This document is relevant for: Inf2, Trn1, Trn1n

PyTorch Neuron (torch-neuronx) release notes#

PyTorch Neuron for Trn1/Inf2 is a software package that enables PyTorch users to train, evaluate, and perform inference on second-generation Neuron hardware (See: NeuronCore-v2).

Release [2.1.2.2.2.0]#

Date: 07/03/2024

Summary#

What’s new in this release#

  • Improvements in ZeRO1 to have FP32 master weights support and BF16 all-gather

  • Added custom SILU enabled via NEURON_CUSTOM_SILU environment variable

  • Neuron Parallel Compile now handle non utf-8 characters in trial-run log and reports compilation time results when enabled with NEURON_PARALLEL_COMPILE_DUMP_RESULTS

  • Support for using DummyStore during PJRT process group initialization by setting TORCH_DIST_INIT_BARRIER=0 and XLA_USE_DUMMY_STORE=1

Known limitations#

The following features are not yet supported in this version of Torch-Neuronx 2.1: * (Training) GSPMD * (Training/Inference) TorchDynamo (torch.compile) * (Training) DDP/FSDP

Resolved Issues#

Resolved an issue with slower loss convergence for GPT-2 pretraining using ZeRO1 tutorial#

Previously with Torch-NeuronX 2.1, we see slower loss convergence in the ZeRO1 tutorial. This issue is now resolved. Customer can now run the tutorial with the recommended flags (NEURON_CC_FLAGS="--distribution-strategy llm-training --model-type transformer").

Resolved an issue with slower loss convergence for NxD LLaMA-2 70B pretraining using ZeRO1 tutorial#

Previously with Torch-NeuronX 2.1, we see slower loss convergence in the LLaMA-2 70B tutorial for neuronx-distributed. This issue is now resolved. Customer can now run the tutorial with the recommended flags (NEURON_CC_FLAGS="--distribution-strategy llm-training --model-type transformer") and turning on functionalization (XLA_DISABLE_FUNCTIONALIZATION=0). Turning on functionalization results in slightly higher device memory usage and ~11% lower in performance due to a known issue with torch-xla 2.1 (https://github.com/pytorch/xla/issues/7174). The higher device memory usage also limits LLaMA-2 70B tutorial to run on 16 trn1.32xlarge nodes at the minimum, and running on 8 nodes would result in out-of-memory error. See the list of environment variables for more information about XLA_DISABLE_FUNCTIONALIZATION.

Resolved an issue where upon a compiler error during XLA JIT execution, the framework process exits with a stack dump followed by a core dump#

Previously, when there’s a compiler error during XLA JIT execution, the framework process exits with a stack dump following by a core dump:

2024-06-10 04:31:49.733004: F ./torch_xla/csrc/runtime/debug_macros.h:20] Non-OK-status: status.status() status: INTERNAL: RunNeuronCCImpl: error condition error != 0: <class 'subprocess.CalledProcessError'>: Command '' died with <Signals.SIGHUP: 1>.
*** Begin stack trace ***
        tsl::CurrentStackTrace()
        std::unique_ptr<xla::PjRtLoadedExecutable, std::default_delete<xla::PjRtLoadedExecutable> > ConsumeValue<std::unique_ptr<xla::PjRtLoadedExecutable, std::default_delete<xla::PjRtLoadedExecutable> > >(absl::lts_20230125::StatusOr<std::unique_ptr<xla::PjRtLoadedExecutable, std::default_delete<xla::PjRtLoadedExecutable> > >&&)
        torch_xla::runtime::PjRtComputationClient::Compile(std::vector<torch_xla::runtime::ComputationClient::CompileInstance, std::allocator<torch_xla::runtime::ComputationClient::CompileInstance> >)
        ...
        Py_RunMain
        Py_BytesMain
        _start
*** End stack trace ***
Aborted (core dumped)

This is now fixed so that the above error is more succinct:

RuntimeError: Bad StatusOr access: INTERNAL: RunNeuronCCImpl: error condition error != 0: <class 'subprocess.CalledProcessError'>: Command '' died with <Signals.SIGHUP: 1>.

Resolved an issue where S3 caching during distributed training can lead to S3 throttling error#

When using S3 location as Neuron Cache path (specified via NEURON_COMPILE_CACHE_URL or –cache_dir option in NEURON_CC_FLAGS), you may get the error An error occurred (SlowDown) when calling the PutObject operation as in:

2024-04-18 01:51:38.231524: F ./torch_xla/csrc/runtime/debug_macros.h:20] Non-OK-status: status.status() status: INVALID_ARGUMENT: RunNeuronCCImpl: error condition !(error != 400): <class 'boto3.exceptions.S3UploadFailedError'>: Failed to upload /tmp/tmp4d8d4r2d/model.hlo to bucket/llama-compile-cache/neuronxcc-2.13.68.0+6dfecc895/MODULE_9048582265414220701+5d2d81ce/model.hlo_module.pb: An error occurred (SlowDown) when calling the PutObject operation (reached max retries: 4): Please reduce your request rate.

This issue is now resolved in release 2.19.

Resolved error “ImportError: cannot import name ‘packaging’ from ‘pkg_resources’” when using latest setuptools version 70#

As reported in https://github.com/aws-neuron/aws-neuron-sdk/issues/893, When running examples in environment where the latest setuptools version 70 is installed, you may get the following error:

ImportError: cannot import name 'packaging' from 'pkg_resources' (/home/ubuntu/aws_neuron_venv_pytorch/lib/python3.8/site-packages/pkg_resources/__init__.py)

In release 2.19 torch-neuronx now depends on setuptools version <= 69.5.1.

Resolved compiler assertion error when training using Hugging Face deepmind/language-perceiver model#

The follow assertion error when training with Hugging Face deepmind/language-perceiver model is now resolved in release 2.19 compiler:

ERROR 176659 [NeuronAssert]: Assertion failure in usr/lib/python3.8/multiprocessing/process.py at line 108 with exception:
Unsupported batch-norm-training op: tensor_op_name: _batch-norm-training.852 | hlo_id: 852| file_name:  | Line: 0 | Column: 0 | .

Resolved lower accuracy for BERT-base finetuning using HF Trainer API#

With release 2.19 compiler, the MRPC dataset accuracy for BERT-base finetuning after 5 epochs is now 87% as expected.

Resolved the issue with increased in Neuron Parallel Compile time#

Torch-NeuronX 2.1, the time to run Neuron Parallel Compile for some model configuration has decreased.

Known issues#

Please see the Introducing PyTorch 2.1 Support for a full list of known issues.

Slower loss convergence for NxD LLaMA-3 70B pretraining using ZeRO1 tutorial#

Currently, with Torch-NeuronX 2.1, we see slower loss convergence in the LLaMA-3 70B tutorial for neuronx-distributed when using the recommended flags (NEURON_CC_FLAGS="--distribution-strategy llm-training --model-type transformer"). To work-around this issue, please only use --model-type transformer flag (NEURON_CC_FLAGS="--model-type transformer").

