RL Test Script

torchtitan/grpo/test/gsm8k_server.py

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+import random
+import time
+from typing import Dict, List, Optional, Tuple, TypedDict, Union
+
+from datasets import load_dataset
+from latex2sympy2_extended import NormalizationConfig
+from math_verify import LatexExtractionConfig, parse, verify
+from tqdm.asyncio import tqdm_asyncio
+
+from atroposlib.envs.base import (
+    APIServerConfig,
+    BaseEnv,
+    BaseEnvConfig,
+    ScoredDataGroup,
+)
+from atroposlib.type_definitions import Item
+
+system_prompt = (
+    "You are a deep thinking AI, you may use extremely long chains of thought "
+    "to deeply consider the problem and deliberate with yourself via systematic "
+    "reasoning processes to help come to a correct solution prior to answering. "
+    "You should enclose your thoughts and internal monologue inside <think> </think> "
+    "tags, and then provide your solution or response to the problem.\n\n"
+)
+
+system_prompt += """You are allocated a maximum of 2048 tokens, please strive to use less.
+
+You will then provide your answer like this: \\boxed{your answer here}
+It is important that you provide your answer in the correct format.
+If you do not, you will not receive credit for your answer.
+So please end your answer with \\boxed{your answer here}"""
+
+
+class GSM8kRow(TypedDict):
+    question: str
+    answer: str
+
+
+class GSM8kEnv(BaseEnv):
+
+    name = "gsm8k"
+
+    def __init__(
+        self,
+        config: BaseEnvConfig,
+        server_configs: List[APIServerConfig],
+        slurm=True,
+        testing=False,
+    ):
+        super().__init__(config, server_configs, slurm, testing)
+        print(f"DEBUG: GSM8kEnv initialized with {len(self.server.servers)} servers")
+        for i, server in enumerate(self.server.servers):
+            if hasattr(server, 'config'):
+                print(f"DEBUG: Server {i}: {server.config.base_url}")
+        self.percent_correct_buffer = list()
+        self.eval_metrics = list()
+        # Add tracking for wandb visualizations
+        self.rollouts_for_wandb = []
+        self.completion_lengths = []
+
+    @classmethod
+    def config_init(cls) -> Tuple[BaseEnvConfig, List[APIServerConfig]]:
+        env_config = BaseEnvConfig(
+            tokenizer_name="Qwen/Qwen2.5-7B",
+            group_size=8,
+            use_wandb=True,
+            rollout_server_url="http://localhost:8000",
+            total_steps=1000,
+            batch_size=12,
+            steps_per_eval=100,
+            max_token_length=2048,
+            wandb_name="gsm8k_qwen3_test",
+        )
+        server_configs = [
+            APIServerConfig(
+                model_name="Qwen/Qwen2.5-7B",
+                base_url="http://localhost:9001/v1",
+                api_key="x",
+                num_requests_for_eval=256,
+            ),
+        ]
+
+        return env_config, server_configs
+
+    async def wandb_log(self, wandb_metrics: Optional[Dict] = None):
+        if wandb_metrics is None:
+            wandb_metrics = {}
+
+        # Try to calculate percent_correct, pass if there's a division by zero
+        try:
+            wandb_metrics["train/percent_correct"] = sum(
+                self.percent_correct_buffer
+            ) / len(self.percent_correct_buffer)
+        except ZeroDivisionError:
+            # Skip if buffer is empty
+            pass
+
+        self.percent_correct_buffer = list()
+        for item in self.eval_metrics:
+            wandb_metrics[item[0]] = item[1]
+        self.eval_metrics = list()
+        # Call the parent method to handle the server metrics
+        await super().wandb_log(wandb_metrics)
+
+    async def setup(self):
+        self.train = load_dataset("gsm8k", "main", split="train").shuffle(seed=42)
+        test_data = load_dataset("gsm8k", "main", split="test").shuffle(seed=42)
+        self.test = list()
+        for item in test_data:
+            self.test.append(
+                {
+                    "question": item["question"],
+                    "gold_answer": item["answer"]
+                    .split("#")[-1]
+                    .strip()
+                    .replace(",", ""),
+                }
+            )
+        self.iter = 0
+
+    def save_checkpoint(self, step, data=None):
+        if data is None:
+            data = {}
+        data["iter"] = self.iter
+        super().save_checkpoint(step, data)
+
+    async def rollout_and_score_eval(self, question: str, answer: str) -> dict:
+        """Rollout and score evaluation with detailed sample data collection."""
