Neuron Compiler (neuronx-cc) release notes
Contents
This document is relevant for: Inf2
, Trn1
, Trn1n
Neuron Compiler (neuronx-cc
) release notes#
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.The new compiler option
distribution-strategy
can be used to better support models using the PyTorch 2.x Fully Sharded Data Parallel (FSDP) APIs.Added support for the HLO operator
BitcastConvertType
and also added support forTopK
(sampling mode) operator.
Neuron Compiler [2.5.0.28]#
Date: 03/28/2023
This release introduces the
trn1n
option argument to the compilertarget
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 theauto-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 compilertarget
option to specify that it should generate code for an inf2 instance type. Example usage:neuronx-cc compile --target=inf2 ...
Theinf2
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
.EXPERIMENTAL:
--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
andDynamicUpdateSlice
(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
, Trn1n