Gradient accumulation is not yet supported for Stable Diffusion due to a compiler error#

Currently, with torch-neuronx 2.1, we are seeing a compiler assertion error with Stable Diffusion training when gradient accumulation is enabled. To train Stable Diffusion with gradient accumulation, please use torch-neuronx 1.13 instead of 2.1.

Enabling functionalization (XLA_DISABLE_FUNCTIONALIZATION=0) results in 15% lower performance and non-convergence for the BERT pretraining tutorial#

Currently, with torch-neuronx 2.1, enabling functionalization (XLA_DISABLE_FUNCTIONALIZATION=0) would result in 15% lower performance and non-convergence for the BERT pretraining tutorial. The lower performance is due to missing aliasing for gradient accumulation and is a known issue with torch-xla 2.1 (https://github.com/pytorch/xla/issues/7174). The non-convergence is due to an issue in marking weights as static (buffer address not changing), which can be worked around by setting NEURON_TRANSFER_WITH_STATIC_RING_OPS to empty string (NEURON_TRANSFER_WITH_STATIC_RING_OPS="". See the list of environment variables for more information about XLA_DISABLE_FUNCTIONALIZATION. and NEURON_TRANSFER_WITH_STATIC_RING_OPS.

export NEURON_TRANSFER_WITH_STATIC_RING_OPS=""

GlibC error on Amazon Linux 2#

If using Torch-NeuronX 2.1 on Amazon Linux 2, you will see a GlibC error below. Please switch to a newer supported OS such as Ubuntu 20, Ubuntu 22, or Amazon Linux 2023.

ImportError: /lib64/libc.so.6: version `GLIBC_2.27' not found (required by /tmp/debug/_XLAC.cpython-38-x86_64-linux-gnu.so)

"EOFError: Ran out of input" or "_pickle.UnpicklingError: invalid load key, '!'" errors during Neuron Parallel Compile#

With torch-neuronx 2.1, HF Trainer API’s use of XLA function .mesh_reduce causes "EOFError: Ran out of input" or "_pickle.UnpicklingError: invalid load key, '!'" errors during Neuron Parallel Compile. To work-around this issue, you can add the following code snippet (after python imports) to replace xm.mesh_reduce with a form that uses xm.all_gather instead of xm.rendezvous() with payload. This will add additional small on-device graphs (as opposed to the original xm.mesh_reduce which runs on CPU).

import copy
import torch_xla.core.xla_model as xm
def mesh_reduce(tag, data, reduce_fn):
    xm.rendezvous(tag)
    xdatain = copy.deepcopy(data)
    xdatain = xdatain.to("xla")
    xdata = xm.all_gather(xdatain, pin_layout=False)
    cpu_xdata = xdata.detach().to("cpu")
    cpu_xdata_split = torch.split(cpu_xdata, xdatain.shape[0])
    xldata = [x for x in cpu_xdata_split]
    return reduce_fn(xldata)
xm.mesh_reduce = mesh_reduce

Check failed: tensor_data error during when using torch.utils.data.DataLoader with shuffle=True#

With torch-neuronx 2.1, using torch.utils.data.DataLoader with shuffle=True would cause the following error in synchronize_rng_states (i.e. ZeRO1 tutorial):

RuntimeError: torch_xla/csrc/xla_graph_executor.cpp:562 : Check failed: tensor_data

This is due to synchronize_rng_states using xm.mesh_reduce to synchronize RNG states. xm.mesh_reduce in turn uses xm.rendezvous() with payload, which as noted in 2.x migration guide, would result in extra graphs that could lead to lower performance due to change in xm.rendezvous() in torch-xla 2.x. In the case of ZeRO1 tutorial, using xm.rendezvous() with payload also lead to the error above. This limitation will be fixed in an upcoming release. For now, to work around the issue, please disable shuffle in DataLoader when NEURON_EXTRACT_GRAPHS_ONLY environment is set automatically by Neuron Parallel Compile:

train_dataloader = DataLoader(
    train_dataset, shuffle=(os.environ.get("NEURON_EXTRACT_GRAPHS_ONLY", None) == None), collate_fn=default_data_collator, batch_size=args.per_device_train_batch_size
)

Additionally, as in the previous section, you can add the following code snippet (after python imports) to replace xm.mesh_reduce with a form that uses xm.all_gather instead of xm.rendezvous() with payload. This will add additional small on-device graphs (as opposed to the original xm.mesh_reduce which runs on CPU).

import copy
import torch_xla.core.xla_model as xm
def mesh_reduce(tag, data, reduce_fn):
    xm.rendezvous(tag)
    xdatain = copy.deepcopy(data)
    xdatain = xdatain.to("xla")
    xdata = xm.all_gather(xdatain, pin_layout=False)
    cpu_xdata = xdata.detach().to("cpu")
    cpu_xdata_split = torch.split(cpu_xdata, xdatain.shape[0])
    xldata = [x for x in cpu_xdata_split]
    return reduce_fn(xldata)
xm.mesh_reduce = mesh_reduce

Compiler error when torch_neuronx.xla_impl.ops.set_unload_prior_neuron_models_mode(True)#

Currently with torch-neuronx 2.1, using the torch_neuronx.xla_impl.ops.set_unload_prior_neuron_models_mode(True) (as previously done in the ZeRO1 tutorial) to unload graphs during execution would cause a compilation error Expecting value: line 1 column 1 (char 0). You can remove this line as it is not recommended for use. Please see the updated ZeRO1 tutorial in release 2.18.

Compiler assertion error when running Stable Diffusion training#

Currently, with torch-neuronx 2.1, we are seeing the following compiler assertion error with Stable Diffusion training when gradient accumulation is enabled. This will be fixed in an upcoming release. For now, if you would like to run Stable Diffusion training with Neuron SDK release 2.18, please use torch-neuronx==1.13.* or disable gradient accumulation in torch-neuronx 2.1.

ERROR 222163 [NeuronAssert]: Assertion failure in usr/lib/python3.8/concurrent/futures/process.py at line 239 with exception:
too many partition dims! {{0,+,960}[10],+,10560}[10]

Lower performance for BERT-Large#

Currently we see 8% less performance when running the BERT-Large pre-training tutorial with Torch-NeuronX 2.1 as compared to Torch-NeuronX 1.13.

Release [1.13.1.2.10.12.0]#

Date: 07/03/2024

Summary#

What’s new in this release#

Improvements in ZeRO1 to have FP32 master weights support and BF16 all-gather Added custom SILU enabled via NEURON_CUSTOM_SILU environment variable Neuron Parallel Compile now handle non utf-8 characters in trial-run log and reports compilation time results when enabled with NEURON_PARALLEL_COMPILE_DUMP_RESULTS

Resolved Issues#

Known Issues and Limitations#

Memory leaking in glibc#

glibc malloc memory leaks affect Neuron and may be temporarily limited by setting MALLOC_ARENA_MAX or using jemalloc library (see https://github.com/aws-neuron/aws-neuron-sdk/issues/728).

