QINCo implementation in CPU Faiss (facebookresearch#3608)

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

demos/demo_qinco.py

@@ -0,0 +1,77 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+This demonstrates how to reproduce the QINCo paper results using the Faiss
+QINCo implementation. The code loads the reference model because training 
+is not implemented in Faiss.
+
+Prepare the data with
+
+cd /tmp
+
+# get the reference qinco code
+git clone https://github.com/facebookresearch/Qinco.git
+
+# get the data
+wget https://dl.fbaipublicfiles.com/QINCo/datasets/bigann/bigann1M.bvecs
+
+# get the model
+wget https://dl.fbaipublicfiles.com/QINCo/models/bigann_8x8_L2.pt
+
+"""
+
+import numpy as np
+from faiss.contrib.vecs_io import bvecs_mmap
+import sys
+import time
+import torch
+import faiss
+
+# make sure pickle deserialization will work
+sys.path.append("/tmp/Qinco")
+import model_qinco
+
+with torch.no_grad():
+
+    qinco = torch.load("/tmp/bigann_8x8_L2.pt")
+    qinco.eval()
+    # print(qinco)
+    if True:
+        torch.set_num_threads(1)
+        faiss.omp_set_num_threads(1)
+
+    x_base = bvecs_mmap("/tmp/bigann1M.bvecs")[:1000].astype('float32')
+    x_scaled = torch.from_numpy(x_base) / qinco.db_scale
+
+    t0 = time.time()
+    codes, _ = qinco.encode(x_scaled)
+    x_decoded_scaled = qinco.decode(codes)
+    print(f"Pytorch encode {time.time() - t0:.3f} s")
+    # multi-thread: 1.13s, single-thread: 7.744
+
+    x_decoded = x_decoded_scaled.numpy() * qinco.db_scale
+
+    err = ((x_decoded - x_base) ** 2).sum(1).mean()
+    print("MSE=", err)  # = 14211.956, near the L=2 result in Fig 4 of the paper
+
+    qinco2 = faiss.QINCo(qinco)
+    t0 = time.time()
+    codes2 = qinco2.encode(faiss.Tensor2D(x_scaled))
+    x_decoded2 = qinco2.decode(codes2).numpy() * qinco.db_scale
+    print(f"Faiss encode {time.time() - t0:.3f} s")
+    # multi-thread: 3.2s, single thread: 7.019
+
+    # these tests don't work because there are outlier encodings
+    # np.testing.assert_array_equal(codes.numpy(), codes2.numpy())
+    # np.testing.assert_allclose(x_decoded, x_decoded2)
+
+    ndiff = (codes.numpy() != codes2.numpy()).sum() / codes.numel()
+    assert ndiff < 0.01
+    ndiff = (((x_decoded - x_decoded2) ** 2).sum(1) > 1e-5).sum()
+    assert ndiff / len(x_base) < 0.01
+
+    err = ((x_decoded2 - x_base) ** 2).sum(1).mean()
+    print("MSE=", err)  # = 14213.551

faiss/CMakeLists.txt

@@ -46,6 +46,7 @@ set(FAISS_SRC
   IndexScalarQuantizer.cpp
   IndexShards.cpp
   IndexShardsIVF.cpp
+  IndexNeuralNetCodec.cpp
   MatrixStats.cpp
   MetaIndexes.cpp
   VectorTransform.cpp
@@ -81,6 +82,7 @@ set(FAISS_SRC
   invlists/InvertedLists.cpp
   invlists/InvertedListsIOHook.cpp
   utils/Heap.cpp
+  utils/NeuralNet.cpp
   utils/WorkerThread.cpp
   utils/distances.cpp
   utils/distances_simd.cpp

