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 colossalai/kernel/triton/rms_layernorm.py]
  1 import torch
  2 
  3 try:
  4     import triton
  5     import triton.language as tl
  6 
  7     HAS_TRITON = True
  8 except ImportError:
  9     HAS_TRITON = False
  10     print("please install triton from https://github.com/openai/triton")
  11 
  12 if HAS_TRITON:
  13     # CREDITS: These functions are adapted from the Triton tutorial
  14     # https://triton-lang.org/main/getting-started/tutorials/05-layer-norm.html
  15 
  16     @triton.jit
  17     def _rmsnorm_kernel(
  18         X,  # pointer to the input
  19         Y,  # pointer to the output
  20         W,  # pointer to the weights
  21         stride,  # how much to increase the pointer when moving by 1 row
  22         N,  # number of columns in X
  23         eps,  # epsilon to avoid division by zero
  24         BLOCK_SIZE: tl.constexpr,
  25     ):
  26 
  27         # This triton kernel implements Root Mean Square Layer Norm (RMSNorm).
  28 
  29         # Map the program id to the row of X and Y it should compute.
  30         row = tl.program_id(0)
  31         Y += row * stride
  32         X += row * stride
  33         # Compute variance
  34         _var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
  35         for off in range(0, N, BLOCK_SIZE):
  36             cols = off + tl.arange(0, BLOCK_SIZE)
  37             x = tl.load(X + cols, mask=cols < N, other=0.0).to(tl.float32)
  38             x = tl.where(cols < N, x, 0.0)
  39             _var += x * x
  40         var = tl.sum(_var, axis=0) / N
  41         rstd = 1 / tl.sqrt(var + eps)
  42         # Normalize and apply linear transformation
  43         for off in range(0, N, BLOCK_SIZE):
  44             cols = off + tl.arange(0, BLOCK_SIZE)
  45             mask = cols < N
  46             w = tl.load(W + cols, mask=mask)
  47             x = tl.load(X + cols, mask=mask, other=0.0).to(tl.float32)
  48             x_hat = x * rstd
  49             y = x_hat * w
  50             # Write output
  51             tl.store(Y + cols, y.to(tl.float16), mask=mask)
  52 
  53     @torch.no_grad()
  54     def rms_layernorm(x, weight, eps):
  55         # allocate output
  56         y = torch.empty_like(x)
  57         # reshape input data into 2D tensor
  58         x_arg = x.reshape(-1, x.shape[-1])
  59         M, N = x_arg.shape
  60         # Less than 64KB per feature: enqueue fused kernel
  61         MAX_FUSED_SIZE = 65536 // x.element_size()
  62         BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
  63         if N > BLOCK_SIZE:
  64             raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.")
  65         # heuristics for number of warps
  66         num_warps = min(max(BLOCK_SIZE // 256, 1), 8)
  67         # enqueue kernel
  68         _rmsnorm_kernel[(M,)](
  69             x_arg, y, weight, x_arg.stride(0), N, eps, BLOCK_SIZE=BLOCK_SIZE, num_warps=num_warps
  70         )
  71         return y
  72
  [end of colossalai/kernel/triton/rms_layernorm.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/colossalai/kernel/triton/rms_layernorm.py b/colossalai/kernel/triton/rms_layernorm.py
  --- a/colossalai/kernel/triton/rms_layernorm.py
  +++ b/colossalai/kernel/triton/rms_layernorm.py
  @@ -23,7 +23,6 @@
           eps,  # epsilon to avoid division by zero
           BLOCK_SIZE: tl.constexpr,
       ):
  -
           # This triton kernel implements Root Mean Square Layer Norm (RMSNorm).
   
           # Map the program id to the row of X and Y it should compute.
  @@ -54,18 +53,19 @@
       def rms_layernorm(x, weight, eps):
           # allocate output
           y = torch.empty_like(x)
  -        # reshape input data into 2D tensor
  +        # reshape input data into 2D tensor, (total token, hidden_size)
           x_arg = x.reshape(-1, x.shape[-1])
           M, N = x_arg.shape
           # Less than 64KB per feature: enqueue fused kernel
           MAX_FUSED_SIZE = 65536 // x.element_size()
  +
           BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
  -        if N > BLOCK_SIZE:
  +        if N > MAX_FUSED_SIZE:
               raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.")
