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Decorrelate partitioning hashes from internal hashes in grouped aggregations and joins - #24396
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WalkthroughThe change defines a distinct internal algorithm hash seed and uses it in row hashing and nested operations. Streaming partition APIs now accept an optional seed and pass it to their C++ partitioning paths. ChangesHash seed handling
Priority: ⬇️ Low Estimated code review effort: 3 (Moderate) | ~20 minutes Change: Bug fix Merge Risk: 🔵 Low · up to The new optional seed parameter shifts the position of 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
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Review comments at @python/cudf_streaming/cudf_streaming/partition_utils.pyx:
- Line 137: Move the seed parameter after reservation in partition_and_pack so
existing positional calls continue to bind reservation correctly. Keep the
parameter order consistent in the Cython declaration and stub, while preserving
keyword-based seed calls.
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python/cudf_polars/cudf_polars/streaming/actor_graph/collectives/shuffle.pypython/cudf_streaming/cudf_streaming/partition.pyipython/cudf_streaming/cudf_streaming/partition.pyxpython/cudf_streaming/cudf_streaming/partition_utils.pxdpython/cudf_streaming/cudf_streaming/partition_utils.pyipython/cudf_streaming/cudf_streaming/partition_utils.pyx
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| int num_partitions, | ||
| Stream stream, | ||
| BufferResource br, | ||
| uint32_t seed = cpp_DEFAULT_HASH_SEED, |
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🎯 Functional Correctness | 🟠 Major | ⚡ Quick win
Preserve the positional reservation argument.
Adding seed before reservation breaks the existing public call partition_and_pack(table, columns, count, stream, br, reservation). The reservation now binds to uint32_t seed, so the call fails instead of using the supplied reservation.
Place seed after reservation. Apply the same parameter order in python/cudf_streaming/cudf_streaming/partition_utils.pxd and python/cudf_streaming/cudf_streaming/partition_utils.pyi. The supplied Polars caller uses keywords and remains compatible.
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Review comment at @python/cudf_streaming/cudf_streaming/partition_utils.pyx at
line 137:
Move the seed parameter after reservation in partition_and_pack so existing
positional calls continue to bind reservation correctly. Keep the parameter
order consistent in the Cython declaration and stub, while preserving
keyword-based seed calls.
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| num_partitions=self._manager.num_partitions, | ||
| # Decorrelate hash from the hash function used inside | ||
| # groupby and/or join. | ||
| seed=plc.hashing.LIBCUDF_DEFAULT_HASH_SEED + 42, |
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TODO/Note: the "is compatibly partitioned" checks don't acutally record either the hash function or hash seed used, so we have to rely on this never being exposed to the user or changing.
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Review comments at @cpp/include/cudf/detail/row_operator/hashing.cuh:
- Around line 280-282: Add [[nodiscard]] to the device_hasher method declaration
so callers are warned when its returned hasher is ignored.
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| Nullate nullate = {}, | ||
| cuda::std::invoke_result_t<hash_function<int32_t>, int32_t> seed = | ||
| hashing::detail::DEFAULT_ALGORITHM_HASH_SEED) const |
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🔎 Supported by static analysis
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Length of output: 41515
Mark device_hasher as [[nodiscard]].
device_hasher is side-effect-free and returns a hasher. Add [[nodiscard]] to comply with the applicable guideline.
Suggested fix
- DeviceRowHasher<hash_function, Nullate> device_hasher(
+ [[nodiscard]] DeviceRowHasher<hash_function, Nullate> device_hasher(🤖 Prompt for AI Agents
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Review comment at @cpp/include/cudf/detail/row_operator/hashing.cuh around lines
280 - 282:
Add [[nodiscard]] to the device_hasher method declaration so callers are warned
when its returned hasher is ignored.
After applying the fix, consider running `coderabbit review --agent` for local
review. Visit https://docs.coderabbit.ai/cli?utm_source=ghpr
Let h be the hash key determined from some 32bit hash function. Let P be
the number of partitions we shuffle into, and let C be the capacity chosen
by a consumer of this hash key (for example a join). Hash partitioning
assigns, by modulo, to every hash key h, a partition id p \in [0, ..., P).
Hence every hash key has the form h = p + jP for some j.
Suppose now that the hash key used for shuffle routing is the same key used
in a subsequent groupby or join. For the groupby code-path, the bucket
selection in the hash table is by modulo with C. So the chosen bucket is
b = h mod C = (p + jP) mod C.
Let d = gcd(C, P), and hence, because both P and C are multiples of d we
have that
b ≡ p mod d
Thus, for our hash key h, although we have C buckets, we initially only
select C / d of those buckets.
To be able to use all the buckets for the initial probing we therefore need
that P and C are coprime. Although the smem aggregations pick a
prime for C, gmem aggregations do not, and so in the general case we cannot
guarantee that P and C are coprime.
