Add GRPO and Support RLVR for PPO (#6186)

* add grpo, support rlvr

* add grpo, support rlvr

* tested deepseek r1 pipeline

* add ci

* verify grpo r1

* verify grpo r1

* update readme, remove unused code

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* remove path

* clean code

* fix circular import

* fix ci OOM

* fix ci OOM

* skip kto tp, fix qwen generation

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

applications/ColossalChat/coati/dataset/tokenization_utils.py

@@ -147,7 +147,6 @@ def tokenize_prompt(
         ignore_index: the ignore index when calculate loss during training
         max_length: the maximum context length
     """
-
     messages = data_point["messages"]
     template = deepcopy(conversation_template)
     template.messages = []
@@ -167,7 +166,6 @@ def tokenize_prompt(
     if len(template.messages) % 2 != 1:
         # exclude the answer if provided. keep only the prompt
         template.messages = template.messages[:-1]
-
     # Prepare data
     prompt = template.get_prompt(length=len(template.messages), add_generation_prompt=True)
     tokenized = tokenizer([prompt], add_special_tokens=False)["input_ids"][0]
@@ -185,12 +183,21 @@ def tokenize_prompt(
         )
 
     # `inputs_decode` can be used to check whether the tokenization method is true.
-    return dict(
-        input_ids=tokenized,
-        inputs_decode=prompt,
-        seq_length=len(tokenized),
-        seq_category=data_point["category"] if "category" in data_point else "None",
-    )
+    if "gt_answer" in data_point:
+        return dict(
+            input_ids=tokenized,
+            inputs_decode=prompt,
+            seq_length=len(tokenized),
+            seq_category=data_point["category"] if "category" in data_point else "None",
+            gt_answer=data_point["gt_answer"],
+        )
+    else:
+        return dict(
+            input_ids=tokenized,
+            inputs_decode=prompt,
+            seq_length=len(tokenized),
+            seq_category=data_point["category"] if "category" in data_point else "None",
+        )
 
 
 def apply_rlhf_data_format(template: Conversation, tokenizer: Any):