+
+        async with self.server.managed_server(tokenizer=self.tokenizer) as managed:
+            completion = await managed.chat_completion(
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": question},
+                ],
+                n=1,
+                max_tokens=self.config.max_token_length,
+                temperature=0.6,
+            )
+
+            response_content = completion.choices[0].message.content
+
+        # Parse gold answer
+        gold_parsed = parse(
+            "\\boxed{" + answer + "}",
+            extraction_mode="first_match",
+            extraction_config=[LatexExtractionConfig()],
+        )
+
+        # Parse model answer
+        answer_parsed = parse(
+            response_content.split("</think>")[-1],
+            extraction_config=[
+                LatexExtractionConfig(
+                    normalization_config=NormalizationConfig(
+                        nits=False,
+                        malformed_operators=False,
+                        basic_latex=True,
+                        equations=True,
+                        boxed="all",
+                        units=True,
+                    ),
+                    boxed_match_priority=0,
+                    try_extract_without_anchor=False,
+                )
+            ],
+            extraction_mode="first_match",
+        )
+
+        score = 1 if verify(answer_parsed, gold_parsed) else 0
+
+        sample = {
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": question},
+                {"role": "assistant", "content": response_content},
+            ],
+            "question": question,
+            "gold_answer": answer,
+            "gold_parsed": str(gold_parsed) if gold_parsed else None,
+            "model_parsed": str(answer_parsed) if answer_parsed else None,
+            "score": int(score),
+            "correct": bool(score),
+            "finish_reason": completion.choices[0].finish_reason,
+            "response_after_think": (
+                response_content.split("</think>")[-1]
+                if "</think>" in response_content
+                else response_content
+            ),
+        }
+
+        return {"score": score, "sample": sample}
+
+    async def evaluate(self, *args, **kwargs):
+        start_time = time.time()
+
+        eval_tasks = []
+        for item in self.test:
+            eval_tasks.append(
+                self.rollout_and_score_eval(item["question"], item["gold_answer"])
+            )
+        results = await tqdm_asyncio.gather(*eval_tasks)
+
+        # Extract scores and samples
+        scores = [result["score"] for result in results]
+        samples = [result["sample"] for result in results]
+
+        percent_correct = sum(scores) / len(scores)
+
+        end_time = time.time()
+
+        # Add to existing metrics for wandb
+        self.eval_metrics.append(("eval/percent_correct", percent_correct))
+
+        # Log evaluation results
+        eval_metrics = {
+            "eval/percent_correct": percent_correct,
+        }
+
+        await self.evaluate_log(
+            metrics=eval_metrics,
+            samples=samples,
+            start_time=start_time,
+            end_time=end_time,
+            generation_parameters={
+                "temperature": 0.0,
+                "max_tokens": self.config.max_token_length,
+            },
+        )
+
+    async def collect_trajectories(
+        self, item: GSM8kRow
+    ) -> Tuple[ScoredDataGroup, list[Item]]:
+        print(f"DEBUG: collect_trajectories() called for question: {item['question'][:80]}...")
+        try:
+            user_message = {"role": "user", "content": item["question"]}
+            gold_answer = (
+                "\\boxed{" + item["answer"].split("#")[-1].strip().replace(",", "") + "}"
+            )
+
+            print(f"DEBUG: About to call managed_server...")
+            async with self.server.managed_server(tokenizer=self.tokenizer) as managed:
+                print(f"DEBUG: Inside managed_server context, about to call chat_completion...")