DDP shows slow convergence#

Currently we see that the models converge slowly with DDP when compared to the scripts that don’t use DDP. We also see a throughput drop with DDP. This is a known issue with torch-xla: https://pytorch.org/xla/release/1.13/index.html#mnist-with-real-data

Runtime crash when we use too many workers per node with DDP#

Currently, if we use 32 workers with DDP, we see that each worker generates its own graph. This causes an error in the runtime, and you may see errors that look like this:

bootstrap.cc:86 CCOM WARN Call to accept failed : Too many open files``.

Hence, it is recommended to use fewer workers per node with DDP.

Known Issues and Limitations (Inference)#

Torchscript serialization error with compiled artifacts larger than 4GB#

When using torch_neuronx.trace(), compiled artifacts which exceed 4GB cannot be serialized. Serializing the torchscript artifact will trigger a segfault. This issue is resolved in torch but is not yet released: https://github.com/pytorch/pytorch/pull/99104

Release [2.1.2.2.1.0]#

Date: 04/01/2024

Summary#

This release of 2.1 includes support for Neuron Profiler, multi-instance distributed training, Nemo Megatron, and HuggingFace Trainer API.

What’s new in this release#

In addition to previously supported features (Transformers-NeuronX, Torch-NeuronX Trace API, Torch-NeuronX training, NeuronX-Distributed training), torch-neuronx 2.1 now includes support for:

  • (Inference) NeuronX-Distributed inference

  • (Training/Inference) Neuron Profiler

  • (Training) Multi-instance distributed training

  • (Training) Nemo Megatron

  • (Training) analyze feature in neuron_parallel_compile

  • (Training) HuggingFace Trainer API

Additionally, auto-bucketing is a new feature for torch-neuronx and Neuronx-Distributed allowing users to define bucket models that can be serialized into a single model for multi-shape inference.

Known limitations#

The following features are not yet supported in this version of Torch-NeuronX 2.1:

  • (Training) GSPMD

  • (Training) TorchDynamo (torch.compile)

  • (Training) DDP/FSDP

  • (Training) S3 caching during distributed training can lead to throttling issues

Resolved issues#

“Attempted to access the data pointer on an invalid python storage”#

When using Hugging Face Trainer API with transformers version >= 4.35 and < 4.37.3, user would see the error "Attempted to access the data pointer on an invalid python storage" during model checkpoint saving. This issue is fixed in transformers version >= 4.37.3. See https://github.com/huggingface/transformers/issues/27578 for more information.

Too many graph compilations when using HF Trainer API#

When using Hugging Face transformers version >= 4.35 and < 4.37.3, user would see many graph compilations (see https://github.com/aws-neuron/aws-neuron-sdk/issues/813 for more information). To work around this issue, in transformers version >= 4.37.3, user can add the option --save_safetensors False to Trainer API function call and modify the installed trainer.py as follows (don’t move model to CPU before saving checkpoint):

# Workaround https://github.com/aws-neuron/aws-neuron-sdk/issues/813
sed -i "s/model\.to(\"cpu\")//" `python -c "import site; print(site.getsitepackages()[0])"`/trainer.py

Divergence (non-convergence) of loss for BERT/LLaMA when using release 2.16 compiler#

With release 2.18, the divergence (non-convergence) of BERT/LLaMA loss is resolved. No compiler flag change is required.

Known Issues#

Please see the Introducing PyTorch 2.1 Support for a full list of known issues.

GlibC error on Amazon Linux 2#

If using Torch-NeuronX 2.1 on Amazon Linux 2, you will see a GlibC error below. Please switch to a newer supported OS such as Ubuntu 20, Ubuntu 22, or Amazon Linux 2023.

ImportError: /lib64/libc.so.6: version `GLIBC_2.27' not found (required by /tmp/debug/_XLAC.cpython-38-x86_64-linux-gnu.so)

"EOFError: Ran out of input" or "_pickle.UnpicklingError: invalid load key, '!'" errors during Neuron Parallel Compile#

With torch-neuronx 2.1, HF Trainer API’s use of XLA function .mesh_reduce causes "EOFError: Ran out of input" or "_pickle.UnpicklingError: invalid load key, '!'" errors during Neuron Parallel Compile. This is an issue with the trial execution of empty NEFFs and should not affect the normal execution of the training script.

Check failed: tensor_data error during when using torch.utils.data.DataLoader with shuffle=True#

With torch-neuronx 2.1, using torch.utils.data.DataLoader with shuffle=True would cause the following error in synchronize_rng_states (i.e. ZeRO1 tutorial):

RuntimeError: torch_xla/csrc/xla_graph_executor.cpp:562 : Check failed: tensor_data

This is due to synchronize_rng_states using xm.mesh_reduce to synchronize RNG states. xm.mesh_reduce in turn uses xm.rendezvous() with payload, which as noted in 2.x migration guide, would result in extra graphs that could lead to lower performance due to change in xm.rendezvous() in torch-xla 2.x. In the case of ZeRO1 tutorial, using xm.rendezvous() with payload also lead to the error above. This limitation will be fixed in an upcoming release. For now, to work around the issue, please disable shuffle in DataLoader when NEURON_EXTRACT_GRAPHS_ONLY environment is set automatically by Neuron Parallel Compile:

train_dataloader = DataLoader(
    train_dataset, shuffle=(os.environ.get("NEURON_EXTRACT_GRAPHS_ONLY", None) == None), collate_fn=default_data_collator, batch_size=args.per_device_train_batch_size
)

Additionally, you can add the following code snippet (after python imports) to replace xm.mesh_reduce with a form that uses xm.all_gather instead of xm.rendezvous() with payload. This will add additional small on-device graphs (as opposed to the original xm.mesh_reduce which runs on CPU).

import copy
import torch_xla.core.xla_model as xm
def mesh_reduce(tag, data, reduce_fn):
    xm.rendezvous(tag)
    xdatain = copy.deepcopy(data)
    xdatain = xdatain.to("xla")
    xdata = xm.all_gather(xdatain, pin_layout=False)
    cpu_xdata = xdata.detach().to("cpu")
    cpu_xdata_split = torch.split(cpu_xdata, xdatain.shape[0])
    xldata = [x for x in cpu_xdata_split]
    return reduce_fn(xldata)
xm.mesh_reduce = mesh_reduce

Compiler error when torch_neuronx.xla_impl.ops.set_unload_prior_neuron_models_mode(True)#

Currently with torch-neuronx 2.1, using the torch_neuronx.xla_impl.ops.set_unload_prior_neuron_models_mode(True) (as previously done in the ZeRO1 tutorial) to unload graphs during execution would cause a compilation error Expecting value: line 1 column 1 (char 0). You can remove this line as it is not recommended for use. Please see the updated ZeRO1 tutorial in release 2.18.

