Signed-off-by: Sahil Jain <sahilj@nvidia.com>

Validated in jobs 11363285 (bake) + 11363286 (inference):
greedy generation against DeepSeek-V4-Flash-Base produced coherent text
("The capital of France is" -> " Paris. The capital of Germany is
Berlin..."). The original `_load_w13: size of tensor a (2048) must
match tensor b (16) at dim 0` blocker is now cleared at the vLLM half;
Automodel half still pending teammate's branch.

## What changed

### pyproject.toml + uv.lock — torch 2.10 rollback

Larkz's vllm wheel `vllm-0.19.2rc1.dev219+gc0879d948.cu129-cp313-cp313`
ships METADATA `Requires-Dist: torch==2.11.0` but its `_C.abi3.so` was
compiled in a `torch==2.10.0` venv (per larkz/nemorl-ds4/pyproject.toml
pin) and references the OLD 4-arg `MessageLogger(char const*, int, int,
bool)` constructor that torch 2.11's libc10 dropped. First bake (job
11362567) failed at venv rebuild with `undefined symbol:
_ZN3c1013MessageLoggerC1EPKciib`.

Reverted in 5 sites:
- project.dependencies torch 2.11.0 -> 2.10.0
- project.dependencies torchvision 0.26.0 -> 0.25.0
- dependency-groups.build torch 2.11.0 -> 2.10.0
- tool.uv.override-dependencies torch / torchaudio -> 2.10.0
- tool.uv.override-dependencies + explicit torchvision==0.25.0

uv lock confirmed: torch / torchaudio / torchvision rolled to 2.10.0+cu129
/ 2.10.0+cu129 / 0.25.0+cu129; nccl 2.28.9 -> 2.27.5 transitively.

### tools/patch_vllm_dsv4_base_fp8_quick.sh — post-#40860 anchors

Anchor #7 rewritten: upstream's rewritten `load_weights` now captures
the result before calling `self.model.finalize_mega_moe_weights()`, so
"return loader.load_weights(...)" no longer matches; new anchor
"loaded_params = loader.load_weights(...)".

Added 8th anchor: forces `use_mega_moe = False` when
`VLLM_DSV4_BASE_FP8=1`. Without this, post-load
`finalize_mega_moe_weights()` would invoke `experts.finalize_weights()`
on a non-MegaMoE FusedMoE layer (Base routes through `Fp8MoEMethod`,
not `DeepseekV4MegaMoEExperts`). Self-contained env gating means
recipes don't also need to override `moe_backend`.

Verified against the larkz wheel: 9 anchors apply cleanly,
`py_compile` clean.

### env_refresh_dsv4.sh — bake the Base patch

Now applies two patches over the freshly-built worker venv before
`--container-save`:
1. `tools/vllm_deepseek_v4_config_patch.py` (defensive superset of
   upstream main's narrow rope kwargs fix)
2. `tools/patch_vllm_dsv4_base_fp8_quick.sh` (env-gated Base FP8
   support; idempotent, no-op when `VLLM_DSV4_BASE_FP8` unset)

### tools/test_vllm_dsv4_base_inference.py (new)

Counterpart to test_vllm_dsv4_inference.py for Flash. Sets
`VLLM_DSV4_BASE_FP8=1` before vllm import, re-applies the patch script
defensively (idempotent), runs greedy generation through standalone
`vllm.LLM`. Ran clean against the new sqsh.

