Refactor on config_manager.py

docs/sphinx_doc/source/tutorial/example_data_functionalities.md

@@ -42,7 +42,7 @@ data:
   # database related. The result dataset will be stored in the database.
   db_url: 'postgresql://{user_name}@localhost:5432/{db_name}'
   # downstream loading related
-  total_epoch: 1
+  total_epochs: 1
   batch_size: 96
   default_workflow_type: 'math_workflow'
 ```
@@ -53,7 +53,7 @@ Here you can set the basic information for the GSM-8K dataset, database informat
 + `dataset_config`: extra config arguments for loading the raw dataset. Mainly for the `load_dataset` method in HuggingFace `datasets` library.
 + `format_config`: some dataset format config items, which are used to map original data field names to unified ones.
 + `db_url`: the URL of the postgresql database to store the result dataset.
-+ `total_epoch`: the total number of epochs to train on this dataset.
++ `total_epochs`: the total number of epochs to train on this dataset.
 + `batch_size`: the training batch size.
 + `default_workflow_type`: the default exploring workflow type. Please refer to [programming guide](trinity_programming_guide.md) for more details.
 
@@ -74,7 +74,7 @@ data:
   # database related. The result dataset will be stored in the database.
   db_url: 'postgresql://{user_name}@localhost:5432/{db_name}'
   # downstream loading related
-  total_epoch: 1
+  total_epochs: 1
   batch_size: 96
   default_workflow_type: 'math_workflow'
 
@@ -120,7 +120,7 @@ data:
   # database related. The result dataset will be stored in the database.
   db_url: 'postgresql://{user_name}@localhost:5432/{db_name}'
   # downstream loading related
-  total_epoch: 1
+  total_epochs: 1
   batch_size: 96
   default_workflow_type: 'math_workflow'
 
@@ -199,7 +199,7 @@ data:
   # database related. The result dataset will be stored in the database.
   db_url: 'postgresql://{user_name}@localhost:5432/{db_name}'
   # downstream loading related
-  total_epoch: 20
+  total_epochs: 20
   batch_size: 32
   default_workflow_type: 'math_workflow'
 ```

docs/sphinx_doc/source/tutorial/trinity_configs.md

@@ -3,6 +3,18 @@
 The following is the main config file for Trinity-RFT. Take `countdown.yaml` as an example.
 
