Example models using DeepSpeed
This report presents the forensic synthetic code analysis of deepspeedai/DeepSpeedExamples, a Python project with 6,834 GitHub stars. SynthScan v2.0 examined 103,715 lines of code across 736 source files, recording 1346 pattern matches distributed across 21 syntactic categories. The overall adjusted score of 26.1 places this repository in the Moderate AI signal band.
The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).
Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.
Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.
This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.
The scanner identified 1346 distinct pattern matches across 21 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.
Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | …aining/data_efficiency/vit_finetuning/main_imagenet.py | 0 | computes the accuracy over the k top predictions for the specified values of k | STRING |
| HIGH | training/data_efficiency/vit_finetuning/utils/utils.py | 0 | computes the accuracy over the k top predictions for the specified values of k | STRING |
| HIGH | training/imagenet/main.py | 0 | computes the accuracy over the k top predictions for the specified values of k | STRING |
| HIGH | training/data_efficiency/gpt_finetuning/analyze_data.py | 0 | fine-tuning the library models for causal language modeling (gpt, gpt-2, ctrl, ...) on a text file or a dataset without | STRING |
| HIGH | …g/data_efficiency/gpt_finetuning/run_clm_no_trainer.py | 0 | fine-tuning the library models for causal language modeling (gpt, gpt-2, ctrl, ...) on a text file or a dataset without | STRING |
| HIGH | compression/gpt2/run_clm_no_trainer.py | 0 | fine-tuning the library models for causal language modeling (gpt, gpt-2, ctrl, ...) on a text file or a dataset without | STRING |
| HIGH | training/bing_bert/deepspeed_train.py | 0 | utility function for checkpointing model + optimizer dictionaries the main purpose for this is to be able to resume trai | STRING |
| HIGH | training/bing_bert/run_glue_classifier_bert_large.py | 0 | utility function for checkpointing model + optimizer dictionaries the main purpose for this is to be able to resume trai | STRING |
| HIGH | training/BingBertGlue/run_glue_classifier_bert_large.py | 0 | utility function for checkpointing model + optimizer dictionaries the main purpose for this is to be able to resume trai | STRING |
| HIGH | training/bing_bert/run_glue_classifier_bert_base.py | 0 | a single training/test example for simple sequence classification. | STRING |
| HIGH | training/bing_bert/run_glue_classifier_bert_large.py | 0 | a single training/test example for simple sequence classification. | STRING |
| HIGH | training/BingBertGlue/run_glue_classifier_bert_base.py | 0 | a single training/test example for simple sequence classification. | STRING |
| HIGH | training/BingBertGlue/run_glue_classifier_bert_large.py | 0 | a single training/test example for simple sequence classification. | STRING |
| HIGH | training/bing_bert/run_glue_classifier_bert_base.py | 0 | constructs a inputexample. args: guid: unique id for the example. text_a: string. the untokenized text of the first sequ | STRING |
| HIGH | training/bing_bert/run_glue_classifier_bert_large.py | 0 | constructs a inputexample. args: guid: unique id for the example. text_a: string. the untokenized text of the first sequ | STRING |
| HIGH | training/BingBertGlue/run_glue_classifier_bert_base.py | 0 | constructs a inputexample. args: guid: unique id for the example. text_a: string. the untokenized text of the first sequ | STRING |
| HIGH | training/BingBertGlue/run_glue_classifier_bert_large.py | 0 | constructs a inputexample. args: guid: unique id for the example. text_a: string. the untokenized text of the first sequ | STRING |
| HIGH | training/bing_bert/run_glue_classifier_bert_base.py | 0 | base class for data converters for sequence classification data sets. | STRING |
| HIGH | training/bing_bert/run_glue_classifier_bert_large.py | 0 | base class for data converters for sequence classification data sets. | STRING |
| HIGH | training/BingBertGlue/run_glue_classifier_bert_base.py | 0 | base class for data converters for sequence classification data sets. | STRING |
| HIGH | training/BingBertGlue/run_glue_classifier_bert_large.py | 0 | base class for data converters for sequence classification data sets. | STRING |
| HIGH | training/bing_bert/run_glue_classifier_bert_base.py | 0 | gets a collection of `inputexample`s for the train set. | STRING |
| HIGH | training/bing_bert/run_glue_classifier_bert_large.py | 0 | gets a collection of `inputexample`s for the train set. | STRING |
| HIGH | training/BingBertGlue/run_glue_classifier_bert_base.py | 0 | gets a collection of `inputexample`s for the train set. | STRING |
| HIGH | training/BingBertGlue/run_glue_classifier_bert_large.py | 0 | gets a collection of `inputexample`s for the train set. | STRING |
| HIGH | training/bing_bert/run_glue_classifier_bert_base.py | 0 | gets a collection of `inputexample`s for the dev set. | STRING |
| HIGH | training/bing_bert/run_glue_classifier_bert_large.py | 0 | gets a collection of `inputexample`s for the dev set. | STRING |
| HIGH | training/BingBertGlue/run_glue_classifier_bert_base.py | 0 | gets a collection of `inputexample`s for the dev set. | STRING |
| HIGH | training/BingBertGlue/run_glue_classifier_bert_large.py | 0 | gets a collection of `inputexample`s for the dev set. | STRING |
| HIGH | training/bing_bert/run_glue_classifier_bert_base.py | 0 | processor for the multinli mismatched data set (glue version). | STRING |
| HIGH | training/bing_bert/run_glue_classifier_bert_large.py | 0 | processor for the multinli mismatched data set (glue version). | STRING |
| HIGH | training/BingBertGlue/run_glue_classifier_bert_base.py | 0 | processor for the multinli mismatched data set (glue version). | STRING |
