class_docstr: Instances of :class:`autocast` serve as context managers or decorators that
  allow regions of your script to run in mixed precision.
  
  In these regions, ops run in an op-specific dtype chosen by autocast
  to improve performance while maintaining accuracy.
  See the :ref:`Autocast Op Reference<autocast-op-reference>` for details.
  
  When entering an autocast-enabled region, Tensors may be any type.
  You should not call ``half()`` or ``bfloat16()`` on your model(s) or inputs when using autocasting.
  
  :class:`autocast` should wrap only the forward pass(es) of your network, including the loss
  computation(s).  Backward passes under autocast are not recommended.
  Backward ops run in the same type that autocast used for corresponding forward ops.
  
  Example for CUDA Devices::
  
      # Creates model and optimizer in default precision
      model = Net().cuda()
      optimizer = optim.SGD(model.parameters(), ...)
  
      for input, target in data:
          optimizer.zero_grad()
  
          # Enables autocasting for the forward pass (model + loss)
          with torch.autocast(device_type="cuda"):
              output = model(input)
              loss = loss_fn(output, target)
  
          # Exits the context manager before backward()
          loss.backward()
          optimizer.step()
  
  See the :ref:`CUDA Automatic Mixed Precision examples<amp-examples>` for usage (along with gradient scaling)
  in more complex scenarios (e.g., gradient penalty, multiple models/losses, custom autograd functions).
  
  :class:`autocast` can also be used as a decorator, e.g., on the ``forward`` method of your model::
  
      class AutocastModel(nn.Module):
          ...
          @torch.autocast(device_type="cuda")
          def forward(self, input):
              ...
  
  Floating-point Tensors produced in an autocast-enabled region may be ``float16``.
  After returning to an autocast-disabled region, using them with floating-point
  Tensors of different dtypes may cause type mismatch errors.  If so, cast the Tensor(s)
  produced in the autocast region back to ``float32`` (or other dtype if desired).
  If a Tensor from the autocast region is already ``float32``, the cast is a no-op,
  and incurs no additional overhead.
  CUDA Example::
  
      # Creates some tensors in default dtype (here assumed to be float32)
      a_float32 = torch.rand((8, 8), device="cuda")
      b_float32 = torch.rand((8, 8), device="cuda")
      c_float32 = torch.rand((8, 8), device="cuda")
      d_float32 = torch.rand((8, 8), device="cuda")
  
      with torch.autocast(device_type="cuda"):
          # torch.mm is on autocast's list of ops that should run in float16.
          # Inputs are float32, but the op runs in float16 and produces float16 output.
          # No manual casts are required.
          e_float16 = torch.mm(a_float32, b_float32)
          # Also handles mixed input types
          f_float16 = torch.mm(d_float32, e_float16)
  
      # After exiting autocast, calls f_float16.float() to use with d_float32
      g_float32 = torch.mm(d_float32, f_float16.float())
  
  CPU Training Example::
  
      # Creates model and optimizer in default precision
      model = Net()
      optimizer = optim.SGD(model.parameters(), ...)
  
      for epoch in epochs:
          for input, target in data:
              optimizer.zero_grad()
  
              # Runs the forward pass with autocasting.
              with torch.autocast(device_type="cpu", dtype=torch.bfloat16):
                  output = model(input)
                  loss = loss_fn(output, target)
  
              loss.backward()
              optimizer.step()
  
  
  CPU Inference Example::
  
      # Creates model in default precision
      model = Net().eval()
  
      with torch.autocast(device_type="cpu", dtype=torch.bfloat16):
          for input in data:
              # Runs the forward pass with autocasting.
              output = model(input)
  
  CPU Inference Example with Jit Trace::
  
      class TestModel(nn.Module):
          def __init__(self, input_size, num_classes):
              super().__init__()
              self.fc1 = nn.Linear(input_size, num_classes)
          def forward(self, x):
              return self.fc1(x)
  
      input_size = 2
      num_classes = 2
      model = TestModel(input_size, num_classes).eval()
  
      # For now, we suggest to disable the Jit Autocast Pass,
      # As the issue: https://github.com/pytorch/pytorch/issues/75956
      torch._C._jit_set_autocast_mode(False)
  
      with torch.cpu.amp.autocast(cache_enabled=False):
          model = torch.jit.trace(model, torch.randn(1, input_size))
      model = torch.jit.freeze(model)
      # Models Run
      for _ in range(3):
          model(torch.randn(1, input_size))
  
  Type mismatch errors *in* an autocast-enabled region are a bug; if this is what you observe,
  please file an issue.
  
