ci: Update transformers to latest version 5.5.3#1789
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svcnvidia-nemo-ci wants to merge 24 commits intomainfrom
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ci: Update transformers to latest version 5.5.3#1789svcnvidia-nemo-ci wants to merge 24 commits intomainfrom
svcnvidia-nemo-ci wants to merge 24 commits intomainfrom
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feat: integrate NeMo-Run launcher for managed job submission (#1668) * feat: integrate NeMo-Run launcher for managed job submission Rewrite the NeMo-Run launcher to use nemo_run.Script with inline torchrun commands instead of run.Partial, enabling remote execution via any NeMo-Run executor (Slurm, Kubernetes, local, etc.). Named executors are loaded from $NEMORUN_HOME/executors.py and YAML overrides (nodes, devices, container_image, time, mounts, env_vars) are applied on top. nemo-run is an optional dependency — all imports are guarded behind try/except so the package is never required at import time. * refactor: remove standalone nemorun example, add conversion guide in docs Replace the separate example YAML with an inline guide showing how to convert any existing config to use NeMo-Run by adding a nemo_run: block. * fix: remove secret-like test values flagged by CI scanner * docs: update container image to nvcr.io/nvidia/nemo-automodel:26.02 * feat: native Torchrun launcher, PatternPackager, and generic executor overrides - Use NeMo-Run's native Torchrun launcher instead of hand-rolled inline torchrun scripts. NeMo-Run now manages rendezvous, node ranks, and nproc-per-node automatically. - Ship the training config via PatternPackager so it is extracted to /nemo_run/code/ and available inside the container. - Replace hardcoded override fields (nodes, devices, partition, etc.) with a generic overrides dict — any YAML key not recognised as a launcher setting is applied to the executor via setattr. Dicts are merged, lists are extended, scalars are replaced. Verified end-to-end: Qwen3-MoE-30B fine-tuning completed successfully with W&B logging on a remote Slurm cluster. * fix: use executor.nproc_per_node() for generic device count Use the base Executor.nproc_per_node() method instead of checking executor-specific attribute names. This works across all NeMo-Run backends (Slurm, Kubernetes, Docker, local). * fix: narrow exception catch to NotImplementedError, fix docs to use native executor attrs - Catch only NotImplementedError in nproc_per_node() fallback so real configuration errors propagate instead of silently defaulting to 1. - Remove misleading 'devices' field from docs; all overrides use native executor attribute names (ntasks_per_node, gpus_per_node, etc.) via generic setattr. * fix: use container_mounts instead of mounts in docs example The override key must match the executor's actual attribute name since apply_overrides uses setattr directly. * fix: CI failures — unused import, spec None guard, toctree entry - Remove unused `tempfile` import (linting failure). - Guard against `spec_from_file_location` returning None (type check failure and review comment). - Add nemo-run.md to docs/index.md toctree (docs build failure). * fix: also catch AttributeError in nproc_per_node fallback Handle executors that don't define nproc_per_node() at all (e.g. third-party or future backends). * style: apply ruff-format to config.py and utils.py --------- Signed-off-by: Hemil Desai <hemild@nvidia.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Hemil Desai <hemild@nvidia.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
fix: mute warning spam (#1721) * structure plan logging * mute pydantic warnings * mute pydantic warnings and rank > 0 if started with torchrun * mute pydantic warnings and rank > 0 if started with torchrun * mute warnings * Update parallelizer.py --------- Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Alexandros Koumparoulis <153118171+akoumpa@users.noreply.github.com>
