PaddleFormers is an easy-to-use library of pre-trained large language model zoo based on PaddlePaddle.
This report presents the forensic synthetic code analysis of PaddlePaddle/PaddleFormers, a Python project with 12,984 GitHub stars. SynthScan v2.0 examined 364,874 lines of code across 1287 source files, recording 6967 pattern matches distributed across 23 syntactic categories. The overall adjusted score of 26.4 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).
This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.
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 6967 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.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tests/testing_utils.py | 82 | def softmax_with_cross_entropy(logits, label, soft_label=False, axis=-1, ignore_index=-1): | CODE |
| LOW | tests/testing_utils.py | 467 | def require_paddle_non_multi_gpu(test_case): | CODE |
| LOW | tests/mergekit/test_merge_model.py | 128 | def test_fuse_qkv_lora_merge_torch(self): | CODE |
| LOW | tests/nn/test_mlp.py | 39 | def test_initialization_default(self): | CODE |
| LOW | tests/nn/test_mlp.py | 52 | def test_initialization_fuse_ffn(self): | CODE |
| LOW | tests/nn/test_mlp.py | 63 | def test_initialization_non_fuse_ffn(self): | CODE |
| LOW⚡ | tests/nn/test_criterion.py | 38 | def test_forward_non_fuse_subbatch_sft(self): | CODE |
| LOW⚡ | tests/nn/test_criterion.py | 45 | def test_forward_with_loss_mask(self): | CODE |
| LOW⚡ | tests/nn/test_criterion.py | 50 | def test_forward_with_recompute(self): | CODE |
| LOW⚡ | tests/nn/test_norm.py | 30 | def test_layer_norm_initialization(self): | CODE |
| LOW⚡ | tests/nn/test_norm.py | 40 | def test_layer_norm_sequence_parallel(self): | CODE |
| LOW⚡ | tests/nn/test_norm.py | 50 | def test_rms_norm_initialization(self): | CODE |
| LOW⚡ | tests/nn/test_norm.py | 59 | def test_rms_norm_sequence_parallel(self): | CODE |
| LOW | tests/nn/test_lm_head.py | 24 | def test_initialization_default(self): | CODE |
| LOW | tests/nn/test_lm_head.py | 35 | def test_initialization_with_tie_word_embeddings(self): | CODE |
| LOW | tests/nn/test_lm_head.py | 50 | def test_forward_fused_head_loss(self): | CODE |
| LOW⚡ | tests/nn/test_attention.py | 203 | def test_forward_calls_correct_function(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_dpo_loss.py | 114 | def test_loss_impl_calls_loss_func(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_dpo_loss.py | 274 | def test_invalid_loss_type_raises(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_dpo_loss.py | 289 | def test_sigmoid_with_offset_alpha(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_dpo_loss.py | 305 | def test_pref_loss_ratio_applied(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_dpo_loss.py | 322 | def test_label_smoothing_applied(self): | CODE |
| LOW⚡ | tests/ai_edited_test/nn/test_ai_generation_utils.py | 17 | def test_single_eos_token_not_finished(self): | CODE |
| LOW⚡ | tests/ai_edited_test/nn/test_ai_generation_utils.py | 26 | def test_single_eos_token_finished(self): | CODE |
| LOW⚡ | tests/ai_edited_test/nn/test_ai_generation_utils.py | 35 | def test_already_finished_stays_finished(self): | CODE |
| LOW⚡ | tests/ai_edited_test/nn/test_ai_generation_utils.py | 54 | def test_list_eos_tokens_none_match(self): | CODE |
| LOW⚡ | tests/ai_edited_test/nn/test_ai_generation_utils.py | 63 | def test_nested_list_eos_tokens(self): | CODE |
| LOW⚡ | tests/ai_edited_test/nn/test_ai_generation_utils.py | 73 | def test_nested_list_eos_none_match(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_generation_utils.py | 139 | def test_add_better_than_worst(self): | CODE |
| LOW⚡ | tests/ai_edited_test/nn/test_ai_generation_utils.py | 152 | def test_is_done_not_enough_beams(self): | CODE |
| LOW⚡ | tests/ai_edited_test/nn/test_ai_generation_utils.py | 159 | def test_is_done_early_stopping(self): | CODE |
| LOW⚡ | tests/ai_edited_test/nn/test_ai_generation_utils.py | 168 | def test_is_done_no_early_stopping_better_available(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_generation_utils.py | 187 | def test_is_done_with_origin_len(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_generation_utils.py | 207 | def test_with_past_key_values(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_generation_utils.py | 233 | def test_window_size_larger_than_seq(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_generation_utils.py | 247 | def test_init_validation_num_beams(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_generation_utils.py | 254 | def test_init_validation_beam_groups(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_generation_utils.py | 270 | def test_init_with_beam_groups(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_sdpa_attention.py | 58 | def test_is_causal_inferred_multi_token(self, mock_sdpa): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_sdpa_attention.py | 72 | def test_is_causal_single_token(self, mock_sdpa): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_sdpa_attention.py | 87 | def test_explicit_is_causal_false(self, mock_sdpa): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_sdpa_attention.py | 151 | def test_dropout_passed_through(self, mock_sdpa): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_sdpa_attention.py | 166 | def test_training_passed_through(self, mock_sdpa): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_loss_utils.py | 19 | def test_subbatch_small_input_no_split(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_loss_utils.py | 31 | def test_subbatch_splits_and_concatenates(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_loss_utils.py | 44 | def test_subbatch_multiple_args(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_loss_utils.py | 57 | def test_subbatch_same_arg_idx(self): | CODE |
| LOW⚡ | tests/ai_edited_test/nn/test_ai_loss_utils.py | 74 | def test_subbatch_preserves_function_name(self): | CODE |
| LOW⚡ | tests/ai_edited_test/nn/test_ai_loss_utils.py | 84 | def test_subbatch_axis_width_mismatch_raises(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_loss_utils.py | 97 | def test_subbatch_same_arg_idx_invalid_raises(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_loss_utils.py | 111 | def test_subbatch_with_recompute(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_loss_utils.py | 141 | def test_calc_lm_head_logits_basic(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_loss_utils.py | 153 | def test_calc_lm_head_logits_with_bias(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_loss_utils.py | 167 | def test_calc_lm_head_logits_sequence_parallel(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_loss_utils.py | 181 | def test_calc_lm_head_logits_tensor_parallel_output_override(self): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_moe_all_gather.py | 36 | def test_single_rank_returns_clone(self, mock_fleet): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_moe_all_gather.py | 51 | def test_group_none_uses_model_parallel(self, mock_fleet): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_moe_all_gather.py | 66 | def test_multi_rank_returns_correct_shape(self, mock_fleet, mock_all_gather): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_moe_all_gather.py | 95 | def test_single_rank_returns_clone(self, mock_fleet): | CODE |
| LOW | tests/ai_edited_test/nn/test_ai_moe_all_gather.py | 110 | def test_group_none_uses_model_parallel(self, mock_fleet): | CODE |
| 3051 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | tests/ai_edited_test/cli/test_ai_load_hf_ckpt.py | 0 | load a module directly from file path without going through __init__.py. | STRING |
| HIGH | tests/ai_edited_test/cli/test_ai_deepseek_v3_fp8.py | 0 | load a module directly from file path without going through __init__.py. | STRING |
| HIGH | tests/ai_edited_test/cli/test_ai_convert_ckpt_sft.py | 0 | load a module directly from file path without going through __init__.py. | STRING |
| HIGH | …ts/ai_edited_test/cli/test_ai_deepseek_v3_moe_utils.py | 0 | load a module directly from file path without going through __init__.py. | STRING |
| HIGH | tests/ai_edited_test/cli/test_ai_deepseek_v3_config.py | 0 | load a module directly from file path without going through __init__.py. | STRING |
| HIGH | tests/ai_edited_test/cli/test_ai_deepseek_v3_kernel.py | 0 | load a module directly from file path without going through __init__.py. | STRING |
| HIGH | …ts/ai_edited_test/cli/test_ai_deepseek_v3_moe_layer.py | 0 | load a module directly from file path without going through __init__.py. | STRING |
| HIGH | …sts/ai_edited_test/cli/test_ai_deepseek_v3_workflow.py | 0 | load a module directly from file path without going through __init__.py. | STRING |
| HIGH | …s/ai_edited_test/cli/test_ai_deepseek_v3_token_disp.py | 0 | load a module directly from file path without going through __init__.py. | STRING |
| HIGH | …sts/ai_edited_test/cli/test_ai_deepseek_v3_moe_gate.py | 0 | load a module directly from file path without going through __init__.py. | STRING |
| HIGH | tests/transformers/kimi_k25/test_processor.py | 0 | tests that special vision tokens do not get truncated when `truncation=true` is set. | STRING |
| HIGH | tests/transformers/qwen2_5_vl/test_processor.py | 0 | tests that special vision tokens do not get truncated when `truncation=true` is set. | STRING |
| HIGH | tests/transformers/qwen2_vl/test_processor.py | 0 | tests that special vision tokens do not get truncated when `truncation=true` is set. | STRING |
| HIGH | tests/transformers/qwen3_vl/test_processor.py | 0 | tests that special vision tokens do not get truncated when `truncation=true` is set. | STRING |
| HIGH | tests/transformers/qwen2_5_vl/test_modeling.py | 0 | tests that vlms through an error with explicit message saying what is wrong when number of images don't match number of | STRING |
| HIGH | tests/transformers/qwen3_vl_moe/test_modeling.py | 0 | tests that vlms through an error with explicit message saying what is wrong when number of images don't match number of | STRING |
| HIGH | tests/transformers/qwen3_vl/test_modeling.py | 0 | tests that vlms through an error with explicit message saying what is wrong when number of images don't match number of | STRING |
| HIGH | tests/transformers/paddleocr_vl/test_modeling.py | 0 | tests that vlms through an error with explicit message saying what is wrong when number of images don't match number of | STRING |
