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Types & classes150 in github.com/Alibaba-NLP/VRAG

↓ 21 callersClassDataProto
A DataProto is a data structure that aims to provide a standard protocol for data exchange between functions. It contains a batch (TensorDict
VRAG-RL/verl/protocol.py:176
↓ 7 callersClassParallelLlamaRMSNorm
VRAG-RL/verl/models/llama/megatron/layers/parallel_rmsnorm.py:25
↓ 7 callersClassParallelQwen2RMSNorm
VRAG-RL/verl/models/qwen2/megatron/layers/parallel_rmsnorm.py:25
↓ 6 callersClassRayClassWithInitArgs
VRAG-RL/verl/single_controller/ray/base.py:148
↓ 4 callersClassFSDPUlyssesShardingManager
Sharding manager to support data resharding when using FSDP + Ulysses
VRAG-RL/verl/workers/sharding_manager/fsdp_ulysses.py:32
↓ 4 callersClassFlopsCounter
Used to count mfu during training loop Example: flops_counter = FlopsCounter(config) flops_achieved, flops_promised = flops_
VRAG-RL/verl/utils/flops_counter.py:54
↓ 3 callersClassEncoderOutput
search_engine/models/GVE/models.py:27
↓ 3 callersClassGenerationConfig
VRAG-RL/vrag_agent/generation.py:45
↓ 3 callersClassModel_Role
VRAG-RL/scripts/data_construct_pipeline.py:115
↓ 3 callersClassRayResourcePool
VRAG-RL/verl/single_controller/ray/base.py:69
↓ 3 callersClassvLLMRollout
VRAG-RL/verl/workers/rollout/vllm_rollout/vllm_rollout.py:57
↓ 2 callersClassDataParallelPPOActor
VRAG-RL/verl/workers/actor/dp_actor.py:39
↓ 2 callersClassDataProtoFuture
DataProtoFuture aims to eliminate actual data fetching on driver. By doing so, the driver doesn't have to wait for data so that asynchronous
VRAG-RL/verl/protocol.py:765
↓ 2 callersClassFSDPCheckpointManager
A checkpoint manager that saves and loads - model - optimizer - lr_scheduler - extra_states in a SPMD way. We save
VRAG-RL/verl/utils/checkpoint/fsdp_checkpoint_manager.py:32
↓ 2 callersClassLLMGenerationManager
VRAG-RL/vrag_agent/generation.py:55
↓ 2 callersClassMegatronPPOActor
VRAG-RL/verl/workers/actor/megatron_actor.py:53
↓ 2 callersClassMemoryBuffer
A memory buffer is a contiguous torch tensor that may combine multiple tensors sharing with the underlying memory. It must have a unique type
VRAG-RL/verl/utils/memory_buffer.py:24
↓ 2 callersClassParallelLlamaDecoderLayerRmPad
VRAG-RL/verl/models/llama/megatron/layers/parallel_decoder.py:102
↓ 2 callersClassParallelLlamaMLP
VRAG-RL/verl/models/llama/megatron/layers/parallel_mlp.py:31
↓ 2 callersClassParallelQwen2DecoderLayerRmPad
VRAG-RL/verl/models/qwen2/megatron/layers/parallel_decoder.py:102
↓ 2 callersClassParallelQwen2MLP
VRAG-RL/verl/models/qwen2/megatron/layers/parallel_mlp.py:31
↓ 2 callersClassRLHFDataset
We assume the dataset contains a column that contains prompts and other information
VRAG-RL/verl/utils/dataset/rl_dataset.py:75
↓ 2 callersClassRMManager
VRAG-RL/verl/workers/reward_manager/rm.py:123
↓ 2 callersClassSFTDataset
This is an in-memory SFTDataset
VRAG-RL/verl/utils/dataset/sft_dataset.py:34
↓ 2 callersClassSearchEngine
search_engine/search_engine.py:10
↓ 2 callersClassState
VRAG-RL/verl/utils/seqlen_balancing.py:49
↓ 2 callersClassTracking
VRAG-RL/verl/utils/tracking.py:24
↓ 2 callersClassVRAG
demo/vrag_agent.py:14
↓ 2 callersClassVimRAG
VimRAG: Visual Memory RAG Agent with DAG-based reasoning.