+
+                chat_completions = await managed.chat_completion(
+                    messages=[{"role": "system", "content": system_prompt}, user_message],
+                    n=self.config.group_size,
+                    max_tokens=self.config.max_token_length,
+                    temperature=1.0,
+                )
+                print(f"DEBUG: chat_completion returned, got {len(chat_completions.choices)} completions")
+
+                state = managed.get_state()
+                nodes = state["nodes"]
+                print(f"DEBUG: Got state with {len(nodes)} nodes")
+        except Exception as e:
+            print(f"ERROR in collect_trajectories: {type(e).__name__}: {e}")
+            import traceback
+            traceback.print_exc()
+            return None, []
+
+        to_score = list()
+        to_backlog = list()
+        for i, chat_completion in enumerate(chat_completions.choices):
+            messages = (
+                {"role": "system", "content": system_prompt},
+                user_message,
+                {"role": "assistant", "content": chat_completion.message.content},
+            )
+            to_score.append(
+                {
+                    "messages": messages,
+                    "gold_answer": gold_answer,
+                    "finish_reason": chat_completion.finish_reason,
+                    "tokens": nodes[i].tokens,
+                    "masks": nodes[i].masked_tokens,
+                    "logprobs": nodes[i].logprobs,
+                }
+            )
+        to_postprocess = await self.score(to_score)
+        return to_postprocess, to_backlog
+
+    async def score(
+        self, rollout_group_data
+    ) -> Union[Optional[ScoredDataGroup], List[Optional[ScoredDataGroup]]]:
+        print(f"DEBUG: score() called with {len(rollout_group_data)} rollouts")
+        scores = ScoredDataGroup()
+        scores["tokens"] = list()
+        scores["masks"] = list()
+        scores["scores"] = list()
+        scores["inference_logprobs"] = list()
+        gold_parsed = parse(
+            rollout_group_data[0]["gold_answer"],
+            extraction_mode="first_match",
+            extraction_config=[LatexExtractionConfig()],
+        )
+        print(f"DEBUG: Gold answer parsed: {len(gold_parsed)} elements")
+        if len(gold_parsed) != 0:
+            # We require the answer to be provided in correct latex (no malformed operators)
+            random.shuffle(rollout_group_data)
+            for item in rollout_group_data:
+                # print(item[0][-1]["content"])
+                answer_parsed = parse(
+                    item["messages"][-1]["content"].split("</think>")[-1],
+                    extraction_config=[
+                        LatexExtractionConfig(
+                            normalization_config=NormalizationConfig(
+                                nits=False,
+                                malformed_operators=False,
+                                basic_latex=True,
+                                equations=True,
+                                boxed="all",
+                                units=True,
+                            ),
+                            # Ensures that boxed is tried first
+                            boxed_match_priority=0,
+                            try_extract_without_anchor=False,
+                        )
+                    ],
+                    extraction_mode="first_match",
+                )
+                # Reward 1 if the content is the same as the ground truth, 0 otherwise
+                reward = verify(answer_parsed, gold_parsed)
+
+                tokens = item["tokens"]
+                masks = item["masks"]
+                logprobs = item["logprobs"]
+
+                # remove obviously bad examples
+                num_valid_tokens = len([1 for i in masks if i != -100])
+                if num_valid_tokens < 5:  # Lowered from 10 to 5 to be less strict
+                    print(f"Filtering out sample with only {num_valid_tokens} valid tokens")
+                    continue
+                scores["tokens"].append(tokens)
+                scores["masks"].append(masks)
+                scores["inference_logprobs"].append(logprobs)
+                scores["scores"].append(1.0 if reward else -1.0)
+
+                if len(scores["tokens"]) >= self.config.group_size:
+                    break
+
+            # Check if we have enough valid samples after filtering
+            if len(scores["tokens"]) < self.config.group_size:
+                print(f"Warning: Only got {len(scores['tokens'])} samples after filtering, need {self.config.group_size}")
+                return None
+
+            for score in scores["scores"]:
+                self.percent_correct_buffer.append(max(score, 0))
+
+            # check if all the same
+            # print(scores['scores'])
+            if all([score == 1 for score in scores["scores"]]):
+                # Do length penalty :)
+                token_lengths = [len(token) for token in scores["tokens"]]
+                if max(token_lengths) == 0:
+                    # What? But don't want to crash a run so just in case...
+                    return None
+
+                # Get max allowed token length from config
+                max_allowed_length = self.config.max_token_length
+                # Set threshold at 50% of max_token_length - no penalty below this
+                length_threshold = max_allowed_length * 0.5
+
+                # Apply modified length penalty with threshold
+                scores["scores"] = []
+                for length in token_lengths:
+                    if length <= length_threshold:
+                        # No penalty for responses under threshold
+                        scores["scores"].append(1.0)
+                    else:
+                        # Calculate how far we are between threshold and max as a percentage
+                        percentage_of_range = (length - length_threshold) / (
+                            max_allowed_length - length_threshold
+                        )
+                        # Cap at 1.0 in case length exceeds max_allowed_length
+                        percentage_of_range = min(percentage_of_range, 1.0)
+                        # Apply linear penalty scaling from 1.0 down to 0.0
+                        scores["scores"].append(1.0 - percentage_of_range)
+            # allow training even when all scores are identical
+            # if all([scores["scores"][0] == score for score in scores["scores"]]):
+            #     return None  # If all the same, we return None
+            print(f"DEBUG: Returning scores with {len(scores['tokens'])} samples, scores: {scores['scores']}")
+            return scores
+        else:
+            # If the gold solution is not parseable, we return None
+            print("DEBUG: Gold solution not parseable, returning None")
+            return None
+
+    async def get_next_item(self) -> GSM8kRow:
+        next_item = self.train[self.iter % len(self.train)]
+        self.iter += 1
+        return next_item
+
+
+if __name__ == "__main__":
+    GSM8kEnv.cli()