Algorithm for multi-stage rechunking

rechunker/__init__.py

@@ -4,4 +4,5 @@
 except ImportError:
     __version__ = "unknown"
 
+from .algorithm import multistage_rechunking_plan, rechunking_plan
 from .api import Rechunked, rechunk

rechunker/algorithm.py

@@ -1,8 +1,12 @@
 """Core rechunking algorithm stuff."""
 import logging
+import warnings
+from math import ceil, floor, prod
 from typing import List, Optional, Sequence, Tuple
 
-from rechunker.compat import prod
+import numpy as np
+
+from rechunker.compat import lcm
 
 logger = logging.getLogger(__name__)
 
@@ -91,38 +95,124 @@ def consolidate_chunks(
     return tuple(new_chunks)
 
 
-def rechunking_plan(
+def _calculate_shared_chunks(
+    read_chunks: Sequence[int], write_chunks: Sequence[int]
+) -> Tuple[int, ...]:
+    # Intermediate chunks are the smallest possible chunks which fit
+    # into both read_chunks and write_chunks.
+    # Example:
+    #   read_chunks:            (20, 5)
+    #   target_chunks:          (4, 25)
+    #   intermediate_chunks:    (4, 5)
+    # We don't need to check their memory usage: they are guaranteed to be smaller
+    # than both read and write chunks.
+    return tuple(
+        min(c_read, c_target) for c_read, c_target in zip(read_chunks, write_chunks)
+    )
+
+
+def calculate_stage_chunks(
+    read_chunks: Tuple[int, ...],
+    write_chunks: Tuple[int, ...],
+    stage_count: int = 1,
+) -> List[Tuple[int, ...]]:
+    """
+    Calculate chunks after each stage of a multi-stage rechunking.
+
+    Each stage consists of "split" step followed by a "consolidate" step.
+
+    The strategy used here is to progressively enlarge or shrink chunks along
+    each dimension by the same multiple in each stage (geometric spacing). This
+    should roughly minimize the total number of arrays resulting from "split"
+    steps in a multi-stage pipeline. It also keeps the total number of elements
+    in each chunk constant, up to rounding error, so memory usage should remain
+    constant.
+
+    Examples::
+
+        >>> calculate_stage_chunks((1_000_000, 1), (1, 1_000_000), stage_count=2)
+        [(1000, 1000)]
+        >>> calculate_stage_chunks((1_000_000, 1), (1, 1_000_000), stage_count=3)
+        [(10000, 100), (100, 10000)]
+        >>> calculate_stage_chunks((1_000_000, 1), (1, 1_000_000), stage_count=4)
+        [(31623, 32), (1000, 1000), (32, 31623)]
+
+    TODO: consider more sophisticated algorithms. In particular, exact geometric
+    spacing often requires irregular intermediate chunk sizes, which (currently)
+    cannot be stored in Zarr arrays.
+    """
+    approx_stages = np.geomspace(read_chunks, write_chunks, num=stage_count + 1)
+    return [tuple(floor(c) for c in stage) for stage in approx_stages[1:-1]]
+
+
+def _count_intermediate_chunks(source_chunk: int, target_chunk: int, size: int) -> int:
+    """Count intermediate chunks required for rechunking along a dimension.
+
+    Intermediate chunks must divide both the source and target chunks, and in
+    general do not need to have a regular size. The number of intermediate
+    chunks is proportional to the number of required read/write operations.
+
+    For example, suppose we want to rechunk an array of size 20 from size 5
+    chunks to size 7 chunks. We can draw out how the array elements are divided:
+        0 1 2 3 4|5 6 7 8 9|10 11 12 13 14|15 16 17 18 19   (4 chunks)
+        0 1 2 3 4 5 6|7 8 9 10 11 12 13|14 15 16 17 18 19   (3 chunks)
+
+    To transfer these chunks, we would need to divide the array into irregular
+    intermediate chunks that fit into both the source and target:
+       0 1 2 3 4|5 6|7 8 9|10 11 12 13|14|15 16 17 18 19    (6 chunks)
+
+    This matches what ``_count_intermediate_chunks()`` calculates::
+
+        >>> _count_intermediate_chunks(5, 7, 20)
+        6
+    """
+    multiple = lcm(source_chunk, target_chunk)
+    splits_per_lcm = multiple // source_chunk + multiple // target_chunk - 1
+    lcm_count, remainder = divmod(size, multiple)
+    if remainder:
+        splits_in_remainder = (
+            ceil(remainder / source_chunk) + ceil(remainder / target_chunk) - 1
+        )
+    else:
+        splits_in_remainder = 0
+    return lcm_count * splits_per_lcm + splits_in_remainder
+
+
+def calculate_single_stage_io_ops(
+    shape: Sequence[int], in_chunks: Sequence[int], out_chunks: Sequence[int]
+) -> int:
+    """Count the number of read/write operations required for rechunking."""
+    return prod(map(_count_intermediate_chunks, in_chunks, out_chunks, shape))
+
+
+# not a tight upper bound, but ensures that the loop in
+# multistage_rechunking_plan always terminates.
+MAX_STAGES = 100
+
+
+_MultistagePlan = List[Tuple[Tuple[int, ...], Tuple[int, ...], Tuple[int, ...]]]
+
+
+class ExcessiveIOWarning(Warning):
+    pass
+
+
+def multistage_rechunking_plan(
     shape: Sequence[int],
     source_chunks: Sequence[int],
     target_chunks: Sequence[int],
     itemsize: int,
+    min_mem: int,
     max_mem: int,
     consolidate_reads: bool = True,
     consolidate_writes: bool = True,
-) -> Tuple[Tuple[int, ...], Tuple[int, ...], Tuple[int, ...]]:
-    """
-    Parameters
-    ----------
-    shape : Tuple
-        Array shape
-    source_chunks : Tuple
-        Original chunk shape (must be in form (5, 10, 20), no irregular chunks)
-    target_chunks : Tuple
-        Target chunk shape (must be in form (5, 10, 20), no irregular chunks)
-    itemsize: int
-        Number of bytes used to represent a single array element
-    max_mem : Int
-        Maximum permissible chunk memory size, measured in units of itemsize
-    consolidate_reads: bool, optional
-        Whether to apply read chunk consolidation
-    consolidate_writes: bool, optional
-        Whether to apply write chunk consolidation
+) -> _MultistagePlan:
+    """Caculate a rechunking plan that can use multiple split/consolidate steps.
 