Compiler assertion error when running Stable Diffusion training#

Currently, with torch-neuronx 2.1, we are seeing the following compiler assertion error with Stable Diffusion training. This will be fixed in an upcoming release. For now, if you would like to run Stable Diffusion training with Neuron SDK release 2.18, please use torch-neuronx==1.13.*.

ERROR 222163 [NeuronAssert]: Assertion failure in usr/lib/python3.8/concurrent/futures/process.py at line 239 with exception:
too many partition dims! {{0,+,960}[10],+,10560}[10]

Compiler assertion error when training using Hugging Face deepmind/language-perceiver model#

Currently, with torch-neuronx 2.1, we are seeing the following compiler assertion error when training with Hugging Face deepmind/language-perceiver model. This will be fixed in an upcoming release. For now, if you would like to train Hugging Face deepmind/language-perceiver model with Neuron SDK release 2.18, please use torch-neuronx==1.13.*.

ERROR 176659 [NeuronAssert]: Assertion failure in usr/lib/python3.8/multiprocessing/process.py at line 108 with exception:
Unsupported batch-norm-training op: tensor_op_name: _batch-norm-training.852 | hlo_id: 852| file_name:  | Line: 0 | Column: 0 | .

Lower performance for BERT-Large#

Currently we see 8% less performance when running the BERT-Large pre-training tutorial with Torch-NeuronX 2.1 as compared to Torch-NeuronX 1.13.

Lower accuracy for BERT-base finetuning using HF Trainer API#

Currently, with Torch-NeuronX 2.1, MRPC dataset accuracy for BERT-base finetuning after 5 epochs is 83% instead of 87%. A work-around is to remove the option --model-type=transformer from NEURON_CC_FLAGS. This will be fixed in an upcoming release.

Increased in Neuron Parallel Compile time#

Currently, with Torch-NeuronX 2.1, the time to run Neuron Parallel Compile for some model configuration is increased. In one example, the Neuron Parallel Compile time for NeuronX Nemo-Megatron LLaMA 13B is 2x compared to when using Torch-NeuronX 1.13. This will be fixed in an upcoming release.

Release [1.13.1.1.14.0]#

Date: 04/01/2024

Summary#

Auto-bucketing is a new feature for torch-neuronx and Neuronx-Distributed allowing users to define bucket models that can be serialized into a single model for multi-shape inference.

Resolved issues#

  • (Inference) Fixed an issue where transformers-neuronx inference errors could crash the application and cause it to hang. Inference errors should now correctly throw a runtime exception.

  • (Inference/Training) Fixed an issue where torch.argmin() produced incorrect results.

  • (Training) neuron_parallel_compile tool now use traceback.print_exc instead of format to support Python 3.10.

  • (Training) Fixed an issue in ZeRO1 when sharded params are initialized with torch.double.

Known issues and limitations#

Memory leaking in glibc#

glibc malloc memory leaks affect Neuron and may be temporarily limited by setting MALLOC_ARENA_MAX or using jemalloc library (see https://github.com/aws-neuron/aws-neuron-sdk/issues/728).

DDP shows slow convergence#

Currently we see that the models converge slowly with DDP when compared to the scripts that don’t use DDP. We also see a throughput drop with DDP. This is a known issue with torch-xla: https://pytorch.org/xla/release/1.13/index.html#mnist-with-real-data

Runtime crash when we use too many workers per node with DDP#

Currently, if we use 32 workers with DDP, we see that each worker generates its own graph. This causes an error in the runtime, and you may see errors that look like this:

bootstrap.cc:86 CCOM WARN Call to accept failed : Too many open files``.

Hence, it is recommended to use fewer workers per node with DDP.

Known issues and limitations (Inference)#

Torchscript serialization error with compiled artifacts larger than 4GB#

When using torch_neuronx.trace(), compiled artifacts that exceed 4GB cannot be serialized. Serializing the TorchScript artifact triggers a segmentation fault. This issue is resolved in PyTorch but is not yet released: https://github.com/pytorch/pytorch/pull/99104

Release [2.1.1.2.0.0b0] (Beta)#

Date: 12/21/2023

Summary#

Introducing the beta release of Torch-NeuronX with PyTorch 2.1 support.

What’s new in this release#

This version of Torch-NeuronX 2.1 supports:

  • (Inference) Transformers-NeuronX

  • (Inference) Torch-NeuronX Trace API

  • (Training) NeuronX-Distributed training

  • (Training) Torch-NeuronX training

  • (Training) New snapshotting capability enabled via the XLA_FLAGS environment variable (see debug guide)

Known limitations#

The following features are not yet supported in this version of Torch-NeuronX 2.1:

  • (Training/Inference) Neuron Profiler

  • (Inference) NeuronX-Distributed inference

  • (Training) Nemo Megatron

  • (Training) GSPMD

  • (Training) TorchDynamo (torch.compile)

  • (Training) analyze feature in neuron_parallel_compile

  • (Training) HuggingFace Trainer API (see Known Issues below)

Additional limitations are noted in the Known Issues section below.

Known Issues#

Please see the Introducing PyTorch 2.1 Support (Beta) for a full list of known issues.

Lower performance for BERT-Large#

Currently we see 8% less performance when running the BERT-Large pre-training tutorial with Torch-NeuronX 2.1 as compared to Torch-NeuronX 1.13.

Divergence (non-convergence) of loss for BERT/LLaMA when using release 2.16 compiler#

Currently, when using release 2.16 compiler version 2.12.54.0+f631c2365, you may see divergence (non-convergence) of loss curve. To workaround this issue, please use release 2.15 compiler version 2.11.0.35+4f5279863.

Error “Attempted to access the data pointer on an invalid python storage” when using HF Trainer API#

Currently, if using HuggingFace Transformers Trainer API to train (i.e. HuggingFace Trainer API fine-tuning tutorial), you may see the error “Attempted to access the data pointer on an invalid python storage”. This is a known issue https://github.com/huggingface/transformers/issues/27578 and will be fixed in a future release.

Release [1.13.1.1.13.0]#

Date: 12/21/2023

Summary#

What’s new in this release#

  • Added Weight Replacement API For Inference)

Resolved issues#

Known issues and limitations#

Memory leaking in glibc#

glibc malloc memory leaks affect Neuron and may be temporarily limited by setting MALLOC_ARENA_MAX or using jemalloc library (see https://github.com/aws-neuron/aws-neuron-sdk/issues/728).

DDP shows slow convergence#

Currently we see that the models converge slowly with DDP when compared to the scripts that don’t use DDP. We also see a throughput drop with DDP. This is a known issue with torch-xla: https://pytorch.org/xla/release/1.13/index.html#mnist-with-real-data

Runtime crash when we use too many workers per node with DDP#

Currently, if we use 32 workers with DDP, we see that each worker generates its own graph. This causes an error in the runtime, and you may see errors that look like this:

bootstrap.cc:86 CCOM WARN Call to accept failed : Too many open files``.

Hence, it is recommended to use fewer workers per node with DDP.

Known issues and limitations (Inference)#

torch.argmin() produces incorrect results#

torch.argmin() produces incorrect results.

