Repository Analysis

NVIDIA/Model-Optimizer

A unified library of SOTA model optimization techniques like quantization, distillation, pruning, neural architecture search, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.

27.7 Moderate AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of NVIDIA/Model-Optimizer, a Python project with 3,230 GitHub stars. SynthScan v2.0 examined 359,617 lines of code across 1770 source files, recording 7674 pattern matches distributed across 23 syntactic categories. The overall adjusted score of 27.7 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).

27.7
Adjusted Score
27.7
Raw Score
100%
Time Factor
2026-07-14
Last Push
3.2K
Stars
Python
Language
359.6K
Lines of Code
1.8K
Files
7.7K
Pattern Hits
2026-07-14
Scan Date
0.07
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

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.

No multi-scan history yet — run the scanner again to build trend data.

Severity Breakdown

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.

CRITICAL 1HIGH 127MEDIUM 707LOW 6839

Directory Score Breakdown

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.

Pattern Findings

The scanner identified 7674 distinct pattern matches across 23 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.

Hyper-Verbose Identifiers3146 hits · 3075 pts
SeverityFileLineSnippetContext
LOWexperimental/dms/tests/test_chunked_prefill.py62 def test_chunked_matches_unchunked(self, decoder_layers, seed):CODE
LOWexperimental/dms/tests/test_dms_utils.py44 def test_decisions_are_binary(self):CODE
LOWexperimental/dms/tests/test_dms_utils.py55 def test_probs_in_unit_interval(self):CODE
LOWexperimental/dms/tests/test_dms_utils.py78 def test_gradient_flows_through_decisions(self):CODE
LOWexperimental/dms/tests/test_prefill_and_generate.py38def fake_flash_attn_with_kvcache(CODE
LOWexperimental/dms/tests/test_prefill_and_generate.py111def _simple_code_for_dms_exact_attention(q, k, v, d, a, state, attn_scaling, window_size):CODE
LOWexperimental/dms/tests/test_prefill_and_generate.py169def _simple_code_for_dms_fast_attention_inference(q, k, v, d, a, attn_scaling, window_size):CODE
LOWexperimental/dms/tests/test_prefill_and_generate.py198def _generate_random_test_params(seed):CODE
LOWexperimental/dms/tests/test_prefill_and_generate.py305 def test_prefill_matches_reference(self, seed):CODE
LOWexperimental/dms/tests/test_prefill_and_generate.py317 def test_generate_after_prefill_matches_reference(self, seed):CODE
LOWexperimental/dms/tests/test_paged_cache.py194 def test_fast_update_matches_regular_update(self, seed):CODE
LOWexperimental/dms/tests/test_paged_cache.py255def _run_paged_cache_update_test(seed, disable_eviction):CODE
LOWexperimental/dms/tests/test_paged_cache.py410 def test_update_with_eviction(self, seed):CODE
LOWexperimental/dms/tests/test_paged_cache.py415 def test_update_without_eviction(self, seed):CODE
LOWexperimental/dms/models/qwen3/train.py105def _parse_data_blend_elements(blend_string: str) -> list[DataBlendElement]:CODE
LOWexperimental/dms/dms/attention_prefill.py34def rewrite_cache_in_left_padding_style(CODE
LOWexperimental/dms/dms/attention_prefill.py73def _rewrite_cache_in_left_padding_style_aux(CODE
LOWexperimental/dms/dms/core.py37def setup_compile_limit_for_dms(compile_limit: int = 72):CODE
LOWexperimental/dms/dms/core.py225def post_process_attention_output(CODE
LOWexperimental/dms/dms/core.py287def dms_perform_chunked_prefill(CODE
LOWexperimental/dms/dms/cache_paged.py220def _aux_update_many_handle_single_chunk(CODE
LOWexperimental/dms/dms/cache_paged.py786 def fast_update_ignore_eviction(CODE
LOWexperimental/dms/dms/training/engine.py666 def get_parameters_to_optimize(self):CODE
LOWexperimental/dms/dms/training/engine.py673 def gradient_checkpointing_enable(self, gradient_checkpointing_kwargs=None):CODE
LOWexperimental/dms/dms/training/data.py63def _load_or_create_cached_dataset(CODE
LOWtools/launcher/core.py174def _explicit_slurm_fields_from_yaml(yaml_path: str | None, task_name: str) -> set[str] | None:CODE
LOWtools/launcher/core.py404 def _inject_controlmaster_rsh(self, command: str) -> str:CODE
LOWtools/launcher/tests/test_core_extended.py48 def test_missing_factory_raises(self, tmp_yaml):CODE
LOWtools/launcher/tests/test_core_extended.py59 def test_missing_slurm_config_raises(self, tmp_yaml):STRING
LOWtools/launcher/tests/test_core_extended.py67 def test_environment_preserved(self, tmp_yaml):STRING
LOWtools/launcher/tests/test_core_extended.py88 def test_dict_environment_interpolation(self):CODE
LOWtools/launcher/tests/test_core_extended.py114 def test_no_global_vars_no_error(self):CODE
LOWtools/launcher/tests/test_core_extended.py124 def test_slurm_repair_preserves_explicit_nullable_gpus(self, monkeypatch):CODE
LOWtools/launcher/tests/test_core_extended.py146 def test_slurm_repair_uses_raw_yaml_keys_for_default_equal_values(self, monkeypatch, tmp_yaml):CODE
LOWtools/launcher/tests/test_core_extended.py191 def test_valid_git_repo_from_nested_directory(self):CODE
LOWtools/launcher/tests/test_core_extended.py197 def test_nonexistent_directory(self):CODE
LOWtools/launcher/tests/test_core_extended.py216 def test_environment_list_merged_to_env(self, mock_docker, mock_exp, tmp_path):CODE
LOWtools/launcher/tests/test_core_extended.py254 def test_none_env_values_converted_to_empty_string(self, mock_docker, mock_exp, tmp_path):CODE
LOWtools/launcher/tests/test_core_extended.py288 def test_test_level_filters_pipeline(self, mock_docker, mock_exp, tmp_path):CODE
LOWtools/launcher/tests/test_core_extended.py320 def test_skipped_pipeline_not_run(self, mock_docker, mock_exp, tmp_path):CODE
LOWtools/launcher/tests/test_core_extended.py342 def test_detach_flag_passed_to_experiment(self, mock_docker, mock_exp, tmp_path):CODE
LOWtools/launcher/tests/test_core_extended.py372 def test_version_report_called(self, mock_docker, mock_exp, tmp_path, capsys):CODE
LOWtools/launcher/tests/test_docker_launch.py37 def test_echo_script_via_launch(self, tmp_path):CODE
LOWtools/launcher/tests/test_docker_launch.py83 def test_failing_script_via_launch(self, tmp_path):STRING
LOWtools/launcher/tests/test_core.py76 def test_task_slots_collected(self):CODE
LOWtools/launcher/tests/test_core.py88 def test_global_vars_interpolation_in_environment(self):CODE
LOWtools/launcher/tests/test_core.py99 def test_global_vars_interpolation_in_args(self):CODE
LOWtools/launcher/tests/test_core.py110 def test_global_vars_unresolved_passthrough(self):CODE
LOWtools/launcher/tests/test_core.py122 def test_skip_and_allow_to_fail(self):CODE
LOWtools/launcher/tests/test_core.py141 def test_create_task_from_yaml_uses_registry(self, tmp_yaml):CODE
LOWtools/launcher/tests/test_core.py161 def test_task_configs_resolved_via_registry(self, tmp_yaml):STRING
LOWtools/launcher/tests/test_core.py232 def test_runs_on_nonexistent_dir(self, capsys):CODE
LOWtools/launcher/tests/test_slurm_executor.py35 def test_controlmaster_env_selects_controlmaster_tunnel(CODE
LOWtools/launcher/tests/test_slurm_executor.py77 def test_connect_reuses_live_controlmaster(self, monkeypatch, tmp_path):CODE
LOWtools/launcher/tests/test_slurm_executor.py99 def test_connect_requires_reauth_when_controlmaster_missing(self, monkeypatch, tmp_path):CODE
LOWtools/launcher/tests/test_slurm_executor.py113 def test_connect_requires_reauth_when_controlmaster_check_times_out(CODE
LOWtools/launcher/tests/test_slurm_executor.py139 def test_installed_mode_skips_modelopt_source_mounts(self, mock_tunnel, mock_executor):CODE
LOWtools/launcher/tests/test_slurm_executor.py185 def test_source_mode_adds_modelopt_source_mounts(self, mock_tunnel, mock_executor):CODE
LOWtools/launcher/tests/test_slurm_executor.py230 def test_scratch_path_uses_experiment_title(self, mock_tunnel, mock_executor):CODE
LOWtools/launcher/tests/test_slurm_executor.py266 def test_tunnel_created_with_correct_params(self, mock_tunnel, mock_executor):CODE
3086 more matches not shown…
Decorative Section Separators503 hits · 1753 pts
SeverityFileLineSnippetContext
MEDIUMnoxfile.py56# ─── CPU unit tests ───────────────────────────────────────────────────────────COMMENT
MEDIUMnoxfile.py95# ─── GPU sessions (run inside containers — no new venv) ──────────────────────COMMENT
MEDIUMnoxfile.py162# ─── Code quality ─────────────────────────────────────────────────────────────COMMENT
MEDIUMnoxfile.py175# ─── Docs ─────────────────────────────────────────────────────────────────────COMMENT
MEDIUMnoxfile.py203# ─── Wheel build ──────────────────────────────────────────────────────────────COMMENT
MEDIUMexperimental/dms/tests/test_paged_cache.py29# ---------------------------------------------------------------------------COMMENT
MEDIUMexperimental/dms/tests/test_paged_cache.py31# ---------------------------------------------------------------------------COMMENT
MEDIUMexperimental/dms/tests/test_paged_cache.py185# ---------------------------------------------------------------------------COMMENT
MEDIUMexperimental/dms/tests/test_paged_cache.py187# ---------------------------------------------------------------------------COMMENT
MEDIUMexperimental/dms/tests/test_paged_cache.py250# ---------------------------------------------------------------------------COMMENT
MEDIUMexperimental/dms/tests/test_paged_cache.py252# ---------------------------------------------------------------------------COMMENT
MEDIUMexperimental/dms/models/qwen3/train.py62# =============================================================================COMMENT
MEDIUMexperimental/dms/models/qwen3/train.py64# =============================================================================COMMENT
MEDIUMexperimental/dms/models/qwen3/train.py100# =============================================================================COMMENT
MEDIUMexperimental/dms/models/qwen3/train.py102# =============================================================================COMMENT
MEDIUMexperimental/dms/models/qwen3/train.py144# =============================================================================COMMENT
MEDIUMexperimental/dms/models/qwen3/train.py146# =============================================================================COMMENT
MEDIUMexperimental/dms/models/qwen3/train.py202# =============================================================================COMMENT
MEDIUMexperimental/dms/models/qwen3/train.py204# =============================================================================COMMENT
MEDIUMexperimental/dms/models/qwen3/train.py256# =============================================================================COMMENT
MEDIUMexperimental/dms/models/qwen3/train.py258# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/attention_prefill.py29# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/attention_prefill.py31# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/attention_prefill.py142# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/attention_prefill.py144# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/attention.py45# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/attention.py47# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/attention.py98# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/attention.py100# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/attention.py176# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/attention.py178# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/cache.py31# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/cache.py33# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/cache.py154# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/cache.py156# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/cache.py412# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/cache.py414# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/core.py32# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/core.py34# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/core.py46# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/core.py48# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/core.py76# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/core.py78# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/core.py114# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/core.py116# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/core.py256# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/core.py258# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/training/engine.py41# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/training/engine.py43# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/training/engine.py186# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/training/engine.py188# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/training/engine.py249# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/training/engine.py251# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/training/engine.py342# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/training/engine.py344# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/training/engine.py431# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/training/engine.py433# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/training/engine.py576# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/training/engine.py578# =============================================================================COMMENT
