GPU memory allocation — JAX documentation (original) (raw)

GPU memory allocation#

JAX will preallocate 75% of the total GPU memory when the first JAX operation is run. Preallocating minimizes allocation overhead and memory fragmentation, but can sometimes cause out-of-memory (OOM) errors. If your JAX process fails with OOM, the following environment variables can be used to override the default behavior:

XLA_PYTHON_CLIENT_PREALLOCATE=false

This disables the preallocation behavior. JAX will instead allocate GPU memory as needed, potentially decreasing the overall memory usage. However, this behavior is more prone to GPU memory fragmentation, meaning a JAX program that uses most of the available GPU memory may OOM with preallocation disabled.

XLA_PYTHON_CLIENT_MEM_FRACTION=.XX

If preallocation is enabled, this makes JAX preallocate XX% of the total GPU memory, instead of the default 75%. Lowering the amount preallocated can fix OOMs that occur when the JAX program starts.

XLA_PYTHON_CLIENT_ALLOCATOR=platform

This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed (note that this is the only configuration that will deallocate GPU memory, instead of reusing it). This is very slow, so is not recommended for general use, but may be useful for running with the minimal possible GPU memory footprint or debugging OOM failures.

Common causes of OOM failures#

Running multiple JAX processes concurrently.

Either use XLA_PYTHON_CLIENT_MEM_FRACTION to give each process an appropriate amount of memory, or setXLA_PYTHON_CLIENT_PREALLOCATE=false.

Running JAX and GPU TensorFlow concurrently.

TensorFlow also preallocates by default, so this is similar to running multiple JAX processes concurrently.

One solution is to use CPU-only TensorFlow (e.g. if you’re only doing data loading with TF). You can prevent TensorFlow from using the GPU with the commandtf.config.experimental.set_visible_devices([], "GPU")

Alternatively, use XLA_PYTHON_CLIENT_MEM_FRACTION orXLA_PYTHON_CLIENT_PREALLOCATE. There are also similar options to configure TensorFlow’s GPU memory allocation (gpu_memory_fractionand allow_growthin TF1, which should be set in a tf.ConfigProto passed totf.Session. SeeUsing GPUs: Limiting GPU memory growthfor TF2).

Running JAX on the display GPU.

Use XLA_PYTHON_CLIENT_MEM_FRACTION orXLA_PYTHON_CLIENT_PREALLOCATE.

Disabling rematerialization HLO pass

Sometimes disabling the automatic rematerialization HLO pass is favorable to avoid poor remat choices by the compiler. The pass can be enable/disable by settingjax.config.update('enable_remat_opt_pass', True) orjax.config.update('enable_remat_opt_pass', False) respectively. Enabling or disabling the automatic remat pass produces different trade-offs between compute and memory. Note however, that the algorithm is basic and you can often get better trade-off between compute and memory by disabling the automatic remat pass and doing it manually with the jax.remat API

Experimental features#

Features here are experimental and must be tried with caution.

TF_GPU_ALLOCATOR=cuda_malloc_async

This replace XLA’s own BFC memory allocator with cudaMallocAsync. This will remove the big fixed pre-allocation and use a memory pool that grows. The expected benefit is no need to set XLA_PYTHON_CLIENT_MEM_FRACTION.

The risk are:

• that memory fragmentation is different, so if you are close to the limit, the exact OOM case due to fragmentation will be different.
• The allocation time won’t be all paid at the start, but be incurred when the memory pool need to be increased. So you could experience less speed stability at the start and for benchmarks it will be even more important to ignore the first few iterations.

The risks can be mitigated by pre-allocating a signigicant chunk and still get the benefit of having a growing memory pool. This can be done with TF_CUDA_MALLOC_ASYNC_SUPPORTED_PREALLOC=N. If N is -1it will preallocate the same as what was allocatedy by default. Otherwise, it is the size in bytes that you want to preallocate.