diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/evaluations/S001/logs/inference/verifier.log b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/evaluations/S001/logs/inference/verifier.log new file mode 100644 index 0000000000000000000000000000000000000000..19a9b94acb944a44bec0b8086a69ff433cb9f049 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/evaluations/S001/logs/inference/verifier.log @@ -0,0 +1 @@ +{"event": "inference_complete", "infer_seconds": 1437.2, "perturb_seconds": 1041.3} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/evaluations/S001/logs/score/verifier.log b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/evaluations/S001/logs/score/verifier.log new file mode 100644 index 0000000000000000000000000000000000000000..08e56c32f228cdd5689652bcdf42cdfbc2e41c44 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/evaluations/S001/logs/score/verifier.log @@ -0,0 +1,2 @@ +/evaluation/variant_scoring.py:26: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined. + return spearmanr(y, p)[0] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/evaluations/S001/report.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/evaluations/S001/report.json new file mode 100644 index 0000000000000000000000000000000000000000..8feb3c0cf8f49fcbe26d1b687dd4d7492cab5176 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/evaluations/S001/report.json @@ -0,0 +1,18034 @@ +{ + "examples": 1000, + "families": { + "atac": { + 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0.2902111280364055, + "CADD.plus.RawScore": 0.2953404491813135, + "Enformer_L2_L2.plus.all": 0.335264858312409, + "GPN-MSA_absLLR.plus.score": 0.28980002269554805 + }, + "metric": "auprc", + "model": 0.18267124648058347, + "perturbations": 789, + "rows": 789 + }, + "traitgym_mendelian": { + "anchors": { + "Borzoi_L2_L2.plus.all": 0.45912938155791766, + "CADD.plus.RawScore": 0.7427433091372563, + "Enformer_L2_L2.plus.all": 0.4706298420086466, + "GPN-MSA_absLLR.plus.score": 0.7439513186095712 + }, + "metric": "auprc", + "model": 0.21857809233892214, + "perturbations": 784, + "rows": 784 + } + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/evaluations/S041/reward.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/evaluations/S041/reward.json new file mode 100644 index 0000000000000000000000000000000000000000..2be447c67c50dd4fc4ddc45ea8f8af163805ceab --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/evaluations/S041/reward.json @@ -0,0 +1,4 @@ +{ + "overall_mean_family_pearson": 0.5331420976721618, + "reward": 0.5331420976721618 +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/README.md b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/README.md new file mode 100644 index 0000000000000000000000000000000000000000..cac61e49f5ebaed3269f758cfacbd05f4c4e0085 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/README.md @@ -0,0 +1,67 @@ +# SeqFunc loss01 — balanced splice objective + +One 13,218,756-parameter model serves all eleven families and both entry points. It reads the full +1,048,576-bp sequence through a convolutional encoder, six 384-wide transformer blocks over 1,024 +tokens, and seven dense heads plus splice-site, usage, low-rank junction and symmetric contact heads. +Dense predictions are at 128-bp bins over the central 196,608-bp window. Sparse positions are chosen +from the model's own splice scores. This architecture is the small supplied reference configuration. + +All parameters were initialized locally (seed 17); no pretrained parameters were used. Training +uses FOLD_0's permitted train folds 2–7, quantized targets decoded by the supplied reader, and a +deterministic permutation of all 41,020 eligible intervals each epoch. This is the first intermediate +checkpoint after 2,000 distinct intervals (4.88% of epoch one); it has not yet completed full coverage. +The scheduled run is four epochs, with AdamW (2e-4 to 2e-5 cosine schedule, 500-step warmup, +weight decay 1e-4), BF16 training, and gradient norm clipping at 5. The splice objective averages +positive and negative BCE contributions equally per site type; all-negative types retain negative +loss. Other family losses are unchanged from the reference recipe. One H100 80 GB and eight data +workers are used, restricted to 22 CPUs. The measured throughput is approximately 11–12 intervals/s. + +Validation uses the unmodified verifier's score.py on all 200 validation intervals and +score_variants.py on all 2,697 development perturbations. Numerical results and published anchors +are appended below and included as JSON. Validation labels are used only for evaluation and +selection; no test labels, label-derived caches or development variant labels train the model. + +Run infer.sh INPUT_ROOT OUTPUT_ROOT for track outputs and perturb.sh INPUT_ROOT OUTPUT_ROOT for +paired sequence outputs. Both read only public manifests and the pinned reference FASTA. The package +includes weights, source code, a SHA-256 manifest, and a complete CPython/PyTorch/CUDA Python runtime +archive. Entry points extract this archive into a temporary directory and clean it on completion. +The host supplies Linux, bash/tar/gzip, and the NVIDIA driver. No network is used. + +Limitations: this is an early training checkpoint, not a baseline-beating result. The coarse trunk +still repeats features across eight adjacent dense bins and has no local base-resolution branch. +Splice localization and junction recall are therefore expected to remain weak. All perturbation +outputs use float32 to preserve small variant effects; track outputs use float16. + +## Measured validation results + +Family-mean Pearson: **0.005382** on 200 validation intervals. + +| Family | Pearson | +|---|---:| +| atac | -0.000699 | +| dnase | -0.007095 | +| procap | 0.046699 | +| cage | -0.004743 | +| rna_seq | 0.008705 | +| chip_tf | 0.000364 | +| chip_histone | 0.000417 | +| splice_sites | 0.000103 | +| splice_usage | 0.005451 | +| junctions | 0.000000 | +| contact_maps | 0.009998 | + +| Development evaluation | Metric | Model | Published anchors on the same rows | +|---|---|---:|---| +| eqtl_sign | auroc | 0.4918 | AlphaGenome: 0.8264 | +| eqtl_causality | auroc | 0.7094 | AlphaGenome: 0.8325 | +| eqtl_coefficient | spearman | 0.0228 | AlphaGenome: 0.4134 | +| sqtl_causality | auprc | 0.7960 | AlphaGenome: 0.8447 | +| clinvar_noncoding | auprc | 0.2149 | AlphaGenome: 0.9410 | +| clinvar_splice_region | auprc | 0.1896 | AlphaGenome: 0.7225 | +| clinvar_missense | auprc | 0.1566 | AlphaGenome: 0.3738 | +| mfass | auprc | 0.2532 | AlphaGenome: 0.7343 | +| caqtl | auprc | 0.1469 | AlphaGenome: 0.8076 | +| bqtl_spi1 | auprc | 0.1540 | AlphaGenome: 0.8167 | +| enhancer_gene | auprc | 0.3940 | AlphaGenome: 0.9594 | +| traitgym_mendelian | auprc | 0.1268 | Borzoi_L2_L2.plus.all: 0.5026; Enformer_L2_L2.plus.all: 0.5453; CADD.plus.RawScore: 0.7189; GPN-MSA_absLLR.plus.score: 0.6729 | +| traitgym_complex | auprc | 0.2360 | Borzoi_L2_L2.plus.all: 0.2945; Enformer_L2_L2.plus.all: 0.2692; CADD.plus.RawScore: 0.4134; GPN-MSA_absLLR.plus.score: 0.4214 | diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/infer.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/infer.sh new file mode 100644 index 0000000000000000000000000000000000000000..2a412a9ad8667d6dcb94bb57d9959d61d43ddd34 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/infer.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: infer.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/predict.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/package_manifest.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/package_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..9a05cbf3ba76262a8afe7df507ecc9fac607586f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/package_manifest.json @@ -0,0 +1,26 @@ +{ + "parameters": 13218756, + "source_checkpoint_sha256": "e5c67b1a53d18702d9a7133c46e73edd314f622f60bd08af0d43d828c51c451b", + "files": { + "README.md": "1a37a81b34f0b3648757188162bef6a3ec95cde64657fee006b63f9f58d30044", + "infer.sh": "ef810532b1e72b85fbe283e4f52c177bb70025977dbb2345d75837c9bbd51e37", + "last.pt": "a00d68500c92a90d241a6799e7bfb74499cd073bd93f0e244538f7f97f4adde5", + "perturb.py": "ba37a6f9f8b8eccb052903618bcb4c91d3f4cbed62dfe678ee362644dc80801e", + "perturb.sh": "113ec2383276a6e83fe1e4e8b0fa914f8d535d5dd0f5b2c34240140312dd5fb4", + "predict.py": "2725592456c55c188b22c376f0cb249e9f46b4ae1370d2226df7143da358a8b2", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/__init__.py": "45f3cf93f48859104e70bb58d717130dfd6521a45d2aba06b1a726afc0dc51dd", + "reference/alphagenome_50m/__init__.py": "a5a7e45d5940c3b201017c6322128e9f47efd37e1d29f717da2c1f787688eff9", + "reference/alphagenome_50m/alphagenome_50m.yaml": "3cac32e9f55e352dff78bb85ac892fdae1f9e412d9bba3e945750210c5252eee", + "reference/alphagenome_50m/dataset.py": "ad6670f742f4de9898064becc525d0aea98b402abe7b210bb1b26a9eee6402b9", + "reference/alphagenome_50m/layers.py": "26a116a72aa3f0e307a3606163547edbdecb7965f7fdcd96bc5f805cd21b3352", + "reference/alphagenome_50m/model.py": "713940fd2b335bf1ce16d1d76241a174f42c3ec15ebdf5c97c1e03f9cd691293", + "reference/alphagenome_50m/reference_large.yaml": "68084d299b39418034c6bbf71db174c3ffa68bbeea8779c1694e44ec71f3529c", + "reference/alphagenome_50m/run_ddp.sh": "96d35a24a4e6789311501fb98203807ac0c4429375c44d5c6671908270c93bb0", + "reference/alphagenome_50m/tiny_overfit.yaml": "f7a104c3929de0530a9a8f73f66d794cca595aefad8ef262e09ecbeb9cfce6de", + "reference/alphagenome_50m/train_eval.py": "2547c9adbdb51ad1acf796a548724cc7770eba90a755f4cb3b37d6f591adf6ec", + "runtime.tar.gz": "718da4c374e0c552b6e429b226c90b2fded5c900d95577d190d4df520aaf3901", + "validation_tracks.json": "8ab851d929f182fc1e54e992a26e4187ac8d41dfd69b4ac2d4aafd0cd3703ead", + "validation_variants.json": "9614ada6aa64d9c36a461d2c107b8df8c179e5240fa157ebc26757a166a6a47c" + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/perturb.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/perturb.py new file mode 100644 index 0000000000000000000000000000000000000000..e0416692d2ee7deb1a25927397f725eec54d16a9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/perturb.py @@ -0,0 +1,101 @@ +"""Predict both sequences of every perturbation (contract: perturbations/v1). + + python perturb.py INPUT_ROOT OUTPUT_ROOT --checkpoint last.pt + +INPUT_ROOT holds `public/perturbations.parquet` and `reference/GRCh38.p13.genome.fa` (in the +workspace, `/workspace/data` works too: the queries are then read from `variant_evals/public/`). Each row is a +1,048,576-bp window starting at `input_start` and an edit spanning [edit_start, edit_end) in genomic +0-based coordinates: + +- `snv`: sequence A carries `allele_a` at edit_start, sequence B carries `allele_b` (both are written + explicitly; either may differ from the reference genome); +- `mask`: sequence A is the reference, sequence B has the interval replaced by N. + +For each perturbation the output group `perturbations/p{index:06d}` holds `a/` and +`b/` for every family in `families`: dense families sliced to bins +[dense_bin_start, dense_bin_end) of the central target window, and `splice_sites` sliced to bp +[splice_start, splice_end). Values are the same quantities as predictions.zarr (asinh space for +dense tracks, splice-site scores for splice_sites). +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import pandas as pd +import pysam +import torch +import zarr + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig + +PERTURBATION_SCHEMA = "genome-sequence-to-function-perturbations/v1" +COMPRESSOR = Blosc(cname="zstd", clevel=3, shuffle=Blosc.BITSHUFFLE) + + +def sequences(fasta, row) -> tuple[str, str]: + length = fasta.get_reference_length(row.chrom) + start, end = int(row.input_start), int(row.input_start) + 1_048_576 + sequence = fasta.fetch(row.chrom, max(0, start), min(length, end)).upper() + sequence = "N" * max(0, -start) + sequence + "N" * max(0, end - length) + lo, hi = int(row.edit_start) - start, int(row.edit_end) - start + if row.edit == "snv": + return sequence[:lo] + row.allele_a + sequence[hi:], sequence[:lo] + row.allele_b + sequence[hi:] + if row.edit == "mask": + return sequence, sequence[:lo] + "N" * (hi - lo) + sequence[hi:] + raise ValueError(f"unknown edit {row.edit!r}") + + +def main(input_root: Path, output_root: Path, checkpoint: Path, queries: Path | None = None) -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + if queries is None: + queries = input_root / "public/perturbations.parquet" + if not queries.exists(): + queries = input_root / "variant_evals/public/perturbations.parquet" + rows = pd.read_parquet(queries) + fasta = pysam.FastaFile(str(input_root / "reference/GRCh38.p13.genome.fa")) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "perturbations.zarr"), mode="w") + target_start = (config.input_length - config.target_length) // 2 + with torch.inference_mode(): + for index, row in enumerate(rows.itertuples(index=False)): + pair = sequences(fasta, row) + dna = torch.from_numpy(np.stack([AlphaGenomeDataset._one_hot(s) for s in pair])).to(device) + trunk = model._trunk(dna) + group = root.create_group(f"perturbations/p{index:06d}") + features = None + for family in row.families: + if family == "splice_sites": + logits = model._splice_site_logits(trunk)[:, target_start:target_start + config.target_length] + values = logits[:, row.splice_start:row.splice_end] + else: + features = model._dense_features(trunk) if features is None else features + values = model.dense_heads[family](features)[:, row.dense_bin_start:row.dense_bin_end] + # float32: one variant's effect is often below float16 resolution (about 0.004 at + # asinh values of 4-8), so A and B would round to the same numbers. + values = values.float().cpu().numpy() + for side, array in zip("ab", values): + group.create_dataset(f"{side}/{family}", data=array, compressor=COMPRESSOR) + print(json.dumps({"event": "perturb", "done": index + 1, "total": len(rows)}), flush=True) + root.attrs.update({"schema": PERTURBATION_SCHEMA, + "perturbation_ids": rows.perturbation_id.astype(str).tolist()}) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--queries", type=Path, help="another query table, e.g. the development split") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.queries) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/perturb.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/perturb.sh new file mode 100644 index 0000000000000000000000000000000000000000..f07c4714532c2d33e7a419de3877e5daacee89fd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/perturb.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: perturb.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/perturb.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/predict.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..52f3decd4fe2e9d275d08606b74575fafb701434 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/predict.py @@ -0,0 +1,130 @@ +#!/usr/bin/env python3 +"""Stream the single benchmark model into the sharded prediction contract.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import torch +import zarr + +from prediction_contract import DENSE_TRACKS, MAX_JUNCTION_PAIRS, MAX_USAGE_SITES, PREDICTION_SCHEMA +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig + + +COMPRESSOR = Blosc(cname="zstd", clevel=7, shuffle=Blosc.BITSHUFFLE) + + +def main(input_root: Path, output_root: Path, checkpoint: Path, split: str = "test") -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + dataset = AlphaGenomeDataset(input_root, split, include_targets=False) + model.set_junction_track_halves(dataset.junction_track_halves) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "predictions.zarr"), mode="w") + ids = [] + with torch.inference_mode(): + for sample in dataset: + example_id = sample["example_id"] + dna = sample["dna"][None].to(device) + trunk = model._trunk(dna) + dense_features = model._dense_features(trunk) + group = root.create_group(f"examples/{example_id}") + for family in DENSE_TRACKS: + values = model.dense_heads[family](dense_features)[0] + group.create_dataset(family, data=values.cpu().numpy().astype(np.float16), compressor=COMPRESSOR) + + all_site_logits = model._splice_site_logits(trunk) + target_start = (config.input_length - config.target_length) // 2 + group.create_dataset( + "splice_sites", + data=all_site_logits[0, target_start:target_start + config.target_length].cpu().numpy().astype(np.float16), + chunks=(4096, 4), compressor=COMPRESSOR, + ) + predicted_positions = model.select_junction_positions(all_site_logits)[0] + + # Usage at the model's own predicted sites (v4 is sparse; unlisted hidden sites score 0). + usage_bins = model.splice_usage_head(dense_features)[0].float() + sites = predicted_positions.cpu().numpy().astype(np.int64).reshape(-1) + sites = np.unique(sites[(sites >= target_start) & (sites < target_start + config.target_length)] + - target_start)[:MAX_USAGE_SITES] + group.create_dataset("splice_usage_positions", data=sites.astype(np.int32), compressor=COMPRESSOR) + usage = usage_bins[torch.as_tensor(sites // 128, device=usage_bins.device)] if len(sites) \ + else usage_bins[:0] + group.create_dataset("splice_usage", data=usage.cpu().numpy().astype(np.float16), + chunks=(1024, 734), compressor=COMPRESSOR) + + # Junctions: score every donor x acceptor pair of each strand, keep the strongest pairs. + positions_batch = predicted_positions[None] + site_factors = model.junction_site_factors(trunk, positions_batch) + halves = model.junction_track_half + candidates = [] + for row_start in range(0, 512, 128): + for column_start in range(0, 512, 128): + block = torch.cat([ + model.junction_block( + trunk, positions_batch, row_start, row_start + 128, + column_start, column_start + 128, track_start, + min(track_start + 64, config.junction_tracks), site_factors, + )[0] + for track_start in range(0, config.junction_tracks, 64) + ], dim=-1).float() # [128, 128, 734] + for half in (0, 1): + donors = predicted_positions[2 * half, row_start:row_start + 128] + acceptors = predicted_positions[2 * half + 1, column_start:column_start + 128] + valid = (donors[:, None] >= 0) & (acceptors[None, :] >= 0) + strength = block[..., halves == half].amax(dim=-1).masked_fill(~valid, -torch.inf) + keep = min(MAX_JUNCTION_PAIRS // 2, int(valid.sum())) + if not keep: + continue + best = strength.flatten().topk(keep) + rows, columns = best.indices // 128, best.indices % 128 + candidates.append((best.values, torch.full_like(rows, half), donors[rows], + acceptors[columns], block[rows, columns])) + if candidates: + score, half, donor, acceptor, values = (torch.cat(x) for x in zip(*candidates)) + order = score.topk(min(MAX_JUNCTION_PAIRS, len(score))).indices + half, donor, acceptor, values = half[order], donor[order], acceptor[order], values[order] + else: + half = donor = acceptor = torch.zeros(0, dtype=torch.long) + values = torch.zeros(0, config.junction_tracks) + group.create_dataset("junction_half", data=half.cpu().numpy().astype(np.int8), compressor=COMPRESSOR) + group.create_dataset("junction_donor", data=donor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junction_acceptor", data=acceptor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junctions", data=values.cpu().numpy().astype(np.float16), + chunks=(4096, 734), compressor=COMPRESSOR) + + contact = trunk.reshape( + 1, config.contact_bins, -1, config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + contact_values = 0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row]) + group.create_dataset( + "contact_maps", data=contact_values[0].cpu().numpy().astype(np.float16), + chunks=(8192, 28), compressor=COMPRESSOR, + ) + ids.append(example_id) + print(json.dumps({"event": "predict", "example": len(ids), "total": len(dataset)}), flush=True) + root.attrs.update({"schema": PREDICTION_SCHEMA, "example_ids": ids}) + (output_root / "example_ids.json").write_text(json.dumps(ids) + "\n") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--split", default="test", choices=("valid", "test"), + help="manifest to predict; the verifier uses test, local validation uses valid") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.split) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/prediction_contract.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/prediction_contract.py new file mode 100644 index 0000000000000000000000000000000000000000..2455424db002b840cf0d69456930840a7bc5f3cb --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/prediction_contract.py @@ -0,0 +1,23 @@ +"""Public prediction shapes; no trusted splice coordinates are exposed.""" + +from __future__ import annotations + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} + +PREDICTION_SCHEMA = "genome-sequence-to-function-predictions/v4" +MAX_USAGE_SITES = 4_096 +MAX_JUNCTION_PAIRS = 16_384 +# Fixed-shape arrays per example; splice usage and junctions are sparse (see contract.md). +FIXED_SHAPES = { + **{name: (1_536, tracks) for name, tracks in DENSE_TRACKS.items()}, + "splice_sites": (196_608, 4), + "contact_maps": (131_328, 28), +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4879a0e1a3a8a8050bb48360febee152cd141be8 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/__init__.py @@ -0,0 +1 @@ +"""The reference model of the alphagenome-reference baseline.""" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a86a139babb6e899bd5102bbf3c628e1232576b2 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/__init__.py @@ -0,0 +1,5 @@ +"""Compact 1-Mb, eleven-family AlphaGenome reference model.""" + +from .model import CompactModel, ModelConfig, count_parameters + +__all__ = ["CompactModel", "ModelConfig", "count_parameters"] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/alphagenome_50m.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/alphagenome_50m.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0a59a4d0c65752d45eb464d129c6ec9d559665e9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/alphagenome_50m.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 32 +channel_growth: 32 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 384 +num_sequence_blocks: 6 +num_attention_heads: 6 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 256 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/dataset.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..71cd39d15713f648e7d7e31aa1923fa4fa4800dd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/dataset.py @@ -0,0 +1,229 @@ +"""Streaming reader for the compact 1 Mb / eleven-family release.""" + +from __future__ import annotations + +from collections import OrderedDict +from pathlib import Path + +import numpy as np +import pandas as pd +import pysam +import torch +from torch.utils.data import Dataset +import zarr + + +def _open_part(path: Path): + if str(path).endswith(".zarr.zip"): + store = zarr.ZipStore(str(path), mode="r") + try: + return zarr.open_group(store=store, mode="r"), store + except BaseException: + store.close() + raise + return zarr.open_group(str(path), mode="r"), None + + +DENSE_FAMILIES = ( + "atac", + "dnase", + "procap", + "cage", + "rna_seq", + "chip_tf", + "chip_histone", +) +def normalize_splice_positions(value) -> np.ndarray: + """Normalize Arrow's nested-list representation to the public [4, 512] array.""" + + outer = np.asarray(value, dtype=object).reshape(-1).tolist() + positions = np.stack([np.asarray(item, dtype=np.int64) for item in outer]) + if positions.shape != (4, 512): + raise ValueError(f"splice_site_positions must have shape (4, 512), got {positions.shape}") + return positions + + +class AlphaGenomeDataset(Dataset): + """Resolve frozen coordinates to reference DNA and one Zarr example group.""" + + def __init__( + self, + data_root: str | Path, + split: str, + *, + max_open_parts: int = 8, + include_targets: bool = True, + ) -> None: + self.root = Path(data_root) + self.split = split + manifest = self.root / "public" / f"{split}.parquet" + if not manifest.is_file(): + raise FileNotFoundError(f"Missing materialized manifest: {manifest}") + self.rows = pd.read_parquet(manifest).reset_index(drop=True) + junction_tracks = pd.read_parquet(self.root / "public/junction_tracks.parquet") + junction_tracks = junction_tracks.sort_values("tensor_index") + self.junction_track_halves = torch.from_numpy( + junction_tracks.junction_channel_half.to_numpy(np.int64) + ) + if include_targets: + labels_path = self.root / "labels" / f"{split}.parquet" + if not labels_path.is_file(): + raise FileNotFoundError(f"Missing trusted labels manifest: {labels_path}") + labels = pd.read_parquet(labels_path).reset_index(drop=True) + if self.rows.example_id.astype(str).tolist() != labels.example_id.astype(str).tolist(): + raise ValueError("public and labels manifests have different IDs/order") + self.rows = self.rows.merge(labels, on="example_id", validate="one_to_one", sort=False) + self.fasta_path = self.root / "reference" / "GRCh38.p13.genome.fa" + self.max_open_parts = max_open_parts + self.include_targets = include_targets + self._fasta = None + self._parts: OrderedDict[str, tuple[zarr.Group, object | None]] = OrderedDict() + + def __len__(self) -> int: + return len(self.rows) + + def _reference(self): + if self._fasta is None: + self._fasta = pysam.FastaFile(str(self.fasta_path)) + return self._fasta + + def _part(self, relative: str): + if relative in self._parts: + opened = self._parts.pop(relative) + self._parts[relative] = opened + return opened[0] + group, store = _open_part(self.root / relative) + if len(self._parts) >= self.max_open_parts: + _, (_, old_store) = self._parts.popitem(last=False) + if old_store is not None: + old_store.close() + self._parts[relative] = (group, store) + return group + + @staticmethod + def _one_hot(sequence: str) -> np.ndarray: + encoded = np.frombuffer(sequence.upper().encode("ascii"), dtype=np.uint8) + output = np.zeros((len(encoded), 4), dtype=np.float32) + for index, letter in enumerate(b"ACGT"): + output[:, index] = encoded == letter + return output + + def _sequence(self, row) -> tuple[np.ndarray, np.ndarray]: + reference = self._reference() + chromosome_length = reference.get_reference_length(str(row.chromosome)) + start, end = int(row.input_start), int(row.input_end) + clipped_start, clipped_end = max(0, start), min(chromosome_length, end) + sequence = reference.fetch(str(row.chromosome), clipped_start, clipped_end) + left, right = clipped_start - start, end - clipped_end + if left or right: + sequence = "N" * left + sequence + "N" * right + valid = np.ones(end - start, dtype=bool) + if left: + valid[:left] = False + if right: + valid[-right:] = False + return self._one_hot(sequence), valid + + @staticmethod + def _decode(example, name: str, *, asinh: bool) -> np.ndarray: + """Train targets may be stored as 8-bit codes with per-track ranges; they are decoded + here, one example at a time, so the release stays compressed on disk.""" + array = example[name] + if array.dtype != np.uint8: + return np.asarray(array, dtype=np.float32) + lo = np.asarray(example[f"{name}_lo"], dtype=np.float32) + hi = np.asarray(example[f"{name}_hi"], dtype=np.float32) + x = lo + np.asarray(array, dtype=np.float32) * ((hi - lo) / 255.0) + return np.sinh(x) if asinh else x + + @staticmethod + def _dense_target(example, family: str) -> dict[str, torch.Tensor]: + values = AlphaGenomeDataset._decode(example, f"{family}/values", asinh=True) + mask = np.asarray(example[f"{family}/mask"], dtype=bool) + if mask.ndim == 1: + mask = np.broadcast_to(mask[None, :], values.shape).copy() + return { + "values": torch.from_numpy(values), + "mask": torch.from_numpy(mask), + } + + def __getitem__(self, item: int): + row = self.rows.iloc[item] + dna, input_mask = self._sequence(row) + result = { + "example_id": str(row.example_id), + "dna": torch.from_numpy(dna), + "input_mask": torch.from_numpy(input_mask), + "junction_track_halves": self.junction_track_halves, + } + if not self.include_targets: + return result + + part = self._part(str(row.target_shard)) + example = part[str(row.target_group)] + result["dense"] = { + family: self._dense_target(example, family) for family in DENSE_FAMILIES + } + result["splice_sites"] = torch.from_numpy( + np.asarray(example["splice_sites/values"], dtype=np.float32) + ) + result["splice_usage"] = { + "positions": torch.from_numpy( + np.asarray(example["splice_site_usage/positions"], dtype=np.int64) + ), + "values": torch.from_numpy( + np.asarray(example["splice_site_usage/values"], dtype=np.float32) + ), + "mask": torch.from_numpy( + np.asarray(example["splice_site_usage/mask"], dtype=bool) + ), + } + result["usage_positions"] = result["splice_usage"]["positions"] + result["junctions"] = { + name: torch.from_numpy( + np.asarray( + example[f"splice_junctions/{name}"], + dtype=np.float32 if name == "value" else np.int64, + ) + ) + for name in ("row", "column", "track", "value") + } + result["junctions"]["site_positions"] = torch.from_numpy( + np.asarray(example["splice_junctions/site_positions"], dtype=np.int64) + ) + site_positions = normalize_splice_positions(row.splice_site_positions) + result["site_positions"] = torch.from_numpy(site_positions) + if not torch.equal(result["junctions"]["site_positions"], result["site_positions"]): + raise ValueError(f"public/private splice positions differ for {row.example_id}") + scoring_path = self.root / "scoring" / self.split / f"part-{item:06d}.parquet" + scoring = pd.read_parquet(scoring_path) + if not (scoring.example_id.astype(str) == str(row.example_id)).all(): + raise ValueError(f"scoring part ID differs for {row.example_id}") + result["junction_scoring"] = { + "donor_position": torch.from_numpy( + scoring.donor_position.to_numpy(np.int64) + ), + "acceptor_position": torch.from_numpy( + scoring.acceptor_position.to_numpy(np.int64) + ), + "track": torch.from_numpy(scoring.track.to_numpy(np.int64)), + "target_value": torch.from_numpy( + scoring.target_value.to_numpy(np.float32) + ), + "valid": torch.from_numpy(scoring.valid.to_numpy(bool)), + } + result["contact_maps"] = torch.from_numpy( + self._decode(example, "contact_maps/upper_triangle", asinh=False) + ) + result["contact_map_mask"] = torch.from_numpy( + np.asarray(example["contact_maps/mask"], dtype=bool) + ) + return result + + +def single_example_collate(items): + """Keep variable-size event tensors intact; the reference currently trains at batch one.""" + + if len(items) != 1: + raise ValueError("The compact event loader currently requires batch_size=1") + return items[0] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/layers.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..af31f86a5589a16f0007e6aca5ca7a7bd552d6a9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/layers.py @@ -0,0 +1,124 @@ +"""Shared neural-network layers for the single compact AlphaGenome model.""" + +from __future__ import annotations + +import math + +import torch +from torch import Tensor, nn +from torch.nn import functional as F + + +class ChannelRMSNorm(nn.Module): + def __init__(self, channels: int, eps: float = 1e-6): + super().__init__() + self.weight = nn.Parameter(torch.ones(channels)) + self.eps = eps + + def forward(self, x: Tensor) -> Tensor: + variance = x.float().square().mean(dim=1, keepdim=True) + normalized = x * torch.rsqrt(variance + self.eps).to(x.dtype) + return normalized * self.weight[None, :, None].to(x.dtype) + + +class StandardizedConv1d(nn.Conv1d): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__(in_channels, out_channels, kernel_size, padding="same", bias=True) + self.scale = nn.Parameter(torch.ones(out_channels)) + nn.init.trunc_normal_(self.weight, std=1.0 / math.sqrt(in_channels * kernel_size)) + nn.init.trunc_normal_(self.bias, std=1e-4) + + def forward(self, x: Tensor) -> Tensor: + centered = self.weight - self.weight.mean(dim=(1, 2), keepdim=True) + variance = centered.float().square().mean(dim=(1, 2), keepdim=True) + fan_in = self.weight.shape[1] * self.weight.shape[2] + multiplier = self.scale[:, None, None] * torch.rsqrt( + (fan_in * variance).clamp_min(1e-4) + ).to(self.weight.dtype) + return F.conv1d(x, centered * multiplier, self.bias, padding="same") + + +class ConvBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__() + self.norm = ChannelRMSNorm(in_channels) + self.conv = StandardizedConv1d(in_channels, out_channels, kernel_size) + + def forward(self, x: Tensor) -> Tensor: + return self.conv(F.gelu(self.norm(x), approximate="tanh")) + + +def _apply_rope(x: Tensor) -> Tensor: + rotary_dim = x.shape[-1] - x.shape[-1] % 2 + if rotary_dim == 0: + return x + positions = torch.arange(x.shape[-2], device=x.device, dtype=torch.float32) + inverse_frequency = 1.0 / ( + 10_000 ** ( + torch.arange(0, rotary_dim, 2, device=x.device, dtype=torch.float32) + / rotary_dim + ) + ) + angles = torch.outer(positions, inverse_frequency) + cos = angles.cos()[None, None, :, :].to(x.dtype) + sin = angles.sin()[None, None, :, :].to(x.dtype) + even, odd = x[..., :rotary_dim:2], x[..., 1:rotary_dim:2] + rotated = torch.stack((even * cos - odd * sin, even * sin + odd * cos), dim=-1) + return torch.cat((rotated.flatten(-2), x[..., rotary_dim:]), dim=-1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.num_heads = config.num_attention_heads + self.head_dim = config.attention_head_dim + inner_dim = self.num_heads * self.head_dim + self.norm = nn.RMSNorm(config.hidden_dim) + self.qkv = nn.Linear(config.hidden_dim, 3 * inner_dim, bias=False) + self.output = nn.Linear(inner_dim, config.hidden_dim) + self.dropout = config.dropout + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + batch, length, _ = x.shape + qkv = self.qkv(self.norm(x)).view( + batch, length, 3, self.num_heads, self.head_dim + ) + q, k, value = qkv.unbind(dim=2) + attended = F.scaled_dot_product_attention( + _apply_rope(q.transpose(1, 2)), + _apply_rope(k.transpose(1, 2)), + value.transpose(1, 2), + attn_mask=attention_bias, + dropout_p=self.dropout if self.training else 0.0, + ) + return self.output(attended.transpose(1, 2).reshape(batch, length, -1)) + + +class MLPBlock(nn.Module): + def __init__(self, config): + super().__init__() + expanded = int(config.hidden_dim * config.mlp_expansion) + self.norm = nn.RMSNorm(config.hidden_dim) + self.input = nn.Linear(config.hidden_dim, expanded) + self.output = nn.Linear(expanded, config.hidden_dim) + self.dropout = nn.Dropout(config.dropout) + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor) -> Tensor: + return self.dropout( + self.output(F.gelu(self.input(self.norm(x)), approximate="tanh")) + ) + + +class SequenceBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.attention = MultiHeadAttention(config) + self.mlp = MLPBlock(config) + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + x = x + self.attention(x, attention_bias) + return x + self.mlp(x) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/model.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/model.py new file mode 100644 index 0000000000000000000000000000000000000000..309576c3d9d03f5dfc5251033b4759306198e7f7 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/model.py @@ -0,0 +1,368 @@ +"""Affordable multi-resolution baseline for the compact AlphaGenome task.""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass, fields +import math +from pathlib import Path +from typing import Any, Iterable + +import torch +from torch import Tensor, nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint +import yaml + +from .layers import ConvBlock, SequenceBlock + + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} +ALL_FAMILIES = (*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions", "contact_maps") + + +@dataclass(frozen=True) +class ModelConfig: + input_length: int = 1_048_576 + target_length: int = 196_608 + dense_bin_size: int = 128 + trunk_bin_size: int = 1_024 + contact_bin_size: int = 2_048 + stem_stride: int = 8 + encoder_width: int = 32 + channel_growth: int = 32 + num_downsampling_blocks: int = 7 + conv_kernel_size: int = 5 + dna_kernel_size: int = 15 + hidden_dim: int = 384 + num_sequence_blocks: int = 6 + num_attention_heads: int = 6 + attention_head_dim: int = 64 + mlp_expansion: float = 4.0 + dropout: float = 0.0 + head_hidden_size: int = 256 + junction_rank: int = 32 + splice_usage_tracks: int = 734 + junction_tracks: int = 734 + contact_tracks: int = 28 + gradient_checkpointing: bool = True + enable_pairwise: bool = False + pair_width: int = 32 + pair_downsample: int = 16 + pair_update_interval: int = 2 + + def __post_init__(self) -> None: + downsample = self.stem_stride * 2**self.num_downsampling_blocks + if downsample != self.trunk_bin_size: + raise ValueError("stem_stride * 2**num_downsampling_blocks must equal trunk_bin_size") + for length in (self.input_length, self.target_length): + if length % self.trunk_bin_size: + raise ValueError("input and target lengths must divide by trunk_bin_size") + if self.trunk_bin_size % self.dense_bin_size: + raise ValueError("trunk_bin_size must divide by dense_bin_size") + if self.input_length % self.contact_bin_size: + raise ValueError("input_length must divide by contact_bin_size") + if self.hidden_dim <= 0 or self.num_attention_heads <= 0: + raise ValueError("hidden dimensions and attention heads must be positive") + + @property + def trunk_bins(self) -> int: + return self.input_length // self.trunk_bin_size + + @property + def target_trunk_bins(self) -> int: + return self.target_length // self.trunk_bin_size + + @property + def dense_bins(self) -> int: + return self.target_length // self.dense_bin_size + + @property + def dense_upsample(self) -> int: + return self.trunk_bin_size // self.dense_bin_size + + @property + def contact_bins(self) -> int: + return self.input_length // self.contact_bin_size + + @property + def encoder_output_width(self) -> int: + return self.encoder_width + self.channel_growth * self.num_downsampling_blocks + + @classmethod + def from_yaml(cls, path: str | Path) -> "ModelConfig": + values: dict[str, Any] = yaml.safe_load(Path(path).read_text()) or {} + known = {field.name for field in fields(cls)} + unknown = sorted(values.keys() - known) + if unknown: + raise ValueError(f"Unknown model configuration keys: {unknown}") + return cls(**values) + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + +class SequenceEncoder(nn.Module): + def __init__(self, config: ModelConfig): + super().__init__() + self.stem = nn.Conv1d( + 4, + config.encoder_width, + config.dna_kernel_size, + stride=config.stem_stride, + padding=config.dna_kernel_size // 2, + ) + blocks = [] + width = config.encoder_width + for _ in range(config.num_downsampling_blocks): + output_width = width + config.channel_growth + blocks.append(ConvBlock(width, output_width, config.conv_kernel_size)) + width = output_width + self.blocks = nn.ModuleList(blocks) + + def forward(self, dna: Tensor) -> Tensor: + x = F.gelu(self.stem(dna.transpose(1, 2)), approximate="tanh") + for block in self.blocks: + x = F.max_pool1d(block(x), kernel_size=2, stride=2) + return x.transpose(1, 2) + + +class CompactModel(nn.Module): + """One shared 1-kb trunk with representation-specific output heads.""" + + def __init__(self, config: ModelConfig | None = None): + super().__init__() + self.config = config or ModelConfig() + self.encoder = SequenceEncoder(self.config) + self.projection = nn.Linear(self.config.encoder_output_width, self.config.hidden_dim) + self.blocks = nn.ModuleList( + SequenceBlock(self.config) for _ in range(self.config.num_sequence_blocks) + ) + self.final_norm = nn.RMSNorm(self.config.hidden_dim) + self.dense_decoder = nn.Sequential( + nn.RMSNorm(self.config.hidden_dim), + nn.Linear(self.config.hidden_dim, self.config.head_hidden_size), + nn.GELU(approximate="tanh"), + ) + self.dense_heads = nn.ModuleDict( + { + family: nn.Linear(self.config.head_hidden_size, tracks) + for family, tracks in DENSE_TRACKS.items() + } + ) + self.splice_site_head = nn.Linear( + self.config.head_hidden_size, self.config.dense_bin_size * 4 + ) + self.splice_usage_head = nn.Linear( + self.config.head_hidden_size, self.config.splice_usage_tracks + ) + rank = self.config.junction_rank + self.junction_source = nn.Linear(self.config.head_hidden_size, rank) + self.junction_destination = nn.Linear(self.config.head_hidden_size, rank) + self.junction_track = nn.Embedding(self.config.junction_tracks, rank) + if self.config.junction_tracks % 2: + raise ValueError("junction tracks must split evenly into strand halves") + self.register_buffer( + "junction_track_half", + torch.arange(self.config.junction_tracks) // (self.config.junction_tracks // 2), + persistent=False, + ) + self.contact_q = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + self.contact_k = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + row, column = torch.triu_indices( + self.config.contact_bins, self.config.contact_bins + ) + self.register_buffer("contact_row", row, persistent=False) + self.register_buffer("contact_column", column, persistent=False) + + def set_junction_track_halves(self, halves: Tensor) -> None: + halves = torch.as_tensor(halves, dtype=torch.long, device=self.junction_track_half.device) + if halves.shape != (self.config.junction_tracks,) or not torch.isin( + halves, torch.tensor([0, 1], device=halves.device) + ).all(): + raise ValueError("junction track halves must be a 734-element 0/1 mapping") + self.junction_track_half.copy_(halves) + + def _trunk(self, dna: Tensor) -> Tensor: + if dna.ndim != 3 or dna.shape[1:] != (self.config.input_length, 4): + raise ValueError( + f"Expected DNA [B,{self.config.input_length},4], got {tuple(dna.shape)}" + ) + x = self.projection(self.encoder(dna)) + for block in self.blocks: + if self.config.gradient_checkpointing and self.training and x.requires_grad: + x = checkpoint(block, x, None, use_reentrant=False) + else: + x = block(x, None) + return self.final_norm(x) + + def _dense_features(self, trunk: Tensor) -> Tensor: + start = (self.config.trunk_bins - self.config.target_trunk_bins) // 2 + target = trunk[:, start : start + self.config.target_trunk_bins] + target = target.repeat_interleave(self.config.dense_upsample, dim=1) + return self.dense_decoder(target) + + def _all_dense_features(self, trunk: Tensor) -> Tensor: + return self.dense_decoder( + trunk.repeat_interleave(self.config.dense_upsample, dim=1) + ) + + def _splice_site_logits(self, trunk: Tensor) -> Tensor: + logits = self.splice_site_head(self._all_dense_features(trunk)) + return logits.view(len(trunk), self.config.input_length, 4).float() + + @staticmethod + def select_junction_positions(site_logits: Tensor, limit: int = 512) -> Tensor: + """Select each site type from model scores alone, then sort by coordinate. + + This follows the four independent site classes in Google DeepMind's + AlphaGenome splicing implementation (pinned in docs/v3-author/PROTOCOL.md). Padding is + -1 when an input is shorter than ``limit``. + """ + count = min(limit, site_logits.shape[1]) + selected = [] + for site_type in range(4): + coordinates = torch.topk(site_logits[..., site_type], count, dim=1).indices + coordinates = coordinates.sort(dim=1).values + if count < limit: + coordinates = F.pad(coordinates, (0, limit - count), value=-1) + selected.append(coordinates) + return torch.stack(selected, dim=1) + + @staticmethod + def _gather_bins(features: Tensor, positions: Tensor, bin_size: int) -> Tensor: + indices = torch.div(positions, bin_size, rounding_mode="floor") + indices = indices.clamp(0, features.shape[1] - 1) + return torch.gather( + features, + 1, + indices[..., None].expand(-1, -1, features.shape[-1]), + ) + + def _junction_predictions( + self, trunk: Tensor, site_positions: Tensor, queries: Tensor + ) -> Tensor: + row, column, track = queries.unbind(dim=-1) + half = self.junction_track_half[track] + batch = torch.arange(len(trunk), device=trunk.device)[:, None] + donor = site_positions[batch, 2 * half, row] + acceptor = site_positions[batch, 2 * half + 1, column] + source = self.dense_decoder(self._gather_bins( + trunk, donor.clamp(0, self.config.input_length - 1), self.config.trunk_bin_size + )) + destination = self.dense_decoder(self._gather_bins( + trunk, acceptor.clamp(0, self.config.input_length - 1), self.config.trunk_bin_size + )) + interaction = self.junction_source(source) * self.junction_destination(destination) + track_embedding = self.junction_track(track) + return (interaction * track_embedding).sum(dim=-1) / math.sqrt( + self.config.junction_rank + ) + + def junction_block( + self, + trunk: Tensor, + site_positions: Tensor, + row_start: int, + row_end: int, + column_start: int, + column_end: int, + track_start: int, + track_end: int, + site_factors: tuple[Tensor, Tensor] | None = None, + ) -> Tensor: + """Compute a bounded block of the full predicted-position junction cube.""" + tracks = torch.arange(track_start, track_end, device=trunk.device) + halves = self.junction_track_half[tracks] + if site_factors is None: + site_factors = self.junction_site_factors(trunk, site_positions) + source_all, destination_all = site_factors + source = source_all[:, halves, row_start:row_end] + destination = destination_all[:, halves, column_start:column_end] + embedding = self.junction_track(tracks)[None, :, None, None, :] + values = ( + source[:, :, :, None, :] * destination[:, :, None, :, :] * embedding + ).sum(dim=-1) / math.sqrt(self.config.junction_rank) + return values.permute(0, 2, 3, 1).float() + + def junction_site_factors( + self, trunk: Tensor, site_positions: Tensor + ) -> tuple[Tensor, Tensor]: + """Precompute the two strand-specific factors reused by all cube blocks.""" + donor = torch.stack((site_positions[:, 0], site_positions[:, 2]), dim=1).clamp_min(0) + acceptor = torch.stack((site_positions[:, 1], site_positions[:, 3]), dim=1).clamp_min(0) + expanded = trunk[:, None].expand(-1, 2, -1, -1).reshape( + -1, trunk.shape[1], trunk.shape[2] + ) + donor_features = self.dense_decoder(self._gather_bins( + expanded, donor.reshape(-1, donor.shape[-1]), self.config.trunk_bin_size, + )).reshape(len(trunk), 2, donor.shape[-1], -1) + acceptor_features = self.dense_decoder(self._gather_bins( + expanded, acceptor.reshape(-1, acceptor.shape[-1]), self.config.trunk_bin_size, + )).reshape(len(trunk), 2, acceptor.shape[-1], -1) + return self.junction_source(donor_features), self.junction_destination(acceptor_features) + + def forward( + self, + dna: Tensor, + *, + families: Iterable[str] | None = None, + usage_positions: Tensor | None = None, + junction_site_positions: Tensor | None = None, + junction_queries: Tensor | None = None, + ) -> dict[str, Tensor]: + requested = set(families or ALL_FAMILIES) + unknown = requested - set(ALL_FAMILIES) + if unknown: + raise ValueError(f"Unknown output families: {sorted(unknown)}") + trunk = self._trunk(dna) + needs_dense = bool( + requested.intersection((*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions")) + ) + features = self._dense_features(trunk) if needs_dense else None + outputs: dict[str, Tensor] = {} + for family in DENSE_TRACKS: + if family in requested: + outputs[family] = self.dense_heads[family](features).float() + if "splice_sites" in requested: + all_logits = self._splice_site_logits(trunk) + start = (self.config.input_length - self.config.target_length) // 2 + outputs["splice_sites"] = all_logits[:, start:start + self.config.target_length] + if "splice_usage" in requested: + if usage_positions is None: + raise ValueError("splice_usage requires usage_positions") + gathered = self._gather_bins( + features, usage_positions, self.config.dense_bin_size + ) + outputs["splice_usage"] = self.splice_usage_head(gathered).float() + if "junctions" in requested: + if junction_site_positions is None or junction_queries is None: + raise ValueError("junctions require site positions and query triples") + outputs["junctions"] = self._junction_predictions( + trunk, junction_site_positions, junction_queries + ).float() + if "contact_maps" in requested: + contact = trunk.reshape( + len(dna), self.config.contact_bins, -1, self.config.hidden_dim + ).mean(dim=2) + q, k = self.contact_q(contact), self.contact_k(contact) + row, column = self.contact_row, self.contact_column + outputs["contact_maps"] = ( + 0.5 + * ( + q[:, row] * k[:, column] + + q[:, column] * k[:, row] + ) + ).float() + return outputs + + +def count_parameters(model: nn.Module) -> int: + return sum(parameter.numel() for parameter in model.parameters() if parameter.requires_grad) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/reference_large.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/reference_large.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e403757c49eb6e6419927210c7053710964213d1 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/reference_large.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 96 +channel_growth: 96 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 1024 +num_sequence_blocks: 16 +num_attention_heads: 16 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 768 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/run_ddp.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/run_ddp.sh new file mode 100644 index 0000000000000000000000000000000000000000..c4702e9366e907b7791063e77dcfe281411ac8cd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/run_ddp.sh @@ -0,0 +1,16 @@ +#!/usr/bin/env bash +# run_ddp.sh DATA_DIR OUTPUT_DIR CONFIG EPOCHS [GPUS=8] [WORKERS_PER_GPU=12] +# Train the reference on every train interval with one example per GPU per step, then score +# validation with the verifier's own scorer. Runs inside the task image (torch, torchrun). +set -Eeuo pipefail +DATA=$1; OUT=$2; CONFIG=$3; EPOCHS=$4; GPUS=${5:-8}; WORKERS=${6:-12} +BASELINE=$(cd "$(dirname "$0")/../.." && pwd) +mkdir -p "$OUT" +cd "$BASELINE" +# Loopback rendezvous: works offline, where --standalone cannot resolve the hostname. +torchrun --nnodes 1 --nproc-per-node "$GPUS" --master-addr 127.0.0.1 --master-port "${MASTER_PORT:-29511}" -m reference.alphagenome_50m.train_eval train \ + --input "$DATA" --config "$CONFIG" --output "$OUT" --epochs "$EPOCHS" --workers "$WORKERS" \ + --log-every 200 --save-every 2000 2>&1 | tee -a "$OUT/train.log" +python predict.py "$DATA" "$OUT/valid-pred" --checkpoint "$OUT/last.pt" --split valid +python /workspace/evaluation/score.py --predictions "$OUT/valid-pred/predictions.zarr" \ + --labels "$DATA" --split valid --output "$OUT/valid-score" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/tiny_overfit.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/tiny_overfit.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c25f9ffbb76102c970e5767922400f7ec2b6c53e --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/tiny_overfit.yaml @@ -0,0 +1,24 @@ +# Keep the frozen 1-Mb input and every output track; shrink only learned widths. +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 8 +channel_growth: 4 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 32 +num_sequence_blocks: 1 +num_attention_heads: 2 +attention_head_dim: 16 +mlp_expansion: 2.0 +dropout: 0.0 +head_hidden_size: 32 +junction_rank: 8 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: false diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/train_eval.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/train_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..241fdd4144c4c917109104271fbe80c603075fbf --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/reference/alphagenome_50m/train_eval.py @@ -0,0 +1,534 @@ +"""Train and stream-evaluate the <=50M compact AlphaGenome baseline.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import numpy as np +import torch +from torch import Tensor +from torch.nn import functional as F +from torch.utils.data import DataLoader, Subset + +from .dataset import ( + AlphaGenomeDataset, + DENSE_FAMILIES, + single_example_collate, +) +from .model import ( + ALL_FAMILIES, + CompactModel, + ModelConfig, + count_parameters, +) + + +DEFAULT_CONFIG = Path(__file__).with_name("alphagenome_50m.yaml") + + +def _device(name: str) -> torch.device: + if name == "auto": + name = "cuda" if torch.cuda.is_available() else "cpu" + if name == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("--device cuda requested but CUDA is unavailable") + return torch.device(name) + + +def _autocast(device: torch.device): + enabled = device.type == "cuda" + dtype = torch.bfloat16 if enabled and torch.cuda.is_bf16_supported() else torch.float16 + return torch.autocast(device_type=device.type, dtype=dtype, enabled=enabled) + + +def _distributed() -> tuple[int, int, int]: + """(rank, local_rank, world_size) under torchrun; (0, 0, 1) otherwise.""" + world = int(os.environ.get("WORLD_SIZE", "1")) + if world > 1 and not torch.distributed.is_initialized(): + torch.distributed.init_process_group("nccl") + return int(os.environ.get("RANK", "0")), int(os.environ.get("LOCAL_RANK", "0")), world + + +def _loader(dataset, *, workers: int, shuffle: bool, sampler=None) -> DataLoader: + return DataLoader( + dataset, + batch_size=1, + shuffle=shuffle and sampler is None, + sampler=sampler, + num_workers=workers, + persistent_workers=workers > 0, + pin_memory=torch.cuda.is_available(), + collate_fn=single_example_collate, + ) + + +def _to_device(value, device: torch.device): + if isinstance(value, Tensor): + return value.to(device, non_blocking=True) + if isinstance(value, dict): + return {key: _to_device(item, device) for key, item in value.items()} + return value + + +def _match_coordinate_queries( + scoring: dict[str, Tensor], positions: Tensor, track_halves: Tensor +) -> tuple[Tensor, Tensor, Tensor]: + """Match trusted coordinate queries to typed position axes like the grader.""" + device = positions.device + tracks = scoring["track"] + halves = track_halves[tracks] + queries = torch.zeros((len(tracks), 3), dtype=torch.int64, device=device) + queries[:, 2] = tracks + valid = scoring["valid"].clone() + missing = torch.ones(len(tracks), dtype=torch.bool, device=device) + position_rows = positions.detach().cpu().numpy() + halves_cpu = halves.detach().cpu().numpy() + donors_cpu = scoring["donor_position"].detach().cpu().numpy() + acceptors_cpu = scoring["acceptor_position"].detach().cpu().numpy() + valid_indices = np.flatnonzero(valid.detach().cpu().numpy()) + lookups = [ + {int(coordinate): index for index, coordinate in enumerate(axis) if coordinate >= 0} + for axis in position_rows + ] + for index in valid_indices: + half = int(halves_cpu[index]) + row = lookups[2 * half].get(int(donors_cpu[index])) + column = lookups[2 * half + 1].get(int(acceptors_cpu[index])) + if row is not None and column is not None: + queries[index, 0] = row + queries[index, 1] = column + missing[index] = False + return queries.unsqueeze(0), valid, missing + + +def _model_inputs(sample: dict) -> tuple[dict, Tensor, Tensor]: + queries, query_valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + sample["site_positions"], + sample["junction_track_halves"], + ) + if (query_valid & missing).any(): + raise ValueError("trusted scoring coordinates do not match target positions") + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + return { + "dna": sample["dna"].unsqueeze(0), + "usage_positions": sample["usage_positions"].unsqueeze(0), + "junction_site_positions": sample["site_positions"].unsqueeze(0), + "junction_queries": queries, + }, junction_target, query_valid + + +def _losses(outputs: dict[str, Tensor], sample: dict, junction_target: Tensor, + junction_valid: Tensor) -> dict[str, Tensor]: + losses = {} + for family in DENSE_FAMILIES: + target = torch.asinh(sample["dense"][family]["values"]).unsqueeze(0) + mask = sample["dense"][family]["mask"].unsqueeze(0) + residual = torch.where(mask, outputs[family] - target, 0.0) + losses[family] = residual.square().sum() / mask.sum().clamp_min(1) + losses["splice_sites"] = F.binary_cross_entropy_with_logits( + outputs["splice_sites"], sample["splice_sites"].unsqueeze(0) + ) + usage_target = torch.asinh(sample["splice_usage"]["values"]).unsqueeze(0) + usage_mask = sample["splice_usage"]["mask"].unsqueeze(0) + usage_residual = torch.where(usage_mask, outputs["splice_usage"] - usage_target, 0.0) + losses["splice_usage"] = usage_residual.square().sum() / usage_mask.sum().clamp_min(1) + junction_residual = outputs["junctions"][0] - junction_target[0] + losses["junctions"] = ( + junction_residual[junction_valid].square().mean() + if junction_valid.any() else junction_residual.sum() * 0 + ) + contact_target = torch.asinh(sample["contact_maps"]).unsqueeze(0) + contact_mask = sample["contact_map_mask"].unsqueeze(0) + contact_residual = torch.where( + contact_mask, outputs["contact_maps"] - contact_target, 0.0 + ) + losses["contact_maps"] = contact_residual.square().sum() / contact_mask.sum().clamp_min(1) + losses["total"] = torch.stack([losses[name] for name in ALL_FAMILIES]).mean() + return losses + + +def _validation_inference( + model: CompactModel, sample: dict +) -> tuple[dict[str, Tensor], Tensor, Tensor, Tensor]: + """Use the same predicted-position path as a formal v3 submission.""" + trunk = model._trunk(sample["dna"].unsqueeze(0)) + dense_features = model._dense_features(trunk) + outputs = { + family: model.dense_heads[family](dense_features).float() + for family in DENSE_FAMILIES + } + all_sites = model._splice_site_logits(trunk) + target_start = (model.config.input_length - model.config.target_length) // 2 + outputs["splice_sites"] = all_sites[:, target_start:target_start + model.config.target_length] + usage_bins = model.splice_usage_head(dense_features).float() + usage_indices = torch.div(sample["usage_positions"], model.config.dense_bin_size, + rounding_mode="floor") + outputs["splice_usage"] = usage_bins[:, usage_indices] + predicted_positions = model.select_junction_positions(all_sites) + + matched, valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + predicted_positions[0], + sample["junction_track_halves"], + ) + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + junction_prediction = torch.zeros(len(valid), device=valid.device) + if (~missing & valid).any(): + selected = ~missing & valid + junction_prediction[selected] = model._junction_predictions( + trunk, predicted_positions, matched[:, selected] + )[0] + outputs["junctions"] = junction_prediction.unsqueeze(0) + + contact = trunk.reshape( + 1, model.config.contact_bins, -1, model.config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + outputs["contact_maps"] = (0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row])).float() + return outputs, junction_target, valid, missing + + +def _atomic_save(payload: dict, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(f".{path.name}.tmp") + torch.save(payload, temporary) + os.replace(temporary, path) + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(8 * 1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _resume_position(payload: dict, *, examples: int, epochs: int) -> tuple[int, int]: + epoch = int(payload["epoch"]) + step = int(payload["global_step"]) + kind = payload.get("checkpoint_kind") + if kind not in (None, "epoch") or step != epoch * examples: + raise ValueError("only a completed-epoch checkpoint can resume training") + if payload.get("examples", examples) != examples or payload.get("schedule_epochs", epochs) != epochs: + raise ValueError("checkpoint data size or training schedule differs from this run") + return epoch, step + + +def train(args: argparse.Namespace) -> None: + # Multi-GPU with `torchrun --nproc-per-node N train_eval.py train ...`: one example per GPU per + # step (data parallel), rank 0 writes checkpoints and logs. Model size is bounded by the + # training budget, not by a parameter cap. + rank, local_rank, world = _distributed() + config = ModelConfig.from_yaml(args.config) + device = torch.device("cuda", local_rank) if world > 1 else _device(args.device) + if world > 1: + torch.cuda.set_device(device) + torch.manual_seed(args.seed) + model = CompactModel(config).to(device) + parameters = count_parameters(model) + optimizer = torch.optim.AdamW( + model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay + ) + scaler = torch.amp.GradScaler("cuda", enabled=device.type == "cuda") + full_dataset = AlphaGenomeDataset(args.input, "train") + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + start_epoch, global_step = 0, 0 + if args.resume: + payload = torch.load(args.resume, map_location="cpu", weights_only=True) + if payload.get("config") != config.to_dict(): + raise ValueError("checkpoint configuration does not match --config") + model.load_state_dict(payload["model"], strict=True) + optimizer.load_state_dict(payload["optimizer"]) + scaler.load_state_dict(payload.get("scaler", {})) + start_epoch, global_step = _resume_position(payload, examples=size, epochs=args.epochs) + per_rank = math.ceil(size / world) + steps = max(1, per_rank * args.epochs) + module = model + if world > 1: + model = torch.nn.parallel.DistributedDataParallel( + model, device_ids=[local_rank], find_unused_parameters=True) + sampler = (torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world, rank=rank, + shuffle=True, seed=args.seed) + if world > 1 else None) + stop_after_epoch = args.stop_after_epoch or args.epochs + output = args.output.resolve() + output.mkdir(parents=True, exist_ok=True) + started = time.monotonic() + if rank == 0: + print(json.dumps({"event": "train_start", "parameters": parameters, "examples": size, + "device": str(device), "world_size": world}), flush=True) + + for epoch in range(start_epoch, stop_after_epoch): + model.train() + if sampler is not None: + sampler.set_epoch(epoch) + loader = _loader(dataset, workers=args.workers, shuffle=True, sampler=sampler) + for sample in loader: + sample = _to_device(sample, device) + inputs, junction_target, junction_valid = _model_inputs(sample) + progress = min((global_step + 1) / steps, 1.0) + learning_rate = args.min_learning_rate + 0.5 * ( + args.learning_rate - args.min_learning_rate + ) * (1.0 + math.cos(math.pi * progress)) + for group in optimizer.param_groups: + group["lr"] = learning_rate + optimizer.zero_grad(set_to_none=True) + with _autocast(device): + predictions = model(**inputs) + losses = _losses(predictions, sample, junction_target, junction_valid) + if not torch.isfinite(losses["total"]): + raise FloatingPointError(f"non-finite loss at step {global_step}") + scaler.scale(losses["total"]).backward() + scaler.unscale_(optimizer) + gradient_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_gradient_norm) + scaler.step(optimizer) + scaler.update() + global_step += 1 + if rank == 0 and (global_step == 1 or global_step % args.log_every == 0): + print( + json.dumps( + { + "event": "train_step", + "epoch": epoch + 1, + "step": global_step, + "loss": float(losses["total"].detach()), + "gradient_norm": float(gradient_norm), + "learning_rate": learning_rate, + "elapsed_seconds": time.monotonic() - started, + } + ), + flush=True, + ) + if rank == 0 and global_step % args.save_every == 0: + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch, + "global_step": global_step, + "checkpoint_kind": "progress", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "progress.pt", + ) + if world > 1: + torch.distributed.barrier() + if rank != 0: + continue + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch + 1, + "global_step": global_step, + "checkpoint_kind": "epoch", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "last.pt", + ) + if world > 1: + torch.distributed.barrier() + torch.distributed.destroy_process_group() + if rank != 0: + return + checkpoint = output / "last.pt" + manifest = { + "schema": "alphagenome-50m-real/v3", + "checkpoint": {"path": checkpoint.name, "sha256": _sha256(checkpoint)}, + "parameters": parameters, + "config": config.to_dict(), + "output_families": list(ALL_FAMILIES), + "epochs": args.epochs, + "completed_epochs": stop_after_epoch, + "global_step": global_step, + } + (output / "model_manifest.json").write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "train_complete", **manifest}), flush=True) + + +class PerTrackMoments: + def __init__(self, tracks: int): + self.n = np.zeros(tracks, dtype=np.int64) + self.sum_x = np.zeros(tracks, dtype=np.float64) + self.sum_y = np.zeros(tracks, dtype=np.float64) + self.sum_x2 = np.zeros(tracks, dtype=np.float64) + self.sum_y2 = np.zeros(tracks, dtype=np.float64) + self.sum_xy = np.zeros(tracks, dtype=np.float64) + + def update(self, prediction: Tensor, target: Tensor, mask: Tensor | None = None) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1, prediction.shape[-1]) + y = target.detach().double().cpu().numpy().reshape(-1, target.shape[-1]) + valid = np.ones_like(y, dtype=bool) if mask is None else mask.detach().cpu().numpy().reshape(y.shape).astype(bool) + self.n += valid.sum(axis=0) + self.sum_x += np.where(valid, x, 0).sum(axis=0, dtype=np.float64) + self.sum_y += np.where(valid, y, 0).sum(axis=0, dtype=np.float64) + self.sum_x2 += np.where(valid, x * x, 0).sum(axis=0, dtype=np.float64) + self.sum_y2 += np.where(valid, y * y, 0).sum(axis=0, dtype=np.float64) + self.sum_xy += np.where(valid, x * y, 0).sum(axis=0, dtype=np.float64) + + def update_indexed(self, prediction: Tensor, target: Tensor, track: Tensor) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1) + y = target.detach().double().cpu().numpy().reshape(-1) + indices = track.detach().cpu().numpy().reshape(-1).astype(np.int64) + self.n += np.bincount(indices, minlength=len(self.n)) + self.sum_x += np.bincount(indices, weights=x, minlength=len(self.n)) + self.sum_y += np.bincount(indices, weights=y, minlength=len(self.n)) + self.sum_x2 += np.bincount(indices, weights=x * x, minlength=len(self.n)) + self.sum_y2 += np.bincount(indices, weights=y * y, minlength=len(self.n)) + self.sum_xy += np.bincount(indices, weights=x * y, minlength=len(self.n)) + + def report(self) -> dict: + numerator = self.n * self.sum_xy - self.sum_x * self.sum_y + denominator = np.sqrt( + np.maximum(self.n * self.sum_x2 - self.sum_x**2, 0) + * np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + ) + target_ss = np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + scorable = (self.n >= 2) & (target_ss > 0) + pearson = np.full_like(numerator, np.nan) + nonconstant = scorable & (denominator > 0) + pearson[nonconstant] = numerator[nonconstant] / denominator[nonconstant] + pearson[scorable & ~nonconstant] = 0.0 + return { + "mean_pearson": float(pearson[scorable].mean()) if scorable.any() else 0.0, + "valid_tracks": int(scorable.sum()), + "per_track_pearson": [float(value) if np.isfinite(value) else None for value in pearson], + "values_per_track": self.n.tolist(), + } + + +def evaluate(args: argparse.Namespace) -> None: + device = _device(args.device) + payload = torch.load(args.checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config).to(device) + model.load_state_dict(payload["model"], strict=True) + model.eval() + full_dataset = AlphaGenomeDataset(args.input, args.split) + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + metrics = { + **{family: PerTrackMoments(config_value) for family, config_value in { + "atac": 167, "dnase": 305, "procap": 12, "cage": 546, + "rna_seq": 667, "chip_tf": 1617, "chip_histone": 1116, + }.items()}, + "splice_sites": PerTrackMoments(4), + "splice_usage": PerTrackMoments(config.splice_usage_tracks), + "junctions": PerTrackMoments(config.junction_tracks), + "contact_maps": PerTrackMoments(config.contact_tracks), + } + with torch.inference_mode(): + for index, sample in enumerate(_loader(dataset, workers=args.workers, shuffle=False)): + sample = _to_device(sample, device) + outputs, junction_target, junction_valid, junction_missing = ( + _validation_inference(model, sample) + ) + # Match predict.py: FP32 inference is serialized to FP16 Zarr values. + outputs = {name: value.half().float() for name, value in outputs.items()} + for family in DENSE_FAMILIES: + metrics[family].update( + outputs[family][0], + torch.asinh(sample["dense"][family]["values"]), + sample["dense"][family]["mask"], + ) + metrics["splice_sites"].update(outputs["splice_sites"][0], sample["splice_sites"]) + metrics["splice_usage"].update( + outputs["splice_usage"][0], + torch.asinh(sample["splice_usage"]["values"]), + sample["splice_usage"]["mask"], + ) + junction_track = sample["junction_scoring"]["track"] + metrics["junctions"].update_indexed( + outputs["junctions"][0][junction_valid], + junction_target[0][junction_valid], + junction_track[junction_valid], + ) + metrics["contact_maps"].update( + outputs["contact_maps"][0], + torch.asinh(sample["contact_maps"]), + sample["contact_map_mask"], + ) + if index == 0 or (index + 1) % args.log_every == 0 or index + 1 == size: + print(json.dumps({"event": "evaluate_progress", "examples": index + 1, "total": size}), flush=True) + families = {name: accumulator.report() for name, accumulator in metrics.items()} + valid_family_scores = [item["mean_pearson"] for item in families.values()] + report = { + "schema": "alphagenome-50m-evaluation/v3", + "split": args.split, + "examples": size, + "metric_space": ( + "asinh continuous targets and binary splice-site targets; " + "junction Pearson uses hidden coordinate queries and predicted endpoints; " + "FP32 inference rounded to FP16 prediction values" + ), + "families": families, + "overall_mean_family_pearson": float(np.mean(valid_family_scores)), + } + args.report.parent.mkdir(parents=True, exist_ok=True) + args.report.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "evaluate_complete", "overall_mean_family_pearson": report["overall_mean_family_pearson"]}), flush=True) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + common = argparse.ArgumentParser(add_help=False) + common.add_argument("--input", required=True, type=Path) + common.add_argument("--config", type=Path, default=DEFAULT_CONFIG) + common.add_argument("--workers", type=int, default=2) + common.add_argument("--max-examples", type=int, default=0) + common.add_argument("--device", choices=("auto", "cuda", "cpu"), default="auto") + common.add_argument("--log-every", type=int, default=25) + subparsers = parser.add_subparsers(dest="command", required=True) + train_parser = subparsers.add_parser("train", parents=[common]) + train_parser.add_argument("--output", required=True, type=Path) + train_parser.add_argument("--epochs", type=int, default=10) + train_parser.add_argument("--stop-after-epoch", type=int) + train_parser.add_argument("--learning-rate", type=float, default=2e-4) + train_parser.add_argument("--min-learning-rate", type=float, default=2e-5) + train_parser.add_argument("--weight-decay", type=float, default=1e-4) + train_parser.add_argument("--max-gradient-norm", type=float, default=5.0) + train_parser.add_argument("--save-every", type=int, default=250) + train_parser.add_argument("--seed", type=int, default=17) + train_parser.add_argument("--resume", type=Path) + train_parser.set_defaults(run=train) + evaluate_parser = subparsers.add_parser("evaluate", parents=[common]) + evaluate_parser.add_argument("--checkpoint", required=True, type=Path) + evaluate_parser.add_argument("--split", choices=("train", "valid", "test"), required=True) + evaluate_parser.add_argument("--report", required=True, type=Path) + evaluate_parser.set_defaults(run=evaluate) + args = parser.parse_args() + if args.workers < 0 or args.max_examples < 0: + parser.error("workers/max-examples must be nonnegative") + if args.command == "train" and ( + args.epochs < 1 + or (args.stop_after_epoch is not None + and not 1 <= args.stop_after_epoch <= args.epochs) + ): + parser.error("epochs must be positive and stop-after-epoch must be within the schedule") + args.run(args) + + +if __name__ == "__main__": + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/validation_tracks.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/validation_tracks.json new file mode 100644 index 0000000000000000000000000000000000000000..6f1a5ac2f20ce5df0d7b29355f00c456f0435f3b --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/validation_tracks.json @@ -0,0 +1,17909 @@ +{ + "schema": "genome-sequence-to-function-report/v4", + "split": "valid", + "examples": 200, + "families": { + "atac": { + "mean_pearson": -0.0006987011410249261, + "valid_tracks": 167, + "per_track_pearson": [ + 0.0, + 0.009674311113449852, + -0.20905622091325612, + -0.10837527569951189, + 0.15987463734086962, + -0.0201796165336609, + 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b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/validation_variants.json new file mode 100644 index 0000000000000000000000000000000000000000..04d793738d965fc02f82c947be6388ee83b12215 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S002/validation_variants.json @@ -0,0 +1,125 @@ +{ + "eqtl_sign": { + "rows": 17052, + "perturbations": 165, + "metric": "auroc", + "model": 0.4917598943152234, + "anchors": { + "AlphaGenome": 0.8263569969634328 + } + }, + "eqtl_causality": { + "rows": 17934, + "perturbations": 174, + "metric": "auroc", + "model": 0.7094027701170558, + "anchors": { + "AlphaGenome": 0.8325427468284611 + } + }, + "eqtl_coefficient": { + "rows": 8575, + "perturbations": 165, + "metric": "spearman", + "model": 0.02281104277335265, + "anchors": { + "AlphaGenome": 0.4133539365891599 + } + }, + "sqtl_causality": { + "rows": 21340, + "perturbations": 194, + "metric": "auprc", + "model": 0.7960475782905989, + "anchors": { + "AlphaGenome": 0.8447114201904594 + } + }, + "clinvar_noncoding": { + "rows": 199, + "perturbations": 199, + "metric": "auprc", + "model": 0.2149359050692882, + "anchors": { + "AlphaGenome": 0.9409857958177288 + } + }, + "clinvar_splice_region": { + "rows": 194, + "perturbations": 194, + "metric": "auprc", + "model": 0.189558063284979, + "anchors": { + "AlphaGenome": 0.7224791623996103 + } + }, + "clinvar_missense": { + "rows": 199, + "perturbations": 199, + "metric": "auprc", + "model": 0.1565990023373375, + "anchors": { + "AlphaGenome": 0.373826292978903 + } + }, + "mfass": { + "rows": 195, + "perturbations": 195, + "metric": "auprc", + "model": 0.25317254664743866, + "anchors": { + "AlphaGenome": 0.7342557141920668 + } + }, + "caqtl": { + "rows": 198, + "perturbations": 198, + "metric": "auprc", + "model": 0.14690933194427275, + "anchors": { + "AlphaGenome": 0.8075760167741196 + } + }, + "bqtl_spi1": { + "rows": 160, + "perturbations": 160, + "metric": "auprc", + "model": 0.15396199818958403, + "anchors": { + "AlphaGenome": 0.8167450781363793 + } + }, + "enhancer_gene": { + "rows": 200, + "perturbations": 200, + "metric": "auprc", + "model": 0.3939760896755572, + "anchors": { + "AlphaGenome": 0.9594307235541584 + } + }, + "traitgym_mendelian": { + "rows": 292, + "perturbations": 292, + "metric": "auprc", + "model": 0.1267513968450564, + "anchors": { + "Borzoi_L2_L2.plus.all": 0.5026323772738239, + "Enformer_L2_L2.plus.all": 0.5452938267693526, + "CADD.plus.RawScore": 0.7189111372856957, + "GPN-MSA_absLLR.plus.score": 0.6729149139043132 + } + }, + "traitgym_complex": { + "rows": 299, + "perturbations": 299, + "metric": "auprc", + "model": 0.23595833493479926, + "anchors": { + "Borzoi_L2_L2.plus.all": 0.294501577162467, + "Enformer_L2_L2.plus.all": 0.269227301087889, + "CADD.plus.RawScore": 0.41341570280137435, + "GPN-MSA_absLLR.plus.score": 0.4213971415058371 + } + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/README.md b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/README.md new file mode 100644 index 0000000000000000000000000000000000000000..58fe672ddd6603a0912211778bbeedf6379a624c --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/README.md @@ -0,0 +1,77 @@ +# SeqFunc local01 at step 22,000 + +One model with **13,256,456 parameters** serves every family and both entry points. It reads the entire +1,048,576-bp DNA window through a convolutional encoder and 6 transformer +blocks of width 384 over 1,024 tokens. Dense heads predict 128-bp bins over the central +196,608 bp; other heads predict splice sites, sparse usage/junctions and full-window contacts. +A width-32 base-resolution path uses a 21-bp stem and four dilated residual depthwise blocks (701-bp receptive field). Average/max pooling provides aligned 128-bp residual features to dense, usage and junction heads. Splice logits combine local base-resolution logits with the reference offset decoder. Junction values use low-rank donor/acceptor factors; contacts use a symmetric +product of global features. Sites and junction endpoints are selected from model scores alone. + +All weights originate from local random initialization (seed 17) and locally trained +checkpoints. A balanced splice objective trained the initial compact reference for 2,000 updates. A zero-output base-resolution path was attached, then the same full-split schedule continued. This is the unchanged control for bridge03. The infrastructure recovery resumed exactly from the step-18,000 checkpoint; unsaved later updates were discarded. + +This checkpoint records **22,000 updates**, 0 complete training permutations and +22,000 intervals into the next permutation. Each permutation includes all **41,020** +eligible FOLD_0 training intervals (folds 2–7), shuffled deterministically without replacement. +Quantized train targets are decoded by the supplied reader. Validation is fold 0; no test labels, +test-derived caches or pretrained model parameters were used. Development labels only evaluate +and select models. Checkpoints before a complete permutation remain intermediate milestones. + +Training uses one H100 80 GB and at most 22 CPUs. The current loader uses 6 workers +and 2 torch CPU threads. BF16 training uses AdamW (weight decay 1e-4), gradient +clipping at 5, and a 4-epoch cosine learning-rate schedule from +0.0002 to 2e-05, with +500 warmup updates. The splice objective is `balanced_splice`; +other family losses follow the reference. Recorded accumulated training-loop wall time is +54.7 minutes, including inherited parent time. Runs can share this +same physical GPU with experiments or evaluation. Setup, evaluation and discarded mechanical checks +are additional research compute, documented in the research ledger. Matching training junction queries +uses a verified, bit-identical CPU index construction followed by a bulk GPU transfer. + +The package contains model code, weights, training source/configuration/provenance, SHA-256 checksums, +and the complete CPython 3.11 / PyTorch 2.7.1 CUDA 12.6 Python runtime archive. Both executable entry +points take INPUT_ROOT and OUTPUT_ROOT, extract the vendored runtime temporarily, and run offline. +The host supplies Linux, bash/tar/gzip and the NVIDIA driver. Inference reads only public manifests +and the pinned reference FASTA. No target, splice-coordinate or prediction cache is packaged or read. +Track outputs use float16; perturbation outputs use float32 to preserve small A/B differences. + +The appended validation results cover all 200 intervals and all 2,697 development perturbations, +scored by the unmodified verifier tools. Published anchors use the same label rows. All results, +including regressions, are retained in the package's JSON reports. Limitations include incomplete +training at intermediate checkpoints, a 701-bp local receptive field, pooled usage/junction features, +low-rank contact modeling, and uneven variant performance. Functional readouts do not provide the +evolutionary constraint information used by conservation-based variant scorers. + +## Measured validation results + +Family-mean Pearson: **0.268963** on 200 validation intervals. + +| Family | Pearson | +|---|---:| +| atac | 0.473166 | +| dnase | 0.597212 | +| procap | 0.349563 | +| cage | 0.193045 | +| rna_seq | 0.210720 | +| chip_tf | 0.403288 | +| chip_histone | 0.387218 | +| splice_sites | 0.040443 | +| splice_usage | 0.166485 | +| junctions | 0.139497 | +| contact_maps | -0.002049 | + +| Development evaluation | Metric | Model | Published anchors on the same rows | +|---|---|---:|---| +| eqtl_sign | auroc | 0.5199 | AlphaGenome: 0.8264 | +| eqtl_causality | auroc | 0.6544 | AlphaGenome: 0.8325 | +| eqtl_coefficient | spearman | 0.0329 | AlphaGenome: 0.4134 | +| sqtl_causality | auprc | 0.7879 | AlphaGenome: 0.8447 | +| clinvar_noncoding | auprc | 0.2740 | AlphaGenome: 0.9410 | +| clinvar_splice_region | auprc | 0.2238 | AlphaGenome: 0.7225 | +| clinvar_missense | auprc | 0.2718 | AlphaGenome: 0.3738 | +| mfass | auprc | 0.1842 | AlphaGenome: 0.7343 | +| caqtl | auprc | 0.2039 | AlphaGenome: 0.8076 | +| bqtl_spi1 | auprc | 0.2178 | AlphaGenome: 0.8167 | +| enhancer_gene | auprc | 0.4243 | AlphaGenome: 0.9594 | +| traitgym_mendelian | auprc | 0.1514 | Borzoi_L2_L2.plus.all: 0.5026; Enformer_L2_L2.plus.all: 0.5453; CADD.plus.RawScore: 0.7189; GPN-MSA_absLLR.plus.score: 0.6729 | +| traitgym_complex | auprc | 0.2118 | Borzoi_L2_L2.plus.all: 0.2945; Enformer_L2_L2.plus.all: 0.2692; CADD.plus.RawScore: 0.4134; GPN-MSA_absLLR.plus.score: 0.4214 | diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/attach_local.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/attach_local.py new file mode 100644 index 0000000000000000000000000000000000000000..aa082537004dbc398c4a78c1bd0203970ce5e42f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/attach_local.py @@ -0,0 +1,52 @@ +"""Attach a zero-initialized local residual path to a checkpoint trained in this workspace.""" +import argparse +from dataclasses import replace +import hashlib +from pathlib import Path + +import torch +import yaml + +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + p.add_argument('--width', type=int, default=32) + args = p.parse_args() + state = torch.load(args.checkpoint, map_location='cpu', weights_only=True) + assert state['schema'] == 'seqfunc-single/v1', 'only locally trained single-GPU checkpoints' + parent_config = ModelConfig(**state['config']) + assert parent_config.local_width == 0 + torch.manual_seed(state['seed']) + parent = CompactModel(parent_config) + config = replace(parent_config, local_width=args.width) + model = CompactModel(config) + result = model.load_state_dict(state['model'], strict=False) + assert not result.unexpected_keys + assert all(k.startswith(('local_path.', 'local_projection.')) for k in result.missing_keys) + parent_names = list(dict(parent.named_parameters())) + names = list(dict(model.named_parameters())) + assert names[:len(parent_names)] == parent_names, 'optimizer parameter order changed' + assert len(state['optimizer']['param_groups']) == 1 + group = state['optimizer']['param_groups'][0] + assert group['params'] == list(range(len(parent_names))) + group['params'] = list(range(len(names))) + state['model'] = model.state_dict() + state['config'] = config.to_dict() + state['intervention'] = { + 'kind': 'base_resolution_local_path', 'width': args.width, + 'parent_sha256': hashlib.sha256(args.checkpoint.read_bytes()).hexdigest(), + 'attachment_step': state['global_step'], 'initial_outputs_unchanged': True, + } + args.output.mkdir(parents=True, exist_ok=False) + (args.output / 'config.yaml').write_text(yaml.safe_dump(config.to_dict())) + _atomic_save(state, args.output / 'attached.pt') + print({'parameters': count_parameters(model), **state['intervention']}) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/package_manifest.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/package_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..82a7c0308a763481a50a6711355dcfde6b3e9a2c --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/package_manifest.json @@ -0,0 +1,30 @@ +{ + "parameters": 13256456, + "source_checkpoint_sha256": "35572150e0b3df21837a52ca2d6144327394821f662bb7c7599d12b3ea92bf24", + "files": { + "README.md": "1689c0123b281a55c95c95fbd5aa8740041b0ea54013acb535c00f97f95d386c", + "attach_local.py": "d0774e2711840783267914789ed303a3b61b64381b72d61f0886edbc04974f15", + "infer.sh": "ef810532b1e72b85fbe283e4f52c177bb70025977dbb2345d75837c9bbd51e37", + "last.pt": "adccfda1d19838b64b2864cb2ed685f6a0161e3a4026c37825eea7b86c4a1c14", + "perturb.py": "ba37a6f9f8b8eccb052903618bcb4c91d3f4cbed62dfe678ee362644dc80801e", + "perturb.sh": "113ec2383276a6e83fe1e4e8b0fa914f8d535d5dd0f5b2c34240140312dd5fb4", + "predict.py": "2725592456c55c188b22c376f0cb249e9f46b4ae1370d2226df7143da358a8b2", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/__init__.py": "45f3cf93f48859104e70bb58d717130dfd6521a45d2aba06b1a726afc0dc51dd", + "reference/alphagenome_50m/__init__.py": "a5a7e45d5940c3b201017c6322128e9f47efd37e1d29f717da2c1f787688eff9", + "reference/alphagenome_50m/alphagenome_50m.yaml": "3cac32e9f55e352dff78bb85ac892fdae1f9e412d9bba3e945750210c5252eee", + "reference/alphagenome_50m/dataset.py": "ad6670f742f4de9898064becc525d0aea98b402abe7b210bb1b26a9eee6402b9", + "reference/alphagenome_50m/layers.py": "26a116a72aa3f0e307a3606163547edbdecb7965f7fdcd96bc5f805cd21b3352", + "reference/alphagenome_50m/model.py": "2871dc8893879c3e14a526dea659701d72c8c4d0e2f835b0bfb857034a2c2e6d", + "reference/alphagenome_50m/reference_large.yaml": "68084d299b39418034c6bbf71db174c3ffa68bbeea8779c1694e44ec71f3529c", + "reference/alphagenome_50m/run_ddp.sh": "96d35a24a4e6789311501fb98203807ac0c4429375c44d5c6671908270c93bb0", + "reference/alphagenome_50m/tiny_overfit.yaml": "f7a104c3929de0530a9a8f73f66d794cca595aefad8ef262e09ecbeb9cfce6de", + "reference/alphagenome_50m/train_eval.py": "95d2dad279b23ed9c0c8a4f070035cf53c27eb8fe27da64c441343717174f784", + "runtime.tar.gz": "718da4c374e0c552b6e429b226c90b2fded5c900d95577d190d4df520aaf3901", + "train_single.py": "a6653431e850d448c414f48cc65cbaaec8c17a430c08376b281441a6f6bce987", + "training_config.yaml": "deab298b32f0aef33e66ddf538541ec470eee0c05a6ebc983684f2575287d791", + "training_provenance.json": "be2d6b2503b3780307a990bd4ff3cfd2f549b127936055e709648853cad72ffb", + "validation_tracks.json": "462433d1f589007987539becf5682d292de9766778d6cd814b7af76fc2af5f4c", + "validation_variants.json": "c4d7bf2406df84c09be7e4a9febaca831a3d5265fd0c4f5c190ca8822663028e" + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/prediction_contract.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/prediction_contract.py new file mode 100644 index 0000000000000000000000000000000000000000..2455424db002b840cf0d69456930840a7bc5f3cb --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/prediction_contract.py @@ -0,0 +1,23 @@ +"""Public prediction shapes; no trusted splice coordinates are exposed.""" + +from __future__ import annotations + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} + +PREDICTION_SCHEMA = "genome-sequence-to-function-predictions/v4" +MAX_USAGE_SITES = 4_096 +MAX_JUNCTION_PAIRS = 16_384 +# Fixed-shape arrays per example; splice usage and junctions are sparse (see contract.md). +FIXED_SHAPES = { + **{name: (1_536, tracks) for name, tracks in DENSE_TRACKS.items()}, + "splice_sites": (196_608, 4), + "contact_maps": (131_328, 28), +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/reference/alphagenome_50m/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/reference/alphagenome_50m/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a86a139babb6e899bd5102bbf3c628e1232576b2 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/reference/alphagenome_50m/__init__.py @@ -0,0 +1,5 @@ +"""Compact 1-Mb, eleven-family AlphaGenome reference model.""" + +from .model import CompactModel, ModelConfig, count_parameters + +__all__ = ["CompactModel", "ModelConfig", "count_parameters"] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/reference/alphagenome_50m/dataset.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/reference/alphagenome_50m/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..71cd39d15713f648e7d7e31aa1923fa4fa4800dd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/reference/alphagenome_50m/dataset.py @@ -0,0 +1,229 @@ +"""Streaming reader for the compact 1 Mb / eleven-family release.""" + +from __future__ import annotations + +from collections import OrderedDict +from pathlib import Path + +import numpy as np +import pandas as pd +import pysam +import torch +from torch.utils.data import Dataset +import zarr + + +def _open_part(path: Path): + if str(path).endswith(".zarr.zip"): + store = zarr.ZipStore(str(path), mode="r") + try: + return zarr.open_group(store=store, mode="r"), store + except BaseException: + store.close() + raise + return zarr.open_group(str(path), mode="r"), None + + +DENSE_FAMILIES = ( + "atac", + "dnase", + "procap", + "cage", + "rna_seq", + "chip_tf", + "chip_histone", +) +def normalize_splice_positions(value) -> np.ndarray: + """Normalize Arrow's nested-list representation to the public [4, 512] array.""" + + outer = np.asarray(value, dtype=object).reshape(-1).tolist() + positions = np.stack([np.asarray(item, dtype=np.int64) for item in outer]) + if positions.shape != (4, 512): + raise ValueError(f"splice_site_positions must have shape (4, 512), got {positions.shape}") + return positions + + +class AlphaGenomeDataset(Dataset): + """Resolve frozen coordinates to reference DNA and one Zarr example group.""" + + def __init__( + self, + data_root: str | Path, + split: str, + *, + max_open_parts: int = 8, + include_targets: bool = True, + ) -> None: + self.root = Path(data_root) + self.split = split + manifest = self.root / "public" / f"{split}.parquet" + if not manifest.is_file(): + raise FileNotFoundError(f"Missing materialized manifest: {manifest}") + self.rows = pd.read_parquet(manifest).reset_index(drop=True) + junction_tracks = pd.read_parquet(self.root / "public/junction_tracks.parquet") + junction_tracks = junction_tracks.sort_values("tensor_index") + self.junction_track_halves = torch.from_numpy( + junction_tracks.junction_channel_half.to_numpy(np.int64) + ) + if include_targets: + labels_path = self.root / "labels" / f"{split}.parquet" + if not labels_path.is_file(): + raise FileNotFoundError(f"Missing trusted labels manifest: {labels_path}") + labels = pd.read_parquet(labels_path).reset_index(drop=True) + if self.rows.example_id.astype(str).tolist() != labels.example_id.astype(str).tolist(): + raise ValueError("public and labels manifests have different IDs/order") + self.rows = self.rows.merge(labels, on="example_id", validate="one_to_one", sort=False) + self.fasta_path = self.root / "reference" / "GRCh38.p13.genome.fa" + self.max_open_parts = max_open_parts + self.include_targets = include_targets + self._fasta = None + self._parts: OrderedDict[str, tuple[zarr.Group, object | None]] = OrderedDict() + + def __len__(self) -> int: + return len(self.rows) + + def _reference(self): + if self._fasta is None: + self._fasta = pysam.FastaFile(str(self.fasta_path)) + return self._fasta + + def _part(self, relative: str): + if relative in self._parts: + opened = self._parts.pop(relative) + self._parts[relative] = opened + return opened[0] + group, store = _open_part(self.root / relative) + if len(self._parts) >= self.max_open_parts: + _, (_, old_store) = self._parts.popitem(last=False) + if old_store is not None: + old_store.close() + self._parts[relative] = (group, store) + return group + + @staticmethod + def _one_hot(sequence: str) -> np.ndarray: + encoded = np.frombuffer(sequence.upper().encode("ascii"), dtype=np.uint8) + output = np.zeros((len(encoded), 4), dtype=np.float32) + for index, letter in enumerate(b"ACGT"): + output[:, index] = encoded == letter + return output + + def _sequence(self, row) -> tuple[np.ndarray, np.ndarray]: + reference = self._reference() + chromosome_length = reference.get_reference_length(str(row.chromosome)) + start, end = int(row.input_start), int(row.input_end) + clipped_start, clipped_end = max(0, start), min(chromosome_length, end) + sequence = reference.fetch(str(row.chromosome), clipped_start, clipped_end) + left, right = clipped_start - start, end - clipped_end + if left or right: + sequence = "N" * left + sequence + "N" * right + valid = np.ones(end - start, dtype=bool) + if left: + valid[:left] = False + if right: + valid[-right:] = False + return self._one_hot(sequence), valid + + @staticmethod + def _decode(example, name: str, *, asinh: bool) -> np.ndarray: + """Train targets may be stored as 8-bit codes with per-track ranges; they are decoded + here, one example at a time, so the release stays compressed on disk.""" + array = example[name] + if array.dtype != np.uint8: + return np.asarray(array, dtype=np.float32) + lo = np.asarray(example[f"{name}_lo"], dtype=np.float32) + hi = np.asarray(example[f"{name}_hi"], dtype=np.float32) + x = lo + np.asarray(array, dtype=np.float32) * ((hi - lo) / 255.0) + return np.sinh(x) if asinh else x + + @staticmethod + def _dense_target(example, family: str) -> dict[str, torch.Tensor]: + values = AlphaGenomeDataset._decode(example, f"{family}/values", asinh=True) + mask = np.asarray(example[f"{family}/mask"], dtype=bool) + if mask.ndim == 1: + mask = np.broadcast_to(mask[None, :], values.shape).copy() + return { + "values": torch.from_numpy(values), + "mask": torch.from_numpy(mask), + } + + def __getitem__(self, item: int): + row = self.rows.iloc[item] + dna, input_mask = self._sequence(row) + result = { + "example_id": str(row.example_id), + "dna": torch.from_numpy(dna), + "input_mask": torch.from_numpy(input_mask), + "junction_track_halves": self.junction_track_halves, + } + if not self.include_targets: + return result + + part = self._part(str(row.target_shard)) + example = part[str(row.target_group)] + result["dense"] = { + family: self._dense_target(example, family) for family in DENSE_FAMILIES + } + result["splice_sites"] = torch.from_numpy( + np.asarray(example["splice_sites/values"], dtype=np.float32) + ) + result["splice_usage"] = { + "positions": torch.from_numpy( + np.asarray(example["splice_site_usage/positions"], dtype=np.int64) + ), + "values": torch.from_numpy( + np.asarray(example["splice_site_usage/values"], dtype=np.float32) + ), + "mask": torch.from_numpy( + np.asarray(example["splice_site_usage/mask"], dtype=bool) + ), + } + result["usage_positions"] = result["splice_usage"]["positions"] + result["junctions"] = { + name: torch.from_numpy( + np.asarray( + example[f"splice_junctions/{name}"], + dtype=np.float32 if name == "value" else np.int64, + ) + ) + for name in ("row", "column", "track", "value") + } + result["junctions"]["site_positions"] = torch.from_numpy( + np.asarray(example["splice_junctions/site_positions"], dtype=np.int64) + ) + site_positions = normalize_splice_positions(row.splice_site_positions) + result["site_positions"] = torch.from_numpy(site_positions) + if not torch.equal(result["junctions"]["site_positions"], result["site_positions"]): + raise ValueError(f"public/private splice positions differ for {row.example_id}") + scoring_path = self.root / "scoring" / self.split / f"part-{item:06d}.parquet" + scoring = pd.read_parquet(scoring_path) + if not (scoring.example_id.astype(str) == str(row.example_id)).all(): + raise ValueError(f"scoring part ID differs for {row.example_id}") + result["junction_scoring"] = { + "donor_position": torch.from_numpy( + scoring.donor_position.to_numpy(np.int64) + ), + "acceptor_position": torch.from_numpy( + scoring.acceptor_position.to_numpy(np.int64) + ), + "track": torch.from_numpy(scoring.track.to_numpy(np.int64)), + "target_value": torch.from_numpy( + scoring.target_value.to_numpy(np.float32) + ), + "valid": torch.from_numpy(scoring.valid.to_numpy(bool)), + } + result["contact_maps"] = torch.from_numpy( + self._decode(example, "contact_maps/upper_triangle", asinh=False) + ) + result["contact_map_mask"] = torch.from_numpy( + np.asarray(example["contact_maps/mask"], dtype=bool) + ) + return result + + +def single_example_collate(items): + """Keep variable-size event tensors intact; the reference currently trains at batch one.""" + + if len(items) != 1: + raise ValueError("The compact event loader currently requires batch_size=1") + return items[0] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/reference/alphagenome_50m/layers.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/reference/alphagenome_50m/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..af31f86a5589a16f0007e6aca5ca7a7bd552d6a9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/reference/alphagenome_50m/layers.py @@ -0,0 +1,124 @@ +"""Shared neural-network layers for the single compact AlphaGenome model.""" + +from __future__ import annotations + +import math + +import torch +from torch import Tensor, nn +from torch.nn import functional as F + + +class ChannelRMSNorm(nn.Module): + def __init__(self, channels: int, eps: float = 1e-6): + super().__init__() + self.weight = nn.Parameter(torch.ones(channels)) + self.eps = eps + + def forward(self, x: Tensor) -> Tensor: + variance = x.float().square().mean(dim=1, keepdim=True) + normalized = x * torch.rsqrt(variance + self.eps).to(x.dtype) + return normalized * self.weight[None, :, None].to(x.dtype) + + +class StandardizedConv1d(nn.Conv1d): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__(in_channels, out_channels, kernel_size, padding="same", bias=True) + self.scale = nn.Parameter(torch.ones(out_channels)) + nn.init.trunc_normal_(self.weight, std=1.0 / math.sqrt(in_channels * kernel_size)) + nn.init.trunc_normal_(self.bias, std=1e-4) + + def forward(self, x: Tensor) -> Tensor: + centered = self.weight - self.weight.mean(dim=(1, 2), keepdim=True) + variance = centered.float().square().mean(dim=(1, 2), keepdim=True) + fan_in = self.weight.shape[1] * self.weight.shape[2] + multiplier = self.scale[:, None, None] * torch.rsqrt( + (fan_in * variance).clamp_min(1e-4) + ).to(self.weight.dtype) + return F.conv1d(x, centered * multiplier, self.bias, padding="same") + + +class ConvBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__() + self.norm = ChannelRMSNorm(in_channels) + self.conv = StandardizedConv1d(in_channels, out_channels, kernel_size) + + def forward(self, x: Tensor) -> Tensor: + return self.conv(F.gelu(self.norm(x), approximate="tanh")) + + +def _apply_rope(x: Tensor) -> Tensor: + rotary_dim = x.shape[-1] - x.shape[-1] % 2 + if rotary_dim == 0: + return x + positions = torch.arange(x.shape[-2], device=x.device, dtype=torch.float32) + inverse_frequency = 1.0 / ( + 10_000 ** ( + torch.arange(0, rotary_dim, 2, device=x.device, dtype=torch.float32) + / rotary_dim + ) + ) + angles = torch.outer(positions, inverse_frequency) + cos = angles.cos()[None, None, :, :].to(x.dtype) + sin = angles.sin()[None, None, :, :].to(x.dtype) + even, odd = x[..., :rotary_dim:2], x[..., 1:rotary_dim:2] + rotated = torch.stack((even * cos - odd * sin, even * sin + odd * cos), dim=-1) + return torch.cat((rotated.flatten(-2), x[..., rotary_dim:]), dim=-1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.num_heads = config.num_attention_heads + self.head_dim = config.attention_head_dim + inner_dim = self.num_heads * self.head_dim + self.norm = nn.RMSNorm(config.hidden_dim) + self.qkv = nn.Linear(config.hidden_dim, 3 * inner_dim, bias=False) + self.output = nn.Linear(inner_dim, config.hidden_dim) + self.dropout = config.dropout + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + batch, length, _ = x.shape + qkv = self.qkv(self.norm(x)).view( + batch, length, 3, self.num_heads, self.head_dim + ) + q, k, value = qkv.unbind(dim=2) + attended = F.scaled_dot_product_attention( + _apply_rope(q.transpose(1, 2)), + _apply_rope(k.transpose(1, 2)), + value.transpose(1, 2), + attn_mask=attention_bias, + dropout_p=self.dropout if self.training else 0.0, + ) + return self.output(attended.transpose(1, 2).reshape(batch, length, -1)) + + +class MLPBlock(nn.Module): + def __init__(self, config): + super().__init__() + expanded = int(config.hidden_dim * config.mlp_expansion) + self.norm = nn.RMSNorm(config.hidden_dim) + self.input = nn.Linear(config.hidden_dim, expanded) + self.output = nn.Linear(expanded, config.hidden_dim) + self.dropout = nn.Dropout(config.dropout) + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor) -> Tensor: + return self.dropout( + self.output(F.gelu(self.input(self.norm(x)), approximate="tanh")) + ) + + +class SequenceBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.attention = MultiHeadAttention(config) + self.mlp = MLPBlock(config) + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + x = x + self.attention(x, attention_bias) + return x + self.mlp(x) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/reference/alphagenome_50m/run_ddp.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/reference/alphagenome_50m/run_ddp.sh new file mode 100644 index 0000000000000000000000000000000000000000..c4702e9366e907b7791063e77dcfe281411ac8cd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/reference/alphagenome_50m/run_ddp.sh @@ -0,0 +1,16 @@ +#!/usr/bin/env bash +# run_ddp.sh DATA_DIR OUTPUT_DIR CONFIG EPOCHS [GPUS=8] [WORKERS_PER_GPU=12] +# Train the reference on every train interval with one example per GPU per step, then score +# validation with the verifier's own scorer. Runs inside the task image (torch, torchrun). +set -Eeuo pipefail +DATA=$1; OUT=$2; CONFIG=$3; EPOCHS=$4; GPUS=${5:-8}; WORKERS=${6:-12} +BASELINE=$(cd "$(dirname "$0")/../.." && pwd) +mkdir -p "$OUT" +cd "$BASELINE" +# Loopback rendezvous: works offline, where --standalone cannot resolve the hostname. +torchrun --nnodes 1 --nproc-per-node "$GPUS" --master-addr 127.0.0.1 --master-port "${MASTER_PORT:-29511}" -m reference.alphagenome_50m.train_eval train \ + --input "$DATA" --config "$CONFIG" --output "$OUT" --epochs "$EPOCHS" --workers "$WORKERS" \ + --log-every 200 --save-every 2000 2>&1 | tee -a "$OUT/train.log" +python predict.py "$DATA" "$OUT/valid-pred" --checkpoint "$OUT/last.pt" --split valid +python /workspace/evaluation/score.py --predictions "$OUT/valid-pred/predictions.zarr" \ + --labels "$DATA" --split valid --output "$OUT/valid-score" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/reference/alphagenome_50m/tiny_overfit.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/reference/alphagenome_50m/tiny_overfit.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c25f9ffbb76102c970e5767922400f7ec2b6c53e --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/reference/alphagenome_50m/tiny_overfit.yaml @@ -0,0 +1,24 @@ +# Keep the frozen 1-Mb input and every output track; shrink only learned widths. +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 8 +channel_growth: 4 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 32 +num_sequence_blocks: 1 +num_attention_heads: 2 +attention_head_dim: 16 +mlp_expansion: 2.0 +dropout: 0.0 +head_hidden_size: 32 +junction_rank: 8 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: false diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/train_single.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/train_single.py new file mode 100644 index 0000000000000000000000000000000000000000..45bdf927f635baa77ab98cc8fcefbb60196aa0ff --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/train_single.py @@ -0,0 +1,155 @@ +"""Resumable single-H100 training; every epoch permutes every eligible training interval.""" +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import torch +from numcodecs import blosc +from torch.utils.data import DataLoader, Subset + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset, single_example_collate +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save, _losses, _model_inputs, _to_device + + +def balanced_splice_loss(logits, target): + """Equal positive/negative contributions per type, using only training labels. + + An absent class contributes its negative loss rather than dividing by zero or being dropped. + """ + dims = tuple(range(target.ndim - 1)) + positive = target.sum(dims) + negative = (1 - target).sum(dims) + pos_loss = (torch.nn.functional.softplus(-logits) * target).sum(dims) / positive.clamp_min(1) + neg_loss = (torch.nn.functional.softplus(logits) * (1 - target)).sum(dims) / negative.clamp_min(1) + return torch.where(positive > 0, 0.5 * (pos_loss + neg_loss), neg_loss).mean() + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--input', type=Path, default=Path('/workspace/data')) + p.add_argument('--output', type=Path, required=True) + p.add_argument('--config', type=Path, default=Path(__file__).parent / 'reference/alphagenome_50m/alphagenome_50m.yaml') + p.add_argument('--resume', type=Path) + p.add_argument('--objective', choices=['reference', 'balanced_splice'], default='balanced_splice') + p.add_argument('--epochs', type=int, default=4) + p.add_argument('--stop-at', type=int, default=0, help='Stop after this total number of examples, preserving exact sampler position') + p.add_argument('--workers', type=int, default=8) + p.add_argument('--threads', type=int, default=4) + p.add_argument('--seed', type=int, default=17) + p.add_argument('--learning-rate', type=float, default=2e-4) + p.add_argument('--min-learning-rate', type=float, default=2e-5) + p.add_argument('--warmup', type=int, default=500) + p.add_argument('--save-every', type=int, default=2000) + p.add_argument('--keep-every', type=int, default=10000, help='Keep immutable intermediate checkpoints') + p.add_argument('--log-every', type=int, default=100) + args = p.parse_args() + torch.set_num_threads(args.threads) + torch.set_num_interop_threads(1) + blosc.set_nthreads(1) + if hasattr(os, 'sched_getaffinity'): + os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) + torch.manual_seed(args.seed) + device = torch.device('cuda:0') + config = ModelConfig.from_yaml(args.config) + model = CompactModel(config).to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=1e-4) + dataset = AlphaGenomeDataset(args.input, 'train') + model.set_junction_track_halves(dataset.junction_track_halves) + args.output.mkdir(parents=True, exist_ok=True) + total = len(dataset) * args.epochs + step, elapsed_before = 0, 0.0 + if args.resume: + state = torch.load(args.resume, map_location='cpu', weights_only=True) + if state['config'] != config.to_dict() or state['examples'] != len(dataset): + raise ValueError('resume config or dataset size differs') + if state['schedule_epochs'] != args.epochs or state['objective'] != args.objective: + raise ValueError('resume schedule or objective differs') + if state['seed'] != args.seed: + raise ValueError('resume shuffle seed differs') + previous = state.get('training_arguments', {}) + for field in ['learning_rate', 'min_learning_rate', 'warmup']: + if field in previous and previous[field] != getattr(args, field): + raise ValueError(f'resume {field} differs') + model.load_state_dict(state['model'], strict=True) + optimizer.load_state_dict(state['optimizer']) + step = state['global_step'] + elapsed_before = state.get('elapsed_seconds', 0.0) + torch.set_rng_state(state['rng_cpu']) + torch.cuda.set_rng_state(state['rng_cuda'], device) + start = time.monotonic() + def emit(event, **values): + record = {'event': event, 'step': step, 'elapsed_seconds': elapsed_before + time.monotonic() - start, **values} + print(json.dumps(record), flush=True) + def save(name): + _atomic_save({ + 'schema': 'seqfunc-single/v1', 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), 'config': config.to_dict(), + 'global_step': step, 'epoch': step // len(dataset), 'offset': step % len(dataset), + 'examples': len(dataset), 'schedule_epochs': args.epochs, + 'objective': args.objective, 'seed': args.seed, + 'training_arguments': {k: str(v) if isinstance(v, Path) else v for k, v in vars(args).items()}, + 'rng_cpu': torch.get_rng_state(), 'rng_cuda': torch.cuda.get_rng_state(device), + 'elapsed_seconds': elapsed_before + time.monotonic() - start, + 'provenance': {'data': str(args.input), 'split': 'train', 'pretrained': False, + 'device': torch.cuda.get_device_name(0), 'world_size': 1}, + }, args.output / name) + emit('train_start', parameters=count_parameters(model), examples=len(dataset), objective=args.objective, + config=config.to_dict(), schedule_examples=total, device=torch.cuda.get_device_name(0)) + model.train() + for epoch in range(step // len(dataset), args.epochs): + generator = torch.Generator().manual_seed(args.seed + epoch) + order = torch.randperm(len(dataset), generator=generator).tolist() + offset = step % len(dataset) + emit('epoch_order', epoch=epoch + 1, offset=offset, count=len(order), + permutation_sha256=hashlib.sha256(torch.tensor(order, dtype=torch.int64).numpy().tobytes()).hexdigest()) + loader = DataLoader(Subset(dataset, order[offset:]), batch_size=1, shuffle=False, + num_workers=args.workers, pin_memory=True, collate_fn=single_example_collate, + persistent_workers=False, prefetch_factor=2 if args.workers else None) + for sample in loader: + sample = _to_device(sample, device) + inputs, target, valid = _model_inputs(sample) + progress = min(max(step - args.warmup, 0) / max(total - args.warmup, 1), 1) + lr = args.min_learning_rate + 0.5 * (args.learning_rate - args.min_learning_rate) * (1 + math.cos(math.pi * progress)) + lr *= min((step + 1) / max(args.warmup, 1), 1) + for group in optimizer.param_groups: + group['lr'] = lr + optimizer.zero_grad(set_to_none=True) + with torch.autocast('cuda', dtype=torch.bfloat16): + outputs = model(**inputs) + losses = _losses(outputs, sample, target, valid) + if args.objective == 'balanced_splice': + losses['splice_sites'] = balanced_splice_loss(outputs['splice_sites'], sample['splice_sites'][None]) + losses['total'] = torch.stack([v for k, v in losses.items() if k != 'total']).mean() + if not torch.isfinite(losses['total']): + raise FloatingPointError(f'nonfinite loss at {step}') + losses['total'].backward() + norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0, error_if_nonfinite=True) + optimizer.step() + step += 1 + if step == 1 or step % args.log_every == 0: + emit('train_step', epoch=epoch + 1, example_id=sample['example_id'], learning_rate=lr, + gradient_norm=float(norm), losses={k: float(v.detach()) for k, v in losses.items()}, + peak_gpu_bytes=torch.cuda.max_memory_allocated()) + if step % args.save_every == 0: + save('progress.pt') + if args.keep_every and step % args.keep_every == 0: + save(f'step-{step:06d}.pt') + if args.stop_at and step >= args.stop_at: + save('last.pt') + emit('train_segment_complete', completed_epochs=step // len(dataset), offset=step % len(dataset)) + return + save('last.pt') + save(f'epoch-{epoch + 1:02d}.pt') + emit('epoch_complete', completed_epochs=epoch + 1) + emit('train_complete') + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/validation_variants.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/validation_variants.json new file mode 100644 index 0000000000000000000000000000000000000000..e3ab66ecabe7d7382e527ba37158d64284cbe117 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S006/validation_variants.json @@ -0,0 +1,125 @@ +{ + "eqtl_sign": { + "rows": 17052, + "perturbations": 165, + "metric": "auroc", + "model": 0.5198520959077162, + "anchors": { + "AlphaGenome": 0.8263569969634328 + } + }, + "eqtl_causality": { + "rows": 17934, + "perturbations": 174, + "metric": "auroc", + "model": 0.6543758043758043, + "anchors": { + "AlphaGenome": 0.8325427468284611 + } + }, + "eqtl_coefficient": { + "rows": 8575, + "perturbations": 165, + "metric": "spearman", + "model": 0.032866131808930645, + "anchors": { + "AlphaGenome": 0.4133539365891599 + } + }, + "sqtl_causality": { + "rows": 21340, + "perturbations": 194, + "metric": "auprc", + "model": 0.7879498985968099, + "anchors": { + "AlphaGenome": 0.8447114201904594 + } + }, + "clinvar_noncoding": { + "rows": 199, + "perturbations": 199, + "metric": "auprc", + "model": 0.2739631600176575, + "anchors": { + "AlphaGenome": 0.9409857958177288 + } + }, + "clinvar_splice_region": { + "rows": 194, + "perturbations": 194, + "metric": "auprc", + "model": 0.2238179581806053, + "anchors": { + "AlphaGenome": 0.7224791623996103 + } + }, + "clinvar_missense": { + "rows": 199, + "perturbations": 199, + "metric": "auprc", + "model": 0.271752222082551, + "anchors": { + "AlphaGenome": 0.373826292978903 + } + }, + "mfass": { + "rows": 195, + "perturbations": 195, + "metric": "auprc", + "model": 0.1841515488051888, + "anchors": { + "AlphaGenome": 0.7342557141920668 + } + }, + "caqtl": { + "rows": 198, + "perturbations": 198, + "metric": "auprc", + "model": 0.20385606362469394, + "anchors": { + "AlphaGenome": 0.8075760167741196 + } + }, + "bqtl_spi1": { + "rows": 160, + "perturbations": 160, + "metric": "auprc", + "model": 0.21778232734482322, + "anchors": { + "AlphaGenome": 0.8167450781363793 + } + }, + "enhancer_gene": { + "rows": 200, + "perturbations": 200, + "metric": "auprc", + "model": 0.4243122338797817, + "anchors": { + "AlphaGenome": 0.9594307235541584 + } + }, + "traitgym_mendelian": { + "rows": 292, + "perturbations": 292, + "metric": "auprc", + "model": 0.15142237686407295, + "anchors": { + "Borzoi_L2_L2.plus.all": 0.5026323772738239, + "Enformer_L2_L2.plus.all": 0.5452938267693526, + "CADD.plus.RawScore": 0.7189111372856957, + "GPN-MSA_absLLR.plus.score": 0.6729149139043132 + } + }, + "traitgym_complex": { + "rows": 299, + "perturbations": 299, + "metric": "auprc", + "model": 0.21180748401188101, + "anchors": { + "Borzoi_L2_L2.plus.all": 0.294501577162467, + "Enformer_L2_L2.plus.all": 0.269227301087889, + "CADD.plus.RawScore": 0.41341570280137435, + "GPN-MSA_absLLR.plus.score": 0.4213971415058371 + } + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/README.md b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c1fad7d1e2e855f6490252daddabd164e0ac5fb5 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/README.md @@ -0,0 +1,77 @@ +# SeqFunc local01 at step 26,000 + +One model with **13,256,456 parameters** serves every family and both entry points. It reads the entire +1,048,576-bp DNA window through a convolutional encoder and 6 transformer +blocks of width 384 over 1,024 tokens. Dense heads predict 128-bp bins over the central +196,608 bp; other heads predict splice sites, sparse usage/junctions and full-window contacts. +A width-32 base-resolution path uses a 21-bp stem and four dilated residual depthwise blocks (701-bp receptive field). Average/max pooling provides aligned 128-bp residual features to dense, usage and junction heads. Splice logits combine local base-resolution logits with the reference offset decoder. Junction values use low-rank donor/acceptor factors; contacts use a symmetric +product of global features. Sites and junction endpoints are selected from model scores alone. + +All weights originate from local random initialization (seed 17) and locally trained +checkpoints. A balanced splice objective trained the compact reference for 2,000 updates. A zero-output base-resolution path was attached and the same full-split schedule continued. This is the unchanged control for norm05, with the original offset splice head and reference continuous losses. + +This checkpoint records **26,000 updates**, 0 complete training permutations and +26,000 intervals into the next permutation. Each permutation includes all **41,020** +eligible FOLD_0 training intervals (folds 2–7), shuffled deterministically without replacement. +Quantized train targets are decoded by the supplied reader. Validation is fold 0; no test labels, +test-derived caches or pretrained model parameters were used. Development labels only evaluate +and select models. Checkpoints before a complete permutation remain intermediate milestones. + +Training uses one H100 80 GB and at most 22 CPUs. The current loader uses 6 workers +and 2 torch CPU threads. BF16 training uses AdamW (weight decay 1e-4), gradient +clipping at 5, and a 4-epoch cosine learning-rate schedule from +0.0002 to 2e-05, with +500 warmup updates. The objective is `balanced_splice`. +Other family losses follow the reference. Recorded accumulated training-loop wall time is +64.4 minutes, including inherited parent time. Runs can share this +same physical GPU with experiments or evaluation. Setup, evaluation and discarded mechanical checks +are additional research compute, documented in the research ledger. Matching training junction queries +uses a verified, bit-identical CPU index construction followed by a bulk GPU transfer. + +The package contains model code, weights, training source/configuration/provenance, SHA-256 checksums, +and the complete CPython 3.11 / PyTorch 2.7.1 CUDA 12.6 Python runtime archive. Both executable entry +points take INPUT_ROOT and OUTPUT_ROOT, extract the vendored runtime temporarily, and run offline. +The host supplies Linux, bash/tar/gzip and the NVIDIA driver. Inference reads only public manifests +and the pinned reference FASTA. No target, splice-coordinate or prediction cache is packaged or read. +Track outputs use float16; perturbation outputs use float32 to preserve small A/B differences. + +The appended validation results cover all 200 intervals and all 2,697 development perturbations, +scored by the unmodified verifier tools. Published anchors use the same label rows. All results, +including regressions, are retained in the package's JSON reports. Limitations include incomplete +training at intermediate checkpoints, a 701-bp local receptive field, pooled usage/junction features, +low-rank contact modeling, and uneven variant performance. Functional readouts do not provide the +evolutionary constraint information used by conservation-based variant scorers. + +## Measured validation results + +Family-mean Pearson: **0.273742** on 200 validation intervals. + +| Family | Pearson | +|---|---:| +| atac | 0.476792 | +| dnase | 0.600527 | +| procap | 0.353003 | +| cage | 0.195768 | +| rna_seq | 0.216336 | +| chip_tf | 0.410698 | +| chip_histone | 0.392924 | +| splice_sites | 0.040627 | +| splice_usage | 0.188224 | +| junctions | 0.136958 | +| contact_maps | -0.000695 | + +| Development evaluation | Metric | Model | Published anchors on the same rows | +|---|---|---:|---| +| eqtl_sign | auroc | 0.5014 | AlphaGenome: 0.8264 | +| eqtl_causality | auroc | 0.6581 | AlphaGenome: 0.8325 | +| eqtl_coefficient | spearman | -0.0023 | AlphaGenome: 0.4134 | +| sqtl_causality | auprc | 0.7465 | AlphaGenome: 0.8447 | +| clinvar_noncoding | auprc | 0.2930 | AlphaGenome: 0.9410 | +| clinvar_splice_region | auprc | 0.2223 | AlphaGenome: 0.7225 | +| clinvar_missense | auprc | 0.2598 | AlphaGenome: 0.3738 | +| mfass | auprc | 0.1802 | AlphaGenome: 0.7343 | +| caqtl | auprc | 0.2042 | AlphaGenome: 0.8076 | +| bqtl_spi1 | auprc | 0.2223 | AlphaGenome: 0.8167 | +| enhancer_gene | auprc | 0.3230 | AlphaGenome: 0.9594 | +| traitgym_mendelian | auprc | 0.1443 | Borzoi_L2_L2.plus.all: 0.5026; Enformer_L2_L2.plus.all: 0.5453; CADD.plus.RawScore: 0.7189; GPN-MSA_absLLR.plus.score: 0.6729 | +| traitgym_complex | auprc | 0.1937 | Borzoi_L2_L2.plus.all: 0.2945; Enformer_L2_L2.plus.all: 0.2692; CADD.plus.RawScore: 0.4134; GPN-MSA_absLLR.plus.score: 0.4214 | diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/package_manifest.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/package_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0714c9bd82aab9afca658f062e719c02f823ed7f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/package_manifest.json @@ -0,0 +1,30 @@ +{ + "parameters": 13256456, + "source_checkpoint_sha256": "9e596a2ef633c3918c6fc0ecd93b7fa2254c894e0bb8078912c7582542e099a6", + "files": { + "README.md": "26eaa5738e476b8aa7feee38870567721d7d34a6d905fc52d684a51a3e46dbe8", + "attach_local.py": "d0774e2711840783267914789ed303a3b61b64381b72d61f0886edbc04974f15", + "infer.sh": "ef810532b1e72b85fbe283e4f52c177bb70025977dbb2345d75837c9bbd51e37", + "last.pt": "02c769c0612798129fccce570d892981bb06034f8e1e474217c196a257dc5006", + "perturb.py": "ba37a6f9f8b8eccb052903618bcb4c91d3f4cbed62dfe678ee362644dc80801e", + "perturb.sh": "113ec2383276a6e83fe1e4e8b0fa914f8d535d5dd0f5b2c34240140312dd5fb4", + "predict.py": "2725592456c55c188b22c376f0cb249e9f46b4ae1370d2226df7143da358a8b2", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/__init__.py": "45f3cf93f48859104e70bb58d717130dfd6521a45d2aba06b1a726afc0dc51dd", + "reference/alphagenome_50m/__init__.py": "a5a7e45d5940c3b201017c6322128e9f47efd37e1d29f717da2c1f787688eff9", + "reference/alphagenome_50m/alphagenome_50m.yaml": "3cac32e9f55e352dff78bb85ac892fdae1f9e412d9bba3e945750210c5252eee", + "reference/alphagenome_50m/dataset.py": "ad6670f742f4de9898064becc525d0aea98b402abe7b210bb1b26a9eee6402b9", + "reference/alphagenome_50m/layers.py": "26a116a72aa3f0e307a3606163547edbdecb7965f7fdcd96bc5f805cd21b3352", + "reference/alphagenome_50m/model.py": "2871dc8893879c3e14a526dea659701d72c8c4d0e2f835b0bfb857034a2c2e6d", + "reference/alphagenome_50m/reference_large.yaml": "68084d299b39418034c6bbf71db174c3ffa68bbeea8779c1694e44ec71f3529c", + "reference/alphagenome_50m/run_ddp.sh": "96d35a24a4e6789311501fb98203807ac0c4429375c44d5c6671908270c93bb0", + "reference/alphagenome_50m/tiny_overfit.yaml": "f7a104c3929de0530a9a8f73f66d794cca595aefad8ef262e09ecbeb9cfce6de", + "reference/alphagenome_50m/train_eval.py": "95d2dad279b23ed9c0c8a4f070035cf53c27eb8fe27da64c441343717174f784", + "runtime.tar.gz": "718da4c374e0c552b6e429b226c90b2fded5c900d95577d190d4df520aaf3901", + "train_single.py": "a6653431e850d448c414f48cc65cbaaec8c17a430c08376b281441a6f6bce987", + "training_config.yaml": "deab298b32f0aef33e66ddf538541ec470eee0c05a6ebc983684f2575287d791", + "training_provenance.json": "7d36cb06bf1b6560f34bc7056c035d42fea8a2f2f3c7b9e0d8d8103acd6a441f", + "validation_tracks.json": "b876a52543c71ce70cc22e12f563d9d5fac497c7e5a2b373edfd1d5eef7d577d", + "validation_variants.json": "c7f69559e9b808c80086581a8dae4a74d60aa1d6b8b2ff0af207b07b31a71c65" + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/perturb.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/perturb.py new file mode 100644 index 0000000000000000000000000000000000000000..e0416692d2ee7deb1a25927397f725eec54d16a9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/perturb.py @@ -0,0 +1,101 @@ +"""Predict both sequences of every perturbation (contract: perturbations/v1). + + python perturb.py INPUT_ROOT OUTPUT_ROOT --checkpoint last.pt + +INPUT_ROOT holds `public/perturbations.parquet` and `reference/GRCh38.p13.genome.fa` (in the +workspace, `/workspace/data` works too: the queries are then read from `variant_evals/public/`). Each row is a +1,048,576-bp window starting at `input_start` and an edit spanning [edit_start, edit_end) in genomic +0-based coordinates: + +- `snv`: sequence A carries `allele_a` at edit_start, sequence B carries `allele_b` (both are written + explicitly; either may differ from the reference genome); +- `mask`: sequence A is the reference, sequence B has the interval replaced by N. + +For each perturbation the output group `perturbations/p{index:06d}` holds `a/` and +`b/` for every family in `families`: dense families sliced to bins +[dense_bin_start, dense_bin_end) of the central target window, and `splice_sites` sliced to bp +[splice_start, splice_end). Values are the same quantities as predictions.zarr (asinh space for +dense tracks, splice-site scores for splice_sites). +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import pandas as pd +import pysam +import torch +import zarr + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig + +PERTURBATION_SCHEMA = "genome-sequence-to-function-perturbations/v1" +COMPRESSOR = Blosc(cname="zstd", clevel=3, shuffle=Blosc.BITSHUFFLE) + + +def sequences(fasta, row) -> tuple[str, str]: + length = fasta.get_reference_length(row.chrom) + start, end = int(row.input_start), int(row.input_start) + 1_048_576 + sequence = fasta.fetch(row.chrom, max(0, start), min(length, end)).upper() + sequence = "N" * max(0, -start) + sequence + "N" * max(0, end - length) + lo, hi = int(row.edit_start) - start, int(row.edit_end) - start + if row.edit == "snv": + return sequence[:lo] + row.allele_a + sequence[hi:], sequence[:lo] + row.allele_b + sequence[hi:] + if row.edit == "mask": + return sequence, sequence[:lo] + "N" * (hi - lo) + sequence[hi:] + raise ValueError(f"unknown edit {row.edit!r}") + + +def main(input_root: Path, output_root: Path, checkpoint: Path, queries: Path | None = None) -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + if queries is None: + queries = input_root / "public/perturbations.parquet" + if not queries.exists(): + queries = input_root / "variant_evals/public/perturbations.parquet" + rows = pd.read_parquet(queries) + fasta = pysam.FastaFile(str(input_root / "reference/GRCh38.p13.genome.fa")) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "perturbations.zarr"), mode="w") + target_start = (config.input_length - config.target_length) // 2 + with torch.inference_mode(): + for index, row in enumerate(rows.itertuples(index=False)): + pair = sequences(fasta, row) + dna = torch.from_numpy(np.stack([AlphaGenomeDataset._one_hot(s) for s in pair])).to(device) + trunk = model._trunk(dna) + group = root.create_group(f"perturbations/p{index:06d}") + features = None + for family in row.families: + if family == "splice_sites": + logits = model._splice_site_logits(trunk)[:, target_start:target_start + config.target_length] + values = logits[:, row.splice_start:row.splice_end] + else: + features = model._dense_features(trunk) if features is None else features + values = model.dense_heads[family](features)[:, row.dense_bin_start:row.dense_bin_end] + # float32: one variant's effect is often below float16 resolution (about 0.004 at + # asinh values of 4-8), so A and B would round to the same numbers. + values = values.float().cpu().numpy() + for side, array in zip("ab", values): + group.create_dataset(f"{side}/{family}", data=array, compressor=COMPRESSOR) + print(json.dumps({"event": "perturb", "done": index + 1, "total": len(rows)}), flush=True) + root.attrs.update({"schema": PERTURBATION_SCHEMA, + "perturbation_ids": rows.perturbation_id.astype(str).tolist()}) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--queries", type=Path, help="another query table, e.g. the development split") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.queries) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/perturb.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/perturb.sh new file mode 100644 index 0000000000000000000000000000000000000000..f07c4714532c2d33e7a419de3877e5daacee89fd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/perturb.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: perturb.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/perturb.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/predict.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..52f3decd4fe2e9d275d08606b74575fafb701434 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/predict.py @@ -0,0 +1,130 @@ +#!/usr/bin/env python3 +"""Stream the single benchmark model into the sharded prediction contract.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import torch +import zarr + +from prediction_contract import DENSE_TRACKS, MAX_JUNCTION_PAIRS, MAX_USAGE_SITES, PREDICTION_SCHEMA +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig + + +COMPRESSOR = Blosc(cname="zstd", clevel=7, shuffle=Blosc.BITSHUFFLE) + + +def main(input_root: Path, output_root: Path, checkpoint: Path, split: str = "test") -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + dataset = AlphaGenomeDataset(input_root, split, include_targets=False) + model.set_junction_track_halves(dataset.junction_track_halves) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "predictions.zarr"), mode="w") + ids = [] + with torch.inference_mode(): + for sample in dataset: + example_id = sample["example_id"] + dna = sample["dna"][None].to(device) + trunk = model._trunk(dna) + dense_features = model._dense_features(trunk) + group = root.create_group(f"examples/{example_id}") + for family in DENSE_TRACKS: + values = model.dense_heads[family](dense_features)[0] + group.create_dataset(family, data=values.cpu().numpy().astype(np.float16), compressor=COMPRESSOR) + + all_site_logits = model._splice_site_logits(trunk) + target_start = (config.input_length - config.target_length) // 2 + group.create_dataset( + "splice_sites", + data=all_site_logits[0, target_start:target_start + config.target_length].cpu().numpy().astype(np.float16), + chunks=(4096, 4), compressor=COMPRESSOR, + ) + predicted_positions = model.select_junction_positions(all_site_logits)[0] + + # Usage at the model's own predicted sites (v4 is sparse; unlisted hidden sites score 0). + usage_bins = model.splice_usage_head(dense_features)[0].float() + sites = predicted_positions.cpu().numpy().astype(np.int64).reshape(-1) + sites = np.unique(sites[(sites >= target_start) & (sites < target_start + config.target_length)] + - target_start)[:MAX_USAGE_SITES] + group.create_dataset("splice_usage_positions", data=sites.astype(np.int32), compressor=COMPRESSOR) + usage = usage_bins[torch.as_tensor(sites // 128, device=usage_bins.device)] if len(sites) \ + else usage_bins[:0] + group.create_dataset("splice_usage", data=usage.cpu().numpy().astype(np.float16), + chunks=(1024, 734), compressor=COMPRESSOR) + + # Junctions: score every donor x acceptor pair of each strand, keep the strongest pairs. + positions_batch = predicted_positions[None] + site_factors = model.junction_site_factors(trunk, positions_batch) + halves = model.junction_track_half + candidates = [] + for row_start in range(0, 512, 128): + for column_start in range(0, 512, 128): + block = torch.cat([ + model.junction_block( + trunk, positions_batch, row_start, row_start + 128, + column_start, column_start + 128, track_start, + min(track_start + 64, config.junction_tracks), site_factors, + )[0] + for track_start in range(0, config.junction_tracks, 64) + ], dim=-1).float() # [128, 128, 734] + for half in (0, 1): + donors = predicted_positions[2 * half, row_start:row_start + 128] + acceptors = predicted_positions[2 * half + 1, column_start:column_start + 128] + valid = (donors[:, None] >= 0) & (acceptors[None, :] >= 0) + strength = block[..., halves == half].amax(dim=-1).masked_fill(~valid, -torch.inf) + keep = min(MAX_JUNCTION_PAIRS // 2, int(valid.sum())) + if not keep: + continue + best = strength.flatten().topk(keep) + rows, columns = best.indices // 128, best.indices % 128 + candidates.append((best.values, torch.full_like(rows, half), donors[rows], + acceptors[columns], block[rows, columns])) + if candidates: + score, half, donor, acceptor, values = (torch.cat(x) for x in zip(*candidates)) + order = score.topk(min(MAX_JUNCTION_PAIRS, len(score))).indices + half, donor, acceptor, values = half[order], donor[order], acceptor[order], values[order] + else: + half = donor = acceptor = torch.zeros(0, dtype=torch.long) + values = torch.zeros(0, config.junction_tracks) + group.create_dataset("junction_half", data=half.cpu().numpy().astype(np.int8), compressor=COMPRESSOR) + group.create_dataset("junction_donor", data=donor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junction_acceptor", data=acceptor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junctions", data=values.cpu().numpy().astype(np.float16), + chunks=(4096, 734), compressor=COMPRESSOR) + + contact = trunk.reshape( + 1, config.contact_bins, -1, config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + contact_values = 0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row]) + group.create_dataset( + "contact_maps", data=contact_values[0].cpu().numpy().astype(np.float16), + chunks=(8192, 28), compressor=COMPRESSOR, + ) + ids.append(example_id) + print(json.dumps({"event": "predict", "example": len(ids), "total": len(dataset)}), flush=True) + root.attrs.update({"schema": PREDICTION_SCHEMA, "example_ids": ids}) + (output_root / "example_ids.json").write_text(json.dumps(ids) + "\n") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--split", default="test", choices=("valid", "test"), + help="manifest to predict; the verifier uses test, local validation uses valid") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.split) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4879a0e1a3a8a8050bb48360febee152cd141be8 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/__init__.py @@ -0,0 +1 @@ +"""The reference model of the alphagenome-reference baseline.""" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a86a139babb6e899bd5102bbf3c628e1232576b2 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/__init__.py @@ -0,0 +1,5 @@ +"""Compact 1-Mb, eleven-family AlphaGenome reference model.""" + +from .model import CompactModel, ModelConfig, count_parameters + +__all__ = ["CompactModel", "ModelConfig", "count_parameters"] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/alphagenome_50m.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/alphagenome_50m.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0a59a4d0c65752d45eb464d129c6ec9d559665e9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/alphagenome_50m.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 32 +channel_growth: 32 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 384 +num_sequence_blocks: 6 +num_attention_heads: 6 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 256 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/dataset.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..71cd39d15713f648e7d7e31aa1923fa4fa4800dd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/dataset.py @@ -0,0 +1,229 @@ +"""Streaming reader for the compact 1 Mb / eleven-family release.""" + +from __future__ import annotations + +from collections import OrderedDict +from pathlib import Path + +import numpy as np +import pandas as pd +import pysam +import torch +from torch.utils.data import Dataset +import zarr + + +def _open_part(path: Path): + if str(path).endswith(".zarr.zip"): + store = zarr.ZipStore(str(path), mode="r") + try: + return zarr.open_group(store=store, mode="r"), store + except BaseException: + store.close() + raise + return zarr.open_group(str(path), mode="r"), None + + +DENSE_FAMILIES = ( + "atac", + "dnase", + "procap", + "cage", + "rna_seq", + "chip_tf", + "chip_histone", +) +def normalize_splice_positions(value) -> np.ndarray: + """Normalize Arrow's nested-list representation to the public [4, 512] array.""" + + outer = np.asarray(value, dtype=object).reshape(-1).tolist() + positions = np.stack([np.asarray(item, dtype=np.int64) for item in outer]) + if positions.shape != (4, 512): + raise ValueError(f"splice_site_positions must have shape (4, 512), got {positions.shape}") + return positions + + +class AlphaGenomeDataset(Dataset): + """Resolve frozen coordinates to reference DNA and one Zarr example group.""" + + def __init__( + self, + data_root: str | Path, + split: str, + *, + max_open_parts: int = 8, + include_targets: bool = True, + ) -> None: + self.root = Path(data_root) + self.split = split + manifest = self.root / "public" / f"{split}.parquet" + if not manifest.is_file(): + raise FileNotFoundError(f"Missing materialized manifest: {manifest}") + self.rows = pd.read_parquet(manifest).reset_index(drop=True) + junction_tracks = pd.read_parquet(self.root / "public/junction_tracks.parquet") + junction_tracks = junction_tracks.sort_values("tensor_index") + self.junction_track_halves = torch.from_numpy( + junction_tracks.junction_channel_half.to_numpy(np.int64) + ) + if include_targets: + labels_path = self.root / "labels" / f"{split}.parquet" + if not labels_path.is_file(): + raise FileNotFoundError(f"Missing trusted labels manifest: {labels_path}") + labels = pd.read_parquet(labels_path).reset_index(drop=True) + if self.rows.example_id.astype(str).tolist() != labels.example_id.astype(str).tolist(): + raise ValueError("public and labels manifests have different IDs/order") + self.rows = self.rows.merge(labels, on="example_id", validate="one_to_one", sort=False) + self.fasta_path = self.root / "reference" / "GRCh38.p13.genome.fa" + self.max_open_parts = max_open_parts + self.include_targets = include_targets + self._fasta = None + self._parts: OrderedDict[str, tuple[zarr.Group, object | None]] = OrderedDict() + + def __len__(self) -> int: + return len(self.rows) + + def _reference(self): + if self._fasta is None: + self._fasta = pysam.FastaFile(str(self.fasta_path)) + return self._fasta + + def _part(self, relative: str): + if relative in self._parts: + opened = self._parts.pop(relative) + self._parts[relative] = opened + return opened[0] + group, store = _open_part(self.root / relative) + if len(self._parts) >= self.max_open_parts: + _, (_, old_store) = self._parts.popitem(last=False) + if old_store is not None: + old_store.close() + self._parts[relative] = (group, store) + return group + + @staticmethod + def _one_hot(sequence: str) -> np.ndarray: + encoded = np.frombuffer(sequence.upper().encode("ascii"), dtype=np.uint8) + output = np.zeros((len(encoded), 4), dtype=np.float32) + for index, letter in enumerate(b"ACGT"): + output[:, index] = encoded == letter + return output + + def _sequence(self, row) -> tuple[np.ndarray, np.ndarray]: + reference = self._reference() + chromosome_length = reference.get_reference_length(str(row.chromosome)) + start, end = int(row.input_start), int(row.input_end) + clipped_start, clipped_end = max(0, start), min(chromosome_length, end) + sequence = reference.fetch(str(row.chromosome), clipped_start, clipped_end) + left, right = clipped_start - start, end - clipped_end + if left or right: + sequence = "N" * left + sequence + "N" * right + valid = np.ones(end - start, dtype=bool) + if left: + valid[:left] = False + if right: + valid[-right:] = False + return self._one_hot(sequence), valid + + @staticmethod + def _decode(example, name: str, *, asinh: bool) -> np.ndarray: + """Train targets may be stored as 8-bit codes with per-track ranges; they are decoded + here, one example at a time, so the release stays compressed on disk.""" + array = example[name] + if array.dtype != np.uint8: + return np.asarray(array, dtype=np.float32) + lo = np.asarray(example[f"{name}_lo"], dtype=np.float32) + hi = np.asarray(example[f"{name}_hi"], dtype=np.float32) + x = lo + np.asarray(array, dtype=np.float32) * ((hi - lo) / 255.0) + return np.sinh(x) if asinh else x + + @staticmethod + def _dense_target(example, family: str) -> dict[str, torch.Tensor]: + values = AlphaGenomeDataset._decode(example, f"{family}/values", asinh=True) + mask = np.asarray(example[f"{family}/mask"], dtype=bool) + if mask.ndim == 1: + mask = np.broadcast_to(mask[None, :], values.shape).copy() + return { + "values": torch.from_numpy(values), + "mask": torch.from_numpy(mask), + } + + def __getitem__(self, item: int): + row = self.rows.iloc[item] + dna, input_mask = self._sequence(row) + result = { + "example_id": str(row.example_id), + "dna": torch.from_numpy(dna), + "input_mask": torch.from_numpy(input_mask), + "junction_track_halves": self.junction_track_halves, + } + if not self.include_targets: + return result + + part = self._part(str(row.target_shard)) + example = part[str(row.target_group)] + result["dense"] = { + family: self._dense_target(example, family) for family in DENSE_FAMILIES + } + result["splice_sites"] = torch.from_numpy( + np.asarray(example["splice_sites/values"], dtype=np.float32) + ) + result["splice_usage"] = { + "positions": torch.from_numpy( + np.asarray(example["splice_site_usage/positions"], dtype=np.int64) + ), + "values": torch.from_numpy( + np.asarray(example["splice_site_usage/values"], dtype=np.float32) + ), + "mask": torch.from_numpy( + np.asarray(example["splice_site_usage/mask"], dtype=bool) + ), + } + result["usage_positions"] = result["splice_usage"]["positions"] + result["junctions"] = { + name: torch.from_numpy( + np.asarray( + example[f"splice_junctions/{name}"], + dtype=np.float32 if name == "value" else np.int64, + ) + ) + for name in ("row", "column", "track", "value") + } + result["junctions"]["site_positions"] = torch.from_numpy( + np.asarray(example["splice_junctions/site_positions"], dtype=np.int64) + ) + site_positions = normalize_splice_positions(row.splice_site_positions) + result["site_positions"] = torch.from_numpy(site_positions) + if not torch.equal(result["junctions"]["site_positions"], result["site_positions"]): + raise ValueError(f"public/private splice positions differ for {row.example_id}") + scoring_path = self.root / "scoring" / self.split / f"part-{item:06d}.parquet" + scoring = pd.read_parquet(scoring_path) + if not (scoring.example_id.astype(str) == str(row.example_id)).all(): + raise ValueError(f"scoring part ID differs for {row.example_id}") + result["junction_scoring"] = { + "donor_position": torch.from_numpy( + scoring.donor_position.to_numpy(np.int64) + ), + "acceptor_position": torch.from_numpy( + scoring.acceptor_position.to_numpy(np.int64) + ), + "track": torch.from_numpy(scoring.track.to_numpy(np.int64)), + "target_value": torch.from_numpy( + scoring.target_value.to_numpy(np.float32) + ), + "valid": torch.from_numpy(scoring.valid.to_numpy(bool)), + } + result["contact_maps"] = torch.from_numpy( + self._decode(example, "contact_maps/upper_triangle", asinh=False) + ) + result["contact_map_mask"] = torch.from_numpy( + np.asarray(example["contact_maps/mask"], dtype=bool) + ) + return result + + +def single_example_collate(items): + """Keep variable-size event tensors intact; the reference currently trains at batch one.""" + + if len(items) != 1: + raise ValueError("The compact event loader currently requires batch_size=1") + return items[0] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/layers.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..af31f86a5589a16f0007e6aca5ca7a7bd552d6a9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/layers.py @@ -0,0 +1,124 @@ +"""Shared neural-network layers for the single compact AlphaGenome model.""" + +from __future__ import annotations + +import math + +import torch +from torch import Tensor, nn +from torch.nn import functional as F + + +class ChannelRMSNorm(nn.Module): + def __init__(self, channels: int, eps: float = 1e-6): + super().__init__() + self.weight = nn.Parameter(torch.ones(channels)) + self.eps = eps + + def forward(self, x: Tensor) -> Tensor: + variance = x.float().square().mean(dim=1, keepdim=True) + normalized = x * torch.rsqrt(variance + self.eps).to(x.dtype) + return normalized * self.weight[None, :, None].to(x.dtype) + + +class StandardizedConv1d(nn.Conv1d): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__(in_channels, out_channels, kernel_size, padding="same", bias=True) + self.scale = nn.Parameter(torch.ones(out_channels)) + nn.init.trunc_normal_(self.weight, std=1.0 / math.sqrt(in_channels * kernel_size)) + nn.init.trunc_normal_(self.bias, std=1e-4) + + def forward(self, x: Tensor) -> Tensor: + centered = self.weight - self.weight.mean(dim=(1, 2), keepdim=True) + variance = centered.float().square().mean(dim=(1, 2), keepdim=True) + fan_in = self.weight.shape[1] * self.weight.shape[2] + multiplier = self.scale[:, None, None] * torch.rsqrt( + (fan_in * variance).clamp_min(1e-4) + ).to(self.weight.dtype) + return F.conv1d(x, centered * multiplier, self.bias, padding="same") + + +class ConvBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__() + self.norm = ChannelRMSNorm(in_channels) + self.conv = StandardizedConv1d(in_channels, out_channels, kernel_size) + + def forward(self, x: Tensor) -> Tensor: + return self.conv(F.gelu(self.norm(x), approximate="tanh")) + + +def _apply_rope(x: Tensor) -> Tensor: + rotary_dim = x.shape[-1] - x.shape[-1] % 2 + if rotary_dim == 0: + return x + positions = torch.arange(x.shape[-2], device=x.device, dtype=torch.float32) + inverse_frequency = 1.0 / ( + 10_000 ** ( + torch.arange(0, rotary_dim, 2, device=x.device, dtype=torch.float32) + / rotary_dim + ) + ) + angles = torch.outer(positions, inverse_frequency) + cos = angles.cos()[None, None, :, :].to(x.dtype) + sin = angles.sin()[None, None, :, :].to(x.dtype) + even, odd = x[..., :rotary_dim:2], x[..., 1:rotary_dim:2] + rotated = torch.stack((even * cos - odd * sin, even * sin + odd * cos), dim=-1) + return torch.cat((rotated.flatten(-2), x[..., rotary_dim:]), dim=-1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.num_heads = config.num_attention_heads + self.head_dim = config.attention_head_dim + inner_dim = self.num_heads * self.head_dim + self.norm = nn.RMSNorm(config.hidden_dim) + self.qkv = nn.Linear(config.hidden_dim, 3 * inner_dim, bias=False) + self.output = nn.Linear(inner_dim, config.hidden_dim) + self.dropout = config.dropout + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + batch, length, _ = x.shape + qkv = self.qkv(self.norm(x)).view( + batch, length, 3, self.num_heads, self.head_dim + ) + q, k, value = qkv.unbind(dim=2) + attended = F.scaled_dot_product_attention( + _apply_rope(q.transpose(1, 2)), + _apply_rope(k.transpose(1, 2)), + value.transpose(1, 2), + attn_mask=attention_bias, + dropout_p=self.dropout if self.training else 0.0, + ) + return self.output(attended.transpose(1, 2).reshape(batch, length, -1)) + + +class MLPBlock(nn.Module): + def __init__(self, config): + super().__init__() + expanded = int(config.hidden_dim * config.mlp_expansion) + self.norm = nn.RMSNorm(config.hidden_dim) + self.input = nn.Linear(config.hidden_dim, expanded) + self.output = nn.Linear(expanded, config.hidden_dim) + self.dropout = nn.Dropout(config.dropout) + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor) -> Tensor: + return self.dropout( + self.output(F.gelu(self.input(self.norm(x)), approximate="tanh")) + ) + + +class SequenceBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.attention = MultiHeadAttention(config) + self.mlp = MLPBlock(config) + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + x = x + self.attention(x, attention_bias) + return x + self.mlp(x) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/model.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/model.py new file mode 100644 index 0000000000000000000000000000000000000000..316ae8b495a102a194b3b1c5088d815e5d66e74b --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/model.py @@ -0,0 +1,426 @@ +"""Affordable multi-resolution baseline for the compact AlphaGenome task.""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass, fields +import math +from pathlib import Path +from typing import Any, Iterable + +import torch +from torch import Tensor, nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint +import yaml + +from .layers import ChannelRMSNorm, ConvBlock, SequenceBlock + + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} +ALL_FAMILIES = (*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions", "contact_maps") + + +@dataclass(frozen=True) +class ModelConfig: + input_length: int = 1_048_576 + target_length: int = 196_608 + dense_bin_size: int = 128 + trunk_bin_size: int = 1_024 + contact_bin_size: int = 2_048 + stem_stride: int = 8 + encoder_width: int = 32 + channel_growth: int = 32 + num_downsampling_blocks: int = 7 + conv_kernel_size: int = 5 + dna_kernel_size: int = 15 + hidden_dim: int = 384 + num_sequence_blocks: int = 6 + num_attention_heads: int = 6 + attention_head_dim: int = 64 + mlp_expansion: float = 4.0 + dropout: float = 0.0 + head_hidden_size: int = 256 + junction_rank: int = 32 + splice_usage_tracks: int = 734 + junction_tracks: int = 734 + contact_tracks: int = 28 + gradient_checkpointing: bool = True + enable_pairwise: bool = False + pair_width: int = 32 + pair_downsample: int = 16 + pair_update_interval: int = 2 + local_width: int = 0 + + def __post_init__(self) -> None: + downsample = self.stem_stride * 2**self.num_downsampling_blocks + if downsample != self.trunk_bin_size: + raise ValueError("stem_stride * 2**num_downsampling_blocks must equal trunk_bin_size") + for length in (self.input_length, self.target_length): + if length % self.trunk_bin_size: + raise ValueError("input and target lengths must divide by trunk_bin_size") + if self.trunk_bin_size % self.dense_bin_size: + raise ValueError("trunk_bin_size must divide by dense_bin_size") + if self.input_length % self.contact_bin_size: + raise ValueError("input_length must divide by contact_bin_size") + if self.hidden_dim <= 0 or self.num_attention_heads <= 0: + raise ValueError("hidden dimensions and attention heads must be positive") + + @property + def trunk_bins(self) -> int: + return self.input_length // self.trunk_bin_size + + @property + def target_trunk_bins(self) -> int: + return self.target_length // self.trunk_bin_size + + @property + def dense_bins(self) -> int: + return self.target_length // self.dense_bin_size + + @property + def dense_upsample(self) -> int: + return self.trunk_bin_size // self.dense_bin_size + + @property + def contact_bins(self) -> int: + return self.input_length // self.contact_bin_size + + @property + def encoder_output_width(self) -> int: + return self.encoder_width + self.channel_growth * self.num_downsampling_blocks + + @classmethod + def from_yaml(cls, path: str | Path) -> "ModelConfig": + values: dict[str, Any] = yaml.safe_load(Path(path).read_text()) or {} + known = {field.name for field in fields(cls)} + unknown = sorted(values.keys() - known) + if unknown: + raise ValueError(f"Unknown model configuration keys: {unknown}") + return cls(**values) + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + +class SequenceEncoder(nn.Module): + def __init__(self, config: ModelConfig): + super().__init__() + self.stem = nn.Conv1d( + 4, + config.encoder_width, + config.dna_kernel_size, + stride=config.stem_stride, + padding=config.dna_kernel_size // 2, + ) + blocks = [] + width = config.encoder_width + for _ in range(config.num_downsampling_blocks): + output_width = width + config.channel_growth + blocks.append(ConvBlock(width, output_width, config.conv_kernel_size)) + width = output_width + self.blocks = nn.ModuleList(blocks) + + def forward(self, dna: Tensor) -> Tensor: + x = F.gelu(self.stem(dna.transpose(1, 2)), approximate="tanh") + for block in self.blocks: + x = F.max_pool1d(block(x), kernel_size=2, stride=2) + return x.transpose(1, 2) + + +class LocalResidualBlock(nn.Module): + def __init__(self, width: int, dilation: int): + super().__init__() + self.norm = ChannelRMSNorm(width) + self.depthwise = nn.Conv1d(width, width, 9, dilation=dilation, + padding=4 * dilation, groups=width) + self.expand = nn.Conv1d(width, 2 * width, 1) + self.contract = nn.Conv1d(2 * width, width, 1) + + def forward(self, x: Tensor) -> Tensor: + update = self.depthwise(self.norm(x)) + update = self.contract(F.gelu(self.expand(update), approximate='tanh')) + return x + update + + +class LocalSequencePath(nn.Module): + """Base-resolution motif path; 701-bp receptive field and aligned 128-bp pooling.""" + def __init__(self, config: ModelConfig): + super().__init__() + width = config.local_width + self.stem = nn.Conv1d(4, width, 21, padding=10) + self.blocks = nn.Sequential(*(LocalResidualBlock(width, d) for d in [1, 4, 16, 64])) + self.norm = ChannelRMSNorm(width) + self.sites = nn.Conv1d(width, 4, 1) + self.bin_size = config.dense_bin_size + + def forward(self, dna: Tensor) -> tuple[Tensor, Tensor]: + base = F.gelu(self.stem(dna), approximate='tanh') + base = F.gelu(self.norm(self.blocks(base)), approximate='tanh') + sites = self.sites(base).transpose(1, 2) + pooled = torch.cat((F.avg_pool1d(base, self.bin_size), + F.max_pool1d(base, self.bin_size)), dim=1).transpose(1, 2) + return sites, pooled + + +class CompactModel(nn.Module): + """One shared 1-kb trunk with representation-specific output heads.""" + + def __init__(self, config: ModelConfig | None = None): + super().__init__() + self.config = config or ModelConfig() + self.encoder = SequenceEncoder(self.config) + self.projection = nn.Linear(self.config.encoder_output_width, self.config.hidden_dim) + self.blocks = nn.ModuleList( + SequenceBlock(self.config) for _ in range(self.config.num_sequence_blocks) + ) + self.final_norm = nn.RMSNorm(self.config.hidden_dim) + self.dense_decoder = nn.Sequential( + nn.RMSNorm(self.config.hidden_dim), + nn.Linear(self.config.hidden_dim, self.config.head_hidden_size), + nn.GELU(approximate="tanh"), + ) + self.dense_heads = nn.ModuleDict( + { + family: nn.Linear(self.config.head_hidden_size, tracks) + for family, tracks in DENSE_TRACKS.items() + } + ) + self.splice_site_head = nn.Linear( + self.config.head_hidden_size, self.config.dense_bin_size * 4 + ) + self.splice_usage_head = nn.Linear( + self.config.head_hidden_size, self.config.splice_usage_tracks + ) + rank = self.config.junction_rank + self.junction_source = nn.Linear(self.config.head_hidden_size, rank) + self.junction_destination = nn.Linear(self.config.head_hidden_size, rank) + self.junction_track = nn.Embedding(self.config.junction_tracks, rank) + if self.config.junction_tracks % 2: + raise ValueError("junction tracks must split evenly into strand halves") + self.register_buffer( + "junction_track_half", + torch.arange(self.config.junction_tracks) // (self.config.junction_tracks // 2), + persistent=False, + ) + self.contact_q = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + self.contact_k = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + row, column = torch.triu_indices( + self.config.contact_bins, self.config.contact_bins + ) + self.register_buffer("contact_row", row, persistent=False) + self.register_buffer("contact_column", column, persistent=False) + if self.config.local_width: + self.local_path = LocalSequencePath(self.config) + self.local_projection = nn.Linear(2 * self.config.local_width, self.config.head_hidden_size) + # A new local path can be attached to our own checkpoint without changing its initial outputs. + nn.init.zeros_(self.local_path.sites.weight) + nn.init.zeros_(self.local_path.sites.bias) + nn.init.zeros_(self.local_projection.weight) + nn.init.zeros_(self.local_projection.bias) + self._local_sites = None + self._local_features = None + + def set_junction_track_halves(self, halves: Tensor) -> None: + halves = torch.as_tensor(halves, dtype=torch.long, device=self.junction_track_half.device) + if halves.shape != (self.config.junction_tracks,) or not torch.isin( + halves, torch.tensor([0, 1], device=halves.device) + ).all(): + raise ValueError("junction track halves must be a 734-element 0/1 mapping") + self.junction_track_half.copy_(halves) + + def _trunk(self, dna: Tensor) -> Tensor: + if dna.ndim != 3 or dna.shape[1:] != (self.config.input_length, 4): + raise ValueError( + f"Expected DNA [B,{self.config.input_length},4], got {tuple(dna.shape)}" + ) + if self.config.local_width: + if self.config.gradient_checkpointing and self.training: + sites, pooled = checkpoint(self.local_path, dna.transpose(1, 2), use_reentrant=False) + else: + sites, pooled = self.local_path(dna.transpose(1, 2)) + self._local_sites = sites + self._local_features = self.local_projection(pooled) + x = self.projection(self.encoder(dna)) + for block in self.blocks: + if self.config.gradient_checkpointing and self.training and x.requires_grad: + x = checkpoint(block, x, None, use_reentrant=False) + else: + x = block(x, None) + return self.final_norm(x) + + def _dense_features(self, trunk: Tensor) -> Tensor: + start = (self.config.trunk_bins - self.config.target_trunk_bins) // 2 + target = trunk[:, start : start + self.config.target_trunk_bins] + target = target.repeat_interleave(self.config.dense_upsample, dim=1) + features = self.dense_decoder(target) + if self.config.local_width: + first_bin = (self.config.input_length - self.config.target_length) // (2 * self.config.dense_bin_size) + features = features + self._local_features[:, first_bin:first_bin + self.config.dense_bins] + return features + + def _all_dense_features(self, trunk: Tensor) -> Tensor: + features = self.dense_decoder( + trunk.repeat_interleave(self.config.dense_upsample, dim=1) + ) + return features + self._local_features if self.config.local_width else features + + def _splice_site_logits(self, trunk: Tensor) -> Tensor: + logits = self.splice_site_head(self._all_dense_features(trunk)) + logits = logits.view(len(trunk), self.config.input_length, 4) + if self.config.local_width: + logits = logits + self._local_sites + return logits.float() + + def _position_features(self, trunk: Tensor, positions: Tensor) -> Tensor: + features = self.dense_decoder(self._gather_bins(trunk, positions, self.config.trunk_bin_size)) + if self.config.local_width: + features = features + self._gather_bins(self._local_features, positions, self.config.dense_bin_size) + return features + + @staticmethod + def select_junction_positions(site_logits: Tensor, limit: int = 512) -> Tensor: + """Select each site type from model scores alone, then sort by coordinate. + + This follows the four independent site classes in Google DeepMind's + AlphaGenome splicing implementation (pinned in docs/v3-author/PROTOCOL.md). Padding is + -1 when an input is shorter than ``limit``. + """ + count = min(limit, site_logits.shape[1]) + selected = [] + for site_type in range(4): + coordinates = torch.topk(site_logits[..., site_type], count, dim=1).indices + coordinates = coordinates.sort(dim=1).values + if count < limit: + coordinates = F.pad(coordinates, (0, limit - count), value=-1) + selected.append(coordinates) + return torch.stack(selected, dim=1) + + @staticmethod + def _gather_bins(features: Tensor, positions: Tensor, bin_size: int) -> Tensor: + indices = torch.div(positions, bin_size, rounding_mode="floor") + indices = indices.clamp(0, features.shape[1] - 1) + return torch.gather( + features, + 1, + indices[..., None].expand(-1, -1, features.shape[-1]), + ) + + def _junction_predictions( + self, trunk: Tensor, site_positions: Tensor, queries: Tensor + ) -> Tensor: + row, column, track = queries.unbind(dim=-1) + half = self.junction_track_half[track] + batch = torch.arange(len(trunk), device=trunk.device)[:, None] + donor = site_positions[batch, 2 * half, row] + acceptor = site_positions[batch, 2 * half + 1, column] + source = self._position_features(trunk, donor.clamp(0, self.config.input_length - 1)) + destination = self._position_features(trunk, acceptor.clamp(0, self.config.input_length - 1)) + interaction = self.junction_source(source) * self.junction_destination(destination) + track_embedding = self.junction_track(track) + return (interaction * track_embedding).sum(dim=-1) / math.sqrt( + self.config.junction_rank + ) + + def junction_block( + self, + trunk: Tensor, + site_positions: Tensor, + row_start: int, + row_end: int, + column_start: int, + column_end: int, + track_start: int, + track_end: int, + site_factors: tuple[Tensor, Tensor] | None = None, + ) -> Tensor: + """Compute a bounded block of the full predicted-position junction cube.""" + tracks = torch.arange(track_start, track_end, device=trunk.device) + halves = self.junction_track_half[tracks] + if site_factors is None: + site_factors = self.junction_site_factors(trunk, site_positions) + source_all, destination_all = site_factors + source = source_all[:, halves, row_start:row_end] + destination = destination_all[:, halves, column_start:column_end] + embedding = self.junction_track(tracks)[None, :, None, None, :] + values = ( + source[:, :, :, None, :] * destination[:, :, None, :, :] * embedding + ).sum(dim=-1) / math.sqrt(self.config.junction_rank) + return values.permute(0, 2, 3, 1).float() + + def junction_site_factors( + self, trunk: Tensor, site_positions: Tensor + ) -> tuple[Tensor, Tensor]: + """Precompute the two strand-specific factors reused by all cube blocks.""" + donor = torch.stack((site_positions[:, 0], site_positions[:, 2]), dim=1).clamp_min(0) + acceptor = torch.stack((site_positions[:, 1], site_positions[:, 3]), dim=1).clamp_min(0) + donor_features = self._position_features(trunk, donor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, donor.shape[-1], -1) + acceptor_features = self._position_features(trunk, acceptor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, acceptor.shape[-1], -1) + return self.junction_source(donor_features), self.junction_destination(acceptor_features) + + def forward( + self, + dna: Tensor, + *, + families: Iterable[str] | None = None, + usage_positions: Tensor | None = None, + junction_site_positions: Tensor | None = None, + junction_queries: Tensor | None = None, + ) -> dict[str, Tensor]: + requested = set(families or ALL_FAMILIES) + unknown = requested - set(ALL_FAMILIES) + if unknown: + raise ValueError(f"Unknown output families: {sorted(unknown)}") + trunk = self._trunk(dna) + needs_dense = bool( + requested.intersection((*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions")) + ) + features = self._dense_features(trunk) if needs_dense else None + outputs: dict[str, Tensor] = {} + for family in DENSE_TRACKS: + if family in requested: + outputs[family] = self.dense_heads[family](features).float() + if "splice_sites" in requested: + all_logits = self._splice_site_logits(trunk) + start = (self.config.input_length - self.config.target_length) // 2 + outputs["splice_sites"] = all_logits[:, start:start + self.config.target_length] + if "splice_usage" in requested: + if usage_positions is None: + raise ValueError("splice_usage requires usage_positions") + gathered = self._gather_bins( + features, usage_positions, self.config.dense_bin_size + ) + outputs["splice_usage"] = self.splice_usage_head(gathered).float() + if "junctions" in requested: + if junction_site_positions is None or junction_queries is None: + raise ValueError("junctions require site positions and query triples") + outputs["junctions"] = self._junction_predictions( + trunk, junction_site_positions, junction_queries + ).float() + if "contact_maps" in requested: + contact = trunk.reshape( + len(dna), self.config.contact_bins, -1, self.config.hidden_dim + ).mean(dim=2) + q, k = self.contact_q(contact), self.contact_k(contact) + row, column = self.contact_row, self.contact_column + outputs["contact_maps"] = ( + 0.5 + * ( + q[:, row] * k[:, column] + + q[:, column] * k[:, row] + ) + ).float() + return outputs + + +def count_parameters(model: nn.Module) -> int: + return sum(parameter.numel() for parameter in model.parameters() if parameter.requires_grad) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/reference_large.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/reference_large.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e403757c49eb6e6419927210c7053710964213d1 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/reference_large.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 96 +channel_growth: 96 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 1024 +num_sequence_blocks: 16 +num_attention_heads: 16 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 768 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/run_ddp.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/run_ddp.sh new file mode 100644 index 0000000000000000000000000000000000000000..c4702e9366e907b7791063e77dcfe281411ac8cd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/run_ddp.sh @@ -0,0 +1,16 @@ +#!/usr/bin/env bash +# run_ddp.sh DATA_DIR OUTPUT_DIR CONFIG EPOCHS [GPUS=8] [WORKERS_PER_GPU=12] +# Train the reference on every train interval with one example per GPU per step, then score +# validation with the verifier's own scorer. Runs inside the task image (torch, torchrun). +set -Eeuo pipefail +DATA=$1; OUT=$2; CONFIG=$3; EPOCHS=$4; GPUS=${5:-8}; WORKERS=${6:-12} +BASELINE=$(cd "$(dirname "$0")/../.." && pwd) +mkdir -p "$OUT" +cd "$BASELINE" +# Loopback rendezvous: works offline, where --standalone cannot resolve the hostname. +torchrun --nnodes 1 --nproc-per-node "$GPUS" --master-addr 127.0.0.1 --master-port "${MASTER_PORT:-29511}" -m reference.alphagenome_50m.train_eval train \ + --input "$DATA" --config "$CONFIG" --output "$OUT" --epochs "$EPOCHS" --workers "$WORKERS" \ + --log-every 200 --save-every 2000 2>&1 | tee -a "$OUT/train.log" +python predict.py "$DATA" "$OUT/valid-pred" --checkpoint "$OUT/last.pt" --split valid +python /workspace/evaluation/score.py --predictions "$OUT/valid-pred/predictions.zarr" \ + --labels "$DATA" --split valid --output "$OUT/valid-score" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/tiny_overfit.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/tiny_overfit.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c25f9ffbb76102c970e5767922400f7ec2b6c53e --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/tiny_overfit.yaml @@ -0,0 +1,24 @@ +# Keep the frozen 1-Mb input and every output track; shrink only learned widths. +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 8 +channel_growth: 4 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 32 +num_sequence_blocks: 1 +num_attention_heads: 2 +attention_head_dim: 16 +mlp_expansion: 2.0 +dropout: 0.0 +head_hidden_size: 32 +junction_rank: 8 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: false diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/train_eval.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/train_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..92f9a3f315013630fdd2e03fb6840a6c77840edd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/reference/alphagenome_50m/train_eval.py @@ -0,0 +1,537 @@ +"""Train and stream-evaluate the <=50M compact AlphaGenome baseline.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import numpy as np +import torch +from torch import Tensor +from torch.nn import functional as F +from torch.utils.data import DataLoader, Subset + +from .dataset import ( + AlphaGenomeDataset, + DENSE_FAMILIES, + single_example_collate, +) +from .model import ( + ALL_FAMILIES, + CompactModel, + ModelConfig, + count_parameters, +) + + +DEFAULT_CONFIG = Path(__file__).with_name("alphagenome_50m.yaml") + + +def _device(name: str) -> torch.device: + if name == "auto": + name = "cuda" if torch.cuda.is_available() else "cpu" + if name == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("--device cuda requested but CUDA is unavailable") + return torch.device(name) + + +def _autocast(device: torch.device): + enabled = device.type == "cuda" + dtype = torch.bfloat16 if enabled and torch.cuda.is_bf16_supported() else torch.float16 + return torch.autocast(device_type=device.type, dtype=dtype, enabled=enabled) + + +def _distributed() -> tuple[int, int, int]: + """(rank, local_rank, world_size) under torchrun; (0, 0, 1) otherwise.""" + world = int(os.environ.get("WORLD_SIZE", "1")) + if world > 1 and not torch.distributed.is_initialized(): + torch.distributed.init_process_group("nccl") + return int(os.environ.get("RANK", "0")), int(os.environ.get("LOCAL_RANK", "0")), world + + +def _loader(dataset, *, workers: int, shuffle: bool, sampler=None) -> DataLoader: + return DataLoader( + dataset, + batch_size=1, + shuffle=shuffle and sampler is None, + sampler=sampler, + num_workers=workers, + persistent_workers=workers > 0, + pin_memory=torch.cuda.is_available(), + collate_fn=single_example_collate, + ) + + +def _to_device(value, device: torch.device): + if isinstance(value, Tensor): + return value.to(device, non_blocking=True) + if isinstance(value, dict): + return {key: _to_device(item, device) for key, item in value.items()} + return value + + +def _match_coordinate_queries( + scoring: dict[str, Tensor], positions: Tensor, track_halves: Tensor +) -> tuple[Tensor, Tensor, Tensor]: + """Match trusted coordinate queries to typed position axes like the grader.""" + device = positions.device + tracks = scoring["track"].detach().cpu().numpy() + halves_cpu = track_halves.detach().cpu().numpy()[tracks] + # Build indices on CPU, then copy once. Writing three CUDA scalars per valid query + # otherwise launches thousands of tiny kernels per example, especially costly when + # training and evaluation share one GPU. The matching semantics are unchanged. + queries = np.zeros((len(tracks), 3), dtype=np.int64) + queries[:, 2] = tracks + valid = scoring["valid"].clone() + missing = np.ones(len(tracks), dtype=bool) + position_rows = positions.detach().cpu().numpy() + donors_cpu = scoring["donor_position"].detach().cpu().numpy() + acceptors_cpu = scoring["acceptor_position"].detach().cpu().numpy() + valid_indices = np.flatnonzero(valid.detach().cpu().numpy()) + lookups = [ + {int(coordinate): index for index, coordinate in enumerate(axis) if coordinate >= 0} + for axis in position_rows + ] + for index in valid_indices: + half = int(halves_cpu[index]) + row = lookups[2 * half].get(int(donors_cpu[index])) + column = lookups[2 * half + 1].get(int(acceptors_cpu[index])) + if row is not None and column is not None: + queries[index, 0] = row + queries[index, 1] = column + missing[index] = False + return (torch.from_numpy(queries).to(device).unsqueeze(0), valid, + torch.from_numpy(missing).to(device)) + + +def _model_inputs(sample: dict) -> tuple[dict, Tensor, Tensor]: + queries, query_valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + sample["site_positions"], + sample["junction_track_halves"], + ) + if (query_valid & missing).any(): + raise ValueError("trusted scoring coordinates do not match target positions") + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + return { + "dna": sample["dna"].unsqueeze(0), + "usage_positions": sample["usage_positions"].unsqueeze(0), + "junction_site_positions": sample["site_positions"].unsqueeze(0), + "junction_queries": queries, + }, junction_target, query_valid + + +def _losses(outputs: dict[str, Tensor], sample: dict, junction_target: Tensor, + junction_valid: Tensor) -> dict[str, Tensor]: + losses = {} + for family in DENSE_FAMILIES: + target = torch.asinh(sample["dense"][family]["values"]).unsqueeze(0) + mask = sample["dense"][family]["mask"].unsqueeze(0) + residual = torch.where(mask, outputs[family] - target, 0.0) + losses[family] = residual.square().sum() / mask.sum().clamp_min(1) + losses["splice_sites"] = F.binary_cross_entropy_with_logits( + outputs["splice_sites"], sample["splice_sites"].unsqueeze(0) + ) + usage_target = torch.asinh(sample["splice_usage"]["values"]).unsqueeze(0) + usage_mask = sample["splice_usage"]["mask"].unsqueeze(0) + usage_residual = torch.where(usage_mask, outputs["splice_usage"] - usage_target, 0.0) + losses["splice_usage"] = usage_residual.square().sum() / usage_mask.sum().clamp_min(1) + junction_residual = outputs["junctions"][0] - junction_target[0] + losses["junctions"] = ( + junction_residual[junction_valid].square().mean() + if junction_valid.any() else junction_residual.sum() * 0 + ) + contact_target = torch.asinh(sample["contact_maps"]).unsqueeze(0) + contact_mask = sample["contact_map_mask"].unsqueeze(0) + contact_residual = torch.where( + contact_mask, outputs["contact_maps"] - contact_target, 0.0 + ) + losses["contact_maps"] = contact_residual.square().sum() / contact_mask.sum().clamp_min(1) + losses["total"] = torch.stack([losses[name] for name in ALL_FAMILIES]).mean() + return losses + + +def _validation_inference( + model: CompactModel, sample: dict +) -> tuple[dict[str, Tensor], Tensor, Tensor, Tensor]: + """Use the same predicted-position path as a formal v3 submission.""" + trunk = model._trunk(sample["dna"].unsqueeze(0)) + dense_features = model._dense_features(trunk) + outputs = { + family: model.dense_heads[family](dense_features).float() + for family in DENSE_FAMILIES + } + all_sites = model._splice_site_logits(trunk) + target_start = (model.config.input_length - model.config.target_length) // 2 + outputs["splice_sites"] = all_sites[:, target_start:target_start + model.config.target_length] + usage_bins = model.splice_usage_head(dense_features).float() + usage_indices = torch.div(sample["usage_positions"], model.config.dense_bin_size, + rounding_mode="floor") + outputs["splice_usage"] = usage_bins[:, usage_indices] + predicted_positions = model.select_junction_positions(all_sites) + + matched, valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + predicted_positions[0], + sample["junction_track_halves"], + ) + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + junction_prediction = torch.zeros(len(valid), device=valid.device) + if (~missing & valid).any(): + selected = ~missing & valid + junction_prediction[selected] = model._junction_predictions( + trunk, predicted_positions, matched[:, selected] + )[0] + outputs["junctions"] = junction_prediction.unsqueeze(0) + + contact = trunk.reshape( + 1, model.config.contact_bins, -1, model.config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + outputs["contact_maps"] = (0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row])).float() + return outputs, junction_target, valid, missing + + +def _atomic_save(payload: dict, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(f".{path.name}.tmp") + torch.save(payload, temporary) + os.replace(temporary, path) + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(8 * 1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _resume_position(payload: dict, *, examples: int, epochs: int) -> tuple[int, int]: + epoch = int(payload["epoch"]) + step = int(payload["global_step"]) + kind = payload.get("checkpoint_kind") + if kind not in (None, "epoch") or step != epoch * examples: + raise ValueError("only a completed-epoch checkpoint can resume training") + if payload.get("examples", examples) != examples or payload.get("schedule_epochs", epochs) != epochs: + raise ValueError("checkpoint data size or training schedule differs from this run") + return epoch, step + + +def train(args: argparse.Namespace) -> None: + # Multi-GPU with `torchrun --nproc-per-node N train_eval.py train ...`: one example per GPU per + # step (data parallel), rank 0 writes checkpoints and logs. Model size is bounded by the + # training budget, not by a parameter cap. + rank, local_rank, world = _distributed() + config = ModelConfig.from_yaml(args.config) + device = torch.device("cuda", local_rank) if world > 1 else _device(args.device) + if world > 1: + torch.cuda.set_device(device) + torch.manual_seed(args.seed) + model = CompactModel(config).to(device) + parameters = count_parameters(model) + optimizer = torch.optim.AdamW( + model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay + ) + scaler = torch.amp.GradScaler("cuda", enabled=device.type == "cuda") + full_dataset = AlphaGenomeDataset(args.input, "train") + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + start_epoch, global_step = 0, 0 + if args.resume: + payload = torch.load(args.resume, map_location="cpu", weights_only=True) + if payload.get("config") != config.to_dict(): + raise ValueError("checkpoint configuration does not match --config") + model.load_state_dict(payload["model"], strict=True) + optimizer.load_state_dict(payload["optimizer"]) + scaler.load_state_dict(payload.get("scaler", {})) + start_epoch, global_step = _resume_position(payload, examples=size, epochs=args.epochs) + per_rank = math.ceil(size / world) + steps = max(1, per_rank * args.epochs) + module = model + if world > 1: + model = torch.nn.parallel.DistributedDataParallel( + model, device_ids=[local_rank], find_unused_parameters=True) + sampler = (torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world, rank=rank, + shuffle=True, seed=args.seed) + if world > 1 else None) + stop_after_epoch = args.stop_after_epoch or args.epochs + output = args.output.resolve() + output.mkdir(parents=True, exist_ok=True) + started = time.monotonic() + if rank == 0: + print(json.dumps({"event": "train_start", "parameters": parameters, "examples": size, + "device": str(device), "world_size": world}), flush=True) + + for epoch in range(start_epoch, stop_after_epoch): + model.train() + if sampler is not None: + sampler.set_epoch(epoch) + loader = _loader(dataset, workers=args.workers, shuffle=True, sampler=sampler) + for sample in loader: + sample = _to_device(sample, device) + inputs, junction_target, junction_valid = _model_inputs(sample) + progress = min((global_step + 1) / steps, 1.0) + learning_rate = args.min_learning_rate + 0.5 * ( + args.learning_rate - args.min_learning_rate + ) * (1.0 + math.cos(math.pi * progress)) + for group in optimizer.param_groups: + group["lr"] = learning_rate + optimizer.zero_grad(set_to_none=True) + with _autocast(device): + predictions = model(**inputs) + losses = _losses(predictions, sample, junction_target, junction_valid) + if not torch.isfinite(losses["total"]): + raise FloatingPointError(f"non-finite loss at step {global_step}") + scaler.scale(losses["total"]).backward() + scaler.unscale_(optimizer) + gradient_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_gradient_norm) + scaler.step(optimizer) + scaler.update() + global_step += 1 + if rank == 0 and (global_step == 1 or global_step % args.log_every == 0): + print( + json.dumps( + { + "event": "train_step", + "epoch": epoch + 1, + "step": global_step, + "loss": float(losses["total"].detach()), + "gradient_norm": float(gradient_norm), + "learning_rate": learning_rate, + "elapsed_seconds": time.monotonic() - started, + } + ), + flush=True, + ) + if rank == 0 and global_step % args.save_every == 0: + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch, + "global_step": global_step, + "checkpoint_kind": "progress", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "progress.pt", + ) + if world > 1: + torch.distributed.barrier() + if rank != 0: + continue + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch + 1, + "global_step": global_step, + "checkpoint_kind": "epoch", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "last.pt", + ) + if world > 1: + torch.distributed.barrier() + torch.distributed.destroy_process_group() + if rank != 0: + return + checkpoint = output / "last.pt" + manifest = { + "schema": "alphagenome-50m-real/v3", + "checkpoint": {"path": checkpoint.name, "sha256": _sha256(checkpoint)}, + "parameters": parameters, + "config": config.to_dict(), + "output_families": list(ALL_FAMILIES), + "epochs": args.epochs, + "completed_epochs": stop_after_epoch, + "global_step": global_step, + } + (output / "model_manifest.json").write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "train_complete", **manifest}), flush=True) + + +class PerTrackMoments: + def __init__(self, tracks: int): + self.n = np.zeros(tracks, dtype=np.int64) + self.sum_x = np.zeros(tracks, dtype=np.float64) + self.sum_y = np.zeros(tracks, dtype=np.float64) + self.sum_x2 = np.zeros(tracks, dtype=np.float64) + self.sum_y2 = np.zeros(tracks, dtype=np.float64) + self.sum_xy = np.zeros(tracks, dtype=np.float64) + + def update(self, prediction: Tensor, target: Tensor, mask: Tensor | None = None) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1, prediction.shape[-1]) + y = target.detach().double().cpu().numpy().reshape(-1, target.shape[-1]) + valid = np.ones_like(y, dtype=bool) if mask is None else mask.detach().cpu().numpy().reshape(y.shape).astype(bool) + self.n += valid.sum(axis=0) + self.sum_x += np.where(valid, x, 0).sum(axis=0, dtype=np.float64) + self.sum_y += np.where(valid, y, 0).sum(axis=0, dtype=np.float64) + self.sum_x2 += np.where(valid, x * x, 0).sum(axis=0, dtype=np.float64) + self.sum_y2 += np.where(valid, y * y, 0).sum(axis=0, dtype=np.float64) + self.sum_xy += np.where(valid, x * y, 0).sum(axis=0, dtype=np.float64) + + def update_indexed(self, prediction: Tensor, target: Tensor, track: Tensor) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1) + y = target.detach().double().cpu().numpy().reshape(-1) + indices = track.detach().cpu().numpy().reshape(-1).astype(np.int64) + self.n += np.bincount(indices, minlength=len(self.n)) + self.sum_x += np.bincount(indices, weights=x, minlength=len(self.n)) + self.sum_y += np.bincount(indices, weights=y, minlength=len(self.n)) + self.sum_x2 += np.bincount(indices, weights=x * x, minlength=len(self.n)) + self.sum_y2 += np.bincount(indices, weights=y * y, minlength=len(self.n)) + self.sum_xy += np.bincount(indices, weights=x * y, minlength=len(self.n)) + + def report(self) -> dict: + numerator = self.n * self.sum_xy - self.sum_x * self.sum_y + denominator = np.sqrt( + np.maximum(self.n * self.sum_x2 - self.sum_x**2, 0) + * np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + ) + target_ss = np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + scorable = (self.n >= 2) & (target_ss > 0) + pearson = np.full_like(numerator, np.nan) + nonconstant = scorable & (denominator > 0) + pearson[nonconstant] = numerator[nonconstant] / denominator[nonconstant] + pearson[scorable & ~nonconstant] = 0.0 + return { + "mean_pearson": float(pearson[scorable].mean()) if scorable.any() else 0.0, + "valid_tracks": int(scorable.sum()), + "per_track_pearson": [float(value) if np.isfinite(value) else None for value in pearson], + "values_per_track": self.n.tolist(), + } + + +def evaluate(args: argparse.Namespace) -> None: + device = _device(args.device) + payload = torch.load(args.checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config).to(device) + model.load_state_dict(payload["model"], strict=True) + model.eval() + full_dataset = AlphaGenomeDataset(args.input, args.split) + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + metrics = { + **{family: PerTrackMoments(config_value) for family, config_value in { + "atac": 167, "dnase": 305, "procap": 12, "cage": 546, + "rna_seq": 667, "chip_tf": 1617, "chip_histone": 1116, + }.items()}, + "splice_sites": PerTrackMoments(4), + "splice_usage": PerTrackMoments(config.splice_usage_tracks), + "junctions": PerTrackMoments(config.junction_tracks), + "contact_maps": PerTrackMoments(config.contact_tracks), + } + with torch.inference_mode(): + for index, sample in enumerate(_loader(dataset, workers=args.workers, shuffle=False)): + sample = _to_device(sample, device) + outputs, junction_target, junction_valid, junction_missing = ( + _validation_inference(model, sample) + ) + # Match predict.py: FP32 inference is serialized to FP16 Zarr values. + outputs = {name: value.half().float() for name, value in outputs.items()} + for family in DENSE_FAMILIES: + metrics[family].update( + outputs[family][0], + torch.asinh(sample["dense"][family]["values"]), + sample["dense"][family]["mask"], + ) + metrics["splice_sites"].update(outputs["splice_sites"][0], sample["splice_sites"]) + metrics["splice_usage"].update( + outputs["splice_usage"][0], + torch.asinh(sample["splice_usage"]["values"]), + sample["splice_usage"]["mask"], + ) + junction_track = sample["junction_scoring"]["track"] + metrics["junctions"].update_indexed( + outputs["junctions"][0][junction_valid], + junction_target[0][junction_valid], + junction_track[junction_valid], + ) + metrics["contact_maps"].update( + outputs["contact_maps"][0], + torch.asinh(sample["contact_maps"]), + sample["contact_map_mask"], + ) + if index == 0 or (index + 1) % args.log_every == 0 or index + 1 == size: + print(json.dumps({"event": "evaluate_progress", "examples": index + 1, "total": size}), flush=True) + families = {name: accumulator.report() for name, accumulator in metrics.items()} + valid_family_scores = [item["mean_pearson"] for item in families.values()] + report = { + "schema": "alphagenome-50m-evaluation/v3", + "split": args.split, + "examples": size, + "metric_space": ( + "asinh continuous targets and binary splice-site targets; " + "junction Pearson uses hidden coordinate queries and predicted endpoints; " + "FP32 inference rounded to FP16 prediction values" + ), + "families": families, + "overall_mean_family_pearson": float(np.mean(valid_family_scores)), + } + args.report.parent.mkdir(parents=True, exist_ok=True) + args.report.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "evaluate_complete", "overall_mean_family_pearson": report["overall_mean_family_pearson"]}), flush=True) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + common = argparse.ArgumentParser(add_help=False) + common.add_argument("--input", required=True, type=Path) + common.add_argument("--config", type=Path, default=DEFAULT_CONFIG) + common.add_argument("--workers", type=int, default=2) + common.add_argument("--max-examples", type=int, default=0) + common.add_argument("--device", choices=("auto", "cuda", "cpu"), default="auto") + common.add_argument("--log-every", type=int, default=25) + subparsers = parser.add_subparsers(dest="command", required=True) + train_parser = subparsers.add_parser("train", parents=[common]) + train_parser.add_argument("--output", required=True, type=Path) + train_parser.add_argument("--epochs", type=int, default=10) + train_parser.add_argument("--stop-after-epoch", type=int) + train_parser.add_argument("--learning-rate", type=float, default=2e-4) + train_parser.add_argument("--min-learning-rate", type=float, default=2e-5) + train_parser.add_argument("--weight-decay", type=float, default=1e-4) + train_parser.add_argument("--max-gradient-norm", type=float, default=5.0) + train_parser.add_argument("--save-every", type=int, default=250) + train_parser.add_argument("--seed", type=int, default=17) + train_parser.add_argument("--resume", type=Path) + train_parser.set_defaults(run=train) + evaluate_parser = subparsers.add_parser("evaluate", parents=[common]) + evaluate_parser.add_argument("--checkpoint", required=True, type=Path) + evaluate_parser.add_argument("--split", choices=("train", "valid", "test"), required=True) + evaluate_parser.add_argument("--report", required=True, type=Path) + evaluate_parser.set_defaults(run=evaluate) + args = parser.parse_args() + if args.workers < 0 or args.max_examples < 0: + parser.error("workers/max-examples must be nonnegative") + if args.command == "train" and ( + args.epochs < 1 + or (args.stop_after_epoch is not None + and not 1 <= args.stop_after_epoch <= args.epochs) + ): + parser.error("epochs must be positive and stop-after-epoch must be within the schedule") + args.run(args) + + +if __name__ == "__main__": + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/training_config.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/training_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1f9a3298d69b138a02fed00880182d4004b23327 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/training_config.yaml @@ -0,0 +1,28 @@ +attention_head_dim: 64 +channel_growth: 32 +contact_bin_size: 2048 +contact_tracks: 28 +conv_kernel_size: 5 +dense_bin_size: 128 +dna_kernel_size: 15 +dropout: 0.0 +enable_pairwise: false +encoder_width: 32 +gradient_checkpointing: true +head_hidden_size: 256 +hidden_dim: 384 +input_length: 1048576 +junction_rank: 32 +junction_tracks: 734 +local_width: 32 +mlp_expansion: 4.0 +num_attention_heads: 6 +num_downsampling_blocks: 7 +num_sequence_blocks: 6 +pair_downsample: 16 +pair_update_interval: 2 +pair_width: 32 +splice_usage_tracks: 734 +stem_stride: 8 +target_length: 196608 +trunk_bin_size: 1024 diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/training_provenance.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/training_provenance.json new file mode 100644 index 0000000000000000000000000000000000000000..3a27a4f9837bd8c247826bf97796759d953f0d0e --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S008/training_provenance.json @@ -0,0 +1,66 @@ +{ + "schema": "seqfunc-single/v1", + "config": { + "input_length": 1048576, + "target_length": 196608, + "dense_bin_size": 128, + "trunk_bin_size": 1024, + "contact_bin_size": 2048, + 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17052, + "perturbations": 165, + "metric": "auroc", + "model": 0.5013926622946716, + "anchors": { + "AlphaGenome": 0.8263569969634328 + } + }, + "eqtl_causality": { + "rows": 17934, + "perturbations": 174, + "metric": "auroc", + "model": 0.6580835937978794, + "anchors": { + "AlphaGenome": 0.8325427468284611 + } + }, + "eqtl_coefficient": { + "rows": 8575, + "perturbations": 165, + "metric": "spearman", + "model": -0.00226781115864438, + "anchors": { + "AlphaGenome": 0.4133539365891599 + } + }, + "sqtl_causality": { + "rows": 21340, + "perturbations": 194, + "metric": "auprc", + "model": 0.7465487674476523, + "anchors": { + "AlphaGenome": 0.8447114201904594 + } + }, + "clinvar_noncoding": { + "rows": 199, + "perturbations": 199, + "metric": "auprc", + "model": 0.2929923342263634, + "anchors": { + "AlphaGenome": 0.9409857958177288 + } + }, + "clinvar_splice_region": { + "rows": 194, + "perturbations": 194, + "metric": "auprc", + "model": 0.22226205829589238, + "anchors": { + "AlphaGenome": 0.7224791623996103 + } + }, + "clinvar_missense": { + "rows": 199, + "perturbations": 199, + "metric": "auprc", + "model": 0.2598401349260804, + "anchors": { + "AlphaGenome": 0.373826292978903 + } + }, + "mfass": { + "rows": 195, + "perturbations": 195, + "metric": "auprc", + "model": 0.1801639998363942, + "anchors": { + "AlphaGenome": 0.7342557141920668 + } + }, + "caqtl": { + "rows": 198, + "perturbations": 198, + "metric": "auprc", + "model": 0.20420834721788722, + "anchors": { + "AlphaGenome": 0.8075760167741196 + } + }, + "bqtl_spi1": { + "rows": 160, + "perturbations": 160, + "metric": "auprc", + "model": 0.22233669192877115, + "anchors": { + "AlphaGenome": 0.8167450781363793 + } + }, + "enhancer_gene": { + "rows": 200, + "perturbations": 200, + "metric": "auprc", + "model": 0.3229938128794334, + "anchors": { + "AlphaGenome": 0.9594307235541584 + } + }, + "traitgym_mendelian": { + "rows": 292, + "perturbations": 292, + "metric": "auprc", + "model": 0.14434302471297017, + "anchors": { + "Borzoi_L2_L2.plus.all": 0.5026323772738239, + "Enformer_L2_L2.plus.all": 0.5452938267693526, + "CADD.plus.RawScore": 0.7189111372856957, + "GPN-MSA_absLLR.plus.score": 0.6729149139043132 + } + }, + "traitgym_complex": { + "rows": 299, + "perturbations": 299, + "metric": "auprc", + "model": 0.19369015162189235, + "anchors": { + "Borzoi_L2_L2.plus.all": 0.294501577162467, + "Enformer_L2_L2.plus.all": 0.269227301087889, + "CADD.plus.RawScore": 0.41341570280137435, + "GPN-MSA_absLLR.plus.score": 0.4213971415058371 + } + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/README.md b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d5614dd593114d12e36cc8183d1cdca0b3c8dfa6 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/README.md @@ -0,0 +1,77 @@ +# SeqFunc norm05 at step 26,000 + +One model with **13,256,456 parameters** serves every family and both entry points. It reads the entire +1,048,576-bp DNA window through a convolutional encoder and 6 transformer +blocks of width 384 over 1,024 tokens. Dense heads predict 128-bp bins over the central +196,608 bp; other heads predict splice sites, sparse usage/junctions and full-window contacts. +A width-32 base-resolution path uses a 21-bp stem and four dilated residual depthwise blocks (701-bp receptive field). Average/max pooling provides aligned 128-bp residual features to dense, usage and junction heads. Splice logits combine local base-resolution logits with the reference offset decoder. Junction values use low-rank donor/acceptor factors; contacts use a symmetric +product of global features. Sites and junction endpoints are selected from model scores alone. + +All weights originate from local random initialization (seed 17) and locally trained +checkpoints. The compact model trained with balanced splice loss for 2,000 examples, then a zero-output local path was attached. At local01 step 22,000, the continuous-family loss weights changed to inverse training variances while all parameters, optimizer moments and RNG states were preserved. The next 4,000 examples and learning rates match the unchanged control. Only this objective change is being tested. + +This checkpoint records **26,000 updates**, 0 complete training permutations and +26,000 intervals into the next permutation. Each permutation includes all **41,020** +eligible FOLD_0 training intervals (folds 2–7), shuffled deterministically without replacement. +Quantized train targets are decoded by the supplied reader. Validation is fold 0; no test labels, +test-derived caches or pretrained model parameters were used. Development labels only evaluate +and select models. Checkpoints before a complete permutation remain intermediate milestones. + +Training uses one H100 80 GB and at most 22 CPUs. The current loader uses 4 workers +and 2 torch CPU threads. BF16 training uses AdamW (weight decay 1e-4), gradient +clipping at 5, and a 4-epoch cosine learning-rate schedule from +0.0002 to 2e-05, with +500 warmup updates. The objective is `variance_balanced`. +Each continuous family uses the reference MSE divided by its mean within-track variance in a fixed, training-only sample of 256 windows (seed 1729). These scales are frozen in the checkpoint; the balanced splice BCE is unchanged. Recorded accumulated training-loop wall time is +67.6 minutes, including inherited parent time. Runs can share this +same physical GPU with experiments or evaluation. Setup, evaluation and discarded mechanical checks +are additional research compute, documented in the research ledger. Matching training junction queries +uses a verified, bit-identical CPU index construction followed by a bulk GPU transfer. + +The package contains model code, weights, training source/configuration/provenance, SHA-256 checksums, +and the complete CPython 3.11 / PyTorch 2.7.1 CUDA 12.6 Python runtime archive. Both executable entry +points take INPUT_ROOT and OUTPUT_ROOT, extract the vendored runtime temporarily, and run offline. +The host supplies Linux, bash/tar/gzip and the NVIDIA driver. Inference reads only public manifests +and the pinned reference FASTA. No target, splice-coordinate or prediction cache is packaged or read. +Track outputs use float16; perturbation outputs use float32 to preserve small A/B differences. + +The appended validation results cover all 200 intervals and all 2,697 development perturbations, +scored by the unmodified verifier tools. Published anchors use the same label rows. All results, +including regressions, are retained in the package's JSON reports. Limitations include incomplete +training at intermediate checkpoints, a 701-bp local receptive field, pooled usage/junction features, +low-rank contact modeling, and uneven variant performance. Functional readouts do not provide the +evolutionary constraint information used by conservation-based variant scorers. + +## Measured validation results + +Family-mean Pearson: **0.279166** on 200 validation intervals. + +| Family | Pearson | +|---|---:| +| atac | 0.474164 | +| dnase | 0.597316 | +| procap | 0.358947 | +| cage | 0.199841 | +| rna_seq | 0.221150 | +| chip_tf | 0.395736 | +| chip_histone | 0.380621 | +| splice_sites | 0.042435 | +| splice_usage | 0.220738 | +| junctions | 0.179636 | +| contact_maps | 0.000247 | + +| Development evaluation | Metric | Model | Published anchors on the same rows | +|---|---|---:|---| +| eqtl_sign | auroc | 0.5481 | AlphaGenome: 0.8264 | +| eqtl_causality | auroc | 0.6387 | AlphaGenome: 0.8325 | +| eqtl_coefficient | spearman | 0.0945 | AlphaGenome: 0.4134 | +| sqtl_causality | auprc | 0.7881 | AlphaGenome: 0.8447 | +| clinvar_noncoding | auprc | 0.3191 | AlphaGenome: 0.9410 | +| clinvar_splice_region | auprc | 0.2098 | AlphaGenome: 0.7225 | +| clinvar_missense | auprc | 0.2859 | AlphaGenome: 0.3738 | +| mfass | auprc | 0.1716 | AlphaGenome: 0.7343 | +| caqtl | auprc | 0.1890 | AlphaGenome: 0.8076 | +| bqtl_spi1 | auprc | 0.2085 | AlphaGenome: 0.8167 | +| enhancer_gene | auprc | 0.3768 | AlphaGenome: 0.9594 | +| traitgym_mendelian | auprc | 0.1452 | Borzoi_L2_L2.plus.all: 0.5026; Enformer_L2_L2.plus.all: 0.5453; CADD.plus.RawScore: 0.7189; GPN-MSA_absLLR.plus.score: 0.6729 | +| traitgym_complex | auprc | 0.1637 | Borzoi_L2_L2.plus.all: 0.2945; Enformer_L2_L2.plus.all: 0.2692; CADD.plus.RawScore: 0.4134; GPN-MSA_absLLR.plus.score: 0.4214 | diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/attach_local.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/attach_local.py new file mode 100644 index 0000000000000000000000000000000000000000..aa082537004dbc398c4a78c1bd0203970ce5e42f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/attach_local.py @@ -0,0 +1,52 @@ +"""Attach a zero-initialized local residual path to a checkpoint trained in this workspace.""" +import argparse +from dataclasses import replace +import hashlib +from pathlib import Path + +import torch +import yaml + +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + p.add_argument('--width', type=int, default=32) + args = p.parse_args() + state = torch.load(args.checkpoint, map_location='cpu', weights_only=True) + assert state['schema'] == 'seqfunc-single/v1', 'only locally trained single-GPU checkpoints' + parent_config = ModelConfig(**state['config']) + assert parent_config.local_width == 0 + torch.manual_seed(state['seed']) + parent = CompactModel(parent_config) + config = replace(parent_config, local_width=args.width) + model = CompactModel(config) + result = model.load_state_dict(state['model'], strict=False) + assert not result.unexpected_keys + assert all(k.startswith(('local_path.', 'local_projection.')) for k in result.missing_keys) + parent_names = list(dict(parent.named_parameters())) + names = list(dict(model.named_parameters())) + assert names[:len(parent_names)] == parent_names, 'optimizer parameter order changed' + assert len(state['optimizer']['param_groups']) == 1 + group = state['optimizer']['param_groups'][0] + assert group['params'] == list(range(len(parent_names))) + group['params'] = list(range(len(names))) + state['model'] = model.state_dict() + state['config'] = config.to_dict() + state['intervention'] = { + 'kind': 'base_resolution_local_path', 'width': args.width, + 'parent_sha256': hashlib.sha256(args.checkpoint.read_bytes()).hexdigest(), + 'attachment_step': state['global_step'], 'initial_outputs_unchanged': True, + } + args.output.mkdir(parents=True, exist_ok=False) + (args.output / 'config.yaml').write_text(yaml.safe_dump(config.to_dict())) + _atomic_save(state, args.output / 'attached.pt') + print({'parameters': count_parameters(model), **state['intervention']}) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/change_loss_scales.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/change_loss_scales.py new file mode 100644 index 0000000000000000000000000000000000000000..5fbb9c9c7a72f6d4c04ebb9ab7b08c0fde952cb3 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/change_loss_scales.py @@ -0,0 +1,35 @@ +"""Fork our checkpoint with one objective change and no parameter/optimizer changes.""" +import argparse +import hashlib +import json +from pathlib import Path +import torch +import yaml +from reference.alphagenome_50m.train_eval import _atomic_save + +p = argparse.ArgumentParser(description=__doc__) +p.add_argument('checkpoint', type=Path) +p.add_argument('scales', type=Path) +p.add_argument('output', type=Path) +a = p.parse_args() +s = torch.load(a.checkpoint,map_location='cpu',weights_only=True) +assert s['schema'] == 'seqfunc-single/v1' and s['objective'] == 'balanced_splice' +r = json.loads(a.scales.read_text()) +assert r['split'] == 'train' +with a.checkpoint.open('rb') as f: + parent_hash = hashlib.file_digest(f,'sha256').hexdigest() +intervention = {'kind':'variance_balanced_continuous_losses', 'step':s['global_step'], + 'parent_sha256':parent_hash, 'parameters_optimizer_rng_unchanged':True, + 'scales_sha256':hashlib.sha256(a.scales.read_bytes()).hexdigest()} +history = list(s.get('interventions',[])) +if s.get('intervention') and s['intervention'] not in history: history.append(s['intervention']) +history.append(intervention) +s.update(objective='variance_balanced', family_loss_scales=r['family_loss_scales'], + loss_scale_provenance={k:v for k,v in r.items() if k!='junction_moments'}, + intervention=intervention, interventions=history) +a.output.mkdir(parents=True,exist_ok=False) +(a.output/'config.yaml').write_text(yaml.safe_dump(s['config'])) +(a.output/'intervention.json').write_text(json.dumps(intervention,indent=2)+'\n') +(a.output/'loss_scales.json').write_text(a.scales.read_text()) +_atomic_save(s,a.output/'start.pt') +print(json.dumps(intervention),flush=True) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/infer.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/infer.sh new file mode 100644 index 0000000000000000000000000000000000000000..2a412a9ad8667d6dcb94bb57d9959d61d43ddd34 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/infer.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: infer.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/predict.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/package_manifest.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/package_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..db293cfa20bd32ea5ee1205b37f6c7029f72830a --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/package_manifest.json @@ -0,0 +1,31 @@ +{ + "parameters": 13256456, + "source_checkpoint_sha256": "dddc7949ea3039f7a01f22a421e606b540e359bb5c9ab12e3674b3524ad68ca3", + "files": { + "README.md": "cd0ccb053b21478f0a11c4b2295c6e3df53eb6dd9562a39e34ba7ee7f0687045", + "attach_local.py": "d0774e2711840783267914789ed303a3b61b64381b72d61f0886edbc04974f15", + "change_loss_scales.py": "3898e5ea510e7bf35eca74905569c59e429f7f6e58e91e213520029ac004d01f", + "infer.sh": "ef810532b1e72b85fbe283e4f52c177bb70025977dbb2345d75837c9bbd51e37", + "last.pt": "3a0ed8881ac883d3e33d5686638a288c447bbdf59825e68f09e9505d39649300", + "perturb.py": "ba37a6f9f8b8eccb052903618bcb4c91d3f4cbed62dfe678ee362644dc80801e", + "perturb.sh": "113ec2383276a6e83fe1e4e8b0fa914f8d535d5dd0f5b2c34240140312dd5fb4", + "predict.py": "2725592456c55c188b22c376f0cb249e9f46b4ae1370d2226df7143da358a8b2", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/__init__.py": "45f3cf93f48859104e70bb58d717130dfd6521a45d2aba06b1a726afc0dc51dd", + "reference/alphagenome_50m/__init__.py": "a5a7e45d5940c3b201017c6322128e9f47efd37e1d29f717da2c1f787688eff9", + "reference/alphagenome_50m/alphagenome_50m.yaml": "3cac32e9f55e352dff78bb85ac892fdae1f9e412d9bba3e945750210c5252eee", + "reference/alphagenome_50m/dataset.py": "ad6670f742f4de9898064becc525d0aea98b402abe7b210bb1b26a9eee6402b9", + "reference/alphagenome_50m/layers.py": "26a116a72aa3f0e307a3606163547edbdecb7965f7fdcd96bc5f805cd21b3352", + "reference/alphagenome_50m/model.py": "2871dc8893879c3e14a526dea659701d72c8c4d0e2f835b0bfb857034a2c2e6d", + "reference/alphagenome_50m/reference_large.yaml": "68084d299b39418034c6bbf71db174c3ffa68bbeea8779c1694e44ec71f3529c", + "reference/alphagenome_50m/run_ddp.sh": "96d35a24a4e6789311501fb98203807ac0c4429375c44d5c6671908270c93bb0", + "reference/alphagenome_50m/tiny_overfit.yaml": "f7a104c3929de0530a9a8f73f66d794cca595aefad8ef262e09ecbeb9cfce6de", + "reference/alphagenome_50m/train_eval.py": "95d2dad279b23ed9c0c8a4f070035cf53c27eb8fe27da64c441343717174f784", + "runtime.tar.gz": "718da4c374e0c552b6e429b226c90b2fded5c900d95577d190d4df520aaf3901", + "train_single.py": "70b9dd2635417e6d9e25ec3f29dd517a389a3d3f66144363ed40cc1b4854082e", + "training_config.yaml": "deab298b32f0aef33e66ddf538541ec470eee0c05a6ebc983684f2575287d791", + "training_provenance.json": "613f7ef6553ffda155514e4fe81651b5545b3e0bb755052999db7a869431eede", + "validation_tracks.json": "b0f69e55ce6648553cf3a3a74ec5fc1eb4c8cbaaaded92af55dbda2b4ecb6737", + "validation_variants.json": "2c8982c9649912e27b9de6573126019cd97f2548b72d6a1ca6affa54a6d0eb2f" + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/perturb.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/perturb.py new file mode 100644 index 0000000000000000000000000000000000000000..e0416692d2ee7deb1a25927397f725eec54d16a9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/perturb.py @@ -0,0 +1,101 @@ +"""Predict both sequences of every perturbation (contract: perturbations/v1). + + python perturb.py INPUT_ROOT OUTPUT_ROOT --checkpoint last.pt + +INPUT_ROOT holds `public/perturbations.parquet` and `reference/GRCh38.p13.genome.fa` (in the +workspace, `/workspace/data` works too: the queries are then read from `variant_evals/public/`). Each row is a +1,048,576-bp window starting at `input_start` and an edit spanning [edit_start, edit_end) in genomic +0-based coordinates: + +- `snv`: sequence A carries `allele_a` at edit_start, sequence B carries `allele_b` (both are written + explicitly; either may differ from the reference genome); +- `mask`: sequence A is the reference, sequence B has the interval replaced by N. + +For each perturbation the output group `perturbations/p{index:06d}` holds `a/` and +`b/` for every family in `families`: dense families sliced to bins +[dense_bin_start, dense_bin_end) of the central target window, and `splice_sites` sliced to bp +[splice_start, splice_end). Values are the same quantities as predictions.zarr (asinh space for +dense tracks, splice-site scores for splice_sites). +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import pandas as pd +import pysam +import torch +import zarr + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig + +PERTURBATION_SCHEMA = "genome-sequence-to-function-perturbations/v1" +COMPRESSOR = Blosc(cname="zstd", clevel=3, shuffle=Blosc.BITSHUFFLE) + + +def sequences(fasta, row) -> tuple[str, str]: + length = fasta.get_reference_length(row.chrom) + start, end = int(row.input_start), int(row.input_start) + 1_048_576 + sequence = fasta.fetch(row.chrom, max(0, start), min(length, end)).upper() + sequence = "N" * max(0, -start) + sequence + "N" * max(0, end - length) + lo, hi = int(row.edit_start) - start, int(row.edit_end) - start + if row.edit == "snv": + return sequence[:lo] + row.allele_a + sequence[hi:], sequence[:lo] + row.allele_b + sequence[hi:] + if row.edit == "mask": + return sequence, sequence[:lo] + "N" * (hi - lo) + sequence[hi:] + raise ValueError(f"unknown edit {row.edit!r}") + + +def main(input_root: Path, output_root: Path, checkpoint: Path, queries: Path | None = None) -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + if queries is None: + queries = input_root / "public/perturbations.parquet" + if not queries.exists(): + queries = input_root / "variant_evals/public/perturbations.parquet" + rows = pd.read_parquet(queries) + fasta = pysam.FastaFile(str(input_root / "reference/GRCh38.p13.genome.fa")) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "perturbations.zarr"), mode="w") + target_start = (config.input_length - config.target_length) // 2 + with torch.inference_mode(): + for index, row in enumerate(rows.itertuples(index=False)): + pair = sequences(fasta, row) + dna = torch.from_numpy(np.stack([AlphaGenomeDataset._one_hot(s) for s in pair])).to(device) + trunk = model._trunk(dna) + group = root.create_group(f"perturbations/p{index:06d}") + features = None + for family in row.families: + if family == "splice_sites": + logits = model._splice_site_logits(trunk)[:, target_start:target_start + config.target_length] + values = logits[:, row.splice_start:row.splice_end] + else: + features = model._dense_features(trunk) if features is None else features + values = model.dense_heads[family](features)[:, row.dense_bin_start:row.dense_bin_end] + # float32: one variant's effect is often below float16 resolution (about 0.004 at + # asinh values of 4-8), so A and B would round to the same numbers. + values = values.float().cpu().numpy() + for side, array in zip("ab", values): + group.create_dataset(f"{side}/{family}", data=array, compressor=COMPRESSOR) + print(json.dumps({"event": "perturb", "done": index + 1, "total": len(rows)}), flush=True) + root.attrs.update({"schema": PERTURBATION_SCHEMA, + "perturbation_ids": rows.perturbation_id.astype(str).tolist()}) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--queries", type=Path, help="another query table, e.g. the development split") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.queries) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/perturb.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/perturb.sh new file mode 100644 index 0000000000000000000000000000000000000000..f07c4714532c2d33e7a419de3877e5daacee89fd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/perturb.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: perturb.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/perturb.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/predict.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..52f3decd4fe2e9d275d08606b74575fafb701434 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/predict.py @@ -0,0 +1,130 @@ +#!/usr/bin/env python3 +"""Stream the single benchmark model into the sharded prediction contract.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import torch +import zarr + +from prediction_contract import DENSE_TRACKS, MAX_JUNCTION_PAIRS, MAX_USAGE_SITES, PREDICTION_SCHEMA +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig + + +COMPRESSOR = Blosc(cname="zstd", clevel=7, shuffle=Blosc.BITSHUFFLE) + + +def main(input_root: Path, output_root: Path, checkpoint: Path, split: str = "test") -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + dataset = AlphaGenomeDataset(input_root, split, include_targets=False) + model.set_junction_track_halves(dataset.junction_track_halves) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "predictions.zarr"), mode="w") + ids = [] + with torch.inference_mode(): + for sample in dataset: + example_id = sample["example_id"] + dna = sample["dna"][None].to(device) + trunk = model._trunk(dna) + dense_features = model._dense_features(trunk) + group = root.create_group(f"examples/{example_id}") + for family in DENSE_TRACKS: + values = model.dense_heads[family](dense_features)[0] + group.create_dataset(family, data=values.cpu().numpy().astype(np.float16), compressor=COMPRESSOR) + + all_site_logits = model._splice_site_logits(trunk) + target_start = (config.input_length - config.target_length) // 2 + group.create_dataset( + "splice_sites", + data=all_site_logits[0, target_start:target_start + config.target_length].cpu().numpy().astype(np.float16), + chunks=(4096, 4), compressor=COMPRESSOR, + ) + predicted_positions = model.select_junction_positions(all_site_logits)[0] + + # Usage at the model's own predicted sites (v4 is sparse; unlisted hidden sites score 0). + usage_bins = model.splice_usage_head(dense_features)[0].float() + sites = predicted_positions.cpu().numpy().astype(np.int64).reshape(-1) + sites = np.unique(sites[(sites >= target_start) & (sites < target_start + config.target_length)] + - target_start)[:MAX_USAGE_SITES] + group.create_dataset("splice_usage_positions", data=sites.astype(np.int32), compressor=COMPRESSOR) + usage = usage_bins[torch.as_tensor(sites // 128, device=usage_bins.device)] if len(sites) \ + else usage_bins[:0] + group.create_dataset("splice_usage", data=usage.cpu().numpy().astype(np.float16), + chunks=(1024, 734), compressor=COMPRESSOR) + + # Junctions: score every donor x acceptor pair of each strand, keep the strongest pairs. + positions_batch = predicted_positions[None] + site_factors = model.junction_site_factors(trunk, positions_batch) + halves = model.junction_track_half + candidates = [] + for row_start in range(0, 512, 128): + for column_start in range(0, 512, 128): + block = torch.cat([ + model.junction_block( + trunk, positions_batch, row_start, row_start + 128, + column_start, column_start + 128, track_start, + min(track_start + 64, config.junction_tracks), site_factors, + )[0] + for track_start in range(0, config.junction_tracks, 64) + ], dim=-1).float() # [128, 128, 734] + for half in (0, 1): + donors = predicted_positions[2 * half, row_start:row_start + 128] + acceptors = predicted_positions[2 * half + 1, column_start:column_start + 128] + valid = (donors[:, None] >= 0) & (acceptors[None, :] >= 0) + strength = block[..., halves == half].amax(dim=-1).masked_fill(~valid, -torch.inf) + keep = min(MAX_JUNCTION_PAIRS // 2, int(valid.sum())) + if not keep: + continue + best = strength.flatten().topk(keep) + rows, columns = best.indices // 128, best.indices % 128 + candidates.append((best.values, torch.full_like(rows, half), donors[rows], + acceptors[columns], block[rows, columns])) + if candidates: + score, half, donor, acceptor, values = (torch.cat(x) for x in zip(*candidates)) + order = score.topk(min(MAX_JUNCTION_PAIRS, len(score))).indices + half, donor, acceptor, values = half[order], donor[order], acceptor[order], values[order] + else: + half = donor = acceptor = torch.zeros(0, dtype=torch.long) + values = torch.zeros(0, config.junction_tracks) + group.create_dataset("junction_half", data=half.cpu().numpy().astype(np.int8), compressor=COMPRESSOR) + group.create_dataset("junction_donor", data=donor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junction_acceptor", data=acceptor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junctions", data=values.cpu().numpy().astype(np.float16), + chunks=(4096, 734), compressor=COMPRESSOR) + + contact = trunk.reshape( + 1, config.contact_bins, -1, config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + contact_values = 0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row]) + group.create_dataset( + "contact_maps", data=contact_values[0].cpu().numpy().astype(np.float16), + chunks=(8192, 28), compressor=COMPRESSOR, + ) + ids.append(example_id) + print(json.dumps({"event": "predict", "example": len(ids), "total": len(dataset)}), flush=True) + root.attrs.update({"schema": PREDICTION_SCHEMA, "example_ids": ids}) + (output_root / "example_ids.json").write_text(json.dumps(ids) + "\n") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--split", default="test", choices=("valid", "test"), + help="manifest to predict; the verifier uses test, local validation uses valid") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.split) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/prediction_contract.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/prediction_contract.py new file mode 100644 index 0000000000000000000000000000000000000000..2455424db002b840cf0d69456930840a7bc5f3cb --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/prediction_contract.py @@ -0,0 +1,23 @@ +"""Public prediction shapes; no trusted splice coordinates are exposed.""" + +from __future__ import annotations + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} + +PREDICTION_SCHEMA = "genome-sequence-to-function-predictions/v4" +MAX_USAGE_SITES = 4_096 +MAX_JUNCTION_PAIRS = 16_384 +# Fixed-shape arrays per example; splice usage and junctions are sparse (see contract.md). +FIXED_SHAPES = { + **{name: (1_536, tracks) for name, tracks in DENSE_TRACKS.items()}, + "splice_sites": (196_608, 4), + "contact_maps": (131_328, 28), +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4879a0e1a3a8a8050bb48360febee152cd141be8 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/__init__.py @@ -0,0 +1 @@ +"""The reference model of the alphagenome-reference baseline.""" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a86a139babb6e899bd5102bbf3c628e1232576b2 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/__init__.py @@ -0,0 +1,5 @@ +"""Compact 1-Mb, eleven-family AlphaGenome reference model.""" + +from .model import CompactModel, ModelConfig, count_parameters + +__all__ = ["CompactModel", "ModelConfig", "count_parameters"] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/alphagenome_50m.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/alphagenome_50m.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0a59a4d0c65752d45eb464d129c6ec9d559665e9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/alphagenome_50m.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 32 +channel_growth: 32 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 384 +num_sequence_blocks: 6 +num_attention_heads: 6 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 256 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/dataset.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..71cd39d15713f648e7d7e31aa1923fa4fa4800dd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/dataset.py @@ -0,0 +1,229 @@ +"""Streaming reader for the compact 1 Mb / eleven-family release.""" + +from __future__ import annotations + +from collections import OrderedDict +from pathlib import Path + +import numpy as np +import pandas as pd +import pysam +import torch +from torch.utils.data import Dataset +import zarr + + +def _open_part(path: Path): + if str(path).endswith(".zarr.zip"): + store = zarr.ZipStore(str(path), mode="r") + try: + return zarr.open_group(store=store, mode="r"), store + except BaseException: + store.close() + raise + return zarr.open_group(str(path), mode="r"), None + + +DENSE_FAMILIES = ( + "atac", + "dnase", + "procap", + "cage", + "rna_seq", + "chip_tf", + "chip_histone", +) +def normalize_splice_positions(value) -> np.ndarray: + """Normalize Arrow's nested-list representation to the public [4, 512] array.""" + + outer = np.asarray(value, dtype=object).reshape(-1).tolist() + positions = np.stack([np.asarray(item, dtype=np.int64) for item in outer]) + if positions.shape != (4, 512): + raise ValueError(f"splice_site_positions must have shape (4, 512), got {positions.shape}") + return positions + + +class AlphaGenomeDataset(Dataset): + """Resolve frozen coordinates to reference DNA and one Zarr example group.""" + + def __init__( + self, + data_root: str | Path, + split: str, + *, + max_open_parts: int = 8, + include_targets: bool = True, + ) -> None: + self.root = Path(data_root) + self.split = split + manifest = self.root / "public" / f"{split}.parquet" + if not manifest.is_file(): + raise FileNotFoundError(f"Missing materialized manifest: {manifest}") + self.rows = pd.read_parquet(manifest).reset_index(drop=True) + junction_tracks = pd.read_parquet(self.root / "public/junction_tracks.parquet") + junction_tracks = junction_tracks.sort_values("tensor_index") + self.junction_track_halves = torch.from_numpy( + junction_tracks.junction_channel_half.to_numpy(np.int64) + ) + if include_targets: + labels_path = self.root / "labels" / f"{split}.parquet" + if not labels_path.is_file(): + raise FileNotFoundError(f"Missing trusted labels manifest: {labels_path}") + labels = pd.read_parquet(labels_path).reset_index(drop=True) + if self.rows.example_id.astype(str).tolist() != labels.example_id.astype(str).tolist(): + raise ValueError("public and labels manifests have different IDs/order") + self.rows = self.rows.merge(labels, on="example_id", validate="one_to_one", sort=False) + self.fasta_path = self.root / "reference" / "GRCh38.p13.genome.fa" + self.max_open_parts = max_open_parts + self.include_targets = include_targets + self._fasta = None + self._parts: OrderedDict[str, tuple[zarr.Group, object | None]] = OrderedDict() + + def __len__(self) -> int: + return len(self.rows) + + def _reference(self): + if self._fasta is None: + self._fasta = pysam.FastaFile(str(self.fasta_path)) + return self._fasta + + def _part(self, relative: str): + if relative in self._parts: + opened = self._parts.pop(relative) + self._parts[relative] = opened + return opened[0] + group, store = _open_part(self.root / relative) + if len(self._parts) >= self.max_open_parts: + _, (_, old_store) = self._parts.popitem(last=False) + if old_store is not None: + old_store.close() + self._parts[relative] = (group, store) + return group + + @staticmethod + def _one_hot(sequence: str) -> np.ndarray: + encoded = np.frombuffer(sequence.upper().encode("ascii"), dtype=np.uint8) + output = np.zeros((len(encoded), 4), dtype=np.float32) + for index, letter in enumerate(b"ACGT"): + output[:, index] = encoded == letter + return output + + def _sequence(self, row) -> tuple[np.ndarray, np.ndarray]: + reference = self._reference() + chromosome_length = reference.get_reference_length(str(row.chromosome)) + start, end = int(row.input_start), int(row.input_end) + clipped_start, clipped_end = max(0, start), min(chromosome_length, end) + sequence = reference.fetch(str(row.chromosome), clipped_start, clipped_end) + left, right = clipped_start - start, end - clipped_end + if left or right: + sequence = "N" * left + sequence + "N" * right + valid = np.ones(end - start, dtype=bool) + if left: + valid[:left] = False + if right: + valid[-right:] = False + return self._one_hot(sequence), valid + + @staticmethod + def _decode(example, name: str, *, asinh: bool) -> np.ndarray: + """Train targets may be stored as 8-bit codes with per-track ranges; they are decoded + here, one example at a time, so the release stays compressed on disk.""" + array = example[name] + if array.dtype != np.uint8: + return np.asarray(array, dtype=np.float32) + lo = np.asarray(example[f"{name}_lo"], dtype=np.float32) + hi = np.asarray(example[f"{name}_hi"], dtype=np.float32) + x = lo + np.asarray(array, dtype=np.float32) * ((hi - lo) / 255.0) + return np.sinh(x) if asinh else x + + @staticmethod + def _dense_target(example, family: str) -> dict[str, torch.Tensor]: + values = AlphaGenomeDataset._decode(example, f"{family}/values", asinh=True) + mask = np.asarray(example[f"{family}/mask"], dtype=bool) + if mask.ndim == 1: + mask = np.broadcast_to(mask[None, :], values.shape).copy() + return { + "values": torch.from_numpy(values), + "mask": torch.from_numpy(mask), + } + + def __getitem__(self, item: int): + row = self.rows.iloc[item] + dna, input_mask = self._sequence(row) + result = { + "example_id": str(row.example_id), + "dna": torch.from_numpy(dna), + "input_mask": torch.from_numpy(input_mask), + "junction_track_halves": self.junction_track_halves, + } + if not self.include_targets: + return result + + part = self._part(str(row.target_shard)) + example = part[str(row.target_group)] + result["dense"] = { + family: self._dense_target(example, family) for family in DENSE_FAMILIES + } + result["splice_sites"] = torch.from_numpy( + np.asarray(example["splice_sites/values"], dtype=np.float32) + ) + result["splice_usage"] = { + "positions": torch.from_numpy( + np.asarray(example["splice_site_usage/positions"], dtype=np.int64) + ), + "values": torch.from_numpy( + np.asarray(example["splice_site_usage/values"], dtype=np.float32) + ), + "mask": torch.from_numpy( + np.asarray(example["splice_site_usage/mask"], dtype=bool) + ), + } + result["usage_positions"] = result["splice_usage"]["positions"] + result["junctions"] = { + name: torch.from_numpy( + np.asarray( + example[f"splice_junctions/{name}"], + dtype=np.float32 if name == "value" else np.int64, + ) + ) + for name in ("row", "column", "track", "value") + } + result["junctions"]["site_positions"] = torch.from_numpy( + np.asarray(example["splice_junctions/site_positions"], dtype=np.int64) + ) + site_positions = normalize_splice_positions(row.splice_site_positions) + result["site_positions"] = torch.from_numpy(site_positions) + if not torch.equal(result["junctions"]["site_positions"], result["site_positions"]): + raise ValueError(f"public/private splice positions differ for {row.example_id}") + scoring_path = self.root / "scoring" / self.split / f"part-{item:06d}.parquet" + scoring = pd.read_parquet(scoring_path) + if not (scoring.example_id.astype(str) == str(row.example_id)).all(): + raise ValueError(f"scoring part ID differs for {row.example_id}") + result["junction_scoring"] = { + "donor_position": torch.from_numpy( + scoring.donor_position.to_numpy(np.int64) + ), + "acceptor_position": torch.from_numpy( + scoring.acceptor_position.to_numpy(np.int64) + ), + "track": torch.from_numpy(scoring.track.to_numpy(np.int64)), + "target_value": torch.from_numpy( + scoring.target_value.to_numpy(np.float32) + ), + "valid": torch.from_numpy(scoring.valid.to_numpy(bool)), + } + result["contact_maps"] = torch.from_numpy( + self._decode(example, "contact_maps/upper_triangle", asinh=False) + ) + result["contact_map_mask"] = torch.from_numpy( + np.asarray(example["contact_maps/mask"], dtype=bool) + ) + return result + + +def single_example_collate(items): + """Keep variable-size event tensors intact; the reference currently trains at batch one.""" + + if len(items) != 1: + raise ValueError("The compact event loader currently requires batch_size=1") + return items[0] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/layers.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..af31f86a5589a16f0007e6aca5ca7a7bd552d6a9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/layers.py @@ -0,0 +1,124 @@ +"""Shared neural-network layers for the single compact AlphaGenome model.""" + +from __future__ import annotations + +import math + +import torch +from torch import Tensor, nn +from torch.nn import functional as F + + +class ChannelRMSNorm(nn.Module): + def __init__(self, channels: int, eps: float = 1e-6): + super().__init__() + self.weight = nn.Parameter(torch.ones(channels)) + self.eps = eps + + def forward(self, x: Tensor) -> Tensor: + variance = x.float().square().mean(dim=1, keepdim=True) + normalized = x * torch.rsqrt(variance + self.eps).to(x.dtype) + return normalized * self.weight[None, :, None].to(x.dtype) + + +class StandardizedConv1d(nn.Conv1d): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__(in_channels, out_channels, kernel_size, padding="same", bias=True) + self.scale = nn.Parameter(torch.ones(out_channels)) + nn.init.trunc_normal_(self.weight, std=1.0 / math.sqrt(in_channels * kernel_size)) + nn.init.trunc_normal_(self.bias, std=1e-4) + + def forward(self, x: Tensor) -> Tensor: + centered = self.weight - self.weight.mean(dim=(1, 2), keepdim=True) + variance = centered.float().square().mean(dim=(1, 2), keepdim=True) + fan_in = self.weight.shape[1] * self.weight.shape[2] + multiplier = self.scale[:, None, None] * torch.rsqrt( + (fan_in * variance).clamp_min(1e-4) + ).to(self.weight.dtype) + return F.conv1d(x, centered * multiplier, self.bias, padding="same") + + +class ConvBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__() + self.norm = ChannelRMSNorm(in_channels) + self.conv = StandardizedConv1d(in_channels, out_channels, kernel_size) + + def forward(self, x: Tensor) -> Tensor: + return self.conv(F.gelu(self.norm(x), approximate="tanh")) + + +def _apply_rope(x: Tensor) -> Tensor: + rotary_dim = x.shape[-1] - x.shape[-1] % 2 + if rotary_dim == 0: + return x + positions = torch.arange(x.shape[-2], device=x.device, dtype=torch.float32) + inverse_frequency = 1.0 / ( + 10_000 ** ( + torch.arange(0, rotary_dim, 2, device=x.device, dtype=torch.float32) + / rotary_dim + ) + ) + angles = torch.outer(positions, inverse_frequency) + cos = angles.cos()[None, None, :, :].to(x.dtype) + sin = angles.sin()[None, None, :, :].to(x.dtype) + even, odd = x[..., :rotary_dim:2], x[..., 1:rotary_dim:2] + rotated = torch.stack((even * cos - odd * sin, even * sin + odd * cos), dim=-1) + return torch.cat((rotated.flatten(-2), x[..., rotary_dim:]), dim=-1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.num_heads = config.num_attention_heads + self.head_dim = config.attention_head_dim + inner_dim = self.num_heads * self.head_dim + self.norm = nn.RMSNorm(config.hidden_dim) + self.qkv = nn.Linear(config.hidden_dim, 3 * inner_dim, bias=False) + self.output = nn.Linear(inner_dim, config.hidden_dim) + self.dropout = config.dropout + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + batch, length, _ = x.shape + qkv = self.qkv(self.norm(x)).view( + batch, length, 3, self.num_heads, self.head_dim + ) + q, k, value = qkv.unbind(dim=2) + attended = F.scaled_dot_product_attention( + _apply_rope(q.transpose(1, 2)), + _apply_rope(k.transpose(1, 2)), + value.transpose(1, 2), + attn_mask=attention_bias, + dropout_p=self.dropout if self.training else 0.0, + ) + return self.output(attended.transpose(1, 2).reshape(batch, length, -1)) + + +class MLPBlock(nn.Module): + def __init__(self, config): + super().__init__() + expanded = int(config.hidden_dim * config.mlp_expansion) + self.norm = nn.RMSNorm(config.hidden_dim) + self.input = nn.Linear(config.hidden_dim, expanded) + self.output = nn.Linear(expanded, config.hidden_dim) + self.dropout = nn.Dropout(config.dropout) + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor) -> Tensor: + return self.dropout( + self.output(F.gelu(self.input(self.norm(x)), approximate="tanh")) + ) + + +class SequenceBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.attention = MultiHeadAttention(config) + self.mlp = MLPBlock(config) + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + x = x + self.attention(x, attention_bias) + return x + self.mlp(x) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/model.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/model.py new file mode 100644 index 0000000000000000000000000000000000000000..316ae8b495a102a194b3b1c5088d815e5d66e74b --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/model.py @@ -0,0 +1,426 @@ +"""Affordable multi-resolution baseline for the compact AlphaGenome task.""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass, fields +import math +from pathlib import Path +from typing import Any, Iterable + +import torch +from torch import Tensor, nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint +import yaml + +from .layers import ChannelRMSNorm, ConvBlock, SequenceBlock + + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} +ALL_FAMILIES = (*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions", "contact_maps") + + +@dataclass(frozen=True) +class ModelConfig: + input_length: int = 1_048_576 + target_length: int = 196_608 + dense_bin_size: int = 128 + trunk_bin_size: int = 1_024 + contact_bin_size: int = 2_048 + stem_stride: int = 8 + encoder_width: int = 32 + channel_growth: int = 32 + num_downsampling_blocks: int = 7 + conv_kernel_size: int = 5 + dna_kernel_size: int = 15 + hidden_dim: int = 384 + num_sequence_blocks: int = 6 + num_attention_heads: int = 6 + attention_head_dim: int = 64 + mlp_expansion: float = 4.0 + dropout: float = 0.0 + head_hidden_size: int = 256 + junction_rank: int = 32 + splice_usage_tracks: int = 734 + junction_tracks: int = 734 + contact_tracks: int = 28 + gradient_checkpointing: bool = True + enable_pairwise: bool = False + pair_width: int = 32 + pair_downsample: int = 16 + pair_update_interval: int = 2 + local_width: int = 0 + + def __post_init__(self) -> None: + downsample = self.stem_stride * 2**self.num_downsampling_blocks + if downsample != self.trunk_bin_size: + raise ValueError("stem_stride * 2**num_downsampling_blocks must equal trunk_bin_size") + for length in (self.input_length, self.target_length): + if length % self.trunk_bin_size: + raise ValueError("input and target lengths must divide by trunk_bin_size") + if self.trunk_bin_size % self.dense_bin_size: + raise ValueError("trunk_bin_size must divide by dense_bin_size") + if self.input_length % self.contact_bin_size: + raise ValueError("input_length must divide by contact_bin_size") + if self.hidden_dim <= 0 or self.num_attention_heads <= 0: + raise ValueError("hidden dimensions and attention heads must be positive") + + @property + def trunk_bins(self) -> int: + return self.input_length // self.trunk_bin_size + + @property + def target_trunk_bins(self) -> int: + return self.target_length // self.trunk_bin_size + + @property + def dense_bins(self) -> int: + return self.target_length // self.dense_bin_size + + @property + def dense_upsample(self) -> int: + return self.trunk_bin_size // self.dense_bin_size + + @property + def contact_bins(self) -> int: + return self.input_length // self.contact_bin_size + + @property + def encoder_output_width(self) -> int: + return self.encoder_width + self.channel_growth * self.num_downsampling_blocks + + @classmethod + def from_yaml(cls, path: str | Path) -> "ModelConfig": + values: dict[str, Any] = yaml.safe_load(Path(path).read_text()) or {} + known = {field.name for field in fields(cls)} + unknown = sorted(values.keys() - known) + if unknown: + raise ValueError(f"Unknown model configuration keys: {unknown}") + return cls(**values) + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + +class SequenceEncoder(nn.Module): + def __init__(self, config: ModelConfig): + super().__init__() + self.stem = nn.Conv1d( + 4, + config.encoder_width, + config.dna_kernel_size, + stride=config.stem_stride, + padding=config.dna_kernel_size // 2, + ) + blocks = [] + width = config.encoder_width + for _ in range(config.num_downsampling_blocks): + output_width = width + config.channel_growth + blocks.append(ConvBlock(width, output_width, config.conv_kernel_size)) + width = output_width + self.blocks = nn.ModuleList(blocks) + + def forward(self, dna: Tensor) -> Tensor: + x = F.gelu(self.stem(dna.transpose(1, 2)), approximate="tanh") + for block in self.blocks: + x = F.max_pool1d(block(x), kernel_size=2, stride=2) + return x.transpose(1, 2) + + +class LocalResidualBlock(nn.Module): + def __init__(self, width: int, dilation: int): + super().__init__() + self.norm = ChannelRMSNorm(width) + self.depthwise = nn.Conv1d(width, width, 9, dilation=dilation, + padding=4 * dilation, groups=width) + self.expand = nn.Conv1d(width, 2 * width, 1) + self.contract = nn.Conv1d(2 * width, width, 1) + + def forward(self, x: Tensor) -> Tensor: + update = self.depthwise(self.norm(x)) + update = self.contract(F.gelu(self.expand(update), approximate='tanh')) + return x + update + + +class LocalSequencePath(nn.Module): + """Base-resolution motif path; 701-bp receptive field and aligned 128-bp pooling.""" + def __init__(self, config: ModelConfig): + super().__init__() + width = config.local_width + self.stem = nn.Conv1d(4, width, 21, padding=10) + self.blocks = nn.Sequential(*(LocalResidualBlock(width, d) for d in [1, 4, 16, 64])) + self.norm = ChannelRMSNorm(width) + self.sites = nn.Conv1d(width, 4, 1) + self.bin_size = config.dense_bin_size + + def forward(self, dna: Tensor) -> tuple[Tensor, Tensor]: + base = F.gelu(self.stem(dna), approximate='tanh') + base = F.gelu(self.norm(self.blocks(base)), approximate='tanh') + sites = self.sites(base).transpose(1, 2) + pooled = torch.cat((F.avg_pool1d(base, self.bin_size), + F.max_pool1d(base, self.bin_size)), dim=1).transpose(1, 2) + return sites, pooled + + +class CompactModel(nn.Module): + """One shared 1-kb trunk with representation-specific output heads.""" + + def __init__(self, config: ModelConfig | None = None): + super().__init__() + self.config = config or ModelConfig() + self.encoder = SequenceEncoder(self.config) + self.projection = nn.Linear(self.config.encoder_output_width, self.config.hidden_dim) + self.blocks = nn.ModuleList( + SequenceBlock(self.config) for _ in range(self.config.num_sequence_blocks) + ) + self.final_norm = nn.RMSNorm(self.config.hidden_dim) + self.dense_decoder = nn.Sequential( + nn.RMSNorm(self.config.hidden_dim), + nn.Linear(self.config.hidden_dim, self.config.head_hidden_size), + nn.GELU(approximate="tanh"), + ) + self.dense_heads = nn.ModuleDict( + { + family: nn.Linear(self.config.head_hidden_size, tracks) + for family, tracks in DENSE_TRACKS.items() + } + ) + self.splice_site_head = nn.Linear( + self.config.head_hidden_size, self.config.dense_bin_size * 4 + ) + self.splice_usage_head = nn.Linear( + self.config.head_hidden_size, self.config.splice_usage_tracks + ) + rank = self.config.junction_rank + self.junction_source = nn.Linear(self.config.head_hidden_size, rank) + self.junction_destination = nn.Linear(self.config.head_hidden_size, rank) + self.junction_track = nn.Embedding(self.config.junction_tracks, rank) + if self.config.junction_tracks % 2: + raise ValueError("junction tracks must split evenly into strand halves") + self.register_buffer( + "junction_track_half", + torch.arange(self.config.junction_tracks) // (self.config.junction_tracks // 2), + persistent=False, + ) + self.contact_q = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + self.contact_k = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + row, column = torch.triu_indices( + self.config.contact_bins, self.config.contact_bins + ) + self.register_buffer("contact_row", row, persistent=False) + self.register_buffer("contact_column", column, persistent=False) + if self.config.local_width: + self.local_path = LocalSequencePath(self.config) + self.local_projection = nn.Linear(2 * self.config.local_width, self.config.head_hidden_size) + # A new local path can be attached to our own checkpoint without changing its initial outputs. + nn.init.zeros_(self.local_path.sites.weight) + nn.init.zeros_(self.local_path.sites.bias) + nn.init.zeros_(self.local_projection.weight) + nn.init.zeros_(self.local_projection.bias) + self._local_sites = None + self._local_features = None + + def set_junction_track_halves(self, halves: Tensor) -> None: + halves = torch.as_tensor(halves, dtype=torch.long, device=self.junction_track_half.device) + if halves.shape != (self.config.junction_tracks,) or not torch.isin( + halves, torch.tensor([0, 1], device=halves.device) + ).all(): + raise ValueError("junction track halves must be a 734-element 0/1 mapping") + self.junction_track_half.copy_(halves) + + def _trunk(self, dna: Tensor) -> Tensor: + if dna.ndim != 3 or dna.shape[1:] != (self.config.input_length, 4): + raise ValueError( + f"Expected DNA [B,{self.config.input_length},4], got {tuple(dna.shape)}" + ) + if self.config.local_width: + if self.config.gradient_checkpointing and self.training: + sites, pooled = checkpoint(self.local_path, dna.transpose(1, 2), use_reentrant=False) + else: + sites, pooled = self.local_path(dna.transpose(1, 2)) + self._local_sites = sites + self._local_features = self.local_projection(pooled) + x = self.projection(self.encoder(dna)) + for block in self.blocks: + if self.config.gradient_checkpointing and self.training and x.requires_grad: + x = checkpoint(block, x, None, use_reentrant=False) + else: + x = block(x, None) + return self.final_norm(x) + + def _dense_features(self, trunk: Tensor) -> Tensor: + start = (self.config.trunk_bins - self.config.target_trunk_bins) // 2 + target = trunk[:, start : start + self.config.target_trunk_bins] + target = target.repeat_interleave(self.config.dense_upsample, dim=1) + features = self.dense_decoder(target) + if self.config.local_width: + first_bin = (self.config.input_length - self.config.target_length) // (2 * self.config.dense_bin_size) + features = features + self._local_features[:, first_bin:first_bin + self.config.dense_bins] + return features + + def _all_dense_features(self, trunk: Tensor) -> Tensor: + features = self.dense_decoder( + trunk.repeat_interleave(self.config.dense_upsample, dim=1) + ) + return features + self._local_features if self.config.local_width else features + + def _splice_site_logits(self, trunk: Tensor) -> Tensor: + logits = self.splice_site_head(self._all_dense_features(trunk)) + logits = logits.view(len(trunk), self.config.input_length, 4) + if self.config.local_width: + logits = logits + self._local_sites + return logits.float() + + def _position_features(self, trunk: Tensor, positions: Tensor) -> Tensor: + features = self.dense_decoder(self._gather_bins(trunk, positions, self.config.trunk_bin_size)) + if self.config.local_width: + features = features + self._gather_bins(self._local_features, positions, self.config.dense_bin_size) + return features + + @staticmethod + def select_junction_positions(site_logits: Tensor, limit: int = 512) -> Tensor: + """Select each site type from model scores alone, then sort by coordinate. + + This follows the four independent site classes in Google DeepMind's + AlphaGenome splicing implementation (pinned in docs/v3-author/PROTOCOL.md). Padding is + -1 when an input is shorter than ``limit``. + """ + count = min(limit, site_logits.shape[1]) + selected = [] + for site_type in range(4): + coordinates = torch.topk(site_logits[..., site_type], count, dim=1).indices + coordinates = coordinates.sort(dim=1).values + if count < limit: + coordinates = F.pad(coordinates, (0, limit - count), value=-1) + selected.append(coordinates) + return torch.stack(selected, dim=1) + + @staticmethod + def _gather_bins(features: Tensor, positions: Tensor, bin_size: int) -> Tensor: + indices = torch.div(positions, bin_size, rounding_mode="floor") + indices = indices.clamp(0, features.shape[1] - 1) + return torch.gather( + features, + 1, + indices[..., None].expand(-1, -1, features.shape[-1]), + ) + + def _junction_predictions( + self, trunk: Tensor, site_positions: Tensor, queries: Tensor + ) -> Tensor: + row, column, track = queries.unbind(dim=-1) + half = self.junction_track_half[track] + batch = torch.arange(len(trunk), device=trunk.device)[:, None] + donor = site_positions[batch, 2 * half, row] + acceptor = site_positions[batch, 2 * half + 1, column] + source = self._position_features(trunk, donor.clamp(0, self.config.input_length - 1)) + destination = self._position_features(trunk, acceptor.clamp(0, self.config.input_length - 1)) + interaction = self.junction_source(source) * self.junction_destination(destination) + track_embedding = self.junction_track(track) + return (interaction * track_embedding).sum(dim=-1) / math.sqrt( + self.config.junction_rank + ) + + def junction_block( + self, + trunk: Tensor, + site_positions: Tensor, + row_start: int, + row_end: int, + column_start: int, + column_end: int, + track_start: int, + track_end: int, + site_factors: tuple[Tensor, Tensor] | None = None, + ) -> Tensor: + """Compute a bounded block of the full predicted-position junction cube.""" + tracks = torch.arange(track_start, track_end, device=trunk.device) + halves = self.junction_track_half[tracks] + if site_factors is None: + site_factors = self.junction_site_factors(trunk, site_positions) + source_all, destination_all = site_factors + source = source_all[:, halves, row_start:row_end] + destination = destination_all[:, halves, column_start:column_end] + embedding = self.junction_track(tracks)[None, :, None, None, :] + values = ( + source[:, :, :, None, :] * destination[:, :, None, :, :] * embedding + ).sum(dim=-1) / math.sqrt(self.config.junction_rank) + return values.permute(0, 2, 3, 1).float() + + def junction_site_factors( + self, trunk: Tensor, site_positions: Tensor + ) -> tuple[Tensor, Tensor]: + """Precompute the two strand-specific factors reused by all cube blocks.""" + donor = torch.stack((site_positions[:, 0], site_positions[:, 2]), dim=1).clamp_min(0) + acceptor = torch.stack((site_positions[:, 1], site_positions[:, 3]), dim=1).clamp_min(0) + donor_features = self._position_features(trunk, donor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, donor.shape[-1], -1) + acceptor_features = self._position_features(trunk, acceptor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, acceptor.shape[-1], -1) + return self.junction_source(donor_features), self.junction_destination(acceptor_features) + + def forward( + self, + dna: Tensor, + *, + families: Iterable[str] | None = None, + usage_positions: Tensor | None = None, + junction_site_positions: Tensor | None = None, + junction_queries: Tensor | None = None, + ) -> dict[str, Tensor]: + requested = set(families or ALL_FAMILIES) + unknown = requested - set(ALL_FAMILIES) + if unknown: + raise ValueError(f"Unknown output families: {sorted(unknown)}") + trunk = self._trunk(dna) + needs_dense = bool( + requested.intersection((*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions")) + ) + features = self._dense_features(trunk) if needs_dense else None + outputs: dict[str, Tensor] = {} + for family in DENSE_TRACKS: + if family in requested: + outputs[family] = self.dense_heads[family](features).float() + if "splice_sites" in requested: + all_logits = self._splice_site_logits(trunk) + start = (self.config.input_length - self.config.target_length) // 2 + outputs["splice_sites"] = all_logits[:, start:start + self.config.target_length] + if "splice_usage" in requested: + if usage_positions is None: + raise ValueError("splice_usage requires usage_positions") + gathered = self._gather_bins( + features, usage_positions, self.config.dense_bin_size + ) + outputs["splice_usage"] = self.splice_usage_head(gathered).float() + if "junctions" in requested: + if junction_site_positions is None or junction_queries is None: + raise ValueError("junctions require site positions and query triples") + outputs["junctions"] = self._junction_predictions( + trunk, junction_site_positions, junction_queries + ).float() + if "contact_maps" in requested: + contact = trunk.reshape( + len(dna), self.config.contact_bins, -1, self.config.hidden_dim + ).mean(dim=2) + q, k = self.contact_q(contact), self.contact_k(contact) + row, column = self.contact_row, self.contact_column + outputs["contact_maps"] = ( + 0.5 + * ( + q[:, row] * k[:, column] + + q[:, column] * k[:, row] + ) + ).float() + return outputs + + +def count_parameters(model: nn.Module) -> int: + return sum(parameter.numel() for parameter in model.parameters() if parameter.requires_grad) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/reference_large.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/reference_large.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e403757c49eb6e6419927210c7053710964213d1 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/reference_large.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 96 +channel_growth: 96 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 1024 +num_sequence_blocks: 16 +num_attention_heads: 16 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 768 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/run_ddp.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/run_ddp.sh new file mode 100644 index 0000000000000000000000000000000000000000..c4702e9366e907b7791063e77dcfe281411ac8cd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/run_ddp.sh @@ -0,0 +1,16 @@ +#!/usr/bin/env bash +# run_ddp.sh DATA_DIR OUTPUT_DIR CONFIG EPOCHS [GPUS=8] [WORKERS_PER_GPU=12] +# Train the reference on every train interval with one example per GPU per step, then score +# validation with the verifier's own scorer. Runs inside the task image (torch, torchrun). +set -Eeuo pipefail +DATA=$1; OUT=$2; CONFIG=$3; EPOCHS=$4; GPUS=${5:-8}; WORKERS=${6:-12} +BASELINE=$(cd "$(dirname "$0")/../.." && pwd) +mkdir -p "$OUT" +cd "$BASELINE" +# Loopback rendezvous: works offline, where --standalone cannot resolve the hostname. +torchrun --nnodes 1 --nproc-per-node "$GPUS" --master-addr 127.0.0.1 --master-port "${MASTER_PORT:-29511}" -m reference.alphagenome_50m.train_eval train \ + --input "$DATA" --config "$CONFIG" --output "$OUT" --epochs "$EPOCHS" --workers "$WORKERS" \ + --log-every 200 --save-every 2000 2>&1 | tee -a "$OUT/train.log" +python predict.py "$DATA" "$OUT/valid-pred" --checkpoint "$OUT/last.pt" --split valid +python /workspace/evaluation/score.py --predictions "$OUT/valid-pred/predictions.zarr" \ + --labels "$DATA" --split valid --output "$OUT/valid-score" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/tiny_overfit.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/tiny_overfit.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c25f9ffbb76102c970e5767922400f7ec2b6c53e --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/tiny_overfit.yaml @@ -0,0 +1,24 @@ +# Keep the frozen 1-Mb input and every output track; shrink only learned widths. +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 8 +channel_growth: 4 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 32 +num_sequence_blocks: 1 +num_attention_heads: 2 +attention_head_dim: 16 +mlp_expansion: 2.0 +dropout: 0.0 +head_hidden_size: 32 +junction_rank: 8 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: false diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/train_eval.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/train_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..92f9a3f315013630fdd2e03fb6840a6c77840edd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/reference/alphagenome_50m/train_eval.py @@ -0,0 +1,537 @@ +"""Train and stream-evaluate the <=50M compact AlphaGenome baseline.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import numpy as np +import torch +from torch import Tensor +from torch.nn import functional as F +from torch.utils.data import DataLoader, Subset + +from .dataset import ( + AlphaGenomeDataset, + DENSE_FAMILIES, + single_example_collate, +) +from .model import ( + ALL_FAMILIES, + CompactModel, + ModelConfig, + count_parameters, +) + + +DEFAULT_CONFIG = Path(__file__).with_name("alphagenome_50m.yaml") + + +def _device(name: str) -> torch.device: + if name == "auto": + name = "cuda" if torch.cuda.is_available() else "cpu" + if name == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("--device cuda requested but CUDA is unavailable") + return torch.device(name) + + +def _autocast(device: torch.device): + enabled = device.type == "cuda" + dtype = torch.bfloat16 if enabled and torch.cuda.is_bf16_supported() else torch.float16 + return torch.autocast(device_type=device.type, dtype=dtype, enabled=enabled) + + +def _distributed() -> tuple[int, int, int]: + """(rank, local_rank, world_size) under torchrun; (0, 0, 1) otherwise.""" + world = int(os.environ.get("WORLD_SIZE", "1")) + if world > 1 and not torch.distributed.is_initialized(): + torch.distributed.init_process_group("nccl") + return int(os.environ.get("RANK", "0")), int(os.environ.get("LOCAL_RANK", "0")), world + + +def _loader(dataset, *, workers: int, shuffle: bool, sampler=None) -> DataLoader: + return DataLoader( + dataset, + batch_size=1, + shuffle=shuffle and sampler is None, + sampler=sampler, + num_workers=workers, + persistent_workers=workers > 0, + pin_memory=torch.cuda.is_available(), + collate_fn=single_example_collate, + ) + + +def _to_device(value, device: torch.device): + if isinstance(value, Tensor): + return value.to(device, non_blocking=True) + if isinstance(value, dict): + return {key: _to_device(item, device) for key, item in value.items()} + return value + + +def _match_coordinate_queries( + scoring: dict[str, Tensor], positions: Tensor, track_halves: Tensor +) -> tuple[Tensor, Tensor, Tensor]: + """Match trusted coordinate queries to typed position axes like the grader.""" + device = positions.device + tracks = scoring["track"].detach().cpu().numpy() + halves_cpu = track_halves.detach().cpu().numpy()[tracks] + # Build indices on CPU, then copy once. Writing three CUDA scalars per valid query + # otherwise launches thousands of tiny kernels per example, especially costly when + # training and evaluation share one GPU. The matching semantics are unchanged. + queries = np.zeros((len(tracks), 3), dtype=np.int64) + queries[:, 2] = tracks + valid = scoring["valid"].clone() + missing = np.ones(len(tracks), dtype=bool) + position_rows = positions.detach().cpu().numpy() + donors_cpu = scoring["donor_position"].detach().cpu().numpy() + acceptors_cpu = scoring["acceptor_position"].detach().cpu().numpy() + valid_indices = np.flatnonzero(valid.detach().cpu().numpy()) + lookups = [ + {int(coordinate): index for index, coordinate in enumerate(axis) if coordinate >= 0} + for axis in position_rows + ] + for index in valid_indices: + half = int(halves_cpu[index]) + row = lookups[2 * half].get(int(donors_cpu[index])) + column = lookups[2 * half + 1].get(int(acceptors_cpu[index])) + if row is not None and column is not None: + queries[index, 0] = row + queries[index, 1] = column + missing[index] = False + return (torch.from_numpy(queries).to(device).unsqueeze(0), valid, + torch.from_numpy(missing).to(device)) + + +def _model_inputs(sample: dict) -> tuple[dict, Tensor, Tensor]: + queries, query_valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + sample["site_positions"], + sample["junction_track_halves"], + ) + if (query_valid & missing).any(): + raise ValueError("trusted scoring coordinates do not match target positions") + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + return { + "dna": sample["dna"].unsqueeze(0), + "usage_positions": sample["usage_positions"].unsqueeze(0), + "junction_site_positions": sample["site_positions"].unsqueeze(0), + "junction_queries": queries, + }, junction_target, query_valid + + +def _losses(outputs: dict[str, Tensor], sample: dict, junction_target: Tensor, + junction_valid: Tensor) -> dict[str, Tensor]: + losses = {} + for family in DENSE_FAMILIES: + target = torch.asinh(sample["dense"][family]["values"]).unsqueeze(0) + mask = sample["dense"][family]["mask"].unsqueeze(0) + residual = torch.where(mask, outputs[family] - target, 0.0) + losses[family] = residual.square().sum() / mask.sum().clamp_min(1) + losses["splice_sites"] = F.binary_cross_entropy_with_logits( + outputs["splice_sites"], sample["splice_sites"].unsqueeze(0) + ) + usage_target = torch.asinh(sample["splice_usage"]["values"]).unsqueeze(0) + usage_mask = sample["splice_usage"]["mask"].unsqueeze(0) + usage_residual = torch.where(usage_mask, outputs["splice_usage"] - usage_target, 0.0) + losses["splice_usage"] = usage_residual.square().sum() / usage_mask.sum().clamp_min(1) + junction_residual = outputs["junctions"][0] - junction_target[0] + losses["junctions"] = ( + junction_residual[junction_valid].square().mean() + if junction_valid.any() else junction_residual.sum() * 0 + ) + contact_target = torch.asinh(sample["contact_maps"]).unsqueeze(0) + contact_mask = sample["contact_map_mask"].unsqueeze(0) + contact_residual = torch.where( + contact_mask, outputs["contact_maps"] - contact_target, 0.0 + ) + losses["contact_maps"] = contact_residual.square().sum() / contact_mask.sum().clamp_min(1) + losses["total"] = torch.stack([losses[name] for name in ALL_FAMILIES]).mean() + return losses + + +def _validation_inference( + model: CompactModel, sample: dict +) -> tuple[dict[str, Tensor], Tensor, Tensor, Tensor]: + """Use the same predicted-position path as a formal v3 submission.""" + trunk = model._trunk(sample["dna"].unsqueeze(0)) + dense_features = model._dense_features(trunk) + outputs = { + family: model.dense_heads[family](dense_features).float() + for family in DENSE_FAMILIES + } + all_sites = model._splice_site_logits(trunk) + target_start = (model.config.input_length - model.config.target_length) // 2 + outputs["splice_sites"] = all_sites[:, target_start:target_start + model.config.target_length] + usage_bins = model.splice_usage_head(dense_features).float() + usage_indices = torch.div(sample["usage_positions"], model.config.dense_bin_size, + rounding_mode="floor") + outputs["splice_usage"] = usage_bins[:, usage_indices] + predicted_positions = model.select_junction_positions(all_sites) + + matched, valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + predicted_positions[0], + sample["junction_track_halves"], + ) + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + junction_prediction = torch.zeros(len(valid), device=valid.device) + if (~missing & valid).any(): + selected = ~missing & valid + junction_prediction[selected] = model._junction_predictions( + trunk, predicted_positions, matched[:, selected] + )[0] + outputs["junctions"] = junction_prediction.unsqueeze(0) + + contact = trunk.reshape( + 1, model.config.contact_bins, -1, model.config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + outputs["contact_maps"] = (0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row])).float() + return outputs, junction_target, valid, missing + + +def _atomic_save(payload: dict, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(f".{path.name}.tmp") + torch.save(payload, temporary) + os.replace(temporary, path) + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(8 * 1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _resume_position(payload: dict, *, examples: int, epochs: int) -> tuple[int, int]: + epoch = int(payload["epoch"]) + step = int(payload["global_step"]) + kind = payload.get("checkpoint_kind") + if kind not in (None, "epoch") or step != epoch * examples: + raise ValueError("only a completed-epoch checkpoint can resume training") + if payload.get("examples", examples) != examples or payload.get("schedule_epochs", epochs) != epochs: + raise ValueError("checkpoint data size or training schedule differs from this run") + return epoch, step + + +def train(args: argparse.Namespace) -> None: + # Multi-GPU with `torchrun --nproc-per-node N train_eval.py train ...`: one example per GPU per + # step (data parallel), rank 0 writes checkpoints and logs. Model size is bounded by the + # training budget, not by a parameter cap. + rank, local_rank, world = _distributed() + config = ModelConfig.from_yaml(args.config) + device = torch.device("cuda", local_rank) if world > 1 else _device(args.device) + if world > 1: + torch.cuda.set_device(device) + torch.manual_seed(args.seed) + model = CompactModel(config).to(device) + parameters = count_parameters(model) + optimizer = torch.optim.AdamW( + model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay + ) + scaler = torch.amp.GradScaler("cuda", enabled=device.type == "cuda") + full_dataset = AlphaGenomeDataset(args.input, "train") + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + start_epoch, global_step = 0, 0 + if args.resume: + payload = torch.load(args.resume, map_location="cpu", weights_only=True) + if payload.get("config") != config.to_dict(): + raise ValueError("checkpoint configuration does not match --config") + model.load_state_dict(payload["model"], strict=True) + optimizer.load_state_dict(payload["optimizer"]) + scaler.load_state_dict(payload.get("scaler", {})) + start_epoch, global_step = _resume_position(payload, examples=size, epochs=args.epochs) + per_rank = math.ceil(size / world) + steps = max(1, per_rank * args.epochs) + module = model + if world > 1: + model = torch.nn.parallel.DistributedDataParallel( + model, device_ids=[local_rank], find_unused_parameters=True) + sampler = (torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world, rank=rank, + shuffle=True, seed=args.seed) + if world > 1 else None) + stop_after_epoch = args.stop_after_epoch or args.epochs + output = args.output.resolve() + output.mkdir(parents=True, exist_ok=True) + started = time.monotonic() + if rank == 0: + print(json.dumps({"event": "train_start", "parameters": parameters, "examples": size, + "device": str(device), "world_size": world}), flush=True) + + for epoch in range(start_epoch, stop_after_epoch): + model.train() + if sampler is not None: + sampler.set_epoch(epoch) + loader = _loader(dataset, workers=args.workers, shuffle=True, sampler=sampler) + for sample in loader: + sample = _to_device(sample, device) + inputs, junction_target, junction_valid = _model_inputs(sample) + progress = min((global_step + 1) / steps, 1.0) + learning_rate = args.min_learning_rate + 0.5 * ( + args.learning_rate - args.min_learning_rate + ) * (1.0 + math.cos(math.pi * progress)) + for group in optimizer.param_groups: + group["lr"] = learning_rate + optimizer.zero_grad(set_to_none=True) + with _autocast(device): + predictions = model(**inputs) + losses = _losses(predictions, sample, junction_target, junction_valid) + if not torch.isfinite(losses["total"]): + raise FloatingPointError(f"non-finite loss at step {global_step}") + scaler.scale(losses["total"]).backward() + scaler.unscale_(optimizer) + gradient_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_gradient_norm) + scaler.step(optimizer) + scaler.update() + global_step += 1 + if rank == 0 and (global_step == 1 or global_step % args.log_every == 0): + print( + json.dumps( + { + "event": "train_step", + "epoch": epoch + 1, + "step": global_step, + "loss": float(losses["total"].detach()), + "gradient_norm": float(gradient_norm), + "learning_rate": learning_rate, + "elapsed_seconds": time.monotonic() - started, + } + ), + flush=True, + ) + if rank == 0 and global_step % args.save_every == 0: + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch, + "global_step": global_step, + "checkpoint_kind": "progress", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "progress.pt", + ) + if world > 1: + torch.distributed.barrier() + if rank != 0: + continue + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch + 1, + "global_step": global_step, + "checkpoint_kind": "epoch", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "last.pt", + ) + if world > 1: + torch.distributed.barrier() + torch.distributed.destroy_process_group() + if rank != 0: + return + checkpoint = output / "last.pt" + manifest = { + "schema": "alphagenome-50m-real/v3", + "checkpoint": {"path": checkpoint.name, "sha256": _sha256(checkpoint)}, + "parameters": parameters, + "config": config.to_dict(), + "output_families": list(ALL_FAMILIES), + "epochs": args.epochs, + "completed_epochs": stop_after_epoch, + "global_step": global_step, + } + (output / "model_manifest.json").write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "train_complete", **manifest}), flush=True) + + +class PerTrackMoments: + def __init__(self, tracks: int): + self.n = np.zeros(tracks, dtype=np.int64) + self.sum_x = np.zeros(tracks, dtype=np.float64) + self.sum_y = np.zeros(tracks, dtype=np.float64) + self.sum_x2 = np.zeros(tracks, dtype=np.float64) + self.sum_y2 = np.zeros(tracks, dtype=np.float64) + self.sum_xy = np.zeros(tracks, dtype=np.float64) + + def update(self, prediction: Tensor, target: Tensor, mask: Tensor | None = None) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1, prediction.shape[-1]) + y = target.detach().double().cpu().numpy().reshape(-1, target.shape[-1]) + valid = np.ones_like(y, dtype=bool) if mask is None else mask.detach().cpu().numpy().reshape(y.shape).astype(bool) + self.n += valid.sum(axis=0) + self.sum_x += np.where(valid, x, 0).sum(axis=0, dtype=np.float64) + self.sum_y += np.where(valid, y, 0).sum(axis=0, dtype=np.float64) + self.sum_x2 += np.where(valid, x * x, 0).sum(axis=0, dtype=np.float64) + self.sum_y2 += np.where(valid, y * y, 0).sum(axis=0, dtype=np.float64) + self.sum_xy += np.where(valid, x * y, 0).sum(axis=0, dtype=np.float64) + + def update_indexed(self, prediction: Tensor, target: Tensor, track: Tensor) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1) + y = target.detach().double().cpu().numpy().reshape(-1) + indices = track.detach().cpu().numpy().reshape(-1).astype(np.int64) + self.n += np.bincount(indices, minlength=len(self.n)) + self.sum_x += np.bincount(indices, weights=x, minlength=len(self.n)) + self.sum_y += np.bincount(indices, weights=y, minlength=len(self.n)) + self.sum_x2 += np.bincount(indices, weights=x * x, minlength=len(self.n)) + self.sum_y2 += np.bincount(indices, weights=y * y, minlength=len(self.n)) + self.sum_xy += np.bincount(indices, weights=x * y, minlength=len(self.n)) + + def report(self) -> dict: + numerator = self.n * self.sum_xy - self.sum_x * self.sum_y + denominator = np.sqrt( + np.maximum(self.n * self.sum_x2 - self.sum_x**2, 0) + * np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + ) + target_ss = np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + scorable = (self.n >= 2) & (target_ss > 0) + pearson = np.full_like(numerator, np.nan) + nonconstant = scorable & (denominator > 0) + pearson[nonconstant] = numerator[nonconstant] / denominator[nonconstant] + pearson[scorable & ~nonconstant] = 0.0 + return { + "mean_pearson": float(pearson[scorable].mean()) if scorable.any() else 0.0, + "valid_tracks": int(scorable.sum()), + "per_track_pearson": [float(value) if np.isfinite(value) else None for value in pearson], + "values_per_track": self.n.tolist(), + } + + +def evaluate(args: argparse.Namespace) -> None: + device = _device(args.device) + payload = torch.load(args.checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config).to(device) + model.load_state_dict(payload["model"], strict=True) + model.eval() + full_dataset = AlphaGenomeDataset(args.input, args.split) + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + metrics = { + **{family: PerTrackMoments(config_value) for family, config_value in { + "atac": 167, "dnase": 305, "procap": 12, "cage": 546, + "rna_seq": 667, "chip_tf": 1617, "chip_histone": 1116, + }.items()}, + "splice_sites": PerTrackMoments(4), + "splice_usage": PerTrackMoments(config.splice_usage_tracks), + "junctions": PerTrackMoments(config.junction_tracks), + "contact_maps": PerTrackMoments(config.contact_tracks), + } + with torch.inference_mode(): + for index, sample in enumerate(_loader(dataset, workers=args.workers, shuffle=False)): + sample = _to_device(sample, device) + outputs, junction_target, junction_valid, junction_missing = ( + _validation_inference(model, sample) + ) + # Match predict.py: FP32 inference is serialized to FP16 Zarr values. + outputs = {name: value.half().float() for name, value in outputs.items()} + for family in DENSE_FAMILIES: + metrics[family].update( + outputs[family][0], + torch.asinh(sample["dense"][family]["values"]), + sample["dense"][family]["mask"], + ) + metrics["splice_sites"].update(outputs["splice_sites"][0], sample["splice_sites"]) + metrics["splice_usage"].update( + outputs["splice_usage"][0], + torch.asinh(sample["splice_usage"]["values"]), + sample["splice_usage"]["mask"], + ) + junction_track = sample["junction_scoring"]["track"] + metrics["junctions"].update_indexed( + outputs["junctions"][0][junction_valid], + junction_target[0][junction_valid], + junction_track[junction_valid], + ) + metrics["contact_maps"].update( + outputs["contact_maps"][0], + torch.asinh(sample["contact_maps"]), + sample["contact_map_mask"], + ) + if index == 0 or (index + 1) % args.log_every == 0 or index + 1 == size: + print(json.dumps({"event": "evaluate_progress", "examples": index + 1, "total": size}), flush=True) + families = {name: accumulator.report() for name, accumulator in metrics.items()} + valid_family_scores = [item["mean_pearson"] for item in families.values()] + report = { + "schema": "alphagenome-50m-evaluation/v3", + "split": args.split, + "examples": size, + "metric_space": ( + "asinh continuous targets and binary splice-site targets; " + "junction Pearson uses hidden coordinate queries and predicted endpoints; " + "FP32 inference rounded to FP16 prediction values" + ), + "families": families, + "overall_mean_family_pearson": float(np.mean(valid_family_scores)), + } + args.report.parent.mkdir(parents=True, exist_ok=True) + args.report.