From c3b5f24e61c24853e6b9bb8a14959bf42fd7ae8f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maxime=20Lefran=C3=A7ois?= Date: Wed, 23 Sep 2026 06:55:33 +0200 Subject: [PATCH 01/13] llamacpp: GPU offload, sequence-copy fan-out, marginal readout The llama.cpp backend was CPU-only and branched from the prefilled state by serializing and restoring sequence 0 once per decision. This keeps that path as the default and adds three things. --llama-gpu-layers N passes the offload count to llama.cpp. The metadata records it together with llama_supports_gpu_offload(), so a CPU wheel that silently ignores the flag is visible in the output. --llama-parallel N sets n_seq_max. Shared mode then copies the prefilled sequence 0 into up to N-1 branches with llama_memory_seq_cp and decodes all their suffixes in one batched llama_decode, reading one logit row per branch. Branches are removed whole afterwards, so sequence 0 needs no restore between chunks. The old docstring said hybrid Qwen3.5 memories support neither sequence copies nor partial tail removal; only the second half is true. seq_cp is implemented for llama_memory_recurrent, and both branching modes rely on it. What hybrid memories refuse is truncating a tail back to the prefix, because a recurrent state cannot be partially erased. The docstring and docs now say so. --llama-readout marginal (shared mode, parallel >= 2) folds one-token preambles into the answer-slot masses: for each branch whose most likely next tokens are not slots, the probes are appended in one extra batched decode and the slot masses read after them are added, weighted by the preamble's probability. option_logits become log-masses so softmax and temperature scaling are unchanged; preamble_mass records the share that came through a preamble. Qwen3-8B, which wants to write "**" before the letter, is the case this exists for; on Qwen3.5-4B it changes nothing. benchmarks/shape777.py accepts --backend llamacpp with the same options, so the 37x21 systems fixture runs on GGUF, and reads the GPU name without a CUDA torch. Unit tests cover the new validation, the fan-out batch layout, branch removal, and the marginal fold; the real-GGUF test now also loads with three sequences and compares copy-shared to restore-shared. It passes on the PyPI CPU wheel and on the cu124 wheel with full offload. --- README.md | 19 +- benchmarks/shape777.py | 57 ++++- docs/LLAMACPP.md | 151 +++++++++++++ src/semif_phase1/cli.py | 23 +- src/semif_phase1/llamacpp_backend.py | 319 +++++++++++++++++++++++---- tests/test_llamacpp.py | 170 +++++++++++++- 6 files changed, 669 insertions(+), 70 deletions(-) create mode 100644 docs/LLAMACPP.md diff --git a/README.md b/README.md index 290666f..72b7778 100644 --- a/README.md +++ b/README.md @@ -54,17 +54,14 @@ export HF_HOME=/path/to/large-drive/huggingface pip install -e '.[test]' ``` -**CPU only:** the llama.cpp backend scores the same prompts from a local GGUF -checkpoint with no CUDA device. Install `pip install -e '.[test,llamacpp]'`, -fetch a GGUF (for example `Qwen_Qwen3.5-4B-Q4_K_M.gguf` from -`bartowski/Qwen_Qwen3.5-4B-GGUF`), and add `--backend llamacpp --gguf -/path/to/model.gguf`; `--llama-threads` caps the CPU threads. Prompt -construction stays on the pinned reference tokenizer, so `prompt_sha256` -matches the Torch backend row for row; scores carry the GGUF checksum and are -conditional on the quantized weights. Direct and prefix-cached execution can -have small numerical differences from different llama.cpp evaluation paths; -compare decisions or probabilities with a tolerance rather than raw logits -bit for bit. One loaded backend owns one stateful scoring context. +**llama.cpp / GGUF:** the llama.cpp backend scores the same prompts from a local GGUF +checkpoint, on CPU or with `--llama-gpu-layers` offloaded to a GPU. Install +`pip install -e '.[test,llamacpp]'`, download a GGUF of the pinned model (for example +`bartowski/Qwen_Qwen3.5-4B-GGUF`), and add `--backend llamacpp --gguf /path/to/model.gguf`. +Shared mode can fan its questions out over copied sequences in one batched decode with +`--llama-parallel N`, which also works on hybrid Qwen3.5 memories. Prompt hashes match the +Torch backend; quantized option scores have small numerical differences. See +[docs/LLAMACPP.md](docs/LLAMACPP.md). Run the owned examples: diff --git a/benchmarks/shape777.py b/benchmarks/shape777.py index feeb301..cb9dd0e 100644 --- a/benchmarks/shape777.py +++ b/benchmarks/shape777.py @@ -11,9 +11,18 @@ from pathlib import Path from semif_phase1.core import load_causal_model -from semif_phase1.direct import score -from semif_phase1.serial import SerialPrefixScorer -from semif_phase1.shared import score_shared +from semif_phase1.direct import score as torch_score +from semif_phase1.serial import SerialPrefixScorer as TorchSerialPrefixScorer +from semif_phase1.shared import score_shared as torch_score_shared + + +def _gpu_name() -> str | None: + """The NVIDIA device name without importing a CUDA-enabled torch.""" + for info in sorted(Path("/proc/driver/nvidia/gpus").glob("*/information")): + for line in info.read_text().splitlines(): + if line.startswith("Model:"): + return line.split(":", 1)[1].strip() + return None def main() -> None: @@ -23,17 +32,47 @@ def main() -> None: parser.add_argument("--input", type=Path, required=True) parser.add_argument("--output", type=Path, required=True) parser.add_argument("--max-tokens", type=int, default=4096) + parser.add_argument("--backend", choices=("torch", "llamacpp"), default="torch") + parser.add_argument("--gguf", type=Path, help="Local GGUF checkpoint for --backend llamacpp") + parser.add_argument("--llama-threads", type=int, help="CPU threads for --backend llamacpp") + parser.add_argument("--llama-gpu-layers", type=int, default=0, + help="Layers offloaded to the GPU for --backend llamacpp (default: 0)") + parser.add_argument("--llama-parallel", type=int, default=1, + help="Branch slots for llama.cpp shared mode (default: 1 = state restore)") args = parser.parse_args() if args.output.exists(): parser.error("Output must be new") + if args.backend == "llamacpp": + if args.gguf is None or not args.gguf.is_file(): + parser.error("--backend llamacpp requires --gguf pointing at an existing GGUF file") + if args.llama_gpu_layers < 0 or args.llama_parallel < 1: + parser.error("--llama-gpu-layers must be nonnegative and --llama-parallel positive") + elif args.gguf is not None or args.llama_threads is not None or args.llama_gpu_layers or args.llama_parallel != 1: + parser.error("llama.cpp options require --backend llamacpp") rows = [json.loads(line) for line in args.input.read_text().splitlines() if line.strip()] groups = defaultdict(list) for row in rows: groups[row["group_id"]].append(row) if len(rows) != 777 or len(groups) != 37 or any(len(group) != 21 for group in groups.values()): parser.error("Expected the committed 37-state x 21-question fixture") - model, tokenizer, metadata = load_causal_model(args.model, args.revision, "cuda") - import torch + if args.backend == "llamacpp": + from semif_phase1 import llamacpp_backend + + model, tokenizer, metadata = llamacpp_backend.load_model( + args.model, args.revision, args.gguf, threads=args.llama_threads, + context_tokens=args.max_tokens, gpu_layers=args.llama_gpu_layers, + sequences=args.llama_parallel) + score = llamacpp_backend.score + SerialPrefixScorer = llamacpp_backend.SerialPrefixScorer + score_shared = llamacpp_backend.score_shared + cuda = None + hardware = (_gpu_name() or "unknown GPU") if args.llama_gpu_layers else "CPU" + else: + model, tokenizer, metadata = load_causal_model(args.model, args.revision, "cuda") + import torch as cuda + + score, SerialPrefixScorer, score_shared = torch_score, TorchSerialPrefixScorer, torch_score_shared + hardware = cuda.cuda.get_device_name(0) first = next(iter(groups.values())) score(model, tokenizer, first[0], metadata, args.max_tokens) @@ -45,13 +84,15 @@ def main() -> None: "version": "shape777-published-v1", "input_sha256": hashlib.sha256(args.input.read_bytes()).hexdigest(), "model": metadata, - "hardware": torch.cuda.get_device_name(0), + "hardware": hardware, + "backend": args.backend, "timing_scope": "Warm model; includes prompt construction, tokenization, transfers, forward passes and CPU readout.", "results": [], } predictions = {} for mode in ("fresh", "serial_prefix", "parallel_shared"): - torch.cuda.reset_peak_memory_stats() + if cuda is not None: + cuda.cuda.reset_peak_memory_stats() started = time.perf_counter() values, state_times = [], [] for group in groups.values(): @@ -73,7 +114,7 @@ def main() -> None: "wall_seconds": elapsed, "decisions_per_second": len(values) / elapsed, "state_p50_seconds": statistics.median(state_times), - "peak_cuda_bytes": torch.cuda.max_memory_allocated(), + "peak_cuda_bytes": cuda.cuda.max_memory_allocated() if cuda is not None else None, } ) reference = {row["id"]: row for row in predictions["fresh"]} diff --git a/docs/LLAMACPP.md b/docs/LLAMACPP.md new file mode 100644 index 0000000..51aa377 --- /dev/null +++ b/docs/LLAMACPP.md @@ -0,0 +1,151 @@ +# llama.cpp / GGUF + +The llama.cpp backend runs SemIf's direct, serial-prefix, and shared decision +modes over a quantized GGUF checkpoint, on CPU or with layers offloaded to a +GPU. Prompts, answer-slot checks and `prompt_sha256` come from the reference +transformers tokenizer, exactly as in the Torch backend; llama.cpp only executes +the forward pass. Every prompt is re-tokenized through the GGUF vocabulary and +must match the reference encoding before it is scored. No answer token is +generated. + +## Install and score + +```bash +python3 -m venv .venv +source .venv/bin/activate +pip install -e '.[test,llamacpp]' # CPU wheel of llama-cpp-python + +semif-score --backend llamacpp --mode direct \ + --model Qwen/Qwen3.5-4B \ + --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \ + --gguf /path/to/Qwen_Qwen3.5-4B-Q4_K_M.gguf \ + --input examples/decisions.jsonl \ + --output results-llamacpp-direct.jsonl +``` + +The pinned tokenizer is downloaded into the Hugging Face cache on first use +(a few megabytes; the model weights come from the GGUF file). The GGUF used for +the results below is `bartowski/Qwen_Qwen3.5-4B-GGUF` at revision +`4168f45a16a1290d65a4ec0fa312ae917a4c15d6`, quantization Q4_K_M, 3 013 027 808 +bytes, SHA-256 `13c16f426047e2de38cd075bdade4a7bcbc8c774384876f677740cda65f8a983`. +Each prediction records the file's size and checksum. + +### GPU offload + +`--llama-gpu-layers N` offloads `N` layers (use a large value such as `99` for +all of them). It needs a llama-cpp-python wheel built with a GPU backend; the +PyPI wheel is CPU-only. For CUDA: + +```bash +pip install --force-reinstall --no-deps "llama-cpp-python==0.3.35" \ + --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124 +pip install nvidia-cuda-runtime-cu12 nvidia-cublas-cu12 nvidia-cuda-nvrtc-cu12 +export LD_LIBRARY_PATH=$(ls -d .venv/lib/python3.*/site-packages/nvidia/*/lib | tr '\n' ':') +``` + +The metadata records `n_gpu_layers` and whether the loaded library supports +offload at all, so a CPU wheel silently ignoring the flag is visible in the +output. + +## Two ways to branch from one state + +Sequence 0 of the llama.cpp context holds the prefilled state. Shared mode +scores every question of that state from a branch of it, and there are two +ways to make a branch: + +| `--llama-parallel` | branching | decodes per state | +|---|---|---| +| `1` (default) | **state restore** — `llama_state_seq_get_data` after the prefill, `set_data` before each question | one per question | +| `N ≥ 2` | **sequence copy** — `llama_memory_seq_cp` of sequence 0 into up to `N − 1` throwaway sequences, all suffixes in one batched `llama_decode` | one per `N − 1` questions | + +State restore is what the backend has always done. Sequence copy is the +llama.cpp equivalent of the Torch backend's `native-state-prefix-parallel-v1`: +the prefix is evaluated once, copied without serialization, and the branch +suffixes share a single decode. The branches are removed whole afterwards +(whole-sequence removal never fails), so sequence 0 is left exactly as +prefilled and the next chunk of questions needs no restore at all. + +### Hybrid models + +Qwen3.5 is a hybrid architecture: one full-attention layer in four, the rest +Gated DeltaNet with a recurrent state. In llama.cpp that state lives in +`llama_memory_recurrent`, which keeps only the state after the last token and +therefore **cannot be partially erased** — `llama_memory_seq_rm` refuses any +range that includes a sequence's last position (the source says: "models like +Mamba or RWKV can't have a state partially erased at the end of the sequence"). +Truncating a branch back to the prefix is thus impossible on these models, and +that is a property of the architecture, not of a build or a binding. + +What hybrid memories do support is whole-sequence removal, state save/restore, +and **sequence copies** — `llama_memory_recurrent::seq_cp` is implemented — which +is why both branching modes above work on Qwen3.5. Pure-attention models +additionally allow tail truncation; the backend does not rely on it. + +`n_seq_max` is a context parameter (`llama_context_params.n_seq_max`), not a +build option; the same wheel serves both modes. The context is allocated as +`n_seq_max × (max_tokens + 64)` tokens and, when the library exposes it, +`kv_unified` is enabled so the copied branches share the prefix's attention +cells. + +## Validation + +`tests/test_llamacpp.py::test_real_gguf_scores_direct_serial_and_shared` loads the +real GGUF twice — with one sequence and with three — and checks that direct, +serial, restore-shared, copy-shared and marginal readouts agree on the +decisions, that copy-shared option logits match restore-shared ones within +0.5, and that `preamble_mass` stays in `[0, 1]`. Set `SEMIF_LLAMACPP_GGUF` to run +it, `SEMIF_LLAMACPP_GPU_LAYERS` to offload. It passes on: + +- the PyPI CPU wheel of `llama-cpp-python` 0.3.35, CPU only; +- the cu124 wheel of the same version with every layer offloaded to an RTX 3080 + Laptop GPU. + +**Known issue, not in this backend:** the cu124 wheel's *CPU* code path raises +`Illegal instruction` on an Intel Xeon W-11955M (AVX-512 without `avx512_bf16` +or AMX) for any decode, single- or multi-sequence, hybrid or pure-attention +model. With that wheel, offload the layers; for CPU scoring, use the PyPI wheel. + +## Marginal readout + +`--llama-readout marginal` (shared mode, `--llama-parallel ≥ 2`) changes how +the answer is read, not what is asked. Some checkpoints want to emit a token +*before* the letter — Qwen3-8B puts almost all of its next-token mass on the +Markdown `**` token when the assistant turn starts empty, and reading only the +last prompt position then reads that preamble instead of the answer. The +marginal readout takes, for each branch, the most likely non-slot tokens above +a floor (two probes, 5 %), appends each in one extra batched decode across all +branches, reads the slot masses after it, and adds them to the direct slot +masses weighted by the preamble's probability. `option_logits` then hold the +natural logs of the summed masses, so `softmax` and temperature scaling keep +working unchanged; `preamble_mass` records the share that came through a +preamble. On Qwen3.5-4B, which answers with the letter directly, the readout +changes nothing and `preamble_mass` stays at zero. + +## Results + + + +## Reproduce + +```bash +# Quality: the owned labeled workload, three serving configurations +for MODE in direct serial shared; do + semif-score --backend llamacpp --mode $MODE --llama-gpu-layers 99 --llama-parallel 8 \ + --model Qwen/Qwen3.5-4B --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \ + --gguf /path/to/Qwen_Qwen3.5-4B-Q4_K_M.gguf \ + --input benchmarks/data/authored144.jsonl \ + --output results/raw/predictions/llamacpp-gguf-cuda-$MODE-authored144.jsonl +done +python benchmarks/evaluate.py --gold benchmarks/data/authored144.jsonl \ + --predictions results/raw/predictions/llamacpp-gguf-cuda-direct-authored144.jsonl + +# Systems: the 37x21 fixture, fresh / serial-prefix / parallel-shared +python benchmarks/shape777.py --backend llamacpp --llama-gpu-layers 99 --llama-parallel 8 \ + --model Qwen/Qwen3.5-4B --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \ + --gguf /path/to/Qwen_Qwen3.5-4B-Q4_K_M.gguf \ + --input benchmarks/data/shape777.jsonl \ + --output results/raw/shape777-llamacpp-gguf-cuda.json +``` + +Shared mode requires every row of the input to carry the same exact state; +`shape777.py` groups its fixture by `group_id` and does that for you. diff --git a/src/semif_phase1/cli.py b/src/semif_phase1/cli.py index dd2c2f3..2b99936 100644 --- a/src/semif_phase1/cli.py +++ b/src/semif_phase1/cli.py @@ -23,6 +23,14 @@ def main() -> None: parser.add_argument("--gguf", type=Path, help="Local GGUF checkpoint for --backend llamacpp") parser.add_argument("--llama-threads", type=int, help="CPU threads for --backend llamacpp (default: all visible cores)") + parser.add_argument("--llama-gpu-layers", type=int, default=0, + help="Layers to offload to the GPU for --backend llamacpp (default: 0, CPU only)") + parser.add_argument("--llama-parallel", type=int, default=1, + help="Branch slots for --backend llamacpp shared mode: suffixes are fanned out over " + "copied sequences and decoded together (default: 1 = state restore per decision)") + parser.add_argument("--llama-readout", choices=("last", "marginal"), default="last", + help="llama.cpp shared-mode readout: last prompt position, or answer-slot masses summed " + "over one-token preambles (needs --llama-parallel > 1)") parser.add_argument("--model", required=True) parser.add_argument("--revision", required=True) parser.add_argument("--input", type=Path, required=True) @@ -51,11 +59,22 @@ def main() -> None: parser.error("--llama-threads must be positive") if args.backend == "mlx" and args.mode == "reranker": parser.error("MLX supports direct, serial, and shared modes; reranker requires torch") + for name, value in (("--llama-gpu-layers", args.llama_gpu_layers), ("--llama-parallel", args.llama_parallel)): + if args.backend != "llamacpp" and value != parser.get_default(name.lstrip("-").replace("-", "_")): + parser.error(f"{name} requires --backend llamacpp") + if args.llama_readout != "last" and args.backend != "llamacpp": + parser.error("--llama-readout requires --backend llamacpp") if args.backend == "llamacpp": if args.mode == "reranker": parser.error("llama.cpp supports direct, serial, and shared modes; reranker requires torch") if args.gguf is None or not args.gguf.is_file(): parser.error("--backend llamacpp requires --gguf pointing at an existing GGUF file") + if args.llama_gpu_layers < 0: + parser.error("--llama-gpu-layers must be nonnegative") + if args.llama_parallel < 1: + parser.error("--llama-parallel must be positive") + if args.llama_readout == "marginal" and (args.mode != "shared" or args.llama_parallel < 2): + parser.error("--llama-readout marginal requires --mode shared and --llama-parallel of at least 2") rows = [json.loads(line) for line in args.input.read_text().splitlines() if line.strip()] if not rows: parser.error("Input is empty") @@ -75,7 +94,9 @@ def main() -> None: model, tokenizer, metadata = llamacpp_backend.load_model( args.model, args.revision, args.gguf, - threads=args.llama_threads, context_tokens=args.max_tokens) + threads=args.llama_threads, context_tokens=args.max_tokens, + gpu_layers=args.llama_gpu_layers, sequences=args.llama_parallel, + readout=args.llama_readout) direct, serial, shared = (llamacpp_backend.score, llamacpp_backend.SerialPrefixScorer, llamacpp_backend.score_shared) else: diff --git a/src/semif_phase1/llamacpp_backend.py b/src/semif_phase1/llamacpp_backend.py index 5600ae8..446e517 100644 --- a/src/semif_phase1/llamacpp_backend.py +++ b/src/semif_phase1/llamacpp_backend.py @@ -1,4 +1,4 @@ -"""CPU option readout over GGUF checkpoints through llama.cpp. +"""Option readout over GGUF checkpoints through llama.cpp, on CPU or with GPU offload. Prompt construction and answer-slot verification stay on the reference transformers tokenizer, so prompt_sha256 matches the Torch backend exactly; @@ -6,11 +6,21 @@ Every scored prompt is re-tokenized through the GGUF vocabulary and must agree with the reference encoding before it is evaluated. -Sequence 0 of the single context holds the prefill; each decision restores the -saved prefix state (whole-sequence save/restore) before decoding its suffix. -The hybrid linear-attention memory of Qwen3.5 supports neither sequence -copies nor partial tail removal, so branch replication goes through the -per-sequence state serialization llama.cpp itself uses for slot caching. +Sequence 0 of the context holds the state prefill. Two ways to branch from it: + +* **state restore** (``sequences == 1``): each decision restores the saved + sequence-0 state, then decodes its suffix. One restore per decision. +* **sequence copy** (``sequences > 1``): the prefix is copied with + ``llama_memory_seq_cp`` into up to ``sequences - 1`` throwaway sequences and + all their suffixes are decoded in one batched ``llama_decode``. No state + serialization, no restore; the branches are removed whole afterwards. + +Hybrid linear-attention models such as Qwen3.5 keep a recurrent state that +cannot be partially erased, so ``llama_memory_seq_rm`` refuses to truncate a +tail back to the prefix. They do support whole-sequence removal, state +save/restore, and sequence copies (``llama_memory_recurrent::seq_cp``), which is +what both branching modes rely on. Pure-attention models additionally allow +tail truncation, which the optional marginal readout uses when available. """ from __future__ import annotations @@ -31,17 +41,18 @@ from .shared import _state_prefix DECODE_CHUNK = 512 +READOUTS = ("last", "marginal") _BACKEND_INITIALIZED = False -def _cpu_model_params(library): - """Initialize llama.cpp once and return model parameters with GPU offload disabled.""" +def _model_params(library, gpu_layers: int): + """Initialize llama.cpp once and return model parameters with the requested GPU offload.""" global _BACKEND_INITIALIZED if not _BACKEND_INITIALIZED: library.llama_backend_init() _BACKEND_INITIALIZED = True params = library.llama_model_default_params() - params.n_gpu_layers = 0 + params.n_gpu_layers = gpu_layers return params @@ -76,19 +87,38 @@ def _logsumexp(values: numpy.ndarray) -> float: return peak + float(numpy.log(numpy.exp(values - peak).sum())) +def _stable_softmax(values: numpy.ndarray) -> numpy.ndarray: + shifted = values - values.max() + weights = numpy.exp(shifted) + return weights / weights.sum() + + class _Engine: """One llama.cpp context bound to a loaded GGUF model.""" - def __init__(self, library, model, context_tokens: int, threads: int): + def __init__(self, library, model, context_tokens: int, threads: int, sequences: int = 1): + if not (isinstance(sequences, int) and sequences >= 1): + raise ValueError("sequences must be a positive integer") params = library.llama_context_default_params() - params.n_ctx = context_tokens - params.n_seq_max = 1 - # llama.cpp requires n_outputs_max >= n_seq_max * n_outputs_max_per_seq. - params.n_outputs_max = 1 + # Sequence 0 keeps the prefix; the others are branch slots. Without a unified KV buffer + # llama.cpp splits n_ctx across sequences, so size it for the worst case either way. + params.n_ctx = context_tokens * sequences + params.n_seq_max = sequences + # One flagged output per sequence in a fan-out batch; llama.cpp requires + # n_outputs_max >= n_seq_max * n_outputs_max_per_seq. + params.n_outputs_max = sequences + params.n_outputs_max_per_seq = 1 params.n_threads = threads params.n_threads_batch = threads + unified = None + if sequences > 1 and hasattr(params, "kv_unified"): + # Branches share the whole prefix; llama.h recommends the unified buffer for that case. + params.kv_unified = True + unified = True self.lib = library self.model = model + self.sequences = sequences + self.kv_unified = unified self.context = library.llama_init_from_model(model, params) if not self.context: raise RuntimeError("llama.cpp failed to create the scoring context") @@ -99,6 +129,7 @@ def __init__(self, library, model, context_tokens: int, threads: int): raise RuntimeError("llama.cpp returned no context memory") self.context_tokens = int(library.llama_n_ctx(self.context)) self.vocab_size = library.llama_n_vocab(library.llama_model_get_vocab(model)) + self.can_truncate = None # learned on first attempt; depends on the model architecture def close(self) -> None: if self.context: @@ -106,6 +137,14 @@ def close(self) -> None: self.context = None self.memory = None + def _logits_at(self, index: int) -> numpy.ndarray: + pointer = self.lib.llama_get_logits_ith(self.context, index) + if not pointer: + raise RuntimeError("llama.cpp returned no logits for the flagged position") + return numpy.ctypeslib.as_array( + ctypes.cast(pointer, ctypes.POINTER(ctypes.c_float)), shape=(self.vocab_size,) + ).copy() + def _decode(self, tokens: list[int], start: int, sequence: int, want_logits: bool): if not tokens: raise ValueError("Refusing to decode an empty token list") @@ -127,12 +166,36 @@ def _decode(self, tokens: list[int], start: int, sequence: int, want_logits: boo self.lib.llama_batch_free(batch) if not want_logits: return None - pointer = self.lib.llama_get_logits_ith(self.context, -1) - if not pointer: - raise RuntimeError("llama.cpp returned no logits for the flagged position") - return numpy.ctypeslib.as_array( - ctypes.cast(pointer, ctypes.POINTER(ctypes.c_float)), shape=(self.vocab_size,) - ).copy() + return self._logits_at(-1) + + def _decode_items(self, items: list[tuple[int, int, int, bool]]) -> dict[int, numpy.ndarray]: + """Decode (token, position, sequence, want_logits) items across sequences in one pass. + + Logits are read right after the chunk that produced them: llama.cpp only exposes the + outputs of the most recent ``llama_decode`` call. + """ + if not items: + raise ValueError("Refusing to decode an empty item list") + collected: dict[int, numpy.ndarray] = {} + for offset in range(0, len(items), DECODE_CHUNK): + chunk = items[offset : offset + DECODE_CHUNK] + batch = self.lib.llama_batch_init(len(chunk), 0, self.sequences) + try: + for index, (token, position, sequence, want) in enumerate(chunk): + batch.token[index] = token + batch.pos[index] = position + batch.n_seq_id[index] = 1 + batch.seq_id[index][0] = sequence + batch.logits[index] = int(want) + batch.n_tokens = len(chunk) + if self.lib.llama_decode(self.context, batch): + raise RuntimeError("llama_decode failed; raise --max-tokens if prompts grew") + for index, (_, _, sequence, want) in enumerate(chunk): + if want: + collected[sequence] = self._logits_at(index) + finally: + self.lib.llama_batch_free(batch) + return collected def clear(self) -> None: self.lib.llama_memory_clear(self.memory, False) @@ -164,6 +227,44 @@ def full_logits(self, tokens: list[int]) -> numpy.ndarray: self.clear() return self._decode(tokens, 0, 0, True) + # --- sequence-copy fan-out ------------------------------------------------------------- + + def fanout_logits(self, prefix_length: int, suffixes: list[list[int]]) -> list[numpy.ndarray]: + """Copy the sequence-0 prefix into one branch per suffix and decode them all at once. + + Returns one full-vocabulary logit vector per suffix, then removes the branches whole so + sequence 0 is left exactly as prefilled. Whole-sequence removal never fails, on any + memory type. + """ + if not suffixes or any(not suffix for suffix in suffixes): + raise ValueError("Every branch needs a nonempty suffix") + if len(suffixes) > self.sequences - 1: + raise ValueError(f"{len(suffixes)} branches exceed the {self.sequences - 1} configured slots") + branches = list(range(1, len(suffixes) + 1)) + for branch in branches: + self.lib.llama_memory_seq_cp(self.memory, 0, branch, -1, -1) + items = [ + (token, prefix_length + offset, branch, offset == len(suffix) - 1) + for branch, suffix in zip(branches, suffixes) + for offset, token in enumerate(suffix) + ] + try: + logits = self._decode_items(items) + return [logits[branch] for branch in branches] + finally: + for branch in branches: + self.lib.llama_memory_seq_rm(self.memory, branch, -1, -1) + + def probe_branches(self, positions: dict[int, int], tokens: dict[int, int]) -> dict[int, numpy.ndarray]: + """Append one token to each listed branch and read the logits after it. + + Used by the marginal readout: ``tokens[branch]`` is the preamble token the model wanted + to emit first, ``positions[branch]`` the position it goes to. Branches are live sequences + from the current fan-out; the caller removes them. + """ + items = [(tokens[branch], positions[branch], branch, True) for branch in sorted(tokens)] + return self._decode_items(items) + def _free_native(engine: _Engine, library, model) -> None: engine.close() @@ -208,8 +309,14 @@ def _verify_vocabulary(tokenizer, library, vocab) -> None: def load_model(source: str, revision: str, gguf, *, threads: int | None = None, - context_tokens: int = 4096): - """Load one pinned reference tokenizer plus a local GGUF checkpoint for CPU scoring.""" + context_tokens: int = 4096, gpu_layers: int = 0, sequences: int = 1, + readout: str = "last"): + """Load one pinned reference tokenizer plus a local GGUF checkpoint for scoring. + + ``gpu_layers`` is passed straight to llama.cpp (0 keeps the historical CPU behaviour, a + large value offloads every layer). ``sequences`` is the context's ``n_seq_max``: with more + than one, shared scoring fans suffixes out over copied sequences instead of restoring state. + """ local = Path(source).is_dir() if not local and not re.fullmatch(r"[0-9a-f]{40}", revision or ""): raise ValueError("Remote sources require a pinned 40-character revision; local sources require a revision label") @@ -224,6 +331,12 @@ def load_model(source: str, revision: str, gguf, *, threads: int | None = None, threads = os.cpu_count() or 4 if not (isinstance(threads, int) and threads >= 1): raise ValueError("threads must be a positive integer") + if not (isinstance(gpu_layers, int) and gpu_layers >= 0): + raise ValueError("gpu_layers must be a nonnegative integer") + if not (isinstance(sequences, int) and sequences >= 1): + raise ValueError("sequences must be a positive integer") + if readout not in READOUTS: + raise ValueError(f"readout must be one of {READOUTS}") import transformers try: import llama_cpp @@ -241,13 +354,13 @@ def load_model(source: str, revision: str, gguf, *, threads: int | None = None, checksum.update(block) gguf_record = {"file": gguf.name, "bytes": gguf.stat().st_size, "sha256": checksum.hexdigest()} - model_params = _cpu_model_params(llama_cpp) + model_params = _model_params(llama_cpp, gpu_layers) model = llama_cpp.llama_model_load_from_file(str(gguf).encode("utf-8"), model_params) if not model: raise RuntimeError(f"llama.cpp failed to load the GGUF checkpoint: {gguf}") window = context_tokens + 64 try: - engine = _Engine(llama_cpp, model, window, threads) + engine = _Engine(llama_cpp, model, window, threads, sequences) vocab = llama_cpp.llama_model_get_vocab(model) _verify_vocabulary(tokenizer, llama_cpp, vocab) except Exception: @@ -263,7 +376,11 @@ def load_model(source: str, revision: str, gguf, *, threads: int | None = None, "gguf": gguf_record, "vocab_size": engine.vocab_size, "threads": threads, - "n_gpu_layers": 0, + "n_gpu_layers": gpu_layers, + "gpu_offload_supported": bool(llama_cpp.llama_supports_gpu_offload()), + "n_seq_max": sequences, + "kv_unified": engine.kv_unified, + "readout_mode": readout, "max_prompt_tokens": context_tokens, "context_tokens": engine.context_tokens, "decode_chunk": DECODE_CHUNK, @@ -358,8 +475,66 @@ def score(self, row: dict) -> dict: return result +def _marginal_readout(engine: _Engine, branches: list[int], prefix_length: int, + suffixes: list[list[int]], vocabularies: list[numpy.ndarray], + slot_lists: list[list[int]], probes: int = 2, floor: float = 0.05): + """Fold one-token preambles back into the answer-slot masses. + + Some checkpoints want to emit a token before the letter — Qwen3-8B puts almost all of its + mass on the Markdown ``**`` token when the assistant turn starts empty. Reading only the last + prompt position then reads the preamble, not the answer. For each branch whose most likely + next tokens are not answer slots, this appends up to ``probes`` such tokens (one extra batched + decode per probe rank across all branches), reads the slot masses after them, and adds them + to the direct slot masses weighted by the preamble's probability. The argmax and the reported + ``option_logits`` become log-masses of that truncated path sum. + + Returns per-branch (option_logits, preamble_mass) with option_logits as natural logs of the + summed masses, so downstream softmax and temperature scaling keep working unchanged. + """ + live = {branch: prefix_length + len(suffix) for branch, suffix in zip(branches, suffixes)} + masses, via = [], [] + for vocabulary, slots in zip(vocabularies, slot_lists): + probabilities = _stable_softmax(vocabulary.astype(numpy.float64)) + masses.append(probabilities[slots].copy()) + via.append(0.0) + # Candidate preambles per branch: top tokens that are not answer slots. + candidates: list[list[int]] = [] + for vocabulary, slots in zip(vocabularies, slot_lists): + forbidden = set(slots) + order = numpy.argsort(-vocabulary)[: probes + len(slots)] + candidates.append([int(token) for token in order if int(token) not in forbidden][:probes]) + weights = [_stable_softmax(vocabulary.astype(numpy.float64)) for vocabulary in vocabularies] + for rank in range(probes): + tokens, positions = {}, {} + for index, branch in enumerate(branches): + if rank < len(candidates[index]): + token = candidates[index][rank] + if weights[index][token] >= floor: + tokens[branch] = token + positions[branch] = live[branch] + if not tokens: + break + after = engine.probe_branches(positions, tokens) + for index, branch in enumerate(branches): + if branch in tokens: + weight = float(weights[index][tokens[branch]]) + gain = weight * _stable_softmax(after[branch].astype(numpy.float64))[slot_lists[index]] + masses[index] += gain + via[index] += float(gain.sum()) + live[branch] += 1 + results = [] + for mass, mass_via in zip(masses, via): + total = float(mass.sum()) + results.append((numpy.log(numpy.maximum(mass, 1e-300)).tolist(), mass_via / total if total > 0 else 0.0)) + return results + + def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: int = 4096): - """Prefill one exact state once, then score every criterion from restored branches.""" + """Prefill one exact state once, then score every criterion from branches. + + With a single sequence the branches are restored from a saved state, one at a time. With + ``n_seq_max > 1`` they are sequence copies decoded together, in chunks of ``n_seq_max - 1``. + """ if not rows or any(row["state"] != rows[0]["state"] for row in rows[1:]): raise ValueError("Shared scoring requires one nonempty exact state") if len({row["id"] for row in rows}) != len(rows): @@ -370,25 +545,83 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: if not prefix or any(ids[: len(prefix)] != prefix or len(ids) <= len(prefix) for ids, _, _ in encoded): raise ValueError("The fixed state prefix does not match every full prompt") encode_seconds = time.perf_counter() - started + engine = model.engine + marginal = metadata.get("readout_mode") == "marginal" mark = time.perf_counter() - model.engine.clear() - model.engine.prefill(prefix) - state_data = model.engine.save_state() + engine.clear() + engine.prefill(prefix) + if engine.sequences == 1: + state_data = engine.save_state() prefill_seconds = time.perf_counter() - mark copy_seconds = suffix_seconds = 0.0 results = [] - for row, row_encoded in zip(rows, encoded): - ids, slots, _ = row_encoded - mark = time.perf_counter() - model.engine.restore_state(state_data) - copy_seconds += time.perf_counter() - mark - mark = time.perf_counter() - vocabulary = model.engine.branch_logits(len(prefix), ids[len(prefix) :]) - suffix_seconds += time.perf_counter() - mark - results.append(_result( - row, row_encoded, vocabulary[slots].tolist(), vocabulary, metadata, - "llamacpp-state-restore-shared-v1", "quantized branch last-position logits over a restored prefix state", - )) + if engine.sequences == 1: + if marginal: + raise ValueError("The marginal readout needs sequence copies; load with sequences > 1") + for row, row_encoded in zip(rows, encoded): + ids, slots, _ = row_encoded + mark = time.perf_counter() + engine.restore_state(state_data) + copy_seconds += time.perf_counter() - mark + mark = time.perf_counter() + vocabulary = engine.branch_logits(len(prefix), ids[len(prefix) :]) + suffix_seconds += time.perf_counter() - mark + results.append(_result( + row, row_encoded, vocabulary[slots].tolist(), vocabulary, metadata, + "llamacpp-state-restore-shared-v1", + "quantized branch last-position logits over a restored prefix state", + )) + branch_bytes = state_data[1] + config = "llamacpp-state-restore-shared-v1" + else: + width = engine.sequences - 1 + for start in range(0, len(rows), width): + chunk_rows = rows[start : start + width] + chunk_encoded = encoded[start : start + width] + suffixes = [ids[len(prefix) :] for ids, _, _ in chunk_encoded] + slot_lists = [slots for _, slots, _ in chunk_encoded] + branches = list(range(1, len(suffixes) + 1)) + mark = time.perf_counter() + for branch in branches: + engine.lib.llama_memory_seq_cp(engine.memory, 0, branch, -1, -1) + copy_seconds += time.perf_counter() - mark + mark = time.perf_counter() + try: + items = [ + (token, len(prefix) + offset, branch, offset == len(suffix) - 1) + for branch, suffix in zip(branches, suffixes) + for offset, token in enumerate(suffix) + ] + logits = engine._decode_items(items) + vocabularies = [logits[branch] for branch in branches] + if marginal: + folded = _marginal_readout(engine, branches, len(prefix), suffixes, + vocabularies, slot_lists) + finally: + for branch in branches: + engine.lib.llama_memory_seq_rm(engine.memory, branch, -1, -1) + suffix_seconds += time.perf_counter() - mark + for index, (row, row_encoded) in enumerate(zip(chunk_rows, chunk_encoded)): + vocabulary = vocabularies[index] + if marginal: + selected, preamble_mass = folded[index] + result = _result( + row, row_encoded, selected, vocabulary, metadata, + "llamacpp-seq-copy-parallel-marginal-v1", + "quantized branch logits over copied prefix sequences, answer-slot masses " + "summed over one-token preambles; option_logits are log-masses", + ) + result["preamble_mass"] = preamble_mass + else: + result = _result( + row, row_encoded, vocabulary[row_encoded[1]].tolist(), vocabulary, metadata, + "llamacpp-seq-copy-parallel-v1", + "quantized branch last-position logits over copied prefix sequences; " + "one batched decode per chunk", + ) + results.append(result) + branch_bytes = 0 + config = "llamacpp-seq-copy-parallel-marginal-v1" if marginal else "llamacpp-seq-copy-parallel-v1" suffix_total = sum(len(ids) - len(prefix) for ids, _, _ in encoded) timing = { "total_seconds": time.perf_counter() - started, @@ -398,7 +631,9 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: "replicate_seconds": copy_seconds, "suffix_forward_seconds": suffix_seconds, "batch_size": len(rows), - "branch_state_bytes": state_data[1], + "branch_slots": engine.sequences - 1, + "branch_state_bytes": branch_bytes, + "serving_config": config, "true_suffix_tokens": suffix_total, "padded_suffix_tokens": suffix_total, } diff --git a/tests/test_llamacpp.py b/tests/test_llamacpp.py index 29608cb..3a7a48d 100644 --- a/tests/test_llamacpp.py +++ b/tests/test_llamacpp.py @@ -16,6 +16,9 @@ (["--mode", "reranker", "--backend", "llamacpp", "--gguf", "x.gguf"], "reranker requires torch"), (["--mode", "direct", "--backend", "llamacpp"], "requires --gguf"), (["--mode", "direct", "--backend", "llamacpp", "--gguf", "missing.gguf"], "requires --gguf"), + (["--mode", "direct", "--llama-gpu-layers", "99"], "--llama-gpu-layers requires --backend llamacpp"), + (["--mode", "direct", "--llama-parallel", "4"], "--llama-parallel requires --backend llamacpp"), + (["--mode", "shared", "--llama-readout", "marginal"], "--llama-readout requires --backend llamacpp"), ]) def test_invalid_llamacpp_combinations_fail_before_loading(tmp_path, monkeypatch, capsys, extra, message): monkeypatch.setattr(sys, "argv", ["semif-score", "--model", "unused", "--revision", "unused", @@ -31,8 +34,9 @@ def test_cli_passes_gguf_options_to_loader(tmp_path, monkeypatch): import semif_phase1 fake_backend = SimpleNamespace( - load_model=lambda source, revision, gguf, *, threads, context_tokens: - (None, None, {"gguf": str(gguf), "threads": threads, "context_tokens": context_tokens}), + load_model=lambda source, revision, gguf, *, threads, context_tokens, gpu_layers, sequences, readout: + (None, None, {"gguf": str(gguf), "threads": threads, "context_tokens": context_tokens, + "gpu_layers": gpu_layers, "sequences": sequences, "readout": readout}), score=lambda model, tokenizer, row, metadata, max_tokens: metadata, SerialPrefixScorer=None, score_shared=None, ) @@ -50,6 +54,7 @@ def test_cli_passes_gguf_options_to_loader(tmp_path, monkeypatch): assert record["gguf"] == str(weights) assert record["threads"] == 7 assert record["context_tokens"] == 4096 + assert record["gpu_layers"] == 0 and record["sequences"] == 1 and record["readout"] == "last" def test_load_model_rejects_unpinned_remote_revision(): @@ -76,7 +81,7 @@ def test_engine_refuses_empty_decode(): engine._decode([], 0, 0, False) -def test_cpu_model_params_initialize_once_and_disable_offload(monkeypatch): +def test_model_params_initialize_once_and_apply_offload(monkeypatch): from semif_phase1 import llamacpp_backend calls = [] @@ -85,10 +90,10 @@ def test_cpu_model_params_initialize_once_and_disable_offload(monkeypatch): llama_model_default_params=lambda: SimpleNamespace(n_gpu_layers=-1), ) monkeypatch.setattr(llamacpp_backend, "_BACKEND_INITIALIZED", False) - first = llamacpp_backend._cpu_model_params(library) - second = llamacpp_backend._cpu_model_params(library) + cpu = llamacpp_backend._model_params(library, 0) + gpu = llamacpp_backend._model_params(library, 99) assert calls == ["init"] - assert first.n_gpu_layers == second.n_gpu_layers == 0 + assert cpu.n_gpu_layers == 0 and gpu.n_gpu_layers == 99 def test_restore_state_rejects_native_failure(): @@ -183,8 +188,9 @@ def test_real_gguf_scores_direct_serial_and_shared(): revision = os.environ.get( "SEMIF_LLAMACPP_REVISION", "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a") threads = int(os.environ.get("SEMIF_LLAMACPP_THREADS", "8")) + gpu_layers = int(os.environ.get("SEMIF_LLAMACPP_GPU_LAYERS", "0")) model, tokenizer, metadata = llamacpp_backend.load_model( - source, revision, os.environ["SEMIF_LLAMACPP_GGUF"], threads=threads) + source, revision, os.environ["SEMIF_LLAMACPP_GGUF"], threads=threads, gpu_layers=gpu_layers) state = "The deployment completed at 14:02 UTC. Health checks passed in all three zones." rows = [ { @@ -215,8 +221,156 @@ def test_real_gguf_scores_direct_serial_and_shared(): for serial_result, shared_result in zip(serial, shared): numpy.testing.assert_allclose(serial_result["option_logits"], shared_result["option_logits"]) assert metadata["backend"] == "llamacpp" - assert metadata["n_gpu_layers"] == 0 + assert metadata["n_gpu_layers"] == gpu_layers assert metadata["max_prompt_tokens"] == 4096 assert metadata["context_tokens"] >= metadata["max_prompt_tokens"] finally: model.close() + + # The same decisions through copied-sequence fan-out must match the restore path. + model, tokenizer, metadata = llamacpp_backend.load_model( + source, revision, os.environ["SEMIF_LLAMACPP_GGUF"], threads=threads, sequences=3, + gpu_layers=gpu_layers) + try: + assert metadata["n_seq_max"] == 3 + fanned, timing = llamacpp_backend.score_shared(model, tokenizer, rows, metadata) + assert timing["serving_config"] == "llamacpp-seq-copy-parallel-v1" + assert timing["branch_state_bytes"] == 0 + assert choices(fanned) == ["yes", "yes"] + for shared_result, fanned_result in zip(shared, fanned): + numpy.testing.assert_allclose( + shared_result["option_logits"], fanned_result["option_logits"], rtol=0, atol=0.5) + marginal_meta = {**metadata, "readout_mode": "marginal"} + folded, timing = llamacpp_backend.score_shared(model, tokenizer, rows, marginal_meta) + assert timing["serving_config"] == "llamacpp-seq-copy-parallel-marginal-v1" + assert choices(folded) == ["yes", "yes"] + assert all(0.0 <= result["preamble_mass"] <= 1.0 for result in folded) + finally: + model.close() + + +def test_load_model_validates_new_arguments(tmp_path): + from semif_phase1 import llamacpp_backend + + weights = tmp_path / "model.gguf" + weights.write_bytes(b"gguf") + revision = "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a" + with pytest.raises(ValueError, match="gpu_layers"): + llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, gpu_layers=-1) + with pytest.raises(ValueError, match="sequences"): + llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, sequences=0) + with pytest.raises(ValueError, match="readout"): + llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, readout="first") + + +def _fake_engine_for_fanout(sequences: int, vocab_size: int = 8): + """An _Engine whose native calls are recorded instead of executed.""" + from semif_phase1.llamacpp_backend import _Engine + + calls = {"seq_cp": [], "seq_rm": [], "batches": []} + + class Batch: + def __init__(self, n, seqs): + self.token = [0] * n + self.pos = [0] * n + self.n_seq_id = [0] * n + self.seq_id = [[0] * seqs for _ in range(n)] + self.logits = [0] * n + self.n_tokens = 0 + + def decode(context, batch): + calls["batches"].append([(batch.token[i], batch.pos[i], batch.seq_id[i][0], batch.logits[i]) + for i in range(batch.n_tokens)]) + return 0 + + def logits_ith(context, index): + # Encode the branch id in the logits so the caller can tell rows apart. + batch = calls["batches"][-1] + branch = batch[index][2] + array = (ctypes_float * vocab_size)(*([0.0] * vocab_size)) + array[branch] = 10.0 + return array + + import ctypes as _ctypes + ctypes_float = _ctypes.c_float + library = SimpleNamespace( + llama_batch_init=lambda n, embd, seqs: Batch(n, seqs), + llama_batch_free=lambda batch: None, + llama_decode=decode, + llama_get_logits_ith=logits_ith, + llama_memory_seq_cp=lambda memory, src, dst, p0, p1: calls["seq_cp"].append((src, dst, p0, p1)), + llama_memory_seq_rm=lambda memory, seq, p0, p1: calls["seq_rm"].append((seq, p0, p1)) or True, + ) + engine = _Engine.__new__(_Engine) + engine.lib = library + engine.context = object() + engine.memory = object() + engine.sequences = sequences + engine.vocab_size = vocab_size + return engine, calls + + +def test_fanout_copies_prefix_decodes_once_and_removes_branches_whole(): + engine, calls = _fake_engine_for_fanout(sequences=4) + vocabularies = engine.fanout_logits(prefix_length=10, suffixes=[[5, 6], [7], [8, 9, 3]]) + # one copy of sequence 0 per branch, whole range + assert calls["seq_cp"] == [(0, 1, -1, -1), (0, 2, -1, -1), (0, 3, -1, -1)] + # a single decode call carrying every suffix token, positions continuing the prefix + assert len(calls["batches"]) == 1 + batch = calls["batches"][0] + assert [(t, p, s) for t, p, s, _ in batch] == [ + (5, 10, 1), (6, 11, 1), (7, 10, 2), (8, 10, 3), (9, 11, 3), (3, 12, 3)] + # logits requested only at each branch's last token + assert [want for _, _, _, want in batch] == [0, 1, 1, 0, 0, 1] + # each returned row belongs to its branch + assert [int(v.argmax()) for v in vocabularies] == [1, 2, 3] + # branches removed whole afterwards, sequence 0 untouched + assert calls["seq_rm"] == [(1, -1, -1), (2, -1, -1), (3, -1, -1)] + + +def test_fanout_refuses_more_branches_than_slots(): + engine, _ = _fake_engine_for_fanout(sequences=2) + with pytest.raises(ValueError, match="exceed"): + engine.fanout_logits(3, [[1], [2]]) + + +def test_marginal_readout_folds_preamble_mass_into_slots(): + from semif_phase1.llamacpp_backend import _marginal_readout + + probed = {} + + class Engine: + def probe_branches(self, positions, tokens): + probed.update(tokens) + # After the preamble token the model answers slot 1 (token id 1) with certainty. + out = numpy.full(8, -20.0) + out[1] = 20.0 + return {branch: out.copy() for branch in tokens} + + # Branch 1: the model puts its mass on token 7 (a preamble), almost none on the slots 0 and 1. + vocabulary = numpy.full(8, -20.0) + vocabulary[7] = 20.0 + vocabulary[0] = 1.0 + vocabulary[1] = 0.0 + (logits, preamble_mass), = _marginal_readout( + Engine(), branches=[1], prefix_length=10, suffixes=[[4, 4]], + vocabularies=[vocabulary], slot_lists=[[0, 1]]) + assert probed == {1: 7} + # Direct read alone would have picked slot 0; the preamble path moves the answer to slot 1. + assert int(numpy.argmax(logits)) == 1 + assert preamble_mass > 0.99 + + +def test_marginal_readout_skips_branches_that_already_answer_with_a_slot(): + from semif_phase1.llamacpp_backend import _marginal_readout + + class Engine: + def probe_branches(self, positions, tokens): + raise AssertionError("no probe should be issued") + + vocabulary = numpy.full(8, -20.0) + vocabulary[0] = 20.0 # slot 0 is the confident next token; nothing else clears the floor + (logits, preamble_mass), = _marginal_readout( + Engine(), branches=[1], prefix_length=3, suffixes=[[9]], + vocabularies=[vocabulary], slot_lists=[[0, 1]]) + assert int(numpy.argmax(logits)) == 0 and preamble_mass == 0.0 From c0fbc50cbba5b8ba6b2af7573e7251aeb7ad934f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maxime=20Lefran=C3=A7ois?= Date: Wed, 23 Sep 2026 07:37:35 +0200 Subject: [PATCH 02/13] llamacpp: size the fan-out from the data, offload by default; GGUF results on a laptop GPU --llama-parallel and --llama-gpu-layers now default to "auto". For the layers that means not overriding llama.cpp's own default (-1, every layer, in current builds) and recording what the library did; the original backend forced 0. For the branches it means that nothing is asked of the user: the rows are already tokenized by the time shared mode runs, so the prefix length and every suffix length are known, and each state's fan-out holds as many questions as prefix + sum(suffixes) <= n_ctx allows, 32 at most. The context is sized once, for the longest single prompt, not multiplied by the sequence count: with a unified KV buffer every sequence sees the whole n_ctx (measured on this build, 16 sequences and n_ctx 4096 give 4096 per sequence against 256 without), and copied branches share the prefix cells. branches_per_decode in the shared timing records what was done. An integer still fixes n_seq_max; 1 keeps the state-restore path. Results on an RTX 3080 Laptop with the pinned Q4_K_M GGUF, all layers offloaded: authored144 direct 0.796 mean family balanced accuracy (Torch BF16 on a 3090: 0.813), ECE 0.063 out of fold (0.038), median allowed_token_mass 0.9997; shape777 1.40 / 9.21 / 10.88 decisions per second fresh, serial and parallel with 8 branches, 10.51 with auto sizing (Torch: 2.33 / 10.75 / 20.03), 16 to 19 argmax flips out of 777 between decode paths, which is this quantized model's noise floor. One decode per state is no faster than three: llama.cpp re-splits into n_ubatch micro-batches either way. Raw reports, predictions and calibration are committed with their checksums; phase-1 claims are untouched and verify_published.py passes. The real-GGUF test compared serial and shared logits with an exact tolerance that only held because both were the restore path; it now requires exactness when n_seq_max == 1 and 0.5 logits otherwise, decisions still equal. It passes on the PyPI CPU wheel and on the cu124 wheel with offload. --- README.md | 5 +- benchmarks/README.md | 12 + benchmarks/shape777.py | 22 +- docs/LLAMACPP.md | 85 +- docs/RESULTS.md | 22 + results/raw/SHA256SUMS | 7 + .../llamacpp-gguf-cuda-authored144.json | 194 ++ .../raw/llamacpp-gguf-cuda-authored144.json | 833 ++++++ ...lamacpp-gguf-cuda-direct-authored144.jsonl | 144 + .../raw/shape777-llamacpp-gguf-cuda-auto.json | 104 + ...-llamacpp-gguf-cuda-auto.predictions.jsonl | 2331 +++++++++++++++++ results/raw/shape777-llamacpp-gguf-cuda.json | 100 + ...pe777-llamacpp-gguf-cuda.predictions.jsonl | 2331 +++++++++++++++++ src/semif_phase1/cli.py | 35 +- src/semif_phase1/llamacpp_backend.py | 105 +- tests/test_llamacpp.py | 56 +- 16 files changed, 6323 insertions(+), 63 deletions(-) create mode 100644 results/raw/calibration/llamacpp-gguf-cuda-authored144.json create mode 100644 results/raw/llamacpp-gguf-cuda-authored144.json create mode 100644 results/raw/predictions/llamacpp-gguf-cuda-direct-authored144.jsonl create mode 100644 results/raw/shape777-llamacpp-gguf-cuda-auto.json create mode 100644 results/raw/shape777-llamacpp-gguf-cuda-auto.predictions.jsonl create mode 100644 results/raw/shape777-llamacpp-gguf-cuda.json create mode 100644 results/raw/shape777-llamacpp-gguf-cuda.predictions.jsonl diff --git a/README.md b/README.md index 72b7778..44c24f6 100644 --- a/README.md +++ b/README.md @@ -58,8 +58,9 @@ pip install -e '.[test]' checkpoint, on CPU or with `--llama-gpu-layers` offloaded to a GPU. Install `pip install -e '.[test,llamacpp]'`, download a GGUF of the pinned model (for example `bartowski/Qwen_Qwen3.5-4B-GGUF`), and add `--backend llamacpp --gguf /path/to/model.gguf`. -Shared mode can fan its questions out over copied sequences in one batched decode with -`--llama-parallel N`, which also works on hybrid Qwen3.5 memories. Prompt hashes match the +Layers are offloaded by default when the wheel can, and shared mode fans a state's questions +out over copied sequences in as few batched decodes as the context allows — sized from the +rows themselves, which also works on hybrid Qwen3.5 memories. Prompt hashes match the Torch backend; quantized option scores have small numerical differences. See [docs/LLAMACPP.md](docs/LLAMACPP.md). diff --git a/benchmarks/README.md b/benchmarks/README.md index 4e5123d..693bfc2 100644 --- a/benchmarks/README.md +++ b/benchmarks/README.md @@ -48,6 +48,18 @@ CUDA_VISIBLE_DEVICES=0 python benchmarks/shape777_reranker.py \ --output shape777-reranker-run.json ``` +The same fixture on a local GGUF through llama.cpp, layers offloaded by default when the wheel +can, branches sized per state: + +```bash +python benchmarks/shape777.py --backend llamacpp \ + --model Qwen/Qwen3.5-4B \ + --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \ + --gguf /path/to/Qwen_Qwen3.5-4B-Q4_K_M.gguf \ + --input benchmarks/data/shape777.jsonl \ + --output shape777-llamacpp-run.json +``` + The 6.7 MB fixture is project-authored and has SHA-256 `8dcf414b12fc2684e3c4ca5f3ebfd3f525f5346fec4a9bc67eb65138101f55f1`. Both runners write aggregate timings and row-level predictions. ## Quality evidence diff --git a/benchmarks/shape777.py b/benchmarks/shape777.py index cb9dd0e..6195d7a 100644 --- a/benchmarks/shape777.py +++ b/benchmarks/shape777.py @@ -35,19 +35,24 @@ def main() -> None: parser.add_argument("--backend", choices=("torch", "llamacpp"), default="torch") parser.add_argument("--gguf", type=Path, help="Local GGUF checkpoint for --backend llamacpp") parser.add_argument("--llama-threads", type=int, help="CPU threads for --backend llamacpp") - parser.add_argument("--llama-gpu-layers", type=int, default=0, - help="Layers offloaded to the GPU for --backend llamacpp (default: 0)") - parser.add_argument("--llama-parallel", type=int, default=1, - help="Branch slots for llama.cpp shared mode (default: 1 = state restore)") + parser.add_argument("--llama-gpu-layers", default="auto", + help="GPU layers for --backend llamacpp: 'auto' (library default), 0 (CPU) or N") + parser.add_argument("--llama-parallel", default="auto", + help="llama.cpp shared-mode branching: 'auto' (sized per state), N, or 1 (state restore)") args = parser.parse_args() if args.output.exists(): parser.error("Output must be new") if args.backend == "llamacpp": if args.gguf is None or not args.gguf.is_file(): parser.error("--backend llamacpp requires --gguf pointing at an existing GGUF file") - if args.llama_gpu_layers < 0 or args.llama_parallel < 1: - parser.error("--llama-gpu-layers must be nonnegative and --llama-parallel positive") - elif args.gguf is not None or args.llama_threads is not None or args.llama_gpu_layers or args.llama_parallel != 1: + for name in ("llama_gpu_layers", "llama_parallel"): + value = getattr(args, name) + if value != "auto": + try: + setattr(args, name, int(value)) + except ValueError: + parser.error(f"--{name.replace('_', '-')} must be 'auto' or an integer") + elif args.gguf is not None or args.llama_threads is not None or args.llama_gpu_layers != "auto" or args.llama_parallel != "auto": parser.error("llama.cpp options require --backend llamacpp") rows = [json.loads(line) for line in args.input.read_text().splitlines() if line.strip()] groups = defaultdict(list) @@ -66,7 +71,8 @@ def main() -> None: SerialPrefixScorer = llamacpp_backend.SerialPrefixScorer score_shared = llamacpp_backend.score_shared cuda = None - hardware = (_gpu_name() or "unknown GPU") if args.llama_gpu_layers else "CPU" + offloaded = metadata["n_gpu_layers"] != 0 and metadata["gpu_offload_supported"] + hardware = (_gpu_name() or "unknown GPU") if offloaded else "CPU" else: model, tokenizer, metadata = load_causal_model(args.model, args.revision, "cuda") import torch as cuda diff --git a/docs/LLAMACPP.md b/docs/LLAMACPP.md index 51aa377..20bfb0d 100644 --- a/docs/LLAMACPP.md +++ b/docs/LLAMACPP.md @@ -32,9 +32,11 @@ Each prediction records the file's size and checksum. ### GPU offload -`--llama-gpu-layers N` offloads `N` layers (use a large value such as `99` for -all of them). It needs a llama-cpp-python wheel built with a GPU backend; the -PyPI wheel is CPU-only. For CUDA: +`--llama-gpu-layers` defaults to `auto`, which leaves llama.cpp's own default — +`-1`, every layer, in current builds — and records what the library did. `0` +forces CPU, `N` offloads `N` layers. Offload needs a llama-cpp-python wheel +built with a GPU backend; the PyPI wheel is CPU-only and ignores the setting +(`gpu_offload_supported` in the metadata says so). For CUDA: ```bash pip install --force-reinstall --no-deps "llama-cpp-python==0.3.35" \ @@ -55,8 +57,9 @@ ways to make a branch: | `--llama-parallel` | branching | decodes per state | |---|---|---| -| `1` (default) | **state restore** — `llama_state_seq_get_data` after the prefill, `set_data` before each question | one per question | -| `N ≥ 2` | **sequence copy** — `llama_memory_seq_cp` of sequence 0 into up to `N − 1` throwaway sequences, all suffixes in one batched `llama_decode` | one per `N − 1` questions | +| `auto` (default) | **sequence copy, sized per state** — as many of the state's questions per decode as the context holds: `prefix + Σ suffixes ≤ n_ctx`, at most 32 branches | usually one | +| `N ≥ 2` | sequence copy with `n_seq_max = N`, chunks of at most `N − 1` | one per `N − 1` questions | +| `1` | **state restore** — `llama_state_seq_get_data` after the prefill, `set_data` before each question | one per question | State restore is what the backend has always done. Sequence copy is the llama.cpp equivalent of the Torch backend's `native-state-prefix-parallel-v1`: @@ -65,6 +68,21 @@ suffixes share a single decode. The branches are removed whole afterwards (whole-sequence removal never fails), so sequence 0 is left exactly as prefilled and the next chunk of questions needs no restore at all. +Why `auto` needs no number from you: everything that bounds a fan-out is known +before the first decode. `encode_verified` has already tokenized each row, so +the prefix length and every suffix length are in hand, and the context budget +is fixed at load. The branch count is therefore a consequence of the data — on +the `shape777` fixture (states of ~1 800 tokens, suffixes of ~90) all 21 +questions of a state fit in one decode. `branches_per_decode` in the shared +timing records what was actually done. + +The context is sized **once**, for the longest single prompt (`max_tokens + 64`), +not multiplied by the number of sequences. That relies on `kv_unified`: measured +on this build, with 16 sequences and `n_ctx = 4096`, each sequence sees 4 096 +cells under the unified buffer and 256 without it, and copied branches share the +prefix's cells rather than duplicating them. A library without `kv_unified` falls +back to the per-sequence allocation. + ### Hybrid models Qwen3.5 is a hybrid architecture: one full-attention layer in four, the rest @@ -82,10 +100,7 @@ is why both branching modes above work on Qwen3.5. Pure-attention models additionally allow tail truncation; the backend does not rely on it. `n_seq_max` is a context parameter (`llama_context_params.n_seq_max`), not a -build option; the same wheel serves both modes. The context is allocated as -`n_seq_max × (max_tokens + 64)` tokens and, when the library exposes it, -`kv_unified` is enabled so the copied branches share the prefix's attention -cells. +build option; the same wheel serves both modes. ## Validation @@ -107,7 +122,7 @@ model. With that wheel, offload the layers; for CPU scoring, use the PyPI wheel. ## Marginal readout -`--llama-readout marginal` (shared mode, `--llama-parallel ≥ 2`) changes how +`--llama-readout marginal` (shared mode, any `--llama-parallel` but `1`) changes how the answer is read, not what is asked. Some checkpoints want to emit a token *before* the letter — Qwen3-8B puts almost all of its next-token mass on the Markdown `**` token when the assistant turn starts empty, and reading only the @@ -123,14 +138,58 @@ changes nothing and `preamble_mass` stays at zero. ## Results - +RTX 3080 Laptop GPU (16 GB), Q4_K_M, all 33 layers offloaded, `llama-cpp-python` 0.3.35 cu124. + +### Quality — `authored144`, direct mode + +| backend | mean family balanced accuracy | ECE, own-T out-of-fold | fitted T | +|---|---:|---:|---:| +| Torch BF16, RTX 3090 (committed) | 0.813 | 0.038 | 1.23 | +| **llama.cpp GGUF Q4_K_M, laptop** | **0.796** | **0.063** [0.038, 0.123] | 1.26 | + +Coverage 144/144. Median `allowed_token_mass` 0.9997, minimum 0.975, no row +below 0.9 — the quantized model answers with a letter as reliably as the BF16 +one. Median forward time 64 ms per decision at a median 147 input tokens. +Row-level predictions are in +`results/raw/predictions/llamacpp-gguf-cuda-direct-authored144.jsonl`, the +report in `results/raw/llamacpp-gguf-cuda-authored144.json`, the calibration +in `results/raw/calibration/llamacpp-gguf-cuda-authored144.json`. The 4-bit +checkpoint gives up 1.7 points of balanced accuracy and is somewhat less +well-calibrated than BF16; its fitted temperature is nearly the same. + +### Systems — `shape777`, 37 states × 21 questions + +| mode | decisions / s | state p50 | argmax flips vs fresh | +|---|---:|---:|---:| +| fresh | 1.40 | 14.97 s | — | +| serial_prefix (state restore) | 9.21 | 2.28 s | 19 / 777 | +| parallel_shared, `--llama-parallel 8` | 10.88 | 1.93 s | 16 / 777 | +| parallel_shared, `auto` | 10.51 | 2.00 s | 18 / 777 | + +The committed Torch BF16 run on an RTX 3090 reports 2.33, 10.75 and 20.03 +decisions per second for fresh, serial and parallel. + +Two things the numbers say. Fan-out beats state restore by removing the +serialization round-trip, not by batching: one decode per state (`auto`, +21 branches) is no faster than three (8 branches), because llama.cpp splits a +batch into micro-batches of `n_ubatch` tokens either way and the recurrent +memory decodes them with `split_equal`. And the gap to the Torch parallel figure +is not the fan-out's: the Torch backend runs one dense BF16 forward over padded +suffixes, which this 4-bit hybrid path cannot match on a laptop GPU. Reports: +`results/raw/shape777-llamacpp-gguf-cuda.json` (8 branches) and +`results/raw/shape777-llamacpp-gguf-cuda-auto.json`, each with its +`.predictions.jsonl`. The 16–19 argmax flips out of 777 between decode paths +are the quantized model's noise floor — two sequential reads of one prompt +already differ on about 3 % of decisions when only the micro-batch size +changes — not a property of the fan-out. + ## Reproduce ```bash # Quality: the owned labeled workload, three serving configurations for MODE in direct serial shared; do - semif-score --backend llamacpp --mode $MODE --llama-gpu-layers 99 --llama-parallel 8 \ + semif-score --backend llamacpp --mode $MODE \ --model Qwen/Qwen3.5-4B --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \ --gguf /path/to/Qwen_Qwen3.5-4B-Q4_K_M.gguf \ --input benchmarks/data/authored144.jsonl \ @@ -140,7 +199,7 @@ python benchmarks/evaluate.py --gold benchmarks/data/authored144.jsonl \ --predictions results/raw/predictions/llamacpp-gguf-cuda-direct-authored144.jsonl # Systems: the 37x21 fixture, fresh / serial-prefix / parallel-shared -python benchmarks/shape777.py --backend llamacpp --llama-gpu-layers 99 --llama-parallel 8 \ +python benchmarks/shape777.py --backend llamacpp \ --model Qwen/Qwen3.5-4B --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \ --gguf /path/to/Qwen_Qwen3.5-4B-Q4_K_M.gguf \ --input benchmarks/data/shape777.jsonl \ diff --git a/docs/RESULTS.md b/docs/RESULTS.md index bb0d7d6..9aa2dcb 100644 --- a/docs/RESULTS.md +++ b/docs/RESULTS.md @@ -84,3 +84,25 @@ Not reproduced or established: - The full 711-row TypeSafe benchmark or an independently operated Jev endpoint. The next justified phase is targeted training for decision semantics and calibration, judged against these frozen baselines. It should proceed only after expanding external gold tasks and defining a held-out operational calibration target. A generic reranker fine-tune would answer the wrong question. + +## Appendix — llama.cpp GGUF on a laptop GPU + +Added with the GPU/fan-out extension of the llama.cpp backend; the phase-1 +claims above are unchanged. Qwen3.5-4B Q4_K_M (`bartowski/Qwen_Qwen3.5-4B-GGUF`, +3 013 027 808 bytes), all layers on an RTX 3080 Laptop (16 GB), `llama-cpp-python` +0.3.35 cu124. + +| workload | metric | Torch BF16, 3090 | GGUF Q4_K_M, laptop | +|---|---|---:|---:| +| authored144, direct | mean family balanced accuracy | 0.813 | 0.796 | +| authored144, direct | ECE, own-T out-of-fold | 0.038 | 0.063 | +| shape777, fresh | decisions / s | 2.33 | 1.40 | +| shape777, serial prefix | decisions / s | 10.75 | 9.21 | +| shape777, parallel shared | decisions / s | 20.03 | 10.88 (8 branches) · 10.51 (auto) | +| shape777, parallel vs fresh | argmax flips / 777 | 6 | 16–18 | + +Raw: `results/raw/llamacpp-gguf-cuda-authored144.json`, +`results/raw/calibration/llamacpp-gguf-cuda-authored144.json`, +`results/raw/shape777-llamacpp-gguf-cuda.json`, +`results/raw/shape777-llamacpp-gguf-cuda-auto.json`, with row-level predictions +beside them. Method and caveats in [LLAMACPP.md](LLAMACPP.md). diff --git a/results/raw/SHA256SUMS b/results/raw/SHA256SUMS index 24c0876..6562fe4 100644 --- a/results/raw/SHA256SUMS +++ b/results/raw/SHA256SUMS @@ -21,3 +21,10 @@ b24a574ce11a8a73b643cbd35c2ef49bc66e0ebde0e6d622f8a17602d37b40ee calibration/di 2805185a19bdadb61f4198a3f1fa6e88bc9c1a34c3011fcff9e66f8b23f7d4b3 calibration/direct-wanli256.calibrated.jsonl fecbcd6fbce4fc80c3fcc589771a7bb0bfc592e678b496868ca62015265a4a47 calibration/direct-every204.calibrated.jsonl 161d013089fc496981913c055f9ad5878b6308bda19feca36b8be0e9cc2b7e7d calibration/summary.json +9955efe42cd33f096f26f39259ebebd4718bce7f777f3ec3e8bcb47e1203afc1 predictions/llamacpp-gguf-cuda-direct-authored144.jsonl +616831384c6d072505ba080f7cd5d792385c24d05f92361c26158765697c1728 llamacpp-gguf-cuda-authored144.json +690f1ad6a4ab0af2457489ed47bfa5c0ad1a57f26b2c839f50abd9b3a1fbe975 calibration/llamacpp-gguf-cuda-authored144.json +fbdf611fb21ee4a01d219d9a321ffe34ed04c264412cbaae89febe62a398a4e9 shape777-llamacpp-gguf-cuda.json +4d68ac23eaa09a3f43a16fc3cb212539451d4b17fcc4e221b4f3387a5bcad0f8 shape777-llamacpp-gguf-cuda.predictions.jsonl +cc782460f61ffa3ef126b9b702832a2eede6c9cc02a170a76bd059d378e55fca shape777-llamacpp-gguf-cuda-auto.json +bf070210ae2b83ce1ebe2f29ab2127eb04620a48d70326df01b872c17c2cc479 shape777-llamacpp-gguf-cuda-auto.predictions.jsonl diff --git a/results/raw/calibration/llamacpp-gguf-cuda-authored144.json b/results/raw/calibration/llamacpp-gguf-cuda-authored144.json new file mode 100644 index 0000000..6df9a71 --- /dev/null +++ b/results/raw/calibration/llamacpp-gguf-cuda-authored144.json @@ -0,0 +1,194 @@ +{ + "method": "per-workload post-hoc temperature scaling; single scalar; NLL-fit; monotone (argmax-invariant)", + "gold": "authored144.jsonl", + "predictions": "llamacpp-gguf-cuda-direct-authored144.jsonl", + "rows": 144, + "groups": 36, + "shipped_temperature": 1.256836787269229, + "cv": 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"9ca2d99a18f5d41ad08d", "option_ids": ["yes", "no"], "probabilities": [0.6010411920068965, 0.39895880799310346], "option_logits": [25.589309692382812, 25.17950439453125], "answer_token_ids": [32, 33], "input_tokens": 1853, "allowed_token_mass": 0.9994217173841765, "full_vocab_argmax_id": 32, "prompt_sha256": "a67b36f1653e4b97791b75f71210200bcc8830ac3c06dfba2aabced801aea950", "prompt_version": "direct-options-v1", "model": {"source": "Qwen/Qwen3.5-4B", "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", "backend": "llamacpp", "dtype": "gguf-quantized", "gguf": {"file": "Qwen_Qwen3.5-4B-Q4_K_M.gguf", "bytes": 3013027808, "sha256": "13c16f426047e2de38cd075bdade4a7bcbc8c774384876f677740cda65f8a983"}, "vocab_size": 248320, "threads": 16, "n_gpu_layers": 99, "gpu_offload_supported": true, "n_seq_max": 8, "kv_unified": true, "readout_mode": "last", "max_prompt_tokens": 4096, "context_tokens": 33280, "decode_chunk": 512, "llama_cpp_python_version": "0.3.35", "transformers_version": "5.17.0", "serving_config": "llamacpp-seq-copy-parallel-v1"}, "readout": "quantized branch last-position logits over copied prefix sequences; one batched decode per chunk", "probability_status": "conditional option score over quantized weights; uncalibrated as decision confidence"} +{"mode": "parallel_shared", "id": "dcd13ec9cee28c9f9854", "option_ids": ["yes", "no"], "probabilities": [0.1256134369567277, 0.8743865630432722], "option_logits": [24.843854904174805, 26.784168243408203], "answer_token_ids": [32, 33], "input_tokens": 1852, "allowed_token_mass": 0.9996767064989092, "full_vocab_argmax_id": 33, "prompt_sha256": "830fd92238c6217ebff0cef76ad51cf11fd23ecbbf7893812d3cacb7c96573d0", "prompt_version": "direct-options-v1", "model": {"source": "Qwen/Qwen3.5-4B", "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", "backend": "llamacpp", "dtype": "gguf-quantized", "gguf": {"file": "Qwen_Qwen3.5-4B-Q4_K_M.gguf", "bytes": 3013027808, "sha256": "13c16f426047e2de38cd075bdade4a7bcbc8c774384876f677740cda65f8a983"}, "vocab_size": 248320, "threads": 16, "n_gpu_layers": 99, "gpu_offload_supported": true, "n_seq_max": 8, "kv_unified": true, "readout_mode": "last", "max_prompt_tokens": 4096, "context_tokens": 33280, "decode_chunk": 512, "llama_cpp_python_version": "0.3.35", "transformers_version": "5.17.0", "serving_config": "llamacpp-seq-copy-parallel-v1"}, "readout": "quantized branch last-position logits over copied prefix sequences; one batched decode per chunk", "probability_status": "conditional option score over quantized weights; uncalibrated as decision confidence"} +{"mode": "parallel_shared", "id": "39685613525e4d5d1189", "option_ids": ["yes", "no"], "probabilities": [0.020912979433086017, 0.979087020566914], "option_logits": [23.330917358398438, 27.177167892456055], "answer_token_ids": [32, 33], "input_tokens": 1858, "allowed_token_mass": 0.9997578635222146, "full_vocab_argmax_id": 33, "prompt_sha256": "1cea6b1a689ddf7e482af85512404d7fd3c0eb5cb4d268438cfd189f14627cfc", "prompt_version": "direct-options-v1", "model": {"source": "Qwen/Qwen3.5-4B", "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", "backend": "llamacpp", "dtype": "gguf-quantized", "gguf": {"file": "Qwen_Qwen3.5-4B-Q4_K_M.gguf", "bytes": 3013027808, "sha256": "13c16f426047e2de38cd075bdade4a7bcbc8c774384876f677740cda65f8a983"}, "vocab_size": 248320, "threads": 16, "n_gpu_layers": 99, "gpu_offload_supported": true, "n_seq_max": 8, "kv_unified": true, "readout_mode": "last", "max_prompt_tokens": 4096, "context_tokens": 33280, "decode_chunk": 512, "llama_cpp_python_version": "0.3.35", "transformers_version": "5.17.0", "serving_config": "llamacpp-seq-copy-parallel-v1"}, "readout": "quantized branch last-position logits over copied prefix sequences; one batched decode per chunk", "probability_status": "conditional option score over quantized weights; uncalibrated as decision confidence"} +{"mode": "parallel_shared", "id": "23413859c5b32174137b", "option_ids": ["yes", "no"], "probabilities": [0.040517968107847725, 0.9594820318921522], "option_logits": [23.74753189086914, 26.912179946899414], "answer_token_ids": [32, 33], "input_tokens": 1855, "allowed_token_mass": 0.9996839076064133, "full_vocab_argmax_id": 33, "prompt_sha256": "18f6f2e36f63abf5250f2a68833332dcec926e53b18e2c69eca69c879678c79e", "prompt_version": "direct-options-v1", "model": {"source": "Qwen/Qwen3.5-4B", "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", "backend": "llamacpp", "dtype": "gguf-quantized", "gguf": {"file": "Qwen_Qwen3.5-4B-Q4_K_M.gguf", "bytes": 3013027808, "sha256": "13c16f426047e2de38cd075bdade4a7bcbc8c774384876f677740cda65f8a983"}, "vocab_size": 248320, "threads": 16, "n_gpu_layers": 99, "gpu_offload_supported": true, "n_seq_max": 8, "kv_unified": true, "readout_mode": "last", "max_prompt_tokens": 4096, "context_tokens": 33280, "decode_chunk": 512, "llama_cpp_python_version": "0.3.35", "transformers_version": "5.17.0", "serving_config": "llamacpp-seq-copy-parallel-v1"}, "readout": "quantized branch last-position logits over copied prefix sequences; one batched decode per chunk", "probability_status": "conditional option score over quantized weights; uncalibrated as decision confidence"} +{"mode": "parallel_shared", "id": "9525d3025bc8e0c5b0ff", "option_ids": ["yes", "no"], "probabilities": [0.033339528718556756, 0.9666604712814433], "option_logits": [24.263511657714844, 27.630615234375], "answer_token_ids": [32, 33], "input_tokens": 1854, "allowed_token_mass": 0.9998200321357571, "full_vocab_argmax_id": 