 
+## Monitor
+
+```yaml
+monitor:
+  project: "Trinity-RFT-countdown"
+  name: "qwen2.5-1.5B-countdown"
+```
+
+- `monitor.project`: The project name. It must be set manually.
+- `monitor.name`: The name of the experiment. It must be set manually.
+
+
 ## Monitor
 
 ```yaml
@@ -33,7 +45,7 @@ data:
   max_retry_times: 3
   max_retry_interval: 1
 
-  total_epoch: 20
+  total_epochs: 20
   batch_size: 96
   default_workflow_type: 'math_workflow'
   default_reward_fn_type: 'countdown_reward'
@@ -47,7 +59,7 @@ data:
 - `data.db_url`: The URL of the database.
 - `data.max_retry_times`: The maximum number of retries when loading the dataset from database.
 - `data.max_retry_interval`: The maximum interval between retries when loading the dataset from database.
-- `data.total_epoch`: The total number of epochs to explore the dataset. Default is `1`. It should be set manually.
+- `data.total_epochs`: The total number of epochs to explore the dataset. Default is `1`. It should be set manually.
 - `data.batch_size`: The number of `Task` in one training batch. The real batch size used in training is `data.batch_size` * `actor_rollout_ref.rollout.n` Default is `1`. It should be set manually.
 - `data.default_workflow_type`: The default workflow type used for training.
 - `data.default_reward_fn_type`: The default reward function type used for training.
@@ -345,10 +357,14 @@ algorithm:
   gamma: 1.0
   lam: 1.0
   adv_estimator: gae
+  norm_adv_by_std_in_grpo: True
+  use_kl_in_reward: False
   kl_penalty: kl  # how to estimate kl divergence
   kl_ctrl:
     type: fixed
     kl_coef: 0.001
+    horizon: 10000
+    target_kl: 0.1
 
 trainer:
   balance_batch: True
@@ -363,7 +379,7 @@ trainer:
   save_freq: 100
   # auto: find the last ckpt to resume. If can't find, start from scratch
   resume_mode: auto # or auto or resume_path if
-  resume_from_path: False
+  resume_from_path: ""
   test_freq: 100
   critic_warmup: 0
   default_hdfs_dir: null
@@ -383,8 +399,9 @@ trainer:
 - `actor_rollout_ref.actor.grad_clip`: Gradient clip for actor model training.
 - `actor_rollout_ref.actor.clip_ratio`: Used for compute policy loss.
 - `actor_rollout_ref.actor.entropy_coeff`: Used for compute policy loss.
-- `actor_rollout_ref.actor.use_kl_loss`: True for GRPO.
-- `actor_rollout_ref.actor.kl_loss_coef`: Used for GRPO, optional value is `kl`, `abs`, `mse` or `low_var_kl`.
+- `actor_rollout_ref.actor.use_kl_loss`: Whether to enable kl loss.
+- `actor_rollout_ref.actor.kl_loss_coef`: The coefficient of kl loss.
+- `actor_rollout_ref.actor.kl_loss_type`: How to compute kl loss, optional value is `kl`, `abs`, `mse` or `low_var_kl`.
 - `actor_rollout_ref.actor.ulysses_sequence_parallel_size`: Ulysses sequence parallel size.
 - `actor_rollout_ref.actor.alg_type`: Used for OPMD, optional value is `ppo`, `opmd` or `pairwise_opmd`.
 - `actor_rollout_ref.actor.tau`: strength of regularization w.r.t. old / ref policy.

examples/dpo_humanlike/dpo.yaml

@@ -1,6 +1,6 @@
 mode: train
 data:
-  total_epoch: 20
+  total_epochs: 20
   batch_size: 32 # NOTE
   train_split: "train"
   dataset_path: ''
@@ -22,7 +22,6 @@ buffer:
   train_dataset:
     name: dpo_buffer
     storage_type: file
-    algorithm_type: dpo
     path: '/PATH/TO/DATASET/'
     kwargs:
       prompt_type: plaintext # plaintext/messages

examples/dpo_humanlike/train_dpo.yaml

@@ -173,7 +173,6 @@ trainer:
   save_freq: 30
   # auto: find the last ckpt to resume. If can't find, start from scratch
   resume_mode: auto # or auto or resume_path if
-  resume_from_path: False
   test_freq: 5
   critic_warmup: 0
   default_hdfs_dir: null

examples/grpo_alfworld/alfworld.yaml

@@ -1,5 +1,5 @@
 data:
-  total_epoch: 20
+  total_epochs: 20
   batch_size: 4
   dataset_path: 'scripts/data_prepare/alfworld_data'
   default_workflow_type: 'alfworld_workflow'
@@ -21,7 +21,6 @@ buffer:
   train_dataset:
     name: alfworld_buffer
     storage_type: queue
-    algorithm_type: ppo
     path: 'sqlite:///alfworld.db'
 explorer:
   engine_type: vllm_async

examples/grpo_alfworld/train_alfworld.yaml

@@ -172,7 +172,6 @@ trainer:
   save_freq: 1
   # auto: find the last ckpt to resume. If can't find, start from scratch
   resume_mode: auto # or auto or resume_path if
-  resume_from_path: False
   test_freq: 100
   critic_warmup: 0
   default_hdfs_dir: null

examples/grpo_gsm8k/gsm8k.yaml

@@ -18,7 +18,7 @@ data:
   # db related
   db_url: ''
   # downstream loading related
-  total_epoch: 1
+  total_epochs: 1
   batch_size: 96
   default_workflow_type: 'math_workflow'
 model:
@@ -35,12 +35,10 @@ buffer:
   train_dataset:
     name: gsm8k_buffer