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "evaluate_complete", "overall_mean_family_pearson": report["overall_mean_family_pearson"]}), flush=True) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + common = argparse.ArgumentParser(add_help=False) + common.add_argument("--input", required=True, type=Path) + common.add_argument("--config", type=Path, default=DEFAULT_CONFIG) + common.add_argument("--workers", type=int, default=2) + common.add_argument("--max-examples", type=int, default=0) + common.add_argument("--device", choices=("auto", "cuda", "cpu"), default="auto") + common.add_argument("--log-every", type=int, default=25) + subparsers = parser.add_subparsers(dest="command", required=True) + train_parser = subparsers.add_parser("train", parents=[common]) + train_parser.add_argument("--output", required=True, type=Path) + train_parser.add_argument("--epochs", type=int, default=10) + train_parser.add_argument("--stop-after-epoch", type=int) + train_parser.add_argument("--learning-rate", type=float, default=2e-4) + train_parser.add_argument("--min-learning-rate", type=float, default=2e-5) + train_parser.add_argument("--weight-decay", type=float, default=1e-4) + train_parser.add_argument("--max-gradient-norm", type=float, default=5.0) + train_parser.add_argument("--save-every", type=int, default=250) + train_parser.add_argument("--seed", type=int, default=17) + train_parser.add_argument("--resume", type=Path) + train_parser.set_defaults(run=train) + evaluate_parser = subparsers.add_parser("evaluate", parents=[common]) + evaluate_parser.add_argument("--checkpoint", required=True, type=Path) + evaluate_parser.add_argument("--split", choices=("train", "valid", "test"), required=True) + evaluate_parser.add_argument("--report", required=True, type=Path) + evaluate_parser.set_defaults(run=evaluate) + args = parser.parse_args() + if args.workers < 0 or args.max_examples < 0: + parser.error("workers/max-examples must be nonnegative") + if args.command == "train" and ( + args.epochs < 1 + or (args.stop_after_epoch is not None + and not 1 <= args.stop_after_epoch <= args.epochs) + ): + parser.error("epochs must be positive and stop-after-epoch must be within the schedule") + args.run(args) + + +if __name__ == "__main__": + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/train_single.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/train_single.py new file mode 100644 index 0000000000000000000000000000000000000000..80536df3fe6ad9f1974ed7ef8952ac6fdf140b0b --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/train_single.py @@ -0,0 +1,170 @@ +"""Resumable single-H100 training; every epoch permutes every eligible training interval.""" +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import torch +from numcodecs import blosc +from torch.utils.data import DataLoader, Subset + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset, single_example_collate +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save, _losses, _model_inputs, _to_device + + +def balanced_splice_loss(logits, target): + """Equal positive/negative contributions per type, using only training labels. + + An absent class contributes its negative loss rather than dividing by zero or being dropped. + """ + dims = tuple(range(target.ndim - 1)) + positive = target.sum(dims) + negative = (1 - target).sum(dims) + pos_loss = (torch.nn.functional.softplus(-logits) * target).sum(dims) / positive.clamp_min(1) + neg_loss = (torch.nn.functional.softplus(logits) * (1 - target)).sum(dims) / negative.clamp_min(1) + return torch.where(positive > 0, 0.5 * (pos_loss + neg_loss), neg_loss).mean() + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--input', type=Path, default=Path('/workspace/data')) + p.add_argument('--output', type=Path, required=True) + p.add_argument('--config', type=Path, default=Path(__file__).parent / 'reference/alphagenome_50m/alphagenome_50m.yaml') + p.add_argument('--resume', type=Path) + p.add_argument('--objective', choices=['reference', 'balanced_splice', 'variance_balanced'], default='balanced_splice') + p.add_argument('--epochs', type=int, default=4) + p.add_argument('--stop-at', type=int, default=0, help='Stop after this total number of examples, preserving exact sampler position') + p.add_argument('--workers', type=int, default=8) + p.add_argument('--threads', type=int, default=4) + p.add_argument('--seed', type=int, default=17) + p.add_argument('--learning-rate', type=float, default=2e-4) + p.add_argument('--min-learning-rate', type=float, default=2e-5) + p.add_argument('--warmup', type=int, default=500) + p.add_argument('--save-every', type=int, default=2000) + p.add_argument('--keep-every', type=int, default=10000, help='Keep immutable intermediate checkpoints') + p.add_argument('--log-every', type=int, default=100) + args = p.parse_args() + torch.set_num_threads(args.threads) + torch.set_num_interop_threads(1) + blosc.set_nthreads(1) + if hasattr(os, 'sched_getaffinity'): + os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) + torch.manual_seed(args.seed) + device = torch.device('cuda:0') + config = ModelConfig.from_yaml(args.config) + model = CompactModel(config).to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=1e-4) + dataset = AlphaGenomeDataset(args.input, 'train') + model.set_junction_track_halves(dataset.junction_track_halves) + args.output.mkdir(parents=True, exist_ok=True) + total = len(dataset) * args.epochs + step, elapsed_before = 0, 0.0 + lineage, family_loss_scales = {}, {} + if args.resume: + state = torch.load(args.resume, map_location='cpu', weights_only=True) + lineage = {k:state[k] for k in ['intervention', 'interventions', 'loss_scale_provenance'] if k in state} + family_loss_scales = state.get('family_loss_scales', {}) + if state['config'] != config.to_dict() or state['examples'] != len(dataset): + raise ValueError('resume config or dataset size differs') + if state['schedule_epochs'] != args.epochs or state['objective'] != args.objective: + raise ValueError('resume schedule or objective differs') + if state['seed'] != args.seed: + raise ValueError('resume shuffle seed differs') + previous = state.get('training_arguments', {}) + for field in ['learning_rate', 'min_learning_rate', 'warmup']: + if field in previous and previous[field] != getattr(args, field): + raise ValueError(f'resume {field} differs') + model.load_state_dict(state['model'], strict=True) + optimizer.load_state_dict(state['optimizer']) + step = state['global_step'] + elapsed_before = state.get('elapsed_seconds', 0.0) + torch.set_rng_state(state['rng_cpu']) + torch.cuda.set_rng_state(state['rng_cuda'], device) + if args.objective == 'variance_balanced': + expected = set(model.dense_heads) | {'splice_usage', 'junctions', 'contact_maps'} + if set(family_loss_scales) != expected or not all(math.isfinite(v) and v > 0 for v in family_loss_scales.values()): + raise ValueError('variance_balanced requires fixed positive scales for every continuous family') + start = time.monotonic() + def emit(event, **values): + record = {'event': event, 'step': step, 'elapsed_seconds': elapsed_before + time.monotonic() - start, **values} + print(json.dumps(record), flush=True) + def save(name): + _atomic_save({ + **lineage, 'family_loss_scales': family_loss_scales, + 'schema': 'seqfunc-single/v1', 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), 'config': config.to_dict(), + 'global_step': step, 'epoch': step // len(dataset), 'offset': step % len(dataset), + 'examples': len(dataset), 'schedule_epochs': args.epochs, + 'objective': args.objective, 'seed': args.seed, + 'training_arguments': {k: str(v) if isinstance(v, Path) else v for k, v in vars(args).items()}, + 'rng_cpu': torch.get_rng_state(), 'rng_cuda': torch.cuda.get_rng_state(device), + 'elapsed_seconds': elapsed_before + time.monotonic() - start, + 'provenance': {'data': str(args.input), 'split': 'train', 'pretrained': False, + 'device': torch.cuda.get_device_name(0), 'world_size': 1}, + }, args.output / name) + emit('train_start', parameters=count_parameters(model), examples=len(dataset), objective=args.objective, + config=config.to_dict(), schedule_examples=total, device=torch.cuda.get_device_name(0), + family_loss_scales=family_loss_scales) + model.train() + for epoch in range(step // len(dataset), args.epochs): + generator = torch.Generator().manual_seed(args.seed + epoch) + order = torch.randperm(len(dataset), generator=generator).tolist() + offset = step % len(dataset) + emit('epoch_order', epoch=epoch + 1, offset=offset, count=len(order), + permutation_sha256=hashlib.sha256(torch.tensor(order, dtype=torch.int64).numpy().tobytes()).hexdigest()) + loader = DataLoader(Subset(dataset, order[offset:]), batch_size=1, shuffle=False, + num_workers=args.workers, pin_memory=True, collate_fn=single_example_collate, + persistent_workers=False, prefetch_factor=2 if args.workers else None) + for sample in loader: + sample = _to_device(sample, device) + inputs, target, valid = _model_inputs(sample) + progress = min(max(step - args.warmup, 0) / max(total - args.warmup, 1), 1) + lr = args.min_learning_rate + 0.5 * (args.learning_rate - args.min_learning_rate) * (1 + math.cos(math.pi * progress)) + lr *= min((step + 1) / max(args.warmup, 1), 1) + for group in optimizer.param_groups: + group['lr'] = lr + optimizer.zero_grad(set_to_none=True) + with torch.autocast('cuda', dtype=torch.bfloat16): + outputs = model(**inputs) + losses = _losses(outputs, sample, target, valid) + if args.objective in ['balanced_splice', 'variance_balanced']: + losses['splice_sites'] = balanced_splice_loss(outputs['splice_sites'], sample['splice_sites'][None]) + losses['total'] = torch.stack([v for k, v in losses.items() if k != 'total']).mean() + raw_losses = dict(losses) + if args.objective == 'variance_balanced': + for name, scale in family_loss_scales.items(): + losses[name] = losses[name] * scale + losses['total'] = torch.stack([v for k, v in losses.items() if k != 'total']).mean() + if not torch.isfinite(losses['total']): + raise FloatingPointError(f'nonfinite loss at {step}') + losses['total'].backward() + norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0, error_if_nonfinite=True) + optimizer.step() + step += 1 + if step == 1 or step % args.log_every == 0: + emit('train_step', epoch=epoch + 1, example_id=sample['example_id'], learning_rate=lr, + gradient_norm=float(norm), losses={k: float(v.detach()) for k, v in losses.items()}, + raw_losses={k: float(v.detach()) for k, v in raw_losses.items()}, + peak_gpu_bytes=torch.cuda.max_memory_allocated()) + if step % args.save_every == 0: + save('progress.pt') + if args.keep_every and step % args.keep_every == 0: + save(f'step-{step:06d}.pt') + if args.stop_at and step >= args.stop_at: + save('last.pt') + emit('train_segment_complete', completed_epochs=step // len(dataset), offset=step % len(dataset)) + return + save('last.pt') + save(f'epoch-{epoch + 1:02d}.pt') + emit('epoch_complete', completed_epochs=epoch + 1) + emit('train_complete') + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/training_config.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/training_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1f9a3298d69b138a02fed00880182d4004b23327 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/training_config.yaml @@ -0,0 +1,28 @@ +attention_head_dim: 64 +channel_growth: 32 +contact_bin_size: 2048 +contact_tracks: 28 +conv_kernel_size: 5 +dense_bin_size: 128 +dna_kernel_size: 15 +dropout: 0.0 +enable_pairwise: false +encoder_width: 32 +gradient_checkpointing: true +head_hidden_size: 256 +hidden_dim: 384 +input_length: 1048576 +junction_rank: 32 +junction_tracks: 734 +local_width: 32 +mlp_expansion: 4.0 +num_attention_heads: 6 +num_downsampling_blocks: 7 +num_sequence_blocks: 6 +pair_downsample: 16 +pair_update_interval: 2 +pair_width: 32 +splice_usage_tracks: 734 +stem_stride: 8 +target_length: 196608 +trunk_bin_size: 1024 diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/training_provenance.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/training_provenance.json new file mode 100644 index 0000000000000000000000000000000000000000..68a3c002f4c8aaf1d5dfaab4a778bd3e64efbff8 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S009/training_provenance.json @@ -0,0 +1,126 @@ +{ + "schema": "seqfunc-single/v1", + "config": { + "input_length": 1048576, + "target_length": 196608, + "dense_bin_size": 128, + "trunk_bin_size": 1024, + "contact_bin_size": 2048, + "stem_stride": 8, + "encoder_width": 32, + "channel_growth": 32, + "num_downsampling_blocks": 7, + "conv_kernel_size": 5, + "dna_kernel_size": 15, + "hidden_dim": 384, + "num_sequence_blocks": 6, + "num_attention_heads": 6, + "attention_head_dim": 64, + "mlp_expansion": 4.0, + "dropout": 0.0, + "head_hidden_size": 256, + "junction_rank": 32, + "splice_usage_tracks": 734, + "junction_tracks": 734, + "contact_tracks": 28, + "gradient_checkpointing": true, + "enable_pairwise": false, + "pair_width": 32, + "pair_downsample": 16, + "pair_update_interval": 2, + "local_width": 32 + }, + "epoch": 0, + "offset": 26000, + "global_step": 26000, + "examples": 41020, + "schedule_epochs": 4, + "objective": "variance_balanced", + "seed": 17, + "training_arguments": { + "input": "/workspace/data", + "output": "/workspace/research/experiments/norm05", + "config": "/workspace/research/experiments/norm05/config.yaml", + "resume": "/workspace/research/experiments/norm05/start.pt", + "objective": "variance_balanced", + "epochs": 4, + "stop_at": 26000, + "workers": 4, + "threads": 2, + "seed": 17, + "learning_rate": 0.0002, + "min_learning_rate": 2e-05, + "warmup": 500, + "save_every": 2000, + "keep_every": 10000, + "log_every": 100 + }, + "elapsed_seconds": 4055.450142032001, + "provenance": { + "data": "/workspace/data", + "split": "train", + "pretrained": false, + 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+ }, + "clinvar_missense": { + "rows": 199, + "perturbations": 199, + "metric": "auprc", + "model": 0.28588521268669786, + "anchors": { + "AlphaGenome": 0.373826292978903 + } + }, + "mfass": { + "rows": 195, + "perturbations": 195, + "metric": "auprc", + "model": 0.17155869823197722, + "anchors": { + "AlphaGenome": 0.7342557141920668 + } + }, + "caqtl": { + "rows": 198, + "perturbations": 198, + "metric": "auprc", + "model": 0.189017859760019, + "anchors": { + "AlphaGenome": 0.8075760167741196 + } + }, + "bqtl_spi1": { + "rows": 160, + "perturbations": 160, + "metric": "auprc", + "model": 0.2085184509961343, + "anchors": { + "AlphaGenome": 0.8167450781363793 + } + }, + "enhancer_gene": { + "rows": 200, + "perturbations": 200, + "metric": "auprc", + "model": 0.37683453604744793, + "anchors": { + "AlphaGenome": 0.9594307235541584 + } + }, + "traitgym_mendelian": { + "rows": 292, + "perturbations": 292, + "metric": "auprc", + "model": 0.14523491139951616, + "anchors": { + "Borzoi_L2_L2.plus.all": 0.5026323772738239, + "Enformer_L2_L2.plus.all": 0.5452938267693526, + "CADD.plus.RawScore": 0.7189111372856957, + "GPN-MSA_absLLR.plus.score": 0.6729149139043132 + } + }, + "traitgym_complex": { + "rows": 299, + "perturbations": 299, + "metric": "auprc", + "model": 0.1636743345537799, + "anchors": { + "Borzoi_L2_L2.plus.all": 0.294501577162467, + "Enformer_L2_L2.plus.all": 0.269227301087889, + "CADD.plus.RawScore": 0.41341570280137435, + "GPN-MSA_absLLR.plus.score": 0.4213971415058371 + } + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/README.md b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/README.md new file mode 100644 index 0000000000000000000000000000000000000000..69abe73e2e69ac7fb55e46081e0d57157c941ba5 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/README.md @@ -0,0 +1,82 @@ +# SeqFunc local01 after one complete training epoch with transcript-direction junction decoding + +One model with **13,256,456 parameters** serves every family and both entry points. It reads the entire +1,048,576-bp DNA window through a convolutional encoder and 6 transformer +blocks of width 384 over 1,024 tokens. Dense heads predict 128-bp bins over the central +196,608 bp; other heads predict splice sites, sparse usage/junctions and full-window contacts. +A width-32 base-resolution path uses a 21-bp stem and 4 dilated residual depthwise blocks (701-bp receptive field). Average/max pooling provides aligned 128-bp residual features to dense, usage and junction heads. Splice logits combine local base-resolution logits with the reference offset decoder. Junction values use low-rank donor/acceptor factors; contacts use a symmetric +product of global features. Sites and junction endpoints are selected from model scores alone. + +All weights originate from local random initialization (seed 17) and locally trained +checkpoints. The compact model trained for 2,000 examples with balanced splice loss, then a zero-output local path was attached. Training continued through all 41,020 eligible intervals. Optimizer, sampler and RNG states were restored after infrastructure interruptions; unsaved updates were discarded. + +This checkpoint records **41,020 updates**, 1 complete training permutations and +0 intervals into the next permutation. Each permutation includes all **41,020** +eligible FOLD_0 training intervals (folds 2–7), shuffled deterministically without replacement. +Quantized train targets are decoded by the supplied reader. Validation is fold 0; no test labels, +test-derived caches or pretrained model parameters were used. Development labels only evaluate +and select models. Checkpoints before a complete permutation remain intermediate milestones. + +Training uses one H100 80 GB and at most 22 CPUs. The current loader uses 4 workers +and 2 torch CPU threads. BF16 training uses AdamW (weight decay 1e-4), gradient +clipping at 5, and a 4-epoch cosine learning-rate schedule from +0.0002 to 2e-05, with +500 warmup training examples. The objective is `balanced_splice`. +Other family losses follow the reference. Recorded accumulated training-loop wall time is +112.3 minutes, including inherited parent time. Runs can share this +same physical GPU with experiments or evaluation. Setup, evaluation and discarded mechanical checks +are additional research compute, documented in the research ledger. Matching training junction queries +uses a verified, bit-identical CPU index construction followed by a bulk GPU transfer. + +The package contains model code, weights, training source/configuration/provenance, SHA-256 checksums, +and the complete CPython 3.11 / PyTorch 2.7.1 CUDA 12.6 Python runtime archive. Both executable entry +points take INPUT_ROOT and OUTPUT_ROOT, extract the vendored runtime temporarily, and run offline. +The host supplies Linux, bash/tar/gzip and the NVIDIA driver. Inference reads only public manifests +and the pinned reference FASTA. No target, splice-coordinate or prediction cache is packaged or read. +Track outputs use float16; perturbation outputs use float32 to preserve small A/B differences. + +The appended validation results cover all 200 intervals and all 2,697 development perturbations, +scored by the unmodified verifier tools. Published anchors use the same label rows. All results, +including regressions, are retained in the package's JSON reports. Limitations include incomplete +training at intermediate checkpoints, a 701-bp local receptive field, pooled usage/junction features, +low-rank contact modeling, and uneven variant performance. Functional readouts do not provide the +evolutionary constraint information used by conservation-based variant scorers. + +Junction selection requires the donor to precede the acceptor on plus-strand transcripts and +follow it on minus-strand transcripts. This fixed biological constraint uses model-selected +coordinates and public strand halves. It changes no model parameters, usage selection or +perturbation computation. The rest of the decoder and candidate budget are unchanged. + +## Measured validation results + +Family-mean Pearson: **0.313219** on 200 validation intervals. + +| Family | Pearson | +|---|---:| +| atac | 0.500901 | +| dnase | 0.614570 | +| procap | 0.377495 | +| cage | 0.209317 | +| rna_seq | 0.273101 | +| chip_tf | 0.435083 | +| chip_histone | 0.421491 | +| splice_sites | 0.042569 | +| splice_usage | 0.248031 | +| junctions | 0.189779 | +| contact_maps | 0.133076 | + +| Development evaluation | Metric | Model | Published anchors on the same rows | +|---|---|---:|---| +| eqtl_sign | auroc | 0.5794 | AlphaGenome: 0.8264 | +| eqtl_causality | auroc | 0.6302 | AlphaGenome: 0.8325 | +| eqtl_coefficient | spearman | 0.1320 | AlphaGenome: 0.4134 | +| sqtl_causality | auprc | 0.8095 | AlphaGenome: 0.8447 | +| clinvar_noncoding | auprc | 0.2918 | AlphaGenome: 0.9410 | +| clinvar_splice_region | auprc | 0.2285 | AlphaGenome: 0.7225 | +| clinvar_missense | auprc | 0.2900 | AlphaGenome: 0.3738 | +| mfass | auprc | 0.2301 | AlphaGenome: 0.7343 | +| caqtl | auprc | 0.1677 | AlphaGenome: 0.8076 | +| bqtl_spi1 | auprc | 0.2263 | AlphaGenome: 0.8167 | +| enhancer_gene | auprc | 0.5832 | AlphaGenome: 0.9594 | +| traitgym_mendelian | auprc | 0.1547 | Borzoi_L2_L2.plus.all: 0.5026; Enformer_L2_L2.plus.all: 0.5453; CADD.plus.RawScore: 0.7189; GPN-MSA_absLLR.plus.score: 0.6729 | +| traitgym_complex | auprc | 0.1918 | Borzoi_L2_L2.plus.all: 0.2945; Enformer_L2_L2.plus.all: 0.2692; CADD.plus.RawScore: 0.4134; GPN-MSA_absLLR.plus.score: 0.4214 | diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/attach_local.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/attach_local.py new file mode 100644 index 0000000000000000000000000000000000000000..aa082537004dbc398c4a78c1bd0203970ce5e42f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/attach_local.py @@ -0,0 +1,52 @@ +"""Attach a zero-initialized local residual path to a checkpoint trained in this workspace.""" +import argparse +from dataclasses import replace +import hashlib +from pathlib import Path + +import torch +import yaml + +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + p.add_argument('--width', type=int, default=32) + args = p.parse_args() + state = torch.load(args.checkpoint, map_location='cpu', weights_only=True) + assert state['schema'] == 'seqfunc-single/v1', 'only locally trained single-GPU checkpoints' + parent_config = ModelConfig(**state['config']) + assert parent_config.local_width == 0 + torch.manual_seed(state['seed']) + parent = CompactModel(parent_config) + config = replace(parent_config, local_width=args.width) + model = CompactModel(config) + result = model.load_state_dict(state['model'], strict=False) + assert not result.unexpected_keys + assert all(k.startswith(('local_path.', 'local_projection.')) for k in result.missing_keys) + parent_names = list(dict(parent.named_parameters())) + names = list(dict(model.named_parameters())) + assert names[:len(parent_names)] == parent_names, 'optimizer parameter order changed' + assert len(state['optimizer']['param_groups']) == 1 + group = state['optimizer']['param_groups'][0] + assert group['params'] == list(range(len(parent_names))) + group['params'] = list(range(len(names))) + state['model'] = model.state_dict() + state['config'] = config.to_dict() + state['intervention'] = { + 'kind': 'base_resolution_local_path', 'width': args.width, + 'parent_sha256': hashlib.sha256(args.checkpoint.read_bytes()).hexdigest(), + 'attachment_step': state['global_step'], 'initial_outputs_unchanged': True, + } + args.output.mkdir(parents=True, exist_ok=False) + (args.output / 'config.yaml').write_text(yaml.safe_dump(config.to_dict())) + _atomic_save(state, args.output / 'attached.pt') + print({'parameters': count_parameters(model), **state['intervention']}) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/decoding_policy.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/decoding_policy.json new file mode 100644 index 0000000000000000000000000000000000000000..430778c1cbb3c9662fdda1bb7e43279a2c3a56a6 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/decoding_policy.json @@ -0,0 +1,9 @@ +{ + "kind": "junction_transcript_direction", + "parent_package": "/workspace/research/experiments/local01-epoch1/raw-package", + "weights_unchanged": true, + "plus": "donor < acceptor", + "minus": "donor > acceptor", + "inference_inputs": "model-selected coordinates and public track strand halves only", + "training_evidence": "64 training windows: 30,616 positive junction queries, all in transcript direction" +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/infer.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/infer.sh new file mode 100644 index 0000000000000000000000000000000000000000..2a412a9ad8667d6dcb94bb57d9959d61d43ddd34 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/infer.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: infer.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/predict.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/package_manifest.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/package_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..780cb48af2cc796544b278b6547327c201a0d6d4 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/package_manifest.json @@ -0,0 +1,31 @@ +{ + "parameters": 13256456, + "source_checkpoint_sha256": "1d2e8ee5cf27aad6d8a4b9f4617c9fe4725a3ac42e9d9c13c43e8b295d208dd5", + "files": { + "README.md": "be0b5dbfea253dadc1baabbf058f02416abe57a3bdffe602893627311e69b316", + "attach_local.py": "d0774e2711840783267914789ed303a3b61b64381b72d61f0886edbc04974f15", + "infer.sh": "ef810532b1e72b85fbe283e4f52c177bb70025977dbb2345d75837c9bbd51e37", + "last.pt": "8d4ccf2f3ac91e272d2de59872447d8ee6d2c1245114634549b1eae9d6c140a6", + "perturb.py": "ba37a6f9f8b8eccb052903618bcb4c91d3f4cbed62dfe678ee362644dc80801e", + "perturb.sh": "113ec2383276a6e83fe1e4e8b0fa914f8d535d5dd0f5b2c34240140312dd5fb4", + "predict.py": "c257bd031d41b946053e37d5a5759c6c1416d22d9b4d3f403e1fd13e0f63968b", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/__init__.py": "45f3cf93f48859104e70bb58d717130dfd6521a45d2aba06b1a726afc0dc51dd", + "reference/alphagenome_50m/__init__.py": "a5a7e45d5940c3b201017c6322128e9f47efd37e1d29f717da2c1f787688eff9", + "reference/alphagenome_50m/alphagenome_50m.yaml": "3cac32e9f55e352dff78bb85ac892fdae1f9e412d9bba3e945750210c5252eee", + "reference/alphagenome_50m/dataset.py": "ad6670f742f4de9898064becc525d0aea98b402abe7b210bb1b26a9eee6402b9", + "reference/alphagenome_50m/layers.py": "26a116a72aa3f0e307a3606163547edbdecb7965f7fdcd96bc5f805cd21b3352", + "reference/alphagenome_50m/model.py": "2871dc8893879c3e14a526dea659701d72c8c4d0e2f835b0bfb857034a2c2e6d", + "reference/alphagenome_50m/reference_large.yaml": "68084d299b39418034c6bbf71db174c3ffa68bbeea8779c1694e44ec71f3529c", + "reference/alphagenome_50m/run_ddp.sh": "96d35a24a4e6789311501fb98203807ac0c4429375c44d5c6671908270c93bb0", + "reference/alphagenome_50m/tiny_overfit.yaml": "f7a104c3929de0530a9a8f73f66d794cca595aefad8ef262e09ecbeb9cfce6de", + "reference/alphagenome_50m/train_eval.py": "95d2dad279b23ed9c0c8a4f070035cf53c27eb8fe27da64c441343717174f784", + "runtime.tar.gz": "718da4c374e0c552b6e429b226c90b2fded5c900d95577d190d4df520aaf3901", + "train_single.py": "a6653431e850d448c414f48cc65cbaaec8c17a430c08376b281441a6f6bce987", + "training_config.yaml": "deab298b32f0aef33e66ddf538541ec470eee0c05a6ebc983684f2575287d791", + "training_provenance.json": "20fcec7edebf4d173f1ad7454477334d597d2f53c2ac3c294d29baa40e12a814", + "decoding_policy.json": "fa72d3cf028143d91ea15a9317253624d1717f1f762a8d4572cacb14d0a764cf", + "validation_tracks.json": "1e24b3a2b6399af5b4552929fa970156ed52795245bf52487050170e8f483e8e", + "validation_variants.json": "f8daed428566c72b0b494e1136bec44f0083099832c25386a2a04eb5e3e99eaf" + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/perturb.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/perturb.py new file mode 100644 index 0000000000000000000000000000000000000000..e0416692d2ee7deb1a25927397f725eec54d16a9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/perturb.py @@ -0,0 +1,101 @@ +"""Predict both sequences of every perturbation (contract: perturbations/v1). + + python perturb.py INPUT_ROOT OUTPUT_ROOT --checkpoint last.pt + +INPUT_ROOT holds `public/perturbations.parquet` and `reference/GRCh38.p13.genome.fa` (in the +workspace, `/workspace/data` works too: the queries are then read from `variant_evals/public/`). Each row is a +1,048,576-bp window starting at `input_start` and an edit spanning [edit_start, edit_end) in genomic +0-based coordinates: + +- `snv`: sequence A carries `allele_a` at edit_start, sequence B carries `allele_b` (both are written + explicitly; either may differ from the reference genome); +- `mask`: sequence A is the reference, sequence B has the interval replaced by N. + +For each perturbation the output group `perturbations/p{index:06d}` holds `a/` and +`b/` for every family in `families`: dense families sliced to bins +[dense_bin_start, dense_bin_end) of the central target window, and `splice_sites` sliced to bp +[splice_start, splice_end). Values are the same quantities as predictions.zarr (asinh space for +dense tracks, splice-site scores for splice_sites). +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import pandas as pd +import pysam +import torch +import zarr + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig + +PERTURBATION_SCHEMA = "genome-sequence-to-function-perturbations/v1" +COMPRESSOR = Blosc(cname="zstd", clevel=3, shuffle=Blosc.BITSHUFFLE) + + +def sequences(fasta, row) -> tuple[str, str]: + length = fasta.get_reference_length(row.chrom) + start, end = int(row.input_start), int(row.input_start) + 1_048_576 + sequence = fasta.fetch(row.chrom, max(0, start), min(length, end)).upper() + sequence = "N" * max(0, -start) + sequence + "N" * max(0, end - length) + lo, hi = int(row.edit_start) - start, int(row.edit_end) - start + if row.edit == "snv": + return sequence[:lo] + row.allele_a + sequence[hi:], sequence[:lo] + row.allele_b + sequence[hi:] + if row.edit == "mask": + return sequence, sequence[:lo] + "N" * (hi - lo) + sequence[hi:] + raise ValueError(f"unknown edit {row.edit!r}") + + +def main(input_root: Path, output_root: Path, checkpoint: Path, queries: Path | None = None) -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + if queries is None: + queries = input_root / "public/perturbations.parquet" + if not queries.exists(): + queries = input_root / "variant_evals/public/perturbations.parquet" + rows = pd.read_parquet(queries) + fasta = pysam.FastaFile(str(input_root / "reference/GRCh38.p13.genome.fa")) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "perturbations.zarr"), mode="w") + target_start = (config.input_length - config.target_length) // 2 + with torch.inference_mode(): + for index, row in enumerate(rows.itertuples(index=False)): + pair = sequences(fasta, row) + dna = torch.from_numpy(np.stack([AlphaGenomeDataset._one_hot(s) for s in pair])).to(device) + trunk = model._trunk(dna) + group = root.create_group(f"perturbations/p{index:06d}") + features = None + for family in row.families: + if family == "splice_sites": + logits = model._splice_site_logits(trunk)[:, target_start:target_start + config.target_length] + values = logits[:, row.splice_start:row.splice_end] + else: + features = model._dense_features(trunk) if features is None else features + values = model.dense_heads[family](features)[:, row.dense_bin_start:row.dense_bin_end] + # float32: one variant's effect is often below float16 resolution (about 0.004 at + # asinh values of 4-8), so A and B would round to the same numbers. + values = values.float().cpu().numpy() + for side, array in zip("ab", values): + group.create_dataset(f"{side}/{family}", data=array, compressor=COMPRESSOR) + print(json.dumps({"event": "perturb", "done": index + 1, "total": len(rows)}), flush=True) + root.attrs.update({"schema": PERTURBATION_SCHEMA, + "perturbation_ids": rows.perturbation_id.astype(str).tolist()}) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--queries", type=Path, help="another query table, e.g. the development split") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.queries) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/perturb.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/perturb.sh new file mode 100644 index 0000000000000000000000000000000000000000..f07c4714532c2d33e7a419de3877e5daacee89fd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/perturb.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: perturb.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/perturb.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/predict.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..dc0f0b20833fc99253b7dcf6023fd5e7a61b1128 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/predict.py @@ -0,0 +1,133 @@ +#!/usr/bin/env python3 +"""Stream the single benchmark model into the sharded prediction contract.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import torch +import zarr + +from prediction_contract import DENSE_TRACKS, MAX_JUNCTION_PAIRS, MAX_USAGE_SITES, PREDICTION_SCHEMA +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig + + +COMPRESSOR = Blosc(cname="zstd", clevel=7, shuffle=Blosc.BITSHUFFLE) + + +def main(input_root: Path, output_root: Path, checkpoint: Path, split: str = "test") -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + dataset = AlphaGenomeDataset(input_root, split, include_targets=False) + model.set_junction_track_halves(dataset.junction_track_halves) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "predictions.zarr"), mode="w") + ids = [] + with torch.inference_mode(): + for sample in dataset: + example_id = sample["example_id"] + dna = sample["dna"][None].to(device) + trunk = model._trunk(dna) + dense_features = model._dense_features(trunk) + group = root.create_group(f"examples/{example_id}") + for family in DENSE_TRACKS: + values = model.dense_heads[family](dense_features)[0] + group.create_dataset(family, data=values.cpu().numpy().astype(np.float16), compressor=COMPRESSOR) + + all_site_logits = model._splice_site_logits(trunk) + target_start = (config.input_length - config.target_length) // 2 + group.create_dataset( + "splice_sites", + data=all_site_logits[0, target_start:target_start + config.target_length].cpu().numpy().astype(np.float16), + chunks=(4096, 4), compressor=COMPRESSOR, + ) + predicted_positions = model.select_junction_positions(all_site_logits)[0] + + # Usage at the model's own predicted sites (v4 is sparse; unlisted hidden sites score 0). + usage_bins = model.splice_usage_head(dense_features)[0].float() + sites = predicted_positions.cpu().numpy().astype(np.int64).reshape(-1) + sites = np.unique(sites[(sites >= target_start) & (sites < target_start + config.target_length)] + - target_start)[:MAX_USAGE_SITES] + group.create_dataset("splice_usage_positions", data=sites.astype(np.int32), compressor=COMPRESSOR) + usage = usage_bins[torch.as_tensor(sites // 128, device=usage_bins.device)] if len(sites) \ + else usage_bins[:0] + group.create_dataset("splice_usage", data=usage.cpu().numpy().astype(np.float16), + chunks=(1024, 734), compressor=COMPRESSOR) + + # Junctions: score every donor x acceptor pair of each strand, keep the strongest pairs. + positions_batch = predicted_positions[None] + site_factors = model.junction_site_factors(trunk, positions_batch) + halves = model.junction_track_half + candidates = [] + for row_start in range(0, 512, 128): + for column_start in range(0, 512, 128): + block = torch.cat([ + model.junction_block( + trunk, positions_batch, row_start, row_start + 128, + column_start, column_start + 128, track_start, + min(track_start + 64, config.junction_tracks), site_factors, + )[0] + for track_start in range(0, config.junction_tracks, 64) + ], dim=-1).float() # [128, 128, 734] + for half in (0, 1): + donors = predicted_positions[2 * half, row_start:row_start + 128] + acceptors = predicted_positions[2 * half + 1, column_start:column_start + 128] + valid = (donors[:, None] >= 0) & (acceptors[None, :] >= 0) + # Transcript direction is determined by the public strand half. + forward = donors[:, None] < acceptors[None, :] if half == 0 else donors[:, None] > acceptors[None, :] + valid = valid & forward + strength = block[..., halves == half].amax(dim=-1).masked_fill(~valid, -torch.inf) + keep = min(MAX_JUNCTION_PAIRS // 2, int(valid.sum())) + if not keep: + continue + best = strength.flatten().topk(keep) + rows, columns = best.indices // 128, best.indices % 128 + candidates.append((best.values, torch.full_like(rows, half), donors[rows], + acceptors[columns], block[rows, columns])) + if candidates: + score, half, donor, acceptor, values = (torch.cat(x) for x in zip(*candidates)) + order = score.topk(min(MAX_JUNCTION_PAIRS, len(score))).indices + half, donor, acceptor, values = half[order], donor[order], acceptor[order], values[order] + else: + half = donor = acceptor = torch.zeros(0, dtype=torch.long) + values = torch.zeros(0, config.junction_tracks) + group.create_dataset("junction_half", data=half.cpu().numpy().astype(np.int8), compressor=COMPRESSOR) + group.create_dataset("junction_donor", data=donor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junction_acceptor", data=acceptor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junctions", data=values.cpu().numpy().astype(np.float16), + chunks=(4096, 734), compressor=COMPRESSOR) + + contact = trunk.reshape( + 1, config.contact_bins, -1, config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + contact_values = 0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row]) + group.create_dataset( + "contact_maps", data=contact_values[0].cpu().numpy().astype(np.float16), + chunks=(8192, 28), compressor=COMPRESSOR, + ) + ids.append(example_id) + print(json.dumps({"event": "predict", "example": len(ids), "total": len(dataset)}), flush=True) + root.attrs.update({"schema": PREDICTION_SCHEMA, "example_ids": ids}) + (output_root / "example_ids.json").write_text(json.dumps(ids) + "\n") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--split", default="test", choices=("valid", "test"), + help="manifest to predict; the verifier uses test, local validation uses valid") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.split) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/prediction_contract.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/prediction_contract.py new file mode 100644 index 0000000000000000000000000000000000000000..2455424db002b840cf0d69456930840a7bc5f3cb --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/prediction_contract.py @@ -0,0 +1,23 @@ +"""Public prediction shapes; no trusted splice coordinates are exposed.""" + +from __future__ import annotations + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} + +PREDICTION_SCHEMA = "genome-sequence-to-function-predictions/v4" +MAX_USAGE_SITES = 4_096 +MAX_JUNCTION_PAIRS = 16_384 +# Fixed-shape arrays per example; splice usage and junctions are sparse (see contract.md). +FIXED_SHAPES = { + **{name: (1_536, tracks) for name, tracks in DENSE_TRACKS.items()}, + "splice_sites": (196_608, 4), + "contact_maps": (131_328, 28), +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4879a0e1a3a8a8050bb48360febee152cd141be8 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/__init__.py @@ -0,0 +1 @@ +"""The reference model of the alphagenome-reference baseline.""" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a86a139babb6e899bd5102bbf3c628e1232576b2 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/__init__.py @@ -0,0 +1,5 @@ +"""Compact 1-Mb, eleven-family AlphaGenome reference model.""" + +from .model import CompactModel, ModelConfig, count_parameters + +__all__ = ["CompactModel", "ModelConfig", "count_parameters"] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/alphagenome_50m.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/alphagenome_50m.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0a59a4d0c65752d45eb464d129c6ec9d559665e9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/alphagenome_50m.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 32 +channel_growth: 32 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 384 +num_sequence_blocks: 6 +num_attention_heads: 6 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 256 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/dataset.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..71cd39d15713f648e7d7e31aa1923fa4fa4800dd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/dataset.py @@ -0,0 +1,229 @@ +"""Streaming reader for the compact 1 Mb / eleven-family release.""" + +from __future__ import annotations + +from collections import OrderedDict +from pathlib import Path + +import numpy as np +import pandas as pd +import pysam +import torch +from torch.utils.data import Dataset +import zarr + + +def _open_part(path: Path): + if str(path).endswith(".zarr.zip"): + store = zarr.ZipStore(str(path), mode="r") + try: + return zarr.open_group(store=store, mode="r"), store + except BaseException: + store.close() + raise + return zarr.open_group(str(path), mode="r"), None + + +DENSE_FAMILIES = ( + "atac", + "dnase", + "procap", + "cage", + "rna_seq", + "chip_tf", + "chip_histone", +) +def normalize_splice_positions(value) -> np.ndarray: + """Normalize Arrow's nested-list representation to the public [4, 512] array.""" + + outer = np.asarray(value, dtype=object).reshape(-1).tolist() + positions = np.stack([np.asarray(item, dtype=np.int64) for item in outer]) + if positions.shape != (4, 512): + raise ValueError(f"splice_site_positions must have shape (4, 512), got {positions.shape}") + return positions + + +class AlphaGenomeDataset(Dataset): + """Resolve frozen coordinates to reference DNA and one Zarr example group.""" + + def __init__( + self, + data_root: str | Path, + split: str, + *, + max_open_parts: int = 8, + include_targets: bool = True, + ) -> None: + self.root = Path(data_root) + self.split = split + manifest = self.root / "public" / f"{split}.parquet" + if not manifest.is_file(): + raise FileNotFoundError(f"Missing materialized manifest: {manifest}") + self.rows = pd.read_parquet(manifest).reset_index(drop=True) + junction_tracks = pd.read_parquet(self.root / "public/junction_tracks.parquet") + junction_tracks = junction_tracks.sort_values("tensor_index") + self.junction_track_halves = torch.from_numpy( + junction_tracks.junction_channel_half.to_numpy(np.int64) + ) + if include_targets: + labels_path = self.root / "labels" / f"{split}.parquet" + if not labels_path.is_file(): + raise FileNotFoundError(f"Missing trusted labels manifest: {labels_path}") + labels = pd.read_parquet(labels_path).reset_index(drop=True) + if self.rows.example_id.astype(str).tolist() != labels.example_id.astype(str).tolist(): + raise ValueError("public and labels manifests have different IDs/order") + self.rows = self.rows.merge(labels, on="example_id", validate="one_to_one", sort=False) + self.fasta_path = self.root / "reference" / "GRCh38.p13.genome.fa" + self.max_open_parts = max_open_parts + self.include_targets = include_targets + self._fasta = None + self._parts: OrderedDict[str, tuple[zarr.Group, object | None]] = OrderedDict() + + def __len__(self) -> int: + return len(self.rows) + + def _reference(self): + if self._fasta is None: + self._fasta = pysam.FastaFile(str(self.fasta_path)) + return self._fasta + + def _part(self, relative: str): + if relative in self._parts: + opened = self._parts.pop(relative) + self._parts[relative] = opened + return opened[0] + group, store = _open_part(self.root / relative) + if len(self._parts) >= self.max_open_parts: + _, (_, old_store) = self._parts.popitem(last=False) + if old_store is not None: + old_store.close() + self._parts[relative] = (group, store) + return group + + @staticmethod + def _one_hot(sequence: str) -> np.ndarray: + encoded = np.frombuffer(sequence.upper().encode("ascii"), dtype=np.uint8) + output = np.zeros((len(encoded), 4), dtype=np.float32) + for index, letter in enumerate(b"ACGT"): + output[:, index] = encoded == letter + return output + + def _sequence(self, row) -> tuple[np.ndarray, np.ndarray]: + reference = self._reference() + chromosome_length = reference.get_reference_length(str(row.chromosome)) + start, end = int(row.input_start), int(row.input_end) + clipped_start, clipped_end = max(0, start), min(chromosome_length, end) + sequence = reference.fetch(str(row.chromosome), clipped_start, clipped_end) + left, right = clipped_start - start, end - clipped_end + if left or right: + sequence = "N" * left + sequence + "N" * right + valid = np.ones(end - start, dtype=bool) + if left: + valid[:left] = False + if right: + valid[-right:] = False + return self._one_hot(sequence), valid + + @staticmethod + def _decode(example, name: str, *, asinh: bool) -> np.ndarray: + """Train targets may be stored as 8-bit codes with per-track ranges; they are decoded + here, one example at a time, so the release stays compressed on disk.""" + array = example[name] + if array.dtype != np.uint8: + return np.asarray(array, dtype=np.float32) + lo = np.asarray(example[f"{name}_lo"], dtype=np.float32) + hi = np.asarray(example[f"{name}_hi"], dtype=np.float32) + x = lo + np.asarray(array, dtype=np.float32) * ((hi - lo) / 255.0) + return np.sinh(x) if asinh else x + + @staticmethod + def _dense_target(example, family: str) -> dict[str, torch.Tensor]: + values = AlphaGenomeDataset._decode(example, f"{family}/values", asinh=True) + mask = np.asarray(example[f"{family}/mask"], dtype=bool) + if mask.ndim == 1: + mask = np.broadcast_to(mask[None, :], values.shape).copy() + return { + "values": torch.from_numpy(values), + "mask": torch.from_numpy(mask), + } + + def __getitem__(self, item: int): + row = self.rows.iloc[item] + dna, input_mask = self._sequence(row) + result = { + "example_id": str(row.example_id), + "dna": torch.from_numpy(dna), + "input_mask": torch.from_numpy(input_mask), + "junction_track_halves": self.junction_track_halves, + } + if not self.include_targets: + return result + + part = self._part(str(row.target_shard)) + example = part[str(row.target_group)] + result["dense"] = { + family: self._dense_target(example, family) for family in DENSE_FAMILIES + } + result["splice_sites"] = torch.from_numpy( + np.asarray(example["splice_sites/values"], dtype=np.float32) + ) + result["splice_usage"] = { + "positions": torch.from_numpy( + np.asarray(example["splice_site_usage/positions"], dtype=np.int64) + ), + "values": torch.from_numpy( + np.asarray(example["splice_site_usage/values"], dtype=np.float32) + ), + "mask": torch.from_numpy( + np.asarray(example["splice_site_usage/mask"], dtype=bool) + ), + } + result["usage_positions"] = result["splice_usage"]["positions"] + result["junctions"] = { + name: torch.from_numpy( + np.asarray( + example[f"splice_junctions/{name}"], + dtype=np.float32 if name == "value" else np.int64, + ) + ) + for name in ("row", "column", "track", "value") + } + result["junctions"]["site_positions"] = torch.from_numpy( + np.asarray(example["splice_junctions/site_positions"], dtype=np.int64) + ) + site_positions = normalize_splice_positions(row.splice_site_positions) + result["site_positions"] = torch.from_numpy(site_positions) + if not torch.equal(result["junctions"]["site_positions"], result["site_positions"]): + raise ValueError(f"public/private splice positions differ for {row.example_id}") + scoring_path = self.root / "scoring" / self.split / f"part-{item:06d}.parquet" + scoring = pd.read_parquet(scoring_path) + if not (scoring.example_id.astype(str) == str(row.example_id)).all(): + raise ValueError(f"scoring part ID differs for {row.example_id}") + result["junction_scoring"] = { + "donor_position": torch.from_numpy( + scoring.donor_position.to_numpy(np.int64) + ), + "acceptor_position": torch.from_numpy( + scoring.acceptor_position.to_numpy(np.int64) + ), + "track": torch.from_numpy(scoring.track.to_numpy(np.int64)), + "target_value": torch.from_numpy( + scoring.target_value.to_numpy(np.float32) + ), + "valid": torch.from_numpy(scoring.valid.to_numpy(bool)), + } + result["contact_maps"] = torch.from_numpy( + self._decode(example, "contact_maps/upper_triangle", asinh=False) + ) + result["contact_map_mask"] = torch.from_numpy( + np.asarray(example["contact_maps/mask"], dtype=bool) + ) + return result + + +def single_example_collate(items): + """Keep variable-size event tensors intact; the reference currently trains at batch one.""" + + if len(items) != 1: + raise ValueError("The compact event loader currently requires batch_size=1") + return items[0] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/layers.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..af31f86a5589a16f0007e6aca5ca7a7bd552d6a9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/layers.py @@ -0,0 +1,124 @@ +"""Shared neural-network layers for the single compact AlphaGenome model.""" + +from __future__ import annotations + +import math + +import torch +from torch import Tensor, nn +from torch.nn import functional as F + + +class ChannelRMSNorm(nn.Module): + def __init__(self, channels: int, eps: float = 1e-6): + super().__init__() + self.weight = nn.Parameter(torch.ones(channels)) + self.eps = eps + + def forward(self, x: Tensor) -> Tensor: + variance = x.float().square().mean(dim=1, keepdim=True) + normalized = x * torch.rsqrt(variance + self.eps).to(x.dtype) + return normalized * self.weight[None, :, None].to(x.dtype) + + +class StandardizedConv1d(nn.Conv1d): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__(in_channels, out_channels, kernel_size, padding="same", bias=True) + self.scale = nn.Parameter(torch.ones(out_channels)) + nn.init.trunc_normal_(self.weight, std=1.0 / math.sqrt(in_channels * kernel_size)) + nn.init.trunc_normal_(self.bias, std=1e-4) + + def forward(self, x: Tensor) -> Tensor: + centered = self.weight - self.weight.mean(dim=(1, 2), keepdim=True) + variance = centered.float().square().mean(dim=(1, 2), keepdim=True) + fan_in = self.weight.shape[1] * self.weight.shape[2] + multiplier = self.scale[:, None, None] * torch.rsqrt( + (fan_in * variance).clamp_min(1e-4) + ).to(self.weight.dtype) + return F.conv1d(x, centered * multiplier, self.bias, padding="same") + + +class ConvBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__() + self.norm = ChannelRMSNorm(in_channels) + self.conv = StandardizedConv1d(in_channels, out_channels, kernel_size) + + def forward(self, x: Tensor) -> Tensor: + return self.conv(F.gelu(self.norm(x), approximate="tanh")) + + +def _apply_rope(x: Tensor) -> Tensor: + rotary_dim = x.shape[-1] - x.shape[-1] % 2 + if rotary_dim == 0: + return x + positions = torch.arange(x.shape[-2], device=x.device, dtype=torch.float32) + inverse_frequency = 1.0 / ( + 10_000 ** ( + torch.arange(0, rotary_dim, 2, device=x.device, dtype=torch.float32) + / rotary_dim + ) + ) + angles = torch.outer(positions, inverse_frequency) + cos = angles.cos()[None, None, :, :].to(x.dtype) + sin = angles.sin()[None, None, :, :].to(x.dtype) + even, odd = x[..., :rotary_dim:2], x[..., 1:rotary_dim:2] + rotated = torch.stack((even * cos - odd * sin, even * sin + odd * cos), dim=-1) + return torch.cat((rotated.flatten(-2), x[..., rotary_dim:]), dim=-1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.num_heads = config.num_attention_heads + self.head_dim = config.attention_head_dim + inner_dim = self.num_heads * self.head_dim + self.norm = nn.RMSNorm(config.hidden_dim) + self.qkv = nn.Linear(config.hidden_dim, 3 * inner_dim, bias=False) + self.output = nn.Linear(inner_dim, config.hidden_dim) + self.dropout = config.dropout + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + batch, length, _ = x.shape + qkv = self.qkv(self.norm(x)).view( + batch, length, 3, self.num_heads, self.head_dim + ) + q, k, value = qkv.unbind(dim=2) + attended = F.scaled_dot_product_attention( + _apply_rope(q.transpose(1, 2)), + _apply_rope(k.transpose(1, 2)), + value.transpose(1, 2), + attn_mask=attention_bias, + dropout_p=self.dropout if self.training else 0.0, + ) + return self.output(attended.transpose(1, 2).reshape(batch, length, -1)) + + +class MLPBlock(nn.Module): + def __init__(self, config): + super().__init__() + expanded = int(config.hidden_dim * config.mlp_expansion) + self.norm = nn.RMSNorm(config.hidden_dim) + self.input = nn.Linear(config.hidden_dim, expanded) + self.output = nn.Linear(expanded, config.hidden_dim) + self.dropout = nn.Dropout(config.dropout) + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor) -> Tensor: + return self.dropout( + self.output(F.gelu(self.input(self.norm(x)), approximate="tanh")) + ) + + +class SequenceBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.attention = MultiHeadAttention(config) + self.mlp = MLPBlock(config) + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + x = x + self.attention(x, attention_bias) + return x + self.mlp(x) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/model.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/model.py new file mode 100644 index 0000000000000000000000000000000000000000..316ae8b495a102a194b3b1c5088d815e5d66e74b --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/model.py @@ -0,0 +1,426 @@ +"""Affordable multi-resolution baseline for the compact AlphaGenome task.""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass, fields +import math +from pathlib import Path +from typing import Any, Iterable + +import torch +from torch import Tensor, nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint +import yaml + +from .layers import ChannelRMSNorm, ConvBlock, SequenceBlock + + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} +ALL_FAMILIES = (*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions", "contact_maps") + + +@dataclass(frozen=True) +class ModelConfig: + input_length: int = 1_048_576 + target_length: int = 196_608 + dense_bin_size: int = 128 + trunk_bin_size: int = 1_024 + contact_bin_size: int = 2_048 + stem_stride: int = 8 + encoder_width: int = 32 + channel_growth: int = 32 + num_downsampling_blocks: int = 7 + conv_kernel_size: int = 5 + dna_kernel_size: int = 15 + hidden_dim: int = 384 + num_sequence_blocks: int = 6 + num_attention_heads: int = 6 + attention_head_dim: int = 64 + mlp_expansion: float = 4.0 + dropout: float = 0.0 + head_hidden_size: int = 256 + junction_rank: int = 32 + splice_usage_tracks: int = 734 + junction_tracks: int = 734 + contact_tracks: int = 28 + gradient_checkpointing: bool = True + enable_pairwise: bool = False + pair_width: int = 32 + pair_downsample: int = 16 + pair_update_interval: int = 2 + local_width: int = 0 + + def __post_init__(self) -> None: + downsample = self.stem_stride * 2**self.num_downsampling_blocks + if downsample != self.trunk_bin_size: + raise ValueError("stem_stride * 2**num_downsampling_blocks must equal trunk_bin_size") + for length in (self.input_length, self.target_length): + if length % self.trunk_bin_size: + raise ValueError("input and target lengths must divide by trunk_bin_size") + if self.trunk_bin_size % self.dense_bin_size: + raise ValueError("trunk_bin_size must divide by dense_bin_size") + if self.input_length % self.contact_bin_size: + raise ValueError("input_length must divide by contact_bin_size") + if self.hidden_dim <= 0 or self.num_attention_heads <= 0: + raise ValueError("hidden dimensions and attention heads must be positive") + + @property + def trunk_bins(self) -> int: + return self.input_length // self.trunk_bin_size + + @property + def target_trunk_bins(self) -> int: + return self.target_length // self.trunk_bin_size + + @property + def dense_bins(self) -> int: + return self.target_length // self.dense_bin_size + + @property + def dense_upsample(self) -> int: + return self.trunk_bin_size // self.dense_bin_size + + @property + def contact_bins(self) -> int: + return self.input_length // self.contact_bin_size + + @property + def encoder_output_width(self) -> int: + return self.encoder_width + self.channel_growth * self.num_downsampling_blocks + + @classmethod + def from_yaml(cls, path: str | Path) -> "ModelConfig": + values: dict[str, Any] = yaml.safe_load(Path(path).read_text()) or {} + known = {field.name for field in fields(cls)} + unknown = sorted(values.keys() - known) + if unknown: + raise ValueError(f"Unknown model configuration keys: {unknown}") + return cls(**values) + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + +class SequenceEncoder(nn.Module): + def __init__(self, config: ModelConfig): + super().__init__() + self.stem = nn.Conv1d( + 4, + config.encoder_width, + config.dna_kernel_size, + stride=config.stem_stride, + padding=config.dna_kernel_size // 2, + ) + blocks = [] + width = config.encoder_width + for _ in range(config.num_downsampling_blocks): + output_width = width + config.channel_growth + blocks.append(ConvBlock(width, output_width, config.conv_kernel_size)) + width = output_width + self.blocks = nn.ModuleList(blocks) + + def forward(self, dna: Tensor) -> Tensor: + x = F.gelu(self.stem(dna.transpose(1, 2)), approximate="tanh") + for block in self.blocks: + x = F.max_pool1d(block(x), kernel_size=2, stride=2) + return x.transpose(1, 2) + + +class LocalResidualBlock(nn.Module): + def __init__(self, width: int, dilation: int): + super().__init__() + self.norm = ChannelRMSNorm(width) + self.depthwise = nn.Conv1d(width, width, 9, dilation=dilation, + padding=4 * dilation, groups=width) + self.expand = nn.Conv1d(width, 2 * width, 1) + self.contract = nn.Conv1d(2 * width, width, 1) + + def forward(self, x: Tensor) -> Tensor: + update = self.depthwise(self.norm(x)) + update = self.contract(F.gelu(self.expand(update), approximate='tanh')) + return x + update + + +class LocalSequencePath(nn.Module): + """Base-resolution motif path; 701-bp receptive field and aligned 128-bp pooling.""" + def __init__(self, config: ModelConfig): + super().__init__() + width = config.local_width + self.stem = nn.Conv1d(4, width, 21, padding=10) + self.blocks = nn.Sequential(*(LocalResidualBlock(width, d) for d in [1, 4, 16, 64])) + self.norm = ChannelRMSNorm(width) + self.sites = nn.Conv1d(width, 4, 1) + self.bin_size = config.dense_bin_size + + def forward(self, dna: Tensor) -> tuple[Tensor, Tensor]: + base = F.gelu(self.stem(dna), approximate='tanh') + base = F.gelu(self.norm(self.blocks(base)), approximate='tanh') + sites = self.sites(base).transpose(1, 2) + pooled = torch.cat((F.avg_pool1d(base, self.bin_size), + F.max_pool1d(base, self.bin_size)), dim=1).transpose(1, 2) + return sites, pooled + + +class CompactModel(nn.Module): + """One shared 1-kb trunk with representation-specific output heads.""" + + def __init__(self, config: ModelConfig | None = None): + super().__init__() + self.config = config or ModelConfig() + self.encoder = SequenceEncoder(self.config) + self.projection = nn.Linear(self.config.encoder_output_width, self.config.hidden_dim) + self.blocks = nn.ModuleList( + SequenceBlock(self.config) for _ in range(self.config.num_sequence_blocks) + ) + self.final_norm = nn.RMSNorm(self.config.hidden_dim) + self.dense_decoder = nn.Sequential( + nn.RMSNorm(self.config.hidden_dim), + nn.Linear(self.config.hidden_dim, self.config.head_hidden_size), + nn.GELU(approximate="tanh"), + ) + self.dense_heads = nn.ModuleDict( + { + family: nn.Linear(self.config.head_hidden_size, tracks) + for family, tracks in DENSE_TRACKS.items() + } + ) + self.splice_site_head = nn.Linear( + self.config.head_hidden_size, self.config.dense_bin_size * 4 + ) + self.splice_usage_head = nn.Linear( + self.config.head_hidden_size, self.config.splice_usage_tracks + ) + rank = self.config.junction_rank + self.junction_source = nn.Linear(self.config.head_hidden_size, rank) + self.junction_destination = nn.Linear(self.config.head_hidden_size, rank) + self.junction_track = nn.Embedding(self.config.junction_tracks, rank) + if self.config.junction_tracks % 2: + raise ValueError("junction tracks must split evenly into strand halves") + self.register_buffer( + "junction_track_half", + torch.arange(self.config.junction_tracks) // (self.config.junction_tracks // 2), + persistent=False, + ) + self.contact_q = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + self.contact_k = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + row, column = torch.triu_indices( + self.config.contact_bins, self.config.contact_bins + ) + self.register_buffer("contact_row", row, persistent=False) + self.register_buffer("contact_column", column, persistent=False) + if self.config.local_width: + self.local_path = LocalSequencePath(self.config) + self.local_projection = nn.Linear(2 * self.config.local_width, self.config.head_hidden_size) + # A new local path can be attached to our own checkpoint without changing its initial outputs. + nn.init.zeros_(self.local_path.sites.weight) + nn.init.zeros_(self.local_path.sites.bias) + nn.init.zeros_(self.local_projection.weight) + nn.init.zeros_(self.local_projection.bias) + self._local_sites = None + self._local_features = None + + def set_junction_track_halves(self, halves: Tensor) -> None: + halves = torch.as_tensor(halves, dtype=torch.long, device=self.junction_track_half.device) + if halves.shape != (self.config.junction_tracks,) or not torch.isin( + halves, torch.tensor([0, 1], device=halves.device) + ).all(): + raise ValueError("junction track halves must be a 734-element 0/1 mapping") + self.junction_track_half.copy_(halves) + + def _trunk(self, dna: Tensor) -> Tensor: + if dna.ndim != 3 or dna.shape[1:] != (self.config.input_length, 4): + raise ValueError( + f"Expected DNA [B,{self.config.input_length},4], got {tuple(dna.shape)}" + ) + if self.config.local_width: + if self.config.gradient_checkpointing and self.training: + sites, pooled = checkpoint(self.local_path, dna.transpose(1, 2), use_reentrant=False) + else: + sites, pooled = self.local_path(dna.transpose(1, 2)) + self._local_sites = sites + self._local_features = self.local_projection(pooled) + x = self.projection(self.encoder(dna)) + for block in self.blocks: + if self.config.gradient_checkpointing and self.training and x.requires_grad: + x = checkpoint(block, x, None, use_reentrant=False) + else: + x = block(x, None) + return self.final_norm(x) + + def _dense_features(self, trunk: Tensor) -> Tensor: + start = (self.config.trunk_bins - self.config.target_trunk_bins) // 2 + target = trunk[:, start : start + self.config.target_trunk_bins] + target = target.repeat_interleave(self.config.dense_upsample, dim=1) + features = self.dense_decoder(target) + if self.config.local_width: + first_bin = (self.config.input_length - self.config.target_length) // (2 * self.config.dense_bin_size) + features = features + self._local_features[:, first_bin:first_bin + self.config.dense_bins] + return features + + def _all_dense_features(self, trunk: Tensor) -> Tensor: + features = self.dense_decoder( + trunk.repeat_interleave(self.config.dense_upsample, dim=1) + ) + return features + self._local_features if self.config.local_width else features + + def _splice_site_logits(self, trunk: Tensor) -> Tensor: + logits = self.splice_site_head(self._all_dense_features(trunk)) + logits = logits.view(len(trunk), self.config.input_length, 4) + if self.config.local_width: + logits = logits + self._local_sites + return logits.float() + + def _position_features(self, trunk: Tensor, positions: Tensor) -> Tensor: + features = self.dense_decoder(self._gather_bins(trunk, positions, self.config.trunk_bin_size)) + if self.config.local_width: + features = features + self._gather_bins(self._local_features, positions, self.config.dense_bin_size) + return features + + @staticmethod + def select_junction_positions(site_logits: Tensor, limit: int = 512) -> Tensor: + """Select each site type from model scores alone, then sort by coordinate. + + This follows the four independent site classes in Google DeepMind's + AlphaGenome splicing implementation (pinned in docs/v3-author/PROTOCOL.md). Padding is + -1 when an input is shorter than ``limit``. + """ + count = min(limit, site_logits.shape[1]) + selected = [] + for site_type in range(4): + coordinates = torch.topk(site_logits[..., site_type], count, dim=1).indices + coordinates = coordinates.sort(dim=1).values + if count < limit: + coordinates = F.pad(coordinates, (0, limit - count), value=-1) + selected.append(coordinates) + return torch.stack(selected, dim=1) + + @staticmethod + def _gather_bins(features: Tensor, positions: Tensor, bin_size: int) -> Tensor: + indices = torch.div(positions, bin_size, rounding_mode="floor") + indices = indices.clamp(0, features.shape[1] - 1) + return torch.gather( + features, + 1, + indices[..., None].expand(-1, -1, features.shape[-1]), + ) + + def _junction_predictions( + self, trunk: Tensor, site_positions: Tensor, queries: Tensor + ) -> Tensor: + row, column, track = queries.unbind(dim=-1) + half = self.junction_track_half[track] + batch = torch.arange(len(trunk), device=trunk.device)[:, None] + donor = site_positions[batch, 2 * half, row] + acceptor = site_positions[batch, 2 * half + 1, column] + source = self._position_features(trunk, donor.clamp(0, self.config.input_length - 1)) + destination = self._position_features(trunk, acceptor.clamp(0, self.config.input_length - 1)) + interaction = self.junction_source(source) * self.junction_destination(destination) + track_embedding = self.junction_track(track) + return (interaction * track_embedding).sum(dim=-1) / math.sqrt( + self.config.junction_rank + ) + + def junction_block( + self, + trunk: Tensor, + site_positions: Tensor, + row_start: int, + row_end: int, + column_start: int, + column_end: int, + track_start: int, + track_end: int, + site_factors: tuple[Tensor, Tensor] | None = None, + ) -> Tensor: + """Compute a bounded block of the full predicted-position junction cube.""" + tracks = torch.arange(track_start, track_end, device=trunk.device) + halves = self.junction_track_half[tracks] + if site_factors is None: + site_factors = self.junction_site_factors(trunk, site_positions) + source_all, destination_all = site_factors + source = source_all[:, halves, row_start:row_end] + destination = destination_all[:, halves, column_start:column_end] + embedding = self.junction_track(tracks)[None, :, None, None, :] + values = ( + source[:, :, :, None, :] * destination[:, :, None, :, :] * embedding + ).sum(dim=-1) / math.sqrt(self.config.junction_rank) + return values.permute(0, 2, 3, 1).float() + + def junction_site_factors( + self, trunk: Tensor, site_positions: Tensor + ) -> tuple[Tensor, Tensor]: + """Precompute the two strand-specific factors reused by all cube blocks.""" + donor = torch.stack((site_positions[:, 0], site_positions[:, 2]), dim=1).clamp_min(0) + acceptor = torch.stack((site_positions[:, 1], site_positions[:, 3]), dim=1).clamp_min(0) + donor_features = self._position_features(trunk, donor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, donor.shape[-1], -1) + acceptor_features = self._position_features(trunk, acceptor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, acceptor.shape[-1], -1) + return self.junction_source(donor_features), self.junction_destination(acceptor_features) + + def forward( + self, + dna: Tensor, + *, + families: Iterable[str] | None = None, + usage_positions: Tensor | None = None, + junction_site_positions: Tensor | None = None, + junction_queries: Tensor | None = None, + ) -> dict[str, Tensor]: + requested = set(families or ALL_FAMILIES) + unknown = requested - set(ALL_FAMILIES) + if unknown: + raise ValueError(f"Unknown output families: {sorted(unknown)}") + trunk = self._trunk(dna) + needs_dense = bool( + requested.intersection((*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions")) + ) + features = self._dense_features(trunk) if needs_dense else None + outputs: dict[str, Tensor] = {} + for family in DENSE_TRACKS: + if family in requested: + outputs[family] = self.dense_heads[family](features).float() + if "splice_sites" in requested: + all_logits = self._splice_site_logits(trunk) + start = (self.config.input_length - self.config.target_length) // 2 + outputs["splice_sites"] = all_logits[:, start:start + self.config.target_length] + if "splice_usage" in requested: + if usage_positions is None: + raise ValueError("splice_usage requires usage_positions") + gathered = self._gather_bins( + features, usage_positions, self.config.dense_bin_size + ) + outputs["splice_usage"] = self.splice_usage_head(gathered).float() + if "junctions" in requested: + if junction_site_positions is None or junction_queries is None: + raise ValueError("junctions require site positions and query triples") + outputs["junctions"] = self._junction_predictions( + trunk, junction_site_positions, junction_queries + ).float() + if "contact_maps" in requested: + contact = trunk.reshape( + len(dna), self.config.contact_bins, -1, self.config.hidden_dim + ).mean(dim=2) + q, k = self.contact_q(contact), self.contact_k(contact) + row, column = self.contact_row, self.contact_column + outputs["contact_maps"] = ( + 0.5 + * ( + q[:, row] * k[:, column] + + q[:, column] * k[:, row] + ) + ).float() + return outputs + + +def count_parameters(model: nn.Module) -> int: + return sum(parameter.numel() for parameter in model.parameters() if parameter.requires_grad) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/reference_large.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/reference_large.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e403757c49eb6e6419927210c7053710964213d1 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/reference_large.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 96 +channel_growth: 96 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 1024 +num_sequence_blocks: 16 +num_attention_heads: 16 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 768 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/run_ddp.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/run_ddp.sh new file mode 100644 index 0000000000000000000000000000000000000000..c4702e9366e907b7791063e77dcfe281411ac8cd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/run_ddp.sh @@ -0,0 +1,16 @@ +#!/usr/bin/env bash +# run_ddp.sh DATA_DIR OUTPUT_DIR CONFIG EPOCHS [GPUS=8] [WORKERS_PER_GPU=12] +# Train the reference on every train interval with one example per GPU per step, then score +# validation with the verifier's own scorer. Runs inside the task image (torch, torchrun). +set -Eeuo pipefail +DATA=$1; OUT=$2; CONFIG=$3; EPOCHS=$4; GPUS=${5:-8}; WORKERS=${6:-12} +BASELINE=$(cd "$(dirname "$0")/../.." && pwd) +mkdir -p "$OUT" +cd "$BASELINE" +# Loopback rendezvous: works offline, where --standalone cannot resolve the hostname. +torchrun --nnodes 1 --nproc-per-node "$GPUS" --master-addr 127.0.0.1 --master-port "${MASTER_PORT:-29511}" -m reference.alphagenome_50m.train_eval train \ + --input "$DATA" --config "$CONFIG" --output "$OUT" --epochs "$EPOCHS" --workers "$WORKERS" \ + --log-every 200 --save-every 2000 2>&1 | tee -a "$OUT/train.log" +python predict.py "$DATA" "$OUT/valid-pred" --checkpoint "$OUT/last.pt" --split valid +python /workspace/evaluation/score.py --predictions "$OUT/valid-pred/predictions.zarr" \ + --labels "$DATA" --split valid --output "$OUT/valid-score" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/tiny_overfit.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/tiny_overfit.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c25f9ffbb76102c970e5767922400f7ec2b6c53e --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/tiny_overfit.yaml @@ -0,0 +1,24 @@ +# Keep the frozen 1-Mb input and every output track; shrink only learned widths. +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 8 +channel_growth: 4 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 32 +num_sequence_blocks: 1 +num_attention_heads: 2 +attention_head_dim: 16 +mlp_expansion: 2.0 +dropout: 0.0 +head_hidden_size: 32 +junction_rank: 8 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: false diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/train_eval.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/train_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..92f9a3f315013630fdd2e03fb6840a6c77840edd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/reference/alphagenome_50m/train_eval.py @@ -0,0 +1,537 @@ +"""Train and stream-evaluate the <=50M compact AlphaGenome baseline.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import numpy as np +import torch +from torch import Tensor +from torch.nn import functional as F +from torch.utils.data import DataLoader, Subset + +from .dataset import ( + AlphaGenomeDataset, + DENSE_FAMILIES, + single_example_collate, +) +from .model import ( + ALL_FAMILIES, + CompactModel, + ModelConfig, + count_parameters, +) + + +DEFAULT_CONFIG = Path(__file__).with_name("alphagenome_50m.yaml") + + +def _device(name: str) -> torch.device: + if name == "auto": + name = "cuda" if torch.cuda.is_available() else "cpu" + if name == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("--device cuda requested but CUDA is unavailable") + return torch.device(name) + + +def _autocast(device: torch.device): + enabled = device.type == "cuda" + dtype = torch.bfloat16 if enabled and torch.cuda.is_bf16_supported() else torch.float16 + return torch.autocast(device_type=device.type, dtype=dtype, enabled=enabled) + + +def _distributed() -> tuple[int, int, int]: + """(rank, local_rank, world_size) under torchrun; (0, 0, 1) otherwise.""" + world = int(os.environ.get("WORLD_SIZE", "1")) + if world > 1 and not torch.distributed.is_initialized(): + torch.distributed.init_process_group("nccl") + return int(os.environ.get("RANK", "0")), int(os.environ.get("LOCAL_RANK", "0")), world + + +def _loader(dataset, *, workers: int, shuffle: bool, sampler=None) -> DataLoader: + return DataLoader( + dataset, + batch_size=1, + shuffle=shuffle and sampler is None, + sampler=sampler, + num_workers=workers, + persistent_workers=workers > 0, + pin_memory=torch.cuda.is_available(), + collate_fn=single_example_collate, + ) + + +def _to_device(value, device: torch.device): + if isinstance(value, Tensor): + return value.to(device, non_blocking=True) + if isinstance(value, dict): + return {key: _to_device(item, device) for key, item in value.items()} + return value + + +def _match_coordinate_queries( + scoring: dict[str, Tensor], positions: Tensor, track_halves: Tensor +) -> tuple[Tensor, Tensor, Tensor]: + """Match trusted coordinate queries to typed position axes like the grader.""" + device = positions.device + tracks = scoring["track"].detach().cpu().numpy() + halves_cpu = track_halves.detach().cpu().numpy()[tracks] + # Build indices on CPU, then copy once. Writing three CUDA scalars per valid query + # otherwise launches thousands of tiny kernels per example, especially costly when + # training and evaluation share one GPU. The matching semantics are unchanged. + queries = np.zeros((len(tracks), 3), dtype=np.int64) + queries[:, 2] = tracks + valid = scoring["valid"].clone() + missing = np.ones(len(tracks), dtype=bool) + position_rows = positions.detach().cpu().numpy() + donors_cpu = scoring["donor_position"].detach().cpu().numpy() + acceptors_cpu = scoring["acceptor_position"].detach().cpu().numpy() + valid_indices = np.flatnonzero(valid.detach().cpu().numpy()) + lookups = [ + {int(coordinate): index for index, coordinate in enumerate(axis) if coordinate >= 0} + for axis in position_rows + ] + for index in valid_indices: + half = int(halves_cpu[index]) + row = lookups[2 * half].get(int(donors_cpu[index])) + column = lookups[2 * half + 1].get(int(acceptors_cpu[index])) + if row is not None and column is not None: + queries[index, 0] = row + queries[index, 1] = column + missing[index] = False + return (torch.from_numpy(queries).to(device).unsqueeze(0), valid, + torch.from_numpy(missing).to(device)) + + +def _model_inputs(sample: dict) -> tuple[dict, Tensor, Tensor]: + queries, query_valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + sample["site_positions"], + sample["junction_track_halves"], + ) + if (query_valid & missing).any(): + raise ValueError("trusted scoring coordinates do not match target positions") + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + return { + "dna": sample["dna"].unsqueeze(0), + "usage_positions": sample["usage_positions"].unsqueeze(0), + "junction_site_positions": sample["site_positions"].unsqueeze(0), + "junction_queries": queries, + }, junction_target, query_valid + + +def _losses(outputs: dict[str, Tensor], sample: dict, junction_target: Tensor, + junction_valid: Tensor) -> dict[str, Tensor]: + losses = {} + for family in DENSE_FAMILIES: + target = torch.asinh(sample["dense"][family]["values"]).unsqueeze(0) + mask = sample["dense"][family]["mask"].unsqueeze(0) + residual = torch.where(mask, outputs[family] - target, 0.0) + losses[family] = residual.square().sum() / mask.sum().clamp_min(1) + losses["splice_sites"] = F.binary_cross_entropy_with_logits( + outputs["splice_sites"], sample["splice_sites"].unsqueeze(0) + ) + usage_target = torch.asinh(sample["splice_usage"]["values"]).unsqueeze(0) + usage_mask = sample["splice_usage"]["mask"].unsqueeze(0) + usage_residual = torch.where(usage_mask, outputs["splice_usage"] - usage_target, 0.0) + losses["splice_usage"] = usage_residual.square().sum() / usage_mask.sum().clamp_min(1) + junction_residual = outputs["junctions"][0] - junction_target[0] + losses["junctions"] = ( + junction_residual[junction_valid].square().mean() + if junction_valid.any() else junction_residual.sum() * 0 + ) + contact_target = torch.asinh(sample["contact_maps"]).unsqueeze(0) + contact_mask = sample["contact_map_mask"].unsqueeze(0) + contact_residual = torch.where( + contact_mask, outputs["contact_maps"] - contact_target, 0.0 + ) + losses["contact_maps"] = contact_residual.square().sum() / contact_mask.sum().clamp_min(1) + losses["total"] = torch.stack([losses[name] for name in ALL_FAMILIES]).mean() + return losses + + +def _validation_inference( + model: CompactModel, sample: dict +) -> tuple[dict[str, Tensor], Tensor, Tensor, Tensor]: + """Use the same predicted-position path as a formal v3 submission.""" + trunk = model._trunk(sample["dna"].unsqueeze(0)) + dense_features = model._dense_features(trunk) + outputs = { + family: model.dense_heads[family](dense_features).float() + for family in DENSE_FAMILIES + } + all_sites = model._splice_site_logits(trunk) + target_start = (model.config.input_length - model.config.target_length) // 2 + outputs["splice_sites"] = all_sites[:, target_start:target_start + model.config.target_length] + usage_bins = model.splice_usage_head(dense_features).float() + usage_indices = torch.div(sample["usage_positions"], model.config.dense_bin_size, + rounding_mode="floor") + outputs["splice_usage"] = usage_bins[:, usage_indices] + predicted_positions = model.select_junction_positions(all_sites) + + matched, valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + predicted_positions[0], + sample["junction_track_halves"], + ) + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + junction_prediction = torch.zeros(len(valid), device=valid.device) + if (~missing & valid).any(): + selected = ~missing & valid + junction_prediction[selected] = model._junction_predictions( + trunk, predicted_positions, matched[:, selected] + )[0] + outputs["junctions"] = junction_prediction.unsqueeze(0) + + contact = trunk.reshape( + 1, model.config.contact_bins, -1, model.config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + outputs["contact_maps"] = (0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row])).float() + return outputs, junction_target, valid, missing + + +def _atomic_save(payload: dict, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(f".{path.name}.tmp") + torch.save(payload, temporary) + os.replace(temporary, path) + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(8 * 1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _resume_position(payload: dict, *, examples: int, epochs: int) -> tuple[int, int]: + epoch = int(payload["epoch"]) + step = int(payload["global_step"]) + kind = payload.get("checkpoint_kind") + if kind not in (None, "epoch") or step != epoch * examples: + raise ValueError("only a completed-epoch checkpoint can resume training") + if payload.get("examples", examples) != examples or payload.get("schedule_epochs", epochs) != epochs: + raise ValueError("checkpoint data size or training schedule differs from this run") + return epoch, step + + +def train(args: argparse.Namespace) -> None: + # Multi-GPU with `torchrun --nproc-per-node N train_eval.py train ...`: one example per GPU per + # step (data parallel), rank 0 writes checkpoints and logs. Model size is bounded by the + # training budget, not by a parameter cap. + rank, local_rank, world = _distributed() + config = ModelConfig.from_yaml(args.config) + device = torch.device("cuda", local_rank) if world > 1 else _device(args.device) + if world > 1: + torch.cuda.set_device(device) + torch.manual_seed(args.seed) + model = CompactModel(config).to(device) + parameters = count_parameters(model) + optimizer = torch.optim.AdamW( + model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay + ) + scaler = torch.amp.GradScaler("cuda", enabled=device.type == "cuda") + full_dataset = AlphaGenomeDataset(args.input, "train") + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + start_epoch, global_step = 0, 0 + if args.resume: + payload = torch.load(args.resume, map_location="cpu", weights_only=True) + if payload.get("config") != config.to_dict(): + raise ValueError("checkpoint configuration does not match --config") + model.load_state_dict(payload["model"], strict=True) + optimizer.load_state_dict(payload["optimizer"]) + scaler.load_state_dict(payload.get("scaler", {})) + start_epoch, global_step = _resume_position(payload, examples=size, epochs=args.epochs) + per_rank = math.ceil(size / world) + steps = max(1, per_rank * args.epochs) + module = model + if world > 1: + model = torch.nn.parallel.DistributedDataParallel( + model, device_ids=[local_rank], find_unused_parameters=True) + sampler = (torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world, rank=rank, + shuffle=True, seed=args.seed) + if world > 1 else None) + stop_after_epoch = args.stop_after_epoch or args.epochs + output = args.output.resolve() + output.mkdir(parents=True, exist_ok=True) + started = time.monotonic() + if rank == 0: + print(json.dumps({"event": "train_start", "parameters": parameters, "examples": size, + "device": str(device), "world_size": world}), flush=True) + + for epoch in range(start_epoch, stop_after_epoch): + model.train() + if sampler is not None: + sampler.set_epoch(epoch) + loader = _loader(dataset, workers=args.workers, shuffle=True, sampler=sampler) + for sample in loader: + sample = _to_device(sample, device) + inputs, junction_target, junction_valid = _model_inputs(sample) + progress = min((global_step + 1) / steps, 1.0) + learning_rate = args.min_learning_rate + 0.5 * ( + args.learning_rate - args.min_learning_rate + ) * (1.0 + math.cos(math.pi * progress)) + for group in optimizer.param_groups: + group["lr"] = learning_rate + optimizer.zero_grad(set_to_none=True) + with _autocast(device): + predictions = model(**inputs) + losses = _losses(predictions, sample, junction_target, junction_valid) + if not torch.isfinite(losses["total"]): + raise FloatingPointError(f"non-finite loss at step {global_step}") + scaler.scale(losses["total"]).backward() + scaler.unscale_(optimizer) + gradient_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_gradient_norm) + scaler.step(optimizer) + scaler.update() + global_step += 1 + if rank == 0 and (global_step == 1 or global_step % args.log_every == 0): + print( + json.dumps( + { + "event": "train_step", + "epoch": epoch + 1, + "step": global_step, + "loss": float(losses["total"].detach()), + "gradient_norm": float(gradient_norm), + "learning_rate": learning_rate, + "elapsed_seconds": time.monotonic() - started, + } + ), + flush=True, + ) + if rank == 0 and global_step % args.save_every == 0: + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch, + "global_step": global_step, + "checkpoint_kind": "progress", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "progress.pt", + ) + if world > 1: + torch.distributed.barrier() + if rank != 0: + continue + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch + 1, + "global_step": global_step, + "checkpoint_kind": "epoch", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "last.pt", + ) + if world > 1: + torch.distributed.barrier() + torch.distributed.destroy_process_group() + if rank != 0: + return + checkpoint = output / "last.pt" + manifest = { + "schema": "alphagenome-50m-real/v3", + "checkpoint": {"path": checkpoint.name, "sha256": _sha256(checkpoint)}, + "parameters": parameters, + "config": config.to_dict(), + "output_families": list(ALL_FAMILIES), + "epochs": args.epochs, + "completed_epochs": stop_after_epoch, + "global_step": global_step, + } + (output / "model_manifest.json").write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "train_complete", **manifest}), flush=True) + + +class PerTrackMoments: + def __init__(self, tracks: int): + self.n = np.zeros(tracks, dtype=np.int64) + self.sum_x = np.zeros(tracks, dtype=np.float64) + self.sum_y = np.zeros(tracks, dtype=np.float64) + self.sum_x2 = np.zeros(tracks, dtype=np.float64) + self.sum_y2 = np.zeros(tracks, dtype=np.float64) + self.sum_xy = np.zeros(tracks, dtype=np.float64) + + def update(self, prediction: Tensor, target: Tensor, mask: Tensor | None = None) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1, prediction.shape[-1]) + y = target.detach().double().cpu().numpy().reshape(-1, target.shape[-1]) + valid = np.ones_like(y, dtype=bool) if mask is None else mask.detach().cpu().numpy().reshape(y.shape).astype(bool) + self.n += valid.sum(axis=0) + self.sum_x += np.where(valid, x, 0).sum(axis=0, dtype=np.float64) + self.sum_y += np.where(valid, y, 0).sum(axis=0, dtype=np.float64) + self.sum_x2 += np.where(valid, x * x, 0).sum(axis=0, dtype=np.float64) + self.sum_y2 += np.where(valid, y * y, 0).sum(axis=0, dtype=np.float64) + self.sum_xy += np.where(valid, x * y, 0).sum(axis=0, dtype=np.float64) + + def update_indexed(self, prediction: Tensor, target: Tensor, track: Tensor) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1) + y = target.detach().double().cpu().numpy().reshape(-1) + indices = track.detach().cpu().numpy().reshape(-1).astype(np.int64) + self.n += np.bincount(indices, minlength=len(self.n)) + self.sum_x += np.bincount(indices, weights=x, minlength=len(self.n)) + self.sum_y += np.bincount(indices, weights=y, minlength=len(self.n)) + self.sum_x2 += np.bincount(indices, weights=x * x, minlength=len(self.n)) + self.sum_y2 += np.bincount(indices, weights=y * y, minlength=len(self.n)) + self.sum_xy += np.bincount(indices, weights=x * y, minlength=len(self.n)) + + def report(self) -> dict: + numerator = self.n * self.sum_xy - self.sum_x * self.sum_y + denominator = np.sqrt( + np.maximum(self.n * self.sum_x2 - self.sum_x**2, 0) + * np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + ) + target_ss = np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + scorable = (self.n >= 2) & (target_ss > 0) + pearson = np.full_like(numerator, np.nan) + nonconstant = scorable & (denominator > 0) + pearson[nonconstant] = numerator[nonconstant] / denominator[nonconstant] + pearson[scorable & ~nonconstant] = 0.0 + return { + "mean_pearson": float(pearson[scorable].mean()) if scorable.any() else 0.0, + "valid_tracks": int(scorable.sum()), + "per_track_pearson": [float(value) if np.isfinite(value) else None for value in pearson], + "values_per_track": self.n.tolist(), + } + + +def evaluate(args: argparse.Namespace) -> None: + device = _device(args.device) + payload = torch.load(args.checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config).to(device) + model.load_state_dict(payload["model"], strict=True) + model.eval() + full_dataset = AlphaGenomeDataset(args.input, args.split) + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + metrics = { + **{family: PerTrackMoments(config_value) for family, config_value in { + "atac": 167, "dnase": 305, "procap": 12, "cage": 546, + "rna_seq": 667, "chip_tf": 1617, "chip_histone": 1116, + }.items()}, + "splice_sites": PerTrackMoments(4), + "splice_usage": PerTrackMoments(config.splice_usage_tracks), + "junctions": PerTrackMoments(config.junction_tracks), + "contact_maps": PerTrackMoments(config.contact_tracks), + } + with torch.inference_mode(): + for index, sample in enumerate(_loader(dataset, workers=args.workers, shuffle=False)): + sample = _to_device(sample, device) + outputs, junction_target, junction_valid, junction_missing = ( + _validation_inference(model, sample) + ) + # Match predict.py: FP32 inference is serialized to FP16 Zarr values. + outputs = {name: value.half().float() for name, value in outputs.items()} + for family in DENSE_FAMILIES: + metrics[family].update( + outputs[family][0], + torch.asinh(sample["dense"][family]["values"]), + sample["dense"][family]["mask"], + ) + metrics["splice_sites"].update(outputs["splice_sites"][0], sample["splice_sites"]) + metrics["splice_usage"].update( + outputs["splice_usage"][0], + torch.asinh(sample["splice_usage"]["values"]), + sample["splice_usage"]["mask"], + ) + junction_track = sample["junction_scoring"]["track"] + metrics["junctions"].update_indexed( + outputs["junctions"][0][junction_valid], + junction_target[0][junction_valid], + junction_track[junction_valid], + ) + metrics["contact_maps"].update( + outputs["contact_maps"][0], + torch.asinh(sample["contact_maps"]), + sample["contact_map_mask"], + ) + if index == 0 or (index + 1) % args.log_every == 0 or index + 1 == size: + print(json.dumps({"event": "evaluate_progress", "examples": index + 1, "total": size}), flush=True) + families = {name: accumulator.report() for name, accumulator in metrics.items()} + valid_family_scores = [item["mean_pearson"] for item in families.values()] + report = { + "schema": "alphagenome-50m-evaluation/v3", + "split": args.split, + "examples": size, + "metric_space": ( + "asinh continuous targets and binary splice-site targets; " + "junction Pearson uses hidden coordinate queries and predicted endpoints; " + "FP32 inference rounded to FP16 prediction values" + ), + "families": families, + "overall_mean_family_pearson": float(np.mean(valid_family_scores)), + } + args.report.parent.mkdir(parents=True, exist_ok=True) + args.report.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "evaluate_complete", "overall_mean_family_pearson": report["overall_mean_family_pearson"]}), flush=True) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + common = argparse.ArgumentParser(add_help=False) + common.add_argument("--input", required=True, type=Path) + common.add_argument("--config", type=Path, default=DEFAULT_CONFIG) + common.add_argument("--workers", type=int, default=2) + common.add_argument("--max-examples", type=int, default=0) + common.add_argument("--device", choices=("auto", "cuda", "cpu"), default="auto") + common.add_argument("--log-every", type=int, default=25) + subparsers = parser.add_subparsers(dest="command", required=True) + train_parser = subparsers.add_parser("train", parents=[common]) + train_parser.add_argument("--output", required=True, type=Path) + train_parser.add_argument("--epochs", type=int, default=10) + train_parser.add_argument("--stop-after-epoch", type=int) + train_parser.add_argument("--learning-rate", type=float, default=2e-4) + train_parser.add_argument("--min-learning-rate", type=float, default=2e-5) + train_parser.add_argument("--weight-decay", type=float, default=1e-4) + train_parser.add_argument("--max-gradient-norm", type=float, default=5.0) + train_parser.add_argument("--save-every", type=int, default=250) + train_parser.add_argument("--seed", type=int, default=17) + train_parser.add_argument("--resume", type=Path) + train_parser.set_defaults(run=train) + evaluate_parser = subparsers.add_parser("evaluate", parents=[common]) + evaluate_parser.add_argument("--checkpoint", required=True, type=Path) + evaluate_parser.add_argument("--split", choices=("train", "valid", "test"), required=True) + evaluate_parser.add_argument("--report", required=True, type=Path) + evaluate_parser.set_defaults(run=evaluate) + args = parser.parse_args() + if args.workers < 0 or args.max_examples < 0: + parser.error("workers/max-examples must be nonnegative") + if args.command == "train" and ( + args.epochs < 1 + or (args.stop_after_epoch is not None + and not 1 <= args.stop_after_epoch <= args.epochs) + ): + parser.error("epochs must be positive and stop-after-epoch must be within the schedule") + args.run(args) + + +if __name__ == "__main__": + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/train_single.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/train_single.py new file mode 100644 index 0000000000000000000000000000000000000000..45bdf927f635baa77ab98cc8fcefbb60196aa0ff --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/train_single.py @@ -0,0 +1,155 @@ +"""Resumable single-H100 training; every epoch permutes every eligible training interval.""" +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import torch +from numcodecs import blosc +from torch.utils.data import DataLoader, Subset + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset, single_example_collate +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save, _losses, _model_inputs, _to_device + + +def balanced_splice_loss(logits, target): + """Equal positive/negative contributions per type, using only training labels. + + An absent class contributes its negative loss rather than dividing by zero or being dropped. + """ + dims = tuple(range(target.ndim - 1)) + positive = target.sum(dims) + negative = (1 - target).sum(dims) + pos_loss = (torch.nn.functional.softplus(-logits) * target).sum(dims) / positive.clamp_min(1) + neg_loss = (torch.nn.functional.softplus(logits) * (1 - target)).sum(dims) / negative.clamp_min(1) + return torch.where(positive > 0, 0.5 * (pos_loss + neg_loss), neg_loss).mean() + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--input', type=Path, default=Path('/workspace/data')) + p.add_argument('--output', type=Path, required=True) + p.add_argument('--config', type=Path, default=Path(__file__).parent / 'reference/alphagenome_50m/alphagenome_50m.yaml') + p.add_argument('--resume', type=Path) + p.add_argument('--objective', choices=['reference', 'balanced_splice'], default='balanced_splice') + p.add_argument('--epochs', type=int, default=4) + p.add_argument('--stop-at', type=int, default=0, help='Stop after this total number of examples, preserving exact sampler position') + p.add_argument('--workers', type=int, default=8) + p.add_argument('--threads', type=int, default=4) + p.add_argument('--seed', type=int, default=17) + p.add_argument('--learning-rate', type=float, default=2e-4) + p.add_argument('--min-learning-rate', type=float, default=2e-5) + p.add_argument('--warmup', type=int, default=500) + p.add_argument('--save-every', type=int, default=2000) + p.add_argument('--keep-every', type=int, default=10000, help='Keep immutable intermediate checkpoints') + p.add_argument('--log-every', type=int, default=100) + args = p.parse_args() + torch.set_num_threads(args.threads) + torch.set_num_interop_threads(1) + blosc.set_nthreads(1) + if hasattr(os, 'sched_getaffinity'): + os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) + torch.manual_seed(args.seed) + device = torch.device('cuda:0') + config = ModelConfig.from_yaml(args.config) + model = CompactModel(config).to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=1e-4) + dataset = AlphaGenomeDataset(args.input, 'train') + model.set_junction_track_halves(dataset.junction_track_halves) + args.output.mkdir(parents=True, exist_ok=True) + total = len(dataset) * args.epochs + step, elapsed_before = 0, 0.0 + if args.resume: + state = torch.load(args.resume, map_location='cpu', weights_only=True) + if state['config'] != config.to_dict() or state['examples'] != len(dataset): + raise ValueError('resume config or dataset size differs') + if state['schedule_epochs'] != args.epochs or state['objective'] != args.objective: + raise ValueError('resume schedule or objective differs') + if state['seed'] != args.seed: + raise ValueError('resume shuffle seed differs') + previous = state.get('training_arguments', {}) + for field in ['learning_rate', 'min_learning_rate', 'warmup']: + if field in previous and previous[field] != getattr(args, field): + raise ValueError(f'resume {field} differs') + model.load_state_dict(state['model'], strict=True) + optimizer.load_state_dict(state['optimizer']) + step = state['global_step'] + elapsed_before = state.get('elapsed_seconds', 0.0) + torch.set_rng_state(state['rng_cpu']) + torch.cuda.set_rng_state(state['rng_cuda'], device) + start = time.monotonic() + def emit(event, **values): + record = {'event': event, 'step': step, 'elapsed_seconds': elapsed_before + time.monotonic() - start, **values} + print(json.dumps(record), flush=True) + def save(name): + _atomic_save({ + 'schema': 'seqfunc-single/v1', 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), 'config': config.to_dict(), + 'global_step': step, 'epoch': step // len(dataset), 'offset': step % len(dataset), + 'examples': len(dataset), 'schedule_epochs': args.epochs, + 'objective': args.objective, 'seed': args.seed, + 'training_arguments': {k: str(v) if isinstance(v, Path) else v for k, v in vars(args).items()}, + 'rng_cpu': torch.get_rng_state(), 'rng_cuda': torch.cuda.get_rng_state(device), + 'elapsed_seconds': elapsed_before + time.monotonic() - start, + 'provenance': {'data': str(args.input), 'split': 'train', 'pretrained': False, + 'device': torch.cuda.get_device_name(0), 'world_size': 1}, + }, args.output / name) + emit('train_start', parameters=count_parameters(model), examples=len(dataset), objective=args.objective, + config=config.to_dict(), schedule_examples=total, device=torch.cuda.get_device_name(0)) + model.train() + for epoch in range(step // len(dataset), args.epochs): + generator = torch.Generator().manual_seed(args.seed + epoch) + order = torch.randperm(len(dataset), generator=generator).tolist() + offset = step % len(dataset) + emit('epoch_order', epoch=epoch + 1, offset=offset, count=len(order), + permutation_sha256=hashlib.sha256(torch.tensor(order, dtype=torch.int64).numpy().tobytes()).hexdigest()) + loader = DataLoader(Subset(dataset, order[offset:]), batch_size=1, shuffle=False, + num_workers=args.workers, pin_memory=True, collate_fn=single_example_collate, + persistent_workers=False, prefetch_factor=2 if args.workers else None) + for sample in loader: + sample = _to_device(sample, device) + inputs, target, valid = _model_inputs(sample) + progress = min(max(step - args.warmup, 0) / max(total - args.warmup, 1), 1) + lr = args.min_learning_rate + 0.5 * (args.learning_rate - args.min_learning_rate) * (1 + math.cos(math.pi * progress)) + lr *= min((step + 1) / max(args.warmup, 1), 1) + for group in optimizer.param_groups: + group['lr'] = lr + optimizer.zero_grad(set_to_none=True) + with torch.autocast('cuda', dtype=torch.bfloat16): + outputs = model(**inputs) + losses = _losses(outputs, sample, target, valid) + if args.objective == 'balanced_splice': + losses['splice_sites'] = balanced_splice_loss(outputs['splice_sites'], sample['splice_sites'][None]) + losses['total'] = torch.stack([v for k, v in losses.items() if k != 'total']).mean() + if not torch.isfinite(losses['total']): + raise FloatingPointError(f'nonfinite loss at {step}') + losses['total'].backward() + norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0, error_if_nonfinite=True) + optimizer.step() + step += 1 + if step == 1 or step % args.log_every == 0: + emit('train_step', epoch=epoch + 1, example_id=sample['example_id'], learning_rate=lr, + gradient_norm=float(norm), losses={k: float(v.detach()) for k, v in losses.items()}, + peak_gpu_bytes=torch.cuda.max_memory_allocated()) + if step % args.save_every == 0: + save('progress.pt') + if args.keep_every and step % args.keep_every == 0: + save(f'step-{step:06d}.pt') + if args.stop_at and step >= args.stop_at: + save('last.pt') + emit('train_segment_complete', completed_epochs=step // len(dataset), offset=step % len(dataset)) + return + save('last.pt') + save(f'epoch-{epoch + 1:02d}.pt') + emit('epoch_complete', completed_epochs=epoch + 1) + emit('train_complete') + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/training_config.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/training_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1f9a3298d69b138a02fed00880182d4004b23327 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/training_config.yaml @@ -0,0 +1,28 @@ +attention_head_dim: 64 +channel_growth: 32 +contact_bin_size: 2048 +contact_tracks: 28 +conv_kernel_size: 5 +dense_bin_size: 128 +dna_kernel_size: 15 +dropout: 0.0 +enable_pairwise: false +encoder_width: 32 +gradient_checkpointing: true +head_hidden_size: 256 +hidden_dim: 384 +input_length: 1048576 +junction_rank: 32 +junction_tracks: 734 +local_width: 32 +mlp_expansion: 4.0 +num_attention_heads: 6 +num_downsampling_blocks: 7 +num_sequence_blocks: 6 +pair_downsample: 16 +pair_update_interval: 2 +pair_width: 32 +splice_usage_tracks: 734 +stem_stride: 8 +target_length: 196608 +trunk_bin_size: 1024 diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/validation_tracks.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/validation_tracks.json new file mode 100644 index 0000000000000000000000000000000000000000..ab5de538f2b25130539075bcec5bf39dddc89836 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/validation_tracks.json @@ -0,0 +1,17909 @@ +{ + "schema": "genome-sequence-to-function-report/v4", + "split": "valid", + "examples": 200, + "families": { + "atac": { + "mean_pearson": 0.5009007147028535, + "valid_tracks": 167, + "per_track_pearson": [ + 0.6185203140767616, + 0.6337654747904041, + 0.38362857080914775, + 0.5790602015579601, + 0.3785107106968191, + 0.37329687865475725, + 0.3922836515403589, + 0.47342841060425916, + 0.5116515651217635, + 0.5698325213158345, + 0.495687329983454, + 0.4595066391936, + 0.3225648356885936, + 0.42936757138601406, + 0.6157349616896458, + 0.40888137920540646, + 0.4670245230622458, + 0.428230918496098, + 0.4666147049336215, + 0.4496039339739506, + 0.4249727768129564, + 0.38549676377292624, + 0.44427677258478765, + 0.4386540143388153, + 0.4277492993481239, + 0.4124615871381051, + 0.3981701613222936, + 0.41942435737204864, + 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b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S012/validation_variants.json @@ -0,0 +1,125 @@ +{ + "eqtl_sign": { + "rows": 17052, + "perturbations": 165, + "metric": "auroc", + "model": 0.5793698733965188, + "anchors": { + "AlphaGenome": 0.8263569969634328 + } + }, + "eqtl_causality": { + "rows": 17934, + "perturbations": 174, + "metric": "auroc", + "model": 0.630227676656248, + "anchors": { + "AlphaGenome": 0.8325427468284611 + } + }, + "eqtl_coefficient": { + "rows": 8575, + "perturbations": 165, + "metric": "spearman", + "model": 0.13204937683811374, + "anchors": { + "AlphaGenome": 0.4133539365891599 + } + }, + "sqtl_causality": { + "rows": 21340, + "perturbations": 194, + "metric": "auprc", + "model": 0.8095265085304872, + "anchors": { + "AlphaGenome": 0.8447114201904594 + } + }, + "clinvar_noncoding": { + "rows": 199, + "perturbations": 199, + "metric": "auprc", + "model": 0.29178879876255137, + "anchors": { + 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0.5831934548667065, + "anchors": { + "AlphaGenome": 0.9594307235541584 + } + }, + "traitgym_mendelian": { + "rows": 292, + "perturbations": 292, + "metric": "auprc", + "model": 0.154692680776655, + "anchors": { + "Borzoi_L2_L2.plus.all": 0.5026323772738239, + "Enformer_L2_L2.plus.all": 0.5452938267693526, + "CADD.plus.RawScore": 0.7189111372856957, + "GPN-MSA_absLLR.plus.score": 0.6729149139043132 + } + }, + "traitgym_complex": { + "rows": 299, + "perturbations": 299, + "metric": "auprc", + "model": 0.19178224857656814, + "anchors": { + "Borzoi_L2_L2.plus.all": 0.294501577162467, + "Enformer_L2_L2.plus.all": 0.269227301087889, + "CADD.plus.RawScore": 0.41341570280137435, + "GPN-MSA_absLLR.plus.score": 0.4213971415058371 + } + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S013/infer.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S013/infer.sh new file mode 100644 index 0000000000000000000000000000000000000000..2a412a9ad8667d6dcb94bb57d9959d61d43ddd34 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S013/infer.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: infer.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/predict.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S013/splice_output.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S013/splice_output.py new file mode 100644 index 0000000000000000000000000000000000000000..1081264d9cf4b4473f4fe53c97ff02b2c3b249e3 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S013/splice_output.py @@ -0,0 +1,11 @@ +"""Shared splice output link; constants estimated only from training targets.""" +import json +from pathlib import Path +import torch + +_OFFSETS = json.loads((Path(__file__).with_name('splice_calibration.json')).read_text())['logit_offset'] + +def splice_output_scores(logits): + logits = logits.float() + offset = torch.tensor(_OFFSETS, device=logits.device, dtype=logits.dtype) + return torch.sigmoid(logits + offset) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/README.md b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e08c264e2b3a23a90195f449c8018935407926da --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/README.md @@ -0,0 +1,111 @@ +# SeqFunc rc17: forward/reverse-complement mean + +One model with **13,265,544 parameters** serves every family and both entry points. It reads the entire +1,048,576-bp DNA window through a convolutional encoder and 6 transformer +blocks of width 384 over 1,024 tokens. Dense heads predict 128-bp bins over the central +196,608 bp; other heads predict splice sites, sparse usage/junctions and full-window contacts. +A width-32 base-resolution path uses a 21-bp stem and 6 dilated residual depthwise blocks (10,941-bp receptive field). Average/max pooling provides aligned 128-bp residual features to dense, usage and junction heads. Splice logits combine local base-resolution logits with the reference offset decoder. Junction values use low-rank donor/acceptor factors; contacts use a symmetric +product of global features. Sites and junction endpoints are selected from model scores alone. + +All weights originate from local random initialization (seed 17) and locally trained +checkpoints. The compact balanced-splice model gained a zero-output local path at step 2,000. At step 40,000, two identity-initialized residual blocks extended its local receptive field from 701 to 10,941 bp. All old parameters and optimizer moments were preserved, with optimizer IDs remapped by parameter name. The next 4,000 examples and learning rates match local01 through step 44,000. + +This checkpoint records **44,000 updates**, 1 complete training permutations and +2,980 intervals into the next permutation. Each permutation includes all **41,020** +eligible FOLD_0 training intervals (folds 2–7), shuffled deterministically without replacement. +Quantized train targets are decoded by the supplied reader. Validation is fold 0; no test labels, +test-derived caches or pretrained model parameters were used. Development labels only evaluate +and select models. Checkpoints before a complete permutation remain intermediate milestones. + +Training uses one H100 80 GB and at most 22 CPUs. The current loader uses 3 workers +and 2 torch CPU threads. BF16 training uses AdamW (weight decay 1e-4), gradient +clipping at 5, and a 4-epoch cosine learning-rate schedule from +0.0002 to 2e-05, with +500 warmup training examples. The objective is `balanced_splice`. +Other family losses follow the reference. Recorded accumulated training-loop wall time is +127.2 minutes, including inherited parent time. Runs can share this +same physical GPU with experiments or evaluation. Setup, evaluation and discarded mechanical checks +are additional research compute, documented in the research ledger. Matching training junction queries +uses a verified, bit-identical CPU index construction followed by a bulk GPU transfer. + +The package contains model code, weights, training source/configuration/provenance, SHA-256 checksums, +and the complete CPython 3.11 / PyTorch 2.7.1 CUDA 12.6 Python runtime archive. Both executable entry +points take INPUT_ROOT and OUTPUT_ROOT, extract the vendored runtime temporarily, and run offline. +The host supplies Linux, bash/tar/gzip and the NVIDIA driver. Inference reads only public manifests +and the pinned reference FASTA. No target, splice-coordinate or prediction cache is packaged or read. +Track outputs use float16; perturbation outputs use float32 to preserve small A/B differences. + +The appended validation results cover all 200 intervals and all 2,697 development perturbations, +scored by the unmodified verifier tools. Published anchors use the same label rows. All results, +including regressions, are retained in the package's JSON reports. Limitations include incomplete +training at intermediate checkpoints, a 10,941-bp local receptive field, pooled usage/junction features, +low-rank contact modeling, and uneven variant performance. Functional readouts do not provide the +evolutionary constraint information used by conservation-based variant scorers. + +Junction selection requires the donor to precede the acceptor on plus-strand transcripts and +follow it on minus-strand transcripts. This fixed biological constraint uses model-selected +coordinates and public strand halves. It changes no model parameters, usage selection or +perturbation computation. The rest of the decoder and candidate budget are unchanged. + +The splice-family output is a probability, using the same fixed link in both entry points: +`sigmoid(raw_logits + offset)`. Four offsets are estimated from 256 training windows (seed 1729) +to correct the marginal prior induced by balanced splice BCE. All network weights are unchanged. +The training prevalence, effective balanced-loss prior, offsets and sampling hash are included in +`splice_calibration.json`. This is an approximate marginal correction because the loss balances +each training window separately. No development labels fit these constants. + +Every family and both entry points average the same checkpoint on the original input and its +reverse complement. Public metadata supplies the opposite-strand track map, vendored in +`strand_permutations.json`. No extra flanking DNA, target coordinates or inference labels are +used. There are 13,265,544 unique learned parameters and two deterministic orientation views. +The weights are fixed at one half; no development labels fit them. This approximately doubles +inference work relative to the parent. Dense/contact values average in the contract's asinh +space; splice scores average the training-prior-corrected probabilities of both orientations. + +Each view selects its sparse sites and strand-consistent junctions independently. After mapping +coordinates and tracks back, sparse functions are averaged over their union, with zero at +unlisted coordinates. The usage union fits the 4,096-position limit. The 16,384 junctions with +the highest averaged strength in their own strand half are retained. This can trade candidate +recall against score dilution when only one orientation lists a coordinate. All readouts are +computed in float32 before final track storage in float16. Perturbations remain float32. + +Mechanical checks cover strand/coordinate involution, direct contact-matrix reversal, sparse +zero-fill averaging and candidate budgets. The extracted single-view decoder reproduced all +15 parent arrays on public DNA. Dense/site readouts matched bit-for-bit between track and +perturbation code and were exactly reverse-complement equivariant. Both vendored shell entry +points succeeded on two public windows and five public perturbations; recovery reproduced all +30 track arrays and 22 perturbation arrays exactly, including a partial perturbation restart. + +## Measured validation results + +Family-mean Pearson: **0.358265** on 200 validation intervals. + +| Family | Pearson | +|---|---:| +| atac | 0.523357 | +| dnase | 0.627183 | +| procap | 0.408468 | +| cage | 0.220098 | +| rna_seq | 0.340182 | +| chip_tf | 0.441846 | +| chip_histone | 0.434650 | +| splice_sites | 0.233956 | +| splice_usage | 0.330321 | +| junctions | 0.242530 | +| contact_maps | 0.138321 | + +| Development evaluation | Metric | Model | Published anchors on the same rows | +|---|---|---:|---| +| eqtl_sign | auroc | 0.5646 | AlphaGenome: 0.8264 | +| eqtl_causality | auroc | 0.6866 | AlphaGenome: 0.8325 | +| eqtl_coefficient | spearman | 0.1704 | AlphaGenome: 0.4134 | +| sqtl_causality | auprc | 0.7725 | AlphaGenome: 0.8447 | +| clinvar_noncoding | auprc | 0.4066 | AlphaGenome: 0.9410 | +| clinvar_splice_region | auprc | 0.3243 | AlphaGenome: 0.7225 | +| clinvar_missense | auprc | 0.2080 | AlphaGenome: 0.3738 | +| mfass | auprc | 0.4002 | AlphaGenome: 0.7343 | +| caqtl | auprc | 0.1684 | AlphaGenome: 0.8076 | +| bqtl_spi1 | auprc | 0.2426 | AlphaGenome: 0.8167 | +| enhancer_gene | auprc | 0.7734 | AlphaGenome: 0.9594 | +| traitgym_mendelian | auprc | 0.1661 | Borzoi_L2_L2.plus.all: 0.5026; Enformer_L2_L2.plus.all: 0.5453; CADD.plus.RawScore: 0.7189; GPN-MSA_absLLR.plus.score: 0.6729 | +| traitgym_complex | auprc | 0.1893 | Borzoi_L2_L2.plus.all: 0.2945; Enformer_L2_L2.plus.all: 0.2692; CADD.plus.RawScore: 0.4134; GPN-MSA_absLLR.plus.score: 0.4214 | diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/attach_local.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/attach_local.py new file mode 100644 index 0000000000000000000000000000000000000000..aa082537004dbc398c4a78c1bd0203970ce5e42f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/attach_local.py @@ -0,0 +1,52 @@ +"""Attach a zero-initialized local residual path to a checkpoint trained in this workspace.""" +import argparse +from dataclasses import replace +import hashlib +from pathlib import Path + +import torch +import yaml + +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + p.add_argument('--width', type=int, default=32) + args = p.parse_args() + state = torch.load(args.checkpoint, map_location='cpu', weights_only=True) + assert state['schema'] == 'seqfunc-single/v1', 'only locally trained single-GPU checkpoints' + parent_config = ModelConfig(**state['config']) + assert parent_config.local_width == 0 + torch.manual_seed(state['seed']) + parent = CompactModel(parent_config) + config = replace(parent_config, local_width=args.width) + model = CompactModel(config) + result = model.load_state_dict(state['model'], strict=False) + assert not result.unexpected_keys + assert all(k.startswith(('local_path.', 'local_projection.')) for k in result.missing_keys) + parent_names = list(dict(parent.named_parameters())) + names = list(dict(model.named_parameters())) + assert names[:len(parent_names)] == parent_names, 'optimizer parameter order changed' + assert len(state['optimizer']['param_groups']) == 1 + group = state['optimizer']['param_groups'][0] + assert group['params'] == list(range(len(parent_names))) + group['params'] = list(range(len(names))) + state['model'] = model.state_dict() + state['config'] = config.to_dict() + state['intervention'] = { + 'kind': 'base_resolution_local_path', 'width': args.width, + 'parent_sha256': hashlib.sha256(args.checkpoint.read_bytes()).hexdigest(), + 'attachment_step': state['global_step'], 'initial_outputs_unchanged': True, + } + args.output.mkdir(parents=True, exist_ok=False) + (args.output / 'config.yaml').write_text(yaml.safe_dump(config.to_dict())) + _atomic_save(state, args.output / 'attached.pt') + print({'parameters': count_parameters(model), **state['intervention']}) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/decoding_policy.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/decoding_policy.json new file mode 100644 index 0000000000000000000000000000000000000000..b303311b59a8500d1f68011fe9d5cd8ace69018e --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/decoding_policy.json @@ -0,0 +1,9 @@ +{ + "kind": "junction_transcript_direction", + "parent_package": "/workspace/research/experiments/context09/raw-package", + "weights_unchanged": true, + "plus": "donor < acceptor", + "minus": "donor > acceptor", + "inference_inputs": "model-selected coordinates and public track strand halves only", + "training_evidence": "64 training windows: 30,616 positive junction queries, all in transcript direction" +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/extend_local_context.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/extend_local_context.py new file mode 100644 index 0000000000000000000000000000000000000000..3651698e8a120b3308566d05b4523733fa621d48 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/extend_local_context.py @@ -0,0 +1,70 @@ +"""Extend our trained local path with identity-initialized dilated residual blocks.""" +import argparse +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import torch +import yaml +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + +def extend(state, blocks): + old_config = ModelConfig(**state['config']) + assert old_config.local_width and blocks > old_config.local_context_blocks + config = replace(old_config, local_context_blocks=blocks) + torch.manual_seed(state['seed']) + parent = CompactModel(old_config) + model = CompactModel(config) + missing = model.load_state_dict(state['model'], strict=False) + new_prefixes = tuple(f'local_path.blocks.{i}.' for i in range(old_config.local_context_blocks, blocks)) + assert not missing.unexpected_keys + assert all(k.startswith(new_prefixes) for k in missing.missing_keys) + for i in range(old_config.local_context_blocks, blocks): + torch.nn.init.zeros_(model.local_path.blocks[i].contract.weight) + torch.nn.init.zeros_(model.local_path.blocks[i].contract.bias) + old_names = list(dict(parent.named_parameters())) + new_names = list(dict(model.named_parameters())) + assert len(state['optimizer']['param_groups']) == 1 + group = state['optimizer']['param_groups'][0] + assert group['params'] == list(range(len(old_names))) + # New blocks shift following norm/head IDs; preserve moments by parameter name. + remap = {old_id: new_names.index(name) for old_id, name in enumerate(old_names)} + state['optimizer']['state'] = {remap[old_id]: values + for old_id, values in state['optimizer']['state'].items()} + group['params'] = list(range(len(new_names))) + for name, value in state['model'].items(): + assert torch.equal(model.state_dict()[name], value) + state.update(config=config.to_dict(), model=model.state_dict()) + return state, model, remap + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + p.add_argument('--blocks', type=int, default=6) + a = p.parse_args() + state = torch.load(a.checkpoint, map_location='cpu', weights_only=True) + old_blocks = ModelConfig(**state['config']).local_context_blocks + state, model, remap = extend(state, a.blocks) + with a.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() + change = {'kind': 'extended_local_context', 'parent_sha256': digest, + 'step': state['global_step'], 'blocks_before': old_blocks, 'blocks_after': a.blocks, + 'receptive_field_bp': 21+8*sum(4**i for i in range(a.blocks)), + 'parent_parameters_optimizer_moments_rng_preserved': True, + 'new_blocks_initially_identity': True, 'optimizer_ids_remapped_by_parameter_name': True} + history = list(state.get('interventions', [])) + if state.get('intervention') and state['intervention'] not in history: + history.append(state['intervention']) + history.append(change) + state.update(intervention=change, interventions=history) + a.output.mkdir(parents=True, exist_ok=False) + (a.output/'config.yaml').write_text(yaml.safe_dump(state['config'])) + (a.output/'intervention.json').write_text(json.dumps(change, indent=2)+'\n') + (a.output/'optimizer_id_remap.json').write_text(json.dumps(remap, indent=2)+'\n') + _atomic_save(state, a.output/'start.pt') + print(json.dumps({'parameters': count_parameters(model), **change}), flush=True) + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/infer.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/infer.sh new file mode 100644 index 0000000000000000000000000000000000000000..2a412a9ad8667d6dcb94bb57d9959d61d43ddd34 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/infer.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: infer.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/predict.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/package_manifest.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/package_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..620f3bdcd88d2f68ecfbb0996a9bf39dcd907a49 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/package_manifest.json @@ -0,0 +1,40 @@ +{ + "parameters": 13265544, + "source_checkpoint_sha256": "f8223af7bb526621769ac713df53b54cfe8d9d0cf0a34e2b1d18b9080d58cf8c", + "files": { + "README.md": "7917ac60a67fa46f55c6124bd8f0e26158f4bac7c2b2244ebd497e7b4f8c2580", + "attach_local.py": "d0774e2711840783267914789ed303a3b61b64381b72d61f0886edbc04974f15", + "decoding_policy.json": "56749f91c8b0d85201820dc20c59a1f685a8955cf467576fff326f67fe27f76f", + "extend_local_context.py": "aa720fdbd8625a900f24bc2c007ec0acf8c2164591971d007d2c01a58792d701", + "infer.sh": "ef810532b1e72b85fbe283e4f52c177bb70025977dbb2345d75837c9bbd51e37", + "last.pt": "3554a6c79b220b41087f34adaa35dfe72d58f96f8060c78ea6c6786ed9324620", + "perturb.py": "49065dc43816920ecac9de3ebcc4735673e4ff6594cf3f6bd34233b88c9bed73", + "perturb.sh": "113ec2383276a6e83fe1e4e8b0fa914f8d535d5dd0f5b2c34240140312dd5fb4", + "predict.py": "4f5dbddf5b6b701079aeaf38920fe414ce980f7161997f4d5c80952a6d834e51", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "rc_ensemble.py": "84a4d0e94ab86622e2c25e96f8835b2ef2bba77731ff7ff0bdc1c2de02cdcef4", + "rc_ensemble_policy.json": "eebea69f19ed21c2026e3a5e8bd54e5b683a665f99f1d02bb75eac72cbbc7259", + "reference/__init__.py": "45f3cf93f48859104e70bb58d717130dfd6521a45d2aba06b1a726afc0dc51dd", + "reference/alphagenome_50m/__init__.py": "a5a7e45d5940c3b201017c6322128e9f47efd37e1d29f717da2c1f787688eff9", + "reference/alphagenome_50m/alphagenome_50m.yaml": "3cac32e9f55e352dff78bb85ac892fdae1f9e412d9bba3e945750210c5252eee", + "reference/alphagenome_50m/dataset.py": "ad6670f742f4de9898064becc525d0aea98b402abe7b210bb1b26a9eee6402b9", + "reference/alphagenome_50m/layers.py": "26a116a72aa3f0e307a3606163547edbdecb7965f7fdcd96bc5f805cd21b3352", + "reference/alphagenome_50m/model.py": "bcd41eba9c2f952250f76aac8812a9dd995ee206791fbe640c751fa073428b8b", + "reference/alphagenome_50m/reference_large.yaml": "68084d299b39418034c6bbf71db174c3ffa68bbeea8779c1694e44ec71f3529c", + "reference/alphagenome_50m/run_ddp.sh": "96d35a24a4e6789311501fb98203807ac0c4429375c44d5c6671908270c93bb0", + "reference/alphagenome_50m/tiny_overfit.yaml": "f7a104c3929de0530a9a8f73f66d794cca595aefad8ef262e09ecbeb9cfce6de", + "reference/alphagenome_50m/train_eval.py": "95d2dad279b23ed9c0c8a4f070035cf53c27eb8fe27da64c441343717174f784", + "runtime.tar.gz": "718da4c374e0c552b6e429b226c90b2fded5c900d95577d190d4df520aaf3901", + 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"validation_variants.json": "8b411b049d65142396ec337f549536f2794a99431e06e204073cb426721bacc7" + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/perturb.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/perturb.py new file mode 100644 index 0000000000000000000000000000000000000000..b8ee571ad9dab90aded5077affbb42419869da16 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/perturb.py @@ -0,0 +1,92 @@ +"""Predict both sequences of every perturbation (contract: perturbations/v1). + + python perturb.py INPUT_ROOT OUTPUT_ROOT --checkpoint last.pt + +INPUT_ROOT holds `public/perturbations.parquet` and `reference/GRCh38.p13.genome.fa` (in the +workspace, `/workspace/data` works too: the queries are then read from `variant_evals/public/`). Each row is a +1,048,576-bp window starting at `input_start` and an edit spanning [edit_start, edit_end) in genomic +0-based coordinates: + +- `snv`: sequence A carries `allele_a` at edit_start, sequence B carries `allele_b` (both are written + explicitly; either may differ from the reference genome); +- `mask`: sequence A is the reference, sequence B has the interval replaced by N. + +For each perturbation the output group `perturbations/p{index:06d}` holds `a/` and +`b/` for every family in `families`: dense families sliced to bins +[dense_bin_start, dense_bin_end) of the central target window, and `splice_sites` sliced to bp +[splice_start, splice_end). Values are the same quantities as predictions.zarr (asinh space for +dense tracks, splice-site scores for splice_sites). +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import pandas as pd +import pysam +import torch +import zarr + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig +from rc_ensemble import predict_requested + +PERTURBATION_SCHEMA = "genome-sequence-to-function-perturbations/v1" +COMPRESSOR = Blosc(cname="zstd", clevel=3, shuffle=Blosc.BITSHUFFLE) + + +def sequences(fasta, row) -> tuple[str, str]: + length = fasta.get_reference_length(row.chrom) + start, end = int(row.input_start), int(row.input_start) + 1_048_576 + sequence = fasta.fetch(row.chrom, max(0, start), min(length, end)).upper() + sequence = "N" * max(0, -start) + sequence + "N" * max(0, end - length) + lo, hi = int(row.edit_start) - start, int(row.edit_end) - start + if row.edit == "snv": + return sequence[:lo] + row.allele_a + sequence[hi:], sequence[:lo] + row.allele_b + sequence[hi:] + if row.edit == "mask": + return sequence, sequence[:lo] + "N" * (hi - lo) + sequence[hi:] + raise ValueError(f"unknown edit {row.edit!r}") + + +def main(input_root: Path, output_root: Path, checkpoint: Path, queries: Path | None = None) -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + if queries is None: + queries = input_root / "public/perturbations.parquet" + if not queries.exists(): + queries = input_root / "variant_evals/public/perturbations.parquet" + rows = pd.read_parquet(queries) + fasta = pysam.FastaFile(str(input_root / "reference/GRCh38.p13.genome.fa")) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "perturbations.zarr"), mode="w") + target_start = (config.input_length - config.target_length) // 2 + with torch.inference_mode(): + for index, row in enumerate(rows.itertuples(index=False)): + pair = sequences(fasta, row) + dna = torch.from_numpy(np.stack([AlphaGenomeDataset._one_hot(s) for s in pair])).to(device) + group = root.create_group(f"perturbations/p{index:06d}") + for family, values in predict_requested(model, dna, row).items(): + values = np.asarray(values, dtype=np.float32) + for side, array in zip("ab", values): + group.create_dataset(f"{side}/{family}", data=array, compressor=COMPRESSOR) + print(json.dumps({"event": "perturb", "done": index + 1, "total": len(rows)}), flush=True) + root.attrs.update({"schema": PERTURBATION_SCHEMA, + "perturbation_ids": rows.perturbation_id.astype(str).tolist()}) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--queries", type=Path, help="another query table, e.g. the development split") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.queries) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/perturb.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/perturb.sh new file mode 100644 index 0000000000000000000000000000000000000000..f07c4714532c2d33e7a419de3877e5daacee89fd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/perturb.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: perturb.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/perturb.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/predict.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..665fb186f3a62b1ff130abb034ac25a3a924c7c8 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/predict.py @@ -0,0 +1,52 @@ +"""Stream one shared-checkpoint forward/reverse-complement mean for every output family.""" +from __future__ import annotations +import argparse +import json +from pathlib import Path +from numcodecs import Blosc +import numpy as np +import torch +import zarr +from prediction_contract import PREDICTION_SCHEMA +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig +from rc_ensemble import engine_for + +COMPRESSOR = Blosc(cname="zstd", clevel=7, shuffle=Blosc.BITSHUFFLE) + + +def main(input_root: Path, output_root: Path, checkpoint: Path, split: str = "test") -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + model = CompactModel(ModelConfig(**payload['config'])) + model.load_state_dict(payload['model'], strict=True) + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + model.to(device).eval() + dataset = AlphaGenomeDataset(input_root, split, include_targets=False) + model.set_junction_track_halves(dataset.junction_track_halves) + engine = engine_for(model) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "predictions.zarr"), mode="w") + ids = [] + with torch.inference_mode(): + for sample in dataset: + example_id = sample['example_id'] + values = engine.tracks(sample['dna'][None].to(device)) + group = root.create_group(f"examples/{example_id}") + for name, array in values.items(): + dtype = np.int8 if name == 'junction_half' else np.int32 if name in ( + 'splice_usage_positions', 'junction_donor', 'junction_acceptor') else np.float16 + group.create_dataset(name, data=array.astype(dtype), compressor=COMPRESSOR) + ids.append(example_id) + print(json.dumps({'event': 'predict', 'example': len(ids), 'total': len(dataset)}), flush=True) + root.attrs.update({'schema': PREDICTION_SCHEMA, 'example_ids': ids}) + (output_root/'example_ids.json').write_text(json.dumps(ids)+'\n') + + +if __name__ == '__main__': + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('input_root', type=Path) + p.add_argument('output_root', type=Path) + p.add_argument('--checkpoint', type=Path, default=Path(__file__).resolve().parent/'last.pt') + p.add_argument('--split', default='test', choices=['valid', 'test']) + a = p.parse_args() + main(a.input_root, a.output_root, a.checkpoint, a.split) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/prediction_contract.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/prediction_contract.py new file mode 100644 index 0000000000000000000000000000000000000000..2455424db002b840cf0d69456930840a7bc5f3cb --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/prediction_contract.py @@ -0,0 +1,23 @@ +"""Public prediction shapes; no trusted splice coordinates are exposed.""" + +from __future__ import annotations + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} + +PREDICTION_SCHEMA = "genome-sequence-to-function-predictions/v4" +MAX_USAGE_SITES = 4_096 +MAX_JUNCTION_PAIRS = 16_384 +# Fixed-shape arrays per example; splice usage and junctions are sparse (see contract.md). +FIXED_SHAPES = { + **{name: (1_536, tracks) for name, tracks in DENSE_TRACKS.items()}, + "splice_sites": (196_608, 4), + "contact_maps": (131_328, 28), +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/rc_ensemble.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/rc_ensemble.py new file mode 100644 index 0000000000000000000000000000000000000000..f076804c66e481eb823e6ca5e721f39fce50bad5 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/rc_ensemble.py @@ -0,0 +1,111 @@ +"""One shared checkpoint evaluated on DNA and its reverse complement for every output.""" +import json +from pathlib import Path +import numpy as np +import torch +from prediction_contract import DENSE_TRACKS, MAX_JUNCTION_PAIRS, MAX_USAGE_SITES +from single_prediction import single_prediction +from splice_output import splice_output_scores + + +def contact_reversal(bins): + row, column = np.triu_indices(bins) + lookup = np.empty((bins, bins), dtype=np.int64) + lookup[row, column] = np.arange(len(row)) + lookup[column, row] = np.arange(len(row)) + return lookup[bins-1-row, bins-1-column] + + +def reverse_readout(arrays, permutations, input_length, target_length, contact_bins): + """Map a reverse-complement prediction back into the original genomic coordinates.""" + out = {name: arrays[name][::-1, permutations[name]].copy() for name in DENSE_TRACKS} + out['splice_sites'] = arrays['splice_sites'][::-1, permutations['splice_sites']].copy() + positions = target_length-1-arrays['splice_usage_positions'] + order = np.argsort(positions) + out['splice_usage_positions'] = positions[order].astype(np.int32) + out['splice_usage'] = arrays['splice_usage'][order][:, permutations['splice_usage']].copy() + out['junction_half'] = (1-arrays['junction_half']).astype(np.int8) + for key in ['junction_donor', 'junction_acceptor']: + out[key] = (input_length-1-arrays[key]).astype(np.int32) + out['junctions'] = arrays['junctions'][:, permutations['junctions']].copy() + out['contact_maps'] = arrays['contact_maps'][contact_reversal(contact_bins)][:, permutations['contact_maps']].copy() + return out + + +def combine_readouts(a, b, track_halves): + """Average sparse functions with zero at unlisted coordinates, then enforce output budgets.""" + out = {name: (a[name]+b[name])*np.float32(.5) + for name in [*DENSE_TRACKS, 'splice_sites', 'contact_maps']} + usage_positions, inverse = np.unique(np.concatenate([a['splice_usage_positions'], b['splice_usage_positions']]), return_inverse=True) + assert len(usage_positions) <= MAX_USAGE_SITES + usage = np.zeros((len(usage_positions), a['splice_usage'].shape[-1]), dtype=np.float32) + np.add.at(usage, inverse, np.concatenate([a['splice_usage'], b['splice_usage']])*np.float32(.5)) + out.update(splice_usage_positions=usage_positions.astype(np.int32), splice_usage=usage) + keys = np.concatenate([np.stack([x['junction_half'], x['junction_donor'], x['junction_acceptor']], axis=1) for x in [a, b]]) + keys, inverse = np.unique(keys, axis=0, return_inverse=True) + values = np.zeros((len(keys), len(track_halves)), dtype=np.float32) + np.add.at(values, inverse, np.concatenate([a['junctions'], b['junctions']])*np.float32(.5)) + strength = np.empty(len(keys), dtype=np.float32) + for half in [0, 1]: + keep = keys[:, 0] == half + strength[keep] = values[keep][:, track_halves == half].max(axis=1) + # Stable sorting makes ties deterministic in the canonical coordinate order. + order = np.argsort(-strength, kind='stable')[:MAX_JUNCTION_PAIRS] + keys, values = keys[order], values[order] + out.update(junction_half=keys[:, 0].astype(np.int8), + junction_donor=keys[:, 1].astype(np.int32), + junction_acceptor=keys[:, 2].astype(np.int32), junctions=values) + return out + + +class ReverseComplementMean: + def __init__(self, model): + self.model = model + data = json.loads(Path(__file__).with_name('strand_permutations.json').read_text()) + self.permutations = {name: np.asarray(values, dtype=np.int64) for name, values in data['permutations'].items()} + for name, indices in self.permutations.items(): + assert np.array_equal(indices[indices], np.arange(len(indices))), name + + def tracks(self, dna): + assert len(dna) == 1 + config = self.model.config + original = single_prediction(self.model, dna) + reverse = single_prediction(self.model, dna.flip((1, 2))) + reverse = reverse_readout(reverse, self.permutations, config.input_length, config.target_length, config.contact_bins) + return combine_readouts(original, reverse, self.model.junction_track_half.cpu().numpy()) + + def dense_and_sites(self, dna, families): + """Return central readouts for both alleles; this same probability/asinh averaging defines tracks.""" + config = self.model.config + start = (config.input_length-config.target_length)//2 + predictions = [] + for reverse in [False, True]: + trunk = self.model._trunk(dna.flip((1, 2)) if reverse else dna) + features = None + values = {} + for family in families: + if family == 'splice_sites': + logits = self.model._splice_site_logits(trunk)[:, start:start+config.target_length] + array = splice_output_scores(logits).float().cpu().numpy() + else: + if features is None: + features = self.model._dense_features(trunk) + array = self.model.dense_heads[family](features).float().cpu().numpy() + if reverse: + array = array[:, ::-1][:, :, self.permutations[family]].copy() + values[family] = array + predictions.append(values) + return {family: (predictions[0][family]+predictions[1][family])*np.float32(.5) for family in families} + + +def engine_for(model): + if not hasattr(model, '_rc_prediction_engine'): + # Plain helper, not a child nn.Module; the backbone state dictionary stays unchanged. + model._rc_prediction_engine = ReverseComplementMean(model) + return model._rc_prediction_engine + + +def predict_requested(model, dna, row): + values = engine_for(model).dense_and_sites(dna, row.families) + return {family: array[:, row.splice_start:row.splice_end] if family == 'splice_sites' + else array[:, row.dense_bin_start:row.dense_bin_end] for family, array in values.items()} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/rc_ensemble_policy.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/rc_ensemble_policy.json new file mode 100644 index 0000000000000000000000000000000000000000..7b38e71a588633405d9411daeae45df029402433 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/rc_ensemble_policy.json @@ -0,0 +1,16 @@ +{ + "created_utc": "2026-10-24T21:57:45.334214+00:00", + "parent_package": "/workspace/research/experiments/calib14/package", + "method": "Equal mean of the same checkpoint evaluated on DNA and its reverse complement, mapped to original positions and public-metadata strand channels.", + "unique_learned_parameters": 13265544, + "orientation_views": 2, + "fitted_ensemble_weights": false, + "weights": [ + 0.5, + 0.5 + ], + "dna_input": "Same provided 1,048,576bp, reversed and complemented; no extra flanking sequence.", + "site_scores": "Apply the existing training-prior probability link per orientation, then average probabilities.", + "sparse_outputs": "Each orientation chooses its own positions/pairs. Average the union with zero for unlisted coordinates; retain up to16,384 junctions by averaged predicted strength. Usage union has at most4,096 coordinates.", + "outputs": "All families and both entry points use the same two-view rule. No labels or targets used at inference." +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4879a0e1a3a8a8050bb48360febee152cd141be8 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/__init__.py @@ -0,0 +1 @@ +"""The reference model of the alphagenome-reference baseline.""" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a86a139babb6e899bd5102bbf3c628e1232576b2 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/__init__.py @@ -0,0 +1,5 @@ +"""Compact 1-Mb, eleven-family AlphaGenome reference model.""" + +from .model import CompactModel, ModelConfig, count_parameters + +__all__ = ["CompactModel", "ModelConfig", "count_parameters"] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/alphagenome_50m.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/alphagenome_50m.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0a59a4d0c65752d45eb464d129c6ec9d559665e9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/alphagenome_50m.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 32 +channel_growth: 32 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 384 +num_sequence_blocks: 6 +num_attention_heads: 6 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 256 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/dataset.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..71cd39d15713f648e7d7e31aa1923fa4fa4800dd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/dataset.py @@ -0,0 +1,229 @@ +"""Streaming reader for the compact 1 Mb / eleven-family release.""" + +from __future__ import annotations + +from collections import OrderedDict +from pathlib import Path + +import numpy as np +import pandas as pd +import pysam +import torch +from torch.utils.data import Dataset +import zarr + + +def _open_part(path: Path): + if str(path).endswith(".zarr.zip"): + store = zarr.ZipStore(str(path), mode="r") + try: + return zarr.open_group(store=store, mode="r"), store + except BaseException: + store.close() + raise + return zarr.open_group(str(path), mode="r"), None + + +DENSE_FAMILIES = ( + "atac", + "dnase", + "procap", + "cage", + "rna_seq", + "chip_tf", + "chip_histone", +) +def normalize_splice_positions(value) -> np.ndarray: + """Normalize Arrow's nested-list representation to the public [4, 512] array.""" + + outer = np.asarray(value, dtype=object).reshape(-1).tolist() + positions = np.stack([np.asarray(item, dtype=np.int64) for item in outer]) + if positions.shape != (4, 512): + raise ValueError(f"splice_site_positions must have shape (4, 512), got {positions.shape}") + return positions + + +class AlphaGenomeDataset(Dataset): + """Resolve frozen coordinates to reference DNA and one Zarr example group.""" + + def __init__( + self, + data_root: str | Path, + split: str, + *, + max_open_parts: int = 8, + include_targets: bool = True, + ) -> None: + self.root = Path(data_root) + self.split = split + manifest = self.root / "public" / f"{split}.parquet" + if not manifest.is_file(): + raise FileNotFoundError(f"Missing materialized manifest: {manifest}") + self.rows = pd.read_parquet(manifest).reset_index(drop=True) + junction_tracks = pd.read_parquet(self.root / "public/junction_tracks.parquet") + junction_tracks = junction_tracks.sort_values("tensor_index") + self.junction_track_halves = torch.from_numpy( + junction_tracks.junction_channel_half.to_numpy(np.int64) + ) + if include_targets: + labels_path = self.root / "labels" / f"{split}.parquet" + if not labels_path.is_file(): + raise FileNotFoundError(f"Missing trusted labels manifest: {labels_path}") + labels = pd.read_parquet(labels_path).reset_index(drop=True) + if self.rows.example_id.astype(str).tolist() != labels.example_id.astype(str).tolist(): + raise ValueError("public and labels manifests have different IDs/order") + self.rows = self.rows.merge(labels, on="example_id", validate="one_to_one", sort=False) + self.fasta_path = self.root / "reference" / "GRCh38.p13.genome.fa" + self.max_open_parts = max_open_parts + self.include_targets = include_targets + self._fasta = None + self._parts: OrderedDict[str, tuple[zarr.Group, object | None]] = OrderedDict() + + def __len__(self) -> int: + return len(self.rows) + + def _reference(self): + if self._fasta is None: + self._fasta = pysam.FastaFile(str(self.fasta_path)) + return self._fasta + + def _part(self, relative: str): + if relative in self._parts: + opened = self._parts.pop(relative) + self._parts[relative] = opened + return opened[0] + group, store = _open_part(self.root / relative) + if len(self._parts) >= self.max_open_parts: + _, (_, old_store) = self._parts.popitem(last=False) + if old_store is not None: + old_store.close() + self._parts[relative] = (group, store) + return group + + @staticmethod + def _one_hot(sequence: str) -> np.ndarray: + encoded = np.frombuffer(sequence.upper().encode("ascii"), dtype=np.uint8) + output = np.zeros((len(encoded), 4), dtype=np.float32) + for index, letter in enumerate(b"ACGT"): + output[:, index] = encoded == letter + return output + + def _sequence(self, row) -> tuple[np.ndarray, np.ndarray]: + reference = self._reference() + chromosome_length = reference.get_reference_length(str(row.chromosome)) + start, end = int(row.input_start), int(row.input_end) + clipped_start, clipped_end = max(0, start), min(chromosome_length, end) + sequence = reference.fetch(str(row.chromosome), clipped_start, clipped_end) + left, right = clipped_start - start, end - clipped_end + if left or right: + sequence = "N" * left + sequence + "N" * right + valid = np.ones(end - start, dtype=bool) + if left: + valid[:left] = False + if right: + valid[-right:] = False + return self._one_hot(sequence), valid + + @staticmethod + def _decode(example, name: str, *, asinh: bool) -> np.ndarray: + """Train targets may be stored as 8-bit codes with per-track ranges; they are decoded + here, one example at a time, so the release stays compressed on disk.""" + array = example[name] + if array.dtype != np.uint8: + return np.asarray(array, dtype=np.float32) + lo = np.asarray(example[f"{name}_lo"], dtype=np.float32) + hi = np.asarray(example[f"{name}_hi"], dtype=np.float32) + x = lo + np.asarray(array, dtype=np.float32) * ((hi - lo) / 255.0) + return np.sinh(x) if asinh else x + + @staticmethod + def _dense_target(example, family: str) -> dict[str, torch.Tensor]: + values = AlphaGenomeDataset._decode(example, f"{family}/values", asinh=True) + mask = np.asarray(example[f"{family}/mask"], dtype=bool) + if mask.ndim == 1: + mask = np.broadcast_to(mask[None, :], values.shape).copy() + return { + "values": torch.from_numpy(values), + "mask": torch.from_numpy(mask), + } + + def __getitem__(self, item: int): + row = self.rows.iloc[item] + dna, input_mask = self._sequence(row) + result = { + "example_id": str(row.example_id), + "dna": torch.from_numpy(dna), + "input_mask": torch.from_numpy(input_mask), + "junction_track_halves": self.junction_track_halves, + } + if not self.include_targets: + return result + + part = self._part(str(row.target_shard)) + example = part[str(row.target_group)] + result["dense"] = { + family: self._dense_target(example, family) for family in DENSE_FAMILIES + } + result["splice_sites"] = torch.from_numpy( + np.asarray(example["splice_sites/values"], dtype=np.float32) + ) + result["splice_usage"] = { + "positions": torch.from_numpy( + np.asarray(example["splice_site_usage/positions"], dtype=np.int64) + ), + "values": torch.from_numpy( + np.asarray(example["splice_site_usage/values"], dtype=np.float32) + ), + "mask": torch.from_numpy( + np.asarray(example["splice_site_usage/mask"], dtype=bool) + ), + } + result["usage_positions"] = result["splice_usage"]["positions"] + result["junctions"] = { + name: torch.from_numpy( + np.asarray( + example[f"splice_junctions/{name}"], + dtype=np.float32 if name == "value" else np.int64, + ) + ) + for name in ("row", "column", "track", "value") + } + result["junctions"]["site_positions"] = torch.from_numpy( + np.asarray(example["splice_junctions/site_positions"], dtype=np.int64) + ) + site_positions = normalize_splice_positions(row.splice_site_positions) + result["site_positions"] = torch.from_numpy(site_positions) + if not torch.equal(result["junctions"]["site_positions"], result["site_positions"]): + raise ValueError(f"public/private splice positions differ for {row.example_id}") + scoring_path = self.root / "scoring" / self.split / f"part-{item:06d}.parquet" + scoring = pd.read_parquet(scoring_path) + if not (scoring.example_id.astype(str) == str(row.example_id)).all(): + raise ValueError(f"scoring part ID differs for {row.example_id}") + result["junction_scoring"] = { + "donor_position": torch.from_numpy( + scoring.donor_position.to_numpy(np.int64) + ), + "acceptor_position": torch.from_numpy( + scoring.acceptor_position.to_numpy(np.int64) + ), + "track": torch.from_numpy(scoring.track.to_numpy(np.int64)), + "target_value": torch.from_numpy( + scoring.target_value.to_numpy(np.float32) + ), + "valid": torch.from_numpy(scoring.valid.to_numpy(bool)), + } + result["contact_maps"] = torch.from_numpy( + self._decode(example, "contact_maps/upper_triangle", asinh=False) + ) + result["contact_map_mask"] = torch.from_numpy( + np.asarray(example["contact_maps/mask"], dtype=bool) + ) + return result + + +def single_example_collate(items): + """Keep variable-size event tensors intact; the reference currently trains at batch one.""" + + if len(items) != 1: + raise ValueError("The compact event loader currently requires batch_size=1") + return items[0] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/layers.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..af31f86a5589a16f0007e6aca5ca7a7bd552d6a9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/layers.py @@ -0,0 +1,124 @@ +"""Shared neural-network layers for the single compact AlphaGenome model.""" + +from __future__ import annotations + +import math + +import torch +from torch import Tensor, nn +from torch.nn import functional as F + + +class ChannelRMSNorm(nn.Module): + def __init__(self, channels: int, eps: float = 1e-6): + super().__init__() + self.weight = nn.Parameter(torch.ones(channels)) + self.eps = eps + + def forward(self, x: Tensor) -> Tensor: + variance = x.float().square().mean(dim=1, keepdim=True) + normalized = x * torch.rsqrt(variance + self.eps).to(x.dtype) + return normalized * self.weight[None, :, None].to(x.dtype) + + +class StandardizedConv1d(nn.Conv1d): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__(in_channels, out_channels, kernel_size, padding="same", bias=True) + self.scale = nn.Parameter(torch.ones(out_channels)) + nn.init.trunc_normal_(self.weight, std=1.0 / math.sqrt(in_channels * kernel_size)) + nn.init.trunc_normal_(self.bias, std=1e-4) + + def forward(self, x: Tensor) -> Tensor: + centered = self.weight - self.weight.mean(dim=(1, 2), keepdim=True) + variance = centered.float().square().mean(dim=(1, 2), keepdim=True) + fan_in = self.weight.shape[1] * self.weight.shape[2] + multiplier = self.scale[:, None, None] * torch.rsqrt( + (fan_in * variance).clamp_min(1e-4) + ).to(self.weight.dtype) + return F.conv1d(x, centered * multiplier, self.bias, padding="same") + + +class ConvBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__() + self.norm = ChannelRMSNorm(in_channels) + self.conv = StandardizedConv1d(in_channels, out_channels, kernel_size) + + def forward(self, x: Tensor) -> Tensor: + return self.conv(F.gelu(self.norm(x), approximate="tanh")) + + +def _apply_rope(x: Tensor) -> Tensor: + rotary_dim = x.shape[-1] - x.shape[-1] % 2 + if rotary_dim == 0: + return x + positions = torch.arange(x.shape[-2], device=x.device, dtype=torch.float32) + inverse_frequency = 1.0 / ( + 10_000 ** ( + torch.arange(0, rotary_dim, 2, device=x.device, dtype=torch.float32) + / rotary_dim + ) + ) + angles = torch.outer(positions, inverse_frequency) + cos = angles.cos()[None, None, :, :].to(x.dtype) + sin = angles.sin()[None, None, :, :].to(x.dtype) + even, odd = x[..., :rotary_dim:2], x[..., 1:rotary_dim:2] + rotated = torch.stack((even * cos - odd * sin, even * sin + odd * cos), dim=-1) + return torch.cat((rotated.flatten(-2), x[..., rotary_dim:]), dim=-1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.num_heads = config.num_attention_heads + self.head_dim = config.attention_head_dim + inner_dim = self.num_heads * self.head_dim + self.norm = nn.RMSNorm(config.hidden_dim) + self.qkv = nn.Linear(config.hidden_dim, 3 * inner_dim, bias=False) + self.output = nn.Linear(inner_dim, config.hidden_dim) + self.dropout = config.dropout + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + batch, length, _ = x.shape + qkv = self.qkv(self.norm(x)).view( + batch, length, 3, self.num_heads, self.head_dim + ) + q, k, value = qkv.unbind(dim=2) + attended = F.scaled_dot_product_attention( + _apply_rope(q.transpose(1, 2)), + _apply_rope(k.transpose(1, 2)), + value.transpose(1, 2), + attn_mask=attention_bias, + dropout_p=self.dropout if self.training else 0.0, + ) + return self.output(attended.transpose(1, 2).reshape(batch, length, -1)) + + +class MLPBlock(nn.Module): + def __init__(self, config): + super().__init__() + expanded = int(config.hidden_dim * config.mlp_expansion) + self.norm = nn.RMSNorm(config.hidden_dim) + self.input = nn.Linear(config.hidden_dim, expanded) + self.output = nn.Linear(expanded, config.hidden_dim) + self.dropout = nn.Dropout(config.dropout) + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor) -> Tensor: + return self.dropout( + self.output(F.gelu(self.input(self.norm(x)), approximate="tanh")) + ) + + +class SequenceBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.attention = MultiHeadAttention(config) + self.mlp = MLPBlock(config) + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + x = x + self.attention(x, attention_bias) + return x + self.mlp(x) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/model.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/model.py new file mode 100644 index 0000000000000000000000000000000000000000..9c3c2effcf45b2aa0eec7e31aa3f668107d7ff90 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/model.py @@ -0,0 +1,430 @@ +"""Affordable multi-resolution baseline for the compact AlphaGenome task.""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass, fields +import math +from pathlib import Path +from typing import Any, Iterable + +import torch +from torch import Tensor, nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint +import yaml + +from .layers import ChannelRMSNorm, ConvBlock, SequenceBlock + + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} +ALL_FAMILIES = (*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions", "contact_maps") + + +@dataclass(frozen=True) +class ModelConfig: + input_length: int = 1_048_576 + target_length: int = 196_608 + dense_bin_size: int = 128 + trunk_bin_size: int = 1_024 + contact_bin_size: int = 2_048 + stem_stride: int = 8 + encoder_width: int = 32 + channel_growth: int = 32 + num_downsampling_blocks: int = 7 + conv_kernel_size: int = 5 + dna_kernel_size: int = 15 + hidden_dim: int = 384 + num_sequence_blocks: int = 6 + num_attention_heads: int = 6 + attention_head_dim: int = 64 + mlp_expansion: float = 4.0 + dropout: float = 0.0 + head_hidden_size: int = 256 + junction_rank: int = 32 + splice_usage_tracks: int = 734 + junction_tracks: int = 734 + contact_tracks: int = 28 + gradient_checkpointing: bool = True + enable_pairwise: bool = False + pair_width: int = 32 + pair_downsample: int = 16 + pair_update_interval: int = 2 + local_width: int = 0 + local_context_blocks: int = 4 + + def __post_init__(self) -> None: + if not 1 <= self.local_context_blocks <= 7: + raise ValueError('local_context_blocks must be between 1 and 7') + downsample = self.stem_stride * 2**self.num_downsampling_blocks + if downsample != self.trunk_bin_size: + raise ValueError("stem_stride * 2**num_downsampling_blocks must equal trunk_bin_size") + for length in (self.input_length, self.target_length): + if length % self.trunk_bin_size: + raise ValueError("input and target lengths must divide by trunk_bin_size") + if self.trunk_bin_size % self.dense_bin_size: + raise ValueError("trunk_bin_size must divide by dense_bin_size") + if self.input_length % self.contact_bin_size: + raise ValueError("input_length must divide by contact_bin_size") + if self.hidden_dim <= 0 or self.num_attention_heads <= 0: + raise ValueError("hidden dimensions and attention heads must be positive") + + @property + def trunk_bins(self) -> int: + return self.input_length // self.trunk_bin_size + + @property + def target_trunk_bins(self) -> int: + return self.target_length // self.trunk_bin_size + + @property + def dense_bins(self) -> int: + return self.target_length // self.dense_bin_size + + @property + def dense_upsample(self) -> int: + return self.trunk_bin_size // self.dense_bin_size + + @property + def contact_bins(self) -> int: + return self.input_length // self.contact_bin_size + + @property + def encoder_output_width(self) -> int: + return self.encoder_width + self.channel_growth * self.num_downsampling_blocks + + @classmethod + def from_yaml(cls, path: str | Path) -> "ModelConfig": + values: dict[str, Any] = yaml.safe_load(Path(path).read_text()) or {} + known = {field.name for field in fields(cls)} + unknown = sorted(values.keys() - known) + if unknown: + raise ValueError(f"Unknown model configuration keys: {unknown}") + return cls(**values) + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + +class SequenceEncoder(nn.Module): + def __init__(self, config: ModelConfig): + super().__init__() + self.stem = nn.Conv1d( + 4, + config.encoder_width, + config.dna_kernel_size, + stride=config.stem_stride, + padding=config.dna_kernel_size // 2, + ) + blocks = [] + width = config.encoder_width + for _ in range(config.num_downsampling_blocks): + output_width = width + config.channel_growth + blocks.append(ConvBlock(width, output_width, config.conv_kernel_size)) + width = output_width + self.blocks = nn.ModuleList(blocks) + + def forward(self, dna: Tensor) -> Tensor: + x = F.gelu(self.stem(dna.transpose(1, 2)), approximate="tanh") + for block in self.blocks: + x = F.max_pool1d(block(x), kernel_size=2, stride=2) + return x.transpose(1, 2) + + +class LocalResidualBlock(nn.Module): + def __init__(self, width: int, dilation: int): + super().__init__() + self.norm = ChannelRMSNorm(width) + self.depthwise = nn.Conv1d(width, width, 9, dilation=dilation, + padding=4 * dilation, groups=width) + self.expand = nn.Conv1d(width, 2 * width, 1) + self.contract = nn.Conv1d(2 * width, width, 1) + + def forward(self, x: Tensor) -> Tensor: + update = self.depthwise(self.norm(x)) + update = self.contract(F.gelu(self.expand(update), approximate='tanh')) + return x + update + + +class LocalSequencePath(nn.Module): + """Base-resolution motif path with powers-of-four dilation and aligned pooling.""" + def __init__(self, config: ModelConfig): + super().__init__() + width = config.local_width + self.stem = nn.Conv1d(4, width, 21, padding=10) + self.blocks = nn.Sequential(*(LocalResidualBlock(width, 4**i) + for i in range(config.local_context_blocks))) + self.norm = ChannelRMSNorm(width) + self.sites = nn.Conv1d(width, 4, 1) + self.bin_size = config.dense_bin_size + + def forward(self, dna: Tensor) -> tuple[Tensor, Tensor]: + base = F.gelu(self.stem(dna), approximate='tanh') + base = F.gelu(self.norm(self.blocks(base)), approximate='tanh') + sites = self.sites(base).transpose(1, 2) + pooled = torch.cat((F.avg_pool1d(base, self.bin_size), + F.max_pool1d(base, self.bin_size)), dim=1).transpose(1, 2) + return sites, pooled + + +class CompactModel(nn.Module): + """One shared 1-kb trunk with representation-specific output heads.""" + + def __init__(self, config: ModelConfig | None = None): + super().__init__() + self.config = config or ModelConfig() + self.encoder = SequenceEncoder(self.config) + self.projection = nn.Linear(self.config.encoder_output_width, self.config.hidden_dim) + self.blocks = nn.ModuleList( + SequenceBlock(self.config) for _ in range(self.config.num_sequence_blocks) + ) + self.final_norm = nn.RMSNorm(self.config.hidden_dim) + self.dense_decoder = nn.Sequential( + nn.RMSNorm(self.config.hidden_dim), + nn.Linear(self.config.hidden_dim, self.config.head_hidden_size), + nn.GELU(approximate="tanh"), + ) + self.dense_heads = nn.ModuleDict( + { + family: nn.Linear(self.config.head_hidden_size, tracks) + for family, tracks in DENSE_TRACKS.items() + } + ) + self.splice_site_head = nn.Linear( + self.config.head_hidden_size, self.config.dense_bin_size * 4 + ) + self.splice_usage_head = nn.Linear( + self.config.head_hidden_size, self.config.splice_usage_tracks + ) + rank = self.config.junction_rank + self.junction_source = nn.Linear(self.config.head_hidden_size, rank) + self.junction_destination = nn.Linear(self.config.head_hidden_size, rank) + self.junction_track = nn.Embedding(self.config.junction_tracks, rank) + if self.config.junction_tracks % 2: + raise ValueError("junction tracks must split evenly into strand halves") + self.register_buffer( + "junction_track_half", + torch.arange(self.config.junction_tracks) // (self.config.junction_tracks // 2), + persistent=False, + ) + self.contact_q = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + self.contact_k = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + row, column = torch.triu_indices( + self.config.contact_bins, self.config.contact_bins + ) + self.register_buffer("contact_row", row, persistent=False) + self.register_buffer("contact_column", column, persistent=False) + if self.config.local_width: + self.local_path = LocalSequencePath(self.config) + self.local_projection = nn.Linear(2 * self.config.local_width, self.config.head_hidden_size) + # A new local path can be attached to our own checkpoint without changing its initial outputs. + nn.init.zeros_(self.local_path.sites.weight) + nn.init.zeros_(self.local_path.sites.bias) + nn.init.zeros_(self.local_projection.weight) + nn.init.zeros_(self.local_projection.bias) + self._local_sites = None + self._local_features = None + + def set_junction_track_halves(self, halves: Tensor) -> None: + halves = torch.as_tensor(halves, dtype=torch.long, device=self.junction_track_half.device) + if halves.shape != (self.config.junction_tracks,) or not torch.isin( + halves, torch.tensor([0, 1], device=halves.device) + ).all(): + raise ValueError("junction track halves must be a 734-element 0/1 mapping") + self.junction_track_half.copy_(halves) + + def _trunk(self, dna: Tensor) -> Tensor: + if dna.ndim != 3 or dna.shape[1:] != (self.config.input_length, 4): + raise ValueError( + f"Expected DNA [B,{self.config.input_length},4], got {tuple(dna.shape)}" + ) + if self.config.local_width: + if self.config.gradient_checkpointing and self.training: + sites, pooled = checkpoint(self.local_path, dna.transpose(1, 2), use_reentrant=False) + else: + sites, pooled = self.local_path(dna.transpose(1, 2)) + self._local_sites = sites + self._local_features = self.local_projection(pooled) + x = self.projection(self.encoder(dna)) + for block in self.blocks: + if self.config.gradient_checkpointing and self.training and x.requires_grad: + x = checkpoint(block, x, None, use_reentrant=False) + else: + x = block(x, None) + return self.final_norm(x) + + def _dense_features(self, trunk: Tensor) -> Tensor: + start = (self.config.trunk_bins - self.config.target_trunk_bins) // 2 + target = trunk[:, start : start + self.config.target_trunk_bins] + target = target.repeat_interleave(self.config.dense_upsample, dim=1) + features = self.dense_decoder(target) + if self.config.local_width: + first_bin = (self.config.input_length - self.config.target_length) // (2 * self.config.dense_bin_size) + features = features + self._local_features[:, first_bin:first_bin + self.config.dense_bins] + return features + + def _all_dense_features(self, trunk: Tensor) -> Tensor: + features = self.dense_decoder( + trunk.repeat_interleave(self.config.dense_upsample, dim=1) + ) + return features + self._local_features if self.config.local_width else features + + def _splice_site_logits(self, trunk: Tensor) -> Tensor: + logits = self.splice_site_head(self._all_dense_features(trunk)) + logits = logits.view(len(trunk), self.config.input_length, 4) + if self.config.local_width: + logits = logits + self._local_sites + return logits.float() + + def _position_features(self, trunk: Tensor, positions: Tensor) -> Tensor: + features = self.dense_decoder(self._gather_bins(trunk, positions, self.config.trunk_bin_size)) + if self.config.local_width: + features = features + self._gather_bins(self._local_features, positions, self.config.dense_bin_size) + return features + + @staticmethod + def select_junction_positions(site_logits: Tensor, limit: int = 512) -> Tensor: + """Select each site type from model scores alone, then sort by coordinate. + + This follows the four independent site classes in Google DeepMind's + AlphaGenome splicing implementation (pinned in docs/v3-author/PROTOCOL.md). Padding is + -1 when an input is shorter than ``limit``. + """ + count = min(limit, site_logits.shape[1]) + selected = [] + for site_type in range(4): + coordinates = torch.topk(site_logits[..., site_type], count, dim=1).indices + coordinates = coordinates.sort(dim=1).values + if count < limit: + coordinates = F.pad(coordinates, (0, limit - count), value=-1) + selected.append(coordinates) + return torch.stack(selected, dim=1) + + @staticmethod + def _gather_bins(features: Tensor, positions: Tensor, bin_size: int) -> Tensor: + indices = torch.div(positions, bin_size, rounding_mode="floor") + indices = indices.clamp(0, features.shape[1] - 1) + return torch.gather( + features, + 1, + indices[..., None].expand(-1, -1, features.shape[-1]), + ) + + def _junction_predictions( + self, trunk: Tensor, site_positions: Tensor, queries: Tensor + ) -> Tensor: + row, column, track = queries.unbind(dim=-1) + half = self.junction_track_half[track] + batch = torch.arange(len(trunk), device=trunk.device)[:, None] + donor = site_positions[batch, 2 * half, row] + acceptor = site_positions[batch, 2 * half + 1, column] + source = self._position_features(trunk, donor.clamp(0, self.config.input_length - 1)) + destination = self._position_features(trunk, acceptor.clamp(0, self.config.input_length - 1)) + interaction = self.junction_source(source) * self.junction_destination(destination) + track_embedding = self.junction_track(track) + return (interaction * track_embedding).sum(dim=-1) / math.sqrt( + self.config.junction_rank + ) + + def junction_block( + self, + trunk: Tensor, + site_positions: Tensor, + row_start: int, + row_end: int, + column_start: int, + column_end: int, + track_start: int, + track_end: int, + site_factors: tuple[Tensor, Tensor] | None = None, + ) -> Tensor: + """Compute a bounded block of the full predicted-position junction cube.""" + tracks = torch.arange(track_start, track_end, device=trunk.device) + halves = self.junction_track_half[tracks] + if site_factors is None: + site_factors = self.junction_site_factors(trunk, site_positions) + source_all, destination_all = site_factors + source = source_all[:, halves, row_start:row_end] + destination = destination_all[:, halves, column_start:column_end] + embedding = self.junction_track(tracks)[None, :, None, None, :] + values = ( + source[:, :, :, None, :] * destination[:, :, None, :, :] * embedding + ).sum(dim=-1) / math.sqrt(self.config.junction_rank) + return values.permute(0, 2, 3, 1).float() + + def junction_site_factors( + self, trunk: Tensor, site_positions: Tensor + ) -> tuple[Tensor, Tensor]: + """Precompute the two strand-specific factors reused by all cube blocks.""" + donor = torch.stack((site_positions[:, 0], site_positions[:, 2]), dim=1).clamp_min(0) + acceptor = torch.stack((site_positions[:, 1], site_positions[:, 3]), dim=1).clamp_min(0) + donor_features = self._position_features(trunk, donor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, donor.shape[-1], -1) + acceptor_features = self._position_features(trunk, acceptor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, acceptor.shape[-1], -1) + return self.junction_source(donor_features), self.junction_destination(acceptor_features) + + def forward( + self, + dna: Tensor, + *, + families: Iterable[str] | None = None, + usage_positions: Tensor | None = None, + junction_site_positions: Tensor | None = None, + junction_queries: Tensor | None = None, + ) -> dict[str, Tensor]: + requested = set(families or ALL_FAMILIES) + unknown = requested - set(ALL_FAMILIES) + if unknown: + raise ValueError(f"Unknown output families: {sorted(unknown)}") + trunk = self._trunk(dna) + needs_dense = bool( + requested.intersection((*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions")) + ) + features = self._dense_features(trunk) if needs_dense else None + outputs: dict[str, Tensor] = {} + for family in DENSE_TRACKS: + if family in requested: + outputs[family] = self.dense_heads[family](features).float() + if "splice_sites" in requested: + all_logits = self._splice_site_logits(trunk) + start = (self.config.input_length - self.config.target_length) // 2 + outputs["splice_sites"] = all_logits[:, start:start + self.config.target_length] + if "splice_usage" in requested: + if usage_positions is None: + raise ValueError("splice_usage requires usage_positions") + gathered = self._gather_bins( + features, usage_positions, self.config.dense_bin_size + ) + outputs["splice_usage"] = self.splice_usage_head(gathered).float() + if "junctions" in requested: + if junction_site_positions is None or junction_queries is None: + raise ValueError("junctions require site positions and query triples") + outputs["junctions"] = self._junction_predictions( + trunk, junction_site_positions, junction_queries + ).float() + if "contact_maps" in requested: + contact = trunk.reshape( + len(dna), self.config.contact_bins, -1, self.config.hidden_dim + ).mean(dim=2) + q, k = self.contact_q(contact), self.contact_k(contact) + row, column = self.contact_row, self.contact_column + outputs["contact_maps"] = ( + 0.5 + * ( + q[:, row] * k[:, column] + + q[:, column] * k[:, row] + ) + ).float() + return outputs + + +def count_parameters(model: nn.Module) -> int: + return sum(parameter.numel() for parameter in model.parameters() if parameter.requires_grad) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/reference_large.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/reference_large.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e403757c49eb6e6419927210c7053710964213d1 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/reference_large.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 96 +channel_growth: 96 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 1024 +num_sequence_blocks: 16 +num_attention_heads: 16 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 768 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/run_ddp.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/run_ddp.sh new file mode 100644 index 0000000000000000000000000000000000000000..c4702e9366e907b7791063e77dcfe281411ac8cd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/run_ddp.sh @@ -0,0 +1,16 @@ +#!/usr/bin/env bash +# run_ddp.sh DATA_DIR OUTPUT_DIR CONFIG EPOCHS [GPUS=8] [WORKERS_PER_GPU=12] +# Train the reference on every train interval with one example per GPU per step, then score +# validation with the verifier's own scorer. Runs inside the task image (torch, torchrun). +set -Eeuo pipefail +DATA=$1; OUT=$2; CONFIG=$3; EPOCHS=$4; GPUS=${5:-8}; WORKERS=${6:-12} +BASELINE=$(cd "$(dirname "$0")/../.." && pwd) +mkdir -p "$OUT" +cd "$BASELINE" +# Loopback rendezvous: works offline, where --standalone cannot resolve the hostname. +torchrun --nnodes 1 --nproc-per-node "$GPUS" --master-addr 127.0.0.1 --master-port "${MASTER_PORT:-29511}" -m reference.alphagenome_50m.train_eval train \ + --input "$DATA" --config "$CONFIG" --output "$OUT" --epochs "$EPOCHS" --workers "$WORKERS" \ + --log-every 200 --save-every 2000 2>&1 | tee -a "$OUT/train.log" +python predict.py "$DATA" "$OUT/valid-pred" --checkpoint "$OUT/last.pt" --split valid +python /workspace/evaluation/score.py --predictions "$OUT/valid-pred/predictions.zarr" \ + --labels "$DATA" --split valid --output "$OUT/valid-score" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/tiny_overfit.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/tiny_overfit.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c25f9ffbb76102c970e5767922400f7ec2b6c53e --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/tiny_overfit.yaml @@ -0,0 +1,24 @@ +# Keep the frozen 1-Mb input and every output track; shrink only learned widths. +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 8 +channel_growth: 4 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 32 +num_sequence_blocks: 1 +num_attention_heads: 2 +attention_head_dim: 16 +mlp_expansion: 2.0 +dropout: 0.0 +head_hidden_size: 32 +junction_rank: 8 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: false diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/train_eval.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/train_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..92f9a3f315013630fdd2e03fb6840a6c77840edd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/reference/alphagenome_50m/train_eval.py @@ -0,0 +1,537 @@ +"""Train and stream-evaluate the <=50M compact AlphaGenome baseline.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import numpy as np +import torch +from torch import Tensor +from torch.nn import functional as F +from torch.utils.data import DataLoader, Subset + +from .dataset import ( + AlphaGenomeDataset, + DENSE_FAMILIES, + single_example_collate, +) +from .model import ( + ALL_FAMILIES, + CompactModel, + ModelConfig, + count_parameters, +) + + +DEFAULT_CONFIG = Path(__file__).with_name("alphagenome_50m.yaml") + + +def _device(name: str) -> torch.device: + if name == "auto": + name = "cuda" if torch.cuda.is_available() else "cpu" + if name == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("--device cuda requested but CUDA is unavailable") + return torch.device(name) + + +def _autocast(device: torch.device): + enabled = device.type == "cuda" + dtype = torch.bfloat16 if enabled and torch.cuda.is_bf16_supported() else torch.float16 + return torch.autocast(device_type=device.type, dtype=dtype, enabled=enabled) + + +def _distributed() -> tuple[int, int, int]: + """(rank, local_rank, world_size) under torchrun; (0, 0, 1) otherwise.""" + world = int(os.environ.get("WORLD_SIZE", "1")) + if world > 1 and not torch.distributed.is_initialized(): + torch.distributed.init_process_group("nccl") + return int(os.environ.get("RANK", "0")), int(os.environ.get("LOCAL_RANK", "0")), world + + +def _loader(dataset, *, workers: int, shuffle: bool, sampler=None) -> DataLoader: + return DataLoader( + dataset, + batch_size=1, + shuffle=shuffle and sampler is None, + sampler=sampler, + num_workers=workers, + persistent_workers=workers > 0, + pin_memory=torch.cuda.is_available(), + collate_fn=single_example_collate, + ) + + +def _to_device(value, device: torch.device): + if isinstance(value, Tensor): + return value.to(device, non_blocking=True) + if isinstance(value, dict): + return {key: _to_device(item, device) for key, item in value.items()} + return value + + +def _match_coordinate_queries( + scoring: dict[str, Tensor], positions: Tensor, track_halves: Tensor +) -> tuple[Tensor, Tensor, Tensor]: + """Match trusted coordinate queries to typed position axes like the grader.""" + device = positions.device + tracks = scoring["track"].detach().cpu().numpy() + halves_cpu = track_halves.detach().cpu().numpy()[tracks] + # Build indices on CPU, then copy once. Writing three CUDA scalars per valid query + # otherwise launches thousands of tiny kernels per example, especially costly when + # training and evaluation share one GPU. The matching semantics are unchanged. + queries = np.zeros((len(tracks), 3), dtype=np.int64) + queries[:, 2] = tracks + valid = scoring["valid"].clone() + missing = np.ones(len(tracks), dtype=bool) + position_rows = positions.detach().cpu().numpy() + donors_cpu = scoring["donor_position"].detach().cpu().numpy() + acceptors_cpu = scoring["acceptor_position"].detach().cpu().numpy() + valid_indices = np.flatnonzero(valid.detach().cpu().numpy()) + lookups = [ + {int(coordinate): index for index, coordinate in enumerate(axis) if coordinate >= 0} + for axis in position_rows + ] + for index in valid_indices: + half = int(halves_cpu[index]) + row = lookups[2 * half].get(int(donors_cpu[index])) + column = lookups[2 * half + 1].get(int(acceptors_cpu[index])) + if row is not None and column is not None: + queries[index, 0] = row + queries[index, 1] = column + missing[index] = False + return (torch.from_numpy(queries).to(device).unsqueeze(0), valid, + torch.from_numpy(missing).to(device)) + + +def _model_inputs(sample: dict) -> tuple[dict, Tensor, Tensor]: + queries, query_valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + sample["site_positions"], + sample["junction_track_halves"], + ) + if (query_valid & missing).any(): + raise ValueError("trusted scoring coordinates do not match target positions") + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + return { + "dna": sample["dna"].unsqueeze(0), + "usage_positions": sample["usage_positions"].unsqueeze(0), + "junction_site_positions": sample["site_positions"].unsqueeze(0), + "junction_queries": queries, + }, junction_target, query_valid + + +def _losses(outputs: dict[str, Tensor], sample: dict, junction_target: Tensor, + junction_valid: Tensor) -> dict[str, Tensor]: + losses = {} + for family in DENSE_FAMILIES: + target = torch.asinh(sample["dense"][family]["values"]).unsqueeze(0) + mask = sample["dense"][family]["mask"].unsqueeze(0) + residual = torch.where(mask, outputs[family] - target, 0.0) + losses[family] = residual.square().sum() / mask.sum().clamp_min(1) + losses["splice_sites"] = F.binary_cross_entropy_with_logits( + outputs["splice_sites"], sample["splice_sites"].unsqueeze(0) + ) + usage_target = torch.asinh(sample["splice_usage"]["values"]).unsqueeze(0) + usage_mask = sample["splice_usage"]["mask"].unsqueeze(0) + usage_residual = torch.where(usage_mask, outputs["splice_usage"] - usage_target, 0.0) + losses["splice_usage"] = usage_residual.square().sum() / usage_mask.sum().clamp_min(1) + junction_residual = outputs["junctions"][0] - junction_target[0] + losses["junctions"] = ( + junction_residual[junction_valid].square().mean() + if junction_valid.any() else junction_residual.sum() * 0 + ) + contact_target = torch.asinh(sample["contact_maps"]).unsqueeze(0) + contact_mask = sample["contact_map_mask"].unsqueeze(0) + contact_residual = torch.where( + contact_mask, outputs["contact_maps"] - contact_target, 0.0 + ) + losses["contact_maps"] = contact_residual.square().sum() / contact_mask.sum().clamp_min(1) + losses["total"] = torch.stack([losses[name] for name in ALL_FAMILIES]).mean() + return losses + + +def _validation_inference( + model: CompactModel, sample: dict +) -> tuple[dict[str, Tensor], Tensor, Tensor, Tensor]: + """Use the same predicted-position path as a formal v3 submission.""" + trunk = model._trunk(sample["dna"].unsqueeze(0)) + dense_features = model._dense_features(trunk) + outputs = { + family: model.dense_heads[family](dense_features).float() + for family in DENSE_FAMILIES + } + all_sites = model._splice_site_logits(trunk) + target_start = (model.config.input_length - model.config.target_length) // 2 + outputs["splice_sites"] = all_sites[:, target_start:target_start + model.config.target_length] + usage_bins = model.splice_usage_head(dense_features).float() + usage_indices = torch.div(sample["usage_positions"], model.config.dense_bin_size, + rounding_mode="floor") + outputs["splice_usage"] = usage_bins[:, usage_indices] + predicted_positions = model.select_junction_positions(all_sites) + + matched, valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + predicted_positions[0], + sample["junction_track_halves"], + ) + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + junction_prediction = torch.zeros(len(valid), device=valid.device) + if (~missing & valid).any(): + selected = ~missing & valid + junction_prediction[selected] = model._junction_predictions( + trunk, predicted_positions, matched[:, selected] + )[0] + outputs["junctions"] = junction_prediction.unsqueeze(0) + + contact = trunk.reshape( + 1, model.config.contact_bins, -1, model.config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + outputs["contact_maps"] = (0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row])).float() + return outputs, junction_target, valid, missing + + +def _atomic_save(payload: dict, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(f".{path.name}.tmp") + torch.save(payload, temporary) + os.replace(temporary, path) + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(8 * 1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _resume_position(payload: dict, *, examples: int, epochs: int) -> tuple[int, int]: + epoch = int(payload["epoch"]) + step = int(payload["global_step"]) + kind = payload.get("checkpoint_kind") + if kind not in (None, "epoch") or step != epoch * examples: + raise ValueError("only a completed-epoch checkpoint can resume training") + if payload.get("examples", examples) != examples or payload.get("schedule_epochs", epochs) != epochs: + raise ValueError("checkpoint data size or training schedule differs from this run") + return epoch, step + + +def train(args: argparse.Namespace) -> None: + # Multi-GPU with `torchrun --nproc-per-node N train_eval.py train ...`: one example per GPU per + # step (data parallel), rank 0 writes checkpoints and logs. Model size is bounded by the + # training budget, not by a parameter cap. + rank, local_rank, world = _distributed() + config = ModelConfig.from_yaml(args.config) + device = torch.device("cuda", local_rank) if world > 1 else _device(args.device) + if world > 1: + torch.cuda.set_device(device) + torch.manual_seed(args.seed) + model = CompactModel(config).to(device) + parameters = count_parameters(model) + optimizer = torch.optim.AdamW( + model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay + ) + scaler = torch.amp.GradScaler("cuda", enabled=device.type == "cuda") + full_dataset = AlphaGenomeDataset(args.input, "train") + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + start_epoch, global_step = 0, 0 + if args.resume: + payload = torch.load(args.resume, map_location="cpu", weights_only=True) + if payload.get("config") != config.to_dict(): + raise ValueError("checkpoint configuration does not match --config") + model.load_state_dict(payload["model"], strict=True) + optimizer.load_state_dict(payload["optimizer"]) + scaler.load_state_dict(payload.get("scaler", {})) + start_epoch, global_step = _resume_position(payload, examples=size, epochs=args.epochs) + per_rank = math.ceil(size / world) + steps = max(1, per_rank * args.epochs) + module = model + if world > 1: + model = torch.nn.parallel.DistributedDataParallel( + model, device_ids=[local_rank], find_unused_parameters=True) + sampler = (torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world, rank=rank, + shuffle=True, seed=args.seed) + if world > 1 else None) + stop_after_epoch = args.stop_after_epoch or args.epochs + output = args.output.resolve() + output.mkdir(parents=True, exist_ok=True) + started = time.monotonic() + if rank == 0: + print(json.dumps({"event": "train_start", "parameters": parameters, "examples": size, + "device": str(device), "world_size": world}), flush=True) + + for epoch in range(start_epoch, stop_after_epoch): + model.train() + if sampler is not None: + sampler.set_epoch(epoch) + loader = _loader(dataset, workers=args.workers, shuffle=True, sampler=sampler) + for sample in loader: + sample = _to_device(sample, device) + inputs, junction_target, junction_valid = _model_inputs(sample) + progress = min((global_step + 1) / steps, 1.0) + learning_rate = args.min_learning_rate + 0.5 * ( + args.learning_rate - args.min_learning_rate + ) * (1.0 + math.cos(math.pi * progress)) + for group in optimizer.param_groups: + group["lr"] = learning_rate + optimizer.zero_grad(set_to_none=True) + with _autocast(device): + predictions = model(**inputs) + losses = _losses(predictions, sample, junction_target, junction_valid) + if not torch.isfinite(losses["total"]): + raise FloatingPointError(f"non-finite loss at step {global_step}") + scaler.scale(losses["total"]).backward() + scaler.unscale_(optimizer) + gradient_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_gradient_norm) + scaler.step(optimizer) + scaler.update() + global_step += 1 + if rank == 0 and (global_step == 1 or global_step % args.log_every == 0): + print( + json.dumps( + { + "event": "train_step", + "epoch": epoch + 1, + "step": global_step, + "loss": float(losses["total"].detach()), + "gradient_norm": float(gradient_norm), + "learning_rate": learning_rate, + "elapsed_seconds": time.monotonic() - started, + } + ), + flush=True, + ) + if rank == 0 and global_step % args.save_every == 0: + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch, + "global_step": global_step, + "checkpoint_kind": "progress", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "progress.pt", + ) + if world > 1: + torch.distributed.barrier() + if rank != 0: + continue + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch + 1, + "global_step": global_step, + "checkpoint_kind": "epoch", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "last.pt", + ) + if world > 1: + torch.distributed.barrier() + torch.distributed.destroy_process_group() + if rank != 0: + return + checkpoint = output / "last.pt" + manifest = { + "schema": "alphagenome-50m-real/v3", + "checkpoint": {"path": checkpoint.name, "sha256": _sha256(checkpoint)}, + "parameters": parameters, + "config": config.to_dict(), + "output_families": list(ALL_FAMILIES), + "epochs": args.epochs, + "completed_epochs": stop_after_epoch, + "global_step": global_step, + } + (output / "model_manifest.json").write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "train_complete", **manifest}), flush=True) + + +class PerTrackMoments: + def __init__(self, tracks: int): + self.n = np.zeros(tracks, dtype=np.int64) + self.sum_x = np.zeros(tracks, dtype=np.float64) + self.sum_y = np.zeros(tracks, dtype=np.float64) + self.sum_x2 = np.zeros(tracks, dtype=np.float64) + self.sum_y2 = np.zeros(tracks, dtype=np.float64) + self.sum_xy = np.zeros(tracks, dtype=np.float64) + + def update(self, prediction: Tensor, target: Tensor, mask: Tensor | None = None) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1, prediction.shape[-1]) + y = target.detach().double().cpu().numpy().reshape(-1, target.shape[-1]) + valid = np.ones_like(y, dtype=bool) if mask is None else mask.detach().cpu().numpy().reshape(y.shape).astype(bool) + self.n += valid.sum(axis=0) + self.sum_x += np.where(valid, x, 0).sum(axis=0, dtype=np.float64) + self.sum_y += np.where(valid, y, 0).sum(axis=0, dtype=np.float64) + self.sum_x2 += np.where(valid, x * x, 0).sum(axis=0, dtype=np.float64) + self.sum_y2 += np.where(valid, y * y, 0).sum(axis=0, dtype=np.float64) + self.sum_xy += np.where(valid, x * y, 0).sum(axis=0, dtype=np.float64) + + def update_indexed(self, prediction: Tensor, target: Tensor, track: Tensor) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1) + y = target.detach().double().cpu().numpy().reshape(-1) + indices = track.detach().cpu().numpy().reshape(-1).astype(np.int64) + self.n += np.bincount(indices, minlength=len(self.n)) + self.sum_x += np.bincount(indices, weights=x, minlength=len(self.n)) + self.sum_y += np.bincount(indices, weights=y, minlength=len(self.n)) + self.sum_x2 += np.bincount(indices, weights=x * x, minlength=len(self.n)) + self.sum_y2 += np.bincount(indices, weights=y * y, minlength=len(self.n)) + self.sum_xy += np.bincount(indices, weights=x * y, minlength=len(self.n)) + + def report(self) -> dict: + numerator = self.n * self.sum_xy - self.sum_x * self.sum_y + denominator = np.sqrt( + np.maximum(self.n * self.sum_x2 - self.sum_x**2, 0) + * np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + ) + target_ss = np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + scorable = (self.n >= 2) & (target_ss > 0) + pearson = np.full_like(numerator, np.nan) + nonconstant = scorable & (denominator > 0) + pearson[nonconstant] = numerator[nonconstant] / denominator[nonconstant] + pearson[scorable & ~nonconstant] = 0.0 + return { + "mean_pearson": float(pearson[scorable].mean()) if scorable.any() else 0.0, + "valid_tracks": int(scorable.sum()), + "per_track_pearson": [float(value) if np.isfinite(value) else None for value in pearson], + "values_per_track": self.n.tolist(), + } + + +def evaluate(args: argparse.Namespace) -> None: + device = _device(args.device) + payload = torch.load(args.checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config).to(device) + model.load_state_dict(payload["model"], strict=True) + model.eval() + full_dataset = AlphaGenomeDataset(args.input, args.split) + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + metrics = { + **{family: PerTrackMoments(config_value) for family, config_value in { + "atac": 167, "dnase": 305, "procap": 12, "cage": 546, + "rna_seq": 667, "chip_tf": 1617, "chip_histone": 1116, + }.items()}, + "splice_sites": PerTrackMoments(4), + "splice_usage": PerTrackMoments(config.splice_usage_tracks), + "junctions": PerTrackMoments(config.junction_tracks), + "contact_maps": PerTrackMoments(config.contact_tracks), + } + with torch.inference_mode(): + for index, sample in enumerate(_loader(dataset, workers=args.workers, shuffle=False)): + sample = _to_device(sample, device) + outputs, junction_target, junction_valid, junction_missing = ( + _validation_inference(model, sample) + ) + # Match predict.py: FP32 inference is serialized to FP16 Zarr values. + outputs = {name: value.half().float() for name, value in outputs.items()} + for family in DENSE_FAMILIES: + metrics[family].update( + outputs[family][0], + torch.asinh(sample["dense"][family]["values"]), + sample["dense"][family]["mask"], + ) + metrics["splice_sites"].update(outputs["splice_sites"][0], sample["splice_sites"]) + metrics["splice_usage"].update( + outputs["splice_usage"][0], + torch.asinh(sample["splice_usage"]["values"]), + sample["splice_usage"]["mask"], + ) + junction_track = sample["junction_scoring"]["track"] + metrics["junctions"].update_indexed( + outputs["junctions"][0][junction_valid], + junction_target[0][junction_valid], + junction_track[junction_valid], + ) + metrics["contact_maps"].update( + outputs["contact_maps"][0], + torch.asinh(sample["contact_maps"]), + sample["contact_map_mask"], + ) + if index == 0 or (index + 1) % args.log_every == 0 or index + 1 == size: + print(json.dumps({"event": "evaluate_progress", "examples": index + 1, "total": size}), flush=True) + families = {name: accumulator.report() for name, accumulator in metrics.items()} + valid_family_scores = [item["mean_pearson"] for item in families.values()] + report = { + "schema": "alphagenome-50m-evaluation/v3", + "split": args.split, + "examples": size, + "metric_space": ( + "asinh continuous targets and binary splice-site targets; " + "junction Pearson uses hidden coordinate queries and predicted endpoints; " + "FP32 inference rounded to FP16 prediction values" + ), + "families": families, + "overall_mean_family_pearson": float(np.mean(valid_family_scores)), + } + args.report.parent.mkdir(parents=True, exist_ok=True) + args.report.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "evaluate_complete", "overall_mean_family_pearson": report["overall_mean_family_pearson"]}), flush=True) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + common = argparse.ArgumentParser(add_help=False) + common.add_argument("--input", required=True, type=Path) + common.add_argument("--config", type=Path, default=DEFAULT_CONFIG) + common.add_argument("--workers", type=int, default=2) + common.add_argument("--max-examples", type=int, default=0) + common.add_argument("--device", choices=("auto", "cuda", "cpu"), default="auto") + common.add_argument("--log-every", type=int, default=25) + subparsers = parser.add_subparsers(dest="command", required=True) + train_parser = subparsers.add_parser("train", parents=[common]) + train_parser.add_argument("--output", required=True, type=Path) + train_parser.add_argument("--epochs", type=int, default=10) + train_parser.add_argument("--stop-after-epoch", type=int) + train_parser.add_argument("--learning-rate", type=float, default=2e-4) + train_parser.add_argument("--min-learning-rate", type=float, default=2e-5) + train_parser.add_argument("--weight-decay", type=float, default=1e-4) + train_parser.add_argument("--max-gradient-norm", type=float, default=5.0) + train_parser.add_argument("--save-every", type=int, default=250) + train_parser.add_argument("--seed", type=int, default=17) + train_parser.add_argument("--resume", type=Path) + train_parser.set_defaults(run=train) + evaluate_parser = subparsers.add_parser("evaluate", parents=[common]) + evaluate_parser.add_argument("--checkpoint", required=True, type=Path) + evaluate_parser.add_argument("--split", choices=("train", "valid", "test"), required=True) + evaluate_parser.add_argument("--report", required=True, type=Path) + evaluate_parser.set_defaults(run=evaluate) + args = parser.parse_args() + if args.workers < 0 or args.max_examples < 0: + parser.error("workers/max-examples must be nonnegative") + if args.command == "train" and ( + args.epochs < 1 + or (args.stop_after_epoch is not None + and not 1 <= args.stop_after_epoch <= args.epochs) + ): + parser.error("epochs must be positive and stop-after-epoch must be within the schedule") + args.run(args) + + +if __name__ == "__main__": + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/single_prediction.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/single_prediction.py new file mode 100644 index 0000000000000000000000000000000000000000..a47022029461e5c8097b3a653af8b3407196d67d --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/single_prediction.py @@ -0,0 +1,97 @@ +"""The parent single-orientation decoder, preserving float32 until ensemble averaging.""" +import numpy as np +import torch +from prediction_contract import DENSE_TRACKS, MAX_JUNCTION_PAIRS, MAX_USAGE_SITES +from splice_output import splice_output_scores + +COMPRESSOR = None + +class ArrayCollector: + def __init__(self): + self.arrays = {} + def create_dataset(self, name, *, data, **kwargs): + self.arrays[name] = np.asarray(data) + +def single_prediction(model, dna): + config = model.config + group = ArrayCollector() + trunk = model._trunk(dna) + dense_features = model._dense_features(trunk) + for family in DENSE_TRACKS: + values = model.dense_heads[family](dense_features)[0] + group.create_dataset(family, data=values.cpu().numpy(), compressor=COMPRESSOR) + + all_site_logits = model._splice_site_logits(trunk) + target_start = (config.input_length - config.target_length) // 2 + group.create_dataset( + "splice_sites", + data=splice_output_scores(all_site_logits[0, target_start:target_start + config.target_length]).cpu().numpy(), + chunks=(4096, 4), compressor=COMPRESSOR, + ) + predicted_positions = model.select_junction_positions(all_site_logits)[0] + + # Usage at the model's own predicted sites (v4 is sparse; unlisted hidden sites score 0). + usage_bins = model.splice_usage_head(dense_features)[0].float() + sites = predicted_positions.cpu().numpy().astype(np.int64).reshape(-1) + sites = np.unique(sites[(sites >= target_start) & (sites < target_start + config.target_length)] + - target_start)[:MAX_USAGE_SITES] + group.create_dataset("splice_usage_positions", data=sites.astype(np.int32), compressor=COMPRESSOR) + usage = usage_bins[torch.as_tensor(sites // 128, device=usage_bins.device)] if len(sites) \ + else usage_bins[:0] + group.create_dataset("splice_usage", data=usage.cpu().numpy(), + chunks=(1024, 734), compressor=COMPRESSOR) + + # Junctions: score every donor x acceptor pair of each strand, keep the strongest pairs. + positions_batch = predicted_positions[None] + site_factors = model.junction_site_factors(trunk, positions_batch) + halves = model.junction_track_half + candidates = [] + for row_start in range(0, 512, 128): + for column_start in range(0, 512, 128): + block = torch.cat([ + model.junction_block( + trunk, positions_batch, row_start, row_start + 128, + column_start, column_start + 128, track_start, + min(track_start + 64, config.junction_tracks), site_factors, + )[0] + for track_start in range(0, config.junction_tracks, 64) + ], dim=-1).float() # [128, 128, 734] + for half in (0, 1): + donors = predicted_positions[2 * half, row_start:row_start + 128] + acceptors = predicted_positions[2 * half + 1, column_start:column_start + 128] + valid = (donors[:, None] >= 0) & (acceptors[None, :] >= 0) + # Transcript direction is determined by the public strand half. + forward = donors[:, None] < acceptors[None, :] if half == 0 else donors[:, None] > acceptors[None, :] + valid = valid & forward + strength = block[..., halves == half].amax(dim=-1).masked_fill(~valid, -torch.inf) + keep = min(MAX_JUNCTION_PAIRS // 2, int(valid.sum())) + if not keep: + continue + best = strength.flatten().topk(keep) + rows, columns = best.indices // 128, best.indices % 128 + candidates.append((best.values, torch.full_like(rows, half), donors[rows], + acceptors[columns], block[rows, columns])) + if candidates: + score, half, donor, acceptor, values = (torch.cat(x) for x in zip(*candidates)) + order = score.topk(min(MAX_JUNCTION_PAIRS, len(score))).indices + half, donor, acceptor, values = half[order], donor[order], acceptor[order], values[order] + else: + half = donor = acceptor = torch.zeros(0, dtype=torch.long) + values = torch.zeros(0, config.junction_tracks) + group.create_dataset("junction_half", data=half.cpu().numpy().astype(np.int8), compressor=COMPRESSOR) + group.create_dataset("junction_donor", data=donor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junction_acceptor", data=acceptor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junctions", data=values.cpu().numpy(), + chunks=(4096, 734), compressor=COMPRESSOR) + + contact = trunk.reshape( + 1, config.contact_bins, -1, config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + contact_values = 0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row]) + group.create_dataset( + "contact_maps", data=contact_values[0].cpu().numpy(), + chunks=(8192, 28), compressor=COMPRESSOR, + ) + return group.arrays diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/splice_calibration.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/splice_calibration.json new file mode 100644 index 0000000000000000000000000000000000000000..470734a574a4b9b9f2f34fd3bcae0007db3737f6 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/splice_calibration.json @@ -0,0 +1,33 @@ +{ + "training_examples": 256, + "training_indices_sha256": "b82df9ae11e95d9be5d6e5f85839cf23e25bbbe0a195eaee266ff52d366800dc", + "training_positive_counts": [ + 3968.0, + 4160.0, + 3874.0, + 3921.0 + ], + "training_prior": [ + 7.883707682291667e-05, + 8.265177408854167e-05, + 7.696946461995442e-05, + 7.790327072143555e-05 + ], + "effective_balanced_loss_prior": [ + 0.3203125, + 0.3125, + 0.30859375, + 0.30859375 + ], + "logit_offset": [ + -8.695712089538574, + -8.612334251403809, + -8.665323257446289, + -8.653263092041016 + ], + "caveat": "marginal prior correction; balanced loss is normalized separately in each window", + "link": "sigmoid(logits + training_prior_offset)", + "training_split": "train", + "seed": 1729, + "development_labels_used_to_fit_offsets": false +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/splice_output.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/splice_output.py new file mode 100644 index 0000000000000000000000000000000000000000..1081264d9cf4b4473f4fe53c97ff02b2c3b249e3 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/splice_output.py @@ -0,0 +1,11 @@ +"""Shared splice output link; constants estimated only from training targets.""" +import json +from pathlib import Path +import torch + +_OFFSETS = json.loads((Path(__file__).with_name('splice_calibration.json')).read_text())['logit_offset'] + +def splice_output_scores(logits): + logits = logits.float() + offset = torch.tensor(_OFFSETS, device=logits.device, dtype=logits.dtype) + return torch.sigmoid(logits + offset) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/strand_permutations.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/strand_permutations.json new file mode 100644 index 0000000000000000000000000000000000000000..044c090773f20347601dcd4f0983c4dfd4c0ecc2 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/strand_permutations.json @@ -0,0 +1,5959 @@ +{ + "schema": "seqfunc-strand-mapping/v1", + "source": "public track metadata; no target values", + "source_sha256": "23450e6ff9463c64d271d7014f8a798875bc95186c36aeb34609c7dc1d62cd5f", + "permutations": { + "atac": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 83, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + 91, + 92, + 93, + 94, + 95, + 96, + 97, + 98, + 99, + 100, + 101, + 102, + 103, + 104, 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a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/train_single.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/train_single.py new file mode 100644 index 0000000000000000000000000000000000000000..03024393206b918eac3ae23a87c09173848b0d15 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/train_single.py @@ -0,0 +1,158 @@ +"""Resumable single-H100 training; every epoch permutes every eligible training interval.""" +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import torch +from numcodecs import blosc +from torch.utils.data import DataLoader, Subset + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset, single_example_collate +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save, _losses, _model_inputs, _to_device + + +def balanced_splice_loss(logits, target): + """Equal positive/negative contributions per type, using only training labels. + + An absent class contributes its negative loss rather than dividing by zero or being dropped. + """ + dims = tuple(range(target.ndim - 1)) + positive = target.sum(dims) + negative = (1 - target).sum(dims) + pos_loss = (torch.nn.functional.softplus(-logits) * target).sum(dims) / positive.clamp_min(1) + neg_loss = (torch.nn.functional.softplus(logits) * (1 - target)).sum(dims) / negative.clamp_min(1) + return torch.where(positive > 0, 0.5 * (pos_loss + neg_loss), neg_loss).mean() + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--input', type=Path, default=Path('/workspace/data')) + p.add_argument('--output', type=Path, required=True) + p.add_argument('--config', type=Path, default=Path(__file__).parent / 'reference/alphagenome_50m/alphagenome_50m.yaml') + p.add_argument('--resume', type=Path) + p.add_argument('--objective', choices=['reference', 'balanced_splice'], default='balanced_splice') + p.add_argument('--epochs', type=int, default=4) + p.add_argument('--stop-at', type=int, default=0, help='Stop after this total number of examples, preserving exact sampler position') + p.add_argument('--workers', type=int, default=8) + p.add_argument('--threads', type=int, default=4) + p.add_argument('--seed', type=int, default=17) + p.add_argument('--learning-rate', type=float, default=2e-4) + p.add_argument('--min-learning-rate', type=float, default=2e-5) + p.add_argument('--warmup', type=int, default=500) + p.add_argument('--save-every', type=int, default=2000) + p.add_argument('--keep-every', type=int, default=10000, help='Keep immutable intermediate checkpoints') + p.add_argument('--log-every', type=int, default=100) + args = p.parse_args() + torch.set_num_threads(args.threads) + torch.set_num_interop_threads(1) + blosc.set_nthreads(1) + if hasattr(os, 'sched_getaffinity'): + os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) + torch.manual_seed(args.seed) + device = torch.device('cuda:0') + config = ModelConfig.from_yaml(args.config) + model = CompactModel(config).to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=1e-4) + dataset = AlphaGenomeDataset(args.input, 'train') + model.set_junction_track_halves(dataset.junction_track_halves) + args.output.mkdir(parents=True, exist_ok=True) + total = len(dataset) * args.epochs + step, elapsed_before = 0, 0.0 + lineage = {} + if args.resume: + state = torch.load(args.resume, map_location='cpu', weights_only=True) + lineage = {k: state[k] for k in ['intervention', 'interventions'] if k in state} + if state['config'] != config.to_dict() or state['examples'] != len(dataset): + raise ValueError('resume config or dataset size differs') + if state['schedule_epochs'] != args.epochs or state['objective'] != args.objective: + raise ValueError('resume schedule or objective differs') + if state['seed'] != args.seed: + raise ValueError('resume shuffle seed differs') + previous = state.get('training_arguments', {}) + for field in ['learning_rate', 'min_learning_rate', 'warmup']: + if field in previous and previous[field] != getattr(args, field): + raise ValueError(f'resume {field} differs') + model.load_state_dict(state['model'], strict=True) + optimizer.load_state_dict(state['optimizer']) + step = state['global_step'] + elapsed_before = state.get('elapsed_seconds', 0.0) + torch.set_rng_state(state['rng_cpu']) + torch.cuda.set_rng_state(state['rng_cuda'], device) + start = time.monotonic() + def emit(event, **values): + record = {'event': event, 'step': step, 'elapsed_seconds': elapsed_before + time.monotonic() - start, **values} + print(json.dumps(record), flush=True) + def save(name): + _atomic_save({ + **lineage, + 'schema': 'seqfunc-single/v1', 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), 'config': config.to_dict(), + 'global_step': step, 'epoch': step // len(dataset), 'offset': step % len(dataset), + 'examples': len(dataset), 'schedule_epochs': args.epochs, + 'objective': args.objective, 'seed': args.seed, + 'training_arguments': {k: str(v) if isinstance(v, Path) else v for k, v in vars(args).items()}, + 'rng_cpu': torch.get_rng_state(), 'rng_cuda': torch.cuda.get_rng_state(device), + 'elapsed_seconds': elapsed_before + time.monotonic() - start, + 'provenance': {'data': str(args.input), 'split': 'train', 'pretrained': False, + 'device': torch.cuda.get_device_name(0), 'world_size': 1}, + }, args.output / name) + emit('train_start', parameters=count_parameters(model), examples=len(dataset), objective=args.objective, + config=config.to_dict(), schedule_examples=total, device=torch.cuda.get_device_name(0)) + model.train() + for epoch in range(step // len(dataset), args.epochs): + generator = torch.Generator().manual_seed(args.seed + epoch) + order = torch.randperm(len(dataset), generator=generator).tolist() + offset = step % len(dataset) + emit('epoch_order', epoch=epoch + 1, offset=offset, count=len(order), + permutation_sha256=hashlib.sha256(torch.tensor(order, dtype=torch.int64).numpy().tobytes()).hexdigest()) + loader = DataLoader(Subset(dataset, order[offset:]), batch_size=1, shuffle=False, + num_workers=args.workers, pin_memory=True, collate_fn=single_example_collate, + persistent_workers=False, prefetch_factor=2 if args.workers else None) + for sample in loader: + sample = _to_device(sample, device) + inputs, target, valid = _model_inputs(sample) + progress = min(max(step - args.warmup, 0) / max(total - args.warmup, 1), 1) + lr = args.min_learning_rate + 0.5 * (args.learning_rate - args.min_learning_rate) * (1 + math.cos(math.pi * progress)) + lr *= min((step + 1) / max(args.warmup, 1), 1) + for group in optimizer.param_groups: + group['lr'] = lr + optimizer.zero_grad(set_to_none=True) + with torch.autocast('cuda', dtype=torch.bfloat16): + outputs = model(**inputs) + losses = _losses(outputs, sample, target, valid) + if args.objective == 'balanced_splice': + losses['splice_sites'] = balanced_splice_loss(outputs['splice_sites'], sample['splice_sites'][None]) + losses['total'] = torch.stack([v for k, v in losses.items() if k != 'total']).mean() + if not torch.isfinite(losses['total']): + raise FloatingPointError(f'nonfinite loss at {step}') + losses['total'].backward() + norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0, error_if_nonfinite=True) + optimizer.step() + step += 1 + if step == 1 or step % args.log_every == 0: + emit('train_step', epoch=epoch + 1, example_id=sample['example_id'], learning_rate=lr, + gradient_norm=float(norm), losses={k: float(v.detach()) for k, v in losses.items()}, + peak_gpu_bytes=torch.cuda.max_memory_allocated()) + if step % args.save_every == 0: + save('progress.pt') + if args.keep_every and step % args.keep_every == 0: + save(f'step-{step:06d}.pt') + if args.stop_at and step >= args.stop_at: + save('last.pt') + emit('train_segment_complete', completed_epochs=step // len(dataset), offset=step % len(dataset)) + return + save('last.pt') + save(f'epoch-{epoch + 1:02d}.pt') + emit('epoch_complete', completed_epochs=epoch + 1) + emit('train_complete') + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/training_config.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/training_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e5a5cee0af43ebd5a92833bed8d5dcc8b264a8d3 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/training_config.yaml @@ -0,0 +1,29 @@ +attention_head_dim: 64 +channel_growth: 32 +contact_bin_size: 2048 +contact_tracks: 28 +conv_kernel_size: 5 +dense_bin_size: 128 +dna_kernel_size: 15 +dropout: 0.0 +enable_pairwise: false +encoder_width: 32 +gradient_checkpointing: true +head_hidden_size: 256 +hidden_dim: 384 +input_length: 1048576 +junction_rank: 32 +junction_tracks: 734 +local_context_blocks: 6 +local_width: 32 +mlp_expansion: 4.0 +num_attention_heads: 6 +num_downsampling_blocks: 7 +num_sequence_blocks: 6 +pair_downsample: 16 +pair_update_interval: 2 +pair_width: 32 +splice_usage_tracks: 734 +stem_stride: 8 +target_length: 196608 +trunk_bin_size: 1024 diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/training_provenance.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/training_provenance.json new file mode 100644 index 0000000000000000000000000000000000000000..4e116d0456eb51c5525e13f6b75b10e78c9bc33a --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/training_provenance.json @@ -0,0 +1,91 @@ +{ + "schema": "seqfunc-single/v1", + "config": { + "input_length": 1048576, + "target_length": 196608, + "dense_bin_size": 128, + "trunk_bin_size": 1024, + "contact_bin_size": 2048, + "stem_stride": 8, + "encoder_width": 32, + "channel_growth": 32, + "num_downsampling_blocks": 7, + "conv_kernel_size": 5, + "dna_kernel_size": 15, + "hidden_dim": 384, + "num_sequence_blocks": 6, + "num_attention_heads": 6, + "attention_head_dim": 64, + "mlp_expansion": 4.0, + "dropout": 0.0, + "head_hidden_size": 256, + "junction_rank": 32, + "splice_usage_tracks": 734, + "junction_tracks": 734, + "contact_tracks": 28, + "gradient_checkpointing": true, + "enable_pairwise": false, + "pair_width": 32, + "pair_downsample": 16, + "pair_update_interval": 2, + "local_width": 32, + "local_context_blocks": 6 + }, + "epoch": 1, + "offset": 2980, + "global_step": 44000, + "examples": 41020, + "schedule_epochs": 4, + "objective": "balanced_splice", + "seed": 17, + "training_arguments": { + "input": "/workspace/data", + "output": "/workspace/research/experiments/context09", + "config": "/workspace/research/experiments/context09/config.yaml", + "resume": "/workspace/research/experiments/context09/start.pt", + "objective": "balanced_splice", + "epochs": 4, + "stop_at": 44000, + "workers": 3, + "threads": 2, + "seed": 17, + "learning_rate": 0.0002, + "min_learning_rate": 2e-05, + "warmup": 500, + "save_every": 100, + "keep_every": 10000, + "log_every": 100 + }, + "elapsed_seconds": 7631.602510094148, + "provenance": { + "data": "/workspace/data", + "split": "train", + "pretrained": false, + "device": "NVIDIA H100 80GB HBM3", + "world_size": 1 + }, + "intervention": { + "kind": "extended_local_context", + "parent_sha256": "72ef8003d6d4a54f8b583bbc01b7300ecbd141a5a4fdb6598f43e190d9dd5eff", + "step": 40000, + "blocks_before": 4, + "blocks_after": 6, + "receptive_field_bp": 10941, + "parent_parameters_optimizer_moments_rng_preserved": true, + "new_blocks_initially_identity": true, + "optimizer_ids_remapped_by_parameter_name": true + }, + "interventions": [ + { + "kind": "extended_local_context", + "parent_sha256": "72ef8003d6d4a54f8b583bbc01b7300ecbd141a5a4fdb6598f43e190d9dd5eff", + "step": 40000, + "blocks_before": 4, + "blocks_after": 6, + "receptive_field_bp": 10941, + "parent_parameters_optimizer_moments_rng_preserved": true, + "new_blocks_initially_identity": true, + "optimizer_ids_remapped_by_parameter_name": true + } + ] +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/validation_checks/entrypoint-check.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/validation_checks/entrypoint-check.json new file mode 100644 index 0000000000000000000000000000000000000000..19ca456e15ebe2f24e1b72b293c80352df12f0b2 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/validation_checks/entrypoint-check.json @@ -0,0 +1,36 @@ +{ + "time_utc": "2026-10-24T22:02:14.388174+00:00", + "vendored_shell_entrypoints_succeeded": true, + "all_arrays_bit_identical": true, + "partial_perturbation_resume_succeeded": true, + "array_counts": { + "tracks": 30, + "perturbations": 22 + }, + "phases": [ + { + "phase": "verify-package", + "elapsed_seconds": 4.719556925003417 + }, + { + "phase": "offline-infer", + "elapsed_seconds": 69.64677276299335 + }, + { + "phase": "offline-perturb", + "elapsed_seconds": 63.509883274004096 + }, + { + "phase": "resumed-tracks", + "elapsed_seconds": 19.518164098000852 + }, + { + "phase": "perturb-prefix", + "elapsed_seconds": 8.539132490986958 + }, + { + "phase": "perturb-resume", + "elapsed_seconds": 9.81999238699791 + } + ] +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/validation_checks/gpu-mechanical-check.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/validation_checks/gpu-mechanical-check.json new file mode 100644 index 0000000000000000000000000000000000000000..e4b877ac18f082043964f1864474d0eabd3391de --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/validation_checks/gpu-mechanical-check.json @@ -0,0 +1,15 @@ +{ + "time_utc": "2026-10-24T21:58:19.120800+00:00", + "all_track_maps_involutive": true, + "all_readouts_double_reversal_exact": true, + "contacts_checked_against_direct_matrix_reversal": 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b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/validation_variants.json new file mode 100644 index 0000000000000000000000000000000000000000..b4a9d01b3e7a347b780e46da2a19bf3a62abfa00 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S016/validation_variants.json @@ -0,0 +1,125 @@ +{ + "eqtl_sign": { + "rows": 17052, + "perturbations": 165, + "metric": "auroc", + "model": 0.564602677543126, + "anchors": { + "AlphaGenome": 0.8263569969634328 + } + }, + "eqtl_causality": { + "rows": 17934, + "perturbations": 174, + "metric": "auroc", + "model": 0.6865968008825152, + "anchors": { + "AlphaGenome": 0.8325427468284611 + } + }, + "eqtl_coefficient": { + "rows": 8575, + "perturbations": 165, + "metric": "spearman", + "model": 0.1704386903924102, + "anchors": { + "AlphaGenome": 0.4133539365891599 + } + }, + "sqtl_causality": { + "rows": 21340, + 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diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/attach_bridge.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/attach_bridge.py new file mode 100644 index 0000000000000000000000000000000000000000..2c2bea67a58e19676fa73ba2a0607002ea2d5419 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/attach_bridge.py @@ -0,0 +1,48 @@ +"""Attach a zero-output local-to-global connection to our own training checkpoint.""" +import argparse +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import torch +import yaml +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + +p = argparse.ArgumentParser(description=__doc__) +p.add_argument('checkpoint', type=Path) +p.add_argument('output', type=Path) +a = p.parse_args() +s = torch.load(a.checkpoint, map_location='cpu', weights_only=True) +assert s['schema'] == 'seqfunc-single/v1' +parent_config = ModelConfig(**s['config']) +assert parent_config.local_width > 0 and not parent_config.local_to_trunk +torch.manual_seed(s['seed']) +parent = CompactModel(parent_config) +config = replace(parent_config, local_to_trunk=True) +model = CompactModel(config) +missing = model.load_state_dict(s['model'], strict=False) +assert not missing.unexpected_keys +assert set(missing.missing_keys) == {'local_trunk_projection.weight', 'local_trunk_projection.bias'} +old_names = list(dict(parent.named_parameters())) +new_names = list(dict(model.named_parameters())) +assert new_names[:len(old_names)] == old_names +assert len(s['optimizer']['param_groups']) == 1 +group = s['optimizer']['param_groups'][0] +assert group['params'] == list(range(len(old_names))) +group['params'] = list(range(len(new_names))) +with a.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() +intervention = {'kind': 'local_to_global_trunk', 'parent_sha256': digest, + 'step': s['global_step'], 'initial_outputs_unchanged': True, + 'computation': 'average aligned local features into 1-kb tokens, zero linear projection, add before transformer'} +history = list(s.get('interventions', [])) +if s.get('intervention') and s['intervention'] not in history: + history.append(s['intervention']) +history.append(intervention) +s.update(config=config.to_dict(), model=model.state_dict(), intervention=intervention, interventions=history) +a.output.mkdir(parents=True, exist_ok=False) +(a.output/'config.yaml').write_text(yaml.safe_dump(config.to_dict())) +(a.output/'intervention.json').write_text(json.dumps(intervention, indent=2) + '\n') +_atomic_save(s, a.output/'start.pt') +print(json.dumps({'parameters': count_parameters(model), **intervention}), flush=True) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/attach_local.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/attach_local.py new file mode 100644 index 0000000000000000000000000000000000000000..aa082537004dbc398c4a78c1bd0203970ce5e42f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/attach_local.py @@ -0,0 +1,52 @@ +"""Attach a zero-initialized local residual path to a checkpoint trained in this workspace.""" +import argparse +from dataclasses import replace +import hashlib +from pathlib import Path + +import torch +import yaml + +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + p.add_argument('--width', type=int, default=32) + args = p.parse_args() + state = torch.load(args.checkpoint, map_location='cpu', weights_only=True) + assert state['schema'] == 'seqfunc-single/v1', 'only locally trained single-GPU checkpoints' + parent_config = ModelConfig(**state['config']) + assert parent_config.local_width == 0 + torch.manual_seed(state['seed']) + parent = CompactModel(parent_config) + config = replace(parent_config, local_width=args.width) + model = CompactModel(config) + result = model.load_state_dict(state['model'], strict=False) + assert not result.unexpected_keys + assert all(k.startswith(('local_path.', 'local_projection.')) for k in result.missing_keys) + parent_names = list(dict(parent.named_parameters())) + names = list(dict(model.named_parameters())) + assert names[:len(parent_names)] == parent_names, 'optimizer parameter order changed' + assert len(state['optimizer']['param_groups']) == 1 + group = state['optimizer']['param_groups'][0] + assert group['params'] == list(range(len(parent_names))) + group['params'] = list(range(len(names))) + state['model'] = model.state_dict() + state['config'] = config.to_dict() + state['intervention'] = { + 'kind': 'base_resolution_local_path', 'width': args.width, + 'parent_sha256': hashlib.sha256(args.checkpoint.read_bytes()).hexdigest(), + 'attachment_step': state['global_step'], 'initial_outputs_unchanged': True, + } + args.output.mkdir(parents=True, exist_ok=False) + (args.output / 'config.yaml').write_text(yaml.safe_dump(config.to_dict())) + _atomic_save(state, args.output / 'attached.pt') + print({'parameters': count_parameters(model), **state['intervention']}) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/change_residual_cap.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/change_residual_cap.py new file mode 100644 index 0000000000000000000000000000000000000000..cd282275702aaf5d5eede4b3904add879aad846a --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/change_residual_cap.py @@ -0,0 +1,40 @@ +"""Change only a parameter-free branch bound on our own trained checkpoint.""" +import argparse +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import torch +import yaml +from reference.alphagenome_50m.model import CompactModel, ModelConfig +from reference.alphagenome_50m.train_eval import _atomic_save + +p = argparse.ArgumentParser(description=__doc__) +p.add_argument('checkpoint', type=Path) +p.add_argument('output', type=Path) +p.add_argument('--cap', type=float, default=0.1) +a = p.parse_args() +s = torch.load(a.checkpoint, map_location='cpu', weights_only=True) +config = ModelConfig(**s['config']) +assert config.residual_rms_cap == 0 and a.cap > 0 +config = replace(config, residual_rms_cap=a.cap) +model = CompactModel(config) +model.load_state_dict(s['model'], strict=True) +assert len(s['optimizer']['param_groups'][0]['params']) == len(list(model.parameters())) +assert all(torch.equal(v, s['model'][k]) for k, v in model.state_dict().items()) +with a.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() +change = {'kind': 'bounded_transformer_residuals', 'parent_sha256': digest, + 'step': s['global_step'], 'cap': a.cap, + 'parameters_optimizer_rng_unchanged': True, + 'computation': 'each attention and MLP update multiplied by min(1, cap/token_RMS)'} +history = list(s.get('interventions', [])) +if s.get('intervention') and s['intervention'] not in history: + history.append(s['intervention']) +history.append(change) +s.update(config=config.to_dict(), intervention=change, interventions=history) +a.output.mkdir(parents=True, exist_ok=False) +(a.output/'config.yaml').write_text(yaml.safe_dump(config.to_dict())) +(a.output/'intervention.json').write_text(json.dumps(change, indent=2) + '\n') +_atomic_save(s, a.output/'start.pt') +print(json.dumps(change), flush=True) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/decoding_policy.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/decoding_policy.json new file mode 100644 index 0000000000000000000000000000000000000000..b91d9d7a3a86f14d40ac35e0b6a5e7076524db13 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/decoding_policy.json @@ -0,0 +1,9 @@ +{ + "kind": "junction_transcript_direction", + "parent_package": "/workspace/research/experiments/scale13-small-epoch01/raw-package", + "weights_unchanged": true, + "plus": "donor < acceptor", + "minus": "donor > acceptor", + "inference_inputs": "model-selected coordinates and public track strand halves only", + "training_evidence": "64 training windows: 30,616 positive junction queries, all in transcript direction" +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/infer.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/infer.sh new file mode 100644 index 0000000000000000000000000000000000000000..2a412a9ad8667d6dcb94bb57d9959d61d43ddd34 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/infer.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: infer.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/predict.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/perturb.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/perturb.py new file mode 100644 index 0000000000000000000000000000000000000000..e0416692d2ee7deb1a25927397f725eec54d16a9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/perturb.py @@ -0,0 +1,101 @@ +"""Predict both sequences of every perturbation (contract: perturbations/v1). + + python perturb.py INPUT_ROOT OUTPUT_ROOT --checkpoint last.pt + +INPUT_ROOT holds `public/perturbations.parquet` and `reference/GRCh38.p13.genome.fa` (in the +workspace, `/workspace/data` works too: the queries are then read from `variant_evals/public/`). Each row is a +1,048,576-bp window starting at `input_start` and an edit spanning [edit_start, edit_end) in genomic +0-based coordinates: + +- `snv`: sequence A carries `allele_a` at edit_start, sequence B carries `allele_b` (both are written + explicitly; either may differ from the reference genome); +- `mask`: sequence A is the reference, sequence B has the interval replaced by N. + +For each perturbation the output group `perturbations/p{index:06d}` holds `a/` and +`b/` for every family in `families`: dense families sliced to bins +[dense_bin_start, dense_bin_end) of the central target window, and `splice_sites` sliced to bp +[splice_start, splice_end). Values are the same quantities as predictions.zarr (asinh space for +dense tracks, splice-site scores for splice_sites). +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import pandas as pd +import pysam +import torch +import zarr + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig + +PERTURBATION_SCHEMA = "genome-sequence-to-function-perturbations/v1" +COMPRESSOR = Blosc(cname="zstd", clevel=3, shuffle=Blosc.BITSHUFFLE) + + +def sequences(fasta, row) -> tuple[str, str]: + length = fasta.get_reference_length(row.chrom) + start, end = int(row.input_start), int(row.input_start) + 1_048_576 + sequence = fasta.fetch(row.chrom, max(0, start), min(length, end)).upper() + sequence = "N" * max(0, -start) + sequence + "N" * max(0, end - length) + lo, hi = int(row.edit_start) - start, int(row.edit_end) - start + if row.edit == "snv": + return sequence[:lo] + row.allele_a + sequence[hi:], sequence[:lo] + row.allele_b + sequence[hi:] + if row.edit == "mask": + return sequence, sequence[:lo] + "N" * (hi - lo) + sequence[hi:] + raise ValueError(f"unknown edit {row.edit!r}") + + +def main(input_root: Path, output_root: Path, checkpoint: Path, queries: Path | None = None) -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + if queries is None: + queries = input_root / "public/perturbations.parquet" + if not queries.exists(): + queries = input_root / "variant_evals/public/perturbations.parquet" + rows = pd.read_parquet(queries) + fasta = pysam.FastaFile(str(input_root / "reference/GRCh38.p13.genome.fa")) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "perturbations.zarr"), mode="w") + target_start = (config.input_length - config.target_length) // 2 + with torch.inference_mode(): + for index, row in enumerate(rows.itertuples(index=False)): + pair = sequences(fasta, row) + dna = torch.from_numpy(np.stack([AlphaGenomeDataset._one_hot(s) for s in pair])).to(device) + trunk = model._trunk(dna) + group = root.create_group(f"perturbations/p{index:06d}") + features = None + for family in row.families: + if family == "splice_sites": + logits = model._splice_site_logits(trunk)[:, target_start:target_start + config.target_length] + values = logits[:, row.splice_start:row.splice_end] + else: + features = model._dense_features(trunk) if features is None else features + values = model.dense_heads[family](features)[:, row.dense_bin_start:row.dense_bin_end] + # float32: one variant's effect is often below float16 resolution (about 0.004 at + # asinh values of 4-8), so A and B would round to the same numbers. + values = values.float().cpu().numpy() + for side, array in zip("ab", values): + group.create_dataset(f"{side}/{family}", data=array, compressor=COMPRESSOR) + print(json.dumps({"event": "perturb", "done": index + 1, "total": len(rows)}), flush=True) + root.attrs.update({"schema": PERTURBATION_SCHEMA, + "perturbation_ids": rows.perturbation_id.astype(str).tolist()}) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--queries", type=Path, help="another query table, e.g. the development split") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.queries) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/predict.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..ee5863c429777d27a02e4c88e05b2120fafe6b89 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/predict.py @@ -0,0 +1,134 @@ +#!/usr/bin/env python3 +"""Stream the single benchmark model into the sharded prediction contract.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import torch +import zarr + +from prediction_contract import DENSE_TRACKS, MAX_JUNCTION_PAIRS, MAX_USAGE_SITES, PREDICTION_SCHEMA +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig + + +COMPRESSOR = Blosc(cname="zstd", clevel=7, shuffle=Blosc.BITSHUFFLE) + + +def main(input_root: Path, output_root: Path, checkpoint: Path, split: str = "test") -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + dataset = AlphaGenomeDataset(input_root, split, include_targets=False) + model.set_junction_track_halves(dataset.junction_track_halves) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "predictions.zarr"), mode="w") + ids = [] + with torch.inference_mode(): + for sample in dataset: + example_id = sample["example_id"] + dna = sample["dna"][None].to(device) + trunk = model._trunk(dna) + dense_features = model._dense_features(trunk) + group = root.create_group(f"examples/{example_id}") + for family in DENSE_TRACKS: + values = model.dense_heads[family](dense_features)[0] + group.create_dataset(family, data=values.cpu().numpy().astype(np.float16), compressor=COMPRESSOR) + + all_site_logits = model._splice_site_logits(trunk) + target_start = (config.input_length - config.target_length) // 2 + group.create_dataset( + "splice_sites", + data=all_site_logits[0, target_start:target_start + config.target_length].cpu().numpy().astype(np.float16), + chunks=(4096, 4), compressor=COMPRESSOR, + ) + predicted_positions = model.select_junction_positions(all_site_logits)[0] + + # Usage at the model's own predicted sites (v4 is sparse; unlisted hidden sites score 0). + usage_bins = model.splice_usage_head(dense_features)[0].float() + # Usage has its own central-window budget; junction axes are unchanged. + central_scores = all_site_logits[0, target_start:target_start + config.target_length].amax(dim=-1) + count = min(MAX_USAGE_SITES, config.target_length) + sites = central_scores.topk(count).indices.sort().values.cpu().numpy().astype(np.int64) + group.create_dataset("splice_usage_positions", data=sites.astype(np.int32), compressor=COMPRESSOR) + usage = usage_bins[torch.as_tensor(sites // 128, device=usage_bins.device)] if len(sites) \ + else usage_bins[:0] + group.create_dataset("splice_usage", data=usage.cpu().numpy().astype(np.float16), + chunks=(1024, 734), compressor=COMPRESSOR) + + # Junctions: score every donor x acceptor pair of each strand, keep the strongest pairs. + positions_batch = predicted_positions[None] + site_factors = model.junction_site_factors(trunk, positions_batch) + halves = model.junction_track_half + candidates = [] + for row_start in range(0, 512, 128): + for column_start in range(0, 512, 128): + block = torch.cat([ + model.junction_block( + trunk, positions_batch, row_start, row_start + 128, + column_start, column_start + 128, track_start, + min(track_start + 64, config.junction_tracks), site_factors, + )[0] + for track_start in range(0, config.junction_tracks, 64) + ], dim=-1).float() # [128, 128, 734] + for half in (0, 1): + donors = predicted_positions[2 * half, row_start:row_start + 128] + acceptors = predicted_positions[2 * half + 1, column_start:column_start + 128] + valid = (donors[:, None] >= 0) & (acceptors[None, :] >= 0) + # Transcript direction is determined by the public strand half. + forward = donors[:, None] < acceptors[None, :] if half == 0 else donors[:, None] > acceptors[None, :] + valid = valid & forward + strength = block[..., halves == half].amax(dim=-1).masked_fill(~valid, -torch.inf) + keep = min(MAX_JUNCTION_PAIRS // 2, int(valid.sum())) + if not keep: + continue + best = strength.flatten().topk(keep) + rows, columns = best.indices // 128, best.indices % 128 + candidates.append((best.values, torch.full_like(rows, half), donors[rows], + acceptors[columns], block[rows, columns])) + if candidates: + score, half, donor, acceptor, values = (torch.cat(x) for x in zip(*candidates)) + order = score.topk(min(MAX_JUNCTION_PAIRS, len(score))).indices + half, donor, acceptor, values = half[order], donor[order], acceptor[order], values[order] + else: + half = donor = acceptor = torch.zeros(0, dtype=torch.long) + values = torch.zeros(0, config.junction_tracks) + group.create_dataset("junction_half", data=half.cpu().numpy().astype(np.int8), compressor=COMPRESSOR) + group.create_dataset("junction_donor", data=donor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junction_acceptor", data=acceptor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junctions", data=values.cpu().numpy().astype(np.float16), + chunks=(4096, 734), compressor=COMPRESSOR) + + contact = trunk.reshape( + 1, config.contact_bins, -1, config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + contact_values = 0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row]) + group.create_dataset( + "contact_maps", data=contact_values[0].cpu().numpy().astype(np.float16), + chunks=(8192, 28), compressor=COMPRESSOR, + ) + ids.append(example_id) + print(json.dumps({"event": "predict", "example": len(ids), "total": len(dataset)}), flush=True) + root.attrs.update({"schema": PREDICTION_SCHEMA, "example_ids": ids}) + (output_root / "example_ids.json").write_text(json.dumps(ids) + "\n") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--split", default="test", choices=("valid", "test"), + help="manifest to predict; the verifier uses test, local validation uses valid") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.split) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4879a0e1a3a8a8050bb48360febee152cd141be8 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/__init__.py @@ -0,0 +1 @@ +"""The reference model of the alphagenome-reference baseline.""" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a86a139babb6e899bd5102bbf3c628e1232576b2 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/__init__.py @@ -0,0 +1,5 @@ +"""Compact 1-Mb, eleven-family AlphaGenome reference model.""" + +from .model import CompactModel, ModelConfig, count_parameters + +__all__ = ["CompactModel", "ModelConfig", "count_parameters"] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/alphagenome_50m.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/alphagenome_50m.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0a59a4d0c65752d45eb464d129c6ec9d559665e9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/alphagenome_50m.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 32 +channel_growth: 32 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 384 +num_sequence_blocks: 6 +num_attention_heads: 6 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 256 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/dataset.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..155ef4138af508a739d6af8d7cd283308fb7b4a9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/dataset.py @@ -0,0 +1,293 @@ +"""Streaming reader for the compact 1 Mb / eleven-family release.""" + +from __future__ import annotations + +from collections import OrderedDict +from pathlib import Path + +import numpy as np +import pandas as pd +import pysam +import torch +from torch.utils.data import Dataset +import zarr + + +import os +from concurrent.futures import ThreadPoolExecutor + + +_pools = {} + + +def process_pool(workers): + key = (os.getpid(), workers) + if key not in _pools: + _pools[key] = ThreadPoolExecutor(max_workers=workers, thread_name_prefix='zarr-read') + return _pools[key] + + +class DirectReadDirectoryStore(zarr.DirectoryStore): + def __getitem__(self, key): + key = self._normalize_key(key) + try: + return self._fromfile(os.path.join(self.path, key)) + except (FileNotFoundError, IsADirectoryError, NotADirectoryError): + raise KeyError(key) from None + + def getitems(self, keys, *, contexts): + result = {} + for key in keys: + try: + result[key] = self[key] + except KeyError: + pass + return result + + def __setitem__(self, key, value): + raise PermissionError('DirectReadDirectoryStore is read-only') + + def __delitem__(self, key): + raise PermissionError('DirectReadDirectoryStore is read-only') + + def is_writeable(self): + return False + + def is_erasable(self): + return False + + +class ParallelDirectReadDirectoryStore(DirectReadDirectoryStore): + """Bounded I/O threads shared by all read stores within one loader process.""" + def __init__(self, path, *, io_workers=4): + super().__init__(path) + if io_workers < 1: + raise ValueError('io_workers must be positive') + self.io_workers = io_workers + + def getitems(self, keys, *, contexts): + def fetch(key): + try: + return key, self[key] + except KeyError: + return key, None + if self.io_workers == 1: + return super().getitems(keys, contexts=contexts) + return {key: value for key, value in process_pool(self.io_workers).map(fetch, keys) + if value is not None} + + +def _open_part(path: Path): + if str(path).endswith(".zarr.zip"): + store = zarr.ZipStore(str(path), mode="r") + try: + return zarr.open_group(store=store, mode="r"), store + except BaseException: + store.close() + raise + return zarr.open_group(store=ParallelDirectReadDirectoryStore(str(path), io_workers=4), mode="r"), None + + +DENSE_FAMILIES = ( + "atac", + "dnase", + "procap", + "cage", + "rna_seq", + "chip_tf", + "chip_histone", +) +def normalize_splice_positions(value) -> np.ndarray: + """Normalize Arrow's nested-list representation to the public [4, 512] array.""" + + outer = np.asarray(value, dtype=object).reshape(-1).tolist() + positions = np.stack([np.asarray(item, dtype=np.int64) for item in outer]) + if positions.shape != (4, 512): + raise ValueError(f"splice_site_positions must have shape (4, 512), got {positions.shape}") + return positions + + +class AlphaGenomeDataset(Dataset): + """Resolve frozen coordinates to reference DNA and one Zarr example group.""" + + def __init__( + self, + data_root: str | Path, + split: str, + *, + max_open_parts: int = 8, + include_targets: bool = True, + ) -> None: + self.root = Path(data_root) + self.split = split + manifest = self.root / "public" / f"{split}.parquet" + if not manifest.is_file(): + raise FileNotFoundError(f"Missing materialized manifest: {manifest}") + self.rows = pd.read_parquet(manifest).reset_index(drop=True) + junction_tracks = pd.read_parquet(self.root / "public/junction_tracks.parquet") + junction_tracks = junction_tracks.sort_values("tensor_index") + self.junction_track_halves = torch.from_numpy( + junction_tracks.junction_channel_half.to_numpy(np.int64) + ) + if include_targets: + labels_path = self.root / "labels" / f"{split}.parquet" + if not labels_path.is_file(): + raise FileNotFoundError(f"Missing trusted labels manifest: {labels_path}") + labels = pd.read_parquet(labels_path).reset_index(drop=True) + if self.rows.example_id.astype(str).tolist() != labels.example_id.astype(str).tolist(): + raise ValueError("public and labels manifests have different IDs/order") + self.rows = self.rows.merge(labels, on="example_id", validate="one_to_one", sort=False) + self.fasta_path = self.root / "reference" / "GRCh38.p13.genome.fa" + self.max_open_parts = max_open_parts + self.include_targets = include_targets + self._fasta = None + self._parts: OrderedDict[str, tuple[zarr.Group, object | None]] = OrderedDict() + + def __len__(self) -> int: + return len(self.rows) + + def _reference(self): + if self._fasta is None: + self._fasta = pysam.FastaFile(str(self.fasta_path)) + return self._fasta + + def _part(self, relative: str): + if relative in self._parts: + opened = self._parts.pop(relative) + self._parts[relative] = opened + return opened[0] + group, store = _open_part(self.root / relative) + if len(self._parts) >= self.max_open_parts: + _, (_, old_store) = self._parts.popitem(last=False) + if old_store is not None: + old_store.close() + self._parts[relative] = (group, store) + return group + + @staticmethod + def _one_hot(sequence: str) -> np.ndarray: + encoded = np.frombuffer(sequence.upper().encode("ascii"), dtype=np.uint8) + output = np.zeros((len(encoded), 4), dtype=np.float32) + for index, letter in enumerate(b"ACGT"): + output[:, index] = encoded == letter + return output + + def _sequence(self, row) -> tuple[np.ndarray, np.ndarray]: + reference = self._reference() + chromosome_length = reference.get_reference_length(str(row.chromosome)) + start, end = int(row.input_start), int(row.input_end) + clipped_start, clipped_end = max(0, start), min(chromosome_length, end) + sequence = reference.fetch(str(row.chromosome), clipped_start, clipped_end) + left, right = clipped_start - start, end - clipped_end + if left or right: + sequence = "N" * left + sequence + "N" * right + valid = np.ones(end - start, dtype=bool) + if left: + valid[:left] = False + if right: + valid[-right:] = False + return self._one_hot(sequence), valid + + @staticmethod + def _decode(example, name: str, *, asinh: bool) -> np.ndarray: + """Train targets may be stored as 8-bit codes with per-track ranges; they are decoded + here, one example at a time, so the release stays compressed on disk.""" + array = example[name] + if array.dtype != np.uint8: + return np.asarray(array, dtype=np.float32) + lo = np.asarray(example[f"{name}_lo"], dtype=np.float32) + hi = np.asarray(example[f"{name}_hi"], dtype=np.float32) + x = lo + np.asarray(array, dtype=np.float32) * ((hi - lo) / 255.0) + return np.sinh(x) if asinh else x + + @staticmethod + def _dense_target(example, family: str) -> dict[str, torch.Tensor]: + values = AlphaGenomeDataset._decode(example, f"{family}/values", asinh=True) + mask = np.asarray(example[f"{family}/mask"], dtype=bool) + if mask.ndim == 1: + mask = np.broadcast_to(mask[None, :], values.shape).copy() + return { + "values": torch.from_numpy(values), + "mask": torch.from_numpy(mask), + } + + def __getitem__(self, item: int): + row = self.rows.iloc[item] + dna, input_mask = self._sequence(row) + result = { + "example_id": str(row.example_id), + "dna": torch.from_numpy(dna), + "input_mask": torch.from_numpy(input_mask), + "junction_track_halves": self.junction_track_halves, + } + if not self.include_targets: + return result + + part = self._part(str(row.target_shard)) + example = part[str(row.target_group)] + result["dense"] = { + family: self._dense_target(example, family) for family in DENSE_FAMILIES + } + result["splice_sites"] = torch.from_numpy( + np.asarray(example["splice_sites/values"], dtype=np.float32) + ) + result["splice_usage"] = { + "positions": torch.from_numpy( + np.asarray(example["splice_site_usage/positions"], dtype=np.int64) + ), + "values": torch.from_numpy( + np.asarray(example["splice_site_usage/values"], dtype=np.float32) + ), + "mask": torch.from_numpy( + np.asarray(example["splice_site_usage/mask"], dtype=bool) + ), + } + result["usage_positions"] = result["splice_usage"]["positions"] + result["junctions"] = { + name: torch.from_numpy( + np.asarray( + example[f"splice_junctions/{name}"], + dtype=np.float32 if name == "value" else np.int64, + ) + ) + for name in ("row", "column", "track", "value") + } + result["junctions"]["site_positions"] = torch.from_numpy( + np.asarray(example["splice_junctions/site_positions"], dtype=np.int64) + ) + site_positions = normalize_splice_positions(row.splice_site_positions) + result["site_positions"] = torch.from_numpy(site_positions) + if not torch.equal(result["junctions"]["site_positions"], result["site_positions"]): + raise ValueError(f"public/private splice positions differ for {row.example_id}") + scoring_path = self.root / "scoring" / self.split / f"part-{item:06d}.parquet" + scoring = pd.read_parquet(scoring_path) + if not (scoring.example_id.astype(str) == str(row.example_id)).all(): + raise ValueError(f"scoring part ID differs for {row.example_id}") + result["junction_scoring"] = { + "donor_position": torch.from_numpy( + scoring.donor_position.to_numpy(np.int64) + ), + "acceptor_position": torch.from_numpy( + scoring.acceptor_position.to_numpy(np.int64) + ), + "track": torch.from_numpy(scoring.track.to_numpy(np.int64)), + "target_value": torch.from_numpy( + scoring.target_value.to_numpy(np.float32) + ), + "valid": torch.from_numpy(scoring.valid.to_numpy(bool)), + } + result["contact_maps"] = torch.from_numpy( + self._decode(example, "contact_maps/upper_triangle", asinh=False) + ) + result["contact_map_mask"] = torch.from_numpy( + np.asarray(example["contact_maps/mask"], dtype=bool) + ) + return result + + +def single_example_collate(items): + """Keep variable-size event tensors intact; the reference currently trains at batch one.""" + + if len(items) != 1: + raise ValueError("The compact event loader currently requires batch_size=1") + return items[0] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/layers.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..7ef7b4e45a81eb774e4e92ac36e30c5abdebbe46 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/layers.py @@ -0,0 +1,134 @@ +"""Shared neural-network layers for the single compact AlphaGenome model.""" + +from __future__ import annotations + +import math + +import torch +from torch import Tensor, nn +from torch.nn import functional as F + + +class ChannelRMSNorm(nn.Module): + def __init__(self, channels: int, eps: float = 1e-6): + super().__init__() + self.weight = nn.Parameter(torch.ones(channels)) + self.eps = eps + + def forward(self, x: Tensor) -> Tensor: + variance = x.float().square().mean(dim=1, keepdim=True) + normalized = x * torch.rsqrt(variance + self.eps).to(x.dtype) + return normalized * self.weight[None, :, None].to(x.dtype) + + +class StandardizedConv1d(nn.Conv1d): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__(in_channels, out_channels, kernel_size, padding="same", bias=True) + self.scale = nn.Parameter(torch.ones(out_channels)) + nn.init.trunc_normal_(self.weight, std=1.0 / math.sqrt(in_channels * kernel_size)) + nn.init.trunc_normal_(self.bias, std=1e-4) + + def forward(self, x: Tensor) -> Tensor: + centered = self.weight - self.weight.mean(dim=(1, 2), keepdim=True) + variance = centered.float().square().mean(dim=(1, 2), keepdim=True) + fan_in = self.weight.shape[1] * self.weight.shape[2] + multiplier = self.scale[:, None, None] * torch.rsqrt( + (fan_in * variance).clamp_min(1e-4) + ).to(self.weight.dtype) + return F.conv1d(x, centered * multiplier, self.bias, padding="same") + + +class ConvBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__() + self.norm = ChannelRMSNorm(in_channels) + self.conv = StandardizedConv1d(in_channels, out_channels, kernel_size) + + def forward(self, x: Tensor) -> Tensor: + return self.conv(F.gelu(self.norm(x), approximate="tanh")) + + +def _apply_rope(x: Tensor) -> Tensor: + rotary_dim = x.shape[-1] - x.shape[-1] % 2 + if rotary_dim == 0: + return x + positions = torch.arange(x.shape[-2], device=x.device, dtype=torch.float32) + inverse_frequency = 1.0 / ( + 10_000 ** ( + torch.arange(0, rotary_dim, 2, device=x.device, dtype=torch.float32) + / rotary_dim + ) + ) + angles = torch.outer(positions, inverse_frequency) + cos = angles.cos()[None, None, :, :].to(x.dtype) + sin = angles.sin()[None, None, :, :].to(x.dtype) + even, odd = x[..., :rotary_dim:2], x[..., 1:rotary_dim:2] + rotated = torch.stack((even * cos - odd * sin, even * sin + odd * cos), dim=-1) + return torch.cat((rotated.flatten(-2), x[..., rotary_dim:]), dim=-1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.num_heads = config.num_attention_heads + self.head_dim = config.attention_head_dim + inner_dim = self.num_heads * self.head_dim + self.norm = nn.RMSNorm(config.hidden_dim) + self.qkv = nn.Linear(config.hidden_dim, 3 * inner_dim, bias=False) + self.output = nn.Linear(inner_dim, config.hidden_dim) + self.dropout = config.dropout + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + batch, length, _ = x.shape + qkv = self.qkv(self.norm(x)).view( + batch, length, 3, self.num_heads, self.head_dim + ) + q, k, value = qkv.unbind(dim=2) + attended = F.scaled_dot_product_attention( + _apply_rope(q.transpose(1, 2)), + _apply_rope(k.transpose(1, 2)), + value.transpose(1, 2), + attn_mask=attention_bias, + dropout_p=self.dropout if self.training else 0.0, + ) + return self.output(attended.transpose(1, 2).reshape(batch, length, -1)) + + +class MLPBlock(nn.Module): + def __init__(self, config): + super().__init__() + expanded = int(config.hidden_dim * config.mlp_expansion) + self.norm = nn.RMSNorm(config.hidden_dim) + self.input = nn.Linear(config.hidden_dim, expanded) + self.output = nn.Linear(expanded, config.hidden_dim) + self.dropout = nn.Dropout(config.dropout) + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor) -> Tensor: + return self.dropout( + self.output(F.gelu(self.input(self.norm(x)), approximate="tanh")) + ) + + +def bound_residual_rms(update: Tensor, cap: float) -> Tensor: + """Cap each token's branch RMS, preserving small updates and their directions.""" + if cap == 0: + return update + rms = update.float().square().mean(dim=-1, keepdim=True).clamp_min(1e-12).sqrt() + scale = (cap / rms).clamp(max=1.0) + return update * scale.to(update.dtype) + + +class SequenceBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.attention = MultiHeadAttention(config) + self.mlp = MLPBlock(config) + self.residual_rms_cap = config.residual_rms_cap + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + x = x + bound_residual_rms(self.attention(x, attention_bias), self.residual_rms_cap) + return x + bound_residual_rms(self.mlp(x), self.residual_rms_cap) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/model.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/model.py new file mode 100644 index 0000000000000000000000000000000000000000..6f211fc29de3fa559db45ce6c2ee3d40816f4a72 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/model.py @@ -0,0 +1,460 @@ +"""Affordable multi-resolution baseline for the compact AlphaGenome task.""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass, fields +import math +from pathlib import Path +from typing import Any, Iterable + +import torch +from torch import Tensor, nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint +import yaml + +from .layers import ChannelRMSNorm, ConvBlock, SequenceBlock + + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} +ALL_FAMILIES = (*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions", "contact_maps") + + +@dataclass(frozen=True) +class ModelConfig: + input_length: int = 1_048_576 + target_length: int = 196_608 + dense_bin_size: int = 128 + trunk_bin_size: int = 1_024 + contact_bin_size: int = 2_048 + stem_stride: int = 8 + encoder_width: int = 32 + channel_growth: int = 32 + num_downsampling_blocks: int = 7 + conv_kernel_size: int = 5 + dna_kernel_size: int = 15 + hidden_dim: int = 384 + num_sequence_blocks: int = 6 + num_attention_heads: int = 6 + attention_head_dim: int = 64 + mlp_expansion: float = 4.0 + dropout: float = 0.0 + head_hidden_size: int = 256 + junction_rank: int = 32 + splice_usage_tracks: int = 734 + junction_tracks: int = 734 + contact_tracks: int = 28 + gradient_checkpointing: bool = True + enable_pairwise: bool = False + pair_width: int = 32 + pair_downsample: int = 16 + pair_update_interval: int = 2 + local_width: int = 0 + local_context_blocks: int = 4 + splice_context_mode: str = 'offset' + local_to_trunk: bool = False + residual_rms_cap: float = 0.0 + + def __post_init__(self) -> None: + if not 1 <= self.local_context_blocks <= 7: + raise ValueError('local_context_blocks must be between 1 and 7') + if not math.isfinite(self.residual_rms_cap) or self.residual_rms_cap < 0: + raise ValueError('residual_rms_cap must be finite and nonnegative') + if self.local_to_trunk and not self.local_width: + raise ValueError('local_to_trunk requires local features') + if self.splice_context_mode not in ('offset', 'uniform'): + raise ValueError('unknown splice_context_mode') + if self.splice_context_mode == 'uniform' and not self.local_width: + raise ValueError('uniform splice context requires a base-resolution path') + downsample = self.stem_stride * 2**self.num_downsampling_blocks + if downsample != self.trunk_bin_size: + raise ValueError("stem_stride * 2**num_downsampling_blocks must equal trunk_bin_size") + for length in (self.input_length, self.target_length): + if length % self.trunk_bin_size: + raise ValueError("input and target lengths must divide by trunk_bin_size") + if self.trunk_bin_size % self.dense_bin_size: + raise ValueError("trunk_bin_size must divide by dense_bin_size") + if self.input_length % self.contact_bin_size: + raise ValueError("input_length must divide by contact_bin_size") + if self.hidden_dim <= 0 or self.num_attention_heads <= 0: + raise ValueError("hidden dimensions and attention heads must be positive") + + @property + def trunk_bins(self) -> int: + return self.input_length // self.trunk_bin_size + + @property + def target_trunk_bins(self) -> int: + return self.target_length // self.trunk_bin_size + + @property + def dense_bins(self) -> int: + return self.target_length // self.dense_bin_size + + @property + def dense_upsample(self) -> int: + return self.trunk_bin_size // self.dense_bin_size + + @property + def contact_bins(self) -> int: + return self.input_length // self.contact_bin_size + + @property + def encoder_output_width(self) -> int: + return self.encoder_width + self.channel_growth * self.num_downsampling_blocks + + @classmethod + def from_yaml(cls, path: str | Path) -> "ModelConfig": + values: dict[str, Any] = yaml.safe_load(Path(path).read_text()) or {} + known = {field.name for field in fields(cls)} + unknown = sorted(values.keys() - known) + if unknown: + raise ValueError(f"Unknown model configuration keys: {unknown}") + return cls(**values) + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + +class SequenceEncoder(nn.Module): + def __init__(self, config: ModelConfig): + super().__init__() + self.stem = nn.Conv1d( + 4, + config.encoder_width, + config.dna_kernel_size, + stride=config.stem_stride, + padding=config.dna_kernel_size // 2, + ) + blocks = [] + width = config.encoder_width + for _ in range(config.num_downsampling_blocks): + output_width = width + config.channel_growth + blocks.append(ConvBlock(width, output_width, config.conv_kernel_size)) + width = output_width + self.blocks = nn.ModuleList(blocks) + + def forward(self, dna: Tensor) -> Tensor: + x = F.gelu(self.stem(dna.transpose(1, 2)), approximate="tanh") + for block in self.blocks: + x = F.max_pool1d(block(x), kernel_size=2, stride=2) + return x.transpose(1, 2) + + +class LocalResidualBlock(nn.Module): + def __init__(self, width: int, dilation: int): + super().__init__() + self.norm = ChannelRMSNorm(width) + self.depthwise = nn.Conv1d(width, width, 9, dilation=dilation, + padding=4 * dilation, groups=width) + self.expand = nn.Conv1d(width, 2 * width, 1) + self.contract = nn.Conv1d(2 * width, width, 1) + + def forward(self, x: Tensor) -> Tensor: + update = self.depthwise(self.norm(x)) + update = self.contract(F.gelu(self.expand(update), approximate='tanh')) + return x + update + + +class LocalSequencePath(nn.Module): + """Base-resolution motif path with powers-of-four dilation and aligned pooling.""" + def __init__(self, config: ModelConfig): + super().__init__() + width = config.local_width + self.stem = nn.Conv1d(4, width, 21, padding=10) + self.blocks = nn.Sequential(*(LocalResidualBlock(width, 4**i) + for i in range(config.local_context_blocks))) + self.norm = ChannelRMSNorm(width) + self.sites = nn.Conv1d(width, 4, 1) + self.bin_size = config.dense_bin_size + self.gradient_checkpointing = config.gradient_checkpointing + + def forward(self, dna: Tensor) -> tuple[Tensor, Tensor]: + base = F.gelu(self.stem(dna), approximate='tanh') + for block in self.blocks: + if self.gradient_checkpointing and self.training: + base = checkpoint(block, base, use_reentrant=False) + else: + base = block(base) + base = F.gelu(self.norm(base), approximate='tanh') + sites = self.sites(base).transpose(1, 2) + pooled = torch.cat((F.avg_pool1d(base, self.bin_size), + F.max_pool1d(base, self.bin_size)), dim=1).transpose(1, 2) + return sites, pooled + + +class CompactModel(nn.Module): + """One shared 1-kb trunk with representation-specific output heads.""" + + def __init__(self, config: ModelConfig | None = None): + super().__init__() + self.config = config or ModelConfig() + self.encoder = SequenceEncoder(self.config) + self.projection = nn.Linear(self.config.encoder_output_width, self.config.hidden_dim) + self.blocks = nn.ModuleList( + SequenceBlock(self.config) for _ in range(self.config.num_sequence_blocks) + ) + self.final_norm = nn.RMSNorm(self.config.hidden_dim) + self.dense_decoder = nn.Sequential( + nn.RMSNorm(self.config.hidden_dim), + nn.Linear(self.config.hidden_dim, self.config.head_hidden_size), + nn.GELU(approximate="tanh"), + ) + self.dense_heads = nn.ModuleDict( + { + family: nn.Linear(self.config.head_hidden_size, tracks) + for family, tracks in DENSE_TRACKS.items() + } + ) + self.splice_site_head = nn.Linear( + self.config.head_hidden_size, self.config.dense_bin_size * 4 + ) + self.splice_usage_head = nn.Linear( + self.config.head_hidden_size, self.config.splice_usage_tracks + ) + rank = self.config.junction_rank + self.junction_source = nn.Linear(self.config.head_hidden_size, rank) + self.junction_destination = nn.Linear(self.config.head_hidden_size, rank) + self.junction_track = nn.Embedding(self.config.junction_tracks, rank) + if self.config.junction_tracks % 2: + raise ValueError("junction tracks must split evenly into strand halves") + self.register_buffer( + "junction_track_half", + torch.arange(self.config.junction_tracks) // (self.config.junction_tracks // 2), + persistent=False, + ) + self.contact_q = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + self.contact_k = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + row, column = torch.triu_indices( + self.config.contact_bins, self.config.contact_bins + ) + self.register_buffer("contact_row", row, persistent=False) + self.register_buffer("contact_column", column, persistent=False) + if self.config.local_width: + self.local_path = LocalSequencePath(self.config) + self.local_projection = nn.Linear(2 * self.config.local_width, self.config.head_hidden_size) + # A new local path can be attached to our own checkpoint without changing its initial outputs. + nn.init.zeros_(self.local_path.sites.weight) + nn.init.zeros_(self.local_path.sites.bias) + nn.init.zeros_(self.local_projection.weight) + nn.init.zeros_(self.local_projection.bias) + if self.config.local_to_trunk: + self.local_trunk_projection = nn.Linear(2 * self.config.local_width, self.config.hidden_dim) + nn.init.zeros_(self.local_trunk_projection.weight) + nn.init.zeros_(self.local_trunk_projection.bias) + self._local_sites = None + self._local_features = None + + def set_junction_track_halves(self, halves: Tensor) -> None: + halves = torch.as_tensor(halves, dtype=torch.long, device=self.junction_track_half.device) + if halves.shape != (self.config.junction_tracks,) or not torch.isin( + halves, torch.tensor([0, 1], device=halves.device) + ).all(): + raise ValueError("junction track halves must be a 734-element 0/1 mapping") + self.junction_track_half.copy_(halves) + + def _trunk(self, dna: Tensor) -> Tensor: + if dna.ndim != 3 or dna.shape[1:] != (self.config.input_length, 4): + raise ValueError( + f"Expected DNA [B,{self.config.input_length},4], got {tuple(dna.shape)}" + ) + if self.config.local_width: + sites, pooled = self.local_path(dna.transpose(1, 2)) + self._local_sites = sites + self._local_features = self.local_projection(pooled) + x = self.projection(self.encoder(dna)) + if self.config.local_to_trunk: + local_tokens = pooled.reshape(len(dna), self.config.trunk_bins, + self.config.dense_upsample, -1).mean(dim=2) + x = x + self.local_trunk_projection(local_tokens) + for block in self.blocks: + if self.config.gradient_checkpointing and self.training and x.requires_grad: + x = checkpoint(block, x, None, use_reentrant=False) + else: + x = block(x, None) + return self.final_norm(x) + + def _dense_features(self, trunk: Tensor) -> Tensor: + start = (self.config.trunk_bins - self.config.target_trunk_bins) // 2 + target = trunk[:, start : start + self.config.target_trunk_bins] + target = target.repeat_interleave(self.config.dense_upsample, dim=1) + features = self.dense_decoder(target) + if self.config.local_width: + first_bin = (self.config.input_length - self.config.target_length) // (2 * self.config.dense_bin_size) + features = features + self._local_features[:, first_bin:first_bin + self.config.dense_bins] + return features + + def _all_dense_features(self, trunk: Tensor) -> Tensor: + features = self.dense_decoder( + trunk.repeat_interleave(self.config.dense_upsample, dim=1) + ) + return features + self._local_features if self.config.local_width else features + + def _splice_site_logits(self, trunk: Tensor) -> Tensor: + if self.config.splice_context_mode == 'uniform': + # The global trunk supplies one gene-context bias per class and kb. Base-resolution + # localization comes from the local path, with no arbitrary 128-bp offset decoder + # and no pooled dense-feature contribution to site logits. + context = self.splice_site_head(self.dense_decoder(trunk)) + context = context.view(len(trunk), self.config.trunk_bins, self.config.dense_bin_size, 4).mean(dim=2) + logits = context.repeat_interleave(self.config.trunk_bin_size, dim=1) + self._local_sites + return logits.float() + logits = self.splice_site_head(self._all_dense_features(trunk)) + logits = logits.view(len(trunk), self.config.input_length, 4) + if self.config.local_width: + logits = logits + self._local_sites + return logits.float() + + def _position_features(self, trunk: Tensor, positions: Tensor) -> Tensor: + features = self.dense_decoder(self._gather_bins(trunk, positions, self.config.trunk_bin_size)) + if self.config.local_width: + features = features + self._gather_bins(self._local_features, positions, self.config.dense_bin_size) + return features + + @staticmethod + def select_junction_positions(site_logits: Tensor, limit: int = 512) -> Tensor: + """Select each site type from model scores alone, then sort by coordinate. + + This follows the four independent site classes in Google DeepMind's + AlphaGenome splicing implementation (pinned in docs/v3-author/PROTOCOL.md). Padding is + -1 when an input is shorter than ``limit``. + """ + count = min(limit, site_logits.shape[1]) + selected = [] + for site_type in range(4): + coordinates = torch.topk(site_logits[..., site_type], count, dim=1).indices + coordinates = coordinates.sort(dim=1).values + if count < limit: + coordinates = F.pad(coordinates, (0, limit - count), value=-1) + selected.append(coordinates) + return torch.stack(selected, dim=1) + + @staticmethod + def _gather_bins(features: Tensor, positions: Tensor, bin_size: int) -> Tensor: + indices = torch.div(positions, bin_size, rounding_mode="floor") + indices = indices.clamp(0, features.shape[1] - 1) + return torch.gather( + features, + 1, + indices[..., None].expand(-1, -1, features.shape[-1]), + ) + + def _junction_predictions( + self, trunk: Tensor, site_positions: Tensor, queries: Tensor + ) -> Tensor: + row, column, track = queries.unbind(dim=-1) + half = self.junction_track_half[track] + batch = torch.arange(len(trunk), device=trunk.device)[:, None] + donor = site_positions[batch, 2 * half, row] + acceptor = site_positions[batch, 2 * half + 1, column] + source = self._position_features(trunk, donor.clamp(0, self.config.input_length - 1)) + destination = self._position_features(trunk, acceptor.clamp(0, self.config.input_length - 1)) + interaction = self.junction_source(source) * self.junction_destination(destination) + track_embedding = self.junction_track(track) + return (interaction * track_embedding).sum(dim=-1) / math.sqrt( + self.config.junction_rank + ) + + def junction_block( + self, + trunk: Tensor, + site_positions: Tensor, + row_start: int, + row_end: int, + column_start: int, + column_end: int, + track_start: int, + track_end: int, + site_factors: tuple[Tensor, Tensor] | None = None, + ) -> Tensor: + """Compute a bounded block of the full predicted-position junction cube.""" + tracks = torch.arange(track_start, track_end, device=trunk.device) + halves = self.junction_track_half[tracks] + if site_factors is None: + site_factors = self.junction_site_factors(trunk, site_positions) + source_all, destination_all = site_factors + source = source_all[:, halves, row_start:row_end] + destination = destination_all[:, halves, column_start:column_end] + embedding = self.junction_track(tracks)[None, :, None, None, :] + values = ( + source[:, :, :, None, :] * destination[:, :, None, :, :] * embedding + ).sum(dim=-1) / math.sqrt(self.config.junction_rank) + return values.permute(0, 2, 3, 1).float() + + def junction_site_factors( + self, trunk: Tensor, site_positions: Tensor + ) -> tuple[Tensor, Tensor]: + """Precompute the two strand-specific factors reused by all cube blocks.""" + donor = torch.stack((site_positions[:, 0], site_positions[:, 2]), dim=1).clamp_min(0) + acceptor = torch.stack((site_positions[:, 1], site_positions[:, 3]), dim=1).clamp_min(0) + donor_features = self._position_features(trunk, donor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, donor.shape[-1], -1) + acceptor_features = self._position_features(trunk, acceptor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, acceptor.shape[-1], -1) + return self.junction_source(donor_features), self.junction_destination(acceptor_features) + + def forward( + self, + dna: Tensor, + *, + families: Iterable[str] | None = None, + usage_positions: Tensor | None = None, + junction_site_positions: Tensor | None = None, + junction_queries: Tensor | None = None, + ) -> dict[str, Tensor]: + requested = set(families or ALL_FAMILIES) + unknown = requested - set(ALL_FAMILIES) + if unknown: + raise ValueError(f"Unknown output families: {sorted(unknown)}") + trunk = self._trunk(dna) + needs_dense = bool( + requested.intersection((*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions")) + ) + features = self._dense_features(trunk) if needs_dense else None + outputs: dict[str, Tensor] = {} + for family in DENSE_TRACKS: + if family in requested: + outputs[family] = self.dense_heads[family](features).float() + if "splice_sites" in requested: + all_logits = self._splice_site_logits(trunk) + start = (self.config.input_length - self.config.target_length) // 2 + outputs["splice_sites"] = all_logits[:, start:start + self.config.target_length] + if "splice_usage" in requested: + if usage_positions is None: + raise ValueError("splice_usage requires usage_positions") + gathered = self._gather_bins( + features, usage_positions, self.config.dense_bin_size + ) + outputs["splice_usage"] = self.splice_usage_head(gathered).float() + if "junctions" in requested: + if junction_site_positions is None or junction_queries is None: + raise ValueError("junctions require site positions and query triples") + outputs["junctions"] = self._junction_predictions( + trunk, junction_site_positions, junction_queries + ).float() + if "contact_maps" in requested: + contact = trunk.reshape( + len(dna), self.config.contact_bins, -1, self.config.hidden_dim + ).mean(dim=2) + q, k = self.contact_q(contact), self.contact_k(contact) + row, column = self.contact_row, self.contact_column + outputs["contact_maps"] = ( + 0.5 + * ( + q[:, row] * k[:, column] + + q[:, column] * k[:, row] + ) + ).float() + return outputs + + +def count_parameters(model: nn.Module) -> int: + return sum(parameter.numel() for parameter in model.parameters() if parameter.requires_grad) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/reference_large.