This document is relevant for: Inf2, Trn1, Trn2

Neuron Compiler (neuronx-cc) release notes#

Neuron Compiler [2.17.194.0]#

Date: 04/03/2025

  • Minor bug fixes and performance enhancements for both the trn1 and trn2 platforms.

Neuron Compiler [2.16.372.0]#

Date: 01/14/2025

  • Minor bug fixes and performance enhancements for the trn2 platform.

Neuron Compiler [2.16.345.0]#

Date: 12/20/2024

  • Minor bug fixes and performance enhancements for the trn2 platform.

Neuron Compiler#

Date: 12/03/2024

  • This release introduces the trn2 option argument to the compiler --target option to specify that the compiler should generate code for a trn2 instance family. Example usage: neuronx-cc compile --target=trn2 ...

  • This release introduces the --logical-nc-config or -lnc compiler command line option in support of the Logical NeuronCore Configuration feature available in Trainium2 instances. The compiler’s default is LNC=2. Note: Use of this option is available only for Trainium2 instances.

Neuron Compiler [2.15.128.0]#

Date: 09/16/2024

  • This release introduces memory optimization that will reduce the generated compiler artifacts size (i.e., NEFFs) and the models’ memory footprint. It is possible that some models may experience unexpected performance degradation. If this occurs, these optimizations can be disabled using the –disable-dge compiler command line option or the framework-level option additional_compile_opt=" --disable-dge"

Neuron Compiler [2.14.213.0]#

Date: 07/03/2024

  • Minor bug fixes and performance enhancements.

  • Improved flash attention kernel performance.

Neuron Compiler [2.13.72.0]#

Date: 04/25/2024

  • Minor bug fixes and enhancements.

Neuron Compiler [2.13.68.0]#

Date: 04/10/2024

  • This release fixes hang issues related to Triton Inference Server.

Neuron Compiler [2.13.66.0]#

Date: 04/01/2024

  • This release introduces a new --enable-mixed-precision-accumulation compiler option. This option instructs the compiler to perform intermediate calculations of reduction operators (such as the dot or reduce operators) in FP32 regardless of the operation’s defined datatype. The final result of the operator will be cast from FP32 to the model-designated datatype (e.g., BF16). This helps to improve the operator’s resulting acccuracy.

Neuron Compiler [2.12.68.0]#

Date: 01/18/2024

  • Patch release with bug fixes.

Neuron Compiler [2.12.54.0]#

Date: 12/21/2023

  • The compiler now generates instructions to check if a model references an embedding table with an illegal index. The check is made at model execution time. If an attempted invalid table index is encountered, the model execution will continue and the user will see an error similar to:

    WARNING: Received notification generated at runtime: failed to run scatter/gather (indirect memory copy with branch_label_id = xx), due to out-of-bound access.

When this occurs, users are encouraged to review the model’s gather/scatter input values to determine if there is a coding error.

Neuron Compiler [2.11.0.35]#

Date: 11/17/2023

  • This release addresses performance related issues when training through neuronx-nemo-megatron library.

Neuron Compiler [2.11.0.34]#

Date: 10/26/2023

  • This release introduces the option-argument llm-training to the existing --distribution_strategy compiler option. This option-argument allows the compiler to make specific optimizations related to training distributed models. This new option-argument is equivalent to the previously introduced nemo option-argument, which will be deprecated in a future release.

Neuron Compiler [2.10.0.35]#

Date: 09/26/2023

  • This release addresses a compilation regression for certain configurations of Llama and Llama-2 inference models when it fails compilation with this error “IndirectLoad/Save requires contiguous indirect access per partition” .

There is still a known issue for some configurations of the model with the error “Too many instructions after unroll for function sg0000” . To mitigate this, recompile using the --optlevel 1 (-O1) option. A complete fix will be coming in the future release which will not require this option

Neuron Compiler [2.10.0.34]#

Date: 09/15/2023

  • This release introduces a new --optlevel (-O) compiler option. This option allows the user to balance between compile-time and optimizations performed. Three levels are supported. Level --optlevel 1 (-O1) aims to minimize compile-time and allow for a more rapid model development cycle. Model execution time may be reduced. Level --optlevel 3 (-O3) performs whole-model optimization. This level will deliver the best performance however there will be longer compile-times and the compiler will use more host DRAM, potentially requiring a larger instance to compile the model. The default is --optlevel 2 (-O2) which provides a balance between model performance and compile time.

    The previous —enable-experimental-O1 flag introduced in the 02/08/2023 Neuron Compiler [2.4.0.21] release is now deprecated. Using this flag will generate a message similar to:

    WARNING: Option —enable-experimental-O1 is deprecated and will be removed in a future release.” Use --optlevel 1 (-O1) instead.

Neuron Compiler [2.9.0.16]#

Date: 08/28/2023

  • This release fixes an issue where any initial seed passed into the Random Number Generator operator was not honored. The RngBitGenerator operator now correctly accepts and uses setting the seed. Note that the current RNG implementation only supports 32-bit seeds.

Neuron Compiler [2.8.0.25]#

Date: 07/19/2023

  • This release introduces a new optional --distribution_strategy compiler option. This option informs the compiler what type of distributed APIs are used to shard the model and allows the compiler to make API-specific optimizations. Currently following option-arguments are supported: nemo.

