prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  [tensor] fix some unittests
  
  [tensor] fix some unittests
  
  [tensor] fix some unittests
  
  
  </issue>
  <code>
  [start of examples/inference/benchmark_ops/benchmark_rmsnorm.py]
  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 of examples/inference/benchmark_ops/benchmark_rmsnorm.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/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"}]}