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/reference_large.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e403757c49eb6e6419927210c7053710964213d1 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/reference_large.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 96 +channel_growth: 96 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 1024 +num_sequence_blocks: 16 +num_attention_heads: 16 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 768 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/run_ddp.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/run_ddp.sh new file mode 100644 index 0000000000000000000000000000000000000000..c4702e9366e907b7791063e77dcfe281411ac8cd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/run_ddp.sh @@ -0,0 +1,16 @@ +#!/usr/bin/env bash +# run_ddp.sh DATA_DIR OUTPUT_DIR CONFIG EPOCHS [GPUS=8] [WORKERS_PER_GPU=12] +# Train the reference on every train interval with one example per GPU per step, then score +# validation with the verifier's own scorer. Runs inside the task image (torch, torchrun). +set -Eeuo pipefail +DATA=$1; OUT=$2; CONFIG=$3; EPOCHS=$4; GPUS=${5:-8}; WORKERS=${6:-12} +BASELINE=$(cd "$(dirname "$0")/../.." && pwd) +mkdir -p "$OUT" +cd "$BASELINE" +# Loopback rendezvous: works offline, where --standalone cannot resolve the hostname. +torchrun --nnodes 1 --nproc-per-node "$GPUS" --master-addr 127.0.0.1 --master-port "${MASTER_PORT:-29511}" -m reference.alphagenome_50m.train_eval train \ + --input "$DATA" --config "$CONFIG" --output "$OUT" --epochs "$EPOCHS" --workers "$WORKERS" \ + --log-every 200 --save-every 2000 2>&1 | tee -a "$OUT/train.log" +python predict.py "$DATA" "$OUT/valid-pred" --checkpoint "$OUT/last.pt" --split valid +python /workspace/evaluation/score.py --predictions "$OUT/valid-pred/predictions.zarr" \ + --labels "$DATA" --split valid --output "$OUT/valid-score" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/tiny_overfit.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/tiny_overfit.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c25f9ffbb76102c970e5767922400f7ec2b6c53e --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/tiny_overfit.yaml @@ -0,0 +1,24 @@ +# Keep the frozen 1-Mb input and every output track; shrink only learned widths. +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 8 +channel_growth: 4 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 32 +num_sequence_blocks: 1 +num_attention_heads: 2 +attention_head_dim: 16 +mlp_expansion: 2.0 +dropout: 0.0 +head_hidden_size: 32 +junction_rank: 8 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: false diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/train_eval.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/train_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..92f9a3f315013630fdd2e03fb6840a6c77840edd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/reference/alphagenome_50m/train_eval.py @@ -0,0 +1,537 @@ +"""Train and stream-evaluate the <=50M compact AlphaGenome baseline.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import numpy as np +import torch +from torch import Tensor +from torch.nn import functional as F +from torch.utils.data import DataLoader, Subset + +from .dataset import ( + AlphaGenomeDataset, + DENSE_FAMILIES, + single_example_collate, +) +from .model import ( + ALL_FAMILIES, + CompactModel, + ModelConfig, + count_parameters, +) + + +DEFAULT_CONFIG = Path(__file__).with_name("alphagenome_50m.yaml") + + +def _device(name: str) -> torch.device: + if name == "auto": + name = "cuda" if torch.cuda.is_available() else "cpu" + if name == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("--device cuda requested but CUDA is unavailable") + return torch.device(name) + + +def _autocast(device: torch.device): + enabled = device.type == "cuda" + dtype = torch.bfloat16 if enabled and torch.cuda.is_bf16_supported() else torch.float16 + return torch.autocast(device_type=device.type, dtype=dtype, enabled=enabled) + + +def _distributed() -> tuple[int, int, int]: + """(rank, local_rank, world_size) under torchrun; (0, 0, 1) otherwise.""" + world = int(os.environ.get("WORLD_SIZE", "1")) + if world > 1 and not torch.distributed.is_initialized(): + torch.distributed.init_process_group("nccl") + return int(os.environ.get("RANK", "0")), int(os.environ.get("LOCAL_RANK", "0")), world + + +def _loader(dataset, *, workers: int, shuffle: bool, sampler=None) -> DataLoader: + return DataLoader( + dataset, + batch_size=1, + shuffle=shuffle and sampler is None, + sampler=sampler, + num_workers=workers, + persistent_workers=workers > 0, + pin_memory=torch.cuda.is_available(), + collate_fn=single_example_collate, + ) + + +def _to_device(value, device: torch.device): + if isinstance(value, Tensor): + return value.to(device, non_blocking=True) + if isinstance(value, dict): + return {key: _to_device(item, device) for key, item in value.items()} + return value + + +def _match_coordinate_queries( + scoring: dict[str, Tensor], positions: Tensor, track_halves: Tensor +) -> tuple[Tensor, Tensor, Tensor]: + """Match trusted coordinate queries to typed position axes like the grader.""" + device = positions.device + tracks = scoring["track"].detach().cpu().numpy() + halves_cpu = track_halves.detach().cpu().numpy()[tracks] + # Build indices on CPU, then copy once. Writing three CUDA scalars per valid query + # otherwise launches thousands of tiny kernels per example, especially costly when + # training and evaluation share one GPU. The matching semantics are unchanged. + queries = np.zeros((len(tracks), 3), dtype=np.int64) + queries[:, 2] = tracks + valid = scoring["valid"].clone() + missing = np.ones(len(tracks), dtype=bool) + position_rows = positions.detach().cpu().numpy() + donors_cpu = scoring["donor_position"].detach().cpu().numpy() + acceptors_cpu = scoring["acceptor_position"].detach().cpu().numpy() + valid_indices = np.flatnonzero(valid.detach().cpu().numpy()) + lookups = [ + {int(coordinate): index for index, coordinate in enumerate(axis) if coordinate >= 0} + for axis in position_rows + ] + for index in valid_indices: + half = int(halves_cpu[index]) + row = lookups[2 * half].get(int(donors_cpu[index])) + column = lookups[2 * half + 1].get(int(acceptors_cpu[index])) + if row is not None and column is not None: + queries[index, 0] = row + queries[index, 1] = column + missing[index] = False + return (torch.from_numpy(queries).to(device).unsqueeze(0), valid, + torch.from_numpy(missing).to(device)) + + +def _model_inputs(sample: dict) -> tuple[dict, Tensor, Tensor]: + queries, query_valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + sample["site_positions"], + sample["junction_track_halves"], + ) + if (query_valid & missing).any(): + raise ValueError("trusted scoring coordinates do not match target positions") + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + return { + "dna": sample["dna"].unsqueeze(0), + "usage_positions": sample["usage_positions"].unsqueeze(0), + "junction_site_positions": sample["site_positions"].unsqueeze(0), + "junction_queries": queries, + }, junction_target, query_valid + + +def _losses(outputs: dict[str, Tensor], sample: dict, junction_target: Tensor, + junction_valid: Tensor) -> dict[str, Tensor]: + losses = {} + for family in DENSE_FAMILIES: + target = torch.asinh(sample["dense"][family]["values"]).unsqueeze(0) + mask = sample["dense"][family]["mask"].unsqueeze(0) + residual = torch.where(mask, outputs[family] - target, 0.0) + losses[family] = residual.square().sum() / mask.sum().clamp_min(1) + losses["splice_sites"] = F.binary_cross_entropy_with_logits( + outputs["splice_sites"], sample["splice_sites"].unsqueeze(0) + ) + usage_target = torch.asinh(sample["splice_usage"]["values"]).unsqueeze(0) + usage_mask = sample["splice_usage"]["mask"].unsqueeze(0) + usage_residual = torch.where(usage_mask, outputs["splice_usage"] - usage_target, 0.0) + losses["splice_usage"] = usage_residual.square().sum() / usage_mask.sum().clamp_min(1) + junction_residual = outputs["junctions"][0] - junction_target[0] + losses["junctions"] = ( + junction_residual[junction_valid].square().mean() + if junction_valid.any() else junction_residual.sum() * 0 + ) + contact_target = torch.asinh(sample["contact_maps"]).unsqueeze(0) + contact_mask = sample["contact_map_mask"].unsqueeze(0) + contact_residual = torch.where( + contact_mask, outputs["contact_maps"] - contact_target, 0.0 + ) + losses["contact_maps"] = contact_residual.square().sum() / contact_mask.sum().clamp_min(1) + losses["total"] = torch.stack([losses[name] for name in ALL_FAMILIES]).mean() + return losses + + +def _validation_inference( + model: CompactModel, sample: dict +) -> tuple[dict[str, Tensor], Tensor, Tensor, Tensor]: + """Use the same predicted-position path as a formal v3 submission.""" + trunk = model._trunk(sample["dna"].unsqueeze(0)) + dense_features = model._dense_features(trunk) + outputs = { + family: model.dense_heads[family](dense_features).float() + for family in DENSE_FAMILIES + } + all_sites = model._splice_site_logits(trunk) + target_start = (model.config.input_length - model.config.target_length) // 2 + outputs["splice_sites"] = all_sites[:, target_start:target_start + model.config.target_length] + usage_bins = model.splice_usage_head(dense_features).float() + usage_indices = torch.div(sample["usage_positions"], model.config.dense_bin_size, + rounding_mode="floor") + outputs["splice_usage"] = usage_bins[:, usage_indices] + predicted_positions = model.select_junction_positions(all_sites) + + matched, valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + predicted_positions[0], + sample["junction_track_halves"], + ) + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + junction_prediction = torch.zeros(len(valid), device=valid.device) + if (~missing & valid).any(): + selected = ~missing & valid + junction_prediction[selected] = model._junction_predictions( + trunk, predicted_positions, matched[:, selected] + )[0] + outputs["junctions"] = junction_prediction.unsqueeze(0) + + contact = trunk.reshape( + 1, model.config.contact_bins, -1, model.config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + outputs["contact_maps"] = (0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row])).float() + return outputs, junction_target, valid, missing + + +def _atomic_save(payload: dict, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(f".{path.name}.tmp") + torch.save(payload, temporary) + os.replace(temporary, path) + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(8 * 1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _resume_position(payload: dict, *, examples: int, epochs: int) -> tuple[int, int]: + epoch = int(payload["epoch"]) + step = int(payload["global_step"]) + kind = payload.get("checkpoint_kind") + if kind not in (None, "epoch") or step != epoch * examples: + raise ValueError("only a completed-epoch checkpoint can resume training") + if payload.get("examples", examples) != examples or payload.get("schedule_epochs", epochs) != epochs: + raise ValueError("checkpoint data size or training schedule differs from this run") + return epoch, step + + +def train(args: argparse.Namespace) -> None: + # Multi-GPU with `torchrun --nproc-per-node N train_eval.py train ...`: one example per GPU per + # step (data parallel), rank 0 writes checkpoints and logs. Model size is bounded by the + # training budget, not by a parameter cap. + rank, local_rank, world = _distributed() + config = ModelConfig.from_yaml(args.config) + device = torch.device("cuda", local_rank) if world > 1 else _device(args.device) + if world > 1: + torch.cuda.set_device(device) + torch.manual_seed(args.seed) + model = CompactModel(config).to(device) + parameters = count_parameters(model) + optimizer = torch.optim.AdamW( + model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay + ) + scaler = torch.amp.GradScaler("cuda", enabled=device.type == "cuda") + full_dataset = AlphaGenomeDataset(args.input, "train") + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + start_epoch, global_step = 0, 0 + if args.resume: + payload = torch.load(args.resume, map_location="cpu", weights_only=True) + if payload.get("config") != config.to_dict(): + raise ValueError("checkpoint configuration does not match --config") + model.load_state_dict(payload["model"], strict=True) + optimizer.load_state_dict(payload["optimizer"]) + scaler.load_state_dict(payload.get("scaler", {})) + start_epoch, global_step = _resume_position(payload, examples=size, epochs=args.epochs) + per_rank = math.ceil(size / world) + steps = max(1, per_rank * args.epochs) + module = model + if world > 1: + model = torch.nn.parallel.DistributedDataParallel( + model, device_ids=[local_rank], find_unused_parameters=True) + sampler = (torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world, rank=rank, + shuffle=True, seed=args.seed) + if world > 1 else None) + stop_after_epoch = args.stop_after_epoch or args.epochs + output = args.output.resolve() + output.mkdir(parents=True, exist_ok=True) + started = time.monotonic() + if rank == 0: + print(json.dumps({"event": "train_start", "parameters": parameters, "examples": size, + "device": str(device), "world_size": world}), flush=True) + + for epoch in range(start_epoch, stop_after_epoch): + model.train() + if sampler is not None: + sampler.set_epoch(epoch) + loader = _loader(dataset, workers=args.workers, shuffle=True, sampler=sampler) + for sample in loader: + sample = _to_device(sample, device) + inputs, junction_target, junction_valid = _model_inputs(sample) + progress = min((global_step + 1) / steps, 1.0) + learning_rate = args.min_learning_rate + 0.5 * ( + args.learning_rate - args.min_learning_rate + ) * (1.0 + math.cos(math.pi * progress)) + for group in optimizer.param_groups: + group["lr"] = learning_rate + optimizer.zero_grad(set_to_none=True) + with _autocast(device): + predictions = model(**inputs) + losses = _losses(predictions, sample, junction_target, junction_valid) + if not torch.isfinite(losses["total"]): + raise FloatingPointError(f"non-finite loss at step {global_step}") + scaler.scale(losses["total"]).backward() + scaler.unscale_(optimizer) + gradient_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_gradient_norm) + scaler.step(optimizer) + scaler.update() + global_step += 1 + if rank == 0 and (global_step == 1 or global_step % args.log_every == 0): + print( + json.dumps( + { + "event": "train_step", + "epoch": epoch + 1, + "step": global_step, + "loss": float(losses["total"].detach()), + "gradient_norm": float(gradient_norm), + "learning_rate": learning_rate, + "elapsed_seconds": time.monotonic() - started, + } + ), + flush=True, + ) + if rank == 0 and global_step % args.save_every == 0: + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch, + "global_step": global_step, + "checkpoint_kind": "progress", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "progress.pt", + ) + if world > 1: + torch.distributed.barrier() + if rank != 0: + continue + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch + 1, + "global_step": global_step, + "checkpoint_kind": "epoch", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "last.pt", + ) + if world > 1: + torch.distributed.barrier() + torch.distributed.destroy_process_group() + if rank != 0: + return + checkpoint = output / "last.pt" + manifest = { + "schema": "alphagenome-50m-real/v3", + "checkpoint": {"path": checkpoint.name, "sha256": _sha256(checkpoint)}, + "parameters": parameters, + "config": config.to_dict(), + "output_families": list(ALL_FAMILIES), + "epochs": args.epochs, + "completed_epochs": stop_after_epoch, + "global_step": global_step, + } + (output / "model_manifest.json").write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "train_complete", **manifest}), flush=True) + + +class PerTrackMoments: + def __init__(self, tracks: int): + self.n = np.zeros(tracks, dtype=np.int64) + self.sum_x = np.zeros(tracks, dtype=np.float64) + self.sum_y = np.zeros(tracks, dtype=np.float64) + self.sum_x2 = np.zeros(tracks, dtype=np.float64) + self.sum_y2 = np.zeros(tracks, dtype=np.float64) + self.sum_xy = np.zeros(tracks, dtype=np.float64) + + def update(self, prediction: Tensor, target: Tensor, mask: Tensor | None = None) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1, prediction.shape[-1]) + y = target.detach().double().cpu().numpy().reshape(-1, target.shape[-1]) + valid = np.ones_like(y, dtype=bool) if mask is None else mask.detach().cpu().numpy().reshape(y.shape).astype(bool) + self.n += valid.sum(axis=0) + self.sum_x += np.where(valid, x, 0).sum(axis=0, dtype=np.float64) + self.sum_y += np.where(valid, y, 0).sum(axis=0, dtype=np.float64) + self.sum_x2 += np.where(valid, x * x, 0).sum(axis=0, dtype=np.float64) + self.sum_y2 += np.where(valid, y * y, 0).sum(axis=0, dtype=np.float64) + self.sum_xy += np.where(valid, x * y, 0).sum(axis=0, dtype=np.float64) + + def update_indexed(self, prediction: Tensor, target: Tensor, track: Tensor) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1) + y = target.detach().double().cpu().numpy().reshape(-1) + indices = track.detach().cpu().numpy().reshape(-1).astype(np.int64) + self.n += np.bincount(indices, minlength=len(self.n)) + self.sum_x += np.bincount(indices, weights=x, minlength=len(self.n)) + self.sum_y += np.bincount(indices, weights=y, minlength=len(self.n)) + self.sum_x2 += np.bincount(indices, weights=x * x, minlength=len(self.n)) + self.sum_y2 += np.bincount(indices, weights=y * y, minlength=len(self.n)) + self.sum_xy += np.bincount(indices, weights=x * y, minlength=len(self.n)) + + def report(self) -> dict: + numerator = self.n * self.sum_xy - self.sum_x * self.sum_y + denominator = np.sqrt( + np.maximum(self.n * self.sum_x2 - self.sum_x**2, 0) + * np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + ) + target_ss = np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + scorable = (self.n >= 2) & (target_ss > 0) + pearson = np.full_like(numerator, np.nan) + nonconstant = scorable & (denominator > 0) + pearson[nonconstant] = numerator[nonconstant] / denominator[nonconstant] + pearson[scorable & ~nonconstant] = 0.0 + return { + "mean_pearson": float(pearson[scorable].mean()) if scorable.any() else 0.0, + "valid_tracks": int(scorable.sum()), + "per_track_pearson": [float(value) if np.isfinite(value) else None for value in pearson], + "values_per_track": self.n.tolist(), + } + + +def evaluate(args: argparse.Namespace) -> None: + device = _device(args.device) + payload = torch.load(args.checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config).to(device) + model.load_state_dict(payload["model"], strict=True) + model.eval() + full_dataset = AlphaGenomeDataset(args.input, args.split) + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + metrics = { + **{family: PerTrackMoments(config_value) for family, config_value in { + "atac": 167, "dnase": 305, "procap": 12, "cage": 546, + "rna_seq": 667, "chip_tf": 1617, "chip_histone": 1116, + }.items()}, + "splice_sites": PerTrackMoments(4), + "splice_usage": PerTrackMoments(config.splice_usage_tracks), + "junctions": PerTrackMoments(config.junction_tracks), + "contact_maps": PerTrackMoments(config.contact_tracks), + } + with torch.inference_mode(): + for index, sample in enumerate(_loader(dataset, workers=args.workers, shuffle=False)): + sample = _to_device(sample, device) + outputs, junction_target, junction_valid, junction_missing = ( + _validation_inference(model, sample) + ) + # Match predict.py: FP32 inference is serialized to FP16 Zarr values. + outputs = {name: value.half().float() for name, value in outputs.items()} + for family in DENSE_FAMILIES: + metrics[family].update( + outputs[family][0], + torch.asinh(sample["dense"][family]["values"]), + sample["dense"][family]["mask"], + ) + metrics["splice_sites"].update(outputs["splice_sites"][0], sample["splice_sites"]) + metrics["splice_usage"].update( + outputs["splice_usage"][0], + torch.asinh(sample["splice_usage"]["values"]), + sample["splice_usage"]["mask"], + ) + junction_track = sample["junction_scoring"]["track"] + metrics["junctions"].update_indexed( + outputs["junctions"][0][junction_valid], + junction_target[0][junction_valid], + junction_track[junction_valid], + ) + metrics["contact_maps"].update( + outputs["contact_maps"][0], + torch.asinh(sample["contact_maps"]), + sample["contact_map_mask"], + ) + if index == 0 or (index + 1) % args.log_every == 0 or index + 1 == size: + print(json.dumps({"event": "evaluate_progress", "examples": index + 1, "total": size}), flush=True) + families = {name: accumulator.report() for name, accumulator in metrics.items()} + valid_family_scores = [item["mean_pearson"] for item in families.values()] + report = { + "schema": "alphagenome-50m-evaluation/v3", + "split": args.split, + "examples": size, + "metric_space": ( + "asinh continuous targets and binary splice-site targets; " + "junction Pearson uses hidden coordinate queries and predicted endpoints; " + "FP32 inference rounded to FP16 prediction values" + ), + "families": families, + "overall_mean_family_pearson": float(np.mean(valid_family_scores)), + } + args.report.parent.mkdir(parents=True, exist_ok=True) + args.report.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "evaluate_complete", "overall_mean_family_pearson": report["overall_mean_family_pearson"]}), flush=True) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + common = argparse.ArgumentParser(add_help=False) + common.add_argument("--input", required=True, type=Path) + common.add_argument("--config", type=Path, default=DEFAULT_CONFIG) + common.add_argument("--workers", type=int, default=2) + common.add_argument("--max-examples", type=int, default=0) + common.add_argument("--device", choices=("auto", "cuda", "cpu"), default="auto") + common.add_argument("--log-every", type=int, default=25) + subparsers = parser.add_subparsers(dest="command", required=True) + train_parser = subparsers.add_parser("train", parents=[common]) + train_parser.add_argument("--output", required=True, type=Path) + train_parser.add_argument("--epochs", type=int, default=10) + train_parser.add_argument("--stop-after-epoch", type=int) + train_parser.add_argument("--learning-rate", type=float, default=2e-4) + train_parser.add_argument("--min-learning-rate", type=float, default=2e-5) + train_parser.add_argument("--weight-decay", type=float, default=1e-4) + train_parser.add_argument("--max-gradient-norm", type=float, default=5.0) + train_parser.add_argument("--save-every", type=int, default=250) + train_parser.add_argument("--seed", type=int, default=17) + train_parser.add_argument("--resume", type=Path) + train_parser.set_defaults(run=train) + evaluate_parser = subparsers.add_parser("evaluate", parents=[common]) + evaluate_parser.add_argument("--checkpoint", required=True, type=Path) + evaluate_parser.add_argument("--split", choices=("train", "valid", "test"), required=True) + evaluate_parser.add_argument("--report", required=True, type=Path) + evaluate_parser.set_defaults(run=evaluate) + args = parser.parse_args() + if args.workers < 0 or args.max_examples < 0: + parser.error("workers/max-examples must be nonnegative") + if args.command == "train" and ( + args.epochs < 1 + or (args.stop_after_epoch is not None + and not 1 <= args.stop_after_epoch <= args.epochs) + ): + parser.error("epochs must be positive and stop-after-epoch must be within the schedule") + args.run(args) + + +if __name__ == "__main__": + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/train_batched.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/train_batched.py new file mode 100644 index 0000000000000000000000000000000000000000..df00b798c655555704cd6c8abbd7e4eacedd0ba3 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/train_batched.py @@ -0,0 +1,168 @@ +"""Resumable single-H100 training with equal-per-example batched losses. + +global_step counts training examples, optimizer_steps counts updates. Every epoch +uses the complete deterministic permutation, including its final partial batch. +""" +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time +import torch +from numcodecs import blosc +from torch.utils.data import DataLoader, Subset +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save, _to_device +from batched_training import list_collate, prepare_batch, equal_example_losses + +def main(): + p=argparse.ArgumentParser(description=__doc__) + p.add_argument('--input',type=Path,default=Path('/workspace/data')) + p.add_argument('--output',type=Path,required=True) + p.add_argument('--config',type=Path,required=True) + p.add_argument('--resume',type=Path) + p.add_argument('--batch-size',type=int,default=8) + p.add_argument('--epochs',type=int,default=14) + p.add_argument('--stop-at',type=int,default=0) + p.add_argument('--workers',type=int,default=6) + p.add_argument('--threads',type=int,default=2) + p.add_argument('--seed',type=int,default=17) + p.add_argument('--learning-rate',type=float,default=2e-4) + p.add_argument('--min-learning-rate',type=float,default=2e-5) + p.add_argument('--warmup',type=int,default=4000,help='Warmup in training examples') + p.add_argument('--objective',choices=['balanced_splice','variance_balanced'],default='balanced_splice') + p.add_argument('--loss-scales',type=Path) + p.add_argument('--save-every',type=int,default=10000,help='Training examples between progress checkpoints') + p.add_argument('--keep-every',type=int,default=50000) + p.add_argument('--log-every',type=int,default=50,help='Optimizer updates between logs') + args=p.parse_args() + if min(args.batch_size,args.epochs,args.save_every,args.log_every) < 1: + raise ValueError('batch, epoch, checkpoint and log intervals must be positive') + os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:22]) + torch.set_num_threads(args.threads);torch.set_num_interop_threads(1);blosc.set_nthreads(1) + torch.manual_seed(args.seed) + device=torch.device('cuda:0') + config=ModelConfig.from_yaml(args.config) + model=CompactModel(config).to(device) + optimizer=torch.optim.AdamW(model.parameters(),lr=args.learning_rate,weight_decay=1e-4) + dataset=AlphaGenomeDataset(args.input,'train') + model.set_junction_track_halves(dataset.junction_track_halves) + args.output.mkdir(parents=True,exist_ok=True) + total=len(dataset)*args.epochs + step,updates,elapsed_before=0,0,0. + lineage,scales={},{} + if args.loss_scales: + scale_source=json.loads(args.loss_scales.read_text()) + assert scale_source['split']=='train' + scales=scale_source['family_loss_scales'] + lineage['loss_scale_provenance']={k:v for k,v in scale_source.items() if k!='junction_moments'} + if args.resume: + state=torch.load(args.resume,map_location='cpu',weights_only=True) + if state['schema'] not in ['seqfunc-single/v1','seqfunc-batched/v1']: + raise ValueError('only locally trained checkpoints may resume') + if ModelConfig(**state['config']).to_dict()!=config.to_dict() or state['examples']!=len(dataset): + raise ValueError('resume architecture or dataset differs') + if state['schedule_epochs']!=args.epochs or state['objective']!=args.objective or state['seed']!=args.seed: + raise ValueError('resume schedule, objective or seed differs') + previous=state.get('training_arguments',{}) + for field in ['learning_rate','min_learning_rate','warmup']: + if field in previous and previous[field]!=getattr(args,field): + raise ValueError(f'resume {field} differs') + if state['schema']=='seqfunc-batched/v1' and previous['batch_size']!=args.batch_size: + raise ValueError('batched resume must keep its batch size') + if scales and scales!=state.get('family_loss_scales',{}): + raise ValueError('resume family scales differ') + scales=state.get('family_loss_scales',{}) + lineage.update({k:state[k] for k in ['intervention','interventions','loss_scale_provenance'] if k in state}) + model.load_state_dict(state['model'],strict=True) + optimizer.load_state_dict(state['optimizer']) + step=state['global_step'];updates=state.get('optimizer_steps',step) + elapsed_before=state.get('elapsed_seconds',0.) + torch.set_rng_state(state['rng_cpu']);torch.cuda.set_rng_state(state['rng_cuda'],device) + if state['schema']=='seqfunc-single/v1': + with args.resume.open('rb') as f: + digest=hashlib.file_digest(f,'sha256').hexdigest() + history=list(lineage.get('interventions',[])) + if lineage.get('intervention') and lineage['intervention'] not in history: + history.append(lineage['intervention']) + change={'kind':'batch_size_change','from':1,'to':args.batch_size,'at_example':step, + 'parent_sha256':digest,'parameters_optimizer_rng_unchanged_at_start':True} + history.append(change);lineage.update(intervention=change,interventions=history) + if args.objective=='variance_balanced': + expected=set(model.dense_heads)|{'splice_usage','junctions','contact_maps'} + if set(scales)!=expected or not all(math.isfinite(v) and v>0 for v in scales.values()): + raise ValueError('fixed positive training-derived scales required') + elif scales: + raise ValueError('unscaled objective cannot carry family scales') + start=time.monotonic() + def emit(event,**values): + print(json.dumps({'event':event,'step':step,'optimizer_steps':updates, + 'elapsed_seconds':elapsed_before+time.monotonic()-start,**values}),flush=True) + def save(name): + _atomic_save({**lineage,'schema':'seqfunc-batched/v1','model':model.state_dict(), + 'optimizer':optimizer.state_dict(),'config':config.to_dict(), + 'global_step':step,'optimizer_steps':updates,'epoch':step//len(dataset),'offset':step%len(dataset), + 'examples':len(dataset),'schedule_epochs':args.epochs,'objective':args.objective,'seed':args.seed, + 'family_loss_scales':scales, + 'training_arguments':{k:str(v) if isinstance(v,Path) else v for k,v in vars(args).items()}, + 'rng_cpu':torch.get_rng_state(),'rng_cuda':torch.cuda.get_rng_state(device), + 'elapsed_seconds':elapsed_before+time.monotonic()-start, + 'provenance':{'data':str(args.input),'split':'train','pretrained':False,'world_size':1, + 'device':torch.cuda.get_device_name(0),'batch_reduction':'equal per example', + 'train_manifest_sha256':hashlib.sha256((args.input/'public/train.parquet').read_bytes()).hexdigest()}, + },args.output/name) + emit('train_start',parameters=count_parameters(model),examples=len(dataset),batch_size=args.batch_size, + objective=args.objective,config=config.to_dict(),schedule_examples=total,family_loss_scales=scales) + if args.stop_at and step>=args.stop_at: + raise ValueError('stop-at must exceed the resumed example count') + model.train() + for epoch in range(step//len(dataset),args.epochs): + order=torch.randperm(len(dataset),generator=torch.Generator().manual_seed(args.seed+epoch)).tolist() + offset=step%len(dataset) + emit('epoch_order',epoch=epoch+1,offset=offset,count=len(order), + permutation_sha256=hashlib.sha256(torch.tensor(order,dtype=torch.int64).numpy().tobytes()).hexdigest()) + loader=DataLoader(Subset(dataset,order[offset:]),batch_size=args.batch_size,shuffle=False, + num_workers=args.workers,pin_memory=True,collate_fn=list_collate, + persistent_workers=False,prefetch_factor=2 if args.workers else None) + for cpu_samples in loader: + if args.stop_at: + cpu_samples=cpu_samples[:args.stop_at-step] + inputs,targets,valid=prepare_batch(cpu_samples) + inputs=_to_device(inputs,device) + targets=[x.to(device,non_blocking=True) for x in targets] + valid=[x.to(device,non_blocking=True) for x in valid] + samples=[_to_device(s,device) for s in cpu_samples] + progress=min(max(step-args.warmup,0)/max(total-args.warmup,1),1) + lr=args.min_learning_rate+.5*(args.learning_rate-args.min_learning_rate)*(1+math.cos(math.pi*progress)) + lr*=min((step+len(samples))/max(args.warmup,1),1) + for group in optimizer.param_groups: group['lr']=lr + optimizer.zero_grad(set_to_none=True) + with torch.autocast('cuda',dtype=torch.bfloat16): + outputs=model(**inputs) + losses,raw=equal_example_losses(outputs,samples,targets,valid,scales=scales) + if not torch.isfinite(losses['total']): + raise FloatingPointError(f'nonfinite loss at {step}') + losses['total'].backward() + norm=torch.nn.utils.clip_grad_norm_(model.parameters(),5,error_if_nonfinite=True) + optimizer.step() + before=step;step+=len(samples);updates+=1 + if updates==1 or updates%args.log_every==0: + emit('train_step',epoch=epoch+1,example_ids=[s['example_id'] for s in samples], + learning_rate=lr,gradient_norm=float(norm),actual_batch_size=len(samples), + losses={k:float(v.detach()) for k,v in losses.items()}, + raw_losses={k:float(v.detach()) for k,v in raw.items()}, + peak_gpu_bytes=torch.cuda.max_memory_allocated()) + if step//args.save_every>before//args.save_every: save('progress.pt') + if args.keep_every and step//args.keep_every>before//args.keep_every: save(f'step-{step:06d}.pt') + if step%len(dataset)==0: + save('last.pt');save(f'epoch-{epoch+1:02d}.pt') + emit('epoch_complete',completed_epochs=epoch+1) + if args.stop_at and step>=args.stop_at: + save('last.pt');emit('train_segment_complete',completed_epochs=step//len(dataset),offset=step%len(dataset)) + return + emit('train_complete') + +if __name__=='__main__': main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/train_single.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/train_single.py new file mode 100644 index 0000000000000000000000000000000000000000..03024393206b918eac3ae23a87c09173848b0d15 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/train_single.py @@ -0,0 +1,158 @@ +"""Resumable single-H100 training; every epoch permutes every eligible training interval.""" +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import torch +from numcodecs import blosc +from torch.utils.data import DataLoader, Subset + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset, single_example_collate +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save, _losses, _model_inputs, _to_device + + +def balanced_splice_loss(logits, target): + """Equal positive/negative contributions per type, using only training labels. + + An absent class contributes its negative loss rather than dividing by zero or being dropped. + """ + dims = tuple(range(target.ndim - 1)) + positive = target.sum(dims) + negative = (1 - target).sum(dims) + pos_loss = (torch.nn.functional.softplus(-logits) * target).sum(dims) / positive.clamp_min(1) + neg_loss = (torch.nn.functional.softplus(logits) * (1 - target)).sum(dims) / negative.clamp_min(1) + return torch.where(positive > 0, 0.5 * (pos_loss + neg_loss), neg_loss).mean() + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--input', type=Path, default=Path('/workspace/data')) + p.add_argument('--output', type=Path, required=True) + p.add_argument('--config', type=Path, default=Path(__file__).parent / 'reference/alphagenome_50m/alphagenome_50m.yaml') + p.add_argument('--resume', type=Path) + p.add_argument('--objective', choices=['reference', 'balanced_splice'], default='balanced_splice') + p.add_argument('--epochs', type=int, default=4) + p.add_argument('--stop-at', type=int, default=0, help='Stop after this total number of examples, preserving exact sampler position') + p.add_argument('--workers', type=int, default=8) + p.add_argument('--threads', type=int, default=4) + p.add_argument('--seed', type=int, default=17) + p.add_argument('--learning-rate', type=float, default=2e-4) + p.add_argument('--min-learning-rate', type=float, default=2e-5) + p.add_argument('--warmup', type=int, default=500) + p.add_argument('--save-every', type=int, default=2000) + p.add_argument('--keep-every', type=int, default=10000, help='Keep immutable intermediate checkpoints') + p.add_argument('--log-every', type=int, default=100) + args = p.parse_args() + torch.set_num_threads(args.threads) + torch.set_num_interop_threads(1) + blosc.set_nthreads(1) + if hasattr(os, 'sched_getaffinity'): + os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) + torch.manual_seed(args.seed) + device = torch.device('cuda:0') + config = ModelConfig.from_yaml(args.config) + model = CompactModel(config).to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=1e-4) + dataset = AlphaGenomeDataset(args.input, 'train') + model.set_junction_track_halves(dataset.junction_track_halves) + args.output.mkdir(parents=True, exist_ok=True) + total = len(dataset) * args.epochs + step, elapsed_before = 0, 0.0 + lineage = {} + if args.resume: + state = torch.load(args.resume, map_location='cpu', weights_only=True) + lineage = {k: state[k] for k in ['intervention', 'interventions'] if k in state} + if state['config'] != config.to_dict() or state['examples'] != len(dataset): + raise ValueError('resume config or dataset size differs') + if state['schedule_epochs'] != args.epochs or state['objective'] != args.objective: + raise ValueError('resume schedule or objective differs') + if state['seed'] != args.seed: + raise ValueError('resume shuffle seed differs') + previous = state.get('training_arguments', {}) + for field in ['learning_rate', 'min_learning_rate', 'warmup']: + if field in previous and previous[field] != getattr(args, field): + raise ValueError(f'resume {field} differs') + model.load_state_dict(state['model'], strict=True) + optimizer.load_state_dict(state['optimizer']) + step = state['global_step'] + elapsed_before = state.get('elapsed_seconds', 0.0) + torch.set_rng_state(state['rng_cpu']) + torch.cuda.set_rng_state(state['rng_cuda'], device) + start = time.monotonic() + def emit(event, **values): + record = {'event': event, 'step': step, 'elapsed_seconds': elapsed_before + time.monotonic() - start, **values} + print(json.dumps(record), flush=True) + def save(name): + _atomic_save({ + **lineage, + 'schema': 'seqfunc-single/v1', 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), 'config': config.to_dict(), + 'global_step': step, 'epoch': step // len(dataset), 'offset': step % len(dataset), + 'examples': len(dataset), 'schedule_epochs': args.epochs, + 'objective': args.objective, 'seed': args.seed, + 'training_arguments': {k: str(v) if isinstance(v, Path) else v for k, v in vars(args).items()}, + 'rng_cpu': torch.get_rng_state(), 'rng_cuda': torch.cuda.get_rng_state(device), + 'elapsed_seconds': elapsed_before + time.monotonic() - start, + 'provenance': {'data': str(args.input), 'split': 'train', 'pretrained': False, + 'device': torch.cuda.get_device_name(0), 'world_size': 1}, + }, args.output / name) + emit('train_start', parameters=count_parameters(model), examples=len(dataset), objective=args.objective, + config=config.to_dict(), schedule_examples=total, device=torch.cuda.get_device_name(0)) + model.train() + for epoch in range(step // len(dataset), args.epochs): + generator = torch.Generator().manual_seed(args.seed + epoch) + order = torch.randperm(len(dataset), generator=generator).tolist() + offset = step % len(dataset) + emit('epoch_order', epoch=epoch + 1, offset=offset, count=len(order), + permutation_sha256=hashlib.sha256(torch.tensor(order, dtype=torch.int64).numpy().tobytes()).hexdigest()) + loader = DataLoader(Subset(dataset, order[offset:]), batch_size=1, shuffle=False, + num_workers=args.workers, pin_memory=True, collate_fn=single_example_collate, + persistent_workers=False, prefetch_factor=2 if args.workers else None) + for sample in loader: + sample = _to_device(sample, device) + inputs, target, valid = _model_inputs(sample) + progress = min(max(step - args.warmup, 0) / max(total - args.warmup, 1), 1) + lr = args.min_learning_rate + 0.5 * (args.learning_rate - args.min_learning_rate) * (1 + math.cos(math.pi * progress)) + lr *= min((step + 1) / max(args.warmup, 1), 1) + for group in optimizer.param_groups: + group['lr'] = lr + optimizer.zero_grad(set_to_none=True) + with torch.autocast('cuda', dtype=torch.bfloat16): + outputs = model(**inputs) + losses = _losses(outputs, sample, target, valid) + if args.objective == 'balanced_splice': + losses['splice_sites'] = balanced_splice_loss(outputs['splice_sites'], sample['splice_sites'][None]) + losses['total'] = torch.stack([v for k, v in losses.items() if k != 'total']).mean() + if not torch.isfinite(losses['total']): + raise FloatingPointError(f'nonfinite loss at {step}') + losses['total'].backward() + norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0, error_if_nonfinite=True) + optimizer.step() + step += 1 + if step == 1 or step % args.log_every == 0: + emit('train_step', epoch=epoch + 1, example_id=sample['example_id'], learning_rate=lr, + gradient_norm=float(norm), losses={k: float(v.detach()) for k, v in losses.items()}, + peak_gpu_bytes=torch.cuda.max_memory_allocated()) + if step % args.save_every == 0: + save('progress.pt') + if args.keep_every and step % args.keep_every == 0: + save(f'step-{step:06d}.pt') + if args.stop_at and step >= args.stop_at: + save('last.pt') + emit('train_segment_complete', completed_epochs=step // len(dataset), offset=step % len(dataset)) + return + save('last.pt') + save(f'epoch-{epoch + 1:02d}.pt') + emit('epoch_complete', completed_epochs=epoch + 1) + emit('train_complete') + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/block-checkpoint-cpu-check.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/block-checkpoint-cpu-check.json new file mode 100644 index 0000000000000000000000000000000000000000..588d9318734995508b1ece2948b790e2d0ce5172 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/block-checkpoint-cpu-check.json @@ -0,0 +1,10 @@ +{ + "all_11_training_and_evaluation_outputs_bit_exact": true, + "max_absolute_gradient_error": 0.0, + "nonzero_local_heads_and_bridge": true, + "six_local_blocks": true, + "active_branch_caps": true, + "unequal_and_empty_event_queries": true, + "precision": "float32", + "device": "CPU" +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/block-checkpoint-gpu-check.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/block-checkpoint-gpu-check.json new file mode 100644 index 0000000000000000000000000000000000000000..144a03aa8b8a1b0624d424c2cf577788896fdca1 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/block-checkpoint-gpu-check.json @@ -0,0 +1,198 @@ +{ + "weights_discarded": true, + "source_training_examples": 1000, + "same_nonzero_trained_parameters": true, + "precision": "bfloat16 training", + "batch_size": 4, + "input_bp": 1048576, + "training_indices": [ + 37958, + 33184, + 21022, + 30377 + ], + "objective": "variance_balanced", + "benchmarks": [ + { + "policy": "whole_path", + "step_seconds": [ + 1.681253916991409, + 0.45803058601450175, + 0.4365358139912132, + 0.439815385005204, + 0.4368974999815691, + 0.4549180749745574 + ], + "steady_seconds_per_example": 0.11130986799835227, + "peak_allocated_GiB": 40.11026668548584, + "all_11_outputs_and_losses_bit_exact": null, + "all_parameter_gradients_bit_exact": null, + "max_absolute_gradient_error": null, + "gradient_relative_L2_error": null, + "gradient_cosine": null, + "gradient_difference_top10": [] + }, + { + "policy": "whole_path_repeat", + "step_seconds": [ + 2.481021937011974, + 0.4393464530003257, + 0.48240915301721543, + 0.43610669201007113, + 0.4453651010117028, + 0.44543533099931665 + ], + "steady_seconds_per_example": 0.11243313650193158, + "peak_allocated_GiB": 40.10498094558716, + "all_11_outputs_and_losses_bit_exact": true, + "all_parameter_gradients_bit_exact": false, + "max_absolute_gradient_error": 1.52587890625e-05, + "gradient_relative_L2_error": 0.0009534162545958643, + "gradient_cosine": 0.999999545498656, + "gradient_difference_top10": [ + { + "parameter": "local_path.stem.weight", + "max_absolute_error": 1.52587890625e-05, + "relative_L2_error": 0.0011708050442353522, + "reference_L2": 0.038250174700771404 + }, + { + "parameter": "local_path.stem.bias", + "max_absolute_error": 1.52587890625e-05, + "relative_L2_error": 0.0011145246032800173, + "reference_L2": 0.015316175233840698 + }, + { + "parameter": "local_path.blocks.0.contract.weight", + "max_absolute_error": 1.52587890625e-05, + "relative_L2_error": 0.0008722401804490078, + "reference_L2": 0.03117670960545457 + }, + { + "parameter": "local_path.blocks.0.contract.bias", + "max_absolute_error": 1.52587890625e-05, + "relative_L2_error": 0.0007519333551147762, + "reference_L2": 0.020936495583310583 + }, + { + "parameter": "local_projection.weight", + "max_absolute_error": 1.52587890625e-05, + "relative_L2_error": 0.0029496872658462915, + "reference_L2": 0.1874866973862457 + }, + { + "parameter": "encoder.stem.weight", + "max_absolute_error": 7.62939453125e-06, + "relative_L2_error": 0.00357048619228398, + "reference_L2": 0.02370183607081833 + }, + { + "parameter": "encoder.stem.bias", + "max_absolute_error": 7.62939453125e-06, + "relative_L2_error": 0.002108935810380534, + "reference_L2": 0.009654671844638604 + }, + { + "parameter": "local_path.blocks.0.expand.weight", + "max_absolute_error": 7.62939453125e-06, + "relative_L2_error": 0.0008152801893268156, + "reference_L2": 0.01960642126710496 + }, + { + "parameter": "local_path.blocks.1.contract.weight", + "max_absolute_error": 7.62939453125e-06, + "relative_L2_error": 0.0007619146919563889, + "reference_L2": 0.02294275974276798 + }, + { + "parameter": "local_path.blocks.1.contract.bias", + "max_absolute_error": 7.62939453125e-06, + "relative_L2_error": 0.0004888368419478168, + "reference_L2": 0.01560724126448807 + } + ] + }, + { + "policy": "per_block", + "step_seconds": [ + 2.334278088004794, + 0.4152768689964432, + 0.41614379500970244, + 0.4367661690048408, + 0.41646649700123817, + 0.4161676469957456 + ], + "steady_seconds_per_example": 0.10504104885039851, + "peak_allocated_GiB": 15.99086332321167, + "all_11_outputs_and_losses_bit_exact": true, + "all_parameter_gradients_bit_exact": false, + "max_absolute_gradient_error": 1.52587890625e-05, + "gradient_relative_L2_error": 0.0009547321603675331, + "gradient_cosine": 0.9999995442418171, + "gradient_difference_top10": [ + { + "parameter": "local_path.stem.weight", + "max_absolute_error": 1.52587890625e-05, + "relative_L2_error": 0.0010200615858954154, + "reference_L2": 0.038250174700771404 + }, + { + "parameter": "local_path.blocks.0.contract.bias", + "max_absolute_error": 1.52587890625e-05, + "relative_L2_error": 0.0007522781995975294, + "reference_L2": 0.020936495583310583 + }, + { + "parameter": "local_projection.weight", + "max_absolute_error": 1.52587890625e-05, + "relative_L2_error": 0.002942487910648785, + "reference_L2": 0.1874866973862457 + }, + { + "parameter": "encoder.stem.weight", + "max_absolute_error": 7.62939453125e-06, + "relative_L2_error": 0.0036607697988758504, + "reference_L2": 0.02370183607081833 + }, + { + "parameter": "encoder.stem.bias", + "max_absolute_error": 7.62939453125e-06, + "relative_L2_error": 0.00198984390485536, + "reference_L2": 0.009654671844638604 + }, + { + "parameter": "local_path.blocks.0.expand.weight", + "max_absolute_error": 7.62939453125e-06, + "relative_L2_error": 0.0009106435365869254, + "reference_L2": 0.01960642126710496 + }, + { + "parameter": "local_path.blocks.0.contract.weight", + "max_absolute_error": 7.62939453125e-06, + "relative_L2_error": 0.0006495164608475437, + "reference_L2": 0.03117670960545457 + }, + { + "parameter": "local_path.blocks.1.contract.bias", + "max_absolute_error": 7.62939453125e-06, + "relative_L2_error": 0.0005038814832610538, + "reference_L2": 0.01560724126448807 + }, + { + "parameter": "local_path.blocks.2.contract.weight", + "max_absolute_error": 7.62939453125e-06, + "relative_L2_error": 0.0006085661912956853, + "reference_L2": 0.02301358982961947 + }, + { + "parameter": "encoder.blocks.0.conv.weight", + "max_absolute_error": 4.121946403756738e-06, + "relative_L2_error": 0.00431087128397906, + "reference_L2": 0.027548808788221385 + } + ] + } + ], + "additional_gpu_consumers": [], + "complete": true +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/execution_change.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/execution_change.json new file mode 100644 index 0000000000000000000000000000000000000000..93f72a0c4f5f51e9192005535fc45e6aa3bbd671 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/execution_change.json @@ -0,0 +1,10 @@ +{ + "adopted_utc": "2026-10-24T21:51:59.762636+00:00", + "change": "Activation checkpoint boundaries only; all model operations, parameters, objective, data and schedule unchanged.", + "example_boundary": 1000, + "before": "Recompute whole local path during backward.", + "after": "Recompute each local residual block during backward.", + "evidence": "CPU gradients and all eleven outputs exactly match. Full-shape BF16 outputs/losses are bit-exact; gradient relative L2 discrepancy .0009547 versus .0009534 for an unchanged repeat, consistent with existing reduction variability. Peak memory40.11 to15.99GiB; isolated compute .11131 to.10504seconds/example.", + "initial_source_manifest_sha256": "18329ef35e89fc5aeb50ccc045b08db690e6c8ef40d661b01ddb74c7f32b6f0b", + "no_benchmark_weights_retained": true +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/make_usage_selection_candidate.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/make_usage_selection_candidate.py new file mode 100644 index 0000000000000000000000000000000000000000..e8c429281f7dd9ff6f304acfd2e05d57e0453b7e --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/make_usage_selection_candidate.py @@ -0,0 +1,85 @@ +"""Change only the sparse usage selection budget on one frozen packaged model.""" +import argparse +import hashlib +import json +from pathlib import Path +import shutil + +from verify_package import verify_package + + +def digest(path): + with Path(path).open('rb') as stream: + return hashlib.file_digest(stream, 'sha256').hexdigest() + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('plan', type=Path) + a = p.parse_args() + plan = json.loads(a.plan.read_text()) + parent, output = Path(plan['parent_package']), Path(plan['output'])/'package' + assert plan['policy'] == 'central4096_max_raw_logit' + assert not output.exists() + verify_package(parent) + assert digest(parent/'last.pt') == plan['parent_weights_sha256'] + assert digest(parent/'predict.py') == plan['parent_predict_sha256'] + assert not (parent/'rc_ensemble.py').exists() + before = ''' sites = predicted_positions.cpu().numpy().astype(np.int64).reshape(-1) + sites = np.unique(sites[(sites >= target_start) & (sites < target_start + config.target_length)] + - target_start)[:MAX_USAGE_SITES] +''' + after = ''' # Usage has its own central-window budget; junction axes are unchanged. + central_scores = all_site_logits[0, target_start:target_start + config.target_length].amax(dim=-1) + count = min(MAX_USAGE_SITES, config.target_length) + sites = central_scores.topk(count).indices.sort().values.cpu().numpy().astype(np.int64) +''' + text = (parent/'predict.py').read_text() + assert text.count(before) == 1 + changed = text.replace(before, after) + compile(changed, str(output/'predict.py'), 'exec') + shutil.copytree(parent, output, ignore=shutil.ignore_patterns('__pycache__')) + (output/'predict.py').write_text(changed) + assert digest(output/'last.pt') == plan['parent_weights_sha256'] + assert digest(output/'perturb.py') == digest(parent/'perturb.py') + for name in ['validation_tracks.json', 'validation_variants.json']: + (output/name).unlink(missing_ok=True) + (output/'usage_selection_policy.json').write_text(json.dumps(plan, indent=2)+'\n') + history = output/'training_history' + history.mkdir(exist_ok=True) + shutil.copyfile(a.plan, history/'usage34-study.json') + shutil.copyfile(__file__, history/'make_usage_selection_candidate.py') + readme = (parent/'README.md').read_text().split('\n## Measured validation results')[0] + heading, body = readme.split('\n', 1) + readme = heading+' with expanded usage-site selection\n'+body + readme += ''' +The only change in usage34 is the sparse usage-position selection rule. It returns the 4,096 +central-window bases with the largest maximum raw splice logit across the four site types, +sorted by coordinate. Conditional usage values still come from the same 128-bp pooled head. +The previous rule reused the central subset of 512 full-window candidates per splice type. +All weights, junction candidates, other track computations and perturbation inference are +unchanged. Both entry points use this one frozen model; no coordinates or targets are supplied +at inference. This rule was selected for a full validation trial after a fixed 256-training-window +diagnostic (seed 3217), with no fitted thresholds or development-label coefficients. + +The earlier 4,000-example model did not benefit from the same larger budget; this experiment +revisits the fixed rule after a complete training epoch. Greater site recall can also expose +incorrect conditional predictions, so no validation improvement is assumed. The known-position +training diagnostic is never used at inference and is not a performance upper bound. Training +provenance and compute are inherited unchanged; this candidate adds no optimizer updates. +Native/recovery equivalence and exact agreement with the parent's unaffected outputs precede +the full 200-window validation. Perturbation outputs may be reused only after all relevant code, +weights and runtime hashes match, then every development variant metric is rescored. +''' + (output/'README.md').write_text(readme) + manifest = json.loads((output/'package_manifest.json').read_text()) + manifest['usage_selection_policy'] = plan['policy'] + manifest['files'] = {str(path.relative_to(output)): digest(path) + for path in sorted(output.rglob('*')) if path.is_file() + and path.name != 'package_manifest.json' and '__pycache__' not in path.parts} + (output/'package_manifest.json').write_text(json.dumps(manifest, indent=2)+'\n') + print(json.dumps({'event': 'usage_candidate_built', **verify_package(output)}), flush=True) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/memory-check-interruption.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/memory-check-interruption.json new file mode 100644 index 0000000000000000000000000000000000000000..19bc02cd2784b11f5f3e658efa28017639c2efeb --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/memory-check-interruption.json @@ -0,0 +1,19 @@ +{ + "time_utc": "2026-10-24T21:49:06.154124+00:00", + "reason": "Mechanical activation-checkpoint memory/equivalence check; optimizer, data order and model math preserved.", + "process_group": 4169, + "checkpoints": [ + { + "run": "scale13-small", + "step": 1000, + "path": "/workspace/research/experiments/scale13-small/step-001000.pt", + "sha256": "a53ea6d92c21c06576c734dc05eac6e3f21d33701f25bfc335817a6e6ec197fc" + }, + { + "run": "scale13-large", + "step": 1000, + "path": 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"all_ordered_ids_and_tensor_bytes_exact": true + } + ], + "total_read_hash_and_transfer_seconds": { + "reference": 69.81505457599997, + "parallel": 37.28158475100645 + }, + "parallel_over_reference_time": 0.5340049503280426, + "reader_sha256": "ad6670f742f4de9898064becc525d0aea98b402abe7b210bb1b26a9eee6402b9", + "adapter_sha256": "ab182b3b687a3b470ff1bbc311a9130cc791974366bdc0a400df5e0e28602c9c", + "caveat": "Alternating paired readers under shared training load, including worker startup, full per-example hashing and pinned tensor IPC. File caches are shared; no GPU computation included." +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/reader-before-parallel-io.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/reader-before-parallel-io.py new file mode 100644 index 0000000000000000000000000000000000000000..71cd39d15713f648e7d7e31aa1923fa4fa4800dd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_history/reader-before-parallel-io.py @@ -0,0 +1,229 @@ +"""Streaming reader for the compact 1 Mb / eleven-family release.""" + +from __future__ import annotations + +from collections import OrderedDict +from pathlib import Path + +import numpy as np +import pandas as pd +import pysam +import torch +from torch.utils.data import Dataset +import zarr + + +def _open_part(path: Path): + if str(path).endswith(".zarr.zip"): + store = zarr.ZipStore(str(path), mode="r") + try: + return zarr.open_group(store=store, mode="r"), store + except BaseException: + store.close() + raise + return zarr.open_group(str(path), mode="r"), None + + +DENSE_FAMILIES = ( + "atac", + "dnase", + "procap", + "cage", + "rna_seq", + "chip_tf", + "chip_histone", +) +def normalize_splice_positions(value) -> np.ndarray: + """Normalize Arrow's nested-list representation to the public [4, 512] array.""" + + outer = np.asarray(value, dtype=object).reshape(-1).tolist() + positions = np.stack([np.asarray(item, dtype=np.int64) for item in outer]) + if positions.shape != (4, 512): + raise ValueError(f"splice_site_positions must have shape (4, 512), got {positions.shape}") + return positions + + +class AlphaGenomeDataset(Dataset): + """Resolve frozen coordinates to reference DNA and one Zarr example group.""" + + def __init__( + self, + data_root: str | Path, + split: str, + *, + max_open_parts: int = 8, + include_targets: bool = True, + ) -> None: + self.root = Path(data_root) + self.split = split + manifest = self.root / "public" / f"{split}.parquet" + if not manifest.is_file(): + raise FileNotFoundError(f"Missing materialized manifest: {manifest}") + self.rows = pd.read_parquet(manifest).reset_index(drop=True) + junction_tracks = pd.read_parquet(self.root / "public/junction_tracks.parquet") + junction_tracks = junction_tracks.sort_values("tensor_index") + self.junction_track_halves = torch.from_numpy( + junction_tracks.junction_channel_half.to_numpy(np.int64) + ) + if include_targets: + labels_path = self.root / "labels" / f"{split}.parquet" + if not labels_path.is_file(): + raise FileNotFoundError(f"Missing trusted labels manifest: {labels_path}") + labels = pd.read_parquet(labels_path).reset_index(drop=True) + if self.rows.example_id.astype(str).tolist() != labels.example_id.astype(str).tolist(): + raise ValueError("public and labels manifests have different IDs/order") + self.rows = self.rows.merge(labels, on="example_id", validate="one_to_one", sort=False) + self.fasta_path = self.root / "reference" / "GRCh38.p13.genome.fa" + self.max_open_parts = max_open_parts + self.include_targets = include_targets + self._fasta = None + self._parts: OrderedDict[str, tuple[zarr.Group, object | None]] = OrderedDict() + + def __len__(self) -> int: + return len(self.rows) + + def _reference(self): + if self._fasta is None: + self._fasta = pysam.FastaFile(str(self.fasta_path)) + return self._fasta + + def _part(self, relative: str): + if relative in self._parts: + opened = self._parts.pop(relative) + self._parts[relative] = opened + return opened[0] + group, store = _open_part(self.root / relative) + if len(self._parts) >= self.max_open_parts: + _, (_, old_store) = self._parts.popitem(last=False) + if old_store is not None: + old_store.close() + self._parts[relative] = (group, store) + return group + + @staticmethod + def _one_hot(sequence: str) -> np.ndarray: + encoded = np.frombuffer(sequence.upper().encode("ascii"), dtype=np.uint8) + output = np.zeros((len(encoded), 4), dtype=np.float32) + for index, letter in enumerate(b"ACGT"): + output[:, index] = encoded == letter + return output + + def _sequence(self, row) -> tuple[np.ndarray, np.ndarray]: + reference = self._reference() + chromosome_length = reference.get_reference_length(str(row.chromosome)) + start, end = int(row.input_start), int(row.input_end) + clipped_start, clipped_end = max(0, start), min(chromosome_length, end) + sequence = reference.fetch(str(row.chromosome), clipped_start, clipped_end) + left, right = clipped_start - start, end - clipped_end + if left or right: + sequence = "N" * left + sequence + "N" * right + valid = np.ones(end - start, dtype=bool) + if left: + valid[:left] = False + if right: + valid[-right:] = False + return self._one_hot(sequence), valid + + @staticmethod + def _decode(example, name: str, *, asinh: bool) -> np.ndarray: + """Train targets may be stored as 8-bit codes with per-track ranges; they are decoded + here, one example at a time, so the release stays compressed on disk.""" + array = example[name] + if array.dtype != np.uint8: + return np.asarray(array, dtype=np.float32) + lo = np.asarray(example[f"{name}_lo"], dtype=np.float32) + hi = np.asarray(example[f"{name}_hi"], dtype=np.float32) + x = lo + np.asarray(array, dtype=np.float32) * ((hi - lo) / 255.0) + return np.sinh(x) if asinh else x + + @staticmethod + def _dense_target(example, family: str) -> dict[str, torch.Tensor]: + values = AlphaGenomeDataset._decode(example, f"{family}/values", asinh=True) + mask = np.asarray(example[f"{family}/mask"], dtype=bool) + if mask.ndim == 1: + mask = np.broadcast_to(mask[None, :], values.shape).copy() + return { + "values": torch.from_numpy(values), + "mask": torch.from_numpy(mask), + } + + def __getitem__(self, item: int): + row = self.rows.iloc[item] + dna, input_mask = self._sequence(row) + result = { + "example_id": str(row.example_id), + "dna": torch.from_numpy(dna), + "input_mask": torch.from_numpy(input_mask), + "junction_track_halves": self.junction_track_halves, + } + if not self.include_targets: + return result + + part = self._part(str(row.target_shard)) + example = part[str(row.target_group)] + result["dense"] = { + family: self._dense_target(example, family) for family in DENSE_FAMILIES + } + result["splice_sites"] = torch.from_numpy( + np.asarray(example["splice_sites/values"], dtype=np.float32) + ) + result["splice_usage"] = { + "positions": torch.from_numpy( + np.asarray(example["splice_site_usage/positions"], dtype=np.int64) + ), + "values": torch.from_numpy( + np.asarray(example["splice_site_usage/values"], dtype=np.float32) + ), + "mask": torch.from_numpy( + np.asarray(example["splice_site_usage/mask"], dtype=bool) + ), + } + result["usage_positions"] = result["splice_usage"]["positions"] + result["junctions"] = { + name: torch.from_numpy( + np.asarray( + example[f"splice_junctions/{name}"], + dtype=np.float32 if name == "value" else np.int64, + ) + ) + for name in ("row", "column", "track", "value") + } + result["junctions"]["site_positions"] = torch.from_numpy( + np.asarray(example["splice_junctions/site_positions"], dtype=np.int64) + ) + site_positions = normalize_splice_positions(row.splice_site_positions) + result["site_positions"] = torch.from_numpy(site_positions) + if not torch.equal(result["junctions"]["site_positions"], result["site_positions"]): + raise ValueError(f"public/private splice positions differ for {row.example_id}") + scoring_path = self.root / "scoring" / self.split / f"part-{item:06d}.parquet" + scoring = pd.read_parquet(scoring_path) + if not (scoring.example_id.astype(str) == str(row.example_id)).all(): + raise ValueError(f"scoring part ID differs for {row.example_id}") + result["junction_scoring"] = { + "donor_position": torch.from_numpy( + scoring.donor_position.to_numpy(np.int64) + ), + "acceptor_position": torch.from_numpy( + scoring.acceptor_position.to_numpy(np.int64) + ), + "track": torch.from_numpy(scoring.track.to_numpy(np.int64)), + "target_value": torch.from_numpy( + scoring.target_value.to_numpy(np.float32) + ), + "valid": torch.from_numpy(scoring.valid.to_numpy(bool)), + } + result["contact_maps"] = torch.from_numpy( + self._decode(example, "contact_maps/upper_triangle", asinh=False) + ) + result["contact_map_mask"] = torch.from_numpy( + np.asarray(example["contact_maps/mask"], dtype=bool) + ) + return result + + +def single_example_collate(items): + """Keep variable-size event tensors intact; the reference currently trains at batch one.""" + + if len(items) != 1: + raise ValueError("The compact event loader currently requires batch_size=1") + return items[0] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_loss_scales.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_loss_scales.json new file mode 100644 index 0000000000000000000000000000000000000000..a8f484c060a0d4300b25557736abf741d6d67b15 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_loss_scales.json @@ -0,0 +1,32 @@ +{ + "split": "train", + "examples": 256, + "seed": 1729, + "source_moments_sha256": "7b7543c4d4d8d82ec9f9bc3f942ca65595671322f62c19391591df827d8fc588", + "training_indices_sha256": "b82df9ae11e95d9be5d6e5f85839cf23e25bbbe0a195eaee266ff52d366800dc", + "method": "reference MSE times inverse mean within-track training variance; floor0.01; splice BCE unchanged", + "mean_track_variance": { + "atac": 0.9602319499041136, + "dnase": 1.0768856864858252, + "procap": 0.3380534137610864, + "cage": 0.3114663911919963, + "rna_seq": 0.66617486157613, + "chip_tf": 2.7198812117366296, + "chip_histone": 3.405612880716664, + "contact_maps": 0.14704202117243276, + "splice_usage": 0.07913355074122039, + "junctions": 0.17919098248535248 + }, + "family_loss_scales": { + "atac": 1.041415045708339, + "dnase": 0.9286036694045732, + "procap": 2.9581124144681272, + "cage": 3.2106192779675315, + "rna_seq": 1.5011073783751905, + "chip_tf": 0.36766311546433506, + "chip_histone": 0.29363290398101966, + "contact_maps": 6.800777029767044, + "splice_usage": 12.636865029223356, + "junctions": 5.5806379658738825 + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_provenance.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_provenance.json new file mode 100644 index 0000000000000000000000000000000000000000..810530f44b9dc68922a0b0b575320531240b21cb --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/training_provenance.json @@ -0,0 +1,119 @@ +{ + "schema": "seqfunc-batched/v1", + "config": { + "input_length": 1048576, + "target_length": 196608, + "dense_bin_size": 128, + "trunk_bin_size": 1024, + "contact_bin_size": 2048, + "stem_stride": 8, + "encoder_width": 32, + "channel_growth": 32, + "num_downsampling_blocks": 7, + "conv_kernel_size": 5, + "dna_kernel_size": 15, + "hidden_dim": 384, + "num_sequence_blocks": 6, + "num_attention_heads": 6, + "attention_head_dim": 64, + "mlp_expansion": 4.0, + "dropout": 0.0, + "head_hidden_size": 256, + "junction_rank": 32, + "splice_usage_tracks": 734, + "junction_tracks": 734, + "contact_tracks": 28, + "gradient_checkpointing": true, + "enable_pairwise": false, + "pair_width": 32, + "pair_downsample": 16, + "pair_update_interval": 2, + "local_width": 32, + "local_context_blocks": 6, + "splice_context_mode": "offset", + "local_to_trunk": true, + "residual_rms_cap": 0.1 + }, + "epoch": 1, + "offset": 0, + "global_step": 41020, + "examples": 41020, + "schedule_epochs": 14, + "objective": "variance_balanced", + "seed": 17, + "training_arguments": { + "input": "/workspace/data", + "output": "/workspace/research/experiments/scale13-small", + "config": "/workspace/research/studies/scale13/scale13-small.yaml", + "resume": "/workspace/research/experiments/scale13-small/progress.pt", + "batch_size": 4, + "epochs": 14, + "stop_at": 0, + "workers": 4, + "threads": 2, + "seed": 17, + "learning_rate": 0.0002, + "min_learning_rate": 2e-05, + "warmup": 4000, + "objective": "variance_balanced", + "loss_scales": "/workspace/research/studies/scale13/loss_scales.json", + "save_every": 1000, + "keep_every": 0, + "log_every": 100 + }, + "elapsed_seconds": 10612.629490868014, + "provenance": { + "data": "/workspace/data", + "split": "train", + "pretrained": false, + "world_size": 1, + "device": "NVIDIA H100 80GB HBM3", + "batch_reduction": "equal per example", + "train_manifest_sha256": "48133d27eedfb4b1d275cfca41f69dd075a2a0305ab2e8591e8e5e7529f36c64" + }, + "optimizer_steps": 10255, + "family_loss_scales": { + "atac": 1.041415045708339, + "dnase": 0.9286036694045732, + "procap": 2.9581124144681272, + "cage": 3.2106192779675315, + "rna_seq": 1.5011073783751905, + "chip_tf": 0.36766311546433506, + "chip_histone": 0.29363290398101966, + "contact_maps": 6.800777029767044, + "splice_usage": 12.636865029223356, + "junctions": 5.5806379658738825 + }, + "loss_scale_provenance": { + "split": "train", + "examples": 256, + "seed": 1729, + "source_moments_sha256": "7b7543c4d4d8d82ec9f9bc3f942ca65595671322f62c19391591df827d8fc588", + "training_indices_sha256": "b82df9ae11e95d9be5d6e5f85839cf23e25bbbe0a195eaee266ff52d366800dc", + "method": "reference MSE times inverse mean within-track training variance; floor0.01; splice BCE unchanged", + "mean_track_variance": { + "atac": 0.9602319499041136, + "dnase": 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0000000000000000000000000000000000000000..83c0d49a0311e20ebec0fab260a9f20f0b133a5d --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S019/usage_selection_policy.json @@ -0,0 +1,55 @@ +{ + "created_utc": "2026-10-25T02:26:23.204684+00:00", + "candidate": "usage34", + "parent_package": "/workspace/research/experiments/scale13-small-epoch01/package", + "parent_evaluation": "/workspace/research/experiments/scale13-small-epoch01", + "parent_native_check": "/workspace/research/studies/scale13/native-small-epoch01", + "parent_weights_sha256": "0595a0feea648c3d64e4428515828b4eb404a55ed1e2dfb27e2ff73d126e03c8", + "parent_predict_sha256": "c257bd031d41b946053e37d5a5759c6c1416d22d9b4d3f403e1fd13e0f63968b", + "output": "/workspace/research/experiments/usage34", + "policy": "central4096_max_raw_logit", + "change": "Only usage position selection: full4096central positions ranked by max raw splice logit; conditional head, other outputs and both-entrypoint weights unchanged. No added training.", + "training_diagnostic": "/workspace/research/analysis/scale13-small-usage-discovery-train256.json", + "training_diagnostic_sha256": "3902f5ca897c06aef68bca94abbf1d4b6d34b423777bb3bf97cbbd4e5dd16865", + "training_evidence": { + "training_windows": 256, + "seed": 3217, + "policy_results": { + "current_global512_per_type": { + "mean_pearson": 0.4218272982468924, + "position_recall": 0.35608867675689254, + "positive_observation_recall": 0.4792380748608904, + "positive_asinh_target_mass_recall": 0.5198967478509209 + }, + "central4096_max_raw_logit": { + "mean_pearson": 0.4882935722456697, + "position_recall": 0.8279218740187151, + "positive_observation_recall": 0.9061336794240705, + "positive_asinh_target_mass_recall": 0.9335667731077516 + }, + "known_positions_oracle": { + "mean_pearson": 0.4640852573367183, + "position_recall": 1.0, + "positive_observation_recall": 1.0, + "positive_asinh_target_mass_recall": 1.0 + } + }, + "caveat": "Training windows only, no validation claim. Known-position readout bypasses discovery but is not an upper bound: it can expose false-positive values at negative queried positions, so its Pearson can be lower than a sparse policy." + }, + "evaluation_lock": "/workspace/research/studies/scale13/evaluation.lock", + "source_sha256": { + "make_usage_selection_candidate.py": "f687c04439ca3a68b6b2fd135078efe92550b3ffc0c8ef4cbf1c1a0698a7b074", + "run_usage_selection_study.py": "298acc5632d8a7e48a20758608bf700ee3ca1ebaa20d4cab641f5020cd6c0323", + "check_native_recovery.py": "47adc6fd7695f8d7e5e05fb9450b41194ce3695ba369dda07dbbd43188ff6965", + "evaluate_package.py": "38885699310241dc84b4d412f14abf240d9708bb8b2de97bc365754df65c5a50", + "promote_if_better.py": "fb863e77b3da094f24de62dcfd08ed8e384b253ee884759babddbab2cb5f283d" + }, + "full_validation_required": { + "tracks": 200, + "variant_queries": 2697, + "variant_metrics": 13 + }, + "variant_reuse_policy": "Only byte-identical weights, inference code and runtime allow reuse of all parent predictions; run score_variants.py again. 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a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/README.md b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/README.md new file mode 100644 index 0000000000000000000000000000000000000000..60c3a4cba4d04688a1797bf45ba299d18a50bf9a --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/README.md @@ -0,0 +1,85 @@ +# SeqFunc scale13-small after 2 complete epochs with transcript-direction junction decoding + +One model with **13,290,504 parameters** serves every family and both entry points. It reads the entire +1,048,576-bp DNA window through a convolutional encoder and 6 transformer +blocks of width 384 over 1,024 tokens. Dense heads predict 128-bp bins over the central +196,608 bp; other heads predict splice sites, sparse usage/junctions and full-window contacts. +A width-32 base-resolution path uses a 21-bp stem and 6 dilated residual depthwise blocks (10,941-bp receptive field). Average/max pooling provides aligned 128-bp residual features to dense, usage and junction heads. Splice logits combine local base-resolution logits with the reference offset decoder. Local average/max features are also averaged into 1-kb tokens and linearly projected into the global trunk before its transformer blocks. The projection is initialized with zero output; any later checkpoint interventions are described in the lineage below. Each transformer attention and MLP update is capped at a per-token RMS of 0.1; smaller updates are unchanged. This parameter-free bound limits residual growth and preserves the incoming sequence features. Junction values use low-rank donor/acceptor factors; contacts use a symmetric +product of global features. Sites and junction endpoints are selected from model scores alone. + +All weights originate from local random initialization (seed 17) and locally trained +checkpoints. Provisional combined recipe: a 10,941-bp local path, local-to-global projection, branch RMS cap 0.1, and fixed training-variance family weights with balanced splice loss. Component ablations are documented in the research ledger. Both scales start from local random initialization; the paired comparison isolates model capacity within this combined recipe. After the first 1,000 examples, both runs use per-local-block activation recomputation; the same mathematical model and objective are preserved. Numerical and memory checks, including an unchanged BF16 repeat, are included in training_history. At40,000examples both scales resume unchanged checkpoints using four bounded I/O threads per data worker. Ordered IDs and all decoded tensor bytes were verified identical; model operations, objective, batch order and LR schedule are unchanged. 13,290,504 parameters; local width 32, trunk width 384, 6 transformer blocks. Parameters are initialized locally with seed 17. The paired scale uses the same batch-four interval order, objective, learning-rate schedule and full training permutations. Only encoder/trunk/head/local-path width and transformer depth differ between scales. + +This checkpoint records **82,040 training examples in 20,510 optimizer updates**, 2 complete training permutations and +0 intervals into the next permutation. Each permutation includes all **41,020** +eligible FOLD_0 training intervals (folds 2–7), shuffled deterministically without replacement. +Quantized train targets are decoded by the supplied reader. Validation is fold 0; no test labels, +test-derived caches or pretrained model parameters were used. Development labels only evaluate +and select models. Checkpoints before a complete permutation remain intermediate milestones. + +Training uses one H100 80 GB and at most 22 CPUs. The current loader uses 4 workers +and 2 torch CPU threads. Batches contain up to 4 examples; losses are averaged equally over examples, with padded event queries excluded. Every epoch includes its final partial batch. BF16 training uses AdamW (weight decay 1e-4), gradient +clipping at 5, and a 14-epoch cosine learning-rate schedule from +0.0002 to 2e-05, with +4000 warmup training examples. The objective is `variance_balanced`. +Each continuous family uses the reference MSE divided by its mean within-track variance in a fixed, training-only sample of 256 windows (seed 1729). These scales are frozen in the checkpoint; the balanced splice BCE is unchanged. Recorded accumulated training-loop wall time is +296.0 minutes, including inherited parent time. Runs can share this +same physical GPU with experiments or evaluation. Setup, evaluation and discarded mechanical checks +are additional research compute, documented in the research ledger. Matching training junction queries +uses a verified, bit-identical CPU index construction followed by a bulk GPU transfer. + +The package contains model code, weights, training source/configuration/provenance, SHA-256 checksums, +and the complete CPython 3.11 / PyTorch 2.7.1 CUDA 12.6 Python runtime archive. Both executable entry +points take INPUT_ROOT and OUTPUT_ROOT, extract the vendored runtime temporarily, and run offline. +The host supplies Linux, bash/tar/gzip and the NVIDIA driver. Inference reads only public manifests +and the pinned reference FASTA. No target, splice-coordinate or prediction cache is packaged or read. +Track outputs use float16; perturbation outputs use float32 to preserve small A/B differences. + +The appended validation results cover all 200 intervals and all 2,697 development perturbations, +scored by the unmodified verifier tools. Published anchors use the same label rows. All results, +including regressions, are retained in the package's JSON reports. Limitations include incomplete +training at intermediate checkpoints, a 10,941-bp local receptive field, pooled usage/junction features, +low-rank contact modeling, and uneven variant performance. Functional readouts do not provide the +evolutionary constraint information used by conservation-based variant scorers. +The release has no targets for the 54 GTEx RNA tracks, so those output heads receive no direct +supervision and are excluded from the scored RNA track mean. The verifier's eQTL metrics use +supervised ENCODE RNA tracks selected by public tissue ontology and gene strand. + +Junction selection requires the donor to precede the acceptor on plus-strand transcripts and +follow it on minus-strand transcripts. This fixed biological constraint uses model-selected +coordinates and public strand halves. It changes no model parameters, usage selection or +perturbation computation. The rest of the decoder and candidate budget are unchanged. + +## Measured validation results + +Family-mean Pearson: **0.422853** on 200 validation intervals. + +| Family | Pearson | +|---|---:| +| atac | 0.555700 | +| dnase | 0.644481 | +| procap | 0.516338 | +| cage | 0.296582 | +| rna_seq | 0.574258 | +| chip_tf | 0.441460 | +| chip_histone | 0.469698 | +| splice_sites | 0.050608 | +| splice_usage | 0.454583 | +| junctions | 0.344881 | +| contact_maps | 0.302800 | + +| Development evaluation | Metric | Model | Published anchors on the same rows | +|---|---|---:|---| +| eqtl_sign | auroc | 0.6192 | AlphaGenome: 0.8264 | +| eqtl_causality | auroc | 0.5299 | AlphaGenome: 0.8325 | +| eqtl_coefficient | spearman | 0.1702 | AlphaGenome: 0.4134 | +| sqtl_causality | auprc | 0.7109 | AlphaGenome: 0.8447 | +| clinvar_noncoding | auprc | 0.3587 | AlphaGenome: 0.9410 | +| clinvar_splice_region | auprc | 0.2597 | AlphaGenome: 0.7225 | +| clinvar_missense | auprc | 0.3309 | AlphaGenome: 0.3738 | +| mfass | auprc | 0.1944 | AlphaGenome: 0.7343 | +| caqtl | auprc | 0.1899 | AlphaGenome: 0.8076 | +| bqtl_spi1 | auprc | 0.2134 | AlphaGenome: 0.8167 | +| enhancer_gene | auprc | 0.4208 | AlphaGenome: 0.9594 | +| traitgym_mendelian | auprc | 0.2089 | Borzoi_L2_L2.plus.all: 0.5026; Enformer_L2_L2.plus.all: 0.5453; CADD.plus.RawScore: 0.7189; GPN-MSA_absLLR.plus.score: 0.6729 | +| traitgym_complex | auprc | 0.2281 | Borzoi_L2_L2.plus.all: 0.2945; Enformer_L2_L2.plus.all: 0.2692; CADD.plus.RawScore: 0.4134; GPN-MSA_absLLR.plus.score: 0.4214 | diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/attach_bridge.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/attach_bridge.py new file mode 100644 index 0000000000000000000000000000000000000000..2c2bea67a58e19676fa73ba2a0607002ea2d5419 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/attach_bridge.py @@ -0,0 +1,48 @@ +"""Attach a zero-output local-to-global connection to our own training checkpoint.""" +import argparse +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import torch +import yaml +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + +p = argparse.ArgumentParser(description=__doc__) +p.add_argument('checkpoint', type=Path) +p.add_argument('output', type=Path) +a = p.parse_args() +s = torch.load(a.checkpoint, map_location='cpu', weights_only=True) +assert s['schema'] == 'seqfunc-single/v1' +parent_config = ModelConfig(**s['config']) +assert parent_config.local_width > 0 and not parent_config.local_to_trunk +torch.manual_seed(s['seed']) +parent = CompactModel(parent_config) +config = replace(parent_config, local_to_trunk=True) +model = CompactModel(config) +missing = model.load_state_dict(s['model'], strict=False) +assert not missing.unexpected_keys +assert set(missing.missing_keys) == {'local_trunk_projection.weight', 'local_trunk_projection.bias'} +old_names = list(dict(parent.named_parameters())) +new_names = list(dict(model.named_parameters())) +assert new_names[:len(old_names)] == old_names +assert len(s['optimizer']['param_groups']) == 1 +group = s['optimizer']['param_groups'][0] +assert group['params'] == list(range(len(old_names))) +group['params'] = list(range(len(new_names))) +with a.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() +intervention = {'kind': 'local_to_global_trunk', 'parent_sha256': digest, + 'step': s['global_step'], 'initial_outputs_unchanged': True, + 'computation': 'average aligned local features into 1-kb tokens, zero linear projection, add before transformer'} +history = list(s.get('interventions', [])) +if s.get('intervention') and s['intervention'] not in history: + history.append(s['intervention']) +history.append(intervention) +s.update(config=config.to_dict(), model=model.state_dict(), intervention=intervention, interventions=history) +a.output.mkdir(parents=True, exist_ok=False) +(a.output/'config.yaml').write_text(yaml.safe_dump(config.to_dict())) +(a.output/'intervention.json').write_text(json.dumps(intervention, indent=2) + '\n') +_atomic_save(s, a.output/'start.pt') +print(json.dumps({'parameters': count_parameters(model), **intervention}), flush=True) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/attach_local.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/attach_local.py new file mode 100644 index 0000000000000000000000000000000000000000..aa082537004dbc398c4a78c1bd0203970ce5e42f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/attach_local.py @@ -0,0 +1,52 @@ +"""Attach a zero-initialized local residual path to a checkpoint trained in this workspace.""" +import argparse +from dataclasses import replace +import hashlib +from pathlib import Path + +import torch +import yaml + +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + p.add_argument('--width', type=int, default=32) + args = p.parse_args() + state = torch.load(args.checkpoint, map_location='cpu', weights_only=True) + assert state['schema'] == 'seqfunc-single/v1', 'only locally trained single-GPU checkpoints' + parent_config = ModelConfig(**state['config']) + assert parent_config.local_width == 0 + torch.manual_seed(state['seed']) + parent = CompactModel(parent_config) + config = replace(parent_config, local_width=args.width) + model = CompactModel(config) + result = model.load_state_dict(state['model'], strict=False) + assert not result.unexpected_keys + assert all(k.startswith(('local_path.', 'local_projection.')) for k in result.missing_keys) + parent_names = list(dict(parent.named_parameters())) + names = list(dict(model.named_parameters())) + assert names[:len(parent_names)] == parent_names, 'optimizer parameter order changed' + assert len(state['optimizer']['param_groups']) == 1 + group = state['optimizer']['param_groups'][0] + assert group['params'] == list(range(len(parent_names))) + group['params'] = list(range(len(names))) + state['model'] = model.state_dict() + state['config'] = config.to_dict() + state['intervention'] = { + 'kind': 'base_resolution_local_path', 'width': args.width, + 'parent_sha256': hashlib.sha256(args.checkpoint.read_bytes()).hexdigest(), + 'attachment_step': state['global_step'], 'initial_outputs_unchanged': True, + } + args.output.mkdir(parents=True, exist_ok=False) + (args.output / 'config.yaml').write_text(yaml.safe_dump(config.to_dict())) + _atomic_save(state, args.output / 'attached.pt') + print({'parameters': count_parameters(model), **state['intervention']}) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/batched_training.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/batched_training.py new file mode 100644 index 0000000000000000000000000000000000000000..e5c609c3680baa61f04739e9f22c03a801d7b294 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/batched_training.py @@ -0,0 +1,38 @@ +"""Batch variable event queries while retaining equal-per-example reference losses.""" +import torch +from torch.nn import functional as F +from reference.alphagenome_50m.train_eval import _model_inputs, _losses +from train_single import balanced_splice_loss + +def list_collate(samples): + return samples + +def prepare_batch(samples): + """Prepare on CPU before a bulk transfer; padding never contributes to a loss.""" + prepared = [_model_inputs(sample) for sample in samples] + max_usage = max(item[0]['usage_positions'].shape[1] for item in prepared) + max_queries = max(item[0]['junction_queries'].shape[1] for item in prepared) + inputs = { + 'dna': torch.cat([x[0]['dna'] for x in prepared]), + 'usage_positions': torch.cat([F.pad(x[0]['usage_positions'], (0, max_usage-x[0]['usage_positions'].shape[1])) for x in prepared]), + 'junction_site_positions': torch.cat([x[0]['junction_site_positions'] for x in prepared]), + 'junction_queries': torch.cat([F.pad(x[0]['junction_queries'], (0, 0, 0, max_queries-x[0]['junction_queries'].shape[1])) for x in prepared]), + } + return inputs, [x[1] for x in prepared], [x[2] for x in prepared] + +def equal_example_losses(outputs, samples, junction_targets, junction_valid, *, balanced=True, scales=None): + per_example = [] + for i, (sample, target, valid) in enumerate(zip(samples, junction_targets, junction_valid)): + sliced = {name:values[i:i+1] for name,values in outputs.items()} + sliced['splice_usage'] = sliced['splice_usage'][:, :sample['usage_positions'].numel()] + sliced['junctions'] = sliced['junctions'][:, :target.shape[1]] + losses = _losses(sliced, sample, target, valid) + if balanced: + losses['splice_sites'] = balanced_splice_loss(sliced['splice_sites'], sample['splice_sites'][None]) + per_example.append(losses) + raw = {name:torch.stack([loss[name] for loss in per_example]).mean() + for name in outputs} + raw['total'] = torch.stack(list(raw.values())).mean() + scaled = {name:value * (scales or {}).get(name, 1.) for name,value in raw.items() if name != 'total'} + scaled['total'] = torch.stack(list(scaled.values())).mean() + return scaled, raw diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/change_residual_cap.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/change_residual_cap.py new file mode 100644 index 0000000000000000000000000000000000000000..cd282275702aaf5d5eede4b3904add879aad846a --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/change_residual_cap.py @@ -0,0 +1,40 @@ +"""Change only a parameter-free branch bound on our own trained checkpoint.""" +import argparse +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import torch +import yaml +from reference.alphagenome_50m.model import CompactModel, ModelConfig +from reference.alphagenome_50m.train_eval import _atomic_save + +p = argparse.ArgumentParser(description=__doc__) +p.add_argument('checkpoint', type=Path) +p.add_argument('output', type=Path) +p.add_argument('--cap', type=float, default=0.1) +a = p.parse_args() +s = torch.load(a.checkpoint, map_location='cpu', weights_only=True) +config = ModelConfig(**s['config']) +assert config.residual_rms_cap == 0 and a.cap > 0 +config = replace(config, residual_rms_cap=a.cap) +model = CompactModel(config) +model.load_state_dict(s['model'], strict=True) +assert len(s['optimizer']['param_groups'][0]['params']) == len(list(model.parameters())) +assert all(torch.equal(v, s['model'][k]) for k, v in model.state_dict().items()) +with a.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() +change = {'kind': 'bounded_transformer_residuals', 'parent_sha256': digest, + 'step': s['global_step'], 'cap': a.cap, + 'parameters_optimizer_rng_unchanged': True, + 'computation': 'each attention and MLP update multiplied by min(1, cap/token_RMS)'} +history = list(s.get('interventions', [])) +if s.get('intervention') and s['intervention'] not in history: + history.append(s['intervention']) +history.append(change) +s.update(config=config.to_dict(), intervention=change, interventions=history) +a.output.mkdir(parents=True, exist_ok=False) +(a.output/'config.yaml').write_text(yaml.safe_dump(config.to_dict())) +(a.output/'intervention.json').write_text(json.dumps(change, indent=2) + '\n') +_atomic_save(s, a.output/'start.pt') +print(json.dumps(change), flush=True) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/extend_local_context.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/extend_local_context.py new file mode 100644 index 0000000000000000000000000000000000000000..3651698e8a120b3308566d05b4523733fa621d48 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/extend_local_context.py @@ -0,0 +1,70 @@ +"""Extend our trained local path with identity-initialized dilated residual blocks.""" +import argparse +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import torch +import yaml +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + +def extend(state, blocks): + old_config = ModelConfig(**state['config']) + assert old_config.local_width and blocks > old_config.local_context_blocks + config = replace(old_config, local_context_blocks=blocks) + torch.manual_seed(state['seed']) + parent = CompactModel(old_config) + model = CompactModel(config) + missing = model.load_state_dict(state['model'], strict=False) + new_prefixes = tuple(f'local_path.blocks.{i}.' for i in range(old_config.local_context_blocks, blocks)) + assert not missing.unexpected_keys + assert all(k.startswith(new_prefixes) for k in missing.missing_keys) + for i in range(old_config.local_context_blocks, blocks): + torch.nn.init.zeros_(model.local_path.blocks[i].contract.weight) + torch.nn.init.zeros_(model.local_path.blocks[i].contract.bias) + old_names = list(dict(parent.named_parameters())) + new_names = list(dict(model.named_parameters())) + assert len(state['optimizer']['param_groups']) == 1 + group = state['optimizer']['param_groups'][0] + assert group['params'] == list(range(len(old_names))) + # New blocks shift following norm/head IDs; preserve moments by parameter name. + remap = {old_id: new_names.index(name) for old_id, name in enumerate(old_names)} + state['optimizer']['state'] = {remap[old_id]: values + for old_id, values in state['optimizer']['state'].items()} + group['params'] = list(range(len(new_names))) + for name, value in state['model'].items(): + assert torch.equal(model.state_dict()[name], value) + state.update(config=config.to_dict(), model=model.state_dict()) + return state, model, remap + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + p.add_argument('--blocks', type=int, default=6) + a = p.parse_args() + state = torch.load(a.checkpoint, map_location='cpu', weights_only=True) + old_blocks = ModelConfig(**state['config']).local_context_blocks + state, model, remap = extend(state, a.blocks) + with a.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() + change = {'kind': 'extended_local_context', 'parent_sha256': digest, + 'step': state['global_step'], 'blocks_before': old_blocks, 'blocks_after': a.blocks, + 'receptive_field_bp': 21+8*sum(4**i for i in range(a.blocks)), + 'parent_parameters_optimizer_moments_rng_preserved': True, + 'new_blocks_initially_identity': True, 'optimizer_ids_remapped_by_parameter_name': True} + history = list(state.get('interventions', [])) + if state.get('intervention') and state['intervention'] not in history: + history.append(state['intervention']) + history.append(change) + state.update(intervention=change, interventions=history) + a.output.mkdir(parents=True, exist_ok=False) + (a.output/'config.yaml').write_text(yaml.safe_dump(state['config'])) + (a.output/'intervention.json').write_text(json.dumps(change, indent=2)+'\n') + (a.output/'optimizer_id_remap.json').write_text(json.dumps(remap, indent=2)+'\n') + _atomic_save(state, a.output/'start.pt') + print(json.dumps({'parameters': count_parameters(model), **change}), flush=True) + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/infer.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/infer.sh new file mode 100644 index 0000000000000000000000000000000000000000..2a412a9ad8667d6dcb94bb57d9959d61d43ddd34 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/infer.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: infer.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/predict.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/package_manifest.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/package_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..4baf917458919ea586e0b1892a22dea9eb9bb10b --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/package_manifest.json @@ -0,0 +1,47 @@ +{ + "parameters": 13290504, + "source_checkpoint_sha256": "ea695c62d648264868443acbfba188e1449f3d0044a806566b47a521426e45a4", + "files": { + "README.md": "26fc65dbbc1d32e6860d5232d5290ca585cbe96ea160b090437064f68eded6a3", + "attach_bridge.py": "0740f17d8041d4886ad4437e83f1b781e941cc76a15db7cd5280a5983c36fc7c", + "attach_local.py": "d0774e2711840783267914789ed303a3b61b64381b72d61f0886edbc04974f15", + "batched_training.py": "217255d8f1d4fbaacc9a9bcf7ce252432ffa834b6eb47b7eed1b7471bbf3d5f7", + "change_residual_cap.py": "7c277370f54f352fd9df81ef241bfb100e123d57a56c87d5b85eac420c55be2d", + "change_splice_context.py": "4a7a1bf746b17abb75e0c611b4b19740541f825fe63a750fc44684351efadf32", + "extend_local_context.py": "aa720fdbd8625a900f24bc2c007ec0acf8c2164591971d007d2c01a58792d701", + "infer.sh": "ef810532b1e72b85fbe283e4f52c177bb70025977dbb2345d75837c9bbd51e37", + "last.pt": "775b5cebc2335ac6732b582234e6665b189ae7ca56f246c4fe3be6d0e9d1011e", + "perturb.py": "ba37a6f9f8b8eccb052903618bcb4c91d3f4cbed62dfe678ee362644dc80801e", + "perturb.sh": "113ec2383276a6e83fe1e4e8b0fa914f8d535d5dd0f5b2c34240140312dd5fb4", + "predict.py": "c257bd031d41b946053e37d5a5759c6c1416d22d9b4d3f403e1fd13e0f63968b", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/__init__.py": "45f3cf93f48859104e70bb58d717130dfd6521a45d2aba06b1a726afc0dc51dd", + 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"6d0407bd7a3fb102c0c548081d7d85527460e75b9dfb096f6db8f92cfadeb991" + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/perturb.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/perturb.py new file mode 100644 index 0000000000000000000000000000000000000000..e0416692d2ee7deb1a25927397f725eec54d16a9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/perturb.py @@ -0,0 +1,101 @@ +"""Predict both sequences of every perturbation (contract: perturbations/v1). + + python perturb.py INPUT_ROOT OUTPUT_ROOT --checkpoint last.pt + +INPUT_ROOT holds `public/perturbations.parquet` and `reference/GRCh38.p13.genome.fa` (in the +workspace, `/workspace/data` works too: the queries are then read from `variant_evals/public/`). Each row is a +1,048,576-bp window starting at `input_start` and an edit spanning [edit_start, edit_end) in genomic +0-based coordinates: + +- `snv`: sequence A carries `allele_a` at edit_start, sequence B carries `allele_b` (both are written + explicitly; either may differ from the reference genome); +- `mask`: sequence A is the reference, sequence B has the interval replaced by N. + +For each perturbation the output group `perturbations/p{index:06d}` holds `a/` and +`b/` for every family in `families`: dense families sliced to bins +[dense_bin_start, dense_bin_end) of the central target window, and `splice_sites` sliced to bp +[splice_start, splice_end). Values are the same quantities as predictions.zarr (asinh space for +dense tracks, splice-site scores for splice_sites). +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import pandas as pd +import pysam +import torch +import zarr + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig + +PERTURBATION_SCHEMA = "genome-sequence-to-function-perturbations/v1" +COMPRESSOR = Blosc(cname="zstd", clevel=3, shuffle=Blosc.BITSHUFFLE) + + +def sequences(fasta, row) -> tuple[str, str]: + length = fasta.get_reference_length(row.chrom) + start, end = int(row.input_start), int(row.input_start) + 1_048_576 + sequence = fasta.fetch(row.chrom, max(0, start), min(length, end)).upper() + sequence = "N" * max(0, -start) + sequence + "N" * max(0, end - length) + lo, hi = int(row.edit_start) - start, int(row.edit_end) - start + if row.edit == "snv": + return sequence[:lo] + row.allele_a + sequence[hi:], sequence[:lo] + row.allele_b + sequence[hi:] + if row.edit == "mask": + return sequence, sequence[:lo] + "N" * (hi - lo) + sequence[hi:] + raise ValueError(f"unknown edit {row.edit!r}") + + +def main(input_root: Path, output_root: Path, checkpoint: Path, queries: Path | None = None) -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + if queries is None: + queries = input_root / "public/perturbations.parquet" + if not queries.exists(): + queries = input_root / "variant_evals/public/perturbations.parquet" + rows = pd.read_parquet(queries) + fasta = pysam.FastaFile(str(input_root / "reference/GRCh38.p13.genome.fa")) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "perturbations.zarr"), mode="w") + target_start = (config.input_length - config.target_length) // 2 + with torch.inference_mode(): + for index, row in enumerate(rows.itertuples(index=False)): + pair = sequences(fasta, row) + dna = torch.from_numpy(np.stack([AlphaGenomeDataset._one_hot(s) for s in pair])).to(device) + trunk = model._trunk(dna) + group = root.create_group(f"perturbations/p{index:06d}") + features = None + for family in row.families: + if family == "splice_sites": + logits = model._splice_site_logits(trunk)[:, target_start:target_start + config.target_length] + values = logits[:, row.splice_start:row.splice_end] + else: + features = model._dense_features(trunk) if features is None else features + values = model.dense_heads[family](features)[:, row.dense_bin_start:row.dense_bin_end] + # float32: one variant's effect is often below float16 resolution (about 0.004 at + # asinh values of 4-8), so A and B would round to the same numbers. + values = values.float().cpu().numpy() + for side, array in zip("ab", values): + group.create_dataset(f"{side}/{family}", data=array, compressor=COMPRESSOR) + print(json.dumps({"event": "perturb", "done": index + 1, "total": len(rows)}), flush=True) + root.attrs.update({"schema": PERTURBATION_SCHEMA, + "perturbation_ids": rows.perturbation_id.astype(str).tolist()}) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--queries", type=Path, help="another query table, e.g. the development split") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.queries) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/perturb.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/perturb.sh new file mode 100644 index 0000000000000000000000000000000000000000..f07c4714532c2d33e7a419de3877e5daacee89fd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/perturb.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: perturb.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/perturb.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/predict.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..dc0f0b20833fc99253b7dcf6023fd5e7a61b1128 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/predict.py @@ -0,0 +1,133 @@ +#!/usr/bin/env python3 +"""Stream the single benchmark model into the sharded prediction contract.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import torch +import zarr + +from prediction_contract import DENSE_TRACKS, MAX_JUNCTION_PAIRS, MAX_USAGE_SITES, PREDICTION_SCHEMA +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig + + +COMPRESSOR = Blosc(cname="zstd", clevel=7, shuffle=Blosc.BITSHUFFLE) + + +def main(input_root: Path, output_root: Path, checkpoint: Path, split: str = "test") -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + dataset = AlphaGenomeDataset(input_root, split, include_targets=False) + model.set_junction_track_halves(dataset.junction_track_halves) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "predictions.zarr"), mode="w") + ids = [] + with torch.inference_mode(): + for sample in dataset: + example_id = sample["example_id"] + dna = sample["dna"][None].to(device) + trunk = model._trunk(dna) + dense_features = model._dense_features(trunk) + group = root.create_group(f"examples/{example_id}") + for family in DENSE_TRACKS: + values = model.dense_heads[family](dense_features)[0] + group.create_dataset(family, data=values.cpu().numpy().astype(np.float16), compressor=COMPRESSOR) + + all_site_logits = model._splice_site_logits(trunk) + target_start = (config.input_length - config.target_length) // 2 + group.create_dataset( + "splice_sites", + data=all_site_logits[0, target_start:target_start + config.target_length].cpu().numpy().astype(np.float16), + chunks=(4096, 4), compressor=COMPRESSOR, + ) + predicted_positions = model.select_junction_positions(all_site_logits)[0] + + # Usage at the model's own predicted sites (v4 is sparse; unlisted hidden sites score 0). + usage_bins = model.splice_usage_head(dense_features)[0].float() + sites = predicted_positions.cpu().numpy().astype(np.int64).reshape(-1) + sites = np.unique(sites[(sites >= target_start) & (sites < target_start + config.target_length)] + - target_start)[:MAX_USAGE_SITES] + group.create_dataset("splice_usage_positions", data=sites.astype(np.int32), compressor=COMPRESSOR) + usage = usage_bins[torch.as_tensor(sites // 128, device=usage_bins.device)] if len(sites) \ + else usage_bins[:0] + group.create_dataset("splice_usage", data=usage.cpu().numpy().astype(np.float16), + chunks=(1024, 734), compressor=COMPRESSOR) + + # Junctions: score every donor x acceptor pair of each strand, keep the strongest pairs. + positions_batch = predicted_positions[None] + site_factors = model.junction_site_factors(trunk, positions_batch) + halves = model.junction_track_half + candidates = [] + for row_start in range(0, 512, 128): + for column_start in range(0, 512, 128): + block = torch.cat([ + model.junction_block( + trunk, positions_batch, row_start, row_start + 128, + column_start, column_start + 128, track_start, + min(track_start + 64, config.junction_tracks), site_factors, + )[0] + for track_start in range(0, config.junction_tracks, 64) + ], dim=-1).float() # [128, 128, 734] + for half in (0, 1): + donors = predicted_positions[2 * half, row_start:row_start + 128] + acceptors = predicted_positions[2 * half + 1, column_start:column_start + 128] + valid = (donors[:, None] >= 0) & (acceptors[None, :] >= 0) + # Transcript direction is determined by the public strand half. + forward = donors[:, None] < acceptors[None, :] if half == 0 else donors[:, None] > acceptors[None, :] + valid = valid & forward + strength = block[..., halves == half].amax(dim=-1).masked_fill(~valid, -torch.inf) + keep = min(MAX_JUNCTION_PAIRS // 2, int(valid.sum())) + if not keep: + continue + best = strength.flatten().topk(keep) + rows, columns = best.indices // 128, best.indices % 128 + candidates.append((best.values, torch.full_like(rows, half), donors[rows], + acceptors[columns], block[rows, columns])) + if candidates: + score, half, donor, acceptor, values = (torch.cat(x) for x in zip(*candidates)) + order = score.topk(min(MAX_JUNCTION_PAIRS, len(score))).indices + half, donor, acceptor, values = half[order], donor[order], acceptor[order], values[order] + else: + half = donor = acceptor = torch.zeros(0, dtype=torch.long) + values = torch.zeros(0, config.junction_tracks) + group.create_dataset("junction_half", data=half.cpu().numpy().astype(np.int8), compressor=COMPRESSOR) + group.create_dataset("junction_donor", data=donor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junction_acceptor", data=acceptor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junctions", data=values.cpu().numpy().astype(np.float16), + chunks=(4096, 734), compressor=COMPRESSOR) + + contact = trunk.reshape( + 1, config.contact_bins, -1, config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + contact_values = 0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row]) + group.create_dataset( + "contact_maps", data=contact_values[0].cpu().numpy().astype(np.float16), + chunks=(8192, 28), compressor=COMPRESSOR, + ) + ids.append(example_id) + print(json.dumps({"event": "predict", "example": len(ids), "total": len(dataset)}), flush=True) + root.attrs.update({"schema": PREDICTION_SCHEMA, "example_ids": ids}) + (output_root / "example_ids.json").write_text(json.dumps(ids) + "\n") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--split", default="test", choices=("valid", "test"), + help="manifest to predict; the verifier uses test, local validation uses valid") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.split) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/prediction_contract.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/prediction_contract.py new file mode 100644 index 0000000000000000000000000000000000000000..2455424db002b840cf0d69456930840a7bc5f3cb --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/prediction_contract.py @@ -0,0 +1,23 @@ +"""Public prediction shapes; no trusted splice coordinates are exposed.""" + +from __future__ import annotations + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} + +PREDICTION_SCHEMA = "genome-sequence-to-function-predictions/v4" +MAX_USAGE_SITES = 4_096 +MAX_JUNCTION_PAIRS = 16_384 +# Fixed-shape arrays per example; splice usage and junctions are sparse (see contract.md). +FIXED_SHAPES = { + **{name: (1_536, tracks) for name, tracks in DENSE_TRACKS.items()}, + "splice_sites": (196_608, 4), + "contact_maps": (131_328, 28), +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/reference/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/reference/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4879a0e1a3a8a8050bb48360febee152cd141be8 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/reference/__init__.py @@ -0,0 +1 @@ +"""The reference model of the alphagenome-reference baseline.""" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/reference/alphagenome_50m/layers.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/reference/alphagenome_50m/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..7ef7b4e45a81eb774e4e92ac36e30c5abdebbe46 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/reference/alphagenome_50m/layers.py @@ -0,0 +1,134 @@ +"""Shared neural-network layers for the single compact AlphaGenome model.""" + +from __future__ import annotations + +import math + +import torch +from torch import Tensor, nn +from torch.nn import functional as F + + +class ChannelRMSNorm(nn.Module): + def __init__(self, channels: int, eps: float = 1e-6): + super().__init__() + self.weight = nn.Parameter(torch.ones(channels)) + self.eps = eps + + def forward(self, x: Tensor) -> Tensor: + variance = x.float().square().mean(dim=1, keepdim=True) + normalized = x * torch.rsqrt(variance + self.eps).to(x.dtype) + return normalized * self.weight[None, :, None].to(x.dtype) + + +class StandardizedConv1d(nn.Conv1d): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__(in_channels, out_channels, kernel_size, padding="same", bias=True) + self.scale = nn.Parameter(torch.ones(out_channels)) + nn.init.trunc_normal_(self.weight, std=1.0 / math.sqrt(in_channels * kernel_size)) + nn.init.trunc_normal_(self.bias, std=1e-4) + + def forward(self, x: Tensor) -> Tensor: + centered = self.weight - self.weight.mean(dim=(1, 2), keepdim=True) + variance = centered.float().square().mean(dim=(1, 2), keepdim=True) + fan_in = self.weight.shape[1] * self.weight.shape[2] + multiplier = self.scale[:, None, None] * torch.rsqrt( + (fan_in * variance).clamp_min(1e-4) + ).to(self.weight.dtype) + return F.conv1d(x, centered * multiplier, self.bias, padding="same") + + +class ConvBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__() + self.norm = ChannelRMSNorm(in_channels) + self.conv = StandardizedConv1d(in_channels, out_channels, kernel_size) + + def forward(self, x: Tensor) -> Tensor: + return self.conv(F.gelu(self.norm(x), approximate="tanh")) + + +def _apply_rope(x: Tensor) -> Tensor: + rotary_dim = x.shape[-1] - x.shape[-1] % 2 + if rotary_dim == 0: + return x + positions = torch.arange(x.shape[-2], device=x.device, dtype=torch.float32) + inverse_frequency = 1.0 / ( + 10_000 ** ( + torch.arange(0, rotary_dim, 2, device=x.device, dtype=torch.float32) + / rotary_dim + ) + ) + angles = torch.outer(positions, inverse_frequency) + cos = angles.cos()[None, None, :, :].to(x.dtype) + sin = angles.sin()[None, None, :, :].to(x.dtype) + even, odd = x[..., :rotary_dim:2], x[..., 1:rotary_dim:2] + rotated = torch.stack((even * cos - odd * sin, even * sin + odd * cos), dim=-1) + return torch.cat((rotated.flatten(-2), x[..., rotary_dim:]), dim=-1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.num_heads = config.num_attention_heads + self.head_dim = config.attention_head_dim + inner_dim = self.num_heads * self.head_dim + self.norm = nn.RMSNorm(config.hidden_dim) + self.qkv = nn.Linear(config.hidden_dim, 3 * inner_dim, bias=False) + self.output = nn.Linear(inner_dim, config.hidden_dim) + self.dropout = config.dropout + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + batch, length, _ = x.shape + qkv = self.qkv(self.norm(x)).view( + batch, length, 3, self.num_heads, self.head_dim + ) + q, k, value = qkv.unbind(dim=2) + attended = F.scaled_dot_product_attention( + _apply_rope(q.transpose(1, 2)), + _apply_rope(k.transpose(1, 2)), + value.transpose(1, 2), + attn_mask=attention_bias, + dropout_p=self.dropout if self.training else 0.0, + ) + return self.output(attended.transpose(1, 2).reshape(batch, length, -1)) + + +class MLPBlock(nn.Module): + def __init__(self, config): + super().__init__() + expanded = int(config.hidden_dim * config.mlp_expansion) + self.norm = nn.RMSNorm(config.hidden_dim) + self.input = nn.Linear(config.hidden_dim, expanded) + self.output = nn.Linear(expanded, config.hidden_dim) + self.dropout = nn.Dropout(config.dropout) + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor) -> Tensor: + return self.dropout( + self.output(F.gelu(self.input(self.norm(x)), approximate="tanh")) + ) + + +def bound_residual_rms(update: Tensor, cap: float) -> Tensor: + """Cap each token's branch RMS, preserving small updates and their directions.""" + if cap == 0: + return update + rms = update.float().square().mean(dim=-1, keepdim=True).clamp_min(1e-12).sqrt() + scale = (cap / rms).clamp(max=1.0) + return update * scale.to(update.dtype) + + +class SequenceBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.attention = MultiHeadAttention(config) + self.mlp = MLPBlock(config) + self.residual_rms_cap = config.residual_rms_cap + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + x = x + bound_residual_rms(self.attention(x, attention_bias), self.residual_rms_cap) + return x + bound_residual_rms(self.mlp(x), self.residual_rms_cap) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/train_single.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/train_single.py new file mode 100644 index 0000000000000000000000000000000000000000..03024393206b918eac3ae23a87c09173848b0d15 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/train_single.py @@ -0,0 +1,158 @@ +"""Resumable single-H100 training; every epoch permutes every eligible training interval.""" +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import torch +from numcodecs import blosc +from torch.utils.data import DataLoader, Subset + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset, single_example_collate +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save, _losses, _model_inputs, _to_device + + +def balanced_splice_loss(logits, target): + """Equal positive/negative contributions per type, using only training labels. + + An absent class contributes its negative loss rather than dividing by zero or being dropped. + """ + dims = tuple(range(target.ndim - 1)) + positive = target.sum(dims) + negative = (1 - target).sum(dims) + pos_loss = (torch.nn.functional.softplus(-logits) * target).sum(dims) / positive.clamp_min(1) + neg_loss = (torch.nn.functional.softplus(logits) * (1 - target)).sum(dims) / negative.clamp_min(1) + return torch.where(positive > 0, 0.5 * (pos_loss + neg_loss), neg_loss).mean() + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--input', type=Path, default=Path('/workspace/data')) + p.add_argument('--output', type=Path, required=True) + p.add_argument('--config', type=Path, default=Path(__file__).parent / 'reference/alphagenome_50m/alphagenome_50m.yaml') + p.add_argument('--resume', type=Path) + p.add_argument('--objective', choices=['reference', 'balanced_splice'], default='balanced_splice') + p.add_argument('--epochs', type=int, default=4) + p.add_argument('--stop-at', type=int, default=0, help='Stop after this total number of examples, preserving exact sampler position') + p.add_argument('--workers', type=int, default=8) + p.add_argument('--threads', type=int, default=4) + p.add_argument('--seed', type=int, default=17) + p.add_argument('--learning-rate', type=float, default=2e-4) + p.add_argument('--min-learning-rate', type=float, default=2e-5) + p.add_argument('--warmup', type=int, default=500) + p.add_argument('--save-every', type=int, default=2000) + p.add_argument('--keep-every', type=int, default=10000, help='Keep immutable intermediate checkpoints') + p.add_argument('--log-every', type=int, default=100) + args = p.parse_args() + torch.set_num_threads(args.threads) + torch.set_num_interop_threads(1) + blosc.set_nthreads(1) + if hasattr(os, 'sched_getaffinity'): + os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) + torch.manual_seed(args.seed) + device = torch.device('cuda:0') + config = ModelConfig.from_yaml(args.config) + model = CompactModel(config).to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=1e-4) + dataset = AlphaGenomeDataset(args.input, 'train') + model.set_junction_track_halves(dataset.junction_track_halves) + args.output.mkdir(parents=True, exist_ok=True) + total = len(dataset) * args.epochs + step, elapsed_before = 0, 0.0 + lineage = {} + if args.resume: + state = torch.load(args.resume, map_location='cpu', weights_only=True) + lineage = {k: state[k] for k in ['intervention', 'interventions'] if k in state} + if state['config'] != config.to_dict() or state['examples'] != len(dataset): + raise ValueError('resume config or dataset size differs') + if state['schedule_epochs'] != args.epochs or state['objective'] != args.objective: + raise ValueError('resume schedule or objective differs') + if state['seed'] != args.seed: + raise ValueError('resume shuffle seed differs') + previous = state.get('training_arguments', {}) + for field in ['learning_rate', 'min_learning_rate', 'warmup']: + if field in previous and previous[field] != getattr(args, field): + raise ValueError(f'resume {field} differs') + model.load_state_dict(state['model'], strict=True) + optimizer.load_state_dict(state['optimizer']) + step = state['global_step'] + elapsed_before = state.get('elapsed_seconds', 0.0) + torch.set_rng_state(state['rng_cpu']) + torch.cuda.set_rng_state(state['rng_cuda'], device) + start = time.monotonic() + def emit(event, **values): + record = {'event': event, 'step': step, 'elapsed_seconds': elapsed_before + time.monotonic() - start, **values} + print(json.dumps(record), flush=True) + def save(name): + _atomic_save({ + **lineage, + 'schema': 'seqfunc-single/v1', 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), 'config': config.to_dict(), + 'global_step': step, 'epoch': step // len(dataset), 'offset': step % len(dataset), + 'examples': len(dataset), 'schedule_epochs': args.epochs, + 'objective': args.objective, 'seed': args.seed, + 'training_arguments': {k: str(v) if isinstance(v, Path) else v for k, v in vars(args).items()}, + 'rng_cpu': torch.get_rng_state(), 'rng_cuda': torch.cuda.get_rng_state(device), + 'elapsed_seconds': elapsed_before + time.monotonic() - start, + 'provenance': {'data': str(args.input), 'split': 'train', 'pretrained': False, + 'device': torch.cuda.get_device_name(0), 'world_size': 1}, + }, args.output / name) + emit('train_start', parameters=count_parameters(model), examples=len(dataset), objective=args.objective, + config=config.to_dict(), schedule_examples=total, device=torch.cuda.get_device_name(0)) + model.train() + for epoch in range(step // len(dataset), args.epochs): + generator = torch.Generator().manual_seed(args.seed + epoch) + order = torch.randperm(len(dataset), generator=generator).tolist() + offset = step % len(dataset) + emit('epoch_order', epoch=epoch + 1, offset=offset, count=len(order), + permutation_sha256=hashlib.sha256(torch.tensor(order, dtype=torch.int64).numpy().tobytes()).hexdigest()) + loader = DataLoader(Subset(dataset, order[offset:]), batch_size=1, shuffle=False, + num_workers=args.workers, pin_memory=True, collate_fn=single_example_collate, + persistent_workers=False, prefetch_factor=2 if args.workers else None) + for sample in loader: + sample = _to_device(sample, device) + inputs, target, valid = _model_inputs(sample) + progress = min(max(step - args.warmup, 0) / max(total - args.warmup, 1), 1) + lr = args.min_learning_rate + 0.5 * (args.learning_rate - args.min_learning_rate) * (1 + math.cos(math.pi * progress)) + lr *= min((step + 1) / max(args.warmup, 1), 1) + for group in optimizer.param_groups: + group['lr'] = lr + optimizer.zero_grad(set_to_none=True) + with torch.autocast('cuda', dtype=torch.bfloat16): + outputs = model(**inputs) + losses = _losses(outputs, sample, target, valid) + if args.objective == 'balanced_splice': + losses['splice_sites'] = balanced_splice_loss(outputs['splice_sites'], sample['splice_sites'][None]) + losses['total'] = torch.stack([v for k, v in losses.items() if k != 'total']).mean() + if not torch.isfinite(losses['total']): + raise FloatingPointError(f'nonfinite loss at {step}') + losses['total'].backward() + norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0, error_if_nonfinite=True) + optimizer.step() + step += 1 + if step == 1 or step % args.log_every == 0: + emit('train_step', epoch=epoch + 1, example_id=sample['example_id'], learning_rate=lr, + gradient_norm=float(norm), losses={k: float(v.detach()) for k, v in losses.items()}, + peak_gpu_bytes=torch.cuda.max_memory_allocated()) + if step % args.save_every == 0: + save('progress.pt') + if args.keep_every and step % args.keep_every == 0: + save(f'step-{step:06d}.pt') + if args.stop_at and step >= args.stop_at: + save('last.pt') + emit('train_segment_complete', completed_epochs=step // len(dataset), offset=step % len(dataset)) + return + save('last.pt') + save(f'epoch-{epoch + 1:02d}.pt') + emit('epoch_complete', completed_epochs=epoch + 1) + emit('train_complete') + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/training_config.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/training_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fa30b0f9f20f1dd25a2c8426ff18d3afab3ba47f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/training_config.yaml @@ -0,0 +1,32 @@ +attention_head_dim: 64 +channel_growth: 32 +contact_bin_size: 2048 +contact_tracks: 28 +conv_kernel_size: 5 +dense_bin_size: 128 +dna_kernel_size: 15 +dropout: 0.0 +enable_pairwise: false +encoder_width: 32 +gradient_checkpointing: true +head_hidden_size: 256 +hidden_dim: 384 +input_length: 1048576 +junction_rank: 32 +junction_tracks: 734 +local_context_blocks: 6 +local_to_trunk: true +local_width: 32 +mlp_expansion: 4.0 +num_attention_heads: 6 +num_downsampling_blocks: 7 +num_sequence_blocks: 6 +pair_downsample: 16 +pair_update_interval: 2 +pair_width: 32 +residual_rms_cap: 0.1 +splice_context_mode: offset +splice_usage_tracks: 734 +stem_stride: 8 +target_length: 196608 +trunk_bin_size: 1024 diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/training_history/execution_change.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/training_history/execution_change.json new file mode 100644 index 0000000000000000000000000000000000000000..93f72a0c4f5f51e9192005535fc45e6aa3bbd671 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/training_history/execution_change.json @@ -0,0 +1,10 @@ +{ + "adopted_utc": "2026-10-24T21:51:59.762636+00:00", + "change": "Activation checkpoint boundaries only; all model operations, parameters, objective, data and schedule unchanged.", + "example_boundary": 1000, + "before": "Recompute whole local path during backward.", + "after": "Recompute each local residual block during backward.", + "evidence": "CPU gradients and all eleven outputs exactly match. Full-shape BF16 outputs/losses are bit-exact; gradient relative L2 discrepancy .0009547 versus .0009534 for an unchanged repeat, consistent with existing reduction variability. Peak memory40.11 to15.99GiB; isolated compute .11131 to.10504seconds/example.", + "initial_source_manifest_sha256": "18329ef35e89fc5aeb50ccc045b08db690e6c8ef40d661b01ddb74c7f32b6f0b", + "no_benchmark_weights_retained": true +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/training_history/memory-check-interruption.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/training_history/memory-check-interruption.json new file mode 100644 index 0000000000000000000000000000000000000000..19bc02cd2784b11f5f3e658efa28017639c2efeb --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/training_history/memory-check-interruption.json @@ -0,0 +1,19 @@ +{ + "time_utc": "2026-10-24T21:49:06.154124+00:00", + "reason": "Mechanical activation-checkpoint memory/equivalence check; optimizer, data order and model math preserved.", + "process_group": 4169, + "checkpoints": [ + { + "run": "scale13-small", + "step": 1000, + "path": "/workspace/research/experiments/scale13-small/step-001000.pt", + "sha256": "a53ea6d92c21c06576c734dc05eac6e3f21d33701f25bfc335817a6e6ec197fc" + }, + { + "run": "scale13-large", + "step": 1000, + "path": "/workspace/research/experiments/scale13-large/step-001000.pt", + "sha256": "7398dd28297d6cc3134b8d3b0cea703fb0bb0a8e60b4c0c2d81abe04c1d7dc43" + } + ] +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/training_loss_scales.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/training_loss_scales.json new file mode 100644 index 0000000000000000000000000000000000000000..a8f484c060a0d4300b25557736abf741d6d67b15 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S020/training_loss_scales.json @@ -0,0 +1,32 @@ +{ + "split": "train", + "examples": 256, + "seed": 1729, + "source_moments_sha256": "7b7543c4d4d8d82ec9f9bc3f942ca65595671322f62c19391591df827d8fc588", + "training_indices_sha256": "b82df9ae11e95d9be5d6e5f85839cf23e25bbbe0a195eaee266ff52d366800dc", + "method": "reference MSE times inverse mean within-track training variance; floor0.01; splice BCE unchanged", + "mean_track_variance": { + "atac": 0.9602319499041136, + "dnase": 1.0768856864858252, + "procap": 0.3380534137610864, + "cage": 0.3114663911919963, + "rna_seq": 0.66617486157613, + "chip_tf": 2.7198812117366296, + "chip_histone": 3.405612880716664, + "contact_maps": 0.14704202117243276, + "splice_usage": 0.07913355074122039, + "junctions": 0.17919098248535248 + }, + "family_loss_scales": { + "atac": 1.041415045708339, + "dnase": 0.9286036694045732, + "procap": 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b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/reference/alphagenome_50m/alphagenome_50m.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 32 +channel_growth: 32 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 384 +num_sequence_blocks: 6 +num_attention_heads: 6 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 256 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/reference/alphagenome_50m/cached_read_store.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/reference/alphagenome_50m/cached_read_store.py new file mode 100644 index 0000000000000000000000000000000000000000..9c3677c1e8518ea97db0dd6759f3d636f5fff27c --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/reference/alphagenome_50m/cached_read_store.py @@ -0,0 +1,71 @@ +"""Optional local byte cache for immutable training Zarr stores. + +Source storage stays read-only. Cached entries contain a key fingerprint, length, +and SHA256 of the original compressed bytes; every hit verifies all three. Writes +replace a private temporary inode atomically. A missing/corrupt/unwritable cache +falls back to the original store, so this layer changes neither values nor keys. +Only training loaders may opt into it; it is not an inference asset. +""" +import hashlib +import os +from pathlib import Path +import struct +import tempfile + +from .direct_read_store import ParallelDirectReadDirectoryStore + +MAGIC = b'SFBC0001' +HEADER = struct.Struct('<8sQ32s32s') + + +class CachedReadDirectoryStore(ParallelDirectReadDirectoryStore): + def __init__(self, path, *, cache_root, namespace, io_workers=4): + super().__init__(path, io_workers=io_workers) + if not namespace: + raise ValueError('An immutable input namespace is required') + self.cache_root = Path(cache_root) + self.namespace = namespace + self.store_key = hashlib.sha256((namespace+'\0'+os.path.abspath(path)).encode()).digest() + self.statistics = {'hit': 0, 'miss': 0, 'corrupt': 0, 'write_failure': 0} + + def cache_location(self, key): + key = self._normalize_key(key) + identity = hashlib.sha256(self.store_key+key.encode()).digest() + name = identity.hex() + return self.cache_root/name[:2]/name[2:], identity + + def __getitem__(self, key): + key = self._normalize_key(key) + destination, identity = self.cache_location(key) + try: + blob = destination.read_bytes() + if len(blob) >= HEADER.size: + magic, size, cached_key, checksum = HEADER.unpack_from(blob) + payload = blob[HEADER.size:] + if (magic == MAGIC and size == len(payload) and cached_key == identity + and hashlib.sha256(payload).digest() == checksum): + self.statistics['hit'] += 1 + return payload + self.statistics['corrupt'] += 1 + except OSError: + pass + self.statistics['miss'] += 1 + payload = super().__getitem__(key) + temporary = None + try: + destination.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile(prefix='.pending-', dir=destination.parent, delete=False) as stream: + temporary = stream.name + stream.write(HEADER.pack(MAGIC, len(payload), identity, hashlib.sha256(payload).digest())) + stream.write(payload) + os.replace(temporary, destination) + temporary = None + except OSError: + self.statistics['write_failure'] += 1 + finally: + if temporary is not None: + try: + os.unlink(temporary) + except OSError: + pass + return payload diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/reference/alphagenome_50m/reference_large.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/reference/alphagenome_50m/reference_large.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e403757c49eb6e6419927210c7053710964213d1 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/reference/alphagenome_50m/reference_large.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 96 +channel_growth: 96 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 1024 +num_sequence_blocks: 16 +num_attention_heads: 16 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 768 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/reference/alphagenome_50m/train_eval.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/reference/alphagenome_50m/train_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..92f9a3f315013630fdd2e03fb6840a6c77840edd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/reference/alphagenome_50m/train_eval.py @@ -0,0 +1,537 @@ +"""Train and stream-evaluate the <=50M compact AlphaGenome baseline.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import numpy as np +import torch +from torch import Tensor +from torch.nn import functional as F +from torch.utils.data import DataLoader, Subset + +from .dataset import ( + AlphaGenomeDataset, + DENSE_FAMILIES, + single_example_collate, +) +from .model import ( + ALL_FAMILIES, + CompactModel, + ModelConfig, + count_parameters, +) + + +DEFAULT_CONFIG = Path(__file__).with_name("alphagenome_50m.yaml") + + +def _device(name: str) -> torch.device: + if name == "auto": + name = "cuda" if torch.cuda.is_available() else "cpu" + if name == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("--device cuda requested but CUDA is unavailable") + return torch.device(name) + + +def _autocast(device: torch.device): + enabled = device.type == "cuda" + dtype = torch.bfloat16 if enabled and torch.cuda.is_bf16_supported() else torch.float16 + return torch.autocast(device_type=device.type, dtype=dtype, enabled=enabled) + + +def _distributed() -> tuple[int, int, int]: + """(rank, local_rank, world_size) under torchrun; (0, 0, 1) otherwise.""" + world = int(os.environ.get("WORLD_SIZE", "1")) + if world > 1 and not torch.distributed.is_initialized(): + torch.distributed.init_process_group("nccl") + return int(os.environ.get("RANK", "0")), int(os.environ.get("LOCAL_RANK", "0")), world + + +def _loader(dataset, *, workers: int, shuffle: bool, sampler=None) -> DataLoader: + return DataLoader( + dataset, + batch_size=1, + shuffle=shuffle and sampler is None, + sampler=sampler, + num_workers=workers, + persistent_workers=workers > 0, + pin_memory=torch.cuda.is_available(), + collate_fn=single_example_collate, + ) + + +def _to_device(value, device: torch.device): + if isinstance(value, Tensor): + return value.to(device, non_blocking=True) + if isinstance(value, dict): + return {key: _to_device(item, device) for key, item in value.items()} + return value + + +def _match_coordinate_queries( + scoring: dict[str, Tensor], positions: Tensor, track_halves: Tensor +) -> tuple[Tensor, Tensor, Tensor]: + """Match trusted coordinate queries to typed position axes like the grader.""" + device = positions.device + tracks = scoring["track"].detach().cpu().numpy() + halves_cpu = track_halves.detach().cpu().numpy()[tracks] + # Build indices on CPU, then copy once. Writing three CUDA scalars per valid query + # otherwise launches thousands of tiny kernels per example, especially costly when + # training and evaluation share one GPU. The matching semantics are unchanged. + queries = np.zeros((len(tracks), 3), dtype=np.int64) + queries[:, 2] = tracks + valid = scoring["valid"].clone() + missing = np.ones(len(tracks), dtype=bool) + position_rows = positions.detach().cpu().numpy() + donors_cpu = scoring["donor_position"].detach().cpu().numpy() + acceptors_cpu = scoring["acceptor_position"].detach().cpu().numpy() + valid_indices = np.flatnonzero(valid.detach().cpu().numpy()) + lookups = [ + {int(coordinate): index for index, coordinate in enumerate(axis) if coordinate >= 0} + for axis in position_rows + ] + for index in valid_indices: + half = int(halves_cpu[index]) + row = lookups[2 * half].get(int(donors_cpu[index])) + column = lookups[2 * half + 1].get(int(acceptors_cpu[index])) + if row is not None and column is not None: + queries[index, 0] = row + queries[index, 1] = column + missing[index] = False + return (torch.from_numpy(queries).to(device).unsqueeze(0), valid, + torch.from_numpy(missing).to(device)) + + +def _model_inputs(sample: dict) -> tuple[dict, Tensor, Tensor]: + queries, query_valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + sample["site_positions"], + sample["junction_track_halves"], + ) + if (query_valid & missing).any(): + raise ValueError("trusted scoring coordinates do not match target positions") + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + return { + "dna": sample["dna"].unsqueeze(0), + "usage_positions": sample["usage_positions"].unsqueeze(0), + "junction_site_positions": sample["site_positions"].unsqueeze(0), + "junction_queries": queries, + }, junction_target, query_valid + + +def _losses(outputs: dict[str, Tensor], sample: dict, junction_target: Tensor, + junction_valid: Tensor) -> dict[str, Tensor]: + losses = {} + for family in DENSE_FAMILIES: + target = torch.asinh(sample["dense"][family]["values"]).unsqueeze(0) + mask = sample["dense"][family]["mask"].unsqueeze(0) + residual = torch.where(mask, outputs[family] - target, 0.0) + losses[family] = residual.square().sum() / mask.sum().clamp_min(1) + losses["splice_sites"] = F.binary_cross_entropy_with_logits( + outputs["splice_sites"], sample["splice_sites"].unsqueeze(0) + ) + usage_target = torch.asinh(sample["splice_usage"]["values"]).unsqueeze(0) + usage_mask = sample["splice_usage"]["mask"].unsqueeze(0) + usage_residual = torch.where(usage_mask, outputs["splice_usage"] - usage_target, 0.0) + losses["splice_usage"] = usage_residual.square().sum() / usage_mask.sum().clamp_min(1) + junction_residual = outputs["junctions"][0] - junction_target[0] + losses["junctions"] = ( + junction_residual[junction_valid].square().mean() + if junction_valid.any() else junction_residual.sum() * 0 + ) + contact_target = torch.asinh(sample["contact_maps"]).unsqueeze(0) + contact_mask = sample["contact_map_mask"].unsqueeze(0) + contact_residual = torch.where( + contact_mask, outputs["contact_maps"] - contact_target, 0.0 + ) + losses["contact_maps"] = contact_residual.square().sum() / contact_mask.sum().clamp_min(1) + losses["total"] = torch.stack([losses[name] for name in ALL_FAMILIES]).mean() + return losses + + +def _validation_inference( + model: CompactModel, sample: dict +) -> tuple[dict[str, Tensor], Tensor, Tensor, Tensor]: + """Use the same predicted-position path as a formal v3 submission.""" + trunk = model._trunk(sample["dna"].unsqueeze(0)) + dense_features = model._dense_features(trunk) + outputs = { + family: model.dense_heads[family](dense_features).float() + for family in DENSE_FAMILIES + } + all_sites = model._splice_site_logits(trunk) + target_start = (model.config.input_length - model.config.target_length) // 2 + outputs["splice_sites"] = all_sites[:, target_start:target_start + model.config.target_length] + usage_bins = model.splice_usage_head(dense_features).float() + usage_indices = torch.div(sample["usage_positions"], model.config.dense_bin_size, + rounding_mode="floor") + outputs["splice_usage"] = usage_bins[:, usage_indices] + predicted_positions = model.select_junction_positions(all_sites) + + matched, valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + predicted_positions[0], + sample["junction_track_halves"], + ) + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + junction_prediction = torch.zeros(len(valid), device=valid.device) + if (~missing & valid).any(): + selected = ~missing & valid + junction_prediction[selected] = model._junction_predictions( + trunk, predicted_positions, matched[:, selected] + )[0] + outputs["junctions"] = junction_prediction.unsqueeze(0) + + contact = trunk.reshape( + 1, model.config.contact_bins, -1, model.config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + outputs["contact_maps"] = (0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row])).float() + return outputs, junction_target, valid, missing + + +def _atomic_save(payload: dict, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(f".{path.name}.tmp") + torch.save(payload, temporary) + os.replace(temporary, path) + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(8 * 1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _resume_position(payload: dict, *, examples: int, epochs: int) -> tuple[int, int]: + epoch = int(payload["epoch"]) + step = int(payload["global_step"]) + kind = payload.get("checkpoint_kind") + if kind not in (None, "epoch") or step != epoch * examples: + raise ValueError("only a completed-epoch checkpoint can resume training") + if payload.get("examples", examples) != examples or payload.get("schedule_epochs", epochs) != epochs: + raise ValueError("checkpoint data size or training schedule differs from this run") + return epoch, step + + +def train(args: argparse.Namespace) -> None: + # Multi-GPU with `torchrun --nproc-per-node N train_eval.py train ...`: one example per GPU per + # step (data parallel), rank 0 writes checkpoints and logs. Model size is bounded by the + # training budget, not by a parameter cap. + rank, local_rank, world = _distributed() + config = ModelConfig.from_yaml(args.config) + device = torch.device("cuda", local_rank) if world > 1 else _device(args.device) + if world > 1: + torch.cuda.set_device(device) + torch.manual_seed(args.seed) + model = CompactModel(config).to(device) + parameters = count_parameters(model) + optimizer = torch.optim.AdamW( + model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay + ) + scaler = torch.amp.GradScaler("cuda", enabled=device.type == "cuda") + full_dataset = AlphaGenomeDataset(args.input, "train") + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + start_epoch, global_step = 0, 0 + if args.resume: + payload = torch.load(args.resume, map_location="cpu", weights_only=True) + if payload.get("config") != config.to_dict(): + raise ValueError("checkpoint configuration does not match --config") + model.load_state_dict(payload["model"], strict=True) + optimizer.load_state_dict(payload["optimizer"]) + scaler.load_state_dict(payload.get("scaler", {})) + start_epoch, global_step = _resume_position(payload, examples=size, epochs=args.epochs) + per_rank = math.ceil(size / world) + steps = max(1, per_rank * args.epochs) + module = model + if world > 1: + model = torch.nn.parallel.DistributedDataParallel( + model, device_ids=[local_rank], find_unused_parameters=True) + sampler = (torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world, rank=rank, + shuffle=True, seed=args.seed) + if world > 1 else None) + stop_after_epoch = args.stop_after_epoch or args.epochs + output = args.output.resolve() + output.mkdir(parents=True, exist_ok=True) + started = time.monotonic() + if rank == 0: + print(json.dumps({"event": "train_start", "parameters": parameters, "examples": size, + "device": str(device), "world_size": world}), flush=True) + + for epoch in range(start_epoch, stop_after_epoch): + model.train() + if sampler is not None: + sampler.set_epoch(epoch) + loader = _loader(dataset, workers=args.workers, shuffle=True, sampler=sampler) + for sample in loader: + sample = _to_device(sample, device) + inputs, junction_target, junction_valid = _model_inputs(sample) + progress = min((global_step + 1) / steps, 1.0) + learning_rate = args.min_learning_rate + 0.5 * ( + args.learning_rate - args.min_learning_rate + ) * (1.0 + math.cos(math.pi * progress)) + for group in optimizer.param_groups: + group["lr"] = learning_rate + optimizer.zero_grad(set_to_none=True) + with _autocast(device): + predictions = model(**inputs) + losses = _losses(predictions, sample, junction_target, junction_valid) + if not torch.isfinite(losses["total"]): + raise FloatingPointError(f"non-finite loss at step {global_step}") + scaler.scale(losses["total"]).backward() + scaler.unscale_(optimizer) + gradient_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_gradient_norm) + scaler.step(optimizer) + scaler.update() + global_step += 1 + if rank == 0 and (global_step == 1 or global_step % args.log_every == 0): + print( + json.dumps( + { + "event": "train_step", + "epoch": epoch + 1, + "step": global_step, + "loss": float(losses["total"].detach()), + "gradient_norm": float(gradient_norm), + "learning_rate": learning_rate, + "elapsed_seconds": time.monotonic() - started, + } + ), + flush=True, + ) + if rank == 0 and global_step % args.save_every == 0: + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch, + "global_step": global_step, + "checkpoint_kind": "progress", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "progress.pt", + ) + if world > 1: + torch.distributed.barrier() + if rank != 0: + continue + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch + 1, + "global_step": global_step, + "checkpoint_kind": "epoch", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "last.pt", + ) + if world > 1: + torch.distributed.barrier() + torch.distributed.destroy_process_group() + if rank != 0: + return + checkpoint = output / "last.pt" + manifest = { + "schema": "alphagenome-50m-real/v3", + "checkpoint": {"path": checkpoint.name, "sha256": _sha256(checkpoint)}, + "parameters": parameters, + "config": config.to_dict(), + "output_families": list(ALL_FAMILIES), + "epochs": args.epochs, + "completed_epochs": stop_after_epoch, + "global_step": global_step, + } + (output / "model_manifest.json").write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "train_complete", **manifest}), flush=True) + + +class PerTrackMoments: + def __init__(self, tracks: int): + self.n = np.zeros(tracks, dtype=np.int64) + self.sum_x = np.zeros(tracks, dtype=np.float64) + self.sum_y = np.zeros(tracks, dtype=np.float64) + self.sum_x2 = np.zeros(tracks, dtype=np.float64) + self.sum_y2 = np.zeros(tracks, dtype=np.float64) + self.sum_xy = np.zeros(tracks, dtype=np.float64) + + def update(self, prediction: Tensor, target: Tensor, mask: Tensor | None = None) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1, prediction.shape[-1]) + y = target.detach().double().cpu().numpy().reshape(-1, target.shape[-1]) + valid = np.ones_like(y, dtype=bool) if mask is None else mask.detach().cpu().numpy().reshape(y.shape).astype(bool) + self.n += valid.sum(axis=0) + self.sum_x += np.where(valid, x, 0).sum(axis=0, dtype=np.float64) + self.sum_y += np.where(valid, y, 0).sum(axis=0, dtype=np.float64) + self.sum_x2 += np.where(valid, x * x, 0).sum(axis=0, dtype=np.float64) + self.sum_y2 += np.where(valid, y * y, 0).sum(axis=0, dtype=np.float64) + self.sum_xy += np.where(valid, x * y, 0).sum(axis=0, dtype=np.float64) + + def update_indexed(self, prediction: Tensor, target: Tensor, track: Tensor) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1) + y = target.detach().double().cpu().numpy().reshape(-1) + indices = track.detach().cpu().numpy().reshape(-1).astype(np.int64) + self.n += np.bincount(indices, minlength=len(self.n)) + self.sum_x += np.bincount(indices, weights=x, minlength=len(self.n)) + self.sum_y += np.bincount(indices, weights=y, minlength=len(self.n)) + self.sum_x2 += np.bincount(indices, weights=x * x, minlength=len(self.n)) + self.sum_y2 += np.bincount(indices, weights=y * y, minlength=len(self.n)) + self.sum_xy += np.bincount(indices, weights=x * y, minlength=len(self.n)) + + def report(self) -> dict: + numerator = self.n * self.sum_xy - self.sum_x * self.sum_y + denominator = np.sqrt( + np.maximum(self.n * self.sum_x2 - self.sum_x**2, 0) + * np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + ) + target_ss = np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + scorable = (self.n >= 2) & (target_ss > 0) + pearson = np.full_like(numerator, np.nan) + nonconstant = scorable & (denominator > 0) + pearson[nonconstant] = numerator[nonconstant] / denominator[nonconstant] + pearson[scorable & ~nonconstant] = 0.0 + return { + "mean_pearson": float(pearson[scorable].mean()) if scorable.any() else 0.0, + "valid_tracks": int(scorable.sum()), + "per_track_pearson": [float(value) if np.isfinite(value) else None for value in pearson], + "values_per_track": self.n.tolist(), + } + + +def evaluate(args: argparse.Namespace) -> None: + device = _device(args.device) + payload = torch.load(args.checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config).to(device) + model.load_state_dict(payload["model"], strict=True) + model.eval() + full_dataset = AlphaGenomeDataset(args.input, args.split) + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + metrics = { + **{family: PerTrackMoments(config_value) for family, config_value in { + "atac": 167, "dnase": 305, "procap": 12, "cage": 546, + "rna_seq": 667, "chip_tf": 1617, "chip_histone": 1116, + }.items()}, + "splice_sites": PerTrackMoments(4), + "splice_usage": PerTrackMoments(config.splice_usage_tracks), + "junctions": PerTrackMoments(config.junction_tracks), + "contact_maps": PerTrackMoments(config.contact_tracks), + } + with torch.inference_mode(): + for index, sample in enumerate(_loader(dataset, workers=args.workers, shuffle=False)): + sample = _to_device(sample, device) + outputs, junction_target, junction_valid, junction_missing = ( + _validation_inference(model, sample) + ) + # Match predict.py: FP32 inference is serialized to FP16 Zarr values. + outputs = {name: value.half().float() for name, value in outputs.items()} + for family in DENSE_FAMILIES: + metrics[family].update( + outputs[family][0], + torch.asinh(sample["dense"][family]["values"]), + sample["dense"][family]["mask"], + ) + metrics["splice_sites"].update(outputs["splice_sites"][0], sample["splice_sites"]) + metrics["splice_usage"].update( + outputs["splice_usage"][0], + torch.asinh(sample["splice_usage"]["values"]), + sample["splice_usage"]["mask"], + ) + junction_track = sample["junction_scoring"]["track"] + metrics["junctions"].update_indexed( + outputs["junctions"][0][junction_valid], + junction_target[0][junction_valid], + junction_track[junction_valid], + ) + metrics["contact_maps"].update( + outputs["contact_maps"][0], + torch.asinh(sample["contact_maps"]), + sample["contact_map_mask"], + ) + if index == 0 or (index + 1) % args.log_every == 0 or index + 1 == size: + print(json.dumps({"event": "evaluate_progress", "examples": index + 1, "total": size}), flush=True) + families = {name: accumulator.report() for name, accumulator in metrics.items()} + valid_family_scores = [item["mean_pearson"] for item in families.values()] + report = { + "schema": "alphagenome-50m-evaluation/v3", + "split": args.split, + "examples": size, + "metric_space": ( + "asinh continuous targets and binary splice-site targets; " + "junction Pearson uses hidden coordinate queries and predicted endpoints; " + "FP32 inference rounded to FP16 prediction values" + ), + "families": families, + "overall_mean_family_pearson": float(np.mean(valid_family_scores)), + } + args.report.parent.mkdir(parents=True, exist_ok=True) + args.report.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "evaluate_complete", "overall_mean_family_pearson": report["overall_mean_family_pearson"]}), flush=True) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + common = argparse.ArgumentParser(add_help=False) + common.add_argument("--input", required=True, type=Path) + common.add_argument("--config", type=Path, default=DEFAULT_CONFIG) + common.add_argument("--workers", type=int, default=2) + common.add_argument("--max-examples", type=int, default=0) + common.add_argument("--device", choices=("auto", "cuda", "cpu"), default="auto") + common.add_argument("--log-every", type=int, default=25) + subparsers = parser.add_subparsers(dest="command", required=True) + train_parser = subparsers.add_parser("train", parents=[common]) + train_parser.add_argument("--output", required=True, type=Path) + train_parser.add_argument("--epochs", type=int, default=10) + train_parser.add_argument("--stop-after-epoch", type=int) + train_parser.add_argument("--learning-rate", type=float, default=2e-4) + train_parser.add_argument("--min-learning-rate", type=float, default=2e-5) + train_parser.add_argument("--weight-decay", type=float, default=1e-4) + train_parser.add_argument("--max-gradient-norm", type=float, default=5.0) + train_parser.add_argument("--save-every", type=int, default=250) + train_parser.add_argument("--seed", type=int, default=17) + train_parser.add_argument("--resume", type=Path) + train_parser.set_defaults(run=train) + evaluate_parser = subparsers.add_parser("evaluate", parents=[common]) + evaluate_parser.add_argument("--checkpoint", required=True, type=Path) + evaluate_parser.add_argument("--split", choices=("train", "valid", "test"), required=True) + evaluate_parser.add_argument("--report", required=True, type=Path) + evaluate_parser.set_defaults(run=evaluate) + args = parser.parse_args() + if args.workers < 0 or args.max_examples < 0: + parser.error("workers/max-examples must be nonnegative") + if args.command == "train" and ( + args.epochs < 1 + or (args.stop_after_epoch is not None + and not 1 <= args.stop_after_epoch <= args.epochs) + ): + parser.error("epochs must be positive and stop-after-epoch must be within the schedule") + args.run(args) + + +if __name__ == "__main__": + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/training_history/bytecache-integration_check.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/training_history/bytecache-integration_check.json new file mode 100644 index 0000000000000000000000000000000000000000..1377348921feb42310e66b133df8ebee9f0d591c --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/training_history/bytecache-integration_check.json @@ -0,0 +1,166 @@ +{ + "created_utc": "2026-10-25T03:48:07.725319+00:00", + "preparation_sha256": "aeb7f326fdddd2dfce6e9df162fd214565b11bb03049d982f0253290d7217caa", + "source_sha256": "b0921bfd3d3dc47f3cb82debc0b492d8291693c40e0cef028764c713c1257e85", + "original_full256_report_sha256": "e6620ff13b00a36857dad22fa0c9d1ad5c2b266eca70773650d280998ba20005", + "examples": 16, + "worker_processes": 4, + "ordered_indices": [ + 38509, + 39126, + 38926, + 17432, + 6551, + 22599, + 24749, + 10790, + 7022, + 30789, + 17340, + 40258, + 26222, + 22674, + 23658, + 17167 + ], + "all_returned_training_ids_and_tensor_bytes_exact": true, + "reference_and_cache_both_match_original_report": true, + "public_inference_and_validation_cache_disabled_despite_environment": true, + "default_cache_disabled": true, + "models_and_trainers_unchanged": true, + "rows": { + "reference": [ + { + "id": "agv2-8556145a51c1c60ee1ccc11e", + "sha256": "3793afb7f6108df3c7483c045a40be7bf1dc65dd49fa5cd7eaf6ce88ab83cec7" + }, + { + "id": "agv2-d6dcfb9c16994d850e7f7602", + "sha256": "bf0d4a3ecd1fa488d961dcd1ceb3b311c915512d8decece7c5426dd49714b06f" + }, + { + "id": "agv2-618847475be4ec5e05f1f4f9", + "sha256": "da8a54e2f8c77bbe46053a7d4cf009cbd52aae638c68faa05b1f11e8bed10977" + }, + { + "id": 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"id": "agv2-618847475be4ec5e05f1f4f9", + "sha256": "da8a54e2f8c77bbe46053a7d4cf009cbd52aae638c68faa05b1f11e8bed10977" + }, + { + "id": "agv2-043fd8c479dd35aa36eaa03b", + "sha256": "10f5df0a669885f382939a29b545124c6606905a6fc302a6bb5cffab31c3b4c5" + }, + { + "id": "agv2-e402a204ca2ba47b2d67412d", + "sha256": "272ea0ea2267a059e535b027edaedb866d39ffb3bded7059be0d28df59446aaf" + }, + { + "id": "agv2-83b57c27b623d818719d9a3c", + "sha256": "d9a208c403b8715bb61f3739d32db0d8e5d367549e1cce65f632fb25adf311c3" + }, + { + "id": "agv2-9f4bc2e53b583656ea30af1c", + "sha256": "283e30c384714c96dea332bbdbe7a36a9496ffe830bdff766092692f97a05aef" + }, + { + "id": "agv2-50625982f9cf30a65da05e6a", + "sha256": "18f71a8f272728f6b492eb238d5509527774ace00708f018d81e2543a1159056" + }, + { + "id": "agv2-c2ac73f0cfe05dd0667f3ae3", + "sha256": "964c067f998b523ed7e2e5e077e71f3dbbe20816fa060f977ac8ad733eda80bb" + }, + { + "id": "agv2-93075cb0a1390d16d5e3ff5f", + "sha256": "54bb0017a5f439f16b9bd771963f4d4db975215ac682952409b6127ef60d6423" + }, + { + "id": "agv2-6d48b3a065397dd93057abd7", + "sha256": "3785743dda56f1e2c4a75c38ee69923a0cf43202846cb37a7503727ff332b199" + }, + { + "id": "agv2-8776ebdc2068929446d1d127", + "sha256": "f2a1d3aa93269a1480deeeb7dcbb07d1c13b04722a5b97456f2dafd4867875f0" + }, + { + "id": "agv2-346477626eeb3a049727a743", + "sha256": "c97666f9ee8f56ff62818ddcab1ed806f7a7deb6f06c5a9dc537561a7a8a0e80" + }, + { + "id": "agv2-00537d263d837f5824cc44ee", + "sha256": "8d8cc2d1a3f69383d73a17391e65360e07fb11b4bd5f0cd1dd6ef28c2f00e46d" + }, + { + "id": "agv2-ae12b59b048cc5fc83b77de5", + "sha256": "dc90aa36afd13521189d81265ee4cdfbb7bf2aee75e24e5acba3224a8c672ef2" + }, + { + "id": "agv2-a85d0f912d8551638f7074cf", + "sha256": "db7758c1aa4b30fe6dce5d8911217063cee3a6f163ec23f458601ad355d29768" + } + ] + }, + "no_active_source_or_training_changes": true +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/training_history/execution_change.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/training_history/execution_change.json new file mode 100644 index 0000000000000000000000000000000000000000..93f72a0c4f5f51e9192005535fc45e6aa3bbd671 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/training_history/execution_change.json @@ -0,0 +1,10 @@ +{ + "adopted_utc": "2026-10-24T21:51:59.762636+00:00", + "change": "Activation checkpoint boundaries only; all model operations, parameters, objective, data and schedule unchanged.", + "example_boundary": 1000, + "before": "Recompute whole local path during backward.", + "after": "Recompute each local residual block during backward.", + "evidence": "CPU gradients and all eleven outputs exactly match. Full-shape BF16 outputs/losses are bit-exact; gradient relative L2 discrepancy .0009547 versus .0009534 for an unchanged repeat, consistent with existing reduction variability. Peak memory40.11 to15.99GiB; isolated compute .11131 to.10504seconds/example.", + "initial_source_manifest_sha256": "18329ef35e89fc5aeb50ccc045b08db690e6c8ef40d661b01ddb74c7f32b6f0b", + "no_benchmark_weights_retained": true +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/training_history/migrate_scale_bytecache.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/training_history/migrate_scale_bytecache.py new file mode 100644 index 0000000000000000000000000000000000000000..05b927345326f5b660fa5b28118ce867eee97c88 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/training_history/migrate_scale_bytecache.py @@ -0,0 +1,235 @@ +"""Checkpoint-safe opt-in byte-cache amendment, preserving an active evaluation.""" +import argparse +from datetime import datetime, timezone +import fcntl +import hashlib +import json +import os +from pathlib import Path +import shutil +import signal +import sys +import time + +import torch + + +def digest(path): + with Path(path).open('rb') as stream: + return hashlib.file_digest(stream, 'sha256').hexdigest() + + +def identity(pid): + p = Path(f'/proc/{pid}') + stat = (p/'stat').read_text().split(') ', 1)[1].split() + return {'pid': pid, 'starttime_ticks': int(stat[19]), + 'cmdline_sha256': hashlib.sha256((p/'cmdline').read_bytes()).hexdigest()} + + +def command(pid): + return Path(f'/proc/{pid}/cmdline').read_bytes().replace(b'\0', b' ').decode() + + +def alive(pid): + try: + return Path(f'/proc/{pid}/stat').read_text().split(') ', 1)[1][0] not in 'ZX' + except FileNotFoundError: + return False + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('plan', type=Path) + args = p.parse_args() + plan = json.loads(args.plan.read_text()) + r = Path('/workspace/research') + root = args.plan.parent + os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) + torch.set_num_threads(1) + for name, value in plan['helper_sha256'].items(): + assert digest(r/'seqfunc'/name) == value, name + assert digest(plan['original_study']) == plan['original_study_sha256'] + original = json.loads(Path(plan['original_study']).read_text()) + prepared = json.loads(Path(plan['preparation']).read_text()) + integrated = json.loads(Path(plan['integration_check']).read_text()) + timing = json.loads(Path(plan['timing_report']).read_text()) + assert integrated['all_returned_training_ids_and_tensor_bytes_exact'] and integrated['examples'] == 16 + assert integrated['public_inference_and_validation_cache_disabled_despite_environment'] + assert timing['cache_over_reference'] < .8 and timing['all_probe_updates_discarded'] + source = Path(prepared['new_source']) + for name, value in prepared['new_files_sha256'].items(): + assert digest(source/name) == value, name + for path in Path(original['source']).rglob('*'): + if path.is_file() and '__pycache__' not in path.parts and '.pytest_cache' not in path.parts: + relative = path.relative_to(original['source']) + if str(relative) != 'reference/alphagenome_50m/dataset.py': + assert digest(path) == digest(source/relative), str(relative) + old_controller = plan['old_controller_pid'] + assert 'run_scale_study.py' in command(old_controller) + assert os.getpgid(old_controller) != os.getpgrp() + processes = json.loads((root/'study-state.json').read_text()) + for name, target in plan['boundaries'].items(): + assert target % 1000 == 0 and target % 4 == 0 + pid = processes[name+'-train']['pid'] + assert str(Path(original['source'])/'train_batched.py') in command(pid) + assert str(r/'experiments'/name) in command(pid) + stopped, snapshots, seen = set(), {}, {} + killed = False + controller_stopped = False + begun = time.monotonic() + + def status(phase, **extra): + record = {'phase': phase, 'utc': datetime.now(timezone.utc).isoformat(), + 'boundaries': plan['boundaries'], 'checkpoints': snapshots, **extra} + temporary = root/'cached-io-migration-state.tmp' + temporary.write_text(json.dumps(record, indent=2)+'\n') + temporary.replace(root/'cached-io-migration-state.json') + print(json.dumps(record), flush=True) + + def interrupted(signum, frame): + raise SystemExit(f'Migration interrupted by signal {signum}') + signal.signal(signal.SIGTERM, interrupted) + signal.signal(signal.SIGINT, interrupted) + status('waiting_for_saved_boundaries') + try: + while len(snapshots) < len(plan['boundaries']): + assert time.monotonic()-begun < 3600, 'Boundary wait exceeded one hour' + for name, target in plan['boundaries'].items(): + if name in snapshots: + continue + pid = processes[name+'-train']['pid'] + assert alive(pid), f'Trainer {name} exited before capture' + progress = r/'experiments'/name/'progress.pt' + with progress.open('rb') as stream: + stat = os.fstat(stream.fileno()) + signature = (stat.st_ino, stat.st_mtime_ns, stat.st_size) + if signature == seen.get(name): + continue + state = torch.load(f'/proc/self/fd/{stream.fileno()}', mmap=True, + map_location='cpu', weights_only=True) + step = state['global_step'] + assert step <= target, f'{name} missed boundary at {step}' + seen[name] = signature + if step == target: + assert state['optimizer_steps'] == step//4 + assert state['objective'] == 'variance_balanced' + os.kill(pid, signal.SIGSTOP) + stopped.add(pid) + destination = progress.with_name(f'bytecache-resume-{step:06d}.pt') + assert not destination.exists() + temporary = destination.with_suffix('.tmp') + stream.seek(0) + with temporary.open('wb') as output: + shutil.copyfileobj(stream, output, length=8*1024*1024) + temporary.replace(destination) + snapshots[name] = {'step': step, 'path': str(destination), + 'sha256': digest(destination), 'single_inode_copy': True} + status('captured_saved_boundary', trainer=name) + del state + time.sleep(1) + # All pending later evaluations still wait for checkpoints. Preserve the + # already staged small epoch2 evaluation, which belongs to the old reader. + keep = 'scale13-small-eval02' + adopted = {} + keep_directory = r/'experiments/scale13-small-epoch02' + if json.loads((keep_directory/'pipeline-state.json').read_text())['phase'] != 'complete': + adopted[keep] = identity(processes[keep]['pid']) + assert 'run_candidate.py' in command(processes[keep]['pid']) + terminate_waiters = [] + for name, record in processes.items(): + if '-eval' not in name or name in adopted or record['returncode'] is not None: + continue + pid = record['pid'] + directory = r/'experiments'/name.replace('-eval', '-epoch') + if json.loads((directory/'pipeline-state.json').read_text())['phase'] == 'complete': + continue + assert json.loads((directory/'pipeline-state.json').read_text())['phase'] == 'waiting_for_checkpoint' + assert not Path(f'/proc/{pid}/task/{pid}/children').read_text().strip() + terminate_waiters.append(pid) + # Keep this old session alive until its adopted evaluation finishes. + # Its monitor is stopped, so only the new controller owns training status. + old_identity = identity(old_controller) + os.kill(old_controller, signal.SIGSTOP) + controller_stopped = True + worker_pids = [] + for name in plan['boundaries']: + pid = processes[name+'-train']['pid'] + worker_pids += [int(p) for p in Path(f'/proc/{pid}/task/{pid}/children').read_text().split()] + terminated = terminate_waiters+worker_pids+[processes[name+'-train']['pid'] for name in plan['boundaries']] + status('replacing_trainers_and_idle_waiters', preserved_evaluations=adopted, terminated_pids=terminated) + for pid in terminated: + if alive(pid): + os.kill(pid, signal.SIGTERM) + os.kill(pid, signal.SIGCONT) + killed = True + stopped.clear() + deadline = time.monotonic()+60 + while any(alive(pid) for pid in terminated): + assert time.monotonic() < deadline, 'Old training or idle waiter did not exit' + time.sleep(1) + for name, record in snapshots.items(): + assert digest(r/'experiments'/name/'progress.pt') == record['sha256'] + amendment = {'adopted_utc': datetime.now(timezone.utc).isoformat(), + 'change': 'Optional verified compressed-byte cache, training only; every hit checks source-byte digest, key and length.', + 'source_before': original['source'], 'source_after': str(source), + 'checkpoints': snapshots, 'cache_root': plan['cache_root'], + 'model_optimizer_rng_exact_at_each_resume': True, + 'model_trainer_loss_sources_data_order_objective_batch_schedule_unchanged': True, + 'different_resume_boundaries_are_IO_only': True, + 'integration_check': plan['integration_check'], 'timing_report': plan['timing_report'], + 'timing_cache_over_reference': timing['cache_over_reference'], + 'timing_caveat': timing['caveats'], + 'cache_not_an_inference_asset': True, + 'adopted_evaluations_without_interruption': adopted, + 'old_monitor_retained_stopped_until_adopted_evaluation_finishes': old_identity} + new = dict(original) + new.update(source=str(source), training_bytecache_root=plan['cache_root'], + cache_execution_amendment=amendment, adopted_evaluations=adopted, + retired_monitor_identity=old_identity) + new['execution_amendments'] = list(original.get('execution_amendments', []))+[amendment] + new['provenance'] += (' At the saved small96k/large78k boundaries, an optional training-only byte cache ' + 'was enabled after exact256example and integrated16example tensor/ID checks. ' + 'Source compressed bytes are verified on every cache hit, with read-only source fallback. ' + 'Model/trainer/loss code, complete epoch permutations, batch and schedule are unchanged. ' + 'The cache is disposable and is disabled for validation/public inference.') + history = source/'training_history' + (history/'bytecache-io-amendment.json').write_text(json.dumps(amendment, indent=2)+'\n') + for name in ['preparation', 'integration_check', 'timing_report']: + path = Path(plan[name]);shutil.copyfile(path, history/('bytecache-'+name+'.json')) + shutil.copyfile(args.plan, history/'bytecache-migration-plan.json') + shutil.copyfile(Path(__file__), history/Path(__file__).name) + shutil.copyfile(r/'seqfunc/run_scale_study_cached_io.py', history/'run_scale_study_cached_io.py') + new_path = root/'study-cached-io.json' + new_path.write_text(json.dumps(new, indent=2)+'\n') + manifest = {str(p.relative_to(source)): digest(p) for p in sorted(source.rglob('*')) + if p.is_file() and '__pycache__' not in p.parts and '.pytest_cache' not in p.parts} + (root/'source_manifest-cached-io.json').write_text(json.dumps(manifest, indent=2)+'\n') + (root/'cached-io-amendment.json').write_text(json.dumps(amendment, indent=2)+'\n') + with Path('/workspace/.submission-promotion.lock').open('a') as lock: + fcntl.flock(lock, fcntl.LOCK_EX) + p = r/'STATE.json'; state = json.loads(p.read_text()) + state['scale_study'].update(plan=str(new_path), source=str(source), + controller_pid=os.getpid(), status='Resuming original trajectories with verified opt-in training byte cache', + cached_reader_amendment=str(root/'cached-io-amendment.json'), + previous_controller_retained_stopped=old_controller) + state['scale_study']['session'] = state['scale_study']['bytecache_migration']['session'] + state['scale_study']['bytecache_migration']['status'] = 'Checkpoints captured; replacement controller starting' + temporary=p.with_suffix('.tmp');temporary.write_text(json.dumps(state,indent=2)+'\n');temporary.replace(p) + with (r/'LEDGER.md').open('a') as ledger: + ledger.write('\n'+amendment['adopted_utc']+': saved small96k/large78k trajectories resumed with optional verified training byte cache; original scientific plans unchanged. Active small epoch2 evaluation preserved. Original study/source/checkpoints retained; full amendment at studies/scale13/cached-io-amendment.json.\n') + status('replacement_controller_starting', source=str(source), new_study=str(new_path)) + log = (root/'study-cached-io.log').open('a') + os.dup2(log.fileno(), 1);os.dup2(log.fileno(), 2) + os.execv(sys.executable,[sys.executable,'-u',str(r/'seqfunc/run_scale_study_cached_io.py'),str(new_path)]) + except BaseException as error: + status('failed_or_interrupted', error=repr(error), old_trainers_terminated=killed) + raise + finally: + if not killed: + for pid in stopped: + if alive(pid):os.kill(pid,signal.SIGCONT) + if controller_stopped and alive(old_controller):os.kill(old_controller,signal.SIGCONT) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/training_history/parallel-io-integration-check.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/training_history/parallel-io-integration-check.json new file mode 100644 index 0000000000000000000000000000000000000000..652abba42a037aa1738675ff6c33ac0a769ffe0f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S021/training_history/parallel-io-integration-check.json @@ -0,0 +1,92 @@ +{ + "created_utc": "2026-10-25T00:29:21.312924+00:00", + "all_returned_training_tensor_bytes_exact": true, + "source_sha256": { + "original": "ad6670f742f4de9898064becc525d0aea98b402abe7b210bb1b26a9eee6402b9", + "parallel": "dc4263a10fecca979811161da97e731c471468465fe9480bbf2a9aacca68fb7f" + }, + "seed": 8585, + "examples": [ + { + "index": 12814, + "id": "agv2-b546f0182590dcfe5eec816c", + "sha256": "7a9dc9eca02761b7f5e14c311863c3974971cddf58a1b6549a2347d0c26bc001" + }, + { + "index": 1383, + "id": 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"0711ba4727d71ffcc86bd336ddf1b250c4fde37ed74cc4b0067f75d4ddd20f23" + }, + { + "index": 37802, + "id": "agv2-7bc22c9b204aac797187f069", + "sha256": "7b7b9b848661f6840139affae8c60fe1748a17a74e4bcb9871fb7ecf1a767a56" + } + ], + "active_trainers_unchanged": true +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/attach_local.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/attach_local.py new file mode 100644 index 0000000000000000000000000000000000000000..aa082537004dbc398c4a78c1bd0203970ce5e42f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/attach_local.py @@ -0,0 +1,52 @@ +"""Attach a zero-initialized local residual path to a checkpoint trained in this workspace.""" +import argparse +from dataclasses import replace +import hashlib +from pathlib import Path + +import torch +import yaml + +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + p.add_argument('--width', type=int, default=32) + args = p.parse_args() + state = torch.load(args.checkpoint, map_location='cpu', weights_only=True) + assert state['schema'] == 'seqfunc-single/v1', 'only locally trained single-GPU checkpoints' + parent_config = ModelConfig(**state['config']) + assert parent_config.local_width == 0 + torch.manual_seed(state['seed']) + parent = CompactModel(parent_config) + config = replace(parent_config, local_width=args.width) + model = CompactModel(config) + result = model.load_state_dict(state['model'], strict=False) + assert not result.unexpected_keys + assert all(k.startswith(('local_path.', 'local_projection.')) for k in result.missing_keys) + parent_names = list(dict(parent.named_parameters())) + names = list(dict(model.named_parameters())) + assert names[:len(parent_names)] == parent_names, 'optimizer parameter order changed' + assert len(state['optimizer']['param_groups']) == 1 + group = state['optimizer']['param_groups'][0] + assert group['params'] == list(range(len(parent_names))) + group['params'] = list(range(len(names))) + state['model'] = model.state_dict() + state['config'] = config.to_dict() + state['intervention'] = { + 'kind': 'base_resolution_local_path', 'width': args.width, + 'parent_sha256': hashlib.sha256(args.checkpoint.read_bytes()).hexdigest(), + 'attachment_step': state['global_step'], 'initial_outputs_unchanged': True, + } + args.output.mkdir(parents=True, exist_ok=False) + (args.output / 'config.yaml').write_text(yaml.safe_dump(config.to_dict())) + _atomic_save(state, args.output / 'attached.pt') + print({'parameters': count_parameters(model), **state['intervention']}) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/batched_training.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/batched_training.py new file mode 100644 index 0000000000000000000000000000000000000000..e5c609c3680baa61f04739e9f22c03a801d7b294 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/batched_training.py @@ -0,0 +1,38 @@ +"""Batch variable event queries while retaining equal-per-example reference losses.""" +import torch +from torch.nn import functional as F +from reference.alphagenome_50m.train_eval import _model_inputs, _losses +from train_single import balanced_splice_loss + +def list_collate(samples): + return samples + +def prepare_batch(samples): + """Prepare on CPU before a bulk transfer; padding never contributes to a loss.""" + prepared = [_model_inputs(sample) for sample in samples] + max_usage = max(item[0]['usage_positions'].shape[1] for item in prepared) + max_queries = max(item[0]['junction_queries'].shape[1] for item in prepared) + inputs = { + 'dna': torch.cat([x[0]['dna'] for x in prepared]), + 'usage_positions': torch.cat([F.pad(x[0]['usage_positions'], (0, max_usage-x[0]['usage_positions'].shape[1])) for x in prepared]), + 'junction_site_positions': torch.cat([x[0]['junction_site_positions'] for x in prepared]), + 'junction_queries': torch.cat([F.pad(x[0]['junction_queries'], (0, 0, 0, max_queries-x[0]['junction_queries'].shape[1])) for x in prepared]), + } + return inputs, [x[1] for x in prepared], [x[2] for x in prepared] + +def equal_example_losses(outputs, samples, junction_targets, junction_valid, *, balanced=True, scales=None): + per_example = [] + for i, (sample, target, valid) in enumerate(zip(samples, junction_targets, junction_valid)): + sliced = {name:values[i:i+1] for name,values in outputs.items()} + sliced['splice_usage'] = sliced['splice_usage'][:, :sample['usage_positions'].numel()] + sliced['junctions'] = sliced['junctions'][:, :target.shape[1]] + losses = _losses(sliced, sample, target, valid) + if balanced: + losses['splice_sites'] = balanced_splice_loss(sliced['splice_sites'], sample['splice_sites'][None]) + per_example.append(losses) + raw = {name:torch.stack([loss[name] for loss in per_example]).mean() + for name in outputs} + raw['total'] = torch.stack(list(raw.values())).mean() + scaled = {name:value * (scales or {}).get(name, 1.) for name,value in raw.items() if name != 'total'} + scaled['total'] = torch.stack(list(scaled.values())).mean() + return scaled, raw diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/change_residual_cap.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/change_residual_cap.py new file mode 100644 index 0000000000000000000000000000000000000000..cd282275702aaf5d5eede4b3904add879aad846a --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/change_residual_cap.py @@ -0,0 +1,40 @@ +"""Change only a parameter-free branch bound on our own trained checkpoint.""" +import argparse +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import torch +import yaml +from reference.alphagenome_50m.model import CompactModel, ModelConfig +from reference.alphagenome_50m.train_eval import _atomic_save + +p = argparse.ArgumentParser(description=__doc__) +p.add_argument('checkpoint', type=Path) +p.add_argument('output', type=Path) +p.add_argument('--cap', type=float, default=0.1) +a = p.parse_args() +s = torch.load(a.checkpoint, map_location='cpu', weights_only=True) +config = ModelConfig(**s['config']) +assert config.residual_rms_cap == 0 and a.cap > 0 +config = replace(config, residual_rms_cap=a.cap) +model = CompactModel(config) +model.load_state_dict(s['model'], strict=True) +assert len(s['optimizer']['param_groups'][0]['params']) == len(list(model.parameters())) +assert all(torch.equal(v, s['model'][k]) for k, v in model.state_dict().items()) +with a.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() +change = {'kind': 'bounded_transformer_residuals', 'parent_sha256': digest, + 'step': s['global_step'], 'cap': a.cap, + 'parameters_optimizer_rng_unchanged': True, + 'computation': 'each attention and MLP update multiplied by min(1, cap/token_RMS)'} +history = list(s.get('interventions', [])) +if s.get('intervention') and s['intervention'] not in history: + history.append(s['intervention']) +history.append(change) +s.update(config=config.to_dict(), intervention=change, interventions=history) +a.output.mkdir(parents=True, exist_ok=False) +(a.output/'config.yaml').write_text(yaml.safe_dump(config.to_dict())) +(a.output/'intervention.json').write_text(json.dumps(change, indent=2) + '\n') +_atomic_save(s, a.output/'start.pt') +print(json.dumps(change), flush=True) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/package_manifest.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/package_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f214e4ce893480709a85eb87029be926d5a79ca8 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/package_manifest.json @@ -0,0 +1,57 @@ +{ + "parameters": 13290504, + "source_checkpoint_sha256": "448b4cc0f5f00c0cde7ab9f2136a6c69268c9b8ae80e55fddfa119b7ee06a9f3", + "files": { + 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"4a0e3480d69aa61d1d1f17c72c2f02ef830b651a0051c3a49aca8e862a192e3d", + "training_provenance.json": "30c05badb228e6d94b3e70113c4ea671ac2cac370c380d4e4e245ca2a00b00b5", + "training_assets_manifest.json": "7e4a33e58edeb9c25fb59ec5b1f3b65ff2b1ca83abb9c3fc0770ec4f57af06c9", + "decoding_policy.json": "da508efb5250cedabc5d1f63740e06465dc8fbf6d3646385da9409dd3c3c45a6", + "validation_tracks.json": "3a45ae11ac8ad3897b926283073b5e755b3013a7796aa9837954dec842d7a942", + "validation_variants.json": "19224eae63273b62b0421fc48c32704b86418a2bfb21adda2a90449cfa0988d8" + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/predict.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..dc0f0b20833fc99253b7dcf6023fd5e7a61b1128 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/predict.py @@ -0,0 +1,133 @@ +#!/usr/bin/env python3 +"""Stream the single benchmark model into the sharded prediction contract.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import torch +import zarr + +from prediction_contract import DENSE_TRACKS, MAX_JUNCTION_PAIRS, MAX_USAGE_SITES, PREDICTION_SCHEMA +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig + + +COMPRESSOR = Blosc(cname="zstd", clevel=7, shuffle=Blosc.BITSHUFFLE) + + +def main(input_root: Path, output_root: Path, checkpoint: Path, split: str = "test") -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + dataset = AlphaGenomeDataset(input_root, split, include_targets=False) + model.set_junction_track_halves(dataset.junction_track_halves) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "predictions.zarr"), mode="w") + ids = [] + with torch.inference_mode(): + for sample in dataset: + example_id = sample["example_id"] + dna = sample["dna"][None].to(device) + trunk = model._trunk(dna) + dense_features = model._dense_features(trunk) + group = root.create_group(f"examples/{example_id}") + for family in DENSE_TRACKS: + values = model.dense_heads[family](dense_features)[0] + group.create_dataset(family, data=values.cpu().numpy().astype(np.float16), compressor=COMPRESSOR) + + all_site_logits = model._splice_site_logits(trunk) + target_start = (config.input_length - config.target_length) // 2 + group.create_dataset( + "splice_sites", + data=all_site_logits[0, target_start:target_start + config.target_length].cpu().numpy().astype(np.float16), + chunks=(4096, 4), compressor=COMPRESSOR, + ) + predicted_positions = model.select_junction_positions(all_site_logits)[0] + + # Usage at the model's own predicted sites (v4 is sparse; unlisted hidden sites score 0). + usage_bins = model.splice_usage_head(dense_features)[0].float() + sites = predicted_positions.cpu().numpy().astype(np.int64).reshape(-1) + sites = np.unique(sites[(sites >= target_start) & (sites < target_start + config.target_length)] + - target_start)[:MAX_USAGE_SITES] + group.create_dataset("splice_usage_positions", data=sites.astype(np.int32), compressor=COMPRESSOR) + usage = usage_bins[torch.as_tensor(sites // 128, device=usage_bins.device)] if len(sites) \ + else usage_bins[:0] + group.create_dataset("splice_usage", data=usage.cpu().numpy().astype(np.float16), + chunks=(1024, 734), compressor=COMPRESSOR) + + # Junctions: score every donor x acceptor pair of each strand, keep the strongest pairs. + positions_batch = predicted_positions[None] + site_factors = model.junction_site_factors(trunk, positions_batch) + halves = model.junction_track_half + candidates = [] + for row_start in range(0, 512, 128): + for column_start in range(0, 512, 128): + block = torch.cat([ + model.junction_block( + trunk, positions_batch, row_start, row_start + 128, + column_start, column_start + 128, track_start, + min(track_start + 64, config.junction_tracks), site_factors, + )[0] + for track_start in range(0, config.junction_tracks, 64) + ], dim=-1).float() # [128, 128, 734] + for half in (0, 1): + donors = predicted_positions[2 * half, row_start:row_start + 128] + acceptors = predicted_positions[2 * half + 1, column_start:column_start + 128] + valid = (donors[:, None] >= 0) & (acceptors[None, :] >= 0) + # Transcript direction is determined by the public strand half. + forward = donors[:, None] < acceptors[None, :] if half == 0 else donors[:, None] > acceptors[None, :] + valid = valid & forward + strength = block[..., halves == half].amax(dim=-1).masked_fill(~valid, -torch.inf) + keep = min(MAX_JUNCTION_PAIRS // 2, int(valid.sum())) + if not keep: + continue + best = strength.flatten().topk(keep) + rows, columns = best.indices // 128, best.indices % 128 + candidates.append((best.values, torch.full_like(rows, half), donors[rows], + acceptors[columns], block[rows, columns])) + if candidates: + score, half, donor, acceptor, values = (torch.cat(x) for x in zip(*candidates)) + order = score.topk(min(MAX_JUNCTION_PAIRS, len(score))).indices + half, donor, acceptor, values = half[order], donor[order], acceptor[order], values[order] + else: + half = donor = acceptor = torch.zeros(0, dtype=torch.long) + values = torch.zeros(0, config.junction_tracks) + group.create_dataset("junction_half", data=half.cpu().numpy().astype(np.int8), compressor=COMPRESSOR) + group.create_dataset("junction_donor", data=donor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junction_acceptor", data=acceptor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junctions", data=values.cpu().numpy().astype(np.float16), + chunks=(4096, 734), compressor=COMPRESSOR) + + contact = trunk.reshape( + 1, config.contact_bins, -1, config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + contact_values = 0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row]) + group.create_dataset( + "contact_maps", data=contact_values[0].cpu().numpy().astype(np.float16), + chunks=(8192, 28), compressor=COMPRESSOR, + ) + ids.append(example_id) + print(json.dumps({"event": "predict", "example": len(ids), "total": len(dataset)}), flush=True) + root.attrs.update({"schema": PREDICTION_SCHEMA, "example_ids": ids}) + (output_root / "example_ids.json").write_text(json.dumps(ids) + "\n") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--split", default="test", choices=("valid", "test"), + help="manifest to predict; the verifier uses test, local validation uses valid") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.split) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/prediction_contract.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/prediction_contract.py new file mode 100644 index 0000000000000000000000000000000000000000..2455424db002b840cf0d69456930840a7bc5f3cb --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/prediction_contract.py @@ -0,0 +1,23 @@ +"""Public prediction shapes; no trusted splice coordinates are exposed.""" + +from __future__ import annotations + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} + +PREDICTION_SCHEMA = "genome-sequence-to-function-predictions/v4" +MAX_USAGE_SITES = 4_096 +MAX_JUNCTION_PAIRS = 16_384 +# Fixed-shape arrays per example; splice usage and junctions are sparse (see contract.md). +FIXED_SHAPES = { + **{name: (1_536, tracks) for name, tracks in DENSE_TRACKS.items()}, + "splice_sites": (196_608, 4), + "contact_maps": (131_328, 28), +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/cached_read_store.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/cached_read_store.py new file mode 100644 index 0000000000000000000000000000000000000000..9c3677c1e8518ea97db0dd6759f3d636f5fff27c --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/cached_read_store.py @@ -0,0 +1,71 @@ +"""Optional local byte cache for immutable training Zarr stores. + +Source storage stays read-only. Cached entries contain a key fingerprint, length, +and SHA256 of the original compressed bytes; every hit verifies all three. Writes +replace a private temporary inode atomically. A missing/corrupt/unwritable cache +falls back to the original store, so this layer changes neither values nor keys. +Only training loaders may opt into it; it is not an inference asset. +""" +import hashlib +import os +from pathlib import Path +import struct +import tempfile + +from .direct_read_store import ParallelDirectReadDirectoryStore + +MAGIC = b'SFBC0001' +HEADER = struct.Struct('<8sQ32s32s') + + +class CachedReadDirectoryStore(ParallelDirectReadDirectoryStore): + def __init__(self, path, *, cache_root, namespace, io_workers=4): + super().__init__(path, io_workers=io_workers) + if not namespace: + raise ValueError('An immutable input namespace is required') + self.cache_root = Path(cache_root) + self.namespace = namespace + self.store_key = hashlib.sha256((namespace+'\0'+os.path.abspath(path)).encode()).digest() + self.statistics = {'hit': 0, 'miss': 0, 'corrupt': 0, 'write_failure': 0} + + def cache_location(self, key): + key = self._normalize_key(key) + identity = hashlib.sha256(self.store_key+key.encode()).digest() + name = identity.hex() + return self.cache_root/name[:2]/name[2:], identity + + def __getitem__(self, key): + key = self._normalize_key(key) + destination, identity = self.cache_location(key) + try: + blob = destination.read_bytes() + if len(blob) >= HEADER.size: + magic, size, cached_key, checksum = HEADER.unpack_from(blob) + payload = blob[HEADER.size:] + if (magic == MAGIC and size == len(payload) and cached_key == identity + and hashlib.sha256(payload).digest() == checksum): + self.statistics['hit'] += 1 + return payload + self.statistics['corrupt'] += 1 + except OSError: + pass + self.statistics['miss'] += 1 + payload = super().__getitem__(key) + temporary = None + try: + destination.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile(prefix='.pending-', dir=destination.parent, delete=False) as stream: + temporary = stream.name + stream.write(HEADER.pack(MAGIC, len(payload), identity, hashlib.sha256(payload).digest())) + stream.write(payload) + os.replace(temporary, destination) + temporary = None + except OSError: + self.statistics['write_failure'] += 1 + finally: + if temporary is not None: + try: + os.unlink(temporary) + except OSError: + pass + return payload diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/dataset.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..8a3f7d4644e5d9f02162bac9e859443ba6c33658 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/dataset.py @@ -0,0 +1,312 @@ +"""Streaming reader for the compact 1 Mb / eleven-family release.""" + +from __future__ import annotations + +from collections import OrderedDict +from pathlib import Path + +import numpy as np +import pandas as pd +import pysam +import torch +from torch.utils.data import Dataset +import zarr + + +import os +from concurrent.futures import ThreadPoolExecutor + + +_pools = {} + + +def process_pool(workers): + key = (os.getpid(), workers) + if key not in _pools: + _pools[key] = ThreadPoolExecutor(max_workers=workers, thread_name_prefix='zarr-read') + return _pools[key] + + +class DirectReadDirectoryStore(zarr.DirectoryStore): + def __getitem__(self, key): + key = self._normalize_key(key) + try: + return self._fromfile(os.path.join(self.path, key)) + except (FileNotFoundError, IsADirectoryError, NotADirectoryError): + raise KeyError(key) from None + + def getitems(self, keys, *, contexts): + result = {} + for key in keys: + try: + result[key] = self[key] + except KeyError: + pass + return result + + def __setitem__(self, key, value): + raise PermissionError('DirectReadDirectoryStore is read-only') + + def __delitem__(self, key): + raise PermissionError('DirectReadDirectoryStore is read-only') + + def is_writeable(self): + return False + + def is_erasable(self): + return False + + +class ParallelDirectReadDirectoryStore(DirectReadDirectoryStore): + """Bounded I/O threads shared by all read stores within one loader process.""" + def __init__(self, path, *, io_workers=4): + super().__init__(path) + if io_workers < 1: + raise ValueError('io_workers must be positive') + self.io_workers = io_workers + + def getitems(self, keys, *, contexts): + def fetch(key): + try: + return key, self[key] + except KeyError: + return key, None + if self.io_workers == 1: + return super().getitems(keys, contexts=contexts) + return {key: value for key, value in process_pool(self.io_workers).map(fetch, keys) + if value is not None} + + +def _open_part(path: Path): + if str(path).endswith(".zarr.zip"): + store = zarr.ZipStore(str(path), mode="r") + try: + return zarr.open_group(store=store, mode="r"), store + except BaseException: + store.close() + raise + return zarr.open_group(store=ParallelDirectReadDirectoryStore(str(path), io_workers=4), mode="r"), None + + +DENSE_FAMILIES = ( + "atac", + "dnase", + "procap", + "cage", + "rna_seq", + "chip_tf", + "chip_histone", +) +def normalize_splice_positions(value) -> np.ndarray: + """Normalize Arrow's nested-list representation to the public [4, 512] array.""" + + outer = np.asarray(value, dtype=object).reshape(-1).tolist() + positions = np.stack([np.asarray(item, dtype=np.int64) for item in outer]) + if positions.shape != (4, 512): + raise ValueError(f"splice_site_positions must have shape (4, 512), got {positions.shape}") + return positions + + +class AlphaGenomeDataset(Dataset): + """Resolve frozen coordinates to reference DNA and one Zarr example group.""" + + def __init__( + self, + data_root: str | Path, + split: str, + *, + max_open_parts: int = 8, + include_targets: bool = True, + ) -> None: + self.root = Path(data_root) + self.split = split + manifest = self.root / "public" / f"{split}.parquet" + if not manifest.is_file(): + raise FileNotFoundError(f"Missing materialized manifest: {manifest}") + self.rows = pd.read_parquet(manifest).reset_index(drop=True) + junction_tracks = pd.read_parquet(self.root / "public/junction_tracks.parquet") + junction_tracks = junction_tracks.sort_values("tensor_index") + self.junction_track_halves = torch.from_numpy( + junction_tracks.junction_channel_half.to_numpy(np.int64) + ) + if include_targets: + labels_path = self.root / "labels" / f"{split}.parquet" + if not labels_path.is_file(): + raise FileNotFoundError(f"Missing trusted labels manifest: {labels_path}") + labels = pd.read_parquet(labels_path).reset_index(drop=True) + if self.rows.example_id.astype(str).tolist() != labels.example_id.astype(str).tolist(): + raise ValueError("public and labels manifests have different IDs/order") + self.rows = self.rows.merge(labels, on="example_id", validate="one_to_one", sort=False) + self.fasta_path = self.root / "reference" / "GRCh38.p13.genome.fa" + self.max_open_parts = max_open_parts + self.include_targets = include_targets + # Optional training-only compressed-byte cache; off for all public inference. + self.training_bytecache = None + requested_cache = os.environ.get('SEQFUNC_TRAIN_BYTECACHE') + if requested_cache and split == 'train' and include_targets: + import hashlib + import json + from .cached_read_store import CachedReadDirectoryStore + manifests = {str(path): hashlib.sha256(path.read_bytes()).hexdigest() for path in + [self.root/'public/train.parquet', self.root/'labels/train.parquet']} + namespace = hashlib.sha256(json.dumps(manifests, sort_keys=True).encode()).hexdigest() + self.training_bytecache = (requested_cache, namespace) + self._fasta = None + self._parts: OrderedDict[str, tuple[zarr.Group, object | None]] = OrderedDict() + + def __len__(self) -> int: + return len(self.rows) + + def _reference(self): + if self._fasta is None: + self._fasta = pysam.FastaFile(str(self.fasta_path)) + return self._fasta + + def _part(self, relative: str): + if relative in self._parts: + opened = self._parts.pop(relative) + self._parts[relative] = opened + return opened[0] + path = self.root / relative + if self.training_bytecache is not None and not str(path).endswith('.zarr.zip'): + from .cached_read_store import CachedReadDirectoryStore + cache_root, namespace = self.training_bytecache + cached_store = CachedReadDirectoryStore(str(path), cache_root=cache_root, + namespace=namespace, io_workers=4) + group, store = zarr.open_group(store=cached_store, mode='r'), None + else: + group, store = _open_part(path) + if len(self._parts) >= self.max_open_parts: + _, (_, old_store) = self._parts.popitem(last=False) + if old_store is not None: + old_store.close() + self._parts[relative] = (group, store) + return group + + @staticmethod + def _one_hot(sequence: str) -> np.ndarray: + encoded = np.frombuffer(sequence.upper().encode("ascii"), dtype=np.uint8) + output = np.zeros((len(encoded), 4), dtype=np.float32) + for index, letter in enumerate(b"ACGT"): + output[:, index] = encoded == letter + return output + + def _sequence(self, row) -> tuple[np.ndarray, np.ndarray]: + reference = self._reference() + chromosome_length = reference.get_reference_length(str(row.chromosome)) + start, end = int(row.input_start), int(row.input_end) + clipped_start, clipped_end = max(0, start), min(chromosome_length, end) + sequence = reference.fetch(str(row.chromosome), clipped_start, clipped_end) + left, right = clipped_start - start, end - clipped_end + if left or right: + sequence = "N" * left + sequence + "N" * right + valid = np.ones(end - start, dtype=bool) + if left: + valid[:left] = False + if right: + valid[-right:] = False + return self._one_hot(sequence), valid + + @staticmethod + def _decode(example, name: str, *, asinh: bool) -> np.ndarray: + """Train targets may be stored as 8-bit codes with per-track ranges; they are decoded + here, one example at a time, so the release stays compressed on disk.""" + array = example[name] + if array.dtype != np.uint8: + return np.asarray(array, dtype=np.float32) + lo = np.asarray(example[f"{name}_lo"], dtype=np.float32) + hi = np.asarray(example[f"{name}_hi"], dtype=np.float32) + x = lo + np.asarray(array, dtype=np.float32) * ((hi - lo) / 255.0) + return np.sinh(x) if asinh else x + + @staticmethod + def _dense_target(example, family: str) -> dict[str, torch.Tensor]: + values = AlphaGenomeDataset._decode(example, f"{family}/values", asinh=True) + mask = np.asarray(example[f"{family}/mask"], dtype=bool) + if mask.ndim == 1: + mask = np.broadcast_to(mask[None, :], values.shape).copy() + return { + "values": torch.from_numpy(values), + "mask": torch.from_numpy(mask), + } + + def __getitem__(self, item: int): + row = self.rows.iloc[item] + dna, input_mask = self._sequence(row) + result = { + "example_id": str(row.example_id), + "dna": torch.from_numpy(dna), + "input_mask": torch.from_numpy(input_mask), + "junction_track_halves": self.junction_track_halves, + } + if not self.include_targets: + return result + + part = self._part(str(row.target_shard)) + example = part[str(row.target_group)] + result["dense"] = { + family: self._dense_target(example, family) for family in DENSE_FAMILIES + } + result["splice_sites"] = torch.from_numpy( + np.asarray(example["splice_sites/values"], dtype=np.float32) + ) + result["splice_usage"] = { + "positions": torch.from_numpy( + np.asarray(example["splice_site_usage/positions"], dtype=np.int64) + ), + "values": torch.from_numpy( + np.asarray(example["splice_site_usage/values"], dtype=np.float32) + ), + "mask": torch.from_numpy( + np.asarray(example["splice_site_usage/mask"], dtype=bool) + ), + } + result["usage_positions"] = result["splice_usage"]["positions"] + result["junctions"] = { + name: torch.from_numpy( + np.asarray( + example[f"splice_junctions/{name}"], + dtype=np.float32 if name == "value" else np.int64, + ) + ) + for name in ("row", "column", "track", "value") + } + result["junctions"]["site_positions"] = torch.from_numpy( + np.asarray(example["splice_junctions/site_positions"], dtype=np.int64) + ) + site_positions = normalize_splice_positions(row.splice_site_positions) + result["site_positions"] = torch.from_numpy(site_positions) + if not torch.equal(result["junctions"]["site_positions"], result["site_positions"]): + raise ValueError(f"public/private splice positions differ for {row.example_id}") + scoring_path = self.root / "scoring" / self.split / f"part-{item:06d}.parquet" + scoring = pd.read_parquet(scoring_path) + if not (scoring.example_id.astype(str) == str(row.example_id)).all(): + raise ValueError(f"scoring part ID differs for {row.example_id}") + result["junction_scoring"] = { + "donor_position": torch.from_numpy( + scoring.donor_position.to_numpy(np.int64) + ), + "acceptor_position": torch.from_numpy( + scoring.acceptor_position.to_numpy(np.int64) + ), + "track": torch.from_numpy(scoring.track.to_numpy(np.int64)), + "target_value": torch.from_numpy( + scoring.target_value.to_numpy(np.float32) + ), + "valid": torch.from_numpy(scoring.valid.to_numpy(bool)), + } + result["contact_maps"] = torch.from_numpy( + self._decode(example, "contact_maps/upper_triangle", asinh=False) + ) + result["contact_map_mask"] = torch.from_numpy( + np.asarray(example["contact_maps/mask"], dtype=bool) + ) + return result + + +def single_example_collate(items): + """Keep variable-size event tensors intact; the reference currently trains at batch one.""" + + if len(items) != 1: + raise ValueError("The compact event loader currently requires batch_size=1") + return items[0] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/direct_read_store.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/direct_read_store.py new file mode 100644 index 0000000000000000000000000000000000000000..c560febc6a15909a523c8e2f3d1f861aeec309a7 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/direct_read_store.py @@ -0,0 +1,64 @@ +"""Read-only Zarr directory store avoiding redundant per-chunk stat calls.""" +import os +from concurrent.futures import ThreadPoolExecutor + +from zarr.storage import DirectoryStore + +_pools = {} + + +def process_pool(workers): + key = (os.getpid(), workers) + if key not in _pools: + _pools[key] = ThreadPoolExecutor(max_workers=workers, thread_name_prefix='zarr-read') + return _pools[key] + + +class DirectReadDirectoryStore(DirectoryStore): + def __getitem__(self, key): + key = self._normalize_key(key) + try: + return self._fromfile(os.path.join(self.path, key)) + except (FileNotFoundError, IsADirectoryError, NotADirectoryError): + raise KeyError(key) from None + + def getitems(self, keys, *, contexts): + result = {} + for key in keys: + try: + result[key] = self[key] + except KeyError: + pass + return result + + def __setitem__(self, key, value): + raise PermissionError('DirectReadDirectoryStore is read-only') + + def __delitem__(self, key): + raise PermissionError('DirectReadDirectoryStore is read-only') + + def is_writeable(self): + return False + + def is_erasable(self): + return False + + +class ParallelDirectReadDirectoryStore(DirectReadDirectoryStore): + """Bounded I/O threads shared by all read stores within one loader process.""" + def __init__(self, path, *, io_workers=4): + super().__init__(path) + if io_workers < 1: + raise ValueError('io_workers must be positive') + self.io_workers = io_workers + + def getitems(self, keys, *, contexts): + def fetch(key): + try: + return key, self[key] + except KeyError: + return key, None + if self.io_workers == 1: + return super().getitems(keys, contexts=contexts) + return {key: value for key, value in process_pool(self.io_workers).map(fetch, keys) + if value is not None} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/model.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/model.py new file mode 100644 index 0000000000000000000000000000000000000000..6f211fc29de3fa559db45ce6c2ee3d40816f4a72 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/model.py @@ -0,0 +1,460 @@ +"""Affordable multi-resolution baseline for the compact AlphaGenome task.""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass, fields +import math +from pathlib import Path +from typing import Any, Iterable + +import torch +from torch import Tensor, nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint +import yaml + +from .layers import ChannelRMSNorm, ConvBlock, SequenceBlock + + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} +ALL_FAMILIES = (*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions", "contact_maps") + + +@dataclass(frozen=True) +class ModelConfig: + input_length: int = 1_048_576 + target_length: int = 196_608 + dense_bin_size: int = 128 + trunk_bin_size: int = 1_024 + contact_bin_size: int = 2_048 + stem_stride: int = 8 + encoder_width: int = 32 + channel_growth: int = 32 + num_downsampling_blocks: int = 7 + conv_kernel_size: int = 5 + dna_kernel_size: int = 15 + hidden_dim: int = 384 + num_sequence_blocks: int = 6 + num_attention_heads: int = 6 + attention_head_dim: int = 64 + mlp_expansion: float = 4.0 + dropout: float = 0.0 + head_hidden_size: int = 256 + junction_rank: int = 32 + splice_usage_tracks: int = 734 + junction_tracks: int = 734 + contact_tracks: int = 28 + gradient_checkpointing: bool = True + enable_pairwise: bool = False + pair_width: int = 32 + pair_downsample: int = 16 + pair_update_interval: int = 2 + local_width: int = 0 + local_context_blocks: int = 4 + splice_context_mode: str = 'offset' + local_to_trunk: bool = False + residual_rms_cap: float = 0.0 + + def __post_init__(self) -> None: + if not 1 <= self.local_context_blocks <= 7: + raise ValueError('local_context_blocks must be between 1 and 7') + if not math.isfinite(self.residual_rms_cap) or self.residual_rms_cap < 0: + raise ValueError('residual_rms_cap must be finite and nonnegative') + if self.local_to_trunk and not self.local_width: + raise ValueError('local_to_trunk requires local features') + if self.splice_context_mode not in ('offset', 'uniform'): + raise ValueError('unknown splice_context_mode') + if self.splice_context_mode == 'uniform' and not self.local_width: + raise ValueError('uniform splice context requires a base-resolution path') + downsample = self.stem_stride * 2**self.num_downsampling_blocks + if downsample != self.trunk_bin_size: + raise ValueError("stem_stride * 2**num_downsampling_blocks must equal trunk_bin_size") + for length in (self.input_length, self.target_length): + if length % self.trunk_bin_size: + raise ValueError("input and target lengths must divide by trunk_bin_size") + if self.trunk_bin_size % self.dense_bin_size: + raise ValueError("trunk_bin_size must divide by dense_bin_size") + if self.input_length % self.contact_bin_size: + raise ValueError("input_length must divide by contact_bin_size") + if self.hidden_dim <= 0 or self.num_attention_heads <= 0: + raise ValueError("hidden dimensions and attention heads must be positive") + + @property + def trunk_bins(self) -> int: + return self.input_length // self.trunk_bin_size + + @property + def target_trunk_bins(self) -> int: + return self.target_length // self.trunk_bin_size + + @property + def dense_bins(self) -> int: + return self.target_length // self.dense_bin_size + + @property + def dense_upsample(self) -> int: + return self.trunk_bin_size // self.dense_bin_size + + @property + def contact_bins(self) -> int: + return self.input_length // self.contact_bin_size + + @property + def encoder_output_width(self) -> int: + return self.encoder_width + self.channel_growth * self.num_downsampling_blocks + + @classmethod + def from_yaml(cls, path: str | Path) -> "ModelConfig": + values: dict[str, Any] = yaml.safe_load(Path(path).read_text()) or {} + known = {field.name for field in fields(cls)} + unknown = sorted(values.keys() - known) + if unknown: + raise ValueError(f"Unknown model configuration keys: {unknown}") + return cls(**values) + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + +class SequenceEncoder(nn.Module): + def __init__(self, config: ModelConfig): + super().__init__() + self.stem = nn.Conv1d( + 4, + config.encoder_width, + config.dna_kernel_size, + stride=config.stem_stride, + padding=config.dna_kernel_size // 2, + ) + blocks = [] + width = config.encoder_width + for _ in range(config.num_downsampling_blocks): + output_width = width + config.channel_growth + blocks.append(ConvBlock(width, output_width, config.conv_kernel_size)) + width = output_width + self.blocks = nn.ModuleList(blocks) + + def forward(self, dna: Tensor) -> Tensor: + x = F.gelu(self.stem(dna.transpose(1, 2)), approximate="tanh") + for block in self.blocks: + x = F.max_pool1d(block(x), kernel_size=2, stride=2) + return x.transpose(1, 2) + + +class LocalResidualBlock(nn.Module): + def __init__(self, width: int, dilation: int): + super().__init__() + self.norm = ChannelRMSNorm(width) + self.depthwise = nn.Conv1d(width, width, 9, dilation=dilation, + padding=4 * dilation, groups=width) + self.expand = nn.Conv1d(width, 2 * width, 1) + self.contract = nn.Conv1d(2 * width, width, 1) + + def forward(self, x: Tensor) -> Tensor: + update = self.depthwise(self.norm(x)) + update = self.contract(F.gelu(self.expand(update), approximate='tanh')) + return x + update + + +class LocalSequencePath(nn.Module): + """Base-resolution motif path with powers-of-four dilation and aligned pooling.""" + def __init__(self, config: ModelConfig): + super().__init__() + width = config.local_width + self.stem = nn.Conv1d(4, width, 21, padding=10) + self.blocks = nn.Sequential(*(LocalResidualBlock(width, 4**i) + for i in range(config.local_context_blocks))) + self.norm = ChannelRMSNorm(width) + self.sites = nn.Conv1d(width, 4, 1) + self.bin_size = config.dense_bin_size + self.gradient_checkpointing = config.gradient_checkpointing + + def forward(self, dna: Tensor) -> tuple[Tensor, Tensor]: + base = F.gelu(self.stem(dna), approximate='tanh') + for block in self.blocks: + if self.gradient_checkpointing and self.training: + base = checkpoint(block, base, use_reentrant=False) + else: + base = block(base) + base = F.gelu(self.norm(base), approximate='tanh') + sites = self.sites(base).transpose(1, 2) + pooled = torch.cat((F.avg_pool1d(base, self.bin_size), + F.max_pool1d(base, self.bin_size)), dim=1).transpose(1, 2) + return sites, pooled + + +class CompactModel(nn.Module): + """One shared 1-kb trunk with representation-specific output heads.""" + + def __init__(self, config: ModelConfig | None = None): + super().__init__() + self.config = config or ModelConfig() + self.encoder = SequenceEncoder(self.config) + self.projection = nn.Linear(self.config.encoder_output_width, self.config.hidden_dim) + self.blocks = nn.ModuleList( + SequenceBlock(self.config) for _ in range(self.config.num_sequence_blocks) + ) + self.final_norm = nn.RMSNorm(self.config.hidden_dim) + self.dense_decoder = nn.Sequential( + nn.RMSNorm(self.config.hidden_dim), + nn.Linear(self.config.hidden_dim, self.config.head_hidden_size), + nn.GELU(approximate="tanh"), + ) + self.dense_heads = nn.ModuleDict( + { + family: nn.Linear(self.config.head_hidden_size, tracks) + for family, tracks in DENSE_TRACKS.items() + } + ) + self.splice_site_head = nn.Linear( + self.config.head_hidden_size, self.config.dense_bin_size * 4 + ) + self.splice_usage_head = nn.Linear( + self.config.head_hidden_size, self.config.splice_usage_tracks + ) + rank = self.config.junction_rank + self.junction_source = nn.Linear(self.config.head_hidden_size, rank) + self.junction_destination = nn.Linear(self.config.head_hidden_size, rank) + self.junction_track = nn.Embedding(self.config.junction_tracks, rank) + if self.config.junction_tracks % 2: + raise ValueError("junction tracks must split evenly into strand halves") + self.register_buffer( + "junction_track_half", + torch.arange(self.config.junction_tracks) // (self.config.junction_tracks // 2), + persistent=False, + ) + self.contact_q = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + self.contact_k = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + row, column = torch.triu_indices( + self.config.contact_bins, self.config.contact_bins + ) + self.register_buffer("contact_row", row, persistent=False) + self.register_buffer("contact_column", column, persistent=False) + if self.config.local_width: + self.local_path = LocalSequencePath(self.config) + self.local_projection = nn.Linear(2 * self.config.local_width, self.config.head_hidden_size) + # A new local path can be attached to our own checkpoint without changing its initial outputs. + nn.init.zeros_(self.local_path.sites.weight) + nn.init.zeros_(self.local_path.sites.bias) + nn.init.zeros_(self.local_projection.weight) + nn.init.zeros_(self.local_projection.bias) + if self.config.local_to_trunk: + self.local_trunk_projection = nn.Linear(2 * self.config.local_width, self.config.hidden_dim) + nn.init.zeros_(self.local_trunk_projection.weight) + nn.init.zeros_(self.local_trunk_projection.bias) + self._local_sites = None + self._local_features = None + + def set_junction_track_halves(self, halves: Tensor) -> None: + halves = torch.as_tensor(halves, dtype=torch.long, device=self.junction_track_half.device) + if halves.shape != (self.config.junction_tracks,) or not torch.isin( + halves, torch.tensor([0, 1], device=halves.device) + ).all(): + raise ValueError("junction track halves must be a 734-element 0/1 mapping") + self.junction_track_half.copy_(halves) + + def _trunk(self, dna: Tensor) -> Tensor: + if dna.ndim != 3 or dna.shape[1:] != (self.config.input_length, 4): + raise ValueError( + f"Expected DNA [B,{self.config.input_length},4], got {tuple(dna.shape)}" + ) + if self.config.local_width: + sites, pooled = self.local_path(dna.transpose(1, 2)) + self._local_sites = sites + self._local_features = self.local_projection(pooled) + x = self.projection(self.encoder(dna)) + if self.config.local_to_trunk: + local_tokens = pooled.reshape(len(dna), self.config.trunk_bins, + self.config.dense_upsample, -1).mean(dim=2) + x = x + self.local_trunk_projection(local_tokens) + for block in self.blocks: + if self.config.gradient_checkpointing and self.training and x.requires_grad: + x = checkpoint(block, x, None, use_reentrant=False) + else: + x = block(x, None) + return self.final_norm(x) + + def _dense_features(self, trunk: Tensor) -> Tensor: + start = (self.config.trunk_bins - self.config.target_trunk_bins) // 2 + target = trunk[:, start : start + self.config.target_trunk_bins] + target = target.repeat_interleave(self.config.dense_upsample, dim=1) + features = self.dense_decoder(target) + if self.config.local_width: + first_bin = (self.config.input_length - self.config.target_length) // (2 * self.config.dense_bin_size) + features = features + self._local_features[:, first_bin:first_bin + self.config.dense_bins] + return features + + def _all_dense_features(self, trunk: Tensor) -> Tensor: + features = self.dense_decoder( + trunk.repeat_interleave(self.config.dense_upsample, dim=1) + ) + return features + self._local_features if self.config.local_width else features + + def _splice_site_logits(self, trunk: Tensor) -> Tensor: + if self.config.splice_context_mode == 'uniform': + # The global trunk supplies one gene-context bias per class and kb. Base-resolution + # localization comes from the local path, with no arbitrary 128-bp offset decoder + # and no pooled dense-feature contribution to site logits. + context = self.splice_site_head(self.dense_decoder(trunk)) + context = context.view(len(trunk), self.config.trunk_bins, self.config.dense_bin_size, 4).mean(dim=2) + logits = context.repeat_interleave(self.config.trunk_bin_size, dim=1) + self._local_sites + return logits.float() + logits = self.splice_site_head(self._all_dense_features(trunk)) + logits = logits.view(len(trunk), self.config.input_length, 4) + if self.config.local_width: + logits = logits + self._local_sites + return logits.float() + + def _position_features(self, trunk: Tensor, positions: Tensor) -> Tensor: + features = self.dense_decoder(self._gather_bins(trunk, positions, self.config.trunk_bin_size)) + if self.config.local_width: + features = features + self._gather_bins(self._local_features, positions, self.config.dense_bin_size) + return features + + @staticmethod + def select_junction_positions(site_logits: Tensor, limit: int = 512) -> Tensor: + """Select each site type from model scores alone, then sort by coordinate. + + This follows the four independent site classes in Google DeepMind's + AlphaGenome splicing implementation (pinned in docs/v3-author/PROTOCOL.md). Padding is + -1 when an input is shorter than ``limit``. + """ + count = min(limit, site_logits.shape[1]) + selected = [] + for site_type in range(4): + coordinates = torch.topk(site_logits[..., site_type], count, dim=1).indices + coordinates = coordinates.sort(dim=1).values + if count < limit: + coordinates = F.pad(coordinates, (0, limit - count), value=-1) + selected.append(coordinates) + return torch.stack(selected, dim=1) + + @staticmethod + def _gather_bins(features: Tensor, positions: Tensor, bin_size: int) -> Tensor: + indices = torch.div(positions, bin_size, rounding_mode="floor") + indices = indices.clamp(0, features.shape[1] - 1) + return torch.gather( + features, + 1, + indices[..., None].expand(-1, -1, features.shape[-1]), + ) + + def _junction_predictions( + self, trunk: Tensor, site_positions: Tensor, queries: Tensor + ) -> Tensor: + row, column, track = queries.unbind(dim=-1) + half = self.junction_track_half[track] + batch = torch.arange(len(trunk), device=trunk.device)[:, None] + donor = site_positions[batch, 2 * half, row] + acceptor = site_positions[batch, 2 * half + 1, column] + source = self._position_features(trunk, donor.clamp(0, self.config.input_length - 1)) + destination = self._position_features(trunk, acceptor.clamp(0, self.config.input_length - 1)) + interaction = self.junction_source(source) * self.junction_destination(destination) + track_embedding = self.junction_track(track) + return (interaction * track_embedding).sum(dim=-1) / math.sqrt( + self.config.junction_rank + ) + + def junction_block( + self, + trunk: Tensor, + site_positions: Tensor, + row_start: int, + row_end: int, + column_start: int, + column_end: int, + track_start: int, + track_end: int, + site_factors: tuple[Tensor, Tensor] | None = None, + ) -> Tensor: + """Compute a bounded block of the full predicted-position junction cube.""" + tracks = torch.arange(track_start, track_end, device=trunk.device) + halves = self.junction_track_half[tracks] + if site_factors is None: + site_factors = self.junction_site_factors(trunk, site_positions) + source_all, destination_all = site_factors + source = source_all[:, halves, row_start:row_end] + destination = destination_all[:, halves, column_start:column_end] + embedding = self.junction_track(tracks)[None, :, None, None, :] + values = ( + source[:, :, :, None, :] * destination[:, :, None, :, :] * embedding + ).sum(dim=-1) / math.sqrt(self.config.junction_rank) + return values.permute(0, 2, 3, 1).float() + + def junction_site_factors( + self, trunk: Tensor, site_positions: Tensor + ) -> tuple[Tensor, Tensor]: + """Precompute the two strand-specific factors reused by all cube blocks.""" + donor = torch.stack((site_positions[:, 0], site_positions[:, 2]), dim=1).clamp_min(0) + acceptor = torch.stack((site_positions[:, 1], site_positions[:, 3]), dim=1).clamp_min(0) + donor_features = self._position_features(trunk, donor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, donor.shape[-1], -1) + acceptor_features = self._position_features(trunk, acceptor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, acceptor.shape[-1], -1) + return self.junction_source(donor_features), self.junction_destination(acceptor_features) + + def forward( + self, + dna: Tensor, + *, + families: Iterable[str] | None = None, + usage_positions: Tensor | None = None, + junction_site_positions: Tensor | None = None, + junction_queries: Tensor | None = None, + ) -> dict[str, Tensor]: + requested = set(families or ALL_FAMILIES) + unknown = requested - set(ALL_FAMILIES) + if unknown: + raise ValueError(f"Unknown output families: {sorted(unknown)}") + trunk = self._trunk(dna) + needs_dense = bool( + requested.intersection((*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions")) + ) + features = self._dense_features(trunk) if needs_dense else None + outputs: dict[str, Tensor] = {} + for family in DENSE_TRACKS: + if family in requested: + outputs[family] = self.dense_heads[family](features).float() + if "splice_sites" in requested: + all_logits = self._splice_site_logits(trunk) + start = (self.config.input_length - self.config.target_length) // 2 + outputs["splice_sites"] = all_logits[:, start:start + self.config.target_length] + if "splice_usage" in requested: + if usage_positions is None: + raise ValueError("splice_usage requires usage_positions") + gathered = self._gather_bins( + features, usage_positions, self.config.dense_bin_size + ) + outputs["splice_usage"] = self.splice_usage_head(gathered).float() + if "junctions" in requested: + if junction_site_positions is None or junction_queries is None: + raise ValueError("junctions require site positions and query triples") + outputs["junctions"] = self._junction_predictions( + trunk, junction_site_positions, junction_queries + ).float() + if "contact_maps" in requested: + contact = trunk.reshape( + len(dna), self.config.contact_bins, -1, self.config.hidden_dim + ).mean(dim=2) + q, k = self.contact_q(contact), self.contact_k(contact) + row, column = self.contact_row, self.contact_column + outputs["contact_maps"] = ( + 0.5 + * ( + q[:, row] * k[:, column] + + q[:, column] * k[:, row] + ) + ).float() + return outputs + + +def count_parameters(model: nn.Module) -> int: + return sum(parameter.numel() for parameter in model.parameters() if parameter.requires_grad) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/tiny_overfit.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/tiny_overfit.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c25f9ffbb76102c970e5767922400f7ec2b6c53e --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/tiny_overfit.yaml @@ -0,0 +1,24 @@ +# Keep the frozen 1-Mb input and every output track; shrink only learned widths. +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 8 +channel_growth: 4 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 32 +num_sequence_blocks: 1 +num_attention_heads: 2 +attention_head_dim: 16 +mlp_expansion: 2.0 +dropout: 0.0 +head_hidden_size: 32 +junction_rank: 8 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: false diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/train_eval.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/train_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..92f9a3f315013630fdd2e03fb6840a6c77840edd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/reference/alphagenome_50m/train_eval.py @@ -0,0 +1,537 @@ +"""Train and stream-evaluate the <=50M compact AlphaGenome baseline.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import numpy as np +import torch +from torch import Tensor +from torch.nn import functional as F +from torch.utils.data import DataLoader, Subset + +from .dataset import ( + AlphaGenomeDataset, + DENSE_FAMILIES, + single_example_collate, +) +from .model import ( + ALL_FAMILIES, + CompactModel, + ModelConfig, + count_parameters, +) + + +DEFAULT_CONFIG = Path(__file__).with_name("alphagenome_50m.yaml") + + +def _device(name: str) -> torch.device: + if name == "auto": + name = "cuda" if torch.cuda.is_available() else "cpu" + if name == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("--device cuda requested but CUDA is unavailable") + return torch.device(name) + + +def _autocast(device: torch.device): + enabled = device.type == "cuda" + dtype = torch.bfloat16 if enabled and torch.cuda.is_bf16_supported() else torch.float16 + return torch.autocast(device_type=device.type, dtype=dtype, enabled=enabled) + + +def _distributed() -> tuple[int, int, int]: + """(rank, local_rank, world_size) under torchrun; (0, 0, 1) otherwise.""" + world = int(os.environ.get("WORLD_SIZE", "1")) + if world > 1 and not torch.distributed.is_initialized(): + torch.distributed.init_process_group("nccl") + return int(os.environ.get("RANK", "0")), int(os.environ.get("LOCAL_RANK", "0")), world + + +def _loader(dataset, *, workers: int, shuffle: bool, sampler=None) -> DataLoader: + return DataLoader( + dataset, + batch_size=1, + shuffle=shuffle and sampler is None, + sampler=sampler, + num_workers=workers, + persistent_workers=workers > 0, + pin_memory=torch.cuda.is_available(), + collate_fn=single_example_collate, + ) + + +def _to_device(value, device: torch.device): + if isinstance(value, Tensor): + return value.to(device, non_blocking=True) + if isinstance(value, dict): + return {key: _to_device(item, device) for key, item in value.items()} + return value + + +def _match_coordinate_queries( + scoring: dict[str, Tensor], positions: Tensor, track_halves: Tensor +) -> tuple[Tensor, Tensor, Tensor]: + """Match trusted coordinate queries to typed position axes like the grader.""" + device = positions.device + tracks = scoring["track"].detach().cpu().numpy() + halves_cpu = track_halves.detach().cpu().numpy()[tracks] + # Build indices on CPU, then copy once. Writing three CUDA scalars per valid query + # otherwise launches thousands of tiny kernels per example, especially costly when + # training and evaluation share one GPU. The matching semantics are unchanged. + queries = np.zeros((len(tracks), 3), dtype=np.int64) + queries[:, 2] = tracks + valid = scoring["valid"].clone() + missing = np.ones(len(tracks), dtype=bool) + position_rows = positions.detach().cpu().numpy() + donors_cpu = scoring["donor_position"].detach().cpu().numpy() + acceptors_cpu = scoring["acceptor_position"].detach().cpu().numpy() + valid_indices = np.flatnonzero(valid.detach().cpu().numpy()) + lookups = [ + {int(coordinate): index for index, coordinate in enumerate(axis) if coordinate >= 0} + for axis in position_rows + ] + for index in valid_indices: + half = int(halves_cpu[index]) + row = lookups[2 * half].get(int(donors_cpu[index])) + column = lookups[2 * half + 1].get(int(acceptors_cpu[index])) + if row is not None and column is not None: + queries[index, 0] = row + queries[index, 1] = column + missing[index] = False + return (torch.from_numpy(queries).to(device).unsqueeze(0), valid, + torch.from_numpy(missing).to(device)) + + +def _model_inputs(sample: dict) -> tuple[dict, Tensor, Tensor]: + queries, query_valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + sample["site_positions"], + sample["junction_track_halves"], + ) + if (query_valid & missing).any(): + raise ValueError("trusted scoring coordinates do not match target positions") + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + return { + "dna": sample["dna"].unsqueeze(0), + "usage_positions": sample["usage_positions"].unsqueeze(0), + "junction_site_positions": sample["site_positions"].unsqueeze(0), + "junction_queries": queries, + }, junction_target, query_valid + + +def _losses(outputs: dict[str, Tensor], sample: dict, junction_target: Tensor, + junction_valid: Tensor) -> dict[str, Tensor]: + losses = {} + for family in DENSE_FAMILIES: + target = torch.asinh(sample["dense"][family]["values"]).unsqueeze(0) + mask = sample["dense"][family]["mask"].unsqueeze(0) + residual = torch.where(mask, outputs[family] - target, 0.0) + losses[family] = residual.square().sum() / mask.sum().clamp_min(1) + losses["splice_sites"] = F.binary_cross_entropy_with_logits( + outputs["splice_sites"], sample["splice_sites"].unsqueeze(0) + ) + usage_target = torch.asinh(sample["splice_usage"]["values"]).unsqueeze(0) + usage_mask = sample["splice_usage"]["mask"].unsqueeze(0) + usage_residual = torch.where(usage_mask, outputs["splice_usage"] - usage_target, 0.0) + losses["splice_usage"] = usage_residual.square().sum() / usage_mask.sum().clamp_min(1) + junction_residual = outputs["junctions"][0] - junction_target[0] + losses["junctions"] = ( + junction_residual[junction_valid].square().mean() + if junction_valid.any() else junction_residual.sum() * 0 + ) + contact_target = torch.asinh(sample["contact_maps"]).unsqueeze(0) + contact_mask = sample["contact_map_mask"].unsqueeze(0) + contact_residual = torch.where( + contact_mask, outputs["contact_maps"] - contact_target, 0.0 + ) + losses["contact_maps"] = contact_residual.square().sum() / contact_mask.sum().clamp_min(1) + losses["total"] = torch.stack([losses[name] for name in ALL_FAMILIES]).mean() + return losses + + +def _validation_inference( + model: CompactModel, sample: dict +) -> tuple[dict[str, Tensor], Tensor, Tensor, Tensor]: + """Use the same predicted-position path as a formal v3 submission.""" + trunk = model._trunk(sample["dna"].unsqueeze(0)) + dense_features = model._dense_features(trunk) + outputs = { + family: model.dense_heads[family](dense_features).float() + for family in DENSE_FAMILIES + } + all_sites = model._splice_site_logits(trunk) + target_start = (model.config.input_length - model.config.target_length) // 2 + outputs["splice_sites"] = all_sites[:, target_start:target_start + model.config.target_length] + usage_bins = model.splice_usage_head(dense_features).float() + usage_indices = torch.div(sample["usage_positions"], model.config.dense_bin_size, + rounding_mode="floor") + outputs["splice_usage"] = usage_bins[:, usage_indices] + predicted_positions = model.select_junction_positions(all_sites) + + matched, valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + predicted_positions[0], + sample["junction_track_halves"], + ) + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + junction_prediction = torch.zeros(len(valid), device=valid.device) + if (~missing & valid).any(): + selected = ~missing & valid + junction_prediction[selected] = model._junction_predictions( + trunk, predicted_positions, matched[:, selected] + )[0] + outputs["junctions"] = junction_prediction.unsqueeze(0) + + contact = trunk.reshape( + 1, model.config.contact_bins, -1, model.config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + outputs["contact_maps"] = (0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row])).float() + return outputs, junction_target, valid, missing + + +def _atomic_save(payload: dict, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(f".{path.name}.tmp") + torch.save(payload, temporary) + os.replace(temporary, path) + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(8 * 1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _resume_position(payload: dict, *, examples: int, epochs: int) -> tuple[int, int]: + epoch = int(payload["epoch"]) + step = int(payload["global_step"]) + kind = payload.get("checkpoint_kind") + if kind not in (None, "epoch") or step != epoch * examples: + raise ValueError("only a completed-epoch checkpoint can resume training") + if payload.get("examples", examples) != examples or payload.get("schedule_epochs", epochs) != epochs: + raise ValueError("checkpoint data size or training schedule differs from this run") + return epoch, step + + +def train(args: argparse.Namespace) -> None: + # Multi-GPU with `torchrun --nproc-per-node N train_eval.py train ...`: one example per GPU per + # step (data parallel), rank 0 writes checkpoints and logs. Model size is bounded by the + # training budget, not by a parameter cap. + rank, local_rank, world = _distributed() + config = ModelConfig.from_yaml(args.config) + device = torch.device("cuda", local_rank) if world > 1 else _device(args.device) + if world > 1: + torch.cuda.set_device(device) + torch.manual_seed(args.seed) + model = CompactModel(config).to(device) + parameters = count_parameters(model) + optimizer = torch.optim.AdamW( + model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay + ) + scaler = torch.amp.GradScaler("cuda", enabled=device.type == "cuda") + full_dataset = AlphaGenomeDataset(args.input, "train") + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + start_epoch, global_step = 0, 0 + if args.resume: + payload = torch.load(args.resume, map_location="cpu", weights_only=True) + if payload.get("config") != config.to_dict(): + raise ValueError("checkpoint configuration does not match --config") + model.load_state_dict(payload["model"], strict=True) + optimizer.load_state_dict(payload["optimizer"]) + scaler.load_state_dict(payload.get("scaler", {})) + start_epoch, global_step = _resume_position(payload, examples=size, epochs=args.epochs) + per_rank = math.ceil(size / world) + steps = max(1, per_rank * args.epochs) + module = model + if world > 1: + model = torch.nn.parallel.DistributedDataParallel( + model, device_ids=[local_rank], find_unused_parameters=True) + sampler = (torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world, rank=rank, + shuffle=True, seed=args.seed) + if world > 1 else None) + stop_after_epoch = args.stop_after_epoch or args.epochs + output = args.output.resolve() + output.mkdir(parents=True, exist_ok=True) + started = time.monotonic() + if rank == 0: + print(json.dumps({"event": "train_start", "parameters": parameters, "examples": size, + "device": str(device), "world_size": world}), flush=True) + + for epoch in range(start_epoch, stop_after_epoch): + model.train() + if sampler is not None: + sampler.set_epoch(epoch) + loader = _loader(dataset, workers=args.workers, shuffle=True, sampler=sampler) + for sample in loader: + sample = _to_device(sample, device) + inputs, junction_target, junction_valid = _model_inputs(sample) + progress = min((global_step + 1) / steps, 1.0) + learning_rate = args.min_learning_rate + 0.5 * ( + args.learning_rate - args.min_learning_rate + ) * (1.0 + math.cos(math.pi * progress)) + for group in optimizer.param_groups: + group["lr"] = learning_rate + optimizer.zero_grad(set_to_none=True) + with _autocast(device): + predictions = model(**inputs) + losses = _losses(predictions, sample, junction_target, junction_valid) + if not torch.isfinite(losses["total"]): + raise FloatingPointError(f"non-finite loss at step {global_step}") + scaler.scale(losses["total"]).backward() + scaler.unscale_(optimizer) + gradient_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_gradient_norm) + scaler.step(optimizer) + scaler.update() + global_step += 1 + if rank == 0 and (global_step == 1 or global_step % args.log_every == 0): + print( + json.dumps( + { + "event": "train_step", + "epoch": epoch + 1, + "step": global_step, + "loss": float(losses["total"].detach()), + "gradient_norm": float(gradient_norm), + "learning_rate": learning_rate, + "elapsed_seconds": time.monotonic() - started, + } + ), + flush=True, + ) + if rank == 0 and global_step % args.save_every == 0: + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch, + "global_step": global_step, + "checkpoint_kind": "progress", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "progress.pt", + ) + if world > 1: + torch.distributed.barrier() + if rank != 0: + continue + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch + 1, + "global_step": global_step, + "checkpoint_kind": "epoch", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "last.pt", + ) + if world > 1: + torch.distributed.barrier() + torch.distributed.destroy_process_group() + if rank != 0: + return + checkpoint = output / "last.pt" + manifest = { + "schema": "alphagenome-50m-real/v3", + "checkpoint": {"path": checkpoint.name, "sha256": _sha256(checkpoint)}, + "parameters": parameters, + "config": config.to_dict(), + "output_families": list(ALL_FAMILIES), + "epochs": args.epochs, + "completed_epochs": stop_after_epoch, + "global_step": global_step, + } + (output / "model_manifest.json").write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "train_complete", **manifest}), flush=True) + + +class PerTrackMoments: + def __init__(self, tracks: int): + self.n = np.zeros(tracks, dtype=np.int64) + self.sum_x = np.zeros(tracks, dtype=np.float64) + self.sum_y = np.zeros(tracks, dtype=np.float64) + self.sum_x2 = np.zeros(tracks, dtype=np.float64) + self.sum_y2 = np.zeros(tracks, dtype=np.float64) + self.sum_xy = np.zeros(tracks, dtype=np.float64) + + def update(self, prediction: Tensor, target: Tensor, mask: Tensor | None = None) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1, prediction.shape[-1]) + y = target.detach().double().cpu().numpy().reshape(-1, target.shape[-1]) + valid = np.ones_like(y, dtype=bool) if mask is None else mask.detach().cpu().numpy().reshape(y.shape).astype(bool) + self.n += valid.sum(axis=0) + self.sum_x += np.where(valid, x, 0).sum(axis=0, dtype=np.float64) + self.sum_y += np.where(valid, y, 0).sum(axis=0, dtype=np.float64) + self.sum_x2 += np.where(valid, x * x, 0).sum(axis=0, dtype=np.float64) + self.sum_y2 += np.where(valid, y * y, 0).sum(axis=0, dtype=np.float64) + self.sum_xy += np.where(valid, x * y, 0).sum(axis=0, dtype=np.float64) + + def update_indexed(self, prediction: Tensor, target: Tensor, track: Tensor) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1) + y = target.detach().double().cpu().numpy().reshape(-1) + indices = track.detach().cpu().numpy().reshape(-1).astype(np.int64) + self.n += np.bincount(indices, minlength=len(self.n)) + self.sum_x += np.bincount(indices, weights=x, minlength=len(self.n)) + self.sum_y += np.bincount(indices, weights=y, minlength=len(self.n)) + self.sum_x2 += np.bincount(indices, weights=x * x, minlength=len(self.n)) + self.sum_y2 += np.bincount(indices, weights=y * y, minlength=len(self.n)) + self.sum_xy += np.bincount(indices, weights=x * y, minlength=len(self.n)) + + def report(self) -> dict: + numerator = self.n * self.sum_xy - self.sum_x * self.sum_y + denominator = np.sqrt( + np.maximum(self.n * self.sum_x2 - self.sum_x**2, 0) + * np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + ) + target_ss = np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + scorable = (self.n >= 2) & (target_ss > 0) + pearson = np.full_like(numerator, np.nan) + nonconstant = scorable & (denominator > 0) + pearson[nonconstant] = numerator[nonconstant] / denominator[nonconstant] + pearson[scorable & ~nonconstant] = 0.0 + return { + "mean_pearson": float(pearson[scorable].mean()) if scorable.any() else 0.0, + "valid_tracks": int(scorable.sum()), + "per_track_pearson": [float(value) if np.isfinite(value) else None for value in pearson], + "values_per_track": self.n.tolist(), + } + + +def evaluate(args: argparse.Namespace) -> None: + device = _device(args.device) + payload = torch.load(args.checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config).to(device) + model.load_state_dict(payload["model"], strict=True) + model.eval() + full_dataset = AlphaGenomeDataset(args.input, args.split) + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + metrics = { + **{family: PerTrackMoments(config_value) for family, config_value in { + "atac": 167, "dnase": 305, "procap": 12, "cage": 546, + "rna_seq": 667, "chip_tf": 1617, "chip_histone": 1116, + }.items()}, + "splice_sites": PerTrackMoments(4), + "splice_usage": PerTrackMoments(config.splice_usage_tracks), + "junctions": PerTrackMoments(config.junction_tracks), + "contact_maps": PerTrackMoments(config.contact_tracks), + } + with torch.inference_mode(): + for index, sample in enumerate(_loader(dataset, workers=args.workers, shuffle=False)): + sample = _to_device(sample, device) + outputs, junction_target, junction_valid, junction_missing = ( + _validation_inference(model, sample) + ) + # Match predict.py: FP32 inference is serialized to FP16 Zarr values. + outputs = {name: value.half().float() for name, value in outputs.items()} + for family in DENSE_FAMILIES: + metrics[family].update( + outputs[family][0], + torch.asinh(sample["dense"][family]["values"]), + sample["dense"][family]["mask"], + ) + metrics["splice_sites"].update(outputs["splice_sites"][0], sample["splice_sites"]) + metrics["splice_usage"].update( + outputs["splice_usage"][0], + torch.asinh(sample["splice_usage"]["values"]), + sample["splice_usage"]["mask"], + ) + junction_track = sample["junction_scoring"]["track"] + metrics["junctions"].update_indexed( + outputs["junctions"][0][junction_valid], + junction_target[0][junction_valid], + junction_track[junction_valid], + ) + metrics["contact_maps"].update( + outputs["contact_maps"][0], + torch.asinh(sample["contact_maps"]), + sample["contact_map_mask"], + ) + if index == 0 or (index + 1) % args.log_every == 0 or index + 1 == size: + print(json.dumps({"event": "evaluate_progress", "examples": index + 1, "total": size}), flush=True) + families = {name: accumulator.report() for name, accumulator in metrics.items()} + valid_family_scores = [item["mean_pearson"] for item in families.values()] + report = { + "schema": "alphagenome-50m-evaluation/v3", + "split": args.split, + "examples": size, + "metric_space": ( + "asinh continuous targets and binary splice-site targets; " + "junction Pearson uses hidden coordinate queries and predicted endpoints; " + "FP32 inference rounded to FP16 prediction values" + ), + "families": families, + "overall_mean_family_pearson": float(np.mean(valid_family_scores)), + } + args.report.parent.mkdir(parents=True, exist_ok=True) + args.report.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "evaluate_complete", "overall_mean_family_pearson": report["overall_mean_family_pearson"]}), flush=True) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + common = argparse.ArgumentParser(add_help=False) + common.add_argument("--input", required=True, type=Path) + common.add_argument("--config", type=Path, default=DEFAULT_CONFIG) + common.add_argument("--workers", type=int, default=2) + common.add_argument("--max-examples", type=int, default=0) + common.add_argument("--device", choices=("auto", "cuda", "cpu"), default="auto") + common.add_argument("--log-every", type=int, default=25) + subparsers = parser.add_subparsers(dest="command", required=True) + train_parser = subparsers.add_parser("train", parents=[common]) + train_parser.add_argument("--output", required=True, type=Path) + train_parser.add_argument("--epochs", type=int, default=10) + train_parser.add_argument("--stop-after-epoch", type=int) + train_parser.add_argument("--learning-rate", type=float, default=2e-4) + train_parser.add_argument("--min-learning-rate", type=float, default=2e-5) + train_parser.add_argument("--weight-decay", type=float, default=1e-4) + train_parser.add_argument("--max-gradient-norm", type=float, default=5.0) + train_parser.add_argument("--save-every", type=int, default=250) + train_parser.add_argument("--seed", type=int, default=17) + train_parser.add_argument("--resume", type=Path) + train_parser.set_defaults(run=train) + evaluate_parser = subparsers.add_parser("evaluate", parents=[common]) + evaluate_parser.add_argument("--checkpoint", required=True, type=Path) + evaluate_parser.add_argument("--split", choices=("train", "valid", "test"), required=True) + evaluate_parser.add_argument("--report", required=True, type=Path) + evaluate_parser.set_defaults(run=evaluate) + args = parser.parse_args() + if args.workers < 0 or args.max_examples < 0: + parser.error("workers/max-examples must be nonnegative") + if args.command == "train" and ( + args.epochs < 1 + or (args.stop_after_epoch is not None + and not 1 <= args.stop_after_epoch <= args.epochs) + ): + parser.error("epochs must be positive and stop-after-epoch must be within the schedule") + args.run(args) + + +if __name__ == "__main__": + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/train_batched.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/train_batched.py new file mode 100644 index 0000000000000000000000000000000000000000..df00b798c655555704cd6c8abbd7e4eacedd0ba3 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/train_batched.py @@ -0,0 +1,168 @@ +"""Resumable single-H100 training with equal-per-example batched losses. + +global_step counts training examples, optimizer_steps counts updates. Every epoch +uses the complete deterministic permutation, including its final partial batch. +""" +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time +import torch +from numcodecs import blosc +from torch.utils.data import DataLoader, Subset +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save, _to_device +from batched_training import list_collate, prepare_batch, equal_example_losses + +def main(): + p=argparse.ArgumentParser(description=__doc__) + p.add_argument('--input',type=Path,default=Path('/workspace/data')) + p.add_argument('--output',type=Path,required=True) + p.add_argument('--config',type=Path,required=True) + p.add_argument('--resume',type=Path) + p.add_argument('--batch-size',type=int,default=8) + p.add_argument('--epochs',type=int,default=14) + p.add_argument('--stop-at',type=int,default=0) + p.add_argument('--workers',type=int,default=6) + p.add_argument('--threads',type=int,default=2) + p.add_argument('--seed',type=int,default=17) + p.add_argument('--learning-rate',type=float,default=2e-4) + p.add_argument('--min-learning-rate',type=float,default=2e-5) + p.add_argument('--warmup',type=int,default=4000,help='Warmup in training examples') + p.add_argument('--objective',choices=['balanced_splice','variance_balanced'],default='balanced_splice') + p.add_argument('--loss-scales',type=Path) + p.add_argument('--save-every',type=int,default=10000,help='Training examples between progress checkpoints') + p.add_argument('--keep-every',type=int,default=50000) + p.add_argument('--log-every',type=int,default=50,help='Optimizer updates between logs') + args=p.parse_args() + if min(args.batch_size,args.epochs,args.save_every,args.log_every) < 1: + raise ValueError('batch, epoch, checkpoint and log intervals must be positive') + os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:22]) + torch.set_num_threads(args.threads);torch.set_num_interop_threads(1);blosc.set_nthreads(1) + torch.manual_seed(args.seed) + device=torch.device('cuda:0') + config=ModelConfig.from_yaml(args.config) + model=CompactModel(config).to(device) + optimizer=torch.optim.AdamW(model.parameters(),lr=args.learning_rate,weight_decay=1e-4) + dataset=AlphaGenomeDataset(args.input,'train') + model.set_junction_track_halves(dataset.junction_track_halves) + args.output.mkdir(parents=True,exist_ok=True) + total=len(dataset)*args.epochs + step,updates,elapsed_before=0,0,0. + lineage,scales={},{} + if args.loss_scales: + scale_source=json.loads(args.loss_scales.read_text()) + assert scale_source['split']=='train' + scales=scale_source['family_loss_scales'] + lineage['loss_scale_provenance']={k:v for k,v in scale_source.items() if k!='junction_moments'} + if args.resume: + state=torch.load(args.resume,map_location='cpu',weights_only=True) + if state['schema'] not in ['seqfunc-single/v1','seqfunc-batched/v1']: + raise ValueError('only locally trained checkpoints may resume') + if ModelConfig(**state['config']).to_dict()!=config.to_dict() or state['examples']!=len(dataset): + raise ValueError('resume architecture or dataset differs') + if state['schedule_epochs']!=args.epochs or state['objective']!=args.objective or state['seed']!=args.seed: + raise ValueError('resume schedule, objective or seed differs') + previous=state.get('training_arguments',{}) + for field in ['learning_rate','min_learning_rate','warmup']: + if field in previous and previous[field]!=getattr(args,field): + raise ValueError(f'resume {field} differs') + if state['schema']=='seqfunc-batched/v1' and previous['batch_size']!=args.batch_size: + raise ValueError('batched resume must keep its batch size') + if scales and scales!=state.get('family_loss_scales',{}): + raise ValueError('resume family scales differ') + scales=state.get('family_loss_scales',{}) + lineage.update({k:state[k] for k in ['intervention','interventions','loss_scale_provenance'] if k in state}) + model.load_state_dict(state['model'],strict=True) + optimizer.load_state_dict(state['optimizer']) + step=state['global_step'];updates=state.get('optimizer_steps',step) + elapsed_before=state.get('elapsed_seconds',0.) + torch.set_rng_state(state['rng_cpu']);torch.cuda.set_rng_state(state['rng_cuda'],device) + if state['schema']=='seqfunc-single/v1': + with args.resume.open('rb') as f: + digest=hashlib.file_digest(f,'sha256').hexdigest() + history=list(lineage.get('interventions',[])) + if lineage.get('intervention') and lineage['intervention'] not in history: + history.append(lineage['intervention']) + change={'kind':'batch_size_change','from':1,'to':args.batch_size,'at_example':step, + 'parent_sha256':digest,'parameters_optimizer_rng_unchanged_at_start':True} + history.append(change);lineage.update(intervention=change,interventions=history) + if args.objective=='variance_balanced': + expected=set(model.dense_heads)|{'splice_usage','junctions','contact_maps'} + if set(scales)!=expected or not all(math.isfinite(v) and v>0 for v in scales.values()): + raise ValueError('fixed positive training-derived scales required') + elif scales: + raise ValueError('unscaled objective cannot carry family scales') + start=time.monotonic() + def emit(event,**values): + print(json.dumps({'event':event,'step':step,'optimizer_steps':updates, + 'elapsed_seconds':elapsed_before+time.monotonic()-start,**values}),flush=True) + def save(name): + _atomic_save({**lineage,'schema':'seqfunc-batched/v1','model':model.state_dict(), + 'optimizer':optimizer.state_dict(),'config':config.to_dict(), + 'global_step':step,'optimizer_steps':updates,'epoch':step//len(dataset),'offset':step%len(dataset), + 'examples':len(dataset),'schedule_epochs':args.epochs,'objective':args.objective,'seed':args.seed, + 'family_loss_scales':scales, + 'training_arguments':{k:str(v) if isinstance(v,Path) else v for k,v in vars(args).items()}, + 'rng_cpu':torch.get_rng_state(),'rng_cuda':torch.cuda.get_rng_state(device), + 'elapsed_seconds':elapsed_before+time.monotonic()-start, + 'provenance':{'data':str(args.input),'split':'train','pretrained':False,'world_size':1, + 'device':torch.cuda.get_device_name(0),'batch_reduction':'equal per example', + 'train_manifest_sha256':hashlib.sha256((args.input/'public/train.parquet').read_bytes()).hexdigest()}, + },args.output/name) + emit('train_start',parameters=count_parameters(model),examples=len(dataset),batch_size=args.batch_size, + objective=args.objective,config=config.to_dict(),schedule_examples=total,family_loss_scales=scales) + if args.stop_at and step>=args.stop_at: + raise ValueError('stop-at must exceed the resumed example count') + model.train() + for epoch in range(step//len(dataset),args.epochs): + order=torch.randperm(len(dataset),generator=torch.Generator().manual_seed(args.seed+epoch)).tolist() + offset=step%len(dataset) + emit('epoch_order',epoch=epoch+1,offset=offset,count=len(order), + permutation_sha256=hashlib.sha256(torch.tensor(order,dtype=torch.int64).numpy().tobytes()).hexdigest()) + loader=DataLoader(Subset(dataset,order[offset:]),batch_size=args.batch_size,shuffle=False, + num_workers=args.workers,pin_memory=True,collate_fn=list_collate, + persistent_workers=False,prefetch_factor=2 if args.workers else None) + for cpu_samples in loader: + if args.stop_at: + cpu_samples=cpu_samples[:args.stop_at-step] + inputs,targets,valid=prepare_batch(cpu_samples) + inputs=_to_device(inputs,device) + targets=[x.to(device,non_blocking=True) for x in targets] + valid=[x.to(device,non_blocking=True) for x in valid] + samples=[_to_device(s,device) for s in cpu_samples] + progress=min(max(step-args.warmup,0)/max(total-args.warmup,1),1) + lr=args.min_learning_rate+.5*(args.learning_rate-args.min_learning_rate)*(1+math.cos(math.pi*progress)) + lr*=min((step+len(samples))/max(args.warmup,1),1) + for group in optimizer.param_groups: group['lr']=lr + optimizer.zero_grad(set_to_none=True) + with torch.autocast('cuda',dtype=torch.bfloat16): + outputs=model(**inputs) + losses,raw=equal_example_losses(outputs,samples,targets,valid,scales=scales) + if not torch.isfinite(losses['total']): + raise FloatingPointError(f'nonfinite loss at {step}') + losses['total'].backward() + norm=torch.nn.utils.clip_grad_norm_(model.parameters(),5,error_if_nonfinite=True) + optimizer.step() + before=step;step+=len(samples);updates+=1 + if updates==1 or updates%args.log_every==0: + emit('train_step',epoch=epoch+1,example_ids=[s['example_id'] for s in samples], + learning_rate=lr,gradient_norm=float(norm),actual_batch_size=len(samples), + losses={k:float(v.detach()) for k,v in losses.items()}, + raw_losses={k:float(v.detach()) for k,v in raw.items()}, + peak_gpu_bytes=torch.cuda.max_memory_allocated()) + if step//args.save_every>before//args.save_every: save('progress.pt') + if args.keep_every and step//args.keep_every>before//args.keep_every: save(f'step-{step:06d}.pt') + if step%len(dataset)==0: + save('last.pt');save(f'epoch-{epoch+1:02d}.pt') + emit('epoch_complete',completed_epochs=epoch+1) + if args.stop_at and step>=args.stop_at: + save('last.pt');emit('train_segment_complete',completed_epochs=step//len(dataset),offset=step%len(dataset)) + return + emit('train_complete') + +if __name__=='__main__': main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_assets_manifest.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_assets_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..8e314c842cca039da1d2be8da7fff6a146178651 --- /dev/null +++ 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+head_hidden_size: 256 +hidden_dim: 384 +input_length: 1048576 +junction_rank: 32 +junction_tracks: 734 +local_context_blocks: 6 +local_to_trunk: true +local_width: 32 +mlp_expansion: 4.0 +num_attention_heads: 6 +num_downsampling_blocks: 7 +num_sequence_blocks: 6 +pair_downsample: 16 +pair_update_interval: 2 +pair_width: 32 +residual_rms_cap: 0.1 +splice_context_mode: offset +splice_usage_tracks: 734 +stem_stride: 8 +target_length: 196608 +trunk_bin_size: 1024 diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/block-checkpoint-cpu-check.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/block-checkpoint-cpu-check.json new file mode 100644 index 0000000000000000000000000000000000000000..588d9318734995508b1ece2948b790e2d0ce5172 --- /dev/null +++ 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"agv2-ae12b59b048cc5fc83b77de5", + "sha256": "dc90aa36afd13521189d81265ee4cdfbb7bf2aee75e24e5acba3224a8c672ef2" + }, + { + "id": "agv2-a85d0f912d8551638f7074cf", + "sha256": "db7758c1aa4b30fe6dce5d8911217063cee3a6f163ec23f458601ad355d29768" + } + ] + }, + "no_active_source_or_training_changes": true +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/bytecache-io-amendment.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/bytecache-io-amendment.json new file mode 100644 index 0000000000000000000000000000000000000000..6b231a5e416976f48c6cca5b572937b4761cdf31 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/bytecache-io-amendment.json @@ -0,0 +1,41 @@ +{ + "adopted_utc": "2026-10-25T04:07:41.689004+00:00", + "change": "Optional verified compressed-byte cache, training only; every hit checks source-byte digest, key and length.", + "source_before": "/workspace/research/studies/scale13/source-parallel-io", + "source_after": "/workspace/research/studies/loader_io/cached-integration/source", + "checkpoints": { + "scale13-small": { + "step": 96000, + "path": "/workspace/research/experiments/scale13-small/bytecache-resume-096000.pt", + "sha256": "62c322bf950af508fafbed8267a7036cd22cedb784ac3226830f4b1111774fba", + "single_inode_copy": true + }, + "scale13-large": { + "step": 78000, + "path": "/workspace/research/experiments/scale13-large/bytecache-resume-078000.pt", + "sha256": "bcac37454809ef1ba3bf6013722ae4569bd6c712729b6d86f507b31a1dd0b034", + "single_inode_copy": true + } + }, + "cache_root": "/tmp/seqfunc-bytecache-train256-20261025T0148", + "model_optimizer_rng_exact_at_each_resume": true, + "model_trainer_loss_sources_data_order_objective_batch_schedule_unchanged": true, + "different_resume_boundaries_are_IO_only": true, + "integration_check": "/workspace/research/studies/loader_io/cached-integration/integration-check.json", + "timing_report": "/workspace/research/analysis/cached-training-recheck.json", + "timing_cache_over_reference": 0.23811392449245397, + "timing_caveat": "Shared GPU load, fixed256preselected training examples and an already populated byte cache; reference/cache/cache/reference order. Includes data delivery, transfer, forward/backward and AdamW after16warmup examples per mode. Excludes source loading/setup, tensor-digest testing and cache population. Does not measure long-tail storage outages or guarantee gains on the full training dataset.", + "cache_not_an_inference_asset": true, + "adopted_evaluations_without_interruption": { + "scale13-small-eval02": { + "pid": 12624, + "starttime_ticks": 21982067, + "cmdline_sha256": "7c6eb145cc8f7c825796945e00d17a6c41868e152f337b404477a8ad6e1b6c73" + } + }, + "old_monitor_retained_stopped_until_adopted_evaluation_finishes": { + "pid": 12248, + "starttime_ticks": 21911693, + "cmdline_sha256": "fa05426c3f41db24f960797d8171d2d369bf0ad15c957c24fcb8fb307282e14a" + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/bytecache-migration-plan.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/bytecache-migration-plan.json new file mode 100644 index 0000000000000000000000000000000000000000..f66141e5eaa8ffa0691022fb3e727f2d75e7542a --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/bytecache-migration-plan.json @@ -0,0 +1,26 @@ +{ + "created_utc": "2026-10-25T03:51:39.643333+00:00", + "scope": "Execution-only checkpoint-safe cache adoption for both original scale trajectories; no scientific or evaluation change", + "original_study": "/workspace/research/studies/scale13/study.json", + "original_study_sha256": "61c9c4c2025b3c83067a27a35fdd58f00efef51ee3d9beb06711425d30cc8200", + "preparation": "/workspace/research/studies/loader_io/cached-integration/preparation.json", + "integration_check": "/workspace/research/studies/loader_io/cached-integration/integration-check.json", + "timing_report": "/workspace/research/analysis/cached-training-recheck.json", + "cache_root": "/tmp/seqfunc-bytecache-train256-20261025T0148", + "old_controller_pid": 12248, + "boundaries": { + "scale13-small": 96000, + "scale13-large": 78000 + }, + "helper_sha256": { + "migrate_scale_bytecache.py": "8c0a181c44fc6ad480dc0d74f2a803dc41d860b16007753bccc35dad17f78c0a", + "run_scale_study_cached_io.py": "adc1bf9c5d3054f63425b605e7146d2b83a24f933a7f9e795083fde0ad98174b" + }, + "controller_adoption_check": "/workspace/research/studies/loader_io/cached-integration/adoption-controller-check.json", + "decision": "Adopt verified exact-data optional reader after complete ABBA training probe; ratio.2381under shared load is evidence of current stalls, not a promised sustained gain. Cold population cost and full-working-set performance remain to measure.", + "unchanged_runs": [ + "qknorm35" + ], + "preserved_evaluation": "scale13-small-epoch02", + "old_monitor_policy": "Suspend old monitor after captures; retain its session while the adopted evaluation completes; then retire. Stop only original trainers/their data workers and idle future checkpoint waiters." +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/bytecache-preparation.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/bytecache-preparation.json new file mode 100644 index 0000000000000000000000000000000000000000..9ce7b02b6f484c9b2d4f61c5bad46e4ed719abf6 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/bytecache-preparation.json @@ -0,0 +1,22 @@ +{ + "created_utc": "2026-10-25T03:47:11.178799+00:00", + "status": "Prepared only, active sources untouched; no migration decision before complete timing and integrated equality check", + "old_source": "/workspace/research/studies/scale13/source-parallel-io", + "new_source": "/workspace/research/studies/loader_io/cached-integration/source", + "only_modified_existing_source": [ + "reference/alphagenome_50m/dataset.py" + ], + "new_files": [ + "reference/alphagenome_50m/direct_read_store.py", + "reference/alphagenome_50m/cached_read_store.py" + ], + "model_trainer_objective_sources_exact": true, + "cache_activation": "Explicit SEQFUNC_TRAIN_BYTECACHE environment path and split=train and include_targets=True; otherwise exact original reader. Zipstore path remains original.", + "namespace": "SHA256ofpublic+labels training manifest digests and original absolute store path/key; every hit checks original bytes SHA/length/key", + "old_dataset_sha256": "dc4263a10fecca979811161da97e731c471468465fe9480bbf2a9aacca68fb7f", + "new_files_sha256": { + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919", + "reference/alphagenome_50m/direct_read_store.py": "ab182b3b687a3b470ff1bbc311a9130cc791974366bdc0a400df5e0e28602c9c", + "reference/alphagenome_50m/cached_read_store.py": "53beb273318ad3603c1a7eec0a66f82887e6b05cabc523cbc33c7a80aa58e688" + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/bytecache-timing_report.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/bytecache-timing_report.json new file mode 100644 index 0000000000000000000000000000000000000000..f65930cac3b7e9dbbaeb4ba5001dcb5488f5bca0 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/bytecache-timing_report.json @@ -0,0 +1,85 @@ +{ + "created_utc": "2026-10-25T03:47:08.875281+00:00", + "plan_sha256": "6e0c89b326b3b5d254a8206167e4ec067a5c6cf5921489a4fa15a55816020e46", + "checkpoint_sha256": "31b67fe7d1b3b691f3d74876ad49e6fc8d096858c646df27aa27ff51c8999cb5", + "modes": [ + { + "repeat": 0, + "mode": "reference", + "fresh_parent_loaded": true, + "initial_optimizer_step_min": 10255.0, + "initial_optimizer_step_max": 10255.0, + "ordered_training_ids_exact": true, + "warmup_examples": 16, + "timed_examples": 240, + "timed_seconds": 139.6908002400014, + "seconds_per_example": 0.5820450010000059, + "timed_dataloader_wait_seconds": 79.04790870507713, + "last_weighted_loss": 0.6381173133850098, + "peak_allocated_GiB": 6.530080318450928, + "peak_reserved_GiB": 9.171875 + }, + { + "repeat": 1, + "mode": "existing_cache", + "fresh_parent_loaded": true, + "initial_optimizer_step_min": 10255.0, + "initial_optimizer_step_max": 10255.0, + "ordered_training_ids_exact": true, + "warmup_examples": 16, + "timed_examples": 240, + "timed_seconds": 32.168527343979804, + "seconds_per_example": 0.13403553059991585, + "timed_dataloader_wait_seconds": 0.005411982972873375, + "last_weighted_loss": 0.6376754641532898, + "peak_allocated_GiB": 6.532010555267334, + "peak_reserved_GiB": 8.953125 + }, + { + "repeat": 2, + "mode": "existing_cache", + "fresh_parent_loaded": true, + "initial_optimizer_step_min": 10255.0, + "initial_optimizer_step_max": 10255.0, + "ordered_training_ids_exact": true, + "warmup_examples": 16, + "timed_examples": 240, + "timed_seconds": 17.468506193021312, + "seconds_per_example": 0.07278544247092214, + "timed_dataloader_wait_seconds": 0.005700364970834926, + "last_weighted_loss": 0.63838130235672, + "peak_allocated_GiB": 6.534998416900635, + "peak_reserved_GiB": 8.9453125 + }, + { + "repeat": 3, + "mode": "reference", + "fresh_parent_loaded": true, + "initial_optimizer_step_min": 10255.0, + "initial_optimizer_step_max": 10255.0, + "ordered_training_ids_exact": true, + "warmup_examples": 16, + "timed_examples": 240, + "timed_seconds": 68.76838014100213, + "seconds_per_example": 0.2865349172541755, + "timed_dataloader_wait_seconds": 5.523634409008082, + "last_weighted_loss": 0.6376829743385315, + "peak_allocated_GiB": 6.534746170043945, + "peak_reserved_GiB": 8.470703125 + } + ], + "total_timed_seconds": { + "reference": 208.45918038100353, + "existing_cache": 49.637033537001116 + }, + "cache_over_reference": 0.23811392449245397, + "all_probe_updates_discarded": true, + "checkpoint_or_weights_written": false, + "actual_training_runs_changed": false, + "split": "train only", + "precision": { + "autocast": "bfloat16", + "allow_tf32": false + }, + "caveats": "Shared GPU load, fixed256preselected training examples and an already populated byte cache; reference/cache/cache/reference order. Includes data delivery, transfer, forward/backward and AdamW after16warmup examples per mode. Excludes source loading/setup, tensor-digest testing and cache population. Does not measure long-tail storage outages or guarantee gains on the full training dataset." +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/execution_change.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/execution_change.json new file mode 100644 index 0000000000000000000000000000000000000000..93f72a0c4f5f51e9192005535fc45e6aa3bbd671 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/execution_change.json @@ -0,0 +1,10 @@ +{ + "adopted_utc": "2026-10-24T21:51:59.762636+00:00", + "change": "Activation checkpoint boundaries only; all model operations, parameters, objective, data and schedule unchanged.", + "example_boundary": 1000, + "before": "Recompute whole local path during backward.", + "after": "Recompute each local residual block during backward.", + "evidence": "CPU gradients and all eleven outputs exactly match. Full-shape BF16 outputs/losses are bit-exact; gradient relative L2 discrepancy .0009547 versus .0009534 for an unchanged repeat, consistent with existing reduction variability. Peak memory40.11 to15.99GiB; isolated compute .11131 to.10504seconds/example.", + "initial_source_manifest_sha256": "18329ef35e89fc5aeb50ccc045b08db690e6c8ef40d661b01ddb74c7f32b6f0b", + "no_benchmark_weights_retained": true +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/memory-check-interruption.