MEDIUMexperimental/dms/dms/training/data.py53# =============================================================================COMMENT
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Over-Commented Block1504 hits · 1482 pts
SeverityFileLineSnippetContext
LOWnoxfile.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/__init__.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/tests/conftest.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/tests/test_chunked_prefill.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/tests/test_dms_utils.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/tests/utils.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/tests/test_prefill_and_generate.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/tests/test_paged_cache.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/models/__init__.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOW…perimental/dms/models/qwen3/configuration_qwen3_dms.py1# Adapted from https://github.com/huggingface/transformers/blob/47b0e478f324b54f177ea7998a0791870fdd0324/src/transformerCOMMENT
LOWexperimental/dms/models/qwen3/__init__.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/models/qwen3/train.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/models/qwen3/extract.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/models/qwen3/modeling_qwen3_dms.py1# Adapted from https://github.com/huggingface/transformers/blob/47b0e478f324b54f177ea7998a0791870fdd0324/src/transformerCOMMENT
LOWexperimental/dms/scripts/train.sh1#!/bin/bashCOMMENT
LOWexperimental/dms/scripts/evaluate.sh1#!/bin/bashCOMMENT
LOWexperimental/dms/scripts/train_small.sh1#!/bin/bashCOMMENT
LOWexperimental/dms/dms/attention_prefill.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/dms/logging.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/dms/attention.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/dms/cache.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/dms/__init__.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/dms/core.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/dms/cache_paged.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/dms/training/__init__.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/dms/training/engine.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWexperimental/dms/dms/training/data.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/slurm_config.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/__init__.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/core.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/launch.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/tests/conftest.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/tests/test_core_extended.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/tests/test_docker_launch.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/tests/test_core.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/tests/__init__.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/tests/test_slurm_executor.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/tests/test_docker_execution.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/tests/test_slurm_config.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/tests/test_yaml_formats.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/tests/test_examples_resolve.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/common/query.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/common/check_regression.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/common/service_utils.sh1#!/bin/bashCOMMENT
LOWtools/launcher/common/smoke/hostname.sh1#!/bin/bashCOMMENT
LOWtools/launcher/common/smoke/nvidia_smi.sh1#!/bin/bashCOMMENT
LOWtools/launcher/common/megatron_bridge/import/import.sh1#!/bin/bashCOMMENT
LOWtools/launcher/common/megatron_lm/quantize/task.py1# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.COMMENT
LOWtools/launcher/common/megatron_lm/quantize/quantize.sh1#!/bin/bashCOMMENT
LOWtools/launcher/common/megatron_lm/train/sft.sh1#!/bin/bashCOMMENT
LOWtools/launcher/common/megatron_lm/train/sft.sh21util_install_extra_depCOMMENT
LOWtools/launcher/common/megatron_lm/train/sft.sh61###################################################################################################COMMENT
LOWtools/launcher/common/megatron_lm/export/export.sh1#!/bin/bashCOMMENT
LOWtools/launcher/common/megatron_lm/export/export.sh21util_install_extra_depCOMMENT
LOWtools/launcher/common/eagle3/dump_offline_data_vllm.sh1#!/bin/bashCOMMENT
LOWtools/launcher/common/eagle3/dump_offline_data_vllm.sh21COMMENT
LOWtools/launcher/common/eagle3/train_eagle_streaming.sh1#!/bin/bashCOMMENT
LOWtools/launcher/common/eagle3/train_eagle_streaming.sh21#COMMENT
LOWtools/launcher/common/eagle3/train_eagle_streaming.sh41#COMMENT
LOWtools/launcher/common/eagle3/hf_ptq.sh1#!/bin/bashCOMMENT
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Unused Imports526 hits · 518 pts
SeverityFileLineSnippetContext
LOWtools/mcp/tests/test_bridge.py18CODE
LOWtools/mcp/modelopt_mcp/server.py33CODE
LOWtools/mcp/modelopt_mcp/bridge.py34CODE
LOWtools/mcp/modelopt_mcp/__init__.py51CODE
LOWtools/precommit/check_modelopt_recipes.py33CODE
LOWtools/precommit/normalize_yaml_ext.py23CODE
LOWtools/precommit/check_launcher_yaml.py36CODE
LOWmodelopt/onnx/__init__.py24CODE
LOWmodelopt/onnx/__init__.py25CODE
LOWmodelopt/onnx/autocast/__init__.py18CODE
LOWmodelopt/onnx/autocast/__init__.py18CODE
LOWmodelopt/onnx/autocast/__init__.py19CODE
LOWmodelopt/onnx/quantization/__init__.py19CODE
LOWmodelopt/onnx/quantization/__init__.py20CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py24CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py24CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py24CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py28CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py29CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py29CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py30CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py30CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py30CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py30CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py30CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py30CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py30CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py30CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py30CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py41CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py41CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py41CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py41CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py47CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py48CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py18CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py18CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py19CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py19CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py19CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py19CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py19CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py19CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py19CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py19CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py19CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py19CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py19CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py19CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py19CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py19CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py19CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py19CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py37CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py38CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py38CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py38CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py38CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py38CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py38CODE
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Deep Nesting469 hits · 411 pts
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LOWexperimental/dms/tests/test_paged_cache.py255CODE
LOWexperimental/dms/dms/core.py287CODE
LOWtools/launcher/core.py623CODE
LOWtools/launcher/core.py713CODE
LOWtools/launcher/core.py243CODE
LOWtools/launcher/common/query.py130CODE
LOWtools/mcp/modelopt_mcp/bridge.py598CODE
LOWtools/precommit/check_modelopt_recipes.py50CODE
LOWtools/precommit/check_modelopt_recipes.py131CODE
LOWtools/precommit/check_modelopt_recipes.py203CODE
LOWmodelopt/onnx/trt_utils.py167CODE
LOWmodelopt/onnx/trt_utils.py294CODE
LOWmodelopt/onnx/trt_utils.py423CODE
LOWmodelopt/onnx/utils.py319CODE
LOWmodelopt/onnx/utils.py424CODE
LOWmodelopt/onnx/utils.py812CODE
LOWmodelopt/onnx/utils.py1120CODE
LOWmodelopt/onnx/utils.py1296CODE
LOWmodelopt/onnx/utils.py1317CODE
LOWmodelopt/onnx/utils.py1400CODE
LOWmodelopt/onnx/utils.py1782CODE
LOWmodelopt/onnx/utils.py833CODE
LOWmodelopt/onnx/autocast/graphsanitizer.py554CODE
LOWmodelopt/onnx/autocast/utils.py84CODE
LOWmodelopt/onnx/autocast/referencerunner.py161CODE
LOWmodelopt/onnx/autocast/precisionconverter.py289CODE
LOWmodelopt/onnx/autocast/precisionconverter.py483CODE
LOWmodelopt/onnx/autocast/precisionconverter.py532CODE
LOWmodelopt/onnx/autocast/precisionconverter.py863CODE
LOWmodelopt/onnx/autocast/precisionconverter.py894CODE
LOWmodelopt/onnx/autocast/precisionconverter.py1036CODE
LOWmodelopt/onnx/autocast/precisionconverter.py1083CODE
LOWmodelopt/onnx/autocast/precisionconverter.py1148CODE
LOWmodelopt/onnx/autocast/precisionconverter.py295CODE
LOWmodelopt/onnx/autocast/precisionconverter.py331CODE
LOWmodelopt/onnx/autocast/nodeclassifier.py303CODE
LOWmodelopt/onnx/quantization/qdq_utils.py799CODE
LOWmodelopt/onnx/quantization/qdq_utils.py1022CODE
LOWmodelopt/onnx/quantization/qdq_utils.py1080CODE
LOWmodelopt/onnx/quantization/qdq_utils.py1249CODE
LOWmodelopt/onnx/quantization/qdq_utils.py1413CODE
LOWmodelopt/onnx/quantization/ort_utils.py155CODE
LOWmodelopt/onnx/quantization/ort_utils.py227CODE
LOWmodelopt/onnx/quantization/ort_utils.py317CODE
LOWmodelopt/onnx/quantization/ort_utils.py423CODE
LOWmodelopt/onnx/quantization/quantize.py97CODE
LOWmodelopt/onnx/quantization/partitioning.py363CODE
LOWmodelopt/onnx/quantization/partitioning.py404CODE
LOWmodelopt/onnx/quantization/graph_utils.py305CODE
LOWmodelopt/onnx/quantization/graph_utils.py399CODE
LOWmodelopt/onnx/quantization/graph_utils.py507CODE
LOWmodelopt/onnx/quantization/graph_utils.py578CODE
LOWmodelopt/onnx/quantization/graph_utils.py657CODE
LOWmodelopt/onnx/quantization/graph_utils.py833CODE
