aws-neuron/torch-neuronx-model-support

torch-neuronx model support

Which Hugging Face Hub models run unmodified on AWS Trainium, through the native PyTorch backend torch-neuronx, for each neuronx-cc and library version tested. The only library tested so far is transformers.

To look up a model, use the Space.

What is tested

transformers

Each run loads a model on a trn2 instance and generates 32 tokens greedily from the prompt "The future of AI is", sent as a user message for chat models. The model is sharded over the smallest power-of-two number of NeuronCores that fits its bf16 weights, one rank per core: with tp_plan="auto", or expert-parallel for mixture-of-experts models without a tensor-parallel plan. Generation uses torch.compile(backend="neuron") with a static cache and chunked prefill. An encoder-decoder model runs its encoder once on the prompt, then generates from its decoder, on one NeuronCore.

A text encoder, such as a BERT, RoBERTa or DeBERTa model, runs one forward pass instead, on one NeuronCore, compiled with torch.compile(backend="neuron"). Its input is the sentence "My name is Wolfgang and I live in Berlin.", padded to 128 tokens, with Berlin masked for a masked language model, and after the question "Where do I live?" for a question-answering one. It loads with the head class its checkpoint declares, or without a head.

For now, only models that fit on 4 Trainium2 devices are tested, 16 NeuronCores at most.

result	Meaning
PASS	The model loaded, generate() or an encoder's forward pass returned, and the output check passed or could not run.
FAIL(load)	from_pretrained raised.
FAIL(gen)	generate() raised.
FAIL(forward)	An encoder's forward pass raised.
FAIL(output)	generate() or the forward pass returned, but the output check failed.
FAIL(other)	The run crashed, or the model's extra Python packages could not be installed.
EXCLUDED	Not tested: the sweep's selection left the model out, for the reason in notes. A later run of the model replaces this row.

The output check compares a run on one NeuronCore with a CPU run of the same checkpoint and prompt: the tokens must match, or differ first at a near-tie. On more cores, or when the CPU run cannot complete, a small instruct model judges whether the text is fluent English. PASS says nothing about throughput or about longer generations.

For an encoder, the CPU runs the same forward pass in fp32, the reference, and in bf16, which shows how far bf16 alone drifts from it: far, for some models. Neuron, in bf16, is held to what the CPU achieves in bf16. The predictions must be the CPU's, in fp32 or in bf16, or differ from fp32's only at a near-tie: the masked word, each token's label, the class or the answer span. A head with a single output, such as a reranker's score, must stay within 1.0 of fp32's, or within twice the bf16 CPU run's distance to it. Without a head, each token's hidden state must have a cosine similarity of at least 0.99 with fp32's, or a cosine distance to it at most twice the bf16 CPU run's.

The models are the top text-generation and image-text-to-text models on the Hub by downloads and trending, reselected before each sweep: the 100 best-ranked form the selection. The sweep's list continues with the models of these tasks, of translation and summarization, encoder-decoder models included, and of the text encoder tasks (fill-mask, token-classification, text-classification, question-answering, zero-shot-classification, feature-extraction, sentence-similarity and text-ranking), down to rank 1000 among the Hub's transformers models of their task, by downloads or trending. Each sweep goes down the list in order until it has run 100 models, skipping those whose test script has not changed since their last run. Quantized checkpoints and models that need trust_remote_code are left out. A model that leaves the list keeps the results it already has.

Layout

There is one file per tested (neuronx-cc, library version) pair:

data/neuronxcc-<version>/<library>-<version>.parquet

Each sweep tests the current Neuron SDK with the library version of the day, so most pairs are never tested. A model missing from a pair was not tested there, which does not mean it fails. A model the sweep left out has an EXCLUDED row instead, with no neuron_cores or runner.

Column	Content
model_id, library, neuron_cores, runner	The model, the library that loads it, the number of NeuronCores it was sharded over (its tensor- or expert-parallel degree), and the load class (run_dense: AutoModelForCausalLM, run_multimodal: AutoModelForImageTextToText, run_seq2seq: AutoModelForSeq2SeqLM, run_dense_ep: expert-parallel, run_encoder: the head class the checkpoint declares, or AutoModel).
selection_rank	The model's rank, from 1, in the selection of the latest sweep of the pair, empty if that selection does not hold it.
result, notes	The outcome, and the start of the generated text, of an encoder's prediction or of the error, or why the model was excluded.
exclusion	The category of the rule that excluded an EXCLUDED model, such as quantized or trust-remote-code.
error	The full exception message of a FAIL(load), FAIL(gen) or FAIL(forward) run.
cpu_match	Runs on one NeuronCore only: Y if the tokens, or an encoder's predictions, match a CPU run, ~ near-tie … if they differ only at a near-tie, N … if not.
english	The judge's verdict, Y or N, when the CPU comparison did not run.
wall_s, hbm_peak_gb, hbm_capacity_gb	Wall time of the run, including compilation, and peak device memory against what its NeuronCores provide.
neuronx_cc, library_version, transformers, accelerate, torch_neuronx, torch, nki, python, neuron_driver	The stack the run used. transformers is also set for the libraries built on it.
tested_at	When the run started (UTC).
run_py	The standalone script that ran.

Querying

import pandas as pd

df = pd.read_parquet("hf://datasets/aws-neuron/torch-neuronx-model-support/data")
df[df.model_id == "Qwen/Qwen3-0.6B"][["neuronx_cc", "library", "library_version", "neuron_cores", "result"]]

Reproducing a run

Each run_py is a standalone script. Its header lists the Neuron stack it ran on and a pip install line for the packages it adds on top. On a Trainium2 instance with that stack, for example the AWS Neuron Deep Learning Container, install those packages, then:

python3 run.py

On a trn2 instance, the run can skip compilation by first copying the compiled graphs of its compiler, from the public NEFF cache:

hf buckets sync hf://buckets/aws-neuron/torch-neuronx-neff-cache/neuronxcc-<neuronx_cc> /tmp/neff_cache

On more than one NeuronCore, the script launches its own torch.distributed ranks.