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/memory-check-interruption.json new file mode 100644 index 0000000000000000000000000000000000000000..19bc02cd2784b11f5f3e658efa28017639c2efeb --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/memory-check-interruption.json @@ -0,0 +1,19 @@ +{ + "time_utc": "2026-10-24T21:49:06.154124+00:00", + "reason": "Mechanical activation-checkpoint memory/equivalence check; optimizer, data order and model math preserved.", + "process_group": 4169, + "checkpoints": [ + { + "run": "scale13-small", + "step": 1000, + "path": "/workspace/research/experiments/scale13-small/step-001000.pt", + "sha256": "a53ea6d92c21c06576c734dc05eac6e3f21d33701f25bfc335817a6e6ec197fc" + }, + { + "run": "scale13-large", + "step": 1000, + "path": "/workspace/research/experiments/scale13-large/step-001000.pt", + "sha256": "7398dd28297d6cc3134b8d3b0cea703fb0bb0a8e60b4c0c2d81abe04c1d7dc43" + } + ] +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/migrate_scale_bytecache.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/migrate_scale_bytecache.py new file mode 100644 index 0000000000000000000000000000000000000000..05b927345326f5b660fa5b28118ce867eee97c88 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/migrate_scale_bytecache.py @@ -0,0 +1,235 @@ +"""Checkpoint-safe opt-in byte-cache amendment, preserving an active evaluation.""" +import argparse +from datetime import datetime, timezone +import fcntl +import hashlib +import json +import os +from pathlib import Path +import shutil +import signal +import sys +import time + +import torch + + +def digest(path): + with Path(path).open('rb') as stream: + return hashlib.file_digest(stream, 'sha256').hexdigest() + + +def identity(pid): + p = Path(f'/proc/{pid}') + stat = (p/'stat').read_text().split(') ', 1)[1].split() + return {'pid': pid, 'starttime_ticks': int(stat[19]), + 'cmdline_sha256': hashlib.sha256((p/'cmdline').read_bytes()).hexdigest()} + + +def command(pid): + return Path(f'/proc/{pid}/cmdline').read_bytes().replace(b'\0', b' ').decode() + + +def alive(pid): + try: + return Path(f'/proc/{pid}/stat').read_text().split(') ', 1)[1][0] not in 'ZX' + except FileNotFoundError: + return False + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('plan', type=Path) + args = p.parse_args() + plan = json.loads(args.plan.read_text()) + r = Path('/workspace/research') + root = args.plan.parent + os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) + torch.set_num_threads(1) + for name, value in plan['helper_sha256'].items(): + assert digest(r/'seqfunc'/name) == value, name + assert digest(plan['original_study']) == plan['original_study_sha256'] + original = json.loads(Path(plan['original_study']).read_text()) + prepared = json.loads(Path(plan['preparation']).read_text()) + integrated = json.loads(Path(plan['integration_check']).read_text()) + timing = json.loads(Path(plan['timing_report']).read_text()) + assert integrated['all_returned_training_ids_and_tensor_bytes_exact'] and integrated['examples'] == 16 + assert integrated['public_inference_and_validation_cache_disabled_despite_environment'] + assert timing['cache_over_reference'] < .8 and timing['all_probe_updates_discarded'] + source = Path(prepared['new_source']) + for name, value in prepared['new_files_sha256'].items(): + assert digest(source/name) == value, name + for path in Path(original['source']).rglob('*'): + if path.is_file() and '__pycache__' not in path.parts and '.pytest_cache' not in path.parts: + relative = path.relative_to(original['source']) + if str(relative) != 'reference/alphagenome_50m/dataset.py': + assert digest(path) == digest(source/relative), str(relative) + old_controller = plan['old_controller_pid'] + assert 'run_scale_study.py' in command(old_controller) + assert os.getpgid(old_controller) != os.getpgrp() + processes = json.loads((root/'study-state.json').read_text()) + for name, target in plan['boundaries'].items(): + assert target % 1000 == 0 and target % 4 == 0 + pid = processes[name+'-train']['pid'] + assert str(Path(original['source'])/'train_batched.py') in command(pid) + assert str(r/'experiments'/name) in command(pid) + stopped, snapshots, seen = set(), {}, {} + killed = False + controller_stopped = False + begun = time.monotonic() + + def status(phase, **extra): + record = {'phase': phase, 'utc': datetime.now(timezone.utc).isoformat(), + 'boundaries': plan['boundaries'], 'checkpoints': snapshots, **extra} + temporary = root/'cached-io-migration-state.tmp' + temporary.write_text(json.dumps(record, indent=2)+'\n') + temporary.replace(root/'cached-io-migration-state.json') + print(json.dumps(record), flush=True) + + def interrupted(signum, frame): + raise SystemExit(f'Migration interrupted by signal {signum}') + signal.signal(signal.SIGTERM, interrupted) + signal.signal(signal.SIGINT, interrupted) + status('waiting_for_saved_boundaries') + try: + while len(snapshots) < len(plan['boundaries']): + assert time.monotonic()-begun < 3600, 'Boundary wait exceeded one hour' + for name, target in plan['boundaries'].items(): + if name in snapshots: + continue + pid = processes[name+'-train']['pid'] + assert alive(pid), f'Trainer {name} exited before capture' + progress = r/'experiments'/name/'progress.pt' + with progress.open('rb') as stream: + stat = os.fstat(stream.fileno()) + signature = (stat.st_ino, stat.st_mtime_ns, stat.st_size) + if signature == seen.get(name): + continue + state = torch.load(f'/proc/self/fd/{stream.fileno()}', mmap=True, + map_location='cpu', weights_only=True) + step = state['global_step'] + assert step <= target, f'{name} missed boundary at {step}' + seen[name] = signature + if step == target: + assert state['optimizer_steps'] == step//4 + assert state['objective'] == 'variance_balanced' + os.kill(pid, signal.SIGSTOP) + stopped.add(pid) + destination = progress.with_name(f'bytecache-resume-{step:06d}.pt') + assert not destination.exists() + temporary = destination.with_suffix('.tmp') + stream.seek(0) + with temporary.open('wb') as output: + shutil.copyfileobj(stream, output, length=8*1024*1024) + temporary.replace(destination) + snapshots[name] = {'step': step, 'path': str(destination), + 'sha256': digest(destination), 'single_inode_copy': True} + status('captured_saved_boundary', trainer=name) + del state + time.sleep(1) + # All pending later evaluations still wait for checkpoints. Preserve the + # already staged small epoch2 evaluation, which belongs to the old reader. + keep = 'scale13-small-eval02' + adopted = {} + keep_directory = r/'experiments/scale13-small-epoch02' + if json.loads((keep_directory/'pipeline-state.json').read_text())['phase'] != 'complete': + adopted[keep] = identity(processes[keep]['pid']) + assert 'run_candidate.py' in command(processes[keep]['pid']) + terminate_waiters = [] + for name, record in processes.items(): + if '-eval' not in name or name in adopted or record['returncode'] is not None: + continue + pid = record['pid'] + directory = r/'experiments'/name.replace('-eval', '-epoch') + if json.loads((directory/'pipeline-state.json').read_text())['phase'] == 'complete': + continue + assert json.loads((directory/'pipeline-state.json').read_text())['phase'] == 'waiting_for_checkpoint' + assert not Path(f'/proc/{pid}/task/{pid}/children').read_text().strip() + terminate_waiters.append(pid) + # Keep this old session alive until its adopted evaluation finishes. + # Its monitor is stopped, so only the new controller owns training status. + old_identity = identity(old_controller) + os.kill(old_controller, signal.SIGSTOP) + controller_stopped = True + worker_pids = [] + for name in plan['boundaries']: + pid = processes[name+'-train']['pid'] + worker_pids += [int(p) for p in Path(f'/proc/{pid}/task/{pid}/children').read_text().split()] + terminated = terminate_waiters+worker_pids+[processes[name+'-train']['pid'] for name in plan['boundaries']] + status('replacing_trainers_and_idle_waiters', preserved_evaluations=adopted, terminated_pids=terminated) + for pid in terminated: + if alive(pid): + os.kill(pid, signal.SIGTERM) + os.kill(pid, signal.SIGCONT) + killed = True + stopped.clear() + deadline = time.monotonic()+60 + while any(alive(pid) for pid in terminated): + assert time.monotonic() < deadline, 'Old training or idle waiter did not exit' + time.sleep(1) + for name, record in snapshots.items(): + assert digest(r/'experiments'/name/'progress.pt') == record['sha256'] + amendment = {'adopted_utc': datetime.now(timezone.utc).isoformat(), + 'change': 'Optional verified compressed-byte cache, training only; every hit checks source-byte digest, key and length.', + 'source_before': original['source'], 'source_after': str(source), + 'checkpoints': snapshots, 'cache_root': plan['cache_root'], + 'model_optimizer_rng_exact_at_each_resume': True, + 'model_trainer_loss_sources_data_order_objective_batch_schedule_unchanged': True, + 'different_resume_boundaries_are_IO_only': True, + 'integration_check': plan['integration_check'], 'timing_report': plan['timing_report'], + 'timing_cache_over_reference': timing['cache_over_reference'], + 'timing_caveat': timing['caveats'], + 'cache_not_an_inference_asset': True, + 'adopted_evaluations_without_interruption': adopted, + 'old_monitor_retained_stopped_until_adopted_evaluation_finishes': old_identity} + new = dict(original) + new.update(source=str(source), training_bytecache_root=plan['cache_root'], + cache_execution_amendment=amendment, adopted_evaluations=adopted, + retired_monitor_identity=old_identity) + new['execution_amendments'] = list(original.get('execution_amendments', []))+[amendment] + new['provenance'] += (' At the saved small96k/large78k boundaries, an optional training-only byte cache ' + 'was enabled after exact256example and integrated16example tensor/ID checks. ' + 'Source compressed bytes are verified on every cache hit, with read-only source fallback. ' + 'Model/trainer/loss code, complete epoch permutations, batch and schedule are unchanged. ' + 'The cache is disposable and is disabled for validation/public inference.') + history = source/'training_history' + (history/'bytecache-io-amendment.json').write_text(json.dumps(amendment, indent=2)+'\n') + for name in ['preparation', 'integration_check', 'timing_report']: + path = Path(plan[name]);shutil.copyfile(path, history/('bytecache-'+name+'.json')) + shutil.copyfile(args.plan, history/'bytecache-migration-plan.json') + shutil.copyfile(Path(__file__), history/Path(__file__).name) + shutil.copyfile(r/'seqfunc/run_scale_study_cached_io.py', history/'run_scale_study_cached_io.py') + new_path = root/'study-cached-io.json' + new_path.write_text(json.dumps(new, indent=2)+'\n') + manifest = {str(p.relative_to(source)): digest(p) for p in sorted(source.rglob('*')) + if p.is_file() and '__pycache__' not in p.parts and '.pytest_cache' not in p.parts} + (root/'source_manifest-cached-io.json').write_text(json.dumps(manifest, indent=2)+'\n') + (root/'cached-io-amendment.json').write_text(json.dumps(amendment, indent=2)+'\n') + with Path('/workspace/.submission-promotion.lock').open('a') as lock: + fcntl.flock(lock, fcntl.LOCK_EX) + p = r/'STATE.json'; state = json.loads(p.read_text()) + state['scale_study'].update(plan=str(new_path), source=str(source), + controller_pid=os.getpid(), status='Resuming original trajectories with verified opt-in training byte cache', + cached_reader_amendment=str(root/'cached-io-amendment.json'), + previous_controller_retained_stopped=old_controller) + state['scale_study']['session'] = state['scale_study']['bytecache_migration']['session'] + state['scale_study']['bytecache_migration']['status'] = 'Checkpoints captured; replacement controller starting' + temporary=p.with_suffix('.tmp');temporary.write_text(json.dumps(state,indent=2)+'\n');temporary.replace(p) + with (r/'LEDGER.md').open('a') as ledger: + ledger.write('\n'+amendment['adopted_utc']+': saved small96k/large78k trajectories resumed with optional verified training byte cache; original scientific plans unchanged. Active small epoch2 evaluation preserved. Original study/source/checkpoints retained; full amendment at studies/scale13/cached-io-amendment.json.\n') + status('replacement_controller_starting', source=str(source), new_study=str(new_path)) + log = (root/'study-cached-io.log').open('a') + os.dup2(log.fileno(), 1);os.dup2(log.fileno(), 2) + os.execv(sys.executable,[sys.executable,'-u',str(r/'seqfunc/run_scale_study_cached_io.py'),str(new_path)]) + except BaseException as error: + status('failed_or_interrupted', error=repr(error), old_trainers_terminated=killed) + raise + finally: + if not killed: + for pid in stopped: + if alive(pid):os.kill(pid,signal.SIGCONT) + if controller_stopped and alive(old_controller):os.kill(old_controller,signal.SIGCONT) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/model_before_block_checkpoint.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/model_before_block_checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..31f17eb68837a9caa6492ec862feda39d068a43f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/model_before_block_checkpoint.py @@ -0,0 +1,457 @@ +"""Affordable multi-resolution baseline for the compact AlphaGenome task.""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass, fields +import math +from pathlib import Path +from typing import Any, Iterable + +import torch +from torch import Tensor, nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint +import yaml + +from .layers import ChannelRMSNorm, ConvBlock, SequenceBlock + + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} +ALL_FAMILIES = (*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions", "contact_maps") + + +@dataclass(frozen=True) +class ModelConfig: + input_length: int = 1_048_576 + target_length: int = 196_608 + dense_bin_size: int = 128 + trunk_bin_size: int = 1_024 + contact_bin_size: int = 2_048 + stem_stride: int = 8 + encoder_width: int = 32 + channel_growth: int = 32 + num_downsampling_blocks: int = 7 + conv_kernel_size: int = 5 + dna_kernel_size: int = 15 + hidden_dim: int = 384 + num_sequence_blocks: int = 6 + num_attention_heads: int = 6 + attention_head_dim: int = 64 + mlp_expansion: float = 4.0 + dropout: float = 0.0 + head_hidden_size: int = 256 + junction_rank: int = 32 + splice_usage_tracks: int = 734 + junction_tracks: int = 734 + contact_tracks: int = 28 + gradient_checkpointing: bool = True + enable_pairwise: bool = False + pair_width: int = 32 + pair_downsample: int = 16 + pair_update_interval: int = 2 + local_width: int = 0 + local_context_blocks: int = 4 + splice_context_mode: str = 'offset' + local_to_trunk: bool = False + residual_rms_cap: float = 0.0 + + def __post_init__(self) -> None: + if not 1 <= self.local_context_blocks <= 7: + raise ValueError('local_context_blocks must be between 1 and 7') + if not math.isfinite(self.residual_rms_cap) or self.residual_rms_cap < 0: + raise ValueError('residual_rms_cap must be finite and nonnegative') + if self.local_to_trunk and not self.local_width: + raise ValueError('local_to_trunk requires local features') + if self.splice_context_mode not in ('offset', 'uniform'): + raise ValueError('unknown splice_context_mode') + if self.splice_context_mode == 'uniform' and not self.local_width: + raise ValueError('uniform splice context requires a base-resolution path') + downsample = self.stem_stride * 2**self.num_downsampling_blocks + if downsample != self.trunk_bin_size: + raise ValueError("stem_stride * 2**num_downsampling_blocks must equal trunk_bin_size") + for length in (self.input_length, self.target_length): + if length % self.trunk_bin_size: + raise ValueError("input and target lengths must divide by trunk_bin_size") + if self.trunk_bin_size % self.dense_bin_size: + raise ValueError("trunk_bin_size must divide by dense_bin_size") + if self.input_length % self.contact_bin_size: + raise ValueError("input_length must divide by contact_bin_size") + if self.hidden_dim <= 0 or self.num_attention_heads <= 0: + raise ValueError("hidden dimensions and attention heads must be positive") + + @property + def trunk_bins(self) -> int: + return self.input_length // self.trunk_bin_size + + @property + def target_trunk_bins(self) -> int: + return self.target_length // self.trunk_bin_size + + @property + def dense_bins(self) -> int: + return self.target_length // self.dense_bin_size + + @property + def dense_upsample(self) -> int: + return self.trunk_bin_size // self.dense_bin_size + + @property + def contact_bins(self) -> int: + return self.input_length // self.contact_bin_size + + @property + def encoder_output_width(self) -> int: + return self.encoder_width + self.channel_growth * self.num_downsampling_blocks + + @classmethod + def from_yaml(cls, path: str | Path) -> "ModelConfig": + values: dict[str, Any] = yaml.safe_load(Path(path).read_text()) or {} + known = {field.name for field in fields(cls)} + unknown = sorted(values.keys() - known) + if unknown: + raise ValueError(f"Unknown model configuration keys: {unknown}") + return cls(**values) + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + +class SequenceEncoder(nn.Module): + def __init__(self, config: ModelConfig): + super().__init__() + self.stem = nn.Conv1d( + 4, + config.encoder_width, + config.dna_kernel_size, + stride=config.stem_stride, + padding=config.dna_kernel_size // 2, + ) + blocks = [] + width = config.encoder_width + for _ in range(config.num_downsampling_blocks): + output_width = width + config.channel_growth + blocks.append(ConvBlock(width, output_width, config.conv_kernel_size)) + width = output_width + self.blocks = nn.ModuleList(blocks) + + def forward(self, dna: Tensor) -> Tensor: + x = F.gelu(self.stem(dna.transpose(1, 2)), approximate="tanh") + for block in self.blocks: + x = F.max_pool1d(block(x), kernel_size=2, stride=2) + return x.transpose(1, 2) + + +class LocalResidualBlock(nn.Module): + def __init__(self, width: int, dilation: int): + super().__init__() + self.norm = ChannelRMSNorm(width) + self.depthwise = nn.Conv1d(width, width, 9, dilation=dilation, + padding=4 * dilation, groups=width) + self.expand = nn.Conv1d(width, 2 * width, 1) + self.contract = nn.Conv1d(2 * width, width, 1) + + def forward(self, x: Tensor) -> Tensor: + update = self.depthwise(self.norm(x)) + update = self.contract(F.gelu(self.expand(update), approximate='tanh')) + return x + update + + +class LocalSequencePath(nn.Module): + """Base-resolution motif path with powers-of-four dilation and aligned pooling.""" + def __init__(self, config: ModelConfig): + super().__init__() + width = config.local_width + self.stem = nn.Conv1d(4, width, 21, padding=10) + self.blocks = nn.Sequential(*(LocalResidualBlock(width, 4**i) + for i in range(config.local_context_blocks))) + self.norm = ChannelRMSNorm(width) + self.sites = nn.Conv1d(width, 4, 1) + self.bin_size = config.dense_bin_size + + def forward(self, dna: Tensor) -> tuple[Tensor, Tensor]: + base = F.gelu(self.stem(dna), approximate='tanh') + base = F.gelu(self.norm(self.blocks(base)), approximate='tanh') + sites = self.sites(base).transpose(1, 2) + pooled = torch.cat((F.avg_pool1d(base, self.bin_size), + F.max_pool1d(base, self.bin_size)), dim=1).transpose(1, 2) + return sites, pooled + + +class CompactModel(nn.Module): + """One shared 1-kb trunk with representation-specific output heads.""" + + def __init__(self, config: ModelConfig | None = None): + super().__init__() + self.config = config or ModelConfig() + self.encoder = SequenceEncoder(self.config) + self.projection = nn.Linear(self.config.encoder_output_width, self.config.hidden_dim) + self.blocks = nn.ModuleList( + SequenceBlock(self.config) for _ in range(self.config.num_sequence_blocks) + ) + self.final_norm = nn.RMSNorm(self.config.hidden_dim) + self.dense_decoder = nn.Sequential( + nn.RMSNorm(self.config.hidden_dim), + nn.Linear(self.config.hidden_dim, self.config.head_hidden_size), + nn.GELU(approximate="tanh"), + ) + self.dense_heads = nn.ModuleDict( + { + family: nn.Linear(self.config.head_hidden_size, tracks) + for family, tracks in DENSE_TRACKS.items() + } + ) + self.splice_site_head = nn.Linear( + self.config.head_hidden_size, self.config.dense_bin_size * 4 + ) + self.splice_usage_head = nn.Linear( + self.config.head_hidden_size, self.config.splice_usage_tracks + ) + rank = self.config.junction_rank + self.junction_source = nn.Linear(self.config.head_hidden_size, rank) + self.junction_destination = nn.Linear(self.config.head_hidden_size, rank) + self.junction_track = nn.Embedding(self.config.junction_tracks, rank) + if self.config.junction_tracks % 2: + raise ValueError("junction tracks must split evenly into strand halves") + self.register_buffer( + "junction_track_half", + torch.arange(self.config.junction_tracks) // (self.config.junction_tracks // 2), + persistent=False, + ) + self.contact_q = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + self.contact_k = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + row, column = torch.triu_indices( + self.config.contact_bins, self.config.contact_bins + ) + self.register_buffer("contact_row", row, persistent=False) + self.register_buffer("contact_column", column, persistent=False) + if self.config.local_width: + self.local_path = LocalSequencePath(self.config) + self.local_projection = nn.Linear(2 * self.config.local_width, self.config.head_hidden_size) + # A new local path can be attached to our own checkpoint without changing its initial outputs. + nn.init.zeros_(self.local_path.sites.weight) + nn.init.zeros_(self.local_path.sites.bias) + nn.init.zeros_(self.local_projection.weight) + nn.init.zeros_(self.local_projection.bias) + if self.config.local_to_trunk: + self.local_trunk_projection = nn.Linear(2 * self.config.local_width, self.config.hidden_dim) + nn.init.zeros_(self.local_trunk_projection.weight) + nn.init.zeros_(self.local_trunk_projection.bias) + self._local_sites = None + self._local_features = None + + def set_junction_track_halves(self, halves: Tensor) -> None: + halves = torch.as_tensor(halves, dtype=torch.long, device=self.junction_track_half.device) + if halves.shape != (self.config.junction_tracks,) or not torch.isin( + halves, torch.tensor([0, 1], device=halves.device) + ).all(): + raise ValueError("junction track halves must be a 734-element 0/1 mapping") + self.junction_track_half.copy_(halves) + + def _trunk(self, dna: Tensor) -> Tensor: + if dna.ndim != 3 or dna.shape[1:] != (self.config.input_length, 4): + raise ValueError( + f"Expected DNA [B,{self.config.input_length},4], got {tuple(dna.shape)}" + ) + if self.config.local_width: + if self.config.gradient_checkpointing and self.training: + sites, pooled = checkpoint(self.local_path, dna.transpose(1, 2), use_reentrant=False) + else: + sites, pooled = self.local_path(dna.transpose(1, 2)) + self._local_sites = sites + self._local_features = self.local_projection(pooled) + x = self.projection(self.encoder(dna)) + if self.config.local_to_trunk: + local_tokens = pooled.reshape(len(dna), self.config.trunk_bins, + self.config.dense_upsample, -1).mean(dim=2) + x = x + self.local_trunk_projection(local_tokens) + for block in self.blocks: + if self.config.gradient_checkpointing and self.training and x.requires_grad: + x = checkpoint(block, x, None, use_reentrant=False) + else: + x = block(x, None) + return self.final_norm(x) + + def _dense_features(self, trunk: Tensor) -> Tensor: + start = (self.config.trunk_bins - self.config.target_trunk_bins) // 2 + target = trunk[:, start : start + self.config.target_trunk_bins] + target = target.repeat_interleave(self.config.dense_upsample, dim=1) + features = self.dense_decoder(target) + if self.config.local_width: + first_bin = (self.config.input_length - self.config.target_length) // (2 * self.config.dense_bin_size) + features = features + self._local_features[:, first_bin:first_bin + self.config.dense_bins] + return features + + def _all_dense_features(self, trunk: Tensor) -> Tensor: + features = self.dense_decoder( + trunk.repeat_interleave(self.config.dense_upsample, dim=1) + ) + return features + self._local_features if self.config.local_width else features + + def _splice_site_logits(self, trunk: Tensor) -> Tensor: + if self.config.splice_context_mode == 'uniform': + # The global trunk supplies one gene-context bias per class and kb. Base-resolution + # localization comes from the local path, with no arbitrary 128-bp offset decoder + # and no pooled dense-feature contribution to site logits. + context = self.splice_site_head(self.dense_decoder(trunk)) + context = context.view(len(trunk), self.config.trunk_bins, self.config.dense_bin_size, 4).mean(dim=2) + logits = context.repeat_interleave(self.config.trunk_bin_size, dim=1) + self._local_sites + return logits.float() + logits = self.splice_site_head(self._all_dense_features(trunk)) + logits = logits.view(len(trunk), self.config.input_length, 4) + if self.config.local_width: + logits = logits + self._local_sites + return logits.float() + + def _position_features(self, trunk: Tensor, positions: Tensor) -> Tensor: + features = self.dense_decoder(self._gather_bins(trunk, positions, self.config.trunk_bin_size)) + if self.config.local_width: + features = features + self._gather_bins(self._local_features, positions, self.config.dense_bin_size) + return features + + @staticmethod + def select_junction_positions(site_logits: Tensor, limit: int = 512) -> Tensor: + """Select each site type from model scores alone, then sort by coordinate. + + This follows the four independent site classes in Google DeepMind's + AlphaGenome splicing implementation (pinned in docs/v3-author/PROTOCOL.md). Padding is + -1 when an input is shorter than ``limit``. + """ + count = min(limit, site_logits.shape[1]) + selected = [] + for site_type in range(4): + coordinates = torch.topk(site_logits[..., site_type], count, dim=1).indices + coordinates = coordinates.sort(dim=1).values + if count < limit: + coordinates = F.pad(coordinates, (0, limit - count), value=-1) + selected.append(coordinates) + return torch.stack(selected, dim=1) + + @staticmethod + def _gather_bins(features: Tensor, positions: Tensor, bin_size: int) -> Tensor: + indices = torch.div(positions, bin_size, rounding_mode="floor") + indices = indices.clamp(0, features.shape[1] - 1) + return torch.gather( + features, + 1, + indices[..., None].expand(-1, -1, features.shape[-1]), + ) + + def _junction_predictions( + self, trunk: Tensor, site_positions: Tensor, queries: Tensor + ) -> Tensor: + row, column, track = queries.unbind(dim=-1) + half = self.junction_track_half[track] + batch = torch.arange(len(trunk), device=trunk.device)[:, None] + donor = site_positions[batch, 2 * half, row] + acceptor = site_positions[batch, 2 * half + 1, column] + source = self._position_features(trunk, donor.clamp(0, self.config.input_length - 1)) + destination = self._position_features(trunk, acceptor.clamp(0, self.config.input_length - 1)) + interaction = self.junction_source(source) * self.junction_destination(destination) + track_embedding = self.junction_track(track) + return (interaction * track_embedding).sum(dim=-1) / math.sqrt( + self.config.junction_rank + ) + + def junction_block( + self, + trunk: Tensor, + site_positions: Tensor, + row_start: int, + row_end: int, + column_start: int, + column_end: int, + track_start: int, + track_end: int, + site_factors: tuple[Tensor, Tensor] | None = None, + ) -> Tensor: + """Compute a bounded block of the full predicted-position junction cube.""" + tracks = torch.arange(track_start, track_end, device=trunk.device) + halves = self.junction_track_half[tracks] + if site_factors is None: + site_factors = self.junction_site_factors(trunk, site_positions) + source_all, destination_all = site_factors + source = source_all[:, halves, row_start:row_end] + destination = destination_all[:, halves, column_start:column_end] + embedding = self.junction_track(tracks)[None, :, None, None, :] + values = ( + source[:, :, :, None, :] * destination[:, :, None, :, :] * embedding + ).sum(dim=-1) / math.sqrt(self.config.junction_rank) + return values.permute(0, 2, 3, 1).float() + + def junction_site_factors( + self, trunk: Tensor, site_positions: Tensor + ) -> tuple[Tensor, Tensor]: + """Precompute the two strand-specific factors reused by all cube blocks.""" + donor = torch.stack((site_positions[:, 0], site_positions[:, 2]), dim=1).clamp_min(0) + acceptor = torch.stack((site_positions[:, 1], site_positions[:, 3]), dim=1).clamp_min(0) + donor_features = self._position_features(trunk, donor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, donor.shape[-1], -1) + acceptor_features = self._position_features(trunk, acceptor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, acceptor.shape[-1], -1) + return self.junction_source(donor_features), self.junction_destination(acceptor_features) + + def forward( + self, + dna: Tensor, + *, + families: Iterable[str] | None = None, + usage_positions: Tensor | None = None, + junction_site_positions: Tensor | None = None, + junction_queries: Tensor | None = None, + ) -> dict[str, Tensor]: + requested = set(families or ALL_FAMILIES) + unknown = requested - set(ALL_FAMILIES) + if unknown: + raise ValueError(f"Unknown output families: {sorted(unknown)}") + trunk = self._trunk(dna) + needs_dense = bool( + requested.intersection((*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions")) + ) + features = self._dense_features(trunk) if needs_dense else None + outputs: dict[str, Tensor] = {} + for family in DENSE_TRACKS: + if family in requested: + outputs[family] = self.dense_heads[family](features).float() + if "splice_sites" in requested: + all_logits = self._splice_site_logits(trunk) + start = (self.config.input_length - self.config.target_length) // 2 + outputs["splice_sites"] = all_logits[:, start:start + self.config.target_length] + if "splice_usage" in requested: + if usage_positions is None: + raise ValueError("splice_usage requires usage_positions") + gathered = self._gather_bins( + features, usage_positions, self.config.dense_bin_size + ) + outputs["splice_usage"] = self.splice_usage_head(gathered).float() + if "junctions" in requested: + if junction_site_positions is None or junction_queries is None: + raise ValueError("junctions require site positions and query triples") + outputs["junctions"] = self._junction_predictions( + trunk, junction_site_positions, junction_queries + ).float() + if "contact_maps" in requested: + contact = trunk.reshape( + len(dna), self.config.contact_bins, -1, self.config.hidden_dim + ).mean(dim=2) + q, k = self.contact_q(contact), self.contact_k(contact) + row, column = self.contact_row, self.contact_column + outputs["contact_maps"] = ( + 0.5 + * ( + q[:, row] * k[:, column] + + q[:, column] * k[:, row] + ) + ).float() + return outputs + + +def count_parameters(model: nn.Module) -> int: + return sum(parameter.numel() for parameter in model.parameters() if parameter.requires_grad) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/parallel-io-amendment.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/parallel-io-amendment.json new file mode 100644 index 0000000000000000000000000000000000000000..48ca3e0d57ff557a5fd2bd2bc517508c6de05b0f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/parallel-io-amendment.json @@ -0,0 +1,27 @@ +{ + "adopted_utc": "2026-10-25T00:42:46.303812+00:00", + "example_boundary": 40000, + "change": "I/O only: direct read and four bounded chunk-read threads per loader process.", + "source_before": "/workspace/research/studies/scale13/source-block-checkpoint", + "source_after": "/workspace/research/studies/scale13/source-parallel-io", + "model_parameters_optimizer_RNG_data_order_objective_schedule_unchanged": true, + "loader_worker_counts_unchanged": true, + "common_immutable_checkpoints": { + "scale13-small": { + "step": 40000, + "path": "/workspace/research/experiments/scale13-small/io-resume-040000.pt", + "sha256": "62daa49a4dc329d0b7cf3ecea0a30834346f714079d975bc636bdd81102f38f2", + "single_inode_copy": true + }, + "scale13-large": { + "step": 40000, + "path": "/workspace/research/experiments/scale13-large/io-resume-040000.pt", + "sha256": "db6c7394de11aba07278c907f328bcf18200c01ab1fb23578b12d293eb7219f4", + "single_inode_copy": true + } + }, + "evidence": "256 full examples through four forked workers and16 integrated-source examples, every tensor byte and ordered ID identical; missing-chunk fixtures pass.", + "loader_parallel_over_reference_time": 0.5340049503280426, + "timing_caveat": "Alternating paired readers under shared training load, including worker startup, full per-example hashing and pinned tensor IPC. 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"all_ordered_ids_and_tensor_bytes_exact": true + } + ], + "total_read_hash_and_transfer_seconds": { + "reference": 69.81505457599997, + "parallel": 37.28158475100645 + }, + "parallel_over_reference_time": 0.5340049503280426, + "reader_sha256": "ad6670f742f4de9898064becc525d0aea98b402abe7b210bb1b26a9eee6402b9", + "adapter_sha256": "ab182b3b687a3b470ff1bbc311a9130cc791974366bdc0a400df5e0e28602c9c", + "caveat": "Alternating paired readers under shared training load, including worker startup, full per-example hashing and pinned tensor IPC. File caches are shared; no GPU computation included." +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/reader-before-parallel-io.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/reader-before-parallel-io.py new file mode 100644 index 0000000000000000000000000000000000000000..71cd39d15713f648e7d7e31aa1923fa4fa4800dd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/reader-before-parallel-io.py @@ -0,0 +1,229 @@ +"""Streaming reader for the compact 1 Mb / eleven-family release.""" + +from __future__ import annotations + +from collections import OrderedDict +from pathlib import Path + +import numpy as np +import pandas as pd +import pysam +import torch +from torch.utils.data import Dataset +import zarr + + +def _open_part(path: Path): + if str(path).endswith(".zarr.zip"): + store = zarr.ZipStore(str(path), mode="r") + try: + return zarr.open_group(store=store, mode="r"), store + except BaseException: + store.close() + raise + return zarr.open_group(str(path), mode="r"), None + + +DENSE_FAMILIES = ( + "atac", + "dnase", + "procap", + "cage", + "rna_seq", + "chip_tf", + "chip_histone", +) +def normalize_splice_positions(value) -> np.ndarray: + """Normalize Arrow's nested-list representation to the public [4, 512] array.""" + + outer = np.asarray(value, dtype=object).reshape(-1).tolist() + positions = np.stack([np.asarray(item, dtype=np.int64) for item in outer]) + if positions.shape != (4, 512): + raise ValueError(f"splice_site_positions must have shape (4, 512), got {positions.shape}") + return positions + + +class AlphaGenomeDataset(Dataset): + """Resolve frozen coordinates to reference DNA and one Zarr example group.""" + + def __init__( + self, + data_root: str | Path, + split: str, + *, + max_open_parts: int = 8, + include_targets: bool = True, + ) -> None: + self.root = Path(data_root) + self.split = split + manifest = self.root / "public" / f"{split}.parquet" + if not manifest.is_file(): + raise FileNotFoundError(f"Missing materialized manifest: {manifest}") + self.rows = pd.read_parquet(manifest).reset_index(drop=True) + junction_tracks = pd.read_parquet(self.root / "public/junction_tracks.parquet") + junction_tracks = junction_tracks.sort_values("tensor_index") + self.junction_track_halves = torch.from_numpy( + junction_tracks.junction_channel_half.to_numpy(np.int64) + ) + if include_targets: + labels_path = self.root / "labels" / f"{split}.parquet" + if not labels_path.is_file(): + raise FileNotFoundError(f"Missing trusted labels manifest: {labels_path}") + labels = pd.read_parquet(labels_path).reset_index(drop=True) + if self.rows.example_id.astype(str).tolist() != labels.example_id.astype(str).tolist(): + raise ValueError("public and labels manifests have different IDs/order") + self.rows = self.rows.merge(labels, on="example_id", validate="one_to_one", sort=False) + self.fasta_path = self.root / "reference" / "GRCh38.p13.genome.fa" + self.max_open_parts = max_open_parts + self.include_targets = include_targets + self._fasta = None + self._parts: OrderedDict[str, tuple[zarr.Group, object | None]] = OrderedDict() + + def __len__(self) -> int: + return len(self.rows) + + def _reference(self): + if self._fasta is None: + self._fasta = pysam.FastaFile(str(self.fasta_path)) + return self._fasta + + def _part(self, relative: str): + if relative in self._parts: + opened = self._parts.pop(relative) + self._parts[relative] = opened + return opened[0] + group, store = _open_part(self.root / relative) + if len(self._parts) >= self.max_open_parts: + _, (_, old_store) = self._parts.popitem(last=False) + if old_store is not None: + old_store.close() + self._parts[relative] = (group, store) + return group + + @staticmethod + def _one_hot(sequence: str) -> np.ndarray: + encoded = np.frombuffer(sequence.upper().encode("ascii"), dtype=np.uint8) + output = np.zeros((len(encoded), 4), dtype=np.float32) + for index, letter in enumerate(b"ACGT"): + output[:, index] = encoded == letter + return output + + def _sequence(self, row) -> tuple[np.ndarray, np.ndarray]: + reference = self._reference() + chromosome_length = reference.get_reference_length(str(row.chromosome)) + start, end = int(row.input_start), int(row.input_end) + clipped_start, clipped_end = max(0, start), min(chromosome_length, end) + sequence = reference.fetch(str(row.chromosome), clipped_start, clipped_end) + left, right = clipped_start - start, end - clipped_end + if left or right: + sequence = "N" * left + sequence + "N" * right + valid = np.ones(end - start, dtype=bool) + if left: + valid[:left] = False + if right: + valid[-right:] = False + return self._one_hot(sequence), valid + + @staticmethod + def _decode(example, name: str, *, asinh: bool) -> np.ndarray: + """Train targets may be stored as 8-bit codes with per-track ranges; they are decoded + here, one example at a time, so the release stays compressed on disk.""" + array = example[name] + if array.dtype != np.uint8: + return np.asarray(array, dtype=np.float32) + lo = np.asarray(example[f"{name}_lo"], dtype=np.float32) + hi = np.asarray(example[f"{name}_hi"], dtype=np.float32) + x = lo + np.asarray(array, dtype=np.float32) * ((hi - lo) / 255.0) + return np.sinh(x) if asinh else x + + @staticmethod + def _dense_target(example, family: str) -> dict[str, torch.Tensor]: + values = AlphaGenomeDataset._decode(example, f"{family}/values", asinh=True) + mask = np.asarray(example[f"{family}/mask"], dtype=bool) + if mask.ndim == 1: + mask = np.broadcast_to(mask[None, :], values.shape).copy() + return { + "values": torch.from_numpy(values), + "mask": torch.from_numpy(mask), + } + + def __getitem__(self, item: int): + row = self.rows.iloc[item] + dna, input_mask = self._sequence(row) + result = { + "example_id": str(row.example_id), + "dna": torch.from_numpy(dna), + "input_mask": torch.from_numpy(input_mask), + "junction_track_halves": self.junction_track_halves, + } + if not self.include_targets: + return result + + part = self._part(str(row.target_shard)) + example = part[str(row.target_group)] + result["dense"] = { + family: self._dense_target(example, family) for family in DENSE_FAMILIES + } + result["splice_sites"] = torch.from_numpy( + np.asarray(example["splice_sites/values"], dtype=np.float32) + ) + result["splice_usage"] = { + "positions": torch.from_numpy( + np.asarray(example["splice_site_usage/positions"], dtype=np.int64) + ), + "values": torch.from_numpy( + np.asarray(example["splice_site_usage/values"], dtype=np.float32) + ), + "mask": torch.from_numpy( + np.asarray(example["splice_site_usage/mask"], dtype=bool) + ), + } + result["usage_positions"] = result["splice_usage"]["positions"] + result["junctions"] = { + name: torch.from_numpy( + np.asarray( + example[f"splice_junctions/{name}"], + dtype=np.float32 if name == "value" else np.int64, + ) + ) + for name in ("row", "column", "track", "value") + } + result["junctions"]["site_positions"] = torch.from_numpy( + np.asarray(example["splice_junctions/site_positions"], dtype=np.int64) + ) + site_positions = normalize_splice_positions(row.splice_site_positions) + result["site_positions"] = torch.from_numpy(site_positions) + if not torch.equal(result["junctions"]["site_positions"], result["site_positions"]): + raise ValueError(f"public/private splice positions differ for {row.example_id}") + scoring_path = self.root / "scoring" / self.split / f"part-{item:06d}.parquet" + scoring = pd.read_parquet(scoring_path) + if not (scoring.example_id.astype(str) == str(row.example_id)).all(): + raise ValueError(f"scoring part ID differs for {row.example_id}") + result["junction_scoring"] = { + "donor_position": torch.from_numpy( + scoring.donor_position.to_numpy(np.int64) + ), + "acceptor_position": torch.from_numpy( + scoring.acceptor_position.to_numpy(np.int64) + ), + "track": torch.from_numpy(scoring.track.to_numpy(np.int64)), + "target_value": torch.from_numpy( + scoring.target_value.to_numpy(np.float32) + ), + "valid": torch.from_numpy(scoring.valid.to_numpy(bool)), + } + result["contact_maps"] = torch.from_numpy( + self._decode(example, "contact_maps/upper_triangle", asinh=False) + ) + result["contact_map_mask"] = torch.from_numpy( + np.asarray(example["contact_maps/mask"], dtype=bool) + ) + return result + + +def single_example_collate(items): + """Keep variable-size event tensors intact; the reference currently trains at batch one.""" + + if len(items) != 1: + raise ValueError("The compact event loader currently requires batch_size=1") + return items[0] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/run_scale_study_cached_io.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/run_scale_study_cached_io.py new file mode 100644 index 0000000000000000000000000000000000000000..3a64b3a086363780b355fe0c827bab67bc691b68 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_history/run_scale_study_cached_io.py @@ -0,0 +1,151 @@ +"""Train two locally initialized scales on an identical recipe and evaluate selected full epochs. + +This script requires a pre-reviewed study.json; it never chooses scientific options itself. +It resumes checkpoints and preserves an independently replayable package for every evaluation. +""" +import argparse +import hashlib +import json +import os +from pathlib import Path +import subprocess +import sys +import time +import torch + +p = argparse.ArgumentParser(description=__doc__) +p.add_argument('study', type=Path) +a = p.parse_args() +plan = json.loads(a.study.read_text()) +assert plan.get('training_bytecache_root'), 'This amended runner requires an explicit training-only cache path' +assert plan['schema'] == 'seqfunc-matched-scale/v1' +assert plan['pretrained'] is False and plan['examples_per_epoch'] == 41020 +assert len(plan['runs']) == 2 and plan['shared']['batch_size'] == 4 +r = Path('/workspace/research') +here = Path(__file__).resolve().parent +source = Path(plan.get('source', r/'seqfunc-scale')) +sys.path.insert(0, str(source)) +from reference.alphagenome_50m.model import ModelConfig +configs = [ModelConfig.from_yaml(run['config']).to_dict() for run in plan['runs']] +differences = {key for key in configs[0] if configs[0][key] != configs[1][key]} +assert differences and differences <= {'encoder_width', 'channel_growth', 'hidden_dim', + 'num_sequence_blocks', 'num_attention_heads', 'head_hidden_size', + 'local_width'} +env = dict(os.environ, OPENBLAS_NUM_THREADS='1', OMP_NUM_THREADS='2', MKL_NUM_THREADS='2') +os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) +env.pop('SEQFUNC_TRAIN_BYTECACHE', None) +children, handles = {}, [] + +class AdoptedEvaluation: + def __init__(self, identity, directory): + self.identity, self.directory = identity, directory + self.pid = identity['pid'] + def poll(self): + try: + path = Path(f'/proc/{self.pid}') + stat = (path/'stat').read_text().split(') ', 1)[1].split() + active = (stat[0] not in 'ZX' and int(stat[19]) == self.identity['starttime_ticks'] + and hashlib.sha256((path/'cmdline').read_bytes()).hexdigest() == self.identity['cmdline_sha256']) + except OSError: + active = False + if active: + return None + try: + done = json.loads((self.directory/'pipeline-state.json').read_text())['phase'] == 'complete' + except (OSError, KeyError, json.JSONDecodeError): + done = False + return 0 if done else 1 + @property + def returncode(self): + return self.poll() + + +def launch(name, arguments, logfile, *, training=False): + handle = logfile.open('a') + handles.append(handle) + child_environment = dict(env) + if training: + child_environment['SEQFUNC_TRAIN_BYTECACHE'] = plan['training_bytecache_root'] + proc = subprocess.Popen([sys.executable, *map(str, arguments)], env=child_environment, + stdout=handle, stderr=subprocess.STDOUT) + children[name] = proc + print(json.dumps({'event': 'study_launch', 'name': name, 'pid': proc.pid}), flush=True) + +for run in plan['runs']: + directory = Path(run['output']) + directory.mkdir(parents=True, exist_ok=True) + checkpoints = [directory/name for name in ['progress.pt', 'last.pt']] + epochs = sorted(directory.glob('epoch-*.pt')) + if epochs: + checkpoints.append(epochs[-1]) + available = [(torch.load(path, map_location='cpu', weights_only=True)['global_step'], path) + for path in checkpoints if path.exists()] + step, checkpoint = max(available) if available else (0, None) + original = plan['cache_execution_amendment']['checkpoints'][run['name']] + assert step >= original['step'] + if step == original['step']: + checkpoint = Path(original['path']) + with checkpoint.open('rb') as stream: + assert hashlib.file_digest(stream, 'sha256').hexdigest() == original['sha256'] + shared = plan['shared'] + if step < shared['epochs']*plan['examples_per_epoch']: + args = [source/'train_batched.py', '--output', directory, '--config', run['config'], + '--workers', run['workers'], '--threads', 2, '--save-every', 1000, + '--keep-every', 0, '--log-every', 100] + for key in ['batch_size', 'epochs', 'seed', 'learning_rate', 'min_learning_rate', 'warmup', 'objective']: + args += ['--'+key.replace('_', '-'), shared[key]] + if shared.get('loss_scales'): + args += ['--loss-scales', shared['loss_scales']] + if checkpoint: + args += ['--resume', checkpoint] + launch(run['name']+'-train', args, directory/'train.log', training=True) + for epoch in plan['evaluation_epochs']: + evaluation = directory.parent/(run['name']+f'-epoch{epoch:02d}') + evaluation.mkdir(exist_ok=True) + name = run['name']+f'-eval{epoch:02d}' + state_path = evaluation/'pipeline-state.json' + if state_path.exists() and json.loads(state_path.read_text()).get('phase') == 'complete': + print(json.dumps({'event': 'completed_evaluation_preserved', 'name': name}), flush=True) + continue + if name in plan.get('adopted_evaluations', {}): + children[name] = AdoptedEvaluation(plan['adopted_evaluations'][name], evaluation) + print(json.dumps({'event': 'evaluation_adopted_without_interruption', 'name': name, 'pid': children[name].pid}), flush=True) + continue + args = [here/'run_candidate.py', directory/f'epoch-{epoch:02d}.pt', evaluation, + '--source', source, '--step', epoch*plan['examples_per_epoch'], + '--evaluation-lock', a.study.with_name('evaluation.lock'), + '--name', f"SeqFunc {run['name']} after {epoch} complete epochs", '--orientation', + '--lineage', plan['provenance']+' '+run['description']+ + ' Parameters are initialized locally with seed 17. The paired scale uses the same ' + 'batch-four interval order, objective, learning-rate schedule and full training permutations. ' + 'Only encoder/trunk/head/local-path width and transformer depth differ between scales.'] + launch(run['name']+f'-eval{epoch:02d}', args, evaluation/'pipeline.log') + +monitor_retired = False +while True: + status = {name: {'pid': proc.pid, 'returncode': proc.poll()} for name,proc in children.items()} + temporary = a.study.with_name('study-state-cached.tmp') + temporary.write_text(json.dumps(status, indent=2)+'\n') + temporary.replace(a.study.with_name('study-state-cached.json')) + if not monitor_retired and all(proc.poll() is not None for proc in children.values() + if isinstance(proc, AdoptedEvaluation)): + import signal + old = plan['retired_monitor_identity'] + try: + path = Path(f'/proc/{old["pid"]}') + stat = (path/'stat').read_text().split(') ', 1)[1].split() + if (int(stat[19]) == old['starttime_ticks'] + and hashlib.sha256((path/'cmdline').read_bytes()).hexdigest() == old['cmdline_sha256']): + os.kill(old['pid'], signal.SIGTERM) + os.kill(old['pid'], signal.SIGCONT) + except (FileNotFoundError, ProcessLookupError): + pass + monitor_retired = True + print(json.dumps({'event': 'old_monitor_retired_after_preserved_evaluation', 'pid': old['pid']}), flush=True) + if all(proc.poll() is not None for proc in children.values()): + break + time.sleep(5) +for handle in handles: + handle.close() +if any(proc.returncode for proc in children.values()): + raise RuntimeError('one or more study processes failed; inspect study-state.json and individual logs') diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_provenance.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_provenance.json new file mode 100644 index 0000000000000000000000000000000000000000..d2355852a548a0e8690b7ce5ed7be3beb0252a73 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S023/training_provenance.json @@ -0,0 +1,119 @@ +{ + "schema": "seqfunc-batched/v1", + "config": { + "input_length": 1048576, + "target_length": 196608, + "dense_bin_size": 128, + "trunk_bin_size": 1024, + "contact_bin_size": 2048, + "stem_stride": 8, + "encoder_width": 32, + "channel_growth": 32, + "num_downsampling_blocks": 7, + "conv_kernel_size": 5, + "dna_kernel_size": 15, + "hidden_dim": 384, + "num_sequence_blocks": 6, + "num_attention_heads": 6, + "attention_head_dim": 64, + "mlp_expansion": 4.0, + "dropout": 0.0, + "head_hidden_size": 256, + "junction_rank": 32, + "splice_usage_tracks": 734, + "junction_tracks": 734, + "contact_tracks": 28, + "gradient_checkpointing": true, + "enable_pairwise": false, + "pair_width": 32, + "pair_downsample": 16, + "pair_update_interval": 2, + "local_width": 32, + "local_context_blocks": 6, + "splice_context_mode": "offset", + "local_to_trunk": true, + "residual_rms_cap": 0.1 + }, + "epoch": 4, + "offset": 0, + "global_step": 164080, + "examples": 41020, + "schedule_epochs": 14, + "objective": "variance_balanced", + "seed": 17, + "training_arguments": { + "input": "/workspace/data", + "output": "/workspace/research/experiments/scale13-small", + "config": "/workspace/research/studies/scale13/scale13-small.yaml", + "resume": "/workspace/research/experiments/scale13-small/bytecache-resume-096000.pt", + "batch_size": 4, + "epochs": 14, + "stop_at": 0, + "workers": 4, + "threads": 2, + "seed": 17, + "learning_rate": 0.0002, + "min_learning_rate": 2e-05, + "warmup": 4000, + "objective": "variance_balanced", + "loss_scales": "/workspace/research/studies/scale13/loss_scales.json", + "save_every": 1000, + "keep_every": 0, + "log_every": 100 + }, + "elapsed_seconds": 40368.013349663, + "provenance": { + "data": "/workspace/data", + "split": "train", + "pretrained": false, + "world_size": 1, + "device": "NVIDIA H100 80GB HBM3", + "batch_reduction": "equal per example", + "train_manifest_sha256": "48133d27eedfb4b1d275cfca41f69dd075a2a0305ab2e8591e8e5e7529f36c64" + }, + "optimizer_steps": 41020, + "family_loss_scales": { + "atac": 1.041415045708339, + "dnase": 0.9286036694045732, + "procap": 2.9581124144681272, + "cage": 3.2106192779675315, + "rna_seq": 1.5011073783751905, + "chip_tf": 0.36766311546433506, + "chip_histone": 0.29363290398101966, + "contact_maps": 6.800777029767044, + "splice_usage": 12.636865029223356, + "junctions": 5.5806379658738825 + }, + "loss_scale_provenance": { + "split": "train", + "examples": 256, + "seed": 1729, + "source_moments_sha256": "7b7543c4d4d8d82ec9f9bc3f942ca65595671322f62c19391591df827d8fc588", + "training_indices_sha256": "b82df9ae11e95d9be5d6e5f85839cf23e25bbbe0a195eaee266ff52d366800dc", + "method": "reference MSE times inverse mean within-track training variance; floor0.01; splice BCE unchanged", + "mean_track_variance": { + "atac": 0.9602319499041136, + 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+ 20485378, + 19944390, + 19750895, + 19989917, + 19835794 + ], + "missing_prediction_ratio": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ] + } + }, + "overall_mean_family_pearson": 0.44610804541871724, + "invalid_reason": null +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/README.md b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/README.md new file mode 100644 index 0000000000000000000000000000000000000000..5a64d8f0184d36b4def723d4f25e0ed175fee341 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/README.md @@ -0,0 +1,106 @@ +# SeqFunc small after three complete epochs with transcript-direction junction decoding with calibrated splice probabilities + +One model with **13,290,504 parameters** serves every family and both entry points. It reads the entire +1,048,576-bp DNA window through a convolutional encoder and 6 transformer +blocks of width 384 over 1,024 tokens. Dense heads predict 128-bp bins over the central +196,608 bp; other heads predict splice sites, sparse usage/junctions and full-window contacts. +A width-32 base-resolution path uses a 21-bp stem and 6 dilated residual depthwise blocks (10,941-bp receptive field). Average/max pooling provides aligned 128-bp residual features to dense, usage and junction heads. Splice logits combine local base-resolution logits with the reference offset decoder. Local average/max features are also averaged into 1-kb tokens and linearly projected into the global trunk before its transformer blocks. The projection is initialized with zero output; any later checkpoint interventions are described in the lineage below. Each transformer attention and MLP update is capped at a per-token RMS of 0.1; smaller updates are unchanged. This parameter-free bound limits residual growth and preserves the incoming sequence features. Junction values use low-rank donor/acceptor factors; contacts use a symmetric +product of global features. Sites and junction endpoints are selected from model scores alone. + +All weights originate from local random initialization (seed 17) and locally trained +checkpoints. Unchanged small control from the original matched scale13 study, trained from local random initialization with seed 17, batch four, fixed variance-normalized family losses and the original 14-epoch learning-rate schedule. This additional immutable three-epoch evaluation is the fixed equal-exposure control for context43; training is not restarted or altered. It retains six local blocks (10,941-bp receptive field), the local-to-trunk bridge, residual RMS cap 0.1, raw splice logits and original sparse decoding. Complete native/recovery and full validation checks precede any publication. + +This checkpoint records **123,060 training examples in 30,765 optimizer updates**, 3 complete training permutations and +0 intervals into the next permutation. Each permutation includes all **41,020** +eligible FOLD_0 training intervals (folds 2–7), shuffled deterministically without replacement. +Quantized train targets are decoded by the supplied reader. Validation is fold 0; no test labels, +test-derived caches or pretrained model parameters were used. Development labels only evaluate +and select models. Checkpoints before a complete permutation remain intermediate milestones. + +Training uses one H100 80 GB and at most 22 CPUs. The current loader uses 4 workers +and 2 torch CPU threads. Batches contain up to 4 examples; losses are averaged equally over examples, with padded event queries excluded. Every epoch includes its final partial batch. BF16 training uses AdamW (weight decay 1e-4), gradient +clipping at 5, and a 14-epoch cosine learning-rate schedule from +0.0002 to 2e-05, with +4000 warmup training examples. The objective is `variance_balanced`. +Each continuous family uses the reference MSE divided by its mean within-track variance in a fixed, training-only sample of 256 windows (seed 1729). These scales are frozen in the checkpoint; the balanced splice BCE is unchanged. Recorded accumulated training-loop wall time is +494.6 minutes, including inherited parent time. Runs can share this +same physical GPU with experiments or evaluation. Setup, evaluation and discarded mechanical checks +are additional research compute, documented in the research ledger. Matching training junction queries +uses a verified, bit-identical CPU index construction followed by a bulk GPU transfer. + +The package contains model code, weights, training source/configuration/provenance, SHA-256 checksums, +and the complete CPython 3.11 / PyTorch 2.7.1 CUDA 12.6 Python runtime archive. Both executable entry +points take INPUT_ROOT and OUTPUT_ROOT, extract the vendored runtime temporarily, and run offline. +The host supplies Linux, bash/tar/gzip and the NVIDIA driver. Inference reads only public manifests +and the pinned reference FASTA. No target, splice-coordinate or prediction cache is packaged or read. +Track outputs use float16; perturbation outputs use float32 to preserve small A/B differences. + +The appended validation results cover all 200 intervals and all 2,697 development perturbations, +scored by the unmodified verifier tools. Published anchors use the same label rows. All results, +including regressions, are retained in the package's JSON reports. Limitations include incomplete +training at intermediate checkpoints, a 10,941-bp local receptive field, pooled usage/junction features, +low-rank contact modeling, and uneven variant performance. Functional readouts do not provide the +evolutionary constraint information used by conservation-based variant scorers. +The release has no targets for the 54 GTEx RNA tracks, so those output heads receive no direct +supervision and are excluded from the scored RNA track mean. The verifier's eQTL metrics use +supervised ENCODE RNA tracks selected by public tissue ontology and gene strand. + +Junction selection requires the donor to precede the acceptor on plus-strand transcripts and +follow it on minus-strand transcripts. This fixed biological constraint uses model-selected +coordinates and public strand halves. It changes no model parameters, usage selection or +perturbation computation. The rest of the decoder and candidate budget are unchanged. + +The splice-family output is a probability, using the same fixed link in both entry points: +`sigmoid(raw_logits + offset)`. Four offsets are estimated from 256 training windows (seed 1729) +to correct the marginal prior induced by balanced splice BCE. All network weights are unchanged. +The training prevalence, effective balanced-loss prior, offsets and sampling hash are included in +`splice_calibration.json`. This is an approximate marginal correction because the loss balances +each training window separately. No development labels fit these constants. + +Site candidates remain ranked by the underlying logits, preserving ordering without probability +rounding ties. Usage, junction and all dense/contact computations are unchanged. The verifier +applies its standard sigmoid when scoring splice perturbations; that scoring code is unmodified. +The complete reports below measure the resulting track and variant tradeoffs. In preliminary +diagnostics, MFASS and site Pearson improved while sQTL and some ClinVar metrics regressed. + +This fixed-link follow-up uses the preselected complete three-epoch small model. +It reuses the original 256-training-window offsets without refitting or changing +network parameters, site selection, usage or junction decoding. Earlier experiment +selection pools are unchanged. Both entrypoints share the same probability link. +Research-only perturbation reuse is verified by exact code/weight checks, the +earlier complete native proof, and fresh native fixtures for this candidate. +All 200 validation windows and all 13 development metrics are scored in full. + +## Measured validation results + +Family-mean Pearson: **0.466809** on 200 validation intervals. + +| Family | Pearson | +|---|---:| +| atac | 0.569696 | +| dnase | 0.650974 | +| procap | 0.533521 | +| cage | 0.311111 | +| rna_seq | 0.592299 | +| chip_tf | 0.454037 | +| chip_histone | 0.482122 | +| splice_sites | 0.320284 | +| splice_usage | 0.502744 | +| junctions | 0.401823 | +| contact_maps | 0.316291 | + +| Development evaluation | Metric | Model | Published anchors on the same rows | +|---|---|---:|---| +| eqtl_sign | auroc | 0.6334 | AlphaGenome: 0.8264 | +| eqtl_causality | auroc | 0.5343 | AlphaGenome: 0.8325 | +| eqtl_coefficient | spearman | 0.3016 | AlphaGenome: 0.4134 | +| sqtl_causality | auprc | 0.7196 | AlphaGenome: 0.8447 | +| clinvar_noncoding | auprc | 0.3501 | AlphaGenome: 0.9410 | +| clinvar_splice_region | auprc | 0.3471 | AlphaGenome: 0.7225 | +| clinvar_missense | auprc | 0.3025 | AlphaGenome: 0.3738 | +| mfass | auprc | 0.3506 | AlphaGenome: 0.7343 | +| caqtl | auprc | 0.1656 | AlphaGenome: 0.8076 | +| bqtl_spi1 | auprc | 0.1923 | AlphaGenome: 0.8167 | +| enhancer_gene | auprc | 0.4281 | AlphaGenome: 0.9594 | +| traitgym_mendelian | auprc | 0.1721 | Borzoi_L2_L2.plus.all: 0.5026; Enformer_L2_L2.plus.all: 0.5453; CADD.plus.RawScore: 0.7189; GPN-MSA_absLLR.plus.score: 0.6729 | +| traitgym_complex | auprc | 0.2188 | Borzoi_L2_L2.plus.all: 0.2945; Enformer_L2_L2.plus.all: 0.2692; CADD.plus.RawScore: 0.4134; GPN-MSA_absLLR.plus.score: 0.4214 | diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/attach_bridge.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/attach_bridge.py new file mode 100644 index 0000000000000000000000000000000000000000..2c2bea67a58e19676fa73ba2a0607002ea2d5419 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/attach_bridge.py @@ -0,0 +1,48 @@ +"""Attach a zero-output local-to-global connection to our own training checkpoint.""" +import argparse +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import torch +import yaml +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + +p = argparse.ArgumentParser(description=__doc__) +p.add_argument('checkpoint', type=Path) +p.add_argument('output', type=Path) +a = p.parse_args() +s = torch.load(a.checkpoint, map_location='cpu', weights_only=True) +assert s['schema'] == 'seqfunc-single/v1' +parent_config = ModelConfig(**s['config']) +assert parent_config.local_width > 0 and not parent_config.local_to_trunk +torch.manual_seed(s['seed']) +parent = CompactModel(parent_config) +config = replace(parent_config, local_to_trunk=True) +model = CompactModel(config) +missing = model.load_state_dict(s['model'], strict=False) +assert not missing.unexpected_keys +assert set(missing.missing_keys) == {'local_trunk_projection.weight', 'local_trunk_projection.bias'} +old_names = list(dict(parent.named_parameters())) +new_names = list(dict(model.named_parameters())) +assert new_names[:len(old_names)] == old_names +assert len(s['optimizer']['param_groups']) == 1 +group = s['optimizer']['param_groups'][0] +assert group['params'] == list(range(len(old_names))) +group['params'] = list(range(len(new_names))) +with a.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() +intervention = {'kind': 'local_to_global_trunk', 'parent_sha256': digest, + 'step': s['global_step'], 'initial_outputs_unchanged': True, + 'computation': 'average aligned local features into 1-kb tokens, zero linear projection, add before transformer'} +history = list(s.get('interventions', [])) +if s.get('intervention') and s['intervention'] not in history: + history.append(s['intervention']) +history.append(intervention) +s.update(config=config.to_dict(), model=model.state_dict(), intervention=intervention, interventions=history) +a.output.mkdir(parents=True, exist_ok=False) +(a.output/'config.yaml').write_text(yaml.safe_dump(config.to_dict())) +(a.output/'intervention.json').write_text(json.dumps(intervention, indent=2) + '\n') +_atomic_save(s, a.output/'start.pt') +print(json.dumps({'parameters': count_parameters(model), **intervention}), flush=True) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/attach_local.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/attach_local.py new file mode 100644 index 0000000000000000000000000000000000000000..aa082537004dbc398c4a78c1bd0203970ce5e42f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/attach_local.py @@ -0,0 +1,52 @@ +"""Attach a zero-initialized local residual path to a checkpoint trained in this workspace.""" +import argparse +from dataclasses import replace +import hashlib +from pathlib import Path + +import torch +import yaml + +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + p.add_argument('--width', type=int, default=32) + args = p.parse_args() + state = torch.load(args.checkpoint, map_location='cpu', weights_only=True) + assert state['schema'] == 'seqfunc-single/v1', 'only locally trained single-GPU checkpoints' + parent_config = ModelConfig(**state['config']) + assert parent_config.local_width == 0 + torch.manual_seed(state['seed']) + parent = CompactModel(parent_config) + config = replace(parent_config, local_width=args.width) + model = CompactModel(config) + result = model.load_state_dict(state['model'], strict=False) + assert not result.unexpected_keys + assert all(k.startswith(('local_path.', 'local_projection.')) for k in result.missing_keys) + parent_names = list(dict(parent.named_parameters())) + names = list(dict(model.named_parameters())) + assert names[:len(parent_names)] == parent_names, 'optimizer parameter order changed' + assert len(state['optimizer']['param_groups']) == 1 + group = state['optimizer']['param_groups'][0] + assert group['params'] == list(range(len(parent_names))) + group['params'] = list(range(len(names))) + state['model'] = model.state_dict() + state['config'] = config.to_dict() + state['intervention'] = { + 'kind': 'base_resolution_local_path', 'width': args.width, + 'parent_sha256': hashlib.sha256(args.checkpoint.read_bytes()).hexdigest(), + 'attachment_step': state['global_step'], 'initial_outputs_unchanged': True, + } + args.output.mkdir(parents=True, exist_ok=False) + (args.output / 'config.yaml').write_text(yaml.safe_dump(config.to_dict())) + _atomic_save(state, args.output / 'attached.pt') + print({'parameters': count_parameters(model), **state['intervention']}) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/batched_training.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/batched_training.py new file mode 100644 index 0000000000000000000000000000000000000000..e5c609c3680baa61f04739e9f22c03a801d7b294 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/batched_training.py @@ -0,0 +1,38 @@ +"""Batch variable event queries while retaining equal-per-example reference losses.""" +import torch +from torch.nn import functional as F +from reference.alphagenome_50m.train_eval import _model_inputs, _losses +from train_single import balanced_splice_loss + +def list_collate(samples): + return samples + +def prepare_batch(samples): + """Prepare on CPU before a bulk transfer; padding never contributes to a loss.""" + prepared = [_model_inputs(sample) for sample in samples] + max_usage = max(item[0]['usage_positions'].shape[1] for item in prepared) + max_queries = max(item[0]['junction_queries'].shape[1] for item in prepared) + inputs = { + 'dna': torch.cat([x[0]['dna'] for x in prepared]), + 'usage_positions': torch.cat([F.pad(x[0]['usage_positions'], (0, max_usage-x[0]['usage_positions'].shape[1])) for x in prepared]), + 'junction_site_positions': torch.cat([x[0]['junction_site_positions'] for x in prepared]), + 'junction_queries': torch.cat([F.pad(x[0]['junction_queries'], (0, 0, 0, max_queries-x[0]['junction_queries'].shape[1])) for x in prepared]), + } + return inputs, [x[1] for x in prepared], [x[2] for x in prepared] + +def equal_example_losses(outputs, samples, junction_targets, junction_valid, *, balanced=True, scales=None): + per_example = [] + for i, (sample, target, valid) in enumerate(zip(samples, junction_targets, junction_valid)): + sliced = {name:values[i:i+1] for name,values in outputs.items()} + sliced['splice_usage'] = sliced['splice_usage'][:, :sample['usage_positions'].numel()] + sliced['junctions'] = sliced['junctions'][:, :target.shape[1]] + losses = _losses(sliced, sample, target, valid) + if balanced: + losses['splice_sites'] = balanced_splice_loss(sliced['splice_sites'], sample['splice_sites'][None]) + per_example.append(losses) + raw = {name:torch.stack([loss[name] for loss in per_example]).mean() + for name in outputs} + raw['total'] = torch.stack(list(raw.values())).mean() + scaled = {name:value * (scales or {}).get(name, 1.) for name,value in raw.items() if name != 'total'} + scaled['total'] = torch.stack(list(scaled.values())).mean() + return scaled, raw diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/change_residual_cap.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/change_residual_cap.py new file mode 100644 index 0000000000000000000000000000000000000000..cd282275702aaf5d5eede4b3904add879aad846a --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/change_residual_cap.py @@ -0,0 +1,40 @@ +"""Change only a parameter-free branch bound on our own trained checkpoint.""" +import argparse +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import torch +import yaml +from reference.alphagenome_50m.model import CompactModel, ModelConfig +from reference.alphagenome_50m.train_eval import _atomic_save + +p = argparse.ArgumentParser(description=__doc__) +p.add_argument('checkpoint', type=Path) +p.add_argument('output', type=Path) +p.add_argument('--cap', type=float, default=0.1) +a = p.parse_args() +s = torch.load(a.checkpoint, map_location='cpu', weights_only=True) +config = ModelConfig(**s['config']) +assert config.residual_rms_cap == 0 and a.cap > 0 +config = replace(config, residual_rms_cap=a.cap) +model = CompactModel(config) +model.load_state_dict(s['model'], strict=True) +assert len(s['optimizer']['param_groups'][0]['params']) == len(list(model.parameters())) +assert all(torch.equal(v, s['model'][k]) for k, v in model.state_dict().items()) +with a.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() +change = {'kind': 'bounded_transformer_residuals', 'parent_sha256': digest, + 'step': s['global_step'], 'cap': a.cap, + 'parameters_optimizer_rng_unchanged': True, + 'computation': 'each attention and MLP update multiplied by min(1, cap/token_RMS)'} +history = list(s.get('interventions', [])) +if s.get('intervention') and s['intervention'] not in history: + history.append(s['intervention']) +history.append(change) +s.update(config=config.to_dict(), intervention=change, interventions=history) +a.output.mkdir(parents=True, exist_ok=False) +(a.output/'config.yaml').write_text(yaml.safe_dump(config.to_dict())) +(a.output/'intervention.json').write_text(json.dumps(change, indent=2) + '\n') +_atomic_save(s, a.output/'start.pt') +print(json.dumps(change), flush=True) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/change_splice_context.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/change_splice_context.py new file mode 100644 index 0000000000000000000000000000000000000000..1a6927a721cd0f49f08768666b710dcdab774bd7 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/change_splice_context.py @@ -0,0 +1,39 @@ +"""Create a controlled fork that changes only the splice context computation.""" +import argparse +import hashlib +import json +from pathlib import Path +import torch +import yaml + +from reference.alphagenome_50m.model import CompactModel, ModelConfig +from reference.alphagenome_50m.train_eval import _atomic_save + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + args = p.parse_args() + state = torch.load(args.checkpoint, map_location='cpu', weights_only=True) + assert state['schema'] == 'seqfunc-single/v1' and state['config']['local_width'] > 0 + state['config']['splice_context_mode'] = 'uniform' + config = ModelConfig(**state['config']) + model = CompactModel(config) + model.load_state_dict(state['model'], strict=True) + with args.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() + state['intervention'] = { + 'kind': 'uniform_splice_context', 'parent_sha256': digest, + 'step': state['global_step'], 'parameters_and_optimizer_unchanged': True, + 'computation': 'mean legacy offset weights into a per-class global bias; remove pooled local features from site context', + } + args.output.mkdir(parents=True, exist_ok=False) + (args.output/'config.yaml').write_text(yaml.safe_dump(config.to_dict())) + (args.output/'intervention.json').write_text(json.dumps(state['intervention'], indent=2)) + _atomic_save(state, args.output/'start.pt') + print(json.dumps(state['intervention']), flush=True) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/decoding_policy.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/decoding_policy.json new file mode 100644 index 0000000000000000000000000000000000000000..ebb1bbde8898494f04485b6fdb6d2e5756809e86 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/decoding_policy.json @@ -0,0 +1,9 @@ +{ + "kind": "junction_transcript_direction", + "parent_package": "/workspace/research/experiments/scale13-small-epoch03/raw-package", + "weights_unchanged": true, + "plus": "donor < acceptor", + "minus": "donor > acceptor", + "inference_inputs": "model-selected coordinates and public track strand halves only", + "training_evidence": "64 training windows: 30,616 positive junction queries, all in transcript direction" +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/extend_local_context.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/extend_local_context.py new file mode 100644 index 0000000000000000000000000000000000000000..3651698e8a120b3308566d05b4523733fa621d48 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/extend_local_context.py @@ -0,0 +1,70 @@ +"""Extend our trained local path with identity-initialized dilated residual blocks.""" +import argparse +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import torch +import yaml +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + +def extend(state, blocks): + old_config = ModelConfig(**state['config']) + assert old_config.local_width and blocks > old_config.local_context_blocks + config = replace(old_config, local_context_blocks=blocks) + torch.manual_seed(state['seed']) + parent = CompactModel(old_config) + model = CompactModel(config) + missing = model.load_state_dict(state['model'], strict=False) + new_prefixes = tuple(f'local_path.blocks.{i}.' for i in range(old_config.local_context_blocks, blocks)) + assert not missing.unexpected_keys + assert all(k.startswith(new_prefixes) for k in missing.missing_keys) + for i in range(old_config.local_context_blocks, blocks): + torch.nn.init.zeros_(model.local_path.blocks[i].contract.weight) + torch.nn.init.zeros_(model.local_path.blocks[i].contract.bias) + old_names = list(dict(parent.named_parameters())) + new_names = list(dict(model.named_parameters())) + assert len(state['optimizer']['param_groups']) == 1 + group = state['optimizer']['param_groups'][0] + assert group['params'] == list(range(len(old_names))) + # New blocks shift following norm/head IDs; preserve moments by parameter name. + remap = {old_id: new_names.index(name) for old_id, name in enumerate(old_names)} + state['optimizer']['state'] = {remap[old_id]: values + for old_id, values in state['optimizer']['state'].items()} + group['params'] = list(range(len(new_names))) + for name, value in state['model'].items(): + assert torch.equal(model.state_dict()[name], value) + state.update(config=config.to_dict(), model=model.state_dict()) + return state, model, remap + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + p.add_argument('--blocks', type=int, default=6) + a = p.parse_args() + state = torch.load(a.checkpoint, map_location='cpu', weights_only=True) + old_blocks = ModelConfig(**state['config']).local_context_blocks + state, model, remap = extend(state, a.blocks) + with a.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() + change = {'kind': 'extended_local_context', 'parent_sha256': digest, + 'step': state['global_step'], 'blocks_before': old_blocks, 'blocks_after': a.blocks, + 'receptive_field_bp': 21+8*sum(4**i for i in range(a.blocks)), + 'parent_parameters_optimizer_moments_rng_preserved': True, + 'new_blocks_initially_identity': True, 'optimizer_ids_remapped_by_parameter_name': True} + history = list(state.get('interventions', [])) + if state.get('intervention') and state['intervention'] not in history: + history.append(state['intervention']) + history.append(change) + state.update(intervention=change, interventions=history) + a.output.mkdir(parents=True, exist_ok=False) + (a.output/'config.yaml').write_text(yaml.safe_dump(state['config'])) + (a.output/'intervention.json').write_text(json.dumps(change, indent=2)+'\n') + (a.output/'optimizer_id_remap.json').write_text(json.dumps(remap, indent=2)+'\n') + _atomic_save(state, a.output/'start.pt') + print(json.dumps({'parameters': count_parameters(model), **change}), flush=True) + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/infer.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/infer.sh new file mode 100644 index 0000000000000000000000000000000000000000..2a412a9ad8667d6dcb94bb57d9959d61d43ddd34 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/infer.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: infer.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/predict.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/package_manifest.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/package_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b1cf3d9830d004b383c651078b8d412e87c9b66b --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/package_manifest.json @@ -0,0 +1,60 @@ +{ + "parameters": 13290504, + "source_checkpoint_sha256": "6e9091a83e34d161017bb7662329d849ce2bd54fe3dd9870336645a63da1f71f", + "files": { + "README.md": "34ac632318814a7e0964fc0641366c4a29304b7d2d0618bf876db9af3aecf64a", + "attach_bridge.py": "0740f17d8041d4886ad4437e83f1b781e941cc76a15db7cd5280a5983c36fc7c", + "attach_local.py": "d0774e2711840783267914789ed303a3b61b64381b72d61f0886edbc04974f15", + "batched_training.py": "217255d8f1d4fbaacc9a9bcf7ce252432ffa834b6eb47b7eed1b7471bbf3d5f7", + "change_residual_cap.py": "7c277370f54f352fd9df81ef241bfb100e123d57a56c87d5b85eac420c55be2d", + "change_splice_context.py": "4a7a1bf746b17abb75e0c611b4b19740541f825fe63a750fc44684351efadf32", + "extend_local_context.py": "aa720fdbd8625a900f24bc2c007ec0acf8c2164591971d007d2c01a58792d701", + "infer.sh": 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then read from `variant_evals/public/`). Each row is a +1,048,576-bp window starting at `input_start` and an edit spanning [edit_start, edit_end) in genomic +0-based coordinates: + +- `snv`: sequence A carries `allele_a` at edit_start, sequence B carries `allele_b` (both are written + explicitly; either may differ from the reference genome); +- `mask`: sequence A is the reference, sequence B has the interval replaced by N. + +For each perturbation the output group `perturbations/p{index:06d}` holds `a/` and +`b/` for every family in `families`: dense families sliced to bins +[dense_bin_start, dense_bin_end) of the central target window, and `splice_sites` sliced to bp +[splice_start, splice_end). Values are the same quantities as predictions.zarr (asinh space for +dense tracks, splice-site scores for splice_sites). +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import pandas as pd +import pysam +import torch +import zarr + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig +from splice_output import splice_output_scores + +PERTURBATION_SCHEMA = "genome-sequence-to-function-perturbations/v1" +COMPRESSOR = Blosc(cname="zstd", clevel=3, shuffle=Blosc.BITSHUFFLE) + + +def sequences(fasta, row) -> tuple[str, str]: + length = fasta.get_reference_length(row.chrom) + start, end = int(row.input_start), int(row.input_start) + 1_048_576 + sequence = fasta.fetch(row.chrom, max(0, start), min(length, end)).upper() + sequence = "N" * max(0, -start) + sequence + "N" * max(0, end - length) + lo, hi = int(row.edit_start) - start, int(row.edit_end) - start + if row.edit == "snv": + return sequence[:lo] + row.allele_a + sequence[hi:], sequence[:lo] + row.allele_b + sequence[hi:] + if row.edit == "mask": + return sequence, sequence[:lo] + "N" * (hi - lo) + sequence[hi:] + raise ValueError(f"unknown edit {row.edit!r}") + + +def main(input_root: Path, output_root: Path, checkpoint: Path, queries: Path | None = None) -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + if queries is None: + queries = input_root / "public/perturbations.parquet" + if not queries.exists(): + queries = input_root / "variant_evals/public/perturbations.parquet" + rows = pd.read_parquet(queries) + fasta = pysam.FastaFile(str(input_root / "reference/GRCh38.p13.genome.fa")) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "perturbations.zarr"), mode="w") + target_start = (config.input_length - config.target_length) // 2 + with torch.inference_mode(): + for index, row in enumerate(rows.itertuples(index=False)): + pair = sequences(fasta, row) + dna = torch.from_numpy(np.stack([AlphaGenomeDataset._one_hot(s) for s in pair])).to(device) + trunk = model._trunk(dna) + group = root.create_group(f"perturbations/p{index:06d}") + features = None + for family in row.families: + if family == "splice_sites": + logits = model._splice_site_logits(trunk)[:, target_start:target_start + config.target_length] + values = splice_output_scores(logits[:, row.splice_start:row.splice_end]) + else: + features = model._dense_features(trunk) if features is None else features + values = model.dense_heads[family](features)[:, row.dense_bin_start:row.dense_bin_end] + # float32: one variant's effect is often below float16 resolution (about 0.004 at + # asinh values of 4-8), so A and B would round to the same numbers. + values = values.float().cpu().numpy() + for side, array in zip("ab", values): + group.create_dataset(f"{side}/{family}", data=array, compressor=COMPRESSOR) + print(json.dumps({"event": "perturb", "done": index + 1, "total": len(rows)}), flush=True) + root.attrs.update({"schema": PERTURBATION_SCHEMA, + "perturbation_ids": rows.perturbation_id.astype(str).tolist()}) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--queries", type=Path, help="another query table, e.g. the development split") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.queries) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/perturb.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/perturb.sh new file mode 100644 index 0000000000000000000000000000000000000000..f07c4714532c2d33e7a419de3877e5daacee89fd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/perturb.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: perturb.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/perturb.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/predict.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..c4be95252c0f0b42bed5a0f6067e2503133c2fa2 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/predict.py @@ -0,0 +1,134 @@ +#!/usr/bin/env python3 +"""Stream the single benchmark model into the sharded prediction contract.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import torch +import zarr + +from prediction_contract import DENSE_TRACKS, MAX_JUNCTION_PAIRS, MAX_USAGE_SITES, PREDICTION_SCHEMA +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig +from splice_output import splice_output_scores + + +COMPRESSOR = Blosc(cname="zstd", clevel=7, shuffle=Blosc.BITSHUFFLE) + + +def main(input_root: Path, output_root: Path, checkpoint: Path, split: str = "test") -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + dataset = AlphaGenomeDataset(input_root, split, include_targets=False) + model.set_junction_track_halves(dataset.junction_track_halves) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "predictions.zarr"), mode="w") + ids = [] + with torch.inference_mode(): + for sample in dataset: + example_id = sample["example_id"] + dna = sample["dna"][None].to(device) + trunk = model._trunk(dna) + dense_features = model._dense_features(trunk) + group = root.create_group(f"examples/{example_id}") + for family in DENSE_TRACKS: + values = model.dense_heads[family](dense_features)[0] + group.create_dataset(family, data=values.cpu().numpy().astype(np.float16), compressor=COMPRESSOR) + + all_site_logits = model._splice_site_logits(trunk) + target_start = (config.input_length - config.target_length) // 2 + group.create_dataset( + "splice_sites", + data=splice_output_scores(all_site_logits[0, target_start:target_start + config.target_length]).cpu().numpy().astype(np.float16), + chunks=(4096, 4), compressor=COMPRESSOR, + ) + predicted_positions = model.select_junction_positions(all_site_logits)[0] + + # Usage at the model's own predicted sites (v4 is sparse; unlisted hidden sites score 0). + usage_bins = model.splice_usage_head(dense_features)[0].float() + sites = predicted_positions.cpu().numpy().astype(np.int64).reshape(-1) + sites = np.unique(sites[(sites >= target_start) & (sites < target_start + config.target_length)] + - target_start)[:MAX_USAGE_SITES] + group.create_dataset("splice_usage_positions", data=sites.astype(np.int32), compressor=COMPRESSOR) + usage = usage_bins[torch.as_tensor(sites // 128, device=usage_bins.device)] if len(sites) \ + else usage_bins[:0] + group.create_dataset("splice_usage", data=usage.cpu().numpy().astype(np.float16), + chunks=(1024, 734), compressor=COMPRESSOR) + + # Junctions: score every donor x acceptor pair of each strand, keep the strongest pairs. + positions_batch = predicted_positions[None] + site_factors = model.junction_site_factors(trunk, positions_batch) + halves = model.junction_track_half + candidates = [] + for row_start in range(0, 512, 128): + for column_start in range(0, 512, 128): + block = torch.cat([ + model.junction_block( + trunk, positions_batch, row_start, row_start + 128, + column_start, column_start + 128, track_start, + min(track_start + 64, config.junction_tracks), site_factors, + )[0] + for track_start in range(0, config.junction_tracks, 64) + ], dim=-1).float() # [128, 128, 734] + for half in (0, 1): + donors = predicted_positions[2 * half, row_start:row_start + 128] + acceptors = predicted_positions[2 * half + 1, column_start:column_start + 128] + valid = (donors[:, None] >= 0) & (acceptors[None, :] >= 0) + # Transcript direction is determined by the public strand half. + forward = donors[:, None] < acceptors[None, :] if half == 0 else donors[:, None] > acceptors[None, :] + valid = valid & forward + strength = block[..., halves == half].amax(dim=-1).masked_fill(~valid, -torch.inf) + keep = min(MAX_JUNCTION_PAIRS // 2, int(valid.sum())) + if not keep: + continue + best = strength.flatten().topk(keep) + rows, columns = best.indices // 128, best.indices % 128 + candidates.append((best.values, torch.full_like(rows, half), donors[rows], + acceptors[columns], block[rows, columns])) + if candidates: + score, half, donor, acceptor, values = (torch.cat(x) for x in zip(*candidates)) + order = score.topk(min(MAX_JUNCTION_PAIRS, len(score))).indices + half, donor, acceptor, values = half[order], donor[order], acceptor[order], values[order] + else: + half = donor = acceptor = torch.zeros(0, dtype=torch.long) + values = torch.zeros(0, config.junction_tracks) + group.create_dataset("junction_half", data=half.cpu().numpy().astype(np.int8), compressor=COMPRESSOR) + group.create_dataset("junction_donor", data=donor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junction_acceptor", data=acceptor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junctions", data=values.cpu().numpy().astype(np.float16), + chunks=(4096, 734), compressor=COMPRESSOR) + + contact = trunk.reshape( + 1, config.contact_bins, -1, config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + contact_values = 0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row]) + group.create_dataset( + "contact_maps", data=contact_values[0].cpu().numpy().astype(np.float16), + chunks=(8192, 28), compressor=COMPRESSOR, + ) + ids.append(example_id) + print(json.dumps({"event": "predict", "example": len(ids), "total": len(dataset)}), flush=True) + root.attrs.update({"schema": PREDICTION_SCHEMA, "example_ids": ids}) + (output_root / "example_ids.json").write_text(json.dumps(ids) + "\n") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--split", default="test", choices=("valid", "test"), + help="manifest to predict; the verifier uses test, local validation uses valid") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.split) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/prediction_contract.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/prediction_contract.py new file mode 100644 index 0000000000000000000000000000000000000000..2455424db002b840cf0d69456930840a7bc5f3cb --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/prediction_contract.py @@ -0,0 +1,23 @@ +"""Public prediction shapes; no trusted splice coordinates are exposed.""" + +from __future__ import annotations + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} + +PREDICTION_SCHEMA = "genome-sequence-to-function-predictions/v4" +MAX_USAGE_SITES = 4_096 +MAX_JUNCTION_PAIRS = 16_384 +# Fixed-shape arrays per example; splice usage and junctions are sparse (see contract.md). +FIXED_SHAPES = { + **{name: (1_536, tracks) for name, tracks in DENSE_TRACKS.items()}, + "splice_sites": (196_608, 4), + "contact_maps": (131_328, 28), +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4879a0e1a3a8a8050bb48360febee152cd141be8 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/__init__.py @@ -0,0 +1 @@ +"""The reference model of the alphagenome-reference baseline.""" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a86a139babb6e899bd5102bbf3c628e1232576b2 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/__init__.py @@ -0,0 +1,5 @@ +"""Compact 1-Mb, eleven-family AlphaGenome reference model.""" + +from .model import CompactModel, ModelConfig, count_parameters + +__all__ = ["CompactModel", "ModelConfig", "count_parameters"] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/alphagenome_50m.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/alphagenome_50m.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0a59a4d0c65752d45eb464d129c6ec9d559665e9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/alphagenome_50m.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 32 +channel_growth: 32 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 384 +num_sequence_blocks: 6 +num_attention_heads: 6 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 256 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/cached_read_store.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/cached_read_store.py new file mode 100644 index 0000000000000000000000000000000000000000..9c3677c1e8518ea97db0dd6759f3d636f5fff27c --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/cached_read_store.py @@ -0,0 +1,71 @@ +"""Optional local byte cache for immutable training Zarr stores. + +Source storage stays read-only. Cached entries contain a key fingerprint, length, +and SHA256 of the original compressed bytes; every hit verifies all three. Writes +replace a private temporary inode atomically. A missing/corrupt/unwritable cache +falls back to the original store, so this layer changes neither values nor keys. +Only training loaders may opt into it; it is not an inference asset. +""" +import hashlib +import os +from pathlib import Path +import struct +import tempfile + +from .direct_read_store import ParallelDirectReadDirectoryStore + +MAGIC = b'SFBC0001' +HEADER = struct.Struct('<8sQ32s32s') + + +class CachedReadDirectoryStore(ParallelDirectReadDirectoryStore): + def __init__(self, path, *, cache_root, namespace, io_workers=4): + super().__init__(path, io_workers=io_workers) + if not namespace: + raise ValueError('An immutable input namespace is required') + self.cache_root = Path(cache_root) + self.namespace = namespace + self.store_key = hashlib.sha256((namespace+'\0'+os.path.abspath(path)).encode()).digest() + self.statistics = {'hit': 0, 'miss': 0, 'corrupt': 0, 'write_failure': 0} + + def cache_location(self, key): + key = self._normalize_key(key) + identity = hashlib.sha256(self.store_key+key.encode()).digest() + name = identity.hex() + return self.cache_root/name[:2]/name[2:], identity + + def __getitem__(self, key): + key = self._normalize_key(key) + destination, identity = self.cache_location(key) + try: + blob = destination.read_bytes() + if len(blob) >= HEADER.size: + magic, size, cached_key, checksum = HEADER.unpack_from(blob) + payload = blob[HEADER.size:] + if (magic == MAGIC and size == len(payload) and cached_key == identity + and hashlib.sha256(payload).digest() == checksum): + self.statistics['hit'] += 1 + return payload + self.statistics['corrupt'] += 1 + except OSError: + pass + self.statistics['miss'] += 1 + payload = super().__getitem__(key) + temporary = None + try: + destination.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile(prefix='.pending-', dir=destination.parent, delete=False) as stream: + temporary = stream.name + stream.write(HEADER.pack(MAGIC, len(payload), identity, hashlib.sha256(payload).digest())) + stream.write(payload) + os.replace(temporary, destination) + temporary = None + except OSError: + self.statistics['write_failure'] += 1 + finally: + if temporary is not None: + try: + os.unlink(temporary) + except OSError: + pass + return payload diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/dataset.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..8a3f7d4644e5d9f02162bac9e859443ba6c33658 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/dataset.py @@ -0,0 +1,312 @@ +"""Streaming reader for the compact 1 Mb / eleven-family release.""" + +from __future__ import annotations + +from collections import OrderedDict +from pathlib import Path + +import numpy as np +import pandas as pd +import pysam +import torch +from torch.utils.data import Dataset +import zarr + + +import os +from concurrent.futures import ThreadPoolExecutor + + +_pools = {} + + +def process_pool(workers): + key = (os.getpid(), workers) + if key not in _pools: + _pools[key] = ThreadPoolExecutor(max_workers=workers, thread_name_prefix='zarr-read') + return _pools[key] + + +class DirectReadDirectoryStore(zarr.DirectoryStore): + def __getitem__(self, key): + key = self._normalize_key(key) + try: + return self._fromfile(os.path.join(self.path, key)) + except (FileNotFoundError, IsADirectoryError, NotADirectoryError): + raise KeyError(key) from None + + def getitems(self, keys, *, contexts): + result = {} + for key in keys: + try: + result[key] = self[key] + except KeyError: + pass + return result + + def __setitem__(self, key, value): + raise PermissionError('DirectReadDirectoryStore is read-only') + + def __delitem__(self, key): + raise PermissionError('DirectReadDirectoryStore is read-only') + + def is_writeable(self): + return False + + def is_erasable(self): + return False + + +class ParallelDirectReadDirectoryStore(DirectReadDirectoryStore): + """Bounded I/O threads shared by all read stores within one loader process.""" + def __init__(self, path, *, io_workers=4): + super().__init__(path) + if io_workers < 1: + raise ValueError('io_workers must be positive') + self.io_workers = io_workers + + def getitems(self, keys, *, contexts): + def fetch(key): + try: + return key, self[key] + except KeyError: + return key, None + if self.io_workers == 1: + return super().getitems(keys, contexts=contexts) + return {key: value for key, value in process_pool(self.io_workers).map(fetch, keys) + if value is not None} + + +def _open_part(path: Path): + if str(path).endswith(".zarr.zip"): + store = zarr.ZipStore(str(path), mode="r") + try: + return zarr.open_group(store=store, mode="r"), store + except BaseException: + store.close() + raise + return zarr.open_group(store=ParallelDirectReadDirectoryStore(str(path), io_workers=4), mode="r"), None + + +DENSE_FAMILIES = ( + "atac", + "dnase", + "procap", + "cage", + "rna_seq", + "chip_tf", + "chip_histone", +) +def normalize_splice_positions(value) -> np.ndarray: + """Normalize Arrow's nested-list representation to the public [4, 512] array.""" + + outer = np.asarray(value, dtype=object).reshape(-1).tolist() + positions = np.stack([np.asarray(item, dtype=np.int64) for item in outer]) + if positions.shape != (4, 512): + raise ValueError(f"splice_site_positions must have shape (4, 512), got {positions.shape}") + return positions + + +class AlphaGenomeDataset(Dataset): + """Resolve frozen coordinates to reference DNA and one Zarr example group.""" + + def __init__( + self, + data_root: str | Path, + split: str, + *, + max_open_parts: int = 8, + include_targets: bool = True, + ) -> None: + self.root = Path(data_root) + self.split = split + manifest = self.root / "public" / f"{split}.parquet" + if not manifest.is_file(): + raise FileNotFoundError(f"Missing materialized manifest: {manifest}") + self.rows = pd.read_parquet(manifest).reset_index(drop=True) + junction_tracks = pd.read_parquet(self.root / "public/junction_tracks.parquet") + junction_tracks = junction_tracks.sort_values("tensor_index") + self.junction_track_halves = torch.from_numpy( + junction_tracks.junction_channel_half.to_numpy(np.int64) + ) + if include_targets: + labels_path = self.root / "labels" / f"{split}.parquet" + if not labels_path.is_file(): + raise FileNotFoundError(f"Missing trusted labels manifest: {labels_path}") + labels = pd.read_parquet(labels_path).reset_index(drop=True) + if self.rows.example_id.astype(str).tolist() != labels.example_id.astype(str).tolist(): + raise ValueError("public and labels manifests have different IDs/order") + self.rows = self.rows.merge(labels, on="example_id", validate="one_to_one", sort=False) + self.fasta_path = self.root / "reference" / "GRCh38.p13.genome.fa" + self.max_open_parts = max_open_parts + self.include_targets = include_targets + # Optional training-only compressed-byte cache; off for all public inference. + self.training_bytecache = None + requested_cache = os.environ.get('SEQFUNC_TRAIN_BYTECACHE') + if requested_cache and split == 'train' and include_targets: + import hashlib + import json + from .cached_read_store import CachedReadDirectoryStore + manifests = {str(path): hashlib.sha256(path.read_bytes()).hexdigest() for path in + [self.root/'public/train.parquet', self.root/'labels/train.parquet']} + namespace = hashlib.sha256(json.dumps(manifests, sort_keys=True).encode()).hexdigest() + self.training_bytecache = (requested_cache, namespace) + self._fasta = None + self._parts: OrderedDict[str, tuple[zarr.Group, object | None]] = OrderedDict() + + def __len__(self) -> int: + return len(self.rows) + + def _reference(self): + if self._fasta is None: + self._fasta = pysam.FastaFile(str(self.fasta_path)) + return self._fasta + + def _part(self, relative: str): + if relative in self._parts: + opened = self._parts.pop(relative) + self._parts[relative] = opened + return opened[0] + path = self.root / relative + if self.training_bytecache is not None and not str(path).endswith('.zarr.zip'): + from .cached_read_store import CachedReadDirectoryStore + cache_root, namespace = self.training_bytecache + cached_store = CachedReadDirectoryStore(str(path), cache_root=cache_root, + namespace=namespace, io_workers=4) + group, store = zarr.open_group(store=cached_store, mode='r'), None + else: + group, store = _open_part(path) + if len(self._parts) >= self.max_open_parts: + _, (_, old_store) = self._parts.popitem(last=False) + if old_store is not None: + old_store.close() + self._parts[relative] = (group, store) + return group + + @staticmethod + def _one_hot(sequence: str) -> np.ndarray: + encoded = np.frombuffer(sequence.upper().encode("ascii"), dtype=np.uint8) + output = np.zeros((len(encoded), 4), dtype=np.float32) + for index, letter in enumerate(b"ACGT"): + output[:, index] = encoded == letter + return output + + def _sequence(self, row) -> tuple[np.ndarray, np.ndarray]: + reference = self._reference() + chromosome_length = reference.get_reference_length(str(row.chromosome)) + start, end = int(row.input_start), int(row.input_end) + clipped_start, clipped_end = max(0, start), min(chromosome_length, end) + sequence = reference.fetch(str(row.chromosome), clipped_start, clipped_end) + left, right = clipped_start - start, end - clipped_end + if left or right: + sequence = "N" * left + sequence + "N" * right + valid = np.ones(end - start, dtype=bool) + if left: + valid[:left] = False + if right: + valid[-right:] = False + return self._one_hot(sequence), valid + + @staticmethod + def _decode(example, name: str, *, asinh: bool) -> np.ndarray: + """Train targets may be stored as 8-bit codes with per-track ranges; they are decoded + here, one example at a time, so the release stays compressed on disk.""" + array = example[name] + if array.dtype != np.uint8: + return np.asarray(array, dtype=np.float32) + lo = np.asarray(example[f"{name}_lo"], dtype=np.float32) + hi = np.asarray(example[f"{name}_hi"], dtype=np.float32) + x = lo + np.asarray(array, dtype=np.float32) * ((hi - lo) / 255.0) + return np.sinh(x) if asinh else x + + @staticmethod + def _dense_target(example, family: str) -> dict[str, torch.Tensor]: + values = AlphaGenomeDataset._decode(example, f"{family}/values", asinh=True) + mask = np.asarray(example[f"{family}/mask"], dtype=bool) + if mask.ndim == 1: + mask = np.broadcast_to(mask[None, :], values.shape).copy() + return { + "values": torch.from_numpy(values), + "mask": torch.from_numpy(mask), + } + + def __getitem__(self, item: int): + row = self.rows.iloc[item] + dna, input_mask = self._sequence(row) + result = { + "example_id": str(row.example_id), + "dna": torch.from_numpy(dna), + "input_mask": torch.from_numpy(input_mask), + "junction_track_halves": self.junction_track_halves, + } + if not self.include_targets: + return result + + part = self._part(str(row.target_shard)) + example = part[str(row.target_group)] + result["dense"] = { + family: self._dense_target(example, family) for family in DENSE_FAMILIES + } + result["splice_sites"] = torch.from_numpy( + np.asarray(example["splice_sites/values"], dtype=np.float32) + ) + result["splice_usage"] = { + "positions": torch.from_numpy( + np.asarray(example["splice_site_usage/positions"], dtype=np.int64) + ), + "values": torch.from_numpy( + np.asarray(example["splice_site_usage/values"], dtype=np.float32) + ), + "mask": torch.from_numpy( + np.asarray(example["splice_site_usage/mask"], dtype=bool) + ), + } + result["usage_positions"] = result["splice_usage"]["positions"] + result["junctions"] = { + name: torch.from_numpy( + np.asarray( + example[f"splice_junctions/{name}"], + dtype=np.float32 if name == "value" else np.int64, + ) + ) + for name in ("row", "column", "track", "value") + } + result["junctions"]["site_positions"] = torch.from_numpy( + np.asarray(example["splice_junctions/site_positions"], dtype=np.int64) + ) + site_positions = normalize_splice_positions(row.splice_site_positions) + result["site_positions"] = torch.from_numpy(site_positions) + if not torch.equal(result["junctions"]["site_positions"], result["site_positions"]): + raise ValueError(f"public/private splice positions differ for {row.example_id}") + scoring_path = self.root / "scoring" / self.split / f"part-{item:06d}.parquet" + scoring = pd.read_parquet(scoring_path) + if not (scoring.example_id.astype(str) == str(row.example_id)).all(): + raise ValueError(f"scoring part ID differs for {row.example_id}") + result["junction_scoring"] = { + "donor_position": torch.from_numpy( + scoring.donor_position.to_numpy(np.int64) + ), + "acceptor_position": torch.from_numpy( + scoring.acceptor_position.to_numpy(np.int64) + ), + "track": torch.from_numpy(scoring.track.to_numpy(np.int64)), + "target_value": torch.from_numpy( + scoring.target_value.to_numpy(np.float32) + ), + "valid": torch.from_numpy(scoring.valid.to_numpy(bool)), + } + result["contact_maps"] = torch.from_numpy( + self._decode(example, "contact_maps/upper_triangle", asinh=False) + ) + result["contact_map_mask"] = torch.from_numpy( + np.asarray(example["contact_maps/mask"], dtype=bool) + ) + return result + + +def single_example_collate(items): + """Keep variable-size event tensors intact; the reference currently trains at batch one.""" + + if len(items) != 1: + raise ValueError("The compact event loader currently requires batch_size=1") + return items[0] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/direct_read_store.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/direct_read_store.py new file mode 100644 index 0000000000000000000000000000000000000000..c560febc6a15909a523c8e2f3d1f861aeec309a7 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/direct_read_store.py @@ -0,0 +1,64 @@ +"""Read-only Zarr directory store avoiding redundant per-chunk stat calls.""" +import os +from concurrent.futures import ThreadPoolExecutor + +from zarr.storage import DirectoryStore + +_pools = {} + + +def process_pool(workers): + key = (os.getpid(), workers) + if key not in _pools: + _pools[key] = ThreadPoolExecutor(max_workers=workers, thread_name_prefix='zarr-read') + return _pools[key] + + +class DirectReadDirectoryStore(DirectoryStore): + def __getitem__(self, key): + key = self._normalize_key(key) + try: + return self._fromfile(os.path.join(self.path, key)) + except (FileNotFoundError, IsADirectoryError, NotADirectoryError): + raise KeyError(key) from None + + def getitems(self, keys, *, contexts): + result = {} + for key in keys: + try: + result[key] = self[key] + except KeyError: + pass + return result + + def __setitem__(self, key, value): + raise PermissionError('DirectReadDirectoryStore is read-only') + + def __delitem__(self, key): + raise PermissionError('DirectReadDirectoryStore is read-only') + + def is_writeable(self): + return False + + def is_erasable(self): + return False + + +class ParallelDirectReadDirectoryStore(DirectReadDirectoryStore): + """Bounded I/O threads shared by all read stores within one loader process.""" + def __init__(self, path, *, io_workers=4): + super().__init__(path) + if io_workers < 1: + raise ValueError('io_workers must be positive') + self.io_workers = io_workers + + def getitems(self, keys, *, contexts): + def fetch(key): + try: + return key, self[key] + except KeyError: + return key, None + if self.io_workers == 1: + return super().getitems(keys, contexts=contexts) + return {key: value for key, value in process_pool(self.io_workers).map(fetch, keys) + if value is not None} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/layers.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..7ef7b4e45a81eb774e4e92ac36e30c5abdebbe46 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/layers.py @@ -0,0 +1,134 @@ +"""Shared neural-network layers for the single compact AlphaGenome model.""" + +from __future__ import annotations + +import math + +import torch +from torch import Tensor, nn +from torch.nn import functional as F + + +class ChannelRMSNorm(nn.Module): + def __init__(self, channels: int, eps: float = 1e-6): + super().__init__() + self.weight = nn.Parameter(torch.ones(channels)) + self.eps = eps + + def forward(self, x: Tensor) -> Tensor: + variance = x.float().square().mean(dim=1, keepdim=True) + normalized = x * torch.rsqrt(variance + self.eps).to(x.dtype) + return normalized * self.weight[None, :, None].to(x.dtype) + + +class StandardizedConv1d(nn.Conv1d): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__(in_channels, out_channels, kernel_size, padding="same", bias=True) + self.scale = nn.Parameter(torch.ones(out_channels)) + nn.init.trunc_normal_(self.weight, std=1.0 / math.sqrt(in_channels * kernel_size)) + nn.init.trunc_normal_(self.bias, std=1e-4) + + def forward(self, x: Tensor) -> Tensor: + centered = self.weight - self.weight.mean(dim=(1, 2), keepdim=True) + variance = centered.float().square().mean(dim=(1, 2), keepdim=True) + fan_in = self.weight.shape[1] * self.weight.shape[2] + multiplier = self.scale[:, None, None] * torch.rsqrt( + (fan_in * variance).clamp_min(1e-4) + ).to(self.weight.dtype) + return F.conv1d(x, centered * multiplier, self.bias, padding="same") + + +class ConvBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__() + self.norm = ChannelRMSNorm(in_channels) + self.conv = StandardizedConv1d(in_channels, out_channels, kernel_size) + + def forward(self, x: Tensor) -> Tensor: + return self.conv(F.gelu(self.norm(x), approximate="tanh")) + + +def _apply_rope(x: Tensor) -> Tensor: + rotary_dim = x.shape[-1] - x.shape[-1] % 2 + if rotary_dim == 0: + return x + positions = torch.arange(x.shape[-2], device=x.device, dtype=torch.float32) + inverse_frequency = 1.0 / ( + 10_000 ** ( + torch.arange(0, rotary_dim, 2, device=x.device, dtype=torch.float32) + / rotary_dim + ) + ) + angles = torch.outer(positions, inverse_frequency) + cos = angles.cos()[None, None, :, :].to(x.dtype) + sin = angles.sin()[None, None, :, :].to(x.dtype) + even, odd = x[..., :rotary_dim:2], x[..., 1:rotary_dim:2] + rotated = torch.stack((even * cos - odd * sin, even * sin + odd * cos), dim=-1) + return torch.cat((rotated.flatten(-2), x[..., rotary_dim:]), dim=-1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.num_heads = config.num_attention_heads + self.head_dim = config.attention_head_dim + inner_dim = self.num_heads * self.head_dim + self.norm = nn.RMSNorm(config.hidden_dim) + self.qkv = nn.Linear(config.hidden_dim, 3 * inner_dim, bias=False) + self.output = nn.Linear(inner_dim, config.hidden_dim) + self.dropout = config.dropout + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + batch, length, _ = x.shape + qkv = self.qkv(self.norm(x)).view( + batch, length, 3, self.num_heads, self.head_dim + ) + q, k, value = qkv.unbind(dim=2) + attended = F.scaled_dot_product_attention( + _apply_rope(q.transpose(1, 2)), + _apply_rope(k.transpose(1, 2)), + value.transpose(1, 2), + attn_mask=attention_bias, + dropout_p=self.dropout if self.training else 0.0, + ) + return self.output(attended.transpose(1, 2).reshape(batch, length, -1)) + + +class MLPBlock(nn.Module): + def __init__(self, config): + super().__init__() + expanded = int(config.hidden_dim * config.mlp_expansion) + self.norm = nn.RMSNorm(config.hidden_dim) + self.input = nn.Linear(config.hidden_dim, expanded) + self.output = nn.Linear(expanded, config.hidden_dim) + self.dropout = nn.Dropout(config.dropout) + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor) -> Tensor: + return self.dropout( + self.output(F.gelu(self.input(self.norm(x)), approximate="tanh")) + ) + + +def bound_residual_rms(update: Tensor, cap: float) -> Tensor: + """Cap each token's branch RMS, preserving small updates and their directions.""" + if cap == 0: + return update + rms = update.float().square().mean(dim=-1, keepdim=True).clamp_min(1e-12).sqrt() + scale = (cap / rms).clamp(max=1.0) + return update * scale.to(update.dtype) + + +class SequenceBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.attention = MultiHeadAttention(config) + self.mlp = MLPBlock(config) + self.residual_rms_cap = config.residual_rms_cap + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + x = x + bound_residual_rms(self.attention(x, attention_bias), self.residual_rms_cap) + return x + bound_residual_rms(self.mlp(x), self.residual_rms_cap) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/model.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/model.py new file mode 100644 index 0000000000000000000000000000000000000000..6f211fc29de3fa559db45ce6c2ee3d40816f4a72 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/model.py @@ -0,0 +1,460 @@ +"""Affordable multi-resolution baseline for the compact AlphaGenome task.""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass, fields +import math +from pathlib import Path +from typing import Any, Iterable + +import torch +from torch import Tensor, nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint +import yaml + +from .layers import ChannelRMSNorm, ConvBlock, SequenceBlock + + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} +ALL_FAMILIES = (*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions", "contact_maps") + + +@dataclass(frozen=True) +class ModelConfig: + input_length: int = 1_048_576 + target_length: int = 196_608 + dense_bin_size: int = 128 + trunk_bin_size: int = 1_024 + contact_bin_size: int = 2_048 + stem_stride: int = 8 + encoder_width: int = 32 + channel_growth: int = 32 + num_downsampling_blocks: int = 7 + conv_kernel_size: int = 5 + dna_kernel_size: int = 15 + hidden_dim: int = 384 + num_sequence_blocks: int = 6 + num_attention_heads: int = 6 + attention_head_dim: int = 64 + mlp_expansion: float = 4.0 + dropout: float = 0.0 + head_hidden_size: int = 256 + junction_rank: int = 32 + splice_usage_tracks: int = 734 + junction_tracks: int = 734 + contact_tracks: int = 28 + gradient_checkpointing: bool = True + enable_pairwise: bool = False + pair_width: int = 32 + pair_downsample: int = 16 + pair_update_interval: int = 2 + local_width: int = 0 + local_context_blocks: int = 4 + splice_context_mode: str = 'offset' + local_to_trunk: bool = False + residual_rms_cap: float = 0.0 + + def __post_init__(self) -> None: + if not 1 <= self.local_context_blocks <= 7: + raise ValueError('local_context_blocks must be between 1 and 7') + if not math.isfinite(self.residual_rms_cap) or self.residual_rms_cap < 0: + raise ValueError('residual_rms_cap must be finite and nonnegative') + if self.local_to_trunk and not self.local_width: + raise ValueError('local_to_trunk requires local features') + if self.splice_context_mode not in ('offset', 'uniform'): + raise ValueError('unknown splice_context_mode') + if self.splice_context_mode == 'uniform' and not self.local_width: + raise ValueError('uniform splice context requires a base-resolution path') + downsample = self.stem_stride * 2**self.num_downsampling_blocks + if downsample != self.trunk_bin_size: + raise ValueError("stem_stride * 2**num_downsampling_blocks must equal trunk_bin_size") + for length in (self.input_length, self.target_length): + if length % self.trunk_bin_size: + raise ValueError("input and target lengths must divide by trunk_bin_size") + if self.trunk_bin_size % self.dense_bin_size: + raise ValueError("trunk_bin_size must divide by dense_bin_size") + if self.input_length % self.contact_bin_size: + raise ValueError("input_length must divide by contact_bin_size") + if self.hidden_dim <= 0 or self.num_attention_heads <= 0: + raise ValueError("hidden dimensions and attention heads must be positive") + + @property + def trunk_bins(self) -> int: + return self.input_length // self.trunk_bin_size + + @property + def target_trunk_bins(self) -> int: + return self.target_length // self.trunk_bin_size + + @property + def dense_bins(self) -> int: + return self.target_length // self.dense_bin_size + + @property + def dense_upsample(self) -> int: + return self.trunk_bin_size // self.dense_bin_size + + @property + def contact_bins(self) -> int: + return self.input_length // self.contact_bin_size + + @property + def encoder_output_width(self) -> int: + return self.encoder_width + self.channel_growth * self.num_downsampling_blocks + + @classmethod + def from_yaml(cls, path: str | Path) -> "ModelConfig": + values: dict[str, Any] = yaml.safe_load(Path(path).read_text()) or {} + known = {field.name for field in fields(cls)} + unknown = sorted(values.keys() - known) + if unknown: + raise ValueError(f"Unknown model configuration keys: {unknown}") + return cls(**values) + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + +class SequenceEncoder(nn.Module): + def __init__(self, config: ModelConfig): + super().__init__() + self.stem = nn.Conv1d( + 4, + config.encoder_width, + config.dna_kernel_size, + stride=config.stem_stride, + padding=config.dna_kernel_size // 2, + ) + blocks = [] + width = config.encoder_width + for _ in range(config.num_downsampling_blocks): + output_width = width + config.channel_growth + blocks.append(ConvBlock(width, output_width, config.conv_kernel_size)) + width = output_width + self.blocks = nn.ModuleList(blocks) + + def forward(self, dna: Tensor) -> Tensor: + x = F.gelu(self.stem(dna.transpose(1, 2)), approximate="tanh") + for block in self.blocks: + x = F.max_pool1d(block(x), kernel_size=2, stride=2) + return x.transpose(1, 2) + + +class LocalResidualBlock(nn.Module): + def __init__(self, width: int, dilation: int): + super().__init__() + self.norm = ChannelRMSNorm(width) + self.depthwise = nn.Conv1d(width, width, 9, dilation=dilation, + padding=4 * dilation, groups=width) + self.expand = nn.Conv1d(width, 2 * width, 1) + self.contract = nn.Conv1d(2 * width, width, 1) + + def forward(self, x: Tensor) -> Tensor: + update = self.depthwise(self.norm(x)) + update = self.contract(F.gelu(self.expand(update), approximate='tanh')) + return x + update + + +class LocalSequencePath(nn.Module): + """Base-resolution motif path with powers-of-four dilation and aligned pooling.""" + def __init__(self, config: ModelConfig): + super().__init__() + width = config.local_width + self.stem = nn.Conv1d(4, width, 21, padding=10) + self.blocks = nn.Sequential(*(LocalResidualBlock(width, 4**i) + for i in range(config.local_context_blocks))) + self.norm = ChannelRMSNorm(width) + self.sites = nn.Conv1d(width, 4, 1) + self.bin_size = config.dense_bin_size + self.gradient_checkpointing = config.gradient_checkpointing + + def forward(self, dna: Tensor) -> tuple[Tensor, Tensor]: + base = F.gelu(self.stem(dna), approximate='tanh') + for block in self.blocks: + if self.gradient_checkpointing and self.training: + base = checkpoint(block, base, use_reentrant=False) + else: + base = block(base) + base = F.gelu(self.norm(base), approximate='tanh') + sites = self.sites(base).transpose(1, 2) + pooled = torch.cat((F.avg_pool1d(base, self.bin_size), + F.max_pool1d(base, self.bin_size)), dim=1).transpose(1, 2) + return sites, pooled + + +class CompactModel(nn.Module): + """One shared 1-kb trunk with representation-specific output heads.""" + + def __init__(self, config: ModelConfig | None = None): + super().__init__() + self.config = config or ModelConfig() + self.encoder = SequenceEncoder(self.config) + self.projection = nn.Linear(self.config.encoder_output_width, self.config.hidden_dim) + self.blocks = nn.ModuleList( + SequenceBlock(self.config) for _ in range(self.config.num_sequence_blocks) + ) + self.final_norm = nn.RMSNorm(self.config.hidden_dim) + self.dense_decoder = nn.Sequential( + nn.RMSNorm(self.config.hidden_dim), + nn.Linear(self.config.hidden_dim, self.config.head_hidden_size), + nn.GELU(approximate="tanh"), + ) + self.dense_heads = nn.ModuleDict( + { + family: nn.Linear(self.config.head_hidden_size, tracks) + for family, tracks in DENSE_TRACKS.items() + } + ) + self.splice_site_head = nn.Linear( + self.config.head_hidden_size, self.config.dense_bin_size * 4 + ) + self.splice_usage_head = nn.Linear( + self.config.head_hidden_size, self.config.splice_usage_tracks + ) + rank = self.config.junction_rank + self.junction_source = nn.Linear(self.config.head_hidden_size, rank) + self.junction_destination = nn.Linear(self.config.head_hidden_size, rank) + self.junction_track = nn.Embedding(self.config.junction_tracks, rank) + if self.config.junction_tracks % 2: + raise ValueError("junction tracks must split evenly into strand halves") + self.register_buffer( + "junction_track_half", + torch.arange(self.config.junction_tracks) // (self.config.junction_tracks // 2), + persistent=False, + ) + self.contact_q = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + self.contact_k = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + row, column = torch.triu_indices( + self.config.contact_bins, self.config.contact_bins + ) + self.register_buffer("contact_row", row, persistent=False) + self.register_buffer("contact_column", column, persistent=False) + if self.config.local_width: + self.local_path = LocalSequencePath(self.config) + self.local_projection = nn.Linear(2 * self.config.local_width, self.config.head_hidden_size) + # A new local path can be attached to our own checkpoint without changing its initial outputs. + nn.init.zeros_(self.local_path.sites.weight) + nn.init.zeros_(self.local_path.sites.bias) + nn.init.zeros_(self.local_projection.weight) + nn.init.zeros_(self.local_projection.bias) + if self.config.local_to_trunk: + self.local_trunk_projection = nn.Linear(2 * self.config.local_width, self.config.hidden_dim) + nn.init.zeros_(self.local_trunk_projection.weight) + nn.init.zeros_(self.local_trunk_projection.bias) + self._local_sites = None + self._local_features = None + + def set_junction_track_halves(self, halves: Tensor) -> None: + halves = torch.as_tensor(halves, dtype=torch.long, device=self.junction_track_half.device) + if halves.shape != (self.config.junction_tracks,) or not torch.isin( + halves, torch.tensor([0, 1], device=halves.device) + ).all(): + raise ValueError("junction track halves must be a 734-element 0/1 mapping") + self.junction_track_half.copy_(halves) + + def _trunk(self, dna: Tensor) -> Tensor: + if dna.ndim != 3 or dna.shape[1:] != (self.config.input_length, 4): + raise ValueError( + f"Expected DNA [B,{self.config.input_length},4], got {tuple(dna.shape)}" + ) + if self.config.local_width: + sites, pooled = self.local_path(dna.transpose(1, 2)) + self._local_sites = sites + self._local_features = self.local_projection(pooled) + x = self.projection(self.encoder(dna)) + if self.config.local_to_trunk: + local_tokens = pooled.reshape(len(dna), self.config.trunk_bins, + self.config.dense_upsample, -1).mean(dim=2) + x = x + self.local_trunk_projection(local_tokens) + for block in self.blocks: + if self.config.gradient_checkpointing and self.training and x.requires_grad: + x = checkpoint(block, x, None, use_reentrant=False) + else: + x = block(x, None) + return self.final_norm(x) + + def _dense_features(self, trunk: Tensor) -> Tensor: + start = (self.config.trunk_bins - self.config.target_trunk_bins) // 2 + target = trunk[:, start : start + self.config.target_trunk_bins] + target = target.repeat_interleave(self.config.dense_upsample, dim=1) + features = self.dense_decoder(target) + if self.config.local_width: + first_bin = (self.config.input_length - self.config.target_length) // (2 * self.config.dense_bin_size) + features = features + self._local_features[:, first_bin:first_bin + self.config.dense_bins] + return features + + def _all_dense_features(self, trunk: Tensor) -> Tensor: + features = self.dense_decoder( + trunk.repeat_interleave(self.config.dense_upsample, dim=1) + ) + return features + self._local_features if self.config.local_width else features + + def _splice_site_logits(self, trunk: Tensor) -> Tensor: + if self.config.splice_context_mode == 'uniform': + # The global trunk supplies one gene-context bias per class and kb. Base-resolution + # localization comes from the local path, with no arbitrary 128-bp offset decoder + # and no pooled dense-feature contribution to site logits. + context = self.splice_site_head(self.dense_decoder(trunk)) + context = context.view(len(trunk), self.config.trunk_bins, self.config.dense_bin_size, 4).mean(dim=2) + logits = context.repeat_interleave(self.config.trunk_bin_size, dim=1) + self._local_sites + return logits.float() + logits = self.splice_site_head(self._all_dense_features(trunk)) + logits = logits.view(len(trunk), self.config.input_length, 4) + if self.config.local_width: + logits = logits + self._local_sites + return logits.float() + + def _position_features(self, trunk: Tensor, positions: Tensor) -> Tensor: + features = self.dense_decoder(self._gather_bins(trunk, positions, self.config.trunk_bin_size)) + if self.config.local_width: + features = features + self._gather_bins(self._local_features, positions, self.config.dense_bin_size) + return features + + @staticmethod + def select_junction_positions(site_logits: Tensor, limit: int = 512) -> Tensor: + """Select each site type from model scores alone, then sort by coordinate. + + This follows the four independent site classes in Google DeepMind's + AlphaGenome splicing implementation (pinned in docs/v3-author/PROTOCOL.md). Padding is + -1 when an input is shorter than ``limit``. + """ + count = min(limit, site_logits.shape[1]) + selected = [] + for site_type in range(4): + coordinates = torch.topk(site_logits[..., site_type], count, dim=1).indices + coordinates = coordinates.sort(dim=1).values + if count < limit: + coordinates = F.pad(coordinates, (0, limit - count), value=-1) + selected.append(coordinates) + return torch.stack(selected, dim=1) + + @staticmethod + def _gather_bins(features: Tensor, positions: Tensor, bin_size: int) -> Tensor: + indices = torch.div(positions, bin_size, rounding_mode="floor") + indices = indices.clamp(0, features.shape[1] - 1) + return torch.gather( + features, + 1, + indices[..., None].expand(-1, -1, features.shape[-1]), + ) + + def _junction_predictions( + self, trunk: Tensor, site_positions: Tensor, queries: Tensor + ) -> Tensor: + row, column, track = queries.unbind(dim=-1) + half = self.junction_track_half[track] + batch = torch.arange(len(trunk), device=trunk.device)[:, None] + donor = site_positions[batch, 2 * half, row] + acceptor = site_positions[batch, 2 * half + 1, column] + source = self._position_features(trunk, donor.clamp(0, self.config.input_length - 1)) + destination = self._position_features(trunk, acceptor.clamp(0, self.config.input_length - 1)) + interaction = self.junction_source(source) * self.junction_destination(destination) + track_embedding = self.junction_track(track) + return (interaction * track_embedding).sum(dim=-1) / math.sqrt( + self.config.junction_rank + ) + + def junction_block( + self, + trunk: Tensor, + site_positions: Tensor, + row_start: int, + row_end: int, + column_start: int, + column_end: int, + track_start: int, + track_end: int, + site_factors: tuple[Tensor, Tensor] | None = None, + ) -> Tensor: + """Compute a bounded block of the full predicted-position junction cube.""" + tracks = torch.arange(track_start, track_end, device=trunk.device) + halves = self.junction_track_half[tracks] + if site_factors is None: + site_factors = self.junction_site_factors(trunk, site_positions) + source_all, destination_all = site_factors + source = source_all[:, halves, row_start:row_end] + destination = destination_all[:, halves, column_start:column_end] + embedding = self.junction_track(tracks)[None, :, None, None, :] + values = ( + source[:, :, :, None, :] * destination[:, :, None, :, :] * embedding + ).sum(dim=-1) / math.sqrt(self.config.junction_rank) + return values.permute(0, 2, 3, 1).float() + + def junction_site_factors( + self, trunk: Tensor, site_positions: Tensor + ) -> tuple[Tensor, Tensor]: + """Precompute the two strand-specific factors reused by all cube blocks.""" + donor = torch.stack((site_positions[:, 0], site_positions[:, 2]), dim=1).clamp_min(0) + acceptor = torch.stack((site_positions[:, 1], site_positions[:, 3]), dim=1).clamp_min(0) + donor_features = self._position_features(trunk, donor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, donor.shape[-1], -1) + acceptor_features = self._position_features(trunk, acceptor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, acceptor.shape[-1], -1) + return self.junction_source(donor_features), self.junction_destination(acceptor_features) + + def forward( + self, + dna: Tensor, + *, + families: Iterable[str] | None = None, + usage_positions: Tensor | None = None, + junction_site_positions: Tensor | None = None, + junction_queries: Tensor | None = None, + ) -> dict[str, Tensor]: + requested = set(families or ALL_FAMILIES) + unknown = requested - set(ALL_FAMILIES) + if unknown: + raise ValueError(f"Unknown output families: {sorted(unknown)}") + trunk = self._trunk(dna) + needs_dense = bool( + requested.intersection((*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions")) + ) + features = self._dense_features(trunk) if needs_dense else None + outputs: dict[str, Tensor] = {} + for family in DENSE_TRACKS: + if family in requested: + outputs[family] = self.dense_heads[family](features).float() + if "splice_sites" in requested: + all_logits = self._splice_site_logits(trunk) + start = (self.config.input_length - self.config.target_length) // 2 + outputs["splice_sites"] = all_logits[:, start:start + self.config.target_length] + if "splice_usage" in requested: + if usage_positions is None: + raise ValueError("splice_usage requires usage_positions") + gathered = self._gather_bins( + features, usage_positions, self.config.dense_bin_size + ) + outputs["splice_usage"] = self.splice_usage_head(gathered).float() + if "junctions" in requested: + if junction_site_positions is None or junction_queries is None: + raise ValueError("junctions require site positions and query triples") + outputs["junctions"] = self._junction_predictions( + trunk, junction_site_positions, junction_queries + ).float() + if "contact_maps" in requested: + contact = trunk.reshape( + len(dna), self.config.contact_bins, -1, self.config.hidden_dim + ).mean(dim=2) + q, k = self.contact_q(contact), self.contact_k(contact) + row, column = self.contact_row, self.contact_column + outputs["contact_maps"] = ( + 0.5 + * ( + q[:, row] * k[:, column] + + q[:, column] * k[:, row] + ) + ).float() + return outputs + + +def count_parameters(model: nn.Module) -> int: + return sum(parameter.numel() for parameter in model.parameters() if parameter.requires_grad) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/reference_large.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/reference_large.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e403757c49eb6e6419927210c7053710964213d1 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/reference_large.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 96 +channel_growth: 96 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 1024 +num_sequence_blocks: 16 +num_attention_heads: 16 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 768 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/run_ddp.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/run_ddp.sh new file mode 100644 index 0000000000000000000000000000000000000000..c4702e9366e907b7791063e77dcfe281411ac8cd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/run_ddp.sh @@ -0,0 +1,16 @@ +#!/usr/bin/env bash +# run_ddp.sh DATA_DIR OUTPUT_DIR CONFIG EPOCHS [GPUS=8] [WORKERS_PER_GPU=12] +# Train the reference on every train interval with one example per GPU per step, then score +# validation with the verifier's own scorer. Runs inside the task image (torch, torchrun). +set -Eeuo pipefail +DATA=$1; OUT=$2; CONFIG=$3; EPOCHS=$4; GPUS=${5:-8}; WORKERS=${6:-12} +BASELINE=$(cd "$(dirname "$0")/../.." && pwd) +mkdir -p "$OUT" +cd "$BASELINE" +# Loopback rendezvous: works offline, where --standalone cannot resolve the hostname. +torchrun --nnodes 1 --nproc-per-node "$GPUS" --master-addr 127.0.0.1 --master-port "${MASTER_PORT:-29511}" -m reference.alphagenome_50m.train_eval train \ + --input "$DATA" --config "$CONFIG" --output "$OUT" --epochs "$EPOCHS" --workers "$WORKERS" \ + --log-every 200 --save-every 2000 2>&1 | tee -a "$OUT/train.log" +python predict.py "$DATA" "$OUT/valid-pred" --checkpoint "$OUT/last.pt" --split valid +python /workspace/evaluation/score.py --predictions "$OUT/valid-pred/predictions.zarr" \ + --labels "$DATA" --split valid --output "$OUT/valid-score" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/tiny_overfit.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/tiny_overfit.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c25f9ffbb76102c970e5767922400f7ec2b6c53e --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/tiny_overfit.yaml @@ -0,0 +1,24 @@ +# Keep the frozen 1-Mb input and every output track; shrink only learned widths. +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 8 +channel_growth: 4 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 32 +num_sequence_blocks: 1 +num_attention_heads: 2 +attention_head_dim: 16 +mlp_expansion: 2.0 +dropout: 0.0 +head_hidden_size: 32 +junction_rank: 8 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: false diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/train_eval.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/train_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..92f9a3f315013630fdd2e03fb6840a6c77840edd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/reference/alphagenome_50m/train_eval.py @@ -0,0 +1,537 @@ +"""Train and stream-evaluate the <=50M compact AlphaGenome baseline.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import numpy as np +import torch +from torch import Tensor +from torch.nn import functional as F +from torch.utils.data import DataLoader, Subset + +from .dataset import ( + AlphaGenomeDataset, + DENSE_FAMILIES, + single_example_collate, +) +from .model import ( + ALL_FAMILIES, + CompactModel, + ModelConfig, + count_parameters, +) + + +DEFAULT_CONFIG = Path(__file__).with_name("alphagenome_50m.yaml") + + +def _device(name: str) -> torch.device: + if name == "auto": + name = "cuda" if torch.cuda.is_available() else "cpu" + if name == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("--device cuda requested but CUDA is unavailable") + return torch.device(name) + + +def _autocast(device: torch.device): + enabled = device.type == "cuda" + dtype = torch.bfloat16 if enabled and torch.cuda.is_bf16_supported() else torch.float16 + return torch.autocast(device_type=device.type, dtype=dtype, enabled=enabled) + + +def _distributed() -> tuple[int, int, int]: + """(rank, local_rank, world_size) under torchrun; (0, 0, 1) otherwise.""" + world = int(os.environ.get("WORLD_SIZE", "1")) + if world > 1 and not torch.distributed.is_initialized(): + torch.distributed.init_process_group("nccl") + return int(os.environ.get("RANK", "0")), int(os.environ.get("LOCAL_RANK", "0")), world + + +def _loader(dataset, *, workers: int, shuffle: bool, sampler=None) -> DataLoader: + return DataLoader( + dataset, + batch_size=1, + shuffle=shuffle and sampler is None, + sampler=sampler, + num_workers=workers, + persistent_workers=workers > 0, + pin_memory=torch.cuda.is_available(), + collate_fn=single_example_collate, + ) + + +def _to_device(value, device: torch.device): + if isinstance(value, Tensor): + return value.to(device, non_blocking=True) + if isinstance(value, dict): + return {key: _to_device(item, device) for key, item in value.items()} + return value + + +def _match_coordinate_queries( + scoring: dict[str, Tensor], positions: Tensor, track_halves: Tensor +) -> tuple[Tensor, Tensor, Tensor]: + """Match trusted coordinate queries to typed position axes like the grader.""" + device = positions.device + tracks = scoring["track"].detach().cpu().numpy() + halves_cpu = track_halves.detach().cpu().numpy()[tracks] + # Build indices on CPU, then copy once. Writing three CUDA scalars per valid query + # otherwise launches thousands of tiny kernels per example, especially costly when + # training and evaluation share one GPU. The matching semantics are unchanged. + queries = np.zeros((len(tracks), 3), dtype=np.int64) + queries[:, 2] = tracks + valid = scoring["valid"].clone() + missing = np.ones(len(tracks), dtype=bool) + position_rows = positions.detach().cpu().numpy() + donors_cpu = scoring["donor_position"].detach().cpu().numpy() + acceptors_cpu = scoring["acceptor_position"].detach().cpu().numpy() + valid_indices = np.flatnonzero(valid.detach().cpu().numpy()) + lookups = [ + {int(coordinate): index for index, coordinate in enumerate(axis) if coordinate >= 0} + for axis in position_rows + ] + for index in valid_indices: + half = int(halves_cpu[index]) + row = lookups[2 * half].get(int(donors_cpu[index])) + column = lookups[2 * half + 1].get(int(acceptors_cpu[index])) + if row is not None and column is not None: + queries[index, 0] = row + queries[index, 1] = column + missing[index] = False + return (torch.from_numpy(queries).to(device).unsqueeze(0), valid, + torch.from_numpy(missing).to(device)) + + +def _model_inputs(sample: dict) -> tuple[dict, Tensor, Tensor]: + queries, query_valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + sample["site_positions"], + sample["junction_track_halves"], + ) + if (query_valid & missing).any(): + raise ValueError("trusted scoring coordinates do not match target positions") + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + return { + "dna": sample["dna"].unsqueeze(0), + "usage_positions": sample["usage_positions"].unsqueeze(0), + "junction_site_positions": sample["site_positions"].unsqueeze(0), + "junction_queries": queries, + }, junction_target, query_valid + + +def _losses(outputs: dict[str, Tensor], sample: dict, junction_target: Tensor, + junction_valid: Tensor) -> dict[str, Tensor]: + losses = {} + for family in DENSE_FAMILIES: + target = torch.asinh(sample["dense"][family]["values"]).unsqueeze(0) + mask = sample["dense"][family]["mask"].unsqueeze(0) + residual = torch.where(mask, outputs[family] - target, 0.0) + losses[family] = residual.square().sum() / mask.sum().clamp_min(1) + losses["splice_sites"] = F.binary_cross_entropy_with_logits( + outputs["splice_sites"], sample["splice_sites"].unsqueeze(0) + ) + usage_target = torch.asinh(sample["splice_usage"]["values"]).unsqueeze(0) + usage_mask = sample["splice_usage"]["mask"].unsqueeze(0) + usage_residual = torch.where(usage_mask, outputs["splice_usage"] - usage_target, 0.0) + losses["splice_usage"] = usage_residual.square().sum() / usage_mask.sum().clamp_min(1) + junction_residual = outputs["junctions"][0] - junction_target[0] + losses["junctions"] = ( + junction_residual[junction_valid].square().mean() + if junction_valid.any() else junction_residual.sum() * 0 + ) + contact_target = torch.asinh(sample["contact_maps"]).unsqueeze(0) + contact_mask = sample["contact_map_mask"].unsqueeze(0) + contact_residual = torch.where( + contact_mask, outputs["contact_maps"] - contact_target, 0.0 + ) + losses["contact_maps"] = contact_residual.square().sum() / contact_mask.sum().clamp_min(1) + losses["total"] = torch.stack([losses[name] for name in ALL_FAMILIES]).mean() + return losses + + +def _validation_inference( + model: CompactModel, sample: dict +) -> tuple[dict[str, Tensor], Tensor, Tensor, Tensor]: + """Use the same predicted-position path as a formal v3 submission.""" + trunk = model._trunk(sample["dna"].unsqueeze(0)) + dense_features = model._dense_features(trunk) + outputs = { + family: model.dense_heads[family](dense_features).float() + for family in DENSE_FAMILIES + } + all_sites = model._splice_site_logits(trunk) + target_start = (model.config.input_length - model.config.target_length) // 2 + outputs["splice_sites"] = all_sites[:, target_start:target_start + model.config.target_length] + usage_bins = model.splice_usage_head(dense_features).float() + usage_indices = torch.div(sample["usage_positions"], model.config.dense_bin_size, + rounding_mode="floor") + outputs["splice_usage"] = usage_bins[:, usage_indices] + predicted_positions = model.select_junction_positions(all_sites) + + matched, valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + predicted_positions[0], + sample["junction_track_halves"], + ) + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + junction_prediction = torch.zeros(len(valid), device=valid.device) + if (~missing & valid).any(): + selected = ~missing & valid + junction_prediction[selected] = model._junction_predictions( + trunk, predicted_positions, matched[:, selected] + )[0] + outputs["junctions"] = junction_prediction.unsqueeze(0) + + contact = trunk.reshape( + 1, model.config.contact_bins, -1, model.config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + outputs["contact_maps"] = (0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row])).float() + return outputs, junction_target, valid, missing + + +def _atomic_save(payload: dict, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(f".{path.name}.tmp") + torch.save(payload, temporary) + os.replace(temporary, path) + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(8 * 1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _resume_position(payload: dict, *, examples: int, epochs: int) -> tuple[int, int]: + epoch = int(payload["epoch"]) + step = int(payload["global_step"]) + kind = payload.get("checkpoint_kind") + if kind not in (None, "epoch") or step != epoch * examples: + raise ValueError("only a completed-epoch checkpoint can resume training") + if payload.get("examples", examples) != examples or payload.get("schedule_epochs", epochs) != epochs: + raise ValueError("checkpoint data size or training schedule differs from this run") + return epoch, step + + +def train(args: argparse.Namespace) -> None: + # Multi-GPU with `torchrun --nproc-per-node N train_eval.py train ...`: one example per GPU per + # step (data parallel), rank 0 writes checkpoints and logs. Model size is bounded by the + # training budget, not by a parameter cap. + rank, local_rank, world = _distributed() + config = ModelConfig.from_yaml(args.config) + device = torch.device("cuda", local_rank) if world > 1 else _device(args.device) + if world > 1: + torch.cuda.set_device(device) + torch.manual_seed(args.seed) + model = CompactModel(config).to(device) + parameters = count_parameters(model) + optimizer = torch.optim.AdamW( + model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay + ) + scaler = torch.amp.GradScaler("cuda", enabled=device.type == "cuda") + full_dataset = AlphaGenomeDataset(args.input, "train") + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + start_epoch, global_step = 0, 0 + if args.resume: + payload = torch.load(args.resume, map_location="cpu", weights_only=True) + if payload.get("config") != config.to_dict(): + raise ValueError("checkpoint configuration does not match --config") + model.load_state_dict(payload["model"], strict=True) + optimizer.load_state_dict(payload["optimizer"]) + scaler.load_state_dict(payload.get("scaler", {})) + start_epoch, global_step = _resume_position(payload, examples=size, epochs=args.epochs) + per_rank = math.ceil(size / world) + steps = max(1, per_rank * args.epochs) + module = model + if world > 1: + model = torch.nn.parallel.DistributedDataParallel( + model, device_ids=[local_rank], find_unused_parameters=True) + sampler = (torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world, rank=rank, + shuffle=True, seed=args.seed) + if world > 1 else None) + stop_after_epoch = args.stop_after_epoch or args.epochs + output = args.output.resolve() + output.mkdir(parents=True, exist_ok=True) + started = time.monotonic() + if rank == 0: + print(json.dumps({"event": "train_start", "parameters": parameters, "examples": size, + "device": str(device), "world_size": world}), flush=True) + + for epoch in range(start_epoch, stop_after_epoch): + model.train() + if sampler is not None: + sampler.set_epoch(epoch) + loader = _loader(dataset, workers=args.workers, shuffle=True, sampler=sampler) + for sample in loader: + sample = _to_device(sample, device) + inputs, junction_target, junction_valid = _model_inputs(sample) + progress = min((global_step + 1) / steps, 1.0) + learning_rate = args.min_learning_rate + 0.5 * ( + args.learning_rate - args.min_learning_rate + ) * (1.0 + math.cos(math.pi * progress)) + for group in optimizer.param_groups: + group["lr"] = learning_rate + optimizer.zero_grad(set_to_none=True) + with _autocast(device): + predictions = model(**inputs) + losses = _losses(predictions, sample, junction_target, junction_valid) + if not torch.isfinite(losses["total"]): + raise FloatingPointError(f"non-finite loss at step {global_step}") + scaler.scale(losses["total"]).backward() + scaler.unscale_(optimizer) + gradient_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_gradient_norm) + scaler.step(optimizer) + scaler.update() + global_step += 1 + if rank == 0 and (global_step == 1 or global_step % args.log_every == 0): + print( + json.dumps( + { + "event": "train_step", + "epoch": epoch + 1, + "step": global_step, + "loss": float(losses["total"].detach()), + "gradient_norm": float(gradient_norm), + "learning_rate": learning_rate, + "elapsed_seconds": time.monotonic() - started, + } + ), + flush=True, + ) + if rank == 0 and global_step % args.save_every == 0: + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch, + "global_step": global_step, + "checkpoint_kind": "progress", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "progress.pt", + ) + if world > 1: + torch.distributed.barrier() + if rank != 0: + continue + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch + 1, + "global_step": global_step, + "checkpoint_kind": "epoch", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "last.pt", + ) + if world > 1: + torch.distributed.barrier() + torch.distributed.destroy_process_group() + if rank != 0: + return + checkpoint = output / "last.pt" + manifest = { + "schema": "alphagenome-50m-real/v3", + "checkpoint": {"path": checkpoint.name, "sha256": _sha256(checkpoint)}, + "parameters": parameters, + "config": config.to_dict(), + "output_families": list(ALL_FAMILIES), + "epochs": args.epochs, + "completed_epochs": stop_after_epoch, + "global_step": global_step, + } + (output / "model_manifest.json").write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "train_complete", **manifest}), flush=True) + + +class PerTrackMoments: + def __init__(self, tracks: int): + self.n = np.zeros(tracks, dtype=np.int64) + self.sum_x = np.zeros(tracks, dtype=np.float64) + self.sum_y = np.zeros(tracks, dtype=np.float64) + self.sum_x2 = np.zeros(tracks, dtype=np.float64) + self.sum_y2 = np.zeros(tracks, dtype=np.float64) + self.sum_xy = np.zeros(tracks, dtype=np.float64) + + def update(self, prediction: Tensor, target: Tensor, mask: Tensor | None = None) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1, prediction.shape[-1]) + y = target.detach().double().cpu().numpy().reshape(-1, target.shape[-1]) + valid = np.ones_like(y, dtype=bool) if mask is None else mask.detach().cpu().numpy().reshape(y.shape).astype(bool) + self.n += valid.sum(axis=0) + self.sum_x += np.where(valid, x, 0).sum(axis=0, dtype=np.float64) + self.sum_y += np.where(valid, y, 0).sum(axis=0, dtype=np.float64) + self.sum_x2 += np.where(valid, x * x, 0).sum(axis=0, dtype=np.float64) + self.sum_y2 += np.where(valid, y * y, 0).sum(axis=0, dtype=np.float64) + self.sum_xy += np.where(valid, x * y, 0).sum(axis=0, dtype=np.float64) + + def update_indexed(self, prediction: Tensor, target: Tensor, track: Tensor) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1) + y = target.detach().double().cpu().numpy().reshape(-1) + indices = track.detach().cpu().numpy().reshape(-1).astype(np.int64) + self.n += np.bincount(indices, minlength=len(self.n)) + self.sum_x += np.bincount(indices, weights=x, minlength=len(self.n)) + self.sum_y += np.bincount(indices, weights=y, minlength=len(self.n)) + self.sum_x2 += np.bincount(indices, weights=x * x, minlength=len(self.n)) + self.sum_y2 += np.bincount(indices, weights=y * y, minlength=len(self.n)) + self.sum_xy += np.bincount(indices, weights=x * y, minlength=len(self.n)) + + def report(self) -> dict: + numerator = self.n * self.sum_xy - self.sum_x * self.sum_y + denominator = np.sqrt( + np.maximum(self.n * self.sum_x2 - self.sum_x**2, 0) + * np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + ) + target_ss = np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + scorable = (self.n >= 2) & (target_ss > 0) + pearson = np.full_like(numerator, np.nan) + nonconstant = scorable & (denominator > 0) + pearson[nonconstant] = numerator[nonconstant] / denominator[nonconstant] + pearson[scorable & ~nonconstant] = 0.0 + return { + "mean_pearson": float(pearson[scorable].mean()) if scorable.any() else 0.0, + "valid_tracks": int(scorable.sum()), + "per_track_pearson": [float(value) if np.isfinite(value) else None for value in pearson], + "values_per_track": self.n.tolist(), + } + + +def evaluate(args: argparse.Namespace) -> None: + device = _device(args.device) + payload = torch.load(args.checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config).to(device) + model.load_state_dict(payload["model"], strict=True) + model.eval() + full_dataset = AlphaGenomeDataset(args.input, args.split) + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + metrics = { + **{family: PerTrackMoments(config_value) for family, config_value in { + "atac": 167, "dnase": 305, "procap": 12, "cage": 546, + "rna_seq": 667, "chip_tf": 1617, "chip_histone": 1116, + }.items()}, + "splice_sites": PerTrackMoments(4), + "splice_usage": PerTrackMoments(config.splice_usage_tracks), + "junctions": PerTrackMoments(config.junction_tracks), + "contact_maps": PerTrackMoments(config.contact_tracks), + } + with torch.inference_mode(): + for index, sample in enumerate(_loader(dataset, workers=args.workers, shuffle=False)): + sample = _to_device(sample, device) + outputs, junction_target, junction_valid, junction_missing = ( + _validation_inference(model, sample) + ) + # Match predict.py: FP32 inference is serialized to FP16 Zarr values. + outputs = {name: value.half().float() for name, value in outputs.items()} + for family in DENSE_FAMILIES: + metrics[family].update( + outputs[family][0], + torch.asinh(sample["dense"][family]["values"]), + sample["dense"][family]["mask"], + ) + metrics["splice_sites"].update(outputs["splice_sites"][0], sample["splice_sites"]) + metrics["splice_usage"].update( + outputs["splice_usage"][0], + torch.asinh(sample["splice_usage"]["values"]), + sample["splice_usage"]["mask"], + ) + junction_track = sample["junction_scoring"]["track"] + metrics["junctions"].update_indexed( + outputs["junctions"][0][junction_valid], + junction_target[0][junction_valid], + junction_track[junction_valid], + ) + metrics["contact_maps"].update( + outputs["contact_maps"][0], + torch.asinh(sample["contact_maps"]), + sample["contact_map_mask"], + ) + if index == 0 or (index + 1) % args.log_every == 0 or index + 1 == size: + print(json.dumps({"event": "evaluate_progress", "examples": index + 1, "total": size}), flush=True) + families = {name: accumulator.report() for name, accumulator in metrics.items()} + valid_family_scores = [item["mean_pearson"] for item in families.values()] + report = { + "schema": "alphagenome-50m-evaluation/v3", + "split": args.split, + "examples": size, + "metric_space": ( + "asinh continuous targets and binary splice-site targets; " + "junction Pearson uses hidden coordinate queries and predicted endpoints; " + "FP32 inference rounded to FP16 prediction values" + ), + "families": families, + "overall_mean_family_pearson": float(np.mean(valid_family_scores)), + } + args.report.parent.mkdir(parents=True, exist_ok=True) + args.report.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "evaluate_complete", "overall_mean_family_pearson": report["overall_mean_family_pearson"]}), flush=True) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + common = argparse.ArgumentParser(add_help=False) + common.add_argument("--input", required=True, type=Path) + common.add_argument("--config", type=Path, default=DEFAULT_CONFIG) + common.add_argument("--workers", type=int, default=2) + common.add_argument("--max-examples", type=int, default=0) + common.add_argument("--device", choices=("auto", "cuda", "cpu"), default="auto") + common.add_argument("--log-every", type=int, default=25) + subparsers = parser.add_subparsers(dest="command", required=True) + train_parser = subparsers.add_parser("train", parents=[common]) + train_parser.add_argument("--output", required=True, type=Path) + train_parser.add_argument("--epochs", type=int, default=10) + train_parser.add_argument("--stop-after-epoch", type=int) + train_parser.add_argument("--learning-rate", type=float, default=2e-4) + train_parser.add_argument("--min-learning-rate", type=float, default=2e-5) + train_parser.add_argument("--weight-decay", type=float, default=1e-4) + train_parser.add_argument("--max-gradient-norm", type=float, default=5.0) + train_parser.add_argument("--save-every", type=int, default=250) + train_parser.add_argument("--seed", type=int, default=17) + train_parser.add_argument("--resume", type=Path) + train_parser.set_defaults(run=train) + evaluate_parser = subparsers.add_parser("evaluate", parents=[common]) + evaluate_parser.add_argument("--checkpoint", required=True, type=Path) + evaluate_parser.add_argument("--split", choices=("train", "valid", "test"), required=True) + evaluate_parser.add_argument("--report", required=True, type=Path) + evaluate_parser.set_defaults(run=evaluate) + args = parser.parse_args() + if args.workers < 0 or args.max_examples < 0: + parser.error("workers/max-examples must be nonnegative") + if args.command == "train" and ( + args.epochs < 1 + or (args.stop_after_epoch is not None + and not 1 <= args.stop_after_epoch <= args.epochs) + ): + parser.error("epochs must be positive and stop-after-epoch must be within the schedule") + args.run(args) + + +if __name__ == "__main__": + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/splice_calibration.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/splice_calibration.json new file mode 100644 index 0000000000000000000000000000000000000000..470734a574a4b9b9f2f34fd3bcae0007db3737f6 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/splice_calibration.json @@ -0,0 +1,33 @@ +{ + "training_examples": 256, + "training_indices_sha256": "b82df9ae11e95d9be5d6e5f85839cf23e25bbbe0a195eaee266ff52d366800dc", + "training_positive_counts": [ + 3968.0, + 4160.0, + 3874.0, + 3921.0 + ], + "training_prior": [ + 7.883707682291667e-05, + 8.265177408854167e-05, + 7.696946461995442e-05, + 7.790327072143555e-05 + ], + "effective_balanced_loss_prior": [ + 0.3203125, + 0.3125, + 0.30859375, + 0.30859375 + ], + "logit_offset": [ + -8.695712089538574, + -8.612334251403809, + -8.665323257446289, + -8.653263092041016 + ], + "caveat": "marginal prior correction; balanced loss is normalized separately in each window", + "link": "sigmoid(logits + training_prior_offset)", + "training_split": "train", + "seed": 1729, + "development_labels_used_to_fit_offsets": false +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/splice_output.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/splice_output.py new file mode 100644 index 0000000000000000000000000000000000000000..1081264d9cf4b4473f4fe53c97ff02b2c3b249e3 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/splice_output.py @@ -0,0 +1,11 @@ +"""Shared splice output link; constants estimated only from training targets.""" +import json +from pathlib import Path +import torch + +_OFFSETS = json.loads((Path(__file__).with_name('splice_calibration.json')).read_text())['logit_offset'] + +def splice_output_scores(logits): + logits = logits.float() + offset = torch.tensor(_OFFSETS, device=logits.device, dtype=logits.dtype) + return torch.sigmoid(logits + offset) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/train_batched.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/train_batched.py new file mode 100644 index 0000000000000000000000000000000000000000..df00b798c655555704cd6c8abbd7e4eacedd0ba3 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/train_batched.py @@ -0,0 +1,168 @@ +"""Resumable single-H100 training with equal-per-example batched losses. + +global_step counts training examples, optimizer_steps counts updates. Every epoch +uses the complete deterministic permutation, including its final partial batch. +""" +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time +import torch +from numcodecs import blosc +from torch.utils.data import DataLoader, Subset +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save, _to_device +from batched_training import list_collate, prepare_batch, equal_example_losses + +def main(): + p=argparse.ArgumentParser(description=__doc__) + p.add_argument('--input',type=Path,default=Path('/workspace/data')) + p.add_argument('--output',type=Path,required=True) + p.add_argument('--config',type=Path,required=True) + p.add_argument('--resume',type=Path) + p.add_argument('--batch-size',type=int,default=8) + p.add_argument('--epochs',type=int,default=14) + p.add_argument('--stop-at',type=int,default=0) + p.add_argument('--workers',type=int,default=6) + p.add_argument('--threads',type=int,default=2) + p.add_argument('--seed',type=int,default=17) + p.add_argument('--learning-rate',type=float,default=2e-4) + p.add_argument('--min-learning-rate',type=float,default=2e-5) + p.add_argument('--warmup',type=int,default=4000,help='Warmup in training examples') + p.add_argument('--objective',choices=['balanced_splice','variance_balanced'],default='balanced_splice') + p.add_argument('--loss-scales',type=Path) + p.add_argument('--save-every',type=int,default=10000,help='Training examples between progress checkpoints') + p.add_argument('--keep-every',type=int,default=50000) + p.add_argument('--log-every',type=int,default=50,help='Optimizer updates between logs') + args=p.parse_args() + if min(args.batch_size,args.epochs,args.save_every,args.log_every) < 1: + raise ValueError('batch, epoch, checkpoint and log intervals must be positive') + os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:22]) + torch.set_num_threads(args.threads);torch.set_num_interop_threads(1);blosc.set_nthreads(1) + torch.manual_seed(args.seed) + device=torch.device('cuda:0') + config=ModelConfig.from_yaml(args.config) + model=CompactModel(config).to(device) + optimizer=torch.optim.AdamW(model.parameters(),lr=args.learning_rate,weight_decay=1e-4) + dataset=AlphaGenomeDataset(args.input,'train') + model.set_junction_track_halves(dataset.junction_track_halves) + args.output.mkdir(parents=True,exist_ok=True) + total=len(dataset)*args.epochs + step,updates,elapsed_before=0,0,0. + lineage,scales={},{} + if args.loss_scales: + scale_source=json.loads(args.loss_scales.read_text()) + assert scale_source['split']=='train' + scales=scale_source['family_loss_scales'] + lineage['loss_scale_provenance']={k:v for k,v in scale_source.items() if k!='junction_moments'} + if args.resume: + state=torch.load(args.resume,map_location='cpu',weights_only=True) + if state['schema'] not in ['seqfunc-single/v1','seqfunc-batched/v1']: + raise ValueError('only locally trained checkpoints may resume') + if ModelConfig(**state['config']).to_dict()!=config.to_dict() or state['examples']!=len(dataset): + raise ValueError('resume architecture or dataset differs') + if state['schedule_epochs']!=args.epochs or state['objective']!=args.objective or state['seed']!=args.seed: + raise ValueError('resume schedule, objective or seed differs') + previous=state.get('training_arguments',{}) + for field in ['learning_rate','min_learning_rate','warmup']: + if field in previous and previous[field]!=getattr(args,field): + raise ValueError(f'resume {field} differs') + if state['schema']=='seqfunc-batched/v1' and previous['batch_size']!=args.batch_size: + raise ValueError('batched resume must keep its batch size') + if scales and scales!=state.get('family_loss_scales',{}): + raise ValueError('resume family scales differ') + scales=state.get('family_loss_scales',{}) + lineage.update({k:state[k] for k in ['intervention','interventions','loss_scale_provenance'] if k in state}) + model.load_state_dict(state['model'],strict=True) + optimizer.load_state_dict(state['optimizer']) + step=state['global_step'];updates=state.get('optimizer_steps',step) + elapsed_before=state.get('elapsed_seconds',0.) + torch.set_rng_state(state['rng_cpu']);torch.cuda.set_rng_state(state['rng_cuda'],device) + if state['schema']=='seqfunc-single/v1': + with args.resume.open('rb') as f: + digest=hashlib.file_digest(f,'sha256').hexdigest() + history=list(lineage.get('interventions',[])) + if lineage.get('intervention') and lineage['intervention'] not in history: + history.append(lineage['intervention']) + change={'kind':'batch_size_change','from':1,'to':args.batch_size,'at_example':step, + 'parent_sha256':digest,'parameters_optimizer_rng_unchanged_at_start':True} + history.append(change);lineage.update(intervention=change,interventions=history) + if args.objective=='variance_balanced': + expected=set(model.dense_heads)|{'splice_usage','junctions','contact_maps'} + if set(scales)!=expected or not all(math.isfinite(v) and v>0 for v in scales.values()): + raise ValueError('fixed positive training-derived scales required') + elif scales: + raise ValueError('unscaled objective cannot carry family scales') + start=time.monotonic() + def emit(event,**values): + print(json.dumps({'event':event,'step':step,'optimizer_steps':updates, + 'elapsed_seconds':elapsed_before+time.monotonic()-start,**values}),flush=True) + def save(name): + _atomic_save({**lineage,'schema':'seqfunc-batched/v1','model':model.state_dict(), + 'optimizer':optimizer.state_dict(),'config':config.to_dict(), + 'global_step':step,'optimizer_steps':updates,'epoch':step//len(dataset),'offset':step%len(dataset), + 'examples':len(dataset),'schedule_epochs':args.epochs,'objective':args.objective,'seed':args.seed, + 'family_loss_scales':scales, + 'training_arguments':{k:str(v) if isinstance(v,Path) else v for k,v in vars(args).items()}, + 'rng_cpu':torch.get_rng_state(),'rng_cuda':torch.cuda.get_rng_state(device), + 'elapsed_seconds':elapsed_before+time.monotonic()-start, + 'provenance':{'data':str(args.input),'split':'train','pretrained':False,'world_size':1, + 'device':torch.cuda.get_device_name(0),'batch_reduction':'equal per example', + 'train_manifest_sha256':hashlib.sha256((args.input/'public/train.parquet').read_bytes()).hexdigest()}, + },args.output/name) + emit('train_start',parameters=count_parameters(model),examples=len(dataset),batch_size=args.batch_size, + objective=args.objective,config=config.to_dict(),schedule_examples=total,family_loss_scales=scales) + if args.stop_at and step>=args.stop_at: + raise ValueError('stop-at must exceed the resumed example count') + model.train() + for epoch in range(step//len(dataset),args.epochs): + order=torch.randperm(len(dataset),generator=torch.Generator().manual_seed(args.seed+epoch)).tolist() + offset=step%len(dataset) + emit('epoch_order',epoch=epoch+1,offset=offset,count=len(order), + permutation_sha256=hashlib.sha256(torch.tensor(order,dtype=torch.int64).numpy().tobytes()).hexdigest()) + loader=DataLoader(Subset(dataset,order[offset:]),batch_size=args.batch_size,shuffle=False, + num_workers=args.workers,pin_memory=True,collate_fn=list_collate, + persistent_workers=False,prefetch_factor=2 if args.workers else None) + for cpu_samples in loader: + if args.stop_at: + cpu_samples=cpu_samples[:args.stop_at-step] + inputs,targets,valid=prepare_batch(cpu_samples) + inputs=_to_device(inputs,device) + targets=[x.to(device,non_blocking=True) for x in targets] + valid=[x.to(device,non_blocking=True) for x in valid] + samples=[_to_device(s,device) for s in cpu_samples] + progress=min(max(step-args.warmup,0)/max(total-args.warmup,1),1) + lr=args.min_learning_rate+.5*(args.learning_rate-args.min_learning_rate)*(1+math.cos(math.pi*progress)) + lr*=min((step+len(samples))/max(args.warmup,1),1) + for group in optimizer.param_groups: group['lr']=lr + optimizer.zero_grad(set_to_none=True) + with torch.autocast('cuda',dtype=torch.bfloat16): + outputs=model(**inputs) + losses,raw=equal_example_losses(outputs,samples,targets,valid,scales=scales) + if not torch.isfinite(losses['total']): + raise FloatingPointError(f'nonfinite loss at {step}') + losses['total'].backward() + norm=torch.nn.utils.clip_grad_norm_(model.parameters(),5,error_if_nonfinite=True) + optimizer.step() + before=step;step+=len(samples);updates+=1 + if updates==1 or updates%args.log_every==0: + emit('train_step',epoch=epoch+1,example_ids=[s['example_id'] for s in samples], + learning_rate=lr,gradient_norm=float(norm),actual_batch_size=len(samples), + losses={k:float(v.detach()) for k,v in losses.items()}, + raw_losses={k:float(v.detach()) for k,v in raw.items()}, + peak_gpu_bytes=torch.cuda.max_memory_allocated()) + if step//args.save_every>before//args.save_every: save('progress.pt') + if args.keep_every and step//args.keep_every>before//args.keep_every: save(f'step-{step:06d}.pt') + if step%len(dataset)==0: + save('last.pt');save(f'epoch-{epoch+1:02d}.pt') + emit('epoch_complete',completed_epochs=epoch+1) + if args.stop_at and step>=args.stop_at: + save('last.pt');emit('train_segment_complete',completed_epochs=step//len(dataset),offset=step%len(dataset)) + return + emit('train_complete') + +if __name__=='__main__': main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/train_single.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/train_single.py new file mode 100644 index 0000000000000000000000000000000000000000..03024393206b918eac3ae23a87c09173848b0d15 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/train_single.py @@ -0,0 +1,158 @@ +"""Resumable single-H100 training; every epoch permutes every eligible training interval.""" +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import torch +from numcodecs import blosc +from torch.utils.data import DataLoader, Subset + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset, single_example_collate +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save, _losses, _model_inputs, _to_device + + +def balanced_splice_loss(logits, target): + """Equal positive/negative contributions per type, using only training labels. + + An absent class contributes its negative loss rather than dividing by zero or being dropped. + """ + dims = tuple(range(target.ndim - 1)) + positive = target.sum(dims) + negative = (1 - target).sum(dims) + pos_loss = (torch.nn.functional.softplus(-logits) * target).sum(dims) / positive.clamp_min(1) + neg_loss = (torch.nn.functional.softplus(logits) * (1 - target)).sum(dims) / negative.clamp_min(1) + return torch.where(positive > 0, 0.5 * (pos_loss + neg_loss), neg_loss).mean() + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--input', type=Path, default=Path('/workspace/data')) + p.add_argument('--output', type=Path, required=True) + p.add_argument('--config', type=Path, default=Path(__file__).parent / 'reference/alphagenome_50m/alphagenome_50m.yaml') + p.add_argument('--resume', type=Path) + p.add_argument('--objective', choices=['reference', 'balanced_splice'], default='balanced_splice') + p.add_argument('--epochs', type=int, default=4) + p.add_argument('--stop-at', type=int, default=0, help='Stop after this total number of examples, preserving exact sampler position') + p.add_argument('--workers', type=int, default=8) + p.add_argument('--threads', type=int, default=4) + p.add_argument('--seed', type=int, default=17) + p.add_argument('--learning-rate', type=float, default=2e-4) + p.add_argument('--min-learning-rate', type=float, default=2e-5) + p.add_argument('--warmup', type=int, default=500) + p.add_argument('--save-every', type=int, default=2000) + p.add_argument('--keep-every', type=int, default=10000, help='Keep immutable intermediate checkpoints') + p.add_argument('--log-every', type=int, default=100) + args = p.parse_args() + torch.set_num_threads(args.threads) + torch.set_num_interop_threads(1) + blosc.set_nthreads(1) + if hasattr(os, 'sched_getaffinity'): + os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) + torch.manual_seed(args.seed) + device = torch.device('cuda:0') + config = ModelConfig.from_yaml(args.config) + model = CompactModel(config).to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=1e-4) + dataset = AlphaGenomeDataset(args.input, 'train') + model.set_junction_track_halves(dataset.junction_track_halves) + args.output.mkdir(parents=True, exist_ok=True) + total = len(dataset) * args.epochs + step, elapsed_before = 0, 0.0 + lineage = {} + if args.resume: + state = torch.load(args.resume, map_location='cpu', weights_only=True) + lineage = {k: state[k] for k in ['intervention', 'interventions'] if k in state} + if state['config'] != config.to_dict() or state['examples'] != len(dataset): + raise ValueError('resume config or dataset size differs') + if state['schedule_epochs'] != args.epochs or state['objective'] != args.objective: + raise ValueError('resume schedule or objective differs') + if state['seed'] != args.seed: + raise ValueError('resume shuffle seed differs') + previous = state.get('training_arguments', {}) + for field in ['learning_rate', 'min_learning_rate', 'warmup']: + if field in previous and previous[field] != getattr(args, field): + raise ValueError(f'resume {field} differs') + model.load_state_dict(state['model'], strict=True) + optimizer.load_state_dict(state['optimizer']) + step = state['global_step'] + elapsed_before = state.get('elapsed_seconds', 0.0) + torch.set_rng_state(state['rng_cpu']) + torch.cuda.set_rng_state(state['rng_cuda'], device) + start = time.monotonic() + def emit(event, **values): + record = {'event': event, 'step': step, 'elapsed_seconds': elapsed_before + time.monotonic() - start, **values} + print(json.dumps(record), flush=True) + def save(name): + _atomic_save({ + **lineage, + 'schema': 'seqfunc-single/v1', 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), 'config': config.to_dict(), + 'global_step': step, 'epoch': step // len(dataset), 'offset': step % len(dataset), + 'examples': len(dataset), 'schedule_epochs': args.epochs, + 'objective': args.objective, 'seed': args.seed, + 'training_arguments': {k: str(v) if isinstance(v, Path) else v for k, v in vars(args).items()}, + 'rng_cpu': torch.get_rng_state(), 'rng_cuda': torch.cuda.get_rng_state(device), + 'elapsed_seconds': elapsed_before + time.monotonic() - start, + 'provenance': {'data': str(args.input), 'split': 'train', 'pretrained': False, + 'device': torch.cuda.get_device_name(0), 'world_size': 1}, + }, args.output / name) + emit('train_start', parameters=count_parameters(model), examples=len(dataset), objective=args.objective, + config=config.to_dict(), schedule_examples=total, device=torch.cuda.get_device_name(0)) + model.train() + for epoch in range(step // len(dataset), args.epochs): + generator = torch.Generator().manual_seed(args.seed + epoch) + order = torch.randperm(len(dataset), generator=generator).tolist() + offset = step % len(dataset) + emit('epoch_order', epoch=epoch + 1, offset=offset, count=len(order), + permutation_sha256=hashlib.sha256(torch.tensor(order, dtype=torch.int64).numpy().tobytes()).hexdigest()) + loader = DataLoader(Subset(dataset, order[offset:]), batch_size=1, shuffle=False, + num_workers=args.workers, pin_memory=True, collate_fn=single_example_collate, + persistent_workers=False, prefetch_factor=2 if args.workers else None) + for sample in loader: + sample = _to_device(sample, device) + inputs, target, valid = _model_inputs(sample) + progress = min(max(step - args.warmup, 0) / max(total - args.warmup, 1), 1) + lr = args.min_learning_rate + 0.5 * (args.learning_rate - args.min_learning_rate) * (1 + math.cos(math.pi * progress)) + lr *= min((step + 1) / max(args.warmup, 1), 1) + for group in optimizer.param_groups: + group['lr'] = lr + optimizer.zero_grad(set_to_none=True) + with torch.autocast('cuda', dtype=torch.bfloat16): + outputs = model(**inputs) + losses = _losses(outputs, sample, target, valid) + if args.objective == 'balanced_splice': + losses['splice_sites'] = balanced_splice_loss(outputs['splice_sites'], sample['splice_sites'][None]) + losses['total'] = torch.stack([v for k, v in losses.items() if k != 'total']).mean() + if not torch.isfinite(losses['total']): + raise FloatingPointError(f'nonfinite loss at {step}') + losses['total'].backward() + norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0, error_if_nonfinite=True) + optimizer.step() + step += 1 + if step == 1 or step % args.log_every == 0: + emit('train_step', epoch=epoch + 1, example_id=sample['example_id'], learning_rate=lr, + gradient_norm=float(norm), losses={k: float(v.detach()) for k, v in losses.items()}, + peak_gpu_bytes=torch.cuda.max_memory_allocated()) + if step % args.save_every == 0: + save('progress.pt') + if args.keep_every and step % args.keep_every == 0: + save(f'step-{step:06d}.pt') + if args.stop_at and step >= args.stop_at: + save('last.pt') + emit('train_segment_complete', completed_epochs=step // len(dataset), offset=step % len(dataset)) + return + save('last.pt') + save(f'epoch-{epoch + 1:02d}.pt') + emit('epoch_complete', completed_epochs=epoch + 1) + emit('train_complete') + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_assets_manifest.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_assets_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..8e314c842cca039da1d2be8da7fff6a146178651 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_assets_manifest.json @@ -0,0 +1,21 @@ +{ + "schema": "seqfunc-training-assets/v1", + "files": { + "training_history/block-checkpoint-cpu-check.json": "d099b0a4d4d6cdd1aab33a6d8efaf3b4a5bddb2be4db0563eed83b3f849a31ef", + "training_history/block-checkpoint-gpu-check.json": "b2e598359fad26fd63c4e7487997432987cfd8c836680726458f62d137df04ce", + "training_history/bytecache-integration_check.json": "a712f359807b414956a87f2486bbc3c6e9288727d1f79e5d0b6fdf321fd9a67b", + 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b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_config.yaml @@ -0,0 +1,32 @@ +attention_head_dim: 64 +channel_growth: 32 +contact_bin_size: 2048 +contact_tracks: 28 +conv_kernel_size: 5 +dense_bin_size: 128 +dna_kernel_size: 15 +dropout: 0.0 +enable_pairwise: false +encoder_width: 32 +gradient_checkpointing: true +head_hidden_size: 256 +hidden_dim: 384 +input_length: 1048576 +junction_rank: 32 +junction_tracks: 734 +local_context_blocks: 6 +local_to_trunk: true +local_width: 32 +mlp_expansion: 4.0 +num_attention_heads: 6 +num_downsampling_blocks: 7 +num_sequence_blocks: 6 +pair_downsample: 16 +pair_update_interval: 2 +pair_width: 32 +residual_rms_cap: 0.1 +splice_context_mode: offset +splice_usage_tracks: 734 +stem_stride: 8 +target_length: 196608 +trunk_bin_size: 1024 diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/block-checkpoint-cpu-check.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/block-checkpoint-cpu-check.json new file mode 100644 index 0000000000000000000000000000000000000000..588d9318734995508b1ece2948b790e2d0ce5172 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/block-checkpoint-cpu-check.json @@ -0,0 +1,10 @@ +{ + "all_11_training_and_evaluation_outputs_bit_exact": true, + "max_absolute_gradient_error": 0.0, + "nonzero_local_heads_and_bridge": true, + "six_local_blocks": true, + "active_branch_caps": true, + "unequal_and_empty_event_queries": true, + "precision": "float32", + "device": "CPU" +} diff --git 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Does not measure long-tail storage outages or guarantee gains on the full training dataset." +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/execution_change.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/execution_change.json new file mode 100644 index 0000000000000000000000000000000000000000..93f72a0c4f5f51e9192005535fc45e6aa3bbd671 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/execution_change.json @@ -0,0 +1,10 @@ +{ + "adopted_utc": "2026-10-24T21:51:59.762636+00:00", + "change": "Activation checkpoint boundaries only; all model operations, parameters, objective, data and schedule unchanged.", + "example_boundary": 1000, + "before": "Recompute whole local path during backward.", + "after": "Recompute each local residual block during backward.", + "evidence": "CPU gradients and all eleven outputs exactly match. Full-shape BF16 outputs/losses are bit-exact; gradient relative L2 discrepancy .0009547 versus .0009534 for an unchanged repeat, consistent with existing reduction variability. Peak memory40.11 to15.99GiB; isolated compute .11131 to.10504seconds/example.", + "initial_source_manifest_sha256": "18329ef35e89fc5aeb50ccc045b08db690e6c8ef40d661b01ddb74c7f32b6f0b", + "no_benchmark_weights_retained": true +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/fixed-link57-plan.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/fixed-link57-plan.json new file mode 100644 index 0000000000000000000000000000000000000000..a6b7bf5ecc16d4f887d74509c5b8632f6069fb71 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/fixed-link57-plan.json @@ -0,0 +1,84 @@ +{ + "created_utc": "2026-10-25T09:02:04.055682+00:00", + "parent_evaluation": "/workspace/research/experiments/scale13-small-epoch03", + "parent_inference_signature": { + "last.pt": "bb5df0fc3e1827e183047345bf246fd33348c79558a51467fb2811528ab3e418", + "predict.py": "c257bd031d41b946053e37d5a5759c6c1416d22d9b4d3f403e1fd13e0f63968b", + "perturb.py": "ba37a6f9f8b8eccb052903618bcb4c91d3f4cbed62dfe678ee362644dc80801e", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/alphagenome_50m/model.py": "c61f50a2a66a4f6950c6f6107a70710a5d0ab99a291b8bfaa1eb5565998d2fa3", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919" + }, + "output": "/workspace/research/experiments/calib57", + "parent_reward": null, + "only_change": "Apply the same frozen sigmoid(raw splice logits + four training-prior offsets) in both entrypoints; keep all weights, raw site selection, sparse usage/junction decoding, other families and orientation fixed.", + "parent_choice": "The already immutable rawsmall3 matched control, declared before its full result. Admit only after its fullnative/full200/13 evaluation completes and reward exceeds rawsmall2 by1e-6. Original candidate pools remain unchanged.", + "no_new_parameter_fitting": true, + "training_prior": "/workspace/research/analysis/prior-calibration-14000.json", + "training_prior_sha256": "dc2fbb82e37768540b6a3d527f73a8071e98c81fe3e0e5b4bff643037a43a235", + "evaluation_lock": "/workspace/research/studies/scale13/evaluation.lock", + "helper_sha256": { + "run_fixed_link_candidate.py": "b63e8f914833a5afd5d4a9fcb76682ba2d23f7fb78bcef65dedff04d256665f4", + "make_splice_probability_candidate.py": "42c0502bc0734ff4ca7fcf69a67e723b8fa83035c91083112845a2bd5758a0c6", + "check_native_recovery.py": "47adc6fd7695f8d7e5e05fb9450b41194ce3695ba369dda07dbbd43188ff6965", + "materialize_splice_link_outputs_v2.py": "d6ffb21a81b4394700adbcebcba1adf95029cc06c09452ed34a4d65e03cbb906", + "check_splice_link_reuse.py": "a9c92658c3a1a9bcc80b594f8b7d2d0e7f93313e811f361d7bbae9cb5e2add17", + "direct_read_store.py": "ab182b3b687a3b470ff1bbc311a9130cc791974366bdc0a400df5e0e28602c9c", + "verify_package.py": "23042e356bddbaa59f2abb1e094196b75154b7a59033284295c02885bb4991d4", + "evaluate_package.py": "38885699310241dc84b4d412f14abf240d9708bb8b2de97bc365754df65c5a50", + "resume_tracks.py": "e539554f7962456d0b06c8d6a1c72b279639a0acd393a015147b7b555e747e3a", + "resume_perturbations.py": "a4c87b25563e08eae1c8486338a45ed5b7d7e3f7930fd7165b44d623bce1d174", + "promote_if_better.py": "fb863e77b3da094f24de62dcfd08ed8e384b253ee884759babddbab2cb5f283d", + "finalize_package.py": "d8c40bdf02e44e8ad33a22ca93b718fa2f1f53a0e5f51cb228287446d15bbb54", + "publish_atomic.py": "34f4f44e21e9130070d1818eeddffdf9210efcc2e034b5be7b41011fe375f289", + "run_fixed_link_followup57.py": "18e93ffcce73900b4c79e6f5b724b571d197a0003cf816bfcca2fb4dfa2494eb" + }, + "materialization": { + "schema": "seqfunc-exact-fixed-link-evaluation-reuse/v1", + "public_queries": "/workspace/research/public-valid/public/perturbations.parquet", + "public_queries_sha256": "a95b37553157f1964d134b64eb30f149d8577f20d6d6e6e834c8b30fe7022aed", + "public_input": "/workspace/research/public-valid", + "complete_native_proof": "/workspace/research/analysis/splice-link-reuse-calib19-proof.json", + "complete_native_proof_sha256": "3361b646308f3af9c77e82ca8ef72cec9cb12be5da5273c826bc958233a04844", + "proof_origin": "Full original package-model evaluation via its recovery runner, already mechanically checked against native entrypoints. All12486arrays match exactly. Every actual reuse candidate additionally gets a fresh standalone vendored perturb.sh fixture check.", + "native_query_indices": [ + 0, + 567, + 1548, + 1746, + 2106, + 2696 + ], + "native_minimum_free_GiB": 12, + "native_timeout_seconds": 300, + "helper_sha256": { + "check_splice_link_reuse.py": "a9c92658c3a1a9bcc80b594f8b7d2d0e7f93313e811f361d7bbae9cb5e2add17", + "direct_read_store.py": "ab182b3b687a3b470ff1bbc311a9130cc791974366bdc0a400df5e0e28602c9c", + "verify_package.py": "23042e356bddbaa59f2abb1e094196b75154b7a59033284295c02885bb4991d4", + "resume_perturbations.py": "a4c87b25563e08eae1c8486338a45ed5b7d7e3f7930fd7165b44d623bce1d174", + "materialize_splice_link_outputs_v2.py": "d6ffb21a81b4394700adbcebcba1adf95029cc06c09452ed34a4d65e03cbb906" + }, + "change": "Apply the unchanged verified fixed-link output derivation to this newly declared parent/candidate only. Full track inference and original official scoring remain required.", + "installation": "Atomic symlink without overwrite; retain study prepared-calib57 outputs while referenced. No cache is an inference asset.", + "labels_read": false, + "not_an_inference_asset": true + }, + "validation": "Fresh native/recovery52-array check; exact link-only code/weight guard, prior12486-array proof plus36fresh native perturbation arrays for exact research-cache derivation; all200 validation tracks and13 official variant scores. Unaffected10family and8variant reports must equalparent before promotion.", + "caveat": "Fixed marginal prior correction is approximate for per-example class weighting, and the verifier applies its unchanged additional sigmoid to the emitted scores. Previous checkpoints show mixed variant effects; no improvement is assumed.", + "parent_native_report": "/workspace/research/studies/context43/native-control/report.json", + "parent_gain_gate": { + "previous_raw_reward": 0.42285346475207175, + "minimum_gain": 1e-06, + "failed_gate_action": "Finish with rejected_at_parent_gain_gate; do not build or evaluate the candidate." + }, + "evidence_sha256": { + "/workspace/research/studies/fixed_link57/construction-proof.json": "ec9561dda0b29ab844340f1a9486173419f33848a5b43f4bbd6f2d59f3441dd7", + "/workspace/research/experiments/calib46/evaluation.json": "adc6b1f35d643d044dcfc4acc53d861d131f7a79b2f34896e3b51cde00e9e876", + "/workspace/research/studies/fixed_link46/matched-comparison.json": "00565bd9b9f26479b9cdf2926814287cc0ed6779d6bc1ad2ee1aa3e31728b490", + "/workspace/research/analysis/calib46-vs-small-epoch02-splice-variants.json": "60e1ef096b62482b87ad416bde3d5c87fb92db199143e8eb8f05d0baef341579", + "/workspace/research/experiments/scale13-small-epoch02/valid-score/report.json": "db460f3cc22b24250402b8c8362983a0c456d4b1c466675c40673f19391c2302", + "/workspace/research/experiments/scale13-small/epoch-03.pt": "6e9091a83e34d161017bb7662329d849ce2bd54fe3dd9870336645a63da1f71f" + }, + "motivation": "Calib46 completed with +.021514reward solelyfromsites and five positive splicevariant pointchanges; onlyMFASS pairedintervals excludezero bothschemes. Test transferof the exact frozen trainingprior to the next completed stronger rawcheckpoint. Do not refit or assume variant gains." +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/memory-check-interruption.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/memory-check-interruption.json new file mode 100644 index 0000000000000000000000000000000000000000..19bc02cd2784b11f5f3e658efa28017639c2efeb --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/memory-check-interruption.json @@ -0,0 +1,19 @@ +{ + "time_utc": "2026-10-24T21:49:06.154124+00:00", + "reason": "Mechanical activation-checkpoint memory/equivalence check; optimizer, data order and model math preserved.", + "process_group": 4169, + "checkpoints": [ + { + "run": "scale13-small", + "step": 1000, + "path": "/workspace/research/experiments/scale13-small/step-001000.pt", + "sha256": "a53ea6d92c21c06576c734dc05eac6e3f21d33701f25bfc335817a6e6ec197fc" + }, + { + "run": "scale13-large", + "step": 1000, + "path": "/workspace/research/experiments/scale13-large/step-001000.pt", + "sha256": "7398dd28297d6cc3134b8d3b0cea703fb0bb0a8e60b4c0c2d81abe04c1d7dc43" + } + ] +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/migrate_scale_bytecache.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/migrate_scale_bytecache.py new file mode 100644 index 0000000000000000000000000000000000000000..05b927345326f5b660fa5b28118ce867eee97c88 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/migrate_scale_bytecache.py @@ -0,0 +1,235 @@ +"""Checkpoint-safe opt-in byte-cache amendment, preserving an active evaluation.""" +import argparse +from datetime import datetime, timezone +import fcntl +import hashlib +import json +import os +from pathlib import Path +import shutil +import signal +import sys +import time + +import torch + + +def digest(path): + with Path(path).open('rb') as stream: + return hashlib.file_digest(stream, 'sha256').hexdigest() + + +def identity(pid): + p = Path(f'/proc/{pid}') + stat = (p/'stat').read_text().split(') ', 1)[1].split() + return {'pid': pid, 'starttime_ticks': int(stat[19]), + 'cmdline_sha256': hashlib.sha256((p/'cmdline').read_bytes()).hexdigest()} + + +def command(pid): + return Path(f'/proc/{pid}/cmdline').read_bytes().replace(b'\0', b' ').decode() + + +def alive(pid): + try: + return Path(f'/proc/{pid}/stat').read_text().split(') ', 1)[1][0] not in 'ZX' + except FileNotFoundError: + return False + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('plan', type=Path) + args = p.parse_args() + plan = json.loads(args.plan.read_text()) + r = Path('/workspace/research') + root = args.plan.parent + os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) + torch.set_num_threads(1) + for name, value in plan['helper_sha256'].items(): + assert digest(r/'seqfunc'/name) == value, name + assert digest(plan['original_study']) == plan['original_study_sha256'] + original = json.loads(Path(plan['original_study']).read_text()) + prepared = json.loads(Path(plan['preparation']).read_text()) + integrated = json.loads(Path(plan['integration_check']).read_text()) + timing = json.loads(Path(plan['timing_report']).read_text()) + assert integrated['all_returned_training_ids_and_tensor_bytes_exact'] and integrated['examples'] == 16 + assert integrated['public_inference_and_validation_cache_disabled_despite_environment'] + assert timing['cache_over_reference'] < .8 and timing['all_probe_updates_discarded'] + source = Path(prepared['new_source']) + for name, value in prepared['new_files_sha256'].items(): + assert digest(source/name) == value, name + for path in Path(original['source']).rglob('*'): + if path.is_file() and '__pycache__' not in path.parts and '.pytest_cache' not in path.parts: + relative = path.relative_to(original['source']) + if str(relative) != 'reference/alphagenome_50m/dataset.py': + assert digest(path) == digest(source/relative), str(relative) + old_controller = plan['old_controller_pid'] + assert 'run_scale_study.py' in command(old_controller) + assert os.getpgid(old_controller) != os.getpgrp() + processes = json.loads((root/'study-state.json').read_text()) + for name, target in plan['boundaries'].items(): + assert target % 1000 == 0 and target % 4 == 0 + pid = processes[name+'-train']['pid'] + assert str(Path(original['source'])/'train_batched.py') in command(pid) + assert str(r/'experiments'/name) in command(pid) + stopped, snapshots, seen = set(), {}, {} + killed = False + controller_stopped = False + begun = time.monotonic() + + def status(phase, **extra): + record = {'phase': phase, 'utc': datetime.now(timezone.utc).isoformat(), + 'boundaries': plan['boundaries'], 'checkpoints': snapshots, **extra} + temporary = root/'cached-io-migration-state.tmp' + temporary.write_text(json.dumps(record, indent=2)+'\n') + temporary.replace(root/'cached-io-migration-state.json') + print(json.dumps(record), flush=True) + + def interrupted(signum, frame): + raise SystemExit(f'Migration interrupted by signal {signum}') + signal.signal(signal.SIGTERM, interrupted) + signal.signal(signal.SIGINT, interrupted) + status('waiting_for_saved_boundaries') + try: + while len(snapshots) < len(plan['boundaries']): + assert time.monotonic()-begun < 3600, 'Boundary wait exceeded one hour' + for name, target in plan['boundaries'].items(): + if name in snapshots: + continue + pid = processes[name+'-train']['pid'] + assert alive(pid), f'Trainer {name} exited before capture' + progress = r/'experiments'/name/'progress.pt' + with progress.open('rb') as stream: + stat = os.fstat(stream.fileno()) + signature = (stat.st_ino, stat.st_mtime_ns, stat.st_size) + if signature == seen.get(name): + continue + state = torch.load(f'/proc/self/fd/{stream.fileno()}', mmap=True, + map_location='cpu', weights_only=True) + step = state['global_step'] + assert step <= target, f'{name} missed boundary at {step}' + seen[name] = signature + if step == target: + assert state['optimizer_steps'] == step//4 + assert state['objective'] == 'variance_balanced' + os.kill(pid, signal.SIGSTOP) + stopped.add(pid) + destination = progress.with_name(f'bytecache-resume-{step:06d}.pt') + assert not destination.exists() + temporary = destination.with_suffix('.tmp') + stream.seek(0) + with temporary.open('wb') as output: + shutil.copyfileobj(stream, output, length=8*1024*1024) + temporary.replace(destination) + snapshots[name] = {'step': step, 'path': str(destination), + 'sha256': digest(destination), 'single_inode_copy': True} + status('captured_saved_boundary', trainer=name) + del state + time.sleep(1) + # All pending later evaluations still wait for checkpoints. Preserve the + # already staged small epoch2 evaluation, which belongs to the old reader. + keep = 'scale13-small-eval02' + adopted = {} + keep_directory = r/'experiments/scale13-small-epoch02' + if json.loads((keep_directory/'pipeline-state.json').read_text())['phase'] != 'complete': + adopted[keep] = identity(processes[keep]['pid']) + assert 'run_candidate.py' in command(processes[keep]['pid']) + terminate_waiters = [] + for name, record in processes.items(): + if '-eval' not in name or name in adopted or record['returncode'] is not None: + continue + pid = record['pid'] + directory = r/'experiments'/name.replace('-eval', '-epoch') + if json.loads((directory/'pipeline-state.json').read_text())['phase'] == 'complete': + continue + assert json.loads((directory/'pipeline-state.json').read_text())['phase'] == 'waiting_for_checkpoint' + assert not Path(f'/proc/{pid}/task/{pid}/children').read_text().strip() + terminate_waiters.append(pid) + # Keep this old session alive until its adopted evaluation finishes. + # Its monitor is stopped, so only the new controller owns training status. + old_identity = identity(old_controller) + os.kill(old_controller, signal.SIGSTOP) + controller_stopped = True + worker_pids = [] + for name in plan['boundaries']: + pid = processes[name+'-train']['pid'] + worker_pids += [int(p) for p in Path(f'/proc/{pid}/task/{pid}/children').read_text().split()] + terminated = terminate_waiters+worker_pids+[processes[name+'-train']['pid'] for name in plan['boundaries']] + status('replacing_trainers_and_idle_waiters', preserved_evaluations=adopted, terminated_pids=terminated) + for pid in terminated: + if alive(pid): + os.kill(pid, signal.SIGTERM) + os.kill(pid, signal.SIGCONT) + killed = True + stopped.clear() + deadline = time.monotonic()+60 + while any(alive(pid) for pid in terminated): + assert time.monotonic() < deadline, 'Old training or idle waiter did not exit' + time.sleep(1) + for name, record in snapshots.items(): + assert digest(r/'experiments'/name/'progress.pt') == record['sha256'] + amendment = {'adopted_utc': datetime.now(timezone.utc).isoformat(), + 'change': 'Optional verified compressed-byte cache, training only; every hit checks source-byte digest, key and length.', + 'source_before': original['source'], 'source_after': str(source), + 'checkpoints': snapshots, 'cache_root': plan['cache_root'], + 'model_optimizer_rng_exact_at_each_resume': True, + 'model_trainer_loss_sources_data_order_objective_batch_schedule_unchanged': True, + 'different_resume_boundaries_are_IO_only': True, + 'integration_check': plan['integration_check'], 'timing_report': plan['timing_report'], + 'timing_cache_over_reference': timing['cache_over_reference'], + 'timing_caveat': timing['caveats'], + 'cache_not_an_inference_asset': True, + 'adopted_evaluations_without_interruption': adopted, + 'old_monitor_retained_stopped_until_adopted_evaluation_finishes': old_identity} + new = dict(original) + new.update(source=str(source), training_bytecache_root=plan['cache_root'], + cache_execution_amendment=amendment, adopted_evaluations=adopted, + retired_monitor_identity=old_identity) + new['execution_amendments'] = list(original.get('execution_amendments', []))+[amendment] + new['provenance'] += (' At the saved small96k/large78k boundaries, an optional training-only byte cache ' + 'was enabled after exact256example and integrated16example tensor/ID checks. ' + 'Source compressed bytes are verified on every cache hit, with read-only source fallback. ' + 'Model/trainer/loss code, complete epoch permutations, batch and schedule are unchanged. ' + 'The cache is disposable and is disabled for validation/public inference.') + history = source/'training_history' + (history/'bytecache-io-amendment.json').write_text(json.dumps(amendment, indent=2)+'\n') + for name in ['preparation', 'integration_check', 'timing_report']: + path = Path(plan[name]);shutil.copyfile(path, history/('bytecache-'+name+'.json')) + shutil.copyfile(args.plan, history/'bytecache-migration-plan.json') + shutil.copyfile(Path(__file__), history/Path(__file__).name) + shutil.copyfile(r/'seqfunc/run_scale_study_cached_io.py', history/'run_scale_study_cached_io.py') + new_path = root/'study-cached-io.json' + new_path.write_text(json.dumps(new, indent=2)+'\n') + manifest = {str(p.relative_to(source)): digest(p) for p in sorted(source.rglob('*')) + if p.is_file() and '__pycache__' not in p.parts and '.pytest_cache' not in p.parts} + (root/'source_manifest-cached-io.json').write_text(json.dumps(manifest, indent=2)+'\n') + (root/'cached-io-amendment.json').write_text(json.dumps(amendment, indent=2)+'\n') + with Path('/workspace/.submission-promotion.lock').open('a') as lock: + fcntl.flock(lock, fcntl.LOCK_EX) + p = r/'STATE.json'; state = json.loads(p.read_text()) + state['scale_study'].update(plan=str(new_path), source=str(source), + controller_pid=os.getpid(), status='Resuming original trajectories with verified opt-in training byte cache', + cached_reader_amendment=str(root/'cached-io-amendment.json'), + previous_controller_retained_stopped=old_controller) + state['scale_study']['session'] = state['scale_study']['bytecache_migration']['session'] + state['scale_study']['bytecache_migration']['status'] = 'Checkpoints captured; replacement controller starting' + temporary=p.with_suffix('.tmp');temporary.write_text(json.dumps(state,indent=2)+'\n');temporary.replace(p) + with (r/'LEDGER.md').open('a') as ledger: + ledger.write('\n'+amendment['adopted_utc']+': saved small96k/large78k trajectories resumed with optional verified training byte cache; original scientific plans unchanged. Active small epoch2 evaluation preserved. Original study/source/checkpoints retained; full amendment at studies/scale13/cached-io-amendment.json.\n') + status('replacement_controller_starting', source=str(source), new_study=str(new_path)) + log = (root/'study-cached-io.log').open('a') + os.dup2(log.fileno(), 1);os.dup2(log.fileno(), 2) + os.execv(sys.executable,[sys.executable,'-u',str(r/'seqfunc/run_scale_study_cached_io.py'),str(new_path)]) + except BaseException as error: + status('failed_or_interrupted', error=repr(error), old_trainers_terminated=killed) + raise + finally: + if not killed: + for pid in stopped: + if alive(pid):os.kill(pid,signal.SIGCONT) + if controller_stopped and alive(old_controller):os.kill(old_controller,signal.SIGCONT) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/model_before_block_checkpoint.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/model_before_block_checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..31f17eb68837a9caa6492ec862feda39d068a43f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/model_before_block_checkpoint.py @@ -0,0 +1,457 @@ +"""Affordable multi-resolution baseline for the compact AlphaGenome task.""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass, fields +import math +from pathlib import Path +from typing import Any, Iterable + +import torch +from torch import Tensor, nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint +import yaml + +from .layers import ChannelRMSNorm, ConvBlock, SequenceBlock + + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} +ALL_FAMILIES = (*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions", "contact_maps") + + +@dataclass(frozen=True) +class ModelConfig: + input_length: int = 1_048_576 + target_length: int = 196_608 + dense_bin_size: int = 128 + trunk_bin_size: int = 1_024 + contact_bin_size: int = 2_048 + stem_stride: int = 8 + encoder_width: int = 32 + channel_growth: int = 32 + num_downsampling_blocks: int = 7 + conv_kernel_size: int = 5 + dna_kernel_size: int = 15 + hidden_dim: int = 384 + num_sequence_blocks: int = 6 + num_attention_heads: int = 6 + attention_head_dim: int = 64 + mlp_expansion: float = 4.0 + dropout: float = 0.0 + head_hidden_size: int = 256 + junction_rank: int = 32 + splice_usage_tracks: int = 734 + junction_tracks: int = 734 + contact_tracks: int = 28 + gradient_checkpointing: bool = True + enable_pairwise: bool = False + pair_width: int = 32 + pair_downsample: int = 16 + pair_update_interval: int = 2 + local_width: int = 0 + local_context_blocks: int = 4 + splice_context_mode: str = 'offset' + local_to_trunk: bool = False + residual_rms_cap: float = 0.0 + + def __post_init__(self) -> None: + if not 1 <= self.local_context_blocks <= 7: + raise ValueError('local_context_blocks must be between 1 and 7') + if not math.isfinite(self.residual_rms_cap) or self.residual_rms_cap < 0: + raise ValueError('residual_rms_cap must be finite and nonnegative') + if self.local_to_trunk and not self.local_width: + raise ValueError('local_to_trunk requires local features') + if self.splice_context_mode not in ('offset', 'uniform'): + raise ValueError('unknown splice_context_mode') + if self.splice_context_mode == 'uniform' and not self.local_width: + raise ValueError('uniform splice context requires a base-resolution path') + downsample = self.stem_stride * 2**self.num_downsampling_blocks + if downsample != self.trunk_bin_size: + raise ValueError("stem_stride * 2**num_downsampling_blocks must equal trunk_bin_size") + for length in (self.input_length, self.target_length): + if length % self.trunk_bin_size: + raise ValueError("input and target lengths must divide by trunk_bin_size") + if self.trunk_bin_size % self.dense_bin_size: + raise ValueError("trunk_bin_size must divide by dense_bin_size") + if self.input_length % self.contact_bin_size: + raise ValueError("input_length must divide by contact_bin_size") + if self.hidden_dim <= 0 or self.num_attention_heads <= 0: + raise ValueError("hidden dimensions and attention heads must be positive") + + @property + def trunk_bins(self) -> int: + return self.input_length // self.trunk_bin_size + + @property + def target_trunk_bins(self) -> int: + return self.target_length // self.trunk_bin_size + + @property + def dense_bins(self) -> int: + return self.target_length // self.dense_bin_size + + @property + def dense_upsample(self) -> int: + return self.trunk_bin_size // self.dense_bin_size + + @property + def contact_bins(self) -> int: + return self.input_length // self.contact_bin_size + + @property + def encoder_output_width(self) -> int: + return self.encoder_width + self.channel_growth * self.num_downsampling_blocks + + @classmethod + def from_yaml(cls, path: str | Path) -> "ModelConfig": + values: dict[str, Any] = yaml.safe_load(Path(path).read_text()) or {} + known = {field.name for field in fields(cls)} + unknown = sorted(values.keys() - known) + if unknown: + raise ValueError(f"Unknown model configuration keys: {unknown}") + return cls(**values) + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + +class SequenceEncoder(nn.Module): + def __init__(self, config: ModelConfig): + super().__init__() + self.stem = nn.Conv1d( + 4, + config.encoder_width, + config.dna_kernel_size, + stride=config.stem_stride, + padding=config.dna_kernel_size // 2, + ) + blocks = [] + width = config.encoder_width + for _ in range(config.num_downsampling_blocks): + output_width = width + config.channel_growth + blocks.append(ConvBlock(width, output_width, config.conv_kernel_size)) + width = output_width + self.blocks = nn.ModuleList(blocks) + + def forward(self, dna: Tensor) -> Tensor: + x = F.gelu(self.stem(dna.transpose(1, 2)), approximate="tanh") + for block in self.blocks: + x = F.max_pool1d(block(x), kernel_size=2, stride=2) + return x.transpose(1, 2) + + +class LocalResidualBlock(nn.Module): + def __init__(self, width: int, dilation: int): + super().__init__() + self.norm = ChannelRMSNorm(width) + self.depthwise = nn.Conv1d(width, width, 9, dilation=dilation, + padding=4 * dilation, groups=width) + self.expand = nn.Conv1d(width, 2 * width, 1) + self.contract = nn.Conv1d(2 * width, width, 1) + + def forward(self, x: Tensor) -> Tensor: + update = self.depthwise(self.norm(x)) + update = self.contract(F.gelu(self.expand(update), approximate='tanh')) + return x + update + + +class LocalSequencePath(nn.Module): + """Base-resolution motif path with powers-of-four dilation and aligned pooling.""" + def __init__(self, config: ModelConfig): + super().__init__() + width = config.local_width + self.stem = nn.Conv1d(4, width, 21, padding=10) + self.blocks = nn.Sequential(*(LocalResidualBlock(width, 4**i) + for i in range(config.local_context_blocks))) + self.norm = ChannelRMSNorm(width) + self.sites = nn.Conv1d(width, 4, 1) + self.bin_size = config.dense_bin_size + + def forward(self, dna: Tensor) -> tuple[Tensor, Tensor]: + base = F.gelu(self.stem(dna), approximate='tanh') + base = F.gelu(self.norm(self.blocks(base)), approximate='tanh') + sites = self.sites(base).transpose(1, 2) + pooled = torch.cat((F.avg_pool1d(base, self.bin_size), + F.max_pool1d(base, self.bin_size)), dim=1).transpose(1, 2) + return sites, pooled + + +class CompactModel(nn.Module): + """One shared 1-kb trunk with representation-specific output heads.""" + + def __init__(self, config: ModelConfig | None = None): + super().__init__() + self.config = config or ModelConfig() + self.encoder = SequenceEncoder(self.config) + self.projection = nn.Linear(self.config.encoder_output_width, self.config.hidden_dim) + self.blocks = nn.ModuleList( + SequenceBlock(self.config) for _ in range(self.config.num_sequence_blocks) + ) + self.final_norm = nn.RMSNorm(self.config.hidden_dim) + self.dense_decoder = nn.Sequential( + nn.RMSNorm(self.config.hidden_dim), + nn.Linear(self.config.hidden_dim, self.config.head_hidden_size), + nn.GELU(approximate="tanh"), + ) + self.dense_heads = nn.ModuleDict( + { + family: nn.Linear(self.config.head_hidden_size, tracks) + for family, tracks in DENSE_TRACKS.items() + } + ) + self.splice_site_head = nn.Linear( + self.config.head_hidden_size, self.config.dense_bin_size * 4 + ) + self.splice_usage_head = nn.Linear( + self.config.head_hidden_size, self.config.splice_usage_tracks + ) + rank = self.config.junction_rank + self.junction_source = nn.Linear(self.config.head_hidden_size, rank) + self.junction_destination = nn.Linear(self.config.head_hidden_size, rank) + self.junction_track = nn.Embedding(self.config.junction_tracks, rank) + if self.config.junction_tracks % 2: + raise ValueError("junction tracks must split evenly into strand halves") + self.register_buffer( + "junction_track_half", + torch.arange(self.config.junction_tracks) // (self.config.junction_tracks // 2), + persistent=False, + ) + self.contact_q = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + self.contact_k = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + row, column = torch.triu_indices( + self.config.contact_bins, self.config.contact_bins + ) + self.register_buffer("contact_row", row, persistent=False) + self.register_buffer("contact_column", column, persistent=False) + if self.config.local_width: + self.local_path = LocalSequencePath(self.config) + self.local_projection = nn.Linear(2 * self.config.local_width, self.config.head_hidden_size) + # A new local path can be attached to our own checkpoint without changing its initial outputs. + nn.init.zeros_(self.local_path.sites.weight) + nn.init.zeros_(self.local_path.sites.bias) + nn.init.zeros_(self.local_projection.weight) + nn.init.zeros_(self.local_projection.bias) + if self.config.local_to_trunk: + self.local_trunk_projection = nn.Linear(2 * self.config.local_width, self.config.hidden_dim) + nn.init.zeros_(self.local_trunk_projection.weight) + nn.init.zeros_(self.local_trunk_projection.bias) + self._local_sites = None + self._local_features = None + + def set_junction_track_halves(self, halves: Tensor) -> None: + halves = torch.as_tensor(halves, dtype=torch.long, device=self.junction_track_half.device) + if halves.shape != (self.config.junction_tracks,) or not torch.isin( + halves, torch.tensor([0, 1], device=halves.device) + ).all(): + raise ValueError("junction track halves must be a 734-element 0/1 mapping") + self.junction_track_half.copy_(halves) + + def _trunk(self, dna: Tensor) -> Tensor: + if dna.ndim != 3 or dna.shape[1:] != (self.config.input_length, 4): + raise ValueError( + f"Expected DNA [B,{self.config.input_length},4], got {tuple(dna.shape)}" + ) + if self.config.local_width: + if self.config.gradient_checkpointing and self.training: + sites, pooled = checkpoint(self.local_path, dna.transpose(1, 2), use_reentrant=False) + else: + sites, pooled = self.local_path(dna.transpose(1, 2)) + self._local_sites = sites + self._local_features = self.local_projection(pooled) + x = self.projection(self.encoder(dna)) + if self.config.local_to_trunk: + local_tokens = pooled.reshape(len(dna), self.config.trunk_bins, + self.config.dense_upsample, -1).mean(dim=2) + x = x + self.local_trunk_projection(local_tokens) + for block in self.blocks: + if self.config.gradient_checkpointing and self.training and x.requires_grad: + x = checkpoint(block, x, None, use_reentrant=False) + else: + x = block(x, None) + return self.final_norm(x) + + def _dense_features(self, trunk: Tensor) -> Tensor: + start = (self.config.trunk_bins - self.config.target_trunk_bins) // 2 + target = trunk[:, start : start + self.config.target_trunk_bins] + target = target.repeat_interleave(self.config.dense_upsample, dim=1) + features = self.dense_decoder(target) + if self.config.local_width: + first_bin = (self.config.input_length - self.config.target_length) // (2 * self.config.dense_bin_size) + features = features + self._local_features[:, first_bin:first_bin + self.config.dense_bins] + return features + + def _all_dense_features(self, trunk: Tensor) -> Tensor: + features = self.dense_decoder( + trunk.repeat_interleave(self.config.dense_upsample, dim=1) + ) + return features + self._local_features if self.config.local_width else features + + def _splice_site_logits(self, trunk: Tensor) -> Tensor: + if self.config.splice_context_mode == 'uniform': + # The global trunk supplies one gene-context bias per class and kb. Base-resolution + # localization comes from the local path, with no arbitrary 128-bp offset decoder + # and no pooled dense-feature contribution to site logits. + context = self.splice_site_head(self.dense_decoder(trunk)) + context = context.view(len(trunk), self.config.trunk_bins, self.config.dense_bin_size, 4).mean(dim=2) + logits = context.repeat_interleave(self.config.trunk_bin_size, dim=1) + self._local_sites + return logits.float() + logits = self.splice_site_head(self._all_dense_features(trunk)) + logits = logits.view(len(trunk), self.config.input_length, 4) + if self.config.local_width: + logits = logits + self._local_sites + return logits.float() + + def _position_features(self, trunk: Tensor, positions: Tensor) -> Tensor: + features = self.dense_decoder(self._gather_bins(trunk, positions, self.config.trunk_bin_size)) + if self.config.local_width: + features = features + self._gather_bins(self._local_features, positions, self.config.dense_bin_size) + return features + + @staticmethod + def select_junction_positions(site_logits: Tensor, limit: int = 512) -> Tensor: + """Select each site type from model scores alone, then sort by coordinate. + + This follows the four independent site classes in Google DeepMind's + AlphaGenome splicing implementation (pinned in docs/v3-author/PROTOCOL.md). Padding is + -1 when an input is shorter than ``limit``. + """ + count = min(limit, site_logits.shape[1]) + selected = [] + for site_type in range(4): + coordinates = torch.topk(site_logits[..., site_type], count, dim=1).indices + coordinates = coordinates.sort(dim=1).values + if count < limit: + coordinates = F.pad(coordinates, (0, limit - count), value=-1) + selected.append(coordinates) + return torch.stack(selected, dim=1) + + @staticmethod + def _gather_bins(features: Tensor, positions: Tensor, bin_size: int) -> Tensor: + indices = torch.div(positions, bin_size, rounding_mode="floor") + indices = indices.clamp(0, features.shape[1] - 1) + return torch.gather( + features, + 1, + indices[..., None].expand(-1, -1, features.shape[-1]), + ) + + def _junction_predictions( + self, trunk: Tensor, site_positions: Tensor, queries: Tensor + ) -> Tensor: + row, column, track = queries.unbind(dim=-1) + half = self.junction_track_half[track] + batch = torch.arange(len(trunk), device=trunk.device)[:, None] + donor = site_positions[batch, 2 * half, row] + acceptor = site_positions[batch, 2 * half + 1, column] + source = self._position_features(trunk, donor.clamp(0, self.config.input_length - 1)) + destination = self._position_features(trunk, acceptor.clamp(0, self.config.input_length - 1)) + interaction = self.junction_source(source) * self.junction_destination(destination) + track_embedding = self.junction_track(track) + return (interaction * track_embedding).sum(dim=-1) / math.sqrt( + self.config.junction_rank + ) + + def junction_block( + self, + trunk: Tensor, + site_positions: Tensor, + row_start: int, + row_end: int, + column_start: int, + column_end: int, + track_start: int, + track_end: int, + site_factors: tuple[Tensor, Tensor] | None = None, + ) -> Tensor: + """Compute a bounded block of the full predicted-position junction cube.""" + tracks = torch.arange(track_start, track_end, device=trunk.device) + halves = self.junction_track_half[tracks] + if site_factors is None: + site_factors = self.junction_site_factors(trunk, site_positions) + source_all, destination_all = site_factors + source = source_all[:, halves, row_start:row_end] + destination = destination_all[:, halves, column_start:column_end] + embedding = self.junction_track(tracks)[None, :, None, None, :] + values = ( + source[:, :, :, None, :] * destination[:, :, None, :, :] * embedding + ).sum(dim=-1) / math.sqrt(self.config.junction_rank) + return values.permute(0, 2, 3, 1).float() + + def junction_site_factors( + self, trunk: Tensor, site_positions: Tensor + ) -> tuple[Tensor, Tensor]: + """Precompute the two strand-specific factors reused by all cube blocks.""" + donor = torch.stack((site_positions[:, 0], site_positions[:, 2]), dim=1).clamp_min(0) + acceptor = torch.stack((site_positions[:, 1], site_positions[:, 3]), dim=1).clamp_min(0) + donor_features = self._position_features(trunk, donor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, donor.shape[-1], -1) + acceptor_features = self._position_features(trunk, acceptor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, acceptor.shape[-1], -1) + return self.junction_source(donor_features), self.junction_destination(acceptor_features) + + def forward( + self, + dna: Tensor, + *, + families: Iterable[str] | None = None, + usage_positions: Tensor | None = None, + junction_site_positions: Tensor | None = None, + junction_queries: Tensor | None = None, + ) -> dict[str, Tensor]: + requested = set(families or ALL_FAMILIES) + unknown = requested - set(ALL_FAMILIES) + if unknown: + raise ValueError(f"Unknown output families: {sorted(unknown)}") + trunk = self._trunk(dna) + needs_dense = bool( + requested.intersection((*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions")) + ) + features = self._dense_features(trunk) if needs_dense else None + outputs: dict[str, Tensor] = {} + for family in DENSE_TRACKS: + if family in requested: + outputs[family] = self.dense_heads[family](features).float() + if "splice_sites" in requested: + all_logits = self._splice_site_logits(trunk) + start = (self.config.input_length - self.config.target_length) // 2 + outputs["splice_sites"] = all_logits[:, start:start + self.config.target_length] + if "splice_usage" in requested: + if usage_positions is None: + raise ValueError("splice_usage requires usage_positions") + gathered = self._gather_bins( + features, usage_positions, self.config.dense_bin_size + ) + outputs["splice_usage"] = self.splice_usage_head(gathered).float() + if "junctions" in requested: + if junction_site_positions is None or junction_queries is None: + raise ValueError("junctions require site positions and query triples") + outputs["junctions"] = self._junction_predictions( + trunk, junction_site_positions, junction_queries + ).float() + if "contact_maps" in requested: + contact = trunk.reshape( + len(dna), self.config.contact_bins, -1, self.config.hidden_dim + ).mean(dim=2) + q, k = self.contact_q(contact), self.contact_k(contact) + row, column = self.contact_row, self.contact_column + outputs["contact_maps"] = ( + 0.5 + * ( + q[:, row] * k[:, column] + + q[:, column] * k[:, row] + ) + ).float() + return outputs + + +def count_parameters(model: nn.Module) -> int: + return sum(parameter.numel() for parameter in model.parameters() if parameter.requires_grad) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/parallel-io-amendment.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/parallel-io-amendment.json new file mode 100644 index 0000000000000000000000000000000000000000..48ca3e0d57ff557a5fd2bd2bc517508c6de05b0f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/parallel-io-amendment.json @@ -0,0 +1,27 @@ +{ + "adopted_utc": "2026-10-25T00:42:46.303812+00:00", + "example_boundary": 40000, + "change": "I/O only: direct read and four bounded chunk-read threads per loader process.", + "source_before": "/workspace/research/studies/scale13/source-block-checkpoint", + "source_after": "/workspace/research/studies/scale13/source-parallel-io", + "model_parameters_optimizer_RNG_data_order_objective_schedule_unchanged": true, + "loader_worker_counts_unchanged": true, + "common_immutable_checkpoints": { + "scale13-small": { + "step": 40000, + "path": "/workspace/research/experiments/scale13-small/io-resume-040000.pt", + "sha256": "62daa49a4dc329d0b7cf3ecea0a30834346f714079d975bc636bdd81102f38f2", + "single_inode_copy": true + }, + "scale13-large": { + "step": 40000, + "path": "/workspace/research/experiments/scale13-large/io-resume-040000.pt", + "sha256": "db6c7394de11aba07278c907f328bcf18200c01ab1fb23578b12d293eb7219f4", + "single_inode_copy": true + } + }, + "evidence": "256 full examples through four forked workers and16 integrated-source examples, every tensor byte and ordered ID identical; missing-chunk fixtures pass.", + "loader_parallel_over_reference_time": 0.5340049503280426, + "timing_caveat": "Alternating paired readers under shared training load, including worker startup, full per-example hashing and pinned tensor IPC. 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"all_ordered_ids_and_tensor_bytes_exact": true + } + ], + "total_read_hash_and_transfer_seconds": { + "reference": 69.81505457599997, + "parallel": 37.28158475100645 + }, + "parallel_over_reference_time": 0.5340049503280426, + "reader_sha256": "ad6670f742f4de9898064becc525d0aea98b402abe7b210bb1b26a9eee6402b9", + "adapter_sha256": "ab182b3b687a3b470ff1bbc311a9130cc791974366bdc0a400df5e0e28602c9c", + "caveat": "Alternating paired readers under shared training load, including worker startup, full per-example hashing and pinned tensor IPC. File caches are shared; no GPU computation included." +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/reader-before-parallel-io.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/reader-before-parallel-io.py new file mode 100644 index 0000000000000000000000000000000000000000..71cd39d15713f648e7d7e31aa1923fa4fa4800dd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/reader-before-parallel-io.py @@ -0,0 +1,229 @@ +"""Streaming reader for the compact 1 Mb / eleven-family release.""" + +from __future__ import annotations + +from collections import OrderedDict +from pathlib import Path + +import numpy as np +import pandas as pd +import pysam +import torch +from torch.utils.data import Dataset +import zarr + + +def _open_part(path: Path): + if str(path).endswith(".zarr.zip"): + store = zarr.ZipStore(str(path), mode="r") + try: + return zarr.open_group(store=store, mode="r"), store + except BaseException: + store.close() + raise + return zarr.open_group(str(path), mode="r"), None + + +DENSE_FAMILIES = ( + "atac", + "dnase", + "procap", + "cage", + "rna_seq", + "chip_tf", + "chip_histone", +) +def normalize_splice_positions(value) -> np.ndarray: + """Normalize Arrow's nested-list representation to the public [4, 512] array.""" + + outer = np.asarray(value, dtype=object).reshape(-1).tolist() + positions = np.stack([np.asarray(item, dtype=np.int64) for item in outer]) + if positions.shape != (4, 512): + raise ValueError(f"splice_site_positions must have shape (4, 512), got {positions.shape}") + return positions + + +class AlphaGenomeDataset(Dataset): + """Resolve frozen coordinates to reference DNA and one Zarr example group.""" + + def __init__( + self, + data_root: str | Path, + split: str, + *, + max_open_parts: int = 8, + include_targets: bool = True, + ) -> None: + self.root = Path(data_root) + self.split = split + manifest = self.root / "public" / f"{split}.parquet" + if not manifest.is_file(): + raise FileNotFoundError(f"Missing materialized manifest: {manifest}") + self.rows = pd.read_parquet(manifest).reset_index(drop=True) + junction_tracks = pd.read_parquet(self.root / "public/junction_tracks.parquet") + junction_tracks = junction_tracks.sort_values("tensor_index") + self.junction_track_halves = torch.from_numpy( + junction_tracks.junction_channel_half.to_numpy(np.int64) + ) + if include_targets: + labels_path = self.root / "labels" / f"{split}.parquet" + if not labels_path.is_file(): + raise FileNotFoundError(f"Missing trusted labels manifest: {labels_path}") + labels = pd.read_parquet(labels_path).reset_index(drop=True) + if self.rows.example_id.astype(str).tolist() != labels.example_id.astype(str).tolist(): + raise ValueError("public and labels manifests have different IDs/order") + self.rows = self.rows.merge(labels, on="example_id", validate="one_to_one", sort=False) + self.fasta_path = self.root / "reference" / "GRCh38.p13.genome.fa" + self.max_open_parts = max_open_parts + self.include_targets = include_targets + self._fasta = None + self._parts: OrderedDict[str, tuple[zarr.Group, object | None]] = OrderedDict() + + def __len__(self) -> int: + return len(self.rows) + + def _reference(self): + if self._fasta is None: + self._fasta = pysam.FastaFile(str(self.fasta_path)) + return self._fasta + + def _part(self, relative: str): + if relative in self._parts: + opened = self._parts.pop(relative) + self._parts[relative] = opened + return opened[0] + group, store = _open_part(self.root / relative) + if len(self._parts) >= self.max_open_parts: + _, (_, old_store) = self._parts.popitem(last=False) + if old_store is not None: + old_store.close() + self._parts[relative] = (group, store) + return group + + @staticmethod + def _one_hot(sequence: str) -> np.ndarray: + encoded = np.frombuffer(sequence.upper().encode("ascii"), dtype=np.uint8) + output = np.zeros((len(encoded), 4), dtype=np.float32) + for index, letter in enumerate(b"ACGT"): + output[:, index] = encoded == letter + return output + + def _sequence(self, row) -> tuple[np.ndarray, np.ndarray]: + reference = self._reference() + chromosome_length = reference.get_reference_length(str(row.chromosome)) + start, end = int(row.input_start), int(row.input_end) + clipped_start, clipped_end = max(0, start), min(chromosome_length, end) + sequence = reference.fetch(str(row.chromosome), clipped_start, clipped_end) + left, right = clipped_start - start, end - clipped_end + if left or right: + sequence = "N" * left + sequence + "N" * right + valid = np.ones(end - start, dtype=bool) + if left: + valid[:left] = False + if right: + valid[-right:] = False + return self._one_hot(sequence), valid + + @staticmethod + def _decode(example, name: str, *, asinh: bool) -> np.ndarray: + """Train targets may be stored as 8-bit codes with per-track ranges; they are decoded + here, one example at a time, so the release stays compressed on disk.""" + array = example[name] + if array.dtype != np.uint8: + return np.asarray(array, dtype=np.float32) + lo = np.asarray(example[f"{name}_lo"], dtype=np.float32) + hi = np.asarray(example[f"{name}_hi"], dtype=np.float32) + x = lo + np.asarray(array, dtype=np.float32) * ((hi - lo) / 255.0) + return np.sinh(x) if asinh else x + + @staticmethod + def _dense_target(example, family: str) -> dict[str, torch.Tensor]: + values = AlphaGenomeDataset._decode(example, f"{family}/values", asinh=True) + mask = np.asarray(example[f"{family}/mask"], dtype=bool) + if mask.ndim == 1: + mask = np.broadcast_to(mask[None, :], values.shape).copy() + return { + "values": torch.from_numpy(values), + "mask": torch.from_numpy(mask), + } + + def __getitem__(self, item: int): + row = self.rows.iloc[item] + dna, input_mask = self._sequence(row) + result = { + "example_id": str(row.example_id), + "dna": torch.from_numpy(dna), + "input_mask": torch.from_numpy(input_mask), + "junction_track_halves": self.junction_track_halves, + } + if not self.include_targets: + return result + + part = self._part(str(row.target_shard)) + example = part[str(row.target_group)] + result["dense"] = { + family: self._dense_target(example, family) for family in DENSE_FAMILIES + } + result["splice_sites"] = torch.from_numpy( + np.asarray(example["splice_sites/values"], dtype=np.float32) + ) + result["splice_usage"] = { + "positions": torch.from_numpy( + np.asarray(example["splice_site_usage/positions"], dtype=np.int64) + ), + "values": torch.from_numpy( + np.asarray(example["splice_site_usage/values"], dtype=np.float32) + ), + "mask": torch.from_numpy( + np.asarray(example["splice_site_usage/mask"], dtype=bool) + ), + } + result["usage_positions"] = result["splice_usage"]["positions"] + result["junctions"] = { + name: torch.from_numpy( + np.asarray( + example[f"splice_junctions/{name}"], + dtype=np.float32 if name == "value" else np.int64, + ) + ) + for name in ("row", "column", "track", "value") + } + result["junctions"]["site_positions"] = torch.from_numpy( + np.asarray(example["splice_junctions/site_positions"], dtype=np.int64) + ) + site_positions = normalize_splice_positions(row.splice_site_positions) + result["site_positions"] = torch.from_numpy(site_positions) + if not torch.equal(result["junctions"]["site_positions"], result["site_positions"]): + raise ValueError(f"public/private splice positions differ for {row.example_id}") + scoring_path = self.root / "scoring" / self.split / f"part-{item:06d}.parquet" + scoring = pd.read_parquet(scoring_path) + if not (scoring.example_id.astype(str) == str(row.example_id)).all(): + raise ValueError(f"scoring part ID differs for {row.example_id}") + result["junction_scoring"] = { + "donor_position": torch.from_numpy( + scoring.donor_position.to_numpy(np.int64) + ), + "acceptor_position": torch.from_numpy( + scoring.acceptor_position.to_numpy(np.int64) + ), + "track": torch.from_numpy(scoring.track.to_numpy(np.int64)), + "target_value": torch.from_numpy( + scoring.target_value.to_numpy(np.float32) + ), + "valid": torch.from_numpy(scoring.valid.to_numpy(bool)), + } + result["contact_maps"] = torch.from_numpy( + self._decode(example, "contact_maps/upper_triangle", asinh=False) + ) + result["contact_map_mask"] = torch.from_numpy( + np.asarray(example["contact_maps/mask"], dtype=bool) + ) + return result + + +def single_example_collate(items): + """Keep variable-size event tensors intact; the reference currently trains at batch one.""" + + if len(items) != 1: + raise ValueError("The compact event loader currently requires batch_size=1") + return items[0] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/run_scale_study_cached_io.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/run_scale_study_cached_io.py new file mode 100644 index 0000000000000000000000000000000000000000..3a64b3a086363780b355fe0c827bab67bc691b68 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_history/run_scale_study_cached_io.py @@ -0,0 +1,151 @@ +"""Train two locally initialized scales on an identical recipe and evaluate selected full epochs. + +This script requires a pre-reviewed study.json; it never chooses scientific options itself. +It resumes checkpoints and preserves an independently replayable package for every evaluation. +""" +import argparse +import hashlib +import json +import os +from pathlib import Path +import subprocess +import sys +import time +import torch + +p = argparse.ArgumentParser(description=__doc__) +p.add_argument('study', type=Path) +a = p.parse_args() +plan = json.loads(a.study.read_text()) +assert plan.get('training_bytecache_root'), 'This amended runner requires an explicit training-only cache path' +assert plan['schema'] == 'seqfunc-matched-scale/v1' +assert plan['pretrained'] is False and plan['examples_per_epoch'] == 41020 +assert len(plan['runs']) == 2 and plan['shared']['batch_size'] == 4 +r = Path('/workspace/research') +here = Path(__file__).resolve().parent +source = Path(plan.get('source', r/'seqfunc-scale')) +sys.path.insert(0, str(source)) +from reference.alphagenome_50m.model import ModelConfig +configs = [ModelConfig.from_yaml(run['config']).to_dict() for run in plan['runs']] +differences = {key for key in configs[0] if configs[0][key] != configs[1][key]} +assert differences and differences <= {'encoder_width', 'channel_growth', 'hidden_dim', + 'num_sequence_blocks', 'num_attention_heads', 'head_hidden_size', + 'local_width'} +env = dict(os.environ, OPENBLAS_NUM_THREADS='1', OMP_NUM_THREADS='2', MKL_NUM_THREADS='2') +os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) +env.pop('SEQFUNC_TRAIN_BYTECACHE', None) +children, handles = {}, [] + +class AdoptedEvaluation: + def __init__(self, identity, directory): + self.identity, self.directory = identity, directory + self.pid = identity['pid'] + def poll(self): + try: + path = Path(f'/proc/{self.pid}') + stat = (path/'stat').read_text().split(') ', 1)[1].split() + active = (stat[0] not in 'ZX' and int(stat[19]) == self.identity['starttime_ticks'] + and hashlib.sha256((path/'cmdline').read_bytes()).hexdigest() == self.identity['cmdline_sha256']) + except OSError: + active = False + if active: + return None + try: + done = json.loads((self.directory/'pipeline-state.json').read_text())['phase'] == 'complete' + except (OSError, KeyError, json.JSONDecodeError): + done = False + return 0 if done else 1 + @property + def returncode(self): + return self.poll() + + +def launch(name, arguments, logfile, *, training=False): + handle = logfile.open('a') + handles.append(handle) + child_environment = dict(env) + if training: + child_environment['SEQFUNC_TRAIN_BYTECACHE'] = plan['training_bytecache_root'] + proc = subprocess.Popen([sys.executable, *map(str, arguments)], env=child_environment, + stdout=handle, stderr=subprocess.STDOUT) + children[name] = proc + print(json.dumps({'event': 'study_launch', 'name': name, 'pid': proc.pid}), flush=True) + +for run in plan['runs']: + directory = Path(run['output']) + directory.mkdir(parents=True, exist_ok=True) + checkpoints = [directory/name for name in ['progress.pt', 'last.pt']] + epochs = sorted(directory.glob('epoch-*.pt')) + if epochs: + checkpoints.append(epochs[-1]) + available = [(torch.load(path, map_location='cpu', weights_only=True)['global_step'], path) + for path in checkpoints if path.exists()] + step, checkpoint = max(available) if available else (0, None) + original = plan['cache_execution_amendment']['checkpoints'][run['name']] + assert step >= original['step'] + if step == original['step']: + checkpoint = Path(original['path']) + with checkpoint.open('rb') as stream: + assert hashlib.file_digest(stream, 'sha256').hexdigest() == original['sha256'] + shared = plan['shared'] + if step < shared['epochs']*plan['examples_per_epoch']: + args = [source/'train_batched.py', '--output', directory, '--config', run['config'], + '--workers', run['workers'], '--threads', 2, '--save-every', 1000, + '--keep-every', 0, '--log-every', 100] + for key in ['batch_size', 'epochs', 'seed', 'learning_rate', 'min_learning_rate', 'warmup', 'objective']: + args += ['--'+key.replace('_', '-'), shared[key]] + if shared.get('loss_scales'): + args += ['--loss-scales', shared['loss_scales']] + if checkpoint: + args += ['--resume', checkpoint] + launch(run['name']+'-train', args, directory/'train.log', training=True) + for epoch in plan['evaluation_epochs']: + evaluation = directory.parent/(run['name']+f'-epoch{epoch:02d}') + evaluation.mkdir(exist_ok=True) + name = run['name']+f'-eval{epoch:02d}' + state_path = evaluation/'pipeline-state.json' + if state_path.exists() and json.loads(state_path.read_text()).get('phase') == 'complete': + print(json.dumps({'event': 'completed_evaluation_preserved', 'name': name}), flush=True) + continue + if name in plan.get('adopted_evaluations', {}): + children[name] = AdoptedEvaluation(plan['adopted_evaluations'][name], evaluation) + print(json.dumps({'event': 'evaluation_adopted_without_interruption', 'name': name, 'pid': children[name].pid}), flush=True) + continue + args = [here/'run_candidate.py', directory/f'epoch-{epoch:02d}.pt', evaluation, + '--source', source, '--step', epoch*plan['examples_per_epoch'], + '--evaluation-lock', a.study.with_name('evaluation.lock'), + '--name', f"SeqFunc {run['name']} after {epoch} complete epochs", '--orientation', + '--lineage', plan['provenance']+' '+run['description']+ + ' Parameters are initialized locally with seed 17. The paired scale uses the same ' + 'batch-four interval order, objective, learning-rate schedule and full training permutations. ' + 'Only encoder/trunk/head/local-path width and transformer depth differ between scales.'] + launch(run['name']+f'-eval{epoch:02d}', args, evaluation/'pipeline.log') + +monitor_retired = False +while True: + status = {name: {'pid': proc.pid, 'returncode': proc.poll()} for name,proc in children.items()} + temporary = a.study.with_name('study-state-cached.tmp') + temporary.write_text(json.dumps(status, indent=2)+'\n') + temporary.replace(a.study.with_name('study-state-cached.json')) + if not monitor_retired and all(proc.poll() is not None for proc in children.values() + if isinstance(proc, AdoptedEvaluation)): + import signal + old = plan['retired_monitor_identity'] + try: + path = Path(f'/proc/{old["pid"]}') + stat = (path/'stat').read_text().split(') ', 1)[1].split() + if (int(stat[19]) == old['starttime_ticks'] + and hashlib.sha256((path/'cmdline').read_bytes()).hexdigest() == old['cmdline_sha256']): + os.kill(old['pid'], signal.SIGTERM) + os.kill(old['pid'], signal.SIGCONT) + except (FileNotFoundError, ProcessLookupError): + pass + monitor_retired = True + print(json.dumps({'event': 'old_monitor_retired_after_preserved_evaluation', 'pid': old['pid']}), flush=True) + if all(proc.poll() is not None for proc in children.values()): + break + time.sleep(5) +for handle in handles: + handle.close() +if any(proc.returncode for proc in children.values()): + raise RuntimeError('one or more study processes failed; inspect study-state.json and individual logs') diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_loss_scales.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_loss_scales.json new file mode 100644 index 0000000000000000000000000000000000000000..a8f484c060a0d4300b25557736abf741d6d67b15 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/training_loss_scales.json @@ -0,0 +1,32 @@ +{ + "split": "train", + "examples": 256, + "seed": 1729, + "source_moments_sha256": "7b7543c4d4d8d82ec9f9bc3f942ca65595671322f62c19391591df827d8fc588", + "training_indices_sha256": "b82df9ae11e95d9be5d6e5f85839cf23e25bbbe0a195eaee266ff52d366800dc", + "method": "reference MSE times inverse mean within-track training variance; floor0.01; splice BCE unchanged", + "mean_track_variance": { + "atac": 0.9602319499041136, + "dnase": 1.0768856864858252, + "procap": 0.3380534137610864, + "cage": 0.3114663911919963, + "rna_seq": 0.66617486157613, + "chip_tf": 2.7198812117366296, + "chip_histone": 3.405612880716664, + "contact_maps": 0.14704202117243276, + "splice_usage": 0.07913355074122039, + "junctions": 0.17919098248535248 + }, + "family_loss_scales": { + "atac": 1.041415045708339, + "dnase": 0.9286036694045732, + "procap": 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+ }, + "overall_mean_family_pearson": 0.4668092624950489, + "invalid_reason": null +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/validation_variants.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/validation_variants.json new file mode 100644 index 0000000000000000000000000000000000000000..258e2686194ec3165142e494e2633ae2c0de6277 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S025/validation_variants.json @@ -0,0 +1,125 @@ +{ + "eqtl_sign": { + "rows": 17052, + "perturbations": 165, + "metric": "auroc", + "model": 0.6333798643277384, + "anchors": { + "AlphaGenome": 0.8263569969634328 + } + }, + "eqtl_causality": { + "rows": 17934, + "perturbations": 174, + "metric": "auroc", + "model": 0.5343276950419807, + "anchors": { + "AlphaGenome": 0.8325427468284611 + } + }, + "eqtl_coefficient": { + "rows": 8575, + "perturbations": 165, + "metric": "spearman", + "model": 0.3015710933758804, + "anchors": { + "AlphaGenome": 0.4133539365891599 + } + }, + "sqtl_causality": { + "rows": 21340, + "perturbations": 194, + "metric": "auprc", + "model": 0.7195676024507723, + "anchors": { + "AlphaGenome": 0.8447114201904594 + } + }, + "clinvar_noncoding": { + "rows": 199, + "perturbations": 199, + "metric": "auprc", + "model": 0.3501028270496376, + "anchors": { + "AlphaGenome": 0.9409857958177288 + } + }, + "clinvar_splice_region": { + "rows": 194, + "perturbations": 194, + "metric": "auprc", + "model": 0.34707375826646103, + "anchors": { + "AlphaGenome": 0.7224791623996103 + } + }, + "clinvar_missense": { + "rows": 199, + "perturbations": 199, + "metric": "auprc", + "model": 0.30254053400113246, + "anchors": { + "AlphaGenome": 0.373826292978903 + } + }, + "mfass": { + "rows": 195, + "perturbations": 195, + "metric": "auprc", + "model": 0.3506289408114316, + "anchors": { + "AlphaGenome": 0.7342557141920668 + } + }, + "caqtl": { + "rows": 198, + "perturbations": 198, + "metric": "auprc", + "model": 0.16564391189385333, + "anchors": { + "AlphaGenome": 0.8075760167741196 + } + }, + "bqtl_spi1": { + "rows": 160, + "perturbations": 160, + "metric": "auprc", + "model": 0.19233754977647055, + "anchors": { + "AlphaGenome": 0.8167450781363793 + } + }, + "enhancer_gene": { + "rows": 200, + "perturbations": 200, + "metric": "auprc", + "model": 0.42809921894538255, + "anchors": { + "AlphaGenome": 0.9594307235541584 + } + }, + "traitgym_mendelian": { + "rows": 292, + "perturbations": 292, + "metric": "auprc", + "model": 0.1721468898124096, + "anchors": { + "Borzoi_L2_L2.plus.all": 0.5026323772738239, + "Enformer_L2_L2.plus.all": 0.5452938267693526, + "CADD.plus.RawScore": 0.7189111372856957, + "GPN-MSA_absLLR.plus.score": 0.6729149139043132 + } + }, + "traitgym_complex": { + "rows": 299, + "perturbations": 299, + "metric": "auprc", + "model": 0.21881652196092666, + "anchors": { + "Borzoi_L2_L2.plus.all": 0.294501577162467, + "Enformer_L2_L2.plus.all": 0.269227301087889, + "CADD.plus.RawScore": 0.41341570280137435, + "GPN-MSA_absLLR.plus.score": 0.4213971415058371 + } + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/README.md b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/README.md new file mode 100644 index 0000000000000000000000000000000000000000..47c16eaab165c08e261c5ab579c92a0f6418b552 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/README.md @@ -0,0 +1,82 @@ +# SeqFunc RC model with selective linear feature decoding + +One model with **13,290,504 unique learned parameters** reads 1,048,576 bp and serves +all eleven families, 5,930 tracks and both offline entrypoints. It uses the same fixed +equal forward/reverse-complement views as RC108. The only new operation is the exact +decoder from experiment111: global features are interpolated at bin centers from +1 kb to128 bp for dense, splice-usage and junction outputs. The original nearest +splice classifier and contact path remain unchanged. No weights or constants are fitted. + +The architecture has a strided convolutional encoder, six width384 transformer blocks +over1,024 global tokens, a width32 base-resolution path with a21-bp stem and six dilated +residual blocks (10,941-bp receptive field), and a local-to-global bridge. Attention and +MLP updates use RMS cap0.1. Dense outputs cover the central196,608bp; contacts and junctions +use low-rank factors. Each orientation applies the four inherited training-prior splice +offsets before averaging probabilities. Dense and contact values average in asinh space. +Sparse coordinates are predicted independently in each view; the union treats an unlisted +view as zero, keeping at most4,096 usage positions and16,384 junction pairs. + +All weights originate from local random initialization, seed17, with **328,160 examples / +82,040 AdamW updates**, eight complete passes through all41,020 FOLD_0 training intervals +in folds2–7. The supplied reader decodes quantized targets per example. Batch4 BF16 training +uses fixed family-variance scales estimated from256 training windows and balanced splice +BCE. AdamW uses weight decay1e-4, clipping5, and the original14-pass cosine schedule from +2e-4 to2e-5 with4,000 warmup examples. The source checkpoint records73,897.9978 accumulated +training-loop seconds (20.53h) on one shared H10080GB within22CPUs. Setup/evaluation cost is +additional. Training recovery preserves checked saved state/order but is not bit-identical. +This candidate adds zero parameters and zero training updates. Restore the recorded +pre-decoder model source to replay the inherited training recipe. + +Run `infer.sh INPUT_ROOT OUTPUT_ROOT` or `perturb.sh INPUT_ROOT OUTPUT_ROOT` offline. +The runtime archive vendors CPython3.11, PyTorch2.7.1/CUDA12.6 and dependencies; the host +supplies Linux, bash/tar/gzip and the NVIDIA driver. Both entrypoints use public manifests +and the pinned reference only, with no targets, supplied splice coordinates, prediction +cache, extra flanking sequence or network access. Track arrays use float16 after float32 +orientation averaging; perturbation arrays remain float32. + +Candidate validation is pending. Its own52-array native/recovery check, six unchanged +native site/contact arrays versus RC108, full200 validation windows, all2,697 development +queries/all13 metrics with same-row anchors,624 native timing arrays, and all three4h +runtime estimates are required. Splice-site/contact family reports and five splice-based +variant reports must exactly match RC108. An all11/all13 paired comparison to RC108 is +required. Parent scores are not candidate scores and gains are not assumed additive. + +Limitations include one-seed exploratory development selection, pooled local features, +finite local context, sparse-union dilution, low-rank contacts and uneven variant effects. +Fifty-four GTEx RNA heads lack direct target supervision and are excluded from scored RNA +means; eQTL scoring uses supervised ENCODE RNA tracks. The model has no conservation input. +Interval overlap with zero does not show equivalence; sampled runtimes are not upper bounds. + +## Measured validation results + +Family-mean Pearson: **0.512570** on 200 validation intervals. + +| Family | Pearson | +|---|---:| +| atac | 0.597240 | +| dnase | 0.671872 | +| procap | 0.558732 | +| cage | 0.340538 | +| rna_seq | 0.645803 | +| chip_tf | 0.475089 | +| chip_histone | 0.509936 | +| splice_sites | 0.431237 | +| splice_usage | 0.632849 | +| junctions | 0.404156 | +| contact_maps | 0.370824 | + +| Development evaluation | Metric | Model | Published anchors on the same rows | +|---|---|---:|---| +| eqtl_sign | auroc | 0.5054 | AlphaGenome: 0.8264 | +| eqtl_causality | auroc | 0.5739 | AlphaGenome: 0.8325 | +| eqtl_coefficient | spearman | -0.0213 | AlphaGenome: 0.4134 | +| sqtl_causality | auprc | 0.7607 | AlphaGenome: 0.8447 | +| clinvar_noncoding | auprc | 0.6116 | AlphaGenome: 0.9410 | +| clinvar_splice_region | auprc | 0.6469 | AlphaGenome: 0.7225 | +| clinvar_missense | auprc | 0.2667 | AlphaGenome: 0.3738 | +| mfass | auprc | 0.4521 | AlphaGenome: 0.7343 | +| caqtl | auprc | 0.1527 | AlphaGenome: 0.8076 | +| bqtl_spi1 | auprc | 0.2084 | AlphaGenome: 0.8167 | +| enhancer_gene | auprc | 0.4671 | AlphaGenome: 0.9594 | +| traitgym_mendelian | auprc | 0.1848 | Borzoi_L2_L2.plus.all: 0.5026; Enformer_L2_L2.plus.all: 0.5453; CADD.plus.RawScore: 0.7189; GPN-MSA_absLLR.plus.score: 0.6729 | +| traitgym_complex | auprc | 0.2247 | Borzoi_L2_L2.plus.all: 0.2945; Enformer_L2_L2.plus.all: 0.2692; CADD.plus.RawScore: 0.4134; GPN-MSA_absLLR.plus.score: 0.4214 | diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/attach_bridge.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/attach_bridge.py new file mode 100644 index 0000000000000000000000000000000000000000..2c2bea67a58e19676fa73ba2a0607002ea2d5419 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/attach_bridge.py @@ -0,0 +1,48 @@ +"""Attach a zero-output local-to-global connection to our own training checkpoint.""" +import argparse +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import torch +import yaml +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + +p = argparse.ArgumentParser(description=__doc__) +p.add_argument('checkpoint', type=Path) +p.add_argument('output', type=Path) +a = p.parse_args() +s = torch.load(a.checkpoint, map_location='cpu', weights_only=True) +assert s['schema'] == 'seqfunc-single/v1' +parent_config = ModelConfig(**s['config']) +assert parent_config.local_width > 0 and not parent_config.local_to_trunk +torch.manual_seed(s['seed']) +parent = CompactModel(parent_config) +config = replace(parent_config, local_to_trunk=True) +model = CompactModel(config) +missing = model.load_state_dict(s['model'], strict=False) +assert not missing.unexpected_keys +assert set(missing.missing_keys) == {'local_trunk_projection.weight', 'local_trunk_projection.bias'} +old_names = list(dict(parent.named_parameters())) +new_names = list(dict(model.named_parameters())) +assert new_names[:len(old_names)] == old_names +assert len(s['optimizer']['param_groups']) == 1 +group = s['optimizer']['param_groups'][0] +assert group['params'] == list(range(len(old_names))) +group['params'] = list(range(len(new_names))) +with a.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() +intervention = {'kind': 'local_to_global_trunk', 'parent_sha256': digest, + 'step': s['global_step'], 'initial_outputs_unchanged': True, + 'computation': 'average aligned local features into 1-kb tokens, zero linear projection, add before transformer'} +history = list(s.get('interventions', [])) +if s.get('intervention') and s['intervention'] not in history: + history.append(s['intervention']) +history.append(intervention) +s.update(config=config.to_dict(), model=model.state_dict(), intervention=intervention, interventions=history) +a.output.mkdir(parents=True, exist_ok=False) +(a.output/'config.yaml').write_text(yaml.safe_dump(config.to_dict())) +(a.output/'intervention.json').write_text(json.dumps(intervention, indent=2) + '\n') +_atomic_save(s, a.output/'start.pt') +print(json.dumps({'parameters': count_parameters(model), **intervention}), flush=True) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/attach_local.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/attach_local.py new file mode 100644 index 0000000000000000000000000000000000000000..aa082537004dbc398c4a78c1bd0203970ce5e42f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/attach_local.py @@ -0,0 +1,52 @@ +"""Attach a zero-initialized local residual path to a checkpoint trained in this workspace.""" +import argparse +from dataclasses import replace +import hashlib +from pathlib import Path + +import torch +import yaml + +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + p.add_argument('--width', type=int, default=32) + args = p.parse_args() + state = torch.load(args.checkpoint, map_location='cpu', weights_only=True) + assert state['schema'] == 'seqfunc-single/v1', 'only locally trained single-GPU checkpoints' + parent_config = ModelConfig(**state['config']) + assert parent_config.local_width == 0 + torch.manual_seed(state['seed']) + parent = CompactModel(parent_config) + config = replace(parent_config, local_width=args.width) + model = CompactModel(config) + result = model.load_state_dict(state['model'], strict=False) + assert not result.unexpected_keys + assert all(k.startswith(('local_path.', 'local_projection.')) for k in result.missing_keys) + parent_names = list(dict(parent.named_parameters())) + names = list(dict(model.named_parameters())) + assert names[:len(parent_names)] == parent_names, 'optimizer parameter order changed' + assert len(state['optimizer']['param_groups']) == 1 + group = state['optimizer']['param_groups'][0] + assert group['params'] == list(range(len(parent_names))) + group['params'] = list(range(len(names))) + state['model'] = model.state_dict() + state['config'] = config.to_dict() + state['intervention'] = { + 'kind': 'base_resolution_local_path', 'width': args.width, + 'parent_sha256': hashlib.sha256(args.checkpoint.read_bytes()).hexdigest(), + 'attachment_step': state['global_step'], 'initial_outputs_unchanged': True, + } + args.output.mkdir(parents=True, exist_ok=False) + (args.output / 'config.yaml').write_text(yaml.safe_dump(config.to_dict())) + _atomic_save(state, args.output / 'attached.pt') + print({'parameters': count_parameters(model), **state['intervention']}) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/batched_training.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/batched_training.py new file mode 100644 index 0000000000000000000000000000000000000000..e5c609c3680baa61f04739e9f22c03a801d7b294 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/batched_training.py @@ -0,0 +1,38 @@ +"""Batch variable event queries while retaining equal-per-example reference losses.""" +import torch +from torch.nn import functional as F +from reference.alphagenome_50m.train_eval import _model_inputs, _losses +from train_single import balanced_splice_loss + +def list_collate(samples): + return samples + +def prepare_batch(samples): + """Prepare on CPU before a bulk transfer; padding never contributes to a loss.""" + prepared = [_model_inputs(sample) for sample in samples] + max_usage = max(item[0]['usage_positions'].shape[1] for item in prepared) + max_queries = max(item[0]['junction_queries'].shape[1] for item in prepared) + inputs = { + 'dna': torch.cat([x[0]['dna'] for x in prepared]), + 'usage_positions': torch.cat([F.pad(x[0]['usage_positions'], (0, max_usage-x[0]['usage_positions'].shape[1])) for x in prepared]), + 'junction_site_positions': torch.cat([x[0]['junction_site_positions'] for x in prepared]), + 'junction_queries': torch.cat([F.pad(x[0]['junction_queries'], (0, 0, 0, max_queries-x[0]['junction_queries'].shape[1])) for x in prepared]), + } + return inputs, [x[1] for x in prepared], [x[2] for x in prepared] + +def equal_example_losses(outputs, samples, junction_targets, junction_valid, *, balanced=True, scales=None): + per_example = [] + for i, (sample, target, valid) in enumerate(zip(samples, junction_targets, junction_valid)): + sliced = {name:values[i:i+1] for name,values in outputs.items()} + sliced['splice_usage'] = sliced['splice_usage'][:, :sample['usage_positions'].numel()] + sliced['junctions'] = sliced['junctions'][:, :target.shape[1]] + losses = _losses(sliced, sample, target, valid) + if balanced: + losses['splice_sites'] = balanced_splice_loss(sliced['splice_sites'], sample['splice_sites'][None]) + per_example.append(losses) + raw = {name:torch.stack([loss[name] for loss in per_example]).mean() + for name in outputs} + raw['total'] = torch.stack(list(raw.values())).mean() + scaled = {name:value * (scales or {}).get(name, 1.) for name,value in raw.items() if name != 'total'} + scaled['total'] = torch.stack(list(scaled.values())).mean() + return scaled, raw diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/change_residual_cap.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/change_residual_cap.py new file mode 100644 index 0000000000000000000000000000000000000000..cd282275702aaf5d5eede4b3904add879aad846a --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/change_residual_cap.py @@ -0,0 +1,40 @@ +"""Change only a parameter-free branch bound on our own trained checkpoint.""" +import argparse +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import torch +import yaml +from reference.alphagenome_50m.model import CompactModel, ModelConfig +from reference.alphagenome_50m.train_eval import _atomic_save + +p = argparse.ArgumentParser(description=__doc__) +p.add_argument('checkpoint', type=Path) +p.add_argument('output', type=Path) +p.add_argument('--cap', type=float, default=0.1) +a = p.parse_args() +s = torch.load(a.checkpoint, map_location='cpu', weights_only=True) +config = ModelConfig(**s['config']) +assert config.residual_rms_cap == 0 and a.cap > 0 +config = replace(config, residual_rms_cap=a.cap) +model = CompactModel(config) +model.load_state_dict(s['model'], strict=True) +assert len(s['optimizer']['param_groups'][0]['params']) == len(list(model.parameters())) +assert all(torch.equal(v, s['model'][k]) for k, v in model.state_dict().items()) +with a.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() +change = {'kind': 'bounded_transformer_residuals', 'parent_sha256': digest, + 'step': s['global_step'], 'cap': a.cap, + 'parameters_optimizer_rng_unchanged': True, + 'computation': 'each attention and MLP update multiplied by min(1, cap/token_RMS)'} +history = list(s.get('interventions', [])) +if s.get('intervention') and s['intervention'] not in history: + history.append(s['intervention']) +history.append(change) +s.update(config=config.to_dict(), intervention=change, interventions=history) +a.output.mkdir(parents=True, exist_ok=False) +(a.output/'config.yaml').write_text(yaml.safe_dump(config.to_dict())) +(a.output/'intervention.json').write_text(json.dumps(change, indent=2) + '\n') +_atomic_save(s, a.output/'start.pt') +print(json.dumps(change), flush=True) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/change_splice_context.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/change_splice_context.py new file mode 100644 index 0000000000000000000000000000000000000000..1a6927a721cd0f49f08768666b710dcdab774bd7 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/change_splice_context.py @@ -0,0 +1,39 @@ +"""Create a controlled fork that changes only the splice context computation.""" +import argparse +import hashlib +import json +from pathlib import Path +import torch +import yaml + +from reference.alphagenome_50m.model import CompactModel, ModelConfig +from reference.alphagenome_50m.train_eval import _atomic_save + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + args = p.parse_args() + state = torch.load(args.checkpoint, map_location='cpu', weights_only=True) + assert state['schema'] == 'seqfunc-single/v1' and state['config']['local_width'] > 0 + state['config']['splice_context_mode'] = 'uniform' + config = ModelConfig(**state['config']) + model = CompactModel(config) + model.load_state_dict(state['model'], strict=True) + with args.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() + state['intervention'] = { + 'kind': 'uniform_splice_context', 'parent_sha256': digest, + 'step': state['global_step'], 'parameters_and_optimizer_unchanged': True, + 'computation': 'mean legacy offset weights into a per-class global bias; remove pooled local features from site context', + } + args.output.mkdir(parents=True, exist_ok=False) + (args.output/'config.yaml').write_text(yaml.safe_dump(config.to_dict())) + (args.output/'intervention.json').write_text(json.dumps(state['intervention'], indent=2)) + _atomic_save(state, args.output/'start.pt') + print(json.dumps(state['intervention']), flush=True) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/decoder113_policy.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/decoder113_policy.json new file mode 100644 index 0000000000000000000000000000000000000000..12e3503e2e1c532cf120ff047fde442b3f3dfd08 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/decoder113_policy.json @@ -0,0 +1,26 @@ +{ + "utc": "2026-10-26T06:54:54.017445+00:00", + "parent_inference_signature": { + "last.pt": "4e81120dd5424c175e43684c0827b8a4f21e5be8b0b554f17df615ac16a027dd", + "predict.py": "4f5dbddf5b6b701079aeaf38920fe414ce980f7161997f4d5c80952a6d834e51", + "perturb.py": "49065dc43816920ecac9de3ebcc4735673e4ff6594cf3f6bd34233b88c9bed73", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/alphagenome_50m/model.py": "c61f50a2a66a4f6950c6f6107a70710a5d0ab99a291b8bfaa1eb5565998d2fa3", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919", + "splice_output.py": "e1b0627e036c60190e4d2a4653a915d9031b166ff3c1de7c23e5774009d70ef0", + "splice_calibration.json": "bd9368b32e739be65ca290d598ffcae5be6387c09f96048e183c57b7d570e641", + "rc_ensemble.py": "84a4d0e94ab86622e2c25e96f8835b2ef2bba77731ff7ff0bdc1c2de02cdcef4", + "single_prediction.py": "10f6216fdd72bf30b3a28ab6de9363e66b415526aa26a2bbfb6a84badbb67bec", + "strand_permutations.json": "afb0f0cf2e897fab3493de66601d7a66dac45a06316c2ac4a6cc828298820c60" + }, + "candidate_model_sha256": "9ddfecf4def3c0ce70a5ac8e831a22c6c5565c90719b5a36dc541acc12b7afb2", + "parameters": 13290504, + "inherited_examples": 328160, + "inherited_optimizer_steps": 82040, + "inherited_complete_passes": 8, + "additional_training_updates": 0, + "only_change": "Replace RC108 nearest global-feature decoding for dense, usage and junction heads with the exact selective linear-bin-center operation from completed111/96. Keep its original nearest splice classifier, contacts, learned tensors, four training-derived offsets and equal forward/RC views.", + "scope": "One fixed network and the same two orientation views serve every family and both entrypoints. No learned or fitted mixture, routing, new splice prior, motif mask, SWA, extra flanking DNA, variant-label training or additional SGD.", + "limitations": "Exploratory one-seed postselection. Interaction can worsen any changed readout. Full native replay, runtime and accuracy must be measured independently; unchanged paths require exact complete reports." +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/decoding_policy.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/decoding_policy.json new file mode 100644 index 0000000000000000000000000000000000000000..8adab1ac9a55de960ee5252eeb36c4b4b5bdf689 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/decoding_policy.json @@ -0,0 +1,9 @@ +{ + "kind": "junction_transcript_direction", + "parent_package": "/workspace/research/experiments/scale13-small-epoch08/raw-package", + "weights_unchanged": true, + "plus": "donor < acceptor", + "minus": "donor > acceptor", + "inference_inputs": "model-selected coordinates and public track strand halves only", + "training_evidence": "64 training windows: 30,616 positive junction queries, all in transcript direction" +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/extend_local_context.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/extend_local_context.py new file mode 100644 index 0000000000000000000000000000000000000000..3651698e8a120b3308566d05b4523733fa621d48 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/extend_local_context.py @@ -0,0 +1,70 @@ +"""Extend our trained local path with identity-initialized dilated residual blocks.""" +import argparse +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import torch +import yaml +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + +def extend(state, blocks): + old_config = ModelConfig(**state['config']) + assert old_config.local_width and blocks > old_config.local_context_blocks + config = replace(old_config, local_context_blocks=blocks) + torch.manual_seed(state['seed']) + parent = CompactModel(old_config) + model = CompactModel(config) + missing = model.load_state_dict(state['model'], strict=False) + new_prefixes = tuple(f'local_path.blocks.{i}.' for i in range(old_config.local_context_blocks, blocks)) + assert not missing.unexpected_keys + assert all(k.startswith(new_prefixes) for k in missing.missing_keys) + for i in range(old_config.local_context_blocks, blocks): + torch.nn.init.zeros_(model.local_path.blocks[i].contract.weight) + torch.nn.init.zeros_(model.local_path.blocks[i].contract.bias) + old_names = list(dict(parent.named_parameters())) + new_names = list(dict(model.named_parameters())) + assert len(state['optimizer']['param_groups']) == 1 + group = state['optimizer']['param_groups'][0] + assert group['params'] == list(range(len(old_names))) + # New blocks shift following norm/head IDs; preserve moments by parameter name. + remap = {old_id: new_names.index(name) for old_id, name in enumerate(old_names)} + state['optimizer']['state'] = {remap[old_id]: values + for old_id, values in state['optimizer']['state'].items()} + group['params'] = list(range(len(new_names))) + for name, value in state['model'].items(): + assert torch.equal(model.state_dict()[name], value) + state.update(config=config.to_dict(), model=model.state_dict()) + return state, model, remap + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + p.add_argument('--blocks', type=int, default=6) + a = p.parse_args() + state = torch.load(a.checkpoint, map_location='cpu', weights_only=True) + old_blocks = ModelConfig(**state['config']).local_context_blocks + state, model, remap = extend(state, a.blocks) + with a.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() + change = {'kind': 'extended_local_context', 'parent_sha256': digest, + 'step': state['global_step'], 'blocks_before': old_blocks, 'blocks_after': a.blocks, + 'receptive_field_bp': 21+8*sum(4**i for i in range(a.blocks)), + 'parent_parameters_optimizer_moments_rng_preserved': True, + 'new_blocks_initially_identity': True, 'optimizer_ids_remapped_by_parameter_name': True} + history = list(state.get('interventions', [])) + if state.get('intervention') and state['intervention'] not in history: + history.append(state['intervention']) + history.append(change) + state.update(intervention=change, interventions=history) + a.output.mkdir(parents=True, exist_ok=False) + (a.output/'config.yaml').write_text(yaml.safe_dump(state['config'])) + (a.output/'intervention.json').write_text(json.dumps(change, indent=2)+'\n') + (a.output/'optimizer_id_remap.json').write_text(json.dumps(remap, indent=2)+'\n') + _atomic_save(state, a.output/'start.pt') + print(json.dumps({'parameters': count_parameters(model), **change}), flush=True) + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/infer.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/infer.sh new file mode 100644 index 0000000000000000000000000000000000000000..2a412a9ad8667d6dcb94bb57d9959d61d43ddd34 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/infer.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: infer.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/predict.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/package_manifest.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/package_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..abd68dd7ba46c51884acf6a457eded656c611dab --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/package_manifest.json @@ -0,0 +1,85 @@ +{ + "parameters": 13290504, + "source_checkpoint_sha256": "8c38a6101b84fca69ac2f7bd9daa19f1a3a5b4cebe6e26258e12ad2a137ed79c", + "files": { + "README.md": "b50ad576c81918543a43d7dc5dff82d996476cdb4a9e76c84623c370b0b19c20", + "attach_bridge.py": "0740f17d8041d4886ad4437e83f1b781e941cc76a15db7cd5280a5983c36fc7c", + "attach_local.py": "d0774e2711840783267914789ed303a3b61b64381b72d61f0886edbc04974f15", + "batched_training.py": "217255d8f1d4fbaacc9a9bcf7ce252432ffa834b6eb47b7eed1b7471bbf3d5f7", + "change_residual_cap.py": "7c277370f54f352fd9df81ef241bfb100e123d57a56c87d5b85eac420c55be2d", + "change_splice_context.py": "4a7a1bf746b17abb75e0c611b4b19740541f825fe63a750fc44684351efadf32", + "extend_local_context.py": "aa720fdbd8625a900f24bc2c007ec0acf8c2164591971d007d2c01a58792d701", + "infer.sh": 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"validation_variants.json": "1d14a3efbdc85a0f8e71ea5c986075ac23f6e1a69945ebd2254765d9430e50f1" + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/perturb.