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Types & classes121 in github.com/Kwai-Klear/AR-GRPO

↓ 16 callersClassModelArgs
autoregressive/models/gpt.py:24
↓ 8 callersClassModelArgs
autoregressive/serve/gpt_model.py:24
↓ 8 callersClassTransformer
autoregressive/serve/gpt_model.py:244
↓ 8 callersClassTransformer
autoregressive/models/gpt.py:260
↓ 8 callersClassTransformerHF
autoregressive/models/gpt_hf.py:5
↓ 6 callersClassRMSNorm
autoregressive/models/gpt.py:137
↓ 6 callersClassResnetBlock
tokenizer_lg/vqgan/layer.py:57
↓ 6 callersClassResnetBlock
tokenizer_lg/tokenizer_image/vq_model.py:279
↓ 5 callersClassImgGenTextImageProcessor
autoregressive/modeling.py:19
↓ 5 callersClassNetLinLayer
A single linear layer which does a 1x1 conv
tokenizer_lg/tokenizer_image/lpips.py:109
↓ 5 callersClassT5Embedder
language/t5.py:16
↓ 4 callersClassAttnBlock
tokenizer_lg/vqgan/layer.py:119
↓ 4 callersClassAttnBlock
tokenizer_lg/tokenizer_image/vq_model.py:317
↓ 4 callersClassModelArgs
tokenizer_lg/tokenizer_image/vq_model.py:13
↓ 3 callersClassFIDStatistics
fid_evaluation.py:71
↓ 2 callersClassLLM
An LLM for generating texts from given prompts and sampling parameters. This class includes a tokenizer, a language model (possibly distributed
autoregressive/serve/llm.py:22
↓ 2 callersClassPrepareDecodeMetadata
autoregressive/serve/model_runner.py:71
↓ 2 callersClassPreparePromptMetadata
autoregressive/serve/model_runner.py:43
↓ 2 callersClassRepeatSampler
Sampler that repeats the indices of a dataset in a structured manner. Args: data_source (`Sized`): Dataset to sample fro
modified_grpo_trainer.py:85
↓ 2 callersClassVQModel
tokenizer_lg/vqgan/model.py:24
↓ 2 callersClassVQModel
tokenizer_lg/tokenizer_image/vq_model.py:28
↓ 2 callersClassVQModelHF
tokenizer_lg/tokenizer_image/vq_model_hf.py:5
↓ 2 callersClassWorker
A worker class that executes (a partition of) the model on a GPU. Each worker is associated with a single GPU. The worker is responsible for
autoregressive/serve/worker.py:27
↓ 1 callersClassARGS
inference_t2i.py:20
↓ 1 callersClassAestheticRewardBatch
img_gen_grpo_rewards.py:544
↓ 1 callersClassAestheticScorer
reward_utils/aesthscore.py:36
↓ 1 callersClassAttention
autoregressive/serve/gpt_model.py:127
↓ 1 callersClassAttention
autoregressive/models/gpt.py:188
↓ 1 callersClassAttentionMonkeyPatch
Note: In vllm, PagedAttention supports head sizes [64, 80, 96, 112, 128, 256]. However, LlamaGen-3B model has head size 100 (for some his
autoregressive/serve/gpt_model.py:178
↓ 1 callersClassBatchIterator
fid_evaluation.py:459
↓ 1 callersClassBlur
tokenizer_lg/tokenizer_image/discriminator.py:238
↓ 1 callersClassBlur
tokenizer_lg/tokenizer_image/discriminator_stylegan.py:84
↓ 1 callersClassCLIPTextReward
img_gen_grpo_rewards.py:132
↓ 1 callersClassCUDAGraphRunner
autoregressive/serve/model_runner.py:1098
↓ 1 callersClassCaptionEmbedder
Embeds text caption into vector representations. Also handles label dropout for classifier-free guidance.
