add use_raft to knn_gpu (torch) (facebookresearch#3509)

Summary:
Add support for `use_raft` in the torch version of `knn_gpu`. The numpy version already has this support, see https://github.com/facebookresearch/faiss/blob/main/faiss/python/gpu_wrappers.py#L59

Pull Request resolved: facebookresearch#3509

Reviewed By: mlomeli1, junjieqi

Differential Revision: D58489851

Pulled By: algoriddle

fbshipit-source-id: cfad722fefd4809b135b765d0d43587cfd782d0e

Author: algoriddle

contrib/torch_utils.py

@@ -492,8 +492,9 @@ def torch_replacement_sa_decode(self, codes, x=None):
         if issubclass(the_class, faiss.Index):
             handle_torch_Index(the_class)
 
+
 # allows torch tensor usage with bfKnn
-def torch_replacement_knn_gpu(res, xq, xb, k, D=None, I=None, metric=faiss.METRIC_L2, device=-1):
+def torch_replacement_knn_gpu(res, xq, xb, k, D=None, I=None, metric=faiss.METRIC_L2, device=-1, use_raft=False):
     if type(xb) is np.ndarray:
         # Forward to faiss __init__.py base method
         return faiss.knn_gpu_numpy(res, xq, xb, k, D, I, metric, device)
@@ -574,6 +575,7 @@ def torch_replacement_knn_gpu(res, xq, xb, k, D=None, I=None, metric=faiss.METRI
     args.outIndices = I_ptr
     args.outIndicesType = I_type
     args.device = device
+    args.use_raft = use_raft
 
     with using_stream(res):
         faiss.bfKnn(res, args)

faiss/gpu/test/torch_test_contrib_gpu.py

@@ -249,7 +249,7 @@ def test_sa_encode_decode(self):
         return
 
 class TestTorchUtilsKnnGpu(unittest.TestCase):
-    def test_knn_gpu(self):
+    def test_knn_gpu(self, use_raft=False):
         torch.manual_seed(10)
         d = 32
         nb = 1024
@@ -286,7 +286,7 @@ def test_knn_gpu(self):
                     else:
                         xb_c = xb_np
 
-                    D, I = faiss.knn_gpu(res, xq_c, xb_c, k)
+                    D, I = faiss.knn_gpu(res, xq_c, xb_c, k, use_raft=use_raft)
 
                     self.assertTrue(torch.equal(torch.from_numpy(I), gt_I))
                     self.assertLess((torch.from_numpy(D) - gt_D).abs().max(), 1e-4)
@@ -312,15 +312,15 @@ def test_knn_gpu(self):
                             xb_c = to_column_major_torch(xb)
                             assert not xb_c.is_contiguous()
 
-                        D, I = faiss.knn_gpu(res, xq_c, xb_c, k)
+                        D, I = faiss.knn_gpu(res, xq_c, xb_c, k, use_raft=use_raft)
 
                         self.assertTrue(torch.equal(I.cpu(), gt_I))
                         self.assertLess((D.cpu() - gt_D).abs().max(), 1e-4)
 
                         # test on subset
                         try:
                             # This internally uses the current pytorch stream
-                            D, I = faiss.knn_gpu(res, xq_c[6:8], xb_c, k)
+                            D, I = faiss.knn_gpu(res, xq_c[6:8], xb_c, k, use_raft=use_raft)
                         except TypeError:
                             if not xq_row_major:
                                 # then it is expected
@@ -331,7 +331,13 @@ def test_knn_gpu(self):
                         self.assertTrue(torch.equal(I.cpu(), gt_I[6:8]))
                         self.assertLess((D.cpu() - gt_D[6:8]).abs().max(), 1e-4)
 
-    def test_knn_gpu_datatypes(self):
+    @unittest.skipUnless(
+        "RAFT" in faiss.get_compile_options(),
+        "only if RAFT is compiled in")
+    def test_knn_gpu_raft(self):
+        self.test_knn_gpu(use_raft=True)
+
+    def test_knn_gpu_datatypes(self, use_raft=False):
         torch.manual_seed(10)
         d = 10
         nb = 1024
@@ -354,7 +360,7 @@ def test_knn_gpu_datatypes(self):
         D = torch.zeros(nq, k, device=xb_c.device, dtype=torch.float32)
         I = torch.zeros(nq, k, device=xb_c.device, dtype=torch.int32)
 
-        faiss.knn_gpu(res, xq_c, xb_c, k, D, I)
+        faiss.knn_gpu(res, xq_c, xb_c, k, D, I, use_raft=use_raft)
 
         self.assertTrue(torch.equal(I.long().cpu(), gt_I))
         self.assertLess((D.float().cpu() - gt_D).abs().max(), 1.5e-3)
@@ -366,7 +372,7 @@ def test_knn_gpu_datatypes(self):
         xb_c = xb.half().numpy()
         xq_c = xq.half().numpy()
 
-        faiss.knn_gpu(res, xq_c, xb_c, k, D, I)
+        faiss.knn_gpu(res, xq_c, xb_c, k, D, I, use_raft=use_raft)
 
         self.assertTrue(torch.equal(torch.from_numpy(I).long(), gt_I))
         self.assertLess((torch.from_numpy(D) - gt_D).abs().max(), 1.5e-3)