SenseNova-U series: Native Unified Paradigm with NEO-unify from the First Principles
This report presents the forensic synthetic code analysis of OpenSenseNova/SenseNova-U1, a Python project with 5,795 GitHub stars. SynthScan v2.0 examined 101,553 lines of code across 337 source files, recording 1809 pattern matches distributed across 21 syntactic categories. The overall adjusted score of 27.6 places this repository in the Moderate AI signal band.
The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).
Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.
Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.
This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.
The scanner identified 1809 distinct pattern matches across 21 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.
Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | pyproject.toml | 99 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM | pyproject.toml | 107 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM | …sensenovavl_moe_chat/configuration_sensenovavl_chat.py | 1 | # -------------------------------------------------------- | COMMENT |
| MEDIUM | …sensenovavl_moe_chat/configuration_sensenovavl_chat.py | 5 | # -------------------------------------------------------- | COMMENT |
| MEDIUM | …enovavl/model/sensenovavl_moe_chat/modeling_neo_vit.py | 1 | # -------------------------------------------------------- | COMMENT |
| MEDIUM | …enovavl/model/sensenovavl_moe_chat/modeling_neo_vit.py | 5 | # -------------------------------------------------------- | COMMENT |
| MEDIUM | …/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py | 1 | # -------------------------------------------------------- | COMMENT |
| MEDIUM | …/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py | 5 | # -------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …ing/sensenovavl/model/sensenovavl_moe_chat/__init__.py | 1 | # -------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …ing/sensenovavl/model/sensenovavl_moe_chat/__init__.py | 5 | # -------------------------------------------------------- | COMMENT |
| MEDIUM | …vl/model/sensenovavl_moe_chat/configuration_neo_vit.py | 1 | # -------------------------------------------------------- | COMMENT |
| MEDIUM | …vl/model/sensenovavl_moe_chat/configuration_neo_vit.py | 5 | # -------------------------------------------------------- | COMMENT |
| MEDIUM | …senovavl/model/sensenovavl_moe_chat/flash_attention.py | 1 | # -------------------------------------------------------- | COMMENT |
| MEDIUM | …senovavl/model/sensenovavl_moe_chat/flash_attention.py | 8 | # -------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | training/sensenovavl/data/cfg_cond_drop_utils.py | 58 | # ------------------------------------------------- | COMMENT |
| MEDIUM⚡ | training/sensenovavl/data/cfg_cond_drop_utils.py | 61 | # ------------------------------------------------- | COMMENT |
| MEDIUM⚡ | training/sensenovavl/data/cfg_cond_drop_utils.py | 77 | # ------------------------------------------------- | COMMENT |
| MEDIUM⚡ | training/sensenovavl/data/cfg_cond_drop_utils.py | 79 | # ------------------------------------------------- | COMMENT |
| MEDIUM⚡ | training/sensenovavl/data/cfg_cond_drop_utils.py | 93 | # ------------------------------------------------- | COMMENT |
| MEDIUM⚡ | training/sensenovavl/data/cfg_cond_drop_utils.py | 95 | # ------------------------------------------------- | COMMENT |
| MEDIUM | training/tools/revert2hf.py | 112 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | training/tools/revert2hf.py | 115 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | training/tools/revert2hf.py | 162 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | training/tools/revert2hf.py | 164 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | training/tools/revert2hf.py | 207 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | training/tools/revert2hf.py | 209 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | training/tools/revert2hf.py | 245 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | training/tools/revert2hf.py | 247 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | training/tools/revert2hf.py | 317 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | training/tools/revert2hf.py | 319 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | training/tools/revert2hf.py | 395 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | training/tools/revert2hf.py | 397 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | training/tools/revert2hf.py | 470 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | training/tools/revert2hf.py | 472 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | training/tools/revert2hf.py | 485 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | training/tools/revert2hf.py | 487 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 10 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 12 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 17 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 19 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 36 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 38 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 45 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 47 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 142 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 144 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 152 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 154 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 159 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 161 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 406 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 408 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 417 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 419 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 63 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 65 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 101 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 103 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 114 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM | …nfigs/sensenovavl_qwen3_gen/sensenovau1_5_8b_mot_pt.py | 116 | # ----------------------------------------------------------------------------- | COMMENT |
| 236 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | training/train_sensenovau1.py | 23 | def patch_inductor_triton_max_block(): | CODE |
