docs: GRPO documentation and Configuration cleanup

README.md

@@ -47,7 +47,7 @@ uv pip install -e '.[dev,test]'
 # Use uv run to launch any runs. 
 # Note that it is recommended to not activate the venv and instead use `uv run` since
 # it ensures consistent environment usage across different shells and sessions.
-uv run python examples/run_grpo.py
+uv run python examples/run_grpo_math.py
 ```
 
 ## Cluster Start

docs/design_docs/design_and_philosophy.md

@@ -9,8 +9,6 @@ Online RL requires coordinating a lot of different pieces of software/models
 
 We refer to each of these pieces of software as an **RL Actor**.
 
-[TODO @sahilj Diagram]
-
 Fundamentally, we need to be able to do 4 things between these RL Actors:
 - Resource them (provide GPUs/CPUs)
 - Isolate them
@@ -32,6 +30,8 @@ We create composable and hackable abstractions for each layer of the tasks above
 
 By creating a common interface for these 4 tasks, **RL algorithm code looks the same from 1 GPU to 1000 GPUs and does not care about the implementation of each RL Actor (Megatron, HF, Grad student with pen and paper)**
 
+![actor-wg-worker-vc](../assets/actor-wg-worker-vc.png)
+
 ### {py:class}`RayVirtualCluster <nemo_reinforcer.distributed.virtual_cluster.RayVirtualCluster>`
 VirtualCluster provides a basic abstraction on top of Ray Placement Groups that allow you to section off a part of your compute resources for WorkerGroups to run on as though they had their own cluster. They support running just one WorkerGroup on each VirtualCluster, or *colocation*, where multiple WorkerGroups share resources (i.e running policy training(hf) and generation(vllm) on the same GPUs in-turn).
 
@@ -84,12 +84,29 @@ class RayWorkerGroup:
     - Support for tied worker groups where multiple workers process the same data
     """
 ```
-[TODO @sahilj Diagram]
-
+`RayWorkerGroup` provides functions like `run_all_workers_single_data` and `run_all_workers_multiple_data` to control and communicate to individual worker processes.
 
 
 ### Single-Controller & Execution Diagram
-
-## Walking through an implementation of GRPO
-
-
+We control the RL Actors using a single-process head controller. Using the aforementioned abstractions, this allows us to represent the main loop of GRPO as though we were working on 1 GPU
+```python
+# data processing/transformations between each step omitted
+def grpo_train(
+    policy: PolicyInterface,
+    policy_generation: GenerationInterface,
+    environment: EnvironmentInterface,
+    dataloader: Iterable[BatchedDataDict[DatumSpec]],
+):
+    loss_fn = GRPOLossFn()
+    for batch in dataloader:
+        batch.repeat_interleave(num_generations_per_prompt) # repeat for GRPO
+        generations = policy_generation.generate(batch) 
+        rewards = environment.step(generations)
+
+        logprobs = policy.get_logprobs(generations)
+        reference_logprobs = policy.get_reference_logprobs(generations)
+
+        training_data = calculate_grpo_trainnig_data(generations, logprobs, reference_logprobs, rewards)
+        policy.train(generations, logprobs, reference_logprobs, GRPOLossFn)
+```
+For a real implementation of grpo (with valiation, checkpointing, memory movement, and the omitted data processing steps), see [grpo_train](../../nemo_reinforcer/algorithms/grpo.py)