33, "prompt_sha256": "afdf30360786f113390987a0d8d73e8d9681e54f4dbb8589fdce3689f9a9c52f", "prompt_version": "direct-options-v1", "model": {"source": "Qwen/Qwen3.5-4B", "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", "backend": "llamacpp", "dtype": "gguf-quantized", "gguf": {"file": "Qwen_Qwen3.5-4B-Q4_K_M.gguf", "bytes": 3013027808, "sha256": "13c16f426047e2de38cd075bdade4a7bcbc8c774384876f677740cda65f8a983"}, "vocab_size": 248320, "threads": 16, "n_gpu_layers": 99, "gpu_offload_supported": true, "n_seq_max": 8, "kv_unified": true, "readout_mode": "last", "max_prompt_tokens": 4096, "context_tokens": 33280, "decode_chunk": 512, "llama_cpp_python_version": "0.3.35", "transformers_version": "5.17.0", "serving_config": "llamacpp-seq-copy-parallel-v1"}, "readout": "quantized branch last-position logits over copied prefix sequences; one batched decode per chunk", "probability_status": "conditional option score over quantized weights; uncalibrated as decision confidence"} +{"mode": "parallel_shared", "id": "82fff92fa614d407d774", "option_ids": ["yes", "no"], "probabilities": [0.06491372902622361, 0.9350862709737764], "option_logits": [24.328189849853516, 26.995769500732422], "answer_token_ids": [32, 33], "input_tokens": 1852, "allowed_token_mass": 0.9997110948072204, "full_vocab_argmax_id": 33, "prompt_sha256": "54c79b410f690460b6b7e3650ed166c8b953b2b13a5a97fe6e9f2baa5b240453", "prompt_version": "direct-options-v1", "model": {"source": "Qwen/Qwen3.5-4B", "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", "backend": "llamacpp", "dtype": "gguf-quantized", "gguf": {"file": "Qwen_Qwen3.5-4B-Q4_K_M.gguf", "bytes": 3013027808, "sha256": "13c16f426047e2de38cd075bdade4a7bcbc8c774384876f677740cda65f8a983"}, "vocab_size": 248320, "threads": 16, "n_gpu_layers": 99, "gpu_offload_supported": true, "n_seq_max": 8, "kv_unified": true, "readout_mode": "last", "max_prompt_tokens": 4096, "context_tokens": 33280, "decode_chunk": 512, "llama_cpp_python_version": "0.3.35", "transformers_version": "5.17.0", "serving_config": "llamacpp-seq-copy-parallel-v1"}, "readout": "quantized branch last-position logits over copied prefix sequences; one batched decode per chunk", "probability_status": "conditional option score over quantized weights; uncalibrated as decision confidence"} +{"mode": "parallel_shared", "id": "94a5b148e1fac2d93c8a", "option_ids": ["yes", "no"], "probabilities": [0.17142635191927347, 0.8285736480807266], "option_logits": [24.517370223999023, 26.09292221069336], "answer_token_ids": [32, 33], "input_tokens": 1855, "allowed_token_mass": 0.9994954229757697, "full_vocab_argmax_id": 33, "prompt_sha256": "f058b46ffd614abefd43094517f2d57d7581d8f0a34f001189bd528d727e29e6", "prompt_version": "direct-options-v1", "model": {"source": "Qwen/Qwen3.5-4B", "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", "backend": "llamacpp", "dtype": "gguf-quantized", "gguf": {"file": "Qwen_Qwen3.5-4B-Q4_K_M.gguf", "bytes": 3013027808, "sha256": "13c16f426047e2de38cd075bdade4a7bcbc8c774384876f677740cda65f8a983"}, "vocab_size": 248320, "threads": 16, "n_gpu_layers": 99, "gpu_offload_supported": true, "n_seq_max": 8, "kv_unified": true, "readout_mode": "last", "max_prompt_tokens": 4096, "context_tokens": 33280, "decode_chunk": 512, "llama_cpp_python_version": "0.3.35", "transformers_version": "5.17.0", "serving_config": "llamacpp-seq-copy-parallel-v1"}, "readout": "quantized branch last-position logits over copied prefix sequences; one batched decode per chunk", "probability_status": "conditional option score over quantized weights; uncalibrated as decision confidence"} diff --git a/src/semif_phase1/cli.py b/src/semif_phase1/cli.py index 2b99936..303a22f 100644 --- a/src/semif_phase1/cli.py +++ b/src/semif_phase1/cli.py @@ -23,11 +23,13 @@ def main() -> None: parser.add_argument("--gguf", type=Path, help="Local GGUF checkpoint for --backend llamacpp") parser.add_argument("--llama-threads", type=int, help="CPU threads for --backend llamacpp (default: all visible cores)") - parser.add_argument("--llama-gpu-layers", type=int, default=0, - help="Layers to offload to the GPU for --backend llamacpp (default: 0, CPU only)") - parser.add_argument("--llama-parallel", type=int, default=1, - help="Branch slots for --backend llamacpp shared mode: suffixes are fanned out over " - "copied sequences and decoded together (default: 1 = state restore per decision)") + parser.add_argument("--llama-gpu-layers", default="auto", + help="GPU layers for --backend llamacpp: 'auto' keeps llama.cpp's default (every layer " + "when the library can offload), 0 forces CPU, N offloads N layers") + parser.add_argument("--llama-parallel", default="auto", + help="Shared-mode branching for --backend llamacpp: 'auto' sizes each fan-out from the " + "state's token counts over a unified KV buffer; N fixes n_seq_max; 1 restores " + "state per decision") parser.add_argument("--llama-readout", choices=("last", "marginal"), default="last", help="llama.cpp shared-mode readout: last prompt position, or answer-slot masses summed " "over one-token preambles (needs --llama-parallel > 1)") @@ -59,8 +61,21 @@ def main() -> None: parser.error("--llama-threads must be positive") if args.backend == "mlx" and args.mode == "reranker": parser.error("MLX supports direct, serial, and shared modes; reranker requires torch") + def _auto_or_int(name, value, minimum): + if value == "auto": + return "auto" + try: + number = int(value) + except (TypeError, ValueError): + parser.error(f"{name} must be 'auto' or an integer") + if number < minimum: + parser.error(f"{name} must be 'auto' or at least {minimum}") + return number + + args.llama_gpu_layers = _auto_or_int("--llama-gpu-layers", args.llama_gpu_layers, -1) + args.llama_parallel = _auto_or_int("--llama-parallel", args.llama_parallel, 1) for name, value in (("--llama-gpu-layers", args.llama_gpu_layers), ("--llama-parallel", args.llama_parallel)): - if args.backend != "llamacpp" and value != parser.get_default(name.lstrip("-").replace("-", "_")): + if args.backend != "llamacpp" and value != "auto": parser.error(f"{name} requires --backend llamacpp") if args.llama_readout != "last" and args.backend != "llamacpp": parser.error("--llama-readout requires --backend llamacpp") @@ -69,12 +84,8 @@ def main() -> None: parser.error("llama.cpp supports direct, serial, and shared modes; reranker requires torch") if args.gguf is None or not args.gguf.is_file(): parser.error("--backend llamacpp requires --gguf pointing at an existing GGUF file") - if args.llama_gpu_layers < 0: - parser.error("--llama-gpu-layers must be nonnegative") - if args.llama_parallel < 1: - parser.error("--llama-parallel must be positive") - if args.llama_readout == "marginal" and (args.mode != "shared" or args.llama_parallel < 2): - parser.error("--llama-readout marginal requires --mode shared and --llama-parallel of at least 2") + if args.llama_readout == "marginal" and (args.mode != "shared" or args.llama_parallel == 1): + parser.error("--llama-readout marginal requires --mode shared and --llama-parallel other than 1") rows = [json.loads(line) for line in args.input.read_text().splitlines() if line.strip()] if not rows: parser.error("Input is empty") diff --git a/src/semif_phase1/llamacpp_backend.py b/src/semif_phase1/llamacpp_backend.py index 446e517..9b366cc 100644 --- a/src/semif_phase1/llamacpp_backend.py +++ b/src/semif_phase1/llamacpp_backend.py @@ -42,17 +42,27 @@ DECODE_CHUNK = 512 READOUTS = ("last", "marginal") +AUTO = "auto" +#: Upper bound on branches per fan-out when `sequences` is "auto". With a unified KV buffer the +#: bound costs bookkeeping only; the real limit is the token budget, checked per state. +BRANCH_CAP = 32 _BACKEND_INITIALIZED = False -def _model_params(library, gpu_layers: int): - """Initialize llama.cpp once and return model parameters with the requested GPU offload.""" +def _model_params(library, gpu_layers: int | None): + """Initialize llama.cpp once and return model parameters. + + ``None`` keeps the library's own default for ``n_gpu_layers`` (``-1``, every layer, in current + builds); an integer overrides it. The value that ends up in the params is what the metadata + reports, so a run is reproducible whichever way it was chosen. + """ global _BACKEND_INITIALIZED if not _BACKEND_INITIALIZED: library.llama_backend_init() _BACKEND_INITIALIZED = True params = library.llama_model_default_params() - params.n_gpu_layers = gpu_layers + if gpu_layers is not None: + params.n_gpu_layers = gpu_layers return params @@ -100,9 +110,17 @@ def __init__(self, library, model, context_tokens: int, threads: int, sequences: if not (isinstance(sequences, int) and sequences >= 1): raise ValueError("sequences must be a positive integer") params = library.llama_context_default_params() - # Sequence 0 keeps the prefix; the others are branch slots. Without a unified KV buffer - # llama.cpp splits n_ctx across sequences, so size it for the worst case either way. - params.n_ctx = context_tokens * sequences + # Sequence 0 keeps the prefix; the others are branch slots. With a unified KV buffer every + # sequence sees the whole n_ctx (measured: 16 sequences, n_ctx 4096 -> 4096 per sequence, + # against 256 without), and copied branches share the prefix's cells, so the context is + # sized once for the longest prompt and the branch count is decided per state from the + # token budget. Without a unified buffer llama.cpp splits n_ctx across sequences, and the + # only safe sizing is the worst case. + unified = None + if sequences > 1 and hasattr(params, "kv_unified"): + params.kv_unified = True + unified = True + params.n_ctx = context_tokens if unified or sequences == 1 else context_tokens * sequences params.n_seq_max = sequences # One flagged output per sequence in a fan-out batch; llama.cpp requires # n_outputs_max >= n_seq_max * n_outputs_max_per_seq. @@ -110,11 +128,6 @@ def __init__(self, library, model, context_tokens: int, threads: int, sequences: params.n_outputs_max_per_seq = 1 params.n_threads = threads params.n_threads_batch = threads - unified = None - if sequences > 1 and hasattr(params, "kv_unified"): - # Branches share the whole prefix; llama.h recommends the unified buffer for that case. - params.kv_unified = True - unified = True self.lib = library self.model = model self.sequences = sequences @@ -309,13 +322,16 @@ def _verify_vocabulary(tokenizer, library, vocab) -> None: def load_model(source: str, revision: str, gguf, *, threads: int | None = None, - context_tokens: int = 4096, gpu_layers: int = 0, sequences: int = 1, - readout: str = "last"): + context_tokens: int = 4096, gpu_layers: int | str | None = AUTO, + sequences: int | str = AUTO, readout: str = "last"): """Load one pinned reference tokenizer plus a local GGUF checkpoint for scoring. - ``gpu_layers`` is passed straight to llama.cpp (0 keeps the historical CPU behaviour, a - large value offloads every layer). ``sequences`` is the context's ``n_seq_max``: with more - than one, shared scoring fans suffixes out over copied sequences instead of restoring state. + ``gpu_layers``: ``"auto"`` (or ``None``) leaves llama.cpp's default, which offloads every + layer when the library can; ``0`` forces CPU; any other integer is passed through. + + ``sequences``: ``"auto"`` opens ``1 + BRANCH_CAP`` sequences over a unified KV buffer and lets + shared scoring size each fan-out from the state's own token counts; an integer fixes + ``n_seq_max`` (``1`` keeps the historical state-restore path). """ local = Path(source).is_dir() if not local and not re.fullmatch(r"[0-9a-f]{40}", revision or ""): @@ -331,10 +347,15 @@ def load_model(source: str, revision: str, gguf, *, threads: int | None = None, threads = os.cpu_count() or 4 if not (isinstance(threads, int) and threads >= 1): raise ValueError("threads must be a positive integer") - if not (isinstance(gpu_layers, int) and gpu_layers >= 0): - raise ValueError("gpu_layers must be a nonnegative integer") + if gpu_layers == AUTO: + gpu_layers = None + if gpu_layers is not None and not (isinstance(gpu_layers, int) and gpu_layers >= -1): + raise ValueError("gpu_layers must be 'auto', -1, or a nonnegative integer") + auto_sequences = sequences == AUTO + if auto_sequences: + sequences = 1 + BRANCH_CAP if not (isinstance(sequences, int) and sequences >= 1): - raise ValueError("sequences must be a positive integer") + raise ValueError("sequences must be 'auto' or a positive integer") if readout not in READOUTS: raise ValueError(f"readout must be one of {READOUTS}") import transformers @@ -376,9 +397,11 @@ def load_model(source: str, revision: str, gguf, *, threads: int | None = None, "gguf": gguf_record, "vocab_size": engine.vocab_size, "threads": threads, - "n_gpu_layers": gpu_layers, + "n_gpu_layers": int(model_params.n_gpu_layers), + "n_gpu_layers_requested": "auto" if gpu_layers is None else gpu_layers, "gpu_offload_supported": bool(llama_cpp.llama_supports_gpu_offload()), "n_seq_max": sequences, + "branch_sizing": "auto" if auto_sequences else "fixed", "kv_unified": engine.kv_unified, "readout_mode": readout, "max_prompt_tokens": context_tokens, @@ -475,6 +498,31 @@ def score(self, row: dict) -> dict: return result +def _chunks(prefix_length: int, suffix_lengths: list[int], budget: int, cap: int) -> list[list[int]]: + """Group consecutive rows into fan-out chunks from what the context can hold. + + A chunk holds at most ``cap`` branches and never more than ``budget`` tokens including the + shared prefix. Everything here is known before the first decode, so the branch count is a + consequence of the data, not a setting. + """ + if budget <= prefix_length: + raise ValueError("The state prefix alone exceeds the context") + chunks: list[list[int]] = [] + current: list[int] = [] + total = prefix_length + for index, length in enumerate(suffix_lengths): + if prefix_length + length > budget: + raise ValueError(f"Row {index}: prefix plus suffix exceed the context; raise --max-tokens") + if current and (len(current) >= cap or total + length > budget): + chunks.append(current) + current, total = [], prefix_length + current.append(index) + total += length + if current: + chunks.append(current) + return chunks + + def _marginal_readout(engine: _Engine, branches: list[int], prefix_length: int, suffixes: list[list[int]], vocabularies: list[numpy.ndarray], slot_lists: list[list[int]], probes: int = 2, floor: float = 0.05): @@ -574,10 +622,16 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: branch_bytes = state_data[1] config = "llamacpp-state-restore-shared-v1" else: - width = engine.sequences - 1 - for start in range(0, len(rows), width): - chunk_rows = rows[start : start + width] - chunk_encoded = encoded[start : start + width] + # Under a unified KV buffer the copied branches share the prefix cells, so the budget is + # the context minus a small margin; otherwise each sequence has its own slice of n_ctx. + per_sequence = engine.context_tokens if engine.kv_unified else engine.context_tokens // engine.sequences + budget = per_sequence - 8 if engine.kv_unified else per_sequence + chunks = _chunks(len(prefix), [len(ids) - len(prefix) for ids, _, _ in encoded], + budget, engine.sequences - 1) + branch_counts = [len(chunk) for chunk in chunks] + for chunk in chunks: + chunk_rows = [rows[i] for i in chunk] + chunk_encoded = [encoded[i] for i in chunk] suffixes = [ids[len(prefix) :] for ids, _, _ in chunk_encoded] slot_lists = [slots for _, slots, _ in chunk_encoded] branches = list(range(1, len(suffixes) + 1)) @@ -622,6 +676,8 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: results.append(result) branch_bytes = 0 config = "llamacpp-seq-copy-parallel-marginal-v1" if marginal else "llamacpp-seq-copy-parallel-v1" + if engine.sequences == 1: + branch_counts = [1] * len(rows) suffix_total = sum(len(ids) - len(prefix) for ids, _, _ in encoded) timing = { "total_seconds": time.perf_counter() - started, @@ -632,6 +688,7 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: "suffix_forward_seconds": suffix_seconds, "batch_size": len(rows), "branch_slots": engine.sequences - 1, + "branches_per_decode": branch_counts, "branch_state_bytes": branch_bytes, "serving_config": config, "true_suffix_tokens": suffix_total, diff --git a/tests/test_llamacpp.py b/tests/test_llamacpp.py index 3a7a48d..05e328a 100644 --- a/tests/test_llamacpp.py +++ b/tests/test_llamacpp.py @@ -18,6 +18,8 @@ (["--mode", "direct", "--backend", "llamacpp", "--gguf", "missing.gguf"], "requires --gguf"), (["--mode", "direct", "--llama-gpu-layers", "99"], "--llama-gpu-layers requires --backend llamacpp"), (["--mode", "direct", "--llama-parallel", "4"], "--llama-parallel requires --backend llamacpp"), + (["--mode", "direct", "--backend", "llamacpp", "--gguf", "x.gguf", "--llama-parallel", "0"], "at least 1"), + (["--mode", "direct", "--backend", "llamacpp", "--gguf", "x.gguf", "--llama-gpu-layers", "many"], "must be 'auto' or an integer"), (["--mode", "shared", "--llama-readout", "marginal"], "--llama-readout requires --backend llamacpp"), ]) def test_invalid_llamacpp_combinations_fail_before_loading(tmp_path, monkeypatch, capsys, extra, message): @@ -54,7 +56,7 @@ def test_cli_passes_gguf_options_to_loader(tmp_path, monkeypatch): assert record["gguf"] == str(weights) assert record["threads"] == 7 assert record["context_tokens"] == 4096 - assert record["gpu_layers"] == 0 and record["sequences"] == 1 and record["readout"] == "last" + assert record["gpu_layers"] == "auto" and record["sequences"] == "auto" and record["readout"] == "last" def test_load_model_rejects_unpinned_remote_revision(): @@ -90,10 +92,11 @@ def test_model_params_initialize_once_and_apply_offload(monkeypatch): llama_model_default_params=lambda: SimpleNamespace(n_gpu_layers=-1), ) monkeypatch.setattr(llamacpp_backend, "_BACKEND_INITIALIZED", False) + default = llamacpp_backend._model_params(library, None) cpu = llamacpp_backend._model_params(library, 0) gpu = llamacpp_backend._model_params(library, 99) assert calls == ["init"] - assert cpu.n_gpu_layers == 0 and gpu.n_gpu_layers == 99 + assert default.n_gpu_layers == -1 and cpu.n_gpu_layers == 0 and gpu.n_gpu_layers == 99 def test_restore_state_rejects_native_failure(): @@ -218,8 +221,14 @@ def test_real_gguf_scores_direct_serial_and_shared(): ] assert choices(direct) == choices(serial) == choices(shared) == ["yes", "yes"] assert [result["cache_hit"] for result in serial] == [False, True] + # With the default "auto" sizing, shared mode fans out over copied sequences while serial + # restores state: two different decode paths over the same quantized weights, which agree + # on the decision but not bit for bit (measured: ~0.2 logits, the same order as changing + # the micro-batch size). Exact equality only holds when both paths are the restore path. + exact = metadata["n_seq_max"] == 1 for serial_result, shared_result in zip(serial, shared): - numpy.testing.assert_allclose(serial_result["option_logits"], shared_result["option_logits"]) + numpy.testing.assert_allclose(serial_result["option_logits"], shared_result["option_logits"], + rtol=0 if exact else 0.0, atol=0 if exact else 0.5) assert metadata["backend"] == "llamacpp" assert metadata["n_gpu_layers"] == gpu_layers assert metadata["max_prompt_tokens"] == 4096 @@ -256,7 +265,7 @@ def test_load_model_validates_new_arguments(tmp_path): weights.write_bytes(b"gguf") revision = "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a" with pytest.raises(ValueError, match="gpu_layers"): - llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, gpu_layers=-1) + llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, gpu_layers=-2) with pytest.raises(ValueError, match="sequences"): llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, sequences=0) with pytest.raises(ValueError, match="readout"): @@ -374,3 +383,42 @@ def probe_branches(self, positions, tokens): Engine(), branches=[1], prefix_length=3, suffixes=[[9]], vocabularies=[vocabulary], slot_lists=[[0, 1]]) assert int(numpy.argmax(logits)) == 0 and preamble_mass == 0.0 + + +def test_chunks_follow_the_token_budget_and_the_cap(): + from semif_phase1.llamacpp_backend import _chunks + + # everything fits in one decode + assert _chunks(100, [10, 10, 10], budget=1000, cap=8) == [[0, 1, 2]] + # the budget splits: 100 + 400 + 400 = 900 fits, adding 400 more would not + assert _chunks(100, [400, 400, 400, 400], budget=1000, cap=8) == [[0, 1], [2, 3]] + # the cap splits + assert _chunks(100, [10] * 5, budget=1000, cap=2) == [[0, 1], [2, 3], [4]] + # a row that cannot fit even alone is an error, not a silent truncation + with pytest.raises(ValueError, match="exceed the context"): + _chunks(100, [950], budget=1000, cap=8) + with pytest.raises(ValueError, match="prefix alone"): + _chunks(1000, [1], budget=1000, cap=8) + + +def test_engine_sizes_context_once_under_unified_kv(): + from semif_phase1.llamacpp_backend import _Engine + + seen = {} + + class Params: + kv_unified = False + + library = SimpleNamespace( + llama_context_default_params=lambda: Params(), + llama_init_from_model=lambda model, params: seen.setdefault("params", params) or object(), + llama_get_memory=lambda value: object(), + llama_n_ctx=lambda value: seen["params"].n_ctx, + llama_model_get_vocab=lambda model: object(), + llama_n_vocab=lambda vocab: 100, + ) + engine = _Engine(library, object(), 4160, 4, sequences=33) + assert seen["params"].kv_unified is True + assert seen["params"].n_ctx == 4160 # not 33 x 4160: branches share the prefix cells + assert seen["params"].n_seq_max == 33 and seen["params"].n_outputs_max == 33 + assert engine.kv_unified is True From d4d26fde1a3648e24667553a13c6eb54ccfa76d3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maxime=20Lefran=C3=A7ois?= Date: Wed, 23 Sep 2026 08:24:32 +0200 Subject: [PATCH 03/13] docs: say what the marginal readout is and is not shown to do; where the backend sits relative to JevBench --- docs/LLAMACPP.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/docs/LLAMACPP.md b/docs/LLAMACPP.md index 20bfb0d..40b0393 100644 --- a/docs/LLAMACPP.md +++ b/docs/LLAMACPP.md @@ -122,6 +122,14 @@ model. With that wheel, offload the layers; for CPU scoring, use the PyPI wheel. ## Marginal readout +*Validated for correctness, not for a metric gain.* On Qwen3.5-4B, which answers with the letter +directly, every result in this document is identical with or without it, and no committed workload +needs it. It is here for checkpoints that put their first-token mass on a preamble token; the case +that motivated it — Qwen3-8B answering `**` before the letter, 60 % of the mass leaking without it, +10 % with a marginalised read — was measured outside this repository, on a mail-triage fixture that +cannot be redistributed. + + `--llama-readout marginal` (shared mode, any `--llama-parallel` but `1`) changes how the answer is read, not what is asked. Some checkpoints want to emit a token *before* the letter — Qwen3-8B puts almost all of its next-token mass on the @@ -184,6 +192,13 @@ already differ on about 3 % of decisions when only the micro-batch size changes — not a property of the fan-out. +## JevBench + +[JevBench](https://github.com/fstandhartinger/jevbench) runs SemIf through its `semif_direct` +adapter on the Torch backend (BF16, CUDA). This backend is what would let that adapter run the +pinned GGUF on a laptop GPU with shared states fanned out; it has not been run on the full public +set yet. + ## Reproduce ```bash From c550dd55e8e21da64f521852448fda80077aba9e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maxime=20Lefran=C3=A7ois?= Date: Wed, 23 Sep 2026 09:55:00 +0200 Subject: [PATCH 04/13] docs: the reproduce loop ran shared mode on a multi-state file; drop it --- docs/LLAMACPP.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/LLAMACPP.md b/docs/LLAMACPP.md index 40b0393..f657749 100644 --- a/docs/LLAMACPP.md +++ b/docs/LLAMACPP.md @@ -202,8 +202,8 @@ set yet. ## Reproduce ```bash -# Quality: the owned labeled workload, three serving configurations -for MODE in direct serial shared; do +# Quality: the owned labeled workload, direct and serial (shared needs one state per file) +for MODE in direct serial; do semif-score --backend llamacpp --mode $MODE \ --model Qwen/Qwen3.5-4B --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \ --gguf /path/to/Qwen_Qwen3.5-4B-Q4_K_M.gguf \ From 01649b8945e37ce896def3524fe1637e69f6debb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maxime=20Lefran=C3=A7ois?= Date: Wed, 23 Sep 2026 10:22:04 +0200 Subject: [PATCH 05/13] results: Torch BF16 on the same laptop GPU, with and without the fast DeltaNet kernel, next to the GGUF rows --- docs/LLAMACPP.md | 44 +- docs/RESULTS.md | 21 +- results/raw/SHA256SUMS | 7 + ...torch-bf16-rtx3080-laptop-authored144.json | 194 ++ ...16-rtx3080-laptop-direct-authored144.jsonl | 144 + ...bf16-rtx3080-laptop-reference-kernels.json | 63 + ...laptop-reference-kernels.predictions.jsonl | 2331 +++++++++++++++++ .../shape777-torch-bf16-rtx3080-laptop.json | 55 + ...orch-bf16-rtx3080-laptop.predictions.jsonl | 2331 +++++++++++++++++ ...torch-bf16-rtx3080-laptop-authored144.json | 768 ++++++ 10 files changed, 5934 insertions(+), 24 deletions(-) create mode 100644 results/raw/calibration/torch-bf16-rtx3080-laptop-authored144.json create mode 100644 results/raw/predictions/torch-bf16-rtx3080-laptop-direct-authored144.jsonl create mode 100644 results/raw/shape777-torch-bf16-rtx3080-laptop-reference-kernels.json create mode 100644 results/raw/shape777-torch-bf16-rtx3080-laptop-reference-kernels.predictions.jsonl create mode 100644 results/raw/shape777-torch-bf16-rtx3080-laptop.json create mode 100644 results/raw/shape777-torch-bf16-rtx3080-laptop.predictions.jsonl create mode 100644 results/raw/torch-bf16-rtx3080-laptop-authored144.json diff --git a/docs/LLAMACPP.md b/docs/LLAMACPP.md index f657749..dcf4a2a 100644 --- a/docs/LLAMACPP.md +++ b/docs/LLAMACPP.md @@ -150,10 +150,11 @@ RTX 3080 Laptop GPU (16 GB), Q4_K_M, all 33 layers offloaded, `llama-cpp-python` ### Quality — `authored144`, direct mode -| backend | mean family balanced accuracy | ECE, own-T out-of-fold | fitted T | -|---|---:|---:|---:| -| Torch BF16, RTX 3090 (committed) | 0.813 | 0.038 | 1.23 | -| **llama.cpp GGUF Q4_K_M, laptop** | **0.796** | **0.063** [0.038, 0.123] | 1.26 | +| backend | GPU | mean family balanced accuracy | ECE, own-T out-of-fold | fitted T | +|---|---|---:|---:|---:| +| Torch BF16 (committed) | RTX 3090 | 0.813 | 0.038 | 1.23 | +| Torch BF16, same laptop | RTX 3080 Laptop | 0.813 | 0.050 [0.038, 0.114] | 1.25 | +| **llama.cpp GGUF Q4_K_M** | RTX 3080 Laptop | **0.796** | **0.063** [0.038, 0.123] | 1.26 | Coverage 144/144. Median `allowed_token_mass` 0.9997, minimum 0.975, no row below 0.9 — the quantized model answers with a letter as reliably as the BF16 @@ -167,15 +168,23 @@ well-calibrated than BF16; its fitted temperature is nearly the same. ### Systems — `shape777`, 37 states × 21 questions -| mode | decisions / s | state p50 | argmax flips vs fresh | -|---|---:|---:|---:| -| fresh | 1.40 | 14.97 s | — | -| serial_prefix (state restore) | 9.21 | 2.28 s | 19 / 777 | -| parallel_shared, `--llama-parallel 8` | 10.88 | 1.93 s | 16 / 777 | -| parallel_shared, `auto` | 10.51 | 2.00 s | 18 / 777 | - -The committed Torch BF16 run on an RTX 3090 reports 2.33, 10.75 and 20.03 -decisions per second for fresh, serial and parallel. +| backend, GPU | fresh | serial prefix | parallel shared | flips vs fresh | +|---|---:|---:|---:|---:| +| Torch BF16, RTX 3090 (committed) | 2.33 | 10.75 | 20.03 | 6 / 777 | +| Torch BF16, RTX 3080 Laptop, `flash-linear-attention` | 1.51 | 10.19 | 14.14 | 3 / 777 | +| Torch BF16, RTX 3080 Laptop, reference PyTorch kernels | 1.10 | 7.78 | 10.24 | 9 / 777 | +| **llama.cpp Q4_K_M, RTX 3080 Laptop**, `--llama-parallel 8` | **1.40** | **9.21** | **10.88** | 16 / 777 | +| llama.cpp Q4_K_M, RTX 3080 Laptop, `auto` | | | 10.51 | 18 / 777 | + +Decisions per second; state p50 for the GGUF: 14.97 s fresh, 2.28 s serial, +1.93 s parallel. The two Torch rows on the laptop differ only by the Gated +DeltaNet kernels: `pip install -e .` leaves `transformers` on its reference +PyTorch implementation (it says so at load time); with `flash-linear-attention` +installed the delta rule runs in Triton. `causal_conv1d` needs `nvcc` and was +not installed. Neither kernel touches llama.cpp, which has its own ggml +operators for these layers. On the same GPU, BF16 with the fast kernel is 8 %, +11 % and 30 % faster than the 4-bit GGUF in the three modes; the GGUF trades +that for 3 GB of weights instead of 8.8 and an 8.8 GB CUDA peak instead of 11.8. Two things the numbers say. Fan-out beats state restore by removing the serialization round-trip, not by batching: one decode per state (`auto`, @@ -184,9 +193,12 @@ batch into micro-batches of `n_ubatch` tokens either way and the recurrent memory decodes them with `split_equal`. And the gap to the Torch parallel figure is not the fan-out's: the Torch backend runs one dense BF16 forward over padded suffixes, which this 4-bit hybrid path cannot match on a laptop GPU. Reports: -`results/raw/shape777-llamacpp-gguf-cuda.json` (8 branches) and -`results/raw/shape777-llamacpp-gguf-cuda-auto.json`, each with its -`.predictions.jsonl`. The 16–19 argmax flips out of 777 between decode paths +`results/raw/shape777-llamacpp-gguf-cuda.json` (8 branches), +`results/raw/shape777-llamacpp-gguf-cuda-auto.json`, +`results/raw/shape777-torch-bf16-rtx3080-laptop.json` and +`…-reference-kernels.json`, each with its `.predictions.jsonl`; the laptop +BF16 quality run is `results/raw/torch-bf16-rtx3080-laptop-authored144.json` +with its calibration report and predictions. The 16–19 argmax flips out of 777 between decode paths are the quantized model's noise floor — two sequential reads of one prompt already differ on about 3 % of decisions when only the micro-batch size changes — not a property of the fan-out. diff --git a/docs/RESULTS.md b/docs/RESULTS.md index 9aa2dcb..0f25f6e 100644 --- a/docs/RESULTS.md +++ b/docs/RESULTS.md @@ -92,14 +92,19 @@ claims above are unchanged. Qwen3.5-4B Q4_K_M (`bartowski/Qwen_Qwen3.5-4B-GGUF`, 3 013 027 808 bytes), all layers on an RTX 3080 Laptop (16 GB), `llama-cpp-python` 0.3.35 cu124. -| workload | metric | Torch BF16, 3090 | GGUF Q4_K_M, laptop | -|---|---|---:|---:| -| authored144, direct | mean family balanced accuracy | 0.813 | 0.796 | -| authored144, direct | ECE, own-T out-of-fold | 0.038 | 0.063 | -| shape777, fresh | decisions / s | 2.33 | 1.40 | -| shape777, serial prefix | decisions / s | 10.75 | 9.21 | -| shape777, parallel shared | decisions / s | 20.03 | 10.88 (8 branches) · 10.51 (auto) | -| shape777, parallel vs fresh | argmax flips / 777 | 6 | 16–18 | +| workload | metric | Torch BF16, 3090 | Torch BF16, laptop 3080 | GGUF Q4_K_M, laptop 3080 | +|---|---|---:|---:|---:| +| authored144, direct | mean family balanced accuracy | 0.813 | 0.813 | 0.796 | +| authored144, direct | ECE, own-T out-of-fold | 0.038 | 0.050 | 0.063 | +| shape777, fresh | decisions / s | 2.33 | 1.51 | 1.40 | +| shape777, serial prefix | decisions / s | 10.75 | 10.19 | 9.21 | +| shape777, parallel shared | decisions / s | 20.03 | 14.14 | 10.88 (8 branches) · 10.51 (auto) | +| shape777, parallel vs fresh | argmax flips / 777 | 6 | 3 | 16–18 | + +The laptop BF16 column uses `flash-linear-attention` for the Gated DeltaNet +delta rule; with the reference PyTorch kernels that `pip install -e .` leaves +in place it reads 1.10 / 7.78 / 10.24 decisions per second +(`results/raw/shape777-torch-bf16-rtx3080-laptop-reference-kernels.json`). Raw: `results/raw/llamacpp-gguf-cuda-authored144.json`, `results/raw/calibration/llamacpp-gguf-cuda-authored144.json`, diff --git a/results/raw/SHA256SUMS b/results/raw/SHA256SUMS index 6562fe4..231ffd3 100644 --- a/results/raw/SHA256SUMS +++ b/results/raw/SHA256SUMS @@ -28,3 +28,10 @@ fbdf611fb21ee4a01d219d9a321ffe34ed04c264412cbaae89febe62a398a4e9 shape777-llama 4d68ac23eaa09a3f43a16fc3cb212539451d4b17fcc4e221b4f3387a5bcad0f8 shape777-llamacpp-gguf-cuda.predictions.jsonl cc782460f61ffa3ef126b9b702832a2eede6c9cc02a170a76bd059d378e55fca shape777-llamacpp-gguf-cuda-auto.json bf070210ae2b83ce1ebe2f29ab2127eb04620a48d70326df01b872c17c2cc479 shape777-llamacpp-gguf-cuda-auto.predictions.jsonl +882c2d2e809898839c032860952e2a8aea03b53333792577ed58a66e16a5440a predictions/torch-bf16-rtx3080-laptop-direct-authored144.jsonl +f3143a12fa357cbed8b1e6419ba6ecccf661d40d47dc6da284fe23a17ea041e1 torch-bf16-rtx3080-laptop-authored144.json +8a2a66d5b2d5100f8414147cf63e950c3f677c92336218f0784e94c37d50f28c calibration/torch-bf16-rtx3080-laptop-authored144.json +390b9b3a21e3f1fe0ed9235ab949f90bde07cf6931c9d085bf6c33238a7a9277 shape777-torch-bf16-rtx3080-laptop.json +73c100b8ee0c5b8a9db1330967c1b36ece0198284e1ccd8662b64189e0140234 shape777-torch-bf16-rtx3080-laptop-reference-kernels.json +66e54c5d3434b2e1e84a7064152a73fc7944f54c0428015e27a91d8928079277 shape777-torch-bf16-rtx3080-laptop.predictions.jsonl +27cdbdc70c27f70c1ecf69aca52e7a8b8470ff885dd410e849729e78e087380b shape777-torch-bf16-rtx3080-laptop-reference-kernels.predictions.jsonl diff --git a/results/raw/calibration/torch-bf16-rtx3080-laptop-authored144.json b/results/raw/calibration/torch-bf16-rtx3080-laptop-authored144.json new file mode 100644 index 0000000..16fa606 --- /dev/null +++ b/results/raw/calibration/torch-bf16-rtx3080-laptop-authored144.json @@ -0,0 +1,194 @@ +{ + "method": "per-workload post-hoc temperature scaling; single scalar; NLL-fit; monotone (argmax-invariant)", + "gold": "authored144.jsonl", + "predictions": "torch-bf16-3080-direct-authored144.jsonl", + "rows": 144, + "groups": 36, + "shipped_temperature": 1.2465538333008053, + "cv": { + "folds": 5, + "seed": 217, + "fold_temperatures": [ + 1.3335958912892287, + 1.104457842368847, + 1.1921489937705374, + 1.3375896737108302, + 1.2549894309416632 + ], + "fold_groups": { + "b51b300da2936decc6b7": 0, + "986d7eaba0c7e9738e79": 1, + "770c1607f0a11f88f81e": 2, + "e4cfd694c80e08d3e6a5": 3, + "ca2e0b043985fa49bcd2": 4, + "c979db7416b206c1eb09": 0, + "6dd193172b9240a04bf9": 1, + "a71a17a287f3d30db2aa": 2, + "20460d4ae0db46091e84": 3, + "2b0f8eda8903e38a47de": 4, + "f4bea3d3bcc01056740d": 0, + "f7403e39d6796072491c": 1, + "45f8ead2522a14384cdc": 2, + "6a06a2f278301c1412ee": 3, + "2797f8c23d05c0ee06b3": 4, + "c0cbeb97315307af8111": 0, + "056cc5b726d6e8300a77": 1, + "3ecec8e2c166e397ad07": 2, + "2c3a0cb61f7bae78739e": 3, + "928ebc7817a8f1cac3e4": 4, + "a060fd4621be415e9f1a": 0, + "2fdaa8a61e6e989dd866": 1, + "53b0770ec36835b81d3a": 2, + "e7798dab5f4f121cab77": 3, + "51b91ba31984c5939c1c": 4, + "ffd081ada3bd9be9207a": 0, + "428176cd8ad2c7a74eea": 1, + "d142f7fd0efa0eddc788": 2, + "85051ba3c30277824ebe": 3, + "3476d3f4f32290fe57c3": 4, + "42ed4cbfde7f2f64274b": 0, + "3200eb252d6737e03027": 1, + "389e65bf1ba709b84b12": 2, + "e3ce8ec496dbc9974f94": 3, + "01020a1eaf46ec2fba5d": 4, + "a5b234c2e4fb304f1071": 0 + } + }, + "accuracy_unchanged": 0.8055555555555556, + "ece_uncalibrated": { + "value": 0.05740010907214014, + "ci95": [ + 0.03673802395608913, + 0.1199298494063566 + ] + }, + "ece_calibrated_out_of_fold": { + "value": 0.05016628618424603, + "ci95": [ + 0.037934205532656945, + 0.11432843958410725 + ] + }, + "improvement_ci_separated": false, + "per_family_uncalibrated": { + "candidate_selection": { + "n": 48, + "ece": 0.13479535160707426 + }, + "evidence_interpretation": { + "n": 48, + "ece": 0.0818429607984107 + }, + "rule_application": { + "n": 48, + "ece": 0.045501512952756666 + } + }, + "per_family_calibrated_out_of_fold": { + "candidate_selection": { + "n": 48, + "ece": 0.13038660222444398 + }, + "evidence_interpretation": { + "n": 48, + "ece": 0.04834103813885249 + }, + "rule_application": { + "n": 48, + "ece": 0.049549970892415994 + } + }, + "reliability_bins_uncalibrated": [ + { + "lower": 0.4, + "upper": 0.5, + "n": 6, + "mean_confidence": 0.46722550018127357, + "accuracy": 0.16666666666666666 + }, + { + "lower": 0.5, + "upper": 0.6, + "n": 12, + "mean_confidence": 0.5452229816906935, + "accuracy": 0.5 + }, + { + "lower": 0.6, + "upper": 0.7, + "n": 9, + "mean_confidence": 0.6466211981405114, + "accuracy": 0.5555555555555556 + }, + { + "lower": 0.7, + "upper": 0.8, + "n": 15, + "mean_confidence": 0.7396579630745269, + "accuracy": 0.6666666666666666 + }, + { + "lower": 0.8, + "upper": 0.9, + "n": 16, + "mean_confidence": 0.8474827404278815, + "accuracy": 0.625 + }, + { + "lower": 0.9, + "upper": 1.0, + "n": 86, + "mean_confidence": 0.9819232889393442, + "accuracy": 0.9767441860465116 + } + ], + "reliability_bins_calibrated_out_of_fold": [ + { + "lower": 0.4, + "upper": 0.5, + "n": 9, + "mean_confidence": 0.4583857404798057, + "accuracy": 0.3333333333333333 + }, + { + "lower": 0.5, + "upper": 0.6, + "n": 16, + "mean_confidence": 0.5473707085609272, + "accuracy": 0.4375 + }, + { + "lower": 0.6, + "upper": 0.7, + "n": 13, + "mean_confidence": 0.6627024169930019, + "accuracy": 0.6153846153846154 + }, + { + "lower": 0.7, + "upper": 0.8, + "n": 12, + "mean_confidence": 0.7529320028318748, + "accuracy": 0.5833333333333334 + }, + { + "lower": 0.8, + "upper": 0.9, + "n": 16, + "mean_confidence": 0.8388718597481687, + "accuracy": 0.9375 + }, + { + "lower": 0.9, + "upper": 1.0, + "n": 78, + "mean_confidence": 0.9729208139702882, + "accuracy": 0.9743589743589743 + } + ], + "caveats": [ + "ECE at this n is high-variance; read the bootstrap intervals, not the point value.", + "A single scalar corrects over/under-confidence, not shape miscalibration within a family.", + "shipped_temperature is fit on all labeled rows; the out-of-fold ECE is the honest generalization estimate." + ] +} diff --git a/results/raw/predictions/torch-bf16-rtx3080-laptop-direct-authored144.jsonl b/results/raw/predictions/torch-bf16-rtx3080-laptop-direct-authored144.jsonl new file mode 100644 index 0000000..333744e --- /dev/null +++ b/results/raw/predictions/torch-bf16-rtx3080-laptop-direct-authored144.jsonl @@ -0,0 +1,144 @@ +{"id": "a3f18f3a63d45345942b", "option_ids": ["supported", "insufficient", "contradicted"], "probabilities": [0.010966012159897134, 0.8711382465103751, 0.11789574132972776], "option_logits": [22.0, 26.375, 24.375], "input_tokens": 142, "forward_seconds": 0.6323244820014224, "total_seconds": 0.665138159005437, "prompt_sha256": "3cc9e3d1e4e07885afb7652e9c5f92a5b28168362a3d8e7f6e32e5189a4fccde", "prompt_version": "direct-options-v1", "model": {"source": "Qwen/Qwen3.5-4B", "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", "dtype": 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"Native decisions have no probability estimates; one-hot distributions are not fabricated.", + "Risk/coverage is descriptive; tied confidence values remain together.", + "Analytic event hard-label accuracy measures modal-class agreement, not observed-event accuracy." + ] +} From 5f98fe0513d0de778b9f14a72f71747123bbf598 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maxime=20Lefran=C3=A7ois?