feat: evaluation implement

docs/guides/eval.md

@@ -0,0 +1,33 @@
+# Evaluation
+
+## Start Evaluation
+
+### Start Script
+```sh
+# To run the evaluation with default config (examples/configs/eval.yaml)
+uv run python examples/run_eval.py
+
+# Specify a custom config file
+uv run python examples/run_eval.py --config path/to/custom_config.yaml
+
+# Override specific config values via command line
+uv run python examples/run_eval.py generation.model_name="Qwen/Qwen2.5-Math-7B-Instruct"
+```
+
+### Example Output
+
+```
+============================================================
+model_name='Qwen2.5-Math-1.5B-Instruct' dataset_name='aime_2024'
+score=0.10 (3.0/30)
+============================================================
+```
+
+## Configuration
+
+An example Evaluation configuration file can be found [here](../../examples/configs/eval.yaml).
+
+### Prompt Template Configuration
+Always remember to use the same `prompt_file` and `system_prompt_file` that were used during training.
+
+For open-source models, we recommend setting `prompt_file=null` and `system_prompt_file=null` to allow them to use their native chat templates.

docs/index.md

@@ -18,6 +18,7 @@ cluster.md
 adding_new_models.md
 guides/sft.md
 guides/grpo.md
+guides/eval.md
 ```
 
 ```{toctree}

examples/configs/eval.yaml

@@ -0,0 +1,30 @@
+# Evaluation Configuration
+generation:
+  backend: "vllm" # only vllm is supported for evaluation
+  max_new_tokens: ${generation.vllm_cfg.max_model_len}
+  temperature: 0.0
+  top_p: 1.0
+  top_k: -1 # disable
+  num_prompts_per_step: -1 # -1 means pass all prompts at once
+  model_name: "Qwen/Qwen2.5-Math-1.5B-Instruct"
+  vllm_cfg:
+    tensor_parallel_size: 1
+    gpu_memory_utilization: 0.9
+    max_model_len: 2048
+
+data:
+  max_input_seq_length: ${generation.vllm_cfg.max_model_len} # useless since we directly use prompts in evaluation
+  prompt_file: null
+  system_prompt_file: null
+  dataset_name: "HuggingFaceH4/aime_2024"
+  dataset_key: "train"
+  problem_key: "problem"
+  solution_key: "answer"
+
+env:
+  math:
+    num_workers: 8
+
+cluster:
+  gpus_per_node: 1
+  num_nodes: 1

examples/run_eval.py

@@ -0,0 +1,145 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import os
+import pprint
+import sys
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from datasets import load_dataset
+from omegaconf import OmegaConf
+from transformers import AutoTokenizer
+
+from examples.run_grpo_math import math_data_processor
+from nemo_reinforcer.data import MathDataConfig
+from nemo_reinforcer.data.datasets import AllTaskProcessedDataset
+from nemo_reinforcer.data.interfaces import TaskDataSpec
+from nemo_reinforcer.data.llm_message_utils import remap_dataset_keys
+from nemo_reinforcer.distributed.virtual_cluster import init_ray
+from nemo_reinforcer.environments.math_environment import MathEnvironment
+from nemo_reinforcer.evals.eval import MasterConfig, run_env_eval, setup
+from nemo_reinforcer.models.generation.interfaces import GenerationConfig
+
+
+def parse_args():
+    """Parse command line arguments."""
+    parser = argparse.ArgumentParser(description="Run Evaluation with configuration")
+    parser.add_argument(
+        "--config", type=str, default=None, help="Path to YAML config file"
+    )
+
+    # Parse known args for the script
+    args, remaining = parser.parse_known_args()
+
+    # Convert remaining args to OmegaConf format
+    overrides = OmegaConf.from_dotlist(remaining)
+
+    return args, overrides
+
+
+def setup_data(
+    data_config: MathDataConfig, generation_config: GenerationConfig, env_configs
+):
+    print("\n▶ Setting up data...")
+    math_task_spec = TaskDataSpec(
+        task_name="math",
+        prompt_file=data_config["prompt_file"],
+        system_prompt_file=data_config["system_prompt_file"],
+    )
+
+    # load dataset
+    base_dataset = load_dataset(data_config["dataset_name"])
+    if data_config["dataset_key"] is not None:
+        base_dataset = base_dataset[data_config["dataset_key"]]
+    # remap problem and solution keys
+    remapped_dataset = remap_dataset_keys(
+        base_dataset,
+        mapping_dict={
+            data_config["problem_key"]: "problem",
+            data_config["solution_key"]: "expected_answer",
+        },
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(generation_config["model_name"])
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+
+    math_env = MathEnvironment.options(
+        runtime_env={"py_executable": MathEnvironment.DEFAULT_PY_EXECUTABLE}
+    ).remote(env_configs["math"])
+
+    dataset = AllTaskProcessedDataset(
+        dataset=remapped_dataset,
+        tokenizer=tokenizer,
+        default_task_data_spec=math_task_spec,
+        task_data_processors=math_data_processor,
+        max_seq_length=data_config["max_input_seq_length"],
+    )
+
+    return dataset, math_env, tokenizer
+
+
+def main():
+    """Main entry point."""
+    # Parse arguments
+    args, overrides = parse_args()
+
+    if not args.config:
+        args.config = os.path.join(os.path.dirname(__file__), "configs", "eval.yaml")
+
+    config = OmegaConf.load(args.config)
+    print(f"Loaded configuration from: {args.config}")
+
+    if overrides:
+        override_conf = OmegaConf.from_cli()
+        print(f"Overrides: {override_conf}")
+        config = OmegaConf.merge(config, override_conf)
+
+    config: MasterConfig = OmegaConf.to_container(config, resolve=True)
+    print("Applied CLI overrides")
+
+    # Print config
+    print("Final config:")
+    pprint.pprint(config)
+
+    # Init ray
+    init_ray()
+
+    # Setup data
+    (
+        dataset,
+        math_env,
+        tokenizer,
+    ) = setup_data(config["data"], config["generation"], config["env"])
+
+    # Setup
+    (
+        vllm_generation,
+        dataloader,
+        master_config,
+    ) = setup(config, tokenizer, dataset)
+
+    # Run evaluation
+    run_env_eval(
+        vllm_generation,
+        dataloader,
+        math_env,
+        master_config,
+    )
+
+
+if __name__ == "__main__":
+    main()

