diff --git a/docs/source/autograd.rst b/docs/source/autograd.rst index 27bc16f5f28..09a8f29b418 100644 --- a/docs/source/autograd.rst +++ b/docs/source/autograd.rst @@ -17,7 +17,7 @@ Functional higher level API ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. warning:: - This API is experimental. Even though the function signatures are very unlikely to change, major + This API is in beta. Even though the function signatures are very unlikely to change, major improvements to performances are planned before we consider this stable. This section contains the higher level API for the autograd that builds on the basic API above diff --git a/docs/source/named_tensor.rst b/docs/source/named_tensor.rst index aee49367b2e..2be41503356 100644 --- a/docs/source/named_tensor.rst +++ b/docs/source/named_tensor.rst @@ -14,7 +14,7 @@ Names can also be used to rearrange dimensions, for example, to support "broadcasting by name" rather than "broadcasting by position". .. warning:: - The named tensor API is experimental and subject to change. + The named tensor API is a prototype feature and subject to change. Creating named tensors ---------------------- diff --git a/docs/source/quantization.rst b/docs/source/quantization.rst index 870f19ae70e..3d729902259 100644 --- a/docs/source/quantization.rst +++ b/docs/source/quantization.rst @@ -3,6 +3,9 @@ Quantization ============ +.. warning :: + Quantization is in beta and subject to change. + Introduction to Quantization ---------------------------- diff --git a/docs/source/rpc.rst b/docs/source/rpc.rst index d65302c896d..4874ceb28d5 100644 --- a/docs/source/rpc.rst +++ b/docs/source/rpc.rst @@ -92,11 +92,12 @@ applications can always explicitly move the input tensors to CPU on the caller and move it to the desired devices on the callee if necessary. .. warning:: - TorchScript support in RPC is experimental and subject to change. Since + TorchScript support in RPC is a prototype feature and subject to change. Since v1.5.0, ``torch.distributed.rpc`` supports calling TorchScript functions as RPC target functions, and this will help improve parallelism on the callee side as executing TorchScript functions does not require GIL. + .. autofunction:: rpc_sync .. autofunction:: rpc_async .. autofunction:: remote @@ -110,7 +111,7 @@ The RPC package also provides decorators which allow applications to specify how a given function should be treated on the callee side. .. warning:: - The ``rpc.functions`` package is experimental and subject to change. + The ``rpc.functions`` package is a prototype feature and subject to change. .. autofunction:: torch.distributed.rpc.functions.async_execution diff --git a/docs/source/sparse.rst b/docs/source/sparse.rst index 581e3244d32..13084fba086 100644 --- a/docs/source/sparse.rst +++ b/docs/source/sparse.rst @@ -7,7 +7,7 @@ torch.sparse .. warning:: - This API is currently experimental and may change in the near future. + This API is in beta and may change in the near future. Torch supports sparse tensors in COO(rdinate) format, which can efficiently store and process tensors for which the majority of elements diff --git a/docs/source/tensor_attributes.rst b/docs/source/tensor_attributes.rst index 81d04e686eb..6050c07a989 100644 --- a/docs/source/tensor_attributes.rst +++ b/docs/source/tensor_attributes.rst @@ -206,9 +206,12 @@ torch.layout .. class:: torch.layout +.. warning:: + The ``torch.layout`` class is in beta and subject to change. + A :class:`torch.layout` is an object that represents the memory layout of a :class:`torch.Tensor`. Currently, we support ``torch.strided`` (dense Tensors) -and have experimental support for ``torch.sparse_coo`` (sparse COO Tensors). +and have beta support for ``torch.sparse_coo`` (sparse COO Tensors). ``torch.strided`` represents dense Tensors and is the memory layout that is most commonly used. Each strided tensor has an associated