MaxPool2D memory leakage on device MPS #125217
Labels
high priority
module: mps
Related to Apple Metal Performance Shaders framework
triaged
This issue has been looked at a team member, and triaged and prioritized into an appropriate module
馃悰 Describe the bug
On MPS device, allocated memory keeps increasing when the model has MaxPool2D modules. It raises an out of memory error when tensor sizes are too big.
Here is an example:
In the above code, allocated memory constantly increases at each iteration when MaxPool2D layers are present. Eventually it crashes due to out of memory. However, when I remove the MaxPool2D layers, it runs without a problem, as the allocated memory remains constant at each iteration
This code runs without a problem.
Versions
PyTorch version: 2.3.0
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: macOS 14.4.1 (arm64)
GCC version: Could not collect
Clang version: 15.0.0 (clang-1500.3.9.4)
CMake version: Could not collect
Libc version: N/A
Python version: 3.11.5 (main, Sep 11 2023, 08:31:25) [Clang 14.0.6 ] (64-bit runtime)
Python platform: macOS-14.4.1-arm64-arm-64bit
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Apple M1 Pro
Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.3.0
[pip3] torchaudio==2.3.0
[pip3] torchvision==0.18.0
[conda] numpy 1.26.4 py311he598dae_0
[conda] numpy-base 1.26.4 py311hfbfe69c_0
[conda] pytorch 2.3.0 py3.11_0 pytorch
[conda] torchaudio 2.3.0 py311_cpu pytorch
[conda] torchvision 0.18.0 py311_cpu pytorch
cc @ezyang @gchanan @zou3519 @kadeng @kulinseth @albanD @malfet @DenisVieriu97 @jhavukainen
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