docs: fix typos

docs/source-fabric/advanced/model_parallel/tp_fsdp.rst

@@ -276,7 +276,7 @@ Next steps
 
 .. displayitem::
     :header: Pipeline Parallelism
-    :description: Coming sooon
+    :description: Coming soon
     :col_css: col-md-4
     :height: 160
     :tag: advanced

docs/source-pytorch/advanced/compile.rst

@@ -262,7 +262,7 @@ Avoid graph breaks
 When ``torch.compile`` looks at the code in your model's ``forward()`` or ``*_step()`` method, it will try to compile as much of the code as possible.
 If there are regions in the code that it doesn't understand, it will introduce a so-called "graph break" that essentially splits the code in optimized and unoptimized parts.
 Graph breaks aren't a deal breaker, since the optimized parts should still run faster.
-But if you want to get the most out of ``torch.compile``, you might want to invest rewriting the problematic section of the code that produce the breaks.
+But if you want to get the most out of ``torch.compile``, you might want to invest rewriting the problematic section of the code that produces the breaks.
 
 You can check whether your model produces graph breaks by calling ``torch.compile`` with ``fullgraph=True``:
 
@@ -332,7 +332,7 @@ Enabling CUDA Graphs often results in a significant speedup, but sometimes also
 
 **Shape padding:** The specific shape/size of the tensors involved in the computation of your model (input, activations, weights, gradients, etc.) can have an impact on the performance.
 With shape padding enabled, ``torch.compile`` can extend the tensors by padding to a size that gives a better memory alignment.
-Naturally, the tradoff here is that it will consume a bit more memory.
+Naturally, the tradeoff here is that it will consume a bit more memory.
 
 .. code-block:: python
 

docs/source-pytorch/advanced/model_parallel/tp_fsdp.rst

@@ -282,7 +282,7 @@ Next steps
 
 .. displayitem::
     :header: Pipeline Parallelism
-    :description: Coming sooon
+    :description: Coming soon
     :col_css: col-md-4
     :height: 160
     :tag: advanced

docs/source-pytorch/advanced/post_training_quantization.rst

@@ -106,7 +106,7 @@ The "approach" parameter in PostTrainingQuantConfig is defined by the user to ma
 Quantize the model
 ==================
 
-The model can be qutized by Intel® Neural Compressor with:
+The model can be quantized by Intel® Neural Compressor with:
 
 .. code-block:: python
 
@@ -126,7 +126,7 @@ At last, the quantized model can be saved by:
 Hands-on Examples
 *****************
 
-Based on the `given example code <https://lightning.ai/docs/pytorch/2.1.0/notebooks/lightning_examples/text-transformers.html>`_, we show how Intel Neural Compressor conduct model quantization on PyTorch Lightning. We first define the basic config of the quantization process.
+Based on the `given example code <https://lightning.ai/docs/pytorch/2.1.0/notebooks/lightning_examples/text-transformers.html>`_, we show how Intel Neural Compressor conducts model quantization on PyTorch Lightning. We first define the basic config of the quantization process.
 
 .. code-block:: python
 

docs/source-pytorch/advanced/pruning_quantization.rst

@@ -32,7 +32,7 @@ You can also perform iterative pruning, apply the `lottery ticket hypothesis <ht
 .. code-block:: python
 
     def compute_amount(epoch):
-        # the sum of all returned values need to be smaller than 1
+        # the sum of all returned values needs to be smaller than 1
         if epoch == 10:
             return 0.5
 
@@ -43,7 +43,7 @@ You can also perform iterative pruning, apply the `lottery ticket hypothesis <ht
             return 0.01
 
 
-    # the amount can be also be a callable
+    # the amount can also be a callable
     trainer = Trainer(callbacks=[ModelPruning("l1_unstructured", amount=compute_amount)])
 
 

docs/source-pytorch/advanced/training_tricks.rst

@@ -46,7 +46,7 @@ If the Trainer's ``gradient_clip_algorithm`` is set to ``'value'`` (``'norm'`` b
     # clip gradients' maximum magnitude to <=0.5
     trainer = Trainer(gradient_clip_val=0.5, gradient_clip_algorithm="value")
 
-Read more about :ref:`Configuring Gradient Clipping <configure_gradient_clipping>` for advanced use-cases.
+Read more about :ref:`Configuring Gradient Clipping <configure_gradient_clipping>` for advanced use cases.
 
 ----------
 

src/lightning/fabric/connector.py

@@ -239,7 +239,7 @@ def _check_config_and_set_final_flags(
                 else:
                     raise TypeError(
                         f"Found invalid type for plugin {plugin}. Expected one of: Precision, "
-                        "CheckpointIO, ClusterEnviroment."
+                        "CheckpointIO, ClusterEnvironment."
                     )
 
             duplicated_plugin_key = [k for k, v in plugins_flags_types.items() if v > 1]

src/lightning/fabric/plugins/precision/bitsandbytes.py

@@ -403,7 +403,7 @@ class _NF4DQLinear(_Linear4bit):
         def __init__(self, *args: Any, **kwargs: Any) -> None:
             super().__init__(*args, quant_type="nf4", compress_statistics=True, **kwargs)
 
-    # these classes are defined programatically like this to avoid importing bitsandbytes in environments that have
+    # these classes are defined programmatically like this to avoid importing bitsandbytes in environments that have
     # it available but will not use it
     classes = {
         "_Linear8bitLt": _Linear8bitLt,

src/lightning/pytorch/trainer/connectors/accelerator_connector.py

@@ -248,7 +248,7 @@ def _check_config_and_set_final_flags(
                 else:
                     raise MisconfigurationException(
                         f"Found invalid type for plugin {plugin}. Expected one of: Precision, "
-                        "CheckpointIO, ClusterEnviroment, or LayerSync."
+                        "CheckpointIO, ClusterEnvironment, or LayerSync."
                     )
 
             duplicated_plugin_key = [k for k, v in plugins_flags_types.items() if v > 1]

tests/tests_fabric/utilities/test_data.py

@@ -53,8 +53,9 @@ def test_has_len():
 def test_replace_dunder_methods_multiple_loaders_without_init():
     """In case of a class, that inherits from a class that we are patching, but doesn't define its own `__init__`
     method (the one we are wrapping), it can happen, that `hasattr(cls, "__old__init__")` is True because of parent
-    class, but it is impossible to delete, because that method is owned by parent class. Furthermore, the error occured
-    only sometimes because it depends on the order in which we are iterating over a set of classes we are patching.
+    class, but it is impossible to delete, because that method is owned by parent class. Furthermore, the error
+    occurred only sometimes because it depends on the order in which we are iterating over a set of classes we are
+    patching.
 
     This test simulates the behavior by generating sufficient number of dummy classes, which do not define `__init__`
     and are children of `DataLoader`. We are testing that a) context manager `_replace_dunder_method` exits cleanly, and