### slurm_jobs/dsv4_base_{bake,inference_test}.sub (new)

Submit scripts for the chained bake + inference flow:
- bake: 1 node 8 GPU 3:55 walltime, --container-save
- inference: 1 node 8 GPU 1:00 walltime, --dependency=afterok

### docs/model-readiness/deepseek-v4* (new + updated)

Tracks the full DSV4 bring-up. Bring-up status doc now reflects:
- vLLM half of Base blocker CLEARED
- Torch ABI rollback chronicled
- New sqsh artifact path
  (.../nemo-rl-dsv4-vllm-c0879d-base-torch210-2026-04-27.sqsh ~99.8 GiB)
- Inference job 11363286 success, generated text logged
- Old broken sqsh marked for forensics

## What still blocks

Automodel-side Base FP8-block-quant state_dict_adapter is pending the
teammate's new branch. Once that lands, sqsh rebake + GRPO smoke can
follow. The vLLM-side recipe override `load_format: auto` may now be
removable since the new wheel includes the upstream dummy-load fix
(`finalize_weights()` 3 hits in main's deepseek_v4.py); validation
deferred until first GRPO smoke attempt.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Signed-off-by: Shuang Yu <shuangy@nvidia.com>

Re-applies the in-scope subset of reverted ef7bfb4 (kept the
docs/scripts out of git per user request).

pyproject.toml + uv.lock — torch 2.11 -> 2.10:
larkz's vllm wheel (vllm-0.19.2rc1.dev219+gc0879d948) ships METADATA
"Requires-Dist: torch==2.11.0" but its _C.abi3.so was compiled in a
torch==2.10 venv (per larkz/nemorl-ds4/pyproject.toml pin) and references
the OLD 4-arg MessageLogger(char const*, int, int, bool) constructor that
torch 2.11's libc10 dropped.  Bake job 11362567 failed at venv rebuild
with `undefined symbol _ZN3c1013MessageLoggerC1EPKciib`.  Reverted in
5 sites (project deps, build group, override-deps).  uv lock confirms
torch / torchaudio / torchvision rolled to 2.10.0+cu129 / 2.10.0+cu129
/ 0.25.0+cu129; nccl 2.28.9 -> 2.27.5 transitively.  Bake job 11363285
+ inference job 11363286 verified the fix end-to-end.

tools/patch_vllm_dsv4_base_fp8_quick.sh — post-#40860 anchors:
- Anchor #7 rewritten: upstream's rewritten load_weights now captures
  the result before calling self.model.finalize_mega_moe_weights(), so
  "return loader.load_weights(...)" no longer matches; new anchor
  "loaded_params = loader.load_weights(...)".
- Added 8th anchor: forces use_mega_moe = False when
  VLLM_DSV4_BASE_FP8=1.  Without this, post-load
  finalize_mega_moe_weights() would invoke experts.finalize_weights()
  on a non-MegaMoE FusedMoE layer (Base routes through Fp8MoEMethod,
  not DeepseekV4MegaMoEExperts).  Self-contained env gating means
  recipes don't also need to override moe_backend.
Verified: 9 anchors apply cleanly against the larkz wheel,
py_compile clean, end-to-end inference passed in job 11363286.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Signed-off-by: Shuang Yu <shuangy@nvidia.com>

…ifecycle

Remove three actor-level wrappers (finish_generation, get_logger_metrics,
clear_logger_metrics) that had zero external callers. The actor's internal
code already calls self.policy_generation.{...} directly at the right
points inside rollout_to_tq; the wrappers added indirection without value.

Rewrite the rollout_to_tq docstring to list all six steps bundled into
the single Ray RPC (reset metrics -> rollout -> flatten -> TQ put ->
release GPU -> capture metrics), making the lifecycle visible without
having to read the method body.

Per yuki-97 PR review (#7, #8).

Signed-off-by: Zhiyu Li <zhiyul@NVIDIA.com>

…metadata

* ``_get_local_node_ip`` now rejects both link-local AND loopback
  addresses. A host whose ``/etc/hosts`` maps the hostname to
  127.0.0.1 would otherwise announce an unroutable address to
  Mooncake peers, causing cross-node ``connection refused``.
  Adds a regression test (``test_local_node_ip_skips_loopback``)
  and updates the module docstring (previously noted loopback was
  NOT skipped).

