QINCo implementation in CPU Faiss #3608
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This pull request was exported from Phabricator. Differential Revision: D59132952 |
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This pull request was exported from Phabricator. Differential Revision: D59132952 |
mdouze
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Jul 2, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Differential Revision: D59132952
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This pull request was exported from Phabricator. Differential Revision: D59132952 |
mdouze
added a commit
to mdouze/faiss
that referenced
this pull request
Jul 5, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Differential Revision: D59132952
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This pull request was exported from Phabricator. Differential Revision: D59132952 |
mdouze
added a commit
to mdouze/faiss
that referenced
this pull request
Jul 5, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Differential Revision: D59132952
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This pull request was exported from Phabricator. Differential Revision: D59132952 |
mdouze
added a commit
to mdouze/faiss
that referenced
this pull request
Jul 5, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Differential Revision: D59132952
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This pull request was exported from Phabricator. Differential Revision: D59132952 |
mdouze
added a commit
to mdouze/faiss
that referenced
this pull request
Jul 5, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Differential Revision: D59132952
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This pull request was exported from Phabricator. Differential Revision: D59132952 |
mdouze
added a commit
to mdouze/faiss
that referenced
this pull request
Jul 5, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Differential Revision: D59132952
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This pull request was exported from Phabricator. Differential Revision: D59132952 |
mdouze
added a commit
to mdouze/faiss
that referenced
this pull request
Jul 5, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Differential Revision: D59132952
|
This pull request was exported from Phabricator. Differential Revision: D59132952 |
mdouze
added a commit
to mdouze/faiss
that referenced
this pull request
Jul 5, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Differential Revision: D59132952
|
This pull request was exported from Phabricator. Differential Revision: D59132952 |
mdouze
added a commit
to mdouze/faiss
that referenced
this pull request
Jul 5, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Differential Revision: D59132952
mdouze
added a commit
to mdouze/faiss
that referenced
this pull request
Jul 8, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Differential Revision: D59132952
|
This pull request was exported from Phabricator. Differential Revision: D59132952 |
mdouze
added a commit
to mdouze/faiss
that referenced
this pull request
Jul 8, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Differential Revision: D59132952
|
This pull request was exported from Phabricator. Differential Revision: D59132952 |
mdouze
added a commit
to mdouze/faiss
that referenced
this pull request
Jul 8, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Differential Revision: D59132952
|
This pull request was exported from Phabricator. Differential Revision: D59132952 |
mdouze
added a commit
to mdouze/faiss
that referenced
this pull request
Jul 8, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Differential Revision: D59132952
Summary: Add dispatcher function to avoid repeating dispatching code for distance computation and result handlers. Differential Revision: D59318865
Summary: Using the new dispatcher functions, add search func to flat codes. To test it, make IndexLattice a subclass of FlatCodes and check the resonstruction there. Differential Revision: D59367989
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This pull request was exported from Phabricator. Differential Revision: D59132952 |
mdouze
added a commit
to mdouze/faiss
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this pull request
Jul 10, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Reviewed By: asadoughi Differential Revision: D59132952
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This pull request was exported from Phabricator. Differential Revision: D59132952 |
mdouze
added a commit
to mdouze/faiss
that referenced
this pull request
Jul 10, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Reviewed By: asadoughi Differential Revision: D59132952
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Reviewed By: asadoughi Differential Revision: D59132952
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This pull request was exported from Phabricator. Differential Revision: D59132952 |
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This pull request has been merged in dd72e41. |
abhinavdangeti
pushed a commit
to blevesearch/faiss
that referenced
this pull request
Jul 12, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Reviewed By: asadoughi Differential Revision: D59132952 fbshipit-source-id: eea4856507a5b7c5f219efcf8d19fe56944df088
ketor
pushed a commit
to dingodb/faiss
that referenced
this pull request
Aug 20, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Reviewed By: asadoughi Differential Revision: D59132952 fbshipit-source-id: eea4856507a5b7c5f219efcf8d19fe56944df088
aalekhpatel07
pushed a commit
to aalekhpatel07/faiss
that referenced
this pull request
Oct 17, 2024
Summary: Pull Request resolved: facebookresearch#3608 This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training). For this, we translate a simplified version of some torch classes: - tensors, restricted to 2D and int32 + float32 - Linear and Embedding layer Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable. There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++. Reviewed By: asadoughi Differential Revision: D59132952 fbshipit-source-id: eea4856507a5b7c5f219efcf8d19fe56944df088
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Summary:
This is a straightforward implementation of QINCo in CPU Faiss, with encoding and decoding capabilities (not training).
For this, we translate a simplified version of some torch classes:
tensors, restricted to 2D and int32 + float32
Linear and Embedding layer
Then the QINCoStep and QINCo can just be defined as C++ objects that are copy-constructable.
There is some plumbing required in the wrapping layers to support the integration. Pytroch tensors are converted to numpy for getting / setting them in C++.
Differential Revision: D59132952