fix: handle dict-typed chat_template in format_chat_template (#1696) Some tokenizers (e.g. Cohere Command R) store multiple named chat templates as a dict instead of a single string. The regex checks on tokenizer.chat_template assumed a string, causing TypeError. Resolve the template string from the dict (using "default" key) before performing regex searches. Signed-off-by: adil-a <adil.asif2000@hotmail.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
fix: handle dict-typed chat_template in format_chat_template (#1696) Some tokenizers (e.g. Cohere Command R) store multiple named chat templates as a dict instead of a single string. The regex checks on tokenizer.chat_template assumed a string, causing TypeError. Resolve the template string from the dict (using "default" key) before performing regex searches. Signed-off-by: adil-a <adil.asif2000@hotmail.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Adil <47084919+adil-a@users.noreply.github.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
feat: adding lora to diffusion (#1653) * update * update * update * Fix tests and linting * fmt * use am peft * delete lora component and use _peft component instead --------- Signed-off-by: Pranav Prashant Thombre <pthombre@nvidia.com> Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: linnan wang <linnanw@nvidia.com> Co-authored-by: Pranav Prashant Thombre <pthombre@nvidia.com> Co-authored-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> Co-authored-by: Alexandros Koumparoulis <153118171+akoumpa@users.noreply.github.com>
…1707) feat: MoE model benchmarks, LoRA configs, and flops calculators (#1676) * feat: add benchmark configs for top MoE models and fix VL model support Add TE+DeepEP benchmark configs for GLM-4.7-Flash, GLM-4.7, GLM-5, MiniMax-M2.5, Mistral Small 4, Qwen3.5 MoE, Qwen3 VL 235B, and Step3.5-Flash. Fix VL composite config handling in benchmark recipe, flops utils, and pipeline sharding to fall back to text_config for models where vocab_size, hidden_size, and num_hidden_layers are nested under text_config (e.g. Qwen3.5 MoE, Qwen3 VL 235B). Add support for custom config classes (e.g. DeepseekV32Config) in benchmark vocab_size inference by respecting the _target_ field. * feat: add LoRA benchmark configs for GLM-4.7-Flash and Qwen3.5 MoE Add TE+DeepEP LoRA benchmark configs with moe_rank_scaling enabled for GLM-4.7-Flash (30B) and Qwen3.5 MoE (35B-A3B). * feat: add renamed LoRA benchmark configs for MoE models * feat: add LoRA configs for Qwen3.5 MoE and GLM-4.7-Flash Rename te_deepep_lora configs to _lora for consistency. * perf: tune gptoss-120b benchmark config for hybridep with TE attention Switch to TE attention backend, hybridep dispatcher with 64 SMs, torch_mm experts, disable activation checkpointing & reshard_after_forward, and reduce local batch size to 1 for better memory/perf profile. * fix: improve Kimi VL model robustness and MoE parallelizer defaults - Fix Kimi K2.5 VL model to handle missing vision_config attributes by falling back to vt_* prefixed and mm_hidden_size attributes - Enable reshard_after_forward for GLM-4.7-Flash LoRA config - Use bf16 reduce_dtype in MoE parallelizer FSDP mixed precision policy * ci: update secrets baseline for nemotron_super_v3_lora.yaml false positive The HuggingFace model ID nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 triggers the Base64 High Entropy String detector as a false positive. * Revert "fix: improve Kimi VL model robustness and MoE parallelizer defaults" This reverts commit a185e34. * test: add unit tests for new FLOPs calculators and update nemotronh values Add 38 new tests covering: - minimax_m2_flops (basic, gbs scaling, MTP, precomputed) - qwen3_5_flops MoE and Dense variants (GDN/full attention hybrid) - mla_moe_flops (Kimi K2, GLM-5, Mistral Small 4) - step3_5_flash_flops (hybrid full/SWA + MoE) - deepseekv3_flops DSA sparse attention extension - _mamba_layer_flops refactored formula - _hybrid_model_flops conditional accumulation - VL composite config text_config fallback in qwen3_flops and qwen3_5_flops - get_flops_formula_for_hf_config dispatch for new model types Update nemotronh precomputed values to match refactored Mamba scan formula. * docs: add copyright headers, recipe