grug/moe: restore aux-loss metrics + add smoke launcher

[dlwh]

Summary

• restore MoE router auxiliary metrics/loss logging in experiments/grug/moe/model.py
• log raw cross-entropy and weighted aux loss from the train loop
• make grug/moe launch TPU type configurable via GRUG_MOE_TPU_TYPE (default v6e-8)
• add experiments/grug/moe/smoke_v6e8_aux_losses.py for small aux-loss smoke launches
• merge latest origin/main into this branch

Validation

• ./infra/pre-commit.py --all-files

[claude]

Claude finished @dlwh's task in 1m 8s —— View job

---

Review

• Read changed files and gather context
• Analyze correctness and check for regressions
• Post review

---

Restores router aux-loss metrics (load balancing, z-loss, entropy, per-layer histograms) through the MoE forward pass and train loop, adds a configurable TPU type for launches, and provides a smoke script. Fulfills the stated objectives.

Findings:

• License header format (experiments/grug/moe/smoke_v6e8_aux_losses.py:1): Uses # Copyright 2025 The Marin Authors but etc/license_header.txt (updated in e986875) requires # Copyright The Marin Authors (no year). Pre-commit already flags this. Fix this →

No other bugs, correctness issues, or guideline violations found. The aux-loss computation (coefficient-weighted sum added to CE loss, with raw and weighted terms logged separately) follows the MaxText/Megatron pattern described in comments. Router stats (_routing_stats, _summarize_router_metrics) are numerically safe.

[dlwh]

@dlwh-golem will you try out this branch on your local machine, modifying to be gpus=1 in launch.py and probably smaller batch. Just get it to run like 10 steps. change to be TinyStories like in the tutorial gpu run

[dlwh-golem]

I updated experiments/grug/moe/launch.py on this PR branch and pushed it:

• Commit: 0f2e57e7b
• Branch: codex/grug-moe-aux-loss-pr
• PR: grug/moe: restore aux-loss metrics + add smoke launcher #3229

What changed for the requested local smoke setup:

• Switched to TinyStories (tutorial-style tokenization + data config).
• Switched resources to single GPU: ResourceConfig.with_gpu("H100", count=1).
• Reduced run size to smoke settings: steps=10, batch_size=4, shorter warmup, eval disabled.
• Included tokenization step in executor_main(...) before the MoE trial step.

Run attempt:

• Command used:
  • UV_CACHE_DIR=/tmp/uv-cache HF_HOME=/tmp/hf-home HF_DATASETS_CACHE=/tmp/hf-home/datasets HUGGINGFACE_HUB_CACHE=/tmp/hf-home/hub WANDB_MODE=disabled uv run python experiments/grug/moe/launch.py --prefix /tmp/grug-moe-local-smoke
• Result:
  • TinyStories tokenization completed successfully.
  • Training step failed before step 1 in this environment with:
    • RuntimeError: No accelerator found. Please run on a TPU or GPU.
    • plus JAX warning: CUDA-enabled jaxlib not installed (fell back to CPU).

So the branch now matches the requested 1-GPU/TinyStories/10-step smoke config, but I couldn’t validate the 10 training steps in this runtime due missing CUDA JAX.

Codex work

• Repository: marin-community/marin
• Trigger: grug/moe: restore aux-loss metrics + add smoke launcher #3229 (comment)
• Request ID: 9de77c5d-b77d-4153-9dac-b5de377bcd24
• Accepted: true

Codex transcript

Completed items:
- none captured

Transcript:
I updated `experiments/grug/moe/launch.py` on this PR branch and pushed it:

- Commit: `0f2e57e7b`  
- Branch: `codex/grug-moe-aux-loss-pr`  
- PR: https://github.com/marin-community/marin/pull/3229

What changed for the requested local smoke setup:
- Switched to TinyStories (tutorial-style tokenization + data config).
- Switched resources to single GPU: `ResourceConfig.with_gpu("H100", count=1)`.
- Reduced run size to smoke settings: `steps=10`, `batch_size=4`, shorter warmup, eval disabled.
- Included tokenization step in `executor_main(...)` before the MoE trial step.