| LOW | training/sensenovavl/utils/checkpoint.py | 327 | def _load_pretrained_internevo_llm(model, model_args, state_dict, has_moe=False): | CODE |
| LOW | training/sensenovavl/utils/checkpoint.py | 508 | def convert_q_k_v_to_wqkv_interleaved( | CODE |
| LOW | training/sensenovavl/utils/checkpoint.py | 1589 | def load_pretrained_fm_modules(model, model_path=None, state_dict=None): | CODE |
| LOW | training/sensenovavl/utils/checkpoint.py | 1623 | def load_pretrained_vit_moe_online(state_dict, model_args): | CODE |
| LOW | training/sensenovavl/utils/checkpoint.py | 1809 | def load_pretrained_llm_moe_offline(model, model_args, model_path): | CODE |
| LOW | training/sensenovavl/utils/checkpoint.py | 1884 | def load_pretrained_llm_moe_online(model, state_dict, model_args, from_hf: bool = True): | CODE |
| LOW | training/sensenovavl/utils/utils.py | 14 | def precompute_rope_freqs_sincos( | CODE |
| LOW | training/sensenovavl/utils/utils.py | 46 | def build_abs_positions_from_grid_hw(grid_hw: torch.Tensor, device=None): | CODE |
| LOW | …/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py | 66 | def _offsets_to_doc_ids_tensor(offsets, has_pad, split_size=1024): | CODE |
| LOW | …/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py | 140 | def create_flex_mask_padding_image_gen(document_ids, modality_indicators, image_gen_indicators, token_pos, dup_boundary, | CODE |
| LOW | …/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py | 235 | def slice_tensor_by_image_lens(tensor: Optional[torch.Tensor], full_image_seq_lens, kept_image_seq_lens, dim: int = 0): | CODE |
| LOW | …/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py | 255 | def align_selected_to_image_seq_lens(selected_mask: torch.Tensor, modality_indicators: torch.Tensor, image_seq_lens): | CODE |
| LOW | …/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py | 274 | def build_modality_indicators_from_context_runs(image_context_mask: torch.Tensor): | CODE |
| LOW | …/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py | 302 | def summarize_image_seq_mismatch(prompt_image_seq_lens, expected_image_seq_lens): | CODE |
| LOW | …/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py | 378 | def unpack_two_branch_sequence( | CODE |
| LOW | …/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py | 586 | def prepare_image_gen_targets(self, pixel_values, image_for_gen_flags, grid_hw): | CODE |
| LOW | …/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py | 682 | def build_image_gen_indicators( | CODE |
| LOW | …/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py | 1175 | def _build_cu_seqlens_from_doc_ids(doc_ids: torch.Tensor) -> torch.Tensor: | CODE |
| LOW | …/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py | 1598 | def build_pipeline_partition_mot_model(**kwargs): | CODE |
| LOW | …aining/sensenovavl/model/sensenovavl_moe_chat/utils.py | 106 | def gather_forward_split_backward(input_, parallel_mode, dim, div=0, mod=0, split_size=None): | CODE |
| LOW | …aining/sensenovavl/model/sensenovavl_moe_chat/utils.py | 151 | def split_forward_gather_backward(input_, parallel_mode, dim, div=0, mod=0, split_size=None): | CODE |
| LOW | training/sensenovavl/model/vit_moe/gshard_layer.py | 666 | def add_multiplicative_jitter(self, jitter_epsilon): | CODE |
| LOW | training/sensenovavl/train/record_metrics.py | 24 | def all_gather_moe_metrics_pp( | CODE |
| LOW | training/sensenovavl/train/record_metrics.py | 58 | def record_current_batch_training_metrics( | CODE |
| LOW⚡ | training/sensenovavl/data/build_dataloader.py | 70 | def maybe_set_torch_sharing_strategy(sharing_strategy): | CODE |
| LOW⚡ | training/sensenovavl/data/build_dataloader.py | 83 | def get_multimodal_streaming_train_loader_items(data_cfg): # NOTE: | CODE |
| LOW | training/sensenovavl/data/build_dataloader.py | 238 | def get_multimodal_packed_streaming_train_loader_items(data_cfg): | CODE |
| LOW | training/sensenovavl/data/build_dataloader.py | 432 | def _build_multimodal_dataloader_kwargs(data_cfg, train_collate_fn): | CODE |
| LOW | training/sensenovavl/data/build_dataloader.py | 465 | def build_train_loader_with_data_type(): | CODE |
| LOW | training/sensenovavl/data/cfg_cond_drop_utils.py | 13 | def find_subsequence_positions(seq: torch.Tensor, pattern: torch.Tensor): | CODE |
| LOW | training/sensenovavl/data/cfg_cond_drop_utils.py | 29 | def build_text_drop_mask_ranges_singleturn( | CODE |
| LOW | training/sensenovavl/data/cfg_cond_drop_utils.py | 125 | def mask_text_for_current_image(image_i: int, user_pos: list, asst_pos: list, image_start_token_positions: List[int], im | CODE |
| LOW | training/sensenovavl/data/dataset.py | 315 | def simulate_jpeg_degradation(quality): | CODE |
| LOW | training/sensenovavl/data/dataset.py | 768 | def dynamic_preprocess_native_resolution(image, size_factor=32, min_pixels=4 * 32 * 32, max_pixels=16384 * 32 * 32, **kw | CODE |
| LOW | training/sensenovavl/data/dataset.py | 799 | def resize_crop_from_candidates(image: Image.Image, candidates, **kwargs): | CODE |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 47 | def seconds_to_minutes_secondswithdot(seconds): | CODE |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 53 | def seconds_to_minutes_seconds(seconds): | CODE |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 226 | def find_closest_aspect_ratio(self, aspect_ratio, target_ratios, width, height): | CODE |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 631 | def image_gen_append_image_size_info( | CODE |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 1191 | def _enable_worker_distributed(self): | CODE |
| LOW | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 1580 | # def get_cu_seqlens_and_indexes( | COMMENT |
| LOW | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 58 | def is_dist_avail_and_initialized(): | CODE |
| LOW | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 1626 | def get_cu_seqlens_and_indexes( | CODE |
| LOW | training/tools/revert2hf.py | 347 | def compute_dense_slice_layout(vit_num_layers: int, llm_num_layers: int): | CODE |
| LOW | training/sensenovalm/checkpoint/load_funcs.py | 19 | def load_hf_llama_pretrained_weights(folder, model): | CODE |
| LOW | training/sensenovalm/checkpoint/load_funcs.py | 234 | def obtain_spliting_parameters(): | CODE |
| LOW | training/sensenovalm/checkpoint/utils.py | 42 | def obtain_not_saved_mtp_state(state, missing_keys: List[str]): | CODE |