  +
           # heuristics for number of warps
  -        num_warps = min(max(BLOCK_SIZE // 256, 1), 8)
  +        num_warps = min(max(triton.next_power_of_2(N) // 256, 8), 32)
  +
           # enqueue kernel
  -        _rmsnorm_kernel[(M,)](
  -            x_arg, y, weight, x_arg.stride(0), N, eps, BLOCK_SIZE=BLOCK_SIZE, num_warps=num_warps
  -        )
  +        _rmsnorm_kernel[(M,)](x_arg, y, weight, x_arg.stride(0), N, eps, BLOCK_SIZE=BLOCK_SIZE, num_warps=num_warps)
           return y
  
verification_info: {"golden_diff": "diff --git a/colossalai/kernel/triton/rms_layernorm.py b/colossalai/kernel/triton/rms_layernorm.py\n--- a/colossalai/kernel/triton/rms_layernorm.py\n+++ b/colossalai/kernel/triton/rms_layernorm.py\n@@ -23,7 +23,6 @@\n         eps,  # epsilon to avoid division by zero\n         BLOCK_SIZE: tl.constexpr,\n     ):\n-\n         # This triton kernel implements Root Mean Square Layer Norm (RMSNorm).\n \n         # Map the program id to the row of X and Y it should compute.\n@@ -54,18 +53,19 @@\n     def rms_layernorm(x, weight, eps):\n         # allocate output\n         y = torch.empty_like(x)\n-        # reshape input data into 2D tensor\n+        # reshape input data into 2D tensor, (total token, hidden_size)\n         x_arg = x.reshape(-1, x.shape[-1])\n         M, N = x_arg.shape\n         # Less than 64KB per feature: enqueue fused kernel\n         MAX_FUSED_SIZE = 65536 // x.element_size()\n+\n         BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))\n-        if N > BLOCK_SIZE:\n+        if N > MAX_FUSED_SIZE:\n             raise RuntimeError(\"This layer norm doesn't support feature dim >= 64KB.\")\n+\n         # heuristics for number of warps\n-        num_warps = min(max(BLOCK_SIZE // 256, 1), 8)\n+        num_warps = min(max(triton.next_power_of_2(N) // 256, 8), 32)\n+\n         # enqueue kernel\n-        _rmsnorm_kernel[(M,)](\n-            x_arg, y, weight, x_arg.stride(0), N, eps, BLOCK_SIZE=BLOCK_SIZE, num_warps=num_warps\n-        )\n+        _rmsnorm_kernel[(M,)](x_arg, y, weight, x_arg.stride(0), N, eps, BLOCK_SIZE=BLOCK_SIZE, num_warps=num_warps)\n         return y\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\ntry:\n    import triton\n    import triton.language as tl\n\n    HAS_TRITON = True\nexcept ImportError:\n    HAS_TRITON = False\n    print(\"please install triton from https://github.com/openai/triton\")\n\nif HAS_TRITON:\n    # CREDITS: These functions are adapted from the Triton tutorial\n    # https://triton-lang.org/main/getting-started/tutorials/05-layer-norm.html\n\n    @triton.jit\n    def _rmsnorm_kernel(\n        X,  # pointer to the input\n        Y,  # pointer to the output\n        W,  # pointer to the weights\n        stride,  # how much to increase the pointer when moving by 1 row\n        N,  # number of columns in X\n        eps,  # epsilon to avoid division by zero\n        BLOCK_SIZE: tl.constexpr,\n    ):\n\n        # This triton kernel implements Root Mean Square Layer Norm (RMSNorm).\n\n        # Map the program id to the row of X and Y it should compute.\n        row = tl.program_id(0)\n        Y += row * stride\n        X += row * stride\n        # Compute variance\n        _var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)\n        for off in range(0, N, BLOCK_SIZE):\n            cols = off + tl.arange(0, BLOCK_SIZE)\n            x = tl.load(X + cols, mask=cols < N, other=0.0).to(tl.float32)\n            x = tl.where(cols < N, x, 0.0)\n            _var += x * x\n        var = tl.sum(_var, axis=0) / N\n        rstd = 1 / tl.sqrt(var + eps)\n        # Normalize and apply linear transformation\n        for off in range(0, N, BLOCK_SIZE):\n            cols = off + tl.arange(0, BLOCK_SIZE)\n            mask = cols < N\n            w = tl.load(W + cols, mask=mask)\n            x = tl.load(X + cols, mask=mask, other=0.0).to(tl.float32)\n            x_hat = x * rstd\n            y = x_hat * w\n            # Write output\n            tl.store(Y + cols, y.to(tl.float16), mask=mask)\n\n    @torch.no_grad()\n    def rms_layernorm(x, weight, eps):\n        # allocate output\n        y = torch.empty_like(x)\n        # reshape input data into 2D tensor\n        x_arg = x.reshape(-1, x.shape[-1])\n        M, N = x_arg.shape\n        # Less than 64KB per feature: enqueue fused kernel\n        MAX_FUSED_SIZE = 65536 // x.element_size()\n        BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))\n        if N > BLOCK_SIZE:\n            raise RuntimeError(\"This layer norm doesn't support feature dim >= 64KB.\")\n        # heuristics for number of warps\n        num_warps = min(max(BLOCK_SIZE // 256, 1), 8)\n        # enqueue kernel\n        _rmsnorm_kernel[(M,)](\n            x_arg, y, weight, x_arg.stride(0), N, eps, BLOCK_SIZE=BLOCK_SIZE, num_warps=num_warps\n        )\n        return y\n", "path": "colossalai/kernel/triton/rms_layernorm.py"}]}