[New Model] Gemma 4 (#21952)

Signed-off-by: Xinyuan Tong <xinyuantong.cs@gmail.com>
Co-authored-by: Pengyu Chen <pychen96@gmail.com>
Co-authored-by: kpham-sgl <khoa.pham@radixark.ai>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Andy Luo <andy.luo@amd.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: adarshxs <adarsh.shirawalmath@gmail.com>

benchmark/mmlu/bench_hf.py

@@ -0,0 +1,151 @@
+"""
+Usage:
+python3 bench_hf.py --model-path meta-llama/Llama-2-7b-hf --data-dir data --ntrain 5
+"""
+
+import argparse
+import json
+import os
+import time
+
+import numpy as np
+import pandas as pd
+import torch
+from tqdm import tqdm
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+choices = ["A", "B", "C", "D"]
+
+
+def format_subject(subject):
+    l = subject.split("_")
+    s = ""
+    for entry in l:
+        s += " " + entry
+    return s
+
+
+def format_example(df, idx, include_answer=True):
+    prompt = df.iloc[idx, 0]
+    k = df.shape[1] - 2
+    for j in range(k):
+        prompt += "\n{}. {}".format(choices[j], df.iloc[idx, j + 1])
+    prompt += "\nAnswer:"
+    if include_answer:
+        prompt += " {}\n\n".format(df.iloc[idx, k + 1])
+    return prompt
+
+
+def gen_prompt(train_df, subject, k=-1):
+    prompt = "The following are multiple choice questions (with answers) about{}.\n\n".format(
+        format_subject(subject)
+    )
+    if k == -1:
+        k = train_df.shape[0]
+    for i in range(k):
+        prompt += format_example(train_df, i)
+    return prompt
+
+
+@torch.no_grad()
+def main(args):
+    print(f"Loading model: {args.model_path}")
+    tokenizer = AutoTokenizer.from_pretrained(args.model_path, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        args.model_path,
+        torch_dtype=torch.bfloat16,
+        trust_remote_code=True,
+        device_map="auto",
+    ).eval()
+
+    subjects = sorted(
+        [
+            f.split("_test.csv")[0]
+            for f in os.listdir(os.path.join(args.data_dir, "test"))
+            if "_test.csv" in f
+        ]
+    )
+
+    all_cors = []
+    num_requests = 0
+    total_latency = 0
+
+    for subject in tqdm(subjects[: args.nsub]):
+        dev_df = pd.read_csv(
+            os.path.join(args.data_dir, "dev", subject + "_dev.csv"), header=None
+        )[: args.ntrain]
+        test_df = pd.read_csv(
+            os.path.join(args.data_dir, "test", subject + "_test.csv"), header=None
+        )
+
+        k = args.ntrain
+        few_shot_examples = gen_prompt(dev_df, subject, k)
+        while len(tokenizer.encode(few_shot_examples)) > 1536:
+            k -= 1
+            if k < 0:
+                break
+            few_shot_examples = gen_prompt(dev_df, subject, k)
+
+        preds = []
+        labels = []
+        tic = time.perf_counter()
+
+        for i in range(test_df.shape[0]):
+            prompt_end = format_example(test_df, i, include_answer=False)
+            prompt = few_shot_examples + prompt_end
+
+            input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
+            output_ids = model.generate(
+                input_ids,
+                max_new_tokens=1,
+                do_sample=False,
+                pad_token_id=tokenizer.eos_token_id,
+            )
+
+            output_str = tokenizer.decode(
+                output_ids[0][input_ids.shape[-1] :], skip_special_tokens=True
+            )
+            preds.append(output_str.strip()[0] if len(output_str.strip()) > 0 else "")
+            labels.append(test_df.iloc[i, test_df.shape[1] - 1])
+
+        latency = time.perf_counter() - tic
+        total_latency += latency
+
+        cors = [pred == label for pred, label in zip(preds, labels)]
+        all_cors.append(cors)
+        num_requests += len(test_df)
+
+        print(
+            f"Subject: {subject}, Accuracy: {np.mean(cors):.3f}, Latency: {latency:.3f}s"
+        )
+
+    weighted_acc = np.mean(np.concatenate(all_cors))
+    print(f"Total Latency: {total_latency:.3f}s")
+    print(f"Average Accuracy: {weighted_acc:.3f}")
+
+    if args.output:
+        with open(args.output, "a") as fout:
+            value = {
+                "task": "mmlu",
+                "backend": "hf",
+                "model": args.model_path,
+                "latency": round(total_latency, 3),
+                "accuracy": round(weighted_acc, 3),
+                "num_requests": num_requests,
+                "other": {
+                    "nsub": args.nsub,
+                    "ntrain": args.ntrain,
+                },
+            }
+            fout.write(json.dumps(value) + "\n")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model-path", type=str, required=True)
+    parser.add_argument("--ntrain", type=int, default=5)
+    parser.add_argument("--data-dir", type=str, default="data")
+    parser.add_argument("--nsub", type=int, default=60)
+    parser.add_argument("--output", type=str, help="Output file path")
+    args = parser.parse_args()
+    main(args)