Upload task_template.py

task_template.py

@@ -0,0 +1,259 @@
+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)
+