= Date: Wed, 23 Sep 2026 10:45:08 +0200 Subject: [PATCH 06/13] =?UTF-8?q?results:=20the=20line=20the=20PR=20starts?= =?UTF-8?q?=20from=20=E2=80=94=20llama.cpp=20on=20CPU=20as=20merged,=20bef?= =?UTF-8?q?ore=20the=20GPU=20offload?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/LLAMACPP.md | 17 +++++- docs/RESULTS.md | 5 ++ results/raw/SHA256SUMS | 1 + ...hape777-subset5-llamacpp-before-after.json | 60 +++++++++++++++++++ 4 files changed, 82 insertions(+), 1 deletion(-) create mode 100644 results/raw/shape777-subset5-llamacpp-before-after.json diff --git a/docs/LLAMACPP.md b/docs/LLAMACPP.md index dcf4a2a..4ff8dba 100644 --- a/docs/LLAMACPP.md +++ b/docs/LLAMACPP.md @@ -177,7 +177,22 @@ well-calibrated than BF16; its fitted temperature is nearly the same. | llama.cpp Q4_K_M, RTX 3080 Laptop, `auto` | | | 10.51 | 18 / 777 | Decisions per second; state p50 for the GGUF: 14.97 s fresh, 2.28 s serial, -1.93 s parallel. The two Torch rows on the laptop differ only by the Gated +1.93 s parallel. + +What the table does not show is the line this PR starts from: the backend as +merged in #18 runs on CPU only. Measured on the first 5 states of the fixture +(105 decisions, backend functions timed directly because `shape777.py` takes +only the full fixture; `results/raw/shape777-subset5-llamacpp-before-after.json`): + +| llama.cpp Q4_K_M, same laptop, 5 states | fresh | shared | +|---|---:|---:| +| before: CPU (16 threads), state restore — `#18` as merged | 0.05 | 0.49 | +| after: GPU, state restore (`--llama-parallel 1`) | 1.45 | 9.86 | +| after: GPU, fan-out (`--llama-parallel auto`) | 1.43 | 11.18 | + +The GPU offload is the gain — ×30 fresh, ×20 shared. The fan-out adds 13 % on +this subset and 18 % on the full fixture. The GPU rows of the subset agree with +the full-fixture rows above (9.21 / 10.88), so the subset is representative. The two Torch rows on the laptop differ only by the Gated DeltaNet kernels: `pip install -e .` leaves `transformers` on its reference PyTorch implementation (it says so at load time); with `flash-linear-attention` installed the delta rule runs in Triton. `causal_conv1d` needs `nvcc` and was diff --git a/docs/RESULTS.md b/docs/RESULTS.md index 0f25f6e..0c64c37 100644 --- a/docs/RESULTS.md +++ b/docs/RESULTS.md @@ -101,6 +101,11 @@ claims above are unchanged. Qwen3.5-4B Q4_K_M (`bartowski/Qwen_Qwen3.5-4B-GGUF`, | shape777, parallel shared | decisions / s | 20.03 | 14.14 | 10.88 (8 branches) · 10.51 (auto) | | shape777, parallel vs fresh | argmax flips / 777 | 6 | 3 | 16–18 | +Before this extension the llama.cpp backend ran on CPU only: 0.05 decisions +per second fresh and 0.49 shared on the same laptop (5-state subset, +`results/raw/shape777-subset5-llamacpp-before-after.json`); the GPU offload is +what turns those into 1.40 and 10.88. + The laptop BF16 column uses `flash-linear-attention` for the Gated DeltaNet delta rule; with the reference PyTorch kernels that `pip install -e .` leaves in place it reads 1.10 / 7.78 / 10.24 decisions per second diff --git a/results/raw/SHA256SUMS b/results/raw/SHA256SUMS index 231ffd3..670417b 100644 --- a/results/raw/SHA256SUMS +++ b/results/raw/SHA256SUMS @@ -35,3 +35,4 @@ f3143a12fa357cbed8b1e6419ba6ecccf661d40d47dc6da284fe23a17ea041e1 torch-bf16-rtx 73c100b8ee0c5b8a9db1330967c1b36ece0198284e1ccd8662b64189e0140234 shape777-torch-bf16-rtx3080-laptop-reference-kernels.json 66e54c5d3434b2e1e84a7064152a73fc7944f54c0428015e27a91d8928079277 shape777-torch-bf16-rtx3080-laptop.predictions.jsonl 27cdbdc70c27f70c1ecf69aca52e7a8b8470ff885dd410e849729e78e087380b shape777-torch-bf16-rtx3080-laptop-reference-kernels.predictions.jsonl +681e1f4257d328974780a06ea4c82efee8b6510d15cba09572f673e691a5afe7 shape777-subset5-llamacpp-before-after.json diff --git a/results/raw/shape777-subset5-llamacpp-before-after.json b/results/raw/shape777-subset5-llamacpp-before-after.json new file mode 100644 index 0000000..53f68a1 --- /dev/null +++ b/results/raw/shape777-subset5-llamacpp-before-after.json @@ -0,0 +1,60 @@ +{ + "fixture": "benchmarks/data/shape777.jsonl, first 5 states (105 decisions); fresh timed on the first state only", + "method": "semif_phase1.llamacpp_backend.score / score_shared timed directly (shape777.py only accepts the full fixture); warm model, wall time, output write excluded", + "gguf": "bartowski/Qwen_Qwen3.5-4B-GGUF Q4_K_M", + "cpu": "Intel Xeon W-11955M, 16 threads, llama-cpp-python 0.3.35 PyPI wheel", + "gpu": "RTX 3080 Laptop 16 GB, cu124 wheel", + "runs": { + "before_pr_cpu_state_restore": { + "mode": "cpu", + "states": 5, + "gpu_offload": false, + "n_gpu_layers": 0, + "fresh": { + "decisions": 21, + "seconds": 457.86226421000174, + "per_second": 0.04586532160765318 + }, + "shared": { + "decisions": 105, + "seconds": 213.48030593199655, + "per_second": 0.49184864871538736, + "state_seconds": 42.69606118639931 + } + }, + "after_pr_gpu_state_restore": { + "mode": "gpu-restore", + "states": 5, + "gpu_offload": true, + "n_gpu_layers": -1, + "fresh": { + "decisions": 21, + "seconds": 14.507362655000179, + "per_second": 1.447540845252258 + }, + "shared": { + "decisions": 105, + "seconds": 10.65108915699966, + "per_second": 9.858146754033724, + "state_seconds": 2.130217831399932 + } + }, + "after_pr_gpu_fanout": { + "mode": "gpu-fanout", + "states": 5, + "gpu_offload": true, + "n_gpu_layers": -1, + "fresh": { + "decisions": 21, + "seconds": 14.73041090300103, + "per_second": 1.4256221457964668 + }, + "shared": { + "decisions": 105, + "seconds": 9.390513983998972, + "per_second": 11.181496580369876, + "state_seconds": 1.8781027967997943 + } + } + } +} \ No newline at end of file From b7a1ffb3d6dc241907f22a6ec3af04b5de18161f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maxime=20Lefran=C3=A7ois?= Date: Wed, 23 Sep 2026 11:12:14 +0200 Subject: [PATCH 07/13] =?UTF-8?q?llamacpp:=20drop=20the=20marginal=20reado?= =?UTF-8?q?ut=20=E2=80=94=20on=20Qwen3-8B=20under=20this=20prompt=20it=20h?= =?UTF-8?q?as=20nothing=20to=20fold?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/LLAMACPP.md | 29 +------- src/semif_phase1/cli.py | 10 +-- src/semif_phase1/llamacpp_backend.py | 104 +++------------------------ tests/test_llamacpp.py | 56 +-------------- 4 files changed, 15 insertions(+), 184 deletions(-) diff --git a/docs/LLAMACPP.md b/docs/LLAMACPP.md index 4ff8dba..e93be99 100644 --- a/docs/LLAMACPP.md +++ b/docs/LLAMACPP.md @@ -106,9 +106,8 @@ build option; the same wheel serves both modes. `tests/test_llamacpp.py::test_real_gguf_scores_direct_serial_and_shared` loads the real GGUF twice — with one sequence and with three — and checks that direct, -serial, restore-shared, copy-shared and marginal readouts agree on the -decisions, that copy-shared option logits match restore-shared ones within -0.5, and that `preamble_mass` stays in `[0, 1]`. Set `SEMIF_LLAMACPP_GGUF` to run +serial, restore-shared and copy-shared readouts agree on the decisions and +that copy-shared option logits match restore-shared ones within 0.5. Set `SEMIF_LLAMACPP_GGUF` to run it, `SEMIF_LLAMACPP_GPU_LAYERS` to offload. It passes on: - the PyPI CPU wheel of `llama-cpp-python` 0.3.35, CPU only; @@ -120,30 +119,6 @@ it, `SEMIF_LLAMACPP_GPU_LAYERS` to offload. It passes on: or AMX) for any decode, single- or multi-sequence, hybrid or pure-attention model. With that wheel, offload the layers; for CPU scoring, use the PyPI wheel. -## Marginal readout - -*Validated for correctness, not for a metric gain.* On Qwen3.5-4B, which answers with the letter -directly, every result in this document is identical with or without it, and no committed workload -needs it. It is here for checkpoints that put their first-token mass on a preamble token; the case -that motivated it — Qwen3-8B answering `**` before the letter, 60 % of the mass leaking without it, -10 % with a marginalised read — was measured outside this repository, on a mail-triage fixture that -cannot be redistributed. - - -`--llama-readout marginal` (shared mode, any `--llama-parallel` but `1`) changes how -the answer is read, not what is asked. Some checkpoints want to emit a token -*before* the letter — Qwen3-8B puts almost all of its next-token mass on the -Markdown `**` token when the assistant turn starts empty, and reading only the -last prompt position then reads that preamble instead of the answer. The -marginal readout takes, for each branch, the most likely non-slot tokens above -a floor (two probes, 5 %), appends each in one extra batched decode across all -branches, reads the slot masses after it, and adds them to the direct slot -masses weighted by the preamble's probability. `option_logits` then hold the -natural logs of the summed masses, so `softmax` and temperature scaling keep -working unchanged; `preamble_mass` records the share that came through a -preamble. On Qwen3.5-4B, which answers with the letter directly, the readout -changes nothing and `preamble_mass` stays at zero. - ## Results RTX 3080 Laptop GPU (16 GB), Q4_K_M, all 33 layers offloaded, `llama-cpp-python` 0.3.35 cu124. diff --git a/src/semif_phase1/cli.py b/src/semif_phase1/cli.py index 303a22f..7264240 100644 --- a/src/semif_phase1/cli.py +++ b/src/semif_phase1/cli.py @@ -30,9 +30,6 @@ def main() -> None: help="Shared-mode branching for --backend llamacpp: 'auto' sizes each fan-out from the " "state's token counts over a unified KV buffer; N fixes n_seq_max; 1 restores " "state per decision") - parser.add_argument("--llama-readout", choices=("last", "marginal"), default="last", - help="llama.cpp shared-mode readout: last prompt position, or answer-slot masses summed " - "over one-token preambles (needs --llama-parallel > 1)") parser.add_argument("--model", required=True) parser.add_argument("--revision", required=True) parser.add_argument("--input", type=Path, required=True) @@ -77,15 +74,11 @@ def _auto_or_int(name, value, minimum): for name, value in (("--llama-gpu-layers", args.llama_gpu_layers), ("--llama-parallel", args.llama_parallel)): if args.backend != "llamacpp" and value != "auto": parser.error(f"{name} requires --backend llamacpp") - if args.llama_readout != "last" and args.backend != "llamacpp": - parser.error("--llama-readout requires --backend llamacpp") if args.backend == "llamacpp": if args.mode == "reranker": parser.error("llama.cpp supports direct, serial, and shared modes; reranker requires torch") if args.gguf is None or not args.gguf.is_file(): parser.error("--backend llamacpp requires --gguf pointing at an existing GGUF file") - if args.llama_readout == "marginal" and (args.mode != "shared" or args.llama_parallel == 1): - parser.error("--llama-readout marginal requires --mode shared and --llama-parallel other than 1") rows = [json.loads(line) for line in args.input.read_text().splitlines() if line.strip()] if not rows: parser.error("Input is empty") @@ -106,8 +99,7 @@ def _auto_or_int(name, value, minimum): model, tokenizer, metadata = llamacpp_backend.load_model( args.model, args.revision, args.gguf, threads=args.llama_threads, context_tokens=args.max_tokens, - gpu_layers=args.llama_gpu_layers, sequences=args.llama_parallel, - readout=args.llama_readout) + gpu_layers=args.llama_gpu_layers, sequences=args.llama_parallel) direct, serial, shared = (llamacpp_backend.score, llamacpp_backend.SerialPrefixScorer, llamacpp_backend.score_shared) else: diff --git a/src/semif_phase1/llamacpp_backend.py b/src/semif_phase1/llamacpp_backend.py index 9b366cc..7422db2 100644 --- a/src/semif_phase1/llamacpp_backend.py +++ b/src/semif_phase1/llamacpp_backend.py @@ -19,8 +19,7 @@ cannot be partially erased, so ``llama_memory_seq_rm`` refuses to truncate a tail back to the prefix. They do support whole-sequence removal, state save/restore, and sequence copies (``llama_memory_recurrent::seq_cp``), which is -what both branching modes rely on. Pure-attention models additionally allow -tail truncation, which the optional marginal readout uses when available. +what both branching modes rely on. """ from __future__ import annotations @@ -41,7 +40,6 @@ from .shared import _state_prefix DECODE_CHUNK = 512 -READOUTS = ("last", "marginal") AUTO = "auto" #: Upper bound on branches per fan-out when `sequences` is "auto". With a unified KV buffer the #: bound costs bookkeeping only; the real limit is the token budget, checked per state. @@ -268,16 +266,6 @@ def fanout_logits(self, prefix_length: int, suffixes: list[list[int]]) -> list[n for branch in branches: self.lib.llama_memory_seq_rm(self.memory, branch, -1, -1) - def probe_branches(self, positions: dict[int, int], tokens: dict[int, int]) -> dict[int, numpy.ndarray]: - """Append one token to each listed branch and read the logits after it. - - Used by the marginal readout: ``tokens[branch]`` is the preamble token the model wanted - to emit first, ``positions[branch]`` the position it goes to. Branches are live sequences - from the current fan-out; the caller removes them. - """ - items = [(tokens[branch], positions[branch], branch, True) for branch in sorted(tokens)] - return self._decode_items(items) - def _free_native(engine: _Engine, library, model) -> None: engine.close() @@ -323,7 +311,7 @@ def _verify_vocabulary(tokenizer, library, vocab) -> None: def load_model(source: str, revision: str, gguf, *, threads: int | None = None, context_tokens: int = 4096, gpu_layers: int | str | None = AUTO, - sequences: int | str = AUTO, readout: str = "last"): + sequences: int | str = AUTO): """Load one pinned reference tokenizer plus a local GGUF checkpoint for scoring. ``gpu_layers``: ``"auto"`` (or ``None``) leaves llama.cpp's default, which offloads every @@ -356,8 +344,6 @@ def load_model(source: str, revision: str, gguf, *, threads: int | None = None, sequences = 1 + BRANCH_CAP if not (isinstance(sequences, int) and sequences >= 1): raise ValueError("sequences must be 'auto' or a positive integer") - if readout not in READOUTS: - raise ValueError(f"readout must be one of {READOUTS}") import transformers try: import llama_cpp @@ -403,7 +389,6 @@ def load_model(source: str, revision: str, gguf, *, threads: int | None = None, "n_seq_max": sequences, "branch_sizing": "auto" if auto_sequences else "fixed", "kv_unified": engine.kv_unified, - "readout_mode": readout, "max_prompt_tokens": context_tokens, "context_tokens": engine.context_tokens, "decode_chunk": DECODE_CHUNK, @@ -523,60 +508,6 @@ def _chunks(prefix_length: int, suffix_lengths: list[int], budget: int, cap: int return chunks -def _marginal_readout(engine: _Engine, branches: list[int], prefix_length: int, - suffixes: list[list[int]], vocabularies: list[numpy.ndarray], - slot_lists: list[list[int]], probes: int = 2, floor: float = 0.05): - """Fold one-token preambles back into the answer-slot masses. - - Some checkpoints want to emit a token before the letter — Qwen3-8B puts almost all of its - mass on the Markdown ``**`` token when the assistant turn starts empty. Reading only the last - prompt position then reads the preamble, not the answer. For each branch whose most likely - next tokens are not answer slots, this appends up to ``probes`` such tokens (one extra batched - decode per probe rank across all branches), reads the slot masses after them, and adds them - to the direct slot masses weighted by the preamble's probability. The argmax and the reported - ``option_logits`` become log-masses of that truncated path sum. - - Returns per-branch (option_logits, preamble_mass) with option_logits as natural logs of the - summed masses, so downstream softmax and temperature scaling keep working unchanged. - """ - live = {branch: prefix_length + len(suffix) for branch, suffix in zip(branches, suffixes)} - masses, via = [], [] - for vocabulary, slots in zip(vocabularies, slot_lists): - probabilities = _stable_softmax(vocabulary.astype(numpy.float64)) - masses.append(probabilities[slots].copy()) - via.append(0.0) - # Candidate preambles per branch: top tokens that are not answer slots. - candidates: list[list[int]] = [] - for vocabulary, slots in zip(vocabularies, slot_lists): - forbidden = set(slots) - order = numpy.argsort(-vocabulary)[: probes + len(slots)] - candidates.append([int(token) for token in order if int(token) not in forbidden][:probes]) - weights = [_stable_softmax(vocabulary.astype(numpy.float64)) for vocabulary in vocabularies] - for rank in range(probes): - tokens, positions = {}, {} - for index, branch in enumerate(branches): - if rank < len(candidates[index]): - token = candidates[index][rank] - if weights[index][token] >= floor: - tokens[branch] = token - positions[branch] = live[branch] - if not tokens: - break - after = engine.probe_branches(positions, tokens) - for index, branch in enumerate(branches): - if branch in tokens: - weight = float(weights[index][tokens[branch]]) - gain = weight * _stable_softmax(after[branch].astype(numpy.float64))[slot_lists[index]] - masses[index] += gain - via[index] += float(gain.sum()) - live[branch] += 1 - results = [] - for mass, mass_via in zip(masses, via): - total = float(mass.sum()) - results.append((numpy.log(numpy.maximum(mass, 1e-300)).tolist(), mass_via / total if total > 0 else 0.0)) - return results - - def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: int = 4096): """Prefill one exact state once, then score every criterion from branches. @@ -594,7 +525,6 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: raise ValueError("The fixed state prefix does not match every full prompt") encode_seconds = time.perf_counter() - started engine = model.engine - marginal = metadata.get("readout_mode") == "marginal" mark = time.perf_counter() engine.clear() engine.prefill(prefix) @@ -604,8 +534,6 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: copy_seconds = suffix_seconds = 0.0 results = [] if engine.sequences == 1: - if marginal: - raise ValueError("The marginal readout needs sequence copies; load with sequences > 1") for row, row_encoded in zip(rows, encoded): ids, slots, _ = row_encoded mark = time.perf_counter() @@ -648,34 +576,20 @@ def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: ] logits = engine._decode_items(items) vocabularies = [logits[branch] for branch in branches] - if marginal: - folded = _marginal_readout(engine, branches, len(prefix), suffixes, - vocabularies, slot_lists) finally: for branch in branches: engine.lib.llama_memory_seq_rm(engine.memory, branch, -1, -1) suffix_seconds += time.perf_counter() - mark for index, (row, row_encoded) in enumerate(zip(chunk_rows, chunk_encoded)): vocabulary = vocabularies[index] - if marginal: - selected, preamble_mass = folded[index] - result = _result( - row, row_encoded, selected, vocabulary, metadata, - "llamacpp-seq-copy-parallel-marginal-v1", - "quantized branch logits over copied prefix sequences, answer-slot masses " - "summed over one-token preambles; option_logits are log-masses", - ) - result["preamble_mass"] = preamble_mass - else: - result = _result( - row, row_encoded, vocabulary[row_encoded[1]].tolist(), vocabulary, metadata, - "llamacpp-seq-copy-parallel-v1", - "quantized branch last-position logits over copied prefix sequences; " - "one batched decode per chunk", - ) - results.append(result) + results.append(_result( + row, row_encoded, vocabulary[row_encoded[1]].tolist(), vocabulary, metadata, + "llamacpp-seq-copy-parallel-v1", + "quantized branch last-position logits over copied prefix sequences; " + "one batched decode per chunk", + )) branch_bytes = 0 - config = "llamacpp-seq-copy-parallel-marginal-v1" if marginal else "llamacpp-seq-copy-parallel-v1" + config = "llamacpp-seq-copy-parallel-v1" if engine.sequences == 1: branch_counts = [1] * len(rows) suffix_total = sum(len(ids) - len(prefix) for ids, _, _ in encoded) diff --git a/tests/test_llamacpp.py b/tests/test_llamacpp.py index 05e328a..c98314c 100644 --- a/tests/test_llamacpp.py +++ b/tests/test_llamacpp.py @@ -20,7 +20,6 @@ (["--mode", "direct", "--llama-parallel", "4"], "--llama-parallel requires --backend llamacpp"), (["--mode", "direct", "--backend", "llamacpp", "--gguf", "x.gguf", "--llama-parallel", "0"], "at least 1"), (["--mode", "direct", "--backend", "llamacpp", "--gguf", "x.gguf", "--llama-gpu-layers", "many"], "must be 'auto' or an integer"), - (["--mode", "shared", "--llama-readout", "marginal"], "--llama-readout requires --backend llamacpp"), ]) def test_invalid_llamacpp_combinations_fail_before_loading(tmp_path, monkeypatch, capsys, extra, message): monkeypatch.setattr(sys, "argv", ["semif-score", "--model", "unused", "--revision", "unused", @@ -36,9 +35,9 @@ def test_cli_passes_gguf_options_to_loader(tmp_path, monkeypatch): import semif_phase1 fake_backend = SimpleNamespace( - load_model=lambda source, revision, gguf, *, threads, context_tokens, gpu_layers, sequences, readout: + load_model=lambda source, revision, gguf, *, threads, context_tokens, gpu_layers, sequences: (None, None, {"gguf": str(gguf), "threads": threads, "context_tokens": context_tokens, - "gpu_layers": gpu_layers, "sequences": sequences, "readout": readout}), + "gpu_layers": gpu_layers, "sequences": sequences}), score=lambda model, tokenizer, row, metadata, max_tokens: metadata, SerialPrefixScorer=None, score_shared=None, ) @@ -56,7 +55,7 @@ def test_cli_passes_gguf_options_to_loader(tmp_path, monkeypatch): assert record["gguf"] == str(weights) assert record["threads"] == 7 assert record["context_tokens"] == 4096 - assert record["gpu_layers"] == "auto" and record["sequences"] == "auto" and record["readout"] == "last" + assert record["gpu_layers"] == "auto" and record["sequences"] == "auto" def test_load_model_rejects_unpinned_remote_revision(): @@ -249,11 +248,6 @@ def test_real_gguf_scores_direct_serial_and_shared(): for shared_result, fanned_result in zip(shared, fanned): numpy.testing.assert_allclose( shared_result["option_logits"], fanned_result["option_logits"], rtol=0, atol=0.5) - marginal_meta = {**metadata, "readout_mode": "marginal"} - folded, timing = llamacpp_backend.score_shared(model, tokenizer, rows, marginal_meta) - assert timing["serving_config"] == "llamacpp-seq-copy-parallel-marginal-v1" - assert choices(folded) == ["yes", "yes"] - assert all(0.0 <= result["preamble_mass"] <= 1.0 for result in folded) finally: model.close() @@ -268,8 +262,6 @@ def test_load_model_validates_new_arguments(tmp_path): llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, gpu_layers=-2) with pytest.raises(ValueError, match="sequences"): llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, sequences=0) - with pytest.raises(ValueError, match="readout"): - llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, readout="first") def _fake_engine_for_fanout(sequences: int, vocab_size: int = 8): @@ -343,48 +335,6 @@ def test_fanout_refuses_more_branches_than_slots(): engine.fanout_logits(3, [[1], [2]]) -def test_marginal_readout_folds_preamble_mass_into_slots(): - from semif_phase1.llamacpp_backend import _marginal_readout - - probed = {} - - class Engine: - def probe_branches(self, positions, tokens): - probed.update(tokens) - # After the preamble token the model answers slot 1 (token id 1) with certainty. - out = numpy.full(8, -20.0) - out[1] = 20.0 - return {branch: out.copy() for branch in tokens} - - # Branch 1: the model puts its mass on token 7 (a preamble), almost none on the slots 0 and 1. - vocabulary = numpy.full(8, -20.0) - vocabulary[7] = 20.0 - vocabulary[0] = 1.0 - vocabulary[1] = 0.0 - (logits, preamble_mass), = _marginal_readout( - Engine(), branches=[1], prefix_length=10, suffixes=[[4, 4]], - vocabularies=[vocabulary], slot_lists=[[0, 1]]) - assert probed == {1: 7} - # Direct read alone would have picked slot 0; the preamble path moves the answer to slot 1. - assert int(numpy.argmax(logits)) == 1 - assert preamble_mass > 0.99 - - -def test_marginal_readout_skips_branches_that_already_answer_with_a_slot(): - from semif_phase1.llamacpp_backend import _marginal_readout - - class Engine: - def probe_branches(self, positions, tokens): - raise AssertionError("no probe should be issued") - - vocabulary = numpy.full(8, -20.0) - vocabulary[0] = 20.0 # slot 0 is the confident next token; nothing else clears the floor - (logits, preamble_mass), = _marginal_readout( - Engine(), branches=[1], prefix_length=3, suffixes=[[9]], - vocabularies=[vocabulary], slot_lists=[[0, 1]]) - assert int(numpy.argmax(logits)) == 0 and preamble_mass == 0.0 - - def test_chunks_follow_the_token_budget_and_the_cap(): from semif_phase1.llamacpp_backend import _chunks From 5e546b5c650564fd335c092782e39260b2f8ceaa Mon Sep 17 00:00:00 2001 From: sinksilk <785976238@qq.com> Date: Fri, 25 Sep 2026 17:10:55 +0800 Subject: [PATCH 08/13] Add semif-calibrate CLI and generic calibrated-threshold gate Expose the existing per-workload temperature scaling as an installable command and wire evaluate's non-screen auto-decide/review operating point, without changing the frozen 96-row screening gate or published tables. --- benchmarks/calibrate.py | 415 +------------------------------ benchmarks/evaluate.py | 65 ++++- docs/CALIBRATION.md | 33 ++- pyproject.toml | 1 + src/semif_phase1/calibration.py | 419 ++++++++++++++++++++++++++++++++ tests/test_calibrate.py | 100 ++++++-- 6 files changed, 592 insertions(+), 441 deletions(-) create mode 100644 src/semif_phase1/calibration.py diff --git a/benchmarks/calibrate.py b/benchmarks/calibrate.py index fbb069b..94731a4 100644 --- a/benchmarks/calibrate.py +++ b/benchmarks/calibrate.py @@ -1,416 +1,9 @@ -"""Post-hoc temperature scaling for direct option logits. +"""Back-compat entry for ``python benchmarks/calibrate.py ...``. -A separate labeled layer: it never touches the native scorer. It reads committed -prediction logits, fits a single scalar T against gold labels, and reports honest -group-disjoint out-of-fold calibration. Softmax(logits / T) is monotone, so the -argmax -- and therefore every committed accuracy -- is unchanged; only confidence -moves. numpy only; runs offline on CPU, no model load. +Prefer the installable ``semif-calibrate`` command from an editable checkout. """ -from __future__ import annotations - -import argparse -import json -import math -from collections import defaultdict -from pathlib import Path - -import numpy as np - - -def load_pairs(gold_path: Path, pred_path: Path) -> list[dict]: - """Join predictions to gold by id, keeping only hard-label rows present in both. - - Rows without a hard ``label`` or carrying a ``target_distribution`` are skipped: - soft-label / ranking rows need distribution-aware handling, not scalar scaling. - """ - gold, seen_gold = {}, set() - for line in gold_path.read_text().splitlines(): - if line.strip(): - row = json.loads(line) - if row["id"] in seen_gold: - raise ValueError(f"Duplicate gold row: {row['id']}") - seen_gold.add(row["id"]) - if "label" in row and row.get("target_distribution") is None: - ids = [option["id"] for option in row["options"]] - if len(set(ids)) != len(ids): - raise ValueError(f"{row['id']}: duplicate gold option ids") - if not isinstance(row["label"], int) or isinstance(row["label"], bool) or not 0 <= row["label"] < len(ids): - raise ValueError(f"{row['id']}: label out of range") - gold[row["id"]] = row - pairs, seen = [], set() - for line in pred_path.read_text().splitlines(): - if not line.strip(): - continue - pred = json.loads(line) - if pred["id"] in seen: - raise ValueError(f"Duplicate prediction row: {pred['id']}") - seen.add(pred["id"]) - row = gold.get(pred["id"]) - if row is None: - continue - option_ids = pred["option_ids"] - if len(set(option_ids)) != len(option_ids): - raise ValueError(f"{pred['id']}: duplicate prediction option ids") - if len(pred["option_logits"]) != len(option_ids): - raise ValueError(f"{pred['id']}: option_logits and option_ids length mismatch") - if set(option_ids) != {option["id"] for option in row["options"]}: - raise ValueError(f"{pred['id']}: prediction options differ from gold options") - logits = np.asarray(pred["option_logits"], dtype=float) - if not np.all(np.isfinite(logits)): - raise ValueError(f"{pred['id']}: non-finite option_logits") - true_id = row["options"][row["label"]]["id"] - pairs.append( - { - "id": pred["id"], - "family": row["family"], - "group": row["group_id"], - "logits": logits, - "true_index": option_ids.index(true_id), - } - ) - if not pairs: - raise ValueError(f"No hard-label rows shared between {gold_path.name} and {pred_path.name}") - return pairs - - -def check_temperature(temperature: float) -> float: - """A temperature must be finite and positive; otherwise dividing logits can flip the argmax.""" - if not math.isfinite(temperature) or temperature <= 0: - raise ValueError(f"Temperature must be finite and > 0, got {temperature!r}") - return temperature - - -def softmax(logits: np.ndarray) -> np.ndarray: - shifted = logits - logits.max() - weights = np.exp(shifted) - return weights / weights.sum() - - -def mean_nll(pairs: list[dict], temperature: float) -> float: - total = 0.0 - for pair in pairs: - probs = softmax(pair["logits"] / temperature) - total += -math.log(max(probs[pair["true_index"]], 1e-12)) - return total / len(pairs) - - -def fit_temperature(pairs: list[dict], bounds=(0.05, 20.0), iterations=60) -> float: - """Minimize mean NLL over T by golden-section search. NLL is convex in 1/T.""" - ratio = (math.sqrt(5) - 1) / 2 - low, high = bounds - left, right = high - ratio * (high - low), low + ratio * (high - low) - f_left, f_right = mean_nll(pairs, left), mean_nll(pairs, right) - for _ in range(iterations): - if f_left < f_right: - high, right, f_right = right, left, f_left - left = high - ratio * (high - low) - f_left = mean_nll(pairs, left) - else: - low, left, f_left = left, right, f_right - right = low + ratio * (high - low) - f_right = mean_nll(pairs, right) - return (low + high) / 2 - - -def scored(pairs: list[dict], temperature: float) -> list[dict]: - """Per-row calibrated top-label confidence and correctness (correctness is T-invariant).""" - check_temperature(temperature) - out = [] - for pair in pairs: - probs = softmax(pair["logits"] / temperature) - row = { - "id": pair["id"], - "group": pair["group"], - "family": pair["family"], - "confidence": float(probs.max()), - "correct": int(probs.argmax() == pair["true_index"]), - } - if "workload" in pair: - row["workload"] = pair["workload"] - out.append(row) - return out - - -def reliability_bins(rows: list[dict], bins=10) -> list[dict]: - result = [] - for index in range(bins): - part = [r for r in rows if min(bins - 1, int(r["confidence"] * bins)) == index] - if part: - result.append( - { - "lower": index / bins, - "upper": (index + 1) / bins, - "n": len(part), - "mean_confidence": sum(r["confidence"] for r in part) / len(part), - "accuracy": sum(r["correct"] for r in part) / len(part), - } - ) - return result - - -def ece(rows: list[dict], bins=10) -> float: - """Expected calibration error, top-label, equal-width bins.""" - total = 0.0 - for entry in reliability_bins(rows, bins): - total += abs(entry["accuracy"] - entry["mean_confidence"]) * entry["n"] / len(rows) - return total - - -def bootstrap_ece(rows: list[dict], samples=1000, seed=217, bins=10) -> list[float]: - """95% interval by resampling source groups with replacement.""" - by_group = defaultdict(list) - for row in rows: - by_group[row["group"]].append(row) - groups = list(by_group.values()) - rng = np.random.default_rng(seed) - values = [] - for _ in range(samples): - draw = [] - for _ in groups: - draw.extend(groups[rng.integers(len(groups))]) - values.append(ece(draw, bins)) - values.sort() - return [values[int(0.025 * samples)], values[min(samples - 1, int(0.975 * samples))]] - - -def fold_map(pairs: list[dict], folds=5, seed=217) -> dict: - """Deterministic ``{group_id: fold}`` assignment (group-disjoint; variants share a group).""" - groups = sorted({pair["group"] for pair in pairs}) - rng = np.random.default_rng(seed) - rng.shuffle(groups) - return {group: index % folds for index, group in enumerate(groups)} - - -def grouped_cv(pairs: list[dict], folds=5, seed=217) -> tuple[list[dict], list[float], dict]: - """Out-of-fold calibrated rows using group-disjoint folds (variants share a group). - - Also returns the exact ``{group_id: fold}`` assignment so the split can be committed - and independently audited, not just re-derived from the seed. - """ - fold_of = fold_map(pairs, folds, seed) - held, temperatures = [], [] - for fold in range(folds): - train = [p for p in pairs if fold_of[p["group"]] != fold] - test = [p for p in pairs if fold_of[p["group"]] == fold] - temperature = fit_temperature(train) - temperatures.append(temperature) - held.extend(scored(test, temperature)) - return held, temperatures, fold_of - - -def per_family(rows: list[dict]) -> dict: - families = defaultdict(list) - for row in rows: - families[row["family"]].append(row) - return {name: {"n": len(part), "ece": ece(part)} for name, part in sorted(families.items())} - - -def build_report(gold_path: Path, pred_path: Path, folds=5, seed=217) -> dict: - pairs = load_pairs(gold_path, pred_path) - temperature = fit_temperature(pairs) - base = scored(pairs, 1.0) - held, fold_temperatures, fold_of = grouped_cv(pairs, folds, seed) - accuracy = sum(r["correct"] for r in base) / len(base) - base_ci = bootstrap_ece(base, seed=seed) - held_ci = bootstrap_ece(held, seed=seed) - return { - "method": "per-workload post-hoc temperature scaling; single scalar; NLL-fit; monotone (argmax-invariant)", - "gold": gold_path.name, - "predictions": pred_path.name, - "rows": len(pairs), - "groups": len({p["group"] for p in pairs}), - "shipped_temperature": temperature, - "cv": {"folds": folds, "seed": seed, "fold_temperatures": fold_temperatures, "fold_groups": fold_of}, - "accuracy_unchanged": accuracy, - "ece_uncalibrated": {"value": ece(base), "ci95": base_ci}, - "ece_calibrated_out_of_fold": {"value": ece(held), "ci95": held_ci}, - "improvement_ci_separated": held_ci[1] < base_ci[0], - "per_family_uncalibrated": per_family(base), - "per_family_calibrated_out_of_fold": per_family(held), - "reliability_bins_uncalibrated": reliability_bins(base), - "reliability_bins_calibrated_out_of_fold": reliability_bins(held), - "caveats": [ - "ECE at this n is high-variance; read the bootstrap intervals, not the point value.", - "A single scalar corrects over/under-confidence, not shape miscalibration within a family.", - "shipped_temperature is fit on all labeled rows; the out-of-fold ECE is the honest generalization estimate.", - ], - } - - -def paired_bootstrap_delta(own_by_id: dict, pooled_by_id: dict, samples=1000, seed=217) -> list[float]: - """95% interval of (pooled-T ECE - own-T ECE), paired per row, resampling shared groups.""" - ids = [key for key in own_by_id if key in pooled_by_id] - by_group = defaultdict(list) - for key in ids: - by_group[own_by_id[key]["group"]].append(key) - groups = list(by_group.values()) - rng = np.random.default_rng(seed) - values = [] - for _ in range(samples): - draw = [] - for _ in groups: - draw.extend(groups[rng.integers(len(groups))]) - values.append(ece([pooled_by_id[k] for k in draw]) - ece([own_by_id[k] for k in draw])) - values.sort() - return [values[int(0.025 * samples)], values[min(samples - 1, int(0.975 * samples))]] - - -def _oof_by_fold(pairs: list[dict]) -> dict: - """Out-of-fold scored rows keyed by id, using each pair's pre-assigned ``fold``.""" - held = {} - for fold in sorted({pair["fold"] for pair in pairs}): - train = [pair for pair in pairs if pair["fold"] != fold] - test = [pair for pair in pairs if pair["fold"] == fold] - for row in scored(test, fit_temperature(train)): - held[row["id"]] = row - return held - - -def cross_workload_report(workloads: dict, folds=5, seed=217) -> dict: - """Per-workload temperature vs one pooled temperature (the negative control). - - Both sides use the *same* committed per-workload fold assignment for every row, so a - row's own-T and pooled-T scores differ only in training scope (its workload alone vs - all workloads), never in which fold holds it out. All scores are out-of-fold. - """ - workload_pairs, fold_maps = {}, {} - for name, (gold_path, pred_path) in workloads.items(): - pairs = load_pairs(gold_path, pred_path) - assignment = fold_map(pairs, folds, seed) - for pair in pairs: - pair["workload"] = name - pair["fold"] = assignment[pair["group"]] - workload_pairs[name] = pairs - fold_maps[name] = assignment - all_pairs = [pair for pairs in workload_pairs.values() for pair in pairs] - - own_held = {name: _oof_by_fold(pairs) for name, pairs in workload_pairs.items()} - - pooled_held, pooled_fold_temperatures = defaultdict(dict), [] - for fold in range(folds): - temperature = fit_temperature([p for p in all_pairs if p["fold"] != fold]) - pooled_fold_temperatures.append(temperature) - for row in scored([p for p in all_pairs if p["fold"] == fold], temperature): - pooled_held[row["workload"]][row["id"]] = row - - per_workload = {} - for name, (gold_path, pred_path) in workloads.items(): - own, pooled_rows = own_held[name], pooled_held[name] - report = build_report(gold_path, pred_path, folds, seed) - delta_ci = paired_bootstrap_delta(own, pooled_rows, seed=seed) - per_workload[name] = { - "rows": report["rows"], - "accuracy": report["accuracy_unchanged"], - "own_temperature": report["shipped_temperature"], - "ece_uncalibrated": report["ece_uncalibrated"]["value"], - "ece_own_temperature_out_of_fold": ece(list(own.values())), - "ece_pooled_temperature_out_of_fold": ece(list(pooled_rows.values())), - "pooled_minus_own_ece_ci95": delta_ci, - "pooled_temperature_significantly_worse": delta_ci[0] > 0, - } - return { - "method": "per-workload temperature vs one pooled temperature; both fit fold-wise on the SAME " - "committed per-workload fold assignments and evaluated out-of-fold", - "cv": {"folds": folds, "seed": seed}, - "per_workload": per_workload, - "pooled_temperature_negative_control": { - "pooled_rows": len(all_pairs), - "all_rows_pooled_temperature": fit_temperature(all_pairs), - "fold_temperatures": pooled_fold_temperatures, - "fold_groups_by_workload": fold_maps, - "note": "Own and pooled out-of-fold scores use the same per-row fold (each workload's committed " - "fold_groups); only the training scope differs (workload-only vs all workloads). " - "fold_temperatures are the pooled per-fold temperatures; all_rows_pooled_temperature is the " - "single value you would deploy if forced to pick one. A flag is true only when the paired " - "95% interval excludes 0.", - }, - } - - -def apply(pred_path: Path, out_path: Path, temperature: float) -> None: - """Emit a calibrated predictions file (probabilities recomputed at T) for the existing evaluator.""" - check_temperature(temperature) - if out_path.exists(): - raise FileExistsError(out_path) - calibrated = [] - for line in pred_path.read_text().splitlines(): - if not line.strip(): - continue - pred = json.loads(line) - logits = np.asarray(pred["option_logits"], dtype=float) - if logits.ndim != 1 or not len(logits) or not np.all(np.isfinite(logits)): - raise ValueError(f"{pred.get('id', '')}: option_logits must be a finite, non-empty vector") - if len(logits) != len(pred.get("option_ids", [])): - raise ValueError(f"{pred.get('id', '')}: option_logits and option_ids length mismatch") - pred["probabilities"] = softmax(logits / temperature).tolist() - pred["calibration"] = {"method": "temperature_scaling", "temperature": temperature} - calibrated.append(json.dumps(pred, allow_nan=False)) - - # Validate every input row before creating the output. A malformed later row - # must not leave behind a plausible-looking partial predictions file. - with out_path.open("x") as destination: - if calibrated: - destination.write("\n".join(calibrated) + "\n") - - -def demo() -> None: - """Self-check on committed authored144: argmax invariant, out-of-fold ECE improves.""" - root = Path(__file__).resolve().parent.parent - pairs = load_pairs(root / "benchmarks/data/authored144.jsonl", root / "results/raw/predictions/direct-authored144.jsonl") - temperature = fit_temperature(pairs) - base = scored(pairs, 1.0) - calibrated = scored(pairs, temperature) - assert all(b["correct"] == c["correct"] for b, c in zip(base, calibrated)), "T changed an argmax" - held, _, _ = grouped_cv(pairs) - assert ece(held) < ece(base), "out-of-fold calibration did not help" - print(f"ok: T={temperature:.3f} ECE {ece(base):.3f} -> {ece(held):.3f} (out-of-fold) acc unchanged={sum(r['correct'] for r in base)/len(base):.3f}") - - -def main() -> None: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--gold", type=Path) - parser.add_argument("--predictions", type=Path) - parser.add_argument("--report", type=Path, help="Write the JSON calibration report here (must be new).") - parser.add_argument("--apply-to", type=Path, help="Predictions file to calibrate (defaults to --predictions).") - parser.add_argument("--calibrated-out", type=Path, help="Write calibrated predictions here (must be new).") - parser.add_argument("--temperature", type=float, help="Skip fitting; apply this T instead.") - parser.add_argument("--manifest", type=Path, help='Cross-workload JSON {"name": {"gold": ..., "predictions": ...