PyTorch Environment Variables
| Variable | Description |
|---|---|
PYTORCH_DEBUG_MPS_ALLOCATOR |
If set to 1, set allocator logging level to verbose. |
PYTORCH_MPS_HIGH_WATERMARK_RATIO |
High watermark ratio for MPS allocator. By default, it is set to 1.7. |
PYTORCH_MPS_LOW_WATERMARK_RATIO |
Low watermark ratio for MPS allocator. By default, it is set to 1.4 if the memory is unified and set to 1.0 if the memory is discrete. |
PYTORCH_MPS_FAST_MATH |
If set to 1, enable fast math for MPS metal kernels. See section 1.6.3 in https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf for precision implications. |
PYTORCH_MPS_PREFER_METAL |
If set to 1, force using metal kernels instead of using MPS Graph APIs. For now this is only used for matmul op. |
PYTORCH_ENABLE_MPS_FALLBACK |
If set to 1, full back operations to CPU when MPS does not support them. |
Note
high watermark ratio is a hard limit for the total allowed allocations
- 0.0 : disables high watermark limit (may cause system failure if system-wide OOM occurs)
- 1.0 : recommended maximum allocation size (i.e., device.recommendedMaxWorkingSetSize)
- >1.0: allows limits beyond the device.recommendedMaxWorkingSetSize
e.g., value 0.95 means we allocate up to 95% of recommended maximum allocation size; beyond that, the allocations would fail with OOM error.
low watermark ratio is a soft limit to attempt limiting memory allocations up to the lower watermark level by garbage collection or committing command buffers more frequently (a.k.a, adaptive commit). Value between 0 to m_high_watermark_ratio (setting 0.0 disables adaptive commit and garbage collection) e.g., value 0.9 means we 'attempt' to limit allocations up to 90% of recommended maximum allocation size.