LOWmodelopt/onnx/quantization/graph_utils.py1155CODE
LOWmodelopt/onnx/quantization/graph_utils.py1415CODE
LOWmodelopt/onnx/quantization/graph_utils.py1892CODE
LOWmodelopt/onnx/quantization/graph_utils.py1909CODE
LOWmodelopt/onnx/quantization/graph_utils.py2009CODE
LOWmodelopt/onnx/quantization/int4.py125CODE
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Self-Referential Comments136 hits · 384 pts
SeverityFileLineSnippetContext
MEDIUMexperimental/dms/models/qwen3/modeling_qwen3_dms.py323 # Create the masksCOMMENT
MEDIUMtools/launcher/tests/test_docker_launch.py39 # Create a trivial scriptCOMMENT
MEDIUMtools/launcher/tests/test_docker_launch.py46 # Create a YAML configCOMMENT
MEDIUMtools/launcher/common/megatron_lm/quantize/quantize.sh82# This function handles the exit status (fails the CI).COMMENT
MEDIUMtools/launcher/common/eagle3/train_eagle.sh38# This function handles the exit status (fails the CI).COMMENT
MEDIUMmodelopt/onnx/logging_config.py22# Create a parent logger for all ONNX componentsCOMMENT
MEDIUMmodelopt/onnx/utils.py844 # Create a temporary model with just this graph for graphsurgeonCOMMENT
MEDIUMmodelopt/onnx/autocast/logging_config.py27# Create a logger for all AutoCast components as a child of modelopt.onnxCOMMENT
MEDIUMmodelopt/onnx/autocast/__main__.py33"""This module provides the command line interface (CLI) entry point for AutoCast."""STRING
MEDIUMmodelopt/onnx/quantization/qdq_utils.py1357 # Create a new scale tensorCOMMENT
MEDIUMmodelopt/onnx/quantization/qdq_utils.py1473 # Create a new scale tensorCOMMENT
MEDIUMmodelopt/onnx/quantization/fp8.py288 # Create a temp file for intermediate modelCOMMENT
MEDIUMmodelopt/onnx/quantization/quantize.py16"""This module is used to convert an ONNX model (w/o QDQ nodes) and calibration data into an ONNX model with QDQ nodes.STRING
MEDIUMmodelopt/onnx/quantization/graph_utils.py251 # Create a dictionary to store tensor producer nodesCOMMENT
MEDIUMmodelopt/onnx/quantization/graph_utils.py294 # Create a dictionary to store tensor consumer nodesCOMMENT
MEDIUMmodelopt/onnx/quantization/int4.py549 # Create a temp file for augmented modelCOMMENT
MEDIUMmodelopt/onnx/quantization/int4.py1106 # Create a temp file for augmented modelCOMMENT
MEDIUMmodelopt/onnx/quantization/ort_patching.py340 # Create the inference session with EP configuration on augmented_modelCOMMENT
MEDIUMmodelopt/onnx/quantization/ort_patching.py347 # Create the inference session with EP configuration on provided model pathCOMMENT
MEDIUMmodelopt/onnx/quantization/ort_patching.py1286 # Create a new onnx modelCOMMENT
MEDIUMmodelopt/onnx/quantization/ort_patching.py1395 # Create a new onnx modelCOMMENT
MEDIUMmodelopt/onnx/quantization/ort_patching.py537 """This function is an auxiliary function of collect_data to solve the OOM issue in the MinMax Calibrator.STRING
MEDIUMmodelopt/onnx/quantization/ort_patching.py567 """This function is an auxiliary function of collect_data to solve the OOM issue in the MinMax Calibrator.STRING
MEDIUMmodelopt/onnx/quantization/int8.py263 # Create a temp file for intermediate modelCOMMENT
MEDIUMmodelopt/onnx/export/nvfp4_exporter.py355 # Create a Cast node to convert the input to FP16/BF16COMMENT
MEDIUMmodelopt/onnx/export/int4_exporter.py182 # Create a new scale tensorCOMMENT
MEDIUM…arsity/attention_sparsity/calibration/ruler_dataset.py972 # Create a simple QA pairCOMMENT
MEDIUMmodelopt/torch/utils/speech_dataset_utils.py128 # Define the DataLoader (batches will be created automatically by DataLoader)COMMENT
MEDIUMmodelopt/torch/utils/plugins/megatron_generate.py314 # Create a static inference context if KV-cache is enabled.COMMENT
MEDIUM…delopt/torch/quantization/plugins/pytorch_geometric.py25 >>> # Create a model with PyG layersSTRING
MEDIUMmodelopt/torch/quantization/plugins/attention.py333 # Create a new class method from bytecodeCOMMENT
MEDIUMmodelopt/torch/quantization/plugins/attention.py348 # Create a new subclass on the flyCOMMENT
MEDIUMmodelopt/torch/quantization/plugins/huggingface.py1539 # Create a QuantDbrxExpertGLU class on the flyCOMMENT
MEDIUMmodelopt/torch/quantization/plugins/huggingface.py1552 # Create a QuantFalconLinear class on the flyCOMMENT
MEDIUMmodelopt/torch/quantization/backends/gemm_registry.py16"""This module provides a registry for low-precision GEMM (General Matrix Multiplication) implementations.STRING
MEDIUMmodelopt/torch/quantization/backends/nvfp4_gemm.py16"""This module provides a GEMM function for nvfp4 quantization."""STRING
MEDIUM…opt/torch/quantization/backends/fp8_per_tensor_gemm.py16"""This module provides a GEMM function for fp8 per tensor quantization."""STRING
MEDIUMmodelopt/torch/quantization/utils/core_utils.py906 # Create a new QFSDPParam or FSDPParam based on weight typeCOMMENT
MEDIUMmodelopt/torch/quantization/utils/core_utils.py662 """This function is directly copied from the latest version of torch FSDP."""STRING
MEDIUMmodelopt/torch/quantization/qtensor/base_qtensor.py166 # Create a QTensorWrapper with the correct metadata during unshardingCOMMENT
MEDIUMmodelopt/torch/quantization/qtensor/mxfp4_tensor.py97 # Create a new tensor with alternating valuesCOMMENT
MEDIUMmodelopt/torch/quantization/qtensor/mxfp4_tensor.py122 # Create a tensor with the E2M1 valuesCOMMENT
MEDIUMmodelopt/torch/_deploy/utils/torch_onnx.py169 # Create a dictionary with all necessary attributesCOMMENT
MEDIUMmodelopt/torch/_deploy/utils/torch_onnx.py191 # Create a new instance without calling __init__ and set the attributesCOMMENT
MEDIUMmodelopt/torch/_deploy/_runtime/common.py23 """This function is supposed to use as a decorator to measure the execution time of another function.STRING
MEDIUMmodelopt/torch/puzzletron/entrypoint.py16"""This module provides the main compression function for a model using MIP-based NAS search algorithm."""STRING
MEDIUMmodelopt/torch/puzzletron/tools/logger.py123# Define a custom function to redirect warnings to loggerCOMMENT
MEDIUM…ch/puzzletron/replacement_library/replacement_utils.py15"""This module provides helper functions for parsing, sorting, and analyzing layer replacementSTRING
MEDIUM…zzletron/bypass_distillation/stitched_model_factory.py529 # Create the student block stitched moduleCOMMENT
MEDIUMmodelopt/torch/opt/conversion.py593 model = ... # Create the model-like objectSTRING
MEDIUMmodelopt/torch/opt/plugins/megatron.py42 # Create a copy to avoid modifying the original metadata dictCOMMENT
MEDIUMmodelopt/torch/speculative/utils.py271 # Initialize tracking variablesCOMMENT
MEDIUMmodelopt/torch/speculative/utils.py502 # Import required modulesCOMMENT
MEDIUMmodelopt/torch/speculative/plugins/hf_medusa.py71 # Create a list of Medusa headsCOMMENT
MEDIUMmodelopt/torch/export/layer_utils.py1620 # Create a dummy module so that is_mlp may catch it.COMMENT
MEDIUMmodelopt/torch/export/convert_hf_config.py243 # Create a hashable key from the layer configCOMMENT
MEDIUMmodelopt/torch/export/unified_export_hf.py458 # Define the dummy forward function for LLMCOMMENT
MEDIUMmodelopt/torch/export/model_config_export.py476 # Create a NFS workspace under the export folder which is also assumed to be NFS.COMMENT
MEDIUMtests/unit/onnx/test_onnx_utils.py283 # Create the ONNX graph with the nodesCOMMENT
MEDIUMtests/unit/onnx/test_onnx_utils.py299 # Create the ONNX initializersCOMMENT
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Modern Structural Boilerplate315 hits · 320 pts
SeverityFileLineSnippetContext
LOWexperimental/__init__.py35__all__ = []CODE
LOWexperimental/dms/models/qwen3/modeling_qwen3_dms.py491__all__ = [CODE
LOWtools/launcher/slurm_config.py27__all__ = ["SlurmConfig", "slurm_factory"]CODE
LOWtools/launcher/__init__.py20__all__ = ["PACKAGE_DIR"]CODE
LOWtools/mcp/modelopt_mcp/__init__.py53__all__ = ["main"]CODE
LOWmodelopt/onnx/autocast/graphsanitizer.py194 def set_ir_version(self, max_ir_version: int | None) -> None:CODE
LOWmodelopt/onnx/autocast/graphsanitizer.py481 def _update_opset_version(self) -> None:CODE
LOWmodelopt/onnx/quantization/quantize.py83__all__ = ["quantize"]CODE
LOWmodelopt/onnx/quantization/int4.py61__all__ = ["quantize"]CODE
LOWmodelopt/onnx/quantization/__main__.py30__all__ = ["main"]CODE
LOWmodelopt/onnx/quantization/autotune/export_utils.py33__all__ = [CODE
LOWmodelopt/onnx/quantization/autotune/__init__.py56__all__ = ["MODE_PRESETS", "StoreWithExplicitFlag", "get_node_filter_list"]CODE
LOWmodelopt/onnx/quantization/autotune/autotuner_base.py226 def set_profile_region(self, region: Region | None, commit: bool = True) -> None:CODE
LOWmodelopt/onnx/graph_surgery/__init__.py131__all__ = [CODE
LOWmodelopt/onnx/graph_surgery/utils/__init__.py47__all__ = [CODE
LOWmodelopt/onnx/export/__init__.py18__all__ = [CODE
LOWmodelopt/recipe/config.py36__all__ = [CODE
LOWmodelopt/recipe/presets.py41__all__ = [CODE
LOWmodelopt/recipe/loader.py38__all__ = ["load_config", "load_recipe"]CODE
LOW…pt/torch/sparsity/attention_sparsity/model_sparsify.py29__all__ = [CODE
LOWmodelopt/torch/sparsity/attention_sparsity/config.py776__all__ = [CODE
LOW…delopt/torch/sparsity/attention_sparsity/conversion.py42def _set_attn_implementation(model: nn.Module, config: SparseAttentionConfig) -> None:CODE
LOW…/torch/sparsity/attention_sparsity/methods/registry.py43 def set_calibration_mode(self, enabled: bool) -> None:CODE
LOW…/torch/sparsity/attention_sparsity/methods/__init__.py20__all__ = [CODE
LOW…ch/sparsity/attention_sparsity/calibration/__init__.py22__all__ = [CODE
LOW…arsity/attention_sparsity/calibration/ruler_dataset.py58logger = logging.getLogger(__name__)CODE
LOW…/torch/sparsity/attention_sparsity/plugins/__init__.py31__all__ = [CODE
LOW…elopt/torch/sparsity/weight_sparsity/sparsification.py29__all__ = ["export", "sparsify"]CODE
LOWmodelopt/torch/sparsity/weight_sparsity/sparsegpt.py214 def _setup_forward_hook(cls, mod: SparseModule) -> None:CODE
LOWmodelopt/torch/sparsity/weight_sparsity/module.py25__all__ = ["SpDMRegistry", "SparseModule"]CODE
LOWmodelopt/torch/kernels/sparsity/attention/__init__.py46def set_skip_softmax_context(active: bool) -> None:CODE
LOWmodelopt/torch/kernels/sparsity/attention/__init__.py56__all__ = [CODE
LOWmodelopt/torch/kernels/quantization/gemm/fp4_kernel.py31__all__ = [CODE
LOW…opt/torch/kernels/quantization/gemm/nvfp4_fp8_sweep.py39__all__ = [CODE
LOW…t/torch/kernels/quantization/gemm/fp4_kernel_hopper.py29__all__ = ["fp4_fake_quant_block"]CODE
LOW…t/torch/kernels/quantization/gemm/gptq_fused_kernel.py35__all__ = ["gptq_fused_block_scalar"]CODE
LOWmodelopt/torch/kernels/common/attention/__init__.py47__all__ = [CODE
LOWmodelopt/torch/kernels/common/attention/triton_fa.py1290__all__ = ["LOG2E", "_apply_mask", "attention"]CODE
LOW…/torch/kernels/common/attention/hf_triton_attention.py302__all__ = [CODE
LOWmodelopt/torch/trace/analyzer.py42__all__ = ["analyze_symbols"]CODE
LOWmodelopt/torch/trace/symbols.py26__all__ = ["SymInfo", "SymMap", "Symbol"]CODE
LOWmodelopt/torch/trace/symbols.py507 def set_symbol(self, mod: nn.Module, name: str, symbol: Symbol) -> None:CODE
LOWmodelopt/torch/trace/tracer.py30__all__ = ["GraphCollection", "RobustTracer", "recursive_trace"]CODE
LOWmodelopt/torch/trace/modules/concat.py43__all__ = ["ConcatNodeProcessor", "ConcatSymbol"]CODE
LOWmodelopt/torch/trace/modules/nn.py24__all__ = ["SymDepth"]CODE
LOWmodelopt/torch/trace/modules/nn.py89 def set_skippable(self, idx: int, val: bool) -> None:CODE
LOWmodelopt/torch/nas/algorithms.py56__all__ = ["profile", "search"]CODE
LOWmodelopt/torch/nas/algorithms.py286 def set_rel_lower_bounds(self, rel_lower: float = 0.85) -> None:CODE
LOWmodelopt/torch/nas/registry.py20__all__ = ["DMRegistry"]CODE
LOWmodelopt/torch/nas/conversion.py37__all__ = ["ExportConfig", "ExportNASModeDescriptor", "convert", "export"]CODE
LOWmodelopt/torch/nas/utils.py46__all__ = [ # noqa: RUF022CODE
LOWmodelopt/torch/nas/search_space.py33__all__ = ["SearchSpace", "generate_search_space"]CODE
LOWmodelopt/torch/nas/traced_hp.py23__all__ = ["TracedHp", "TracedHpRegistry"]CODE
LOWmodelopt/torch/nas/autonas.py64__all__ = [CODE
LOWmodelopt/torch/nas/plugins/megatron_model_stats.py59__all__ = [CODE
LOWmodelopt/torch/nas/plugins/mbridge.py47__all__ = []CODE