Neuron Compiler [2.7.0.40]#

Date: 06/14/2023

  • This release introduces a new --enable-saturate-infinity compiler option. A computation that can generate +/- infinity is at a high risk of generating Not-a-Number (NaN) values when the infinity value is used in subsequent computations. This option helps avoid this by converting +Inf/-Inf values to MAX/MIN_FLOAT before operations that could produce NaN values for +Inf/-Inf inputs on the target architecture. While this option helps to avoid NaN values, there is a potential performance degradation that occurs during model execution when this conversion is enabled.

Neuron Compiler [2.6.0.19]#

Date: 05/01/2023

  • This release introduces a new model-type option argument: unet-inference. This option instructs the compiler to perform model-specific optimizations that produce executable models with improved performance on the specified target instance.

  • Added support for the HLO operator BitcastConvertType and also added support for TopK (sampling mode) operator.

Neuron Compiler [2.5.0.28]#

Date: 03/28/2023

  • This release introduces the trn1n option argument to the compiler target option to specify that it should generate code for a trn1n instance type. Example usage: neuronx-cc compile --target=trn1n ...

  • The compiler’s usage message now includes the inf2 option argument.

  • A new 8-bit floating point data type, fp8_e4m3, is now supported and can be specificed using the auto-cast-type option. This instructs the compiler to convert the FP32 operations selected via the --auto-cast option to a signed FP8 size with 4-bit exponent and 3-bit mantissa. Care must be taken to ensure that the down-casted values are representable within the 8-bit data range.

Neuron Compiler [2.4.0.21]#

Date: 02/24/2023

  • This release introduces the inf2 option argument to the compiler target option to specify that it should generate code for an inf2 instance type. Example usage: neuronx-cc compile --target=inf2 ... The inf2 option argument does not appear in the compiler’s usage message. It will be added in the next release.

Neuron Compiler [2.4.0.21]#

Date: 02/08/2023

  • Added support for the following HLO operators: SelectAndScatter.

  • Beta: --enable-experimental-O1 flag: This option reduces the compile-time with a neglible impact on model execution performance. It allows the compiler to execute compiler passes in parallel to perform the compilation. By default the compiler uses 8 processes. This can be changed via the CLI option --num-parallel-jobs. This option is expected to become the default in a future SDK release.

Neuron Compiler [2.3.0.4]#

Date: 12/09/2022

  • Added support for the following HLO operators: rev (reverse).

  • The pow() function can now handle both integer and floating-point exponents.

  • Optimization enhancements and bug fixes to improve model execution performance.

Neuron Compiler [2.2.0.73]#

Date: 10/27/2022

  • Adding support for the following HLO operators: LogicalNot, atan2 and DynamicUpdateSlice (for constant index).

Neuron Compiler [2.1.0.76]#

Date: 10/5/2022

The Neuron Compiler is an Ahead-of-Time compiler that accelerates models for execution on NeuronCores. This release supports compiling models for training on a Trn1 instance using Pytorch Neuron. Users typically access the compiler via the Framework to perform model compilation, although it can also be run as a command line tool (neuronx-cc).

The Neuron Compiler supports compiling models for mixed precision calculations. The trn1 hardware supports matrix multiplication using FP16, BF16, and FP32 on its Matrix Multiplication Engine, and accumulations using FP32. Operators such as activations or vector operations are supported using FP16, BF16, and FP32. Tensor transpose can be accomplished in FP16, BF16, FP32, or TF32 datatypes. By default, scalar and vector operations on FP32 values will be done in FP32, while matrix multiplications are cast to BF16 and transpose operations are cast to FP32. This default casting will generate the highest performance for a FP32 trained model.

By default, the compiler will target maximum performance by automatically casting the model to mixed precision. It also provides an option (--auto-cast) that allows the user to make tradeoffs between higher performance and optimal accuracy. The decision on what option argument to use with the --auto-cast option will be application specific. Compiler CLI options can be passed to the compiler via the framework.

Known issues#

  • The Random Number Generator operation can be passed an initial seed value, however setting the seed is not supported in this release.

  • The exponent value of the pow() function must be a compile-time integer constant.

  • The compiler treats INT64 datatypes as INT32 by truncating the high-order bits. If possible, cast these values to 32 bits .

  • Model compilation time is proportional to the model size and operators used. For some larger NLP models it may be upwards of 30 minutes.

Supported Operators#

The following XLA operators are supported by the Neuron Compiler. Future releases will broaden model support by providing additional XLA operators defined in https://www.tensorflow.org/xla/operation_semantics.

The list of supported operators can also be retrieved from the command line using neuronx-cc list-operators.

Supported XLA Operators

Notes

Abs

Add

Allgather

Allreduce

Atan2

Batchnorm

Batchnormgrad

Batchnorminference

BitcastConvertType

Broadcast

BroadcastInDim

Ceil

Clamp

Compare

Concatenate

Constant

ConstantLiteral

ConvertElementType

Cos

Customcall

Div

Dot

DotGeneral

DynamicUpdateSlice

Supports only for constant index

Eq

Exp

Floor

Gather

Supports only disjoint start_index_map and remapped_offset_dims

Ge

GetTupleElement

Gt

Iota

Le

Log

LogicalAnd

LogicalNot

Lt

Max

Min

Mul

Ne

Neg

Pad

Pow

Exponent argument must be a compile-time integer constant

Reduce

Min, Max, Add and Mul are the only supported computations. Init_values must be constant

Reshape

Rev (reverse)

RngBitGenerator

Ignores user seed

RngUniform

Rsqrt

Scatter

Select

SelectAndScatter

ShiftRightLogical

Sign

Sin

Slice

Sqrt

Sub

Tanh

Transpose

Tuple

This document is relevant for: Inf2, Trn1, Trn2