docs/guides/grpo.md

@@ -1,3 +1,93 @@
-# GRPO
+# An in-depth walkthrough of GRPO in Reinforcer
 
-placeholder TBD
+## Quickstart: Launch a GRPO Run
+
+If you want to get running quickly, the script [examples/run_grpo_math.py](../../examples/run_grpo_math.py) has an example implementation of using GRPO to train a model on math problems. This script can either be launched locally or via Slurm. For details on how to set up Ray and launch a job using Slurm, refer to the [cluster documentation](../cluster.md).
+
+We recommend launching the job using `uv`:
+```bash
+uv run examples/run_grpo_math.py --config <PATH TO YAML CONFIG> {overrides}
+```
+If not specified, `config` will default to [examples/configs/grpo.yaml](../../examples/configs/grpo.yaml)
+
+## Now, for the details:
+
+In this guide, we'll walk through we handle
+* Data
+* Model training
+* Fast generation
+* Overall Resource Flow
+
+### Data
+We support training with multiple RL "Environments" at the same time.
+
+An [Environment](../../nemo_reinforcer/environments/interfaces.py) is an object that accepts a state/action history and returns an update state and rewards for the step. They run as Ray Remote Actors. Example [MathEnvironment](../../nemo_reinforcer/environments/math_environment.py).
+
+To support this, we need to know:
+* What environments you have
+* Which data should go to which environments
+* How to prepare the data from your dataset into a form we can use
+
+#### Common Data Format
+We define a [DatumSpec](../../nemo_reinforcer/data/interfaces.py) that holds all relevant information for each training example:
+```python
+class DatumSpec(TypedDict):
+    message_log: LLMMessageLogType
+    length: int  # total (concatenated) length of the message tensors
+    extra_env_info: Dict[str, Any] # anything your environment requires goes here, for example the 'answer' of a math problem
+    loss_multiplier: float  # multiplier for the loss for this datum. 0 to mask out (say the sample is invalid)
+    idx: int
+    task_name: Optional[str] = "default"
+    __extra__: Any  # This allows additional fields of any type
+```
+
+#### Data Processors
+We name all distinct "environments your model wants to optimize against" "tasks". So you might define a "math" task or a "code" task. 
+For each task, you should provide a data processor that reads from your dataset and returns a [DatumSpec](../../nemo_reinforcer/data/interfaces.py)
+
+```python
+def my_data_processor(
+    datum_dict: Dict[str, Any], # loaded directly from your dataset (i.e. single line of jsonl data)
+    task_data_spec: TaskDataSpec,
+    tokenizer,
+    max_seq_length: int,
+    idx: int,
+) -> DatumSpec:
+```
+We have an example of this as `math_data_processor` in [run_grpo_math.py](../../examples/run_grpo_math.py)
+
+#### Putting it all together:
+GRPO expects datasets to have the following form:
+```json
+{"task_name": "math", <actual data>}
+```
+Then, you can set data up as such:
+```python
+base_dataset = load_dataset("json", data_files=data_config["dataset_name"])["train"]
+tokenizer = AutoTokenizer.from_pretrained(policy_config["model_name"])
+
+task_data_processors = defaultdict(lambda: (math_task_spec, math_data_processor))
+task_data_processors["math"] = (math_task_spec, math_data_processor)
+
+math_env = MathEnvironment.remote(env_configs["math"]) # ray remote actor
+
+dataset = AllTaskProcessedDataset(
+    base_dataset,
+    tokenizer,
+    math_task_spec,
+    task_data_processors,
+    max_seq_length=data_config["max_input_seq_length"],
+)
+```
+Notice that you provide a mapping of tasks to their processors so the dataset knows what to use when processing samples.
+
+
+### Policy Model
+We define a [PolicyInterface]() that contains everything you need to train a Policy model.
+
+This Policy object holds a [RayWorkerGroup](../../nemo_reinforcer/distributed/worker_groups.py) of SPMD (1 proc/gpu) processes that run HF/MCore, all coordinated by this object so it appears to you like 1 GPU!
+
+### Fast Generation
+We support vLLM through the [VllmGeneration](../../nemo_reinforcer/models/generation/vllm.py) class right now.
+
+The function [grpo_train](../../nemo_reinforcer/algorithms/grpo.py) contains the core GRPO training loop.

docs/guides/sft.md

@@ -6,7 +6,7 @@ The script [examples/run_sft.py](../../examples/run_sft.py) can be used to launc
 
 Be sure to launch the job using `uv`. The command to launch an SFT job is as follows:
 ```bash
-uv run examples/run_sft.py --config <PATH TO YAML CONFIG>
+uv run examples/run_sft.py --config <PATH TO YAML CONFIG> <OVERRIDES>
 ```
 If not specified, `config` will default to [examples/configs/sft.yaml](../../examples/configs/sft.yaml).
 

examples/configs/grpo.yaml → examples/configs/grpo_math_1B.yaml

@@ -1,10 +1,8 @@
 # GRPO Algorithm Configuration
-defaults: "base.yaml"
-
 grpo:
   num_prompts_per_step: 8
   num_generations_per_prompt: 8
-  num_steps: 100
+  max_num_steps: 100
   normalize_rewards: true
   use_leave_one_out_baseline: true
   val_period: 10
@@ -29,16 +27,28 @@ policy:
   train_global_batch_size: 32
   train_micro_batch_size: 4
   generation_batch_size: 32
+  learning_rate: 5.0e-6
   logprob_batch_size: 4
-  max_total_sequence_length: 1024
+  max_total_sequence_length: 512
+
+  scheduler:
+    - name: "torch.optim.lr_scheduler.LinearLR"
+      kwargs:
+        start_factor: 0.1
+        end_factor: 1.0
+        total_iters: 50
+    - name: "torch.optim.lr_scheduler.ConstantLR"
+      kwargs:
+        factor: 1.0
+        total_iters: 10000000000
+    - milestones: [50]
 
   generation:
-    backend: "vllm" # "vllm" or "hf"(to use the hf training framework's generation)
-    max_new_tokens: ${policy.max_total_sequence_length} # upper bound, real truncation occurs at vllm.max_model_len below
+    backend: "vllm"
+    max_new_tokens: ${policy.max_total_sequence_length}
     temperature: 1.0
-    # Don't change since vllm logprobs in V0 runtime are after sampling and in V1 runtime are before sampling.
     top_p: 1.0
-    top_k: null # disable
+    top_k: null
     vllm_cfg:
       tensor_parallel_size: 1
       gpu_memory_utilization: 0.7
@@ -54,3 +64,17 @@ data:
 env:
   math:
     num_workers: 8
+
+logger:
+  log_dir: "logs"  # Base directory for all logs
+  num_val_samples_to_print: 0 # Number of validation samples to pretty print on terminal
+  wandb_enabled: false
+  tensorboard_enabled: false
+  wandb:
+    project: "grpo-dev"
+    name: "grpo-dev-logger"
+  tensorboard: {}
+
+cluster:
+  gpus_per_node: 1
+  num_nodes: 1