demo/vimrag_agent.py:19
↓ 1 callersClassAdaptiveKLController
Adaptive KL controller described in the paper: https://arxiv.org/pdf/1909.08593.pdf
VRAG-RL/verl/trainer/ppo/core_algos.py:28
↓ 1 callersClassAllGatherPPModel
VRAG-RL/verl/workers/sharding_manager/megatron_vllm.py:37
↓ 1 callersClassAutoModelForSentenceEmbeddingTriplet
search_engine/models/GVE/models.py:209
↓ 1 callersClassBaseShardingManager
VRAG-RL/verl/workers/sharding_manager/base.py:21
↓ 1 callersClassCapturing
VRAG-RL/verl/utils/reward_score/prime_code/testing_util.py:74
↓ 1 callersClassDataParallelPPOCritic
VRAG-RL/verl/workers/critic/dp_critic.py:39
↓ 1 callersClassDistGlobalInfo
VRAG-RL/verl/single_controller/base/worker.py:31
↓ 1 callersClassDistRankInfo
VRAG-RL/verl/single_controller/base/worker.py:24
↓ 1 callersClassFSDPSFTTrainer
VRAG-RL/verl/trainer/fsdp_sft_trainer.py:78
↓ 1 callersClassFSDPSGLangShardingManager
VRAG-RL/verl/workers/sharding_manager/fsdp_sglang.py:47
↓ 1 callersClassFSDPVLLMShardingManager
VRAG-RL/verl/workers/sharding_manager/fsdp_vllm.py:37
↓ 1 callersClassFixedKLController
Fixed KL controller.
VRAG-RL/verl/trainer/ppo/core_algos.py:46
↓ 1 callersClassHFRollout
VRAG-RL/verl/workers/rollout/hf_rollout.py:35
↓ 1 callersClassLambdaLayer
VRAG-RL/verl/utils/model.py:28
↓ 1 callersClassLlamaDynamicNTKScalingRotaryEmbedding
LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla
VRAG-RL/verl/models/llama/megatron/layers/parallel_attention.py:91
↓ 1 callersClassLlamaLinearScalingRotaryEmbedding
LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev
VRAG-RL/verl/models/llama/megatron/layers/parallel_attention.py:72
↓ 1 callersClassLlamaRotaryEmbedding
VRAG-RL/verl/models/llama/megatron/layers/parallel_attention.py:35
↓ 1 callersClassLocalLogger
VRAG-RL/verl/utils/logger/aggregate_logger.py:30
↓ 1 callersClassMMRAG
VRAG-RL/scripts/data_construct_pipeline.py:162
↓ 1 callersClassMegatronPPOCritic
VRAG-RL/verl/workers/critic/megatron_critic.py:43
↓ 1 callersClassMegatronRewardModel
VRAG-RL/verl/workers/reward_model/megatron/reward_model.py:32
↓ 1 callersClassMegatronVLLMShardingManager
VRAG-RL/verl/workers/sharding_manager/megatron_vllm.py:254
↓ 1 callersClassMergedColumnParallelLinear
VRAG-RL/verl/models/qwen2/megatron/layers/parallel_linear.py:52
↓ 1 callersClassMergedColumnParallelLinear
VRAG-RL/verl/models/llama/megatron/layers/parallel_linear.py:52
↓ 1 callersClassNestedNamespace
VRAG-RL/verl/utils/py_functional.py:48
↓ 1 callersClassParallelLlamaAttention
Multi-headed attention from 'Attention Is All You Need' paper
VRAG-RL/verl/models/llama/megatron/layers/parallel_attention.py:143
↓ 1 callersClassParallelLlamaAttentionRmPad
VRAG-RL/verl/models/llama/megatron/layers/parallel_attention.py:338
↓ 1 callersClassParallelLlamaDecoderLayer
VRAG-RL/verl/models/llama/megatron/layers/parallel_decoder.py:35
↓ 1 callersClassParallelLlamaModel
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] Args: config: LlamaConfig
VRAG-RL/verl/models/llama/megatron/modeling_llama_megatron.py:74
↓ 1 callersClassParallelLlamaModelRmPad
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] Args: config: LlamaConfig
VRAG-RL/verl/models/llama/megatron/modeling_llama_megatron.py:219
↓ 1 callersClassParallelLlamaModelRmPadPP
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`] This model definition supports pip
VRAG-RL/verl/models/llama/megatron/modeling_llama_megatron.py:405
↓ 1 callersClassParallelQwen2Attention
Multi-headed attention from 'Attention Is All You Need' paper
VRAG-RL/verl/models/qwen2/megatron/layers/parallel_attention.py:143
↓ 1 callersClassParallelQwen2AttentionRmPad
VRAG-RL/verl/models/qwen2/megatron/layers/parallel_attention.py:322
↓ 1 callersClassParallelQwen2DecoderLayer
VRAG-RL/verl/models/qwen2/megatron/layers/parallel_decoder.py:35
↓ 1 callersClassParallelQwen2Model
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`] Args: config: Qwen2Config
VRAG-RL/verl/models/qwen2/megatron/modeling_qwen2_megatron.py:73
↓ 1 callersClassParallelQwen2ModelRmPad
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`] Args: config: Qwen2Config
VRAG-RL/verl/models/qwen2/megatron/modeling_qwen2_megatron.py:218
↓ 1 callersClassParallelQwen2ModelRmPadPP
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`] This model definition supports pip
VRAG-RL/verl/models/qwen2/megatron/modeling_qwen2_megatron.py:404
↓ 1 callersClassQKVParallelLinear
VRAG-RL/verl/models/qwen2/megatron/layers/parallel_linear.py:21
↓ 1 callersClassQKVParallelLinear
VRAG-RL/verl/models/llama/megatron/layers/parallel_linear.py:21
↓ 1 callersClassQwen2RotaryEmbedding
VRAG-RL/verl/models/qwen2/megatron/layers/parallel_attention.py:35
↓ 1 callersClassQwen3VLEmbedder
search_engine/models/Qwen3_VL_Embedding/qwen3_vl_embedding.py:160
↓ 1 callersClassQwen3VLForEmbeddingOutput
search_engine/models/Qwen3_VL_Embedding/qwen3_vl_embedding.py:37
↓ 1 callersClassRayPPOTrainer
Note that this trainer runs on the driver process on a single CPU/GPU node.
