Update the example of human in the loop

docs/sphinx_doc/source/tutorial/example_data_functionalities.md

@@ -212,6 +212,7 @@ Trinity-RFT uses a unified config file to manage all config items. For the data
 In this example, assume that you need to select the chosen and rejected responses for DPO method. So you can set these config items like the following example:
 
 ```yaml
+# using task pipeline to decide the chosen and rejected from human preference
 data_processor:
   # task pipeline related
   task_pipeline:
@@ -239,7 +240,7 @@ data_processor:
           chosen_key: "chosen"  # Chosen field
           rejected_key: "rejected"  # Rejected field
     inputs:  # the output will be set to the explorer input automatically
-      - /PATH/TO/DATA/FILE/TO/BE/ANNOTATED
+      - 'examples/dpo_human_in_the_loop/demo-data.jsonl'
     target_fields: ["prompt"]
 service:
   data_juicer:
@@ -252,6 +253,8 @@ The difference is that we use the data-juicer OP `human_preference_annotation_ma
 
 You can set more config items for this OP (e.g. notification when annotation is finished). For more details, please refer to this [doc](https://github.com/modelscope/data-juicer/tree/main/configs/annotation).
 
+All config items in the `data_processor` section can be found [here](trinity_configs.md). A prepared config file for this example can be found in [the config file](https://github.com/modelscope/Trinity-RFT/tree/main/examples/dpo_human_in_the_loop/dpo.yaml).
+
 ### Start Running
 
 When you start running with the RFT config, the data processor will start the OP `human_preference_annotation_mapper`, and then you can find a new project on the "Projects" page of the label-studio server.

examples/dpo_human_in_the_loop/README.md

@@ -0,0 +1,7 @@
+# DPO with Human in the Loop
+
+This example shows the usage of DPO with human in the loop on a simple example dataset.
+
+For more detailed information, please refer to the [documentation](../../docs/sphinx_doc/source/tutorial/example_data_functionalities.md#example-human-in-the-loop).
+
+The config files are located in [`dpo.yaml`](dpo.yaml) and [`train_dpo.yaml`](train_dpo.yaml). The example dataset is located in [`demo_data.jsonl`](demo-data.jsonl).

examples/dpo_human_in_the_loop/demo-data.jsonl

@@ -0,0 +1,10 @@
+{"prompt": "What is the capital of France?", "answer1": "Paris", "answer2": "Lyon"}
+{"prompt": "Which planet is known as the Red Planet?", "answer1": "Mars", "answer2": "Venus"}
+{"prompt": "What is the chemical symbol for gold?", "answer1": "Au", "answer2": "Ag"}
+{"prompt": "Who wrote 'Romeo and Juliet'?", "answer1": "William Shakespeare", "answer2": "Christopher Marlowe"}
+{"prompt": "What is the largest mammal on Earth?", "answer1": "Blue Whale", "answer2": "African Elephant"}
+{"prompt": "In which year did World War II end?", "answer1": "1945", "answer2": "1944"}
+{"prompt": "What is the square root of 64?", "answer1": "8", "answer2": "6"}
+{"prompt": "Who painted the Mona Lisa?", "answer1": "Leonardo da Vinci", "answer2": "Michelangelo"}
+{"prompt": "What is the main component of the Sun?", "answer1": "Hydrogen", "answer2": "Helium"}
+{"prompt": "Which programming language was created by Guido van Rossum?", "answer1": "Python", "answer2": "Java"}

examples/dpo_human_in_the_loop/dpo.yaml

@@ -0,0 +1,73 @@
+project: "dpo_example"
+name: "trinity_dpo"
+mode: train
+
+# using task pipeline to decide the chosen and rejected from human preference
+data_processor:
+  # task pipeline related
+  task_pipeline:
+    num_process: 1
+    operators:
+      - name: "human_preference_annotation_mapper"
+        args:
+          # general annotation project settings
+          project_name_prefix: "Human_Preference_Annotation_Demo"
+          wait_for_annotations: true  # Whether to wait for annotations to complete
+          timeout: 3600  # Maximum time to wait for annotations in seconds (1 hour)
+          poll_interval: 10  # Time between annotation status checks in seconds
+          max_tasks_per_batch: 10  # Maximum number of tasks in a single batch
+          notification_config:
+            enabled: false
+
+          # label studio connection settings
+          api_url: "http://localhost:7070"  # Default Label Studio URL
+          api_key: "YOUR_API_KEY"  # Your API key for label studuio authentication, which can be set when starting the label-studio service
+
+          # human preference annotation settings
+          prompt_key: "prompt"  # Prompt field
+          answer1_key: "answer1"  # First answer option
+          answer2_key: "answer2"  # Second answer option
+          chosen_key: "chosen"  # Chosen field
+          rejected_key: "rejected"  # Rejected field
+    inputs:  # the output will be set to the explorer input automatically
+      - 'examples/dpo_human_in_the_loop/demo-data.jsonl'
+    target_fields: ["prompt"]
+service:
+  data_juicer:
+    auto_start: true
+
+algorithm:
+  algorithm_type: dpo
+  kl_loss_fn: k1
+  kl_loss_fn_args:
+    kl_coef: 0.1
+checkpoint_root_dir: /PATH/TO/CHECKPOINT/
