prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  [tensor] fix some unittests
  
  [tensor] fix some unittests
  
  [tensor] fix some unittests
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `examples/inference/benchmark_ops/benchmark_rmsnorm.py`
  Content:
  ```
  1 import torch
  2 
  3 from colossalai.kernel.kernel_loader import InferenceOpsLoader
  4 from colossalai.kernel.triton import rms_layernorm
  5 
  6 try:
  7     import triton  # noqa
  8 except ImportError:
  9     print("please install triton from https://github.com/openai/triton")
  10 
  11 inference_ops = InferenceOpsLoader().load()
  12 
  13 # Triton benchmark plot attributions
  14 configs = [
  15     triton.testing.Benchmark(
  16         x_names=["SEQUENCE_TOTAL"],
  17         x_vals=[i for i in range(128, 1025, 128)],
  18         line_arg="provider",
  19         line_vals=[
  20             "vllm_rms_layernorm",
  21             "triton_rms_layernorm",
  22             "cuda_rms_layernorm",
  23             "vllm_rms_layernorm_with_residual",
  24             "triton_rms_layernorm_with_residual",
  25             "cuda_rms_layernorm_with_residual",
  26         ],
  27         line_names=[
  28             "vllm_rms_layernorm",
  29             "triton_rms_layernorm",
  30             "cuda_rms_layernorm",
  31             "vllm_rms_layernorm_with_residual",
  32             "triton_rms_layernorm_with_residual",
  33             "cuda_rms_layernorm_with_residual",
  34         ],
  35         styles=[("red", "-"), ("blue", "-"), ("yellow", "-"), ("red", "--"), ("blue", "--"), ("yellow", "--")],
  36         ylabel="ms",
  37         plot_name=f"RMSNorm benchmarking results",
  38         args={"HIDDEN_SIZE": 1024},
  39     )
  40 ]
  41 
  42 
  43 @triton.testing.perf_report(configs)
  44 def benchmark_rms_layernorm(
  45     provider: str,
  46     SEQUENCE_TOTAL: int,
  47     HIDDEN_SIZE: int,
  48 ):
  49     try:
  50         from vllm.model_executor.layers.layernorm import RMSNorm
  51     except ImportError:
  52         raise ImportError("Please install vllm from https://github.com/vllm-project/vllm")
  53 
  54     warmup = 10
  55     rep = 1000
  56 
  57     dtype = torch.float16
  58     eps = 1e-5
  59     x_shape = (SEQUENCE_TOTAL, HIDDEN_SIZE)
  60     w_shape = (x_shape[-1],)
  61     residual = torch.rand(x_shape, dtype=dtype, device="cuda")
  62     weight = torch.ones(w_shape, dtype=dtype, device="cuda")
  63     vllm_norm = RMSNorm(hidden_size=HIDDEN_SIZE, eps=eps).to(dtype=dtype, device="cuda")
  64     x = -2.3 + 0.5 * torch.randn(x_shape, dtype=dtype, device="cuda")
  65     if provider == "vllm_rms_layernorm":
  66         fn = lambda: vllm_norm(x)
  67     elif provider == "triton_rms_layernorm":
  68         fn = lambda: rms_layernorm(x, weight, eps=eps)
  69     elif provider == "cuda_rms_layernorm":
  70         out = torch.empty_like(x)
  71         fn = lambda: inference_ops.rms_layernorm(out, x, weight, eps)
  72     elif provider == "vllm_rms_layernorm_with_residual":
  73         fn = lambda: vllm_norm(x, residual=residual)
  74     elif provider == "triton_rms_layernorm_with_residual":
  75         fn = lambda: rms_layernorm(x, weight, eps=eps, residual=residual)
  76     elif provider == "cuda_rms_layernorm_with_residual":
  77         fn = lambda: inference_ops.fused_add_rms_layernorm(x, residual, weight, eps)
  78     else:
  79         raise ValueError("Undefined provider.")