| LOW | training/sensenovalm/checkpoint/components.py | 624 | def load_optimizer_checkpoint(folder, optim): | CODE |
| LOW | training/sensenovalm/checkpoint/components.py | 754 | def save_optimizer_checkpoint(optim, state_path): | CODE |
| LOW | training/sensenovalm/checkpoint/checkpoint_manager.py | 235 | def try_load_sensenovalm_ckpt_func(ckpt_mm, load_info, *args, func=None, **kwargs): # pylint: disable=W0613 | CODE |
| LOW | training/sensenovalm/checkpoint/checkpoint_manager.py | 586 | def query_latest_snapshot_step_local(self): | CODE |
| LOW | training/sensenovalm/checkpoint/checkpoint_manager.py | 669 | def try_load_averaged_model_from_checkpoint(self): | CODE |
| LOW | training/sensenovalm/checkpoint/convert_to_hf.py | 779 | def convert_internevo_ckpt_to_hf(src: str, tgt: str): | CODE |
| LOW | training/sensenovalm/core/model_average.py | 30 | def _iter_named_params_and_buffers(model: Module, use_buffers: bool) -> Iterable[Tuple[str, Tensor]]: | CODE |
| LOW | training/sensenovalm/core/model_average.py | 219 | def get_averaged_model_state_dict(self, model: Module) -> Dict[str, Tensor]: | STRING |
| LOW | training/sensenovalm/core/model_average.py | 236 | def load_from_model_state_dict(self, model_state_dict: Dict[str, Tensor]) -> None: | STRING |
| LOW | training/sensenovalm/core/model_average.py | 251 | def load_from_checkpoint_folder( | STRING |
| LOW | training/sensenovalm/core/model_average.py | 274 | def save_to_averaged_model_info(self, folder: str): | STRING |
| LOW | training/sensenovalm/core/trainer_builder.py | 62 | def _is_dataloader_worker_failure(exc: BaseException) -> bool: | CODE |
| 387 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | training/sensenovavl/utils/__init__.py | 1 | CODE | |
| LOW | training/sensenovavl/utils/__init__.py | 1 | CODE | |
| LOW | training/sensenovavl/utils/__init__.py | 1 | CODE | |
| LOW | training/sensenovavl/utils/__init__.py | 1 | CODE | |
| LOW | …/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py | 13 | CODE | |
| LOW | …/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py | 22 | CODE | |
| LOW | …ing/sensenovavl/model/sensenovavl_moe_chat/__init__.py | 7 | CODE | |
| LOW | …ing/sensenovavl/model/sensenovavl_moe_chat/__init__.py | 8 | CODE | |
| LOW | …ing/sensenovavl/model/sensenovavl_moe_chat/__init__.py | 9 | CODE | |
| LOW | …ing/sensenovavl/model/sensenovavl_moe_chat/__init__.py | 10 | CODE | |
| LOW | …ing/sensenovavl/model/sensenovavl_moe_chat/__init__.py | 10 | CODE | |
| LOW | training/sensenovavl/data/build_dataloader.py | 5 | CODE | |
| LOW | training/sensenovavl/data/build_dataloader.py | 10 | CODE | |
| LOW | training/sensenovavl/data/build_dataloader.py | 40 | CODE | |
| LOW | training/sensenovavl/data/build_dataloader.py | 46 | CODE | |
| LOW | training/sensenovavl/data/__init__.py | 1 | CODE | |
| LOW | training/sensenovavl/data/__init__.py | 2 | CODE | |
| LOW | training/sensenovavl/data/dataset.py | 18 | CODE | |
| LOW | training/sensenovavl/data/dataset.py | 28 | CODE | |
| LOW | training/sensenovavl/data/dataset.py | 28 | CODE | |
| LOW | training/sensenovavl/data/dataset.py | 28 | CODE | |
| LOW | training/sensenovavl/data/dataset.py | 28 | CODE | |
| LOW | training/sensenovavl/data/dataset.py | 28 | CODE | |
| LOW | training/sensenovavl/data/dataset.py | 28 | CODE | |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 6 | CODE | |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 12 | CODE | |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 22 | CODE | |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 29 | CODE | |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 41 | CODE | |
| LOW | training/sensenovalm/__init__.py | 1 | CODE | |
| LOW | training/sensenovalm/__init__.py | 2 | CODE | |
| LOW | training/sensenovalm/__init__.py | 2 | CODE | |
| LOW | training/sensenovalm/__init__.py | 2 | CODE | |
| LOW | training/sensenovalm/checkpoint/__init__.py | 1 | CODE | |
| LOW | training/sensenovalm/core/model_average.py | 15 | CODE | |
| LOW | training/sensenovalm/core/__init__.py | 1 | CODE | |
| LOW | training/sensenovalm/core/__init__.py | 2 | CODE | |
| LOW | training/sensenovalm/core/__init__.py | 3 | CODE | |
| LOW | training/sensenovalm/core/trainer.py | 9 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 1 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 1 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 1 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 1 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 1 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 1 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 1 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 1 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 1 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 12 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 12 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 12 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 12 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 12 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 12 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 12 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 12 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 22 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 22 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 22 | CODE | |
| LOW | training/sensenovalm/core/context/__init__.py | 22 | CODE | |
| 211 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | training/sensenovalm/model/modules/mha.py | 157 | CODE | |
| MEDIUM | training/sensenovalm/model/modules/mha.py | 158 | CODE | |
| MEDIUM | training/sensenovalm/model/modules/mha.py | 159 | CODE | |
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| MEDIUM | src/sensenova_u1/models/neo_unify/modeling_qwen3_moe.py | 296 | CODE | |
| MEDIUM | src/sensenova_u1/models/neo_unify/modeling_qwen3_moe.py | 298 | CODE | |
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| LOW | training/train_sensenovau1.py | 36 | except Exception: | CODE |
| LOW | …enovavl/model/sensenovavl_moe_chat/modeling_neo_vit.py | 64 | except Exception: | CODE |
| LOW⚡ | training/sensenovavl/data/build_dataloader.py | 79 | except Exception as exc: # pragma: no cover - depends on runtime env | CODE |
| LOW | training/sensenovavl/data/build_dataloader.py | 394 | except Exception as exc: # pragma: no cover - best effort cleanup | CODE |
| LOW | training/sensenovavl/data/dataset.py | 122 | except Exception: | CODE |
