[Misc] refactor example eagle (#16100)

Signed-off-by: reidliu41 <reid201711@gmail.com>
Co-authored-by: reidliu41 <reid201711@gmail.com>

examples/offline_inference/eagle.py

@@ -7,59 +7,65 @@ from transformers import AutoTokenizer
 
 from vllm import LLM, SamplingParams
 
-parser = argparse.ArgumentParser()
 
-parser.add_argument(
+def load_prompts(dataset_path, num_prompts):
+    if os.path.exists(dataset_path):
+        prompts = []
+        try:
+            with open(dataset_path) as f:
+                for line in f:
+                    data = json.loads(line)
+                    prompts.append(data["turns"][0])
+        except Exception as e:
+            print(f"Error reading dataset: {e}")
+            return []
+    else:
+        prompts = [
+            "The future of AI is", "The president of the United States is"
+        ]
+
+    return prompts[:num_prompts]
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
         "--dataset",
         type=str,
         default="./examples/data/gsm8k.jsonl",
         help="downloaded from the eagle repo " \
         "https://github.com/SafeAILab/EAGLE/blob/main/eagle/data/"
-)
-parser.add_argument("--max_num_seqs", type=int, default=8)
-parser.add_argument("--num_prompts", type=int, default=80)
-parser.add_argument("--num_spec_tokens", type=int, default=2)
-parser.add_argument("--tp", type=int, default=1)
-parser.add_argument("--draft_tp", type=int, default=1)
-parser.add_argument("--enforce_eager", action='store_true')
-parser.add_argument("--enable_chunked_prefill", action='store_true')
-parser.add_argument("--max_num_batched_tokens", type=int, default=2048)
-parser.add_argument("--temp", type=float, default=0)
-
-args = parser.parse_args()
+    )
+    parser.add_argument("--max_num_seqs", type=int, default=8)
+    parser.add_argument("--num_prompts", type=int, default=80)
+    parser.add_argument("--num_spec_tokens", type=int, default=2)
+    parser.add_argument("--tp", type=int, default=1)
+    parser.add_argument("--draft_tp", type=int, default=1)
+    parser.add_argument("--enforce_eager", action='store_true')
+    parser.add_argument("--enable_chunked_prefill", action='store_true')
+    parser.add_argument("--max_num_batched_tokens", type=int, default=2048)
+    parser.add_argument("--temp", type=float, default=0)
+    args = parser.parse_args()
 
-print(args)
+    model_dir = "meta-llama/Meta-Llama-3-8B-Instruct"
+    eagle_dir = "abhigoyal/EAGLE-LLaMA3-Instruct-8B-vllm"
 
-model_dir = "meta-llama/Meta-Llama-3-8B-Instruct"
-eagle_dir = "abhigoyal/EAGLE-LLaMA3-Instruct-8B-vllm"
+    max_model_len = 2048
 
-max_model_len = 2048
+    tokenizer = AutoTokenizer.from_pretrained(model_dir)
 
-tokenizer = AutoTokenizer.from_pretrained(model_dir)
+    prompts = load_prompts(args.dataset, args.num_prompts)
 
-if os.path.exists(args.dataset):
-    prompts = []
-    num_prompts = args.num_prompts
-    with open(args.dataset) as f:
-        for line in f:
-            data = json.loads(line)
-            prompts.append(data["turns"][0])
-else:
-    prompts = ["The future of AI is", "The president of the United States is"]
-
-prompts = prompts[:args.num_prompts]
-num_prompts = len(prompts)
-
-prompt_ids = [
+    prompt_ids = [
         tokenizer.apply_chat_template([{
             "role": "user",
             "content": prompt
         }],
                                       add_generation_prompt=True)
         for prompt in prompts
-]
+    ]
 
-llm = LLM(
+    llm = LLM(
         model=model_dir,
         trust_remote_code=True,
         tensor_parallel_size=args.tp,
@@ -76,20 +82,27 @@ llm = LLM(
             "max_model_len": max_model_len,
         },
         disable_log_stats=False,
-)
+    )
 
-sampling_params = SamplingParams(temperature=args.temp, max_tokens=256)
+    sampling_params = SamplingParams(temperature=args.temp, max_tokens=256)
 
-outputs = llm.generate(prompt_token_ids=prompt_ids,
+    outputs = llm.generate(prompt_token_ids=prompt_ids,
                            sampling_params=sampling_params)
 
-# calculate the average number of accepted tokens per forward pass, +1 is
-# to account for the token from the target model that's always going to be
-# accepted
-acceptance_counts = [0] * (args.num_spec_tokens + 1)
-for output in outputs:
-    for step, count in enumerate(output.metrics.spec_token_acceptance_counts):
+    # calculate the average number of accepted tokens per forward pass, +1 is
+    # to account for the token from the target model that's always going to be
+    # accepted
+    acceptance_counts = [0] * (args.num_spec_tokens + 1)
+    for output in outputs:
+        for step, count in enumerate(
+                output.metrics.spec_token_acceptance_counts):
             acceptance_counts[step] += count
 
-print(f"mean acceptance length: \
+    print("-" * 50)
+    print(f"mean acceptance length: \
         {sum(acceptance_counts) / acceptance_counts[0]:.2f}")
+    print("-" * 50)
+
+
+if __name__ == "__main__":
+    main()