{ "issues": [ { "url": "https://api.github.com/repos/google/tunix/issues/1063", "id": 3910127300, "node_id": "I_kwDOOTDVN87pD87E", "number": 1063, "title": "Qwen3-VL", "user": { "login": "ridcl", "id": 214909705, "node_id": "U_kgDODM9DCQ", "avatar_url": "https://avatars.githubusercontent.com/u/214909705?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ridcl", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274161, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1cQ", "url": "https://api.github.com/repos/google/tunix/labels/type:feature/enhancement", "name": "type:feature/enhancement", "color": "a2eeef", "default": false, "description": "New feature or request" } }, "state": "open", "locked": false, "assignee": null, "assignees": {}, "milestone": null, "comments": 0, "created_at": "2026-02-07T12:07:52Z", "updated_at": "2026-02-09T12:26:46Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "**Is your feature request related to a problem? Please describe.**\n\nCurrently, Tunix supports text-only Qwen3, but not multimodal Qwen3-VL. This makes it harder to compare performance of different VLMs on vision-language tasks.\n\n**Describe the solution you'd like**\n\nAccording to the [technical report](https://arxiv.org/pdf/2511.21631), Qwen3-VL adopts a three-module architecture comprising a vision encoder, an MLP-based vision–language merger, and a large language model (LLM). The vision encoder is SigLIP2, which we already have as WIP in #511. \n\nIt's also worth to mention:\n\n* Interleaved MRoPE, which we don't seem to support yet\n* DeepStack for the vision-language merger, which is a bit more complicated than what we gave for Gemma 3\n* Video timestamp\n\n**Additional context**\n\nA couple of design questions: \n\n1. Should we wait for #511 to be merged or start the work on Qwen3-VL in parallel? Calling @abheesht17 for an opinion. \n2. Should we extend (text-only) Qwen3 or create a totally new model? I'm not sure it will be easy to integrate DeepStack without changing the way of the text-only version. \n\n**Checklist**\n\n- [x] I have searched the existing issues for similar feature requests.\n- [x] This is not a support question (please use the \"bug template\" for that).\n", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/1063/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/1049", "id": 3900632286, "node_id": "I_kwDOOTDVN87ofuze", "number": 1049, "title": "`.sow()` intermediate values don't enable Dead Code Elimination of unused outputs", "user": { "login": "mdda", "id": 362532, "node_id": "MDQ6VXNlcjM2MjUzMg==", "avatar_url": "https://avatars.githubusercontent.com/u/362532?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mdda", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 9550630502, "node_id": "LA_kwDOOTDVN88AAAACOUMOZg", "url": "https://api.github.com/repos/google/tunix/labels/type:performance", "name": "type:performance", "color": "e7b5e4", "default": false, "description": "Make things lean and fast" } }, "state": "open", "locked": false, "assignee": null, "assignees": {}, "milestone": null, "comments": 0, "created_at": "2026-02-05T09:42:38Z", "updated_at": "2026-02-09T12:26:26Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "## Summary\n\nWhen using `output_hidden_states=True` with the Gemma3 model to access intermediate hidden states via `.sow()`, the final logits computation is still materialized even when the logits return value is ignored. This prevents efficient \"headless\" inference where only embeddings are needed (or, for my case, top-k logit extraction without full logit computation).\n\n## Context\n\nThe Gemma3 `Transformer.__call__` method supports outputting hidden states via `.sow()`:\n\n(https://github.com/google/tunix/blob/main/tunix/models/gemma3/model.py#L966)\n\n```python\nif output_hidden_states:\n self.sow(nnx.Intermediate, 'all_hidden_states', x)\nlogits = self.embedder.decode(x)\nreturn logits, new_cache\n```\n\n**Expected Behavior**\n\nWhen wrapping the model to only extract the sowed `all_hidden_states` and ignoring the returned logits, JAX's Dead Code Elimination (DCE) should recognize that:\n1. The logits are never used\n2. The `embedder.decode(x)` matmul can be eliminated\n3. Only the hidden states need to be computed\n\nThis would save significant memory and compute, since the embedding→vocab matmul (e.g., `[B, L, hidden] @ [hidden, 262144]`) is one of the largest operations.\n\n**Actual Behavior**\n\nThe logits matmul is still present in the compiled jaxpr, even when the logits are completely ignored. DCE does not eliminate the unused computation.\n\n**Steps to Reproduce the Problem**\n\nI created a simplified 2-layer NNX model to isolate the issue:\n\n```python\nimport jax\nimport jax.numpy as jnp\nfrom flax import nnx\n\nDIM_INPUT = 10\nDIM_HIDDEN = 20\nDIM_OUTPUT = 1000 # Large - should be DCE'd if unused\n\nclass SimpleTwoLayerModel(nnx.Module):\n def __init__(self, rngs: nnx.Rngs):\n self.layer1 = nnx.Linear(DIM_INPUT, DIM_HIDDEN, rngs=rngs)\n self.layer2 = nnx.Linear(DIM_HIDDEN, DIM_OUTPUT, rngs=rngs)\n\n def __call__(self, x, output_hidden=False):\n hidden = nnx.relu(self.layer1(x))\n if output_hidden:\n self.sow(nnx.Intermediate, 'hidden_state', hidden)\n output = self.layer2(hidden)\n return output\n\nclass HeadlessWrapper(nnx.Module):\n def __init__(self, model):\n self.model = model\n\n def __call__(self, x):\n _ = self.model(x, output_hidden=True) # Ignore output!\n sow_state = nnx.pop(self.model, nnx.Intermediate)\n return sow_state['hidden_state'].value[0]\n\n# Test\nrngs = nnx.Rngs(0)\nbase_model = SimpleTwoLayerModel(rngs)\nheadless = HeadlessWrapper(base_model)\n\n@nnx.jit\ndef run_headless(model, x):\n return model(x)\n\nx = jnp.ones((1, DIM_INPUT))\njaxpr = jax.make_jaxpr(run_headless)(headless, x)\nprint(jaxpr) # Shows matmul with DIM_OUTPUT still present\n```\n\n**Colab notebook with full test:** \n\nhttps://colab.research.google.com/drive/1-gAlL5fHwZvaEVskHtiCfAz0xsLiElKu?usp=sharing \n\n\n**Environment**\n\nper provided Colab:\n\n```txt\nPython 3.12.12\nflax==0.12.3\njax==0.9.0\n```\n\n**Checklist**\n\n- [Y] I have searched the existing issues for a similar bug report.\n- [Y] I have provided all the required information in the \"Environment\" section.\n- [Y] I have provided a minimal, reproducible example.\n\n**Would you like to help us fix it?**\n\nIt seems that (unless there is something obvious that I'm missing) this could be an upstream nnx (or jax) issue, with the resolution largely in Google's hands - but any feedback here would be welcome.", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/1049/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/1040", "id": 3889893835, "node_id": "I_kwDOOTDVN87n2xHL", "number": 1040, "title": "[Tiny Feat] make log_level be configurable", "user": { "login": "aolemila", "id": 214426322, "node_id": "U_kgDODMfi0g", "avatar_url": "https://avatars.githubusercontent.com/u/214426322?v=4", "gravatar_id": "", "url": "https://api.github.com/users/aolemila", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274161, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1cQ", "url": "https://api.github.com/repos/google/tunix/labels/type:feature/enhancement", "name": "type:feature/enhancement", "color": "a2eeef", "default": false, "description": "New feature or request" } }, "state": "open", "locked": false, "assignee": { "login": "aolemila", "id": 214426322, "node_id": "U_kgDODMfi0g", "avatar_url": "https://avatars.githubusercontent.com/u/214426322?v=4", "gravatar_id": "", "url": "https://api.github.com/users/aolemila", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "aolemila", "id": 214426322, "node_id": "U_kgDODMfi0g", "avatar_url": "https://avatars.githubusercontent.com/u/214426322?v=4", "gravatar_id": "", "url": "https://api.github.com/users/aolemila", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 0, "created_at": "2026-02-03T08:22:57Z", "updated_at": "2026-02-03T16:31:21Z", "closed_at": null, "author_association": "COLLABORATOR", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "**Is your feature request related to a problem? Please describe.**\n\nLack of debug logs.\n\n**Describe the solution you'd like**\n\nMake log_level be configurable rather than fixed value which is info.\n\n**Additional context**\n\n\n**Checklist**\n\n- [ ] I have searched the existing issues for similar feature requests.\n- [ ] This is not a support question (please use the \"bug template\" for that).\n", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/1040/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [ 1041 ] }, { "url": "https://api.github.com/repos/google/tunix/issues/995", "id": 3841212230, "node_id": "I_kwDOOTDVN87k9D9G", "number": 995, "title": "[Bug] Fix run_prod for scripts/grpo_demo_llama3_qwen2.py with sglang_jax with LoRA", "user": { "login": "aolemila", "id": 214426322, "node_id": "U_kgDODMfi0g", "avatar_url": "https://avatars.githubusercontent.com/u/214426322?v=4", "gravatar_id": "", "url": "https://api.github.com/users/aolemila", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274152, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1aA", "url": "https://api.github.com/repos/google/tunix/labels/type:bug", "name": "type:bug", "color": "d73a4a", "default": false, "description": "Something isn't working" } }, "state": "open", "locked": false, "assignee": { "login": "aolemila", "id": 214426322, "node_id": "U_kgDODMfi0g", "avatar_url": "https://avatars.githubusercontent.com/u/214426322?v=4", "gravatar_id": "", "url": "https://api.github.com/users/aolemila", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "aolemila", "id": 214426322, "node_id": "U_kgDODMfi0g", "avatar_url": "https://avatars.githubusercontent.com/u/214426322?v=4", "gravatar_id": "", "url": "https://api.github.com/users/aolemila", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 1, "created_at": "2026-01-22T04:42:47Z", "updated_at": "2026-01-22T16:59:06Z", "closed_at": null, "author_association": "COLLABORATOR", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "**Expected Behavior**\n\nPass.\n\n**Actual Behavior**\n\nFailed with the following error:\n\n```bash\n File \"/usr/local/lib/python3.12/site-packages/jax/_src/tree_util.py\", line 361, in tree_map\n return treedef.unflatten(f(*xs) for xs in zip(*all_leaves))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/site-packages/jax/_src/tree_util.py\", line 361, in \n return treedef.unflatten(f(*xs) for xs in zip(*all_leaves))\n ^^^^^^\n File \"/usr/local/lib/python3.12/site-packages/qwix/_src/flax_util.py\", line 293, in fn\n return unbox(x.get_raw_value())\n ^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/site-packages/flax/nnx/variablelib.py\", line 299, in __getattr__\n return getattr(self.raw_value, name)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'jaxlib._jax.ArrayImpl' object has no attribute 'get_raw_value'\nOne or more scripts failed!\n```\nSee more [here](https://github.com/google/tunix/actions/runs/21205448021/job/61000751630#step:7:11846).\n\n**Steps to Reproduce the Problem**\n\n```bash\n unset JAX_PLATFORMS\n pip list | egrep 'jax|flax|libtpu'\n cd ..\n git clone https://github.com/sgl-project/sglang-jax.git && cd sglang-jax/python && pip install -e . && cd ../..\n pip install jax==0.8.1 flax==0.12.0 libtpu==0.0.24\n pip list | egrep 'jax|flax|libtpu'\n cd tunix\n```\n\nRun `python scripts/grpo_demo_llama3_qwen2.py --root-dir=/tmp/grpo_test --num-batches=20 --rollout-engine sglang_jax --enable-lora --lora-target-modules all`.\n\n\n\n**Environment**\n\n- **OS:** [e.g., Ubuntu, etc.]\n- **Project Version:** [e.g., 0.0.1]\n\n**Checklist**\n\n- [ ] I have searched the existing issues for a similar bug report.\n- [ ] I have provided all the required information in the \"Environment\" section.\n- [ ] I have provided a minimal, reproducible example.\n\n**Would you like to help us fix it?**\n", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/995/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [ 996 ] }, { "url": "https://api.github.com/repos/google/tunix/issues/979", "id": 3818556598, "node_id": "I_kwDOOTDVN87jmoy2", "number": 979, "title": "Natural Language to SQL GRPO Example", "user": { "login": "NP2241", "id": 65132325, "node_id": "MDQ6VXNlcjY1MTMyMzI1", "avatar_url": "https://avatars.githubusercontent.com/u/65132325?v=4", "gravatar_id": "", "url": "https://api.github.com/users/NP2241", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274161, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1cQ", "url": "https://api.github.com/repos/google/tunix/labels/type:feature/enhancement", "name": "type:feature/enhancement", "color": "a2eeef", "default": false, "description": "New feature or request" } }, "state": "open", "locked": false, "assignee": null, "assignees": {}, "milestone": null, "comments": 1, "created_at": "2026-01-15T17:55:29Z", "updated_at": "2026-02-07T07:36:37Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "**Is your feature request related to a problem? Please describe.**\n\nTunix currently lacks an example showing how to use GRPO with an external execution environment (e.g., a SQL database), making it unclear how to define structured rewards beyond text only tasks.\n\n**Describe the solution you'd like**\n\nI propose adding a small natural language to SQL GRPO example where the model generates SQL, the SQL is executed against a small SQLite database, and the reward reflects execution success and result correctness. If maintainers are open to this addition, I can implement a full example.\n\n**Additional context**\n\nThis would give users a clear reference for integrating Tunix with external tools and for building verifiable RL tasks beyond GSM8K.\n\n**Checklist**\n\n- [x] I have searched the existing issues for similar feature requests.\n- [x] This is not a support question (please use the \"bug template\" for that).\n", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/979/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [ 997 ] }, { "url": "https://api.github.com/repos/google/tunix/issues/977", "id": 3818446423, "node_id": "I_kwDOOTDVN87jmN5X", "number": 977, "title": "Add GDPO Support", "user": { "login": "kmsgnnew", "id": 16554470, "node_id": "MDQ6VXNlcjE2NTU0NDcw", "avatar_url": "https://avatars.githubusercontent.com/u/16554470?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kmsgnnew", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274161, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1cQ", "url": "https://api.github.com/repos/google/tunix/labels/type:feature/enhancement", "name": "type:feature/enhancement", "color": "a2eeef", "default": false, "description": "New feature or request" } }, "state": "open", "locked": false, "assignee": null, "assignees": {}, "milestone": null, "comments": 0, "created_at": "2026-01-15T17:22:35Z", "updated_at": "2026-01-15T18:27:40Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "**Is your feature request related to a problem? Please describe.**\n\n GRPO inherently compresses the reward signal, causing loss of information in the advantage estimates. Group-wise decoupled normalization of each reward separately better preserves cross-reward distinctions and enables more accurate multi-reward optimization.\n\n**Describe the solution you'd like**\nhttps://arxiv.org/abs/2601.05242\n\n**Additional context**\nAuthors have reported huge improvement in results\n\nHow i came to this --? \nAs part of Google Tunix hackathon i have observed the problem of GRPO with a multi reward optimization. I found this latest paper directly questions the issues of loss of information in advantage estimates in GRPO hence i would love to add this to Tunix!\n\n**Checklist**\n\n- [x] I have searched the existing issues for similar feature requests.\n- [x] This is not a support question (please use the \"bug template\" for that).\n", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/977/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [ 978 ] }, { "url": "https://api.github.com/repos/google/tunix/issues/953", "id": 3790628178, "node_id": "I_kwDOOTDVN87h8GVS", "number": 953, "title": "Feature Request: RLOO (REINFORCE Leave-One-Out) Advantage Estimator", "user": { "login": "42euge", "id": 169945246, "node_id": "U_kgDOCiEong", "avatar_url": "https://avatars.githubusercontent.com/u/169945246?v=4", "gravatar_id": "", "url": "https://api.github.com/users/42euge", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274161, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1cQ", "url": "https://api.github.com/repos/google/tunix/labels/type:feature/enhancement", "name": "type:feature/enhancement", "color": "a2eeef", "default": false, "description": "New feature or request" } }, "state": "open", "locked": false, "assignee": null, "assignees": {}, "milestone": null, "comments": 0, "created_at": "2026-01-07T22:55:43Z", "updated_at": "2026-01-08T05:45:44Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "## Is your feature request related to a problem? Please describe.\n\nI'm working on the Tunix hackathon and running into stability issues training on rubric-based rewards (for the \"show your work\" objective). The current GRPO works well when you have binary correct/wrong signals from a verifier, but my setup uses continuous scores from a reward model evaluating reasoning quality.\n\nThe problem is GRPO's std normalization amplifies noise when rewards are subjective. I'm also seeing reward hacking since there's no KL mechanism to keep the policy from drifting too far to exploit the reward model.\n\n## Describe the solution you'd like\n\nAdd RLOO (REINFORCE Leave-One-Out) as an alternative advantage estimator. The main difference from GRPO:\n\n```python\n# GRPO (current)\nA_i = (R_i - mean(R)) / std(R)\n\n# DrGRPO (removes std, already exists)\nA_i = R_i - mean(R)\n\n# RLOO (leave-one-out baseline + KL in reward)\nA_i = R_i - mean(R_j where j != i) - β * KL_i\n```\n\nRLOO uses a leave-one-out baseline instead of batch mean, which is more numerically stable. Critically, it folds KL directly into the reward `R'_i = R_i - β * KL_i` before advantage computation, rather than adding it to the loss function afterward.\n\n[Ahmadian et al. 2024](https://arxiv.org/abs/2402.14740) showed RLOO is more robust to noisy rewards than PPO/GRPO and outperforms DPO on preference tasks. It's also what [PRIME](https://arxiv.org/abs/2502.01456) uses under the hood.\n\n## Implementation approach\n\nVerified that Tunix already has the infrastructure for this (function_registry.py):\n\n1. Add `advantage_estimator='rloo'` option in `GRPOConfig`\n2. Add `kl_in_reward: bool = False` parameter to control where KL is applied\n3. Register new advantage estimator function with `@function_registry.register_advantage_estimator(\"rloo\")` (following drgrpo_learner.py pattern)\n4. Modify reward computation in `_generate_and_compute_advantage()` to optionally fold KL into rewards when `kl_in_reward=True`\n\nThis follows the existing pluggable advantage estimator pattern and doesn't require a separate learner class.\n\n## Additional context\n\nThis would help hackathon participants working on non-verifiable tasks. Right now GRPO is tuned for math/code with binary rewards - RLOO would extend Tunix to creative/subjective domains.\n\nReference implementations:\n- [verl RLOO](https://github.com/volcengine/verl)\n- [swift RLOO docs](https://swift.readthedocs.io/en/latest/Instruction/GRPO/AdvancedResearch/RLOO.html)\n\n## Checklist\n\n- [x] I have searched the existing issues for similar feature requests.\n- [x] I have verified the codebase already supports pluggable advantage estimators via function_registry.\n- [x] This is not a support question (please use the \"bug template\" for that).\n", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/953/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [ 1000 ] }, { "url": "https://api.github.com/repos/google/tunix/issues/944", "id": 3778540052, "node_id": "I_kwDOOTDVN87hN_IU", "number": 944, "title": "Wrong Python version causing Attribute Error is_integer() in utils.py", "user": { "login": "taesuhpark", "id": 84999437, "node_id": "MDQ6VXNlcjg0OTk5NDM3", "avatar_url": "https://avatars.githubusercontent.com/u/84999437?v=4", "gravatar_id": "", "url": "https://api.github.com/users/taesuhpark", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274152, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1aA", "url": "https://api.github.com/repos/google/tunix/labels/type:bug", "name": "type:bug", "color": "d73a4a", "default": false, "description": "Something isn't working" } }, "state": "open", "locked": false, "assignee": null, "assignees": {}, "milestone": null, "comments": 2, "created_at": "2026-01-03T22:10:15Z", "updated_at": "2026-01-06T01:23:12Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "https://github.com/google/tunix/blob/3b558520bb65e4cd649f8199362f9dc144e541da/pyproject.toml#L8\n\nThis version should be set to '>=3.12' (or higher) at least, because tunix/tunix/rl/utils.py calls value.is_integer() which is safely supported for the base `int` type since Python v3.12. For example, see below:\nhttps://github.com/google/tunix/blob/3b558520bb65e4cd649f8199362f9dc144e541da/tunix/rl/utils.py#L37\n\nI tested it with v3.11 and confirmed it causes Attribute Error. You should upgrade the default Python version to 3.12 (or higher) in pyproject.toml or substitute all value.is_integer() with the more primitive and safe isinstance().", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/944/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/943", "id": 3777548271, "node_id": "I_kwDOOTDVN87hKM_v", "number": 943, "title": "Qwen2.5-7B gets ~50% on GSM8K eval while Qwen2.5-3B gets ~80% using scripts/grpo_demo_llama3_qwen2.py", "user": { "login": "zyh1999", "id": 25893843, "node_id": "MDQ6VXNlcjI1ODkzODQz", "avatar_url": "https://avatars.githubusercontent.com/u/25893843?v=4", "gravatar_id": "", "url": "https://api.github.com/users/zyh1999", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 9550630502, "node_id": "LA_kwDOOTDVN88AAAACOUMOZg", "url": "https://api.github.com/repos/google/tunix/labels/type:performance", "name": "type:performance", "color": "e7b5e4", "default": false, "description": "Make things lean and fast" } }, "state": "open", "locked": false, "assignee": null, "assignees": {}, "milestone": null, "comments": 0, "created_at": "2026-01-03T01:34:08Z", "updated_at": "2026-01-05T04:41:15Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "\n### Description\n\nHi — I’m evaluating GSM8K using:\n\n`scripts/grpo_demo_llama3_qwen2.py`\n\nThis is **eval-only (no training), pass@1**, using the **vanilla inference engine**.