VRAG-RL/verl/trainer/ppo/ray_trainer.py:248
↓ 1 callersClassRayWorkerGroup
VRAG-RL/verl/single_controller/ray/base.py:196
↓ 1 callersClassResourcePoolManager
Define a resource pool specification. Resource pool will be initialized first. Mapping
VRAG-RL/verl/trainer/ppo/ray_trainer.py:75
↓ 1 callersClassSGLangRollout
VRAG-RL/verl/workers/rollout/sglang_rollout/sglang_rollout.py:84
↓ 1 callersClassSet
VRAG-RL/verl/utils/seqlen_balancing.py:27
↓ 1 callersClassTensorConfig
VRAG-RL/vrag_agent/tensor_helper.py:6
↓ 1 callersClassTensorHelper
VRAG-RL/vrag_agent/tensor_helper.py:9
↓ 1 callersClassTimeoutException
VRAG-RL/verl/utils/reward_score/prime_math/grader.py:340
↓ 1 callersClassUnifierMnrlLoss
search_engine/models/GVE/model_utils.py:9
↓ 1 callersClassVLProcessor
search_engine/models/GVE/processor.py:5
↓ 1 callersClassValidationGenerationsLogger
VRAG-RL/verl/utils/tracking.py:168
↓ 1 callersClassWorkerMeta
VRAG-RL/verl/single_controller/base/worker.py:68
↓ 1 callersClass_MlflowLoggingAdapter
VRAG-RL/verl/utils/tracking.py:126
↓ 1 callersClass_TensorboardAdapter
VRAG-RL/verl/utils/tracking.py:108
ClassActorRolloutRefWorker
This worker can be instantiated as a standalone actor or a standalone rollout or a standalone reference policy or a hybrid engine based on th
VRAG-RL/verl/workers/megatron_workers.py:67
ClassActorRolloutRefWorker
This worker can be instantiated as a standalone actor or a standalone rollout or a standalone reference policy or a hybrid engine based on th
VRAG-RL/verl/workers/fsdp_workers.py:70
ClassAdvantageEstimator
Using an enumeration class to avoid spelling errors in adv_estimator
VRAG-RL/verl/trainer/ppo/ray_trainer.py:63
ClassAutoModelForSentenceEmbedding
search_engine/models/GVE/models.py:34
ClassBaseCheckpointManager
A checkpoint manager that saves and loads - model - optimizer - lr_scheduler - extra_states in a SPMD way. We save -
VRAG-RL/verl/utils/checkpoint/checkpoint_manager.py:27
ClassBasePPOActor
VRAG-RL/verl/workers/actor/base.py:26
ClassBasePPOCritic
VRAG-RL/verl/workers/critic/base.py:26
ClassBasePPORewardModel
VRAG-RL/verl/workers/reward_model/base.py:23
ClassBaseRollout
VRAG-RL/verl/workers/rollout/base.py:23
ClassCODE_TYPE
VRAG-RL/verl/utils/reward_score/prime_code/testing_util.py:50
ClassClassWithInitArgs
This class stores a class constructor and the args/kwargs to construct the class. It is used to instantiate the remote class.
VRAG-RL/verl/single_controller/base/worker_group.py:61
ClassCriticWorker
VRAG-RL/verl/workers/megatron_workers.py:486
ClassCriticWorker
VRAG-RL/verl/workers/fsdp_workers.py:616
ClassDataProtoItem
VRAG-RL/verl/protocol.py:168
ClassDispatch
VRAG-RL/verl/single_controller/base/decorator.py:25
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