| HIGH | examples/experiments/paddlefleet/run_pretrain.py | 0 | arguments pertaining to what data we are going to input our model for training and evaluating. using `pdargumentparser` | STRING |
| HIGH | …mples/experiments/deepseek_v3_pretrain/run_pretrain.py | 0 | arguments pertaining to what data we are going to input our model for training and evaluating. using `pdargumentparser` | STRING |
| HIGH | …ddleformers/cli/train/deepseek_v3_pretrain/workflow.py | 0 | arguments pertaining to what data we are going to input our model for training and evaluating. using `pdargumentparser` | STRING |
| HIGH | examples/experiments/paddlefleet/run_pretrain.py | 0 | arguments pertaining to which model/config/tokenizer we are going to pre-train from. | STRING |
| HIGH | …mples/experiments/deepseek_v3_pretrain/run_pretrain.py | 0 | arguments pertaining to which model/config/tokenizer we are going to pre-train from. | STRING |
| HIGH | …ddleformers/cli/train/deepseek_v3_pretrain/workflow.py | 0 | arguments pertaining to which model/config/tokenizer we are going to pre-train from. | STRING |
| HIGH | examples/experiments/deepseek_v3_pretrain/moe_utils.py | 0 | restore the original order of tokens after permutation. if probs are provided, it will also apply them to the tokens bef | STRING |
| HIGH | …dleformers/cli/train/deepseek_v3_pretrain/moe_utils.py | 0 | restore the original order of tokens after permutation. if probs are provided, it will also apply them to the tokens bef | STRING |
| HIGH | paddleformers/transformers/moe_utils.py | 0 | restore the original order of tokens after permutation. if probs are provided, it will also apply them to the tokens bef | STRING |
| HIGH | examples/experiments/deepseek_v3_pretrain/moe_layer.py | 0 | moe layer forward function 1. gate forward. 2. dispatch export. 3. experts forward. args: hidden_state: moe layer input | STRING |
| HIGH | …dleformers/cli/train/deepseek_v3_pretrain/moe_layer.py | 0 | moe layer forward function 1. gate forward. 2. dispatch export. 3. experts forward. args: hidden_state: moe layer input | STRING |
| HIGH | paddleformers/transformers/moe_layer.py | 0 | moe layer forward function 1. gate forward. 2. dispatch export. 3. experts forward. args: hidden_state: moe layer input | STRING |
| HIGH | examples/experiments/deepseek_v3_pretrain/moe_layer.py | 0 | moe expert dispatch from: https://huggingface.co/deepseek-ai/deepseek-v3/blob/main/modeling_deepseek.py | STRING |
| HIGH | …dleformers/cli/train/deepseek_v3_pretrain/moe_layer.py | 0 | moe expert dispatch from: https://huggingface.co/deepseek-ai/deepseek-v3/blob/main/modeling_deepseek.py | STRING |
| HIGH | paddleformers/transformers/moe_layer.py | 0 | moe expert dispatch from: https://huggingface.co/deepseek-ai/deepseek-v3/blob/main/modeling_deepseek.py | STRING |
| HIGH | examples/experiments/deepseek_v3_pretrain/moe_gate.py | 0 | _summary_ the priority is the cumulative sum of the expert indices. this method is used in hunyuan model args: topk_idx | STRING |
| HIGH | paddleformers/nn/moe_deepep/moe_gate.py | 0 | _summary_ the priority is the cumulative sum of the expert indices. this method is used in hunyuan model args: topk_idx | STRING |
| HIGH | …ddleformers/cli/train/deepseek_v3_pretrain/moe_gate.py | 0 | _summary_ the priority is the cumulative sum of the expert indices. this method is used in hunyuan model args: topk_idx | STRING |
| HIGH | paddleformers/transformers/moe_gate.py | 0 | _summary_ the priority is the cumulative sum of the expert indices. this method is used in hunyuan model args: topk_idx | STRING |
| HIGH | paddleformers/transformers/moe_gate_auto.py | 0 | _summary_ the priority is the cumulative sum of the expert indices. this method is used in hunyuan model args: topk_idx | STRING |
| HIGH | examples/experiments/deepseek_v3_pretrain/moe_gate.py | 0 | _summary_ args: scores (paddle.tensor): [bsz*seq_len, n_experts] k (int): select the top k experts returns: tuple[paddle | STRING |
| HIGH | paddleformers/nn/moe_deepep/moe_gate.py | 0 | _summary_ args: scores (paddle.tensor): [bsz*seq_len, n_experts] k (int): select the top k experts returns: tuple[paddle | STRING |
| HIGH | …ddleformers/cli/train/deepseek_v3_pretrain/moe_gate.py | 0 | _summary_ args: scores (paddle.tensor): [bsz*seq_len, n_experts] k (int): select the top k experts returns: tuple[paddle | STRING |
| HIGH | paddleformers/transformers/moe_gate.py | 0 | _summary_ args: scores (paddle.tensor): [bsz*seq_len, n_experts] k (int): select the top k experts returns: tuple[paddle | STRING |
| HIGH | paddleformers/transformers/moe_gate_auto.py | 0 | _summary_ args: scores (paddle.tensor): [bsz*seq_len, n_experts] k (int): select the top k experts returns: tuple[paddle | STRING |
| HIGH | examples/experiments/deepseek_v3_pretrain/moe_gate.py | 0 | _summary_ args: scores (paddle.tensor): [bsz*seq_len, n_experts] k (int): select the top k experts in each group n_group | STRING |
| HIGH | paddleformers/nn/moe_deepep/moe_gate.py | 0 | _summary_ args: scores (paddle.tensor): [bsz*seq_len, n_experts] k (int): select the top k experts in each group n_group | STRING |
| HIGH | …ddleformers/cli/train/deepseek_v3_pretrain/moe_gate.py | 0 | _summary_ args: scores (paddle.tensor): [bsz*seq_len, n_experts] k (int): select the top k experts in each group n_group | STRING |
| HIGH | paddleformers/transformers/moe_gate.py | 0 | _summary_ args: scores (paddle.tensor): [bsz*seq_len, n_experts] k (int): select the top k experts in each group n_group | STRING |
| HIGH | paddleformers/transformers/moe_gate_auto.py | 0 | _summary_ args: scores (paddle.tensor): [bsz*seq_len, n_experts] k (int): select the top k experts in each group n_group | STRING |
| HIGH | examples/experiments/deepseek_v3_pretrain/moe_gate.py | 0 | calculate sequence auxiliary loss. args: logits (paddle.tensor): model output. returns: paddle.tensor: the value of sequ | STRING |
| HIGH | …ddleformers/cli/train/deepseek_v3_pretrain/moe_gate.py | 0 | calculate sequence auxiliary loss. args: logits (paddle.tensor): model output. returns: paddle.tensor: the value of sequ | STRING |
| HIGH | paddleformers/transformers/moe_gate_auto.py | 0 | calculate sequence auxiliary loss. args: logits (paddle.tensor): model output. returns: paddle.tensor: the value of sequ | STRING |
| HIGH | …mples/experiments/deepseek_v3_pretrain/load_hf_ckpt.py | 0 | convert paddle model parameter names to hugging face format name lists args: paddle_name: parameter name in paddle forma | STRING |
| HIGH | …xperiments/deepseek_v3_pretrain/convert_ckpt_to_sft.py | 0 | convert paddle model parameter names to hugging face format name lists args: paddle_name: parameter name in paddle forma | STRING |
| HIGH | …s/cli/train/deepseek_v3_pretrain/utils/load_hf_ckpt.py | 0 | convert paddle model parameter names to hugging face format name lists args: paddle_name: parameter name in paddle forma | STRING |
| HIGH | …rain/deepseek_v3_pretrain/utils/convert_ckpt_to_sft.py | 0 | convert paddle model parameter names to hugging face format name lists args: paddle_name: parameter name in paddle forma | STRING |
| HIGH | examples/experiments/deepseek_v3_pretrain/modeling.py | 0 | get the value of the 'use_casual_mask' environment variable. | STRING |
| HIGH | paddleformers/utils/masking_utils.py | 0 | get the value of the 'use_casual_mask' environment variable. | STRING |
| HIGH | …ddleformers/cli/train/deepseek_v3_pretrain/modeling.py | 0 | get the value of the 'use_casual_mask' environment variable. | STRING |
| HIGH | examples/experiments/deepseek_v3_pretrain/modeling.py | 0 | args: hidden_states (_type_): [batch_size * seq_len, hidden_size] | STRING |
| HIGH | …ddleformers/cli/train/deepseek_v3_pretrain/modeling.py | 0 | args: hidden_states (_type_): [batch_size * seq_len, hidden_size] | STRING |
| 271 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | setup.py | 1 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/conftest.py | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/parallel_launch.py | 1 | # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/testing_utils.py | 1 | # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/common_test.py | 1 | # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/__init__.py | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/check_log_for_exitcode.py | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/glm45_pt_fp8.sh | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/qwen3vl_sft.sh | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/glm45_a100.sh | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/glm45_dpo_lora.sh | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/update_precision.sh | 1 | # Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/qwen.sh | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/check_pr_approval.py | 1 | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/qwen3_single_card.sh | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/qwen3vl_sft_single_card.sh | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/glm45_pt_ep4.sh | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/qwen3vl_lora.sh | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/glm45_dpo.sh | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/qwen3_a100.sh | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/glm45_pt_single_card.sh | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/glm45_sft_cp.sh | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/glm45_pt.sh | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/preprocess.sh | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/glm45_sft.sh | 1 | # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
| LOW | tests/integration_test/check_precision_approval.sh | 1 | # Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved. | COMMENT |