-    Returns
-    -------
-    new_chunks : tuple
-        The new chunks, size guaranteed to be <= mam_mem
+    For best results, max_mem should be significantly larger than min_mem (e.g.,
+    10x). Otherwise an excessive number of rechunking steps will be required.
     """
+
     ndim = len(shape)
     if len(source_chunks) != ndim:
         raise ValueError(f"source_chunks {source_chunks} must have length {ndim}")
@@ -141,6 +231,11 @@ def rechunking_plan(
             f"Target chunk memory ({target_chunk_mem}) exceeds max_mem ({max_mem})"
         )
 
+    if max_mem < min_mem:  # basic sanity check
+        raise ValueError(
+            f"max_mem ({max_mem}) cannot be smaller than min_mem ({min_mem})"
+        )
+
     if consolidate_writes:
         logger.debug(
             f"consolidate_write_chunks({shape}, {target_chunks}, {itemsize}, {max_mem})"
@@ -150,17 +245,16 @@ def rechunking_plan(
         write_chunks = tuple(target_chunks)
 
     if consolidate_reads:
-        read_chunk_limits: List[Optional[int]]
-        read_chunk_limits = []  #
-        for n_ax, (sc, wc) in enumerate(zip(source_chunks, write_chunks)):
-            read_chunk_lim: Optional[int]
+        read_chunk_limits: List[Optional[int]] = []
+        for sc, wc in zip(source_chunks, write_chunks):
+            limit: Optional[int]
             if wc > sc:
                 # consolidate reads over this axis, up to the write chunk size
-                read_chunk_lim = wc
+                limit = wc
             else:
                 # don't consolidate reads over this axis
-                read_chunk_lim = None
-            read_chunk_limits.append(read_chunk_lim)
+                limit = None
+            read_chunk_limits.append(limit)
 
         logger.debug(
             f"consolidate_read_chunks({shape}, {source_chunks}, {itemsize}, {max_mem}, {read_chunk_limits})"
@@ -171,16 +265,101 @@ def rechunking_plan(
     else:
         read_chunks = tuple(source_chunks)
 
