Manually duplicate both tool and non-tool requests.

open_instruct/dataset_transformation.py

@@ -763,7 +763,6 @@ def get_tokenizer_tulu_v2_2(tc: "TokenizerConfig"):
 DEFAULT_SFT_MESSAGES_KEY = "messages"
 GROUND_TRUTHS_KEY = "ground_truth"
 VERIFIER_SOURCE_KEY = "dataset"
-RAW_PROMPT_KEY = "prompt"
 
 
 @dataclass
@@ -815,14 +814,8 @@ def tokenizer(self):
 ATTENTION_MASK_KEY = "attention_mask"
 LABELS_KEY = "labels"
 DATASET_ORIGIN_KEY = "dataset_source"  # just 'dataset' clashes with RLVR stuff (see VERIFIER_SOURCE_KEY)
-TOKENIZED_SFT_DATASET_KEYS = [INPUT_IDS_KEY, ATTENTION_MASK_KEY, LABELS_KEY, RAW_PROMPT_KEY]
-TOKENIZED_SFT_DATASET_KEYS_WITH_SOURCE = [
-    INPUT_IDS_KEY,
-    ATTENTION_MASK_KEY,
-    LABELS_KEY,
-    DATASET_ORIGIN_KEY,
-    RAW_PROMPT_KEY,
-]
+TOKENIZED_SFT_DATASET_KEYS = [INPUT_IDS_KEY, ATTENTION_MASK_KEY, LABELS_KEY]
+TOKENIZED_SFT_DATASET_KEYS_WITH_SOURCE = [INPUT_IDS_KEY, ATTENTION_MASK_KEY, LABELS_KEY, DATASET_ORIGIN_KEY]
 
 
 def remove_dataset_source_field(dataset: Dataset) -> Dataset:
@@ -1192,8 +1185,6 @@ def rlvr_tokenize_v1(
     row[LABELS_KEY] = labels
     row[GROUND_TRUTHS_KEY] = row[ground_truths_key]
     row[VERIFIER_SOURCE_KEY] = row[verifier_source_key]
-    # concatenate all the previous messages as <role>: <content>\n <role>: <content>\n ...
-    row[RAW_PROMPT_KEY] = "\n".join(f"{msg['role']}: {msg['content']}" for msg in prompt)
     return row
 
 
@@ -1221,10 +1212,6 @@ def rlvr_tokenize_v2(
     row[LABELS_KEY] = labels
     row[GROUND_TRUTHS_KEY] = row[ground_truths_key]
     row[VERIFIER_SOURCE_KEY] = row[verifier_source_key]
-    # concatenate all the previous messages as <role>: <content>\n <role>: <content>\n ...
-    # row[DEFAULT_SFT_MESSAGES_KEY] = prompt
-    # concatenate all the previous messages as <role>: <content>\n <role>: <content>\n ...
-    row[RAW_PROMPT_KEY] = "\n".join(f"{msg['role']}: {msg['content']}" for msg in prompt)
     # some basic transformations:
     # if ground truths is a string, make it a list
     if isinstance(row[ground_truths_key], str):
@@ -1686,6 +1673,7 @@ def get_cached_dataset_tulu_with_statistics(
                 frac_or_num_samples = float(frac_or_num_samples)
             else:
                 frac_or_num_samples = int(frac_or_num_samples)
+
             dataset_config = DatasetConfig(
                 dataset_name=dataset_name,
                 dataset_split=dataset_mixer_list_splits[i],

open_instruct/ground_truth_utils.py

@@ -38,14 +38,6 @@
 
 logger = logger_utils.setup_logger(__name__)
 
-# remove excessive logging from liteLLM
-logging.getLogger("LiteLLM").setLevel(logging.WARNING)
-logging.getLogger("litellm").setLevel(logging.ERROR)
-logging.getLogger("litellm.cost_calculator").setLevel(logging.CRITICAL)
-logging.getLogger("litellm._client").setLevel(logging.CRITICAL)
-logging.getLogger("cost_calculator").setLevel(logging.WARNING)
-logging.getLogger("httpx").setLevel(logging.WARNING)
-
 