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| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 150 | x_fp8, x_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 157 | _, _, x_t_fp8, x_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 165 | x_fp8, x_scale, x_t_fp8, x_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 172 | _, _, w1_fp8, w1_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 182 | o2_fp8, o2_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 186 | _, _, w2_t_fp8, w2_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 227 | do3_fp8, do3_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 234 | _, _, do3_t_fp8, do3_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 241 | do3_fp8, do3_scale, do3_t_fp8, do3_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 247 | w2_fp8, w2_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 257 | _, _, o2_t_fp8, o2_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 277 | do1_fp8, do1_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 284 | _, _, do1_t_fp8, do1_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 291 | do1_fp8, do1_scale, do1_t_fp8, do1_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 297 | w1_fp8, w1_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 354 | x_fp8, x_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 358 | _, _, w1_fp8, w1_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 368 | o2_fp8, o2_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 372 | _, _, w2_t_fp8, w2_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 412 | _, _, x_t_fp8, x_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 422 | do3_fp8, do3_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 429 | _, _, do3_t_fp8, do3_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 436 | do3_fp8, do3_scale, do3_t_fp8, do3_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 442 | w2_fp8, w2_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 452 | _, _, o2_t_fp8, o2_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 472 | do1_fp8, do1_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 479 | _, _, do1_t_fp8, do1_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 486 | do1_fp8, do1_scale, do1_t_fp8, do1_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL⚡ | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 492 | w1_fp8, w1_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 400 | _, _, w1_fp8, w1_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …xperiments/ernie_pretrain/models/ernie/modeling_moe.py | 2003 | layer.self_attn.o_proj.dense_experts.down_proj.weight.scale_(factor) | CODE |
| CRITICAL | …xperiments/ernie_pretrain/models/ernie/modeling_moe.py | 2015 | layer.mlp.dense_experts.down_proj.weight.scale_(factor) | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 191 | w1_t_quant, w1_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 201 | x_fp8, x_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 267 | w2_quant, w2_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 279 | o2_quant, o2_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 325 | w2_quant, w2_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 336 | o2_quant, o2_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 384 | bw_w2_quant, bw_w2_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 393 | unzipped_grad_fp8, unzipped_grad_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 438 | bw_w2_quant, bw_w2_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 447 | unzipped_grad_fp8, unzipped_grad_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 508 | bw_w1_quant, bw_w1_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 518 | do1_fp8, do1_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 568 | o2_t_fp8, o2_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 587 | out_grad_fp8, out_grad_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 651 | input_x_fp8, input_x_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 667 | do1_fp8, do1_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 891 | w1_t_quant, w1_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 909 | x_fp8, x_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 966 | w2_quant, w2_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 986 | o2_fp8, o2_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 1031 | bw_w2_quant, bw_w2_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 1040 | unzipped_grad_fp8, unzipped_grad_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 1106 | bw_w1_quant, bw_w1_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 1115 | do1_fp8, do1_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 1187 | o2_t_fp8, o2_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 1203 | do3_t_fp8, do3_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 1250 | input_x_t_fp8, input_x_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
| CRITICAL | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 1266 | do1_t_fp8, do1_t_scale = paddle.incubate.nn.functional.fp8.fp8_quant_blockwise( | CODE |
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| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | tests/nn/test_mlp.py | 25 | # Create a mock config with default values | COMMENT |
| MEDIUM | tests/nn/test_attention.py | 267 | # Create the ground truth dense causal mask for the naive implementation | COMMENT |
| MEDIUM | tests/nn/test_attention.py | 297 | # Create the ground truth dense mask from the FlashMask sparse format | COMMENT |
| MEDIUM | tests/ai_edited_test/nn/test_ai_pp_model.py | 401 | # Create a config where head_dim attribute doesn't exist at all, | COMMENT |
| MEDIUM | tests/ai_edited_test/nn/test_ai_pp_model.py | 691 | # Create a mock layer that has a weight attribute | COMMENT |
| MEDIUM⚡ | tests/ai_edited_test/datasets/test_ai_mm_plugin.py | 188 | # Create a new plugin class | COMMENT |
| MEDIUM | tests/ai_edited_test/utils/test_ai_doc_parser.py | 46 | # Create a temp file with content < 1024 chars | COMMENT |
| MEDIUM | tests/ai_edited_test/utils/test_ai_aistudio_download.py | 237 | # Create the expected cache structure | COMMENT |
| MEDIUM | tests/ai_edited_test/utils/test_ai_optimizer.py | 28 | # Create a simple model and optimizer so we can call the method | COMMENT |
| MEDIUM | tests/ai_edited_test/utils/test_ai_image_utils.py | 63 | # Create a simple test image and its base64 encoding | COMMENT |
| MEDIUM | tests/ai_edited_test/cli/test_ai_ernie_top2_gate.py | 128 | # Create a near-orthogonal weight matrix | COMMENT |
| MEDIUM⚡ | tests/ai_edited_test/cli/test_ai_dataset_formatting.py | 180 | # Create a dataset with unsupported features | COMMENT |
| MEDIUM⚡ | tests/ai_edited_test/cli/test_ai_cli.py | 54 | # Create the partial after mocking print | COMMENT |
| MEDIUM | tests/ai_edited_test/peft/test_ai_loraga_utils.py | 105 | # Create a mock LoRA module | COMMENT |
| MEDIUM | …s/ai_edited_test/transformers/test_ai_moe_gate_auto.py | 140 | # Create a weight parameter for the gate | COMMENT |
| MEDIUM | …_edited_test/transformers/test_ai_kimi_k2_tokenizer.py | 27 | # Create a minimal tiktoken model file for testing | COMMENT |
| MEDIUM⚡ | tests/ai_edited_test/trainer/test_ai_timer.py | 172 | # Create a _Timer first | COMMENT |
| MEDIUM | tests/ai_edited_test/trainer/test_ai_timer.py | 184 | # Create a distinct class to force type mismatch | COMMENT |
| MEDIUM | tests/transformers/test_processing_common.py | 379 | # Define the kwargs for each modality | COMMENT |
| MEDIUM | tests/transformers/test_processing_common.py | 400 | # Define the kwargs for each modality | COMMENT |
| MEDIUM | tests/transformers/test_processing_common.py | 570 | # Define the kwargs for each modality | COMMENT |
| MEDIUM | tests/transformers/test_processing_common.py | 591 | # Define the kwargs for each modality | COMMENT |
| MEDIUM⚡ | tests/transformers/ernie4_5_moe_vl/test_processor.py | 461 | # Define the kwargs for each modality | COMMENT |
| MEDIUM⚡ | tests/transformers/ernie4_5_moe_vl/test_processor.py | 477 | # Define the kwargs for each modality | COMMENT |
| MEDIUM | tests/trainer/test_trainer_callback.py | 16 | # This file is modified from | COMMENT |
| MEDIUM | examples/experiments/paddlefleet/run_pretrain.py | 614 | # Create the learning_rate sheduler and optimizer | COMMENT |
| MEDIUM | …mples/experiments/deepseek_v3_pretrain/run_pretrain.py | 577 | # Create the learning_rate sheduler and optimizer | COMMENT |
| MEDIUM | examples/experiments/deepseek_v3_pretrain/moe_utils.py | 118 | # Create an output tensor filled with zeros | COMMENT |
| MEDIUM | examples/experiments/deepseek_v3_pretrain/moe_utils.py | 335 | # Create an output tensor filled with zeros | COMMENT |
| MEDIUM | examples/experiments/deepseek_v3_pretrain/moe_gate.py | 186 | # Create a mask for 1st's expert per token | COMMENT |
| MEDIUM | examples/experiments/deepseek_v3_pretrain/moe_gate.py | 252 | # Create a mask for 1st's expert per token. | COMMENT |
| MEDIUM | examples/experiments/deepseek_v3_pretrain/moe_gate.py | 257 | # Create a mask for 2nd's expert per token using Gumbel-max trick. | COMMENT |
| MEDIUM | scripts/iluvatar_ci/test_ernie_21b_sft.py | 27 | # Define the exact shell command to execute | COMMENT |
| MEDIUM | scripts/xpu_ci/test_ernie_21b_sft.py | 27 | # Define the exact shell command to execute | COMMENT |
| MEDIUM | scripts/xpu_ci/test_ernie_28b_thinking_sft.py | 27 | # Define the exact shell command to execute | COMMENT |
| MEDIUM | scripts/codestyle/check_dead_links.py | 118 | # Create the symlink | COMMENT |
| MEDIUM | .github/workflows/cherry-pick.yml | 97 | # Create a new branch for the cherry-pick | COMMENT |
| MEDIUM | paddleformers/datasets/template/mm_plugin.py | 561 | # Create an image with a white background and the same size as the original image | COMMENT |
| MEDIUM | paddleformers/utils/download/common.py | 310 | # Create a factory function that returns a Session with configured proxies | STRING |
| MEDIUM | paddleformers/cli/cli.py | 98 | # Create a default GPU list string (e.g., "0,1,2" for 3 GPUs) | COMMENT |
| MEDIUM | …dleformers/cli/train/deepseek_v3_pretrain/moe_utils.py | 118 | # Create an output tensor filled with zeros | COMMENT |
| MEDIUM | …dleformers/cli/train/deepseek_v3_pretrain/moe_utils.py | 335 | # Create an output tensor filled with zeros | COMMENT |
| MEDIUM | …ddleformers/cli/train/deepseek_v3_pretrain/moe_gate.py | 186 | # Create a mask for 1st's expert per token | COMMENT |
| MEDIUM | …ddleformers/cli/train/deepseek_v3_pretrain/moe_gate.py | 252 | # Create a mask for 1st's expert per token. | COMMENT |
| MEDIUM | …ddleformers/cli/train/deepseek_v3_pretrain/moe_gate.py | 257 | # Create a mask for 2nd's expert per token using Gumbel-max trick. | COMMENT |
| MEDIUM | …ddleformers/cli/train/deepseek_v3_pretrain/workflow.py | 507 | # Create the learning_rate sheduler and optimizer | COMMENT |
| MEDIUM | paddleformers/cli/train/auto_parallel/workflow.py | 320 | # Create the learning_rate scheduler and optimizer | COMMENT |