| MEDIUM | training/sensenovavl/data/multimodal_dataset.py | 1204 | def get_sample(self, line) -> Dict[str, torch.Tensor]: | CODE |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 751 | except Exception as e: | CODE |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 1209 | except Exception as e: | CODE |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 1258 | except Exception as e: | CODE |
| MEDIUM | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 247 | def load_image(self, image_path_or_url): | CODE |
| MEDIUM | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 254 | def _get_mmap(self, data_path): | CODE |
| MEDIUM | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 265 | def get_sample(self, line): | CODE |
| MEDIUM | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 945 | def next_data(self, current_dataset_idx): | CODE |
| LOW | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 250 | except Exception as e: | CODE |
| LOW | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 260 | except Exception as e: | CODE |
| LOW | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 271 | except Exception as e: | CODE |
| LOW | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 513 | except Exception as e: | CODE |
| LOW | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 960 | except Exception as e: | CODE |
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| LOW | training/sensenovalm/checkpoint/components.py | 730 | except Exception as e: | CODE |
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| LOW | training/sensenovalm/initialize/launch.py | 913 | except Exception: | CODE |
| MEDIUM | training/sensenovalm/initialize/launch.py | 869 | def get_config_value(config, key, defalut): | CODE |
| MEDIUM | training/sensenovalm/utils/storage_manager.py | 77 | print(f"Error: {mesage}", flush=True) | CODE |
| LOW | training/sensenovalm/utils/storage_manager.py | 484 | except Exception as e: | CODE |
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| MEDIUM | training/sensenovalm/utils/writer.py | 237 | def add_text(self, key, value, step): | CODE |
| LOW | training/sensenovalm/data/tokenized/single_dataset.py | 131 | except Exception as e: | CODE |
| LOW | training/sensenovalm/data/tokenized/single_dataset.py | 154 | except Exception as err: | CODE |
| LOW | evaluation/gen/tiif/eval/eval_with_vlm_mp.py | 75 | except Exception as e: | CODE |
| LOW | evaluation/gen/tiif/eval/eval_with_vlm_mp.py | 148 | except Exception as e: | CODE |
| LOW | evaluation/gen/tiif/eval/eval_with_vlm_mp.py | 219 | except Exception as e: | CODE |
| MEDIUM | evaluation/gen/tiif/eval/eval_with_vlm_mp.py | 185 | def process_task(task, args): | CODE |
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| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | training/sensenovavl/utils/utils.py | 0 | compute patch coordinates (x, y) args: grid_hw: (b, 2) tensor representing (h, w) per image | STRING |
| HIGH | src/sensenova_u1/models/neo_unify/modeling_neo_vit.py | 0 | compute patch coordinates (x, y) args: grid_hw: (b, 2) tensor representing (h, w) per image | STRING |
| HIGH | src/sensenova_u1/models/neo_unify/modeling_neo_chat.py | 0 | compute patch coordinates (x, y) args: grid_hw: (b, 2) tensor representing (h, w) per image | STRING |
| HIGH | training/sensenovavl/model/vit_moe/gshard_layer.py | 0 | the file has been adapted from the following files: https://github.com/microsoft/deepspeed/blob/master/deepspeed/moe/exp | STRING |
| HIGH | training/sensenovalm/model/moe/gshard_layer.py | 0 | the file has been adapted from the following files: https://github.com/microsoft/deepspeed/blob/master/deepspeed/moe/exp | STRING |
| HIGH | training/sensenovalm/model/moe/experts.py | 0 | the file has been adapted from the following files: https://github.com/microsoft/deepspeed/blob/master/deepspeed/moe/exp | STRING |
| HIGH | training/sensenovalm/model/moe/dropless_layer_qwen.py | 0 | the file has been adapted from the following files: https://github.com/microsoft/deepspeed/blob/master/deepspeed/moe/exp | STRING |
| HIGH | training/sensenovavl/model/vit_moe/gshard_layer.py | 0 | gate module which implements top2gating as described in gshard_. :: gate = topkgate(model_dim, num_experts) l_aux, combi | STRING |
| HIGH | training/sensenovalm/model/moe/gshard_layer.py | 0 | gate module which implements top2gating as described in gshard_. :: gate = topkgate(model_dim, num_experts) l_aux, combi | STRING |
| HIGH | training/sensenovalm/model/moe/dropless_layer_qwen.py | 0 | gate module which implements top2gating as described in gshard_. :: gate = topkgate(model_dim, num_experts) l_aux, combi | STRING |
| HIGH | training/sensenovalm/model/ops/cross_entropy.py | 0 | a simple operator selector, used for compatibility with different platforms such as cuda and ascend, as well as whether | STRING |
| HIGH | training/sensenovalm/model/ops/attention.py | 0 | a simple operator selector, used for compatibility with different platforms such as cuda and ascend, as well as whether | STRING |
| HIGH | training/sensenovalm/model/ops/linear.py | 0 | a simple operator selector, used for compatibility with different platforms such as cuda and ascend, as well as whether | STRING |
| HIGH | training/sensenovalm/model/ops/rotary_emb.py | 0 | a simple operator selector, used for compatibility with different platforms such as cuda and ascend, as well as whether | STRING |
| HIGH | examples/interleave/inference.py | 0 | you are a multimodal assistant capable of reasoning with both text and images. you support two modes: think mode: when r | STRING |
| HIGH | evaluation/interleave/OpenING/infer_opening.py | 0 | you are a multimodal assistant capable of reasoning with both text and images. you support two modes: think mode: when r | STRING |
| HIGH | evaluation/interleave/Unimmmu/inference_unimmmu.py | 0 | you are a multimodal assistant capable of reasoning with both text and images. you support two modes: think mode: when r | STRING |
| HIGH | evaluation/interleave/Realunify/inference_realunify.py | 0 | you are a multimodal assistant capable of reasoning with both text and images. you support two modes: think mode: when r | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | training/sensenovavl/utils/checkpoint.py | 32 | # Create a uniform distribution for jittering | COMMENT |
| MEDIUM | training/sensenovavl/utils/utils.py | 62 | # Create the batch index for each patch (B x patch count) | COMMENT |