 @dataclass
 class VerifierConfig:
@@ -671,7 +663,8 @@ async def async_call(
         for attempt in range(max_retries):
             # judges the quality of a response
             try:
-                messages = build_messages(prompt)
+                system_prompt = "Do not generate text between the <think> and </think> tags."  # "You are a concise assistant who gives very short explanations before giving a quality score."
+                messages = build_messages(prompt, system_prompt)
 
                 # Faeze: check if the request would exceed context window
                 # Import the context window checker

open_instruct/grpo_fast.py

@@ -87,7 +87,6 @@
 from open_instruct.dataset_transformation import (
     GROUND_TRUTHS_KEY,
     INPUT_IDS_PROMPT_KEY,
-    RAW_PROMPT_KEY,
     VERIFIER_SOURCE_KEY,
     TokenizerConfig,
     get_cached_dataset_tulu,
@@ -120,6 +119,7 @@
     calibrate_checkpoint_state_dir,
     clean_last_n_checkpoints_deepspeed,
     download_latest_checkpoint_from_gs,
+    extract_user_query,
     get_beaker_whoami,
     get_eval_ds_config,
     get_optimizer_grouped_parameters,
@@ -487,7 +487,6 @@ def next_batch(dataset_indices: List[int], dataset: datasets.Dataset) -> Batch:
         queries=data_next[INPUT_IDS_PROMPT_KEY],
         ground_truths=data_next[GROUND_TRUTHS_KEY],
         datasets=data_next[VERIFIER_SOURCE_KEY],
-        raw_queries=data_next[RAW_PROMPT_KEY],
         indices=dataset_indices,
     )
 
@@ -1280,63 +1279,45 @@ def __init__(self):
         self._map = {}  # dataset_idx -> (query, ground_truth, dataset, count)
         self._lock = threading.Lock()
 
-    def insert(self, dataset_idx, query, ground_truth, dataset, raw_query):
+    def insert(self, dataset_idx, query, ground_truth, dataset):
         """Insert or increment count for a dataset index."""
         with self._lock:
             if dataset_idx in self._map:
                 # Already exists - just increment count
-                existing_query, existing_ground_truth, existing_dataset, existing_raw_query, count = self._map[
-                    dataset_idx
-                ]
-                self._map[dataset_idx] = (
-                    existing_query,
-                    existing_ground_truth,
-                    existing_dataset,
-                    existing_raw_query,
-                    count + 1,
-                )
+                existing_query, existing_ground_truth, existing_dataset, count = self._map[dataset_idx]
+                self._map[dataset_idx] = (existing_query, existing_ground_truth, existing_dataset, count + 1)
             else:
                 # New entry - count starts at 1
-                self._map[dataset_idx] = (query, ground_truth, dataset, raw_query, 1)
+                self._map[dataset_idx] = (query, ground_truth, dataset, 1)
 
-    def insert_many(self, dataset_indices, queries, ground_truths, datasets, raw_queries):
+    def insert_many(self, dataset_indices, queries, ground_truths, datasets):
         """Insert or increment count for multiple dataset indices at once."""
         with self._lock:
             for i, dataset_idx in enumerate(dataset_indices):
-                current_raw_query = raw_queries[i]
-
                 if dataset_idx in self._map:
                     # Already exists - just increment count
-                    existing_query, existing_ground_truth, existing_dataset, existing_raw_query, count = self._map[
-                        dataset_idx
-                    ]
-                    self._map[dataset_idx] = (
-                        existing_query,
-                        existing_ground_truth,
-                        existing_dataset,
-                        existing_raw_query,
-                        count + 1,
-                    )
+                    existing_query, existing_ground_truth, existing_dataset, count = self._map[dataset_idx]
+                    self._map[dataset_idx] = (existing_query, existing_ground_truth, existing_dataset, count + 1)
                 else:
                     # New entry - count starts at 1
-                    self._map[dataset_idx] = (queries[i], ground_truths[i], datasets[i], current_raw_query, 1)
+                    self._map[dataset_idx] = (queries[i], ground_truths[i], datasets[i], 1)
 
     def pop(self, dataset_idx):
         """Retrieve data and decrement count. Removes entry when count reaches 0."""
         with self._lock:
             if dataset_idx not in self._map:
                 raise RuntimeError(f"Dataset index {dataset_idx} not found in pending_queries_map")
 