declaration, and run instructions to benchmark configs Add Apache 2.0 copyright header, recipe: BenchmarkingRecipeForNextTokenPrediction declaration, and automodel run instructions to all 21 new benchmark configs. * ci: update secrets baseline line number after copyright header addition * fix: address claude-bot review — rsplit bug, duplicate trust_remote_code, tests - Fix _infer_vocab_size string _target_ import: use rsplit to correctly extract module_path and class_name instead of discarding class name - Remove duplicate trust_remote_code under config: section in 5 YAML configs (step_3.5_flash, qwen3.5_moe, qwen3.5_moe_te_deepep, qwen3.5_moe_lora, step35flash_lora) - Add unit tests for string _target_ and VL text_config fallback paths in _infer_vocab_size * fix: address round-2 claude-bot review comments - Use from_pretrained() instead of direct constructor call for callable config targets in _infer_vocab_size - Guard mla_moe_flops VL text_config fallback with num_hidden_layers check, consistent with qwen3_flops and qwen3_5_flops patterns * test: add VL composite config fallback test for _precompute_stage_shapes Verify that VL configs without hidden_size at root level correctly fall back to text_config for pipeline stage shape precomputation. * fix: respect moe_layer_freq as frequency in _build_moe_layer_pattern When moe_layer_freq is an integer, use it as a frequency (every Nth layer is MoE) rather than marking all post-dense layers as MoE. * Update nemo_automodel/recipes/llm/benchmark.py * fix: restore _infer_vocab_size callable/string config_target branches The previous commit accidentally broke _infer_vocab_size by removing the callable config_target branch and introducing duplicate code with bad indentation, causing a SyntaxError. Restore the correct if/elif structure. --------- Signed-off-by: hemildesai <hemild@nvidia.com> Signed-off-by: HuiyingLi <willwin.lee@gmail.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Hemil Desai <hemild@nvidia.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> Co-authored-by: HuiyingLi <willwin.lee@gmail.com> Co-authored-by: Alexandros Koumparoulis <153118171+akoumpa@users.noreply.github.com> Co-authored-by: claude[bot] <209825114+claude[bot]@users.noreply.github.com>
fix: fixing the pooling error for non-llama models for biencoder training (#1645) * fix pooling errow for non-llama models * small fix in the biencoder yaml * set add_eos_token to false Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: rnyak <16246900+rnyak@users.noreply.github.com>
ci: Address timeout is ci tests (#1733) Address timeout is ci tests Signed-off-by: Dong Hyuk Chang <9426164+thomasdhc@users.noreply.github.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Dong Hyuk Chang <9426164+thomasdhc@users.noreply.github.com>
…ess_group() (1730)` into `r0.4.0` (#1739) test: Checkpoint robustness skips atexit-registered destroy_process_group() (#1730) * Explicity tear down in checkpoint robustness * Skip atexit-registered destory_process_group --------- Signed-off-by: Dong Hyuk Chang <9426164+thomasdhc@users.noreply.github.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Dong Hyuk Chang <9426164+thomasdhc@users.noreply.github.com>
fix: Qwen3.5 dense CP support and FSDP mixed-dtype fix (#1710) * feat: add neat packing (greedy knapsack) for LLM and VLM datasets Implement sequence packing via min-heap first-fit-decreasing knapsack for both LLM and VLM datasets, with indexed attention masks and flash attention support. Includes unit tests and benchmarks. * feat: add LengthGroupedSampler for token-aware distributed sampling Sort samples by estimated token length (text + media) and shuffle within buckets to keep batch-internal lengths similar, reducing padding waste. Includes accurate image/video token count estimation via smart_resize and comprehensive test suite. * feat: integrate neat packing strategy into LLM finetune recipe Add packing_strategy config field ("neat" or "thd") to select between greedy knapsack packing and existing THD packing in the LLM recipe. * chore: remove benchmark scripts not needed for this PR * fix: lint