Keep each Neuron target's results in its own folder, with a target column

README.md

@@ -9,7 +9,7 @@ configs:
 - config_name: default
   data_files:
   - split: train
-    path: data/*/*.parquet
+    path: data/*/*/*.parquet
 ---
 
 # torch-neuronx model support
@@ -52,7 +52,7 @@ forward pass on `cats_image.jpeg`, the COCO photo of two cats. A model that
 matches images with texts, such as CLIP, scores `"a photo of a cat"` and
 `"a photo of a dog"`.
 
-For now, only models that fit on 4 Trainium2 devices are tested, 16
+For now, only models that fit on 4 devices of the target are tested, 16
 NeuronCores at most.
 
 | `result` | Meaning |
@@ -103,21 +103,23 @@ 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:
+There is one file per Neuron target (`trn2`, `trn3`) and tested
+`(neuronx-cc, library version)` pair:
 
 ```
-data/neuronxcc-<version>/<library>-<version>.parquet
+data/<target>/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
+so most pairs are never tested. A model missing from a target's 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_seq2seq_audio`: `AutoModelForSpeechSeq2Seq`, `run_dense_ep`: expert-parallel, `run_encoder`, `run_encoder_audio` and `run_encoder_image`: the head class the checkpoint declares, `AutoModelForZeroShotImageClassification` for an image model that matches images with texts, 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. |
+| `target` | The Neuron target the run compiled for and ran on: `trn2` (Trainium2) or `trn3` (Trainium3). The sweep picks each model's `neuron_cores` from the memory of the target's NeuronCores, so a model can run on fewer cores of one target than of the other. |
+| `selection_rank` | The model's rank, from 1, in the selection of the latest sweep of the target's 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. |
@@ -134,26 +136,26 @@ there, which does not mean it fails. A model the sweep left out has an
 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"]]
+df[df.model_id == "Qwen/Qwen3-0.6B"][["target", "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:
+on and a `pip install` line for the packages it adds on top. On an instance of
+its `target` with that stack, for example the AWS Neuron Deep Learning
+Container, install those packages, then:
 
 ```bash
 python3 run.py
 ```
 
-On a `trn2` instance, the run can skip compilation by first copying the
-compiled graphs of its compiler, from the public
+The run can skip compilation by first copying the compiled graphs of its
+target and compiler, from the public
 [NEFF cache](https://huggingface.co/buckets/aws-neuron/torch-neuronx-neff-cache):
 
 ```bash
-hf buckets sync hf://buckets/aws-neuron/torch-neuronx-neff-cache/neuronxcc-<neuronx_cc> /tmp/neff_cache
+hf buckets sync hf://buckets/aws-neuron/torch-neuronx-neff-cache/<target>/neuronxcc-<neuronx_cc> /tmp/neff_cache
 ```
 
 On more than one NeuronCore, the script launches its own `torch.distributed`

data/{neuronxcc-2.28.7631.0+8f7b0cf5 → trn2/neuronxcc-2.28.7631.0+8f7b0cf5}/transformers-5.20.1.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2bf6537888379516d5a7ecfaa0399a07aa38af654ef5c4e04284107b063a351c
-size 14644
+oid sha256:8045b9af882eb32098f8955f99169cc162f7ccb01f2ada44ee8086d6764bf2f6
+size 14873

data/{neuronxcc-2.28.7631.0+8f7b0cf5 → trn2/neuronxcc-2.28.7631.0+8f7b0cf5}/transformers-5.20.2.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:94381b8b9ed32001b7a271167e02e877d0a2c008578e15d712b6e4513c15960a
-size 1403779
+oid sha256:9d74be77dffa9d34fa1be99e8a2993cc44793affb4d60fd14032aa16b58e3ef7
+size 1404071

data/{neuronxcc-2.28.7631.0+8f7b0cf5 → trn2/neuronxcc-2.28.7631.0+8f7b0cf5}/transformers-5.21.0.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7f4efb844663de6718692a9141a830aa38edcd6ac546651f32b0dd031438e2f6
-size 2976467
+oid sha256:0b2bf5180259e31d180d6d7310add06b6b53e8dcc6409496ba8181afc99edcad
+size 2976759

data/{neuronxcc-2.28.7631.0+8f7b0cf5 → trn2/neuronxcc-2.28.7631.0+8f7b0cf5}/transformers-5.22.1.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:df2ec85a0a65782cfcb38c4acc1b4f67471dc21a24567ebaa91088df34f9183b
-size 491156
+oid sha256:99535180c097c19701d20a95fe92a5f6ff9fa98a49fbf935532cdc547148afea
+size 491448