Merge branch 'master' into toggle_optimizer

Author: Borda

.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/_legacy-checkpoints.yml

@@ -113,7 +113,7 @@ jobs:
 
       - run: pip install -r requirements/ci.txt
       - name: Upload checkpoints to S3
-        if: secrets.AWS_REGION != ''
+        if: ${{ secrets[AWS_REGION] != '' }}
         working-directory: ${{ env.LEGACY_FOLDER }}
         env:
           AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY }}

.github/workflows/ci-checkpoints.yml renamed to .github/workflows/ci-checkpoints.yml.disabled

@@ -94,6 +94,11 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
+        # 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"]
+        cuda_version: ["12.6.3"]
         include:
           # These are the base images for PL release docker images.
           # Make sure the matrix here matches the one above.

.github/workflows/docker-build.yml

@@ -13,7 +13,7 @@ on:
       - "requirements/ci.txt"
       - ".github/actions/pkg-check/*"
       - ".github/actions/pkg-publish/*"
-      - ".github/workflows/_legacy-checkpoints.yml.yml"
+      - ".github/workflows/_legacy-checkpoints.yml"
       - ".github/workflows/_build-packages.yml"
       - ".github/workflows/release-pypi.yml"
 
@@ -179,12 +179,12 @@ jobs:
         with:
           pkg-folder: dist/${{ steps.folder.outputs.pkg }}
           pypi-token: ${{ secrets[format('PYPI_TOKEN_{0}', matrix.name)] }}
-
-  legacy-checkpoints:
-    needs: [build-packages]
-    uses: ./.github/workflows/_legacy-checkpoints.yml
-    with:
-      push_to_s3: ${{ startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release' }}
-      upload_local: ${{ startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release' }}
-      create_pr: ${{ startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release' }}
-    secrets: inherit
+# FIXME: this is not working suddenly, Unrecognized named-value: 'secrets'
+#  legacy-checkpoints:
+#    needs: [build-packages]
+#    uses: ./.github/workflows/_legacy-checkpoints.yml
+#    with:
+#      push_to_s3: ${{ startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release' }}
+#      upload_local: ${{ startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release' }}
+#      create_pr: ${{ startsWith(github.event.ref, 'refs/tags') || github.event_name == 'release' }}
+#    secrets: inherit

.github/workflows/release-pkg.yml

@@ -198,7 +198,7 @@ node_modules/
 **/events.out.tfevents.*
 examples/**/*.png
 
-# instalation artifacts
+# installation artifacts
 requirements/base.txt
 
 # CI

.gitignore

@@ -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-fabric/advanced/model_parallel/tp_fsdp.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/compile.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/model_parallel/tp_fsdp.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