* ``_patch_tq_actor_runtime_env`` no longer hard-codes the
  ``TransferQueue`` git+SHA pin. It now reads it from nemo-rl's
  installed metadata via ``importlib.metadata.requires("nemo-rl")``
  (new ``_resolve_tq_pin`` helper). pyproject.toml is the single
  source of truth; the two pins cannot drift. Adds a TODO to drop
  the whole patch once the nightly container is published with TQ
  baked in (at that point the injection becomes pure overhead).

* ``_tq()`` import-error hint no longer references a stale ``0.1.6``
  version — points users to pyproject.toml instead.

Per terryk PR review (#7, #8).

Signed-off-by: Zhiyu Li <zhiyul@NVIDIA.com>

…ifecycle

Remove three actor-level wrappers (finish_generation, get_logger_metrics,
clear_logger_metrics) that had zero external callers. The actor's internal
code already calls self.policy_generation.{...} directly at the right
points inside rollout_to_tq; the wrappers added indirection without value.

Rewrite the rollout_to_tq docstring to list all six steps bundled into
the single Ray RPC (reset metrics -> rollout -> flatten -> TQ put ->
release GPU -> capture metrics), making the lifecycle visible without
having to read the method body.

Per yuki-97 PR review (#7, #8).

Signed-off-by: Zhiyu Li <zhiyul@NVIDIA.com>

…metadata

* ``_get_local_node_ip`` now rejects both link-local AND loopback
  addresses. A host whose ``/etc/hosts`` maps the hostname to
  127.0.0.1 would otherwise announce an unroutable address to
  Mooncake peers, causing cross-node ``connection refused``.
  Adds a regression test (``test_local_node_ip_skips_loopback``)
  and updates the module docstring (previously noted loopback was
  NOT skipped).

* ``_patch_tq_actor_runtime_env`` no longer hard-codes the
  ``TransferQueue`` git+SHA pin. It now reads it from nemo-rl's
  installed metadata via ``importlib.metadata.requires("nemo-rl")``
  (new ``_resolve_tq_pin`` helper). pyproject.toml is the single
  source of truth; the two pins cannot drift. Adds a TODO to drop
  the whole patch once the nightly container is published with TQ
  baked in (at that point the injection becomes pure overhead).

* ``_tq()`` import-error hint no longer references a stale ``0.1.6``
  version — points users to pyproject.toml instead.

Per terryk PR review (#7, #8).

Signed-off-by: Zhiyu Li <zhiyul@NVIDIA.com>

…ifecycle

Remove three actor-level wrappers (finish_generation, get_logger_metrics,
clear_logger_metrics) that had zero external callers. The actor's internal
code already calls self.policy_generation.{...} directly at the right
points inside rollout_to_tq; the wrappers added indirection without value.

Rewrite the rollout_to_tq docstring to list all six steps bundled into
the single Ray RPC (reset metrics -> rollout -> flatten -> TQ put ->
release GPU -> capture metrics), making the lifecycle visible without
having to read the method body.

Per yuki-97 PR review (#7, #8).

Signed-off-by: Zhiyu Li <zhiyul@NVIDIA.com>

…metadata

* ``_get_local_node_ip`` now rejects both link-local AND loopback
  addresses. A host whose ``/etc/hosts`` maps the hostname to
  127.0.0.1 would otherwise announce an unroutable address to
  Mooncake peers, causing cross-node ``connection refused``.
  Adds a regression test (``test_local_node_ip_skips_loopback``)
  and updates the module docstring (previously noted loopback was
  NOT skipped).

* ``_patch_tq_actor_runtime_env`` no longer hard-codes the
  ``TransferQueue`` git+SHA pin. It now reads it from nemo-rl's
  installed metadata via ``importlib.metadata.requires("nemo-rl")``
  (new ``_resolve_tq_pin`` helper). pyproject.toml is the single
  source of truth; the two pins cannot drift. Adds a TODO to drop
  the whole patch once the nightly container is published with TQ
  baked in (at that point the injection becomes pure overhead).

* ``_tq()`` import-error hint no longer references a stale ``0.1.6``
  version — points users to pyproject.toml instead.

Per terryk PR review (#7, #8).