  ``autocast(enabled=False)`` subregions can be nested in autocast-enabled regions.
  Locally disabling autocast can be useful, for example, if you want to force a subregion
  to run in a particular ``dtype``.  Disabling autocast gives you explicit control over
  the execution type.  In the subregion, inputs from the surrounding region
  should be cast to ``dtype`` before use::
  
      # Creates some tensors in default dtype (here assumed to be float32)
      a_float32 = torch.rand((8, 8), device="cuda")
      b_float32 = torch.rand((8, 8), device="cuda")
      c_float32 = torch.rand((8, 8), device="cuda")
      d_float32 = torch.rand((8, 8), device="cuda")
  
      with torch.autocast(device_type="cuda"):
          e_float16 = torch.mm(a_float32, b_float32)
          with torch.autocast(device_type="cuda", enabled=False):
              # Calls e_float16.float() to ensure float32 execution
              # (necessary because e_float16 was created in an autocasted region)
              f_float32 = torch.mm(c_float32, e_float16.float())
  
          # No manual casts are required when re-entering the autocast-enabled region.
          # torch.mm again runs in float16 and produces float16 output, regardless of input types.
          g_float16 = torch.mm(d_float32, f_float32)
  
  The autocast state is thread-local.  If you want it enabled in a new thread, the context manager or decorator
  must be invoked in that thread.  This affects :class:`torch.nn.DataParallel` and
  :class:`torch.nn.parallel.DistributedDataParallel` when used with more than one GPU per process
  (see :ref:`Working with Multiple GPUs<amp-multigpu>`).
  
  Args:
      device_type(str, required):  Device type to use. Possible values are: 'cuda', 'cpu', 'xpu' and 'hpu'.
                                   The type is the same as the `type` attribute of a :class:`torch.device`.
                                   Thus, you may obtain the device type of a tensor using `Tensor.device.type`.
      enabled(bool, optional):  Whether autocasting should be enabled in the region.
          Default: ``True``
      dtype(torch_dtype, optional):  Whether to use torch.float16 or torch.bfloat16.
      cache_enabled(bool, optional):  Whether the weight cache inside autocast should be enabled.
          Default: ``True``
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code: def __init__(
      self,
      device_type: str,
      dtype: Optional[_dtype] = None,
      enabled: bool = True,
      cache_enabled: Optional[bool] = None,
  ):
      if torch._jit_internal.is_scripting():
          self._enabled = enabled
          self.device = device_type
          self.fast_dtype = dtype
          # TODO: support get_autocast_gpu/cpu_dtype
          assert dtype is not None
          return
      self.device = device_type
      self.custom_backend_name = (
          torch._C._get_privateuse1_backend_name()
      )
      if self.device == "cuda":
          self.fast_dtype = torch.get_autocast_gpu_dtype()
      elif self.device == "cpu":
          self.fast_dtype = torch.get_autocast_cpu_dtype()
      elif self.device == "xpu":
          self.fast_dtype = torch.xpu.get_autocast_xpu_dtype()  # type: ignore[attr-defined]
      elif self.device == "ipu":
          self.fast_dtype = torch.get_autocast_ipu_dtype()  # type: ignore[attr-defined]
      elif self.device == "hpu":
          self.fast_dtype = torch.hpu.get_autocast_hpu_dtype()  # type: ignore[attr-defined]
      elif self.device == "xla":
          self.fast_dtype = torch.get_autocast_xla_dtype()  # type: ignore[attr-defined]
      elif self.device == self.custom_backend_name:
          necessary_funcs = [
              "is_autocast_enabled",
              "set_autocast_enabled",
              "get_autocast_dtype",
              "set_autocast_dtype",
              "get_amp_supported_dtype",
          ]
          message = f"Tried to use AMP with the `{self.custom_backend_name}` backend, but the backend has not "
          message += "registered a module or  the module miss some necessary funcs. The backend should register "
          message += "a module by `torch._register_device_module`, and the module must have these funcs: \n"
          message += "`is_autocast_enabled() -> bool`, `set_autocast_enabled(bool) -> None`, "
          message += "`get_autocast_dtype() -> torch.dtype`, `set_autocast_dtype(torch.dtype) "
          message += "-> None` and `get_amp_supported_dtype() -> List[torch.dtype]`. \n"
  
