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#
# 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,
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"""Contains data processors for evaluation."""
from typing import Any, cast
import torch
from transformers import PreTrainedTokenizerBase
from nemo_rl.data.interfaces import DatumSpec, LLMMessageLogType, TaskDataSpec
TokenizerType = PreTrainedTokenizerBase
# Example of a generic math data processor
[docs]
def math_data_processor(
datum_dict: dict[str, Any],
task_data_spec: TaskDataSpec,
tokenizer: TokenizerType,
max_seq_length: int,
idx: int,
) -> DatumSpec:
"""Process a datum dictionary (directly loaded from dataset) into a DatumSpec for the Math Environment."""
problem = datum_dict["problem"]
solution = str(datum_dict["expected_answer"])
extra_env_info = {"ground_truth": solution}
message_log: LLMMessageLogType = []
# system prompt
if task_data_spec.system_prompt:
sys_prompt: dict[str, str | torch.Tensor] = {
"role": "system",
"content": task_data_spec.system_prompt,
}
sys = tokenizer.apply_chat_template(
[cast(dict[str, str], sys_prompt)],
tokenize=False,
add_generation_prompt=False,
add_special_tokens=False,
)
sys_prompt["token_ids"] = tokenizer(sys, return_tensors="pt")["input_ids"][0]
message_log.append(sys_prompt)
# 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],
tokenize=False,
add_generation_prompt=True,
add_special_tokens=False,
)
user_message["token_ids"] = tokenizer(message, return_tensors="pt")["input_ids"][0]
user_message["content"] = message
message_log.append(user_message)
length = sum(len(m["token_ids"]) for m in message_log)
loss_multiplier = 1.0
if length > max_seq_length:
# make smaller and mask out
for indiv_message in message_log:
indiv_message["token_ids"] = indiv_message["token_ids"][
: min(4, max_seq_length // len(message_log))
]
loss_multiplier = 0.0
output: DatumSpec = {
"message_log": message_log,
"length": length,
"extra_env_info": extra_env_info,
"loss_multiplier": loss_multiplier,
"idx": idx,
}
if "task_name" in datum_dict:
output["task_name"] = datum_dict["task_name"]
return output
[docs]
def _construct_multichoice_prompt(
prompt: str, question: str, options: dict[str, str]
) -> str:
"""Construct prompt from question and options."""
output = prompt
output += f"\n\nQuestion: {question}\nOptions:\n"
output += "\n".join(
[
f"{letter}) {option}"
for letter, option in options.items()
if option is not None
]
)
return output
[docs]
def multichoice_qa_processor(
datum_dict: dict[str, Any],
task_data_spec: TaskDataSpec,
tokenizer: TokenizerType,
max_seq_length: int,
idx: int,
) -> DatumSpec:
"""Process a datum dictionary (directly loaded from dataset) into a DatumSpec for multiple-choice problems."""
question = datum_dict["question"]
answer = str(datum_dict["answer"])
options = datum_dict["options"]
extra_env_info = {"ground_truth": answer}
if "subject" in datum_dict:
extra_env_info.update({"subject": datum_dict["subject"]})
message_log = []
# system prompt
if task_data_spec.system_prompt:
sys_prompt: dict[str, str | torch.Tensor] = {
"role": "system",
"content": task_data_spec.system_prompt,
}
sys = tokenizer.apply_chat_template(
[cast(dict[str, str], sys_prompt)],
tokenize=False,
add_generation_prompt=False,
add_special_tokens=False,
)
sys_prompt["token_ids"] = tokenizer(sys, return_tensors="pt")["input_ids"][0]
message_log.append(sys_prompt)
# user prompt
if task_data_spec.prompt:
question = _construct_multichoice_prompt(
task_data_spec.prompt, question, options
)
user_message = {"role": "user", "content": question}
message = tokenizer.apply_chat_template(
[user_message],
tokenize=False,
add_generation_prompt=True,
add_special_tokens=False,
)
user_message["token_ids"] = tokenizer(message, return_tensors="pt")["input_ids"][0]
user_message["content"] = message
message_log.append(user_message)
length = sum(len(m["token_ids"]) for m in message_log)
output: DatumSpec = {
"message_log": message_log,
"length": length,
"extra_env_info": extra_env_info,
"loss_multiplier": 1.0,
"idx": idx,
}
if "task_name" in datum_dict:
output["task_name"] = datum_dict["task_name"]
return output