\n\nResults:\n\n| Model | Accuracy | Note |\n|------|----------|------|\n| **Qwen2.5-3B-Instruct** | ~80% | consistent with Qwen’s official report |\n| **Qwen2.5-7B-Instruct** | ~50% | much lower than expected |\n\nTo enable the 3B model I only added a few lines of configuration (no training logic changed). \nThe small patch is here for reference:\n\n👉 **Link:** <[PASTE YOUR PR / GIST HERE](https://github.com/zyh1999/tunix/tree/only-test?tab=readme-ov-file)>\n\n\n### Reproduction\n\npython3 scripts/grpo_demo_llama3_qwen2.py --model-version=Qwen/Qwen2.5-7B-Instruct --rollout-engine=vanilla --global-batch-size=16 --num-test-batches=83 --max-tpu-to-use=4 --eval-only=True\n\npython3 scripts/grpo_demo_llama3_qwen2.py --model-version=Qwen/Qwen2.5-3B-Instruct --rollout-engine=van\nilla --global-batch-size=16 --num-test-batches=83 --max-tpu-to-use=2 --eval-only=True\n\n\n", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/943/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/939", "id": 3762143205, "node_id": "I_kwDOOTDVN87gPb_l", "number": 939, "title": "Clarify differences between examples/rl/grpo GSM8K recipes and scripts/grpo_demo_llama3_qwen2.py", "user": { "login": "zyh1999", "id": 25893843, "node_id": "MDQ6VXNlcjI1ODkzODQz", "avatar_url": "https://avatars.githubusercontent.com/u/25893843?v=4", "gravatar_id": "", "url": "https://api.github.com/users/zyh1999", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274168, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1eA", "url": "https://api.github.com/repos/google/tunix/labels/type:support", "name": "type:support", "color": "d876e3", "default": false, "description": "Further information is requested" } }, "state": "open", "locked": false, "assignee": null, "assignees": {}, "milestone": null, "comments": 2, "created_at": "2025-12-25T15:45:14Z", "updated_at": "2025-12-31T04:01:45Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "Hi, thanks for open-sourcing Tunix!\n\nI’m a bit confused about the two GRPO entry points that seem to cover similar use cases:\n\nShell scripts under examples/rl/grpo/gsm8k (e.g. run_llama3.2_1b.sh, run_gemma3_1b.sh), which call python -m tunix.cli.grpo_main ... with a base_config.yaml, dataset_name=\"gsm8k\", reward_functions=\"['tunix/cli/reward_fn/gsm8k.py']\", and various overrides for steps, LR, checkpointing, etc.\n\nThe Python demo scripts/grpo_demo_llama3_qwen2.py, which sets up GRPO for Llama 3 / Qwen 2 with its own argument parser (e.g. --model-version) .\n\nSo what is the main difference? When should I choose this one instead of the other?", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/939/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/902", "id": 3736740260, "node_id": "I_kwDOOTDVN87euiGk", "number": 902, "title": "RL utils.py has an incorrect check for is_positive_integer", "user": { "login": "Ajay-26", "id": 64776385, "node_id": "MDQ6VXNlcjY0Nzc2Mzg1", "avatar_url": "https://avatars.githubusercontent.com/u/64776385?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Ajay-26", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274152, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1aA", "url": "https://api.github.com/repos/google/tunix/labels/type:bug", "name": "type:bug", "color": "d73a4a", "default": false, "description": "Something isn't working" } }, "state": "open", "locked": false, "assignee": null, "assignees": {}, "milestone": null, "comments": 1, "created_at": "2025-12-16T23:45:28Z", "updated_at": "2025-12-17T22:59:07Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "Hi! Raising this issue to note that there might be a bug in initializing cluster config for RL. \n\nThe following snippet crashes when I run it:\n\n```\n# Training config\ncluster_config = rl_cluster_lib.ClusterConfig(\n role_to_mesh={\n rl_cluster_lib.Role.ACTOR: mesh,\n rl_cluster_lib.Role.REFERENCE: mesh,\n rl_cluster_lib.Role.ROLLOUT: mesh,\n },\n rollout_engine='vanilla',\n offload_to_cpu=False,\n training_config=rl_cluster_lib.RLTrainingConfig(\n actor_optimizer=optimizer,\n eval_every_n_steps=EVAL_EVERY_N_STEPS,\n max_steps=MAX_STEPS,\n mini_batch_size=TRAIN_MICRO_BATCH_SIZE,\n train_micro_batch_size=TRAIN_MICRO_BATCH_SIZE,\n # metrics logging\n metrics_logging_options=metrics_logging_options,\n # checkpoint saving\n checkpoint_root_directory=CKPT_DIR,\n checkpointing_options=checkpointing_options,\n ),\n rollout_config=base_rollout.RolloutConfig(\n max_tokens_to_generate=TOTAL_GENERATION_STEPS,\n max_prompt_length=MAX_PROMPT_LENGTH,\n kv_cache_size=MAX_PROMPT_LENGTH + TOTAL_GENERATION_STEPS + 256,\n temperature=TEMPERATURE,\n top_p=TOP_P,\n top_k=TOP_K,\n eos_tokens=EOS_TOKENS,\n ),\n)\n\ngrpo_config = GRPOConfig(\n num_generations=NUM_GENERATIONS,\n num_iterations=NUM_ITERATIONS,\n beta=BETA,\n epsilon=EPSILON,\n)\n```\n\n**Expected Behavior**\nThe given snippet should simply work. \n\n**Actual Behavior**\nThe snippet raises the following error:\n```\n---------------------------------------------------------------------------\nAttributeError Traceback (most recent call last)\n/tmp/ipykernel_181/593643255.py in ()\n 8 rollout_engine='vanilla',\n 9 offload_to_cpu=False,\n---> 10 training_config=rl_cluster_lib.RLTrainingConfig(\n 11 actor_optimizer=optimizer,\n 12 eval_every_n_steps=EVAL_EVERY_N_STEPS,\n\n/usr/local/lib/python3.11/dist-packages/tunix/rl/rl_cluster.py in __init__(self, eval_every_n_steps, max_steps, gradient_accumulation_steps, checkpoint_root_directory, checkpointing_options, metrics_logging_options, profiler_options, data_sharding_axis, max_inflight_computations, metrics_prefix, pbar_description, actor_optimizer, critic_optimizer, mini_batch_size, train_micro_batch_size, rollout_micro_batch_size, compute_logps_micro_batch_size)\n\n/usr/local/lib/python3.11/dist-packages/tunix/rl/rl_cluster.py in __post_init__(self)\n 112 \"compute_logps_micro_batch_size\",\n 113 ]:\n--> 114 rl_utils.is_positive_integer(getattr(self, name), name)\n 115 \n 116 if self.gradient_accumulation_steps is not None:\n\n/usr/local/lib/python3.11/dist-packages/tunix/rl/utils.py in is_positive_integer(value, name)\n 35 def is_positive_integer(value: int | None, name: str):\n 36 \"\"\"Checks if the value is positive.\"\"\"\n---> 37 if value is not None and (not value.is_integer() or value <= 0):\n 38 raise ValueError(f\"{name} must be a positive integer. Got: {value}\")\n 39 \n\nAttributeError: 'int' object has no attribute 'is_integer'\n```\n\n**Steps to Reproduce the Problem**\n\n1.Run the above provided snippet. It can be found in the link given below\n\n\nI am trying to run the provided example on GRPO that is given [here](https://github.com/google/tunix/blob/main/examples/grpo_gemma.ipynb) on a Kaggle notebook. \n\nI have been able to locate that the issue is in [the is_positive_integer() function here](https://github.com/google/tunix/blob/main/tunix/rl/utils.py) on line 35\n\nHappy to make a PR with the fix. ", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/902/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [ 1000, 930 ] }, { "url": "https://api.github.com/repos/google/tunix/issues/887", "id": 3730152964, "node_id": "I_kwDOOTDVN87eVZ4E", "number": 887, "title": "Feature request: Add Contrastive Representation Distillation (CRD)", "user": { "login": "surajyadav-research", "id": 223458481, "node_id": "U_kgDODVG0sQ", "avatar_url": "https://avatars.githubusercontent.com/u/223458481?v=4", "gravatar_id": "", "url": "https://api.github.com/users/surajyadav-research", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274161, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1cQ", "url": "https://api.github.com/repos/google/tunix/labels/type:feature/enhancement", "name": "type:feature/enhancement", "color": "a2eeef", "default": false, "description": "New feature or request" } }, "state": "open", "locked": false, "assignee": null, "assignees": {}, "milestone": null, "comments": 0, "created_at": "2025-12-15T12:14:20Z", "updated_at": "2025-12-15T16:22:16Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "\nRequesting a built-in **Contrastive Representation Distillation (CRD)** strategy (Tian et al., 2019) for `tunix.distillation.DistillationTrainer`.\n\n## What CRD does\nDistills at the **representation level** using an InfoNCE/contrastive loss:\n- positive pair: (student rep, teacher rep) from the **same sample**\n- negatives: mismatched pairs (e.g., in-batch negatives)\n\nMinimal form:\n- `logits = (z_s @ z_t.T) / tau`\n- `labels = arange(B)`\n- `loss = CE(logits, labels)` (optional symmetric term on `logits.T`)\n\n## Why this helps\n- Complements / improves over logit-KD in many setups\n\n\n## Reference\nTian et al., *Contrastive Representation Distillation*, arXiv:1910.10699 (2019): https://arxiv.org/pdf/1910.10699\n\n\nI can contribute a PR + tests if you’re open to it.", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/887/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/884", "id": 3727014423, "node_id": "I_kwDOOTDVN87eJboX", "number": 884, "title": "Gemma 3 1b model loading asyncio error on kaggle notebook", "user": { "login": "kamalkraj", "id": 17096858, "node_id": "MDQ6VXNlcjE3MDk2ODU4", "avatar_url": "https://avatars.githubusercontent.com/u/17096858?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kamalkraj", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274152, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1aA", "url": "https://api.github.com/repos/google/tunix/labels/type:bug", "name": "type:bug", "color": "d73a4a", "default": false, "description": "Something isn't working" } }, "state": "open", "locked": false, "assignee": { "login": "selamw1", "id": 153962326, "node_id": "U_kgDOCS1HVg", "avatar_url": "https://avatars.githubusercontent.com/u/153962326?v=4", "gravatar_id": "", "url": "https://api.github.com/users/selamw1", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "selamw1", "id": 153962326, "node_id": "U_kgDOCS1HVg", "avatar_url": "https://avatars.githubusercontent.com/u/153962326?v=4", "gravatar_id": "", "url": "https://api.github.com/users/selamw1", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 3, "created_at": "2025-12-14T09:06:42Z", "updated_at": "2025-12-24T07:18:25Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "**Expected Behavior**\nThe model loading without any asyncio error. \n**Actual Behavior**\nThe model is loaded properly. But the notebook cell execution is stopped after the model load cell. Need to manually run after the model loading cells. \n\n**Steps to Reproduce the Problem**\n\n[tunix-error.ipynb](https://github.com/user-attachments/files/24148738/tunix-error.ipynb)\n\n\n\n**Environment**\n\n- **OS:** Kaggle TPU Notebook\n- **Project Version:** \n\n\n| Package Name | Version |\n| :--- | :--- |\n| jax[tpu] | 0.8.1 |\n| flax | 0.12.1 |\n| optax | 0.2.6 |\n| qwix | 0.1.5 |\n| orbax-checkpoint | 0.11.31 |\n| grain | 0.2.15 |\n| google-metrax | 0.2.4 |\n| google-tunix[prod] | 0.1.5 |\n\n**Checklist**\n\n- [x] I have searched the existing issues for a similar bug report.\n- [x] I have provided all the required information in the \"Environment\" section.\n- [x] I have provided a minimal, reproducible example.\n\n**Would you like to help us fix it?**\n", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/884/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/874", "id": 3719120248, "node_id": "I_kwDOOTDVN87drUV4", "number": 874, "title": "Train GRPO through SGLangJax in Single or Multi Hosts with Pathways", "user": { "login": "aolemila", "id": 214426322, "node_id": "U_kgDODMfi0g", "avatar_url": "https://avatars.githubusercontent.com/u/214426322?v=4", "gravatar_id": "", "url": "https://api.github.com/users/aolemila", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274161, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1cQ", "url": "https://api.github.com/repos/google/tunix/labels/type:feature/enhancement", "name": "type:feature/enhancement", "color": "a2eeef", "default": false, "description": "New feature or request" } }, "state": "open", "locked": false, "assignee": { "login": "aolemila", "id": 214426322, "node_id": "U_kgDODMfi0g", "avatar_url": "https://avatars.githubusercontent.com/u/214426322?v=4", "gravatar_id": "", "url": "https://api.github.com/users/aolemila", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "aolemila", "id": 214426322, "node_id": "U_kgDODMfi0g", "avatar_url": "https://avatars.githubusercontent.com/u/214426322?v=4", "gravatar_id": "", "url": "https://api.github.com/users/aolemila", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 0, "created_at": "2025-12-11T12:07:56Z", "updated_at": "2025-12-15T16:24:41Z", "closed_at": null, "author_association": "COLLABORATOR", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "Note: \n- Current situation is we succeed to run `python3 scripts/grpo_demo_llama3_qwen2.py` in single and multi hosts with Pathways. The performance needs to optimize. \n- Multi hosts locates in the same Pod. Currently we have not tested the multi hosts across Pods.\n- If you want to use Pathways, please ensure you have access to GKE. Now allow list is not required.\n- How to setup GKE with Pathways? Maybe you can refer to [run_in_pathways.md](https://github.com/sgl-project/sglang-jax/blob/main/docs/features/run_in_pathways.md).\n\n# Single Host with Pathways\n\n**Environment**\n- topology: 2x2\n- hosts: 1\n- machine_type: ct6e-standard-4t\n\n**Instruction**\n```bash\n# Execute the following script on any hosts where you can connect to GKE.\nJAX_PLATFORMS=proxy \\\nJAX_BACKEND_TARGET=grpc://127.0.0.1:29000 \\\npython3 scripts/grpo_demo_llama3_qwen2.py \\\n--num-batches 4 \\\n--num-test-batches 1 \\\n--root-dir=/home/gcpuser/aolemila \\\n--model-version=meta-llama/Llama-3.2-1B-Instruct \\\n--data-source tfds \\\n--dataset gsm8k \\\n--rollout-engine sglang_jax\n```\n\n**Expected Result**\n\"image\"\n\n\n# Multi Hosts in the Pod with Pathways\n\n**Environment**\n- topology: 4x4\n- hosts: 2\n- machine_type: ct6e-standard-4t\n\n**Instruction**\n```bash\n# Execute the following script on any hosts where you can connect to GKE.\nJAX_PLATFORMS=proxy \\\nJAX_BACKEND_TARGET=grpc://127.0.0.1:29000 \\\npython3 scripts/grpo_demo_llama3_qwen2.py \\\n--num-batches 4 \\\n--num-test-batches 1 \\\n--root-dir=/home/gcpuser/aolemila \\\n--model-version=meta-llama/Llama-3.2-1B-Instruct \\\n--data-source tfds \\\n--dataset gsm8k \\\n--rollout-engine sglang_jax \\\n--rollout-tp 8 \\\n--cluster-setup disaggregated-2-way\n```\n\nNote: `--rollout-tp 8` and `--cluster-setup disaggregated-2-way` are required due to SGLangJax lacks to support DP now. It will support DP soon.\n\n**Expected Result**\n\"Pasted", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/874/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/842", "id": 3697382388, "node_id": "I_kwDOOTDVN87cYZP0", "number": 842, "title": "gemma3-1b-it bad rollout.", "user": { "login": "carlesoctav", "id": 106587439, "node_id": "U_kgDOBlplLw", "avatar_url": "https://avatars.githubusercontent.com/u/106587439?v=4", "gravatar_id": "", "url": "https://api.github.com/users/carlesoctav", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274152, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1aA", "url": "https://api.github.com/repos/google/tunix/labels/type:bug", "name": "type:bug", "color": "d73a4a", "default": false, "description": "Something isn't working" } }, "state": "open", "locked": false, "assignee": null, "assignees": {}, "milestone": null, "comments": 1, "created_at": "2025-12-05T03:46:25Z", "updated_at": "2025-12-27T21:44:04Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "this is from examples/rl/grpo/gsm8k/run_gemma3_1b.sh\n\n1. issue it start with random token like `es` `ery`, and more\n2. it doesnt stop at , adding `eos_token` and token_ids to `eos_token` on the rollout config help stop this but i dont see this issue on gemma2 1b.sh\n3. even on the fullfinetune (without lora) i still get this issue.\n\n```\nI1205 03:40:18.077061 139779238873088 dataset_info.py:707] Load dataset info from data/train/gsm8k/1.0.0\nI1205 03:40:18.081385 139779238873088 dataset_builder.py:892] Found random access formats: . Chose to use FileFormat.ARRAY_RECORD. Overriding file format in the dataset info.\nI1205 03:40:18.117426 139779238873088 rl_learner.py:580] Training with full_batch_size=4, mini_batch_size=4, train_micro_batch_size=2, self._rollout_micro_batch_size=2, self._compute_logps_micro_batch_size=2, grad_acc_steps=2\nI1205 03:41:39.454670 139699820918336 gsm8k.py:112] START ============================\nI1205 03:41:39.455143 139699820918336 gsm8k.py:113] Question: Jane is painting her fingernails. She applies a base coat that takes 2 minutes to dry, two color coats that take 3 minutes each to dry, and a clear top coat that takes 5 minutes to dry. How many minutes total does Jane spend waiting for her nail polish to dry?\nI1205 03:41:39.455501 139699820918336 gsm8k.py:114] Answer: 13\nI1205 03:41:39.455768 139699820918336 gsm8k.py:115] Response: es:\nLet $T_b$ be the time taken for the base coat to dry, $T_c$ be the time taken for the first color coat to dry, and $T_t$ be the time taken for the clear top coat to dry.\nWe are given that $T_b = 2$ minutes, $T_c = 3$ minutes, and $T_t = 5$ minutes.\nThe total time spent painting is the sum of the times taken for each step.\nTotal time = $T_b + T_c + T_t = 2 + 3 + 5 = 10$ minutes.\nTherefore, Jane spends 10 minutes waiting for her nail polish to dry.\n\n10 escortenvironmentalistenvironmentalistenvironmentalistenvironmentalist\n पर्यावरणीय\n\n\n\n\n\n", "closed_by": { "login": "aaghaazkhan", "id": 177999751, "node_id": "U_kgDOCpwPhw", "avatar_url": "https://avatars.githubusercontent.com/u/177999751?v=4", "gravatar_id": "", "url": "https://api.github.com/users/aaghaazkhan", "type": "User", "user_view_type": "public", "site_admin": false }, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/816/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": "reopened", "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/809", "id": 3668872383, "node_id": "I_kwDOOTDVN87aroy_", "number": 809, "title": "Single-item batches truncate sampler token_buffer instead of populating it", "user": { "login": "Cascoopman", "id": 58267444, "node_id": "MDQ6VXNlcjU4MjY3NDQ0", "avatar_url": "https://avatars.githubusercontent.com/u/58267444?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Cascoopman", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274152, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1aA", "url": "https://api.github.com/repos/google/tunix/labels/type:bug", "name": "type:bug", "color": "d73a4a", "default": false, "description": "Something isn't working" } }, "state": "open", "locked": false, "assignee": null, "assignees": {}, "milestone": null, "comments": 0, "created_at": "2025-11-26T19:56:32Z", "updated_at": "2025-11-28T04:10:27Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "I am doing the Tunix Kaggle competition but run into bugs when setting the TRAIN_MICRO_BATCH_SIZE to 1 in this[example notebook](https://www.kaggle.com/code/windmaple/grpo-demo-gemma2-2b).\n\nhttps://github.com/google/tunix/blob/1fe360d47961abd1011d427adeb336a429857fbb/tunix/generate/sampler.py#L328-L330\n\n### Description\nWhen invoking `tunix.generate.sampler.Sampler` with `batch_size=1`, the generated output is always truncated after just a few tokens; the call never reaches `` even though the same prompt works fine with `batch_size=2`. Digging into the sampler code shows that:\n- `token_buffer` is initialized in `init_sample_state()` as `(batch_size, total_sampling_steps)` (e.g., (1, 896)).\n- `_sample()` is supposed to write each new token via `token_buffer = token_buffer.at[:, decoding_step + 1].set(next_token_candidate)`.\n- During the JAX `while_loop` (`_decode_fn`), `_sample_step` repeatedly calls `_sample()`, but when `batch_size=1` the buffer never grows beyond the first 2–4 entries.\n- By the time token extraction runs (`__call__`, lines 735-782), `token_buffer` has shape `(1, 2)` (or `(1, 4)`), so `token_buffer[max_prompt_length:]` only contains a few tokens and `find_first_eos_idx` returns immediately, resulting in truncated outputs. Running the same logic with `batch_size=2` produces a normal-length buffer and completion.\n\nI added debug host-callbacks around `_sample_step` to log `token_buffer.shape` before/after each iteration and the logs confirm that the `.at[:, decoding_step + 1].set(...)` update silently stops writing when there’s a single example. This makes the sampler unusable when `TRAIN_MICRO_BATCH_SIZE=1`, yet it works fine for larger batches.\n\n### Steps to reproduce\n1. Instantiate the sampler used in `gemma2-grpo-original-demo.ipynb`, e.g. the Tunix transformer + tokenizer + cache config.\n2. Call `sampler(input_strings=[prompt], ...)` with `max_generation_steps` large enough to reach ``, using greedy sampling (`temperature=0.0001`, `top_k=1`).\n3. Inspect `result.tokens[0]` / `result.text[0]` – the output is only a few tokens long and often doesn’t end with ``.\n4. Repeat step 2 with `input_strings=[prompt, prompt]` (batch_size=2) and observe a normal-length completion.\n\n### Expected behavior\n`token_buffer` should stay `(1, total_sampling_steps)` and `_sample()` should keep writing tokens until EOS, just like it does when `batch_size=2`.\n\n### Actual behavior\n`token_buffer` stops at ~2-4 slots, so the extractor only returns the first few tokens and the sample is truncated. This happens inside `_sample()` / `_sample_step`, meaning the loop never expands the buffer for single-item batches.