| MEDIUM | paddleformers/cli/train/sft/workflow.py | 695 | # Create the learning_rate sheduler and optimizer | COMMENT |
| MEDIUM | paddleformers/transformers/video_utils.py | 220 | # Create a batched list from single object | COMMENT |
| MEDIUM | paddleformers/transformers/image_transforms.py | 722 | # Create the outer list | COMMENT |
| MEDIUM | paddleformers/transformers/moe_utils.py | 66 | # Create a dense expert-to-token mapping from the sparse token-to-expert mapping | COMMENT |
| MEDIUM | paddleformers/transformers/moe_utils.py | 109 | # Create an output tensor filled with zeros | COMMENT |
| MEDIUM | paddleformers/transformers/processing_utils.py | 471 | # Create a unified `preprocessor_config.json` and save all attributes as a composite config, except for toke | COMMENT |
| MEDIUM | paddleformers/transformers/image_processing_utils.py | 53 | # Define the key methods that should support Paddle tensor return types. | COMMENT |
| MEDIUM | paddleformers/transformers/moe_gate.py | 345 | # Create a mask for 1st's expert per token | COMMENT |
| MEDIUM | paddleformers/transformers/moe_gate.py | 413 | # Create a mask for 1st's expert per token. | COMMENT |
| MEDIUM | paddleformers/transformers/moe_gate.py | 418 | # Create a mask for 2nd's expert per token using Gumbel-max trick. | COMMENT |
| MEDIUM | paddleformers/transformers/moe_gate_auto.py | 348 | # Create a mask for 1st's expert per token | COMMENT |
| MEDIUM | paddleformers/transformers/moe_gate_auto.py | 414 | # Create a mask for 1st's expert per token. | COMMENT |
| MEDIUM | paddleformers/transformers/moe_gate_auto.py | 419 | # Create a mask for 2nd's expert per token using Gumbel-max trick. | COMMENT |
| 26 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | examples/experiments/deepseek_v3_pretrain/fp8_linear.py | 57 | Applies a linear transformation to the incoming data: y = xA^T + b. This function supports specialized implemen | STRING |
| HIGH | examples/experiments/deepseek_v3_pretrain/kernel.py | 101 | Dequantizes the given weight tensor using the provided scale tensor. Args: x (paddle.Tensor): The quant | STRING |
| HIGH | …rnie_pretrain/models/moe/token_dispatcher/moe_utils.py | 110 | Permute tokens based on expert assignment indices. Args: tokens (paddle.Tensor): Input tokens to be permute | STRING |
| HIGH | …rnie_pretrain/models/moe/token_dispatcher/moe_utils.py | 136 | Restore original token order from permuted tokens. Args: permuted_tokens (paddle.Tensor): Permuted tokens t | STRING |
| HIGH | scripts/regression/test_models.py | 116 | Load model configuration from YAML file. Args: model_key: The model identifier to load configuratio | STRING |
| HIGH | paddleformers/nn/pp_model.py | 114 | Determines the layer partitioning scheme for Pipeline Parallelism (PP) and Virtual Pipeline Parallelism (VP) wi | STRING |
| HIGH | paddleformers/nn/attention/sink_impl.py | 561 | A unified, high-performance attention implementation with Sink mechanism support. This function automatically | STRING |
| HIGH | paddleformers/datasets/data_utils.py | 119 | Convert batch dialogue into input_ids. The API support multiple data format: `pt`, `sft. Args: dials ( | STRING |
| HIGH | paddleformers/datasets/DPODataset.py | 161 | Generate packed sequences using greedy strategy. Args: examples: List of examples to pack. | STRING |
| HIGH | paddleformers/datasets/DPODataset.py | 187 | Preprocess DPO training examples Args: example: Raw training example containing: - | STRING |
| HIGH | paddleformers/utils/downloader.py | 94 | Get weights path from WEIGHT_HOME, if not exists, download it from url. Args: url (str): download url | STRING |
| HIGH | paddleformers/utils/tools.py | 148 | Args: version (str): The first version string needed to be compared. The format of version stri | STRING |
| HIGH | paddleformers/utils/distributed.py | 167 | nested all gather function with offload Args: tensor (Any): the desired tensor, list of tensor, dict of ten | STRING |
| HIGH | paddleformers/utils/download/aistudio_hub_download.py | 628 | Checks if a file exists in a repository on the Aistudio Hub. Args: repo_id (`str`): A name | STRING |
| HIGH | paddleformers/cli/hparams/parser.py | 120 | _summary_ Args: parser (PdArgumentParser): _description_ args (Optional[Union[dict[str, Any], list[ | STRING |
| HIGH | …leformers/cli/train/deepseek_v3_pretrain/fp8_linear.py | 57 | Applies a linear transformation to the incoming data: y = xA^T + b. This function supports specialized implemen | STRING |
| HIGH | paddleformers/cli/train/deepseek_v3_pretrain/kernel.py | 101 | Dequantizes the given weight tensor using the provided scale tensor. Args: x (paddle.Tensor): The quant | STRING |
| HIGH | …rnie_pretrain/models/moe/token_dispatcher/moe_utils.py | 110 | Permute tokens based on expert assignment indices. Args: tokens (paddle.Tensor): Input tokens to be permute | STRING |
| HIGH | …rnie_pretrain/models/moe/token_dispatcher/moe_utils.py | 136 | Restore original token order from permuted tokens. Args: permuted_tokens (paddle.Tensor): Permuted tokens t | STRING |
| HIGH | paddleformers/transformers/configuration_utils.py | 1101 | Instantiate a [`PretrainedConfig`] (or a derived class) from a pretrained model configuration. Args: | STRING |
| HIGH | paddleformers/transformers/model_utils.py | 211 | This function chunks the `input_tensors` into smaller input tensor parts of size `chunk_size` over the dimension | STRING |
| HIGH | paddleformers/transformers/feature_extraction_utils.py | 231 | Instantiate a type of [`~feature_extraction_utils.FeatureExtractionMixin`] from a feature extractor, *e.g.* a | STRING |
| HIGH | paddleformers/transformers/fp8_utils.py | 227 | FP8 Linear computation function supporting multiple return modes and quantized/unquantized inputs. Arg | STRING |
| HIGH | paddleformers/transformers/conversion_utils.py | 1400 | get name mapping of PretrainedModel Args: config (PretrainedConfig): the configuration of name-mapp | STRING |
| HIGH | paddleformers/transformers/conversion_utils.py | 1526 | get fused parameter mapping of PretrainedModel Args: config (PretrainedConfig): the configuration o | STRING |
| HIGH | paddleformers/transformers/utils.py | 511 | Tries to locate a file in a local folder and repo, downloads and cache it if necessary. Args: path_or_r | STRING |
| HIGH | paddleformers/transformers/auto/video_processing.py | 87 | Loads the video processor configuration from a pretrained model video processor configuration. Args: p | STRING |
| HIGH | paddleformers/transformers/auto/feature_extraction.py | 91 | Loads the feature extractor configuration from a pretrained model feature extractor configuration. Args: | STRING |
| HIGH | paddleformers/transformers/auto/tokenizer.py | 65 | Loads the tokenizer configuration from a pretrained model tokenizer configuration. Args: pretrained_mo | STRING |
| HIGH | paddleformers/transformers/auto/image_processing.py | 107 | Loads the image processor configuration from a pretrained model image processor configuration. Args: p | STRING |
| HIGH | paddleformers/transformers/legacy/tokenizer_utils.py | 1093 | Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to | STRING |
| HIGH | …dleformers/transformers/legacy/tokenizer_utils_base.py | 839 | Add a dictionary of special tokens (eos, pad, cls, etc.) to the encoder and link them to class attributes. If | STRING |
| HIGH | …dleformers/transformers/legacy/tokenizer_utils_base.py | 933 | Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to | STRING |
| HIGH | paddleformers/transformers/qwen2_5_vl/modeling.py | 1245 | Calculate the 3D rope index based on image and video's temporal, height and width in LLM. Explanation: | STRING |
| HIGH | …/transformers/ernie4_5_moe_vl/model/modeling_moe_vl.py | 1080 | Calculate the 3D rope index based on image and video's temporal, height and width in LLM. Explanation: | STRING |
| HIGH | …/transformers/ernie4_5_moe_vl/model/modeling_moe_pp.py | 93 | Determines the layer partitioning scheme for Pipeline Parallelism (PP) and Virtual Pipeline Parallelism (VP) wi | STRING |
| HIGH | …ansformers/ernie4_5_moe_vl/model/modeling_moe_vl_pp.py | 830 | Convert input data into a format acceptable by the model, including image processing, text processing, etc. | STRING |
| HIGH | …ernie4_5_moe_vl/model/distributed/common_dist_utils.py | 83 | Performs parallel matrix multiplication with tensor model parallelism support. Args: x (paddle.Tensor) | STRING |
| HIGH | …rmers/transformers/ernie4_5_moe_vl/model/utils/misc.py | 89 | TrainingLogs Args: object (_type_): _description_ Raises: AttributeError: _description_ R | STRING |
| HIGH | …rmers/transformers/ernie4_5_moe_vl/model/utils/misc.py | 170 | gate attr Args: attr (_type_): _description_ Raises: AttributeError: _descript | STRING |
| HIGH | …/ernie4_5_moe_vl/model/fusion_ops/common_fusion_ops.py | 49 | Performs fused flash attention with multiple implementation variants. Args: q (paddle.Tensor): Query t | STRING |
| HIGH | paddleformers/transformers/glm4v_moe/modeling.py | 1340 | Calculate the 3D rope index based on image and video's temporal, height and width in LLM. Explanation: | STRING |
| HIGH | paddleformers/transformers/qwen2_vl/vision_process.py | 162 | calculate the number of frames for video used for model inputs. Args: ele (dict): a dict contains the confi | STRING |
| HIGH | paddleformers/transformers/qwen2_vl/vision_process.py | 202 | Calculate the start and end frame indices based on the given time range. Args: ele (dict): A dictionar | STRING |
| HIGH | paddleformers/transformers/paddleocr_vl/modeling.py | 1798 | Calculate the 3D rope index based on image and video's temporal, height and width in LLM. Explanation: | STRING |
| HIGH | paddleformers/data/causal_dataset.py | 36 | Retrieve logits with retry mechanism if no response is received within the specified time Parameters: | STRING |
| HIGH | paddleformers/generation/configuration_utils.py | 346 | Instantiate a [`GenerationConfig`] from a generation configuration file. Args: pretrained_ | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | setup.py | 76 | __all__ = ['show'] | CODE |
| LOW | tests/testing_utils.py | 39 | __all__ = ["get_vocab_list", "stable_softmax", "cross_entropy"] | CODE |
| LOW | tests/common_test.py | 20 | __all__ = ["CommonTest", "CpuCommonTest"] | CODE |