+model:
+  model_path: /PATH/TO/MODEL
+  max_response_tokens: 1024
+  max_model_len: 1536
+cluster:
+  node_num: 1
+  gpu_per_node: 8
+buffer:
+  total_epochs: 2
+  train_batch_size: 64
+  trainer_input:
+    experience_buffer:
+      name: dpo_buffer
+      storage_type: file
+      enable_progress_bar: True
+      path: ./outputs/human_annotation_output/  # the result data after human preference annotation are stored here
+      format:
+        prompt_type: plaintext # plaintext/messages
+        prompt_key: prompt
+        chosen_key: chosen
+        rejected_key: rejected
+synchronizer:
+  sync_method: 'checkpoint'
+  sync_interval: 30
+  sync_timeout: 1200
+trainer:
+  trainer_type: 'verl'
+  trainer_config_path: 'examples/dpo_human_in_the_loop/train_dpo.yaml'
+  save_interval: 30

examples/dpo_human_in_the_loop/train_dpo.yaml

@@ -0,0 +1,51 @@
+actor_rollout_ref:
+  hybrid_engine: True
+  model:
+    external_lib: null
+    override_config: { }
+    enable_gradient_checkpointing: True
+    use_remove_padding: False
+  actor:
+    strategy: fsdp  # This is for backward-compatibility
+    ppo_micro_batch_size_per_gpu: 2
+    use_dynamic_bsz: False
+    ppo_max_token_len_per_gpu: 16384
+    grad_clip: 1.0
+    ppo_epochs: 1
+    shuffle: False
+    ulysses_sequence_parallel_size: 1 # sp size
+    optim:
+      lr: 5e-7
+      lr_warmup_steps_ratio: 0.03  # the total steps will be injected during runtime
+      min_lr_ratio: 0.1   # only useful for warmup with cosine
+      warmup_style: cosine  # select from constant/cosine
+      total_training_steps: 783
+      betas: [0.9, 0.95]
+    fsdp_config:
+      wrap_policy:
+        # transformer_layer_cls_to_wrap: None
+        min_num_params: 0
+      param_offload: False
+      optimizer_offload: False
+      fsdp_size: -1
+  ref:
+    fsdp_config:
+      param_offload: False
+      wrap_policy:
+        # transformer_layer_cls_to_wrap: None
+        min_num_params: 0
+    # log_prob_micro_batch_size: 4 # will be deprecated, use log_prob_micro_batch_size_per_gpu
+    log_prob_micro_batch_size_per_gpu: 2
+    log_prob_use_dynamic_bsz: ${actor_rollout_ref.actor.use_dynamic_bsz}
+    log_prob_max_token_len_per_gpu: ${actor_rollout_ref.actor.ppo_max_token_len_per_gpu}
+    ulysses_sequence_parallel_size: ${actor_rollout_ref.actor.ulysses_sequence_parallel_size} # sp size
+
+trainer:
+  balance_batch: False
+  total_training_steps: 783
+  # auto: find the last ckpt to resume. If can't find, start from scratch
+  resume_mode: auto # or auto or resume_path if
+  default_hdfs_dir: null
+  remove_previous_ckpt_in_save: False
+  del_local_ckpt_after_load: False
+  val_before_train: False

trinity/buffer/pipelines/task_pipeline.py

@@ -5,9 +5,6 @@
 
 
 def check_and_run_task_pipeline(config: Config) -> Dict:
-    if not (config.mode == "explore" or config.mode == "both"):
-        # task pipeline is only available when using Explorer
-        return {}
     if config.data_processor.task_pipeline is None:
         return {}
 

trinity/common/config.py

@@ -688,7 +688,21 @@ def _check_buffer(self) -> None:  # noqa: C901
                 )
         if self.data_processor.task_pipeline is not None:
             if self.data_processor.task_pipeline.output is None:
-                self.data_processor.task_pipeline.output = self.buffer.explorer_input.taskset
+                if self.buffer.explorer_input.taskset.path is not None:
+                    self.data_processor.task_pipeline.output = self.buffer.explorer_input.taskset
+                elif (
+                    self.buffer.trainer_input.experience_buffer.schema_type in {"dpo", "sft"}
+                    and self.buffer.trainer_input.experience_buffer.path is not None
+                ):
+                    self.data_processor.task_pipeline.output = (
+                        self.buffer.trainer_input.experience_buffer
+                    )
+                else:
+                    raise ValueError(
+                        "`data_processor.task_pipeline.output` is required when both "
+                        "`buffer.explorer_input.taskset.path` and `buffer.trainer_input.experience_buffer.path` are "
+                        "None"
+                    )
             if self.data_processor.task_pipeline.output.path and os.path.exists(
                 self.data_processor.task_pipeline.output.path
             ):

trinity/service/data_juicer/server/utils.py

@@ -114,6 +114,8 @@ def _parse_task_pipeline_config(config: DJConfig) -> Namespace:
 
 
 def group_scores(dataset: Dataset) -> Dataset:
+    if Fields.stats not in dataset.features:
+        return dataset
     # for perplexity, normalize them with the max value.
     stats_min_max = {}
     for stats in dataset.features[Fields.stats]:
@@ -165,6 +167,8 @@ def compute_priority_scores(
 
     from data_juicer.utils.constant import Fields
 
+    if Fields.stats not in sample:
+        return sample
     stats = sample[Fields.stats]
     if isinstance(stats, list):
         stats = stats[0]