examples/run_grpo_math.py

@@ -122,6 +122,8 @@ def math_data_processor(
 
     template = task_data_spec.custom_template
     message_log: LLMMessageLogType = []
+
+    # system prompt
     if task_data_spec.system_prompt:
         sys_message = {"role": "system", "content": task_data_spec.system_prompt}
         message = tokenizer.apply_chat_template(
@@ -135,10 +137,11 @@ def math_data_processor(
             0
         ]
         message_log.append(sys_message)
-    user_message = {
-        "role": "user",
-        "content": task_data_spec.prompt.format(problem),
-    }
+
+    # user prompt
+    if task_data_spec.prompt:
+        problem = task_data_spec.prompt.format(problem)
+    user_message = {"role": "user", "content": problem}
     message = tokenizer.apply_chat_template(
         [user_message],
         chat_template=template,
@@ -167,8 +170,9 @@ def math_data_processor(
         "extra_env_info": extra_env_info,
         "loss_multiplier": loss_multiplier,
         "idx": idx,
-        "task_name": datum_dict["task_name"],
     }
+    if "task_name" in datum_dict:
+        output["task_name"] = datum_dict["task_name"]
     return output
 
 

nemo_reinforcer/data/__init__.py

@@ -21,3 +21,8 @@ class DataConfig(TypedDict):
     system_prompt_file: Optional[str]
     dataset_name: str
     val_dataset_name: Optional[str]
+
+
+class MathDataConfig(DataConfig):
+    problem_key: str
+    solution_key: str

nemo_reinforcer/data/datasets.py

@@ -130,3 +130,54 @@ def rl_collate_fn(data_batch: List[DatumSpec]) -> BatchedDataDict:
         batch_max_length=batch_max_length,
     )
     return output
+
+
+def eval_collate_fn(data_batch: List[DatumSpec]) -> BatchedDataDict:
+    """Collate function for evaluation.
+
+    Takes a list of data samples and combines them into a single batched dictionary
+    for model evaluation.
+
+    Args:
+        data_batch: List of data samples with message_log, extra_env_info, and idx fields.
+
+    Returns:
+        BatchedDataDict with message_log, extra_env_info, and idx fields.
+
+    Examples:
+    ```{doctest}
+    >>> import torch
+    >>> from nemo_reinforcer.data.datasets import eval_collate_fn
+    >>> from nemo_reinforcer.data.interfaces import DatumSpec
+    >>> data_batch = [
+    ...     DatumSpec(
+    ...         message_log=[{"role": "user", "content": "Hello", "token_ids": torch.tensor([1, 2, 3])}],
+    ...         extra_env_info={'ground_truth': '1'},
+    ...         idx=0,
+    ...     ),
+    ...     DatumSpec(
+    ...         message_log=[{"role": "assistant", "content": "Hi there", "token_ids": torch.tensor([4, 5, 6, 7])}],
+    ...         extra_env_info={'ground_truth': '2'},
+    ...         idx=1,
+    ...     ),
+    ... ]
+    >>> output = eval_collate_fn(data_batch)
+    >>> output['message_log'][0]
+    [{'role': 'user', 'content': 'Hello', 'token_ids': tensor([1, 2, 3])}]
+    >>> output['message_log'][1]
+    [{'role': 'assistant', 'content': 'Hi there', 'token_ids': tensor([4, 5, 6, 7])}]
+    >>> output['extra_env_info']
+    [{'ground_truth': '1'}, {'ground_truth': '2'}]
+    >>> output['idx']
+    [0, 1]
+    """
+    message_log = [datum_spec["message_log"] for datum_spec in data_batch]
+    extra_env_info = [datum_spec["extra_env_info"] for datum_spec in data_batch]
+    idx = [datum_spec["idx"] for datum_spec in data_batch]
+
+    output = BatchedDataDict(
+        message_log=message_log,
+        extra_env_info=extra_env_info,
+        idx=idx,
+    )
+    return output

nemo_reinforcer/data/llm_message_utils.py

@@ -11,10 +11,10 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from typing import Dict, List, Union
-
+from typing import Dict, List
 
 import torch
+from datasets import Dataset
 
 from nemo_reinforcer.data.interfaces import (
     LLMMessageLogType,
@@ -390,3 +390,27 @@ def get_formatted_message_log(
         prev_formatted_message = formatted_message
 
     return message_log
+
+
+def remap_dataset_keys(
+    dataset: Dataset,
+    mapping_dict: Dict[str, str],
+) -> Dataset:
+    """Remap dataset keys as per mapping.
+
+    Args:
+        dataset: The input dataset to remap keys in
+        mapping_dict: A dictionary mapping input keys to output keys
+
+    Returns:
+        Dataset: A new dataset with remapped keys
+    """
+    # no need to remap if the keys are already correct
+    if all(k == v for k, v in mapping_dict.items()):
+        return dataset
+
+    # return the remapped dataset
+    return dataset.map(
+        lambda x: {v: x[k] for k, v in mapping_dict.items()},
+        remove_columns=list(mapping_dict.keys()),
+    )