tools
-
Solution: Install development tools:
# Ubuntu/Debian sudo apt-get install build-essential python3-dev # CentOS/RHEL sudo yum groupinstall "Development Tools" sudo yum install python3-devel
Alternative: Install from Source
If the above doesn't work, you can build from source:
git clone https://github.com/jettify/adam-atan2.git
cd adam-atan2
pip install -e .
This ensures the C++ extensions are compiled in your specific environment.
Hope this helps! The key is ensuring the CUDA backend is properly compiled during installation. The --no-cache-dir --verbose flags are crucial for debugging and ensuring a fresh build.
