diff --git a/README.md b/README.md index 290666f..44c24f6 100644 --- a/README.md +++ b/README.md @@ -54,17 +54,15 @@ 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`. +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). Run the owned examples: 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 feeb301..6195d7a 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 +32,53 @@ 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", 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") + 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) 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 + 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 + + 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 +90,15 @@ 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 +120,7 @@ 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..e93be99 --- /dev/null +++ b/docs/LLAMACPP.md @@ -0,0 +1,227 @@ +# 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` 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" \ + --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 | +|---|---|---| +| `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`: +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. + +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 +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. + +## 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 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; +- 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. + +## 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 | 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 +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 + +| 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. + +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 +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`, +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), +`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. + + +## 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 +# 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 $MODE \ + --model Qwen/Qwen3.5-4B --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \ + --gguf /path/to/Qwen_Qwen3.5-4B-Q4_K_M.gguf \ + --input benchmarks/data/authored144.jsonl \ + --output results/raw/predictions/llamacpp-gguf-cuda-$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 \ + --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/docs/RESULTS.md b/docs/RESULTS.md index bb0d7d6..0c64c37 100644 --- a/docs/RESULTS.md +++ b/docs/RESULTS.md @@ -84,3 +84,35 @@ 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 | 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 | + +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 +(`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`, +`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..670417b 100644 --- a/results/raw/SHA256SUMS +++ b/results/raw/SHA256SUMS @@ -21,3 +21,18 @@ 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 +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 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+ "predicted_id": "contradicted", + "correct": false, + "confidence": 0.7458145102540846, + "nll": 1.4182783554678657, + "brier": 1.1307507532604308, + "status": "distribution", + "probabilities": [ + 0.7458145102540846, + 0.01205496880026748, + 0.24213052094564783 + ] + }, + { + "id": "2708193212d8a4a523c7", + "group_id": "6a06a2f278301c1412ee", + "family": "rule_application", + "gold_id": "prohibited", + "predicted_id": "permitted", + "correct": false, + "confidence": 0.7661572065563422, + "nll": 1.7663678998071337, + "brier": 1.2782713115860567, + "status": "distribution", + "probabilities": [ + 0.17095278019779026, + 0.7661572065563422, + 0.0628900132458675 + ] + }, + { + "id": "acacd8b46acd25cb954b", + "group_id": "6a06a2f278301c1412ee", + "family": "rule_application", + "gold_id": "permitted", + "predicted_id": "insufficient", + "correct": false, + "confidence": 0.5742128517698835, + "nll": 1.429755129501267, + "brier": 0.9430344292949584, + "status": "distribution", + "probabilities": [ + 0.5742128517698835, + 0.23936752911414957, + 0.186419619115967 + ] + }, + { + "id": "02f2b95a5c5a4777a45c", + "group_id": "2797f8c23d05c0ee06b3", + "family": "candidate_selection", + "gold_id": "A", + "predicted_id": "B", + "correct": false, + "confidence": 0.8421164512966888, + "nll": 2.6718369711000984, + "brier": 1.5835662026295678, + "status": "distribution", + "probabilities": [ + 0.8421164512966888, + 0.06912512774585124, + 0.08875842095745999 + ] + }, + { + "id": "b8c5b9b285cbdddc9f8d", + "group_id": "2797f8c23d05c0ee06b3", + "family": "candidate_selection", + "gold_id": "B", + "predicted_id": "insufficient", + "correct": false, + "confidence": 0.7132171318867825, + "nll": 1.337969372128006, + "brier": 1.0533606117462375, + "status": "distribution", + "probabilities": [ + 0.02440494820080904, + 0.7132171318867825, + 0.26237791991240844 + ] + } + ], + "limitations": [ + "All gold rows count in accuracy, including missing/invalid/unparsed predictions.", + "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." + ] +} diff --git a/src/semif_phase1/cli.py b/src/semif_phase1/cli.py index dd2c2f3..7264240 100644 --- a/src/semif_phase1/cli.py +++ b/src/semif_phase1/cli.py @@ -23,6 +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", 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("--model", required=True) parser.add_argument("--revision", required=True) parser.add_argument("--input", type=Path, required=True) @@ -51,6 +58,22 @@ 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") + 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 != "auto": + parser.error(f"{name} requires --backend llamacpp") if args.backend == "llamacpp": if args.mode == "reranker": parser.error("llama.cpp supports direct, serial, and shared modes; reranker requires torch") @@ -75,7 +98,8 @@ 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) 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..7422db2 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 +6,20 @@ 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. """ from __future__ import annotations @@ -31,17 +40,27 @@ from .shared import _state_prefix DECODE_CHUNK = 512 +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 _cpu_model_params(library): - """Initialize llama.cpp once and return model parameters with GPU offload disabled.""" +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 = 0 + if gpu_layers is not None: + params.n_gpu_layers = gpu_layers return params @@ -76,19 +95,41 @@ 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. 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. + params.n_outputs_max = sequences + params.n_outputs_max_per_seq = 1 params.n_threads = threads params.n_threads_batch = threads 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 +140,7 @@ 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 +148,14 @@ 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 +177,36 @@ 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 +238,34 @@ 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 _free_native(engine: _Engine, library, model) -> None: engine.close() @@ -208,8 +310,17 @@ 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 | str | None = AUTO, + 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 + 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 ""): raise ValueError("Remote sources require a pinned 40-character revision; local sources require a revision label") @@ -224,6 +335,15 @@ 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 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 'auto' or a positive integer") import transformers try: import llama_cpp @@ -241,13 +361,13 @@ 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 +383,12 @@ 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": 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, "max_prompt_tokens": context_tokens, "context_tokens": engine.context_tokens, "decode_chunk": DECODE_CHUNK, @@ -358,8 +483,37 @@ 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 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 +524,74 @@ 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 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: + 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: + # 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)) + 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] + 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] + 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-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, @@ -398,7 +601,10 @@ 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, + "branches_per_decode": branch_counts, + "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..c98314c 100644 --- a/tests/test_llamacpp.py +++ b/tests/test_llamacpp.py @@ -16,6 +16,10 @@ (["--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", "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"), ]) 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 +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: - (None, None, {"gguf": str(gguf), "threads": threads, "context_tokens": context_tokens}), + 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}), score=lambda model, tokenizer, row, metadata, max_tokens: metadata, SerialPrefixScorer=None, score_shared=None, ) @@ -50,6 +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" def test_load_model_rejects_unpinned_remote_revision(): @@ -76,7 +82,7 @@ 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 +91,11 @@ 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) + 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 first.n_gpu_layers == second.n_gpu_layers == 0 + 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(): @@ -183,8 +190,9 @@ 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 = [ { @@ -212,11 +220,155 @@ 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"] == 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) + 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=-2) + with pytest.raises(ValueError, match="sequences"): + llamacpp_backend.load_model("Qwen/Qwen3.5-4B", revision, weights, sequences=0) + + +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_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