Add optional r1-style thinking reward

docs/algorithms/grpo.md

@@ -0,0 +1,21 @@
+# Grouped Relative Policy Optimization (GRPO)
+
+GRPO is an online RL method used in [DeepSeek R1 paper](https://arxiv.org/abs/2501.12948) and its first appearance is in [DeepSeekMath](https://arxiv.org/abs/2402.03300)
+
+`open_instruct/grpo_vllm_thread_ray_gtrl.py` contains an implementation of GRPO.
+
+
+## Get started
+
+
+Here is a command to run GRPO on the Llama3.1-8b on [ai2-adapt-dev/rlvr_gsm8k_zs](https://huggingface.co/datasets/ai2-adapt-dev/rlvr_gsm8k_zs), which is simply a zero-shot version of the RLVR GSM8K dataset.
+
+
+```bash
+bash scripts/train/rlvr/grpo_llama3.1-8b.sh
+```
+
+The results look quite reasonable: with format score, score all going up, KL not exploding, and sequence length seems stable (at least at first)
+
+
+![alt text](grpo_8b.png)

open_instruct/dataset_transformation.py

@@ -182,6 +182,24 @@ def visualize_token(tokens: list[int], tokenizer: PreTrainedTokenizer):
         "{% endif %}"
         "{% endfor %}"
     ),
+    "r1_simple_chat_postpend_think": (
+        "A conversation between User and Assistant. "
+        "The user asks a question, and the Assistant solves it. "
+        "The assistant first thinks about the reasoning process in "
+        "the mind and then provides the user with the answer. "
+        "The reasoning process and answer are enclosed within <think> </think> "
+        "and <answer> </answer> tags, respectively, "
+        "i.e., <think> reasoning process here </think> "
+        "<answer> answer here </answer>."
+        "\n\n"
+        "{% for message in messages %}"
+        "{{ '\n\n' if not loop.first else '' }}"
+        "{{ message['role'].capitalize() + ': ' + message['content'] + '\n' }}"
+        "{% if loop.last and add_generation_prompt %}"
+        "{{ 'Assistant: <think>' }}"
+        "{% endif %}"
+        "{% endfor %}"
+    ),
 }
 # flake8: noqa
 

open_instruct/ground_truth_utils.py

@@ -1,12 +1,21 @@
-'''
+"""
 Collection of 'ground truth rewards' for different datasets/tasks.
 Used to give feedback to the model based on the ground truth answer.
-'''
-import re
+"""
+
 import json
+import re
 import string
-from open_instruct.math_utils import last_boxed_only_string, remove_boxed, get_unnormalized_answer, normalize_final_answer, is_equiv, hendrycks_is_equiv
+
 from open_instruct.if_functions import IF_FUNCTIONS_MAP
+from open_instruct.math_utils import (
+    get_unnormalized_answer,
+    hendrycks_is_equiv,
+    is_equiv,
+    last_boxed_only_string,
+    normalize_final_answer,
+    remove_boxed,
+)
 
 
 def verify_gsm8k_sample(model_output, ground_truth_answer):
@@ -138,11 +147,19 @@ def verify_flan_sample(model_output, ground_truth_answer):
     return normalize_answer(answer_string) == normalize_answer(ground_truth_answer)
 
 
+def soft_format_reward_func(responses: list[str], reward_scale: float = 1.0) -> list[float]:
+    """Reward function that checks if the completion has a specific format."""
+    pattern = r".*?</think>\s*<answer>.*?</answer>"
+    matches = [re.match(pattern, r, re.DOTALL) for r in responses]
+    return [reward_scale if match else 0.0 for match in matches]
+
+
 # debug code
 if __name__ == "__main__":
     from datasets import load_dataset
+
     ds = load_dataset("ai2-adapt-dev/prompts_with_constraints_for_ground_truth")
     test_model_output = "<|assistant|>\nThe answer is $\\boxed{3.14}$"
-    for sample in ds['train']:
+    for sample in ds["train"]:
         print(sample)
-        verify_ifeval_sample(test_model_output, sample['ground_truth'])
+        verify_ifeval_sample(test_model_output, sample["ground_truth"])

open_instruct/grpo_vllm_thread_ray_gtrl.py

@@ -47,6 +47,7 @@
     TokenizerConfig,
     get_cached_dataset_rlvr,
 )
+from open_instruct.ground_truth_utils import soft_format_reward_func
 
 os.environ["NCCL_CUMEM_ENABLE"] = "0"  # NOQA
 
@@ -234,6 +235,10 @@ class Args:
     """the reward model multiplier, for down/upscaling the reward model output"""
     verification_reward: float = 10.0
     """the reward value for verifiable responses"""
+    add_r1_style_format_reward: bool = False
+    """whether to add the R1 style format reward"""
+    r1_style_format_reward: float = 1.0
+    """the reward value for R1 style format reward"""
 