| LOW | …ples/experiments/paddlefleet/deepseek_v3_2_provider.py | 40 | logger = logging.getLogger(__name__) | CODE |
| LOW | examples/experiments/paddlefleet/glm45_provider.py | 27 | logger = logging.getLogger(__name__) | CODE |
| LOW | examples/experiments/paddlefleet/qwen_provider.py | 27 | logger = logging.getLogger(__name__) | CODE |
| LOW | examples/experiments/deepseek_v3_pretrain/fp8_linear.py | 42 | __all__ = [ | CODE |
| LOW | examples/experiments/deepseek_v3_pretrain/modeling.py | 135 | __all__ = [ | CODE |
| LOW | …amples/experiments/deepseek_v3_pretrain/modeling_pp.py | 72 | __all__ = [ | CODE |
| LOW | …periments/deepseek_v3_pretrain/config/configuration.py | 18 | __all__ = [ | CODE |
| LOW | …rnie_pretrain/tools/sharded_to_uc/merge_sharding_ep.py | 33 | logger = logging.getLogger(__name__) | CODE |
| LOW | …amples/experiments/ernie_pretrain/models/fp8_linear.py | 41 | __all__ = ["Fp8FusedMlp"] | CODE |
| LOW | examples/experiments/ernie_pretrain/models/utils.py | 21 | logger = logging.getLogger(__name__) | CODE |
| LOW | …ments/ernie_pretrain/models/sequence_parallel_utils.py | 41 | logger = logging.getLogger(__name__) | CODE |
| LOW | …amples/experiments/ernie_pretrain/models/comm_utils.py | 30 | logger = logging.getLogger(__name__) | CODE |
| LOW | …periments/ernie_pretrain/models/ernie/configuration.py | 24 | logger = logging.getLogger(__name__) | CODE |
| LOW | …periments/ernie_pretrain/models/ernie/configuration.py | 26 | __all__ = [ | CODE |
| LOW | …xperiments/ernie_pretrain/models/ernie/modeling_moe.py | 90 | logger = logging.getLogger(__name__) | CODE |
| LOW | …xperiments/ernie_pretrain/models/ernie/modeling_moe.py | 111 | __all__ = [ | CODE |
| LOW | …es/experiments/ernie_pretrain/models/ernie/modeling.py | 71 | logger = logging.getLogger(__name__) | CODE |
| LOW | …es/experiments/ernie_pretrain/models/ernie/modeling.py | 120 | __all__ = [ | CODE |
| LOW | …experiments/ernie_pretrain/models/ernie/modeling_pp.py | 87 | logger = logging.getLogger(__name__) | CODE |
| LOW | …les/experiments/ernie_pretrain/models/moe/top2_gate.py | 32 | logger = logging.getLogger(__name__) | CODE |
| LOW | …les/experiments/ernie_pretrain/models/moe/moe_layer.py | 53 | logger = logging.getLogger(__name__) | CODE |
| LOW | …rnie_pretrain/models/moe/token_dispatcher/fp8_utils.py | 37 | __all__ = [ | CODE |
| LOW | …mples/experiments/ernie_pretrain/ernie/model_config.py | 18 | __all__ = ["ModelConfig"] | CODE |
| LOW | …e_pretrain/ernie/src/callbacks/moe_logging_callback.py | 42 | __all__ = ["GlobalRNGCallback", "MoeLoggingCallback"] | CODE |
| LOW | …riments/ernie_pretrain/ernie/src/callbacks/__init__.py | 24 | __all__ = [ | CODE |
| LOW | …ernie_pretrain/ernie/src/callbacks/logging_callback.py | 19 | logger = logging.getLogger(__name__) | CODE |
| LOW | …_pretrain/ernie/src/callbacks/sp_grad_sync_callback.py | 25 | logger = logging.getLogger(__name__) | CODE |
| LOW | …e/src/callbacks/moe_correction_bias_adjust_callback.py | 25 | logger = logging.getLogger(__name__) | CODE |
| LOW | …train/ernie/src/callbacks/fp8_quant_weight_callback.py | 21 | logger = logging.getLogger(__name__) | CODE |
| LOW | …/experiments/ernie_pretrain/ernie/src/clip/__init__.py | 17 | __all__ = ["ClipGradForMOEByGlobalNorm"] | CODE |
| LOW | …/experiments/ernie_pretrain/ernie/src/clip/moe_clip.py | 25 | logger = logging.getLogger(__name__) | CODE |
| LOW | …nts/ernie_pretrain/ernie/src/lr_schedulers/__init__.py | 17 | __all__ = ["get_wsd_schedule_with_warmup", "get_cosine_schedule_with_warmup"] | CODE |
| LOW | …les/experiments/ernie_pretrain/ernie/src/utils/misc.py | 23 | logger = logging.getLogger(__name__) | CODE |
| LOW | …experiments/ernie_pretrain/ernie/src/utils/__init__.py | 17 | __all__ = ["logger", "setup_logger_output_file"] | CODE |
| LOW | …ments/ernie_pretrain/ernie/src/utils/training_utils.py | 17 | logger = logging.getLogger(__name__) | CODE |
| LOW | …eriments/ernie_pretrain/ernie/src/trainers/__init__.py | 21 | __all__ = [ | CODE |
| LOW | …nie_pretrain/ernie/src/trainers/pretraining_trainer.py | 15 | __all__ = [ | CODE |
| LOW | …nie_pretrain/ernie/src/trainers/pretraining_trainer.py | 94 | logger = logging.getLogger(__name__) | CODE |
| LOW | …rainers/dygraph_optimizer/hybrid_parallel_optimizer.py | 34 | __all__ = [] | CODE |
| LOW | paddleformers/nn/embedding.py | 21 | __all__ = ["Embedding"] | CODE |
| LOW | paddleformers/nn/linear.py | 26 | __all__ = ["Linear"] | CODE |
| LOW | paddleformers/nn/norm.py | 26 | __all__ = ["Norm"] | CODE |
| LOW | paddleformers/nn/mlp.py | 22 | __all__ = ["MLP"] | CODE |
| LOW | paddleformers/nn/lm_head.py | 24 | __all__ = ["LMHead"] | CODE |
| LOW | paddleformers/nn/attention/interface.py | 19 | __all__ = ["AttentionInterface"] | CODE |
| LOW | paddleformers/nn/moe_deepep/modular_moe_layer.py | 36 | logger = logging.getLogger(__name__) | CODE |
| LOW | paddleformers/nn/moe_deepep/moe_factory.py | 62 | __all__ = ["QuickAccessMoEFactory"] | CODE |
| LOW | paddleformers/nn/moe_deepep/moe_loss.py | 24 | logger = logging.getLogger(__name__) | CODE |
| LOW | paddleformers/datasets/dataset.py | 48 | __all__ = ["MapDataset", "DatasetBuilder", "IterDataset", "load_dataset"] | CODE |
| LOW | paddleformers/datasets/rlhf_datasets/protocol.py | 28 | __all__ = [ | CODE |
| LOW | paddleformers/utils/downloader.py | 44 | __all__ = ["get_weights_path_from_url"] | CODE |
| LOW | paddleformers/utils/moe_hybrid_parallel_optimizer.py | 47 | __all__ = [ | CODE |
| LOW | paddleformers/utils/batch_sampler.py | 19 | __all__ = ["MappingBatchSampler", "MappingDistributedBatchSampler", "DistributedBatchSampler"] | CODE |
| LOW | paddleformers/utils/initializer.py | 26 | __all__ = [ | CODE |
| LOW | paddleformers/utils/safetensors.py | 23 | __all__ = [ | CODE |
| LOW | paddleformers/utils/memory_utils.py | 18 | __all__ = [ | CODE |
| LOW | paddleformers/utils/download/common.py | 44 | logger = logging.getLogger(__name__) | CODE |
| 174 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | setup.py | 220 | except Exception as e: | STRING |
| LOW | tests/ai_edited_test/nn/test_ai_loss_utils.py | 125 | except Exception: | CODE |
| LOW⚡ | tests/ai_edited_test/datasets/test_ai_template.py | 291 | except Exception: | CODE |
| LOW | tests/ai_edited_test/cli/test_ai_ernie_moe_utils.py | 66 | except Exception: | CODE |
| LOW⚡ | …sts/ai_edited_test/cli/test_ai_tensorboard_callback.py | 109 | except Exception: | CODE |
| LOW | tests/ai_edited_test/cli/test_ai_deepseek_v3_kernel.py | 46 | except Exception: | CODE |
| LOW⚡ | tests/ai_edited_test/cli/test_ai_deepseek_v3_kernel.py | 85 | except Exception as e: | CODE |
| LOW⚡ | …_edited_test/transformers/test_ai_kimi_k2_tokenizer.py | 162 | except Exception: | CODE |
| LOW⚡ | …_edited_test/transformers/test_ai_kimi_k2_tokenizer.py | 170 | except Exception: | CODE |
| LOW | tests/peft/test_lora.py | 213 | except Exception as e: | CODE |
| LOW | tests/peft/test_lora.py | 283 | except Exception as e: | CODE |
| LOW | tests/transformers/phi4/test_phi4_modeling.py | 149 | except Exception: | CODE |
| LOW | tests/transformers/phi4/test_phi4_modeling.py | 210 | except Exception: | CODE |
| LOW | tests/transformers/phi4/test_phi4_modeling.py | 426 | except Exception: | CODE |
| LOW⚡ | tests/transformers/intern_lm2_5/test_tokenizer.py | 29 | except Exception: | CODE |
| MEDIUM | tests/transformers/intern_lm2_5/test_tokenizer.py | 26 | def setUpClass(cls): | CODE |
| LOW | tests/transformers/qwen3_vl/test_modeling_fleet.py | 33 | except Exception as error: | CODE |
| LOW | docs/zh/custom_datasets_format_zh.md | 14 | except Exception: | CODE |
| MEDIUM | docs/zh/custom_datasets_format_zh.md | 9 | def load_parquet(file_path): | CODE |
| LOW | …mples/experiments/deepseek_v3_pretrain/load_hf_ckpt.py | 369 | except Exception as e: | CODE |
| LOW | …rnie_pretrain/tools/sharded_to_uc/merge_sharding_ep.py | 612 | except Exception: | CODE |
| MEDIUM | …rnie_pretrain/tools/sharded_to_uc/merge_sharding_ep.py | 614 | print("Error !!!!") | CODE |
| LOW | examples/experiments/ernie_pretrain/ernie/pretrain.py | 539 | except Exception: | CODE |
| LOW | …e_pretrain/ernie/src/callbacks/tensorboard_callback.py | 25 | except Exception: | CODE |
| LOW | examples/best_practices/PaddleOCR-VL/README.md | 565 | except Exception: | CODE |
| LOW | examples/best_practices/PaddleOCR-VL-1.5/region_ocr.md | 153 | except Exception: | CODE |
| LOW | examples/best_practices/PaddleOCR-VL-1.5/region_ocr.md | 231 | except Exception: | CODE |
| LOW | examples/best_practices/PaddleOCR-VL-1.5/region_ocr.md | 950 | except Exception: | CODE |
| MEDIUM | examples/best_practices/PaddleOCR-VL-1.5/region_ocr.md | 146 | def download_image_cv2(url): | CODE |
| LOW | …ples/best_practices/DeepSeek-V4/dequant_fp8_to_bf16.py | 188 | except Exception as e: | CODE |
| LOW | …ples/best_practices/DeepSeek-V4/dequant_fp8_to_bf16.py | 285 | except Exception as e: | CODE |
| LOW | …ces/tutorials/how_to_train_a_visual_grounding_model.md | 151 | except Exception as e: | CODE |
| LOW | …ces/tutorials/how_to_train_a_visual_grounding_model.md | 178 | except Exception as e: | CODE |
| LOW | …ces/tutorials/how_to_train_a_visual_grounding_model.md | 195 | except Exception as e: | CODE |
| LOW | …ces/tutorials/how_to_train_a_visual_grounding_model.md | 296 | except Exception as e: | CODE |
| MEDIUM | …ces/tutorials/how_to_train_a_visual_grounding_model.md | 678 | print(f"Error: Image not found at: {full_image_path}") | CODE |
| LOW | …ces/tutorials/how_to_train_a_visual_grounding_model.md | 688 | except Exception as e: | CODE |
| LOW | scripts/ci_utils/training_utils.py | 92 | except Exception as e: | CODE |
| LOW | scripts/codestyle/check_dead_links.py | 68 | except Exception as e: | CODE |
| MEDIUM | scripts/codestyle/check_dead_links.py | 69 | print(f"Error reading {file_path}: {e}") | CODE |
| MEDIUM | scripts/regression/merge_configs.py | 210 | print(f"Error: {origin_config_path} does not exist") | CODE |
| MEDIUM | scripts/regression/merge_configs.py | 214 | print(f"Error: {update_config_path} does not exist") | CODE |
| LOW | scripts/regression/test_models.py | 212 | except Exception as e: | CODE |
| LOW | scripts/regression/test_models.py | 235 | except Exception as e: | CODE |
| LOW | scripts/regression/test_models.py | 634 | except Exception as e: | CODE |
| MEDIUM⚡ | .github/workflows/cleanup_ci_gists.yml | 55 | print(f"Error: API returned {resp.status_code}: {resp.text}") | CODE |
