This document is relevant for: Trn1

PyTorch Neuron Environment Variables (torch-neuronx)#

Environment variables allow modifications to PyTorch Neuron behavior without requiring code change to user script. It is recommended to set them in code or just before invoking the python process, such as NEURON_FRAMEWORK_DEBUG=1 python3 <script> to avoid inadvertently changing behavior for other scripts. Environment variables specific to PyTorch Neuron are (experimental ones are noted):

NEURON_CC_FLAGS:

  • Compiler options. Full compiler options are described in the mixed-precision-casting-options. Additional options for the persistent cache can be found in the “Persistent cache” section below.

NEURON_FRAMEWORK_DEBUG [Experimental]:

  • Enable dumping of XLA graphs in both HLO format (intermediate representation) and text form for debugging.

NEURON_EXTRACT_GRAPHS_ONLY [Experimental]:

  • Dump the XLA graphs in HLO format (intermediate representation) and execute empty stubs with zero outputs in order to allow multiple XLA graphs to be traced through a trial execution. Used for ahead-of-time graph extraction for parallel compilation in neuron_parallel_compile tool. This environment variable can be checked in the training script to prevent checking of bad outputs during trial run.

NEURON_NUM_RECENT_MODELS_TO_KEEP [Experimental]:

  • Keep only N number of graphs loaded in Neuron runtime for each process, where N is the value this environment variable is set to. Default is to keep all graphs loaded by a process.

NEURON_PARALLEL_COMPILE_MAX_RETRIES [Experimental]:

  • Set the maximum number of retries when using neuron_parallel_compile tool. If set to N, the tool will try compilation N more time(s) if the first graph compilation failed. Example: Set NEURON_PARALLEL_COMPILE_MAX_RETRIES=1 when precompiling on trn1.2xlarge where there’s limited host memory and CPU resources. Default is 0.

NEURON_IGNORE_TRAINING_SCRIPT_ERROR_AND_COMPILE [Experimental]:

  • When using neuron_parallel_compile, if you want to ignore the error in training script and compile the accumulated HLO graphs, you can do so by setting this environment variable. Example: If NEURON_IGNORE_TRAINING_SCRIPT_ERROR_AND_COMPILE=1 is set when using neuron_parallel_compile, a crash in the training script would be ignored and the graphs collected upto the crash would be compiled.

NEURON_DUMP_HLO_SNAPSHOT [Experimental]:

  • Dump the inputs, outputs, and graph in HLO format of a graph execution in a snapshot file. This variable can be set to 1, ON_NRT_ERROR, ON_NRT_ERROR_CPU, ON_NRT_ERROR_HYBRID to dump snapshots at every iteration using CPU memory, or dump only on errors automatically using device, host, and both device and host memory respectively.

NEURON_NC0_ONLY_SNAPSHOT [Experimental]:

  • Dump only the snapshot associated with Neuron Core 0 when NEURON_NC0_ONLY_SNAPSHOT=1 and the NEURON_DUMP_HLO_SNAPSHOT flag is set.

BUCKET_CAP_MB [PyTorch XLA]:

  • If there are many parameters, such as in BERT training, small allreduce sizes can limit performance. To improve performance, you can try increasing the bucket size using BUCKET_CAP_MB environment variable, which is set to 50MB by default. For example, BERT pretraining on multiple instances can see improved performance with BUCKET_CAP_MB=512.

XLA_USE_BF16 [PyTorch XLA]:

  • When XLA_USE_BF16=1, PyTorch Neuron will automatically map both torch.float and torch.double tensors to bfloat16 tensors and turn on Stochastic Rounding mode. This can both reduce memory footprint and improve performance. Example: to enable bfloat16 autocasting and stochastic rounding, set XLA_USE_BF16=1 only, as stochastic rounding mode is on by default when XLA_USE_BF16=1. If you would like to preserve some tensors in float32, see XLA_DOWNCAST_BF16 below.

XLA_DOWNCAST_BF16 [PyTorch XLA]:

  • When XLA_DOWNCAST_BF16=1, PyTorch Neuron will automatically map torch.float tensors to bfloat16 tensors, torch.double tensors to float32 tensors and turn on Stochastic Rounding mode. This can both reduce memory footprint and improve performance, while preserving some tensors in float32. Example: to enable float to bfloat16 and double to float autocasting and stochastic rounding, set XLA_DOWNCAST_BF16=1 only, as stochastic rounding mode is on by default when XLA_DOWNCAST_BF16=1. If you want to cast both torch.float and torch.double to bfloat16, please see XLA_USE_BF16 above.

NEURON_RT_STOCHASTIC_ROUNDING_EN [Neuron Runtime]:

  • When NEURON_RT_STOCHASTIC_ROUNDING_EN=1, PyTorch Neuron will use stochastic rounding instead of round-nearest-even for all internal rounding operations when casting from FP32 to a reduced precision data type (FP16, BF16, FP8, TF32). This feature has been shown to improve training convergence for reduced precision training jobs, such as when bfloat16 autocasting is enabled. This is set to 1 by default by PyTorch Neuron when XLA_USE_BF16=1 or XLA_DOWNCAST_BF16=1. To switch to round-nearest-even mode, please set NEURON_RT_STOCHASTIC_ROUNDING_EN=0.

NEURON_RT_STOCHASTIC_ROUNDING_SEED [Neuron Runtime]:

  • Sets the seed for the random number generator used in stochastic rounding (see previous section). If this environment variable is not set, the seed is set to 0 by default. Please set NEURON_RT_STOCHASTIC_ROUNDING_SEED to a fixed value to ensure reproducibility between runs.

NEURON_RT_VISIBLE_CORES [Neuron Runtime]:

Integer range of specific NeuronCores needed by the process (for example, 0-3 specifies NeuronCores 0, 1, 2, and 3). You this environment variable when using torchrun to limit the launched processs to specific consecutive NeuronCores. To ensure best performance, the multi-core jobs requiring N NeuronCores for collective communication must be placed at the NeuronCore ID that starts at a multiple of N, where N is the world size limited to 1, 2, 8, 32. For example, a process using 2 NeuronCores can be mapped to 2 free NeuronCores starting at NeuronCore id 0, 2, 4, 6, etc, and a process using 8 NeuronCores can be mapped to 8 free NeuronCores starting at NeuronCore id 0, 8, 16, 24.

Additional Neuron runtime environment variables are described in runtime configuration documentation.

Additional XLA runtime environment variables are described in PyTorch-XLA troubleshooting guide.

This document is relevant for: Trn1