| HIGH | training/BingBertGlue/run_glue_classifier_bert_large.py | 0 | processor for the multinli mismatched data set (glue version). | STRING |
| HIGH | training/bing_bert/run_glue_classifier_bert_base.py | 0 | truncates a sequence pair in place to the maximum length. | STRING |
| HIGH | training/bing_bert/run_glue_classifier_bert_large.py | 0 | truncates a sequence pair in place to the maximum length. | STRING |
| HIGH | training/BingBertGlue/run_glue_classifier_bert_base.py | 0 | truncates a sequence pair in place to the maximum length. | STRING |
| HIGH | training/BingBertGlue/run_glue_classifier_bert_large.py | 0 | truncates a sequence pair in place to the maximum length. | STRING |
| HIGH | …ning/bing_bert/pytorch_pretrained_bert/optimization.py | 0 | implements bert version of adam algorithm with weight decay fix. params: lr: learning rate warmup: portion of t_total fo | STRING |
| HIGH | …g/BingBertGlue/pytorch_pretrained_bert/optimization.py | 0 | implements bert version of adam algorithm with weight decay fix. params: lr: learning rate warmup: portion of t_total fo | STRING |
| HIGH | …/BingBertSquad/pytorch_pretrained_bert/optimization.py | 0 | implements bert version of adam algorithm with weight decay fix. params: lr: learning rate warmup: portion of t_total fo | STRING |
| HIGH | …ning/bing_bert/pytorch_pretrained_bert/optimization.py | 0 | performs a single optimization step. arguments: closure (callable, optional): a closure that reevaluates the model and r | STRING |
| HIGH | …g/BingBertGlue/pytorch_pretrained_bert/optimization.py | 0 | performs a single optimization step. arguments: closure (callable, optional): a closure that reevaluates the model and r | STRING |
| HIGH | …/BingBertSquad/pytorch_pretrained_bert/optimization.py | 0 | performs a single optimization step. arguments: closure (callable, optional): a closure that reevaluates the model and r | STRING |
| HIGH | …ning/bing_bert/pytorch_pretrained_bert/tokenization.py | 0 | runs basic whitespace cleaning and splitting on a peice of text. | STRING |
| HIGH | …g/BingBertGlue/pytorch_pretrained_bert/tokenization.py | 0 | runs basic whitespace cleaning and splitting on a peice of text. | STRING |
| HIGH | …/BingBertSquad/pytorch_pretrained_bert/tokenization.py | 0 | runs basic whitespace cleaning and splitting on a peice of text. | STRING |
| HIGH | …ning/bing_bert/pytorch_pretrained_bert/tokenization.py | 0 | runs end-to-end tokenization: punctuation splitting + wordpiece | STRING |
| HIGH | …g/BingBertGlue/pytorch_pretrained_bert/tokenization.py | 0 | runs end-to-end tokenization: punctuation splitting + wordpiece | STRING |
| HIGH | …/BingBertSquad/pytorch_pretrained_bert/tokenization.py | 0 | runs end-to-end tokenization: punctuation splitting + wordpiece | STRING |
| HIGH | …ning/bing_bert/pytorch_pretrained_bert/tokenization.py | 0 | converts a sequence of tokens into ids using the vocab. | STRING |
| HIGH | …g/BingBertGlue/pytorch_pretrained_bert/tokenization.py | 0 | converts a sequence of tokens into ids using the vocab. | STRING |
| HIGH | …/BingBertSquad/pytorch_pretrained_bert/tokenization.py | 0 | converts a sequence of tokens into ids using the vocab. | STRING |
| HIGH | …ning/bing_bert/pytorch_pretrained_bert/tokenization.py | 0 | converts a sequence of ids in wordpiece tokens using the vocab. | STRING |
| HIGH | …g/BingBertGlue/pytorch_pretrained_bert/tokenization.py | 0 | converts a sequence of ids in wordpiece tokens using the vocab. | STRING |
| HIGH | …/BingBertSquad/pytorch_pretrained_bert/tokenization.py | 0 | converts a sequence of ids in wordpiece tokens using the vocab. | STRING |
| HIGH | …ning/bing_bert/pytorch_pretrained_bert/tokenization.py | 0 | instantiate a pretrainedbertmodel from a pre-trained model file. download and cache the pre-trained model file if needed | STRING |
| HIGH | …g/BingBertGlue/pytorch_pretrained_bert/tokenization.py | 0 | instantiate a pretrainedbertmodel from a pre-trained model file. download and cache the pre-trained model file if needed | STRING |
| HIGH | …/BingBertSquad/pytorch_pretrained_bert/tokenization.py | 0 | instantiate a pretrainedbertmodel from a pre-trained model file. download and cache the pre-trained model file if needed | STRING |
| HIGH | …ning/bing_bert/pytorch_pretrained_bert/tokenization.py | 0 | runs basic tokenization (punctuation splitting, lower casing, etc.). | STRING |
| HIGH | …g/BingBertGlue/pytorch_pretrained_bert/tokenization.py | 0 | runs basic tokenization (punctuation splitting, lower casing, etc.). | STRING |
| 242 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …aining/data_efficiency/vit_finetuning/main_imagenet.py | 28 | CODE | |
| LOW | …aining/data_efficiency/vit_finetuning/main_imagenet.py | 30 | CODE | |
| LOW | …aining/data_efficiency/vit_finetuning/main_imagenet.py | 30 | CODE | |
| LOW | training/data_efficiency/vit_finetuning/main_cifar.py | 16 | CODE | |
| LOW | training/data_efficiency/vit_finetuning/main_cifar.py | 20 | CODE | |
| LOW | training/data_efficiency/vit_finetuning/main_cifar.py | 21 | CODE | |
| LOW | training/data_efficiency/vit_finetuning/main_cifar.py | 23 | CODE | |
| LOW | training/data_efficiency/vit_finetuning/main_cifar.py | 29 | CODE | |
| LOW | training/data_efficiency/vit_finetuning/main_cifar.py | 30 | CODE | |
| LOW | training/data_efficiency/vit_finetuning/main_cifar.py | 33 | CODE | |
| LOW | …ining/data_efficiency/vit_finetuning/utils/__init__.py | 15 | CODE | |
| LOW | …ining/data_efficiency/vit_finetuning/utils/__init__.py | 15 | CODE | |
| LOW | …ining/data_efficiency/vit_finetuning/utils/__init__.py | 15 | CODE | |
| LOW | …ining/data_efficiency/vit_finetuning/utils/__init__.py | 15 | CODE | |
| LOW | …ining/data_efficiency/vit_finetuning/utils/__init__.py | 15 | CODE | |
| LOW | …ining/data_efficiency/vit_finetuning/utils/__init__.py | 15 | CODE | |
| LOW | …ining/data_efficiency/vit_finetuning/utils/__init__.py | 15 | CODE | |
| LOW | …ining/data_efficiency/vit_finetuning/utils/__init__.py | 15 | CODE | |