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/perturb.py new file mode 100644 index 0000000000000000000000000000000000000000..b8ee571ad9dab90aded5077affbb42419869da16 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/perturb.py @@ -0,0 +1,92 @@ +"""Predict both sequences of every perturbation (contract: perturbations/v1). + + python perturb.py INPUT_ROOT OUTPUT_ROOT --checkpoint last.pt + +INPUT_ROOT holds `public/perturbations.parquet` and `reference/GRCh38.p13.genome.fa` (in the +workspace, `/workspace/data` works too: the queries are then read from `variant_evals/public/`). Each row is a +1,048,576-bp window starting at `input_start` and an edit spanning [edit_start, edit_end) in genomic +0-based coordinates: + +- `snv`: sequence A carries `allele_a` at edit_start, sequence B carries `allele_b` (both are written + explicitly; either may differ from the reference genome); +- `mask`: sequence A is the reference, sequence B has the interval replaced by N. + +For each perturbation the output group `perturbations/p{index:06d}` holds `a/` and +`b/` for every family in `families`: dense families sliced to bins +[dense_bin_start, dense_bin_end) of the central target window, and `splice_sites` sliced to bp +[splice_start, splice_end). Values are the same quantities as predictions.zarr (asinh space for +dense tracks, splice-site scores for splice_sites). +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import pandas as pd +import pysam +import torch +import zarr + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig +from rc_ensemble import predict_requested + +PERTURBATION_SCHEMA = "genome-sequence-to-function-perturbations/v1" +COMPRESSOR = Blosc(cname="zstd", clevel=3, shuffle=Blosc.BITSHUFFLE) + + +def sequences(fasta, row) -> tuple[str, str]: + length = fasta.get_reference_length(row.chrom) + start, end = int(row.input_start), int(row.input_start) + 1_048_576 + sequence = fasta.fetch(row.chrom, max(0, start), min(length, end)).upper() + sequence = "N" * max(0, -start) + sequence + "N" * max(0, end - length) + lo, hi = int(row.edit_start) - start, int(row.edit_end) - start + if row.edit == "snv": + return sequence[:lo] + row.allele_a + sequence[hi:], sequence[:lo] + row.allele_b + sequence[hi:] + if row.edit == "mask": + return sequence, sequence[:lo] + "N" * (hi - lo) + sequence[hi:] + raise ValueError(f"unknown edit {row.edit!r}") + + +def main(input_root: Path, output_root: Path, checkpoint: Path, queries: Path | None = None) -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + if queries is None: + queries = input_root / "public/perturbations.parquet" + if not queries.exists(): + queries = input_root / "variant_evals/public/perturbations.parquet" + rows = pd.read_parquet(queries) + fasta = pysam.FastaFile(str(input_root / "reference/GRCh38.p13.genome.fa")) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "perturbations.zarr"), mode="w") + target_start = (config.input_length - config.target_length) // 2 + with torch.inference_mode(): + for index, row in enumerate(rows.itertuples(index=False)): + pair = sequences(fasta, row) + dna = torch.from_numpy(np.stack([AlphaGenomeDataset._one_hot(s) for s in pair])).to(device) + group = root.create_group(f"perturbations/p{index:06d}") + for family, values in predict_requested(model, dna, row).items(): + values = np.asarray(values, dtype=np.float32) + for side, array in zip("ab", values): + group.create_dataset(f"{side}/{family}", data=array, compressor=COMPRESSOR) + print(json.dumps({"event": "perturb", "done": index + 1, "total": len(rows)}), flush=True) + root.attrs.update({"schema": PERTURBATION_SCHEMA, + "perturbation_ids": rows.perturbation_id.astype(str).tolist()}) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--queries", type=Path, help="another query table, e.g. the development split") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.queries) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/perturb.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/perturb.sh new file mode 100644 index 0000000000000000000000000000000000000000..f07c4714532c2d33e7a419de3877e5daacee89fd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/perturb.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: perturb.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/perturb.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/predict.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..665fb186f3a62b1ff130abb034ac25a3a924c7c8 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/predict.py @@ -0,0 +1,52 @@ +"""Stream one shared-checkpoint forward/reverse-complement mean for every output family.""" +from __future__ import annotations +import argparse +import json +from pathlib import Path +from numcodecs import Blosc +import numpy as np +import torch +import zarr +from prediction_contract import PREDICTION_SCHEMA +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig +from rc_ensemble import engine_for + +COMPRESSOR = Blosc(cname="zstd", clevel=7, shuffle=Blosc.BITSHUFFLE) + + +def main(input_root: Path, output_root: Path, checkpoint: Path, split: str = "test") -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + model = CompactModel(ModelConfig(**payload['config'])) + model.load_state_dict(payload['model'], strict=True) + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + model.to(device).eval() + dataset = AlphaGenomeDataset(input_root, split, include_targets=False) + model.set_junction_track_halves(dataset.junction_track_halves) + engine = engine_for(model) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "predictions.zarr"), mode="w") + ids = [] + with torch.inference_mode(): + for sample in dataset: + example_id = sample['example_id'] + values = engine.tracks(sample['dna'][None].to(device)) + group = root.create_group(f"examples/{example_id}") + for name, array in values.items(): + dtype = np.int8 if name == 'junction_half' else np.int32 if name in ( + 'splice_usage_positions', 'junction_donor', 'junction_acceptor') else np.float16 + group.create_dataset(name, data=array.astype(dtype), compressor=COMPRESSOR) + ids.append(example_id) + print(json.dumps({'event': 'predict', 'example': len(ids), 'total': len(dataset)}), flush=True) + root.attrs.update({'schema': PREDICTION_SCHEMA, 'example_ids': ids}) + (output_root/'example_ids.json').write_text(json.dumps(ids)+'\n') + + +if __name__ == '__main__': + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('input_root', type=Path) + p.add_argument('output_root', type=Path) + p.add_argument('--checkpoint', type=Path, default=Path(__file__).resolve().parent/'last.pt') + p.add_argument('--split', default='test', choices=['valid', 'test']) + a = p.parse_args() + main(a.input_root, a.output_root, a.checkpoint, a.split) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/prediction_contract.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/prediction_contract.py new file mode 100644 index 0000000000000000000000000000000000000000..2455424db002b840cf0d69456930840a7bc5f3cb --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/prediction_contract.py @@ -0,0 +1,23 @@ +"""Public prediction shapes; no trusted splice coordinates are exposed.""" + +from __future__ import annotations + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} + +PREDICTION_SCHEMA = "genome-sequence-to-function-predictions/v4" +MAX_USAGE_SITES = 4_096 +MAX_JUNCTION_PAIRS = 16_384 +# Fixed-shape arrays per example; splice usage and junctions are sparse (see contract.md). +FIXED_SHAPES = { + **{name: (1_536, tracks) for name, tracks in DENSE_TRACKS.items()}, + "splice_sites": (196_608, 4), + "contact_maps": (131_328, 28), +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/rc_ensemble.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/rc_ensemble.py new file mode 100644 index 0000000000000000000000000000000000000000..f076804c66e481eb823e6ca5e721f39fce50bad5 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/rc_ensemble.py @@ -0,0 +1,111 @@ +"""One shared checkpoint evaluated on DNA and its reverse complement for every output.""" +import json +from pathlib import Path +import numpy as np +import torch +from prediction_contract import DENSE_TRACKS, MAX_JUNCTION_PAIRS, MAX_USAGE_SITES +from single_prediction import single_prediction +from splice_output import splice_output_scores + + +def contact_reversal(bins): + row, column = np.triu_indices(bins) + lookup = np.empty((bins, bins), dtype=np.int64) + lookup[row, column] = np.arange(len(row)) + lookup[column, row] = np.arange(len(row)) + return lookup[bins-1-row, bins-1-column] + + +def reverse_readout(arrays, permutations, input_length, target_length, contact_bins): + """Map a reverse-complement prediction back into the original genomic coordinates.""" + out = {name: arrays[name][::-1, permutations[name]].copy() for name in DENSE_TRACKS} + out['splice_sites'] = arrays['splice_sites'][::-1, permutations['splice_sites']].copy() + positions = target_length-1-arrays['splice_usage_positions'] + order = np.argsort(positions) + out['splice_usage_positions'] = positions[order].astype(np.int32) + out['splice_usage'] = arrays['splice_usage'][order][:, permutations['splice_usage']].copy() + out['junction_half'] = (1-arrays['junction_half']).astype(np.int8) + for key in ['junction_donor', 'junction_acceptor']: + out[key] = (input_length-1-arrays[key]).astype(np.int32) + out['junctions'] = arrays['junctions'][:, permutations['junctions']].copy() + out['contact_maps'] = arrays['contact_maps'][contact_reversal(contact_bins)][:, permutations['contact_maps']].copy() + return out + + +def combine_readouts(a, b, track_halves): + """Average sparse functions with zero at unlisted coordinates, then enforce output budgets.""" + out = {name: (a[name]+b[name])*np.float32(.5) + for name in [*DENSE_TRACKS, 'splice_sites', 'contact_maps']} + usage_positions, inverse = np.unique(np.concatenate([a['splice_usage_positions'], b['splice_usage_positions']]), return_inverse=True) + assert len(usage_positions) <= MAX_USAGE_SITES + usage = np.zeros((len(usage_positions), a['splice_usage'].shape[-1]), dtype=np.float32) + np.add.at(usage, inverse, np.concatenate([a['splice_usage'], b['splice_usage']])*np.float32(.5)) + out.update(splice_usage_positions=usage_positions.astype(np.int32), splice_usage=usage) + keys = np.concatenate([np.stack([x['junction_half'], x['junction_donor'], x['junction_acceptor']], axis=1) for x in [a, b]]) + keys, inverse = np.unique(keys, axis=0, return_inverse=True) + values = np.zeros((len(keys), len(track_halves)), dtype=np.float32) + np.add.at(values, inverse, np.concatenate([a['junctions'], b['junctions']])*np.float32(.5)) + strength = np.empty(len(keys), dtype=np.float32) + for half in [0, 1]: + keep = keys[:, 0] == half + strength[keep] = values[keep][:, track_halves == half].max(axis=1) + # Stable sorting makes ties deterministic in the canonical coordinate order. + order = np.argsort(-strength, kind='stable')[:MAX_JUNCTION_PAIRS] + keys, values = keys[order], values[order] + out.update(junction_half=keys[:, 0].astype(np.int8), + junction_donor=keys[:, 1].astype(np.int32), + junction_acceptor=keys[:, 2].astype(np.int32), junctions=values) + return out + + +class ReverseComplementMean: + def __init__(self, model): + self.model = model + data = json.loads(Path(__file__).with_name('strand_permutations.json').read_text()) + self.permutations = {name: np.asarray(values, dtype=np.int64) for name, values in data['permutations'].items()} + for name, indices in self.permutations.items(): + assert np.array_equal(indices[indices], np.arange(len(indices))), name + + def tracks(self, dna): + assert len(dna) == 1 + config = self.model.config + original = single_prediction(self.model, dna) + reverse = single_prediction(self.model, dna.flip((1, 2))) + reverse = reverse_readout(reverse, self.permutations, config.input_length, config.target_length, config.contact_bins) + return combine_readouts(original, reverse, self.model.junction_track_half.cpu().numpy()) + + def dense_and_sites(self, dna, families): + """Return central readouts for both alleles; this same probability/asinh averaging defines tracks.""" + config = self.model.config + start = (config.input_length-config.target_length)//2 + predictions = [] + for reverse in [False, True]: + trunk = self.model._trunk(dna.flip((1, 2)) if reverse else dna) + features = None + values = {} + for family in families: + if family == 'splice_sites': + logits = self.model._splice_site_logits(trunk)[:, start:start+config.target_length] + array = splice_output_scores(logits).float().cpu().numpy() + else: + if features is None: + features = self.model._dense_features(trunk) + array = self.model.dense_heads[family](features).float().cpu().numpy() + if reverse: + array = array[:, ::-1][:, :, self.permutations[family]].copy() + values[family] = array + predictions.append(values) + return {family: (predictions[0][family]+predictions[1][family])*np.float32(.5) for family in families} + + +def engine_for(model): + if not hasattr(model, '_rc_prediction_engine'): + # Plain helper, not a child nn.Module; the backbone state dictionary stays unchanged. + model._rc_prediction_engine = ReverseComplementMean(model) + return model._rc_prediction_engine + + +def predict_requested(model, dna, row): + values = engine_for(model).dense_and_sites(dna, row.families) + return {family: array[:, row.splice_start:row.splice_end] if family == 'splice_sites' + else array[:, row.dense_bin_start:row.dense_bin_end] for family, array in values.items()} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/rc_ensemble_policy.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/rc_ensemble_policy.json new file mode 100644 index 0000000000000000000000000000000000000000..ea28ca2d1886a20b2d9c56a721efe4d1f50b4a2c --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/rc_ensemble_policy.json @@ -0,0 +1,35 @@ +{ + "method": "Equal mean of the same checkpoint evaluated on DNA and its reverse complement, mapped to original positions and public-metadata strand channels.", + "orientation_views": 2, + "fitted_ensemble_weights": false, + "weights": [ + 0.5, + 0.5 + ], + "dna_input": "Same provided 1,048,576bp, reversed and complemented; no extra flanking sequence.", + "site_scores": "Apply the existing training-prior probability link per orientation, then average probabilities.", + "sparse_outputs": "Each orientation chooses its own positions/pairs. Average the union with zero for unlisted coordinates; retain up to16,384 junctions by averaged predicted strength. Usage union has at most4,096 coordinates.", + "outputs": "All families and both entry points use the same two-view rule. No labels or targets used at inference.", + "created_utc": "2026-10-26T01:07:17.491176+00:00", + "parent_package": "/run-state/workspace/research/experiments/calib81-small-epoch08/package", + "parent_inference_signature": { + "last.pt": "4e81120dd5424c175e43684c0827b8a4f21e5be8b0b554f17df615ac16a027dd", + "predict.py": "5f5507be4efce5af3167616de01c11da89fa4fe2b2c59dbdac0152e1a84d1348", + "perturb.py": "9defff26b93513ebe681c31485b75730aac911d9c0f9983f2c7aa615a01b67f8", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/alphagenome_50m/model.py": "c61f50a2a66a4f6950c6f6107a70710a5d0ab99a291b8bfaa1eb5565998d2fa3", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919", + "splice_output.py": "e1b0627e036c60190e4d2a4653a915d9031b166ff3c1de7c23e5774009d70ef0", + "splice_calibration.json": "bd9368b32e739be65ca290d598ffcae5be6387c09f96048e183c57b7d570e641" + }, + "unique_learned_parameters": 13290504, + "decoder_study": "/run-state/workspace/research/studies/rc17", + "decoder_assets_sha256": { + "predict.py": "4f5dbddf5b6b701079aeaf38920fe414ce980f7161997f4d5c80952a6d834e51", + "perturb.py": "49065dc43816920ecac9de3ebcc4735673e4ff6594cf3f6bd34233b88c9bed73", + "rc_ensemble.py": "84a4d0e94ab86622e2c25e96f8835b2ef2bba77731ff7ff0bdc1c2de02cdcef4", + "single_prediction.py": "10f6216fdd72bf30b3a28ab6de9363e66b415526aa26a2bbfb6a84badbb67bec", + "strand_permutations.json": "afb0f0cf2e897fab3493de66601d7a66dac45a06316c2ac4a6cc828298820c60" + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4879a0e1a3a8a8050bb48360febee152cd141be8 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/__init__.py @@ -0,0 +1 @@ +"""The reference model of the alphagenome-reference baseline.""" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a86a139babb6e899bd5102bbf3c628e1232576b2 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/__init__.py @@ -0,0 +1,5 @@ +"""Compact 1-Mb, eleven-family AlphaGenome reference model.""" + +from .model import CompactModel, ModelConfig, count_parameters + +__all__ = ["CompactModel", "ModelConfig", "count_parameters"] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/alphagenome_50m.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/alphagenome_50m.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0a59a4d0c65752d45eb464d129c6ec9d559665e9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/alphagenome_50m.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 32 +channel_growth: 32 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 384 +num_sequence_blocks: 6 +num_attention_heads: 6 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 256 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/cached_read_store.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/cached_read_store.py new file mode 100644 index 0000000000000000000000000000000000000000..9c3677c1e8518ea97db0dd6759f3d636f5fff27c --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/cached_read_store.py @@ -0,0 +1,71 @@ +"""Optional local byte cache for immutable training Zarr stores. + +Source storage stays read-only. Cached entries contain a key fingerprint, length, +and SHA256 of the original compressed bytes; every hit verifies all three. Writes +replace a private temporary inode atomically. A missing/corrupt/unwritable cache +falls back to the original store, so this layer changes neither values nor keys. +Only training loaders may opt into it; it is not an inference asset. +""" +import hashlib +import os +from pathlib import Path +import struct +import tempfile + +from .direct_read_store import ParallelDirectReadDirectoryStore + +MAGIC = b'SFBC0001' +HEADER = struct.Struct('<8sQ32s32s') + + +class CachedReadDirectoryStore(ParallelDirectReadDirectoryStore): + def __init__(self, path, *, cache_root, namespace, io_workers=4): + super().__init__(path, io_workers=io_workers) + if not namespace: + raise ValueError('An immutable input namespace is required') + self.cache_root = Path(cache_root) + self.namespace = namespace + self.store_key = hashlib.sha256((namespace+'\0'+os.path.abspath(path)).encode()).digest() + self.statistics = {'hit': 0, 'miss': 0, 'corrupt': 0, 'write_failure': 0} + + def cache_location(self, key): + key = self._normalize_key(key) + identity = hashlib.sha256(self.store_key+key.encode()).digest() + name = identity.hex() + return self.cache_root/name[:2]/name[2:], identity + + def __getitem__(self, key): + key = self._normalize_key(key) + destination, identity = self.cache_location(key) + try: + blob = destination.read_bytes() + if len(blob) >= HEADER.size: + magic, size, cached_key, checksum = HEADER.unpack_from(blob) + payload = blob[HEADER.size:] + if (magic == MAGIC and size == len(payload) and cached_key == identity + and hashlib.sha256(payload).digest() == checksum): + self.statistics['hit'] += 1 + return payload + self.statistics['corrupt'] += 1 + except OSError: + pass + self.statistics['miss'] += 1 + payload = super().__getitem__(key) + temporary = None + try: + destination.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile(prefix='.pending-', dir=destination.parent, delete=False) as stream: + temporary = stream.name + stream.write(HEADER.pack(MAGIC, len(payload), identity, hashlib.sha256(payload).digest())) + stream.write(payload) + os.replace(temporary, destination) + temporary = None + except OSError: + self.statistics['write_failure'] += 1 + finally: + if temporary is not None: + try: + os.unlink(temporary) + except OSError: + pass + return payload diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/dataset.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..8a3f7d4644e5d9f02162bac9e859443ba6c33658 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/dataset.py @@ -0,0 +1,312 @@ +"""Streaming reader for the compact 1 Mb / eleven-family release.""" + +from __future__ import annotations + +from collections import OrderedDict +from pathlib import Path + +import numpy as np +import pandas as pd +import pysam +import torch +from torch.utils.data import Dataset +import zarr + + +import os +from concurrent.futures import ThreadPoolExecutor + + +_pools = {} + + +def process_pool(workers): + key = (os.getpid(), workers) + if key not in _pools: + _pools[key] = ThreadPoolExecutor(max_workers=workers, thread_name_prefix='zarr-read') + return _pools[key] + + +class DirectReadDirectoryStore(zarr.DirectoryStore): + def __getitem__(self, key): + key = self._normalize_key(key) + try: + return self._fromfile(os.path.join(self.path, key)) + except (FileNotFoundError, IsADirectoryError, NotADirectoryError): + raise KeyError(key) from None + + def getitems(self, keys, *, contexts): + result = {} + for key in keys: + try: + result[key] = self[key] + except KeyError: + pass + return result + + def __setitem__(self, key, value): + raise PermissionError('DirectReadDirectoryStore is read-only') + + def __delitem__(self, key): + raise PermissionError('DirectReadDirectoryStore is read-only') + + def is_writeable(self): + return False + + def is_erasable(self): + return False + + +class ParallelDirectReadDirectoryStore(DirectReadDirectoryStore): + """Bounded I/O threads shared by all read stores within one loader process.""" + def __init__(self, path, *, io_workers=4): + super().__init__(path) + if io_workers < 1: + raise ValueError('io_workers must be positive') + self.io_workers = io_workers + + def getitems(self, keys, *, contexts): + def fetch(key): + try: + return key, self[key] + except KeyError: + return key, None + if self.io_workers == 1: + return super().getitems(keys, contexts=contexts) + return {key: value for key, value in process_pool(self.io_workers).map(fetch, keys) + if value is not None} + + +def _open_part(path: Path): + if str(path).endswith(".zarr.zip"): + store = zarr.ZipStore(str(path), mode="r") + try: + return zarr.open_group(store=store, mode="r"), store + except BaseException: + store.close() + raise + return zarr.open_group(store=ParallelDirectReadDirectoryStore(str(path), io_workers=4), mode="r"), None + + +DENSE_FAMILIES = ( + "atac", + "dnase", + "procap", + "cage", + "rna_seq", + "chip_tf", + "chip_histone", +) +def normalize_splice_positions(value) -> np.ndarray: + """Normalize Arrow's nested-list representation to the public [4, 512] array.""" + + outer = np.asarray(value, dtype=object).reshape(-1).tolist() + positions = np.stack([np.asarray(item, dtype=np.int64) for item in outer]) + if positions.shape != (4, 512): + raise ValueError(f"splice_site_positions must have shape (4, 512), got {positions.shape}") + return positions + + +class AlphaGenomeDataset(Dataset): + """Resolve frozen coordinates to reference DNA and one Zarr example group.""" + + def __init__( + self, + data_root: str | Path, + split: str, + *, + max_open_parts: int = 8, + include_targets: bool = True, + ) -> None: + self.root = Path(data_root) + self.split = split + manifest = self.root / "public" / f"{split}.parquet" + if not manifest.is_file(): + raise FileNotFoundError(f"Missing materialized manifest: {manifest}") + self.rows = pd.read_parquet(manifest).reset_index(drop=True) + junction_tracks = pd.read_parquet(self.root / "public/junction_tracks.parquet") + junction_tracks = junction_tracks.sort_values("tensor_index") + self.junction_track_halves = torch.from_numpy( + junction_tracks.junction_channel_half.to_numpy(np.int64) + ) + if include_targets: + labels_path = self.root / "labels" / f"{split}.parquet" + if not labels_path.is_file(): + raise FileNotFoundError(f"Missing trusted labels manifest: {labels_path}") + labels = pd.read_parquet(labels_path).reset_index(drop=True) + if self.rows.example_id.astype(str).tolist() != labels.example_id.astype(str).tolist(): + raise ValueError("public and labels manifests have different IDs/order") + self.rows = self.rows.merge(labels, on="example_id", validate="one_to_one", sort=False) + self.fasta_path = self.root / "reference" / "GRCh38.p13.genome.fa" + self.max_open_parts = max_open_parts + self.include_targets = include_targets + # Optional training-only compressed-byte cache; off for all public inference. + self.training_bytecache = None + requested_cache = os.environ.get('SEQFUNC_TRAIN_BYTECACHE') + if requested_cache and split == 'train' and include_targets: + import hashlib + import json + from .cached_read_store import CachedReadDirectoryStore + manifests = {str(path): hashlib.sha256(path.read_bytes()).hexdigest() for path in + [self.root/'public/train.parquet', self.root/'labels/train.parquet']} + namespace = hashlib.sha256(json.dumps(manifests, sort_keys=True).encode()).hexdigest() + self.training_bytecache = (requested_cache, namespace) + self._fasta = None + self._parts: OrderedDict[str, tuple[zarr.Group, object | None]] = OrderedDict() + + def __len__(self) -> int: + return len(self.rows) + + def _reference(self): + if self._fasta is None: + self._fasta = pysam.FastaFile(str(self.fasta_path)) + return self._fasta + + def _part(self, relative: str): + if relative in self._parts: + opened = self._parts.pop(relative) + self._parts[relative] = opened + return opened[0] + path = self.root / relative + if self.training_bytecache is not None and not str(path).endswith('.zarr.zip'): + from .cached_read_store import CachedReadDirectoryStore + cache_root, namespace = self.training_bytecache + cached_store = CachedReadDirectoryStore(str(path), cache_root=cache_root, + namespace=namespace, io_workers=4) + group, store = zarr.open_group(store=cached_store, mode='r'), None + else: + group, store = _open_part(path) + if len(self._parts) >= self.max_open_parts: + _, (_, old_store) = self._parts.popitem(last=False) + if old_store is not None: + old_store.close() + self._parts[relative] = (group, store) + return group + + @staticmethod + def _one_hot(sequence: str) -> np.ndarray: + encoded = np.frombuffer(sequence.upper().encode("ascii"), dtype=np.uint8) + output = np.zeros((len(encoded), 4), dtype=np.float32) + for index, letter in enumerate(b"ACGT"): + output[:, index] = encoded == letter + return output + + def _sequence(self, row) -> tuple[np.ndarray, np.ndarray]: + reference = self._reference() + chromosome_length = reference.get_reference_length(str(row.chromosome)) + start, end = int(row.input_start), int(row.input_end) + clipped_start, clipped_end = max(0, start), min(chromosome_length, end) + sequence = reference.fetch(str(row.chromosome), clipped_start, clipped_end) + left, right = clipped_start - start, end - clipped_end + if left or right: + sequence = "N" * left + sequence + "N" * right + valid = np.ones(end - start, dtype=bool) + if left: + valid[:left] = False + if right: + valid[-right:] = False + return self._one_hot(sequence), valid + + @staticmethod + def _decode(example, name: str, *, asinh: bool) -> np.ndarray: + """Train targets may be stored as 8-bit codes with per-track ranges; they are decoded + here, one example at a time, so the release stays compressed on disk.""" + array = example[name] + if array.dtype != np.uint8: + return np.asarray(array, dtype=np.float32) + lo = np.asarray(example[f"{name}_lo"], dtype=np.float32) + hi = np.asarray(example[f"{name}_hi"], dtype=np.float32) + x = lo + np.asarray(array, dtype=np.float32) * ((hi - lo) / 255.0) + return np.sinh(x) if asinh else x + + @staticmethod + def _dense_target(example, family: str) -> dict[str, torch.Tensor]: + values = AlphaGenomeDataset._decode(example, f"{family}/values", asinh=True) + mask = np.asarray(example[f"{family}/mask"], dtype=bool) + if mask.ndim == 1: + mask = np.broadcast_to(mask[None, :], values.shape).copy() + return { + "values": torch.from_numpy(values), + "mask": torch.from_numpy(mask), + } + + def __getitem__(self, item: int): + row = self.rows.iloc[item] + dna, input_mask = self._sequence(row) + result = { + "example_id": str(row.example_id), + "dna": torch.from_numpy(dna), + "input_mask": torch.from_numpy(input_mask), + "junction_track_halves": self.junction_track_halves, + } + if not self.include_targets: + return result + + part = self._part(str(row.target_shard)) + example = part[str(row.target_group)] + result["dense"] = { + family: self._dense_target(example, family) for family in DENSE_FAMILIES + } + result["splice_sites"] = torch.from_numpy( + np.asarray(example["splice_sites/values"], dtype=np.float32) + ) + result["splice_usage"] = { + "positions": torch.from_numpy( + np.asarray(example["splice_site_usage/positions"], dtype=np.int64) + ), + "values": torch.from_numpy( + np.asarray(example["splice_site_usage/values"], dtype=np.float32) + ), + "mask": torch.from_numpy( + np.asarray(example["splice_site_usage/mask"], dtype=bool) + ), + } + result["usage_positions"] = result["splice_usage"]["positions"] + result["junctions"] = { + name: torch.from_numpy( + np.asarray( + example[f"splice_junctions/{name}"], + dtype=np.float32 if name == "value" else np.int64, + ) + ) + for name in ("row", "column", "track", "value") + } + result["junctions"]["site_positions"] = torch.from_numpy( + np.asarray(example["splice_junctions/site_positions"], dtype=np.int64) + ) + site_positions = normalize_splice_positions(row.splice_site_positions) + result["site_positions"] = torch.from_numpy(site_positions) + if not torch.equal(result["junctions"]["site_positions"], result["site_positions"]): + raise ValueError(f"public/private splice positions differ for {row.example_id}") + scoring_path = self.root / "scoring" / self.split / f"part-{item:06d}.parquet" + scoring = pd.read_parquet(scoring_path) + if not (scoring.example_id.astype(str) == str(row.example_id)).all(): + raise ValueError(f"scoring part ID differs for {row.example_id}") + result["junction_scoring"] = { + "donor_position": torch.from_numpy( + scoring.donor_position.to_numpy(np.int64) + ), + "acceptor_position": torch.from_numpy( + scoring.acceptor_position.to_numpy(np.int64) + ), + "track": torch.from_numpy(scoring.track.to_numpy(np.int64)), + "target_value": torch.from_numpy( + scoring.target_value.to_numpy(np.float32) + ), + "valid": torch.from_numpy(scoring.valid.to_numpy(bool)), + } + result["contact_maps"] = torch.from_numpy( + self._decode(example, "contact_maps/upper_triangle", asinh=False) + ) + result["contact_map_mask"] = torch.from_numpy( + np.asarray(example["contact_maps/mask"], dtype=bool) + ) + return result + + +def single_example_collate(items): + """Keep variable-size event tensors intact; the reference currently trains at batch one.""" + + if len(items) != 1: + raise ValueError("The compact event loader currently requires batch_size=1") + return items[0] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/direct_read_store.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/direct_read_store.py new file mode 100644 index 0000000000000000000000000000000000000000..c560febc6a15909a523c8e2f3d1f861aeec309a7 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/direct_read_store.py @@ -0,0 +1,64 @@ +"""Read-only Zarr directory store avoiding redundant per-chunk stat calls.""" +import os +from concurrent.futures import ThreadPoolExecutor + +from zarr.storage import DirectoryStore + +_pools = {} + + +def process_pool(workers): + key = (os.getpid(), workers) + if key not in _pools: + _pools[key] = ThreadPoolExecutor(max_workers=workers, thread_name_prefix='zarr-read') + return _pools[key] + + +class DirectReadDirectoryStore(DirectoryStore): + def __getitem__(self, key): + key = self._normalize_key(key) + try: + return self._fromfile(os.path.join(self.path, key)) + except (FileNotFoundError, IsADirectoryError, NotADirectoryError): + raise KeyError(key) from None + + def getitems(self, keys, *, contexts): + result = {} + for key in keys: + try: + result[key] = self[key] + except KeyError: + pass + return result + + def __setitem__(self, key, value): + raise PermissionError('DirectReadDirectoryStore is read-only') + + def __delitem__(self, key): + raise PermissionError('DirectReadDirectoryStore is read-only') + + def is_writeable(self): + return False + + def is_erasable(self): + return False + + +class ParallelDirectReadDirectoryStore(DirectReadDirectoryStore): + """Bounded I/O threads shared by all read stores within one loader process.""" + def __init__(self, path, *, io_workers=4): + super().__init__(path) + if io_workers < 1: + raise ValueError('io_workers must be positive') + self.io_workers = io_workers + + def getitems(self, keys, *, contexts): + def fetch(key): + try: + return key, self[key] + except KeyError: + return key, None + if self.io_workers == 1: + return super().getitems(keys, contexts=contexts) + return {key: value for key, value in process_pool(self.io_workers).map(fetch, keys) + if value is not None} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/layers.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..7ef7b4e45a81eb774e4e92ac36e30c5abdebbe46 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/layers.py @@ -0,0 +1,134 @@ +"""Shared neural-network layers for the single compact AlphaGenome model.""" + +from __future__ import annotations + +import math + +import torch +from torch import Tensor, nn +from torch.nn import functional as F + + +class ChannelRMSNorm(nn.Module): + def __init__(self, channels: int, eps: float = 1e-6): + super().__init__() + self.weight = nn.Parameter(torch.ones(channels)) + self.eps = eps + + def forward(self, x: Tensor) -> Tensor: + variance = x.float().square().mean(dim=1, keepdim=True) + normalized = x * torch.rsqrt(variance + self.eps).to(x.dtype) + return normalized * self.weight[None, :, None].to(x.dtype) + + +class StandardizedConv1d(nn.Conv1d): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__(in_channels, out_channels, kernel_size, padding="same", bias=True) + self.scale = nn.Parameter(torch.ones(out_channels)) + nn.init.trunc_normal_(self.weight, std=1.0 / math.sqrt(in_channels * kernel_size)) + nn.init.trunc_normal_(self.bias, std=1e-4) + + def forward(self, x: Tensor) -> Tensor: + centered = self.weight - self.weight.mean(dim=(1, 2), keepdim=True) + variance = centered.float().square().mean(dim=(1, 2), keepdim=True) + fan_in = self.weight.shape[1] * self.weight.shape[2] + multiplier = self.scale[:, None, None] * torch.rsqrt( + (fan_in * variance).clamp_min(1e-4) + ).to(self.weight.dtype) + return F.conv1d(x, centered * multiplier, self.bias, padding="same") + + +class ConvBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__() + self.norm = ChannelRMSNorm(in_channels) + self.conv = StandardizedConv1d(in_channels, out_channels, kernel_size) + + def forward(self, x: Tensor) -> Tensor: + return self.conv(F.gelu(self.norm(x), approximate="tanh")) + + +def _apply_rope(x: Tensor) -> Tensor: + rotary_dim = x.shape[-1] - x.shape[-1] % 2 + if rotary_dim == 0: + return x + positions = torch.arange(x.shape[-2], device=x.device, dtype=torch.float32) + inverse_frequency = 1.0 / ( + 10_000 ** ( + torch.arange(0, rotary_dim, 2, device=x.device, dtype=torch.float32) + / rotary_dim + ) + ) + angles = torch.outer(positions, inverse_frequency) + cos = angles.cos()[None, None, :, :].to(x.dtype) + sin = angles.sin()[None, None, :, :].to(x.dtype) + even, odd = x[..., :rotary_dim:2], x[..., 1:rotary_dim:2] + rotated = torch.stack((even * cos - odd * sin, even * sin + odd * cos), dim=-1) + return torch.cat((rotated.flatten(-2), x[..., rotary_dim:]), dim=-1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.num_heads = config.num_attention_heads + self.head_dim = config.attention_head_dim + inner_dim = self.num_heads * self.head_dim + self.norm = nn.RMSNorm(config.hidden_dim) + self.qkv = nn.Linear(config.hidden_dim, 3 * inner_dim, bias=False) + self.output = nn.Linear(inner_dim, config.hidden_dim) + self.dropout = config.dropout + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + batch, length, _ = x.shape + qkv = self.qkv(self.norm(x)).view( + batch, length, 3, self.num_heads, self.head_dim + ) + q, k, value = qkv.unbind(dim=2) + attended = F.scaled_dot_product_attention( + _apply_rope(q.transpose(1, 2)), + _apply_rope(k.transpose(1, 2)), + value.transpose(1, 2), + attn_mask=attention_bias, + dropout_p=self.dropout if self.training else 0.0, + ) + return self.output(attended.transpose(1, 2).reshape(batch, length, -1)) + + +class MLPBlock(nn.Module): + def __init__(self, config): + super().__init__() + expanded = int(config.hidden_dim * config.mlp_expansion) + self.norm = nn.RMSNorm(config.hidden_dim) + self.input = nn.Linear(config.hidden_dim, expanded) + self.output = nn.Linear(expanded, config.hidden_dim) + self.dropout = nn.Dropout(config.dropout) + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor) -> Tensor: + return self.dropout( + self.output(F.gelu(self.input(self.norm(x)), approximate="tanh")) + ) + + +def bound_residual_rms(update: Tensor, cap: float) -> Tensor: + """Cap each token's branch RMS, preserving small updates and their directions.""" + if cap == 0: + return update + rms = update.float().square().mean(dim=-1, keepdim=True).clamp_min(1e-12).sqrt() + scale = (cap / rms).clamp(max=1.0) + return update * scale.to(update.dtype) + + +class SequenceBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.attention = MultiHeadAttention(config) + self.mlp = MLPBlock(config) + self.residual_rms_cap = config.residual_rms_cap + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + x = x + bound_residual_rms(self.attention(x, attention_bias), self.residual_rms_cap) + return x + bound_residual_rms(self.mlp(x), self.residual_rms_cap) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/model.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/model.py new file mode 100644 index 0000000000000000000000000000000000000000..d71fb760a1db4042cc85d73831b98e3e17a9f8bd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/model.py @@ -0,0 +1,467 @@ +"""Affordable multi-resolution baseline for the compact AlphaGenome task.""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass, fields +import math +from pathlib import Path +from typing import Any, Iterable + +import torch +from torch import Tensor, nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint +import yaml + +from .layers import ChannelRMSNorm, ConvBlock, SequenceBlock + + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} +ALL_FAMILIES = (*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions", "contact_maps") + + +@dataclass(frozen=True) +class ModelConfig: + input_length: int = 1_048_576 + target_length: int = 196_608 + dense_bin_size: int = 128 + trunk_bin_size: int = 1_024 + contact_bin_size: int = 2_048 + stem_stride: int = 8 + encoder_width: int = 32 + channel_growth: int = 32 + num_downsampling_blocks: int = 7 + conv_kernel_size: int = 5 + dna_kernel_size: int = 15 + hidden_dim: int = 384 + num_sequence_blocks: int = 6 + num_attention_heads: int = 6 + attention_head_dim: int = 64 + mlp_expansion: float = 4.0 + dropout: float = 0.0 + head_hidden_size: int = 256 + junction_rank: int = 32 + splice_usage_tracks: int = 734 + junction_tracks: int = 734 + contact_tracks: int = 28 + gradient_checkpointing: bool = True + enable_pairwise: bool = False + pair_width: int = 32 + pair_downsample: int = 16 + pair_update_interval: int = 2 + local_width: int = 0 + local_context_blocks: int = 4 + splice_context_mode: str = 'offset' + local_to_trunk: bool = False + residual_rms_cap: float = 0.0 + + def __post_init__(self) -> None: + if not 1 <= self.local_context_blocks <= 7: + raise ValueError('local_context_blocks must be between 1 and 7') + if not math.isfinite(self.residual_rms_cap) or self.residual_rms_cap < 0: + raise ValueError('residual_rms_cap must be finite and nonnegative') + if self.local_to_trunk and not self.local_width: + raise ValueError('local_to_trunk requires local features') + if self.splice_context_mode not in ('offset', 'uniform'): + raise ValueError('unknown splice_context_mode') + if self.splice_context_mode == 'uniform' and not self.local_width: + raise ValueError('uniform splice context requires a base-resolution path') + downsample = self.stem_stride * 2**self.num_downsampling_blocks + if downsample != self.trunk_bin_size: + raise ValueError("stem_stride * 2**num_downsampling_blocks must equal trunk_bin_size") + for length in (self.input_length, self.target_length): + if length % self.trunk_bin_size: + raise ValueError("input and target lengths must divide by trunk_bin_size") + if self.trunk_bin_size % self.dense_bin_size: + raise ValueError("trunk_bin_size must divide by dense_bin_size") + if self.input_length % self.contact_bin_size: + raise ValueError("input_length must divide by contact_bin_size") + if self.hidden_dim <= 0 or self.num_attention_heads <= 0: + raise ValueError("hidden dimensions and attention heads must be positive") + + @property + def trunk_bins(self) -> int: + return self.input_length // self.trunk_bin_size + + @property + def target_trunk_bins(self) -> int: + return self.target_length // self.trunk_bin_size + + @property + def dense_bins(self) -> int: + return self.target_length // self.dense_bin_size + + @property + def dense_upsample(self) -> int: + return self.trunk_bin_size // self.dense_bin_size + + @property + def contact_bins(self) -> int: + return self.input_length // self.contact_bin_size + + @property + def encoder_output_width(self) -> int: + return self.encoder_width + self.channel_growth * self.num_downsampling_blocks + + @classmethod + def from_yaml(cls, path: str | Path) -> "ModelConfig": + values: dict[str, Any] = yaml.safe_load(Path(path).read_text()) or {} + known = {field.name for field in fields(cls)} + unknown = sorted(values.keys() - known) + if unknown: + raise ValueError(f"Unknown model configuration keys: {unknown}") + return cls(**values) + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + +class SequenceEncoder(nn.Module): + def __init__(self, config: ModelConfig): + super().__init__() + self.stem = nn.Conv1d( + 4, + config.encoder_width, + config.dna_kernel_size, + stride=config.stem_stride, + padding=config.dna_kernel_size // 2, + ) + blocks = [] + width = config.encoder_width + for _ in range(config.num_downsampling_blocks): + output_width = width + config.channel_growth + blocks.append(ConvBlock(width, output_width, config.conv_kernel_size)) + width = output_width + self.blocks = nn.ModuleList(blocks) + + def forward(self, dna: Tensor) -> Tensor: + x = F.gelu(self.stem(dna.transpose(1, 2)), approximate="tanh") + for block in self.blocks: + x = F.max_pool1d(block(x), kernel_size=2, stride=2) + return x.transpose(1, 2) + + +class LocalResidualBlock(nn.Module): + def __init__(self, width: int, dilation: int): + super().__init__() + self.norm = ChannelRMSNorm(width) + self.depthwise = nn.Conv1d(width, width, 9, dilation=dilation, + padding=4 * dilation, groups=width) + self.expand = nn.Conv1d(width, 2 * width, 1) + self.contract = nn.Conv1d(2 * width, width, 1) + + def forward(self, x: Tensor) -> Tensor: + update = self.depthwise(self.norm(x)) + update = self.contract(F.gelu(self.expand(update), approximate='tanh')) + return x + update + + +class LocalSequencePath(nn.Module): + """Base-resolution motif path with powers-of-four dilation and aligned pooling.""" + def __init__(self, config: ModelConfig): + super().__init__() + width = config.local_width + self.stem = nn.Conv1d(4, width, 21, padding=10) + self.blocks = nn.Sequential(*(LocalResidualBlock(width, 4**i) + for i in range(config.local_context_blocks))) + self.norm = ChannelRMSNorm(width) + self.sites = nn.Conv1d(width, 4, 1) + self.bin_size = config.dense_bin_size + self.gradient_checkpointing = config.gradient_checkpointing + + def forward(self, dna: Tensor) -> tuple[Tensor, Tensor]: + base = F.gelu(self.stem(dna), approximate='tanh') + for block in self.blocks: + if self.gradient_checkpointing and self.training: + base = checkpoint(block, base, use_reentrant=False) + else: + base = block(base) + base = F.gelu(self.norm(base), approximate='tanh') + sites = self.sites(base).transpose(1, 2) + pooled = torch.cat((F.avg_pool1d(base, self.bin_size), + F.max_pool1d(base, self.bin_size)), dim=1).transpose(1, 2) + return sites, pooled + + +class CompactModel(nn.Module): + """One shared 1-kb trunk with representation-specific output heads.""" + + def __init__(self, config: ModelConfig | None = None): + super().__init__() + self.config = config or ModelConfig() + self.encoder = SequenceEncoder(self.config) + self.projection = nn.Linear(self.config.encoder_output_width, self.config.hidden_dim) + self.blocks = nn.ModuleList( + SequenceBlock(self.config) for _ in range(self.config.num_sequence_blocks) + ) + self.final_norm = nn.RMSNorm(self.config.hidden_dim) + self.dense_decoder = nn.Sequential( + nn.RMSNorm(self.config.hidden_dim), + nn.Linear(self.config.hidden_dim, self.config.head_hidden_size), + nn.GELU(approximate="tanh"), + ) + self.dense_heads = nn.ModuleDict( + { + family: nn.Linear(self.config.head_hidden_size, tracks) + for family, tracks in DENSE_TRACKS.items() + } + ) + self.splice_site_head = nn.Linear( + self.config.head_hidden_size, self.config.dense_bin_size * 4 + ) + self.splice_usage_head = nn.Linear( + self.config.head_hidden_size, self.config.splice_usage_tracks + ) + rank = self.config.junction_rank + self.junction_source = nn.Linear(self.config.head_hidden_size, rank) + self.junction_destination = nn.Linear(self.config.head_hidden_size, rank) + self.junction_track = nn.Embedding(self.config.junction_tracks, rank) + if self.config.junction_tracks % 2: + raise ValueError("junction tracks must split evenly into strand halves") + self.register_buffer( + "junction_track_half", + torch.arange(self.config.junction_tracks) // (self.config.junction_tracks // 2), + persistent=False, + ) + self.contact_q = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + self.contact_k = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + row, column = torch.triu_indices( + self.config.contact_bins, self.config.contact_bins + ) + self.register_buffer("contact_row", row, persistent=False) + self.register_buffer("contact_column", column, persistent=False) + if self.config.local_width: + self.local_path = LocalSequencePath(self.config) + self.local_projection = nn.Linear(2 * self.config.local_width, self.config.head_hidden_size) + # A new local path can be attached to our own checkpoint without changing its initial outputs. + nn.init.zeros_(self.local_path.sites.weight) + nn.init.zeros_(self.local_path.sites.bias) + nn.init.zeros_(self.local_projection.weight) + nn.init.zeros_(self.local_projection.bias) + if self.config.local_to_trunk: + self.local_trunk_projection = nn.Linear(2 * self.config.local_width, self.config.hidden_dim) + nn.init.zeros_(self.local_trunk_projection.weight) + nn.init.zeros_(self.local_trunk_projection.bias) + self._local_sites = None + self._local_features = None + + def set_junction_track_halves(self, halves: Tensor) -> None: + halves = torch.as_tensor(halves, dtype=torch.long, device=self.junction_track_half.device) + if halves.shape != (self.config.junction_tracks,) or not torch.isin( + halves, torch.tensor([0, 1], device=halves.device) + ).all(): + raise ValueError("junction track halves must be a 734-element 0/1 mapping") + self.junction_track_half.copy_(halves) + + def _trunk(self, dna: Tensor) -> Tensor: + if dna.ndim != 3 or dna.shape[1:] != (self.config.input_length, 4): + raise ValueError( + f"Expected DNA [B,{self.config.input_length},4], got {tuple(dna.shape)}" + ) + if self.config.local_width: + sites, pooled = self.local_path(dna.transpose(1, 2)) + self._local_sites = sites + self._local_features = self.local_projection(pooled) + x = self.projection(self.encoder(dna)) + if self.config.local_to_trunk: + local_tokens = pooled.reshape(len(dna), self.config.trunk_bins, + self.config.dense_upsample, -1).mean(dim=2) + x = x + self.local_trunk_projection(local_tokens) + for block in self.blocks: + if self.config.gradient_checkpointing and self.training and x.requires_grad: + x = checkpoint(block, x, None, use_reentrant=False) + else: + x = block(x, None) + return self.final_norm(x) + + def _global_dense_features(self, trunk: Tensor) -> Tensor: + # Coarse features are centered in 1-kb bins; align_corners=False maps their + # centers onto the aligned 128-bp grid and extends boundary values constantly. + decoded = self.dense_decoder(trunk) + return F.interpolate(decoded.transpose(1, 2), scale_factor=self.config.dense_upsample, + mode='linear', align_corners=False).transpose(1, 2) + + def _dense_features(self, trunk: Tensor) -> Tensor: + first = (self.config.input_length - self.config.target_length) // (2 * self.config.dense_bin_size) + features = self._global_dense_features(trunk)[:, first:first + self.config.dense_bins] + if self.config.local_width: + features = features + self._local_features[:, first:first + self.config.dense_bins] + return features + + def _all_dense_features(self, trunk: Tensor) -> Tensor: + features = self._global_dense_features(trunk) + return features + self._local_features if self.config.local_width else features + + def _splice_site_logits(self, trunk: Tensor) -> Tensor: + if self.config.splice_context_mode == 'uniform': + # The global trunk supplies one gene-context bias per class and kb. Base-resolution + # localization comes from the local path, with no arbitrary 128-bp offset decoder + # and no pooled dense-feature contribution to site logits. + context = self.splice_site_head(self.dense_decoder(trunk)) + context = context.view(len(trunk), self.config.trunk_bins, self.config.dense_bin_size, 4).mean(dim=2) + logits = context.repeat_interleave(self.config.trunk_bin_size, dim=1) + self._local_sites + return logits.float() + # The splice classifier retains the feature grid used during training. + # Dense, usage and junction readouts keep the separate interpolated grid. + features = self.dense_decoder(trunk.repeat_interleave(self.config.dense_upsample, dim=1)) + if self.config.local_width: + features = features + self._local_features + logits = self.splice_site_head(features) + logits = logits.view(len(trunk), self.config.input_length, 4) + if self.config.local_width: + logits = logits + self._local_sites + return logits.float() + + def _position_features(self, trunk: Tensor, positions: Tensor) -> Tensor: + features = self._gather_bins(self._global_dense_features(trunk), positions, self.config.dense_bin_size) + if self.config.local_width: + features = features + self._gather_bins(self._local_features, positions, self.config.dense_bin_size) + return features + + @staticmethod + def select_junction_positions(site_logits: Tensor, limit: int = 512) -> Tensor: + """Select each site type from model scores alone, then sort by coordinate. + + This follows the four independent site classes in Google DeepMind's + AlphaGenome splicing implementation (pinned in docs/v3-author/PROTOCOL.md). Padding is + -1 when an input is shorter than ``limit``. + """ + count = min(limit, site_logits.shape[1]) + selected = [] + for site_type in range(4): + coordinates = torch.topk(site_logits[..., site_type], count, dim=1).indices + coordinates = coordinates.sort(dim=1).values + if count < limit: + coordinates = F.pad(coordinates, (0, limit - count), value=-1) + selected.append(coordinates) + return torch.stack(selected, dim=1) + + @staticmethod + def _gather_bins(features: Tensor, positions: Tensor, bin_size: int) -> Tensor: + indices = torch.div(positions, bin_size, rounding_mode="floor") + indices = indices.clamp(0, features.shape[1] - 1) + return torch.gather( + features, + 1, + indices[..., None].expand(-1, -1, features.shape[-1]), + ) + + def _junction_predictions( + self, trunk: Tensor, site_positions: Tensor, queries: Tensor + ) -> Tensor: + row, column, track = queries.unbind(dim=-1) + half = self.junction_track_half[track] + batch = torch.arange(len(trunk), device=trunk.device)[:, None] + donor = site_positions[batch, 2 * half, row] + acceptor = site_positions[batch, 2 * half + 1, column] + source = self._position_features(trunk, donor.clamp(0, self.config.input_length - 1)) + destination = self._position_features(trunk, acceptor.clamp(0, self.config.input_length - 1)) + interaction = self.junction_source(source) * self.junction_destination(destination) + track_embedding = self.junction_track(track) + return (interaction * track_embedding).sum(dim=-1) / math.sqrt( + self.config.junction_rank + ) + + def junction_block( + self, + trunk: Tensor, + site_positions: Tensor, + row_start: int, + row_end: int, + column_start: int, + column_end: int, + track_start: int, + track_end: int, + site_factors: tuple[Tensor, Tensor] | None = None, + ) -> Tensor: + """Compute a bounded block of the full predicted-position junction cube.""" + tracks = torch.arange(track_start, track_end, device=trunk.device) + halves = self.junction_track_half[tracks] + if site_factors is None: + site_factors = self.junction_site_factors(trunk, site_positions) + source_all, destination_all = site_factors + source = source_all[:, halves, row_start:row_end] + destination = destination_all[:, halves, column_start:column_end] + embedding = self.junction_track(tracks)[None, :, None, None, :] + values = ( + source[:, :, :, None, :] * destination[:, :, None, :, :] * embedding + ).sum(dim=-1) / math.sqrt(self.config.junction_rank) + return values.permute(0, 2, 3, 1).float() + + def junction_site_factors( + self, trunk: Tensor, site_positions: Tensor + ) -> tuple[Tensor, Tensor]: + """Precompute the two strand-specific factors reused by all cube blocks.""" + donor = torch.stack((site_positions[:, 0], site_positions[:, 2]), dim=1).clamp_min(0) + acceptor = torch.stack((site_positions[:, 1], site_positions[:, 3]), dim=1).clamp_min(0) + donor_features = self._position_features(trunk, donor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, donor.shape[-1], -1) + acceptor_features = self._position_features(trunk, acceptor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, acceptor.shape[-1], -1) + return self.junction_source(donor_features), self.junction_destination(acceptor_features) + + def forward( + self, + dna: Tensor, + *, + families: Iterable[str] | None = None, + usage_positions: Tensor | None = None, + junction_site_positions: Tensor | None = None, + junction_queries: Tensor | None = None, + ) -> dict[str, Tensor]: + requested = set(families or ALL_FAMILIES) + unknown = requested - set(ALL_FAMILIES) + if unknown: + raise ValueError(f"Unknown output families: {sorted(unknown)}") + trunk = self._trunk(dna) + needs_dense = bool( + requested.intersection((*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions")) + ) + features = self._dense_features(trunk) if needs_dense else None + outputs: dict[str, Tensor] = {} + for family in DENSE_TRACKS: + if family in requested: + outputs[family] = self.dense_heads[family](features).float() + if "splice_sites" in requested: + all_logits = self._splice_site_logits(trunk) + start = (self.config.input_length - self.config.target_length) // 2 + outputs["splice_sites"] = all_logits[:, start:start + self.config.target_length] + if "splice_usage" in requested: + if usage_positions is None: + raise ValueError("splice_usage requires usage_positions") + gathered = self._gather_bins( + features, usage_positions, self.config.dense_bin_size + ) + outputs["splice_usage"] = self.splice_usage_head(gathered).float() + if "junctions" in requested: + if junction_site_positions is None or junction_queries is None: + raise ValueError("junctions require site positions and query triples") + outputs["junctions"] = self._junction_predictions( + trunk, junction_site_positions, junction_queries + ).float() + if "contact_maps" in requested: + contact = trunk.reshape( + len(dna), self.config.contact_bins, -1, self.config.hidden_dim + ).mean(dim=2) + q, k = self.contact_q(contact), self.contact_k(contact) + row, column = self.contact_row, self.contact_column + outputs["contact_maps"] = ( + 0.5 + * ( + q[:, row] * k[:, column] + + q[:, column] * k[:, row] + ) + ).float() + return outputs + + +def count_parameters(model: nn.Module) -> int: + return sum(parameter.numel() for parameter in model.parameters() if parameter.requires_grad) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/reference_large.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/reference_large.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e403757c49eb6e6419927210c7053710964213d1 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/reference_large.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 96 +channel_growth: 96 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 1024 +num_sequence_blocks: 16 +num_attention_heads: 16 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 768 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/run_ddp.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/run_ddp.sh new file mode 100644 index 0000000000000000000000000000000000000000..c4702e9366e907b7791063e77dcfe281411ac8cd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/run_ddp.sh @@ -0,0 +1,16 @@ +#!/usr/bin/env bash +# run_ddp.sh DATA_DIR OUTPUT_DIR CONFIG EPOCHS [GPUS=8] [WORKERS_PER_GPU=12] +# Train the reference on every train interval with one example per GPU per step, then score +# validation with the verifier's own scorer. Runs inside the task image (torch, torchrun). +set -Eeuo pipefail +DATA=$1; OUT=$2; CONFIG=$3; EPOCHS=$4; GPUS=${5:-8}; WORKERS=${6:-12} +BASELINE=$(cd "$(dirname "$0")/../.." && pwd) +mkdir -p "$OUT" +cd "$BASELINE" +# Loopback rendezvous: works offline, where --standalone cannot resolve the hostname. +torchrun --nnodes 1 --nproc-per-node "$GPUS" --master-addr 127.0.0.1 --master-port "${MASTER_PORT:-29511}" -m reference.alphagenome_50m.train_eval train \ + --input "$DATA" --config "$CONFIG" --output "$OUT" --epochs "$EPOCHS" --workers "$WORKERS" \ + --log-every 200 --save-every 2000 2>&1 | tee -a "$OUT/train.log" +python predict.py "$DATA" "$OUT/valid-pred" --checkpoint "$OUT/last.pt" --split valid +python /workspace/evaluation/score.py --predictions "$OUT/valid-pred/predictions.zarr" \ + --labels "$DATA" --split valid --output "$OUT/valid-score" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/tiny_overfit.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/tiny_overfit.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c25f9ffbb76102c970e5767922400f7ec2b6c53e --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/tiny_overfit.yaml @@ -0,0 +1,24 @@ +# Keep the frozen 1-Mb input and every output track; shrink only learned widths. +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 8 +channel_growth: 4 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 32 +num_sequence_blocks: 1 +num_attention_heads: 2 +attention_head_dim: 16 +mlp_expansion: 2.0 +dropout: 0.0 +head_hidden_size: 32 +junction_rank: 8 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: false diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/train_eval.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/train_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..92f9a3f315013630fdd2e03fb6840a6c77840edd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/reference/alphagenome_50m/train_eval.py @@ -0,0 +1,537 @@ +"""Train and stream-evaluate the <=50M compact AlphaGenome baseline.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import numpy as np +import torch +from torch import Tensor +from torch.nn import functional as F +from torch.utils.data import DataLoader, Subset + +from .dataset import ( + AlphaGenomeDataset, + DENSE_FAMILIES, + single_example_collate, +) +from .model import ( + ALL_FAMILIES, + CompactModel, + ModelConfig, + count_parameters, +) + + +DEFAULT_CONFIG = Path(__file__).with_name("alphagenome_50m.yaml") + + +def _device(name: str) -> torch.device: + if name == "auto": + name = "cuda" if torch.cuda.is_available() else "cpu" + if name == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("--device cuda requested but CUDA is unavailable") + return torch.device(name) + + +def _autocast(device: torch.device): + enabled = device.type == "cuda" + dtype = torch.bfloat16 if enabled and torch.cuda.is_bf16_supported() else torch.float16 + return torch.autocast(device_type=device.type, dtype=dtype, enabled=enabled) + + +def _distributed() -> tuple[int, int, int]: + """(rank, local_rank, world_size) under torchrun; (0, 0, 1) otherwise.""" + world = int(os.environ.get("WORLD_SIZE", "1")) + if world > 1 and not torch.distributed.is_initialized(): + torch.distributed.init_process_group("nccl") + return int(os.environ.get("RANK", "0")), int(os.environ.get("LOCAL_RANK", "0")), world + + +def _loader(dataset, *, workers: int, shuffle: bool, sampler=None) -> DataLoader: + return DataLoader( + dataset, + batch_size=1, + shuffle=shuffle and sampler is None, + sampler=sampler, + num_workers=workers, + persistent_workers=workers > 0, + pin_memory=torch.cuda.is_available(), + collate_fn=single_example_collate, + ) + + +def _to_device(value, device: torch.device): + if isinstance(value, Tensor): + return value.to(device, non_blocking=True) + if isinstance(value, dict): + return {key: _to_device(item, device) for key, item in value.items()} + return value + + +def _match_coordinate_queries( + scoring: dict[str, Tensor], positions: Tensor, track_halves: Tensor +) -> tuple[Tensor, Tensor, Tensor]: + """Match trusted coordinate queries to typed position axes like the grader.""" + device = positions.device + tracks = scoring["track"].detach().cpu().numpy() + halves_cpu = track_halves.detach().cpu().numpy()[tracks] + # Build indices on CPU, then copy once. Writing three CUDA scalars per valid query + # otherwise launches thousands of tiny kernels per example, especially costly when + # training and evaluation share one GPU. The matching semantics are unchanged. + queries = np.zeros((len(tracks), 3), dtype=np.int64) + queries[:, 2] = tracks + valid = scoring["valid"].clone() + missing = np.ones(len(tracks), dtype=bool) + position_rows = positions.detach().cpu().numpy() + donors_cpu = scoring["donor_position"].detach().cpu().numpy() + acceptors_cpu = scoring["acceptor_position"].detach().cpu().numpy() + valid_indices = np.flatnonzero(valid.detach().cpu().numpy()) + lookups = [ + {int(coordinate): index for index, coordinate in enumerate(axis) if coordinate >= 0} + for axis in position_rows + ] + for index in valid_indices: + half = int(halves_cpu[index]) + row = lookups[2 * half].get(int(donors_cpu[index])) + column = lookups[2 * half + 1].get(int(acceptors_cpu[index])) + if row is not None and column is not None: + queries[index, 0] = row + queries[index, 1] = column + missing[index] = False + return (torch.from_numpy(queries).to(device).unsqueeze(0), valid, + torch.from_numpy(missing).to(device)) + + +def _model_inputs(sample: dict) -> tuple[dict, Tensor, Tensor]: + queries, query_valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + sample["site_positions"], + sample["junction_track_halves"], + ) + if (query_valid & missing).any(): + raise ValueError("trusted scoring coordinates do not match target positions") + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + return { + "dna": sample["dna"].unsqueeze(0), + "usage_positions": sample["usage_positions"].unsqueeze(0), + "junction_site_positions": sample["site_positions"].unsqueeze(0), + "junction_queries": queries, + }, junction_target, query_valid + + +def _losses(outputs: dict[str, Tensor], sample: dict, junction_target: Tensor, + junction_valid: Tensor) -> dict[str, Tensor]: + losses = {} + for family in DENSE_FAMILIES: + target = torch.asinh(sample["dense"][family]["values"]).unsqueeze(0) + mask = sample["dense"][family]["mask"].unsqueeze(0) + residual = torch.where(mask, outputs[family] - target, 0.0) + losses[family] = residual.square().sum() / mask.sum().clamp_min(1) + losses["splice_sites"] = F.binary_cross_entropy_with_logits( + outputs["splice_sites"], sample["splice_sites"].unsqueeze(0) + ) + usage_target = torch.asinh(sample["splice_usage"]["values"]).unsqueeze(0) + usage_mask = sample["splice_usage"]["mask"].unsqueeze(0) + usage_residual = torch.where(usage_mask, outputs["splice_usage"] - usage_target, 0.0) + losses["splice_usage"] = usage_residual.square().sum() / usage_mask.sum().clamp_min(1) + junction_residual = outputs["junctions"][0] - junction_target[0] + losses["junctions"] = ( + junction_residual[junction_valid].square().mean() + if junction_valid.any() else junction_residual.sum() * 0 + ) + contact_target = torch.asinh(sample["contact_maps"]).unsqueeze(0) + contact_mask = sample["contact_map_mask"].unsqueeze(0) + contact_residual = torch.where( + contact_mask, outputs["contact_maps"] - contact_target, 0.0 + ) + losses["contact_maps"] = contact_residual.square().sum() / contact_mask.sum().clamp_min(1) + losses["total"] = torch.stack([losses[name] for name in ALL_FAMILIES]).mean() + return losses + + +def _validation_inference( + model: CompactModel, sample: dict +) -> tuple[dict[str, Tensor], Tensor, Tensor, Tensor]: + """Use the same predicted-position path as a formal v3 submission.""" + trunk = model._trunk(sample["dna"].unsqueeze(0)) + dense_features = model._dense_features(trunk) + outputs = { + family: model.dense_heads[family](dense_features).float() + for family in DENSE_FAMILIES + } + all_sites = model._splice_site_logits(trunk) + target_start = (model.config.input_length - model.config.target_length) // 2 + outputs["splice_sites"] = all_sites[:, target_start:target_start + model.config.target_length] + usage_bins = model.splice_usage_head(dense_features).float() + usage_indices = torch.div(sample["usage_positions"], model.config.dense_bin_size, + rounding_mode="floor") + outputs["splice_usage"] = usage_bins[:, usage_indices] + predicted_positions = model.select_junction_positions(all_sites) + + matched, valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + predicted_positions[0], + sample["junction_track_halves"], + ) + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + junction_prediction = torch.zeros(len(valid), device=valid.device) + if (~missing & valid).any(): + selected = ~missing & valid + junction_prediction[selected] = model._junction_predictions( + trunk, predicted_positions, matched[:, selected] + )[0] + outputs["junctions"] = junction_prediction.unsqueeze(0) + + contact = trunk.reshape( + 1, model.config.contact_bins, -1, model.config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + outputs["contact_maps"] = (0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row])).float() + return outputs, junction_target, valid, missing + + +def _atomic_save(payload: dict, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(f".{path.name}.tmp") + torch.save(payload, temporary) + os.replace(temporary, path) + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(8 * 1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _resume_position(payload: dict, *, examples: int, epochs: int) -> tuple[int, int]: + epoch = int(payload["epoch"]) + step = int(payload["global_step"]) + kind = payload.get("checkpoint_kind") + if kind not in (None, "epoch") or step != epoch * examples: + raise ValueError("only a completed-epoch checkpoint can resume training") + if payload.get("examples", examples) != examples or payload.get("schedule_epochs", epochs) != epochs: + raise ValueError("checkpoint data size or training schedule differs from this run") + return epoch, step + + +def train(args: argparse.Namespace) -> None: + # Multi-GPU with `torchrun --nproc-per-node N train_eval.py train ...`: one example per GPU per + # step (data parallel), rank 0 writes checkpoints and logs. Model size is bounded by the + # training budget, not by a parameter cap. + rank, local_rank, world = _distributed() + config = ModelConfig.from_yaml(args.config) + device = torch.device("cuda", local_rank) if world > 1 else _device(args.device) + if world > 1: + torch.cuda.set_device(device) + torch.manual_seed(args.seed) + model = CompactModel(config).to(device) + parameters = count_parameters(model) + optimizer = torch.optim.AdamW( + model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay + ) + scaler = torch.amp.GradScaler("cuda", enabled=device.type == "cuda") + full_dataset = AlphaGenomeDataset(args.input, "train") + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + start_epoch, global_step = 0, 0 + if args.resume: + payload = torch.load(args.resume, map_location="cpu", weights_only=True) + if payload.get("config") != config.to_dict(): + raise ValueError("checkpoint configuration does not match --config") + model.load_state_dict(payload["model"], strict=True) + optimizer.load_state_dict(payload["optimizer"]) + scaler.load_state_dict(payload.get("scaler", {})) + start_epoch, global_step = _resume_position(payload, examples=size, epochs=args.epochs) + per_rank = math.ceil(size / world) + steps = max(1, per_rank * args.epochs) + module = model + if world > 1: + model = torch.nn.parallel.DistributedDataParallel( + model, device_ids=[local_rank], find_unused_parameters=True) + sampler = (torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world, rank=rank, + shuffle=True, seed=args.seed) + if world > 1 else None) + stop_after_epoch = args.stop_after_epoch or args.epochs + output = args.output.resolve() + output.mkdir(parents=True, exist_ok=True) + started = time.monotonic() + if rank == 0: + print(json.dumps({"event": "train_start", "parameters": parameters, "examples": size, + "device": str(device), "world_size": world}), flush=True) + + for epoch in range(start_epoch, stop_after_epoch): + model.train() + if sampler is not None: + sampler.set_epoch(epoch) + loader = _loader(dataset, workers=args.workers, shuffle=True, sampler=sampler) + for sample in loader: + sample = _to_device(sample, device) + inputs, junction_target, junction_valid = _model_inputs(sample) + progress = min((global_step + 1) / steps, 1.0) + learning_rate = args.min_learning_rate + 0.5 * ( + args.learning_rate - args.min_learning_rate + ) * (1.0 + math.cos(math.pi * progress)) + for group in optimizer.param_groups: + group["lr"] = learning_rate + optimizer.zero_grad(set_to_none=True) + with _autocast(device): + predictions = model(**inputs) + losses = _losses(predictions, sample, junction_target, junction_valid) + if not torch.isfinite(losses["total"]): + raise FloatingPointError(f"non-finite loss at step {global_step}") + scaler.scale(losses["total"]).backward() + scaler.unscale_(optimizer) + gradient_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_gradient_norm) + scaler.step(optimizer) + scaler.update() + global_step += 1 + if rank == 0 and (global_step == 1 or global_step % args.log_every == 0): + print( + json.dumps( + { + "event": "train_step", + "epoch": epoch + 1, + "step": global_step, + "loss": float(losses["total"].detach()), + "gradient_norm": float(gradient_norm), + "learning_rate": learning_rate, + "elapsed_seconds": time.monotonic() - started, + } + ), + flush=True, + ) + if rank == 0 and global_step % args.save_every == 0: + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch, + "global_step": global_step, + "checkpoint_kind": "progress", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "progress.pt", + ) + if world > 1: + torch.distributed.barrier() + if rank != 0: + continue + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch + 1, + "global_step": global_step, + "checkpoint_kind": "epoch", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "last.pt", + ) + if world > 1: + torch.distributed.barrier() + torch.distributed.destroy_process_group() + if rank != 0: + return + checkpoint = output / "last.pt" + manifest = { + "schema": "alphagenome-50m-real/v3", + "checkpoint": {"path": checkpoint.name, "sha256": _sha256(checkpoint)}, + "parameters": parameters, + "config": config.to_dict(), + "output_families": list(ALL_FAMILIES), + "epochs": args.epochs, + "completed_epochs": stop_after_epoch, + "global_step": global_step, + } + (output / "model_manifest.json").write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "train_complete", **manifest}), flush=True) + + +class PerTrackMoments: + def __init__(self, tracks: int): + self.n = np.zeros(tracks, dtype=np.int64) + self.sum_x = np.zeros(tracks, dtype=np.float64) + self.sum_y = np.zeros(tracks, dtype=np.float64) + self.sum_x2 = np.zeros(tracks, dtype=np.float64) + self.sum_y2 = np.zeros(tracks, dtype=np.float64) + self.sum_xy = np.zeros(tracks, dtype=np.float64) + + def update(self, prediction: Tensor, target: Tensor, mask: Tensor | None = None) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1, prediction.shape[-1]) + y = target.detach().double().cpu().numpy().reshape(-1, target.shape[-1]) + valid = np.ones_like(y, dtype=bool) if mask is None else mask.detach().cpu().numpy().reshape(y.shape).astype(bool) + self.n += valid.sum(axis=0) + self.sum_x += np.where(valid, x, 0).sum(axis=0, dtype=np.float64) + self.sum_y += np.where(valid, y, 0).sum(axis=0, dtype=np.float64) + self.sum_x2 += np.where(valid, x * x, 0).sum(axis=0, dtype=np.float64) + self.sum_y2 += np.where(valid, y * y, 0).sum(axis=0, dtype=np.float64) + self.sum_xy += np.where(valid, x * y, 0).sum(axis=0, dtype=np.float64) + + def update_indexed(self, prediction: Tensor, target: Tensor, track: Tensor) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1) + y = target.detach().double().cpu().numpy().reshape(-1) + indices = track.detach().cpu().numpy().reshape(-1).astype(np.int64) + self.n += np.bincount(indices, minlength=len(self.n)) + self.sum_x += np.bincount(indices, weights=x, minlength=len(self.n)) + self.sum_y += np.bincount(indices, weights=y, minlength=len(self.n)) + self.sum_x2 += np.bincount(indices, weights=x * x, minlength=len(self.n)) + self.sum_y2 += np.bincount(indices, weights=y * y, minlength=len(self.n)) + self.sum_xy += np.bincount(indices, weights=x * y, minlength=len(self.n)) + + def report(self) -> dict: + numerator = self.n * self.sum_xy - self.sum_x * self.sum_y + denominator = np.sqrt( + np.maximum(self.n * self.sum_x2 - self.sum_x**2, 0) + * np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + ) + target_ss = np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + scorable = (self.n >= 2) & (target_ss > 0) + pearson = np.full_like(numerator, np.nan) + nonconstant = scorable & (denominator > 0) + pearson[nonconstant] = numerator[nonconstant] / denominator[nonconstant] + pearson[scorable & ~nonconstant] = 0.0 + return { + "mean_pearson": float(pearson[scorable].mean()) if scorable.any() else 0.0, + "valid_tracks": int(scorable.sum()), + "per_track_pearson": [float(value) if np.isfinite(value) else None for value in pearson], + "values_per_track": self.n.tolist(), + } + + +def evaluate(args: argparse.Namespace) -> None: + device = _device(args.device) + payload = torch.load(args.checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config).to(device) + model.load_state_dict(payload["model"], strict=True) + model.eval() + full_dataset = AlphaGenomeDataset(args.input, args.split) + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + metrics = { + **{family: PerTrackMoments(config_value) for family, config_value in { + "atac": 167, "dnase": 305, "procap": 12, "cage": 546, + "rna_seq": 667, "chip_tf": 1617, "chip_histone": 1116, + }.items()}, + "splice_sites": PerTrackMoments(4), + "splice_usage": PerTrackMoments(config.splice_usage_tracks), + "junctions": PerTrackMoments(config.junction_tracks), + "contact_maps": PerTrackMoments(config.contact_tracks), + } + with torch.inference_mode(): + for index, sample in enumerate(_loader(dataset, workers=args.workers, shuffle=False)): + sample = _to_device(sample, device) + outputs, junction_target, junction_valid, junction_missing = ( + _validation_inference(model, sample) + ) + # Match predict.py: FP32 inference is serialized to FP16 Zarr values. + outputs = {name: value.half().float() for name, value in outputs.items()} + for family in DENSE_FAMILIES: + metrics[family].update( + outputs[family][0], + torch.asinh(sample["dense"][family]["values"]), + sample["dense"][family]["mask"], + ) + metrics["splice_sites"].update(outputs["splice_sites"][0], sample["splice_sites"]) + metrics["splice_usage"].update( + outputs["splice_usage"][0], + torch.asinh(sample["splice_usage"]["values"]), + sample["splice_usage"]["mask"], + ) + junction_track = sample["junction_scoring"]["track"] + metrics["junctions"].update_indexed( + outputs["junctions"][0][junction_valid], + junction_target[0][junction_valid], + junction_track[junction_valid], + ) + metrics["contact_maps"].update( + outputs["contact_maps"][0], + torch.asinh(sample["contact_maps"]), + sample["contact_map_mask"], + ) + if index == 0 or (index + 1) % args.log_every == 0 or index + 1 == size: + print(json.dumps({"event": "evaluate_progress", "examples": index + 1, "total": size}), flush=True) + families = {name: accumulator.report() for name, accumulator in metrics.items()} + valid_family_scores = [item["mean_pearson"] for item in families.values()] + report = { + "schema": "alphagenome-50m-evaluation/v3", + "split": args.split, + "examples": size, + "metric_space": ( + "asinh continuous targets and binary splice-site targets; " + "junction Pearson uses hidden coordinate queries and predicted endpoints; " + "FP32 inference rounded to FP16 prediction values" + ), + "families": families, + "overall_mean_family_pearson": float(np.mean(valid_family_scores)), + } + args.report.parent.mkdir(parents=True, exist_ok=True) + args.report.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "evaluate_complete", "overall_mean_family_pearson": report["overall_mean_family_pearson"]}), flush=True) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + common = argparse.ArgumentParser(add_help=False) + common.add_argument("--input", required=True, type=Path) + common.add_argument("--config", type=Path, default=DEFAULT_CONFIG) + common.add_argument("--workers", type=int, default=2) + common.add_argument("--max-examples", type=int, default=0) + common.add_argument("--device", choices=("auto", "cuda", "cpu"), default="auto") + common.add_argument("--log-every", type=int, default=25) + subparsers = parser.add_subparsers(dest="command", required=True) + train_parser = subparsers.add_parser("train", parents=[common]) + train_parser.add_argument("--output", required=True, type=Path) + train_parser.add_argument("--epochs", type=int, default=10) + train_parser.add_argument("--stop-after-epoch", type=int) + train_parser.add_argument("--learning-rate", type=float, default=2e-4) + train_parser.add_argument("--min-learning-rate", type=float, default=2e-5) + train_parser.add_argument("--weight-decay", type=float, default=1e-4) + train_parser.add_argument("--max-gradient-norm", type=float, default=5.0) + train_parser.add_argument("--save-every", type=int, default=250) + train_parser.add_argument("--seed", type=int, default=17) + train_parser.add_argument("--resume", type=Path) + train_parser.set_defaults(run=train) + evaluate_parser = subparsers.add_parser("evaluate", parents=[common]) + evaluate_parser.add_argument("--checkpoint", required=True, type=Path) + evaluate_parser.add_argument("--split", choices=("train", "valid", "test"), required=True) + evaluate_parser.add_argument("--report", required=True, type=Path) + evaluate_parser.set_defaults(run=evaluate) + args = parser.parse_args() + if args.workers < 0 or args.max_examples < 0: + parser.error("workers/max-examples must be nonnegative") + if args.command == "train" and ( + args.epochs < 1 + or (args.stop_after_epoch is not None + and not 1 <= args.stop_after_epoch <= args.epochs) + ): + parser.error("epochs must be positive and stop-after-epoch must be within the schedule") + args.run(args) + + +if __name__ == "__main__": + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/single_prediction.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/single_prediction.py new file mode 100644 index 0000000000000000000000000000000000000000..a47022029461e5c8097b3a653af8b3407196d67d --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/single_prediction.py @@ -0,0 +1,97 @@ +"""The parent single-orientation decoder, preserving float32 until ensemble averaging.""" +import numpy as np +import torch +from prediction_contract import DENSE_TRACKS, MAX_JUNCTION_PAIRS, MAX_USAGE_SITES +from splice_output import splice_output_scores + +COMPRESSOR = None + +class ArrayCollector: + def __init__(self): + self.arrays = {} + def create_dataset(self, name, *, data, **kwargs): + self.arrays[name] = np.asarray(data) + +def single_prediction(model, dna): + config = model.config + group = ArrayCollector() + trunk = model._trunk(dna) + dense_features = model._dense_features(trunk) + for family in DENSE_TRACKS: + values = model.dense_heads[family](dense_features)[0] + group.create_dataset(family, data=values.cpu().numpy(), compressor=COMPRESSOR) + + all_site_logits = model._splice_site_logits(trunk) + target_start = (config.input_length - config.target_length) // 2 + group.create_dataset( + "splice_sites", + data=splice_output_scores(all_site_logits[0, target_start:target_start + config.target_length]).cpu().numpy(), + chunks=(4096, 4), compressor=COMPRESSOR, + ) + predicted_positions = model.select_junction_positions(all_site_logits)[0] + + # Usage at the model's own predicted sites (v4 is sparse; unlisted hidden sites score 0). + usage_bins = model.splice_usage_head(dense_features)[0].float() + sites = predicted_positions.cpu().numpy().astype(np.int64).reshape(-1) + sites = np.unique(sites[(sites >= target_start) & (sites < target_start + config.target_length)] + - target_start)[:MAX_USAGE_SITES] + group.create_dataset("splice_usage_positions", data=sites.astype(np.int32), compressor=COMPRESSOR) + usage = usage_bins[torch.as_tensor(sites // 128, device=usage_bins.device)] if len(sites) \ + else usage_bins[:0] + group.create_dataset("splice_usage", data=usage.cpu().numpy(), + chunks=(1024, 734), compressor=COMPRESSOR) + + # Junctions: score every donor x acceptor pair of each strand, keep the strongest pairs. + positions_batch = predicted_positions[None] + site_factors = model.junction_site_factors(trunk, positions_batch) + halves = model.junction_track_half + candidates = [] + for row_start in range(0, 512, 128): + for column_start in range(0, 512, 128): + block = torch.cat([ + model.junction_block( + trunk, positions_batch, row_start, row_start + 128, + column_start, column_start + 128, track_start, + min(track_start + 64, config.junction_tracks), site_factors, + )[0] + for track_start in range(0, config.junction_tracks, 64) + ], dim=-1).float() # [128, 128, 734] + for half in (0, 1): + donors = predicted_positions[2 * half, row_start:row_start + 128] + acceptors = predicted_positions[2 * half + 1, column_start:column_start + 128] + valid = (donors[:, None] >= 0) & (acceptors[None, :] >= 0) + # Transcript direction is determined by the public strand half. + forward = donors[:, None] < acceptors[None, :] if half == 0 else donors[:, None] > acceptors[None, :] + valid = valid & forward + strength = block[..., halves == half].amax(dim=-1).masked_fill(~valid, -torch.inf) + keep = min(MAX_JUNCTION_PAIRS // 2, int(valid.sum())) + if not keep: + continue + best = strength.flatten().topk(keep) + rows, columns = best.indices // 128, best.indices % 128 + candidates.append((best.values, torch.full_like(rows, half), donors[rows], + acceptors[columns], block[rows, columns])) + if candidates: + score, half, donor, acceptor, values = (torch.cat(x) for x in zip(*candidates)) + order = score.topk(min(MAX_JUNCTION_PAIRS, len(score))).indices + half, donor, acceptor, values = half[order], donor[order], acceptor[order], values[order] + else: + half = donor = acceptor = torch.zeros(0, dtype=torch.long) + values = torch.zeros(0, config.junction_tracks) + group.create_dataset("junction_half", data=half.cpu().numpy().astype(np.int8), compressor=COMPRESSOR) + group.create_dataset("junction_donor", data=donor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junction_acceptor", data=acceptor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junctions", data=values.cpu().numpy(), + chunks=(4096, 734), compressor=COMPRESSOR) + + contact = trunk.reshape( + 1, config.contact_bins, -1, config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + contact_values = 0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row]) + group.create_dataset( + "contact_maps", data=contact_values[0].cpu().numpy(), + chunks=(8192, 28), compressor=COMPRESSOR, + ) + return group.arrays diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/splice_calibration.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/splice_calibration.json new file mode 100644 index 0000000000000000000000000000000000000000..470734a574a4b9b9f2f34fd3bcae0007db3737f6 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/splice_calibration.json @@ -0,0 +1,33 @@ +{ + "training_examples": 256, + "training_indices_sha256": "b82df9ae11e95d9be5d6e5f85839cf23e25bbbe0a195eaee266ff52d366800dc", + "training_positive_counts": [ + 3968.0, + 4160.0, + 3874.0, + 3921.0 + ], + "training_prior": [ + 7.883707682291667e-05, + 8.265177408854167e-05, + 7.696946461995442e-05, + 7.790327072143555e-05 + ], + "effective_balanced_loss_prior": [ + 0.3203125, + 0.3125, + 0.30859375, + 0.30859375 + ], + "logit_offset": [ + -8.695712089538574, + -8.612334251403809, + -8.665323257446289, + -8.653263092041016 + ], + "caveat": "marginal prior correction; balanced loss is normalized separately in each window", + "link": "sigmoid(logits + training_prior_offset)", + "training_split": "train", + "seed": 1729, + "development_labels_used_to_fit_offsets": false +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/splice_output.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/splice_output.py new file mode 100644 index 0000000000000000000000000000000000000000..1081264d9cf4b4473f4fe53c97ff02b2c3b249e3 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/splice_output.py @@ -0,0 +1,11 @@ +"""Shared splice output link; constants estimated only from training targets.""" +import json +from pathlib import Path +import torch + +_OFFSETS = json.loads((Path(__file__).with_name('splice_calibration.json')).read_text())['logit_offset'] + +def splice_output_scores(logits): + logits = logits.float() + offset = torch.tensor(_OFFSETS, device=logits.device, dtype=logits.dtype) + return torch.sigmoid(logits + offset) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/strand_permutations.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/strand_permutations.json new file mode 100644 index 0000000000000000000000000000000000000000..044c090773f20347601dcd4f0983c4dfd4c0ecc2 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/strand_permutations.json @@ -0,0 +1,5959 @@ +{ + "schema": "seqfunc-strand-mapping/v1", + "source": "public track metadata; no target values", + "source_sha256": "23450e6ff9463c64d271d7014f8a798875bc95186c36aeb34609c7dc1d62cd5f", + "permutations": { + "atac": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 83, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + 91, + 92, + 93, + 94, + 95, + 96, + 97, + 98, + 99, + 100, + 101, + 102, + 103, + 104, 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a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/train_batched.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/train_batched.py new file mode 100644 index 0000000000000000000000000000000000000000..df00b798c655555704cd6c8abbd7e4eacedd0ba3 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/train_batched.py @@ -0,0 +1,168 @@ +"""Resumable single-H100 training with equal-per-example batched losses. + +global_step counts training examples, optimizer_steps counts updates. Every epoch +uses the complete deterministic permutation, including its final partial batch. +""" +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time +import torch +from numcodecs import blosc +from torch.utils.data import DataLoader, Subset +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save, _to_device +from batched_training import list_collate, prepare_batch, equal_example_losses + +def main(): + p=argparse.ArgumentParser(description=__doc__) + p.add_argument('--input',type=Path,default=Path('/workspace/data')) + p.add_argument('--output',type=Path,required=True) + p.add_argument('--config',type=Path,required=True) + p.add_argument('--resume',type=Path) + p.add_argument('--batch-size',type=int,default=8) + p.add_argument('--epochs',type=int,default=14) + p.add_argument('--stop-at',type=int,default=0) + p.add_argument('--workers',type=int,default=6) + p.add_argument('--threads',type=int,default=2) + p.add_argument('--seed',type=int,default=17) + p.add_argument('--learning-rate',type=float,default=2e-4) + p.add_argument('--min-learning-rate',type=float,default=2e-5) + p.add_argument('--warmup',type=int,default=4000,help='Warmup in training examples') + p.add_argument('--objective',choices=['balanced_splice','variance_balanced'],default='balanced_splice') + p.add_argument('--loss-scales',type=Path) + p.add_argument('--save-every',type=int,default=10000,help='Training examples between progress checkpoints') + p.add_argument('--keep-every',type=int,default=50000) + p.add_argument('--log-every',type=int,default=50,help='Optimizer updates between logs') + args=p.parse_args() + if min(args.batch_size,args.epochs,args.save_every,args.log_every) < 1: + raise ValueError('batch, epoch, checkpoint and log intervals must be positive') + os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:22]) + torch.set_num_threads(args.threads);torch.set_num_interop_threads(1);blosc.set_nthreads(1) + torch.manual_seed(args.seed) + device=torch.device('cuda:0') + config=ModelConfig.from_yaml(args.config) + model=CompactModel(config).to(device) + optimizer=torch.optim.AdamW(model.parameters(),lr=args.learning_rate,weight_decay=1e-4) + dataset=AlphaGenomeDataset(args.input,'train') + model.set_junction_track_halves(dataset.junction_track_halves) + args.output.mkdir(parents=True,exist_ok=True) + total=len(dataset)*args.epochs + step,updates,elapsed_before=0,0,0. + lineage,scales={},{} + if args.loss_scales: + scale_source=json.loads(args.loss_scales.read_text()) + assert scale_source['split']=='train' + scales=scale_source['family_loss_scales'] + lineage['loss_scale_provenance']={k:v for k,v in scale_source.items() if k!='junction_moments'} + if args.resume: + state=torch.load(args.resume,map_location='cpu',weights_only=True) + if state['schema'] not in ['seqfunc-single/v1','seqfunc-batched/v1']: + raise ValueError('only locally trained checkpoints may resume') + if ModelConfig(**state['config']).to_dict()!=config.to_dict() or state['examples']!=len(dataset): + raise ValueError('resume architecture or dataset differs') + if state['schedule_epochs']!=args.epochs or state['objective']!=args.objective or state['seed']!=args.seed: + raise ValueError('resume schedule, objective or seed differs') + previous=state.get('training_arguments',{}) + for field in ['learning_rate','min_learning_rate','warmup']: + if field in previous and previous[field]!=getattr(args,field): + raise ValueError(f'resume {field} differs') + if state['schema']=='seqfunc-batched/v1' and previous['batch_size']!=args.batch_size: + raise ValueError('batched resume must keep its batch size') + if scales and scales!=state.get('family_loss_scales',{}): + raise ValueError('resume family scales differ') + scales=state.get('family_loss_scales',{}) + lineage.update({k:state[k] for k in ['intervention','interventions','loss_scale_provenance'] if k in state}) + model.load_state_dict(state['model'],strict=True) + optimizer.load_state_dict(state['optimizer']) + step=state['global_step'];updates=state.get('optimizer_steps',step) + elapsed_before=state.get('elapsed_seconds',0.) + torch.set_rng_state(state['rng_cpu']);torch.cuda.set_rng_state(state['rng_cuda'],device) + if state['schema']=='seqfunc-single/v1': + with args.resume.open('rb') as f: + digest=hashlib.file_digest(f,'sha256').hexdigest() + history=list(lineage.get('interventions',[])) + if lineage.get('intervention') and lineage['intervention'] not in history: + history.append(lineage['intervention']) + change={'kind':'batch_size_change','from':1,'to':args.batch_size,'at_example':step, + 'parent_sha256':digest,'parameters_optimizer_rng_unchanged_at_start':True} + history.append(change);lineage.update(intervention=change,interventions=history) + if args.objective=='variance_balanced': + expected=set(model.dense_heads)|{'splice_usage','junctions','contact_maps'} + if set(scales)!=expected or not all(math.isfinite(v) and v>0 for v in scales.values()): + raise ValueError('fixed positive training-derived scales required') + elif scales: + raise ValueError('unscaled objective cannot carry family scales') + start=time.monotonic() + def emit(event,**values): + print(json.dumps({'event':event,'step':step,'optimizer_steps':updates, + 'elapsed_seconds':elapsed_before+time.monotonic()-start,**values}),flush=True) + def save(name): + _atomic_save({**lineage,'schema':'seqfunc-batched/v1','model':model.state_dict(), + 'optimizer':optimizer.state_dict(),'config':config.to_dict(), + 'global_step':step,'optimizer_steps':updates,'epoch':step//len(dataset),'offset':step%len(dataset), + 'examples':len(dataset),'schedule_epochs':args.epochs,'objective':args.objective,'seed':args.seed, + 'family_loss_scales':scales, + 'training_arguments':{k:str(v) if isinstance(v,Path) else v for k,v in vars(args).items()}, + 'rng_cpu':torch.get_rng_state(),'rng_cuda':torch.cuda.get_rng_state(device), + 'elapsed_seconds':elapsed_before+time.monotonic()-start, + 'provenance':{'data':str(args.input),'split':'train','pretrained':False,'world_size':1, + 'device':torch.cuda.get_device_name(0),'batch_reduction':'equal per example', + 'train_manifest_sha256':hashlib.sha256((args.input/'public/train.parquet').read_bytes()).hexdigest()}, + },args.output/name) + emit('train_start',parameters=count_parameters(model),examples=len(dataset),batch_size=args.batch_size, + objective=args.objective,config=config.to_dict(),schedule_examples=total,family_loss_scales=scales) + if args.stop_at and step>=args.stop_at: + raise ValueError('stop-at must exceed the resumed example count') + model.train() + for epoch in range(step//len(dataset),args.epochs): + order=torch.randperm(len(dataset),generator=torch.Generator().manual_seed(args.seed+epoch)).tolist() + offset=step%len(dataset) + emit('epoch_order',epoch=epoch+1,offset=offset,count=len(order), + permutation_sha256=hashlib.sha256(torch.tensor(order,dtype=torch.int64).numpy().tobytes()).hexdigest()) + loader=DataLoader(Subset(dataset,order[offset:]),batch_size=args.batch_size,shuffle=False, + num_workers=args.workers,pin_memory=True,collate_fn=list_collate, + persistent_workers=False,prefetch_factor=2 if args.workers else None) + for cpu_samples in loader: + if args.stop_at: + cpu_samples=cpu_samples[:args.stop_at-step] + inputs,targets,valid=prepare_batch(cpu_samples) + inputs=_to_device(inputs,device) + targets=[x.to(device,non_blocking=True) for x in targets] + valid=[x.to(device,non_blocking=True) for x in valid] + samples=[_to_device(s,device) for s in cpu_samples] + progress=min(max(step-args.warmup,0)/max(total-args.warmup,1),1) + lr=args.min_learning_rate+.5*(args.learning_rate-args.min_learning_rate)*(1+math.cos(math.pi*progress)) + lr*=min((step+len(samples))/max(args.warmup,1),1) + for group in optimizer.param_groups: group['lr']=lr + optimizer.zero_grad(set_to_none=True) + with torch.autocast('cuda',dtype=torch.bfloat16): + outputs=model(**inputs) + losses,raw=equal_example_losses(outputs,samples,targets,valid,scales=scales) + if not torch.isfinite(losses['total']): + raise FloatingPointError(f'nonfinite loss at {step}') + losses['total'].backward() + norm=torch.nn.utils.clip_grad_norm_(model.parameters(),5,error_if_nonfinite=True) + optimizer.step() + before=step;step+=len(samples);updates+=1 + if updates==1 or updates%args.log_every==0: + emit('train_step',epoch=epoch+1,example_ids=[s['example_id'] for s in samples], + learning_rate=lr,gradient_norm=float(norm),actual_batch_size=len(samples), + losses={k:float(v.detach()) for k,v in losses.items()}, + raw_losses={k:float(v.detach()) for k,v in raw.items()}, + peak_gpu_bytes=torch.cuda.max_memory_allocated()) + if step//args.save_every>before//args.save_every: save('progress.pt') + if args.keep_every and step//args.keep_every>before//args.keep_every: save(f'step-{step:06d}.pt') + if step%len(dataset)==0: + save('last.pt');save(f'epoch-{epoch+1:02d}.pt') + emit('epoch_complete',completed_epochs=epoch+1) + if args.stop_at and step>=args.stop_at: + save('last.pt');emit('train_segment_complete',completed_epochs=step//len(dataset),offset=step%len(dataset)) + return + emit('train_complete') + +if __name__=='__main__': main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/train_single.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/train_single.py new file mode 100644 index 0000000000000000000000000000000000000000..03024393206b918eac3ae23a87c09173848b0d15 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/train_single.py @@ -0,0 +1,158 @@ +"""Resumable single-H100 training; every epoch permutes every eligible training interval.""" +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import torch +from numcodecs import blosc +from torch.utils.data import DataLoader, Subset + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset, single_example_collate +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save, _losses, _model_inputs, _to_device + + +def balanced_splice_loss(logits, target): + """Equal positive/negative contributions per type, using only training labels. + + An absent class contributes its negative loss rather than dividing by zero or being dropped. + """ + dims = tuple(range(target.ndim - 1)) + positive = target.sum(dims) + negative = (1 - target).sum(dims) + pos_loss = (torch.nn.functional.softplus(-logits) * target).sum(dims) / positive.clamp_min(1) + neg_loss = (torch.nn.functional.softplus(logits) * (1 - target)).sum(dims) / negative.clamp_min(1) + return torch.where(positive > 0, 0.5 * (pos_loss + neg_loss), neg_loss).mean() + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--input', type=Path, default=Path('/workspace/data')) + p.add_argument('--output', type=Path, required=True) + p.add_argument('--config', type=Path, default=Path(__file__).parent / 'reference/alphagenome_50m/alphagenome_50m.yaml') + p.add_argument('--resume', type=Path) + p.add_argument('--objective', choices=['reference', 'balanced_splice'], default='balanced_splice') + p.add_argument('--epochs', type=int, default=4) + p.add_argument('--stop-at', type=int, default=0, help='Stop after this total number of examples, preserving exact sampler position') + p.add_argument('--workers', type=int, default=8) + p.add_argument('--threads', type=int, default=4) + p.add_argument('--seed', type=int, default=17) + p.add_argument('--learning-rate', type=float, default=2e-4) + p.add_argument('--min-learning-rate', type=float, default=2e-5) + p.add_argument('--warmup', type=int, default=500) + p.add_argument('--save-every', type=int, default=2000) + p.add_argument('--keep-every', type=int, default=10000, help='Keep immutable intermediate checkpoints') + p.add_argument('--log-every', type=int, default=100) + args = p.parse_args() + torch.set_num_threads(args.threads) + torch.set_num_interop_threads(1) + blosc.set_nthreads(1) + if hasattr(os, 'sched_getaffinity'): + os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) + torch.manual_seed(args.seed) + device = torch.device('cuda:0') + config = ModelConfig.from_yaml(args.config) + model = CompactModel(config).to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=1e-4) + dataset = AlphaGenomeDataset(args.input, 'train') + model.set_junction_track_halves(dataset.junction_track_halves) + args.output.mkdir(parents=True, exist_ok=True) + total = len(dataset) * args.epochs + step, elapsed_before = 0, 0.0 + lineage = {} + if args.resume: + state = torch.load(args.resume, map_location='cpu', weights_only=True) + lineage = {k: state[k] for k in ['intervention', 'interventions'] if k in state} + if state['config'] != config.to_dict() or state['examples'] != len(dataset): + raise ValueError('resume config or dataset size differs') + if state['schedule_epochs'] != args.epochs or state['objective'] != args.objective: + raise ValueError('resume schedule or objective differs') + if state['seed'] != args.seed: + raise ValueError('resume shuffle seed differs') + previous = state.get('training_arguments', {}) + for field in ['learning_rate', 'min_learning_rate', 'warmup']: + if field in previous and previous[field] != getattr(args, field): + raise ValueError(f'resume {field} differs') + model.load_state_dict(state['model'], strict=True) + optimizer.load_state_dict(state['optimizer']) + step = state['global_step'] + elapsed_before = state.get('elapsed_seconds', 0.0) + torch.set_rng_state(state['rng_cpu']) + torch.cuda.set_rng_state(state['rng_cuda'], device) + start = time.monotonic() + def emit(event, **values): + record = {'event': event, 'step': step, 'elapsed_seconds': elapsed_before + time.monotonic() - start, **values} + print(json.dumps(record), flush=True) + def save(name): + _atomic_save({ + **lineage, + 'schema': 'seqfunc-single/v1', 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), 'config': config.to_dict(), + 'global_step': step, 'epoch': step // len(dataset), 'offset': step % len(dataset), + 'examples': len(dataset), 'schedule_epochs': args.epochs, + 'objective': args.objective, 'seed': args.seed, + 'training_arguments': {k: str(v) if isinstance(v, Path) else v for k, v in vars(args).items()}, + 'rng_cpu': torch.get_rng_state(), 'rng_cuda': torch.cuda.get_rng_state(device), + 'elapsed_seconds': elapsed_before + time.monotonic() - start, + 'provenance': {'data': str(args.input), 'split': 'train', 'pretrained': False, + 'device': torch.cuda.get_device_name(0), 'world_size': 1}, + }, args.output / name) + emit('train_start', parameters=count_parameters(model), examples=len(dataset), objective=args.objective, + config=config.to_dict(), schedule_examples=total, device=torch.cuda.get_device_name(0)) + model.train() + for epoch in range(step // len(dataset), args.epochs): + generator = torch.Generator().manual_seed(args.seed + epoch) + order = torch.randperm(len(dataset), generator=generator).tolist() + offset = step % len(dataset) + emit('epoch_order', epoch=epoch + 1, offset=offset, count=len(order), + permutation_sha256=hashlib.sha256(torch.tensor(order, dtype=torch.int64).numpy().tobytes()).hexdigest()) + loader = DataLoader(Subset(dataset, order[offset:]), batch_size=1, shuffle=False, + num_workers=args.workers, pin_memory=True, collate_fn=single_example_collate, + persistent_workers=False, prefetch_factor=2 if args.workers else None) + for sample in loader: + sample = _to_device(sample, device) + inputs, target, valid = _model_inputs(sample) + progress = min(max(step - args.warmup, 0) / max(total - args.warmup, 1), 1) + lr = args.min_learning_rate + 0.5 * (args.learning_rate - args.min_learning_rate) * (1 + math.cos(math.pi * progress)) + lr *= min((step + 1) / max(args.warmup, 1), 1) + for group in optimizer.param_groups: + group['lr'] = lr + optimizer.zero_grad(set_to_none=True) + with torch.autocast('cuda', dtype=torch.bfloat16): + outputs = model(**inputs) + losses = _losses(outputs, sample, target, valid) + if args.objective == 'balanced_splice': + losses['splice_sites'] = balanced_splice_loss(outputs['splice_sites'], sample['splice_sites'][None]) + losses['total'] = torch.stack([v for k, v in losses.items() if k != 'total']).mean() + if not torch.isfinite(losses['total']): + raise FloatingPointError(f'nonfinite loss at {step}') + losses['total'].backward() + norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0, error_if_nonfinite=True) + optimizer.step() + step += 1 + if step == 1 or step % args.log_every == 0: + emit('train_step', epoch=epoch + 1, example_id=sample['example_id'], learning_rate=lr, + gradient_norm=float(norm), losses={k: float(v.detach()) for k, v in losses.items()}, + peak_gpu_bytes=torch.cuda.max_memory_allocated()) + if step % args.save_every == 0: + save('progress.pt') + if args.keep_every and step % args.keep_every == 0: + save(f'step-{step:06d}.pt') + if args.stop_at and step >= args.stop_at: + save('last.pt') + emit('train_segment_complete', completed_epochs=step // len(dataset), offset=step % len(dataset)) + return + save('last.pt') + save(f'epoch-{epoch + 1:02d}.pt') + emit('epoch_complete', completed_epochs=epoch + 1) + emit('train_complete') + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_assets_manifest.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_assets_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..8e314c842cca039da1d2be8da7fff6a146178651 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_assets_manifest.json @@ -0,0 +1,21 @@ +{ + "schema": "seqfunc-training-assets/v1", + "files": { + "training_history/block-checkpoint-cpu-check.json": "d099b0a4d4d6cdd1aab33a6d8efaf3b4a5bddb2be4db0563eed83b3f849a31ef", + "training_history/block-checkpoint-gpu-check.json": "b2e598359fad26fd63c4e7487997432987cfd8c836680726458f62d137df04ce", + "training_history/bytecache-integration_check.json": "a712f359807b414956a87f2486bbc3c6e9288727d1f79e5d0b6fdf321fd9a67b", + "training_history/bytecache-io-amendment.json": "0cb0d61d2ed7de26df12a62c7d4e4df2b77c6704b786c400eb216ad3735a96ac", + "training_history/bytecache-migration-plan.json": "e90379ec449255094a5789321b14ec9cec7692d61fba6900759510a34cd13035", + "training_history/bytecache-preparation.json": "aeb7f326fdddd2dfce6e9df162fd214565b11bb03049d982f0253290d7217caa", + "training_history/bytecache-timing_report.json": "25e6f24f940b815f4765255a8ebb81ad63eda28a4e7c9c412d81b32d78cf1000", + "training_history/execution_change.json": "5035b72c3dbc0e8d73eba0133232d21180cb03325630a1d95f94d35ab90fcdd0", + "training_history/memory-check-interruption.json": "895de55a7ff7871b2e6a053f6033aee02d6f96fa98eb525c80e3a1eeae407d16", + "training_history/migrate_scale_bytecache.py": "8c0a181c44fc6ad480dc0d74f2a803dc41d860b16007753bccc35dad17f78c0a", + "training_history/model_before_block_checkpoint.py": "2a185c9eea285b0dd6e2e05a00e8181c62887c6e26ab9e0e1bd1ca7c8eef66cf", + "training_history/parallel-io-amendment.json": "97b44cda7f5d3f2b70a91f55cce8772ccc46f4a24a537f07f7aa12cc22977a79", + "training_history/parallel-io-integration-check.json": "aa807f5f087b698e1132eadc6aa8dee82ecffc82d06971734836f6896de0174f", + "training_history/parallel-loader4x4-train256.json": "3ef8e42e6e90fee4320add09c54ea8f83fa426f5e9c8b76c2abb00ab5ae62412", + "training_history/reader-before-parallel-io.py": "ad6670f742f4de9898064becc525d0aea98b402abe7b210bb1b26a9eee6402b9", + "training_history/run_scale_study_cached_io.py": "adc1bf9c5d3054f63425b605e7146d2b83a24f933a7f9e795083fde0ad98174b" + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_config.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fa30b0f9f20f1dd25a2c8426ff18d3afab3ba47f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_config.yaml @@ -0,0 +1,32 @@ +attention_head_dim: 64 +channel_growth: 32 +contact_bin_size: 2048 +contact_tracks: 28 +conv_kernel_size: 5 +dense_bin_size: 128 +dna_kernel_size: 15 +dropout: 0.0 +enable_pairwise: false +encoder_width: 32 +gradient_checkpointing: true +head_hidden_size: 256 +hidden_dim: 384 +input_length: 1048576 +junction_rank: 32 +junction_tracks: 734 +local_context_blocks: 6 +local_to_trunk: true +local_width: 32 +mlp_expansion: 4.0 +num_attention_heads: 6 +num_downsampling_blocks: 7 +num_sequence_blocks: 6 +pair_downsample: 16 +pair_update_interval: 2 +pair_width: 32 +residual_rms_cap: 0.1 +splice_context_mode: offset +splice_usage_tracks: 734 +stem_stride: 8 +target_length: 196608 +trunk_bin_size: 1024 diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/block-checkpoint-cpu-check.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/block-checkpoint-cpu-check.json new file mode 100644 index 0000000000000000000000000000000000000000..588d9318734995508b1ece2948b790e2d0ce5172 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/block-checkpoint-cpu-check.json @@ -0,0 +1,10 @@ +{ + "all_11_training_and_evaluation_outputs_bit_exact": true, + "max_absolute_gradient_error": 0.0, + "nonzero_local_heads_and_bridge": true, + "six_local_blocks": true, + "active_branch_caps": true, + "unequal_and_empty_event_queries": true, + "precision": "float32", + "device": "CPU" +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/block-checkpoint-gpu-check.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/block-checkpoint-gpu-check.json new file mode 100644 index 0000000000000000000000000000000000000000..144a03aa8b8a1b0624d424c2cf577788896fdca1 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/block-checkpoint-gpu-check.json @@ -0,0 +1,198 @@ +{ + "weights_discarded": true, + "source_training_examples": 1000, + "same_nonzero_trained_parameters": true, + "precision": "bfloat16 training", + "batch_size": 4, + "input_bp": 1048576, + "training_indices": [ + 37958, + 33184, + 21022, + 30377 + ], + "objective": "variance_balanced", + "benchmarks": [ + { + "policy": "whole_path", + "step_seconds": [ + 1.681253916991409, + 0.45803058601450175, + 0.4365358139912132, + 0.439815385005204, + 0.4368974999815691, + 0.4549180749745574 + ], + "steady_seconds_per_example": 0.11130986799835227, + "peak_allocated_GiB": 40.11026668548584, + "all_11_outputs_and_losses_bit_exact": null, + "all_parameter_gradients_bit_exact": null, + "max_absolute_gradient_error": null, + "gradient_relative_L2_error": null, + "gradient_cosine": null, + "gradient_difference_top10": [] + }, + { + "policy": "whole_path_repeat", + "step_seconds": [ + 2.481021937011974, + 0.4393464530003257, + 0.48240915301721543, + 0.43610669201007113, + 0.4453651010117028, + 0.44543533099931665 + ], + "steady_seconds_per_example": 0.11243313650193158, + "peak_allocated_GiB": 40.10498094558716, + "all_11_outputs_and_losses_bit_exact": true, + "all_parameter_gradients_bit_exact": false, + "max_absolute_gradient_error": 1.52587890625e-05, + "gradient_relative_L2_error": 0.0009534162545958643, + "gradient_cosine": 0.999999545498656, + "gradient_difference_top10": [ + { + "parameter": "local_path.stem.weight", + "max_absolute_error": 1.52587890625e-05, + "relative_L2_error": 0.0011708050442353522, + "reference_L2": 0.038250174700771404 + }, + { + "parameter": "local_path.stem.bias", + "max_absolute_error": 1.52587890625e-05, + "relative_L2_error": 0.0011145246032800173, + "reference_L2": 0.015316175233840698 + }, + { + "parameter": "local_path.blocks.0.contract.weight", + "max_absolute_error": 1.52587890625e-05, + "relative_L2_error": 0.0008722401804490078, + "reference_L2": 0.03117670960545457 + }, + { + "parameter": "local_path.blocks.0.contract.bias", + "max_absolute_error": 1.52587890625e-05, + "relative_L2_error": 0.0007519333551147762, + "reference_L2": 0.020936495583310583 + }, + { + "parameter": "local_projection.weight", + "max_absolute_error": 1.52587890625e-05, + "relative_L2_error": 0.0029496872658462915, + "reference_L2": 0.1874866973862457 + }, + { + "parameter": "encoder.stem.weight", + 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Stop only original trainers/their data workers and idle future checkpoint waiters." +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/bytecache-preparation.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/bytecache-preparation.json new file mode 100644 index 0000000000000000000000000000000000000000..9ce7b02b6f484c9b2d4f61c5bad46e4ed719abf6 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/bytecache-preparation.json @@ -0,0 +1,22 @@ +{ + "created_utc": "2026-10-25T03:47:11.178799+00:00", + "status": "Prepared only, active sources untouched; no migration decision before complete timing and integrated equality check", + "old_source": "/workspace/research/studies/scale13/source-parallel-io", + "new_source": "/workspace/research/studies/loader_io/cached-integration/source", + "only_modified_existing_source": [ + "reference/alphagenome_50m/dataset.py" + ], + "new_files": [ + "reference/alphagenome_50m/direct_read_store.py", + "reference/alphagenome_50m/cached_read_store.py" + ], + "model_trainer_objective_sources_exact": true, + "cache_activation": "Explicit SEQFUNC_TRAIN_BYTECACHE environment path and split=train and include_targets=True; otherwise exact original reader. Zipstore path remains original.", + "namespace": "SHA256ofpublic+labels training manifest digests and original absolute store path/key; every hit checks original bytes SHA/length/key", + "old_dataset_sha256": "dc4263a10fecca979811161da97e731c471468465fe9480bbf2a9aacca68fb7f", + "new_files_sha256": { + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919", + "reference/alphagenome_50m/direct_read_store.py": "ab182b3b687a3b470ff1bbc311a9130cc791974366bdc0a400df5e0e28602c9c", + "reference/alphagenome_50m/cached_read_store.py": "53beb273318ad3603c1a7eec0a66f82887e6b05cabc523cbc33c7a80aa58e688" + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/bytecache-timing_report.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/bytecache-timing_report.json new file mode 100644 index 0000000000000000000000000000000000000000..f65930cac3b7e9dbbaeb4ba5001dcb5488f5bca0 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/bytecache-timing_report.json @@ -0,0 +1,85 @@ +{ + "created_utc": "2026-10-25T03:47:08.875281+00:00", + "plan_sha256": "6e0c89b326b3b5d254a8206167e4ec067a5c6cf5921489a4fa15a55816020e46", + "checkpoint_sha256": "31b67fe7d1b3b691f3d74876ad49e6fc8d096858c646df27aa27ff51c8999cb5", + "modes": [ + { + "repeat": 0, + "mode": "reference", + "fresh_parent_loaded": true, + "initial_optimizer_step_min": 10255.0, + "initial_optimizer_step_max": 10255.0, + "ordered_training_ids_exact": true, + "warmup_examples": 16, + "timed_examples": 240, + "timed_seconds": 139.6908002400014, + "seconds_per_example": 0.5820450010000059, + "timed_dataloader_wait_seconds": 79.04790870507713, + "last_weighted_loss": 0.6381173133850098, + "peak_allocated_GiB": 6.530080318450928, + "peak_reserved_GiB": 9.171875 + }, + { + "repeat": 1, + "mode": "existing_cache", + "fresh_parent_loaded": true, + "initial_optimizer_step_min": 10255.0, + "initial_optimizer_step_max": 10255.0, + "ordered_training_ids_exact": true, + "warmup_examples": 16, + "timed_examples": 240, + "timed_seconds": 32.168527343979804, + "seconds_per_example": 0.13403553059991585, + "timed_dataloader_wait_seconds": 0.005411982972873375, + "last_weighted_loss": 0.6376754641532898, + "peak_allocated_GiB": 6.532010555267334, + "peak_reserved_GiB": 8.953125 + }, + { + "repeat": 2, + "mode": "existing_cache", + "fresh_parent_loaded": true, + "initial_optimizer_step_min": 10255.0, + "initial_optimizer_step_max": 10255.0, + "ordered_training_ids_exact": true, + "warmup_examples": 16, + "timed_examples": 240, + "timed_seconds": 17.468506193021312, + "seconds_per_example": 0.07278544247092214, + "timed_dataloader_wait_seconds": 0.005700364970834926, + "last_weighted_loss": 0.63838130235672, + "peak_allocated_GiB": 6.534998416900635, + "peak_reserved_GiB": 8.9453125 + }, + { + "repeat": 3, + "mode": "reference", + "fresh_parent_loaded": true, + "initial_optimizer_step_min": 10255.0, + "initial_optimizer_step_max": 10255.0, + "ordered_training_ids_exact": true, + "warmup_examples": 16, + "timed_examples": 240, + "timed_seconds": 68.76838014100213, + "seconds_per_example": 0.2865349172541755, + "timed_dataloader_wait_seconds": 5.523634409008082, + "last_weighted_loss": 0.6376829743385315, + "peak_allocated_GiB": 6.534746170043945, + "peak_reserved_GiB": 8.470703125 + } + ], + "total_timed_seconds": { + "reference": 208.45918038100353, + "existing_cache": 49.637033537001116 + }, + "cache_over_reference": 0.23811392449245397, + "all_probe_updates_discarded": true, + "checkpoint_or_weights_written": false, + "actual_training_runs_changed": false, + "split": "train only", + "precision": { + "autocast": "bfloat16", + "allow_tf32": false + }, + "caveats": "Shared GPU load, fixed256preselected training examples and an already populated byte cache; reference/cache/cache/reference order. Includes data delivery, transfer, forward/backward and AdamW after16warmup examples per mode. Excludes source loading/setup, tensor-digest testing and cache population. Does not measure long-tail storage outages or guarantee gains on the full training dataset." +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/check_decoder111_parent.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/check_decoder111_parent.py new file mode 100644 index 0000000000000000000000000000000000000000..b0b893ff12f3727492b2a2f2b7908a702df34fcc --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/check_decoder111_parent.py @@ -0,0 +1,57 @@ +"""Compare the six unchanged native site/contact arrays with the sole eight-pass parent.""" +import argparse +from datetime import datetime, timezone +import json +import os +from pathlib import Path +import zarr +from numcodecs import blosc +from direct_read_store import DirectReadDirectoryStore +from verify_package import inference_signature +from prepare_decoder111 import digest + + +def main(): + parser=argparse.ArgumentParser(description=__doc__) + parser.add_argument('plan',type=Path) + args=parser.parse_args();plan=json.loads(args.plan.read_text());study=args.plan.parent + assert not (study/'native-parent-check.json').exists() + os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:2]);blosc.set_nthreads(1) + for name,expected in plan['native_public_sha256'].items(): + assert digest(Path(plan['native_public_input'])/name)==expected,name + package=Path(plan['output'])/'package' + candidate=study/'native-recovery';parent=Path(plan['parent_native']) + proof=json.loads((candidate/'report.json').read_text()) + previous=json.loads((parent/'report.json').read_text()) + assert inference_signature(package)==proof['package_inference_signature']==plan['candidate_inference_signature'] + assert previous['package_inference_signature']==plan['parent_inference_signature'] + assert proof['all_arrays_bit_identical'] and proof['arrays']=={'tracks':30,'perturbations':22} + count=0;observations=[] + for kind,filename,attribute in [('tracks','predictions.zarr','example_ids'), + ('perturbations','perturbations.zarr','perturbation_ids')]: + a=zarr.open_group(DirectReadDirectoryStore(str(candidate/('native-'+kind)/filename)),mode='r') + b=zarr.open_group(DirectReadDirectoryStore(str(parent/('native-'+kind)/filename)),mode='r') + assert a.attrs[attribute]==b.attrs[attribute] + arrays={} + def collect(name,obj): + if isinstance(obj,zarr.Array): + arrays[name]=obj + a.visititems(collect) + for name,value in arrays.items(): + if name.rsplit('/',1)[-1] not in ['splice_sites','contact_maps']: + continue + other=b[name] + assert value.shape==other.shape and value.dtype==other.dtype,name + assert value[:].tobytes()==other[:].tobytes(),name + count+=1;observations.append(dict(kind=kind,key=name,shape=list(value.shape))) + assert count==6,observations + report=dict(utc=datetime.now(timezone.utc).isoformat(),plan_sha256=digest(args.plan), + unchanged_native_arrays_exact=count,observations=observations, + native_report_sha256=digest(candidate/'report.json'),parent_native_report_sha256=digest(parent/'report.json'), + labels_read=False,new_GPU_work=False) + path=study/'native-parent-check.json';temporary=path.with_suffix('.tmp') + temporary.write_text(json.dumps(report,indent=2)+'\n');temporary.replace(path) + print(json.dumps(report),flush=True) + + +if __name__=='__main__':main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-CPU-construction-check.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-CPU-construction-check.json new file mode 100644 index 0000000000000000000000000000000000000000..d2057580d1fbf29025830797566ec80c71c9bbe0 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-CPU-construction-check.json @@ -0,0 +1,34 @@ +{ + "utc": "2026-10-26T06:55:07.227765+00:00", + "candidate_inference_signature": { + "last.pt": "4e81120dd5424c175e43684c0827b8a4f21e5be8b0b554f17df615ac16a027dd", + "predict.py": "4f5dbddf5b6b701079aeaf38920fe414ce980f7161997f4d5c80952a6d834e51", + "perturb.py": "49065dc43816920ecac9de3ebcc4735673e4ff6594cf3f6bd34233b88c9bed73", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/alphagenome_50m/model.py": "9ddfecf4def3c0ce70a5ac8e831a22c6c5565c90719b5a36dc541acc12b7afb2", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919", + "splice_output.py": "e1b0627e036c60190e4d2a4653a915d9031b166ff3c1de7c23e5774009d70ef0", + "splice_calibration.json": "bd9368b32e739be65ca290d598ffcae5be6387c09f96048e183c57b7d570e641", + "rc_ensemble.py": "84a4d0e94ab86622e2c25e96f8835b2ef2bba77731ff7ff0bdc1c2de02cdcef4", + "single_prediction.py": "10f6216fdd72bf30b3a28ab6de9363e66b415526aa26a2bbfb6a84badbb67bec", + "strand_permutations.json": "afb0f0cf2e897fab3493de66601d7a66dac45a06316c2ac4a6cc828298820c60" + }, + "all_learned_tensors_exact": true, + "parameters": 13290504, + "synthetic_batch": 4, + "synthetic_input_bp": 8192, + "actual_allele_and_N_edits": true, + "both_forward_and_RC_synthetic_inputs": true, + "all11_outputs_finite": true, + "classifier_contacts_trunk_and_selected_positions_exact_parent": true, + "full_central_and_sparse_features_exact": true, + "source_exact_completed96": true, + "source_and_weights_exact_completed111": true, + "orientation_wrappers_and_policy_exact_RC108": true, + "CUDA_used": false, + "training_updates": 0, + "labels_read": false, + "accepted111_CPU_proof_sha256": "004cf4925d682937f80cb4ffcc5838d0bae0508a5801a7c325ad3640e9c46ed1", + "caveat": "Short synthetic base-network check plus unchanged accepted orientation wrapper. Own full1Mb native recovery, six exact parent output arrays and complete accuracy are still required." +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-admission.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-admission.json new file mode 100644 index 0000000000000000000000000000000000000000..06997a9b13558349fdbd89aa70aed09a5084f1dc --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-admission.json @@ -0,0 +1,18 @@ +{ + "utc": "2026-10-26T06:57:17.752156+00:00", + "admitted": true, + "additional_SGD": 0, + "sole_parent": "/workspace/research/experiments/rc108", + "scientific_reason": "On the same eight-pass weights,111 improved reward by0.002630816 under both track schemes and all seven dense families; enhancer AP improved0.012034669 with gene interval[0.00147553,0.02601987]. RC108 separately improved reward0.017002677, with all13 variant intervals including zero. Test whether the fixed decoder gain transfers to RC108; additivity and variant gains are not assumed.", + "contrary_evidence": "111 usage and junction intervals include zero, caQTL is positive only under block resampling, and its other11 variant intervals include zero. RC108 has negative eQTL coefficient correlation and Mendelian AP below the previous published SWA model. All selection uses the repeatedly inspected development split.", + "update_to_prior_hold": "Relevant completed108 and111 all11/all13 controls now resolve this fixed inference transfer. Pending94 trains a different full interpolation operation; its unchanged pipeline continues. This adds no training fork and enters the existing full-evaluation queue.", + "candidate_policy": "Replace RC108 nearest global-feature decoding for dense, usage and junction heads with the exact selective linear-bin-center operation from completed111/96. Keep its original nearest splice classifier, contacts, learned tensors, four training-derived offsets and equal forward/RC views.", + "no_tuning_or_new_parameters": true, + "requirements": "Own52native/recovery, six exact RC108 native arrays, all200/2697/all13, exact twofamily/fivevariant reports, own624native/runtime with allthree4h estimates, one all11/all13 paired observer against RC108. Atomic monotonic publication only after full scores and runtime.", + "resource_review_sha256": "bcdd8e6e097244ff596ee432ae9152ef8fd3da8a7b730a323b53ba1afa1e4177", + "operator_recovery_context": "/workspace/research/studies/operator_recovery_20261026T064822/recovery.json", + "operator_recovery_context_sha256": "e3edb6bc383d74cc1f6d8d08c48c4d5265c719a5e86d26c51431539c6e18a0d3", + "research_deadline_utc": "2026-10-28T04:29:08+00:00", + "no_training_cadence_or_allocation_change": true, + "zero_interval_is_not_equivalence": true +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-controller-reuse.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-controller-reuse.json new file mode 100644 index 0000000000000000000000000000000000000000..6bb66e77a92b9765da4cd8a6a48f1aff60abd062 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-controller-reuse.json @@ -0,0 +1,10 @@ +{ + "utc": "2026-10-26T06:57:17.752156+00:00", + "controller": "run_decoder111.py", + "controller_sha256": "0c88342f7a15deb2d112139b99200fea34cdca69d9bcdce8239f18e8ef5e64b7", + "parent_checker": "check_decoder111_parent.py", + "parent_checker_sha256": "053a3f6a6e84bd81d8103833b748b325833de0090887be82e1cb6f7c943a73be", + "sources_unchanged": true, + "scope": "Reuse completed111 plan-driven evaluator/checker without code edits. Paths, parent signature, native parent and model hash are supplied by the new113 plan.", + "accepted_controller_completion_sha256": "dcc21750b7079db2b31349b45d828c40a1aad2414ed9cc1ff54506b3299f433f" +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-fixture-check.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-fixture-check.json new file mode 100644 index 0000000000000000000000000000000000000000..75c82bbe514e5783e0a2baec5e796a63c072589e --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-fixture-check.json @@ -0,0 +1,23 @@ +{ + "utc": "2026-10-26T06:57:17.752156+00:00", + "timing_backing": "/workspace/research/studies/native_latency_input_recovery/frozen-validation-public-input", + "timing_rows": [ + 16, + 96 + ], + "timing_rows_and_manifest_hashes_exact": true, + "native_rows": [ + 2, + 5 + ], + "native_public_sha256": { + "public/test.parquet": "ff36ae00a21f7d84bf8f436f6236a3a4da1d71be9e096d4106485097d9a62099", + "public/perturbations.parquet": "fcd349372a6363dc7a7e73f37fdccc05956f5596b488611146a8a3a8fea53e7c", + "public/junction_tracks.parquet": "8b2f8888ae06779b487e086c5c49234e90452d03966debe0bea9767a555a00d4", + "reference/GRCh38.p13.genome.fa.fai": "9293fb33f63b7f09d8fadc055d78e401d07c76de0211243fba778780ff836ff9" + }, + "FAI_bound": true, + "full_reference_hash_claim": false, + "labels_read": false, + "GPU_used": false +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-preparation.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-preparation.json new file mode 100644 index 0000000000000000000000000000000000000000..09116f7b00721b57608c511d7555e3f0150e244d --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-preparation.json @@ -0,0 +1,56 @@ +{ + "utc": "2026-10-26T06:55:07.230154+00:00", + "package": "/workspace/research/experiments/rc_decoder113/package", + "package_verification": { + "files_verified": 68, + "bytes_verified": 3632938236 + }, + "parent_inference_signature": { + "last.pt": "4e81120dd5424c175e43684c0827b8a4f21e5be8b0b554f17df615ac16a027dd", + "predict.py": "4f5dbddf5b6b701079aeaf38920fe414ce980f7161997f4d5c80952a6d834e51", + "perturb.py": "49065dc43816920ecac9de3ebcc4735673e4ff6594cf3f6bd34233b88c9bed73", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/alphagenome_50m/model.py": "c61f50a2a66a4f6950c6f6107a70710a5d0ab99a291b8bfaa1eb5565998d2fa3", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919", + "splice_output.py": "e1b0627e036c60190e4d2a4653a915d9031b166ff3c1de7c23e5774009d70ef0", + "splice_calibration.json": "bd9368b32e739be65ca290d598ffcae5be6387c09f96048e183c57b7d570e641", + "rc_ensemble.py": "84a4d0e94ab86622e2c25e96f8835b2ef2bba77731ff7ff0bdc1c2de02cdcef4", + "single_prediction.py": "10f6216fdd72bf30b3a28ab6de9363e66b415526aa26a2bbfb6a84badbb67bec", + "strand_permutations.json": "afb0f0cf2e897fab3493de66601d7a66dac45a06316c2ac4a6cc828298820c60" + }, + "candidate_inference_signature": { + "last.pt": "4e81120dd5424c175e43684c0827b8a4f21e5be8b0b554f17df615ac16a027dd", + "predict.py": "4f5dbddf5b6b701079aeaf38920fe414ce980f7161997f4d5c80952a6d834e51", + "perturb.py": "49065dc43816920ecac9de3ebcc4735673e4ff6594cf3f6bd34233b88c9bed73", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/alphagenome_50m/model.py": "9ddfecf4def3c0ce70a5ac8e831a22c6c5565c90719b5a36dc541acc12b7afb2", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919", + "splice_output.py": "e1b0627e036c60190e4d2a4653a915d9031b166ff3c1de7c23e5774009d70ef0", + "splice_calibration.json": "bd9368b32e739be65ca290d598ffcae5be6387c09f96048e183c57b7d570e641", + "rc_ensemble.py": "84a4d0e94ab86622e2c25e96f8835b2ef2bba77731ff7ff0bdc1c2de02cdcef4", + "single_prediction.py": "10f6216fdd72bf30b3a28ab6de9363e66b415526aa26a2bbfb6a84badbb67bec", + "strand_permutations.json": "afb0f0cf2e897fab3493de66601d7a66dac45a06316c2ac4a6cc828298820c60" + }, + "evidence_sha256": { + "/workspace/research/experiments/rc108/evaluation.json": "a666d3d6e17cb8cf00585cc5c33a4bd657b5ae81a9492ff91b9efe4e6f795cd3", + "/workspace/research/experiments/rc108/valid-score/report.json": "52fb0735c13904046b56400c4894b672f95d5920f6db01b1b2d101a843c8ee36", + "/workspace/research/experiments/rc108/valid-variant-score/report.json": "2358fb31bd4bf7a6780085edab7765fceec958f4a6f1d36f1e802c2627c0c4e3", + "/workspace/research/studies/rc108/completion-observation.json": "457ae0d8c35be775f898a3f19ef2d97df407290d423f57fb2a625bbe5f086490", + "/workspace/research/studies/rc108/diagnostics/summary.json": "0c754eac92f646930be8c29c6c4dc170713fbc1f17fcab10bf57c74b048a2486", + "/workspace/research/studies/decoder111/completion-observation.json": "dcc21750b7079db2b31349b45d828c40a1aad2414ed9cc1ff54506b3299f433f", + "/workspace/research/studies/decoder111/diagnostics/summary.json": "9ea94a5b471a6d3d824305c905674e603d68b6de4b0e62fbd4026d5311671844", + "/workspace/research/studies/decoder111/CPU-construction-check.json": "004cf4925d682937f80cb4ffcc5838d0bae0508a5801a7c325ad3640e9c46ed1", + "/workspace/research/studies/rc_decoder113/proposed-recipe.json": "c16194b994ec2438075f2f6bdad25ba88a545daef0212f0182a88382253b2e7d", + "/workspace/research/studies/rc_decoder113/CPU-construction-check.json": "6c81af75929b4d556d444799f30685a6ae16588ee212ea2eb4f869a1b6751155", + "/workspace/research/seqfunc/prepare_decoder111.py": "afa19189b21fa7780f5f801df3ef0216b1d004bca9e17a034b8606c28420989e", + "/workspace/research/seqfunc/make_linear_upsample83.py": "2048f81143e187051b20471e3fed1b2068e36ef9f4bbc92b66b51c040fb5da0f", + "/workspace/research/seqfunc/make_splice_grid96.py": "2ae38b29e6c055b724b156240ba38ed22cd93ebdb1bf6da490d427f82c6577ec", + "/workspace/research/seqfunc/prepare_rc_decoder113.py": "c1446c7a45ca60087fa98fc74167aff61c8d30f05d824483615d75de78f26727" + }, + "evaluation_admitted": false, + "controller_created": false, + "GPU_used": false, + "next": "Separate resource/admission decision, freeze original native/full/all13/runtime/paired gates versus RC108; no automatic GPU launch." +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-proposed-recipe.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-proposed-recipe.json new file mode 100644 index 0000000000000000000000000000000000000000..f416e2bb7b4132389d7d9712a4a5ed59484d35d6 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-proposed-recipe.json @@ -0,0 +1,44 @@ +{ + "utc": "2026-10-26T06:54:54.017445+00:00", + "parent": "/workspace/research/experiments/rc108", + "parent_package": "/workspace/research/studies/rc108/paired-documentation/package", + "output": "/workspace/research/experiments/rc_decoder113", + "parameters": 13290504, + "inherited_examples": 328160, + "inherited_optimizer_steps": 82040, + "inherited_complete_passes": 8, + "additional_training_updates": 0, + "parent_inference_signature": { + "last.pt": "4e81120dd5424c175e43684c0827b8a4f21e5be8b0b554f17df615ac16a027dd", + "predict.py": "4f5dbddf5b6b701079aeaf38920fe414ce980f7161997f4d5c80952a6d834e51", + "perturb.py": "49065dc43816920ecac9de3ebcc4735673e4ff6594cf3f6bd34233b88c9bed73", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/alphagenome_50m/model.py": "c61f50a2a66a4f6950c6f6107a70710a5d0ab99a291b8bfaa1eb5565998d2fa3", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919", + "splice_output.py": "e1b0627e036c60190e4d2a4653a915d9031b166ff3c1de7c23e5774009d70ef0", + "splice_calibration.json": "bd9368b32e739be65ca290d598ffcae5be6387c09f96048e183c57b7d570e641", + "rc_ensemble.py": "84a4d0e94ab86622e2c25e96f8835b2ef2bba77731ff7ff0bdc1c2de02cdcef4", + "single_prediction.py": "10f6216fdd72bf30b3a28ab6de9363e66b415526aa26a2bbfb6a84badbb67bec", + "strand_permutations.json": "afb0f0cf2e897fab3493de66601d7a66dac45a06316c2ac4a6cc828298820c60" + }, + "candidate_model_sha256": "9ddfecf4def3c0ce70a5ac8e831a22c6c5565c90719b5a36dc541acc12b7afb2", + "only_change": "Replace RC108 nearest global-feature decoding for dense, usage and junction heads with the exact selective linear-bin-center operation from completed111/96. Keep its original nearest splice classifier, contacts, learned tensors, four training-derived offsets and equal forward/RC views.", + "expected_exact_families": [ + "splice_sites", + "contact_maps" + ], + "expected_exact_variants": [ + "sqtl_causality", + "clinvar_noncoding", + "clinvar_splice_region", + "clinvar_missense", + "mfass" + ], + "motivation": "On the same eight-pass weights,111 improved reward by0.002630816 under both track schemes and all seven dense families; enhancer AP improved0.012034669 with gene interval[0.00147553,0.02601987]. RC108 separately improved reward0.017002677, with all13 variant intervals including zero. Test whether the fixed decoder gain transfers to RC108; additivity and variant gains are not assumed.", + "contrary_evidence": "111 usage and junction intervals include zero, caQTL is positive only under block resampling, and its other11 variant intervals include zero. RC108 has negative eQTL coefficient correlation and Mendelian AP below the previous published SWA model. All selection uses the repeatedly inspected development split.", + "scope": "One fixed network and the same two orientation views serve every family and both entrypoints. No learned or fitted mixture, routing, new splice prior, motif mask, SWA, extra flanking DNA, variant-label training or additional SGD.", + "limitations": "Exploratory one-seed postselection. Interaction can worsen any changed readout. Full native replay, runtime and accuracy must be measured independently; unchanged paths require exact complete reports.", + "evaluation_admitted": false, + "GPU_used": false +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-resource-review.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-resource-review.json new file mode 100644 index 0000000000000000000000000000000000000000..1aa32fc6e144fd6d68ee3d95d72d588eb31146de --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-resource-review.json @@ -0,0 +1,98 @@ +{ + "utc": "2026-10-26T06:57:17.802537+00:00", + "trainers": [ + { + "pid": 887, + "output": "/workspace/research/experiments/scale13-small" + }, + { + "pid": 911, + "output": "/workspace/research/experiments/scale13-large" + }, + { + "pid": 933, + "output": "/workspace/research/experiments/scale13-small" + }, + { + "pid": 935, + "output": "/workspace/research/experiments/scale13-small" + }, + { + "pid": 937, + "output": "/workspace/research/experiments/scale13-small" + }, + { + "pid": 939, + "output": "/workspace/research/experiments/scale13-small" + }, + { + "pid": 998, + "output": "/workspace/research/experiments/scale13-large" + }, + { + "pid": 1000, + "output": "/workspace/research/experiments/scale13-large" + }, + { + "pid": 1002, + "output": "/workspace/research/experiments/scale13-large" + }, + { + "pid": 1007, + "output": "/workspace/research/experiments/scale13-large" + }, + { + "pid": 1009, + "output": "/workspace/research/experiments/scale13-large" + }, + { + "pid": 1011, + "output": "/workspace/research/experiments/scale13-large" + }, + { + "pid": 1013, + "output": "/workspace/research/experiments/scale13-large" + }, + { + "pid": 1016, + "output": "/workspace/research/experiments/scale13-large" + } + ], + "main_rates": { + "small": { + "step": 567600, + "optimizer_steps": 141900, + "samples": 7, + "observed_seconds": 322.3475660174154, + "examples_per_second": 7.445379624396903, + "remaining_main14_hours": 0.24922242372642764, + "scope": "Latest resumed segment only, shared load; not an isolated rate or upper bound." + }, + "large": { + "step": 352400, + "optimizer_steps": 88100, + "samples": 4, + "observed_seconds": 269.8194649373181, + "examples_per_second": 4.447418203422695, + "remaining_main14_hours": 13.858227518586144, + "scope": "Latest resumed segment only, shared load; not an isolated rate or upper bound." + } + }, + "gpu": "36059, 45032, 100", + "memory_current_bytes": 226678050816, + "memory_events": { + "low": "0", + "high": "0", + "max": "0", + "oom": "0", + "oom_kill": "0" + }, + "hours_remaining": 45.53062440111111, + "deadline_utc": "2026-10-28T04:29:08+00:00", + "operator_recovery_context": "/workspace/research/studies/operator_recovery_20261026T064822/recovery.json", + "operator_recovery_sha256": "e3edb6bc383d74cc1f6d8d08c48c4d5265c719a5e86d26c51431539c6e18a0d3", + "main_large_evaluation_reserve_hours": 3, + "additional_planning_allowance_hours": 3, + "proposed_new_training_updates": 0, + "proposed_evaluation_serialized": true +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-training-replay.md b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-training-replay.md new file mode 100644 index 0000000000000000000000000000000000000000..237f88ece1ac07317f0a0a7dbd8866dcf664f92a --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/decoder113-training-replay.md @@ -0,0 +1,5 @@ +All eight inherited training passes used pre-decoder113-model.py at reference/alphagenome_50m/model.py. +Restore that source for the inherited training recipe. Training remains forward-only with the original +objective, seed17, batch4, data order, AdamW state and fourteen-pass learning-rate schedule. +RC108 introduced equal forward/RC inference without SGD. Decoder113 adds only the exact model.py +from completed111/96 via preserve_classifier(interpolate(original_source)); no weights are changed. diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/execution_change.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/execution_change.json new file mode 100644 index 0000000000000000000000000000000000000000..93f72a0c4f5f51e9192005535fc45e6aa3bbd671 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/execution_change.json @@ -0,0 +1,10 @@ +{ + "adopted_utc": "2026-10-24T21:51:59.762636+00:00", + "change": "Activation checkpoint boundaries only; all model operations, parameters, objective, data and schedule unchanged.", + "example_boundary": 1000, + "before": "Recompute whole local path during backward.", + "after": "Recompute each local residual block during backward.", + "evidence": "CPU gradients and all eleven outputs exactly match. Full-shape BF16 outputs/losses are bit-exact; gradient relative L2 discrepancy .0009547 versus .0009534 for an unchanged repeat, consistent with existing reduction variability. Peak memory40.11 to15.99GiB; isolated compute .11131 to.10504seconds/example.", + "initial_source_manifest_sha256": "18329ef35e89fc5aeb50ccc045b08db690e6c8ef40d661b01ddb74c7f32b6f0b", + "no_benchmark_weights_retained": true +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/freeze_rc_decoder113_execution.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/freeze_rc_decoder113_execution.py new file mode 100644 index 0000000000000000000000000000000000000000..7e6afffdc0b3087d4479e8b279a7a8b22c447ca5 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/freeze_rc_decoder113_execution.py @@ -0,0 +1,137 @@ +"""Freeze the completed decoder transfer proof and one queued full evaluation against RC108.""" +import argparse +import ast +from datetime import datetime, timezone, timedelta +import json +from pathlib import Path +import shutil + +import pandas as pd + +from prepare_decoder111 import digest, read, save +from run_seed110_pilot import resource_snapshot, rows +from verify_package import inference_signature, verify_package + + +def main(): + parser=argparse.ArgumentParser(description=__doc__) + parser.add_argument('--recovery-context',type=Path,required=True) + args=parser.parse_args() + root=Path('/workspace/research');here=root/'seqfunc';study=root/'studies/rc_decoder113' + assert not (study/'plan.json').exists() + preparation=read(study/'preparation.json');recipe=read(study/'proposed-recipe.json') + parent=Path(recipe['parent']);output=Path(recipe['output']);package=output/'package' + proof=read(study/'CPU-construction-check.json') + assert proof['source_and_weights_exact_completed111'] and proof['both_forward_and_RC_synthetic_inputs'] + assert proof['orientation_wrappers_and_policy_exact_RC108'] and not proof['CUDA_used'] + for path,expected in preparation['evidence_sha256'].items():assert digest(path)==expected,path + assert inference_signature(package)==preparation['candidate_inference_signature'] + current=read(root/'STATE.json')['current_operator_recovery'] + assert current['sha256']==digest(args.recovery_context) + recovery=read(args.recovery_context) + deadline=datetime.fromisoformat(recovery['new_deadline']) + assert deadline==datetime.fromisoformat(recovery['recovery_utc'])+timedelta(seconds=recovery['remaining_seconds_at_recovery']) + now=datetime.now(timezone.utc) + resources=resource_snapshot({name:str(root/'experiments'/('scale13-'+name)) for name in ['small','large']}) + # Replayed log rows must not inflate a throughput estimate after operator recovery. + rates={} + for name in ['small','large']: + events=rows(root/'experiments'/('scale13-'+name)/'train.log') + start=max(i for i,row in enumerate(events) if row.get('event')=='train_start') + tail=[row for row in events[start+1:] if row.get('event')=='train_step'][-11:] + assert len(tail)>=3,name + seconds=tail[-1]['elapsed_seconds']-tail[0]['elapsed_seconds'] + assert seconds>=120,name + rate=(tail[-1]['step']-tail[0]['step'])/seconds + assert rate>0 + rates[name]=dict(step=tail[-1]['step'],optimizer_steps=tail[-1]['optimizer_steps'], + samples=len(tail),observed_seconds=seconds,examples_per_second=rate, + remaining_main14_hours=(574280-tail[-1]['step'])/rate/3600, + scope='Latest resumed segment only, shared load; not an isolated rate or upper bound.') + resources['main_rates']=rates + resources.update(hours_remaining=(deadline-now).total_seconds()/3600,deadline_utc=deadline.isoformat(), + operator_recovery_context=str(args.recovery_context),operator_recovery_sha256=digest(args.recovery_context), + main_large_evaluation_reserve_hours=3,additional_planning_allowance_hours=3, + proposed_new_training_updates=0,proposed_evaluation_serialized=True) + assert rates['large']['remaining_main14_hours']+61e-6. Parent metadata must match exact whole epochs, all41020intervals, originalseed17/B4/14epochobjective and frozen capacity configs. Freeze actual parent signatures/reports only after completion; do not substitute checkpoints.", + "validation": "Original builder and verifier helpers; fresh native52/recovery, exact link-only core guard, original full12486array proof and36fresh native arrays, then fresh200track inference and full13officialmetrics using verified research-only perturbation reuse. Require10unaffectedfamily/eightvariant reports exact. Reward improvements additionally require624native arrays/allthree4h estimates before atomic promotion.", + "diagnostics": "After full evaluation, use unchanged paired track and remaining-seven variant diagnostics against each own raw parent,2000draws/seed1729. Five splice metrics may change; ca/SPI and eight other official reports are exact. This is not anall13bootstrap. Matched learning/capacity comparisons can be declared separately after both candidates complete.", + "nonimprovement_policy": "A fully scored candidate below the current publication skips optional native latency/publication, but still finishes its declared paired diagnostics. Original raw/other candidate selection pools and GPU schedules are unchanged.", + "limitations": [ + "The fixed marginal correction approximates per-window balanced BCE; no new prior fitting.", + "The verifier applies its unchanged sigmoid to emitted splice probabilities; report all resulting tradeoffs.", + "Intervals are exploratory/unadjusted and one training seed. Runtime extrapolations are shared-load estimates, not guaranteed bounds.", + "No test labels, variant-label training, external parameters or inference caches are used." + ] +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/late-fixed-links81-parent-check.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/late-fixed-links81-parent-check.json new file mode 100644 index 0000000000000000000000000000000000000000..4e85ff1231bb0b51a26679743837f33f5732f829 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/late-fixed-links81-parent-check.json @@ -0,0 +1,53 @@ +{ + "created_utc": "2026-10-25T16:21:07.054587+00:00", + "plan_sha256": "39844848f0b239ada3f89e52a5c1e1e49b473ed6fc4746f20bbb329f194d2bd6", + "actual_completed_parent_checks": [ + { + "experiment": "/workspace/research/experiments/scale13-small-epoch04", + "capacity": "small", + "epoch": 4, + "inference_signature": { + "last.pt": "ae6a15c21e8d9b5900f986f28f91b00962c0030ffc952340b1b3264eef1a6659", + "predict.py": "c257bd031d41b946053e37d5a5759c6c1416d22d9b4d3f403e1fd13e0f63968b", + "perturb.py": "ba37a6f9f8b8eccb052903618bcb4c91d3f4cbed62dfe678ee362644dc80801e", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/alphagenome_50m/model.py": "c61f50a2a66a4f6950c6f6107a70710a5d0ab99a291b8bfaa1eb5565998d2fa3", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919" + }, + "reward": 0.44610804541871724, + "sha256": { + "/workspace/research/experiments/scale13-small-epoch04/evaluation.json": "3193c974189a7ad77a06d0abf64a2c3e002953470660ca20f30d97271e3ffd0a", + "/workspace/research/experiments/scale13-small-epoch04/evaluation-package.json": "e06f0c731873e9ef1a94722d29651c180eefc6c1b54650b52622bad525dd5da7", + "/workspace/research/experiments/scale13-small-epoch04/valid-score/report.json": "3a45ae11ac8ad3897b926283073b5e755b3013a7796aa9837954dec842d7a942", + "/workspace/research/experiments/scale13-small-epoch04/valid-variant-score/report.json": "19224eae63273b62b0421fc48c32704b86418a2bfb21adda2a90449cfa0988d8", + "/workspace/research/experiments/scale13-small-epoch04/package/training_provenance.json": "30c05badb228e6d94b3e70113c4ea671ac2cac370c380d4e4e245ca2a00b00b5" + } + }, + { + "experiment": "/workspace/research/experiments/scale13-large-epoch02", + "capacity": "large", + "epoch": 2, + "inference_signature": { + "last.pt": "6e7c605f2683881b9f59571f042cb7df93a3a0f224d8f2cb4e37be0a123cdb27", + "predict.py": 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0000000000000000000000000000000000000000..241cd648df020f91b007aefc0cf0e8d9f188765a --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/late-fixed-links81-resolved-parents.json @@ -0,0 +1,58 @@ +{ + "master_plan_sha256": "39844848f0b239ada3f89e52a5c1e1e49b473ed6fc4746f20bbb329f194d2bd6", + "member": { + "capacity": "small", + "epoch": 8, + "previous_epoch": 4, + "parent": "/workspace/research/experiments/scale13-small-epoch08", + "previous_raw": "/workspace/research/experiments/scale13-small-epoch04", + "output": "/workspace/research/experiments/calib81-small-epoch08", + "study": "/workspace/research/studies/late_fixed_links81/small-epoch08" + }, + "receipts": { + "parent": { + "experiment": "/workspace/research/experiments/scale13-small-epoch08", + "capacity": "small", + "epoch": 8, + "inference_signature": { + "last.pt": "4e81120dd5424c175e43684c0827b8a4f21e5be8b0b554f17df615ac16a027dd", + "predict.py": "c257bd031d41b946053e37d5a5759c6c1416d22d9b4d3f403e1fd13e0f63968b", + "perturb.py": "ba37a6f9f8b8eccb052903618bcb4c91d3f4cbed62dfe678ee362644dc80801e", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/alphagenome_50m/model.py": "c61f50a2a66a4f6950c6f6107a70710a5d0ab99a291b8bfaa1eb5565998d2fa3", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919" + }, + "reward": 0.4622727224599102, + "sha256": { + "/workspace/research/experiments/scale13-small-epoch08/evaluation.json": "048a4f8e805c8476b4240977d2e6c6e800dd8a509ec1183f099e9726cc51cd11", + "/workspace/research/experiments/scale13-small-epoch08/evaluation-package.json": "84a37d41701a015643c86a120d0c8b1794af718fce1478824f8aaf0798411cf5", + 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"reference/alphagenome_50m/model.py": "c61f50a2a66a4f6950c6f6107a70710a5d0ab99a291b8bfaa1eb5565998d2fa3", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919" + }, + "reward": 0.44610804541871724, + "sha256": { + "/workspace/research/experiments/scale13-small-epoch04/evaluation.json": "3193c974189a7ad77a06d0abf64a2c3e002953470660ca20f30d97271e3ffd0a", + "/workspace/research/experiments/scale13-small-epoch04/evaluation-package.json": "e06f0c731873e9ef1a94722d29651c180eefc6c1b54650b52622bad525dd5da7", + "/workspace/research/experiments/scale13-small-epoch04/valid-score/report.json": "3a45ae11ac8ad3897b926283073b5e755b3013a7796aa9837954dec842d7a942", + "/workspace/research/experiments/scale13-small-epoch04/valid-variant-score/report.json": "19224eae63273b62b0421fc48c32704b86418a2bfb21adda2a90449cfa0988d8", + "/workspace/research/experiments/scale13-small-epoch04/package/training_provenance.json": "30c05badb228e6d94b3e70113c4ea671ac2cac370c380d4e4e245ca2a00b00b5" + } + } + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/late_fixed_links81.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/late_fixed_links81.py new file mode 100644 index 0000000000000000000000000000000000000000..28c50d9e3a33f6accbd9ce3cdeca8bdbb9b481a6 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/late_fixed_links81.py @@ -0,0 +1,145 @@ +"""Predeclare fixed-prior output links for the remaining main training checkpoints.""" +import argparse +from datetime import datetime, timezone +import hashlib +import json +import math +from pathlib import Path + +from verify_package import inference_signature, verify_package + +ROOT = Path('/workspace/research') +STUDY = ROOT / 'studies/late_fixed_links81' +HERE = Path(__file__).resolve().parent + + +def digest(path): + with Path(path).open('rb') as stream: + return hashlib.file_digest(stream, 'sha256').hexdigest() + + +def write_json(path, value): + path = Path(path) + temporary = path.with_suffix(path.suffix + '.tmp') + temporary.write_text(json.dumps(value, indent=2, allow_nan=False) + '\n') + temporary.replace(path) + + +def parent_ready(path): + path = Path(path) + try: + return (json.loads((path / 'pipeline-state.json').read_text())['phase'] == 'complete' + and (path / 'evaluation.json').exists()) + except (FileNotFoundError, json.JSONDecodeError, KeyError): + return False + + +def parent_receipt(path, capacity, epoch, plan): + path = Path(path) + assert parent_ready(path), str(path) + package = path / 'package' + verify_package(package) + signature = inference_signature(package) + assert json.loads((path / 'evaluation.json').read_text())['package_inference_signature'] == signature + provenance = json.loads((package / 'training_provenance.json').read_text()) + assert provenance['config'] == plan['capacities'][capacity]['config'] + assert provenance['global_step'] == epoch * 41020 and provenance['optimizer_steps'] == epoch * 10255 + assert provenance['epoch'] == epoch and provenance['offset'] == 0 + assert provenance['seed'] == 17 and provenance['schedule_epochs'] == 14 + assert provenance['objective'] == 'variance_balanced' and provenance['examples'] == 41020 + assert provenance['training_arguments']['batch_size'] == 4 + assert provenance['family_loss_scales'] == plan['family_loss_scales'] + source = provenance['provenance'] + assert source['split'] == 'train' and source['pretrained'] is False and source['world_size'] == 1 + assert source['train_manifest_sha256'] == plan['train_manifest_sha256'] + manifest = json.loads((package / 'package_manifest.json').read_text()) + assert manifest['parameters'] == plan['capacities'][capacity]['parameters'] + assert not any((package / name).exists() for name in [ + 'splice_calibration.json', 'checkpoint_ensemble_policy.json', 'rc_ensemble_policy.json']) + tracks = json.loads((path / 'valid-score/report.json').read_text()) + variants = json.loads((path / 'valid-variant-score/report.json').read_text()) + assert tracks['examples'] == 200 and tracks['invalid_reason'] is None + assert len(tracks['families']) == 11 and len(variants) == 13 + assert all(math.isfinite(value['model']) for value in variants.values()) + return dict(experiment=str(path), capacity=capacity, epoch=epoch, inference_signature=signature, + reward=tracks['overall_mean_family_pearson'], + sha256={str(p): digest(p) for p in [path / 'evaluation.json', path / 'evaluation-package.json', + path / 'valid-score/report.json', path / 'valid-variant-score/report.json', + package / 'training_provenance.json']}) + + +def prepare(): + STUDY.mkdir(exist_ok=True) + assert not (STUDY / 'plan.json').exists() + link69_path = ROOT / 'studies/fixed_link69/plan.json' + link69 = json.loads(link69_path.read_text()) + link61_path = ROOT / 'studies/fixed_link61/plan.json' + link61 = json.loads(link61_path.read_text()) + members = {} + for capacity, epoch, previous in [('large', 4, 2), ('small', 8, 4), ('large', 8, 4), + ('small', 14, 8), ('large', 14, 8)]: + key = f'{capacity}-epoch{epoch:02d}' + members[key] = dict(capacity=capacity, epoch=epoch, previous_epoch=previous, + parent=str(ROOT / 'experiments' / f'scale13-{capacity}-epoch{epoch:02d}'), + previous_raw=str(ROOT / 'experiments' / f'scale13-{capacity}-epoch{previous:02d}'), + output=str(ROOT / 'experiments' / f'calib81-{key}'), study=str(STUDY / key)) + (STUDY / key).mkdir(exist_ok=True) + capacities, evidence = {}, [link69_path, link61_path, ROOT / 'studies/fixed_link57/complete-summary.json', + ROOT / 'studies/scale13/study-cached-io.json', ROOT / 'studies/scale13/loss_scales.json', + Path('/workspace/data/public/train.parquet'), ROOT / 'experiments/calib57/package/splice_calibration.json'] + for capacity in ['small', 'large']: + package = ROOT / 'experiments' / f'scale13-{capacity}-epoch04/package' + provenance_path = package / 'training_provenance.json' + provenance = json.loads(provenance_path.read_text()) + capacities[capacity] = dict(config=provenance['config'], parameters=13290504 if capacity == 'small' else 215626632) + evidence.append(provenance_path) + helpers = set(link69['helper_sha256']) | { + 'late_fixed_links81.py', 'run_late_fixed_links81.py', 'diagnose_track_uncertainty.py', + 'diagnose_remaining_variant_uncertainty.py'} + materialization = dict(link69['materialization']) + materialization['installation'] = 'Atomic research-only alias to prepared-{candidate-name} within each member study; preserve while referenced. Never an inference asset.' + latency = dict(link61['native_latency_template']) + latency['scope'] = 'Native16-window/96-query replay for a completed late fixed-link candidate; all624arrays must equal complete evaluation and allthree4h estimates pass before any publication.' + latency['memory_evidence'] = 'One unchanged small or large main model plus the original four-offset elementwise splice link; require16GiB free for native probes. This is a scheduling guard, not a guaranteed peak bound.' + latency['minimum_free_GiB'] = 16 + plan = dict(created_utc=datetime.now(timezone.utc).isoformat(), members=members, capacities=capacities, + train_manifest_sha256=digest('/workspace/data/public/train.parquet'), + family_loss_scales=json.loads((ROOT / 'studies/scale13/loss_scales.json').read_text())['family_loss_scales'], + training_prior=link69['training_prior'], training_prior_sha256=link69['training_prior_sha256'], + calibration_reference=str(ROOT / 'experiments/calib57/package/splice_calibration.json'), + helper_sha256={str(HERE / name): digest(HERE / name) for name in sorted(helpers)}, + evidence_sha256={str(path): digest(path) for path in evidence}, materialization=materialization, + native_latency_template=latency, evaluation_lock=str(ROOT / 'studies/scale13/evaluation.lock'), + bounded_lock=str(ROOT / 'studies/junction_discovery39/bounded-diagnostic.lock'), + minimum_free_GiB=16, native_timeout_seconds=1800, materialization_timeout_seconds=1800, + parent_minimum_reward_gain=1e-6, publication_minimum_reward_gain=1e-6, + hypothesis='Transfer the same preselected training-prior sigmoid to the remaining main raw checkpoints. No new network updates, constants, orientation, site selection or sparse decoder changes. Prior link gains do not guarantee later track or variant improvements.', + admission='Wait for the declared raw parent and its preceding same-capacity raw checkpoint to finish200windows/2697queries/all13metrics. Require a raw reward gain>1e-6. Parent metadata must match exact whole epochs, all41020intervals, originalseed17/B4/14epochobjective and frozen capacity configs. Freeze actual parent signatures/reports only after completion; do not substitute checkpoints.', + validation='Original builder and verifier helpers; fresh native52/recovery, exact link-only core guard, original full12486array proof and36fresh native arrays, then fresh200track inference and full13officialmetrics using verified research-only perturbation reuse. Require10unaffectedfamily/eightvariant reports exact. Reward improvements additionally require624native arrays/allthree4h estimates before atomic promotion.', + diagnostics='After full evaluation, use unchanged paired track and remaining-seven variant diagnostics against each own raw parent,2000draws/seed1729. Five splice metrics may change; ca/SPI and eight other official reports are exact. This is not anall13bootstrap. Matched learning/capacity comparisons can be declared separately after both candidates complete.', + nonimprovement_policy='A fully scored candidate below the current publication skips optional native latency/publication, but still finishes its declared paired diagnostics. Original raw/other candidate selection pools and GPU schedules are unchanged.', + limitations=['The fixed marginal correction approximates per-window balanced BCE; no new prior fitting.', + 'The verifier applies its unchanged sigmoid to emitted splice probabilities; report all resulting tradeoffs.', + 'Intervals are exploratory/unadjusted and one training seed. Runtime extrapolations are shared-load estimates, not guaranteed bounds.', + 'No test labels, variant-label training, external parameters or inference caches are used.']) + write_json(STUDY / 'plan.json', plan) + print(json.dumps(dict(plan=str(STUDY / 'plan.json'), sha256=digest(STUDY / 'plan.json'), members=list(members))), flush=True) + + +def audit(): + assert not (STUDY / 'parent-check.json').exists() + plan = json.loads((STUDY / 'plan.json').read_text()) + for path, expected in {**plan['helper_sha256'], **plan['evidence_sha256']}.items(): + assert digest(path) == expected, path + receipts = [parent_receipt(ROOT / 'experiments' / name, capacity, epoch, plan) + for name, capacity, epoch in [('scale13-small-epoch04', 'small', 4), ('scale13-large-epoch02', 'large', 2)]] + write_json(STUDY / 'parent-check.json', dict(created_utc=datetime.now(timezone.utc).isoformat(), + plan_sha256=digest(STUDY / 'plan.json'), actual_completed_parent_checks=receipts, + gpu_used=False, packages_changed=False, no_new_predictions_or_parameter_fitting=True)) + print(json.dumps(dict(phase='parent_audit_complete', report_sha256=digest(STUDY / 'parent-check.json'))), flush=True) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('action', choices=['prepare', 'audit']) + globals()[parser.parse_args().action]() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/make_linear_upsample83.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/make_linear_upsample83.py new file mode 100644 index 0000000000000000000000000000000000000000..110da0ed7b9336e709813bfe66c4f889692d3b46 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/make_linear_upsample83.py @@ -0,0 +1,136 @@ +"""One fixed inference intervention: linearly upsample decoded global features.""" +import argparse +import ast +from datetime import datetime, timezone +import hashlib +import json +from pathlib import Path +import shutil + +from verify_package import inference_signature, verify_package + + +def digest(path): + with Path(path).open('rb') as stream: + return hashlib.file_digest(stream, 'sha256').hexdigest() + + +def changed_source(source): + replacements = { + '_dense_features': ''' def _global_dense_features(self, trunk: Tensor) -> Tensor: + # Coarse features are centered in 1-kb bins; align_corners=False maps their + # centers onto the aligned 128-bp grid and extends boundary values constantly. + decoded = self.dense_decoder(trunk) + return F.interpolate(decoded.transpose(1, 2), scale_factor=self.config.dense_upsample, + mode='linear', align_corners=False).transpose(1, 2) + + def _dense_features(self, trunk: Tensor) -> Tensor: + first = (self.config.input_length - self.config.target_length) // (2 * self.config.dense_bin_size) + features = self._global_dense_features(trunk)[:, first:first + self.config.dense_bins] + if self.config.local_width: + features = features + self._local_features[:, first:first + self.config.dense_bins] + return features''', + '_all_dense_features': ''' def _all_dense_features(self, trunk: Tensor) -> Tensor: + features = self._global_dense_features(trunk) + return features + self._local_features if self.config.local_width else features''', + '_position_features': ''' def _position_features(self, trunk: Tensor, positions: Tensor) -> Tensor: + features = self._gather_bins(self._global_dense_features(trunk), positions, self.config.dense_bin_size) + if self.config.local_width: + features = features + self._gather_bins(self._local_features, positions, self.config.dense_bin_size) + return features''', + } + tree = ast.parse(source) + cls = next(n for n in tree.body if isinstance(n, ast.ClassDef) and n.name == 'CompactModel') + assert not any(isinstance(n, ast.FunctionDef) and n.name == '_global_dense_features' for n in cls.body) + methods = {n.name:n for n in cls.body if isinstance(n, ast.FunctionDef)} + lines = source.splitlines(keepends=True) + for name in sorted(replacements, key=lambda k:methods[k].lineno, reverse=True): + node = methods[name] + lines[node.lineno-1:node.end_lineno] = [replacements[name]+'\n'] + changed = ''.join(lines) + new_tree = ast.parse(changed) + new_cls = next(n for n in new_tree.body if isinstance(n, ast.ClassDef) and n.name == 'CompactModel') + # The module is identical after removing the three replaced methods and new helper. + cls.body = [n for n in cls.body if not isinstance(n, ast.FunctionDef) or n.name not in replacements] + new_cls.body = [n for n in new_cls.body if not isinstance(n, ast.FunctionDef) or n.name not in {*replacements, '_global_dense_features'}] + assert ast.dump(tree, include_attributes=False) == ast.dump(new_tree, include_attributes=False) + return changed + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('plan', type=Path) + args = parser.parse_args() + plan, study = json.loads(args.plan.read_text()), args.plan.parent + parent, output = Path(plan['parent'])/'package', Path(plan['output'])/'package' + assert not output.exists(), output + assert inference_signature(parent) == plan['parent_inference_signature'] + verify_package(parent) + source = changed_source((parent/'reference/alphagenome_50m/model.py').read_text()) + assert hashlib.sha256(source.encode()).hexdigest() == plan['candidate_model_sha256'] + shutil.copytree(parent, output, ignore=shutil.ignore_patterns('__pycache__')) + (output/'reference/alphagenome_50m/model.py').write_text(source) + policy = dict(created_utc=datetime.now(timezone.utc).isoformat(), + parent_inference_signature=plan['parent_inference_signature'], + parameters=plan['parameters'], additional_training_updates=0, + interpolation='Linear decoded global features, align_corners=False, full1024->8192bin grid before central crop or position gathering.', + changed_methods=['_dense_features','_all_dense_features','_position_features'], + new_helper='_global_dense_features', + learned_weights_unchanged=True, local_path_unchanged=True, + both_entrypoints_share_model=True, contact_path_unchanged=True, + scope='Inference-only architectural intervention after the inherited training. No refitting, auxiliary input, label or target access.') + (output/'global_upsampling_policy.json').write_text(json.dumps(policy,indent=2)+'\n') + provenance_path=output/'training_provenance.json' + provenance=json.loads(provenance_path.read_text());provenance['global_upsampling_intervention']=policy + provenance_path.write_text(json.dumps(provenance,indent=2)+'\n') + readme=output/'README.md';body=readme.read_text().split('\n## Measured validation results')[0].split('\n',1)[1] + body=body.replace('In preliminary\ndiagnostics, MFASS and site Pearson improved while sQTL and some ClinVar metrics regressed.', + "The complete reports below measure this checkpoint's track and variant tradeoffs.") + note=''' +This candidate changes one architectural operation after training: decoded global features +at 1 kb are interpolated linearly onto the 128 bp grid instead of repeated eight times. +Interpolation uses bin centers (align_corners=False) over the full input before cropping. +Dense outputs, splice logits, usage and junction position queries share this feature grid. +The local base-resolution path, transformer, output heads, four fixed splice offsets and +all learned weights remain inherited. Contacts use the inherited global trunk directly. + +The intervention adds no parameters or optimizer updates; the inherited training used +repeated global features. A public-DNA-only coordinate-aligned probe motivated the test: +128 bp shifts changed effects much more than 1 kb shifts. Encoder aliasing and changed +edge context can also contribute, so smoother upsampling does not guarantee accuracy or +shift invariance. This package requires its own full track and development variant scores, +native replay and representative runtime checks. No values are fit to development labels. +''' + readme.write_text('# SeqFunc small3 with linear global upsampling\n'+body+note) + history=output/'training_history';history.mkdir(exist_ok=True) + (history/'linear-upsample83-plan.json').write_bytes(args.plan.read_bytes()) + (history/'make_linear_upsample83.py').write_bytes(Path(__file__).read_bytes()) + (history/'pre-linear-upsample-model.py').write_bytes((parent/'reference/alphagenome_50m/model.py').read_bytes()) + for name in ['run_linear_upsample83.py','check_linear_upsample83.py']: + (history/name).write_bytes((Path(__file__).parent/name).read_bytes()) + (history/'linear-upsample-replay.md').write_text( + 'The inherited training used pre-linear-upsample-model.py at reference/alphagenome_50m/model.py.\n' + 'Restore that source when reproducing the inherited training trajectory; all training configuration,\n' + 'objective, interval order and optimizer provenance remain inherited. The installed model.py is\n' + 'the post-training inference intervention, generated by make_linear_upsample83.py.\n' + 'No optimizer update produced the intervention. Native inference uses the installed source.\n') + manifest=json.loads((output/'package_manifest.json').read_text()) + for name in ['validation_tracks.json','validation_variants.json']: + (output/name).unlink(missing_ok=True);manifest['files'].pop(name,None) + changed=['reference/alphagenome_50m/model.py','global_upsampling_policy.json', + 'training_provenance.json','README.md','training_history/linear-upsample83-plan.json', + 'training_history/make_linear_upsample83.py','training_history/pre-linear-upsample-model.py', + 'training_history/run_linear_upsample83.py','training_history/check_linear_upsample83.py', + 'training_history/linear-upsample-replay.md'] + for name in changed:manifest['files'][name]=digest(output/name) + manifest['files']={k:v for k,v in manifest['files'].items() if '__pycache__' not in Path(k).parts} + (output/'package_manifest.json').write_text(json.dumps(manifest,indent=2)+'\n') + actual=inference_signature(output) + assert {k for k in actual if actual[k]!=plan['parent_inference_signature'][k]} == {'reference/alphagenome_50m/model.py'} + for name in ['last.pt','predict.py','perturb.py','infer.sh','perturb.sh','runtime.tar.gz']: + assert digest(output/name)==digest(parent/name),name + print(json.dumps(dict(event='linear_upsample_candidate_built',**verify_package(output))),flush=True) + + +if __name__=='__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/make_splice_grid96.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/make_splice_grid96.py new file mode 100644 index 0000000000000000000000000000000000000000..c4e7cb81bb064023689cc3fdc5b8edf79de8ee21 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/make_splice_grid96.py @@ -0,0 +1,132 @@ +"""Restore the trained nearest-neighbor splice-classifier grid in one Linear83 network.""" +import argparse +from datetime import datetime, timezone +import hashlib +import json +from pathlib import Path +import shutil + +from verify_package import inference_signature, verify_package + + +def digest(path): + with Path(path).open('rb') as stream: + return hashlib.file_digest(stream, 'sha256').hexdigest() + + +def changed_source(source): + before = ' logits = self.splice_site_head(self._all_dense_features(trunk))\n' + after = ''' # The splice classifier retains the feature grid used during training. + # Dense, usage and junction readouts keep the separate interpolated grid. + features = self.dense_decoder(trunk.repeat_interleave(self.config.dense_upsample, dim=1)) + if self.config.local_width: + features = features + self._local_features + logits = self.splice_site_head(features) +''' + assert source.count(before) == 1 + changed = source.replace(before, after) + assert changed.replace(after, before) == source + return changed + + +def main(): + parser=argparse.ArgumentParser(description=__doc__);parser.add_argument('plan',type=Path) + args=parser.parse_args();plan=json.loads(args.plan.read_text());study=args.plan.parent + parent=Path(plan['parent'])/'package';output=Path(plan['output'])/'package' + assert not output.exists() + assert inference_signature(parent)==plan['parent_inference_signature'] + verify_package(parent) + source=changed_source((parent/'reference/alphagenome_50m/model.py').read_text()) + assert hashlib.sha256(source.encode()).hexdigest()==plan['candidate_model_sha256'] + shutil.copytree(parent,output,ignore=shutil.ignore_patterns('__pycache__','*.pyc')) + (output/'reference/alphagenome_50m/model.py').write_text(source) + policy=dict(created_utc=datetime.now(timezone.utc).isoformat(), + parent_inference_signature=plan['parent_inference_signature'],parameters=plan['parameters'], + additional_training_updates=0,learned_weights_unchanged=True, + only_changed_method='_splice_site_logits',splice_classifier_grid='nearest-neighbor repetition, as trained', + dense_usage_junction_grid='inherited bin-center linear interpolation', + local_and_global_backbone_unchanged=True,contacts_unchanged=True, + both_entrypoints_share_one_fixed_model=True, + scope='One fixed internal readout path, independent of input manifest or evaluation identity. ' + 'All outputs are computed from public DNA by the same network. No prediction mixing, fitting or caches.') + (output/'splice_grid_policy.json').write_text(json.dumps(policy,indent=2)+'\n') + provenance=json.loads((output/'training_provenance.json').read_text()) + provenance['splice_classifier_grid_intervention']=policy + (output/'training_provenance.json').write_text(json.dumps(provenance,indent=2)+'\n') + history=output/'training_history' + (history/'pre-splice-grid96-model.py').write_bytes((parent/'reference/alphagenome_50m/model.py').read_bytes()) + (history/'splice-grid96-plan.json').write_bytes(args.plan.read_bytes()) + for name in ['make_splice_grid96.py','check_splice_grid96.py','run_splice_grid96.py']: + (history/name).write_bytes((Path(__file__).parent/name).read_bytes()) + old_global=json.loads((output/'global_upsampling_policy.json').read_text()) + (history/'linear83-global-upsampling-policy.json').write_text(json.dumps(old_global,indent=2)+'\n') + old_global['splice_classifier_grid_refinement']='Restored nearest-neighbor classifier input by splice_grid_policy.json; dense/usage/junction interpolation remains inherited.' + (output/'global_upsampling_policy.json').write_text(json.dumps(old_global,indent=2)+'\n') + (output/'README.md').write_text('''# SeqFunc with linear readouts and the trained splice-classifier grid + +One **13,290,504-parameter** network reads the complete1,048,576-bp DNA window and serves +all eleven output families and both entrypoints. A convolutional encoder and six width384 +transformer blocks process1,024 global tokens. A width32 base-resolution path has a21-bp +stem and six dilated residual blocks, with10,941-bp receptive field. Its pooled features +also enter the global trunk through a learned projection. Transformer updates retain the +inherited per-token RMS cap0.1. + +Decoded global features are interpolated from1kb to128bp at bin centers for the seven +dense families and the usage/junction readouts. The splice classifier uses the original +repeated global features, with its learned128-offset decoder and local base-resolution +logits. These are fixed internal paths of the same backbone and learned heads. Contact +maps retain the symmetric global-feature product. Model-selected splice coordinates and +strand-consistent junction pairs are used; inference receives no splice coordinates. + +Weights come from local random initialization, seed17: **123,060 training examples, +30,765 optimizer updates, three complete passes** over all41,020 eligible FOLD_0 training +intervals (folds2–7). Train8-bit targets are decoded per example by the supplied reader. +Batch4 BF16 AdamW uses weight decay1e-4, clipping5 and the original14-epoch cosine schedule +from2e-4 to2e-5 with4,000-example warmup. Fixed variance-balanced family scales use256 +training windows; splice BCE is balanced. Accumulated inherited training-loop time is +494.6minutes on one shared H10080GB. Setup/evaluation and other experiments are additional +research compute. Training source, configuration, history and provenance are packaged. + +All three training passes used nearest-neighbor global upsampling. Linear83 introduced +interpolation after training. This candidate restores only the splice classifier's trained +grid; it adds **zero parameters and zero training updates**. Four original training-derived +splice offsets and the fixed probability link remain unchanged. They were not refit. +Global usage/junction features still interpolate, but predicted site selection can change; +their track accuracy therefore needs a fresh full evaluation. + +This experiment follows Linear83's higher track reward and lower ClinVar non-coding AP. +It is selected using validation evidence, with one training seed and no multiplicity +adjustment. Its own validation measurements are appended only after complete scoring. +No test labels, externally pretrained weights or variant-label training were used. + +Both executable entrypoints take INPUT_ROOT and OUTPUT_ROOT and run offline from public +manifests and the pinned reference FASTA. CPython3.11,PyTorch2.7.1/CUDA12.6 and dependencies +are vendored in runtime.tar.gz; the host supplies Linux,bash/tar/gzip and an NVIDIA driver. +Track outputs use float16 and perturbations float32. No prediction/target cache is included. +The same fixed model computes every requested output from DNA in both entrypoints. + +Limitations include three-pass training, pooled usage/junction features, low-rank contacts, +the finite local receptive field and uneven variant performance. The54 masked GTEx RNA +tracks have no training targets and are excluded from scored RNA means. Functional readouts +do not supply the conservation information used by strong TraitGym anchors. Native replay, +complete200-window/2,697-query development scoring and representative four-hour runtime +gates are required before publication; runtime estimates are not guaranteed bounds. +''') + manifest=json.loads((output/'package_manifest.json').read_text()) + for name in ['validation_tracks.json','validation_variants.json']: + (output/name).unlink(missing_ok=True);manifest['files'].pop(name,None) + updated=['reference/alphagenome_50m/model.py','splice_grid_policy.json','global_upsampling_policy.json', + 'training_provenance.json','README.md','training_history/pre-splice-grid96-model.py', + 'training_history/splice-grid96-plan.json','training_history/linear83-global-upsampling-policy.json', + *['training_history/'+name for name in ['make_splice_grid96.py','check_splice_grid96.py','run_splice_grid96.py']]] + for name in updated:manifest['files'][name]=digest(output/name) + manifest['files']={k:v for k,v in manifest['files'].items() if '__pycache__' not in Path(k).parts} + (output/'package_manifest.json').write_text(json.dumps(manifest,indent=2)+'\n') + signature=inference_signature(output) + assert {k for k in signature if signature[k]!=plan['parent_inference_signature'][k]}=={'reference/alphagenome_50m/model.py'} + for name in ['last.pt','predict.py','perturb.py','infer.sh','perturb.sh','runtime.tar.gz','splice_calibration.json','splice_output.py']: + assert digest(output/name)==digest(parent/name),name + print(json.dumps(dict(event='splice_grid96_built',**verify_package(output))),flush=True) + + +if __name__=='__main__':main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/memory-check-interruption.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/memory-check-interruption.json new file mode 100644 index 0000000000000000000000000000000000000000..19bc02cd2784b11f5f3e658efa28017639c2efeb --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/memory-check-interruption.json @@ -0,0 +1,19 @@ +{ + "time_utc": "2026-10-24T21:49:06.154124+00:00", + "reason": "Mechanical activation-checkpoint memory/equivalence check; optimizer, data order and model math preserved.", + "process_group": 4169, + "checkpoints": [ + { + "run": "scale13-small", + "step": 1000, + "path": "/workspace/research/experiments/scale13-small/step-001000.pt", + "sha256": "a53ea6d92c21c06576c734dc05eac6e3f21d33701f25bfc335817a6e6ec197fc" + }, + { + "run": "scale13-large", + "step": 1000, + "path": "/workspace/research/experiments/scale13-large/step-001000.pt", + "sha256": "7398dd28297d6cc3134b8d3b0cea703fb0bb0a8e60b4c0c2d81abe04c1d7dc43" + } + ] +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/migrate_scale_bytecache.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/migrate_scale_bytecache.py new file mode 100644 index 0000000000000000000000000000000000000000..05b927345326f5b660fa5b28118ce867eee97c88 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/migrate_scale_bytecache.py @@ -0,0 +1,235 @@ +"""Checkpoint-safe opt-in byte-cache amendment, preserving an active evaluation.""" +import argparse +from datetime import datetime, timezone +import fcntl +import hashlib +import json +import os +from pathlib import Path +import shutil +import signal +import sys +import time + +import torch + + +def digest(path): + with Path(path).open('rb') as stream: + return hashlib.file_digest(stream, 'sha256').hexdigest() + + +def identity(pid): + p = Path(f'/proc/{pid}') + stat = (p/'stat').read_text().split(') ', 1)[1].split() + return {'pid': pid, 'starttime_ticks': int(stat[19]), + 'cmdline_sha256': hashlib.sha256((p/'cmdline').read_bytes()).hexdigest()} + + +def command(pid): + return Path(f'/proc/{pid}/cmdline').read_bytes().replace(b'\0', b' ').decode() + + +def alive(pid): + try: + return Path(f'/proc/{pid}/stat').read_text().split(') ', 1)[1][0] not in 'ZX' + except FileNotFoundError: + return False + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('plan', type=Path) + args = p.parse_args() + plan = json.loads(args.plan.read_text()) + r = Path('/workspace/research') + root = args.plan.parent + os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) + torch.set_num_threads(1) + for name, value in plan['helper_sha256'].items(): + assert digest(r/'seqfunc'/name) == value, name + assert digest(plan['original_study']) == plan['original_study_sha256'] + original = json.loads(Path(plan['original_study']).read_text()) + prepared = json.loads(Path(plan['preparation']).read_text()) + integrated = json.loads(Path(plan['integration_check']).read_text()) + timing = json.loads(Path(plan['timing_report']).read_text()) + assert integrated['all_returned_training_ids_and_tensor_bytes_exact'] and integrated['examples'] == 16 + assert integrated['public_inference_and_validation_cache_disabled_despite_environment'] + assert timing['cache_over_reference'] < .8 and timing['all_probe_updates_discarded'] + source = Path(prepared['new_source']) + for name, value in prepared['new_files_sha256'].items(): + assert digest(source/name) == value, name + for path in Path(original['source']).rglob('*'): + if path.is_file() and '__pycache__' not in path.parts and '.pytest_cache' not in path.parts: + relative = path.relative_to(original['source']) + if str(relative) != 'reference/alphagenome_50m/dataset.py': + assert digest(path) == digest(source/relative), str(relative) + old_controller = plan['old_controller_pid'] + assert 'run_scale_study.py' in command(old_controller) + assert os.getpgid(old_controller) != os.getpgrp() + processes = json.loads((root/'study-state.json').read_text()) + for name, target in plan['boundaries'].items(): + assert target % 1000 == 0 and target % 4 == 0 + pid = processes[name+'-train']['pid'] + assert str(Path(original['source'])/'train_batched.py') in command(pid) + assert str(r/'experiments'/name) in command(pid) + stopped, snapshots, seen = set(), {}, {} + killed = False + controller_stopped = False + begun = time.monotonic() + + def status(phase, **extra): + record = {'phase': phase, 'utc': datetime.now(timezone.utc).isoformat(), + 'boundaries': plan['boundaries'], 'checkpoints': snapshots, **extra} + temporary = root/'cached-io-migration-state.tmp' + temporary.write_text(json.dumps(record, indent=2)+'\n') + temporary.replace(root/'cached-io-migration-state.json') + print(json.dumps(record), flush=True) + + def interrupted(signum, frame): + raise SystemExit(f'Migration interrupted by signal {signum}') + signal.signal(signal.SIGTERM, interrupted) + signal.signal(signal.SIGINT, interrupted) + status('waiting_for_saved_boundaries') + try: + while len(snapshots) < len(plan['boundaries']): + assert time.monotonic()-begun < 3600, 'Boundary wait exceeded one hour' + for name, target in plan['boundaries'].items(): + if name in snapshots: + continue + pid = processes[name+'-train']['pid'] + assert alive(pid), f'Trainer {name} exited before capture' + progress = r/'experiments'/name/'progress.pt' + with progress.open('rb') as stream: + stat = os.fstat(stream.fileno()) + signature = (stat.st_ino, stat.st_mtime_ns, stat.st_size) + if signature == seen.get(name): + continue + state = torch.load(f'/proc/self/fd/{stream.fileno()}', mmap=True, + map_location='cpu', weights_only=True) + step = state['global_step'] + assert step <= target, f'{name} missed boundary at {step}' + seen[name] = signature + if step == target: + assert state['optimizer_steps'] == step//4 + assert state['objective'] == 'variance_balanced' + os.kill(pid, signal.SIGSTOP) + stopped.add(pid) + destination = progress.with_name(f'bytecache-resume-{step:06d}.pt') + assert not destination.exists() + temporary = destination.with_suffix('.tmp') + stream.seek(0) + with temporary.open('wb') as output: + shutil.copyfileobj(stream, output, length=8*1024*1024) + temporary.replace(destination) + snapshots[name] = {'step': step, 'path': str(destination), + 'sha256': digest(destination), 'single_inode_copy': True} + status('captured_saved_boundary', trainer=name) + del state + time.sleep(1) + # All pending later evaluations still wait for checkpoints. Preserve the + # already staged small epoch2 evaluation, which belongs to the old reader. + keep = 'scale13-small-eval02' + adopted = {} + keep_directory = r/'experiments/scale13-small-epoch02' + if json.loads((keep_directory/'pipeline-state.json').read_text())['phase'] != 'complete': + adopted[keep] = identity(processes[keep]['pid']) + assert 'run_candidate.py' in command(processes[keep]['pid']) + terminate_waiters = [] + for name, record in processes.items(): + if '-eval' not in name or name in adopted or record['returncode'] is not None: + continue + pid = record['pid'] + directory = r/'experiments'/name.replace('-eval', '-epoch') + if json.loads((directory/'pipeline-state.json').read_text())['phase'] == 'complete': + continue + assert json.loads((directory/'pipeline-state.json').read_text())['phase'] == 'waiting_for_checkpoint' + assert not Path(f'/proc/{pid}/task/{pid}/children').read_text().strip() + terminate_waiters.append(pid) + # Keep this old session alive until its adopted evaluation finishes. + # Its monitor is stopped, so only the new controller owns training status. + old_identity = identity(old_controller) + os.kill(old_controller, signal.SIGSTOP) + controller_stopped = True + worker_pids = [] + for name in plan['boundaries']: + pid = processes[name+'-train']['pid'] + worker_pids += [int(p) for p in Path(f'/proc/{pid}/task/{pid}/children').read_text().split()] + terminated = terminate_waiters+worker_pids+[processes[name+'-train']['pid'] for name in plan['boundaries']] + status('replacing_trainers_and_idle_waiters', preserved_evaluations=adopted, terminated_pids=terminated) + for pid in terminated: + if alive(pid): + os.kill(pid, signal.SIGTERM) + os.kill(pid, signal.SIGCONT) + killed = True + stopped.clear() + deadline = time.monotonic()+60 + while any(alive(pid) for pid in terminated): + assert time.monotonic() < deadline, 'Old training or idle waiter did not exit' + time.sleep(1) + for name, record in snapshots.items(): + assert digest(r/'experiments'/name/'progress.pt') == record['sha256'] + amendment = {'adopted_utc': datetime.now(timezone.utc).isoformat(), + 'change': 'Optional verified compressed-byte cache, training only; every hit checks source-byte digest, key and length.', + 'source_before': original['source'], 'source_after': str(source), + 'checkpoints': snapshots, 'cache_root': plan['cache_root'], + 'model_optimizer_rng_exact_at_each_resume': True, + 'model_trainer_loss_sources_data_order_objective_batch_schedule_unchanged': True, + 'different_resume_boundaries_are_IO_only': True, + 'integration_check': plan['integration_check'], 'timing_report': plan['timing_report'], + 'timing_cache_over_reference': timing['cache_over_reference'], + 'timing_caveat': timing['caveats'], + 'cache_not_an_inference_asset': True, + 'adopted_evaluations_without_interruption': adopted, + 'old_monitor_retained_stopped_until_adopted_evaluation_finishes': old_identity} + new = dict(original) + new.update(source=str(source), training_bytecache_root=plan['cache_root'], + cache_execution_amendment=amendment, adopted_evaluations=adopted, + retired_monitor_identity=old_identity) + new['execution_amendments'] = list(original.get('execution_amendments', []))+[amendment] + new['provenance'] += (' At the saved small96k/large78k boundaries, an optional training-only byte cache ' + 'was enabled after exact256example and integrated16example tensor/ID checks. ' + 'Source compressed bytes are verified on every cache hit, with read-only source fallback. ' + 'Model/trainer/loss code, complete epoch permutations, batch and schedule are unchanged. ' + 'The cache is disposable and is disabled for validation/public inference.') + history = source/'training_history' + (history/'bytecache-io-amendment.json').write_text(json.dumps(amendment, indent=2)+'\n') + for name in ['preparation', 'integration_check', 'timing_report']: + path = Path(plan[name]);shutil.copyfile(path, history/('bytecache-'+name+'.json')) + shutil.copyfile(args.plan, history/'bytecache-migration-plan.json') + shutil.copyfile(Path(__file__), history/Path(__file__).name) + shutil.copyfile(r/'seqfunc/run_scale_study_cached_io.py', history/'run_scale_study_cached_io.py') + new_path = root/'study-cached-io.json' + new_path.write_text(json.dumps(new, indent=2)+'\n') + manifest = {str(p.relative_to(source)): digest(p) for p in sorted(source.rglob('*')) + if p.is_file() and '__pycache__' not in p.parts and '.pytest_cache' not in p.parts} + (root/'source_manifest-cached-io.json').write_text(json.dumps(manifest, indent=2)+'\n') + (root/'cached-io-amendment.json').write_text(json.dumps(amendment, indent=2)+'\n') + with Path('/workspace/.submission-promotion.lock').open('a') as lock: + fcntl.flock(lock, fcntl.LOCK_EX) + p = r/'STATE.json'; state = json.loads(p.read_text()) + state['scale_study'].update(plan=str(new_path), source=str(source), + controller_pid=os.getpid(), status='Resuming original trajectories with verified opt-in training byte cache', + cached_reader_amendment=str(root/'cached-io-amendment.json'), + previous_controller_retained_stopped=old_controller) + state['scale_study']['session'] = state['scale_study']['bytecache_migration']['session'] + state['scale_study']['bytecache_migration']['status'] = 'Checkpoints captured; replacement controller starting' + temporary=p.with_suffix('.tmp');temporary.write_text(json.dumps(state,indent=2)+'\n');temporary.replace(p) + with (r/'LEDGER.md').open('a') as ledger: + ledger.write('\n'+amendment['adopted_utc']+': saved small96k/large78k trajectories resumed with optional verified training byte cache; original scientific plans unchanged. Active small epoch2 evaluation preserved. Original study/source/checkpoints retained; full amendment at studies/scale13/cached-io-amendment.json.\n') + status('replacement_controller_starting', source=str(source), new_study=str(new_path)) + log = (root/'study-cached-io.log').open('a') + os.dup2(log.fileno(), 1);os.dup2(log.fileno(), 2) + os.execv(sys.executable,[sys.executable,'-u',str(r/'seqfunc/run_scale_study_cached_io.py'),str(new_path)]) + except BaseException as error: + status('failed_or_interrupted', error=repr(error), old_trainers_terminated=killed) + raise + finally: + if not killed: + for pid in stopped: + if alive(pid):os.kill(pid,signal.SIGCONT) + if controller_stopped and alive(old_controller):os.kill(old_controller,signal.SIGCONT) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/model_before_block_checkpoint.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/model_before_block_checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..31f17eb68837a9caa6492ec862feda39d068a43f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/model_before_block_checkpoint.py @@ -0,0 +1,457 @@ +"""Affordable multi-resolution baseline for the compact AlphaGenome task.""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass, fields +import math +from pathlib import Path +from typing import Any, Iterable + +import torch +from torch import Tensor, nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint +import yaml + +from .layers import ChannelRMSNorm, ConvBlock, SequenceBlock + + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} +ALL_FAMILIES = (*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions", "contact_maps") + + +@dataclass(frozen=True) +class ModelConfig: + input_length: int = 1_048_576 + target_length: int = 196_608 + dense_bin_size: int = 128 + trunk_bin_size: int = 1_024 + contact_bin_size: int = 2_048 + stem_stride: int = 8 + encoder_width: int = 32 + channel_growth: int = 32 + num_downsampling_blocks: int = 7 + conv_kernel_size: int = 5 + dna_kernel_size: int = 15 + hidden_dim: int = 384 + num_sequence_blocks: int = 6 + num_attention_heads: int = 6 + attention_head_dim: int = 64 + mlp_expansion: float = 4.0 + dropout: float = 0.0 + head_hidden_size: int = 256 + junction_rank: int = 32 + splice_usage_tracks: int = 734 + junction_tracks: int = 734 + contact_tracks: int = 28 + gradient_checkpointing: bool = True + enable_pairwise: bool = False + pair_width: int = 32 + pair_downsample: int = 16 + pair_update_interval: int = 2 + local_width: int = 0 + local_context_blocks: int = 4 + splice_context_mode: str = 'offset' + local_to_trunk: bool = False + residual_rms_cap: float = 0.0 + + def __post_init__(self) -> None: + if not 1 <= self.local_context_blocks <= 7: + raise ValueError('local_context_blocks must be between 1 and 7') + if not math.isfinite(self.residual_rms_cap) or self.residual_rms_cap < 0: + raise ValueError('residual_rms_cap must be finite and nonnegative') + if self.local_to_trunk and not self.local_width: + raise ValueError('local_to_trunk requires local features') + if self.splice_context_mode not in ('offset', 'uniform'): + raise ValueError('unknown splice_context_mode') + if self.splice_context_mode == 'uniform' and not self.local_width: + raise ValueError('uniform splice context requires a base-resolution path') + downsample = self.stem_stride * 2**self.num_downsampling_blocks + if downsample != self.trunk_bin_size: + raise ValueError("stem_stride * 2**num_downsampling_blocks must equal trunk_bin_size") + for length in (self.input_length, self.target_length): + if length % self.trunk_bin_size: + raise ValueError("input and target lengths must divide by trunk_bin_size") + if self.trunk_bin_size % self.dense_bin_size: + raise ValueError("trunk_bin_size must divide by dense_bin_size") + if self.input_length % self.contact_bin_size: + raise ValueError("input_length must divide by contact_bin_size") + if self.hidden_dim <= 0 or self.num_attention_heads <= 0: + raise ValueError("hidden dimensions and attention heads must be positive") + + @property + def trunk_bins(self) -> int: + return self.input_length // self.trunk_bin_size + + @property + def target_trunk_bins(self) -> int: + return self.target_length // self.trunk_bin_size + + @property + def dense_bins(self) -> int: + return self.target_length // self.dense_bin_size + + @property + def dense_upsample(self) -> int: + return self.trunk_bin_size // self.dense_bin_size + + @property + def contact_bins(self) -> int: + return self.input_length // self.contact_bin_size + + @property + def encoder_output_width(self) -> int: + return self.encoder_width + self.channel_growth * self.num_downsampling_blocks + + @classmethod + def from_yaml(cls, path: str | Path) -> "ModelConfig": + values: dict[str, Any] = yaml.safe_load(Path(path).read_text()) or {} + known = {field.name for field in fields(cls)} + unknown = sorted(values.keys() - known) + if unknown: + raise ValueError(f"Unknown model configuration keys: {unknown}") + return cls(**values) + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + +class SequenceEncoder(nn.Module): + def __init__(self, config: ModelConfig): + super().__init__() + self.stem = nn.Conv1d( + 4, + config.encoder_width, + config.dna_kernel_size, + stride=config.stem_stride, + padding=config.dna_kernel_size // 2, + ) + blocks = [] + width = config.encoder_width + for _ in range(config.num_downsampling_blocks): + output_width = width + config.channel_growth + blocks.append(ConvBlock(width, output_width, config.conv_kernel_size)) + width = output_width + self.blocks = nn.ModuleList(blocks) + + def forward(self, dna: Tensor) -> Tensor: + x = F.gelu(self.stem(dna.transpose(1, 2)), approximate="tanh") + for block in self.blocks: + x = F.max_pool1d(block(x), kernel_size=2, stride=2) + return x.transpose(1, 2) + + +class LocalResidualBlock(nn.Module): + def __init__(self, width: int, dilation: int): + super().__init__() + self.norm = ChannelRMSNorm(width) + self.depthwise = nn.Conv1d(width, width, 9, dilation=dilation, + padding=4 * dilation, groups=width) + self.expand = nn.Conv1d(width, 2 * width, 1) + self.contract = nn.Conv1d(2 * width, width, 1) + + def forward(self, x: Tensor) -> Tensor: + update = self.depthwise(self.norm(x)) + update = self.contract(F.gelu(self.expand(update), approximate='tanh')) + return x + update + + +class LocalSequencePath(nn.Module): + """Base-resolution motif path with powers-of-four dilation and aligned pooling.""" + def __init__(self, config: ModelConfig): + super().__init__() + width = config.local_width + self.stem = nn.Conv1d(4, width, 21, padding=10) + self.blocks = nn.Sequential(*(LocalResidualBlock(width, 4**i) + for i in range(config.local_context_blocks))) + self.norm = ChannelRMSNorm(width) + self.sites = nn.Conv1d(width, 4, 1) + self.bin_size = config.dense_bin_size + + def forward(self, dna: Tensor) -> tuple[Tensor, Tensor]: + base = F.gelu(self.stem(dna), approximate='tanh') + base = F.gelu(self.norm(self.blocks(base)), approximate='tanh') + sites = self.sites(base).transpose(1, 2) + pooled = torch.cat((F.avg_pool1d(base, self.bin_size), + F.max_pool1d(base, self.bin_size)), dim=1).transpose(1, 2) + return sites, pooled + + +class CompactModel(nn.Module): + """One shared 1-kb trunk with representation-specific output heads.""" + + def __init__(self, config: ModelConfig | None = None): + super().__init__() + self.config = config or ModelConfig() + self.encoder = SequenceEncoder(self.config) + self.projection = nn.Linear(self.config.encoder_output_width, self.config.hidden_dim) + self.blocks = nn.ModuleList( + SequenceBlock(self.config) for _ in range(self.config.num_sequence_blocks) + ) + self.final_norm = nn.RMSNorm(self.config.hidden_dim) + self.dense_decoder = nn.Sequential( + nn.RMSNorm(self.config.hidden_dim), + nn.Linear(self.config.hidden_dim, self.config.head_hidden_size), + nn.GELU(approximate="tanh"), + ) + self.dense_heads = nn.ModuleDict( + { + family: nn.Linear(self.config.head_hidden_size, tracks) + for family, tracks in DENSE_TRACKS.items() + } + ) + self.splice_site_head = nn.Linear( + self.config.head_hidden_size, self.config.dense_bin_size * 4 + ) + self.splice_usage_head = nn.Linear( + self.config.head_hidden_size, self.config.splice_usage_tracks + ) + rank = self.config.junction_rank + self.junction_source = nn.Linear(self.config.head_hidden_size, rank) + self.junction_destination = nn.Linear(self.config.head_hidden_size, rank) + self.junction_track = nn.Embedding(self.config.junction_tracks, rank) + if self.config.junction_tracks % 2: + raise ValueError("junction tracks must split evenly into strand halves") + self.register_buffer( + "junction_track_half", + torch.arange(self.config.junction_tracks) // (self.config.junction_tracks // 2), + persistent=False, + ) + self.contact_q = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + self.contact_k = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + row, column = torch.triu_indices( + self.config.contact_bins, self.config.contact_bins + ) + self.register_buffer("contact_row", row, persistent=False) + self.register_buffer("contact_column", column, persistent=False) + if self.config.local_width: + self.local_path = LocalSequencePath(self.config) + self.local_projection = nn.Linear(2 * self.config.local_width, self.config.head_hidden_size) + # A new local path can be attached to our own checkpoint without changing its initial outputs. + nn.init.zeros_(self.local_path.sites.weight) + nn.init.zeros_(self.local_path.sites.bias) + nn.init.zeros_(self.local_projection.weight) + nn.init.zeros_(self.local_projection.bias) + if self.config.local_to_trunk: + self.local_trunk_projection = nn.Linear(2 * self.config.local_width, self.config.hidden_dim) + nn.init.zeros_(self.local_trunk_projection.weight) + nn.init.zeros_(self.local_trunk_projection.bias) + self._local_sites = None + self._local_features = None + + def set_junction_track_halves(self, halves: Tensor) -> None: + halves = torch.as_tensor(halves, dtype=torch.long, device=self.junction_track_half.device) + if halves.shape != (self.config.junction_tracks,) or not torch.isin( + halves, torch.tensor([0, 1], device=halves.device) + ).all(): + raise ValueError("junction track halves must be a 734-element 0/1 mapping") + self.junction_track_half.copy_(halves) + + def _trunk(self, dna: Tensor) -> Tensor: + if dna.ndim != 3 or dna.shape[1:] != (self.config.input_length, 4): + raise ValueError( + f"Expected DNA [B,{self.config.input_length},4], got {tuple(dna.shape)}" + ) + if self.config.local_width: + if self.config.gradient_checkpointing and self.training: + sites, pooled = checkpoint(self.local_path, dna.transpose(1, 2), use_reentrant=False) + else: + sites, pooled = self.local_path(dna.transpose(1, 2)) + self._local_sites = sites + self._local_features = self.local_projection(pooled) + x = self.projection(self.encoder(dna)) + if self.config.local_to_trunk: + local_tokens = pooled.reshape(len(dna), self.config.trunk_bins, + self.config.dense_upsample, -1).mean(dim=2) + x = x + self.local_trunk_projection(local_tokens) + for block in self.blocks: + if self.config.gradient_checkpointing and self.training and x.requires_grad: + x = checkpoint(block, x, None, use_reentrant=False) + else: + x = block(x, None) + return self.final_norm(x) + + def _dense_features(self, trunk: Tensor) -> Tensor: + start = (self.config.trunk_bins - self.config.target_trunk_bins) // 2 + target = trunk[:, start : start + self.config.target_trunk_bins] + target = target.repeat_interleave(self.config.dense_upsample, dim=1) + features = self.dense_decoder(target) + if self.config.local_width: + first_bin = (self.config.input_length - self.config.target_length) // (2 * self.config.dense_bin_size) + features = features + self._local_features[:, first_bin:first_bin + self.config.dense_bins] + return features + + def _all_dense_features(self, trunk: Tensor) -> Tensor: + features = self.dense_decoder( + trunk.repeat_interleave(self.config.dense_upsample, dim=1) + ) + return features + self._local_features if self.config.local_width else features + + def _splice_site_logits(self, trunk: Tensor) -> Tensor: + if self.config.splice_context_mode == 'uniform': + # The global trunk supplies one gene-context bias per class and kb. Base-resolution + # localization comes from the local path, with no arbitrary 128-bp offset decoder + # and no pooled dense-feature contribution to site logits. + context = self.splice_site_head(self.dense_decoder(trunk)) + context = context.view(len(trunk), self.config.trunk_bins, self.config.dense_bin_size, 4).mean(dim=2) + logits = context.repeat_interleave(self.config.trunk_bin_size, dim=1) + self._local_sites + return logits.float() + logits = self.splice_site_head(self._all_dense_features(trunk)) + logits = logits.view(len(trunk), self.config.input_length, 4) + if self.config.local_width: + logits = logits + self._local_sites + return logits.float() + + def _position_features(self, trunk: Tensor, positions: Tensor) -> Tensor: + features = self.dense_decoder(self._gather_bins(trunk, positions, self.config.trunk_bin_size)) + if self.config.local_width: + features = features + self._gather_bins(self._local_features, positions, self.config.dense_bin_size) + return features + + @staticmethod + def select_junction_positions(site_logits: Tensor, limit: int = 512) -> Tensor: + """Select each site type from model scores alone, then sort by coordinate. + + This follows the four independent site classes in Google DeepMind's + AlphaGenome splicing implementation (pinned in docs/v3-author/PROTOCOL.md). Padding is + -1 when an input is shorter than ``limit``. + """ + count = min(limit, site_logits.shape[1]) + selected = [] + for site_type in range(4): + coordinates = torch.topk(site_logits[..., site_type], count, dim=1).indices + coordinates = coordinates.sort(dim=1).values + if count < limit: + coordinates = F.pad(coordinates, (0, limit - count), value=-1) + selected.append(coordinates) + return torch.stack(selected, dim=1) + + @staticmethod + def _gather_bins(features: Tensor, positions: Tensor, bin_size: int) -> Tensor: + indices = torch.div(positions, bin_size, rounding_mode="floor") + indices = indices.clamp(0, features.shape[1] - 1) + return torch.gather( + features, + 1, + indices[..., None].expand(-1, -1, features.shape[-1]), + ) + + def _junction_predictions( + self, trunk: Tensor, site_positions: Tensor, queries: Tensor + ) -> Tensor: + row, column, track = queries.unbind(dim=-1) + half = self.junction_track_half[track] + batch = torch.arange(len(trunk), device=trunk.device)[:, None] + donor = site_positions[batch, 2 * half, row] + acceptor = site_positions[batch, 2 * half + 1, column] + source = self._position_features(trunk, donor.clamp(0, self.config.input_length - 1)) + destination = self._position_features(trunk, acceptor.clamp(0, self.config.input_length - 1)) + interaction = self.junction_source(source) * self.junction_destination(destination) + track_embedding = self.junction_track(track) + return (interaction * track_embedding).sum(dim=-1) / math.sqrt( + self.config.junction_rank + ) + + def junction_block( + self, + trunk: Tensor, + site_positions: Tensor, + row_start: int, + row_end: int, + column_start: int, + column_end: int, + track_start: int, + track_end: int, + site_factors: tuple[Tensor, Tensor] | None = None, + ) -> Tensor: + """Compute a bounded block of the full predicted-position junction cube.""" + tracks = torch.arange(track_start, track_end, device=trunk.device) + halves = self.junction_track_half[tracks] + if site_factors is None: + site_factors = self.junction_site_factors(trunk, site_positions) + source_all, destination_all = site_factors + source = source_all[:, halves, row_start:row_end] + destination = destination_all[:, halves, column_start:column_end] + embedding = self.junction_track(tracks)[None, :, None, None, :] + values = ( + source[:, :, :, None, :] * destination[:, :, None, :, :] * embedding + ).sum(dim=-1) / math.sqrt(self.config.junction_rank) + return values.permute(0, 2, 3, 1).float() + + def junction_site_factors( + self, trunk: Tensor, site_positions: Tensor + ) -> tuple[Tensor, Tensor]: + """Precompute the two strand-specific factors reused by all cube blocks.""" + donor = torch.stack((site_positions[:, 0], site_positions[:, 2]), dim=1).clamp_min(0) + acceptor = torch.stack((site_positions[:, 1], site_positions[:, 3]), dim=1).clamp_min(0) + donor_features = self._position_features(trunk, donor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, donor.shape[-1], -1) + acceptor_features = self._position_features(trunk, acceptor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, acceptor.shape[-1], -1) + return self.junction_source(donor_features), self.junction_destination(acceptor_features) + + def forward( + self, + dna: Tensor, + *, + families: Iterable[str] | None = None, + usage_positions: Tensor | None = None, + junction_site_positions: Tensor | None = None, + junction_queries: Tensor | None = None, + ) -> dict[str, Tensor]: + requested = set(families or ALL_FAMILIES) + unknown = requested - set(ALL_FAMILIES) + if unknown: + raise ValueError(f"Unknown output families: {sorted(unknown)}") + trunk = self._trunk(dna) + needs_dense = bool( + requested.intersection((*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions")) + ) + features = self._dense_features(trunk) if needs_dense else None + outputs: dict[str, Tensor] = {} + for family in DENSE_TRACKS: + if family in requested: + outputs[family] = self.dense_heads[family](features).float() + if "splice_sites" in requested: + all_logits = self._splice_site_logits(trunk) + start = (self.config.input_length - self.config.target_length) // 2 + outputs["splice_sites"] = all_logits[:, start:start + self.config.target_length] + if "splice_usage" in requested: + if usage_positions is None: + raise ValueError("splice_usage requires usage_positions") + gathered = self._gather_bins( + features, usage_positions, self.config.dense_bin_size + ) + outputs["splice_usage"] = self.splice_usage_head(gathered).float() + if "junctions" in requested: + if junction_site_positions is None or junction_queries is None: + raise ValueError("junctions require site positions and query triples") + outputs["junctions"] = self._junction_predictions( + trunk, junction_site_positions, junction_queries + ).float() + if "contact_maps" in requested: + contact = trunk.reshape( + len(dna), self.config.contact_bins, -1, self.config.hidden_dim + ).mean(dim=2) + q, k = self.contact_q(contact), self.contact_k(contact) + row, column = self.contact_row, self.contact_column + outputs["contact_maps"] = ( + 0.5 + * ( + q[:, row] * k[:, column] + + q[:, column] * k[:, row] + ) + ).float() + return outputs + + +def count_parameters(model: nn.Module) -> int: + return sum(parameter.numel() for parameter in model.parameters() if parameter.requires_grad) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/parallel-io-amendment.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/parallel-io-amendment.json new file mode 100644 index 0000000000000000000000000000000000000000..48ca3e0d57ff557a5fd2bd2bc517508c6de05b0f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/parallel-io-amendment.json @@ -0,0 +1,27 @@ +{ + "adopted_utc": "2026-10-25T00:42:46.303812+00:00", + "example_boundary": 40000, + "change": "I/O only: direct read and four bounded chunk-read threads per loader process.", + "source_before": "/workspace/research/studies/scale13/source-block-checkpoint", + "source_after": "/workspace/research/studies/scale13/source-parallel-io", + "model_parameters_optimizer_RNG_data_order_objective_schedule_unchanged": true, + "loader_worker_counts_unchanged": true, + "common_immutable_checkpoints": { + "scale13-small": { + "step": 40000, + "path": "/workspace/research/experiments/scale13-small/io-resume-040000.pt", + "sha256": "62daa49a4dc329d0b7cf3ecea0a30834346f714079d975bc636bdd81102f38f2", + "single_inode_copy": true + }, + "scale13-large": { + "step": 40000, + "path": "/workspace/research/experiments/scale13-large/io-resume-040000.pt", + "sha256": "db6c7394de11aba07278c907f328bcf18200c01ab1fb23578b12d293eb7219f4", + "single_inode_copy": true + } + }, + "evidence": "256 full examples through four forked workers and16 integrated-source examples, every tensor byte and ordered ID identical; missing-chunk fixtures pass.", + "loader_parallel_over_reference_time": 0.5340049503280426, + "timing_caveat": "Alternating paired readers under shared training load, including worker startup, full per-example hashing and pinned tensor IPC. 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"all_ordered_ids_and_tensor_bytes_exact": true + } + ], + "total_read_hash_and_transfer_seconds": { + "reference": 69.81505457599997, + "parallel": 37.28158475100645 + }, + "parallel_over_reference_time": 0.5340049503280426, + "reader_sha256": "ad6670f742f4de9898064becc525d0aea98b402abe7b210bb1b26a9eee6402b9", + "adapter_sha256": "ab182b3b687a3b470ff1bbc311a9130cc791974366bdc0a400df5e0e28602c9c", + "caveat": "Alternating paired readers under shared training load, including worker startup, full per-example hashing and pinned tensor IPC. File caches are shared; no GPU computation included." +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/pre-decoder113-model.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/pre-decoder113-model.py new file mode 100644 index 0000000000000000000000000000000000000000..6f211fc29de3fa559db45ce6c2ee3d40816f4a72 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/pre-decoder113-model.py @@ -0,0 +1,460 @@ +"""Affordable multi-resolution baseline for the compact AlphaGenome task.""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass, fields +import math +from pathlib import Path +from typing import Any, Iterable + +import torch +from torch import Tensor, nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint +import yaml + +from .layers import ChannelRMSNorm, ConvBlock, SequenceBlock + + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} +ALL_FAMILIES = (*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions", "contact_maps") + + +@dataclass(frozen=True) +class ModelConfig: + input_length: int = 1_048_576 + target_length: int = 196_608 + dense_bin_size: int = 128 + trunk_bin_size: int = 1_024 + contact_bin_size: int = 2_048 + stem_stride: int = 8 + encoder_width: int = 32 + channel_growth: int = 32 + num_downsampling_blocks: int = 7 + conv_kernel_size: int = 5 + dna_kernel_size: int = 15 + hidden_dim: int = 384 + num_sequence_blocks: int = 6 + num_attention_heads: int = 6 + attention_head_dim: int = 64 + mlp_expansion: float = 4.0 + dropout: float = 0.0 + head_hidden_size: int = 256 + junction_rank: int = 32 + splice_usage_tracks: int = 734 + junction_tracks: int = 734 + contact_tracks: int = 28 + gradient_checkpointing: bool = True + enable_pairwise: bool = False + pair_width: int = 32 + pair_downsample: int = 16 + pair_update_interval: int = 2 + local_width: int = 0 + local_context_blocks: int = 4 + splice_context_mode: str = 'offset' + local_to_trunk: bool = False + residual_rms_cap: float = 0.0 + + def __post_init__(self) -> None: + if not 1 <= self.local_context_blocks <= 7: + raise ValueError('local_context_blocks must be between 1 and 7') + if not math.isfinite(self.residual_rms_cap) or self.residual_rms_cap < 0: + raise ValueError('residual_rms_cap must be finite and nonnegative') + if self.local_to_trunk and not self.local_width: + raise ValueError('local_to_trunk requires local features') + if self.splice_context_mode not in ('offset', 'uniform'): + raise ValueError('unknown splice_context_mode') + if self.splice_context_mode == 'uniform' and not self.local_width: + raise ValueError('uniform splice context requires a base-resolution path') + downsample = self.stem_stride * 2**self.num_downsampling_blocks + if downsample != self.trunk_bin_size: + raise ValueError("stem_stride * 2**num_downsampling_blocks must equal trunk_bin_size") + for length in (self.input_length, self.target_length): + if length % self.trunk_bin_size: + raise ValueError("input and target lengths must divide by trunk_bin_size") + if self.trunk_bin_size % self.dense_bin_size: + raise ValueError("trunk_bin_size must divide by dense_bin_size") + if self.input_length % self.contact_bin_size: + raise ValueError("input_length must divide by contact_bin_size") + if self.hidden_dim <= 0 or self.num_attention_heads <= 0: + raise ValueError("hidden dimensions and attention heads must be positive") + + @property + def trunk_bins(self) -> int: + return self.input_length // self.trunk_bin_size + + @property + def target_trunk_bins(self) -> int: + return self.target_length // self.trunk_bin_size + + @property + def dense_bins(self) -> int: + return self.target_length // self.dense_bin_size + + @property + def dense_upsample(self) -> int: + return self.trunk_bin_size // self.dense_bin_size + + @property + def contact_bins(self) -> int: + return self.input_length // self.contact_bin_size + + @property + def encoder_output_width(self) -> int: + return self.encoder_width + self.channel_growth * self.num_downsampling_blocks + + @classmethod + def from_yaml(cls, path: str | Path) -> "ModelConfig": + values: dict[str, Any] = yaml.safe_load(Path(path).read_text()) or {} + known = {field.name for field in fields(cls)} + unknown = sorted(values.keys() - known) + if unknown: + raise ValueError(f"Unknown model configuration keys: {unknown}") + return cls(**values) + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + +class SequenceEncoder(nn.Module): + def __init__(self, config: ModelConfig): + super().__init__() + self.stem = nn.Conv1d( + 4, + config.encoder_width, + config.dna_kernel_size, + stride=config.stem_stride, + padding=config.dna_kernel_size // 2, + ) + blocks = [] + width = config.encoder_width + for _ in range(config.num_downsampling_blocks): + output_width = width + config.channel_growth + blocks.append(ConvBlock(width, output_width, config.conv_kernel_size)) + width = output_width + self.blocks = nn.ModuleList(blocks) + + def forward(self, dna: Tensor) -> Tensor: + x = F.gelu(self.stem(dna.transpose(1, 2)), approximate="tanh") + for block in self.blocks: + x = F.max_pool1d(block(x), kernel_size=2, stride=2) + return x.transpose(1, 2) + + +class LocalResidualBlock(nn.Module): + def __init__(self, width: int, dilation: int): + super().__init__() + self.norm = ChannelRMSNorm(width) + self.depthwise = nn.Conv1d(width, width, 9, dilation=dilation, + padding=4 * dilation, groups=width) + self.expand = nn.Conv1d(width, 2 * width, 1) + self.contract = nn.Conv1d(2 * width, width, 1) + + def forward(self, x: Tensor) -> Tensor: + update = self.depthwise(self.norm(x)) + update = self.contract(F.gelu(self.expand(update), approximate='tanh')) + return x + update + + +class LocalSequencePath(nn.Module): + """Base-resolution motif path with powers-of-four dilation and aligned pooling.""" + def __init__(self, config: ModelConfig): + super().__init__() + width = config.local_width + self.stem = nn.Conv1d(4, width, 21, padding=10) + self.blocks = nn.Sequential(*(LocalResidualBlock(width, 4**i) + for i in range(config.local_context_blocks))) + self.norm = ChannelRMSNorm(width) + self.sites = nn.Conv1d(width, 4, 1) + self.bin_size = config.dense_bin_size + self.gradient_checkpointing = config.gradient_checkpointing + + def forward(self, dna: Tensor) -> tuple[Tensor, Tensor]: + base = F.gelu(self.stem(dna), approximate='tanh') + for block in self.blocks: + if self.gradient_checkpointing and self.training: + base = checkpoint(block, base, use_reentrant=False) + else: + base = block(base) + base = F.gelu(self.norm(base), approximate='tanh') + sites = self.sites(base).transpose(1, 2) + pooled = torch.cat((F.avg_pool1d(base, self.bin_size), + F.max_pool1d(base, self.bin_size)), dim=1).transpose(1, 2) + return sites, pooled + + +class CompactModel(nn.Module): + """One shared 1-kb trunk with representation-specific output heads.""" + + def __init__(self, config: ModelConfig | None = None): + super().__init__() + self.config = config or ModelConfig() + self.encoder = SequenceEncoder(self.config) + self.projection = nn.Linear(self.config.encoder_output_width, self.config.hidden_dim) + self.blocks = nn.ModuleList( + SequenceBlock(self.config) for _ in range(self.config.num_sequence_blocks) + ) + self.final_norm = nn.RMSNorm(self.config.hidden_dim) + self.dense_decoder = nn.Sequential( + nn.RMSNorm(self.config.hidden_dim), + nn.Linear(self.config.hidden_dim, self.config.head_hidden_size), + nn.GELU(approximate="tanh"), + ) + self.dense_heads = nn.ModuleDict( + { + family: nn.Linear(self.config.head_hidden_size, tracks) + for family, tracks in DENSE_TRACKS.items() + } + ) + self.splice_site_head = nn.Linear( + self.config.head_hidden_size, self.config.dense_bin_size * 4 + ) + self.splice_usage_head = nn.Linear( + self.config.head_hidden_size, self.config.splice_usage_tracks + ) + rank = self.config.junction_rank + self.junction_source = nn.Linear(self.config.head_hidden_size, rank) + self.junction_destination = nn.Linear(self.config.head_hidden_size, rank) + self.junction_track = nn.Embedding(self.config.junction_tracks, rank) + if self.config.junction_tracks % 2: + raise ValueError("junction tracks must split evenly into strand halves") + self.register_buffer( + "junction_track_half", + torch.arange(self.config.junction_tracks) // (self.config.junction_tracks // 2), + persistent=False, + ) + self.contact_q = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + self.contact_k = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + row, column = torch.triu_indices( + self.config.contact_bins, self.config.contact_bins + ) + self.register_buffer("contact_row", row, persistent=False) + self.register_buffer("contact_column", column, persistent=False) + if self.config.local_width: + self.local_path = LocalSequencePath(self.config) + self.local_projection = nn.Linear(2 * self.config.local_width, self.config.head_hidden_size) + # A new local path can be attached to our own checkpoint without changing its initial outputs. + nn.init.zeros_(self.local_path.sites.weight) + nn.init.zeros_(self.local_path.sites.bias) + nn.init.zeros_(self.local_projection.weight) + nn.init.zeros_(self.local_projection.bias) + if self.config.local_to_trunk: + self.local_trunk_projection = nn.Linear(2 * self.config.local_width, self.config.hidden_dim) + nn.init.zeros_(self.local_trunk_projection.weight) + nn.init.zeros_(self.local_trunk_projection.bias) + self._local_sites = None + self._local_features = None + + def set_junction_track_halves(self, halves: Tensor) -> None: + halves = torch.as_tensor(halves, dtype=torch.long, device=self.junction_track_half.device) + if halves.shape != (self.config.junction_tracks,) or not torch.isin( + halves, torch.tensor([0, 1], device=halves.device) + ).all(): + raise ValueError("junction track halves must be a 734-element 0/1 mapping") + self.junction_track_half.copy_(halves) + + def _trunk(self, dna: Tensor) -> Tensor: + if dna.ndim != 3 or dna.shape[1:] != (self.config.input_length, 4): + raise ValueError( + f"Expected DNA [B,{self.config.input_length},4], got {tuple(dna.shape)}" + ) + if self.config.local_width: + sites, pooled = self.local_path(dna.transpose(1, 2)) + self._local_sites = sites + self._local_features = self.local_projection(pooled) + x = self.projection(self.encoder(dna)) + if self.config.local_to_trunk: + local_tokens = pooled.reshape(len(dna), self.config.trunk_bins, + self.config.dense_upsample, -1).mean(dim=2) + x = x + self.local_trunk_projection(local_tokens) + for block in self.blocks: + if self.config.gradient_checkpointing and self.training and x.requires_grad: + x = checkpoint(block, x, None, use_reentrant=False) + else: + x = block(x, None) + return self.final_norm(x) + + def _dense_features(self, trunk: Tensor) -> Tensor: + start = (self.config.trunk_bins - self.config.target_trunk_bins) // 2 + target = trunk[:, start : start + self.config.target_trunk_bins] + target = target.repeat_interleave(self.config.dense_upsample, dim=1) + features = self.dense_decoder(target) + if self.config.local_width: + first_bin = (self.config.input_length - self.config.target_length) // (2 * self.config.dense_bin_size) + features = features + self._local_features[:, first_bin:first_bin + self.config.dense_bins] + return features + + def _all_dense_features(self, trunk: Tensor) -> Tensor: + features = self.dense_decoder( + trunk.repeat_interleave(self.config.dense_upsample, dim=1) + ) + return features + self._local_features if self.config.local_width else features + + def _splice_site_logits(self, trunk: Tensor) -> Tensor: + if self.config.splice_context_mode == 'uniform': + # The global trunk supplies one gene-context bias per class and kb. Base-resolution + # localization comes from the local path, with no arbitrary 128-bp offset decoder + # and no pooled dense-feature contribution to site logits. + context = self.splice_site_head(self.dense_decoder(trunk)) + context = context.view(len(trunk), self.config.trunk_bins, self.config.dense_bin_size, 4).mean(dim=2) + logits = context.repeat_interleave(self.config.trunk_bin_size, dim=1) + self._local_sites + return logits.float() + logits = self.splice_site_head(self._all_dense_features(trunk)) + logits = logits.view(len(trunk), self.config.input_length, 4) + if self.config.local_width: + logits = logits + self._local_sites + return logits.float() + + def _position_features(self, trunk: Tensor, positions: Tensor) -> Tensor: + features = self.dense_decoder(self._gather_bins(trunk, positions, self.config.trunk_bin_size)) + if self.config.local_width: + features = features + self._gather_bins(self._local_features, positions, self.config.dense_bin_size) + return features + + @staticmethod + def select_junction_positions(site_logits: Tensor, limit: int = 512) -> Tensor: + """Select each site type from model scores alone, then sort by coordinate. + + This follows the four independent site classes in Google DeepMind's + AlphaGenome splicing implementation (pinned in docs/v3-author/PROTOCOL.md). Padding is + -1 when an input is shorter than ``limit``. + """ + count = min(limit, site_logits.shape[1]) + selected = [] + for site_type in range(4): + coordinates = torch.topk(site_logits[..., site_type], count, dim=1).indices + coordinates = coordinates.sort(dim=1).values + if count < limit: + coordinates = F.pad(coordinates, (0, limit - count), value=-1) + selected.append(coordinates) + return torch.stack(selected, dim=1) + + @staticmethod + def _gather_bins(features: Tensor, positions: Tensor, bin_size: int) -> Tensor: + indices = torch.div(positions, bin_size, rounding_mode="floor") + indices = indices.clamp(0, features.shape[1] - 1) + return torch.gather( + features, + 1, + indices[..., None].expand(-1, -1, features.shape[-1]), + ) + + def _junction_predictions( + self, trunk: Tensor, site_positions: Tensor, queries: Tensor + ) -> Tensor: + row, column, track = queries.unbind(dim=-1) + half = self.junction_track_half[track] + batch = torch.arange(len(trunk), device=trunk.device)[:, None] + donor = site_positions[batch, 2 * half, row] + acceptor = site_positions[batch, 2 * half + 1, column] + source = self._position_features(trunk, donor.clamp(0, self.config.input_length - 1)) + destination = self._position_features(trunk, acceptor.clamp(0, self.config.input_length - 1)) + interaction = self.junction_source(source) * self.junction_destination(destination) + track_embedding = self.junction_track(track) + return (interaction * track_embedding).sum(dim=-1) / math.sqrt( + self.config.junction_rank + ) + + def junction_block( + self, + trunk: Tensor, + site_positions: Tensor, + row_start: int, + row_end: int, + column_start: int, + column_end: int, + track_start: int, + track_end: int, + site_factors: tuple[Tensor, Tensor] | None = None, + ) -> Tensor: + """Compute a bounded block of the full predicted-position junction cube.""" + tracks = torch.arange(track_start, track_end, device=trunk.device) + halves = self.junction_track_half[tracks] + if site_factors is None: + site_factors = self.junction_site_factors(trunk, site_positions) + source_all, destination_all = site_factors + source = source_all[:, halves, row_start:row_end] + destination = destination_all[:, halves, column_start:column_end] + embedding = self.junction_track(tracks)[None, :, None, None, :] + values = ( + source[:, :, :, None, :] * destination[:, :, None, :, :] * embedding + ).sum(dim=-1) / math.sqrt(self.config.junction_rank) + return values.permute(0, 2, 3, 1).float() + + def junction_site_factors( + self, trunk: Tensor, site_positions: Tensor + ) -> tuple[Tensor, Tensor]: + """Precompute the two strand-specific factors reused by all cube blocks.""" + donor = torch.stack((site_positions[:, 0], site_positions[:, 2]), dim=1).clamp_min(0) + acceptor = torch.stack((site_positions[:, 1], site_positions[:, 3]), dim=1).clamp_min(0) + donor_features = self._position_features(trunk, donor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, donor.shape[-1], -1) + acceptor_features = self._position_features(trunk, acceptor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, acceptor.shape[-1], -1) + return self.junction_source(donor_features), self.junction_destination(acceptor_features) + + def forward( + self, + dna: Tensor, + *, + families: Iterable[str] | None = None, + usage_positions: Tensor | None = None, + junction_site_positions: Tensor | None = None, + junction_queries: Tensor | None = None, + ) -> dict[str, Tensor]: + requested = set(families or ALL_FAMILIES) + unknown = requested - set(ALL_FAMILIES) + if unknown: + raise ValueError(f"Unknown output families: {sorted(unknown)}") + trunk = self._trunk(dna) + needs_dense = bool( + requested.intersection((*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions")) + ) + features = self._dense_features(trunk) if needs_dense else None + outputs: dict[str, Tensor] = {} + for family in DENSE_TRACKS: + if family in requested: + outputs[family] = self.dense_heads[family](features).float() + if "splice_sites" in requested: + all_logits = self._splice_site_logits(trunk) + start = (self.config.input_length - self.config.target_length) // 2 + outputs["splice_sites"] = all_logits[:, start:start + self.config.target_length] + if "splice_usage" in requested: + if usage_positions is None: + raise ValueError("splice_usage requires usage_positions") + gathered = self._gather_bins( + features, usage_positions, self.config.dense_bin_size + ) + outputs["splice_usage"] = self.splice_usage_head(gathered).float() + if "junctions" in requested: + if junction_site_positions is None or junction_queries is None: + raise ValueError("junctions require site positions and query triples") + outputs["junctions"] = self._junction_predictions( + trunk, junction_site_positions, junction_queries + ).float() + if "contact_maps" in requested: + contact = trunk.reshape( + len(dna), self.config.contact_bins, -1, self.config.hidden_dim + ).mean(dim=2) + q, k = self.contact_q(contact), self.contact_k(contact) + row, column = self.contact_row, self.contact_column + outputs["contact_maps"] = ( + 0.5 + * ( + q[:, row] * k[:, column] + + q[:, column] * k[:, row] + ) + ).float() + return outputs + + +def count_parameters(model: nn.Module) -> int: + return sum(parameter.numel() for parameter in model.parameters() if parameter.requires_grad) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/prepare_decoder111.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/prepare_decoder111.py new file mode 100644 index 0000000000000000000000000000000000000000..1a5b8c2dd386ef19447380be11a7b9018f673595 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/prepare_decoder111.py @@ -0,0 +1,226 @@ +"""CPU-only transfer of the exact grid96 decoder to the fixed eight-pass parent.""" +from datetime import datetime, timezone +import hashlib +import importlib +import importlib.util +import json +import os +from pathlib import Path +import shutil +import sys + +from make_linear_upsample83 import changed_source as interpolate +from make_splice_grid96 import changed_source as preserve_classifier +from verify_package import inference_signature, verify_package + + +def digest(path): + with Path(path).open('rb') as stream: + return hashlib.file_digest(stream, 'sha256').hexdigest() + + +def read(path): + return json.loads(Path(path).read_text()) + + +def save(path, value): + assert not path.exists(), path + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(value, indent=2, allow_nan=False)+'\n') + + +def module(name, path): + spec = importlib.util.spec_from_file_location(name, path) + value = importlib.util.module_from_spec(spec) + sys.modules[name] = value + spec.loader.exec_module(value) + return value + + +def main(): + root = Path('/workspace/research') + here = root/'seqfunc' + study, output = root/'studies/decoder111', root/'experiments/decoder111' + parent = root/'experiments/calib81-small-epoch08' + previous = root/'experiments/splice_grid96' + assert not study.exists() and not output.exists() + assert read(root/'studies/splice_grid96/state.json')['phase'] == 'complete' + assert read(root/'studies/splice_grid96/diagnostics/summary/report.json')['complete_metrics'] == 13 + assert read(root/'studies/late_fixed_links81/small-epoch08/state.json')['phase'] == 'complete' + study.mkdir(); output.mkdir() + package, old = output/'package', parent/'package' + signature = inference_signature(old) + verify_package(old) + assert signature == read(parent/'evaluation.json')['package_inference_signature'] + original_source = (old/'reference/alphagenome_50m/model.py').read_text() + source = preserve_classifier(interpolate(original_source)) + assert source == (previous/'package/reference/alphagenome_50m/model.py').read_text() + old_three = inference_signature(root/'experiments/calib57/package') + assert all(signature[name] == old_three[name] for name in signature if name != 'last.pt') + assert digest(old/'last.pt') == '4e81120dd5424c175e43684c0827b8a4f21e5be8b0b554f17df615ac16a027dd' + now = datetime.now(timezone.utc).isoformat() + specification = dict(utc=now, parent=str(parent), output=str(output), parameters=13290504, + inherited_examples=328160, inherited_optimizer_steps=82040, inherited_complete_passes=8, + additional_training_updates=0, parent_inference_signature=signature, + candidate_model_sha256=hashlib.sha256(source.encode()).hexdigest(), + only_change='Apply the exact grid96 fixed decoder: linear bin-center global features for dense/usage/junction readouts, retaining the original nearest-neighbor splice classifier. Same backbone, heads, weights, four prior offsets and forward view.', + exact_existing_model_source=str(previous/'package/reference/alphagenome_50m/model.py'), + expected_exact_families=['splice_sites','contact_maps'], + expected_exact_variants=['sqtl_causality','clinvar_noncoding','clinvar_splice_region','clinvar_missense','mfass'], + motivation='Grid96 restores the Linear83 ClinVar noncoding regression with positive paired intervals while retaining eight exact83 family reports. Its own reward difference from83 is uncertain. Test this fixed decoder on the stronger eight-pass parent without assuming transfer.', + scope='One fixed network for every family and both entrypoints; no routing among predictions, variant labels, fitted mixture, new offset, RC, SWA or motif mask.', + limitations='Exploratory validation-guided selection, one seed and shared development data. RNA/eQTL and other dense effects can change or regress. No accuracy or runtime benefit presumed.', + evaluation_admitted=False, GPU_used=False) + save(study/'proposed-recipe.json', specification) + shutil.copytree(old, package, ignore=shutil.ignore_patterns('__pycache__','*.pyc')) + (package/'reference/alphagenome_50m/model.py').write_text(source) + policy = {key:specification[key] for key in ['utc','parent_inference_signature','candidate_model_sha256', + 'parameters','inherited_examples','inherited_optimizer_steps','inherited_complete_passes', + 'additional_training_updates','only_change','scope','limitations']} + save(package/'decoder_policy.json', policy) + provenance = read(package/'training_provenance.json') + provenance['fixed_decoder111_intervention'] = policy + (package/'training_provenance.json').write_text(json.dumps(provenance,indent=2)+'\n') + history = package/'training_history'; history.mkdir(exist_ok=True) + (history/'pre-decoder111-model.py').write_text(original_source) + shutil.copy2(study/'proposed-recipe.json',history/'decoder111-proposed-recipe.json') + for name in ['prepare_decoder111.py','make_linear_upsample83.py','make_splice_grid96.py']: + shutil.copy2(here/name,history/name) + (history/'decoder111-training-replay.md').write_text( + 'The eight inherited training passes used pre-decoder111-model.py at reference/alphagenome_50m/model.py.\n' + 'Restore that source to replay the inherited training recipe. All model weights, training order, objective,\n' + 'batch and LR schedule are inherited. The inference-only change is the exact model source from grid96,\n' + 'generated by preserve_classifier(interpolate(original_source)); it adds zero optimizer updates.\n') + (package/'README.md').write_text('''# SeqFunc eight-pass model with the fixed grid96 decoder + +One **13,290,504-parameter** network reads 1,048,576 bp and serves all eleven families +and both entrypoints. A convolutional encoder and six width-384 transformer blocks +process 1,024 global tokens. A width-32 base-resolution path with six dilated blocks +has a 10,941-bp receptive field; pooled local features also enter the global trunk. +Attention and MLP residual updates retain the per-token RMS cap of 0.1. + +This candidate transfers the exact decoder source from experiment96 to the unchanged +eight-pass weights. Decoded global features are interpolated linearly from 1 kb to +128 bp at bin centers for dense, usage and junction outputs. The splice classifier +retains its original repeated global features, learned offset decoder and local logits. +Contacts retain their symmetric global-feature product. All are fixed internal paths +of one network. Both entrypoints use the same model and one forward orientation. +Model scores select splice coordinates; junctions follow transcript direction. + +All weights originate from local random initialization, seed17: **328,160 training +examples, 82,040 optimizer updates and eight complete passes** over all 41,020 eligible +FOLD_0 training intervals (folds2–7). The supplied reader decodes quantized targets. +Batch4 BF16 AdamW uses weight decay1e-4, clipping5 and the original14-pass cosine LR +schedule from2e-4 to2e-5 with4,000-example warmup. Fixed family variance scales use +256 training windows; splice BCE is balanced. Accumulated training-loop wall time is +1231.6 minutes on one shared H10080GB, with at most22CPUs, four loader workers and two +torch threads. Setup and evaluations are additional research compute. Complete inherited +configuration, training history and provenance are included. Recorded recovery preserves +checkpoint state and order; floating-point training replay is not claimed bit-exact. + +The decoder adds **zero parameters and zero training updates**. The original four +training-derived splice offsets and sigmoid output link remain fixed. They were not +refit. The inherited eight training passes used nearest-neighbor global features; +training_history/pre-decoder111-model.py records that source for training replay. + +Grid96 restored an earlier ClinVar noncoding regression at three passes; its small +reward difference from the fully interpolated decoder was uncertain. Transfer to eight +passes is unproven. This is exploratory validation-guided architecture selection on +the same development split and one training seed. No test labels, pretrained external +weights, variant-label fitting, conservation input or prediction caches are used. + +Both executable entrypoints take INPUT_ROOT and OUTPUT_ROOT and run offline using public +manifests and the pinned reference FASTA. runtime.tar.gz vendors CPython3.11, +PyTorch2.7.1/CUDA12.6 and dependencies; the host supplies Linux, bash/tar/gzip and an +NVIDIA driver. Tracks use float16 and perturbations float32. No targets or supplied +splice coordinates are used at inference. + +Own native/recovery checks, all200 validation windows, all2,697 development perturbations, +all13 metrics with same-row published anchors, representative native runtime and paired +comparisons to the sole eight-pass parent are required. Candidate measurements are +appended after scoring. Splice-site/contact family reports and the five splicing variant +reports must exactly match the parent. Other effects can change or regress. Sampled +runtime estimates are not guaranteed bounds. Limitations include finite local context, +pooled usage/junction features, low-rank contacts and uneven variant effects. The54 masked +GTEx RNA tracks receive no direct target supervision; they are excluded from scored RNA +means. Functional readouts do not supply the conservation information used by strong +TraitGym anchors. +''') + manifest = read(package/'package_manifest.json') + for name in list(manifest['files']): + if name.startswith('validation_'): + (package/name).unlink(missing_ok=True); manifest['files'].pop(name) + for path in package.rglob('*'): + if path.is_file() and path.name!='package_manifest.json' and '__pycache__' not in path.parts: + relative=str(path.relative_to(package)) + if relative not in manifest['files'] or relative in ['README.md','training_provenance.json','reference/alphagenome_50m/model.py']: + manifest['files'][relative]=digest(path) + (package/'package_manifest.json').write_text(json.dumps(manifest,indent=2)+'\n') + actual = inference_signature(package) + assert {name for name in actual if actual[name]!=signature[name]} == {'reference/alphagenome_50m/model.py'} + verification = verify_package(package) + # Actual eight-pass learned tensors, short synthetic DNA, CPU only. + import torch + os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:2]) + torch.set_num_threads(2); torch.set_num_interop_threads(1); torch.manual_seed(1729) + sys.path.insert(0,str(package)) + candidate = importlib.import_module('reference.alphagenome_50m.model') + nearest = module('reference.alphagenome_50m.nearest111',old/'reference/alphagenome_50m/model.py') + linear = module('reference.alphagenome_50m.linear111',root/'experiments/linear_upsample83/package/reference/alphagenome_50m/model.py') + payload = torch.load(package/'last.pt',map_location='cpu',weights_only=True) + config = dict(payload['config'],input_length=8192,target_length=4096,gradient_checkpointing=False) + models = [m.CompactModel(m.ModelConfig(**config)).eval() for m in [candidate,nearest,linear]] + for model in models: + model.load_state_dict(payload['model'],strict=True) + assert candidate.count_parameters(model)==13290504 + assert all(torch.equal(value,payload['model'][key]) for key,value in model.state_dict().items()) + dna = torch.nn.functional.one_hot(torch.randint(0,4,(2,8192)),4).float() + dna[1] = dna[0].clone(); dna[1,4096] = torch.roll(dna[1,4096],1); dna[1,6000:6016]=0 + usage = torch.tensor([[0,127,128,2048,4095]]).repeat(2,1) + sites = torch.tensor([[[0,1023,4096],[1024,6144,8191],[128,4095,8191],[0,4096,6144]]]).repeat(2,1,1) + junctions = torch.tensor([[[0,1,0],[1,0,733],[2,2,367]]]).repeat(2,1,1) + with torch.no_grad(): + outputs=[m(dna,usage_positions=usage,junction_site_positions=sites,junction_queries=junctions) for m in models] + assert len(outputs[0])==11 + for name,value in outputs[0].items(): + expected=outputs[1][name] if name in ['splice_sites','contact_maps'] else outputs[2][name] + assert torch.equal(value,expected),name + assert torch.isfinite(value).all(),name + assert any(not torch.equal(outputs[0][name],outputs[1][name]) for name in ['atac','rna_seq','dnase']) + trunks=[m._trunk(dna) for m in models[:2]] + assert torch.equal(trunks[0],trunks[1]) + selected=[m.select_junction_positions(m._splice_site_logits(t),limit=32) for m,t in zip(models[:2],trunks)] + assert torch.equal(*selected) + full=models[0]._all_dense_features(trunks[0]) + assert torch.equal(models[0]._dense_features(trunks[0]),full[:,16:48]) + positions=torch.tensor([[0,127,128,1023,1024,4095,4096,8191]]).repeat(2,1) + assert torch.equal(models[0]._position_features(trunks[0],positions),models[0]._gather_bins(full,positions,128)) + assert not torch.cuda.is_initialized() + cpu=dict(utc=datetime.now(timezone.utc).isoformat(),candidate_inference_signature=actual, + all_learned_tensors_exact=True,parameters=13290504,synthetic_batch=2,synthetic_input_bp=8192, + actual_allele_and_N_edits=True,all11_outputs_finite=True, + classifier_contacts_trunk_and_selected_positions_exact_parent=True, + other_nine_fixed_query_outputs_exact_linear_operator_with_same8pass_weights=True, + full_central_and_sparse_features_exact=True,source_exact_completed96=True, + CUDA_used=False,training_updates=0,labels_read=False, + caveat='Mechanical short synthetic input; own full1Mb native/recovery and full accuracy remain required.') + save(study/'CPU-construction-check.json',cpu) + evidence=[parent/'evaluation.json',parent/'valid-score/report.json',parent/'valid-variant-score/report.json', + root/'studies/late_fixed_links81/small-epoch08/pipeline-completion-observation.json', + root/'studies/splice_grid96/completion-observation.json', + root/'studies/splice_grid96/diagnostics/summary/report.json', + root/'studies/splice_grid96/construction-check.json',study/'proposed-recipe.json',study/'CPU-construction-check.json', + here/'make_linear_upsample83.py',here/'make_splice_grid96.py',Path(__file__)] + save(study/'preparation.json',dict(utc=datetime.now(timezone.utc).isoformat(), + package=str(package),package_verification=verification, + parent_inference_signature=signature,candidate_inference_signature=actual, + evidence_sha256={str(p):digest(p) for p in evidence}, + evaluation_admitted=False,controller_created=False,GPU_used=False, + next='Freeze independent native52, six exact parent arrays, full200/2697/all13, exact twofamily/fivevariant reports, native624/runtime and one all11/all13 paired observer after fresh resource review.')) + print(json.dumps(dict(preparation_sha256=digest(study/'preparation.json'), + CPU_check_sha256=digest(study/'CPU-construction-check.json'),package=verification, + candidate_model_sha256=actual['reference/alphagenome_50m/model.py'],GPU_used=False)),flush=True) + + +if __name__=='__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/prepare_rc_decoder113.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/prepare_rc_decoder113.py new file mode 100644 index 0000000000000000000000000000000000000000..eac0b626dd61ec2ae4a0e47682906630cfbff38d --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/prepare_rc_decoder113.py @@ -0,0 +1,196 @@ +"""CPU-only fixed decoder111 transfer into the completed RC108 model; no evaluation admission.""" +from datetime import datetime, timezone +import importlib +import json +import os +from pathlib import Path +import shutil +import sys + +from prepare_decoder111 import digest, read, save, module +from make_linear_upsample83 import changed_source as interpolate +from make_splice_grid96 import changed_source as preserve_classifier +from verify_package import inference_signature, verify_package + + +def main(): + root = Path('/workspace/research') + here = root/'seqfunc' + study, output = root/'studies/rc_decoder113', root/'experiments/rc_decoder113' + parent = root/'experiments/rc108' + old = root/'studies/rc108/paired-documentation/package' + decoder = root/'experiments/decoder111/package' + assert not study.exists() and not output.exists() + assert read(root/'studies/rc108/completion-observation.json') + assert read(root/'studies/decoder111/completion-observation.json')['status'] == 'all_complete_no_promotion' + assert read(root/'studies/rc108/diagnostics/summary.json')['complete_metrics'] == 13 + assert read(root/'studies/decoder111/diagnostics/summary.json')['complete_metrics'] == 13 + assert read(root/'studies/rc108/state.json')['phase'] == 'complete' + assert read(root/'studies/decoder111/state.json')['phase'] == 'complete' + verify_package(old) + signature = inference_signature(old) + assert signature == inference_signature(parent/'package') == read(parent/'evaluation.json')['package_inference_signature'] + original_source = (old/'reference/alphagenome_50m/model.py').read_text() + changed = preserve_classifier(interpolate(original_source)) + assert changed == (decoder/'reference/alphagenome_50m/model.py').read_text() + assert digest(old/'last.pt') == digest(decoder/'last.pt') == '4e81120dd5424c175e43684c0827b8a4f21e5be8b0b554f17df615ac16a027dd' + assert digest(old/'splice_calibration.json') == digest(decoder/'splice_calibration.json') + orientation = read(old/'rc_ensemble_policy.json') + assert orientation['orientation_views'] == 2 and orientation['weights'] == [.5,.5] + assert orientation['fitted_ensemble_weights'] is False + study.mkdir(); output.mkdir() + package = output/'package' + now = datetime.now(timezone.utc).isoformat() + recipe = dict(utc=now,parent=str(parent),parent_package=str(old),output=str(output),parameters=13290504, + inherited_examples=328160,inherited_optimizer_steps=82040,inherited_complete_passes=8, + additional_training_updates=0,parent_inference_signature=signature, + candidate_model_sha256=digest(decoder/'reference/alphagenome_50m/model.py'), + only_change='Replace RC108 nearest global-feature decoding for dense, usage and junction heads with the exact selective linear-bin-center operation from completed111/96. Keep its original nearest splice classifier, contacts, learned tensors, four training-derived offsets and equal forward/RC views.', + expected_exact_families=['splice_sites','contact_maps'], + expected_exact_variants=['sqtl_causality','clinvar_noncoding','clinvar_splice_region','clinvar_missense','mfass'], + motivation='On the same eight-pass weights,111 improved reward by0.002630816 under both track schemes and all seven dense families; enhancer AP improved0.012034669 with gene interval[0.00147553,0.02601987]. RC108 separately improved reward0.017002677, with all13 variant intervals including zero. Test whether the fixed decoder gain transfers to RC108; additivity and variant gains are not assumed.', + contrary_evidence='111 usage and junction intervals include zero, caQTL is positive only under block resampling, and its other11 variant intervals include zero. RC108 has negative eQTL coefficient correlation and Mendelian AP below the previous published SWA model. All selection uses the repeatedly inspected development split.', + scope='One fixed network and the same two orientation views serve every family and both entrypoints. No learned or fitted mixture, routing, new splice prior, motif mask, SWA, extra flanking DNA, variant-label training or additional SGD.', + limitations='Exploratory one-seed postselection. Interaction can worsen any changed readout. Full native replay, runtime and accuracy must be measured independently; unchanged paths require exact complete reports.', + evaluation_admitted=False,GPU_used=False) + save(study/'proposed-recipe.json',recipe) + shutil.copytree(old,package,ignore=shutil.ignore_patterns('__pycache__','*.pyc')) + (package/'reference/alphagenome_50m/model.py').write_text(changed) + policy={k:recipe[k] for k in ['utc','parent_inference_signature','candidate_model_sha256','parameters', + 'inherited_examples','inherited_optimizer_steps','inherited_complete_passes','additional_training_updates', + 'only_change','scope','limitations']} + save(package/'decoder113_policy.json',policy) + provenance=read(package/'training_provenance.json') + provenance['fixed_decoder113_intervention']=policy + (package/'training_provenance.json').write_text(json.dumps(provenance,indent=2)+'\n') + history=package/'training_history';history.mkdir(exist_ok=True) + (history/'pre-decoder113-model.py').write_text(original_source) + shutil.copy2(study/'proposed-recipe.json',history/'decoder113-proposed-recipe.json') + for name in [Path(__file__).name,'prepare_decoder111.py','make_linear_upsample83.py','make_splice_grid96.py']: + shutil.copy2(here/name,history/name) + (history/'decoder113-training-replay.md').write_text( + 'All eight inherited training passes used pre-decoder113-model.py at reference/alphagenome_50m/model.py.\n' + 'Restore that source for the inherited training recipe. Training remains forward-only with the original\n' + 'objective, seed17, batch4, data order, AdamW state and fourteen-pass learning-rate schedule.\n' + 'RC108 introduced equal forward/RC inference without SGD. Decoder113 adds only the exact model.py\n' + 'from completed111/96 via preserve_classifier(interpolate(original_source)); no weights are changed.\n') + (package/'README.md').write_text('''# SeqFunc RC model with selective linear feature decoding + +One model with **13,290,504 unique learned parameters** reads 1,048,576 bp and serves +all eleven families, 5,930 tracks and both offline entrypoints. It uses the same fixed +equal forward/reverse-complement views as RC108. The only new operation is the exact +decoder from experiment111: global features are interpolated at bin centers from +1 kb to128 bp for dense, splice-usage and junction outputs. The original nearest +splice classifier and contact path remain unchanged. No weights or constants are fitted. + +The architecture has a strided convolutional encoder, six width384 transformer blocks +over1,024 global tokens, a width32 base-resolution path with a21-bp stem and six dilated +residual blocks (10,941-bp receptive field), and a local-to-global bridge. Attention and +MLP updates use RMS cap0.1. Dense outputs cover the central196,608bp; contacts and junctions +use low-rank factors. Each orientation applies the four inherited training-prior splice +offsets before averaging probabilities. Dense and contact values average in asinh space. +Sparse coordinates are predicted independently in each view; the union treats an unlisted +view as zero, keeping at most4,096 usage positions and16,384 junction pairs. + +All weights originate from local random initialization, seed17, with **328,160 examples / +82,040 AdamW updates**, eight complete passes through all41,020 FOLD_0 training intervals +in folds2–7. The supplied reader decodes quantized targets per example. Batch4 BF16 training +uses fixed family-variance scales estimated from256 training windows and balanced splice +BCE. AdamW uses weight decay1e-4, clipping5, and the original14-pass cosine schedule from +2e-4 to2e-5 with4,000 warmup examples. The source checkpoint records73,897.9978 accumulated +training-loop seconds (20.53h) on one shared H10080GB within22CPUs. Setup/evaluation cost is +additional. Training recovery preserves checked saved state/order but is not bit-identical. +This candidate adds zero parameters and zero training updates. Restore the recorded +pre-decoder model source to replay the inherited training recipe. + +Run `infer.sh INPUT_ROOT OUTPUT_ROOT` or `perturb.sh INPUT_ROOT OUTPUT_ROOT` offline. +The runtime archive vendors CPython3.11, PyTorch2.7.1/CUDA12.6 and dependencies; the host +supplies Linux, bash/tar/gzip and the NVIDIA driver. Both entrypoints use public manifests +and the pinned reference only, with no targets, supplied splice coordinates, prediction +cache, extra flanking sequence or network access. Track arrays use float16 after float32 +orientation averaging; perturbation arrays remain float32. + +Candidate validation is pending. Its own52-array native/recovery check, six unchanged +native site/contact arrays versus RC108, full200 validation windows, all2,697 development +queries/all13 metrics with same-row anchors,624 native timing arrays, and all three4h +runtime estimates are required. Splice-site/contact family reports and five splice-based +variant reports must exactly match RC108. An all11/all13 paired comparison to RC108 is +required. Parent scores are not candidate scores and gains are not assumed additive. + +Limitations include one-seed exploratory development selection, pooled local features, +finite local context, sparse-union dilution, low-rank contacts and uneven variant effects. +Fifty-four GTEx RNA heads lack direct target supervision and are excluded from scored RNA +means; eQTL scoring uses supervised ENCODE RNA tracks. The model has no conservation input. +Interval overlap with zero does not show equivalence; sampled runtimes are not upper bounds. +''') + manifest=read(package/'package_manifest.json') + for name in list(manifest['files']): + if name.startswith('validation_'): + (package/name).unlink(missing_ok=True);manifest['files'].pop(name) + for path in package.rglob('*'): + if path.is_file() and path.name!='package_manifest.json' and '__pycache__' not in path.parts: + relative=str(path.relative_to(package)) + if relative not in manifest['files'] or relative in ['README.md','training_provenance.json','reference/alphagenome_50m/model.py']: + manifest['files'][relative]=digest(path) + (package/'package_manifest.json').write_text(json.dumps(manifest,indent=2)+'\n') + actual=inference_signature(package) + assert set(actual)==set(signature) + assert {name for name in actual if actual[name]!=signature[name]}=={'reference/alphagenome_50m/model.py'} + for name in ['rc_ensemble.py','single_prediction.py','strand_permutations.json','rc_ensemble_policy.json','splice_calibration.json']: + assert digest(package/name)==digest(old/name),name + verification=verify_package(package) + + import torch + os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:2]) + torch.set_num_threads(2);torch.set_num_interop_threads(1);torch.manual_seed(1729) + sys.path.insert(0,str(package)) + candidate=importlib.import_module('reference.alphagenome_50m.model') + nearest=module('reference.alphagenome_50m.nearest113',old/'reference/alphagenome_50m/model.py') + payload=torch.load(package/'last.pt',map_location='cpu',weights_only=True) + config=dict(payload['config'],input_length=8192,target_length=4096,gradient_checkpointing=False) + models=[m.CompactModel(m.ModelConfig(**config)).eval() for m in [candidate,nearest]] + for model in models: + model.load_state_dict(payload['model'],strict=True) + assert candidate.count_parameters(model)==13290504 + assert all(torch.equal(v,payload['model'][k]) for k,v in model.state_dict().items()) + forward=torch.nn.functional.one_hot(torch.randint(0,4,(2,8192)),4).float() + forward[1]=forward[0].clone();forward[1,4096]=torch.roll(forward[1,4096],1);forward[1,6000:6016]=0 + dna=torch.cat((forward,torch.flip(forward,dims=[1,2])),dim=0) + usage=torch.tensor([[0,127,128,2048,4095]]).repeat(4,1) + sites=torch.tensor([[[0,1023,4096],[1024,6144,8191],[128,4095,8191],[0,4096,6144]]]).repeat(4,1,1) + junctions=torch.tensor([[[0,1,0],[1,0,733],[2,2,367]]]).repeat(4,1,1) + with torch.no_grad(): + outputs=[m(dna,usage_positions=usage,junction_site_positions=sites,junction_queries=junctions) for m in models] + assert len(outputs[0])==11 and all(torch.isfinite(v).all() for v in outputs[0].values()) + for name in ['splice_sites','contact_maps']:assert torch.equal(outputs[0][name],outputs[1][name]),name + assert any(not torch.equal(outputs[0][name],outputs[1][name]) for name in ['atac','dnase','rna_seq']) + trunks=[m._trunk(dna) for m in models];assert torch.equal(*trunks) + selected=[m.select_junction_positions(m._splice_site_logits(t),limit=32) for m,t in zip(models,trunks)] + assert torch.equal(*selected) + full=models[0]._all_dense_features(trunks[0]);assert torch.equal(models[0]._dense_features(trunks[0]),full[:,16:48]) + positions=torch.tensor([[0,127,128,1023,1024,4095,4096,8191]]).repeat(4,1) + assert torch.equal(models[0]._position_features(trunks[0],positions),models[0]._gather_bins(full,positions,128)) + assert not torch.cuda.is_initialized() + proof=dict(utc=datetime.now(timezone.utc).isoformat(),candidate_inference_signature=actual, + all_learned_tensors_exact=True,parameters=13290504,synthetic_batch=4,synthetic_input_bp=8192, + actual_allele_and_N_edits=True,both_forward_and_RC_synthetic_inputs=True,all11_outputs_finite=True, + classifier_contacts_trunk_and_selected_positions_exact_parent=True,full_central_and_sparse_features_exact=True, + source_exact_completed96=True,source_and_weights_exact_completed111=True, + orientation_wrappers_and_policy_exact_RC108=True,CUDA_used=False,training_updates=0,labels_read=False, + accepted111_CPU_proof_sha256=digest(root/'studies/decoder111/CPU-construction-check.json'), + caveat='Short synthetic base-network check plus unchanged accepted orientation wrapper. Own full1Mb native recovery, six exact parent output arrays and complete accuracy are still required.') + save(study/'CPU-construction-check.json',proof) + evidence=[parent/'evaluation.json',parent/'valid-score/report.json',parent/'valid-variant-score/report.json', + root/'studies/rc108/completion-observation.json',root/'studies/rc108/diagnostics/summary.json', + root/'studies/decoder111/completion-observation.json',root/'studies/decoder111/diagnostics/summary.json', + root/'studies/decoder111/CPU-construction-check.json',study/'proposed-recipe.json',study/'CPU-construction-check.json', + here/'prepare_decoder111.py',here/'make_linear_upsample83.py',here/'make_splice_grid96.py',Path(__file__)] + save(study/'preparation.json',dict(utc=datetime.now(timezone.utc).isoformat(),package=str(package),package_verification=verification, + parent_inference_signature=signature,candidate_inference_signature=actual,evidence_sha256={str(p):digest(p) for p in evidence}, + evaluation_admitted=False,controller_created=False,GPU_used=False, + next='Separate resource/admission decision, freeze original native/full/all13/runtime/paired gates versus RC108; no automatic GPU launch.')) + print(json.dumps(dict(preparation_sha256=digest(study/'preparation.json'),CPU_check_sha256=digest(study/'CPU-construction-check.json'),package=verification,GPU_used=False)),flush=True) + + +if __name__=='__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/reader-before-parallel-io.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/reader-before-parallel-io.py new file mode 100644 index 0000000000000000000000000000000000000000..71cd39d15713f648e7d7e31aa1923fa4fa4800dd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/reader-before-parallel-io.py @@ -0,0 +1,229 @@ +"""Streaming reader for the compact 1 Mb / eleven-family release.""" + +from __future__ import annotations + +from collections import OrderedDict +from pathlib import Path + +import numpy as np +import pandas as pd +import pysam +import torch +from torch.utils.data import Dataset +import zarr + + +def _open_part(path: Path): + if str(path).endswith(".zarr.zip"): + store = zarr.ZipStore(str(path), mode="r") + try: + return zarr.open_group(store=store, mode="r"), store + except BaseException: + store.close() + raise + return zarr.open_group(str(path), mode="r"), None + + +DENSE_FAMILIES = ( + "atac", + "dnase", + "procap", + "cage", + "rna_seq", + "chip_tf", + "chip_histone", +) +def normalize_splice_positions(value) -> np.ndarray: + """Normalize Arrow's nested-list representation to the public [4, 512] array.""" + + outer = np.asarray(value, dtype=object).reshape(-1).tolist() + positions = np.stack([np.asarray(item, dtype=np.int64) for item in outer]) + if positions.shape != (4, 512): + raise ValueError(f"splice_site_positions must have shape (4, 512), got {positions.shape}") + return positions + + +class AlphaGenomeDataset(Dataset): + """Resolve frozen coordinates to reference DNA and one Zarr example group.""" + + def __init__( + self, + data_root: str | Path, + split: str, + *, + max_open_parts: int = 8, + include_targets: bool = True, + ) -> None: + self.root = Path(data_root) + self.split = split + manifest = self.root / "public" / f"{split}.parquet" + if not manifest.is_file(): + raise FileNotFoundError(f"Missing materialized manifest: {manifest}") + self.rows = pd.read_parquet(manifest).reset_index(drop=True) + junction_tracks = pd.read_parquet(self.root / "public/junction_tracks.parquet") + junction_tracks = junction_tracks.sort_values("tensor_index") + self.junction_track_halves = torch.from_numpy( + junction_tracks.junction_channel_half.to_numpy(np.int64) + ) + if include_targets: + labels_path = self.root / "labels" / f"{split}.parquet" + if not labels_path.is_file(): + raise FileNotFoundError(f"Missing trusted labels manifest: {labels_path}") + labels = pd.read_parquet(labels_path).reset_index(drop=True) + if self.rows.example_id.astype(str).tolist() != labels.example_id.astype(str).tolist(): + raise ValueError("public and labels manifests have different IDs/order") + self.rows = self.rows.merge(labels, on="example_id", validate="one_to_one", sort=False) + self.fasta_path = self.root / "reference" / "GRCh38.p13.genome.fa" + self.max_open_parts = max_open_parts + self.include_targets = include_targets + self._fasta = None + self._parts: OrderedDict[str, tuple[zarr.Group, object | None]] = OrderedDict() + + def __len__(self) -> int: + return len(self.rows) + + def _reference(self): + if self._fasta is None: + self._fasta = pysam.FastaFile(str(self.fasta_path)) + return self._fasta + + def _part(self, relative: str): + if relative in self._parts: + opened = self._parts.pop(relative) + self._parts[relative] = opened + return opened[0] + group, store = _open_part(self.root / relative) + if len(self._parts) >= self.max_open_parts: + _, (_, old_store) = self._parts.popitem(last=False) + if old_store is not None: + old_store.close() + self._parts[relative] = (group, store) + return group + + @staticmethod + def _one_hot(sequence: str) -> np.ndarray: + encoded = np.frombuffer(sequence.upper().encode("ascii"), dtype=np.uint8) + output = np.zeros((len(encoded), 4), dtype=np.float32) + for index, letter in enumerate(b"ACGT"): + output[:, index] = encoded == letter + return output + + def _sequence(self, row) -> tuple[np.ndarray, np.ndarray]: + reference = self._reference() + chromosome_length = reference.get_reference_length(str(row.chromosome)) + start, end = int(row.input_start), int(row.input_end) + clipped_start, clipped_end = max(0, start), min(chromosome_length, end) + sequence = reference.fetch(str(row.chromosome), clipped_start, clipped_end) + left, right = clipped_start - start, end - clipped_end + if left or right: + sequence = "N" * left + sequence + "N" * right + valid = np.ones(end - start, dtype=bool) + if left: + valid[:left] = False + if right: + valid[-right:] = False + return self._one_hot(sequence), valid + + @staticmethod + def _decode(example, name: str, *, asinh: bool) -> np.ndarray: + """Train targets may be stored as 8-bit codes with per-track ranges; they are decoded + here, one example at a time, so the release stays compressed on disk.""" + array = example[name] + if array.dtype != np.uint8: + return np.asarray(array, dtype=np.float32) + lo = np.asarray(example[f"{name}_lo"], dtype=np.float32) + hi = np.asarray(example[f"{name}_hi"], dtype=np.float32) + x = lo + np.asarray(array, dtype=np.float32) * ((hi - lo) / 255.0) + return np.sinh(x) if asinh else x + + @staticmethod + def _dense_target(example, family: str) -> dict[str, torch.Tensor]: + values = AlphaGenomeDataset._decode(example, f"{family}/values", asinh=True) + mask = np.asarray(example[f"{family}/mask"], dtype=bool) + if mask.ndim == 1: + mask = np.broadcast_to(mask[None, :], values.shape).copy() + return { + "values": torch.from_numpy(values), + "mask": torch.from_numpy(mask), + } + + def __getitem__(self, item: int): + row = self.rows.iloc[item] + dna, input_mask = self._sequence(row) + result = { + "example_id": str(row.example_id), + "dna": torch.from_numpy(dna), + "input_mask": torch.from_numpy(input_mask), + "junction_track_halves": self.junction_track_halves, + } + if not self.include_targets: + return result + + part = self._part(str(row.target_shard)) + example = part[str(row.target_group)] + result["dense"] = { + family: self._dense_target(example, family) for family in DENSE_FAMILIES + } + result["splice_sites"] = torch.from_numpy( + np.asarray(example["splice_sites/values"], dtype=np.float32) + ) + result["splice_usage"] = { + "positions": torch.from_numpy( + np.asarray(example["splice_site_usage/positions"], dtype=np.int64) + ), + "values": torch.from_numpy( + np.asarray(example["splice_site_usage/values"], dtype=np.float32) + ), + "mask": torch.from_numpy( + np.asarray(example["splice_site_usage/mask"], dtype=bool) + ), + } + result["usage_positions"] = result["splice_usage"]["positions"] + result["junctions"] = { + name: torch.from_numpy( + np.asarray( + example[f"splice_junctions/{name}"], + dtype=np.float32 if name == "value" else np.int64, + ) + ) + for name in ("row", "column", "track", "value") + } + result["junctions"]["site_positions"] = torch.from_numpy( + np.asarray(example["splice_junctions/site_positions"], dtype=np.int64) + ) + site_positions = normalize_splice_positions(row.splice_site_positions) + result["site_positions"] = torch.from_numpy(site_positions) + if not torch.equal(result["junctions"]["site_positions"], result["site_positions"]): + raise ValueError(f"public/private splice positions differ for {row.example_id}") + scoring_path = self.root / "scoring" / self.split / f"part-{item:06d}.parquet" + scoring = pd.read_parquet(scoring_path) + if not (scoring.example_id.astype(str) == str(row.example_id)).all(): + raise ValueError(f"scoring part ID differs for {row.example_id}") + result["junction_scoring"] = { + "donor_position": torch.from_numpy( + scoring.donor_position.to_numpy(np.int64) + ), + "acceptor_position": torch.from_numpy( + scoring.acceptor_position.to_numpy(np.int64) + ), + "track": torch.from_numpy(scoring.track.to_numpy(np.int64)), + "target_value": torch.from_numpy( + scoring.target_value.to_numpy(np.float32) + ), + "valid": torch.from_numpy(scoring.valid.to_numpy(bool)), + } + result["contact_maps"] = torch.from_numpy( + self._decode(example, "contact_maps/upper_triangle", asinh=False) + ) + result["contact_map_mask"] = torch.from_numpy( + np.asarray(example["contact_maps/mask"], dtype=bool) + ) + return result + + +def single_example_collate(items): + """Keep variable-size event tensors intact; the reference currently trains at batch one.""" + + if len(items) != 1: + raise ValueError("The compact event loader currently requires batch_size=1") + return items[0] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/run_decoder111.