Torchscript serialization error with compiled artifacts larger than 4GB#

When using torch_neuronx.trace(), compiled artifacts that exceed 4GB cannot be serialized. Serializing the TorchScript artifact triggers a segmentation fault. This issue is resolved in PyTorch but is not yet released: https://github.com/pytorch/pytorch/pull/99104

Release [2.0.0.2.0.0b0] (Beta)#

Date: 10/26/2023

Summary#

Introducing the beta release of Torch-NeuronX with PyTorch 2.0 and PJRT support.

What’s new in this release#

  • Updating from XRT to PJRT runtime. For more info see: <link to intro pjrt doc>

  • (Inference) Added the ability to partition unsupported ops to CPU during traced inference (See torch_neuronx.trace API guide)

Known issues and limitations#

  • Snapshotting is not supported

  • NEURON_FRAMEWORK_DEBUG=1 is not supported

  • Analyze in neuron_parallel_compile is not supported

  • Neuron Profiler is not supported

  • VGG11 with input sizes 300x300 may show accuracy issues

  • Possible issues with NeMo Megatron checkpointing

  • S3 caching with neuron_parallel_compile may show compilation errors

  • Compiling without neuron_parallel_compile on multiple nodes may show compilation errors

  • GPT2 inference may show errors with torch_neuronx.trace

Release [1.13.1.1.12.0]#

Date: 10/26/2023

Summary#

What’s new in this release#

  • (Training) Added coalescing of all-gather and reduce-scatter inside ZeRO1, which should help in improving performance at high cluster sizes.

  • (Inference) Added the ability to partition unsupported ops to CPU during traced inference. (See torch_neuronx.trace API guide)

  • (Inference) Previously undocumented arguments trace API args state and options are now unsupported (have no effect) and will result in a deprecation warning if used.

Resolved issues#

  • Fixed an issue where torch.topk would fail on specific dimensions

  • (Inference) Fixed an issue where NaNs could be produced when using torch_neuronx.dynamic_batch

  • (Inference) Updated torch_neuronx.dynamic_batch to better support Modules (traced, scripted, and normal modules) with multiple Neuron subgraphs

  • (Inference) Isolate frontend calls to the Neuron compiler to working directories, so concurrent compilations do not conflict by being run from the same directory.

Known issues and limitations (Training)#

Memory leaking in glibc#

glibc malloc memory leaks affect Neuron and may be temporarily limited by setting MALLOC_ARENA_MAX.

DDP shows slow convergence#

Currently we see that the models converge slowly with DDP when compared to the scripts that don’t use DDP. We also see a throughput drop with DDP. This is a known issue with torch-xla: https://pytorch.org/xla/release/1.13/index.html#mnist-with-real-data

Runtime crash when we use too many workers per node with DDP#

Currently, if we use 32 workers with DDP, we see that each worker generates its own graph. This causes an error in the runtime, and you may see errors that look like this:

bootstrap.cc:86 CCOM WARN Call to accept failed : Too many open files``.

Hence, it is recommended to use fewer workers per node with DDP.

Known issues and limitations (Inference)#

torch.argmin() produces incorrect results#

torch.argmin() produces incorrect results.

Torchscript serialization error with compiled artifacts larger than 4GB#

When using torch_neuronx.trace(), compiled artifacts that exceed 4GB cannot be serialized. Serializing the TorchScript artifact triggers a segmentation fault. This issue is resolved in PyTorch but is not yet released: https://github.com/pytorch/pytorch/pull/99104

Release [1.13.1.1.11.0]#

Date: 9/15/2023

Summary#

Resolved issues#

Release [1.13.1.1.10.1]#

Date: 9/01/2023

Summary#

Minor bug fixes and enhancements.

Release [1.13.1.1.10.0]#

Date: 8/28/2023

Summary#

What’s new in this release#

  • Removed support for Python 3.7

  • (Training) Added a neuron_parallel_compile command to clear file locks left behind when a neuron_parallel_compile execution was interrupted (neuron_parallel_compile –command clear-locks)

  • (Training) Seedable dropout now enabled by default

Resolved issues#

  • (Training) Convolution is now supported

  • Fixed segmentation fault when using torch-neuronx to compile models on U22

  • Fixed XLA tensor stride information in torch-xla package, which blocked lowering of log_softmax and similar functions and showed errors like:

File "/home/ubuntu/waldronn/asr/test_env/lib/python3.7/site-packages/torch/nn/functional.py", line 1930, in log_softmax
      ret = input.log_softmax(dim)
  RuntimeError: dimensionality of sizes (3) must match dimensionality of strides (1)

Known issues and limitations (Training)#

Memory leaking in glibc#

glibc malloc memory leaks affect Neuron and may be temporarily limited by setting MALLOC_ARENA_MAX.

DDP shows slow convergence#

Currently we see that the models converge slowly with DDP when compared to the scripts that don’t use DDP. We also see a throughput drop with DDP. This is a known issue with torch-xla: https://pytorch.org/xla/release/1.13/index.html#mnist-with-real-data

Runtime crash when we use too many workers per node with DDP#

Currently, if we use 32 workers with DDP, we see that each worker generates its own graph. This causes an error in the runtime, and you may see errors that look like this:

bootstrap.cc:86 CCOM WARN Call to accept failed : Too many open files``.

Hence, it is recommended to use fewer workers per node with DDP.

Known issues and limitations (Inference)#

torch.argmin() produces incorrect results#

torch.argmin() produces incorrect results.

No automatic partitioning#

Currently, when Neuron encounters an operation that it does not support during torch_neuronx.trace(), it may exit with the following compiler error: “Import of the HLO graph into the Neuron Compiler has failed. This may be caused by unsupported operators or an internal compiler error.” The intended behavior when tracing is to automatically partition the model into separate subgraphs that run on NeuronCores and subgraphs that run on CPU. This will be supported in a future release. See PyTorch Neuron (torch-neuronx) - Supported Operators for a list of supported operators.

Torchscript serialization error with compiled artifacts larger than 4GB#

When using torch_neuronx.trace(), compiled artifacts that exceed 4GB cannot be serialized. Serializing the TorchScript artifact triggers a segmentation fault. This issue is resolved in PyTorch but is not yet released: https://github.com/pytorch/pytorch/pull/99104

Release [1.13.1.1.9.0]#

Date: 7/19/2023

Summary#

What’s new in this release#

Training support:

  • Uses jemalloc as the primary malloc lib to avoid memory leak at checkpointing

  • Added support for ZeRO-1 along with tutorial

Inference support:

  • Add async load and lazy model load options to accelerate model loading

  • Optimize DataParallel API to load onto multiple cores simultaneously when device IDs specified in device_ids are consecutive

Resolved issues (Training)#

  • Remove extra graph creation in torch_neuronx.optim.adamw when the beta/lr parameters values become 0 or 1.

  • Stability improvements and faster failure on hitting a fault in XRT server used by XLA.