| LOW | …ining/data_efficiency/vit_finetuning/utils/__init__.py | 16 | CODE | |
| LOW | training/data_efficiency/vit_finetuning/utils/utils.py | 15 | CODE | |
| LOW | training/data_efficiency/vit_finetuning/utils/utils.py | 17 | CODE | |
| LOW | training/data_efficiency/vit_finetuning/utils/utils.py | 21 | CODE | |
| LOW | training/data_efficiency/vit_finetuning/utils/utils.py | 23 | CODE | |
| LOW | training/data_efficiency/vit_finetuning/utils/utils.py | 24 | CODE | |
| LOW | training/data_efficiency/vit_finetuning/utils/utils.py | 30 | CODE | |
| LOW | training/data_efficiency/vit_finetuning/utils/utils.py | 32 | CODE | |
| LOW | …ning/data_efficiency/vit_finetuning/models/__init__.py | 15 | CODE | |
| LOW | …ning/data_efficiency/vit_finetuning/models/__init__.py | 16 | CODE | |
| LOW | …ning/data_efficiency/vit_finetuning/models/__init__.py | 17 | CODE | |
| LOW | training/data_efficiency/vit_finetuning/models/vit.py | 26 | CODE | |
| LOW | training/data_efficiency/vit_finetuning/models/vit.py | 33 | CODE | |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 27 | CODE | |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 29 | CODE | |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 31 | CODE | |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 32 | CODE | |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 34 | CODE | |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 37 | CODE | |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 37 | CODE | |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 37 | CODE | |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 38 | CODE | |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 39 | CODE | |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 41 | CODE | |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 41 | CODE | |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 41 | CODE | |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 41 | CODE | |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 41 | CODE | |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 55 | CODE | |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 65 | CODE | |
| LOW | …g/data_efficiency/gpt_finetuning/run_clm_no_trainer.py | 31 | CODE | |
| LOW | …g/data_efficiency/gpt_finetuning/run_clm_no_trainer.py | 32 | CODE | |
| LOW | …g/data_efficiency/gpt_finetuning/run_clm_no_trainer.py | 34 | CODE | |
| LOW | …g/data_efficiency/gpt_finetuning/run_clm_no_trainer.py | 39 | CODE | |
| LOW | training/pipeline_parallelism/train.py | 12 | CODE | |
| LOW | training/bing_bert/deepspeed_train.py | 11 | CODE | |
| LOW | training/bing_bert/deepspeed_train.py | 18 | CODE | |
| LOW | training/bing_bert/deepspeed_train.py | 19 | CODE | |
| LOW | training/bing_bert/deepspeed_train.py | 19 | CODE | |
| LOW | training/bing_bert/deepspeed_train.py | 19 | CODE | |
| LOW | training/bing_bert/deepspeed_train.py | 21 | CODE | |
| LOW | training/bing_bert/deepspeed_train.py | 21 | CODE | |
| 348 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | training/data_efficiency/vit_finetuning/models/vit.py | 662 | def _create_vision_transformer(variant, pretrained=False, **kwargs): | CODE |
| LOW | …g/tensor_parallel/hf_integration/train_bench_length.py | 67 | def smart_tokenizer_and_embedding_resize( | CODE |
| LOW | …g/tensor_parallel/hf_integration/train_bench_length.py | 193 | def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer, data_args) -> Dict: | CODE |
| LOW | training/tensor_parallel/hf_integration/train.py | 65 | def smart_tokenizer_and_embedding_resize( | CODE |
| LOW | training/tensor_parallel/hf_integration/train.py | 188 | def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer, data_args) -> Dict: | CODE |
| LOW | …aining/tensor_parallel/basic_example/autotp_example.py | 27 | def get_data_parallel_world_size(self): | CODE |
| LOW | …aining/tensor_parallel/basic_example/autotp_example.py | 30 | def get_model_parallel_world_size(self): | CODE |
| LOW | …tensor_parallel/basic_example/autotp_memory_compare.py | 28 | def get_data_parallel_world_size(self): | CODE |
| LOW | …tensor_parallel/basic_example/autotp_memory_compare.py | 31 | def get_model_parallel_world_size(self): | CODE |
| LOW | …sor_parallel/custom_patterns/autotp_custom_patterns.py | 32 | def get_data_parallel_world_size(self): | CODE |
| LOW | …sor_parallel/custom_patterns/autotp_custom_patterns.py | 35 | def get_model_parallel_world_size(self): | CODE |
| LOW | training/opsd/losses.py | 64 | def chunked_distillation_loss( | CODE |
| LOW | training/opsd/losses.py | 122 | def streamed_distillation_loss( | CODE |
| LOW⚡ | training/opsd/tests/test_losses.py | 28 | def test_positive_when_different(loss_type): | CODE |
| LOW⚡ | training/opsd/tests/test_losses.py | 38 | def test_chunking_equivalent_to_unchunked(loss_type): | CODE |
| LOW⚡ | training/opsd/tests/test_losses.py | 48 | def test_mask_excludes_tokens(): | CODE |
| LOW⚡ | training/opsd/tests/test_losses.py | 58 | def test_gradient_flows_to_student(): | CODE |
| LOW⚡ | training/opsd/tests/test_losses.py | 69 | def test_gradient_does_not_flow_to_teacher_when_detached(): | CODE |
| LOW⚡ | training/opsd/tests/test_losses.py | 79 | def test_unknown_loss_type_raises(): | CODE |
| LOW⚡ | training/opsd/tests/test_losses.py | 87 | def test_shape_mismatch_raises(): | CODE |
| LOW⚡ | training/opsd/tests/test_losses.py | 95 | def test_mask_shape_mismatch_raises(): | CODE |
| LOW⚡ | training/opsd/tests/test_losses.py | 104 | def test_temperature_changes_loss_but_stays_finite(temperature): | CODE |
| LOW⚡ | training/opsd/tests/test_losses.py | 123 | def test_all_zero_mask_returns_zero(): | CODE |
| LOW⚡ | training/opsd/tests/test_losses.py | 132 | def test_build_response_mask_basic(): | CODE |
| LOW⚡ | training/opsd/tests/test_losses.py | 140 | def test_build_response_mask_validates_shapes(): | CODE |