For joins, we use the hashcsr scheme. Its initial bucket for a given hash
key h is
b = floor(C rev_32(h) / 2^32)
Where rev_32(h) is the 32bit-reversal of h. Now, if the partition count P = 2ᵏ
is a power of two, each partition p fixes the low bits of h. After
reversing the bits of h, the possible values for a given partition p are
rev_32(h) = rev_k(p) 2 ^{32 - k} + u
for some u \in [0, ..., 2^{32 - k}). So the initial buckets occupy the
contiguous interval [floor(C rev_k(p) / P]), floor(C (rev_k(p) + 1)/P - C /
2^{32 - k})]. The number of initial buckets is then about C / P.
The same argument can be applied to non-power of two partitions P = 2^k R
for some odd R. We end up confining the buckets to around only C / 2^k
slots.
Although pathological keys can still hit this, we want to avoid the case
where by construction we end up in pathological cases. The easiest way to
do this is to decorrelate the hash key used for partitioning from the hash
key used in the hash-groupby or hash-join. To do this, we introduce a new,
internal, default seed for hashing inside joins, groupbys, and distincts.
This seed is distinct from the external cudf::DEFAULT_HASH_SEED.
Construction of row hashers now uses this internal seed value by default.
External apis that accepted a seed always pass it through, so no change is
observable there.
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The alternative fix to this one (introducing a new "internal" hash seed) is for the user of That is, for cudf-polars, a smaller overall fix. But everyone who does hash-partitioned routing needs to know to do it. This makes things internal, but more opaque. |
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Do you have any test/benchmark to show the changes in performance?
Contributor
I just tested the PR, here are the results:
So performance is essentially the same as it used to be. EDIT: The test in question is the sample from the #24395 bug report. |
Contributor
|
Unfortunately, the test errors seem likely as an effect of the changes from the PR: |
Contributor
Actually, this is just because of the changing seed. I pushed a fix in 62e00d1 . |
Contributor
|
/merge |
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…gations and joins (NVIDIA#24396) Let $h$ be the hash key determined from some 32bit hash function. Let $P$ be the number of partitions we shuffle into, and let $C$ be the capacity chosen by a consumer of this hash key (for example a join). Hash partitioning assigns, by modulo, to every hash key $h$, a partition id $p \in [0, \dots, P)$. Hence every hash key has the form $h = p + jP$ for some $j$. Suppose now that the hash key used for shuffle routing is the same key used in a subsequent groupby or join. For the groupby code-path, the bucket selection in the hash table is by modulo with C. So the chosen bucket is $$ b = h \mod C = (p + jP) \mod C. $$ Let $d = \text{gcd}(C, P)$, and hence, because both $P$ and $C$ are multiples of $d$ we have that $$ b \equiv p \mod d $$ Thus, for our hash key $h$, although we have $C$ buckets, we initially only select $\frac{C}{d}$ of those buckets. To be able to use all the buckets for the initial probing we therefore need that $P$ and $C$ are coprime. Although the smem aggregations pick a prime for $C$, gmem aggregations do not, and so in the general case we cannot guarantee that $P$ and $C$ are coprime. For joins, we use the hashcsr scheme. Its initial bucket for a given hash key $h$ is $$ b = \left\lfloor \frac{C \text{rev}_{32}(h)}{2^{32}} \right\rfloor $$ Where $\text{rev}_{32}(h)$ is the 32bit-reversal of h. Now, if the partition count is a power of two, $P = 2^k$, each partition $p$ fixes the low $k$ bits of $h$. After reversing the bits of $h$, the possible values for a given partition $p$ are $$ \text{rev}_{32}(h) = \text{rev}_k(p) 2^{32 - k} + u $$ for some $u \in [0, \dots, 2^{32 - k})$. So the initial buckets occupy the contiguous interval $$ \left[\left\lfloor\frac{C \text{rev}_k(p)}{P}\right\rfloor, \left\lfloor \frac{C (\text{rev}_k(p) + 1)}{P} - \frac{C}{2^{32 - k}}\right\rfloor\right], $$ and so the number of initial buckets is then about $\frac{C}{P}$. The same argument can be applied to non-power of two partitions $P = 2^k R$ for some odd $R$. We end up confining the buckets to around only $\frac{C}{2^k}$ slots. Although pathological keys can still hit this, we want to avoid the case where by construction we end up in pathological cases. The easiest way to do this is to decorrelate the hash key used for partitioning from the hash key used in the hash-groupby or hash-join. To do this, we introduce a new, internal, default seed for hashing inside joins, groupbys, and distincts. This seed is distinct from the external `cudf::DEFAULT_HASH_SEED`. Construction of row hashers now uses this internal seed value by default. External apis that accepted a seed always pass it through, so no change is observable there. Authors: - Lawrence Mitchell (https://github.com/wence-) - Peter Andreas Entschev (https://github.com/pentschev) Approvers: - Bradley Dice (https://github.com/bdice) - Peter Andreas Entschev (https://github.com/pentschev) - Nghia Truong (https://github.com/ttnghia) URL: NVIDIA#24396 (cherry picked from commit 95d76a7)
