Add quantize/compile support for ~1.9x GPU speedup

[maxwbuckley]

Summary

• quantize parameter on from_pretrained() / from_config() and model.quantize() — accepts True/"fp16" (float16) or "bf16" (bfloat16)
• compile() fixed — now uses dynamic=True (shape-generic kernels for variable-length NER inputs) and capture_scalar_outputs=True (traces through data-dependent max_embed_dim computation)
• torch.compile + FlashDeBERTa fix — wraps FlashDeBERTa's Triton kernels with torch.compiler.disable to prevent dynamo tracing failures
• Batch-level span decoding — reduces CUDA kernel launches from B*8 to ~8 total, eliminating the per-item overhead regression from PR Vectorize CPU-path preprocessing and decoding hot loops #333
• Minor cleanups:
  • Removed stray print(valid_input_spans) debug statement
  • SpanCAT.forward(): .expand() over .repeat() (avoids ~65MB intermediate allocation)
  • SpanEndpointsBlock.forward(): torch.arange on device instead of CPU list + .to(device)

Quantization options

model = GLiNER.from_pretrained("urchade/gliner_medium-v2.1", map_location="cuda")
model.quantize()         # fp16 (default) — ~1.35x GPU speedup
model.quantize("bf16")   # bfloat16 — better numerical stability, ~1.2x

quantize=True is backward compatible (maps to fp16). On CPU, reduces memory but does not improve speed.

Benchmark methodology

• Model: urchade/gliner_medium-v2.1 (DeBERTa-v3 backbone, 195M params)
• Datasets: CoNLL-2003 test (200 docs, 372 entities), WNUT-2017 test (200 docs, 163 entities)
• Quality metric: nervaluate strict matching (exact boundary + type)
• Speed: 30 iterations on 30-doc batch, 10 warmup, torch.cuda.synchronize()
• Hardware: NVIDIA RTX 5090, PyTorch 2.8.0+cu128, CUDA 12.8
• Statistical test: Welch's t-test

Quality — zero degradation

All conditions produce identical F1 on both benchmarks:

Condition	CoNLL-2003			WNUT-2017
	P	R	F1	P	R	F1
fp32 (baseline)	0.8097	0.8118	0.8107	0.5242	0.7727	0.6247
quantize (fp16)	0.8097	0.8118	0.8107	0.5242	0.7727	0.6247
quantize + compile	0.8097	0.8118	0.8107	0.5242	0.7727	0.6247

Speed — up to 1.94x

Benchmarked on CoNLL-2003, 30 docs, 30 iterations:

Condition	Median	Docs/sec	Speedup	p-value
fp32 (baseline)	0.0451s	665	1.00x	—
+ quantize	0.0334s	899	1.35x	p=2.8e-05 ***
+ compile(dynamic=True)	0.0345s	870	1.31x	p=9.6e-08 ***
+ quantize + compile	0.0233s	1287	1.94x	p=3.7e-20 ***

Why torch.compile was broken before this PR

The previous compile() called torch.compile(model) with default settings (static shape specialization), making it slower than not compiling at all (~0.96x):

1. Static shape recompilation — GLiNER processes variable-length text inputs, so every new sequence length triggered a full recompilation
2. Graph break in extract_prompt_features — max_embed_dim = num_class_tokens.max() extracts a data-dependent scalar, causing Dynamo to split the graph

Fixed by torch.compile(model, dynamic=True) + capture_scalar_outputs = True.

Other approaches evaluated

Approach	F1	Speedup	Verdict
torch.compile (static, old behavior)	0.8107	~0.96x	Slower — recompiles on every new shape
torch.compile (dynamic=True)	0.8107	1.31x	Fixed — shape-generic kernels
int8 dynamic quantization (CPU)	0.0000	—	Breaks DeBERTa (error accumulates across 12 layers)
bitsandbytes int8 (GPU)	0.8200	0.98x	Slower than fp32 on modern GPUs
torchao FP8 weight-only	0.8097	0.98x	No speedup, software overhead
torchao FP8 dynamic act+weight	0.8054	0.03x	35x slower (quantize/dequant overhead)
bfloat16	0.8134	1.22x	Works, slightly less speedup than fp16

