krickert opened a new pull request, #1003:
URL: https://github.com/apache/opennlp/pull/1003
## Summary
Make ME classes (TokenizerME, SentenceDetectorME, POSTaggerME, LemmatizerME)
safe for concurrent use by eliminating shared mutable instance state. This
enables reusing ME instances across threads instead of allocating a new
instance per call, reducing allocation overhead in high-throughput pipelines.
The old pattern (`new TokenizerME(model)` per call) continues to work
identically — zero regressions in correctness or performance.
## Motivation
ME classes were documented as not thread-safe due to mutable instance fields
(`bestSequence`, `tokProbs`, `newTokens`, `sentProbs`) that corrupt under
concurrent access. The recommended workaround was either creating a new ME
instance per call (expensive for high-throughput pipelines processing thousands
of sentences in parallel) or using the `ThreadSafe*ME` wrappers (which use
`ThreadLocal` and leak in Jakarta EE / long-running thread environments).
The root cause was mutable state at four layers:
1. **ME classes** — result fields written on every call
2. **Context generators** — per-call caches (`contextsCache`, `wordsKey`,
`buf`, `collectFeats`)
3. **Feature generators** — `CachedFeatureGenerator` with mutable
`prevTokens` and cache
4. **BeamSearch** — shared `probs[]` output buffer and a `contextsCache`
that stored references to the reused buffer (cached values were always stale)
## Approach
Move mutable state to method-local variables at every layer. ME instance
fields are preserved as `volatile` for backward-compatible `probs()` access
(last-writer-wins under concurrency). Caches are removed entirely — they were
small (size 3 typically), not thread-safe, and in BeamSearch's case, buggy.
### Files changed (10 source, 5 test)
| File | Change
|
| -------------------------------------- |
---------------------------------------------------------------------------------
|
| `BeamSearch.java` | Removed shared `probs[]` and
buggy `contextsCache`; added `@ThreadSafe` |
| `DefaultSDContextGenerator.java` | `buf`/`collectFeats` moved to
method-local; `collectFeatures()` signature updated |
| `SentenceContextGenerator.java` (Thai) | Updated to match new
`collectFeatures()` signature |
| `DefaultPOSContextGenerator.java` | Removed `contextsCache` and
`wordsKey` |
| `ConfigurablePOSContextGenerator.java` | Removed `contextsCache` and
`wordsKey` |
| `CachedFeatureGenerator.java` | Removed `prevTokens`,
`contextsCache`, counters; delegates directly |
| `TokenizerME.java` | `newTokens`/`tokProbs` volatile;
`tokenizePos()` uses local lists |
| `SentenceDetectorME.java` | `sentProbs` volatile;
`sentPosDetect()` uses local list |
| `POSTaggerME.java` | `bestSequence` volatile; `tag()`
uses local var; added null guard |
| `LemmatizerME.java` | `bestSequence` volatile;
`predictSES()` uses local var |
### Backward compatibility
- The old pattern (`new ME(model)` per call) is **unchanged** — verified by
regression benchmark
- `probs()` methods preserved (deprecated behavior under concurrency,
correct single-threaded)
- Constructor signatures preserved (`cacheSize` params accepted but ignored,
marked `@Deprecated(since = "3.0.0")`)
- No new dependencies
## Test plan
- [x] All 675 existing tests pass (`mvn test` on opennlp-runtime)
- [x] `ThreadSafetyBenchmarkTest` — JUnit correctness test: shared ME
instances produce identical results to single-threaded baseline across all CPU
cores
- [x] `RegressionBenchmark` — head-to-head stock vs patched,
new-instance-per-call only: zero mismatches, zero errors, performance within
noise on both builds
- [x] `ThreadSafetyBenchmark` — three-way comparison (new-instance-per-call
/ instance-per-thread / shared-single-instance)
- [x] `CachedFeatureGeneratorTest` — updated for removed cache behavior
- [x] Checkstyle violation count unchanged (9,446 pre-existing on both stock
and patched)
- [ ] Full `mvn clean install` at root (checkstyle must be skipped — 9,446
pre-existing violations on main)
### Regression benchmark results (32 threads, new-instance-per-call)
Proves zero regression — stock vs patched, same API pattern:
| Component | stock avg_ms | patched avg_ms | Mismatches | Errors |
| ---------------- | ------------ | -------------- | ---------- | ------ |
| Tokenizer | 16.09 | 16.69 | 0 | 0 |
| SentenceDetector | 9.21 | 9.01 | 0 | 0 |
| POSTagger | 105.76 | 106.58 | 0 | 0 |
### Speedup benchmark results (32 threads, three-way comparison)
#### Approaches
The benchmark compares three strategies for using ME classes in a
multi-threaded environment. All three produce identical output for a given
input — the difference is how ME instances are allocated and shared.
| Approach | Description
| Example code
|
| -------------------------- |
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
-----------------------------------------------------------------------------------------------
|
| **new-instance-per-call** | A fresh ME instance is created for every
single operation. This is the traditional pattern and the baseline. Safe but
expensive — each call pays the full cost of constructing the ME, its
BeamSearch, context generators, and feature generator chain. |
`String[] tags = new POSTaggerME(model).tag(tokens);`
|
| **instance-per-thread** | One ME instance is created per thread and
reused across all operations on that thread. No cross-thread sharing, so no
contention. Eliminates per-call constructor overhead while remaining completely
safe. | `POSTaggerME tagger
= new POSTaggerME(model);`<br>`for (String[] t : sentences) tagger.tag(t);` |
| **shared-single-instance** | A single ME instance is shared across all
threads. Maximum memory efficiency — only one set of internal structures
exists. Works for TokenizerME and SentenceDetectorME. POSTaggerME has known
contention in the feature generator chain at high thread counts. | `POSTaggerME
shared = new POSTaggerME(model);`<br>`// pass shared to all threads`
|
#### Benchmark results
| Component | Approach | avg_ms | Mismatches |
Speedup |
| ---------------- | ------------------------- | ---------- | ---------- |
--------- |
| Tokenizer | new-instance-per-call | 16.63 | 0 |
1.0x |
| Tokenizer | instance-per-thread | 15.92 | 0 |
1.04x |
| Tokenizer | shared-single-instance | 16.24 | 0 |
1.02x |
| SentenceDetector | new-instance-per-call | 9.49 | 0 |
1.0x |
| SentenceDetector | instance-per-thread | 9.28 | 0 |
1.02x |
| SentenceDetector | shared-single-instance | 8.93 | 0 |
1.06x |
| **POSTagger** | **new-instance-per-call** | **133.55** | **0** |
**1.0x** |
| **POSTagger** | **instance-per-thread** | **80.01** | **0** |
**1.67x** |
POSTagger sees the largest gain because its constructor is the heaviest — it
builds a BeamSearch, a ConfigurablePOSContextGenerator, and a full
AdaptiveFeatureGenerator chain on every instantiation. Reusing one instance per
thread eliminates that allocation on every call, yielding a **1.67x speedup**
with zero correctness impact.
Tokenizer and SentenceDetector constructors are lighter, so the per-call
overhead is smaller and all three approaches perform similarly.
See `opennlp-core/opennlp-runtime/BENCHMARKS.md` for full benchmark
instructions.
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