prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  BUG 3D benchmark fails on GPU
  It doesn't call the `cuda()` function (now required) when giving a CUDA tensor input, so the scattering transform errors.
  
  --- 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/3d/compute_speed.py`
  Content:
  ```
  1 """
  2 3D scattering transform benchmark
  3 =================================
  4 We compute scattering transforms for volume maps of size `128`-by-`128`-by-
  5 `128`, with averaging scale `2**2 = 4` and maximum spherical harmonic
  6 order `L = 2`. The volumes are stacked into batches of size `batch_size = 8`
  7 and the transforms are computed `10` times to get an average running time.
  8 """
  9 
  10 ###############################################################################
  11 # Preliminaries
  12 # -------------
  13 # Since kymatio handles PyTorch arrays, we first import `torch`.
  14 
  15 import torch
  16 
  17 ###############################################################################
  18 # To measure the running time of the implementation, we use the `time` package.
  19 
  20 import time
  21 
  22 ###############################################################################
  23 # The performance of the implementation depends on which "backend" is used. We
  24 # therefore want to report the name of the backend when presenting the results.
  25 # Certain backends are also GPU-only, we we want to detect that before running
  26 # the benchmark.
  27 
  28 import kymatio.scattering3d.backend as backend
  29 
  30 ###############################################################################
  31 # Finally, we import the `HarmonicScattering3D` class that computes the scattering
  32 # transform.
  33 
  34 from kymatio import HarmonicScattering3D
  35 ###############################################################################
  36 # Benchmark setup
  37 # --------------------
  38 # First, we set up some basic parameters: the volume width `M`, height `N`,
  39 # and depth 'O', the maximum number of the spherical harmonics `L`, and the
  40 # maximum scale `2**J`. Here, we consider cubic volumes of size `128`, with
  41 # a maximum scale of `2**2 = 4` and maximum spherical harmonic order of `2`.
  42 
  43 M, N, O = 128, 128, 128
  44 J = 2
  45 L = 2
  46 
  47 integral_powers = [1., 2.]
  48 sigma_0 = 1
  49 
  50 ###############################################################################
  51 # To squeeze the maximum performance out of the implementation, we apply it to
  52 # a batch of `8` volumes. Larger batch sizes do not yield increased efficiency,
  53 # but smaller values increases the influence of overhead on the running time.
  54 
  55 batch_size = 8
  56 
  57 ###############################################################################
  58 # We repeat the benchmark `10` times and compute the average running time to
  59 # get a reasonable estimate.
  60 
  61 times = 10
  62 
  63 ###############################################################################
  64 # Determine which devices (CPU or GPU) that are supported by the current
  65 # backend.
  66 
  67 if backend.NAME == 'torch':
  68     devices = ['cpu', 'gpu']
  69 elif backend.NAME == 'skcuda':
  70     devices = ['gpu']
  71 
  72 ###############################################################################
  73 # Set up the scattering object and the test data
  74 # ----------------------------------------------
  75 
  76 ###############################################################################
  77 # Create the `HarmonicScattering3D` object using the given parameters and generate
  78 # some compatible test data with the specified batch size.
  79 
  80 scattering = HarmonicScattering3D(J, shape=(M, N, O), L=L, sigma_0=sigma_0)
  81 
  82 x = torch.randn(batch_size, M, N, O, dtype=torch.float32)
  83 
  84 ###############################################################################
  85 # Run the benchmark
  86 # -----------------
  87 # For each device, we need to convert the Tensor `x` to the appropriate type,
  88 # invoke `times` calls to `scattering.forward` and print the running times.
  89 # Before the timer starts, we add an extra `scattering.forward` call to ensure
  90 # any first-time overhead, such as memory allocation and CUDA kernel
  91 # compilation, is not counted. If the benchmark is running on the GPU, we also
  92 # need to call `torch.cuda.synchronize()` before and after the benchmark to
  93 # make sure that all CUDA kernels have finished executing.
