Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2217
This diff introduces a new Faiss contrib module that contains:
- generic k-means implemented in python (was in distributed_ondisk)
- the two-level clustering code, including a simple function that runs it on a Faiss IVF index.
- sparse clustering code (new)
The main idea is that that code is often re-used so better have it in contrib.
Reviewed By: beauby
Differential Revision: D34170932
fbshipit-source-id: cc297cc56d241b5ef421500ed410d8e2be0f1b77
Summary:
Adds the preassigned add and search python wrappers to contrib.
Adds the preassigned search for the binary case (was missing before).
Also adds a real test for that functionality.
Reviewed By: beauby
Differential Revision: D26560021
fbshipit-source-id: 330b715a9ed0073cfdadbfbcb1c23b10bed963a5
Summary:
Added a few functions in contrib to:
- run range searches by batches on the query or the database side
- emulate range search on GPU: search on GPU with k=1024, if the farthest neighbor is still within range, re-perform search on CPU
- as reference implementations for precision-recall on range search datasets
- optimized code to plot precision-recall plots (ie. sweep over thresholds)
The new functions are mainly in a new `evaluation.py`
Reviewed By: wickedfoo
Differential Revision: D25627619
fbshipit-source-id: 58f90654c32c925557d7bbf8083efbb710712e03
Summary:
IndexPQ and IndexIVFPQ implementations with AVX shuffle instructions.
The training and computing of the codes does not change wrt. the original PQ versions but the code layout is "packed" so that it can be used efficiently by the SIMD computation kernels.
The main changes are:
- new IndexPQFastScan and IndexIVFPQFastScan objects
- simdib.h for an abstraction above the AVX2 intrinsics
- BlockInvertedLists for invlists that are 32-byte aligned and where codes are not sequential
- pq4_fast_scan.h/.cpp: for packing codes and look-up tables + optmized distance comptuation kernels
- simd_result_hander.h: SIMD version of result collection in heaps / reservoirs
Misc changes:
- added contrib.inspect_tools to access fields in C++ objects
- moved .h and .cpp code for inverted lists to an invlists/ subdirectory, and made a .h/.cpp for InvertedListsIOHook
- added a new inverted lists type with 32-byte aligned codes (for consumption by SIMD)
- moved Windows-specific intrinsics to platfrom_macros.h
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1542
Test Plan:
```
buck test mode/opt -j 4 //faiss/tests/:test_fast_scan_ivf //faiss/tests/:test_fast_scan
buck test mode/opt //faiss/manifold/...
```
Reviewed By: wickedfoo
Differential Revision: D25175439
Pulled By: mdouze
fbshipit-source-id: ad1a40c0df8c10f4b364bdec7172e43d71b56c34
Summary:
Pull Request resolved: https://github.com/facebookresearch/faiss/pull/1484
This diff allows for native usage of PyTorch tensors for Faiss indexes on both CPU and GPU. It is currently only implemented in this diff for things that inherit from `faiss.Index`, which covers the non-binary indices, and it patches the same functions on `faiss.Index` that were also covered by `__init__.py` for numpy interoperability.
There must be uniformity among the inputs: if any array input is a Torch tensor, then all array inputs must be Torch tensors. Similarly, if any array input is a numpy ndarray, then all array inputs must be numpy ndarrays.
If `faiss.contrib.torch_utils` is imported, it ensures that `import faiss` has already been performed to patch all of the functions using the base `__init__.py` numpy wrappers, and then patches the following functions again:
```
add
add_with_ids
assign
train
search
remove_ids
reconstruct
reconstruct_n
range_search
update_vectors
search_and_reconstruct
sa_encode
sa_decode
```
to allow usage of PyTorch CPU tensors, and additionally PyTorch GPU tensors if the index being used is on the GPU.
numpy functionality is still available when `faiss.contrib.torch_utils` is imported; we pass through to the original patched numpy function when we detect numpy inputs.
In addition, to allow for better (asynchronous) GPU usage without requiring the CPU to be involved, all of these functions which construct tensors/arrays for output now take optional arguments for storage (numpy or torch.Tensor) to be provided that will contain the output data. `range_search` is the only exception to this, as the size of the output data is indeterminate. The eventual GPU implementation will likely require the user to provide a maximum cap on the output size, and allow that to be passed instead. If the optional pre-allocated output values are presented by the user, they are used; otherwise, new return ndarray / Tensors are constructed as before and used for the return. If this feature were not provided on the GPU, then every execution would be completely serial as we would depend upon the CPU to allocate GPU memory before every operation. Instead, now this can function much like NN graph execution on the GPU, assuming that all of the data requirements are pre-allocated, so the execution will run at the full speed of the GPU and not be stalled sequentially launching kernels.
This diff also exposes the `GpuResources` shared_ptr object owned by a GPU index. This is required for pytorch GPU so that we can perform proper stream ordering in Faiss with respect to the current pytorch stream. So, Faiss indices now perform more or less as any NN operation in Torch does.
Note, however, that a Faiss index has its own setting on current device, and if the pytorch GPU tensor inputs are resident on a different device than what the Faiss index expects, a cross-device copy will be initiated. I may choose to make this an error in the future and require matching device to device.
This diff also found a bug when passing GPU data directly to `train()` for `GpuIndexIVFFlat` and `GpuIndexIVFScalarQuantizer`, as I guess we never tested passing GPU data directly to these functions before. `GpuIndexIVFPQ` was doing the right thing however.
The assign function is now also implemented on the GPU as well, and is now marked `const` to be in line with the `search` function.
Also added better checking of non-contiguous inputs for both Torch tensors and numpy ndarrays.
Updated the `knn_gpu` function with a base implementation always present that allows for usage of numpy arrays, which is overridden when `torch_utils` is imported to allow torch usage. This supports row/column major layout, float32/float16 data and int64/int32 indices for both numpy and torch.
Reviewed By: mdouze
Differential Revision: D24299400
fbshipit-source-id: b4f117b9c120bd1ad83e7702087051ab7b303b29
Summary:
The pytorch interop code was in a test until now. However, it is better if people can rely on it to be updated when the API is updated. Therefore, we move it into contrib.
Also added a README.md
Reviewed By: wickedfoo
Differential Revision: D23392962
fbshipit-source-id: 9b7c0e388a7ea3c0b73dc0018322138f49191673
Matthijs Douze
Jeff Johnson