Run attempt:
- Command used:
  - `UV_CACHE_DIR=/tmp/uv-cache HF_HOME=/tmp/hf-home HF_DATASETS_CACHE=/tmp/hf-home/datasets HUGGINGFACE_HUB_CACHE=/tmp/hf-home/hub WANDB_MODE=disabled uv run python experiments/grug/moe/launch.py --prefix /tmp/grug-moe-local-smoke`
- Result:
  - TinyStories tokenization completed successfully.
  - Training step failed before step 1 in this environment with:
    - `RuntimeError: No accelerator found. Please run on a TPU or GPU.`
    - plus JAX warning: CUDA-enabled `jaxlib` not installed (fell back to CPU).  

So the branch now matches the requested 1-GPU/TinyStories/10-step smoke config, but I couldn’t validate the 10 training steps in this runtime due missing CUDA JAX.

[dlwh]

@dlwh-golem you need to install the gpu packages. follow the tutorial for gpus

[dlwh-golem]

Followed the GPU tutorial setup path and installed the GPU Python stack:

• Ran: uv sync --all-packages --extra=gpu
• This installed CUDA JAX plugin packages (jax-cuda12-plugin, jax-cuda12-pjrt) plus CUDA wheels (nvidia-cudnn-cu12, nvidia-cublas-cu12, etc.).

Then I retried the PR smoke run:

• uv run python experiments/grug/moe/launch.py --prefix /tmp/grug-moe-local-smoke
  with HF_HOME/HF_DATASETS_CACHE/HUGGINGFACE_HUB_CACHE set under /tmp.

Result:

• TinyStories tokenization step succeeds.
• Training still fails before step 1 with:
  • cuInit(0) failed: CUDA_ERROR_OPERATING_SYSTEM
  • then RuntimeError: No accelerator found. Please run on a TPU or GPU.

Quick check on this worker also shows nvidia-smi -> Failed to initialize NVML: Unknown Error, so this looks like host/runtime GPU access, not missing Python packages anymore.

If you want, next I can run the same command outside sandbox on the host runtime (or on a different GPU host) to confirm it reaches 10 steps.

Codex work

• Repository: marin-community/marin
• Trigger: grug/moe: restore aux-loss metrics + add smoke launcher #3229 (comment)
• Request ID: 947ba31e-e687-4e32-b3c1-dc72fe12e966
• Accepted: true

Codex transcript

Completed items:
- none captured

Transcript:
Followed the GPU tutorial setup path and installed the GPU Python stack:

- Ran: `uv sync --all-packages --extra=gpu`
- This installed CUDA JAX plugin packages (`jax-cuda12-plugin`, `jax-cuda12-pjrt`) plus CUDA wheels (`nvidia-cudnn-cu12`, `nvidia-cublas-cu12`, etc.).

Then I retried the PR smoke run:

- `uv run python experiments/grug/moe/launch.py --prefix /tmp/grug-moe-local-smoke`  
  with `HF_HOME/HF_DATASETS_CACHE/HUGGINGFACE_HUB_CACHE` set under `/tmp`.

Result:
- TinyStories tokenization step succeeds.
- Training still fails before step 1 with:
  - `cuInit(0) failed: CUDA_ERROR_OPERATING_SYSTEM`
  - then `RuntimeError: No accelerator found. Please run on a TPU or GPU.`

Quick check on this worker also shows `nvidia-smi` -> `Failed to initialize NVML: Unknown Error`, so this looks like host/runtime GPU access, not missing Python packages anymore.

If you want, next I can run the same command outside sandbox on the host runtime (or on a different GPU host) to confirm it reaches 10 steps.

[dlwh]

@dlwh-golem try one more time. fixed some config on your end

[dlwh-golem]

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Please mention @dlwh-golem again to retry.

[dlwh-golem]

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[dlwh-golem]

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[dlwh-golem]

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[dlwh-golem]

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Please mention @dlwh-golem again to retry.

[dlwh-golem]

Sorry, I hit an internal failure while handling this mention and couldn't complete the request.

Please mention @dlwh-golem again to retry.

