Merge branch 'Lightning-AI:master' into master

Author: SiddhantSadangi

.azure/gpu-tests-pytorch.yml

@@ -117,7 +117,6 @@ jobs:
           set -e
           extra=$(python -c "print({'lightning': 'pytorch-'}.get('$(PACKAGE_NAME)', ''))")
           pip install -e ".[${extra}dev]" pytest-timeout -U --extra-index-url="${TORCH_URL}"
-          pip install setuptools==75.6.0 jsonargparse==4.35.0
         displayName: "Install package & dependencies"
 
       - bash: pip uninstall -y lightning

.github/dependabot.yml

@@ -19,8 +19,6 @@ updates:
       separator: "-"
     # Allow up to 5 open pull requests for pip dependencies
     open-pull-requests-limit: 10
-    reviewers:
-      - "Lightning-AI/teams/core-lightning"
 
   # Enable version updates for GitHub Actions
   - package-ecosystem: "github-actions"
@@ -37,5 +35,3 @@ updates:
       separator: "-"
     # Allow up to 5 open pull requests for GitHub Actions
     open-pull-requests-limit: 10
-    reviewers:
-      - "Lightning-AI/teams/core-lightning"

.github/workflows/docker-build.yml

@@ -97,7 +97,7 @@ jobs:
         # adding dome more images as Thunder mainly using python 3.10,
         # and we need to support integrations as for example LitGPT
         python_version: ["3.10"]
-        pytorch_version: ["2.6.0", "2.7.0"]
+        pytorch_version: ["2.6.0", "2.7.1"]
         cuda_version: ["12.6.3"]
         include:
           # These are the base images for PL release docker images.
@@ -108,7 +108,7 @@ jobs:
           - { python_version: "3.11", pytorch_version: "2.4.1", cuda_version: "12.1.1" }
           - { python_version: "3.12", pytorch_version: "2.5.1", cuda_version: "12.1.1" }
           - { python_version: "3.12", pytorch_version: "2.6.0", cuda_version: "12.4.1" }
-          - { python_version: "3.12", pytorch_version: "2.7.0", cuda_version: "12.6.3" }
+          - { python_version: "3.12", pytorch_version: "2.7.1", cuda_version: "12.6.3" }
     steps:
       - uses: actions/checkout@v4
       - uses: docker/setup-buildx-action@v3

dockers/base-cuda/Dockerfile

@@ -34,7 +34,7 @@ ENV \
     MAKEFLAGS="-j2"
 
 RUN \
-    apt-get update && apt-get install -y wget && \
+    apt-get update --fix-missing && apt-get install -y wget && \
     apt-get update -qq --fix-missing && \
     NCCL_VER=$(dpkg -s libnccl2 | grep '^Version:' | awk -F ' ' '{print $2}' | awk -F '-' '{print $1}' | grep -ve '^\s*$') && \
     CUDA_VERSION_MM=${CUDA_VERSION%.*} && \

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.
 
 ----------