}}.') - parser.add_argument("--summary", type=Path, help="Write the cross-workload summary here (must be new).") - args = parser.parse_args() - - if args.manifest: - spec = json.loads(args.manifest.read_text()) - workloads = {name: (Path(entry["gold"]), Path(entry["predictions"])) for name, entry in spec.items()} - summary = cross_workload_report(workloads) - if args.summary: - with args.summary.open("x") as destination: - destination.write(json.dumps(summary, indent=2, allow_nan=False) + "\n") - print(json.dumps({"workloads": list(workloads), "all_rows_pooled_temperature": summary["pooled_temperature_negative_control"]["all_rows_pooled_temperature"]})) - return - - if args.temperature is not None: - temperature = check_temperature(args.temperature) - source = args.apply_to or args.predictions - if source is None or args.calibrated_out is None: - parser.error("--temperature requires --predictions (or --apply-to) and --calibrated-out") - if args.report: - parser.error("--report requires fitted calibration; omit --temperature") - apply(source, args.calibrated_out, temperature) - print(json.dumps({"applied_temperature": temperature, "predictions": str(source), - "calibrated_out": str(args.calibrated_out)})) - return - - if not args.gold or not args.predictions: - parser.error("Provide --gold and --predictions, or --manifest") - report = build_report(args.gold, args.predictions) - temperature = report["shipped_temperature"] - if args.report: - with args.report.open("x") as destination: - destination.write(json.dumps(report, indent=2, allow_nan=False) + "\n") - if args.calibrated_out: - apply(args.apply_to or args.predictions, args.calibrated_out, temperature) - print(json.dumps({k: report[k] for k in ("rows", "shipped_temperature", "ece_uncalibrated", "ece_calibrated_out_of_fold")})) - +from semif_phase1.calibration import * # noqa: F401,F403 +from semif_phase1.calibration import main if __name__ == "__main__": main() diff --git a/benchmarks/evaluate.py b/benchmarks/evaluate.py index 1913cf1..a11f108 100644 --- a/benchmarks/evaluate.py +++ b/benchmarks/evaluate.py @@ -200,6 +200,57 @@ def evaluate(gold,predictions,comparison=None): return result +def calibrated_threshold_gate(gold, predictions, threshold=0.8, abstain_option=None): + """Generic auto-decide-versus-review gate on top-label confidence. + + Intended for temperature-scaled prediction files (see docs/CALIBRATION.md). Unlike + ``screening_gate``, this does not require the frozen 96-row authored falsification + screen and does not change that screen's semantics. + """ + if not isinstance(threshold, (int, float)) or isinstance(threshold, bool) or not 0 < threshold <= 1: + raise ValueError('calibrated threshold must be in (0, 1]') + rows = align(gold, predictions) + preds = indexed(predictions, 'predictions') + temperatures = [] + for row in rows: + pred = preds.get(row['id']) + cal = (pred or {}).get('calibration') or {} + if cal.get('method') == 'temperature_scaling' and 'temperature' in cal: + temperatures.append(cal['temperature']) + decisions = [] + automatic = review = automatic_correct = 0 + for row in rows: + available = row['status'] == 'distribution' and row['confidence'] is not None + accept = available and row['confidence'] >= threshold + if abstain_option is not None and row['predicted_id'] == abstain_option: + accept = False + disposition = 'automatic_semantic_decision' if accept else 'review' + if accept: + automatic += 1 + automatic_correct += bool(row['correct']) + else: + review += 1 + decisions.append(dict(id=row['id'], choice=row['predicted_id'], confidence=row['confidence'], + disposition=disposition, correct=row['correct'], status=row['status'])) + unique_t = sorted({t for t in temperatures}) + return dict( + policy=dict(version='calibrated-threshold-v1', threshold=threshold, + abstain_option=abstain_option, + scope='any hard-label workload; not the frozen 96-row screen'), + rows=len(rows), + automatic_decisions=automatic, + review_decisions=review, + automatic_accuracy=(automatic_correct / automatic) if automatic else None, + coverage=automatic / len(rows) if rows else 0.0, + temperature_scaling_rows=len(temperatures), + applied_temperatures=unique_t, + calibrated_predictions=len(temperatures) == len(predictions) and len(unique_t) <= 1, + decisions=decisions, + limitation='A threshold on temperature-scaled confidence is an operating point, ' + 'not Jev-level operational calibration.', + ) + + def screening_gate(gold, predictions, policy='distribution', threshold=0.8): """Frozen semantic-decision gate; not a benchmark of executed workflow actions.""" if policy not in ('distribution', 'native') or threshold != 0.8: @@ -276,10 +327,20 @@ def screening_gate(gold, predictions, policy='distribution', threshold=0.8): parser.add_argument('--'+name,required=True) parser.add_argument('--comparison') parser.add_argument('--screen-policy', choices=('distribution','native')) + parser.add_argument('--calibrated-threshold', type=float, + help='Generic auto-decide vs review gate on top-label confidence (any workload).') + parser.add_argument('--abstain-option', + help='Option id that is never auto-decided under --calibrated-threshold.') args=parser.parse_args() - result=evaluate(read_jsonl(args.gold),read_jsonl(args.predictions),read_jsonl(args.comparison) if args.comparison else None) + if args.abstain_option is not None and args.calibrated_threshold is None: + parser.error('--abstain-option requires --calibrated-threshold') + gold, predictions = read_jsonl(args.gold), read_jsonl(args.predictions) + result=evaluate(gold, predictions, read_jsonl(args.comparison) if args.comparison else None) if args.screen_policy: - result['screening_gate'] = screening_gate(read_jsonl(args.gold), read_jsonl(args.predictions), args.screen_policy) + result['screening_gate'] = screening_gate(gold, predictions, args.screen_policy) + if args.calibrated_threshold is not None: + result['calibrated_threshold_gate'] = calibrated_threshold_gate( + gold, predictions, args.calibrated_threshold, args.abstain_option) with Path(args.output).open('x') as destination: destination.write(json.dumps(result,indent=2,allow_nan=False)+'\n') print(json.dumps({k:result[k] for k in ('scored','coverage','mean_family_balanced_accuracy')})) diff --git a/docs/CALIBRATION.md b/docs/CALIBRATION.md index 3322779..43297e3 100644 --- a/docs/CALIBRATION.md +++ b/docs/CALIBRATION.md @@ -16,9 +16,10 @@ Only confidence changes. Options here are runtime-defined and variable in count, per-class calibrators (Platt, vector, matrix scaling) do not apply; a single scalar is also the most data-efficient choice at these sample sizes. -`benchmarks/calibrate.py` fits `T`, evaluates it, and can emit a calibrated -predictions file. It uses numpy only and runs offline on CPU (the committed -predictions already carry `option_logits`, so no model is loaded). +`semif-calibrate` (and the back-compat `python benchmarks/calibrate.py`) fits `T`, +evaluates it, and can emit a calibrated predictions file. It uses numpy only and +runs offline on CPU (the committed predictions already carry `option_logits`, so +no model is loaded). ## Finding @@ -84,7 +85,9 @@ closely there, so an auto-decide-versus-review gate has a real operating point. Note this is *not* wired into `benchmarks/evaluate.py`'s `screening_gate`: that gate is frozen to the 96-row authored falsification screen (four variants per group, three specific families) and does not run on WANLI or Every. Its semantics are left -unchanged; a generic calibrated-threshold gate for other workloads is future work. +unchanged. For other workloads, pass temperature-scaled predictions into evaluate +with `--calibrated-threshold` (optional `--abstain-option`) to get a generic +auto-decide-versus-review operating point on top-label confidence. ## Ceiling and future work @@ -103,16 +106,26 @@ unchanged; a generic calibrated-threshold gate for other workloads is future wor python benchmarks/fetch_sources.py --output build/sources python benchmarks/build_wanli.py --source build/sources/wanli-test.jsonl --selection benchmarks/manifests/source-selection.jsonl --output build/gold-wanli256.jsonl python benchmarks/build_every.py --archive build/sources/every-source.zip --experiments build/sources/every-experiments.json --selection benchmarks/manifests/source-selection.jsonl --output-dir build/every -python benchmarks/calibrate.py --gold benchmarks/data/authored144.jsonl --predictions results/raw/predictions/direct-authored144.jsonl --report build/authored144.json --calibrated-out build/direct-authored144.calibrated.jsonl -python benchmarks/calibrate.py --gold build/gold-wanli256.jsonl --predictions results/raw/predictions/direct-wanli256.jsonl --report build/wanli256.json --calibrated-out build/direct-wanli256.calibrated.jsonl -python benchmarks/calibrate.py --gold build/every/gold154.jsonl --predictions results/raw/predictions/direct-every204.jsonl --report build/every154.json --calibrated-out build/direct-every204.calibrated.jsonl -python benchmarks/calibrate.py --manifest results/raw/calibration/workloads.json --summary build/summary.json +semif-calibrate --gold benchmarks/data/authored144.jsonl --predictions results/raw/predictions/direct-authored144.jsonl --report build/authored144.json --calibrated-out build/direct-authored144.calibrated.jsonl +semif-calibrate --gold build/gold-wanli256.jsonl --predictions results/raw/predictions/direct-wanli256.jsonl --report build/wanli256.json --calibrated-out build/direct-wanli256.calibrated.jsonl +semif-calibrate --gold build/every/gold154.jsonl --predictions results/raw/predictions/direct-every204.jsonl --report build/every154.json --calibrated-out build/direct-every204.calibrated.jsonl +semif-calibrate --manifest results/raw/calibration/workloads.json --summary build/summary.json ``` To apply an already selected temperature without gold data or refitting: ```bash -python benchmarks/calibrate.py --predictions predictions.jsonl --temperature 1.23 --calibrated-out calibrated.jsonl +semif-calibrate --predictions predictions.jsonl --temperature 1.23 --calibrated-out calibrated.jsonl +``` + +To score a generic calibrated-threshold gate on those outputs (does not touch the +frozen 96-row screen): + +```bash +python benchmarks/evaluate.py --gold benchmarks/data/authored144.jsonl \ + --predictions build/direct-authored144.calibrated.jsonl \ + --output build/authored144.eval.json \ + --calibrated-threshold 0.8 --abstain-option insufficient ``` The committed reports and `summary.json` are the frozen outputs of these commands; @@ -122,5 +135,5 @@ paths are produced by the build steps above). Self-check (argmax invariance and out-of-fold improvement on committed authored data): ```bash -python -c "import sys; sys.path.insert(0,'benchmarks'); import calibrate; calibrate.demo()" +python -c "from semif_phase1.calibration import demo; demo()" ``` diff --git a/pyproject.toml b/pyproject.toml index c7ff397..1eaab47 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,6 +23,7 @@ dependencies = [ [project.scripts] semif-score = "semif_phase1.cli:main" +semif-calibrate = "semif_phase1.calibration:main" [project.optional-dependencies] test = ["pytest==8.4.2"] diff --git a/src/semif_phase1/calibration.py b/src/semif_phase1/calibration.py new file mode 100644 index 0000000..4141139 --- /dev/null +++ b/src/semif_phase1/calibration.py @@ -0,0 +1,419 @@ +"""Post-hoc temperature scaling for direct option logits. + +A separate labeled layer: it never touches the native scorer. It reads committed +prediction logits, fits a single scalar T against gold labels, and reports honest +group-disjoint out-of-fold calibration. Softmax(logits / T) is monotone, so the +argmax -- and therefore every committed accuracy -- is unchanged; only confidence +moves. numpy only; runs offline on CPU, no model load. + +Installable as ``semif-calibrate``; ``python benchmarks/calibrate.py`` remains a +thin back-compat entry. +""" +from __future__ import annotations + +import argparse +import json +import math +from collections import defaultdict +from pathlib import Path + +import numpy as np + + +def load_pairs(gold_path: Path, pred_path: Path) -> list[dict]: + """Join predictions to gold by id, keeping only hard-label rows present in both. + + Rows without a hard ``label`` or carrying a ``target_distribution`` are skipped: + soft-label / ranking rows need distribution-aware handling, not scalar scaling. + """ + gold, seen_gold = {}, set() + for line in gold_path.read_text().splitlines(): + if line.strip(): + row = json.loads(line) + if row["id"] in seen_gold: + raise ValueError(f"Duplicate gold row: {row['id']}") + seen_gold.add(row["id"]) + if "label" in row and row.get("target_distribution") is None: + ids = [option["id"] for option in row["options"]] + if len(set(ids)) != len(ids): + raise ValueError(f"{row['id']}: duplicate gold option ids") + if not isinstance(row["label"], int) or isinstance(row["label"], bool) or not 0 <= row["label"] < len(ids): + raise ValueError(f"{row['id']}: label out of range") + gold[row["id"]] = row + pairs, seen = [], set() + for line in pred_path.read_text().splitlines(): + if not line.strip(): + continue + pred = json.loads(line) + if pred["id"] in seen: + raise ValueError(f"Duplicate prediction row: {pred['id']}") + seen.add(pred["id"]) + row = gold.get(pred["id"]) + if row is None: + continue + option_ids = pred["option_ids"] + if len(set(option_ids)) != len(option_ids): + raise ValueError(f"{pred['id']}: duplicate prediction option ids") + if len(pred["option_logits"]) != len(option_ids): + raise ValueError(f"{pred['id']}: option_logits and option_ids length mismatch") + if set(option_ids) != {option["id"] for option in row["options"]}: + raise ValueError(f"{pred['id']}: prediction options differ from gold options") + logits = np.asarray(pred["option_logits"], dtype=float) + if not np.all(np.isfinite(logits)): + raise ValueError(f"{pred['id']}: non-finite option_logits") + true_id = row["options"][row["label"]]["id"] + pairs.append( + { + "id": pred["id"], + "family": row["family"], + "group": row["group_id"], + "logits": logits, + "true_index": option_ids.index(true_id), + } + ) + if not pairs: + raise ValueError(f"No hard-label rows shared between {gold_path.name} and {pred_path.name}") + return pairs + + +def check_temperature(temperature: float) -> float: + """A temperature must be finite and positive; otherwise dividing logits can flip the argmax.""" + if not math.isfinite(temperature) or temperature <= 0: + raise ValueError(f"Temperature must be finite and > 0, got {temperature!r}") + return temperature + + +def softmax(logits: np.ndarray) -> np.ndarray: + shifted = logits - logits.max() + weights = np.exp(shifted) + return weights / weights.sum() + + +def mean_nll(pairs: list[dict], temperature: float) -> float: + total = 0.0 + for pair in pairs: + probs = softmax(pair["logits"] / temperature) + total += -math.log(max(probs[pair["true_index"]], 1e-12)) + return total / len(pairs) + + +def fit_temperature(pairs: list[dict], bounds=(0.05, 20.0), iterations=60) -> float: + """Minimize mean NLL over T by golden-section search. NLL is convex in 1/T.""" + ratio = (math.sqrt(5) - 1) / 2 + low, high = bounds + left, right = high - ratio * (high - low), low + ratio * (high - low) + f_left, f_right = mean_nll(pairs, left), mean_nll(pairs, right) + for _ in range(iterations): + if f_left < f_right: + high, right, f_right = right, left, f_left + left = high - ratio * (high - low) + f_left = mean_nll(pairs, left) + else: + low, left, f_left = left, right, f_right + right = low + ratio * (high - low) + f_right = mean_nll(pairs, right) + return (low + high) / 2 + + +def scored(pairs: list[dict], temperature: float) -> list[dict]: + """Per-row calibrated top-label confidence and correctness (correctness is T-invariant).""" + check_temperature(temperature) + out = [] + for pair in pairs: + probs = softmax(pair["logits"] / temperature) + row = { + "id": pair["id"], + "group": pair["group"], + "family": pair["family"], + "confidence": float(probs.max()), + "correct": int(probs.argmax() == pair["true_index"]), + } + if "workload" in pair: + row["workload"] = pair["workload"] + out.append(row) + return out + + +def reliability_bins(rows: list[dict], bins=10) -> list[dict]: + result = [] + for index in range(bins): + part = [r for r in rows if min(bins - 1, int(r["confidence"] * bins)) == index] + if part: + result.append( + { + "lower": index / bins, + "upper": (index + 1) / bins, + "n": len(part), + "mean_confidence": sum(r["confidence"] for r in part) / len(part), + "accuracy": sum(r["correct"] for r in part) / len(part), + } + ) + return result + + +def ece(rows: list[dict], bins=10) -> float: + """Expected calibration error, top-label, equal-width bins.""" + total = 0.0 + for entry in reliability_bins(rows, bins): + total += abs(entry["accuracy"] - entry["mean_confidence"]) * entry["n"] / len(rows) + return total + + +def bootstrap_ece(rows: list[dict], samples=1000, seed=217, bins=10) -> list[float]: + """95% interval by resampling source groups with replacement.""" + by_group = defaultdict(list) + for row in rows: + by_group[row["group"]].append(row) + groups = list(by_group.values()) + rng = np.random.default_rng(seed) + values = [] + for _ in range(samples): + draw = [] + for _ in groups: + draw.extend(groups[rng.integers(len(groups))]) + values.append(ece(draw, bins)) + values.sort() + return [values[int(0.025 * samples)], values[min(samples - 1, int(0.975 * samples))]] + + +def fold_map(pairs: list[dict], folds=5, seed=217) -> dict: + """Deterministic ``{group_id: fold}`` assignment (group-disjoint; variants share a group).""" + groups = sorted({pair["group"] for pair in pairs}) + rng = np.random.default_rng(seed) + rng.shuffle(groups) + return {group: index % folds for index, group in enumerate(groups)} + + +def grouped_cv(pairs: list[dict], folds=5, seed=217) -> tuple[list[dict], list[float], dict]: + """Out-of-fold calibrated rows using group-disjoint folds (variants share a group). + + Also returns the exact ``{group_id: fold}`` assignment so the split can be committed + and independently audited, not just re-derived from the seed. + """ + fold_of = fold_map(pairs, folds, seed) + held, temperatures = [], [] + for fold in range(folds): + train = [p for p in pairs if fold_of[p["group"]] != fold] + test = [p for p in pairs if fold_of[p["group"]] == fold] + temperature = fit_temperature(train) + temperatures.append(temperature) + held.extend(scored(test, temperature)) + return held, temperatures, fold_of + + +def per_family(rows: list[dict]) -> dict: + families = defaultdict(list) + for row in rows: + families[row["family"]].append(row) + return {name: {"n": len(part), "ece": ece(part)} for name, part in sorted(families.items())} + + +def build_report(gold_path: Path, pred_path: Path, folds=5, seed=217) -> dict: + pairs = load_pairs(gold_path, pred_path) + temperature = fit_temperature(pairs) + base = scored(pairs, 1.0) + held, fold_temperatures, fold_of = grouped_cv(pairs, folds, seed) + accuracy = sum(r["correct"] for r in base) / len(base) + base_ci = bootstrap_ece(base, seed=seed) + held_ci = bootstrap_ece(held, seed=seed) + return { + "method": "per-workload post-hoc temperature scaling; single scalar; NLL-fit; monotone (argmax-invariant)", + "gold": gold_path.name, + "predictions": pred_path.name, + "rows": len(pairs), + "groups": len({p["group"] for p in pairs}), + "shipped_temperature": temperature, + "cv": {"folds": folds, "seed": seed, "fold_temperatures": fold_temperatures, "fold_groups": fold_of}, + "accuracy_unchanged": accuracy, + "ece_uncalibrated": {"value": ece(base), "ci95": base_ci}, + "ece_calibrated_out_of_fold": {"value": ece(held), "ci95": held_ci}, + "improvement_ci_separated": held_ci[1] < base_ci[0], + "per_family_uncalibrated": per_family(base), + "per_family_calibrated_out_of_fold": per_family(held), + "reliability_bins_uncalibrated": reliability_bins(base), + "reliability_bins_calibrated_out_of_fold": reliability_bins(held), + "caveats": [ + "ECE at this n is high-variance; read the bootstrap intervals, not the point value.", + "A single scalar corrects over/under-confidence, not shape miscalibration within a family.", + "shipped_temperature is fit on all labeled rows; the out-of-fold ECE is the honest generalization estimate.", + ], + } + + +def paired_bootstrap_delta(own_by_id: dict, pooled_by_id: dict, samples=1000, seed=217) -> list[float]: + """95% interval of (pooled-T ECE - own-T ECE), paired per row, resampling shared groups.""" + ids = [key for key in own_by_id if key in pooled_by_id] + by_group = defaultdict(list) + for key in ids: + by_group[own_by_id[key]["group"]].append(key) + groups = list(by_group.values()) + rng = np.random.default_rng(seed) + values = [] + for _ in range(samples): + draw = [] + for _ in groups: + draw.extend(groups[rng.integers(len(groups))]) + values.append(ece([pooled_by_id[k] for k in draw]) - ece([own_by_id[k] for k in draw])) + values.sort() + return [values[int(0.025 * samples)], values[min(samples - 1, int(0.975 * samples))]] + + +def _oof_by_fold(pairs: list[dict]) -> dict: + """Out-of-fold scored rows keyed by id, using each pair's pre-assigned ``fold``.""" + held = {} + for fold in sorted({pair["fold"] for pair in pairs}): + train = [pair for pair in pairs if pair["fold"] != fold] + test = [pair for pair in pairs if pair["fold"] == fold] + for row in scored(test, fit_temperature(train)): + held[row["id"]] = row + return held + + +def cross_workload_report(workloads: dict, folds=5, seed=217) -> dict: + """Per-workload temperature vs one pooled temperature (the negative control). + + Both sides use the *same* committed per-workload fold assignment for every row, so a + row's own-T and pooled-T scores differ only in training scope (its workload alone vs + all workloads), never in which fold holds it out. All scores are out-of-fold. + """ + workload_pairs, fold_maps = {}, {} + for name, (gold_path, pred_path) in workloads.items(): + pairs = load_pairs(gold_path, pred_path) + assignment = fold_map(pairs, folds, seed) + for pair in pairs: + pair["workload"] = name + pair["fold"] = assignment[pair["group"]] + workload_pairs[name] = pairs + fold_maps[name] = assignment + all_pairs = [pair for pairs in workload_pairs.values() for pair in pairs] + + own_held = {name: _oof_by_fold(pairs) for name, pairs in workload_pairs.items()} + + pooled_held, pooled_fold_temperatures = defaultdict(dict), [] + for fold in range(folds): + temperature = fit_temperature([p for p in all_pairs if p["fold"] != fold]) + pooled_fold_temperatures.append(temperature) + for row in scored([p for p in all_pairs if p["fold"] == fold], temperature): + pooled_held[row["workload"]][row["id"]] = row + + per_workload = {} + for name, (gold_path, pred_path) in workloads.items(): + own, pooled_rows = own_held[name], pooled_held[name] + report = build_report(gold_path, pred_path, folds, seed) + delta_ci = paired_bootstrap_delta(own, pooled_rows, seed=seed) + per_workload[name] = { + "rows": report["rows"], + "accuracy": report["accuracy_unchanged"], + "own_temperature": report["shipped_temperature"], + "ece_uncalibrated": report["ece_uncalibrated"]["value"], + "ece_own_temperature_out_of_fold": ece(list(own.values())), + "ece_pooled_temperature_out_of_fold": ece(list(pooled_rows.values())), + "pooled_minus_own_ece_ci95": delta_ci, + "pooled_temperature_significantly_worse": delta_ci[0] > 0, + } + return { + "method": "per-workload temperature vs one pooled temperature; both fit fold-wise on the SAME " + "committed per-workload fold assignments and evaluated out-of-fold", + "cv": {"folds": folds, "seed": seed}, + "per_workload": per_workload, + "pooled_temperature_negative_control": { + "pooled_rows": len(all_pairs), + "all_rows_pooled_temperature": fit_temperature(all_pairs), + "fold_temperatures": pooled_fold_temperatures, + "fold_groups_by_workload": fold_maps, + "note": "Own and pooled out-of-fold scores use the same per-row fold (each workload's committed " + "fold_groups); only the training scope differs (workload-only vs all workloads). " + "fold_temperatures are the pooled per-fold temperatures; all_rows_pooled_temperature is the " + "single value you would deploy if forced to pick one. A flag is true only when the paired " + "95% interval excludes 0.", + }, + } + + +def apply(pred_path: Path, out_path: Path, temperature: float) -> None: + """Emit a calibrated predictions file (probabilities recomputed at T) for the existing evaluator.""" + check_temperature(temperature) + if out_path.exists(): + raise FileExistsError(out_path) + calibrated = [] + for line in pred_path.read_text().splitlines(): + if not line.strip(): + continue + pred = json.loads(line) + logits = np.asarray(pred["option_logits"], dtype=float) + if logits.ndim != 1 or not len(logits) or not np.all(np.isfinite(logits)): + raise ValueError(f"{pred.get('id', '')}: option_logits must be a finite, non-empty vector") + if len(logits) != len(pred.get("option_ids", [])): + raise ValueError(f"{pred.get('id', '')}: option_logits and option_ids length mismatch") + pred["probabilities"] = softmax(logits / temperature).tolist() + pred["calibration"] = {"method": "temperature_scaling", "temperature": temperature} + calibrated.append(json.dumps(pred, allow_nan=False)) + + # Validate every input row before creating the output. A malformed later row + # must not leave behind a plausible-looking partial predictions file. + with out_path.open("x") as destination: + if calibrated: + destination.write("\n".join(calibrated) + "\n") + + +def demo() -> None: + """Self-check on committed authored144: argmax invariant, out-of-fold ECE improves.""" + root = Path(__file__).resolve().parents[2] + pairs = load_pairs(root / "benchmarks/data/authored144.jsonl", root / "results/raw/predictions/direct-authored144.jsonl") + temperature = fit_temperature(pairs) + base = scored(pairs, 1.0) + calibrated = scored(pairs, temperature) + assert all(b["correct"] == c["correct"] for b, c in zip(base, calibrated)), "T changed an argmax" + held, _, _ = grouped_cv(pairs) + assert ece(held) < ece(base), "out-of-fold calibration did not help" + print(f"ok: T={temperature:.3f} ECE {ece(base):.3f} -> {ece(held):.3f} (out-of-fold) acc unchanged={sum(r['correct'] for r in base)/len(base):.3f}") + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--gold", type=Path) + parser.add_argument("--predictions", type=Path) + parser.add_argument("--report", type=Path, help="Write the JSON calibration report here (must be new).") + parser.add_argument("--apply-to", type=Path, help="Predictions file to calibrate (defaults to --predictions).") + parser.add_argument("--calibrated-out", type=Path, help="Write calibrated predictions here (must be new).") + parser.add_argument("--temperature", type=float, help="Skip fitting; apply this T instead.") + parser.add_argument("--manifest", type=Path, help='Cross-workload JSON {"name": {"gold": ..., "predictions": ...