RFR: 8346236: Auto vectorization support for various Float16 operations [v6]

[Emanuel Peter]

On Sat, 22 Mar 2025 17:55:27 GMT, Jatin Bhateja <jbhateja at openjdk.org> wrote:

>> This is a follow-up PR for https://github.com/openjdk/jdk/pull/22754
>> 
>> The patch adds support to vectorize various float16 scalar operations (add/subtract/divide/multiply/sqrt/fma).
>> 
>> Summary of changes included with the patch:
>>    1. C2 compiler New Vector IR creation.
>>    2. Auto-vectorization support.
>>    3. x86 backend implementation.
>>    4. New IR verification test for each newly supported vector operation.
>> 
>> Following are the performance numbers of Float16OperationsBenchmark
>> 
>> System : Intel(R) Xeon(R) Processor code-named Granite rapids
>> Frequency fixed at 2.5 GHz
>> 
>> 
>> Baseline
>> Benchmark                                                      (vectorDim)   Mode  Cnt     Score   Error   Units
>> Float16OperationsBenchmark.absBenchmark                               1024  thrpt    2  4191.787          ops/ms
>> Float16OperationsBenchmark.addBenchmark                               1024  thrpt    2  1211.978          ops/ms
>> Float16OperationsBenchmark.cosineSimilarityDequantizedFP16            1024  thrpt    2   493.026          ops/ms
>> Float16OperationsBenchmark.cosineSimilarityDoubleRoundingFP16         1024  thrpt    2   612.430          ops/ms
>> Float16OperationsBenchmark.cosineSimilaritySingleRoundingFP16         1024  thrpt    2   616.012          ops/ms
>> Float16OperationsBenchmark.divBenchmark                               1024  thrpt    2   604.882          ops/ms
>> Float16OperationsBenchmark.dotProductFP16                             1024  thrpt    2   410.798          ops/ms
>> Float16OperationsBenchmark.euclideanDistanceDequantizedFP16           1024  thrpt    2   602.863          ops/ms
>> Float16OperationsBenchmark.euclideanDistanceFP16                      1024  thrpt    2   640.348          ops/ms
>> Float16OperationsBenchmark.fmaBenchmark                               1024  thrpt    2   809.175          ops/ms
>> Float16OperationsBenchmark.getExponentBenchmark                       1024  thrpt    2  2682.764          ops/ms
>> Float16OperationsBenchmark.isFiniteBenchmark                          1024  thrpt    2  3373.901          ops/ms
>> Float16OperationsBenchmark.isFiniteCMovBenchmark                      1024  thrpt    2  1881.652          ops/ms
>> Float16OperationsBenchmark.isFiniteStoreBenchmark                     1024  thrpt    2  2273.745          ops/ms
>> Float16OperationsBenchmark.isInfiniteBenchmark                        1024  thrpt    2  2147.913          ops/ms
>> Float16OperationsBenchmark.isInfiniteCMovBen...
>
> Jatin Bhateja has updated the pull request incrementally with one additional commit since the last revision:
> 
>   Removing Generator dependency on incubation module

I looked at the changes in `Generators.java`, thanks for adding some code there 😊 

Some comments on it:
- You should add some Float16 tests to `test/hotspot/jtreg/testlibrary_tests/generators/tests/TestGenerators.java`.
- I am missing the "mixed distribution" function `float16s()`. As a reference, take `public Generator<Double> doubles()`. The idea is that we have a set of distributions, and we pick a random distribution every time in the tests.
- I'm also missing a "any bits" version, where you would take a random short value and reinterpret it as `Float16`. This ensures that we are getting all possible encodings, including multiple NaN encodings.
- All of this is probably enough code to make a separate PR.

test/hotspot/jtreg/compiler/vectorization/TestFloat16VectorOperations.java line 74:

> 72:         short min_value = float16ToRawShortBits(Float16.MIN_VALUE);
> 73:         short max_value = float16ToRawShortBits(Float16.MAX_VALUE);
> 74:         Generator<Short> gen = G.mixedWithSpecialFloat16s(G.uniformFloat16s(min_value, max_value), 10, 2);

Here you would simply be using the `float16s` random distribution picker. Sometimes you would get uniform,     sometimes special, sometimes mixed, sometimes any-bits, etc.

-------------

PR Review: https://git.openjdk.org/jdk/pull/22755#pullrequestreview-2712740608
PR Review Comment: https://git.openjdk.org/jdk/pull/22755#discussion_r2011516136