\n\n### Proposed fix\nInvestigate how `_sample()` / `_sample_step` behaves under JAX when `batch_size=1`. The `.at` update may be optimized out or shape-inferred incorrectly. Ensuring the `token_buffer` update runs (and that `token_buffer` remains `(batch_size, total_sampling_steps)`) should fix the bug. If necessary, add a test for `batch_size=1` to catch regressions.", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/809/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [ 963 ] }, { "url": "https://api.github.com/repos/google/tunix/issues/802", "id": 3662090092, "node_id": "I_kwDOOTDVN87aRw9s", "number": 802, "title": "[Bug] Fail to run train_deepscaler_nb.py", "user": { "login": "aolemila", "id": 214426322, "node_id": "U_kgDODMfi0g", "avatar_url": "https://avatars.githubusercontent.com/u/214426322?v=4", "gravatar_id": "", "url": "https://api.github.com/users/aolemila", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274152, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1aA", "url": "https://api.github.com/repos/google/tunix/labels/type:bug", "name": "type:bug", "color": "d73a4a", "default": false, "description": "Something isn't working" } }, "state": "open", "locked": false, "assignee": { "login": "vector-one", "id": 165829168, "node_id": "U_kgDOCeJaMA", "avatar_url": "https://avatars.githubusercontent.com/u/165829168?v=4", "gravatar_id": "", "url": "https://api.github.com/users/vector-one", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "vector-one", "id": 165829168, "node_id": "U_kgDOCeJaMA", "avatar_url": "https://avatars.githubusercontent.com/u/165829168?v=4", "gravatar_id": "", "url": "https://api.github.com/users/vector-one", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 2, "created_at": "2025-11-25T08:56:00Z", "updated_at": "2026-01-09T11:49:31Z", "closed_at": null, "author_association": "COLLABORATOR", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "**Expected Behavior**\n\nSuccessfully run `python3 examples/deepscaler/train_deepscaler_nb.py`.\n\n**Actual Behavior**\n\n\"Image\"\n\n**Steps to Reproduce the Problem**\n\n1. `python3 examples/deepscaler/train_deepscaler_nb.py`\n\n\n\n**Environment**\n\n- **OS:** [e.g., Ubuntu, etc.] Ubuntu-22.04, tpu-v6e-4.\n- **Project Version:** [e.g., 0.0.1] main-096080ffbb6529353b8e0897142bc425ed57fb3b\n\n**Checklist**\n\n- [x] I have searched the existing issues for a similar bug report.\n- [x] I have provided all the required information in the \"Environment\" section.\n- [x] I have provided a minimal, reproducible example.\n\n**Would you like to help us fix it?**\n\nMaybe I need a correct google package to run.\n", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/802/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [ 958 ] }, { "url": "https://api.github.com/repos/google/tunix/issues/780", "id": 3649038402, "node_id": "I_kwDOOTDVN87Zf-hC", "number": 780, "title": "vLLM sampler accesses attribute that does not exist", "user": { "login": "darsnack", "id": 4811350, "node_id": "MDQ6VXNlcjQ4MTEzNTA=", "avatar_url": "https://avatars.githubusercontent.com/u/4811350?v=4", "gravatar_id": "", "url": "https://api.github.com/users/darsnack", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274152, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1aA", "url": "https://api.github.com/repos/google/tunix/labels/type:bug", "name": "type:bug", "color": "d73a4a", "default": false, "description": "Something isn't working" } }, "state": "open", "locked": false, "assignee": { "login": "wang2yn84", "id": 13134832, "node_id": "MDQ6VXNlcjEzMTM0ODMy", "avatar_url": "https://avatars.githubusercontent.com/u/13134832?v=4", "gravatar_id": "", "url": "https://api.github.com/users/wang2yn84", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "wang2yn84", "id": 13134832, "node_id": "MDQ6VXNlcjEzMTM0ODMy", "avatar_url": "https://avatars.githubusercontent.com/u/13134832?v=4", "gravatar_id": "", "url": "https://api.github.com/users/wang2yn84", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 1, "created_at": "2025-11-20T20:52:41Z", "updated_at": "2025-11-21T22:21:39Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "**Expected Behavior**\n\nRunning generation (for GRPO) with vLLM sampler should access attributes that actually exist.\n\n**Actual Behavior**\n\nCurrently, attempting to use the vLLM sampler results in:\n```\n return self.llm.llm_engine.model_executor.driver_worker.model_runner\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'MultiprocExecutor' object has no attribute 'driver_worker'\n```\n\nAFAICT, there is no `driver_worker` attribute available. My guess is that there is a version mismatch between the expected and installed vLLM API?\n\n**Steps to Reproduce the Problem**\n\nN/A\n\n**Environment**\n\n- **OS:** Ubuntu\n- **Project Version:** 0.1.4\n\n**Checklist**\n\n- [x] I have searched the existing issues for a similar bug report.\n- [x] I have provided all the required information in the \"Environment\" section.\n- [ ] I have provided a minimal, reproducible example.\n\n**Would you like to help us fix it?**\n\nWith some guidance, I can help.", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/780/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/761", "id": 3637837083, "node_id": "I_kwDOOTDVN87Y1P0b", "number": 761, "title": "Please consider the following when creating a PR:", "user": { "login": "Tsukimarf", "id": 215229009, "node_id": "U_kgDODNQiUQ", "avatar_url": "https://avatars.githubusercontent.com/u/215229009?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Tsukimarf", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274170, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1eg", "url": "https://api.github.com/repos/google/tunix/labels/wontfix", "name": "wontfix", "color": "ffffff", "default": true, "description": "This will not be worked on" } }, "state": "open", "locked": false, "assignee": null, "assignees": {}, "milestone": null, "comments": 0, "created_at": "2025-11-18T12:03:35Z", "updated_at": "2025-11-20T17:15:44Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "Of course! When creating a Pull Request (PR), providing clear and comprehensive context is crucial for a smooth and efficient review process. Here is a detailed guide on what to consider, structured for easy copying and pasting into your PR template.\n\n---\n\nPull Request Creation Checklist & Guidelines\n\nPlease review the following points before submitting your PR. This helps reviewers understand your changes and accelerates the merge process.\n\n1. PR Title & Description\n\n· Title: Use a clear, descriptive title following conventional commit standards (e.g., feat: add user profile endpoint, fix: resolve login crash on iOS).\n· Description:\n · What? Provide a concise summary of the changes.\n · Why? Explain the reason for the change (e.g., bug ticket number, new feature request, refactoring need).\n · How? Briefly explain the implementation approach, if relevant.\n · Screenshots/Recordings: For UI changes, always include before-and-after screenshots or a screen recording.\n · Issue Link: Link to the related GitHub/GitLab/Jira issue using keywords (e.g., Fixes #123, Closes PROJECT-456).\n\n2. Scope & Type of Change\n\n· Type: Please label the PR appropriately (e.g., feat, fix, docs, style, refactor, test, chore).\n· Scope: Is this a breaking change? If yes, it must be clearly highlighted in the title and description.\n\n3. Code Quality & Testing\n\n· Self-Review: I have reviewed my own code.\n· Code Style: The code follows the project's established style and conventions.\n· Comments: Code is self-documenting where possible. Complex logic has clear comments.\n· Tests:\n · I have added/updated unit tests or integration tests that prove my fix is effective or that my feature works.\n · All new and existing tests pass locally.\n · For UI changes, I have tested them manually across major browsers (Chrome, Firefox, Safari, Edge) or relevant platforms.\n\n4. Pre-Submission Checklist\n\n· My code builds without any errors or warnings.\n· I have rebased/merged the latest changes from the target branch (e.g., main, develop).\n· I have resolved any merge conflicts.\n· I have ensured my branch name is meaningful (e.g., fix/login-issue, feature/payment-integration).\n\n5. Dependencies & Documentation\n\n· Dependencies: Are there any new dependencies? If yes, justify their need.\n· Documentation:\n · I have updated the relevant documentation (README, API docs, code comments) to reflect the changes.\n · My changes generate no new warnings in the documentation build.\n\n---\n\nExample PR Description Template\n\nYou can copy and paste the template below directly into your PR description.\n\n```\n## What does this PR do?\n\n\n## Why is this change needed?\n\n\n## How was it implemented?\n\n\n## How can it be tested?\n\n1.\n2.\n3.\n\n## Screenshots (for UI changes)\n| Before | After |\n|--------|-------|\n| | |\n\n## Checklist\n- [ ] I have performed a self-review of my own code.\n- [ ] I have commented my code, particularly in hard-to-understand areas.\n- [ ] I have added/updated tests that prove my fix is effective or that my feature works.\n- [ ] All new and existing tests pass.\n- [ ] My changes require a change to the documentation.\n- [ ] I have updated the documentation accordingly.\n- [ ] I have tested the changes on [relevant platforms/browsers].\n\n**Issue Link:** `Fixes #`\n```\n\nBy providing this level of detail, you show respect for your reviewers' time and significantly increase the chances of a quick, high-quality review. Thank you!", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/761/reactions", "total_count": 2, "+1": 2, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/698", "id": 3594982749, "node_id": "I_kwDOOTDVN87WRxVd", "number": 698, "title": "GRPO training fails in multi-process mode", "user": { "login": "yhtang", "id": 6081524, "node_id": "MDQ6VXNlcjYwODE1MjQ=", "avatar_url": "https://avatars.githubusercontent.com/u/6081524?v=4", "gravatar_id": "", "url": "https://api.github.com/users/yhtang", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274152, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1aA", "url": "https://api.github.com/repos/google/tunix/labels/type:bug", "name": "type:bug", "color": "d73a4a", "default": false, "description": "Something isn't working" } }, "state": "open", "locked": false, "assignee": { "login": "selamw1", "id": 153962326, "node_id": "U_kgDOCS1HVg", "avatar_url": "https://avatars.githubusercontent.com/u/153962326?v=4", "gravatar_id": "", "url": "https://api.github.com/users/selamw1", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "selamw1", "id": 153962326, "node_id": "U_kgDOCS1HVg", "avatar_url": "https://avatars.githubusercontent.com/u/153962326?v=4", "gravatar_id": "", "url": "https://api.github.com/users/selamw1", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 3, "created_at": "2025-11-06T09:51:04Z", "updated_at": "2025-11-07T21:43:26Z", "closed_at": null, "author_association": "COLLABORATOR", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "**Expected Behavior**\nGRPO training with Tunix in multi-process SPMD (e.g., 2 GPU nodes) should generate rollouts and compute advantages without failures.\n\n**Actual Behavior**\nCrash during rollout sampling when iterating a distributed `jax.Array`:\n- `AssertionError` at `jax/_src/array.py:380`: `assert self.is_fully_replicated or self.is_fully_addressable`\n- Triggered in `tunix/generate/sampler.py` while doing `zip(out_tokens, lengths)`\n\n**Steps to Reproduce the Problem**\n1. Add `jax.distributed.initialize()` at the beginning of the GRPO example script.\n2. Run the GRPO demo\n```\nsrun -u --label --ntasks=2 --ntasks-per-node=1 -c${SLURM_CPUS_ON_NODE} python \n```\n\n**Checklist**\n- [x] I have searched the existing issues for a similar bug report.\n- [x] I have provided all the required information in the \"Environment\" section.\n- [x] I have provided a minimal, reproducible example.\n\n**Would you like to help us fix it?**\n", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/698/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [ 834 ] }, { "url": "https://api.github.com/repos/google/tunix/issues/687", "id": 3590019636, "node_id": "I_kwDOOTDVN87V-1o0", "number": 687, "title": "grpo_demo.ipynb: Excessive output", "user": { "login": "cool-RR", "id": 56778, "node_id": "MDQ6VXNlcjU2Nzc4", "avatar_url": "https://avatars.githubusercontent.com/u/56778?v=4", "gravatar_id": "", "url": "https://api.github.com/users/cool-RR", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274161, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1cQ", "url": "https://api.github.com/repos/google/tunix/labels/type:feature/enhancement", "name": "type:feature/enhancement", "color": "a2eeef", "default": false, "description": "New feature or request" } }, "state": "open", "locked": false, "assignee": { "login": "selamw1", "id": 153962326, "node_id": "U_kgDOCS1HVg", "avatar_url": "https://avatars.githubusercontent.com/u/153962326?v=4", "gravatar_id": "", "url": "https://api.github.com/users/selamw1", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "selamw1", "id": 153962326, "node_id": "U_kgDOCS1HVg", "avatar_url": "https://avatars.githubusercontent.com/u/153962326?v=4", "gravatar_id": "", "url": "https://api.github.com/users/selamw1", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 4, "created_at": "2025-11-05T09:10:22Z", "updated_at": "2025-11-06T22:28:28Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "This isn't an outright bug, just something that makes it difficult to work with `grpo_demo.ipynb`.\n\nThe cell with `grpo_trainer.train` produces a huge amount of text output. It becomes 95% of the notebook. This makes it difficult to scroll around the notebook or even tell when it's finished. I understand that you would like to show samples of outputs, but if it's important, I'd suggest showing a much smaller selection of outputs to keep the notebook workable.\n\nIf you agree with this I'll be happy to help.", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/687/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/686", "id": 3590010335, "node_id": "I_kwDOOTDVN87V-zXf", "number": 686, "title": "\"RuntimeError: cannot enter context\" in grpo_demo.ipynb", "user": { "login": "cool-RR", "id": 56778, "node_id": "MDQ6VXNlcjU2Nzc4", "avatar_url": "https://avatars.githubusercontent.com/u/56778?v=4", "gravatar_id": "", "url": "https://api.github.com/users/cool-RR", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274152, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1aA", "url": "https://api.github.com/repos/google/tunix/labels/type:bug", "name": "type:bug", "color": "d73a4a", "default": false, "description": "Something isn't working" } }, "state": "open", "locked": false, "assignee": { "login": "selamw1", "id": 153962326, "node_id": "U_kgDOCS1HVg", "avatar_url": "https://avatars.githubusercontent.com/u/153962326?v=4", "gravatar_id": "", "url": "https://api.github.com/users/selamw1", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "selamw1", "id": 153962326, "node_id": "U_kgDOCS1HVg", "avatar_url": "https://avatars.githubusercontent.com/u/153962326?v=4", "gravatar_id": "", "url": "https://api.github.com/users/selamw1", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 6, "created_at": "2025-11-05T09:07:51Z", "updated_at": "2025-11-07T07:12:04Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "I attempted to run `grpo_demo.ipynb` to reproduce the training you've shown in [your blog post](https://developers.googleblog.com/en/introducing-tunix-a-jax-native-library-for-llm-post-training/).\n\nI've provisioned a v5e-4 machine from GCP and ran the notebook.\n\nIn the code block that invokes `get_gemma_ref_model`, I get lots of error text, including:\n\n```\nERROR:asyncio:Exception in callback Task.__step()\nhandle: \nTraceback (most recent call last):\n File \"/home/ramrachum/.local/share/uv/python/cpython-3.13.9-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n self._context.run(self._callback, *self._args)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nRuntimeError: cannot enter context: <_contextvars.Context object at 0x7f94a16454c0> is already entered\n\n```\n\nI've continued to run the notebook, not sure whether this error is going to adversly affect the results. When I got to the `grpo_trainer.train` cell and ran it, I got similar exceptions:\n\n```\nERROR:asyncio:Exception in callback Task.__step()\nhandle: \nTraceback (most recent call last):\n File \"/home/ramrachum/.local/share/uv/python/cpython-3.13.9-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n self._context.run(self._callback, *self._args)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nRuntimeError: cannot enter context: <_contextvars.Context object at 0x7f94a16454c0> is already entered\n```\n\nHere is an uploaded copy of the notebook showing the error: https://colab.research.google.com/drive/1cMd3jKUiyUUGBvMELtaPthdqIwNImdNy\n\nNote that I did not run this on Colab, I ran it on Jupyterlab on a GCP machine, because Colab does not provide a v5e-4 machine as far as I know. \n\n**Environment**\n\n- **OS:** Ubuntu 22.04.3 LTS\n- **Project Version:** `git+https://github.com/google/tunix@f0d5d1e63e24f42647fd4e6122641d689f8bfd0e`\n\n**Checklist**\n\n- [X] I have searched the existing issues for a similar bug report.\n- [X] I have provided all the required information in the \"Environment\" section.\n- [X] I have provided a minimal, reproducible example.\n\n**Would you like to help us fix it?**\n\nYes.", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/686/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/667", "id": 3581110948, "node_id": "I_kwDOOTDVN87Vc2qk", "number": 667, "title": "Failed build: Tunix Package Tests", "user": { "login": "github-actions[bot]", "id": 41898282, "node_id": "MDM6Qm90NDE4OTgyODI=", "avatar_url": "https://avatars.githubusercontent.com/in/15368?v=4", "gravatar_id": "", "url": "https://api.github.com/users/github-actions%5Bbot%5D", "type": "Bot", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 9574535587, "node_id": "LA_kwDOOTDVN88AAAACOq_Row", "url": "https://api.github.com/repos/google/tunix/labels/build%20failed", "name": "build failed", "color": "ededed", "default": false, "description": null } }, "state": "open", "locked": false, "assignee": { "login": "selamw1", "id": 153962326, "node_id": "U_kgDOCS1HVg", "avatar_url": "https://avatars.githubusercontent.com/u/153962326?v=4", "gravatar_id": "", "url": "https://api.github.com/users/selamw1", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "selamw1", "id": 153962326, "node_id": "U_kgDOCS1HVg", "avatar_url": "https://avatars.githubusercontent.com/u/153962326?v=4", "gravatar_id": "", "url": "https://api.github.com/users/selamw1", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 156, "created_at": "2025-11-03T08:47:17Z", "updated_at": "2026-02-17T13:56:06Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "GitHub Actions workflow [Tunix Package Tests #1439](https://github.com/google/tunix/actions/runs/19027914124) failed.\n\nEvent: schedule\nBranch: [main](https://github.com/google/tunix/tree/main)\nCommit: [1c221aee25158aafb5a83576ea4957d63b2172f4](https://github.com/google/tunix/commit/1c221aee25158aafb5a83576ea4957d63b2172f4)\n\nCreated by [jayqi/failed-build-issue-action](https://github.com/jayqi/failed-build-issue-action)", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/667/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/612", "id": 3540855242, "node_id": "I_kwDOOTDVN87TDSnK", "number": 612, "title": "A unified API for model loading", "user": { "login": "heydaari", "id": 95540171, "node_id": "U_kgDOBbHTyw", "avatar_url": "https://avatars.githubusercontent.com/u/95540171?v=4", "gravatar_id": "", "url": "https://api.github.com/users/heydaari", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274161, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1cQ", "url": "https://api.github.com/repos/google/tunix/labels/type:feature/enhancement", "name": "type:feature/enhancement", "color": "a2eeef", "default": false, "description": "New feature or request" } }, "state": "open", "locked": false, "assignee": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": "https://avatars.githubusercontent.com/u/4454178?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tianshub", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": "https://avatars.githubusercontent.com/u/4454178?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tianshub", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 1, "created_at": "2025-10-22T13:20:47Z", "updated_at": "2025-11-06T17:28:03Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "Currently, for loading different models in Tunix we need to load the model and its params_loader in a very hard python specific module loading (e.g. from tunix.models.qwen3.params import create_model_from_safe_tensors or from tunix.models.qwen3 import model as model_lib)\n\nI think it is based on best practices that creating a higher level abstraction like AutoModel for model creation and loading it weights must be beneficial and unifying all the models under the same abstract AutoModel\n", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/612/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/611", "id": 3540788499, "node_id": "I_kwDOOTDVN87TDCUT", "number": 611, "title": "Gemma3 12B loading error", "user": { "login": "heydaari", "id": 95540171, "node_id": "U_kgDOBbHTyw", "avatar_url": "https://avatars.githubusercontent.com/u/95540171?v=4", "gravatar_id": "", "url": "https://api.github.com/users/heydaari", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274152, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1aA", "url": "https://api.github.com/repos/google/tunix/labels/type:bug", "name": "type:bug", "color": "d73a4a", "default": false, "description": "Something isn't working" } }, "state": "open", "locked": false, "assignee": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": "https://avatars.githubusercontent.com/u/4454178?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tianshub", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": "https://avatars.githubusercontent.com/u/4454178?