Update and document GPU buffer handling

changes/2751.bugfix.rst

@@ -0,0 +1 @@
+Fixed bug with Zarr using device memory, instead of host memory, for storing metadata when using GPUs.

changes/2751.doc.rst

@@ -0,0 +1 @@
+Added new user guide on :ref:`user-guide-gpu`.

changes/2751.feature.rst

@@ -0,0 +1 @@
+Added :meth:`zarr.config.enable_gpu` to update Zarr's configuration to use GPUs.

docs/developers/contributing.rst

@@ -230,6 +230,27 @@ during development at `http://0.0.0.0:8000/ <http://0.0.0.0:8000/>`_. This can b
 
     $ hatch --env docs run serve
 
+.. _changelog:
+
+Changelog
+~~~~~~~~~
+
+zarr-python uses `towncrier`_ to manage release notes. Most pull requests should
+include at least one news fragment describing the changes. To add a release
+note, you'll need the GitHub issue or pull request number and the type of your
+change (``feature``, ``bugfix``, ``doc``, ``removal``, ``misc``). With that, run
+```towncrier create``` with your development environment, which will prompt you
+for the issue number, change type, and the news text::
+
+   towncrier create
+
+Alternatively, you can manually create the files in the ``changes`` directory
+using the naming convention ``{issue-number}.{change-type}.rst``.
+
+See the `towncrier`_ docs for more.
+
+.. _towncrier: https://towncrier.readthedocs.io/en/stable/tutorial.html
+
 Development best practices, policies and procedures
 ---------------------------------------------------
 

docs/user-guide/config.rst

@@ -32,6 +32,7 @@ Configuration options include the following:
 - Whether empty chunks are written to storage ``array.write_empty_chunks``
 - Async and threading options, e.g. ``async.concurrency`` and ``threading.max_workers``
 - Selections of implementations of codecs, codec pipelines and buffers
+- Enabling GPU support with ``zarr.config.enable_gpu()``. See :ref:`user-guide-gpu` for more.
 
 For selecting custom implementations of codecs, pipelines, buffers and ndbuffers,
 first register the implementations in the registry and then select them in the config.

docs/user-guide/gpu.rst

@@ -0,0 +1,37 @@
+.. _user-guide-gpu:
+
+Using GPUs with Zarr
+====================
+
+Zarr can use GPUs to accelerate your workload by running
+:meth:`zarr.config.enable_gpu`.
+
+.. note::
+
+   `zarr-python` currently supports reading the ndarray data into device (GPU)
+   memory as the final stage of the codec pipeline. Data will still be read into
+   or copied to host (CPU) memory for encoding and decoding.
+
+   In the future, codecs will be available compressing and decompressing data on
+   the GPU, avoiding the need to move data between the host and device for
+   compression and decompression.
+
+Reading data into device memory
+-------------------------------
+
+:meth:`zarr.config.enable_gpu` configures Zarr to use GPU memory for the data
+buffers used internally by Zarr.
+
+.. code-block:: python
+
+   >>> import zarr
+   >>> import cupy as cp  # doctest: +SKIP
+   >>> zarr.config.enable_gpu()  # doctest: +SKIP
+   >>> store = zarr.storage.MemoryStore()  # doctest: +SKIP
+   >>> z = zarr.create_array(  # doctest: +SKIP
+   ...     store=store, shape=(100, 100), chunks=(10, 10), dtype="float32",
+   ... )
+   >>> type(z[:10, :10])  # doctest: +SKIP
+   cupy.ndarray
+
+Note that the output type is a ``cupy.ndarray`` rather than a NumPy array.

docs/user-guide/index.rst

@@ -23,6 +23,7 @@ Advanced Topics
     performance
     consolidated_metadata
     extending
+    gpu
 
 
 .. Coming soon

src/zarr/core/array.py

@@ -38,6 +38,7 @@
     NDBuffer,
     default_buffer_prototype,
 )
+from zarr.core.buffer.cpu import buffer_prototype as cpu_buffer_prototype
 from zarr.core.chunk_grids import RegularChunkGrid, _auto_partition, normalize_chunks
 from zarr.core.chunk_key_encodings import (
     ChunkKeyEncoding,
@@ -163,19 +164,20 @@
 ) -> dict[str, JSON]:
     if zarr_format == 2:
         zarray_bytes, zattrs_bytes = await gather(
-            (store_path / ZARRAY_JSON).get(), (store_path / ZATTRS_JSON).get()
+            (store_path / ZARRAY_JSON).get(prototype=cpu_buffer_prototype),
+            (store_path / ZATTRS_JSON).get(prototype=cpu_buffer_prototype),
         )
         if zarray_bytes is None:
             raise FileNotFoundError(store_path)
     elif zarr_format == 3:
-        zarr_json_bytes = await (store_path / ZARR_JSON).get()
+        zarr_json_bytes = await (store_path / ZARR_JSON).get(prototype=cpu_buffer_prototype)
         if zarr_json_bytes is None:
             raise FileNotFoundError(store_path)
     elif zarr_format is None:
         zarr_json_bytes, zarray_bytes, zattrs_bytes = await gather(
-            (store_path / ZARR_JSON).get(),
-            (store_path / ZARRAY_JSON).get(),
-            (store_path / ZATTRS_JSON).get(),
+            (store_path / ZARR_JSON).get(prototype=cpu_buffer_prototype),
+            (store_path / ZARRAY_JSON).get(prototype=cpu_buffer_prototype),
+            (store_path / ZATTRS_JSON).get(prototype=cpu_buffer_prototype),
         )
         if zarr_json_bytes is not None and zarray_bytes is not None:
             # warn and favor v3
@@ -1348,7 +1350,7 @@
         """
         Asynchronously save the array metadata.
         """
-        to_save = metadata.to_buffer_dict(default_buffer_prototype())
+        to_save = metadata.to_buffer_dict(cpu_buffer_prototype)
         awaitables = [set_or_delete(self.store_path / key, value) for key, value in to_save.items()]
 