| LOW | training/opsd/tests/test_teacher_caching.py | 17 | def test_round_trip_preserves_values_within_dtype(): | CODE |
| LOW | training/opsd/tests/test_teacher_caching.py | 29 | def test_chunk_slicing_is_correct(): | CODE |
| LOW | training/opsd/tests/test_teacher_caching.py | 39 | def test_invalid_chunk_bounds_raise(): | CODE |
| LOW⚡ | training/opsd/tests/test_teacher_caching.py | 54 | def test_rejects_gpu_resident_logits(): | CODE |
| LOW⚡ | training/opsd/tests/test_teacher_caching.py | 61 | def test_dtype_override_in_chunk_to_device(): | CODE |
| LOW⚡ | training/opsd/tests/test_teacher_caching.py | 68 | def test_free_releases_buffer(): | CODE |
| LOW | training/opsd/tests/test_teacher_caching.py | 82 | def test_streamed_chunked_loss_matches_full_loss(): | CODE |
| LOW | training/opsd/tests/test_data.py | 36 | def test_collator_left_pads_real_tokens_to_right_edge(): | CODE |
| LOW⚡ | training/opsd/tests/test_data.py | 55 | def test_collator_truncates_to_max_prompt_length(): | CODE |
| LOW⚡ | training/opsd/tests/test_data.py | 64 | def test_collator_falls_back_to_eos_when_no_pad_id(): | CODE |
| LOW⚡ | training/opsd/tests/test_data.py | 74 | def test_collator_raises_without_pad_and_eos(): | CODE |
| LOW⚡ | training/opsd/tests/test_data.py | 80 | def test_prompt_dataset_reads_jsonl(tmp_path): | CODE |
| LOW⚡ | training/opsd/tests/test_data.py | 89 | def test_prompt_dataset_missing_field_raises(tmp_path): | CODE |
| LOW | training/bing_bert/deepspeed_train.py | 366 | def prepare_optimizer_parameters(args, model): | CODE |
| LOW | training/bing_bert/nvidia_bert_dataset_provider.py | 30 | def create_pretraining_dataset(input_file, max_predictions_per_seq, | CODE |
| LOW | training/bing_bert/run_glue_classifier_bert_base.py | 82 | class DataProcessor(object): | CODE |
| LOW | training/bing_bert/run_glue_classifier_bert_base.py | 411 | def convert_examples_to_features(examples, label_list, max_seq_length, | CODE |
| LOW | training/bing_bert/run_glue_classifier_bert_large.py | 109 | class DataProcessor(object): | CODE |
| LOW | training/bing_bert/run_glue_classifier_bert_large.py | 453 | def convert_examples_to_features(examples, label_list, max_seq_length, | CODE |
| LOW | …ch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py | 31 | def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, | CODE |
| LOW | training/bing_bert/nvidia/modelingpreln_layerdrop.py | 79 | def get_sparse_attention_config(args, num_heads): | CODE |
| LOW | training/bing_bert/nvidia/modelingpreln_layerdrop.py | 112 | def get_sparse_attention_utils(sparse_attention_config): | CODE |
| LOW | training/bing_bert/nvidia/modelingpreln.py | 78 | def get_sparse_attention_config(args, num_heads): | CODE |
| LOW | training/bing_bert/nvidia/modelingpreln.py | 110 | def get_sparse_attention_utils(sparse_attention_config): | CODE |
| LOW | training/bing_bert/turing/dataset.py | 344 | def create_masked_lm_predictions(self, tokens): | CODE |
| LOW | training/BingBertGlue/nvidia_bert_dataset_provider.py | 30 | def create_pretraining_dataset(input_file, max_predictions_per_seq, | CODE |
| LOW | training/BingBertGlue/run_glue_classifier_bert_base.py | 81 | class DataProcessor(object): | CODE |
| LOW | training/BingBertGlue/run_glue_classifier_bert_base.py | 410 | def convert_examples_to_features(examples, label_list, max_seq_length, | CODE |
| LOW | training/BingBertGlue/run_glue_classifier_bert_large.py | 110 | class DataProcessor(object): | CODE |
| LOW | training/BingBertGlue/run_glue_classifier_bert_large.py | 454 | def convert_examples_to_features(examples, label_list, max_seq_length, | CODE |
| LOW | …ch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py | 31 | def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, | CODE |
| LOW | training/BingBertGlue/nvidia/modelingpreln_layerdrop.py | 79 | def get_sparse_attention_config(args, num_heads): | CODE |
| LOW | training/BingBertGlue/nvidia/modelingpreln_layerdrop.py | 112 | def get_sparse_attention_utils(sparse_attention_config): | CODE |
| LOW | training/BingBertGlue/nvidia/modelingpreln.py | 78 | def get_sparse_attention_config(args, num_heads): | CODE |
| LOW | training/BingBertGlue/nvidia/modelingpreln.py | 110 | def get_sparse_attention_utils(sparse_attention_config): | CODE |
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| LOW | …aining/data_efficiency/vit_finetuning/main_imagenet.py | 109 | CODE | |
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| LOW | …g/data_efficiency/gpt_finetuning/run_clm_no_trainer.py | 241 | CODE | |
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| LOW | training/DeepSpeed-Domino/domino/language_model.py | 276 | CODE | |
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| LOW | …atch_size_and_lr/variable_batch_size_and_lr_example.py | 141 | "data_sampling": { | COMMENT |
| LOW | …g/data_efficiency/variable_batch_size_and_lr/README.md | 81 | "dynamic_batching": { | COMMENT |
| LOW | training/data_efficiency/vit_finetuning/main_cifar.py | 1 | # Copyright 2021 Google LLC | COMMENT |
| LOW | …ncy/vit_finetuning/bash_script/run_cifar_random_ltd.sh | 1 | #!/bin/bash | COMMENT |
| LOW | …/vit_finetuning/bash_script/run_imagenet_random_ltd.sh | 21 | # --deepspeed \ | COMMENT |
| LOW | …ining/data_efficiency/vit_finetuning/utils/__init__.py | 1 | # Copyright 2021 Google LLC | COMMENT |
| LOW | …ining/data_efficiency/vit_finetuning/utils/get_data.py | 1 | # Copyright 2021 Google LLC | COMMENT |
| LOW | training/data_efficiency/vit_finetuning/utils/utils.py | 1 | # Copyright 2021 Google LLC | COMMENT |
| LOW | …ning/data_efficiency/vit_finetuning/models/__init__.py | 1 | # Copyright 2021 Google LLC | COMMENT |
| LOW | …ining/data_efficiency/gpt_finetuning/learning_rates.py | 1 | # This code is adopted from https://github.com/deepspeedai/Megatron-DeepSpeed/blob/main/megatron/learning_rates.py | COMMENT |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 1 | #!/usr/bin/env python | COMMENT |