-            query, ground_truth, dataset, raw_query, count = self._map[dataset_idx]
+            query, ground_truth, dataset, count = self._map[dataset_idx]
 
             if count > 1:
                 # More results expected - just decrement
-                self._map[dataset_idx] = (query, ground_truth, dataset, raw_query, count - 1)
+                self._map[dataset_idx] = (query, ground_truth, dataset, count - 1)
             else:
                 # Last result - remove entry
                 del self._map[dataset_idx]
 
-            return query, ground_truth, dataset, raw_query
+            return query, ground_truth, dataset
 
     def __len__(self):
         """Return the number of entries in the map."""
@@ -1389,7 +1370,6 @@ def accumulate_inference_batches(
     all_queries = []
     all_ground_truths = []
     all_datasets = []
-    all_raw_queries = []
     for i in tqdm(
         range(args.vllm_num_engines),
         total=args.vllm_num_engines,
@@ -1421,20 +1401,17 @@ def accumulate_inference_batches(
         batch_queries = []
         batch_ground_truths = []
         batch_datasets = []
-        batch_raw_queries = []
 
         for dataset_idx in dataset_indices:
-            query, ground_truth, dataset, raw_query = pending_queries_map.pop(dataset_idx)
+            query, ground_truth, dataset = pending_queries_map.pop(dataset_idx)
             batch_queries.append(query)
             batch_ground_truths.append(ground_truth)
             batch_datasets.append(dataset)
-            batch_raw_queries.append(raw_query)
 
         results.append(result)
         all_queries.extend(batch_queries)
         all_ground_truths.extend(batch_ground_truths)
         all_datasets.extend(batch_datasets)
-        all_raw_queries.extend(batch_raw_queries)
 
     # Combine all results into a single GenerationResult
     combined_responses = []
@@ -1496,7 +1473,6 @@ def accumulate_inference_batches(
         queries=all_queries,
         ground_truths=all_ground_truths,
         datasets=all_datasets,
-        raw_queries=all_raw_queries,
         indices=None,  # Not meaningful for combined results
     )
     return combined_result, batch
@@ -1533,7 +1509,6 @@ def data_preparation_thread(
                 queries=repeat_each(batch.queries, args.num_samples_per_prompt_rollout),
                 ground_truths=repeat_each(batch.ground_truths, args.num_samples_per_prompt_rollout),
                 datasets=repeat_each(batch.datasets, args.num_samples_per_prompt_rollout),
-                raw_queries=repeat_each(batch.raw_queries, args.num_samples_per_prompt_rollout),
                 indices=repeat_each(batch.indices, args.num_samples_per_prompt_rollout) if batch.indices else None,
             )
             good_outputs = [
@@ -1555,7 +1530,8 @@ def data_preparation_thread(
 
         with Timer("🔥 [Data Preparation Thread] Decoding responses", noop=True):
             decoded_responses = tokenizer.batch_decode(result.responses, skip_special_tokens=True)
-            decoded_queries = batch.raw_queries
+            decoded_queries = tokenizer.batch_decode(batch.queries, skip_special_tokens=True)
+            decoded_queries = [extract_user_query(query) for query in decoded_queries]
             stop_rate = sum(int(finish_reason == "stop") for finish_reason in result.finish_reasons) / len(
                 result.finish_reasons
             )
@@ -2071,7 +2047,7 @@ def split_and_insert_batch(
 
         # Store prompts in the map using thread-safe insert_many
         pending_queries_map.insert_many(
-            sub_batch.indices, sub_batch.queries, sub_batch.ground_truths, sub_batch.datasets, sub_batch.raw_queries
+            sub_batch.indices, sub_batch.queries, sub_batch.ground_truths, sub_batch.datasets
         )
 
         # Use PromptRequest for Ray queue with batch-specific dataset_index list
@@ -2883,6 +2859,8 @@ def main(args: Args, tc: TokenizerConfig, model_config: ModelConfig, num_eval_sa
             actor_manager,
             checkpoint_state,
         )
+    except Exception as e:
+        logger.error(f"Error in run_training: {e}", exc_info=True)
     finally:
         cleanup_training_resources(
             stop_event, executor, [inference_results_Q, param_prompt_Q, evaluation_inference_results_Q], actor_manager

open_instruct/judge_utils.py

@@ -31,20 +31,19 @@
 AI assistant to the user query displayed below.
 