         if ensure_parents:
@@ -1360,7 +1362,7 @@
                     [
                         (parent.store_path / key).set_if_not_exists(value)
                         for key, value in parent.metadata.to_buffer_dict(
-                            default_buffer_prototype()
+                            cpu_buffer_prototype
                         ).items()
                     ]
                 )

src/zarr/core/buffer/gpu.py

@@ -13,6 +13,10 @@
 
 from zarr.core.buffer import core
 from zarr.core.buffer.core import ArrayLike, BufferPrototype, NDArrayLike
+from zarr.registry import (
+    register_buffer,
+    register_ndbuffer,
+)
 
 if TYPE_CHECKING:
     from collections.abc import Iterable
@@ -215,3 +219,6 @@ def __setitem__(self, key: Any, value: Any) -> None:
 
 
 buffer_prototype = BufferPrototype(buffer=Buffer, nd_buffer=NDBuffer)
+
+register_buffer(Buffer)
+register_ndbuffer(NDBuffer)

src/zarr/core/config.py

@@ -29,10 +29,13 @@
 
 from __future__ import annotations
 
-from typing import Any, Literal, cast
+from typing import TYPE_CHECKING, Any, Literal, cast
 
 from donfig import Config as DConfig
 
+if TYPE_CHECKING:
+    from donfig.config_obj import ConfigSet
+
 
 class BadConfigError(ValueError):
     _msg = "bad Config: %r"
@@ -56,6 +59,14 @@ def reset(self) -> None:
         self.clear()
         self.refresh()
 
+    def enable_gpu(self) -> ConfigSet:
+        """
+        Configure Zarr to use GPUs where possible.
+        """
+        return self.set(
+            {"buffer": "zarr.core.buffer.gpu.Buffer", "ndbuffer": "zarr.core.buffer.gpu.NDBuffer"}
+        )
+
 
 # The default configuration for zarr
 config = Config(

src/zarr/testing/utils.py

@@ -38,7 +38,7 @@ def has_cupy() -> bool:
         return False
 
 
-T_Callable = TypeVar("T_Callable", bound=Callable[[], Coroutine[Any, Any, None]])
+T_Callable = TypeVar("T_Callable", bound=Callable[..., Coroutine[Any, Any, None] | None])
 
 
 # Decorator for GPU tests

tests/test_api.py

@@ -27,6 +27,7 @@
 from zarr.errors import MetadataValidationError
 from zarr.storage import MemoryStore
 from zarr.storage._utils import normalize_path
+from zarr.testing.utils import gpu_test
 
 
 def test_create(memory_store: Store) -> None:
@@ -1121,3 +1122,40 @@ def test_open_array_with_mode_r_plus(store: Store) -> None:
     assert isinstance(z2, Array)
     assert (z2[:] == 1).all()
     z2[:] = 3
+
+
+@gpu_test
+@pytest.mark.parametrize(
+    "store",
+    ["local", "memory", "zip"],
+    indirect=True,
+)
+@pytest.mark.parametrize("zarr_format", [None, 2, 3])
+def test_gpu_basic(store: Store, zarr_format: ZarrFormat | None) -> None:
+    import cupy as cp
+
+    if zarr_format == 2:
+        # Without this, the zstd codec attempts to convert the cupy
+        # array to bytes.
+        compressors = None
+    else:
+        compressors = "auto"
+
+    with zarr.config.enable_gpu():
+        src = cp.random.uniform(size=(100, 100))  # allocate on the device
+        z = zarr.create_array(
+            store,
+            name="a",
+            shape=src.shape,
+            chunks=(10, 10),
+            dtype=src.dtype,
+            overwrite=True,
+            zarr_format=zarr_format,
+            compressors=compressors,
+        )
+        z[:10, :10] = src[:10, :10]
+
+        result = z[:10, :10]
+        # assert_array_equal doesn't check the type
+        assert isinstance(result, type(src))
+        cp.testing.assert_array_equal(result, src[:10, :10])