Signed-off-by: Zhiyu Li <zhiyul@NVIDIA.com>

Comments addressed: #3, #5, NVIDIA-NeMo#7, NVIDIA-NeMo#8, NVIDIA-NeMo#9, NVIDIA-NeMo#10, NVIDIA-NeMo#11.

- Rename _load_M -> _get_sparse_projection_matrix and
  _load_dense_projection -> _get_topk_projection (later removed in
  favor of module-level cache helpers below).
- Drop unused alignment_student_spans / alignment_teacher_spans
  from the cross-tokenizer batch payload.
- Remove NRL_XTOKEN_LOSS_DUMP_DIR debug-dump side effect.
- Move Fp32SparseMM, chunk_average_log_probs, valid_chunk_mask to a
  new shared module nemo_rl/algorithms/x_token/utils.py.
- Extract projection-file parsing into utils.parse_projection_file;
  tokenalign.py and loss_functions.py both go through it.
- Move per-instance projection-matrix caches to process-local caches
  in utils.get_sparse_projection_matrix / get_topk_projection. The
  driver no longer holds large CUDA tensors; each Ray worker fills
  its own cache on first loss call.

Signed-off-by: Adithya Hanasoge <avenkateshha@nvidia.com>

…ifecycle

Remove three actor-level wrappers (finish_generation, get_logger_metrics,
clear_logger_metrics) that had zero external callers. The actor's internal
code already calls self.policy_generation.{...} directly at the right
points inside rollout_to_tq; the wrappers added indirection without value.

Rewrite the rollout_to_tq docstring to list all six steps bundled into
the single Ray RPC (reset metrics -> rollout -> flatten -> TQ put ->
release GPU -> capture metrics), making the lifecycle visible without
having to read the method body.

Per yuki-97 PR review (#7, #8).

Signed-off-by: Zhiyu Li <zhiyul@NVIDIA.com>

…metadata

* ``_get_local_node_ip`` now rejects both link-local AND loopback
  addresses. A host whose ``/etc/hosts`` maps the hostname to
  127.0.0.1 would otherwise announce an unroutable address to
  Mooncake peers, causing cross-node ``connection refused``.
  Adds a regression test (``test_local_node_ip_skips_loopback``)
  and updates the module docstring (previously noted loopback was
  NOT skipped).

* ``_patch_tq_actor_runtime_env`` no longer hard-codes the
  ``TransferQueue`` git+SHA pin. It now reads it from nemo-rl's
  installed metadata via ``importlib.metadata.requires("nemo-rl")``
  (new ``_resolve_tq_pin`` helper). pyproject.toml is the single
  source of truth; the two pins cannot drift. Adds a TODO to drop
  the whole patch once the nightly container is published with TQ
  baked in (at that point the injection becomes pure overhead).

* ``_tq()`` import-error hint no longer references a stale ``0.1.6``
  version — points users to pyproject.toml instead.

Per terryk PR review (#7, #8).

Signed-off-by: Zhiyu Li <zhiyul@NVIDIA.com>

…ifecycle

Remove three actor-level wrappers (finish_generation, get_logger_metrics,
clear_logger_metrics) that had zero external callers. The actor's internal
code already calls self.policy_generation.{...} directly at the right
points inside rollout_to_tq; the wrappers added indirection without value.

Rewrite the rollout_to_tq docstring to list all six steps bundled into
the single Ray RPC (reset metrics -> rollout -> flatten -> TQ put ->
release GPU -> capture metrics), making the lifecycle visible without
having to read the method body.

Per yuki-97 PR review (#7, #8).

Signed-off-by: Zhiyu Li <zhiyul@NVIDIA.com>

…metadata

* ``_get_local_node_ip`` now rejects both link-local AND loopback
  addresses. A host whose ``/etc/hosts`` maps the hostname to
  127.0.0.1 would otherwise announce an unroutable address to
  Mooncake peers, causing cross-node ``connection refused``.
  Adds a regression test (``test_local_node_ip_skips_loopback``)
  and updates the module docstring (previously noted loopback was
  NOT skipped).