[dlwh-golem]

Retried just now after syncing to the latest branch commit (0f2e57e7b) and rerunning exactly:

UV_CACHE_DIR=/tmp/uv-cache \
HF_HOME=/tmp/hf-home \
HF_DATASETS_CACHE=/tmp/hf-home/datasets \
HUGGINGFACE_HUB_CACHE=/tmp/hf-home/hub \
WANDB_MODE=disabled \
uv run python experiments/grug/moe/launch.py --prefix /tmp/grug-moe-local-smoke

Result is still the same failure in this harness runtime:

• cuInit(0) failed: CUDA_ERROR_OPERATING_SYSTEM
• then RuntimeError: No accelerator found. Please run on a TPU or GPU.

I also attempted to rerun it outside the sandbox (so it can use host GPU drivers directly), but that escalation was not approved in this run, so I’m blocked from validating the 10 training steps here.

Exact next action needed:

• either allow one escalated run for the command above, or
• run that same command directly on the host shell and paste back the log tail from trainer init onward.

Codex work

• Repository: marin-community/marin
• Trigger: grug/moe: restore aux-loss metrics + add smoke launcher #3229 (comment)
• Request ID: 5b768f5c-c3c5-4a22-9f37-eeb7b76685d8
• Accepted: true

Codex transcript

Completed items:
- none captured

Transcript:
Retried just now after syncing to the latest branch commit (`0f2e57e7b`) and rerunning exactly:

```bash
UV_CACHE_DIR=/tmp/uv-cache \
HF_HOME=/tmp/hf-home \
HF_DATASETS_CACHE=/tmp/hf-home/datasets \
HUGGINGFACE_HUB_CACHE=/tmp/hf-home/hub \
WANDB_MODE=disabled \
uv run python experiments/grug/moe/launch.py --prefix /tmp/grug-moe-local-smoke
```

Result is still the same failure in this harness runtime:

- `cuInit(0) failed: CUDA_ERROR_OPERATING_SYSTEM`
- then `RuntimeError: No accelerator found. Please run on a TPU or GPU.`

I also attempted to rerun it outside the sandbox (so it can use host GPU drivers directly), but that escalation was not approved in this run, so I’m blocked from validating the 10 training steps here.

Exact next action needed:
- either allow one escalated run for the command above, or
- run that same command directly on the host shell and paste back the log tail from trainer init onward.

[dlwh]

@dlwh-golem will you try out this branch on your local machine. be sure you instlal the gpu packages following the gpu tutorial

[dlwh-golem]

I retried on this branch locally and followed the GPU tutorial path.

• nvidia-smi now works on this machine (GB10 visible), and nvcc --version reports CUDA 13.0.
• Ran GPU deps install: uv sync --all-packages --extra=gpu (completed successfully).
• Ran smoke command:

UV_CACHE_DIR=/tmp/uv-cache \
HF_HOME=/tmp/hf-home \
HF_DATASETS_CACHE=/tmp/hf-home/datasets \
HUGGINGFACE_HUB_CACHE=/tmp/hf-home/hub \
WANDB_MODE=disabled \
uv run python experiments/grug/moe/launch.py --prefix /tmp/grug-moe-local-smoke

Result:

• Gets past accelerator init (no cuInit/No accelerator found error now).
• Fails before step 1 with:
  • ValueError: grug/moe requires an abstract mesh with axis 'expert'
  • Raised from experiments/grug/moe/model.py:38 via MoEMLP.init.

So GPU package setup is now fine; current blocker is mesh config for the MoE path (missing "expert" axis in the active abstract mesh during launch).

Codex work

• Repository: marin-community/marin
• Trigger: grug/moe: restore aux-loss metrics + add smoke launcher #3229 (comment)
• Request ID: f98ac63e-9d60-454e-93e3-e7d085bb1edc
• Accepted: true

Codex transcript

Completed items:
- none captured

Transcript:
I retried on this branch locally and followed the GPU tutorial path.

- `nvidia-smi` now works on this machine (GB10 visible), and `nvcc --version` reports CUDA 13.0.
- Ran GPU deps install: `uv sync --all-packages --extra=gpu` (completed successfully).
- Ran smoke command:

```bash
UV_CACHE_DIR=/tmp/uv-cache \
HF_HOME=/tmp/hf-home \
HF_DATASETS_CACHE=/tmp/hf-home/datasets \
HUGGINGFACE_HUB_CACHE=/tmp/hf-home/hub \
WANDB_MODE=disabled \
uv run python experiments/grug/moe/launch.py --prefix /tmp/grug-moe-local-smoke
```

Result:
- Gets past accelerator init (no `cuInit`/`No accelerator found` error now).
- Fails before step 1 with:
  - `ValueError: grug/moe requires an abstract mesh with axis 'expert'`
  - Raised from `experiments/grug/moe/model.py:38` via `MoEMLP.init`.

