Merge remote-tracking branch 'upstream/master' into read-preprocssed-…

…micro

.github/CODEOWNERS

@@ -11,7 +11,7 @@
 # NOTE: Add @ray-project/ray-docs to all following docs subdirs.
 /doc/ @ray-project/ray-docs
 /doc/source/use-cases.rst @ericl @pcmoritz
-/doc/source/templates @justinvyu @sofianhnaide
+/doc/source/templates @justinvyu @matthewdeng @sofianhnaide
 
 # ==== Ray core ====
 

python/ray/data/_internal/torch_iterable_dataset.py

@@ -1,13 +1,5 @@
-from typing import TYPE_CHECKING, Dict, Union
-
 from torch.utils.data import IterableDataset
 
-if TYPE_CHECKING:
-    import torch
-
-
-TorchTensorBatchType = Union["torch.Tensor", Dict[str, "torch.Tensor"]]
-
 
 class TorchIterableDataset(IterableDataset):
     def __init__(self, generator_func):

python/ray/data/dataset.py

@@ -18,6 +18,7 @@
     Optional,
     Tuple,
     Type,
+    TypeVar,
     Union,
 )
 from uuid import uuid4
@@ -152,7 +153,6 @@
     from tensorflow_metadata.proto.v0 import schema_pb2
 
     from ray.data._internal.execution.interfaces import Executor, NodeIdStr
-    from ray.data._internal.torch_iterable_dataset import TorchTensorBatchType
     from ray.data.dataset_pipeline import DatasetPipeline
     from ray.data.grouped_data import GroupedData
 
@@ -165,6 +165,9 @@
 
 TensorFlowTensorBatchType = Union["tf.Tensor", Dict[str, "tf.Tensor"]]
 
+CollatedData = TypeVar("CollatedData")
+TorchBatchType = Union[Dict[str, "torch.Tensor"], CollatedData]
+
 
 @PublicAPI
 class Dataset:
@@ -3336,7 +3339,7 @@ def iter_batches(
         drop_last: bool = False,
         local_shuffle_buffer_size: Optional[int] = None,
         local_shuffle_seed: Optional[int] = None,
-        _collate_fn: Optional[Callable[[DataBatch], Any]] = None,
+        _collate_fn: Optional[Callable[[DataBatch], CollatedData]] = None,
         # Deprecated.
         prefetch_blocks: int = 0,
     ) -> Iterator[DataBatch]:
@@ -3408,37 +3411,49 @@ def iter_torch_batches(
         prefetch_batches: int = 1,
         batch_size: Optional[int] = 256,
         dtypes: Optional[Union["torch.dtype", Dict[str, "torch.dtype"]]] = None,
-        device: Optional[str] = None,
-        collate_fn: Optional[Callable[[Dict[str, np.ndarray]], Any]] = None,
+        device: str = "auto",
+        collate_fn: Optional[Callable[[Dict[str, np.ndarray]], CollatedData]] = None,
         drop_last: bool = False,
         local_shuffle_buffer_size: Optional[int] = None,
         local_shuffle_seed: Optional[int] = None,
         # Deprecated
         prefetch_blocks: int = 0,
-    ) -> Iterator["TorchTensorBatchType"]:
+    ) -> Iterator[TorchBatchType]:
         """Return an iterator over batches of data represented as Torch tensors.
 
         This iterator yields batches of type ``Dict[str, torch.Tensor]``.
         For more flexibility, call :meth:`~Dataset.iter_batches` and manually convert
         your data to Torch tensors.
 
         Examples:
+            >>> import ray
+            >>> for batch in ray.data.range(
+            ...     12,
+            ... ).iter_torch_batches(batch_size=4):
+            ...     print(batch)
+            {'id': tensor([0, 1, 2, 3])}
+            {'id': tensor([4, 5, 6, 7])}
+            {'id': tensor([ 8,  9, 10, 11])}
+
+            Use the ``collate_fn`` to customize how the tensor batch is created.
+
+            >>> from typing import Any, Dict
+            >>> import torch
+            >>> import numpy as np
+            >>> import ray
+            >>> def collate_fn(batch: Dict[str, np.ndarray]) -> Any:
+            ...     return torch.stack(
+            ...         [torch.as_tensor(array) for array in batch.values()],
+            ...         axis=1
+            ...     )
+            >>> dataset = ray.data.from_items([
+            ...     {"col_1": 1, "col_2": 2},
+            ...     {"col_1": 3, "col_2": 4}])
+            >>> for batch in dataset.iter_torch_batches(collate_fn=collate_fn):
+            ...     print(batch)
+            tensor([[1, 2],
+                    [3, 4]])
 