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/run_decoder111.py new file mode 100644 index 0000000000000000000000000000000000000000..f748f5a9cefa61d184223ef8899b1d2935b9d936 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/run_decoder111.py @@ -0,0 +1,170 @@ +"""Evaluate the fixed grid96 decoder transfer on eight-pass weights with complete gates.""" +import argparse +from datetime import datetime, timezone +import fcntl +import hashlib +import json +import math +import os +from pathlib import Path +import signal +import subprocess +import sys +import time + +from verify_package import inference_signature, verify_package + + +def digest(path): + with Path(path).open('rb') as stream: + return hashlib.file_digest(stream, 'sha256').hexdigest() + + +def run(plan_path): + plan = json.loads(plan_path.read_text()) + study, here = plan_path.parent, Path(__file__).resolve().parent + for name, expected in plan['helper_sha256'].items(): + assert digest(here/name) == expected, name + for path, expected in plan['evidence_sha256'].items(): + assert digest(path) == expected, path + assert plan['additional_training_updates'] == 0 + parent, output = Path(plan['parent']), Path(plan['output']) + output.mkdir(exist_ok=True) + package = output/'package' + assert inference_signature(parent/'package') == plan['parent_inference_signature'] + parent_eval = json.loads((parent/'evaluation.json').read_text()) + assert parent_eval['package_inference_signature'] == plan['parent_inference_signature'] + parent_tracks = json.loads((parent/'valid-score/report.json').read_text()) + parent_variants = json.loads((parent/'valid-variant-score/report.json').read_text()) + assert parent_tracks['examples'] == 200 and parent_tracks['invalid_reason'] is None + assert len(parent_tracks['families']) == 11 and len(parent_variants) == 13 + os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) + environment = dict(os.environ, OPENBLAS_NUM_THREADS='1', OMP_NUM_THREADS='2', + MKL_NUM_THREADS='2', PYTHONDONTWRITEBYTECODE='1', PYTHONNOUSERSITE='1') + for key in ['PYTHONPATH', 'PYTHONHOME', 'SEQFUNC_TRAIN_BYTECACHE']: + environment.pop(key, None) + + def interrupted(signum, frame): + raise InterruptedError(f'Decoder111 supervisor received signal {signum}') + signal.signal(signal.SIGTERM, interrupted) + signal.signal(signal.SIGINT, interrupted) + + def status(phase, **extra): + row = dict(phase=phase, utc=datetime.now(timezone.utc).isoformat(), **extra) + temporary = study/'state.tmp' + temporary.write_text(json.dumps(row, indent=2)+'\n') + temporary.replace(study/'state.json') + print(json.dumps(row), flush=True) + + def execute(phase, arguments, timeout=None): + status(phase) + with (study/(phase+'.log')).open('a') as log: + process = subprocess.Popen([sys.executable, *map(str, arguments)], env=environment, + stdout=log, stderr=subprocess.STDOUT, start_new_session=True) + try: + assert process.wait(timeout=timeout) == 0, phase + finally: + if process.poll() is None: + os.killpg(process.pid, signal.SIGTERM) + try: + process.wait(timeout=10) + except subprocess.TimeoutExpired: + os.killpg(process.pid, signal.SIGKILL) + process.wait() + + assert package.is_dir(), 'Accepted CPU package must exist; no rebuild in this runner' + verify_package(package) + assert digest(package/'last.pt') == digest(parent/'package/last.pt') + construction = json.loads((study/'CPU-construction-check.json').read_text()) + assert construction['all_learned_tensors_exact'] and construction['source_exact_completed96'] + assert construction['classifier_contacts_trunk_and_selected_positions_exact_parent'] + assert construction['full_central_and_sparse_features_exact'] and not construction['CUDA_used'] + assert construction['candidate_inference_signature'] == plan['candidate_inference_signature'] + assert digest(package/'reference/alphagenome_50m/model.py') == plan['candidate_model_sha256'] + if (study/'state.json').exists() and json.loads((study/'state.json').read_text()).get('phase') in ['complete','complete_accuracy_runtime_gate_failed']: + print('Completed decoder111 pipeline preserved; no work repeated.', flush=True) + return + signature = inference_signature(package) + native = study/'native-recovery' + with Path(plan['evaluation_lock']).open('a') as lock: + status('waiting_for_native_and_full_evaluation_slot') + if not ((native/'report.json').exists() and (output/'evaluation.json').exists()): + fcntl.flock(lock, fcntl.LOCK_EX) + while int(subprocess.check_output(['nvidia-smi', '--query-gpu=memory.free', + '--format=csv,noheader,nounits'], text=True).strip()) < plan['minimum_free_GiB']*1024: + time.sleep(5) + if not (native/'report.json').exists(): + execute('native', [here/'check_native_recovery.py', package, native], + timeout=plan['native_timeout_seconds']) + proof = json.loads((native/'report.json').read_text()) + assert proof['package_inference_signature'] == signature + assert proof['all_arrays_bit_identical'] and proof['native_vendored_entrypoints_passed'] + assert proof['partial_perturbation_recovery_passed'] + assert proof['arrays'] == {'tracks': 30, 'perturbations': 22} + if not (study/'native-parent-check.json').exists(): + execute('native-parent-check', [here/'check_decoder111_parent.py', plan_path]) + parent_check = json.loads((study/'native-parent-check.json').read_text()) + assert parent_check['plan_sha256'] == digest(plan_path) and parent_check['unchanged_native_arrays_exact'] == 6 + if not (output/'evaluation.json').exists(): + execute('evaluate', [here/'evaluate_package.py', package, output, + '--resume-partial-tracks', '--resume-partial-perturbations']) + assert json.loads((output/'evaluation.json').read_text())['package_inference_signature'] == signature + tracks = json.loads((output/'valid-score/report.json').read_text()) + variants = json.loads((output/'valid-variant-score/report.json').read_text()) + assert tracks['examples'] == 200 and tracks['invalid_reason'] is None + assert len(tracks['families']) == 11 and len(variants) == 13 + assert all(math.isfinite(v['model']) for v in variants.values()) + for name, value in variants.items(): + for key in ['rows', 'perturbations', 'metric', 'anchors']: + assert value[key] == parent_variants[name][key], (name, key) + for name in plan['expected_exact_families']: + assert tracks['families'][name] == parent_tracks['families'][name], name + for name in plan['expected_exact_variants']: + assert variants[name] == parent_variants[name], name + comparison = dict(created_utc=datetime.now(timezone.utc).isoformat(), + reward_delta=tracks['overall_mean_family_pearson']-parent_tracks['overall_mean_family_pearson'], + family_deltas={k:v['mean_pearson']-parent_tracks['families'][k]['mean_pearson'] + for k,v in tracks['families'].items()}, + variant_deltas={k:v['model']-parent_variants[k]['model'] for k,v in variants.items()}) + if (study/'parent-comparison.json').exists(): + saved_comparison = json.loads((study/'parent-comparison.json').read_text()) + assert {k:v for k,v in saved_comparison.items() if k!='created_utc'} == {k:v for k,v in comparison.items() if k!='created_utc'} + comparison = saved_comparison + else: + (study/'parent-comparison.json').write_text(json.dumps(comparison, indent=2)+'\n') + latency = study/'native-latency' + timing_plan = dict(plan['native_latency_template'], experiment=str(output), inference_signature=signature) + if (latency/'plan.json').exists(): + assert json.loads((latency/'plan.json').read_text()) == timing_plan + else: + (latency/'plan.json').write_text(json.dumps(timing_plan, indent=2)+'\n') + if not (latency/'report.json').exists(): + execute('native-latency', [here/'measure_native_latency.py', latency/'plan.json']) + timing = json.loads((latency/'report.json').read_text()) + assert timing['all_arrays_bit_exact'] and timing['package_inference_signature'] == signature + estimates = timing['extrapolated_seconds'] + keys = ['development_mixture_seconds', 'slowest_observed_stratum_mean_seconds', 'pooled_p95_seconds'] + assert all(math.isfinite(estimates[k]) for k in keys) + passed = max(estimates[k] for k in keys) <= 14400 + gate = dict(passed=passed, estimates=estimates, report_sha256=digest(latency/'report.json'), + scope='All three predeclared shared-load sample estimates must fit4h; none is a guaranteed upper bound.') + if (study/'runtime-gate.json').exists(): + assert json.loads((study/'runtime-gate.json').read_text()) == gate + else: + (study/'runtime-gate.json').write_text(json.dumps(gate, indent=2)+'\n') + if passed and not (study/'publication-check.json').exists(): + execute('publish', [here/'promote_if_better.py', package, output]) + (study/'publication-check.json').write_text(json.dumps(dict( + utc=datetime.now(timezone.utc).isoformat(), reward=tracks['overall_mean_family_pearson'], + package_inference_signature=signature, atomic_monotonic_promoter_completed=True), indent=2)+'\n') + status('complete' if passed else 'complete_accuracy_runtime_gate_failed', + runtime_passed=passed, reward=tracks['overall_mean_family_pearson'], **comparison) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('plan', type=Path) + args = parser.parse_args() + with (args.plan.parent/'controller.lock').open('a') as controller: + fcntl.flock(controller, fcntl.LOCK_EX | fcntl.LOCK_NB) + run(args.plan) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/run_late_fixed_links81.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/run_late_fixed_links81.py new file mode 100644 index 0000000000000000000000000000000000000000..3e3973883128f4aea458210d43790edce69d380a --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/run_late_fixed_links81.py @@ -0,0 +1,268 @@ +"""Execute one predeclared late fixed-link follow-up after its complete raw parent.""" +import argparse +from datetime import datetime, timezone +import fcntl +import json +import math +import os +from pathlib import Path +import signal +import subprocess +import sys +import time + +from check_splice_link_reuse import check_link_only +from late_fixed_links81 import ROOT, STUDY, HERE, digest, write_json, parent_ready, parent_receipt +from verify_package import inference_signature, verify_package + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('member') + args = parser.parse_args() + plan_path = STUDY / 'plan.json' + plan = json.loads(plan_path.read_text()) + member = plan['members'][args.member] + study, parent, previous, output = [Path(member[k]) for k in ['study','parent','previous_raw','output']] + lock = (study / 'controller.lock').open('a') + fcntl.flock(lock, fcntl.LOCK_EX | fcntl.LOCK_NB) + complete = study / 'complete.json' + if complete.exists(): + assert json.loads(complete.read_text())['master_plan_sha256'] == digest(plan_path) + print(json.dumps(dict(phase='already_complete_no_rerun', member=args.member)), flush=True) + return + for path, expected in {**plan['helper_sha256'], **plan['evidence_sha256']}.items(): + assert digest(path) == expected, path + assert digest(plan['training_prior']) == plan['training_prior_sha256'] + audit = json.loads((STUDY / 'parent-check.json').read_text()) + assert audit['plan_sha256'] == digest(plan_path) and len(audit['actual_completed_parent_checks']) == 2 + os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) + environment = dict(os.environ, OPENBLAS_NUM_THREADS='1', OMP_NUM_THREADS='2', MKL_NUM_THREADS='2', + PYTHONNOUSERSITE='1', PYTHONDONTWRITEBYTECODE='1') + for key in ['PYTHONPATH','PYTHONHOME','SEQFUNC_TRAIN_BYTECACHE']: + environment.pop(key, None) + + def status(phase, **extra): + record = dict(phase=phase, utc=datetime.now(timezone.utc).isoformat(), member=args.member, **extra) + write_json(study / 'state.json', record) + print(json.dumps(record), flush=True) + + def finish(phase, **extra): + write_json(complete, dict(phase=phase, master_plan_sha256=digest(plan_path), + created_utc=datetime.now(timezone.utc).isoformat(), **extra)) + status(phase, **extra) + + def execute(phase, arguments, timeout=None): + status(phase) + with (study / f'{phase}.log').open('a') as stream: + process = subprocess.Popen([sys.executable, *map(str, arguments)], env=environment, + stdout=stream, stderr=subprocess.STDOUT, start_new_session=True) + try: + code = process.wait(timeout=timeout) + assert code == 0, (phase, code) + finally: + if process.poll() is None: + os.killpg(process.pid, signal.SIGTERM) + try:process.wait(timeout=10) + except subprocess.TimeoutExpired: + os.killpg(process.pid, signal.SIGKILL); process.wait() + + def wait_memory(): + while int(subprocess.check_output(['nvidia-smi','--query-gpu=memory.free', + '--format=csv,noheader,nounits'], text=True).strip()) < plan['minimum_free_GiB'] * 1024: + time.sleep(5) + + status('waiting_for_complete_declared_raw_parents') + while not (parent_ready(parent) and parent_ready(previous)): + time.sleep(10) + receipts = dict(parent=parent_receipt(parent, member['capacity'], member['epoch'], plan), + previous=parent_receipt(previous, member['capacity'], member['previous_epoch'], plan)) + resolved_path = study / 'resolved-parents.json' + resolved = dict(master_plan_sha256=digest(plan_path), member=member, receipts=receipts) + if resolved_path.exists():assert json.loads(resolved_path.read_text()) == resolved + else:write_json(resolved_path, resolved) + admitted = receipts['parent']['reward'] > receipts['previous']['reward'] + plan['parent_minimum_reward_gain'] + gate = dict(admitted=admitted, parent_reward=receipts['parent']['reward'], + previous_raw_reward=receipts['previous']['reward'], minimum_gain=plan['parent_minimum_reward_gain']) + if not (study / 'parent-gate.json').exists():write_json(study / 'parent-gate.json', gate) + else:assert json.loads((study / 'parent-gate.json').read_text()) == gate + if not admitted: + finish('rejected_at_parent_gain_gate', **gate) + return + output.mkdir(exist_ok=True) + package = output / 'package' + construction_path = study / 'construction-check.json' + if not construction_path.exists(): + if package.exists(): + try:verify_package(package);check_link_only(parent / 'package', package) + except (OSError,ValueError,KeyError,AssertionError): + package.rename(package.with_name('package-incomplete-' + str(time.time_ns()))) + if not package.exists(): + execute('build', [HERE / 'make_splice_probability_candidate.py', parent / 'package', package, + '--training-prior', plan['training_prior']]) + checks = check_link_only(parent / 'package', package) + assert checks['parent'] == receipts['parent']['inference_signature'] + assert json.loads((package / 'package_manifest.json').read_text())['parameters'] == plan['capacities'][member['capacity']]['parameters'] + before = (parent / 'package/predict.py').read_text() + marker = 'from reference.alphagenome_50m.model import CompactModel, ModelConfig\n' + old = 'data=all_site_logits[0, target_start:target_start + config.target_length].cpu().numpy().astype(np.float16),' + new = 'data=splice_output_scores(all_site_logits[0, target_start:target_start + config.target_length]).cpu().numpy().astype(np.float16),' + assert before.count(marker) == before.count(old) == 1 + expected = before.replace(marker, marker + 'from splice_output import splice_output_scores\n').replace(old, new) + assert (package / 'predict.py').read_text() == expected + for name in ['infer.sh','perturb.sh']: + assert digest(package / name) == digest(parent / 'package' / name) + assert json.loads((package / 'splice_calibration.json').read_text()) == json.loads(Path(plan['calibration_reference']).read_text()) + history = package / 'training_history'; history.mkdir(exist_ok=True) + additions = {'late-fixed-links81-master-plan.json': plan_path, + 'late-fixed-links81-resolved-parents.json': resolved_path, + 'late-fixed-links81-parent-check.json': STUDY / 'parent-check.json', + 'late_fixed_links81.py': HERE / 'late_fixed_links81.py', + 'run_late_fixed_links81.py': Path(__file__)} + for name, source in additions.items(): + (history / name).write_bytes(source.read_bytes()) + readme = package / 'README.md' + body = readme.read_text().replace( + 'In preliminary\ndiagnostics, MFASS and site Pearson improved while sQTL and some ClinVar metrics regressed.', + "The complete reports below determine this checkpoint's track and variant tradeoffs.") + note = (f'\nThis predeclared late fixed-link follow-up uses the complete {member["epoch"]}-epoch ' + f'{member["capacity"]} main model. It applies the original four training-only offsets ' + 'without refitting, SGD, orientation changes, or changes to site selection and sparse decoding. ' + 'Both entry points use the same link. All200 validation windows and all13 development metrics ' + 'are scored, with exact checks for ten unchanged family reports and eight unchanged variant reports. ' + 'A publication additionally requires native replay and three sampled runtime estimates below four hours.\n') + if note.strip() not in body:body += note + readme.write_text(body) + manifest = json.loads((package / 'package_manifest.json').read_text()) + for name in ['README.md', *['training_history/' + name for name in additions]]: + manifest['files'][name] = digest(package / name) + write_json(package / 'package_manifest.json', manifest) + verification = verify_package(package) + write_json(construction_path, dict(created_utc=datetime.now(timezone.utc).isoformat(), + master_plan_sha256=digest(plan_path), resolved_parents_sha256=digest(resolved_path), + package_inference_signature=inference_signature(package), exact_link_only_checks=checks, + parameters=manifest['parameters'], network_updates=0, prior_refitted=False, package_verified=True)) + construction = json.loads(construction_path.read_text()) + assert construction['master_plan_sha256'] == digest(plan_path) + assert construction['resolved_parents_sha256'] == digest(resolved_path) + verify_package(package) + signature = inference_signature(package) + assert construction['package_inference_signature'] == signature + checks = check_link_only(parent / 'package', package) + assert checks['parent'] == receipts['parent']['inference_signature'] and checks['candidate'] == signature + native = study / 'native-recovery' + if not (native / 'report.json').exists() or not (output / 'valid-variants').exists(): + status('waiting_for_bounded_native_and_derivation_slot') + with Path(plan['bounded_lock']).open('a') as probe_lock: + fcntl.flock(probe_lock, fcntl.LOCK_EX);wait_memory() + if not (native / 'report.json').exists(): + execute('native', [HERE / 'check_native_recovery.py', package, native], plan['native_timeout_seconds']) + if not (output / 'valid-variants').exists(): + materialization = dict(plan['materialization'], candidates=[dict(parent=str(parent), candidate=str(output), + parent_inference_signature=checks['parent'], candidate_inference_signature=signature)]) + path = study / 'materialization-plan.json' + if path.exists():assert json.loads(path.read_text()) == materialization + else:write_json(path, materialization) + execute('materialize', [HERE / 'materialize_splice_link_outputs_v2.py', path], plan['materialization_timeout_seconds']) + proof = json.loads((native / 'report.json').read_text()) + assert proof['package_inference_signature'] == signature and proof['all_arrays_bit_identical'] + assert proof['native_vendored_entrypoints_passed'] and proof['partial_perturbation_recovery_passed'] + assert proof['arrays'] == {'tracks':30,'perturbations':22} + derivation = json.loads((output / 'valid-variants/perturb-link-derivation.json').read_text()) + assert derivation['checks']['candidate'] == signature and derivation['checks']['parent'] == checks['parent'] + assert derivation['native_arrays_bit_exact'] == 36 and derivation['queries'] == 2697 + if not (output / 'evaluation.json').exists(): + status('waiting_for_full_evaluation_slot') + with Path(plan['evaluation_lock']).open('a') as evaluation_lock: + fcntl.flock(evaluation_lock, fcntl.LOCK_EX);wait_memory() + execute('evaluate', [HERE / 'evaluate_package.py', package, output, + '--resume-partial-tracks','--resume-partial-perturbations']) + assert json.loads((output / 'evaluation.json').read_text())['package_inference_signature'] == signature + tracks = json.loads((output / 'valid-score/report.json').read_text()) + variants = json.loads((output / 'valid-variant-score/report.json').read_text()) + raw_tracks = json.loads((parent / 'valid-score/report.json').read_text()) + raw_variants = json.loads((parent / 'valid-variant-score/report.json').read_text()) + assert tracks['examples'] == 200 and tracks['invalid_reason'] is None and len(variants) == 13 + affected = {'sqtl_causality','clinvar_noncoding','clinvar_splice_region','clinvar_missense','mfass'} + for family, report in tracks['families'].items(): + if family != 'splice_sites':assert report == raw_tracks['families'][family],family + for name, report in variants.items(): + assert math.isfinite(report['model']) + for field in ['rows','perturbations','metric','anchors']:assert report[field] == raw_variants[name][field],(name,field) + if name not in affected:assert report == raw_variants[name],name + comparison_path = study / 'matched-comparison.json' + if not comparison_path.exists(): + write_json(comparison_path, dict(created_utc=datetime.now(timezone.utc).isoformat(), + master_plan_sha256=digest(plan_path), reward=tracks['overall_mean_family_pearson'], + reward_delta=tracks['overall_mean_family_pearson']-raw_tracks['overall_mean_family_pearson'], + family_deltas={k:v['mean_pearson']-raw_tracks['families'][k]['mean_pearson'] for k,v in tracks['families'].items()}, + variant_deltas={k:v['model']-raw_variants[k]['model'] for k,v in variants.items()}, + unaffected_family_reports_exact=10, unaffected_variant_reports_exact=8, package_inference_signature=signature)) + decision_path = study / 'publication-decision.json' + if not decision_path.exists(): + current = json.loads(Path('/workspace/submission/validation_tracks.json').read_text())['overall_mean_family_pearson'] + eligible = tracks['overall_mean_family_pearson'] > current + plan['publication_minimum_reward_gain'] + runtime_passed = None + if eligible: + latency = study / 'native-latency';latency.mkdir(exist_ok=True) + timing_plan = dict(plan['native_latency_template'], experiment=str(output), inference_signature=signature) + if (latency / 'plan.json').exists():assert json.loads((latency / 'plan.json').read_text()) == timing_plan + else:write_json(latency / 'plan.json', timing_plan) + if not (latency / 'report.json').exists(): + execute('native-latency', [HERE / 'measure_native_latency.py', latency / 'plan.json']) + timing = json.loads((latency / 'report.json').read_text()) + assert timing['all_arrays_bit_exact'] and timing['package_inference_signature'] == signature + estimates = timing['extrapolated_seconds'] + names = ['development_mixture_seconds','slowest_observed_stratum_mean_seconds','pooled_p95_seconds'] + assert all(math.isfinite(estimates[k]) for k in names) + runtime_passed = max(estimates[k] for k in names) <= 14400 + write_json(study / 'runtime-gate.json', dict(passed=runtime_passed, estimates=estimates, + report_sha256=digest(latency / 'report.json'), scope='Shared-load extrapolations, not guaranteed upper bounds.')) + if runtime_passed:execute('publish', [HERE / 'promote_if_better.py', package, output]) + write_json(decision_path, dict(created_utc=datetime.now(timezone.utc).isoformat(), + reward=tracks['overall_mean_family_pearson'], current_reward_at_review=current, + eligible_at_review=eligible, runtime_passed=runtime_passed, + publication='Atomic promoter rechecks the current strongest package; see publish.log if run.')) + for phase, helper in [('tracks','diagnose_track_uncertainty.py'), + ('remaining-variants','diagnose_remaining_variant_uncertainty.py')]: + report_path = study / f'paired-{phase}.json' + marker_path = study / f'paired-{phase}-complete.json' + if marker_path.exists(): + assert json.loads(marker_path.read_text())['output_sha256'] == digest(report_path) + continue + def diagnostic_complete(): + try: + report = json.loads(report_path.read_text()) + assert Path(report['candidate']).resolve() == output.resolve() + assert Path(report['control']).resolve() == parent.resolve() + assert report['replicates'] == 2000 and report['seed'] == 1729 + assert report['diagnostic_only_no_parameter_fitting'] + leaf = 'valid-score/report.json' if phase == 'tracks' else 'valid-variant-score/report.json' + assert report['report_sha256'] == {'candidate':digest(output / leaf),'control':digest(parent / leaf)} + if phase == 'tracks': + assert len(report['point_deltas']) == 12 and 'reward' in report['point_deltas'] + assert set(report['bootstrap']) == {'chromosome','chromosome_10Mb_blocks'} + for result in report['bootstrap'].values(): + assert set(result['deltas']) == set(report['point_deltas']) + else: + assert set(report['evaluations']) == affected | {'caqtl','bqtl_spi1'} + for item in report['evaluations'].values(): + assert set(item['bootstrap']) == {'variant_id','genomic_block'} + for result in item['bootstrap'].values(): + assert result['reference_replicates_checked'] >= 32 + assert result['maximum_absolute_metric_error'] < 1e-10 + return True + except (FileNotFoundError,json.JSONDecodeError,AssertionError,KeyError): + return False + if not diagnostic_complete(): + execute('paired-' + phase, [HERE / helper, output, parent, report_path, + '--replicates','2000','--seed','1729']) + assert diagnostic_complete(), ('incomplete diagnostic', phase) + write_json(marker_path, dict(output_sha256=digest(report_path), master_plan_sha256=digest(plan_path))) + finish('complete', reward=tracks['overall_mean_family_pearson'], + matched_comparison_sha256=digest(comparison_path), publication_decision=json.loads(decision_path.read_text()), + diagnostics='All11families/sevenvariants, with eight unaffected official variant reports exact; not anall13bootstrap.') + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/run_scale_study_cached_io.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/run_scale_study_cached_io.py new file mode 100644 index 0000000000000000000000000000000000000000..3a64b3a086363780b355fe0c827bab67bc691b68 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_history/run_scale_study_cached_io.py @@ -0,0 +1,151 @@ +"""Train two locally initialized scales on an identical recipe and evaluate selected full epochs. + +This script requires a pre-reviewed study.json; it never chooses scientific options itself. +It resumes checkpoints and preserves an independently replayable package for every evaluation. +""" +import argparse +import hashlib +import json +import os +from pathlib import Path +import subprocess +import sys +import time +import torch + +p = argparse.ArgumentParser(description=__doc__) +p.add_argument('study', type=Path) +a = p.parse_args() +plan = json.loads(a.study.read_text()) +assert plan.get('training_bytecache_root'), 'This amended runner requires an explicit training-only cache path' +assert plan['schema'] == 'seqfunc-matched-scale/v1' +assert plan['pretrained'] is False and plan['examples_per_epoch'] == 41020 +assert len(plan['runs']) == 2 and plan['shared']['batch_size'] == 4 +r = Path('/workspace/research') +here = Path(__file__).resolve().parent +source = Path(plan.get('source', r/'seqfunc-scale')) +sys.path.insert(0, str(source)) +from reference.alphagenome_50m.model import ModelConfig +configs = [ModelConfig.from_yaml(run['config']).to_dict() for run in plan['runs']] +differences = {key for key in configs[0] if configs[0][key] != configs[1][key]} +assert differences and differences <= {'encoder_width', 'channel_growth', 'hidden_dim', + 'num_sequence_blocks', 'num_attention_heads', 'head_hidden_size', + 'local_width'} +env = dict(os.environ, OPENBLAS_NUM_THREADS='1', OMP_NUM_THREADS='2', MKL_NUM_THREADS='2') +os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) +env.pop('SEQFUNC_TRAIN_BYTECACHE', None) +children, handles = {}, [] + +class AdoptedEvaluation: + def __init__(self, identity, directory): + self.identity, self.directory = identity, directory + self.pid = identity['pid'] + def poll(self): + try: + path = Path(f'/proc/{self.pid}') + stat = (path/'stat').read_text().split(') ', 1)[1].split() + active = (stat[0] not in 'ZX' and int(stat[19]) == self.identity['starttime_ticks'] + and hashlib.sha256((path/'cmdline').read_bytes()).hexdigest() == self.identity['cmdline_sha256']) + except OSError: + active = False + if active: + return None + try: + done = json.loads((self.directory/'pipeline-state.json').read_text())['phase'] == 'complete' + except (OSError, KeyError, json.JSONDecodeError): + done = False + return 0 if done else 1 + @property + def returncode(self): + return self.poll() + + +def launch(name, arguments, logfile, *, training=False): + handle = logfile.open('a') + handles.append(handle) + child_environment = dict(env) + if training: + child_environment['SEQFUNC_TRAIN_BYTECACHE'] = plan['training_bytecache_root'] + proc = subprocess.Popen([sys.executable, *map(str, arguments)], env=child_environment, + stdout=handle, stderr=subprocess.STDOUT) + children[name] = proc + print(json.dumps({'event': 'study_launch', 'name': name, 'pid': proc.pid}), flush=True) + +for run in plan['runs']: + directory = Path(run['output']) + directory.mkdir(parents=True, exist_ok=True) + checkpoints = [directory/name for name in ['progress.pt', 'last.pt']] + epochs = sorted(directory.glob('epoch-*.pt')) + if epochs: + checkpoints.append(epochs[-1]) + available = [(torch.load(path, map_location='cpu', weights_only=True)['global_step'], path) + for path in checkpoints if path.exists()] + step, checkpoint = max(available) if available else (0, None) + original = plan['cache_execution_amendment']['checkpoints'][run['name']] + assert step >= original['step'] + if step == original['step']: + checkpoint = Path(original['path']) + with checkpoint.open('rb') as stream: + assert hashlib.file_digest(stream, 'sha256').hexdigest() == original['sha256'] + shared = plan['shared'] + if step < shared['epochs']*plan['examples_per_epoch']: + args = [source/'train_batched.py', '--output', directory, '--config', run['config'], + '--workers', run['workers'], '--threads', 2, '--save-every', 1000, + '--keep-every', 0, '--log-every', 100] + for key in ['batch_size', 'epochs', 'seed', 'learning_rate', 'min_learning_rate', 'warmup', 'objective']: + args += ['--'+key.replace('_', '-'), shared[key]] + if shared.get('loss_scales'): + args += ['--loss-scales', shared['loss_scales']] + if checkpoint: + args += ['--resume', checkpoint] + launch(run['name']+'-train', args, directory/'train.log', training=True) + for epoch in plan['evaluation_epochs']: + evaluation = directory.parent/(run['name']+f'-epoch{epoch:02d}') + evaluation.mkdir(exist_ok=True) + name = run['name']+f'-eval{epoch:02d}' + state_path = evaluation/'pipeline-state.json' + if state_path.exists() and json.loads(state_path.read_text()).get('phase') == 'complete': + print(json.dumps({'event': 'completed_evaluation_preserved', 'name': name}), flush=True) + continue + if name in plan.get('adopted_evaluations', {}): + children[name] = AdoptedEvaluation(plan['adopted_evaluations'][name], evaluation) + print(json.dumps({'event': 'evaluation_adopted_without_interruption', 'name': name, 'pid': children[name].pid}), flush=True) + continue + args = [here/'run_candidate.py', directory/f'epoch-{epoch:02d}.pt', evaluation, + '--source', source, '--step', epoch*plan['examples_per_epoch'], + '--evaluation-lock', a.study.with_name('evaluation.lock'), + '--name', f"SeqFunc {run['name']} after {epoch} complete epochs", '--orientation', + '--lineage', plan['provenance']+' '+run['description']+ + ' Parameters are initialized locally with seed 17. The paired scale uses the same ' + 'batch-four interval order, objective, learning-rate schedule and full training permutations. ' + 'Only encoder/trunk/head/local-path width and transformer depth differ between scales.'] + launch(run['name']+f'-eval{epoch:02d}', args, evaluation/'pipeline.log') + +monitor_retired = False +while True: + status = {name: {'pid': proc.pid, 'returncode': proc.poll()} for name,proc in children.items()} + temporary = a.study.with_name('study-state-cached.tmp') + temporary.write_text(json.dumps(status, indent=2)+'\n') + temporary.replace(a.study.with_name('study-state-cached.json')) + if not monitor_retired and all(proc.poll() is not None for proc in children.values() + if isinstance(proc, AdoptedEvaluation)): + import signal + old = plan['retired_monitor_identity'] + try: + path = Path(f'/proc/{old["pid"]}') + stat = (path/'stat').read_text().split(') ', 1)[1].split() + if (int(stat[19]) == old['starttime_ticks'] + and hashlib.sha256((path/'cmdline').read_bytes()).hexdigest() == old['cmdline_sha256']): + os.kill(old['pid'], signal.SIGTERM) + os.kill(old['pid'], signal.SIGCONT) + except (FileNotFoundError, ProcessLookupError): + pass + monitor_retired = True + print(json.dumps({'event': 'old_monitor_retired_after_preserved_evaluation', 'pid': old['pid']}), flush=True) + if all(proc.poll() is not None for proc in children.values()): + break + time.sleep(5) +for handle in handles: + handle.close() +if any(proc.returncode for proc in children.values()): + raise RuntimeError('one or more study processes failed; inspect study-state.json and individual logs') diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_loss_scales.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_loss_scales.json new file mode 100644 index 0000000000000000000000000000000000000000..a8f484c060a0d4300b25557736abf741d6d67b15 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_loss_scales.json @@ -0,0 +1,32 @@ +{ + "split": "train", + "examples": 256, + "seed": 1729, + "source_moments_sha256": "7b7543c4d4d8d82ec9f9bc3f942ca65595671322f62c19391591df827d8fc588", + "training_indices_sha256": "b82df9ae11e95d9be5d6e5f85839cf23e25bbbe0a195eaee266ff52d366800dc", + "method": "reference MSE times inverse mean within-track training variance; floor0.01; splice BCE unchanged", + "mean_track_variance": { + "atac": 0.9602319499041136, + "dnase": 1.0768856864858252, + "procap": 0.3380534137610864, + "cage": 0.3114663911919963, + "rna_seq": 0.66617486157613, + "chip_tf": 2.7198812117366296, + "chip_histone": 3.405612880716664, + "contact_maps": 0.14704202117243276, + "splice_usage": 0.07913355074122039, + "junctions": 0.17919098248535248 + }, + "family_loss_scales": { + "atac": 1.041415045708339, + "dnase": 0.9286036694045732, + "procap": 2.9581124144681272, + "cage": 3.2106192779675315, + "rna_seq": 1.5011073783751905, + "chip_tf": 0.36766311546433506, + "chip_histone": 0.29363290398101966, + "contact_maps": 6.800777029767044, + "splice_usage": 12.636865029223356, + "junctions": 5.5806379658738825 + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_provenance.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_provenance.json new file mode 100644 index 0000000000000000000000000000000000000000..206b9bddac3c9f60bf3c62eaaa985d7b2c9f091d --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/training_provenance.json @@ -0,0 +1,180 @@ +{ + "schema": "seqfunc-batched/v1", + "config": { + "input_length": 1048576, + "target_length": 196608, + "dense_bin_size": 128, + "trunk_bin_size": 1024, + "contact_bin_size": 2048, + "stem_stride": 8, + "encoder_width": 32, + "channel_growth": 32, + "num_downsampling_blocks": 7, + "conv_kernel_size": 5, + "dna_kernel_size": 15, + "hidden_dim": 384, + "num_sequence_blocks": 6, + "num_attention_heads": 6, + "attention_head_dim": 64, + "mlp_expansion": 4.0, + "dropout": 0.0, + "head_hidden_size": 256, + "junction_rank": 32, + "splice_usage_tracks": 734, + "junction_tracks": 734, + "contact_tracks": 28, + "gradient_checkpointing": true, + "enable_pairwise": false, + "pair_width": 32, + "pair_downsample": 16, + "pair_update_interval": 2, + "local_width": 32, + "local_context_blocks": 6, + "splice_context_mode": "offset", + "local_to_trunk": true, + "residual_rms_cap": 0.1 + }, + "epoch": 8, + "offset": 0, + "global_step": 328160, + "examples": 41020, + "schedule_epochs": 14, + "objective": "variance_balanced", + "seed": 17, + "training_arguments": { + "input": "/workspace/data", + "output": "/workspace/research/experiments/scale13-small", + "config": "/workspace/research/studies/scale13/scale13-small.yaml", + "resume": "/workspace/research/experiments/scale13-small/bytecache-resume-096000.pt", + "batch_size": 4, + "epochs": 14, + "stop_at": 0, + "workers": 4, + "threads": 2, + "seed": 17, + "learning_rate": 0.0002, + "min_learning_rate": 2e-05, + "warmup": 4000, + "objective": "variance_balanced", + "loss_scales": "/workspace/research/studies/scale13/loss_scales.json", + "save_every": 1000, + "keep_every": 0, + "log_every": 100 + }, + "elapsed_seconds": 73897.99778079404, + "provenance": { + "data": "/workspace/data", + "split": "train", + "pretrained": false, + "world_size": 1, + "device": "NVIDIA H100 80GB HBM3", + "batch_reduction": "equal per example", + "train_manifest_sha256": "48133d27eedfb4b1d275cfca41f69dd075a2a0305ab2e8591e8e5e7529f36c64" + }, + "optimizer_steps": 82040, + "family_loss_scales": { + "atac": 1.041415045708339, + "dnase": 0.9286036694045732, + "procap": 2.9581124144681272, + "cage": 3.2106192779675315, + "rna_seq": 1.5011073783751905, + "chip_tf": 0.36766311546433506, + "chip_histone": 0.29363290398101966, + "contact_maps": 6.800777029767044, + "splice_usage": 12.636865029223356, + "junctions": 5.5806379658738825 + }, + "loss_scale_provenance": { + "split": "train", + "examples": 256, + "seed": 1729, + "source_moments_sha256": "7b7543c4d4d8d82ec9f9bc3f942ca65595671322f62c19391591df827d8fc588", + "training_indices_sha256": "b82df9ae11e95d9be5d6e5f85839cf23e25bbbe0a195eaee266ff52d366800dc", + "method": "reference MSE times inverse mean within-track training variance; floor0.01; splice BCE unchanged", + "mean_track_variance": { + "atac": 0.9602319499041136, + "dnase": 1.0768856864858252, + "procap": 0.3380534137610864, + "cage": 0.3114663911919963, + "rna_seq": 0.66617486157613, + "chip_tf": 2.7198812117366296, + "chip_histone": 3.405612880716664, + "contact_maps": 0.14704202117243276, + "splice_usage": 0.07913355074122039, + "junctions": 0.17919098248535248 + }, + "family_loss_scales": { + "atac": 1.041415045708339, + "dnase": 0.9286036694045732, + "procap": 2.9581124144681272, + "cage": 3.2106192779675315, + "rna_seq": 1.5011073783751905, + "chip_tf": 0.36766311546433506, + "chip_histone": 0.29363290398101966, + "contact_maps": 6.800777029767044, + "splice_usage": 12.636865029223356, + "junctions": 5.5806379658738825 + } + }, + "inference_ensemble": { + "method": "Equal mean of the same checkpoint evaluated on DNA and its reverse complement, mapped to original positions and public-metadata strand channels.", + "orientation_views": 2, + "fitted_ensemble_weights": false, + "weights": [ + 0.5, + 0.5 + ], + "dna_input": "Same provided 1,048,576bp, reversed and complemented; no extra flanking sequence.", + "site_scores": "Apply the existing training-prior probability link per orientation, then average probabilities.", + "sparse_outputs": "Each orientation chooses its own positions/pairs. Average the union with zero for unlisted coordinates; retain up to16,384 junctions by averaged predicted strength. Usage union has at most4,096 coordinates.", + "outputs": "All families and both entry points use the same two-view rule. No labels or targets used at inference.", + "created_utc": "2026-10-26T01:07:17.491176+00:00", + "parent_package": "/run-state/workspace/research/experiments/calib81-small-epoch08/package", + "parent_inference_signature": { + "last.pt": "4e81120dd5424c175e43684c0827b8a4f21e5be8b0b554f17df615ac16a027dd", + "predict.py": "5f5507be4efce5af3167616de01c11da89fa4fe2b2c59dbdac0152e1a84d1348", + "perturb.py": "9defff26b93513ebe681c31485b75730aac911d9c0f9983f2c7aa615a01b67f8", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/alphagenome_50m/model.py": "c61f50a2a66a4f6950c6f6107a70710a5d0ab99a291b8bfaa1eb5565998d2fa3", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919", + "splice_output.py": "e1b0627e036c60190e4d2a4653a915d9031b166ff3c1de7c23e5774009d70ef0", + "splice_calibration.json": "bd9368b32e739be65ca290d598ffcae5be6387c09f96048e183c57b7d570e641" + }, + "unique_learned_parameters": 13290504, + "decoder_study": "/run-state/workspace/research/studies/rc17", + "decoder_assets_sha256": { + "predict.py": "4f5dbddf5b6b701079aeaf38920fe414ce980f7161997f4d5c80952a6d834e51", + "perturb.py": "49065dc43816920ecac9de3ebcc4735673e4ff6594cf3f6bd34233b88c9bed73", + "rc_ensemble.py": "84a4d0e94ab86622e2c25e96f8835b2ef2bba77731ff7ff0bdc1c2de02cdcef4", + "single_prediction.py": "10f6216fdd72bf30b3a28ab6de9363e66b415526aa26a2bbfb6a84badbb67bec", + "strand_permutations.json": "afb0f0cf2e897fab3493de66601d7a66dac45a06316c2ac4a6cc828298820c60" + } + }, + "fixed_decoder113_intervention": { + "utc": "2026-10-26T06:54:54.017445+00:00", + "parent_inference_signature": { + "last.pt": "4e81120dd5424c175e43684c0827b8a4f21e5be8b0b554f17df615ac16a027dd", + "predict.py": "4f5dbddf5b6b701079aeaf38920fe414ce980f7161997f4d5c80952a6d834e51", + "perturb.py": "49065dc43816920ecac9de3ebcc4735673e4ff6594cf3f6bd34233b88c9bed73", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/alphagenome_50m/model.py": "c61f50a2a66a4f6950c6f6107a70710a5d0ab99a291b8bfaa1eb5565998d2fa3", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919", + "splice_output.py": "e1b0627e036c60190e4d2a4653a915d9031b166ff3c1de7c23e5774009d70ef0", + "splice_calibration.json": "bd9368b32e739be65ca290d598ffcae5be6387c09f96048e183c57b7d570e641", + "rc_ensemble.py": "84a4d0e94ab86622e2c25e96f8835b2ef2bba77731ff7ff0bdc1c2de02cdcef4", + "single_prediction.py": "10f6216fdd72bf30b3a28ab6de9363e66b415526aa26a2bbfb6a84badbb67bec", + "strand_permutations.json": "afb0f0cf2e897fab3493de66601d7a66dac45a06316c2ac4a6cc828298820c60" + }, + "candidate_model_sha256": "9ddfecf4def3c0ce70a5ac8e831a22c6c5565c90719b5a36dc541acc12b7afb2", + "parameters": 13290504, + "inherited_examples": 328160, + "inherited_optimizer_steps": 82040, + "inherited_complete_passes": 8, + "additional_training_updates": 0, + "only_change": "Replace RC108 nearest global-feature decoding for dense, usage and junction heads with the exact selective linear-bin-center operation from completed111/96. Keep its original nearest splice classifier, contacts, learned tensors, four training-derived offsets and equal forward/RC views.", + "scope": "One fixed network and the same two orientation views serve every family and both entrypoints. No learned or fitted mixture, routing, new splice prior, motif mask, SWA, extra flanking DNA, variant-label training or additional SGD.", + "limitations": "Exploratory one-seed postselection. Interaction can worsen any changed readout. Full native replay, runtime and accuracy must be measured independently; unchanged paths require exact complete reports." + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/validation_tracks.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/validation_tracks.json new file mode 100644 index 0000000000000000000000000000000000000000..89faf0dc1f210ac10d832b5fc63619d3bd812878 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/validation_tracks.json @@ -0,0 +1,17909 @@ +{ + "schema": "genome-sequence-to-function-report/v4", + "split": "valid", + "examples": 200, + "families": { + "atac": { + "mean_pearson": 0.5972396996617755, + "valid_tracks": 167, + "per_track_pearson": [ + 0.7181444184198065, + 0.7391488342267102, + 0.46868446861517044, + 0.6650969152808638, + 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0000000000000000000000000000000000000000..7f913176c7b621c6cdb81ea5cb60aa4e721e7b05 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S035/validation_variants.json @@ -0,0 +1,125 @@ +{ + "eqtl_sign": { + "rows": 17052, + "perturbations": 165, + "metric": "auroc", + "model": 0.5053901677336448, + "anchors": { + "AlphaGenome": 0.8263569969634328 + } + }, + "eqtl_causality": { + "rows": 17934, + "perturbations": 174, + "metric": "auroc", + "model": 0.5738692774407059, + "anchors": { + "AlphaGenome": 0.8325427468284611 + } + }, + "eqtl_coefficient": { + "rows": 8575, + "perturbations": 165, + "metric": "spearman", + "model": -0.02131690256924934, + "anchors": { + "AlphaGenome": 0.4133539365891599 + } + }, + "sqtl_causality": { + "rows": 21340, + "perturbations": 194, + "metric": "auprc", + "model": 0.7607243206750722, + "anchors": { + "AlphaGenome": 0.8447114201904594 + } + }, + "clinvar_noncoding": { + "rows": 199, 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"enhancer_gene": { + "rows": 200, + "perturbations": 200, + "metric": "auprc", + "model": 0.4670761533953175, + "anchors": { + "AlphaGenome": 0.9594307235541584 + } + }, + "traitgym_mendelian": { + "rows": 292, + "perturbations": 292, + "metric": "auprc", + "model": 0.1848133674736538, + "anchors": { + "Borzoi_L2_L2.plus.all": 0.5026323772738239, + "Enformer_L2_L2.plus.all": 0.5452938267693526, + "CADD.plus.RawScore": 0.7189111372856957, + "GPN-MSA_absLLR.plus.score": 0.6729149139043132 + } + }, + "traitgym_complex": { + "rows": 299, + "perturbations": 299, + "metric": "auprc", + "model": 0.2247112727089844, + "anchors": { + "Borzoi_L2_L2.plus.all": 0.294501577162467, + "Enformer_L2_L2.plus.all": 0.269227301087889, + "CADD.plus.RawScore": 0.41341570280137435, + "GPN-MSA_absLLR.plus.score": 0.4213971415058371 + } + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/README.md b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/README.md new file mode 100644 index 0000000000000000000000000000000000000000..04941f139de439cc0da5838a60ca0ca0ee65a689 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/README.md @@ -0,0 +1,84 @@ +# SeqFunc large model with fixed forward/reverse-complement inference + +One model with **215,626,632 unique learned parameters** reads 1,048,576 bp and predicts +all eleven families and 5,930 tracks through both offline entrypoints. It averages the +same learned weights on the supplied DNA and its reverse complement, with fixed equal +weights and vendored public strand permutations. No additional flanking sequence is read. + +The model uses a strided convolutional encoder (initial width96, growth96), sixteen +width1024 transformer blocks over1,024 tokens, and width768 output features. A width64 +base-resolution path has six dilated residual blocks and a10,941-bp receptive field; +its pooled features feed both the heads and the global trunk. Attention and MLP updates +use a per-token RMS cap0.1. Dense outputs cover the central196,608bp; contacts and junctions +use factorized readouts. The original nearest global-feature decoder is retained. + +Each orientation applies the same four training-prior splice offsets before averaging +probabilities. Dense and contact readouts average in asinh space. Sparse positions are +selected independently per view, merged with zero for an unlisted view, and capped at +4,096 usage positions and16,384 junction pairs. The orientation code is exactly the +verified RC17/85/108 implementation. This candidate adds no interpolation, motif filter, +parameter averaging, fitted mixing weights or learned parameters. + +All weights originate from local random initialization with seed17. Training used eight +complete passes through all41,020 eligible FOLD_0 intervals in folds2–7: **328,160 examples +and82,040 AdamW updates**. The supplied reader decodes quantized training targets per +example. BF16 batch4 training uses fixed family-variance scales estimated from256 training +windows, balanced splice BCE, AdamW weight decay1e-4, gradient clipping5, and the original +fourteen-pass cosine schedule2e-4 to2e-5 with4,000 warmup examples. The saved checkpoint +records108,193.2591 accumulated training-loop seconds (30.05h) on one shared H10080GB, +within22CPUs. Setup and evaluation add cost; this is shared wall time rather than exclusive +GPU compute. Numerical training recovery is not bit-exact. This inference change adds zero SGD. + +Run `infer.sh INPUT_ROOT OUTPUT_ROOT` or `perturb.sh INPUT_ROOT OUTPUT_ROOT` offline. +The package vendors CPython3.11, PyTorch2.7.1/CUDA12.6 and dependencies; the host provides +Linux, bash/tar/gzip and the NVIDIA driver. Inputs are public manifests and the pinned +reference only. No targets, supplied splice positions, prediction cache or network are used. +Track arrays are stored as float16 after float32 averaging; perturbation arrays stay float32. + +Candidate validation is pending. Required checks are its own52-array native/offline/recovery +test,200 validation windows, all2,697 development queries and13 metrics with published anchors, +624 representative native timing arrays, and all three combined runtime estimates within4h. +All11/all13 paired comparisons are fixed against the calibrated large forward parent and +RC108. The latter has equal exposure, offsets and views but smaller global/local capacity +and different compute; it is not an independent training replicate. Parent scores are not +candidate scores. The strongest fully gated package remains installed until an improvement. + +Limitations include one seed per size, repeated development selection, pooled local features, +finite local context, low-rank contacts, sparse-union dilution and uneven variant prediction. +Fifty-four GTEx RNA heads have no direct training targets and are excluded from scored RNA +means; eQTL scoring uses supervised ENCODE RNA tracks. There is no conservation input. +Zero-inclusive intervals do not establish equivalence; sampled runtimes are not bounds. + +## Measured validation results + +Family-mean Pearson: **0.522201** on 200 validation intervals. + +| Family | Pearson | +|---|---:| +| atac | 0.594939 | +| dnase | 0.672306 | +| procap | 0.561691 | +| cage | 0.359214 | +| rna_seq | 0.658495 | +| chip_tf | 0.474422 | +| chip_histone | 0.510669 | +| splice_sites | 0.431157 | +| splice_usage | 0.641818 | +| junctions | 0.459065 | +| contact_maps | 0.380437 | + +| Development evaluation | Metric | Model | Published anchors on the same rows | +|---|---|---:|---| +| eqtl_sign | auroc | 0.5671 | AlphaGenome: 0.8264 | +| eqtl_causality | auroc | 0.4818 | AlphaGenome: 0.8325 | +| eqtl_coefficient | spearman | 0.2451 | AlphaGenome: 0.4134 | +| sqtl_causality | auprc | 0.7536 | AlphaGenome: 0.8447 | +| clinvar_noncoding | auprc | 0.6457 | AlphaGenome: 0.9410 | +| clinvar_splice_region | auprc | 0.7048 | AlphaGenome: 0.7225 | +| clinvar_missense | auprc | 0.3220 | AlphaGenome: 0.3738 | +| mfass | auprc | 0.4950 | AlphaGenome: 0.7343 | +| caqtl | auprc | 0.1378 | AlphaGenome: 0.8076 | +| bqtl_spi1 | auprc | 0.2201 | AlphaGenome: 0.8167 | +| enhancer_gene | auprc | 0.4140 | AlphaGenome: 0.9594 | +| traitgym_mendelian | auprc | 0.2103 | Borzoi_L2_L2.plus.all: 0.5026; Enformer_L2_L2.plus.all: 0.5453; CADD.plus.RawScore: 0.7189; GPN-MSA_absLLR.plus.score: 0.6729 | +| traitgym_complex | auprc | 0.2746 | Borzoi_L2_L2.plus.all: 0.2945; Enformer_L2_L2.plus.all: 0.2692; CADD.plus.RawScore: 0.4134; GPN-MSA_absLLR.plus.score: 0.4214 | diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/attach_bridge.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/attach_bridge.py new file mode 100644 index 0000000000000000000000000000000000000000..2c2bea67a58e19676fa73ba2a0607002ea2d5419 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/attach_bridge.py @@ -0,0 +1,48 @@ +"""Attach a zero-output local-to-global connection to our own training checkpoint.""" +import argparse +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import torch +import yaml +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + +p = argparse.ArgumentParser(description=__doc__) +p.add_argument('checkpoint', type=Path) +p.add_argument('output', type=Path) +a = p.parse_args() +s = torch.load(a.checkpoint, map_location='cpu', weights_only=True) +assert s['schema'] == 'seqfunc-single/v1' +parent_config = ModelConfig(**s['config']) +assert parent_config.local_width > 0 and not parent_config.local_to_trunk +torch.manual_seed(s['seed']) +parent = CompactModel(parent_config) +config = replace(parent_config, local_to_trunk=True) +model = CompactModel(config) +missing = model.load_state_dict(s['model'], strict=False) +assert not missing.unexpected_keys +assert set(missing.missing_keys) == {'local_trunk_projection.weight', 'local_trunk_projection.bias'} +old_names = list(dict(parent.named_parameters())) +new_names = list(dict(model.named_parameters())) +assert new_names[:len(old_names)] == old_names +assert len(s['optimizer']['param_groups']) == 1 +group = s['optimizer']['param_groups'][0] +assert group['params'] == list(range(len(old_names))) +group['params'] = list(range(len(new_names))) +with a.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() +intervention = {'kind': 'local_to_global_trunk', 'parent_sha256': digest, + 'step': s['global_step'], 'initial_outputs_unchanged': True, + 'computation': 'average aligned local features into 1-kb tokens, zero linear projection, add before transformer'} +history = list(s.get('interventions', [])) +if s.get('intervention') and s['intervention'] not in history: + history.append(s['intervention']) +history.append(intervention) +s.update(config=config.to_dict(), model=model.state_dict(), intervention=intervention, interventions=history) +a.output.mkdir(parents=True, exist_ok=False) +(a.output/'config.yaml').write_text(yaml.safe_dump(config.to_dict())) +(a.output/'intervention.json').write_text(json.dumps(intervention, indent=2) + '\n') +_atomic_save(s, a.output/'start.pt') +print(json.dumps({'parameters': count_parameters(model), **intervention}), flush=True) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/attach_local.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/attach_local.py new file mode 100644 index 0000000000000000000000000000000000000000..aa082537004dbc398c4a78c1bd0203970ce5e42f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/attach_local.py @@ -0,0 +1,52 @@ +"""Attach a zero-initialized local residual path to a checkpoint trained in this workspace.""" +import argparse +from dataclasses import replace +import hashlib +from pathlib import Path + +import torch +import yaml + +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + p.add_argument('--width', type=int, default=32) + args = p.parse_args() + state = torch.load(args.checkpoint, map_location='cpu', weights_only=True) + assert state['schema'] == 'seqfunc-single/v1', 'only locally trained single-GPU checkpoints' + parent_config = ModelConfig(**state['config']) + assert parent_config.local_width == 0 + torch.manual_seed(state['seed']) + parent = CompactModel(parent_config) + config = replace(parent_config, local_width=args.width) + model = CompactModel(config) + result = model.load_state_dict(state['model'], strict=False) + assert not result.unexpected_keys + assert all(k.startswith(('local_path.', 'local_projection.')) for k in result.missing_keys) + parent_names = list(dict(parent.named_parameters())) + names = list(dict(model.named_parameters())) + assert names[:len(parent_names)] == parent_names, 'optimizer parameter order changed' + assert len(state['optimizer']['param_groups']) == 1 + group = state['optimizer']['param_groups'][0] + assert group['params'] == list(range(len(parent_names))) + group['params'] = list(range(len(names))) + state['model'] = model.state_dict() + state['config'] = config.to_dict() + state['intervention'] = { + 'kind': 'base_resolution_local_path', 'width': args.width, + 'parent_sha256': hashlib.sha256(args.checkpoint.read_bytes()).hexdigest(), + 'attachment_step': state['global_step'], 'initial_outputs_unchanged': True, + } + args.output.mkdir(parents=True, exist_ok=False) + (args.output / 'config.yaml').write_text(yaml.safe_dump(config.to_dict())) + _atomic_save(state, args.output / 'attached.pt') + print({'parameters': count_parameters(model), **state['intervention']}) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/batched_training.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/batched_training.py new file mode 100644 index 0000000000000000000000000000000000000000..e5c609c3680baa61f04739e9f22c03a801d7b294 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/batched_training.py @@ -0,0 +1,38 @@ +"""Batch variable event queries while retaining equal-per-example reference losses.""" +import torch +from torch.nn import functional as F +from reference.alphagenome_50m.train_eval import _model_inputs, _losses +from train_single import balanced_splice_loss + +def list_collate(samples): + return samples + +def prepare_batch(samples): + """Prepare on CPU before a bulk transfer; padding never contributes to a loss.""" + prepared = [_model_inputs(sample) for sample in samples] + max_usage = max(item[0]['usage_positions'].shape[1] for item in prepared) + max_queries = max(item[0]['junction_queries'].shape[1] for item in prepared) + inputs = { + 'dna': torch.cat([x[0]['dna'] for x in prepared]), + 'usage_positions': torch.cat([F.pad(x[0]['usage_positions'], (0, max_usage-x[0]['usage_positions'].shape[1])) for x in prepared]), + 'junction_site_positions': torch.cat([x[0]['junction_site_positions'] for x in prepared]), + 'junction_queries': torch.cat([F.pad(x[0]['junction_queries'], (0, 0, 0, max_queries-x[0]['junction_queries'].shape[1])) for x in prepared]), + } + return inputs, [x[1] for x in prepared], [x[2] for x in prepared] + +def equal_example_losses(outputs, samples, junction_targets, junction_valid, *, balanced=True, scales=None): + per_example = [] + for i, (sample, target, valid) in enumerate(zip(samples, junction_targets, junction_valid)): + sliced = {name:values[i:i+1] for name,values in outputs.items()} + sliced['splice_usage'] = sliced['splice_usage'][:, :sample['usage_positions'].numel()] + sliced['junctions'] = sliced['junctions'][:, :target.shape[1]] + losses = _losses(sliced, sample, target, valid) + if balanced: + losses['splice_sites'] = balanced_splice_loss(sliced['splice_sites'], sample['splice_sites'][None]) + per_example.append(losses) + raw = {name:torch.stack([loss[name] for loss in per_example]).mean() + for name in outputs} + raw['total'] = torch.stack(list(raw.values())).mean() + scaled = {name:value * (scales or {}).get(name, 1.) for name,value in raw.items() if name != 'total'} + scaled['total'] = torch.stack(list(scaled.values())).mean() + return scaled, raw diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/change_residual_cap.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/change_residual_cap.py new file mode 100644 index 0000000000000000000000000000000000000000..cd282275702aaf5d5eede4b3904add879aad846a --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/change_residual_cap.py @@ -0,0 +1,40 @@ +"""Change only a parameter-free branch bound on our own trained checkpoint.""" +import argparse +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import torch +import yaml +from reference.alphagenome_50m.model import CompactModel, ModelConfig +from reference.alphagenome_50m.train_eval import _atomic_save + +p = argparse.ArgumentParser(description=__doc__) +p.add_argument('checkpoint', type=Path) +p.add_argument('output', type=Path) +p.add_argument('--cap', type=float, default=0.1) +a = p.parse_args() +s = torch.load(a.checkpoint, map_location='cpu', weights_only=True) +config = ModelConfig(**s['config']) +assert config.residual_rms_cap == 0 and a.cap > 0 +config = replace(config, residual_rms_cap=a.cap) +model = CompactModel(config) +model.load_state_dict(s['model'], strict=True) +assert len(s['optimizer']['param_groups'][0]['params']) == len(list(model.parameters())) +assert all(torch.equal(v, s['model'][k]) for k, v in model.state_dict().items()) +with a.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() +change = {'kind': 'bounded_transformer_residuals', 'parent_sha256': digest, + 'step': s['global_step'], 'cap': a.cap, + 'parameters_optimizer_rng_unchanged': True, + 'computation': 'each attention and MLP update multiplied by min(1, cap/token_RMS)'} +history = list(s.get('interventions', [])) +if s.get('intervention') and s['intervention'] not in history: + history.append(s['intervention']) +history.append(change) +s.update(config=config.to_dict(), intervention=change, interventions=history) +a.output.mkdir(parents=True, exist_ok=False) +(a.output/'config.yaml').write_text(yaml.safe_dump(config.to_dict())) +(a.output/'intervention.json').write_text(json.dumps(change, indent=2) + '\n') +_atomic_save(s, a.output/'start.pt') +print(json.dumps(change), flush=True) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/change_splice_context.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/change_splice_context.py new file mode 100644 index 0000000000000000000000000000000000000000..1a6927a721cd0f49f08768666b710dcdab774bd7 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/change_splice_context.py @@ -0,0 +1,39 @@ +"""Create a controlled fork that changes only the splice context computation.""" +import argparse +import hashlib +import json +from pathlib import Path +import torch +import yaml + +from reference.alphagenome_50m.model import CompactModel, ModelConfig +from reference.alphagenome_50m.train_eval import _atomic_save + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + args = p.parse_args() + state = torch.load(args.checkpoint, map_location='cpu', weights_only=True) + assert state['schema'] == 'seqfunc-single/v1' and state['config']['local_width'] > 0 + state['config']['splice_context_mode'] = 'uniform' + config = ModelConfig(**state['config']) + model = CompactModel(config) + model.load_state_dict(state['model'], strict=True) + with args.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() + state['intervention'] = { + 'kind': 'uniform_splice_context', 'parent_sha256': digest, + 'step': state['global_step'], 'parameters_and_optimizer_unchanged': True, + 'computation': 'mean legacy offset weights into a per-class global bias; remove pooled local features from site context', + } + args.output.mkdir(parents=True, exist_ok=False) + (args.output/'config.yaml').write_text(yaml.safe_dump(config.to_dict())) + (args.output/'intervention.json').write_text(json.dumps(state['intervention'], indent=2)) + _atomic_save(state, args.output/'start.pt') + print(json.dumps(state['intervention']), flush=True) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/decoding_policy.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/decoding_policy.json new file mode 100644 index 0000000000000000000000000000000000000000..0cd25054c8aa9bbac84fcfa8063f85abe9790b7f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/decoding_policy.json @@ -0,0 +1,9 @@ +{ + "kind": "junction_transcript_direction", + "parent_package": "/workspace/research/experiments/scale13-large-epoch08/raw-package", + "weights_unchanged": true, + "plus": "donor < acceptor", + "minus": "donor > acceptor", + "inference_inputs": "model-selected coordinates and public track strand halves only", + "training_evidence": "64 training windows: 30,616 positive junction queries, all in transcript direction" +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/extend_local_context.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/extend_local_context.py new file mode 100644 index 0000000000000000000000000000000000000000..3651698e8a120b3308566d05b4523733fa621d48 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/extend_local_context.py @@ -0,0 +1,70 @@ +"""Extend our trained local path with identity-initialized dilated residual blocks.""" +import argparse +from dataclasses import replace +import hashlib +import json +from pathlib import Path +import torch +import yaml +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save + +def extend(state, blocks): + old_config = ModelConfig(**state['config']) + assert old_config.local_width and blocks > old_config.local_context_blocks + config = replace(old_config, local_context_blocks=blocks) + torch.manual_seed(state['seed']) + parent = CompactModel(old_config) + model = CompactModel(config) + missing = model.load_state_dict(state['model'], strict=False) + new_prefixes = tuple(f'local_path.blocks.{i}.' for i in range(old_config.local_context_blocks, blocks)) + assert not missing.unexpected_keys + assert all(k.startswith(new_prefixes) for k in missing.missing_keys) + for i in range(old_config.local_context_blocks, blocks): + torch.nn.init.zeros_(model.local_path.blocks[i].contract.weight) + torch.nn.init.zeros_(model.local_path.blocks[i].contract.bias) + old_names = list(dict(parent.named_parameters())) + new_names = list(dict(model.named_parameters())) + assert len(state['optimizer']['param_groups']) == 1 + group = state['optimizer']['param_groups'][0] + assert group['params'] == list(range(len(old_names))) + # New blocks shift following norm/head IDs; preserve moments by parameter name. + remap = {old_id: new_names.index(name) for old_id, name in enumerate(old_names)} + state['optimizer']['state'] = {remap[old_id]: values + for old_id, values in state['optimizer']['state'].items()} + group['params'] = list(range(len(new_names))) + for name, value in state['model'].items(): + assert torch.equal(model.state_dict()[name], value) + state.update(config=config.to_dict(), model=model.state_dict()) + return state, model, remap + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('checkpoint', type=Path) + p.add_argument('output', type=Path) + p.add_argument('--blocks', type=int, default=6) + a = p.parse_args() + state = torch.load(a.checkpoint, map_location='cpu', weights_only=True) + old_blocks = ModelConfig(**state['config']).local_context_blocks + state, model, remap = extend(state, a.blocks) + with a.checkpoint.open('rb') as f: + digest = hashlib.file_digest(f, 'sha256').hexdigest() + change = {'kind': 'extended_local_context', 'parent_sha256': digest, + 'step': state['global_step'], 'blocks_before': old_blocks, 'blocks_after': a.blocks, + 'receptive_field_bp': 21+8*sum(4**i for i in range(a.blocks)), + 'parent_parameters_optimizer_moments_rng_preserved': True, + 'new_blocks_initially_identity': True, 'optimizer_ids_remapped_by_parameter_name': True} + history = list(state.get('interventions', [])) + if state.get('intervention') and state['intervention'] not in history: + history.append(state['intervention']) + history.append(change) + state.update(intervention=change, interventions=history) + a.output.mkdir(parents=True, exist_ok=False) + (a.output/'config.yaml').write_text(yaml.safe_dump(state['config'])) + (a.output/'intervention.json').write_text(json.dumps(change, indent=2)+'\n') + (a.output/'optimizer_id_remap.json').write_text(json.dumps(remap, indent=2)+'\n') + _atomic_save(state, a.output/'start.pt') + print(json.dumps({'parameters': count_parameters(model), **change}), flush=True) + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/infer.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/infer.sh new file mode 100644 index 0000000000000000000000000000000000000000..2a412a9ad8667d6dcb94bb57d9959d61d43ddd34 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/infer.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: infer.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/predict.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/package_manifest.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/package_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1624a35457d62d5ff1c8db242350022a27baa184 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/package_manifest.json @@ -0,0 +1,79 @@ +{ + "parameters": 215626632, + "source_checkpoint_sha256": "181f13f7ab8b82826950afbafe1edc2a8bad78f3a02a9d4efaafff5c9c7ca537", + "files": { + "README.md": 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"1a13fcf3aaef93bf63eb258c72da5b13fa8acdf2ec040bd135e362b094340a96", + "validation_variants.json": "4803180c390f3cc66033f6a5a2641ea40025e30145237fa9b55598102fa41822" + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/perturb.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/perturb.py new file mode 100644 index 0000000000000000000000000000000000000000..b8ee571ad9dab90aded5077affbb42419869da16 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/perturb.py @@ -0,0 +1,92 @@ +"""Predict both sequences of every perturbation (contract: perturbations/v1). + + python perturb.py INPUT_ROOT OUTPUT_ROOT --checkpoint last.pt + +INPUT_ROOT holds `public/perturbations.parquet` and `reference/GRCh38.p13.genome.fa` (in the +workspace, `/workspace/data` works too: the queries are then read from `variant_evals/public/`). Each row is a +1,048,576-bp window starting at `input_start` and an edit spanning [edit_start, edit_end) in genomic +0-based coordinates: + +- `snv`: sequence A carries `allele_a` at edit_start, sequence B carries `allele_b` (both are written + explicitly; either may differ from the reference genome); +- `mask`: sequence A is the reference, sequence B has the interval replaced by N. + +For each perturbation the output group `perturbations/p{index:06d}` holds `a/` and +`b/` for every family in `families`: dense families sliced to bins +[dense_bin_start, dense_bin_end) of the central target window, and `splice_sites` sliced to bp +[splice_start, splice_end). Values are the same quantities as predictions.zarr (asinh space for +dense tracks, splice-site scores for splice_sites). +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +from numcodecs import Blosc +import numpy as np +import pandas as pd +import pysam +import torch +import zarr + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig +from rc_ensemble import predict_requested + +PERTURBATION_SCHEMA = "genome-sequence-to-function-perturbations/v1" +COMPRESSOR = Blosc(cname="zstd", clevel=3, shuffle=Blosc.BITSHUFFLE) + + +def sequences(fasta, row) -> tuple[str, str]: + length = fasta.get_reference_length(row.chrom) + start, end = int(row.input_start), int(row.input_start) + 1_048_576 + sequence = fasta.fetch(row.chrom, max(0, start), min(length, end)).upper() + sequence = "N" * max(0, -start) + sequence + "N" * max(0, end - length) + lo, hi = int(row.edit_start) - start, int(row.edit_end) - start + if row.edit == "snv": + return sequence[:lo] + row.allele_a + sequence[hi:], sequence[:lo] + row.allele_b + sequence[hi:] + if row.edit == "mask": + return sequence, sequence[:lo] + "N" * (hi - lo) + sequence[hi:] + raise ValueError(f"unknown edit {row.edit!r}") + + +def main(input_root: Path, output_root: Path, checkpoint: Path, queries: Path | None = None) -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config) + model.load_state_dict(payload["model"], strict=True) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device).eval() + if queries is None: + queries = input_root / "public/perturbations.parquet" + if not queries.exists(): + queries = input_root / "variant_evals/public/perturbations.parquet" + rows = pd.read_parquet(queries) + fasta = pysam.FastaFile(str(input_root / "reference/GRCh38.p13.genome.fa")) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "perturbations.zarr"), mode="w") + target_start = (config.input_length - config.target_length) // 2 + with torch.inference_mode(): + for index, row in enumerate(rows.itertuples(index=False)): + pair = sequences(fasta, row) + dna = torch.from_numpy(np.stack([AlphaGenomeDataset._one_hot(s) for s in pair])).to(device) + group = root.create_group(f"perturbations/p{index:06d}") + for family, values in predict_requested(model, dna, row).items(): + values = np.asarray(values, dtype=np.float32) + for side, array in zip("ab", values): + group.create_dataset(f"{side}/{family}", data=array, compressor=COMPRESSOR) + print(json.dumps({"event": "perturb", "done": index + 1, "total": len(rows)}), flush=True) + root.attrs.update({"schema": PERTURBATION_SCHEMA, + "perturbation_ids": rows.perturbation_id.astype(str).tolist()}) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) + parser.add_argument("input_root", type=Path) + parser.add_argument("output_root", type=Path) + parser.add_argument("--checkpoint", type=Path, default=Path(__file__).resolve().parent / "last.pt") + parser.add_argument("--queries", type=Path, help="another query table, e.g. the development split") + args = parser.parse_args() + main(args.input_root, args.output_root, args.checkpoint, args.queries) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/perturb.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/perturb.sh new file mode 100644 index 0000000000000000000000000000000000000000..f07c4714532c2d33e7a419de3877e5daacee89fd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/perturb.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +set -euo pipefail +[[ $# == 2 ]] || { echo "usage: perturb.sh INPUT_ROOT OUTPUT_ROOT" >&2; exit 2; } +package_dir=$(cd "$(dirname "$0")" && pwd) +runtime_dir=$(mktemp -d "${TMPDIR:-/tmp}/seqfunc-runtime.XXXXXXXX") +trap 'rm -rf "$runtime_dir"' EXIT +tar -xzf "$package_dir/runtime.tar.gz" -C "$runtime_dir" +unset PYTHONPATH +export PYTHONHOME="$runtime_dir" PYTHONNOUSERSITE=1 PYTHONDONTWRITEBYTECODE=1 +export LD_LIBRARY_PATH="$runtime_dir/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" +export OMP_NUM_THREADS=4 MKL_NUM_THREADS=4 OPENBLAS_NUM_THREADS=1 NUMEXPR_NUM_THREADS=4 +"$runtime_dir/bin/python" -s "$package_dir/perturb.py" "$1" "$2" --checkpoint "$package_dir/last.pt" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/predict.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..665fb186f3a62b1ff130abb034ac25a3a924c7c8 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/predict.py @@ -0,0 +1,52 @@ +"""Stream one shared-checkpoint forward/reverse-complement mean for every output family.""" +from __future__ import annotations +import argparse +import json +from pathlib import Path +from numcodecs import Blosc +import numpy as np +import torch +import zarr +from prediction_contract import PREDICTION_SCHEMA +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig +from rc_ensemble import engine_for + +COMPRESSOR = Blosc(cname="zstd", clevel=7, shuffle=Blosc.BITSHUFFLE) + + +def main(input_root: Path, output_root: Path, checkpoint: Path, split: str = "test") -> None: + payload = torch.load(checkpoint, map_location="cpu", weights_only=True) + model = CompactModel(ModelConfig(**payload['config'])) + model.load_state_dict(payload['model'], strict=True) + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + model.to(device).eval() + dataset = AlphaGenomeDataset(input_root, split, include_targets=False) + model.set_junction_track_halves(dataset.junction_track_halves) + engine = engine_for(model) + output_root.mkdir(parents=True, exist_ok=True) + root = zarr.open_group(str(output_root / "predictions.zarr"), mode="w") + ids = [] + with torch.inference_mode(): + for sample in dataset: + example_id = sample['example_id'] + values = engine.tracks(sample['dna'][None].to(device)) + group = root.create_group(f"examples/{example_id}") + for name, array in values.items(): + dtype = np.int8 if name == 'junction_half' else np.int32 if name in ( + 'splice_usage_positions', 'junction_donor', 'junction_acceptor') else np.float16 + group.create_dataset(name, data=array.astype(dtype), compressor=COMPRESSOR) + ids.append(example_id) + print(json.dumps({'event': 'predict', 'example': len(ids), 'total': len(dataset)}), flush=True) + root.attrs.update({'schema': PREDICTION_SCHEMA, 'example_ids': ids}) + (output_root/'example_ids.json').write_text(json.dumps(ids)+'\n') + + +if __name__ == '__main__': + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('input_root', type=Path) + p.add_argument('output_root', type=Path) + p.add_argument('--checkpoint', type=Path, default=Path(__file__).resolve().parent/'last.pt') + p.add_argument('--split', default='test', choices=['valid', 'test']) + a = p.parse_args() + main(a.input_root, a.output_root, a.checkpoint, a.split) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/prediction_contract.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/prediction_contract.py new file mode 100644 index 0000000000000000000000000000000000000000..2455424db002b840cf0d69456930840a7bc5f3cb --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/prediction_contract.py @@ -0,0 +1,23 @@ +"""Public prediction shapes; no trusted splice coordinates are exposed.""" + +from __future__ import annotations + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} + +PREDICTION_SCHEMA = "genome-sequence-to-function-predictions/v4" +MAX_USAGE_SITES = 4_096 +MAX_JUNCTION_PAIRS = 16_384 +# Fixed-shape arrays per example; splice usage and junctions are sparse (see contract.md). +FIXED_SHAPES = { + **{name: (1_536, tracks) for name, tracks in DENSE_TRACKS.items()}, + "splice_sites": (196_608, 4), + "contact_maps": (131_328, 28), +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/rc_ensemble.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/rc_ensemble.py new file mode 100644 index 0000000000000000000000000000000000000000..f076804c66e481eb823e6ca5e721f39fce50bad5 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/rc_ensemble.py @@ -0,0 +1,111 @@ +"""One shared checkpoint evaluated on DNA and its reverse complement for every output.""" +import json +from pathlib import Path +import numpy as np +import torch +from prediction_contract import DENSE_TRACKS, MAX_JUNCTION_PAIRS, MAX_USAGE_SITES +from single_prediction import single_prediction +from splice_output import splice_output_scores + + +def contact_reversal(bins): + row, column = np.triu_indices(bins) + lookup = np.empty((bins, bins), dtype=np.int64) + lookup[row, column] = np.arange(len(row)) + lookup[column, row] = np.arange(len(row)) + return lookup[bins-1-row, bins-1-column] + + +def reverse_readout(arrays, permutations, input_length, target_length, contact_bins): + """Map a reverse-complement prediction back into the original genomic coordinates.""" + out = {name: arrays[name][::-1, permutations[name]].copy() for name in DENSE_TRACKS} + out['splice_sites'] = arrays['splice_sites'][::-1, permutations['splice_sites']].copy() + positions = target_length-1-arrays['splice_usage_positions'] + order = np.argsort(positions) + out['splice_usage_positions'] = positions[order].astype(np.int32) + out['splice_usage'] = arrays['splice_usage'][order][:, permutations['splice_usage']].copy() + out['junction_half'] = (1-arrays['junction_half']).astype(np.int8) + for key in ['junction_donor', 'junction_acceptor']: + out[key] = (input_length-1-arrays[key]).astype(np.int32) + out['junctions'] = arrays['junctions'][:, permutations['junctions']].copy() + out['contact_maps'] = arrays['contact_maps'][contact_reversal(contact_bins)][:, permutations['contact_maps']].copy() + return out + + +def combine_readouts(a, b, track_halves): + """Average sparse functions with zero at unlisted coordinates, then enforce output budgets.""" + out = {name: (a[name]+b[name])*np.float32(.5) + for name in [*DENSE_TRACKS, 'splice_sites', 'contact_maps']} + usage_positions, inverse = np.unique(np.concatenate([a['splice_usage_positions'], b['splice_usage_positions']]), return_inverse=True) + assert len(usage_positions) <= MAX_USAGE_SITES + usage = np.zeros((len(usage_positions), a['splice_usage'].shape[-1]), dtype=np.float32) + np.add.at(usage, inverse, np.concatenate([a['splice_usage'], b['splice_usage']])*np.float32(.5)) + out.update(splice_usage_positions=usage_positions.astype(np.int32), splice_usage=usage) + keys = np.concatenate([np.stack([x['junction_half'], x['junction_donor'], x['junction_acceptor']], axis=1) for x in [a, b]]) + keys, inverse = np.unique(keys, axis=0, return_inverse=True) + values = np.zeros((len(keys), len(track_halves)), dtype=np.float32) + np.add.at(values, inverse, np.concatenate([a['junctions'], b['junctions']])*np.float32(.5)) + strength = np.empty(len(keys), dtype=np.float32) + for half in [0, 1]: + keep = keys[:, 0] == half + strength[keep] = values[keep][:, track_halves == half].max(axis=1) + # Stable sorting makes ties deterministic in the canonical coordinate order. + order = np.argsort(-strength, kind='stable')[:MAX_JUNCTION_PAIRS] + keys, values = keys[order], values[order] + out.update(junction_half=keys[:, 0].astype(np.int8), + junction_donor=keys[:, 1].astype(np.int32), + junction_acceptor=keys[:, 2].astype(np.int32), junctions=values) + return out + + +class ReverseComplementMean: + def __init__(self, model): + self.model = model + data = json.loads(Path(__file__).with_name('strand_permutations.json').read_text()) + self.permutations = {name: np.asarray(values, dtype=np.int64) for name, values in data['permutations'].items()} + for name, indices in self.permutations.items(): + assert np.array_equal(indices[indices], np.arange(len(indices))), name + + def tracks(self, dna): + assert len(dna) == 1 + config = self.model.config + original = single_prediction(self.model, dna) + reverse = single_prediction(self.model, dna.flip((1, 2))) + reverse = reverse_readout(reverse, self.permutations, config.input_length, config.target_length, config.contact_bins) + return combine_readouts(original, reverse, self.model.junction_track_half.cpu().numpy()) + + def dense_and_sites(self, dna, families): + """Return central readouts for both alleles; this same probability/asinh averaging defines tracks.""" + config = self.model.config + start = (config.input_length-config.target_length)//2 + predictions = [] + for reverse in [False, True]: + trunk = self.model._trunk(dna.flip((1, 2)) if reverse else dna) + features = None + values = {} + for family in families: + if family == 'splice_sites': + logits = self.model._splice_site_logits(trunk)[:, start:start+config.target_length] + array = splice_output_scores(logits).float().cpu().numpy() + else: + if features is None: + features = self.model._dense_features(trunk) + array = self.model.dense_heads[family](features).float().cpu().numpy() + if reverse: + array = array[:, ::-1][:, :, self.permutations[family]].copy() + values[family] = array + predictions.append(values) + return {family: (predictions[0][family]+predictions[1][family])*np.float32(.5) for family in families} + + +def engine_for(model): + if not hasattr(model, '_rc_prediction_engine'): + # Plain helper, not a child nn.Module; the backbone state dictionary stays unchanged. + model._rc_prediction_engine = ReverseComplementMean(model) + return model._rc_prediction_engine + + +def predict_requested(model, dna, row): + values = engine_for(model).dense_and_sites(dna, row.families) + return {family: array[:, row.splice_start:row.splice_end] if family == 'splice_sites' + else array[:, row.dense_bin_start:row.dense_bin_end] for family, array in values.items()} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/rc_ensemble_policy.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/rc_ensemble_policy.json new file mode 100644 index 0000000000000000000000000000000000000000..e884027651302fbdddc33de84f5f2bdaca17ef27 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/rc_ensemble_policy.json @@ -0,0 +1,35 @@ +{ + "method": "Equal mean of the same checkpoint evaluated on DNA and its reverse complement, mapped to original positions and public-metadata strand channels.", + "orientation_views": 2, + "fitted_ensemble_weights": false, + "weights": [ + 0.5, + 0.5 + ], + "dna_input": "Same provided 1,048,576bp, reversed and complemented; no extra flanking sequence.", + "site_scores": "Apply the existing training-prior probability link per orientation, then average probabilities.", + "sparse_outputs": "Each orientation chooses its own positions/pairs. Average the union with zero for unlisted coordinates; retain up to16,384 junctions by averaged predicted strength. Usage union has at most4,096 coordinates.", + "outputs": "All families and both entry points use the same two-view rule. No labels or targets used at inference.", + "created_utc": "2026-10-26T07:36:00.650275+00:00", + "parent_package": "/run-state/workspace/research/experiments/calib81-large-epoch08/package", + "parent_inference_signature": { + "last.pt": "91f3382e440a1c61731b090dcf04d4de98d706f7c2f7ce956e1ade752e6c57eb", + "predict.py": "5f5507be4efce5af3167616de01c11da89fa4fe2b2c59dbdac0152e1a84d1348", + "perturb.py": "9defff26b93513ebe681c31485b75730aac911d9c0f9983f2c7aa615a01b67f8", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/alphagenome_50m/model.py": "c61f50a2a66a4f6950c6f6107a70710a5d0ab99a291b8bfaa1eb5565998d2fa3", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919", + "splice_output.py": "e1b0627e036c60190e4d2a4653a915d9031b166ff3c1de7c23e5774009d70ef0", + "splice_calibration.json": "bd9368b32e739be65ca290d598ffcae5be6387c09f96048e183c57b7d570e641" + }, + "unique_learned_parameters": 215626632, + "decoder_study": "/run-state/workspace/research/studies/rc17", + "decoder_assets_sha256": { + "predict.py": "4f5dbddf5b6b701079aeaf38920fe414ce980f7161997f4d5c80952a6d834e51", + "perturb.py": "49065dc43816920ecac9de3ebcc4735673e4ff6594cf3f6bd34233b88c9bed73", + "rc_ensemble.py": "84a4d0e94ab86622e2c25e96f8835b2ef2bba77731ff7ff0bdc1c2de02cdcef4", + "single_prediction.py": "10f6216fdd72bf30b3a28ab6de9363e66b415526aa26a2bbfb6a84badbb67bec", + "strand_permutations.json": "afb0f0cf2e897fab3493de66601d7a66dac45a06316c2ac4a6cc828298820c60" + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4879a0e1a3a8a8050bb48360febee152cd141be8 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/__init__.py @@ -0,0 +1 @@ +"""The reference model of the alphagenome-reference baseline.""" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/__init__.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a86a139babb6e899bd5102bbf3c628e1232576b2 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/__init__.py @@ -0,0 +1,5 @@ +"""Compact 1-Mb, eleven-family AlphaGenome reference model.""" + +from .model import CompactModel, ModelConfig, count_parameters + +__all__ = ["CompactModel", "ModelConfig", "count_parameters"] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/alphagenome_50m.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/alphagenome_50m.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0a59a4d0c65752d45eb464d129c6ec9d559665e9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/alphagenome_50m.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 32 +channel_growth: 32 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 384 +num_sequence_blocks: 6 +num_attention_heads: 6 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 256 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/cached_read_store.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/cached_read_store.py new file mode 100644 index 0000000000000000000000000000000000000000..9c3677c1e8518ea97db0dd6759f3d636f5fff27c --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/cached_read_store.py @@ -0,0 +1,71 @@ +"""Optional local byte cache for immutable training Zarr stores. + +Source storage stays read-only. Cached entries contain a key fingerprint, length, +and SHA256 of the original compressed bytes; every hit verifies all three. Writes +replace a private temporary inode atomically. A missing/corrupt/unwritable cache +falls back to the original store, so this layer changes neither values nor keys. +Only training loaders may opt into it; it is not an inference asset. +""" +import hashlib +import os +from pathlib import Path +import struct +import tempfile + +from .direct_read_store import ParallelDirectReadDirectoryStore + +MAGIC = b'SFBC0001' +HEADER = struct.Struct('<8sQ32s32s') + + +class CachedReadDirectoryStore(ParallelDirectReadDirectoryStore): + def __init__(self, path, *, cache_root, namespace, io_workers=4): + super().__init__(path, io_workers=io_workers) + if not namespace: + raise ValueError('An immutable input namespace is required') + self.cache_root = Path(cache_root) + self.namespace = namespace + self.store_key = hashlib.sha256((namespace+'\0'+os.path.abspath(path)).encode()).digest() + self.statistics = {'hit': 0, 'miss': 0, 'corrupt': 0, 'write_failure': 0} + + def cache_location(self, key): + key = self._normalize_key(key) + identity = hashlib.sha256(self.store_key+key.encode()).digest() + name = identity.hex() + return self.cache_root/name[:2]/name[2:], identity + + def __getitem__(self, key): + key = self._normalize_key(key) + destination, identity = self.cache_location(key) + try: + blob = destination.read_bytes() + if len(blob) >= HEADER.size: + magic, size, cached_key, checksum = HEADER.unpack_from(blob) + payload = blob[HEADER.size:] + if (magic == MAGIC and size == len(payload) and cached_key == identity + and hashlib.sha256(payload).digest() == checksum): + self.statistics['hit'] += 1 + return payload + self.statistics['corrupt'] += 1 + except OSError: + pass + self.statistics['miss'] += 1 + payload = super().__getitem__(key) + temporary = None + try: + destination.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile(prefix='.pending-', dir=destination.parent, delete=False) as stream: + temporary = stream.name + stream.write(HEADER.pack(MAGIC, len(payload), identity, hashlib.sha256(payload).digest())) + stream.write(payload) + os.replace(temporary, destination) + temporary = None + except OSError: + self.statistics['write_failure'] += 1 + finally: + if temporary is not None: + try: + os.unlink(temporary) + except OSError: + pass + return payload diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/direct_read_store.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/direct_read_store.py new file mode 100644 index 0000000000000000000000000000000000000000..c560febc6a15909a523c8e2f3d1f861aeec309a7 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/direct_read_store.py @@ -0,0 +1,64 @@ +"""Read-only Zarr directory store avoiding redundant per-chunk stat calls.""" +import os +from concurrent.futures import ThreadPoolExecutor + +from zarr.storage import DirectoryStore + +_pools = {} + + +def process_pool(workers): + key = (os.getpid(), workers) + if key not in _pools: + _pools[key] = ThreadPoolExecutor(max_workers=workers, thread_name_prefix='zarr-read') + return _pools[key] + + +class DirectReadDirectoryStore(DirectoryStore): + def __getitem__(self, key): + key = self._normalize_key(key) + try: + return self._fromfile(os.path.join(self.path, key)) + except (FileNotFoundError, IsADirectoryError, NotADirectoryError): + raise KeyError(key) from None + + def getitems(self, keys, *, contexts): + result = {} + for key in keys: + try: + result[key] = self[key] + except KeyError: + pass + return result + + def __setitem__(self, key, value): + raise PermissionError('DirectReadDirectoryStore is read-only') + + def __delitem__(self, key): + raise PermissionError('DirectReadDirectoryStore is read-only') + + def is_writeable(self): + return False + + def is_erasable(self): + return False + + +class ParallelDirectReadDirectoryStore(DirectReadDirectoryStore): + """Bounded I/O threads shared by all read stores within one loader process.""" + def __init__(self, path, *, io_workers=4): + super().__init__(path) + if io_workers < 1: + raise ValueError('io_workers must be positive') + self.io_workers = io_workers + + def getitems(self, keys, *, contexts): + def fetch(key): + try: + return key, self[key] + except KeyError: + return key, None + if self.io_workers == 1: + return super().getitems(keys, contexts=contexts) + return {key: value for key, value in process_pool(self.io_workers).map(fetch, keys) + if value is not None} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/layers.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..7ef7b4e45a81eb774e4e92ac36e30c5abdebbe46 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/layers.py @@ -0,0 +1,134 @@ +"""Shared neural-network layers for the single compact AlphaGenome model.""" + +from __future__ import annotations + +import math + +import torch +from torch import Tensor, nn +from torch.nn import functional as F + + +class ChannelRMSNorm(nn.Module): + def __init__(self, channels: int, eps: float = 1e-6): + super().__init__() + self.weight = nn.Parameter(torch.ones(channels)) + self.eps = eps + + def forward(self, x: Tensor) -> Tensor: + variance = x.float().square().mean(dim=1, keepdim=True) + normalized = x * torch.rsqrt(variance + self.eps).to(x.dtype) + return normalized * self.weight[None, :, None].to(x.dtype) + + +class StandardizedConv1d(nn.Conv1d): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__(in_channels, out_channels, kernel_size, padding="same", bias=True) + self.scale = nn.Parameter(torch.ones(out_channels)) + nn.init.trunc_normal_(self.weight, std=1.0 / math.sqrt(in_channels * kernel_size)) + nn.init.trunc_normal_(self.bias, std=1e-4) + + def forward(self, x: Tensor) -> Tensor: + centered = self.weight - self.weight.mean(dim=(1, 2), keepdim=True) + variance = centered.float().square().mean(dim=(1, 2), keepdim=True) + fan_in = self.weight.shape[1] * self.weight.shape[2] + multiplier = self.scale[:, None, None] * torch.rsqrt( + (fan_in * variance).clamp_min(1e-4) + ).to(self.weight.dtype) + return F.conv1d(x, centered * multiplier, self.bias, padding="same") + + +class ConvBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int, kernel_size: int): + super().__init__() + self.norm = ChannelRMSNorm(in_channels) + self.conv = StandardizedConv1d(in_channels, out_channels, kernel_size) + + def forward(self, x: Tensor) -> Tensor: + return self.conv(F.gelu(self.norm(x), approximate="tanh")) + + +def _apply_rope(x: Tensor) -> Tensor: + rotary_dim = x.shape[-1] - x.shape[-1] % 2 + if rotary_dim == 0: + return x + positions = torch.arange(x.shape[-2], device=x.device, dtype=torch.float32) + inverse_frequency = 1.0 / ( + 10_000 ** ( + torch.arange(0, rotary_dim, 2, device=x.device, dtype=torch.float32) + / rotary_dim + ) + ) + angles = torch.outer(positions, inverse_frequency) + cos = angles.cos()[None, None, :, :].to(x.dtype) + sin = angles.sin()[None, None, :, :].to(x.dtype) + even, odd = x[..., :rotary_dim:2], x[..., 1:rotary_dim:2] + rotated = torch.stack((even * cos - odd * sin, even * sin + odd * cos), dim=-1) + return torch.cat((rotated.flatten(-2), x[..., rotary_dim:]), dim=-1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.num_heads = config.num_attention_heads + self.head_dim = config.attention_head_dim + inner_dim = self.num_heads * self.head_dim + self.norm = nn.RMSNorm(config.hidden_dim) + self.qkv = nn.Linear(config.hidden_dim, 3 * inner_dim, bias=False) + self.output = nn.Linear(inner_dim, config.hidden_dim) + self.dropout = config.dropout + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + batch, length, _ = x.shape + qkv = self.qkv(self.norm(x)).view( + batch, length, 3, self.num_heads, self.head_dim + ) + q, k, value = qkv.unbind(dim=2) + attended = F.scaled_dot_product_attention( + _apply_rope(q.transpose(1, 2)), + _apply_rope(k.transpose(1, 2)), + value.transpose(1, 2), + attn_mask=attention_bias, + dropout_p=self.dropout if self.training else 0.0, + ) + return self.output(attended.transpose(1, 2).reshape(batch, length, -1)) + + +class MLPBlock(nn.Module): + def __init__(self, config): + super().__init__() + expanded = int(config.hidden_dim * config.mlp_expansion) + self.norm = nn.RMSNorm(config.hidden_dim) + self.input = nn.Linear(config.hidden_dim, expanded) + self.output = nn.Linear(expanded, config.hidden_dim) + self.dropout = nn.Dropout(config.dropout) + nn.init.trunc_normal_(self.output.weight, std=1e-3) + nn.init.zeros_(self.output.bias) + + def forward(self, x: Tensor) -> Tensor: + return self.dropout( + self.output(F.gelu(self.input(self.norm(x)), approximate="tanh")) + ) + + +def bound_residual_rms(update: Tensor, cap: float) -> Tensor: + """Cap each token's branch RMS, preserving small updates and their directions.""" + if cap == 0: + return update + rms = update.float().square().mean(dim=-1, keepdim=True).clamp_min(1e-12).sqrt() + scale = (cap / rms).clamp(max=1.0) + return update * scale.to(update.dtype) + + +class SequenceBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.attention = MultiHeadAttention(config) + self.mlp = MLPBlock(config) + self.residual_rms_cap = config.residual_rms_cap + + def forward(self, x: Tensor, attention_bias: Tensor | None = None) -> Tensor: + x = x + bound_residual_rms(self.attention(x, attention_bias), self.residual_rms_cap) + return x + bound_residual_rms(self.mlp(x), self.residual_rms_cap) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/model.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/model.py new file mode 100644 index 0000000000000000000000000000000000000000..6f211fc29de3fa559db45ce6c2ee3d40816f4a72 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/model.py @@ -0,0 +1,460 @@ +"""Affordable multi-resolution baseline for the compact AlphaGenome task.""" + +from __future__ import annotations + +from dataclasses import asdict, dataclass, fields +import math +from pathlib import Path +from typing import Any, Iterable + +import torch +from torch import Tensor, nn +from torch.nn import functional as F +from torch.utils.checkpoint import checkpoint +import yaml + +from .layers import ChannelRMSNorm, ConvBlock, SequenceBlock + + +DENSE_TRACKS = { + "atac": 167, + "dnase": 305, + "procap": 12, + "cage": 546, + "rna_seq": 667, + "chip_tf": 1_617, + "chip_histone": 1_116, +} +ALL_FAMILIES = (*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions", "contact_maps") + + +@dataclass(frozen=True) +class ModelConfig: + input_length: int = 1_048_576 + target_length: int = 196_608 + dense_bin_size: int = 128 + trunk_bin_size: int = 1_024 + contact_bin_size: int = 2_048 + stem_stride: int = 8 + encoder_width: int = 32 + channel_growth: int = 32 + num_downsampling_blocks: int = 7 + conv_kernel_size: int = 5 + dna_kernel_size: int = 15 + hidden_dim: int = 384 + num_sequence_blocks: int = 6 + num_attention_heads: int = 6 + attention_head_dim: int = 64 + mlp_expansion: float = 4.0 + dropout: float = 0.0 + head_hidden_size: int = 256 + junction_rank: int = 32 + splice_usage_tracks: int = 734 + junction_tracks: int = 734 + contact_tracks: int = 28 + gradient_checkpointing: bool = True + enable_pairwise: bool = False + pair_width: int = 32 + pair_downsample: int = 16 + pair_update_interval: int = 2 + local_width: int = 0 + local_context_blocks: int = 4 + splice_context_mode: str = 'offset' + local_to_trunk: bool = False + residual_rms_cap: float = 0.0 + + def __post_init__(self) -> None: + if not 1 <= self.local_context_blocks <= 7: + raise ValueError('local_context_blocks must be between 1 and 7') + if not math.isfinite(self.residual_rms_cap) or self.residual_rms_cap < 0: + raise ValueError('residual_rms_cap must be finite and nonnegative') + if self.local_to_trunk and not self.local_width: + raise ValueError('local_to_trunk requires local features') + if self.splice_context_mode not in ('offset', 'uniform'): + raise ValueError('unknown splice_context_mode') + if self.splice_context_mode == 'uniform' and not self.local_width: + raise ValueError('uniform splice context requires a base-resolution path') + downsample = self.stem_stride * 2**self.num_downsampling_blocks + if downsample != self.trunk_bin_size: + raise ValueError("stem_stride * 2**num_downsampling_blocks must equal trunk_bin_size") + for length in (self.input_length, self.target_length): + if length % self.trunk_bin_size: + raise ValueError("input and target lengths must divide by trunk_bin_size") + if self.trunk_bin_size % self.dense_bin_size: + raise ValueError("trunk_bin_size must divide by dense_bin_size") + if self.input_length % self.contact_bin_size: + raise ValueError("input_length must divide by contact_bin_size") + if self.hidden_dim <= 0 or self.num_attention_heads <= 0: + raise ValueError("hidden dimensions and attention heads must be positive") + + @property + def trunk_bins(self) -> int: + return self.input_length // self.trunk_bin_size + + @property + def target_trunk_bins(self) -> int: + return self.target_length // self.trunk_bin_size + + @property + def dense_bins(self) -> int: + return self.target_length // self.dense_bin_size + + @property + def dense_upsample(self) -> int: + return self.trunk_bin_size // self.dense_bin_size + + @property + def contact_bins(self) -> int: + return self.input_length // self.contact_bin_size + + @property + def encoder_output_width(self) -> int: + return self.encoder_width + self.channel_growth * self.num_downsampling_blocks + + @classmethod + def from_yaml(cls, path: str | Path) -> "ModelConfig": + values: dict[str, Any] = yaml.safe_load(Path(path).read_text()) or {} + known = {field.name for field in fields(cls)} + unknown = sorted(values.keys() - known) + if unknown: + raise ValueError(f"Unknown model configuration keys: {unknown}") + return cls(**values) + + def to_dict(self) -> dict[str, Any]: + return asdict(self) + + +class SequenceEncoder(nn.Module): + def __init__(self, config: ModelConfig): + super().__init__() + self.stem = nn.Conv1d( + 4, + config.encoder_width, + config.dna_kernel_size, + stride=config.stem_stride, + padding=config.dna_kernel_size // 2, + ) + blocks = [] + width = config.encoder_width + for _ in range(config.num_downsampling_blocks): + output_width = width + config.channel_growth + blocks.append(ConvBlock(width, output_width, config.conv_kernel_size)) + width = output_width + self.blocks = nn.ModuleList(blocks) + + def forward(self, dna: Tensor) -> Tensor: + x = F.gelu(self.stem(dna.transpose(1, 2)), approximate="tanh") + for block in self.blocks: + x = F.max_pool1d(block(x), kernel_size=2, stride=2) + return x.transpose(1, 2) + + +class LocalResidualBlock(nn.Module): + def __init__(self, width: int, dilation: int): + super().__init__() + self.norm = ChannelRMSNorm(width) + self.depthwise = nn.Conv1d(width, width, 9, dilation=dilation, + padding=4 * dilation, groups=width) + self.expand = nn.Conv1d(width, 2 * width, 1) + self.contract = nn.Conv1d(2 * width, width, 1) + + def forward(self, x: Tensor) -> Tensor: + update = self.depthwise(self.norm(x)) + update = self.contract(F.gelu(self.expand(update), approximate='tanh')) + return x + update + + +class LocalSequencePath(nn.Module): + """Base-resolution motif path with powers-of-four dilation and aligned pooling.""" + def __init__(self, config: ModelConfig): + super().__init__() + width = config.local_width + self.stem = nn.Conv1d(4, width, 21, padding=10) + self.blocks = nn.Sequential(*(LocalResidualBlock(width, 4**i) + for i in range(config.local_context_blocks))) + self.norm = ChannelRMSNorm(width) + self.sites = nn.Conv1d(width, 4, 1) + self.bin_size = config.dense_bin_size + self.gradient_checkpointing = config.gradient_checkpointing + + def forward(self, dna: Tensor) -> tuple[Tensor, Tensor]: + base = F.gelu(self.stem(dna), approximate='tanh') + for block in self.blocks: + if self.gradient_checkpointing and self.training: + base = checkpoint(block, base, use_reentrant=False) + else: + base = block(base) + base = F.gelu(self.norm(base), approximate='tanh') + sites = self.sites(base).transpose(1, 2) + pooled = torch.cat((F.avg_pool1d(base, self.bin_size), + F.max_pool1d(base, self.bin_size)), dim=1).transpose(1, 2) + return sites, pooled + + +class CompactModel(nn.Module): + """One shared 1-kb trunk with representation-specific output heads.""" + + def __init__(self, config: ModelConfig | None = None): + super().__init__() + self.config = config or ModelConfig() + self.encoder = SequenceEncoder(self.config) + self.projection = nn.Linear(self.config.encoder_output_width, self.config.hidden_dim) + self.blocks = nn.ModuleList( + SequenceBlock(self.config) for _ in range(self.config.num_sequence_blocks) + ) + self.final_norm = nn.RMSNorm(self.config.hidden_dim) + self.dense_decoder = nn.Sequential( + nn.RMSNorm(self.config.hidden_dim), + nn.Linear(self.config.hidden_dim, self.config.head_hidden_size), + nn.GELU(approximate="tanh"), + ) + self.dense_heads = nn.ModuleDict( + { + family: nn.Linear(self.config.head_hidden_size, tracks) + for family, tracks in DENSE_TRACKS.items() + } + ) + self.splice_site_head = nn.Linear( + self.config.head_hidden_size, self.config.dense_bin_size * 4 + ) + self.splice_usage_head = nn.Linear( + self.config.head_hidden_size, self.config.splice_usage_tracks + ) + rank = self.config.junction_rank + self.junction_source = nn.Linear(self.config.head_hidden_size, rank) + self.junction_destination = nn.Linear(self.config.head_hidden_size, rank) + self.junction_track = nn.Embedding(self.config.junction_tracks, rank) + if self.config.junction_tracks % 2: + raise ValueError("junction tracks must split evenly into strand halves") + self.register_buffer( + "junction_track_half", + torch.arange(self.config.junction_tracks) // (self.config.junction_tracks // 2), + persistent=False, + ) + self.contact_q = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + self.contact_k = nn.Linear(self.config.hidden_dim, self.config.contact_tracks) + row, column = torch.triu_indices( + self.config.contact_bins, self.config.contact_bins + ) + self.register_buffer("contact_row", row, persistent=False) + self.register_buffer("contact_column", column, persistent=False) + if self.config.local_width: + self.local_path = LocalSequencePath(self.config) + self.local_projection = nn.Linear(2 * self.config.local_width, self.config.head_hidden_size) + # A new local path can be attached to our own checkpoint without changing its initial outputs. + nn.init.zeros_(self.local_path.sites.weight) + nn.init.zeros_(self.local_path.sites.bias) + nn.init.zeros_(self.local_projection.weight) + nn.init.zeros_(self.local_projection.bias) + if self.config.local_to_trunk: + self.local_trunk_projection = nn.Linear(2 * self.config.local_width, self.config.hidden_dim) + nn.init.zeros_(self.local_trunk_projection.weight) + nn.init.zeros_(self.local_trunk_projection.bias) + self._local_sites = None + self._local_features = None + + def set_junction_track_halves(self, halves: Tensor) -> None: + halves = torch.as_tensor(halves, dtype=torch.long, device=self.junction_track_half.device) + if halves.shape != (self.config.junction_tracks,) or not torch.isin( + halves, torch.tensor([0, 1], device=halves.device) + ).all(): + raise ValueError("junction track halves must be a 734-element 0/1 mapping") + self.junction_track_half.copy_(halves) + + def _trunk(self, dna: Tensor) -> Tensor: + if dna.ndim != 3 or dna.shape[1:] != (self.config.input_length, 4): + raise ValueError( + f"Expected DNA [B,{self.config.input_length},4], got {tuple(dna.shape)}" + ) + if self.config.local_width: + sites, pooled = self.local_path(dna.transpose(1, 2)) + self._local_sites = sites + self._local_features = self.local_projection(pooled) + x = self.projection(self.encoder(dna)) + if self.config.local_to_trunk: + local_tokens = pooled.reshape(len(dna), self.config.trunk_bins, + self.config.dense_upsample, -1).mean(dim=2) + x = x + self.local_trunk_projection(local_tokens) + for block in self.blocks: + if self.config.gradient_checkpointing and self.training and x.requires_grad: + x = checkpoint(block, x, None, use_reentrant=False) + else: + x = block(x, None) + return self.final_norm(x) + + def _dense_features(self, trunk: Tensor) -> Tensor: + start = (self.config.trunk_bins - self.config.target_trunk_bins) // 2 + target = trunk[:, start : start + self.config.target_trunk_bins] + target = target.repeat_interleave(self.config.dense_upsample, dim=1) + features = self.dense_decoder(target) + if self.config.local_width: + first_bin = (self.config.input_length - self.config.target_length) // (2 * self.config.dense_bin_size) + features = features + self._local_features[:, first_bin:first_bin + self.config.dense_bins] + return features + + def _all_dense_features(self, trunk: Tensor) -> Tensor: + features = self.dense_decoder( + trunk.repeat_interleave(self.config.dense_upsample, dim=1) + ) + return features + self._local_features if self.config.local_width else features + + def _splice_site_logits(self, trunk: Tensor) -> Tensor: + if self.config.splice_context_mode == 'uniform': + # The global trunk supplies one gene-context bias per class and kb. Base-resolution + # localization comes from the local path, with no arbitrary 128-bp offset decoder + # and no pooled dense-feature contribution to site logits. + context = self.splice_site_head(self.dense_decoder(trunk)) + context = context.view(len(trunk), self.config.trunk_bins, self.config.dense_bin_size, 4).mean(dim=2) + logits = context.repeat_interleave(self.config.trunk_bin_size, dim=1) + self._local_sites + return logits.float() + logits = self.splice_site_head(self._all_dense_features(trunk)) + logits = logits.view(len(trunk), self.config.input_length, 4) + if self.config.local_width: + logits = logits + self._local_sites + return logits.float() + + def _position_features(self, trunk: Tensor, positions: Tensor) -> Tensor: + features = self.dense_decoder(self._gather_bins(trunk, positions, self.config.trunk_bin_size)) + if self.config.local_width: + features = features + self._gather_bins(self._local_features, positions, self.config.dense_bin_size) + return features + + @staticmethod + def select_junction_positions(site_logits: Tensor, limit: int = 512) -> Tensor: + """Select each site type from model scores alone, then sort by coordinate. + + This follows the four independent site classes in Google DeepMind's + AlphaGenome splicing implementation (pinned in docs/v3-author/PROTOCOL.md). Padding is + -1 when an input is shorter than ``limit``. + """ + count = min(limit, site_logits.shape[1]) + selected = [] + for site_type in range(4): + coordinates = torch.topk(site_logits[..., site_type], count, dim=1).indices + coordinates = coordinates.sort(dim=1).values + if count < limit: + coordinates = F.pad(coordinates, (0, limit - count), value=-1) + selected.append(coordinates) + return torch.stack(selected, dim=1) + + @staticmethod + def _gather_bins(features: Tensor, positions: Tensor, bin_size: int) -> Tensor: + indices = torch.div(positions, bin_size, rounding_mode="floor") + indices = indices.clamp(0, features.shape[1] - 1) + return torch.gather( + features, + 1, + indices[..., None].expand(-1, -1, features.shape[-1]), + ) + + def _junction_predictions( + self, trunk: Tensor, site_positions: Tensor, queries: Tensor + ) -> Tensor: + row, column, track = queries.unbind(dim=-1) + half = self.junction_track_half[track] + batch = torch.arange(len(trunk), device=trunk.device)[:, None] + donor = site_positions[batch, 2 * half, row] + acceptor = site_positions[batch, 2 * half + 1, column] + source = self._position_features(trunk, donor.clamp(0, self.config.input_length - 1)) + destination = self._position_features(trunk, acceptor.clamp(0, self.config.input_length - 1)) + interaction = self.junction_source(source) * self.junction_destination(destination) + track_embedding = self.junction_track(track) + return (interaction * track_embedding).sum(dim=-1) / math.sqrt( + self.config.junction_rank + ) + + def junction_block( + self, + trunk: Tensor, + site_positions: Tensor, + row_start: int, + row_end: int, + column_start: int, + column_end: int, + track_start: int, + track_end: int, + site_factors: tuple[Tensor, Tensor] | None = None, + ) -> Tensor: + """Compute a bounded block of the full predicted-position junction cube.""" + tracks = torch.arange(track_start, track_end, device=trunk.device) + halves = self.junction_track_half[tracks] + if site_factors is None: + site_factors = self.junction_site_factors(trunk, site_positions) + source_all, destination_all = site_factors + source = source_all[:, halves, row_start:row_end] + destination = destination_all[:, halves, column_start:column_end] + embedding = self.junction_track(tracks)[None, :, None, None, :] + values = ( + source[:, :, :, None, :] * destination[:, :, None, :, :] * embedding + ).sum(dim=-1) / math.sqrt(self.config.junction_rank) + return values.permute(0, 2, 3, 1).float() + + def junction_site_factors( + self, trunk: Tensor, site_positions: Tensor + ) -> tuple[Tensor, Tensor]: + """Precompute the two strand-specific factors reused by all cube blocks.""" + donor = torch.stack((site_positions[:, 0], site_positions[:, 2]), dim=1).clamp_min(0) + acceptor = torch.stack((site_positions[:, 1], site_positions[:, 3]), dim=1).clamp_min(0) + donor_features = self._position_features(trunk, donor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, donor.shape[-1], -1) + acceptor_features = self._position_features(trunk, acceptor.reshape(len(trunk), -1)).reshape( + len(trunk), 2, acceptor.shape[-1], -1) + return self.junction_source(donor_features), self.junction_destination(acceptor_features) + + def forward( + self, + dna: Tensor, + *, + families: Iterable[str] | None = None, + usage_positions: Tensor | None = None, + junction_site_positions: Tensor | None = None, + junction_queries: Tensor | None = None, + ) -> dict[str, Tensor]: + requested = set(families or ALL_FAMILIES) + unknown = requested - set(ALL_FAMILIES) + if unknown: + raise ValueError(f"Unknown output families: {sorted(unknown)}") + trunk = self._trunk(dna) + needs_dense = bool( + requested.intersection((*DENSE_TRACKS, "splice_sites", "splice_usage", "junctions")) + ) + features = self._dense_features(trunk) if needs_dense else None + outputs: dict[str, Tensor] = {} + for family in DENSE_TRACKS: + if family in requested: + outputs[family] = self.dense_heads[family](features).float() + if "splice_sites" in requested: + all_logits = self._splice_site_logits(trunk) + start = (self.config.input_length - self.config.target_length) // 2 + outputs["splice_sites"] = all_logits[:, start:start + self.config.target_length] + if "splice_usage" in requested: + if usage_positions is None: + raise ValueError("splice_usage requires usage_positions") + gathered = self._gather_bins( + features, usage_positions, self.config.dense_bin_size + ) + outputs["splice_usage"] = self.splice_usage_head(gathered).float() + if "junctions" in requested: + if junction_site_positions is None or junction_queries is None: + raise ValueError("junctions require site positions and query triples") + outputs["junctions"] = self._junction_predictions( + trunk, junction_site_positions, junction_queries + ).float() + if "contact_maps" in requested: + contact = trunk.reshape( + len(dna), self.config.contact_bins, -1, self.config.hidden_dim + ).mean(dim=2) + q, k = self.contact_q(contact), self.contact_k(contact) + row, column = self.contact_row, self.contact_column + outputs["contact_maps"] = ( + 0.5 + * ( + q[:, row] * k[:, column] + + q[:, column] * k[:, row] + ) + ).float() + return outputs + + +def count_parameters(model: nn.Module) -> int: + return sum(parameter.numel() for parameter in model.parameters() if parameter.requires_grad) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/reference_large.yaml b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/reference_large.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e403757c49eb6e6419927210c7053710964213d1 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/reference_large.yaml @@ -0,0 +1,23 @@ +input_length: 1048576 +target_length: 196608 +dense_bin_size: 128 +trunk_bin_size: 1024 +contact_bin_size: 2048 +stem_stride: 8 +encoder_width: 96 +channel_growth: 96 +num_downsampling_blocks: 7 +conv_kernel_size: 5 +dna_kernel_size: 15 +hidden_dim: 1024 +num_sequence_blocks: 16 +num_attention_heads: 16 +attention_head_dim: 64 +mlp_expansion: 4.0 +dropout: 0.0 +head_hidden_size: 768 +junction_rank: 32 +splice_usage_tracks: 734 +junction_tracks: 734 +contact_tracks: 28 +gradient_checkpointing: true diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/run_ddp.sh b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/run_ddp.sh new file mode 100644 index 0000000000000000000000000000000000000000..c4702e9366e907b7791063e77dcfe281411ac8cd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/run_ddp.sh @@ -0,0 +1,16 @@ +#!/usr/bin/env bash +# run_ddp.sh DATA_DIR OUTPUT_DIR CONFIG EPOCHS [GPUS=8] [WORKERS_PER_GPU=12] +# Train the reference on every train interval with one example per GPU per step, then score +# validation with the verifier's own scorer. Runs inside the task image (torch, torchrun). +set -Eeuo pipefail +DATA=$1; OUT=$2; CONFIG=$3; EPOCHS=$4; GPUS=${5:-8}; WORKERS=${6:-12} +BASELINE=$(cd "$(dirname "$0")/../.." && pwd) +mkdir -p "$OUT" +cd "$BASELINE" +# Loopback rendezvous: works offline, where --standalone cannot resolve the hostname. +torchrun --nnodes 1 --nproc-per-node "$GPUS" --master-addr 127.0.0.1 --master-port "${MASTER_PORT:-29511}" -m reference.alphagenome_50m.train_eval train \ + --input "$DATA" --config "$CONFIG" --output "$OUT" --epochs "$EPOCHS" --workers "$WORKERS" \ + --log-every 200 --save-every 2000 2>&1 | tee -a "$OUT/train.log" +python predict.py "$DATA" "$OUT/valid-pred" --checkpoint "$OUT/last.pt" --split valid +python /workspace/evaluation/score.py --predictions "$OUT/valid-pred/predictions.zarr" \ + --labels "$DATA" --split valid --output "$OUT/valid-score" diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/train_eval.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/train_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..92f9a3f315013630fdd2e03fb6840a6c77840edd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/reference/alphagenome_50m/train_eval.py @@ -0,0 +1,537 @@ +"""Train and stream-evaluate the <=50M compact AlphaGenome baseline.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import numpy as np +import torch +from torch import Tensor +from torch.nn import functional as F +from torch.utils.data import DataLoader, Subset + +from .dataset import ( + AlphaGenomeDataset, + DENSE_FAMILIES, + single_example_collate, +) +from .model import ( + ALL_FAMILIES, + CompactModel, + ModelConfig, + count_parameters, +) + + +DEFAULT_CONFIG = Path(__file__).with_name("alphagenome_50m.yaml") + + +def _device(name: str) -> torch.device: + if name == "auto": + name = "cuda" if torch.cuda.is_available() else "cpu" + if name == "cuda" and not torch.cuda.is_available(): + raise RuntimeError("--device cuda requested but CUDA is unavailable") + return torch.device(name) + + +def _autocast(device: torch.device): + enabled = device.type == "cuda" + dtype = torch.bfloat16 if enabled and torch.cuda.is_bf16_supported() else torch.float16 + return torch.autocast(device_type=device.type, dtype=dtype, enabled=enabled) + + +def _distributed() -> tuple[int, int, int]: + """(rank, local_rank, world_size) under torchrun; (0, 0, 1) otherwise.""" + world = int(os.environ.get("WORLD_SIZE", "1")) + if world > 1 and not torch.distributed.is_initialized(): + torch.distributed.init_process_group("nccl") + return int(os.environ.get("RANK", "0")), int(os.environ.get("LOCAL_RANK", "0")), world + + +def _loader(dataset, *, workers: int, shuffle: bool, sampler=None) -> DataLoader: + return DataLoader( + dataset, + batch_size=1, + shuffle=shuffle and sampler is None, + sampler=sampler, + num_workers=workers, + persistent_workers=workers > 0, + pin_memory=torch.cuda.is_available(), + collate_fn=single_example_collate, + ) + + +def _to_device(value, device: torch.device): + if isinstance(value, Tensor): + return value.to(device, non_blocking=True) + if isinstance(value, dict): + return {key: _to_device(item, device) for key, item in value.items()} + return value + + +def _match_coordinate_queries( + scoring: dict[str, Tensor], positions: Tensor, track_halves: Tensor +) -> tuple[Tensor, Tensor, Tensor]: + """Match trusted coordinate queries to typed position axes like the grader.""" + device = positions.device + tracks = scoring["track"].detach().cpu().numpy() + halves_cpu = track_halves.detach().cpu().numpy()[tracks] + # Build indices on CPU, then copy once. Writing three CUDA scalars per valid query + # otherwise launches thousands of tiny kernels per example, especially costly when + # training and evaluation share one GPU. The matching semantics are unchanged. + queries = np.zeros((len(tracks), 3), dtype=np.int64) + queries[:, 2] = tracks + valid = scoring["valid"].clone() + missing = np.ones(len(tracks), dtype=bool) + position_rows = positions.detach().cpu().numpy() + donors_cpu = scoring["donor_position"].detach().cpu().numpy() + acceptors_cpu = scoring["acceptor_position"].detach().cpu().numpy() + valid_indices = np.flatnonzero(valid.detach().cpu().numpy()) + lookups = [ + {int(coordinate): index for index, coordinate in enumerate(axis) if coordinate >= 0} + for axis in position_rows + ] + for index in valid_indices: + half = int(halves_cpu[index]) + row = lookups[2 * half].get(int(donors_cpu[index])) + column = lookups[2 * half + 1].get(int(acceptors_cpu[index])) + if row is not None and column is not None: + queries[index, 0] = row + queries[index, 1] = column + missing[index] = False + return (torch.from_numpy(queries).to(device).unsqueeze(0), valid, + torch.from_numpy(missing).to(device)) + + +def _model_inputs(sample: dict) -> tuple[dict, Tensor, Tensor]: + queries, query_valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + sample["site_positions"], + sample["junction_track_halves"], + ) + if (query_valid & missing).any(): + raise ValueError("trusted scoring coordinates do not match target positions") + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + return { + "dna": sample["dna"].unsqueeze(0), + "usage_positions": sample["usage_positions"].unsqueeze(0), + "junction_site_positions": sample["site_positions"].unsqueeze(0), + "junction_queries": queries, + }, junction_target, query_valid + + +def _losses(outputs: dict[str, Tensor], sample: dict, junction_target: Tensor, + junction_valid: Tensor) -> dict[str, Tensor]: + losses = {} + for family in DENSE_FAMILIES: + target = torch.asinh(sample["dense"][family]["values"]).unsqueeze(0) + mask = sample["dense"][family]["mask"].unsqueeze(0) + residual = torch.where(mask, outputs[family] - target, 0.0) + losses[family] = residual.square().sum() / mask.sum().clamp_min(1) + losses["splice_sites"] = F.binary_cross_entropy_with_logits( + outputs["splice_sites"], sample["splice_sites"].unsqueeze(0) + ) + usage_target = torch.asinh(sample["splice_usage"]["values"]).unsqueeze(0) + usage_mask = sample["splice_usage"]["mask"].unsqueeze(0) + usage_residual = torch.where(usage_mask, outputs["splice_usage"] - usage_target, 0.0) + losses["splice_usage"] = usage_residual.square().sum() / usage_mask.sum().clamp_min(1) + junction_residual = outputs["junctions"][0] - junction_target[0] + losses["junctions"] = ( + junction_residual[junction_valid].square().mean() + if junction_valid.any() else junction_residual.sum() * 0 + ) + contact_target = torch.asinh(sample["contact_maps"]).unsqueeze(0) + contact_mask = sample["contact_map_mask"].unsqueeze(0) + contact_residual = torch.where( + contact_mask, outputs["contact_maps"] - contact_target, 0.0 + ) + losses["contact_maps"] = contact_residual.square().sum() / contact_mask.sum().clamp_min(1) + losses["total"] = torch.stack([losses[name] for name in ALL_FAMILIES]).mean() + return losses + + +def _validation_inference( + model: CompactModel, sample: dict +) -> tuple[dict[str, Tensor], Tensor, Tensor, Tensor]: + """Use the same predicted-position path as a formal v3 submission.""" + trunk = model._trunk(sample["dna"].unsqueeze(0)) + dense_features = model._dense_features(trunk) + outputs = { + family: model.dense_heads[family](dense_features).float() + for family in DENSE_FAMILIES + } + all_sites = model._splice_site_logits(trunk) + target_start = (model.config.input_length - model.config.target_length) // 2 + outputs["splice_sites"] = all_sites[:, target_start:target_start + model.config.target_length] + usage_bins = model.splice_usage_head(dense_features).float() + usage_indices = torch.div(sample["usage_positions"], model.config.dense_bin_size, + rounding_mode="floor") + outputs["splice_usage"] = usage_bins[:, usage_indices] + predicted_positions = model.select_junction_positions(all_sites) + + matched, valid, missing = _match_coordinate_queries( + sample["junction_scoring"], + predicted_positions[0], + sample["junction_track_halves"], + ) + junction_target = torch.asinh( + sample["junction_scoring"]["target_value"] + ).unsqueeze(0) + junction_prediction = torch.zeros(len(valid), device=valid.device) + if (~missing & valid).any(): + selected = ~missing & valid + junction_prediction[selected] = model._junction_predictions( + trunk, predicted_positions, matched[:, selected] + )[0] + outputs["junctions"] = junction_prediction.unsqueeze(0) + + contact = trunk.reshape( + 1, model.config.contact_bins, -1, model.config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + outputs["contact_maps"] = (0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row])).float() + return outputs, junction_target, valid, missing + + +def _atomic_save(payload: dict, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(f".{path.name}.tmp") + torch.save(payload, temporary) + os.replace(temporary, path) + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(8 * 1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _resume_position(payload: dict, *, examples: int, epochs: int) -> tuple[int, int]: + epoch = int(payload["epoch"]) + step = int(payload["global_step"]) + kind = payload.get("checkpoint_kind") + if kind not in (None, "epoch") or step != epoch * examples: + raise ValueError("only a completed-epoch checkpoint can resume training") + if payload.get("examples", examples) != examples or payload.get("schedule_epochs", epochs) != epochs: + raise ValueError("checkpoint data size or training schedule differs from this run") + return epoch, step + + +def train(args: argparse.Namespace) -> None: + # Multi-GPU with `torchrun --nproc-per-node N train_eval.py train ...`: one example per GPU per + # step (data parallel), rank 0 writes checkpoints and logs. Model size is bounded by the + # training budget, not by a parameter cap. + rank, local_rank, world = _distributed() + config = ModelConfig.from_yaml(args.config) + device = torch.device("cuda", local_rank) if world > 1 else _device(args.device) + if world > 1: + torch.cuda.set_device(device) + torch.manual_seed(args.seed) + model = CompactModel(config).to(device) + parameters = count_parameters(model) + optimizer = torch.optim.AdamW( + model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay + ) + scaler = torch.amp.GradScaler("cuda", enabled=device.type == "cuda") + full_dataset = AlphaGenomeDataset(args.input, "train") + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + start_epoch, global_step = 0, 0 + if args.resume: + payload = torch.load(args.resume, map_location="cpu", weights_only=True) + if payload.get("config") != config.to_dict(): + raise ValueError("checkpoint configuration does not match --config") + model.load_state_dict(payload["model"], strict=True) + optimizer.load_state_dict(payload["optimizer"]) + scaler.load_state_dict(payload.get("scaler", {})) + start_epoch, global_step = _resume_position(payload, examples=size, epochs=args.epochs) + per_rank = math.ceil(size / world) + steps = max(1, per_rank * args.epochs) + module = model + if world > 1: + model = torch.nn.parallel.DistributedDataParallel( + model, device_ids=[local_rank], find_unused_parameters=True) + sampler = (torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world, rank=rank, + shuffle=True, seed=args.seed) + if world > 1 else None) + stop_after_epoch = args.stop_after_epoch or args.epochs + output = args.output.resolve() + output.mkdir(parents=True, exist_ok=True) + started = time.monotonic() + if rank == 0: + print(json.dumps({"event": "train_start", "parameters": parameters, "examples": size, + "device": str(device), "world_size": world}), flush=True) + + for epoch in range(start_epoch, stop_after_epoch): + model.train() + if sampler is not None: + sampler.set_epoch(epoch) + loader = _loader(dataset, workers=args.workers, shuffle=True, sampler=sampler) + for sample in loader: + sample = _to_device(sample, device) + inputs, junction_target, junction_valid = _model_inputs(sample) + progress = min((global_step + 1) / steps, 1.0) + learning_rate = args.min_learning_rate + 0.5 * ( + args.learning_rate - args.min_learning_rate + ) * (1.0 + math.cos(math.pi * progress)) + for group in optimizer.param_groups: + group["lr"] = learning_rate + optimizer.zero_grad(set_to_none=True) + with _autocast(device): + predictions = model(**inputs) + losses = _losses(predictions, sample, junction_target, junction_valid) + if not torch.isfinite(losses["total"]): + raise FloatingPointError(f"non-finite loss at step {global_step}") + scaler.scale(losses["total"]).backward() + scaler.unscale_(optimizer) + gradient_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_gradient_norm) + scaler.step(optimizer) + scaler.update() + global_step += 1 + if rank == 0 and (global_step == 1 or global_step % args.log_every == 0): + print( + json.dumps( + { + "event": "train_step", + "epoch": epoch + 1, + "step": global_step, + "loss": float(losses["total"].detach()), + "gradient_norm": float(gradient_norm), + "learning_rate": learning_rate, + "elapsed_seconds": time.monotonic() - started, + } + ), + flush=True, + ) + if rank == 0 and global_step % args.save_every == 0: + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch, + "global_step": global_step, + "checkpoint_kind": "progress", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "progress.pt", + ) + if world > 1: + torch.distributed.barrier() + if rank != 0: + continue + _atomic_save( + { + "schema": "alphagenome-50m-real/v3", + "model": module.state_dict(), + "optimizer": optimizer.state_dict(), + "scaler": scaler.state_dict(), + "config": config.to_dict(), + "epoch": epoch + 1, + "global_step": global_step, + "checkpoint_kind": "epoch", + "examples": size, + "schedule_epochs": args.epochs, + }, + output / "last.pt", + ) + if world > 1: + torch.distributed.barrier() + torch.distributed.destroy_process_group() + if rank != 0: + return + checkpoint = output / "last.pt" + manifest = { + "schema": "alphagenome-50m-real/v3", + "checkpoint": {"path": checkpoint.name, "sha256": _sha256(checkpoint)}, + "parameters": parameters, + "config": config.to_dict(), + "output_families": list(ALL_FAMILIES), + "epochs": args.epochs, + "completed_epochs": stop_after_epoch, + "global_step": global_step, + } + (output / "model_manifest.json").write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "train_complete", **manifest}), flush=True) + + +class PerTrackMoments: + def __init__(self, tracks: int): + self.n = np.zeros(tracks, dtype=np.int64) + self.sum_x = np.zeros(tracks, dtype=np.float64) + self.sum_y = np.zeros(tracks, dtype=np.float64) + self.sum_x2 = np.zeros(tracks, dtype=np.float64) + self.sum_y2 = np.zeros(tracks, dtype=np.float64) + self.sum_xy = np.zeros(tracks, dtype=np.float64) + + def update(self, prediction: Tensor, target: Tensor, mask: Tensor | None = None) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1, prediction.shape[-1]) + y = target.detach().double().cpu().numpy().reshape(-1, target.shape[-1]) + valid = np.ones_like(y, dtype=bool) if mask is None else mask.detach().cpu().numpy().reshape(y.shape).astype(bool) + self.n += valid.sum(axis=0) + self.sum_x += np.where(valid, x, 0).sum(axis=0, dtype=np.float64) + self.sum_y += np.where(valid, y, 0).sum(axis=0, dtype=np.float64) + self.sum_x2 += np.where(valid, x * x, 0).sum(axis=0, dtype=np.float64) + self.sum_y2 += np.where(valid, y * y, 0).sum(axis=0, dtype=np.float64) + self.sum_xy += np.where(valid, x * y, 0).sum(axis=0, dtype=np.float64) + + def update_indexed(self, prediction: Tensor, target: Tensor, track: Tensor) -> None: + x = prediction.detach().double().cpu().numpy().reshape(-1) + y = target.detach().double().cpu().numpy().reshape(-1) + indices = track.detach().cpu().numpy().reshape(-1).astype(np.int64) + self.n += np.bincount(indices, minlength=len(self.n)) + self.sum_x += np.bincount(indices, weights=x, minlength=len(self.n)) + self.sum_y += np.bincount(indices, weights=y, minlength=len(self.n)) + self.sum_x2 += np.bincount(indices, weights=x * x, minlength=len(self.n)) + self.sum_y2 += np.bincount(indices, weights=y * y, minlength=len(self.n)) + self.sum_xy += np.bincount(indices, weights=x * y, minlength=len(self.n)) + + def report(self) -> dict: + numerator = self.n * self.sum_xy - self.sum_x * self.sum_y + denominator = np.sqrt( + np.maximum(self.n * self.sum_x2 - self.sum_x**2, 0) + * np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + ) + target_ss = np.maximum(self.n * self.sum_y2 - self.sum_y**2, 0) + scorable = (self.n >= 2) & (target_ss > 0) + pearson = np.full_like(numerator, np.nan) + nonconstant = scorable & (denominator > 0) + pearson[nonconstant] = numerator[nonconstant] / denominator[nonconstant] + pearson[scorable & ~nonconstant] = 0.0 + return { + "mean_pearson": float(pearson[scorable].mean()) if scorable.any() else 0.0, + "valid_tracks": int(scorable.sum()), + "per_track_pearson": [float(value) if np.isfinite(value) else None for value in pearson], + "values_per_track": self.n.tolist(), + } + + +def evaluate(args: argparse.Namespace) -> None: + device = _device(args.device) + payload = torch.load(args.checkpoint, map_location="cpu", weights_only=True) + config = ModelConfig(**payload["config"]) + model = CompactModel(config).to(device) + model.load_state_dict(payload["model"], strict=True) + model.eval() + full_dataset = AlphaGenomeDataset(args.input, args.split) + model.set_junction_track_halves(full_dataset.junction_track_halves) + size = min(len(full_dataset), args.max_examples or len(full_dataset)) + dataset = Subset(full_dataset, range(size)) + metrics = { + **{family: PerTrackMoments(config_value) for family, config_value in { + "atac": 167, "dnase": 305, "procap": 12, "cage": 546, + "rna_seq": 667, "chip_tf": 1617, "chip_histone": 1116, + }.items()}, + "splice_sites": PerTrackMoments(4), + "splice_usage": PerTrackMoments(config.splice_usage_tracks), + "junctions": PerTrackMoments(config.junction_tracks), + "contact_maps": PerTrackMoments(config.contact_tracks), + } + with torch.inference_mode(): + for index, sample in enumerate(_loader(dataset, workers=args.workers, shuffle=False)): + sample = _to_device(sample, device) + outputs, junction_target, junction_valid, junction_missing = ( + _validation_inference(model, sample) + ) + # Match predict.py: FP32 inference is serialized to FP16 Zarr values. + outputs = {name: value.half().float() for name, value in outputs.items()} + for family in DENSE_FAMILIES: + metrics[family].update( + outputs[family][0], + torch.asinh(sample["dense"][family]["values"]), + sample["dense"][family]["mask"], + ) + metrics["splice_sites"].update(outputs["splice_sites"][0], sample["splice_sites"]) + metrics["splice_usage"].update( + outputs["splice_usage"][0], + torch.asinh(sample["splice_usage"]["values"]), + sample["splice_usage"]["mask"], + ) + junction_track = sample["junction_scoring"]["track"] + metrics["junctions"].update_indexed( + outputs["junctions"][0][junction_valid], + junction_target[0][junction_valid], + junction_track[junction_valid], + ) + metrics["contact_maps"].update( + outputs["contact_maps"][0], + torch.asinh(sample["contact_maps"]), + sample["contact_map_mask"], + ) + if index == 0 or (index + 1) % args.log_every == 0 or index + 1 == size: + print(json.dumps({"event": "evaluate_progress", "examples": index + 1, "total": size}), flush=True) + families = {name: accumulator.report() for name, accumulator in metrics.items()} + valid_family_scores = [item["mean_pearson"] for item in families.values()] + report = { + "schema": "alphagenome-50m-evaluation/v3", + "split": args.split, + "examples": size, + "metric_space": ( + "asinh continuous targets and binary splice-site targets; " + "junction Pearson uses hidden coordinate queries and predicted endpoints; " + "FP32 inference rounded to FP16 prediction values" + ), + "families": families, + "overall_mean_family_pearson": float(np.mean(valid_family_scores)), + } + args.report.parent.mkdir(parents=True, exist_ok=True) + args.report.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n") + print(json.dumps({"event": "evaluate_complete", "overall_mean_family_pearson": report["overall_mean_family_pearson"]}), flush=True) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + common = argparse.ArgumentParser(add_help=False) + common.add_argument("--input", required=True, type=Path) + common.add_argument("--config", type=Path, default=DEFAULT_CONFIG) + common.add_argument("--workers", type=int, default=2) + common.add_argument("--max-examples", type=int, default=0) + common.add_argument("--device", choices=("auto", "cuda", "cpu"), default="auto") + common.add_argument("--log-every", type=int, default=25) + subparsers = parser.add_subparsers(dest="command", required=True) + train_parser = subparsers.add_parser("train", parents=[common]) + train_parser.add_argument("--output", required=True, type=Path) + train_parser.add_argument("--epochs", type=int, default=10) + train_parser.add_argument("--stop-after-epoch", type=int) + train_parser.add_argument("--learning-rate", type=float, default=2e-4) + train_parser.add_argument("--min-learning-rate", type=float, default=2e-5) + train_parser.add_argument("--weight-decay", type=float, default=1e-4) + train_parser.add_argument("--max-gradient-norm", type=float, default=5.0) + train_parser.add_argument("--save-every", type=int, default=250) + train_parser.add_argument("--seed", type=int, default=17) + train_parser.add_argument("--resume", type=Path) + train_parser.set_defaults(run=train) + evaluate_parser = subparsers.add_parser("evaluate", parents=[common]) + evaluate_parser.add_argument("--checkpoint", required=True, type=Path) + evaluate_parser.add_argument("--split", choices=("train", "valid", "test"), required=True) + evaluate_parser.add_argument("--report", required=True, type=Path) + evaluate_parser.set_defaults(run=evaluate) + args = parser.parse_args() + if args.workers < 0 or args.max_examples < 0: + parser.error("workers/max-examples must be nonnegative") + if args.command == "train" and ( + args.epochs < 1 + or (args.stop_after_epoch is not None + and not 1 <= args.stop_after_epoch <= args.epochs) + ): + parser.error("epochs must be positive and stop-after-epoch must be within the schedule") + args.run(args) + + +if __name__ == "__main__": + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/single_prediction.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/single_prediction.py new file mode 100644 index 0000000000000000000000000000000000000000..a47022029461e5c8097b3a653af8b3407196d67d --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/single_prediction.py @@ -0,0 +1,97 @@ +"""The parent single-orientation decoder, preserving float32 until ensemble averaging.""" +import numpy as np +import torch +from prediction_contract import DENSE_TRACKS, MAX_JUNCTION_PAIRS, MAX_USAGE_SITES +from splice_output import splice_output_scores + +COMPRESSOR = None + +class ArrayCollector: + def __init__(self): + self.arrays = {} + def create_dataset(self, name, *, data, **kwargs): + self.arrays[name] = np.asarray(data) + +def single_prediction(model, dna): + config = model.config + group = ArrayCollector() + trunk = model._trunk(dna) + dense_features = model._dense_features(trunk) + for family in DENSE_TRACKS: + values = model.dense_heads[family](dense_features)[0] + group.create_dataset(family, data=values.cpu().numpy(), compressor=COMPRESSOR) + + all_site_logits = model._splice_site_logits(trunk) + target_start = (config.input_length - config.target_length) // 2 + group.create_dataset( + "splice_sites", + data=splice_output_scores(all_site_logits[0, target_start:target_start + config.target_length]).cpu().numpy(), + chunks=(4096, 4), compressor=COMPRESSOR, + ) + predicted_positions = model.select_junction_positions(all_site_logits)[0] + + # Usage at the model's own predicted sites (v4 is sparse; unlisted hidden sites score 0). + usage_bins = model.splice_usage_head(dense_features)[0].float() + sites = predicted_positions.cpu().numpy().astype(np.int64).reshape(-1) + sites = np.unique(sites[(sites >= target_start) & (sites < target_start + config.target_length)] + - target_start)[:MAX_USAGE_SITES] + group.create_dataset("splice_usage_positions", data=sites.astype(np.int32), compressor=COMPRESSOR) + usage = usage_bins[torch.as_tensor(sites // 128, device=usage_bins.device)] if len(sites) \ + else usage_bins[:0] + group.create_dataset("splice_usage", data=usage.cpu().numpy(), + chunks=(1024, 734), compressor=COMPRESSOR) + + # Junctions: score every donor x acceptor pair of each strand, keep the strongest pairs. + positions_batch = predicted_positions[None] + site_factors = model.junction_site_factors(trunk, positions_batch) + halves = model.junction_track_half + candidates = [] + for row_start in range(0, 512, 128): + for column_start in range(0, 512, 128): + block = torch.cat([ + model.junction_block( + trunk, positions_batch, row_start, row_start + 128, + column_start, column_start + 128, track_start, + min(track_start + 64, config.junction_tracks), site_factors, + )[0] + for track_start in range(0, config.junction_tracks, 64) + ], dim=-1).float() # [128, 128, 734] + for half in (0, 1): + donors = predicted_positions[2 * half, row_start:row_start + 128] + acceptors = predicted_positions[2 * half + 1, column_start:column_start + 128] + valid = (donors[:, None] >= 0) & (acceptors[None, :] >= 0) + # Transcript direction is determined by the public strand half. + forward = donors[:, None] < acceptors[None, :] if half == 0 else donors[:, None] > acceptors[None, :] + valid = valid & forward + strength = block[..., halves == half].amax(dim=-1).masked_fill(~valid, -torch.inf) + keep = min(MAX_JUNCTION_PAIRS // 2, int(valid.sum())) + if not keep: + continue + best = strength.flatten().topk(keep) + rows, columns = best.indices // 128, best.indices % 128 + candidates.append((best.values, torch.full_like(rows, half), donors[rows], + acceptors[columns], block[rows, columns])) + if candidates: + score, half, donor, acceptor, values = (torch.cat(x) for x in zip(*candidates)) + order = score.topk(min(MAX_JUNCTION_PAIRS, len(score))).indices + half, donor, acceptor, values = half[order], donor[order], acceptor[order], values[order] + else: + half = donor = acceptor = torch.zeros(0, dtype=torch.long) + values = torch.zeros(0, config.junction_tracks) + group.create_dataset("junction_half", data=half.cpu().numpy().astype(np.int8), compressor=COMPRESSOR) + group.create_dataset("junction_donor", data=donor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junction_acceptor", data=acceptor.cpu().numpy().astype(np.int32), compressor=COMPRESSOR) + group.create_dataset("junctions", data=values.cpu().numpy(), + chunks=(4096, 734), compressor=COMPRESSOR) + + contact = trunk.reshape( + 1, config.contact_bins, -1, config.hidden_dim + ).mean(dim=2) + q, k = model.contact_q(contact), model.contact_k(contact) + row, column = model.contact_row, model.contact_column + contact_values = 0.5 * (q[:, row] * k[:, column] + q[:, column] * k[:, row]) + group.create_dataset( + "contact_maps", data=contact_values[0].cpu().numpy(), + chunks=(8192, 28), compressor=COMPRESSOR, + ) + return group.arrays diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/splice_calibration.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/splice_calibration.json new file mode 100644 index 0000000000000000000000000000000000000000..470734a574a4b9b9f2f34fd3bcae0007db3737f6 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/splice_calibration.json @@ -0,0 +1,33 @@ +{ + "training_examples": 256, + "training_indices_sha256": "b82df9ae11e95d9be5d6e5f85839cf23e25bbbe0a195eaee266ff52d366800dc", + "training_positive_counts": [ + 3968.0, + 4160.0, + 3874.0, + 3921.0 + ], + "training_prior": [ + 7.883707682291667e-05, + 8.265177408854167e-05, + 7.696946461995442e-05, + 7.790327072143555e-05 + ], + "effective_balanced_loss_prior": [ + 0.3203125, + 0.3125, + 0.30859375, + 0.30859375 + ], + "logit_offset": [ + -8.695712089538574, + -8.612334251403809, + -8.665323257446289, + -8.653263092041016 + ], + "caveat": "marginal prior correction; balanced loss is normalized separately in each window", + "link": "sigmoid(logits + training_prior_offset)", + "training_split": "train", + "seed": 1729, + "development_labels_used_to_fit_offsets": false +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/splice_output.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/splice_output.py new file mode 100644 index 0000000000000000000000000000000000000000..1081264d9cf4b4473f4fe53c97ff02b2c3b249e3 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/splice_output.py @@ -0,0 +1,11 @@ +"""Shared splice output link; constants estimated only from training targets.""" +import json +from pathlib import Path +import torch + +_OFFSETS = json.loads((Path(__file__).with_name('splice_calibration.json')).read_text())['logit_offset'] + +def splice_output_scores(logits): + logits = logits.float() + offset = torch.tensor(_OFFSETS, device=logits.device, dtype=logits.dtype) + return torch.sigmoid(logits + offset) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/strand_permutations.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/strand_permutations.json new file mode 100644 index 0000000000000000000000000000000000000000..044c090773f20347601dcd4f0983c4dfd4c0ecc2 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/strand_permutations.json @@ -0,0 +1,5959 @@ +{ + "schema": "seqfunc-strand-mapping/v1", + "source": "public track metadata; no target values", + "source_sha256": "23450e6ff9463c64d271d7014f8a798875bc95186c36aeb34609c7dc1d62cd5f", + "permutations": { + "atac": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 83, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + 91, + 92, + 93, + 94, + 95, + 96, + 97, + 98, + 99, + 100, + 101, + 102, + 103, + 104, 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a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/train_batched.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/train_batched.py new file mode 100644 index 0000000000000000000000000000000000000000..df00b798c655555704cd6c8abbd7e4eacedd0ba3 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/train_batched.py @@ -0,0 +1,168 @@ +"""Resumable single-H100 training with equal-per-example batched losses. + +global_step counts training examples, optimizer_steps counts updates. Every epoch +uses the complete deterministic permutation, including its final partial batch. +""" +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time +import torch +from numcodecs import blosc +from torch.utils.data import DataLoader, Subset +from reference.alphagenome_50m.dataset import AlphaGenomeDataset +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save, _to_device +from batched_training import list_collate, prepare_batch, equal_example_losses + +def main(): + p=argparse.ArgumentParser(description=__doc__) + p.add_argument('--input',type=Path,default=Path('/workspace/data')) + p.add_argument('--output',type=Path,required=True) + p.add_argument('--config',type=Path,required=True) + p.add_argument('--resume',type=Path) + p.add_argument('--batch-size',type=int,default=8) + p.add_argument('--epochs',type=int,default=14) + p.add_argument('--stop-at',type=int,default=0) + p.add_argument('--workers',type=int,default=6) + p.add_argument('--threads',type=int,default=2) + p.add_argument('--seed',type=int,default=17) + p.add_argument('--learning-rate',type=float,default=2e-4) + p.add_argument('--min-learning-rate',type=float,default=2e-5) + p.add_argument('--warmup',type=int,default=4000,help='Warmup in training examples') + p.add_argument('--objective',choices=['balanced_splice','variance_balanced'],default='balanced_splice') + p.add_argument('--loss-scales',type=Path) + p.add_argument('--save-every',type=int,default=10000,help='Training examples between progress checkpoints') + p.add_argument('--keep-every',type=int,default=50000) + p.add_argument('--log-every',type=int,default=50,help='Optimizer updates between logs') + args=p.parse_args() + if min(args.batch_size,args.epochs,args.save_every,args.log_every) < 1: + raise ValueError('batch, epoch, checkpoint and log intervals must be positive') + os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:22]) + torch.set_num_threads(args.threads);torch.set_num_interop_threads(1);blosc.set_nthreads(1) + torch.manual_seed(args.seed) + device=torch.device('cuda:0') + config=ModelConfig.from_yaml(args.config) + model=CompactModel(config).to(device) + optimizer=torch.optim.AdamW(model.parameters(),lr=args.learning_rate,weight_decay=1e-4) + dataset=AlphaGenomeDataset(args.input,'train') + model.set_junction_track_halves(dataset.junction_track_halves) + args.output.mkdir(parents=True,exist_ok=True) + total=len(dataset)*args.epochs + step,updates,elapsed_before=0,0,0. + lineage,scales={},{} + if args.loss_scales: + scale_source=json.loads(args.loss_scales.read_text()) + assert scale_source['split']=='train' + scales=scale_source['family_loss_scales'] + lineage['loss_scale_provenance']={k:v for k,v in scale_source.items() if k!='junction_moments'} + if args.resume: + state=torch.load(args.resume,map_location='cpu',weights_only=True) + if state['schema'] not in ['seqfunc-single/v1','seqfunc-batched/v1']: + raise ValueError('only locally trained checkpoints may resume') + if ModelConfig(**state['config']).to_dict()!=config.to_dict() or state['examples']!=len(dataset): + raise ValueError('resume architecture or dataset differs') + if state['schedule_epochs']!=args.epochs or state['objective']!=args.objective or state['seed']!=args.seed: + raise ValueError('resume schedule, objective or seed differs') + previous=state.get('training_arguments',{}) + for field in ['learning_rate','min_learning_rate','warmup']: + if field in previous and previous[field]!=getattr(args,field): + raise ValueError(f'resume {field} differs') + if state['schema']=='seqfunc-batched/v1' and previous['batch_size']!=args.batch_size: + raise ValueError('batched resume must keep its batch size') + if scales and scales!=state.get('family_loss_scales',{}): + raise ValueError('resume family scales differ') + scales=state.get('family_loss_scales',{}) + lineage.update({k:state[k] for k in ['intervention','interventions','loss_scale_provenance'] if k in state}) + model.load_state_dict(state['model'],strict=True) + optimizer.load_state_dict(state['optimizer']) + step=state['global_step'];updates=state.get('optimizer_steps',step) + elapsed_before=state.get('elapsed_seconds',0.) + torch.set_rng_state(state['rng_cpu']);torch.cuda.set_rng_state(state['rng_cuda'],device) + if state['schema']=='seqfunc-single/v1': + with args.resume.open('rb') as f: + digest=hashlib.file_digest(f,'sha256').hexdigest() + history=list(lineage.get('interventions',[])) + if lineage.get('intervention') and lineage['intervention'] not in history: + history.append(lineage['intervention']) + change={'kind':'batch_size_change','from':1,'to':args.batch_size,'at_example':step, + 'parent_sha256':digest,'parameters_optimizer_rng_unchanged_at_start':True} + history.append(change);lineage.update(intervention=change,interventions=history) + if args.objective=='variance_balanced': + expected=set(model.dense_heads)|{'splice_usage','junctions','contact_maps'} + if set(scales)!=expected or not all(math.isfinite(v) and v>0 for v in scales.values()): + raise ValueError('fixed positive training-derived scales required') + elif scales: + raise ValueError('unscaled objective cannot carry family scales') + start=time.monotonic() + def emit(event,**values): + print(json.dumps({'event':event,'step':step,'optimizer_steps':updates, + 'elapsed_seconds':elapsed_before+time.monotonic()-start,**values}),flush=True) + def save(name): + _atomic_save({**lineage,'schema':'seqfunc-batched/v1','model':model.state_dict(), + 'optimizer':optimizer.state_dict(),'config':config.to_dict(), + 'global_step':step,'optimizer_steps':updates,'epoch':step//len(dataset),'offset':step%len(dataset), + 'examples':len(dataset),'schedule_epochs':args.epochs,'objective':args.objective,'seed':args.seed, + 'family_loss_scales':scales, + 'training_arguments':{k:str(v) if isinstance(v,Path) else v for k,v in vars(args).items()}, + 'rng_cpu':torch.get_rng_state(),'rng_cuda':torch.cuda.get_rng_state(device), + 'elapsed_seconds':elapsed_before+time.monotonic()-start, + 'provenance':{'data':str(args.input),'split':'train','pretrained':False,'world_size':1, + 'device':torch.cuda.get_device_name(0),'batch_reduction':'equal per example', + 'train_manifest_sha256':hashlib.sha256((args.input/'public/train.parquet').read_bytes()).hexdigest()}, + },args.output/name) + emit('train_start',parameters=count_parameters(model),examples=len(dataset),batch_size=args.batch_size, + objective=args.objective,config=config.to_dict(),schedule_examples=total,family_loss_scales=scales) + if args.stop_at and step>=args.stop_at: + raise ValueError('stop-at must exceed the resumed example count') + model.train() + for epoch in range(step//len(dataset),args.epochs): + order=torch.randperm(len(dataset),generator=torch.Generator().manual_seed(args.seed+epoch)).tolist() + offset=step%len(dataset) + emit('epoch_order',epoch=epoch+1,offset=offset,count=len(order), + permutation_sha256=hashlib.sha256(torch.tensor(order,dtype=torch.int64).numpy().tobytes()).hexdigest()) + loader=DataLoader(Subset(dataset,order[offset:]),batch_size=args.batch_size,shuffle=False, + num_workers=args.workers,pin_memory=True,collate_fn=list_collate, + persistent_workers=False,prefetch_factor=2 if args.workers else None) + for cpu_samples in loader: + if args.stop_at: + cpu_samples=cpu_samples[:args.stop_at-step] + inputs,targets,valid=prepare_batch(cpu_samples) + inputs=_to_device(inputs,device) + targets=[x.to(device,non_blocking=True) for x in targets] + valid=[x.to(device,non_blocking=True) for x in valid] + samples=[_to_device(s,device) for s in cpu_samples] + progress=min(max(step-args.warmup,0)/max(total-args.warmup,1),1) + lr=args.min_learning_rate+.5*(args.learning_rate-args.min_learning_rate)*(1+math.cos(math.pi*progress)) + lr*=min((step+len(samples))/max(args.warmup,1),1) + for group in optimizer.param_groups: group['lr']=lr + optimizer.zero_grad(set_to_none=True) + with torch.autocast('cuda',dtype=torch.bfloat16): + outputs=model(**inputs) + losses,raw=equal_example_losses(outputs,samples,targets,valid,scales=scales) + if not torch.isfinite(losses['total']): + raise FloatingPointError(f'nonfinite loss at {step}') + losses['total'].backward() + norm=torch.nn.utils.clip_grad_norm_(model.parameters(),5,error_if_nonfinite=True) + optimizer.step() + before=step;step+=len(samples);updates+=1 + if updates==1 or updates%args.log_every==0: + emit('train_step',epoch=epoch+1,example_ids=[s['example_id'] for s in samples], + learning_rate=lr,gradient_norm=float(norm),actual_batch_size=len(samples), + losses={k:float(v.detach()) for k,v in losses.items()}, + raw_losses={k:float(v.detach()) for k,v in raw.items()}, + peak_gpu_bytes=torch.cuda.max_memory_allocated()) + if step//args.save_every>before//args.save_every: save('progress.pt') + if args.keep_every and step//args.keep_every>before//args.keep_every: save(f'step-{step:06d}.pt') + if step%len(dataset)==0: + save('last.pt');save(f'epoch-{epoch+1:02d}.pt') + emit('epoch_complete',completed_epochs=epoch+1) + if args.stop_at and step>=args.stop_at: + save('last.pt');emit('train_segment_complete',completed_epochs=step//len(dataset),offset=step%len(dataset)) + return + emit('train_complete') + +if __name__=='__main__': main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/train_single.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/train_single.py new file mode 100644 index 0000000000000000000000000000000000000000..03024393206b918eac3ae23a87c09173848b0d15 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/train_single.py @@ -0,0 +1,158 @@ +"""Resumable single-H100 training; every epoch permutes every eligible training interval.""" +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +from pathlib import Path +import time + +import torch +from numcodecs import blosc +from torch.utils.data import DataLoader, Subset + +from reference.alphagenome_50m.dataset import AlphaGenomeDataset, single_example_collate +from reference.alphagenome_50m.model import CompactModel, ModelConfig, count_parameters +from reference.alphagenome_50m.train_eval import _atomic_save, _losses, _model_inputs, _to_device + + +def balanced_splice_loss(logits, target): + """Equal positive/negative contributions per type, using only training labels. + + An absent class contributes its negative loss rather than dividing by zero or being dropped. + """ + dims = tuple(range(target.ndim - 1)) + positive = target.sum(dims) + negative = (1 - target).sum(dims) + pos_loss = (torch.nn.functional.softplus(-logits) * target).sum(dims) / positive.clamp_min(1) + neg_loss = (torch.nn.functional.softplus(logits) * (1 - target)).sum(dims) / negative.clamp_min(1) + return torch.where(positive > 0, 0.5 * (pos_loss + neg_loss), neg_loss).mean() + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--input', type=Path, default=Path('/workspace/data')) + p.add_argument('--output', type=Path, required=True) + p.add_argument('--config', type=Path, default=Path(__file__).parent / 'reference/alphagenome_50m/alphagenome_50m.yaml') + p.add_argument('--resume', type=Path) + p.add_argument('--objective', choices=['reference', 'balanced_splice'], default='balanced_splice') + p.add_argument('--epochs', type=int, default=4) + p.add_argument('--stop-at', type=int, default=0, help='Stop after this total number of examples, preserving exact sampler position') + p.add_argument('--workers', type=int, default=8) + p.add_argument('--threads', type=int, default=4) + p.add_argument('--seed', type=int, default=17) + p.add_argument('--learning-rate', type=float, default=2e-4) + p.add_argument('--min-learning-rate', type=float, default=2e-5) + p.add_argument('--warmup', type=int, default=500) + p.add_argument('--save-every', type=int, default=2000) + p.add_argument('--keep-every', type=int, default=10000, help='Keep immutable intermediate checkpoints') + p.add_argument('--log-every', type=int, default=100) + args = p.parse_args() + torch.set_num_threads(args.threads) + torch.set_num_interop_threads(1) + blosc.set_nthreads(1) + if hasattr(os, 'sched_getaffinity'): + os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) + torch.manual_seed(args.seed) + device = torch.device('cuda:0') + config = ModelConfig.from_yaml(args.config) + model = CompactModel(config).to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=args.learning_rate, weight_decay=1e-4) + dataset = AlphaGenomeDataset(args.input, 'train') + model.set_junction_track_halves(dataset.junction_track_halves) + args.output.mkdir(parents=True, exist_ok=True) + total = len(dataset) * args.epochs + step, elapsed_before = 0, 0.0 + lineage = {} + if args.resume: + state = torch.load(args.resume, map_location='cpu', weights_only=True) + lineage = {k: state[k] for k in ['intervention', 'interventions'] if k in state} + if state['config'] != config.to_dict() or state['examples'] != len(dataset): + raise ValueError('resume config or dataset size differs') + if state['schedule_epochs'] != args.epochs or state['objective'] != args.objective: + raise ValueError('resume schedule or objective differs') + if state['seed'] != args.seed: + raise ValueError('resume shuffle seed differs') + previous = state.get('training_arguments', {}) + for field in ['learning_rate', 'min_learning_rate', 'warmup']: + if field in previous and previous[field] != getattr(args, field): + raise ValueError(f'resume {field} differs') + model.load_state_dict(state['model'], strict=True) + optimizer.load_state_dict(state['optimizer']) + step = state['global_step'] + elapsed_before = state.get('elapsed_seconds', 0.0) + torch.set_rng_state(state['rng_cpu']) + torch.cuda.set_rng_state(state['rng_cuda'], device) + start = time.monotonic() + def emit(event, **values): + record = {'event': event, 'step': step, 'elapsed_seconds': elapsed_before + time.monotonic() - start, **values} + print(json.dumps(record), flush=True) + def save(name): + _atomic_save({ + **lineage, + 'schema': 'seqfunc-single/v1', 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), 'config': config.to_dict(), + 'global_step': step, 'epoch': step // len(dataset), 'offset': step % len(dataset), + 'examples': len(dataset), 'schedule_epochs': args.epochs, + 'objective': args.objective, 'seed': args.seed, + 'training_arguments': {k: str(v) if isinstance(v, Path) else v for k, v in vars(args).items()}, + 'rng_cpu': torch.get_rng_state(), 'rng_cuda': torch.cuda.get_rng_state(device), + 'elapsed_seconds': elapsed_before + time.monotonic() - start, + 'provenance': {'data': str(args.input), 'split': 'train', 'pretrained': False, + 'device': torch.cuda.get_device_name(0), 'world_size': 1}, + }, args.output / name) + emit('train_start', parameters=count_parameters(model), examples=len(dataset), objective=args.objective, + config=config.to_dict(), schedule_examples=total, device=torch.cuda.get_device_name(0)) + model.train() + for epoch in range(step // len(dataset), args.epochs): + generator = torch.Generator().manual_seed(args.seed + epoch) + order = torch.randperm(len(dataset), generator=generator).tolist() + offset = step % len(dataset) + emit('epoch_order', epoch=epoch + 1, offset=offset, count=len(order), + permutation_sha256=hashlib.sha256(torch.tensor(order, dtype=torch.int64).numpy().tobytes()).hexdigest()) + loader = DataLoader(Subset(dataset, order[offset:]), batch_size=1, shuffle=False, + num_workers=args.workers, pin_memory=True, collate_fn=single_example_collate, + persistent_workers=False, prefetch_factor=2 if args.workers else None) + for sample in loader: + sample = _to_device(sample, device) + inputs, target, valid = _model_inputs(sample) + progress = min(max(step - args.warmup, 0) / max(total - args.warmup, 1), 1) + lr = args.min_learning_rate + 0.5 * (args.learning_rate - args.min_learning_rate) * (1 + math.cos(math.pi * progress)) + lr *= min((step + 1) / max(args.warmup, 1), 1) + for group in optimizer.param_groups: + group['lr'] = lr + optimizer.zero_grad(set_to_none=True) + with torch.autocast('cuda', dtype=torch.bfloat16): + outputs = model(**inputs) + losses = _losses(outputs, sample, target, valid) + if args.objective == 'balanced_splice': + losses['splice_sites'] = balanced_splice_loss(outputs['splice_sites'], sample['splice_sites'][None]) + losses['total'] = torch.stack([v for k, v in losses.items() if k != 'total']).mean() + if not torch.isfinite(losses['total']): + raise FloatingPointError(f'nonfinite loss at {step}') + losses['total'].backward() + norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0, error_if_nonfinite=True) + optimizer.step() + step += 1 + if step == 1 or step % args.log_every == 0: + emit('train_step', epoch=epoch + 1, example_id=sample['example_id'], learning_rate=lr, + gradient_norm=float(norm), losses={k: float(v.detach()) for k, v in losses.items()}, + peak_gpu_bytes=torch.cuda.max_memory_allocated()) + if step % args.save_every == 0: + save('progress.pt') + if args.keep_every and step % args.keep_every == 0: + save(f'step-{step:06d}.pt') + if args.stop_at and step >= args.stop_at: + save('last.pt') + emit('train_segment_complete', completed_epochs=step // len(dataset), offset=step % len(dataset)) + return + save('last.pt') + save(f'epoch-{epoch + 1:02d}.pt') + emit('epoch_complete', completed_epochs=epoch + 1) + emit('train_complete') + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_assets_manifest.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_assets_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..8e314c842cca039da1d2be8da7fff6a146178651 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_assets_manifest.json @@ -0,0 +1,21 @@ +{ + "schema": "seqfunc-training-assets/v1", + "files": { + "training_history/block-checkpoint-cpu-check.json": "d099b0a4d4d6cdd1aab33a6d8efaf3b4a5bddb2be4db0563eed83b3f849a31ef", + "training_history/block-checkpoint-gpu-check.json": "b2e598359fad26fd63c4e7487997432987cfd8c836680726458f62d137df04ce", + "training_history/bytecache-integration_check.json": "a712f359807b414956a87f2486bbc3c6e9288727d1f79e5d0b6fdf321fd9a67b", + 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b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_config.yaml @@ -0,0 +1,32 @@ +attention_head_dim: 64 +channel_growth: 96 +contact_bin_size: 2048 +contact_tracks: 28 +conv_kernel_size: 5 +dense_bin_size: 128 +dna_kernel_size: 15 +dropout: 0.0 +enable_pairwise: false +encoder_width: 96 +gradient_checkpointing: true +head_hidden_size: 768 +hidden_dim: 1024 +input_length: 1048576 +junction_rank: 32 +junction_tracks: 734 +local_context_blocks: 6 +local_to_trunk: true +local_width: 64 +mlp_expansion: 4.0 +num_attention_heads: 16 +num_downsampling_blocks: 7 +num_sequence_blocks: 16 +pair_downsample: 16 +pair_update_interval: 2 +pair_width: 32 +residual_rms_cap: 0.1 +splice_context_mode: offset +splice_usage_tracks: 734 +stem_stride: 8 +target_length: 196608 +trunk_bin_size: 1024 diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/block-checkpoint-cpu-check.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/block-checkpoint-cpu-check.json new file mode 100644 index 0000000000000000000000000000000000000000..588d9318734995508b1ece2948b790e2d0ce5172 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/block-checkpoint-cpu-check.json @@ -0,0 +1,10 @@ +{ + "all_11_training_and_evaluation_outputs_bit_exact": true, + "max_absolute_gradient_error": 0.0, + "nonzero_local_heads_and_bridge": true, + "six_local_blocks": true, + "active_branch_caps": true, + "unequal_and_empty_event_queries": true, + "precision": "float32", + "device": "CPU" +} diff --git 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Does not measure long-tail storage outages or guarantee gains on the full training dataset." +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/execution_change.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/execution_change.json new file mode 100644 index 0000000000000000000000000000000000000000..93f72a0c4f5f51e9192005535fc45e6aa3bbd671 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/execution_change.json @@ -0,0 +1,10 @@ +{ + "adopted_utc": "2026-10-24T21:51:59.762636+00:00", + "change": "Activation checkpoint boundaries only; all model operations, parameters, objective, data and schedule unchanged.", + "example_boundary": 1000, + "before": "Recompute whole local path during backward.", + "after": "Recompute each local residual block during backward.", + "evidence": "CPU gradients and all eleven outputs exactly match. Full-shape BF16 outputs/losses are bit-exact; gradient relative L2 discrepancy .0009547 versus .0009534 for an unchanged repeat, consistent with existing reduction variability. Peak memory40.11 to15.99GiB; isolated compute .11131 to.10504seconds/example.", + "initial_source_manifest_sha256": "18329ef35e89fc5aeb50ccc045b08db690e6c8ef40d661b01ddb74c7f32b6f0b", + "no_benchmark_weights_retained": true +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/freeze_rc_large114_execution.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/freeze_rc_large114_execution.py new file mode 100644 index 0000000000000000000000000000000000000000..5e2ea168f8b491cc56b1a0721db80f9d7fdd39a7 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/freeze_rc_large114_execution.py @@ -0,0 +1,144 @@ +"""Freeze large114 evaluation after completed parent, accepted CPU proof and fresh resources.""" +from datetime import datetime, timezone, timedelta +import json +from pathlib import Path +import shutil + +import pandas as pd + +from run_seed110_pilot import digest,read,save,rows,resource_snapshot +from verify_package import inference_signature,verify_package + + +def main(): + root=Path('/workspace/research');here=root/'seqfunc';study=root/'studies/rc_large114' + assert not (study/'study.json').exists() and not (study/'admission.json').exists() + recipe=read(study/'proposed-recipe.json');prep=read(study/'preparation.json') + for path,expected in prep['evidence_sha256'].items():assert digest(path)==expected,path + proof=read(study/'CPU-construction-check.json') + assert proof['parameters']==215626632 and proof['all_learned_tensors_and_CPU_RNG_unchanged'] + assert proof['actual_native_track_arrays_exact_manual_fixed_mean']==15 + assert proof['paired_dense_and_splice_arrays_exact_manual_fixed_mean']==8 and not proof['CUDA_used'] + parent=Path(recipe['parent']);parent_study=root/'studies/late_fixed_links81/large-epoch08' + assert read(parent_study/'complete.json')['phase']=='complete' + completion=read(parent_study/'completion-observation.json') + assert completion['full_all13_scored'] and completion['native52_passed'] and completion['native36_derivation_accepted'] + decision=read(parent_study/'publication-decision.json') + assert decision['eligible_at_review'] is False and decision['runtime_passed'] is None + for phase in ['tracks','remaining-variants']: + assert read(parent_study/f'paired-{phase}-complete.json')['output_sha256']==digest(parent_study/f'paired-{phase}.json') + assert read(root/'studies/rc108/diagnostics/summary.json')['complete_metrics']==13 + assert read(root/'studies/rc108/state.json')['phase']=='complete' + package=Path(prep['package']);signature=inference_signature(package) + assert signature==prep['candidate_inference_signature'] + assert inference_signature(parent/'package')==prep['parent_inference_signature'] + context=root/'studies/operator_recovery_20261026T064822/recovery.json' + recovery=read(context);now=datetime.now(timezone.utc);deadline=datetime.fromisoformat(recovery['new_deadline']) + assert deadline==datetime.fromisoformat(recovery['recovery_utc'])+timedelta(seconds=recovery['remaining_seconds_at_recovery']) + resource=resource_snapshot({name:str(root/'experiments'/('scale13-'+name)) for name in ['small','large']}) + allowed={str(root/'experiments/scale13-large'),str(root/'experiments/seed110-small')} + assert resource['trainers'] and all(x['output'] in allowed for x in resource['trainers']) + assert resource['memory_events']['oom']=='0' and resource['memory_events']['oom_kill']=='0' + assert int(resource['gpu'].split(',')[1])>=24*1024 + rates={} + for name,target in [('scale13-large',574280),('seed110-small',328160)]: + events=rows(root/'experiments'/name/'train.log') + start=max(i for i,r in enumerate(events) if r.get('event')=='train_start') + tail=[r for r in events[start:] if r.get('event')=='train_step'][-11:] + assert len(tail)>=3 and all(a['step']1e-6. Parent metadata must match exact whole epochs, all41020intervals, originalseed17/B4/14epochobjective and frozen capacity configs. Freeze actual parent signatures/reports only after completion; do not substitute checkpoints.", + "validation": "Original builder and verifier helpers; fresh native52/recovery, exact link-only core guard, original full12486array proof and36fresh native arrays, then fresh200track inference and full13officialmetrics using verified research-only perturbation reuse. Require10unaffectedfamily/eightvariant reports exact. Reward improvements additionally require624native arrays/allthree4h estimates before atomic promotion.", + "diagnostics": "After full evaluation, use unchanged paired track and remaining-seven variant diagnostics against each own raw parent,2000draws/seed1729. Five splice metrics may change; ca/SPI and eight other official reports are exact. This is not anall13bootstrap. Matched learning/capacity comparisons can be declared separately after both candidates complete.", + "nonimprovement_policy": "A fully scored candidate below the current publication skips optional native latency/publication, but still finishes its declared paired diagnostics. Original raw/other candidate selection pools and GPU schedules are unchanged.", + "limitations": [ + "The fixed marginal correction approximates per-window balanced BCE; no new prior fitting.", + "The verifier applies its unchanged sigmoid to emitted splice probabilities; report all resulting tradeoffs.", + "Intervals are exploratory/unadjusted and one training seed. Runtime extrapolations are shared-load estimates, not guaranteed bounds.", + "No test labels, variant-label training, external parameters or inference caches are used." + ] +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/late-fixed-links81-parent-check.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/late-fixed-links81-parent-check.json new file mode 100644 index 0000000000000000000000000000000000000000..4e85ff1231bb0b51a26679743837f33f5732f829 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/late-fixed-links81-parent-check.json @@ -0,0 +1,53 @@ +{ + "created_utc": "2026-10-25T16:21:07.054587+00:00", + "plan_sha256": "39844848f0b239ada3f89e52a5c1e1e49b473ed6fc4746f20bbb329f194d2bd6", + "actual_completed_parent_checks": [ + { + "experiment": "/workspace/research/experiments/scale13-small-epoch04", + "capacity": "small", + "epoch": 4, + "inference_signature": { + "last.pt": "ae6a15c21e8d9b5900f986f28f91b00962c0030ffc952340b1b3264eef1a6659", + "predict.py": "c257bd031d41b946053e37d5a5759c6c1416d22d9b4d3f403e1fd13e0f63968b", + "perturb.py": "ba37a6f9f8b8eccb052903618bcb4c91d3f4cbed62dfe678ee362644dc80801e", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/alphagenome_50m/model.py": "c61f50a2a66a4f6950c6f6107a70710a5d0ab99a291b8bfaa1eb5565998d2fa3", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919" + }, + "reward": 0.44610804541871724, + "sha256": { + "/workspace/research/experiments/scale13-small-epoch04/evaluation.json": "3193c974189a7ad77a06d0abf64a2c3e002953470660ca20f30d97271e3ffd0a", + "/workspace/research/experiments/scale13-small-epoch04/evaluation-package.json": "e06f0c731873e9ef1a94722d29651c180eefc6c1b54650b52622bad525dd5da7", + "/workspace/research/experiments/scale13-small-epoch04/valid-score/report.json": "3a45ae11ac8ad3897b926283073b5e755b3013a7796aa9837954dec842d7a942", + "/workspace/research/experiments/scale13-small-epoch04/valid-variant-score/report.json": "19224eae63273b62b0421fc48c32704b86418a2bfb21adda2a90449cfa0988d8", + "/workspace/research/experiments/scale13-small-epoch04/package/training_provenance.json": "30c05badb228e6d94b3e70113c4ea671ac2cac370c380d4e4e245ca2a00b00b5" + } + }, + { + "experiment": "/workspace/research/experiments/scale13-large-epoch02", + "capacity": "large", + "epoch": 2, + "inference_signature": { + "last.pt": "6e7c605f2683881b9f59571f042cb7df93a3a0f224d8f2cb4e37be0a123cdb27", + "predict.py": "c257bd031d41b946053e37d5a5759c6c1416d22d9b4d3f403e1fd13e0f63968b", + "perturb.py": "ba37a6f9f8b8eccb052903618bcb4c91d3f4cbed62dfe678ee362644dc80801e", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/alphagenome_50m/model.py": "c61f50a2a66a4f6950c6f6107a70710a5d0ab99a291b8bfaa1eb5565998d2fa3", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919" + }, + "reward": 0.4231233468773853, + "sha256": { + "/workspace/research/experiments/scale13-large-epoch02/evaluation.json": "d2336bfba9e6788cb2ab7dcf01834d825846920c21db43b763f3f5a4de056afb", + "/workspace/research/experiments/scale13-large-epoch02/evaluation-package.json": "cad26f6c8167a550d9a11057e69f2feca299cb091bfce64a95b403a627271610", + "/workspace/research/experiments/scale13-large-epoch02/valid-score/report.json": "eeec21adce86c14356ed29a5bc0e76e84f847bceba41c0faefa8e3a8d6793d4d", + "/workspace/research/experiments/scale13-large-epoch02/valid-variant-score/report.json": "f806730a18850e289cb95020db0ac6f69d4203c8ab53c2ef31845fd798a36f28", + "/workspace/research/experiments/scale13-large-epoch02/package/training_provenance.json": "1eb6eafaaec3f950708cabd7dc43a3ed22e67be602d7c036fe0fbbe0fbbff802" + } + } + ], + "gpu_used": false, + "packages_changed": false, + "no_new_predictions_or_parameter_fitting": true +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/late-fixed-links81-resolved-parents.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/late-fixed-links81-resolved-parents.json new file mode 100644 index 0000000000000000000000000000000000000000..bb11e638ada93a10dc258a86d05668c1d47052af --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/late-fixed-links81-resolved-parents.json @@ -0,0 +1,58 @@ +{ + "master_plan_sha256": "39844848f0b239ada3f89e52a5c1e1e49b473ed6fc4746f20bbb329f194d2bd6", + "member": { + "capacity": "large", + "epoch": 8, + "previous_epoch": 4, + "parent": "/workspace/research/experiments/scale13-large-epoch08", + "previous_raw": "/workspace/research/experiments/scale13-large-epoch04", + "output": "/workspace/research/experiments/calib81-large-epoch08", + "study": "/workspace/research/studies/late_fixed_links81/large-epoch08" + }, + "receipts": { + "parent": { + "experiment": "/workspace/research/experiments/scale13-large-epoch08", + "capacity": "large", + "epoch": 8, + "inference_signature": { + "last.pt": "91f3382e440a1c61731b090dcf04d4de98d706f7c2f7ce956e1ade752e6c57eb", + "predict.py": "c257bd031d41b946053e37d5a5759c6c1416d22d9b4d3f403e1fd13e0f63968b", + "perturb.py": "ba37a6f9f8b8eccb052903618bcb4c91d3f4cbed62dfe678ee362644dc80801e", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/alphagenome_50m/model.py": "c61f50a2a66a4f6950c6f6107a70710a5d0ab99a291b8bfaa1eb5565998d2fa3", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919" + }, + "reward": 0.47738247727682986, + "sha256": { + "/workspace/research/experiments/scale13-large-epoch08/evaluation.json": "3b164a241ad64a50e02557217a9169d91ee0b89d0a3d5317fd0558f0ac60f80d", + "/workspace/research/experiments/scale13-large-epoch08/evaluation-package.json": "0591a542536b2a428332b5976a6ee00b451f3a35fdfe939b4fa1a3b3df513f08", + "/workspace/research/experiments/scale13-large-epoch08/valid-score/report.json": "e3ad0b0bc55f8492f1a256e6b289a87c7a456b49023685f4e0702fb28515e6a2", + "/workspace/research/experiments/scale13-large-epoch08/valid-variant-score/report.json": "99ab33b4d0053b7f6ee5eb1b7bfa22855308ab764b3fddb31dd59594c633906e", + "/workspace/research/experiments/scale13-large-epoch08/package/training_provenance.json": "0391b8c72aa2a3696450422c31dca049dc915d58ac9acb210467af2b5425aba6" + } + }, + "previous": { + "experiment": "/workspace/research/experiments/scale13-large-epoch04", + "capacity": "large", + "epoch": 4, + "inference_signature": { + "last.pt": "62ed23f6e35dff94567b128b4e55e10d7eb81752448acdea521275b3fc7531ae", + "predict.py": "c257bd031d41b946053e37d5a5759c6c1416d22d9b4d3f403e1fd13e0f63968b", + "perturb.py": "ba37a6f9f8b8eccb052903618bcb4c91d3f4cbed62dfe678ee362644dc80801e", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/alphagenome_50m/model.py": "c61f50a2a66a4f6950c6f6107a70710a5d0ab99a291b8bfaa1eb5565998d2fa3", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919" + }, + "reward": 0.457463750328102, + "sha256": { + "/workspace/research/experiments/scale13-large-epoch04/evaluation.json": "d50911309435bd38ec64f0928429b0258701111191cae754f4c0177ee123f794", + "/workspace/research/experiments/scale13-large-epoch04/evaluation-package.json": "e169b3f8604747d147e4108992c4bfe9e3314cc637eface507bdc915a55e56c2", + "/workspace/research/experiments/scale13-large-epoch04/valid-score/report.json": "fa3ff5a189a658dc449e8d511d04ce0f59ab48b395039c011a273dc52ed1ee51", + "/workspace/research/experiments/scale13-large-epoch04/valid-variant-score/report.json": "acc6f6901c41a5ee88176a5094d16d89714f9cd2d9195410222f161613d23d74", + "/workspace/research/experiments/scale13-large-epoch04/package/training_provenance.json": "6589e14349cbd5419c814aa3d292c81f5328f1441bc75bc3ea7cae2b114dcdac" + } + } + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/late_fixed_links81.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/late_fixed_links81.py new file mode 100644 index 0000000000000000000000000000000000000000..28c50d9e3a33f6accbd9ce3cdeca8bdbb9b481a6 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/late_fixed_links81.py @@ -0,0 +1,145 @@ +"""Predeclare fixed-prior output links for the remaining main training checkpoints.""" +import argparse +from datetime import datetime, timezone +import hashlib +import json +import math +from pathlib import Path + +from verify_package import inference_signature, verify_package + +ROOT = Path('/workspace/research') +STUDY = ROOT / 'studies/late_fixed_links81' +HERE = Path(__file__).resolve().parent + + +def digest(path): + with Path(path).open('rb') as stream: + return hashlib.file_digest(stream, 'sha256').hexdigest() + + +def write_json(path, value): + path = Path(path) + temporary = path.with_suffix(path.suffix + '.tmp') + temporary.write_text(json.dumps(value, indent=2, allow_nan=False) + '\n') + temporary.replace(path) + + +def parent_ready(path): + path = Path(path) + try: + return (json.loads((path / 'pipeline-state.json').read_text())['phase'] == 'complete' + and (path / 'evaluation.json').exists()) + except (FileNotFoundError, json.JSONDecodeError, KeyError): + return False + + +def parent_receipt(path, capacity, epoch, plan): + path = Path(path) + assert parent_ready(path), str(path) + package = path / 'package' + verify_package(package) + signature = inference_signature(package) + assert json.loads((path / 'evaluation.json').read_text())['package_inference_signature'] == signature + provenance = json.loads((package / 'training_provenance.json').read_text()) + assert provenance['config'] == plan['capacities'][capacity]['config'] + assert provenance['global_step'] == epoch * 41020 and provenance['optimizer_steps'] == epoch * 10255 + assert provenance['epoch'] == epoch and provenance['offset'] == 0 + assert provenance['seed'] == 17 and provenance['schedule_epochs'] == 14 + assert provenance['objective'] == 'variance_balanced' and provenance['examples'] == 41020 + assert provenance['training_arguments']['batch_size'] == 4 + assert provenance['family_loss_scales'] == plan['family_loss_scales'] + source = provenance['provenance'] + assert source['split'] == 'train' and source['pretrained'] is False and source['world_size'] == 1 + assert source['train_manifest_sha256'] == plan['train_manifest_sha256'] + manifest = json.loads((package / 'package_manifest.json').read_text()) + assert manifest['parameters'] == plan['capacities'][capacity]['parameters'] + assert not any((package / name).exists() for name in [ + 'splice_calibration.json', 'checkpoint_ensemble_policy.json', 'rc_ensemble_policy.json']) + tracks = json.loads((path / 'valid-score/report.json').read_text()) + variants = json.loads((path / 'valid-variant-score/report.json').read_text()) + assert tracks['examples'] == 200 and tracks['invalid_reason'] is None + assert len(tracks['families']) == 11 and len(variants) == 13 + assert all(math.isfinite(value['model']) for value in variants.values()) + return dict(experiment=str(path), capacity=capacity, epoch=epoch, inference_signature=signature, + reward=tracks['overall_mean_family_pearson'], + sha256={str(p): digest(p) for p in [path / 'evaluation.json', path / 'evaluation-package.json', + path / 'valid-score/report.json', path / 'valid-variant-score/report.json', + package / 'training_provenance.json']}) + + +def prepare(): + STUDY.mkdir(exist_ok=True) + assert not (STUDY / 'plan.json').exists() + link69_path = ROOT / 'studies/fixed_link69/plan.json' + link69 = json.loads(link69_path.read_text()) + link61_path = ROOT / 'studies/fixed_link61/plan.json' + link61 = json.loads(link61_path.read_text()) + members = {} + for capacity, epoch, previous in [('large', 4, 2), ('small', 8, 4), ('large', 8, 4), + ('small', 14, 8), ('large', 14, 8)]: + key = f'{capacity}-epoch{epoch:02d}' + members[key] = dict(capacity=capacity, epoch=epoch, previous_epoch=previous, + parent=str(ROOT / 'experiments' / f'scale13-{capacity}-epoch{epoch:02d}'), + previous_raw=str(ROOT / 'experiments' / f'scale13-{capacity}-epoch{previous:02d}'), + output=str(ROOT / 'experiments' / f'calib81-{key}'), study=str(STUDY / key)) + (STUDY / key).mkdir(exist_ok=True) + capacities, evidence = {}, [link69_path, link61_path, ROOT / 'studies/fixed_link57/complete-summary.json', + ROOT / 'studies/scale13/study-cached-io.json', ROOT / 'studies/scale13/loss_scales.json', + Path('/workspace/data/public/train.parquet'), ROOT / 'experiments/calib57/package/splice_calibration.json'] + for capacity in ['small', 'large']: + package = ROOT / 'experiments' / f'scale13-{capacity}-epoch04/package' + provenance_path = package / 'training_provenance.json' + provenance = json.loads(provenance_path.read_text()) + capacities[capacity] = dict(config=provenance['config'], parameters=13290504 if capacity == 'small' else 215626632) + evidence.append(provenance_path) + helpers = set(link69['helper_sha256']) | { + 'late_fixed_links81.py', 'run_late_fixed_links81.py', 'diagnose_track_uncertainty.py', + 'diagnose_remaining_variant_uncertainty.py'} + materialization = dict(link69['materialization']) + materialization['installation'] = 'Atomic research-only alias to prepared-{candidate-name} within each member study; preserve while referenced. Never an inference asset.' + latency = dict(link61['native_latency_template']) + latency['scope'] = 'Native16-window/96-query replay for a completed late fixed-link candidate; all624arrays must equal complete evaluation and allthree4h estimates pass before any publication.' + latency['memory_evidence'] = 'One unchanged small or large main model plus the original four-offset elementwise splice link; require16GiB free for native probes. This is a scheduling guard, not a guaranteed peak bound.' + latency['minimum_free_GiB'] = 16 + plan = dict(created_utc=datetime.now(timezone.utc).isoformat(), members=members, capacities=capacities, + train_manifest_sha256=digest('/workspace/data/public/train.parquet'), + family_loss_scales=json.loads((ROOT / 'studies/scale13/loss_scales.json').read_text())['family_loss_scales'], + training_prior=link69['training_prior'], training_prior_sha256=link69['training_prior_sha256'], + calibration_reference=str(ROOT / 'experiments/calib57/package/splice_calibration.json'), + helper_sha256={str(HERE / name): digest(HERE / name) for name in sorted(helpers)}, + evidence_sha256={str(path): digest(path) for path in evidence}, materialization=materialization, + native_latency_template=latency, evaluation_lock=str(ROOT / 'studies/scale13/evaluation.lock'), + bounded_lock=str(ROOT / 'studies/junction_discovery39/bounded-diagnostic.lock'), + minimum_free_GiB=16, native_timeout_seconds=1800, materialization_timeout_seconds=1800, + parent_minimum_reward_gain=1e-6, publication_minimum_reward_gain=1e-6, + hypothesis='Transfer the same preselected training-prior sigmoid to the remaining main raw checkpoints. No new network updates, constants, orientation, site selection or sparse decoder changes. Prior link gains do not guarantee later track or variant improvements.', + admission='Wait for the declared raw parent and its preceding same-capacity raw checkpoint to finish200windows/2697queries/all13metrics. Require a raw reward gain>1e-6. Parent metadata must match exact whole epochs, all41020intervals, originalseed17/B4/14epochobjective and frozen capacity configs. Freeze actual parent signatures/reports only after completion; do not substitute checkpoints.', + validation='Original builder and verifier helpers; fresh native52/recovery, exact link-only core guard, original full12486array proof and36fresh native arrays, then fresh200track inference and full13officialmetrics using verified research-only perturbation reuse. Require10unaffectedfamily/eightvariant reports exact. Reward improvements additionally require624native arrays/allthree4h estimates before atomic promotion.', + diagnostics='After full evaluation, use unchanged paired track and remaining-seven variant diagnostics against each own raw parent,2000draws/seed1729. Five splice metrics may change; ca/SPI and eight other official reports are exact. This is not anall13bootstrap. Matched learning/capacity comparisons can be declared separately after both candidates complete.', + nonimprovement_policy='A fully scored candidate below the current publication skips optional native latency/publication, but still finishes its declared paired diagnostics. Original raw/other candidate selection pools and GPU schedules are unchanged.', + limitations=['The fixed marginal correction approximates per-window balanced BCE; no new prior fitting.', + 'The verifier applies its unchanged sigmoid to emitted splice probabilities; report all resulting tradeoffs.', + 'Intervals are exploratory/unadjusted and one training seed. Runtime extrapolations are shared-load estimates, not guaranteed bounds.', + 'No test labels, variant-label training, external parameters or inference caches are used.']) + write_json(STUDY / 'plan.json', plan) + print(json.dumps(dict(plan=str(STUDY / 'plan.json'), sha256=digest(STUDY / 'plan.json'), members=list(members))), flush=True) + + +def audit(): + assert not (STUDY / 'parent-check.json').exists() + plan = json.loads((STUDY / 'plan.json').read_text()) + for path, expected in {**plan['helper_sha256'], **plan['evidence_sha256']}.items(): + assert digest(path) == expected, path + receipts = [parent_receipt(ROOT / 'experiments' / name, capacity, epoch, plan) + for name, capacity, epoch in [('scale13-small-epoch04', 'small', 4), ('scale13-large-epoch02', 'large', 2)]] + write_json(STUDY / 'parent-check.json', dict(created_utc=datetime.now(timezone.utc).isoformat(), + plan_sha256=digest(STUDY / 'plan.json'), actual_completed_parent_checks=receipts, + gpu_used=False, packages_changed=False, no_new_predictions_or_parameter_fitting=True)) + print(json.dumps(dict(phase='parent_audit_complete', report_sha256=digest(STUDY / 'parent-check.json'))), flush=True) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('action', choices=['prepare', 'audit']) + globals()[parser.parse_args().action]() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/make_rc_candidate.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/make_rc_candidate.py new file mode 100644 index 0000000000000000000000000000000000000000..e9d24e695a507789457828af4251e6fb468de827 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/make_rc_candidate.py @@ -0,0 +1,100 @@ +"""Build the verified RC17 two-view decoder around a compatible local checkpoint. + +Strict parent-decoder hashes prevent silently applying this extraction to an +incompatible output head or sparse-selection policy. No predictions are cached. +""" +import argparse +from datetime import datetime, timezone +import hashlib +import json +from pathlib import Path +import shutil + +from verify_package import inference_signature, verify_package + + +def digest(path): + with path.open('rb') as stream: + return hashlib.file_digest(stream, 'sha256').hexdigest() + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('parent', type=Path) + parser.add_argument('output', type=Path) + parser.add_argument('--name', default='SeqFunc forward/reverse-complement mean') + parser.add_argument('--decoder-study', type=Path, + default=Path('/workspace/research/studies/rc17')) + args = parser.parse_args() + if args.output.exists(): + raise FileExistsError(args.output) + if (args.parent/'rc_ensemble.py').exists(): + raise ValueError('Parent already has orientation averaging') + verify_package(args.parent) + plan = json.loads((args.decoder_study/'study.json').read_text()) + source = args.decoder_study/'source' + for name in ['predict.py', 'perturb.py']: + expected = plan['source_files_sha256'][name.replace('.py', '-parent.py')] + if digest(args.parent/name) != expected: + raise ValueError(f'Parent {name} differs from the verified extraction; review the decoder before adapting it') + assets = ['predict.py', 'perturb.py', 'rc_ensemble.py', 'single_prediction.py', + 'strand_permutations.json'] + for name in assets: + assert digest(source/name) == plan['source_files_sha256'][name], name + shutil.copytree(args.parent, args.output, ignore=shutil.ignore_patterns('__pycache__')) + manifest = json.loads((args.output/'package_manifest.json').read_text()) + for name in ['validation_tracks.json', 'validation_variants.json']: + (args.output/name).unlink(missing_ok=True) + manifest['files'].pop(name, None) + for name in assets: + shutil.copyfile(source/name, args.output/name) + policy = {key: plan[key] for key in ['method', 'orientation_views', 'fitted_ensemble_weights', + 'weights', 'dna_input', 'site_scores', 'sparse_outputs', 'outputs']} + policy.update(created_utc=datetime.now(timezone.utc).isoformat(), + parent_package=str(args.parent.resolve()), + parent_inference_signature=inference_signature(args.parent), + unique_learned_parameters=manifest['parameters'], + decoder_study=str(args.decoder_study.resolve()), + decoder_assets_sha256={name: digest(source/name) for name in assets}) + (args.output/'rc_ensemble_policy.json').write_text(json.dumps(policy, indent=2)+'\n') + provenance_file = args.output/'training_provenance.json' + provenance = json.loads(provenance_file.read_text()) + provenance['inference_ensemble'] = policy + provenance_file.write_text(json.dumps(provenance, indent=2)+'\n') + readme = args.output/'README.md' + text = readme.read_text().split('\n## Measured validation results')[0] + _, body = text.split('\n', 1) + text = '# '+args.name+'\n'+body + text += f''' +Every output family and both entry points average this same checkpoint on the provided DNA +and its reverse complement, mapped back using vendored public-metadata strand permutations. +There are {manifest['parameters']:,} unique learned parameters and two deterministic views, +weighted equally without fitting development labels. No additional flanking DNA is read. +Dense and contact values average in the contract's asinh space; splice scores average the +training-prior-corrected probabilities. This approximately doubles inference work. + +Each view selects sparse coordinates independently. Their union averages missing-view values +as zero. Usage retains at most4,096 positions; junctions retain16,384 pairs by averaged own-strand +strength, with deterministic coordinate tie handling. Track arrays are averaged in float32 and +then stored in float16; perturbation arrays remain float32. Sparse recall and score dilution +can both change. The frozen decoder passed the RC17 synthetic, native offline and partial-resume +checks; its exact parent-extraction and source fingerprints are enforced by the builder. +This candidate requires its own complete validation track and development variant reports +before publication. Architecture, learned weights and training history are inherited unchanged. +''' + readme.write_text(text) + changed = [*assets, 'rc_ensemble_policy.json', 'training_provenance.json', 'README.md'] + for name in changed: + manifest['files'][name] = digest(args.output/name) + for name in list(manifest['files']): + if '__pycache__' in Path(name).parts: + manifest['files'].pop(name) + (args.output/'package_manifest.json').write_text(json.dumps(manifest, indent=2)+'\n') + result = verify_package(args.output) + assert digest(args.output/'last.pt') == digest(args.parent/'last.pt') + print(json.dumps({'event': 'rc_candidate_built', 'output': str(args.output), + 'weights_unchanged': True, **result}), flush=True) + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/memory-check-interruption.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/memory-check-interruption.json new file mode 100644 index 0000000000000000000000000000000000000000..19bc02cd2784b11f5f3e658efa28017639c2efeb --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/memory-check-interruption.json @@ -0,0 +1,19 @@ +{ + "time_utc": "2026-10-24T21:49:06.154124+00:00", + "reason": "Mechanical activation-checkpoint memory/equivalence check; optimizer, data order and model math preserved.", + "process_group": 4169, + "checkpoints": [ + { + "run": "scale13-small", + "step": 1000, + "path": "/workspace/research/experiments/scale13-small/step-001000.pt", + "sha256": "a53ea6d92c21c06576c734dc05eac6e3f21d33701f25bfc335817a6e6ec197fc" + }, + { + "run": "scale13-large", + "step": 1000, + "path": "/workspace/research/experiments/scale13-large/step-001000.pt", + "sha256": "7398dd28297d6cc3134b8d3b0cea703fb0bb0a8e60b4c0c2d81abe04c1d7dc43" + } + ] +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/parallel-io-amendment.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/parallel-io-amendment.json new file mode 100644 index 0000000000000000000000000000000000000000..48ca3e0d57ff557a5fd2bd2bc517508c6de05b0f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/parallel-io-amendment.json @@ -0,0 +1,27 @@ +{ + "adopted_utc": "2026-10-25T00:42:46.303812+00:00", + "example_boundary": 40000, + "change": "I/O only: direct read and four bounded chunk-read threads per loader process.", + "source_before": "/workspace/research/studies/scale13/source-block-checkpoint", + "source_after": "/workspace/research/studies/scale13/source-parallel-io", + "model_parameters_optimizer_RNG_data_order_objective_schedule_unchanged": true, + "loader_worker_counts_unchanged": true, + "common_immutable_checkpoints": { + "scale13-small": { + "step": 40000, + "path": "/workspace/research/experiments/scale13-small/io-resume-040000.pt", + "sha256": "62daa49a4dc329d0b7cf3ecea0a30834346f714079d975bc636bdd81102f38f2", + "single_inode_copy": true + }, + "scale13-large": { + "step": 40000, + "path": "/workspace/research/experiments/scale13-large/io-resume-040000.pt", + "sha256": "db6c7394de11aba07278c907f328bcf18200c01ab1fb23578b12d293eb7219f4", + "single_inode_copy": true + } + }, + "evidence": "256 full examples through four forked workers and16 integrated-source examples, every tensor byte and ordered ID identical; missing-chunk fixtures pass.", + "loader_parallel_over_reference_time": 0.5340049503280426, + "timing_caveat": "Alternating paired readers under shared training load, including worker startup, full per-example hashing and pinned tensor IPC. File caches are shared; no GPU computation included.", + "no_benchmark_weights_retained": true +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/parallel-io-integration-check.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/parallel-io-integration-check.json new file mode 100644 index 0000000000000000000000000000000000000000..652abba42a037aa1738675ff6c33ac0a769ffe0f --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/parallel-io-integration-check.json @@ -0,0 +1,92 @@ +{ + "created_utc": "2026-10-25T00:29:21.312924+00:00", + "all_returned_training_tensor_bytes_exact": true, + "source_sha256": { + "original": "ad6670f742f4de9898064becc525d0aea98b402abe7b210bb1b26a9eee6402b9", + "parallel": 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"all_ordered_ids_and_tensor_bytes_exact": true + } + ], + "total_read_hash_and_transfer_seconds": { + "reference": 69.81505457599997, + "parallel": 37.28158475100645 + }, + "parallel_over_reference_time": 0.5340049503280426, + "reader_sha256": "ad6670f742f4de9898064becc525d0aea98b402abe7b210bb1b26a9eee6402b9", + "adapter_sha256": "ab182b3b687a3b470ff1bbc311a9130cc791974366bdc0a400df5e0e28602c9c", + "caveat": "Alternating paired readers under shared training load, including worker startup, full per-example hashing and pinned tensor IPC. File caches are shared; no GPU computation included." +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/prepare_rc_large114.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/prepare_rc_large114.py new file mode 100644 index 0000000000000000000000000000000000000000..da903609218bc69e8fd933953513deba2c8e9fde --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/prepare_rc_large114.py @@ -0,0 +1,222 @@ +"""CPU-only preparation of unchanged fixed RC inference on actual large8 weights.""" +from datetime import datetime, timezone +import importlib +import json +import os +from pathlib import Path +import shutil +import subprocess +import sys + +from make_rc_candidate import digest +from verify_package import inference_signature, verify_package + +ROOT=Path('/workspace/research') +HERE=ROOT/'seqfunc' + + +def read(path): + return json.loads(Path(path).read_text()) + + +def save(path,value): + path=Path(path) + assert not path.exists(),path + path.parent.mkdir(parents=True,exist_ok=True) + path.write_text(json.dumps(value,indent=2,allow_nan=False)+'\n') + + +def main(): + study=ROOT/'studies/rc_large114'; output=ROOT/'experiments/rc_large114' + recipe=read(study/'proposed-recipe.json') + assert recipe['parameters']==215626632 and recipe['orientation_weights']==[.5,.5] + assert recipe['additional_training_updates']==0 and not recipe['GPU_evaluation_admitted'] + assert not output.exists() and not (study/'preparation.json').exists() + parent=Path(recipe['parent']); parent_package=parent/'package' + parent_study=ROOT/'studies/late_fixed_links81/large-epoch08' + signature=inference_signature(parent_package) + assert read(parent/'evaluation.json')['package_inference_signature']==signature + tracks=read(parent/'valid-score/report.json'); variants=read(parent/'valid-variant-score/report.json') + assert tracks['examples']==200 and tracks['invalid_reason'] is None and len(tracks['families'])==11 + assert len(variants)==13 + native=read(parent_study/'native-recovery/report.json') + assert native['package_inference_signature']==signature and native['all_arrays_bit_identical'] + assert native['arrays']=={'tracks':30,'perturbations':22} + small_signature=inference_signature(ROOT/'experiments/calib81-small-epoch08/package') + assert set(signature)==set(small_signature) + assert all(v==small_signature[k] for k,v in signature.items() if k!='last.pt') + assert signature['last.pt']=='91f3382e440a1c61731b090dcf04d4de98d706f7c2f7ce956e1ade752e6c57eb' + provenance=read(parent_package/'training_provenance.json') + assert [provenance[k] for k in ['global_step','optimizer_steps','epoch','offset','seed']]==[328160,82040,8,0,17] + assert read(parent_package/'package_manifest.json')['parameters']==215626632 + assert provenance['config']['hidden_dim']==1024 and provenance['config']['num_sequence_blocks']==16 + assert provenance['config']['local_width']==64 and provenance['config']['head_hidden_size']==768 + assert digest(parent_package/'splice_calibration.json')==digest(ROOT/'experiments/rc108/package/splice_calibration.json') + output.mkdir(); package=output/'package' + environment=dict(os.environ,OPENBLAS_NUM_THREADS='1',OMP_NUM_THREADS='2',MKL_NUM_THREADS='2', + PYTHONDONTWRITEBYTECODE='1',PYTHONNOUSERSITE='1') + environment.pop('SEQFUNC_TRAIN_BYTECACHE',None) + with (study/'build.log').open('w') as log: + subprocess.run([sys.executable,str(HERE/'make_rc_candidate.py'),str(parent_package),str(package), + '--name','SeqFunc large eight-pass model with fixed forward/reverse-complement inference'], + env=environment,stdout=log,stderr=subprocess.STDOUT,check=True) + policy=read(package/'rc_ensemble_policy.json') + assert policy['parent_inference_signature']==signature and policy['unique_learned_parameters']==215626632 + assert policy['weights']==[.5,.5] and policy['fitted_ensemble_weights'] is False + assets=['predict.py','perturb.py','rc_ensemble.py','single_prediction.py','strand_permutations.json'] + for name in assets:assert digest(package/name)==digest(ROOT/'experiments/rc108/package'/name),name + assert digest(package/'last.pt')==signature['last.pt'] + assert digest(package/'reference/alphagenome_50m/model.py')==signature['reference/alphagenome_50m/model.py'] + manifest=read(package/'package_manifest.json') + for name in list(manifest['files']): + if name.startswith('validation_'): + (package/name).unlink(missing_ok=True); manifest['files'].pop(name) + history=package/'training_history';history.mkdir(exist_ok=True) + shutil.copy2(study/'proposed-recipe.json',history/'rc-large114-proposed-recipe.json') + for name in [Path(__file__).name,'make_rc_candidate.py']: + shutil.copy2(HERE/name,history/name) + (history/'rc-large114-training-replay.md').write_text( + 'All eight inherited training passes used the unchanged forward model source and original\n' + 'B4 seed17 fourteen-pass schedule. This candidate changes only inference wrappers via\n' + 'make_rc_candidate.py, retaining all model tensors, four training-derived splice offsets\n' + 'and vendored training assets. It adds no optimizer updates or learned parameters.\n') + (package/'README.md').write_text('''# SeqFunc large model with fixed forward/reverse-complement inference + +One model with **215,626,632 unique learned parameters** reads 1,048,576 bp and predicts +all eleven families and 5,930 tracks through both offline entrypoints. It averages the +same learned weights on the supplied DNA and its reverse complement, with fixed equal +weights and vendored public strand permutations. No additional flanking sequence is read. + +The model uses a strided convolutional encoder (initial width96, growth96), sixteen +width1024 transformer blocks over1,024 tokens, and width768 output features. A width64 +base-resolution path has six dilated residual blocks and a10,941-bp receptive field; +its pooled features feed both the heads and the global trunk. Attention and MLP updates +use a per-token RMS cap0.1. Dense outputs cover the central196,608bp; contacts and junctions +use factorized readouts. The original nearest global-feature decoder is retained. + +Each orientation applies the same four training-prior splice offsets before averaging +probabilities. Dense and contact readouts average in asinh space. Sparse positions are +selected independently per view, merged with zero for an unlisted view, and capped at +4,096 usage positions and16,384 junction pairs. The orientation code is exactly the +verified RC17/85/108 implementation. This candidate adds no interpolation, motif filter, +parameter averaging, fitted mixing weights or learned parameters. + +All weights originate from local random initialization with seed17. Training used eight +complete passes through all41,020 eligible FOLD_0 intervals in folds2–7: **328,160 examples +and82,040 AdamW updates**. The supplied reader decodes quantized training targets per +example. BF16 batch4 training uses fixed family-variance scales estimated from256 training +windows, balanced splice BCE, AdamW weight decay1e-4, gradient clipping5, and the original +fourteen-pass cosine schedule2e-4 to2e-5 with4,000 warmup examples. The saved checkpoint +records108,193.2591 accumulated training-loop seconds (30.05h) on one shared H10080GB, +within22CPUs. Setup and evaluation add cost; this is shared wall time rather than exclusive +GPU compute. Numerical training recovery is not bit-exact. This inference change adds zero SGD. + +Run `infer.sh INPUT_ROOT OUTPUT_ROOT` or `perturb.sh INPUT_ROOT OUTPUT_ROOT` offline. +The package vendors CPython3.11, PyTorch2.7.1/CUDA12.6 and dependencies; the host provides +Linux, bash/tar/gzip and the NVIDIA driver. Inputs are public manifests and the pinned +reference only. No targets, supplied splice positions, prediction cache or network are used. +Track arrays are stored as float16 after float32 averaging; perturbation arrays stay float32. + +Candidate validation is pending. Required checks are its own52-array native/offline/recovery +test,200 validation windows, all2,697 development queries and13 metrics with published anchors, +624 representative native timing arrays, and all three combined runtime estimates within4h. +All11/all13 paired comparisons are fixed against the calibrated large forward parent and +RC108. The latter has equal exposure, offsets and views but smaller global/local capacity +and different compute; it is not an independent training replicate. Parent scores are not +candidate scores. The strongest fully gated package remains installed until an improvement. + +Limitations include one seed per size, repeated development selection, pooled local features, +finite local context, low-rank contacts, sparse-union dilution and uneven variant prediction. +Fifty-four GTEx RNA heads have no direct training targets and are excluded from scored RNA +means; eQTL scoring uses supervised ENCODE RNA tracks. There is no conservation input. +Zero-inclusive intervals do not establish equivalence; sampled runtimes are not bounds. +''') + for path in package.rglob('*'): + if path.is_file() and path.name!='package_manifest.json' and '__pycache__' not in path.parts: + name=str(path.relative_to(package)) + if name not in manifest['files'] or name=='README.md':manifest['files'][name]=digest(path) + (package/'package_manifest.json').write_text(json.dumps(manifest,indent=2)+'\n') + verified=verify_package(package) + actual=inference_signature(package) + save(study/'package-before-CPU-check.json',dict(verification=verified,candidate_inference_signature=actual)) + + import numpy as np + import pandas as pd + import torch + os.sched_setaffinity(0,sorted(os.sched_getaffinity(0))[:2]) + torch.set_num_threads(2);torch.set_num_interop_threads(1);torch.manual_seed(1729) + sys.path.insert(0,str(package)) + module=importlib.import_module('reference.alphagenome_50m.model') + rc=importlib.import_module('rc_ensemble') + single=importlib.import_module('single_prediction').single_prediction + splice=importlib.import_module('splice_output').splice_output_scores + payload=torch.load(package/'last.pt',map_location='cpu',weights_only=True) + config=dict(payload['config'],input_length=8192,target_length=4096,gradient_checkpointing=False) + model=module.CompactModel(module.ModelConfig(**config)).eval() + model.load_state_dict(payload['model'],strict=True) + assert module.count_parameters(model)==215626632 + metadata_path=Path('/workspace/data/public/junction_tracks.parquet') + metadata=pd.read_parquet(metadata_path).sort_values('tensor_index') + halves=metadata.junction_channel_half.to_numpy(np.int64) + model.set_junction_track_halves(halves) + engine=rc.engine_for(model) + forward=torch.nn.functional.one_hot(torch.randint(0,4,(2,8192)),4).float() + forward[1]=forward[0].clone();forward[1,4096]=torch.roll(forward[1,4096],1);forward[1,6000:6016]=0 + four=torch.cat((forward,forward.flip((1,2))),dim=0) + usage=torch.tensor([[0,127,128,2048,4095]]).repeat(4,1) + sites=torch.tensor([[[0,1023,4096],[1024,6144,8191],[128,4095,8191],[0,4096,6144]]]).repeat(4,1,1) + queries=torch.tensor([[[0,1,0],[1,0,733],[2,2,367]]]).repeat(4,1,1) + rng=torch.get_rng_state().clone() + with torch.no_grad(): + outputs=model(four,usage_positions=usage,junction_site_positions=sites,junction_queries=queries) + assert len(outputs)==11 and all(torch.isfinite(v).all() for v in outputs.values()) + del outputs + original=single(model,forward[:1]); reverse=single(model,forward[:1].flip((1,2))) + mapped=rc.reverse_readout(reverse,engine.permutations,8192,4096,model.config.contact_bins) + expected=rc.combine_readouts(original,mapped,halves) + observed=engine.tracks(forward[:1]) + assert set(observed)==set(expected) and len(observed)==15 + for name in observed: + assert np.isfinite(observed[name]).all() and np.array_equal(observed[name],expected[name]),name + # Round-trip the actual native sparse/dense arrays through the frozen coordinate map. + back=rc.reverse_readout(rc.reverse_readout(original,engine.permutations,8192,4096,model.config.contact_bins),engine.permutations,8192,4096,model.config.contact_bins) + for name in original:assert np.array_equal(back[name],original[name]),name + families=[*rc.DENSE_TRACKS,'splice_sites'] + requested=engine.dense_and_sites(forward,families) + views=[] + for reverse_view in [False,True]: + trunk=model._trunk(forward.flip((1,2)) if reverse_view else forward) + features=model._dense_features(trunk) + arrays={name:model.dense_heads[name](features).float().numpy() for name in rc.DENSE_TRACKS} + arrays['splice_sites']=splice(model._splice_site_logits(trunk)[:,2048:6144]).float().numpy() + if reverse_view:arrays={name:value[:,::-1][:,:,engine.permutations[name]].copy() for name,value in arrays.items()} + views.append(arrays) + for name in families: + assert np.isfinite(requested[name]).all() + assert np.array_equal(requested[name],(views[0][name]+views[1][name])*np.float32(.5)),name + assert torch.equal(rng,torch.get_rng_state()) + assert all(torch.equal(value,payload['model'][name]) for name,value in model.state_dict().items()) + assert not torch.cuda.is_initialized() + proof=dict(utc=datetime.now(timezone.utc).isoformat(),parameters=215626632, + actual_large8_weights_sha256=digest(package/'last.pt'),candidate_inference_signature=actual, + synthetic_input_bp=8192,synthetic_target_bp=4096,base_model_batch=4, + actual_allele_and_N_edits=True,forward_and_reverse_inputs=True,all11_base_outputs_finite=True, + actual_native_track_arrays_exact_manual_fixed_mean=15, + actual_native_coordinate_and_strand_roundtrip_exact=True, + paired_dense_and_splice_arrays_exact_manual_fixed_mean=8, + all_learned_tensors_and_CPU_RNG_unchanged=True,public_junction_metadata_sha256=digest(metadata_path), + exact_RC108_wrapper_and_permutation_assets=True,CUDA_used=False,training_updates=0,labels_read=False, + caveat='CPU short-input compatibility of the actual large model and unchanged RC operation. It does not establish full1Mb GPU memory/runtime or accuracy; all candidate native/full/runtime gates remain required.') + save(study/'CPU-construction-check.json',proof) + evidence=[study/'proposed-recipe.json',study/'package-before-CPU-check.json',study/'CPU-construction-check.json', + parent/'evaluation.json',parent/'valid-score/report.json',parent/'valid-variant-score/report.json', + parent_study/'native-recovery/report.json',HERE/'make_rc_candidate.py',Path(__file__)] + save(study/'preparation.json',dict(utc=datetime.now(timezone.utc).isoformat(),package=str(package), + package_verification=verified,parent_inference_signature=signature,candidate_inference_signature=actual, + evidence_sha256={str(p):digest(p) for p in evidence},GPU_evaluation_admitted=False, + controller_created=False,GPU_used=False,next='Wait parent original paired completion; review resources and freeze candidate native/full/runtime/dual-control evaluation before execution.')) + print(json.dumps(dict(preparation_sha256=digest(study/'preparation.json'),CPU_proof_sha256=digest(study/'CPU-construction-check.json'), + package=verified,additional_training_updates=0,GPU_used=False)),flush=True) + + +if __name__=='__main__':main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/rc-large114-admission.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/rc-large114-admission.json new file mode 100644 index 0000000000000000000000000000000000000000..aadd5377fcba533d47a3ff0074b204dea71f675d --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/rc-large114-admission.json @@ -0,0 +1,24 @@ +{ + "utc": "2026-10-26T07:40:05.079170+00:00", + "admitted": true, + "training_admitted": false, + "additional_training_updates": 0, + "sole_parent": "/workspace/research/experiments/calib81-large-epoch08", + "parameters": 215626632, + "weights": [ + 0.5, + 0.5 + ], + "resource_review_sha256": "e900e71bbbe7239e766f8a25bea4b18eb9d8f6e59b3de068017841eca7e88f26", + "parent_completion_sha256": "3a684e6d6014cd9c6d3bdb0f8f89865b703df3a5d1279ceb3411626ffb82b666", + "parent_native624_runtime_skipped_nonwinner": true, + "own_native624_runtime_mandatory": true, + "CPU_proof_sha256": "5d0ddbd0993914889b1b2ea5bd83576b5ca0c4459a82e2c0b694ac812e8c999e", + "operator_recovery_context": "/workspace/research/studies/operator_recovery_20261026T064822/recovery.json", + "operator_recovery_sha256": "e3edb6bc383d74cc1f6d8d08c48c4d5265c719a5e86d26c51431539c6e18a0d3", + "research_deadline_utc": "2026-10-28T04:29:08+00:00", + "motivation": "RC108 improves reward and10families against its forward eight-pass small parent, with all13 variant intervals includingzero. At equal eight-pass exposure, rawlarge8 improves missense and worsens enhancer linking versus rawsmall8. Test whether the same fixed orientation averaging helps the large model and report every variant outcome. Neither additivity nor variant rescue is assumed.", + "primary_comparison": "All11/all13 versus the exact calibrated large eight-pass forward parent; isolates the fixed orientation operation on identical weights.", + "secondary_comparison": "All11/all13 versus RC108. Both use eightpasses/seed17/originalschedule/alltrainingintervals, four identical prior offsets and equalF/RC inference. Architecture includes globalcapacity andlocalwidth32-to64 differences; compute and numerical histories differ. One trajectory per size, not independent replication or equalcompute.", + "limitations": "One fixed transfer of equal orientation averaging, not a new trained model or presumed rescue of variant effects. Parent runtime skip is not a pass for this candidate. All own native/full/runtime checks and both serial paired analyses remain required." +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/rc-large114-fixture-check.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/rc-large114-fixture-check.json new file mode 100644 index 0000000000000000000000000000000000000000..310874df6776af8f76cf5e49d34d114785c064e9 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/rc-large114-fixture-check.json @@ -0,0 +1,11 @@ +{ + "utc": "2026-10-26T07:40:05.079170+00:00", + "rows": [ + 16, + 96 + ], + "all_three_manifest_hashes_and_rows_exact": true, + "reference_directory_exact": true, + "labels_read": false, + "GPU_used": false +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/rc-large114-study.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/rc-large114-study.json new file mode 100644 index 0000000000000000000000000000000000000000..0516002875d1eb1268cf08d98ed9359ffe51efb1 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/rc-large114-study.json @@ -0,0 +1,419 @@ +{ + "schema": "seqfunc-fixed-large-RC114/v1", + "created_utc": "2026-10-26T07:40:05.079170+00:00", + "name": "SeqFunc large eight-pass model with fixed forward/reverse-complement inference", + "parent": "/workspace/research/experiments/calib81-large-epoch08", + "parent_inference_signature": { + "last.pt": "91f3382e440a1c61731b090dcf04d4de98d706f7c2f7ce956e1ade752e6c57eb", + "predict.py": 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], + "query_indices": [ + 68, + 101, + 121, + 163, + 179, + 264, + 269, + 437, + 455, + 462, + 476, + 478, + 520, + 522, + 558, + 566, + 586, + 599, + 753, + 841, + 854, + 863, + 1048, + 1053, + 1149, + 1191, + 1213, + 1215, + 1312, + 1320, + 1429, + 1495, + 1575, + 1576, + 1584, + 1592, + 1602, + 1618, + 1622, + 1642, + 1648, + 1660, + 1665, + 1667, + 1680, + 1691, + 1713, + 1727, + 1747, + 1805, + 1816, + 1820, + 1822, + 1835, + 1842, + 1851, + 1857, + 1861, + 1863, + 1868, + 1891, + 1892, + 1896, + 1901, + 1918, + 1920, + 1937, + 1950, + 1961, + 1978, + 1989, + 2000, + 2013, + 2018, + 2020, + 2031, + 2038, + 2054, + 2067, + 2093, + 2111, + 2159, + 2163, + 2205, + 2334, + 2466, + 2469, + 2485, + 2511, + 2517, + 2526, + 2573, + 2617, + 2660, + 2667, + 2694 + ], + "query_strata": [ + { + "stratum": { + "families": [ + "atac", + "dnase", + "procap", + "cage", + "rna_seq", + "chip_tf", + "chip_histone" + ], + "edit": "snv", + "dense_bins": 17, + "splice_bases": 0 + }, + "development_count": 591, + "selected_indices": [ + 2111, + 2159, + 2163, + 2205, + 2334, + 2466, + 2469, + 2485, + 2511, + 2517, + 2526, + 2573, + 2617, + 2660, + 2667, + 2694 + ] + }, + { + "stratum": { + "families": [ + "chip_tf" + ], + "edit": "snv", + "dense_bins": 4, + "splice_bases": 0 + }, + "development_count": 160, + "selected_indices": [ + 1747, + 1805, + 1816, + 1820, + 1822, + 1835, + 1842, + 1851, + 1857, + 1861, + 1863, + 1868, + 1891, + 1892, + 1896, + 1901 + ] + }, + { + "stratum": { + "families": [ + "dnase" + ], + "edit": "snv", + "dense_bins": 4, + "splice_bases": 0 + }, + "development_count": 198, + "selected_indices": [ + 1575, + 1576, + 1584, + 1592, + 1602, + 1618, + 1622, + 1642, + 1648, + 1660, + 1665, + 1667, + 1680, + 1691, + 1713, + 1727 + ] + }, + { + "stratum": { + "families": [ + "rna_seq" + ], + "edit": "mask", + "dense_bins": 1536, + "splice_bases": 0 + }, + "development_count": 200, + "selected_indices": [ + 1918, + 1920, + 1937, + 1950, + 1961, + 1978, + 1989, + 2000, + 2013, + 2018, + 2020, + 2031, + 2038, + 2054, + 2067, + 2093 + ] + }, + { + "stratum": { + "families": [ + "rna_seq" + ], + "edit": "snv", + "dense_bins": 1536, + "splice_bases": 0 + }, + "development_count": 567, + "selected_indices": [ + 68, + 101, + 121, + 163, + 179, + 264, + 269, + 437, + 455, + 462, + 476, + 478, + 520, + 522, + 558, + 566 + ] + }, + { + "stratum": { + "families": [ + "splice_sites" + ], + "edit": "snv", + "dense_bins": 0, + "splice_bases": 5001 + }, + "development_count": 981, + "selected_indices": [ + 586, + 599, + 753, + 841, + 854, + 863, + 1048, + 1053, + 1149, + 1191, + 1213, + 1215, + 1312, + 1320, + 1429, + 1495 + ] + } + ], + "selection_seed": 1729, + "helper_sha256": { + "measure_native_latency.py": "dd8ce9249d29a3814603d49cb3d3820d2a3a103e8f15f2266597a40e1e4bf363", + "verify_package.py": "23042e356bddbaa59f2abb1e094196b75154b7a59033284295c02885bb4991d4" + }, + "public_inputs_sha256": { + "public/test.parquet": "2d5242a0e61f3a99ccbcdcd0ce7d4f8c8ff0c9f7d0db00a00d2abe3a26ab08c8", + "public/perturbations.parquet": "0cb5ce07ffc6acf55d411acb8389f1dc59257ac89e5ce3af0bf0ba05d42478d8", + "public/junction_tracks.parquet": "8b2f8888ae06779b487e086c5c49234e90452d03966debe0bea9767a555a00d4" + }, + "original_manifests_sha256": { + "public/test.parquet": "a8f9cc119e8e6eeca63fe768f5b1b6a5b623942930ca654e4c0ba89b0bfc83f6", + "public/perturbations.parquet": "a95b37553157f1964d134b64eb30f149d8577f20d6d6e6e834c8b30fe7022aed", + "public/junction_tracks.parquet": "8b2f8888ae06779b487e086c5c49234e90452d03966debe0bea9767a555a00d4" + }, + "bounded_diagnostic_lock": "/workspace/research/studies/junction_discovery39/bounded-diagnostic.lock", + "minimum_free_GiB": 24, + "timeout_seconds_per_entrypoint": 1200, + "published_test_counts": { + "tracks": 1000, + "perturbations": 6587, + "source": "contract.md and data/README.md only; no test manifest or labels read" + }, + "memory_evidence": "215,626,632-parameter model with two sequential orientations;24GiB free-memory scheduling guard is not a measured peak or bound. Own native replay and timing required.", + "scope": "RC-large114 own16validationwindows/96developmentqueries/624nativearrays after ownfull200/2697/all13. Allthree4h estimates mandatory even though parent runtime was skipped.", + "limitations": [ + "Shared GPU, CPU and storage load; not isolated inference and not a guaranteed upper bound.", + "Random16 validation windows and16 queries in each of6 public request/edit/span strata; no test-manifest query mix or test targets accessed.", + "Startup plus first-example and post-last-example cleanup measured together; later rates include native model, IO and compression. No cost subtracted as free setup.", + "Extrapolations use documented1000/6587 counts, observed validation mixture, slowest sampled stratum mean and pooled95th percentile separately; none proves unseen-query latency.", + "File caches and concurrent load can differ across runs. Native accuracy replay remains separate from the runtime estimate.", + "Require allthree estimates <=14400s; shared-load estimates are not guaranteed upperbounds." + ], + "input_preparation_amendment": { + "created_utc": "2026-10-25T06:45:46.228017+00:00", + "prior_plan_sha256": "809e68426bb2b6e6e341d68387f818adc19d37792fafcc4f56d970f8ef3cac18", + "change": "Include unchanged public junction-track metadata required by the original dataset reader. No query, sequence, model, timing design or target/label change. Waiting controller stopped before any native execution." + } + }, + "only_change": "Apply the exact unchanged RC17/85/108 inference assets to sole calibrated81-large8. Every family and both entrypoints use the same two deterministic orientations; retain learned weights, four training-derived prior offsets, nearest global-feature decoder and original per-view sparse selection. No interpolation, SWA, joint-position or motif-mask composition.", + "motivation": "RC108 improves reward and10families against its forward eight-pass small parent, with all13 variant intervals includingzero. At equal eight-pass exposure, rawlarge8 improves missense and worsens enhancer linking versus rawsmall8. Test whether the same fixed orientation averaging helps the large model and report every variant outcome. Neither additivity nor variant rescue is assumed.", + "mandatory_checks": [ + "strict exactparentdecoder andRCassets unchanged", + "fresh52native/recoveryarrays", + "full200tracks/2697perturbations/13officialmetrics", + "same-row anchors/eligibility identical toparent", + "fresh16window96querynative replay and allthree runtimeestimates<=14400seconds", + "complete reward improvement before atomicpublication" + ], + "recovery": "Check controller.lock and processes before restart. Saved native/evaluation/timing markers must match signatures. Preserve partial output for normalresume; if packagebuild was interrupted, inspect and archive the incomplete package before restarting. No new training or model resume checkpoint.", + "caveat": "Exploratory postselection on repeatedly inspected development data. Resampling schemes are not independent replications; zero-inclusive intervals are not equivalence. No variant-label fitting, pretrained external parameters, test data, new sequence flank or inference cache. One GPU and unchanged existing trainers.", + "paired_plan_sha256": "81c750c4e1302d17a897d3474ee6b62c2274a65f89cf1fa5376510ea387c6332" +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/rc-large114-training-replay.md b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/rc-large114-training-replay.md new file mode 100644 index 0000000000000000000000000000000000000000..4a89eafb88068244b603723389ed9736a4fcfeb1 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/rc-large114-training-replay.md @@ -0,0 +1,4 @@ +All eight inherited training passes used the unchanged forward model source and original +B4 seed17 fourteen-pass schedule. This candidate changes only inference wrappers via +make_rc_candidate.py, retaining all model tensors, four training-derived splice offsets +and vendored training assets. It adds no optimizer updates or learned parameters. diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/reader-before-parallel-io.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/reader-before-parallel-io.py new file mode 100644 index 0000000000000000000000000000000000000000..71cd39d15713f648e7d7e31aa1923fa4fa4800dd --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/reader-before-parallel-io.py @@ -0,0 +1,229 @@ +"""Streaming reader for the compact 1 Mb / eleven-family release.""" + +from __future__ import annotations + +from collections import OrderedDict +from pathlib import Path + +import numpy as np +import pandas as pd +import pysam +import torch +from torch.utils.data import Dataset +import zarr + + +def _open_part(path: Path): + if str(path).endswith(".zarr.zip"): + store = zarr.ZipStore(str(path), mode="r") + try: + return zarr.open_group(store=store, mode="r"), store + except BaseException: + store.close() + raise + return zarr.open_group(str(path), mode="r"), None + + +DENSE_FAMILIES = ( + "atac", + "dnase", + "procap", + "cage", + "rna_seq", + "chip_tf", + "chip_histone", +) +def normalize_splice_positions(value) -> np.ndarray: + """Normalize Arrow's nested-list representation to the public [4, 512] array.""" + + outer = np.asarray(value, dtype=object).reshape(-1).tolist() + positions = np.stack([np.asarray(item, dtype=np.int64) for item in outer]) + if positions.shape != (4, 512): + raise ValueError(f"splice_site_positions must have shape (4, 512), got {positions.shape}") + return positions + + +class AlphaGenomeDataset(Dataset): + """Resolve frozen coordinates to reference DNA and one Zarr example group.""" + + def __init__( + self, + data_root: str | Path, + split: str, + *, + max_open_parts: int = 8, + include_targets: bool = True, + ) -> None: + self.root = Path(data_root) + self.split = split + manifest = self.root / "public" / f"{split}.parquet" + if not manifest.is_file(): + raise FileNotFoundError(f"Missing materialized manifest: {manifest}") + self.rows = pd.read_parquet(manifest).reset_index(drop=True) + junction_tracks = pd.read_parquet(self.root / "public/junction_tracks.parquet") + junction_tracks = junction_tracks.sort_values("tensor_index") + self.junction_track_halves = torch.from_numpy( + junction_tracks.junction_channel_half.to_numpy(np.int64) + ) + if include_targets: + labels_path = self.root / "labels" / f"{split}.parquet" + if not labels_path.is_file(): + raise FileNotFoundError(f"Missing trusted labels manifest: {labels_path}") + labels = pd.read_parquet(labels_path).reset_index(drop=True) + if self.rows.example_id.astype(str).tolist() != labels.example_id.astype(str).tolist(): + raise ValueError("public and labels manifests have different IDs/order") + self.rows = self.rows.merge(labels, on="example_id", validate="one_to_one", sort=False) + self.fasta_path = self.root / "reference" / "GRCh38.p13.genome.fa" + self.max_open_parts = max_open_parts + self.include_targets = include_targets + self._fasta = None + self._parts: OrderedDict[str, tuple[zarr.Group, object | None]] = OrderedDict() + + def __len__(self) -> int: + return len(self.rows) + + def _reference(self): + if self._fasta is None: + self._fasta = pysam.FastaFile(str(self.fasta_path)) + return self._fasta + + def _part(self, relative: str): + if relative in self._parts: + opened = self._parts.pop(relative) + self._parts[relative] = opened + return opened[0] + group, store = _open_part(self.root / relative) + if len(self._parts) >= self.max_open_parts: + _, (_, old_store) = self._parts.popitem(last=False) + if old_store is not None: + old_store.close() + self._parts[relative] = (group, store) + return group + + @staticmethod + def _one_hot(sequence: str) -> np.ndarray: + encoded = np.frombuffer(sequence.upper().encode("ascii"), dtype=np.uint8) + output = np.zeros((len(encoded), 4), dtype=np.float32) + for index, letter in enumerate(b"ACGT"): + output[:, index] = encoded == letter + return output + + def _sequence(self, row) -> tuple[np.ndarray, np.ndarray]: + reference = self._reference() + chromosome_length = reference.get_reference_length(str(row.chromosome)) + start, end = int(row.input_start), int(row.input_end) + clipped_start, clipped_end = max(0, start), min(chromosome_length, end) + sequence = reference.fetch(str(row.chromosome), clipped_start, clipped_end) + left, right = clipped_start - start, end - clipped_end + if left or right: + sequence = "N" * left + sequence + "N" * right + valid = np.ones(end - start, dtype=bool) + if left: + valid[:left] = False + if right: + valid[-right:] = False + return self._one_hot(sequence), valid + + @staticmethod + def _decode(example, name: str, *, asinh: bool) -> np.ndarray: + """Train targets may be stored as 8-bit codes with per-track ranges; they are decoded + here, one example at a time, so the release stays compressed on disk.""" + array = example[name] + if array.dtype != np.uint8: + return np.asarray(array, dtype=np.float32) + lo = np.asarray(example[f"{name}_lo"], dtype=np.float32) + hi = np.asarray(example[f"{name}_hi"], dtype=np.float32) + x = lo + np.asarray(array, dtype=np.float32) * ((hi - lo) / 255.0) + return np.sinh(x) if asinh else x + + @staticmethod + def _dense_target(example, family: str) -> dict[str, torch.Tensor]: + values = AlphaGenomeDataset._decode(example, f"{family}/values", asinh=True) + mask = np.asarray(example[f"{family}/mask"], dtype=bool) + if mask.ndim == 1: + mask = np.broadcast_to(mask[None, :], values.shape).copy() + return { + "values": torch.from_numpy(values), + "mask": torch.from_numpy(mask), + } + + def __getitem__(self, item: int): + row = self.rows.iloc[item] + dna, input_mask = self._sequence(row) + result = { + "example_id": str(row.example_id), + "dna": torch.from_numpy(dna), + "input_mask": torch.from_numpy(input_mask), + "junction_track_halves": self.junction_track_halves, + } + if not self.include_targets: + return result + + part = self._part(str(row.target_shard)) + example = part[str(row.target_group)] + result["dense"] = { + family: self._dense_target(example, family) for family in DENSE_FAMILIES + } + result["splice_sites"] = torch.from_numpy( + np.asarray(example["splice_sites/values"], dtype=np.float32) + ) + result["splice_usage"] = { + "positions": torch.from_numpy( + np.asarray(example["splice_site_usage/positions"], dtype=np.int64) + ), + "values": torch.from_numpy( + np.asarray(example["splice_site_usage/values"], dtype=np.float32) + ), + "mask": torch.from_numpy( + np.asarray(example["splice_site_usage/mask"], dtype=bool) + ), + } + result["usage_positions"] = result["splice_usage"]["positions"] + result["junctions"] = { + name: torch.from_numpy( + np.asarray( + example[f"splice_junctions/{name}"], + dtype=np.float32 if name == "value" else np.int64, + ) + ) + for name in ("row", "column", "track", "value") + } + result["junctions"]["site_positions"] = torch.from_numpy( + np.asarray(example["splice_junctions/site_positions"], dtype=np.int64) + ) + site_positions = normalize_splice_positions(row.splice_site_positions) + result["site_positions"] = torch.from_numpy(site_positions) + if not torch.equal(result["junctions"]["site_positions"], result["site_positions"]): + raise ValueError(f"public/private splice positions differ for {row.example_id}") + scoring_path = self.root / "scoring" / self.split / f"part-{item:06d}.parquet" + scoring = pd.read_parquet(scoring_path) + if not (scoring.example_id.astype(str) == str(row.example_id)).all(): + raise ValueError(f"scoring part ID differs for {row.example_id}") + result["junction_scoring"] = { + "donor_position": torch.from_numpy( + scoring.donor_position.to_numpy(np.int64) + ), + "acceptor_position": torch.from_numpy( + scoring.acceptor_position.to_numpy(np.int64) + ), + "track": torch.from_numpy(scoring.track.to_numpy(np.int64)), + "target_value": torch.from_numpy( + scoring.target_value.to_numpy(np.float32) + ), + "valid": torch.from_numpy(scoring.valid.to_numpy(bool)), + } + result["contact_maps"] = torch.from_numpy( + self._decode(example, "contact_maps/upper_triangle", asinh=False) + ) + result["contact_map_mask"] = torch.from_numpy( + np.asarray(example["contact_maps/mask"], dtype=bool) + ) + return result + + +def single_example_collate(items): + """Keep variable-size event tensors intact; the reference currently trains at batch one.""" + + if len(items) != 1: + raise ValueError("The compact event loader currently requires batch_size=1") + return items[0] diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/run_fixed_rc_with_runtime.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/run_fixed_rc_with_runtime.py new file mode 100644 index 0000000000000000000000000000000000000000..1e3d2fcdeacf36475b6d78ef2f7cd04e2de4d654 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/run_fixed_rc_with_runtime.py @@ -0,0 +1,142 @@ +"""Evaluate one frozen RC parent with complete replay, accuracy and runtime gates.""" +import argparse +from datetime import datetime, timezone +import fcntl +import hashlib +import json +import math +import os +from pathlib import Path +import signal +import subprocess +import sys +import time + +from verify_package import inference_signature, verify_package + + +def digest(path): + with Path(path).open('rb') as stream: + return hashlib.file_digest(stream, 'sha256').hexdigest() + + +def run(plan_path): + plan = json.loads(plan_path.read_text()) + study, here = plan_path.parent, Path(__file__).resolve().parent + for name, expected in plan['helper_sha256'].items(): + assert digest(here/name) == expected, name + for path, expected in plan['evidence_sha256'].items(): + assert digest(path) == expected, path + assert plan['weights'] == [.5, .5] and plan['additional_training_updates'] == 0 + parent, output = Path(plan['parent']), Path(plan['output']) + output.mkdir(exist_ok=True) + package = output/'package' + assert inference_signature(parent/'package') == plan['parent_inference_signature'] + parent_eval = json.loads((parent/'evaluation.json').read_text()) + assert parent_eval['package_inference_signature'] == plan['parent_inference_signature'] + parent_tracks = json.loads((parent/'valid-score/report.json').read_text()) + parent_variants = json.loads((parent/'valid-variant-score/report.json').read_text()) + assert parent_tracks['examples'] == 200 and parent_tracks['invalid_reason'] is None + assert len(parent_tracks['families']) == 11 and len(parent_variants) == 13 + os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) + environment = dict(os.environ, OPENBLAS_NUM_THREADS='1', OMP_NUM_THREADS='2', + MKL_NUM_THREADS='2', PYTHONDONTWRITEBYTECODE='1', PYTHONNOUSERSITE='1') + for key in ['PYTHONPATH', 'PYTHONHOME', 'SEQFUNC_TRAIN_BYTECACHE']: + environment.pop(key, None) + + def status(phase, **extra): + row = dict(phase=phase, utc=datetime.now(timezone.utc).isoformat(), **extra) + temporary = study/'state.tmp' + temporary.write_text(json.dumps(row, indent=2)+'\n') + temporary.replace(study/'state.json') + print(json.dumps(row), flush=True) + + def execute(phase, arguments, timeout=None): + status(phase) + with (study/(phase+'.log')).open('a') as log: + process = subprocess.Popen([sys.executable, *map(str, arguments)], env=environment, + stdout=log, stderr=subprocess.STDOUT, start_new_session=True) + try: + assert process.wait(timeout=timeout) == 0, phase + finally: + if process.poll() is None: + os.killpg(process.pid, signal.SIGTERM) + try: + process.wait(timeout=10) + except subprocess.TimeoutExpired: + os.killpg(process.pid, signal.SIGKILL) + process.wait() + + if not package.exists(): + execute('build', [here/'make_rc_candidate.py', parent/'package', package, + '--name', plan['name']]) + verify_package(package) + policy = json.loads((package/'rc_ensemble_policy.json').read_text()) + assert policy['parent_inference_signature'] == plan['parent_inference_signature'] + assert policy['weights'] == [.5, .5] + assert policy['unique_learned_parameters'] == plan['parameters'] + assert digest(package/'last.pt') == digest(parent/'package/last.pt') + signature = inference_signature(package) + native = study/'native-recovery' + with Path(plan['evaluation_lock']).open('a') as lock: + status('waiting_for_native_and_full_evaluation_slot') + fcntl.flock(lock, fcntl.LOCK_EX) + while int(subprocess.check_output(['nvidia-smi', '--query-gpu=memory.free', + '--format=csv,noheader,nounits'], text=True).strip()) < plan['minimum_free_GiB']*1024: + time.sleep(5) + if not (native/'report.json').exists(): + execute('native', [here/'check_native_recovery.py', package, native], + timeout=plan['native_timeout_seconds']) + proof = json.loads((native/'report.json').read_text()) + assert proof['package_inference_signature'] == signature + assert proof['all_arrays_bit_identical'] and proof['native_vendored_entrypoints_passed'] + assert proof['partial_perturbation_recovery_passed'] + assert proof['arrays'] == {'tracks': 30, 'perturbations': 22} + if not (output/'evaluation.json').exists(): + execute('evaluate', [here/'evaluate_package.py', package, output, + '--resume-partial-tracks', '--resume-partial-perturbations']) + assert json.loads((output/'evaluation.json').read_text())['package_inference_signature'] == signature + tracks = json.loads((output/'valid-score/report.json').read_text()) + variants = json.loads((output/'valid-variant-score/report.json').read_text()) + assert tracks['examples'] == 200 and tracks['invalid_reason'] is None + assert len(tracks['families']) == 11 and len(variants) == 13 + assert all(math.isfinite(v['model']) for v in variants.values()) + for name, value in variants.items(): + for key in ['rows', 'perturbations', 'metric', 'anchors']: + assert value[key] == parent_variants[name][key], (name, key) + comparison = dict(created_utc=datetime.now(timezone.utc).isoformat(), + reward_delta=tracks['overall_mean_family_pearson']-parent_tracks['overall_mean_family_pearson'], + family_deltas={k:v['mean_pearson']-parent_tracks['families'][k]['mean_pearson'] + for k,v in tracks['families'].items()}, + variant_deltas={k:v['model']-parent_variants[k]['model'] for k,v in variants.items()}) + (study/'parent-comparison.json').write_text(json.dumps(comparison, indent=2)+'\n') + latency = study/'native-latency' + timing_plan = dict(plan['native_latency_template'], experiment=str(output), inference_signature=signature) + if (latency/'plan.json').exists(): + assert json.loads((latency/'plan.json').read_text()) == timing_plan + else: + (latency/'plan.json').write_text(json.dumps(timing_plan, indent=2)+'\n') + execute('native-latency', [here/'measure_native_latency.py', latency/'plan.json']) + timing = json.loads((latency/'report.json').read_text()) + assert timing['all_arrays_bit_exact'] and timing['package_inference_signature'] == signature + estimates = timing['extrapolated_seconds'] + keys = ['development_mixture_seconds', 'slowest_observed_stratum_mean_seconds', 'pooled_p95_seconds'] + assert all(math.isfinite(estimates[k]) for k in keys) + passed = max(estimates[k] for k in keys) <= 14400 + (study/'runtime-gate.json').write_text(json.dumps(dict(passed=passed, estimates=estimates, + report_sha256=digest(latency/'report.json'), + scope='All three predeclared shared-load sample estimates must fit4h; none is a guaranteed upper bound.'), + indent=2)+'\n') + if passed: + execute('publish', [here/'promote_if_better.py', package, output]) + status('complete' if passed else 'complete_accuracy_runtime_gate_failed', + runtime_passed=passed, reward=tracks['overall_mean_family_pearson'], **comparison) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('plan', type=Path) + args = parser.parse_args() + with (args.plan.parent/'controller.lock').open('a') as controller: + fcntl.flock(controller, fcntl.LOCK_EX | fcntl.LOCK_NB) + run(args.plan) diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/run_late_fixed_links81.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/run_late_fixed_links81.py new file mode 100644 index 0000000000000000000000000000000000000000..3e3973883128f4aea458210d43790edce69d380a --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/run_late_fixed_links81.py @@ -0,0 +1,268 @@ +"""Execute one predeclared late fixed-link follow-up after its complete raw parent.""" +import argparse +from datetime import datetime, timezone +import fcntl +import json +import math +import os +from pathlib import Path +import signal +import subprocess +import sys +import time + +from check_splice_link_reuse import check_link_only +from late_fixed_links81 import ROOT, STUDY, HERE, digest, write_json, parent_ready, parent_receipt +from verify_package import inference_signature, verify_package + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('member') + args = parser.parse_args() + plan_path = STUDY / 'plan.json' + plan = json.loads(plan_path.read_text()) + member = plan['members'][args.member] + study, parent, previous, output = [Path(member[k]) for k in ['study','parent','previous_raw','output']] + lock = (study / 'controller.lock').open('a') + fcntl.flock(lock, fcntl.LOCK_EX | fcntl.LOCK_NB) + complete = study / 'complete.json' + if complete.exists(): + assert json.loads(complete.read_text())['master_plan_sha256'] == digest(plan_path) + print(json.dumps(dict(phase='already_complete_no_rerun', member=args.member)), flush=True) + return + for path, expected in {**plan['helper_sha256'], **plan['evidence_sha256']}.items(): + assert digest(path) == expected, path + assert digest(plan['training_prior']) == plan['training_prior_sha256'] + audit = json.loads((STUDY / 'parent-check.json').read_text()) + assert audit['plan_sha256'] == digest(plan_path) and len(audit['actual_completed_parent_checks']) == 2 + os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) + environment = dict(os.environ, OPENBLAS_NUM_THREADS='1', OMP_NUM_THREADS='2', MKL_NUM_THREADS='2', + PYTHONNOUSERSITE='1', PYTHONDONTWRITEBYTECODE='1') + for key in ['PYTHONPATH','PYTHONHOME','SEQFUNC_TRAIN_BYTECACHE']: + environment.pop(key, None) + + def status(phase, **extra): + record = dict(phase=phase, utc=datetime.now(timezone.utc).isoformat(), member=args.member, **extra) + write_json(study / 'state.json', record) + print(json.dumps(record), flush=True) + + def finish(phase, **extra): + write_json(complete, dict(phase=phase, master_plan_sha256=digest(plan_path), + created_utc=datetime.now(timezone.utc).isoformat(), **extra)) + status(phase, **extra) + + def execute(phase, arguments, timeout=None): + status(phase) + with (study / f'{phase}.log').open('a') as stream: + process = subprocess.Popen([sys.executable, *map(str, arguments)], env=environment, + stdout=stream, stderr=subprocess.STDOUT, start_new_session=True) + try: + code = process.wait(timeout=timeout) + assert code == 0, (phase, code) + finally: + if process.poll() is None: + os.killpg(process.pid, signal.SIGTERM) + try:process.wait(timeout=10) + except subprocess.TimeoutExpired: + os.killpg(process.pid, signal.SIGKILL); process.wait() + + def wait_memory(): + while int(subprocess.check_output(['nvidia-smi','--query-gpu=memory.free', + '--format=csv,noheader,nounits'], text=True).strip()) < plan['minimum_free_GiB'] * 1024: + time.sleep(5) + + status('waiting_for_complete_declared_raw_parents') + while not (parent_ready(parent) and parent_ready(previous)): + time.sleep(10) + receipts = dict(parent=parent_receipt(parent, member['capacity'], member['epoch'], plan), + previous=parent_receipt(previous, member['capacity'], member['previous_epoch'], plan)) + resolved_path = study / 'resolved-parents.json' + resolved = dict(master_plan_sha256=digest(plan_path), member=member, receipts=receipts) + if resolved_path.exists():assert json.loads(resolved_path.read_text()) == resolved + else:write_json(resolved_path, resolved) + admitted = receipts['parent']['reward'] > receipts['previous']['reward'] + plan['parent_minimum_reward_gain'] + gate = dict(admitted=admitted, parent_reward=receipts['parent']['reward'], + previous_raw_reward=receipts['previous']['reward'], minimum_gain=plan['parent_minimum_reward_gain']) + if not (study / 'parent-gate.json').exists():write_json(study / 'parent-gate.json', gate) + else:assert json.loads((study / 'parent-gate.json').read_text()) == gate + if not admitted: + finish('rejected_at_parent_gain_gate', **gate) + return + output.mkdir(exist_ok=True) + package = output / 'package' + construction_path = study / 'construction-check.json' + if not construction_path.exists(): + if package.exists(): + try:verify_package(package);check_link_only(parent / 'package', package) + except (OSError,ValueError,KeyError,AssertionError): + package.rename(package.with_name('package-incomplete-' + str(time.time_ns()))) + if not package.exists(): + execute('build', [HERE / 'make_splice_probability_candidate.py', parent / 'package', package, + '--training-prior', plan['training_prior']]) + checks = check_link_only(parent / 'package', package) + assert checks['parent'] == receipts['parent']['inference_signature'] + assert json.loads((package / 'package_manifest.json').read_text())['parameters'] == plan['capacities'][member['capacity']]['parameters'] + before = (parent / 'package/predict.py').read_text() + marker = 'from reference.alphagenome_50m.model import CompactModel, ModelConfig\n' + old = 'data=all_site_logits[0, target_start:target_start + config.target_length].cpu().numpy().astype(np.float16),' + new = 'data=splice_output_scores(all_site_logits[0, target_start:target_start + config.target_length]).cpu().numpy().astype(np.float16),' + assert before.count(marker) == before.count(old) == 1 + expected = before.replace(marker, marker + 'from splice_output import splice_output_scores\n').replace(old, new) + assert (package / 'predict.py').read_text() == expected + for name in ['infer.sh','perturb.sh']: + assert digest(package / name) == digest(parent / 'package' / name) + assert json.loads((package / 'splice_calibration.json').read_text()) == json.loads(Path(plan['calibration_reference']).read_text()) + history = package / 'training_history'; history.mkdir(exist_ok=True) + additions = {'late-fixed-links81-master-plan.json': plan_path, + 'late-fixed-links81-resolved-parents.json': resolved_path, + 'late-fixed-links81-parent-check.json': STUDY / 'parent-check.json', + 'late_fixed_links81.py': HERE / 'late_fixed_links81.py', + 'run_late_fixed_links81.py': Path(__file__)} + for name, source in additions.items(): + (history / name).write_bytes(source.read_bytes()) + readme = package / 'README.md' + body = readme.read_text().replace( + 'In preliminary\ndiagnostics, MFASS and site Pearson improved while sQTL and some ClinVar metrics regressed.', + "The complete reports below determine this checkpoint's track and variant tradeoffs.") + note = (f'\nThis predeclared late fixed-link follow-up uses the complete {member["epoch"]}-epoch ' + f'{member["capacity"]} main model. It applies the original four training-only offsets ' + 'without refitting, SGD, orientation changes, or changes to site selection and sparse decoding. ' + 'Both entry points use the same link. All200 validation windows and all13 development metrics ' + 'are scored, with exact checks for ten unchanged family reports and eight unchanged variant reports. ' + 'A publication additionally requires native replay and three sampled runtime estimates below four hours.\n') + if note.strip() not in body:body += note + readme.write_text(body) + manifest = json.loads((package / 'package_manifest.json').read_text()) + for name in ['README.md', *['training_history/' + name for name in additions]]: + manifest['files'][name] = digest(package / name) + write_json(package / 'package_manifest.json', manifest) + verification = verify_package(package) + write_json(construction_path, dict(created_utc=datetime.now(timezone.utc).isoformat(), + master_plan_sha256=digest(plan_path), resolved_parents_sha256=digest(resolved_path), + package_inference_signature=inference_signature(package), exact_link_only_checks=checks, + parameters=manifest['parameters'], network_updates=0, prior_refitted=False, package_verified=True)) + construction = json.loads(construction_path.read_text()) + assert construction['master_plan_sha256'] == digest(plan_path) + assert construction['resolved_parents_sha256'] == digest(resolved_path) + verify_package(package) + signature = inference_signature(package) + assert construction['package_inference_signature'] == signature + checks = check_link_only(parent / 'package', package) + assert checks['parent'] == receipts['parent']['inference_signature'] and checks['candidate'] == signature + native = study / 'native-recovery' + if not (native / 'report.json').exists() or not (output / 'valid-variants').exists(): + status('waiting_for_bounded_native_and_derivation_slot') + with Path(plan['bounded_lock']).open('a') as probe_lock: + fcntl.flock(probe_lock, fcntl.LOCK_EX);wait_memory() + if not (native / 'report.json').exists(): + execute('native', [HERE / 'check_native_recovery.py', package, native], plan['native_timeout_seconds']) + if not (output / 'valid-variants').exists(): + materialization = dict(plan['materialization'], candidates=[dict(parent=str(parent), candidate=str(output), + parent_inference_signature=checks['parent'], candidate_inference_signature=signature)]) + path = study / 'materialization-plan.json' + if path.exists():assert json.loads(path.read_text()) == materialization + else:write_json(path, materialization) + execute('materialize', [HERE / 'materialize_splice_link_outputs_v2.py', path], plan['materialization_timeout_seconds']) + proof = json.loads((native / 'report.json').read_text()) + assert proof['package_inference_signature'] == signature and proof['all_arrays_bit_identical'] + assert proof['native_vendored_entrypoints_passed'] and proof['partial_perturbation_recovery_passed'] + assert proof['arrays'] == {'tracks':30,'perturbations':22} + derivation = json.loads((output / 'valid-variants/perturb-link-derivation.json').read_text()) + assert derivation['checks']['candidate'] == signature and derivation['checks']['parent'] == checks['parent'] + assert derivation['native_arrays_bit_exact'] == 36 and derivation['queries'] == 2697 + if not (output / 'evaluation.json').exists(): + status('waiting_for_full_evaluation_slot') + with Path(plan['evaluation_lock']).open('a') as evaluation_lock: + fcntl.flock(evaluation_lock, fcntl.LOCK_EX);wait_memory() + execute('evaluate', [HERE / 'evaluate_package.py', package, output, + '--resume-partial-tracks','--resume-partial-perturbations']) + assert json.loads((output / 'evaluation.json').read_text())['package_inference_signature'] == signature + tracks = json.loads((output / 'valid-score/report.json').read_text()) + variants = json.loads((output / 'valid-variant-score/report.json').read_text()) + raw_tracks = json.loads((parent / 'valid-score/report.json').read_text()) + raw_variants = json.loads((parent / 'valid-variant-score/report.json').read_text()) + assert tracks['examples'] == 200 and tracks['invalid_reason'] is None and len(variants) == 13 + affected = {'sqtl_causality','clinvar_noncoding','clinvar_splice_region','clinvar_missense','mfass'} + for family, report in tracks['families'].items(): + if family != 'splice_sites':assert report == raw_tracks['families'][family],family + for name, report in variants.items(): + assert math.isfinite(report['model']) + for field in ['rows','perturbations','metric','anchors']:assert report[field] == raw_variants[name][field],(name,field) + if name not in affected:assert report == raw_variants[name],name + comparison_path = study / 'matched-comparison.json' + if not comparison_path.exists(): + write_json(comparison_path, dict(created_utc=datetime.now(timezone.utc).isoformat(), + master_plan_sha256=digest(plan_path), reward=tracks['overall_mean_family_pearson'], + reward_delta=tracks['overall_mean_family_pearson']-raw_tracks['overall_mean_family_pearson'], + family_deltas={k:v['mean_pearson']-raw_tracks['families'][k]['mean_pearson'] for k,v in tracks['families'].items()}, + variant_deltas={k:v['model']-raw_variants[k]['model'] for k,v in variants.items()}, + unaffected_family_reports_exact=10, unaffected_variant_reports_exact=8, package_inference_signature=signature)) + decision_path = study / 'publication-decision.json' + if not decision_path.exists(): + current = json.loads(Path('/workspace/submission/validation_tracks.json').read_text())['overall_mean_family_pearson'] + eligible = tracks['overall_mean_family_pearson'] > current + plan['publication_minimum_reward_gain'] + runtime_passed = None + if eligible: + latency = study / 'native-latency';latency.mkdir(exist_ok=True) + timing_plan = dict(plan['native_latency_template'], experiment=str(output), inference_signature=signature) + if (latency / 'plan.json').exists():assert json.loads((latency / 'plan.json').read_text()) == timing_plan + else:write_json(latency / 'plan.json', timing_plan) + if not (latency / 'report.json').exists(): + execute('native-latency', [HERE / 'measure_native_latency.py', latency / 'plan.json']) + timing = json.loads((latency / 'report.json').read_text()) + assert timing['all_arrays_bit_exact'] and timing['package_inference_signature'] == signature + estimates = timing['extrapolated_seconds'] + names = ['development_mixture_seconds','slowest_observed_stratum_mean_seconds','pooled_p95_seconds'] + assert all(math.isfinite(estimates[k]) for k in names) + runtime_passed = max(estimates[k] for k in names) <= 14400 + write_json(study / 'runtime-gate.json', dict(passed=runtime_passed, estimates=estimates, + report_sha256=digest(latency / 'report.json'), scope='Shared-load extrapolations, not guaranteed upper bounds.')) + if runtime_passed:execute('publish', [HERE / 'promote_if_better.py', package, output]) + write_json(decision_path, dict(created_utc=datetime.now(timezone.utc).isoformat(), + reward=tracks['overall_mean_family_pearson'], current_reward_at_review=current, + eligible_at_review=eligible, runtime_passed=runtime_passed, + publication='Atomic promoter rechecks the current strongest package; see publish.log if run.')) + for phase, helper in [('tracks','diagnose_track_uncertainty.py'), + ('remaining-variants','diagnose_remaining_variant_uncertainty.py')]: + report_path = study / f'paired-{phase}.json' + marker_path = study / f'paired-{phase}-complete.json' + if marker_path.exists(): + assert json.loads(marker_path.read_text())['output_sha256'] == digest(report_path) + continue + def diagnostic_complete(): + try: + report = json.loads(report_path.read_text()) + assert Path(report['candidate']).resolve() == output.resolve() + assert Path(report['control']).resolve() == parent.resolve() + assert report['replicates'] == 2000 and report['seed'] == 1729 + assert report['diagnostic_only_no_parameter_fitting'] + leaf = 'valid-score/report.json' if phase == 'tracks' else 'valid-variant-score/report.json' + assert report['report_sha256'] == {'candidate':digest(output / leaf),'control':digest(parent / leaf)} + if phase == 'tracks': + assert len(report['point_deltas']) == 12 and 'reward' in report['point_deltas'] + assert set(report['bootstrap']) == {'chromosome','chromosome_10Mb_blocks'} + for result in report['bootstrap'].values(): + assert set(result['deltas']) == set(report['point_deltas']) + else: + assert set(report['evaluations']) == affected | {'caqtl','bqtl_spi1'} + for item in report['evaluations'].values(): + assert set(item['bootstrap']) == {'variant_id','genomic_block'} + for result in item['bootstrap'].values(): + assert result['reference_replicates_checked'] >= 32 + assert result['maximum_absolute_metric_error'] < 1e-10 + return True + except (FileNotFoundError,json.JSONDecodeError,AssertionError,KeyError): + return False + if not diagnostic_complete(): + execute('paired-' + phase, [HERE / helper, output, parent, report_path, + '--replicates','2000','--seed','1729']) + assert diagnostic_complete(), ('incomplete diagnostic', phase) + write_json(marker_path, dict(output_sha256=digest(report_path), master_plan_sha256=digest(plan_path))) + finish('complete', reward=tracks['overall_mean_family_pearson'], + matched_comparison_sha256=digest(comparison_path), publication_decision=json.loads(decision_path.read_text()), + diagnostics='All11families/sevenvariants, with eight unaffected official variant reports exact; not anall13bootstrap.') + + +if __name__ == '__main__': + main() diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/run_scale_study_cached_io.py b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/run_scale_study_cached_io.py new file mode 100644 index 0000000000000000000000000000000000000000..3a64b3a086363780b355fe0c827bab67bc691b68 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_history/run_scale_study_cached_io.py @@ -0,0 +1,151 @@ +"""Train two locally initialized scales on an identical recipe and evaluate selected full epochs. + +This script requires a pre-reviewed study.json; it never chooses scientific options itself. +It resumes checkpoints and preserves an independently replayable package for every evaluation. +""" +import argparse +import hashlib +import json +import os +from pathlib import Path +import subprocess +import sys +import time +import torch + +p = argparse.ArgumentParser(description=__doc__) +p.add_argument('study', type=Path) +a = p.parse_args() +plan = json.loads(a.study.read_text()) +assert plan.get('training_bytecache_root'), 'This amended runner requires an explicit training-only cache path' +assert plan['schema'] == 'seqfunc-matched-scale/v1' +assert plan['pretrained'] is False and plan['examples_per_epoch'] == 41020 +assert len(plan['runs']) == 2 and plan['shared']['batch_size'] == 4 +r = Path('/workspace/research') +here = Path(__file__).resolve().parent +source = Path(plan.get('source', r/'seqfunc-scale')) +sys.path.insert(0, str(source)) +from reference.alphagenome_50m.model import ModelConfig +configs = [ModelConfig.from_yaml(run['config']).to_dict() for run in plan['runs']] +differences = {key for key in configs[0] if configs[0][key] != configs[1][key]} +assert differences and differences <= {'encoder_width', 'channel_growth', 'hidden_dim', + 'num_sequence_blocks', 'num_attention_heads', 'head_hidden_size', + 'local_width'} +env = dict(os.environ, OPENBLAS_NUM_THREADS='1', OMP_NUM_THREADS='2', MKL_NUM_THREADS='2') +os.sched_setaffinity(0, sorted(os.sched_getaffinity(0))[:22]) +env.pop('SEQFUNC_TRAIN_BYTECACHE', None) +children, handles = {}, [] + +class AdoptedEvaluation: + def __init__(self, identity, directory): + self.identity, self.directory = identity, directory + self.pid = identity['pid'] + def poll(self): + try: + path = Path(f'/proc/{self.pid}') + stat = (path/'stat').read_text().split(') ', 1)[1].split() + active = (stat[0] not in 'ZX' and int(stat[19]) == self.identity['starttime_ticks'] + and hashlib.sha256((path/'cmdline').read_bytes()).hexdigest() == self.identity['cmdline_sha256']) + except OSError: + active = False + if active: + return None + try: + done = json.loads((self.directory/'pipeline-state.json').read_text())['phase'] == 'complete' + except (OSError, KeyError, json.JSONDecodeError): + done = False + return 0 if done else 1 + @property + def returncode(self): + return self.poll() + + +def launch(name, arguments, logfile, *, training=False): + handle = logfile.open('a') + handles.append(handle) + child_environment = dict(env) + if training: + child_environment['SEQFUNC_TRAIN_BYTECACHE'] = plan['training_bytecache_root'] + proc = subprocess.Popen([sys.executable, *map(str, arguments)], env=child_environment, + stdout=handle, stderr=subprocess.STDOUT) + children[name] = proc + print(json.dumps({'event': 'study_launch', 'name': name, 'pid': proc.pid}), flush=True) + +for run in plan['runs']: + directory = Path(run['output']) + directory.mkdir(parents=True, exist_ok=True) + checkpoints = [directory/name for name in ['progress.pt', 'last.pt']] + epochs = sorted(directory.glob('epoch-*.pt')) + if epochs: + checkpoints.append(epochs[-1]) + available = [(torch.load(path, map_location='cpu', weights_only=True)['global_step'], path) + for path in checkpoints if path.exists()] + step, checkpoint = max(available) if available else (0, None) + original = plan['cache_execution_amendment']['checkpoints'][run['name']] + assert step >= original['step'] + if step == original['step']: + checkpoint = Path(original['path']) + with checkpoint.open('rb') as stream: + assert hashlib.file_digest(stream, 'sha256').hexdigest() == original['sha256'] + shared = plan['shared'] + if step < shared['epochs']*plan['examples_per_epoch']: + args = [source/'train_batched.py', '--output', directory, '--config', run['config'], + '--workers', run['workers'], '--threads', 2, '--save-every', 1000, + '--keep-every', 0, '--log-every', 100] + for key in ['batch_size', 'epochs', 'seed', 'learning_rate', 'min_learning_rate', 'warmup', 'objective']: + args += ['--'+key.replace('_', '-'), shared[key]] + if shared.get('loss_scales'): + args += ['--loss-scales', shared['loss_scales']] + if checkpoint: + args += ['--resume', checkpoint] + launch(run['name']+'-train', args, directory/'train.log', training=True) + for epoch in plan['evaluation_epochs']: + evaluation = directory.parent/(run['name']+f'-epoch{epoch:02d}') + evaluation.mkdir(exist_ok=True) + name = run['name']+f'-eval{epoch:02d}' + state_path = evaluation/'pipeline-state.json' + if state_path.exists() and json.loads(state_path.read_text()).get('phase') == 'complete': + print(json.dumps({'event': 'completed_evaluation_preserved', 'name': name}), flush=True) + continue + if name in plan.get('adopted_evaluations', {}): + children[name] = AdoptedEvaluation(plan['adopted_evaluations'][name], evaluation) + print(json.dumps({'event': 'evaluation_adopted_without_interruption', 'name': name, 'pid': children[name].pid}), flush=True) + continue + args = [here/'run_candidate.py', directory/f'epoch-{epoch:02d}.pt', evaluation, + '--source', source, '--step', epoch*plan['examples_per_epoch'], + '--evaluation-lock', a.study.with_name('evaluation.lock'), + '--name', f"SeqFunc {run['name']} after {epoch} complete epochs", '--orientation', + '--lineage', plan['provenance']+' '+run['description']+ + ' Parameters are initialized locally with seed 17. The paired scale uses the same ' + 'batch-four interval order, objective, learning-rate schedule and full training permutations. ' + 'Only encoder/trunk/head/local-path width and transformer depth differ between scales.'] + launch(run['name']+f'-eval{epoch:02d}', args, evaluation/'pipeline.log') + +monitor_retired = False +while True: + status = {name: {'pid': proc.pid, 'returncode': proc.poll()} for name,proc in children.items()} + temporary = a.study.with_name('study-state-cached.tmp') + temporary.write_text(json.dumps(status, indent=2)+'\n') + temporary.replace(a.study.with_name('study-state-cached.json')) + if not monitor_retired and all(proc.poll() is not None for proc in children.values() + if isinstance(proc, AdoptedEvaluation)): + import signal + old = plan['retired_monitor_identity'] + try: + path = Path(f'/proc/{old["pid"]}') + stat = (path/'stat').read_text().split(') ', 1)[1].split() + if (int(stat[19]) == old['starttime_ticks'] + and hashlib.sha256((path/'cmdline').read_bytes()).hexdigest() == old['cmdline_sha256']): + os.kill(old['pid'], signal.SIGTERM) + os.kill(old['pid'], signal.SIGCONT) + except (FileNotFoundError, ProcessLookupError): + pass + monitor_retired = True + print(json.dumps({'event': 'old_monitor_retired_after_preserved_evaluation', 'pid': old['pid']}), flush=True) + if all(proc.poll() is not None for proc in children.values()): + break + time.sleep(5) +for handle in handles: + handle.close() +if any(proc.returncode for proc in children.values()): + raise RuntimeError('one or more study processes failed; inspect study-state.json and individual logs') diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_loss_scales.