So GPU package setup is now fine; current blocker is mesh config for the MoE path (missing `"expert"` axis in the active abstract mesh during launch).

[dlwh]

@dlwh-golem ok fix tghat by adding expert: 1 to the mesh in either train.py or launch.py and then keep fixing things until it runs or you're out of ideas

[dlwh-golem]

Blocked on environment permissions before I can apply the fix.

I attempted to do exactly what you asked:

• Edit experiments/grug/moe/launch.py to add expert: 1 in trainer mesh.
• Re-run and keep iterating on failures.

But this run is in a read-only sandbox, and escalation requests are being rejected, so:

• File edit was denied.
• Even uv run ... --help fails because uv cannot write its cache (/home/golem/.cache/uv permission denied).

Planned code change (not yet applied due the block):

• Add import: from levanter.utils.mesh import MeshConfig
• In TrainerConfig(...) in run_grug_moe_trial, add:
  • mesh=MeshConfig(axes={"expert": 1}),

Exact next action needed:

• Re-run me with write-enabled permissions (or approve one escalated run/edit), and I’ll apply this patch and continue fixing runtime issues until it runs or we hit a true dead end.

Codex work

• Repository: marin-community/marin
• Trigger: grug/moe: restore aux-loss metrics + add smoke launcher #3229 (comment)
• Request ID: 9f466c3f-232b-4fb5-88aa-d92cb3114fa2
• Accepted: true

Codex transcript

Completed items:
- none captured

Transcript:
Blocked on environment permissions before I can apply the fix.

I attempted to do exactly what you asked:
- Edit `experiments/grug/moe/launch.py` to add `expert: 1` in trainer mesh.
- Re-run and keep iterating on failures.

But this run is in a read-only sandbox, and escalation requests are being rejected, so:
- File edit was denied.
- Even `uv run ... --help` fails because `uv` cannot write its cache (`/home/golem/.cache/uv` permission denied).

Planned code change (not yet applied due the block):
- Add import: `from levanter.utils.mesh import MeshConfig`
- In `TrainerConfig(...)` in `run_grug_moe_trial`, add:
  - `mesh=MeshConfig(axes={"expert": 1}),`

Exact next action needed:
- Re-run me with write-enabled permissions (or approve one escalated run/edit), and I’ll apply this patch and continue fixing runtime issues until it runs or we hit a true dead end.

[dlwh]

@dlwh-golem please try again. continue prior work; first read full PR comments/review thread before making changes.”

[dlwh-golem]

Read the full PR thread/reviews first, then continued from prior work.

Pushed one incremental fix commit to this PR branch:

• Commit: e0cc5e6eb
• Branch: codex/grug-moe-aux-loss-pr
• PR: grug/moe: restore aux-loss metrics + add smoke launcher #3229

What I changed in experiments/grug/moe/launch.py:

• Added mesh=MeshConfig(axes={"expert": 1}) in TrainerConfig(...) (as requested).
• Fixed TinyStories validation split keying:
  • TINYSTORIES_DATASET_KEY = os.path.basename(TINYSTORIES_TOKENIZED.name)
  • num_validation_sequences={TINYSTORIES_DATASET_KEY: 64}

Re-run command (same as before):

UV_CACHE_DIR=/tmp/uv-cache HF_HOME=/tmp/hf-home HF_DATASETS_CACHE=/tmp/hf-home/datasets HUGGINGFACE_HUB_CACHE=/tmp/hf-home/hub WANDB_MODE=disabled uv run python experiments/grug/moe/launch.py --prefix /tmp/grug-moe-local-smoke

Current blocker after these fixes:

• Gets past accelerator init, MoE mesh-axis check, and TinyStories validation-key KeyError.
• Fails during training step 0 with JAX sharding mismatch in attention:
  • jax._src.core.ShardingTypeError: broadcast_shardings got incompatible shardings for broadcasting: ('data', None, None, None), (('data', 'expert'), 'model', None, None), (None, None, None, None).
  • Raised at experiments/grug/moe/model.py in attention path (jnp.where inside levanter/grug/attention.py).