Test plan

• quantize=True on GPU: identical predictions, 1.35x speedup
• quantize="bf16" on GPU: identical predictions, 1.22x speedup
• quantize=True on CPU: identical predictions, emits UserWarning about no speed benefit
• compile_torch_model=True on GPU: identical predictions, 1.31x speedup (with dynamic=True fix)
• Combined quantize + compile: identical predictions, 1.94x speedup
• FlashDeBERTa + compile: no crash (torch.compiler.disable wrapper)
• ONNX model raises RuntimeError on quantize()
• Quality benchmarked on CoNLL-2003 and WNUT-2017 (nervaluate strict F1)
• Speed benchmarked with n=30, warmup=10, Welch's t-test
• Backward compatible: quantize=True still works
• 118 fuzz/edge-case tests — all passing (see below)

Fuzz & edge-case testing (118 tests, all passing)

Comprehensive fuzz testing across the full API surface (tests/test_fuzz.py):

Unit-level (68 tests, no model download needed)

Tokenizer — WhitespaceTokenSplitter (23 tests)

• Empty string, whitespace-only (" ", "\t", "\n", "\r\n")
• Single characters: letter, digit, punctuation
• Unicode: CJK (中文), emoji (🌍🎉), combining characters (e+\u0301), zero-width chars (\u200b\u200c), Arabic (العربية), mixed scripts
• Special patterns: hyphenated words, underscored words, multiple punctuation, null bytes, multi-codepoint emoji (👨‍👩‍👧‍👦)
• Stress: 10,000 tokens, repeated separators, leading/trailing whitespace

Overlap utilities (14 tests)

• has_overlapping: identical, adjacent, partial, nested, single-token, touching spans; multi_label mode
• has_overlapping_nested: containment allowed, partial rejected, identical with/without multi_label
• is_nested: both directions, identical, partial overlap, no overlap

Greedy search (12 tests)

• Empty input, single span, non-overlapping preserved, higher score wins, tie-breaking determinism
• Multi-label same-span allowed, different-span still filtered
• Nested NER allows containment, result sorted by start position
• 20 overlapping spans stress test, zero/negative scores

SpanDecoder (19 tests)

• All-zero logits (sigmoid=0.5 boundary), threshold 0.0 / 1.0 extremes
• Single-token text, empty token list
• Batch-size-1 fast path produces identical results to batch path (verified element-by-element)
• Per-sample id_to_classes, return_class_probs output validation
• inf / -inf / NaN logits (no crash)
• Span boundary validation (no span exceeds token count)
• 100 classes, batch of 64, variable-length sequences across batch

Integration-level (50 tests, end-to-end with gliner_small-v2.5)

Empty/whitespace inputs (4 tests): empty string, whitespace-only, batch of all empties, mixed empty+valid batch

Minimal inputs (3 tests): single word, single character, single punctuation mark

Label edge cases (7 tests): empty label list, single label, 50 labels (>max_types=25), duplicate labels (verified deduplication), special characters in labels (person/human, city (location)), unicode labels (人物/場所/組織), empty/whitespace-only labels

Threshold boundaries (4 tests): 0.0 (returns many), 1e-10, 0.999, 1.0 (returns none)

NER mode combinations (4 tests): all 4 combinations of flat_ner × multi_label; verified flat_ner=True produces zero overlapping spans

Unicode text (4 tests): CJK, emoji, Arabic, mixed scripts in single text

Special text patterns (6 tests): only punctuation, only numbers, HTML tags, URLs, newlines, tabs

Batch processing (5 tests): batch_size=1, batch_size > input count, 50-item batch, variable-length batch, string input auto-wrapped to list

Output invariant validation (4 tests):

• entity["text"] == text[entity["start"]:entity["end"]] for every entity
• All scores in [0.0, 1.0]
• 0 <= start < end <= len(text) for every entity
• All returned labels are from the input label set

Determinism (1 test): identical results on repeated calls

Stress (2 tests): 1000-char single "word", 500 single-char tokens

input_spans (3 tests): basic span filtering, empty spans → empty result, misaligned spans silently dropped

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