| LOW⚡ | .github/workflows/cleanup_ci_gists.yml | 60 | except Exception as e: | CODE |
| MEDIUM⚡ | .github/workflows/cleanup_ci_gists.yml | 61 | print(f"Error parsing JSON: {e}") | CODE |
| LOW | paddleformers/nn/pp_model.py | 651 | except Exception: | CODE |
| LOW | paddleformers/nn/moe_deepep/modular_moe_layer.py | 86 | except Exception: | CODE |
| LOW | paddleformers/datasets/collate.py | 615 | except Exception: | CODE |
| MEDIUM | paddleformers/datasets/SFTDataset.py | 239 | def _rss_mb(): | CODE |
| LOW⚡ | paddleformers/datasets/SFTDataset.py | 217 | except Exception as e: | CODE |
| LOW⚡ | paddleformers/datasets/SFTDataset.py | 221 | except Exception: | CODE |
| LOW | paddleformers/datasets/SFTDataset.py | 245 | except Exception: | CODE |
| LOW | paddleformers/datasets/SFTDataset.py | 320 | except Exception as e: | CODE |
| LOW | paddleformers/datasets/SFTDataset.py | 773 | except Exception: | CODE |
| LOW | paddleformers/datasets/SFTDataset.py | 1063 | except Exception as e: | CODE |
| LOW | paddleformers/datasets/SFTDataset.py | 1085 | except Exception as e: | CODE |
| LOW | paddleformers/datasets/SFTDataset.py | 1118 | except Exception: | CODE |
| 118 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM⚡ | tests/dataset/test_collate.py | 97 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM⚡ | tests/dataset/test_collate.py | 107 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/dataset/test_collate.py | 189 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/dataset/test_collate.py | 202 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/dataset/test_collate.py | 258 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/dataset/test_collate.py | 272 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | tests/dataset/test_mix_datasets.py | 35 | # =========================================================================== | COMMENT |
| MEDIUM | tests/dataset/test_mix_datasets.py | 37 | # =========================================================================== | COMMENT |
| MEDIUM⚡ | tests/dataset/test_mix_datasets.py | 211 | # =========================================================================== | COMMENT |
| MEDIUM⚡ | tests/dataset/test_mix_datasets.py | 213 | # =========================================================================== | COMMENT |
| MEDIUM | tests/transformers/glm_ocr/test_modeling.py | 635 | # ------------------------- | COMMENT |
| MEDIUM | tests/transformers/glm_ocr/test_modeling.py | 637 | # ------------------------- | COMMENT |
| MEDIUM | tests/transformers/glm_ocr/test_modeling.py | 665 | # ------------------------- | COMMENT |
| MEDIUM | tests/transformers/glm_ocr/test_modeling.py | 668 | # ------------------------- | COMMENT |
| MEDIUM | tests/transformers/glm_ocr/test_modeling.py | 709 | # ------------------------- | COMMENT |
| MEDIUM | tests/transformers/glm_ocr/test_modeling.py | 711 | # ------------------------- | COMMENT |
| MEDIUM | examples/best_practices/PaddleOCR-VL-1.5/table_ocr.md | 135 | # ========================================== | COMMENT |
| MEDIUM | examples/best_practices/PaddleOCR-VL-1.5/table_ocr.md | 137 | # ========================================== | COMMENT |
| MEDIUM | examples/best_practices/PaddleOCR-VL-1.5/table_ocr.md | 285 | # ========================================== | COMMENT |
| MEDIUM | examples/best_practices/PaddleOCR-VL-1.5/table_ocr.md | 287 | # ========================================== | COMMENT |
| MEDIUM | examples/best_practices/PaddleOCR-VL-1.5/region_ocr.md | 140 | # ========================================== | COMMENT |
| MEDIUM | examples/best_practices/PaddleOCR-VL-1.5/region_ocr.md | 334 | # ========================================== | COMMENT |
| MEDIUM | examples/best_practices/PaddleOCR-VL-1.5/region_ocr.md | 336 | # ========================================== | COMMENT |
| MEDIUM | examples/best_practices/PaddleOCR-VL-1.5/region_ocr.md | 484 | # ========================================== | COMMENT |
| MEDIUM | examples/best_practices/PaddleOCR-VL-1.5/region_ocr.md | 486 | # ========================================== | COMMENT |
| MEDIUM | …ples/best_practices/DeepSeek-V4/dequant_fp8_to_bf16.py | 156 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | …ples/best_practices/DeepSeek-V4/dequant_fp8_to_bf16.py | 158 | # --------------------------------------------------------------------------- | COMMENT |
| MEDIUM | .github/workflows/formers_bot_analysis.yml | 86 | # ── 定时触发路径 ────────────────────────────────────────────────── | COMMENT |
| MEDIUM | .github/workflows/formers_bot_analysis.yml | 114 | # ── PR 触发路径 ─────────────────────────────────────────────────── | COMMENT |
| MEDIUM | .github/workflows/formers_bot_analysis.yml | 297 | # ── 写入 GITHUB_OUTPUT,供 report-to-monitor 步骤读取 ────────── | COMMENT |
| MEDIUM | paddleformers/utils/upcast_downcast_triton.py | 352 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM | paddleformers/utils/upcast_downcast_triton.py | 354 | # ----------------------------------------------------------------------------- | COMMENT |
| MEDIUM | paddleformers/utils/upcast_downcast_triton.py | 476 | # ------------ | COMMENT |
| MEDIUM | paddleformers/transformers/aoa_config_base.py | 525 | # ================================================================== | COMMENT |
| MEDIUM | paddleformers/transformers/aoa_config_base.py | 527 | # ================================================================== | COMMENT |
| MEDIUM⚡ | paddleformers/transformers/paddleocr_vl/modeling.py | 870 | # ========================================== | COMMENT |
| MEDIUM⚡ | paddleformers/transformers/paddleocr_vl/modeling.py | 872 | # ========================================== | COMMENT |
| MEDIUM | paddleformers/transformers/paddleocr_vl/modeling.py | 905 | # ========================================== | COMMENT |
| MEDIUM | paddleformers/transformers/paddleocr_vl/modeling.py | 907 | # ========================================== | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH⚡ | …ai_edited_test/transformers/test_ai_longcontext_ops.py | 33 | heap.push((0, 5)) | CODE |
| HIGH⚡ | …ai_edited_test/transformers/test_ai_longcontext_ops.py | 34 | heap.push((1, 3)) | CODE |
| HIGH⚡ | …ai_edited_test/transformers/test_ai_longcontext_ops.py | 35 | heap.push((2, 8)) | CODE |
| HIGH | scripts/regression/test_models.py | 673 | subprocess.run("pkill -9 -f 'paddleformers/cli/launcher.py' 2>/dev/null || true", shell=True) | CODE |
| HIGH | paddleformers/quantization/quantization_linear.py | 332 | "2) cd PaddleSlim && pip install -e .\n" | CODE |
| HIGH | paddleformers/quantization/quantization_linear.py | 333 | "3) cd csrc && python ./setup_cuda.py install" | CODE |
| HIGH | paddleformers/quantization/quantization_linear.py | 522 | "2) cd PaddleSlim && pip install -e .\n" | CODE |
| HIGH | paddleformers/quantization/quantization_linear.py | 523 | "3) cd csrc && python ./setup_cuda.py install" | CODE |
| HIGH | paddleformers/quantization/quantization_linear.py | 761 | "2) cd PaddleSlim && pip install -e .\n" | CODE |
| HIGH | paddleformers/quantization/quantization_linear.py | 762 | "3) cd csrc && python ./setup_cuda.py install" | CODE |
| HIGH | paddleformers/transformers/video_utils.py | 323 | f" 1. Install dependencies: `conda install ffmpeg -c conda-forge` or `apt-get update && apt-get install f | CODE |
| HIGH | paddleformers/transformers/audio_processing_utils.py | 23 | print("need to install audioread and librosa. Please try: pip install audioread && pip install librosa") | CODE |
| HIGH | paddleformers/transformers/kimi_k25/media_utils.py | 103 | f" 1. Install dependencies: `conda install ffmpeg -c conda-forge` or `apt-get update && apt-get install f | CODE |
| HIGH | …/transformers/ernie4_5_moe_vl/model/longcontext_ops.py | 134 | surplus_piles.push([src_rank, surplus - move_amount]) | CODE |
| HIGH | …/transformers/ernie4_5_moe_vl/model/longcontext_ops.py | 136 | deficit_piles.push([dst_rank, deficit - move_amount]) | CODE |
| HIGH | paddleformers/transformers/qwen2_vl/vision_process.py | 286 | f" 1. Install dependencies: `conda install ffmpeg -c conda-forge` or `apt-get update && apt-get install f | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tests/transformers/test_video_processing_common.py | 277 | # Set sampling to True. Video frames should be sampled with `num_frames` in the output | COMMENT |
| LOW | tests/transformers/test_cache_utils.py | 424 | # Check if they are on the same device as the model parameters | COMMENT |
| LOW | tests/transformers/qwen2_vl/test_video_processor.py | 302 | # Set sampling to True. Video frames should be sampled with `num_frames` in the output | COMMENT |
| LOW | scripts/regression/prepare_datasets.py | 95 | # Check if already exists | COMMENT |
| LOW | scripts/regression/ci_model_unittest.sh | 130 | # Check if file is in transformer directories (don't check file existence, rely on git diff) | COMMENT |
| LOW | .github/workflows/cherry-pick.yml | 90 | # Check if target branch exists on remote | COMMENT |
| LOW | .github/workflows/cherry-pick.yml | 154 | # Check if PR already exists | COMMENT |
| LOW | .github/workflows/_xpu_ci_test.yml | 60 | # Check if anything matching ${REPO_NAME}* still exists | COMMENT |
| LOW | paddleformers/datasets/dataset.py | 206 | # Check if selected name and split is valid in this DatasetBuilder | COMMENT |
| LOW | paddleformers/datasets/SFTDataset.py | 399 | # Set flag to False and yield empty list to signal the end of estimation | COMMENT |
| LOW | paddleformers/datasets/SFTDataset.py | 436 | # Set flag to False and yield empty list to signal the end of estimation | COMMENT |
| LOW | paddleformers/datasets/SFTDataset.py | 468 | # Set flag to False and yield empty list to signal the end of estimation | COMMENT |
| LOW | paddleformers/datasets/SFTDataset.py | 499 | # Set flag to False and yield empty list to signal the end of estimation | COMMENT |
| LOW | paddleformers/datasets/SFTDataset.py | 540 | # Set flag to False and yield empty list to signal the end of estimation | COMMENT |
| LOW | paddleformers/datasets/DPODataset.py | 218 | # Check if multimedia tags are included in "messages"+"chosen_response", and "messages"+"rejected_response" | COMMENT |
| LOW | paddleformers/utils/upcast_downcast_triton.py | 726 | # # Check if blocks and scales are on CPU, and move to GPU if so | COMMENT |
| LOW | paddleformers/utils/import_utils.py | 118 | # Check if the package spec exists and grab its version to avoid importing a local directory | COMMENT |