  80 
  81     ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep)
  82 
  83     return ms
  84 
  85 
  86 if __name__ == "__main__":
  87     benchmark_rms_layernorm.run(save_path=".", print_data=True)
  88
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/examples/inference/benchmark_ops/benchmark_rmsnorm.py b/examples/inference/benchmark_ops/benchmark_rmsnorm.py
  --- a/examples/inference/benchmark_ops/benchmark_rmsnorm.py
  +++ b/examples/inference/benchmark_ops/benchmark_rmsnorm.py
  @@ -35,7 +35,7 @@
           styles=[("red", "-"), ("blue", "-"), ("yellow", "-"), ("red", "--"), ("blue", "--"), ("yellow", "--")],
           ylabel="ms",
           plot_name=f"RMSNorm benchmarking results",
  -        args={"HIDDEN_SIZE": 1024},
  +        args={"HIDDEN_SIZE": 5120},
       )
   ]
  
verification_info: {"golden_diff": "diff --git a/examples/inference/benchmark_ops/benchmark_rmsnorm.py b/examples/inference/benchmark_ops/benchmark_rmsnorm.py\n--- a/examples/inference/benchmark_ops/benchmark_rmsnorm.py\n+++ b/examples/inference/benchmark_ops/benchmark_rmsnorm.py\n@@ -35,7 +35,7 @@\n         styles=[(\"red\", \"-\"), (\"blue\", \"-\"), (\"yellow\", \"-\"), (\"red\", \"--\"), (\"blue\", \"--\"), (\"yellow\", \"--\")],\n         ylabel=\"ms\",\n         plot_name=f\"RMSNorm benchmarking results\",\n-        args={\"HIDDEN_SIZE\": 1024},\n+        args={\"HIDDEN_SIZE\": 5120},\n     )\n ]\n", "issue": "[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n", "before_files": [{"content": "import torch\n\nfrom colossalai.kernel.kernel_loader import InferenceOpsLoader\nfrom colossalai.kernel.triton import rms_layernorm\n\ntry:\n    import triton  # noqa\nexcept ImportError:\n    print(\"please install triton from https://github.com/openai/triton\")\n\ninference_ops = InferenceOpsLoader().load()\n\n# Triton benchmark plot attributions\nconfigs = [\n    triton.testing.Benchmark(\n        x_names=[\"SEQUENCE_TOTAL\"],\n        x_vals=[i for i in range(128, 1025, 128)],\n        line_arg=\"provider\",\n        line_vals=[\n            \"vllm_rms_layernorm\",\n            \"triton_rms_layernorm\",\n            \"cuda_rms_layernorm\",\n            \"vllm_rms_layernorm_with_residual\",\n            \"triton_rms_layernorm_with_residual\",\n            \"cuda_rms_layernorm_with_residual\",\n        ],\n        line_names=[\n            \"vllm_rms_layernorm\",\n            \"triton_rms_layernorm\",\n            \"cuda_rms_layernorm\",\n            \"vllm_rms_layernorm_with_residual\",\n            \"triton_rms_layernorm_with_residual\",\n            \"cuda_rms_layernorm_with_residual\",\n        ],\n        styles=[(\"red\", \"-\"), (\"blue\", \"-\"), (\"yellow\", \"-\"), (\"red\", \"--\"), (\"blue\", \"--\"), (\"yellow\", \"--\")],\n        ylabel=\"ms\",\n        plot_name=f\"RMSNorm benchmarking results\",\n        args={\"HIDDEN_SIZE\": 1024},\n    )\n]\n\n\n@triton.testing.perf_report(configs)\ndef benchmark_rms_layernorm(\n    provider: str,\n    SEQUENCE_TOTAL: int,\n    HIDDEN_SIZE: int,\n):\n    try:\n        from vllm.model_executor.layers.layernorm import RMSNorm\n    except ImportError:\n        raise ImportError(\"Please install vllm from https://github.com/vllm-project/vllm\")\n\n    warmup = 10\n    rep = 1000\n\n    dtype = torch.float16\n    eps = 1e-5\n    x_shape = (SEQUENCE_TOTAL, HIDDEN_SIZE)\n    w_shape = (x_shape[-1],)\n    residual = torch.rand(x_shape, dtype=dtype, device=\"cuda\")\n    weight = torch.ones(w_shape, dtype=dtype, device=\"cuda\")\n    vllm_norm = RMSNorm(hidden_size=HIDDEN_SIZE, eps=eps).to(dtype=dtype, device=\"cuda\")\n    x = -2.3 + 0.5 * torch.randn(x_shape, dtype=dtype, device=\"cuda\")\n    if provider == \"vllm_rms_layernorm\":\n        fn = lambda: vllm_norm(x)\n    elif provider == \"triton_rms_layernorm\":\n        fn = lambda: rms_layernorm(x, weight, eps=eps)\n    elif provider == \"cuda_rms_layernorm\":\n        out = torch.empty_like(x)\n        fn = lambda: inference_ops.rms_layernorm(out, x, weight, eps)\n    elif provider == \"vllm_rms_layernorm_with_residual\":\n        fn = lambda: vllm_norm(x, residual=residual)\n    elif provider == \"triton_rms_layernorm_with_residual\":\n        fn = lambda: rms_layernorm(x, weight, eps=eps, residual=residual)\n    elif provider == \"cuda_rms_layernorm_with_residual\":\n        fn = lambda: inference_ops.fused_add_rms_layernorm(x, residual, weight, eps)\n    else:\n        raise ValueError(\"Undefined provider.