diff --git a/contrib/torch_utils.py b/contrib/torch_utils.py
index 83bd2ac8e..027739958 100644
--- a/contrib/torch_utils.py
+++ b/contrib/torch_utils.py
@@ -33,7 +33,7 @@ def swig_ptr_from_UInt8Tensor(x):
     assert x.is_contiguous()
     assert x.dtype == torch.uint8
     return faiss.cast_integer_to_uint8_ptr(
-        x.storage().data_ptr() + x.storage_offset())
+        x._storage().data_ptr() + x.storage_offset())
 
 def swig_ptr_from_HalfTensor(x):
     """ gets a Faiss SWIG pointer from a pytorch tensor (on CPU or GPU) """
@@ -41,28 +41,28 @@ def swig_ptr_from_HalfTensor(x):
     assert x.dtype == torch.float16
     # no canonical half type in C/C++
     return faiss.cast_integer_to_void_ptr(
-        x.storage().data_ptr() + x.storage_offset() * 4)
+        x._storage().data_ptr() + x.storage_offset() * 2)
 
 def swig_ptr_from_FloatTensor(x):
     """ gets a Faiss SWIG pointer from a pytorch tensor (on CPU or GPU) """
     assert x.is_contiguous()
     assert x.dtype == torch.float32
     return faiss.cast_integer_to_float_ptr(
-        x.storage().data_ptr() + x.storage_offset() * 4)
+        x._storage().data_ptr() + x.storage_offset() * 4)
 
 def swig_ptr_from_IntTensor(x):
     """ gets a Faiss SWIG pointer from a pytorch tensor (on CPU or GPU) """
     assert x.is_contiguous()
     assert x.dtype == torch.int32, 'dtype=%s' % x.dtype
     return faiss.cast_integer_to_int_ptr(
-        x.storage().data_ptr() + x.storage_offset() * 8)
+        x._storage().data_ptr() + x.storage_offset() * 4)
 
 def swig_ptr_from_IndicesTensor(x):
     """ gets a Faiss SWIG pointer from a pytorch tensor (on CPU or GPU) """
     assert x.is_contiguous()
     assert x.dtype == torch.int64, 'dtype=%s' % x.dtype
     return faiss.cast_integer_to_idx_t_ptr(
-        x.storage().data_ptr() + x.storage_offset() * 8)
+        x._storage().data_ptr() + x.storage_offset() * 8)
 
 @contextlib.contextmanager
 def using_stream(res, pytorch_stream=None):
@@ -319,7 +319,10 @@ def handle_torch_Index(the_class):
 
         return x
 
-    def torch_replacement_reconstruct_n(self, n0, ni, x=None):
+    def torch_replacement_reconstruct_n(self, n0=0, ni=-1, x=None):
+        if ni == -1:
+            ni = self.ntotal
+
         # No tensor inputs are required, but with importing this module, we
         # assume that the default should be torch tensors. If we are passed a
         # numpy array, however, assume that the user is overriding this default
diff --git a/faiss/gpu/impl/IndexUtils.cu b/faiss/gpu/impl/IndexUtils.cu
index 6e4f23448..2389e1ad2 100644
--- a/faiss/gpu/impl/IndexUtils.cu
+++ b/faiss/gpu/impl/IndexUtils.cu
@@ -24,7 +24,7 @@ int getMaxKSelection() {
 
 void validateKSelect(idx_t k) {
     FAISS_THROW_IF_NOT_FMT(
-            k > 0 && k < (idx_t)getMaxKSelection(),
+            k > 0 && k <= (idx_t)getMaxKSelection(),
             "GPU index only supports min/max-K selection up to %d (requested %zu)",
             getMaxKSelection(),
             k);
@@ -32,7 +32,7 @@ void validateKSelect(idx_t k) {
 
 void validateNProbe(idx_t nprobe) {
     FAISS_THROW_IF_NOT_FMT(
-            nprobe > 0 && nprobe < (idx_t)getMaxKSelection(),
+            nprobe > 0 && nprobe <= (idx_t)getMaxKSelection(),
             "GPU IVF index only supports nprobe selection up to %d (requested %zu)",
             getMaxKSelection(),
             nprobe);
diff --git a/faiss/gpu/test/TestGpuIndexFlat.cpp b/faiss/gpu/test/TestGpuIndexFlat.cpp
index 617db62d9..bcd49c68d 100644
--- a/faiss/gpu/test/TestGpuIndexFlat.cpp
+++ b/faiss/gpu/test/TestGpuIndexFlat.cpp
@@ -141,6 +141,20 @@ TEST(TestGpuIndexFlat, L2_Float32) {
     }
 }
 
+// At least one test for the k > 1024 select
+TEST(TestGpuIndexFlat, L2_k_2048) {
+    if (faiss::gpu::getMaxKSelection() >= 2048) {
+        TestFlatOptions opt;
+        opt.metric = faiss::MetricType::METRIC_L2;
+        opt.useFloat16 = false;
+        opt.kOverride = 2048;
+        opt.dimOverride = 128;
+        opt.numVecsOverride = 10000;
+
+        testFlat(opt);
+    }
+}
+
 // test specialized k == 1 codepath
 TEST(TestGpuIndexFlat, L2_Float32_K1) {
     for (int tries = 0; tries < 3; ++tries) {