 Notes:
-- Your evaluation should consider factors such as the helpfulness, relevance, accuracy, creativity, appropriate level of detail, and how well the response satisfies the user's explicit constraints or accurately follows their instructions.
-- If there is a system prompt, ensure the AI answer prioritizes following it.
-- Begin your evaluation by providing a short explanation.
-- Be as objective as possible. After providing your short explanation, please output a score on a scale of 1 to 10.
-- Please adhere to the following format.
+1- Your evaluation should consider factors such as the helpfulness, relevance, accuracy, depth, creativity, and level of detail of the response.
+2- Begin your evaluation by providing a short explanation.
+3- Be as objective as possible. After providing your explanation, please rate the response on a scale of 1 to 10.
 
-[Conversation History]
+[Query]
 {input}
 
-[AI Answer]
+[Response]
 {output}
 
 [Your judgement]
-Respond in JSON format. {{"REASONING": "[...]", "SCORE": "<your-score>"}}"""
+Respond in JSON format. {{"REASONING": "[...]", "SCORE": "<your-score>"}}
+"""
 
 
 general_quality_rubric_template = """
@@ -77,18 +76,16 @@
 general_quality_ref_template = """
 ### Task Description
 Please act as an impartial judge and evaluate the quality of the answer provided by an
-AI assistant to the conversation history leading up to the answer displayed below.
-Judge whether the provided answer is good by comparing it to the reference answer.
+AI assistant to the user query displayed below. Judge whether the provided answer is good by comparing it to the reference answer.
 
 Notes:
-- Besides comparing to the reference answer, your evaluation should consider factors such as the helpfulness, relevance, accuracy, creativity, appropriate level of detail, and how well the response satisfies the user's explicit constraints or accurately follows their instructions.
+- Besides comparing to the referennce answer, your evaluation should consider factors such as the helpfulness, relevance, accuracy, depth, creativity, and appropriate level of detail of the response.
 - Note that sometimes the reference answer is not the only answer. So any valid variation of the reference answer is also acceptable and can get a full score.
-- If there is a system prompt, ensure the AI answer prioritizes following it.
 - Begin your evaluation by providing a short explanation.
 - Be as objective as possible. After providing your short explanation, please output a score on a scale of 1 to 10.
 - Please adhere to the following format.
 
-[Conversation History]
+[Query]
 {input}
 
 [AI Answer]

open_instruct/model_utils.py

@@ -54,7 +54,6 @@ class Batch:
     queries: List[List[int]]
     ground_truths: List[List[int]]
     datasets: List[str]
-    raw_queries: Optional[List[str]]
     indices: Optional[List[int]]
 
     def __getitem__(self, key: Union[slice, int, List[int]]) -> "Batch":
@@ -65,7 +64,6 @@ def __getitem__(self, key: Union[slice, int, List[int]]) -> "Batch":
                 queries=self.queries[key],
                 ground_truths=self.ground_truths[key],
                 datasets=self.datasets[key],
-                raw_queries=self.raw_queries[key],
                 indices=self.indices[key] if self.indices else None,
             )
         elif isinstance(key, int):
@@ -74,7 +72,6 @@ def __getitem__(self, key: Union[slice, int, List[int]]) -> "Batch":
                 queries=[self.queries[key]],
                 ground_truths=[self.ground_truths[key]],
                 datasets=[self.datasets[key]],
-                raw_queries=[self.raw_queries[key]],
                 indices=[self.indices[key]] if self.indices else None,
             )
         else:
@@ -83,7 +80,6 @@ def __getitem__(self, key: Union[slice, int, List[int]]) -> "Batch":
                 queries=[self.queries[i] for i in key],
                 ground_truths=[self.ground_truths[i] for i in key],
                 datasets=[self.datasets[i] for i in key],
-                raw_queries=[self.raw_queries[i] for i in key],
                 indices=[self.indices[i] for i in key] if self.indices else None,
             )