-            .. testcode::
-
-                import ray
-
-                # This dataset contains three images.
-                ds = ray.data.read_images("example://image-datasets/simple")
-
-                for batch in ds.iter_torch_batches(batch_size=2):
-                    print(batch)
-
-            .. testoutput::
-                :options: +MOCK
-
-                {'image': <tf.Tensor: shape=(2, 32, 32, 3), dtype=uint8, numpy=array([[[[...]]]], dtype=uint8)>}
-                {'image': <tf.Tensor: shape=(1, 32, 32, 3), dtype=uint8, numpy=array([[[[...]]]], dtype=uint8)>}
 
         Time complexity: O(1)
 
@@ -3451,17 +3466,25 @@ def iter_torch_batches(
                 blocks as batches (blocks may contain different number of rows).
                 The final batch may include fewer than ``batch_size`` rows if
                 ``drop_last`` is ``False``. Defaults to 256.
-            dtypes: The Torch dtype(s) for the created tensor(s); if ``None``, the
-                dtype is inferred from the tensor data.
-            device: The device on which the tensor should be placed; if ``None``, the
-                Torch tensor is constructed on CPU.
+            dtypes: The Torch dtype(s) for the created tensor(s); if ``None``, the dtype
+                is inferred from the tensor data. You can't use this parameter with
+                ``collate_fn``.
+            device: The device on which the tensor should be placed. Defaults to
+                "auto" which moves the tensors to the appropriate device when the
+                Dataset is passed to Ray Train and ``collate_fn`` is not provided.
+                Otherwise, defaults to CPU. You can't use this parameter with
+                ``collate_fn``.
             collate_fn: A function to convert a Numpy batch to a PyTorch tensor batch.
-                Potential use cases include collating along a dimension other than the
-                first, padding sequences of various lengths, or generally handling
-                batches of different length tensors. If not provided, the default
-                collate function is used which simply converts the batch of numpy
-                arrays to a batch of PyTorch tensors. This API is still experimental
-                and is subject to change.
+                When this parameter is specified, the user should manually handle the
+                host to device data transfer outside of collate_fn.
+                This is useful for further processing the data after it has been
+                batched. Potential use cases include collating along a dimension other
+                than the first, padding sequences of various lengths, or generally
+                handling batches of different length tensors. If not provided, the
+                default collate function is used which simply converts the batch of
+                numpy arrays to a batch of PyTorch tensors. This API is still
+                experimental and is subject to change. You can't use this parameter in
+                conjunction with ``dtypes`` or ``device``.
             drop_last: Whether to drop the last batch if it's incomplete.
             local_shuffle_buffer_size: If not ``None``, the data is randomly shuffled
                 using a local in-memory shuffle buffer, and this value serves as the

python/ray/data/iterator.py

@@ -32,8 +32,12 @@
     import tensorflow as tf
     import torch
 
-    from ray.data._internal.torch_iterable_dataset import TorchTensorBatchType
-    from ray.data.dataset import Schema, TensorFlowTensorBatchType
+    from ray.data.dataset import (
+        CollatedData,
+        Schema,
+        TensorFlowTensorBatchType,
+        TorchBatchType,
+    )
 
 
 @PublicAPI(stability="beta")
@@ -93,7 +97,7 @@ def iter_batches(
         drop_last: bool = False,
         local_shuffle_buffer_size: Optional[int] = None,
         local_shuffle_seed: Optional[int] = None,
-        _collate_fn: Optional[Callable[[DataBatch], Any]] = None,
+        _collate_fn: Optional[Callable[[DataBatch], "CollatedData"]] = None,
         _finalize_fn: Optional[Callable[[Any], Any]] = None,
         # Deprecated.
         prefetch_blocks: int = 0,
@@ -255,29 +259,48 @@ def iter_torch_batches(
         prefetch_batches: int = 1,
         batch_size: Optional[int] = 256,
         dtypes: Optional[Union["torch.dtype", Dict[str, "torch.dtype"]]] = None,
-        device: Optional[str] = None,
-        collate_fn: Optional[
-            Callable[[Union[np.ndarray, Dict[str, np.ndarray]]], Any]
-        ] = None,
+        device: str = "auto",
+        collate_fn: Optional[Callable[[Dict[str, np.ndarray]], "CollatedData"]] = None,
         drop_last: bool = False,
         local_shuffle_buffer_size: Optional[int] = None,
         local_shuffle_seed: Optional[int] = None,
         # Deprecated.
         prefetch_blocks: int = 0,
-    ) -> Iterator["TorchTensorBatchType"]:
+    ) -> Iterator["TorchBatchType"]:
         """Return a batched iterator of Torch Tensors over the dataset.
 
