task_template.py

import torch
import pandas as pd
import requests
import sys
import torchvision.models as models
import os

from transformers import AutoTokenizer, PreTrainedModel
from peft import PeftModel
from hf_olmo import OLMoForCausalLM

# --------------------------------
# DATASET
# --------------------------------

"""
Dataset contents:

- 100 subsets of text data, each subset stored under the key "subset_{i}" where i ranges from 0 to 99.
  Each subset is a dictionary with:
    -"sentences": List of 100 sentences in the subset
    -"input_ids": Tensor of tokenized input IDs for the sentences, has shape (100, MAX_LENGTH)
    -"attention_mask": Tensor of attention masks for the tokenized inputs, has shape (100, MAX_LENGTH)
    -"labels": Tensor of true labels for the sentences in the subset, has shape (100)
    -"subset_id": Integer ID of the subset (from 0 to 99)
"""

# Load the dataset
dataset = torch.load("subsets_dataset.pt")

# Example: Acessing subsets
subset_0 = dataset["subset_0"]

print("Subset 0 keys:", subset_0.keys())
print("Subset ID:", subset_0["subset_id"])
print("Labels shape:", subset_0["labels"].shape) 
print("First sentence:", subset_0["sentences"][:1])
print("First 10 labels:", subset_0["labels"][:10])

# --------------------------------
# QUERYING THE CLASSIFIER
# --------------------------------

# You can use the following Code to load and query the LLM with sentences:

# IMPORTANT: adapter_config.json and adapter_model.safetensors must be put in a folder named "LORA"
# Sorry for the inconvenience!

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

BASE_MODEL = "allenai/OLMo-1B"

tokenizer = AutoTokenizer.from_pretrained(
    BASE_MODEL,
    trust_remote_code=True,
)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

def _no_op_mark_tied_weights_as_initialized(self):
    return

PreTrainedModel.mark_tied_weights_as_initialized = (
    _no_op_mark_tied_weights_as_initialized
)

def _empty_tied_weights_keys(self):
    return {}

PreTrainedModel.all_tied_weights_keys = property(_empty_tied_weights_keys)
OLMoForCausalLM.all_tied_weights_keys = property(_empty_tied_weights_keys)

def _no_op_tie_weights(self, *args, **kwargs):
    return

OLMoForCausalLM.tie_weights = _no_op_tie_weights

base_model = OLMoForCausalLM.from_pretrained(
    BASE_MODEL,
    trust_remote_code=True,
    torch_dtype=torch.float16 if DEVICE.type == "cuda" else torch.float32,
)

model = PeftModel.from_pretrained(
    base_model,
    "LORA",
)

model.config.use_cache = False

model.eval()
model.to(DEVICE)

subset = dataset["subset_0"]

input_ids = subset["input_ids"].to(DEVICE)
attention_mask = subset["attention_mask"].to(DEVICE)

with torch.no_grad():
    outputs = model(
        input_ids=input_ids,
        attention_mask=attention_mask,
        return_dict=True,
    )

logits = outputs.logits
last_idx = attention_mask.sum(dim=1) - 1
batch_idx = torch.arange(logits.size(0), device=logits.device)
final_logits = logits[batch_idx, last_idx]

pos_id = tokenizer.encode(" positive", add_special_tokens=False)[0]
neg_id = tokenizer.encode(" negative", add_special_tokens=False)[0]

subset_logits = final_logits[:, [neg_id, pos_id]]

print(f"Logits shape: {subset_logits.shape}") # should be (100, 2)
print(f"First 10 logits: {subset_logits[:10]}")

# # --------------------------------
# # SUBMISSION FORMAT
# # --------------------------------

"""
The submission must be a .csv file with the following format:

-"subset_id": ID of the subset (from 0 to 99)
-"membership": Membership score for each image (float)
"""

# Example Submission:

subset_ids = list(range(len(dataset)))  
membership_scores = torch.rand(len(dataset)).tolist()
submission_df = pd.DataFrame({
    "subset_id": subset_ids,
    "membership": membership_scores
})
submission_df.to_csv("example_submission.csv", index=None)

# --------------------------------
# SUBMISSION PROCESS
# --------------------------------

"""
Example submission script for the LLM Set Membership Inference Task.