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…gations and joins (NVIDIA#24396) Let $h$ be the hash key determined from some 32bit hash function. Let $P$ be the number of partitions we shuffle into, and let $C$ be the capacity chosen by a consumer of this hash key (for example a join). Hash partitioning assigns, by modulo, to every hash key $h$, a partition id $p \in [0, \dots, P)$. Hence every hash key has the form $h = p + jP$ for some $j$. Suppose now that the hash key used for shuffle routing is the same key used in a subsequent groupby or join. For the groupby code-path, the bucket selection in the hash table is by modulo with C. So the chosen bucket is $$ b = h \mod C = (p + jP) \mod C. $$ Let $d = \text{gcd}(C, P)$, and hence, because both $P$ and $C$ are multiples of $d$ we have that $$ b \equiv p \mod d $$ Thus, for our hash key $h$, although we have $C$ buckets, we initially only select $\frac{C}{d}$ of those buckets. To be able to use all the buckets for the initial probing we therefore need that $P$ and $C$ are coprime. Although the smem aggregations pick a prime for $C$, gmem aggregations do not, and so in the general case we cannot guarantee that $P$ and $C$ are coprime. For joins, we use the hashcsr scheme. Its initial bucket for a given hash key $h$ is $$ b = \left\lfloor \frac{C \text{rev}_{32}(h)}{2^{32}} \right\rfloor $$ Where $\text{rev}_{32}(h)$ is the 32bit-reversal of h. Now, if the partition count is a power of two, $P = 2^k$, each partition $p$ fixes the low $k$ bits of $h$. After reversing the bits of $h$, the possible values for a given partition $p$ are $$ \text{rev}_{32}(h) = \text{rev}_k(p) 2^{32 - k} + u $$ for some $u \in [0, \dots, 2^{32 - k})$. So the initial buckets occupy the contiguous interval $$ \left[\left\lfloor\frac{C \text{rev}_k(p)}{P}\right\rfloor, \left\lfloor \frac{C (\text{rev}_k(p) + 1)}{P} - \frac{C}{2^{32 - k}}\right\rfloor\right], $$ and so the number of initial buckets is then about $\frac{C}{P}$. The same argument can be applied to non-power of two partitions $P = 2^k R$ for some odd $R$. We end up confining the buckets to around only $\frac{C}{2^k}$ slots. Although pathological keys can still hit this, we want to avoid the case where by construction we end up in pathological cases. The easiest way to do this is to decorrelate the hash key used for partitioning from the hash key used in the hash-groupby or hash-join. To do this, we introduce a new, internal, default seed for hashing inside joins, groupbys, and distincts. This seed is distinct from the external `cudf::DEFAULT_HASH_SEED`. Construction of row hashers now uses this internal seed value by default. External apis that accepted a seed always pass it through, so no change is observable there. Authors: - Lawrence Mitchell (https://github.com/wence-) - Peter Andreas Entschev (https://github.com/pentschev) Approvers: - Bradley Dice (https://github.com/bdice) - Peter Andreas Entschev (https://github.com/pentschev) - Nghia Truong (https://github.com/ttnghia) URL: NVIDIA#24396 (cherry picked from commit 95d76a7)
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…ashCSR join memory and PyArrow 26 parquet fix (#24652) ## Description Backport of #24231, #24331, #24396, #24655 and #24663 to `hotfix/26.10.1`. 26.10.0 uses about 2.8x the memory of 26.08.1 to hold a `hash_join` and about 3.3x at peak while building it (10M unique bigint keys: 522.8 MB peak / 442.7 MB retained vs 160.0 MB for 26.08.1). #24231 and #24331 fix it on `main`. #24396 is needed with #24331: after #24331, HashCSR slot selection on hash-partitioned input collapses to a fraction of the table (#24395). It is cherry-picked without the `spark_hashing.cuh` hunk, since that file does not exist in 26.10. #24655 fixes the default stream check in `memcpy_batch_async`. #24663 makes `read_parquet(columns=[])` return PyArrow's row count under PyArrow 26, which fixes the PyArrow 26 wheel-test failures on this PR. It is cherry-picked without the removal of the `pyarrow<26` pin, which 26.10 never had. Hotfix criteria: https://docs.nvidia.com/datascience/releases/hotfix/ ## Checklist - [x] I am familiar with the [Contributing Guidelines](https://github.com/rapidsai/cudf/blob/HEAD/CONTRIBUTING.md). - [x] New or existing tests cover these changes. - [x] The documentation is up to date with these changes. --------- Co-authored-by: Yunsong Wang <12716979+PointKernel@users.noreply.github.com> Co-authored-by: Muhammad Haseeb <14217455+mhaseeb123@users.noreply.github.com> Co-authored-by: Lawrence Mitchell <lmitchell@nvidia.com> Co-authored-by: Tom Augspurger <toaugspurger@nvidia.com>
