prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  benchmark_latency.py will hang  when --batchsize=1 and --n=2
  benchmark_latency.py will hang if setting --batchsize=1 and --n=2or4or8.
  should max_num_seqs been set as args.batch_size * args.n ？
  https://github.com/vllm-project/vllm/blob/main/benchmarks/benchmark_latency.py#L23
  
  </issue>
  <code>
  [start of benchmarks/benchmark_latency.py]
  1 """Benchmark the latency of processing a single batch of requests."""
  2 import argparse
  3 import time
  4 
  5 import numpy as np
  6 import torch
  7 from tqdm import tqdm
  8 
  9 from vllm import LLM, SamplingParams
  10 
  11 
  12 def main(args: argparse.Namespace):
  13     print(args)
  14 
  15     # NOTE(woosuk): If the request cannot be processed in a single batch,
  16     # the engine will automatically process the request in multiple batches.
  17     llm = LLM(
  18         model=args.model,
  19         tokenizer=args.tokenizer,
  20         quantization=args.quantization,
  21         tensor_parallel_size=args.tensor_parallel_size,
  22         max_num_seqs=args.batch_size,
  23         trust_remote_code=args.trust_remote_code,
  24         dtype=args.dtype,
  25     )
  26 
  27     sampling_params = SamplingParams(
  28         n=args.n,
  29         temperature=0.0 if args.use_beam_search else 1.0,
  30         top_p=1.0,
  31         use_beam_search=args.use_beam_search,
  32         ignore_eos=True,
  33         max_tokens=args.output_len,
  34     )
  35     print(sampling_params)
  36     dummy_prompt_token_ids = [[0] * args.input_len] * args.batch_size
  37 
  38     def run_to_completion(profile: bool = False):
  39         if profile:
  40             with torch.profiler.profile(activities=[
  41                     torch.profiler.ProfilerActivity.CPU,
  42                     torch.profiler.ProfilerActivity.CUDA,
  43             ]) as p:
  44                 llm.generate(prompt_token_ids=dummy_prompt_token_ids,
  45                              sampling_params=sampling_params,
  46                              use_tqdm=False)
  47             print(p.key_averages())
  48         else:
  49             start_time = time.perf_counter()
  50             llm.generate(prompt_token_ids=dummy_prompt_token_ids,
  51                          sampling_params=sampling_params,
  52                          use_tqdm=False)
  53             end_time = time.perf_counter()
  54             latency = end_time - start_time
  55             return latency
  56 
  57     print("Warming up...")
  58     run_to_completion(profile=False)
  59 
  60     if args.profile:
  61         print("Profiling...")
  62         run_to_completion(profile=True)
  63         return
  64 
  65     # Benchmark.
  66     latencies = []
  67     for _ in tqdm(range(args.num_iters), desc="Profiling iterations"):
  68         latencies.append(run_to_completion(profile=False))
  69     print(f'Avg latency: {np.mean(latencies)} seconds')
  70 
  71 
  72 if __name__ == '__main__':
  73     parser = argparse.ArgumentParser(
  74         description='Benchmark the latency of processing a single batch of '
  75         'requests till completion.')
  76     parser.add_argument('--model', type=str, default='facebook/opt-125m')
  77     parser.add_argument('--tokenizer', type=str, default=None)
  78     parser.add_argument('--quantization',
  79                         '-q',
  80                         choices=['awq', 'squeezellm', None],
  81                         default=None)
  82     parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1)
  83     parser.add_argument('--input-len', type=int, default=32)
  84     parser.add_argument('--output-len', type=int, default=128)
  85     parser.add_argument('--batch-size', type=int, default=8)
  86     parser.add_argument('--n',
  87                         type=int,
  88                         default=1,
  89                         help='Number of generated sequences per prompt.')
  90     parser.add_argument('--use-beam-search', action='store_true')
  91     parser.add_argument('--num-iters',
  92                         type=int,
  93                         default=3,
  94                         help='Number of iterations to run.')
  95     parser.add_argument('--trust-remote-code',
  96                         action='store_true',
  97                         help='trust remote code from huggingface')
  98     parser.add_argument(
  99         '--dtype',
  100         type=str,
  101         default='auto',
  102         choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'],
  103         help='data type for model weights and activations. '
  104         'The "auto" option will use FP16 precision '
  105         'for FP32 and FP16 models, and BF16 precision '
  106         'for BF16 models.')