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tianshub", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 8, "created_at": "2025-10-22T13:02:56Z", "updated_at": "2026-01-12T19:59:07Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "**Actual Behavior**\n```\n---------------------------------------------------------------------------\nValueError Traceback (most recent call last)\nFile /usr/local/lib/python3.12/site-packages/tunix/models/safetensors_loader.py:106, in load_and_create_model(file_dir, model_class, config, key_mapping, mesh, preprocess_fn, dtype)\n 105 v = sf.get_tensor(k_name)\n--> 106 jax_key_mapped, transform = torch_key_to_jax_key(key_map, k_name)\n 108 if transform is not None:\n\nFile /usr/local/lib/python3.12/site-packages/tunix/models/safetensors_loader.py:35, in torch_key_to_jax_key(mapping, source_key)\n 34 if len(subs) != 1:\n---> 35 raise ValueError(f\"Only one key should be found: {subs} for {source_key}\")\n 36 else:\n\nValueError: Only one key should be found: [] for language_model.model.layers.10.input_layernorm.weight\n\nThe above exception was the direct cause of the following exception:\n\nRuntimeError Traceback (most recent call last)\nCell In[7], line 9\n 7 mesh = jax.make_mesh(*MESH)\n 8 with mesh:\n----> 9 gemma3 = params_lib.create_model_from_safe_tensors(\n 10 MODEL_CP_PATH, config, mesh\n 11 )\n 12 nnx.display(gemma3)\n\nFile /usr/local/lib/python3.12/site-packages/tunix/models/gemma3/params_safetensors.py:200, in create_model_from_safe_tensors(file_dir, config, mesh, dtype)\n 194 def create_model_from_safe_tensors(\n 195 file_dir: str,\n 196 config: model_lib.ModelConfig,\n 197 mesh: jax.sharding.Mesh | None = None,\n 198 dtype: jnp.dtype | None = None,\n 199 ):\n--> 200 return safetensors_loader.load_and_create_model(\n 201 file_dir=file_dir,\n 202 model_class=model_lib.Gemma3,\n 203 config=config,\n 204 key_mapping=_get_key_and_transform_mapping,\n 205 mesh=mesh,\n 206 preprocess_fn=_make_preprocess_fn(config),\n 207 dtype=dtype,\n 208 )\n\nFile /usr/local/lib/python3.12/site-packages/tunix/models/safetensors_loader.py:126, in load_and_create_model(file_dir, model_class, config, key_mapping, mesh, preprocess_fn, dtype)\n 123 file_loaded_tensors[jax_key_mapped] = current_arr\n 125 except Exception as e:\n--> 126 raise RuntimeError(\n 127 f\"Failed to load tensor {k_name} from file {f.name}: {e}\"\n 128 ) from e\n 130 # Apply preprocessing if provided (e.g., for MoE expert stacking)\n 131 if preprocess_fn is not None:\n\nRuntimeError: Failed to load tensor language_model.model.layers.10.input_layernorm.weight from file model-00002-of-00005.safetensors: Only one key should be found: [] for language_model.model.layers.10.input_layernorm.weight\n```\n\n**Steps to Reproduce the Problem**\nrun this code on Kaggle TPUs\n\n```python\nimport os\nos.environ['HF_TOKEN'] = \"YO-TOKEN\"\n\nimport functools\nfrom flax import nnx\nfrom huggingface_hub import snapshot_download\nimport humanize\nimport jax\nfrom tunix.models.gemma3 import model as model_lib\nfrom tunix.models.gemma3 import params_safetensors as params_lib\n\nmodel_id = \"google/gemma-3-12b-it\"\nignore_patterns = [\n \"*.pth\", # Ignore PyTorch .pth weight files\n]\nprint(f\"Downloading {model_id} from Hugging Face...\")\nlocal_model_path = snapshot_download(\n repo_id=model_id, ignore_patterns=ignore_patterns\n)\nprint(f\"Model successfully downloaded to: {local_model_path}\")\n\nMODEL_CP_PATH = local_model_path\n\nconfig = (\n model_lib.ModelConfig.gemma3_12b()\n) # pick correponding config based on model version\nMESH = [(8, 1), (\"fsdp\", \"tp\")] # update this based on your # TPU devices\nmesh = jax.make_mesh(*MESH)\nwith mesh:\n gemma3 = params_lib.create_model_from_safe_tensors(\n MODEL_CP_PATH, config, mesh\n )\n nnx.display(gemma3)\n\n```\n\n", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/611/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/587", "id": 3526795928, "node_id": "I_kwDOOTDVN87SNqKY", "number": 587, "title": "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).", "user": { "login": "Tsukimarf", "id": 215229009, "node_id": "U_kgDODNQiUQ", "avatar_url": "https://avatars.githubusercontent.com/u/215229009?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Tsukimarf", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8691895442, "node_id": "LA_kwDOOTDVN88AAAACBhPMkg", "url": "https://api.github.com/repos/google/tunix/labels/CLA:yes", "name": "CLA:yes", "color": "C2E0C6", "default": false, "description": "Need to sign a Contributor License Agreement (CLA)" } }, "state": "open", "locked": false, "assignee": { "login": "wang2yn84", "id": 13134832, "node_id": "MDQ6VXNlcjEzMTM0ODMy", "avatar_url": "https://avatars.githubusercontent.com/u/13134832?v=4", "gravatar_id": "", "url": "https://api.github.com/users/wang2yn84", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "wang2yn84", "id": 13134832, "node_id": "MDQ6VXNlcjEzMTM0ODMy", "avatar_url": "https://avatars.githubusercontent.com/u/13134832?v=4", "gravatar_id": "", "url": "https://api.github.com/users/wang2yn84", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 1, "created_at": "2025-10-17T16:31:28Z", "updated_at": "2025-10-29T20:53:35Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).\n\nView this [failed invocation](https://github.com/google/tunix/pull/585/checks?check_run_id=53025141866) of the CLA check for more information.\n\nFor the most up to date status, view the checks section at the bottom of the pull request.\n\n_Originally posted by @google-cla in https://github.com/google/tunix/pull/585#issuecomment-3415973328_", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/587/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/560", "id": 3506511461, "node_id": "I_kwDOOTDVN87RAR5l", "number": 560, "title": "How to train on multi host tpu pod", "user": { "login": "siddagra", "id": 52118936, "node_id": "MDQ6VXNlcjUyMTE4OTM2", "avatar_url": "https://avatars.githubusercontent.com/u/52118936?v=4", "gravatar_id": "", "url": "https://api.github.com/users/siddagra", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274168, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1eA", "url": "https://api.github.com/repos/google/tunix/labels/type:support", "name": "type:support", "color": "d876e3", "default": false, "description": "Further information is requested" } }, "state": "open", "locked": false, "assignee": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": "https://avatars.githubusercontent.com/u/4454178?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tianshub", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": "https://avatars.githubusercontent.com/u/4454178?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tianshub", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 23, "created_at": "2025-10-12T00:54:22Z", "updated_at": "2025-11-11T00:33:33Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "How to train on multi host tpu pod like TPUv4-32? It says I need to ssh into every machine but I am confused how it works for Tunix library specifically ", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/560/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/510", "id": 3488632386, "node_id": "I_kwDOOTDVN87P8E5C", "number": 510, "title": "VLM support for DPO", "user": { "login": "immuntasir", "id": 12914001, "node_id": "MDQ6VXNlcjEyOTE0MDAx", "avatar_url": "https://avatars.githubusercontent.com/u/12914001?v=4", "gravatar_id": "", "url": "https://api.github.com/users/immuntasir", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274161, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1cQ", "url": "https://api.github.com/repos/google/tunix/labels/type:feature/enhancement", "name": "type:feature/enhancement", "color": "a2eeef", "default": false, "description": "New feature or request" } }, "state": "open", "locked": false, "assignee": { "login": "abheesht17", "id": 31360468, "node_id": "MDQ6VXNlcjMxMzYwNDY4", "avatar_url": "https://avatars.githubusercontent.com/u/31360468?v=4", "gravatar_id": "", "url": "https://api.github.com/users/abheesht17", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "abheesht17", "id": 31360468, "node_id": "MDQ6VXNlcjMxMzYwNDY4", "avatar_url": "https://avatars.githubusercontent.com/u/31360468?v=4", "gravatar_id": "", "url": "https://api.github.com/users/abheesht17", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 1, "created_at": "2025-10-06T18:56:29Z", "updated_at": "2025-10-23T22:08:55Z", "closed_at": null, "author_association": "CONTRIBUTOR", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "DPO (and other SFT and RL methods) only supports text-only models as of now. VLM support will be very helpful.\n\n**Checklist**\n\n- [x ] I have searched the existing issues for similar feature requests.\n- [x ] This is not a support question (please use the \"bug template\" for that).\n", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/510/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [ 511 ] }, { "url": "https://api.github.com/repos/google/tunix/issues/509", "id": 3485856576, "node_id": "I_kwDOOTDVN87PxfNA", "number": 509, "title": "Add larger Qwen 2.5 models", "user": { "login": "abheesht17", "id": 31360468, "node_id": "MDQ6VXNlcjMxMzYwNDY4", "avatar_url": "https://avatars.githubusercontent.com/u/31360468?v=4", "gravatar_id": "", "url": "https://api.github.com/users/abheesht17", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274161, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1cQ", "url": "https://api.github.com/repos/google/tunix/labels/type:feature/enhancement", "name": "type:feature/enhancement", "color": "a2eeef", "default": false, "description": "New feature or request" } }, "state": "open", "locked": false, "assignee": { "login": "wang2yn84", "id": 13134832, "node_id": "MDQ6VXNlcjEzMTM0ODMy", "avatar_url": "https://avatars.githubusercontent.com/u/13134832?v=4", "gravatar_id": "", "url": "https://api.github.com/users/wang2yn84", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "wang2yn84", "id": 13134832, "node_id": "MDQ6VXNlcjEzMTM0ODMy", "avatar_url": "https://avatars.githubusercontent.com/u/13134832?v=4", "gravatar_id": "", "url": "https://api.github.com/users/wang2yn84", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 2, "created_at": "2025-10-06T06:31:51Z", "updated_at": "2025-11-06T17:29:25Z", "closed_at": null, "author_association": "COLLABORATOR", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "Tunix has Qwen 2.5 0.5B, 3B, 7B models. Let's add the larger presets too!", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/509/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/489", "id": 3479372799, "node_id": "I_kwDOOTDVN87PYwP_", "number": 489, "title": "Failed build: Tunix Package Tests", "user": { "login": "github-actions[bot]", "id": 41898282, "node_id": "MDM6Qm90NDE4OTgyODI=", "avatar_url": "https://avatars.githubusercontent.com/in/15368?v=4", "gravatar_id": "", "url": "https://api.github.com/users/github-actions%5Bbot%5D", "type": "Bot", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 9357816432, "node_id": "LA_kwDOOTDVN88AAAACLcTycA", "url": "https://api.github.com/repos/google/tunix/labels/type:build/install", "name": "type:build/install", "color": "ededed", "default": false, "description": "Issue related to build/install" } }, "state": "open", "locked": false, "assignee": { "login": "selamw1", "id": 153962326, "node_id": "U_kgDOCS1HVg", "avatar_url": "https://avatars.githubusercontent.com/u/153962326?v=4", "gravatar_id": "", "url": "https://api.github.com/users/selamw1", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "selamw1", "id": 153962326, "node_id": "U_kgDOCS1HVg", "avatar_url": "https://avatars.githubusercontent.com/u/153962326?v=4", "gravatar_id": "", "url": "https://api.github.com/users/selamw1", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 14, "created_at": "2025-10-03T00:35:04Z", "updated_at": "2025-11-06T17:30:36Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "GitHub Actions workflow [Tunix Package Tests #462](https://github.com/google/tunix/actions/runs/18209232188) failed.\n\nEvent: schedule\nBranch: [main](https://github.com/google/tunix/tree/main)\nCommit: [f8baab9b0e2e7a61e258c325dad304cab9be60b5](https://github.com/google/tunix/commit/f8baab9b0e2e7a61e258c325dad304cab9be60b5)\n\nCreated by [jayqi/failed-build-issue-action](https://github.com/jayqi/failed-build-issue-action)", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/489/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/390", "id": 3455375559, "node_id": "I_kwDOOTDVN87N9NjH", "number": 390, "title": "RL Bug/Issue Tracker", "user": { "login": "abheesht17", "id": 31360468, "node_id": "MDQ6VXNlcjMxMzYwNDY4", "avatar_url": "https://avatars.githubusercontent.com/u/31360468?v=4", "gravatar_id": "", "url": "https://api.github.com/users/abheesht17", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274152, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1aA", "url": "https://api.github.com/repos/google/tunix/labels/type:bug", "name": "type:bug", "color": "d73a4a", "default": false, "description": "Something isn't working" } }, "state": "open", "locked": false, "assignee": { "login": "selamw1", "id": 153962326, "node_id": "U_kgDOCS1HVg", "avatar_url": "https://avatars.githubusercontent.com/u/153962326?v=4", "gravatar_id": "", "url": "https://api.github.com/users/selamw1", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "selamw1", "id": 153962326, "node_id": "U_kgDOCS1HVg", "avatar_url": "https://avatars.githubusercontent.com/u/153962326?v=4", "gravatar_id": "", "url": "https://api.github.com/users/selamw1", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 0, "created_at": "2025-09-26T00:07:28Z", "updated_at": "2025-11-06T17:31:28Z", "closed_at": null, "author_association": "COLLABORATOR", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "This issue is a running tracker of all RL bugs/issues we identify:\n\n- [ ] Logging for metrics returned by loss functions via `aux` is at a `self._iter_steps` level, whereas other metrics are logged at a `self.global_steps` level.\n- [ ] The progress bar for GRPO/PPO is at a `self._iter_steps` level. We should possibly unify this with how we log metrics. It's a bit confusing for the user to pass `max_steps` at a `self._iter_steps`, and not at `self.global_steps` level.", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/390/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [ 491 ] }, { "url": "https://api.github.com/repos/google/tunix/issues/256", "id": 3386809801, "node_id": "I_kwDOOTDVN87J3p3J", "number": 256, "title": "Question: how to run tunix with vLLM?", "user": { "login": "OhadRubin", "id": 4252994, "node_id": "MDQ6VXNlcjQyNTI5OTQ=", "avatar_url": "https://avatars.githubusercontent.com/u/4252994?v=4", "gravatar_id": "", "url": "https://api.github.com/users/OhadRubin", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274152, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1aA", "url": "https://api.github.com/repos/google/tunix/labels/type:bug", "name": "type:bug", "color": "d73a4a", "default": false, "description": "Something isn't working" } }, "state": "open", "locked": false, "assignee": { "login": "wang2yn84", "id": 13134832, "node_id": "MDQ6VXNlcjEzMTM0ODMy", "avatar_url": "https://avatars.githubusercontent.com/u/13134832?v=4", "gravatar_id": "", "url": "https://api.github.com/users/wang2yn84", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "wang2yn84", "id": 13134832, "node_id": "MDQ6VXNlcjEzMTM0ODMy", "avatar_url": "https://avatars.githubusercontent.com/u/13134832?v=4", "gravatar_id": "", "url": "https://api.github.com/users/wang2yn84", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 9, "created_at": "2025-09-05T08:59:39Z", "updated_at": "2026-01-28T20:41:43Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "Hey, \nWould you guys mind pinning the requirements for vLLM usage? or generally providing a bit more information about it?\nI'm not able to use tunix in that setup.\nMinimal steps to recreate on a fresh TPUv4:\n\n\n\n```\ncd ~\ngit clone https://github.com/vllm-project/vllm\ngit clone https://github.com/google/tunix\ncd tunix\njsonl_to_json(){ python -c 'import sys,json;p=sys.argv[1];d=[json.loads(l) for l in open(p)];open(p,\"w\").write(json.dumps(d))' \"$1\"; }\nmkdir -p root-dir/rl/grpo/data\nwget https://raw.githubusercontent.com/openai/grade-school-math/3101c7d5072418e28b9008a6636bde82a006892c/grade_school_math/data/train.jsonl -O root-dir/rl/grpo/data/gsm8k_train2.json\nwget https://raw.githubusercontent.com/openai/grade-school-math/2909d34ef28520753df82a2234c357259d254aa8/grade_school_math/data/test.jsonl -O root-dir/rl/grpo/data/gsm8k_test.json\njsonl_to_json root-dir/rl/grpo/data/gsm8k_train.json\njsonl_to_json root-dir/rl/grpo/data/gsm8k_test.json\n\n# build the vllm docker container like https://docs.vllm.ai/en/v0.5.5/getting_started/tpu-installation.html instructs\n(cd ~/vllm && sudo docker build -f docker/Dockerfile.tpu -t vllm-tpu .) \n# run it in bash mode with the tunix repo mounted\n(cd ~/vllm && sudo docker run --privileged --net host --shm-size=16G -v /home/$USER/tunix:/workspace/tunix -it vllm-tpu)\n# in the bash of the container\ncd /workspace/tunix\npython3 -m pip install git+https://github.com/google/qwix grain tensorboardx jaxtyping huggingface-hub==0.34.4\npython3 -m pip install --no-deps -e . #otherwise this overrides the vllm tpulib 0.18\npython3 scripts/grpo_demo_llama3_qwen2.py --root-dir=root_dir --model-version=Qwen/Qwen2.5-0.5B-Instruct\n```\n\n\nError trace:\n```\nroot@v4-8-node-1:/workspace/tunix# python3 scripts/grpo_demo_llama3_qwen2.py --root-dir=root_dir --model-version=Qwen/Qwen2.5-0.5B-Instruct\n/workspace/tunix/scripts/grpo_demo_llama3_qwen2.py:275: SyntaxWarning: invalid escape sequence '\\$'\n \nThis script is still WIP and you'll need to download all the data tolocal first. Functionality and performance is not guaranteed. Try at your own discretion\nDirectory does not exist: root_dir/rl/grpo/demo/experiments/llama3/training_runs/2\nINFO:absl: - Pathways not available. Using defaultHBM stats collector\nINFO:absl:Using 13.0 KiB / 30.7 GiB (4.0321782714101377e-07) on TPU_0(process=0,(0,0,0,0))\nINFO:absl:Using 13.0 KiB / 30.7 GiB (4.0321782714101377e-07) on TPU_1(process=0,(1,0,0,0))\nINFO:absl:Using 13.0 KiB / 30.7 GiB (4.0321782714101377e-07) on TPU_2(process=0,(0,1,0,0))\nINFO:absl:Using 13.0 KiB / 30.7 GiB (4.0321782714101377e-07) on TPU_3(process=0,(1,1,0,0))\ntrain_dataset size: 1869, val_dataset size:0,test_dataset size: 50\n{'answer': array(['3', '34', '300', '35'], dtype='system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n<|im_start|>user\\nuser\\nYou are given a problem. Think about the problem and provide your reasoning. Place it between and . Then, provide the final answer (i.e., just one numerical value) between and .\\n\\nMaria has 4 dimes, 4 quarters, and 7 nickels in her piggy bank. Her mom gives her 5 quarters. How much money, in dollars, does Maria have now?\\nmodel<|im_end|>\\n<|im_start|>assistant\\n',\n '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n<|im_start|>user\\nuser\\nYou are given a problem. Think about the problem and provide your reasoning. Place it between and . Then, provide the final answer (i.e., just one numerical value) between and .\\n\\nA wildlife team is monitoring the number of birds in a park. There are 3 blackbirds in each of the park’s 7 trees. There are also 13 magpies roaming around the park. How many birds are in the park in total?\\nmodel<|im_end|>\\n<|im_start|>assistant\\n',\n '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n<|im_start|>user\\nuser\\nYou are given a problem. Think about the problem and provide your reasoning. Place it between and . Then, provide the final answer (i.e., just one numerical value) between and .\\n\\nLast year, the school library purchased 50 new books. This year, it purchased 3 times as many books. If the library had 100 books before it purchased new books last year, how many books are in the library now?\\nmodel<|im_end|>\\n<|im_start|>assistant\\n',\n '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n<|im_start|>user\\nuser\\nYou are given a problem. Think about the problem and provide your reasoning. Place it between and . Then, provide the final answer (i.e., just one numerical value) between and .\\n\\nJame gets 20 singing lessons. He gets the first lesson free and after the first 10 paid lessons he only needs to pay for every other lesson. Each lesson is $5. His uncle pays for half. How much does James pay?\\nmodel<|im_end|>\\n<|im_start|>assistant\\n'],\n dtype='\n rl_cluster = rl_cluster_lib.RLCluster(\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/tunix/tunix/rl/rl_cluster.py\", line 166, in __init__\n self._init_cluster()\n File \"/workspace/tunix/tunix/rl/rl_cluster.py\", line 289, in _init_cluster\n self._rollout = vllm_rollout.VllmRollout(\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/tunix/tunix/rl/rollout/vllm_rollout.py\", line 41, in __init__\n self._sampler = vllm_sampler.VllmSampler(\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/tunix/tunix/generate/vllm_sampler.py\", line 94, in __init__\n self.llm = LLM(**self.args)\n ^^^^^^^^^^^^^^^^\n File \"/workspace/vllm/vllm/entrypoints/llm.py\", line 272, in __init__\n self.llm_engine = LLMEngine.from_engine_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/vllm/vllm/engine/llm_engine.py\", line 492, in from_engine_args\n return engine_cls.from_vllm_config(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/vllm/vllm/v1/engine/llm_engine.py\", line 127, in from_vllm_config\n return cls(vllm_config=vllm_config,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/vllm/vllm/v1/engine/llm_engine.py\", line 104, in __init__\n self.engine_core = EngineCoreClient.make_client(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/vllm/vllm/v1/engine/core_client.py\", line 82, in make_client\n return InprocClient(vllm_config, executor_class, log_stats)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/vllm/vllm/v1/engine/core_client.py\", line 245, in __init__\n self.engine_core = EngineCore(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/vllm/vllm/v1/engine/core.py\", line 82, in __init__\n self.model_executor = executor_class(vllm_config)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/vllm/vllm/executor/executor_base.py\", line 54, in __init__\n self._init_executor()\n File \"/workspace/vllm/vllm/v1/executor/multiproc_executor.py\", line 96, in _init_executor\n self.workers = WorkerProc.wait_for_ready(unready_workers)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/vllm/vllm/v1/executor/multiproc_executor.py\", line 487, in wait_for_ready\n raise e from None\nException: WorkerProc initialization failed due to an exception in a background process. See stack trace for root cause.