| MEDIUM | training/sensenovavl/model/vit_moe/gshard_layer.py | 58 | # Create a uniform distribution for jittering | COMMENT |
| MEDIUM | training/sensenovavl/model/vit_moe/gshard_layer.py | 85 | # Create a mask for 1st's expert per token | COMMENT |
| MEDIUM | training/sensenovavl/model/vit_moe/gshard_layer.py | 163 | # Create a mask for 1st's expert per token | COMMENT |
| MEDIUM | training/sensenovavl/model/vit_moe/gshard_layer.py | 171 | # Create a mask for 2nd's expert per token using Gumbel-max trick | COMMENT |
| MEDIUM | training/sensenovavl/model/vit_moe/gshard_layer.py | 254 | # Create a mask by top-k experts | COMMENT |
| MEDIUM | training/sensenovavl/data/dataset.py | 326 | # Define the JPEG compression quality range, pre-create all JPEG compression functions | COMMENT |
| MEDIUM | training/sensenovalm/core/context/parallel_context.py | 143 | """This class provides interface functions for users to get the parallel context, | STRING |
| MEDIUM | training/sensenovalm/core/context/random.py | 20 | """This class is a manager of all random seeds involved in the system.""" | STRING |
| MEDIUM | training/sensenovalm/utils/parallel.py | 128 | r"""This function is changed from colossalai, which is ``sync_model_param``. | STRING |
| MEDIUM | training/sensenovalm/model/metrics.py | 199 | # Create a mask of valid vocab ids (1 means it needs to be masked). | COMMENT |
| MEDIUM | training/sensenovalm/model/ops/cross_entropy.py | 187 | # Create a mask of valid vocab ids (1 means it needs to be masked). | COMMENT |
| MEDIUM | training/sensenovalm/model/moe/gshard_layer.py | 138 | # The following functions are extracted and scripted | COMMENT |
| MEDIUM | training/sensenovalm/model/moe/gshard_layer.py | 183 | # Create a mask for 1st's expert per token | COMMENT |
| MEDIUM | training/sensenovalm/model/moe/gshard_layer.py | 258 | # Create a mask for 1st's expert per token | COMMENT |
| MEDIUM | training/sensenovalm/model/moe/gshard_layer.py | 263 | # Create a mask for 2nd's expert per token using Gumbel-max trick | COMMENT |
| MEDIUM | training/sensenovalm/model/moe/gshard_layer.py | 333 | # Create a mask by top-k experts | COMMENT |
| MEDIUM | training/sensenovalm/model/moe/experts.py | 51 | # The following code is designed for multiple experts. | COMMENT |
| MEDIUM | training/sensenovalm/model/moe/dropless_layer_qwen.py | 812 | # Create a mask of mapping between global and local tokens where each | COMMENT |
| MEDIUM | training/sensenovalm/train/utils.py | 303 | # Create the param MoE groups, leave param assign to next step | COMMENT |
| MEDIUM | training/sensenovalm/train/pipeline.py | 297 | # This function is needed to make sure parameters that are not splitted by tensor parallelism are | COMMENT |
| MEDIUM | training/sensenovalm/data/build_dataloader.py | 158 | # Create the training data loader | COMMENT |
| MEDIUM | scripts/render_requirements.py | 63 | "# This file is generated from pyproject.toml by scripts/render_requirements.py.", | CODE |
| MEDIUM | evaluation/gen/cvtg/unified_metrics_eval.py | 266 | # Create a default zero tensor as placeholder | COMMENT |
| MEDIUM | …uation/interleave/Realunify/inference_realunify_ueg.py | 89 | # Create a small dummy image (32x32 white) | COMMENT |
| MEDIUM | src/sensenova_u1/models/neo_unify/modeling_neo_vit.py | 38 | # Create the batch index for each patch (B x patch count) | COMMENT |
| MEDIUM | src/sensenova_u1/models/neo_unify/modeling_neo_chat.py | 134 | # Create the batch index for each patch (B x patch count) | COMMENT |
| MEDIUM | src/sensenova_u1/models/neo_unify/modeling_qwen3.py | 1147 | # Create the masks | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | docs/u1.5_preview_CN.md | 912 | export OPENAI_API_KEY="your-api-key" | CODE |
| HIGH | docs/u1.5_preview_CN.md | 965 | export OPENAI_API_KEY="your-api-key" | CODE |
| HIGH | docs/u1.5_preview.md | 936 | export OPENAI_API_KEY="your-api-key" | CODE |
| HIGH | docs/u1.5_preview.md | 1002 | export OPENAI_API_KEY="your-api-key" | CODE |
| HIGH | docs/u1.5_best_practices.md | 57 | export PE_MODEL_API_KEY="your-api-key" | CODE |
| HIGH | docs/u1.5_best_practices.md | 133 | export OPENAI_API_KEY="your-api-key" | CODE |
| HIGH | docs/u1.5_best_practices.md | 150 | export OPENAI_API_KEY="your-api-key" | CODE |
| HIGH | docs/u1.5_best_practices_CN.md | 57 | export PE_MODEL_API_KEY="your-api-key" | CODE |
| HIGH | docs/u1.5_best_practices_CN.md | 133 | export OPENAI_API_KEY="your-api-key" | CODE |
| HIGH | docs/u1.5_best_practices_CN.md | 150 | export OPENAI_API_KEY="your-api-key" | CODE |
| HIGH | evaluation/docs/interleaved.md | 93 | --api-key your_api_key \ | CODE |
| HIGH | evaluation/docs/interleaved.md | 189 | export OPENING_JUDGE_API_KEY=your_api_key | CODE |
| HIGH | evaluation/gen/bizgeneval/run_bizgeneval.sh | 11 | API_KEY="your-api-key" | CODE |
| HIGH | evaluation/gen/igenbench/run_igenbench.sh | 11 | API_KEY="your-api-key" | CODE |
| HIGH | src/sensenova_u1_5/caption/caption.py | 8 | export OPENAI_API_KEY="your-api-key" | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …sensenovavl_moe_chat/configuration_sensenovavl_chat.py | 25 | CODE | |
| LOW | …/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py | 390 | CODE | |
| LOW | …/sensenovavl_moe_chat/modeling_sensenovavl_chat_mot.py | 706 | CODE | |
| LOW | …vl/model/sensenovavl_moe_chat/configuration_neo_vit.py | 63 | CODE | |
| LOW | training/sensenovavl/model/vit_moe/gshard_layer.py | 226 | CODE | |
| LOW | training/sensenovavl/model/vit_moe/gshard_layer.py | 420 | CODE | |
| LOW | training/sensenovavl/model/vit_moe/gshard_layer.py | 565 | CODE | |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 1413 | CODE | |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 62 | CODE | |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 947 | CODE | |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 1108 | CODE | |
| LOW | training/sensenovavl/data/multimodal_dataset.py | 1189 | CODE | |
| LOW | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 83 | CODE | |
| LOW | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 442 | CODE | |
| LOW | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 647 | CODE | |
| LOW | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 826 | CODE | |
| LOW | training/sensenovalm/core/parallel/comm/isp.py | 228 | CODE | |
| LOW | training/sensenovalm/core/parallel/comm/tensor.py | 393 | CODE | |
| LOW | training/sensenovalm/core/parallel/comm/utils.py | 105 | CODE | |
| LOW | training/sensenovalm/core/parallel/comm/utils.py | 127 | CODE | |