examples/configs/base.yaml → examples/configs/grpo_math_8B.yaml

@@ -1,12 +1,14 @@
-# Base configuration with common settings
+# GRPO Algorithm Configuration
+defaults: "grpo_math_1B.yaml"
+
 policy:
-  model_name: "meta-llama/Llama-3.2-1B-Instruct"
+  model_name: "meta-llama/Llama-3.1-8B-Instruct"
   train_global_batch_size: 32
-  train_micro_batch_size: 4
+  train_micro_batch_size: 1
   generation_batch_size: 32
   learning_rate: 5.0e-6
-  logprob_batch_size: 4
-  max_total_sequence_length: 8192
+  logprob_batch_size: 2
+  max_total_sequence_length: 4096
 
   scheduler:
     - name: "torch.optim.lr_scheduler.LinearLR"
@@ -28,24 +30,9 @@ policy:
     top_k: null
     vllm_cfg:
       tensor_parallel_size: 1
-      gpu_memory_utilization: 0.7
+      gpu_memory_utilization: 0.6
       max_model_len: ${policy.max_total_sequence_length}
-
-data:
-  max_input_seq_length: ${policy.max_total_sequence_length}
-  prompt_file: "examples/prompts/cot.txt"
-  system_prompt_file: null
-
-logger:
-  log_dir: "logs"  # Base directory for all logs
-  num_val_samples_to_print: 0 # Number of validation samples to pretty print on terminal
-  wandb_enabled: false
-  tensorboard_enabled: false
-  wandb:
-    project: "grpo-dev"
-    name: "grpo-dev-logger"
-  tensorboard: {}
-
+
 cluster:
-  gpus_per_node: 2
+  gpus_per_node: 8
   num_nodes: 1

examples/configs/sft.yaml

@@ -1,6 +1,6 @@
 # SFT Algorithm Configuration
 sft:
-  num_steps: 20
+  max_num_steps: 20
   val_period: 10
   val_batches: 8
   val_global_batch_size: 32

examples/run_grpo_math.py

@@ -170,7 +170,7 @@ def main():
     args, overrides = parse_args()
 
     if not args.config:
-        args.config = os.path.join(os.path.dirname(__file__), "configs", "grpo.yaml")
+        args.config = os.path.join(os.path.dirname(__file__), "configs", "grpo_math_1B.yaml")
 
     config = load_config(args.config)
     print(f"Loaded configuration from: {args.config}")

nemo_reinforcer/algorithms/grpo.py

@@ -69,7 +69,7 @@
 class GRPOConfig(TypedDict):
     num_prompts_per_step: int
     num_generations_per_prompt: int
-    num_steps: int
+    max_num_steps: int
     normalize_rewards: bool
     use_leave_one_out_baseline: bool
     val_period: int
@@ -445,7 +445,7 @@ def grpo_train(
 
     # Run grpo training (single-turn)
     for batch in dataloader:
-        print(f"\n{'=' * 25} Step {step + 1}/{len(dataloader)} {'=' * 25}")
+        print(f"\n{'=' * 25} Step {step + 1}/{min(len(dataloader), master_config['grpo']['max_num_steps'])} {'=' * 25}")
 
         with timer.time("total_step_time"):
             # Prepare batch
@@ -654,7 +654,7 @@ def grpo_train(
 
         timer.reset()
         step += 1
-        if step >= master_config["grpo"]["num_steps"]:
+        if step >= master_config["grpo"]["max_num_steps"]:
             break
 
 

nemo_reinforcer/algorithms/sft.py

@@ -51,7 +51,7 @@ def _default_sft_save_state() -> SFTSaveState:
 
 
 class SFTConfig(TypedDict):
-    num_steps: int
+    max_num_steps: int
     val_period: int
     val_batches: int
     val_global_batch_size: int
@@ -437,5 +437,5 @@ def sft_train(
         timer.reset()
         step += 1
 
-        if step >= master_config["sft"]["num_steps"]:
+        if step >= master_config["sft"]["max_num_steps"]:
             break

tests/functional/grpo.sh

@@ -16,7 +16,7 @@ mkdir -p $LOG_DIR
 cd $PROJECT_ROOT
 uv run $PROJECT_ROOT/examples/run_grpo_math.py \
     cluster.gpus_per_node=2 \
-    grpo.num_steps=10 \
+    grpo.max_num_steps=10 \
     logger.tensorboard_enabled=true \
     logger.log_dir=$LOG_DIR \
     logger.wandb_enabled=false \

tests/functional/sft.sh

@@ -16,7 +16,7 @@ mkdir -p $LOG_DIR
 cd $PROJECT_ROOT
 uv run $PROJECT_ROOT/examples/run_sft.py \
     cluster.gpus_per_node=2 \
-    sft.num_steps=10 \
+    sft.max_num_steps=10 \
     logger.tensorboard_enabled=true \
     logger.log_dir=$LOG_DIR \
     logger.wandb_enabled=false \