     # async setting
     async_mode: bool = True
@@ -1058,7 +1063,9 @@ def vllm_generate(
                 sequence_lengths = []
                 if accelerator.is_main_process:
                     g_response_token_ids = response_ids_Q.get()
-                    DUMMY_PAD_TOKEN = 0  # we can't use tokenizer.pad_token_id because it's outside vocab and `torch.gather(all_logprob, 2, response.unsqueeze(-1))` will error out
+                    DUMMY_PAD_TOKEN = (
+                        args.stop_token_id
+                    )  # we can't use tokenizer.pad_token_id because it's outside vocab and `torch.gather(all_logprob, 2, response.unsqueeze(-1))` will error out
                     g_padded_response_ids = [
                         response + [DUMMY_PAD_TOKEN] * (args.response_length - len(response))
                         for response in g_response_token_ids
@@ -1071,6 +1078,12 @@ def vllm_generate(
                 ]
                 # print(f"{local_vllm_responses.shape=}, {local_vllm_responses=}")
                 query_responses = torch.cat((queries, local_vllm_responses), 1)
+
+                if args.add_r1_style_format_reward:
+                    decoded_response = tokenizer.batch_decode(local_vllm_responses)
+                    format_scores = torch.tensor(
+                        soft_format_reward_func(decoded_response, args.r1_style_format_reward), device=device
+                    )
                 for i in range(0, queries.shape[0], args.local_rollout_forward_batch_size):
                     # print(f"get reward stuff starts {i=}")
                     query = queries[i : i + args.local_rollout_forward_batch_size]
@@ -1122,6 +1135,9 @@ def vllm_generate(
                     else:
                         verifiable_count = torch.tensor([0.0], device=device).float()
 
+                    if args.add_r1_style_format_reward:
+                        score += format_scores[i : i + args.local_rollout_forward_batch_size]
+
                     responses.append(response)
                     postprocessed_responses.append(postprocessed_response)
                     logprobs.append(logprob)
@@ -1140,6 +1156,10 @@ def vllm_generate(
                 verifiable_counts = torch.cat(verifiable_counts, 0)
                 verifiable_correct_rate = verifiable_counts.sum() / queries.shape[0]
                 # print(f"get reward stuff finished")
+                if self.rank == 0:
+                    print(f"{sequence_lengths=}")
+                    print(f"{postprocessed_responses[0]=}")
+                    print(f"{tokenizer.decode(postprocessed_responses[0])=}")
                 del (logprob, ref_logprob, score)
                 gc.collect()
                 torch.cuda.empty_cache()
@@ -1294,6 +1314,8 @@ def vllm_generate(
                 local_metrics.add("val/ratio", ratio_stats.mean())
                 local_metrics.add("val/ratio_var", ratio_stats.var())
                 local_metrics.add("val/stop_token_rate", contain_stop_token.float().mean())
+                if args.add_r1_style_format_reward:
+                    local_metrics.add("val/format_scores", format_scores.float().mean())
 
                 metrics = {
                     "episode": episode,

scripts/train/rlvr/grpo_llama3.1-8b.sh

@@ -0,0 +1,40 @@
+python open_instruct/grpo_vllm_thread_ray_gtrl.py \
+    --exp_name $exp_name \
+    --output_dir /weka/oe-adapt-default/costah/models/$exp_name \
+    --dataset_mixer_list ai2-adapt-dev/rlvr_gsm8k_zs 1.0 \
+    --dataset_mixer_list_splits train \
+    --dataset_mixer_eval_list ai2-adapt-dev/rlvr_gsm8k_zs 1.0 \
+    --dataset_mixer_eval_list_splits train \
+    --max_token_length 2048 \
+    --max_prompt_token_length 2048 \
+    --response_length 2048 \
+    --number_samples_per_prompt 4 \
+    --model_name_or_path meta-llama/Llama-3.1-8B \
+    --stop_strings '"</answer>"' \
+    --add_r1_style_format_reward \
+    --non_stop_penalty False \
+    --stop_token eos \
+    --penalty_reward_value 0.0 \
+    --temperature 0.7 \
+    --ground_truths_key ground_truth \
+    --chat_template_name r1_simple_chat_postpend_think \
+    --sft_messages_key messages \
+    --learning_rate 3e-7 \
+    --total_episodes 1000000 \
+    --deepspeed_stage 3 \
+    --per_device_train_batch_size 1 \
+    --local_rollout_forward_batch_size 1 \
+    --local_mini_batch_size 16 \
+    --local_rollout_batch_size 16 \
+    --num_epochs 1 \
+    --actor_num_gpus_per_node 6 \
+    --vllm_tensor_parallel_size 2 \
+    --beta 0.01 \
+    --apply_verifiable_reward true \
+    --seed 3 \
+    --num_evals 100 \
+    --save_freq 100 \
+    --reward_model_multiplier 0.0 \
+    --no_try_launch_beaker_eval_jobs \
+    --gradient_checkpointing \
+    --with_tracking