errors and broken sampler tests Remove unused import and variable in neat_packing_vlm.py. Fix 13 sampler tests that referenced non-existent bucket_size and shuffle_bucket_size parameters. * style: fix all ruff lint errors across changed files Sort imports, remove unused imports/variables, fix f-strings without placeholders, rename ambiguous variable name. * style: run ruff format on all changed source and test files * style: add missing copyright headers to test files * feat: add meta-dataset loading system with ShareGPT format support Implement LLaMA-Factory style meta JSON dataset loading with support for multiple dataset composition, sampling ratios, ShareGPT format conversion, LMDB image storage, video frame reading via decord, media preloading, and cross-rank data sharding. * feat: add RobustDatasetWrapper with retry and fake image injection RobustDatasetWrapper provides data loading error retry, media preloading, and fake image injection to prevent FSDP/Zero3 hangs on pure-text batches. PreTokenizedDatasetWrapper supports per-sample tokenization in DataLoader workers with overlong sample detection. * enhance: refactor label building with template-based approach Replace BPE context-sensitive pattern matching with token ID-level scanning (build_labels_from_template) for reliable assistant turn detection. Remove qwen2_5 dependency on qwen_vl_utils. Add per-sample media counts (n_images_per_sample/n_videos_per_sample) to collate output for precise PP chunking. Replace truncation with pre-filtering via _drop_overlong_samples. Use decord as video backend globally. * refactor: simplify video timestamp handling with VideoMetadata Replace the manual _fix_video_timestamps regex approach with _build_video_metadata that passes metadata directly to the processor. Also adds second_per_grid_ts to output keys. * feat: add precompute_tokens script for offline tokenization Offline parallel tokenization tool that writes _text_tokens counts to dataset samples, enabling LengthGroupedSampler to use exact token counts instead of heuristic estimation. * feat: wire up configure_packing and attn-aware collaters for neat packing Wire up configure_packing and attn-aware collaters into both LLM and VLM recipes so neat packing correctly enforces per-document attention boundaries with flash_attention_2 and SDPA. Changes: - neat_packed_collater: accept attn_implementation param, keep 2D indexed mask for flash, 4D bool block-causal mask for SDPA - configure_packing: patch create_causal_mask in qwen2/qwen2_5_vl/qwen2_vl/ qwen3_vl/qwen3_vl_moe modules via importlib loop - LLM recipe: call configure_packing when packing_strategy=neat, detect attn backend from cfg_model (backend.attn or attn_implementation) - VLM recipe: add pretokenize + packing path to build_dataloader with cfg_model param, same attn detection logic - Add 3 example recipes: LLM neat packing, VLM 4B neat packing, VLM MoE 30B neat packing Tested: - VLM Qwen3-VL-4B flash: 4.19 -> 1.47 -> 0.49 - VLM Qwen3-VL-4B sdpa: 4.19 -> 1.47 -> 0.49 - VLM Qwen3-VL-30B MoE flash: 1.76 -> 0.41 -> 0.10 - LLM Qwen2.5-0.5B flash+force_hf: 3.72 -> ... -> 2.84 * refactor: move VLM packing config to top-level packed_sequence section Move packing configuration from nested dataset.packing to a top-level packed_sequence: section, matching the LLM recipe pattern. This decouples dataset definition from packing strategy. The VLM recipe's build_dataloader now accepts cfg_ps and reads packing config from there first, falling back to legacy dataset.packing for backward compatibility. Additional fixes from merge: - Fix stale build_labels() call in collate_fns.py (merge artifact) - Revert phi4/kimi collate to use build_labels (not in _IMSTART allowlist) - Comment out decord2 monkey-patch (user removed it for torchcodec testing) - Add TODO on _PACKING_PATCH_MODULES about generality * Switch VLM neat packing example to MedPix-VQA dataset with 8k seqlen Use HF dataset (mmoukouba/MedPix-VQA) instead of local mockdata to demonstrate packed_sequence working with standard HF datasets. Increase pack_size/max_length to 8192 for real image samples. * refactor: deduplicate robust_collate into make_robust_collate Extract the duplicated collate retry logic from PreTokenizedDatasetWrapper and RobustDatasetWrapper into a shared make_robust_collate() function in collate_fns.py. Both classes now delegate to it. * refactor: move media I/O helpers from datasets.py to utils.py Move _resolve_lmdb_image, _read_video_frames, _preload_media, and _build_video_metadata to vlm/utils.py. These are generic media utilities not tied to any specific dataset. * cleanup: move random import to module level, allow pretokenize without packing - Move `import random` from inside make_robust_collate to module-level import in collate_fns.py - Read pretokenize/max_length from cfg_ps regardless of pack_size, enabling pretokenize-only mode without packing * cleanup: remove verbose comments from packing recipe yamls * feat: add Qwen3.5-4B VLM neat packing recipe Tested with 8 GPUs, 8k pack_size, MedPix-VQA dataset. Requires transformers >= 5.3.0 for Qwen3.5 support. * fix: add qwen3_5 to packing patch modules and fix missing import - Add transformers.models.qwen3_5.modeling_qwen3_5 to packing patch list so create_causal_mask is patched for Qwen3.5 dense models - Fix _passthrough_create_causal_mask signature to accept both input_embeds and inputs_embeds (HF 5.3.0 uses inputs_embeds) - Import _lmdb_env_cache from utils.py in datasets.py (missed in earlier media helpers refactor) * remove LLM recipe from VLM data pipeline PR This LLM recipe doesn't belong in the VLM packing PR. * fix: update test imports after media helpers move to utils.py Update test_datasets.py to import _read_video_frames and _preload_media from vlm/utils.py instead of vlm/datasets.py. * test: add unit tests for packing, utils, and collate changes New test files: - test_utils.py: _resolve_lmdb_image (cache, missing key, RGB), _build_video_metadata (empty, no video, preserved fields) - test_packing.py: get_seqlens_in_batch, get_unpad_data, _passthrough_create_causal_mask (both HF signatures), get_attn_implementation (backend vs HF config), configure_packing (noop for sdpa, patches FA2 modules) Extended test_collate_fns.py: - make_robust_collate (success, retry, max_retries exhausted) - neat_packed_vlm_collater attn_implementation variants (2D mask for FA2, 4D for sdpa, fixed max_length, pixel_values concat) * fix: lint errors and missing copyright headers - ruff fix: remove unused imports (copy, BaseVideoProcessor, load_video, as_completed), unused variables (grid_idx, total_text_tokens, total_media_tokens), fix import ordering - Add copyright headers to scripts/precompute_tokens.py and tests/test_meta_dataset_all.py * style: ruff format on all changed files * fix: rename test_utils.py to avoid pytest collection conflict tests/unit_tests/datasets/test_utils.py already exists; having test_utils.py in the vlm/ subdirectory causes a module name collision. * fix: configure cfg_ds.get defaults in build_dataloader tests MagicMock().get() returns a truthy MagicMock by default, which incorrectly triggers the pretokenize path. Configure side_effect to return proper defaults for packing-related keys. * fix: make packing mask patch safe for non-packed forward passes _passthrough_create_causal_mask now checks whether the attention mask is actually a packed mask (4D or indexed with values > 1) before returning it as-is. For normal 2D masks (standard training), it delegates to the original HF create_causal_mask, preventing test pollution where the monkey-patch breaks non-packed Qwen2 tests. * fix: passthrough causal mask for FA2 to avoid breaking validation The previous logic delegated all non-packed 2D masks to HF's create_causal_mask, which produced a mask incompatible with flash_attention_2 during validation. FA2 handles causal masking internally, so always pass through. Delegation to HF is now limited to non-FA2 backends (sdpa/eager) where it is needed. * fix: address code review feedback from claude[bot] - Fix wrong import: _resolve_lmdb_image lives in utils.py not datasets.py - Assign unused sum() results to variables in dataset timing summary - Fix fake_indices bug: _drop_overlong_samples now returns kept indices so callers can filter examples in sync with conversations * fix: log actual processor type before falling back to default * fix: remove unused sum() variables flagged by ruff F841 * feat: add validation and max_steps to VLM packing recipes - Add validation_dataset (MedPix-VQA) and validation_dataloader to qwen3_vl_4b and qwen3_vl_moe_30b recipes - Add max_steps: 100 to both recipes - Switch MoE recipe from mockdata to MedPix-VQA with pack_size 8192 * fix: enable checkpoint with safetensors in qwen3_vl_4b recipe * feat: enable Qwen3.5 dense CP support and fix FSDP mixed-dtype wrapping PR #1631 added _restore_loaded_model_dtype which restores checkpoint dtypes after loading. For Qwen3.5 dense, this puts A_log and norm back to float32 while everything else is bfloat16, breaking FSDP2 which requires uniform dtype per group. Fix by adding Qwen3_5ParallelizationStrategy that: - Moves float32 bare params (A_log) into a _fp32_params submodule so fully_shard_by_dtype can wrap them in a separate FSDP group - When CP>1, swaps HF Qwen3_5GatedDeltaNet to CPAwareGatedDeltaNet (reusing the existing MoE CP implementation) and sets _cp_mesh - Adds a decoder-layer pre-hook to pass position_ids to linear_attn (HF decoder layers don't forward it, but CP needs it) Tested: CP=1 and CP=2 losses match (2.8484 vs 2.8484 step 0, 2.4787 vs 2.4766 step 1, val 2.9792 vs 2.9786). * style: ruff format + add unit tests for Qwen3.5 CP/FSDP patching * fix: address Claude review - test calls real patch_hf_model, clarify thread-safety comment - Rewrote test_fp32_params_moved_to_holder to call the real patch_hf_model function instead of replicating its logic, by monkeypatching the transformers module stubs so isinstance() matches _FakeGatedDeltaNet. - Clarified the thread-safety comment on the globals() swap in Qwen3_5ParallelizationStrategy.parallelize. * fix: address round-2 review - test_no_class_swap calls real patch_hf_model, add defensive assertion - Updated test_no_class_swap_when_cp_disabled to call patch_hf_model with stubs instead of trivially asserting the fake type. - Added defensive assertion that apply_fsdp2_sharding_recursively exists before the globals() swap in Qwen3_5ParallelizationStrategy. * test: add test_class_swap_when_cp_enabled for patch_hf_model cp_enabled=True path * test: add coverage for Qwen3_5ParallelizationStrategy.parallelize() Add tests for the parallelize() method covering: - patch_hf_model call and delegation to super() - globals swap and restore of apply_fsdp2_sharding_recursively - global restore on error (try/finally) - CP mesh assignment when cp_enabled=True - _fsdp_by_dtype ModuleList iteration with fully_shard_by_dtype --------- Signed-off-by: HuiyingLi <willwin.lee@gmail.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Huiying <willwin.lee@gmail.com> Co-authored-by: zhiqil <zhiqil@nvidia.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
feat: Add lora recipes for gemma4 (#1731) Add lora recipes for gemma4 Signed-off-by: Abhishree Thittenamane <athittenaman@cw-dfw-cs-001-login-02.cm.cluster> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Co-authored-by: Abhishree Thittenamane <athittenaman@cw-dfw-cs-001-login-02.cm.cluster>
test: add vLLM deployment tests for checkpoint robustness (#1656) * test: add vLLM deployment tests for checkpoint robustness vLLM deployment verification tests that load consolidated checkpoints and compare greedy output token-for-token against HuggingFace. Supports both full comparison and smoke test mode. Depends on checkpoint robustness PR #1606. * Create deploy-test dependency group * Revert deploy test group * Move configs to recipes and create vllm_launcher * Setup deploy environment * Remove duplicate keys * Add scope to vllm deploy test * Drop needs dependency * Use finetune test name for ckpt dir * Make ckpt checking more robust * Pass arguments correctly * Update arguments * Remove unused file --------- Signed-off-by: adil-a <adil.asif2000@hotmail.com> Signed-off-by: Dong Hyuk Chang <9426164+thomasdhc@users.noreply.github.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Adil <47084919+adil-a@users.noreply.github.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com> Co-authored-by: Dong Hyuk Chang <9426164+thomasdhc@users.noreply.github.com> Co-authored-by: Alexandros Koumparoulis <153118171+akoumpa@users.noreply.github.com>