| LOW | …g/data_efficiency/gpt_finetuning/run_clm_no_trainer.py | 1 | #!/usr/bin/env python | COMMENT |
| LOW | …ency/gpt_finetuning/bash_script/run_base_random_ltd.sh | 21 | # --random_ltd \ | COMMENT |
| LOW | …g/tensor_parallel/hf_integration/train_bench_length.py | 1 | # Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li | COMMENT |
| LOW | training/tensor_parallel/hf_integration/train.py | 1 | # Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li | COMMENT |
| LOW | training/bing_bert/run_glue_classifier_bert_base.py | 1 | # coding=utf-8 | COMMENT |
| LOW | training/bing_bert/run_glue_classifier_bert_base.py | 441 | # tokens: [CLS] the dog is hairy . [SEP] | COMMENT |
| LOW | training/bing_bert/NOTICE.txt | 21 | Open Source License/Copyright Notice. | COMMENT |
| LOW | training/bing_bert/run_glue_classifier_bert_large.py | 1 | # coding=utf-8 | COMMENT |
| LOW | training/bing_bert/run_glue_classifier_bert_large.py | 481 | # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 | COMMENT |
| LOW | …/mpi_train_bert_onebitlamb_bsz32k_seq512_infiniband.sh | 1 | #!/bin/bash | COMMENT |
| LOW | …/mpi_train_bert_onebitlamb_bsz64k_seq128_infiniband.sh | 1 | #!/bin/bash | COMMENT |
| LOW | …et/mpi_train_bert_onebitlamb_bsz32k_seq512_ethernet.sh | 1 | #!/bin/bash | COMMENT |
| LOW | …et/mpi_train_bert_onebitlamb_bsz64k_seq128_ethernet.sh | 1 | #!/bin/bash | COMMENT |
| LOW | …ch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …ning/bing_bert/pytorch_pretrained_bert/optimization.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …ning/bing_bert/pytorch_pretrained_bert/tokenization.py | 1 | # coding=utf-8 | COMMENT |
| LOW | training/bing_bert/pytorch_pretrained_bert/modeling.py | 1 | # coding=utf-8 | COMMENT |
| LOW | training/bing_bert/nvidia/modelingpreln_layerdrop.py | 1 | # DeepSpeed note, code taken from commit 3d59216cec89a363649b4fe3d15295ba936ced0f | COMMENT |
| LOW | training/bing_bert/nvidia/modelingpreln_layerdrop.py | 681 | COMMENT | |
| LOW | training/bing_bert/nvidia/modelingpreln.py | 1 | # DeepSpeed note, code taken from commit 3d59216cec89a363649b4fe3d15295ba936ced0f | COMMENT |
| LOW | training/bing_bert/nvidia/modelingpreln.py | 661 | # super(BertEncoder, self).__init__() | COMMENT |
| LOW | …d/mpi_train_bert_onebitadam_bsz4k_seq128_infiniband.sh | 1 | #!/bin/bash | COMMENT |
| LOW | …net/mpi_train_bert_onebitadam_bsz4k_seq128_ethernet.sh | 1 | #!/bin/bash | COMMENT |
| LOW | training/BingBertGlue/run_glue_classifier_bert_base.py | 1 | # coding=utf-8 | COMMENT |
| LOW | training/BingBertGlue/run_glue_classifier_bert_base.py | 441 | # type_ids: 0 0 0 0 0 0 0 | COMMENT |
| LOW | training/BingBertGlue/run_glue_classifier_bert_large.py | 1 | # coding=utf-8 | COMMENT |
| LOW | training/BingBertGlue/run_glue_classifier_bert_large.py | 481 | # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] | COMMENT |
| LOW | …ch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …g/BingBertGlue/pytorch_pretrained_bert/optimization.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …g/BingBertGlue/pytorch_pretrained_bert/tokenization.py | 1 | # coding=utf-8 | COMMENT |
| LOW | …ining/BingBertGlue/pytorch_pretrained_bert/modeling.py | 1 | # coding=utf-8 | COMMENT |
| LOW | training/BingBertGlue/nvidia/modelingpreln_layerdrop.py | 1 | # DeepSpeed note, code taken from commit 3d59216cec89a363649b4fe3d15295ba936ced0f | COMMENT |
| LOW | training/BingBertGlue/nvidia/modelingpreln_layerdrop.py | 681 | COMMENT | |
| LOW | training/BingBertGlue/nvidia/modeling.py | 1 | # DeepSpeed note, code taken from commit 3d59216cec89a363649b4fe3d15295ba936ced0f | COMMENT |
| LOW | training/BingBertGlue/nvidia/modeling.py | 581 | #class BertEncoder(nn.Module): | COMMENT |
| LOW | training/BingBertGlue/nvidia/modelingpreln.py | 1 | # DeepSpeed note, code taken from commit 3d59216cec89a363649b4fe3d15295ba936ced0f | COMMENT |
| LOW | training/BingBertGlue/nvidia/modelingpreln.py | 661 | COMMENT | |
| LOW | training/BingBertGlue/nvidia/modelingpreln.py | 1021 | COMMENT | |
| LOW | training/imagenet/extract_ILSVRC.sh | 1 | #!/bin/bash | COMMENT |
| LOW | training/imagenet/extract_ILSVRC.sh | 21 | # │ ├── ILSVRC2012_val_00002138.JPEG | COMMENT |
| LOW | training/imagenet/extract_ILSVRC.sh | 41 | # 2. extract and copy contents of .tar file into directory | COMMENT |
| LOW | training/imagenet/extract_ILSVRC.sh | 61 | # get script from soumith and run; this script creates all class directories and moves images into corresponding directo | COMMENT |
| LOW | training/deepspeed_finetune_demo/run_and_evaluate.sh | 1 | #!/bin/bash | COMMENT |
| LOW | training/stable_diffusion/train_sd_distil_lora.py | 1 | #!/usr/bin/env python | COMMENT |
| LOW | …ng/stable_diffusion/local_pipeline_stable_diffusion.py | 1 | # Copyright 2023 The HuggingFace Team. All rights reserved. | COMMENT |
| LOW | training/BingBertSquad/nvidia_run_squad_baseline.py | 1 | # coding=utf-8 | COMMENT |
| LOW | training/BingBertSquad/nvidia_run_squad_baseline.py | 341 | def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, | COMMENT |
| LOW | training/BingBertSquad/nvidia_run_squad_baseline.py | 381 | # Because of the sliding window approach taken to scoring documents, a single | COMMENT |
| LOW | training/BingBertSquad/nvidia_run_squad_baseline.py | 561 | # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So | COMMENT |
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|---|---|---|---|---|
| MEDIUM | training/HelloDeepSpeed/train_bert_ds.py | 149 | # Create the labels first | COMMENT |
| MEDIUM | training/HelloDeepSpeed/train_bert.py | 119 | # Create the labels first | COMMENT |
| MEDIUM | training/DeepSpeed-Domino/pretrain_gpt3_13b.sh | 1 | # This file is adapted from pretrain_gpt.sh in Megatron-LM | COMMENT |
| MEDIUM | training/DeepSpeed-Domino/pretrain_gpt.py | 2 | # This file is adapted from pretrain_gpt.py in Megatron-LM | COMMENT |
| MEDIUM | training/DeepSpeed-Domino/pretrain_gpt3_6.7b.sh | 1 | # This file is adapted from pretrain_gpt.sh in Megatron-LM | COMMENT |