faiss/IndexNeuralNetCodec.cpp

@@ -0,0 +1,56 @@
+/**
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#include <faiss/IndexNeuralNetCodec.h>
+#include <faiss/impl/FaissAssert.h>
+#include <faiss/utils/hamming.h>
+
+namespace faiss {
+
+/*********************************************************
+ * IndexNeuralNetCodec implementation
+ *********************************************************/
+
+IndexNeuralNetCodec::IndexNeuralNetCodec(
+        int d,
+        int M,
+        int nbits,
+        MetricType metric)
+        : IndexFlatCodes((M * nbits + 7) / 8, d, metric), M(M), nbits(nbits) {
+    is_trained = false;
+}
+
+void IndexNeuralNetCodec::train(idx_t n, const float* x) {
+    FAISS_THROW_MSG("Training not implemented in C++, use Pytorch");
+}
+
+void IndexNeuralNetCodec::sa_encode(idx_t n, const float* x, uint8_t* codes)
+        const {
+    nn::Tensor2D x_tensor(n, d, x);
+    nn::Int32Tensor2D codes_tensor = net->encode(x_tensor);
+    pack_bitstrings(n, M, nbits, codes_tensor.data(), codes, code_size);
+}
+
+void IndexNeuralNetCodec::sa_decode(idx_t n, const uint8_t* codes, float* x)
+        const {
+    nn::Int32Tensor2D codes_tensor(n, M);
+    unpack_bitstrings(n, M, nbits, codes, code_size, codes_tensor.data());
+    nn::Tensor2D x_tensor = net->decode(codes_tensor);
+    memcpy(x, x_tensor.data(), d * n * sizeof(float));
+}
+
+/*********************************************************
+ * IndexQINeuralNetCodec implementation
+ *********************************************************/
+
+IndexQINCo::IndexQINCo(int d, int M, int nbits, int L, int h, MetricType metric)
+        : IndexNeuralNetCodec(d, M, nbits, metric),
+          qinco(d, 1 << nbits, L, M, h) {
+    net = &qinco;
+}
+
+} // namespace faiss

faiss/IndexNeuralNetCodec.h

@@ -0,0 +1,49 @@
+/**
+ * Copyright (c) Facebook, Inc. and its affiliates.
+ *
+ * This source code is licensed under the MIT license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include <vector>
+
+#include <faiss/IndexFlatCodes.h>
+#include <faiss/utils/NeuralNet.h>
+
+namespace faiss {
+
+struct IndexNeuralNetCodec : IndexFlatCodes {
+    NeuralNetCodec* net = nullptr;
+    size_t M, nbits;
+
+    explicit IndexNeuralNetCodec(
+            int d = 0,
+            int M = 0,
+            int nbits = 0,
+            MetricType metric = METRIC_L2);
+
+    void train(idx_t n, const float* x) override;
+
+    void sa_encode(idx_t n, const float* x, uint8_t* codes) const override;
+    void sa_decode(idx_t n, const uint8_t* codes, float* x) const override;
+
+    ~IndexNeuralNetCodec() {}
+};
+
+struct IndexQINCo : IndexNeuralNetCodec {
+    QINCo qinco;
+
+    IndexQINCo(
+            int d,
+            int M,
+            int nbits,
+            int L,
+            int h,
+            MetricType metric = METRIC_L2);
+
+    ~IndexQINCo() {}
+};
+
+} // namespace faiss

faiss/impl/ResultHandler.h

@@ -16,6 +16,7 @@
 #include <faiss/impl/IDSelector.h>
 #include <faiss/utils/Heap.h>
 #include <faiss/utils/partitioning.h>
+
 #include <algorithm>
 #include <iostream>
 

faiss/python/__init__.py

@@ -44,6 +44,14 @@
 class_wrappers.handle_IDSelectorSubset(IDSelectorBitmap, class_owns=False, force_int64=False)
 class_wrappers.handle_CodeSet(CodeSet)
 
+class_wrappers.handle_Tensor2D(Tensor2D)
+class_wrappers.handle_Tensor2D(Int32Tensor2D)
+class_wrappers.handle_Embedding(Embedding)
+class_wrappers.handle_Linear(Linear)
+class_wrappers.handle_QINCo(QINCo)
+class_wrappers.handle_QINCoStep(QINCoStep)
+
+
 this_module = sys.modules[__name__]
 
 # handle sub-classes

faiss/python/class_wrappers.py

@@ -1247,3 +1247,152 @@ def replacement_insert(self, codes, inserted=None):
         return inserted
 