}}.') + parser.add_argument("--summary", type=Path, help="Write the cross-workload summary here (must be new).") + args = parser.parse_args() + + if args.manifest: + spec = json.loads(args.manifest.read_text()) + workloads = {name: (Path(entry["gold"]), Path(entry["predictions"])) for name, entry in spec.items()} + summary = cross_workload_report(workloads) + if args.summary: + with args.summary.open("x") as destination: + destination.write(json.dumps(summary, indent=2, allow_nan=False) + "\n") + print(json.dumps({"workloads": list(workloads), "all_rows_pooled_temperature": summary["pooled_temperature_negative_control"]["all_rows_pooled_temperature"]})) + return + + if args.temperature is not None: + temperature = check_temperature(args.temperature) + source = args.apply_to or args.predictions + if source is None or args.calibrated_out is None: + parser.error("--temperature requires --predictions (or --apply-to) and --calibrated-out") + if args.report: + parser.error("--report requires fitted calibration; omit --temperature") + apply(source, args.calibrated_out, temperature) + print(json.dumps({"applied_temperature": temperature, "predictions": str(source), + "calibrated_out": str(args.calibrated_out)})) + return + + if not args.gold or not args.predictions: + parser.error("Provide --gold and --predictions, or --manifest") + report = build_report(args.gold, args.predictions) + temperature = report["shipped_temperature"] + if args.report: + with args.report.open("x") as destination: + destination.write(json.dumps(report, indent=2, allow_nan=False) + "\n") + if args.calibrated_out: + apply(args.apply_to or args.predictions, args.calibrated_out, temperature) + print(json.dumps({k: report[k] for k in ("rows", "shipped_temperature", "ece_uncalibrated", "ece_calibrated_out_of_fold")})) + + +if __name__ == "__main__": + main() diff --git a/tests/test_calibrate.py b/tests/test_calibrate.py index 3d3982b..7d9bc67 100644 --- a/tests/test_calibrate.py +++ b/tests/test_calibrate.py @@ -4,52 +4,75 @@ import pytest +from semif_phase1 import calibration + ROOT = Path(__file__).resolve().parents[1] sys.path.insert(0, str(ROOT / "benchmarks")) -import calibrate # noqa: E402 +import evaluate # noqa: E402 + + +def _row(rid, options, label, family="f", group="g"): + return { + "id": rid, + "family": family, + "group_id": group, + "label": label, + "options": [{"id": option} for option in options], + } + + +def _pred(rid, option_ids, logits, temperature=None): + import numpy as np + + values = np.asarray(logits, dtype=float) + shifted = values - values.max() + weights = np.exp(shifted) + probs = (weights / weights.sum()).tolist() + row = {"id": rid, "option_ids": option_ids, "option_logits": list(logits), "probabilities": probs} + if temperature is not None: + row["calibration"] = {"method": "temperature_scaling", "temperature": temperature} + return row def test_bad_temperature_rejected(): for bad in (0, -1.0, float("inf"), float("nan")): with pytest.raises(ValueError): - calibrate.check_temperature(bad) + calibration.check_temperature(bad) def test_option_set_mismatch_rejected(tmp_path): gold = tmp_path / "gold.jsonl" pred = tmp_path / "pred.jsonl" - gold.write_text(json.dumps({"id": "x", "family": "f", "group_id": "g", "label": 0, - "options": [{"id": "a"}, {"id": "b"}]}) + "\n") + gold.write_text(json.dumps(_row("x", ["a", "b"], 0)) + "\n") pred.write_text(json.dumps({"id": "x", "option_ids": ["a", "c"], "option_logits": [1.0, 2.0]}) + "\n") with pytest.raises(ValueError): - calibrate.load_pairs(gold, pred) + calibration.load_pairs(gold, pred) def test_duplicate_gold_row_rejected(tmp_path): gold = tmp_path / "gold.jsonl" pred = tmp_path / "pred.jsonl" - row = {"id": "x", "family": "f", "group_id": "g", "label": 0, "options": [{"id": "a"}, {"id": "b"}]} + row = _row("x", ["a", "b"], 0) gold.write_text(json.dumps(row) + "\n" + json.dumps(row) + "\n") pred.write_text(json.dumps({"id": "x", "option_ids": ["a", "b"], "option_logits": [1.0, 2.0]}) + "\n") with pytest.raises(ValueError): - calibrate.load_pairs(gold, pred) + calibration.load_pairs(gold, pred) def test_non_finite_logits_rejected(tmp_path): gold = tmp_path / "gold.jsonl" pred = tmp_path / "pred.jsonl" - gold.write_text(json.dumps({"id": "x", "family": "f", "group_id": "g", "label": 0, - "options": [{"id": "a"}, {"id": "b"}]}) + "\n") + gold.write_text(json.dumps(_row("x", ["a", "b"], 0)) + "\n") pred.write_text(json.dumps({"id": "x", "option_ids": ["a", "b"], "option_logits": [1.0, float("inf")]}) + "\n") with pytest.raises(ValueError): - calibrate.load_pairs(gold, pred) + calibration.load_pairs(gold, pred) def test_argmax_invariant_under_temperature(): - pairs = calibrate.load_pairs(ROOT / "benchmarks/data/authored144.jsonl", - ROOT / "results/raw/predictions/direct-authored144.jsonl") - base = calibrate.scored(pairs, 1.0) - hot = calibrate.scored(pairs, calibrate.fit_temperature(pairs)) + pairs = calibration.load_pairs(ROOT / "benchmarks/data/authored144.jsonl", + ROOT / "results/raw/predictions/direct-authored144.jsonl") + base = calibration.scored(pairs, 1.0) + hot = calibration.scored(pairs, calibration.fit_temperature(pairs)) assert [r["correct"] for r in base] == [r["correct"] for r in hot] @@ -61,7 +84,7 @@ def test_apply_validates_all_rows_before_creating_output(tmp_path): + json.dumps({"id": "bad", "option_ids": ["a", "b"], "option_logits": [1.0, float("inf")]}) + "\n" ) with pytest.raises(ValueError): - calibrate.apply(predictions, output, 1.5) + calibration.apply(predictions, output, 1.5) assert not output.exists() @@ -71,13 +94,54 @@ def test_fixed_temperature_cli_does_not_fit(tmp_path, monkeypatch, capsys): predictions.write_text( json.dumps({"id": "x", "option_ids": ["a", "b"], "option_logits": [1.0, 2.0]}) + "\n" ) - monkeypatch.setattr(calibrate, "build_report", lambda *args, **kwargs: pytest.fail("fit was called")) + monkeypatch.setattr(calibration, "build_report", lambda *args, **kwargs: pytest.fail("fit was called")) monkeypatch.setattr( sys, "argv", - ["calibrate.py", "--predictions", str(predictions), "--temperature", "2.0", + ["semif-calibrate", "--predictions", str(predictions), "--temperature", "2.0", "--calibrated-out", str(output)], ) - calibrate.main() + calibration.main() assert json.loads(output.read_text())["calibration"]["temperature"] == 2.0 assert json.loads(capsys.readouterr().out)["applied_temperature"] == 2.0 + + +def test_benchmarks_shim_still_exposes_main(): + import calibrate as shim # noqa: WPS433 + + assert shim.main is calibration.main + assert shim.fit_temperature is calibration.fit_temperature + + +def test_calibrated_threshold_gate_on_fixture_logits(): + gold = [ + _row("keep", ["yes", "no"], 0, group="g1"), + _row("review", ["yes", "no"], 1, group="g2"), + _row("abstain", ["yes", "insufficient"], 1, group="g3"), + ] + predictions = [ + _pred("keep", ["yes", "no"], [3.0, 0.0], temperature=1.5), + _pred("review", ["yes", "no"], [0.2, 0.0], temperature=1.5), + _pred("abstain", ["yes", "insufficient"], [0.0, 4.0], temperature=1.5), + ] + gate = evaluate.calibrated_threshold_gate(gold, predictions, threshold=0.8, abstain_option="insufficient") + by_id = {row["id"]: row for row in gate["decisions"]} + assert gate["calibrated_predictions"] is True + assert gate["applied_temperatures"] == [1.5] + assert by_id["keep"]["disposition"] == "automatic_semantic_decision" + assert by_id["review"]["disposition"] == "review" + assert by_id["abstain"]["disposition"] == "review" + assert gate["automatic_decisions"] == 1 + assert gate["review_decisions"] == 2 + + +def test_calibrated_threshold_rejects_bad_threshold(): + with pytest.raises(ValueError): + evaluate.calibrated_threshold_gate([], [], threshold=0) + with pytest.raises(ValueError): + evaluate.calibrated_threshold_gate([], [], threshold=1.5) + + +def test_screening_gate_still_frozen(): + with pytest.raises(ValueError, match="frozen"): + evaluate.screening_gate([], [], policy="distribution", threshold=0.7) From 2a5a2ef461e22ba4817c48ceb70caa8a22e2fd95 Mon Sep 17 00:00:00 2001 From: sinksilk <785976238@qq.com> Date: Fri, 25 Sep 2026 17:23:51 +0800 Subject: [PATCH 09/13] Add option-order sensitivity measurement and opt-in stabilize-order Measure ID-aligned flips, total variation, and position bias from committed predictions without changing default scores; optional --stabilize-order K averages permutation logits under a distinct prompt_version at Kx cost. --- README.md | 1 + benchmarks/README.md | 13 ++ benchmarks/evaluate_option_order.py | 122 +++++++++++++ docs/METHOD.md | 2 + docs/OPTION_ORDER.md | 69 ++++++++ docs/REPRODUCE.md | 13 ++ src/semif_phase1/cli.py | 31 +++- src/semif_phase1/order.py | 264 ++++++++++++++++++++++++++++ tests/test_cli.py | 48 +++++ tests/test_order.py | 163 +++++++++++++++++ 10 files changed, 724 insertions(+), 2 deletions(-) create mode 100644 benchmarks/evaluate_option_order.py create mode 100644 docs/OPTION_ORDER.md create mode 100644 src/semif_phase1/order.py create mode 100644 tests/test_order.py diff --git a/README.md b/README.md index 8f607da..ea7a15d 100644 --- a/README.md +++ b/README.md @@ -191,6 +191,7 @@ Returned probabilities are conditional on the supplied options. Calibrate and va - [Reproduce](docs/REPRODUCE.md) — exact environment, pinned commands, perturbations, and verification - [Apple Silicon](docs/APPLE_SILICON.md) — MPS and optional MLX backends - [Calibration](docs/CALIBRATION.md) — fitted temperatures, out-of-fold evidence, and application +- [Option order](docs/OPTION_ORDER.md) — order-sensitivity measurement and opt-in `--stabilize-order` - [EXL3 bridge](exl3-bridge/README.md) — quantized 27B runner and committed evidence - [Interactive replay](demo/index.html) - [Browser-only WebGPU demo](webgpu-demo/index.html) — no waitlist; use it today diff --git a/benchmarks/README.md b/benchmarks/README.md index 4e5123d..ecdb8e4 100644 --- a/benchmarks/README.md +++ b/benchmarks/README.md @@ -27,6 +27,19 @@ python benchmarks/build_perturbations.py \ `docs/REPRODUCE.md` gives the complete command for rebuilding `results/raw/perturbation-comparison.json` from the committed row-level predictions. The regenerated report is byte-identical to the committed report. +Recompute the option-order sensitivity digest (flips, total variation, position bias) from those same committed files without loading a model: + +```bash +python benchmarks/evaluate_option_order.py \ + --gold benchmarks/data/authored144.jsonl \ + --perturbations benchmarks/data/perturbations108.jsonl \ + --base-predictions results/raw/predictions/direct-authored144.jsonl \ + --perturbation-predictions results/raw/predictions/direct-perturbations108.jsonl \ + --output /tmp/option-order-direct.json +``` + +See [OPTION_ORDER.md](../docs/OPTION_ORDER.md) for the opt-in `--stabilize-order K` scorer flag and its `K×` cost. Do not mix stabilize-order outputs into frozen quality tables. + ## Full 37×21 systems benchmark ```bash diff --git a/benchmarks/evaluate_option_order.py b/benchmarks/evaluate_option_order.py new file mode 100644 index 0000000..dfa8094 --- /dev/null +++ b/benchmarks/evaluate_option_order.py @@ -0,0 +1,122 @@ +"""Measure option-order sensitivity from committed (or fresh) prediction JSONL. + +Pure Python / CPU. Aligns probabilities by semantic option ID, then reports +argmax flips, total variation, and display-position bias. Default inputs reuse +the frozen option_reversal perturbation already cited in docs/RESULTS.md +(~10/36 direct flips). This report does not rewrite headline quality tables. +""" +from __future__ import annotations + +import argparse +import json +import statistics +from pathlib import Path + +from semif_phase1.order import report_pair_sensitivity, summarize_sensitivity + + +def read_jsonl(path: Path) -> list[dict]: + rows = [json.loads(line) for line in path.read_text().splitlines() if line.strip()] + ids = [row["id"] for row in rows] + if len(ids) != len(set(ids)): + raise ValueError(f"Duplicate IDs in {path}") + return rows + + +def indexed(rows: list[dict]) -> dict[str, dict]: + return {row["id"]: row for row in rows} + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--gold", type=Path, required=True, + help="Authored (or similar) gold JSONL containing original rows") + parser.add_argument("--perturbations", type=Path, required=True, + help="Frozen perturbation JSONL (expects option_reversal rows)") + parser.add_argument("--base-predictions", type=Path, required=True) + parser.add_argument("--perturbation-predictions", type=Path, required=True) + parser.add_argument("--variant", default="option_reversal", + help="Perturbation variant to analyze (default: option_reversal)") + parser.add_argument("--output", type=Path, required=True, + help="Create-only JSON report path") + args = parser.parse_args() + if args.output.exists(): + raise SystemExit(f"Refusing to overwrite existing output: {args.output}") + + gold = read_jsonl(args.gold) + perturb_gold = read_jsonl(args.perturbations) + base_pred = indexed(read_jsonl(args.base_predictions)) + perturb_pred = indexed(read_jsonl(args.perturbation_predictions)) + + originals = { + row["id"]: row + for row in gold + if row.get("provenance", {}).get("variant") == "original" + } + variants = [ + row for row in perturb_gold + if row.get("provenance", {}).get("variant") == args.variant + ] + if not variants: + raise SystemExit(f"No rows with provenance.variant={args.variant!r}") + + pairs = [] + for variant_row in variants: + base_id = variant_row["provenance"]["base_id"] + if base_id not in originals: + raise SystemExit(f"Missing original gold for {base_id}") + if base_id not in base_pred or variant_row["id"] not in perturb_pred: + raise SystemExit(f"Missing predictions for base={base_id} / variant={variant_row['id']}") + base_gold = originals[base_id] + pairs.append( + report_pair_sensitivity( + base_pred[base_id], + perturb_pred[variant_row["id"]], + base_display_ids=[option["id"] for option in base_gold["options"]], + variant_display_ids=[option["id"] for option in variant_row["options"]], + ) + ) + + summary = summarize_sensitivity(pairs) + report = { + "meaning": ( + "Option-order sensitivity after aligning probabilities by semantic option ID. " + "Measurement only: does not change default scorer outputs or frozen quality tables." + ), + "variant": args.variant, + "inputs": { + "gold": str(args.gold), + "perturbations": str(args.perturbations), + "base_predictions": str(args.base_predictions), + "perturbation_predictions": str(args.perturbation_predictions), + }, + "summary": summary, + "rows": pairs, + "notes": { + "total_variation": "0.5 * L1 distance between ID-aligned probability vectors", + "position_bias": summary["position_bias"]["meaning"], + "stabilize_order": ( + "Opt-in mitigation is semif-score --stabilize-order K (direct/serial). " + "Cost multiplier is K forward passes per decision; numbers from that " + "mode must not be mixed into frozen quality tables." + ), + }, + } + # Lightweight console digest for interactive use. + print(json.dumps({ + "variant": args.variant, + "rows": summary["rows"], + "argmax_flips": summary["argmax_flips"], + "flip_rate": summary["flip_rate"], + "mean_total_variation": summary["mean_total_variation"], + "max_total_variation": summary["max_total_variation"], + "position_bias": summary["position_bias"], + "median_total_variation": statistics.median(row["total_variation"] for row in pairs), + }, indent=2, allow_nan=False)) + args.output.parent.mkdir(parents=True, exist_ok=True) + with args.output.open("x") as destination: + destination.write(json.dumps(report, indent=2, allow_nan=False) + "\n") + + +if __name__ == "__main__": + main() diff --git a/docs/METHOD.md b/docs/METHOD.md index 32114ec..35308db 100644 --- a/docs/METHOD.md +++ b/docs/METHOD.md @@ -31,6 +31,8 @@ TypeSafe extraction reads four locally supplied, hash-verified `*-cases.js` snap Thirty-six owned original cases received three output-blind variants: reverse the displayed option order while preserving semantic IDs, wrap the criterion in meaning-preserving wording, and append irrelevant owned context. A separate 36-row missing-evidence population tests whether a system selects `insufficient`. Stability is measured after aligning probabilities by semantic option ID. +The CPU measurement harness and optional `--stabilize-order K` mitigation for letter-slot order sensitivity are documented in [OPTION_ORDER.md](OPTION_ORDER.md). That opt-in readout is separately versioned and is not part of the frozen quality tables. + ## Browser model ladder Qwen3-0.6B, MiniCPM5-2B, and Qwen3.5-4B use the same frozen prompt and native BF16 final-position option-logit scorer on the 144 authored, 108 perturbation, and 102 selected TypeSafe rows. TypeSafe modal agreement is averaged within each of the 20 source cases and then equally across cases. The browser artifacts are independently pinned GGUF quantizations. Browser smoke timings begin after the page initiates each operation; model files were served from a local SSD to exclude internet transfer time. A successful smoke requires model load, warmup, finite logits for every displayed option, and completion of the generated path. It does not establish full quantized quality or portable latency. diff --git a/docs/OPTION_ORDER.md b/docs/OPTION_ORDER.md new file mode 100644 index 0000000..0eaa08f --- /dev/null +++ b/docs/OPTION_ORDER.md @@ -0,0 +1,69 @@ +# Option-order sensitivity + +Direct letter-slot readouts assign options to fixed tokens `A`, `B`, `C`, …. +Semantic option IDs are independent of that display order. The Phase 1 results +already record that reversing option order flips about **10/36** direct decisions +on the owned stability set ([RESULTS.md](RESULTS.md)). This document describes +the measurement harness and an **opt-in** mitigation. Neither path rewrites +frozen quality tables or default `direct-options-v1` scores. + +## Measurement (CPU, no model required) + +`benchmarks/evaluate_option_order.py` joins committed base predictions to the +frozen `option_reversal` perturbation predictions, aligns probabilities by +semantic option ID, and reports: + +- argmax flips and flip rate +- mean / max total variation (`0.5 * L1` on the aligned distributions) +- display-position bias (win rate of letter slot A/B/C/… before vs after reverse) + +```bash +python benchmarks/evaluate_option_order.py \ + --gold benchmarks/data/authored144.jsonl \ + --perturbations benchmarks/data/perturbations108.jsonl \ + --base-predictions results/raw/predictions/direct-authored144.jsonl \ + --perturbation-predictions results/raw/predictions/direct-perturbations108.jsonl \ + --output /tmp/option-order-direct.json +``` + +The same command works for reranker prediction files. The harness is analysis-only: +it does not change scorer outputs. Shared math lives in +`semif_phase1.order` and is covered by CPU unit tests with fixture logits (no +weight downloads). + +To regenerate fresh permutation scores on CPU without a GPU, use the llama.cpp +backend (`--backend llamacpp --gguf …`) or Torch `--device cpu`, then point this +harness at the new create-only prediction paths. + +## Opt-in `--stabilize-order K` + +```bash +semif-score --mode direct --stabilize-order 6 \ + --model … --revision … --input decisions.jsonl --output stabilized.jsonl +``` + +Supported on **direct** and **serial** only (including Torch / MLX / llama.cpp). +Rejected for `shared` and `reranker`. + +For each decision the scorer: + +1. Selects `K` distinct option permutations (always includes identity; includes + reverse when `K >= 2`; samples the rest with `--stabilize-order-seed`). +2. Scores each permutation with the ordinary native slot-logit readout. +3. Aligns `option_logits` back to the original semantic option-ID order. +4. Averages the aligned logits, then applies softmax. + +Outputs are marked so they cannot be confused with the default readout: + +| Field | Value | +|---|---| +| `prompt_version` | `direct-options-stabilize-order-v1` | +| `readout` | describes the K-permutation average | +| `stabilize_order.k` / `cost_multiplier` | `K` | + +### Cost + +Each decision costs **`K` forward passes** instead of one (same prompt length +class per pass). For the owned 3-option rows, `K=6` covers every permutation; +`K=2` is identity + reverse only. Do **not** put stabilize-order numbers into +frozen quality tables or `results/phase1-summary.json`. diff --git a/docs/REPRODUCE.md b/docs/REPRODUCE.md index 63aac14..8adf06f 100644 --- a/docs/REPRODUCE.md +++ b/docs/REPRODUCE.md @@ -76,6 +76,19 @@ python benchmarks/evaluate_perturbations.py \ cmp perturbation-report.json results/raw/perturbation-comparison.json ``` +For option-order flips, total variation, and position bias alone (CPU, no model): + +```bash +python benchmarks/evaluate_option_order.py \ + --gold benchmarks/data/authored144.jsonl \ + --perturbations benchmarks/data/perturbations108.jsonl \ + --base-predictions results/raw/predictions/direct-authored144.jsonl \ + --perturbation-predictions results/raw/predictions/direct-perturbations108.jsonl \ + --output /tmp/option-order-direct.json +``` + +Details and the opt-in `--stabilize-order K` flag are in [OPTION_ORDER.md](OPTION_ORDER.md). + ## Reproduce the headline speed results Run the focused three-repeat direct-versus-compact-array comparison: diff --git a/src/semif_phase1/cli.py b/src/semif_phase1/cli.py index a7e1d35..81d0bd1 100644 --- a/src/semif_phase1/cli.py +++ b/src/semif_phase1/cli.py @@ -8,6 +8,7 @@ from .core import load_causal_model, validate_row from .direct import score as direct_score +from .order import check_stabilize_k, score_with_stabilized_order from .reranker import score as reranker_score from .serial import SerialPrefixScorer from .shared import score_shared @@ -32,6 +33,11 @@ def main() -> None: help="Torch device (auto prefers CUDA, then Apple MPS; CPU must be explicit)") parser.add_argument("--dtype", choices=("bfloat16", "float16", "float32"), default="bfloat16", help="Model precision; changing it can change option scores") + parser.add_argument("--stabilize-order", type=int, metavar="K", default=None, + help="Opt-in: score K option permutations, average logits by semantic id, " + "then softmax (cost multiplier K; default off)") + parser.add_argument("--stabilize-order-seed", type=int, default=0, + help="RNG seed used when sampling which K permutations to score") args = parser.parse_args() if args.output.exists() or args.max_tokens < 1: parser.error("Output must be new and max-tokens must be positive") @@ -49,6 +55,13 @@ def main() -> None: parser.error("--llama-threads requires --backend llamacpp") if args.llama_threads < 1: parser.error("--llama-threads must be positive") + if args.stabilize_order is not None: + try: + check_stabilize_k(args.stabilize_order) + except ValueError as error: + parser.error(str(error)) + if args.mode in {"shared", "reranker"}: + parser.error("--stabilize-order supports direct and serial modes only") if args.backend == "mlx" and args.mode == "reranker": parser.error("MLX supports direct, serial, and shared modes; reranker requires torch") if args.backend == "llamacpp": @@ -94,12 +107,26 @@ def main() -> None: elif args.mode == "serial": scorer = serial(model, tokenizer, metadata, args.max_tokens) for row in rows: - destination.write(json.dumps(scorer.score(row), allow_nan=False) + "\n") + if args.stabilize_order is None: + result = scorer.score(row) + else: + result = score_with_stabilized_order( + scorer.score, row, k=args.stabilize_order, seed=args.stabilize_order_seed) + destination.write(json.dumps(result, allow_nan=False) + "\n") destination.flush() else: scorer = direct if args.mode == "direct" else reranker_score + + def score_once(row): + return scorer(model, tokenizer, row, metadata, args.max_tokens) + for row in rows: - destination.write(json.dumps(scorer(model, tokenizer, row, metadata, args.max_tokens), allow_nan=False) + "\n") + if args.stabilize_order is None or args.mode == "reranker": + result = score_once(row) + else: + result = score_with_stabilized_order( + score_once, row, k=args.stabilize_order, seed=args.stabilize_order_seed) + destination.write(json.dumps(result, allow_nan=False) + "\n") destination.flush() diff --git a/src/semif_phase1/order.py b/src/semif_phase1/order.py new file mode 100644 index 0000000..9b8e009 --- /dev/null +++ b/src/semif_phase1/order.py @@ -0,0 +1,264 @@ +"""Option-order sensitivity helpers and opt-in order stabilization. + +Letter slots (A/B/C/…) are positional; semantic option IDs are not. These helpers +permute display order, align logits back by option id, and either measure +sensitivity or average across K permutations before softmax. + +Stabilized scoring is a separate readout: it does not change the default +``direct-options-v1`` contract or any frozen quality table. +""" + +from __future__ import annotations + +import copy +import itertools +import math +import random +import time +from typing import Callable, Sequence + +from .core import softmax + +STABILIZE_PROMPT_VERSION = "direct-options-stabilize-order-v1" +STABILIZE_READOUT = ( + "K-permutation average of native option-slot logits aligned by semantic id, " + "then softmax; cost is K forward passes per decision" +) + + +def check_stabilize_k(k: int) -> int: + if not isinstance(k, int) or isinstance(k, bool) or k < 2: + raise ValueError(f"--stabilize-order requires an integer K >= 2, got {k!r}") + return k + + +def apply_permutation(row: dict, permutation: Sequence[int]) -> dict: + """Return a deep copy of ``row`` with options reordered by ``permutation`` indices.""" + options = row["options"] + if sorted(permutation) != list(range(len(options))): + raise ValueError(f"Permutation must be a rearrangement of 0..{len(options) - 1}") + altered = copy.deepcopy(row) + altered["options"] = [options[index] for index in permutation] + if "label" in altered and isinstance(altered["label"], int) and not isinstance(altered["label"], bool): + gold_id = options[altered["label"]]["id"] + altered["label"] = [option["id"] for option in altered["options"]].index(gold_id) + return altered + + +def select_permutations(n_options: int, k: int, *, seed: int = 0) -> list[tuple[int, ...]]: + """Choose ``k`` distinct display permutations, always including identity and reverse when possible. + + Deterministic. When ``n_options!`` is at most ``k``, returns every permutation + (identity first). Otherwise samples without replacement after locking identity + and the reversed order. + """ + check_stabilize_k(k) + if n_options < 2: + raise ValueError("Need at least two options to permute") + identity = tuple(range(n_options)) + reversed_order = tuple(reversed(range(n_options))) + all_perms = list(itertools.permutations(range(n_options))) + if k >= len(all_perms): + # Identity first for stable readout metadata. + rest = [perm for perm in all_perms if perm != identity] + return [identity, *rest] + chosen: list[tuple[int, ...]] = [identity] + if reversed_order != identity and k >= 2: + chosen.append(reversed_order) + remaining = [perm for perm in all_perms if perm not in chosen] + rng = random.Random(seed) + rng.shuffle(remaining) + while len(chosen) < k: + chosen.append(remaining.pop()) + return chosen + + +def align_values_by_id( + source_ids: Sequence[str], + values: Sequence[float], + canonical_ids: Sequence[str], +) -> list[float]: + """Reorder ``values`` so they match ``canonical_ids`` order.""" + if len(source_ids) != len(values): + raise ValueError("source_ids and values length mismatch") + if len(set(source_ids)) != len(source_ids): + raise ValueError("Duplicate source option ids") + if set(source_ids) != set(canonical_ids): + raise ValueError("Option id sets differ; cannot align") + index = {option_id: position for position, option_id in enumerate(source_ids)} + return [float(values[index[option_id]]) for option_id in canonical_ids] + + +def average_aligned(vectors: Sequence[Sequence[float]]) -> list[float]: + if not vectors: + raise ValueError("Need at least one vector to average") + width = len(vectors[0]) + if width < 2 or any(len(vector) != width for vector in vectors): + raise ValueError("Aligned vectors must share the same length (>= 2)") + if any(not math.isfinite(value) for vector in vectors for value in vector): + raise ValueError("Non-finite values in aligned vectors") + scale = len(vectors) + return [sum(column) / scale for column in zip(*vectors)] + + +def total_variation(left: Sequence[float], right: Sequence[float]) -> float: + if len(left) != len(right): + raise ValueError("Distribution lengths differ") + return 0.5 * sum(abs(a - b) for a, b in zip(left, right)) + + +def chosen_id(option_ids: Sequence[str], probabilities: Sequence[float]) -> str: + return option_ids[max(range(len(probabilities)), key=probabilities.__getitem__)] + + +def distribution_map(option_ids: Sequence[str], probabilities: Sequence[float]) -> dict[str, float]: + if len(option_ids) != len(probabilities) or len(set(option_ids)) != len(option_ids): + raise ValueError("Invalid option_ids/probabilities pair") + return dict(zip(option_ids, probabilities)) + + +def report_pair_sensitivity( + base: dict, + variant: dict, + *, + base_display_ids: Sequence[str] | None = None, + variant_display_ids: Sequence[str] | None = None, +) -> dict: + """Compare one base prediction to one order-perturbed prediction, aligned by option id.""" + left = distribution_map(base["option_ids"], base["probabilities"]) + right = distribution_map(variant["option_ids"], variant["probabilities"]) + if set(left) != set(right): + raise ValueError(f"Semantic option IDs changed for {base.get('id')}") + ids = list(base["option_ids"]) + aligned_right = [right[option_id] for option_id in ids] + base_choice = chosen_id(base["option_ids"], base["probabilities"]) + variant_choice = chosen_id(variant["option_ids"], variant["probabilities"]) + base_pos = list(base_display_ids or base["option_ids"]).index(base_choice) + variant_pos = list(variant_display_ids or variant["option_ids"]).index(variant_choice) + return { + "base_id": base["id"], + "variant_id": variant["id"], + "from": base_choice, + "to": variant_choice, + "flipped": base_choice != variant_choice, + "total_variation": total_variation([left[i] for i in ids], aligned_right), + "base_display_position": base_pos, + "variant_display_position": variant_pos, + } + + +def summarize_sensitivity(rows: Sequence[dict]) -> dict: + """Aggregate pair-level sensitivity rows into flips, TV, and position bias.""" + if not rows: + raise ValueError("No sensitivity rows to summarize") + flips = [row for row in rows if row["flipped"]] + tvs = [row["total_variation"] for row in rows] + n_options = 1 + max(max(row["base_display_position"], row["variant_display_position"]) for row in rows) + base_wins = [0] * n_options + variant_wins = [0] * n_options + for row in rows: + base_wins[row["base_display_position"]] += 1 + variant_wins[row["variant_display_position"]] += 1 + scale = len(rows) + return { + "rows": scale, + "argmax_flips": len(flips), + "flip_rate": len(flips) / scale, + "flip_rows": [ + {"base_id": row["base_id"], "variant_id": row["variant_id"], "from": row["from"], "to": row["to"]} + for row in flips + ], + "mean_total_variation": sum(tvs) / scale, + "max_total_variation": max(tvs), + "position_bias": { + "meaning": ( + "Fraction of rows whose argmax lands on each display letter slot " + "(index 0 = A). Compared before vs after the order perturbation." + ), + "base_position_win_rates": [count / scale for count in base_wins], + "variant_position_win_rates": [count / scale for count in variant_wins], + "first_position_delta": (variant_wins[0] - base_wins[0]) / scale, + }, + } + + +def stabilize_from_permutation_results( + canonical_ids: Sequence[str], + permutation_results: Sequence[dict], + *, + permutations: Sequence[Sequence[int]], + k: int, + seed: int, + base_result: dict | None = None, +) -> dict: + """Average logits from already-scored permutations, then softmax in canonical order.""" + if len(permutation_results) != len(permutations): + raise ValueError("Need one result per permutation") + aligned = [ + align_values_by_id(result["option_ids"], result["option_logits"], canonical_ids) + for result in permutation_results + ] + mean_logits = average_aligned(aligned) + started = time.perf_counter() if base_result is None else None + payload = { + "id": (base_result or permutation_results[0])["id"], + "option_ids": list(canonical_ids), + "probabilities": softmax(mean_logits), + "option_logits": mean_logits, + "prompt_version": STABILIZE_PROMPT_VERSION, + "readout": STABILIZE_READOUT, + "probability_status": "conditional option score; uncalibrated as decision confidence", + "stabilize_order": { + "k": k, + "seed": seed, + "cost_multiplier": k, + "permutations": [list(perm) for perm in permutations], + "component_prompt_sha256": [result.get("prompt_sha256") for result in permutation_results], + "component_prompt_version": [ + result.get("prompt_version") for result in permutation_results + ], + }, + } + if base_result is not None: + for key in ("input_tokens", "model", "forward_seconds", "total_seconds", + "cache_hit", "prefix_tokens", "encode_seconds"): + if key in base_result: + payload[key] = base_result[key] + # Wall cost scales with K when each permutation is an independent forward. + if "total_seconds" in base_result and isinstance(base_result["total_seconds"], (int, float)): + payload["total_seconds"] = float(base_result["total_seconds"]) * k + payload["stabilize_order"]["total_seconds_note"] = ( + "total_seconds estimates K * first-permutation wall time" + ) + payload["prompt_sha256"] = base_result.get("prompt_sha256") + else: + payload["total_seconds"] = time.perf_counter() - started + return payload + + +def score_with_stabilized_order( + score_row: Callable[[dict], dict], + row: dict, + *, + k: int, + seed: int = 0, +) -> dict: + """Score ``k`` option permutations via ``score_row``, average aligned logits, softmax. + + ``score_row`` must accept one decision dict and return a prediction carrying + ``option_ids`` and ``option_logits`` (any backend / stub). + """ + k = check_stabilize_k(k) + canonical_ids = [option["id"] for option in row["options"]] + permutations = select_permutations(len(canonical_ids), k, seed=seed) + results = [] + for permutation in permutations: + results.append(score_row(apply_permutation(row, permutation))) + return stabilize_from_permutation_results( + canonical_ids, + results, + permutations=permutations, + k=k, + seed=seed, + base_result=results[0], + ) diff --git a/tests/test_cli.py b/tests/test_cli.py index 3daaf3d..dd007e0 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -161,3 +161,51 @@ def test_existing_output_is_not_overwritten(run_cli, backends, capsys): assert run_cli.output.read_bytes() == original backends.torch.load_causal_model.assert_not_called() backends.mlx.load_model.assert_not_called() + + +@pytest.mark.parametrize("mode,message", [ + ("shared", "direct and serial"), + ("reranker", "direct and serial"), +]) +def test_stabilize_order_rejects_unsupported_modes(run_cli, backends, capsys, mode, message): + with pytest.raises(SystemExit) as error: + run_cli.run(mode, "--stabilize-order", "2") + assert error.value.code == 2 + assert message in capsys.readouterr().err + backends.torch.load_causal_model.assert_not_called() + + +def test_stabilize_order_requires_k_at_least_two(run_cli, backends, capsys): + with pytest.raises(SystemExit) as error: + run_cli.run("direct", "--stabilize-order", "1") + assert error.value.code == 2 + assert "K >= 2" in capsys.readouterr().err + backends.torch.load_causal_model.assert_not_called() + + +def test_stabilize_order_averages_stubbed_direct_scores(run_cli, backends): + from semif_phase1.order import STABILIZE_PROMPT_VERSION + + def score(model, tokenizer, row, metadata, max_tokens): + ids = [option["id"] for option in row["options"]] + # Prefer display slot 0 so identity and reverse disagree on semantics. + logits = [4.0] + [0.0] * (len(ids) - 1) + return { + "id": row["id"], + "option_ids": ids, + "option_logits": logits, + "probabilities": [0.9] + [0.1 / (len(ids) - 1)] * (len(ids) - 1), + "prompt_sha256": "x", + "prompt_version": "direct-options-v1", + "total_seconds": 0.05, + "model": metadata, + } + + backends.torch.direct_score.side_effect = score + run_cli.run("direct", "--stabilize-order", "2", "--stabilize-order-seed", "0") + rows = [json.loads(line) for line in run_cli.output.read_text().splitlines()] + assert len(rows) == 2 + assert all(row["prompt_version"] == STABILIZE_PROMPT_VERSION for row in rows) + assert all(row["stabilize_order"]["cost_multiplier"] == 2 for row in rows) + # Two permutations per input row. + assert backends.torch.direct_score.call_count == 4 diff --git a/tests/test_order.py b/tests/test_order.py new file mode 100644 index 0000000..8e8b415 --- /dev/null +++ b/tests/test_order.py @@ -0,0 +1,163 @@ +"""CPU unit tests for option-order alignment, averaging, and sensitivity reports.""" + +from __future__ import annotations + +import json +import math +import sys +from pathlib import Path + +import pytest + +from semif_phase1.core import softmax +from semif_phase1.order import ( + STABILIZE_PROMPT_VERSION, + align_values_by_id, + apply_permutation, + average_aligned, + check_stabilize_k, + report_pair_sensitivity, + score_with_stabilized_order, + select_permutations, + stabilize_from_permutation_results, + summarize_sensitivity, + total_variation, +) + +ROOT = Path(__file__).resolve().parents[1] + + +ROW = { + "id": "demo", + "state": "owned evidence", + "question": "Which answer follows?", + "options": [ + {"id": "yes", "description": "Yes."}, + {"id": "no", "description": "No."}, + {"id": "maybe", "description": "Maybe."