LOWmodelopt/torch/nas/plugins/megatron.py102__all__ = ["get_te_mamba_stack_spec"]CODE
LOWmodelopt/torch/nas/plugins/torch.py23__all__ = []CODE
LOWmodelopt/torch/nas/hparams/concat.py31__all__ = ["ConcatTracedHp"]CODE
LOWmodelopt/torch/nas/hparams/concat.py110 def _set_hp_start_idx(self) -> None:CODE
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Cross-File Repetition63 hits · 315 pts
SeverityFileLineSnippetContext
HIGHmodelopt/torch/sparsity/attention_sparsity/mode.py0returns the value (str representation) of the mode.STRING
HIGHmodelopt/torch/sparsity/weight_sparsity/mode.py0returns the value (str representation) of the mode.STRING
HIGHmodelopt/torch/nas/conversion.py0returns the value (str representation) of the mode.STRING
HIGHmodelopt/torch/nas/autonas.py0returns the value (str representation) of the mode.STRING
HIGHmodelopt/torch/distill/mode.py0returns the value (str representation) of the mode.STRING
HIGHmodelopt/torch/peft/mode.py0returns the value (str representation) of the mode.STRING
HIGHmodelopt/torch/quantization/mode.py0returns the value (str representation) of the mode.STRING
HIGHmodelopt/torch/prune/fastnas.py0returns the value (str representation) of the mode.STRING
HIGHmodelopt/torch/prune/plugins/mcore_minitron.py0returns the value (str representation) of the mode.STRING
HIGHmodelopt/torch/speculative/mode.py0returns the value (str representation) of the mode.STRING
HIGHtests/unit/torch/opt/test_mode_registry.py0returns the value (str representation) of the mode.STRING
HIGHmodelopt/torch/sparsity/attention_sparsity/mode.py0the mode that corresponds to the export mode of this mode.STRING
HIGHmodelopt/torch/sparsity/weight_sparsity/mode.py0the mode that corresponds to the export mode of this mode.STRING
HIGHmodelopt/torch/nas/autonas.py0the mode that corresponds to the export mode of this mode.STRING
HIGHmodelopt/torch/distill/mode.py0the mode that corresponds to the export mode of this mode.STRING
HIGHmodelopt/torch/quantization/mode.py0the mode that corresponds to the export mode of this mode.STRING
HIGHmodelopt/torch/prune/fastnas.py0the mode that corresponds to the export mode of this mode.STRING
HIGHmodelopt/torch/prune/plugins/mcore_minitron.py0the mode that corresponds to the export mode of this mode.STRING
HIGHmodelopt/torch/sparsity/weight_sparsity/mode.py0class to describe the layerwise knowledge-distillation mode. the properties of this mode can be inspected via the sourceSTRING
HIGHmodelopt/torch/nas/conversion.py0class to describe the layerwise knowledge-distillation mode. the properties of this mode can be inspected via the sourceSTRING
HIGHmodelopt/torch/nas/autonas.py0class to describe the layerwise knowledge-distillation mode. the properties of this mode can be inspected via the sourceSTRING
HIGHmodelopt/torch/distill/mode.py0class to describe the layerwise knowledge-distillation mode. the properties of this mode can be inspected via the sourceSTRING
HIGHmodelopt/torch/quantization/mode.py0class to describe the layerwise knowledge-distillation mode. the properties of this mode can be inspected via the sourceSTRING
HIGHmodelopt/torch/prune/fastnas.py0class to describe the layerwise knowledge-distillation mode. the properties of this mode can be inspected via the sourceSTRING
HIGHmodelopt/torch/prune/plugins/mcore_minitron.py0class to describe the layerwise knowledge-distillation mode. the properties of this mode can be inspected via the sourceSTRING
HIGHmodelopt/torch/speculative/mode.py0class to describe the layerwise knowledge-distillation mode. the properties of this mode can be inspected via the sourceSTRING
HIGHmodelopt/torch/nas/autonas.py0specifies the search algorithm to use for this mode (if any).STRING
HIGHmodelopt/torch/opt/mode.py0specifies the search algorithm to use for this mode (if any).STRING
HIGHmodelopt/torch/prune/fastnas.py0specifies the search algorithm to use for this mode (if any).STRING
HIGHmodelopt/torch/nas/autonas.py0the mode's entrypoint for updating the models state before saving.STRING
HIGHmodelopt/torch/quantization/mode.py0the mode's entrypoint for updating the models state before saving.STRING
HIGHmodelopt/torch/prune/fastnas.py0the mode's entrypoint for updating the models state before saving.STRING
HIGHmodelopt/torch/nas/autonas.py0the mode's entrypoint for updating the models state before new mode.STRING
HIGHmodelopt/torch/peft/mode.py0the mode's entrypoint for updating the models state before new mode.STRING
HIGHmodelopt/torch/quantization/mode.py0the mode's entrypoint for updating the models state before new mode.STRING
HIGHmodelopt/torch/prune/fastnas.py0the mode's entrypoint for updating the models state before new mode.STRING
HIGHmodelopt/torch/nas/hparams/concat.py0set the active value with a sanity check for choices and dynamic hparams.STRING
HIGHmodelopt/torch/quantization/algorithms.py0set the active value with a sanity check for choices and dynamic hparams.STRING
HIGHmodelopt/torch/opt/hparam.py0set the active value with a sanity check for choices and dynamic hparams.STRING
HIGHmodelopt/torch/quantization/qtensor/nf4_tensor.py0implements the int4 quantization on tensors for more efficient storage or computation. attributes: quantized_data (torchSTRING
HIGHmodelopt/torch/quantization/qtensor/nvfp4_tensor.py0implements the int4 quantization on tensors for more efficient storage or computation. attributes: quantized_data (torchSTRING
HIGHmodelopt/torch/quantization/qtensor/mxfp4_tensor.py0implements the int4 quantization on tensors for more efficient storage or computation. attributes: quantized_data (torchSTRING
HIGHmodelopt/torch/quantization/qtensor/fp8_tensor.py0implements the int4 quantization on tensors for more efficient storage or computation. attributes: quantized_data (torchSTRING
HIGHmodelopt/torch/quantization/qtensor/int4_tensor.py0implements the int4 quantization on tensors for more efficient storage or computation. attributes: quantized_data (torchSTRING
HIGH…ts/unit/torch/quantization/test_layerwise_calibrate.py0decoder layer that returns a tuple, mimicking huggingface decoder layers.STRING
HIGH…sts/gpu/torch/quantization/test_layerwise_calibrate.py0decoder layer that returns a tuple, mimicking huggingface decoder layers.STRING
HIGH…/gpu/torch/quantization/plugins/test_accelerate_gpu.py0decoder layer that returns a tuple, mimicking huggingface decoder layers.STRING
HIGH…ollect_hidden_states/send_conversations_for_hiddens.py0maximum number of tokens in a conversation. longer conversations will be skipped. defaults to 3072 tokens.STRING
HIGH…/collect_hidden_states/compute_hidden_states_trtllm.py0maximum number of tokens in a conversation. longer conversations will be skipped. defaults to 3072 tokens.STRING
HIGH…ding/collect_hidden_states/compute_hidden_states_hf.py0maximum number of tokens in a conversation. longer conversations will be skipped. defaults to 3072 tokens.STRING
HIGH…speculative_decoding/scripts/send_conversation_vllm.py0maximum number of tokens in a conversation. longer conversations will be skipped. defaults to 3072 tokens.STRING
HIGH…ollect_hidden_states/send_conversations_for_hiddens.py0root directory in which to save the hidden states. the data will be saved as a torch (`.pt`) dump file for each conversaSTRING
HIGH…/collect_hidden_states/compute_hidden_states_trtllm.py0root directory in which to save the hidden states. the data will be saved as a torch (`.pt`) dump file for each conversaSTRING
HIGH…ding/collect_hidden_states/compute_hidden_states_hf.py0root directory in which to save the hidden states. the data will be saved as a torch (`.pt`) dump file for each conversaSTRING
HIGH…speculative_decoding/scripts/send_conversation_vllm.py0root directory in which to save the hidden states. the data will be saved as a torch (`.pt`) dump file for each conversaSTRING
HIGH…ollect_hidden_states/send_conversations_for_hiddens.py0for debugging purposes, limit the number of conversations processed. default is none, meaning no limit.STRING
HIGH…/collect_hidden_states/compute_hidden_states_trtllm.py0for debugging purposes, limit the number of conversations processed. default is none, meaning no limit.STRING
HIGH…ding/collect_hidden_states/compute_hidden_states_hf.py0for debugging purposes, limit the number of conversations processed. default is none, meaning no limit.STRING
HIGH…speculative_decoding/scripts/send_conversation_vllm.py0for debugging purposes, limit the number of conversations processed. default is none, meaning no limit.STRING
HIGH…les/llm_sparsity/weight_sparsity/export_trtllm_ckpt.py0resize tokenizer and embedding. note: this is the unoptimized version that may make your embedding size not be divisibleSTRING
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Structural Annotation Overuse177 hits · 294 pts
SeverityFileLineSnippetContext
LOWtools/launcher/common/megatron_lm/quantize/quantize.sh49# Step 1: quantizeCOMMENT
LOW…examples/moonshotai/Kimi-K2.5/hf_streaming_eagle3.yaml28 # Step 1: Build input conversations (model-agnostic)COMMENT
LOW…examples/moonshotai/Kimi-K2.5/hf_streaming_eagle3.yaml42 # Step 2: Streaming EAGLE3 training (node0 serve TP=4 / node1 train), 4 GPU/nodeCOMMENT
LOW…examples/moonshotai/Kimi-K2.5/hf_streaming_eagle3.yaml84 # Step 3: Benchmark speculative decoding (VLLM backend, Kimi served at TP=4)COMMENT
LOW…r/examples/moonshotai/Kimi-K2.5/hf_offline_dflash.yaml19 # Step 1: Data synthesis via vLLM serverCOMMENT
LOW…uncher/examples/Qwen/Qwen3-0.6B/hf_offline_eagle3.yaml24 # Step 1: Data synthesis via TRT-LLM serverCOMMENT
LOW…uncher/examples/Qwen/Qwen3-0.6B/hf_offline_eagle3.yaml49 # Step 2: Dump hidden states from target modelCOMMENT
LOW…uncher/examples/Qwen/Qwen3-0.6B/hf_offline_eagle3.yaml67 # Step 3: Train EAGLE3 draft head (offline, single task)COMMENT
LOW…uncher/examples/Qwen/Qwen3-0.6B/hf_offline_eagle3.yaml89 # Step 4: Benchmark speculative decoding (VLLM backend)COMMENT
LOW…/launcher/examples/Qwen/Qwen3-8B/hf_online_dflash.yaml34 # Step 1: Online DFlash trainingCOMMENT
LOW…/launcher/examples/Qwen/Qwen3-8B/hf_online_dflash.yaml64 # Step 2: vLLM smoke test (uses exported checkpoint from training)COMMENT
LOW…/launcher/examples/Qwen/Qwen3-8B/hf_online_dflash.yaml80 # Step 3: HF AR evaluationCOMMENT
LOW…cher/examples/Qwen/Qwen3-8B/hf_offline_eagle3_ptq.yaml25 # Step 1: Data synthesis via TRT-LLM serverCOMMENT
LOW…cher/examples/Qwen/Qwen3-8B/hf_offline_eagle3_ptq.yaml49 # Step 2: Dump hidden states from target modelCOMMENT
LOW…cher/examples/Qwen/Qwen3-8B/hf_offline_eagle3_ptq.yaml67 # Step 3: Train EAGLE3 draft head (offline, single task) and export checkpointCOMMENT
LOW…cher/examples/Qwen/Qwen3-8B/hf_offline_eagle3_ptq.yaml92 # Step 4: PTQ — quantize the EAGLE3 model using offline hidden statesCOMMENT
LOW…cher/examples/Qwen/Qwen3-8B/hf_offline_eagle3_ptq.yaml108 # Step 5: Benchmark speculative decoding (VLLM backend)COMMENT
LOW…auncher/examples/Qwen/Qwen3-8B/eagle3_quick_check.yaml21 # Step 1: Data synthesis via TRT-LLM serverCOMMENT
LOW…auncher/examples/Qwen/Qwen3-8B/eagle3_quick_check.yaml44 # Step 2: Dump hidden states from target modelCOMMENT
LOW…auncher/examples/Qwen/Qwen3-8B/eagle3_quick_check.yaml61 # Step 3: Train EAGLE3 draft head (offline, single task)COMMENT
LOW…auncher/examples/Qwen/Qwen3-8B/eagle3_quick_check.yaml83 # Step 4: Benchmark speculative decoding (VLLM backend)COMMENT
LOW…ples/Qwen/Qwen3-8B/hf_streaming_eagle3_multi_node.yaml23 # Step 1: Build input conversationsCOMMENT
LOW…ples/Qwen/Qwen3-8B/hf_streaming_eagle3_multi_node.yaml36 # Step 2: Streaming EAGLE3 training — 2 serve replicas (TP=2) + 2 trainer nodes (2 GPU each).COMMENT
LOW…ples/Qwen/Qwen3-8B/hf_streaming_eagle3_multi_node.yaml68 # Step 3: Benchmark speculative decoding (VLLM backend)COMMENT
LOW…ples/Qwen/Qwen3-8B/hf_streaming_dflash_multi_node.yaml26 # Step 1: Build input conversationsCOMMENT
LOW…ples/Qwen/Qwen3-8B/hf_streaming_dflash_multi_node.yaml39 # Step 2: Streaming DFlash training — 2 serve replicas (TP=2) + 2 trainer nodes (2 GPU each).COMMENT
LOW…ples/Qwen/Qwen3-8B/hf_streaming_dflash_multi_node.yaml85 # Step 3: vLLM smoke test (DFlash, uses exported checkpoint from training)COMMENT