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…ashCSR join memory and PyArrow 26 parquet fix (#24713) ## Description Backport of #24231, #24331, #24396, #24655 and #24663 to `hotfix/26.10.1`. 26.10.0 uses about 2.8x the memory of 26.08.1 to hold a `hash_join` and about 3.3x at peak while building it (10M unique bigint keys: 522.8 MB peak / 442.7 MB retained vs 160.0 MB for 26.08.1). #24231 and #24331 fix it on `main`. #24396 is needed with #24331: after #24331, HashCSR slot selection on hash-partitioned input collapses to a fraction of the table (#24395). It is cherry-picked without the `spark_hashing.cuh` hunk, since that file does not exist in 26.10. #24655 fixes the default stream check in `memcpy_batch_async`. #24663 makes `read_parquet(columns=[])` return PyArrow's row count under PyArrow 26, which fixes the PyArrow 26 wheel-test failures on this PR. It is cherry-picked without the removal of the `pyarrow<26` pin, which 26.10 never had. Hotfix criteria: https://docs.nvidia.com/datascience/releases/hotfix/ ## Checklist - [x] I am familiar with the [Contributing Guidelines](https://github.com/rapidsai/cudf/blob/HEAD/CONTRIBUTING.md). - [x] New or existing tests cover these changes. - [x] The documentation is up to date with these changes. --------- ## Description <!-- Provide a standalone description of changes in this PR. --> <!-- Reference any issues closed by this PR with "closes #1234". --> <!-- Note: The pull request title will be included in the CHANGELOG. --> ## Checklist - [ ] I am familiar with the [Contributing Guidelines](https://github.com/NVIDIA/cudf/blob/HEAD/CONTRIBUTING.md). - [ ] New or existing tests cover these changes. - [ ] The documentation is up to date with these changes. Co-authored-by: Bradley Dice <bdice@bradleydice.com> Co-authored-by: Yunsong Wang <12716979+PointKernel@users.noreply.github.com> Co-authored-by: Muhammad Haseeb <14217455+mhaseeb123@users.noreply.github.com> Co-authored-by: Lawrence Mitchell <lmitchell@nvidia.com> Co-authored-by: Tom Augspurger <toaugspurger@nvidia.com>
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Description
Let$h$ be the hash key determined from some 32bit hash function. Let $P$ be the number of partitions we shuffle into, and let $C$ be the capacity chosen by a consumer of this hash key (for example a join). Hash partitioning assigns, by modulo, to every hash key $h$ , a partition id $p \in [0, \dots, P)$ . Hence every hash key has the form $h = p + jP$ for some $j$ .
Suppose now that the hash key used for shuffle routing is the same key used in a subsequent groupby or join. For the groupby code-path, the bucket selection in the hash table is by modulo with C. So the chosen bucket is
Let$d = \text{gcd}(C, P)$ , and hence, because both $P$ and $C$ are multiples of $d$ we
have that
Thus, for our hash key$h$ , although we have $C$ buckets, we initially only select $\frac{C}{d}$ of those buckets.
To be able to use all the buckets for the initial probing we therefore need that$P$ and $C$ are coprime. Although the smem aggregations pick a prime for $C$ , gmem aggregations do not, and so in the general case we cannot guarantee that $P$ and $C$ are coprime.
For joins, we use the hashcsr scheme. Its initial bucket for a given hash key$h$ is
Where$\text{rev}_{32}(h)$ is the 32bit-reversal of h. Now, if the partition count is a power of two, $P = 2^k$ , each partition $p$ fixes the low $k$ bits of $h$ . After$h$ , the possible values for a given partition $p$ are
reversing the bits of
for some$u \in [0, \dots, 2^{32 - k})$ . So the initial buckets occupy the
contiguous interval
and so the number of initial buckets is then about$\frac{C}{P}$ .
The same argument can be applied to non-power of two partitions$P = 2^k R$ for some odd $R$ . We end up confining the buckets to around only $\frac{C}{2^k}$ slots.
Although pathological keys can still hit this, we want to avoid the case where by construction we end up in pathological cases. The easiest way to do this is to decorrelate the hash key used for partitioning from the hash key used in the hash-groupby or hash-join. To do this, we introduce a new, internal, default seed for hashing inside joins, groupbys, and distincts. This seed is distinct from the external
cudf::DEFAULT_HASH_SEED. Construction of row hashers now uses this internal seed value by default. External apis that accepted a seed always pass it through, so no change isobservable there.
Checklist