Upload task_template.py

task_template.py

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+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("datasets/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 = "models/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 = "models/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 = "models/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 = "models/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 = "models/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   = "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)
+