LOW…s/launcher/examples/Qwen/Qwen3-8B/megatron_lm_ptq.yaml66 # Step 3: TRT-LLM eval MMLU on all exported checkpointsCOMMENT
LOW…/launcher/examples/Qwen/Qwen3-8B/hf_online_domino.yaml30 # Step 1: Build input conversations. example_data_config.yaml enables only theCOMMENT
LOW…/launcher/examples/Qwen/Qwen3-8B/hf_online_domino.yaml46 # Step 2: Online Domino training (the script exports the drafter at the end).COMMENT
LOW…uncher/examples/Qwen/Qwen3-8B/hf_streaming_dflash.yaml28 # Step 1: Build input conversationsCOMMENT
LOW…uncher/examples/Qwen/Qwen3-8B/hf_streaming_dflash.yaml41 # Step 2: Streaming DFlash training — node 0 vllm serve, node 1 trainer.COMMENT
LOW…uncher/examples/Qwen/Qwen3-8B/hf_streaming_dflash.yaml82 # Step 3: vLLM smoke test (DFlash, uses exported checkpoint from training)COMMENT
LOW…launcher/examples/Qwen/Qwen3-8B/hf_offline_eagle3.yaml24 # Step 1: Data synthesis via TRT-LLM serverCOMMENT
LOW…launcher/examples/Qwen/Qwen3-8B/hf_offline_eagle3.yaml48 # Step 2: Dump hidden states from target modelCOMMENT
LOW…launcher/examples/Qwen/Qwen3-8B/hf_offline_eagle3.yaml66 # Step 3: Train EAGLE3 draft head (offline, single task)COMMENT
LOW…launcher/examples/Qwen/Qwen3-8B/hf_offline_eagle3.yaml84 # Step 4: Benchmark speculative decoding (VLLM backend)COMMENT
LOW…Qwen/Qwen3-30B-A3B/hf_streaming_eagle3_multi_node.yaml36 # Step 1: Build input conversationsCOMMENT
LOW…Qwen/Qwen3-30B-A3B/hf_streaming_eagle3_multi_node.yaml49 # Step 2: Streaming EAGLE3 training — 2 serve replicas (TP=2) + 2 trainer nodes (2 GPU each).COMMENT
LOW…Qwen/Qwen3-30B-A3B/hf_streaming_eagle3_multi_node.yaml88 # Step 3: Benchmark speculative decoding (VLLM backend)COMMENT
LOW…Qwen/Qwen3-30B-A3B/hf_streaming_dflash_multi_node.yaml40 # Step 1: Build input conversationsCOMMENT
LOW…Qwen/Qwen3-30B-A3B/hf_streaming_dflash_multi_node.yaml53 # Step 2: Streaming DFlash training — 2 serve replicas (TP=2) + 2 trainer nodes (2 GPU each).COMMENT
LOW…Qwen/Qwen3-30B-A3B/hf_streaming_dflash_multi_node.yaml107 # Step 3: vLLM smoke test (DFlash, uses exported checkpoint from training)COMMENT
LOW…auncher/examples/Qwen/Qwen3.5-4B/hf_online_dflash.yaml22 # Step 1: Online DFlash trainingCOMMENT
LOW…auncher/examples/Qwen/Qwen3.5-4B/hf_online_dflash.yaml55 # Step 2: vLLM smoke test with exported DFlash draftCOMMENT
LOW…auncher/examples/Qwen/Qwen3.5-4B/hf_online_dflash.yaml70 # Step 3: HF AR evaluationCOMMENT
LOW…s/launcher/examples/Qwen/Qwen3.5-4B/specdec_bench.yaml34 # Step 1: qualitative split — quality / acceptance-rate numbers.COMMENT
LOW…s/launcher/examples/Qwen/Qwen3.5-4B/specdec_bench.yaml62 # Step 2: throughput_32k split — long-context throughput.COMMENT
LOW…uncher/examples/Qwen/Qwen3.5-4B/specdec_bench_mtp.yaml28 # Step 1: qualitative split — quality / acceptance-rate numbers with MTP draft=3.COMMENT
LOW…uncher/examples/Qwen/Qwen3.5-4B/specdec_bench_mtp.yaml72 # Step 2: throughput_32k split — long-context throughput with MTP draft=3.COMMENT
LOW…ples/MiniMax/MiniMax-M2.7-DFlash/hf_online_dflash.yaml29 # Step 1: Online DFlash training.COMMENT
LOW…ples/MiniMax/MiniMax-M2.7-DFlash/hf_online_dflash.yaml81 # Step 2: vLLM smoke test with the exported DFlash draft.COMMENT
LOW…ples/MiniMax/MiniMax-M2.7-DFlash/hf_online_dflash.yaml97 # Step 3: HF AR (acceptance-length) evaluation on MT-Bench.COMMENT
LOW…les/MiniMax/MiniMax-M2.7-DFlash/hf_offline_dflash.yaml24 # Step 1: Dump base-model hidden states via vLLM extract_hidden_states (TP=4).COMMENT
LOW…les/MiniMax/MiniMax-M2.7-DFlash/hf_offline_dflash.yaml46 # Step 2: Train DFlash offline on the dumped hidden states. FakeBaseModel avoidsCOMMENT
LOW…xamples/MiniMax/MiniMax-M2.7-DFlash/specdec_bench.yaml32 # Step 1: qualitative split — quality / acceptance-length numbers.COMMENT
LOW…xamples/MiniMax/MiniMax-M2.7-DFlash/specdec_bench.yaml60 # Step 2: throughput_32k split — long-context throughput / AL.COMMENT
LOW…/meta-llama/Llama-3.2-1B-Instruct/megatron_lm_qad.yaml30 # Step 1: PTQ quantize (NVFP4) + MMLU gate. export skipped (QAD only needs theCOMMENT
LOW…/meta-llama/Llama-3.2-1B-Instruct/megatron_lm_qad.yaml52 # Step 2: QAD/SFT training on the quantized checkpoint from task_0. All trainCOMMENT
LOW…openai/gpt-oss-20b/hf_streaming_eagle3_multi_node.yaml33 # Step 1: Build input conversationsCOMMENT
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Excessive Try-Catch Wrapping265 hits · 292 pts
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LOWtools/launcher/common/query.py79 except Exception as e:CODE
LOWtools/mcp/modelopt_mcp/bridge.py2245 except Exception as e:CODE
LOWtools/precommit/check_modelopt_recipes.py92 except Exception:CODE
LOWtools/precommit/check_modelopt_recipes.py159 except Exception as exc:CODE
LOWtools/precommit/check_launcher_yaml.py110 except Exception as exc:CODE
LOWtools/precommit/check_launcher_yaml.py119 except Exception as exc:CODE
LOWmodelopt/onnx/trt_utils.py54 except Exception as e:CODE
LOWmodelopt/onnx/trt_utils.py82 except Exception as e:CODE
LOWmodelopt/onnx/trt_utils.py328 except Exception as e:CODE
LOWmodelopt/onnx/logging_config.py55 except Exception as e:CODE
LOWmodelopt/onnx/utils.py462 except Exception:CODE
LOWmodelopt/onnx/utils.py505 except Exception as e:CODE
LOWmodelopt/onnx/utils.py564 except Exception as e:CODE
LOWmodelopt/onnx/utils.py660 except Exception as e:CODE
LOWmodelopt/onnx/utils.py856 except Exception as e:CODE
LOWmodelopt/onnx/utils.py1037 except Exception as e:CODE
LOWmodelopt/onnx/utils.py1177 except Exception as e:CODE
LOWmodelopt/onnx/utils.py1231 except Exception as e:CODE
LOWmodelopt/onnx/utils.py1954 except Exception as e:CODE
LOWmodelopt/onnx/utils.py1959 except Exception as e:CODE
LOWmodelopt/onnx/autocast/logging_config.py85 except Exception as e:CODE
LOWmodelopt/onnx/autocast/graphsanitizer.py185 except Exception as e:CODE
LOWmodelopt/onnx/autocast/graphsanitizer.py210 except Exception as e:CODE
LOWmodelopt/onnx/autocast/graphsanitizer.py244 except Exception as e:CODE
LOWmodelopt/onnx/autocast/graphsanitizer.py387 except Exception as e:CODE
MEDIUMmodelopt/onnx/autocast/precisionconverter.py319def _can_propagate_type(from_type, to_type):CODE
LOWmodelopt/onnx/autocast/precisionconverter.py327 except Exception as e:CODE
LOWmodelopt/onnx/quantization/qdq_utils.py784 except Exception as e:CODE
LOWmodelopt/onnx/quantization/qdq_utils.py887 except Exception as e:CODE
LOWmodelopt/onnx/quantization/qdq_utils.py927 except Exception as e:CODE
LOWmodelopt/onnx/quantization/qdq_utils.py1007 except Exception as e:CODE
LOWmodelopt/onnx/quantization/ort_utils.py255 except Exception as e:CODE
LOWmodelopt/onnx/quantization/ort_utils.py328 except Exception as e:CODE
LOWmodelopt/onnx/quantization/ort_utils.py342 except Exception as e:CODE
LOWmodelopt/onnx/quantization/ort_utils.py350 except Exception as e:CODE
LOWmodelopt/onnx/quantization/quantize.py182 except Exception as e:CODE
LOWmodelopt/onnx/quantization/quantize.py215 except Exception as e:CODE
LOWmodelopt/onnx/quantization/graph_utils.py675 except Exception:CODE
LOWmodelopt/onnx/quantization/graph_utils.py721 except Exception:CODE
LOWmodelopt/onnx/quantization/int4.py72except Exception as e:CODE
LOWmodelopt/onnx/quantization/extensions.py32except Exception as e:CODE
LOWmodelopt/onnx/quantization/ort_patching.py92 except Exception as e:CODE
LOWmodelopt/onnx/quantization/ort_patching.py510 except Exception as e:CODE
LOWmodelopt/onnx/quantization/calib_utils.py174 except Exception as e:CODE
LOWmodelopt/onnx/quantization/autotune/benchmark.py143 except Exception as e:CODE
LOWmodelopt/onnx/quantization/autotune/benchmark.py235 except Exception as e:CODE
LOWmodelopt/onnx/quantization/autotune/benchmark.py303 except Exception as e:CODE
LOWmodelopt/onnx/quantization/autotune/benchmark.py422 except Exception as e:CODE
LOWmodelopt/onnx/quantization/autotune/benchmark.py716 except Exception as e:CODE
LOWmodelopt/onnx/quantization/autotune/benchmark.py724 except Exception as cleanup_error:CODE
LOWmodelopt/onnx/quantization/autotune/benchmark.py736 except Exception as e:CODE
LOWmodelopt/onnx/quantization/autotune/benchmark.py754 except Exception as e:CODE
LOWmodelopt/onnx/quantization/autotune/region_inspect.py197 except Exception as e:STRING
LOWmodelopt/onnx/quantization/autotune/workflows.py78 except Exception as e:CODE
LOWmodelopt/onnx/quantization/autotune/workflows.py131 except Exception as e:CODE
LOWmodelopt/onnx/quantization/autotune/__main__.py145 except Exception as e:CODE
LOWmodelopt/onnx/graph_surgery/gqa_replacement.py1447 except Exception as e:CODE
LOWmodelopt/onnx/llm_export_utils/export_utils.py74 except Exception:CODE
LOW…/sparsity/attention_sparsity/calibration/calibrator.py234 except Exception as e:CODE
LOW…arsity/attention_sparsity/calibration/ruler_dataset.py747 except Exception as e:CODE
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Docstring Block Structure55 hits · 275 pts
SeverityFileLineSnippetContext
HIGHmodelopt/onnx/utils.py1253Get the target type from a Cast node. Args: cast_node: The Cast node to extract type from. Returns: STRING
HIGHmodelopt/onnx/autocast/utils.py66Get a single consumer node and raise exception if there are multiple consumers. Args: model: The ONNX modelSTRING
HIGHmodelopt/onnx/quantization/qdq_utils.py544Get scale and zero point tensors for a node. Args: node: ONNX node to get scale and zero point for STRING
HIGHmodelopt/onnx/quantization/qdq_utils.py619Convert a weight tensor to INT8/FP8 format based on scale and zero point. Args: weight_array: The weight teSTRING
HIGHmodelopt/onnx/quantization/qdq_utils.py704Convert FP32/FP16 weights of the given ONNX model to INT8/FP8 weights. This function converts a model with QDQ (QuaSTRING
HIGHmodelopt/onnx/quantization/qdq_utils.py802Remove DQ nodes from the input and Q from the output of quantized custom ops for TensorRT compatibility. TensorRT rSTRING
HIGHmodelopt/onnx/quantization/qdq_utils.py951Remove Q nodes from the inputs of a quantized ONNX model. This supports generating quantized models with low-precisSTRING
HIGHmodelopt/onnx/quantization/ort_utils.py52Run a 'trtexec' command via subprocess. Args: args: Arguments to pass to trtexec (without the 'trtexec' comSTRING
HIGHmodelopt/onnx/quantization/ort_utils.py74Check if the `trtexec` CLI tool is available in PATH and is >= min_version. Args: min_version (str): MinimuSTRING
HIGHmodelopt/onnx/quantization/autotune/region_pattern.py130Check if this pattern matches another pattern or region. This method provides three distinct behaviors dependinSTRING
HIGHmodelopt/onnx/quantization/autotune/region_pattern.py201Internal function: Match this pattern against a region. Args: region: The region to match against STRING
HIGHmodelopt/onnx/quantization/autotune/region_pattern.py408Create signature for a single node including its parameters. Includes operation type and key attributes that afSTRING
HIGHmodelopt/onnx/quantization/autotune/common.py756Load pattern cache from a YAML file. Reads a previously saved pattern cache file and reconstructs all pattern STRING
HIGHmodelopt/onnx/quantization/autotune/utils.py54Validate that a file path exists. Args: path: Path string to validate (can be None) description: DeSTRING
HIGHmodelopt/onnx/quantization/autotune/workflows.py41Benchmark ONNX model inference latency using TensorRT Python API. Args: model_path: Path to ONNX model fileSTRING
HIGHmodelopt/onnx/quantization/autotune/autotuner_base.py444Compute Q/DQ insertion points for the best schemes (assuming best=True). Args: best: If True, use tSTRING
HIGHmodelopt/onnx/quantization/autotune/autotuner_base.py563Export ONNX model with Q/DQ nodes inserted according to tested schemes. This method creates a modified version STRING
HIGHmodelopt/onnx/graph_surgery/__init__.py91Run a graph surgery by name. This is the unified entry point for all graph surgeries. It dispatches to the apprSTRING
HIGH…/torch/sparsity/attention_sparsity/methods/registry.py145Extract numeric parts for proper version sorting. Args: version_str: Version string (e.g., "v1", "v2", "v10STRING
HIGH…/torch/sparsity/attention_sparsity/methods/registry.py196Get sparse attention method by name and optional version. Args: name: Method name to retrieve versiSTRING