…1729)` into `r0.4.0` (#1756) build: drop rc0 pre-release tag and add dynamic git versioning (#1729) * build: drop rc0 pre-release tag and add dynamic git versioning Append +<short-sha> to __version__ at import time using only the git binary. Falls back silently if git is unavailable or not in a repo. Set NO_VCS_VERSION=1 to opt out (e.g. for release builds). * ci: pin build-test-publish-wheel to FW-CI-templates@7a6fd6d Temporarily pins to the commit that sets NO_VCS_VERSION=1 in the build step, fixing the sdist/wheel version mismatch introduced by dynamic git versioning. Will be replaced with a version tag once FW-CI-templates NVIDIA-NeMo/FW-CI-templates#443 is released. * style: add blank line before if block (ruff E303) * fix: suppress I001 import-sorting false positive in package_info.py * ci: pin FW-CI-templates to v0.88.1 --------- Signed-off-by: oliver könig <okoenig@nvidia.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: oliver könig <okoenig@nvidia.com> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
…to `r0.4.0` (#1754) fix: Baichuan2 checkpoint robustness test CI failures (#1727) * fix: checkpoint robustness test CI failures - Add trust_remote_code: true to baichuan ci.checkpoint_robustness - Add hf_device_map_auto: true to nemotron nano configs - Bump robustness global_batch_size 16→32 for multi-node compatibility - Remove hardcoded trust_remote_code=False that broke tokenizer loading - Fix dotted keys in ci.checkpoint_robustness being silently ignored (e.g. distributed.tp_size, dataset.limit_dataset_samples) * fix: Baichuan2 checkpoint robustness test CI failures - Register MLP-only TP plan for BaichuanForCausalLM (NormHead is not nn.Linear, W_pack has non-interleaved QKV layout — both incompatible with ColwiseParallel) - Fix HF remote code meta-tensor issue: RotaryEmbedding creates inv_freq/cos_cached/sin_cached as plain attributes that stay on meta device; added _fix_meta_rotary_embeddings helper for Phase 4 - Set appropriate KL/loss thresholds for Baichuan2 with TP=2 * fix: Baichuan2 PEFT checkpoint robustness test CI failures - Apply _fix_meta_rotary_embeddings to PEFT base model loading path - Add KL/loss thresholds to baichuan_2_7b_squad_peft.yaml CI config * fix: remove unused cross-TP/resume settings from Baichuan2 PEFT config Cross-TP and resume assertion are skipped for PEFT models in the test. * fix: add gc.collect() before torch.cuda.empty_cache() in checkpoint robustness test FSDP2/DTensor circular references prevented GPU memory from being freed between test phases, causing OOM on large models (e.g. Nemotron Super 120B) when Phase 4 tries to reload via vanilla HF with device_map="auto". * fix: PEFT checkpoint restore for MoE models with activation checkpointing - Strip _checkpoint_wrapped_module. from FQNs in _get_peft_state_dict and _set_peft_state_dict to match DCP's normalization. Without this, expert LoRA weights are silently skipped on reload when activation checkpointing is enabled (keys mismatch), causing KL divergence of ~0.5. - Wire up no_check_hf flag to skip Phase 4 vanilla HF check when configured - Qwen3 MoE 30B LoRA: reduce to 1 node, add no_check_hf * fix: Qwen3 MoE PEFT adapter HF compatibility via ParamWrapper format Save Qwen3 MoE expert LoRA adapters in PEFT v0.18+ ParamWrapper format so PeftModel.from_pretrained() can load them directly. Previously, adapters were saved with per-expert individual keys (experts.0.gate_proj.lora_A.weight) which vanilla HF couldn't load because Qwen3 MoE uses fused nn.Parameter tensors (experts.gate_up_proj), not individual nn.Module per expert. The new format (default, v4_compatible=False) uses target_parameters in adapter_config.json and 2D fused LoRA tensors matching ParamWrapper's expected key layout. Legacy per-expert format is preserved when v4_compatible=True. Also: reduce Qwen3 MoE CI from 2 nodes to 1, remove dead no_check_hf parsing from test, clean up _extract_target_modules helpers. * fix: remove debug print statement from checkpoint robustness test --------- Signed-off-by: adil-a <adil.asif2000@hotmail.