Known issues and limitations (Training)#

Memory leaking in glibc#

glibc malloc memory leaks affect Neuron and may be temporarily limited by setting MALLOC_ARENA_MAX.

Convolution is not supported#

Convolution is not supported during training.

DDP shows slow convergence#

Currently we see that the models converge slowly with DDP when compared to the scripts that don’t use DDP. We also see a throughput drop with DDP. This is a known issue with torch-xla: https://pytorch.org/xla/release/1.13/index.html#mnist-with-real-data

Runtime crash when we use too many workers per node with DDP#

Currently, if we use 32 workers with DDP, we see that each worker generates its own graph. This causes an error in the runtime, and you may see errors that look like this:

bootstrap.cc:86 CCOM WARN Call to accept failed : Too many open files``.

Hence, it is recommended to use fewer workers per node with DDP.

Known issues and limitations (Inference)#

torch.argmin() produces incorrect results#

torch.argmin() produces incorrect results.

No automatic partitioning#

Currently, when Neuron encounters an operation that it does not support during torch_neuronx.trace(), it may exit with the following compiler error: “Import of the HLO graph into the Neuron Compiler has failed. This may be caused by unsupported operators or an internal compiler error.” The intended behavior when tracing is to automatically partition the model into separate subgraphs that run on NeuronCores and subgraphs that run on CPU. This will be supported in a future release. See PyTorch Neuron (torch-neuronx) - Supported Operators for a list of supported operators.

Torchscript serialization error with compiled artifacts larger than 4GB#

When using torch_neuronx.trace(), compiled artifacts that exceed 4GB cannot be serialized. Serializing the TorchScript artifact triggers a segmentation fault. This issue is resolved in PyTorch but is not yet released: https://github.com/pytorch/pytorch/pull/99104

Release [1.13.1.1.8.0]#

Date: 6/14/2023

Summary#

  • Added s3 caching to NeuronCache.

  • Added extract/compile/analyze phases to neuron_parallel_compile.

What’s new in this release#

Training support:

  • Added S3 caching support to NeuronCache. Removed NeuronCache options –cache_size/cache_ttl (please delete cache directories as needed).

  • Added separate extract and compile phases Neuron Parallel Compile.

  • Added model analyze API to Neuron Parallel Compile.

Known issues and limitations (Training)#

Memory leaking in glibc#

glibc malloc memory leaks affect Neuron and may be temporarily limited by setting MALLOC_ARENA_MAX.

Convolution is not supported#

Convolution is not supported during training.

DDP shows slow convergence#

Currently we see that the models converge slowly with DDP when compared to the scripts that don’t use DDP. We also see a throughput drop with DDP. This is a known issue with torch-xla: https://pytorch.org/xla/release/1.13/index.html#mnist-with-real-data

Runtime crash when we use too many workers per node with DDP#

Currently, if we use 32 workers with DDP, we see that each worker generates its own graph. This causes an error in the runtime, and you may see errors that look like this:

bootstrap.cc:86 CCOM WARN Call to accept failed : Too many open files``.

Hence, it is recommended to use fewer workers per node with DDP.

Known issues and limitations (Inference)#

torch.argmin() produces incorrect results#

torch.argmin() produces incorrect results.

No automatic partitioning#

Currently, when Neuron encounters an operation that it does not support during torch_neuronx.trace(), this will cause an error. The intended behavior when tracing is to automatically partition the model into separate subgraphs that run on NeuronCores and subgraphs that run on CPU. See PyTorch Neuron (torch-neuronx) - Supported Operators for a list of supported operators.

Torchscript serialization error with compiled artifacts larger than 4GB#

When using torch_neuronx.trace(), compiled artifacts that exceed 4GB cannot be serialized. Serializing the TorchScript artifact triggers a segmentation fault. This issue is resolved in PyTorch but is not yet released: https://github.com/pytorch/pytorch/pull/99104

Release [1.13.1.1.7.0]#

Date: 05/01/2023

Summary#

What’s new in this release#

Training support:

  • Added an improved Neuron-optimized AdamW optimizer implementation.

  • Added an improved Neuron-optimized torch.nn.Dropout implementation.

  • Added an assertion when the torch.nn.Dropout argument inplace=True during training. This is currently not supported on Neuron.

  • Added XLA lowering for aten::count_nonzero

Inference support:

Resolved issues (Training)#

Unexpected behavior with torch.autocast#

Fixed an issue where torch.autocast did not correctly autocast when using torch.bfloat16

Resolved slower BERT bf16 Phase 1 Single Node Performance#

As of the Neuron 2.9.0 release, BERT phase 1 pretraining performance has regressed by approximately 8-9% when executed on a single node only (i.e. just one trn1.32xlarge instance). This is resolved in 2.10 release.

Resolved lower throughput for BERT-large training on AL2 instances#

Starting in release 2.7, we see a performance drop of roughly 5-10% for BERT model training on AL2 instances. This is resolved in release 2.10.

Resolved issues (Inference)#

Error when using the original model after torch_neuronx.trace#

Fixed an issue where model parameters would be moved to the Neuron 'xla' device during torch_neuronx.trace() and would no longer be available to execute on the original device. This made it more difficult to compare Neuron models against CPU since previously this would require manually moving parameters back to CPU.

Error when using the xm.xla_device() object followed by using torch_neuronx.trace#

Fixed an issue where XLA device execution and torch_neuronx.trace() could not be performed in the same python process.

Error when executing torch_neuronx.trace with torch.bfloat16 input/output tensors#

Fixed an issue where torch_neuronx.trace() could not compile models which consumed or produced torch.bfloat16 values.

Known issues and limitations (Training)#

Memory leaking in glibc#

glibc malloc memory leaks affect Neuron and may be temporarily limited by setting MALLOC_ARENA_MAX.

Convolution is not supported#

Convolution is not supported during training.

DDP shows slow convergence#

Currently we see that the models converge slowly with DDP when compared to the scripts that don’t use DDP. We also see a throughput drop with DDP. This is a known issue with torch-xla: https://pytorch.org/xla/release/1.13/index.html#mnist-with-real-data

Runtime crash when we use too many workers per node with DDP#

Currently, if we use 32 workers with DDP, we see that each worker generates its own graph. This causes an error in the runtime, and you may see errors that look like this:

bootstrap.cc:86 CCOM WARN Call to accept failed : Too many open files``.

Hence, it is recommended to use fewer workers per node with DDP.

Known issues and limitations (Inference)#

torch.argmin() produces incorrect results#

torch.argmin() produces incorrect results.

No automatic partitioning#

Currently, when Neuron encounters an operation that it does not support during torch_neuronx.trace(), this will cause an error. The intended behavior when tracing is to automatically partition the model into separate subgraphs that run on NeuronCores and subgraphs that run on CPU. See PyTorch Neuron (torch-neuronx) - Supported Operators for a list of supported operators.