  94 
  95 for device in devices:
  96     fmt_str = '==> Testing Float32 with {} backend, on {}, forward'
  97     print(fmt_str.format(backend.NAME, device.upper()))
  98 
  99     if device == 'gpu':
  100         x = x.cuda()
  101     else:
  102         x = x.cpu()
  103 
  104     scattering.method = 'integral'
  105     scattering.integral_powers = integral_powers
  106 
  107     scattering.forward(x)
  108 
  109     if device == 'gpu':
  110         torch.cuda.synchronize()
  111 
  112     t_start = time.time()
  113     for _ in range(times):
  114         scattering.forward(x)
  115 
  116     if device == 'gpu':
  117         torch.cuda.synchronize()
  118 
  119     t_elapsed = time.time() - t_start
  120 
  121     fmt_str = 'Elapsed time: {:2f} [s / {:d} evals], avg: {:.2f} (s/batch)'
  122     print(fmt_str.format(t_elapsed, times, t_elapsed/times))
  123 
  124 ###############################################################################
  125 # The resulting output should be something like
  126 #
  127 # .. code-block:: text
  128 #
  129 #   ==> Testing Float32 with torch backend, on CPU, forward
  130 #   Elapsed time: 109.739110 [s / 10 evals], avg: 10.97 (s/batch)
  131 #   ==> Testing Float32 with torch backend, on GPU, forward
  132 #   Elapsed time: 60.476041 [s / 10 evals], avg: 6.05 (s/batch)
  133
  ```
  --- 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/3d/compute_speed.py b/examples/3d/compute_speed.py
  --- a/examples/3d/compute_speed.py
  +++ b/examples/3d/compute_speed.py
  @@ -97,8 +97,10 @@
       print(fmt_str.format(backend.NAME, device.upper()))
   
       if device == 'gpu':
  +        scattering.cuda()
           x = x.cuda()
       else:
  +        scattering.cpu()
           x = x.cpu()
   
       scattering.method = 'integral'
  
verification_info: {"golden_diff": "diff --git a/examples/3d/compute_speed.py b/examples/3d/compute_speed.py\n--- a/examples/3d/compute_speed.py\n+++ b/examples/3d/compute_speed.py\n@@ -97,8 +97,10 @@\n     print(fmt_str.format(backend.NAME, device.upper()))\n \n     if device == 'gpu':\n+        scattering.cuda()\n         x = x.cuda()\n     else:\n+        scattering.cpu()\n         x = x.cpu()\n \n     scattering.method = 'integral'\n", "issue": "BUG 3D benchmark fails on GPU\nIt doesn't call the `cuda()` function (now required) when giving a CUDA tensor input, so the scattering transform errors.\n", "before_files": [{"content": "\"\"\"\n3D scattering transform benchmark\n=================================\nWe compute scattering transforms for volume maps of size `128`-by-`128`-by-\n`128`, with averaging scale `2**2 = 4` and maximum spherical harmonic\norder `L = 2`. The volumes are stacked into batches of size `batch_size = 8`\nand the transforms are computed `10` times to get an average running time.\n\"\"\"\n\n###############################################################################\n# Preliminaries\n# -------------\n# Since kymatio handles PyTorch arrays, we first import `torch`.\n\nimport torch\n\n###############################################################################\n# To measure the running time of the implementation, we use the `time` package.\n\nimport time\n\n###############################################################################\n# The performance of the implementation depends on which \"backend\" is used. We\n# therefore want to report the name of the backend when presenting the results.\n# Certain backends are also GPU-only, we we want to detect that before running\n# the benchmark.\n\nimport kymatio.scattering3d.backend as backend\n\n###############################################################################\n# Finally, we import the `HarmonicScattering3D` class that computes the scattering\n# transform.\n\nfrom kymatio import HarmonicScattering3D\n###############################################################################\n# Benchmark setup\n# --------------------\n# First, we set up some basic parameters: the volume width `M`, height `N`,\n# and depth 'O', the maximum number of the spherical harmonics `L`, and the\n# maximum scale `2**J`. Here, we consider cubic volumes of size `128`, with\n# a maximum scale of `2**2 = 4` and maximum spherical harmonic order of `2`.