[BUG] kneighbors_graph with ivfflat

[Intron7]

Describe the bug kneighbors_graph with algorithm="ivfflat" fails because _fit_X gives and AttributeError.

/home/severin/conda/envs/rapids-23.10/lib/python3.10/site-packages/cuml/internals/api_decorators.py:190: UserWarning: 
Warning: Approximate Nearest Neighbor methods might be unstable in this version of cuML. This is due to a known issue in the FAISS release that this cuML version is linked to. (see cuML issue #4020)
  return func(*args, **kwargs)
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[1], line 10
      8 nn = NearestNeighbors(n_neighbors=k,algorithm="ivfflat")
      9 nn.fit(dataset)
---> 10 nn.kneighbors_graph(dataset)

File ~/conda/envs/rapids-23.10/lib/python3.10/site-packages/cuml/internals/api_decorators.py:188, in _make_decorator_function.<locals>.decorator_function.<locals>.decorator_closure.<locals>.wrapper(*args, **kwargs)
    185     set_api_output_dtype(output_dtype)
    187 if process_return:
--> 188     ret = func(*args, **kwargs)
    189 else:
    190     return func(*args, **kwargs)

File ~/conda/envs/rapids-23.10/lib/python3.10/site-packages/cuml/internals/api_decorators.py:393, in enable_device_interop.<locals>.dispatch(self, *args, **kwargs)
    391 if hasattr(self, "dispatch_func"):
    392     func_name = gpu_func.__name__
--> 393     return self.dispatch_func(func_name, gpu_func, *args, **kwargs)
    394 else:
    395     return gpu_func(self, *args, **kwargs)

File ~/conda/envs/rapids-23.10/lib/python3.10/site-packages/cuml/internals/api_decorators.py:190, in _make_decorator_function.<locals>.decorator_function.<locals>.decorator_closure.<locals>.wrapper(*args, **kwargs)
    188         ret = func(*args, **kwargs)
    189     else:
--> 190         return func(*args, **kwargs)
    192 return cm.process_return(ret)

File base.pyx:674, in cuml.internals.base.UniversalBase.dispatch_func()

File nearest_neighbors.pyx:853, in cuml.neighbors.nearest_neighbors.NearestNeighbors.kneighbors_graph()

File base.pyx:324, in cuml.internals.base.Base.__getattr__()

Steps/Code to reproduce bug

from cuml.neighbors import NearestNeighbors
import cupy as cp
n_samples = 50000
n_features = 30
k = 10
dataset = cp.random.random_sample((n_samples, n_features),
                                  dtype=cp.float32)
nn = NearestNeighbors(n_neighbors=k,algorithm="ivfflat")
nn.fit(dataset)
nn.kneighbors_graph(dataset)

Expected behavior Works like kneighbors just with sparse matrix output.

Environment details (please complete the following information):

• Environment location: Bare-metal
• Linux Distro/Architecture: [Ubuntu 22.04 amd64]
• GPU Model/Driver: [309 and driver 545.23.06 ]
• CUDA: [11.8]
• Method of cuDF & cuML install: conda

Conda Env

List of packages in environment: "/home/severin/conda/envs/rapids-23.10"

Name Version Build Channel
─────────────────────────────────────────────────────────────────────────────────────────────── _libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_gnu conda-forge aiohttp 3.8.6 py310h2372a71_1 conda-forge aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge anyio 4.0.0 pyhd8ed1ab_0 conda-forge aom 3.6.1 h59595ed_0 conda-forge appdirs 1.4.4 pyh9f0ad1d_0 conda-forge argon2-cffi 23.1.0 pyhd8ed1ab_0 conda-forge argon2-cffi-bindings 21.2.0 py310h2372a71_4 conda-forge arpack 3.8.0 nompi_h0baa96a_101 conda-forge arrow 1.3.0 pyhd8ed1ab_0 conda-forge asciitree 0.3.3 py_2 conda-forge asttokens 2.4.0 pyhd8ed1ab_0 conda-forge async-lru 2.0.4 pyhd8ed1ab_0 conda-forge async-timeout 4.0.3 pyhd8ed1ab_0 conda-forge attrs 23.1.0 pyh71513ae_1 conda-forge aws-c-auth 0.7.3 he2921ad_3 conda-forge aws-c-cal 0.6.2 hc309b26_1 conda-forge aws-c-common 0.9.0 hd590300_0 conda-forge aws-c-compression 0.2.17 h4d4d85c_2 conda-forge aws-c-event-stream 0.3.2 h2e3709c_0 conda-forge aws-c-http 0.7.12 hc865f51_1 conda-forge aws-c-io 0.13.32 h1a03231_3 conda-forge aws-c-mqtt 0.9.5 h3a0376c_1 conda-forge aws-c-s3 0.3.14 h1678ad6_3 conda-forge aws-c-sdkutils 0.1.12 h4d4d85c_1 conda-forge aws-checksums 0.1.17 h4d4d85c_1 conda-forge aws-crt-cpp 0.23.0 h40cdbb9_5 conda-forge aws-sdk-cpp 1.10.57 h6f6b8fa_21 conda-forge babel 2.13.0 pyhd8ed1ab_0 conda-forge backcall 0.2.0 pyh9f0ad1d_0 conda-forge backports 1.0 pyhd8ed1ab_3 conda-forge backports.functools_lru_cache 1.6.5 pyhd8ed1ab_0 conda-forge beautifulsoup4 4.12.2 pyha770c72_0 conda-forge bleach 6.1.0 pyhd8ed1ab_0 conda-forge blosc 1.21.5 h0f2a231_0 conda-forge bokeh 3.2.2 pyhd8ed1ab_0 conda-forge branca 0.6.0 pyhd8ed1ab_0 conda-forge brotli 1.1.0 hd590300_1 conda-forge brotli-bin 1.1.0 hd590300_1 conda-forge brotli-python 1.1.0 py310hc6cd4ac_1 conda-forge brunsli 0.1 h9c3ff4c_0 conda-forge bzip2 1.0.8 h7f98852_4 conda-forge c-ares 1.20.1 hd590300_0 conda-forge c-blosc2 2.10.5 hb4ffafa_0 conda-forge ca-certificates 2023.7.22 hbcca054_0 conda-forge cached-property 1.5.2 hd8ed1ab_1 conda-forge cached_property 1.5.2 pyha770c72_1 conda-forge cachetools 5.3.1 pyhd8ed1ab_0 conda-forge cairo 1.18.0 h3faef2a_0 conda-forge certifi 2023.7.22 pyhd8ed1ab_0 conda-forge cffi 1.16.0 py310h2fee648_0 conda-forge cfitsio 4.3.0 hbdc6101_0 conda-forge charls 2.4.2 h59595ed_0 conda-forge charset-normalizer 3.3.0 pyhd8ed1ab_0 conda-forge click 8.1.7 unix_pyh707e725_0 conda-forge click-plugins 1.1.1 py_0 conda-forge cligj 0.7.2 pyhd8ed1ab_1 conda-forge cloudpickle 2.2.1 pyhd8ed1ab_0 conda-forge colorama 0.4.6 pyhd8ed1ab_0 conda-forge colorcet 3.0.1 pyhd8ed1ab_0 conda-forge comm 0.1.4 pyhd8ed1ab_0 conda-forge contourpy 1.1.1 py310hd41b1e2_1 conda-forge cubinlinker 0.3.0 py310hfdf336d_0 rapidsai
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dask-cudf 23.10.00 cuda11_py310_231011_g9f0c2f452f_0 rapidsai
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