From 1e6036e8c50bd07fa487a34c15ecf288e18bd61e Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Mon, 5 Oct 2026 15:50:56 -0700 Subject: [PATCH 01/24] feat(gift-eval): notebook-aligned replication registry and Modal workflow Add a separate replication config (9 families), S3 prefix, pilot/full entrypoints, and verify/Makefile targets so we can re-run GIFT-Eval with official notebook params without changing the default full-grid configs. Co-authored-by: Cursor --- experiments/gift-eval/Makefile | 15 +++- experiments/gift-eval/README.md | 34 +++++++- .../configs/replication/families.yaml | 11 +++ .../gift-eval/configs/replication/models.yaml | 83 ++++++++++++++++++ .../configs/replication/notebooks.yaml | 30 +++++++ experiments/gift-eval/src/eval/evaluate.py | 7 +- experiments/gift-eval/src/eval/jobs.py | 45 ++++++++++ experiments/gift-eval/src/eval/models.py | 52 ++++++++---- .../gift-eval/src/runners/run_modal.py | 61 +++++++++++++- .../gift-eval/src/runners/run_model.py | 14 +++- .../gift-eval/src/runners/run_verify.py | 84 +++++++++++++++---- .../gift-eval/src/verify/replication_table.py | 10 ++- experiments/gift-eval/src/verify/verify.py | 19 ++--- .../tests/test_replication_config.py | 43 ++++++++++ 14 files changed, 459 insertions(+), 49 deletions(-) create mode 100644 experiments/gift-eval/configs/replication/families.yaml create mode 100644 experiments/gift-eval/configs/replication/models.yaml create mode 100644 experiments/gift-eval/configs/replication/notebooks.yaml create mode 100644 experiments/gift-eval/tests/test_replication_config.py diff --git a/experiments/gift-eval/Makefile b/experiments/gift-eval/Makefile index a907ef3..8562436 100644 --- a/experiments/gift-eval/Makefile +++ b/experiments/gift-eval/Makefile @@ -1,4 +1,6 @@ -.PHONY: download-gift-eval-data upload-data-to-s3 sync-ci-results sync-results verify-all replication-table +.PHONY: download-gift-eval-data upload-data-to-s3 sync-ci-results sync-results sync-replication verify-all replication-table verify-replication-pilot verify-replication-full + +RUN_ID ?= download-gift-eval-data: @hf download Salesforce/GiftEval --repo-type=dataset --local-dir=./data/gift-eval @@ -14,6 +16,17 @@ sync-results: @mkdir -p ./results @aws s3 sync s3://foundationforecast-gift-eval/results ./results +sync-replication: + @test -n "$(RUN_ID)" || (echo "Set RUN_ID=..." && exit 1) + @mkdir -p ./results/replication/$(RUN_ID) + @aws s3 sync s3://foundationforecast-gift-eval/results/replication/$(RUN_ID) ./results/replication/$(RUN_ID) + +verify-replication-pilot: sync-replication + @uv run python -m src.runners.run_verify --replication --run-id $(RUN_ID) --replication-pilot + +verify-replication-full: sync-replication + @uv run python -m src.runners.run_verify --replication --run-id $(RUN_ID) --all --require-complete + verify-all: sync-results @uv run python -m src.runners.run_verify --all diff --git a/experiments/gift-eval/README.md b/experiments/gift-eval/README.md index b93594b..4daa403 100644 --- a/experiments/gift-eval/README.md +++ b/experiments/gift-eval/README.md @@ -10,7 +10,7 @@ src/ ├── verify/ HF reference loading and replication checks └── runners/ CLI and Modal entrypoints tests/ pytest replication checks (imports src.verify) -configs/ models.yaml (full matrix) and ci_subset.yaml (CI) +configs/ models.yaml (full matrix), ci_subset.yaml (CI), replication/ (notebook-aligned) ``` ## Setup @@ -70,6 +70,38 @@ make sync-ci-results # download results for local verify / pytest uv run pytest tests/test_replication.py -n 0 -x ``` +## Replication run (notebook-aligned, one model per family) + +[`configs/replication/`](configs/replication/) holds **9 families** with params traced to +[official gift-eval notebooks](https://github.com/SalesforceAIResearch/gift-eval/tree/main/notebooks). +Results go to **`s3://foundationforecast-gift-eval/results/replication//`** +(not `results/` or `results/ci/`). + +1. Pick a run id, e.g. `2026-04-05-nb-v1`. +2. **Pilot** (9 jobs: `m4_weekly` / `short`): + +```bash +uv run modal run -m src.runners.run_modal::run_replication_pilot --run-id 2026-04-05-nb-v1 +make sync-replication RUN_ID=2026-04-05-nb-v1 +make verify-replication-pilot RUN_ID=2026-04-05-nb-v1 +``` + +3. After pilot verify passes, **full grid** (9 × 97 jobs): + +```bash +uv run modal run -m src.runners.run_modal::run_replication_full --run-id 2026-04-05-nb-v1 +make verify-replication-full RUN_ID=2026-04-05-nb-v1 +``` + +Local single job with replication registry: + +```bash +uv run python -m src.runners.run_model --replication \ + --model-key google--timesfm-3.0-pytorch \ + --dataset-name m4_weekly --term short \ + --output-root ./results/replication/local-test +``` + ## Full benchmark grid (Modal) One GPU job per `(model_key, dataset, term)`: diff --git a/experiments/gift-eval/configs/replication/families.yaml b/experiments/gift-eval/configs/replication/families.yaml new file mode 100644 index 0000000..2b324e6 --- /dev/null +++ b/experiments/gift-eval/configs/replication/families.yaml @@ -0,0 +1,11 @@ +# One representative model per family for notebook-aligned replication runs. +model_keys: + - amazon--chronos-2 + - google--timesfm-3.0-pytorch + - Salesforce--moirai-2.0-R-small + - Datadog--Toto-2.0-313m + - NX-AI--TiRex-2-gifteval-zs + - ibm-granite--granite-timeseries-flowstate-r1 + - ibm-granite--granite-timeseries-patchtst-fm-r2 + - theforecastingcompany--t0-beta + - tafsut-univariate-base diff --git a/experiments/gift-eval/configs/replication/models.yaml b/experiments/gift-eval/configs/replication/models.yaml new file mode 100644 index 0000000..7ae0704 --- /dev/null +++ b/experiments/gift-eval/configs/replication/models.yaml @@ -0,0 +1,83 @@ +# Notebook-aligned params for replication runs only (see configs/replication/notebooks.yaml). +models: + amazon--chronos-2: + class: foundationforecast.models.chronos.Chronos + reference_slug: Chronos-2 + max_length: null + predictor_batch_size: 1024 + kwargs: + repo_id: amazon/chronos-2 + alias: Chronos-2 + batch_size: 1024 + + google--timesfm-3.0-pytorch: + class: foundationforecast.models.timesfm.TimesFM + reference_slug: TimesFM-3 + kwargs: + repo_id: google/timesfm-3.0-pytorch + alias: TimesFM-3 + context_length: 15360 + batch_size: 64 + input_patch_length: 32 + output_patch_length: 64 + use_variate_attention: true + use_sdpa: true + + Salesforce--moirai-2.0-R-small: + class: foundationforecast.models.moirai.Moirai + reference_slug: Moirai2 + predictor_batch_size: 2048 + kwargs: + repo_id: Salesforce/moirai-2.0-R-small + context_length: 4000 + batch_size: 2048 + + Datadog--Toto-2.0-313m: + class: foundationforecast.models.toto.Toto + reference_slug: Toto-2.0-313m + kwargs: + repo_id: Datadog/Toto-2.0-313m + context_length: 4096 + batch_size: 512 + + NX-AI--TiRex-2-gifteval-zs: + class: foundationforecast.models.tirex.TiRex + reference_slug: TiRex-2-Zeroshot + predictor_batch_size: 512 + kwargs: + repo_id: NX-AI/TiRex-2-gifteval-zs + batch_size: 512 + + ibm-granite--granite-timeseries-flowstate-r1: + class: foundationforecast.models.flowstate.FlowState + reference_slug: Granite-FlowState-r1.1 + max_length: null + kwargs: + repo_id: ibm-granite/granite-timeseries-flowstate-r1 + batch_size: 16 + + ibm-granite--granite-timeseries-patchtst-fm-r2: + class: foundationforecast.models.patchtst_fm.PatchTSTFM + reference_slug: Granite-PatchTST-FM-r2 + max_length: 8192 + kwargs: + repo_id: ibm-granite/granite-timeseries-patchtst-fm-r2 + context_length: 8192 + batch_size: 32 + + theforecastingcompany--t0-beta: + class: foundationforecast.models.t0.T0 + reference_slug: t0-beta + kwargs: + repo_id: theforecastingcompany/t0-beta + alias: t0-beta + context_length: 8192 + batch_size: 64 + + tafsut-univariate-base: + class: foundationforecast.models.tafsut.Tafsut + reference_slug: tafsut + kwargs: + repo_id: Tafsut-FM/tafsut-univariate-base + alias: tafsut + batch_size: 128 diff --git a/experiments/gift-eval/configs/replication/notebooks.yaml b/experiments/gift-eval/configs/replication/notebooks.yaml new file mode 100644 index 0000000..2195718 --- /dev/null +++ b/experiments/gift-eval/configs/replication/notebooks.yaml @@ -0,0 +1,30 @@ +# Official GIFT-Eval replication notebooks (SalesforceAIResearch/gift-eval). +# HF results: https://huggingface.co/spaces/Salesforce/GIFT-Eval/tree/main/results/{reference_slug} +sources: + amazon--chronos-2: + notebook: notebooks/chronos-2.ipynb + reference_slug: Chronos-2 + google--timesfm-3.0-pytorch: + notebook: notebooks/timesfm3.ipynb + reference_slug: TimesFM-3 + Salesforce--moirai-2.0-R-small: + notebook: notebooks/moirai2.ipynb + reference_slug: Moirai2 + Datadog--Toto-2.0-313m: + notebook: notebooks/toto_2_0.ipynb + reference_slug: Toto-2.0-313m + NX-AI--TiRex-2-gifteval-zs: + notebook: notebooks/tirex-2.ipynb + reference_slug: TiRex-2-Zeroshot + ibm-granite--granite-timeseries-flowstate-r1: + notebook: notebooks/flowstate.ipynb + reference_slug: Granite-FlowState-r1.1 + ibm-granite--granite-timeseries-patchtst-fm-r2: + notebook: notebooks/patchtst_fm_r2.ipynb + reference_slug: Granite-PatchTST-FM-r2 + theforecastingcompany--t0-beta: + notebook: notebooks/t0-beta.ipynb + reference_slug: t0-beta + tafsut-univariate-base: + notebook: notebooks/tafsut.ipynb + reference_slug: tafsut diff --git a/experiments/gift-eval/src/eval/evaluate.py b/experiments/gift-eval/src/eval/evaluate.py index 9176f86..f632e23 100644 --- a/experiments/gift-eval/src/eval/evaluate.py +++ b/experiments/gift-eval/src/eval/evaluate.py @@ -8,7 +8,7 @@ from timecopilot_gift_eval import GIFTEval, GluonTSPredictor from .jobs import Job, job_output_dir, result_csv, timing_json -from .models import build_model, predictor_batch_size, predictor_max_length +from .models import Registry, build_model, predictor_batch_size, predictor_max_length logger = logging.getLogger(__name__) @@ -23,6 +23,7 @@ def run_gift_eval( storage_path: Path | str, output_root: Path | str = Path("results"), overwrite_results: bool = False, + registry: Registry = "default", ) -> Path: output_path = job_output_dir(job, Path(output_root)) output_path.mkdir(parents=True, exist_ok=True) @@ -34,16 +35,18 @@ def run_gift_eval( job.term, ) - forecaster = build_model(job.model_key) + forecaster = build_model(job.model_key, registry=registry) predictor = GluonTSPredictor( forecaster=forecaster, max_length=predictor_max_length( job.model_key, forecaster, + registry=registry, default=DEFAULT_MAX_LENGTH, ), batch_size=predictor_batch_size( job.model_key, + registry=registry, default=DEFAULT_PREDICTOR_BATCH_SIZE, ), ) diff --git a/experiments/gift-eval/src/eval/jobs.py b/experiments/gift-eval/src/eval/jobs.py index 13a5c7a..f630cbc 100644 --- a/experiments/gift-eval/src/eval/jobs.py +++ b/experiments/gift-eval/src/eval/jobs.py @@ -8,7 +8,10 @@ from timecopilot_gift_eval.utils import DATASETS_WITH_TERMS CONFIGS_DIR = Path(__file__).resolve().parents[2] / "configs" +REPLICATION_CONFIGS_DIR = CONFIGS_DIR / "replication" DEFAULT_RESULTS_ROOT = Path("results") +REPLICATION_PILOT_DATASET = "m4_weekly" +REPLICATION_PILOT_TERM = "short" @dataclass(frozen=True) @@ -28,6 +31,48 @@ def load_models_config() -> dict: return _load_yaml(CONFIGS_DIR / "models.yaml")["models"] +@lru_cache +def load_replication_models_config() -> dict: + return _load_yaml(REPLICATION_CONFIGS_DIR / "models.yaml")["models"] + + +def load_models_config_for_registry(registry: str) -> dict: + if registry == "default": + return load_models_config() + if registry == "replication": + return load_replication_models_config() + raise ValueError(f"Unknown registry {registry!r}") + + +@lru_cache +def load_replication_families() -> tuple[str, ...]: + raw = _load_yaml(REPLICATION_CONFIGS_DIR / "families.yaml")["model_keys"] + return tuple(raw) + + +def load_replication_pilot_jobs() -> list[Job]: + return [ + Job( + model_key=model_key, + dataset_name=REPLICATION_PILOT_DATASET, + term=REPLICATION_PILOT_TERM, + ) + for model_key in load_replication_families() + ] + + +def load_replication_matrix() -> list[Job]: + return [ + Job(model_key=model_key, dataset_name=dataset_name, term=term) + for model_key in load_replication_families() + for dataset_name, term in DATASETS_WITH_TERMS + ] + + +def replication_output_root(run_id: str) -> Path: + return DEFAULT_RESULTS_ROOT / "replication" / run_id + + def load_ci_subset() -> list[Job]: raw = _load_yaml(CONFIGS_DIR / "ci_subset.yaml")["jobs"] return [Job(**job) for job in raw] diff --git a/experiments/gift-eval/src/eval/models.py b/experiments/gift-eval/src/eval/models.py index 8fad6a5..ec0e1bf 100644 --- a/experiments/gift-eval/src/eval/models.py +++ b/experiments/gift-eval/src/eval/models.py @@ -1,11 +1,13 @@ from __future__ import annotations import importlib -from typing import Any +from typing import Any, Literal from timecopilot_gift_eval.protocol import ForecasterProtocol -from .jobs import load_models_config +from .jobs import load_models_config_for_registry + +Registry = Literal["default", "replication"] def _import_class(class_path: str) -> type: @@ -14,13 +16,23 @@ def _import_class(class_path: str) -> type: return getattr(module, class_name) -def build_model(model_key: str) -> ForecasterProtocol: - models = load_models_config() +def _model_spec(model_key: str, registry: Registry) -> dict: + models = load_models_config_for_registry(registry) if model_key not in models: available = ", ".join(sorted(models)) - raise KeyError(f"Unknown model_key {model_key!r}. Available: {available}") + raise KeyError( + f"Unknown model_key {model_key!r} in registry {registry!r}. " + f"Available: {available}" + ) + return models[model_key] + - spec = models[model_key] +def build_model( + model_key: str, + *, + registry: Registry = "default", +) -> ForecasterProtocol: + spec = _model_spec(model_key, registry) model_cls = _import_class(spec["class"]) kwargs: dict[str, Any] = dict(spec.get("kwargs", {})) reference = spec.get("reference_slug") @@ -29,8 +41,13 @@ def build_model(model_key: str) -> ForecasterProtocol: return model_cls(**kwargs) -def predictor_batch_size(model_key: str, *, default: int = 1024) -> int: - spec = load_models_config()[model_key] +def predictor_batch_size( + model_key: str, + *, + registry: Registry = "default", + default: int = 1024, +) -> int: + spec = _model_spec(model_key, registry) if "predictor_batch_size" in spec: return int(spec["predictor_batch_size"]) return default @@ -40,17 +57,24 @@ def predictor_max_length( model_key: str, forecaster: ForecasterProtocol, *, + registry: Registry = "default", default: int = 4096, ) -> int | None: - spec = load_models_config()[model_key] + spec = _model_spec(model_key, registry) if "max_length" in spec: max_length = spec["max_length"] return None if max_length is None else int(max_length) return int(getattr(forecaster, "context_length", default)) -def reference_slug(model_key: str) -> str | None: - models = load_models_config() - if model_key not in models: - raise KeyError(f"Unknown model_key {model_key!r}") - return models[model_key].get("reference_slug") +def reference_slug(model_key: str, *, registry: Registry = "default") -> str | None: + return _model_spec(model_key, registry).get("reference_slug") + + +def model_keys_with_reference(*, registry: Registry = "default") -> list[str]: + models = load_models_config_for_registry(registry) + return [ + model_key + for model_key, spec in models.items() + if spec.get("reference_slug") is not None + ] diff --git a/experiments/gift-eval/src/runners/run_modal.py b/experiments/gift-eval/src/runners/run_modal.py index 454db0e..98e4d4e 100644 --- a/experiments/gift-eval/src/runners/run_modal.py +++ b/experiments/gift-eval/src/runners/run_modal.py @@ -79,6 +79,10 @@ def _resolve_paths() -> tuple[Path, Path]: S3_CI_RESULTS_PREFIX = "results/ci" +def replication_s3_prefix(run_id: str) -> str: + return f"results/replication/{run_id}" + + @app.function( image=image, volumes=volume, @@ -94,6 +98,7 @@ def run_gift_eval_modal( storage_path: str = "/s3-bucket/data/gift-eval", output_root: str = "/s3-bucket/results", force: bool = False, + registry: str = "default", ) -> None: import logging from pathlib import Path @@ -114,6 +119,7 @@ def run_gift_eval_modal( storage_path=storage_path, output_root=Path(output_root), overwrite_results=force, + registry=registry, # type: ignore[arg-type] ) @@ -127,13 +133,14 @@ def _dispatch_jobs( storage_path: str, output_root: str, force: bool, + registry: str = "default", ) -> None: logging.basicConfig(level=logging.INFO) if not jobs: logging.info("No jobs to run") return args = [ - (*job_tuple, storage_path, output_root, force) + (*job_tuple, storage_path, output_root, force, registry) for job_tuple in _job_tuples(jobs) ] results = list( @@ -255,3 +262,55 @@ def run_missing_timing() -> None: output_root=f"/s3-bucket/{S3_RESULTS_PREFIX}", force=True, ) + + +@app.local_entrypoint() +def run_replication_pilot(run_id: str, force: bool = True) -> None: + from src.eval.jobs import load_replication_pilot_jobs + + prefix = replication_s3_prefix(run_id) + jobs = load_replication_pilot_jobs() + logging.info( + "Replication pilot: %s jobs → s3://%s/%s", + len(jobs), + S3_BUCKET, + prefix, + ) + _dispatch_jobs( + jobs, + storage_path="/s3-bucket/data/gift-eval", + output_root=f"/s3-bucket/{prefix}", + force=force, + registry="replication", + ) + + +@app.local_entrypoint() +def run_replication_full(run_id: str, force: bool = False) -> None: + from src.eval.jobs import load_replication_matrix + + prefix = replication_s3_prefix(run_id) + jobs = load_replication_matrix() + if force: + selected = jobs + else: + selected = _jobs_from_s3( + jobs, + bucket=S3_BUCKET, + prefix=prefix, + mode="missing", + ) + logging.info( + "Replication full grid: %s jobs (force=%s) → s3://%s/%s", + len(selected), + force, + S3_BUCKET, + prefix, + ) + _dispatch_jobs( + selected, + storage_path="/s3-bucket/data/gift-eval", + output_root=f"/s3-bucket/{prefix}", + force=force, + registry="replication", + ) diff --git a/experiments/gift-eval/src/runners/run_model.py b/experiments/gift-eval/src/runners/run_model.py index eb4cd2d..10c8b94 100644 --- a/experiments/gift-eval/src/runners/run_model.py +++ b/experiments/gift-eval/src/runners/run_model.py @@ -26,9 +26,21 @@ def main( Path, typer.Option(help="Path to downloaded GIFT-Eval dataset"), ] = Path("data/gift-eval"), + replication: Annotated[ + bool, + typer.Option( + help="Use configs/replication/models.yaml (notebook-aligned