| LOW | training/sensenovalm/core/scheduler/comm/p2p.py | 123 | CODE | |
| LOW | training/sensenovalm/utils/common.py | 49 | CODE | |
| LOW | training/sensenovalm/utils/common.py | 209 | CODE | |
| LOW | training/sensenovalm/utils/writer.py | 117 | CODE | |
| LOW | training/sensenovalm/solver/optimizer/utils.py | 449 | CODE | |
| LOW | training/sensenovalm/model/modeling_qwen3_moe_mot.py | 72 | CODE | |
| LOW | training/sensenovalm/model/modeling_qwen3_moe_mot.py | 436 | CODE | |
| LOW | training/sensenovalm/model/ops/attention.py | 689 | CODE | |
| LOW | training/sensenovalm/model/ops/attention.py | 723 | CODE | |
| LOW | training/sensenovalm/model/ops/norm.py | 114 | CODE | |
| LOW | …ash_attn/zigzag_ring_flash_attn_with_sliding_window.py | 515 | CODE | |
| LOW | …ash_attn/zigzag_ring_flash_attn_with_sliding_window.py | 552 | CODE | |
| LOW | …ash_attn/zigzag_ring_flash_attn_with_sliding_window.py | 588 | CODE | |
| LOW | training/sensenovalm/model/modules/linear.py | 1007 | CODE | |
| LOW | training/sensenovalm/model/modules/mlp.py | 211 | CODE | |
| LOW | training/sensenovalm/model/modules/mlp.py | 63 | CODE | |
| LOW | training/sensenovalm/model/modules/mlp.py | 143 | CODE | |
| LOW | training/sensenovalm/model/modules/mlp.py | 208 | CODE | |
| LOW | training/sensenovalm/model/modules/mha.py | 136 | CODE | |
| LOW | training/sensenovalm/model/modules/mha.py | 431 | CODE | |
| LOW | training/sensenovalm/model/modules/mha.py | 790 | CODE | |
| LOW | training/sensenovalm/model/modules/mha.py | 1259 | CODE | |
| LOW | training/sensenovalm/model/modules/mha.py | 1568 | CODE | |
| LOW | training/sensenovalm/model/moe/gshard_layer.py | 415 | CODE | |
| LOW | training/sensenovalm/model/moe/gshard_layer.py | 522 | CODE | |
| LOW | training/sensenovalm/model/moe/moe.py | 56 | CODE | |
| LOW | training/sensenovalm/model/moe/moe.py | 147 | CODE | |
| LOW | training/sensenovalm/model/moe/moe.py | 229 | CODE | |
| LOW | training/sensenovalm/model/moe/moe.py | 304 | CODE | |
| LOW | training/sensenovalm/model/moe/dropless_layer_qwen.py | 168 | CODE | |
| LOW | training/sensenovalm/model/moe/dropless_layer_qwen.py | 660 | CODE | |
| LOW | training/sensenovalm/data/tokenized/packed_dataset.py | 453 | CODE | |
| LOW | training/sensenovalm/data/tokenized/packed_dataset.py | 519 | CODE | |
| LOW | training/sensenovalm/data/tokenized/packed_dataset.py | 636 | CODE | |
| LOW | examples/t2i/inference.py | 83 | CODE | |
| LOW | examples/t2i/inference.py | 134 | CODE | |
| LOW | examples/editing/inference.py | 215 | CODE | |
| LOW | examples/editing/inference.py | 247 | CODE | |
| LOW | examples/vqa/inference.py | 32 | CODE | |
| LOW | examples/interleave/inference.py | 138 | CODE | |
| 23 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | training/sensenovavl/data/dataset.py | 79 | calculate the number of frames for video used for model inputs. Args: ele (dict): a dict contains the confi | STRING |
| HIGH | training/sensenovalm/core/parallel/comm/isp.py | 759 | Registers a callback function that specifies a prerequisite condition for prefetching parameters before | STRING |
| HIGH | training/sensenovalm/core/context/parallel_context.py | 91 | Reads a python file and constructs a corresponding :class:`Config` object. Args: filename (str): Na | STRING |
| HIGH | training/sensenovalm/model/registry.py | 44 | Retrieves a module with name `module_name` and returns the module if it has already been registered before. | STRING |
| HIGH | training/sensenovalm/data/train_state.py | 30 | Args: dataloader (torch.utils.data.Dataset): Raises: ValueError: Only support tokenized/stream | STRING |
| HIGH | training/sensenovalm/data/tokenized/collaters.py | 7 | Collate function for packed input sequences. Args: batch (List[Dict]): List of dictionaries representi | STRING |
| HIGH | training/sensenovalm/data/tokenized/dataset.py | 12 | Return a dictionary of Datasets from a folder containing data files for validation. Args: folder (str) | STRING |
| HIGH | …u1/prompt_enhance/adapters/chat_completions_adapter.py | 154 | Extract the assistant message text from a chat/completions response. Handles both plain-string and list-of-cont | STRING |
| HIGH | …u1/prompt_enhance/adapters/chat_completions_adapter.py | 191 | Call the ``/chat/completions`` endpoint with vision content. Args: user_prompt: User-facing text in | STRING |
| HIGH | src/sensenova_u1/prompt_enhance/adapters/utils.py | 13 | Read raw image bytes from a path or return bytes unchanged. Args: image: File path to an image, or raw imag | STRING |
| HIGH | …senova_u1/prompt_enhance/adapters/anthropic_adapter.py | 160 | Extract the assistant message text from Anthropic Messages response. Handles responses with or without content | STRING |
| HIGH | …senova_u1/prompt_enhance/adapters/anthropic_adapter.py | 193 | Call the ``/v1/messages`` endpoint with vision content. Args: user_prompt: User-facing text instruc | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | training/sensenovavl/utils/__init__.py | 8 | __all__ = [ | CODE |
| LOW⚡ | …ing/sensenovavl/model/sensenovavl_moe_chat/__init__.py | 12 | __all__ = [ | CODE |
| LOW | training/sensenovavl/model/vit_moe/__init__.py | 9 | __all__ = ["new_moe_layer"] | CODE |
| LOW | training/sensenovavl/data/__init__.py | 4 | __all__ = ["build_train_loader_with_data_type", "get_train_state"] | CODE |
| LOW | training/sensenovavl/data/distributed_sampler.py | 12 | __all__ = ["DistributedSampler"] | CODE |
| LOW | training/sensenovavl/data/distributed_sampler.py | 140 | def set_epoch(self, epoch: int, consumed_samples=0) -> None: | STRING |
| LOW | training/sensenovalm/__init__.py | 4 | __all__ = [ | CODE |
| LOW | training/sensenovalm/checkpoint/__init__.py | 3 | __all__ = ["CheckpointManager"] | CODE |
| LOW | training/sensenovalm/checkpoint/checkpoint_manager.py | 658 | def set_averaged_model(self, averaged_model) -> None: | CODE |
| LOW | training/sensenovalm/core/model_average.py | 171 | def set_extra_state(self, state: Dict[str, object]) -> None: | STRING |
| LOW | training/sensenovalm/core/__init__.py | 5 | __all__ = [ | CODE |
| LOW | training/sensenovalm/core/context/__init__.py | 33 | __all__ = [ | CODE |
| LOW | training/sensenovalm/core/scheduler/__init__.py | 9 | __all__ = [ | CODE |
| LOW | training/sensenovalm/core/scheduler/comm/__init__.py | 17 | __all__ = [ | CODE |
| LOW | training/sensenovalm/initialize/__init__.py | 10 | __all__ = [ | CODE |