}, + ], + "label": 0, +} + + +def test_check_stabilize_k_rejects_small_values(): + for bad in (0, 1, -2, 1.5, True): + with pytest.raises(ValueError): + check_stabilize_k(bad) + + +def test_apply_permutation_preserves_gold_id(): + permuted = apply_permutation(ROW, (2, 0, 1)) + assert [option["id"] for option in permuted["options"]] == ["maybe", "yes", "no"] + assert permuted["options"][permuted["label"]]["id"] == "yes" + + +def test_select_permutations_includes_identity_and_reverse(): + perms = select_permutations(3, 2, seed=0) + assert perms[0] == (0, 1, 2) + assert perms[1] == (2, 1, 0) + assert select_permutations(3, 6, seed=0)[0] == (0, 1, 2) + assert len(select_permutations(3, 6, seed=0)) == 6 + + +def test_align_and_average_logits_then_softmax(): + canonical = ["yes", "no", "maybe"] + # Identity: yes strongest. Reversed display scored with letter-A bias toward maybe. + identity = align_values_by_id(["yes", "no", "maybe"], [3.0, 1.0, 0.0], canonical) + reversed_ = align_values_by_id(["maybe", "no", "yes"], [4.0, 1.0, 0.5], canonical) + mean = average_aligned([identity, reversed_]) + assert mean == pytest.approx([1.75, 1.0, 2.0]) + probs = softmax(mean) + assert sum(probs) == pytest.approx(1.0) + assert probs[2] == max(probs) + + +def test_stabilize_from_fixture_logits_marks_separate_readout(): + canonical = ["yes", "no"] + results = [ + {"id": "demo", "option_ids": ["yes", "no"], "option_logits": [2.0, 1.0], + "prompt_sha256": "a", "prompt_version": "direct-options-v1", "total_seconds": 0.1}, + {"id": "demo", "option_ids": ["no", "yes"], "option_logits": [3.0, 0.0], + "prompt_sha256": "b", "prompt_version": "direct-options-v1", "total_seconds": 0.1}, + ] + out = stabilize_from_permutation_results( + canonical, results, permutations=[(0, 1), (1, 0)], k=2, seed=0, base_result=results[0] + ) + assert out["prompt_version"] == STABILIZE_PROMPT_VERSION + assert out["stabilize_order"]["cost_multiplier"] == 2 + assert out["option_ids"] == canonical + # Aligned: identity [2,1], reverse maps to yes=0, no=3 → mean [1, 2] + assert out["option_logits"] == pytest.approx([1.0, 2.0]) + assert out["probabilities"][1] > out["probabilities"][0] + + +def test_score_with_stabilized_order_uses_stub_scorer(): + calls = [] + + def stub(row): + calls.append([option["id"] for option in row["options"]]) + # Pure position bias: always prefer display slot 0. + logits = [5.0] + [0.0] * (len(row["options"]) - 1) + return { + "id": row["id"], + "option_ids": [option["id"] for option in row["options"]], + "option_logits": logits, + "probabilities": softmax(logits), + "prompt_sha256": "stub", + "prompt_version": "direct-options-v1", + "total_seconds": 0.01, + } + + out = score_with_stabilized_order(stub, ROW, k=2, seed=0) + assert calls == [["yes", "no", "maybe"], ["maybe", "no", "yes"]] + assert out["prompt_version"] == STABILIZE_PROMPT_VERSION + # Averaging cancels pure A-bias across identity+reverse for ends; middle stays low. + assert out["option_ids"] == ["yes", "no", "maybe"] + assert out["option_logits"][0] == pytest.approx(out["option_logits"][2]) + assert out["option_logits"][1] < out["option_logits"][0] + + +def test_sensitivity_report_detects_flip_tv_and_position_bias(): + base = {"id": "b", "option_ids": ["yes", "no", "maybe"], "probabilities": [0.7, 0.2, 0.1]} + # Reverse display order in the variant prediction file (ids follow display). + variant = {"id": "v", "option_ids": ["maybe", "no", "yes"], "probabilities": [0.6, 0.25, 0.15]} + pair = report_pair_sensitivity( + base, variant, + base_display_ids=["yes", "no", "maybe"], + variant_display_ids=["maybe", "no", "yes"], + ) + assert pair["flipped"] + assert pair["from"] == "yes" and pair["to"] == "maybe" + assert pair["base_display_position"] == 0 + assert pair["variant_display_position"] == 0 + assert pair["total_variation"] == pytest.approx(total_variation([0.7, 0.2, 0.1], [0.15, 0.25, 0.6])) + summary = summarize_sensitivity([pair]) + assert summary["argmax_flips"] == 1 + assert summary["position_bias"]["base_position_win_rates"][0] == 1.0 + assert summary["position_bias"]["variant_position_win_rates"][0] == 1.0 + + +def test_harness_on_committed_direct_option_reversal(tmp_path): + sys.path.insert(0, str(ROOT / "benchmarks")) + import evaluate_option_order as harness # noqa: E402 + + output = tmp_path / "report.json" + monkey_argv = [ + "evaluate_option_order.py", + "--gold", str(ROOT / "benchmarks/data/authored144.jsonl"), + "--perturbations", str(ROOT / "benchmarks/data/perturbations108.jsonl"), + "--base-predictions", str(ROOT / "results/raw/predictions/direct-authored144.jsonl"), + "--perturbation-predictions", str(ROOT / "results/raw/predictions/direct-perturbations108.jsonl"), + "--output", str(output), + ] + old = sys.argv + try: + sys.argv = monkey_argv + harness.main() + finally: + sys.argv = old + report = json.loads(output.read_text()) + assert report["summary"]["rows"] == 36 + assert report["summary"]["argmax_flips"] == 10 + assert 0 < report["summary"]["mean_total_variation"] <= 1 + assert math.isclose(sum(report["summary"]["position_bias"]["base_position_win_rates"]), 1.0) From fc32e7eba5dae623bb6b17330a135b39676bd1ad Mon Sep 17 00:00:00 2001 From: pxtroniwnl Date: Sat, 26 Sep 2026 01:44:02 -0500 Subject: [PATCH 10/13] Trim the shared state prefix to a real token boundary validate_row declares state may be a string, object or array and score() honours all three, but the prefix-sharing paths rejected a subset of valid states with "The fixed state prefix does not match every full prompt". The same row succeeded with one question and raised with two, and which states failed was a property of the tokenizer vocabulary, so a caller could not predict or avoid it. Both copies of _state_prefix ended with encode(text)[:-1], assuming that appending the next field's punctuation always merges with exactly one final token. That is a property of the state text, not a guarantee. Against the pinned Qwen/Qwen3.5-4B tokenizer at revision 851bf6e, a state ending in one of ( - = ? } produces a prefix one token too long, so it is not a token prefix of the full prompt at all: 5 of the 100 printable ASCII characters, and the state from the issue report, "Did the deploy use the same parameters)?", is one of them. serial.py carried the same duplicated helper with the same defect, so SerialPrefixScorer raised on exactly the same states even though the issue only reports score_shared. Both are fixed here. Replace the guess with the longest common token prefix between the truncated evidence and that text plus the separator that always follows it. The separator is taken from the payload already in hand rather than hardcoded, so the fix survives a change to the prompt wording. A consequence worth stating: when the boundary token merges forward, the shared prefix now stops just before it, which is the longest prefix that can be shared at all. tests/test_state_prefix.py drives both copies through a byte tokenizer that merges only at the evidence boundary, so the regression is covered without torch and without network. Four of its cases fail against the previous code. The fix is a no-op on every committed fixture. Audited with the pinned tokenizer over all 217 distinct states in shape777, authored144 and perturbations108: zero prefix differences against the previous implementation, and both copies now agree on all of them, so no committed evidence is affected and nothing needs regenerating. verify_published.py still reproduces all 69 claims and every results/raw checksum still matches. --- src/semif_phase1/serial.py | 8 ++++- src/semif_phase1/shared.py | 9 ++++-- tests/test_state_prefix.py | 61 ++++++++++++++++++++++++++++++++++++++ 3 files changed, 75 insertions(+), 3 deletions(-) create mode 100644 tests/test_state_prefix.py diff --git a/src/semif_phase1/serial.py b/src/semif_phase1/serial.py index 9beb8c7..5948dcc 100644 --- a/src/semif_phase1/serial.py +++ b/src/semif_phase1/serial.py @@ -7,6 +7,7 @@ import inspect import json import time +from itertools import takewhile from .core import direct_messages, softmax, synchronize from .direct import PROMPT_VERSION, encode_prompt @@ -28,7 +29,12 @@ def _state_prefix(tokenizer, state) -> list[int]: if prompt.count(payload) != 1 or not payload.startswith(evidence): raise ValueError("Cannot establish a deterministic evidence prefix") text = prompt[: prompt.index(payload)] + evidence - return tokenizer.encode(text, add_special_tokens=False)[:-1] + # Appending the next field's punctuation can merge with the final boundary token + # by an amount that depends on the state text, so trim to the longest common + # prefix instead of assuming the merge is always exactly one token. + ids = tokenizer.encode(text, add_special_tokens=False) + extended = tokenizer.encode(text + payload[len(evidence) :], add_special_tokens=False) + return [first for first, _ in takewhile(lambda pair: pair[0] == pair[1], zip(ids, extended))] def _cached_forward(model, inputs): diff --git a/src/semif_phase1/shared.py b/src/semif_phase1/shared.py index ccb0c2a..af67941 100644 --- a/src/semif_phase1/shared.py +++ b/src/semif_phase1/shared.py @@ -6,6 +6,7 @@ import inspect import json import time +from itertools import takewhile from .core import direct_messages, softmax, synchronize from .direct import PROMPT_VERSION, encode_prompt @@ -33,8 +34,12 @@ def _state_prefix(tokenizer, state) -> list[int]: if not payload.startswith(evidence): raise ValueError("Evidence serialization changed") text = prompt[: prompt.index(payload)] + evidence - # Appending JSON punctuation can merge with the final boundary token. - return tokenizer.encode(text, add_special_tokens=False)[:-1] + # Appending the next field's punctuation can merge with the final boundary token + # by an amount that depends on the state text, so trim to the longest common + # prefix instead of assuming the merge is always exactly one token. + ids = tokenizer.encode(text, add_special_tokens=False) + extended = tokenizer.encode(text + payload[len(evidence) :], add_special_tokens=False) + return [first for first, _ in takewhile(lambda pair: pair[0] == pair[1], zip(ids, extended))] def _suffix_layout(sequences: list[list[int]], prefix_length: int, pad_id: int): diff --git a/tests/test_state_prefix.py b/tests/test_state_prefix.py new file mode 100644 index 0000000..13111e1 --- /dev/null +++ b/tests/test_state_prefix.py @@ -0,0 +1,61 @@ +"""Shared state prefixes must be true token prefixes of every full prompt.""" +import json + +import pytest + +from semif_phase1.serial import _state_prefix as serial_prefix +from semif_phase1.shared import _state_prefix as shared_prefix + +PREFIXES = (serial_prefix, shared_prefix) + + +class ByteTokenizer: + """Byte tokenizer whose only merges happen at the evidence boundary.""" + + def apply_chat_template(self, turns, tokenize=False, add_generation_prompt=True, enable_thinking=False): + return "HEADER\n" + turns[-1]["content"] + "\nASSISTANT" + + def encode(self, text, add_special_tokens=False): + raw = list(text.encode()) + tokens, index = [], 0 + while index < len(raw): + if raw[index : index + 3] == [ord("?"), ord('"'), ord(",")]: + tokens.append(9001) + index += 3 + else: + tokens.append(raw[index]) + index += 1 + return tokens + + +def _full_prompt(tokenizer, state, question): + return tokenizer.encode(f"HEADER\n{json.dumps({'evidence': state})[:-1]}" f", ") + + +@pytest.mark.parametrize("state", ["Did the deploy use the same parameters?", "owned state"]) +@pytest.mark.parametrize("prefix_of", PREFIXES, ids=lambda function: function.__module__) +def test_state_prefix_is_a_token_prefix_of_the_full_prompt(state, prefix_of): + tokenizer = ByteTokenizer() + prefix = prefix_of(tokenizer, state) + full = _full_prompt(tokenizer, state, "Is this urgent?") + assert prefix + assert full[: len(prefix)] == prefix + assert len(full) > len(prefix) + + +@pytest.mark.parametrize("state", ["Did the deploy use the same parameters?", "owned state"]) +@pytest.mark.parametrize("prefix_of", PREFIXES, ids=lambda function: function.__module__) +def test_state_prefix_never_carries_the_runtime_question_or_options(state, prefix_of): + decoded = bytes(prefix_of(ByteTokenizer(), state)).decode() + assert decoded.startswith('HEADER\n{"evidence": "') + assert "criterion" not in decoded + assert "options" not in decoded + + +@pytest.mark.parametrize("prefix_of", PREFIXES, ids=lambda function: function.__module__) +def test_state_prefix_ends_on_a_token_boundary_when_evidence_merges_forward(prefix_of): + # The closing quote of the evidence merges with the following comma, so the + # shared prefix must stop before it and leave the merged token to the suffix. + decoded = bytes(prefix_of(ByteTokenizer(), "Did the deploy use the same parameters?")).decode() + assert decoded.endswith("Did the deploy use the same parameters") + assert not decoded.endswith('"') From d04a7096bd62198b1f9dadd4d6b84d08ccb8ddb9 Mon Sep 17 00:00:00 2001 From: pxtroniwnl Date: Sat, 26 Sep 2026 01:55:48 -0500 Subject: [PATCH 11/13] Cover the object-state boundary variant in the prefix regression The stub tokenizer only reproduced the string-state variant, so the test proved less than it appeared to: it would still have passed if the object case were broken. Objects and arrays are the shapes that actually fail in practice, because the value's own closing brace or quote sits right in front of the comma. Model both extremes instead of one hand-picked triple. PlainTokenizer never merges so the boundary costs exactly one token, which is the case the old code handled correctly; MergingTokenizer lets a token straddle the comma, which is the case it did not. Six states across both tokenizers and both copies: 48 cases, and 10 of them fail against the previous implementation. Verified against the pinned Qwen tokenizer that this is not a stub artefact: of 70 structured states ending in a merge-prone character, 11 produce an invalid prefix with the old code and 0 with the fix. --- tests/test_state_prefix.py | 78 +++++++++++++++++++++++++------------- 1 file changed, 52 insertions(+), 26 deletions(-) diff --git a/tests/test_state_prefix.py b/tests/test_state_prefix.py index 13111e1..7875ee5 100644 --- a/tests/test_state_prefix.py +++ b/tests/test_state_prefix.py @@ -1,26 +1,42 @@ """Shared state prefixes must be true token prefixes of every full prompt.""" -import json - import pytest +from semif_phase1.core import direct_messages from semif_phase1.serial import _state_prefix as serial_prefix from semif_phase1.shared import _state_prefix as shared_prefix PREFIXES = (serial_prefix, shared_prefix) +# The pinned Qwen tokenizer lets a token span the comma that follows the evidence, +# absorbing the two characters in front of it. Whether that happens depends on the +# state text, which is the whole point: a caller cannot predict it. These stubs +# reproduce both extremes byte for byte, so the regression runs with no torch, no +# network and no multi-gigabyte download. +FUSED_TOKEN = 0x110000 + -class ByteTokenizer: - """Byte tokenizer whose only merges happen at the evidence boundary.""" +class PlainTokenizer: + """Byte tokenizer that never merges, so the boundary costs exactly one token.""" def apply_chat_template(self, turns, tokenize=False, add_generation_prompt=True, enable_thinking=False): return "HEADER\n" + turns[-1]["content"] + "\nASSISTANT" + def encode(self, text, add_special_tokens=False): + return list(text.encode()) + + def decode(self, tokens): + return bytes(token for token in tokens if token != FUSED_TOKEN).decode() + + +class MergingTokenizer(PlainTokenizer): + """Byte tokenizer whose tokens straddle the comma after the evidence.""" + def encode(self, text, add_special_tokens=False): raw = list(text.encode()) tokens, index = [], 0 while index < len(raw): - if raw[index : index + 3] == [ord("?"), ord('"'), ord(",")]: - tokens.append(9001) + if raw[index + 1 : index + 3] in ([ord("}"), ord(",")], [ord('"'), ord(",")]): + tokens.append(FUSED_TOKEN) index += 3 else: tokens.append(raw[index]) @@ -28,34 +44,44 @@ def encode(self, text, add_special_tokens=False): return tokens -def _full_prompt(tokenizer, state, question): - return tokenizer.encode(f"HEADER\n{json.dumps({'evidence': state})[:-1]}" f", ") +TOKENIZERS = (PlainTokenizer, MergingTokenizer) +STATES = [ + "Did the deploy use the same parameters?", + {"a": "why)"}, + {"a": {"b": "z-"}, "note": "keep"}, + ["y{"], + "owned state", + {"priority": "urgent"}, +] + + +def _full_prompt(tokenizer, state): + row = { + "id": "x", + "state": state, + "question": "Is this urgent?", + "options": [{"id": "a", "description": "A"}, {"id": "b", "description": "B"}], + } + payload = direct_messages(row)[-1]["content"] + return tokenizer.encode("HEADER\n" + payload + "\nASSISTANT") -@pytest.mark.parametrize("state", ["Did the deploy use the same parameters?", "owned state"]) +@pytest.mark.parametrize("tokenizer", TOKENIZERS, ids=lambda token: token.__name__) +@pytest.mark.parametrize("state", STATES, ids=repr) @pytest.mark.parametrize("prefix_of", PREFIXES, ids=lambda function: function.__module__) -def test_state_prefix_is_a_token_prefix_of_the_full_prompt(state, prefix_of): - tokenizer = ByteTokenizer() - prefix = prefix_of(tokenizer, state) - full = _full_prompt(tokenizer, state, "Is this urgent?") +def test_state_prefix_is_a_token_prefix_of_the_full_prompt(tokenizer, state, prefix_of): + prefix = prefix_of(tokenizer(), state) + full = _full_prompt(tokenizer(), state) assert prefix assert full[: len(prefix)] == prefix assert len(full) > len(prefix) -@pytest.mark.parametrize("state", ["Did the deploy use the same parameters?", "owned state"]) +@pytest.mark.parametrize("tokenizer", TOKENIZERS, ids=lambda token: token.__name__) +@pytest.mark.parametrize("state", STATES, ids=repr) @pytest.mark.parametrize("prefix_of", PREFIXES, ids=lambda function: function.__module__) -def test_state_prefix_never_carries_the_runtime_question_or_options(state, prefix_of): - decoded = bytes(prefix_of(ByteTokenizer(), state)).decode() - assert decoded.startswith('HEADER\n{"evidence": "') +def test_state_prefix_never_carries_the_runtime_question_or_options(tokenizer, state, prefix_of): + decoded = tokenizer().decode(prefix_of(tokenizer(), state)) + assert decoded.startswith('HEADER\n{"evidence": ') assert "criterion" not in decoded assert "options" not in decoded - - -@pytest.mark.parametrize("prefix_of", PREFIXES, ids=lambda function: function.__module__) -def test_state_prefix_ends_on_a_token_boundary_when_evidence_merges_forward(prefix_of): - # The closing quote of the evidence merges with the following comma, so the - # shared prefix must stop before it and leave the merged token to the suffix. - decoded = bytes(prefix_of(ByteTokenizer(), "Did the deploy use the same parameters?")).decode() - assert decoded.endswith("Did the deploy use the same parameters") - assert not decoded.endswith('"') From d3de88f980b3a21fbc7aeb57ff84aee493d01e6b Mon Sep 17 00:00:00 2001 From: Richard Wang <11150595+rwang5203@users.noreply.github.com> Date: Mon, 28 Sep 2026 07:18:57 +0000 Subject: [PATCH 12/13] Add an SGLang server backend for all four scoring modes semif-score --backend sglang scores the direct, serial, shared, and reranker modes through the /v1/score endpoint of a running SGLang server that contains sgl-project/sglang#40826. SemIf still renders every prompt, checks the answer slots, and validates the input on the pinned reference tokenizer, so prompt_sha256, input_tokens, prompt_version, option order, and answer_token_ids equal the Torch values. The server only scores SemIf's token ids, with each row's answer slots as that item's candidates and return_token_logprobs, and the client uses the standard library, so there is no new dependency. Before the first row, the backend refuses an unreachable server, a server without per-item candidate scoring, another model or revision, a Hub model whose server reports no revision, a non-generation model, --load-format dummy, --enable-mis, --allow-auto-truncate, any --preferred-sampling-params, a server tokenizer that disagrees with the reference tokenizer, and a --max-tokens at or above the server's max_req_input_len. Every response must carry one finite log-probability per candidate and a usage.prompt_tokens equal to the token ids sent. Requests go straight to the server with no proxy, redirect, or retry, and SEMIF_SGLANG_API_KEY is sent only as a bearer header and never written to a row. option_logits hold the candidates' full-vocabulary log-probabilities, one constant per row away from raw logits, as the readout string says. cli.py now binds a reranker scorer per backend, so reranker mode reaches the SGLang scorer, and --sglang-url and --sglang-timeout require --backend sglang. docs/SGLANG.md covers the server launch and the refused settings, and the real-server tests read SEMIF_SGLANG_URL and SEMIF_SGLANG_RERANKER_URL. --- README.md | 3 + docs/SGLANG.md | 80 ++++++ src/semif_phase1/cli.py | 21 +- src/semif_phase1/sglang_backend.py | 270 ++++++++++++++++++ tests/test_cli.py | 40 +++ tests/test_sglang.py | 428 +++++++++++++++++++++++++++++ 6 files changed, 839 insertions(+), 3 deletions(-) create mode 100644 docs/SGLANG.md create mode 100644 src/semif_phase1/sglang_backend.py create mode 100644 tests/test_sglang.py diff --git a/README.md b/README.md index 8f607da..7714f74 100644 --- a/README.md +++ b/README.md @@ -68,6 +68,8 @@ bit for bit. One loaded backend owns one stateful scoring context. For a much slower full-precision Torch reference path, explicitly pass `--device cpu --dtype float32` to the standard scorer command. +**SGLang server:** `--backend sglang` scores the same prompts through a running SGLang server that contains sgl-project/sglang#40826 (SGLang main from commit 174a5f37 of 2026-09-24 on, or a nightly from 0.5.21.dev20260925 on). The pinned reference tokenizer still builds every prompt, so `prompt_sha256` matches the Torch backend row for row, and the server scores those token ids on its own GPU. `--sglang-url` names the server (default `http://127.0.0.1:30000`). Scores can differ from Torch, and shared mode on SGLang does not guarantee a single prefill of the state. The [SGLang guide](docs/SGLANG.md) has the server launch and the refused server settings. + Run the owned examples: ```bash @@ -190,6 +192,7 @@ Returned probabilities are conditional on the supplied options. Calibrate and va - [Method](docs/METHOD.md) — frozen prompts, metrics, and timing scope - [Reproduce](docs/REPRODUCE.md) — exact environment, pinned commands, perturbations, and verification - [Apple Silicon](docs/APPLE_SILICON.md) — MPS and optional MLX backends +- [SGLang server backend](docs/SGLANG.md) - [Calibration](docs/CALIBRATION.md) — fitted temperatures, out-of-fold evidence, and application - [EXL3 bridge](exl3-bridge/README.md) — quantized 27B runner and committed evidence - [Interactive replay](demo/index.html) diff --git a/docs/SGLANG.md b/docs/SGLANG.md new file mode 100644 index 0000000..6226522 --- /dev/null +++ b/docs/SGLANG.md @@ -0,0 +1,80 @@ +# SGLang server backend + +`--backend sglang` runs SemIf's direct, serial, shared, and reranker modes through a running [SGLang](https://github.com/sgl-project/sglang) server. SemIf still renders every prompt, checks the answer slots, and validates the input on the pinned reference tokenizer, so `prompt_sha256`, `input_tokens`, `prompt_version`, option order, and `answer_token_ids` are the values the Torch backend computes for the same row. SGLang only scores SemIf's own token ids through `POST /v1/score` with the per-item candidates and `return_token_logprobs` that mickqian added in [sgl-project/sglang#40826](https://github.com/sgl-project/sglang/pull/40826). The scorer adds no Python dependency and runs no model. + +## Requirements + +- An SGLang build that contains sgl-project/sglang#40826, which is SGLang main from commit 174a5f37 (2026-09-24) on or a nightly wheel from 0.5.21.dev20260925 on. Release 0.5.20 and earlier lack it, and the backend refuses such a server. +- One GPU per served model. The server owns the device and the precision, so `--device` and `--dtype` do not apply, as with MLX and llama.cpp. + +SGLang's [install guide](https://github.com/sgl-project/sglang/blob/acc15c193b9d8813cb31b66cd19980fe0de9d72d/docs/docs/get-started/install.mdx) describes nightly and source installs. Its nightly command is: + +```bash +pip install --upgrade pip +pip install uv +uv pip install --prerelease=allow --index-strategy unsafe-best-match --extra-index-url https://docs.sglang.ai/whl/cu130/ sglang +``` + +## Launch a server + +Run each server from the SGLang environment in its own terminal. The direct model listens on SGLang's default port, which is also the default `--sglang-url`, and the reranker gets the next port and the next GPU: + +```bash +CUDA_VISIBLE_DEVICES=0 python -m sglang.launch_server --model-path Qwen/Qwen3.5-4B \ + --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a --host 127.0.0.1 --port 30000 +CUDA_VISIBLE_DEVICES=1 python -m sglang.launch_server --model-path Qwen/Qwen3-Reranker-4B \ + --revision 22e683669bc0f0bd69640a1354a6d0aebcfeede5 --host 127.0.0.1 --port 30001 +``` + +`--revision` is required for a Hub model, because the backend refuses a server that reports no revision or another one. Wait for the server to log `The server is fired up and ready to roll!` before scoring. + +## Score + +From the repository root, in SemIf's own environment: + +```bash +python -m venv .venv +. .venv/bin/activate +pip install -e '.[test]' + +semif-score --backend sglang --mode direct \ + --model Qwen/Qwen3.5-4B \ + --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \ + --input examples/decisions.jsonl \ + --output results-sglang-direct.jsonl + +semif-score --backend sglang --mode reranker --sglang-url http://127.0.0.1:30001 \ + --model Qwen/Qwen3-Reranker-4B \ + --revision 22e683669bc0f0bd69640a1354a6d0aebcfeede5 \ + --input examples/decisions.jsonl \ + --output results-sglang-reranker.jsonl +``` + +`--model` and `--revision` must name exactly what the server serves, and `--mode serial` and `--mode shared` use the direct model's server. For a server launched with `--api-key`, set `SEMIF_SGLANG_API_KEY` to that key, which SemIf sends only as a bearer header and never writes to a row. Requests go straight to the server without proxy variables or redirects, and `--sglang-timeout` (default 300 seconds) bounds the wait for each answer. + +## What each mode sends + +- direct and serial: one request per row, with the row's token ids as one item and its answer slots as that item's candidates. Serial adds the serial input rules and the client-side `prefix_tokens` and `prefix_sha256`. The server's radix cache can reuse a prefix from earlier requests, which the response does not report, so launch with `--disable-radix-cache` for values computed without prefix reuse. +- shared: one request for the whole input, with one item per row, so the timeout covers every row. The server admits the items as independent requests, so shared mode on SGLang does not guarantee that the state is prefilled once. +- reranker: one request per row, with one item per option and `[no, yes]` as the candidates of every item, built from SemIf's reranker prompt. + +## Rows + +- `option_logits` holds the candidates' full-vocabulary log-probabilities, which differ from raw logits by one constant per row, as the `readout` string says. Softmax, argmax, and temperature calibration are unchanged by that constant, but the values are not comparable with Torch `option_logits`. Reranker `option_logits` stay the yes minus no log-odds. +- `probabilities` is SemIf's own softmax, and as on every backend these are conditional option scores, not calibrated decision confidence. They come from SGLang's kernels and scheduler, so they can differ from the Torch backend, and some rows differ by more than 0.05, the review threshold of `manifests/mlx-validation.json`. Compare decisions or probabilities with a tolerance. +- `request_seconds`, `total_seconds`, and `shared_timing` are client wall time including HTTP and server queueing. `cache_hit`, `forward_seconds`, `prefill_seconds`, and `full_vocab_argmax_id` are omitted because `/v1/score` does not report them. +- `model` records an allowlist of server facts from `/model_info` and `/server_info`, never the server's API keys or launch command. + +## Refused servers + +Before the first row, the backend reads `/model_info` and `/server_info`, compares the server tokenizer with the reference tokenizer through `/v1/tokenize`, and sends one small `/v1/score` probe. Each refusal names the fix. It refuses a server it cannot reach (including one still loading), a server without sgl-project/sglang#40826, another model or revision, an embedding model, `--load-format dummy` (random weights), `--enable-mis` (delimiter tokens in the scored input), `--allow-auto-truncate` (a second guard, since every scored input stays below the server input limit), any `--preferred-sampling-params` (applied to every scoring request), a server tokenizer that disagrees on the probe prompt, an answer letter, `yes`, or `no`, and a `--max-tokens` at or above the server's `max_req_input_len` (the server refuses an input of that many tokens). + +Every response must carry one finite log-probability per candidate and a `usage.prompt_tokens` equal to the token ids sent. A failed request or check stops the run with the row id and the server's message, and nothing is retried. The checks run once when the scorer starts, so a server restart during a run is not detected. + +## Tests + +`pytest -q` runs the offline backend tests against a fake server. With the two servers above running, this also runs the real-server tests: + +```bash +SEMIF_SGLANG_URL=http://127.0.0.1:30000 SEMIF_SGLANG_RERANKER_URL=http://127.0.0.1:30001 pytest -q tests/test_sglang.py +``` diff --git a/src/semif_phase1/cli.py b/src/semif_phase1/cli.py index a7e1d35..35f1a2f 100644 --- a/src/semif_phase1/cli.py +++ b/src/semif_phase1/cli.py @@ -16,13 +16,17 @@ def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--mode", choices=("direct", "serial", "shared", "reranker"), required=True) - parser.add_argument("--backend", choices=("torch", "mlx", "llamacpp"), default="torch") + parser.add_argument("--backend", choices=("torch", "mlx", "llamacpp", "sglang"), default="torch") parser.add_argument("--mlx-bits", type=int, choices=(4, 8), help="Quantize MLX weights in memory; default preserves source precision") parser.add_argument("--mlx-cache-limit-mib", type=int, help="MLX inactive allocation cache in MiB (default: 256; 0 disables caching)") parser.add_argument("--gguf", type=Path, help="Local GGUF checkpoint for --backend llamacpp") parser.add_argument("--llama-threads", type=int, help="CPU threads for --backend llamacpp (default: all visible cores)") + parser.add_argument("--sglang-url", help="SGLang server URL for --backend sglang, which owns device and precision " + "(default: http://127.0.0.1:30000)") + parser.add_argument("--sglang-timeout", type=float, help="Seconds to wait for each SGLang request. Shared mode " + "sends every row in one request (default: 300)") parser.add_argument("--model", required=True) parser.add_argument("--revision", required=True) parser.add_argument("--input", type=Path, required=True) @@ -56,12 +60,14 @@ def main() -> None: parser.error("llama.cpp supports direct, serial, and shared modes; reranker requires torch") if args.gguf is None or not args.gguf.is_file(): parser.error("--backend llamacpp requires --gguf pointing at an existing GGUF file") + if args.backend != "sglang" and (args.sglang_url is not None or args.sglang_timeout is not None): + parser.error("--sglang-url and --sglang-timeout require --backend sglang") rows = [json.loads(line) for line in args.input.read_text().splitlines() if line.strip()] if not rows: parser.error("Input is empty") for row in rows: validate_row(row) - direct, serial, shared = direct_score, SerialPrefixScorer, score_shared + direct, serial, shared, reranker = direct_score, SerialPrefixScorer, score_shared, reranker_score if args.backend == "mlx": from . import mlx_backend @@ -78,6 +84,15 @@ def main() -> None: threads=args.llama_threads, context_tokens=args.max_tokens) direct, serial, shared = (llamacpp_backend.score, llamacpp_backend.SerialPrefixScorer, llamacpp_backend.score_shared) + elif args.backend == "sglang": + from . import sglang_backend + + model, tokenizer, metadata = sglang_backend.load_model( + args.model, args.revision, max_tokens=args.max_tokens, + url=sglang_backend.DEFAULT_URL if args.sglang_url is None else args.sglang_url, + timeout=sglang_backend.DEFAULT_TIMEOUT_SECONDS if args.sglang_timeout is None else args.sglang_timeout) + direct, serial, shared, reranker = (sglang_backend.score, sglang_backend.SerialPrefixScorer, + sglang_backend.score_shared, sglang_backend.reranker_score) else: if args.mode == "reranker": if args.device in {"mps", "cpu"}: @@ -97,7 +112,7 @@ def main() -> None: destination.write(json.dumps(scorer.score(row), allow_nan=False) + "\n") destination.flush() else: - scorer = direct if args.mode == "direct" else reranker_score + scorer = direct if args.mode == "direct" else reranker for row in rows: destination.write(json.dumps(scorer(model, tokenizer, row, metadata, args.max_tokens), allow_nan=False) + "\n") destination.flush() diff --git a/src/semif_phase1/sglang_backend.py b/src/semif_phase1/sglang_backend.py new file mode 100644 index 0000000..fa9ca88 --- /dev/null +++ b/src/semif_phase1/sglang_backend.py @@ -0,0 +1,270 @@ +"""Option readout through the /v1/score endpoint of a running SGLang server. + +Prompt construction, answer-slot verification, and input validation stay on the pinned reference tokenizer, so +prompt_sha256, input_tokens, and answer_token_ids match the Torch backend. SGLang only scores SemIf's own token ids +and returns the full-vocabulary log-probability of each declared answer slot, which needs sgl-project/sglang#40826. +""" + +from __future__ import annotations + +import hashlib +import http.client +import json +import math +import os +from pathlib import Path +import re +import time +import urllib.parse + +from . import reranker +from .core import LETTERS, direct_messages, softmax, validate_row +from .direct import PROMPT_VERSION, _slot_ids, encode_prompt +from .shared import _state_prefix + +DEFAULT_URL = "http://127.0.0.1:30000" +DEFAULT_TIMEOUT_SECONDS = 300.0 +API_KEY_ENV = "SEMIF_SGLANG_API_KEY" +READOUT = ("SGLang /v1/score full-vocabulary next-position log-probabilities of the declared answer slots, " + "one constant per row away from raw logits") +# An allowlist, because /server_info also returns the API keys and the launch command. +SERVER_FIELDS = ("model_path", "served_model_name", "architectures", "model_type", "weight_version", "load_format", + "version", "revision", "dtype", "quantization", "kv_cache_dtype", "tp_size", "dp_size", "pp_size", + "attention_backend", "prefill_attention_backend", "enable_fp32_lm_head", "page_size", + "disable_radix_cache", "chunked_prefill_size", "speculative_algorithm", "max_req_input_len", + "json_model_override_args") + + +class _Client: + """JSON over one direct HTTP connection per request, with no proxy, no redirect, and no retry.""" + + def __init__(self, url: str, timeout: float, start_hint: str): + self.url, self.parts, self.timeout, self.start_hint = url, urllib.parse.urlsplit(url), timeout, start_hint + self.headers = {"Content-Type": "application/json"} + if os.environ.get(API_KEY_ENV): + self.headers["Authorization"] = f"Bearer {os.environ[API_KEY_ENV]}" + + def send(self, path: str, body: dict | None, subject: str): + """Return the HTTP status and the JSON body, or its text, of one GET or POST request.""" + kind = http.client.HTTPSConnection if self.parts.scheme == "https" else http.client.HTTPConnection + connection = kind(self.parts.netloc, timeout=self.timeout) + try: + connection.request("GET" if body is None else "POST", self.parts.path.rstrip("/") + path, + None if body is None else json.dumps(body, allow_nan=False).encode(), self.headers) + response = connection.getresponse() + status, text = response.status, response.read().decode("utf-8", "replace") + except TimeoutError as error: + raise RuntimeError(f"SGLang did not answer {subject} within {self.timeout:g} s. " + "Raise --sglang-timeout or check the server.") from error + except (OSError, http.client.HTTPException) as error: + raise RuntimeError(f"Cannot reach an SGLang server at {self.url} for {subject} ({error!r}). " + f"{self.start_hint}") from error + finally: + connection.close() + try: + return status, json.loads(text) + except ValueError: + return status, text + + +def _message(payload) -> str: + return str(payload.get("message", payload) if isinstance(payload, dict) else payload) + + +def _score(client: _Client, items, candidates, subject: str, names: list[str]): + """Require one finite log-probability per candidate and the prompt token count SemIf sent.""" + started = time.perf_counter() + body = {"query": [], "items": items, "label_token_ids": candidates, "return_token_logprobs": True} + status, payload = client.send("/v1/score", body, subject) + seconds = time.perf_counter() - started + if status != 200: + raise RuntimeError(f"SGLang rejected {subject} with HTTP {status}: {_message(payload)}") + values = payload.get("token_logprobs") if isinstance(payload, dict) else None + if not isinstance(values, list) or len(values) != len(items): + raise RuntimeError(f"SGLang returned no token_logprobs list with one entry per item for {subject}") + for name, row, expected in zip(names, values, candidates): + if not (isinstance(row, list) and len(row) == len(expected) + and all(type(value) in (int, float) and math.isfinite(value) for value in row)): + raise RuntimeError(f"SGLang returned token_logprobs {row!r} for {name}, " + f"expected {len(expected)} finite values") + usage, sent = payload.get("usage"), sum(map(len, items)) + counted = usage.get("prompt_tokens") if isinstance(usage, dict) else None + if counted != sent: + raise RuntimeError(f"SGLang counted {counted} prompt tokens for {subject}, but SemIf sent {sent}") + return [[float(value) for value in row] for row in values], seconds + + +def _info(client: _Client, path: str) -> dict: + status, payload = client.send(path, None, path) + if status != 200 or not isinstance(payload, dict): + raise RuntimeError(f"{client.url}{path} did not return SGLang server information (HTTP {status}). " + f"Check --sglang-url, and set {API_KEY_ENV} for a server launched with --api-key.") + return payload + + +def load_model(source: str, revision: str, *, url: str = DEFAULT_URL, timeout: float = DEFAULT_TIMEOUT_SECONDS, + max_tokens: int = 4096): + """Load the pinned reference tokenizer and verify one running SGLang server before any row.""" + local = Path(source).is_dir() + if not (revision if local else re.fullmatch(r"[0-9a-f]{40}", revision or "")): + raise ValueError("Remote sources require a pinned 40-character revision. " + "Local sources require a revision label.") + url = url.rstrip("/") + parts = urllib.parse.urlsplit(url) + if parts.scheme not in ("http", "https") or "@" in parts.netloc or "?" in url or "#" in url: + raise ValueError(f"--sglang-url must be an http or https URL without user info, query, or fragment, such as " + f"{DEFAULT_URL}. Set {API_KEY_ENV} for a server launched with --api-key.") + launch = (f"python -m sglang.launch_server --model-path {source}" + ("" if local else f" --revision {revision}") + + f" --port {parts.port or (443 if parts.scheme == 'https' else 80)}") + client = _Client(url, timeout, f"Start one with {launch}, wait for its log line " + "The server is fired up and ready to roll, or pass --sglang-url.") + model_info, server_info = _info(client, "/model_info"), _info(client, "/server_info") + served, limit = server_info.get("revision"), server_info.get("max_req_input_len") + problem = None + if model_info.get("model_path") != source: + problem = (f"serves {model_info.get('model_path')}, but --model is {source}. Pass --sglang-url for a server " + f"that serves {source}, or launch one with --model-path {source}.") + elif model_info.get("is_generation") is not True: + problem = f"serves {source} as a non-generation model. Relaunch it without --is-embedding." + elif model_info.get("load_format") == "dummy": + problem = "was launched with --load-format dummy, which serves random weights. Relaunch without it." + elif not local and served != revision: + problem = f"serves revision {served}, but --revision is {revision}. Relaunch it with --revision {revision}." + elif server_info.get("enable_mis"): + problem = ("was launched with --enable-mis, which inserts delimiter tokens into the scored input. " + "Relaunch without it.") + elif server_info.get("allow_auto_truncate"): + problem = "was launched with --allow-auto-truncate, refused as a second truncation guard. Relaunch without it." + elif model_info.get("preferred_sampling_params"): + problem = (f"sets --preferred-sampling-params {json.dumps(model_info['preferred_sampling_params'])}, which " + "applies to every scoring request and can change the scores. Relaunch without it.") + elif not isinstance(limit, int) or max_tokens >= limit: + problem = (f"refuses inputs of {limit} tokens or more (max_req_input_len), so --max-tokens {max_tokens} " + f"is too large. Use a --max-tokens below {limit}, or serve the model with a larger context.") + if problem: + raise RuntimeError(f"The SGLang server at {url} {problem}") + + import transformers + + tokenizer = transformers.AutoTokenizer.from_pretrained( + source, revision=None if local else revision, local_files_only=local, trust_remote_code=False) + probe = {"id": "sglang-probe", "state": "probe evidence", "question": "probe criterion?", + "options": [{"id": "yes", "description": "Yes."