Submission Requirements (read carefully to avoid automatic rejection):

1. CSV FORMAT
----------------
- The file **must be a CSV** with extension `.csv`.
- It must contain **exactly two columns**, named:
      subset_id, membership
  → Column names must match exactly (lowercase, no extra spaces).
  → Column order does not matter, but both must be present.

2. ROW COUNT AND IDENTIFIERS
-------------------------------
- Your file must contain **exactly 100 rows**.
- Each row corresponds to one unique `subset_id` in the range **0–99** (inclusive).
- Every subset_id must appear **exactly once**.
- Do **not** add, remove, or rename any IDs.
- Do **not** include duplicates or missing entries.
- The evaluator checks:
      subset_id.min() == 0
      subset_id.max() == 99
      subset_id.unique().size == 100

3. MEMBERSHIP SCORES
----------------------
- The `membership` column must contain **numeric values** representing your model’s predicted confidence
  that the corresponding subset is a **member** of the training set.

  Examples of valid membership values:
    - Probabilities: values in [0.0, 1.0]
    - Raw model scores: any finite numeric values (will be ranked for TPR@FPR=0.05)

- Do **not** submit string labels like "yes"/"no" or "member"/"non-member".
- The evaluator converts your `membership` column to numeric using `pd.to_numeric()`.
  → Any non-numeric, NaN, or infinite entries will cause automatic rejection.

4. TECHNICAL LIMITS
----------------------
- Maximum file size: **20 MB**
- Encoding: UTF-8 recommended.
- Avoid extra columns, blank lines, or formulas.
- Ensure all values are numeric and finite.
- Supported data types: int, float (e.g., float32, float64)

5. VALIDATION SUMMARY
------------------------
Your submission will fail if:
- Columns don’t match exactly ("subset_id", "membership")
- Row count differs from 100
- Any subset_id is missing, duplicated, or outside [0, 99]
- Any membership value is NaN, Inf, or non-numeric
- File is too large or not a valid CSV

Two key metrics are computed:
  1. **ROC-AUC (Area Under the ROC Curve)** — measures overall discriminative ability.
  2. **TPR@FPR=0.05** — true positive rate when the false positive rate is at 5%.

"""

BASE_URL  = "http://35.192.205.84:80"
API_KEY   = "YOUR_API_KEY_HERE"  # replace with your actual API key

TASK_ID   = "14-llm-dataset-inference"
FILE_PATH = "Your-Submission-File.csv"  # replace with your actual file path

SUBMIT     = False  # Set to True to enable submission

def die(msg):
    print(f"{msg}", file=sys.stderr)
    sys.exit(1)

if SUBMIT:
    if not os.path.isfile(FILE_PATH):
        die(f"File not found: {FILE_PATH}")

    try:
        with open(FILE_PATH, "rb") as f:
            files = {
                # (fieldname) -> (filename, fileobj, content_type)
                "file": (os.path.basename(FILE_PATH), f, "csv"),
            }
            resp = requests.post(
                f"{BASE_URL}/submit/{TASK_ID}",
                headers={"X-API-Key": API_KEY},
                files=files,
                timeout=(10, 120),  # (connect timeout, read timeout)
            )
        # Helpful output even on non-2xx
        try:
            body = resp.json()
        except Exception:
            body = {"raw_text": resp.text}

        if resp.status_code == 413:
            die("Upload rejected: file too large (HTTP 413). Reduce size and try again.")

        resp.raise_for_status()

        submission_id = body.get("submission_id")
        print("Successfully submitted.")
        print("Server response:", body)
        if submission_id:
            print(f"Submission ID: {submission_id}")

    except requests.exceptions.RequestException as e:
        detail = getattr(e, "response", None)
        print(f"Submission error: {e}")
        if detail is not None:
            try:
                print("Server response:", detail.json())
            except Exception:
                print("Server response (text):", detail.text)
        sys.exit(1)