Delete task_template.py

task_template.py

@@ -1,314 +0,0 @@
-import torch
-import pandas as pd
-import requests
-import sys
-import torchvision.models as models
-import os
-
-from transformers import AutoTokenizer, PreTrainedModel, AutoModelForSequenceClassification
-
-import utils
-
-# --------------------------------
-# DATASET
-# --------------------------------
-
-"""
-Dataset contents:
-
-- 1000 subsets of text data, each subset stored under the key "subset_{i}" where i ranges from 0 to 999.
-  Each subset is a dictionary with:
-    -"prompts": List of 100 prompts in the subset
-    -"labels": Tensor of true labels for the prompts in the subset, has shape (100)
-    -"subset_id": Integer ID of the subset (from 0 to 999)
-"""
-
-# Load the dataset
-dataset = torch.load("fulltuning.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 length:", len(subset_0["labels"])) 
-print("First prompts:", subset_0["prompts"][:5])
-print("First 5 labels:", subset_0["labels"][:5])
-
-# --------------------------------
-# QUERYING THE CLASSIFIER
-# --------------------------------
-
-# This Code can be used to load and query the fully fine-tuned models. You also need to the available utils.py file.
-
-#|---------------------------------------------------------------------------------------------------|
-#|    NOTE: "Missing or unexpected params" warnings are no reason for concern. They stem from the    | 
-#|    fact that the model is first loaded without a classifier head, which is added afterwards.      |
-#|---------------------------------------------------------------------------------------------------|
-
-# Use this tokenizer for OLMO...
-
-tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-1B-hf", trust_remote_code=True)
-
-# ...and this one for Pythia
-
-tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-410m", trust_remote_code=True)
-
-
-tokenizer.padding_side = "left"
-
-if tokenizer.pad_token is None:
-    tokenizer.pad_token = tokenizer.eos_token
-
-
-# Usage example (fulltuning):
-
-model_path = "olmo-fulltuning"
-
-model = utils.get_fulltuning_model(model_path, model_type="olmo") # model_type can be "olmo" or "pythia"
-
-example_prompt = "I think, therefore I am.\n\nI am."
-
-inputs = tokenizer(example_prompt, return_tensors="pt", truncation=True)
-inputs = {k: v.to(model.device) for k, v in inputs.items()}
-with torch.no_grad():
-    outputs = model(**inputs)
-
-logits = outputs.logits
-
-print(f"Logits shape: {logits.shape}")
-print(f"Logits: {logits}")
-
-
-
-# Usage example (softprompt):
-
-model_path = "olmo-softprompt"
-
-model = utils.get_peft_model(model_path, model_type="olmo") # model_type can be "olmo" or "pythia"
-
-example_prompt = "I think, but do I exist?\n\nSince you think, you exist."
-
-inputs = tokenizer(example_prompt, return_tensors="pt", truncation=True)
-inputs = {k: v.to(model.device) for k, v in inputs.items()}
-with torch.no_grad():
-    outputs = model(**inputs)
-
-logits = outputs.logits
-
-print(f"Logits shape: {logits.shape}")
-print(f"Logits: {logits}")
-
-
-
-# Usage example (lora):
-
-model_path = "olmo-lora"
-
-model = utils.get_peft_model(model_path, model_type="olmo") # model_type can be "olmo" or "pythia"
-
-example_prompt = "Who am I?\n\nWhat am I?"
-
-inputs = tokenizer(example_prompt, return_tensors="pt", truncation=True)
-inputs = {k: v.to(model.device) for k, v in inputs.items()}
-with torch.no_grad():
-    outputs = model(**inputs)
-
-logits = outputs.logits
-
-print(f"Logits shape: {logits.shape}")
-print(f"Logits: {logits}")
-
-
-
-# Usage example (lastlayer):
-
-model_path = "olmo-lastlayer"
-
-model = utils.get_peft_model(model_path, model_type="olmo") # model_type can be "olmo" or "pythia"
-
-example_prompt = "I love to exist!"
-
-inputs = tokenizer(example_prompt, return_tensors="pt", truncation=True)
-inputs = {k: v.to(model.device) for k, v in inputs.items()}
-with torch.no_grad():
-    outputs = model(**inputs)
-
-logits = outputs.logits
-
-print(f"Logits shape: {logits.shape}")
-print(f"Logits: {logits}")
-
-
-
-# Usage example (prefix):
-
-model_path = "olmo-prefix"
-
-model = utils.get_peft_model(model_path, model_type="olmo") # model_type can be "olmo" or "pythia"
-
-example_prompt = "I will exist yesterday."
-
-inputs = tokenizer(example_prompt, return_tensors="pt", truncation=True)
-inputs = {k: v.to(model.device) for k, v in inputs.items()}
-with torch.no_grad():
-    outputs = utils.forward_peft_seqcls(model, **inputs)
-
-logits = outputs.logits
-
-print(f"Logits shape: {logits.shape}")
-print(f"Logits: {logits}")
-
-# --------------------------------
-# SUBMISSION FORMAT
-# --------------------------------
-
-"""
-The submission must be a .csv file with the following format:
-
--"type": Name of the model (e.g., "softprompt", "fulltuning", etc.)
--"subset_id": ID of the subset (from 0 to 999, per type)
--"membership": Membership score for each subset (float)
-"""
-
-# Example Submission:
-
-types = ["softprompt", "fulltuning", "lora", "lastlayer", "prefix"]
-type_list = []
-
-for t in types:
-    type_list.extend([t] * 1000)
-
-subset_ids = []
-for _ in types:
-    subset_ids.extend(list(range(1000)))
-
-membership_scores = torch.rand(5000).tolist()
-submission_df = pd.DataFrame({
-    "type": type_list,
-    "subset_id": subset_ids,
-    "membership": membership_scores
-})
-submission_df.to_csv("example_submission.csv", index=None)
-
-# --------------------------------
-# SUBMISSION PROCESS
-# --------------------------------
-
-"""
-Example submission script for the LLM Dataset 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 three columns**, named:
-      type, subset_id, membership
-  → Column names must match exactly (lowercase, no extra spaces).
-  → Column order does not matter, but all three must be present.
-
-2. ROW COUNT AND IDENTIFIERS
--------------------------------
-- Your file must contain **exactly 5000 rows**.
-- Each row corresponds to one unique `subset_id`/`type` pair, with ids in the range **0–999** (inclusive).
-- Every subset_id must appear **exactly once** for each type.
-- 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() == 999
-      subset_id.unique().size == 1000
-
-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 ("type", "subset_id", "membership")
-- Row count differs from 5000
-- Any type name is unexpected or not in the allowed set
-- Any subset_id is missing, duplicated, or outside [0, 999] for any type
-- 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   = "77ab4a807d03d48e1d85881b27496871"  # replace with your actual API key
-
-TASK_ID   = "14-llm-dataset-inference"
-FILE_PATH = "example_submission.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)
-