mirror of
https://github.com/zebrajr/pytorch.git
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46eaf4be7d
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97024 Approved by: https://github.com/Skylion007, https://github.com/soulitzer
374 lines
18 KiB
Python
374 lines
18 KiB
Python
"""
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``torch.autograd`` provides classes and functions implementing automatic
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differentiation of arbitrary scalar valued functions. It requires minimal
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changes to the existing code - you only need to declare :class:`Tensor` s
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for which gradients should be computed with the ``requires_grad=True`` keyword.
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As of now, we only support autograd for floating point :class:`Tensor` types (
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half, float, double and bfloat16) and complex :class:`Tensor` types (cfloat, cdouble).
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"""
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import torch
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import warnings
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from torch.types import _TensorOrTensors, _size
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from typing import Any, Callable, List, Optional, Sequence, Tuple, Union, cast
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from .variable import Variable
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from .function import Function, NestedIOFunction
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from .gradcheck import gradcheck, gradgradcheck
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from .grad_mode import (
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no_grad, enable_grad, set_grad_enabled, inference_mode, set_multithreading_enabled, _force_original_view_tracking,
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_unsafe_preserve_version_counter
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)
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from .anomaly_mode import detect_anomaly, set_detect_anomaly
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from ..overrides import has_torch_function, handle_torch_function, is_tensor_like
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from . import functional
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from . import forward_ad
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from . import graph
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from .. import _vmap_internals
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__all__ = ['Variable', 'Function', 'backward', 'grad_mode']
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_OptionalTensor = Optional[torch.Tensor]
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_ShapeorNestedShape = Union[_size, Sequence[_size], torch.Tensor]
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def _calculate_shape(output: torch.Tensor, grad: torch.Tensor,
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is_grads_batched: bool) -> Tuple[_ShapeorNestedShape, _ShapeorNestedShape]:
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# is_same_size ensures that both tensors are either nested or non nested
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if output.is_nested:
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if is_grads_batched:
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raise RuntimeError("Batched grads are not supported with Nested Tensor.")
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out_shape = output._nested_tensor_size()
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grad_shape = grad._nested_tensor_size()
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return out_shape, grad_shape
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reg_out_shape = output.shape
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reg_grad_shape = grad.shape if not is_grads_batched else grad.shape[1:]
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return reg_out_shape, reg_grad_shape
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def _make_grads(outputs: Sequence[torch.Tensor], grads: Sequence[_OptionalTensor],
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is_grads_batched: bool) -> Tuple[_OptionalTensor, ...]:
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new_grads: List[_OptionalTensor] = []
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for out, grad in zip(outputs, grads):
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if isinstance(grad, torch.Tensor):
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first_grad = grad if not is_grads_batched else grad[0]
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if not torch.is_same_size(out, first_grad):
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out_shape, grad_shape = _calculate_shape(out, first_grad, is_grads_batched)
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if is_grads_batched:
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raise RuntimeError("If `is_grads_batched=True`, we interpret the first "
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"dimension of each grad_output as the batch dimension. "
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"The sizes of the remaining dimensions are expected to match "
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"the shape of corresponding output, but a mismatch "
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"was detected: grad_output["
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+ str(grads.index(grad)) + "] has a shape of "
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+ str(grad_shape) + " and output["
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+ str(outputs.index(out)) + "] has a shape of "
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+ str(out_shape) + ". "
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"If you only want some tensors in `grad_output` to be considered "
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"batched, consider using vmap.")
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else:
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raise RuntimeError("Mismatch in shape: grad_output["
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+ str(grads.index(grad)) + "] has a shape of "
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+ str(grad_shape) + " and output["
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+ str(outputs.index(out)) + "] has a shape of "
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+ str(out_shape) + ".")
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if out.dtype.is_complex != grad.dtype.is_complex:
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raise RuntimeError("For complex Tensors, both grad_output and output"
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" are required to have the same dtype."
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" Mismatch in dtype: grad_output["
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+ str(grads.index(grad)) + "] has a dtype of "
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+ str(grad.dtype) + " and output["
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+ str(outputs.index(out)) + "] has a dtype of "
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+ str(out.dtype) + ".")
