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# Copyright 2025-present the HuggingFace Inc. team.
#
# 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.
"""
Data handling utilities for PEFT benchmarking.
"""
import json
import os
from typing import Optional
from transformers import PreTrainedTokenizer
from utils import BenchmarkConfig
DEFAULT_PROMPTS_PATH = os.path.join(os.path.dirname(__file__), "configs", "prompts.json")
def load_test_prompts(config: dict) -> dict[str, list[str]]:
"""
Load prompts from JSON file.
Args:
config: Configuration containing prompts file path
Returns:
dictionary with prompts by category
"""
prompts_file = getattr(config, "prompts_file", DEFAULT_PROMPTS_PATH)
with open(prompts_file) as f:
prompts = json.load(f)
return prompts
def truncate_prompt_for_model(
prompt: str,
tokenizer: PreTrainedTokenizer,
max_length: Optional[int] = None,
reserve_output_tokens: int = 50,
) -> str:
"""
Truncate a prompt to fit within the model's context window.
Args:
prompt: Input prompt
tokenizer: Model tokenizer
max_length: Maximum sequence length (if None, uses model's max_length)
reserve_output_tokens: Number of tokens to reserve for response
Returns:
Truncated prompt
"""
if max_length is None:
if hasattr(tokenizer, "model_max_length"):
max_length = tokenizer.model_max_length
else:
max_length = 2048
max_prompt_length = max_length - reserve_output_tokens
input_ids = tokenizer.encode(prompt, return_tensors="pt")[0]
if len(input_ids) <= max_prompt_length:
return prompt
truncated_ids = input_ids[:max_prompt_length]
truncated_prompt = tokenizer.decode(truncated_ids, skip_special_tokens=True)
return truncated_prompt
def prepare_benchmark_prompts(
config: BenchmarkConfig,
tokenizer: PreTrainedTokenizer,
max_input_length: Optional[int] = None,
seed: int = 42,
) -> dict[str, list[str]]:
"""
Prepare prompts for benchmarking, ensuring appropriate length and variety.
Always returns all prompt categories for consistent benchmarking.
Args:
config: Benchmark configuration
tokenizer: Model tokenizer
max_input_length: Maximum input length (overrides model default if provided)
seed: Random seed (kept for backwards compatibility)
Returns:
Dictionary with processed prompts by category (all categories included)
"""
all_prompts = load_test_prompts(config)
processed_prompts = {}
for category, prompts in all_prompts.items():
truncated_prompts = [
truncate_prompt_for_model(
prompt,
tokenizer,
max_length=max_input_length,
reserve_output_tokens=getattr(config, "reserve_output_tokens", 50),
)
for prompt in prompts
]
processed_prompts[category] = truncated_prompts
return processed_prompts
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