Change default branch references from master to main. (#2029)

Summary:
This is required for the renaming of the default branch from `master` to `main`, in accordance with the new Facebook OSS guidelines.

Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2029

Reviewed By: mdouze

Differential Revision: D30672862

Pulled By: beauby

fbshipit-source-id: 0b6458a4ff02a12aae14cf94057e85fdcbcbff96

.circleci/config.yml

@@ -426,7 +426,7 @@ workflows:
           filters:
             branches:
               only:
-                - master
+                - main
     jobs:
       - deploy_linux:
           name: Linux nightlies

.github/workflows/update-doxygen.yml

@@ -2,7 +2,7 @@ name: update-doxygen
 on:
   push:
     branches:
-      - master
+      - main
     paths:
       - 'faiss/**'
 jobs:
@@ -35,6 +35,6 @@ jobs:
           git add xml cpp_api
           if [ -n "$(git status --porcelain)" ]
           then
-            git commit -m "Update API docs ($(git rev-parse --short master))."
+            git commit -m "Update API docs ($(git rev-parse --short main))."
             git push origin gh-pages
           fi

CHANGELOG.md

@@ -53,9 +53,9 @@ Tensor Core operations (mixed-precision arithmetic) are enabled on supported
 hardware when operating with float16 data.
 - Support k-means clustering with encoded vectors. This makes it possible to
 train on larger datasets without decompressing them in RAM, and is especially
-useful for binary datasets (see https://github.com/facebookresearch/faiss/blob/master/tests/test_build_blocks.py#L92).
+useful for binary datasets (see https://github.com/facebookresearch/faiss/blob/main/tests/test_build_blocks.py#L92).
 - Support weighted k-means. Weights can be associated to each training point
-(see https://github.com/facebookresearch/faiss/blob/master/tests/test_build_blocks.py).
+(see https://github.com/facebookresearch/faiss/blob/main/tests/test_build_blocks.py).
 - Serialize callback in python, to write to pipes or sockets (see
 https://github.com/facebookresearch/faiss/wiki/Index-IO,-cloning-and-hyper-parameter-tuning).
 - Reconstruct arbitrary ids from IndexIVF + efficient remove of a small number
@@ -63,12 +63,12 @@ of ids. This avoids 2 inefficiencies: O(ntotal) removal of vectors and
 IndexIDMap2 on top of indexIVF. Documentation here:
 https://github.com/facebookresearch/faiss/wiki/Special-operations-on-indexes.
 - Support inner product as a metric in IndexHNSW (see
-https://github.com/facebookresearch/faiss/blob/master/tests/test_index.py#L490).
+https://github.com/facebookresearch/faiss/blob/main/tests/test_index.py#L490).
 - Support PQ of sizes other than 8 bit in IndexIVFPQ.
 - Demo on how to perform searches sequentially on an IVF index. This is useful
 for an OnDisk index with a very large batch of queries. In that case, it is
 worthwhile to scan the index sequentially (see
-https://github.com/facebookresearch/faiss/blob/master/tests/test_ivflib.py#L62).
+https://github.com/facebookresearch/faiss/blob/main/tests/test_ivflib.py#L62).
 - Range search support for most binary indexes.
 - Support for hashing-based binary indexes (see
 https://github.com/facebookresearch/faiss/wiki/Binary-indexes).

CONTRIBUTING.md

@@ -14,7 +14,7 @@ We welcome pull requests that add significant value to Faiss. If you plan to do
 a major development and contribute it back to Faiss, please contact us first before
 putting too much effort into it.
 
-1. Fork the repo and create your branch from `master`.
+1. Fork the repo and create your branch from `main`.
 2. If you've added code that should be tested, add tests.
 3. If you've changed APIs, update the documentation.
 4. Ensure the test suite passes.
@@ -50,4 +50,3 @@ outlined on that page and do not file a public issue.
 
 By contributing to Faiss, you agree that your contributions will be licensed
 under the LICENSE file in the root directory of this source tree.
-

INSTALL.md

@@ -175,7 +175,7 @@ $ PYTHONPATH="$(ls -d ./build/faiss/python/build/lib*/)" pytest tests/test_*.py
 ### Basic example
 
 A basic usage example is available in
-[`demos/demo_ivfpq_indexing.cpp`](https://github.com/facebookresearch/faiss/blob/master/demos/demo_ivfpq_indexing.cpp).
+[`demos/demo_ivfpq_indexing.cpp`](https://github.com/facebookresearch/faiss/blob/main/demos/demo_ivfpq_indexing.cpp).
 
 It creates a small index, stores it and performs some searches. A normal runtime
 is around 20s. With a fast machine and Intel MKL's BLAS it runs in 2.5s.

contrib/datasets.py

@@ -202,7 +202,7 @@ class DatasetBigANN(Dataset):
 class DatasetDeep1B(Dataset):
     """
     See
-    https://github.com/facebookresearch/faiss/tree/master/benchs#getting-deep1b
+    https://github.com/facebookresearch/faiss/tree/main/benchs#getting-deep1b
     on how to get the data
     """