  107     parser.add_argument(
  108         '--profile',
  109         action='store_true',
  110         help='profile the generation process of a single batch')
  111     args = parser.parse_args()
  112     main(args)
  113
  [end of benchmarks/benchmark_latency.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/benchmarks/benchmark_latency.py b/benchmarks/benchmark_latency.py
  --- a/benchmarks/benchmark_latency.py
  +++ b/benchmarks/benchmark_latency.py
  @@ -19,7 +19,6 @@
           tokenizer=args.tokenizer,
           quantization=args.quantization,
           tensor_parallel_size=args.tensor_parallel_size,
  -        max_num_seqs=args.batch_size,
           trust_remote_code=args.trust_remote_code,
           dtype=args.dtype,
       )
  
verification_info: {"golden_diff": "diff --git a/benchmarks/benchmark_latency.py b/benchmarks/benchmark_latency.py\n--- a/benchmarks/benchmark_latency.py\n+++ b/benchmarks/benchmark_latency.py\n@@ -19,7 +19,6 @@\n         tokenizer=args.tokenizer,\n         quantization=args.quantization,\n         tensor_parallel_size=args.tensor_parallel_size,\n-        max_num_seqs=args.batch_size,\n         trust_remote_code=args.trust_remote_code,\n         dtype=args.dtype,\n     )\n", "issue": "benchmark_latency.py will hang  when --batchsize=1 and --n=2\nbenchmark_latency.py will hang if setting --batchsize=1 and --n=2or4or8.\r\nshould max_num_seqs been set as args.batch_size * args.n \uff1f\r\nhttps://github.com/vllm-project/vllm/blob/main/benchmarks/benchmark_latency.py#L23\n", "before_files": [{"content": "\"\"\"Benchmark the latency of processing a single batch of requests.\"\"\"\nimport argparse\nimport time\n\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\n\nfrom vllm import LLM, SamplingParams\n\n\ndef main(args: argparse.Namespace):\n    print(args)\n\n    # NOTE(woosuk): If the request cannot be processed in a single batch,\n    # the engine will automatically process the request in multiple batches.\n    llm = LLM(\n        model=args.model,\n        tokenizer=args.tokenizer,\n        quantization=args.quantization,\n        tensor_parallel_size=args.tensor_parallel_size,\n        max_num_seqs=args.batch_size,\n        trust_remote_code=args.trust_remote_code,\n        dtype=args.dtype,\n    )\n\n    sampling_params = SamplingParams(\n        n=args.n,\n        temperature=0.0 if args.use_beam_search else 1.0,\n        top_p=1.0,\n        use_beam_search=args.use_beam_search,\n        ignore_eos=True,\n        max_tokens=args.output_len,\n    )\n    print(sampling_params)\n    dummy_prompt_token_ids = [[0] * args.input_len] * args.batch_size\n\n    def run_to_completion(profile: bool = False):\n        if profile:\n            with torch.profiler.profile(activities=[\n                    torch.profiler.ProfilerActivity.CPU,\n                    torch.profiler.ProfilerActivity.CUDA,\n            ]) as p:\n                llm.generate(prompt_token_ids=dummy_prompt_token_ids,\n                             sampling_params=sampling_params,\n                             use_tqdm=False)\n            print(p.key_averages())\n        else:\n            start_time = time.perf_counter()\n            llm.generate(prompt_token_ids=dummy_prompt_token_ids,\n                         sampling_params=sampling_params,\n                         use_tqdm=False)\n            end_time = time.perf_counter()\n            latency = end_time - start_time\n            return latency\n\n    print(\"Warming up...\")\n    run_to_completion(profile=False)\n\n    if args.profile:\n        print(\"Profiling...\")\n        run_to_completion(profile=True)\n        return\n\n    # Benchmark.\n    latencies = []\n    for _ in tqdm(range(args.num_iters), desc=\"Profiling iterations\"):\n        latencies.append(run_to_completion(profile=False))\n    print(f'Avg latency: {np.mean(latencies)} seconds')\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(\n        description='Benchmark the latency of processing a single batch of '\n        'requests till completion.')\n    parser.add_argument('--model', type=str, default='facebook/opt-125m')\n    parser.add_argument('--tokenizer', type=str, default=None)\n    parser.add_argument('--quantization',\n                        '-q',\n                        choices=['awq', 'squeezellm', None],\n                        default=None)\n    parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1)\n    parser.add_argument('--input-len', type=int, default=32)\n    parser.add_argument('--output-len', type=int, default=128)\n    parser.add_argument('--batch-size', type=int, default=8)\n    parser.add_argument('--n',\n                        type=int,\n                        default=1,\n                        help='Number of generated sequences per prompt.')\n    parser.add_argument('--use-beam-search', action='store_true')\n    parser.add_argument('--num-iters',\n                        type=int,\n                        default=3,\n                        help='Number of iterations to run.')\n    parser.add_argument('--trust-remote-code',\n                        action='store_true',\n                        help='trust remote code from huggingface')\n    parser.add_argument(\n        '--dtype',\n        type=str,\n        default='auto',\n        choices=['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'],\n        help='data type for model weights and activations. '\n        'The \"auto\" option will use FP16 precision '\n        'for FP32 and FP16 models, and BF16 precision '\n        'for BF16 models.')\n    parser.add_argument(\n        '--profile',\n        action='store_true',\n        help='profile the generation process of a single batch')\n    args = parser.parse_args()\n    main(args)\n", "path": "benchmarks/benchmark_latency.py"}]}