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new_grads.append(grad)
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elif grad is None:
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if out.requires_grad:
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if out.numel() != 1:
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raise RuntimeError("grad can be implicitly created only for scalar outputs")
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if not out.dtype.is_floating_point:
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msg = ("grad can be implicitly created only for real scalar outputs"
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f" but got {out.dtype}")
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raise RuntimeError(msg)
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new_grads.append(torch.ones_like(out, memory_format=torch.preserve_format))
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else:
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new_grads.append(None)
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else:
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raise TypeError("gradients can be either Tensors or None, but got " +
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type(grad).__name__)
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return tuple(new_grads)
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def _tensor_or_tensors_to_tuple(tensors: Optional[_TensorOrTensors], length: int) -> Tuple[_OptionalTensor, ...]:
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if tensors is None:
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return (None, ) * length
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if isinstance(tensors, torch.Tensor):
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return (tensors, )
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return tuple(tensors)
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def backward(
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tensors: _TensorOrTensors,
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grad_tensors: Optional[_TensorOrTensors] = None,
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retain_graph: Optional[bool] = None,
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create_graph: bool = False,
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grad_variables: Optional[_TensorOrTensors] = None,
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inputs: Optional[_TensorOrTensors] = None,
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) -> None:
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r"""Computes the sum of gradients of given tensors with respect to graph
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leaves.
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The graph is differentiated using the chain rule. If any of ``tensors``
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are non-scalar (i.e. their data has more than one element) and require
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gradient, then the Jacobian-vector product would be computed, in this
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case the function additionally requires specifying ``grad_tensors``.
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It should be a sequence of matching length, that contains the "vector"
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in the Jacobian-vector product, usually the gradient of the differentiated
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function w.r.t. corresponding tensors (``None`` is an acceptable value for
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all tensors that don't need gradient tensors).
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This function accumulates gradients in the leaves - you might need to zero
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``.grad`` attributes or set them to ``None`` before calling it.
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See :ref:`Default gradient layouts<default-grad-layouts>`
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for details on the memory layout of accumulated gradients.
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.. note::
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Using this method with ``create_graph=True`` will create a reference cycle
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between the parameter and its gradient which can cause a memory leak.
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We recommend using ``autograd.grad`` when creating the graph to avoid this.
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If you have to use this function, make sure to reset the ``.grad`` fields of your
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parameters to ``None`` after use to break the cycle and avoid the leak.
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.. note::
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If you run any forward ops, create ``grad_tensors``, and/or call ``backward``
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in a user-specified CUDA stream context, see
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:ref:`Stream semantics of backward passes<bwd-cuda-stream-semantics>`.
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.. note::
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When ``inputs`` are provided and a given input is not a leaf,
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the current implementation will call its grad_fn (even though it is not strictly needed to get this gradients).
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It is an implementation detail on which the user should not rely.
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See https://github.com/pytorch/pytorch/pull/60521#issuecomment-867061780 for more details.
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Args:
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tensors (Sequence[Tensor] or Tensor): Tensors of which the derivative will be
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computed.
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grad_tensors (Sequence[Tensor or None] or Tensor, optional): The "vector" in
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the Jacobian-vector product, usually gradients w.r.t. each element of
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corresponding tensors. None values can be specified for scalar Tensors or
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ones that don't require grad. If a None value would be acceptable for all
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grad_tensors, then this argument is optional.
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retain_graph (bool, optional): If ``False``, the graph used to compute the grad
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will be freed. Note that in nearly all cases setting this option to ``True``
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is not needed and often can be worked around in a much more efficient
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way. Defaults to the value of ``create_graph``.
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create_graph (bool, optional): If ``True``, graph of the derivative will
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be constructed, allowing to compute higher order derivative products.
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Defaults to ``False``.
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inputs (Sequence[Tensor] or Tensor, optional): Inputs w.r.t. which the gradient
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be will accumulated into ``.grad``. All other Tensors will be ignored. If
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not provided, the gradient is accumulated into all the leaf Tensors that
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were used to compute the attr::tensors.
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"""
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if torch._C._are_functorch_transforms_active():
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raise RuntimeError(
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"backward() called inside a functorch transform. This is not "
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"supported, please use functorch.grad or functorch.vjp instead "
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"or call backward() outside of functorch transforms.")
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if grad_variables is not None:
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warnings.warn("'grad_variables' is deprecated. Use 'grad_tensors' instead.")