HIGH…arsity/attention_sparsity/calibration/ruler_dataset.py111Load Paul Graham essays from local files. Reads essay .txt files from data_dir/essays. Files must be downloadedSTRING
HIGH…arsity/attention_sparsity/calibration/ruler_dataset.py506Generate target lengths as descending powers of 2. Args: max_seqlen: Maximum sequence length num_leSTRING
HIGH…h/sparsity/attention_sparsity/calibration/calibrate.py45Extract tokenizer name/path from model config. Args: model: Model to extract tokenizer from Returns: STRING
HIGH…h/sparsity/attention_sparsity/calibration/calibrate.py66Extract and validate calibration config from sparse_cfg. Args: config: Sparse attention configuration dict STRING
HIGH…/torch/kernels/quantization/conv/implicit_gemm_cuda.py98Conv3D via implicit GEMM with BF16 WMMA tensor cores. Args: x: Input tensor [N, Cin, D, H, W] w: WeSTRING
HIGHmodelopt/torch/kernels/quantization/gemm/fp8_kernel.py81Dequantizes the given weight tensor using the provided scale tensor. Reference: https://github.com/deepseek-ai/DeepSTRING
HIGHmodelopt/torch/kernels/common/attention/triton_fa.py1173Variable-length flash attention with GQA, autograd, optional sparsity, and paged KV. Args: q: [total_q_tokeSTRING
HIGHmodelopt/torch/fastgen/factory.py58Return a trainable fake-score network initialized from the teacher. This is the unit-test / single-script path; fraSTRING
HIGHmodelopt/torch/utils/memory_monitor.py65Initialize a NVIDIA GPU memory monitor. This class monitors the memory usage of NVIDIA GPUs at specified intervSTRING
HIGHmodelopt/torch/utils/regex.py32Check if a name matches any of the given patterns. This utility function checks if a given name (e.g., module name,STRING
HIGHmodelopt/torch/quantization/config.py255Find the last entry in a ``quant_cfg`` list whose ``quantizer_name`` key equals the query. This performs an **exactSTRING
HIGHmodelopt/torch/quantization/config.py1373Normalize a raw quant_cfg into a list of :class:`QuantizerCfgEntry` instances. Supports the following input forms: STRING
HIGHmodelopt/torch/_deploy/utils/torch_onnx.py433Real quantizes the weights in the onnx model. Applies weight quantization to an ONNX model based on the quantizatioSTRING
HIGH…lopt/torch/_deploy/_runtime/tensorrt/engine_builder.py43Run a 'trtexec' command via subprocess, logging the cmd and any failure output. The 'trtexec' binary is hardcoded aSTRING
HIGH…etron/tools/validate_puzzle_with_multi_replacements.py67Validate puzzle solutions by applying layer replacements and evaluating model performance. Args: args: ConfSTRING
HIGHmodelopt/torch/puzzletron/tools/validate_model.py74Validate a language model on a dataset by calculating loss and optionally capturing activations. Args: argsSTRING
HIGH…/anymodel/model_descriptor/model_descriptor_factory.py41Resolve the model descriptor by loading the checkpoint config and mapping model_type. Args: pretrained: PatSTRING
HIGHmodelopt/torch/opt/conversion.py558Load the modelopt state from a file. Args: modelopt_state_path: Target file location. **kwargs: addSTRING
HIGHmodelopt/torch/opt/mode.py138The mode's entrypoint for converting a model. The function signature of the convert entrypoint is described belSTRING
HIGHmodelopt/torch/opt/mode.py166The mode's entrypoint for restoring a model. The function signature of the restore entrypoint is described beloSTRING
HIGH…torch/prune/importance_hooks/compare_module_outputs.py206Compute RMSE and cosine similarity for a layer's outputs. Args: ref_data: List of reference tensors. STRING
HIGHmodelopt/torch/export/model_utils.py86Check if a model is a Vision-Language Model (VLM) or multimodal model. This function detects various multimodal modSTRING
HIGHmodelopt/torch/export/model_utils.py132Extract the language model lineage from a Vision-Language Model (VLM). This function handles the common patterns foSTRING
HIGHmodelopt/torch/export/diffusers_utils.py610Extract the parent attention block path and QKV type for grouping. QKV projections should only be fused within the STRING
HIGHmodelopt/torch/export/diffusers_utils.py1025Detect the diffusion model type for merge function dispatch. To add a new model type, add a detection clause here aSTRING
HIGHexamples/hf_ptq/cast_mxfp4_to_nvfp4.py112Walk the source MXFP4 checkpoint and build the per-layer amax map. Args: checkpoint_dir: Path to a Hugging STRING
HIGH…users/fastgen/preprocess/processors/caption_loaders.py123 Factory method to get the appropriate caption loader. Args: format_name: One of 'sidecar',STRING
HIGH…es/diffusers/fastgen/preprocess/processors/registry.py73 Get a processor instance by name. Args: name: Registered processor name Returns: STRING
HIGH…es/diffusers/fastgen/preprocess/processors/registry.py92 Get a processor class by name (without instantiating). Args: name: Registered processor naSTRING
HIGHexamples/onnx_ptq/evaluation.py129Evaluate a model for the given dataset. Args: model: PyTorch model or DeviceModel to evaluate. tranSTRING
HIGHexamples/onnx_ptq/evaluation.py176Evaluate the accuracy of the model on the validation dataset. Args: model: Model to evaluate. val_lSTRING
HIGH…amples/puzzletron/evaluation/hf_deployable_anymodel.py187Generate text based on the provided input prompts. This method processes input prompts through the loaded pipelSTRING
HIGH…amples/puzzletron/evaluation/hf_deployable_anymodel.py569Perform inference using Ray with dictionary inputs and outputs. Args: inputs (dict[Any, Any]): DictSTRING
HIGH…acy_benchmark/perplexity_metrics/perplexity_metrics.py63 Evaluate perplexity of a HuggingFace model on the WikiText-2 dataset. This function computes perplexity using STRING
HIGHexamples/specdec_bench/specdec_bench/datasets/base.py44Prepare and save the dataset to the specified output directory. Downloads any external data, resolves all referSTRING
Verbosity Indicators143 hits · 244 pts
SeverityFileLineSnippetContext
LOWtools/launcher/common/megatron_lm/quantize/quantize.sh49# Step 1: quantizeCOMMENT
LOW…examples/moonshotai/Kimi-K2.5/hf_streaming_eagle3.yaml28 # Step 1: Build input conversations (model-agnostic)COMMENT
LOW…examples/moonshotai/Kimi-K2.5/hf_streaming_eagle3.yaml42 # Step 2: Streaming EAGLE3 training (node0 serve TP=4 / node1 train), 4 GPU/nodeCOMMENT
LOW…examples/moonshotai/Kimi-K2.5/hf_streaming_eagle3.yaml84 # Step 3: Benchmark speculative decoding (VLLM backend, Kimi served at TP=4)COMMENT
LOW…r/examples/moonshotai/Kimi-K2.5/hf_offline_dflash.yaml19 # Step 1: Data synthesis via vLLM serverCOMMENT
LOW…uncher/examples/Qwen/Qwen3-0.6B/hf_offline_eagle3.yaml24 # Step 1: Data synthesis via TRT-LLM serverCOMMENT
LOW…uncher/examples/Qwen/Qwen3-0.6B/hf_offline_eagle3.yaml49 # Step 2: Dump hidden states from target modelCOMMENT
LOW…uncher/examples/Qwen/Qwen3-0.6B/hf_offline_eagle3.yaml67 # Step 3: Train EAGLE3 draft head (offline, single task)COMMENT
LOW…uncher/examples/Qwen/Qwen3-0.6B/hf_offline_eagle3.yaml89 # Step 4: Benchmark speculative decoding (VLLM backend)COMMENT
LOW…/launcher/examples/Qwen/Qwen3-8B/hf_online_dflash.yaml34 # Step 1: Online DFlash trainingCOMMENT
LOW…/launcher/examples/Qwen/Qwen3-8B/hf_online_dflash.yaml64 # Step 2: vLLM smoke test (uses exported checkpoint from training)COMMENT
LOW…/launcher/examples/Qwen/Qwen3-8B/hf_online_dflash.yaml80 # Step 3: HF AR evaluationCOMMENT
LOW…cher/examples/Qwen/Qwen3-8B/hf_offline_eagle3_ptq.yaml25 # Step 1: Data synthesis via TRT-LLM serverCOMMENT
LOW…cher/examples/Qwen/Qwen3-8B/hf_offline_eagle3_ptq.yaml49 # Step 2: Dump hidden states from target modelCOMMENT
LOW…cher/examples/Qwen/Qwen3-8B/hf_offline_eagle3_ptq.yaml67 # Step 3: Train EAGLE3 draft head (offline, single task) and export checkpointCOMMENT
LOW…cher/examples/Qwen/Qwen3-8B/hf_offline_eagle3_ptq.yaml92 # Step 4: PTQ — quantize the EAGLE3 model using offline hidden statesCOMMENT
LOW…cher/examples/Qwen/Qwen3-8B/hf_offline_eagle3_ptq.yaml108 # Step 5: Benchmark speculative decoding (VLLM backend)COMMENT
LOW…auncher/examples/Qwen/Qwen3-8B/eagle3_quick_check.yaml21 # Step 1: Data synthesis via TRT-LLM serverCOMMENT
LOW…auncher/examples/Qwen/Qwen3-8B/eagle3_quick_check.yaml44 # Step 2: Dump hidden states from target modelCOMMENT
LOW…auncher/examples/Qwen/Qwen3-8B/eagle3_quick_check.yaml61 # Step 3: Train EAGLE3 draft head (offline, single task)COMMENT
LOW…auncher/examples/Qwen/Qwen3-8B/eagle3_quick_check.yaml83 # Step 4: Benchmark speculative decoding (VLLM backend)COMMENT
LOW…ples/Qwen/Qwen3-8B/hf_streaming_eagle3_multi_node.yaml23 # Step 1: Build input conversationsCOMMENT
LOW…ples/Qwen/Qwen3-8B/hf_streaming_eagle3_multi_node.yaml36 # Step 2: Streaming EAGLE3 training — 2 serve replicas (TP=2) + 2 trainer nodes (2 GPU each).COMMENT
LOW…ples/Qwen/Qwen3-8B/hf_streaming_eagle3_multi_node.yaml68 # Step 3: Benchmark speculative decoding (VLLM backend)COMMENT
LOW…ples/Qwen/Qwen3-8B/hf_streaming_dflash_multi_node.yaml26 # Step 1: Build input conversationsCOMMENT
LOW…ples/Qwen/Qwen3-8B/hf_streaming_dflash_multi_node.yaml39 # Step 2: Streaming DFlash training — 2 serve replicas (TP=2) + 2 trainer nodes (2 GPU each).COMMENT
LOW…ples/Qwen/Qwen3-8B/hf_streaming_dflash_multi_node.yaml85 # Step 3: vLLM smoke test (DFlash, uses exported checkpoint from training)COMMENT
LOW…s/launcher/examples/Qwen/Qwen3-8B/megatron_lm_ptq.yaml66 # Step 3: TRT-LLM eval MMLU on all exported checkpointsCOMMENT
LOW…/launcher/examples/Qwen/Qwen3-8B/hf_online_domino.yaml30 # Step 1: Build input conversations. example_data_config.yaml enables only theCOMMENT
LOW…/launcher/examples/Qwen/Qwen3-8B/hf_online_domino.yaml46 # Step 2: Online Domino training (the script exports the drafter at the end).COMMENT
LOW…uncher/examples/Qwen/Qwen3-8B/hf_streaming_dflash.yaml28 # Step 1: Build input conversationsCOMMENT
LOW…uncher/examples/Qwen/Qwen3-8B/hf_streaming_dflash.yaml41 # Step 2: Streaming DFlash training — node 0 vllm serve, node 1 trainer.COMMENT
LOW…uncher/examples/Qwen/Qwen3-8B/hf_streaming_dflash.yaml82 # Step 3: vLLM smoke test (DFlash, uses exported checkpoint from training)COMMENT
LOW…launcher/examples/Qwen/Qwen3-8B/hf_offline_eagle3.yaml24 # Step 1: Data synthesis via TRT-LLM serverCOMMENT
LOW…launcher/examples/Qwen/Qwen3-8B/hf_offline_eagle3.yaml48 # Step 2: Dump hidden states from target modelCOMMENT
LOW…launcher/examples/Qwen/Qwen3-8B/hf_offline_eagle3.yaml66 # Step 3: Train EAGLE3 draft head (offline, single task)COMMENT
LOW…launcher/examples/Qwen/Qwen3-8B/hf_offline_eagle3.yaml84 # Step 4: Benchmark speculative decoding (VLLM backend)COMMENT
LOW…Qwen/Qwen3-30B-A3B/hf_streaming_eagle3_multi_node.yaml36 # Step 1: Build input conversationsCOMMENT
LOW…Qwen/Qwen3-30B-A3B/hf_streaming_eagle3_multi_node.yaml49 # Step 2: Streaming EAGLE3 training — 2 serve replicas (TP=2) + 2 trainer nodes (2 GPU each).COMMENT
LOW…Qwen/Qwen3-30B-A3B/hf_streaming_eagle3_multi_node.yaml88 # Step 3: Benchmark speculative decoding (VLLM backend)COMMENT
LOW…Qwen/Qwen3-30B-A3B/hf_streaming_dflash_multi_node.yaml40 # Step 1: Build input conversationsCOMMENT
LOW…Qwen/Qwen3-30B-A3B/hf_streaming_dflash_multi_node.yaml53 # Step 2: Streaming DFlash training — 2 serve replicas (TP=2) + 2 trainer nodes (2 GPU each).COMMENT
LOW…Qwen/Qwen3-30B-A3B/hf_streaming_dflash_multi_node.yaml107 # Step 3: vLLM smoke test (DFlash, uses exported checkpoint from training)COMMENT
LOW…auncher/examples/Qwen/Qwen3.5-4B/hf_online_dflash.yaml22 # Step 1: Online DFlash trainingCOMMENT
LOW…auncher/examples/Qwen/Qwen3.5-4B/hf_online_dflash.yaml55 # Step 2: vLLM smoke test with exported DFlash draftCOMMENT
LOW…auncher/examples/Qwen/Qwen3.5-4B/hf_online_dflash.yaml70 # Step 3: HF AR evaluationCOMMENT
LOW…s/launcher/examples/Qwen/Qwen3.5-4B/specdec_bench.yaml34 # Step 1: qualitative split — quality / acceptance-rate numbers.COMMENT
LOW…s/launcher/examples/Qwen/Qwen3.5-4B/specdec_bench.yaml62 # Step 2: throughput_32k split — long-context throughput.COMMENT
LOW…uncher/examples/Qwen/Qwen3.5-4B/specdec_bench_mtp.yaml28 # Step 1: qualitative split — quality / acceptance-rate numbers with MTP draft=3.COMMENT
LOW…uncher/examples/Qwen/Qwen3.5-4B/specdec_bench_mtp.yaml72 # Step 2: throughput_32k split — long-context throughput with MTP draft=3.COMMENT