* ``_patch_tq_actor_runtime_env`` no longer hard-codes the
  ``TransferQueue`` git+SHA pin. It now reads it from nemo-rl's
  installed metadata via ``importlib.metadata.requires("nemo-rl")``
  (new ``_resolve_tq_pin`` helper). pyproject.toml is the single
  source of truth; the two pins cannot drift. Adds a TODO to drop
  the whole patch once the nightly container is published with TQ
  baked in (at that point the injection becomes pure overhead).

* ``_tq()`` import-error hint no longer references a stale ``0.1.6``
  version — points users to pyproject.toml instead.

Per terryk PR review (#7, #8).

Signed-off-by: Zhiyu Li <zhiyul@NVIDIA.com>

Comments addressed: #3, #5, NVIDIA-NeMo#7, NVIDIA-NeMo#8, NVIDIA-NeMo#9, NVIDIA-NeMo#10, NVIDIA-NeMo#11.

- Rename _load_M -> _get_sparse_projection_matrix and
  _load_dense_projection -> _get_topk_projection (later removed in
  favor of module-level cache helpers below).
- Drop unused alignment_student_spans / alignment_teacher_spans
  from the cross-tokenizer batch payload.
- Remove NRL_XTOKEN_LOSS_DUMP_DIR debug-dump side effect.
- Move Fp32SparseMM, chunk_average_log_probs, valid_chunk_mask to a
  new shared module nemo_rl/algorithms/x_token/utils.py.
- Extract projection-file parsing into utils.parse_projection_file;
  tokenalign.py and loss_functions.py both go through it.
- Move per-instance projection-matrix caches to process-local caches
  in utils.get_sparse_projection_matrix / get_topk_projection. The
  driver no longer holds large CUDA tensors; each Ray worker fills
  its own cache on first loss call.

Signed-off-by: Adithya Hanasoge <avenkateshha@nvidia.com>

Comments addressed: #3, #5, #7, #8, #9, #10, #11.

- Rename _load_M -> _get_sparse_projection_matrix and
  _load_dense_projection -> _get_topk_projection (later removed in
  favor of module-level cache helpers below).
- Drop unused alignment_student_spans / alignment_teacher_spans
  from the cross-tokenizer batch payload.
- Remove NRL_XTOKEN_LOSS_DUMP_DIR debug-dump side effect.
- Move Fp32SparseMM, chunk_average_log_probs, valid_chunk_mask to a
  new shared module nemo_rl/algorithms/x_token/utils.py.
- Extract projection-file parsing into utils.parse_projection_file;
  tokenalign.py and loss_functions.py both go through it.
- Move per-instance projection-matrix caches to process-local caches
  in utils.get_sparse_projection_matrix / get_topk_projection. The
  driver no longer holds large CUDA tensors; each Ray worker fills
  its own cache on first loss call.

Signed-off-by: Adithya Hanasoge <avenkateshha@nvidia.com>

Comments addressed: #3, #5, #7, #8, #9, #10, #11.

- Rename _load_M -> _get_sparse_projection_matrix and
  _load_dense_projection -> _get_topk_projection (later removed in
  favor of module-level cache helpers below).
- Drop unused alignment_student_spans / alignment_teacher_spans
  from the cross-tokenizer batch payload.
- Remove NRL_XTOKEN_LOSS_DUMP_DIR debug-dump side effect.
- Move Fp32SparseMM, chunk_average_log_probs, valid_chunk_mask to a
  new shared module nemo_rl/algorithms/x_token/utils.py.
- Extract projection-file parsing into utils.parse_projection_file;
  tokenalign.py and loss_functions.py both go through it.
- Move per-instance projection-matrix caches to process-local caches
  in utils.get_sparse_projection_matrix / get_topk_projection. The
  driver no longer holds large CUDA tensors; each Ray worker fills
  its own cache on first loss call.

Signed-off-by: Adithya Hanasoge <avenkateshha@nvidia.com>