-        This iterator will yield single-tensor batches if the underlying dataset
-        consists of a single column; otherwise, it will yield a dictionary of
-        column-tensors. If looking for more flexibility in the tensor conversion (e.g.
-        casting dtypes) or the batch format, try using `.iter_batches` directly.
+        This iterator yields a dictionary of column-tensors. If you are looking for
+        more flexibility in the tensor conversion (e.g. casting dtypes) or the batch
+        format, try using :meth:`~ray.data.iterator.DataIterator.iter_batches` directly.
 
         Examples:
             >>> import ray
-            >>> for row in ray.data.range(
-            ...     1000000
-            ... ).iterator().iter_rows(): # doctest: +SKIP
-            ...     print(row) # doctest: +SKIP
+            >>> for batch in ray.data.range(
+            ...     12,
+            ... ).iterator().iter_torch_batches(batch_size=4):
+            ...     print(batch)
+            {'id': tensor([0, 1, 2, 3])}
+            {'id': tensor([4, 5, 6, 7])}
+            {'id': tensor([ 8,  9, 10, 11])}
+
+            Use the ``collate_fn`` to customize how the tensor batch is created.
+
+            >>> from typing import Any, Dict
+            >>> import torch
+            >>> import numpy as np
+            >>> import ray
+            >>> def collate_fn(batch: Dict[str, np.ndarray]) -> Any:
+            ...     return torch.stack(
+            ...         [torch.as_tensor(array) for array in batch.values()],
+            ...         axis=1
+            ...     )
+            >>> iterator = ray.data.from_items([
+            ...     {"col_1": 1, "col_2": 2},
+            ...     {"col_1": 3, "col_2": 4}]).iterator()
+            >>> for batch in iterator.iter_torch_batches(collate_fn=collate_fn):
+            ...     print(batch)
+            tensor([[1, 2],
+                    [3, 4]])
 
         Time complexity: O(1)
 
@@ -293,17 +316,24 @@ def iter_torch_batches(
                 The final batch may include fewer than ``batch_size`` rows if
                 ``drop_last`` is ``False``. Defaults to 256.
             dtypes: The Torch dtype(s) for the created tensor(s); if None, the dtype
-                will be inferred from the tensor data.
-            device: The device on which the tensor should be placed; if None, the Torch
-                tensor will be constructed on the CPU.
-            collate_fn: A function to apply to each data batch before returning it. When
-                this parameter is specified, the user should manually handle the host
-                to device data transfer outside of collate_fn. Potential use cases
-                include collating along a dimension other than the first, padding
-                sequences of various lengths, or generally handling batches of different
-                length tensors. This API is still experimental and is subject to change.
-                This parameter cannot be used in conjunction with ``dtypes`` or
-                ``device``.
+                will be inferred from the tensor data. You can't use this parameter
+                with ``collate_fn``.
+            device: The device on which the tensor should be placed. Defaults to
+                "auto" which moves the tensors to the appropriate device when the
+                Dataset is passed to Ray Train and ``collate_fn`` is not provided.
+                Otherwise, defaults to CPU. You can't use this parameter with
+                ``collate_fn``.
+            collate_fn: A function to convert a Numpy batch to a PyTorch tensor batch.
+                When this parameter is specified, the user should manually handle the
+                host to device data transfer outside of ``collate_fn``.
+                This is useful for further processing the data after it has been
+                batched. Potential use cases include collating along a dimension other
+                than the first, padding sequences of various lengths, or generally
+                handling batches of different length tensors. If not provided, the
+                default collate function is used which simply converts the batch of
+                numpy arrays to a batch of PyTorch tensors. This API is still
+                experimental and is subject to change. You can't use this parameter in
+                conjunction with ``dtypes`` or ``device``.
             drop_last: Whether to drop the last batch if it's incomplete.
             local_shuffle_buffer_size: If non-None, the data will be randomly shuffled
                 using a local in-memory shuffle buffer, and this value will serve as the
@@ -324,20 +354,19 @@ def iter_torch_batches(
             get_device,
         )
 
-        if collate_fn is not None and (dtypes is not None or device is not None):
+        if collate_fn is not None and (dtypes is not None or device != "auto"):
             raise ValueError(
                 "collate_fn cannot be used with dtypes and device."
                 "You should manually move the output Torch tensors to the"
                 "desired dtype and device outside of collate_fn."
             )
 