I tried additional sharding-alignment edits and reruns, but they did not resolve this cleanly, so I reverted those speculative changes and left only the two confirmed fixes above.

Codex work

• Repository: marin-community/marin
• Trigger: grug/moe: restore aux-loss metrics + add smoke launcher #3229 (comment)
• Request ID: b276f1b2-c6fe-44f6-8088-19a358361579
• Accepted: true

Codex transcript

Completed items:
- none captured

Transcript:
Read the full PR thread/reviews first, then continued from prior work.

Pushed one incremental fix commit to this PR branch:

- Commit: `e0cc5e6eb`
- Branch: `codex/grug-moe-aux-loss-pr`
- PR: https://github.com/marin-community/marin/pull/3229

What I changed in `experiments/grug/moe/launch.py`:

- Added `mesh=MeshConfig(axes={"expert": 1})` in `TrainerConfig(...)` (as requested).
- Fixed TinyStories validation split keying:
  - `TINYSTORIES_DATASET_KEY = os.path.basename(TINYSTORIES_TOKENIZED.name)`
  - `num_validation_sequences={TINYSTORIES_DATASET_KEY: 64}`

Re-run command (same as before):

`UV_CACHE_DIR=/tmp/uv-cache HF_HOME=/tmp/hf-home HF_DATASETS_CACHE=/tmp/hf-home/datasets HUGGINGFACE_HUB_CACHE=/tmp/hf-home/hub WANDB_MODE=disabled uv run python experiments/grug/moe/launch.py --prefix /tmp/grug-moe-local-smoke`

Current blocker after these fixes:

- Gets past accelerator init, MoE mesh-axis check, and TinyStories validation-key `KeyError`.
- Fails during training step 0 with JAX sharding mismatch in attention:
  - `jax._src.core.ShardingTypeError: broadcast_shardings got incompatible shardings for broadcasting: ('data', None, None, None), (('data', 'expert'), 'model', None, None), (None, None, None, None).`
  - Raised at `experiments/grug/moe/model.py` in attention path (`jnp.where` inside `levanter/grug/attention.py`).

I tried additional sharding-alignment edits and reruns, but they did not resolve this cleanly, so I reverted those speculative changes and left only the two confirmed fixes above.

[dlwh]

@dlwh-golem i fixed that issue but now there's a triton lowering thing. can you at least minimize and ideally fix

  File "/home/golem/oa-jder-bot/worktrees/github.com/marin-community/marin/3229/.venv/lib/python3.11/site-packages/jax/_src/pallas/pallas_call.py", line 1448, in _pallas_call_lowering
    return mlir.lower_per_platform(ctx, "pallas_call",
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/golem/oa-jder-bot/worktrees/github.com/marin-community/marin/3229/.venv/lib/python3.11/site-packages/jax/_src/pallas/pallas_call.py", line 1444, in gpu_lowering
    return pallas_call_registration.pallas_call_lowering(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/golem/oa-jder-bot/worktrees/github.com/marin-community/marin/3229/.venv/lib/python3.11/site-packages/jax/_src/pallas/triton/pallas_call_registration.py", line 65, in pallas_call_lowering
    raise NotImplementedError(
NotImplementedError: dynamic grid bounds not supported in the Triton backend
--------------------

[dlwh-golem]

Minimized to a single call site:

• experiments/grug/moe/model.py calls fused_linear_softmax_cross_entropy_loss(...)
• That goes to lib/levanter/src/levanter/grug/loss.py
• In that function, fused_cross_entropy_loss_and_logsumexp_penalty(...) is currently on auto-impl selection.
• On GPU, lib/levanter/src/levanter/kernels/pallas/fused_cross_entropy_loss/api.py prefers pallas_gpu first, which reaches pallas_call in .../pallas_gpu.py and throws:
  • NotImplementedError: dynamic grid bounds not supported in the Triton backend

So the minimal workaround is to force this path to XLA in grug loss:

loss = fused_cross_entropy_loss_and_logsumexp_penalty(
    ...,
    implementation="xla",
)

I attempted to apply exactly that edit in lib/levanter/src/levanter/grug/loss.py and re-run, but this run is read-only and my escalation request to write was rejected, so I’m blocked from pushing the fix in this turn.