LOW…ples/MiniMax/MiniMax-M2.7-DFlash/hf_online_dflash.yaml29 # Step 1: Online DFlash training.COMMENT
LOW…ples/MiniMax/MiniMax-M2.7-DFlash/hf_online_dflash.yaml81 # Step 2: vLLM smoke test with the exported DFlash draft.COMMENT
LOW…ples/MiniMax/MiniMax-M2.7-DFlash/hf_online_dflash.yaml97 # Step 3: HF AR (acceptance-length) evaluation on MT-Bench.COMMENT
LOW…les/MiniMax/MiniMax-M2.7-DFlash/hf_offline_dflash.yaml24 # Step 1: Dump base-model hidden states via vLLM extract_hidden_states (TP=4).COMMENT
LOW…les/MiniMax/MiniMax-M2.7-DFlash/hf_offline_dflash.yaml46 # Step 2: Train DFlash offline on the dumped hidden states. FakeBaseModel avoidsCOMMENT
LOW…xamples/MiniMax/MiniMax-M2.7-DFlash/specdec_bench.yaml32 # Step 1: qualitative split — quality / acceptance-length numbers.COMMENT
LOW…xamples/MiniMax/MiniMax-M2.7-DFlash/specdec_bench.yaml60 # Step 2: throughput_32k split — long-context throughput / AL.COMMENT
LOW…/meta-llama/Llama-3.2-1B-Instruct/megatron_lm_qad.yaml30 # Step 1: PTQ quantize (NVFP4) + MMLU gate. export skipped (QAD only needs theCOMMENT
LOW…/meta-llama/Llama-3.2-1B-Instruct/megatron_lm_qad.yaml52 # Step 2: QAD/SFT training on the quantized checkpoint from task_0. All trainCOMMENT
LOW…openai/gpt-oss-20b/hf_streaming_eagle3_multi_node.yaml33 # Step 1: Build input conversationsCOMMENT
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Redundant / Tautological Comments138 hits · 209 pts
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LOWexperimental/dms/configs/qwen3_8b.yaml7# Set resume_from_checkpoint to "auto" below, or pass an explicit path.COMMENT
LOW…xamples/MiniMax/MiniMax-M2.7-DFlash/specdec_bench.yaml15# Set draft_model to the trained, exported draft checkpoint (an exported-checkpoint-*COMMENT
LOWtools/precommit/check_modelopt_recipes.py213 # Check if this file is inside a directory-format recipe.COMMENT
LOWtools/debugger/client.sh120 # Check if server is still aliveCOMMENT
LOWtools/debugger/client.sh214 # Check if there's a running commandCOMMENT
LOWmodelopt/onnx/trt_utils.py368 # Check if there's a custom TensorRT op in the ONNX model. If so, make it ORT compatible by addingCOMMENT
LOWmodelopt/onnx/utils.py670 # Set ir_version to 10, remove it once ORT supports ir_version 11COMMENT
LOWmodelopt/onnx/utils.py985 # Check if this node has subgraphs (GRAPH or GRAPHS attributes)COMMENT
LOWmodelopt/onnx/utils.py1408 # Check if the cast output is also a graph outputCOMMENT
LOWmodelopt/onnx/autocast/logging_config.py44 # Check if parent logger (modelopt.onnx) already has handlers configuredCOMMENT
LOWmodelopt/onnx/autocast/convert.py242 # Check if Q/DQ nodes require a higher opsetCOMMENT
LOWmodelopt/onnx/autocast/graphsanitizer.py204 # Set ir_version to 10, remove it once ORT supports ir_version 11COMMENT
LOWmodelopt/onnx/autocast/graphsanitizer.py566 # Check if casting to FP64, change to FP32COMMENT
LOWmodelopt/onnx/autocast/graphsanitizer.py574 # Check if the value attribute uses FP64COMMENT
LOWmodelopt/onnx/autocast/referencerunner.py196 # Check if model has external data by checking:COMMENT
LOWmodelopt/onnx/autocast/precisionconverter.py877 # Check if input comes from an initializer - don't remove cast in that caseCOMMENT
LOWmodelopt/onnx/autocast/precisionconverter.py1257 # Check if we should skip this input for low precision conversionCOMMENT
LOWmodelopt/onnx/quantization/qdq_utils.py1099 # Check if second input (weight) is from DequantizeLinearCOMMENT
LOWmodelopt/onnx/quantization/qdq_utils.py1317 # Check if there's an optional Cast node between Reshape and Transpose/MatMul/GemmCOMMENT
LOWmodelopt/onnx/quantization/quantize.py220 # Check if data-dependent shape ops are present in the modelCOMMENT
LOWmodelopt/onnx/quantization/quantize.py744 # Check if intermediate files should be deletedCOMMENT
LOWmodelopt/onnx/quantization/quantize.py753 # Check if the quantized model is validCOMMENT
LOWmodelopt/onnx/quantization/partitioning.py211 # Check if the node is already in some other partitionCOMMENT
LOWmodelopt/onnx/quantization/graph_utils.py128 # Check if current non-wild node type does not match the expected path typeCOMMENT
LOWmodelopt/onnx/quantization/graph_utils.py162 # Check if any child (forward path) or parent (backward path) can match the remaining path typesCOMMENT
LOWmodelopt/onnx/quantization/graph_utils.py383 # Check if the first input (activation) comes from a CASK partitionCOMMENT
LOWmodelopt/onnx/quantization/graph_utils.py465 # Check if partition head is of default quantizable typeCOMMENT
LOWmodelopt/onnx/quantization/graph_utils.py1965 # Check if concat output is quantizedSTRING
LOWmodelopt/onnx/quantization/int4.py691 # Set ir_version to 10, remove it once ORT supports ir_version 11COMMENT
LOWmodelopt/onnx/quantization/int4.py1419 # Set ir_version to 10, remove it once ORT supports ir_version 11COMMENT
LOWmodelopt/onnx/quantization/int4.py1515 # Check if block_size is None and set default to 128COMMENT
LOWmodelopt/onnx/quantization/ort_patching.py244 # Set ir_version to 10, remove it once ORT supports ir_version 11COMMENT
LOWmodelopt/onnx/quantization/autotune/region_search.py668 # Check if convergence creates a reasonable-sized regionSTRING
LOWmodelopt/onnx/quantization/autotune/common.py520 # Check if this scheme is too similar to any already-filtered schemeCOMMENT
LOWmodelopt/onnx/graph_surgery/gqa_replacement.py144 # Check if output is a graph outputCOMMENT
LOWmodelopt/onnx/graph_surgery/gqa_replacement.py1383 # Check if opset 21 is being used (requires IR version >= 10)COMMENT
LOWmodelopt/onnx/graph_surgery/dq_transpose.py213 # Check if DQ output is the weight input (input[1] for MatMul/Gemm)COMMENT
LOWmodelopt/onnx/graph_surgery/dq_transpose.py251 # Check if 2DCOMMENT
LOWmodelopt/onnx/graph_surgery/utils/whisper_utils.py111 # Check if file already existsCOMMENT
LOWmodelopt/onnx/graph_surgery/utils/whisper_utils.py365 # Check if file already existsCOMMENT
LOWmodelopt/onnx/graph_surgery/utils/graph_utils.py314 # Check if ANY of the node's outputs are consumedCOMMENT
LOWmodelopt/onnx/export/int4_exporter.py73 # Check if there's an optional Cast node between Reshape and Transpose/MatMul/GemmCOMMENT
LOW…h/sparsity/attention_sparsity/calibration/calibrate.py343 # Check if any calibration succeededCOMMENT
LOW…rch/sparsity/attention_sparsity/plugins/huggingface.py78 # Check if this is an attention module by nameCOMMENT
LOWmodelopt/torch/distill/distillation_model.py85 # Assign loss to specified modules.COMMENT
LOWmodelopt/torch/distill/distillation.py78 # Check if the model is a distillation model.COMMENT
LOWmodelopt/torch/utils/graph.py45 # Check if input and output degrees match.COMMENT
LOWmodelopt/torch/utils/graph.py51 # Check if the node operator matches.COMMENT
LOWmodelopt/torch/utils/graph.py55 # Check if the node target matches.COMMENT
LOWmodelopt/torch/utils/graph.py66 # Check if we have already matched these nodes in the current traversal.COMMENT
LOWmodelopt/torch/utils/graph.py112 # Check if the number of nodes match.COMMENT
LOWmodelopt/torch/utils/plugins/megatron_generate.py375 # Check if this is a VLM model (vision inputs only passed at step 0 / prefill)COMMENT
LOWmodelopt/torch/peft/convert.py65 # Check if model is already in PEFT mode by looking for LoRA modulesCOMMENT
LOWmodelopt/torch/quantization/algorithms.py1465 # Check if we meet the constraintCOMMENT
LOWmodelopt/torch/quantization/mode.py348 # Check if _calib_func is defined as a class attributeCOMMENT
LOWmodelopt/torch/quantization/nn/modules/quant_linear.py224 # Check if real-quant GEMM is availableCOMMENT
LOWmodelopt/torch/quantization/nn/modules/quant_linear.py293 # Check if fn is a tensor_cast_fun and print warning if soCOMMENT
LOW…lopt/torch/quantization/nn/modules/tensor_quantizer.py1199 # Check if the input tensor is contiguousCOMMENT
LOWmodelopt/torch/quantization/plugins/attention.py290 # Check if the method is a decorated methodCOMMENT
LOWmodelopt/torch/quantization/plugins/attention.py309 # Check if this is the method with the right freevarsCOMMENT
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AI Structural Patterns117 hits · 104 pts
SeverityFileLineSnippetContext
LOW…perimental/dms/models/qwen3/configuration_qwen3_dms.py40CODE
LOWexperimental/dms/models/qwen3/modeling_qwen3_dms.py274CODE
LOWexperimental/dms/models/qwen3/modeling_qwen3_dms.py392CODE
LOWexperimental/dms/dms/training/engine.py573CODE
LOWtools/launcher/slurm_config.py66CODE
LOWtools/launcher/launch.py125CODE
LOWtools/mcp/modelopt_mcp/server.py160CODE
LOWtools/mcp/modelopt_mcp/bridge.py1018CODE
LOWmodelopt/onnx/autocast/convert.py47CODE
LOWmodelopt/onnx/autocast/precisionconverter.py86CODE
LOWmodelopt/onnx/autocast/nodeclassifier.py403CODE
LOWmodelopt/onnx/quantization/fp8.py162CODE
LOWmodelopt/onnx/quantization/quantize.py269CODE
LOWmodelopt/onnx/quantization/quantize.py329CODE
LOWmodelopt/onnx/quantization/int4.py1431CODE
LOWmodelopt/onnx/quantization/ort_patching.py1543CODE
LOWmodelopt/onnx/quantization/int8.py118CODE
LOWmodelopt/onnx/quantization/autotune/workflows.py162CODE
LOWmodelopt/onnx/graph_surgery/gqa_replacement.py788CODE
LOWmodelopt/onnx/graph_surgery/encoder_cross_kv.py319CODE
LOWmodelopt/onnx/graph_surgery/utils/whisper_utils.py144CODE
LOW…arsity/attention_sparsity/calibration/ruler_dataset.py228CODE
LOWmodelopt/torch/kernels/common/attention/triton_fa.py229CODE
LOWmodelopt/torch/kernels/common/attention/triton_fa.py1147CODE
LOWmodelopt/torch/utils/dataset_utils.py742CODE
LOWmodelopt/torch/utils/dataset_utils.py1175CODE
LOWmodelopt/torch/utils/vlm_dataset_utils.py354CODE
LOWmodelopt/torch/utils/plugins/megatron_generate.py262CODE
LOW…delopt/torch/utils/plugins/megatron_preprocess_data.py512CODE
LOWmodelopt/torch/quantization/model_quant.py269CODE
LOWmodelopt/torch/quantization/export_onnx.py353CODE
LOWmodelopt/torch/quantization/tensor_quant.py153CODE
LOWmodelopt/torch/quantization/tensor_quant.py213CODE
LOWmodelopt/torch/quantization/tensor_quant.py324CODE
LOWmodelopt/torch/quantization/tensor_quant.py350CODE
LOWmodelopt/torch/quantization/model_calib.py799CODE
LOWmodelopt/torch/quantization/plugins/vllm.py251CODE
LOWmodelopt/torch/quantization/plugins/peft.py99CODE
LOWmodelopt/torch/quantization/plugins/huggingface.py808CODE
LOW…lopt/torch/quantization/plugins/diffusion/diffusers.py218CODE
LOW…lopt/torch/quantization/plugins/diffusion/diffusers.py251CODE
LOWmodelopt/torch/quantization/backends/nvfp4_gemm.py147CODE
LOW…opt/torch/quantization/backends/fp8_per_tensor_gemm.py170CODE
LOWmodelopt/torch/quantization/utils/calib_utils.py163CODE
LOWmodelopt/torch/_deploy/utils/torch_onnx.py482CODE
LOW…lopt/torch/_deploy/_runtime/tensorrt/engine_builder.py139CODE
LOW…torch/puzzletron/tools/bypassed_training/child_init.py387CODE
LOW…torch/puzzletron/subblock_stats/calc_subblock_stats.py251CODE
LOWmodelopt/torch/puzzletron/sewing_kit/passage.py68CODE
LOWmodelopt/torch/puzzletron/sewing_kit/passage.py112CODE
LOWmodelopt/torch/puzzletron/sewing_kit/passage.py123CODE
LOWmodelopt/torch/puzzletron/sewing_kit/passage.py182CODE
LOWmodelopt/torch/puzzletron/sewing_kit/passage.py251CODE
LOWmodelopt/torch/puzzletron/sewing_kit/passage.py288CODE
LOWmodelopt/torch/puzzletron/sewing_kit/passage.py384CODE
LOWmodelopt/torch/puzzletron/sewing_kit/passage.py471CODE
LOWmodelopt/torch/puzzletron/sewing_kit/core.py101CODE
LOWmodelopt/torch/puzzletron/sewing_kit/core.py128CODE
LOWmodelopt/torch/puzzletron/sewing_kit/core.py164CODE
LOWmodelopt/torch/puzzletron/sewing_kit/core.py180CODE
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Example Usage Blocks54 hits · 86 pts
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LOWexperimental/dms/configs/qwen3_8b.yaml3# Usage:COMMENT
LOWexperimental/dms/scripts/train.sh20# Usage:COMMENT
LOWexperimental/dms/scripts/evaluate.sh23# Usage:COMMENT
LOWtools/launcher/common/vllm/query.sh23# Usage:COMMENT
LOWtools/launcher/common/tensorrt_llm/query.sh23# Usage:COMMENT
LOWtools/launcher/common/hf/ptq.sh19# Usage:COMMENT
LOW…er/examples/moonshotai/Kimi-K2.5/hf_dflash_dryrun.yaml5# Usage:COMMENT
LOW…r/examples/moonshotai/Kimi-K2.5/hf_offline_dflash.yaml7# Usage:COMMENT
LOW…er/examples/moonshotai/Kimi-K2.5/hf_eagle3_dryrun.yaml31# Usage:COMMENT