| LOW | paddleformers/utils/import_utils.py | 131 | # Check if the version contains "dev" | COMMENT |
| LOW | paddleformers/utils/import_utils.py | 161 | # Check if the package spec exists and grab its version to avoid importing a local directory | COMMENT |
| LOW | paddleformers/utils/import_utils.py | 174 | # Check if the version contains "dev" | COMMENT |
| LOW⚡ | paddleformers/utils/download/aistudio_hub_download.py | 714 | # Check if file is cached as "no_exist" | COMMENT |
| LOW⚡ | paddleformers/utils/download/aistudio_hub_download.py | 718 | # Check if revision folder exists | COMMENT |
| LOW⚡ | paddleformers/utils/download/aistudio_hub_download.py | 726 | # Check if file exists in cache | COMMENT |
| LOW | paddleformers/cli/export/export.py | 97 | # Check if the output directory is a valid model directory (contains .safetensors or .pdparams files) | COMMENT |
| LOW | paddleformers/transformers/model_utils.py | 1741 | # Check if README file already exist in repo | STRING |
| LOW | paddleformers/transformers/model_utils.py | 3009 | # Check if `_keep_in_fp32_modules` is not None | STRING |
| LOW | …dleformers/transformers/image_processing_utils_fast.py | 605 | # Check if we have nested structure, assuming the nesting is consistent | COMMENT |
| LOW | paddleformers/transformers/image_utils.py | 752 | # Check if image is in (n_channels, height, width) or (height, width, n_channels) format | COMMENT |
| LOW | paddleformers/transformers/image_utils.py | 762 | # Check if cropped area is within image boundaries | COMMENT |
| LOW | paddleformers/transformers/image_transforms.py | 438 | # Check if cropped area is within image boundaries | COMMENT |
| LOW | paddleformers/transformers/feature_extraction_utils.py | 174 | # Check if the args are a device or a dtype | STRING |
| LOW | paddleformers/transformers/processing_utils.py | 913 | # Check if tokenizer is fast - use backend attribute if available, otherwise fall back to class name | COMMENT |
| LOW | paddleformers/transformers/utils.py | 690 | # Check if the model is already cached or not. We only try the last checkpoint, this should cover most cases of | STRING |
| LOW | paddleformers/transformers/gpt_provider.py | 210 | # Check if mtp_block_spec parameter is supported | COMMENT |
| LOW | paddleformers/transformers/masking_utils.py | 247 | # Check if the mask can be safely skipped | COMMENT |
| LOW | paddleformers/transformers/kimi_k2/modeling.py | 186 | # Check if mtp_block_spec parameter is supported | COMMENT |
| LOW | paddleformers/transformers/kimi_k2/modeling.py | 228 | # Check if mtp_block_spec parameter is supported | COMMENT |
| LOW | …dleformers/transformers/legacy/tokenizer_utils_base.py | 1981 | # Check if README file already exist in repo | COMMENT |
| LOW | paddleformers/transformers/ernie4_5_moe_vl/tokenizer.py | 240 | # Check if padding is needed | COMMENT |
| LOW | …ddleformers/transformers/qwen3_vl_moe/configuration.py | 320 | # Check if the attribute exists in sub-configs | COMMENT |
| LOW | …ddleformers/transformers/qwen3_vl_moe/configuration.py | 356 | # Check if user has set a top-level override | COMMENT |
| LOW | paddleformers/transformers/qwen3_vl/configuration.py | 309 | # Check if the attribute exists in sub-configs | COMMENT |
| LOW | paddleformers/transformers/qwen3_vl/configuration.py | 345 | # Check if user has set a top-level override | COMMENT |
| LOW | paddleformers/data/data_collator.py | 571 | # Check if padding is necessary. | COMMENT |
| LOW | paddleformers/data/data_collator.py | 607 | # Check if padding is necessary. | COMMENT |
| LOW | paddleformers/trainer/trainer.py | 2168 | # Check if continuing training from a checkpoint | COMMENT |
| LOW | paddleformers/trainer/trainer.py | 4613 | # Check if we should delete older checkpoint(s) | COMMENT |
| LOW | paddleformers/trainer/trainer.py | 4639 | # Check if we should delete older hf checkpoint(s) | COMMENT |
| LOW | paddleformers/trainer/trainer.py | 5226 | # Set back to None to begin a new accumulation | COMMENT |
| LOW | paddleformers/trainer/integrations.py | 287 | # Check if a Weights & Biases (wandb) API key is provided in the training arguments | COMMENT |
| LOW | paddleformers/trainer/integrations.py | 453 | # Check if SwanLab is disabled via environment variable | COMMENT |
| LOW | paddleformers/generation/utils.py | 282 | # Check if we are done so that we can save a pad step if all(done) | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | tests/nn/test_attention.py | 163 | # Step 1: Reshape tensors from [B, S, H, D] to [B, H, S, D] for matrix multiplication | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 168 | # Step 2: Transpose key for matmul: [B, H, S, D] -> [B, H, D, S] | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 171 | # Step 3: Calculate attention scores (Query @ Key^T) and apply scaling | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 174 | # Step 4: Apply the attention mask if provided | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 179 | # Step 5: Prepare and concatenate the sink logits. The sink is a special token | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 184 | # Step 6: Apply softmax over the combined logits (scores + sink) | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 188 | # Step 7: Separate the attention probabilities from the sink probabilities | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 191 | # Step 8: Apply dropout to the scores | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 194 | # Step 9: Compute the weighted sum of values (Scores @ Value) | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 197 | # Step 10: Reshape the output back to [B, S, H, D] and flatten the head dimension | COMMENT |
| LOW | paddleformers/datasets/dataset.py | 669 | # We need to check if the example contains label column and confirm its name. | COMMENT |
| LOW | paddleformers/datasets/dataset.py | 705 | # We need to check if the example contains label column and confirm its name. | COMMENT |
| LOW⚡ | paddleformers/transformers/minimax_m2/modeling.py | 1088 | # Step 1: Transpose fleet weight | COMMENT |
| LOW⚡ | paddleformers/transformers/minimax_m2/modeling.py | 1093 | # Step 2: Split into per-group chunks along axis=0 | COMMENT |
| LOW | paddleformers/transformers/minimax_m2/modeling.py | 1115 | # Step 3: Reassemble q_proj (interleaved Q+Gate) | COMMENT |
| LOW | paddleformers/transformers/auto/feature_extraction.py | 176 | # not all of these are nested. We need to check if it was saved recently as nested or if it is legacy style | COMMENT |
| LOW⚡ | paddleformers/transformers/deepseek_v4/modeling.py | 976 | # Step 1: split up_gate_proj back to w1/w3 (intermediate, no transpose yet) | COMMENT |
| LOW⚡ | paddleformers/transformers/deepseek_v4/modeling.py | 982 | # Step 2: transpose each piece to HF shape | COMMENT |
| LOW⚡ | paddleformers/transformers/qwen3_5/modeling.py | 254 | # Step 1: Split q_proj into 2*num_heads equal chunks (each = head_dim) | COMMENT |
| LOW⚡ | paddleformers/transformers/qwen3_5/modeling.py | 259 | # Step 2: Split k_proj and v_proj into num_kv_heads chunks | COMMENT |
| LOW⚡ | paddleformers/transformers/qwen3_5/modeling.py | 264 | # Step 3: Assemble per-group in fleet order and concat | COMMENT |
| LOW⚡ | paddleformers/transformers/qwen3_5/modeling.py | 335 | # Step 2: 6 sources (q, k, v, z, b, a) → fused_in_proj with TP interleaving | COMMENT |
| LOW⚡ | paddleformers/transformers/qwen3_5/modeling.py | 339 | # Step 3: Transpose to Fleet layout [hidden, in_proj_dim] | COMMENT |
| LOW⚡ | paddleformers/transformers/qwen3_5/modeling.py | 624 | # Step 1: Transpose fleet weight [in, out] -> [out, in] | COMMENT |
| LOW⚡ | paddleformers/transformers/qwen3_5/modeling.py | 627 | # Step 2: Split into per-group chunks along axis=0 | COMMENT |
| LOW | paddleformers/transformers/qwen3_5/modeling.py | 279 | # Step 4: Transpose the fused weight | COMMENT |
| LOW | paddleformers/transformers/qwen3_5/modeling.py | 319 | # Step 1: Split in_proj_qkv [qk_dim+qk_dim+v_dim, hidden] into q, k, v along axis=0 | COMMENT |
| LOW | paddleformers/transformers/qwen3_5/modeling.py | 644 | # Step 3: Reassemble into HF format | COMMENT |
| LOW⚡ | paddleformers/trainer/utils/offload_optimizer.py | 46 | # Step 1: mock _add_accumulator | COMMENT |
| LOW⚡ | paddleformers/trainer/utils/offload_optimizer.py | 56 | # Step 2: mock _C_ops.adamw_ and _C_ops.adamw | COMMENT |
| LOW⚡ | paddleformers/trainer/utils/offload_optimizer.py | 177 | # Step 1: mock _add_accumulator | COMMENT |
| LOW⚡ | paddleformers/trainer/utils/offload_optimizer.py | 187 | # Step 2: mock _C_ops.adamw_ and _C_ops.adamw | COMMENT |
| LOW | paddleformers/trainer/utils/offload_optimizer.py | 77 | # Step 3: mock _insert_sync | COMMENT |
| LOW | paddleformers/trainer/utils/offload_optimizer.py | 95 | # Step 4: mock Muon._muon_update and Muon._apply_optimize | COMMENT |
| LOW | paddleformers/trainer/utils/offload_optimizer.py | 207 | # Step 3: mock _insert_sync | COMMENT |
| LOW | paddleformers/trainer/utils/offload_optimizer.py | 225 | # Step 4: mock Muon._muon_update and Muon._apply_optimize | COMMENT |
| LOW⚡ | paddleformers/generation/utils.py | 389 | # Step 1: Process input mask to generate basic expanded mask | STRING |
| LOW | paddleformers/generation/utils.py | 447 | # Step 2: Convert boolean mask to numerical mask (adapt to different devices) | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | tests/nn/test_attention.py | 163 | # Step 1: Reshape tensors from [B, S, H, D] to [B, H, S, D] for matrix multiplication | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 168 | # Step 2: Transpose key for matmul: [B, H, S, D] -> [B, H, D, S] | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 171 | # Step 3: Calculate attention scores (Query @ Key^T) and apply scaling | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 174 | # Step 4: Apply the attention mask if provided | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 179 | # Step 5: Prepare and concatenate the sink logits. The sink is a special token | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 184 | # Step 6: Apply softmax over the combined logits (scores + sink) | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 188 | # Step 7: Separate the attention probabilities from the sink probabilities | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 191 | # Step 8: Apply dropout to the scores | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 194 | # Step 9: Compute the weighted sum of values (Scores @ Value) | COMMENT |