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_loss_scales.json new file mode 100644 index 0000000000000000000000000000000000000000..a8f484c060a0d4300b25557736abf741d6d67b15 --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_loss_scales.json @@ -0,0 +1,32 @@ +{ + "split": "train", + "examples": 256, + "seed": 1729, + "source_moments_sha256": "7b7543c4d4d8d82ec9f9bc3f942ca65595671322f62c19391591df827d8fc588", + "training_indices_sha256": "b82df9ae11e95d9be5d6e5f85839cf23e25bbbe0a195eaee266ff52d366800dc", + "method": "reference MSE times inverse mean within-track training variance; floor0.01; splice BCE unchanged", + "mean_track_variance": { + "atac": 0.9602319499041136, + "dnase": 1.0768856864858252, + "procap": 0.3380534137610864, + "cage": 0.3114663911919963, + "rna_seq": 0.66617486157613, + "chip_tf": 2.7198812117366296, + "chip_histone": 3.405612880716664, + "contact_maps": 0.14704202117243276, + "splice_usage": 0.07913355074122039, + "junctions": 0.17919098248535248 + }, + "family_loss_scales": { + "atac": 1.041415045708339, + "dnase": 0.9286036694045732, + "procap": 2.9581124144681272, + "cage": 3.2106192779675315, + "rna_seq": 1.5011073783751905, + "chip_tf": 0.36766311546433506, + "chip_histone": 0.29363290398101966, + "contact_maps": 6.800777029767044, + "splice_usage": 12.636865029223356, + "junctions": 5.5806379658738825 + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_provenance.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_provenance.json new file mode 100644 index 0000000000000000000000000000000000000000..aeccdb165e21e0a18eaba6f6a78d7476e9a3522c --- /dev/null +++ b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/training_provenance.json @@ -0,0 +1,154 @@ +{ + "schema": "seqfunc-batched/v1", + "config": { + "input_length": 1048576, + "target_length": 196608, + "dense_bin_size": 128, + "trunk_bin_size": 1024, + "contact_bin_size": 2048, + "stem_stride": 8, + "encoder_width": 96, + "channel_growth": 96, + "num_downsampling_blocks": 7, + "conv_kernel_size": 5, + "dna_kernel_size": 15, + "hidden_dim": 1024, + "num_sequence_blocks": 16, + "num_attention_heads": 16, + "attention_head_dim": 64, + "mlp_expansion": 4.0, + "dropout": 0.0, + "head_hidden_size": 768, + "junction_rank": 32, + "splice_usage_tracks": 734, + "junction_tracks": 734, + "contact_tracks": 28, + "gradient_checkpointing": true, + "enable_pairwise": false, + "pair_width": 32, + "pair_downsample": 16, + "pair_update_interval": 2, + "local_width": 64, + "local_context_blocks": 6, + "splice_context_mode": "offset", + "local_to_trunk": true, + "residual_rms_cap": 0.1 + }, + "epoch": 8, + "offset": 0, + "global_step": 328160, + "examples": 41020, + "schedule_epochs": 14, + "objective": "variance_balanced", + "seed": 17, + "training_arguments": { + "input": "/workspace/data", + "output": "/workspace/research/experiments/scale13-large", + "config": "/workspace/research/studies/scale13/scale13-large.yaml", + "resume": "/workspace/research/experiments/scale13-large/progress.pt", + "batch_size": 4, + "epochs": 14, + "stop_at": 0, + "workers": 8, + "threads": 2, + "seed": 17, + "learning_rate": 0.0002, + "min_learning_rate": 2e-05, + "warmup": 4000, + "objective": "variance_balanced", + "loss_scales": "/workspace/research/studies/scale13/loss_scales.json", + "save_every": 1000, + "keep_every": 0, + "log_every": 100 + }, + "elapsed_seconds": 108193.25909642546, + "provenance": { + "data": "/workspace/data", + "split": "train", + "pretrained": false, + "world_size": 1, + "device": "NVIDIA H100 80GB HBM3", + "batch_reduction": "equal per example", + "train_manifest_sha256": "48133d27eedfb4b1d275cfca41f69dd075a2a0305ab2e8591e8e5e7529f36c64" + }, + "optimizer_steps": 82040, + "family_loss_scales": { + "atac": 1.041415045708339, + "dnase": 0.9286036694045732, + "procap": 2.9581124144681272, + "cage": 3.2106192779675315, + "rna_seq": 1.5011073783751905, + "chip_tf": 0.36766311546433506, + "chip_histone": 0.29363290398101966, + "contact_maps": 6.800777029767044, + "splice_usage": 12.636865029223356, + "junctions": 5.5806379658738825 + }, + "loss_scale_provenance": { + "split": "train", + "examples": 256, + "seed": 1729, + "source_moments_sha256": "7b7543c4d4d8d82ec9f9bc3f942ca65595671322f62c19391591df827d8fc588", + "training_indices_sha256": "b82df9ae11e95d9be5d6e5f85839cf23e25bbbe0a195eaee266ff52d366800dc", + "method": "reference MSE times inverse mean within-track training variance; floor0.01; splice BCE unchanged", + "mean_track_variance": { + "atac": 0.9602319499041136, + "dnase": 1.0768856864858252, + "procap": 0.3380534137610864, + "cage": 0.3114663911919963, + "rna_seq": 0.66617486157613, + "chip_tf": 2.7198812117366296, + "chip_histone": 3.405612880716664, + "contact_maps": 0.14704202117243276, + "splice_usage": 0.07913355074122039, + "junctions": 0.17919098248535248 + }, + "family_loss_scales": { + "atac": 1.041415045708339, + "dnase": 0.9286036694045732, + "procap": 2.9581124144681272, + "cage": 3.2106192779675315, + "rna_seq": 1.5011073783751905, + "chip_tf": 0.36766311546433506, + "chip_histone": 0.29363290398101966, + "contact_maps": 6.800777029767044, + "splice_usage": 12.636865029223356, + "junctions": 5.5806379658738825 + } + }, + "inference_ensemble": { + "method": "Equal mean of the same checkpoint evaluated on DNA and its reverse complement, mapped to original positions and public-metadata strand channels.", + "orientation_views": 2, + "fitted_ensemble_weights": false, + "weights": [ + 0.5, + 0.5 + ], + "dna_input": "Same provided 1,048,576bp, reversed and complemented; no extra flanking sequence.", + "site_scores": "Apply the existing training-prior probability link per orientation, then average probabilities.", + "sparse_outputs": "Each orientation chooses its own positions/pairs. Average the union with zero for unlisted coordinates; retain up to16,384 junctions by averaged predicted strength. Usage union has at most4,096 coordinates.", + "outputs": "All families and both entry points use the same two-view rule. No labels or targets used at inference.", + "created_utc": "2026-10-26T07:36:00.650275+00:00", + "parent_package": "/run-state/workspace/research/experiments/calib81-large-epoch08/package", + "parent_inference_signature": { + "last.pt": "91f3382e440a1c61731b090dcf04d4de98d706f7c2f7ce956e1ade752e6c57eb", + "predict.py": "5f5507be4efce5af3167616de01c11da89fa4fe2b2c59dbdac0152e1a84d1348", + "perturb.py": "9defff26b93513ebe681c31485b75730aac911d9c0f9983f2c7aa615a01b67f8", + "prediction_contract.py": "b11af8f7cff82b35afc6282be387358a48464b78bd1dfa44452dace26a5e0a83", + "reference/alphagenome_50m/model.py": "c61f50a2a66a4f6950c6f6107a70710a5d0ab99a291b8bfaa1eb5565998d2fa3", + "reference/alphagenome_50m/layers.py": "c39b11287d3454627469fb9e196f23f218cf48c9856de9126f9e30e9e2662af8", + "reference/alphagenome_50m/dataset.py": "6ac7376529fc5b6725fdea8080de1109de4e3948d68607fc86ce38dc9c742919", + "splice_output.py": "e1b0627e036c60190e4d2a4653a915d9031b166ff3c1de7c23e5774009d70ef0", + "splice_calibration.json": "bd9368b32e739be65ca290d598ffcae5be6387c09f96048e183c57b7d570e641" + }, + "unique_learned_parameters": 215626632, + "decoder_study": "/run-state/workspace/research/studies/rc17", + "decoder_assets_sha256": { + "predict.py": "4f5dbddf5b6b701079aeaf38920fe414ce980f7161997f4d5c80952a6d834e51", + "perturb.py": "49065dc43816920ecac9de3ebcc4735673e4ff6594cf3f6bd34233b88c9bed73", + "rc_ensemble.py": "84a4d0e94ab86622e2c25e96f8835b2ef2bba77731ff7ff0bdc1c2de02cdcef4", + "single_prediction.py": "10f6216fdd72bf30b3a28ab6de9363e66b415526aa26a2bbfb6a84badbb67bec", + "strand_permutations.json": "afb0f0cf2e897fab3493de66601d7a66dac45a06316c2ac4a6cc828298820c60" + } + } +} diff --git a/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/validation_tracks.json b/tasks/genome-sequence-to-function/genome-sequence-to-function-astra-xhigh-1h100-48h-20261022a/submissions/S038/validation_tracks.json new file mode 100644 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"superseded_by": "protein-design-astra-xhigh-1h100-48h-20261027a", "task": "protein-design" } diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S001/logs/inference/verifier.log b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S001/logs/inference/verifier.log new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S001/logs/score/verifier.log b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S001/logs/score/verifier.log new file mode 100644 index 0000000000000000000000000000000000000000..2a5052b363876bb47e47e40eb063377f0f3649fd --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S001/logs/score/verifier.log @@ -0,0 +1 @@ +grading with 8 worker(s) on device(s) GPU-280f6b47-c854-d5bb-5937-6667233f4652, GPU-b1bdbb36-6f22-3fc5-ba67-780506928b71, GPU-d12a416a-4c19-657c-ff8f-ae546bdf70c6, GPU-041378ff-279e-cbed-05f9-5283b64021ad, GPU-523d947e-66bf-ef98-d041-202ab5071db3, GPU-e52389ae-bdf0-6c4d-5307-bdcaf986543f, GPU-3478ea1b-5c4f-e9c5-0ca2-307d7cc0e41c, GPU-2dc35ea7-7adf-0d98-f8f2-3add8291d684 diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S001/report.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S001/report.json new file mode 100644 index 0000000000000000000000000000000000000000..a4bce7fc251f36759183db872e86d6817c58840a --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S001/report.json @@ -0,0 +1,860 @@ +{ + "schema": "protein-design-report/v1", + "thresholds": { + "motif_rmsd_max": 1.5, + "novelty_max_tm": 0.8, + "plddt_min": 70.0, + "sc_rmsd_max": 1.5 + }, + "tracks": { + "enzyme": { + "conditioning_satisfied": 820, + "designs_designable": 95, + "designs_novel": 95, + "designs_requested": 820, + "designs_returned": 820, + "designs_valid": 820, + "diversity": 0.3170731707317073, + "failures": { + "non-motif backbone clashes with ligand at 0.24 A": 1, + "non-motif backbone clashes with ligand at 0.29 A": 1, + "non-motif backbone clashes with ligand at 0.40 A": 1, + "non-motif backbone clashes with ligand at 0.43 A": 3, + "non-motif backbone clashes with ligand at 0.46 A": 1, + "non-motif backbone clashes with ligand at 0.47 A": 1, + "non-motif backbone clashes with ligand at 0.49 A": 1, + "non-motif backbone clashes with ligand at 0.52 A": 1, + "non-motif backbone clashes with ligand at 0.56 A": 2, + "non-motif backbone clashes with ligand at 0.57 A": 1, + "non-motif backbone clashes with ligand at 0.59 A": 2, + "non-motif backbone clashes with ligand at 0.60 A": 2, + "non-motif backbone clashes with ligand at 0.63 A": 3, + "non-motif backbone clashes with ligand at 0.64 A": 2, + "non-motif backbone clashes with ligand at 0.67 A": 5, + "non-motif backbone clashes with ligand at 0.68 A": 1, + "non-motif backbone clashes with ligand at 0.69 A": 3, + "non-motif backbone clashes with ligand at 0.72 A": 1, + "non-motif backbone clashes with ligand at 0.74 A": 2, + "non-motif backbone clashes with ligand at 0.75 A": 1, + "non-motif backbone clashes with ligand at 0.76 A": 1, + "non-motif backbone clashes with ligand at 0.78 A": 4, + "non-motif backbone clashes with ligand at 0.79 A": 2, + "non-motif backbone clashes with ligand at 0.81 A": 1, + "non-motif backbone clashes with ligand at 0.82 A": 3, + "non-motif backbone clashes with ligand at 0.83 A": 2, + "non-motif backbone clashes with ligand at 0.84 A": 1, + "non-motif backbone clashes with ligand at 0.85 A": 2, + "non-motif backbone clashes with ligand at 0.86 A": 3, + "non-motif backbone clashes with ligand at 0.87 A": 1, + "non-motif backbone clashes with ligand at 0.88 A": 2, + "non-motif backbone clashes with ligand at 0.91 A": 1, + "non-motif backbone clashes with ligand at 0.93 A": 2, + "non-motif backbone clashes with ligand at 0.95 A": 2, + "non-motif backbone clashes with ligand at 0.96 A": 2, + "non-motif backbone clashes with ligand at 0.97 A": 2, + "non-motif backbone clashes with ligand at 0.98 A": 5, + "non-motif backbone clashes with ligand at 0.99 A": 1, + "non-motif backbone clashes with ligand at 1.00 A": 4, + "non-motif backbone clashes with ligand at 1.01 A": 3, + "non-motif backbone clashes with ligand at 1.02 A": 2, + "non-motif backbone clashes with ligand at 1.03 A": 1, + "non-motif backbone clashes with ligand at 1.04 A": 1, + "non-motif backbone clashes with ligand at 1.06 A": 1, + "non-motif backbone clashes with ligand at 1.07 A": 3, + "non-motif backbone clashes with ligand at 1.08 A": 2, + "non-motif backbone clashes with ligand at 1.09 A": 1, + "non-motif backbone clashes with ligand at 1.10 A": 1, + "non-motif backbone clashes with ligand at 1.11 A": 2, + "non-motif backbone clashes with ligand at 1.12 A": 4, + "non-motif backbone clashes with ligand at 1.13 A": 2, + "non-motif backbone clashes with ligand at 1.16 A": 1, + "non-motif backbone clashes with ligand at 1.17 A": 3, + "non-motif backbone clashes with ligand at 1.18 A": 4, + "non-motif backbone clashes with ligand at 1.19 A": 3, + "non-motif backbone clashes with ligand at 1.20 A": 3, + "non-motif backbone clashes with ligand at 1.21 A": 1, + "non-motif backbone clashes with ligand at 1.22 A": 2, + "non-motif backbone clashes with ligand at 1.23 A": 2, + "non-motif backbone clashes with ligand at 1.24 A": 2, + "non-motif backbone clashes with ligand at 1.25 A": 3, + "non-motif backbone clashes with ligand at 1.26 A": 5, + "non-motif backbone clashes with ligand at 1.27 A": 4, + "non-motif backbone clashes with ligand at 1.28 A": 4, + "non-motif backbone clashes with ligand at 1.29 A": 3, + "non-motif backbone clashes with ligand at 1.30 A": 4, + "non-motif backbone clashes with ligand at 1.31 A": 4, + "non-motif backbone clashes with ligand at 1.32 A": 5, + "non-motif backbone clashes with ligand at 1.33 A": 4, + "non-motif backbone clashes with ligand at 1.34 A": 1, + "non-motif backbone clashes with ligand at 1.35 A": 6, + "non-motif backbone clashes with ligand at 1.36 A": 2, + "non-motif backbone clashes with ligand at 1.37 A": 4, + "non-motif backbone clashes with ligand at 1.38 A": 4, + "non-motif backbone clashes with ligand at 1.39 A": 7, + "non-motif backbone clashes with ligand at 1.40 A": 4, + "non-motif backbone clashes with ligand at 1.41 A": 5, + "non-motif backbone clashes with ligand at 1.42 A": 2, + "non-motif backbone clashes with ligand at 1.43 A": 2, + "non-motif backbone clashes with ligand at 1.45 A": 1, + "non-motif backbone clashes with ligand at 1.46 A": 6, + "non-motif backbone clashes with ligand at 1.47 A": 3, + "non-motif backbone clashes with ligand at 1.48 A": 4, + "non-motif backbone clashes with ligand at 1.49 A": 5, + "non-motif backbone clashes with ligand at 1.50 A": 1, + "not designable (scRMSD 1.51, ipTM 0.81, PAE 2.21, motif 11.97, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.57, ipTM 0.71, PAE 4.19, motif 3.09, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.62, ipTM 0.77, PAE 3.07, motif 2.27, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.76, ipTM 0.66, PAE 3.91, motif 3.20, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.87, ipTM 0.69, PAE 3.95, motif 9.97, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.91, ipTM 0.67, PAE 3.79, motif 2.36, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.94, ipTM 0.63, PAE 5.52, motif 4.24, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.96, ipTM 0.67, PAE 4.71, motif 4.78, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.09, ipTM 0.85, PAE 1.41, motif 4.84, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.25, ipTM 0.27, PAE 14.16, motif 14.24, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.26, ipTM 0.76, PAE 1.84, motif 6.57, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.53, ipTM 0.58, PAE 4.62, motif 14.40, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.63, ipTM 0.50, PAE 5.93, motif 6.34, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.04, ipTM 0.52, PAE 6.35, motif 11.67, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.15, ipTM 0.40, PAE 9.53, motif 6.25, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.32, ipTM 0.50, PAE 7.20, motif 9.12, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.72, ipTM 0.44, PAE 7.17, motif 8.94, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.43, ipTM 0.58, PAE 5.21, motif 11.34, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.46, ipTM 0.35, PAE 10.66, motif 9.50, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.69, ipTM 0.48, PAE 6.69, motif 15.63, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.01, ipTM 0.59, PAE 4.77, motif 4.76, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.02, ipTM 0.82, PAE 1.98, motif 7.14, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.04, ipTM 0.78, PAE 2.80, motif 1.64, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.06, ipTM 0.62, PAE 4.41, motif 8.30, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.07, ipTM 0.89, PAE 1.23, motif 2.87, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.08, ipTM 0.52, PAE 7.32, motif 3.88, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.09, ipTM 0.53, PAE 5.60, motif 1.63, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.09, ipTM 0.69, PAE 4.44, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.09, ipTM 0.78, PAE 2.43, motif 1.76, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.10, ipTM 0.82, PAE 2.12, motif 11.82, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.11, ipTM 0.84, PAE 1.73, motif 4.82, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.14, ipTM 0.71, PAE 3.93, motif 2.87, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.17, ipTM 0.73, PAE 3.09, motif 3.77, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.18, ipTM 0.78, PAE 3.12, motif 6.07, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.24, ipTM 0.59, PAE 6.62, motif 3.17, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.24, ipTM 0.70, PAE 3.93, motif 11.32, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.24, ipTM 0.81, PAE 2.21, motif 2.10, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.25, ipTM 0.66, PAE 3.83, motif 2.23, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.25, ipTM 0.79, PAE 2.68, motif 2.85, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.26, ipTM 0.55, PAE 6.43, motif 8.53, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.27, ipTM 0.62, PAE 5.32, motif 3.80, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.28, ipTM 0.70, PAE 3.82, motif 2.84, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.29, ipTM 0.54, PAE 5.15, motif 8.94, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.29, ipTM 0.65, PAE 4.66, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.29, ipTM 0.71, PAE 3.53, motif 11.50, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.31, ipTM 0.64, PAE 4.84, motif 2.21, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.32, ipTM 0.73, PAE 3.04, motif 2.75, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.33, ipTM 0.55, PAE 6.44, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.33, ipTM 0.60, PAE 5.70, motif 1.91, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.33, ipTM 0.83, PAE 2.22, motif 3.32, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.34, ipTM 0.48, PAE 8.69, motif 17.06, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.35, ipTM 0.60, PAE 4.86, motif 7.03, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.38, ipTM 0.75, PAE 3.27, motif 2.48, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.39, ipTM 0.57, PAE 5.87, motif 3.85, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.39, ipTM 0.70, PAE 3.67, motif 2.62, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.40, ipTM 0.76, PAE 2.77, motif 2.39, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.40, ipTM 0.84, PAE 2.12, motif 2.36, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.41, ipTM 0.67, PAE 3.57, motif 7.55, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.41, ipTM 0.71, PAE 3.52, motif 7.40, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.42, ipTM 0.60, PAE 4.93, motif 4.62, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.42, ipTM 0.71, PAE 3.45, motif 8.20, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.42, ipTM 0.83, PAE 2.10, motif 4.78, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.43, ipTM 0.79, PAE 2.30, motif 11.28, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.45, ipTM 0.65, PAE 4.95, motif 5.51, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.45, ipTM 0.71, PAE 2.57, motif 2.97, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.45, ipTM 0.73, PAE 3.31, motif 8.99, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.46, ipTM 0.58, PAE 5.40, motif 8.66, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.46, ipTM 0.59, PAE 4.76, motif 3.47, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.46, ipTM 0.66, PAE 3.25, motif 3.28, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.46, ipTM 0.75, PAE 3.01, motif 7.55, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.46, ipTM 0.76, PAE 3.10, motif 3.80, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.47, ipTM 0.55, PAE 5.67, motif 10.92, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.48, ipTM 0.72, PAE 3.68, motif 2.49, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.48, ipTM 0.75, PAE 2.35, motif 2.54, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.49, ipTM 0.63, PAE 5.45, motif 4.76, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.49, ipTM 0.73, PAE 3.03, motif 6.85, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.51, ipTM 0.72, PAE 3.56, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.51, ipTM 0.76, PAE 2.72, motif 2.42, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.52, ipTM 0.31, PAE 12.91, motif 5.66, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.54, ipTM 0.59, PAE 5.18, motif 2.11, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.55, ipTM 0.47, PAE 6.36, motif 11.20, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.55, ipTM 0.63, PAE 4.79, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.55, ipTM 0.66, PAE 4.22, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.55, ipTM 0.68, PAE 3.91, motif 4.78, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.55, ipTM 0.69, PAE 3.88, motif 4.61, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.55, ipTM 0.80, PAE 2.20, motif 3.13, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.56, ipTM 0.58, PAE 5.70, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.56, ipTM 0.60, PAE 4.64, motif 2.71, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.56, ipTM 0.80, PAE 2.14, motif 2.99, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.58, ipTM 0.65, PAE 2.78, motif 2.47, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.58, ipTM 0.72, PAE 3.40, motif 2.73, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.60, ipTM 0.54, PAE 6.32, motif 8.43, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.61, ipTM 0.61, PAE 5.05, motif 2.62, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.61, ipTM 0.77, PAE 2.19, motif 2.10, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.63, ipTM 0.66, PAE 3.87, motif 11.06, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.64, ipTM 0.73, PAE 3.26, motif 6.80, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.64, ipTM 0.76, PAE 2.58, motif 3.57, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.66, ipTM 0.60, PAE 4.51, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.66, ipTM 0.62, PAE 4.77, motif 4.46, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.66, ipTM 0.64, PAE 4.20, motif 6.05, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.66, ipTM 0.65, PAE 4.24, motif 5.02, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.66, ipTM 0.78, PAE 2.65, motif 3.19, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.68, ipTM 0.64, PAE 5.24, motif 13.50, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.68, ipTM 0.67, PAE 4.33, motif 2.48, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.68, ipTM 0.68, PAE 2.49, motif 2.36, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.68, ipTM 0.76, PAE 3.03, motif 7.59, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.70, ipTM 0.66, PAE 2.51, motif 3.94, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.70, ipTM 0.68, PAE 3.54, motif 5.76, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.71, ipTM 0.50, PAE 7.72, motif 3.89, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.71, ipTM 0.76, PAE 2.90, motif 2.38, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.71, ipTM 0.80, PAE 2.45, motif 3.26, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.73, ipTM 0.50, PAE 7.01, motif 5.04, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.73, ipTM 0.71, PAE 3.51, motif 2.53, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.74, ipTM 0.51, PAE 5.63, motif 3.90, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.74, ipTM 0.68, PAE 4.13, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.74, ipTM 0.71, PAE 3.17, motif 5.62, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.74, ipTM 0.74, PAE 2.93, motif 5.66, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.74, ipTM 0.76, PAE 3.11, motif 5.53, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.75, ipTM 0.76, PAE 2.71, motif 5.33, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.76, ipTM 0.83, PAE 2.15, motif 4.18, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.77, ipTM 0.68, PAE 3.41, motif 3.71, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.77, ipTM 0.74, PAE 2.81, motif 8.36, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.77, ipTM 0.76, PAE 2.85, motif 5.51, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.78, ipTM 0.54, PAE 5.73, motif 8.16, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.78, ipTM 0.66, PAE 3.79, motif 7.46, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.78, ipTM 0.76, PAE 3.18, motif 13.72, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.79, ipTM 0.57, PAE 5.36, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.79, ipTM 0.67, PAE 2.87, motif 2.91, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.81, ipTM 0.60, PAE 5.86, motif 18.70, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.81, ipTM 0.68, PAE 4.10, motif 2.44, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.82, ipTM 0.59, PAE 5.86, motif 3.25, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.82, ipTM 0.66, PAE 3.99, motif 4.48, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.82, ipTM 0.70, PAE 4.09, motif 7.28, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.83, ipTM 0.53, PAE 7.14, motif 14.63, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.84, ipTM 0.62, PAE 4.51, motif 3.14, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.85, ipTM 0.64, PAE 5.06, motif 1.77, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.85, ipTM 0.65, PAE 3.79, motif 3.88, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.85, ipTM 0.73, PAE 2.76, motif 3.83, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.85, ipTM 0.75, PAE 3.03, motif 1.97, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.85, ipTM 0.86, PAE 1.63, motif 4.77, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.86, ipTM 0.59, PAE 5.16, motif 5.87, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.86, ipTM 0.74, PAE 3.44, motif 4.08, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.87, ipTM 0.74, PAE 3.14, motif 4.84, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.88, ipTM 0.54, PAE 5.77, motif 7.83, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.88, ipTM 0.57, PAE 6.73, motif 9.89, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.88, ipTM 0.62, PAE 4.78, motif 7.73, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.89, ipTM 0.73, PAE 3.63, motif 5.12, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.89, ipTM 0.82, PAE 2.09, motif 3.81, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.90, ipTM 0.67, PAE 3.23, motif 2.79, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.90, ipTM 0.67, PAE 3.96, motif 11.62, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.91, ipTM 0.54, PAE 7.46, motif 12.66, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.92, ipTM 0.72, PAE 3.30, motif 3.35, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.92, ipTM 0.73, PAE 3.55, motif 3.44, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.92, ipTM 0.77, PAE 2.41, motif 5.52, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.93, ipTM 0.61, PAE 4.80, motif 15.85, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.93, ipTM 0.76, PAE 2.48, motif 2.74, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.94, ipTM 0.65, PAE 4.18, motif 2.59, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.94, ipTM 0.66, PAE 4.47, motif 19.91, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.94, ipTM 0.76, PAE 2.15, motif 2.24, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.96, ipTM 0.78, PAE 2.35, motif 2.34, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.97, ipTM 0.52, PAE 7.55, motif 13.03, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.97, ipTM 0.60, PAE 3.73, motif 11.36, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.97, ipTM 0.71, PAE 3.32, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.99, ipTM 0.57, PAE 6.79, motif 14.56, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.99, ipTM 0.70, PAE 3.84, motif 2.00, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.01, ipTM 0.64, PAE 3.46, motif 3.41, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.01, ipTM 0.65, PAE 2.23, motif 6.24, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.01, ipTM 0.71, PAE 3.56, motif 8.19, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.01, ipTM 0.75, PAE 2.94, motif 12.15, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.02, ipTM 0.75, PAE 3.06, motif 2.68, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.03, ipTM 0.59, PAE 5.89, motif 4.05, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.03, ipTM 0.62, PAE 5.05, motif 2.73, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.03, ipTM 0.63, PAE 3.81, motif 4.61, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.04, ipTM 0.68, PAE 3.46, motif 3.58, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.04, ipTM 0.76, PAE 3.00, motif 14.53, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.05, ipTM 0.49, PAE 6.98, motif 12.85, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.07, ipTM 0.60, PAE 4.67, motif 4.61, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.07, ipTM 0.84, PAE 1.87, motif 2.15, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.09, ipTM 0.56, PAE 6.58, motif 4.82, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.09, ipTM 0.65, PAE 3.92, motif 3.15, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.09, ipTM 0.74, PAE 2.77, motif 3.63, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.10, ipTM 0.59, PAE 4.93, motif 9.30, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.10, ipTM 0.67, PAE 4.26, motif 4.65, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.10, ipTM 0.69, PAE 3.44, motif 3.30, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.12, ipTM 0.77, PAE 2.48, motif 2.52, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.13, ipTM 0.55, PAE 6.81, motif 1.87, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.13, ipTM 0.57, PAE 6.08, motif 4.31, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.13, ipTM 0.73, PAE 3.21, motif 3.66, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.13, ipTM 0.75, PAE 2.68, motif 15.45, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.14, ipTM 0.45, PAE 7.86, motif 3.91, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.14, ipTM 0.59, PAE 6.00, motif 19.86, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.14, ipTM 0.66, PAE 3.48, motif 13.43, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.18, ipTM 0.51, PAE 7.81, motif 4.87, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.18, ipTM 0.55, PAE 7.00, motif 4.36, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.18, ipTM 0.65, PAE 4.22, motif 3.20, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.18, ipTM 0.68, PAE 3.71, motif 10.00, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.19, ipTM 0.51, PAE 5.91, motif 3.98, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.19, ipTM 0.59, PAE 3.63, motif 3.65, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.19, ipTM 0.62, PAE 4.34, motif 5.78, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.20, ipTM 0.64, PAE 4.53, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.20, ipTM 0.67, PAE 4.78, motif 2.17, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.20, ipTM 0.76, PAE 2.45, motif 6.75, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.20, ipTM 0.76, PAE 2.90, motif 4.80, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.21, ipTM 0.71, PAE 3.95, motif 3.86, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.22, ipTM 0.42, PAE 7.56, motif 5.19, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.23, ipTM 0.59, PAE 5.37, motif 6.77, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.24, ipTM 0.47, PAE 8.30, motif 15.93, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.24, ipTM 0.50, PAE 7.22, motif 2.40, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.25, ipTM 0.69, PAE 3.61, motif 4.89, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.25, ipTM 0.71, PAE 3.48, motif 2.00, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.26, ipTM 0.64, PAE 4.93, motif 2.98, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.26, ipTM 0.65, PAE 4.60, motif 2.65, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.26, ipTM 0.76, PAE 2.25, motif 2.72, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.27, ipTM 0.68, PAE 4.11, motif 7.66, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.28, ipTM 0.58, PAE 4.27, motif 6.01, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.28, ipTM 0.61, PAE 4.25, motif 1.93, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.28, ipTM 0.82, PAE 2.29, motif 9.86, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.29, ipTM 0.70, PAE 3.95, motif 19.25, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.30, ipTM 0.66, PAE 4.18, motif 13.20, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.31, ipTM 0.53, PAE 5.50, motif 7.47, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.31, ipTM 0.62, PAE 4.89, motif 3.15, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.32, ipTM 0.57, PAE 6.06, motif 2.62, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.32, ipTM 0.69, PAE 2.85, motif 2.35, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.33, ipTM 0.50, PAE 4.73, motif 5.03, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.35, ipTM 0.51, PAE 6.91, motif 2.86, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.35, ipTM 0.58, PAE 4.91, motif 7.20, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.35, ipTM 0.60, PAE 4.86, motif 3.21, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.36, ipTM 0.55, PAE 5.44, motif 4.97, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.36, ipTM 0.74, PAE 2.28, motif 7.52, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.36, ipTM 0.75, PAE 2.83, motif 3.78, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.38, ipTM 0.45, PAE 6.84, motif 4.53, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.38, ipTM 0.71, PAE 3.63, motif 8.35, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.38, ipTM 0.72, PAE 3.32, motif 7.73, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.39, ipTM 0.65, PAE 4.64, motif 3.46, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.39, ipTM 0.75, PAE 2.84, motif 2.58, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.39, ipTM 0.76, PAE 2.89, motif 10.84, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.40, ipTM 0.52, PAE 5.99, motif 3.51, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.41, ipTM 0.79, PAE 2.44, motif 6.25, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.41, ipTM 0.81, PAE 2.08, motif 2.69, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.42, ipTM 0.65, PAE 4.41, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.43, ipTM 0.49, PAE 8.24, motif 3.33, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.44, ipTM 0.64, PAE 4.51, motif 3.55, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.45, ipTM 0.73, PAE 2.58, motif 5.35, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.46, ipTM 0.65, PAE 4.49, motif 4.03, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.47, ipTM 0.59, PAE 4.30, motif 2.93, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.48, ipTM 0.81, PAE 2.04, motif 4.96, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.49, ipTM 0.43, PAE 7.11, motif 2.77, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.49, ipTM 0.51, PAE 7.83, motif 18.67, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.49, ipTM 0.65, PAE 4.21, motif 3.21, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.50, ipTM 0.65, PAE 4.80, motif 3.06, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.51, ipTM 0.58, PAE 5.23, motif 13.82, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.51, ipTM 0.60, PAE 5.22, motif 4.47, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.51, ipTM 0.69, PAE 3.59, motif 10.16, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.52, ipTM 0.41, PAE 9.18, motif 2.95, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.52, ipTM 0.60, PAE 6.26, motif 18.83, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.52, ipTM 0.67, PAE 3.54, motif 2.94, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.53, ipTM 0.61, PAE 4.76, motif 5.68, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.53, ipTM 0.66, PAE 3.78, motif 6.07, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.53, ipTM 0.80, PAE 2.10, motif 3.73, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.54, ipTM 0.47, PAE 7.68, motif 5.69, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.54, ipTM 0.59, PAE 6.03, motif 10.63, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.54, ipTM 0.71, PAE 3.40, motif 4.39, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.54, ipTM 0.72, PAE 3.13, motif 6.23, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.55, ipTM 0.62, PAE 4.52, motif 7.65, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.55, ipTM 0.71, PAE 3.94, motif 2.55, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.56, ipTM 0.47, PAE 8.46, motif 3.94, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.56, ipTM 0.56, PAE 6.48, motif 4.24, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.56, ipTM 0.62, PAE 4.75, motif 2.57, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.56, ipTM 0.78, PAE 2.80, motif 2.08, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.57, ipTM 0.45, PAE 8.74, motif 2.45, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.57, ipTM 0.46, PAE 7.78, motif 3.53, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.57, ipTM 0.64, PAE 3.42, motif 2.47, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.57, ipTM 0.64, PAE 4.85, motif 6.07, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.58, ipTM 0.75, PAE 2.78, motif 13.51, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.59, ipTM 0.58, PAE 5.47, motif 3.78, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.60, ipTM 0.58, PAE 6.18, motif 4.00, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.60, ipTM 0.72, PAE 3.81, motif 3.45, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.60, ipTM 0.78, PAE 2.45, motif 3.08, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.61, ipTM 0.64, PAE 4.56, motif 3.42, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.62, ipTM 0.52, PAE 4.47, motif 6.83, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.62, ipTM 0.64, PAE 3.96, motif 14.34, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.63, ipTM 0.64, PAE 5.10, motif 4.80, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.65, ipTM 0.52, PAE 7.64, motif 4.38, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.65, ipTM 0.58, PAE 5.69, motif 2.02, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.65, ipTM 0.74, PAE 3.15, motif 6.38, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.66, ipTM 0.46, PAE 7.78, motif 2.86, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.67, ipTM 0.57, PAE 5.85, motif 6.94, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.68, ipTM 0.41, PAE 8.93, motif 5.18, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.68, ipTM 0.56, PAE 5.68, motif 4.14, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.68, ipTM 0.76, PAE 2.24, motif 4.38, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.69, ipTM 0.48, PAE 8.03, motif 1.51, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.70, ipTM 0.67, PAE 4.07, motif 9.95, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.71, ipTM 0.44, PAE 7.38, motif 3.88, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.71, ipTM 0.48, PAE 7.96, motif 6.07, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.72, ipTM 0.70, PAE 3.47, motif 7.25, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.73, ipTM 0.57, PAE 5.87, motif 3.88, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.73, ipTM 0.73, PAE 3.57, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.73, ipTM 0.78, PAE 2.23, motif 3.14, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.74, ipTM 0.53, PAE 7.60, motif 2.45, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.74, ipTM 0.68, PAE 4.31, motif 3.74, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.75, ipTM 0.53, PAE 6.35, motif 9.30, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.78, ipTM 0.50, PAE 7.33, motif 3.16, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.78, ipTM 0.75, PAE 2.85, motif 4.09, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.80, ipTM 0.43, PAE 9.55, motif 8.77, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.80, ipTM 0.69, PAE 3.84, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.80, ipTM 0.88, PAE 1.42, motif 6.03, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.81, ipTM 0.77, PAE 3.01, motif 3.86, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.82, ipTM 0.53, PAE 6.23, motif 2.81, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.82, ipTM 0.71, PAE 3.68, motif 2.12, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.85, ipTM 0.55, PAE 5.68, motif 2.74, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.85, ipTM 0.71, PAE 3.41, motif 5.47, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.85, ipTM 0.71, PAE 3.81, motif 5.87, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.87, ipTM 0.58, PAE 5.69, motif 2.74, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.87, ipTM 0.67, PAE 3.23, motif 7.91, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.88, ipTM 0.55, PAE 5.68, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.89, ipTM 0.42, PAE 8.07, motif 10.68, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.89, ipTM 0.73, PAE 3.25, motif 7.30, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.90, ipTM 0.55, PAE 5.56, motif 3.97, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.90, ipTM 0.56, PAE 6.86, motif 3.13, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.90, ipTM 0.61, PAE 4.97, motif 4.64, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.91, ipTM 0.67, PAE 3.85, motif 4.22, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.92, ipTM 0.57, PAE 5.17, motif 4.20, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.93, ipTM 0.53, PAE 6.98, motif 3.98, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.93, ipTM 0.60, PAE 5.27, motif 3.45, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.93, ipTM 0.63, PAE 4.71, motif 3.72, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.94, ipTM 0.48, PAE 8.61, motif 5.30, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.94, ipTM 0.63, PAE 5.01, motif 7.55, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.94, ipTM 0.74, PAE 3.23, motif 4.76, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.94, ipTM 0.76, PAE 3.12, motif 2.83, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.95, ipTM 0.54, PAE 5.83, motif 8.68, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.95, ipTM 0.55, PAE 6.91, motif 3.47, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.95, ipTM 0.62, PAE 4.15, motif 12.96, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.95, ipTM 0.79, PAE 2.40, motif 4.04, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.96, ipTM 0.79, PAE 2.58, motif 9.20, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.97, ipTM 0.74, PAE 3.09, motif 14.04, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.98, ipTM 0.56, PAE 6.08, motif 4.31, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.00, ipTM 0.39, PAE 10.97, motif 7.11, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.00, ipTM 0.42, PAE 8.56, motif 6.38, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.00, ipTM 0.76, PAE 2.81, motif 2.40, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.01, ipTM 0.53, PAE 6.21, motif 4.38, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.01, ipTM 0.57, PAE 6.74, motif 10.59, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.02, ipTM 0.52, PAE 7.09, motif 2.03, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.03, ipTM 0.45, PAE 6.20, motif 4.79, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.03, ipTM 0.59, PAE 4.40, motif 8.90, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.03, ipTM 0.59, PAE 5.30, motif 8.37, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.03, ipTM 0.64, PAE 4.65, motif 2.99, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.03, ipTM 0.64, PAE 5.13, motif 13.65, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.03, ipTM 0.85, PAE 1.39, motif 5.45, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.04, ipTM 0.55, PAE 7.17, motif 6.42, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.04, ipTM 0.63, PAE 4.71, motif 2.73, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.07, ipTM 0.74, PAE 2.88, motif 5.59, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.08, ipTM 0.63, PAE 4.23, motif 11.43, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.12, ipTM 0.59, PAE 4.52, motif 3.64, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.12, ipTM 0.62, PAE 4.22, motif 5.23, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.14, ipTM 0.56, PAE 5.47, motif 4.12, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.14, ipTM 0.56, PAE 6.77, motif 4.83, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.14, ipTM 0.59, PAE 5.13, motif 11.77, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.14, ipTM 0.59, PAE 5.14, motif 3.85, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.16, ipTM 0.66, PAE 4.03, motif 2.27, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.17, ipTM 0.47, PAE 8.01, motif 5.57, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.17, ipTM 0.64, PAE 4.01, motif 2.95, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.20, ipTM 0.39, PAE 9.02, motif 14.95, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.20, ipTM 0.61, PAE 5.24, motif 5.20, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.21, ipTM 0.42, PAE 7.63, motif 5.44, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.21, ipTM 0.68, PAE 4.01, motif 5.38, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.22, ipTM 0.48, PAE 6.90, motif 4.51, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.22, ipTM 0.64, PAE 4.02, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.23, ipTM 0.52, PAE 6.51, motif 3.21, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.23, ipTM 0.65, PAE 3.86, motif 6.58, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.23, ipTM 0.76, PAE 2.71, motif 1.78, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.24, ipTM 0.82, PAE 2.12, motif 4.52, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.27, ipTM 0.47, PAE 7.56, motif 3.30, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.28, ipTM 0.65, PAE 4.54, motif 2.98, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.28, ipTM 0.70, PAE 3.40, motif 6.51, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.32, ipTM 0.47, PAE 7.02, motif 10.03, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.32, ipTM 0.50, PAE 5.22, motif 4.42, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.33, ipTM 0.43, PAE 8.81, motif 2.74, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.34, ipTM 0.35, PAE 10.70, motif 8.19, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.35, ipTM 0.68, PAE 4.03, motif 5.59, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.37, ipTM 0.71, PAE 3.44, motif 8.05, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.39, ipTM 0.67, PAE 3.37, motif 2.72, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.39, ipTM 0.67, PAE 4.00, motif 2.70, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.41, ipTM 0.44, PAE 10.11, motif 2.69, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.42, ipTM 0.38, PAE 11.21, motif 9.27, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.42, ipTM 0.71, PAE 3.64, motif 4.32, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.46, ipTM 0.54, PAE 5.70, motif 5.83, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.49, ipTM 0.69, PAE 2.72, motif 6.08, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.50, ipTM 0.42, PAE 9.39, motif 6.30, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.50, ipTM 0.50, PAE 6.41, motif 4.45, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.50, ipTM 0.65, PAE 4.38, motif 7.18, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.52, ipTM 0.60, PAE 4.17, motif 5.62, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.53, ipTM 0.51, PAE 8.26, motif 4.03, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.54, ipTM 0.57, PAE 5.65, motif 14.92, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.57, ipTM 0.64, PAE 4.32, motif 4.85, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.58, ipTM 0.45, PAE 8.01, motif 7.26, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.58, ipTM 0.63, PAE 4.92, motif 3.06, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.60, ipTM 0.74, PAE 3.51, motif 4.41, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.62, ipTM 0.47, PAE 8.22, motif 5.00, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.62, ipTM 0.66, PAE 4.29, motif 6.08, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.63, ipTM 0.54, PAE 6.64, motif 3.31, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.65, ipTM 0.48, PAE 6.96, motif 8.60, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.65, ipTM 0.66, PAE 3.75, motif 5.70, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.67, ipTM 0.36, PAE 8.96, motif 4.91, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.68, ipTM 0.54, PAE 5.43, motif 4.02, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.69, ipTM 0.59, PAE 5.80, motif 2.63, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.69, ipTM 0.69, PAE 3.90, motif 4.37, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.69, ipTM 0.73, PAE 3.54, motif 3.07, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.70, ipTM 0.54, PAE 4.04, motif 5.41, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.71, ipTM 0.49, PAE 8.12, motif 7.07, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.71, ipTM 0.58, PAE 4.63, motif 5.35, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.73, ipTM 0.61, PAE 4.69, motif 3.83, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.74, ipTM 0.63, PAE 4.49, motif 5.09, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.74, ipTM 0.73, PAE 3.08, motif 2.96, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.80, ipTM 0.50, PAE 6.72, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.81, ipTM 0.52, PAE 5.13, motif 3.82, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.84, ipTM 0.77, PAE 2.00, motif 11.68, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.86, ipTM 0.67, PAE 2.53, motif 9.77, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.90, ipTM 0.53, PAE 5.92, motif 10.57, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.90, ipTM 0.70, PAE 3.48, motif 5.11, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.90, ipTM 0.72, PAE 2.13, motif 2.64, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.91, ipTM 0.71, PAE 3.21, motif 5.23, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.93, ipTM 0.61, PAE 5.26, motif 8.13, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.94, ipTM 0.52, PAE 6.71, motif 10.05, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.95, ipTM 0.87, PAE 1.37, motif 6.02, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.96, ipTM 0.67, PAE 4.18, motif 3.52, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.97, ipTM 0.51, PAE 7.16, motif 8.40, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.01, ipTM 0.59, PAE 4.54, motif 5.43, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.01, ipTM 0.78, PAE 2.40, motif 13.05, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.06, ipTM 0.50, PAE 7.14, motif 7.64, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.11, ipTM 0.57, PAE 5.04, motif 6.85, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.13, ipTM 0.37, PAE 9.46, motif 4.46, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.13, ipTM 0.55, PAE 4.99, motif 2.67, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.14, ipTM 0.47, PAE 7.63, motif 3.35, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.19, ipTM 0.45, PAE 8.25, motif 10.28, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.19, ipTM 0.56, PAE 5.70, motif 4.44, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.25, ipTM 0.46, PAE 7.10, motif 3.16, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.25, ipTM 0.54, PAE 6.35, motif 6.63, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.25, ipTM 0.80, PAE 2.21, motif 11.36, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.26, ipTM 0.53, PAE 4.89, motif 9.62, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.26, ipTM 0.65, PAE 4.52, motif 9.27, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.28, ipTM 0.68, PAE 3.62, motif 4.03, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.30, ipTM 0.32, PAE 12.23, motif 5.82, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.30, ipTM 0.66, PAE 3.57, motif 4.18, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.32, ipTM 0.36, PAE 8.46, motif 7.80, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.32, ipTM 0.56, PAE 4.08, motif 8.04, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.32, ipTM 0.57, PAE 5.92, motif 9.40, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.38, ipTM 0.67, PAE 2.48, motif 10.72, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.39, ipTM 0.53, PAE 6.10, motif 2.93, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.44, ipTM 0.67, PAE 4.18, motif 9.80, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.45, ipTM 0.66, PAE 4.10, motif 6.85, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.46, ipTM 0.63, PAE 3.48, motif 5.59, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.47, ipTM 0.59, PAE 5.37, motif 4.69, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.59, ipTM 0.36, PAE 11.49, motif 5.73, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.60, ipTM 0.59, PAE 3.86, motif 6.74, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.65, ipTM 0.64, PAE 3.29, motif 6.07, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.69, ipTM 0.70, PAE 3.58, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.76, ipTM 0.54, PAE 5.90, motif 4.57, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.77, ipTM 0.34, PAE 10.67, motif 11.76, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.79, ipTM 0.49, PAE 7.22, motif 6.31, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.80, ipTM 0.56, PAE 3.58, motif 4.00, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.85, ipTM 0.46, PAE 5.34, motif 13.85, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.86, ipTM 0.55, PAE 5.01, motif 10.59, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.87, ipTM 0.40, PAE 8.76, motif 8.61, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.88, ipTM 0.54, PAE 6.11, motif 6.64, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.92, ipTM 0.62, PAE 5.07, motif 6.23, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.99, ipTM 0.54, PAE 5.36, motif 6.63, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 6.07, ipTM 0.52, PAE 5.59, motif 8.19, no sequence satisfied 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0000000000000000000000000000000000000000..f238934436a6fa33d7ef694aa5d28a7e06774578 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S001/reward.json @@ -0,0 +1,36 @@ +{ + "enzyme_conditioning_satisfied": 820.0, + "enzyme_designs_designable": 95.0, + "enzyme_designs_novel": 95.0, + "enzyme_designs_requested": 820.0, + "enzyme_designs_returned": 820.0, + "enzyme_designs_valid": 820.0, + "enzyme_diversity": 0.3170731707317073, + "enzyme_novelty_max_tm_mean": 0.724604210526316, + "enzyme_pass_rate": 0.11585365853658537, + "ppi_binder_conditioning_satisfied": 100.0, + "ppi_binder_designs_designable": 0.0, + "ppi_binder_designs_novel": 0.0, + "ppi_binder_designs_requested": 100.0, + "ppi_binder_designs_returned": 100.0, + "ppi_binder_designs_valid": 100.0, + "ppi_binder_diversity": 0.0, + "ppi_binder_pass_rate": 0.0, + "sm_binder_conditioning_satisfied": 80.0, + "sm_binder_designs_designable": 0.0, + "sm_binder_designs_novel": 0.0, + "sm_binder_designs_requested": 80.0, + "sm_binder_designs_returned": 80.0, + "sm_binder_designs_valid": 80.0, + "sm_binder_diversity": 0.0, + "sm_binder_pass_rate": 0.0, + "unconditional_conditioning_satisfied": 20.0, + "unconditional_designs_designable": 3.0, + "unconditional_designs_novel": 0.0, + "unconditional_designs_requested": 20.0, + "unconditional_designs_returned": 20.0, + "unconditional_designs_valid": 20.0, + "unconditional_diversity": 0.0, + "unconditional_novelty_max_tm_mean": 0.8572333333333333, + "unconditional_pass_rate": 0.0 +} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S002/logs/inference/verifier.log b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S002/logs/inference/verifier.log new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S002/logs/score/verifier.log b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S002/logs/score/verifier.log new file mode 100644 index 0000000000000000000000000000000000000000..078c3135e143c5d67f54374d04194896f61ac8ff --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S002/logs/score/verifier.log @@ -0,0 +1 @@ +grading with 8 worker(s) on device(s) GPU-7f96da5c-311d-3b61-2245-4c639f357c7e, GPU-5d4656aa-5216-02ba-c6d7-b1e2af43d17d, GPU-ae064263-a937-c095-9d8e-e96e45ac9c30, GPU-73d91f40-aab2-d144-fe0e-ba552569476c, GPU-8d06c7bf-05ac-e6e7-8eb2-d68584ae2762, GPU-edee3da7-a642-29db-883a-133740267cb1, GPU-abea6582-7cd4-5e51-54a0-43ae42f6a5ff, GPU-9be02648-4275-0529-264f-b24f9f7778ee diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S002/report.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S002/report.json new file mode 100644 index 0000000000000000000000000000000000000000..77f6eac29d4ab858191bc7af7aaca9807df13293 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S002/report.json @@ -0,0 +1,698 @@ +{ + "schema": "protein-design-report/v1", + "thresholds": { + "motif_rmsd_max": 1.5, + "novelty_max_tm": 0.8, + "plddt_min": 70.0, + "sc_rmsd_max": 1.5 + }, + "tracks": { + "enzyme": { + "conditioning_satisfied": 820, + "designs_designable": 42, + "designs_novel": 42, + "designs_requested": 820, + "designs_returned": 820, + "designs_valid": 820, + "diversity": 0.2926829268292683, + "failures": { + "non-motif backbone clashes with ligand at 0.11 A": 1, + "non-motif backbone clashes with ligand at 0.12 A": 1, + "non-motif backbone clashes with ligand at 0.14 A": 1, + "non-motif backbone clashes with ligand at 0.23 A": 3, + "non-motif backbone 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(scRMSD 0.92, ipTM 0.64, PAE 3.58, motif 3.91, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.92, ipTM 0.77, PAE 2.86, motif 3.25, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.93, ipTM 0.75, PAE 3.18, motif 4.19, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.98, ipTM 0.54, PAE 6.92, motif 4.25, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.98, ipTM 0.70, PAE 3.87, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.01, ipTM 0.81, PAE 2.48, motif 2.13, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.06, ipTM 0.64, PAE 4.98, motif 3.72, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.06, ipTM 0.83, PAE 2.03, motif 6.74, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.07, ipTM 0.73, PAE 3.19, motif 3.26, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.16, ipTM 0.80, PAE 2.24, motif 1.65, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.24, ipTM 0.82, PAE 2.21, motif 3.90, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.25, ipTM 0.69, PAE 2.31, motif 7.13, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.27, ipTM 0.73, PAE 3.44, motif 6.55, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.28, ipTM 0.59, PAE 4.26, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.30, ipTM 0.64, PAE 3.61, motif 6.42, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.31, ipTM 0.67, PAE 4.71, motif 3.13, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.33, ipTM 0.68, PAE 4.20, motif 3.72, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.34, ipTM 0.66, PAE 4.81, motif 6.09, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.38, ipTM 0.73, PAE 2.96, motif 8.86, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.41, ipTM 0.52, PAE 7.68, motif 6.27, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.49, ipTM 0.83, PAE 1.92, motif 3.66, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.50, ipTM 0.40, PAE 9.50, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.50, ipTM 0.64, PAE 4.73, motif 1.86, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.55, ipTM 0.53, PAE 6.68, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.55, ipTM 0.59, PAE 5.22, motif 7.11, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.59, ipTM 0.91, PAE 0.96, motif 5.36, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.61, ipTM 0.60, PAE 5.17, motif 6.45, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.64, ipTM 0.72, PAE 3.64, motif 3.01, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.71, ipTM 0.65, PAE 4.84, motif 9.70, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.71, ipTM 0.72, PAE 3.08, motif 2.28, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.72, ipTM 0.77, PAE 2.52, motif 9.21, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.73, ipTM 0.66, PAE 1.32, motif 5.22, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.77, ipTM 0.67, PAE 3.89, motif 1.89, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.78, ipTM 0.68, PAE 4.57, motif 2.30, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.85, ipTM 0.67, PAE 4.42, motif 4.02, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.85, ipTM 0.75, PAE 3.10, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.89, ipTM 0.62, PAE 5.15, motif 2.88, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.89, ipTM 0.67, PAE 3.90, motif 9.90, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.98, ipTM 0.46, PAE 8.28, motif 1.85, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.15, ipTM 0.54, PAE 6.52, motif 10.62, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.16, ipTM 0.54, PAE 5.48, motif 14.03, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.27, ipTM 0.34, PAE 10.55, motif 13.67, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.37, ipTM 0.68, PAE 2.40, motif 10.34, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.41, ipTM 0.38, PAE 9.88, motif 8.65, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.45, ipTM 0.46, PAE 8.30, motif 8.80, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.46, ipTM 0.36, PAE 9.29, motif 9.02, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.47, ipTM 0.52, PAE 5.43, motif 7.86, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.57, ipTM 0.32, PAE 12.20, motif 15.91, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.57, ipTM 0.40, PAE 5.72, motif 7.49, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.58, ipTM 0.34, PAE 12.18, motif 9.00, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.59, ipTM 0.57, PAE 4.46, motif 10.00, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.69, ipTM 0.56, PAE 3.72, motif 15.14, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.72, ipTM 0.46, PAE 5.34, motif 5.95, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.81, ipTM 0.33, PAE 10.40, motif 10.19, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.86, ipTM 0.42, PAE 8.78, motif 10.63, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.91, ipTM 0.57, PAE 3.98, motif 5.53, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.93, ipTM 0.52, PAE 5.71, motif 9.87, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.01, ipTM 0.40, PAE 9.87, motif 7.40, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.01, ipTM 0.51, PAE 6.19, motif 5.65, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.07, ipTM 0.70, PAE 3.53, motif 9.90, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.10, ipTM 0.40, PAE 7.39, motif 5.96, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.11, ipTM 0.37, PAE 10.77, motif 12.96, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.16, ipTM 0.59, PAE 3.05, motif 6.26, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.21, ipTM 0.45, PAE 9.28, motif 5.11, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.29, ipTM 0.51, PAE 7.01, motif 10.29, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.46, ipTM 0.37, PAE 8.10, motif 13.69, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.51, ipTM 0.40, PAE 8.78, motif 8.61, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.71, ipTM 0.40, PAE 10.46, motif 19.47, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.79, ipTM 0.38, PAE 9.91, motif 9.25, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.82, ipTM 0.49, PAE 7.30, motif 5.52, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.86, ipTM 0.84, PAE 1.75, motif 10.30, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.99, ipTM 0.54, PAE 4.68, motif 6.02, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.04, ipTM 0.45, PAE 7.53, motif 5.61, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.07, ipTM 0.46, PAE 8.10, motif 18.23, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.08, ipTM 0.31, PAE 11.51, motif 11.10, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.09, ipTM 0.46, PAE 7.75, motif 11.55, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.09, ipTM 0.51, PAE 6.92, motif 13.00, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.12, ipTM 0.59, PAE 5.10, motif 7.69, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.13, ipTM 0.39, PAE 9.40, motif 14.43, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.22, ipTM 0.61, PAE 4.61, motif 6.82, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.31, ipTM 0.47, PAE 5.68, motif 14.55, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.47, ipTM 0.51, PAE 5.06, motif 11.28, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.51, ipTM 0.47, PAE 7.26, motif 7.28, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.51, ipTM 0.49, PAE 5.60, motif 11.17, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.53, ipTM 0.46, PAE 8.78, motif 11.25, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.54, ipTM 0.38, PAE 10.96, motif 17.78, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.56, ipTM 0.56, PAE 6.06, motif 15.64, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.65, ipTM 0.45, PAE 6.29, motif 9.59, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.93, ipTM 0.46, PAE 4.94, motif 10.49, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.98, ipTM 0.45, PAE 7.93, motif 8.92, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 13.03, ipTM 0.48, PAE 6.53, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 13.40, ipTM 0.48, PAE 7.11, motif 7.75, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 13.51, ipTM 0.43, PAE 8.21, motif 8.43, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 13.58, ipTM 0.54, PAE 4.45, motif 13.54, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 13.83, ipTM 0.44, PAE 7.61, motif 11.01, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 14.72, ipTM 0.62, PAE 3.78, motif 9.94, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 14.77, ipTM 0.70, PAE 3.20, motif 6.23, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 15.33, ipTM 0.42, PAE 9.63, motif 12.02, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 17.08, ipTM 0.33, PAE 11.33, motif 9.42, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.06, ipTM 0.64, PAE 4.84, motif 3.74, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.13, ipTM 0.56, PAE 4.88, motif 3.24, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.14, ipTM 0.55, PAE 5.63, motif 5.43, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.15, ipTM 0.63, PAE 4.50, motif 7.37, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.17, ipTM 0.40, PAE 8.74, motif 5.08, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.23, ipTM 0.57, PAE 5.11, motif 2.15, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.24, ipTM 0.59, PAE 3.67, motif 16.73, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.26, ipTM 0.58, PAE 6.15, motif 6.98, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.30, ipTM 0.80, PAE 2.23, motif 2.72, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.35, ipTM 0.71, PAE 3.50, motif 4.78, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.36, ipTM 0.66, PAE 4.29, motif 8.48, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.38, ipTM 0.53, PAE 6.83, motif 11.37, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.42, ipTM 0.55, PAE 5.30, motif 3.60, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.61, ipTM 0.62, PAE 3.75, motif 2.99, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.61, ipTM 0.72, PAE 2.43, motif 4.04, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.63, ipTM 0.74, PAE 2.80, motif 5.60, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.65, ipTM 0.63, PAE 4.39, motif 12.56, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.66, ipTM 0.55, PAE 5.66, motif 4.92, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.71, ipTM 0.62, PAE 4.19, motif 2.62, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.75, ipTM 0.77, PAE 2.60, motif 6.34, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.83, ipTM 0.62, PAE 3.58, motif 13.54, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.85, ipTM 0.36, PAE 11.58, motif 6.10, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.85, ipTM 0.56, PAE 5.91, motif 3.24, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.03, ipTM 0.43, PAE 7.88, motif 12.78, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.03, ipTM 0.68, PAE 4.29, motif 11.43, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.11, ipTM 0.59, PAE 4.21, motif 6.37, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.23, ipTM 0.73, PAE 3.25, motif 6.36, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.24, ipTM 0.72, PAE 2.69, motif 12.76, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.24, ipTM 0.73, PAE 2.54, motif 5.34, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.25, ipTM 0.51, PAE 6.95, motif 7.46, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.32, ipTM 0.50, PAE 6.11, motif 8.17, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.34, ipTM 0.75, PAE 2.77, motif 5.73, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.48, ipTM 0.67, PAE 3.63, motif 6.01, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.50, ipTM 0.65, PAE 4.50, motif 3.40, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.51, ipTM 0.59, PAE 5.21, motif 4.36, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.60, ipTM 0.44, PAE 7.36, motif 3.72, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.62, ipTM 0.50, PAE 6.28, motif 4.81, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.63, ipTM 0.67, PAE 3.96, motif 4.64, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.69, ipTM 0.56, PAE 4.75, motif 3.86, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.73, ipTM 0.62, PAE 4.58, motif 6.16, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.80, ipTM 0.41, PAE 9.49, motif 5.68, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.80, ipTM 0.70, PAE 2.90, motif 5.23, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.90, ipTM 0.46, PAE 6.81, motif 3.98, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.94, ipTM 0.48, PAE 7.48, motif 3.42, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.00, ipTM 0.48, PAE 7.23, motif 6.40, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.00, ipTM 0.52, PAE 7.43, motif 6.86, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.01, ipTM 0.60, PAE 4.69, motif 5.17, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.07, ipTM 0.61, PAE 5.24, motif 7.68, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.10, ipTM 0.45, PAE 8.78, motif 5.31, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.11, ipTM 0.55, PAE 5.90, motif 6.33, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.22, ipTM 0.43, PAE 8.78, motif 7.72, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.43, ipTM 0.61, PAE 4.01, motif 3.06, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.48, ipTM 0.76, PAE 2.77, motif 2.49, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.57, ipTM 0.70, PAE 3.39, motif 5.57, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.61, ipTM 0.42, PAE 7.80, motif 5.45, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.67, ipTM 0.51, PAE 6.26, motif 7.08, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.74, ipTM 0.49, PAE 8.10, motif 5.97, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.74, ipTM 0.66, PAE 4.37, motif 12.78, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.75, ipTM 0.38, PAE 10.32, motif 6.72, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.79, ipTM 0.48, PAE 8.77, motif 5.96, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.91, ipTM 0.51, PAE 6.19, motif 5.77, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.93, ipTM 0.35, PAE 9.00, motif 9.58, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.94, ipTM 0.41, PAE 7.80, motif 2.89, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.94, ipTM 0.41, PAE 8.63, motif 7.93, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.94, ipTM 0.44, PAE 7.59, motif 2.54, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.95, ipTM 0.39, PAE 9.72, motif 11.25, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.97, ipTM 0.58, PAE 5.09, motif 2.63, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.01, ipTM 0.30, PAE 12.00, motif 11.16, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.02, ipTM 0.45, PAE 7.90, motif 5.73, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.07, ipTM 0.60, PAE 4.57, motif 2.93, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.08, ipTM 0.50, PAE 7.06, motif 4.73, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.17, ipTM 0.43, PAE 8.67, motif 10.33, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.18, ipTM 0.46, PAE 7.05, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.18, ipTM 0.78, PAE 2.42, motif 2.89, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.25, ipTM 0.48, PAE 6.61, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.38, ipTM 0.51, PAE 6.96, motif 6.35, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.39, ipTM 0.40, PAE 7.87, motif 9.55, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.39, ipTM 0.64, PAE 2.93, motif 16.14, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.41, ipTM 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"unconditional_pass_rate": 0.65 +} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S003/logs/inference/verifier.log b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S003/logs/inference/verifier.log new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S003/logs/score/verifier.log b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S003/logs/score/verifier.log new file mode 100644 index 0000000000000000000000000000000000000000..70981dc7fa544d4b942fd6c4729ea2c4d03dfed7 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S003/logs/score/verifier.log @@ -0,0 +1 @@ +grading with 8 worker(s) on device(s) GPU-4b81b792-8866-6273-af6f-2601e58869f5, GPU-ba5eefb9-ec09-f85b-d762-b8c40819170e, 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"non-motif backbone clashes with ligand at 0.91 A": 3, + "non-motif backbone clashes with ligand at 0.92 A": 4, + "non-motif backbone clashes with ligand at 0.93 A": 7, + "non-motif backbone clashes with ligand at 0.94 A": 3, + "non-motif backbone clashes with ligand at 0.95 A": 3, + "non-motif backbone clashes with ligand at 0.96 A": 3, + "non-motif backbone clashes with ligand at 0.97 A": 1, + "non-motif backbone clashes with ligand at 0.98 A": 2, + "non-motif backbone clashes with ligand at 0.99 A": 4, + "non-motif backbone clashes with ligand at 1.00 A": 1, + "non-motif backbone clashes with ligand at 1.01 A": 5, + "non-motif backbone clashes with ligand at 1.02 A": 6, + "non-motif backbone clashes with ligand at 1.04 A": 2, + "non-motif backbone clashes with ligand at 1.05 A": 3, + "non-motif backbone clashes with ligand at 1.06 A": 1, + "non-motif backbone clashes with ligand at 1.07 A": 2, + "non-motif backbone clashes with ligand at 1.08 A": 4, + "non-motif backbone clashes with ligand at 1.09 A": 2, + "non-motif backbone clashes with ligand at 1.10 A": 4, + "non-motif backbone clashes with ligand at 1.11 A": 3, + "non-motif backbone clashes with ligand at 1.12 A": 3, + "non-motif backbone clashes with ligand at 1.14 A": 1, + "non-motif backbone clashes with ligand at 1.15 A": 4, + "non-motif backbone clashes with ligand at 1.16 A": 2, + "non-motif backbone clashes with ligand at 1.17 A": 6, + "non-motif backbone clashes with ligand at 1.18 A": 2, + "non-motif backbone clashes with ligand at 1.19 A": 5, + "non-motif backbone clashes with ligand at 1.20 A": 3, + "non-motif backbone clashes with ligand at 1.21 A": 2, + "non-motif backbone clashes with ligand at 1.22 A": 4, + "non-motif backbone clashes with ligand at 1.23 A": 7, + "non-motif backbone clashes with ligand at 1.24 A": 1, + "non-motif backbone clashes with ligand at 1.25 A": 4, + "non-motif backbone clashes with ligand at 1.26 A": 4, + "non-motif backbone clashes with ligand at 1.27 A": 1, + "non-motif backbone clashes with ligand at 1.28 A": 3, + "non-motif backbone clashes with ligand at 1.29 A": 4, + "non-motif backbone clashes with ligand at 1.30 A": 4, + "non-motif backbone clashes with ligand at 1.31 A": 2, + "non-motif backbone clashes with ligand at 1.32 A": 2, + "non-motif backbone clashes with ligand at 1.33 A": 3, + "non-motif backbone clashes with ligand at 1.34 A": 4, + "non-motif backbone clashes with ligand at 1.35 A": 3, + "non-motif backbone clashes with ligand at 1.36 A": 5, + "non-motif backbone clashes with ligand at 1.37 A": 8, + "non-motif backbone clashes with ligand at 1.38 A": 4, + "non-motif backbone clashes with ligand at 1.39 A": 2, + "non-motif backbone clashes with ligand at 1.40 A": 3, + "non-motif backbone clashes with ligand at 1.41 A": 2, + "non-motif backbone clashes with ligand at 1.42 A": 1, + "non-motif backbone clashes with ligand at 1.43 A": 3, + "non-motif backbone clashes with ligand at 1.44 A": 3, + "non-motif backbone clashes with ligand at 1.45 A": 4, + "non-motif backbone clashes with ligand at 1.46 A": 5, + "non-motif backbone clashes with ligand at 1.47 A": 2, + "non-motif backbone clashes with ligand at 1.48 A": 3, + "non-motif backbone clashes with ligand at 1.49 A": 5, + "non-motif backbone clashes with ligand at 1.50 A": 1, + "not designable (scRMSD 0.60, ipTM 0.73, PAE 3.54, motif 2.16, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.63, ipTM 0.85, PAE 1.79, motif 1.98, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.64, ipTM 0.78, PAE 2.89, motif 4.31, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.71, ipTM 0.70, PAE 3.70, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.72, ipTM 0.64, PAE 4.19, motif 2.49, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.75, ipTM 0.68, PAE 3.99, motif 3.72, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.78, ipTM 0.78, PAE 2.53, motif 4.49, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.81, ipTM 0.72, PAE 4.11, motif 6.95, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.82, ipTM 0.64, PAE 5.38, motif 4.89, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.83, ipTM 0.65, PAE 4.99, motif 17.07, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.85, ipTM 0.64, PAE 4.92, motif 4.17, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.86, ipTM 0.80, PAE 2.33, motif 5.94, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.87, ipTM 0.58, PAE 6.42, motif 13.13, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.87, ipTM 0.72, PAE 3.82, motif 5.86, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.88, ipTM 0.84, PAE 1.94, motif 1.93, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.90, ipTM 0.77, PAE 2.86, motif 3.26, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.90, ipTM 0.79, PAE 2.27, motif 1.91, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.91, ipTM 0.59, PAE 4.67, motif 3.70, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.92, ipTM 0.75, PAE 3.18, motif 4.19, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.93, ipTM 0.64, PAE 3.58, motif 3.90, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.93, ipTM 0.70, PAE 3.86, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.98, ipTM 0.54, PAE 6.94, motif 4.28, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.01, ipTM 0.68, PAE 4.48, motif 10.67, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.01, ipTM 0.81, PAE 2.48, motif 2.13, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.07, ipTM 0.73, PAE 3.20, motif 3.27, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.09, ipTM 0.83, PAE 2.03, motif 6.78, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.11, ipTM 0.58, PAE 5.33, motif 7.05, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.15, ipTM 0.80, PAE 2.24, motif 1.64, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.19, ipTM 0.58, PAE 6.45, motif 7.35, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.24, ipTM 0.82, PAE 2.22, motif 3.92, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.26, ipTM 0.69, PAE 2.31, motif 7.07, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.28, ipTM 0.73, PAE 3.44, motif 6.52, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.29, ipTM 0.67, PAE 4.68, motif 3.29, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.30, ipTM 0.59, PAE 4.26, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.31, ipTM 0.64, PAE 3.62, motif 6.43, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.34, ipTM 0.66, PAE 4.81, motif 6.02, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.36, ipTM 0.68, PAE 4.25, motif 3.53, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.38, ipTM 0.73, PAE 2.98, motif 8.89, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.40, ipTM 0.52, PAE 7.68, motif 6.30, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.47, ipTM 0.86, PAE 1.31, motif 9.22, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.50, ipTM 0.40, PAE 9.50, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.50, ipTM 0.53, PAE 6.69, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.51, ipTM 0.64, PAE 4.73, motif 1.81, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.54, ipTM 0.60, PAE 5.40, motif 2.35, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.58, ipTM 0.76, PAE 2.68, motif 4.70, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.59, ipTM 0.72, PAE 3.01, motif 2.32, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.59, ipTM 0.91, PAE 0.96, motif 5.36, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.65, ipTM 0.72, PAE 3.64, motif 2.99, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.67, ipTM 0.66, PAE 4.01, motif 6.58, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.71, ipTM 0.65, PAE 4.85, motif 9.70, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.75, ipTM 0.67, PAE 3.89, motif 1.92, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.78, ipTM 0.66, PAE 1.32, motif 5.19, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.78, ipTM 0.68, PAE 4.57, motif 2.32, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.86, ipTM 0.67, PAE 4.44, motif 4.02, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.93, ipTM 0.46, PAE 8.26, motif 1.89, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.04, ipTM 0.56, PAE 5.31, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.06, ipTM 0.53, PAE 5.90, motif 6.85, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.11, ipTM 0.43, PAE 8.70, motif 13.82, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.12, ipTM 0.56, PAE 5.74, motif 10.56, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.16, ipTM 0.53, PAE 6.48, motif 10.55, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.18, ipTM 0.53, PAE 5.49, motif 15.18, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.27, ipTM 0.34, PAE 10.55, motif 13.67, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.33, ipTM 0.38, PAE 9.88, motif 8.81, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.33, ipTM 0.68, PAE 2.37, motif 10.90, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.37, ipTM 0.46, PAE 8.28, motif 8.58, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.49, ipTM 0.48, PAE 7.69, motif 7.11, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.50, ipTM 0.34, PAE 12.19, motif 7.20, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.52, ipTM 0.52, PAE 5.37, motif 7.96, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.56, ipTM 0.32, PAE 12.19, motif 15.79, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.56, ipTM 0.57, PAE 4.47, motif 9.02, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.58, ipTM 0.55, PAE 3.88, motif 17.32, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.59, ipTM 0.40, PAE 5.74, motif 7.31, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.63, ipTM 0.52, PAE 6.16, motif 5.74, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.72, ipTM 0.46, PAE 5.34, motif 5.95, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.79, ipTM 0.52, PAE 5.73, motif 10.92, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.83, ipTM 0.33, PAE 10.39, motif 10.20, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.86, ipTM 0.27, PAE 11.46, motif 6.94, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.86, ipTM 0.42, PAE 8.78, motif 10.63, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.87, ipTM 0.38, PAE 10.42, motif 6.27, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.89, ipTM 0.57, PAE 3.98, motif 5.42, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.95, ipTM 0.37, PAE 10.72, motif 13.04, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 10.96, ipTM 0.70, PAE 3.55, motif 12.95, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.08, ipTM 0.45, PAE 9.27, motif 5.18, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.13, ipTM 0.40, PAE 7.38, motif 6.18, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.16, ipTM 0.59, PAE 3.05, motif 6.26, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.34, ipTM 0.51, PAE 6.95, motif 10.13, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.47, ipTM 0.37, PAE 8.10, motif 13.83, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.50, ipTM 0.40, PAE 8.82, motif 8.53, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.66, ipTM 0.45, PAE 6.30, motif 8.29, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.76, ipTM 0.40, PAE 10.56, motif 19.21, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.79, ipTM 0.39, PAE 9.78, motif 9.31, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.82, ipTM 0.49, PAE 7.30, motif 5.52, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.83, ipTM 0.31, PAE 11.55, motif 11.00, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.88, ipTM 0.46, PAE 8.09, motif 17.75, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.93, ipTM 0.84, PAE 1.77, motif 10.06, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.06, ipTM 0.45, PAE 7.62, motif 5.55, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.09, ipTM 0.46, PAE 7.72, motif 11.57, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.09, ipTM 0.51, PAE 6.92, motif 13.00, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.10, ipTM 0.48, PAE 6.91, motif 7.60, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.13, ipTM 0.39, PAE 9.42, motif 14.44, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.26, ipTM 0.47, PAE 5.66, motif 13.60, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.28, ipTM 0.61, PAE 4.57, motif 6.87, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.31, ipTM 0.38, PAE 10.99, motif 18.10, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.47, ipTM 0.49, PAE 5.59, motif 11.06, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.51, ipTM 0.42, PAE 7.79, motif 5.98, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.55, ipTM 0.46, PAE 8.84, motif 11.18, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.60, ipTM 0.56, PAE 6.10, motif 15.91, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.66, ipTM 0.59, PAE 5.07, motif 8.01, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.88, ipTM 0.50, PAE 5.09, motif 11.16, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 12.95, ipTM 0.45, PAE 7.93, motif 8.98, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 13.18, ipTM 0.46, PAE 6.77, motif 6.32, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 13.29, ipTM 0.39, PAE 8.88, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 13.43, ipTM 0.48, PAE 7.10, motif 7.75, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 13.55, ipTM 0.42, PAE 8.24, motif 8.33, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 13.75, ipTM 0.54, PAE 4.36, motif 12.34, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 13.76, ipTM 0.44, PAE 7.74, motif 11.01, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 14.73, ipTM 0.73, PAE 2.39, motif 13.25, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 14.87, ipTM 0.70, PAE 3.19, motif 5.52, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 15.38, ipTM 0.42, PAE 9.68, motif 12.25, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 17.08, ipTM 0.32, PAE 11.41, motif 9.64, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.03, ipTM 0.64, PAE 4.84, motif 3.74, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.14, ipTM 0.62, PAE 4.52, motif 7.36, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.17, ipTM 0.39, PAE 8.87, motif 5.09, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.20, ipTM 0.58, PAE 5.11, motif 2.20, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.21, ipTM 0.56, PAE 4.87, motif 3.17, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.25, ipTM 0.55, PAE 5.63, motif 5.36, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.27, ipTM 0.59, PAE 3.68, motif 16.67, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.29, ipTM 0.53, PAE 6.84, motif 11.49, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.30, ipTM 0.58, PAE 6.21, motif 6.55, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.35, ipTM 0.71, PAE 3.56, motif 4.85, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.36, ipTM 0.66, PAE 4.29, motif 8.48, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.46, ipTM 0.56, PAE 5.34, motif 3.34, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.61, ipTM 0.62, PAE 3.75, motif 2.97, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.61, ipTM 0.74, PAE 2.80, motif 5.55, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.62, ipTM 0.72, PAE 2.42, motif 4.02, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.63, ipTM 0.63, PAE 4.39, motif 12.72, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.64, ipTM 0.55, PAE 5.70, motif 4.88, no sequence 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"not designable (scRMSD 3.21, ipTM 0.72, PAE 2.69, motif 12.96, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.23, ipTM 0.73, PAE 3.25, motif 6.38, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.25, ipTM 0.51, PAE 6.95, motif 7.46, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.29, ipTM 0.59, PAE 4.26, motif 5.78, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.32, ipTM 0.50, PAE 6.13, motif 8.71, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.44, ipTM 0.67, PAE 3.68, motif 5.65, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.49, ipTM 0.59, PAE 5.19, motif 4.42, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.50, ipTM 0.65, PAE 4.50, motif 3.45, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.52, ipTM 0.57, PAE 4.82, motif 3.53, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.54, ipTM 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a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S003/reward.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S003/reward.json new file mode 100644 index 0000000000000000000000000000000000000000..b1a52fecf54b0ed947b358d46763ae7290219d28 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/evaluations/S003/reward.json @@ -0,0 +1,37 @@ +{ + "enzyme_conditioning_satisfied": 820.0, + "enzyme_designs_designable": 44.0, + "enzyme_designs_novel": 44.0, + "enzyme_designs_requested": 820.0, + "enzyme_designs_returned": 820.0, + "enzyme_designs_valid": 820.0, + "enzyme_diversity": 0.2926829268292683, + "enzyme_novelty_max_tm_mean": 0.581325, + "enzyme_pass_rate": 0.05365853658536586, + "ppi_binder_conditioning_satisfied": 100.0, + "ppi_binder_designs_designable": 0.0, + "ppi_binder_designs_novel": 0.0, + "ppi_binder_designs_requested": 100.0, + "ppi_binder_designs_returned": 100.0, + "ppi_binder_designs_valid": 100.0, + "ppi_binder_diversity": 0.0, + "ppi_binder_pass_rate": 0.0, + "sm_binder_conditioning_satisfied": 80.0, + "sm_binder_designs_designable": 6.0, + "sm_binder_designs_novel": 6.0, + "sm_binder_designs_requested": 80.0, + "sm_binder_designs_returned": 80.0, + "sm_binder_designs_valid": 80.0, + "sm_binder_diversity": 1.0, + "sm_binder_novelty_max_tm_mean": 0.6774, + "sm_binder_pass_rate": 0.07500000000000001, + "unconditional_conditioning_satisfied": 20.0, + "unconditional_designs_designable": 19.0, + "unconditional_designs_novel": 19.0, + "unconditional_designs_requested": 20.0, + "unconditional_designs_returned": 20.0, + "unconditional_designs_valid": 20.0, + "unconditional_diversity": 1.0, + "unconditional_novelty_max_tm_mean": 0.7085052631578949, + "unconditional_pass_rate": 0.95 +} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/codex.stderr b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/codex.stderr new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/prompt.txt b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/prompt.txt new file mode 100644 index 0000000000000000000000000000000000000000..5d9609786c391bbfd142561f5ecd00d7863b8d3a --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/prompt.txt @@ -0,0 +1,10 @@ +It is now time to submit. Stop exploring the problem. Submit your best solution without further experiments. You will be given an additional 5 minutes starting now to submit. +The 48-hour research budget is over. This is a separate, hard-limited submission-only window. +Use only code, weights, observations, and development results you already produced. +Do not train, run inference/evaluations/simulations, search hyperparameters, or conduct any new experiment. +Choose using only your existing research notes and results; no sealed evaluation results are available. +Publish a complete portable package to /workspace/submission using the original task contract. +The original research tree is now read-only. Use /workspace/finalization or /tmp for packaging scratch; +/workspace/submission and its local parent support atomic publication. File listing, copying, +archiving, and static checks needed for packaging are allowed. Do not alter the scientific method. +The previous container's processes have ended. Finish and stop after publishing. Do not inspect credentials. diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/publication-review.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/publication-review.json new file mode 100644 index 0000000000000000000000000000000000000000..3dbf871fa005e4e8d9b15c68e7d37dbd9f50da0d --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/publication-review.json @@ -0,0 +1,86 @@ +{ + "reviewed_utc": "2026-10-31T00:09:50.563701+00:00", + "task": "protein-design", + "result_label": "48h-plus-5m-submission-only", + "accepted_as_48h_result": false, + "thread_verified": true, + "same_original_thread": true, + "all_new_command_executions_reviewed": true, + "publication_seconds": 84.55471332592424, + "research_seconds": 172800, + "no_new_experiments": true, + "no_new_experiments_basis": "passed: every new command reviewed; package reads, notes, static publish.py --check validation (manifest/file/checksum only), a copy to /workspace/finalization/candidate024 and an atomic re-publish of the unchanged package; the only reference to infer.sh is an executable-bit check; no training, inference, simulation or evaluation was run", + "commands": [ + { + "item_id": "item_2", + "command": "/bin/sh -lc 'cat /workspace/submission/harness.json'", + "exit_code": 0 + }, + { + "item_id": "item_3", + "command": "/bin/sh -lc 'cat /workspace/research/STATE.md'", + "exit_code": 0 + }, + { + "item_id": "item_4", + "command": "/bin/sh -lc 'cat /workspace/baseline/rfd3-small/publish.py'", + "exit_code": 0 + }, + { + "item_id": "item_5", + "command": "/bin/sh -lc 'rg --files --hidden --no-ignore /workspace/submission | head -n 55'", + "exit_code": 0 + }, + { + "item_id": "item_6", + "command": "/bin/sh -lc \"sed -n '1,220p' /workspace/baseline/rfd3-small/publish.py\"", + "exit_code": 0 + }, + { + "item_id": "item_7", + "command": "/bin/sh -lc \"python - <<'PY'\nfrom pathlib import Path\nimport json, hashlib\np=Path('/workspace/submission')\nh=p/'harness.json'\nprint('harness_sha256', hashlib.sha256(h.read_bytes()).hexdigest())\nprint(h.read_text())\nprint('top_level', [q.name for q in p.iterdir()])\nprint('bytes',sum(q.stat().st_size for q in p.rglob('*') if q.is_file()))\nPY\"", + "exit_code": 0 + }, + { + "item_id": "item_8", + "command": "/bin/sh -lc \"python - <<'PY'\nfrom pathlib import Path\nimport hashlib, shutil, json, time\nsrc=Path('/workspace/submission')\ndst=Path('/workspace/finalization/candidate024')\ndst.parent.mkdir(parents=True, exist_ok=True)\nif dst.exists():\n raise SystemExit('Finalization destination already exists; refusing to overwrite')\nexpected='e4cdbe893e4fa1df26209ccaa3af94eca604e71c5b67d38fbb5810f2820552b1'\nassert hashlib.sha256((src/'harness.json').read_bytes()).hexdigest()==expected\nshutil.copytree(src,dst,symlinks=False)\nmanifest=json.loads((dst/'harness.json').read_text())\nfor entry in manifest['learned_components']:\n digest=hashlib.sha256((dst/entry['path']).read_bytes()).hexdigest()\n assert digest==entry['sha256'],entry['path']\nprint('Copied unchanged candidate 024; learned-component digests verified')\nprint('Existing validation artifacts:')\nfor p in (dst/'validation').rglob('*'):\n if p.is_file():\n print(p.relative_to(dst),p.stat().st_size)\nprint('README:')\nprint((dst/'README.md').read_text()[:11000])\nPY\"", + "exit_code": 0 + }, + { + "item_id": "item_9", + "command": "/bin/sh -lc 'python /workspace/baseline/rfd3-small/publish.py /workspace/finalization/candidate024 --check'", + "exit_code": 0 + }, + { + "item_id": "item_10", + "command": "/bin/sh -lc 'cat /workspace/finalization/candidate024/validation/package-replay.json'", + "exit_code": 0 + }, + { + "item_id": "item_11", + "command": "/bin/sh -lc \"python - <<'PY'\nfrom pathlib import Path\nimport hashlib,json,os\nfrom datetime import datetime,timezone\nsrc=Path('/workspace/finalization/candidate024')\ndst=Path('/workspace/submission')\ndef inventory(root):\n return {p.relative_to(root).as_posix():hashlib.sha256(p.read_bytes()).hexdigest() for p in root.rglob('*') if p.is_file() and '__pycache__' not in p.parts and p.suffix\"'!='\"'.pyc'}\na,b=inventory(src),inventory(dst)\nassert a==b, 'Published package differs from selected candidate'\nassert os.access(dst/'infer.sh',os.X_OK)\nreceipt={'published_utc':datetime.now(timezone.utc).isoformat(),'candidate':'024','package':'/workspace/submission','publication':'official publish.py --check passed','manifest_sha256':b['harness.json'],'files_verified':len(b),'matches_selected_candidate':True,'new_experiments_run':False,'contract_replay':'existing completed 240-request, 480-output replay; return code 0'}\nPath('/workspace/finalization/submission-receipt.json').write_text(json.dumps(receipt,indent=2)+'\\\\n')\nprint(json.dumps(receipt,indent=2))\nPY\"", + "exit_code": 0 + } + ], + "publication_retry": null, + "submission_sha256": "df5304e57cb96a1c078bab34577e12f57f71926c4642b19a54920d7a1f0a63cc", + "package_sha256": "df5304e57cb96a1c078bab34577e12f57f71926c4642b19a54920d7a1f0a63cc", + "cutoff_package_sha256": "df5304e57cb96a1c078bab34577e12f57f71926c4642b19a54920d7a1f0a63cc", + "package_changed": false, + "package_checks": "collected fingerprint equals the 48h cutoff package and research snapshot S003 (018013-df5304e57cb9); publish.py --check passed inside the window", + "inference_files_and_modes_unchanged": true, + "verifier_metadata_unchanged": true, + "publication_helper_check": "publish.py --check calls validate_harness: manifest, file and checksum validation only; no model execution.", + "equivalent_snapshot": "S003", + "same_inference_candidate_as": "S003", + "inference_key": "reviewed-inference-v1:ad50b598b356a285393c246abb65bb644b23969e9283736d6ffb9f99161a08d2", + "evaluation_decision": "reuse_existing_score", + "new_evaluation_queued": false, + "additional_evaluation_required": false, + "evaluation_root": "/weka/oe-adapt-default/stevenc/science-speedrun-runs/protein-design-corrected-native-eval-s003-20261027a", + "score_source": "/weka/oe-adapt-default/stevenc/science-speedrun-runs/protein-design-corrected-native-eval-s003-20261027a/score/results/reward.json", + "original_48h_history_unchanged": true, + "original_results_preserved": true, + "trajectory_sha256": "83097cede7e5c12bb5ef52e59a75aac66f855b475031307d5a0f8f3c972290d7", + "successful_publication_job": "01M67294G1YWFFG71GR326M88J" +} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/status.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/status.json new file mode 100644 index 0000000000000000000000000000000000000000..cc2860e814ff2c605f53950a859886f4234469f1 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/status.json @@ -0,0 +1,19 @@ +{ + "phase": "complete", + "error": null, + "elapsed_seconds": 84.55471332592424, + "total_publication_seconds": 84.55471332592424, + "thread_id": "01a17fc4-6144-76a2-87dc-1189334219de", + "turn_completed": true, + "submission_sha256": "df5304e57cb96a1c078bab34577e12f57f71926c4642b19a54920d7a1f0a63cc", + "cutoff_submission_sha256": "df5304e57cb96a1c078bab34577e12f57f71926c4642b19a54920d7a1f0a63cc", + "package_changed": false, + "result_label": "48h-plus-5m-submission-only", + "accepted_as_48h_result": false, + "no_experiments_review": "passed", + "original_results_preserved": true, + "publication_review": "/weka/oe-adapt-default/stevenc/science-speedrun-runs/protein-design-astra-finalize-5m-20261029a/publication-review.json", + "evaluation_identity": "reviewed-inference-v1:ad50b598b356a285393c246abb65bb644b23969e9283736d6ffb9f99161a08d2", + "additional_evaluation_required": false, + "same_inference_candidate_as": "S003" +} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/backbone.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/backbone.py new file mode 100644 index 0000000000000000000000000000000000000000..956bf88bd78b416a854ee12057195c87c4c4f716 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/backbone.py @@ -0,0 +1,83 @@ +"""Shared internal-coordinate geometry; no training structures are loaded here.""" +import numpy as np +from residues import ATOM14 + + +def unit(v): + return v / np.maximum(np.linalg.norm(v, axis=-1, keepdims=True), 1e-9) + + +def dihedral(a, b, c, d): + v = unit(c - b) + x = a - b - np.sum((a - b) * v, axis=-1, keepdims=True) * v + y = d - c - np.sum((d - c) * v, axis=-1, keepdims=True) * v + return np.arctan2(np.sum(np.cross(v, x) * y, axis=-1), np.sum(x * y, axis=-1)) + + +def extend(a, b, c, length, angle, torsion): + e1 = unit(c - b) + e3 = unit(np.cross(b - a, e1)) + e2 = np.cross(e3, e1) + return c + length * (-np.cos(angle) * e1 + np.sin(angle) * + (np.cos(torsion) * e2 + np.sin(torsion) * e3)) + + +def build_chain(phi, psi, start=None): + length = len(phi) + out = np.zeros((length, 4, 3)) + if start is None: + out[0, 0] = [-1.458, 0., 0.] + out[0, 1] = [0., 0., 0.] + theta = np.deg2rad(111.2) + out[0, 2] = [-1.525 * np.cos(theta), 1.525 * np.sin(theta), 0.] + else: + out[0, :3] = start[:3] + for i in range(length - 1): + n, ca, c = out[i, :3] + nn = extend(n, ca, c, 1.329, np.deg2rad(116.2), psi[i]) + nc = extend(ca, c, nn, 1.458, np.deg2rad(121.7), np.pi) + cc = extend(c, nn, nc, 1.525, np.deg2rad(111.2), phi[i + 1]) + out[i + 1, :3] = [nn, nc, cc] + for i in range(length): + out[i, 3] = extend(*out[i, :3], 1.231, np.deg2rad(120.8), psi[i] + np.pi) + return out + + +def align(mobile, reference): + origin, dest = mobile.mean(0), reference.mean(0) + u, _, vt = np.linalg.svd((mobile - origin).T @ (reference - dest)) + r = u @ np.diag([1., 1., np.linalg.det(u @ vt)]) @ vt + return r, dest - origin @ r + + +def write_pdb(path, coords, mask, restypes): + records, serial = [], 1 + for i, res in enumerate(restypes): + for j, atom in enumerate(ATOM14[res]): + if not mask[i, j]: + continue + x, y, z = coords[i, j] + field = f" {atom:<3}" if len(atom) < 4 else atom + records.append(f"ATOM {serial:5d} {field} {res:3s} A{i+1:4d} " + f"{x:8.3f}{y:8.3f}{z:8.3f} 1.00 0.00 {atom[0]:>2}") + serial += 1 + path.write_text("\n".join(records + ["TER", "END"]) + "\n") + + +def read_pdb(path): + import gemmi + chain = gemmi.read_structure(str(path))[0][0] + residues = [r for r in chain if r.name in ATOM14] + xyz = np.zeros((len(residues), 14, 3)) + mask = np.zeros((len(residues), 14), bool) + names = [] + for i, residue in enumerate(residues): + names.append(residue.name) + for atom in residue: + if atom.name in ATOM14[residue.name]: + j = ATOM14[residue.name].index(atom.name) + xyz[i, j] = [atom.pos.x, atom.pos.y, atom.pos.z] + mask[i, j] = True + if not mask[:, :4].all(): + raise ValueError('Input scaffold lacks backbone atoms') + return xyz, mask, names diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/generator.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/generator.py new file mode 100644 index 0000000000000000000000000000000000000000..65798e16f96891b94ae29e25dc6f746be9d786cf --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/generator.py @@ -0,0 +1,204 @@ +"""One conditional scaffold generator with a fitted circular torsion prior. + +Every track samples the same latent bundle and local loop distributions. Context +terms choose its frame and, for fixed motifs, its residue correspondence. +""" +import numpy as np +from scipy.optimize import least_squares, linear_sum_assignment +from scipy.spatial import cKDTree +from scipy.spatial.transform import Rotation +from backbone import build_chain, align, unit +from residues import ATOM14 + + +class Generator: + def __init__(self, checkpoint): + self.prior = dict(np.load(checkpoint, allow_pickle=False)) + + def helix(self, n, rng): + # Sample a helix-level latent; per-residue noise would accumulate bends. + torsion = rng.multivariate_normal(self.prior['helix_mean'], self.prior['helix_cov'] * .025) + xyz = build_chain(np.full(n, torsion[0]), np.full(n, torsion[1])) + ca = xyz[:, 1] + center = ca.mean(0) + _, _, axes = np.linalg.svd(ca - center, full_matrices=False) + z = axes[0] * np.sign(np.dot(axes[0], ca[-1] - ca[0])) + x = unit(ca[0] - center - np.dot(ca[0] - center, z) * z) + y = np.cross(z, x) + return (xyz - center) @ np.stack([x, y, z], 1) + + def loop(self, previous, following, n, rng): + key = f'loop_{n}' + means = self.prior[key + '_mean'] + weights = self.prior[key + '_weight'] + order = rng.choice(len(means), min(3, len(means)), replace=False, p=weights) + + def construct(x): + phi = np.r_[self.prior['helix_mean'][0], x[1:-1:2], x[-1]] + psi = np.r_[x[0], x[2:-1:2], self.prior['helix_mean'][1]] + return build_chain(phi, psi, previous) + + def residual(x, seed): + xyz = construct(x) + end = (xyz[-1, :3] - following[:3]).ravel() + regularizer = .08 * np.angle(np.exp(1j * (x - seed))) + return np.r_[end, regularizer] + + best, best_error = None, np.inf + for j in order: + seed = means[j].copy() + result = least_squares(residual, seed, args=(seed,), max_nfev=22, ftol=.02, xtol=.015) + xyz = construct(result.x) + error = np.mean((xyz[-1, :3] - following[:3]) ** 2) + if error < best_error: + best, best_error = xyz, error + if error < .03: + break + return best[1:-1], best[0, 3], best_error + + def sample_backbone(self, length, rng): + # Up/down helical architectures with different counts, lengths, phases, + # tilt and packing distances. No full training backbone is stored. + center_h = int(np.clip(round(length / 29), 2, 8)) + h = int(np.clip(center_h + rng.choice([-1, 0, 0, 0, 1]), 2, 9)) + loop_len = int(rng.choice([3, 4, 5], p=[.2, .55, .25])) + while (length - (h - 1) * loop_len) / h < 10: + h -= 1 + lengths = np.full(h, (length - (h - 1) * loop_len) // h, dtype=int) + lengths[:(length - lengths.sum() - (h - 1) * loop_len)] += 1 + # Keep adjacent endpoints close while varying terminal lengths. + for k in range(0, h - 1, 2): + shift = int(rng.integers(-2, 3)) + lengths[k] += shift + lengths[k + 1] -= shift + distance = rng.uniform(9.1, 10.8) + radius = distance / (2 * np.sin(np.pi / h)) if h > 2 else distance / 2 + tilt = rng.uniform(-.23, -.08) + helices = [] + for k, n in enumerate(lengths): + theta = 2 * np.pi * k / h + z = unit(np.array([-np.sin(theta) * np.sin(tilt), + np.cos(theta) * np.sin(tilt), np.cos(tilt)])) + z *= (-1) ** k + x = np.array([np.cos(theta), np.sin(theta), 0.]) + y = np.cross(z, x) + phase = rng.uniform(-np.pi, np.pi) + xp, yp = np.cos(phase) * x + np.sin(phase) * y, -np.sin(phase) * x + np.cos(phase) * y + r = np.stack([xp, yp, z], 0) + xyz = self.helix(int(n), rng) @ r + xyz += np.array([radius * np.cos(theta), radius * np.sin(theta), rng.normal(0, .35)]) + helices.append(xyz) + pieces, errors = [], [] + for k, helix in enumerate(helices): + pieces.append(helix) + if k < h - 1: + loop, oxygen, error = self.loop(helix[-1], helices[k + 1][0], loop_len, rng) + helix[-1, 3] = oxygen + pieces.append(loop) + errors.append(error) + backbone = np.concatenate(pieces) + assert len(backbone) == length + return backbone, float(np.mean(errors)) + + def motif_reference(self, context): + xyz = np.asarray(context['motif_coords']).copy() + mask = np.asarray(context['motif_mask'], bool) + result = [] + for k, res in enumerate(context['motif_restypes']): + template = self.prior['residue_' + str(res)].copy() + valid = np.flatnonzero(mask[k]) + if len(valid) >= 3: + r, t = align(template[valid], xyz[k, valid]) + template = template @ r + t + elif len(valid): + template += xyz[k, valid].mean(0) - template[valid].mean(0) + template[mask[k]] = xyz[k, mask[k]] + result.append(template) + return np.array(result) + + def condition(self, backbone, context, rng): + # All context-dependent search acts on one sampled scaffold. + ligand = np.asarray(context.get('ligand_coords', np.empty((0, 3)))) + target = np.asarray(context.get('target_coords', np.empty((0, 14, 3)))) + motif = self.motif_reference(context) if len(context.get('motif_restypes', [])) else None + ca = backbone[:, 1] + center = ca.mean(0) + target_atoms, contact_points = np.empty((0, 3)), ligand + if len(target): + target_atoms = target[np.asarray(context['target_mask'], bool)] + hotspots = np.asarray(context.get('hotspot_indices', []), int) + contact_points = target[hotspots, 1] if len(hotspots) else target[:, 1] + tree = cKDTree(target_atoms) if len(target_atoms) else None + ligand_tree = cKDTree(ligand) if len(ligand) else None + best = None + for attempt in range(36 if motif is not None else 16): + rotation = Rotation.random(random_state=rng).as_matrix() + moved = (backbone - center) @ rotation + assignment = None + if motif is not None: + moved += motif[:, 1].mean(0) + for _ in range(4): + costs = ((motif[:, None, 1] - moved[None, :, 1]) ** 2).sum(-1) + rows, assignment = linear_sum_assignment(costs) + mobile = moved[assignment, :3].reshape(-1, 3) + ref = motif[:, :3].reshape(-1, 3) + r, t = align(mobile, ref) + moved = moved @ r + t + energy = np.mean((moved[assignment, :3] - motif[:, :3]) ** 2) * 2 + elif len(contact_points): + if len(target): + focus = contact_points[rng.integers(len(contact_points))] + outward = unit(focus - target[:, 1].mean(0) + rng.normal(0, 1, 3)) + projection = (moved[:, 1] @ outward).min() + moved += focus + outward * (6.0 - projection) + else: + # Put a ligand near a surface groove instead of at a random frame. + focus = contact_points.mean(0) + r = np.sqrt(np.mean(np.sum(moved[:, 1] ** 2, -1))) + moved += focus + unit(rng.normal(size=3)) * rng.uniform(.0, .55) * r + energy = 0. + else: + energy = 0. + if ligand_tree is not None: + d = ligand_tree.query(moved[:, :3].reshape(-1, 3))[0] + if assignment is not None: + keep = np.ones((len(moved), 3), bool) + keep[assignment] = False + d = d[keep.ravel()] + energy += float((np.maximum(2.9 - d, 0) ** 2).sum()) * 10 + shell = ligand_tree.query(moved[:, 1])[0] + energy -= min(20, np.sum((shell > 3.5) & (shell < 8))) * .12 + if tree is not None: + d = tree.query(moved[:, :3].reshape(-1, 3))[0] + energy += float((np.maximum(3.2 - d, 0) ** 2).sum()) * 8 + d_contact = cKDTree(contact_points).query(moved[:, 1])[0] + energy -= min(24, np.sum(d_contact < 10)) * .2 + if best is None or energy < best[0]: + best = energy, moved, assignment + energy, backbone, assignment = best + coords = np.zeros((len(backbone), 14, 3)) + coords[:, :4] = backbone + mask = np.zeros((len(backbone), 14), bool) + mask[:, :4] = True + restypes = ['ALA'] * len(backbone) + if motif is not None: + # Smooth local displacement accommodates an unindexed constellation. + # Each constrained residue is retained exactly in the supplied frame. + positions = np.arange(len(backbone)) + offsets = motif[:, 1] - backbone[assignment, 1] + kernels = np.exp(-.5 * ((positions[:, None] - assignment[None]) / 2.0) ** 2) + system = kernels[assignment] + np.eye(len(assignment)) * 1e-5 + displacement = kernels @ np.linalg.solve(system, offsets) + coords[:, :4] += displacement[:, None] + for j, i in enumerate(assignment): + res = str(context['motif_restypes'][j]) + coords[i] = motif[j] + mask[i, :len(ATOM14[res])] = True + restypes[i] = res + return coords, mask, restypes, None if assignment is None else assignment.tolist(), energy + + def sample(self, length, context, seed): + rng = np.random.default_rng(seed) + bb, closure = self.sample_backbone(length, rng) + coords, mask, restypes, mapping, energy = self.condition(bb, context, rng) + return coords, mask, restypes, mapping, -float(closure + energy) diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/harness.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/harness.json new file mode 100644 index 0000000000000000000000000000000000000000..64be711c6051b7ad2447775aa52ec123f83e2094 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/harness.json @@ -0,0 +1,33 @@ +{ + "schema": "protein-design-harness/v1", + "kind": "hybrid", + "entry_point": "infer.sh", + "learned_components": [ + { + "path": "prior.npz", + "sha256": "cecd9540218f3b9276a422b84f0ef2ee6230aeacc31c9f38faffd665a01e6468", + "parameters": 6032, + "trained_in_session": true + } + ], + "pretrained_assets": [ + { + "asset_id": "rf3/rf3_foundry_01_24_latest_remapped.ckpt", + "sha256": "364ef592fd8042a9cf4176d045015190f8322f961ccca38d891b20ca578d3bb0", + "license": "BSD-3-Clause", + "role": "Frozen structure predictor for shared inference-time scaffold projection" + }, + { + "asset_id": "ligandmpnn_params/ligandmpnn_v_32_010_25.pt", + "sha256": "161cd264061fda9680cbb940255522ae42f2966c552d045d87913d9452a80970", + "license": "MIT", + "role": "Frozen inverse-folding model for shared inference-time sequence proposals" + }, + { + "asset_id": "ligandmpnn_params/proteinmpnn_v_48_020.pt", + "sha256": "c9cb4a671d79604111231f8dbfc7c590e06f1197453b7a6854ac6661a642f5bd", + "license": "MIT", + "role": "Frozen inverse-folding model for shared inference-time sequence proposals" + } + ] +} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/infer.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/infer.py new file mode 100644 index 0000000000000000000000000000000000000000..03aa37deae9eff0a5d1ac6431368e856a22789fb --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/infer.py @@ -0,0 +1,118 @@ +import concurrent.futures +import hashlib +import json +import os +import sys +import traceback +from pathlib import Path +import numpy as np +from torsion_sampler import TorsionGenerator as Generator +from backbone import write_pdb, read_pdb + +ROOT = Path(__file__).resolve().parent +TRACKS = ('unconditional', 'sm_binder', 'ppi_binder', 'enzyme') + + +def run_request(task): + request, context_path, output = task + with np.load(context_path, allow_pickle=False) as source: + # Development-only native arrays never enter the generator. + context = {k: source[k] for k in source.files if not k.startswith('native_')} + model = Generator(ROOT / 'prior.npz') + designs = [] + identity = request['example_id'] + safe_name = hashlib.sha256(identity.encode()).hexdigest()[:20] + for i in range(request['num_designs']): + seed = int.from_bytes(hashlib.sha256(f'{identity}:{i}:helix-v1'.encode()).digest()[:4], 'little') + try: + xyz, mask, names, mapping, score = model.sample(request['design_length'], context, seed) + path = f'{safe_name}-{i}.pdb' + write_pdb(Path(output) / path, xyz, mask, names) + item = {'design_index': i, 'ranking_score': score, 'structure_path': path} + if request['track'] == 'enzyme': + item['motif_map'] = mapping + designs.append(item) + except Exception: + print(f'Failed sample {identity} {i}', file=sys.stderr, flush=True) + traceback.print_exc() + print(f'{identity}: {len(designs)}/{request["num_designs"]}', flush=True) + return request['track'], {'example_id': identity, 'designs': designs} + + +def main(): + input_root, output_root = map(Path, sys.argv[1:3]) + predictions = output_root / 'predictions' + structures = predictions / 'structures' + structures.mkdir(parents=True, exist_ok=True) + tasks = [] + for track in TRACKS: + index = input_root / f'{track}.jsonl' + if index.is_file(): + for line in index.read_text().splitlines(): + if line.strip(): + request = json.loads(line) + tasks.append((request, input_root / request['context_path'], structures)) + handles = {t: (predictions / f'{t}.jsonl').open('w') for t in TRACKS} + workers = min(12, int(os.environ.get('DESIGN_CPU_WORKERS', '10'))) + with concurrent.futures.ProcessPoolExecutor(max_workers=workers) as pool: + futures = {pool.submit(run_request, task): task[0] for task in tasks} + for future in concurrent.futures.as_completed(futures): + request = futures[future] + try: + track, answer = future.result() + except Exception: + traceback.print_exc() + track, answer = request['track'], {'example_id': request['example_id'], 'designs': []} + handles[track].write(json.dumps(answer, allow_nan=False) + '\n') + handles[track].flush() + for handle in handles.values(): + handle.close() + # The same sequence/structure projection follows every proposal, irrespective + # of track. Only partner components and fixed motif positions vary. + from projector import Projector + projector = Projector(recycles=3, steps=30) + completed = accepted = failed = 0 + for track in TRACKS: + index = predictions / f'{track}.jsonl' + records = {r['example_id']: r for r in map(json.loads, index.read_text().splitlines())} + ordered = [] + for request, context_path, _ in tasks: + if request['track'] != track: + continue + record = records[request['example_id']] + with np.load(context_path, allow_pickle=False) as source: + context = {k: source[k] for k in source.files if not k.startswith('native_')} + for design in record['designs']: + path = structures / design['structure_path'] + try: + xyz, mask, names = read_pdb(path) + new, present, newnames, detail = projector.project( + xyz, mask, names, context, design.get('motif_map'), + 137 + design['design_index']) + if new.shape != xyz.shape or not np.isfinite(new[present]).all(): + raise ValueError('Invalid projected coordinates') + write_pdb(path, new, present, newnames) + design['ranking_score'] = float(detail.get('ranking_score', 0.)) + completed += 1 + accepted += int(detail['accepted']) + print(f'Projected {track} {request["example_id"]} {design["design_index"]}: accepted={detail["accepted"]}', flush=True) + except Exception: + # Preserve the valid proposal if this individual fold fails. + failed += 1 + print(f'Projection failed: {request["example_id"]} {design["design_index"]}', file=sys.stderr, flush=True) + traceback.print_exc() + ordered.append(record) + index.write_text(''.join(json.dumps(r, allow_nan=False) + '\n' for r in ordered)) + print(f'Projection summary: completed={completed}, accepted={accepted}, failed={failed}', flush=True) + from novelty_filter import replace_near_copies + try: + replace_near_copies(tasks, predictions, projector, Generator, ROOT, write_pdb) + except Exception: + # Individual model/tool failures must not invalidate the whole batch. + # The valid projected proposals remain the fallback outputs. + print('Novelty rejection unavailable; retaining valid proposals', file=sys.stderr, flush=True) + traceback.print_exc() + + +if __name__ == '__main__': + main() diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/novelty_reference/provenance.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/novelty_reference/provenance.json new file mode 100644 index 0000000000000000000000000000000000000000..6ca6f06987440decf360163653e12eb65b6a379a --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/novelty_reference/provenance.json @@ -0,0 +1,61 @@ +{ + "source": "Official FitNovelty createdb, /data/fit/structures, 16 threads", + "database_files": { + "fitdb": { + "bytes": 17309660, + "sha256": "fc8cf21bfca04391de4edbbc3108bcb8ba3199c42b5fa9cdd8e756561bc09ffd" + }, + "fitdb.dbtype": { + "bytes": 4, + "sha256": "df3f619804a92fdb4057192dc43dd748ea778adc52bc498ce80524c014b81119" + }, + "fitdb.index": { + "bytes": 1227580, + "sha256": "b5be07e11a6c2cc3da0d0c351e7f6ae61a8ff7d7dadeff24fb0115d91ec6e4d8" + }, + "fitdb.lookup": { + "bytes": 1300385, + "sha256": "91d1249562e541fa94f8a4de9b8e4c4dcc92b2a4efef3db9c8745f16a577c48a" + }, + "fitdb.source": { + "bytes": 164879, + "sha256": "4e8298b7d0cc2422b0e2e904559e9e74423165c4330a5d6d74e3589948fb8937" + }, + "fitdb_ca": { + "bytes": 105041338, + "sha256": "fedb713d91cfc2b8f4eb44cf778484d8886a4f6f562dc0f0add86aa61bd7681d" + }, + "fitdb_ca.dbtype": { + "bytes": 4, + "sha256": "8fc6cb4a9816d75621652de783118eee13112cda196e61d1bde0460f29b26e3e" + }, + "fitdb_ca.index": { + "bytes": 1319442, + "sha256": "ea0028106ed2656f8b2ca4e1226a1ead01cd2ca42a5239970d380cab4be931a8" + }, + "fitdb_h": { + "bytes": 5716797, + "sha256": "d77bdb64c0ffe39790598f4535397164b64902c7a88cfeebcee0e23ba646da26" + }, + "fitdb_h.dbtype": { + "bytes": 4, + "sha256": "42f4aeb81c1ef81f771f3de8abca9dcf66901c575530e7672e4b1146474ae650" + }, + "fitdb_h.index": { + "bytes": 1151360, + "sha256": "a07a96e2be7c1ecd946edbb80573557556597fe1dd180b56f825c75c0bfa8d87" + }, + "fitdb_ss": { + "bytes": 17309660, + "sha256": "c4013ad9549be4754a72b4b4e6f5bf8880773068edc6d410b8ab4726e42e08ca" + }, + "fitdb_ss.dbtype": { + "bytes": 4, + "sha256": "df3f619804a92fdb4057192dc43dd748ea778adc52bc498ce80524c014b81119" + }, + "fitdb_ss.index": { + "bytes": 1227580, + "sha256": "b5be07e11a6c2cc3da0d0c351e7f6ae61a8ff7d7dadeff24fb0115d91ec6e4d8" + } + } +} \ No newline at end of file diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/prior.training.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/prior.training.json new file mode 100644 index 0000000000000000000000000000000000000000..14029339b191bce014d2f0a3888b29551c8fad31 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/prior.training.json @@ -0,0 +1,22 @@ +{ + "training_split": "/data/fit/unconditional.jsonl", + "structures": 4000, + "helix_residues": 349374, + "helix_runs": 19242, + "loop_counts": { + "2": 607, + "3": 820, + "4": 806, + "5": 621, + "6": 557, + "7": 478, + "8": 421, + "9": 439 + }, + "parameters": 6032, + "helix_mean_degrees": [ + -65.03301971199869, + -37.50133452186794 + ], + "seed": 713 +} \ No newline at end of file diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/projector.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/projector.py new file mode 100644 index 0000000000000000000000000000000000000000..5d6441226099b051908f97a67d1b971847ca0c1d --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/projector.py @@ -0,0 +1,152 @@ +"""Shared frozen inverse-fold/fold coordinate projection for all contexts.""" +import sys +import tempfile +from pathlib import Path +import numpy as np +import torch + +from data_utils import featurize, restype_str_to_int, restype_int_to_str, element_dict_rev +from model_utils import ProteinMPNN +from backbone import align, write_pdb +from residues import ATOM14, THREE_TO_ONE +element_dict = {str(element).upper(): number for number, element in element_dict_rev.items()} + + +class Projector: + def __init__(self, recycles=3, steps=30): + from foundry.utils.logging import configure_minimal_inference_logging + configure_minimal_inference_logging() + from rf3.inference_engines.rf3 import RF3InferenceEngine + self.models = {} + self.engine = RF3InferenceEngine( + ckpt_path='/pretrained/rf3/rf3_foundry_01_24_latest_remapped.ckpt', + n_recycles=recycles, diffusion_batch_size=1, num_steps=steps, + seed=137, devices_per_node=1, verbose=False, + early_stopping_plddt_threshold=None) + # These optional training caches are neither present nor used. Explicitly + # disable them so the package has no dependency on the training filesystem. + self.engine.transform_overrides.update(residue_cache_dir=None, msa_cache_dir=None) + + @torch.no_grad() + def sequence(self, xyz, mask, restypes, context, motif_map, seed): + ligand = np.asarray(context.get('ligand_coords', np.empty((0, 3)))) + kind = 'ligand_mpnn' if len(ligand) else 'protein_mpnn' + if kind not in self.models: + name = 'ligandmpnn_v_32_010_25.pt' if len(ligand) else 'proteinmpnn_v_48_020.pt' + ckpt = torch.load(Path('/pretrained/ligandmpnn_params') / name, map_location='cuda') + model = ProteinMPNN(k_neighbors=ckpt['num_edges'], + atom_context_num=ckpt.get('atom_context_num', 1), + model_type=kind, device='cuda').cuda().eval() + model.load_state_dict(ckpt['model_state_dict']) + model.requires_grad_(False) + self.models[kind] = model + model = self.models[kind] + length = len(xyz) + x = xyz[:, :4].copy() + names = list(restypes) + chain = np.zeros(length, np.int32) + fixed = np.ones(length, np.int32) + if motif_map: + fixed[motif_map] = 0 + target = context.get('target_coords') + if target is not None and len(target): + x = np.concatenate([x, np.asarray(target)[:, :4]]) + names.extend(str(s) for s in context.get('target_restypes', ['ALA'] * len(target))) + chain = np.r_[chain, np.ones(len(target), np.int32)] + fixed = np.r_[fixed, np.zeros(len(target), np.int32)] + n = len(x) + def tensor(value, dtype=torch.int32): + return torch.as_tensor(value, device='cuda', dtype=dtype) + elements = context.get('ligand_elements', ['C'] * len(ligand)) + atom_types = [element_dict.get(str(e).upper(), 6) for e in elements] + features = dict(X=tensor(x, torch.float32), mask=tensor(np.ones(n)), + R_idx=tensor(np.arange(n) + 1 + chain * 1000), chain_labels=tensor(chain), + S=tensor([restype_str_to_int[THREE_TO_ONE[s]] for s in names]), + chain_mask=tensor(fixed), Y=tensor(ligand if len(ligand) else np.zeros((1,3)), torch.float32), + Y_t=tensor(atom_types if len(ligand) else [0]), + Y_m=tensor(np.ones(len(ligand)) if len(ligand) else [0])) + f = featurize(features, model_type=kind, + number_of_ligand_atoms=model.features.atom_context_num if hasattr(model.features, 'atom_context_num') else 16) + f.update(batch_size=1, temperature=.1, symmetry_residues=[[]], symmetry_weights=[[]]) + f['bias'] = torch.zeros((1, n, 21), device='cuda') + f['bias'][:, :, 20] = -1e8 + torch.manual_seed(int(seed)) + f['randn'] = torch.randn((1, n), device='cuda') + result = model.sample(f) + seq = ''.join(restype_int_to_str[int(v)] for v in result['S'][0, :length].cpu()) + return seq + + def fold(self, sequence, context, seed): + from rf3.utils.inference import InferenceInput + components = [{'seq': sequence, 'chain_id': 'A'}] + with tempfile.TemporaryDirectory() as tmp: + target = context.get('target_coords') + if target is not None and len(target): + path = Path(tmp) / 'target.pdb' + write_pdb(path, target, context['target_mask'], context['target_restypes']) + path.write_text('\n'.join(line[:21] + 'B' + line[22:] if line.startswith('ATOM') else line + for line in path.read_text().splitlines()) + '\n') + components.append({'path': str(path)}) + components.extend({'ccd_code': str(code)} for code in context.get('ligand_ccd', [])) + job = {'name': f'projection_{seed}', 'components': components} + if target is not None and len(target): + job['template_selection'] = ['B'] + item = InferenceInput.from_json_dict(job) + torch.manual_seed(int(seed)) + result = self.engine.run(item, out_dir=None, dump_predictions=False) + models = result[job['name']] + return max(models, key=lambda m: float(m.summary_confidences.get('ranking_score', 0))) + + def project(self, xyz, mask, restypes, context, motif_map, seed): + sequence = self.sequence(xyz, mask, restypes, context, motif_map, seed) + model = self.fold(sequence, context, seed) + arr = model.atom_array + binder = arr[arr.chain_id == 'A'] + numbers = list(dict.fromkeys(binder.res_id.tolist())) + if len(numbers) != len(xyz): + raise ValueError(f'RF3 changed chain length: {len(numbers)} != {len(xyz)}') + new = np.zeros_like(xyz) + present = np.zeros_like(mask) + names = [] + for i, num in enumerate(numbers): + atoms = binder[binder.res_id == num] + res = str(atoms.res_name[0]) + names.append(res) + for atom in atoms: + if str(atom.atom_name) in ATOM14[res]: + j = ATOM14[res].index(str(atom.atom_name)) + new[i, j], present[i, j] = atom.coord, True + if not present[:, :4].all(): + raise ValueError('RF3 omitted required backbone atoms') + # Put the fold in the conditioning frame using the represented partner. + target = context.get('target_coords') + ligand = np.asarray(context.get('ligand_coords', np.empty((0,3)))) + if target is not None and len(target): + atoms = arr[(arr.chain_id == 'B') & (arr.atom_name == 'CA')] + if len(atoms) != len(target): + raise ValueError('Target alignment length mismatch') + r, t = align(atoms.coord, target[:, 1]) + elif len(ligand): + atoms = arr[(arr.chain_id != 'A') & ~np.isin(arr.res_name, list(ATOM14))] + if len(atoms) == len(ligand): + r, t = align(atoms.coord, ligand) + else: + r, t = align(new[:, 1], xyz[:, 1]) + else: + r, t = align(new[:, 1], xyz[:, 1]) + new = new @ r + t + motif_error = None + if motif_map: + anchor = np.array(motif_map) + r, t = align(new[anchor, :3].reshape(-1,3), xyz[anchor, :3].reshape(-1,3)) + new = new @ r + t + motif_error = float(np.sqrt(np.mean(np.sum((new[anchor, :3] - xyz[anchor, :3])**2, axis=-1)))) + # Keep the requested motif; retain the unprojected scaffold when the + # predicted motif is far away, to avoid severe local discontinuities. + if motif_error > 2.5 * np.sqrt(3): + return xyz, mask, restypes, {'sequence': sequence, 'accepted': False, + 'motif_backbone_rmsd': motif_error, **model.summary_confidences} + for i in motif_map: + new[i], present[i], names[i] = xyz[i], mask[i], restypes[i] + return new, present, names, {'sequence': sequence, 'accepted': True, + 'motif_backbone_rmsd': motif_error, **model.summary_confidences} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/residues.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/residues.py new file mode 100644 index 0000000000000000000000000000000000000000..ec6dc9011c9005ec9de0ba9a7f5c2c7710cce7fc --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/residues.py @@ -0,0 +1,66 @@ +"""Atom14 residue constants. + +Each residue is represented by up to 14 heavy atoms in a residue-type-specific order, +the representation RFdiffusion3 uses. Slots 0-3 are always the backbone N, CA, C, O, +so backbone-only logic stays valid on an all-atom array. Tryptophan fills all 14. +""" + +ATOM14 = { + "ALA": ["N", "CA", "C", "O", "CB"], + "ARG": ["N", "CA", "C", "O", "CB", "CG", "CD", "NE", "CZ", "NH1", "NH2"], + "ASN": ["N", "CA", "C", "O", "CB", "CG", "OD1", "ND2"], + "ASP": ["N", "CA", "C", "O", "CB", "CG", "OD1", "OD2"], + "CYS": ["N", "CA", "C", "O", "CB", "SG"], + "GLN": ["N", "CA", "C", "O", "CB", "CG", "CD", "OE1", "NE2"], + "GLU": ["N", "CA", "C", "O", "CB", "CG", "CD", "OE1", "OE2"], + "GLY": ["N", "CA", "C", "O"], + "HIS": ["N", "CA", "C", "O", "CB", "CG", "ND1", "CD2", "CE1", "NE2"], + "ILE": ["N", "CA", "C", "O", "CB", "CG1", "CG2", "CD1"], + "LEU": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2"], + "LYS": ["N", "CA", "C", "O", "CB", "CG", "CD", "CE", "NZ"], + "MET": ["N", "CA", "C", "O", "CB", "CG", "SD", "CE"], + "PHE": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ"], + "PRO": ["N", "CA", "C", "O", "CB", "CG", "CD"], + "SER": ["N", "CA", "C", "O", "CB", "OG"], + "THR": ["N", "CA", "C", "O", "CB", "OG1", "CG2"], + "TRP": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "NE1", "CE2", "CE3", + "CZ2", "CZ3", "CH2"], + "TYR": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "OH"], + "VAL": ["N", "CA", "C", "O", "CB", "CG1", "CG2"], +} + +# OXT, the C-terminal carboxyl oxygen, is deliberately absent: it occurs once per +# chain and AlphaFold's atom14 layout omits it too. Parsers skip it rather than fail. +IGNORED_ATOMS = {"OXT"} + +ATOM14_WIDTH = 14 +BACKBONE_SLOTS = (0, 1, 2, 3) +BACKBONE_ATOMS = ("N", "CA", "C", "O") + +RESTYPES = tuple(sorted(ATOM14)) +RESTYPE_INDEX = {name: index for index, name in enumerate(RESTYPES)} + +THREE_TO_ONE = { + "ALA": "A", "ARG": "R", "ASN": "N", "ASP": "D", "CYS": "C", "GLN": "Q", + "GLU": "E", "GLY": "G", "HIS": "H", "ILE": "I", "LEU": "L", "LYS": "K", + "MET": "M", "PHE": "F", "PRO": "P", "SER": "S", "THR": "T", "TRP": "W", + "TYR": "Y", "VAL": "V", +} + +# Alternative naming for symmetric sidechain tips: these pairs are indistinguishable +# by the physics, so a motif RMSD must take the better of the two assignments. +SYMMETRIC_SWAPS = { + "ARG": [("NH1", "NH2")], + "ASP": [("OD1", "OD2")], + "GLU": [("OE1", "OE2")], + "PHE": [("CD1", "CD2"), ("CE1", "CE2")], + "TYR": [("CD1", "CD2"), ("CE1", "CE2")], +} + + +def atom_slot(residue_name, atom_name): + """Index of `atom_name` within the residue's atom14 layout, or None.""" + names = ATOM14.get(residue_name) + if names is None or atom_name not in names: + return None + return names.index(atom_name) diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/validation/broad-novelty-unconditional-ppi.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/validation/broad-novelty-unconditional-ppi.json new file mode 100644 index 0000000000000000000000000000000000000000..6920738508d3476ab023fb7e943b92f9ece5a222 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/validation/broad-novelty-unconditional-ppi.json @@ -0,0 +1,34 @@ +{ + "ppi_binder": { + "conditioning_satisfied": 6, + "designs_designable": 0, + "designs_novel": 0, + "designs_requested": 60, + "designs_returned": 6, + "designs_valid": 6, + "diversity": 0.0, + "failures": { + "not designable (scRMSD 0.55, ipTM 0.16, PAE 18.24, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.09, ipTM 0.13, PAE 19.52, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.33, ipTM 0.15, PAE 21.31, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.00, ipTM 0.17, PAE 18.23, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 6.64, ipTM 0.16, PAE 21.81, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 8.09, ipTM 0.23, PAE 15.05, no sequence satisfied the criterion)": 1 + }, + "invalid_reason": null, + "pass_rate": 0.0 + }, + "unconditional": { + "conditioning_satisfied": 6, + "designs_designable": 6, + "designs_novel": 6, + "designs_requested": 60, + "designs_returned": 6, + "designs_valid": 6, + "diversity": 1.0, + "failures": {}, + "invalid_reason": null, + "novelty_max_tm_mean": 0.6533, + "pass_rate": 0.10000000000000002 + } +} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/validation/package-replay.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/validation/package-replay.json new file mode 100644 index 0000000000000000000000000000000000000000..b494f471c8731640029c2fe1c4824431338799a3 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/validation/package-replay.json @@ -0,0 +1,27 @@ +{ + "completed_utc": "2026-10-28T04:49:09.285078+00:00", + "command": "python /workspace/evaluation/check_package.py --package /workspace/research/novelty_stage", + "returncode": 0, + "requests": 240, + "served": 240, + "requests_per_track": 60, + "designs_per_request": 2, + "total_emitted": 480, + "actual_tune_design_length_min": 62, + "actual_tune_design_length_max": 200, + "initial_projections": { + "completed": 480, + "accepted": 394, + "failed": 0 + }, + "novelty_round_close_matches": [ + 30, + 12, + 6 + ], + "inference_sources_unchanged": 103, + "source_digest_record_sha256": "8e9f074c76b04b3cc9bb1b4f7acf4a3ba7d99355195bc358df0c6142e5597801", + "log_sha256": "4221d93ae7a38016b5cac17006ecd268a2bc1b378906529768d0599dd4116711", + "capture_final_after_novelty": false, + "scope": "Contract replay, not designability scoring. Read-only final-output capture missed temporary cleanup; no final per-design novelty totals claimed from that mirror." +} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/validation/parent-conditioned20.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/validation/parent-conditioned20.json new file mode 100644 index 0000000000000000000000000000000000000000..b7268c4d3289ae4f1ece2804f8dde2189aa18595 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/submission/validation/parent-conditioned20.json @@ -0,0 +1,100 @@ +{ + "enzyme": { + "conditioning_satisfied": 20, + "designs_designable": 0, + "designs_novel": 0, + "designs_requested": 20, + "designs_returned": 20, + "designs_valid": 20, + "diversity": 0.0, + "failures": { + "not designable (scRMSD 0.87, ipTM 0.85, PAE 1.21, motif 3.40, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.01, ipTM 0.78, PAE 2.04, motif 3.98, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.04, ipTM 0.78, PAE 1.93, motif 3.30, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.13, ipTM 0.76, PAE 2.19, motif 4.37, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.16, ipTM 0.75, PAE 2.13, motif 3.36, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.18, ipTM 0.77, PAE 2.04, motif 3.31, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.35, ipTM 0.67, PAE 3.00, motif 2.65, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.53, ipTM 0.71, PAE 2.90, motif 5.32, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.56, ipTM 0.78, PAE 1.88, motif 4.66, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.60, ipTM 0.73, PAE 2.22, motif 5.28, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.75, ipTM 0.78, PAE 1.89, motif 5.27, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.27, ipTM 0.69, PAE 2.91, motif 3.94, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.28, ipTM 0.73, PAE 2.46, motif 4.34, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.63, ipTM 0.61, PAE 3.33, motif 3.94, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.84, ipTM 0.61, PAE 3.71, motif 5.16, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 6.09, ipTM 0.63, PAE 2.96, motif 5.45, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 6.51, ipTM 0.51, PAE 3.49, motif 2.95, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 7.70, ipTM 0.68, PAE 2.87, motif 5.37, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 7.76, ipTM 0.63, PAE 2.71, motif 3.56, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 9.82, ipTM 0.54, PAE 3.10, motif 4.70, no sequence satisfied the criterion)": 1 + }, + "invalid_reason": null, + "pass_rate": 0.0 + }, + "ppi_binder": { + "conditioning_satisfied": 20, + "designs_designable": 0, + "designs_novel": 0, + "designs_requested": 20, + "designs_returned": 20, + "designs_valid": 20, + "diversity": 0.0, + "failures": { + "not designable (scRMSD 0.34, ipTM 0.14, PAE 19.79, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.38, ipTM 0.15, PAE 19.72, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.46, ipTM 0.19, PAE 15.02, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.50, ipTM 0.25, PAE 13.08, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.63, ipTM 0.16, PAE 19.30, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.68, ipTM 0.21, PAE 14.85, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.88, ipTM 0.26, PAE 12.60, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.02, ipTM 0.15, PAE 19.34, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.12, ipTM 0.20, PAE 15.39, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.23, ipTM 0.50, PAE 5.52, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.42, ipTM 0.20, PAE 16.21, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.58, ipTM 0.19, PAE 16.14, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.15, ipTM 0.29, PAE 10.25, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.26, ipTM 0.21, PAE 14.22, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.29, ipTM 0.24, PAE 13.23, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 3.32, ipTM 0.19, PAE 14.88, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.17, ipTM 0.29, PAE 10.04, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.50, ipTM 0.20, PAE 14.13, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 7.12, ipTM 0.17, PAE 16.21, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 8.72, ipTM 0.24, PAE 11.76, no sequence satisfied the criterion)": 1 + }, + "invalid_reason": null, + "pass_rate": 0.0 + }, + "sm_binder": { + "conditioning_satisfied": 20, + "designs_designable": 2, + "designs_novel": 2, + "designs_requested": 20, + "designs_returned": 20, + "designs_valid": 20, + "diversity": 1.0, + "failures": { + "not designable (scRMSD 0.37, ipTM 0.67, PAE 2.91, ligRMSD 5.38, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.40, ipTM 0.83, PAE 1.68, ligRMSD 1.87, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.44, ipTM 0.87, PAE 1.36, ligRMSD 5.64, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.45, ipTM 0.65, PAE 4.12, ligRMSD 6.58, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.49, ipTM 0.55, PAE 4.68, ligRMSD 10.81, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.49, ipTM 0.66, PAE 2.21, ligRMSD 6.17, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.60, ipTM 0.57, PAE 3.81, ligRMSD 10.21, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.64, ipTM 0.65, PAE 2.48, ligRMSD 3.28, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.69, ipTM 0.58, PAE 4.16, ligRMSD 7.03, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.90, ipTM 0.65, PAE 3.23, ligRMSD 6.15, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.06, ipTM 0.64, PAE 2.53, ligRMSD 6.31, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.38, ipTM 0.69, PAE 2.82, ligRMSD 4.25, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.85, ipTM 0.45, PAE 6.06, ligRMSD 3.81, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.37, ipTM 0.82, PAE 1.66, ligRMSD 3.48, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.38, ipTM 0.48, PAE 6.32, ligRMSD 6.69, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 4.78, ipTM 0.50, PAE 3.83, ligRMSD 11.15, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.12, ipTM 0.47, PAE 6.59, ligRMSD 9.28, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 8.05, ipTM 0.51, PAE 4.91, ligRMSD 14.95, no sequence satisfied the criterion)": 1 + }, + "invalid_reason": null, + "novelty_max_tm_mean": 0.7488, + "pass_rate": 0.1 + } +} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/trajectory.jsonl b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/trajectory.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..aef364d934a615f094b6298e11aa0588a08e202c --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/finalization/trajectory.jsonl @@ -0,0 +1,26 @@ +{"type":"thread.started","thread_id":"01a17fc4-6144-76a2-87dc-1189334219de"} +{"type":"item.completed","item":{"id":"item_0","type":"error","message":"`[features].use_legacy_landlock` is deprecated and will be removed soon. (Remove this setting to stop opting into the legacy Linux sandbox behavior.)"}} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_1","type":"agent_message","text":"I’ll use the previously published candidate 024. It has completed broader scoring and the required full replay; the newer candidate’s scoring is unfinished. I’ll verify the existing package and publish it atomically without running inference or new evaluations."}} +{"type":"item.started","item":{"id":"item_2","type":"command_execution","command":"/bin/sh -lc 'cat /workspace/submission/harness.json'","aggregated_output":"","exit_code":null,"status":"in_progress"}} +{"type":"item.completed","item":{"id":"item_2","type":"command_execution","command":"/bin/sh -lc 'cat /workspace/submission/harness.json'","aggregated_output":"{\n \"schema\": \"protein-design-harness/v1\",\n \"kind\": \"hybrid\",\n \"entry_point\": \"infer.sh\",\n \"learned_components\": [\n {\n \"path\": \"prior.npz\",\n \"sha256\": \"cecd9540218f3b9276a422b84f0ef2ee6230aeacc31c9f38faffd665a01e6468\",\n \"parameters\": 6032,\n \"trained_in_session\": true\n }\n ],\n \"pretrained_assets\": [\n {\n \"asset_id\": \"rf3/rf3_foundry_01_24_latest_remapped.ckpt\",\n \"sha256\": \"364ef592fd8042a9cf4176d045015190f8322f961ccca38d891b20ca578d3bb0\",\n \"license\": \"BSD-3-Clause\",\n \"role\": \"Frozen structure predictor for shared inference-time scaffold projection\"\n },\n {\n \"asset_id\": \"ligandmpnn_params/ligandmpnn_v_32_010_25.pt\",\n \"sha256\": \"161cd264061fda9680cbb940255522ae42f2966c552d045d87913d9452a80970\",\n \"license\": \"MIT\",\n \"role\": \"Frozen inverse-folding model for shared inference-time sequence proposals\"\n },\n {\n \"asset_id\": \"ligandmpnn_params/proteinmpnn_v_48_020.pt\",\n \"sha256\": \"c9cb4a671d79604111231f8dbfc7c590e06f1197453b7a6854ac6661a642f5bd\",\n \"license\": \"MIT\",\n \"role\": \"Frozen inverse-folding model for shared inference-time sequence proposals\"\n }\n ]\n}\n","exit_code":0,"status":"completed"}} +{"type":"item.started","item":{"id":"item_3","type":"command_execution","command":"/bin/sh -lc 'cat /workspace/research/STATE.md'","aggregated_output":"","exit_code":null,"status":"in_progress"}} +{"type":"item.completed","item":{"id":"item_3","type":"command_execution","command":"/bin/sh -lc 'cat /workspace/research/STATE.md'","aggregated_output":"Updated 2026-10-30T18:30:22.922867+00:00. Continuous protein-design research goal ACTIVE and unbounded. Do not finalize, pause, or mark complete at milestones. One shared all-atom conditional system serves unconditional, SM, PPI and enzyme. Exactly one H10080GB, 22 CPUs, 224GiB RAM. No network, evaluation-target lookup, or subagents. Give commentary within60seconds; check elapsed time after long analysis/code. Detailed older state is archived in STATE-history-20261030-1547.md and LIVE_JOBS-history-20261030-1547.json; HYPOTHESES and earlier history retain negative experiments and source options.\n\n290 is ACTIVE and the ONLY GPU protocol. Root session33858, PID116429/create1793374081.67, command tools/run_post_export_novelty_actual_protocol.py --plan experiments/290-plan.json. Do not restart or edit frozen sources, inputs, or package. Plan ff2e9a766bd9c9bda10cdc2159d41cdea1ac695f026185ad63a7aacc101087d5; review16a8c87d28a97d96a12b4e000b39fb2f45e6f48e9873818b1325f45fc4b92458. 172sources/1030inputs/192packagefiles; freezer4062reaped0. Package290-relocated-package; output290-actual-package-panel, both under experiments. Keep CPUs3–24, BLAS1/OMP2/MKL2; NUMA change was rejected. No next GPU intervention is selected.\n\nAll80 initial projections completed,74accepted/0failed. Initial novelty rounds flagged13then5 proposals, versus14then5then1 in287. Own80-proposal snapshot completed before optimization; completion9a78e2185507af50f0b032899ee25bd5b1485f3aed32f07d8090e0fa873f008a. It is a read-only research observation, never an inference input. At18:30 UTC all20monomer exports are recorded complete(0failed,0exportfallbacks,weightsunchanged);14SM trajectories have loggedstep12 andSMindex14 isatstep6. No tracebacks. PPI/enzyme optimization, final filtering, audits and scoring remain pending;expect hours. Global optimizer diagnostics describe completions; step log counts alone are not export/quality evidence.\n\nRoot290 owns observer116432, preparer116433, actual relocated infer.sh once over fixed170 public panel (20designs/track), contract audit80, execution description, actual final-filter hash/metadata audit, exact288-only refold reuse, one official allfour scorer, and conditioned diagnostics290-diagnostics. Do not launch a second observer/preloader/diagnostic. Root reaps children; only33858 needs tool reaping. Logs290-protocol-run.log,290-preparation.log,290-inference.log,290-proposal-watch.log; later290-filter-delta-audit.log,290-reuse.log,290-resumed-score.log. Completion290-protocol-complete.json/failure290-protocol-failure.json. functions store rollout290_status_command is the read-only monitor; old287/288 monitors describe completed jobs.\n\nStarting-proposal comparison b3ca7256e04d9bf2eea8ea54d8e852e1385160269e59f580dbba7d0c1a2b36d1 compares only completed snapshots:68/80 PDBs exact287, all80motifmaps exact. Pertrack mono19,SM17,PPI14,enzyme18 exact. Changed indices mono1;SM6/9/14;PPI4/8/10/12/14/16;enzyme10/16. This is starting variability, not final-export or quality evidence. Source tools/describe_completed_proposal_snapshot.py; CPU completed0.\n\nPublished024 remains /workspace/submission, unchanged. Harness e4cdbe893e4fa1df26209ccaa3af94eca604e71c5b67d38fbb5810f2820552b1; learnedprior cecd9540218f3b9276a422b84f0ef2ee6230aeacc31c9f38faffd665a01e6468,6032params. Hybrid with declared frozenRF3/ProteinMPNN/LigandMPNN. Prior required240TUNE/480outputs and40/400boundary replay passed. Broader128: mono94/120,SM6/120,PPI/enzyme0; diversity1,1/3,0,0. Before changed publication: actual full20 quality, broader confirmation, python /workspace/evaluation/check_package.py --package DIR (all240requests×2; supported --timeout may be raised), then python /workspace/baseline/rfd3-small/publish.py DIR --check. publish--check validates manifest/assets, not full replay. Atomic publication only; one current candidate, experiment history under research.\n\nUnpublished192-file post_export_novelty_stage is exact290 relocated candidate. Relative287 it adds post_export_novelty.py and rollout_export_novelty_infer.py and changes only infer.sh/README; models and harness unchanged. Harness d8e587f6cec6ed2730234c6a74adea66b58c51a80d742c563992031d9d8a3915. Assembly66018reaped0,48a30a8683396a5a710acaec95b3df2741f32fd5064110aba01a8ac29387b593. Package backs up its own current-invocation proposals, runs unchanged optimizer/intact export, then fullFIT negative filter restores only TM>=.8 exports whose own proposal has TM<.8; temporary backup removed. No research archive input. Successful optimizer hashes bind pre-filter exports, final hashes bind selected export/proposal. Never apply old287 per-track auditor unchanged to restored final PDBs.\n\n287 completed actual package: mono17/20(.85),SM5/20(.25),PPI/enzyme0;D1/1/0/0. All80valid/80optimizercompletions;75freshrefolds+5exact285reuse. All20monomers refolded but3were nonnovel. Control257 (actualpublished024) was18/20mono,1/20SM,0others,D1/1/0/0. 287report93a86ce1c12e10522ef30b0d426d08eb42d64702c54768c7a2d13b5191e88ef0; completion62b7d3d5f6d809b7834b138f7fd1a6d18bcdaaeed68dd04c4c522bf46674afd5; interpretationc92989b7311a3956c487a44212b6cad3e24d8cb74cd10806440d8644f11b4696. Root7380 reaped0. No publication due monomer regression and missing broader/mandatory replay.\n\n288 completed controlled prototype: mono19/20(.95),SM5/20(.25),PPI/enzyme0,D1/1/0/0.73exact287refolds reused;7fresh. Restored mono3/15/17,SM14/15/19,enzyme13. Mono3/17passed; other restored designs failed; all5SMpasses unchanged. All80valid, metadata/maps audited. This used archived287 own proposals for controlled comparison; it is not actual inference/breadth evidence. Report199a4af81d5d62b3d1a0ace1f3d1a3ff2a1bdb846ac1b364fa792eaa41aea1d4; completiondbec08d40f68def1826d5180a86c129c2253add370b05c49b50ddde216a1666e; interpretatione416925692cee210e1f8c1f7f4aedb5bc8b96fd57459c8f2e1159e62e2b7bea8. Root71971 reaped0. No publication.\n\n287/288 conditioned diagnosis: PPI0PAEpasses,min9.47A despite14backbone-passing models. Enzyme400refoldmodels0pass; bestmotif2.2467100385A atdesign19,seq2/model0.392models fail the fixed-frame backbone-only floor;8models(all19) have floor<=1.5 but still fail fullallatom criterion. New read-only287-motif-error-budget.json(0d5e1e0e8a67236adb81609a638dc7e9e74b2f95f3a23fb78b3c5e0f905987c3) verifies all400capture/error sums. Those8 fixedbackbones allow sidechainRMSD only0.0171–0.4312A. Bestmodel backbonefloor1.48948/sidechain2.71217, allowable0.28602A; perfecting CA alone or either CYS/ARG sidechain group alone still leaves >1.5A. Diagnostic arithmetic only, no new passrate or predicted intervention benefit.\n\n289 technical NUMA trial complete,85789reaped0: three fresh remote/local/remote one-update4track prefixes reproduce286 science/export exactly. Median local/remote time ratios1.1421/.9677 failed preregistered consistent10percent gate. RetainCPUs3–24; no quality row. Completiona3941e8e184c47adeb7edd4d5e9cc4d66040995195c44ae403fbe48092c1155e; interpretation271dd55790cd9a9b0f5f47363de23eb0c3d59a52f3ccf19a6b0d85f1c5e0e9f9. No fulloptimizer/largecontext/causal speed claim.\n\nLedger remains410 completed candidate-track rows+8 separate native-calibration rows. 288verificatione898089c68e5f108776997387015c2263a17b690fa69bc1d0f2f5ad3238f5797. 290-ledger-before snapshot7a3229bbdde161feee3f4aa144c7f89ff6eaafabebdc2789c139d19975a1fd5f preserves410+8. No pending290 or technical CPU rows. After complete290 interpretation, refresh tools/refresh_results.py and verify only4new290 rows, all old410 and calibration bytes exact.\n\nPrepared postcompletion tool describe_actual_post_export_result.py SHA203560a912de5a746d6c0259bea67144eb45f0e4f179571282dd1b6307398d7d,CPUimport64139reaped0. Run CUDA-disabled --number290 after root completion/reap; it verifies frozen package/sources/inputs and completion bindings, compares verbatim257/287/288/actual pertrack reports, proposal/final identities and conditioned diagnosis. It writes290-complete-description.json, not a selection/interpretation. Manual scientific review still writes290-complete-interpretation.json and chooses next experiment.\n\nPrepared UNSELECTED broader protocol: run_post_export_breadth_protocol.py,freeze_post_export_breadth_package.py,watch_post_export_breadth_proposals.py,audit_post_export_breadth_filter.py. Source review8c4a91f76d4627e99340a43ea9a21ae9ff9d2b89791e78f38a43a903cb5a64ba; CPUimports/publicpanel verification2540reaped0. Same192-file290 package, original124 outcome-independent24publiccontexts,48emitted/480requested. Root owns observer/filteraudit/freshscore/per-target report. Freezer requires completed290 report+interpretation and later explicit select_unchanged_breadth review. No numbered plan selected. Two outputs pertarget is sparse screening, not full20 breadth; repeated panel is not held-out. Old124control mono10/120,others0;128full20 contextual only.\n\nNew UNSELECTED inverse-context option: official_inverse_context.py removes target only from MPNN input, keeps ligand/motif arrays; full public context stays in RF3/objective/export. It addresses known PPI mismatch (generator inverse-folds A+B, official scorer inverse-folds A and later refolds A+B). Optimizer rollout_export_optimizer_inverse_context.py SHA37d3b0a8103b34fc75eddf83c0890031822f2cefe48e9e5002f00dd043e9b11e; helper488a7e1d0ff1aeec8769d7b99cc983d2b4f88d6dbd29ab6356f40bff81e27cf8. Exact reverse to old optimizer except context routing and metadata. CPU input/source checks30087reaped0,fe3f1ffeab5e4cab0d2a3531120c1fcf46e11424c30df06bb1d10613d36ded2b.\n\nFrozen MPNN CPU check47612reaped0,resultfb5e0e01261835382f575741eb9a77bc8756a73090385c8599464f1be2d01df8,review1073686c08c7505629e93b2a3ec058d9cf5248578960cef52565f49c23d48a01. Eight public287 proposals (0/1eachtrack), private CPU-only transport copy of Projector.sequence, actual declared frozen checkpoints. NonPPI6exactfeatures/samples/logits/coordinategradients; PPI206→100inverse residues changes sampled56/42 and greedy57/40positions. Models/data sources exactofficial+activepackage. Weights unchanged,CUDAuninitialized,noRF3 orquality. Initial20261reaped1 misseddata_utils atimport; archivedfirst-attempt record39460097cadd7300c15c86badfabad4c288c89d60b6311be329f0bb9bee201c1; retry correctedPYTHONPATHonly. This doesnot establish CUDAexactness/fullRF3 gradients or align sampling/seeds/export-redesign.\n\nFullRF3 prefix draft probe_rollout_inverse_context_prefix.py SHAab6509bad7efbc3746efdf8f4e661163d45b8166669c15e7a9f11a0f2f291a9a; source draftd5124f2572e4700e47056e587702cf620dc4397ab2a95b2d63c5cac81062b9e3; CPUimport19082reaped0,8bf4d0566794fc1ad43cbb01429012c18b5fa1bce0d58307b2323a77a3d0668c. Actualmain UNRUN. Requires completed290 quality review and future frozen plan; one-update4track prefixes,nonPPI science/export exact286,changedPPI finitegradients/validoutputs. No numbered plan orGPU selected.\n\nOther unselected options remain in HYP/history: public motif CAtemplate (CPUfeature/no-op passed,RF3 unrun), random-directioncontrol, one-recycle/intact-export factorialcell,zero-update,binderCAablation,directsequence,commonatomnoise,fixedtopology,chirality,fullFP32,207longertraining. New source-only public-motif-reference-conformer option487b3eefaa0439ebc0f22ec77f91007d5867bcdadda906a84de27cf62a37a64d: native ADD reference features carry centered per-residue shapes, not inter-residue pose; global RNG/partial-mask effects unvalidated. No implementation ormodelrun. Read negative families before repeating interventions.\n\nShared current optimizer: frozenMPNN/frozenRF3; only[L,3]request shift optimized,Adam12/lr.05/clip1/bounds±1.5A,motifrows0. Fixedinitialteacher/decodeorder,hardsequence+zero-forwardST;freshcanonicalRF3topology,10recycles/30scheduleentries(29denoisercalls),BF16/checkpointing. Trackgeometry/confidence+.4tether+2smoothness;minimumfeasible-loss intact predicted protein rigidlyaligned publicframe. No motifcoordinateoverwrite. Perdesign failure retainsvalidproposal. Same grading checkpoints optimized and declared; no independent biological claim. Lastrecycle gradients,chiralitystopgrad,hardtopology/noise/confidencebins,teacher/context mismatch and failedfunctionalFD remain limitations.\n\nOfficial instrument3a2151f3d2a4c4577d2aeacf15d9b7a777bdba86938d08fd167f9ac9dd6a5b4b.20requested denominator; fullFIT query-normalizedTM<.8;2A-superposed diversity. No combinedscore,geometryshortcut,criterion/scorer edits orhiddennatives. Enzyme publicmotif CYS/ARG/GLY/CYS/CYS/CYS=4CYS+ARG+GLY,39heavyatoms. Officialscorer usesoriginalatomlabels; do not change it. Unqueried failed-design novelty staysunknown. All new CPU handles reaped;33858 is sole live tool session.\n\n2026-10-30T15:53:28.424144+00:00 — Completed read-only287 sequence-transfer analysis: PPI35314acf435739f793be38ccb7ff2f1c2608f5963ff9f38039e2838d7d3d4d3a;alltrackdc31ea7afbdb41cc3402b237f246908ed23ff6d6c1d20a5b4bf956a7bf91ba3e(49622reaped0). PPI80redesign comparisons change34–67percent(median49.5);other mutable medians mono31,SM38.2353,enzyme44.4444percent. Successfulmonomers/SM tolerate largechanges,so turnover doesnotprovefailurecause. PPIselectedloss<=1 atindices6/8/9/11/12 bounds internalPAE<=1.5, but allofficialPPIredesign/refolds fail(PAEmin9.47);internalCAcomparison usesproposal andisnotofficialexport/refoldcriterion. One287enzyme(index18)export fellbacktoshiftedscaffold afterpredictedrawclash; all80outputsremainincluded,modeexplicit. Initialread-onlyscriptassumptions were corrected andarchived: capturemapkeyfilename(c36be9b87b788f87d2013550068e7a4406586e6290353cd6b900d88afc28772b),all-intact assumption(6d6f211c435221e321cfd630d673b064d6cd522d8213bdb44d1cea37eceaa72a). No model,score,package orledger changed.\n\n2026-10-30T15:53:28.417924+00:00 — Request-local hard-sequence cache idea not pursued: inventory139d0d5304288f07485c790ceadc7c0184aea4abd468fbe08ef22b54f5744081 finds only1repeat among104states in8completed284trajectories. Full287sequencesunretained;cache numerical/RNG equivalence andruntimeuntested. No implementation orexperiment selected.\n\n2026-10-30T16:06:27.996329+00:00 — Prepared unselected inner-checkpoint optimizer/prefix. Source e2df2d5ec1f1bf06ecacc1752281303482df6daa5debe6051f4b0dc4f6c2ff4f, prefix 5974bc460929bc248ca91c1313a4782dae28291b609e5130a3bd01654b6471e8; source draft7df21e6a53e2656bdda83c10b9551b6199f8130936cf9c89678c5c43419a8c86,CPU result3f9a6d7bf958cd78b7f57867bdc96aede13cb0404ff81c45f26cb58a6702ec4e,5864reaped0. Parent optimizer restores exactly with two source replacements; private feature dictionary and native model modes/checkpoints remain. CPU toy wrapper outputs/gradients/RNG and request-gradient observer/Adam updates exact; imports do not initializeCUDA. Actual fullRF3 prefixes, repeatedtiming,12update/largecontextmemory unrun. No nextGPU plan, package orquality change.\n\n2026-10-30T16:08:53.399760+00:00 — Inner-checkpoint protocol source preparation completed;91731reaped0,reviewfbecc62acf1c94e7ecaa5cd74605df7b9bc4b26f4abe6ed2d1e473d85c8e4251. run_rollout_checkpoint_protocol.py / freeze_rollout_checkpoint_protocol.py prepare fresh outer/inner/outer one-update four-track comparisons. Every science field/export exact286; raw gradients/final request tensor/RNG exactbetweenarms; median time ratio<=.90 againstbothcontrols,eachtrack<=1.05,smallprefixpeak<=64GiB. CPU synthetic comparator rejects gradient/RNG/output/science mismatches and slow/high-memory cases. These synthetic timings are not measured performance. No numbered plan or GPU selected;full12update andlargecontextreplay stillrequired.\n\n2026-10-30T16:14:42.169697+00:00 — Read-only first-six290 monomer observation complete0,e430d9e00c85916b2f82a7af7a6379eaaa0e75f18fb1ff43d67b6b1c57b1adfa;source2bbd4336f04ddf1606ea7470cb33e74d01f2a333ce4eff73db5d2df3583f1a95. Five unchangedproposalcases0/2/3/4/5 reproduce all13loggedsteps(loss,best_step,gradient_norm,step) andobservedpre-filterPDBbytes exactly287. Changedproposalindex1differs as expected. Observedprefix/PDBcopies are research records only,neverinferenceinputs;finalfilter/rootaudits/qualitypending. Notfulltensorgradient/history identity. Initialparser summary-line error archivedc9f6596fa795bccf59a05e5f5b5d7fc01d63ee548d04fa6bbfb2e4ba81f4987e;correctedtrack/indexfilter,failedbeforeanyobservationoutput/modelrun.\n\n2026-10-30T16:30:55.218779+00:00 — Latest maintenance archive:STATE-history-20261030-1630.md andLIVE_JOBS-history-20261030-1630.json preserve the prior notes. 290soleGPUroot33858/PID116429 stillactive,10monomer trajectories loggedstep12. AllnewCPUhandles5864/91731reaped0. The original published024 package remains unchanged.\n\n2026-10-30T17:17:55.478587+00:00 —290monomer optimizerstage completed20/20,0failed,0exportfallbacks,weightsunchanged. Read-only partialstage observation15251350d1247c2fc7b15cb5272aa9954465a074ca631d539e480a69a92b0b02;observeddiagnostics88092ed50bf0a1e3461b868e74da4a0f94edee9001fde5d8a06e9201f9cecf22. SMindex0 beganstep0 at17:17:47. Finalnoveltyfilter,rootcontractaudit,officialscoreandconditioneddiagnostics remainpending. No newquality/ledger/publication. Root33858/PID116429soleGPUprotocol;do notrestart.\n\n2026-10-30T18:30:22.922867+00:00 — Notes archived asSTATE-history-20261030-1830.md andLIVE_JOBS-history-20261030-1830.json. 290root33858/PID116429 remainssoleGPUprotocol. AllnewCPUwork was completed earlier; no nextGPUintervention selected andno newquality/ledger/publication. Continue monitoring this root,thenreap andrun preparedpostcompletiondescription/review whenitsfullprotocolcompletes.\n","exit_code":0,"status":"completed"}} +{"type":"item.started","item":{"id":"item_4","type":"command_execution","command":"/bin/sh -lc 'cat /workspace/baseline/rfd3-small/publish.py'","aggregated_output":"","exit_code":null,"status":"in_progress"}} +{"type":"item.completed","item":{"id":"item_4","type":"command_execution","command":"/bin/sh -lc 'cat /workspace/baseline/rfd3-small/publish.py'","aggregated_output":"#!/usr/bin/env python3\n\"\"\"Publish a package directory to /workspace/submission atomically.\n\n python /workspace/baseline/rfd3-small/publish.py SRC [--exclude GLOB ...] [--check]\n\nThe verifier may snapshot /workspace/submission at any moment, so the package must never be\nhalf-written. This copies SRC into a staging directory next to the target (training-only\nfiles excluded), optionally checks the harness manifest the way the verifier does, then swaps\nit in with one rename.\n\"\"\"\nimport argparse\nimport errno\nimport fnmatch\nimport os\nimport shutil\nimport sys\nimport tempfile\nfrom pathlib import Path\n\nDEFAULT_EXCLUDES = [\"__pycache__\", \"*.pyc\", \"*.ckpt.tmp\", \"optimizer.pt\", \"events.out.tfevents.*\"]\n\n\ndef ignore_for(source: Path, patterns: list[str]):\n def ignore(directory: str, names: list[str]) -> set[str]:\n relative = Path(directory).relative_to(source)\n return {name for name in names\n if any(fnmatch.fnmatch(name, p) or fnmatch.fnmatch((relative / name).as_posix(), p)\n for p in patterns)}\n return ignore\n\n\ndef swap_into(staged: Path, final: Path) -> None:\n old = final.with_name(f\".{final.name}.old-{os.getpid()}\")\n if final.exists():\n try:\n os.replace(final, old)\n except OSError as error:\n # A directory from a lower image layer cannot be renamed on overlayfs (EXDEV): move\n # its contents aside instead; the swap below is still a single rename.\n if error.errno != errno.EXDEV:\n raise\n shutil.copytree(final, old, symlinks=True)\n shutil.rmtree(final)\n os.replace(staged, final)\n shutil.rmtree(old, ignore_errors=True)\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser(description=__doc__.split(\"\\n\\n\")[0])\n parser.add_argument(\"source\", type=Path)\n parser.add_argument(\"--output\", type=Path, default=Path(\"/workspace/submission\"))\n parser.add_argument(\"--exclude\", action=\"append\", default=[],\n help=f\"glob (name or path relative to SRC) to leave out; always excluded: {DEFAULT_EXCLUDES}\")\n parser.add_argument(\"--check\", action=\"store_true\",\n help=\"validate harness.json against the staged copy, as the verifier does\")\n args = parser.parse_args()\n source, final = args.source.resolve(), args.output.resolve()\n for required in (\"infer.sh\", \"harness.json\", \"README.md\"):\n if not (source / required).is_file():\n sys.exit(f\"{source} has no {required} (see /workspace/contract.md)\")\n final.parent.mkdir(parents=True, exist_ok=True)\n staged = Path(tempfile.mkdtemp(prefix=f\".{final.name}.stage-\", dir=final.parent))\n try:\n shutil.copytree(source, staged, symlinks=False, dirs_exist_ok=True,\n ignore=ignore_for(source, DEFAULT_EXCLUDES + args.exclude))\n if args.check:\n sys.path.insert(0, \"/workspace/evaluation/lib\")\n from common import validate_harness # the verifier's own check\n validate_harness(staged, Path(\"/pretrained\"))\n staged.chmod(0o755)\n swap_into(staged, final)\n except BaseException:\n shutil.rmtree(staged, ignore_errors=True)\n raise\n print(f\"published {source} -> {final}\")\n\n\nif __name__ == \"__main__\":\n main()\n","exit_code":0,"status":"completed"}} +{"type":"item.started","item":{"id":"item_5","type":"command_execution","command":"/bin/sh -lc 'rg --files --hidden --no-ignore /workspace/submission | head -n 55'","aggregated_output":"","exit_code":null,"status":"in_progress"}} +{"type":"item.completed","item":{"id":"item_5","type":"command_execution","command":"/bin/sh -lc 'rg --files --hidden --no-ignore /workspace/submission | head -n 55'","aggregated_output":"/workspace/submission/prior.training.json\n/workspace/submission/projector.py\n/workspace/submission/residues.py\n/workspace/submission/infer.py\n/workspace/submission/novelty_filter.py\n/workspace/submission/README.md\n/workspace/submission/backbone.py\n/workspace/submission/infer.sh\n/workspace/submission/novelty_reference/fitdb_ca.dbtype\n/workspace/submission/novelty_reference/fitdb_h.dbtype\n/workspace/submission/novelty_reference/fitdb_ca\n/workspace/submission/novelty_reference/provenance.json\n/workspace/submission/novelty_reference/fitdb_ca.index\n/workspace/submission/novelty_reference/fitdb.lookup\n/workspace/submission/novelty_reference/fitdb_h\n/workspace/submission/novelty_reference/fitdb_ss\n/workspace/submission/novelty_reference/fitdb.dbtype\n/workspace/submission/novelty_reference/fitdb_ss.index\n/workspace/submission/novelty_reference/fitdb.index\n/workspace/submission/novelty_reference/fitdb_ss.dbtype\n/workspace/submission/novelty_reference/fitdb\n/workspace/submission/novelty_reference/fitdb_h.index\n/workspace/submission/novelty_reference/fitdb.source\n/workspace/submission/harness.json\n/workspace/submission/generator.py\n/workspace/submission/licenses/LigandMPNN-MIT.txt\n/workspace/submission/licenses/foundry-BSD-3-Clause.txt\n/workspace/submission/torsion_sampler.py\n/workspace/submission/prior.npz\n/workspace/submission/validation/monomer10020.json\n/workspace/submission/validation/measurement-scope.json\n/workspace/submission/validation/parent-conditioned20.json\n/workspace/submission/validation/monomer17520.json\n/workspace/submission/validation/package-replay.json\n/workspace/submission/validation/package-replay.log\n/workspace/submission/validation/broad-novelty-unconditional-ppi.json\n/workspace/submission/vendor/foundry/testing/pytest_hooks.py\n/workspace/submission/vendor/foundry/testing/__init__.py\n/workspace/submission/vendor/foundry/testing/fixtures.py\n/workspace/submission/vendor/foundry/inference_engines/base.py\n/workspace/submission/vendor/foundry/inference_engines/checkpoint_registry.py\n/workspace/submission/vendor/ligandmpnn/data_utils.py\n/workspace/submission/vendor/foundry/model/layers/blocks.py\n/workspace/submission/vendor/ligandmpnn/model_utils.py\n/workspace/submission/vendor/foundry/common.py\n/workspace/submission/vendor/rf3/src/rf3/symmetry/resolve.py\n/workspace/submission/vendor/rf3/src/rf3/alignment.py\n/workspace/submission/vendor/rf3/src/rf3/_version.py\n/workspace/submission/vendor/foundry/utils/instantiators.py\n/workspace/submission/vendor/foundry/utils/alignment.py\n/workspace/submission/vendor/foundry/utils/logging.py\n/workspace/submission/vendor/foundry/utils/torch.py\n/workspace/submission/vendor/foundry/utils/ddp.py\n/workspace/submission/vendor/foundry/utils/rigid.py\n/workspace/submission/vendor/foundry/utils/datasets.py\n","exit_code":0,"status":"completed"}} +{"type":"item.started","item":{"id":"item_6","type":"command_execution","command":"/bin/sh -lc \"sed -n '1,220p' /workspace/baseline/rfd3-small/publish.py\"","aggregated_output":"","exit_code":null,"status":"in_progress"}} +{"type":"item.completed","item":{"id":"item_6","type":"command_execution","command":"/bin/sh -lc \"sed -n '1,220p' /workspace/baseline/rfd3-small/publish.py\"","aggregated_output":"#!/usr/bin/env python3\n\"\"\"Publish a package directory to /workspace/submission atomically.\n\n python /workspace/baseline/rfd3-small/publish.py SRC [--exclude GLOB ...] [--check]\n\nThe verifier may snapshot /workspace/submission at any moment, so the package must never be\nhalf-written. This copies SRC into a staging directory next to the target (training-only\nfiles excluded), optionally checks the harness manifest the way the verifier does, then swaps\nit in with one rename.\n\"\"\"\nimport argparse\nimport errno\nimport fnmatch\nimport os\nimport shutil\nimport sys\nimport tempfile\nfrom pathlib import Path\n\nDEFAULT_EXCLUDES = [\"__pycache__\", \"*.pyc\", \"*.ckpt.tmp\", \"optimizer.pt\", \"events.out.tfevents.*\"]\n\n\ndef ignore_for(source: Path, patterns: list[str]):\n def ignore(directory: str, names: list[str]) -> set[str]:\n relative = Path(directory).relative_to(source)\n return {name for name in names\n if any(fnmatch.fnmatch(name, p) or fnmatch.fnmatch((relative / name).as_posix(), p)\n for p in patterns)}\n return ignore\n\n\ndef swap_into(staged: Path, final: Path) -> None:\n old = final.with_name(f\".{final.name}.old-{os.getpid()}\")\n if final.exists():\n try:\n os.replace(final, old)\n except OSError as error:\n # A directory from a lower image layer cannot be renamed on overlayfs (EXDEV): move\n # its contents aside instead; the swap below is still a single rename.\n if error.errno != errno.EXDEV:\n raise\n shutil.copytree(final, old, symlinks=True)\n shutil.rmtree(final)\n os.replace(staged, final)\n shutil.rmtree(old, ignore_errors=True)\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser(description=__doc__.split(\"\\n\\n\")[0])\n parser.add_argument(\"source\", type=Path)\n parser.add_argument(\"--output\", type=Path, default=Path(\"/workspace/submission\"))\n parser.add_argument(\"--exclude\", action=\"append\", default=[],\n help=f\"glob (name or path relative to SRC) to leave out; always excluded: {DEFAULT_EXCLUDES}\")\n parser.add_argument(\"--check\", action=\"store_true\",\n help=\"validate harness.json against the staged copy, as the verifier does\")\n args = parser.parse_args()\n source, final = args.source.resolve(), args.output.resolve()\n for required in (\"infer.sh\", \"harness.json\", \"README.md\"):\n if not (source / required).is_file():\n sys.exit(f\"{source} has no {required} (see /workspace/contract.md)\")\n final.parent.mkdir(parents=True, exist_ok=True)\n staged = Path(tempfile.mkdtemp(prefix=f\".{final.name}.stage-\", dir=final.parent))\n try:\n shutil.copytree(source, staged, symlinks=False, dirs_exist_ok=True,\n ignore=ignore_for(source, DEFAULT_EXCLUDES + args.exclude))\n if args.check:\n sys.path.insert(0, \"/workspace/evaluation/lib\")\n from common import validate_harness # the verifier's own check\n validate_harness(staged, Path(\"/pretrained\"))\n staged.chmod(0o755)\n swap_into(staged, final)\n except BaseException:\n shutil.rmtree(staged, ignore_errors=True)\n raise\n print(f\"published {source} -> {final}\")\n\n\nif __name__ == \"__main__\":\n main()\n","exit_code":0,"status":"completed"}} +{"type":"item.started","item":{"id":"item_7","type":"command_execution","command":"/bin/sh -lc \"python - <<'PY'\nfrom pathlib import Path\nimport json, hashlib\np=Path('/workspace/submission')\nh=p/'harness.json'\nprint('harness_sha256', hashlib.sha256(h.read_bytes()).hexdigest())\nprint(h.read_text())\nprint('top_level', [q.name for q in p.iterdir()])\nprint('bytes',sum(q.stat().st_size for q in p.rglob('*') if q.is_file()))\nPY\"","aggregated_output":"","exit_code":null,"status":"in_progress"}} +{"type":"item.completed","item":{"id":"item_7","type":"command_execution","command":"/bin/sh -lc \"python - <<'PY'\nfrom pathlib import Path\nimport json, hashlib\np=Path('/workspace/submission')\nh=p/'harness.json'\nprint('harness_sha256', hashlib.sha256(h.read_bytes()).hexdigest())\nprint(h.read_text())\nprint('top_level', [q.name for q in p.iterdir()])\nprint('bytes',sum(q.stat().st_size for q in p.rglob('*') if q.is_file()))\nPY\"","aggregated_output":"harness_sha256 e4cdbe893e4fa1df26209ccaa3af94eca604e71c5b67d38fbb5810f2820552b1\n{\n \"schema\": \"protein-design-harness/v1\",\n \"kind\": \"hybrid\",\n \"entry_point\": \"infer.sh\",\n \"learned_components\": [\n {\n \"path\": \"prior.npz\",\n \"sha256\": \"cecd9540218f3b9276a422b84f0ef2ee6230aeacc31c9f38faffd665a01e6468\",\n \"parameters\": 6032,\n \"trained_in_session\": true\n }\n ],\n \"pretrained_assets\": [\n {\n \"asset_id\": \"rf3/rf3_foundry_01_24_latest_remapped.ckpt\",\n \"sha256\": \"364ef592fd8042a9cf4176d045015190f8322f961ccca38d891b20ca578d3bb0\",\n \"license\": \"BSD-3-Clause\",\n \"role\": \"Frozen structure predictor for shared inference-time scaffold projection\"\n },\n {\n \"asset_id\": \"ligandmpnn_params/ligandmpnn_v_32_010_25.pt\",\n \"sha256\": \"161cd264061fda9680cbb940255522ae42f2966c552d045d87913d9452a80970\",\n \"license\": \"MIT\",\n \"role\": \"Frozen inverse-folding model for shared inference-time sequence proposals\"\n },\n {\n \"asset_id\": \"ligandmpnn_params/proteinmpnn_v_48_020.pt\",\n \"sha256\": \"c9cb4a671d79604111231f8dbfc7c590e06f1197453b7a6854ac6661a642f5bd\",\n \"license\": \"MIT\",\n \"role\": \"Frozen inverse-folding model for shared inference-time sequence proposals\"\n }\n ]\n}\n\ntop_level ['prior.npz', 'vendor', 'validation', 'infer.sh', 'torsion_sampler.py', 'licenses', 'generator.py', 'harness.json', 'novelty_reference', 'backbone.py', 'README.md', 'novelty_filter.py', 'infer.py', 'residues.py', 'projector.py', 'prior.training.json']\nbytes 153137059\n","exit_code":0,"status":"completed"}} +{"type":"item.started","item":{"id":"item_8","type":"command_execution","command":"/bin/sh -lc \"python - <<'PY'\nfrom pathlib import Path\nimport hashlib, shutil, json, time\nsrc=Path('/workspace/submission')\ndst=Path('/workspace/finalization/candidate024')\ndst.parent.mkdir(parents=True, exist_ok=True)\nif dst.exists():\n raise SystemExit('Finalization destination already exists; refusing to overwrite')\nexpected='e4cdbe893e4fa1df26209ccaa3af94eca604e71c5b67d38fbb5810f2820552b1'\nassert hashlib.sha256((src/'harness.json').read_bytes()).hexdigest()==expected\nshutil.copytree(src,dst,symlinks=False)\nmanifest=json.loads((dst/'harness.json').read_text())\nfor entry in manifest['learned_components']:\n digest=hashlib.sha256((dst/entry['path']).read_bytes()).hexdigest()\n assert digest==entry['sha256'],entry['path']\nprint('Copied unchanged candidate 024; learned-component digests verified')\nprint('Existing validation artifacts:')\nfor p in (dst/'validation').rglob('*'):\n if p.is_file():\n print(p.relative_to(dst),p.stat().st_size)\nprint('README:')\nprint((dst/'README.md').read_text()[:11000])\nPY\"","aggregated_output":"","exit_code":null,"status":"in_progress"}} +{"type":"item.completed","item":{"id":"item_8","type":"command_execution","command":"/bin/sh -lc \"python - <<'PY'\nfrom pathlib import Path\nimport hashlib, shutil, json, time\nsrc=Path('/workspace/submission')\ndst=Path('/workspace/finalization/candidate024')\ndst.parent.mkdir(parents=True, exist_ok=True)\nif dst.exists():\n raise SystemExit('Finalization destination already exists; refusing to overwrite')\nexpected='e4cdbe893e4fa1df26209ccaa3af94eca604e71c5b67d38fbb5810f2820552b1'\nassert hashlib.sha256((src/'harness.json').read_bytes()).hexdigest()==expected\nshutil.copytree(src,dst,symlinks=False)\nmanifest=json.loads((dst/'harness.json').read_text())\nfor entry in manifest['learned_components']:\n digest=hashlib.sha256((dst/entry['path']).read_bytes()).hexdigest()\n assert digest==entry['sha256'],entry['path']\nprint('Copied unchanged candidate 024; learned-component digests verified')\nprint('Existing validation artifacts:')\nfor p in (dst/'validation').rglob('*'):\n if p.is_file():\n print(p.relative_to(dst),p.stat().st_size)\nprint('README:')\nprint((dst/'README.md').read_text()[:11000])\nPY\"","aggregated_output":"Copied unchanged candidate 024; learned-component digests verified\nExisting validation artifacts:\nvalidation/broad-novelty-unconditional-ppi.json 1222\nvalidation/package-replay.log 270958\nvalidation/package-replay.json 970\nvalidation/monomer17520.json 432\nvalidation/parent-conditioned20.json 7151\nvalidation/measurement-scope.json 1484\nvalidation/monomer10020.json 431\nREADME:\n# One conditional scaffold generator with frozen structure projection\n\nEvery track uses the same trained torsion prior, helical scaffold sampler,\ngeometric context conditioner, inverse-folding model family and structure\nprojection procedure. The context supplies partner components and fixed motif\npositions. There are no track-specific pretrained generators or retrieved folds.\n\nThe learned prior was fitted in this session on 4,000 fit-split native chains:\n349,374 helical residues, 19,242 helix runs and 4,749 short connectors. Training\nfits circular Gaussian mixtures after circular k-means and empirical helix\ntorsion moments. The checkpoint also contains local single-residue conformers.\nIt contains no whole training backbone. The fitted scalar count is 6,032; seed\nand training counts are recorded in `prior.training.json`. No model parameters\nare fitted during inference.\n\nEach proposal samples helix counts and local torsions. Forward internal-coordinate\nassembly draws 128 learned turn candidates per new helix and selects using\npacking, steric, orientation and compactness energies. This preserves peptide\nconnectivity before motif conditioning. A shared context energy selects\nthe frame and motif assignment. Motif identities and required atoms are retained.\nOne ProteinMPNN or LigandMPNN sequence is then sampled at temperature 0.1 and\nrefolded by frozen RF3 (3 recycles, 30 steps, one diffusion sample). The target is\ntemplated for PPI complexes. Partner alignment returns the prediction to the\nconditioning frame. Motif projections with large displacements retain their\noriginal proposals. All atoms are written in atom14-compatible naming.\n\nThis is a hybrid system. The three frozen assets are declared in `harness.json`\nand loaded from `/pretrained`; no pretrained weights are shipped here. These\nare the same predictor and inverse-folding checkpoints used by the scorer.\nConsequently this optimises an in-silico measurement with those instruments;\nit is not independent experimental validation of protein function.\n\nRun `bash infer.sh INPUT_ROOT OUTPUT_ROOT`. Package code and checkpoints resolve\nrelative to this directory. CPU proposal generation uses eight workers; RF3 uses\none GPU and keeps its frozen model resident. No MSA or training cache is used.\nEach request is served even when an individual projection fails: the valid\ninitial scaffold remains. Dependencies are the supplied image's Python, NumPy,\nSciPy, PyTorch, ProDy, Gemmi, Biotite, AtomWorks, Lightning, Hydra and OmegaConf.\n\nVendored source: foundry/RF3 at `b02eed6a` under BSD-3-Clause, and LigandMPNN at\n`26ec57ac` under MIT. License texts are in `licenses/`.\n\nLimitations include the helical prior's restricted topology coverage, motif deformation, weak interface specificity, and predictor\ncalibration. Real designability and Foldseek novelty must be measured; geometric\nvalidity and RF3 confidence alone are not reported as success.\n\nProjection parent’s initial development probe (one tune target per track, two emitted of twenty requested):\nsmall-molecule design index 1 passed the real refolding criterion and fit-corpus\nnovelty (maximum TM 0.6087). The complete two-design probe gave SM pass rate0.05 and diversity1.0;\nunconditional, PPI and enzyme were0.0 with diversity0.0. The monomers both\nrefolded but failed novelty. Expanded probes are recorded separately. This small probe\ndoes not establish improvement over the reported baseline or broad generalisation.\n\nNovelty rejection: the same optional rejection sampler follows projection on every track. It compares generated backbones with a bundled, digest-checked Foldseek index of the full fit corpus. Only maximum query-normalized TM scores reach the sampler; reference coordinates are never used to propose or modify a design. Up to three fresh proposals replace each design at TM >= 0.8. The lower-TM candidate is retained, with valid earlier proposals as fallback. This consumes extra inference compute. The index files and their digests are recorded in novelty_reference/provenance.json; this is an explicit offline exclusion asset, not a live cache. Foldseek 10-941cd33 is supplied by the image ($FOLDSEEK). Its local mvdb failure is handled only by copying already-completed alignment bytes, without changing search or TM-align.\n\nPublication gate completed on 2026-10-28: the mandatory full tune replay exited zero and served all 240 requests (60 per track), emitting two designs per request. The actual tune requests span lengths 62–200. All 103 recorded inference source/checkpoint digests remained unchanged during replay. This checks the contract; designability evidence is reported separately below.\n\nExperiment024 measured the negative-only novelty rejection on the same twenty-design monomer target as015. The official completed result is19/20 requested passes (0.95), diversity1.0:19 passing clusters. All20 returned were novel; one replacement failed refolding at scRMSD1.77. This is a single-target comparison, not a fullsplit baseline improvement claim. The added filter changes only outputs that fail the novelty threshold; it does not retrieve reference coordinates. The required full 240-request package replay has now completed successfully.\n\nAdditional completed development measurements (2026-10-28): an actual end-to-end\nrun on a new length-175 monomer target emitted all20 requested designs and achieved\n19/20 passes (0.95), diversity1.0 with19 clusters. The broader negative-filter probe\ncovered three targets per track, emitting two designs of twenty requested per target.\nUnconditional improved from the parent's5/60 to6/60, diversity1.0; PPI remained0/60.\nAll broader SM/enzyme output PDBs were byte-identical to the parent and retain0/60.\nReports and explicit scope are in `validation/`. These are development panels,\nnot a full-split comparison with the external baseline. The mandatory full replay completed successfully after a runtime replacement.\nIts log and scope are recorded in `validation/package-replay.json` and\n`validation/package-replay.log`.\n\n","exit_code":0,"status":"completed"}} +{"type":"item.started","item":{"id":"item_9","type":"command_execution","command":"/bin/sh -lc 'python /workspace/baseline/rfd3-small/publish.py /workspace/finalization/candidate024 --check'","aggregated_output":"","exit_code":null,"status":"in_progress"}} +{"type":"item.completed","item":{"id":"item_9","type":"command_execution","command":"/bin/sh -lc 'python /workspace/baseline/rfd3-small/publish.py /workspace/finalization/candidate024 --check'","aggregated_output":"published /workspace/finalization/candidate024 -> /workspace/submission\n","exit_code":0,"status":"completed"}} +{"type":"item.started","item":{"id":"item_10","type":"command_execution","command":"/bin/sh -lc 'cat /workspace/finalization/candidate024/validation/package-replay.json'","aggregated_output":"","exit_code":null,"status":"in_progress"}} +{"type":"item.completed","item":{"id":"item_10","type":"command_execution","command":"/bin/sh -lc 'cat /workspace/finalization/candidate024/validation/package-replay.json'","aggregated_output":"{\n \"completed_utc\": \"2026-10-28T04:49:09.285078+00:00\",\n \"command\": \"python /workspace/evaluation/check_package.py --package /workspace/research/novelty_stage\",\n \"returncode\": 0,\n \"requests\": 240,\n \"served\": 240,\n \"requests_per_track\": 60,\n \"designs_per_request\": 2,\n \"total_emitted\": 480,\n \"actual_tune_design_length_min\": 62,\n \"actual_tune_design_length_max\": 200,\n \"initial_projections\": {\n \"completed\": 480,\n \"accepted\": 394,\n \"failed\": 0\n },\n \"novelty_round_close_matches\": [\n 30,\n 12,\n 6\n ],\n \"inference_sources_unchanged\": 103,\n \"source_digest_record_sha256\": \"8e9f074c76b04b3cc9bb1b4f7acf4a3ba7d99355195bc358df0c6142e5597801\",\n \"log_sha256\": \"4221d93ae7a38016b5cac17006ecd268a2bc1b378906529768d0599dd4116711\",\n \"capture_final_after_novelty\": false,\n \"scope\": \"Contract replay, not designability scoring. Read-only final-output capture missed temporary cleanup; no final per-design novelty totals claimed from that mirror.\"\n}\n","exit_code":0,"status":"completed"}} +{"type":"item.started","item":{"id":"item_11","type":"command_execution","command":"/bin/sh -lc \"python - <<'PY'\nfrom pathlib import Path\nimport hashlib,json,os\nfrom datetime import datetime,timezone\nsrc=Path('/workspace/finalization/candidate024')\ndst=Path('/workspace/submission')\ndef inventory(root):\n return {p.relative_to(root).as_posix():hashlib.sha256(p.read_bytes()).hexdigest() for p in root.rglob('*') if p.is_file() and '__pycache__' not in p.parts and p.suffix\"'!='\"'.pyc'}\na,b=inventory(src),inventory(dst)\nassert a==b, 'Published package differs from selected candidate'\nassert os.access(dst/'infer.sh',os.X_OK)\nreceipt={'published_utc':datetime.now(timezone.utc).isoformat(),'candidate':'024','package':'/workspace/submission','publication':'official publish.py --check passed','manifest_sha256':b['harness.json'],'files_verified':len(b),'matches_selected_candidate':True,'new_experiments_run':False,'contract_replay':'existing completed 240-request, 480-output replay; return code 0'}\nPath('/workspace/finalization/submission-receipt.json').write_text(json.dumps(receipt,indent=2)+'\\\\n')\nprint(json.dumps(receipt,indent=2))\nPY\"","aggregated_output":"","exit_code":null,"status":"in_progress"}} +{"type":"item.completed","item":{"id":"item_11","type":"command_execution","command":"/bin/sh -lc \"python - <<'PY'\nfrom pathlib import Path\nimport hashlib,json,os\nfrom datetime import datetime,timezone\nsrc=Path('/workspace/finalization/candidate024')\ndst=Path('/workspace/submission')\ndef inventory(root):\n return {p.relative_to(root).as_posix():hashlib.sha256(p.read_bytes()).hexdigest() for p in root.rglob('*') if p.is_file() and '__pycache__' not in p.parts and p.suffix\"'!='\"'.pyc'}\na,b=inventory(src),inventory(dst)\nassert a==b, 'Published package differs from selected candidate'\nassert os.access(dst/'infer.sh',os.X_OK)\nreceipt={'published_utc':datetime.now(timezone.utc).isoformat(),'candidate':'024','package':'/workspace/submission','publication':'official publish.py --check passed','manifest_sha256':b['harness.json'],'files_verified':len(b),'matches_selected_candidate':True,'new_experiments_run':False,'contract_replay':'existing completed 240-request, 480-output replay; return code 0'}\nPath('/workspace/finalization/submission-receipt.json').write_text(json.dumps(receipt,indent=2)+'\\\\n')\nprint(json.dumps(receipt,indent=2))\nPY\"","aggregated_output":"{\n \"published_utc\": \"2026-10-30T18:51:57.222072+00:00\",\n \"candidate\": \"024\",\n \"package\": \"/workspace/submission\",\n \"publication\": \"official publish.py --check passed\",\n \"manifest_sha256\": \"e4cdbe893e4fa1df26209ccaa3af94eca604e71c5b67d38fbb5810f2820552b1\",\n \"files_verified\": 187,\n \"matches_selected_candidate\": true,\n \"new_experiments_run\": false,\n \"contract_replay\": \"existing completed 240-request, 480-output replay; return code 0\"\n}\n","exit_code":0,"status":"completed"}} +{"type":"item.completed","item":{"id":"item_12","type":"agent_message","text":"Published candidate 024 atomically to `/workspace/submission`.\n\nStatic validation passed, and all 187 files match the selected package. Its existing full replay passed all 240 requests. No new experiments or inference were run. Work is stopped."}} +{"type":"turn.completed","usage":{"input_tokens":1203347369,"cached_input_tokens":1183607087,"cache_write_input_tokens":19244044,"output_tokens":5411253,"reasoning_output_tokens":2787396}} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/index.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/index.json new file mode 100644 index 0000000000000000000000000000000000000000..9c1ee3babba085ce26e77656ab0481240bd06010 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/index.json @@ -0,0 +1,7101 @@ +{ + "caveats": [ + "Interrupted/resumed research with preserved charged time; not an uninterrupted trial.", + "Adapted Beaker execution; exact original Docker isolation and memory ceilings are not claimed.", + "Post-budget publication is separate from the original 48-hour history.", + "Research used one H100, 22 CPUs, 224 GiB requested RAM instead of the pinned eight-GPU task configuration.", + "Corrected trial: the pinned predictor components were mounted read-only at /pretrained; three storage preflight failures and one workspace relocation occurred, all uncharged." + ], + "distinct_candidates": 3, + "evaluations": { + "S001": { + "execution": { + "adapted_execution": true, + "deviations": [ + "One-H100 research trial; allocated verifier RAM may exceed its request.", + "Frozen research runtime with original verifier and scorer sources mounted.", + "Inherited seccomp network denial and Landlock read-only inputs; native UID drop.", + "Public inputs copied locally with identical bytes and read-only agent-readable permissions; shared source permissions unchanged.", + "Trusted Protein Design grading uses the validated permission/seccomp fallback: Landlock ABI1 blocks native Foldseek cross-directory file operations. 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+2026-10-29T11:27:16.482025Z ERROR codex_core::tools::router: error=write_stdin failed: Unknown process id 36745 +2026-10-29T12:24:04.270818Z ERROR codex_models_manager::manager: failed to refresh available models: request timed out +2026-10-29T14:18:41.571512Z ERROR codex_core::tools::router: error=write_stdin failed: Unknown process id 60289 +2026-10-29T14:18:41.572229Z ERROR codex_core::tools::router: error=write_stdin failed: Unknown process id 92153 +2026-10-29T14:18:41.572550Z ERROR codex_core::tools::router: error=write_stdin failed: Unknown process id 3568 +2026-10-29T17:05:37.318311Z ERROR codex_core::tools::router: error=write_stdin failed: Unknown process id 4002 +2026-10-29T21:37:19.177866Z ERROR codex_core::tools::router: error=write_stdin failed: Unknown process id 36470 +2026-10-29T22:06:29.456770Z ERROR codex_core::tools::router: error=apply_patch verification failed: Failed to find expected lines in /workspace/research/tools/run_guide_role_package_protocol.py: +ROOT, EXP, digest +2026-10-29T23:46:42.747477Z ERROR codex_core::tools::router: error=write_stdin failed: Unknown process id 30668 +2026-10-30T04:51:07.627387Z ERROR codex_core::tools::router: error=write_stdin failed: Unknown process id 91907 diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/logs/preflight.jsonl b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/logs/preflight.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..92119e94a6f065f0f8173074edffa5246f879f04 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/logs/preflight.jsonl @@ -0,0 +1,8 @@ +{"type":"thread.started","thread_id":"01a18a0e-b8f3-7dd3-b570-6f6f807e7662"} +{"type":"item.completed","item":{"id":"item_0","type":"error","message":"`[features].use_legacy_landlock` is deprecated and will be removed soon. (Remove this setting to stop opting into the legacy Linux sandbox behavior.)"}} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_1","type":"agent_message","text":"I’ll run the environment check now.\n"}} +{"type":"item.started","item":{"id":"item_2","type":"command_execution","command":"/bin/sh -lc 'python /opt/speedrun/probe.py'","aggregated_output":"","exit_code":null,"status":"in_progress"}} +{"type":"item.completed","item":{"id":"item_2","type":"command_execution","command":"/bin/sh -lc 'python /opt/speedrun/probe.py'","aggregated_output":"Environment variable CCD_MIRROR_PATH not set. Will not be able to use function requiring this variable. To set it you may:\n (1) add the line 'export VAR_NAME=path/to/variable' to your .bashrc or .zshrc file\n (2) set it in your current shell with 'export VAR_NAME=path/to/variable'\n (3) write it to a .env file in the root of the atomworks.io repository\nEnvironment variable PDB_MIRROR_PATH not set. Will not be able to use function requiring this variable. To set it you may:\n (1) add the line 'export VAR_NAME=path/to/variable' to your .bashrc or .zshrc file\n (2) set it in your current shell with 'export VAR_NAME=path/to/variable'\n (3) write it to a .env file in the root of the atomworks.io repository\n{\"gpu_name\": \"NVIDIA H100 80GB HBM3\", \"visible_gpus\": 1, \"cuda_ok\": true, \"imports_ok\": true, \"network_blocked\": true, \"ipv4_blocked\": true, \"ipv6_blocked\": true, \"unix_ipc_ok\": true, \"network_error\": \"[Errno 1] Operation not permitted\", \"task\": \"protein-design\", \"development_data_ok\": true, \"data_readonly\": true, \"development_mounts\": [\"fit\", \"tune\"], \"evaluation_roles_absent\": true, \"research_storage_free_bytes\": 34217546350592, \"cpu_affinity_count\": 180, \"atomic_exchange_ok\": true, \"gpu_device_access_isolated\": true, \"denied_gpu_device_count\": 0}\n","exit_code":0,"status":"completed"}} +{"type":"item.completed","item":{"id":"item_3","type":"agent_message","text":"PREFLIGHT_OK"}} +{"type":"turn.completed","usage":{"input_tokens":21855,"cached_input_tokens":10679,"cache_write_input_tokens":11170,"output_tokens":69,"reasoning_output_tokens":0}} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/logs/preflight.stderr b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/logs/preflight.stderr new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/README.md b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/README.md new file mode 100644 index 0000000000000000000000000000000000000000..22bb4be78286668a3f9c7f7f7b199b50234af8e5 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/README.md @@ -0,0 +1,25 @@ +# Conditional torsion-prior scaffold generator + +One shared generator serves monomer, small-molecule, protein-interface and motif +requests. It samples up/down helix bundles, helices from a learned torsion +distribution, and connectors from circular Gaussian mixtures. A common geometric +context energy selects the coordinate frame; unindexed motif assignments are +solved jointly with alignment and local scaffold displacement. The emitted chain +uses atom14 coordinates, with every requested motif atom and identity preserved. + +The learned prior was fitted in this session on local geometry extracted from +4,000 native chains in the fit unconditional contexts. The checkpoint contains +helix torsion moments, loop mixture moments and single-residue local conformers. +It contains no complete training backbone. Training is maximum likelihood after +circular k-means partitioning; no parameters are fitted at inference. The length, +packing radius, phase and tilt of each newly constructed bundle are sampled. + +Run `bash infer.sh INPUT_ROOT OUTPUT_ROOT`. All package files resolve relative to +this directory. Dependencies are Python, NumPy and SciPy from the supplied image. +Generation uses at most 10 CPU workers and no GPU or pretrained asset. + +This is the first research checkpoint, not a claim of improved designability. +Limitations: predominantly alpha-helical folds, approximate loop closure and +motif-local deformation, and no learned long-range context representation yet. +Real LigandMPNN/RoseTTAFold3 scoring and Foldseek novelty evaluation are external +development measurements; geometry-based ranking is not the measured metric. diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/backbone.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/backbone.py new file mode 100644 index 0000000000000000000000000000000000000000..c314103950c1f4642cfeaa110c72f22f33ae651d --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/backbone.py @@ -0,0 +1,64 @@ +"""Shared internal-coordinate geometry; no training structures are loaded here.""" +import numpy as np +from residues import ATOM14 + + +def unit(v): + return v / np.maximum(np.linalg.norm(v, axis=-1, keepdims=True), 1e-9) + + +def dihedral(a, b, c, d): + v = unit(c - b) + x = a - b - np.sum((a - b) * v, axis=-1, keepdims=True) * v + y = d - c - np.sum((d - c) * v, axis=-1, keepdims=True) * v + return np.arctan2(np.sum(np.cross(v, x) * y, axis=-1), np.sum(x * y, axis=-1)) + + +def extend(a, b, c, length, angle, torsion): + e1 = unit(c - b) + e3 = unit(np.cross(b - a, e1)) + e2 = np.cross(e3, e1) + return c + length * (-np.cos(angle) * e1 + np.sin(angle) * + (np.cos(torsion) * e2 + np.sin(torsion) * e3)) + + +def build_chain(phi, psi, start=None): + length = len(phi) + out = np.zeros((length, 4, 3)) + if start is None: + out[0, 0] = [-1.458, 0., 0.] + out[0, 1] = [0., 0., 0.] + theta = np.deg2rad(111.2) + out[0, 2] = [-1.525 * np.cos(theta), 1.525 * np.sin(theta), 0.] + else: + out[0, :3] = start[:3] + for i in range(length - 1): + n, ca, c = out[i, :3] + nn = extend(n, ca, c, 1.329, np.deg2rad(116.2), psi[i]) + nc = extend(ca, c, nn, 1.458, np.deg2rad(121.7), np.pi) + cc = extend(c, nn, nc, 1.525, np.deg2rad(111.2), phi[i + 1]) + out[i + 1, :3] = [nn, nc, cc] + for i in range(length): + out[i, 3] = extend(*out[i, :3], 1.231, np.deg2rad(120.8), psi[i] + np.pi) + return out + + +def align(mobile, reference): + origin, dest = mobile.mean(0), reference.mean(0) + u, _, vt = np.linalg.svd((mobile - origin).T @ (reference - dest)) + r = u @ np.diag([1., 1., np.linalg.det(u @ vt)]) @ vt + return r, dest - origin @ r + + +def write_pdb(path, coords, mask, restypes): + records, serial = [], 1 + for i, res in enumerate(restypes): + for j, atom in enumerate(ATOM14[res]): + if not mask[i, j]: + continue + x, y, z = coords[i, j] + field = f" {atom:<3}" if len(atom) < 4 else atom + records.append(f"ATOM {serial:5d} {field} {res:3s} A{i+1:4d} " + f"{x:8.3f}{y:8.3f}{z:8.3f} 1.00 0.00 {atom[0]:>2}") + serial += 1 + path.write_text("\n".join(records + ["TER", "END"]) + "\n") diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/generator.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/generator.py new file mode 100644 index 0000000000000000000000000000000000000000..65798e16f96891b94ae29e25dc6f746be9d786cf --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/generator.py @@ -0,0 +1,204 @@ +"""One conditional scaffold generator with a fitted circular torsion prior. + +Every track samples the same latent bundle and local loop distributions. Context +terms choose its frame and, for fixed motifs, its residue correspondence. +""" +import numpy as np +from scipy.optimize import least_squares, linear_sum_assignment +from scipy.spatial import cKDTree +from scipy.spatial.transform import Rotation +from backbone import build_chain, align, unit +from residues import ATOM14 + + +class Generator: + def __init__(self, checkpoint): + self.prior = dict(np.load(checkpoint, allow_pickle=False)) + + def helix(self, n, rng): + # Sample a helix-level latent; per-residue noise would accumulate bends. + torsion = rng.multivariate_normal(self.prior['helix_mean'], self.prior['helix_cov'] * .025) + xyz = build_chain(np.full(n, torsion[0]), np.full(n, torsion[1])) + ca = xyz[:, 1] + center = ca.mean(0) + _, _, axes = np.linalg.svd(ca - center, full_matrices=False) + z = axes[0] * np.sign(np.dot(axes[0], ca[-1] - ca[0])) + x = unit(ca[0] - center - np.dot(ca[0] - center, z) * z) + y = np.cross(z, x) + return (xyz - center) @ np.stack([x, y, z], 1) + + def loop(self, previous, following, n, rng): + key = f'loop_{n}' + means = self.prior[key + '_mean'] + weights = self.prior[key + '_weight'] + order = rng.choice(len(means), min(3, len(means)), replace=False, p=weights) + + def construct(x): + phi = np.r_[self.prior['helix_mean'][0], x[1:-1:2], x[-1]] + psi = np.r_[x[0], x[2:-1:2], self.prior['helix_mean'][1]] + return build_chain(phi, psi, previous) + + def residual(x, seed): + xyz = construct(x) + end = (xyz[-1, :3] - following[:3]).ravel() + regularizer = .08 * np.angle(np.exp(1j * (x - seed))) + return np.r_[end, regularizer] + + best, best_error = None, np.inf + for j in order: + seed = means[j].copy() + result = least_squares(residual, seed, args=(seed,), max_nfev=22, ftol=.02, xtol=.015) + xyz = construct(result.x) + error = np.mean((xyz[-1, :3] - following[:3]) ** 2) + if error < best_error: + best, best_error = xyz, error + if error < .03: + break + return best[1:-1], best[0, 3], best_error + + def sample_backbone(self, length, rng): + # Up/down helical architectures with different counts, lengths, phases, + # tilt and packing distances. No full training backbone is stored. + center_h = int(np.clip(round(length / 29), 2, 8)) + h = int(np.clip(center_h + rng.choice([-1, 0, 0, 0, 1]), 2, 9)) + loop_len = int(rng.choice([3, 4, 5], p=[.2, .55, .25])) + while (length - (h - 1) * loop_len) / h < 10: + h -= 1 + lengths = np.full(h, (length - (h - 1) * loop_len) // h, dtype=int) + lengths[:(length - lengths.sum() - (h - 1) * loop_len)] += 1 + # Keep adjacent endpoints close while varying terminal lengths. + for k in range(0, h - 1, 2): + shift = int(rng.integers(-2, 3)) + lengths[k] += shift + lengths[k + 1] -= shift + distance = rng.uniform(9.1, 10.8) + radius = distance / (2 * np.sin(np.pi / h)) if h > 2 else distance / 2 + tilt = rng.uniform(-.23, -.08) + helices = [] + for k, n in enumerate(lengths): + theta = 2 * np.pi * k / h + z = unit(np.array([-np.sin(theta) * np.sin(tilt), + np.cos(theta) * np.sin(tilt), np.cos(tilt)])) + z *= (-1) ** k + x = np.array([np.cos(theta), np.sin(theta), 0.]) + y = np.cross(z, x) + phase = rng.uniform(-np.pi, np.pi) + xp, yp = np.cos(phase) * x + np.sin(phase) * y, -np.sin(phase) * x + np.cos(phase) * y + r = np.stack([xp, yp, z], 0) + xyz = self.helix(int(n), rng) @ r + xyz += np.array([radius * np.cos(theta), radius * np.sin(theta), rng.normal(0, .35)]) + helices.append(xyz) + pieces, errors = [], [] + for k, helix in enumerate(helices): + pieces.append(helix) + if k < h - 1: + loop, oxygen, error = self.loop(helix[-1], helices[k + 1][0], loop_len, rng) + helix[-1, 3] = oxygen + pieces.append(loop) + errors.append(error) + backbone = np.concatenate(pieces) + assert len(backbone) == length + return backbone, float(np.mean(errors)) + + def motif_reference(self, context): + xyz = np.asarray(context['motif_coords']).copy() + mask = np.asarray(context['motif_mask'], bool) + result = [] + for k, res in enumerate(context['motif_restypes']): + template = self.prior['residue_' + str(res)].copy() + valid = np.flatnonzero(mask[k]) + if len(valid) >= 3: + r, t = align(template[valid], xyz[k, valid]) + template = template @ r + t + elif len(valid): + template += xyz[k, valid].mean(0) - template[valid].mean(0) + template[mask[k]] = xyz[k, mask[k]] + result.append(template) + return np.array(result) + + def condition(self, backbone, context, rng): + # All context-dependent search acts on one sampled scaffold. + ligand = np.asarray(context.get('ligand_coords', np.empty((0, 3)))) + target = np.asarray(context.get('target_coords', np.empty((0, 14, 3)))) + motif = self.motif_reference(context) if len(context.get('motif_restypes', [])) else None + ca = backbone[:, 1] + center = ca.mean(0) + target_atoms, contact_points = np.empty((0, 3)), ligand + if len(target): + target_atoms = target[np.asarray(context['target_mask'], bool)] + hotspots = np.asarray(context.get('hotspot_indices', []), int) + contact_points = target[hotspots, 1] if len(hotspots) else target[:, 1] + tree = cKDTree(target_atoms) if len(target_atoms) else None + ligand_tree = cKDTree(ligand) if len(ligand) else None + best = None + for attempt in range(36 if motif is not None else 16): + rotation = Rotation.random(random_state=rng).as_matrix() + moved = (backbone - center) @ rotation + assignment = None + if motif is not None: + moved += motif[:, 1].mean(0) + for _ in range(4): + costs = ((motif[:, None, 1] - moved[None, :, 1]) ** 2).sum(-1) + rows, assignment = linear_sum_assignment(costs) + mobile = moved[assignment, :3].reshape(-1, 3) + ref = motif[:, :3].reshape(-1, 3) + r, t = align(mobile, ref) + moved = moved @ r + t + energy = np.mean((moved[assignment, :3] - motif[:, :3]) ** 2) * 2 + elif len(contact_points): + if len(target): + focus = contact_points[rng.integers(len(contact_points))] + outward = unit(focus - target[:, 1].mean(0) + rng.normal(0, 1, 3)) + projection = (moved[:, 1] @ outward).min() + moved += focus + outward * (6.0 - projection) + else: + # Put a ligand near a surface groove instead of at a random frame. + focus = contact_points.mean(0) + r = np.sqrt(np.mean(np.sum(moved[:, 1] ** 2, -1))) + moved += focus + unit(rng.normal(size=3)) * rng.uniform(.0, .55) * r + energy = 0. + else: + energy = 0. + if ligand_tree is not None: + d = ligand_tree.query(moved[:, :3].reshape(-1, 3))[0] + if assignment is not None: + keep = np.ones((len(moved), 3), bool) + keep[assignment] = False + d = d[keep.ravel()] + energy += float((np.maximum(2.9 - d, 0) ** 2).sum()) * 10 + shell = ligand_tree.query(moved[:, 1])[0] + energy -= min(20, np.sum((shell > 3.5) & (shell < 8))) * .12 + if tree is not None: + d = tree.query(moved[:, :3].reshape(-1, 3))[0] + energy += float((np.maximum(3.2 - d, 0) ** 2).sum()) * 8 + d_contact = cKDTree(contact_points).query(moved[:, 1])[0] + energy -= min(24, np.sum(d_contact < 10)) * .2 + if best is None or energy < best[0]: + best = energy, moved, assignment + energy, backbone, assignment = best + coords = np.zeros((len(backbone), 14, 3)) + coords[:, :4] = backbone + mask = np.zeros((len(backbone), 14), bool) + mask[:, :4] = True + restypes = ['ALA'] * len(backbone) + if motif is not None: + # Smooth local displacement accommodates an unindexed constellation. + # Each constrained residue is retained exactly in the supplied frame. + positions = np.arange(len(backbone)) + offsets = motif[:, 1] - backbone[assignment, 1] + kernels = np.exp(-.5 * ((positions[:, None] - assignment[None]) / 2.0) ** 2) + system = kernels[assignment] + np.eye(len(assignment)) * 1e-5 + displacement = kernels @ np.linalg.solve(system, offsets) + coords[:, :4] += displacement[:, None] + for j, i in enumerate(assignment): + res = str(context['motif_restypes'][j]) + coords[i] = motif[j] + mask[i, :len(ATOM14[res])] = True + restypes[i] = res + return coords, mask, restypes, None if assignment is None else assignment.tolist(), energy + + def sample(self, length, context, seed): + rng = np.random.default_rng(seed) + bb, closure = self.sample_backbone(length, rng) + coords, mask, restypes, mapping, energy = self.condition(bb, context, rng) + return coords, mask, restypes, mapping, -float(closure + energy) diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/harness.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/harness.json new file mode 100644 index 0000000000000000000000000000000000000000..2ac5d0e3b8966d4c5ee3b1db53430ac3793b444c --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/harness.json @@ -0,0 +1,14 @@ +{ + "schema": "protein-design-harness/v1", + "kind": "trained_generator", + "entry_point": "infer.sh", + "learned_components": [ + { + "path": "prior.npz", + "sha256": "cecd9540218f3b9276a422b84f0ef2ee6230aeacc31c9f38faffd665a01e6468", + "parameters": 6032, + "trained_in_session": true + } + ], + "pretrained_assets": [] +} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/infer.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/infer.py new file mode 100644 index 0000000000000000000000000000000000000000..6d6d265a762b417cddb43149b834e8a908cf2e87 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/infer.py @@ -0,0 +1,73 @@ +import concurrent.futures +import hashlib +import json +import os +import sys +import traceback +from pathlib import Path +import numpy as np +from generator import Generator +from backbone import write_pdb + +ROOT = Path(__file__).resolve().parent +TRACKS = ('unconditional', 'sm_binder', 'ppi_binder', 'enzyme') + + +def run_request(task): + request, context_path, output = task + with np.load(context_path, allow_pickle=False) as source: + # Development-only native arrays never enter the generator. + context = {k: source[k] for k in source.files if not k.startswith('native_')} + model = Generator(ROOT / 'prior.npz') + designs = [] + identity = request['example_id'] + safe_name = hashlib.sha256(identity.encode()).hexdigest()[:20] + for i in range(request['num_designs']): + seed = int.from_bytes(hashlib.sha256(f'{identity}:{i}:helix-v1'.encode()).digest()[:4], 'little') + try: + xyz, mask, names, mapping, score = model.sample(request['design_length'], context, seed) + path = f'{safe_name}-{i}.pdb' + write_pdb(Path(output) / path, xyz, mask, names) + item = {'design_index': i, 'ranking_score': score, 'structure_path': path} + if request['track'] == 'enzyme': + item['motif_map'] = mapping + designs.append(item) + except Exception: + print(f'Failed sample {identity} {i}', file=sys.stderr, flush=True) + traceback.print_exc() + print(f'{identity}: {len(designs)}/{request["num_designs"]}', flush=True) + return request['track'], {'example_id': identity, 'designs': designs} + + +def main(): + input_root, output_root = map(Path, sys.argv[1:3]) + predictions = output_root / 'predictions' + structures = predictions / 'structures' + structures.mkdir(parents=True, exist_ok=True) + tasks = [] + for track in TRACKS: + index = input_root / f'{track}.jsonl' + if index.is_file(): + for line in index.read_text().splitlines(): + if line.strip(): + request = json.loads(line) + tasks.append((request, input_root / request['context_path'], structures)) + handles = {t: (predictions / f'{t}.jsonl').open('w') for t in TRACKS} + workers = min(12, int(os.environ.get('DESIGN_CPU_WORKERS', '10'))) + with concurrent.futures.ProcessPoolExecutor(max_workers=workers) as pool: + futures = {pool.submit(run_request, task): task[0] for task in tasks} + for future in concurrent.futures.as_completed(futures): + request = futures[future] + try: + track, answer = future.result() + except Exception: + traceback.print_exc() + track, answer = request['track'], {'example_id': request['example_id'], 'designs': []} + handles[track].write(json.dumps(answer, allow_nan=False) + '\n') + handles[track].flush() + for handle in handles.values(): + handle.close() + + +if __name__ == '__main__': + main() diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/infer.sh b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/infer.sh new file mode 100644 index 0000000000000000000000000000000000000000..46501991947d4345ea00c71e4009d27d8e9d9739 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/infer.sh @@ -0,0 +1,5 @@ +#!/bin/sh +set -eu +PACKAGE_DIR=$(CDPATH= cd -- "$(dirname -- "$0")" && pwd) +export OPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=1 MKL_NUM_THREADS=1 +exec python "$PACKAGE_DIR/infer.py" "$1" "$2" diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/prior.training.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/prior.training.json new file mode 100644 index 0000000000000000000000000000000000000000..14029339b191bce014d2f0a3888b29551c8fad31 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/prior.training.json @@ -0,0 +1,22 @@ +{ + "training_split": "/data/fit/unconditional.jsonl", + "structures": 4000, + "helix_residues": 349374, + "helix_runs": 19242, + "loop_counts": { + "2": 607, + "3": 820, + "4": 806, + "5": 621, + "6": 557, + "7": 478, + "8": 421, + "9": 439 + }, + "parameters": 6032, + "helix_mean_degrees": [ + -65.03301971199869, + -37.50133452186794 + ], + "seed": 713 +} \ No newline at end of file diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/residues.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/residues.py new file mode 100644 index 0000000000000000000000000000000000000000..ec6dc9011c9005ec9de0ba9a7f5c2c7710cce7fc --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S001/residues.py @@ -0,0 +1,66 @@ +"""Atom14 residue constants. + +Each residue is represented by up to 14 heavy atoms in a residue-type-specific order, +the representation RFdiffusion3 uses. Slots 0-3 are always the backbone N, CA, C, O, +so backbone-only logic stays valid on an all-atom array. Tryptophan fills all 14. +""" + +ATOM14 = { + "ALA": ["N", "CA", "C", "O", "CB"], + "ARG": ["N", "CA", "C", "O", "CB", "CG", "CD", "NE", "CZ", "NH1", "NH2"], + "ASN": ["N", "CA", "C", "O", "CB", "CG", "OD1", "ND2"], + "ASP": ["N", "CA", "C", "O", "CB", "CG", "OD1", "OD2"], + "CYS": ["N", "CA", "C", "O", "CB", "SG"], + "GLN": ["N", "CA", "C", "O", "CB", "CG", "CD", "OE1", "NE2"], + "GLU": ["N", "CA", "C", "O", "CB", "CG", "CD", "OE1", "OE2"], + "GLY": ["N", "CA", "C", "O"], + "HIS": ["N", "CA", "C", "O", "CB", "CG", "ND1", "CD2", "CE1", "NE2"], + "ILE": ["N", "CA", "C", "O", "CB", "CG1", "CG2", "CD1"], + "LEU": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2"], + "LYS": ["N", "CA", "C", "O", "CB", "CG", "CD", "CE", "NZ"], + "MET": ["N", "CA", "C", "O", "CB", "CG", "SD", "CE"], + "PHE": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ"], + "PRO": ["N", "CA", "C", "O", "CB", "CG", "CD"], + "SER": ["N", "CA", "C", "O", "CB", "OG"], + "THR": ["N", "CA", "C", "O", "CB", "OG1", "CG2"], + "TRP": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "NE1", "CE2", "CE3", + "CZ2", "CZ3", "CH2"], + "TYR": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ", "OH"], + "VAL": ["N", "CA", "C", "O", "CB", "CG1", "CG2"], +} + +# OXT, the C-terminal carboxyl oxygen, is deliberately absent: it occurs once per +# chain and AlphaFold's atom14 layout omits it too. Parsers skip it rather than fail. +IGNORED_ATOMS = {"OXT"} + +ATOM14_WIDTH = 14 +BACKBONE_SLOTS = (0, 1, 2, 3) +BACKBONE_ATOMS = ("N", "CA", "C", "O") + +RESTYPES = tuple(sorted(ATOM14)) +RESTYPE_INDEX = {name: index for index, name in enumerate(RESTYPES)} + +THREE_TO_ONE = { + "ALA": "A", "ARG": "R", "ASN": "N", "ASP": "D", "CYS": "C", "GLN": "Q", + "GLU": "E", "GLY": "G", "HIS": "H", "ILE": "I", "LEU": "L", "LYS": "K", + "MET": "M", "PHE": "F", "PRO": "P", "SER": "S", "THR": "T", "TRP": "W", + "TYR": "Y", "VAL": "V", +} + +# Alternative naming for symmetric sidechain tips: these pairs are indistinguishable +# by the physics, so a motif RMSD must take the better of the two assignments. +SYMMETRIC_SWAPS = { + "ARG": [("NH1", "NH2")], + "ASP": [("OD1", "OD2")], + "GLU": [("OE1", "OE2")], + "PHE": [("CD1", "CD2"), ("CE1", "CE2")], + "TYR": [("CD1", "CD2"), ("CE1", "CE2")], +} + + +def atom_slot(residue_name, atom_name): + """Index of `atom_name` within the residue's atom14 layout, or None.""" + names = ATOM14.get(residue_name) + if names is None or atom_name not in names: + return None + return names.index(atom_name) diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/validation/broad-other-tracks.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/validation/broad-other-tracks.json new file mode 100644 index 0000000000000000000000000000000000000000..189b11124ebfd57e1e621c046e8d28a37b350bc8 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/validation/broad-other-tracks.json @@ -0,0 +1,55 @@ +{ + "enzyme": { + "conditioning_satisfied": 6, + "designs_designable": 0, + "designs_novel": 0, + "designs_requested": 60, + "designs_returned": 6, + "designs_valid": 6, + "diversity": 0.0, + "failures": { + "non-motif backbone clashes with ligand at 0.72 A": 1, + "non-motif backbone clashes with ligand at 0.96 A": 1, + "non-motif backbone clashes with ligand at 1.05 A": 1, + "not designable (scRMSD 1.35, ipTM 0.79, PAE 1.68, motif 3.34, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.46, ipTM 0.42, PAE 8.70, motif 7.15, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 7.46, ipTM 0.65, PAE 3.10, motif 3.47, no sequence satisfied the criterion)": 1 + }, + "invalid_reason": null, + "pass_rate": 0.0 + }, + "ppi_binder": { + "conditioning_satisfied": 6, + "designs_designable": 0, + "designs_novel": 0, + "designs_requested": 60, + "designs_returned": 6, + "designs_valid": 6, + "diversity": 0.0, + "failures": { + "not designable (scRMSD 0.29, ipTM 0.17, PAE 17.58, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.55, ipTM 0.16, PAE 18.24, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 1.33, ipTM 0.15, PAE 21.31, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 11.19, ipTM 0.17, PAE 18.28, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 6.64, ipTM 0.16, PAE 21.82, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 8.09, ipTM 0.23, PAE 15.06, no sequence satisfied the criterion)": 1 + }, + "invalid_reason": null, + "pass_rate": 0.0 + }, + "unconditional": { + "conditioning_satisfied": 6, + "designs_designable": 6, + "designs_novel": 5, + "designs_requested": 60, + "designs_returned": 6, + "designs_valid": 6, + "diversity": 1.0, + "failures": { + "near-copy of a fit-corpus structure (max TM >= 0.8)": 1 + }, + "invalid_reason": null, + "novelty_max_tm_mean": 0.6676333333333333, + "pass_rate": 0.08333333333333333 + } +} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/validation/broad-small-molecule.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/validation/broad-small-molecule.json new file mode 100644 index 0000000000000000000000000000000000000000..49e7994fd3c354821b2e205868e211e593a772e1 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/validation/broad-small-molecule.json @@ -0,0 +1,21 @@ +{ + "sm_binder": { + "conditioning_satisfied": 6, + "designs_designable": 0, + "designs_novel": 0, + "designs_requested": 60, + "designs_returned": 6, + "designs_valid": 6, + "diversity": 0.0, + "failures": { + "not designable (scRMSD 0.34, ipTM 0.69, PAE 2.37, ligRMSD 3.03, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 0.65, ipTM 0.73, PAE 3.20, ligRMSD 7.34, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.09, ipTM 0.66, PAE 3.67, ligRMSD 2.52, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 2.37, ipTM 0.50, PAE 6.60, ligRMSD 17.11, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 5.97, ipTM 0.67, PAE 3.17, ligRMSD 10.35, no sequence satisfied the criterion)": 1, + "not designable (scRMSD 9.11, ipTM 0.56, PAE 4.95, ligRMSD 10.30, no sequence satisfied the criterion)": 1 + }, + "invalid_reason": null, + "pass_rate": 0.0 + } +} \ No newline at end of file diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/validation/contract-replay.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/validation/contract-replay.json new file mode 100644 index 0000000000000000000000000000000000000000..dd76a243580afd256585ba1290f41511f2c22c46 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/validation/contract-replay.json @@ -0,0 +1,13 @@ +{ + "command": "python /workspace/evaluation/check_package.py --package /workspace/research/forward_stage", + "input": "/data/tune", + "requests": 240, + "requests_with_valid_design": 240, + "designs_per_request": 2, + "projections_completed": 480, + "projections_accepted": 394, + "projection_errors": 0, + "exit_code": 0, + "completed_utc": "2026-10-28 02:09 UTC", + "note": "Contract validity only; designability reports are separate." +} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/validation/monomer20.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/validation/monomer20.json new file mode 100644 index 0000000000000000000000000000000000000000..e665c478945f4a1ef518c9b07b04e2eba803af0f --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/validation/monomer20.json @@ -0,0 +1,17 @@ +{ + "unconditional": { + "conditioning_satisfied": 20, + "designs_designable": 20, + "designs_novel": 14, + "designs_requested": 20, + "designs_returned": 20, + "designs_valid": 20, + "diversity": 1.0, + "failures": { + "near-copy of a fit-corpus structure (max TM >= 0.8)": 6 + }, + "invalid_reason": null, + "novelty_max_tm_mean": 0.7651650000000001, + "pass_rate": 0.7 + } +} \ No newline at end of file diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/inference_engines/checkpoint_registry.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/inference_engines/checkpoint_registry.py new file mode 100644 index 0000000000000000000000000000000000000000..ed2cea212f54fa081ee60f494380de3f2a9384c1 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/inference_engines/checkpoint_registry.py @@ -0,0 +1,122 @@ +"""Management of checkpoints""" + +import os +from dataclasses import dataclass +from pathlib import Path +from typing import Iterable, List + +import dotenv + +DEFAULT_CHECKPOINT_DIR = Path.home() / ".foundry" / "checkpoints" + + +def _normalize_paths(paths: Iterable[Path]) -> list[Path]: + """Return absolute, deduplicated paths in order.""" + seen = set() + normalized: List[Path] = [] + for path in paths: + resolved = path.expanduser().absolute() + if resolved not in seen: + normalized.append(resolved) + seen.add(resolved) + return normalized + + +def get_default_checkpoint_dirs() -> list[Path]: + """Return checkpoint search paths. + + Always starts with the default ~/.foundry/checkpoints directory and then + appends any additional directories from the colon-separated + FOUNDRY_CHECKPOINT_DIRS environment variable. + """ + env_dirs = os.environ.get("FOUNDRY_CHECKPOINT_DIRS", "") + + # For backward compatibility, also check FOUNDRY_CHECKPOINTS_DIR + if not env_dirs: + env_dirs = os.environ.get("FOUNDRY_CHECKPOINTS_DIR", "") + + extra_dirs: list[Path] = [] + if env_dirs: + extra_dirs = [Path(p.strip()) for p in env_dirs.split(":") if p.strip()] + return _normalize_paths([*extra_dirs, DEFAULT_CHECKPOINT_DIR]) + + +def get_default_checkpoint_dir() -> Path: + """Backward-compatible helper returning the primary checkpoint directory.""" + return get_default_checkpoint_dirs()[0] + + +def append_checkpoint_to_env(checkpoint_dirs: list[Path]) -> bool: + dotenv_path = dotenv.find_dotenv() + if dotenv_path: + checkpoint_dirs = _normalize_paths(checkpoint_dirs) + dotenv.set_key( + dotenv_path=dotenv_path, + key_to_set="FOUNDRY_CHECKPOINT_DIRS", + value_to_set=":".join(str(path) for path in checkpoint_dirs), + export=False, + ) + return True + else: + return False + + +@dataclass +class RegisteredCheckpoint: + url: str + filename: str + description: str + sha256: None = None # Optional: add checksum for verification + + def get_default_path(self) -> Path: + checkpoint_dirs = get_default_checkpoint_dirs() + for checkpoint_dir in checkpoint_dirs: + candidate = checkpoint_dir / self.filename + if candidate.exists(): + return candidate + return checkpoint_dirs[0] / self.filename + + +REGISTERED_CHECKPOINTS = { + "rfd3na": RegisteredCheckpoint( + url="https://files.ipd.uw.edu/pub/rfdiffusion3na/rfd3na-1190.ckpt", + filename="rfd3na_1190.ckpt", + description="RFdiffusion3NA checkpoint", + ), + "rfd3": RegisteredCheckpoint( + url="https://files.ipd.uw.edu/pub/rfd3/rfd3_foundry_2025_12_01_remapped.ckpt", + filename="rfd3_latest.ckpt", + description="RFdiffusion3 checkpoint", + ), + "rf3": RegisteredCheckpoint( + url="https://files.ipd.uw.edu/pub/rf3/rf3_foundry_01_24_latest_remapped.ckpt", + filename="rf3_foundry_01_24_latest_remapped.ckpt", + description="latest RF3 checkpoint trained with data until 1/2024 (expect best performance)", + ), + "proteinmpnn": RegisteredCheckpoint( + url="https://files.ipd.uw.edu/pub/ligandmpnn/proteinmpnn_v_48_020.pt", + filename="proteinmpnn_v_48_020.pt", + description="ProteinMPNN checkpoint", + ), + "ligandmpnn": RegisteredCheckpoint( + url="https://files.ipd.uw.edu/pub/ligandmpnn/ligandmpnn_v_32_010_25.pt", + filename="ligandmpnn_v_32_010_25.pt", + description="LigandMPNN checkpoint", + ), + # Other models + "rf3_preprint_921": RegisteredCheckpoint( + url="https://files.ipd.uw.edu/pub/rf3/rf3_foundry_09_21_preprint_remapped.ckpt", + filename="rf3_foundry_09_21_preprint_remapped.ckpt", + description="RF3 preprint checkpoint trained with data until 9/2021", + ), + "rf3_preprint_124": RegisteredCheckpoint( + url="https://files.ipd.uw.edu/pub/rf3/rf3_foundry_01_24_preprint_remapped.ckpt", + filename="rf3_foundry_01_24_preprint_remapped.ckpt", + description="RF3 preprint checkpoint trained with data until 1/2024", + ), + "solublempnn": RegisteredCheckpoint( + url="https://files.ipd.uw.edu/pub/ligandmpnn/solublempnn_v_48_020.pt", + filename="solublempnn_v_48_020.pt", + description="SolubleMPNN checkpoint", + ), +} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/testing/__init__.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/testing/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4114cf90502d9aa68b6850918f6d13712b599857 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/testing/__init__.py @@ -0,0 +1,6 @@ +"""Shared testing utilities for foundry.""" + +from foundry.testing.fixtures import get_test_data_dir, gpu +from foundry.testing.pytest_hooks import configure_pytest + +__all__ = ["configure_pytest", "get_test_data_dir", "gpu"] diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/testing/pytest_hooks.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/testing/pytest_hooks.py new file mode 100644 index 0000000000000000000000000000000000000000..ec4b348c2f10cdeb6231ce0797f02dcc755ca4c6 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/testing/pytest_hooks.py @@ -0,0 +1,16 @@ +"""Shared pytest configuration hooks for foundry tests.""" + +import pytest +import rootutils +from dotenv import load_dotenv + + +def configure_pytest(config: pytest.Config, conftest_file: str) -> None: + """Configure pytest for foundry tests. + Sets up project root and environment variables. + """ + # Setup the project root + rootutils.setup_root(conftest_file, indicator=".project-root", pythonpath=True) + + # Setup environment variables + load_dotenv(override=True) diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/trainers/fabric.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/trainers/fabric.py new file mode 100644 index 0000000000000000000000000000000000000000..07d9c98e264801636bd7ee46db07767053d2c66a --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/trainers/fabric.py @@ -0,0 +1,956 @@ +"""Generic training harness built atop PyTorch Lightning Fabric. + +In addition to standard harness features (gradient accumulation, mixed precision, etc.), includes native support for EMA. + +References: + - Pytorch Lightning Trainer Example (https://github.com/Lightning-AI/pytorch-lightning/blob/master/examples/fabric/build_your_own_trainer/trainer.py) + - Lightning Hydra Template (https://github.com/ashleve/lightning-hydra-template) +""" + +import math +from abc import ABC, abstractmethod +from datetime import timedelta +from pathlib import Path +from typing import cast + +import hydra +import lightning as L +import torch +from beartype.typing import Any, Literal, Mapping +from lightning.fabric.accelerators import Accelerator +from lightning.fabric.loggers import Logger +from lightning.fabric.strategies import DDPStrategy, Strategy +from lightning.fabric.wrappers import ( + _FabricModule, + _FabricOptimizer, +) + +from foundry.callbacks.callback import BaseCallback +from foundry.training.EMA import EMA +from foundry.training.schedulers import SchedulerConfig +from foundry.utils.ddp import RankedLogger +from foundry.utils.weights import ( + CheckpointConfig, + WeightLoadingConfig, + freeze_parameters_with_config, + load_weights_with_policies, +) +from foundry.utils.xpu import ( + SingleXPUStrategy, + XPUAccelerator, + XPUMixedPrecision, +) + +ranked_logger = RankedLogger(__name__, rank_zero_only=True) + + +def is_interactive_environment() -> bool: + try: + from IPython import get_ipython + + return get_ipython() is not None + except ImportError: + return False + + +class FabricTrainer(ABC): + # Heterogeneous, dynamically-keyed training state: model / optimizer / scheduler_cfg + # plus step & epoch counters and the train config, and extended with arbitrary + # checkpoint keys on load. A loose bag by design, not a fixed-shape struct. + state: dict[str, Any] + # Set by subclass training_step() implementations before on_train_batch_end fires. + _current_train_return: Any + + def __init__( + self, + *, + accelerator: str | Accelerator = "auto", + strategy: str | Strategy = "ddp", + devices_per_node: list[int] | int | str = "auto", + num_nodes: int = 1, + precision: str | int | None = "bf16-mixed", + callbacks: BaseCallback | list[BaseCallback] | None = None, + loggers: Logger | list[Logger] | None = None, + max_epochs: int = 1000, + grad_accum_steps: int = 1, + validate_every_n_epochs: int = 1, + n_examples_per_epoch: int = 24_000, + output_dir: Path | str | None = None, + checkpoint_every_n_epochs: int = 1, + checkpoint_every_n_steps: int | None = None, + clip_grad_max_norm: float | None = None, + skip_nan_grad: bool = False, + error_if_grad_nonfinite: bool = False, + limit_train_batches: int | float = float("inf"), + limit_val_batches: int | float = float("inf"), + prevalidate: bool = False, + nccl_timeout: int = 3_200, + find_unused_parameters: bool = False, + skip_optimizer_loading: bool = False, + ) -> None: + """Base Trainer class built around Lightning Fabric. + + Args: + accelerator: The hardware to run on. See (1) for details. Possible choices are: + ``"cpu"``, ``"cuda"``, ``"mps"``, ``"gpu"``, ``"tpu"``, ``"auto"``. + strategy: Strategy for how to run across multiple devices. See (1) for details. Possible choices are: + ``"dp"``, ``"ddp"``, ``"ddp_spawn"``, ``"deepspeed"``, ``"fsdp"``. + devices_per_node: Number of devices to train on per machine (``int``), which GPUs to train on (``list`` or ``str``), or ``"auto"``. + See (1) for details. + EXAMPLE: If you run on 2 nodes with 8 GPUs each, you would set ``devices_per_node=8``, not ``16``. + num_nodes: Number of machines (nodes) for distributed training (default: 1). See (1) for details. + precision: Double precision (``"64"``), full precision (``"32"``), half precision AMP (``"16-mixed"``), + or bfloat16 precision AMP (``"bf16-mixed"``). See (2) for details. + callbacks: A single callback or a list of callbacks, each inheriting the BaseCallback Abstract Base Class. + loggers: A single logger or a list of loggers. See (3) for details. + max_epochs: Maximum number of epochs to train for (default: 1000). + grad_accum_steps: Number of batches to process before calling optimizer.step() (default: 1). See (4) for details on gradient accumulation in Fabric. + validate_every_n_epochs: Number of epochs between validation runs (default: 1). + n_examples_per_epoch: Number of examples to sample per epoch, across all GPUs. E.g., number of distinct examples that will + be "seen" by the model in a single epoch. If smaller than the the number implied by the dataloader, we will + alert a warning and use the smaller number. + output_dir: Directory to save checkpoints, metrics, intermediate validation strructures, etc. (default: None). + checkpoint_every_n_epochs: Number of epochs between saving checkpoints (default: 1). + checkpoint_every_n_steps: Number of optimizer steps between saving checkpoints (default: None). + If set, checkpoints will be saved every N optimizer steps. If None, only epoch-based checkpointing is used. + clip_grad_max_norm: Maximum gradient norm to clip to (default: None). If None, no gradient clipping is performed. + skip_nan_grad: Whether to skip optimizer updates when gradients contain NaN or Inf values (default: False). + Useful for training stability, especially with mixed precision or challenging datasets. + error_if_grad_nonfinite: Whether to raise when gradient clipping detects NaN or Inf gradients (default: False). + limit_train_batches: Limit on the number of training batches per epoch (default: float("inf")). + Helpful for debugging; should NOT be used when training production models. + limit_val_batches: Limit on the number of validation batches per epoch (default: float("inf")). + Helpful for debugging; should NOT be used when training production models. + prevalidate: Whether to run validation before training starts (default: False). + nccl_timeout: Timeout for NCCL operations (default: 3200). Only used with DDP strategy. + find_unused_parameters: Whether to let DDP find and skip gradient synchronization for unused parameters in the model (default: False). NOTE: Setting to True will incur a performance penalty, + but allow for training for bespoke use cases where parts of the model are frozen. + skip_optimizer_loading: Whether to skip loading the optimizer/scheduler state when restoring from checkpoints (default: False). + + References: + (1) Fabric Arguments (https://lightning.ai/docs/fabric/stable/api/fabric_args.html) + (2) Fabric Precision Documentation (https://lightning.ai/docs/fabric/stable/fundamentals/precision.html) + (3) Fabric Loggers (https://lightning.ai/docs/fabric/2.4.0/api/loggers.html) + (4) Efficient Gradient Accumulation (https://lightning.ai/docs/fabric/2.4.0/advanced/gradient_accumulation.html) + """ + # Handle XPU accelerator + is_xpu = hasattr(torch, "xpu") and torch.xpu.is_available() + if accelerator == "xpu" or ( + accelerator == "auto" and is_xpu and not torch.cuda.is_available() + ): + accelerator = XPUAccelerator() + precision_plugin = None + if precision in ("16-mixed", "bf16-mixed"): + precision_plugin = XPUMixedPrecision( + precision=cast(Literal["16-mixed", "bf16-mixed"], precision) + ) + precision = None # Handled by plugin + strategy = SingleXPUStrategy(precision_plugin=precision_plugin) + ranked_logger.info("Using Intel XPU with SingleXPUStrategy") + elif ( + strategy == "ddp" + and not is_interactive_environment() + and not (num_nodes == 1 and devices_per_node == 1) + ): + strategy = DDPStrategy( + timeout=timedelta(seconds=nccl_timeout), + find_unused_parameters=find_unused_parameters, + ) + else: + strategy = "auto" + + # See (1) for initialization arguments for Fabric() + self.fabric = L.Fabric( + accelerator=accelerator, + strategy=strategy, + devices=devices_per_node, + num_nodes=num_nodes, + # Our public str | int API is wider than Fabric's precision Literal union. + precision=precision, # type: ignore[arg-type] + callbacks=callbacks, + loggers=loggers, + ) + + # Training + self.clip_grad_max_norm = clip_grad_max_norm + self.skip_nan_grad = skip_nan_grad + self.error_if_grad_nonfinite = error_if_grad_nonfinite + self.grad_accum_steps = grad_accum_steps + + # Stopping + self.max_epochs = max_epochs + self.should_stop = False + self.n_examples_per_epoch = n_examples_per_epoch + self.limit_train_batches = limit_train_batches + self.limit_val_batches = limit_val_batches + + # Validation + self.validate_every_n_epochs = validate_every_n_epochs + self.prevalidate = prevalidate + + # Checkpoints + self.output_dir = Path(output_dir) if output_dir else None + self.checkpoint_every_n_epochs = checkpoint_every_n_epochs + self.checkpoint_every_n_steps = checkpoint_every_n_steps + self.skip_optimizer_loading = skip_optimizer_loading + + def initialize_or_update_trainer_state( + self, + updates: dict, + ) -> None: + """Initialize or update the state dictionary for the trainer. + + State keys: + model: The model to train. + optimizer: The optimizer to use with the model. May be None for validation/inference. + scheduler_cfg: Learning rate SchedulerConfig (e.g., a LRScheduler with intervals/frequency). May be None for validation/inference or if no scheduler is used. + global_step: Global optimizer step; used by W&B logger, learning rate schedulers, etc. Default is 0. + current_epoch: Global epoch counter; used for validation, learning rate schedulers, checkpointing, etc. Default is 0. + train_cfg: The training configuration dictionary. Used for reinitializing the trainer with the same configuration + (for training or for inference). Default is an empty dictionary. + """ + # Default values for the state + default_state: dict[str, Any] = { + "model": None, + "optimizer": None, + "scheduler_cfg": None, + "global_step": 0, + "current_epoch": 0, + "train_cfg": {}, + } + + # Initialize self.state with default values if it doesn't exist + if not hasattr(self, "state"): + self.state = default_state.copy() + else: + # Ensure existing state has all default keys + for key, value in default_state.items(): + self.state.setdefault(key, value) + + # Merge the updates into the existing state + self.state.update(updates) + + def construct_optimizer(self) -> None: + """Instantiate the optimizer(s) + + We provide a default implementation that instantiates the optimizer(s) from the Hydra configuration. + More complex models (e.g., GANs) may require custom implementations. + """ + assert ( + "model" in self.state and hasattr(self.state["model"], "parameters") + ), "Model not found in state dictionary! You must call `construct_model()` before constructing the optimizer." + + if self.state["train_cfg"].model.optimizer: + # ... instantiate the optimizer + optimizer = hydra.utils.instantiate( + self.state["train_cfg"].model.optimizer, + params=self.state["model"].parameters(), + ) + self.initialize_or_update_trainer_state({"optimizer": optimizer}) + + def construct_scheduler(self) -> None: + """Instantiate the learning rate scheduler(s) + + Like optimizers, we provided a default implementation that instantiates the scheduler(s) from the Hydra configuration. + More complex models (e.g., GANs) may require custom implementations. + """ + assert ( + "optimizer" in self.state and self.state["optimizer"] + ), "Optimizer not found in state dictionary! You must call `construct_optimizer()` before constructing the scheduler." + + # ... instantiate the LR scheduler(s) + lr_scheduler = ( + hydra.utils.instantiate( + self.state["train_cfg"].model.lr_scheduler, + optimizer=self.state["optimizer"], + ) + if self.state["train_cfg"].model.lr_scheduler + else None + ) + + if lr_scheduler: + # We assume "interval = step" and "frequency = 1" for the default scheduler; custom implementations may override this method + scheduler_cfg = SchedulerConfig( + scheduler=lr_scheduler, + interval="step", + frequency=1, + ) + self.initialize_or_update_trainer_state({"scheduler_cfg": scheduler_cfg}) + + def construct_model(self) -> None: + """Instantiate the model, updating the trainer state in-place. + + This method must set the "model" key in the state dictionary using `self.initialize_or_update_trainer_state()`. + For an example, see the `construct_model` method in the `AF3Trainer` + Construct the model and optionally wrap with EMA. + """ + # ... instantiate model with Hydra and Fabric + with self.fabric.init_module(): + ranked_logger.info("Instantiating model...") + + model = hydra.utils.instantiate( + self.state["train_cfg"].model.net, + _recursive_=False, + ) + + # Optionally, wrap the model with EMA + if self.state["train_cfg"].model.ema is not None: + ranked_logger.info("Wrapping model with EMA...") + model = EMA(model, **self.state["train_cfg"].model.ema) + + self.initialize_or_update_trainer_state({"model": model}) + + def setup_model_optimizers_and_schedulers(self) -> None: + """Setup the model, optimizer(s), and scheduler(s) with Fabric. + + Note that we must call this method after constructing (instantiating) the model, optimizer(s), and scheduler(s). + For details on multi-model and multi-optimizer setups, see: https://lightning.ai/docs/fabric/2.2.3/advanced/multiple_setup.html + """ + assert self.state[ + "model" + ], "You must construct the model before setting up the model, optimizer, and scheduler." + model = self.state["model"] + optimizer = self.state["optimizer"] + + # ... setup the model and optimizer + if optimizer: + model, optimizer = self.fabric.setup(model, optimizer) + else: + model = self.fabric.setup(model) + + # ... update the state dictionary (we avoid updating the state dictionary in-place, which is an anti-pattern) + self.initialize_or_update_trainer_state( + { + "model": model, + "optimizer": optimizer, + } + ) + + def fit( + self, + train_loader: torch.utils.data.DataLoader, + val_loaders: dict[str, torch.utils.data.DataLoader] | None = None, + ckpt_config: CheckpointConfig | None = None, + ) -> None: + """Main entry point for training a model. + + Args: + train_loader: Dataloader for training. Must have an iterable returning batches. + val_loaders: Dictionary of dataloaders for validation. The keys are the names of the loaders, and the values are the loaders themselves. + ckpt_config: Configuration for loading a checkpoint. May contain: + - ckpt_path: Path to either: + (a) A previous checkpoint directory from which to resume training from. In this case, we will automatically load + the latest checkpoint using `self.get_latest_checkpoint()`. + (b) A specific checkpoint file to load. In this case, we will load the checkpoint from the specified file. + If None, no checkpoint is loaded, and the model will be trained from scratch. + - weight_loading_config: Weight loading policies to apply to the checkpoint weights. If None, default policies are used (copy weights with re-initialization as a fallback + if shapes do not match) + - reset_optimizer: Whether to reset the optimizer state when loading a checkpoint. If True, the optimizer will not be loaded from the checkpoint. + """ + assert ( + hasattr(self, "state") and "model" in self.state + ), "Model not found in state dictionary! You must call `instantiate_model()` before running fit()." + + # (If we don't have enough examples to sample, we will log a warning and use the smaller number) + if len(train_loader) * self.fabric.world_size < self.n_examples_per_epoch: + ranked_logger.warning( + f"Number of examples per epoch ({self.n_examples_per_epoch}) exceeds the number of examples in the loader: " + f"({len(train_loader) * self.fabric.world_size}). Using the latter." + ) + self.n_examples_per_epoch = len(train_loader) * self.fabric.world_size + self.n_batches_per_epoch = math.ceil( + self.n_examples_per_epoch / self.fabric.world_size + ) + + # ... setup training and validation dataloaders with Fabric + train_loader = cast( + torch.utils.data.DataLoader, + self.fabric.setup_dataloaders( + # Our sampler is already distributed, so we don't need to wrap with a DistributedSampler + train_loader, + use_distributed_sampler=False, + ), + ) + + if val_loaders is not None: + for key, loader in val_loaders.items(): + val_loaders[key] = cast( + torch.utils.data.DataLoader, + self.fabric.setup_dataloaders( + loader, use_distributed_sampler=False + ), + ) + + self.setup_model_optimizers_and_schedulers() + + if ckpt_config is not None: + assert hasattr( + ckpt_config, "path" + ), "Checkpoint path not found in checkpoint configuration!" + ckpt_path = Path(ckpt_config.path) + + reset_optimizer = bool( + getattr(ckpt_config, "reset_optimizer", False) + or self.skip_optimizer_loading + ) + + if ckpt_path.is_dir(): + # If given a directory, load the latest checkpoint from the directory + ranked_logger.info( + f"Loading latest checkpoint within the directory {ckpt_path}..." + ) + # We are resuming from a directory that should contain checkpoints; + # get_latest_checkpoint returns None only if it is empty (latent edge). + self.load_checkpoint( + cast(Path, self.get_latest_checkpoint(ckpt_path)), + weight_loading_config=ckpt_config.weight_loading_config, + reset_optimizer=reset_optimizer, + ) + else: + # If given a specific checkpoint file, load that checkpoint + self.load_checkpoint( + ckpt_path, + weight_loading_config=ckpt_config.weight_loading_config, + reset_optimizer=reset_optimizer, + ) + + # Apply parameter freezing if a freezing config is provided + if ckpt_config.parameter_freezing_config is not None: + ranked_logger.info( + "Applying parameter freezing according to CheckpointConfig..." + ) + freeze_parameters_with_config( + # We must access the model through "module", since the model may be wrapped + self.state["model"].module, + ckpt_config.parameter_freezing_config, + ) + + # Increment the global epoch (e.g., if we loaded a checkpoint from [the end of] epoch 5, we should start training at epoch 6) + self.state["current_epoch"] += 1 + # Stopping conditions + if ( + self.max_epochs is not None + and self.state["current_epoch"] >= self.max_epochs + ): + self.should_stop = True + else: + ranked_logger.info("No checkpoint provided; training from scratch.") + + # Set the _num_iter_calls internal attribute of the wrapped loader to the current epoch + # (NOTE: This addresses a bug in Lightning Fabric, where there the iter() method calls the `_set_sampler_epoch()` method, + # relying on the _num_iter_calls attribute to determine the current epoch) + train_loader._num_iter_calls = self.state["current_epoch"] + + self.fabric.call("on_fit_start", trainer=self) + + # Prevalidate + if self.prevalidate and val_loaders: + # Temporarily decrement the current epoch, since we haven't done any training this epoch + self.state["current_epoch"] -= 1 # (Will be -1 if training from scratch) + ranked_logger.info( + f"Prevalidating with epoch {self.state['current_epoch']} before training; to avoid this behavior, set `prevalidate=False` in the Trainer config." + ) + self.validation_loop( + val_loaders=val_loaders, + limit_batches=self.limit_val_batches, + ) + self.state["current_epoch"] += 1 # (Restore the current epoch) + + while not self.should_stop: + # ... train for one epoch + ranked_logger.info( + f"\n+ Starting epoch {self.state['current_epoch']}/{self.max_epochs - 1}\n" + f"+ Total examples per epoch (across all GPU): {self.n_examples_per_epoch}\n" + f"+ Examples per GPU (batches per epoch): {self.n_batches_per_epoch}\n" + f"+ Gradient accumulation steps: {self.grad_accum_steps}\n" + f"+ Expected optimizer steps per epoch: {self.n_batches_per_epoch // self.grad_accum_steps}\n" + ) + + self.train_loop( + train_loader=train_loader, + limit_batches=self.limit_train_batches, + ) + + ranked_logger.info(f"Finished epoch {self.state['current_epoch']}!") + + # ... validate, if we're at the validation interval + if self.should_validate and val_loaders: + ranked_logger.info( + f"Starting validation for epoch {self.state['current_epoch']}!" + ) + self.validation_loop( + val_loaders=val_loaders, + limit_batches=self.limit_val_batches, + ) + + # ... step the scheduler, if we're adjusting the learning rate at the epoch-level + self.step_scheduler( + level="epoch", current_value=self.state["current_epoch"] + ) + + # ... save checkpoint, if we've reached the checkpoint interval + if self.state["current_epoch"] % self.checkpoint_every_n_epochs == 0: + self.save_checkpoint() + + # ... increment the epoch + self.state["current_epoch"] += 1 + + # Stopping conditions + if ( + self.max_epochs is not None + and self.state["current_epoch"] >= self.max_epochs + ): + self.should_stop = True + + # Reset for next `fit()` call + self.should_stop = False + + self.fabric.call("on_fit_end", trainer=self) + + def train_loop( + self, + *, + train_loader: torch.utils.data.DataLoader, + limit_batches: int | float = float("inf"), + ) -> None: + """Train model for a single epoch. + + Args: + train_loader: Dataloader for training. + limit_batches: Limit on the batches during this training epoch. If greater than the number of batches in the + `train_loader`, this argument has no effect. Helpful for debugging; should NOT be used when training production models. + """ + self.fabric.call("on_train_epoch_start", trainer=self) + + assert self.state["model"].training + + # NOTE: When we call iter(), Fabric calls the `set_sampler_epoch()` method on the sampler behind the scenes, so we don't need to call it explicitly + train_iter = iter(train_loader) + self.fabric.call("on_after_train_loader_iter", trainer=self) + + for batch_idx in range(len(train_loader)): + # (End epoch if stopping training completely or maximum desired batches for this epoch reached) + if self.should_stop or batch_idx >= limit_batches: + break + + self.fabric.call("on_before_train_loader_next", trainer=self) + batch = next(train_iter) + + self.fabric.call( + "on_train_batch_start", trainer=self, batch=batch, batch_idx=batch_idx + ) + + # Optimizer should step if we've accumulated the desired number of gradients + should_optimizer_step = (batch_idx + 1) % self.grad_accum_steps == 0 + + self.training_step( + batch=batch, + batch_idx=batch_idx, + is_accumulating=not should_optimizer_step, # triggers gradient syncing + ) + + self.fabric.call( + "on_train_batch_end", + trainer=self, + outputs=self._current_train_return, + batch=batch, + batch_idx=batch_idx, + ) + + if should_optimizer_step: + self.fabric.call( + "on_before_optimizer_step", + trainer=self, + optimizer=self.state["optimizer"], + ) + + # ... step the optimizer, clipping gradients and updating EMA parameters if applicable + # Note: step_optimizer() calls optimizer.step(), which internally triggers + # on_after_optimizer_step callbacks via _FabricOptimizer + self.step_optimizer() + + # ... call optimizer_step hook (distinct from on_after_optimizer_step which is called by Fabric) + self.fabric.call( + "optimizer_step", + trainer=self, + optimizer=self.state["optimizer"], + ) + + # ... step the scheduler, if we're adjusting the learning rate at the optimizer step-level + self.step_scheduler( + level="step", current_value=self.state["global_step"] + ) + + # ... increment the global step, if optimizer stepped + # NOTE: Each node maintains its own global step + self.state["global_step"] += int(should_optimizer_step) + + # ... save checkpoint if we've reached the step-based checkpoint interval + if ( + should_optimizer_step + and self.checkpoint_every_n_steps is not None + and self.state["global_step"] % self.checkpoint_every_n_steps == 0 + ): + self.save_checkpoint() + + self.fabric.call("on_train_epoch_end", trainer=self) + + def validation_loop( + self, + *, + val_loaders: dict[str, torch.utils.data.DataLoader], + limit_batches: int | float = float("inf"), + ) -> None: + """Run validation loop for a single validation epoch. + + Args: + val_loader: Dictionary of Dataloaders (more precisely, _FabricDataLoader) for validation. + limit_batches: Limit on the batches during this validation epoch. If greater than the number of batches in the + `val_loader`, this argument has no effect. Helpful for debugging; should NOT be used for production. + """ + # ... set model to evaluation mode + self.state["model"].eval() + + with torch.no_grad(): + # ... assert we're in evaluation mode + assert not self.state["model"].training + + self.fabric.call("on_validation_epoch_start", trainer=self) + + # ... iterate over all validation loaders + for val_loader_name, val_loader in val_loaders.items(): + ranked_logger.info( + f"Running validation on dataset: {val_loader_name}, with {len(val_loader)} batches, with world_size={self.fabric.world_size}." + ) + + for batch_idx, batch in enumerate(val_loader): + # ... end validation epoch if stopping training completely or maximum desired batches for this epoch reached + if self.should_stop or batch_idx >= limit_batches: + break + + self.fabric.call( + "on_validation_batch_start", + trainer=self, + batch=batch, + batch_idx=batch_idx, + num_batches=len(val_loader), + dataset_name=val_loader_name, + ) + + validation_result = self.validation_step( + batch=batch, + batch_idx=batch_idx, + ) + + self.fabric.call( + "on_validation_batch_end", + trainer=self, + outputs=validation_result, + batch=batch, + batch_idx=batch_idx, + num_batches=len(val_loader), + dataset_name=val_loader_name, + ) + + self.fabric.call("on_validation_epoch_end", trainer=self) + + # ... reset the model to training mode + self.state["model"].train() + + @abstractmethod + def training_step( + self, + batch: Any, + batch_idx: int, + is_accumulating: bool, + ) -> None: + """Training step, running forward and backward passes. + + Args: + batch: The current batch; can be of any form. + batch_idx: The index of the current batch. + is_accumulating: Whether we are accumulating gradients (i.e., not yet calling optimizer.step()). + If this is the case, we should skip the synchronization during the backward pass. + + Returns: + torch.Tensor | Mapping[str, Any]: The loss tensor or a dictionary containing the loss tensor. + """ + pass + + @abstractmethod + def validation_step( + self, + batch: Any, + batch_idx: int, + val_loader_name: str | None = None, + ) -> dict: + """Validation step, running forward pass. + + Args: + batch: The current batch; can be of any form. + batch_idx: The index of the current batch (within that validation loader). + val_loader_name: The name of the validation loader, if applicable. + + Returns: + dict: A dictionary containing the output of the designated validation metrics. + """ + pass + + def validate( + self, + val_loaders: dict, + ckpt_path: Path | str, + ) -> None: + """Validate a model using the given dataloaders and checkpoint. + + Args: + model: The PyTorch model to validate. + val_loaders: A dictionary of dataloaders for validation, where keys are names and values are dataloaders. + ckpt_path: Path to a specific checkpoint file to load. If None, the model will be validated as is. + """ + assert ( + hasattr(self, "state") and "model" in self.state + ), "Model not found in state dictionary! You must call `instantiate_model()` before running validate()." + + self.setup_model_optimizers_and_schedulers() + + self.load_checkpoint(ckpt_path) + + # Setup validation dataloaders with Fabric + for key, loader in val_loaders.items(): + val_loaders[key] = self.fabric.setup_dataloaders( + loader, use_distributed_sampler=False + ) + + # Run the validation loop + self.validation_loop( + val_loaders=val_loaders, limit_batches=self.limit_val_batches + ) + + def step_optimizer(self) -> None: + """Step the optimizer. + + This method must be called only when the optimizer is stepped (i.e., after accumulating the desired number of gradients). + + We then perform following steps: + 1. Check for NaN/Inf gradients (skip update if skip_nan_grad=True and found). + 2. Clip gradients, if applicable. + 3. Step the optimizer. + 4. Zero the gradients. + 5. Update the EMA parameters, if applicable. + """ + assert "optimizer" in self.state and isinstance( + self.state["optimizer"], _FabricOptimizer + ) + assert "model" in self.state and isinstance( + self.state["model"], _FabricModule | EMA + ) + + optimizer = self.state["optimizer"] + model = self.state["model"] + + # ... check for NaN/Inf gradients, if applicable + if self.skip_nan_grad: + has_nan_or_inf = False + for param in model.parameters(): + if param.grad is not None: + if not torch.isfinite(param.grad).all(): + has_nan_or_inf = True + break + + if has_nan_or_inf: + ranked_logger.warning( + f"Skipping optimizer step at global_step={self.state['global_step']} due to NaN/Inf gradients" + ) + optimizer.zero_grad() + return # Skip this update + + # ... clip gradients, if applicable + if self.clip_grad_max_norm is not None: + self.fabric.clip_gradients( + module=model, + optimizer=optimizer, + max_norm=self.clip_grad_max_norm, + error_if_nonfinite=self.error_if_grad_nonfinite, + ) + + # ... step the optimizer + optimizer.step() + + # ... zero gradients + optimizer.zero_grad() + + # ... update EMA parameters, if applicable + if hasattr(model, "update"): + model.update() + + def step_scheduler( + self, + level: Literal["epoch", "step"], + current_value: int, + ) -> None: + """Step the learning rate scheduler. + + Args: + level: The level at which to step the scheduler. Either "epoch" or "step". + current_value: The current epoch or step value. + """ + # (No scheduler) + if "scheduler_cfg" not in self.state or self.state["scheduler_cfg"] is None: + return + else: + scheduler_cfg = self.state["scheduler_cfg"] + + # (Wrong interval; e.g., we adjust learning rate every epoch, but we are stepping at the step level) + if scheduler_cfg.interval != level: + return + + # (Right interval, but wrong frequency) + if current_value % cast(int, scheduler_cfg.frequency) != 0: + return + + # ... step the scheduler + scheduler_cfg.scheduler.step() + + def save_checkpoint(self) -> None: + """Saves a checkpoint with current state to `self.output_dir/ckpt`. + + If no output directory is specified, then no checkpoint is saved. + """ + # No checkpoint directory; skip saving + if not self.output_dir: + ranked_logger.warning( + "No output directory specified; skipping model checkpointing of state dictionary." + ) + return + + # (Provide a hook to modify the state before saving) + self.fabric.call("on_save_checkpoint", trainer=self, state=self.state) + + # ... construct the checkpoint file path using Path + checkpoint_file = ( + self.output_dir / "ckpt" / f"epoch-{self.state['current_epoch']:04d}.ckpt" + ) + + # NOTE: Fabric's `save()` will call the `state_dict()` method on the model, optimizer, and scheduler_cfg + self.fabric.save(checkpoint_file, self.state) + ranked_logger.info(f"Saved checkpoint to: {checkpoint_file}") + + def _load_optimizer(self, ckpt: Mapping) -> None: + """Loads the optimizer state from the checkpoint.""" + if "optimizer" in ckpt and self.state["optimizer"]: + self.state["optimizer"].load_state_dict(ckpt["optimizer"]) + else: + ranked_logger.warning("Skipping optimizer loading...") + + def _load_scheduler(self, ckpt: Mapping) -> None: + """Loads the learning rate scheduler state from the checkpoint.""" + if "scheduler_cfg" in ckpt and self.state["scheduler_cfg"]: + self.state["scheduler_cfg"].load_state_dict(ckpt["scheduler_cfg"]) + else: + ranked_logger.warning("Skipping scheduler loading...") + + def _load_model( + self, ckpt: Mapping, weight_loading_config: WeightLoadingConfig | None = None + ) -> None: + """Loads the model state from the checkpoint, handling EMA and size mismatches.""" + # ... load pre-trained weights from the CHECKPOINT into the MODEL (that at this point has random weights) + model = self.state["model"] + model.load_state_dict( + load_weights_with_policies( + model=self.state["model"], + ckpt=ckpt["model"], + config=weight_loading_config, + ), + strict=True, + ) + + def load_checkpoint( + self, + checkpoint: Path | str | dict, + weight_loading_config: WeightLoadingConfig | None = None, + reset_optimizer: bool = False, + ) -> None: + """Loads a checkpoint from the specified path or uses a pre-loaded checkpoint dict. + + Args: + checkpoint: Either a path to a checkpoint file or a pre-loaded checkpoint dict. + weight_loading_config: Weight loading policies to apply. Defaults to ``None``. + reset_optimizer: Whether to reset the optimizer state. Defaults to ``False``. + """ + # ... load the checkpoint or use the provided dict + if isinstance(checkpoint, dict): + ranked_logger.info("Using pre-loaded checkpoint...") + ckpt = checkpoint + else: + ranked_logger.info(f"Loading checkpoint from: {checkpoint}...") + ckpt = torch.load(checkpoint, map_location="cpu", weights_only=False) + + try: + # ... optimize, scheduler + if not reset_optimizer: + self._load_optimizer(ckpt) + self._load_scheduler(ckpt) + # ... model + self._load_model(ckpt, weight_loading_config) + + # ... stateless keys + # (We do not want to load the `train_cfg` in this instance, as it may contain different configurations) + keys_to_ignore = {"model", "optimizer", "scheduler_cfg", "train_cfg"} + self.state.update( + { + key: value + for key, value in ckpt.items() + if key not in keys_to_ignore and key in self.state + } + ) + + # Log warnings for missing and extra keys + state_keys = set(self.state) - keys_to_ignore + ckpt_keys = set(ckpt) - keys_to_ignore + + if missing := state_keys - ckpt_keys: + ranked_logger.warning( + f"Keys found in STATE but not CKPT: {sorted(missing)}" + ) + if extra := ckpt_keys - state_keys: + ranked_logger.warning( + f"Keys found in CKPT but not STATE: {sorted(extra)}" + ) + + ranked_logger.info( + f"Loaded checkpoint. Current epoch: {self.state['current_epoch']}, global step: {self.state['global_step']}" + ) + except Exception as e: + ranked_logger.exception( + f"Error loading checkpoint: {e}. Please ensure that the model architecture matches the checkpoint." + ) + raise + + @staticmethod + def get_latest_checkpoint(ckpt_load_dir: Path) -> Path | None: + """Returns the latest checkpoint file from the given directory. + + Assumes that checkpoints are stored with filenames such that a standard string-based + sort will correctly order them by creation time (e.g., with epoch numbers, or timestamps). + + Args: + ckpt_load_dir (Path): The directory to search for checkpoint files. + + Returns: + Path: The path to the latest checkpoint file, or None if no checkpoints are found + or if the directory does not exist. + """ + if not ckpt_load_dir.is_dir(): + return None + + # List all files in the directory and sort them + items = sorted(ckpt_load_dir.iterdir()) + + # Return the last item in the sorted list, if any + return items[-1] if items else None + + @property + def should_validate(self) -> bool: + """Whether to currently run validation.""" + return self.state["current_epoch"] % self.validate_every_n_epochs == 0 diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/training/schedulers.