     replace_method(the_class, 'insert', replacement_insert)
+
+######################################################
+# Syntatic sugar for NeuralNet classes
+######################################################
+
+
+def handle_Tensor2D(the_class):
+    the_class.original_init = the_class.__init__
+
+    def replacement_init(self, *args):
+        if len(args) == 1:
+            array, = args
+            n, d = array.shape
+            self.original_init(n, d)
+            faiss.copy_array_to_vector(
+                np.ascontiguousarray(array).ravel(), self.v)
+        else:
+            self.original_init(*args)
+
+    def numpy(self):
+        shape = np.zeros(2, dtype=np.int64)
+        faiss.memcpy(faiss.swig_ptr(shape), self.shape, shape.nbytes)
+        return faiss.vector_to_array(self.v).reshape(shape[0], shape[1])
+
+    the_class.__init__ = replacement_init
+    the_class.numpy = numpy
+
+
+def handle_Embedding(the_class):
+    the_class.original_init = the_class.__init__
+
+    def replacement_init(self, *args):
+        if len(args) != 1 or args[0].__class__ == the_class:
+            self.original_init(*args)
+            return
+        # assume it's a torch.Embedding
+        emb = args[0]
+        self.original_init(emb.num_embeddings, emb.embedding_dim)
+        self.from_torch(emb)
+
+    def from_torch(self, emb):
+        """ copy weights from torch.Embedding """
+        assert emb.weight.shape == (self.num_embeddings, self.embedding_dim)
+        faiss.copy_array_to_vector(
+            np.ascontiguousarray(emb.weight.data).ravel(), self.weight)
+
+    def from_array(self, array):
+        """ copy weights from numpy array """
+        assert array.shape == (self.num_embeddings, self.embedding_dim)
+        faiss.copy_array_to_vector(
+            np.ascontiguousarray(array).ravel(), self.weight)
+
+    the_class.from_array = from_array
+    the_class.from_torch = from_torch
+    the_class.__init__ = replacement_init
+
+
+def handle_Linear(the_class):
+    the_class.original_init = the_class.__init__
+
+    def replacement_init(self, *args):
+        if len(args) != 1 or args[0].__class__ == the_class:
+            self.original_init(*args)
+            return
+        # assume it's a torch.Linear
+        linear = args[0]
+        bias = linear.bias is not None
+        self.original_init(linear.in_features, linear.out_features, bias)
+        self.from_torch(linear)
+
+    def from_torch(self, linear):
+        """ copy weights from torch.Linear """
+        assert linear.weight.shape == (self.out_features, self.in_features)
+        faiss.copy_array_to_vector(
+            linear.weight.data.numpy().ravel(), self.weight)
+        if linear.bias is not None:
+            assert linear.bias.shape == (self.out_features,)
+            faiss.copy_array_to_vector(linear.bias.data.numpy(), self.bias)
+
+    def from_array(self, array, bias=None):
+        """ copy weights from numpy array """
+        assert array.shape == (self.out_features, self.in_features)
+        faiss.copy_array_to_vector(
+            np.ascontiguousarray(array).ravel(), self.weight)
+        if bias is not None:
+            assert bias.shape == (self.out_features,)
+            faiss.copy_array_to_vector(bias, self.bias)
+
+    the_class.__init__ = replacement_init
+    the_class.from_array = from_array
+    the_class.from_torch = from_torch
+
+######################################################
+# Syntatic sugar for QINCo and QINCoStep
+######################################################
+
+def handle_QINCoStep(the_class):
+    the_class.original_init = the_class.__init__
+
+    def replacement_init(self, *args):
+        if len(args) != 1 or args[0].__class__ == the_class:
+            self.original_init(*args)
+            return
+        step = args[0]
+        # assume it's a Torch QINCoStep
+        self.original_init(step.d, step.K, step.L, step.h)
+        self.from_torch(step)
+
+    def from_torch(self, step):
+        """ copy weights from torch.QINCoStep """
+        assert (step.d, step.K, step.L, step.h) == (self.d, self.K, self.L, self.h)
+        self.codebook.from_torch(step.codebook)
+        self.MLPconcat.from_torch(step.MLPconcat)
+
+        for l in range(step.L):
+            src = step.residual_blocks[l]
+            dest = self.get_residual_block(l)
+            dest.linear1.from_torch(src[0])
+            dest.linear2.from_torch(src[2])
+
+    the_class.__init__ = replacement_init
+    the_class.from_torch = from_torch
+
+
+def handle_QINCo(the_class):
+    the_class.original_init = the_class.__init__
+
+    def replacement_init(self, *args):
+        if len(args) != 1 or args[0].__class__ == the_class:
+            self.original_init(*args)
+            return
+
+        # assume it's a Torch QINCo
+        qinco = args[0]
+        self.original_init(qinco.d, qinco.K, qinco.L, qinco.M, qinco.h)
+        self.from_torch(qinco)
+
+    def from_torch(self, qinco):
+        """ copy weights from torch.QINCo """
+        assert (
+            (qinco.d, qinco.K, qinco.L, qinco.M, qinco.h) ==
+            (self.d, self.K, self.L, self.M, self.h)
+        )
+        self.codebook0.from_torch(qinco.codebook0)
+        for m in range(qinco.M - 1):
+            self.get_step(m).from_torch(qinco.steps[m])
+
+    the_class.__init__ = replacement_init
+    the_class.from_torch = from_torch