          assert hasattr(torch, self.custom_backend_name), message
          self.custom_device_mod = getattr(
              torch, self.custom_backend_name
          )
          for func in necessary_funcs:
              assert hasattr(self.custom_device_mod, func), (
                  message + f"But the func `{func}` is missing. \n"
              )
  
          self.fast_dtype = self.custom_device_mod.get_autocast_dtype()
      else:
          raise RuntimeError(
              f"User specified an unsupported autocast device_type '{self.device}'"
          )
      self._cache_enabled = torch.is_autocast_cache_enabled()
      if (
          enabled
          and torch.cuda.amp.common.amp_definitely_not_available()
          and self.device == "cuda"
      ):
          warnings.warn(
              "User provided device_type of 'cuda', but CUDA is not available. Disabling"
          )
          enabled = False
      if dtype is not None:
          self.fast_dtype = dtype
      if cache_enabled is not None:
          self._cache_enabled = cache_enabled
  
      if self.device == "cpu":
          supported_dtype = [torch.bfloat16, torch.float16]
          if self.fast_dtype not in supported_dtype and enabled:
              error_message = "In CPU autocast, but the target dtype is not supported. Disabling autocast.\n"
              error_message += "CPU Autocast only supports dtype of "
              error_message += (
                  ", ".join(str(dtype) for dtype in supported_dtype)
                  + " currently."
              )
              warnings.warn(error_message)
              enabled = False
      elif self.device == "xpu":
          supported_dtype = [torch.bfloat16, torch.float16]
          if self.fast_dtype not in supported_dtype:
              error_message = "In XPU autocast, but the target dtype is not supported. Disabling autocast.\n"
              error_message += "XPU Autocast only supports dtypes of torch.bfloat16 and torch.float16 currently."
              warnings.warn(error_message)
              enabled = False
      elif self.device == "ipu":
          supported_dtypes = [torch.bfloat16, torch.float16]
          if self.fast_dtype not in supported_dtypes:
              error_message = "In IPU autocast, but the target dtype is not supported. Disabling autocast.\n"
              error_message += "IPU Autocast only supports dtypes of torch.bfloat16 and torch.float16 currently."
              warnings.warn(error_message)
              enabled = False
      elif self.device == "hpu":
          supported_dtype = [torch.bfloat16, torch.float16]
          if self.fast_dtype not in supported_dtype:
              error_message = "In HPU autocast, but the target dtype is not supported. Disabling autocast.\n"
              error_message += "HPU Autocast only supports dtypes of torch.bfloat16 and torch.float16 currently."
              warnings.warn(error_message)
              enabled = False
      elif self.device == self.custom_backend_name:
          supported_dtype = (
              self.custom_device_mod.get_amp_supported_dtype()
          )
          if self.fast_dtype not in supported_dtype:
              error_message = f"In {self.custom_backend_name} autocast, but the target dtype is not supported. "
              error_message += f"Disabling autocast.\n {self.custom_backend_name} Autocast only supports dtypes of "
              error_message += (
                  ", ".join(str(dtype) for dtype in supported_dtype)
                  + " currently."
              )
              warnings.warn(error_message)
              enabled = False
      elif self.device == "cuda":
          if (
              enabled
              and self.fast_dtype == torch.bfloat16
              and not torch.cuda.is_bf16_supported()
          ):
              raise RuntimeError(
                  "Current CUDA Device does not support bfloat16. Please switch dtype to float16."
              )
      elif self.device == "xla":
          supported_dtype = [torch.float16, torch.bfloat16]
          if self.fast_dtype not in supported_dtype:
              error_message = "In XLA autocast, but the target dtype is not supported. Disabling autocast.\n"
              error_message += "XLA Autocast only supports dtype of torch.bfloat16 currently."
              warnings.warn(error_message)
              enabled = False
      self._enabled = enabled
  
file_path: pytorch/torch/amp/autocast_mode.py
imports: import functools
  import warnings
  from typing import Any, Optional
  import torch
  from torch.types import _dtype
label_desc: Use image node_id 1 to create a new autocast object  with example: obj = autocast(device_type, dtype, enabled, cache_enabled)