params)" + ), + ] = False, ) -> None: job = Job(model_key=model_key, dataset_name=dataset_name, term=term) - run_gift_eval(job, storage_path=storage_path, output_root=output_root) + registry = "replication" if replication else "default" + run_gift_eval( + job, + storage_path=storage_path, + output_root=output_root, + registry=registry, # type: ignore[arg-type] + ) if __name__ == "__main__": diff --git a/experiments/gift-eval/src/runners/run_verify.py b/experiments/gift-eval/src/runners/run_verify.py index 513cf89..1863091 100644 --- a/experiments/gift-eval/src/runners/run_verify.py +++ b/experiments/gift-eval/src/runners/run_verify.py @@ -6,14 +6,17 @@ import typer -from src.eval.jobs import ci_output_root, load_ci_subset -from src.verify.replication_table import write_replication_table -from src.verify.verify import ( - ReplicationSkip, - model_keys_with_reference, - verify_all, - verify_model, +from src.eval.jobs import ( + ci_output_root, + load_ci_subset, + load_replication_families, + load_replication_matrix, + load_replication_pilot_jobs, + replication_output_root, ) +from src.eval.models import model_keys_with_reference +from src.verify.replication_table import write_replication_table +from src.verify.verify import ReplicationSkip, verify_all, verify_model logging.basicConfig(level=logging.INFO) app = typer.Typer() @@ -26,22 +29,43 @@ def _resolve_model_keys( model_key: str | None, all_models: bool, ci: bool, -) -> tuple[list[str], Path]: + replication: bool, + run_id: str | None, + replication_pilot: bool, +) -> tuple[list[str], Path, list | None]: + if replication: + if not run_id: + raise typer.BadParameter("--replication requires --run-id RUN_ID") + root = replication_output_root(run_id) + if replication_pilot: + jobs = load_replication_pilot_jobs() + elif all_models or not model_key: + jobs = load_replication_matrix() + else: + jobs = None + if model_key: + keys = [model_key] + elif replication_pilot: + keys = sorted({job.model_key for job in jobs or []}) + else: + keys = list(load_replication_families()) + return keys, root, jobs + if ci: jobs = load_ci_subset() - return sorted({job.model_key for job in jobs}), ci_output_root() + return sorted({job.model_key for job in jobs}), ci_output_root(), jobs if model_key and all_models: raise typer.BadParameter("Use either --model-key or --all, not both") if model_key: - return [model_key], Path("results") + return [model_key], Path("results"), None if all_models: - return model_keys_with_reference(), Path("results") + return model_keys_with_reference(), Path("results"), None raise typer.BadParameter( - "Specify --model-key KEY, --all, or --ci. " + "Specify --model-key KEY, --all, --ci, or --replication --run-id. " "Example: uv run python -m src.runners.run_verify --all" ) @@ -51,6 +75,7 @@ def _run_verify_models( output_root: Path, *, require_complete: bool, + registry: str = "default", ) -> None: passed: list[str] = [] skipped: list[tuple[str, str]] = [] @@ -62,6 +87,7 @@ def _run_verify_models( key, output_root, require_complete=require_complete, + registry=registry, # type: ignore[arg-type] ) passed.append(key) except ReplicationSkip as exc: @@ -97,6 +123,20 @@ def main( bool, typer.Option(help="Verify CI subset jobs (per-job layout under results/ci)"), ] = False, + replication: Annotated[ + bool, + typer.Option(help="Verify replication run under results/replication/RUN_ID"), + ] = False, + run_id: Annotated[ + str | None, + typer.Option(help="Replication run id (required with --replication)"), + ] = None, + replication_pilot: Annotated[ + bool, + typer.Option( + help="With --replication, verify pilot jobs only (m4_weekly/short)" + ), + ] = False, output_root: Annotated[ Path | None, typer.Option(help="Root directory containing benchmark outputs"), @@ -116,23 +156,39 @@ def main( ), ] = False, ) -> None: - model_keys, default_root = _resolve_model_keys( + model_keys, default_root, replication_jobs = _resolve_model_keys( model_key=model_key, all_models=all_models, ci=ci, + replication=replication, + run_id=run_id, + replication_pilot=replication_pilot, ) resolved_output_root = output_root or default_root + registry = "replication" if replication else "default" if ci and not verify_only: verify_all(load_ci_subset(), resolved_output_root) + elif replication and replication_pilot and not verify_only: + verify_all( + replication_jobs or load_replication_pilot_jobs(), + resolved_output_root, + registry=registry, # type: ignore[arg-type] + ) elif not verify_only: _run_verify_models( model_keys, resolved_output_root, require_complete=require_complete, + registry=registry, ) - write_replication_table(model_keys, resolved_output_root, table_output) + write_replication_table( + model_keys, + resolved_output_root, + table_output, + registry=registry, # type: ignore[arg-type] + ) if __name__ == "__main__": diff --git a/experiments/gift-eval/src/verify/replication_table.py b/experiments/gift-eval/src/verify/replication_table.py index 0362bd5..9b264f2 100644 --- a/experiments/gift-eval/src/verify/replication_table.py +++ b/experiments/gift-eval/src/verify/replication_table.py @@ -6,7 +6,7 @@ import pandas as pd -from src.eval.models import reference_slug +from src.eval.models import Registry, reference_slug from src.verify.reference import CRPS_COL, MASE_COL, load_reference_results logger = logging.getLogger(__name__) @@ -48,12 +48,14 @@ def load_timing_map(model_key: str, output_root: Path) -> dict[str, float]: def build_replication_table( model_keys: list[str], output_root: Path, + *, + registry: Registry = "default", ) -> pd.DataFrame: from src.verify.verify import load_actual_results rows: list[dict] = [] for model_key in model_keys: - slug = reference_slug(model_key) + slug = reference_slug(model_key, registry=registry) if slug is None: logger.warning("Skipping %s: no reference_slug", model_key) continue @@ -105,8 +107,10 @@ def write_replication_table( model_keys: list[str], output_root: Path, table_path: Path, + *, + registry: Registry = "default", ) -> pd.DataFrame: - table = build_replication_table(model_keys, output_root) + table = build_replication_table(model_keys, output_root, registry=registry) table_path.parent.mkdir(parents=True, exist_ok=True) table.to_csv(table_path, index=False) logger.info("Wrote replication table (%s rows) to %s", len(table), table_path) diff --git a/experiments/gift-eval/src/verify/verify.py b/experiments/gift-eval/src/verify/verify.py index 05ff8ba..eb1b578 100644 --- a/experiments/gift-eval/src/verify/verify.py +++ b/experiments/gift-eval/src/verify/verify.py @@ -7,7 +7,7 @@ from timecopilot_gift_eval import GIFTEval from src.eval.jobs import Job, result_csv -from src.eval.models import load_models_config, reference_slug +from src.eval.models import Registry, reference_slug from .reference import ( REPLICATION_ATOL, REPLICATION_METRIC_COLS, @@ -39,8 +39,9 @@ def verify_job( storage_path: Path | str | None = None, atol: float = REPLICATION_ATOL, rtol: float = REPLICATION_RTOL, + registry: Registry = "default", ) -> None: - slug = reference_slug(job.model_key) + slug = reference_slug(job.model_key, registry=registry) if slug is None: raise ReplicationSkip(f"No reference slug for model_key={job.model_key!r}") @@ -74,6 +75,7 @@ def verify_all( storage_path: Path | str | None = None, atol: float = REPLICATION_ATOL, rtol: float = REPLICATION_RTOL, + registry: Registry = "default", ) -> None: for job in jobs: verify_job( @@ -82,6 +84,7 @@ def verify_all( storage_path=storage_path, atol=atol, rtol=rtol, + registry=registry, ) @@ -110,8 +113,9 @@ def verify_model( atol: float = REPLICATION_ATOL, rtol: float = REPLICATION_RTOL, require_complete: bool = False, + registry: Registry = "default", ) -> None: - slug = reference_slug(model_key) + slug = reference_slug(model_key, registry=registry) if slug is None: raise ReplicationSkip(f"No reference slug for model_key={model_key!r}") @@ -145,12 +149,3 @@ def verify_model( len(expected), slug, ) - - -def model_keys_with_reference() -> list[str]: - models = load_models_config() - return [ - model_key - for model_key, spec in models.items() - if spec.get("reference_slug") is not None - ] diff --git a/experiments/gift-eval/tests/test_replication_config.py b/experiments/gift-eval/tests/test_replication_config.py new file mode 100644 index 0000000..410da30 --- /dev/null +++ b/experiments/gift-eval/tests/test_replication_config.py @@ -0,0 +1,43 @@ +import pytest +from src.eval.jobs import load_replication_families, load_replication_pilot_jobs +from src.eval.models import build_model, reference_slug +from src.verify.reference import load_reference_results + + +@pytest.mark.parametrize("model_key", load_replication_families()) +def test_replication_alias_matches_hf_reference_model_column(model_key: str) -> None: + slug = reference_slug(model_key, registry="replication") + assert slug is not None, f"missing reference_slug for {model_key}" + expected_name = load_reference_results(slug)["model"].iloc[0] + forecaster = build_model(model_key, registry="replication") + assert forecaster.alias == expected_name + + +def test_replication_pilot_job_count() -> None: + families = load_replication_families() + pilot = load_replication_pilot_jobs() + assert len(pilot) == len(families) + assert {j.model_key for j in pilot} == set(families) + + +def test_timesfm3_replication_context_length() -> None: + from src.eval.jobs import load_replication_models_config + + spec = load_replication_models_config()["google--timesfm-3.0-pytorch"] + assert spec["kwargs"]["context_length"] == 15360 + + +def test_moirai2_replication_context_and_batch() -> None: + from src.eval.jobs import load_replication_models_config + + spec = load_replication_models_config()["Salesforce--moirai-2.0-R-small"] + assert spec["kwargs"]["context_length"] == 4000 + assert spec["kwargs"]["batch_size"] == 2048 + + +def test_chronos2_replication_no_gluonts_max_length_cap() -> None: + from src.eval.jobs import load_replication_models_config + + spec = load_replication_models_config()["amazon--chronos-2"] + assert spec["max_length"] is None + assert spec["kwargs"]["alias"] == "Chronos-2" From 3085a97f7657fd7c51c286f2e3e2dade9bb619bf Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Tue, 6 Oct 2026 13:00:58 -0700 Subject: [PATCH 02/24] feat(gift-eval): aggregate replication verify and slim Modal runs Use leaderboard-style geomean checks for the full replication grid while keeping strict per-job verify for CI and pilot. Drop Chronos-2 and FlowState from the FF replication matrix and install foundationforecast from PyPI on Modal. Co-authored-by: Cursor --- experiments/gift-eval/README.md | 20 +- .../configs/replication/families.yaml | 4 +- .../gift-eval/configs/replication/models.yaml | 18 -- .../configs/replication/notebooks.yaml | 6 - .../gift-eval/src/runners/run_modal.py | 49 +++-- .../gift-eval/src/runners/run_verify.py | 8 + .../src/verify/replication_aggregate.py | 197 ++++++++++++++++++ .../tests/test_replication_aggregate.py | 135 ++++++++++++ .../tests/test_replication_config.py | 7 +- 9 files changed, 392 insertions(+), 52 deletions(-) create mode 100644 experiments/gift-eval/src/verify/replication_aggregate.py create mode 100644 experiments/gift-eval/tests/test_replication_aggregate.py diff --git a/experiments/gift-eval/README.md b/experiments/gift-eval/README.md index 4daa403..4ebd295 100644 --- a/experiments/gift-eval/README.md +++ b/experiments/gift-eval/README.md @@ -72,13 +72,16 @@ uv run pytest tests/test_replication.py -n 0 -x ## Replication run (notebook-aligned, one model per family) -[`configs/replication/`](configs/replication/) holds **9 families** with params traced to +[`configs/replication/`](configs/replication/) holds **7 families** (FF-replicable) with params traced to [official gift-eval notebooks](https://github.com/SalesforceAIResearch/gift-eval/tree/main/notebooks). Results go to **`s3://foundationforecast-gift-eval/results/replication//`** (not `results/` or `results/ci/`). +Modal installs **`foundationforecast` from PyPI** (version pinned from the monorepo root) and +only uploads `experiments/gift-eval` (`src/`, `configs/`) — not the full repo. + 1. Pick a run id, e.g. `2026-04-05-nb-v1`. -2. **Pilot** (9 jobs: `m4_weekly` / `short`): +2. **Pilot** (7 jobs: `m4_weekly` / `short`): ```bash uv run