\")\n\n    ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep)\n\n    return ms\n\n\nif __name__ == \"__main__\":\n    benchmark_rms_layernorm.run(save_path=\".\", print_data=True)\n", "path": "examples/inference/benchmark_ops/benchmark_rmsnorm.py"}], "after_files": [{"content": "import torch\n\nfrom colossalai.kernel.kernel_loader import InferenceOpsLoader\nfrom colossalai.kernel.triton import rms_layernorm\n\ntry:\n    import triton  # noqa\nexcept ImportError:\n    print(\"please install triton from https://github.com/openai/triton\")\n\ninference_ops = InferenceOpsLoader().load()\n\n# Triton benchmark plot attributions\nconfigs = [\n    triton.testing.Benchmark(\n        x_names=[\"SEQUENCE_TOTAL\"],\n        x_vals=[i for i in range(128, 1025, 128)],\n        line_arg=\"provider\",\n        line_vals=[\n            \"vllm_rms_layernorm\",\n            \"triton_rms_layernorm\",\n            \"cuda_rms_layernorm\",\n            \"vllm_rms_layernorm_with_residual\",\n            \"triton_rms_layernorm_with_residual\",\n            \"cuda_rms_layernorm_with_residual\",\n        ],\n        line_names=[\n            \"vllm_rms_layernorm\",\n            \"triton_rms_layernorm\",\n            \"cuda_rms_layernorm\",\n            \"vllm_rms_layernorm_with_residual\",\n            \"triton_rms_layernorm_with_residual\",\n            \"cuda_rms_layernorm_with_residual\",\n        ],\n        styles=[(\"red\", \"-\"), (\"blue\", \"-\"), (\"yellow\", \"-\"), (\"red\", \"--\"), (\"blue\", \"--\"), (\"yellow\", \"--\")],\n        ylabel=\"ms\",\n        plot_name=f\"RMSNorm benchmarking results\",\n        args={\"HIDDEN_SIZE\": 5120},\n    )\n]\n\n\n@triton.testing.perf_report(configs)\ndef benchmark_rms_layernorm(\n    provider: str,\n    SEQUENCE_TOTAL: int,\n    HIDDEN_SIZE: int,\n):\n    try:\n        from vllm.model_executor.layers.layernorm import RMSNorm\n    except ImportError:\n        raise ImportError(\"Please install vllm from https://github.com/vllm-project/vllm\")\n\n    warmup = 10\n    rep = 1000\n\n    dtype = torch.float16\n    eps = 1e-5\n    x_shape = (SEQUENCE_TOTAL, HIDDEN_SIZE)\n    w_shape = (x_shape[-1],)\n    residual = torch.rand(x_shape, dtype=dtype, device=\"cuda\")\n    weight = torch.ones(w_shape, dtype=dtype, device=\"cuda\")\n    vllm_norm = RMSNorm(hidden_size=HIDDEN_SIZE, eps=eps).to(dtype=dtype, device=\"cuda\")\n    x = -2.3 + 0.5 * torch.randn(x_shape, dtype=dtype, device=\"cuda\")\n    if provider == \"vllm_rms_layernorm\":\n        fn = lambda: vllm_norm(x)\n    elif provider == \"triton_rms_layernorm\":\n        fn = lambda: rms_layernorm(x, weight, eps=eps)\n    elif provider == \"cuda_rms_layernorm\":\n        out = torch.empty_like(x)\n        fn = lambda: inference_ops.rms_layernorm(out, x, weight, eps)\n    elif provider == \"vllm_rms_layernorm_with_residual\":\n        fn = lambda: vllm_norm(x, residual=residual)\n    elif provider == \"triton_rms_layernorm_with_residual\":\n        fn = lambda: rms_layernorm(x, weight, eps=eps, residual=residual)\n    elif provider == \"cuda_rms_layernorm_with_residual\":\n        fn = lambda: inference_ops.fused_add_rms_layernorm(x, residual, weight, eps)\n    else:\n        raise ValueError(\"Undefined provider.\")\n\n    ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep)\n\n    return ms\n\n\nif __name__ == \"__main__\":\n    benchmark_rms_layernorm.run(save_path=\".\", print_data=True)\n", "path": "examples/inference/benchmark_ops/benchmark_rmsnorm.py"}]}