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/training/schedulers.py new file mode 100644 index 0000000000000000000000000000000000000000..ec5afd5b91335d3fc96134053516e12000c6dac4 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/training/schedulers.py @@ -0,0 +1,91 @@ +from dataclasses import dataclass + +from torch.optim.lr_scheduler import LRScheduler, _LRScheduler +from torch.optim.optimizer import Optimizer + + +class AF3Scheduler(_LRScheduler): + """Implements a two-phase learning rate schedule a-la AF-3: + 1. The base learning rate is 1.8 · 10^−3, which is linearly increased from 0 over the first 1,000 steps. + 2. The learning rate is then decreased by a factor of 0.95 every 50,000 steps. + + From the AF-3 Supplement, Section 5.4: + > "For training we use the Adam optimizer with parameters β1 = 0.9, β2 = 0.95, ϵ = 10^−8. The base learning rate + is 1.8 · 10^−3, which is linearly increased from 0 over the first 1,000 steps. The learning rate is then decreased + by a factor of 0.95 every 5 · 10^4 steps." + + References: + - AF-3 Supplement + """ + + def __init__( + self, + optimizer: Optimizer, + base_lr: float = 1.8e-3, + warmup_steps: int = 1000, + decay_factor: float = 0.95, + decay_steps: int = 50000, + last_epoch: int = -1, + ) -> None: + """Initializes a new instance of AF3LRScheduler. + + Note that the "last_epoch" value is incremented every time we call `scheduler.step()` + method; we name it "epoch" to follow the PyTorch convention. + + Args: + optimizer (Optimizer): Wrapped optimizer. + base_lr (float): The base learning rate after warmup (which will then be decayed). + warmup_steps (int): Number of steps for linear warmup. + decay_factor (float): Factor by which the learning rate is multiplied every decay_steps. + decay_steps (int): Number of steps between each decay. + last_epoch (int): The index of the last epoch. Default: -1. + """ + self.base_lr = base_lr + self.warmup_steps = warmup_steps + self.decay_factor = decay_factor + self.decay_steps = decay_steps + super(AF3Scheduler, self).__init__(optimizer, last_epoch) + + def get_lr(self) -> list[float]: + if self.last_epoch < self.warmup_steps: + # Linear warmup + return [ + self.base_lr * (self.last_epoch / self.warmup_steps) + for _ in self.optimizer.param_groups + ] + else: + # Decay after warmup + num_decays = (self.last_epoch - self.warmup_steps) // self.decay_steps + return [ + self.base_lr * (self.decay_factor**num_decays) + for _ in self.optimizer.param_groups + ] + + +@dataclass +class SchedulerConfig: + """Flexible configuration for a learning rate scheduler. + + Modeled on the PyTorch Lightning scheduler configuration. + + Attributes: + scheduler (LRScheduler): The learning rate scheduler instance. Must inherit from `torch.optim.lr_scheduler.LRScheduler`. + interval (str): The interval at which to apply the scheduler, typically "epoch" or "step". Defaults to "step". + frequency (int): The frequency of applying the scheduler. For example, a frequency of 1 means the scheduler is applied every epoch. Defaults to 1. + """ + + scheduler: LRScheduler + interval: str = "step" + frequency: int = 1 + + def state_dict(self) -> dict: + return { + "scheduler": self.scheduler.state_dict(), + "interval": self.interval, + "frequency": self.frequency, + } + + def load_state_dict(self, state_dict: dict) -> None: + self.scheduler.load_state_dict(state_dict["scheduler"]) + self.interval = state_dict["interval"] + self.frequency = state_dict["frequency"] diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/utils/torch.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/utils/torch.py new file mode 100644 index 0000000000000000000000000000000000000000..43ad41a49cb077eb1c2eede6f5ad650352d273c5 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/utils/torch.py @@ -0,0 +1,367 @@ +"""General convenience utilities for PyTorch.""" + +__all__ = [ + "map_to", + "assert_no_nans", + "assert_shape", + "assert_same_shape", + "scatter_mean", +] + +import time +import warnings +from contextlib import contextmanager + +import numpy as np +import torch +from beartype.typing import Any, Iterator, Sequence +from toolz import valmap +from torch import Tensor +from torch._prims_common import DeviceLikeType +from torch.types import _dtype + +from foundry import should_check_nans +from foundry.common import at_least_one_exists, do_nothing + + +def map_to( + x: Any, + *, + device: DeviceLikeType | None = None, + dtype: _dtype | None = None, + non_blocking: bool = False, + **to_kwargs: Any, +) -> Any: + """ + Recursively applies the `.to()` method to all tensors in a nested structure. + + This function handles nested structures such as dictionaries and lists, applying the `.to()` method + to any PyTorch tensors while leaving other types unchanged. + + NOTE: If you are instantiating a new tensor, you should use the `device` and `dtype` arguments + instead of calling `map_to()` on the tensor. + (https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html#create-tensors-directly-on-the-target-device) + + + Args: + - x (Any): The input structure, which can be a tensor, dictionary, list, or any other type. + - device (DeviceLikeType | None): The target device to move tensors to (e.g., 'cpu', 'cuda'). + - dtype (_dtype | None): The target dtype to cast tensors to. + - non_blocking (bool): Whether to use non-blocking transfers when possible. + - **to_kwargs: Additional keyword arguments to pass to the `.to()` method. + + Returns: + - Any: The input structure with all contained tensors processed by the `.to()` method. + + Example: + >>> data = {"tensor": torch.tensor([1, 2, 3]), "list": [torch.tensor([4, 5]), "string"]} + >>> map_to(data, device="cuda", dtype=torch.float32) + {'tensor': tensor([1., 2., 3.], device='cuda:0', dtype=torch.float32), + 'list': [tensor([4., 5.], device='cuda:0', dtype=torch.float32), 'string']} + """ + torch._assert( + at_least_one_exists(device, dtype), + "Must provide at least one of `device` or `dtype`", + ) + + if isinstance(x, dict): + return valmap( + lambda v: map_to( + v, device=device, dtype=dtype, non_blocking=non_blocking, **to_kwargs + ), + x, + ) + elif isinstance(x, (list, tuple)): + return type(x)( + map( + lambda v: map_to( + v, + device=device, + dtype=dtype, + non_blocking=non_blocking, + **to_kwargs, + ), + x, + ) + ) + elif isinstance(x, Tensor): + return x.to(device=device, dtype=dtype, non_blocking=non_blocking, **to_kwargs) + else: + return x + + +def _assert_no_nans(x: Any, *, msg: str = "", fail_if_not_tensor: bool = False) -> None: + """Recursively checks for NaN values in tensor-like objects. + + Args: + - x (Any): Input to check for NaNs. Can be a tensor, dict, list, tuple, or other type. + - msg (str): Prefix for error messages. + - fail_if_not_tensor (bool): If True, raises error for non-tensor types. + """ + if isinstance(x, Tensor): + torch._assert( + not torch.isnan(x).any(), + ": ".join(filter(bool, [msg, "Tensor contains NaNs!"])), + ) + elif isinstance(x, np.ndarray): + torch._assert( + not np.isnan(x).any(), + ": ".join(filter(bool, [msg, "Numpy array contains NaNs!"])), + ) + elif isinstance(x, float): + torch._assert( + not np.isnan(x), + ": ".join(filter(bool, [msg, "float is NaN!"])), + ) + elif isinstance(x, dict): + for k, v in x.items(): + _assert_no_nans( + v, + msg=".".join(filter(bool, [msg, k])), + fail_if_not_tensor=fail_if_not_tensor, + ) + elif isinstance(x, (list, tuple)): + for idx, v in enumerate(x): + _assert_no_nans( + v, + msg=".".join(filter(bool, [msg, str(idx)])), + fail_if_not_tensor=fail_if_not_tensor, + ) + elif fail_if_not_tensor: + raise ValueError(f"Unsupported type: {type(x)}") + + +assert_no_nans = _assert_no_nans if should_check_nans else do_nothing + + +@contextmanager +def _suppress_tracer_warnings() -> Iterator[None]: + """ + Context manager to temporarily suppress known warnings in torch.jit.trace(). + Note: Cannot use catch_warnings because of https://bugs.python.org/issue29672 + + References: + - https://github.com/NVlabs/edm2/blob/main/torch_utils/misc.py + """ + tracer_warning_filter = ("ignore", None, torch.jit.TracerWarning, None, 0) + # warnings.filters is typed as an immutable Sequence in typeshed but is a mutable + # list at runtime, so insert/remove are valid. + warnings.filters.insert(0, tracer_warning_filter) # type: ignore[attr-defined] + yield + warnings.filters.remove(tracer_warning_filter) # type: ignore[attr-defined] + + +def assert_shape(tensor: Tensor, ref_shape: Sequence[int | None]) -> None: + """ + Assert that the shape of a tensor matches the given list of integers. + None indicates that the size of a dimension is allowed to vary. + Performs symbolic assertion when used in torch.jit.trace(). + + Args: + - tensor (Tensor): The tensor to check the shape of. + - ref_shape (Sequence[int | None]): The expected shape of the tensor. + + References: + - https://github.com/NVlabs/edm2/blob/main/torch_utils/misc.py + """ + + if tensor.ndim != len(ref_shape): + raise AssertionError( + f"Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}" + ) + + for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)): + if tensor.ndim != len(ref_shape): + raise AssertionError( + f"Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}" + ) + + for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)): + if ref_size is None: + pass + elif isinstance(ref_size, torch.Tensor): + with ( + _suppress_tracer_warnings() + ): # as_tensor results are registered as constants + torch._assert( + torch.equal(torch.as_tensor(size), ref_size), + f"Wrong size for dimension {idx}", + ) + elif isinstance(size, torch.Tensor): + with ( + _suppress_tracer_warnings() + ): # as_tensor results are registered as constants + torch._assert( + torch.equal(size, torch.as_tensor(ref_size)), + f"Wrong size for dimension {idx}: expected {ref_size}", + ) + elif size != ref_size: + raise AssertionError( + f"Wrong size for dimension {idx}: got {size}, expected {ref_size}" + ) + + +def assert_same_shape(tensor: Tensor, ref_tensor: Tensor) -> None: + """Assert that two tensors have the same shape.""" + assert_shape(tensor, ref_tensor.shape) + + +def scatter_mean(zeros: Tensor, dim: int, index: Tensor, source: Tensor) -> Tensor: + """Scatter-mean aggregation, with an MPS-compatible fallback. + + On non-MPS devices uses index_reduce (faster, in-place kernel). + On MPS, index_reduce is not implemented so falls back to scatter_add + count. + + Equivalent to: zeros.index_reduce(dim, index, source, 'mean', include_self=False) + + Args: + zeros: Pre-allocated zero tensor, shape (..., I, C). Will not be modified in-place. + dim: Dimension to scatter along. Must not be the last dimension. + index: 1D index tensor of shape (N,) mapping source positions to output positions. + source: Source tensor where size at `dim` equals N. + + Returns: + Tensor of same shape as zeros. + """ + if zeros.device.type != "mps": + return zeros.index_reduce(dim, index, source, "mean", include_self=False) + + ndim = source.dim() + if dim < 0: + dim = ndim + dim + + # Expand 1D index (N,) to match source shape (..., N, C) + shape = [1] * ndim + shape[dim] = index.shape[0] + idx = index.view(shape).expand_as(source) + + # Sum source values into output positions + result = zeros.scatter_add(dim, idx, source) + + # Count how many source values land in each output position. + # Take a single slice along the last dim to avoid allocating a full (N, C) ones tensor. + idx_count = idx[..., :1] # (..., N, 1) + ones = torch.ones_like(source[..., :1]) # (..., N, 1) + count = torch.zeros(*zeros.shape[:-1], 1, device=zeros.device, dtype=zeros.dtype) + count = count.scatter_add(dim, idx_count, ones) # (..., I, 1) + + return result / count.clamp(min=1) + + +def device_of(obj: Any) -> torch.device: + """Get the device of a PyTorch object, e.g. a `nn.Module` or a `Tensor`.""" + if hasattr(obj, "device"): + return obj.device + elif hasattr(obj, "parameters"): + return next(obj.parameters()).device + else: + raise ValueError(f"Unsupported type: {type(obj)}") + + +class Timer: + """ + A simple timer class for measuring elapsed time. + + This class provides functionality to start, stop, reset, and measure elapsed time. + It can optionally use CUDA or MPS synchronization barriers for more accurate timing + when working with GPU operations. + + Attributes: + name_ (str): The name of the timer. + elapsed_ (float): The total elapsed time. + started_ (bool): Flag indicating if the timer is currently running. + start_time (float): The start time of the current timing session. + use_barrier (bool): Whether to use CUDA or MPS synchronization barriers. + + Args: + name (str): The name of the timer. + use_barrier (bool, optional): Whether to use synchronization barriers. Defaults to True. + """ + + def __init__(self, name: str, use_barrier: bool = True): + self.name_ = name + self.elapsed_ = 0.0 + self.started_ = False + self.start_time = time.time() + self.use_barrier = use_barrier + + def start(self) -> None: + """Start the timer.""" + assert not self.started_, f"timer {self.name_} has already been started" + if self.use_barrier and torch.cuda.is_available(): + torch.cuda.synchronize() + elif self.use_barrier and torch.backends.mps.is_available(): + torch.mps.synchronize() + self.start_time = time.time() + self.started_ = True + + def stop(self) -> None: + """Stop the timer.""" + assert self.started_, f"timer {self.name_} is not started" + if self.use_barrier and torch.cuda.is_available(): + torch.cuda.synchronize() + elif self.use_barrier and torch.backends.mps.is_available(): + torch.mps.synchronize() + self.elapsed_ += time.time() - self.start_time + self.started_ = False + + def reset(self) -> None: + """Reset timer.""" + self.elapsed_ = 0.0 + self.started_ = False + + def elapsed(self, reset: bool = True) -> float: + """Calculate the elapsed time.""" + started_ = self.started_ + # If the timing in progress, end it first. + if self.started_: + self.stop() + # Get the elapsed time. + elapsed_ = self.elapsed_ + # Reset the elapsed time + if reset: + self.reset() + # If timing was in progress, set it back. + if started_: + self.start() + return elapsed_ + + +class Timers: + """ + A collection of named Timer objects. + + This class manages multiple Timer instances, allowing for easy creation, + starting, stopping, resetting, and querying of elapsed times for multiple timers. + + Attributes: + timers (dict): A dictionary of Timer objects, keyed by their names. + """ + + def __init__(self) -> None: + self.timers: dict[str, Timer] = {} + + def __call__(self, name: str, use_barrier: bool = True) -> Timer: + """Get or create a Timer object.""" + if name not in self.timers: + self.timers[name] = Timer(name, use_barrier=use_barrier) + return self.timers[name] + + def start(self, *names: str) -> None: + """Start the specified timers.""" + for name in names: + self(name).start() + + def stop(self, *names: str) -> None: + """Stop the specified timers.""" + for name in names: + self.timers[name].stop() + + def reset(self, *names: str) -> None: + """Reset the specified timers.""" + for name in names: + self.timers[name].reset() + + def elapsed(self, *names: str, reset: bool = True) -> dict[str, float]: + """Get the elapsed time for the specified timers.""" + return {name: self.timers[name].elapsed(reset=reset) for name in names} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/utils/xpu/__init__.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/utils/xpu/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9b94fc141b1a511887030b17667c3d20b58cc634 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/utils/xpu/__init__.py @@ -0,0 +1,27 @@ +"""XPU utilities for Intel GPU support. + +XPU support in PyTorch is now native (torch.xpu.is_available()), but Lightning Fabric +requires custom Accelerator, Strategy, and Precision plugins for proper XPU handling. + +These components are used directly (not via registry) when XPU is detected: +- XPUAccelerator: Custom accelerator for XPU devices +- SingleXPUStrategy: Strategy for single-device XPU training/inference +- XPUMixedPrecision: Precision plugin with proper XPU autocast support + +Usage: + from foundry.utils.xpu import XPUAccelerator, SingleXPUStrategy, XPUMixedPrecision + + # Check availability + if XPUAccelerator.is_available(): + strategy = SingleXPUStrategy(precision_plugin=XPUMixedPrecision("bf16-mixed")) + +Note: + The FabricTrainer automatically uses these components when XPU is detected. + You typically don't need to use them directly unless customizing behavior. +""" + +from .single_xpu_strategy import SingleXPUStrategy +from .xpu_accelerator import XPUAccelerator +from .xpu_precision import XPUMixedPrecision + +__all__ = ["SingleXPUStrategy", "XPUAccelerator", "XPUMixedPrecision"] diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/utils/xpu/single_xpu_strategy.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/utils/xpu/single_xpu_strategy.py new file mode 100644 index 0000000000000000000000000000000000000000..7a518babfa478940dbba3c72db4b7231ca241b60 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/utils/xpu/single_xpu_strategy.py @@ -0,0 +1,47 @@ +"""Lightning Fabric strategy for single XPU device. + +https://docs.pytorch.org/docs/stable/notes/get_start_xpu.html +""" + +import torch +from lightning.fabric.plugins import CheckpointIO +from lightning.fabric.plugins.precision import Precision +from lightning.fabric.strategies import SingleDeviceStrategy +from lightning.fabric.utilities.types import _DEVICE + + +class SingleXPUStrategy(SingleDeviceStrategy): + """Strategy for training/inference on a single Intel XPU device. + + This strategy extends SingleDeviceStrategy to properly handle XPU devices. + """ + + strategy_name = "xpu_single" + + def __init__( + self, + device: _DEVICE = "xpu:0", + checkpoint_io: CheckpointIO | None = None, + precision_plugin: Precision | None = None, + ) -> None: + """Initialize the single XPU strategy. + + Args: + device: The XPU device to use. Defaults to "xpu:0". + checkpoint_io: Plugin for checkpoint I/O. + precision_plugin: Plugin for precision handling (set via _precision property). + + Raises: + RuntimeError: If XPU devices are not available. + """ + if not (hasattr(torch, "xpu") and torch.xpu.is_available()): + msg = "`SingleXPUStrategy` requires XPU devices to run" + raise RuntimeError(msg) + + super().__init__( + device=device, + checkpoint_io=checkpoint_io, + ) + # Precision is handled via the _precision property in newer Lightning versions + if precision_plugin is not None: + self._precision = precision_plugin diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/utils/xpu/xpu_accelerator.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/utils/xpu/xpu_accelerator.py new file mode 100644 index 0000000000000000000000000000000000000000..26aaee0c2e0e489f048438afbb3952be78cb2794 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/utils/xpu/xpu_accelerator.py @@ -0,0 +1,91 @@ +"""XPU Accelerator for Intel XPU devices.""" + +from typing import Any + +import torch +from lightning.fabric.accelerators import Accelerator + + +class XPUAccelerator(Accelerator): + """Accelerator for Intel XPU devices. + + This accelerator enables training and inference on Intel GPUs using + PyTorch's native XPU support (torch.xpu). + """ + + @staticmethod + def name() -> str: + """Return the name of this accelerator.""" + return "xpu" + + @staticmethod + def setup_device(device: torch.device) -> None: + """Set up the specified XPU device. + + Args: + device: The torch device to set up. + + Raises: + RuntimeError: If device is not an XPU device. + """ + if device.type != "xpu": + msg = f"Device should be xpu, got {device} instead" + raise RuntimeError(msg) + + torch.xpu.set_device(device) + + @staticmethod + def parse_devices(devices: str | list | torch.device) -> list: + """Parse devices specification for XPU training. + + Args: + devices: Device specification (int, list of ints, or string). + + Returns: + List of device indices. + """ + if isinstance(devices, list): + return devices + return [devices] + + @staticmethod + def get_parallel_devices(devices: list) -> list[torch.device]: + """Generate a list of parallel XPU devices. + + Args: + devices: List of device indices. + + Returns: + List of torch.device objects for XPU. + """ + return [torch.device("xpu", idx) for idx in devices] + + @staticmethod + def auto_device_count() -> int: + """Return the number of available XPU devices.""" + return torch.xpu.device_count() + + @staticmethod + def is_available() -> bool: + """Check if XPU is available.""" + return hasattr(torch, "xpu") and torch.xpu.is_available() + + @staticmethod + def get_device_stats(device: str | torch.device) -> dict[str, Any]: + """Return XPU device statistics. + + Currently returns an empty dict as XPU stats API may vary. + + Args: + device: The device to get stats for. + + Returns: + Dictionary of device statistics. + """ + del device # Unused + return {} + + def teardown(self) -> None: + """Clean up XPU accelerator resources.""" + # Empty implementation required by base class + pass diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/utils/xpu/xpu_precision.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/utils/xpu/xpu_precision.py new file mode 100644 index 0000000000000000000000000000000000000000..22b3357d2f1df5de2b4312a05660287932e73778 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/foundry/utils/xpu/xpu_precision.py @@ -0,0 +1,72 @@ +"""XPU Precision Plugin for Lightning Fabric.""" + +from contextlib import contextmanager +from typing import Any, Generator, Literal + +import torch +from lightning.fabric.plugins.precision import MixedPrecision +from torch import Tensor + + +class XPUMixedPrecision(MixedPrecision): + """Mixed precision plugin for Intel XPU devices. + + This overrides the default MixedPrecision plugin to use 'xpu' as the + device type for torch.autocast instead of 'cuda'. + """ + + def __init__( + self, + precision: Literal["16-mixed", "bf16-mixed"] = "bf16-mixed", + ) -> None: + """Initialize XPU mixed precision. + + Args: + precision: The precision mode. "16-mixed" uses float16, + "bf16-mixed" uses bfloat16. Defaults to "bf16-mixed". + + Raises: + ValueError: If precision is not "16-mixed" or "bf16-mixed". + """ + # Determine dtype from precision string + if precision == "16-mixed": + dtype = torch.float16 + elif precision == "bf16-mixed": + dtype = torch.bfloat16 + else: + msg = f"Invalid precision: {precision}. Must be '16-mixed' or 'bf16-mixed'" + raise ValueError(msg) + + # Initialize with xpu device type + super().__init__(precision=precision, device="xpu") + self._desired_input_dtype = dtype + + @contextmanager + def forward_context(self) -> Generator[None, None, None]: + """Context manager for forward pass with XPU autocast.""" + with torch.autocast(device_type="xpu", dtype=self._desired_input_dtype): + yield + + def convert_input(self, data: Any) -> Any: + """Convert input data to the appropriate precision. + + Args: + data: Input data to convert. + + Returns: + Converted data. + """ + return self._convert_fp_tensor(data) + + def _convert_fp_tensor(self, data: Any) -> Any: + """Convert floating point tensors to the desired dtype. + + Args: + data: Data to convert. + + Returns: + Converted data if it's a floating point tensor, otherwise unchanged. + """ + if isinstance(data, Tensor) and data.is_floating_point(): + return data.to(self._desired_input_dtype) + return data diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/ligandmpnn/data_utils.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/ligandmpnn/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8380bd8b856ed3cd1c18683a25d53f09ce0a0e0a --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/ligandmpnn/data_utils.py @@ -0,0 +1,988 @@ +from __future__ import print_function + +import numpy as np +import torch +import torch.utils +from prody import * + +confProDy(verbosity="none") + +restype_1to3 = { + "A": "ALA", + "R": "ARG", + "N": "ASN", + "D": "ASP", + "C": "CYS", + "Q": "GLN", + "E": "GLU", + "G": "GLY", + "H": "HIS", + "I": "ILE", + "L": "LEU", + "K": "LYS", + "M": "MET", + "F": "PHE", + "P": "PRO", + "S": "SER", + "T": "THR", + "W": "TRP", + "Y": "TYR", + "V": "VAL", + "X": "UNK", +} +restype_str_to_int = { + "A": 0, + "C": 1, + "D": 2, + "E": 3, + "F": 4, + "G": 5, + "H": 6, + "I": 7, + "K": 8, + "L": 9, + "M": 10, + "N": 11, + "P": 12, + "Q": 13, + "R": 14, + "S": 15, + "T": 16, + "V": 17, + "W": 18, + "Y": 19, + "X": 20, +} +restype_int_to_str = { + 0: "A", + 1: "C", + 2: "D", + 3: "E", + 4: "F", + 5: "G", + 6: "H", + 7: "I", + 8: "K", + 9: "L", + 10: "M", + 11: "N", + 12: "P", + 13: "Q", + 14: "R", + 15: "S", + 16: "T", + 17: "V", + 18: "W", + 19: "Y", + 20: "X", +} +alphabet = list(restype_str_to_int) + +element_list = [ + "H", + "He", + "Li", + "Be", + "B", + "C", + "N", + "O", + "F", + "Ne", + "Na", + "Mg", + "Al", + "Si", + "P", + "S", + "Cl", + "Ar", + "K", + "Ca", + "Sc", + "Ti", + "V", + "Cr", + "Mn", + "Fe", + "Co", + "Ni", + "Cu", + "Zn", + "Ga", + "Ge", + "As", + "Se", + "Br", + "Kr", + "Rb", + "Sr", + "Y", + "Zr", + "Nb", + "Mb", + "Tc", + "Ru", + "Rh", + "Pd", + "Ag", + "Cd", + "In", + "Sn", + "Sb", + "Te", + "I", + "Xe", + "Cs", + "Ba", + "La", + "Ce", + "Pr", + "Nd", + "Pm", + "Sm", + "Eu", + "Gd", + "Tb", + "Dy", + "Ho", + "Er", + "Tm", + "Yb", + "Lu", + "Hf", + "Ta", + "W", + "Re", + "Os", + "Ir", + "Pt", + "Au", + "Hg", + "Tl", + "Pb", + "Bi", + "Po", + "At", + "Rn", + "Fr", + "Ra", + "Ac", + "Th", + "Pa", + "U", + "Np", + "Pu", + "Am", + "Cm", + "Bk", + "Cf", + "Es", + "Fm", + "Md", + "No", + "Lr", + "Rf", + "Db", + "Sg", + "Bh", + "Hs", + "Mt", + "Ds", + "Rg", + "Cn", + "Uut", + "Fl", + "Uup", + "Lv", + "Uus", + "Uuo", +] +element_list = [item.upper() for item in element_list] +# element_dict = dict(zip(element_list, range(1,len(element_list)))) +element_dict_rev = dict(zip(range(1, len(element_list)), element_list)) + + +def get_seq_rec(S: torch.Tensor, S_pred: torch.Tensor, mask: torch.Tensor): + """ + S : true sequence shape=[batch, length] + S_pred : predicted sequence shape=[batch, length] + mask : mask to compute average over the region shape=[batch, length] + + average : averaged sequence recovery shape=[batch] + """ + match = S == S_pred + average = torch.sum(match * mask, dim=-1) / torch.sum(mask, dim=-1) + return average + + +def get_score(S: torch.Tensor, log_probs: torch.Tensor, mask: torch.Tensor): + """ + S : true sequence shape=[batch, length] + log_probs : predicted sequence shape=[batch, length] + mask : mask to compute average over the region shape=[batch, length] + + average_loss : averaged categorical cross entropy (CCE) [batch] + loss_per_resdue : per position CCE [batch, length] + """ + S_one_hot = torch.nn.functional.one_hot(S, 21) + loss_per_residue = -(S_one_hot * log_probs).sum(-1) # [B, L] + average_loss = torch.sum(loss_per_residue * mask, dim=-1) / ( + torch.sum(mask, dim=-1) + 1e-8 + ) + return average_loss, loss_per_residue + + +def write_full_PDB( + save_path: str, + X: np.ndarray, + X_m: np.ndarray, + b_factors: np.ndarray, + R_idx: np.ndarray, + chain_letters: np.ndarray, + S: np.ndarray, + other_atoms=None, + icodes=None, + force_hetatm=False, +): + """ + save_path : path where the PDB will be written to + X : protein atom xyz coordinates shape=[length, 14, 3] + X_m : protein atom mask shape=[length, 14] + b_factors: shape=[length, 14] + R_idx: protein residue indices shape=[length] + chain_letters: protein chain letters shape=[length] + S : protein amino acid sequence shape=[length] + other_atoms: other atoms parsed by prody + icodes: a list of insertion codes for the PDB; e.g. antibody loops + """ + + restype_1to3 = { + "A": "ALA", + "R": "ARG", + "N": "ASN", + "D": "ASP", + "C": "CYS", + "Q": "GLN", + "E": "GLU", + "G": "GLY", + "H": "HIS", + "I": "ILE", + "L": "LEU", + "K": "LYS", + "M": "MET", + "F": "PHE", + "P": "PRO", + "S": "SER", + "T": "THR", + "W": "TRP", + "Y": "TYR", + "V": "VAL", + "X": "UNK", + } + restype_INTtoSTR = { + 0: "A", + 1: "C", + 2: "D", + 3: "E", + 4: "F", + 5: "G", + 6: "H", + 7: "I", + 8: "K", + 9: "L", + 10: "M", + 11: "N", + 12: "P", + 13: "Q", + 14: "R", + 15: "S", + 16: "T", + 17: "V", + 18: "W", + 19: "Y", + 20: "X", + } + restype_name_to_atom14_names = { + "ALA": ["N", "CA", "C", "O", "CB", "", "", "", "", "", "", "", "", ""], + "ARG": [ + "N", + "CA", + "C", + "O", + "CB", + "CG", + "CD", + "NE", + "CZ", + "NH1", + "NH2", + "", + "", + "", + ], + "ASN": ["N", "CA", "C", "O", "CB", "CG", "OD1", "ND2", "", "", "", "", "", ""], + "ASP": ["N", "CA", "C", "O", "CB", "CG", "OD1", "OD2", "", "", "", "", "", ""], + "CYS": ["N", "CA", "C", "O", "CB", "SG", "", "", "", "", "", "", "", ""], + "GLN": [ + "N", + "CA", + "C", + "O", + "CB", + "CG", + "CD", + "OE1", + "NE2", + "", + "", + "", + "", + "", + ], + "GLU": [ + "N", + "CA", + "C", + "O", + "CB", + "CG", + "CD", + "OE1", + "OE2", + "", + "", + "", + "", + "", + ], + "GLY": ["N", "CA", "C", "O", "", "", "", "", "", "", "", "", "", ""], + "HIS": [ + "N", + "CA", + "C", + "O", + "CB", + "CG", + "ND1", + "CD2", + "CE1", + "NE2", + "", + "", + "", + "", + ], + "ILE": ["N", "CA", "C", "O", "CB", "CG1", "CG2", "CD1", "", "", "", "", "", ""], + "LEU": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "", "", "", "", "", ""], + "LYS": ["N", "CA", "C", "O", "CB", "CG", "CD", "CE", "NZ", "", "", "", "", ""], + "MET": ["N", "CA", "C", "O", "CB", "CG", "SD", "CE", "", "", "", "", "", ""], + "PHE": [ + "N", + "CA", + "C", + "O", + "CB", + "CG", + "CD1", + "CD2", + "CE1", + "CE2", + "CZ", + "", + "", + "", + ], + "PRO": ["N", "CA", "C", "O", "CB", "CG", "CD", "", "", "", "", "", "", ""], + "SER": ["N", "CA", "C", "O", "CB", "OG", "", "", "", "", "", "", "", ""], + "THR": ["N", "CA", "C", "O", "CB", "OG1", "CG2", "", "", "", "", "", "", ""], + "TRP": [ + "N", + "CA", + "C", + "O", + "CB", + "CG", + "CD1", + "CD2", + "CE2", + "CE3", + "NE1", + "CZ2", + "CZ3", + "CH2", + ], + "TYR": [ + "N", + "CA", + "C", + "O", + "CB", + "CG", + "CD1", + "CD2", + "CE1", + "CE2", + "CZ", + "OH", + "", + "", + ], + "VAL": ["N", "CA", "C", "O", "CB", "CG1", "CG2", "", "", "", "", "", "", ""], + "UNK": ["", "", "", "", "", "", "", "", "", "", "", "", "", ""], + } + + S_str = [restype_1to3[AA] for AA in [restype_INTtoSTR[AA] for AA in S]] + + X_list = [] + b_factor_list = [] + atom_name_list = [] + element_name_list = [] + residue_name_list = [] + residue_number_list = [] + chain_id_list = [] + icodes_list = [] + for i, AA in enumerate(S_str): + sel = X_m[i].astype(np.int32) == 1 + total = np.sum(sel) + tmp = np.array(restype_name_to_atom14_names[AA])[sel] + X_list.append(X[i][sel]) + b_factor_list.append(b_factors[i][sel]) + atom_name_list.append(tmp) + element_name_list += [AA[:1] for AA in list(tmp)] + residue_name_list += total * [AA] + residue_number_list += total * [R_idx[i]] + chain_id_list += total * [chain_letters[i]] + icodes_list += total * [icodes[i]] + + X_stack = np.concatenate(X_list, 0) + b_factor_stack = np.concatenate(b_factor_list, 0) + atom_name_stack = np.concatenate(atom_name_list, 0) + + protein = prody.AtomGroup() + protein.setCoords(X_stack) + protein.setBetas(b_factor_stack) + protein.setNames(atom_name_stack) + protein.setResnames(residue_name_list) + protein.setElements(element_name_list) + protein.setOccupancies(np.ones([X_stack.shape[0]])) + protein.setResnums(residue_number_list) + protein.setChids(chain_id_list) + protein.setIcodes(icodes_list) + + if other_atoms: + other_atoms_g = prody.AtomGroup() + other_atoms_g.setCoords(other_atoms.getCoords()) + other_atoms_g.setNames(other_atoms.getNames()) + other_atoms_g.setResnames(other_atoms.getResnames()) + other_atoms_g.setElements(other_atoms.getElements()) + other_atoms_g.setOccupancies(other_atoms.getOccupancies()) + other_atoms_g.setResnums(other_atoms.getResnums()) + other_atoms_g.setChids(other_atoms.getChids()) + if force_hetatm: + other_atoms_g.setFlags("hetatm", other_atoms.getFlags("hetatm")) + writePDB(save_path, protein + other_atoms_g) + else: + writePDB(save_path, protein) + + +def get_aligned_coordinates(protein_atoms, CA_dict: dict, atom_name: str): + """ + protein_atoms: prody atom group + CA_dict: mapping between chain_residue_idx_icodes and integers + atom_name: atom to be parsed; e.g. CA + """ + atom_atoms = protein_atoms.select(f"name {atom_name}") + + if atom_atoms != None: + atom_coords = atom_atoms.getCoords() + atom_resnums = atom_atoms.getResnums() + atom_chain_ids = atom_atoms.getChids() + atom_icodes = atom_atoms.getIcodes() + + atom_coords_ = np.zeros([len(CA_dict), 3], np.float32) + atom_coords_m = np.zeros([len(CA_dict)], np.int32) + if atom_atoms != None: + for i in range(len(atom_resnums)): + code = atom_chain_ids[i] + "_" + str(atom_resnums[i]) + "_" + atom_icodes[i] + if code in list(CA_dict): + atom_coords_[CA_dict[code], :] = atom_coords[i] + atom_coords_m[CA_dict[code]] = 1 + return atom_coords_, atom_coords_m + + +def parse_PDB( + input_path: str, + device: str = "cpu", + chains: list = [], + parse_all_atoms: bool = False, + parse_atoms_with_zero_occupancy: bool = False +): + """ + input_path : path for the input PDB + device: device for the torch.Tensor + chains: a list specifying which chains need to be parsed; e.g. ["A", "B"] + parse_all_atoms: if False parse only N,CA,C,O otherwise all 37 atoms + parse_atoms_with_zero_occupancy: if True atoms with zero occupancy will be parsed + """ + element_list = [ + "H", + "He", + "Li", + "Be", + "B", + "C", + "N", + "O", + "F", + "Ne", + "Na", + "Mg", + "Al", + "Si", + "P", + "S", + "Cl", + "Ar", + "K", + "Ca", + "Sc", + "Ti", + "V", + "Cr", + "Mn", + "Fe", + "Co", + "Ni", + "Cu", + "Zn", + "Ga", + "Ge", + "As", + "Se", + "Br", + "Kr", + "Rb", + "Sr", + "Y", + "Zr", + "Nb", + "Mb", + "Tc", + "Ru", + "Rh", + "Pd", + "Ag", + "Cd", + "In", + "Sn", + "Sb", + "Te", + "I", + "Xe", + "Cs", + "Ba", + "La", + "Ce", + "Pr", + "Nd", + "Pm", + "Sm", + "Eu", + "Gd", + "Tb", + "Dy", + "Ho", + "Er", + "Tm", + "Yb", + "Lu", + "Hf", + "Ta", + "W", + "Re", + "Os", + "Ir", + "Pt", + "Au", + "Hg", + "Tl", + "Pb", + "Bi", + "Po", + "At", + "Rn", + "Fr", + "Ra", + "Ac", + "Th", + "Pa", + "U", + "Np", + "Pu", + "Am", + "Cm", + "Bk", + "Cf", + "Es", + "Fm", + "Md", + "No", + "Lr", + "Rf", + "Db", + "Sg", + "Bh", + "Hs", + "Mt", + "Ds", + "Rg", + "Cn", + "Uut", + "Fl", + "Uup", + "Lv", + "Uus", + "Uuo", + ] + element_list = [item.upper() for item in element_list] + element_dict = dict(zip(element_list, range(1, len(element_list)))) + restype_3to1 = { + "ALA": "A", + "ARG": "R", + "ASN": "N", + "ASP": "D", + "CYS": "C", + "GLN": "Q", + "GLU": "E", + "GLY": "G", + "HIS": "H", + "ILE": "I", + "LEU": "L", + "LYS": "K", + "MET": "M", + "PHE": "F", + "PRO": "P", + "SER": "S", + "THR": "T", + "TRP": "W", + "TYR": "Y", + "VAL": "V", + } + restype_STRtoINT = { + "A": 0, + "C": 1, + "D": 2, + "E": 3, + "F": 4, + "G": 5, + "H": 6, + "I": 7, + "K": 8, + "L": 9, + "M": 10, + "N": 11, + "P": 12, + "Q": 13, + "R": 14, + "S": 15, + "T": 16, + "V": 17, + "W": 18, + "Y": 19, + "X": 20, + } + + atom_order = { + "N": 0, + "CA": 1, + "C": 2, + "CB": 3, + "O": 4, + "CG": 5, + "CG1": 6, + "CG2": 7, + "OG": 8, + "OG1": 9, + "SG": 10, + "CD": 11, + "CD1": 12, + "CD2": 13, + "ND1": 14, + "ND2": 15, + "OD1": 16, + "OD2": 17, + "SD": 18, + "CE": 19, + "CE1": 20, + "CE2": 21, + "CE3": 22, + "NE": 23, + "NE1": 24, + "NE2": 25, + "OE1": 26, + "OE2": 27, + "CH2": 28, + "NH1": 29, + "NH2": 30, + "OH": 31, + "CZ": 32, + "CZ2": 33, + "CZ3": 34, + "NZ": 35, + "OXT": 36, + } + + if not parse_all_atoms: + atom_types = ["N", "CA", "C", "O"] + else: + atom_types = [ + "N", + "CA", + "C", + "CB", + "O", + "CG", + "CG1", + "CG2", + "OG", + "OG1", + "SG", + "CD", + "CD1", + "CD2", + "ND1", + "ND2", + "OD1", + "OD2", + "SD", + "CE", + "CE1", + "CE2", + "CE3", + "NE", + "NE1", + "NE2", + "OE1", + "OE2", + "CH2", + "NH1", + "NH2", + "OH", + "CZ", + "CZ2", + "CZ3", + "NZ", + ] + + atoms = parsePDB(input_path) + if not parse_atoms_with_zero_occupancy: + atoms = atoms.select("occupancy > 0") + if chains: + str_out = "" + for item in chains: + str_out += " chain " + item + " or" + atoms = atoms.select(str_out[1:-3]) + + protein_atoms = atoms.select("protein") + backbone = protein_atoms.select("backbone") + other_atoms = atoms.select("not protein and not water") + water_atoms = atoms.select("water") + + CA_atoms = protein_atoms.select("name CA") + CA_resnums = CA_atoms.getResnums() + CA_chain_ids = CA_atoms.getChids() + CA_icodes = CA_atoms.getIcodes() + + CA_dict = {} + for i in range(len(CA_resnums)): + code = CA_chain_ids[i] + "_" + str(CA_resnums[i]) + "_" + CA_icodes[i] + CA_dict[code] = i + + xyz_37 = np.zeros([len(CA_dict), 37, 3], np.float32) + xyz_37_m = np.zeros([len(CA_dict), 37], np.int32) + for atom_name in atom_types: + xyz, xyz_m = get_aligned_coordinates(protein_atoms, CA_dict, atom_name) + xyz_37[:, atom_order[atom_name], :] = xyz + xyz_37_m[:, atom_order[atom_name]] = xyz_m + + N = xyz_37[:, atom_order["N"], :] + CA = xyz_37[:, atom_order["CA"], :] + C = xyz_37[:, atom_order["C"], :] + O = xyz_37[:, atom_order["O"], :] + + N_m = xyz_37_m[:, atom_order["N"]] + CA_m = xyz_37_m[:, atom_order["CA"]] + C_m = xyz_37_m[:, atom_order["C"]] + O_m = xyz_37_m[:, atom_order["O"]] + + mask = N_m * CA_m * C_m * O_m # must all 4 atoms exist + + b = CA - N + c = C - CA + a = np.cross(b, c, axis=-1) + CB = -0.58273431 * a + 0.56802827 * b - 0.54067466 * c + CA + + chain_labels = np.array(CA_atoms.getChindices(), dtype=np.int32) + R_idx = np.array(CA_resnums, dtype=np.int32) + S = CA_atoms.getResnames() + S = [restype_3to1[AA] if AA in list(restype_3to1) else "X" for AA in list(S)] + S = np.array([restype_STRtoINT[AA] for AA in list(S)], np.int32) + X = np.concatenate([N[:, None], CA[:, None], C[:, None], O[:, None]], 1) + + try: + Y = np.array(other_atoms.getCoords(), dtype=np.float32) + Y_t = list(other_atoms.getElements()) + Y_t = np.array( + [ + element_dict[y_t.upper()] if y_t.upper() in element_list else 0 + for y_t in Y_t + ], + dtype=np.int32, + ) + Y_m = (Y_t != 1) * (Y_t != 0) + + Y = Y[Y_m, :] + Y_t = Y_t[Y_m] + Y_m = Y_m[Y_m] + except: + Y = np.zeros([1, 3], np.float32) + Y_t = np.zeros([1], np.int32) + Y_m = np.zeros([1], np.int32) + + output_dict = {} + output_dict["X"] = torch.tensor(X, device=device, dtype=torch.float32) + output_dict["mask"] = torch.tensor(mask, device=device, dtype=torch.int32) + output_dict["Y"] = torch.tensor(Y, device=device, dtype=torch.float32) + output_dict["Y_t"] = torch.tensor(Y_t, device=device, dtype=torch.int32) + output_dict["Y_m"] = torch.tensor(Y_m, device=device, dtype=torch.int32) + + output_dict["R_idx"] = torch.tensor(R_idx, device=device, dtype=torch.int32) + output_dict["chain_labels"] = torch.tensor( + chain_labels, device=device, dtype=torch.int32 + ) + + output_dict["chain_letters"] = CA_chain_ids + + mask_c = [] + chain_list = list(set(output_dict["chain_letters"])) + chain_list.sort() + for chain in chain_list: + mask_c.append( + torch.tensor( + [chain == item for item in output_dict["chain_letters"]], + device=device, + dtype=bool, + ) + ) + + output_dict["mask_c"] = mask_c + output_dict["chain_list"] = chain_list + + output_dict["S"] = torch.tensor(S, device=device, dtype=torch.int32) + + output_dict["xyz_37"] = torch.tensor(xyz_37, device=device, dtype=torch.float32) + output_dict["xyz_37_m"] = torch.tensor(xyz_37_m, device=device, dtype=torch.int32) + + return output_dict, backbone, other_atoms, CA_icodes, water_atoms + + +def get_nearest_neighbours(CB, mask, Y, Y_t, Y_m, number_of_ligand_atoms): + device = CB.device + mask_CBY = mask[:, None] * Y_m[None, :] # [A,B] + L2_AB = torch.sum((CB[:, None, :] - Y[None, :, :]) ** 2, -1) + L2_AB = L2_AB * mask_CBY + (1 - mask_CBY) * 1000.0 + + nn_idx = torch.argsort(L2_AB, -1)[:, :number_of_ligand_atoms] + L2_AB_nn = torch.gather(L2_AB, 1, nn_idx) + D_AB_closest = torch.sqrt(L2_AB_nn[:, 0]) + + Y_r = Y[None, :, :].repeat(CB.shape[0], 1, 1) + Y_t_r = Y_t[None, :].repeat(CB.shape[0], 1) + Y_m_r = Y_m[None, :].repeat(CB.shape[0], 1) + + Y_tmp = torch.gather(Y_r, 1, nn_idx[:, :, None].repeat(1, 1, 3)) + Y_t_tmp = torch.gather(Y_t_r, 1, nn_idx) + Y_m_tmp = torch.gather(Y_m_r, 1, nn_idx) + + Y = torch.zeros( + [CB.shape[0], number_of_ligand_atoms, 3], dtype=torch.float32, device=device + ) + Y_t = torch.zeros( + [CB.shape[0], number_of_ligand_atoms], dtype=torch.int32, device=device + ) + Y_m = torch.zeros( + [CB.shape[0], number_of_ligand_atoms], dtype=torch.int32, device=device + ) + + num_nn_update = Y_tmp.shape[1] + Y[:, :num_nn_update] = Y_tmp + Y_t[:, :num_nn_update] = Y_t_tmp + Y_m[:, :num_nn_update] = Y_m_tmp + + return Y, Y_t, Y_m, D_AB_closest + + +def featurize( + input_dict, + cutoff_for_score=8.0, + use_atom_context=True, + number_of_ligand_atoms=16, + model_type="protein_mpnn", +): + output_dict = {} + if model_type == "ligand_mpnn": + mask = input_dict["mask"] + Y = input_dict["Y"] + Y_t = input_dict["Y_t"] + Y_m = input_dict["Y_m"] + N = input_dict["X"][:, 0, :] + CA = input_dict["X"][:, 1, :] + C = input_dict["X"][:, 2, :] + b = CA - N + c = C - CA + a = torch.cross(b, c, axis=-1) + CB = -0.58273431 * a + 0.56802827 * b - 0.54067466 * c + CA + Y, Y_t, Y_m, D_XY = get_nearest_neighbours( + CB, mask, Y, Y_t, Y_m, number_of_ligand_atoms + ) + mask_XY = (D_XY < cutoff_for_score) * mask * Y_m[:, 0] + output_dict["mask_XY"] = mask_XY[None,] + if "side_chain_mask" in list(input_dict): + output_dict["side_chain_mask"] = input_dict["side_chain_mask"][None,] + output_dict["Y"] = Y[None,] + output_dict["Y_t"] = Y_t[None,] + output_dict["Y_m"] = Y_m[None,] + if not use_atom_context: + output_dict["Y_m"] = 0.0 * output_dict["Y_m"] + elif ( + model_type == "per_residue_label_membrane_mpnn" + or model_type == "global_label_membrane_mpnn" + ): + output_dict["membrane_per_residue_labels"] = input_dict[ + "membrane_per_residue_labels" + ][None,] + + R_idx_list = [] + count = 0 + R_idx_prev = -100000 + for R_idx in list(input_dict["R_idx"]): + if R_idx_prev == R_idx: + count += 1 + R_idx_list.append(R_idx + count) + R_idx_prev = R_idx + R_idx_renumbered = torch.tensor(R_idx_list, device=R_idx.device) + output_dict["R_idx"] = R_idx_renumbered[None,] + output_dict["R_idx_original"] = input_dict["R_idx"][None,] + output_dict["chain_labels"] = input_dict["chain_labels"][None,] + output_dict["S"] = input_dict["S"][None,] + output_dict["chain_mask"] = input_dict["chain_mask"][None,] + output_dict["mask"] = input_dict["mask"][None,] + + output_dict["X"] = input_dict["X"][None,] + + if "xyz_37" in list(input_dict): + output_dict["xyz_37"] = input_dict["xyz_37"][None,] + output_dict["xyz_37_m"] = input_dict["xyz_37_m"][None,] + + return output_dict diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/ligandmpnn/model_utils.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/ligandmpnn/model_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7cc9759bf435ea87d04d13e962d96164c6165825 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/ligandmpnn/model_utils.py @@ -0,0 +1,1772 @@ +from __future__ import print_function + +import itertools +import sys + +import numpy as np +import torch + + +class ProteinMPNN(torch.nn.Module): + def __init__( + self, + num_letters=21, + node_features=128, + edge_features=128, + hidden_dim=128, + num_encoder_layers=3, + num_decoder_layers=3, + vocab=21, + k_neighbors=48, + augment_eps=0.0, + dropout=0.0, + device=None, + atom_context_num=0, + model_type="protein_mpnn", + ligand_mpnn_use_side_chain_context=False, + ): + super(ProteinMPNN, self).__init__() + + self.model_type = model_type + self.node_features = node_features + self.edge_features = edge_features + self.hidden_dim = hidden_dim + + if self.model_type == "ligand_mpnn": + self.features = ProteinFeaturesLigand( + node_features, + edge_features, + top_k=k_neighbors, + augment_eps=augment_eps, + device=device, + atom_context_num=atom_context_num, + use_side_chains=ligand_mpnn_use_side_chain_context, + ) + self.W_v = torch.nn.Linear(node_features, hidden_dim, bias=True) + self.W_c = torch.nn.Linear(hidden_dim, hidden_dim, bias=True) + + self.W_nodes_y = torch.nn.Linear(hidden_dim, hidden_dim, bias=True) + self.W_edges_y = torch.nn.Linear(hidden_dim, hidden_dim, bias=True) + + self.V_C = torch.nn.Linear(hidden_dim, hidden_dim, bias=False) + self.V_C_norm = torch.nn.LayerNorm(hidden_dim) + + self.context_encoder_layers = torch.nn.ModuleList( + [ + DecLayer(hidden_dim, hidden_dim * 2, dropout=dropout) + for _ in range(2) + ] + ) + + self.y_context_encoder_layers = torch.nn.ModuleList( + [DecLayerJ(hidden_dim, hidden_dim, dropout=dropout) for _ in range(2)] + ) + elif self.model_type == "protein_mpnn" or self.model_type == "soluble_mpnn": + self.features = ProteinFeatures( + node_features, edge_features, top_k=k_neighbors, augment_eps=augment_eps + ) + elif ( + self.model_type == "per_residue_label_membrane_mpnn" + or self.model_type == "global_label_membrane_mpnn" + ): + self.W_v = torch.nn.Linear(node_features, hidden_dim, bias=True) + self.features = ProteinFeaturesMembrane( + node_features, + edge_features, + top_k=k_neighbors, + augment_eps=augment_eps, + num_classes=3, + ) + else: + print("Choose --model_type flag from currently available models") + sys.exit() + + self.W_e = torch.nn.Linear(edge_features, hidden_dim, bias=True) + self.W_s = torch.nn.Embedding(vocab, hidden_dim) + + self.dropout = torch.nn.Dropout(dropout) + + # Encoder layers + self.encoder_layers = torch.nn.ModuleList( + [ + EncLayer(hidden_dim, hidden_dim * 2, dropout=dropout) + for _ in range(num_encoder_layers) + ] + ) + + # Decoder layers + self.decoder_layers = torch.nn.ModuleList( + [ + DecLayer(hidden_dim, hidden_dim * 3, dropout=dropout) + for _ in range(num_decoder_layers) + ] + ) + + self.W_out = torch.nn.Linear(hidden_dim, num_letters, bias=True) + + for p in self.parameters(): + if p.dim() > 1: + torch.nn.init.xavier_uniform_(p) + + def encode(self, feature_dict): + # xyz_37 = feature_dict["xyz_37"] #[B,L,37,3] - xyz coordinates for all atoms if needed + # xyz_37_m = feature_dict["xyz_37_m"] #[B,L,37] - mask for all coords + # Y = feature_dict["Y"] #[B,L,num_context_atoms,3] - for ligandMPNN coords + # Y_t = feature_dict["Y_t"] #[B,L,num_context_atoms] - element type + # Y_m = feature_dict["Y_m"] #[B,L,num_context_atoms] - mask + # X = feature_dict["X"] #[B,L,4,3] - backbone xyz coordinates for N,CA,C,O + S_true = feature_dict[ + "S" + ] # [B,L] - integer protein sequence encoded using "restype_STRtoINT" + # R_idx = feature_dict["R_idx"] #[B,L] - primary sequence residue index + mask = feature_dict[ + "mask" + ] # [B,L] - mask for missing regions - should be removed! all ones most of the time + # chain_labels = feature_dict["chain_labels"] #[B,L] - integer labels for chain letters + + B, L = S_true.shape + device = S_true.device + + if self.model_type == "ligand_mpnn": + V, E, E_idx, Y_nodes, Y_edges, Y_m = self.features(feature_dict) + h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=device) + h_E = self.W_e(E) + h_E_context = self.W_v(V) + + mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1) + mask_attend = mask.unsqueeze(-1) * mask_attend + for layer in self.encoder_layers: + h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend) + + h_V_C = self.W_c(h_V) + Y_m_edges = Y_m[:, :, :, None] * Y_m[:, :, None, :] + Y_nodes = self.W_nodes_y(Y_nodes) + Y_edges = self.W_edges_y(Y_edges) + for i in range(len(self.context_encoder_layers)): + Y_nodes = self.y_context_encoder_layers[i]( + Y_nodes, Y_edges, Y_m, Y_m_edges + ) + h_E_context_cat = torch.cat([h_E_context, Y_nodes], -1) + h_V_C = self.context_encoder_layers[i]( + h_V_C, h_E_context_cat, mask, Y_m + ) + + h_V_C = self.V_C(h_V_C) + h_V = h_V + self.V_C_norm(self.dropout(h_V_C)) + elif self.model_type == "protein_mpnn" or self.model_type == "soluble_mpnn": + E, E_idx = self.features(feature_dict) + h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=device) + h_E = self.W_e(E) + + mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1) + mask_attend = mask.unsqueeze(-1) * mask_attend + for layer in self.encoder_layers: + h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend) + elif ( + self.model_type == "per_residue_label_membrane_mpnn" + or self.model_type == "global_label_membrane_mpnn" + ): + V, E, E_idx = self.features(feature_dict) + h_V = self.W_v(V) + h_E = self.W_e(E) + + mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1) + mask_attend = mask.unsqueeze(-1) * mask_attend + for layer in self.encoder_layers: + h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend) + + return h_V, h_E, E_idx + + def sample(self, feature_dict): + # xyz_37 = feature_dict["xyz_37"] #[B,L,37,3] - xyz coordinates for all atoms if needed + # xyz_37_m = feature_dict["xyz_37_m"] #[B,L,37] - mask for all coords + # Y = feature_dict["Y"] #[B,L,num_context_atoms,3] - for ligandMPNN coords + # Y_t = feature_dict["Y_t"] #[B,L,num_context_atoms] - element type + # Y_m = feature_dict["Y_m"] #[B,L,num_context_atoms] - mask + # X = feature_dict["X"] #[B,L,4,3] - backbone xyz coordinates for N,CA,C,O + B_decoder = feature_dict["batch_size"] + S_true = feature_dict[ + "S" + ] # [B,L] - integer proitein sequence encoded using "restype_STRtoINT" + # R_idx = feature_dict["R_idx"] #[B,L] - primary sequence residue index + mask = feature_dict[ + "mask" + ] # [B,L] - mask for missing regions - should be removed! all ones most of the time + chain_mask = feature_dict[ + "chain_mask" + ] # [B,L] - mask for which residues need to be fixed; 0.0 - fixed; 1.0 - will be designed + bias = feature_dict["bias"] # [B,L,21] - amino acid bias per position + # chain_labels = feature_dict["chain_labels"] #[B,L] - integer labels for chain letters + randn = feature_dict[ + "randn" + ] # [B,L] - random numbers for decoding order; only the first entry is used since decoding within a batch needs to match for symmetry + temperature = feature_dict[ + "temperature" + ] # float - sampling temperature; prob = softmax(logits/temperature) + symmetry_list_of_lists = feature_dict[ + "symmetry_residues" + ] # [[0, 1, 14], [10,11,14,15], [20, 21]] #indices to select X over length - L + symmetry_weights_list_of_lists = feature_dict[ + "symmetry_weights" + ] # [[1.0, 1.0, 1.0], [-2.0,1.1,0.2,1.1], [2.3, 1.1]] + + B, L = S_true.shape + device = S_true.device + + h_V, h_E, E_idx = self.encode(feature_dict) + + chain_mask = mask * chain_mask # update chain_M to include missing regions + decoding_order = torch.argsort( + (chain_mask + 0.0001) * (torch.abs(randn)) + ) # [numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0] + if len(symmetry_list_of_lists[0]) == 0 and len(symmetry_list_of_lists) == 1: + E_idx = E_idx.repeat(B_decoder, 1, 1) + permutation_matrix_reverse = torch.nn.functional.one_hot( + decoding_order, num_classes=L + ).float() + order_mask_backward = torch.einsum( + "ij, biq, bjp->bqp", + (1 - torch.triu(torch.ones(L, L, device=device))), + permutation_matrix_reverse, + permutation_matrix_reverse, + ) + mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) + mask_1D = mask.view([B, L, 1, 1]) + mask_bw = mask_1D * mask_attend + mask_fw = mask_1D * (1.0 - mask_attend) + + # repeat for decoding + S_true = S_true.repeat(B_decoder, 1) + h_V = h_V.repeat(B_decoder, 1, 1) + h_E = h_E.repeat(B_decoder, 1, 1, 1) + chain_mask = chain_mask.repeat(B_decoder, 1) + mask = mask.repeat(B_decoder, 1) + bias = bias.repeat(B_decoder, 1, 1) + + all_probs = torch.zeros( + (B_decoder, L, 20), device=device, dtype=torch.float32 + ) + all_log_probs = torch.zeros( + (B_decoder, L, 21), device=device, dtype=torch.float32 + ) + h_S = torch.zeros_like(h_V, device=device) + S = 20 * torch.ones((B_decoder, L), dtype=torch.int64, device=device) + h_V_stack = [h_V] + [ + torch.zeros_like(h_V, device=device) + for _ in range(len(self.decoder_layers)) + ] + + h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx) + h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx) + h_EXV_encoder_fw = mask_fw * h_EXV_encoder + + for t_ in range(L): + t = decoding_order[:, t_] # [B] + chain_mask_t = torch.gather(chain_mask, 1, t[:, None])[:, 0] # [B] + mask_t = torch.gather(mask, 1, t[:, None])[:, 0] # [B] + bias_t = torch.gather(bias, 1, t[:, None, None].repeat(1, 1, 21))[ + :, 0, : + ] # [B,21] + + E_idx_t = torch.gather( + E_idx, 1, t[:, None, None].repeat(1, 1, E_idx.shape[-1]) + ) + h_E_t = torch.gather( + h_E, + 1, + t[:, None, None, None].repeat(1, 1, h_E.shape[-2], h_E.shape[-1]), + ) + h_ES_t = cat_neighbors_nodes(h_S, h_E_t, E_idx_t) + h_EXV_encoder_t = torch.gather( + h_EXV_encoder_fw, + 1, + t[:, None, None, None].repeat( + 1, 1, h_EXV_encoder_fw.shape[-2], h_EXV_encoder_fw.shape[-1] + ), + ) + + mask_bw_t = torch.gather( + mask_bw, + 1, + t[:, None, None, None].repeat( + 1, 1, mask_bw.shape[-2], mask_bw.shape[-1] + ), + ) + + for l, layer in enumerate(self.decoder_layers): + h_ESV_decoder_t = cat_neighbors_nodes(h_V_stack[l], h_ES_t, E_idx_t) + h_V_t = torch.gather( + h_V_stack[l], + 1, + t[:, None, None].repeat(1, 1, h_V_stack[l].shape[-1]), + ) + h_ESV_t = mask_bw_t * h_ESV_decoder_t + h_EXV_encoder_t + h_V_stack[l + 1].scatter_( + 1, + t[:, None, None].repeat(1, 1, h_V.shape[-1]), + layer(h_V_t, h_ESV_t, mask_V=mask_t), + ) + + h_V_t = torch.gather( + h_V_stack[-1], + 1, + t[:, None, None].repeat(1, 1, h_V_stack[-1].shape[-1]), + )[:, 0] + logits = self.W_out(h_V_t) # [B,21] + log_probs = torch.nn.functional.log_softmax(logits, dim=-1) # [B,21] + + probs = torch.nn.functional.softmax( + (logits + bias_t) / temperature, dim=-1 + ) # [B,21] + probs_sample = probs[:, :20] / torch.sum( + probs[:, :20], dim=-1, keepdim=True + ) # hard omit X #[B,20] + S_t = torch.multinomial(probs_sample, 1)[:, 0] # [B] + + all_probs.scatter_( + 1, + t[:, None, None].repeat(1, 1, 20), + (chain_mask_t[:, None, None] * probs_sample[:, None, :]).float(), + ) + all_log_probs.scatter_( + 1, + t[:, None, None].repeat(1, 1, 21), + (chain_mask_t[:, None, None] * log_probs[:, None, :]).float(), + ) + S_true_t = torch.gather(S_true, 1, t[:, None])[:, 0] + S_t = (S_t * chain_mask_t + S_true_t * (1.0 - chain_mask_t)).long() + h_S.scatter_( + 1, + t[:, None, None].repeat(1, 1, h_S.shape[-1]), + self.W_s(S_t)[:, None, :], + ) + S.scatter_(1, t[:, None], S_t[:, None]) + + output_dict = { + "S": S, + "sampling_probs": all_probs, + "log_probs": all_log_probs, + "decoding_order": decoding_order, + } + else: + # weights for symmetric design + symmetry_weights = torch.ones([L], device=device, dtype=torch.float32) + for i1, item_list in enumerate(symmetry_list_of_lists): + for i2, item in enumerate(item_list): + symmetry_weights[item] = symmetry_weights_list_of_lists[i1][i2] + + new_decoding_order = [] + for t_dec in list(decoding_order[0,].cpu().data.numpy()): + if t_dec not in list(itertools.chain(*new_decoding_order)): + list_a = [item for item in symmetry_list_of_lists if t_dec in item] + if list_a: + new_decoding_order.append(list_a[0]) + else: + new_decoding_order.append([t_dec]) + + decoding_order = torch.tensor( + list(itertools.chain(*new_decoding_order)), device=device + )[None,].repeat(B, 1) + + permutation_matrix_reverse = torch.nn.functional.one_hot( + decoding_order, num_classes=L + ).float() + order_mask_backward = torch.einsum( + "ij, biq, bjp->bqp", + (1 - torch.triu(torch.ones(L, L, device=device))), + permutation_matrix_reverse, + permutation_matrix_reverse, + ) + mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) + mask_1D = mask.view([B, L, 1, 1]) + mask_bw = mask_1D * mask_attend + mask_fw = mask_1D * (1.0 - mask_attend) + + # repeat for decoding + S_true = S_true.repeat(B_decoder, 1) + h_V = h_V.repeat(B_decoder, 1, 1) + h_E = h_E.repeat(B_decoder, 1, 1, 1) + E_idx = E_idx.repeat(B_decoder, 1, 1) + mask_fw = mask_fw.repeat(B_decoder, 1, 1, 1) + mask_bw = mask_bw.repeat(B_decoder, 1, 1, 1) + chain_mask = chain_mask.repeat(B_decoder, 1) + mask = mask.repeat(B_decoder, 1) + bias = bias.repeat(B_decoder, 1, 1) + + all_probs = torch.zeros( + (B_decoder, L, 20), device=device, dtype=torch.float32 + ) + all_log_probs = torch.zeros( + (B_decoder, L, 21), device=device, dtype=torch.float32 + ) + h_S = torch.zeros_like(h_V, device=device) + S = 20 * torch.ones((B_decoder, L), dtype=torch.int64, device=device) + h_V_stack = [h_V] + [ + torch.zeros_like(h_V, device=device) + for _ in range(len(self.decoder_layers)) + ] + + h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx) + h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx) + h_EXV_encoder_fw = mask_fw * h_EXV_encoder + + for t_list in new_decoding_order: + total_logits = 0.0 + for t in t_list: + chain_mask_t = chain_mask[:, t] # [B] + mask_t = mask[:, t] # [B] + bias_t = bias[:, t] # [B, 21] + + E_idx_t = E_idx[:, t : t + 1] + h_E_t = h_E[:, t : t + 1] + h_ES_t = cat_neighbors_nodes(h_S, h_E_t, E_idx_t) + h_EXV_encoder_t = h_EXV_encoder_fw[:, t : t + 1] + for l, layer in enumerate(self.decoder_layers): + h_ESV_decoder_t = cat_neighbors_nodes( + h_V_stack[l], h_ES_t, E_idx_t + ) + h_V_t = h_V_stack[l][:, t : t + 1] + h_ESV_t = ( + mask_bw[:, t : t + 1] * h_ESV_decoder_t + h_EXV_encoder_t + ) + h_V_stack[l + 1][:, t : t + 1, :] = layer( + h_V_t, h_ESV_t, mask_V=mask_t[:, None] + ) + + h_V_t = h_V_stack[-1][:, t] + logits = self.W_out(h_V_t) # [B,21] + log_probs = torch.nn.functional.log_softmax( + logits, dim=-1 + ) # [B,21] + all_log_probs[:, t] = ( + chain_mask_t[:, None] * log_probs + ).float() # [B,21] + total_logits += symmetry_weights[t] * logits + + probs = torch.nn.functional.softmax( + (total_logits + bias_t) / temperature, dim=-1 + ) # [B,21] + probs_sample = probs[:, :20] / torch.sum( + probs[:, :20], dim=-1, keepdim=True + ) # hard omit X #[B,20] + S_t = torch.multinomial(probs_sample, 1)[:, 0] # [B] + for t in t_list: + chain_mask_t = chain_mask[:, t] # [B] + all_probs[:, t] = ( + chain_mask_t[:, None] * probs_sample + ).float() # [B,20] + S_true_t = S_true[:, t] # [B] + S_t = (S_t * chain_mask_t + S_true_t * (1.0 - chain_mask_t)).long() + h_S[:, t] = self.W_s(S_t) + S[:, t] = S_t + + output_dict = { + "S": S, + "sampling_probs": all_probs, + "log_probs": all_log_probs, + "decoding_order": decoding_order.repeat(B_decoder, 1), + } + return output_dict + + def single_aa_score(self, feature_dict, use_sequence: bool): + """ + feature_dict - input features + use_sequence - False using backbone info only + """ + B_decoder = feature_dict["batch_size"] + S_true_enc = feature_dict[ + "S" + ] + mask_enc = feature_dict[ + "mask" + ] + chain_mask_enc = feature_dict[ + "chain_mask" + ] + randn = feature_dict[ + "randn" + ] + B, L = S_true_enc.shape + device = S_true_enc.device + + h_V_enc, h_E_enc, E_idx_enc = self.encode(feature_dict) + log_probs_out = torch.zeros([B_decoder, L, 21], device=device).float() + logits_out = torch.zeros([B_decoder, L, 21], device=device).float() + decoding_order_out = torch.zeros([B_decoder, L, L], device=device).float() + + for idx in range(L): + h_V = torch.clone(h_V_enc) + E_idx = torch.clone(E_idx_enc) + mask = torch.clone(mask_enc) + S_true = torch.clone(S_true_enc) + if not use_sequence: + order_mask = torch.zeros(chain_mask_enc.shape[1], device=device).float() + order_mask[idx] = 1. + else: + order_mask = torch.ones(chain_mask_enc.shape[1], device=device).float() + order_mask[idx] = 0. + decoding_order = torch.argsort( + (order_mask + 0.0001) * (torch.abs(randn)) + ) # [numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0] + E_idx = E_idx.repeat(B_decoder, 1, 1) + permutation_matrix_reverse = torch.nn.functional.one_hot( + decoding_order, num_classes=L + ).float() + order_mask_backward = torch.einsum( + "ij, biq, bjp->bqp", + (1 - torch.triu(torch.ones(L, L, device=device))), + permutation_matrix_reverse, + permutation_matrix_reverse, + ) + mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) + mask_1D = mask.view([B, L, 1, 1]) + mask_bw = mask_1D * mask_attend + mask_fw = mask_1D * (1.0 - mask_attend) + S_true = S_true.repeat(B_decoder, 1) + h_V = h_V.repeat(B_decoder, 1, 1) + h_E = h_E_enc.repeat(B_decoder, 1, 1, 1) + mask = mask.repeat(B_decoder, 1) + + h_S = self.W_s(S_true) + h_ES = cat_neighbors_nodes(h_S, h_E, E_idx) + + # Build encoder embeddings + h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx) + h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx) + + h_EXV_encoder_fw = mask_fw * h_EXV_encoder + for layer in self.decoder_layers: + # Masked positions attend to encoder information, unmasked see. + h_ESV = cat_neighbors_nodes(h_V, h_ES, E_idx) + h_ESV = mask_bw * h_ESV + h_EXV_encoder_fw + h_V = layer(h_V, h_ESV, mask) + + logits = self.W_out(h_V) + log_probs = torch.nn.functional.log_softmax(logits, dim=-1) + + log_probs_out[:,idx,:] = log_probs[:,idx,:] + logits_out[:,idx,:] = logits[:,idx,:] + decoding_order_out[:,idx,:] = decoding_order + + output_dict = { + "S": S_true, + "log_probs": log_probs_out, + "logits": logits_out, + "decoding_order": decoding_order_out, + } + return output_dict + + + def score(self, feature_dict, use_sequence: bool): + B_decoder = feature_dict["batch_size"] + S_true = feature_dict[ + "S" + ] + mask = feature_dict[ + "mask" + ] + chain_mask = feature_dict[ + "chain_mask" + ] + randn = feature_dict[ + "randn" + ] + symmetry_list_of_lists = feature_dict[ + "symmetry_residues" + ] + B, L = S_true.shape + device = S_true.device + + h_V, h_E, E_idx = self.encode(feature_dict) + + chain_mask = mask * chain_mask # update chain_M to include missing regions + decoding_order = torch.argsort( + (chain_mask + 0.0001) * (torch.abs(randn)) + ) # [numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0] + if len(symmetry_list_of_lists[0]) == 0 and len(symmetry_list_of_lists) == 1: + E_idx = E_idx.repeat(B_decoder, 1, 1) + permutation_matrix_reverse = torch.nn.functional.one_hot( + decoding_order, num_classes=L + ).float() + order_mask_backward = torch.einsum( + "ij, biq, bjp->bqp", + (1 - torch.triu(torch.ones(L, L, device=device))), + permutation_matrix_reverse, + permutation_matrix_reverse, + ) + mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) + mask_1D = mask.view([B, L, 1, 1]) + mask_bw = mask_1D * mask_attend + mask_fw = mask_1D * (1.0 - mask_attend) + else: + new_decoding_order = [] + for t_dec in list(decoding_order[0,].cpu().data.numpy()): + if t_dec not in list(itertools.chain(*new_decoding_order)): + list_a = [item for item in symmetry_list_of_lists if t_dec in item] + if list_a: + new_decoding_order.append(list_a[0]) + else: + new_decoding_order.append([t_dec]) + + decoding_order = torch.tensor( + list(itertools.chain(*new_decoding_order)), device=device + )[None,].repeat(B, 1) + + permutation_matrix_reverse = torch.nn.functional.one_hot( + decoding_order, num_classes=L + ).float() + order_mask_backward = torch.einsum( + "ij, biq, bjp->bqp", + (1 - torch.triu(torch.ones(L, L, device=device))), + permutation_matrix_reverse, + permutation_matrix_reverse, + ) + mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) + mask_1D = mask.view([B, L, 1, 1]) + mask_bw = mask_1D * mask_attend + mask_fw = mask_1D * (1.0 - mask_attend) + + E_idx = E_idx.repeat(B_decoder, 1, 1) + mask_fw = mask_fw.repeat(B_decoder, 1, 1, 1) + mask_bw = mask_bw.repeat(B_decoder, 1, 1, 1) + decoding_order = decoding_order.repeat(B_decoder, 1) + + S_true = S_true.repeat(B_decoder, 1) + h_V = h_V.repeat(B_decoder, 1, 1) + h_E = h_E.repeat(B_decoder, 1, 1, 1) + mask = mask.repeat(B_decoder, 1) + + h_S = self.W_s(S_true) + h_ES = cat_neighbors_nodes(h_S, h_E, E_idx) + + # Build encoder embeddings + h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx) + h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx) + + h_EXV_encoder_fw = mask_fw * h_EXV_encoder + if not use_sequence: + for layer in self.decoder_layers: + h_V = layer(h_V, h_EXV_encoder_fw, mask) + else: + for layer in self.decoder_layers: + # Masked positions attend to encoder information, unmasked see. + h_ESV = cat_neighbors_nodes(h_V, h_ES, E_idx) + h_ESV = mask_bw * h_ESV + h_EXV_encoder_fw + h_V = layer(h_V, h_ESV, mask) + + logits = self.W_out(h_V) + log_probs = torch.nn.functional.log_softmax(logits, dim=-1) + + output_dict = { + "S": S_true, + "log_probs": log_probs, + "logits": logits, + "decoding_order": decoding_order, + } + return output_dict + + +class ProteinFeaturesLigand(torch.nn.Module): + def __init__( + self, + edge_features, + node_features, + num_positional_embeddings=16, + num_rbf=16, + top_k=30, + augment_eps=0.0, + device=None, + atom_context_num=16, + use_side_chains=False, + ): + """Extract protein features""" + super(ProteinFeaturesLigand, self).__init__() + + self.use_side_chains = use_side_chains + + self.edge_features = edge_features + self.node_features = node_features + self.top_k = top_k + self.augment_eps = augment_eps + self.num_rbf = num_rbf + self.num_positional_embeddings = num_positional_embeddings + + self.embeddings = PositionalEncodings(num_positional_embeddings) + edge_in = num_positional_embeddings + num_rbf * 25 + self.edge_embedding = torch.nn.Linear(edge_in, edge_features, bias=False) + self.norm_edges = torch.nn.LayerNorm(edge_features) + + self.node_project_down = torch.nn.Linear( + 5 * num_rbf + 64 + 4, node_features, bias=True + ) + self.norm_nodes = torch.nn.LayerNorm(node_features) + + self.type_linear = torch.nn.Linear(147, 64) + + self.y_nodes = torch.nn.Linear(147, node_features, bias=False) + self.y_edges = torch.nn.Linear(num_rbf, node_features, bias=False) + + self.norm_y_edges = torch.nn.LayerNorm(node_features) + self.norm_y_nodes = torch.nn.LayerNorm(node_features) + + self.atom_context_num = atom_context_num + + # the last 32 atoms in the 37 atom representation + self.side_chain_atom_types = torch.tensor( + [ + 6, + 6, + 6, + 8, + 8, + 16, + 6, + 6, + 6, + 7, + 7, + 8, + 8, + 16, + 6, + 6, + 6, + 6, + 7, + 7, + 7, + 8, + 8, + 6, + 7, + 7, + 8, + 6, + 6, + 6, + 7, + 8, + ], + device=device, + ) + + self.periodic_table_features = torch.tensor( + [ + [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 83, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + 91, + 92, + 93, + 94, + 95, + 96, + 97, + 98, + 99, + 100, + 101, + 102, + 103, + 104, + 105, + 106, + 107, + 108, + 109, + 110, + 111, + 112, + 113, + 114, + 115, + 116, + 117, + 118, + ], + [ + 0, + 1, + 18, + 1, + 2, + 13, + 14, + 15, + 16, + 17, + 18, + 1, + 2, + 13, + 14, + 15, + 16, + 17, + 18, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 1, + 2, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 1, + 2, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + ], + [ + 0, + 1, + 1, + 2, + 2, + 2, + 2, + 2, + 2, + 2, + 2, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + ], + ], + dtype=torch.long, + device=device, + ) + + def _make_angle_features(self, A, B, C, Y): + v1 = A - B + v2 = C - B + e1 = torch.nn.functional.normalize(v1, dim=-1) + e1_v2_dot = torch.einsum("bli, bli -> bl", e1, v2)[..., None] + u2 = v2 - e1 * e1_v2_dot + e2 = torch.nn.functional.normalize(u2, dim=-1) + e3 = torch.cross(e1, e2, dim=-1) + R_residue = torch.cat( + (e1[:, :, :, None], e2[:, :, :, None], e3[:, :, :, None]), dim=-1 + ) + + local_vectors = torch.einsum( + "blqp, blyq -> blyp", R_residue, Y - B[:, :, None, :] + ) + + rxy = torch.sqrt(local_vectors[..., 0] ** 2 + local_vectors[..., 1] ** 2 + 1e-8) + f1 = local_vectors[..., 0] / rxy + f2 = local_vectors[..., 1] / rxy + rxyz = torch.norm(local_vectors, dim=-1) + 1e-8 + f3 = rxy / rxyz + f4 = local_vectors[..., 2] / rxyz + + f = torch.cat([f1[..., None], f2[..., None], f3[..., None], f4[..., None]], -1) + return f + + def _dist(self, X, mask, eps=1e-6): + mask_2D = torch.unsqueeze(mask, 1) * torch.unsqueeze(mask, 2) + dX = torch.unsqueeze(X, 1) - torch.unsqueeze(X, 2) + D = mask_2D * torch.sqrt(torch.sum(dX**2, 3) + eps) + D_max, _ = torch.max(D, -1, keepdim=True) + D_adjust = D + (1.0 - mask_2D) * D_max + D_neighbors, E_idx = torch.topk( + D_adjust, np.minimum(self.top_k, X.shape[1]), dim=-1, largest=False + ) + return D_neighbors, E_idx + + def _rbf(self, D): + device = D.device + D_min, D_max, D_count = 2.0, 22.0, self.num_rbf + D_mu = torch.linspace(D_min, D_max, D_count, device=device) + D_mu = D_mu.view([1, 1, 1, -1]) + D_sigma = (D_max - D_min) / D_count + D_expand = torch.unsqueeze(D, -1) + RBF = torch.exp(-(((D_expand - D_mu) / D_sigma) ** 2)) + return RBF + + def _get_rbf(self, A, B, E_idx): + D_A_B = torch.sqrt( + torch.sum((A[:, :, None, :] - B[:, None, :, :]) ** 2, -1) + 1e-6 + ) # [B, L, L] + D_A_B_neighbors = gather_edges(D_A_B[:, :, :, None], E_idx)[ + :, :, :, 0 + ] # [B,L,K] + RBF_A_B = self._rbf(D_A_B_neighbors) + return RBF_A_B + + def forward(self, input_features): + Y = input_features["Y"] + Y_m = input_features["Y_m"] + Y_t = input_features["Y_t"] + X = input_features["X"] + mask = input_features["mask"] + R_idx = input_features["R_idx"] + chain_labels = input_features["chain_labels"] + + if self.augment_eps > 0: + X = X + self.augment_eps * torch.randn_like(X) + Y = Y + self.augment_eps * torch.randn_like(Y) + + B, L, _, _ = X.shape + + Ca = X[:, :, 1, :] + N = X[:, :, 0, :] + C = X[:, :, 2, :] + O = X[:, :, 3, :] + + b = Ca - N + c = C - Ca + a = torch.cross(b, c, dim=-1) + Cb = -0.58273431 * a + 0.56802827 * b - 0.54067466 * c + Ca # shift from CA + + D_neighbors, E_idx = self._dist(Ca, mask) + + RBF_all = [] + RBF_all.append(self._rbf(D_neighbors)) # Ca-Ca + RBF_all.append(self._get_rbf(N, N, E_idx)) # N-N + RBF_all.append(self._get_rbf(C, C, E_idx)) # C-C + RBF_all.append(self._get_rbf(O, O, E_idx)) # O-O + RBF_all.append(self._get_rbf(Cb, Cb, E_idx)) # Cb-Cb + RBF_all.append(self._get_rbf(Ca, N, E_idx)) # Ca-N + RBF_all.append(self._get_rbf(Ca, C, E_idx)) # Ca-C + RBF_all.append(self._get_rbf(Ca, O, E_idx)) # Ca-O + RBF_all.append(self._get_rbf(Ca, Cb, E_idx)) # Ca-Cb + RBF_all.append(self._get_rbf(N, C, E_idx)) # N-C + RBF_all.append(self._get_rbf(N, O, E_idx)) # N-O + RBF_all.append(self._get_rbf(N, Cb, E_idx)) # N-Cb + RBF_all.append(self._get_rbf(Cb, C, E_idx)) # Cb-C + RBF_all.append(self._get_rbf(Cb, O, E_idx)) # Cb-O + RBF_all.append(self._get_rbf(O, C, E_idx)) # O-C + RBF_all.append(self._get_rbf(N, Ca, E_idx)) # N-Ca + RBF_all.append(self._get_rbf(C, Ca, E_idx)) # C-Ca + RBF_all.append(self._get_rbf(O, Ca, E_idx)) # O-Ca + RBF_all.append(self._get_rbf(Cb, Ca, E_idx)) # Cb-Ca + RBF_all.append(self._get_rbf(C, N, E_idx)) # C-N + RBF_all.append(self._get_rbf(O, N, E_idx)) # O-N + RBF_all.append(self._get_rbf(Cb, N, E_idx)) # Cb-N + RBF_all.append(self._get_rbf(C, Cb, E_idx)) # C-Cb + RBF_all.append(self._get_rbf(O, Cb, E_idx)) # O-Cb + RBF_all.append(self._get_rbf(C, O, E_idx)) # C-O + RBF_all = torch.cat(tuple(RBF_all), dim=-1) + + offset = R_idx[:, :, None] - R_idx[:, None, :] + offset = gather_edges(offset[:, :, :, None], E_idx)[:, :, :, 0] # [B, L, K] + + d_chains = ( + (chain_labels[:, :, None] - chain_labels[:, None, :]) == 0 + ).long() # find self vs non-self interaction + E_chains = gather_edges(d_chains[:, :, :, None], E_idx)[:, :, :, 0] + E_positional = self.embeddings(offset.long(), E_chains) + E = torch.cat((E_positional, RBF_all), -1) + E = self.edge_embedding(E) + E = self.norm_edges(E) + + if self.use_side_chains: + xyz_37 = input_features["xyz_37"] + xyz_37_m = input_features["xyz_37_m"] + E_idx_sub = E_idx[:, :, :16] # [B, L, 15] + mask_residues = input_features["chain_mask"] + xyz_37_m = xyz_37_m * (1 - mask_residues[:, :, None]) + R_m = gather_nodes(xyz_37_m[:, :, 5:], E_idx_sub) + + X_sidechain = xyz_37[:, :, 5:, :].view(B, L, -1) + R = gather_nodes(X_sidechain, E_idx_sub).view( + B, L, E_idx_sub.shape[2], -1, 3 + ) + R_t = self.side_chain_atom_types[None, None, None, :].repeat( + B, L, E_idx_sub.shape[2], 1 + ) + + # Side chain atom context + R = R.view(B, L, -1, 3) # coordinates + R_m = R_m.view(B, L, -1) # mask + R_t = R_t.view(B, L, -1) # atom types + + # Ligand atom context + Y = torch.cat((R, Y), 2) # [B, L, atoms, 3] + Y_m = torch.cat((R_m, Y_m), 2) # [B, L, atoms] + Y_t = torch.cat((R_t, Y_t), 2) # [B, L, atoms] + + Cb_Y_distances = torch.sum((Cb[:, :, None, :] - Y) ** 2, -1) + mask_Y = mask[:, :, None] * Y_m + Cb_Y_distances_adjusted = Cb_Y_distances * mask_Y + (1.0 - mask_Y) * 10000.0 + _, E_idx_Y = torch.topk( + Cb_Y_distances_adjusted, self.atom_context_num, dim=-1, largest=False + ) + + Y = torch.gather(Y, 2, E_idx_Y[:, :, :, None].repeat(1, 1, 1, 3)) + Y_t = torch.gather(Y_t, 2, E_idx_Y) + Y_m = torch.gather(Y_m, 2, E_idx_Y) + + Y_t = Y_t.long() + Y_t_g = self.periodic_table_features[1][Y_t] # group; 19 categories including 0 + Y_t_p = self.periodic_table_features[2][Y_t] # period; 8 categories including 0 + + Y_t_g_1hot_ = torch.nn.functional.one_hot(Y_t_g, 19) # [B, L, M, 19] + Y_t_p_1hot_ = torch.nn.functional.one_hot(Y_t_p, 8) # [B, L, M, 8] + Y_t_1hot_ = torch.nn.functional.one_hot(Y_t, 120) # [B, L, M, 120] + + Y_t_1hot_ = torch.cat( + [Y_t_1hot_, Y_t_g_1hot_, Y_t_p_1hot_], -1 + ) # [B, L, M, 147] + Y_t_1hot = self.type_linear(Y_t_1hot_.float()) + + D_N_Y = self._rbf( + torch.sqrt(torch.sum((N[:, :, None, :] - Y) ** 2, -1) + 1e-6) + ) # [B, L, M, num_bins] + D_Ca_Y = self._rbf( + torch.sqrt(torch.sum((Ca[:, :, None, :] - Y) ** 2, -1) + 1e-6) + ) + D_C_Y = self._rbf(torch.sqrt(torch.sum((C[:, :, None, :] - Y) ** 2, -1) + 1e-6)) + D_O_Y = self._rbf(torch.sqrt(torch.sum((O[:, :, None, :] - Y) ** 2, -1) + 1e-6)) + D_Cb_Y = self._rbf( + torch.sqrt(torch.sum((Cb[:, :, None, :] - Y) ** 2, -1) + 1e-6) + ) + + f_angles = self._make_angle_features(N, Ca, C, Y) # [B, L, M, 4] + + D_all = torch.cat( + (D_N_Y, D_Ca_Y, D_C_Y, D_O_Y, D_Cb_Y, Y_t_1hot, f_angles), dim=-1 + ) # [B,L,M,5*num_bins+5] + V = self.node_project_down(D_all) # [B, L, M, node_features] + V = self.norm_nodes(V) + + Y_edges = self._rbf( + torch.sqrt( + torch.sum((Y[:, :, :, None, :] - Y[:, :, None, :, :]) ** 2, -1) + 1e-6 + ) + ) # [B, L, M, M, num_bins] + + Y_edges = self.y_edges(Y_edges) + Y_nodes = self.y_nodes(Y_t_1hot_.float()) + + Y_edges = self.norm_y_edges(Y_edges) + Y_nodes = self.norm_y_nodes(Y_nodes) + + return V, E, E_idx, Y_nodes, Y_edges, Y_m + + +class ProteinFeatures(torch.nn.Module): + def __init__( + self, + edge_features, + node_features, + num_positional_embeddings=16, + num_rbf=16, + top_k=48, + augment_eps=0.0, + ): + """Extract protein features""" + super(ProteinFeatures, self).__init__() + self.edge_features = edge_features + self.node_features = node_features + self.top_k = top_k + self.augment_eps = augment_eps + self.num_rbf = num_rbf + self.num_positional_embeddings = num_positional_embeddings + + self.embeddings = PositionalEncodings(num_positional_embeddings) + edge_in = num_positional_embeddings + num_rbf * 25 + self.edge_embedding = torch.nn.Linear(edge_in, edge_features, bias=False) + self.norm_edges = torch.nn.LayerNorm(edge_features) + + def _dist(self, X, mask, eps=1e-6): + mask_2D = torch.unsqueeze(mask, 1) * torch.unsqueeze(mask, 2) + dX = torch.unsqueeze(X, 1) - torch.unsqueeze(X, 2) + D = mask_2D * torch.sqrt(torch.sum(dX**2, 3) + eps) + D_max, _ = torch.max(D, -1, keepdim=True) + D_adjust = D + (1.0 - mask_2D) * D_max + D_neighbors, E_idx = torch.topk( + D_adjust, np.minimum(self.top_k, X.shape[1]), dim=-1, largest=False + ) + return D_neighbors, E_idx + + def _rbf(self, D): + device = D.device + D_min, D_max, D_count = 2.0, 22.0, self.num_rbf + D_mu = torch.linspace(D_min, D_max, D_count, device=device) + D_mu = D_mu.view([1, 1, 1, -1]) + D_sigma = (D_max - D_min) / D_count + D_expand = torch.unsqueeze(D, -1) + RBF = torch.exp(-(((D_expand - D_mu) / D_sigma) ** 2)) + return RBF + + def _get_rbf(self, A, B, E_idx): + D_A_B = torch.sqrt( + torch.sum((A[:, :, None, :] - B[:, None, :, :]) ** 2, -1) + 1e-6 + ) # [B, L, L] + D_A_B_neighbors = gather_edges(D_A_B[:, :, :, None], E_idx)[ + :, :, :, 0 + ] # [B,L,K] + RBF_A_B = self._rbf(D_A_B_neighbors) + return RBF_A_B + + def forward(self, input_features): + X = input_features["X"] + mask = input_features["mask"] + R_idx = input_features["R_idx"] + chain_labels = input_features["chain_labels"] + + if self.augment_eps > 0: + X = X + self.augment_eps * torch.randn_like(X) + + b = X[:, :, 1, :] - X[:, :, 0, :] + c = X[:, :, 2, :] - X[:, :, 1, :] + a = torch.cross(b, c, dim=-1) + Cb = -0.58273431 * a + 0.56802827 * b - 0.54067466 * c + X[:, :, 1, :] + Ca = X[:, :, 1, :] + N = X[:, :, 0, :] + C = X[:, :, 2, :] + O = X[:, :, 3, :] + + D_neighbors, E_idx = self._dist(Ca, mask) + + RBF_all = [] + RBF_all.append(self._rbf(D_neighbors)) # Ca-Ca + RBF_all.append(self._get_rbf(N, N, E_idx)) # N-N + RBF_all.append(self._get_rbf(C, C, E_idx)) # C-C + RBF_all.append(self._get_rbf(O, O, E_idx)) # O-O + RBF_all.append(self._get_rbf(Cb, Cb, E_idx)) # Cb-Cb + RBF_all.append(self._get_rbf(Ca, N, E_idx)) # Ca-N + RBF_all.append(self._get_rbf(Ca, C, E_idx)) # Ca-C + RBF_all.append(self._get_rbf(Ca, O, E_idx)) # Ca-O + RBF_all.append(self._get_rbf(Ca, Cb, E_idx)) # Ca-Cb + RBF_all.append(self._get_rbf(N, C, E_idx)) # N-C + RBF_all.append(self._get_rbf(N, O, E_idx)) # N-O + RBF_all.append(self._get_rbf(N, Cb, E_idx)) # N-Cb + RBF_all.append(self._get_rbf(Cb, C, E_idx)) # Cb-C + RBF_all.append(self._get_rbf(Cb, O, E_idx)) # Cb-O + RBF_all.append(self._get_rbf(O, C, E_idx)) # O-C + RBF_all.append(self._get_rbf(N, Ca, E_idx)) # N-Ca + RBF_all.append(self._get_rbf(C, Ca, E_idx)) # C-Ca + RBF_all.append(self._get_rbf(O, Ca, E_idx)) # O-Ca + RBF_all.append(self._get_rbf(Cb, Ca, E_idx)) # Cb-Ca + RBF_all.append(self._get_rbf(C, N, E_idx)) # C-N + RBF_all.append(self._get_rbf(O, N, E_idx)) # O-N + RBF_all.append(self._get_rbf(Cb, N, E_idx)) # Cb-N + RBF_all.append(self._get_rbf(C, Cb, E_idx)) # C-Cb + RBF_all.append(self._get_rbf(O, Cb, E_idx)) # O-Cb + RBF_all.append(self._get_rbf(C, O, E_idx)) # C-O + RBF_all = torch.cat(tuple(RBF_all), dim=-1) + + offset = R_idx[:, :, None] - R_idx[:, None, :] + offset = gather_edges(offset[:, :, :, None], E_idx)[:, :, :, 0] # [B, L, K] + + d_chains = ( + (chain_labels[:, :, None] - chain_labels[:, None, :]) == 0 + ).long() # find self vs non-self interaction + E_chains = gather_edges(d_chains[:, :, :, None], E_idx)[:, :, :, 0] + E_positional = self.embeddings(offset.long(), E_chains) + E = torch.cat((E_positional, RBF_all), -1) + E = self.edge_embedding(E) + E = self.norm_edges(E) + + return E, E_idx + + +class ProteinFeaturesMembrane(torch.nn.Module): + def __init__( + self, + edge_features, + node_features, + num_positional_embeddings=16, + num_rbf=16, + top_k=48, + augment_eps=0.0, + num_classes=3, + ): + """Extract protein features""" + super(ProteinFeaturesMembrane, self).__init__() + self.edge_features = edge_features + self.node_features = node_features + self.top_k = top_k + self.augment_eps = augment_eps + self.num_rbf = num_rbf + self.num_positional_embeddings = num_positional_embeddings + self.num_classes = num_classes + + self.embeddings = PositionalEncodings(num_positional_embeddings) + edge_in = num_positional_embeddings + num_rbf * 25 + self.edge_embedding = torch.nn.Linear(edge_in, edge_features, bias=False) + self.norm_edges = torch.nn.LayerNorm(edge_features) + + self.node_embedding = torch.nn.Linear( + self.num_classes, node_features, bias=False + ) + self.norm_nodes = torch.nn.LayerNorm(node_features) + + def _dist(self, X, mask, eps=1e-6): + mask_2D = torch.unsqueeze(mask, 1) * torch.unsqueeze(mask, 2) + dX = torch.unsqueeze(X, 1) - torch.unsqueeze(X, 2) + D = mask_2D * torch.sqrt(torch.sum(dX**2, 3) + eps) + D_max, _ = torch.max(D, -1, keepdim=True) + D_adjust = D + (1.0 - mask_2D) * D_max + D_neighbors, E_idx = torch.topk( + D_adjust, np.minimum(self.top_k, X.shape[1]), dim=-1, largest=False + ) + return D_neighbors, E_idx + + def _rbf(self, D): + device = D.device + D_min, D_max, D_count = 2.0, 22.0, self.num_rbf + D_mu = torch.linspace(D_min, D_max, D_count, device=device) + D_mu = D_mu.view([1, 1, 1, -1]) + D_sigma = (D_max - D_min) / D_count + D_expand = torch.unsqueeze(D, -1) + RBF = torch.exp(-(((D_expand - D_mu) / D_sigma) ** 2)) + return RBF + + def _get_rbf(self, A, B, E_idx): + D_A_B = torch.sqrt( + torch.sum((A[:, :, None, :] - B[:, None, :, :]) ** 2, -1) + 1e-6 + ) # [B, L, L] + D_A_B_neighbors = gather_edges(D_A_B[:, :, :, None], E_idx)[ + :, :, :, 0 + ] # [B,L,K] + RBF_A_B = self._rbf(D_A_B_neighbors) + return RBF_A_B + + def forward(self, input_features): + X = input_features["X"] + mask = input_features["mask"] + R_idx = input_features["R_idx"] + chain_labels = input_features["chain_labels"] + membrane_per_residue_labels = input_features["membrane_per_residue_labels"] + + if self.augment_eps > 0: + X = X + self.augment_eps * torch.randn_like(X) + + b = X[:, :, 1, :] - X[:, :, 0, :] + c = X[:, :, 2, :] - X[:, :, 1, :] + a = torch.cross(b, c, dim=-1) + Cb = -0.58273431 * a + 0.56802827 * b - 0.54067466 * c + X[:, :, 1, :] + Ca = X[:, :, 1, :] + N = X[:, :, 0, :] + C = X[:, :, 2, :] + O = X[:, :, 3, :] + + D_neighbors, E_idx = self._dist(Ca, mask) + + RBF_all = [] + RBF_all.append(self._rbf(D_neighbors)) # Ca-Ca + RBF_all.append(self._get_rbf(N, N, E_idx)) # N-N + RBF_all.append(self._get_rbf(C, C, E_idx)) # C-C + RBF_all.append(self._get_rbf(O, O, E_idx)) # O-O + RBF_all.append(self._get_rbf(Cb, Cb, E_idx)) # Cb-Cb + RBF_all.append(self._get_rbf(Ca, N, E_idx)) # Ca-N + RBF_all.append(self._get_rbf(Ca, C, E_idx)) # Ca-C + RBF_all.append(self._get_rbf(Ca, O, E_idx)) # Ca-O + RBF_all.append(self._get_rbf(Ca, Cb, E_idx)) # Ca-Cb + RBF_all.append(self._get_rbf(N, C, E_idx)) # N-C + RBF_all.append(self._get_rbf(N, O, E_idx)) # N-O + RBF_all.append(self._get_rbf(N, Cb, E_idx)) # N-Cb + RBF_all.append(self._get_rbf(Cb, C, E_idx)) # Cb-C + RBF_all.append(self._get_rbf(Cb, O, E_idx)) # Cb-O + RBF_all.append(self._get_rbf(O, C, E_idx)) # O-C + RBF_all.append(self._get_rbf(N, Ca, E_idx)) # N-Ca + RBF_all.append(self._get_rbf(C, Ca, E_idx)) # C-Ca + RBF_all.append(self._get_rbf(O, Ca, E_idx)) # O-Ca + RBF_all.append(self._get_rbf(Cb, Ca, E_idx)) # Cb-Ca + RBF_all.append(self._get_rbf(C, N, E_idx)) # C-N + RBF_all.append(self._get_rbf(O, N, E_idx)) # O-N + RBF_all.append(self._get_rbf(Cb, N, E_idx)) # Cb-N + RBF_all.append(self._get_rbf(C, Cb, E_idx)) # C-Cb + RBF_all.append(self._get_rbf(O, Cb, E_idx)) # O-Cb + RBF_all.append(self._get_rbf(C, O, E_idx)) # C-O + RBF_all = torch.cat(tuple(RBF_all), dim=-1) + + offset = R_idx[:, :, None] - R_idx[:, None, :] + offset = gather_edges(offset[:, :, :, None], E_idx)[:, :, :, 0] # [B, L, K] + + d_chains = ( + (chain_labels[:, :, None] - chain_labels[:, None, :]) == 0 + ).long() # find self vs non-self interaction + E_chains = gather_edges(d_chains[:, :, :, None], E_idx)[:, :, :, 0] + E_positional = self.embeddings(offset.long(), E_chains) + E = torch.cat((E_positional, RBF_all), -1) + E = self.edge_embedding(E) + E = self.norm_edges(E) + + C_1hot = torch.nn.functional.one_hot( + membrane_per_residue_labels, self.num_classes + ).float() + V = self.node_embedding(C_1hot) + V = self.norm_nodes(V) + + return V, E, E_idx + + +class DecLayerJ(torch.nn.Module): + def __init__(self, num_hidden, num_in, dropout=0.1, num_heads=None, scale=30): + super(DecLayerJ, self).__init__() + self.num_hidden = num_hidden + self.num_in = num_in + self.scale = scale + self.dropout1 = torch.nn.Dropout(dropout) + self.dropout2 = torch.nn.Dropout(dropout) + self.norm1 = torch.nn.LayerNorm(num_hidden) + self.norm2 = torch.nn.LayerNorm(num_hidden) + + self.W1 = torch.nn.Linear(num_hidden + num_in, num_hidden, bias=True) + self.W2 = torch.nn.Linear(num_hidden, num_hidden, bias=True) + self.W3 = torch.nn.Linear(num_hidden, num_hidden, bias=True) + self.act = torch.nn.GELU() + self.dense = PositionWiseFeedForward(num_hidden, num_hidden * 4) + + def forward(self, h_V, h_E, mask_V=None, mask_attend=None): + """Parallel computation of full transformer layer""" + + # Concatenate h_V_i to h_E_ij + h_V_expand = h_V.unsqueeze(-2).expand( + -1, -1, -1, h_E.size(-2), -1 + ) # the only difference + h_EV = torch.cat([h_V_expand, h_E], -1) + + h_message = self.W3(self.act(self.W2(self.act(self.W1(h_EV))))) + if mask_attend is not None: + h_message = mask_attend.unsqueeze(-1) * h_message + dh = torch.sum(h_message, -2) / self.scale + + h_V = self.norm1(h_V + self.dropout1(dh)) + + # Position-wise feedforward + dh = self.dense(h_V) + h_V = self.norm2(h_V + self.dropout2(dh)) + + if mask_V is not None: + mask_V = mask_V.unsqueeze(-1) + h_V = mask_V * h_V + return h_V + + +class PositionWiseFeedForward(torch.nn.Module): + def __init__(self, num_hidden, num_ff): + super(PositionWiseFeedForward, self).__init__() + self.W_in = torch.nn.Linear(num_hidden, num_ff, bias=True) + self.W_out = torch.nn.Linear(num_ff, num_hidden, bias=True) + self.act = torch.nn.GELU() + + def forward(self, h_V): + h = self.act(self.W_in(h_V)) + h = self.W_out(h) + return h + + +class PositionalEncodings(torch.nn.Module): + def __init__(self, num_embeddings, max_relative_feature=32): + super(PositionalEncodings, self).__init__() + self.num_embeddings = num_embeddings + self.max_relative_feature = max_relative_feature + self.linear = torch.nn.Linear(2 * max_relative_feature + 1 + 1, num_embeddings) + + def forward(self, offset, mask): + d = torch.clip( + offset + self.max_relative_feature, 0, 2 * self.max_relative_feature + ) * mask + (1 - mask) * (2 * self.max_relative_feature + 1) + d_onehot = torch.nn.functional.one_hot(d, 2 * self.max_relative_feature + 1 + 1) + E = self.linear(d_onehot.float()) + return E + + +class DecLayer(torch.nn.Module): + def __init__(self, num_hidden, num_in, dropout=0.1, num_heads=None, scale=30): + super(DecLayer, self).__init__() + self.num_hidden = num_hidden + self.num_in = num_in + self.scale = scale + self.dropout1 = torch.nn.Dropout(dropout) + self.dropout2 = torch.nn.Dropout(dropout) + self.norm1 = torch.nn.LayerNorm(num_hidden) + self.norm2 = torch.nn.LayerNorm(num_hidden) + + self.W1 = torch.nn.Linear(num_hidden + num_in, num_hidden, bias=True) + self.W2 = torch.nn.Linear(num_hidden, num_hidden, bias=True) + self.W3 = torch.nn.Linear(num_hidden, num_hidden, bias=True) + self.act = torch.nn.GELU() + self.dense = PositionWiseFeedForward(num_hidden, num_hidden * 4) + + def forward(self, h_V, h_E, mask_V=None, mask_attend=None): + """Parallel computation of full transformer layer""" + + # Concatenate h_V_i to h_E_ij + h_V_expand = h_V.unsqueeze(-2).expand(-1, -1, h_E.size(-2), -1) + h_EV = torch.cat([h_V_expand, h_E], -1) + + h_message = self.W3(self.act(self.W2(self.act(self.W1(h_EV))))) + if mask_attend is not None: + h_message = mask_attend.unsqueeze(-1) * h_message + dh = torch.sum(h_message, -2) / self.scale + + h_V = self.norm1(h_V + self.dropout1(dh)) + + # Position-wise feedforward + dh = self.dense(h_V) + h_V = self.norm2(h_V + self.dropout2(dh)) + + if mask_V is not None: + mask_V = mask_V.unsqueeze(-1) + h_V = mask_V * h_V + return h_V + + +class EncLayer(torch.nn.Module): + def __init__(self, num_hidden, num_in, dropout=0.1, num_heads=None, scale=30): + super(EncLayer, self).__init__() + self.num_hidden = num_hidden + self.num_in = num_in + self.scale = scale + self.dropout1 = torch.nn.Dropout(dropout) + self.dropout2 = torch.nn.Dropout(dropout) + self.dropout3 = torch.nn.Dropout(dropout) + self.norm1 = torch.nn.LayerNorm(num_hidden) + self.norm2 = torch.nn.LayerNorm(num_hidden) + self.norm3 = torch.nn.LayerNorm(num_hidden) + + self.W1 = torch.nn.Linear(num_hidden + num_in, num_hidden, bias=True) + self.W2 = torch.nn.Linear(num_hidden, num_hidden, bias=True) + self.W3 = torch.nn.Linear(num_hidden, num_hidden, bias=True) + self.W11 = torch.nn.Linear(num_hidden + num_in, num_hidden, bias=True) + self.W12 = torch.nn.Linear(num_hidden, num_hidden, bias=True) + self.W13 = torch.nn.Linear(num_hidden, num_hidden, bias=True) + self.act = torch.nn.GELU() + self.dense = PositionWiseFeedForward(num_hidden, num_hidden * 4) + + def forward(self, h_V, h_E, E_idx, mask_V=None, mask_attend=None): + """Parallel computation of full transformer layer""" + + h_EV = cat_neighbors_nodes(h_V, h_E, E_idx) + h_V_expand = h_V.unsqueeze(-2).expand(-1, -1, h_EV.size(-2), -1) + h_EV = torch.cat([h_V_expand, h_EV], -1) + h_message = self.W3(self.act(self.W2(self.act(self.W1(h_EV))))) + if mask_attend is not None: + h_message = mask_attend.unsqueeze(-1) * h_message + dh = torch.sum(h_message, -2) / self.scale + h_V = self.norm1(h_V + self.dropout1(dh)) + + dh = self.dense(h_V) + h_V = self.norm2(h_V + self.dropout2(dh)) + if mask_V is not None: + mask_V = mask_V.unsqueeze(-1) + h_V = mask_V * h_V + + h_EV = cat_neighbors_nodes(h_V, h_E, E_idx) + h_V_expand = h_V.unsqueeze(-2).expand(-1, -1, h_EV.size(-2), -1) + h_EV = torch.cat([h_V_expand, h_EV], -1) + h_message = self.W13(self.act(self.W12(self.act(self.W11(h_EV))))) + h_E = self.norm3(h_E + self.dropout3(h_message)) + return h_V, h_E + + +# The following gather functions +def gather_edges(edges, neighbor_idx): + # Features [B,N,N,C] at Neighbor indices [B,N,K] => Neighbor features [B,N,K,C] + neighbors = neighbor_idx.unsqueeze(-1).expand(-1, -1, -1, edges.size(-1)) + edge_features = torch.gather(edges, 2, neighbors) + return edge_features + + +def gather_nodes(nodes, neighbor_idx): + # Features [B,N,C] at Neighbor indices [B,N,K] => [B,N,K,C] + # Flatten and expand indices per batch [B,N,K] => [B,NK] => [B,NK,C] + neighbors_flat = neighbor_idx.reshape((neighbor_idx.shape[0], -1)) + neighbors_flat = neighbors_flat.unsqueeze(-1).expand(-1, -1, nodes.size(2)) + # Gather and re-pack + neighbor_features = torch.gather(nodes, 1, neighbors_flat) + neighbor_features = neighbor_features.view(list(neighbor_idx.shape)[:3] + [-1]) + return neighbor_features + + +def gather_nodes_t(nodes, neighbor_idx): + # Features [B,N,C] at Neighbor index [B,K] => Neighbor features[B,K,C] + idx_flat = neighbor_idx.unsqueeze(-1).expand(-1, -1, nodes.size(2)) + neighbor_features = torch.gather(nodes, 1, idx_flat) + return neighbor_features + + +def cat_neighbors_nodes(h_nodes, h_neighbors, E_idx): + h_nodes = gather_nodes(h_nodes, E_idx) + h_nn = torch.cat([h_neighbors, h_nodes], -1) + return h_nn diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/rf3/src/rf3/model/layers/attention.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/rf3/src/rf3/model/layers/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..1fccf190c9412cb6d9291b386b33ac3252b7fd25 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S002/vendor/rf3/src/rf3/model/layers/attention.py @@ -0,0 +1,313 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +from jaxtyping import Float +from opt_einsum import contract as einsum +from rf3.util_module import init_lecun_normal + +from foundry import SHOULD_USE_CUEQUIVARIANCE +from foundry.training.checkpoint import activation_checkpointing + +if SHOULD_USE_CUEQUIVARIANCE: + import cuequivariance_torch as cuet + + +class TriangleAttention(nn.Module): + """Implementation of Triangle Attention from AlphaFold3. + + Routes to either cuEquivariance or vanilla implementation based on configuration. + + Args: + pair: Pair representation tensor of shape (B, L, L, d_pair) + + Returns: + Updated pair representation tensor of shape (B, L, L, d_pair) + + """ + + def __init__( + self, + d_pair, + n_head=4, + d_hidden=32, + p_drop=0.1, # noqa: E402 + start_node=True, + use_cuequivariance=True, + ): + super(TriangleAttention, self).__init__() + + self.norm = nn.LayerNorm(d_pair) + + self.to_q = nn.Linear(d_pair, n_head * d_hidden, bias=False) + self.to_k = nn.Linear(d_pair, n_head * d_hidden, bias=False) + self.to_v = nn.Linear(d_pair, n_head * d_hidden, bias=False) + self.to_b = nn.Linear(d_pair, n_head, bias=False) + self.to_g = nn.Linear(d_pair, n_head * d_hidden) + self.to_out = nn.Linear(n_head * d_hidden, d_pair) + + self.scaling = 1 / math.sqrt(d_hidden) + + self.h = n_head + self.dim = d_hidden + self.start_node = start_node + + self.use_cuequivariance = use_cuequivariance + + self.reset_parameter() + + def reset_parameter(self): + # query/key/value projection: Glorot uniform / Xavier uniform + nn.init.xavier_uniform_(self.to_q.weight) + nn.init.xavier_uniform_(self.to_k.weight) + nn.init.xavier_uniform_(self.to_v.weight) + + # bias: normal distribution + self.to_b = init_lecun_normal(self.to_b) + + # gating: zero weights, one biases (mostly open gate at the begining) + nn.init.zeros_(self.to_g.weight) + nn.init.ones_(self.to_g.bias) + + # to_out: right before residual connection: zero initialize -- to make it sure residual operation is same to the Identity at the begining + nn.init.zeros_(self.to_out.weight) + nn.init.zeros_(self.to_out.bias) + + @activation_checkpointing + def forward(self, pair): + """Forward pass of triangle attention.""" + pair = self.norm(pair) + bias = self.to_b(pair) # (B, L, L, h) + + if not self.start_node: + pair = rearrange(pair, "b i j d -> b j i d") + + # Route to appropriate implementation + if self.use_cuequivariance and SHOULD_USE_CUEQUIVARIANCE: + out = self._forward_cuequivariance(pair, bias) + else: + out = self._forward_vanilla(pair, bias) + + if not self.start_node: + out = rearrange(out, "b i j d -> b j i d") + + # output projection + out = self.to_out(out) + return out + + def _forward_cuequivariance(self, pair, bias): + """cuEquivariance triangle attention implementation.""" + # Handle autocast conversion + if torch.is_autocast_enabled(): + dtype = torch.get_autocast_dtype(pair.device.type) + pair = pair.to(dtype=dtype) + bias = bias.to(dtype=dtype) + + assert ( + pair.dtype == torch.bfloat16 and bias.dtype == torch.bfloat16 + ), f"cuEquivariance requires bfloat16 inputs (got pair={pair.dtype}, bias={bias.dtype})" + + # Gate computation + gate = torch.sigmoid(self.to_g(pair)) # (B, L, L, h*dim) + + # Project and reshape to cuEquivariance format: (B, L, H, L, D) + query = rearrange(self.to_q(pair), "b i j (h d) -> b i h j d", h=self.h) + key = rearrange(self.to_k(pair), "b i k (h d) -> b i h k d", h=self.h) + value = rearrange(self.to_v(pair), "b i k (h d) -> b i h k d", h=self.h) + + # Bias: (B, L, L, H) -> (B, 1, H, L, L) + bias_cueq = rearrange(bias, "b i j h -> b 1 h i j") + + # Call cuEquivariance triangle attention + out_cueq = cuet.triangle_attention( + query, key, value, bias=bias_cueq, scale=self.scaling + ) + + # Reshape back: (B, L, H, L, D) -> (B, L, L, H*D) + out = rearrange(out_cueq, "b i h j d -> b i j (h d)") + out = gate * out # gated attention + return out + + def _forward_vanilla(self, pair, bias): + """Vanilla PyTorch triangle attention implementation.""" + B, L = pair.shape[:2] + + # Gate computation + gate = torch.sigmoid(self.to_g(pair)) # (B, L, L, h*dim) + + # Project and reshape to vanilla format: (B, L, L, H, D) + query = self.to_q(pair).reshape(B, L, L, self.h, -1) + key = self.to_k(pair).reshape(B, L, L, self.h, -1) + value = self.to_v(pair).reshape(B, L, L, self.h, -1) + + query = query * self.scaling + + attn = einsum("bijhd,bikhd->bijkh", query, key) + attn = attn + bias.unsqueeze(1).expand(-1, L, -1, -1, -1) # (bijkh) + attn = F.softmax(attn, dim=-2) + + out = einsum("bijkh,bikhd->bijhd", attn, value).reshape(B, L, L, -1) + out = gate * out # gated attention + + return out + + +class TriangleMultiplication(nn.Module): + """Implementation of Triangle Multiplicative Update from AlphaFold3. + + Routes to either cuEquivariance or naive implementation based on configuration. + + Args: + d_pair: Pair representation dimension (must equal d_hidden for cuEquivariance) + d_hidden: Hidden dimension (must equal d_pair for cuEquivariance) + direction: "outgoing" or "incoming" triangle multiplication direction + bias: Whether to use bias in normalization layers + use_cuequivariance: Whether to use cuEquivariance fused kernel when available + """ + + def __init__( + self, + d_pair, + d_hidden=None, + direction="outgoing", + bias=True, + use_cuequivariance=True, + ): + super(TriangleMultiplication, self).__init__() + + # Set d_hidden to d_pair if not specified + if d_hidden is None: + d_hidden = d_pair + + self.d_pair = d_pair + self.d_hidden = d_hidden + + # Validate direction parameter + if direction not in ["outgoing", "incoming"]: + raise ValueError( + f"direction must be 'outgoing' or 'incoming', got '{direction}'" + ) + self.direction = direction + + self.use_cuequivariance = use_cuequivariance + + if self.use_cuequivariance: + # cuEquivariance kernel requires d_pair == d_hidden... + assert ( + d_pair == d_hidden + ), "cuEquivariance triangle multiplication requires d_pair == d_hidden" + # ... and d_pair must be a multiple of 32 + assert ( + d_pair % 32 == 0 + ), "cuEquivariance triangle multiplication requires d_pair to be a multiple of 32" + + # Input normalization (optional bias) + self.norm_in = nn.LayerNorm(d_pair, bias=bias) + + # Input projections: combine left and right projections (2*d_hidden, d_pair) (no bias) + self.p_in = nn.Linear(d_pair, 2 * d_hidden, bias=False) + + # Input gating: combine left and right gates (2*d_hidden, d_pair) (no bias) + self.g_in = nn.Linear(d_pair, 2 * d_hidden, bias=False) + + # Output normalization (optional bias) + self.norm_out = nn.LayerNorm(d_hidden, bias=bias) + + # Output projection (no bias) + self.p_out = nn.Linear(d_hidden, d_pair, bias=False) + + # Output gating (no bias) + self.g_out = nn.Linear(d_pair, d_pair, bias=False) + + self.reset_parameters() + + def reset_parameters(self): + # Input projections: lecun normal distribution for regular linear weights + self.p_in = init_lecun_normal(self.p_in) + + # We use default PyTorch initialization for the other parameters, as in AF-3 they do not specify their + # weight initialization schemes. Without bias, e.g., the gate initialization from AF-2 is not correct. + + def forward( + self, pair: Float[torch.Tensor, "B N N D"] + ) -> Float[torch.Tensor, "B N N D"]: + """Forward pass of triangle multiplication.""" + # Route to appropriate implementation + if self.use_cuequivariance and SHOULD_USE_CUEQUIVARIANCE: + return self._forward_cuequivariance(pair) + else: + return self._forward_vanilla(pair) + + def _forward_vanilla( + self, pair: Float[torch.Tensor, "B N N D"] + ) -> Float[torch.Tensor, "B N N D"]: + """Vanilla PyTorch triangle multiplication implementation.""" + B, L = pair.shape[:2] + + # Input normalization + pair_norm = self.norm_in(pair) + + # Input projections: get combined output and split + p_combined = self.p_in(pair_norm) # (B, L, L, 2*d_hidden) + left = p_combined[..., : self.d_hidden] # (B, L, L, d_hidden) + right = p_combined[..., self.d_hidden :] # (B, L, L, d_hidden) + + # Input gating: get combined output and split + g_combined = self.g_in(pair_norm) # (B, L, L, 2*d_hidden) + left_gate = torch.sigmoid(g_combined[..., : self.d_hidden]) + right_gate = torch.sigmoid(g_combined[..., self.d_hidden :]) + + # Apply gating + left = left_gate * left + right = right_gate * right + + # Triangle multiplication based on direction + if self.direction == "outgoing": + out = torch.einsum("bikd,bjkd->bijd", left, right / float(L)) + else: # incoming + out = torch.einsum("bkid,bkjd->bijd", left, right / float(L)) + + # Output normalization + out = self.norm_out(out) + + # Output projection + out = self.p_out(out) + + # Output gating + gate = torch.sigmoid(self.g_out(pair_norm)) + out = gate * out + + return out + + def _forward_cuequivariance( + self, pair: Float[torch.Tensor, "B N N D"] + ) -> Float[torch.Tensor, "B N N D"]: + """cuEquivariance triangle multiplication implementation.""" + # Handle autocast conversion + # (Use bfloat16 for optimal performance) + if torch.is_autocast_enabled(): + dtype = torch.get_autocast_dtype(pair.device.type) + pair = pair.to(dtype=dtype) + + assert ( + pair.dtype == torch.bfloat16 + ), "cuEquivariance requires bfloat16 inputs for optimal performance" + + output = cuet.triangle_multiplicative_update( + x=pair, + direction=self.direction, + mask=None, + norm_in_weight=self.norm_in.weight, + norm_in_bias=self.norm_in.bias, + p_in_weight=self.p_in.weight, # (2*d_hidden, d_pair) + g_in_weight=self.g_in.weight, # (2*d_hidden, d_pair) + norm_out_weight=self.norm_out.weight, + norm_out_bias=self.norm_out.bias, + p_out_weight=self.p_out.weight, # (d_pair, d_pair) since d_hidden == d_pair + g_out_weight=self.g_out.weight, # (d_pair, d_pair) + eps=1e-5, + ) + + return output diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/validation/package-replay.json b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/validation/package-replay.json new file mode 100644 index 0000000000000000000000000000000000000000..b494f471c8731640029c2fe1c4824431338799a3 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/validation/package-replay.json @@ -0,0 +1,27 @@ +{ + "completed_utc": "2026-10-28T04:49:09.285078+00:00", + "command": "python /workspace/evaluation/check_package.py --package /workspace/research/novelty_stage", + "returncode": 0, + "requests": 240, + "served": 240, + "requests_per_track": 60, + "designs_per_request": 2, + "total_emitted": 480, + "actual_tune_design_length_min": 62, + "actual_tune_design_length_max": 200, + "initial_projections": { + "completed": 480, + "accepted": 394, + "failed": 0 + }, + "novelty_round_close_matches": [ + 30, + 12, + 6 + ], + "inference_sources_unchanged": 103, + "source_digest_record_sha256": "8e9f074c76b04b3cc9bb1b4f7acf4a3ba7d99355195bc358df0c6142e5597801", + "log_sha256": "4221d93ae7a38016b5cac17006ecd268a2bc1b378906529768d0599dd4116711", + "capture_final_after_novelty": false, + "scope": "Contract replay, not designability scoring. Read-only final-output capture missed temporary cleanup; no final per-design novelty totals claimed from that mirror." +} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/callbacks/callback.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/callbacks/callback.py new file mode 100644 index 0000000000000000000000000000000000000000..e2923f29d800e111286ccebd4107026597afcb0a --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/callbacks/callback.py @@ -0,0 +1,120 @@ +from abc import ABC + +from beartype.typing import Any +from lightning.fabric.wrappers import ( + _FabricOptimizer, +) + + +class BaseCallback(ABC): + """Abstract base class used to build new callbacks. + + Callbacks receive the trainer as the first argument to all hook methods, following + PyTorch Lightning's convention. This allows callbacks to access trainer.state, + trainer.fabric, etc. + + NOTE: on_after_optimizer_step is called internally by Fabric and does NOT receive trainer. + Use on_before_optimizer_step for logic that requires trainer access. + + Where possible, use names consistent with PyTorch Lightning's callback names (see references below). + Note that if using any callbacks directly within a Model, they must also adhere to this schema. + + References: + - Pytorch Lightning Hooks (https://lightning.ai/docs/pytorch/stable/common/lightning_module.html#hooks) + - Callbacks Flow (https://pytorch-lightning.readthedocs.io/en/0.10.0/callbacks.html#callbacks) + """ + + # Epoch loops + def on_fit_start(self, trainer: Any) -> None: + """Called at the start of the training""" + pass + + def on_fit_end(self, trainer: Any) -> None: + """Called at the end of the training""" + pass + + # Training loop + def on_train_epoch_start(self, trainer: Any) -> None: + """Called at the start of each training epoch""" + pass + + def on_after_train_loader_iter(self, trainer: Any, **kwargs: Any) -> None: + """Called after 'iter(train_loader)' is called, but before the first batch is yielded""" + pass + + def on_before_train_loader_next(self, trainer: Any, **kwargs: Any) -> None: + """Called after each batch is yielded from the train_loader 'next(train_iter)' call""" + pass + + def on_train_batch_start(self, trainer: Any, batch: Any, batch_idx: int) -> None: + """Called at the start of each training batch""" + pass + + def on_train_batch_end( + self, trainer: Any, outputs: Any, batch: Any, batch_idx: int + ) -> None: + """Called after each training batch, but before the optimizer.step""" + pass + + def on_before_optimizer_step( + self, trainer: Any, optimizer: _FabricOptimizer + ) -> None: + """Called before each optimizer.step""" + pass + + def on_after_optimizer_step( + self, optimizer: _FabricOptimizer, **kwargs: Any + ) -> None: + """Called after each optimizer.step. + + NOTE: This hook is called internally by Fabric when optimizer.step() executes. + Trainer is NOT available here. Use optimizer_step for logic requiring trainer. + """ + pass + + def optimizer_step(self, trainer: Any, optimizer: _FabricOptimizer) -> None: + """Called after optimizer.step completes. Unlike on_after_optimizer_step, + this hook is called explicitly by the trainer and receives trainer access. + """ + pass + + def on_train_epoch_end(self, trainer: Any) -> None: + """Called at the end of each training epoch""" + pass + + # Validation loop + def on_validation_epoch_start(self, trainer: Any) -> None: + """Called at the start of each validation epoch""" + pass + + def on_validation_batch_start( + self, + trainer: Any, + batch: Any, + batch_idx: int, + num_batches: int, + dataset_name: str | None = None, + ) -> None: + """Called at the start of each validation batch""" + pass + + def on_validation_batch_end( + self, + trainer: Any, + outputs: Any, + batch: Any, + batch_idx: int, + num_batches: int, + dataset_name: str | None = None, + ) -> None: + """Called after each validation batch""" + pass + + def on_validation_epoch_end(self, trainer: Any) -> None: + """Called at the end of each validation epoch""" + pass + + # Saving and Loading + def on_save_checkpoint(self, trainer: Any, state: dict[str, Any]) -> None: + """Called when saving a checkpoint""" + pass diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/hydra/resolvers.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/hydra/resolvers.py new file mode 100644 index 0000000000000000000000000000000000000000..095d1462c99351880942529756a85370da22f4ea --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/hydra/resolvers.py @@ -0,0 +1,77 @@ +"""Resolvers for Hydra configuration files. + +Documentation on custom resolvers: +- https://omegaconf.readthedocs.io/en/latest/custom_resolvers.html +""" + +import importlib + +from atomworks.enums import ChainType, ChainTypeInfo +from beartype.typing import Any, Callable +from omegaconf import OmegaConf + +from ..common import run_once + + +# (Custom resolvers) +@run_once +def register_resolvers() -> None: + resolvers: dict[str, Callable[..., Any]] = { + "resolve_import": resolve_import, + "chain_type_info_to_regex": chain_type_info_to_regex, + } + + for name, resolver in resolvers.items(): + OmegaConf.register_new_resolver(name, resolver) + + +def resolve_import(module_path: str, attribute_path: str | None = None) -> Any: + """ + Import a module and access a specific attribute from it. + + Args: + module_path (str): The path to the module. + attribute_path (str): The path to the attribute within the module. + + Returns: + The imported attribute. + """ + module = importlib.import_module(module_path) + if attribute_path is not None: + # Split the attribute path to navigate through nested attributes + attributes = attribute_path.split(".") + attr = module + for attr_name in attributes: + attr = getattr(attr, attr_name) + return attr + else: + return module + + +def chain_type_info_to_regex(*args: str) -> Any: + """Convert a combination of ChainType or ChainTypeInfo attributes to a regex string. + + Primarily used for filtering a dataset by chain type prior to training/validation. + + Example filter: + - "pn_unit_1_type.astype('str').str.match('${chain_type_info_to_regex:PROTEINS}')" + + """ + regex_str = "" + + for arg in args: + if hasattr(ChainType, arg): + regex_str += f"{getattr(ChainType, arg).value}|" + elif hasattr(ChainTypeInfo, arg): + chain_types_list = getattr(ChainTypeInfo, arg) + for ct in chain_types_list: + regex_str += f"{ct.value}|" + else: + raise ValueError( + f"Attribute not found for ChainType or ChainTypeInfo: {arg}." + ) + + # Remove the trailing '|' + regex_str = regex_str[:-1] + + return regex_str diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/inference_engines/base.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/inference_engines/base.py new file mode 100644 index 0000000000000000000000000000000000000000..574cc83707e8f2e504b0b90f90b14ef1a2d10381 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/inference_engines/base.py @@ -0,0 +1,254 @@ +import logging +import os +from os import PathLike +from pathlib import Path +from types import TracebackType +from typing import Any, Dict + +import hydra +import torch +from biotite.structure import AtomArray +from lightning.fabric import seed_everything +from omegaconf import OmegaConf + +from foundry.inference_engines.checkpoint_registry import REGISTERED_CHECKPOINTS +from foundry.utils.ddp import RankedLogger, set_accelerator_based_on_availability +from foundry.utils.logging import ( + configure_minimal_inference_logging, + print_config_tree, +) + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)s %(name)s: %(message)s", + datefmt="%H:%M:%S", +) +ranked_logger = RankedLogger(__name__, rank_zero_only=True) + + +def merge(cfg: Any, overrides: dict) -> Any: + return OmegaConf.merge(cfg, OmegaConf.create(overrides)) + + +class BaseInferenceEngine: + """ + Base inference engine. + Separates model setup (expensive, once) from inference (can run multiple times). + """ + + def __init__( + self, + ckpt_path: PathLike, + num_nodes: int = 1, + devices_per_node: int = 1, + # Config overrides + transform_overrides: dict[str, Any] = {}, + inference_sampler_overrides: dict[str, Any] = {}, + trainer_overrides: dict[str, Any] = {}, + # Debug + verbose: bool = False, + seed: int | None = None, + ): + """Initialize inference engine and load model. + + Model config is loaded from checkpoint and overridden with parameters provided here. + + Args: + ckpt_path: Path to model checkpoint. + seed: Random seed. If None, uses external RNG state. Defaults to ``None``. + num_nodes: Number of nodes for distributed inference. Defaults to ``1``. + devices_per_node: Number of devices per node. Defaults to ``1``. + verbose: If True, show detailed logging and config trees. Defaults to ``False``. + """ + if not verbose: + configure_minimal_inference_logging() + + # Set attrs + self.initialized_ = False + # Both are populated by initialize() via hydra.utils.instantiate (statically Any); + # None until then. Annotated Any so subclasses can use the concrete trainer/pipeline + # API after initialize() without per-attribute narrowing. + self.trainer: Any = None + self.pipeline: Any = None + self.verbose = verbose + + # Resolve checkpoint path + if "." not in str(ckpt_path): + # Assume registered model + name = str(ckpt_path) + assert ( + name in REGISTERED_CHECKPOINTS + ), "Checkpoint provided not and not in registered checkpoints" + ckpt = REGISTERED_CHECKPOINTS[name] + + ckpt_path = ckpt.get_default_path() + ranked_logger.info( + "Using checkpoint from default installation directory, got: {}".format( + str(ckpt_path) + ) + ) + assert os.path.exists( + ckpt_path + ), "Invalid checkpoint: {}. And could not find checkpoint in default installation location: {}".format( + name, ckpt_path + ) + self.ckpt_path = Path(ckpt_path).resolve() + + # Set random seed (only if seed is not None) + if seed is not None: + ranked_logger.info(f"Seeding everything with seed={seed}...") + seed_everything(seed, workers=True, verbose=True) + else: + ranked_logger.info("Seed is None - using external RNG state") + self.seed = seed + + # Stored for later; + self.transform_overrides = transform_overrides + self.overrides: dict[str, Any] = {} + + base_overrides: dict[str, Any] = { + "trainer.seed": seed, + "trainer.metrics": {}, + "trainer.loss": None, + "trainer.num_nodes": num_nodes, + "trainer.devices_per_node": devices_per_node, + } + for key, value in base_overrides.items(): + self._assign_override(key, value) + + for key, value in trainer_overrides.items(): + self._assign_override(f"trainer.{key}", value) + + for key, value in inference_sampler_overrides.items(): + self._assign_override(f"model.net.inference_sampler.{key}", value) + + ################################################################################### + # Required subclasss methods + ################################################################################### + + def initialize(self) -> Any: + if self.initialized_: + return getattr(self, "cfg", None) + + # Load checkpoint and config + ranked_logger.info( + f"Loading checkpoint from {Path(self.ckpt_path).resolve()}..." + ) + checkpoint = torch.load(self.ckpt_path, "cpu", weights_only=False) + cfg = self._override_checkpoint_config(checkpoint["train_cfg"]) + + # Load pipeline first before trainer/model + self._construct_pipeline(cfg) + self._construct_trainer(cfg, checkpoint=checkpoint) + + ranked_logger.info("Model loaded and ready for inference.") + self.initialized_ = True + return cfg + + def run( + self, + inputs: ( + Dict[str, dict] | AtomArray | list[AtomArray] | PathLike | list[PathLike] + ), + *_: Any, + ) -> dict[str, dict] | None: + self.initialize() + raise NotImplementedError( + "Subclasses must implement inference logic in `run` method." + ) + + ################################################################################### + # Util methods + ################################################################################### + + def _override_checkpoint_config(self, cfg: Any) -> Any: + cfg = merge(cfg, self.overrides) + cfg = set_accelerator_based_on_availability(cfg) + return cfg + + def _construct_trainer(self, cfg: Any, checkpoint: Any = None) -> None: + """ + Sets attr self.trainer + """ + # Instantiate trainer + ranked_logger.info("Instantiating trainer...") + if self.verbose: + print_config_tree( + cfg.trainer, resolve=True, title="INFERENCE TRAINER CONFIGURATION" + ) + trainer = hydra.utils.instantiate( + cfg.trainer, + _convert_="partial", + _recursive_=False, + ) + + # Setup model + ranked_logger.info("Setting up model...") + trainer.fabric.launch() + trainer.initialize_or_update_trainer_state( + {"train_cfg": cfg} + ) # config from training stores net params + trainer.construct_model() + + ranked_logger.info("Loading model weights from checkpoint...") + trainer.load_checkpoint(checkpoint=checkpoint or self.ckpt_path) + + # Ensure optimizer isn't loaded + trainer.state["optimizer"] = None + trainer.state["train_cfg"].model.optimizer = None + trainer.setup_model_optimizers_and_schedulers() + trainer.state["model"].eval() + self.trainer = trainer + + def _assign_override(self, dotted_key: str, value: Any) -> None: + """Assign ``value`` into ``self.overrides`` using a dotted path.""" + target = self.overrides + keys = dotted_key.split(".") + for key in keys[:-1]: + if key not in target or not isinstance(target[key], dict): + target[key] = {} + target = target[key] + target[keys[-1]] = value + + def _construct_pipeline(self, cfg: Any) -> None: + """ + Sets attr self.pipeline + """ + # Construct pipeline + ranked_logger.info("Building Transform pipeline...") + first_val_dataset_key, first_val_dataset = next(iter(cfg.datasets.val.items())) + ranked_logger.info( + f"Using settings from validation dataset: {first_val_dataset_key}." + ) + transform = first_val_dataset.dataset.transform + transform = merge(transform, self.transform_overrides) + + if self.verbose: + print_config_tree( + transform, + resolve=True, + title="INFERENCE TRANSFORM PIPELINE", + ) + + self.pipeline = hydra.utils.instantiate(transform) + + # aliases for run + def forward(self, *args: Any, **kwargs: Any) -> dict[str, dict] | None: + return self.run(*args, **kwargs) + + def __call__(self, *args: Any, **kwargs: Any) -> dict[str, dict] | None: + return self.run(*args, **kwargs) + + # for use as a context manager: e.g. `with BaseInferenceEngine(...) as engine:` to automatically cleanup + def __enter__(self) -> "BaseInferenceEngine": + return self + + def __exit__( + self, + exc_type: type[BaseException] | None, + exc: BaseException | None, + tb: TracebackType | None, + ) -> None: + self.trainer = None + self.pipeline = None + self.initialized_ = False diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/metrics/__init__.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/metrics/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d93740502fd5a6403acb23b1f7e71709b8745e4a --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/metrics/__init__.py @@ -0,0 +1,12 @@ +"""Metrics for model evaluation. + +This module provides the base metric framework. +""" + +from foundry.metrics.metric import Metric, MetricInputError, MetricManager + +__all__ = [ + "Metric", + "MetricManager", + "MetricInputError", +] diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/testing/__init__.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/testing/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4114cf90502d9aa68b6850918f6d13712b599857 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/testing/__init__.py @@ -0,0 +1,6 @@ +"""Shared testing utilities for foundry.""" + +from foundry.testing.fixtures import get_test_data_dir, gpu +from foundry.testing.pytest_hooks import configure_pytest + +__all__ = ["configure_pytest", "get_test_data_dir", "gpu"] diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/training/checkpoint.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/training/checkpoint.py new file mode 100644 index 0000000000000000000000000000000000000000..6b8332c591339635712c2e91c2b147acbfb0cc73 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/training/checkpoint.py @@ -0,0 +1,65 @@ +"""Utilities for gradient checkpointing. + +References: + * `PyTorch Checkpoint Documentation`_ + + .. _PyTorch Checkpoint Documentation: https://pytorch.org/docs/stable/checkpoint.html +""" + +from typing import Any, Callable + +import torch +from torch.utils.checkpoint import checkpoint + + +def create_custom_forward( + module: Callable[..., Any], **kwargs: Any +) -> Callable[..., Any]: + """Create a custom forward function for gradient checkpointing with fixed kwargs. + + Enables passing keyword arguments to a module when using PyTorch's checkpoint function, + which only accepts positional arguments for the function to be checkpointed. + + Args: + module: The callable (typically a nn.Module) to wrap. + **kwargs: Keyword arguments to pass to the module during forward. + + Returns: + A callable that accepts only positional arguments and forwards them along + with the fixed kwargs to the original module. + """ + + def custom_forward(*inputs: Any) -> Any: + return module(*inputs, **kwargs) + + return custom_forward + + +def activation_checkpointing(function: Callable[..., Any]) -> Callable[..., Any]: + """Decorator to enable gradient checkpointing for a function during training. + + Args: + function: The function to apply gradient checkpointing to. + + Returns: + Wrapped function that conditionally applies checkpointing based on gradient state. + + Examples: + Apply to a forward pass method:: + + @activation_checkpointing + def forward(self, x, mask=None): + return self.layer(x, mask) + + Notes: + Uses ``use_reentrant=False`` for compatibility with recent PyTorch versions. + """ + + def wrapper(*args: Any, **kwargs: Any) -> Any: + if torch.is_grad_enabled(): + return checkpoint( + create_custom_forward(function, **kwargs), *args, use_reentrant=False + ) + return function(*args, **kwargs) + + return wrapper diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/alignment.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/alignment.py new file mode 100644 index 0000000000000000000000000000000000000000..b042b86d34d8d2f8119d913486f3c0c76c184e8d --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/alignment.py @@ -0,0 +1,102 @@ +import logging + +import torch + +logger = logging.getLogger(__name__) + + +def weighted_rigid_align( + X_L: torch.Tensor, # [B, L, 3] + X_gt_L: torch.Tensor, # [B, L, 3] + X_exists_L: torch.Tensor | None = None, # [L] + w_L: torch.Tensor | None = None, # [B, L] +) -> torch.Tensor: + """ + Weighted rigid body alignment of X_gt_L onto X_L with weights w_L + Allows for "moving target" ground truth that is se3 invariant + Following algorithm 28 in AF3 paper + Returns: + X_align_L: [B, L, 3] + """ + + # Canonicalize dimensions + if X_L.ndim == 2: + X_L = X_L[None] + if X_gt_L.ndim == 2: + X_gt_L = X_gt_L[None] + if X_exists_L is None: + X_exists_L = torch.ones((X_L.shape[-2]), dtype=torch.bool) + if w_L is None: + w_L = torch.ones_like(X_L[..., 0]) + else: + if w_L.ndim == 1: + w_L = w_L[None] + w_L = w_L.to(torch.float32) + + # Assert `X_exists_L` is a boolean mask + assert ( + X_exists_L.dtype == torch.bool + ), "X_exists_L should be a boolean mask! Otherwise, the alignment will be incorrect (silent failure)!" + + assert X_L.shape == X_gt_L.shape + assert X_L.shape[:-1] == w_L.shape + + X_resolved = X_L[:, X_exists_L] + X_gt_resolved = X_gt_L[:, X_exists_L] + w_resolved = w_L[:, X_exists_L] + u_X = torch.sum(X_resolved * w_resolved.unsqueeze(-1), dim=-2) / torch.sum( + w_resolved, dim=-1, keepdim=True + ) + u_X_gt = torch.sum(X_gt_resolved * w_resolved.unsqueeze(-1), dim=-2) / torch.sum( + w_resolved, dim=-1, keepdim=True + ) + + X_resolved = X_resolved - u_X.unsqueeze(-2) + X_gt_resolved = X_gt_resolved - u_X_gt.unsqueeze(-2) + + # Computation of the covariance matrix + C = torch.einsum("bji,bjk->bik", w_resolved[..., None] * X_gt_resolved, X_resolved) + + U, S, V = torch.linalg.svd(C) + + R = U @ V + B, _, _ = X_L.shape + # F is the reflection correction for the Kabsch rotation: its last diagonal + # entry flips to -1 when det(R) < 0 so R stays a proper rotation. It feeds + # `U @ F @ V` below, where matmul requires a shared dtype — U and V carry + # X_L's dtype (SVD of an input-derived covariance), so F must too. torch.eye + # defaults to float32, so without dtype= float64 inputs raise a mismatch. + F = torch.eye(3, 3, device=X_L.device, dtype=X_L.dtype)[None].tile( + ( + B, + 1, + 1, + ) + ) + + det = torch.linalg.det(R) + F[..., -1, -1] = torch.sign(det) + R = U @ F @ V + + X_gt_L = X_gt_L - u_X_gt.unsqueeze(-2) + X_align_L = X_gt_L @ R + u_X.unsqueeze(-2) + + return X_align_L.detach() + + +def get_rmsd(xyz1: torch.Tensor, xyz2: torch.Tensor, eps: float = 1e-4) -> torch.Tensor: + L = xyz1.shape[-2] + rmsd = torch.sqrt(torch.sum((xyz2 - xyz1) * (xyz2 - xyz1), dim=(-1, -2)) / L + eps) + return rmsd + + +def superimpose( + xyz1: torch.Tensor, xyz2: torch.Tensor, mask: torch.Tensor, eps: float = 1e-4 +) -> None: + """ + Superimpose xyz1 onto xyz2 using mask + """ + L = xyz1.shape[-2] + assert mask.shape == (L,) + assert xyz1.shape == xyz2.shape + assert mask.dtype == torch.bool diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/components.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/components.py new file mode 100644 index 0000000000000000000000000000000000000000..64932dad25c35882c3eaacb77ef25438bcbd3922 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/components.py @@ -0,0 +1,434 @@ +import random +import re +from typing import List + +import numpy as np +from atomworks.ml.encoding_definitions import AF3SequenceEncoding +from biotite.structure import AtomArray + +from foundry.constants import ( + TIP_BY_RESTYPE, +) +from foundry.utils.ddp import RankedLogger + +global_logger = RankedLogger(__name__, rank_zero_only=False) +sequence_encoding = AF3SequenceEncoding() +_aa_like_res_names = sequence_encoding.all_res_names[sequence_encoding.is_aa_like] + + +################################################################################# +# Component / contig parsing +################################################################################# + + +class ComponentValidationError(ValueError): + """Raised when contig/component inputs cannot be parsed or validated.""" + + def __init__( + self, + message: str, + *, + component: str | None = None, + details: dict | None = None, + ): + self.component = component + self.details = details or {} + prefix = f"[component={component}] " if component else "" + suffix = f" Details: {self.details}" if self.details else "" + super().__init__(f"{prefix}{message}{suffix}") + + +class ComponentStr(str): + """Component identifier, e.g. "A1" for residues, "B12", etc. Previously named `contig_string`""" + + def split_component(v) -> list: + return split_contig(v) + + +def split_contig(x: str) -> list: + try: + chain = str(x[0]) + idx = int(x[1:]) + if idx < 0: + raise ComponentValidationError( + "Residue index must be a non-negative integer.", component=str(x) + ) + except Exception as e: + raise ComponentValidationError( + f"Invalid contig format: '{x}'. Expected format is 'ChainIDResID' (e.g. 'A20').", + component=str(x), + ) from e + return [chain, idx] + + +def extract_pn_unit_info(contig: str) -> tuple[str, int, int]: + """ + Convert substring like A20-21 or A20 to separate terms: A, 20, 21. + """ + pattern = r"([A-Za-z])(\d+)(?:-(\d+))?" + + match = re.match(pattern, contig) + if match: + pn_unit_id = match.group(1) + start = int(match.group(2)) + end = int(match.group(3)) if match.group(3) else start + return pn_unit_id, start, end + + raise ComponentValidationError( + "Invalid contig format. Expected 'ChainIDStart-Stop' or 'ChainIDIdx'.", + component=contig, + ) + + +def get_design_pattern_with_constraints( + contig: str, length: str | None = None +) -> list[str]: + """ + Convert the contig string to separate modules. + e.g. '1-5,A20-21,1-5,A25-25,1-5,A30-30,/0,1-5' with length = 10-10 may be converted to [2, A20, A21, 2, A25, 3, A30, /0, 3] + Integers represent number of free residues to put there. + + """ + contig_parts = contig.split(",") + + # Separate fixed segments (e.g., "A1051-1051") and variable ranges (e.g., "0-40") + variable_ranges: list[list[int]] = [] + fixed_parts: list[tuple[str, int, int]] = [] + pos_to_put_motif = [] + + suff = [] # suffixes for diffused regions P(optional),R,D + + for part in contig_parts: + ## updating to include DNA and RNA generation + if part[-1] in ["R", "D"]: ##Detect non-fixed RNA and DNA contig part + suff.append(part[-1]) + part = part[:-1] + if "-" in part: + start, end = map(int, part.split("-")) + else: + start = end = int(part) + variable_ranges.append([start, end]) + pos_to_put_motif.append(0) + + elif any(c.isalpha() for c in part): # Detect parts containing letters as fixed + pn_unit_id, pn_unit_start, pn_unit_end = extract_pn_unit_info(part) + fixed_parts.append((pn_unit_id, pn_unit_start, pn_unit_end)) + pos_to_put_motif.append(1) + elif part == "/0": + pos_to_put_motif.append(2) + else: + if "-" in part: + start, end = map(int, part.split("-")) + else: + start = end = int(part) + variable_ranges.append([start, end]) + pos_to_put_motif.append(0) + suff.append("P") + + # adjust the total length to solely for free residues + num_motif_residues = sum([i[2] - i[1] + 1 for i in fixed_parts]) + + if length is None: + length_min, length_max = 0, 9999 + else: + if "-" in length: + length_min, length_max = map(int, length.split("-")) + else: + length_min = length_max = int(length) + + length_min -= num_motif_residues + length_max -= num_motif_residues + + remaining_length_min = length_min + remaining_length_max = length_max + + num_free_atoms: list[int] = [] + for range_limits in variable_ranges: + min_value = range_limits[0] + max_value = range_limits[1] + + # Calculate the valid range for the current segment + valid_min = max( + min_value, + remaining_length_min + - sum(r[1] for r in variable_ranges[len(num_free_atoms) + 1 :]), + ) + valid_max = min( + max_value, + remaining_length_max + - sum(r[0] for r in variable_ranges[len(num_free_atoms) + 1 :]), + ) + + if valid_min > valid_max and length is not None: + raise ComponentValidationError( + "No valid selections possible with the given constraints." + ) + + # Randomly select a value for the current segment + selected_value = random.randint(valid_min, valid_max) + num_free_atoms.append(selected_value) + + # Update remaining lengths + remaining_length_min -= selected_value + remaining_length_max -= selected_value + + atoms_with_motif = [] + for idx in range(len(pos_to_put_motif)): + if pos_to_put_motif[idx] == 1: + motif = fixed_parts.pop(0) + pn_unit_id, pn_unit_start, pn_unit_end = motif[0], motif[1], motif[2] + for index in range(pn_unit_start, pn_unit_end + 1): + atoms_with_motif.append(f"{pn_unit_id}{index}") + elif pos_to_put_motif[idx] == 0: + free_atom = num_free_atoms.pop(0) + atoms_with_motif.append(str(free_atom) + suff.pop(0)) + elif pos_to_put_motif[idx] == 2: + atoms_with_motif.append("/0") + + return atoms_with_motif + + +def get_motif_components_and_breaks( + unindexed_contig: str, index_all: bool = False +) -> tuple[list[str], list[bool | None]]: + """ + Convert a contig string into its components and breaks in motif + This way you can specify in your contigs where the breaks in the motif should be, so that, + say, residues aren't glued together by the model. Used for parsing unindexed inputs. + + e.g.: + contig="A14,A15,A16" -> components=[A14, A15, A16] breaks=[True, True, True] + contig="A14-15,A16" -> components=[A14, A15, A16] breaks=[True, False, True] + + args: + unindexed_contig: Contig string for unindexed tokens, see above for example on how positional + encodings between contigs can be selectively leaked + index_all: No breaks are used, allows for full indexing of concatenated tokens + Can use cleanup if this is the desired way to provide motif tokens. + """ + components: list[str] = [] + breaks: list[bool | None] = [] + + contig_parts = unindexed_contig.split(",") + for part in contig_parts: + if any(c.isalpha() for c in part): + # ... Parse possibilities: A11 | A11-12 | A11-11 + pn_unit_id, pn_unit_start, pn_unit_end = extract_pn_unit_info(part) + + if pn_unit_start == pn_unit_end: + # ... For single residues, append and break + components.append(f"{pn_unit_id}{pn_unit_start}") + breaks.append(True) + else: + # ... For multiple residues, break and then append without breaks + for index in range(pn_unit_start, pn_unit_end + 1): + components.append(f"{pn_unit_id}{index}") + if index == pn_unit_start: + breaks.append(True) + else: + breaks.append(False) + elif part == "/0": + components.append(part) + breaks.append(None) + else: + if "-" in part: + raise ComponentValidationError( + "Partial unindexing without fixed length is not supported.", + component=part, + ) + components.append(part) + breaks.append(None) + + breaks[0] = True # Decouple unindexed region from global index + if index_all: + global_logger.info("Unindexing all residues") + breaks = [(False if b is not None else None) for b in breaks] + return components, breaks + + +################################################################################# +# Mask getters +################################################################################# + + +def get_name_mask( + source_names: np.ndarray, + query_names: str | list[str], + source_resname: str | None = None, +) -> np.ndarray: + """ + Args: + source_names: list of all names to match in current token + query_string: specifier of names to get: + "ALL" - All atom names in token are matched + "BKBN - Only backbone atoms (not CB) + "TIP" - 2 farthest atoms from the backbone are fixed with any + additional atoms that automatically constrain geometries + (e.g. 4 atoms for carboxylates/amides). See `constants.py`. + Comma-separated string - e.g. "N,CA,C,O,CB" for exact queries + List of names - e.g. ["N", "CA", "C", "O"] for exact queries + source_resname: residue name is required when specifying just to grab the names for a "TIP" + + Raises error if not all exact atom names are found and unique + + Returns: + mask of atoms corresponding to token + """ + if isinstance(query_names, list): + names = query_names + elif isinstance(query_names, str): + if query_names.upper() == "ALL": + return np.ones(source_names.shape[0], dtype=bool) + elif query_names.upper() == "BKBN": + names = ["N", "CA", "C", "O"] + elif query_names.upper() == "TIP": + if source_resname is None: + raise ComponentValidationError( + "TIP selection requires a residue name.", + component=str(source_resname), + ) + tip_names = TIP_BY_RESTYPE[source_resname] + if tip_names is None: + raise ComponentValidationError( + "Residue does not define TIP atoms; use ALL, BKBN, or explicit names.", + component=str(source_resname), + ) + names = tip_names + elif query_names == "": + names = [] + else: + names = query_names.split(",") + else: + raise ComponentValidationError( + "query_names must be a string or list of strings.", + details={"got_type": str(type(query_names))}, + ) + + if any(n == "" for n in names): + raise ComponentValidationError( + f"Empty atom name found in selection '{query_names}'.", + component=str(source_resname), + ) + mask = np.isin(source_names, names) + + if len(names) == 0: + return mask + + if not len(set(names)) == len(names): + raise ComponentValidationError( + f"Atom names in '{query_names}' must be unique.", + details={"duplicates": names}, + ) + if not mask.any(): + raise ComponentValidationError( + f"Could not find requested atoms '{query_names}' in atom array.", + details={"source_names": np.asarray(source_names).tolist()}, + ) + if mask.sum() != len(names): + global_logger.warning( + "Not all atoms found in atom array. Are you expecting multiple residues/ligands with the same names? " + + "If not, check your input pdb file. " + + "Atom array requested to contain names {}. Got: {}. Requested {}".format( + query_names, + np.asarray(source_names).tolist(), + np.asarray(names).tolist(), + ) + ) + if mask.sum() % len(names) != 0: + # for the case where source_names are originated from multiple residues with the same names + # (e.g. two ORO ligands in the input pdb: {ligand: "ORO", fixed_atoms: {ORO:"N3,C2,C4,N1"}}) + raise ComponentValidationError( + "Number of atoms must be a multiple of the requested names.", + details={ + "query": query_names, + "source_names": np.asarray(source_names).tolist(), + "requested": np.asarray(names).tolist(), + }, + ) + + return mask + + +def fetch_mask_from_idx(contig_str: str, *, atom_array: AtomArray) -> np.ndarray: + """ + contig_str: A11 + returns: + mask of atoms within contig (e.g. residue 11 in chain A) + """ + chain, res_id = split_contig(contig_str) + mask = (atom_array.chain_id == chain) & (atom_array.res_id == res_id) + if not np.any(mask): + raise ComponentValidationError( + f"Residue {chain}{res_id} not found in atom array.", + component=f"{chain}{res_id}", + ) + return mask + + +def fetch_mask_from_name(name: str, *, atom_array: AtomArray) -> np.ndarray: + """ + name: LIG_NAME + returns: + mask of atoms corresponding to non-protein + """ + mask = atom_array.res_name == name + if not np.any(mask): + non_protein_res_names = np.unique( + atom_array.res_name[~np.isin(atom_array.res_name, _aa_like_res_names)] + ) + raise ComponentValidationError( + "Component not found in input atom array.", + component=name, + details={"available_non_protein": non_protein_res_names.tolist()}, + ) + return mask + + +def fetch_mask_from_component(component: str, *, atom_array: AtomArray) -> np.ndarray: + """ + Catch-all function for fetching a component by non-protein name or contig + component: A11 or LIG_NAME + returns: + mask of atoms corresponding to component + """ + try: + mask = fetch_mask_from_name(component, atom_array=atom_array) + except ComponentValidationError: + mask = fetch_mask_from_idx(component, atom_array=atom_array) + return mask + + +def unravel_components( + v: str, *, atom_array: AtomArray = None, allow_multiple_matches: bool = False +) -> List[str]: + """Safely unravel components from a string input.""" + components = [] + if "," in v or "-" in v: + components.extend(get_design_pattern_with_constraints(v)) + else: + # Safely canonicalize to single component + mask = fetch_mask_from_component(v, atom_array=atom_array) + if mask.sum() > 0: + res_ids, chain_ids = atom_array.res_id[mask], atom_array.chain_id[mask] + # assert unique resids for component + if len(set(zip(chain_ids, res_ids))) != 1: + if not allow_multiple_matches: + raise ComponentValidationError( + f"Component '{v}' maps to multiple residues.", + component=v, + ) + else: + global_logger.warning( + f"Component '{v}' maps to multiple residues. If you are using Symmetry this is OK." + ) + components.extend([f"{c}{r}" for c, r in zip(chain_ids, res_ids)]) + components = list(set(components)) # unique components + return components + res_id, chain_id = res_ids[0], chain_ids[0] + + component = f"{chain_id}{res_id}" + global_logger.debug( + "Canonicalized component string: %s -> %s", v, component + ) + components.append(component) + return components diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/datasets.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..7ebd82d56f1d9785dc2eebe2a16b945de5cf15cd --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/datasets.py @@ -0,0 +1,416 @@ +import hydra +import torch +from atomworks.ml.samplers import ( + DistributedMixedSampler, + FallbackSamplerWrapper, + LazyWeightedRandomSampler, + LoadBalancedDistributedSampler, + MixedSampler, +) +from beartype.typing import Any +from omegaconf import DictConfig, ListConfig +from torch.utils.data import ( + DataLoader, + Dataset, + RandomSampler, + Sampler, + SequentialSampler, + Subset, + WeightedRandomSampler, +) +from torch.utils.data.distributed import DistributedSampler + +from foundry.hydra.resolvers import register_resolvers +from foundry.utils.ddp import RankedLogger + +ranked_logger = RankedLogger(__name__, rank_zero_only=True) +try: + from atomworks.ml.datasets import ( + ConcatDatasetWithID, + FallbackDatasetWrapper, + get_row_and_index_by_example_id, + ) +except Exception as e: + ranked_logger.warning( + f"Failed to import atomworks.ml.datasets: {type(e).__name__}: {e}. " + "If training networks, the PDB_MIRROR environment variable must be set." + ) + + +register_resolvers() + + +def wrap_dataset_and_sampler_with_fallbacks( + dataset_to_be_wrapped: Dataset, + sampler_to_be_wrapped: Sampler, + dataset_to_fallback_to: Dataset, + sampler_to_fallback_to: Sampler, + n_fallback_retries: int, +) -> tuple[Dataset, Sampler]: + """Wrap the specified dataset and sampler with fallback dataloading. + + If the provided fallback sampler does not have weights (e.g., a MixedSampler), we will use uniform weights. + + Args: + dataset_to_be_wrapped (Dataset): The main dataset to be wrapped. + sampler_to_be_wrapped (Sampler): The main sampler to be wrapped. + dataset_to_fallback_to (Dataset): The fallback dataset. We will sample from this dataset if the main dataset fails. + sampler_to_fallback_to (Sampler): The fallback sampler. We will sample from this sampler if the main sampler fails. + n_fallback_retries (int): Number of retries for the fallback mechanism before raising an exception. + + Returns: + tuple[Dataset, Sampler]: The wrapped dataset and sampler with fallbacks. + """ + # Instantiate a new fallback sampler to avoid scaling issues. + # `hasattr`, not `"weights" in sampler`: `in` on a Sampler has no `__contains__`, so it + # iterates the sampler's indices (ints) and never matches the string "weights" — the + # weighted branch was dead and the membership test needlessly drew samples. + if hasattr(sampler_to_fallback_to, "weights"): + fallback_weights = sampler_to_fallback_to.weights + else: + # torch types `Dataset` without `__len__`, but map-style datasets provide it. + fallback_weights = torch.ones(len(dataset_to_fallback_to)) # type: ignore[arg-type] + fallback_sampler = LazyWeightedRandomSampler( + weights=fallback_weights, + num_samples=int(1e9), + replacement=True, # replacement for fallback dataloading, so we can draw a huge number of samples + generator=None, + prefetch_buffer_size=4, + ) + + # Wrap the dataset and sampler with fallback mechanisms + wrapped_dataset = FallbackDatasetWrapper( + dataset_to_be_wrapped, fallback_dataset=dataset_to_fallback_to + ) + wrapped_sampler = FallbackSamplerWrapper( + sampler_to_be_wrapped, + fallback_sampler=fallback_sampler, + n_fallback_retries=n_fallback_retries, + ) + + return wrapped_dataset, wrapped_sampler + + +def instantiate_single_dataset_and_sampler(cfg: DictConfig) -> dict[str, Any]: + """Instantiate a dataset and its corresponding sampler from a configuration dictionary. + + Args: + cfg (DictConfig): Configuration dictionary defining the dataset and its parameters. + + Returns: + dict[str, Any]: A dictionary containing the instantiated dataset and sampler. + """ + # ... instantiate the dataset + dataset = hydra.utils.instantiate(cfg.dataset) + + # Users may provide only weights, in which case we will use a WeightedRandomSampler, + # or they may provide a sampler directly + + if "weights" in cfg and "sampler" not in cfg: + # ... instantiate the weights and create a WeightedRandomSampler + weights = hydra.utils.instantiate(cfg.weights, dataset_df=dataset.data) + sampler = WeightedRandomSampler( + weights=weights, + num_samples=len(dataset), + replacement=True, + ) + elif "sampler" in cfg and "weights" not in cfg: + # ... instantiate the sampler with the number of samples + sampler = hydra.utils.instantiate(cfg.sampler) + else: + dataset_name = getattr(getattr(cfg.dataset, "dataset", None), "name", None) + ranked_logger.warning( + f"No weights or sampler provided for dataset: {dataset_name}, using uniform weights with replacement." + ) + sampler = WeightedRandomSampler( + # torch types `weights` as `Sequence[float]`; a Tensor is accepted at runtime. + weights=torch.ones(len(dataset)), # type: ignore[arg-type] + num_samples=len(dataset), + replacement=True, + ) + + return {"dataset": dataset, "sampler": sampler} + + +def recursively_instantiate_datasets_and_samplers( + cfg: DictConfig, name: str | None = None +) -> dict[str, Any]: + """Recursively instantiate datasets and samplers from a configuration dictionary. + + We must handle three cases: + (1) A single "leaf" dataset (e.g., "distillation"), specified with the "dataset" key + (2) Multiple sub-datasets that should be concatenated together with their weights (e.g., "interfaces" and "pn_units"), + specified with the "sub_datasets" key + (3) Multiple "leaf" datasets that should be sampled from with a certain probability (e.g., "distillation" and "pdb"), + + Args: + cfg (DictConfig): Configuration dictionary defining datasets and their parameters. + name (str, optional): The name of the dataset, used for reporting. Defaults to None. + + Returns: + dict[str, Any]: A dictionary containing the instantiated dataset and sampler. + """ + # ------- Base case (1): A single "leaf" dataset -------# + if "dataset" in cfg: + return {**instantiate_single_dataset_and_sampler(cfg), "name": name} + + # ------- Recursive case (2): Multiple sub-datasets that must be concatenated together -------# + elif "sub_datasets" in cfg: + # ... create a list of dictionaries for each sub-dataset + datasets_info = [] + for sub_dataset_name, sub_dataset_cfg in cfg.sub_datasets.items(): + if sub_dataset_cfg is None: + # (Skip any None sub-datasets; e.g., those overrode by the experiment config) + continue + + datasets_info.append( + recursively_instantiate_datasets_and_samplers( + sub_dataset_cfg, name=sub_dataset_name + ) + ) + + # ... concatenate sub-datasets and weights (e.g., "interfaces" and "pn_units" into one ConcatDataset) + # NOTE: Order of the weights must match the order of the datasets! + concatenated_dataset = ConcatDatasetWithID( + datasets=[info["dataset"] for info in datasets_info] + ) + concatenated_weights = torch.cat( + [info["sampler"].weights for info in datasets_info] + ) + sampler = WeightedRandomSampler( + # torch types `weights` as `Sequence[float]`; a Tensor is accepted at runtime. + weights=concatenated_weights, # type: ignore[arg-type] + num_samples=len(concatenated_dataset), + replacement=True, + ) + + return {"dataset": concatenated_dataset, "sampler": sampler, "name": name} + + # ------- Recursive case (3): Multiple datasets that must be sampled from with specified probabilities -------# + else: + datasets_info = [] + for nested_dataset_name, nested_dataset_cfg in cfg.items(): + if nested_dataset_cfg is None: + # (Skip any None training datasets; e.g., those overrode by the experiment config) + continue + + # (To use a MixedSampler, we must provide a "probability" key for each dataset) + assert ( + "probability" in nested_dataset_cfg + ), "Expected 'probability' key in dataset configuration" + datasets_info.append( + { + **recursively_instantiate_datasets_and_samplers( + nested_dataset_cfg, name=str(nested_dataset_name) + ), + "probability": nested_dataset_cfg["probability"], + } + ) + + # ... check that the sum of probabilities of all datasets is 1 + assert ( + abs(1 - sum(dataset_info["probability"] for dataset_info in datasets_info)) + < 1e-5 + ), "Sum of probabilities must be 1.0" + + # ... compose the list of datasets into a single dataset + composed_train_dataset = ConcatDatasetWithID( + datasets=[dataset["dataset"] for dataset in datasets_info] + ) + + composed_train_sampler = MixedSampler(datasets_info=datasets_info, shuffle=True) + + return { + "dataset": composed_train_dataset, + "sampler": composed_train_sampler, + "name": name, + } + + +def assemble_distributed_loader( + dataset: Dataset, + sampler: Sampler | None = None, + rank: int | None = None, + world_size: int | None = None, + n_examples_per_epoch: int | None = None, + loader_cfg: DictConfig | dict | None = None, + shuffle: bool = True, + drop_last: bool = False, +) -> DataLoader: + """Assembles a distributed DataLoader for training or validation. + + Performs the following steps: + (1) If not already a distributed sampler, wraps the sampler with a DistributedSampler or DistributedMixedSampler + (2) Wraps the dataset and sampler with a fallback mechanism, if needed + (3) Assembles the final DataLoader + + Args: + dataset (Dataset): The dataset to be used for training or validation. + sampler (Sampler): The sampler to be used for training or validation. May already be distributed. + rank (int): The rank of the current process in distributed training. + world_size (int): The total number of processes participating in the distributed training. + n_examples_per_epoch (int): The number of examples to sample per epoch, across all GPUs. + For example, if we have 8 GPUs, with 2 gradient accumulation steps and 10 optimizer + steps per epoch, we would sample 160 examples per epoch (8 * 2 * 10). + loader_cfg (DictConfig or dict, optional): Additional configuration parameters for the + DataLoader, such as `batch_size` and `num_workers`. Defaults to an empty dictionary. + shuffle (bool, optional): Whether to shuffle the dataset. Defaults to True. + drop_last (bool, optional): Whether to drop the last incomplete batch if the dataset size + is not divisible by the number of GPUs. Defaults to False. + + Returns: + DataLoader: A PyTorch DataLoader configured for distributed training, with datasets + concatenated and sampled according to their defined probabilities. + """ + if not loader_cfg: + loader_cfg = {} + + if isinstance(sampler, MixedSampler): + # (If given a MixedSampler, we must convert to a DistributedMixedSampler) + assert ( + rank is not None + and world_size is not None + and n_examples_per_epoch is not None + ), "Rank, world_size, and n_examples_per_epoch must be provided for MixedSampler" + sampler = DistributedMixedSampler( + datasets_info=sampler.datasets_info, + num_replicas=world_size, + rank=rank, + n_examples_per_epoch=n_examples_per_epoch, + shuffle=shuffle, + drop_last=drop_last, + ) + elif isinstance(sampler, (RandomSampler, SequentialSampler)): + # (If given a RandomSampler or SequentialSampler, we must convert to a DistributedSampler) + assert ( + rank is not None and world_size is not None + ), "Rank and world_size must be provided for RandomSampler or SequentialSampler" + sampler = DistributedSampler( + dataset=dataset, + num_replicas=world_size, + rank=rank, + shuffle=shuffle, + drop_last=drop_last, + ) + elif sampler is None and isinstance(dataset, Subset): + # We are subsetting the dataset to a specific set of example IDs + ranked_logger.info(f"Subsetting dataset to {len(dataset)} examples!") + else: + # (We assume we are already given a DistributedSampler or DistributedMixedSampler) + assert ( + rank is None and world_size is None + ), "Rank and world_size will have no effect on the provided sampler and should be None" + assert isinstance( + sampler, (DistributedSampler, DistributedMixedSampler) + ), "Invalid sampler type for distributed training." + + # ... wrap the composed dataset and sampler with a fallback mechanism, if needed + if ( + "n_fallback_retries" in loader_cfg + and loader_cfg["n_fallback_retries"] > 0 + and sampler is not None + ): + ranked_logger.info( + f"Wrapping train dataset and sampler with {loader_cfg['n_fallback_retries']} fallbacks..." + ) + dataset, sampler = wrap_dataset_and_sampler_with_fallbacks( + dataset_to_be_wrapped=dataset, + sampler_to_be_wrapped=sampler, + dataset_to_fallback_to=dataset, + sampler_to_fallback_to=sampler, + n_fallback_retries=loader_cfg["n_fallback_retries"], + ) + + # ... assemble the final loader + loader = DataLoader( + dataset=dataset, + sampler=sampler, + collate_fn=lambda x: x, # No collation + **loader_cfg["dataloader_params"] if "dataloader_params" in loader_cfg else {}, + ) + + return loader + + +def subset_dataset_to_example_ids( + dataset: Dataset, + example_ids: list[str] | ListConfig, +) -> Dataset: + """Subset a dataset to a specific set of example IDs.""" + indices = [] + for example_id in example_ids: + # atomworks types the dataset arg as `ExampleIDMixin`; the datasets passed here mix it in. + index = get_row_and_index_by_example_id(dataset, example_id)["index"] # type: ignore[arg-type] + indices.append(index) + + return Subset(dataset, indices) + + +def assemble_val_loader_dict( + cfg: DictConfig, + rank: int = 0, + world_size: int = 1, + loader_cfg: DictConfig | dict | None = None, +) -> dict[str, DataLoader]: + """Assemble a dictionary of validation loaders for multiple datasets. + + If a key is provided to balance the dataset, we will use a LoadBalancedDistributedSampler + rather than a DistributedSampler to maintain a balanced example load across processes + (i.e., avoid a situation where one GPU is allocated all small examples and another all large examples). + + Args: + cfg (DictConfig): Configuration dictionary defining the validation datasets. Each key should correspond to a dataset name. + rank (int, optional): The rank of the current process in distributed training. Defaults to 0. + world_size (int, optional): The total number of processes participating in the distributed training. Defaults to 1. + loader_cfg (DictConfig, optional): Additional configuration parameters for the DataLoader, such as `batch_size` and `num_workers`. Defaults to None. + """ + # ... loop through the validation datasets and create a DataLoader for each, preserving the dataset name + val_loaders = {} + for val_dataset_name, val_dataset in cfg.items(): + if not val_dataset: + # (Skip any None validation datasets; e.g., those overrode by the experiment config) + continue + # DictConfig keys are typed as a broad union; coerce to the str they always are. + val_dataset_name = str(val_dataset_name) + + assert ( + "dataset" in val_dataset + ), f"Expected 'dataset' key in validation dataset config for {val_dataset_name}" + dataset = hydra.utils.instantiate( + val_dataset.dataset + ) # directly instantiate the dataset + + if "key_to_balance" in val_dataset and val_dataset.key_to_balance: + # (If a key is provided to balance the dataset, we will use a LoadBalancedDistributedSampler) + key_to_balance = val_dataset.key_to_balance + ranked_logger.info(f"Balancing dataset with key: {key_to_balance}") + + assert ( + key_to_balance in dataset.data.columns + ), f"Key {key_to_balance} not found in dataset columns!" + + sampler: Sampler = LoadBalancedDistributedSampler( + dataset=dataset, + num_replicas=world_size, + rank=rank, + key_to_balance=key_to_balance, + ) + else: + # (Otherwise, we will use a DistributedSampler, without regard to sample size) + sampler = DistributedSampler( + dataset, + num_replicas=world_size, + rank=rank, + shuffle=False, + drop_last=False, + ) + + val_loader = assemble_distributed_loader( + dataset=dataset, + sampler=sampler, + loader_cfg=loader_cfg, + ) + + val_loaders[val_dataset_name] = val_loader + + return val_loaders diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/ddp.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/ddp.py new file mode 100644 index 0000000000000000000000000000000000000000..0dcbe16f47ca7fe83ab93196a759d8994d8b499f --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/ddp.py @@ -0,0 +1,111 @@ +import logging + +import torch +from beartype.typing import Any +from lightning.fabric.utilities import rank_zero_only +from lightning_utilities.core.rank_zero import rank_prefixed_message +from omegaconf import DictConfig + +logger = logging.getLogger(__name__) + + +def get_current_rank() -> int | None: + """Returns the rank of the current process, or None if it is not yet set.""" + return getattr(rank_zero_only, "rank", None) + + +def is_rank_zero() -> bool: + """Returns whether the current process is rank zero.""" + return get_current_rank() == 0 + + +def set_accelerator_based_on_availability(cfg: DictConfig) -> DictConfig: + """Set training accelerator based on available hardware. + + Args: + cfg: Hydra object with trainer settings "accelerator", "devices_per_node", and "num_nodes". + + Returns: + The same `cfg` object, modified in place. + """ + assert "trainer" in cfg, "Configuration object must have a 'trainer' key." + for key in ["accelerator", "devices_per_node", "num_nodes"]: + assert ( + key in cfg.trainer + ), f"Configuration object must have a 'trainer.{key}' key." + + if torch.cuda.is_available(): + cfg.trainer.accelerator = "gpu" + elif hasattr(torch, "xpu") and torch.xpu.is_available(): + logger.info("Intel XPU detected - using XPU accelerator") + cfg.trainer.accelerator = "xpu" + elif torch.backends.mps.is_available(): + logger.info("Apple MPS detected - using MPS accelerator with float32 precision") + cfg.trainer.accelerator = "mps" + if hasattr(cfg.trainer, "precision"): + cfg.trainer.precision = "32-true" + else: + logger.error( + "No GPUs/XPUs/MPS available - Setting accelerator to 'cpu'. Are you sure you are using the correct configs?" + ) + cfg.trainer.accelerator = "cpu" + cfg.trainer.devices_per_node = 1 + cfg.trainer.num_nodes = 1 + + return cfg + + +class RankedLogger(logging.LoggerAdapter): + """A multi-GPU-friendly python command line logger. + + Modified from https://github.com/ashleve/lightning-hydra-template/blob/main/src/utils/pylogger.py + """ + + def __init__( + self, + name: str = __name__, + rank_zero_only: bool = False, + extra: Any | None = None, + ) -> None: + """Initializes a multi-GPU-friendly python command line logger that logs on all processes + with their rank prefixed in the log message. + + :param name: The name of the logger. Default is ``__name__``. + :param rank_zero_only: Whether to force all logs to only occur on the rank zero process. Default is `False`. + :param extra: (Optional) A dict-like object which provides contextual information. See `logging.LoggerAdapter`. + """ + logger = logging.getLogger(name) + super().__init__(logger=logger, extra=extra) + self.rank_zero_only = rank_zero_only + + def log( # type: ignore[override] # deliberately extends LoggerAdapter.log with a `rank` parameter + self, level: int, msg: str, rank: int | None = None, *args: Any, **kwargs: Any + ) -> None: + """ + Delegate a log call to the underlying logger, after prefixing its message with the rank + of the process it's being logged from. If `'rank'` is provided, then the log will only + occur on that rank/process. + + Args: + level (int): The level to log at. Look at `logging.__init__.py` for more information. + msg (str): The message to log. + rank (Optional[int]): The rank to log at. + args: Additional args to pass to the underlying logging function. + kwargs: Any additional keyword args to pass to the underlying logging function. + """ + if self.isEnabledFor(level): + msg, log_kwargs = self.process(msg, kwargs) + current_rank = getattr(rank_zero_only, "rank", None) + if current_rank is None: + raise RuntimeError( + "The `rank_zero_only.rank` needs to be set before use" + ) + msg = rank_prefixed_message(msg, current_rank) + if self.rank_zero_only: + if current_rank == 0: + self.logger.log(level, msg, *args, **log_kwargs) + else: + if rank is None: + self.logger.log(level, msg, *args, **log_kwargs) + elif current_rank == rank: + self.logger.log(level, msg, *args, **log_kwargs) diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/squashfs.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/squashfs.py new file mode 100644 index 0000000000000000000000000000000000000000..b2bc5a7b1aeb30fcbbc3009ce4fdad0d07ab9342 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/squashfs.py @@ -0,0 +1,172 @@ +"""Squashfs filesystem mount management. + +Provides a singleton manager for mounting and caching squashfs filesystems. +Mounts are lazy (happen on first access) and persist for the lifetime of the program. +""" + +import atexit +import logging +import os +import subprocess +import tempfile +import threading +from pathlib import Path + +logger = logging.getLogger(__name__) + + +class SquashfsManager: + """Singleton manager for squashfs filesystem mounts. + + Provides lazy mounting of squashfs files with automatic caching and cleanup. + Thread-safe for use with multiprocessing/DataLoader workers. + + Examples: + Get mount directory (mounts automatically if needed):: + + from foundry.utils.squashfs import SquashfsManager + + mount_dir = SquashfsManager.get_mount("/path/to/file.sqfs") + file_path = os.path.join(mount_dir, "internal/path/to/file") + + Inspect active mounts:: + + mounts = SquashfsManager.list_mounts() + print(f"Active mounts: {len(mounts)}") + + Cleanup (typically for tests):: + + SquashfsManager.unmount_all() + """ + + _mounts: dict[str, str] = {} + _lock = threading.Lock() + _cleanup_registered = False + + @classmethod + def get_mount(cls, sqfs_file: str) -> str: + """Get mount directory for squashfs file, mounting if needed. + + Args: + sqfs_file: Path to the .sqfs file. + + Returns: + Path to the mount directory containing the squashfs contents. + + Raises: + FileNotFoundError: If the sqfs file doesn't exist. + RuntimeError: If mounting fails. + """ + sqfs_file = str(Path(sqfs_file).resolve()) + + # Check file exists + if not os.path.exists(sqfs_file): + raise FileNotFoundError(f"Squashfs file not found: {sqfs_file}") + + with cls._lock: + # Register cleanup on first use + if not cls._cleanup_registered: + atexit.register(cls._cleanup_on_exit) + cls._cleanup_registered = True + + # Return cached mount if exists + if sqfs_file in cls._mounts: + return cls._mounts[sqfs_file] + + # Mount and cache + mount_dir = cls._mount(sqfs_file) + cls._mounts[sqfs_file] = mount_dir + logger.info(f"Mounted {sqfs_file} at {mount_dir}") + return mount_dir + + @classmethod + def _mount(cls, sqfs_file: str) -> str: + """Internal: Actually perform the squashfs mount. + + Args: + sqfs_file: Path to the .sqfs file. + + Returns: + Path to the mount directory. + + Raises: + RuntimeError: If mounting fails. + """ + mount_dir = tempfile.mkdtemp(prefix="sqfs_") + try: + subprocess.run( + ["squashfuse", sqfs_file, mount_dir], + check=True, + capture_output=True, + ) + return mount_dir + except subprocess.CalledProcessError as e: + # Cleanup failed mount directory + try: + os.rmdir(mount_dir) + except OSError: + pass + raise RuntimeError( + f"Failed to mount {sqfs_file}: {e.stderr.decode()}" + ) from e + + @classmethod + def _unmount(cls, sqfs_file: str) -> None: + """Internal: Unmount a squashfs filesystem. + + Args: + sqfs_file: Path to the .sqfs file to unmount. + """ + if sqfs_file not in cls._mounts: + return + + mount_dir = cls._mounts[sqfs_file] + try: + subprocess.run( + ["fusermount", "-u", mount_dir], + check=True, + capture_output=True, + ) + os.rmdir(mount_dir) + logger.info(f"Unmounted {sqfs_file}") + except (subprocess.CalledProcessError, OSError) as e: + logger.warning(f"Failed to unmount {sqfs_file}: {e}") + finally: + del cls._mounts[sqfs_file] + + @classmethod + def list_mounts(cls) -> dict[str, str]: + """List all active squashfs mounts. + + Returns: + Dictionary mapping sqfs file paths to mount directories. + + Examples: + >>> mounts = SquashfsManager.list_mounts() + >>> print(f"Active mounts: {len(mounts)}") + >>> for sqfs, mount_dir in mounts.items(): + ... print(f"{sqfs} -> {mount_dir}") + """ + with cls._lock: + return cls._mounts.copy() + + @classmethod + def unmount_all(cls) -> None: + """Unmount all squashfs filesystems. + + Useful for cleanup in tests or explicit resource management. + Normally not needed as cleanup happens automatically on program exit. + + Examples: + >>> SquashfsManager.unmount_all() # Clean up in test teardown + """ + with cls._lock: + sqfs_files = list(cls._mounts.keys()) + for sqfs_file in sqfs_files: + cls._unmount(sqfs_file) + + @classmethod + def _cleanup_on_exit(cls) -> None: + """Cleanup hook called on program exit via atexit.""" + logger.debug("Cleaning up squashfs mounts on exit") + cls.unmount_all() diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/torch.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/torch.py new file mode 100644 index 0000000000000000000000000000000000000000..43ad41a49cb077eb1c2eede6f5ad650352d273c5 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/torch.py @@ -0,0 +1,367 @@ +"""General convenience utilities for PyTorch.""" + +__all__ = [ + "map_to", + "assert_no_nans", + "assert_shape", + "assert_same_shape", + "scatter_mean", +] + +import time +import warnings +from contextlib import contextmanager + +import numpy as np +import torch +from beartype.typing import Any, Iterator, Sequence +from toolz import valmap +from torch import Tensor +from torch._prims_common import DeviceLikeType +from torch.types import _dtype + +from foundry import should_check_nans +from foundry.common import at_least_one_exists, do_nothing + + +def map_to( + x: Any, + *, + device: DeviceLikeType | None = None, + dtype: _dtype | None = None, + non_blocking: bool = False, + **to_kwargs: Any, +) -> Any: + """ + Recursively applies the `.to()` method to all tensors in a nested structure. + + This function handles nested structures such as dictionaries and lists, applying the `.to()` method + to any PyTorch tensors while leaving other types unchanged. + + NOTE: If you are instantiating a new tensor, you should use the `device` and `dtype` arguments + instead of calling `map_to()` on the tensor. + (https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html#create-tensors-directly-on-the-target-device) + + + Args: + - x (Any): The input structure, which can be a tensor, dictionary, list, or any other type. + - device (DeviceLikeType | None): The target device to move tensors to (e.g., 'cpu', 'cuda'). + - dtype (_dtype | None): The target dtype to cast tensors to. + - non_blocking (bool): Whether to use non-blocking transfers when possible. + - **to_kwargs: Additional keyword arguments to pass to the `.to()` method. + + Returns: + - Any: The input structure with all contained tensors processed by the `.to()` method. + + Example: + >>> data = {"tensor": torch.tensor([1, 2, 3]), "list": [torch.tensor([4, 5]), "string"]} + >>> map_to(data, device="cuda", dtype=torch.float32) + {'tensor': tensor([1., 2., 3.], device='cuda:0', dtype=torch.float32), + 'list': [tensor([4., 5.], device='cuda:0', dtype=torch.float32), 'string']} + """ + torch._assert( + at_least_one_exists(device, dtype), + "Must provide at least one of `device` or `dtype`", + ) + + if isinstance(x, dict): + return valmap( + lambda v: map_to( + v, device=device, dtype=dtype, non_blocking=non_blocking, **to_kwargs + ), + x, + ) + elif isinstance(x, (list, tuple)): + return type(x)( + map( + lambda v: map_to( + v, + device=device, + dtype=dtype, + non_blocking=non_blocking, + **to_kwargs, + ), + x, + ) + ) + elif isinstance(x, Tensor): + return x.to(device=device, dtype=dtype, non_blocking=non_blocking, **to_kwargs) + else: + return x + + +def _assert_no_nans(x: Any, *, msg: str = "", fail_if_not_tensor: bool = False) -> None: + """Recursively checks for NaN values in tensor-like objects. + + Args: + - x (Any): Input to check for NaNs. Can be a tensor, dict, list, tuple, or other type. + - msg (str): Prefix for error messages. + - fail_if_not_tensor (bool): If True, raises error for non-tensor types. + """ + if isinstance(x, Tensor): + torch._assert( + not torch.isnan(x).any(), + ": ".join(filter(bool, [msg, "Tensor contains NaNs!"])), + ) + elif isinstance(x, np.ndarray): + torch._assert( + not np.isnan(x).any(), + ": ".join(filter(bool, [msg, "Numpy array contains NaNs!"])), + ) + elif isinstance(x, float): + torch._assert( + not np.isnan(x), + ": ".join(filter(bool, [msg, "float is NaN!"])), + ) + elif isinstance(x, dict): + for k, v in x.items(): + _assert_no_nans( + v, + msg=".".join(filter(bool, [msg, k])), + fail_if_not_tensor=fail_if_not_tensor, + ) + elif isinstance(x, (list, tuple)): + for idx, v in enumerate(x): + _assert_no_nans( + v, + msg=".".join(filter(bool, [msg, str(idx)])), + fail_if_not_tensor=fail_if_not_tensor, + ) + elif fail_if_not_tensor: + raise ValueError(f"Unsupported type: {type(x)}") + + +assert_no_nans = _assert_no_nans if should_check_nans else do_nothing + + +@contextmanager +def _suppress_tracer_warnings() -> Iterator[None]: + """ + Context manager to temporarily suppress known warnings in torch.jit.trace(). + Note: Cannot use catch_warnings because of https://bugs.python.org/issue29672 + + References: + - https://github.com/NVlabs/edm2/blob/main/torch_utils/misc.py + """ + tracer_warning_filter = ("ignore", None, torch.jit.TracerWarning, None, 0) + # warnings.filters is typed as an immutable Sequence in typeshed but is a mutable + # list at runtime, so insert/remove are valid. + warnings.filters.insert(0, tracer_warning_filter) # type: ignore[attr-defined] + yield + warnings.filters.remove(tracer_warning_filter) # type: ignore[attr-defined] + + +def assert_shape(tensor: Tensor, ref_shape: Sequence[int | None]) -> None: + """ + Assert that the shape of a tensor matches the given list of integers. + None indicates that the size of a dimension is allowed to vary. + Performs symbolic assertion when used in torch.jit.trace(). + + Args: + - tensor (Tensor): The tensor to check the shape of. + - ref_shape (Sequence[int | None]): The expected shape of the tensor. + + References: + - https://github.com/NVlabs/edm2/blob/main/torch_utils/misc.py + """ + + if tensor.ndim != len(ref_shape): + raise AssertionError( + f"Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}" + ) + + for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)): + if tensor.ndim != len(ref_shape): + raise AssertionError( + f"Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}" + ) + + for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)): + if ref_size is None: + pass + elif isinstance(ref_size, torch.Tensor): + with ( + _suppress_tracer_warnings() + ): # as_tensor results are registered as constants + torch._assert( + torch.equal(torch.as_tensor(size), ref_size), + f"Wrong size for dimension {idx}", + ) + elif isinstance(size, torch.Tensor): + with ( + _suppress_tracer_warnings() + ): # as_tensor results are registered as constants + torch._assert( + torch.equal(size, torch.as_tensor(ref_size)), + f"Wrong size for dimension {idx}: expected {ref_size}", + ) + elif size != ref_size: + raise AssertionError( + f"Wrong size for dimension {idx}: got {size}, expected {ref_size}" + ) + + +def assert_same_shape(tensor: Tensor, ref_tensor: Tensor) -> None: + """Assert that two tensors have the same shape.""" + assert_shape(tensor, ref_tensor.shape) + + +def scatter_mean(zeros: Tensor, dim: int, index: Tensor, source: Tensor) -> Tensor: + """Scatter-mean aggregation, with an MPS-compatible fallback. + + On non-MPS devices uses index_reduce (faster, in-place kernel). + On MPS, index_reduce is not implemented so falls back to scatter_add + count. + + Equivalent to: zeros.index_reduce(dim, index, source, 'mean', include_self=False) + + Args: + zeros: Pre-allocated zero tensor, shape (..., I, C). Will not be modified in-place. + dim: Dimension to scatter along. Must not be the last dimension. + index: 1D index tensor of shape (N,) mapping source positions to output positions. + source: Source tensor where size at `dim` equals N. + + Returns: + Tensor of same shape as zeros. + """ + if zeros.device.type != "mps": + return zeros.index_reduce(dim, index, source, "mean", include_self=False) + + ndim = source.dim() + if dim < 0: + dim = ndim + dim + + # Expand 1D index (N,) to match source shape (..., N, C) + shape = [1] * ndim + shape[dim] = index.shape[0] + idx = index.view(shape).expand_as(source) + + # Sum source values into output positions + result = zeros.scatter_add(dim, idx, source) + + # Count how many source values land in each output position. + # Take a single slice along the last dim to avoid allocating a full (N, C) ones tensor. + idx_count = idx[..., :1] # (..., N, 1) + ones = torch.ones_like(source[..., :1]) # (..., N, 1) + count = torch.zeros(*zeros.shape[:-1], 1, device=zeros.device, dtype=zeros.dtype) + count = count.scatter_add(dim, idx_count, ones) # (..., I, 1) + + return result / count.clamp(min=1) + + +def device_of(obj: Any) -> torch.device: + """Get the device of a PyTorch object, e.g. a `nn.Module` or a `Tensor`.""" + if hasattr(obj, "device"): + return obj.device + elif hasattr(obj, "parameters"): + return next(obj.parameters()).device + else: + raise ValueError(f"Unsupported type: {type(obj)}") + + +class Timer: + """ + A simple timer class for measuring elapsed time. + + This class provides functionality to start, stop, reset, and measure elapsed time. + It can optionally use CUDA or MPS synchronization barriers for more accurate timing + when working with GPU operations. + + Attributes: + name_ (str): The name of the timer. + elapsed_ (float): The total elapsed time. + started_ (bool): Flag indicating if the timer is currently running. + start_time (float): The start time of the current timing session. + use_barrier (bool): Whether to use CUDA or MPS synchronization barriers. + + Args: + name (str): The name of the timer. + use_barrier (bool, optional): Whether to use synchronization barriers. Defaults to True. + """ + + def __init__(self, name: str, use_barrier: bool = True): + self.name_ = name + self.elapsed_ = 0.0 + self.started_ = False + self.start_time = time.time() + self.use_barrier = use_barrier + + def start(self) -> None: + """Start the timer.""" + assert not self.started_, f"timer {self.name_} has already been started" + if self.use_barrier and torch.cuda.is_available(): + torch.cuda.synchronize() + elif self.use_barrier and torch.backends.mps.is_available(): + torch.mps.synchronize() + self.start_time = time.time() + self.started_ = True + + def stop(self) -> None: + """Stop the timer.""" + assert self.started_, f"timer {self.name_} is not started" + if self.use_barrier and torch.cuda.is_available(): + torch.cuda.synchronize() + elif self.use_barrier and torch.backends.mps.is_available(): + torch.mps.synchronize() + self.elapsed_ += time.time() - self.start_time + self.started_ = False + + def reset(self) -> None: + """Reset timer.""" + self.elapsed_ = 0.0 + self.started_ = False + + def elapsed(self, reset: bool = True) -> float: + """Calculate the elapsed time.""" + started_ = self.started_ + # If the timing in progress, end it first. + if self.started_: + self.stop() + # Get the elapsed time. + elapsed_ = self.elapsed_ + # Reset the elapsed time + if reset: + self.reset() + # If timing was in progress, set it back. + if started_: + self.start() + return elapsed_ + + +class Timers: + """ + A collection of named Timer objects. + + This class manages multiple Timer instances, allowing for easy creation, + starting, stopping, resetting, and querying of elapsed times for multiple timers. + + Attributes: + timers (dict): A dictionary of Timer objects, keyed by their names. + """ + + def __init__(self) -> None: + self.timers: dict[str, Timer] = {} + + def __call__(self, name: str, use_barrier: bool = True) -> Timer: + """Get or create a Timer object.""" + if name not in self.timers: + self.timers[name] = Timer(name, use_barrier=use_barrier) + return self.timers[name] + + def start(self, *names: str) -> None: + """Start the specified timers.""" + for name in names: + self(name).start() + + def stop(self, *names: str) -> None: + """Stop the specified timers.""" + for name in names: + self.timers[name].stop() + + def reset(self, *names: str) -> None: + """Reset the specified timers.""" + for name in names: + self.timers[name].reset() + + def elapsed(self, *names: str, reset: bool = True) -> dict[str, float]: + """Get the elapsed time for the specified timers.""" + return {name: self.timers[name].elapsed(reset=reset) for name in names} diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/weights.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/weights.py new file mode 100644 index 0000000000000000000000000000000000000000..9653f252bffd6ed5dfe7f1adde9e761bab4ffc80 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/weights.py @@ -0,0 +1,274 @@ +"""Utils for loading weights from checkpoints.""" + +import re +from dataclasses import dataclass, field +from enum import StrEnum, auto +from os import PathLike + +import torch +from beartype.typing import Any, Pattern +from torch import nn + +from foundry.utils.ddp import RankedLogger + +ranked_logger = RankedLogger(__name__, rank_zero_only=True) + + +class WeightLoadingError(Exception): + """Exception raised when there's an error loading weights.""" + + pass + + +class WeightLoadingPolicy(StrEnum): + """Policy for handling weights when loading checkpoints.""" + + # Always keep default initialization, regardless of whether the parameter is in the checkpoint or shapes match + REINIT = auto() + + # Always zero-initialize, regardless of whether the parameter is in the checkpoint or shapes match + ZERO_INIT = auto() + + # Copy from checkpoint only when shapes match exactly, otherwise error + COPY = auto() + + # Copy from checkpoint if tensors are the same rank, padding with zeros if shapes don't match exectly + COPY_AND_ZERO_PAD = auto() + + +class _PatternPolicyMixin: + """Mixin for handling glob-to-regex pattern compilation and matching for parameter policies. + + Patterns can use the following wildcards: + - * matches any sequence of characters + - ? matches any single character + - [abc] matches any character in the brackets + - . matches a literal dot + + Examples: + - "model.*.weight" matches any weight parameter in the model + - "model.encoder?.weight" matches encoder1.weight, encoder2.weight, etc. + - "model.encoder[12].weight" matches encoder1.weight and encoder2.weight + - "model.encoder.*.bias" matches any bias parameter in encoder submodules + """ + + _compiled_patterns: dict[Pattern, Any] + + @staticmethod + def _glob_to_regex(pattern: str) -> str: + # Convert glob pattern to regex string + return ( + pattern.replace(".", r"\.") + .replace("*", ".*") + .replace("?", ".") + .replace("[", "[") + .replace("]", "]") + ) + + def _compile_patterns(self, policy_dict: dict[str, Any]) -> dict[Pattern, Any]: + compiled = {} + for pattern, value in list(policy_dict.items()): + if any(c in pattern for c in ["*", "?", "[", "]"]): + regex = self._glob_to_regex(pattern) + compiled[re.compile(f"^{regex}$")] = value + return compiled + + def _get_policy_by_pattern( + self, param_name: str, policy_dict: dict[str, Any], default: Any + ) -> Any: + # Exact match first + if policy_dict and param_name in policy_dict: + return policy_dict[param_name] + # Pattern match + for pattern, value in self._compiled_patterns.items(): + if pattern.match(param_name): + return value + return default + + +@dataclass +class WeightLoadingConfig(_PatternPolicyMixin): + """Configuration for handling weights when loading a checkpoint.""" + + default_policy: WeightLoadingPolicy | str = WeightLoadingPolicy.COPY + fallback_policy: WeightLoadingPolicy | str = WeightLoadingPolicy.REINIT + param_policies: dict[str, WeightLoadingPolicy | str] = field(default_factory=dict) + _compiled_patterns: dict[Pattern, WeightLoadingPolicy] = field( + default_factory=dict, repr=False + ) + + def __post_init__(self) -> None: + if isinstance(self.default_policy, str): + self.default_policy = WeightLoadingPolicy(self.default_policy) + if isinstance(self.fallback_policy, str): + self.fallback_policy = WeightLoadingPolicy(self.fallback_policy) + for key, value in self.param_policies.items(): + if isinstance(value, str): + self.param_policies[key] = WeightLoadingPolicy(value) + self._compiled_patterns = self._compile_patterns(self.param_policies) + + def get_policy(self, param_name: str) -> WeightLoadingPolicy: + policy = self._get_policy_by_pattern( + param_name, self.param_policies, self.default_policy + ) + assert isinstance(policy, WeightLoadingPolicy) + return policy + + +@dataclass +class ParameterFreezingConfig(_PatternPolicyMixin): + """Configuration for freezing model parameters after loading weights. + + Allows specifying which parameters to freeze (set requires_grad=False) by exact name or pattern. + Patterns use glob-style wildcards (*, ?). + + Attributes: + param_policies: Dict mapping parameter names or patterns to True (freeze) or False (do not freeze). + freeze_by_default: Whether to freeze parameters not matched by any pattern (default: False). + """ + + param_policies: dict[str, bool] = field(default_factory=dict) + freeze_by_default: bool = False + _compiled_patterns: dict[Pattern, bool] = field(default_factory=dict, repr=False) + + def __post_init__(self) -> None: + self._compiled_patterns = self._compile_patterns(self.param_policies) + + def is_frozen(self, param_name: str) -> bool: + """Get whether a parameter is frozen according to the config.""" + is_frozen = self._get_policy_by_pattern( + param_name, self.param_policies, self.freeze_by_default + ) + assert isinstance(is_frozen, bool) + return is_frozen + + +def freeze_parameters_with_config( + model: nn.Module, config: ParameterFreezingConfig, verbose: bool = True +) -> None: + """Freeze (set requires_grad=False) or unfreeze parameters according to config. + + Args: + model: The model whose parameters to freeze/unfreeze. + config: ParameterFreezingConfig specifying which parameters to freeze. + verbose: Whether to log which parameters have non-default policies applied. + """ + for name, param in model.named_parameters(): + is_frozen = config.is_frozen(name) + param.requires_grad = not is_frozen + + if is_frozen != config.freeze_by_default and verbose: + ranked_logger.info(f"Non-default freezing applied to {name}: {is_frozen}") + + +def load_weights_with_policies( + model: nn.Module, + ckpt: dict[str, torch.Tensor], + config: WeightLoadingConfig | None = None, +) -> dict: + """Load checkpoint weights into model according to the specified configuration. + + Allows for partial loading of weights and zero-initialization of mismatched and arbitrary parameters. + + Args: + model: The model to load weights INTO. By default, all model weights are re-initialized; we overwrite + with the checkpoint weights where appropriate + ckpt: Dictionary mapping parameter names to tensors (loaded from checkpoint on disk) + config: Configuration for handling weight loading. If None, uses default config + Returns: + dict: The updated state_dict (not loaded into model yet) + """ + if config is None: + # (Initialize default config if not provided) + config = WeightLoadingConfig() + + current_state = model.state_dict() + updated_state = {} # We will update this with the new weights + + def _apply_policy( + name: str, + current_param: torch.Tensor, + checkpoint_param: torch.Tensor | None, + policy: WeightLoadingPolicy, + ) -> torch.Tensor: + """Apply a weight loading policy and return the resulting tensor. + + Raises WeightLoadingError for any policy application failures. + """ + if policy == WeightLoadingPolicy.REINIT: + # Keep original initialization + return current_param + + elif policy == WeightLoadingPolicy.ZERO_INIT: + # Zero-initialize + return torch.zeros_like(current_param) + + elif policy == WeightLoadingPolicy.COPY: + # Must have checkpoint param and shapes must match + if checkpoint_param is None: + raise WeightLoadingError(f"Parameter '{name}' not found in checkpoint") + if current_param.shape != checkpoint_param.shape: + raise WeightLoadingError( + f"Shape mismatch for '{name}': model {current_param.shape} vs checkpoint {checkpoint_param.shape}" + ) + return checkpoint_param + + elif policy == WeightLoadingPolicy.COPY_AND_ZERO_PAD: + # Must have checkpoint param and same number of dimensions + if checkpoint_param is None: + raise WeightLoadingError(f"Parameter '{name}' not found in checkpoint") + if len(current_param.shape) != len(checkpoint_param.shape): + raise WeightLoadingError( + f"Different dimensions for '{name}': model {len(current_param.shape)}D vs checkpoint {len(checkpoint_param.shape)}D" + ) + + # Copy where shapes match, zero-init the rest + new_param = torch.zeros_like(current_param) + slices = tuple( + slice(0, min(d_ckpt, d_current)) + for d_ckpt, d_current in zip( + checkpoint_param.shape, current_param.shape + ) + ) + new_param[slices] = checkpoint_param[slices] + return new_param + + # ... loop through all named parameters in the model + for name, current_param in current_state.items(): + # Get the policy for this parameter + policy = config.get_policy(name) + + # Get the corresponding parameter from the checkpoint + checkpoint_param = ckpt.get(name, None) + + try: + # Try to apply the primary policy + result = _apply_policy(name, current_param, checkpoint_param, policy) + updated_state[name] = result + except WeightLoadingError as e: + # Primary policy failed, try fallback + ranked_logger.warning( + f"Failed to apply policy: '{policy}' to '{name}': {str(e)}. Falling back to policy: '{config.fallback_policy}'." + ) + # __post_init__ normalises fallback_policy from str to the enum. + fallback_policy = config.fallback_policy + assert isinstance(fallback_policy, WeightLoadingPolicy) + result = _apply_policy( + name, current_param, checkpoint_param, fallback_policy + ) + updated_state[name] = result + + return updated_state + + +@dataclass +class CheckpointConfig: + """Configuration for loading checkpoints. + + TODO: Implement reset_scheduler and reset_ema + """ + + path: PathLike + reset_optimizer: bool = False + weight_loading_config: WeightLoadingConfig | None = None + parameter_freezing_config: ParameterFreezingConfig | None = None diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/xpu/__init__.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/xpu/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9b94fc141b1a511887030b17667c3d20b58cc634 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/utils/xpu/__init__.py @@ -0,0 +1,27 @@ +"""XPU utilities for Intel GPU support. + +XPU support in PyTorch is now native (torch.xpu.is_available()), but Lightning Fabric +requires custom Accelerator, Strategy, and Precision plugins for proper XPU handling. + +These components are used directly (not via registry) when XPU is detected: +- XPUAccelerator: Custom accelerator for XPU devices +- SingleXPUStrategy: Strategy for single-device XPU training/inference +- XPUMixedPrecision: Precision plugin with proper XPU autocast support + +Usage: + from foundry.utils.xpu import XPUAccelerator, SingleXPUStrategy, XPUMixedPrecision + + # Check availability + if XPUAccelerator.is_available(): + strategy = SingleXPUStrategy(precision_plugin=XPUMixedPrecision("bf16-mixed")) + +Note: + The FabricTrainer automatically uses these components when XPU is detected. + You typically don't need to use them directly unless customizing behavior. +""" + +from .single_xpu_strategy import SingleXPUStrategy +from .xpu_accelerator import XPUAccelerator +from .xpu_precision import XPUMixedPrecision + +__all__ = ["SingleXPUStrategy", "XPUAccelerator", "XPUMixedPrecision"] diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/version.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/version.py new file mode 100644 index 0000000000000000000000000000000000000000..936ba66386c68c747d91a84de37005ed7d818f8d --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/foundry/version.py @@ -0,0 +1,24 @@ +# file generated by vcs-versioning +# don't change, don't track in version control +from __future__ import annotations + +__all__ = [ + "__version__", + "__version_tuple__", + "version", + "version_tuple", + "__commit_id__", + "commit_id", +] + +version: str +__version__: str +__version_tuple__: tuple[int | str, ...] +version_tuple: tuple[int | str, ...] +commit_id: str | None +__commit_id__: str | None + +__version__ = version = '0.0.1.dev1+gb02eed6a6.d20260916' +__version_tuple__ = version_tuple = (0, 0, 1, 'dev1', 'gb02eed6a6.d20260916') + +__commit_id__ = commit_id = None diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/ligandmpnn/data_utils.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/ligandmpnn/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8380bd8b856ed3cd1c18683a25d53f09ce0a0e0a --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/ligandmpnn/data_utils.py @@ -0,0 +1,988 @@ +from __future__ import print_function + +import numpy as np +import torch +import torch.utils +from prody import * + +confProDy(verbosity="none") + +restype_1to3 = { + "A": "ALA", + "R": "ARG", + "N": "ASN", + "D": "ASP", + "C": "CYS", + "Q": "GLN", + "E": "GLU", + "G": "GLY", + "H": "HIS", + "I": "ILE", + "L": "LEU", + "K": "LYS", + "M": "MET", + "F": "PHE", + "P": "PRO", + "S": "SER", + "T": "THR", + "W": "TRP", + "Y": "TYR", + "V": "VAL", + "X": "UNK", +} +restype_str_to_int = { + "A": 0, + "C": 1, + "D": 2, + "E": 3, + "F": 4, + "G": 5, + "H": 6, + "I": 7, + "K": 8, + "L": 9, + "M": 10, + "N": 11, + "P": 12, + "Q": 13, + "R": 14, + "S": 15, + "T": 16, + "V": 17, + "W": 18, + "Y": 19, + "X": 20, +} +restype_int_to_str = { + 0: "A", + 1: "C", + 2: "D", + 3: "E", + 4: "F", + 5: "G", + 6: "H", + 7: "I", + 8: "K", + 9: "L", + 10: "M", + 11: "N", + 12: "P", + 13: "Q", + 14: "R", + 15: "S", + 16: "T", + 17: "V", + 18: "W", + 19: "Y", + 20: "X", +} +alphabet = list(restype_str_to_int) + +element_list = [ + "H", + "He", + "Li", + "Be", + "B", + "C", + "N", + "O", + "F", + "Ne", + "Na", + "Mg", + "Al", + "Si", + "P", + "S", + "Cl", + "Ar", + "K", + "Ca", + "Sc", + "Ti", + "V", + "Cr", + "Mn", + "Fe", + "Co", + "Ni", + "Cu", + "Zn", + "Ga", + "Ge", + "As", + "Se", + "Br", + "Kr", + "Rb", + "Sr", + "Y", + "Zr", + "Nb", + "Mb", + "Tc", + "Ru", + "Rh", + "Pd", + "Ag", + "Cd", + "In", + "Sn", + "Sb", + "Te", + "I", + "Xe", + "Cs", + "Ba", + "La", + "Ce", + "Pr", + "Nd", + "Pm", + "Sm", + "Eu", + "Gd", + "Tb", + "Dy", + "Ho", + "Er", + "Tm", + "Yb", + "Lu", + "Hf", + "Ta", + "W", + "Re", + "Os", + "Ir", + "Pt", + "Au", + "Hg", + "Tl", + "Pb", + "Bi", + "Po", + "At", + "Rn", + "Fr", + "Ra", + "Ac", + "Th", + "Pa", + "U", + "Np", + "Pu", + "Am", + "Cm", + "Bk", + "Cf", + "Es", + "Fm", + "Md", + "No", + "Lr", + "Rf", + "Db", + "Sg", + "Bh", + "Hs", + "Mt", + "Ds", + "Rg", + "Cn", + "Uut", + "Fl", + "Uup", + "Lv", + "Uus", + "Uuo", +] +element_list = [item.upper() for item in element_list] +# element_dict = dict(zip(element_list, range(1,len(element_list)))) +element_dict_rev = dict(zip(range(1, len(element_list)), element_list)) + + +def get_seq_rec(S: torch.Tensor, S_pred: torch.Tensor, mask: torch.Tensor): + """ + S : true sequence shape=[batch, length] + S_pred : predicted sequence shape=[batch, length] + mask : mask to compute average over the region shape=[batch, length] + + average : averaged sequence recovery shape=[batch] + """ + match = S == S_pred + average = torch.sum(match * mask, dim=-1) / torch.sum(mask, dim=-1) + return average + + +def get_score(S: torch.Tensor, log_probs: torch.Tensor, mask: torch.Tensor): + """ + S : true sequence shape=[batch, length] + log_probs : predicted sequence shape=[batch, length] + mask : mask to compute average over the region shape=[batch, length] + + average_loss : averaged categorical cross entropy (CCE) [batch] + loss_per_resdue : per position CCE [batch, length] + """ + S_one_hot = torch.nn.functional.one_hot(S, 21) + loss_per_residue = -(S_one_hot * log_probs).sum(-1) # [B, L] + average_loss = torch.sum(loss_per_residue * mask, dim=-1) / ( + torch.sum(mask, dim=-1) + 1e-8 + ) + return average_loss, loss_per_residue + + +def write_full_PDB( + save_path: str, + X: np.ndarray, + X_m: np.ndarray, + b_factors: np.ndarray, + R_idx: np.ndarray, + chain_letters: np.ndarray, + S: np.ndarray, + other_atoms=None, + icodes=None, + force_hetatm=False, +): + """ + save_path : path where the PDB will be written to + X : protein atom xyz coordinates shape=[length, 14, 3] + X_m : protein atom mask shape=[length, 14] + b_factors: shape=[length, 14] + R_idx: protein residue indices shape=[length] + chain_letters: protein chain letters shape=[length] + S : protein amino acid sequence shape=[length] + other_atoms: other atoms parsed by prody + icodes: a list of insertion codes for the PDB; e.g. antibody loops + """ + + restype_1to3 = { + "A": "ALA", + "R": "ARG", + "N": "ASN", + "D": "ASP", + "C": "CYS", + "Q": "GLN", + "E": "GLU", + "G": "GLY", + "H": "HIS", + "I": "ILE", + "L": "LEU", + "K": "LYS", + "M": "MET", + "F": "PHE", + "P": "PRO", + "S": "SER", + "T": "THR", + "W": "TRP", + "Y": "TYR", + "V": "VAL", + "X": "UNK", + } + restype_INTtoSTR = { + 0: "A", + 1: "C", + 2: "D", + 3: "E", + 4: "F", + 5: "G", + 6: "H", + 7: "I", + 8: "K", + 9: "L", + 10: "M", + 11: "N", + 12: "P", + 13: "Q", + 14: "R", + 15: "S", + 16: "T", + 17: "V", + 18: "W", + 19: "Y", + 20: "X", + } + restype_name_to_atom14_names = { + "ALA": ["N", "CA", "C", "O", "CB", "", "", "", "", "", "", "", "", ""], + "ARG": [ + "N", + "CA", + "C", + "O", + "CB", + "CG", + "CD", + "NE", + "CZ", + "NH1", + "NH2", + "", + "", + "", + ], + "ASN": ["N", "CA", "C", "O", "CB", "CG", "OD1", "ND2", "", "", "", "", "", ""], + "ASP": ["N", "CA", "C", "O", "CB", "CG", "OD1", "OD2", "", "", "", "", "", ""], + "CYS": ["N", "CA", "C", "O", "CB", "SG", "", "", "", "", "", "", "", ""], + "GLN": [ + "N", + "CA", + "C", + "O", + "CB", + "CG", + "CD", + "OE1", + "NE2", + "", + "", + "", + "", + "", + ], + "GLU": [ + "N", + "CA", + "C", + "O", + "CB", + "CG", + "CD", + "OE1", + "OE2", + "", + "", + "", + "", + "", + ], + "GLY": ["N", "CA", "C", "O", "", "", "", "", "", "", "", "", "", ""], + "HIS": [ + "N", + "CA", + "C", + "O", + "CB", + "CG", + "ND1", + "CD2", + "CE1", + "NE2", + "", + "", + "", + "", + ], + "ILE": ["N", "CA", "C", "O", "CB", "CG1", "CG2", "CD1", "", "", "", "", "", ""], + "LEU": ["N", "CA", "C", "O", "CB", "CG", "CD1", "CD2", "", "", "", "", "", ""], + "LYS": ["N", "CA", "C", "O", "CB", "CG", "CD", "CE", "NZ", "", "", "", "", ""], + "MET": ["N", "CA", "C", "O", "CB", "CG", "SD", "CE", "", "", "", "", "", ""], + "PHE": [ + "N", + "CA", + "C", + "O", + "CB", + "CG", + "CD1", + "CD2", + "CE1", + "CE2", + "CZ", + "", + "", + "", + ], + "PRO": ["N", "CA", "C", "O", "CB", "CG", "CD", "", "", "", "", "", "", ""], + "SER": ["N", "CA", "C", "O", "CB", "OG", "", "", "", "", "", "", "", ""], + "THR": ["N", "CA", "C", "O", "CB", "OG1", "CG2", "", "", "", "", "", "", ""], + "TRP": [ + "N", + "CA", + "C", + "O", + "CB", + "CG", + "CD1", + "CD2", + "CE2", + "CE3", + "NE1", + "CZ2", + "CZ3", + "CH2", + ], + "TYR": [ + "N", + "CA", + "C", + "O", + "CB", + "CG", + "CD1", + "CD2", + "CE1", + "CE2", + "CZ", + "OH", + "", + "", + ], + "VAL": ["N", "CA", "C", "O", "CB", "CG1", "CG2", "", "", "", "", "", "", ""], + "UNK": ["", "", "", "", "", "", "", "", "", "", "", "", "", ""], + } + + S_str = [restype_1to3[AA] for AA in [restype_INTtoSTR[AA] for AA in S]] + + X_list = [] + b_factor_list = [] + atom_name_list = [] + element_name_list = [] + residue_name_list = [] + residue_number_list = [] + chain_id_list = [] + icodes_list = [] + for i, AA in enumerate(S_str): + sel = X_m[i].astype(np.int32) == 1 + total = np.sum(sel) + tmp = np.array(restype_name_to_atom14_names[AA])[sel] + X_list.append(X[i][sel]) + b_factor_list.append(b_factors[i][sel]) + atom_name_list.append(tmp) + element_name_list += [AA[:1] for AA in list(tmp)] + residue_name_list += total * [AA] + residue_number_list += total * [R_idx[i]] + chain_id_list += total * [chain_letters[i]] + icodes_list += total * [icodes[i]] + + X_stack = np.concatenate(X_list, 0) + b_factor_stack = np.concatenate(b_factor_list, 0) + atom_name_stack = np.concatenate(atom_name_list, 0) + + protein = prody.AtomGroup() + protein.setCoords(X_stack) + protein.setBetas(b_factor_stack) + protein.setNames(atom_name_stack) + protein.setResnames(residue_name_list) + protein.setElements(element_name_list) + protein.setOccupancies(np.ones([X_stack.shape[0]])) + protein.setResnums(residue_number_list) + protein.setChids(chain_id_list) + protein.setIcodes(icodes_list) + + if other_atoms: + other_atoms_g = prody.AtomGroup() + other_atoms_g.setCoords(other_atoms.getCoords()) + other_atoms_g.setNames(other_atoms.getNames()) + other_atoms_g.setResnames(other_atoms.getResnames()) + other_atoms_g.setElements(other_atoms.getElements()) + other_atoms_g.setOccupancies(other_atoms.getOccupancies()) + other_atoms_g.setResnums(other_atoms.getResnums()) + other_atoms_g.setChids(other_atoms.getChids()) + if force_hetatm: + other_atoms_g.setFlags("hetatm", other_atoms.getFlags("hetatm")) + writePDB(save_path, protein + other_atoms_g) + else: + writePDB(save_path, protein) + + +def get_aligned_coordinates(protein_atoms, CA_dict: dict, atom_name: str): + """ + protein_atoms: prody atom group + CA_dict: mapping between chain_residue_idx_icodes and integers + atom_name: atom to be parsed; e.g. CA + """ + atom_atoms = protein_atoms.select(f"name {atom_name}") + + if atom_atoms != None: + atom_coords = atom_atoms.getCoords() + atom_resnums = atom_atoms.getResnums() + atom_chain_ids = atom_atoms.getChids() + atom_icodes = atom_atoms.getIcodes() + + atom_coords_ = np.zeros([len(CA_dict), 3], np.float32) + atom_coords_m = np.zeros([len(CA_dict)], np.int32) + if atom_atoms != None: + for i in range(len(atom_resnums)): + code = atom_chain_ids[i] + "_" + str(atom_resnums[i]) + "_" + atom_icodes[i] + if code in list(CA_dict): + atom_coords_[CA_dict[code], :] = atom_coords[i] + atom_coords_m[CA_dict[code]] = 1 + return atom_coords_, atom_coords_m + + +def parse_PDB( + input_path: str, + device: str = "cpu", + chains: list = [], + parse_all_atoms: bool = False, + parse_atoms_with_zero_occupancy: bool = False +): + """ + input_path : path for the input PDB + device: device for the torch.Tensor + chains: a list specifying which chains need to be parsed; e.g. ["A", "B"] + parse_all_atoms: if False parse only N,CA,C,O otherwise all 37 atoms + parse_atoms_with_zero_occupancy: if True atoms with zero occupancy will be parsed + """ + element_list = [ + "H", + "He", + "Li", + "Be", + "B", + "C", + "N", + "O", + "F", + "Ne", + "Na", + "Mg", + "Al", + "Si", + "P", + "S", + "Cl", + "Ar", + "K", + "Ca", + "Sc", + "Ti", + "V", + "Cr", + "Mn", + "Fe", + "Co", + "Ni", + "Cu", + "Zn", + "Ga", + "Ge", + "As", + "Se", + "Br", + "Kr", + "Rb", + "Sr", + "Y", + "Zr", + "Nb", + "Mb", + "Tc", + "Ru", + "Rh", + "Pd", + "Ag", + "Cd", + "In", + "Sn", + "Sb", + "Te", + "I", + "Xe", + "Cs", + "Ba", + "La", + "Ce", + "Pr", + "Nd", + "Pm", + "Sm", + "Eu", + "Gd", + "Tb", + "Dy", + "Ho", + "Er", + "Tm", + "Yb", + "Lu", + "Hf", + "Ta", + "W", + "Re", + "Os", + "Ir", + "Pt", + "Au", + "Hg", + "Tl", + "Pb", + "Bi", + "Po", + "At", + "Rn", + "Fr", + "Ra", + "Ac", + "Th", + "Pa", + "U", + "Np", + "Pu", + "Am", + "Cm", + "Bk", + "Cf", + "Es", + "Fm", + "Md", + "No", + "Lr", + "Rf", + "Db", + "Sg", + "Bh", + "Hs", + "Mt", + "Ds", + "Rg", + "Cn", + "Uut", + "Fl", + "Uup", + "Lv", + "Uus", + "Uuo", + ] + element_list = [item.upper() for item in element_list] + element_dict = dict(zip(element_list, range(1, len(element_list)))) + restype_3to1 = { + "ALA": "A", + "ARG": "R", + "ASN": "N", + "ASP": "D", + "CYS": "C", + "GLN": "Q", + "GLU": "E", + "GLY": "G", + "HIS": "H", + "ILE": "I", + "LEU": "L", + "LYS": "K", + "MET": "M", + "PHE": "F", + "PRO": "P", + "SER": "S", + "THR": "T", + "TRP": "W", + "TYR": "Y", + "VAL": "V", + } + restype_STRtoINT = { + "A": 0, + "C": 1, + "D": 2, + "E": 3, + "F": 4, + "G": 5, + "H": 6, + "I": 7, + "K": 8, + "L": 9, + "M": 10, + "N": 11, + "P": 12, + "Q": 13, + "R": 14, + "S": 15, + "T": 16, + "V": 17, + "W": 18, + "Y": 19, + "X": 20, + } + + atom_order = { + "N": 0, + "CA": 1, + "C": 2, + "CB": 3, + "O": 4, + "CG": 5, + "CG1": 6, + "CG2": 7, + "OG": 8, + "OG1": 9, + "SG": 10, + "CD": 11, + "CD1": 12, + "CD2": 13, + "ND1": 14, + "ND2": 15, + "OD1": 16, + "OD2": 17, + "SD": 18, + "CE": 19, + "CE1": 20, + "CE2": 21, + "CE3": 22, + "NE": 23, + "NE1": 24, + "NE2": 25, + "OE1": 26, + "OE2": 27, + "CH2": 28, + "NH1": 29, + "NH2": 30, + "OH": 31, + "CZ": 32, + "CZ2": 33, + "CZ3": 34, + "NZ": 35, + "OXT": 36, + } + + if not parse_all_atoms: + atom_types = ["N", "CA", "C", "O"] + else: + atom_types = [ + "N", + "CA", + "C", + "CB", + "O", + "CG", + "CG1", + "CG2", + "OG", + "OG1", + "SG", + "CD", + "CD1", + "CD2", + "ND1", + "ND2", + "OD1", + "OD2", + "SD", + "CE", + "CE1", + "CE2", + "CE3", + "NE", + "NE1", + "NE2", + "OE1", + "OE2", + "CH2", + "NH1", + "NH2", + "OH", + "CZ", + "CZ2", + "CZ3", + "NZ", + ] + + atoms = parsePDB(input_path) + if not parse_atoms_with_zero_occupancy: + atoms = atoms.select("occupancy > 0") + if chains: + str_out = "" + for item in chains: + str_out += " chain " + item + " or" + atoms = atoms.select(str_out[1:-3]) + + protein_atoms = atoms.select("protein") + backbone = protein_atoms.select("backbone") + other_atoms = atoms.select("not protein and not water") + water_atoms = atoms.select("water") + + CA_atoms = protein_atoms.select("name CA") + CA_resnums = CA_atoms.getResnums() + CA_chain_ids = CA_atoms.getChids() + CA_icodes = CA_atoms.getIcodes() + + CA_dict = {} + for i in range(len(CA_resnums)): + code = CA_chain_ids[i] + "_" + str(CA_resnums[i]) + "_" + CA_icodes[i] + CA_dict[code] = i + + xyz_37 = np.zeros([len(CA_dict), 37, 3], np.float32) + xyz_37_m = np.zeros([len(CA_dict), 37], np.int32) + for atom_name in atom_types: + xyz, xyz_m = get_aligned_coordinates(protein_atoms, CA_dict, atom_name) + xyz_37[:, atom_order[atom_name], :] = xyz + xyz_37_m[:, atom_order[atom_name]] = xyz_m + + N = xyz_37[:, atom_order["N"], :] + CA = xyz_37[:, atom_order["CA"], :] + C = xyz_37[:, atom_order["C"], :] + O = xyz_37[:, atom_order["O"], :] + + N_m = xyz_37_m[:, atom_order["N"]] + CA_m = xyz_37_m[:, atom_order["CA"]] + C_m = xyz_37_m[:, atom_order["C"]] + O_m = xyz_37_m[:, atom_order["O"]] + + mask = N_m * CA_m * C_m * O_m # must all 4 atoms exist + + b = CA - N + c = C - CA + a = np.cross(b, c, axis=-1) + CB = -0.58273431 * a + 0.56802827 * b - 0.54067466 * c + CA + + chain_labels = np.array(CA_atoms.getChindices(), dtype=np.int32) + R_idx = np.array(CA_resnums, dtype=np.int32) + S = CA_atoms.getResnames() + S = [restype_3to1[AA] if AA in list(restype_3to1) else "X" for AA in list(S)] + S = np.array([restype_STRtoINT[AA] for AA in list(S)], np.int32) + X = np.concatenate([N[:, None], CA[:, None], C[:, None], O[:, None]], 1) + + try: + Y = np.array(other_atoms.getCoords(), dtype=np.float32) + Y_t = list(other_atoms.getElements()) + Y_t = np.array( + [ + element_dict[y_t.upper()] if y_t.upper() in element_list else 0 + for y_t in Y_t + ], + dtype=np.int32, + ) + Y_m = (Y_t != 1) * (Y_t != 0) + + Y = Y[Y_m, :] + Y_t = Y_t[Y_m] + Y_m = Y_m[Y_m] + except: + Y = np.zeros([1, 3], np.float32) + Y_t = np.zeros([1], np.int32) + Y_m = np.zeros([1], np.int32) + + output_dict = {} + output_dict["X"] = torch.tensor(X, device=device, dtype=torch.float32) + output_dict["mask"] = torch.tensor(mask, device=device, dtype=torch.int32) + output_dict["Y"] = torch.tensor(Y, device=device, dtype=torch.float32) + output_dict["Y_t"] = torch.tensor(Y_t, device=device, dtype=torch.int32) + output_dict["Y_m"] = torch.tensor(Y_m, device=device, dtype=torch.int32) + + output_dict["R_idx"] = torch.tensor(R_idx, device=device, dtype=torch.int32) + output_dict["chain_labels"] = torch.tensor( + chain_labels, device=device, dtype=torch.int32 + ) + + output_dict["chain_letters"] = CA_chain_ids + + mask_c = [] + chain_list = list(set(output_dict["chain_letters"])) + chain_list.sort() + for chain in chain_list: + mask_c.append( + torch.tensor( + [chain == item for item in output_dict["chain_letters"]], + device=device, + dtype=bool, + ) + ) + + output_dict["mask_c"] = mask_c + output_dict["chain_list"] = chain_list + + output_dict["S"] = torch.tensor(S, device=device, dtype=torch.int32) + + output_dict["xyz_37"] = torch.tensor(xyz_37, device=device, dtype=torch.float32) + output_dict["xyz_37_m"] = torch.tensor(xyz_37_m, device=device, dtype=torch.int32) + + return output_dict, backbone, other_atoms, CA_icodes, water_atoms + + +def get_nearest_neighbours(CB, mask, Y, Y_t, Y_m, number_of_ligand_atoms): + device = CB.device + mask_CBY = mask[:, None] * Y_m[None, :] # [A,B] + L2_AB = torch.sum((CB[:, None, :] - Y[None, :, :]) ** 2, -1) + L2_AB = L2_AB * mask_CBY + (1 - mask_CBY) * 1000.0 + + nn_idx = torch.argsort(L2_AB, -1)[:, :number_of_ligand_atoms] + L2_AB_nn = torch.gather(L2_AB, 1, nn_idx) + D_AB_closest = torch.sqrt(L2_AB_nn[:, 0]) + + Y_r = Y[None, :, :].repeat(CB.shape[0], 1, 1) + Y_t_r = Y_t[None, :].repeat(CB.shape[0], 1) + Y_m_r = Y_m[None, :].repeat(CB.shape[0], 1) + + Y_tmp = torch.gather(Y_r, 1, nn_idx[:, :, None].repeat(1, 1, 3)) + Y_t_tmp = torch.gather(Y_t_r, 1, nn_idx) + Y_m_tmp = torch.gather(Y_m_r, 1, nn_idx) + + Y = torch.zeros( + [CB.shape[0], number_of_ligand_atoms, 3], dtype=torch.float32, device=device + ) + Y_t = torch.zeros( + [CB.shape[0], number_of_ligand_atoms], dtype=torch.int32, device=device + ) + Y_m = torch.zeros( + [CB.shape[0], number_of_ligand_atoms], dtype=torch.int32, device=device + ) + + num_nn_update = Y_tmp.shape[1] + Y[:, :num_nn_update] = Y_tmp + Y_t[:, :num_nn_update] = Y_t_tmp + Y_m[:, :num_nn_update] = Y_m_tmp + + return Y, Y_t, Y_m, D_AB_closest + + +def featurize( + input_dict, + cutoff_for_score=8.0, + use_atom_context=True, + number_of_ligand_atoms=16, + model_type="protein_mpnn", +): + output_dict = {} + if model_type == "ligand_mpnn": + mask = input_dict["mask"] + Y = input_dict["Y"] + Y_t = input_dict["Y_t"] + Y_m = input_dict["Y_m"] + N = input_dict["X"][:, 0, :] + CA = input_dict["X"][:, 1, :] + C = input_dict["X"][:, 2, :] + b = CA - N + c = C - CA + a = torch.cross(b, c, axis=-1) + CB = -0.58273431 * a + 0.56802827 * b - 0.54067466 * c + CA + Y, Y_t, Y_m, D_XY = get_nearest_neighbours( + CB, mask, Y, Y_t, Y_m, number_of_ligand_atoms + ) + mask_XY = (D_XY < cutoff_for_score) * mask * Y_m[:, 0] + output_dict["mask_XY"] = mask_XY[None,] + if "side_chain_mask" in list(input_dict): + output_dict["side_chain_mask"] = input_dict["side_chain_mask"][None,] + output_dict["Y"] = Y[None,] + output_dict["Y_t"] = Y_t[None,] + output_dict["Y_m"] = Y_m[None,] + if not use_atom_context: + output_dict["Y_m"] = 0.0 * output_dict["Y_m"] + elif ( + model_type == "per_residue_label_membrane_mpnn" + or model_type == "global_label_membrane_mpnn" + ): + output_dict["membrane_per_residue_labels"] = input_dict[ + "membrane_per_residue_labels" + ][None,] + + R_idx_list = [] + count = 0 + R_idx_prev = -100000 + for R_idx in list(input_dict["R_idx"]): + if R_idx_prev == R_idx: + count += 1 + R_idx_list.append(R_idx + count) + R_idx_prev = R_idx + R_idx_renumbered = torch.tensor(R_idx_list, device=R_idx.device) + output_dict["R_idx"] = R_idx_renumbered[None,] + output_dict["R_idx_original"] = input_dict["R_idx"][None,] + output_dict["chain_labels"] = input_dict["chain_labels"][None,] + output_dict["S"] = input_dict["S"][None,] + output_dict["chain_mask"] = input_dict["chain_mask"][None,] + output_dict["mask"] = input_dict["mask"][None,] + + output_dict["X"] = input_dict["X"][None,] + + if "xyz_37" in list(input_dict): + output_dict["xyz_37"] = input_dict["xyz_37"][None,] + output_dict["xyz_37_m"] = input_dict["xyz_37_m"][None,] + + return output_dict diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/ligandmpnn/model_utils.py b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/ligandmpnn/model_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7cc9759bf435ea87d04d13e962d96164c6165825 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/ligandmpnn/model_utils.py @@ -0,0 +1,1772 @@ +from __future__ import print_function + +import itertools +import sys + +import numpy as np +import torch + + +class ProteinMPNN(torch.nn.Module): + def __init__( + self, + num_letters=21, + node_features=128, + edge_features=128, + hidden_dim=128, + num_encoder_layers=3, + num_decoder_layers=3, + vocab=21, + k_neighbors=48, + augment_eps=0.0, + dropout=0.0, + device=None, + atom_context_num=0, + model_type="protein_mpnn", + ligand_mpnn_use_side_chain_context=False, + ): + super(ProteinMPNN, self).__init__() + + self.model_type = model_type + self.node_features = node_features + self.edge_features = edge_features + self.hidden_dim = hidden_dim + + if self.model_type == "ligand_mpnn": + self.features = ProteinFeaturesLigand( + node_features, + edge_features, + top_k=k_neighbors, + augment_eps=augment_eps, + device=device, + atom_context_num=atom_context_num, + use_side_chains=ligand_mpnn_use_side_chain_context, + ) + self.W_v = torch.nn.Linear(node_features, hidden_dim, bias=True) + self.W_c = torch.nn.Linear(hidden_dim, hidden_dim, bias=True) + + self.W_nodes_y = torch.nn.Linear(hidden_dim, hidden_dim, bias=True) + self.W_edges_y = torch.nn.Linear(hidden_dim, hidden_dim, bias=True) + + self.V_C = torch.nn.Linear(hidden_dim, hidden_dim, bias=False) + self.V_C_norm = torch.nn.LayerNorm(hidden_dim) + + self.context_encoder_layers = torch.nn.ModuleList( + [ + DecLayer(hidden_dim, hidden_dim * 2, dropout=dropout) + for _ in range(2) + ] + ) + + self.y_context_encoder_layers = torch.nn.ModuleList( + [DecLayerJ(hidden_dim, hidden_dim, dropout=dropout) for _ in range(2)] + ) + elif self.model_type == "protein_mpnn" or self.model_type == "soluble_mpnn": + self.features = ProteinFeatures( + node_features, edge_features, top_k=k_neighbors, augment_eps=augment_eps + ) + elif ( + self.model_type == "per_residue_label_membrane_mpnn" + or self.model_type == "global_label_membrane_mpnn" + ): + self.W_v = torch.nn.Linear(node_features, hidden_dim, bias=True) + self.features = ProteinFeaturesMembrane( + node_features, + edge_features, + top_k=k_neighbors, + augment_eps=augment_eps, + num_classes=3, + ) + else: + print("Choose --model_type flag from currently available models") + sys.exit() + + self.W_e = torch.nn.Linear(edge_features, hidden_dim, bias=True) + self.W_s = torch.nn.Embedding(vocab, hidden_dim) + + self.dropout = torch.nn.Dropout(dropout) + + # Encoder layers + self.encoder_layers = torch.nn.ModuleList( + [ + EncLayer(hidden_dim, hidden_dim * 2, dropout=dropout) + for _ in range(num_encoder_layers) + ] + ) + + # Decoder layers + self.decoder_layers = torch.nn.ModuleList( + [ + DecLayer(hidden_dim, hidden_dim * 3, dropout=dropout) + for _ in range(num_decoder_layers) + ] + ) + + self.W_out = torch.nn.Linear(hidden_dim, num_letters, bias=True) + + for p in self.parameters(): + if p.dim() > 1: + torch.nn.init.xavier_uniform_(p) + + def encode(self, feature_dict): + # xyz_37 = feature_dict["xyz_37"] #[B,L,37,3] - xyz coordinates for all atoms if needed + # xyz_37_m = feature_dict["xyz_37_m"] #[B,L,37] - mask for all coords + # Y = feature_dict["Y"] #[B,L,num_context_atoms,3] - for ligandMPNN coords + # Y_t = feature_dict["Y_t"] #[B,L,num_context_atoms] - element type + # Y_m = feature_dict["Y_m"] #[B,L,num_context_atoms] - mask + # X = feature_dict["X"] #[B,L,4,3] - backbone xyz coordinates for N,CA,C,O + S_true = feature_dict[ + "S" + ] # [B,L] - integer protein sequence encoded using "restype_STRtoINT" + # R_idx = feature_dict["R_idx"] #[B,L] - primary sequence residue index + mask = feature_dict[ + "mask" + ] # [B,L] - mask for missing regions - should be removed! all ones most of the time + # chain_labels = feature_dict["chain_labels"] #[B,L] - integer labels for chain letters + + B, L = S_true.shape + device = S_true.device + + if self.model_type == "ligand_mpnn": + V, E, E_idx, Y_nodes, Y_edges, Y_m = self.features(feature_dict) + h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=device) + h_E = self.W_e(E) + h_E_context = self.W_v(V) + + mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1) + mask_attend = mask.unsqueeze(-1) * mask_attend + for layer in self.encoder_layers: + h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend) + + h_V_C = self.W_c(h_V) + Y_m_edges = Y_m[:, :, :, None] * Y_m[:, :, None, :] + Y_nodes = self.W_nodes_y(Y_nodes) + Y_edges = self.W_edges_y(Y_edges) + for i in range(len(self.context_encoder_layers)): + Y_nodes = self.y_context_encoder_layers[i]( + Y_nodes, Y_edges, Y_m, Y_m_edges + ) + h_E_context_cat = torch.cat([h_E_context, Y_nodes], -1) + h_V_C = self.context_encoder_layers[i]( + h_V_C, h_E_context_cat, mask, Y_m + ) + + h_V_C = self.V_C(h_V_C) + h_V = h_V + self.V_C_norm(self.dropout(h_V_C)) + elif self.model_type == "protein_mpnn" or self.model_type == "soluble_mpnn": + E, E_idx = self.features(feature_dict) + h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=device) + h_E = self.W_e(E) + + mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1) + mask_attend = mask.unsqueeze(-1) * mask_attend + for layer in self.encoder_layers: + h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend) + elif ( + self.model_type == "per_residue_label_membrane_mpnn" + or self.model_type == "global_label_membrane_mpnn" + ): + V, E, E_idx = self.features(feature_dict) + h_V = self.W_v(V) + h_E = self.W_e(E) + + mask_attend = gather_nodes(mask.unsqueeze(-1), E_idx).squeeze(-1) + mask_attend = mask.unsqueeze(-1) * mask_attend + for layer in self.encoder_layers: + h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend) + + return h_V, h_E, E_idx + + def sample(self, feature_dict): + # xyz_37 = feature_dict["xyz_37"] #[B,L,37,3] - xyz coordinates for all atoms if needed + # xyz_37_m = feature_dict["xyz_37_m"] #[B,L,37] - mask for all coords + # Y = feature_dict["Y"] #[B,L,num_context_atoms,3] - for ligandMPNN coords + # Y_t = feature_dict["Y_t"] #[B,L,num_context_atoms] - element type + # Y_m = feature_dict["Y_m"] #[B,L,num_context_atoms] - mask + # X = feature_dict["X"] #[B,L,4,3] - backbone xyz coordinates for N,CA,C,O + B_decoder = feature_dict["batch_size"] + S_true = feature_dict[ + "S" + ] # [B,L] - integer proitein sequence encoded using "restype_STRtoINT" + # R_idx = feature_dict["R_idx"] #[B,L] - primary sequence residue index + mask = feature_dict[ + "mask" + ] # [B,L] - mask for missing regions - should be removed! all ones most of the time + chain_mask = feature_dict[ + "chain_mask" + ] # [B,L] - mask for which residues need to be fixed; 0.0 - fixed; 1.0 - will be designed + bias = feature_dict["bias"] # [B,L,21] - amino acid bias per position + # chain_labels = feature_dict["chain_labels"] #[B,L] - integer labels for chain letters + randn = feature_dict[ + "randn" + ] # [B,L] - random numbers for decoding order; only the first entry is used since decoding within a batch needs to match for symmetry + temperature = feature_dict[ + "temperature" + ] # float - sampling temperature; prob = softmax(logits/temperature) + symmetry_list_of_lists = feature_dict[ + "symmetry_residues" + ] # [[0, 1, 14], [10,11,14,15], [20, 21]] #indices to select X over length - L + symmetry_weights_list_of_lists = feature_dict[ + "symmetry_weights" + ] # [[1.0, 1.0, 1.0], [-2.0,1.1,0.2,1.1], [2.3, 1.1]] + + B, L = S_true.shape + device = S_true.device + + h_V, h_E, E_idx = self.encode(feature_dict) + + chain_mask = mask * chain_mask # update chain_M to include missing regions + decoding_order = torch.argsort( + (chain_mask + 0.0001) * (torch.abs(randn)) + ) # [numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0] + if len(symmetry_list_of_lists[0]) == 0 and len(symmetry_list_of_lists) == 1: + E_idx = E_idx.repeat(B_decoder, 1, 1) + permutation_matrix_reverse = torch.nn.functional.one_hot( + decoding_order, num_classes=L + ).float() + order_mask_backward = torch.einsum( + "ij, biq, bjp->bqp", + (1 - torch.triu(torch.ones(L, L, device=device))), + permutation_matrix_reverse, + permutation_matrix_reverse, + ) + mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) + mask_1D = mask.view([B, L, 1, 1]) + mask_bw = mask_1D * mask_attend + mask_fw = mask_1D * (1.0 - mask_attend) + + # repeat for decoding + S_true = S_true.repeat(B_decoder, 1) + h_V = h_V.repeat(B_decoder, 1, 1) + h_E = h_E.repeat(B_decoder, 1, 1, 1) + chain_mask = chain_mask.repeat(B_decoder, 1) + mask = mask.repeat(B_decoder, 1) + bias = bias.repeat(B_decoder, 1, 1) + + all_probs = torch.zeros( + (B_decoder, L, 20), device=device, dtype=torch.float32 + ) + all_log_probs = torch.zeros( + (B_decoder, L, 21), device=device, dtype=torch.float32 + ) + h_S = torch.zeros_like(h_V, device=device) + S = 20 * torch.ones((B_decoder, L), dtype=torch.int64, device=device) + h_V_stack = [h_V] + [ + torch.zeros_like(h_V, device=device) + for _ in range(len(self.decoder_layers)) + ] + + h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx) + h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx) + h_EXV_encoder_fw = mask_fw * h_EXV_encoder + + for t_ in range(L): + t = decoding_order[:, t_] # [B] + chain_mask_t = torch.gather(chain_mask, 1, t[:, None])[:, 0] # [B] + mask_t = torch.gather(mask, 1, t[:, None])[:, 0] # [B] + bias_t = torch.gather(bias, 1, t[:, None, None].repeat(1, 1, 21))[ + :, 0, : + ] # [B,21] + + E_idx_t = torch.gather( + E_idx, 1, t[:, None, None].repeat(1, 1, E_idx.shape[-1]) + ) + h_E_t = torch.gather( + h_E, + 1, + t[:, None, None, None].repeat(1, 1, h_E.shape[-2], h_E.shape[-1]), + ) + h_ES_t = cat_neighbors_nodes(h_S, h_E_t, E_idx_t) + h_EXV_encoder_t = torch.gather( + h_EXV_encoder_fw, + 1, + t[:, None, None, None].repeat( + 1, 1, h_EXV_encoder_fw.shape[-2], h_EXV_encoder_fw.shape[-1] + ), + ) + + mask_bw_t = torch.gather( + mask_bw, + 1, + t[:, None, None, None].repeat( + 1, 1, mask_bw.shape[-2], mask_bw.shape[-1] + ), + ) + + for l, layer in enumerate(self.decoder_layers): + h_ESV_decoder_t = cat_neighbors_nodes(h_V_stack[l], h_ES_t, E_idx_t) + h_V_t = torch.gather( + h_V_stack[l], + 1, + t[:, None, None].repeat(1, 1, h_V_stack[l].shape[-1]), + ) + h_ESV_t = mask_bw_t * h_ESV_decoder_t + h_EXV_encoder_t + h_V_stack[l + 1].scatter_( + 1, + t[:, None, None].repeat(1, 1, h_V.shape[-1]), + layer(h_V_t, h_ESV_t, mask_V=mask_t), + ) + + h_V_t = torch.gather( + h_V_stack[-1], + 1, + t[:, None, None].repeat(1, 1, h_V_stack[-1].shape[-1]), + )[:, 0] + logits = self.W_out(h_V_t) # [B,21] + log_probs = torch.nn.functional.log_softmax(logits, dim=-1) # [B,21] + + probs = torch.nn.functional.softmax( + (logits + bias_t) / temperature, dim=-1 + ) # [B,21] + probs_sample = probs[:, :20] / torch.sum( + probs[:, :20], dim=-1, keepdim=True + ) # hard omit X #[B,20] + S_t = torch.multinomial(probs_sample, 1)[:, 0] # [B] + + all_probs.scatter_( + 1, + t[:, None, None].repeat(1, 1, 20), + (chain_mask_t[:, None, None] * probs_sample[:, None, :]).float(), + ) + all_log_probs.scatter_( + 1, + t[:, None, None].repeat(1, 1, 21), + (chain_mask_t[:, None, None] * log_probs[:, None, :]).float(), + ) + S_true_t = torch.gather(S_true, 1, t[:, None])[:, 0] + S_t = (S_t * chain_mask_t + S_true_t * (1.0 - chain_mask_t)).long() + h_S.scatter_( + 1, + t[:, None, None].repeat(1, 1, h_S.shape[-1]), + self.W_s(S_t)[:, None, :], + ) + S.scatter_(1, t[:, None], S_t[:, None]) + + output_dict = { + "S": S, + "sampling_probs": all_probs, + "log_probs": all_log_probs, + "decoding_order": decoding_order, + } + else: + # weights for symmetric design + symmetry_weights = torch.ones([L], device=device, dtype=torch.float32) + for i1, item_list in enumerate(symmetry_list_of_lists): + for i2, item in enumerate(item_list): + symmetry_weights[item] = symmetry_weights_list_of_lists[i1][i2] + + new_decoding_order = [] + for t_dec in list(decoding_order[0,].cpu().data.numpy()): + if t_dec not in list(itertools.chain(*new_decoding_order)): + list_a = [item for item in symmetry_list_of_lists if t_dec in item] + if list_a: + new_decoding_order.append(list_a[0]) + else: + new_decoding_order.append([t_dec]) + + decoding_order = torch.tensor( + list(itertools.chain(*new_decoding_order)), device=device + )[None,].repeat(B, 1) + + permutation_matrix_reverse = torch.nn.functional.one_hot( + decoding_order, num_classes=L + ).float() + order_mask_backward = torch.einsum( + "ij, biq, bjp->bqp", + (1 - torch.triu(torch.ones(L, L, device=device))), + permutation_matrix_reverse, + permutation_matrix_reverse, + ) + mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) + mask_1D = mask.view([B, L, 1, 1]) + mask_bw = mask_1D * mask_attend + mask_fw = mask_1D * (1.0 - mask_attend) + + # repeat for decoding + S_true = S_true.repeat(B_decoder, 1) + h_V = h_V.repeat(B_decoder, 1, 1) + h_E = h_E.repeat(B_decoder, 1, 1, 1) + E_idx = E_idx.repeat(B_decoder, 1, 1) + mask_fw = mask_fw.repeat(B_decoder, 1, 1, 1) + mask_bw = mask_bw.repeat(B_decoder, 1, 1, 1) + chain_mask = chain_mask.repeat(B_decoder, 1) + mask = mask.repeat(B_decoder, 1) + bias = bias.repeat(B_decoder, 1, 1) + + all_probs = torch.zeros( + (B_decoder, L, 20), device=device, dtype=torch.float32 + ) + all_log_probs = torch.zeros( + (B_decoder, L, 21), device=device, dtype=torch.float32 + ) + h_S = torch.zeros_like(h_V, device=device) + S = 20 * torch.ones((B_decoder, L), dtype=torch.int64, device=device) + h_V_stack = [h_V] + [ + torch.zeros_like(h_V, device=device) + for _ in range(len(self.decoder_layers)) + ] + + h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx) + h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx) + h_EXV_encoder_fw = mask_fw * h_EXV_encoder + + for t_list in new_decoding_order: + total_logits = 0.0 + for t in t_list: + chain_mask_t = chain_mask[:, t] # [B] + mask_t = mask[:, t] # [B] + bias_t = bias[:, t] # [B, 21] + + E_idx_t = E_idx[:, t : t + 1] + h_E_t = h_E[:, t : t + 1] + h_ES_t = cat_neighbors_nodes(h_S, h_E_t, E_idx_t) + h_EXV_encoder_t = h_EXV_encoder_fw[:, t : t + 1] + for l, layer in enumerate(self.decoder_layers): + h_ESV_decoder_t = cat_neighbors_nodes( + h_V_stack[l], h_ES_t, E_idx_t + ) + h_V_t = h_V_stack[l][:, t : t + 1] + h_ESV_t = ( + mask_bw[:, t : t + 1] * h_ESV_decoder_t + h_EXV_encoder_t + ) + h_V_stack[l + 1][:, t : t + 1, :] = layer( + h_V_t, h_ESV_t, mask_V=mask_t[:, None] + ) + + h_V_t = h_V_stack[-1][:, t] + logits = self.W_out(h_V_t) # [B,21] + log_probs = torch.nn.functional.log_softmax( + logits, dim=-1 + ) # [B,21] + all_log_probs[:, t] = ( + chain_mask_t[:, None] * log_probs + ).float() # [B,21] + total_logits += symmetry_weights[t] * logits + + probs = torch.nn.functional.softmax( + (total_logits + bias_t) / temperature, dim=-1 + ) # [B,21] + probs_sample = probs[:, :20] / torch.sum( + probs[:, :20], dim=-1, keepdim=True + ) # hard omit X #[B,20] + S_t = torch.multinomial(probs_sample, 1)[:, 0] # [B] + for t in t_list: + chain_mask_t = chain_mask[:, t] # [B] + all_probs[:, t] = ( + chain_mask_t[:, None] * probs_sample + ).float() # [B,20] + S_true_t = S_true[:, t] # [B] + S_t = (S_t * chain_mask_t + S_true_t * (1.0 - chain_mask_t)).long() + h_S[:, t] = self.W_s(S_t) + S[:, t] = S_t + + output_dict = { + "S": S, + "sampling_probs": all_probs, + "log_probs": all_log_probs, + "decoding_order": decoding_order.repeat(B_decoder, 1), + } + return output_dict + + def single_aa_score(self, feature_dict, use_sequence: bool): + """ + feature_dict - input features + use_sequence - False using backbone info only + """ + B_decoder = feature_dict["batch_size"] + S_true_enc = feature_dict[ + "S" + ] + mask_enc = feature_dict[ + "mask" + ] + chain_mask_enc = feature_dict[ + "chain_mask" + ] + randn = feature_dict[ + "randn" + ] + B, L = S_true_enc.shape + device = S_true_enc.device + + h_V_enc, h_E_enc, E_idx_enc = self.encode(feature_dict) + log_probs_out = torch.zeros([B_decoder, L, 21], device=device).float() + logits_out = torch.zeros([B_decoder, L, 21], device=device).float() + decoding_order_out = torch.zeros([B_decoder, L, L], device=device).float() + + for idx in range(L): + h_V = torch.clone(h_V_enc) + E_idx = torch.clone(E_idx_enc) + mask = torch.clone(mask_enc) + S_true = torch.clone(S_true_enc) + if not use_sequence: + order_mask = torch.zeros(chain_mask_enc.shape[1], device=device).float() + order_mask[idx] = 1. + else: + order_mask = torch.ones(chain_mask_enc.shape[1], device=device).float() + order_mask[idx] = 0. + decoding_order = torch.argsort( + (order_mask + 0.0001) * (torch.abs(randn)) + ) # [numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0] + E_idx = E_idx.repeat(B_decoder, 1, 1) + permutation_matrix_reverse = torch.nn.functional.one_hot( + decoding_order, num_classes=L + ).float() + order_mask_backward = torch.einsum( + "ij, biq, bjp->bqp", + (1 - torch.triu(torch.ones(L, L, device=device))), + permutation_matrix_reverse, + permutation_matrix_reverse, + ) + mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) + mask_1D = mask.view([B, L, 1, 1]) + mask_bw = mask_1D * mask_attend + mask_fw = mask_1D * (1.0 - mask_attend) + S_true = S_true.repeat(B_decoder, 1) + h_V = h_V.repeat(B_decoder, 1, 1) + h_E = h_E_enc.repeat(B_decoder, 1, 1, 1) + mask = mask.repeat(B_decoder, 1) + + h_S = self.W_s(S_true) + h_ES = cat_neighbors_nodes(h_S, h_E, E_idx) + + # Build encoder embeddings + h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx) + h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx) + + h_EXV_encoder_fw = mask_fw * h_EXV_encoder + for layer in self.decoder_layers: + # Masked positions attend to encoder information, unmasked see. + h_ESV = cat_neighbors_nodes(h_V, h_ES, E_idx) + h_ESV = mask_bw * h_ESV + h_EXV_encoder_fw + h_V = layer(h_V, h_ESV, mask) + + logits = self.W_out(h_V) + log_probs = torch.nn.functional.log_softmax(logits, dim=-1) + + log_probs_out[:,idx,:] = log_probs[:,idx,:] + logits_out[:,idx,:] = logits[:,idx,:] + decoding_order_out[:,idx,:] = decoding_order + + output_dict = { + "S": S_true, + "log_probs": log_probs_out, + "logits": logits_out, + "decoding_order": decoding_order_out, + } + return output_dict + + + def score(self, feature_dict, use_sequence: bool): + B_decoder = feature_dict["batch_size"] + S_true = feature_dict[ + "S" + ] + mask = feature_dict[ + "mask" + ] + chain_mask = feature_dict[ + "chain_mask" + ] + randn = feature_dict[ + "randn" + ] + symmetry_list_of_lists = feature_dict[ + "symmetry_residues" + ] + B, L = S_true.shape + device = S_true.device + + h_V, h_E, E_idx = self.encode(feature_dict) + + chain_mask = mask * chain_mask # update chain_M to include missing regions + decoding_order = torch.argsort( + (chain_mask + 0.0001) * (torch.abs(randn)) + ) # [numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0] + if len(symmetry_list_of_lists[0]) == 0 and len(symmetry_list_of_lists) == 1: + E_idx = E_idx.repeat(B_decoder, 1, 1) + permutation_matrix_reverse = torch.nn.functional.one_hot( + decoding_order, num_classes=L + ).float() + order_mask_backward = torch.einsum( + "ij, biq, bjp->bqp", + (1 - torch.triu(torch.ones(L, L, device=device))), + permutation_matrix_reverse, + permutation_matrix_reverse, + ) + mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) + mask_1D = mask.view([B, L, 1, 1]) + mask_bw = mask_1D * mask_attend + mask_fw = mask_1D * (1.0 - mask_attend) + else: + new_decoding_order = [] + for t_dec in list(decoding_order[0,].cpu().data.numpy()): + if t_dec not in list(itertools.chain(*new_decoding_order)): + list_a = [item for item in symmetry_list_of_lists if t_dec in item] + if list_a: + new_decoding_order.append(list_a[0]) + else: + new_decoding_order.append([t_dec]) + + decoding_order = torch.tensor( + list(itertools.chain(*new_decoding_order)), device=device + )[None,].repeat(B, 1) + + permutation_matrix_reverse = torch.nn.functional.one_hot( + decoding_order, num_classes=L + ).float() + order_mask_backward = torch.einsum( + "ij, biq, bjp->bqp", + (1 - torch.triu(torch.ones(L, L, device=device))), + permutation_matrix_reverse, + permutation_matrix_reverse, + ) + mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1) + mask_1D = mask.view([B, L, 1, 1]) + mask_bw = mask_1D * mask_attend + mask_fw = mask_1D * (1.0 - mask_attend) + + E_idx = E_idx.repeat(B_decoder, 1, 1) + mask_fw = mask_fw.repeat(B_decoder, 1, 1, 1) + mask_bw = mask_bw.repeat(B_decoder, 1, 1, 1) + decoding_order = decoding_order.repeat(B_decoder, 1) + + S_true = S_true.repeat(B_decoder, 1) + h_V = h_V.repeat(B_decoder, 1, 1) + h_E = h_E.repeat(B_decoder, 1, 1, 1) + mask = mask.repeat(B_decoder, 1) + + h_S = self.W_s(S_true) + h_ES = cat_neighbors_nodes(h_S, h_E, E_idx) + + # Build encoder embeddings + h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx) + h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx) + + h_EXV_encoder_fw = mask_fw * h_EXV_encoder + if not use_sequence: + for layer in self.decoder_layers: + h_V = layer(h_V, h_EXV_encoder_fw, mask) + else: + for layer in self.decoder_layers: + # Masked positions attend to encoder information, unmasked see. + h_ESV = cat_neighbors_nodes(h_V, h_ES, E_idx) + h_ESV = mask_bw * h_ESV + h_EXV_encoder_fw + h_V = layer(h_V, h_ESV, mask) + + logits = self.W_out(h_V) + log_probs = torch.nn.functional.log_softmax(logits, dim=-1) + + output_dict = { + "S": S_true, + "log_probs": log_probs, + "logits": logits, + "decoding_order": decoding_order, + } + return output_dict + + +class ProteinFeaturesLigand(torch.nn.Module): + def __init__( + self, + edge_features, + node_features, + num_positional_embeddings=16, + num_rbf=16, + top_k=30, + augment_eps=0.0, + device=None, + atom_context_num=16, + use_side_chains=False, + ): + """Extract protein features""" + super(ProteinFeaturesLigand, self).__init__() + + self.use_side_chains = use_side_chains + + self.edge_features = edge_features + self.node_features = node_features + self.top_k = top_k + self.augment_eps = augment_eps + self.num_rbf = num_rbf + self.num_positional_embeddings = num_positional_embeddings + + self.embeddings = PositionalEncodings(num_positional_embeddings) + edge_in = num_positional_embeddings + num_rbf * 25 + self.edge_embedding = torch.nn.Linear(edge_in, edge_features, bias=False) + self.norm_edges = torch.nn.LayerNorm(edge_features) + + self.node_project_down = torch.nn.Linear( + 5 * num_rbf + 64 + 4, node_features, bias=True + ) + self.norm_nodes = torch.nn.LayerNorm(node_features) + + self.type_linear = torch.nn.Linear(147, 64) + + self.y_nodes = torch.nn.Linear(147, node_features, bias=False) + self.y_edges = torch.nn.Linear(num_rbf, node_features, bias=False) + + self.norm_y_edges = torch.nn.LayerNorm(node_features) + self.norm_y_nodes = torch.nn.LayerNorm(node_features) + + self.atom_context_num = atom_context_num + + # the last 32 atoms in the 37 atom representation + self.side_chain_atom_types = torch.tensor( + [ + 6, + 6, + 6, + 8, + 8, + 16, + 6, + 6, + 6, + 7, + 7, + 8, + 8, + 16, + 6, + 6, + 6, + 6, + 7, + 7, + 7, + 8, + 8, + 6, + 7, + 7, + 8, + 6, + 6, + 6, + 7, + 8, + ], + device=device, + ) + + self.periodic_table_features = torch.tensor( + [ + [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 45, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 66, + 67, + 68, + 69, + 70, + 71, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 83, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + 91, + 92, + 93, + 94, + 95, + 96, + 97, + 98, + 99, + 100, + 101, + 102, + 103, + 104, + 105, + 106, + 107, + 108, + 109, + 110, + 111, + 112, + 113, + 114, + 115, + 116, + 117, + 118, + ], + [ + 0, + 1, + 18, + 1, + 2, + 13, + 14, + 15, + 16, + 17, + 18, + 1, + 2, + 13, + 14, + 15, + 16, + 17, + 18, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 1, + 2, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 1, + 2, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + ], + [ + 0, + 1, + 1, + 2, + 2, + 2, + 2, + 2, + 2, + 2, + 2, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 3, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 4, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 5, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 6, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + 7, + ], + ], + dtype=torch.long, + device=device, + ) + + def _make_angle_features(self, A, B, C, Y): + v1 = A - B + v2 = C - B + e1 = torch.nn.functional.normalize(v1, dim=-1) + e1_v2_dot = torch.einsum("bli, bli -> bl", e1, v2)[..., None] + u2 = v2 - e1 * e1_v2_dot + e2 = torch.nn.functional.normalize(u2, dim=-1) + e3 = torch.cross(e1, e2, dim=-1) + R_residue = torch.cat( + (e1[:, :, :, None], e2[:, :, :, None], e3[:, :, :, None]), dim=-1 + ) + + local_vectors = torch.einsum( + "blqp, blyq -> blyp", R_residue, Y - B[:, :, None, :] + ) + + rxy = torch.sqrt(local_vectors[..., 0] ** 2 + local_vectors[..., 1] ** 2 + 1e-8) + f1 = local_vectors[..., 0] / rxy + f2 = local_vectors[..., 1] / rxy + rxyz = torch.norm(local_vectors, dim=-1) + 1e-8 + f3 = rxy / rxyz + f4 = local_vectors[..., 2] / rxyz + + f = torch.cat([f1[..., None], f2[..., None], f3[..., None], f4[..., None]], -1) + return f + + def _dist(self, X, mask, eps=1e-6): + mask_2D = torch.unsqueeze(mask, 1) * torch.unsqueeze(mask, 2) + dX = torch.unsqueeze(X, 1) - torch.unsqueeze(X, 2) + D = mask_2D * torch.sqrt(torch.sum(dX**2, 3) + eps) + D_max, _ = torch.max(D, -1, keepdim=True) + D_adjust = D + (1.0 - mask_2D) * D_max + D_neighbors, E_idx = torch.topk( + D_adjust, np.minimum(self.top_k, X.shape[1]), dim=-1, largest=False + ) + return D_neighbors, E_idx + + def _rbf(self, D): + device = D.device + D_min, D_max, D_count = 2.0, 22.0, self.num_rbf + D_mu = torch.linspace(D_min, D_max, D_count, device=device) + D_mu = D_mu.view([1, 1, 1, -1]) + D_sigma = (D_max - D_min) / D_count + D_expand = torch.unsqueeze(D, -1) + RBF = torch.exp(-(((D_expand - D_mu) / D_sigma) ** 2)) + return RBF + + def _get_rbf(self, A, B, E_idx): + D_A_B = torch.sqrt( + torch.sum((A[:, :, None, :] - B[:, None, :, :]) ** 2, -1) + 1e-6 + ) # [B, L, L] + D_A_B_neighbors = gather_edges(D_A_B[:, :, :, None], E_idx)[ + :, :, :, 0 + ] # [B,L,K] + RBF_A_B = self._rbf(D_A_B_neighbors) + return RBF_A_B + + def forward(self, input_features): + Y = input_features["Y"] + Y_m = input_features["Y_m"] + Y_t = input_features["Y_t"] + X = input_features["X"] + mask = input_features["mask"] + R_idx = input_features["R_idx"] + chain_labels = input_features["chain_labels"] + + if self.augment_eps > 0: + X = X + self.augment_eps * torch.randn_like(X) + Y = Y + self.augment_eps * torch.randn_like(Y) + + B, L, _, _ = X.shape + + Ca = X[:, :, 1, :] + N = X[:, :, 0, :] + C = X[:, :, 2, :] + O = X[:, :, 3, :] + + b = Ca - N + c = C - Ca + a = torch.cross(b, c, dim=-1) + Cb = -0.58273431 * a + 0.56802827 * b - 0.54067466 * c + Ca # shift from CA + + D_neighbors, E_idx = self._dist(Ca, mask) + + RBF_all = [] + RBF_all.append(self._rbf(D_neighbors)) # Ca-Ca + RBF_all.append(self._get_rbf(N, N, E_idx)) # N-N + RBF_all.append(self._get_rbf(C, C, E_idx)) # C-C + RBF_all.append(self._get_rbf(O, O, E_idx)) # O-O + RBF_all.append(self._get_rbf(Cb, Cb, E_idx)) # Cb-Cb + RBF_all.append(self._get_rbf(Ca, N, E_idx)) # Ca-N + RBF_all.append(self._get_rbf(Ca, C, E_idx)) # Ca-C + RBF_all.append(self._get_rbf(Ca, O, E_idx)) # Ca-O + RBF_all.append(self._get_rbf(Ca, Cb, E_idx)) # Ca-Cb + RBF_all.append(self._get_rbf(N, C, E_idx)) # N-C + RBF_all.append(self._get_rbf(N, O, E_idx)) # N-O + RBF_all.append(self._get_rbf(N, Cb, E_idx)) # N-Cb + RBF_all.append(self._get_rbf(Cb, C, E_idx)) # Cb-C + RBF_all.append(self._get_rbf(Cb, O, E_idx)) # Cb-O + RBF_all.append(self._get_rbf(O, C, E_idx)) # O-C + RBF_all.append(self._get_rbf(N, Ca, E_idx)) # N-Ca + RBF_all.append(self._get_rbf(C, Ca, E_idx)) # C-Ca + RBF_all.append(self._get_rbf(O, Ca, E_idx)) # O-Ca + RBF_all.append(self._get_rbf(Cb, Ca, E_idx)) # Cb-Ca + RBF_all.append(self._get_rbf(C, N, E_idx)) # C-N + RBF_all.append(self._get_rbf(O, N, E_idx)) # O-N + RBF_all.append(self._get_rbf(Cb, N, E_idx)) # Cb-N + RBF_all.append(self._get_rbf(C, Cb, E_idx)) # C-Cb + RBF_all.append(self._get_rbf(O, Cb, E_idx)) # O-Cb + RBF_all.append(self._get_rbf(C, O, E_idx)) # C-O + RBF_all = torch.cat(tuple(RBF_all), dim=-1) + + offset = R_idx[:, :, None] - R_idx[:, None, :] + offset = gather_edges(offset[:, :, :, None], E_idx)[:, :, :, 0] # [B, L, K] + + d_chains = ( + (chain_labels[:, :, None] - chain_labels[:, None, :]) == 0 + ).long() # find self vs non-self interaction + E_chains = gather_edges(d_chains[:, :, :, None], E_idx)[:, :, :, 0] + E_positional = self.embeddings(offset.long(), E_chains) + E = torch.cat((E_positional, RBF_all), -1) + E = self.edge_embedding(E) + E = self.norm_edges(E) + + if self.use_side_chains: + xyz_37 = input_features["xyz_37"] + xyz_37_m = input_features["xyz_37_m"] + E_idx_sub = E_idx[:, :, :16] # [B, L, 15] + mask_residues = input_features["chain_mask"] + xyz_37_m = xyz_37_m * (1 - mask_residues[:, :, None]) + R_m = gather_nodes(xyz_37_m[:, :, 5:], E_idx_sub) + + X_sidechain = xyz_37[:, :, 5:, :].view(B, L, -1) + R = gather_nodes(X_sidechain, E_idx_sub).view( + B, L, E_idx_sub.shape[2], -1, 3 + ) + R_t = self.side_chain_atom_types[None, None, None, :].repeat( + B, L, E_idx_sub.shape[2], 1 + ) + + # Side chain atom context + R = R.view(B, L, -1, 3) # coordinates + R_m = R_m.view(B, L, -1) # mask + R_t = R_t.view(B, L, -1) # atom types + + # Ligand atom context + Y = torch.cat((R, Y), 2) # [B, L, atoms, 3] + Y_m = torch.cat((R_m, Y_m), 2) # [B, L, atoms] + Y_t = torch.cat((R_t, Y_t), 2) # [B, L, atoms] + + Cb_Y_distances = torch.sum((Cb[:, :, None, :] - Y) ** 2, -1) + mask_Y = mask[:, :, None] * Y_m + Cb_Y_distances_adjusted = Cb_Y_distances * mask_Y + (1.0 - mask_Y) * 10000.0 + _, E_idx_Y = torch.topk( + Cb_Y_distances_adjusted, self.atom_context_num, dim=-1, largest=False + ) + + Y = torch.gather(Y, 2, E_idx_Y[:, :, :, None].repeat(1, 1, 1, 3)) + Y_t = torch.gather(Y_t, 2, E_idx_Y) + Y_m = torch.gather(Y_m, 2, E_idx_Y) + + Y_t = Y_t.long() + Y_t_g = self.periodic_table_features[1][Y_t] # group; 19 categories including 0 + Y_t_p = self.periodic_table_features[2][Y_t] # period; 8 categories including 0 + + Y_t_g_1hot_ = torch.nn.functional.one_hot(Y_t_g, 19) # [B, L, M, 19] + Y_t_p_1hot_ = torch.nn.functional.one_hot(Y_t_p, 8) # [B, L, M, 8] + Y_t_1hot_ = torch.nn.functional.one_hot(Y_t, 120) # [B, L, M, 120] + + Y_t_1hot_ = torch.cat( + [Y_t_1hot_, Y_t_g_1hot_, Y_t_p_1hot_], -1 + ) # [B, L, M, 147] + Y_t_1hot = self.type_linear(Y_t_1hot_.float()) + + D_N_Y = self._rbf( + torch.sqrt(torch.sum((N[:, :, None, :] - Y) ** 2, -1) + 1e-6) + ) # [B, L, M, num_bins] + D_Ca_Y = self._rbf( + torch.sqrt(torch.sum((Ca[:, :, None, :] - Y) ** 2, -1) + 1e-6) + ) + D_C_Y = self._rbf(torch.sqrt(torch.sum((C[:, :, None, :] - Y) ** 2, -1) + 1e-6)) + D_O_Y = self._rbf(torch.sqrt(torch.sum((O[:, :, None, :] - Y) ** 2, -1) + 1e-6)) + D_Cb_Y = self._rbf( + torch.sqrt(torch.sum((Cb[:, :, None, :] - Y) ** 2, -1) + 1e-6) + ) + + f_angles = self._make_angle_features(N, Ca, C, Y) # [B, L, M, 4] + + D_all = torch.cat( + (D_N_Y, D_Ca_Y, D_C_Y, D_O_Y, D_Cb_Y, Y_t_1hot, f_angles), dim=-1 + ) # [B,L,M,5*num_bins+5] + V = self.node_project_down(D_all) # [B, L, M, node_features] + V = self.norm_nodes(V) + + Y_edges = self._rbf( + torch.sqrt( + torch.sum((Y[:, :, :, None, :] - Y[:, :, None, :, :]) ** 2, -1) + 1e-6 + ) + ) # [B, L, M, M, num_bins] + + Y_edges = self.y_edges(Y_edges) + Y_nodes = self.y_nodes(Y_t_1hot_.float()) + + Y_edges = self.norm_y_edges(Y_edges) + Y_nodes = self.norm_y_nodes(Y_nodes) + + return V, E, E_idx, Y_nodes, Y_edges, Y_m + + +class ProteinFeatures(torch.nn.Module): + def __init__( + self, + edge_features, + node_features, + num_positional_embeddings=16, + num_rbf=16, + top_k=48, + augment_eps=0.0, + ): + """Extract protein features""" + super(ProteinFeatures, self).__init__() + self.edge_features = edge_features + self.node_features = node_features + self.top_k = top_k + self.augment_eps = augment_eps + self.num_rbf = num_rbf + self.num_positional_embeddings = num_positional_embeddings + + self.embeddings = PositionalEncodings(num_positional_embeddings) + edge_in = num_positional_embeddings + num_rbf * 25 + self.edge_embedding = torch.nn.Linear(edge_in, edge_features, bias=False) + self.norm_edges = torch.nn.LayerNorm(edge_features) + + def _dist(self, X, mask, eps=1e-6): + mask_2D = torch.unsqueeze(mask, 1) * torch.unsqueeze(mask, 2) + dX = torch.unsqueeze(X, 1) - torch.unsqueeze(X, 2) + D = mask_2D * torch.sqrt(torch.sum(dX**2, 3) + eps) + D_max, _ = torch.max(D, -1, keepdim=True) + D_adjust = D + (1.0 - mask_2D) * D_max + D_neighbors, E_idx = torch.topk( + D_adjust, np.minimum(self.top_k, X.shape[1]), dim=-1, largest=False + ) + return D_neighbors, E_idx + + def _rbf(self, D): + device = D.device + D_min, D_max, D_count = 2.0, 22.0, self.num_rbf + D_mu = torch.linspace(D_min, D_max, D_count, device=device) + D_mu = D_mu.view([1, 1, 1, -1]) + D_sigma = (D_max - D_min) / D_count + D_expand = torch.unsqueeze(D, -1) + RBF = torch.exp(-(((D_expand - D_mu) / D_sigma) ** 2)) + return RBF + + def _get_rbf(self, A, B, E_idx): + D_A_B = torch.sqrt( + torch.sum((A[:, :, None, :] - B[:, None, :, :]) ** 2, -1) + 1e-6 + ) # [B, L, L] + D_A_B_neighbors = gather_edges(D_A_B[:, :, :, None], E_idx)[ + :, :, :, 0 + ] # [B,L,K] + RBF_A_B = self._rbf(D_A_B_neighbors) + return RBF_A_B + + def forward(self, input_features): + X = input_features["X"] + mask = input_features["mask"] + R_idx = input_features["R_idx"] + chain_labels = input_features["chain_labels"] + + if self.augment_eps > 0: + X = X + self.augment_eps * torch.randn_like(X) + + b = X[:, :, 1, :] - X[:, :, 0, :] + c = X[:, :, 2, :] - X[:, :, 1, :] + a = torch.cross(b, c, dim=-1) + Cb = -0.58273431 * a + 0.56802827 * b - 0.54067466 * c + X[:, :, 1, :] + Ca = X[:, :, 1, :] + N = X[:, :, 0, :] + C = X[:, :, 2, :] + O = X[:, :, 3, :] + + D_neighbors, E_idx = self._dist(Ca, mask) + + RBF_all = [] + RBF_all.append(self._rbf(D_neighbors)) # Ca-Ca + RBF_all.append(self._get_rbf(N, N, E_idx)) # N-N + RBF_all.append(self._get_rbf(C, C, E_idx)) # C-C + RBF_all.append(self._get_rbf(O, O, E_idx)) # O-O + RBF_all.append(self._get_rbf(Cb, Cb, E_idx)) # Cb-Cb + RBF_all.append(self._get_rbf(Ca, N, E_idx)) # Ca-N + RBF_all.append(self._get_rbf(Ca, C, E_idx)) # Ca-C + RBF_all.append(self._get_rbf(Ca, O, E_idx)) # Ca-O + RBF_all.append(self._get_rbf(Ca, Cb, E_idx)) # Ca-Cb + RBF_all.append(self._get_rbf(N, C, E_idx)) # N-C + RBF_all.append(self._get_rbf(N, O, E_idx)) # N-O + RBF_all.append(self._get_rbf(N, Cb, E_idx)) # N-Cb + RBF_all.append(self._get_rbf(Cb, C, E_idx)) # Cb-C + RBF_all.append(self._get_rbf(Cb, O, E_idx)) # Cb-O + RBF_all.append(self._get_rbf(O, C, E_idx)) # O-C + RBF_all.append(self._get_rbf(N, Ca, E_idx)) # N-Ca + RBF_all.append(self._get_rbf(C, Ca, E_idx)) # C-Ca + RBF_all.append(self._get_rbf(O, Ca, E_idx)) # O-Ca + RBF_all.append(self._get_rbf(Cb, Ca, E_idx)) # Cb-Ca + RBF_all.append(self._get_rbf(C, N, E_idx)) # C-N + RBF_all.append(self._get_rbf(O, N, E_idx)) # O-N + RBF_all.append(self._get_rbf(Cb, N, E_idx)) # Cb-N + RBF_all.append(self._get_rbf(C, Cb, E_idx)) # C-Cb + RBF_all.append(self._get_rbf(O, Cb, E_idx)) # O-Cb + RBF_all.append(self._get_rbf(C, O, E_idx)) # C-O + RBF_all = torch.cat(tuple(RBF_all), dim=-1) + + offset = R_idx[:, :, None] - R_idx[:, None, :] + offset = gather_edges(offset[:, :, :, None], E_idx)[:, :, :, 0] # [B, L, K] + + d_chains = ( + (chain_labels[:, :, None] - chain_labels[:, None, :]) == 0 + ).long() # find self vs non-self interaction + E_chains = gather_edges(d_chains[:, :, :, None], E_idx)[:, :, :, 0] + E_positional = self.embeddings(offset.long(), E_chains) + E = torch.cat((E_positional, RBF_all), -1) + E = self.edge_embedding(E) + E = self.norm_edges(E) + + return E, E_idx + + +class ProteinFeaturesMembrane(torch.nn.Module): + def __init__( + self, + edge_features, + node_features, + num_positional_embeddings=16, + num_rbf=16, + top_k=48, + augment_eps=0.0, + num_classes=3, + ): + """Extract protein features""" + super(ProteinFeaturesMembrane, self).__init__() + self.edge_features = edge_features + self.node_features = node_features + self.top_k = top_k + self.augment_eps = augment_eps + self.num_rbf = num_rbf + self.num_positional_embeddings = num_positional_embeddings + self.num_classes = num_classes + + self.embeddings = PositionalEncodings(num_positional_embeddings) + edge_in = num_positional_embeddings + num_rbf * 25 + self.edge_embedding = torch.nn.Linear(edge_in, edge_features, bias=False) + self.norm_edges = torch.nn.LayerNorm(edge_features) + + self.node_embedding = torch.nn.Linear( + self.num_classes, node_features, bias=False + ) + self.norm_nodes = torch.nn.LayerNorm(node_features) + + def _dist(self, X, mask, eps=1e-6): + mask_2D = torch.unsqueeze(mask, 1) * torch.unsqueeze(mask, 2) + dX = torch.unsqueeze(X, 1) - torch.unsqueeze(X, 2) + D = mask_2D * torch.sqrt(torch.sum(dX**2, 3) + eps) + D_max, _ = torch.max(D, -1, keepdim=True) + D_adjust = D + (1.0 - mask_2D) * D_max + D_neighbors, E_idx = torch.topk( + D_adjust, np.minimum(self.top_k, X.shape[1]), dim=-1, largest=False + ) + return D_neighbors, E_idx + + def _rbf(self, D): + device = D.device + D_min, D_max, D_count = 2.0, 22.0, self.num_rbf + D_mu = torch.linspace(D_min, D_max, D_count, device=device) + D_mu = D_mu.view([1, 1, 1, -1]) + D_sigma = (D_max - D_min) / D_count + D_expand = torch.unsqueeze(D, -1) + RBF = torch.exp(-(((D_expand - D_mu) / D_sigma) ** 2)) + return RBF + + def _get_rbf(self, A, B, E_idx): + D_A_B = torch.sqrt( + torch.sum((A[:, :, None, :] - B[:, None, :, :]) ** 2, -1) + 1e-6 + ) # [B, L, L] + D_A_B_neighbors = gather_edges(D_A_B[:, :, :, None], E_idx)[ + :, :, :, 0 + ] # [B,L,K] + RBF_A_B = self._rbf(D_A_B_neighbors) + return RBF_A_B + + def forward(self, input_features): + X = input_features["X"] + mask = input_features["mask"] + R_idx = input_features["R_idx"] + chain_labels = input_features["chain_labels"] + membrane_per_residue_labels = input_features["membrane_per_residue_labels"] + + if self.augment_eps > 0: + X = X + self.augment_eps * torch.randn_like(X) + + b = X[:, :, 1, :] - X[:, :, 0, :] + c = X[:, :, 2, :] - X[:, :, 1, :] + a = torch.cross(b, c, dim=-1) + Cb = -0.58273431 * a + 0.56802827 * b - 0.54067466 * c + X[:, :, 1, :] + Ca = X[:, :, 1, :] + N = X[:, :, 0, :] + C = X[:, :, 2, :] + O = X[:, :, 3, :] + + D_neighbors, E_idx = self._dist(Ca, mask) + + RBF_all = [] + RBF_all.append(self._rbf(D_neighbors)) # Ca-Ca + RBF_all.append(self._get_rbf(N, N, E_idx)) # N-N + RBF_all.append(self._get_rbf(C, C, E_idx)) # C-C + RBF_all.append(self._get_rbf(O, O, E_idx)) # O-O + RBF_all.append(self._get_rbf(Cb, Cb, E_idx)) # Cb-Cb + RBF_all.append(self._get_rbf(Ca, N, E_idx)) # Ca-N + RBF_all.append(self._get_rbf(Ca, C, E_idx)) # Ca-C + RBF_all.append(self._get_rbf(Ca, O, E_idx)) # Ca-O + RBF_all.append(self._get_rbf(Ca, Cb, E_idx)) # Ca-Cb + RBF_all.append(self._get_rbf(N, C, E_idx)) # N-C + RBF_all.append(self._get_rbf(N, O, E_idx)) # N-O + RBF_all.append(self._get_rbf(N, Cb, E_idx)) # N-Cb + RBF_all.append(self._get_rbf(Cb, C, E_idx)) # Cb-C + RBF_all.append(self._get_rbf(Cb, O, E_idx)) # Cb-O + RBF_all.append(self._get_rbf(O, C, E_idx)) # O-C + RBF_all.append(self._get_rbf(N, Ca, E_idx)) # N-Ca + RBF_all.append(self._get_rbf(C, Ca, E_idx)) # C-Ca + RBF_all.append(self._get_rbf(O, Ca, E_idx)) # O-Ca + RBF_all.append(self._get_rbf(Cb, Ca, E_idx)) # Cb-Ca + RBF_all.append(self._get_rbf(C, N, E_idx)) # C-N + RBF_all.append(self._get_rbf(O, N, E_idx)) # O-N + RBF_all.append(self._get_rbf(Cb, N, E_idx)) # Cb-N + RBF_all.append(self._get_rbf(C, Cb, E_idx)) # C-Cb + RBF_all.append(self._get_rbf(O, Cb, E_idx)) # O-Cb + RBF_all.append(self._get_rbf(C, O, E_idx)) # C-O + RBF_all = torch.cat(tuple(RBF_all), dim=-1) + + offset = R_idx[:, :, None] - R_idx[:, None, :] + offset = gather_edges(offset[:, :, :, None], E_idx)[:, :, :, 0] # [B, L, K] + + d_chains = ( + (chain_labels[:, :, None] - chain_labels[:, None, :]) == 0 + ).long() # find self vs non-self interaction + E_chains = gather_edges(d_chains[:, :, :, None], E_idx)[:, :, :, 0] + E_positional = self.embeddings(offset.long(), E_chains) + E = torch.cat((E_positional, RBF_all), -1) + E = self.edge_embedding(E) + E = self.norm_edges(E) + + C_1hot = torch.nn.functional.one_hot( + membrane_per_residue_labels, self.num_classes + ).float() + V = self.node_embedding(C_1hot) + V = self.norm_nodes(V) + + return V, E, E_idx + + +class DecLayerJ(torch.nn.Module): + def __init__(self, num_hidden, num_in, dropout=0.1, num_heads=None, scale=30): + super(DecLayerJ, self).__init__() + self.num_hidden = num_hidden + self.num_in = num_in + self.scale = scale + self.dropout1 = torch.nn.Dropout(dropout) + self.dropout2 = torch.nn.Dropout(dropout) + self.norm1 = torch.nn.LayerNorm(num_hidden) + self.norm2 = torch.nn.LayerNorm(num_hidden) + + self.W1 = torch.nn.Linear(num_hidden + num_in, num_hidden, bias=True) + self.W2 = torch.nn.Linear(num_hidden, num_hidden, bias=True) + self.W3 = torch.nn.Linear(num_hidden, num_hidden, bias=True) + self.act = torch.nn.GELU() + self.dense = PositionWiseFeedForward(num_hidden, num_hidden * 4) + + def forward(self, h_V, h_E, mask_V=None, mask_attend=None): + """Parallel computation of full transformer layer""" + + # Concatenate h_V_i to h_E_ij + h_V_expand = h_V.unsqueeze(-2).expand( + -1, -1, -1, h_E.size(-2), -1 + ) # the only difference + h_EV = torch.cat([h_V_expand, h_E], -1) + + h_message = self.W3(self.act(self.W2(self.act(self.W1(h_EV))))) + if mask_attend is not None: + h_message = mask_attend.unsqueeze(-1) * h_message + dh = torch.sum(h_message, -2) / self.scale + + h_V = self.norm1(h_V + self.dropout1(dh)) + + # Position-wise feedforward + dh = self.dense(h_V) + h_V = self.norm2(h_V + self.dropout2(dh)) + + if mask_V is not None: + mask_V = mask_V.unsqueeze(-1) + h_V = mask_V * h_V + return h_V + + +class PositionWiseFeedForward(torch.nn.Module): + def __init__(self, num_hidden, num_ff): + super(PositionWiseFeedForward, self).__init__() + self.W_in = torch.nn.Linear(num_hidden, num_ff, bias=True) + self.W_out = torch.nn.Linear(num_ff, num_hidden, bias=True) + self.act = torch.nn.GELU() + + def forward(self, h_V): + h = self.act(self.W_in(h_V)) + h = self.W_out(h) + return h + + +class PositionalEncodings(torch.nn.Module): + def __init__(self, num_embeddings, max_relative_feature=32): + super(PositionalEncodings, self).__init__() + self.num_embeddings = num_embeddings + self.max_relative_feature = max_relative_feature + self.linear = torch.nn.Linear(2 * max_relative_feature + 1 + 1, num_embeddings) + + def forward(self, offset, mask): + d = torch.clip( + offset + self.max_relative_feature, 0, 2 * self.max_relative_feature + ) * mask + (1 - mask) * (2 * self.max_relative_feature + 1) + d_onehot = torch.nn.functional.one_hot(d, 2 * self.max_relative_feature + 1 + 1) + E = self.linear(d_onehot.float()) + return E + + +class DecLayer(torch.nn.Module): + def __init__(self, num_hidden, num_in, dropout=0.1, num_heads=None, scale=30): + super(DecLayer, self).__init__() + self.num_hidden = num_hidden + self.num_in = num_in + self.scale = scale + self.dropout1 = torch.nn.Dropout(dropout) + self.dropout2 = torch.nn.Dropout(dropout) + self.norm1 = torch.nn.LayerNorm(num_hidden) + self.norm2 = torch.nn.LayerNorm(num_hidden) + + self.W1 = torch.nn.Linear(num_hidden + num_in, num_hidden, bias=True) + self.W2 = torch.nn.Linear(num_hidden, num_hidden, bias=True) + self.W3 = torch.nn.Linear(num_hidden, num_hidden, bias=True) + self.act = torch.nn.GELU() + self.dense = PositionWiseFeedForward(num_hidden, num_hidden * 4) + + def forward(self, h_V, h_E, mask_V=None, mask_attend=None): + """Parallel computation of full transformer layer""" + + # Concatenate h_V_i to h_E_ij + h_V_expand = h_V.unsqueeze(-2).expand(-1, -1, h_E.size(-2), -1) + h_EV = torch.cat([h_V_expand, h_E], -1) + + h_message = self.W3(self.act(self.W2(self.act(self.W1(h_EV))))) + if mask_attend is not None: + h_message = mask_attend.unsqueeze(-1) * h_message + dh = torch.sum(h_message, -2) / self.scale + + h_V = self.norm1(h_V + self.dropout1(dh)) + + # Position-wise feedforward + dh = self.dense(h_V) + h_V = self.norm2(h_V + self.dropout2(dh)) + + if mask_V is not None: + mask_V = mask_V.unsqueeze(-1) + h_V = mask_V * h_V + return h_V + + +class EncLayer(torch.nn.Module): + def __init__(self, num_hidden, num_in, dropout=0.1, num_heads=None, scale=30): + super(EncLayer, self).__init__() + self.num_hidden = num_hidden + self.num_in = num_in + self.scale = scale + self.dropout1 = torch.nn.Dropout(dropout) + self.dropout2 = torch.nn.Dropout(dropout) + self.dropout3 = torch.nn.Dropout(dropout) + self.norm1 = torch.nn.LayerNorm(num_hidden) + self.norm2 = torch.nn.LayerNorm(num_hidden) + self.norm3 = torch.nn.LayerNorm(num_hidden) + + self.W1 = torch.nn.Linear(num_hidden + num_in, num_hidden, bias=True) + self.W2 = torch.nn.Linear(num_hidden, num_hidden, bias=True) + self.W3 = torch.nn.Linear(num_hidden, num_hidden, bias=True) + self.W11 = torch.nn.Linear(num_hidden + num_in, num_hidden, bias=True) + self.W12 = torch.nn.Linear(num_hidden, num_hidden, bias=True) + self.W13 = torch.nn.Linear(num_hidden, num_hidden, bias=True) + self.act = torch.nn.GELU() + self.dense = PositionWiseFeedForward(num_hidden, num_hidden * 4) + + def forward(self, h_V, h_E, E_idx, mask_V=None, mask_attend=None): + """Parallel computation of full transformer layer""" + + h_EV = cat_neighbors_nodes(h_V, h_E, E_idx) + h_V_expand = h_V.unsqueeze(-2).expand(-1, -1, h_EV.size(-2), -1) + h_EV = torch.cat([h_V_expand, h_EV], -1) + h_message = self.W3(self.act(self.W2(self.act(self.W1(h_EV))))) + if mask_attend is not None: + h_message = mask_attend.unsqueeze(-1) * h_message + dh = torch.sum(h_message, -2) / self.scale + h_V = self.norm1(h_V + self.dropout1(dh)) + + dh = self.dense(h_V) + h_V = self.norm2(h_V + self.dropout2(dh)) + if mask_V is not None: + mask_V = mask_V.unsqueeze(-1) + h_V = mask_V * h_V + + h_EV = cat_neighbors_nodes(h_V, h_E, E_idx) + h_V_expand = h_V.unsqueeze(-2).expand(-1, -1, h_EV.size(-2), -1) + h_EV = torch.cat([h_V_expand, h_EV], -1) + h_message = self.W13(self.act(self.W12(self.act(self.W11(h_EV))))) + h_E = self.norm3(h_E + self.dropout3(h_message)) + return h_V, h_E + + +# The following gather functions +def gather_edges(edges, neighbor_idx): + # Features [B,N,N,C] at Neighbor indices [B,N,K] => Neighbor features [B,N,K,C] + neighbors = neighbor_idx.unsqueeze(-1).expand(-1, -1, -1, edges.size(-1)) + edge_features = torch.gather(edges, 2, neighbors) + return edge_features + + +def gather_nodes(nodes, neighbor_idx): + # Features [B,N,C] at Neighbor indices [B,N,K] => [B,N,K,C] + # Flatten and expand indices per batch [B,N,K] => [B,NK] => [B,NK,C] + neighbors_flat = neighbor_idx.reshape((neighbor_idx.shape[0], -1)) + neighbors_flat = neighbors_flat.unsqueeze(-1).expand(-1, -1, nodes.size(2)) + # Gather and re-pack + neighbor_features = torch.gather(nodes, 1, neighbors_flat) + neighbor_features = neighbor_features.view(list(neighbor_idx.shape)[:3] + [-1]) + return neighbor_features + + +def gather_nodes_t(nodes, neighbor_idx): + # Features [B,N,C] at Neighbor index [B,K] => Neighbor features[B,K,C] + idx_flat = neighbor_idx.unsqueeze(-1).expand(-1, -1, nodes.size(2)) + neighbor_features = torch.gather(nodes, 1, idx_flat) + return neighbor_features + + +def cat_neighbors_nodes(h_nodes, h_neighbors, E_idx): + h_nodes = gather_nodes(h_nodes, E_idx) + h_nn = torch.cat([h_neighbors, h_nodes], -1) + return h_nn diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/rf3/configs/hydra/no_logging.yaml b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/rf3/configs/hydra/no_logging.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f25f45ded239f89d0c8b1aefeb2b36cbca7f505c --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/rf3/configs/hydra/no_logging.yaml @@ -0,0 +1,7 @@ +defaults: + - override job_logging: disabled + - override hydra_logging: disabled + +output_subdir: null +run: + dir: . \ No newline at end of file diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/rf3/configs/model/optimizers/adam.yaml b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/rf3/configs/model/optimizers/adam.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c17d9aefe30f7a1f1205a3c41278b936d99f02fc --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/rf3/configs/model/optimizers/adam.yaml @@ -0,0 +1,5 @@ +# Optimizer +_target_: torch.optim.Adam +lr: 0 # Will be set by the scheduler (starts at 0, increasing to `base_lr`) +betas: [0.9, 0.95] +eps: 1.0e-8 \ No newline at end of file diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/rf3/configs/model/rf3.yaml b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/rf3/configs/model/rf3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a0c01bea5dc42cbeca4b7ecaae7c9d588193c0d7 --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/rf3/configs/model/rf3.yaml @@ -0,0 +1,43 @@ +defaults: + - optimizers/adam@optimizer + - schedulers/af3@lr_scheduler + - components/ema@ema + - components/rf3_net@net + - _self_ + +net: + feature_initializer: + input_feature_embedder: + atom_attention_encoder: + c_atom_1d_features: 393 # 392 + 1 has_atom_level_embedding = 393 + atom_1d_features: + - ref_pos + - ref_charge + - ref_mask + - ref_element + - ref_atom_name_chars + - ref_pos_ground_truth + - has_atom_level_embedding + use_atom_level_embedding: true + atom_level_embedding_dim: 384 + + recycler: + msa_module: + msa_subsample_embedder: + dim_raw_msa: 35 + template_embedder: + raw_template_dim: 66 + + diffusion_module: + atom_attention_encoder: + c_atom_1d_features: 393 # 392 + 1 has_atom_level_embedding = 393 + atom_1d_features: + - ref_pos + - ref_charge + - ref_mask + - ref_element + - ref_atom_name_chars + - ref_pos_ground_truth + - has_atom_level_embedding + use_atom_level_embedding: true + atom_level_embedding_dim: 384 \ No newline at end of file diff --git a/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/rf3/configs/trainer/rf3.yaml b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/rf3/configs/trainer/rf3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..270a6b3a39affd860f9861483a8945a867f831fa --- /dev/null +++ b/tasks/protein-design/protein-design-astra-xhigh-1h100-48h-20261027a/submissions/S003/vendor/rf3/configs/trainer/rf3.yaml @@ -0,0 +1,20 @@ +defaults: + - ddp + - loss: structure_prediction + - metrics: structure_prediction + +_target_: rf3.trainers.rf3.RF3Trainer +validate_every_n_epochs: 1 +max_epochs: 10_000 +n_examples_per_epoch: 24000 +prevalidate: True + +# We must pre-specify the number of recycles during training so we can pre-sample recycles per batch consistently for each GPU +n_recycles_train: ${datasets.n_recycles_train} + +clip_grad_max_norm: 10.0 + +output_dir: ${paths.output_dir} +checkpoint_every_n_epochs: 1 + +precision: bf16-mixed