docs: fix typos

Author: omahs

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

@@ -53,7 +53,7 @@ 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
+    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__`