\n/usr/local/lib/python3.12/multiprocessing/resource_tracker.py:279: UserWarning: resource_tracker: There appear to be 3 leaked shared_memory objects to clean up at shutdown\n warnings.warn('resource_tracker: There appear to be %d '\nroot@v4-8-node-1:/workspace/tunix# \n```", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/256/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/137", "id": 3266528148, "node_id": "I_kwDOOTDVN87Cs0OU", "number": 137, "title": "Wandb", "user": { "login": "shivajid", "id": 7957968, "node_id": "MDQ6VXNlcjc5NTc5Njg=", "avatar_url": "https://avatars.githubusercontent.com/u/7957968?v=4", "gravatar_id": "", "url": "https://api.github.com/users/shivajid", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274161, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1cQ", "url": "https://api.github.com/repos/google/tunix/labels/type:feature/enhancement", "name": "type:feature/enhancement", "color": "a2eeef", "default": false, "description": "New feature or request" } }, "state": "open", "locked": false, "assignee": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": "https://avatars.githubusercontent.com/u/4454178?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tianshub", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": "https://avatars.githubusercontent.com/u/4454178?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tianshub", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 1, "created_at": "2025-07-27T04:04:43Z", "updated_at": "2025-11-06T17:32:33Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "## Expected Behavior\nClean execution\n\n## Actual Behavior\nAdding the metrics logger throws wandb errors.\n\nwandb: WARNING Tried to log to step 0 that is less than the current step 3. Steps must be monotonically increasing, so this data will be ignored. See https://wandb.me/define-metric to log data out of order.\n\nLater has some some unexpected behavior\n\n```\n File \"/home/shivajid/workshop/tensor_crunch/sample_trainer.py\", line 223, in \n out_data = sampler(\n ^^^^^^^^\n File \"/home/shivajid/tunixenv/lib/python3.11/site-packages/tunix/generate/sampler.py\", line 705, in __call__\n sampling_state = self._compiled_prefill_fn(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/shivajid/tunixenv/lib/python3.11/site-packages/jax/_src/monitoring.py\", line 97, in record_scalar\n callback(event, value, **kwargs)\n File \"/home/shivajid/tunixenv/lib/python3.11/site-packages/tunix/sft/metrics_logger.py\", line 90, in log_to_wandb\n wandb.log({event: scalar_value}, step=current_step)\n File \"/home/shivajid/tunixenv/lib/python3.11/site-packages/wandb/sdk/lib/preinit.py\", line 36, in preinit_wrapper\n raise wandb.Error(f\"You must call wandb.init() before {name}()\")\nwandb.errors.errors.Error: You must call wandb.init() before wandb.log()\n```\n\n\n## Steps to Reproduce the Problem\n\n1. Add wandb to your import and login\n1.\n1.\n\n## Specifications\n\n- Version:\n- Platform: TPUv6e-8\n", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/137/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/94", "id": 3205477204, "node_id": "I_kwDOOTDVN86_D7NU", "number": 94, "title": "Qwen Example relies on HF Transformer and Accelerate, which is removed", "user": { "login": "shivajid", "id": 7957968, "node_id": "MDQ6VXNlcjc5NTc5Njg=", "avatar_url": "https://avatars.githubusercontent.com/u/7957968?v=4", "gravatar_id": "", "url": "https://api.github.com/users/shivajid", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 9357816432, "node_id": "LA_kwDOOTDVN88AAAACLcTycA", "url": "https://api.github.com/repos/google/tunix/labels/type:build/install", "name": "type:build/install", "color": "ededed", "default": false, "description": "Issue related to build/install" } }, "state": "open", "locked": false, "assignee": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": "https://avatars.githubusercontent.com/u/4454178?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tianshub", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": "https://avatars.githubusercontent.com/u/4454178?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tianshub", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 1, "created_at": "2025-07-05T19:33:53Z", "updated_at": "2025-11-06T17:32:53Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "I am trying to run the Tunix Qwen Example on TPUv6e, it is asking me to install PyTorch XLA. Is there. pure Jax way of doing this?", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/94/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/93", "id": 3202053996, "node_id": "I_kwDOOTDVN86-23ds", "number": 93, "title": "Adding linting configuration file", "user": { "login": "Practicinginhell", "id": 147235329, "node_id": "U_kgDOCMaiAQ", "avatar_url": "https://avatars.githubusercontent.com/u/147235329?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Practicinginhell", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274161, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1cQ", "url": "https://api.github.com/repos/google/tunix/labels/type:feature/enhancement", "name": "type:feature/enhancement", "color": "a2eeef", "default": false, "description": "New feature or request" } }, "state": "open", "locked": false, "assignee": { "login": "Limeload", "id": 80426069, "node_id": "MDQ6VXNlcjgwNDI2MDY5", "avatar_url": "https://avatars.githubusercontent.com/u/80426069?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Limeload", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "Limeload", "id": 80426069, "node_id": "MDQ6VXNlcjgwNDI2MDY5", "avatar_url": "https://avatars.githubusercontent.com/u/80426069?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Limeload", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 1, "created_at": "2025-07-04T09:56:24Z", "updated_at": "2025-11-06T17:33:23Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "Firstly, thank you for the amazing repo. Could you provide a linting configuration file for tools like Pylint or Flake8. ", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/93/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] }, { "url": "https://api.github.com/repos/google/tunix/issues/45", "id": 3093673887, "node_id": "I_kwDOOTDVN864Zbef", "number": 45, "title": "Qwen example notebook", "user": { "login": "peregilk", "id": 9079808, "node_id": "MDQ6VXNlcjkwNzk4MDg=", "avatar_url": "https://avatars.githubusercontent.com/u/9079808?v=4", "gravatar_id": "", "url": "https://api.github.com/users/peregilk", "type": "User", "user_view_type": "public", "site_admin": false }, "labels": { "0": { "id": 8394274168, "node_id": "LA_kwDOOTDVN88AAAAB9FZ1eA", "url": "https://api.github.com/repos/google/tunix/labels/type:support", "name": "type:support", "color": "d876e3", "default": false, "description": "Further information is requested" } }, "state": "open", "locked": false, "assignee": { "login": "selamw1", "id": 153962326, "node_id": "U_kgDOCS1HVg", "avatar_url": "https://avatars.githubusercontent.com/u/153962326?v=4", "gravatar_id": "", "url": "https://api.github.com/users/selamw1", "type": "User", "user_view_type": "public", "site_admin": false }, "assignees": { "0": { "login": "selamw1", "id": 153962326, "node_id": "U_kgDOCS1HVg", "avatar_url": "https://avatars.githubusercontent.com/u/153962326?v=4", "gravatar_id": "", "url": "https://api.github.com/users/selamw1", "type": "User", "user_view_type": "public", "site_admin": false } }, "milestone": null, "comments": 1, "created_at": "2025-05-27T11:58:24Z", "updated_at": "2025-11-06T17:33:48Z", "closed_at": null, "author_association": "NONE", "type": null, "active_lock_reason": null, "sub_issues_summary": { "total": 0, "completed": 0, "percent_completed": 0 }, "issue_dependencies_summary": { "blocked_by": 0, "total_blocked_by": 0, "blocking": 0, "total_blocking": 0 }, "body": "I am trying out your Qwen example notebook. It links to Kaggle but as far I can see, there are no JAX/Flax checkpoints there. How do I convert/find these?", "closed_by": null, "reactions": { "url": "https://api.github.com/repos/google/tunix/issues/45/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }, "performed_via_github_app": null, "state_reason": null, "pinned_comment": null, "linked_prs": [] } ], "pulls": [ { "url": "https://api.github.com/repos/google/tunix/pulls/1105", "id": 3294496148, "node_id": "PR_kwDOOTDVN87EXgWU", "number": 1105, "state": "open", "locked": false, "title": "Added a GPU demo for PEFT with QLoRA on Llama 3_1", "user": { "login": "katjasrz", "id": 47559975, "node_id": "MDQ6VXNlcjQ3NTU5OTc1", "avatar_url": "https://avatars.githubusercontent.com/u/47559975?v=4", "gravatar_id": "", "url": "https://api.github.com/users/katjasrz", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "Resolves #N/A (example notebook addition; no prior issue)\r\n\r\n**Summary**\r\n\r\nAdd `examples/qlora_llama3_gpu.ipynb`: a GPU-focused tutorial for parameter-efficient fine-tuning (LoRA/QLoRA) of Llama 3.1-8B using JAX + Tunix + Qwix on NVIDIA GPUs.\r\n\r\n**Why**\r\n\r\nTunix already includes QLoRA examples (e.g., Gemma), but a Llama 3.1 GPU end-to-end notebook is useful for users training on NVIDIA hardware.\r\n\r\n**What’s included**\r\n\r\nGPU preflight (nvidia-smi), HF token UI + authentication, gated-model access instructions, device mesh setup (1 GPU → no sharding; multi-GPU → FSDP/TP), model load via AutoModel, and a small training run (e.g., 100 steps) with TensorBoard + sanity-check inference.\r\n\r\nNotebook is an example and is not intended to be executed in CI.\r\n\r\n**Reference**\r\n\r\n- LoRA paper: Hu et al., 2021 (“LoRA: Low-Rank Adaptation…”)\r\n- QLoRA paper: Dettmers et al., 2023 (“QLoRA…”)\r\n- Existing QLoRA example notebook: `examples/qlora_gemma.ipynb` (in upstream google/tunix)\r\n- Llama 3.1 model card [https://huggingface.co/meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B)\r\n\r\n**Colab Notebook**\r\n\r\n[https://colab.research.google.com/github/katjasrz/tunix/blob/main/examples/qlora_llama3_gpu.ipynb](https://colab.research.google.com/github/katjasrz/tunix/blob/main/examples/qlora_llama3_gpu.ipynb)\r\n\r\n**Reproducing**\r\n\r\nExecuted the notebook in the NGC container `nvcr.io/nvidia/jax:26.01-maxtext-py3` on a cluster node with 8 H100 NVIDIA GPUs as well as on a single DGX Spark, CUDA 13.1, driver 580.105.08, JAX 0.8.1.dev20260217.\r\n\r\nVerified:\r\n\r\n- PEFT runs for 100 steps with LoRA and QLoRA and writes checkpoints/logs.\r\n- TensorBoard logs are created.\r\n- Inference sanity check runs and produces output.\r\n\r\nTo reproduce, execute as follows:\r\n\r\n```bash\r\ncd \r\n\r\n# clone this repo\r\ngit clone https://github.com/katjasrz/tunix.git\r\n\r\nexport ROOT_DIR=$(pwd)\r\nexport PROJECT_DIR=$ROOT_DIR/tunix/examples\r\nexport HF_CACHE_DIR=$ROOT_DIR/huggingface\r\n\r\ndocker run -it --rm --ipc=host \\\r\n --gpus=all \\\r\n -p 8889:8889 \\\r\n -p 6006:6006 \\\r\n --shm-size=16g \\\r\n --ulimit memlock=-1 \\\r\n -v \"$PROJECT_DIR\":/workspace \\\r\n -v \"$HF_CACHE_DIR\":/hf_cache \\\r\n -e HF_HOME=/hf_cache \\\r\n -e LOCAL_UID=$(id -u) \\\r\n -e LOCAL_GID=$(id -g) \\\r\n nvcr.io/nvidia/jax:26.01-maxtext-py3 \\\r\n bash -lc 'set -e\r\n groupadd -g $LOCAL_GID hostgrp 2>/dev/null || true\r\n useradd -u $LOCAL_UID -g $LOCAL_GID -M -d /workspace hostusr 2>/dev/null || true\r\n \r\n python3 -m pip install --upgrade pip\r\n pip install jupyterlab ipywidgets\r\n pip install -U git+https://github.com/google/tunix\r\n \r\n su hostusr -c \"cd /workspace && HOME=/workspace HF_HOME=/hf_cache \\\r\n jupyter lab --ip=0.0.0.0 --port=8889 --no-browser\"'\r\n```\r\n\r\nThen follow the instructions in the jupyter notebook /examples/qlora_llama3_gpu.ipynb\r\n\r\n**Checklist**\r\n\r\n- [ x] I have added all the necessary unit tests for my change.\r\n- [x ] I have verified that my change does not break existing code and all unit tests pass.\r\n- [ x] I have added all appropriate doc-strings/documentation.\r\n- [ x] My PR is based on the latest changes of the main branch (if unsure, rebase the code).\r\n- [ x] I have signed the [Contributor License Agreement](https://cla.developers.google.com/about).\r\n- [x ] I have followed [Contribution Guidelines](https://github.com/google/tunix/blob/main/docs/contributing.md).\r\n", "created_at": "2026-02-17T14:02:01Z", "updated_at": "2026-02-17T14:03:56Z", "closed_at": null, "merged_at": null, "merge_commit_sha": "9f698a4471001c49b257b138ceebc6e3497fce1a", "assignee": null, "assignees": {}, "requested_reviewers": { "0": { "login": "jiangyangmu", "id": 1014774, "node_id": "MDQ6VXNlcjEwMTQ3NzQ=", "avatar_url": "https://avatars.githubusercontent.com/u/1014774?v=4", "gravatar_id": "", "url": "https://api.github.com/users/jiangyangmu", "type": "User", "user_view_type": 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Guidelines](https://github.com/google/tunix/blob/main/docs/contributing.md).\r\n", "created_at": "2026-02-03T08:24:10Z", "updated_at": "2026-02-03T08:45:13Z", "closed_at": null, "merged_at": null, "merge_commit_sha": "64ec55d631ffbb07443ff16349db236fbe64b43d", "assignee": null, "assignees": {}, "requested_reviewers": { "0": { "login": "jiangyangmu", "id": 1014774, "node_id": "MDQ6VXNlcjEwMTQ3NzQ=", "avatar_url": "https://avatars.githubusercontent.com/u/1014774?v=4", "gravatar_id": "", "url": "https://api.github.com/users/jiangyangmu", "type": "User", "user_view_type": "public", "site_admin": false }, "1": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": "https://avatars.githubusercontent.com/u/4454178?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tianshub", "type": "User", "user_view_type": "public", "site_admin": false }, "2": { "login": "wang2yn84", "id": 13134832, "node_id": "MDQ6VXNlcjEzMTM0ODMy", "avatar_url": 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"active_lock_reason": null, "linked_issues": [] }, { "url": "https://api.github.com/repos/google/tunix/pulls/1000", "id": 3207201749, "node_id": "PR_kwDOOTDVN86_KgPV", "number": 1000, "state": "open", "locked": false, "title": "fix(rl): robust integer validation for utils.py (Fixes #953)", "user": { "login": "abdulwahabahmedkhanyusufzai", "id": 175131310, "node_id": "U_kgDOCnBKrg", "avatar_url": "https://avatars.githubusercontent.com/u/175131310?v=4", "gravatar_id": "", "url": "https://api.github.com/users/abdulwahabahmedkhanyusufzai", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "This PR fixes an AttributeError crash encountered when using is_positive_integer with Numpy types (e.g., np.int64) or on Python 3.11+ where certain types lack the .is_integer() method.\r\n\r\nThis supersedes stale PRs #903 and #930. I have consolidated the fix and added the missing unit tests requested by maintainers.\r\n\r\nChanges:\r\n\r\nImplemented a safe is_integer_value helper that handles int, float, and numpy types correctly.\r\n\r\nAdded tests/rl/rl_utils_test.py to verify the fix across all types.\r\n\r\nRelated Issues:\r\n\r\nFixes #953\r\n\r\nResolves #903\r\n\r\nResolves #902\r\n\r\nTest Plan\r\nCreated a new test file tests/rl/rl_utils_test.py. Verified locally with python3 -m unittest tests/rl/rl_utils_test.py.\r\n\r\nResults:\r\n\r\n✅ int (Standard) - Passed\r\n\r\n✅ np.int64 (Numpy) - Passed (Previously crashed)\r\n\r\n✅ float (Standard) - Passed\r\n\r\n✅ bool - Rejected correctly", "created_at": "2026-01-24T19:09:28Z", "updated_at": "2026-01-28T22:32:23Z", "closed_at": null, "merged_at": null, "merge_commit_sha": "f87e892dbb41bd777b5164527ee5ac1c94aefa02", "assignee": null, "assignees": {}, "requested_reviewers": { "0": { "login": "jiangyangmu", "id": 1014774, "node_id": "MDQ6VXNlcjEwMTQ3NzQ=", "avatar_url": "https://avatars.githubusercontent.com/u/1014774?v=4", "gravatar_id": "", "url": "https://api.github.com/users/jiangyangmu", "type": "User", "user_view_type": "public", "site_admin": false }, "1": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": "https://avatars.githubusercontent.com/u/4454178?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tianshub", "type": "User", 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"https://api.github.com/users/NP2241", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "Resolves #979\r\n\r\n> It's a good idea to open an issue first for discussion.\r\n\r\nThis PR adds a new GRPO example demonstrating natural language to SQL training with an external execution environment. The example shows how to integrate Tunix GRPO with a structured task where rewards are computed by executing generated SQL against a small SQLite database and checking result correctness.\r\n\r\nThe example is intentionally minimal and mirrors the structure of the existing GSM8K GRPO example to serve as a clear reference for users.\r\n\r\n**Reference**\r\n- GitHub issue: https://github.com/google/tunix/issues/979\r\n- Existing GRPO example: `examples/rl/grpo/gsm8k/`\r\n\r\n**Colab Notebook**\r\nN/A — this PR adds an example recipe under `examples/` and does not introduce a new public API.\r\n\r\n**Checklist**\r\n\r\n- [x] I have added all the necessary unit tests for my change. \r\n _N/A — this PR adds an example and does not modify core library logic._\r\n- [x] I have verified that my change does not break existing code and all unit tests pass.\r\n- [x] I have added all appropriate doc-strings/documentation.\r\n- [x] My PR is based on the latest changes of the main branch.\r\n- [x] I have signed the [Contributor License Agreement](https://cla.developers.google.com/about).\r\n- [x] I have followed [Contribution Guidelines](https://github.com/google/tunix/blob/main/CONTRIBUTING.md).\r\n", "created_at": "2026-01-22T18:39:18Z", "updated_at": "2026-01-22T18:39:19Z", "closed_at": null, "merged_at": null, "merge_commit_sha": "12636d9009165990a007b7b96f231d4d9a7fc900", "assignee": null, "assignees": {}, "requested_reviewers": { "0": { "login": "jiangyangmu", "id": 1014774, "node_id": "MDQ6VXNlcjEwMTQ3NzQ=", "avatar_url": "https://avatars.githubusercontent.com/u/1014774?v=4", "gravatar_id": "", "url": "https://api.github.com/users/jiangyangmu", "type": "User", "user_view_type": "public", "site_admin": false }, "1": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": "https://avatars.githubusercontent.com/u/4454178?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tianshub", "type": "User", "user_view_type": "public", 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"PR_kwDOOTDVN869bqqH", "number": 978, "state": "open", "locked": false, "title": "Feature/gdpo", "user": { "login": "kmsgnnew", "id": 16554470, "node_id": "MDQ6VXNlcjE2NTU0NDcw", "avatar_url": "https://avatars.githubusercontent.com/u/16554470?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kmsgnnew", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "Resolves #977 \r\n\r\nhttps://github.com/google/tunix/issues/977\r\n\r\n\r\nAdded implementation of GDPO. Shared below reference link and implementation. This paper improves upon GRPO.\r\n\r\n**Reference**\r\nReference paper\r\nhttps://arxiv.org/abs/2601.05242\r\nReference Implementation \r\nhttps://github.com/NVlabs/GDPO/blob/b080e63d0126870ad08acc8ebc3f04b728175a9e/trl-GDPO/trl-0.18.0-gdpo/trl/trainer/grpo_trainer.py#L1222\r\n\r\n**Colab Notebook**\r\nNiL\r\n\r\n**Checklist**\r\n\r\n\r\n- [x] I have added all the necessary unit tests for my change.\r\n- [x] I have verified that my change does not break existing code and all unit tests pass.\r\n- [x] I have added all appropriate doc-strings/documentation.\r\n- [x] My PR is based on the latest changes of the main branch (if unsure, rebase the code).\r\n- [x] I have signed the [Contributor License Agreement](https://cla.developers.google.com/about).\r\n- [x] I have followed [Contribution Guidelines](https://github.com/google/tunix/blob/main/CONTRIBUTING.md).\r\n", "created_at": "2026-01-15T17:27:39Z", "updated_at": "2026-02-08T17:18:10Z", "closed_at": null, "merged_at": null, 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When loading the base model state, it should load all the safetensor file instead of a hardcoded one.", "user": { "login": "copybara-service[bot]", "id": 56741989, "node_id": "MDM6Qm90NTY3NDE5ODk=", "avatar_url": "https://avatars.githubusercontent.com/in/44061?v=4", "gravatar_id": "", "url": "https://api.github.com/users/copybara-service%5Bbot%5D", "type": "Bot", "user_view_type": "public", "site_admin": false }, "body": "[Tunix] Fix the safetensors saving. When loading the base model state, it should load all the safetensor file instead of a hardcoded one.\n", "created_at": "2026-01-13T18:19:47Z", "updated_at": "2026-01-13T18:19:49Z", "closed_at": null, "merged_at": null, "merge_commit_sha": null, "assignee": null, "assignees": {}, "requested_reviewers": { "0": { "login": "jiangyangmu", "id": 1014774, "node_id": "MDQ6VXNlcjEwMTQ3NzQ=", "avatar_url": "https://avatars.githubusercontent.com/u/1014774?v=4", "gravatar_id": "", "url": "https://api.github.com/users/jiangyangmu", "type": "User", "user_view_type": "public", "site_admin": false }, "1": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": "https://avatars.githubusercontent.com/u/4454178?