modal run -m src.runners.run_modal::run_replication_pilot --run-id 2026-04-05-nb-v1 @@ -86,13 +89,24 @@ make sync-replication RUN_ID=2026-04-05-nb-v1 make verify-replication-pilot RUN_ID=2026-04-05-nb-v1 ``` -3. After pilot verify passes, **full grid** (9 × 97 jobs): +3. After pilot verify passes, **full grid** (7 × 97 jobs): + +Chronos-2 and Granite FlowState are **not** in the replication list (they need native GIFT-Eval notebook runners). ```bash uv run modal run -m src.runners.run_modal::run_replication_full --run-id 2026-04-05-nb-v1 make verify-replication-full RUN_ID=2026-04-05-nb-v1 ``` +**Pilot** uses the same **per-job** MASE/CRPS check as CI (`atol=0.01`, `rtol=2.5%`). + +**Full grid** uses a **leaderboard aggregate** check per model: +geomean(`MASE/Seasonal_Naive`) and geomean(`CRPS/Seasonal_Naive`) on the same +dataset set as the HF submission, compared with the same default tolerances. +Individual job drift is allowed if the aggregate still matches (see +`src/verify/replication_aggregate.py`). CI pytest (`tests/test_replication.py`) +still enforces strict per-job replication on the CI subset only. + Local single job with replication registry: ```bash diff --git a/experiments/gift-eval/configs/replication/families.yaml b/experiments/gift-eval/configs/replication/families.yaml index 2b324e6..52af6ca 100644 --- a/experiments/gift-eval/configs/replication/families.yaml +++ b/experiments/gift-eval/configs/replication/families.yaml @@ -1,11 +1,11 @@ # One representative model per family for notebook-aligned replication runs. +# Excluded (need native GIFT-Eval notebook runners, not foundationforecast): +# amazon--chronos-2, ibm-granite--granite-timeseries-flowstate-r1 model_keys: - - amazon--chronos-2 - google--timesfm-3.0-pytorch - Salesforce--moirai-2.0-R-small - Datadog--Toto-2.0-313m - NX-AI--TiRex-2-gifteval-zs - - ibm-granite--granite-timeseries-flowstate-r1 - ibm-granite--granite-timeseries-patchtst-fm-r2 - theforecastingcompany--t0-beta - tafsut-univariate-base diff --git a/experiments/gift-eval/configs/replication/models.yaml b/experiments/gift-eval/configs/replication/models.yaml index 7ae0704..9545f76 100644 --- a/experiments/gift-eval/configs/replication/models.yaml +++ b/experiments/gift-eval/configs/replication/models.yaml @@ -1,15 +1,5 @@ # Notebook-aligned params for replication runs only (see configs/replication/notebooks.yaml). models: - amazon--chronos-2: - class: foundationforecast.models.chronos.Chronos - reference_slug: Chronos-2 - max_length: null - predictor_batch_size: 1024 - kwargs: - repo_id: amazon/chronos-2 - alias: Chronos-2 - batch_size: 1024 - google--timesfm-3.0-pytorch: class: foundationforecast.models.timesfm.TimesFM reference_slug: TimesFM-3 @@ -48,14 +38,6 @@ models: repo_id: NX-AI/TiRex-2-gifteval-zs batch_size: 512 - ibm-granite--granite-timeseries-flowstate-r1: - class: foundationforecast.models.flowstate.FlowState - reference_slug: Granite-FlowState-r1.1 - max_length: null - kwargs: - repo_id: ibm-granite/granite-timeseries-flowstate-r1 - batch_size: 16 - ibm-granite--granite-timeseries-patchtst-fm-r2: class: foundationforecast.models.patchtst_fm.PatchTSTFM reference_slug: Granite-PatchTST-FM-r2 diff --git a/experiments/gift-eval/configs/replication/notebooks.yaml b/experiments/gift-eval/configs/replication/notebooks.yaml index 2195718..d92e644 100644 --- a/experiments/gift-eval/configs/replication/notebooks.yaml +++ b/experiments/gift-eval/configs/replication/notebooks.yaml @@ -1,9 +1,6 @@ # Official GIFT-Eval replication notebooks (SalesforceAIResearch/gift-eval). # HF results: https://huggingface.co/spaces/Salesforce/GIFT-Eval/tree/main/results/{reference_slug} sources: - amazon--chronos-2: - notebook: notebooks/chronos-2.ipynb - reference_slug: Chronos-2 google--timesfm-3.0-pytorch: notebook: notebooks/timesfm3.ipynb reference_slug: TimesFM-3 @@ -16,9 +13,6 @@ sources: NX-AI--TiRex-2-gifteval-zs: notebook: notebooks/tirex-2.ipynb reference_slug: TiRex-2-Zeroshot - ibm-granite--granite-timeseries-flowstate-r1: - notebook: notebooks/flowstate.ipynb - reference_slug: Granite-FlowState-r1.1 ibm-granite--granite-timeseries-patchtst-fm-r2: notebook: notebooks/patchtst_fm_r2.ipynb reference_slug: Granite-PatchTST-FM-r2 diff --git a/experiments/gift-eval/src/runners/run_modal.py b/experiments/gift-eval/src/runners/run_modal.py index 98e4d4e..bd6f52f 100644 --- a/experiments/gift-eval/src/runners/run_modal.py +++ b/experiments/gift-eval/src/runners/run_modal.py @@ -2,23 +2,34 @@ from pathlib import Path import modal +import tomllib -_MODAL_MONOREPO = "/root/monorepo" -_MODAL_GIFT_EVAL = f"{_MODAL_MONOREPO}/experiments/gift-eval" +_MODAL_GIFT_EVAL = "/root/gift-eval" -def _resolve_paths() -> tuple[Path, Path]: +def _resolve_gift_eval_root() -> Path: here = Path(__file__).resolve() try: gift_eval_root = here.parents[2] if (gift_eval_root / "pyproject.toml").exists(): - return gift_eval_root, gift_eval_root.parent.parent + return gift_eval_root except IndexError: pass - return Path(_MODAL_GIFT_EVAL), Path(_MODAL_MONOREPO) + return Path(_MODAL_GIFT_EVAL) -_GIFT_EVAL_ROOT, _REPO_ROOT = _resolve_paths() +def _foundationforecast_pypi_version(gift_eval_root: Path) -> str: + repo_pyproject = gift_eval_root.parent.parent / "pyproject.toml" + if repo_pyproject.is_file(): + with repo_pyproject.open("rb") as f: + return tomllib.load(f)["project"]["version"] + from importlib.metadata import version + + return version("foundationforecast") + + +_GIFT_EVAL_ROOT = _resolve_gift_eval_root() +_FF_VERSION = _foundationforecast_pypi_version(_GIFT_EVAL_ROOT) app = modal.App(name="foundationforecast-gift-eval") image = ( @@ -28,35 +39,35 @@ def _resolve_paths() -> tuple[Path, Path]: ) .apt_install("git") .pip_install("uv") - .add_local_file( - _REPO_ROOT / "pyproject.toml", - remote_path=f"{_MODAL_MONOREPO}/pyproject.toml", - copy=True, + .run_commands( + "uv pip install --system --compile-bytecode " + f"foundationforecast=={_FF_VERSION} " + "'timecopilot-gift-eval>=0.3.1' modal pyyaml s3fs typer", ) .add_local_file( - _REPO_ROOT / "README.md", - remote_path=f"{_MODAL_MONOREPO}/README.md", + _GIFT_EVAL_ROOT / "pyproject.toml", + remote_path=f"{_MODAL_GIFT_EVAL}/pyproject.toml", copy=True, ) .add_local_file( - _REPO_ROOT / "uv.lock", - remote_path=f"{_MODAL_MONOREPO}/uv.lock", + _GIFT_EVAL_ROOT / "README.md", + remote_path=f"{_MODAL_GIFT_EVAL}/README.md", copy=True, ) .add_local_dir( - _REPO_ROOT / "foundationforecast", - remote_path=f"{_MODAL_MONOREPO}/foundationforecast", + _GIFT_EVAL_ROOT / "src", + remote_path=f"{_MODAL_GIFT_EVAL}/src", copy=True, ) .add_local_dir( - _GIFT_EVAL_ROOT, - remote_path=_MODAL_GIFT_EVAL, + _GIFT_EVAL_ROOT / "configs", + remote_path=f"{_MODAL_GIFT_EVAL}/configs", copy=True, ) .workdir(_MODAL_GIFT_EVAL) .env({"PYTHONPATH": _MODAL_GIFT_EVAL}) .run_commands( - "uv pip install --system --compile-bytecode -e .", + "uv pip install --system --no-deps --compile-bytecode -e .", ) ) secret = modal.Secret.from_name( diff --git a/experiments/gift-eval/src/runners/run_verify.py b/experiments/gift-eval/src/runners/run_verify.py index 1863091..a4e55d4 100644 --- a/experiments/gift-eval/src/runners/run_verify.py +++ b/experiments/gift-eval/src/runners/run_verify.py @@ -15,6 +15,7 @@ replication_output_root, ) from src.eval.models import model_keys_with_reference +from src.verify.replication_aggregate import verify_replication_aggregates from src.verify.replication_table import write_replication_table from src.verify.verify import ReplicationSkip, verify_all, verify_model @@ -175,6 +176,13 @@ def main( resolved_output_root, registry=registry, # type: ignore[arg-type] ) + elif replication and not replication_pilot and not verify_only: + verify_replication_aggregates( + model_keys, + resolved_output_root, + require_complete=require_complete, + registry=registry, # type: ignore[arg-type] + ) elif not verify_only: _run_verify_models( model_keys, diff --git a/experiments/gift-eval/src/verify/replication_aggregate.py b/experiments/gift-eval/src/verify/replication_aggregate.py new file mode 100644 index 0000000..f5a3e21 --- /dev/null +++ b/experiments/gift-eval/src/verify/replication_aggregate.py @@ -0,0 +1,197 @@ +"""Leaderboard-style aggregate replication checks (experiment grid only).""" + +from __future__ import annotations + +import logging +from dataclasses import dataclass +from functools import lru_cache + +import numpy as np +import pandas as pd +from numpy.testing import assert_allclose +from scipy import stats + +from src.eval.jobs import Job, load_replication_matrix, result_csv +from src.eval.models import Registry, reference_slug +from src.verify.reference import CRPS_COL, MASE_COL, load_reference_results +from src.verify.verify import ReplicationSkip, load_actual_results + +logger = logging.getLogger(__name__) + +SEASONAL_NAIVE_REFERENCE_SLUG = "Seasonal_Naive" + +# Geomean(MASE/SN, CRPS/SN) vs the HF submission on the same dataset set. +# Slightly looser than per-job verify is unnecessary at aggregate level; same +# rtol keeps the experiment aligned with CI semantics while tolerating outlier jobs. +REPLICATION_AGGREGATE_ATOL = 1e-3 +REPLICATION_AGGREGATE_RTOL = 2.5e-2 + + +@dataclass(frozen=True) +class AggregateScores: + norm_mase: float + norm_crps: float + n_configs: int + + +@lru_cache +def seasonal_naive_by_dataset() -> pd.DataFrame: + sn = load_reference_results(SEASONAL_NAIVE_REFERENCE_SLUG) + return sn.rename( + columns={ + MASE_COL: "sn_mase", + CRPS_COL: "sn_crps", + } + )[["dataset", "sn_mase", "sn_crps"]] + + +def geomean_positive(series: pd.Series) -> float: + values = series.astype(float) + values = values[values > 0] + if values.empty: + return float("nan") + return float(stats.gmean(values)) + + +def leaderboard_aggregate(df: pd.DataFrame, sn: pd.DataFrame) -> AggregateScores: + merged = df.merge(sn, on="dataset", how="inner") + if merged.empty: + return AggregateScores(float("nan"), float("nan"), 0) + norm_mase = merged[MASE_COL] / merged["sn_mase"] + norm_crps = merged[CRPS_COL] / merged["sn_crps"] + return AggregateScores( + geomean_positive(norm_mase), + geomean_positive(norm_crps), + len(merged), + ) + + +def missing_matrix_jobs(model_key: str, output_root) -> list[Job]: + from pathlib import Path + + root = Path(output_root) + missing: list[Job] = [] + for job in load_replication_matrix(): + if job.model_key != model_key: + continue + if not result_csv(job, root).exists(): + missing.append(job) + return missing + + +def verify_replication_aggregate( + model_key: str, + output_root, + *, + require_complete: bool = False, + registry: Registry = "replication", + atol: float = REPLICATION_AGGREGATE_ATOL, + rtol: float = REPLICATION_AGGREGATE_RTOL, +) -> AggregateScores: + from pathlib import Path + + root = Path(output_root) + slug = reference_slug(model_key, registry=registry) + if slug is None: + raise ReplicationSkip(f"No reference slug for model_key={model_key!r}") + + if require_complete: + missing = missing_matrix_jobs(model_key, root) + if missing: + sample = ", ".join(f"{j.dataset_name}/{j.term}" for j in missing[:5]) + suffix = "..." if len(missing) > 5 else "" + raise AssertionError( + f"{model_key}: missing {len(missing)} replication matrix jobs " + f"(e.g. {sample}{suffix})" + ) + + actual = load_actual_results(model_key, root) + expected = load_reference_results(slug) + datasets = sorted(set(actual["dataset"]) & set(expected["dataset"])) + if not datasets: + raise AssertionError(f"{model_key}: no overlapping datasets with HF reference") + + sn = seasonal_naive_by_dataset() + actual_sub = actual[actual["dataset"].isin(datasets)] + expected_sub = expected[expected["dataset"].isin(datasets)] + + rep = leaderboard_aggregate(actual_sub, sn) + hf = leaderboard_aggregate(expected_sub, sn) + if rep.n_configs != hf.n_configs or rep.n_configs != len(datasets): + raise AssertionError( + f"{model_key}: aggregate config count mismatch " + f"(rep={rep.n_configs}, hf={hf.n_configs}, overlap={len(datasets)})" + ) + + for label, rep_val, hf_val in ( + ("geomean(MASE/Seasonal_Naive)", rep.norm_mase, hf.norm_mase), + ("geomean(CRPS/Seasonal_Naive)", rep.norm_crps, hf.norm_crps), + ): + try: + assert_allclose( + np.array([rep_val], dtype=float), + np.array([hf_val], dtype=float), + atol=atol, + rtol=rtol, + ) + except