Fix typo under docs directory and RELEASE.md (pytorch#85896)

kiszk · pytorchmergebot · commit bc57306bdd6a · 2022-09-29T21:41:59.000Z
This PR fixes typo in rst files under docs directory and `RELEASE.md`. Pull Request resolved: pytorch#85896 Approved by: https://github.com/kit1980
diff --git a/RELEASE.md b/RELEASE.md
@@ -14,7 +14,7 @@
     - [Release Candidate health validation](#release-candidate-health-validation)
     - [Cherry Picking Fixes](#cherry-picking-fixes)
   - [Promoting RCs to Stable](#promoting-rcs-to-stable)
-  - [Additonal Steps to prepare for release day](#additonal-steps-to-prepare-for-release-day)
+  - [Additional Steps to prepare for release day](#additional-steps-to-prepare-for-release-day)
     - [Modify release matrix](#modify-release-matrix)
     - [Open Google Colab issue](#open-google-colab-issue)
 - [Patch Releases](#patch-releases)
@@ -186,7 +186,7 @@ Promotion should occur in two steps:
 
 **NOTE**: The promotion of wheels to PyPI can only be done once so take caution when attempting to promote wheels to PyPI, (see https://github.com/pypa/warehouse/issues/726 for a discussion on potential draft releases within PyPI)
 
-## Additonal Steps to prepare for release day
+## Additional Steps to prepare for release day
 
 The following should be prepared for the release day
 
@@ -264,7 +264,7 @@ For versions of Python that we support we follow the [NEP 29 policy](https://num
 
 ## Accelerator Software
 
-For acclerator software like CUDA and ROCm we will typically use the following criteria:
+For accelerator software like CUDA and ROCm we will typically use the following criteria:
 * Support latest 2 minor versions
 
 ### Special support cases
diff --git a/docs/cpp/source/notes/tensor_cuda_stream.rst b/docs/cpp/source/notes/tensor_cuda_stream.rst
@@ -61,7 +61,7 @@ Pytorch's C++ API provides the following ways to set CUDA stream:
 
 .. attention::
 
-  This function may have nosthing to do with the current device. It only changes the current stream on the stream's device.
+  This function may have nothing to do with the current device. It only changes the current stream on the stream's device.
   We recommend using ``CUDAStreamGuard``, instead, since it switches to the stream's device and makes it the current stream on that device.
   ``CUDAStreamGuard`` will also restore the current device and stream when it's destroyed
 
diff --git a/docs/source/notes/autograd.rst b/docs/source/notes/autograd.rst
@@ -203,7 +203,7 @@ grad mode in the next forward pass.
 
 The implementations in :ref:`nn-init-doc` also
 rely on no-grad mode when initializing the parameters as to avoid
-autograd tracking when updating the intialized parameters in-place.
+autograd tracking when updating the initialized parameters in-place.
 
 Inference Mode
 ^^^^^^^^^^^^^^
diff --git a/docs/source/quantization-support.rst b/docs/source/quantization-support.rst
@@ -543,7 +543,7 @@ as follows:
 
 where :math:`\text{clamp}(.)` is the same as :func:`~torch.clamp` while the
 scale :math:`s` and zero point :math:`z` are then computed
-as decribed in :class:`~torch.ao.quantization.observer.MinMaxObserver`, specifically:
+as described in :class:`~torch.ao.quantization.observer.MinMaxObserver`, specifically:
 