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if grad_tensors is None:
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grad_tensors = grad_variables
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else:
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raise RuntimeError("'grad_tensors' and 'grad_variables' (deprecated) "
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"arguments both passed to backward(). Please only "
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"use 'grad_tensors'.")
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if inputs is not None and len(inputs) == 0:
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raise RuntimeError("'inputs' argument to backward() cannot be empty.")
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tensors = (tensors,) if isinstance(tensors, torch.Tensor) else tuple(tensors)
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inputs = (inputs,) if isinstance(inputs, torch.Tensor) else \
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tuple(inputs) if inputs is not None else tuple()
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grad_tensors_ = _tensor_or_tensors_to_tuple(grad_tensors, len(tensors))
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grad_tensors_ = _make_grads(tensors, grad_tensors_, is_grads_batched=False)
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if retain_graph is None:
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retain_graph = create_graph
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# The reason we repeat the same comment below is that
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# some Python versions print out the first line of a multi-line function
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# calls in the traceback and some print out the last line
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Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
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tensors, grad_tensors_, retain_graph, create_graph, inputs,
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allow_unreachable=True, accumulate_grad=True) # Calls into the C++ engine to run the backward pass
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def grad(
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outputs: _TensorOrTensors,
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inputs: _TensorOrTensors,
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grad_outputs: Optional[_TensorOrTensors] = None,
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retain_graph: Optional[bool] = None,
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create_graph: bool = False,
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only_inputs: bool = True,
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allow_unused: bool = False,
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is_grads_batched: bool = False
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) -> Tuple[torch.Tensor, ...]:
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r"""Computes and returns the sum of gradients of outputs with respect to
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the inputs.
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``grad_outputs`` should be a sequence of length matching ``output``
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containing the "vector" in vector-Jacobian product, usually the pre-computed
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gradients w.r.t. each of the outputs. If an output doesn't require_grad,
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then the gradient can be ``None``).
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.. note::
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If you run any forward ops, create ``grad_outputs``, and/or call ``grad``
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in a user-specified CUDA stream context, see
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:ref:`Stream semantics of backward passes<bwd-cuda-stream-semantics>`.
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.. note::
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``only_inputs`` argument is deprecated and is ignored now (defaults to ``True``).
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To accumulate gradient for other parts of the graph, please use
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``torch.autograd.backward``.
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Args:
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outputs (sequence of Tensor): outputs of the differentiated function.
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inputs (sequence of Tensor): Inputs w.r.t. which the gradient will be
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returned (and not accumulated into ``.grad``).
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grad_outputs (sequence of Tensor): The "vector" in the vector-Jacobian product.
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Usually gradients w.r.t. each output. None values can be specified for scalar
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Tensors or ones that don't require grad. If a None value would be acceptable
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for all grad_tensors, then this argument is optional. Default: None.
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retain_graph (bool, optional): If ``False``, the graph used to compute the grad
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will be freed. Note that in nearly all cases setting this option to ``True``
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is not needed and often can be worked around in a much more efficient
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way. Defaults to the value of ``create_graph``.
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create_graph (bool, optional): If ``True``, graph of the derivative will
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be constructed, allowing to compute higher order derivative products.
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Default: ``False``.
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allow_unused (bool, optional): If ``False``, specifying inputs that were not
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used when computing outputs (and therefore their grad is always zero)
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is an error. Defaults to ``False``.
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is_grads_batched (bool, optional): If ``True``, the first dimension of each
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tensor in ``grad_outputs`` will be interpreted as the batch dimension.
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Instead of computing a single vector-Jacobian product, we compute a
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batch of vector-Jacobian products for each "vector" in the batch.
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We use the vmap prototype feature as the backend to vectorize calls
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to the autograd engine so that this computation can be performed in a
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single call. This should lead to performance improvements when compared
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to manually looping and performing backward multiple times. Note that
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due to this feature being experimental, there may be performance
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cliffs. Please use ``torch._C._debug_only_display_vmap_fallback_warnings(True)``
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to show any performance warnings and file an issue on github if warnings exist
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for your use case. Defaults to ``False``.