Torchscript serialization error with compiled artifacts larger than 4GB#

When using torch_neuronx.trace(), compiled artifacts that exceed 4GB cannot be serialized. Serializing the TorchScript artifact triggers a segmentation fault. This issue is resolved in PyTorch but is not yet released: https://github.com/pytorch/pytorch/pull/99104

Release [1.13.0.1.6.1]#

Date: 04/19/2023

Summary#

What’s new in this release#

Training support:

  • No changes

Inference support:

Release [1.13.0.1.6.1]#

Date: 04/19/2023

Summary#

What’s new in this release#

Training support:

  • No changes

Inference support:

Release [1.13.0.1.6.0]#

Date: 03/28/2023

Summary#

What’s new in this release#

Training support:

  • Added pipeline parallelism support in AWS Samples for Megatron-LM

Inference support:

  • Added model analysis API: torch_neuronx.analyze

  • Added HLO opcode support for:

    • kAtan2

    • kAfterAll

    • kMap

  • Added XLA lowering support for:

    • aten::glu

    • aten::scatter_reduce

  • Updated torch.nn.MSELoss to promote input data types to a compatible type

Resolved issues (Training)#

GRPC timeout errors when running Megatron-LM GPT 6.7B tutorial on multiple instances#

When running AWS Samples for Megatron-LM GPT 6.7B tutorial over multiple instances, you may encounter GRPC timeout errors like below:

E0302 01:10:20.511231294  138645 chttp2_transport.cc:1098]   Received a GOAWAY with error code ENHANCE_YOUR_CALM and debug data equal to "too_many_pings"
2023-03-02 01:10:20.511500: W tensorflow/core/distributed_runtime/rpc/grpc_remote_master.cc:157] RPC failed with status = "UNAVAILABLE: Too many pings" and grpc_error_string = "{"created":"@1677719420.511317309","description":"Error received from peer ipv4:10.1.35.105:54729","file":"external/com_github_grpc_grpc/src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Too many pings","grpc_status":14}", maybe retrying the RPC

or:

2023-03-08 21:18:27.040863: F tensorflow/compiler/xla/xla_client/xrt_computation_client.cc:476] Non-OK-status: session->session()->Run(session_work->feed_inputs, session_work->outputs_handles, &outputs) status: UNKNOWN: Stream removed

This is due to excessive DNS lookups during execution, and is fixed in this release.

NaNs seen with transformers version >= 4.21.0 when running HF GPT fine-tuning or pretraining with XLA_USE_BF16=1 or XLA_DOWNCAST_BF16=1#

Using Hugging Face transformers version >= 4.21.0 can produce NaN outputs for GPT models when using full BF16 (XLA_USE_BF16=1 or XLA_DOWNCAST_BF16=1) plus stochastic rounding. This issue occurs due to large negative constants used to implement attention masking (https://github.com/huggingface/transformers/pull/17306). To workaround this issue, please use transformers version <= 4.20.0.

Resolved issues (Inference)#

torch.argmax() now supports single argument call variant#

Previously only the 3 argument variant of torch.argmax() was supported. Now the single argument call variant is supported.

Known issues and limitations (Training)#

Slower BERT bf16 Phase 1 Single Node Performance#

In the Neuron 2.9.0 release, BERT phase 1 pretraining performance has regressed by approximately 8-9% when executed on a single node only (i.e. just one trn1.32xlarge instance).

Convolution is not supported#

In this release, convolution is not supported.

DDP shows slow convergence#

Currently we see that the models converge slowly with DDP when compared to the scripts that don’t use DDP. We also see a throughput drop with DDP. This is a known issue with torch-xla: https://pytorch.org/xla/release/1.13/index.html#mnist-with-real-data

Runtime crash when we use too many workers per node with DDP#

Currently, if we use 32 workers with DDP, we see that each worker generates its own graph. This causes an error in the runtime, and you may see errors that look like this: bootstrap.cc:86 CCOM WARN Call to accept failed : Too many open files.

Hence, it is recommended to use fewer workers per node with DDP.

Lower throughput for BERT-large training on AL2 instances#

We see a performance drop of roughly 5-10% for BERT model training on AL2 instances. This is because of the increase in time required for tracing the model.

Known issues and limitations (Inference)#

torch.argmin() produces incorrect results#

torch.argmin() now supports both the single argument call variant and the 3 argument variant. However, torch.argmin() currently produces incorrect results.

Error when using the xm.xla_device() object followed by using torch_neuronx.trace#

Executing a model using the xm.xla_device() object followed by using torch_neuronx.trace in the same process can produce errors in specific situations due to torch-xla caching behavior. It is recommended that only one type of execution is used per process.

Error when executing torch_neuronx.trace with torch.bfloat16 input/output tensors#

Executing torch_neuronx.trace with torch.bfloat16 input/output tensors can cause an error. It is currently recommended to use an alternative torch data type in combination with compiler casting flags instead.

No automatic partitioning#

Currently, there’s no automatic partitioning of a model into subgraphs that run on NeuronCores and subgraphs that run on CPU Operations in the model that are not supported by Neuron would result in compilation error. Please see PyTorch Neuron (torch-neuronx) - Supported Operators for a list of supported operators.

Release [1.13.0.1.5.0]#

Date: 02/24/2023

Summary#

What’s new in this release#

Training support:

  • Added SPMD flag for XLA backend to generate global collective-compute replica groups

Inference support:

  • Expanded inference support to inf2

  • Added Dynamic Batching

Resolved issues#

Known issues and limitations (Training)#

Convolution is not supported#

In this release, convolution is not supported.

DDP shows slow convergence#

Currently we see that the models converge slowly with DDP when compared to the scripts that don’t use DDP. We also see a throughput drop with DDP. This is a known issue with torch-xla: https://pytorch.org/xla/release/1.13/index.html#mnist-with-real-data

Runtime crash when we use too many workers per node with DDP#

Currently, if we use 32 workers with DDP, we see that each worker generates its own graph. This causes an error in the runtime, and you may see errors that look like this: bootstrap.cc:86 CCOM WARN Call to accept failed : Too many open files.

Hence, it is recommended to use fewer workers per node with DDP.

Lower throughput for BERT-large training on AL2 instances#

We see a performance drop of roughly 5-10% for BERT model training on AL2 instances. This is because of the increase in time required for tracing the model.

Known issues and limitations (Inference)#

torch.argmax() and torch.argmin() do not support the single argument call variant#

torch.argmax() and torch.argmin() do not support the single argument call variant. Only the 3 argument variant of these functions is supported. The dim argument must be specified or this function will fail at the call-site. Secondly, torch.argmin() may produce incorrect results.

No automatic partitioning#

Currently, there’s no automatic partitioning of a model into subgraphs that run on NeuronCores and subgraphs that run on CPU Operations in the model that are not supported by Neuron would result in compilation error. Please see PyTorch Neuron (torch-neuronx) - Supported Operators for a list of supported operators.