| LOW⚡ | tests/nn/test_attention.py | 197 | # Step 10: Reshape the output back to [B, S, H, D] and flatten the head dimension | COMMENT |
| LOW⚡ | paddleformers/transformers/minimax_m2/modeling.py | 1088 | # Step 1: Transpose fleet weight | COMMENT |
| LOW⚡ | paddleformers/transformers/minimax_m2/modeling.py | 1093 | # Step 2: Split into per-group chunks along axis=0 | COMMENT |
| LOW | paddleformers/transformers/minimax_m2/modeling.py | 1115 | # Step 3: Reassemble q_proj (interleaved Q+Gate) | COMMENT |
| LOW⚡ | paddleformers/transformers/deepseek_v4/modeling.py | 976 | # Step 1: split up_gate_proj back to w1/w3 (intermediate, no transpose yet) | COMMENT |
| LOW⚡ | paddleformers/transformers/deepseek_v4/modeling.py | 982 | # Step 2: transpose each piece to HF shape | COMMENT |
| LOW⚡ | paddleformers/transformers/qwen3_5/modeling.py | 254 | # Step 1: Split q_proj into 2*num_heads equal chunks (each = head_dim) | COMMENT |
| LOW⚡ | paddleformers/transformers/qwen3_5/modeling.py | 259 | # Step 2: Split k_proj and v_proj into num_kv_heads chunks | COMMENT |
| LOW⚡ | paddleformers/transformers/qwen3_5/modeling.py | 264 | # Step 3: Assemble per-group in fleet order and concat | COMMENT |
| LOW⚡ | paddleformers/transformers/qwen3_5/modeling.py | 335 | # Step 2: 6 sources (q, k, v, z, b, a) → fused_in_proj with TP interleaving | COMMENT |
| LOW⚡ | paddleformers/transformers/qwen3_5/modeling.py | 339 | # Step 3: Transpose to Fleet layout [hidden, in_proj_dim] | COMMENT |
| LOW⚡ | paddleformers/transformers/qwen3_5/modeling.py | 624 | # Step 1: Transpose fleet weight [in, out] -> [out, in] | COMMENT |
| LOW⚡ | paddleformers/transformers/qwen3_5/modeling.py | 627 | # Step 2: Split into per-group chunks along axis=0 | COMMENT |
| LOW | paddleformers/transformers/qwen3_5/modeling.py | 279 | # Step 4: Transpose the fused weight | COMMENT |
| LOW | paddleformers/transformers/qwen3_5/modeling.py | 319 | # Step 1: Split in_proj_qkv [qk_dim+qk_dim+v_dim, hidden] into q, k, v along axis=0 | COMMENT |
| LOW | paddleformers/transformers/qwen3_5/modeling.py | 644 | # Step 3: Reassemble into HF format | COMMENT |
| LOW⚡ | paddleformers/trainer/utils/offload_optimizer.py | 46 | # Step 1: mock _add_accumulator | COMMENT |
| LOW⚡ | paddleformers/trainer/utils/offload_optimizer.py | 56 | # Step 2: mock _C_ops.adamw_ and _C_ops.adamw | COMMENT |
| LOW⚡ | paddleformers/trainer/utils/offload_optimizer.py | 177 | # Step 1: mock _add_accumulator | COMMENT |
| LOW⚡ | paddleformers/trainer/utils/offload_optimizer.py | 187 | # Step 2: mock _C_ops.adamw_ and _C_ops.adamw | COMMENT |
| LOW | paddleformers/trainer/utils/offload_optimizer.py | 77 | # Step 3: mock _insert_sync | COMMENT |
| LOW | paddleformers/trainer/utils/offload_optimizer.py | 95 | # Step 4: mock Muon._muon_update and Muon._apply_optimize | COMMENT |
| LOW | paddleformers/trainer/utils/offload_optimizer.py | 207 | # Step 3: mock _insert_sync | COMMENT |
| LOW | paddleformers/trainer/utils/offload_optimizer.py | 225 | # Step 4: mock Muon._muon_update and Muon._apply_optimize | COMMENT |
| LOW⚡ | paddleformers/generation/utils.py | 389 | # Step 1: Process input mask to generate basic expanded mask | STRING |
| LOW | paddleformers/generation/utils.py | 447 | # Step 2: Convert boolean mask to numerical mask (adapt to different devices) | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | …e_pretrain/ernie/src/callbacks/moe_logging_callback.py | 77 | CODE | |
| MEDIUM | …e_pretrain/ernie/src/callbacks/moe_logging_callback.py | 79 | CODE | |
| MEDIUM | …e_pretrain/ernie/src/callbacks/moe_logging_callback.py | 80 | CODE | |
| MEDIUM | …e_pretrain/ernie/src/callbacks/moe_logging_callback.py | 85 | CODE | |
| MEDIUM | …e_pretrain/ernie/src/callbacks/moe_logging_callback.py | 90 | CODE | |
| MEDIUM | …e_pretrain/ernie/src/callbacks/moe_logging_callback.py | 91 | CODE | |
| MEDIUM | …e_pretrain/ernie/src/callbacks/moe_logging_callback.py | 99 | CODE | |
| MEDIUM | …e_pretrain/ernie/src/callbacks/moe_logging_callback.py | 111 | CODE | |
| MEDIUM | …e_pretrain/ernie/src/callbacks/moe_logging_callback.py | 112 | CODE | |
| MEDIUM | …e_pretrain/ernie/src/callbacks/moe_logging_callback.py | 116 | CODE | |
| MEDIUM | …e_pretrain/ernie/src/callbacks/moe_logging_callback.py | 117 | CODE | |
| MEDIUM | …e_pretrain/ernie/src/callbacks/moe_logging_callback.py | 118 | CODE | |
| MEDIUM | …e_pretrain/ernie/src/callbacks/moe_logging_callback.py | 120 | CODE | |
| MEDIUM | …n/ernie_pretrain/src/callbacks/moe_logging_callback.py | 78 | CODE | |
| MEDIUM | …n/ernie_pretrain/src/callbacks/moe_logging_callback.py | 80 | CODE | |
| MEDIUM | …n/ernie_pretrain/src/callbacks/moe_logging_callback.py | 81 | CODE | |
| MEDIUM | …n/ernie_pretrain/src/callbacks/moe_logging_callback.py | 86 | CODE | |
| MEDIUM | …n/ernie_pretrain/src/callbacks/moe_logging_callback.py | 91 | CODE | |
| MEDIUM | …n/ernie_pretrain/src/callbacks/moe_logging_callback.py | 92 | CODE | |
| MEDIUM | …n/ernie_pretrain/src/callbacks/moe_logging_callback.py | 100 | CODE | |
| MEDIUM | …n/ernie_pretrain/src/callbacks/moe_logging_callback.py | 112 | CODE | |
| MEDIUM | …n/ernie_pretrain/src/callbacks/moe_logging_callback.py | 113 | CODE | |
| MEDIUM | …n/ernie_pretrain/src/callbacks/moe_logging_callback.py | 117 | CODE | |
| MEDIUM | …n/ernie_pretrain/src/callbacks/moe_logging_callback.py | 118 | CODE | |
| MEDIUM | …n/ernie_pretrain/src/callbacks/moe_logging_callback.py | 119 | CODE | |
| MEDIUM | …n/ernie_pretrain/src/callbacks/moe_logging_callback.py | 121 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | tests/ai_edited_test/datasets/test_ai_mm_plugin.py | 165 | mock_response.content = b"fake_data" | CODE |
| LOW | …ctices/tutorials/how_to_train_a_function_call_model.md | 96 | {"messages": [{"role": "user", "content": "I need to generate an invoice for a customer named John Doe. He bought 2 appl | CODE |
| LOW | …ansformers/ernie4_5_moe_vl/model/modeling_moe_vl_pp.py | 969 | fake_data = paddle.ones([pp_sd_group.nranks, 1]) | CODE |
| LOW | …ansformers/ernie4_5_moe_vl/model/modeling_moe_vl_pp.py | 971 | dist.alltoall(fake_out, fake_data, pp_sd_group) | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 151 | fake_data = [nested_reduce_tensor(data)] | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 157 | fake_data = [None] | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 160 | fake_data = [nested_reduce_tensor(data)] | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 166 | fake_data = [None] | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 169 | fake_data, | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 175 | fake_data, | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 180 | fake_data = fake_data[0] | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 181 | if fake_data is None: | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 187 | data = nested_empty_tensor(fake_data) | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 190 | data = nested_empty_tensor(fake_data) | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 436 | fake_data = [nested_reduce_tensor(data)] | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 440 | fake_data = [None] | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 443 | fake_data = [nested_reduce_tensor(data)] | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 447 | fake_data = [None] | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 450 | fake_data, | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 456 | fake_data, | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 461 | fake_data = fake_data[0] | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 462 | if fake_data is None: | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 468 | data = nested_empty_tensor(fake_data) | CODE |
| LOW⚡ | paddleformers/data/dist_dataloader.py | 471 | data = nested_empty_tensor(fake_data) | CODE |
| LOW | paddleformers/data/dist_dataloader.py | 387 | fake_data = [None] * self._dataset_world_size | CODE |
| LOW | paddleformers/data/dist_dataloader.py | 389 | fake_data, src=self._stream_data_src, group=self._stream_data_group | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | tests/generation/test_logits_process.py | 330 | # scores = 0 to facilitate checks | COMMENT |
| LOW | paddleformers/datasets/DPODataset.py | 386 | # The sequnece is too long, just return None | COMMENT |
| LOW | paddleformers/peft/lora/lora_model.py | 101 | # If paddle_xpu is not installed, just use PaddleFormers's native lora layers | COMMENT |
| LOW | paddleformers/transformers/linear_utils.py | 82 | # If paddle_xpu is not installed, just use Paddle's native Linear implementations | COMMENT |
| MEDIUM | paddleformers/transformers/auto/tokenizer.py | 234 | # First, let's see whether the tokenizer_type is passed so that we can leverage it | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | paddleformers/nn/moe_deepep/modular_moe_layer.py | 314 | # the current expert. We need to make sure to multiply the output hidden | COMMENT |
| LOW | paddleformers/transformers/qwen2_moe/modeling.py | 370 | # the current expert. We need to make sure to multiply the output hidden | COMMENT |
| LOW | paddleformers/transformers/qwen3_moe/modeling.py | 368 | # the current expert. We need to make sure to multiply the output hidden | COMMENT |
| LOW | paddleformers/trainer/training_args.py | 353 | If you set this value, `greater_is_better` will default to `True`. Don't forget to set it to `False` if | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | tests/ai_edited_test/nn/test_ai_loss_utils.py | 78 | def my_function(x): | CODE |
| LOW | tests/ai_edited_test/trainer/test_ai_zero_cost_ckpt.py | 109 | def my_function(state_dict): | CODE |
| LOW | paddleformers/data/data_collator.py | 507 | def process_data(self, data, pad_idx, max_len): | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | paddleformers/nn/moe/moe_alltoall_layer.py | 82 | # TODO: implement reduce in cuda ops | COMMENT |
| LOW | …rs/transformers/ernie4_5_moe_vl/model/moe/moe_layer.py | 133 | # TODO: implement reduce in cuda ops | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | paddleformers/transformers/tokenizer_utils.py | 227 | "documents": [] # RAG context documents | STRING |