LOW…auncher/examples/Qwen/Qwen3-0.6B/hf_online_eagle3.yaml11# Usage:COMMENT
LOW…auncher/examples/Qwen/Qwen3-0.6B/hf_online_dflash.yaml20# Usage:COMMENT
LOW…uncher/examples/Qwen/Qwen3-0.6B/hf_offline_dflash.yaml12# Usage:COMMENT
LOW…uncher/examples/Qwen/Qwen3-0.6B/hf_offline_eagle3.yaml11# Usage:COMMENT
LOWtools/launcher/examples/Qwen/Qwen3-8B/hf_synth.yaml3# Usage:COMMENT
LOW…/launcher/examples/Qwen/Qwen3-8B/hf_online_eagle3.yaml11# Usage:COMMENT
LOW…/launcher/examples/Qwen/Qwen3-8B/hf_online_dflash.yaml25# Usage:COMMENT
LOW…cher/examples/Qwen/Qwen3-8B/hf_offline_eagle3_ptq.yaml12# Usage:COMMENT
LOW…uncher/examples/Qwen/Qwen3-8B/hf_streaming_eagle3.yaml11# Usage:COMMENT
LOW…auncher/examples/Qwen/Qwen3-8B/eagle3_quick_check.yaml8# Usage:COMMENT
LOW…ples/Qwen/Qwen3-8B/hf_streaming_eagle3_multi_node.yaml11# Usage:COMMENT
LOW…ples/Qwen/Qwen3-8B/hf_streaming_dflash_multi_node.yaml14# Usage:COMMENT
LOW…/launcher/examples/Qwen/Qwen3-8B/hf_eagle3_dryrun.yaml28# Usage:COMMENT
LOW…s/launcher/examples/Qwen/Qwen3-8B/megatron_lm_ptq.yaml8# Usage:COMMENT
LOW…/launcher/examples/Qwen/Qwen3-8B/hf_online_domino.yaml21# Usage:COMMENT
LOW…uncher/examples/Qwen/Qwen3-8B/hf_streaming_dflash.yaml16# Usage:COMMENT
LOW…launcher/examples/Qwen/Qwen3-8B/hf_offline_eagle3.yaml11# Usage:COMMENT
LOW…cher/examples/Qwen/Qwen3-8B/megatron_lm_ptq_local.yaml3# Usage:COMMENT
LOW…Qwen/Qwen3-30B-A3B/hf_streaming_eagle3_multi_node.yaml24# Usage:COMMENT
LOW…Qwen/Qwen3-30B-A3B/hf_streaming_dflash_multi_node.yaml28# Usage:COMMENT
LOW…ncher/examples/Qwen/Qwen3-30B-A3B/megatron_lm_ptq.yaml6# Usage:COMMENT
LOW…auncher/examples/Qwen/Qwen3.5-4B/hf_online_dflash.yaml13# Usage:COMMENT
LOW…ples/MiniMax/MiniMax-M2.7-DFlash/hf_online_dflash.yaml20# Usage:COMMENT
LOW…les/MiniMax/MiniMax-M2.7-DFlash/hf_offline_dflash.yaml16# Usage:COMMENT
LOW…xamples/MiniMax/MiniMax-M2.7-DFlash/specdec_bench.yaml18# Usage:COMMENT
LOW…/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16-AutoQuantize.yaml4# Usage:COMMENT
LOW…VIDIA-Nemotron-3-Nano-30B-A3B-BF16/nemo_evaluator.yaml17# Usage:COMMENT
LOW…IDIA-Nemotron-3-Nano-30B-A3B-BF16/megatron_lm_ptq.yaml31# Usage:COMMENT
LOW…A-Nemotron-3-Super-120B-A12B-BF16/megatron_lm_ptq.yaml12# Usage:COMMENT
LOW…ron-3-Super-120B-A12B-BF16/megatron_bridge_import.yaml7# Usage:COMMENT
LOW…A-Nemotron-3-Ultra-550B-A55B-BF16/megatron_lm_ptq.yaml13# Usage:COMMENT
LOW…/meta-llama/Llama-3.2-1B-Instruct/megatron_lm_qad.yaml21# Usage:COMMENT
LOW…openai/gpt-oss-20b/hf_streaming_eagle3_multi_node.yaml21# Usage:COMMENT
LOW…openai/gpt-oss-20b/hf_streaming_dflash_multi_node.yaml26# Usage:COMMENT
LOWtools/debugger/client.sh21# Usage:COMMENT
LOW.agents/scripts/sync-upstream-skills.sh23# Usage:COMMENT
LOW.agents/skills/common/remote_exec.sh19# Usage:COMMENT
LOW…s/skills/evaluation/recipes/examples/example_eval.yaml12# Usage:COMMENT
LOWexamples/speculative_decoding/launch_train.sh17# Usage:COMMENT
LOW…VIDIA-Nemotron-3-Nano-30B-A3B-BF16/nemo_evaluator.yaml17# Usage:COMMENT
LOW…amples/llm_qat/configs/train/lr/lr_config_example.yaml14# Usage:COMMENT
LOW…es/diffusers/distillation/configs/accelerate/fsdp.yaml6# Usage:COMMENT
LOWexamples/llm_eval/run_lm_eval_vllm.sh21# Usage:COMMENT
LOW…initron/NVIDIA-Nemotron-Nano-9B-v2/nemo_evaluator.yaml8# Usage:COMMENT
LOW…acy_benchmark/perplexity_metrics/perplexity_metrics.py556# Example usage:COMMENT
AI Slop Vocabulary28 hits · 80 pts
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MEDIUMexperimental/dms/scripts/evaluate.sh18# Evaluate a trained DMS model using lm-eval-harness.COMMENT
MEDIUM…uncher/examples/Qwen/Qwen3.5-4B/specdec_bench_mtp.yaml53 # actually running. Set them in the harness (planned for Phase 2 inCOMMENT
MEDIUM…uncher/examples/Qwen/Qwen3.5-4B/specdec_bench_mtp.yaml63 # When run standalone (without the harness), dump_env falls back toCOMMENT
MEDIUM…/meta-llama/Llama-3.2-1B-Instruct/megatron_lm_qad.yaml12# args below, so flags pass via `args:` (robust) rather than space-containing envCOMMENT
MEDIUMmodelopt/onnx/quantization/fp8.py220 # Either of m or n in matmul is 1, this matmul cannot utilize TensorCores.COMMENT
MEDIUMmodelopt/onnx/quantization/int8.py167 # Either of m or n in matmul is 1, this matmul cannot utilize TensorCores.COMMENT
MEDIUMmodelopt/torch/trace/tracer.py34 """A robust graph tracer for tracing dependencies between layers.STRING
MEDIUMmodelopt/torch/utils/robust_json.py17"""Provides a robust JSON encoder that can handle various types of objects.STRING
LOWmodelopt/torch/utils/distributed.py292 # NCCL has an issue with calling barrier. So we just use the gloo backebnd for group barriers.COMMENT
LOWmodelopt/torch/utils/network.py411 # simply return as args/kwargs in this caseCOMMENT
LOWmodelopt/torch/utils/vlm_dataset_utils.py215 # If it's already a PIL/numpy/torch image-like object, just return it and let the processor validate.COMMENT
MEDIUM…elopt/torch/quantization/plugins/transformer_engine.py79 # signature — robust to TE versions that insert positional args between themCOMMENT
MEDIUM…elopt/torch/quantization/plugins/transformer_engine.py164 # signature — robust to TE versions that move the m_splits/non_tensor_argsCOMMENT
MEDIUM…pt/torch/prune/importance_hooks/base_hooks_analysis.py55 >>> # Collect multiple batches for robust evaluationSTRING
MEDIUM…/torch/kernels/quantization/conv/test_implicit_gemm.py79 # Check mean diff is small (more robust than quantile for large tensors)COMMENT
MEDIUM…rch/prune/plugins/test_mcore_mamba_minitron_pruning.py393 # demonstrating that active_params-first ranking can elevate 4-layer above 3-layer modelsCOMMENT
MEDIUM…s/skills/evaluation/recipes/examples/example_eval.yaml9# Includes (default): ns_gpqa (nemo-skills harness, num_repeats=16, AA mcq-4choices).COMMENT
MEDIUM…s/skills/evaluation/recipes/examples/example_eval.yaml166 # prompt (golden Nemotron-3-Ultra harness); repeat count kept at this recipe'sCOMMENT
MEDIUM…elopt_recipes/general/speculative_decoding/dspark.yaml45 # export + the offline acceptance-length harness instead.COMMENT
MEDIUMexamples/hf_ptq/scripts/huggingface_example.sh262 # Flags instructed by https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/llama3#paperCOMMENT
MEDIUMexamples/diffusers/fastgen/dmd2_finetune.py32# ``python .../dmd2_finetune.py``; this makes that explicit and robust to other invocations.)COMMENT
MEDIUM…/diffusers/fastgen/preprocess/processors/qwen_image.py172 # real prompt length; keeping the true mask makes the contract explicit and robust.COMMENT
MEDIUMexamples/llm_eval/lm_eval_hf.py1# Adapted from https://github.com/EleutherAI/lm-evaluation-harness/tree/aa457edc3d64d81530159cd3a182932320c78f8cCOMMENT
MEDIUMexamples/llm_eval/run_lm_eval_vllm.sh19# Script to run lm-evaluation-harness against a running vLLM OpenAI-compatible server.COMMENT
MEDIUMexamples/specdec_bench/specdec_bench/utils.py292 # Each *_sha and modelopt_version prefers an env var set by the harnessCOMMENT
MEDIUMexamples/specdec_bench/specdec_bench/utils.py295 # harness. container_image and nmm_sandbox_sha are env-only — there is noCOMMENT
MEDIUMexamples/specdec_bench/specdec_bench/utils.py317 # runs. The harness sets JIRA_TICKET only after verifying the checkpointCOMMENT
MEDIUMexamples/torch_trt/torch_tensorrt_accuracy.py55# Reuse the ImageNet accuracy harness from the onnx_ptq example (sibling dir).COMMENT
Cross-Language Confusion9 hits · 58 pts
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HIGHtools/mcp/modelopt_mcp/bridge.py865 argv += [target, "whoami && hostname"]CODE
HIGH…h/deploy/_runtime/tensorrt/test_layerwise_profiling.py37 {"Conv_88 || Conv_59": 0.01},CODE
HIGH…h/deploy/_runtime/tensorrt/test_layerwise_profiling.py145 {"Conv_757 || Conv_635": 0.01, "Conv_757 || Conv_635 output reformatter 0": 0.02},CODE
HIGH…h/deploy/_runtime/tensorrt/test_layerwise_profiling.py146 {"Conv_757 || Conv_635": 0.03},CODE
HIGH…h/deploy/_runtime/tensorrt/test_layerwise_profiling.py164 "Conv_705 || Conv_733": 0.01,CODE
HIGH…h/deploy/_runtime/tensorrt/test_layerwise_profiling.py165 "Reformatting CopyNode for Output Tensor 0 to Conv_705 || Conv_733": 0.02,CODE
HIGH…h/deploy/_runtime/tensorrt/test_layerwise_profiling.py167 {"Conv_705 || Conv_733": 0.03},CODE
HIGHtests/examples/specdec_bench/test_upload_to_s3.py135 (run / "configuration.json").write_text('{"container_image": null}')CODE
HIGH.agents/skills/day0-release/scripts/gate_run.py30 "score": float | null, # canonical score, if extractedSTRING
Modern AI Meta-Vocabulary16 hits · 44 pts
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MEDIUMREADME.md33- [2026/03/11] Model Optimizer quantized Nemotron-3-Super checkpoints are available on Hugging Face for download: [FP8](CODE
MEDIUMtools/launcher/common/query.py196 # Restore the full reasoning trace for the last generated assistant turn.COMMENT
MEDIUMmodelopt/torch/utils/plugins/megatron_mmlu.py118 # Group dev examples by subject for few-shot prompt construction.COMMENT
MEDIUM…t/torch/prune/importance_hooks/expert_removal_hooks.py296 # Compute zero-shot expert ranks (double argsort converts counts to rank positions)COMMENT
MEDIUM…/unit/torch/puzzletron/test_bypass_checkpoint_utils.py178# save_bypass_checkpoint — orchestration: symlink, args.json, markerCOMMENT
MEDIUM.agents/scripts/nel-next.sh41# [harbor] = agentic/sandbox deps; [export] pulls mlflow for `mlflow-push`.COMMENT
MEDIUM.agents/skills/evaluation/references/nel-next.md1# nel-next (nemo-evaluator 0.3.x) — shared reference for harbor / agentic benchmarksCOMMENT
MEDIUM…/evaluation/recipes/tasks/aa_next/swebench_verified.md64### Deployment proxy (multi-turn agentic)COMMENT
MEDIUM…lls/evaluation/recipes/examples/example_eval_next.yaml1# nel-next (nemo-evaluator 0.3.x) agentic AA eval template — NOT the 0.2.6 schema.COMMENT
MEDIUM…lls/evaluation/recipes/examples/example_eval_next.yaml28 - "--enable-auto-tool-choice" # agentic = tool-callingCODE
MEDIUMexamples/speculative_decoding/README.md252# Direct SFT training mix (~3.9M rows, includes agentic/tool-use datasets):COMMENT
MEDIUM…rials/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/ABLATIONS.md4> Every benchmark table in this document is from evaluations run **without** eval-time tool calling (no Python sandbox).CODE
MEDIUMexamples/dataset/MEGATRON_DATA_PREP.md261Datasets below are from the [Nemotron Post-Training v3 collection](https://huggingface.co/collections/nvidia/nemotron-poCODE
MEDIUMexamples/dataset/augmentations.yaml85 # /no_think: suppresses chain-of-thought in models that support it (e.g. Qwen3).COMMENT
MEDIUMexamples/llm_eval/mmlu.py207 # Skip examples that do not fit even at zero-shot, otherwise the backend rejectsCOMMENT
MEDIUMexamples/llm_eval/modeling.py546 ctx = prompt # Special format has lower few-shot performanceCODE
Hallucination Indicators1 hit · 10 pts
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CRITICALmodelopt/torch/fastgen/methods/dmd.py108 "modelopt.torch.fastgen.plugins.qwen_image.attach_feature_capture(teacher, ...) "CODE
Fake / Example Data6 hits · 8 pts
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LOWmodelopt/torch/trace/analyzer.py1235 inputs = [node for node in graph.nodes if node.op == "placeholder"]CODE
LOWmodelopt/torch/utils/graph.py38 "placeholder": lambda _: None,CODE
LOWmodelopt/torch/utils/graph.py78 if nx.op == "placeholder":CODE
LOWtests/_test_utils/torch/puzzletron/utils.py247 {"role": "user", "content": "please cite Lorem Ipsum?"},CODE
LOWtests/_test_utils/torch/puzzletron/utils.py251 "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed in blandit ante. "CODE
LOWtests/_test_utils/torch/puzzletron/utils.py251 "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed in blandit ante. "CODE
Slop Phrases1 hit · 3 pts
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MEDIUMexamples/hf_ptq/scripts/parser.sh152 # Now you can use the variables $GPU, $MODEL, and $TASKS in your scriptCOMMENT
TODO Padding1 hit · 2 pts
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LOWmodelopt/torch/peft/conversion.py27# TODO: Add test cases to cover these functionsCOMMENT
Overly Generic Function Names1 hit · 1 pts
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LOWmodelopt/torch/nas/autonas.py247 def process_data(data):CODE