| MEDIUM | training/DeepSpeed-Domino/domino/language_model.py | 1 | # This file is adapted from language_model.py in Megatron-LM | COMMENT |
| MEDIUM | training/DeepSpeed-Domino/domino/gpt_model.py | 1 | # This file is adapted from gpt_model.py in Megatron-LM | COMMENT |
| MEDIUM | training/DeepSpeed-Domino/domino/training.py | 2 | # This file is adapted from training.py in Megatron-LM | COMMENT |
| MEDIUM | training/stable_diffusion/train_sd_distil_lora.py | 1235 | # Create the pipeline using using the trained modules and save it. | STRING |
| MEDIUM | training/cifar/cifar10_deepspeed.py | 237 | # Define the test dataloader. | COMMENT |
| MEDIUM | training/cifar/cifar10_deepspeed.py | 356 | # Define the Classification Cross-Entropy loss function. | COMMENT |
| MEDIUM | …chmarks/inference/deepspeedometer/tests/test_config.py | 22 | # Create a config that would generate 6 benchmark settings | COMMENT |
| MEDIUM | …ce/deepspeedometer/src/deepspeedometer/sample_input.py | 95 | # Define the dataset and data collator | COMMENT |
| MEDIUM | …ce/deepspeedometer/src/deepspeedometer/sample_input.py | 99 | # Define the training arguments | COMMENT |
| MEDIUM | benchmarks/inference/mii/src/sample_input.py | 95 | # Define the dataset and data collator | COMMENT |
| MEDIUM | benchmarks/inference/mii/src/sample_input.py | 99 | # Define the training arguments | COMMENT |
| MEDIUM | benchmarks/inference/mii/src/utils.py | 150 | # Create the parser, inheriting from the server and/or client parsers | COMMENT |
| MEDIUM | benchmarks/autosp/distributed_attention.py | 65 | # Create the attention mask from padding mask + causal mask | COMMENT |
| MEDIUM | compression/cifar/utils.py | 5 | # This file is part of PyHessian library. | COMMENT |
| MEDIUM | …lications/DeepSpeed-VisualChat/utils/model/vis_proj.py | 28 | # The following code is adopted from QWen-Clip | COMMENT |
| MEDIUM | …ons/DeepSpeed-VisualChat/utils/data/ocr_vqa_dataset.py | 1 | # This file is adapted from https://github.com/open-mmlab/Multimodal-GPT | COMMENT |
| MEDIUM | …eepSpeed-VisualChat/utils/data/cc_sbu_align_dataset.py | 1 | # This file is adapted from https://github.com/open-mmlab/Multimodal-GPT | COMMENT |
| MEDIUM | …ations/DeepSpeed-VisualChat/utils/data/dial_dataset.py | 1 | # This file is adapted from https://github.com/open-mmlab/Multimodal-GPT | COMMENT |
| MEDIUM | …plications/DeepSpeed-VisualChat/utils/data/__init__.py | 1 | # This file is adapted from https://github.com/open-mmlab/Multimodal-GPT | COMMENT |
| MEDIUM | applications/DeepSpeed-VisualChat/utils/data/builder.py | 1 | # This file is adapted from https://github.com/open-mmlab/Multimodal-GPT | COMMENT |
| MEDIUM | …cations/DeepSpeed-VisualChat/utils/data/vqa_dataset.py | 1 | # This file is adapted from https://github.com/open-mmlab/Multimodal-GPT | COMMENT |
| MEDIUM | …ions/DeepSpeed-VisualChat/utils/data/aokvqa_dataset.py | 1 | # This file is adapted from https://github.com/open-mmlab/Multimodal-GPT | COMMENT |
| MEDIUM | …tions/DeepSpeed-VisualChat/utils/data/llava_dataset.py | 1 | # This file is adapted from https://github.com/open-mmlab/Multimodal-GPT | COMMENT |
| MEDIUM | …eepSpeed-VisualChat/utils/data/coco_caption_dataset.py | 1 | # This file is adapted from https://github.com/open-mmlab/Multimodal-GPT | COMMENT |
| MEDIUM | applications/DeepSpeed-Chat/dschat/utils/utils.py | 152 | # This function is a modified version of code available in the from_pretrained API of HuggingFace Transformers | COMMENT |
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| LOW | …ining/data_efficiency/vit_finetuning/utils/__init__.py | 18 | __all__ = [ "get_dataset", "ImageMemFolder", "AverageMeter", "ProgressMeter", "accuracy", "get_optimizer", "get_schedule | CODE |
| LOW | training/data_efficiency/vit_finetuning/models/vit.py | 1097 | __all__ = [ 'vits16r224', 'vits16r384', 'lvits16r224','lvits16r384', 'Block'] | CODE |
| LOW | training/data_efficiency/gpt_finetuning/analyze_data.py | 68 | logger = logging.getLogger(__name__) | CODE |
| LOW | …g/data_efficiency/gpt_finetuning/run_clm_no_trainer.py | 65 | logger = logging.getLogger(__name__) | CODE |
| LOW | training/bing_bert/run_glue_classifier_bert_base.py | 48 | logger = logging.getLogger(__name__) | CODE |
| LOW | training/bing_bert/run_glue_classifier_bert_large.py | 48 | logger = logging.getLogger(__name__) | CODE |
| LOW | …ning/bing_bert/pytorch_pretrained_bert/tokenization.py | 28 | logger = logging.getLogger(__name__) | CODE |
| LOW | …aining/bing_bert/pytorch_pretrained_bert/file_utils.py | 24 | logger = logging.getLogger(__name__) # pylint: disable=invalid-name | CODE |
| LOW | training/bing_bert/pytorch_pretrained_bert/modeling.py | 37 | logger = logging.getLogger(__name__) | CODE |
| LOW | training/bing_bert/nvidia/modelingpreln_layerdrop.py | 48 | logger = logging.getLogger(__name__) | CODE |
| LOW | training/bing_bert/nvidia/modelingpreln.py | 47 | logger = logging.getLogger(__name__) | CODE |
| LOW | training/bing_bert/turing/logger.py | 8 | logger = logging.getLogger(__name__) | CODE |
| LOW | training/bing_bert/turing/file_utils.py | 39 | logger = logging.getLogger(__name__) # pylint: disable=invalid-name | CODE |
| LOW | training/BingBertGlue/run_glue_classifier_bert_base.py | 47 | logger = logging.getLogger(__name__) | CODE |
| LOW | training/BingBertGlue/run_glue_classifier_bert_large.py | 49 | logger = logging.getLogger(__name__) | CODE |
| LOW | …g/BingBertGlue/pytorch_pretrained_bert/tokenization.py | 28 | logger = logging.getLogger(__name__) | CODE |
| LOW | …ing/BingBertGlue/pytorch_pretrained_bert/file_utils.py | 24 | logger = logging.getLogger(__name__) # pylint: disable=invalid-name | CODE |
| LOW | …ining/BingBertGlue/pytorch_pretrained_bert/modeling.py | 37 | logger = logging.getLogger(__name__) | CODE |
| LOW | training/BingBertGlue/nvidia/modelingpreln_layerdrop.py | 48 | logger = logging.getLogger(__name__) | CODE |
