This document is relevant for: Inf2, Trn1, Trn1n

Neuron Persistent Cache#

PyTorch Neuron (torch-neuronx) uses torch-xla, and torch-xla operates in lazy mode. In other words, every operation in training script is recorded in a graph. The graph is executed only when the results are requested by the user when they use print or xm.mark_step. Requesting results tells torch-xla that the recorded graph needs to be executed.

Before executing the graph, torch-xla would call Neuron Compiler (neuronx-cc) to compile the graph into Neuron specific graph. Then the graph is executed on the NeuronCore/s. Compiling the graph involves running optimizations that can make use of the NeuronCore/s efficiently. Running these optimizations can be expensive and can result in long compile times. To save the users from compiling these graphs at every iteration, torch-xla maintains an in-memory cache called Just in Time (JIT) cache. When the user re-runs the same graph (eg. 2nd iteration of the training run), torch-xla would check in this JIT cache and re-use the cached compilation result, thereby avoiding the wait times.

Since the JIT cache is an in-memory cache, it needs to be constructed every time the training script is run. Hence, if the user re-runs the training script, a new JIT cache is created. This causes a compilation for the first training graph. To avoid such compilations across training runs, PyTorch Neuron (torch-neuronx) has built an on-disk Neuron Persistent Cache. Since this cache is on-disk, its persistent across training runs. So now, when a graph is compiled for the fist time, the compilation result is saved in Neuron Persistent Cache. When the user re-runs the training script, since the JIT cache is not ready, it would send the graph for compilation. PyTorch Neuron (torch-neuronx) would then check if the compiled result is present in the Neuron Persistent Cache, if yes, it would return with the compiled result. This on-disk cache thereby avoids compilations across training runs. This cache is enabled by default and the default cache directory is /var/tmp/neuron-compile-cache.

Look at the diagram below on the end to end flow:


As seen from the diagram, the operations are recorded in a graph in lazy mode and only when a mark_step is hit, the graph is executed. Before execution, the graph passes through two caches to check if we have compiled the graph sometime in the past. If yes, we reuse the compilation result and execute with it. This avoid duplicate compilations. One thing to note, both JIT cache and Neuron Cache are complementary to each other. JIT cache prevents duplicate compilation within a run and Neuron Cache prevents duplicate compilations across training runs. For example, within a training script, we have a training loop that iterates through the dataset. The first iteration would trace a unique graph and the following iteration would trace a graph that is similar to the first one. In this case, the subsequent iterations would hit the JIT cache and reuse the result. However, to save users from compiling for the first iteration graph, Neuron Persistent Cache would be used. In this case, the very first time when the script is run, the Neuron Persistent Cache would be updated. Going forward when we re-run the training script, compilation results from Neuron Persistent Cache would be used.

To better understand how Neuron Persistent Cache works, consider the example below:

import torch
import torch_xla
import torch_xla.core.xla_model as xm
device = xm.xla_device()
t1 = torch.randn(3, 3).to(device)
t2 = t1 / 0.5
x = t2.cpu()

Running the above example produces the following logs:

2022-07-17 17:59:53.000541: INFO ||NCC_WRAPPER||: No candidate found under /var/tmp/neuron-compile-cache/USER_neuroncc-
2022-07-17 17:59:53.000541: INFO ||NCC_WRAPPER||: Cache dir for the neff: /var/tmp/neuron-compile-cache/USER_neuroncc-
Compiler status PASS
2022-07-17 18:00:01.000117: INFO ||NCC_WRAPPER||: Exiting with a successfully compiled graph

Re-running the above script would fetch the graph from the neuron cache and you would see logs as follows:

2022-07-17 18:05:37.000179: INFO ||NCC_WRAPPER||: Using a cached neff at /var/tmp/neuron-compile-cache/USER_neuroncc- Exiting with a successfully compiled graph

As you can see, the next run picks the compiled graph from cache, thereby saving the compilation time. The cache uses hash of the Neuron compiler flags and XLA graph as the key. If the Neuron compiler version or XLA graph changes, you will see recompilation. Examples of changes that would cause XLA graph change include:

  • Model type and size

  • Batch size

  • Optimizer and optimizer hyperparameters

  • Location of xm.mark_step()

All compilation results are saved in the cache. To disable the cache, you can pass --no_cache option via NEURON_CC_FLAGS:

os.environ['NEURON_CC_FLAGS'] = os.environ.get('NEURON_CC_FLAGS', '') + ' --no_cache'

To change the cache’s root directory, pass --cache_dir=<root dir> option via NEURON_CC_FLAGS (the actual cache directory will be in <root dir>/neuron-compile-cache):

os.environ['NEURON_CC_FLAGS'] = os.environ.get('NEURON_CC_FLAGS', '') + ' --cache_dir=<root dir>'

Stale cached compiled graphs (NEFFs) are deleted from the cache whenever the size of cache is above default cache size of 100GB . The deletion order is based on least-recently-used first. To change the cache size, pass --cache_size=SIZE_IN_BYTES. For example, to change the cache size to 16 MB:

os.environ['NEURON_CC_FLAGS'] = os.environ.get('NEURON_CC_FLAGS', '') + ' --cache_size=16777216'

A cache entry considered stale if the last used time is older than a time-to-live value, currently default to 30 days. If the last used time is earlier than the time-to-live value, then it is not deleted even if cache size exceeds cache size limit. To change cache time-to-live, set the option --cache_ttl to the number of days desired:

os.environ['NEURON_CC_FLAGS'] = os.environ.get('NEURON_CC_FLAGS', '') + ' --cache_ttl=60'

You can change the verbose level of the compiler by adding log_level to either WARNING, INFO or ERROR. This can be done as follows:

os.environ['NEURON_CC_FLAGS'] = os.environ.get('NEURON_CC_FLAGS', '') + ' --log_level=INFO'

Note: All compilation results are saved in the cache. In other words even if there is a failed compilation, its result would be saved in the cache. If you want to retry a failed compilation, you can do so by using --retry_failed_compilation.

os.environ['NEURON_CC_FLAGS'] = os.environ.get('NEURON_CC_FLAGS', '') + ' --retry_failed_compilation'

Setting the above flag, would retry all the failed compilations and save fresh results in the cache.

This document is relevant for: Inf2, Trn1, Trn1n