| LOW | training/sensenovalm/solver/__init__.py | 7 | __all__ = ["Beta2Scheduler", "FineTuneCosineAnnealingWarmupLR", "HybridZeroOptimizer"] | CODE |
| LOW | training/sensenovalm/solver/optimizer/store.py | 174 | def set_param_to_rank(self, tensor: Tensor, rank: int) -> None: | CODE |
| LOW | training/sensenovalm/solver/optimizer/__init__.py | 7 | __all__ = ["FSDPadaptOptimizer", "HybridZeroOptimizer"] | CODE |
| LOW | training/sensenovalm/solver/schedulers/__init__.py | 8 | __all__ = ["Beta2Scheduler", "WarmupScheduler", "CosineAnnealingWarmupLR", "FineTuneCosineAnnealingWarmupLR"] | CODE |
| LOW | training/sensenovalm/accelerator/__init__.py | 5 | __all__ = [ | CODE |
| LOW | training/sensenovalm/model/losses/__init__.py | 3 | __all__ = [ | CODE |
| LOW | …ning/sensenovalm/model/ops/ring_flash_attn/__init__.py | 7 | __all__ = [ | CODE |
| LOW⚡ | training/sensenovalm/model/ops/ring_flash_attn/utils.py | 16 | __all__ = ["update_out_and_lse", "RingComm"] | CODE |
| LOW | training/sensenovalm/model/moe/__init__.py | 5 | __all__ = [ | CODE |
| LOW | training/sensenovalm/model/moe/monitor/__init__.py | 5 | __all__ = ["BaseMonitor", "MoEMonitor"] | CODE |
| LOW | training/sensenovalm/train/__init__.py | 13 | __all__ = [ | CODE |
| LOW | training/sensenovalm/data/__init__.py | 7 | __all__ = [ | CODE |
| LOW | training/sensenovalm/data/tokenized/__init__.py | 6 | __all__ = [ | CODE |
| LOW | training/sensenovalm/data/tokenized/batch_sampler.py | 103 | def set_epoch(self, epoch: int) -> None: | CODE |
| LOW | examples/interleave/inference.py | 70 | def _set_seed(seed: int) -> None: | CODE |
| LOW | apps/comfyui/__init__.py | 33 | __all__ = ["comfy_entrypoint", "WEB_DIRECTORY"] | CODE |
| LOW | src/sensenova_u1/__init__.py | 27 | __all__ = [ | CODE |
| LOW | src/sensenova_u1/utils/__init__.py | 39 | __all__ = [ | CODE |
| LOW | src/sensenova_u1/utils/comparison.py | 11 | __all__ = ["ensure_cjk_font", "make_comparison", "save_compare"] | CODE |
| LOW | src/sensenova_u1/utils/layer_offload.py | 49 | logger = logging.getLogger(__name__) | CODE |
| LOW⚡ | src/sensenova_u1/utils/layer_offload.py | 607 | def _setup(self) -> None: | CODE |
| LOW | src/sensenova_u1/utils/layer_offload.py | 863 | def set_inference_phase(self, phase: str) -> None: | CODE |
| LOW | src/sensenova_u1/utils/profiler.py | 123 | def set_config(self, config: Mapping[str, object]) -> None: | CODE |
| LOW⚡ | src/sensenova_u1/utils/profiler.py | 219 | def update_last_batch(self, n: int) -> None: | CODE |
| LOW | src/sensenova_u1/models/__init__.py | 5 | __all__ = ["neo_unify"] | CODE |
| LOW | src/sensenova_u1/models/neo_unify/modeling_qwen3_moe.py | 541 | __all__ = [ | CODE |
| LOW | src/sensenova_u1/models/neo_unify/__init__.py | 16 | __all__ = [ | CODE |
| LOW | src/sensenova_u1/models/neo_unify/modeling_qwen3.py | 1291 | __all__ = [ | CODE |
| LOW | src/sensenova_u1/prompt_enhance/__init__.py | 10 | __all__ = [ | CODE |
| LOW | …u1/prompt_enhance/adapters/chat_completions_adapter.py | 31 | logger = logging.getLogger(__name__) | CODE |
| LOW | src/sensenova_u1/prompt_enhance/adapters/__init__.py | 5 | __all__ = ["AnthropicVlmAdapter", "ChatCompletionsVlmAdapter", "VlmAdapter"] | CODE |
| LOW | …senova_u1/prompt_enhance/adapters/anthropic_adapter.py | 30 | logger = logging.getLogger(__name__) | CODE |
| LOW | src/sensenova_u1_5/edit/__init__.py | 5 | __all__ = ["enhance_edit_prompt"] | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | evaluation/interleave/BabyVision/eval_babyvision.py | 49 | - If the extracted answer is empty or null, return false. | CODE |
| HIGH | evaluation/interleave/OpenING/eval_opening.py | 952 | help="Retry existing results whose score metrics are missing, null, or non-numeric", | CODE |
| HIGH⚡ | evaluation/interleave/OpenING/summarize_GPT_scores.py | 60 | # Check if score is null, empty, or contains null scores | COMMENT |
| HIGH⚡ | evaluation/interleave/OpenING/summarize_GPT_scores.py | 60 | # Check if score is null, empty, or contains null scores | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 41 | # 其中,内层不设置token数量限制,但是会根据图像数量限制对文档进行切割 | COMMENT |
| LOW | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 1581 | # data_index: torch.LongTensor, # (seq_len,) | COMMENT |
| LOW | …ining/sensenovavl/data/dataset_interleaved_iterable.py | 1601 | # assert num_tokens > 0 | COMMENT |
| LOW | training/sensenovalm/utils/parallel.py | 241 | # f"and rank{i}:{value_list[i]}", | COMMENT |
| LOW | …ning/sensenovalm/solver/optimizer/hybrid_zero_optim.py | 221 | flat_tensor = flatten(tensor_list) | COMMENT |
| LOW | training/sensenovalm/model/modeling_qwen3_moe_mot.py | 981 | # router_prob_per_expert = torch.mean(routing_weights, dim=1) | COMMENT |
| LOW | training/sensenovalm/model/modeling_qwen3_moe_mot.py | 1001 | COMMENT | |
| LOW | training/sensenovalm/model/modeling_qwen3_moe_mot.py | 1021 | COMMENT | |
| LOW | training/sensenovalm/model/modeling_qwen3_moe_mot.py | 1041 | # l_aux_image = torch.sum(image_tokens_per_expert * image_router_prob_per_expert) * num_experts | COMMENT |
| LOW | training/sensenovalm/model/modeling_qwen3_moe_mot.py | 1061 | COMMENT | |
| LOW | training/sensenovalm/model/modeling_qwen3_moe_mot.py | 1081 | # logger.info( | COMMENT |
| LOW | training/sensenovalm/model/ops/cross_entropy.py | 1 | # -------------------------------------------------------- | COMMENT |
| LOW | training/sensenovalm/model/modules/embedding.py | 1 | #!/usr/bin/env python | COMMENT |
| LOW | training/sensenovalm/model/modules/mha.py | 1581 | # k_t[image_gen_indicators] = self.wk_mot_gen(x[image_gen_indicators]) | COMMENT |
| LOW | training/sensenovalm/model/modules/mha.py | 1601 | # kv_seq_len = v.size(0) | COMMENT |
| LOW | training/sensenovalm/model/modules/mha.py | 1621 | # q_h, k_h = apply_norm(q_h, k_h, self.q_norm_h, self.q_norm_h_mot_gen, self.k_norm_h, self.k_norm_h_mot_gen | COMMENT |
| LOW | training/sensenovalm/model/modules/mha.py | 1641 | COMMENT | |
| LOW | .github/workflows/publish-comfyui.yml | 1 | # Sync apps/comfyui/ to the standalone publish mirror | COMMENT |
| LOW | evaluation/easi/scripts/setup.sh | 1 | #!/usr/bin/env bash | COMMENT |
| LOW | evaluation/easi/scripts/setup.sh | 21 | # evaluation/easi/EASI/VLMEvalKit/vlmeval/sensenova_models.py | COMMENT |
| LOW | evaluation/easi/scripts/setup.sh | 241 | # Re-activate LightLLM venv only if we set it up earlier. | COMMENT |
| LOW | evaluation/easi/scripts/serve_lb.sh | 1 | #!/usr/bin/env bash | COMMENT |
| LOW | evaluation/easi/scripts/download_weights.sh | 1 | #!/usr/bin/env bash | COMMENT |