\n\nM, N, O = 128, 128, 128\nJ = 2\nL = 2\n\nintegral_powers = [1., 2.]\nsigma_0 = 1\n\n###############################################################################\n# To squeeze the maximum performance out of the implementation, we apply it to\n# a batch of `8` volumes. Larger batch sizes do not yield increased efficiency,\n# but smaller values increases the influence of overhead on the running time.\n\nbatch_size = 8\n\n###############################################################################\n# We repeat the benchmark `10` times and compute the average running time to\n# get a reasonable estimate.\n\ntimes = 10\n\n###############################################################################\n# Determine which devices (CPU or GPU) that are supported by the current\n# backend.\n\nif backend.NAME == 'torch':\n    devices = ['cpu', 'gpu']\nelif backend.NAME == 'skcuda':\n    devices = ['gpu']\n\n###############################################################################\n# Set up the scattering object and the test data\n# ----------------------------------------------\n\n###############################################################################\n# Create the `HarmonicScattering3D` object using the given parameters and generate\n# some compatible test data with the specified batch size.\n\nscattering = HarmonicScattering3D(J, shape=(M, N, O), L=L, sigma_0=sigma_0)\n\nx = torch.randn(batch_size, M, N, O, dtype=torch.float32)\n\n###############################################################################\n# Run the benchmark\n# -----------------\n# For each device, we need to convert the Tensor `x` to the appropriate type,\n# invoke `times` calls to `scattering.forward` and print the running times.\n# Before the timer starts, we add an extra `scattering.forward` call to ensure\n# any first-time overhead, such as memory allocation and CUDA kernel\n# compilation, is not counted. If the benchmark is running on the GPU, we also\n# need to call `torch.cuda.synchronize()` before and after the benchmark to\n# make sure that all CUDA kernels have finished executing.\n\nfor device in devices:\n    fmt_str = '==> Testing Float32 with {} backend, on {}, forward'\n    print(fmt_str.format(backend.NAME, device.upper()))\n\n    if device == 'gpu':\n        x = x.cuda()\n    else:\n        x = x.cpu()\n\n    scattering.method = 'integral'\n    scattering.integral_powers = integral_powers\n\n    scattering.forward(x)\n\n    if device == 'gpu':\n        torch.cuda.synchronize()\n\n    t_start = time.time()\n    for _ in range(times):\n        scattering.forward(x)\n\n    if device == 'gpu':\n        torch.cuda.synchronize()\n\n    t_elapsed = time.time() - t_start\n\n    fmt_str = 'Elapsed time: {:2f} [s / {:d} evals], avg: {:.2f} (s/batch)'\n    print(fmt_str.format(t_elapsed, times, t_elapsed/times))\n\n###############################################################################\n# The resulting output should be something like\n#\n# .. code-block:: text\n#\n#   ==> Testing Float32 with torch backend, on CPU, forward\n#   Elapsed time: 109.739110 [s / 10 evals], avg: 10.97 (s/batch)\n#   ==> Testing Float32 with torch backend, on GPU, forward\n#   Elapsed time: 60.476041 [s / 10 evals], avg: 6.05 (s/batch)\n", "path": "examples/3d/compute_speed.py"}], "after_files": [{"content": "\"\"\"\n3D scattering transform benchmark\n=================================\nWe compute scattering transforms for volume maps of size `128`-by-`128`-by-\n`128`, with averaging scale `2**2 = 4` and maximum spherical harmonic\norder `L = 2`. The volumes are stacked into batches of size `batch_size = 8`\nand the transforms are computed `10` times to get an average running time.\n\"\"\"\n\n###############################################################################\n# Preliminaries\n# -------------\n# Since kymatio handles PyTorch arrays, we first import `torch`.\n\nimport torch\n\n###############################################################################\n# To measure the running time of the implementation, we use the `time` package.\n\nimport time\n\n###############################################################################\n# The performance of the implementation depends on which \"backend\" is used. We\n# therefore want to report the name of the backend when presenting the results.