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tianshub", "type": "User", "user_view_type": "public", "site_admin": false }, "2": { "login": "wang2yn84", "id": 13134832, "node_id": "MDQ6VXNlcjEzMTM0ODMy", "avatar_url": 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"open_issues": 148, "watchers": 2155, "default_branch": "main" } }, "_links": { "self": { "href": "https://api.github.com/repos/google/tunix/pulls/972" }, "html": { "href": "https://github.com/google/tunix/pull/972" }, "issue": { "href": "https://api.github.com/repos/google/tunix/issues/972" }, "comments": { "href": "https://api.github.com/repos/google/tunix/issues/972/comments" }, "review_comments": { "href": "https://api.github.com/repos/google/tunix/pulls/972/comments" }, "review_comment": { "href": "https://api.github.com/repos/google/tunix/pulls/comments{/number}" }, "commits": { "href": "https://api.github.com/repos/google/tunix/pulls/972/commits" }, "statuses": { "href": "https://api.github.com/repos/google/tunix/statuses/40b4ab473baa79477e94fb68e02699c1dbdb1216" } }, "author_association": "NONE", "auto_merge": null, "active_lock_reason": null, "linked_issues": [] }, { "url": "https://api.github.com/repos/google/tunix/pulls/967", "id": 3166654897, "node_id": "PR_kwDOOTDVN868v1Gx", "number": 967, "state": "open", "locked": false, "title": "Fix PeftTrainer input sharding for raw inputs and add regression test", "user": { "login": "jayshrivastava0", "id": 98177190, "node_id": "U_kgDOBdoQpg", "avatar_url": "https://avatars.githubusercontent.com/u/98177190?v=4", "gravatar_id": "", "url": "https://api.github.com/users/jayshrivastava0", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "Resolves #965 \r\n\r\n\r\n\r\n### Description\r\nThis PR fixes a `TypeError` when training with datasets containing raw inputs (e.g., strings) that require processing via `gen_model_input_fn` before becoming valid JAX arrays.\r\n\r\n**The Problem:**\r\nPreviously, `sharding_utils.shard_input` was executed on the dataset items *before* `gen_model_input_fn` had a chance to transform them. Since raw types like `str` are not valid JAX types, the sharding utility crashed. The transformation logic was incorrectly placed inside the JIT-compiled `_train_step` and `_eval_step`.\r\n\r\n**The Fix:**\r\n- Modified `tunix/sft/peft_trainer.py`:\r\n - Moved the execution of `self.gen_model_input_fn(inputs)` out of `_train_step` and `_eval_step`.\r\n - Placed the execution inside the `train()` and `_run_eval()` loops, explicitly **after** `_prepare_inputs` and **before** `sharding_utils.shard_input`.\r\n- This ensures that `shard_input` always receives valid JAX/NumPy arrays.\r\n\r\n**Verification:**\r\n- Added a new regression test `test_input_processing_order` in `tunix/tests/sft/peft_trainer_test.py`.\r\n- The test simulates a user passing raw text data (`[{\"raw_text\": \"...\"}]`) and defines a custom input generator to tokenize it.\r\n- Confirmed that the test passes with this fix (previously it would crash).\r\n- Confirmed that existing tests (e.g., `test_compile_once`, `test_dist_training`) still pass.\r\n\r\n\r\n**Reference**\r\n\r\nIssue #965: Confused about with_gen_model_input_fn()\r\n\r\n\r\n**Colab Notebook**\r\n\r\nN/A (The error is reproduced and verified via the new unit test `test_input_processing_order` added in this PR).\r\n\r\n**Checklist**\r\n\r\n\r\n- [x] I have added all the necessary unit tests for my change.\r\n- [x] I have verified that my change does not break existing code and all unit tests pass.\r\n- [x] I have added all appropriate doc-strings/documentation.\r\n- [x] My PR is based on the latest changes of the main branch (if unsure, rebase the code).\r\n- [x] I have signed the [Contributor License Agreement](https://cla.developers.google.com/about).\r\n- [x] I have followed [Contribution Guidelines](https://github.com/google/tunix/blob/main/CONTRIBUTING.md).\r\n", "created_at": "2026-01-12T17:14:25Z", "updated_at": "2026-01-12T17:14:27Z", "closed_at": null, "merged_at": null, "merge_commit_sha": "b674509e2f58a6196f8f3e66f4a4240ab2d037bb", "assignee": null, "assignees": {}, "requested_reviewers": { "0": { 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"https://api.github.com/users/Green0-0", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "This PR should NOT be looked at.\r\n\r\nI am only creating this PR because I was told at https://www.kaggle.com/competitions/google-tunix-hackathon/discussion/666550 that I must submit a PR to have my competition submission be eligible.\r\n\r\nOtherwise I would not recommend anyone even spend time looking at the code, this PR has a bunch of things jumbled together (including personal files for the competition submission), most of it is low-quality and screws with the current codebase.\r\n\r\nOnce the competition concludes and I see that my notebook actually works, I'll try to break apart this PR into individual features (or open issues instead of trying to PR code) that work with the codebase.\r\n\r\nFor those curious, this PR is supposed to implement the following:\r\nhttps://unsloth.ai/blog/gradient (Gradient accumulation fix)\r\nhttps://github.com/apple/ml-cross-entropy (Cut cross entropy)\r\nhttps://github.com/jax-ml/jax/pull/28099/files/940747949e3b8ddf60cf82480ba681cd8c11e129#diff-5e175da3ab167ed5441d24c3e92faf1162c3583ffcbf0feaee161ffae0b8721a (Flash attention with softcap for gemma2)\r\nFull model rematerialization\r\nhttps://arxiv.org/abs/2405.14734 (SimPO)\r\n\r\nThe notebook attached with this PR should in theory work, if anyone actually wants to test. \r\n\r\nAlso, I'm very sorry for not following the contributing guidelines.", "created_at": "2026-01-12T00:52:38Z", "updated_at": "2026-01-12T00:52:44Z", "closed_at": null, "merged_at": null, "merge_commit_sha": null, "assignee": null, "assignees": {}, "requested_reviewers": { "0": { "login": "jiangyangmu", "id": 1014774, "node_id": "MDQ6VXNlcjEwMTQ3NzQ=", "avatar_url": "https://avatars.githubusercontent.com/u/1014774?v=4", "gravatar_id": "", "url": "https://api.github.com/users/jiangyangmu", "type": "User", "user_view_type": "public", 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"key": "apache-2.0", "name": "Apache License 2.0", "spdx_id": "Apache-2.0", "url": "https://api.github.com/licenses/apache-2.0", "node_id": "MDc6TGljZW5zZTI=" }, "allow_forking": true, "is_template": false, "web_commit_signoff_required": false, "has_pull_requests": true, "pull_request_creation_policy": "all", "topics": {}, "visibility": "public", "forks": 239, "open_issues": 148, "watchers": 2155, "default_branch": "main" } }, "_links": { "self": { "href": "https://api.github.com/repos/google/tunix/pulls/966" }, "html": { "href": "https://github.com/google/tunix/pull/966" }, "issue": { "href": "https://api.github.com/repos/google/tunix/issues/966" }, "comments": { "href": "https://api.github.com/repos/google/tunix/issues/966/comments" }, "review_comments": { "href": "https://api.github.com/repos/google/tunix/pulls/966/comments" }, "review_comment": { "href": "https://api.github.com/repos/google/tunix/pulls/comments{/number}" }, "commits": { "href": "https://api.github.com/repos/google/tunix/pulls/966/commits" }, "statuses": { "href": "https://api.github.com/repos/google/tunix/statuses/b15927af5cd397f9c470d69a178981cc576ea9f2" } }, "author_association": "NONE", "auto_merge": null, "active_lock_reason": null, "linked_issues": [] }, { "url": "https://api.github.com/repos/google/tunix/pulls/964", "id": 3163127541, "node_id": "PR_kwDOOTDVN868iX71", "number": 964, "state": "open", "locked": false, "title": "fix: Add truncation support to pad_to_length for consistent prompt shapes", "user": { "login": "Cascoopman", "id": 58267444, "node_id": "MDQ6VXNlcjU4MjY3NDQ0", "avatar_url": "https://avatars.githubusercontent.com/u/58267444?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Cascoopman", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "## Problem\r\n\r\nWhen `max_prompt_length` is explicitly specified (e.g., 128), the sampler could produce inconsistent prompt shapes across batches:\r\n\r\n1. `pad_to_length()` in `generate/utils.py` returned inputs unchanged if they exceeded `target_length`\r\n2. The sampler auto-increased `max_prompt_length` via `next_power_of_2()` when prompts were longer\r\n3. Different micro-batches could have different prompt shapes (e.g., batch 1: `(N, 128)`, batch 2: `(N, 256)`)\r\n4. `rl_cluster.generate()` failed when concatenating micro-batch outputs due to shape mismatch\r\n\r\n\r\n## Solution\r\n\r\n### 1. Add `truncate` parameter to `pad_to_length()` (`generate/utils.py`)\r\n\r\n```python\r\ndef pad_to_length(x, target_length, pad_value=0, left=False, axis=0, truncate=False):\r\n # When truncate=True and left=True: keeps last target_length elements\r\n # When truncate=True and left=False: keeps first target_length elements\r\n```\r\n\r\n### 2. Update sampler to use truncation when length is specified (`generate/sampler.py`)\r\n\r\n```python\r\nuser_specified_length = max_prompt_length is not None\r\nif max_prompt_length is None or max_prompt_length < max_tokens_length:\r\n if user_specified_length:\r\n pass # Keep user-specified length, will truncate\r\n else:\r\n max_prompt_length = utils.next_power_of_2(max_tokens_length)\r\n\r\nall_input_ids = np.array([\r\n utils.pad_to_length(x, ..., truncate=user_specified_length)\r\n for x in tokens\r\n])\r\n```\r\n\r\n## Backward Compatibility\r\n\r\n- If `max_prompt_length` is `None`, the sampler still auto-sizes to accommodate all prompts (original behavior)\r\n- If `max_prompt_length` is explicitly specified, it's now respected via truncation (new behavior)\r\n\r\n## Testing\r\n\r\n- All existing sampler tests pass\r\n- All GRPO learner tests pass\r\n- Verified truncation behavior with unit tests\r\n\r\n## Related\r\n\r\nFixes the shape mismatch error when using `MAX_PROMPT_LENGTH <= 128` in GRPO training.", "created_at": "2026-01-11T11:35:26Z", "updated_at": "2026-01-11T11:38:51Z", "closed_at": null, "merged_at": null, "merge_commit_sha": "2085325416429edaf6c319f012530b021148049d", "assignee": null, "assignees": {}, "requested_reviewers": { "0": { "login": "jiangyangmu", "id": 1014774, "node_id": "MDQ6VXNlcjEwMTQ3NzQ=", "avatar_url": "https://avatars.githubusercontent.com/u/1014774?v=4", "gravatar_id": "", "url": "https://api.github.com/users/jiangyangmu", "type": "User", "user_view_type": "public", "site_admin": false }, "1": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": "https://avatars.githubusercontent.com/u/4454178?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tianshub", "type": "User", "user_view_type": "public", "site_admin": false }, "2": { "login": "wang2yn84", "id": 13134832, "node_id": "MDQ6VXNlcjEzMTM0ODMy", "avatar_url": "https://avatars.githubusercontent.com/u/13134832?v=4", "gravatar_id": 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"description": "A Lightweight LLM Post-Training Library", "fork": false, "url": "https://api.github.com/repos/google/tunix", "created_at": "2025-04-02T22:19:15Z", "updated_at": "2026-02-15T21:28:44Z", "pushed_at": "2026-02-17T10:58:30Z", "homepage": "", "size": 99707, "stargazers_count": 2155, "watchers_count": 2155, "language": "Python", "has_issues": true, "has_projects": true, "has_downloads": true, "has_wiki": false, "has_pages": false, "has_discussions": true, "forks_count": 239, "archived": false, "disabled": false, "open_issues_count": 148, "license": { "key": "apache-2.0", "name": "Apache License 2.0", "spdx_id": "Apache-2.0", "url": "https://api.github.com/licenses/apache-2.0", "node_id": "MDc6TGljZW5zZTI=" }, "allow_forking": true, "is_template": false, "web_commit_signoff_required": false, "has_pull_requests": true, "pull_request_creation_policy": "all", "topics": {}, "visibility": "public", "forks": 239, "open_issues": 148, "watchers": 2155, "default_branch": "main" } }, "_links": { "self": { "href": "https://api.github.com/repos/google/tunix/pulls/964" }, "html": { "href": "https://github.com/google/tunix/pull/964" }, "issue": { "href": "https://api.github.com/repos/google/tunix/issues/964" }, "comments": { "href": "https://api.github.com/repos/google/tunix/issues/964/comments" }, "review_comments": { "href": "https://api.github.com/repos/google/tunix/pulls/964/comments" }, "review_comment": { "href": "https://api.github.com/repos/google/tunix/pulls/comments{/number}" }, "commits": { "href": "https://api.github.com/repos/google/tunix/pulls/964/commits" }, "statuses": { "href": "https://api.github.com/repos/google/tunix/statuses/f2537ef6cbbebab1b997f0c7c6e2a0411f223168" } }, "author_association": "NONE", "auto_merge": null, "active_lock_reason": null, "linked_issues": [] }, { "url": "https://api.github.com/repos/google/tunix/pulls/963", "id": 3163063183, "node_id": "PR_kwDOOTDVN868iIOP", "number": 963, "state": "open", "locked": false, "title": "Fix batch_size=1 token_buffer truncation in sampler", "user": { "login": "Cascoopman", "id": 58267444, "node_id": "MDQ6VXNlcjU4MjY3NDQ0", "avatar_url": "https://avatars.githubusercontent.com/u/58267444?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Cascoopman", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "## Summary\n\nFixes #809\n\nWhen using the generic sampler with `batch_size=1`, the `token_buffer` was being truncated instead of properly populated during the `while_loop`. This made the sampler unusable when `TRAIN_MICRO_BATCH_SIZE=1`, yet it worked fine for larger batches.\n\n## Root Cause\n\nThe issue was caused by using Python integers for `decoding_step` indexing inside `jax.lax.while_loop`. When JAX traces through a while_loop, using Python integers for dynamic indexing can cause issues with how the traced computation is specialized. With `batch_size=1`, JAX may incorrectly optimize or trace the array update operations (`.at[:, decoding_step + 1].set(...)`).\n\nThe Gemma-specific sampler (`tunix/tunix/models/gemma/sampler.py`) already had this fix at line 440:\n```python\ndecoding_step = jnp.asarray(sampler_state.decoding_step, dtype=jnp.int32)\n```\n\nBut the generic sampler was missing this conversion.\n\n## Changes\n\n1. **`tunix/generate/sampler.py`**:\n - Initialize `decoding_step` as `jnp.int32(num_input_tokens - 1)` in `init_sample_state`\n - Add explicit `jnp.asarray()` conversion in `_sample()` method\n - Add explicit `jnp.asarray()` conversion in `_sample_step()` method with a comment referencing the issue\n\n2. **`tests/generate/sampler_test.py`**:\n - Added `test_batch_size_one()` test case\n - Added `test_batch_size_one_with_echo()` test case\n\n## Testing\n\n- Ran all existing sampler tests - all pass\n- Ran new batch_size=1 tests - all pass\n- Verified fix with standalone reproduction script testing while_loop behavior\n\n## Related\n\nThis fix aligns the generic sampler with the Gemma sampler implementation which already had this fix.", "created_at": "2026-01-11T10:15:49Z", "updated_at": "2026-01-11T10:15:50Z", "closed_at": null, "merged_at": null, "merge_commit_sha": "01fa0df161b93e37c034b83578047c188660de50", "assignee": null, "assignees": {}, "requested_reviewers": { "0": { "login": "jiangyangmu", "id": 1014774, "node_id": "MDQ6VXNlcjEwMTQ3NzQ=", "avatar_url": "https://avatars.githubusercontent.com/u/1014774?v=4", "gravatar_id": "", "url": "https://api.github.com/users/jiangyangmu", "type": "User", "user_view_type": "public", "site_admin": false }, "1": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": "https://avatars.githubusercontent.com/u/4454178?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tianshub", "type": "User", "user_view_type": "public", "site_admin": false }, "2": { "login": "wang2yn84", "id": 13134832, 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"linked_issues": [ 8, 809 ] }, { "url": "https://api.github.com/repos/google/tunix/pulls/961", "id": 3163042480, "node_id": "PR_kwDOOTDVN868iDKw", "number": 961, "state": "open", "locked": false, "title": "feat(agentic): Add tool calling support for GRPO training", "user": { "login": "Cascoopman", "id": 58267444, "node_id": "MDQ6VXNlcjU4MjY3NDQ0", "avatar_url": "https://avatars.githubusercontent.com/u/58267444?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Cascoopman", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "## Summary\n\nThis PR graduates the experimental tool calling feature to a stable API and adds comprehensive support for training LLMs to use tools during reasoning.\n\n## New Features\n\n### ExpressionCalculatorTool\n- Safe mathematical expression evaluator using Python's AST\n- Supports +, -, *, /, parentheses, and negative numbers\n- No use of eval() for security\n\n### AgenticGRPOLearner\n- Extends GRPO to support multi-turn tool interactions\n- Configurable tool_map parameter for custom tools\n- Supports async rollout for efficient parallel training\n- Compatible with existing GRPO reward functions\n\n### New Configuration Options\n- `max_tool_steps`: Maximum tool interaction turns per episode\n- `tool_parser_name`: Parser for tool call tags (qwen, gemini)\n- `tool_map`: Dictionary of available tools\n\n## Files Added\n- `tunix/rl/agentic/agentic_grpo_learner.py`\n- `tunix/rl/agentic/tools/expression_calculator_tool.py`\n- `tunix/rl/agentic/__init__.py`\n- `tunix/rl/agentic/tools/__init__.py`\n- `examples/agentic/tool_calling_grpo_demo.py`\n- `tests/rl/agentic/agentic_grpo_learner_test.py`\n- `tests/rl/agentic/tools/expression_calculator_test.py`\n\n## Usage Example\n```python\nfrom tunix.rl.agentic import AgenticGRPOConfig, AgenticGRPOLearner\nfrom tunix.rl.agentic.tools import ExpressionCalculatorTool\n\n# Define tools\ntool_map = {'calculator': ExpressionCalculatorTool}\n\n# Create config with tool calling enabled\nconfig = AgenticGRPOConfig(\n num_generations=4,\n num_iterations=2,\n system_prompt='You have access to a calculator tool...',\n max_concurrency=4,\n)\n\n# Create learner with tool support\nlearner = AgenticGRPOLearner(\n rl_cluster=rl_cluster,\n reward_fns=my_reward_fns,\n algo_config=config,\n tool_map=tool_map,\n)\n\nlearner.train(train_dataset, eval_dataset)\n```\n\n## Testing\n- 22 unit tests for ExpressionCalculatorTool (all passing)\n- 10 unit tests for AgenticGRPOConfig (all passing)\n- Full training loop tested on Mac CPU/Metal\n\n## Breaking Changes\nNone - this is purely additive functionality.\n", "created_at": "2026-01-11T09:49:21Z", "updated_at": "2026-01-11T09:56:43Z", "closed_at": null, "merged_at": null, "merge_commit_sha": null, "assignee": null, "assignees": {}, "requested_reviewers": { "0": { "login": "jiangyangmu", "id": 1014774, "node_id": "MDQ6VXNlcjEwMTQ3NzQ=", "avatar_url": "https://avatars.githubusercontent.com/u/1014774?v=4", "gravatar_id": 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`ImportError` when running example scripts in OSS environment.\r\n\r\n**Issues Fixed:**\r\n1. `ImportError: cannot import name 'storage' from 'google.cloud'` at `train_deepscaler_nb.py:151`\r\n2. `ModuleNotFoundError: No module named 'gcsfs'` at `train_deepscaler_nb.py:234`\r\n\r\n**Changes:**\r\n- Added `google-cloud-storage` to dependencies (required for GCS file operations in OSS environment)\r\n- Added `gcsfs` to dependencies (required for accessing `gs://` URIs via fsspec)\r\n\r\nBoth dependencies are used by example notebooks (`train_deepscaler_nb.py`, `math_eval_nb.py`) to access Google Cloud Storage buckets for data and models.\r\n\r\n**Reference**\r\n- Issue: #802\r\n- Affected files: `examples/deepscaler/train_deepscaler_nb.py`, `examples/deepscaler/math_eval_nb.py`\r\n\r\n**Colab Notebook**\r\nN/A - This is a dependency fix, not a new API addition.\r\n\r\n**Checklist**\r\n\r\n- [ ] I have added all the necessary unit tests for my change.\r\n- [x] I have verified that my 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captured via `sowed_module`, combined with the supervised loss. #887\r\n\r\n## Implementation (CRD)\r\n- Wraps selected student/teacher layers with `sowed_module` to capture intermediates.\r\n- Pools each captured feature leaf to `(B, C)`:\r\n - `(B, C)` → use as-is \r\n - `(B, T, C)` + `input_mask` → masked mean over `T` \r\n - otherwise mean over non-batch/non-channel dims (e.g. `(B, H, W, C)`).\r\n- Stacks pooled leaves to `(N, B, C)` and averages over `N` to get `(B, C)`.\r\n- Projects to a shared embedding space (MLP/linear), L2-normalizes, and computes **InfoNCE with in-batch negatives** (optional symmetric).\r\n- Training loss: `alpha * crd_loss + (1 - alpha) * task_ce`. Teacher features use `stop_gradient`.\r\n- `pre_process_models` wraps models + adds heads; `post_process_models` unwraps and restores original models.\r\n\r\n## Tests\r\n- `feature_extraction_test.py`\r\n - Avg-pooling (VALID/SAME + include/exclude pad) + error cases\r\n - `sowed_module` wrap/pop/unwrap (single + multi-leaf capture; shape-safe matching)\r\n - Feature projection setup/removal integration\r\n- `strategies_test.py`\r\n - LogitStrategy matches manual KL+CE\r\n - Smoke tests: FeaturePoolingStrategy, FeatureProjectionStrategy, and CRD\r\n - Verifies losses are scalars and sowed wrappers are removed in post-process\r\n\r\n## Testing\r\n```bash\r\npython -m ruff format --check \\\r\n tunix/distillation/strategies/crd_strategy.py \\\r\n tests/distillation/feature_extraction_test.py \\\r\n tests/distillation/strategies_test.py\r\n\r\npython -m pytest -q \\\r\n tests/distillation/feature_extraction_test.py \\\r\n tests/distillation/strategies_test.py", "created_at": 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"topics": {}, "visibility": "public", "forks": 239, "open_issues": 148, "watchers": 2155, "default_branch": "main" } }, "_links": { "self": { "href": "https://api.github.com/repos/google/tunix/pulls/898" }, "html": { "href": "https://github.com/google/tunix/pull/898" }, "issue": { "href": "https://api.github.com/repos/google/tunix/issues/898" }, "comments": { "href": "https://api.github.com/repos/google/tunix/issues/898/comments" }, "review_comments": { "href": "https://api.github.com/repos/google/tunix/pulls/898/comments" }, "review_comment": { "href": "https://api.github.com/repos/google/tunix/pulls/comments{/number}" }, "commits": { "href": "https://api.github.com/repos/google/tunix/pulls/898/commits" }, "statuses": { "href": "https://api.github.com/repos/google/tunix/statuses/1646ab74cd097d5968e8fab311d9b11057ccc5be" } }, "author_association": "NONE", "auto_merge": null, "active_lock_reason": null, "linked_issues": [] }, { "url": "https://api.github.com/repos/google/tunix/pulls/895", "id": 3106568189, "node_id": "PR_kwDOOTDVN865Knf9", "number": 895, "state": "open", "locked": false, "title": "Upgrade GitHub Actions to latest versions", "user": { "login": "salmanmkc", "id": 32169182, "node_id": "MDQ6VXNlcjMyMTY5MTgy", "avatar_url": "https://avatars.githubusercontent.com/u/32169182?v=4", "gravatar_id": "", "url": "https://api.github.com/users/salmanmkc", "type": "User", "user_view_type": "public", "site_admin": true }, "body": "## Summary\n\nUpgrade GitHub Actions to their latest versions for improved features, bug fixes, and security updates.