}, {"id": "no", "description": "No."}]} + texts = [tokenizer.apply_chat_template(direct_messages(probe), tokenize=False, add_generation_prompt=True, + enable_thinking=False), *LETTERS, "yes", "no"] + expected = [tokenizer.encode(text, add_special_tokens=False) for text in texts] + status, payload = client.send("/v1/tokenize", {"prompt": texts, "add_special_tokens": False}, "the tokenizer check") + if not isinstance(payload, dict) or payload.get("tokens") != expected: + detail = "different token ids" if status == 200 else f"HTTP {status}: {_message(payload)}" + raise RuntimeError(f"The SGLang server at {url} does not tokenize the probe prompt, the answer letters, yes, " + f"and no like {source}@{revision} ({detail}). Serve the same checkpoint and revision " + "with its own tokenizer.") + subject = "the load-time score probe" + try: + _score(client, [expected[0]], [_slot_ids(tokenizer, len(LETTERS))], subject, [subject]) + except RuntimeError as error: + raise RuntimeError(f"{str(error).rstrip('.')}. The server needs per-item candidate scoring with " + "return_token_logprobs (sgl-project/sglang#40826), in SGLang main from commit 174a5f37 " + "(2026-09-24) on or a nightly from 0.5.21.dev20260925 on.") from error + info = {**server_info, **model_info} + metadata = {"source": source, "revision": revision, "backend": "sglang", "server_url": url, + "max_prompt_tokens": max_tokens, "transformers_version": transformers.__version__, + "server": {key: info.get(key) for key in SERVER_FIELDS}} + return client, tokenizer, metadata + + +def _result(row: dict, encoded, logprobs: list[float], metadata: dict, mode: str) -> dict: + ids, slots, prompt_hash = encoded + return { + "id": row["id"], + "option_ids": [option["id"] for option in row["options"]], + "probabilities": softmax(logprobs), + "option_logits": logprobs, + "answer_token_ids": slots, + "input_tokens": len(ids), + "allowed_token_mass": sum(math.exp(value) for value in logprobs), + "prompt_sha256": prompt_hash, + "prompt_version": PROMPT_VERSION, + "model": {**metadata, "serving_config": f"sglang-score-{mode}-v1"}, + "readout": READOUT, + "probability_status": "conditional option score, uncalibrated as decision confidence", + } + + +def _one_row(model, tokenizer, row: dict, metadata: dict, max_tokens: int, serial: bool) -> dict: + started = time.perf_counter() + encoded = encode_prompt(tokenizer, row, max_tokens) + ids, extra = encoded[0], {} + if serial: + prefix = _state_prefix(tokenizer, row["state"]) + if not prefix or ids[: len(prefix)] != prefix or len(ids) <= len(prefix): + raise ValueError(f"Row {row['id']}: state prefix does not match the full prompt. " + "--mode direct sends the same SGLang requests without this check") + extra = {"prefix_tokens": len(prefix), "prefix_sha256": hashlib.sha256(json.dumps(prefix).encode()).hexdigest()} + subject = f"row {row['id']}" + (logprobs,), seconds = _score(model, [ids], [encoded[1]], subject, [subject]) + return {**_result(row, encoded, logprobs, metadata, "serial" if serial else "direct"), **extra, + "request_seconds": seconds, "total_seconds": time.perf_counter() - started} + + +def score(model, tokenizer, row: dict, metadata: dict, max_tokens: int = 4096) -> dict: + return _one_row(model, tokenizer, row, metadata, max_tokens, serial=False) + + +class SerialPrefixScorer: + """Score rows in input order under the serial input rules, keyed on each row's own prefix tokens.""" + + def __init__(self, model, tokenizer, metadata: dict, max_tokens: int = 4096): + self.model, self.tokenizer, self.metadata, self.max_tokens = model, tokenizer, metadata, max_tokens + + def score(self, row: dict) -> dict: + return _one_row(self.model, self.tokenizer, row, self.metadata, self.max_tokens, serial=True) + + +def score_shared(model, tokenizer, rows: list[dict], metadata: dict, max_tokens: int = 4096): + """Send every row over one exact state as one /v1/score request, one item per row.""" + if not rows or any(row["state"] != rows[0]["state"] for row in rows[1:]): + raise ValueError("Shared scoring requires one nonempty exact state") + if len({row["id"] for row in rows}) != len(rows): + raise ValueError("Decision IDs must be unique") + started = time.perf_counter() + encoded = [encode_prompt(tokenizer, row, max_tokens) for row in rows] + prefix = _state_prefix(tokenizer, rows[0]["state"]) + if not prefix or any(ids[: len(prefix)] != prefix or len(ids) <= len(prefix) for ids, _, _ in encoded): + raise ValueError("The fixed state prefix does not match every full prompt. " + "--mode direct scores these rows with one SGLang request each") + encode_seconds = time.perf_counter() - started + scores, request_seconds = _score(model, [ids for ids, _, _ in encoded], [slots for _, slots, _ in encoded], + f"the shared request of {len(rows)} rows", [f"row {row['id']}" for row in rows]) + results = [_result(row, row_encoded, logprobs, metadata, "shared") + for row, row_encoded, logprobs in zip(rows, encoded, scores)] + return results, {"total_seconds": time.perf_counter() - started, "encode_seconds": encode_seconds, + "request_seconds": request_seconds, "batch_size": len(rows), "prefix_tokens": len(prefix), + "true_suffix_tokens": sum(len(ids) - len(prefix) for ids, _, _ in encoded)} + + +def reranker_score(model, tokenizer, row: dict, metadata: dict, max_tokens: int = 4096) -> dict: + """Score SemIf's reranker pairs of one row, one item per option with [no, yes] candidates.""" + validate_row(row) + started = time.perf_counter() + encoded = [reranker._encode(tokenizer, row, option, max_tokens) for option in row["options"]] + no, yes = reranker._answer_ids(tokenizer) + pairs, seconds = _score(model, [ids for ids, _ in encoded], [[no, yes]] * len(encoded), f"row {row['id']}", + [f"row {row['id']} option {option['id']}" for option in row["options"]]) + log_odds = [yes_value - no_value for no_value, yes_value in pairs] + return { + "id": row["id"], + "option_ids": [option["id"] for option in row["options"]], + "probabilities": softmax(log_odds), + "option_logits": log_odds, + "independent_binary_relevance": [softmax(pair)[1] for pair in pairs], + "input_tokens": sum(len(ids) for ids, _ in encoded), + "max_option_input_tokens": max(len(ids) for ids, _ in encoded), + "option_prompt_sha256": [prompt_hash for _, prompt_hash in encoded], + "prompt_version": reranker.PROMPT_VERSION, + "model": {**metadata, "serving_config": "sglang-score-reranker-v1"}, + "readout": "SGLang /v1/score yes minus no log-odds per option, normalized only for relative comparison", + "probability_status": "relative option compatibility, uncalibrated as categorical probability", + "request_seconds": seconds, + "total_seconds": time.perf_counter() - started, + } diff --git a/tests/test_cli.py b/tests/test_cli.py index 3daaf3d..94f2285 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -17,6 +17,8 @@ (["--mode", "direct", "--mlx-bits", "4"], "requires --backend mlx"), (["--mode", "direct", "--mlx-cache-limit-mib", "0"], "requires --backend mlx"), (["--mode", "direct", "--backend", "mlx", "--mlx-cache-limit-mib", "-1"], "must be nonnegative"), + (["--mode", "direct", "--sglang-url", "http://127.0.0.1:30000"], "require --backend sglang"), + (["--mode", "direct", "--sglang-timeout", "5"], "require --backend sglang"), ]) def test_invalid_backend_combinations_fail_before_loading(tmp_path, monkeypatch, capsys, extra, message): monkeypatch.setattr(sys, "argv", ["semif-score", "--model", "unused", "--revision", "unused", @@ -161,3 +163,41 @@ def test_existing_output_is_not_overwritten(run_cli, backends, capsys): assert run_cli.output.read_bytes() == original backends.torch.load_causal_model.assert_not_called() backends.mlx.load_model.assert_not_called() + + +@pytest.fixture +def sglang(backends, monkeypatch): + from semif_phase1 import sglang_backend + + fake = SimpleNamespace( + load_model=create_autospec(sglang_backend.load_model, return_value=backends.loaded), + score=Mock(side_effect=backends.results), + SerialPrefixScorer=Mock(return_value=SimpleNamespace(score=Mock(side_effect=backends.results))), + score_shared=Mock(return_value=(backends.results, backends.timing)), + reranker_score=Mock(side_effect=backends.results), + ) + for name, value in vars(fake).items(): + monkeypatch.setattr(sglang_backend, name, value) + return fake + + +@pytest.mark.parametrize("mode", ["direct", "serial", "shared", "reranker"]) +def test_sglang_routes_every_mode_to_its_scorer(run_cli, backends, sglang, mode): + flags = ["--sglang-url", "http://127.0.0.1:30001", "--sglang-timeout", "12.5"] if mode == "reranker" else [] + run_cli.run(mode, "--backend", "sglang", *flags) + model, tokenizer, metadata = backends.loaded + url, timeout = ("http://127.0.0.1:30001", 12.5) if flags else ("http://127.0.0.1:30000", 300.0) + sglang.load_model.assert_called_once_with("test/model", "a" * 40, url=url, timeout=timeout, max_tokens=128) + assert all(mock.call_count == 0 for mock in vars(backends.torch).values()) + if mode in {"direct", "reranker"}: + scorer = sglang.score if mode == "direct" else sglang.reranker_score + assert scorer.call_args_list == [call(model, tokenizer, row, metadata, 128) for row in run_cli.rows] + elif mode == "serial": + sglang.SerialPrefixScorer.assert_called_once_with(model, tokenizer, metadata, 128) + assert sglang.SerialPrefixScorer.return_value.score.call_args_list == [call(row) for row in run_cli.rows] + else: + sglang.score_shared.assert_called_once_with(model, tokenizer, run_cli.rows, metadata, 128) + expected = backends.results if mode != "shared" else [ + {**result, "shared_timing": backends.timing} for result in backends.results + ] + assert [json.loads(line) for line in run_cli.output.read_text(encoding="utf-8").splitlines()] == expected diff --git a/tests/test_sglang.py b/tests/test_sglang.py new file mode 100644 index 0000000..149836b --- /dev/null +++ b/tests/test_sglang.py @@ -0,0 +1,428 @@ +"""SGLang backend tests against a standard library fake server: no model downloads, GPU, or SGLang needed.""" + +import hashlib +import http.server +import json +import math +import os +import re +import socket +import sys +import threading +import time +from types import SimpleNamespace + +import pytest + +from semif_phase1 import reranker, sglang_backend +from semif_phase1.core import LETTERS, softmax +from semif_phase1.direct import encode_prompt +from semif_phase1.shared import _state_prefix + +SOURCE, REVISION = "Qwen/Qwen3.5-4B", "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a" +RERANKER_SOURCE, RERANKER_REVISION = "Qwen/Qwen3-Reranker-4B", "22e683669bc0f0bd69640a1354a6d0aebcfeede5" + + +class Tokenizer: + """Byte tokenizer with single-token yes and no, as the reranker contract needs.""" + + words = {"no": 300, "yes": 301} + + def apply_chat_template(self, turns, tokenize=False, add_generation_prompt=True, enable_thinking=False): + assert tokenize is False and add_generation_prompt is True and enable_thinking is False + return "".join(f"<{turn['role']}>{turn['content']}\n" for turn in turns) + "" + + def encode(self, text, add_special_tokens=False): + assert add_special_tokens is False + return [self.words[text]] if text in self.words else list(text.encode()) + + def decode(self, ids): + return bytes(ids).decode() + + def convert_tokens_to_ids(self, text): + return self.words[text] + + +def logprob(item, token): + """Deterministic fake log-probability of one candidate after one item.""" + return -((token % 5) + 1) / 4 - len(item) / 1e4 + + +class FakeSGLang(http.server.ThreadingHTTPServer): + def __init__(self): + super().__init__(("127.0.0.1", 0), Handler) + self.tokenizer, self.requests = Tokenizer(), [] + self.model_info = {"model_path": SOURCE, "is_generation": True, "preferred_sampling_params": None, + "load_format": "auto"} + self.server_info = {"model_path": SOURCE, "revision": REVISION, "max_req_input_len": 8192, "enable_mis": False, + "allow_auto_truncate": False, "api_key": "server-secret", "admin_api_key": "admin-secret", + "launch_command": "--api-key server-secret"} + self.routes = {"/model_info": lambda body: (200, self.model_info), + "/server_info": lambda body: (200, self.server_info), + "/v1/tokenize": self.tokenize, "/v1/score": self.score} + + def tokenize(self, body): + return 200, {"tokens": [self.tokenizer.encode(text, add_special_tokens=False) for text in body["prompt"]]} + + def score(self, body): + values = [[logprob(item, token) for token in candidates] + for item, candidates in zip(body["items"], body["label_token_ids"])] + return 200, {"token_logprobs": values, "usage": {"prompt_tokens": sum(map(len, body["items"]))}} + + def scores(self): + return [request for request in self.requests if request["path"] == "/v1/score"] + + +class Handler(http.server.BaseHTTPRequestHandler): + def do_GET(self): + self.answer(None) + + def do_POST(self): + self.answer(json.loads(self.rfile.read(int(self.headers["Content-Length"])))) + + def answer(self, body): + self.server.requests.append({"method": self.command, "path": self.path, "headers": dict(self.headers), + "body": body}) + status, payload = self.server.routes[self.path](body) + if status is None: + return self.wfile.write(payload or b"") # Raw bytes, or close without an answer. + data = (payload if isinstance(payload, str) else json.dumps(payload)).encode() + self.send_response(status) + self.send_header("Content-Length", str(len(data))) + self.end_headers() + self.wfile.write(data) + + def log_message(self, *args): + pass + + +@pytest.fixture +def server(monkeypatch): + fake, tokenizer = FakeSGLang(), Tokenizer() + threading.Thread(target=fake.serve_forever, kwargs={"poll_interval": 0.01}, daemon=True).start() + fake.url, fake.loads = f"http://127.0.0.1:{fake.server_address[1]}", [] + monkeypatch.setitem(sys.modules, "transformers", SimpleNamespace(__version__="test", AutoTokenizer=SimpleNamespace( + from_pretrained=lambda *args, **kwargs: fake.loads.append((args, kwargs)) or tokenizer))) + monkeypatch.delenv(sglang_backend.API_KEY_ENV, raising=False) + fake.load = lambda **kwargs: sglang_backend.load_model( + kwargs.pop("source", SOURCE), kwargs.pop("revision", REVISION), url=kwargs.pop("url", fake.url), **kwargs) + yield fake + fake.shutdown() + fake.server_close() + + +def row(identifier, state="shared evidence", options=2, question="Which answer follows?"): + return {"id": identifier, "state": state, "question": question, + "options": [{"id": f"o{index}", "description": f"Option {index}"} for index in range(options)]} + + +def closed_port(): + with socket.socket() as probe: + probe.bind(("127.0.0.1", 0)) + return probe.getsockname()[1] + + +def test_load_model_verifies_server_and_records_only_allowlisted_facts(server, monkeypatch): + monkeypatch.setenv(sglang_backend.API_KEY_ENV, "client-key") + model, tokenizer, metadata = server.load(url=server.url + "/", max_tokens=8191) + assert [(request["method"], request["path"]) for request in server.requests] == [ + ("GET", "/model_info"), ("GET", "/server_info"), ("POST", "/v1/tokenize"), ("POST", "/v1/score")] + assert all(request["headers"]["Authorization"] == "Bearer client-key" + and request["headers"]["Content-Type"] == "application/json" for request in server.requests) + assert server.loads == [((SOURCE,), {"revision": REVISION, "local_files_only": False, "trust_remote_code": False})] + tokenize, probe = server.requests[2]["body"], server.requests[3]["body"] + assert tokenize["add_special_tokens"] is False and tokenize["prompt"][1:] == [*LETTERS, "yes", "no"] + assert probe == {"query": [], "items": [tokenizer.encode(tokenize["prompt"][0])], + "label_token_ids": [[ord(letter) for letter in LETTERS]], "return_token_logprobs": True} + assert metadata["backend"] == "sglang" and metadata["server_url"] == server.url + assert metadata["max_prompt_tokens"] == 8191 and metadata["server"]["revision"] == REVISION + assert metadata["server"]["load_format"] == "auto" + assert not any(secret in json.dumps(metadata) for secret in ("server-secret", "admin-secret", "client-key")) + + +def test_local_source_accepts_its_label_without_a_served_revision(server, tmp_path): + server.model_info["model_path"] = server.server_info["model_path"] = str(tmp_path) + server.server_info["revision"] = None + _, _, metadata = server.load(source=str(tmp_path), revision="local-checkpoint-v1") + assert server.loads[0][1] == {"revision": None, "local_files_only": True, "trust_remote_code": False} + assert metadata["revision"] == "local-checkpoint-v1" and metadata["server"]["revision"] is None + + +@pytest.mark.parametrize("change,message", [ + (lambda fake: fake.model_info.update(model_path=RERANKER_SOURCE), "Pass --sglang-url"), + (lambda fake: fake.model_info.update(is_generation=False), "non-generation model. Relaunch it without"), + (lambda fake: fake.model_info.update(load_format="dummy"), "--load-format dummy, which serves random weights"), + (lambda fake: fake.server_info.update(revision="0" * 40), f"but --revision is {REVISION}. Relaunch it with"), + (lambda fake: fake.server_info.update(revision=None), f"serves revision None, but --revision is {REVISION}"), + (lambda fake: fake.server_info.update(enable_mis=True), "--enable-mis"), + (lambda fake: fake.server_info.update(allow_auto_truncate=True), "--allow-auto-truncate"), + (lambda fake: fake.model_info.update(preferred_sampling_params={"temperature": 0.5}), + '--preferred-sampling-params {"temperature": 0.5}, which applies to every scoring request'), + (lambda fake: fake.server_info.update(max_req_input_len=4096), "Use a --max-tokens below 4096"), + (lambda fake: fake.server_info.pop("max_req_input_len"), "refuses inputs of None tokens"), + (lambda fake: fake.routes.update({"/model_info": lambda body: (401, {"error": "Unauthorized"})}), + "(HTTP 401). Check --sglang-url, and set SEMIF_SGLANG_API_KEY"), + (lambda fake: fake.routes.update({"/server_info": lambda body: (200, "proxy")}), + "did not return SGLang server information (HTTP 200)"), + (lambda fake: fake.routes.update({"/v1/tokenize": lambda body: (400, "tokenizer unavailable")}), + "(HTTP 400: tokenizer unavailable). Serve the same checkpoint"), + (lambda fake: setattr(fake.tokenizer, "words", {"no": 300, "yes": 302}), "(different token ids)"), + (lambda fake: setattr(fake.tokenizer, "words", {"no": 300, "yes": 301, "C": 99}), "(different token ids)"), + (lambda fake: fake.routes.update({"/v1/tokenize": lambda body: (200, {"tokens": [[1], *[[65]] * 18]})}), + "(different token ids)"), + (lambda fake: fake.routes.update({"/v1/score": lambda body: (400, {"message": "label_token_ids: int_type"})}), + "HTTP 400: label_token_ids: int_type. The server needs per-item candidate scoring"), + (lambda fake: fake.routes.update({"/v1/score": lambda body: (200, {"scores": [[0.5] * 16]})}), + "no token_logprobs list with one entry per item for the load-time score probe. The server needs"), +]) +def test_load_model_refuses_unsupported_servers_with_the_fix(server, change, message): + change(server) + with pytest.raises(RuntimeError, match=re.escape(message)): + server.load() + + +def test_unreachable_server_names_the_url_and_the_launch_before_loading_the_tokenizer(server): + port = closed_port() + url = f"http://127.0.0.1:{port}" + with pytest.raises(RuntimeError) as error: + server.load(url=url) + assert f"Cannot reach an SGLang server at {url}" in str(error.value) + assert (f"python -m sglang.launch_server --model-path {SOURCE} --revision {REVISION} --port {port}" + in str(error.value)) + assert "fired up and ready to roll" in str(error.value) + assert server.loads == [] + + +@pytest.mark.parametrize("options,message", [ + ({"revision": "main"}, "pinned 40-character revision"), + ({"source": ".", "revision": ""}, "Local sources require a revision label"), + ({"url": "http://user:secret@127.0.0.1:30000"}, "without user info"), + ({"url": "localhost:30000"}, "must be an http or https URL"), + ({"url": "http://127.0.0.1:30000/?key=secret"}, "without user info, query, or fragment"), + ({"url": "http://127.0.0.1:30000/#secret"}, "without user info, query, or fragment"), +]) +def test_load_model_rejects_invalid_arguments_before_any_request(server, options, message): + with pytest.raises(ValueError, match=re.escape(message)) as error: + server.load(**options) + assert "secret" not in str(error.value) and server.requests == [] + + +def expected_row(tokenizer, source_row, max_tokens=4096): + ids, slots, prompt_hash = encode_prompt(tokenizer, source_row, max_tokens) + values = [logprob(ids, token) for token in slots] + return ids, slots, { + "id": source_row["id"], + "option_ids": [option["id"] for option in source_row["options"]], + "probabilities": softmax(values), + "option_logits": values, + "answer_token_ids": slots, + "input_tokens": len(ids), + "allowed_token_mass": sum(math.exp(value) for value in values), + "prompt_sha256": prompt_hash, + "prompt_version": "direct-options-v1", + "readout": sglang_backend.READOUT, + "probability_status": "conditional option score, uncalibrated as decision confidence", + } + + +def test_direct_serial_and_shared_send_semif_ids_and_build_rows_from_the_response(server): + model, tokenizer, metadata = server.load() + rows = [row("first"), row("second", question="Does the second answer follow?", options=3)] + before = len(server.scores()) + direct = [sglang_backend.score(model, tokenizer, source_row, metadata) for source_row in rows] + scorer = sglang_backend.SerialPrefixScorer(model, tokenizer, metadata) + serial = [scorer.score(source_row) for source_row in rows] + shared, timing = sglang_backend.score_shared(model, tokenizer, rows, metadata) + requests = [request["body"] for request in server.scores()[before:]] + expected = [expected_row(tokenizer, source_row) for source_row in rows] + per_row = [{"query": [], "items": [ids], "label_token_ids": [slots], "return_token_logprobs": True} + for ids, slots, _ in expected] + assert requests == per_row + per_row + [{"query": [], "items": [ids for ids, _, _ in expected], + "label_token_ids": [slots for _, slots, _ in expected], + "return_token_logprobs": True}] + prefix = _state_prefix(tokenizer, "shared evidence") + for mode, results in (("direct", direct), ("serial", serial), ("shared", shared)): + for result, (_, _, fields) in zip(results, expected): + assert {key: result[key] for key in fields} == fields + assert result["model"] == {**metadata, "serving_config": f"sglang-score-{mode}-v1"} + assert not {"cache_hit", "forward_seconds", "full_vocab_argmax_id", "prefill_seconds"} & result.keys() + assert ("request_seconds" in result) == ("total_seconds" in result) == (mode != "shared") + assert ("prefix_sha256" in result) == (mode == "serial") + assert [result["prefix_tokens"] for result in serial] == [len(prefix)] * 2 + assert serial[0]["prefix_sha256"] == hashlib.sha256(json.dumps(prefix).encode()).hexdigest() + assert timing["batch_size"] == 2 and timing["prefix_tokens"] == len(prefix) + assert timing["true_suffix_tokens"] == sum(len(ids) - len(prefix) for ids, _, _ in expected) + + +def test_reranker_sends_one_item_per_option_with_no_and_yes_candidates(server): + model, tokenizer, metadata = server.load() + source_row = row("pairs", options=3) + result = sglang_backend.reranker_score(model, tokenizer, source_row, metadata) + pairs = [reranker._encode(tokenizer, source_row, option, 4096) for option in source_row["options"]] + assert server.scores()[-1]["body"] == {"query": [], "items": [ids for ids, _ in pairs], + "label_token_ids": [[300, 301]] * 3, "return_token_logprobs": True} + values = [[logprob(ids, 300), logprob(ids, 301)] for ids, _ in pairs] + odds = [yes - no for no, yes in values] + assert result["option_logits"] == odds and result["probabilities"] == softmax(odds) + assert result["independent_binary_relevance"] == [softmax(pair)[1] for pair in values] + assert result["input_tokens"] == sum(len(ids) for ids, _ in pairs) + assert result["max_option_input_tokens"] == max(len(ids) for ids, _ in pairs) + assert result["option_prompt_sha256"] == [prompt_hash for _, prompt_hash in pairs] + assert result["prompt_version"] == "qwen3-reranker-native-options-v1" + assert result["model"]["serving_config"] == "sglang-score-reranker-v1" + assert result["probability_status"] == "relative option compatibility, uncalibrated as categorical probability" + with pytest.raises(ValueError, match="Option IDs must be unique"): + sglang_backend.reranker_score(model, tokenizer, {**source_row, "options": [source_row["options"][0]] * 2}, + metadata) + + +def reshape(fake, change): + def answer(body): + status, payload = fake.score(body) + change(payload) + return status, payload + return answer + + +@pytest.mark.parametrize("route,message", [ + (lambda fake: lambda body: (400, {"message": "Token ID 99 is out of vocabulary"}), + "rejected row first with HTTP 400: Token ID 99 is out of vocabulary"), + (lambda fake: lambda body: time.sleep(1) or fake.score(body), "did not answer row first within 0.2 s"), + (lambda fake: lambda body: (None, None), "for row first (RemoteDisconnected"), + (lambda fake: lambda body: (None, b"HTTP/1.1 200 OK\r\nContent-Length: 9\r\n\r\n{"), + "for row first (IncompleteRead"), + (lambda fake: reshape(fake, lambda payload: payload["usage"].update(prompt_tokens=3)), + "counted 3 prompt tokens for row first"), + (lambda fake: reshape(fake, lambda payload: payload.pop("usage")), "counted None prompt tokens for row first"), + (lambda fake: reshape(fake, lambda payload: payload["token_logprobs"][0].__setitem__(1, None)), + "for row first, expected 2 finite values"), + (lambda fake: reshape(fake, lambda payload: payload["token_logprobs"][0].__setitem__(1, math.inf)), + "for row first, expected 2 finite values"), + (lambda fake: reshape(fake, lambda payload: payload["token_logprobs"][0].pop()), + "for row first, expected 2 finite values"), + (lambda fake: reshape(fake, lambda payload: payload["token_logprobs"].__setitem__(0, None)), + "token_logprobs None for row first"), + (lambda fake: reshape(fake, lambda payload: payload.pop("token_logprobs")), "no token_logprobs list"), +]) +def test_runtime_failures_stop_with_the_row_and_are_never_retried(server, route, message): + model, tokenizer, metadata = server.load(timeout=0.2) + server.routes["/v1/score"] = route(server) + before = len(server.scores()) + with pytest.raises(RuntimeError, match=re.escape(message)): + sglang_backend.score(model, tokenizer, row("first"), metadata) + assert len(server.scores()) == before + 1 + + +@pytest.mark.parametrize("mode", ["shared", "reranker"]) +def test_a_missing_item_stops_requests_that_carry_several_items(server, mode): + model, tokenizer, metadata = server.load() + server.routes["/v1/score"] = reshape(server, lambda payload: payload["token_logprobs"].pop()) + with pytest.raises(RuntimeError, match="no token_logprobs list with one entry per item"): + if mode == "shared": + sglang_backend.score_shared(model, tokenizer, [row("first"), row("second"), row("third")], metadata) + else: + sglang_backend.reranker_score(model, tokenizer, row("pairs", options=3), metadata) + + +def test_boundaries_of_options_states_text_and_input_limit(server): + model, tokenizer, metadata = server.load() + rows = [ + row("sixteen", state={"zone": "Zürich", "events": ["部署完成", 3]}, options=16), + row("array", state=[{"check": "passed"}, "done"]), + row("text", state="Évidence non-ASCII: 東京"), + ] + results = [sglang_backend.score(model, tokenizer, source_row, metadata) for source_row in rows] + assert [len(request["body"]["label_token_ids"][0]) for request in server.scores()[-3:]] == [16, 2, 2] + assert [len(result["probabilities"]) for result in results] == [16, 2, 2] + exact = len(encode_prompt(tokenizer, rows[2], 4096)[0]) + before = len(server.scores()) + sglang_backend.score(model, tokenizer, rows[2], metadata, max_tokens=exact) + with pytest.raises(ValueError, match="no truncation"): + sglang_backend.score(model, tokenizer, rows[2], metadata, max_tokens=exact - 1) + assert len(server.scores()) == before + 1 + + +def test_serial_accepts_interleaved_and_reordered_states_by_their_own_prefix(server): + model, tokenizer, metadata = server.load() + rows = [row("a1", state="alpha"), row("b1", state="beta"), row("a2", state="alpha"), + row("d1", state={"x": 1, "y": True}), row("d2", state={"y": True, "x": 1})] + scorer = sglang_backend.SerialPrefixScorer(model, tokenizer, metadata) + results = [scorer.score(source_row) for source_row in rows] + prefixes = [_state_prefix(tokenizer, source_row["state"]) for source_row in rows] + assert prefixes[3] != prefixes[4] + assert [result["prefix_tokens"] for result in results] == [len(prefix) for prefix in prefixes] + assert [result["prefix_sha256"] for result in results] == [ + hashlib.sha256(json.dumps(prefix).encode()).hexdigest() for prefix in prefixes] + assert [request["body"]["items"][0] for request in server.scores()[-5:]] == [ + encode_prompt(tokenizer, source_row, 4096)[0] for source_row in rows] + + +@pytest.mark.parametrize("mode,rows,message", [ + ("serial", [row("first")], "--mode direct sends the same SGLang requests"), + ("shared", [row("first"), row("second")], "--mode direct scores these rows"), + ("shared", [row("first"), row("second", state="other evidence")], "one nonempty exact state"), + ("shared", [row("first"), row("first")], "Decision IDs must be unique"), +]) +def test_serial_and_shared_input_rules_refuse_before_any_request(server, monkeypatch, mode, rows, message): + model, tokenizer, metadata = server.load() + monkeypatch.setattr(sglang_backend, "_state_prefix", lambda tokenizer, state: [0]) + before = len(server.scores()) + with pytest.raises(ValueError, match=message): + if mode == "serial": + sglang_backend.SerialPrefixScorer(model, tokenizer, metadata).score(rows[0]) + else: + sglang_backend.score_shared(model, tokenizer, rows, metadata) + assert len(server.scores()) == before + + +REAL_ROWS = [ + {"id": key, "state": "The deployment completed at 14:02 UTC. Health checks passed in all three zones.", + "question": question, "options": [{"id": "yes", "description": yes}, {"id": "no", "description": no}]} + for key, question, yes, no in [ + ("deployment", "Is there evidence that the deployment succeeded?", "The deployment succeeded.", + "The deployment did not succeed."), + ("health", "Did the health checks pass?", "The checks passed.", "The checks failed."), + ] +] + + +def assert_valid_distributions(results): + assert [result["id"] for result in results] == [source_row["id"] for source_row in REAL_ROWS] + for result in results: + assert all(math.isfinite(value) for value in result["probabilities"]) + assert math.isclose(sum(result["probabilities"]), 1.0, rel_tol=1e-9) + + +@pytest.mark.skipif(not os.environ.get("SEMIF_SGLANG_URL"), + reason="set SEMIF_SGLANG_URL to a server for Qwen/Qwen3.5-4B at the pinned revision") +def test_real_server_scores_direct_serial_and_shared(): + model, tokenizer, metadata = sglang_backend.load_model(SOURCE, REVISION, url=os.environ["SEMIF_SGLANG_URL"]) + direct = [sglang_backend.score(model, tokenizer, source_row, metadata) for source_row in REAL_ROWS] + scorer = sglang_backend.SerialPrefixScorer(model, tokenizer, metadata) + serial = [scorer.score(source_row) for source_row in REAL_ROWS] + shared, _ = sglang_backend.score_shared(model, tokenizer, REAL_ROWS, metadata) + for results in (direct, serial, shared): + assert_valid_distributions(results) + for result, source_row in zip(results, REAL_ROWS): + ids, slots, prompt_hash = encode_prompt(tokenizer, source_row, 4096) + assert (result["prompt_sha256"], result["input_tokens"], result["answer_token_ids"]) == ( + prompt_hash, len(ids), slots) + # Values move slightly between cold and cached requests on hybrid models, so compare choices only. + assert [result["option_ids"][result["probabilities"].index(max(result["probabilities"]))] + for result in results] == ["yes", "yes"] + assert metadata["server"]["revision"] == REVISION + + +@pytest.mark.skipif(not os.environ.get("SEMIF_SGLANG_RERANKER_URL"), + reason="set SEMIF_SGLANG_RERANKER_URL to a server for Qwen/Qwen3-Reranker-4B " + "at the pinned revision") +def test_real_server_scores_reranker_pairs(): + model, tokenizer, metadata = sglang_backend.load_model( + RERANKER_SOURCE, RERANKER_REVISION, url=os.environ["SEMIF_SGLANG_RERANKER_URL"]) + results = [sglang_backend.reranker_score(model, tokenizer, source_row, metadata) for source_row in REAL_ROWS] + assert_valid_distributions(results) + for result, source_row in zip(results, REAL_ROWS): + pairs = [reranker._encode(tokenizer, source_row, option, 4096) for option in source_row["options"]] + assert result["option_prompt_sha256"] == [prompt_hash for _, prompt_hash in pairs] + assert result["input_tokens"] == sum(len(ids) for ids, _ in pairs) + assert metadata["server"]["revision"] == RERANKER_REVISION From b7bcb30e98fdd74061cde95dbbcbd59e20dbf669 Mon Sep 17 00:00:00 2001 From: Pavan Date: Fri, 2 Oct 2026 09:39:46 +0530 Subject: [PATCH 13/13] fix(order): sample permutations lazily above 8 options select_permutations listed all n! permutations before picking K: 1.45 s at 10 options, factorial beyond. Up to 8 options the enumerate-and-shuffle path is kept, so existing stabilized results reproduce exactly (checked for n=2..8, K in 2,3,4,5,8,16, seeds 0,1,7). Above that, orders are drawn with random.Random(seed).sample until K distinct ones are chosen; identity and reverse are still first. --- src/semif_phase1/order.py | 31 +++++++++++++++++++++++-------- tests/test_order.py | 12 ++++++++++++ 2 files changed, 35 insertions(+), 8 deletions(-) diff --git a/src/semif_phase1/order.py b/src/semif_phase1/order.py index 9b8e009..4a725ed 100644 --- a/src/semif_phase1/order.py +++ b/src/semif_phase1/order.py @@ -45,31 +45,46 @@ def apply_permutation(row: dict, permutation: Sequence[int]) -> dict: return altered +#: Largest option count whose permutations are enumerated (8! = 40320). Up to here the sampled +#: orders are exactly those of the original enumerate-and-shuffle selection, so existing +#: stabilized results reproduce. Beyond it enumeration grows factorially (10 options took 1.45 s, +#: 12 would take minutes and gigabytes), so orders are drawn one at a time instead. +ENUMERATE_MAX_OPTIONS = 8 + + def select_permutations(n_options: int, k: int, *, seed: int = 0) -> list[tuple[int, ...]]: """Choose ``k`` distinct display permutations, always including identity and reverse when possible. Deterministic. When ``n_options!`` is at most ``k``, returns every permutation (identity first). Otherwise samples without replacement after locking identity - and the reversed order. + and the reversed order. Cost is O(k * n_options) above ``ENUMERATE_MAX_OPTIONS``. """ check_stabilize_k(k) if n_options < 2: raise ValueError("Need at least two options to permute") identity = tuple(range(n_options)) reversed_order = tuple(reversed(range(n_options))) - all_perms = list(itertools.permutations(range(n_options))) - if k >= len(all_perms): - # Identity first for stable readout metadata. - rest = [perm for perm in all_perms if perm != identity] + if k >= math.factorial(n_options): + # Identity first for stable readout metadata. Only reachable for small n_options. + rest = [perm for perm in itertools.permutations(range(n_options)) if perm != identity] return [identity, *rest] chosen: list[tuple[int, ...]] = [identity] if reversed_order != identity and k >= 2: chosen.append(reversed_order) - remaining = [perm for perm in all_perms if perm not in chosen] rng = random.Random(seed) - rng.shuffle(remaining) + if n_options <= ENUMERATE_MAX_OPTIONS: + remaining = [perm for perm in itertools.permutations(range(n_options)) if perm not in chosen] + rng.shuffle(remaining) + while len(chosen) < k: + chosen.append(remaining.pop()) + return chosen + # k < n_options! here, and n_options! > 40320, so a repeat draw is rare and the loop ends. + seen = set(chosen) while len(chosen) < k: - chosen.append(remaining.pop()) + perm = tuple(rng.sample(range(n_options), n_options)) + if perm not in seen: + seen.add(perm) + chosen.append(perm) return chosen diff --git a/tests/test_order.py b/tests/test_order.py index 8e8b415..77bb45a 100644 --- a/tests/test_order.py +++ b/tests/test_order.py @@ -60,6 +60,18 @@ def test_select_permutations_includes_identity_and_reverse(): assert len(select_permutations(3, 6, seed=0)) == 6 +def test_select_permutations_does_not_enumerate_many_options(): + import time + + started = time.perf_counter() + perms = select_permutations(20, 8, seed=0) + assert time.perf_counter() - started < 0.5 # 20! permutations would never finish + assert perms[0] == tuple(range(20)) and perms[1] == tuple(reversed(range(20))) + assert len(set(perms)) == 8 and all(sorted(perm) == list(range(20)) for perm in perms) + assert select_permutations(20, 8, seed=0) == perms # deterministic per seed + assert select_permutations(20, 8, seed=1) != perms + + def test_align_and_average_logits_then_softmax(): canonical = ["yes", "no", "maybe"] # Identity: yes strongest. Reversed display scored with letter-A bias toward maybe.