autoregressive/models/gpt.py:89
↓ 1 callersClassCustomDataset
language/extract_t5_feature.py:23
↓ 1 callersClassCustomDataset
autoregressive/train/extract_codes_t2i.py:25
↓ 1 callersClassDeQARewardBatch
img_gen_grpo_rewards.py:522
↓ 1 callersClassDecoder
tokenizer_lg/vqgan/layer.py:269
↓ 1 callersClassDecoder
tokenizer_lg/tokenizer_image/vq_model.py:128
↓ 1 callersClassDiscriminatorBlock
tokenizer_lg/tokenizer_image/discriminator.py:212
↓ 1 callersClassDiscriminatorBlock
tokenizer_lg/tokenizer_image/discriminator_stylegan.py:57
↓ 1 callersClassDistanceBlock
Calculate pairwise distances between vectors. Adapted from https://github.com/kynkaat/improved-precision-and-recall-metric/blob/f60f25e5ad93
fid_evaluation.py:366
↓ 1 callersClassDownsample
tokenizer_lg/vqgan/layer.py:35
↓ 1 callersClassDownsample
tokenizer_lg/tokenizer_image/vq_model.py:381
↓ 1 callersClassDropPath
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
utils/drop_path.py:24
↓ 1 callersClassEncoder
tokenizer_lg/vqgan/layer.py:175
↓ 1 callersClassEncoder
tokenizer_lg/tokenizer_image/vq_model.py:64
↓ 1 callersClassEvaluator
fid_evaluation.py:122
↓ 1 callersClassFeedForward
autoregressive/serve/gpt_model.py:98
↓ 1 callersClassFeedForward
autoregressive/models/gpt.py:151
↓ 1 callersClassHPSV21TextReward
img_gen_grpo_rewards.py:226
↓ 1 callersClassImage
reward_utils/maniqa.py:11
↓ 1 callersClassImageCrops
benchmark/reward-server/reward_server/gen_eval.py:64
↓ 1 callersClassImageCrops
benchmark/geneval/evaluation/evaluate_images.py:82
↓ 1 callersClassImagePathDataset
reward_utils/fid.py:49
↓ 1 callersClassImageRewardBatch
img_gen_grpo_rewards.py:472
↓ 1 callersClassImageRewardScorer
reward_utils/imagereward.py:6
↓ 1 callersClassImgGen_HF
autoregressive/modeling.py:114
↓ 1 callersClassKVCache
autoregressive/models/gpt.py:170
↓ 1 callersClassLPIPS
tokenizer_lg/tokenizer_image/lpips.py:53
↓ 1 callersClassLabelEmbedder
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
autoregressive/serve/gpt_model.py:50
↓ 1 callersClassLabelEmbedder
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
autoregressive/models/gpt.py:56
↓ 1 callersClassLazyImageSupervisedDataset
lazy_dataset.py:60
↓ 1 callersClassLazySupervisedDataset
lazy_dataset.py:77
↓ 1 callersClassMLP
reward_utils/aesthscore.py:17
↓ 1 callersClassMLP
autoregressive/models/gpt.py:118
↓ 1 callersClassManifoldEstimator
A helper for comparing manifolds of feature vectors. Adapted from https://github.com/kynkaat/improved-precision-and-recall-metric/blob/f60f2
fid_evaluation.py:209
↓ 1 callersClassModelRunner
autoregressive/serve/model_runner.py:103
↓ 1 callersClassPatchGANDiscriminator
Defines a PatchGAN discriminator as in Pix2Pix --> see https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
tokenizer_lg/tokenizer_image/discriminator.py:17
↓ 1 callersClassPickScoreScorer
reward_utils/pickscore.py:11
↓ 1 callersClassPickscoreRewardBatch
img_gen_grpo_rewards.py:458
↓ 1 callersClassSampler
Samples the next tokens from the model's outputs. This layer does the following: 1. Discard the hidden states that are not used for sampling
autoregressive/serve/sampler.py:17
↓ 1 callersClassScalingLayer
tokenizer_lg/tokenizer_image/lpips.py:99
↓ 1 callersClassSingleFolderDataset
tokenizer_lg/vae/reconstruction_vae_ddp.py:22
↓ 1 callersClassSingleFolderDataset
tokenizer_lg/vqgan/reconstruction_vqgan_ddp.py:24
↓ 1 callersClassSingleFolderDataset
tokenizer_lg/validation/val_ddp.py:17
↓ 1 callersClassSingleFolderDataset
tokenizer_lg/consistencydecoder/reconstruction_cd_ddp.py:22
↓ 1 callersClassStreamingNpzArrayReader
fid_evaluation.py:471
↓ 1 callersClassStyleGANDiscriminator
tokenizer_lg/tokenizer_image/discriminator.py:168
↓ 1 callersClassTransformerBlock
autoregressive/serve/gpt_model.py:227
↓ 1 callersClassTransformerBlock
autoregressive/models/gpt.py:244
↓ 1 callersClassUnifiedRewardBatch
img_gen_grpo_rewards.py:431
↓ 1 callersClassUpsample
tokenizer_lg/vqgan/layer.py:17
↓ 1 callersClassUpsample
tokenizer_lg/tokenizer_image/vq_model.py:367
↓ 1 callersClassVQLoss
tokenizer_lg/tokenizer_image/vq_loss.py:49
↓ 1 callersClassVectorQuantizer
see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py __________________________________
tokenizer_lg/vqgan/quantize.py:9
↓ 1 callersClassVectorQuantizer
tokenizer_lg/tokenizer_image/vq_model.py:197
↓ 1 callersClassvgg16
tokenizer_lg/tokenizer_image/lpips.py:118
ClassActNorm
tokenizer_lg/tokenizer_image/discriminator.py:79
ClassActNorm
tokenizer_lg/tokenizer_image/discriminator_patchgan.py:70
ClassBatchType
autoregressive/serve/model_runner.py:94
ClassCLIPDistanceReward
img_gen_grpo_rewards.py:71
ClassCLIP_FIDReward
img_gen_grpo_rewards.py:648
ClassCMMDReward
img_gen_grpo_rewards.py:638
ClassDiscriminator
tokenizer_lg/tokenizer_image/discriminator_stylegan.py:13
ClassFIDReward
img_gen_grpo_rewards.py:594
ClassGPUExecutor
autoregressive/serve/gpu_executor.py:17
ClassGPUExecutorAsync
autoregressive/serve/gpu_executor.py:187
ClassGRPORewardFunc
img_gen_grpo_rewards.py:32
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