\n\n## Changes\n\n| Action | Old Version(s) | New Version | Files |\n|--------|---------------|-------------|-------|\n| `pypa/gh-action-pypi-publish` | `release/v1` | [`ed0c539`](https://github.com/pypa/gh-action-pypi-publish/commit/ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e) | workflow files |\n\n## Why upgrade?\n\nKeeping GitHub Actions up to date ensures:\n- **Security**: Latest security patches and fixes\n- **Features**: Access to new functionality and improvements\n- **Compatibility**: Better support for current GitHub features\n- **Performance**: Optimizations and efficiency improvements\n\n### Note on pypa/gh-action-pypi-publish\n\nThis action uses **branch-based versioning** (`release/v1.x`) rather than tags. 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|\n|--------|---------------|-------------|---------|-------|\n| `actions/cache` | [`v4`](https://github.com/actions/cache/releases/tag/v4) | [`v5`](https://github.com/actions/cache/releases/tag/v5) | [Release](https://github.com/actions/cache/releases/tag/v5) | tpu-nightly-regression.yml, tpu-tests.yml |\n| `actions/checkout` | [`v4`](https://github.com/actions/checkout/releases/tag/v4) | [`v6`](https://github.com/actions/checkout/releases/tag/v6) | [Release](https://github.com/actions/checkout/releases/tag/v6) | cpu-tests.yml, pypi_release.yml, tpu-nightly-regression.yml, tpu-tests.yml |\n| `actions/setup-python` | [`v4`](https://github.com/actions/setup-python/releases/tag/v4), [`v5`](https://github.com/actions/setup-python/releases/tag/v5) | [`v6`](https://github.com/actions/setup-python/releases/tag/v6) | [Release](https://github.com/actions/setup-python/releases/tag/v6) | build_package.yml, cpu-tests.yml, pypi_release.yml |\n\n## Context\n\nPer [GitHub's announcement](https://github.blog/changelog/2025-09-19-deprecation-of-node-20-on-github-actions-runners/), Node 20 is being deprecated and runners will begin using Node 24 by default starting March 4th, 2026.\n\n### Why this matters\n\n- **Node 20 EOL**: April 2026\n- **Node 24 default**: March 4th, 2026\n- **Action**: Update to latest action versions that support Node 24\n\n### Security Note\n\nActions that were previously pinned to commit SHAs remain pinned to SHAs (updated to the latest release SHA) to maintain the security benefits of immutable references.\n\n### Testing\n\nThese changes only affect CI/CD workflow configurations and should not impact application functionality. 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"state": "open", "locked": false, "title": "Fdpo", "user": { "login": "xinzhuo20", "id": 71267422, "node_id": "MDQ6VXNlcjcxMjY3NDIy", "avatar_url": "https://avatars.githubusercontent.com/u/71267422?v=4", "gravatar_id": "", "url": "https://api.github.com/users/xinzhuo20", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "#fDPO Implementation\r\n\r\nFine-tuning examples using Google Tunix with **fDPO** - a novel fine-grained preference learning algorithm for segment-level preference optimization.\r\n\r\n## fDPO Overview\r\n\r\nfDPO (Fine-grained Direct Preference Optimization) extends traditional DPO by introducing **segment-level preference granularity**. Instead of applying a single global trade-off parameter β uniformly across all reasoning steps, fDPO separates responses into distinct components (description and reasoning) and applies adaptive, segment-specific β values.\r\n\r\n### Key Features\r\n\r\n- **Segment-Level Optimization**: Separates responses into description (`R_desc`) and reasoning (`R_reason`) components\r\n- **Adaptive β Values**: Dynamically computes `β_desc` and `β_reason` based on preference differentials\r\n- **Balanced Learning**: Prevents overfitting to simpler descriptive responses while properly optimizing complex reasoning paths\r\n\r\n**Reference**\r\nhttps://plan-lab.github.io/projects/spatialreasoner/\r\n", "created_at": "2025-12-16T00:05:28Z", "updated_at": "2025-12-17T00:47:47Z", "closed_at": null, "merged_at": null, "merge_commit_sha": "4368cb4774c3591bcfd3f4f17cdf20c208331488", "assignee": null, "assignees": {}, "requested_reviewers": { "0": { "login": "jiangyangmu", "id": 1014774, "node_id": 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Introduce a method in MetricsLogger to retrieve the latest metrics and integrate metrics logging during training and evaluation steps in PeftTrainer. This improves the tracking of model performance over time.\r\n\r\n\r\n**Checklist**\r\n\r\n\r\n- [x] I have added all the necessary unit tests for my change.\r\n- [x] I have verified that my change does not break existing code and all unit tests pass.\r\n- [x] I have added all appropriate doc-strings/documentation.\r\n- [x] My PR is based on the latest changes of the main branch (if unsure, rebase the code).\r\n- [x] I have signed the [Contributor License Agreement](https://cla.developers.google.com/about).\r\n- [x] I have followed [Contribution Guidelines](https://github.com/google/tunix/blob/main/CONTRIBUTING.md).\r\n", "created_at": "2025-12-14T14:52:51Z", "updated_at": "2025-12-20T07:31:55Z", "closed_at": null, "merged_at": null, "merge_commit_sha": null, "assignee": null, "assignees": {}, "requested_reviewers": { "0": { "login": "jiangyangmu", "id": 1014774, "node_id": "MDQ6VXNlcjEwMTQ3NzQ=", "avatar_url": "https://avatars.githubusercontent.com/u/1014774?v=4", "gravatar_id": "", 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"login": "kamalkraj", "id": 17096858, "node_id": "MDQ6VXNlcjE3MDk2ODU4", "avatar_url": "https://avatars.githubusercontent.com/u/17096858?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kamalkraj", "type": "User", "user_view_type": "public", "site_admin": false }, "repo": { "id": 1116242094, "node_id": "R_kgDOQoiArg", "name": "tunix", "full_name": "kamalkraj/tunix", "private": false, "owner": { "login": "kamalkraj", "id": 17096858, "node_id": "MDQ6VXNlcjE3MDk2ODU4", "avatar_url": "https://avatars.githubusercontent.com/u/17096858?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kamalkraj", "type": "User", "user_view_type": "public", "site_admin": false }, "description": "A Lightweight LLM Post-Training Library", "fork": true, "url": "https://api.github.com/repos/kamalkraj/tunix", "created_at": "2025-12-14T13:38:25Z", "updated_at": "2026-01-08T07:32:48Z", "pushed_at": "2026-01-08T08:25:52Z", "homepage": "", "size": 14583, "stargazers_count": 0, "watchers_count": 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"type": "Organization", "user_view_type": "public", "site_admin": false }, "repo": { "id": 959501623, "node_id": "R_kgDOOTDVNw", "name": "tunix", "full_name": "google/tunix", "private": false, "owner": { "login": "google", "id": 1342004, "node_id": "MDEyOk9yZ2FuaXphdGlvbjEzNDIwMDQ=", "avatar_url": "https://avatars.githubusercontent.com/u/1342004?v=4", "gravatar_id": "", "url": "https://api.github.com/users/google", "type": "Organization", "user_view_type": "public", "site_admin": false }, "description": "A Lightweight LLM Post-Training Library", "fork": false, "url": "https://api.github.com/repos/google/tunix", "created_at": "2025-04-02T22:19:15Z", "updated_at": "2026-02-15T21:28:44Z", "pushed_at": "2026-02-17T10:58:30Z", "homepage": "", "size": 99707, "stargazers_count": 2155, "watchers_count": 2155, "language": "Python", "has_issues": true, "has_projects": true, "has_downloads": true, "has_wiki": false, "has_pages": false, "has_discussions": true, "forks_count": 239, "archived": false, "disabled": false, "open_issues_count": 148, "license": { "key": "apache-2.0", "name": "Apache License 2.0", "spdx_id": "Apache-2.0", "url": "https://api.github.com/licenses/apache-2.0", "node_id": "MDc6TGljZW5zZTI=" }, "allow_forking": true, "is_template": false, "web_commit_signoff_required": false, "has_pull_requests": true, "pull_request_creation_policy": "all", "topics": {}, "visibility": "public", "forks": 239, "open_issues": 148, "watchers": 2155, "default_branch": "main" } }, "_links": { "self": { "href": "https://api.github.com/repos/google/tunix/pulls/885" }, "html": { "href": "https://github.com/google/tunix/pull/885" }, "issue": { "href": "https://api.github.com/repos/google/tunix/issues/885" }, "comments": { "href": "https://api.github.com/repos/google/tunix/issues/885/comments" }, "review_comments": { "href": "https://api.github.com/repos/google/tunix/pulls/885/comments" }, "review_comment": { "href": "https://api.github.com/repos/google/tunix/pulls/comments{/number}" }, "commits": { "href": "https://api.github.com/repos/google/tunix/pulls/885/commits" }, "statuses": { "href": "https://api.github.com/repos/google/tunix/statuses/664e79a3ade4ba51a9492fd00178461ce8247e82" } }, "author_association": "NONE", "auto_merge": null, "active_lock_reason": null, "linked_issues": [] }, { "url": "https://api.github.com/repos/google/tunix/pulls/871", "id": 3088498971, "node_id": "PR_kwDOOTDVN864FsEb", "number": 871, "state": "open", "locked": false, "title": "Add Qwen3 235B-A22B model config", "user": { "login": "yhtang", "id": 6081524, "node_id": "MDQ6VXNlcjYwODE1MjQ=", "avatar_url": "https://avatars.githubusercontent.com/u/6081524?v=4", "gravatar_id": "", "url": "https://api.github.com/users/yhtang", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "Add model config for the Qwen3-235B-A22B MoE model. This is implemented as a new `qwen3_235b_a22b` classmethod on `tunix.models.qwen3.model.ModelConfig`, modeled after the existing `qwen3_30b` classmethod and parameterized from the upstream HF config.\r\n\r\n**Reference**\r\n\r\nThe [raw specs from HF](https://huggingface.co/Qwen/Qwen3-235B-A22B/raw/main/config.json) are:\r\n```\r\n{\r\n \"architectures\": [\r\n \"Qwen3MoeForCausalLM\"\r\n ],\r\n \"attention_bias\": false,\r\n \"attention_dropout\": 0.0,\r\n \"bos_token_id\": 151643,\r\n \"decoder_sparse_step\": 1,\r\n \"eos_token_id\": 151645,\r\n \"head_dim\": 128,\r\n \"hidden_act\": \"silu\",\r\n \"hidden_size\": 4096,\r\n \"initializer_range\": 0.02,\r\n \"intermediate_size\": 12288,\r\n \"max_position_embeddings\": 40960,\r\n \"max_window_layers\": 94,\r\n \"mlp_only_layers\": [],\r\n \"model_type\": \"qwen3_moe\",\r\n \"moe_intermediate_size\": 1536,\r\n \"norm_topk_prob\": true,\r\n \"num_attention_heads\": 64,\r\n \"num_experts\": 128,\r\n \"num_experts_per_tok\": 8,\r\n \"num_hidden_layers\": 94,\r\n \"num_key_value_heads\": 4,\r\n \"output_router_logits\": false,\r\n \"rms_norm_eps\": 1e-06,\r\n \"rope_scaling\": null,\r\n \"rope_theta\": 1000000.0,\r\n \"router_aux_loss_coef\": 0.001,\r\n \"sliding_window\": null,\r\n \"tie_word_embeddings\": false,\r\n \"torch_dtype\": \"bfloat16\",\r\n \"transformers_version\": \"4.51.0\",\r\n \"use_cache\": true,\r\n \"use_sliding_window\": false,\r\n \"vocab_size\": 151936\r\n}\r\n```\r\n\r\n**Checklist**\r\n\r\n\r\n- [x] I have added all the necessary unit tests for my change.\r\n- [x] I have verified that my change does not break existing code and all unit tests pass.\r\n- [x] I have added all appropriate doc-strings/documentation.\r\n- [x] My PR is based on the latest changes of the main branch (if unsure, rebase the code).\r\n- [x] I have signed the [Contributor License Agreement](https://cla.developers.google.com/about).\r\n- [x] I have followed [Contribution Guidelines](https://github.com/google/tunix/blob/main/CONTRIBUTING.md).\r\n", "created_at": "2025-12-10T08:55:08Z", "updated_at": "2025-12-19T00:41:45Z", "closed_at": null, "merged_at": null, "merge_commit_sha": "8dc1f55ecfa6423488ab70bea620df4da9f97617", "assignee": null, "assignees": {}, "requested_reviewers": { "0": { "login": "jiangyangmu", "id": 1014774, "node_id": "MDQ6VXNlcjEwMTQ3NzQ=", "avatar_url": "https://avatars.githubusercontent.com/u/1014774?v=4", "gravatar_id": "", "url": "https://api.github.com/users/jiangyangmu", "type": "User", "user_view_type": "public", "site_admin": false }, "1": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": "https://avatars.githubusercontent.com/u/4454178?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tianshub", "type": "User", "user_view_type": "public", "site_admin": false }, "2": { "login": "wang2yn84", "id": 13134832, "node_id": "MDQ6VXNlcjEzMTM0ODMy", "avatar_url": 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the same `jax.distributed.initialize` session with each controlling distinct GPUs.\r\n\r\n**Background**\r\n\r\nRunning multi-controller on GPUs (e.g., 2 processes × N GPU each) previously failed with:\r\n- `AssertionError` when iterating over non-fully-addressable arrays\r\n- `ValueError: device_put's first argument must be a fully addressable array`\r\n\r\n**Changes**\r\n\r\n1. `tunix/generate/sampler.py`\r\n - Decode strings from local shards (`addressable_shards`) instead of iterating over global arrays\r\n - Maintains DP sharding: each process decodes only its local batch slice\r\n - Fallback to `device_get` for single-controller/fully-addressable cases\r\n2. `tunix/sft/peft_trainer.py`\r\n - Updated `_shard_input` to handle global JAX arrays from rollout\r\n - Use `jax.device_put(x, target_sharding)` for cross-mesh resharding instead of `make_array_from_process_local_data`\r\n - Preserves existing behavior for NumPy/local arrays\r\n\r\n**Status**\r\n\r\nThis PR should be 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This PR removes the excessive outputs from the notebook and the size now goes back to normal.\r\n\r\n\r\n\r\n**Reference**\r\n\r\n\r\n**Colab Notebook**\r\n\r\n\r\n**Checklist**\r\n\r\n\r\n- [x] I have added all the necessary unit tests for my change.\r\n- [x] I have verified that my change does not break existing code and all unit tests pass.\r\n- [x] I have added all appropriate doc-strings/documentation.\r\n- [x] My PR is based on the latest changes of the main branch (if unsure, rebase the code).\r\n- [x] I have signed the [Contributor License Agreement](https://cla.developers.google.com/about).\r\n- [x] I have followed [Contribution Guidelines](https://github.com/google/tunix/blob/main/CONTRIBUTING.md).\r\n", "created_at": "2025-12-03T01:10:44Z", "updated_at": "2025-12-03T01:15:53Z", "closed_at": null, "merged_at": null, "merge_commit_sha": null, "assignee": null, "assignees": {}, "requested_reviewers": { "0": { "login": "jiangyangmu", "id": 1014774, "node_id": 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"license": { "key": "apache-2.0", "name": "Apache License 2.0", "spdx_id": "Apache-2.0", "url": "https://api.github.com/licenses/apache-2.0", "node_id": "MDc6TGljZW5zZTI=" }, "allow_forking": true, "is_template": false, "web_commit_signoff_required": false, "has_pull_requests": true, "pull_request_creation_policy": "all", "topics": {}, "visibility": "public", "forks": 239, "open_issues": 148, "watchers": 2155, "default_branch": "main" } }, "_links": { "self": { "href": "https://api.github.com/repos/google/tunix/pulls/830" }, "html": { "href": "https://github.com/google/tunix/pull/830" }, "issue": { "href": "https://api.github.com/repos/google/tunix/issues/830" }, "comments": { "href": "https://api.github.com/repos/google/tunix/issues/830/comments" }, "review_comments": { "href": "https://api.github.com/repos/google/tunix/pulls/830/comments" }, "review_comment": { "href": "https://api.github.com/repos/google/tunix/pulls/comments{/number}" }, "commits": { "href": 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This allows users to supply a `maxtext_config` via `additional_config` without needing explicit key mappings.\r\n\r\nResolves #\\\r\n\r\n> It's a good idea to open an issue first for discussion.\r\n\r\n\r\n\r\n**Reference**\r\n\r\n\r\n**Colab Notebook**\r\n\r\n\r\n**Checklist**\r\n\r\n\r\n- [ ] I have added all the necessary unit tests for my change.\r\n- [ ] I have verified that my change does not break existing code and all unit tests pass.\r\n- [ ] I have added all appropriate doc-strings/documentation.\r\n- [ ] My PR is based on the latest changes of the main branch (if unsure, rebase the code).\r\n- [ ] I have signed the [Contributor License Agreement](https://cla.developers.google.com/about).\r\n- [ ] I have followed [Contribution Guidelines](https://github.com/google/tunix/blob/main/CONTRIBUTING.md).\r\n", "created_at": "2025-12-01T21:39:18Z", "updated_at": "2025-12-01T21:39:18Z", "closed_at": null, "merged_at": null, "merge_commit_sha": null, "assignee": null, "assignees": {}, 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"sha": "480d94659c04658671d5ede8e1bd26c052ff127e", "user": { "login": "google", "id": 1342004, "node_id": "MDEyOk9yZ2FuaXphdGlvbjEzNDIwMDQ=", "avatar_url": "https://avatars.githubusercontent.com/u/1342004?v=4", "gravatar_id": "", "url": "https://api.github.com/users/google", "type": "Organization", "user_view_type": "public", "site_admin": false }, "repo": { "id": 959501623, "node_id": "R_kgDOOTDVNw", "name": "tunix", "full_name": "google/tunix", "private": false, "owner": { "login": "google", "id": 1342004, "node_id": "MDEyOk9yZ2FuaXphdGlvbjEzNDIwMDQ=", "avatar_url": "https://avatars.githubusercontent.com/u/1342004?v=4", "gravatar_id": "", "url": "https://api.github.com/users/google", "type": "Organization", "user_view_type": "public", "site_admin": false }, "description": "A Lightweight LLM Post-Training Library", "fork": false, "url": "https://api.github.com/repos/google/tunix", "created_at": "2025-04-02T22:19:15Z", "updated_at": "2026-02-15T21:28:44Z", "pushed_at": 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"https://api.github.com/repos/google/tunix/issues/821" }, "comments": { "href": "https://api.github.com/repos/google/tunix/issues/821/comments" }, "review_comments": { "href": "https://api.github.com/repos/google/tunix/pulls/821/comments" }, "review_comment": { "href": "https://api.github.com/repos/google/tunix/pulls/comments{/number}" }, "commits": { "href": "https://api.github.com/repos/google/tunix/pulls/821/commits" }, "statuses": { "href": "https://api.github.com/repos/google/tunix/statuses/f6213d89329f713eea9dac892b87819b4fec682b" } }, "author_association": "COLLABORATOR", "auto_merge": null, "active_lock_reason": null, "linked_issues": [] }, { "url": "https://api.github.com/repos/google/tunix/pulls/820", "id": 3060824150, "node_id": "PR_kwDOOTDVN862cHhW", "number": 820, "state": "open", "locked": false, "title": "Add GRPO micro-batch size validation to prevent incorrect grouping and advantage computation", "user": { "login": "aaghaazkhan", "id": 177999751, "node_id": "U_kgDOCpwPhw", "avatar_url": "https://avatars.githubusercontent.com/u/177999751?v=4", "gravatar_id": "", "url": "https://api.github.com/users/aaghaazkhan", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "## Summary\r\n\r\nThis PR fixes issue #816 by adding a defensive validation to ensure GRPO's required grouping\r\ninvariants are not silently violated during rollout or log-probability\r\ncomputation.\r\n\r\nGRPO requires that for each prompt, the policy generates `G` completions\r\n(`num_generations`). These `G` completions must stay grouped together\r\nthroughout rollout, reward computation, and log-prob computation.\r\n\r\nIf the configured micro-batch sizes are smaller than `num_generations`,\r\nthe framework silently reshapes rewards/logprobs incorrectly, mixing\r\ngenerations from different prompts. This leads to invalid advantage\r\ncomputations and results in the model failing to learn (e.g., 0%\r\naccuracy for all metrics).\r\n\r\nThis PR introduces explicit validation checks to stop training early if\r\nincorrect batching configurations are used.\r\n\r\n---\r\n\r\n## What This Fix Does\r\n\r\n### Adds two safety checks:\r\n\r\n1. Validate that rollout micro-batch size is large enough:\r\n```python\r\nif self._rollout_micro_batch_size < self.algo_config.num_generations:\r\n raise ValueError(...)\r\n```\r\n\r\n2. Validate that log-prob computation micro-batch size is also large enough:\r\n```python\r\nif self._compute_logps_micro_batch_size < self.algo_config.num_generations:\r\n raise ValueError(...)\r\n```\r\n\r\nThese checks prevent GRPO from running with invalid batching\r\nconfiguration that silently corrupts reward grouping.\r\n\r\n## Why This Matters\r\n\r\n### Without these checks:\r\n\r\n- Batches are split incorrectly\r\n- Rewards are grouped incorrectly\r\n- Advantages are computed with wrong (prompt, generation) alignment\r\n- Training converges to 0 accuracy even for simple tasks\r\n- Debugging is very difficult because failure is something very unexpected\r\n\r\nWith this fix, the error is explicit, descriptive, and actionable.\r\n\r\n---\r\n\r\n## Checklist:\r\n\r\n- [x] I have added all the necessary unit tests for my change.\r\n- [x] I have verified that my change does not break existing code and all unit tests pass.\r\n- [x] I have added all appropriate doc-strings/documentation.\r\n- [x] My PR is based on the latest changes of the main branch (if unsure, rebase the code).\r\n- [x] I have signed the [Contributor License Agreement](https://cla.developers.google.com/about).\r\n- [x] I have followed [Contribution Guidelines](https://github.com/google/tunix/blob/main/CONTRIBUTING.md).\r\n", "created_at": "2025-12-01T20:36:28Z", "updated_at": "2025-12-13T15:03:53Z", "closed_at": null, "merged_at": null, "merge_commit_sha": "abe973e1a3e6d329a83e69d72babf73fa3ff26e8", "assignee": null, "assignees": {}, "requested_reviewers": { "0": { "login": "jiangyangmu", "id": 1014774, "node_id": "MDQ6VXNlcjEwMTQ3NzQ=", "avatar_url": "https://avatars.githubusercontent.com/u/1014774?v=4", "gravatar_id": "", "url": "https://api.github.com/users/jiangyangmu", "type": "User", "user_view_type": "public", "site_admin": false }, "1": { "login": "tianshub", "id": 4454178, "node_id": "MDQ6VXNlcjQ0NTQxNzg=", "avatar_url": 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"https://api.github.com/repos/google/tunix/statuses/d2227af3abe5c8aeb11603c719302540c7826771" } }, "author_association": "NONE", "auto_merge": null, "active_lock_reason": null, "linked_issues": [ 8 ] }, { "url": "https://api.github.com/repos/google/tunix/pulls/812", "id": 3055320171, "node_id": "PR_kwDOOTDVN862HHxr", "number": 812, "state": "open", "locked": false, "title": "Add fixed basic inference example using Sampler with dummy Gemma model", "user": { "login": "Prasanth-ADS", "id": 178168493, "node_id": "U_kgDOCp6irQ", "avatar_url": "https://avatars.githubusercontent.com/u/178168493?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Prasanth-ADS", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "\r\n\r\n