-    # Intermediate chunks  are the smallest possible chunks which fit
-    # into both read_chunks and write_chunks.
-    # Example:
-    #   read_chunks:            (20, 5)
-    #   target_chunks:          (4, 25)
-    #   intermediate_chunks:    (4, 5)
-    # We don't need to check their memory usage: they are guaranteed to be smaller
-    # than both read and write chunks.
-    intermediate_chunks = [
-        min(c_read, c_target) for c_read, c_target in zip(read_chunks, write_chunks)
-    ]
+    prev_io_ops: Optional[float] = None
+    prev_plan: Optional[_MultistagePlan] = None
+
+    # increase the number of stages until min_mem is exceeded
+    for stage_count in range(1, MAX_STAGES):
+
+        stage_chunks = calculate_stage_chunks(read_chunks, write_chunks, stage_count)
+        pre_chunks = [read_chunks] + stage_chunks
+        post_chunks = stage_chunks + [write_chunks]
+
+        int_chunks = [
+            _calculate_shared_chunks(pre, post)
+            for pre, post in zip(pre_chunks, post_chunks)
+        ]
+        plan = list(zip(pre_chunks, int_chunks, post_chunks))
+
+        int_mem = min(itemsize * prod(chunks) for chunks in int_chunks)
+        if int_mem >= min_mem:
+            return plan  # success!
+
+        io_ops = sum(
+            calculate_single_stage_io_ops(shape, pre, post)
+            for pre, post in zip(pre_chunks, post_chunks)
+        )
+        if prev_io_ops is not None and io_ops > prev_io_ops:
+            warnings.warn(
+                "Search for multi-stage rechunking plan terminated before "
+                "achieving the minimum memory requirement due to increasing IO "
+                f"requirements. Smallest intermediates have size {int_mem}. "
+                f"Consider decreasing min_mem ({min_mem}) or increasing "
+                f"({max_mem}) to find a more efficient plan.",
+                category=ExcessiveIOWarning,
+            )
+            assert prev_plan is not None
+            return prev_plan
 
-    return read_chunks, tuple(intermediate_chunks), write_chunks
+        prev_io_ops = io_ops
+        prev_plan = plan
+
+    raise AssertionError(
+        "Failed to find a feasible multi-staging rechunking scheme satisfying "
+        f"min_mem ({min_mem}) and max_mem ({max_mem}) constraints. "
+        "Please file a bug report on GitHub: "
+        "https://github.com/pangeo-data/rechunker/issues\n\n"
+        "Include the following debugging info:\n"
+        f"shape={shape}, source_chunks={source_chunks}, "
+        f"target_chunks={target_chunks}, itemsize={itemsize}, "
+        f"min_mem={min_mem}, max_mem={max_mem}, "
+        f"consolidate_reads={consolidate_reads}, "
+        f"consolidate_writes={consolidate_writes}"
+    )
+
+
+def rechunking_plan(
+    shape: Sequence[int],
+    source_chunks: Sequence[int],
+    target_chunks: Sequence[int],
+    itemsize: int,
+    max_mem: int,
+    consolidate_reads: bool = True,
+    consolidate_writes: bool = True,
+) -> Tuple[Tuple[int, ...], Tuple[int, ...], Tuple[int, ...]]:
+    """
+    Calculate a plan for rechunking arrays.
+
+    Parameters
+    ----------
+    shape : Tuple
+        Array shape
+    source_chunks : Tuple
+        Original chunk shape (must be in form (5, 10, 20), no irregular chunks)
+    target_chunks : Tuple
+        Target chunk shape (must be in form (5, 10, 20), no irregular chunks)
+    itemsize: int
+        Number of bytes used to represent a single array element
+    max_mem : int
+        Maximum permissible chunk memory size, measured in units of itemsize
+    consolidate_reads: bool, optional
+        Whether to apply read chunk consolidation
+    consolidate_writes: bool, optional
+        Whether to apply write chunk consolidation
+
+    Returns
+    -------
+    new_chunks : tuple
+        The new chunks, size guaranteed to be <= mam_mem
+    """
+    (stage,) = multistage_rechunking_plan(
+        shape,
+        source_chunks,
+        target_chunks,
+        itemsize=itemsize,
+        min_mem=itemsize,
+        max_mem=max_mem,
+        consolidate_writes=consolidate_writes,
+        consolidate_reads=consolidate_reads,
+    )
+    return stage

rechunker/compat.py

@@ -1,13 +1,10 @@
-import operator
-from functools import reduce
-from typing import Sequence
+import math
 
+try:
+    from math import lcm  # type: ignore  # Python 3.9
+except ImportError:
 
-def prod(iterable: Sequence[int]) -> int:
-    """Implementation of `math.prod()` all Python versions."""
-    try:
-        from math import prod as mathprod  # type: ignore # Python 3.8
-
-        return mathprod(iterable)
-    except ImportError:
-        return reduce(operator.mul, iterable, 1)
+    def lcm(a: int, b: int) -> int:  # type: ignore
+        """Implementation of `math.lcm()` for all Python versions."""
+        # https://stackoverflow.com/a/51716959/809705
+        return a * b // math.gcd(a, b)