Exact next action needed:

• re-run me with write-enabled permissions (or approve escalation), and I’ll:

1. apply this one-line fix,
2. run the same experiments/grug/moe/launch.py smoke command,
3. push the commit to codex/grug-moe-aux-loss-pr with results.

Codex work

• Repository: marin-community/marin
• Trigger: grug/moe: restore aux-loss metrics + add smoke launcher #3229 (comment)
• Request ID: 73b0487e-b99d-49b4-8c2a-549fda891463
• Accepted: true

Codex transcript

Completed items:
- none captured

Transcript:
Minimized to a single call site:

- `experiments/grug/moe/model.py` calls `fused_linear_softmax_cross_entropy_loss(...)`
- That goes to `lib/levanter/src/levanter/grug/loss.py`
- In that function, `fused_cross_entropy_loss_and_logsumexp_penalty(...)` is currently on auto-impl selection.
- On GPU, `lib/levanter/src/levanter/kernels/pallas/fused_cross_entropy_loss/api.py` prefers `pallas_gpu` first, which reaches `pallas_call` in `.../pallas_gpu.py` and throws:
  - `NotImplementedError: dynamic grid bounds not supported in the Triton backend`

So the minimal workaround is to force this path to XLA in grug loss:

```python
loss = fused_cross_entropy_loss_and_logsumexp_penalty(
    ...,
    implementation="xla",
)
```

I attempted to apply exactly that edit in `lib/levanter/src/levanter/grug/loss.py` and re-run, but this run is read-only and my escalation request to write was rejected, so I’m blocked from pushing the fix in this turn.

Exact next action needed:
- re-run me with write-enabled permissions (or approve escalation), and I’ll:
1. apply this one-line fix,
2. run the same `experiments/grug/moe/launch.py` smoke command,
3. push the commit to `codex/grug-moe-aux-loss-pr` with results.

[dlwh]

@dlwh-golem i thought we gave you full auto

[dlwh-golem]

You’re right to expect full-auto. This runner is still enforcing manual escalation and is rejecting every write command, so I can’t execute the fix.

What I attempted immediately after your note (all rejected by the runner):

• git pull --ff-only origin codex/grug-moe-aux-loss-pr (local is behind remote by 1 commit: 9805cf009)
• in-place edit to set implementation="xla" in lib/levanter/src/levanter/grug/loss.py

Exact next action needed to unblock me:

• enable write/escalated commands for this harness run (or fix approval policy so escalations are auto-approved), then I will:

1. fast-forward to 9805cf009,
2. apply the one-line Triton workaround (implementation="xla"),
3. rerun experiments/grug/moe/launch.py,
4. keep iterating and push results to this PR branch in the same turn.

Codex work

• Repository: marin-community/marin
• Trigger: grug/moe: restore aux-loss metrics + add smoke launcher #3229 (comment)
• Request ID: e0548928-227c-45ca-9726-57d54f06e707
• Accepted: true

Codex transcript

Completed items:
- none captured

Transcript:
You’re right to expect full-auto. This runner is still enforcing manual escalation and is rejecting every write command, so I can’t execute the fix.

What I attempted immediately after your note (all rejected by the runner):
- `git pull --ff-only origin codex/grug-moe-aux-loss-pr` (local is behind remote by 1 commit: `9805cf009`)
- in-place edit to set `implementation="xla"` in [`lib/levanter/src/levanter/grug/loss.py`](/home/golem/oa-jder-bot/worktrees/github.com/marin-community/marin/3229/lib/levanter/src/levanter/grug/loss.py)

Exact next action needed to unblock me:
- enable write/escalated commands for this harness run (or fix approval policy so escalations are auto-approved), then I will:
1. fast-forward to `9805cf009`,
2. apply the one-line Triton workaround (`implementation="xla"`),
3. rerun `experiments/grug/moe/launch.py`,
4. keep iterating and push results to this PR branch in the same turn.