Add a generic ViT model to BioNeMo Recipes. (#1103)

### Description

<!-- Provide a detailed description of the changes in this PR -->

- Demonstrate how to use Megatron-FSDP + TransformerEngine on a generic
ViT model adapted from the TIMM library, check it out here:
https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py
- OSRB NVBug No. 5499906 ✅ 

#### TODO

- TransformerEngine CP
- Need to update the ViT to be entirely sequence parallel after the
patch tokenization.
- DCP <> HF Checkpointing
-
https://pytorch.org/blog/huggingface-safetensors-support-in-pytorch-distributed-checkpointing/
- Performance
- Have not benchmarked this on large models to show improvement vs. FSDP
or vanilla Transformer. For small models, TE seems slower than a vanilla
Transformer due to overhead likely.

### Usage

<!--- How does a user interact with the changed code -->

- Build and run the Docker container: 
```
docker run -it --rm --gpus=all --shm-size=16G gitlab-master.nvidia.com:5005/clara-discovery/bionemo-recipes/recipes/vit:timm
```
- Run training (on 2 GPUs):
```
torchrun --nproc-per-node 2 train.py --config-name vit_te_base_patch16_224 distributed.dp_shard=2
```
- Run inference (on 1 GPU):
```
# Convert DCP checkpoint to torch.save format.
python -m torch.distributed.checkpoint.format_utils dcp_to_torch step_25_loss_2.533 torch_ckpt_test.pt
# Update inference.checkpoint.path=torch_ckpt_test.pt in config and run inference.
torchrun --nproc-per-node 1 infer.py --config-name=vit_te_base_patch16_224
```

### Type of changes

<!-- Mark the relevant option with an [x] -->

- [ ] Bug fix (non-breaking change which fixes an issue)
- [x] New feature (non-breaking change which adds functionality)
- [ ] Refactor
- [ ] Documentation update
- [ ] Other (please describe):

### CI Pipeline Configuration

Configure CI behavior by applying the relevant labels:

-
[SKIP_CI](https://github.com/NVIDIA/bionemo-framework/blob/main/docs/docs/user-guide/contributing/contributing.md#skip_ci)
- Skip all continuous integration tests
-
[INCLUDE_NOTEBOOKS_TESTS](https://github.com/NVIDIA/bionemo-framework/blob/main/docs/docs/user-guide/contributing/contributing.md#include_notebooks_tests)
- Execute notebook validation tests in pytest
-
[INCLUDE_SLOW_TESTS](https://github.com/NVIDIA/bionemo-framework/blob/main/docs/docs/user-guide/contributing/contributing.md#include_slow_tests)
- Execute tests labelled as slow in pytest for extensive testing

> [!NOTE]
> By default, the notebooks validation tests are skipped unless
explicitly enabled.

#### Authorizing CI Runs

We use
[copy-pr-bot](https://docs.gha-runners.nvidia.com/apps/copy-pr-bot/#automation)
to manage authorization of CI
runs on NVIDIA's compute resources.

- If a pull request is opened by a trusted user and contains only
trusted changes, the pull request's code will
automatically be copied to a pull-request/ prefixed branch in the source
repository (e.g. pull-request/123)
- If a pull request is opened by an untrusted user or contains untrusted
changes, an NVIDIA org member must leave an
`/ok to test` comment on the pull request to trigger CI. This will need
to be done for each new commit.

### Pre-submit Checklist

<!--- Ensure all items are completed before submitting -->

- [x] I have tested these changes locally
- [x] I have updated the documentation accordingly
- [x] I have added/updated tests as needed
- [x] All existing tests pass successfully


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

- **New Features**
- ViT recipe: configurable Vision Transformer with training and
inference entry points, distributed Megatron-FSDP support, optional
Transformer Engine, and DCP↔Torch checkpointing with auto-resume.
  - Beans dataset pipeline and infinite dataloader helper.

- **Documentation**
- New README with setup (Docker/PIP), training/inference commands,
checkpointing & conversion guidance.

- **Tests**
- End-to-end training and inference tests validating configs,
checkpointing, and auto-resume.

- **Chores**
- Added dependencies, lint config, license/third-party notices,
.gitignore patterns, and increased CI shared memory.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Cory Ye <cye@nvidia.com>

Author: cspades

.github/workflows/unit-tests-recipes.yml

@@ -131,6 +131,7 @@ jobs:
     if: ${{ needs.changed-dirs.outputs.dirs != '[]' }}
     container:
       image: ${{ matrix.recipe.image }}
+      options: --shm-size=16G
     strategy:
       matrix:
         recipe: ${{ fromJson(needs.changed-dirs.outputs.dirs) }}

LICENSE/third_party.txt

@@ -0,0 +1,5 @@
+checkpoints/
+wandb/
+outputs/
+__pycache__/
+.ruff_cache/

recipes/vit/.gitignore

@@ -0,0 +1,3 @@
+extend = "../.ruff.toml"
+[lint]
+ignore = ["D", "N", "C901", "PLW2901"]

recipes/vit/.ruff.toml

@@ -0,0 +1,9 @@
+FROM nvcr.io/nvidia/pytorch:25.06-py3
+
+RUN --mount=type=secret,id=netrc,target=/root/.netrc \
+    --mount=type=cache,target=/root/.cache/pip \
+    --mount=type=bind,source=requirements.txt,target=/requirements.txt \
+    PIP_CONSTRAINT= pip install -r /requirements.txt
+
+WORKDIR /workspace
+COPY . .

recipes/vit/Dockerfile

@@ -0,0 +1,226 @@
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+   Copyright 2019 Ross Wightman (huggingface/pytorch-image-models)
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