| LOW | evaluation/easi/scripts/serve.sh | 1 | #!/usr/bin/env bash | COMMENT |
| LOW | evaluation/easi/scripts/serve.sh | 21 | # | COMMENT |
| LOW | evaluation/easi/scripts/serve.sh | 41 | # LIGHTLLM_LOG_LEVEL debug|info|warning|error (default: info) | COMMENT |
| LOW | evaluation/interleave/Unimmmu/run.sh | 1 | #!/bin/bash | COMMENT |
| LOW | evaluation/interleave/Realunify/run.sh | 1 | #!/bin/bash | COMMENT |
| LOW | apps/comfyui/requirements.txt | 1 | httpx | COMMENT |
| LOW | src/sensenova_u1/utils/param_count.py | 41 | model_path: str | COMMENT |
| LOW | …c/sensenova_u1/models/neo_unify/modeling_fm_modules.py | 81 | h = self.mlp(h) | COMMENT |
| LOW | …c/sensenova_u1/models/neo_unify/modeling_fm_modules.py | 101 | # super().__init__() | COMMENT |
| LOW | …c/sensenova_u1/models/neo_unify/modeling_fm_modules.py | 121 | COMMENT | |
| LOW | …c/sensenova_u1/models/neo_unify/modeling_fm_modules.py | 141 | # nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0) | COMMENT |
| LOW | src/sensenova_u1/models/neo_unify/modeling_qwen3.py | 501 | ) | COMMENT |
| LOW | src/sensenova_u1/models/neo_unify/modeling_qwen3.py | 521 | # query_states_t, query_states_hw = query_states.chunk(2, dim=-1) | COMMENT |
| LOW | src/sensenova_u1/models/neo_unify/modeling_qwen3.py | 541 | # query_states_w, key_states_w = apply_rotary_pos_emb(query_states_w, key_states_w, cos_w, sin_w) | COMMENT |
| LOW | src/sensenova_u1/models/neo_unify/modeling_qwen3.py | 561 | # value_states = torch.cat([past_v, value_states], dim=2) | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | training/sensenovavl/data/conversation.py | 57 | # The number of few shot examples | COMMENT |
| MEDIUM⚡ | docs/u1_infographic_model.md | 585 | <td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; o | CODE |
| MEDIUM⚡ | docs/u1_infographic_model.md | 594 | <td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; o | CODE |
| MEDIUM⚡ | docs/u1_infographic_model.md | 594 | <td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; o | CODE |
| MEDIUM⚡ | docs/u1_infographic_model_CN.md | 583 | <td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; o | CODE |
| MEDIUM⚡ | docs/u1_infographic_model_CN.md | 592 | <td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; o | CODE |
| MEDIUM⚡ | docs/u1_infographic_model_CN.md | 592 | <td colspan="2" valign="top"><details><summary><b>Prompt</b></summary><div style="text-align: left; max-height: 200px; o | CODE |
| MEDIUM | src/sensenova_u1/models/neo_unify/conversation.py | 52 | # The number of few shot examples | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | training/sensenovavl/data/cfg_cond_drop_utils.py | 59 | # Step 1: Build image spans within user interval | COMMENT |
| LOW⚡ | training/sensenovavl/data/cfg_cond_drop_utils.py | 78 | # Step 2: Merge overlapping or adjacent spans | COMMENT |
| LOW⚡ | training/sensenovavl/data/cfg_cond_drop_utils.py | 94 | # Step 3: Compute complement = regions to mask | COMMENT |
| LOW | evaluation/interleave/Realunify/inference_realunify.py | 535 | # Step 1: Edit image | COMMENT |
| LOW | evaluation/interleave/Realunify/inference_realunify.py | 569 | # Step 2: Image QA using edited image | COMMENT |
| LOW⚡ | …uation/interleave/Realunify/inference_realunify_ueg.py | 182 | # Step 1: Understanding - refine prompt | COMMENT |
| LOW⚡ | …uation/interleave/Realunify/inference_realunify_ueg.py | 185 | # Step 2: T2I generation | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | training/sensenovavl/data/cfg_cond_drop_utils.py | 59 | # Step 1: Build image spans within user interval | COMMENT |
| LOW⚡ | training/sensenovavl/data/cfg_cond_drop_utils.py | 78 | # Step 2: Merge overlapping or adjacent spans | COMMENT |
| LOW⚡ | training/sensenovavl/data/cfg_cond_drop_utils.py | 94 | # Step 3: Compute complement = regions to mask | COMMENT |
| LOW | evaluation/interleave/Realunify/inference_realunify.py | 535 | # Step 1: Edit image | COMMENT |
| LOW | evaluation/interleave/Realunify/inference_realunify.py | 569 | # Step 2: Image QA using edited image | COMMENT |
| LOW⚡ | …uation/interleave/Realunify/inference_realunify_ueg.py | 182 | # Step 1: Understanding - refine prompt | COMMENT |
| LOW⚡ | …uation/interleave/Realunify/inference_realunify_ueg.py | 185 | # Step 2: T2I generation | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | training/sensenovalm/checkpoint/components.py | 740 | # for new added empty group, since it has no params, just create it fakely | COMMENT |
| MEDIUM | …/sensenovalm/core/context/process_group_initializer.py | 35 | # this is added to facilitate amp and grad clipping in hybrid parallel | COMMENT |
| LOW | training/sensenovalm/utils/megatron_timers.py | 97 | # polutes the runs list, so we just add each as a scalar | COMMENT |
| MEDIUM | …yui/example_workflows/infographic_series_t2i_edit.json | 250 | "You are a world-renowned \"Senior Visual Information Architect\" and \"AI Image Prompt Engineering Expert.\" Yo | CODE |
| MEDIUM | …s/comfyui/example_workflows/sensenova_api_gen_t2i.json | 117 | "You are a world-renowned \"Senior Visual Information Architect\" and \"AI Image Prompt Engineering Expert.\" Yo | CODE |
| MEDIUM | src/sensenova_u1/utils/param_count.py | 166 | # parameter, which is robust to mixed-dtype checkpoints (e.g. norms | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| CRITICAL | training/sensenovalm/train/pipeline.py | 765 | scaler = engine.optimizer.optim.grad_scaler._scale.item() | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …ining/sensenovalm/core/scheduler/pipeline_scheduler.py | 1582 | # Check if it is the last model chunk of the last pipeline stage, no need to send forward output. | COMMENT |
| LOW | …ining/sensenovalm/core/scheduler/pipeline_scheduler.py | 1587 | # Check if it needs to receive the results from the previous rank. | COMMENT |
| LOW⚡ | evaluation/interleave/OpenING/summarize_GPT_scores.py | 60 | # Check if score is null, empty, or contains null scores | COMMENT |
| LOW⚡ | evaluation/interleave/OpenING/summarize_GPT_scores.py | 68 | # Check if required metrics exist and any metric_data['Score'] is None | COMMENT |
| LOW | evaluation/interleave/OpenING/summarize_GPT_scores.py | 123 | # Print results | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | evaluation/easi/scripts/setup.sh | 34 | # Usage: | COMMENT |
| LOW | evaluation/easi/scripts/download_weights.sh | 4 | # Usage: | COMMENT |
| LOW | evaluation/easi/scripts/serve.sh | 17 | # Usage: | COMMENT |