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"""
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t_outputs = cast(Tuple[torch.Tensor, ...], (outputs,) if is_tensor_like(outputs) else tuple(outputs))
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t_inputs = cast(Tuple[torch.Tensor, ...], (inputs,) if is_tensor_like(inputs) else tuple(inputs))
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overridable_args = t_outputs + t_inputs
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if has_torch_function(overridable_args):
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return handle_torch_function(
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grad,
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overridable_args,
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t_outputs,
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t_inputs,
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grad_outputs=grad_outputs,
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retain_graph=retain_graph,
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create_graph=create_graph,
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only_inputs=only_inputs,
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allow_unused=allow_unused,
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is_grads_batched=is_grads_batched,
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)
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if not only_inputs:
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warnings.warn("only_inputs argument is deprecated and is ignored now "
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"(defaults to True). To accumulate gradient for other "
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"parts of the graph, please use torch.autograd.backward.")
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grad_outputs_ = _tensor_or_tensors_to_tuple(grad_outputs, len(t_outputs))
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grad_outputs_ = _make_grads(t_outputs, grad_outputs_, is_grads_batched=is_grads_batched)
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if retain_graph is None:
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retain_graph = create_graph
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# The reason we repeat the same comment several times below is because
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# some Python versions print out the first line of multi-line function
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# calls in the traceback and some print out the last line
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if is_grads_batched:
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def vjp(gO):
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return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
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t_outputs, gO, retain_graph, create_graph, t_inputs,
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allow_unused, accumulate_grad=False) # Calls into the C++ engine to run the backward pass
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return _vmap_internals._vmap(vjp, 0, 0, allow_none_pass_through=True)(grad_outputs_)
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else:
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return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
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t_outputs, grad_outputs_, retain_graph, create_graph, t_inputs,
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allow_unused, accumulate_grad=False) # Calls into the C++ engine to run the backward pass
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# This function applies in case of gradient checkpointing for memory
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# optimization. Currently, gradient checkpointing is supported only if the
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# execution engine is invoked through torch.autograd.backward() and its
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# inputs argument is not passed. It is not supported for torch.autograd.grad().
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# This is because if inputs are specified, the gradient won't be calculated for
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# anything else e.g. model parameters like weights, bias etc.
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#
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# This function returns whether the checkpointing is valid i.e. torch.autograd.backward
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# or not i.e. torch.autograd.grad. The implementation works by maintaining a thread
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# local variable in torch/csrc/autograd/engine.cpp which looks at the NodeTask
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# in the stack and before a NodeTask is executed in evaluate_function, it
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# checks for whether reentrant backwards is imperative or not.
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# See https://github.com/pytorch/pytorch/pull/4594 for more discussion/context
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def _is_checkpoint_valid():
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return Variable._execution_engine.is_checkpoint_valid()
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def variable(*args, **kwargs):
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raise RuntimeError("torch.autograd.variable(...) is deprecated, use torch.tensor(...) instead")
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# Monkey patching variable.Variable to fix FX codegen. FX generates a call by roughly doing
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# f"{fn.__module__}.{fn.__name__}(...). This yields torch.autograd.variable.Variable(...) in the
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# output of an FX graph. Unfortunately the module name torch.autograd.variable is shadowed by the
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# deprecated function - variable(...).
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variable.Variable = Variable # type: ignore[attr-defined]
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if not torch._C._autograd_init():
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raise RuntimeError("autograd initialization failed")
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# Import all native method/classes
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from torch._C._autograd import (
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_add_metadata_json,
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_disable_profiler,
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_disable_profiler_legacy,
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_enable_profiler,
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_enable_profiler_legacy,
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_enable_record_function,
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_kineto_step,
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_KinetoEvent,
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_pop_saved_tensors_default_hooks,
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_prepare_profiler,
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_profiler_enabled,
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_ProfilerResult,
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_push_saved_tensors_default_hooks,
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_record_function_with_args_enter,
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_record_function_with_args_exit,
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_set_empty_test_observer,
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_supported_activities,
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DeviceType,
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kineto_available,
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ProfilerEvent,
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SavedTensor,
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)
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from torch._C._profiler import ProfilerActivity, ProfilerConfig, ProfilerState
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from . import profiler
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def _register_py_tensor_class_for_device(device, cls):
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if not isinstance(cls, type):
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raise RuntimeError("cls isn't a typeinfo object")
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torch._C._register_py_class_for_device(device, cls)
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