| LOW | training/BingBertGlue/nvidia/modeling.py | 47 | logger = logging.getLogger(__name__) | CODE |
| LOW | training/BingBertGlue/nvidia/modelingpreln.py | 47 | logger = logging.getLogger(__name__) | CODE |
| LOW | training/BingBertGlue/turing/logger.py | 8 | logger = logging.getLogger(__name__) | CODE |
| LOW | training/BingBertGlue/turing/file_utils.py | 39 | logger = logging.getLogger(__name__) # pylint: disable=invalid-name | CODE |
| LOW | …aining/BingBertSquad/convert_bert_ckpt_to_deepspeed.py | 15 | logger = logging.getLogger(__name__) | CODE |
| LOW | training/BingBertSquad/nvidia_run_squad_baseline.py | 51 | logger = logging.getLogger(__name__) | CODE |
| LOW | training/BingBertSquad/nvidia_run_squad_deepspeed.py | 54 | logger = logging.getLogger(__name__) | CODE |
| LOW | …/BingBertSquad/pytorch_pretrained_bert/tokenization.py | 28 | logger = logging.getLogger(__name__) | CODE |
| LOW | …ng/BingBertSquad/pytorch_pretrained_bert/file_utils.py | 24 | logger = logging.getLogger(__name__) # pylint: disable=invalid-name | CODE |
| LOW | …ning/BingBertSquad/pytorch_pretrained_bert/modeling.py | 38 | logger = logging.getLogger(__name__) | CODE |
| LOW | …aining/BingBertSquad/turing/modelingpreln_layerdrop.py | 48 | logger = logging.getLogger(__name__) | CODE |
| LOW | training/BingBertSquad/turing/nvidia_modeling.py | 47 | logger = logging.getLogger(__name__) | CODE |
| LOW | training/BingBertSquad/turing/file_utils.py | 39 | logger = logging.getLogger(__name__) # pylint: disable=invalid-name | CODE |
| LOW | training/BingBertSquad/turing/nvidia_modelingpreln.py | 47 | logger = logging.getLogger(__name__) | CODE |
| LOW | training/MoQ/run_glue.py | 68 | logger = logging.getLogger(__name__) | CODE |
| LOW | …me/model_checkpoint/torch/serialization_orig_v2.6.0.py | 41 | __all__ = [ | CODE |
| LOW | …me/model_checkpoint/torch/serialization_fast_v2.6.0.py | 41 | __all__ = [ | CODE |
| LOW | benchmarks/autosp/ring_attention.py | 14 | __all__ = ["update_out_and_lse", "RingComm", "get_default_args"] | CODE |
| LOW | compression/bert/util.py | 16 | logger = logging.getLogger(__name__) | CODE |
| LOW | compression/bert/run_glue_lkd.py | 54 | logger = logging.getLogger(__name__) | CODE |
| LOW | compression/bert/run_glue_no_trainer.py | 54 | logger = logging.getLogger(__name__) | CODE |
| LOW | compression/reasoning_aware_compression/rac/__init__.py | 24 | __all__ = [ | CODE |
| LOW | compression/gpt2/run_clm_no_trainer.py | 60 | logger = logging.getLogger(__name__) | CODE |
| LOW | compression/cifar/resnet.py | 13 | __all__ = ['resnet'] | CODE |
| LOW | …at/training/step1_supervised_finetuning/prompt_eval.py | 16 | logger = logging.getLogger(__name__) | CODE |
| LOW | …eneration/run-generation-script/test-run-generation.py | 51 | logger = logging.getLogger(__name__) | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | training/opsd/trainer.py | 77 | # ------------------------------------------------------------------ | COMMENT |
| MEDIUM | training/opsd/trainer.py | 79 | # ------------------------------------------------------------------ | COMMENT |
| MEDIUM | training/opsd/trainer.py | 97 | # ------------------------------------------------------------------ | COMMENT |
| MEDIUM | training/opsd/trainer.py | 99 | # ------------------------------------------------------------------ | COMMENT |
| MEDIUM | training/opsd/trainer.py | 187 | # ------------------------------------------------------------------ | COMMENT |
| MEDIUM | training/opsd/trainer.py | 189 | # ------------------------------------------------------------------ | COMMENT |
| MEDIUM | training/cifar/cifar10_tutorial.py | 298 | # ---------------- | COMMENT |
| MEDIUM⚡ | training/cifar/cifar10_tutorial.py | 342 | # ------------------------- | COMMENT |
| MEDIUM⚡ | training/cifar/cifar10_tutorial.py | 347 | # ------------------- | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | …ng/stable_diffusion/local_pipeline_stable_diffusion.py | 538 | Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[st | STRING |
| HIGH | …me/model_checkpoint/torch/serialization_orig_v2.6.0.py | 673 | Restores `storage` using a deserializer function registered for the `location`. This function looks in the reg | STRING |
| HIGH | …me/model_checkpoint/torch/serialization_fast_v2.6.0.py | 673 | Restores `storage` using a deserializer function registered for the `location`. This function looks in the reg | STRING |
| HIGH | …ce/stable-diffusion/local_pipeline_stable_diffusion.py | 540 | Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[st | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …ining/data_efficiency/gpt_finetuning/learning_rates.py | 66 | # If the learning rate is constant, just return the initial value. | COMMENT |
| LOW | training/BingBertSquad/nvidia_run_squad_baseline.py | 365 | # "Japanese", we just use "Japanese" as the annotation. This is fairly rare | COMMENT |
| LOW | training/BingBertSquad/nvidia_run_squad_baseline.py | 523 | # just create a nonce prediction in this case to avoid failure. | COMMENT |
| LOW | training/BingBertSquad/nvidia_run_squad_baseline.py | 583 | # can fail in certain cases in which case we just return `orig_text`. | COMMENT |
| LOW | training/BingBertSquad/nvidia_run_squad_deepspeed.py | 369 | # "Japanese", we just use "Japanese" as the annotation. This is fairly rare | COMMENT |
| LOW | training/BingBertSquad/nvidia_run_squad_deepspeed.py | 527 | # just create a nonce prediction in this case to avoid failure. | COMMENT |
| LOW | training/BingBertSquad/nvidia_run_squad_deepspeed.py | 587 | # can fail in certain cases in which case we just return `orig_text`. | COMMENT |
| MEDIUM⚡ | training/cifar/cifar10_tutorial.py | 350 | # - `Train a state-of-the-art ResNet network on imagenet`_ | COMMENT |
| MEDIUM⚡ | training/cifar/cifar10_tutorial.py | 358 | # .. _Train a state-of-the-art ResNet network on imagenet: https://github.com/pytorch/examples/tree/master/imagenet | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | training/DeepSpeed-SuperOffload/finetune_zero3.py | 220 | except Exception as e: | CODE |