PR Summary

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This pull request introduces a fully working & beginner-friendly inference example for Tunix — resolving the errors caused by outdated example notebooks.

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What This PR Fixes

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Previous inference examples depended on non-existent functions such as sample()
\r\n Users frequently ran into ImportError, tokenizer loading failures & missing model loading steps
\r\n No end-to-end example worked without requiring pretrained model downloads

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What This PR Adds

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Full Running Inference Example

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    Basic notebook and Python script version

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    Works locally, using dummy_model_creator + Gemma model config

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    Sampler correctly initialized with dummy model + cache config

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    Tokenizer now falls back to a mock tokenizer if a real tokenizer path is unavailable

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Better UX for Developers

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    Handles missing dependencies with readable warnings

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    Supports multiple prompts at once

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    Clear printed output with both prompt & generated text

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    Eliminates confusion for new contributors & first-time Tunix users

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Impact

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This PR dramatically improves onboarding for both contributors and researchers
\r\n🔹 New users can now run inference immediately without needing model download or TPU/GPU
\r\n🔹 Becomes a reference implementation for future inference examples & tutorials

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Included Files

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\r\nType | File\r\n-- | --\r\nNotebook | examples/01_basic_inference_example.ipynb\r\nPython Script | examples/01_basic_inference_example_fixed.py\r\nSupport | Tokenizer fallback + dummy model usage\r\n\r\n
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Reproducibility

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No external downloads required.

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python examples/01_basic_inference_example_fixed.py
\r\nworks in CPU-only systems.

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PR Metadata

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    Follows contribution guidelines

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    Does not break existing code

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    Adds documentation explaining behavior

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    Signed Google CLA

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Future Opportunities

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This example can later be extended to support:

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    Beam search comparison plots

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    Reasoning step visualization

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    Reward-based generation demos

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Final Note

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This PR meaningfully improves the usability of Tunix by giving new developers something that works out-of-the-box — no configuration headaches, no silent failures.

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"linked_issues": [] }, { "url": "https://api.github.com/repos/google/tunix/pulls/557", "id": 2906955531, "node_id": "PR_kwDOOTDVN86tRJ8L", "number": 557, "state": "open", "locked": false, "title": "Added Dr. GRPO and Magistral improvements", "user": { "login": "siddagra", "id": 52118936, "node_id": "MDQ6VXNlcjUyMTE4OTM2", "avatar_url": "https://avatars.githubusercontent.com/u/52118936?v=4", "gravatar_id": "", "url": "https://api.github.com/users/siddagra", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "\r\n\r\nThis PR adds several new configuration options to the GRPO learner, enabling support for recent algorithmic improvements from the literature:\r\n\r\n- **epsilon_high**: Defaults to Epsilon. Epsilon value for clipping upper bound. If None, defaults to epsilon for symmetric clipping. Set higher than epsilon for asymmetric clipping. \r\n *Clip Higher introduced in DAPO ([arxiv:2503.14476](https://arxiv.org/pdf/2503.14476)), also used in Magistral and VAPO ([arxiv:2504.05118](https://arxiv.org/abs/2504.05118)).*\r\n\r\n- **dr_grpo**: Default False. If True, use DR-GRPO variant which:\r\n - Only subtracts mean rewards (no std dev normalization)\r\n - Normalizes loss by max_tokens instead of actual token counts \r\n *See DR-GRPO ([arxiv:2503.20783](https://arxiv.org/pdf/2503.20783)).*\r\n\r\n- **max_tokens**: Only used if Dr-GRPO is True. Maximum generation tokens for DR-GRPO normalization. If None and dr_grpo is True, will attempt to infer from rollout config.\r\n\r\n- **magistral_adv_norm**: Default False. If True, normalize advantages by mini-batch std dev instead of group std dev after subtracting group mean. \r\n *See Magistral ([arxiv:2506.10910](https://arxiv.org/pdf/2506.10910)).*\r\n\r\n- **eliminate_non_diverse_groups**: Default False. If True, groups where all rewards are identical are masked instead, as they provide no/noisey learning signal. Done this by setting loss for this group to 0 via masking. \r\n *Introduced in Magistral ([arxiv:2506.10910](https://arxiv.org/pdf/2506.10910)).*\r\n\r\n- **debug**: Default False. If True, print detailed debug information during training.\r\n\r\nMost of these improvements are supported both theoretically and empirically in the respective papers.\r\n\r\nAll of these features are kept False by default in order to mantain original functionality unless the user decides to enable some feature.\r\n\r\nThe reason for keeping these terms independent is twofold:\r\n1. To enable ablation studies with certain features on/off.\r\n2. To allow combining multiple theoretically sound corrections from all of these papers under the same framework.\r\n\r\n**Reference**\r\n- [GRPO](https://arxiv.org/abs/2402.03300)\r\n- [GSPO](https://www.arxiv.org/pdf/2507.18071)\r\n- [Magistral](https://arxiv.org/pdf/2506.10910)\r\n- [DR-GRPO](https://arxiv.org/pdf/2503.20783)\r\n- [DAPO (Clip Higher)](https://arxiv.org/pdf/2503.14476)\r\n- [VAPO](https://arxiv.org/abs/2504.05118)\r\n\r\n**Colab Notebook**\r\n\r\n\r\n**Checklist**\r\n\r\n\r\n- [x] I have added all the necessary unit tests for my change.\r\n- [Y] I have verified that my change does not break existing code and all unit tests pass.\r\n- [Y] I have added all appropriate doc-strings/documentation.\r\n- [Y] My PR is based on the latest changes of the main branch (if unsure, rebase the code).\r\n- [Y] I have signed the [Contributor License Agreement](https://cla.developers.google.com/about).\r\n- [Y] I have followed [Contribution Guidelines](https://github.com/google/tunix/blob/main/CONTRIBUTING.md).\r\n\r\nLet me know if you want to further customize or add more details!\r\nAlso let me know how I should add unit tests more concretely. I conduct unit tests in a main function but I assume that is not fine for this. 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"PR_kwDOOTDVN86r37ia", "number": 491, "state": "open", "locked": false, "title": "Fix logging inconsistency: unify metrics logging to use global_steps", "user": { "login": "joaobenedetmachado", "id": 105388117, "node_id": "U_kgDOBkgYVQ", "avatar_url": "https://avatars.githubusercontent.com/u/105388117?v=4", "gravatar_id": "", "url": "https://api.github.com/users/joaobenedetmachado", "type": "User", "user_view_type": "public", "site_admin": false }, "body": "# Problem\r\nLogging for metrics returned by loss functions via aux was at a `_iter_steps` level,\r\nwhereas other metrics were logged at a `global_steps` level. The progress bar for\r\nGRPO/PPO was at a `_iter_steps` level. This inconsistency made it confusing for\r\nusers to pass `max_steps` at a `_iter_steps` level, and not at `global_steps` level.\r\n\r\n## Solution\r\nUnified logging to use `global_steps` consistently across all components:\r\n- Add global_steps property to PeftTrainer (returns train_steps for compatibility)\r\n- Update DPO trainer to use global_steps for aux metrics logging\r\n- Update progress bar to use global_steps for initial_steps\r\n- Ensures consistent step counting across all RL algorithms\r\n- Resolves confusion about max_steps parameter interpretation\r\n\r\n## Files Changed\r\n- `tunix/sft/peft_trainer.py` - Added global_steps property and updated progress bar\r\n- `tunix/sft/dpo/dpo_trainer.py` - Updated logging methods to use global_steps\r\n\r\n## Testing\r\n- All code verification tests pass\r\n- Backward compatibility maintained (global_steps = train_steps)\r\n- Demo notebook available in Colab\r\n\r\nResolves #390 \r\n\r\n**Colab 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"https://avatars.githubusercontent.com/u/148087983?u=5f2f878d51edd644dc1d7a6c6feca3e31197af5d&v=4" } }, { "id": "D_kwDOOTDVN84AjL-S", "number": 856, "title": "Interested in contributing — looking for guidance on where to begin", "body": "Hi! I'm interested in contributing to the Tunix library.\r\nI'm an intermediate Python user with a fair understanding of JAX and FLAX ,although I'm new to open-source, I'd love to start getting involved with the project.\r\n\r\nI'm looking for recommendations on good entry points where I can make meaningful contributions.I'm open to working on documentation, examples, small features, cleanup, or anything else that'd be helpful for the project.\r\n\r\nI'm looking forward to learning the workflow, collaborating with the team and contributing to the project's growth.\r\n\r\nThank you!", "created_at": "2025-12-08T12:58:12Z", "updated_at": "2025-12-09T21:15:47Z", "category": { "name": "General", "emoji": ":speech_balloon:" }, "answer": null, "user": { "login": "SarveshMahalingam", "avatar_url": "https://avatars.githubusercontent.com/u/90710042?v=4" } }, { "id": "D_kwDOOTDVN84Ai9pV", "number": 787, "title": "What difference between Tunix and EasyDeL?", "body": "It seems there is already a mature training framework \r\nhttps://github.com/erfanzar/EasyDeL", "created_at": "2025-11-21T18:42:23Z", "updated_at": "2025-11-21T18:42:25Z", "category": { "name": "General", "emoji": ":speech_balloon:" }, "answer": null, "user": { "login": "MoFHeka", "avatar_url": "https://avatars.githubusercontent.com/u/90189118?u=fc671f83739885a57a7b7c41201a6b1b546855d9&v=4" } }, { "id": "D_kwDOOTDVN84Aiysp", "number": 705, "title": "RFC: GPU Enablement for Tunix (Jax GPU)", "body": "Status: Draft for community feedback\r\nAuthors: Lance Wang\r\n\r\n1. Motivation\r\n\r\nTunix focuses on TPU-first training/inference for SFT and RL (DPO/PPO/GRPO/GSPO/...). Community users and contributors have expressed strong interest in running Tunix on GPUs (A100/H100, 4090/4080, L4, etc.). GPU support broadens accessibility, enables on‑prem/smaller‑scale runs, and increases contributor velocity.\r\n\r\n2. Goals & Non‑Goals\r\nGoals\r\n\r\nModularized design: clear boundary between GPU and TPU\r\n\r\nParity path: Run the full Tunix SFT + RL stack on single- and multi‑GPU via XLA:GPU (PJRT), starting from unit tests to the notebook/script examples: https://github.com/google/tunix/tree/main/examples\r\n\r\nPerformance-minded: Competitive throughput/latency using bf16/fp16, Flash‑Attention, and fused optimizers.\r\n\r\nSimple install: Docker and Conda instructions; reproducible envs for CUDA 12.x/13.x + cuDNN 9 or ROCm.\r\n\r\nCI coverage: Smoke tests on single‑GPU; nightly correctness on multi‑GPU via self-hosted runners.\r\n\r\nDocs: Clear “Getting Started on GPU” & troubleshooting.\r\n\r\nNon‑Goals\r\n\r\nNon‑JAX training backends (e.g., PyTorch) for core Tunix trainers.\r\n\r\nPerfect performance parity with TPU on day one.\r\n\r\n3. User Stories\r\n\r\nUS1 (Single‑GPU dev): A researcher with a 4090 runs SFT and minimal GRPO locally.\r\n\r\nUS2 (Multi‑GPU node): A lab with 8xH100 trains 1B–30B using FSDP+TP.\r\n\r\nUS3 (Multi‑host): A cluster with multiple H100 nodes runs distributed RL with NCCL over InfiniBand/RoCE.\r\n\r\nUS4 (Eval/Serve): Run RL learning and eval loops on GPU without TPU dependencies.\r\n\r\n4. Requirements\r\n\r\nR1: Support CUDA 12/13; cuDNN 9.x.\r\n\r\nR2: Support bf16 on Ampere/Hopper; fp16 fallback on consumer GPUs if needed.\r\n\r\nR3: PJRT runtime on XLA:GPU; NCCL collectives for dp/fsdp/tp.\r\n\r\nR4: Flash‑Attention path (jax-labs/pallas or vendor kernels) for attention-heavy models.\r\n\r\nR5: CI smoke tests on single GPU; perf sanity benchmarks; correctness parity on small models.", "created_at": "2025-11-08T23:14:03Z", "updated_at": "2025-11-11T00:31:06Z", "category": { "name": "Ideas", "emoji": ":bulb:" }, "answer": null, "user": { "login": "wang2yn84", "avatar_url": "https://avatars.githubusercontent.com/u/13134832?v=4" } }, { "id": "D_kwDOOTDVN84AiytZ", "number": 706, "title": "RFC: Multi-Controller JAX (McJAX) Support for Tunix", "body": "**Status:** Draft for community feedback\r\n**Authors:** Lance Wang @wang2yn84 \r\n\r\n---\r\n\r\n## 1. Motivation\r\n\r\nTunix currently leverages [**Pathways on Cloud**](https://docs.cloud.google.com/ai-hypercomputer/docs/workloads/pathways-on-cloud/pathways-intro) for multi-host training, which offers robust distributed runtime and TPU support. However, there are strong reasons to add **Multi-Controller JAX (McJAX)** as an alternative multi-host backend:\r\n\r\n1. **TRC Compatibility:** Google’s [**TRC (Tensor Research Cloud)**](https://sites.research.google/trc/about/) clusters do not support GKE/Pathways setups, but do support McJAX. \r\n2. **Broader Familiarity:** Many open-source and academic users already use McJAX for distributed runs (via SSH or Slurm launchers). \r\n3. **Open Availability:** Pathways is not yet fully open-sourced, , limiting reproducibility for non-Google users. \r\n\r\nAdding McJAX support enables Tunix to run seamlessly on **TPUs, GPUs, and CPUs** in **multi-host environments** without relying on GKE or proprietary infra.\r\n\r\n---\r\n\r\n## 2. Goals & Non-Goals\r\n\r\n### Goals\r\n- Provide **multi-host training** for Tunix using **McJAX**, supporting both **TPU** and **GPU** platforms.\r\n- Maintain **API compatibility** with existing Pathways code (resharding API).\r\n- Offer **launcher scripts** for SSH and Slurm clusters (e.g., TRC).\r\n- Support **fault-tolerant checkpointing** via Orbax for McJAX topologies.\r\n- Ensure **interoperability** — Pathways and McJAX can co-exist via environment flag.\r\n\r\n### Non-Goals\r\n- Re-implement Pathways runtime features such as autoscaling or elastic jobs. \r\n- Guarantee full feature parity across all hardware configurations at day one.\r\n\r\n---\r\n\r\n## 3. Background\r\n\r\n- **Pathways** provides a cloud-native single-controller runtime, integrating with GKE and TPU VM topologies. It’s widely used internally but has limited public availability.\r\n- **McJAX** is a simpler, open alternative for distributed JAX training across multiple controllers, communicating over **gRPC** or **RDMA** or **NCCL**.\r\n\r\n---\r\n\r\n## 4 Implementation Phases\r\n- **M0 | Identify the suitable library for distributed system handling\r\n- **M1 | McJAX env prototype on single host | Launch single controller successfully\r\n- **M2 | 2-host TPU v5e-8 | Verify distributed Jax works\r\n- **M3 | End-to-end SFT training\r\n- **M4 | Multi-host GRPO | Verify reward aggregation & gradients\r\n- **M5 | Orbax distributed checkpointing | Reliable recovery\r\n- **M6 | Public Docker + Docs | Release setup guide for TRC and Slurm\r\n", "created_at": "2025-11-08T23:41:47Z", "updated_at": "2025-11-08T23:41:48Z", "category": { "name": "Ideas", "emoji": ":bulb:" }, "answer": null, "user": { "login": "wang2yn84", "avatar_url": "https://avatars.githubusercontent.com/u/13134832?v=4" } }, { "id": "D_kwDOOTDVN84Ais14", "number": 659, "title": "Running GRPO training on Gemma 2 2B-IT", "body": "Hi guys!\r\n\r\nMy name is Ram Rachum, and I'm a research intern at CHAI, UCB. I'm working on research on RL explainability, and I want to use Tunix as case study.\r\n\r\nI'm trying to replicate your GPRO training of Gemma 2 2B-IT. I ran your `grpo_demo.ipynb` notebook on a v5litepod-4 machine on GCP. I had to stop it because it was filling the entire browser with output, and Jupyter isn't very good in handling that.\r\n\r\nQuestions:\r\n\r\n1. I understand that you ran this on `v5litepod-8`. How long did running the entire notebook take?\r\n2. Do you think this would run on `v5litepod-4`? I'm still waiting for GCP staff to allow me to use `v5litepod-8`. If you have any way to help with, or could connect me to a program that grants GCP compute credits, that would be helpful.\r\n\r\nI already ran a simplified version of `grpo_demo.ipynb` locally on my RTX 5080, but I could not get it to improve the GSM8K score of any LLM.", "created_at": "2025-11-01T21:39:06Z", "updated_at": "2025-11-05T10:28:12Z", "category": { "name": "Q&A", "emoji": ":pray:" }, "answer": { "id": "DC_kwDOOTDVN84A4u5J", "body": "The notebook should be finished in around 1 hour on `v5litepod-4`." }, "user": { "login": "cool-RR", "avatar_url": "https://avatars.githubusercontent.com/u/56778?u=3ec6c1c47cb6b10fc1c9371f8ff65ebf69d13672&v=4" } }, { "id": "D_kwDOOTDVN84AhSzE", "number": 197, "title": "Does tunix support multi-host training?", "body": "Does tunix support training on Cloud TPU slice like v5p-256?", "created_at": "2025-08-14T07:19:56Z", "updated_at": "2025-10-24T01:28:48Z", "category": { "name": "General", "emoji": ":speech_balloon:" }, "answer": null, "user": { "login": "weirayao", "avatar_url": "https://avatars.githubusercontent.com/u/22192355?u=1dac08d15b8370b87753343747d03f32ee7ec7c9&v=4" } } ], "details": { "id": 959501623, "node_id": "R_kgDOOTDVNw", "name": "tunix", "full_name": "google/tunix", "private": false, "owner": { "login": "google", "id": 1342004, "node_id": "MDEyOk9yZ2FuaXphdGlvbjEzNDIwMDQ=", "avatar_url": "https://avatars.githubusercontent.com/u/1342004?v=4", "gravatar_id": "", "url": "https://api.github.com/users/google", "type": "Organization", "user_view_type": "public", "site_admin": false }, "description": "A Lightweight LLM Post-Training Library", "fork": false, "url": "https://api.github.com/repos/google/tunix", "created_at": "2025-04-02T22:19:15Z", "updated_at": "2026-02-15T21:28:44Z", "pushed_at": "2026-02-17T10:58:30Z", "homepage": "", "size": 99707, "stargazers_count": 2155, "watchers_count": 2155, "language": "Python", "has_issues": true, "has_projects": true, "has_downloads": true, "has_wiki": false, "has_pages": false, "has_discussions": true, "forks_count": 239, "archived": false, "disabled": false, "open_issues_count": 148, "license": { "key": "apache-2.0", "name": "Apache License 2.0", "spdx_id": "Apache-2.0", "url": "https://api.github.com/licenses/apache-2.0", "node_id": "MDc6TGljZW5zZTI=" }, "allow_forking": true, "is_template": false, "web_commit_signoff_required": false, "has_pull_requests": true, "pull_request_creation_policy": "all", "topics": {}, "visibility": "public", "forks": 239, "open_issues": 148, "watchers": 2155, "default_branch": "main", "permissions": { "admin": false, "maintain": false, "push": false, "triage": false, "pull": true }, "temp_clone_token": "", "custom_properties": { "ai_agent_config_bypass": "false", "ghss_scan_exemption": { "0": "direct-push" }, "requires_action_scanning": "false", "requires_two_party_review": "false" }, "organization": { "login": "google", "id": 1342004, "node_id": "MDEyOk9yZ2FuaXphdGlvbjEzNDIwMDQ=", "avatar_url": "https://avatars.githubusercontent.com/u/1342004?v=4", "gravatar_id": "", "url": "https://api.github.com/users/google", "type": "Organization", "user_view_type": "public", "site_admin": false }, "network_count": 239, "subscribers_count": 19 }, "lastFetched": 1771350150862 }