Update README.md

README.md

@@ -34,8 +34,8 @@ With this dataset, we provide ONNX text for universal-NAS regression training ov
   - FBNet: 5000
   - Hiaml: 4629
   - Inception: 580
-  - NASBench101: 423624
-  - NASBench201: 15625
+  - NASBench101 (NB101): 423624
+  - NASBench201 (NB201): 15625
   - NASNet: 4846
   - OfaMB: 7491
   - OfaPN: 8206
@@ -54,12 +54,67 @@ With this dataset, we provide ONNX text for universal-NAS regression training ov
 ## How to load with 🤗 Datasets
 ```python
 from datasets import load_dataset
+ds = load_dataset("akhauriyash/GraphArch-Regression")
+```
+
+## Testing Graph Architecture Regression with a basic Gemma RLM model
+
+Use the code below as reference for evaluating a basic RegressLM model ( better, more models to come! :) )
+
+Note that the best practice is to fine-tune this base model on more NAS ONNX graph data, and few-shot transfer to the target search space (Say NASNet, etc.). 
+If we want to finetune on 16 examples from say, ENAS, the optimal strategy we found was to construct a small NAS dataset of e.g., DARTS, NASNet, Amoeba, ENAS and use ~(1024, 1024, 1024, 16) samples from each, and up-sample (repeat) the 16 ENAS samples 8 times. Random-shuffle the dataset and fine-tune the RLM with 1e-4 LR (cosine decay) to avoid catastrophic forgetting.
+The code below is just illustrative to demonstrate non-trivial NAS performance
+
+```
+import torch
+import numpy as np
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from scipy.stats import spearmanr
+from tqdm import tqdm
+
+REPO_ID = "akhauriyash/RLM-GemmaS-Code-v0"
+DATASET = "akhauriyash/GraphArch-Regression"
+dataset = load_dataset(DATASET, split="train")
+tok = AutoTokenizer.from_pretrained(REPO_ID, trust_remote_code=True)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = AutoModelForSeq2SeqLM.from_pretrained(REPO_ID, trust_remote_code=True).to(device).eval()
+MAX_ITEMS, BATCH_SIZE, spaces, results = 512, 4, ["NASBench101", "ENAS", "NASNet"], {}
+n_out_tokens = getattr(model.config, "num_tokens_per_obj", 8) * getattr(model.config, "max_num_objs", 1)
+n_out_tokens = model.config.num_tokens_per_obj * model.config.max_num_objs
+
+for SPACE in spaces:
+    inputs, targets = [], []
+    for row in tqdm(dataset, desc=f"Processing {SPACE} till {MAX_ITEMS} items"):
+        if row.get("space") == SPACE and "input" in row and "val_accuracy" in row:
+            try:
+                targets.append(float(row["val_accuracy"]))
+                inputs.append(f"{SPACE}\n\n{row['input']}")
+            except: continue
+            if len(inputs) >= MAX_ITEMS: break
+    preds = []
+    for i in tqdm(range(0, len(inputs), BATCH_SIZE)):
+        enc = tok(inputs[i:i+BATCH_SIZE], return_tensors="pt", truncation=True, padding=True, max_length=4096).to(device)
+        batch_preds = []
+        for _ in range(8):
+            out = model.generate(**enc, max_new_tokens=n_out_tokens, min_new_tokens=n_out_tokens, do_sample=True, top_p=0.95, temperature=1.0)
+            decoded = [tok.token_ids_to_floats(seq.tolist()) for seq in out]
+            decoded = [d[0] if isinstance(d, list) and d else float("nan") for d in decoded]
+            batch_preds.append(decoded)
+        preds.extend(torch.tensor(batch_preds).median(dim=0).values.tolist())
+    spear, _ = spearmanr(np.array(targets), np.array(preds))
+    results[SPACE] = spear; print(f"Spearman ρ for {SPACE}: {spear:.3f}")
+
+print("Spearman ρ | NASBench101 | ENAS | NASNet")
+print(f"{REPO_ID} | " + " | ".join(f"{results[s]:.3f}" for s in spaces))
+```
 
-# After you upload this folder to a dataset repo, e.g. your-username/GraphArch-Regression
-ds = load_dataset("your-username/GraphArch-Regression")
 
-# Or from a local clone:
-# ds = load_dataset("json", data_files="GraphArch-Regression/data.jsonl", split="train")
+We got the following results when testing on a random subset of the GraphArch-Regression dataset.
+
+```
+Model ID                                 | NASBench101  | ENAS  | NASNet
+akhauriyash/RegressLM-gemma-s-RLM-table3 | 0.384        | 0.211 | 0.209 
 ```
 
 Credits