| LOW | training/DeepSpeed-SuperOffload/finetune_zero3.py | 370 | except Exception as e: | CODE |
| LOW | training/DeepSpeed-SuperOffload/finetune_zero3.py | 377 | except Exception as e: | CODE |
| MEDIUM | training/bf16_master_weight/gather_memory.py | 65 | print(f"Error: Log directory '{log_dir}' does not exist") | CODE |
| LOW | …me/model_checkpoint/torch/serialization_orig_v2.6.0.py | 513 | except Exception as e: | STRING |
| LOW | …me/model_checkpoint/torch/serialization_orig_v2.6.0.py | 1534 | except Exception: # saving the source is optional, so we can ignore any errors | CODE |
| MEDIUM | …me/model_checkpoint/torch/serialization_orig_v2.6.0.py | 1531 | def _check_container_source(container_type, source_file, original_source): | CODE |
| LOW | …me/model_checkpoint/torch/serialization_fast_v2.6.0.py | 513 | except Exception as e: | STRING |
| LOW | …me/model_checkpoint/torch/serialization_fast_v2.6.0.py | 1538 | except Exception: # saving the source is optional, so we can ignore any errors | CODE |
| MEDIUM | …me/model_checkpoint/torch/serialization_fast_v2.6.0.py | 1535 | def _check_container_source(container_type, source_file, original_source): | CODE |
| MEDIUM | benchmarks/inference/collect_results.py | 121 | print(f"Error found in {file_path}, collecting error info...") | CODE |
| LOW | …dometer/src/deepspeedometer/clients/azure_ml_client.py | 72 | except Exception as e: | CODE |
| LOW | benchmarks/inference/mii/src/client.py | 254 | except Exception as e: | CODE |
| LOW | benchmarks/autosp/correctness/correctness_run.py | 32 | except Exception: | CODE |
| MEDIUM | benchmarks/autosp/correctness/correctness_run.py | 26 | def get_host_ip(): | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH⚡ | training/opsd/losses.py | 12 | * ``forward_kl`` = D_KL(teacher || student) — mode-covering for student | STRING |
| HIGH⚡ | training/opsd/losses.py | 13 | * ``reverse_kl`` = D_KL(student || teacher) — mode-seeking for student | STRING |
| HIGH⚡ | training/opsd/losses.py | 14 | * ``jsd`` = 0.5 * D_KL(P || M) + 0.5 * D_KL(Q || M), M = (P+Q)/2 | STRING |
| HIGH | training/opsd/tests/test_losses.py | 8 | cd examples/opsd && python -m pytest tests/test_losses.py -v | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | compression/cifar/run_compress.sh | 5 | ## Step 1: obtain a checkpoint (to be compressed) | COMMENT |
| LOW⚡ | compression/cifar/run_compress.sh | 11 | ### Step 2: compress: channel pruning | COMMENT |
| LOW⚡ | compression/cifar/run_compress.sh | 25 | ### Step 1: obtain a checkpoint (to be compressed) | COMMENT |
| LOW⚡ | compression/cifar/run_compress.sh | 32 | #### Step 2: compress: channel pruning | COMMENT |
| LOW | applications/DeepSpeed-Chat/training/README.md | 7 | ### Step 1: Supervised Finetuning | COMMENT |
| LOW | applications/DeepSpeed-Chat/training/README.md | 16 | ### Step 2: Reward Model Finetuning | COMMENT |
| LOW | applications/DeepSpeed-Chat/training/README.md | 34 | ### Step 3: RLHF finetuning | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | training/cifar/cifar10_tutorial.py | 213 | # But we need to check if the network has learnt anything at all. | COMMENT |
| LOW⚡ | compression/cifar/run_compress.sh | 5 | ## Step 1: obtain a checkpoint (to be compressed) | COMMENT |
| LOW⚡ | compression/cifar/run_compress.sh | 11 | ### Step 2: compress: channel pruning | COMMENT |
| LOW⚡ | compression/cifar/run_compress.sh | 25 | ### Step 1: obtain a checkpoint (to be compressed) | COMMENT |
| LOW⚡ | compression/cifar/run_compress.sh | 32 | #### Step 2: compress: channel pruning | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | …g/tensor_parallel/hf_integration/train_bench_length.py | 243 | # Print message except when distributed but not rank 0 | COMMENT |
| LOW | training/tensor_parallel/hf_integration/train.py | 238 | # Print message except when distributed but not rank 0 | COMMENT |
| LOW | training/DeepSpeed-Domino/domino/training.py | 660 | # Set grad to zero. | COMMENT |
| LOW | …mpression/bash_script/collect_traces_zero_inference.sh | 25 | # Set NVME_OFFLOAD_DIR to spill parameters to NVMe instead of CPU memory. | COMMENT |
| LOW | …ions/DeepSpeed-Chat/dschat/utils/model/reward_model.py | 99 | # Check if there is any padding otherwise take length of sequence | COMMENT |
| LOW | …cations/DeepSpeed-Chat/dschat/utils/data/data_utils.py | 364 | if sft_train_datasets: # Check if sft_train_datasets is not empty | CODE |
| LOW | …cations/DeepSpeed-Chat/dschat/utils/data/data_utils.py | 370 | if sft_eval_datasets: # Check if sft_eval_datasets is not empty | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | …/huggingface/stable-diffusion/test-stable-diffusion.py | 8 | # In this example the SD inference pipeline is optimized based on recommendations in the research paper | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | training/bf16_master_weight/run_comparison.sh | 4 | # Usage: | COMMENT |
| LOW | benchmarks/autosp/correctness/correctness.sh | 10 | # Usage: | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | …re_compression/bash_script/run_calibration_ablation.sh | 11 | # rac those prompts plus the dense model's own chain-of-thought | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | training/stable_diffusion/train_sd_distil_lora.py | 850 | # make sure to pop weight so that corresponding model is not saved again | STRING |
| LOW | benchmarks/inference/mii/src/utils.py | 200 | # If we are not running the benchmark, we need to make sure to only have one | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | …pression/bert/huggingface_transformer/modeling_bert.py | 1386 | dummy_token = torch.full( | STRING |
| LOW⚡ | …pression/bert/huggingface_transformer/modeling_bert.py | 1389 | input_ids = torch.cat([input_ids, dummy_token], dim=1) | STRING |