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Adil <47084919+adil-a@users.noreply.github.com> Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…1758) ci: Address container and source code cve (#1753) * Address container and source code cve * Update uv lock --------- Signed-off-by: Dong Hyuk Chang <9426164+thomasdhc@users.noreply.github.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Dong Hyuk Chang <9426164+thomasdhc@users.noreply.github.com> Co-authored-by: NeMo Bot <nemo-bot@nvidia.com>
….4.0` (#1759) ci: Update test timeout and add ci_tests readme (#1752) * Update test timeout and add ci_tests readme * Space out the finetune logging --------- Signed-off-by: Dong Hyuk Chang <9426164+thomasdhc@users.noreply.github.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Dong Hyuk Chang <9426164+thomasdhc@users.noreply.github.com>
fix: Update lora configs for gemma4 (#1748) Update lora configs for gemma4 Signed-off-by: Abhishree Thittenamane <athittenaman@cw-dfw-cs-001-login-02.cm.cluster> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Abhishree Thittenamane <47577437+athitten@users.noreply.github.com> Co-authored-by: Abhishree Thittenamane <athittenaman@cw-dfw-cs-001-login-02.cm.cluster>
…`r0.4.0` (#1772) fix: launcher option from being consumed as a config override. (#1766) --nproc-per-node goes to app not yaml Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Alexandros Koumparoulis <153118171+akoumpa@users.noreply.github.com>
…0` (#1771) fix: skip embedding[padding_idx] = 0 with TP (#1675) * skip embedding[padding_idx] = 0 * fix * remove code --------- Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Alexandros Koumparoulis <153118171+akoumpa@users.noreply.github.com>
ci: add missing recipe owners (#1775) * add missing owners to recipes for CI notifications. --------- Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Alexandros Koumparoulis <153118171+akoumpa@users.noreply.github.com>
ci: Resolve cve and remove uv cache (#1774) * Resolve commit and remove uv cache * Update uv lock --------- Signed-off-by: Dong Hyuk Chang <9426164+thomasdhc@users.noreply.github.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Dong Hyuk Chang <9426164+thomasdhc@users.noreply.github.com> Co-authored-by: NeMo Bot <nemo-bot@nvidia.com>
feat: FSDP2 w weight prefetching and async TP optimization (#1711) * feat: FSDP2 w weight prefetching and async TP optimization * remove deferred rs feature * add datapoints * lint * fix unit tests * address claude review * remove invalid tests and better readbility * skip unused fsdp flag * Apply suggestions from code review * refactor: use nn.Module.compile() and consolidate compile paths in infrastructure * refactor: remove fsdp_layer_group_size flag * derive pp_enabled * lint * update cp and fix * lint * update perf * update * update perf * fix test --------- Signed-off-by: Zhiyu Li <zhiyul@NVIDIA.com> Co-authored-by: Alexandros Koumparoulis <153118171+akoumpa@users.noreply.github.com> Co-authored-by: jgerh <163925524+jgerh@users.noreply.github.com> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
fix: update yamls for vllm_deploy (#1780) * update configuration * ensure checkpoint is saved as consolidated safetensors * fix ci --------- Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com> Signed-off-by: NeMo Bot <nemo-bot@nvidia.com> Co-authored-by: Alexandros Koumparoulis <153118171+akoumpa@users.noreply.github.com>
Signed-off-by: gitlab-runner <gitlab-runner@nvidia.com>
svcnvidia-nemo-ci
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HuiyingLi,
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akoumpa,
hemildesai and
snowmanwwg
as code owners
svcnvidia-nemo-ci
requested review from
a team,
ZhiyuLi-Nvidia,
jgerh and
pthombre
as code owners
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beep boop 🤖: Updating transformers to latest version on pypi