\n# Certain backends are also GPU-only, we we want to detect that before running\n# the benchmark.\n\nimport kymatio.scattering3d.backend as backend\n\n###############################################################################\n# Finally, we import the `HarmonicScattering3D` class that computes the scattering\n# transform.\n\nfrom kymatio import HarmonicScattering3D\n###############################################################################\n# Benchmark setup\n# --------------------\n# First, we set up some basic parameters: the volume width `M`, height `N`,\n# and depth 'O', the maximum number of the spherical harmonics `L`, and the\n# maximum scale `2**J`. Here, we consider cubic volumes of size `128`, with\n# a maximum scale of `2**2 = 4` and maximum spherical harmonic order of `2`.\n\nM, N, O = 128, 128, 128\nJ = 2\nL = 2\n\nintegral_powers = [1., 2.]\nsigma_0 = 1\n\n###############################################################################\n# To squeeze the maximum performance out of the implementation, we apply it to\n# a batch of `8` volumes. Larger batch sizes do not yield increased efficiency,\n# but smaller values increases the influence of overhead on the running time.\n\nbatch_size = 8\n\n###############################################################################\n# We repeat the benchmark `10` times and compute the average running time to\n# get a reasonable estimate.\n\ntimes = 10\n\n###############################################################################\n# Determine which devices (CPU or GPU) that are supported by the current\n# backend.\n\nif backend.NAME == 'torch':\n    devices = ['cpu', 'gpu']\nelif backend.NAME == 'skcuda':\n    devices = ['gpu']\n\n###############################################################################\n# Set up the scattering object and the test data\n# ----------------------------------------------\n\n###############################################################################\n# Create the `HarmonicScattering3D` object using the given parameters and generate\n# some compatible test data with the specified batch size.\n\nscattering = HarmonicScattering3D(J, shape=(M, N, O), L=L, sigma_0=sigma_0)\n\nx = torch.randn(batch_size, M, N, O, dtype=torch.float32)\n\n###############################################################################\n# Run the benchmark\n# -----------------\n# For each device, we need to convert the Tensor `x` to the appropriate type,\n# invoke `times` calls to `scattering.forward` and print the running times.\n# Before the timer starts, we add an extra `scattering.forward` call to ensure\n# any first-time overhead, such as memory allocation and CUDA kernel\n# compilation, is not counted. If the benchmark is running on the GPU, we also\n# need to call `torch.cuda.synchronize()` before and after the benchmark to\n# make sure that all CUDA kernels have finished executing.\n\nfor device in devices:\n    fmt_str = '==> Testing Float32 with {} backend, on {}, forward'\n    print(fmt_str.format(backend.NAME, device.upper()))\n\n    if device == 'gpu':\n        scattering.cuda()\n        x = x.cuda()\n    else:\n        scattering.cpu()\n        x = x.cpu()\n\n    scattering.method = 'integral'\n    scattering.integral_powers = integral_powers\n\n    scattering.forward(x)\n\n    if device == 'gpu':\n        torch.cuda.synchronize()\n\n    t_start = time.time()\n    for _ in range(times):\n        scattering.forward(x)\n\n    if device == 'gpu':\n        torch.cuda.synchronize()\n\n    t_elapsed = time.time() - t_start\n\n    fmt_str = 'Elapsed time: {:2f} [s / {:d} evals], avg: {:.2f} (s/batch)'\n    print(fmt_str.format(t_elapsed, times, t_elapsed/times))\n\n###############################################################################\n# The resulting output should be something like\n#\n# .. code-block:: text\n#\n#   ==> Testing Float32 with torch backend, on CPU, forward\n#   Elapsed time: 109.739110 [s / 10 evals], avg: 10.97 (s/batch)\n#   ==> Testing Float32 with torch backend, on GPU, forward\n#   Elapsed time: 60.476041 [s / 10 evals], avg: 6.05 (s/batch)\n", "path": "examples/3d/compute_speed.py"}]}