     .. math::
 
diff --git a/docs/source/quantization.rst b/docs/source/quantization.rst
@@ -80,7 +80,7 @@ The following table compares the differences between Eager Mode Quantization and
 |                 |Static, Dynamic,   |Static, Dynamic,   |
 |                 |Weight Only        |Weight Only        |
 |                 |                   |                   |
-|                 |Quantiztion Aware  |Quantiztion Aware  |
+|                 |Quantization Aware |Quantization Aware |
 |                 |Training:          |Training:          |
 |                 |Static             |Static             |
 +-----------------+-------------------+-------------------+
@@ -632,15 +632,15 @@ Quantization Mode Support
 |                             |Quantization                                          |Dataset         | Works Best For | Accuracy   |      Notes      |
 |                             |Mode                                                  |Requirement     |                |            |                 |
 +-----------------------------+---------------------------------+--------------------+----------------+----------------+------------+-----------------+
-|Post Training Quantization   |Dyanmic/Weight Only Quantization |activation          |None            |LSTM, MLP,      |good        |Easy to use,     |
+|Post Training Quantization   |Dynamic/Weight Only Quantization |activation          |None            |LSTM, MLP,      |good        |Easy to use,     |
 |                             |                                 |dynamically         |                |Embedding,      |            |close to static  |
 |                             |                                 |quantized (fp16,    |                |Transformer     |            |quantization when|
 |                             |                                 |int8) or not        |                |                |            |performance is   |
 |                             |                                 |quantized, weight   |                |                |            |compute or memory|
 |                             |                                 |statically quantized|                |                |            |bound due to     |
 |                             |                                 |(fp16, int8, in4)   |                |                |            |weights          |
 |                             +---------------------------------+--------------------+----------------+----------------+------------+-----------------+
-|                             |Static Quantization              |acivation and       |calibration     |CNN             |good        |Provides best    |
+|                             |Static Quantization              |activation and      |calibration     |CNN             |good        |Provides best    |
 |                             |                                 |weights statically  |dataset         |                |            |perf, may have   |
 |                             |                                 |quantized (int8)    |                |                |            |big impact on    |
 |                             |                                 |                    |                |                |            |accuracy, good   |
@@ -652,7 +652,7 @@ Quantization Mode Support
 |                             |                                 |weight are fake     |dataset         |                |            |for now          |
 |                             |                                 |quantized           |                |                |            |                 |
 |                             +---------------------------------+--------------------+----------------+----------------+------------+-----------------+
-|                             |Static Quantization              |activatio nand      |fine-tuning     |CNN, MLP,       |best        |Typically used   |
+|                             |Static Quantization              |activation and      |fine-tuning     |CNN, MLP,       |best        |Typically used   |
 |                             |                                 |weight are fake     |dataset         |Embedding       |            |when static      |
 |                             |                                 |quantized           |                |                |            |quantization     |
 |                             |                                 |                    |                |                |            |leads to bad     |
@@ -736,7 +736,7 @@ Backend/Hardware Support
 +-----------------+---------------+------------+------------+------------+
 |server GPU       |TensorRT (early|Not support |Supported   |Static      |
 |                 |prototype)     |this it     |            |Quantization|
-|                 |               |requries a  |            |            |
+|                 |               |requires a  |            |            |
 |                 |               |graph       |            |            |
 +-----------------+---------------+------------+------------+------------+
 
diff --git a/docs/source/rpc.rst b/docs/source/rpc.rst
@@ -16,7 +16,7 @@ machines.
     CUDA support was introduced in PyTorch 1.9 and is still a **beta** feature.
     Not all features of the RPC package are yet compatible with CUDA support and
     thus their use is discouraged. These unsupported features include: RRefs,
-    JIT compatibility, dist autograd and dist optimizier, and profiling. These
+    JIT compatibility, dist autograd and dist optimizer, and profiling. These
     shortcomings will be addressed in future releases.
 
 .. note ::
diff --git a/docs/source/sparse.rst b/docs/source/sparse.rst
@@ -470,7 +470,7 @@ ncols, *densesize)`` where ``len(batchsize) == B`` and
 
    The batches of sparse CSR tensors are dependent: the number of
    specified elements in all batches must be the same. This somewhat
-   artifical constraint allows efficient storage of the indices of
+   artificial constraint allows efficient storage of the indices of
    different CSR batches.
 
 .. note::