Release [1.13.0.1.4.0]#

Date: 02/08/2023

Summary#

What’s new in this release#

Training support:

  • Added support for PyTorch 1.13

  • Added support for Python version 3.9

  • Added support for torch.nn.parallel.DistributedDataParallel (DDP) along with a tutorial

  • Added optimized lowering for Softmax activation

  • Added support for LAMB optimizer in BF16 mode

Added initial support for inference on Trn1, including the following features:

  • Trace API (torch_neuronx.trace)

  • Core placement API (Beta)

  • Python 3.7, 3.8 and 3.9 support

  • Support for tracing models larger than 2 GB

The following inference features are not included in this release:

  • Automatic partitioning of a model into subgraphs that run on NeuronCores and subgraphs that run on CPU

  • cxx11 ABI wheels

Resolved issues#

Known issues and limitations#

Convolution is not supported#

In this release, convolution is not supported.

DDP shows slow convergence#

Currently we see that the models converge slowly with DDP when compared to the scripts that don’t use DDP. We also see a throughput drop with DDP. This is a known issue with torch-xla: https://pytorch.org/xla/release/1.13/index.html#mnist-with-real-data

Runtime crash when we use too many workers per node with DDP#

Currently, if we use 32 workers with DDP, we see that each worker generates its own graph. This causes an error in the runtime, and you may see errors that look like this: bootstrap.cc:86 CCOM WARN Call to accept failed : Too many open files.

Hence, it is recommended to use fewer workers per node with DDP.

Lower throughput for BERT-large training on AL2 instances#

We see a performance drop of roughly 5-10% for BERT model training on AL2 instances. This is because of the increase in time required for tracing the model.

Release [1.12.0.1.4.0]#

Date: 12/12/2022

Summary#

What’s new in this release#

  • Added support for PyTorch 1.12.

  • Setting XLA_DOWNCAST_BF16=1 now also enables stochastic rounding by default (as done with XLA_USE_BF16=1).

  • Added support for capturing snapshots of inputs, outputs and graph HLO for debug.

  • Fixed issue with parallel compile error when both train and evaluation are enabled in HuggingFace fine-tuning tutorial.

  • Added support for LAMB optimizer in FP32 mode.

Resolved issues#

NaNs seen with transformers version >= 4.21.0 when running HF BERT fine-tuning or pretraining with XLA_USE_BF16=1 or XLA_DOWNCAST_BF16=1#

When running HuggingFace BERT (any size) fine-tuning tutorial or pretraining tutorial with transformers version >= 4.21.0 and using XLA_USE_BF16=1 or XLA_DOWNCAST_BF16=1, you will see NaNs in the loss immediately at the first step. More details on the issue can be found at pytorch/xla#4152. The workaround is to use 4.20.0 or earlier (the tutorials currently recommend version 4.15.0) or add the line transformers.modeling_utils.get_parameter_dtype = lambda x: torch.bfloat16 to your Python training script (as now done in latest tutorials). A permanent fix will become part of an upcoming HuggingFace transformers release.

Known issues and limitations#

Convolution is not supported#

In this release, convolution is not supported.

Number of data parallel training workers on one Trn1 instance#

The number of workers used in single-instance data parallel training can be one of the following values: 1 or 2 for trn1.2xlarge and 1, 2, 8 or 32 for trn1.32xlarge.

Release [1.11.0.1.2.0]#

Date: 10/27/2022

Summary#

What’s new in this release#

  • Added support for argmax.

  • Clarified error messages for runtime errors NRT_UNINITIALIZED and NRT_CLOSED.

  • When multi-worker training is launched using torchrun on one instance, framework now handles runtime state cleanup at end of training.

Resolved issues#

Drop-out rate ignored in dropout operation#

A known issue in the compiler’s implementation of dropout caused drop-rate to be ignored in the last release. It is fixed in the current release.

Runtime error “invalid offset in Coalesced_memloc_…” followed by “Failed to process dma block: 1703”#

Previously, when running MRPC fine-tuning tutorial with bert-base-* model, you would encounter runtime error “invalid offset in Coalesced_memloc_…” followed by “Failed to process dma block: 1703”. This is fixed in the current release.

Compilation error: “TongaSBTensor[0x7fb2a46e0830]:TongaSB partitions[0] uint8 %138392[128, 512]”#

Previously, when compiling MRPC fine-tuning tutorial with bert-large-* and FP32 (no XLA_USE_BF16=1) for two workers or more, you would encounter compiler error that looks like Error message:  TongaSBTensor[0x7fb2a46e0830]:TongaSB partitions[0] uint8 %138392[128, 512] followed by Error class:    KeyError. Single worker fine-tuning is not affected. This is fixed in the current release.

Known issues and limitations#

Convolution is not supported#

In this release, convolution is not supported.

Number of data parallel training workers on one Trn1 instance#

The number of workers used in single-instance data parallel training can be one of the following values: 1 or 2 for trn1.2xlarge and 1, 2, 8 or 32 for trn1.32xlarge.

Release [1.11.0.1.1.1]#

Date: 10/10/2022

Summary#

This is the initial release of PyTorch Neuron that supports Trainium for users to train their models on the new EC2 Trn1 instances.

What’s new in this release#

Announcing the first PyTorch Neuron release for training.

  • XLA device support for Trainium

  • PyTorch 1.11 with XLA backend support in torch.distributed

  • torch-xla distributed support

  • Single-instance and multi-instance distributed training using torchrun

  • Support for ParallelCluster and SLURM with node-level scheduling granularity

  • Persistent cache for compiled graph

  • neuron_parallel_compile utility to help speed up compilation

  • Optimizer support: SGD, AdamW

  • Loss functions supported: NLLLoss

  • Python versions supported: 3.7, 3.8

  • Multi-instance training support with EFA

  • Support PyTorch’s BF16 automatic mixed precision

Known issues and limitations#

Convolution is not supported#

In this release, convolution is not supported.

Number of data parallel training workers on one Trn1 instance#

The number of workers used in single-instance data parallel training can be one of the following values: 1 or 2 for trn1.2xlarge and 1, 2, 8 or 32 for trn1.32xlarge.

Drop-out rate ignored in dropout operation#

A known issue in the compiler’s implementation of dropout caused drop-rate to be ignored. Will be fixed in a follow-on release.

Runtime error “invalid offset in Coalesced_memloc_…” followed by “Failed to process dma block: 1703”#

Currently, when running MRPC fine-tuning tutorial with bert-base-* model, you will encounter runtime error “invalid offset in Coalesced_memloc_…” followed by “Failed to process dma block: 1703”. This issue will be fixed in an upcoming release.

Compilation error: “TongaSBTensor[0x7fb2a46e0830]:TongaSB partitions[0] uint8 %138392[128, 512]”#

When compiling MRPC fine-tuning tutorial with bert-large-* and FP32 (no XLA_USE_BF16=1) for two workers or more, you will encounter compiler error that looks like Error message:  TongaSBTensor[0x7fb2a46e0830]:TongaSB partitions[0] uint8 %138392[128, 512] followed by Error class:    KeyError. Single worker fine-tuning is not affected. This issue will be fixed in an upcoming release.

This document is relevant for: Inf2, Trn1, Trn1n