-        if collate_fn is None:
-            # Automatically move torch tensors to the appropriate device.
-            if device is None:
-                default_device = get_device()
-                if default_device.type != "cpu":
-                    device = default_device
+        if device == "auto":
+            # Use the appropriate device for Ray Train, or falls back to CPU if
+            # Ray Train is not being used.
+            device = get_device()
 
+        if collate_fn is None:
             # The default collate_fn handles formatting and Tensor creation.
             # Here, we set device=None to defer host to device data transfer
             # to the subsequent finalize_fn.

python/ray/serve/scripts.py

@@ -124,7 +124,7 @@ def convert_args_to_dict(args: Tuple[str]) -> Dict[str, str]:
 
 @click.group(
     help="CLI for managing Serve applications on a Ray cluster.",
-    context_settings=dict(help_option_names=["-h", "--help"]),
+    context_settings=dict(help_option_names=["--help", "-h"]),
 )
 def cli():
     pass

release/nightly_tests/dataset/image_loader_microbenchmark.py

@@ -171,10 +171,10 @@ def crop_and_flip_image_batch(image_batch):
         args.data_root, batch_size=args.batch_size, image_size=FULL_IMAGE_SIZE
     )
     for i in range(args.num_epochs):
-        iterate(tf_dataset, "tf.data", args.batch_size, metrics)
+        iterate(tf_dataset, "tf_data", args.batch_size, metrics)
     tf_dataset = tf_dataset.map(lambda img, label: (tf_crop_and_flip(img), label))
     for i in range(args.num_epochs):
-        iterate(tf_dataset, "tf.data+transform", args.batch_size, metrics)
+        iterate(tf_dataset, "tf_data+transform", args.batch_size, metrics)
 
     torch_dataset = build_torch_dataset(
         args.data_root, args.batch_size, transform=torchvision.transforms.ToTensor()
@@ -193,7 +193,7 @@ def crop_and_flip_image_batch(image_batch):
     for i in range(args.num_epochs):
         iterate(
             ray_dataset.iter_torch_batches(batch_size=args.batch_size),
-            "ray.data+transform",
+            "ray_data+transform",
             args.batch_size,
             metrics,
         )
@@ -204,7 +204,7 @@ def crop_and_flip_image_batch(image_batch):
     for i in range(args.num_epochs):
         iterate(
             ray_dataset.iter_torch_batches(batch_size=args.batch_size),
-            "ray.data+transform+zerocopy",
+            "ray_data+transform+zerocopy",
             args.batch_size,
             metrics,
         )
@@ -213,7 +213,7 @@ def crop_and_flip_image_batch(image_batch):
     for i in range(args.num_epochs):
         iterate(
             ray_dataset.iter_torch_batches(batch_size=args.batch_size),
-            "ray.data",
+            "ray_data",
             args.batch_size,
             metrics,
         )
@@ -224,7 +224,7 @@ def crop_and_flip_image_batch(image_batch):
     for i in range(args.num_epochs):
         iterate(
             ray_dataset.iter_torch_batches(batch_size=args.batch_size),
-            "ray.data+dummy_pyarrow_transform",
+            "ray_data+dummy_pyarrow_transform",
             args.batch_size,
             metrics,
         )
@@ -235,7 +235,7 @@ def crop_and_flip_image_batch(image_batch):
     for i in range(args.num_epochs):
         iterate(
             ray_dataset.iter_torch_batches(batch_size=args.batch_size),
-            "ray.data+dummy_np_transform",
+            "ray_data+dummy_np_transform",
             args.batch_size,
             metrics,
         )
@@ -257,7 +257,7 @@ def load(batch):
     for i in range(args.num_epochs):
         iterate(
             ray_dataset.iter_torch_batches(batch_size=args.batch_size),
-            "ray.data_manual_load",
+            "ray_data_manual_load",
             args.batch_size,
             metrics,
         )

release/nightly_tests/dataset/preprocessed_image_loader_microbenchmark.py

@@ -231,7 +231,7 @@ def build_ray_dataset(data_root, batch_size):
     # Tf.data.
     tf_ds = build_tf_dataset(args.tf_data_root, args.batch_size)
     for i in range(args.num_epochs):
-        iterate(tf_ds, "tf.data", args.batch_size, metrics)
+        iterate(tf_ds, "tf_data", args.batch_size, metrics)
 
     # ray.data.
     ray_ds = build_ray_dataset(args.tf_data_root, args.batch_size)