     storage_type: queue
-    algorithm_type: ppo
     path: 'sqlite:///gsm8k.db'
   # sft_warmup_dataset: # Uncomment these to enable sft warmup
   #   name: warmup_data
   #   storage_type: file
-  #   algorithm_type: sft
   #   path: '/PATH/TO/WARMUP_DATA/'
   #   kwargs:
   #     prompt_type: plaintext

examples/grpo_gsm8k/train_gsm8k.yaml

@@ -177,7 +177,6 @@ trainer:
   save_freq: 100
   # auto: find the last ckpt to resume. If can't find, start from scratch
   resume_mode: auto # or auto or resume_path if
-  resume_from_path: False
   test_freq: 5
   critic_warmup: 0
   default_hdfs_dir: null

examples/grpo_math/math.yaml

@@ -10,7 +10,7 @@ data:
   # db related
   db_url: ''
   # downstream loading related
-  total_epoch: 20
+  total_epochs: 20
   batch_size: 288
   default_workflow_type: 'math_workflow'
 model:
@@ -27,8 +27,7 @@ buffer:
   train_dataset:
     name: math_buffer
     storage_type: queue
-    algorithm_type: ppo
-    path: 'sqlite:////math.db'
+    path: 'sqlite:///math.db'
 explorer:
   engine_type: vllm_async
   engine_num: 2

examples/grpo_math/train_math.yaml

@@ -169,7 +169,6 @@ trainer:
   save_freq: 100
   # auto: find the last ckpt to resume. If can't find, start from scratch
   resume_mode: auto # or auto or resume_path if
-  resume_from_path: False
   test_freq: 5
   critic_warmup: 0
   default_hdfs_dir: null

examples/grpo_sciworld/sciworld.yaml

@@ -1,5 +1,5 @@
 data:
-  total_epoch: 20
+  total_epochs: 20
   batch_size: 4
   dataset_path: 'scripts/data_prepare/sciworld_data'
   default_workflow_type: 'sciworld_workflow'
@@ -21,7 +21,6 @@ buffer:
   train_dataset:
     name: sciworld_buffer
     storage_type: queue
-    algorithm_type: ppo
     path: 'sqlite:///sciworld.db'
 explorer:
   engine_type: vllm_async

examples/grpo_sciworld/train_sciworld.yaml

@@ -167,7 +167,6 @@ trainer:
   save_freq: 1
   # auto: find the last ckpt to resume. If can't find, start from scratch
   resume_mode: auto # or auto or resume_path if
-  resume_from_path: False
   test_freq: 100
   critic_warmup: 0
   default_hdfs_dir: null

examples/grpo_webshop/train_webshop.yaml

@@ -172,7 +172,6 @@ trainer:
   save_freq: 1
   # auto: find the last ckpt to resume. If can't find, start from scratch
   resume_mode: auto # or auto or resume_path if
-  resume_from_path: False
   test_freq: 100
   critic_warmup: 0
   default_hdfs_dir: null

examples/grpo_webshop/webshop.yaml

@@ -1,5 +1,5 @@
 data:
-  total_epoch: 20
+  total_epochs: 20
   batch_size: 4
   dataset_path: 'scripts/data_prepare/webshop_data'
   default_workflow_type: 'webshop_workflow'
@@ -21,7 +21,6 @@ buffer:
   train_dataset:
     name: webshop_buffer
     storage_type: queue
-    algorithm_type: ppo
     path: 'sqlite:///webshop.db'
 explorer:
   engine_type: vllm_async

examples/opmd_gsm8k/opmd_gsm8k.yaml

@@ -1,5 +1,5 @@
 data:
-  total_epoch: 1
+  total_epochs: 1
   batch_size: 96
   dataset_path: '{path to datasets}/gsm8k'
   default_workflow_type: 'math_workflow'
@@ -20,7 +20,6 @@ buffer:
   train_dataset:
     name: gsm8k_buffer
     storage_type: queue
-    algorithm_type: opmd
     path: 'sqlite:///gsm8k_opmd.db'
 explorer:
   engine_type: vllm_async

examples/opmd_gsm8k/train_opmd_gsm8k.yaml

@@ -204,7 +204,6 @@ trainer:
   save_freq: 100
   # auto: find the last ckpt to resume. If can't find, start from scratch
   resume_mode: auto # or auto or resume_path if
-  resume_from_path: False
   test_freq: 100
   critic_warmup: 0
   default_hdfs_dir: null

examples/ppo_countdown/countdown.yaml

@@ -1,5 +1,5 @@
 data:
-  total_epoch: 20
+  total_epochs: 20
   batch_size: 96
   dataset_path: 'countdown_dataset/oneshot-split'
   default_workflow_type: 'math_workflow'
@@ -23,8 +23,7 @@ buffer:
   train_dataset:
     name: countdown_buffer
     storage_type: queue
-    algorithm_type: ppo
-    path: 'sqlite:////countdown.db'
+    path: 'sqlite:///countdown.db'
 explorer:
   engine_type: vllm_async
   engine_num: 2

examples/ppo_countdown/train_countdown.yaml

@@ -179,7 +179,6 @@ trainer:
   save_freq: 100
   # auto: find the last ckpt to resume. If can't find, start from scratch
   resume_mode: auto # or auto or resume_path if
-  resume_from_path: False
   test_freq: 100
   critic_warmup: 0
   default_hdfs_dir: null

pyproject.toml

@@ -32,6 +32,7 @@ dependencies = [
     "math_verify",
     "ninja",
     "fire",
+    "streamlit",
     "flask",
     "requests",
     "tensorboard",

tests/common/tmp/template_config.yaml

@@ -1,7 +1,7 @@
 mode: both
 data:
   dataset_path: ''
-  total_epoch: 1
+  total_epochs: 1
   batch_size: 32
   train_split: 'train'
   eval_split: ''