AssertionError as exc: + delta_pct = 100 * (rep_val - hf_val) / hf_val if hf_val else float("nan") + raise AssertionError( + f"{model_key}: {label} differs from HF submission aggregate " + f"(rep={rep_val:.6g}, hf={hf_val:.6g}, delta={delta_pct:+.2f}%, " + f"n={rep.n_configs}, atol={atol}, rtol={rtol})" + ) from exc + + logger.info( + "%s: aggregate OK on %s configs — rep MASE/SN=%.4f CRPS/SN=%.4f " + "(HF %.4f / %.4f)", + model_key, + rep.n_configs, + rep.norm_mase, + rep.norm_crps, + hf.norm_mase, + hf.norm_crps, + ) + return rep + + +def verify_replication_aggregates( + model_keys: list[str], + output_root, + *, + require_complete: bool = False, + registry: Registry = "replication", + atol: float = REPLICATION_AGGREGATE_ATOL, + rtol: float = REPLICATION_AGGREGATE_RTOL, +) -> None: + passed: list[str] = [] + skipped: list[tuple[str, str]] = [] + failed: list[tuple[str, str]] = [] + + for key in model_keys: + try: + verify_replication_aggregate( + key, + output_root, + require_complete=require_complete, + registry=registry, + atol=atol, + rtol=rtol, + ) + passed.append(key) + except ReplicationSkip as exc: + skipped.append((key, str(exc))) + logger.warning("Skipped %s: %s", key, exc) + except Exception as exc: + failed.append((key, str(exc))) + logger.error("Failed %s: %s", key, exc) + + logger.info( + "Aggregate verify summary: passed=%s skipped=%s failed=%s", + len(passed), + len(skipped), + len(failed), + ) + if failed: + details = "\n".join(f" {key}: {error}" for key, error in failed) + raise AssertionError(f"Aggregate replication verification failed:\n{details}") diff --git a/experiments/gift-eval/tests/test_replication_aggregate.py b/experiments/gift-eval/tests/test_replication_aggregate.py new file mode 100644 index 0000000..c55644e --- /dev/null +++ b/experiments/gift-eval/tests/test_replication_aggregate.py @@ -0,0 +1,135 @@ +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest +from scipy import stats +from src.eval.jobs import Job +from src.verify.reference import CRPS_COL, MASE_COL +from src.verify.replication_aggregate import ( + REPLICATION_AGGREGATE_RTOL, + leaderboard_aggregate, + verify_replication_aggregate, +) + + +def test_leaderboard_aggregate_geomean_normalized() -> None: + sn = pd.DataFrame( + { + "dataset": ["cfg/a", "cfg/b"], + "sn_mase": [2.0, 0.5], + "sn_crps": [4.0, 1.0], + } + ) + df = pd.DataFrame( + { + "dataset": ["cfg/a", "cfg/b"], + MASE_COL: [1.0, 0.5], + CRPS_COL: [2.0, 0.5], + } + ) + scores = leaderboard_aggregate(df, sn) + assert scores.n_configs == 2 + assert np.isclose(scores.norm_mase, stats.gmean([0.5, 1.0])) + assert np.isclose(scores.norm_crps, stats.gmean([0.5, 0.5])) + + +def test_verify_replication_aggregate_passes_with_outlier_jobs( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + model_key = "test--model" + datasets = ["cfg/a", "cfg/b"] + sn = pd.DataFrame( + { + "dataset": datasets, + "sn_mase": [1.0, 1.0], + "sn_crps": [1.0, 1.0], + } + ) + # HF submission: geomean MASE/SN = gmean(0.7, 0.7) = 0.7 + hf_rows = pd.DataFrame( + { + "dataset": datasets, + MASE_COL: [0.7, 0.7], + CRPS_COL: [0.5, 0.5], + } + ) + # Per-job values differ (>2.5% rtol) but geomean stays near 0.7 + rep_rows = pd.DataFrame( + { + "dataset": datasets, + MASE_COL: [0.85, 0.58], + CRPS_COL: [0.5, 0.5], + } + ) + + model_root = tmp_path / model_key / "m4_weekly" / "short" + model_root.mkdir(parents=True) + rep_rows.to_csv(model_root / "all_results.csv", index=False) + + monkeypatch.setattr( + "src.verify.replication_aggregate.reference_slug", + lambda _key, registry="replication": "hf-slug", + ) + monkeypatch.setattr( + "src.verify.replication_aggregate.load_reference_results", + lambda slug: hf_rows.copy(), + ) + monkeypatch.setattr( + "src.verify.replication_aggregate.seasonal_naive_by_dataset", + lambda: sn.copy(), + ) + monkeypatch.setattr( + "src.verify.replication_aggregate.load_replication_matrix", + lambda: [ + Job(model_key=model_key, dataset_name="m4_weekly", term="short"), + ], + ) + + verify_replication_aggregate( + model_key, + tmp_path, + require_complete=True, + rtol=REPLICATION_AGGREGATE_RTOL, + ) + + +def test_verify_replication_aggregate_fails_when_aggregate_drifts( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + model_key = "test--model" + datasets = ["cfg/a"] + sn = pd.DataFrame({"dataset": datasets, "sn_mase": [1.0], "sn_crps": [1.0]}) + hf_rows = pd.DataFrame({"dataset": datasets, MASE_COL: [0.5], CRPS_COL: [0.5]}) + rep_rows = pd.DataFrame({"dataset": datasets, MASE_COL: [0.8], CRPS_COL: [0.5]}) + + model_root = tmp_path / model_key / "m4_weekly" / "short" + model_root.mkdir(parents=True) + rep_rows.to_csv(model_root / "all_results.csv", index=False) + + monkeypatch.setattr( + "src.verify.replication_aggregate.reference_slug", + lambda _key, registry="replication": "hf-slug", + ) + monkeypatch.setattr( + "src.verify.replication_aggregate.load_reference_results", + lambda slug: hf_rows.copy(), + ) + monkeypatch.setattr( + "src.verify.replication_aggregate.seasonal_naive_by_dataset", + lambda: sn.copy(), + ) + monkeypatch.setattr( + "src.verify.replication_aggregate.load_replication_matrix", + lambda: [ + Job(model_key=model_key, dataset_name="m4_weekly", term="short"), + ], + ) + + with pytest.raises(AssertionError, match="MASE/Seasonal_Naive"): + verify_replication_aggregate( + model_key, + tmp_path, + require_complete=True, + rtol=REPLICATION_AGGREGATE_RTOL, + ) diff --git a/experiments/gift-eval/tests/test_replication_config.py b/experiments/gift-eval/tests/test_replication_config.py index 410da30..686379b 100644 --- a/experiments/gift-eval/tests/test_replication_config.py +++ b/experiments/gift-eval/tests/test_replication_config.py @@ -35,9 +35,8 @@ def test_moirai2_replication_context_and_batch() -> None: assert spec["kwargs"]["batch_size"] == 2048 -def test_chronos2_replication_no_gluonts_max_length_cap() -> None: +def test_toto_replication_batch_size() -> None: from src.eval.jobs import load_replication_models_config - spec = load_replication_models_config()["amazon--chronos-2"] - assert spec["max_length"] is None - assert spec["kwargs"]["alias"] == "Chronos-2" + spec = load_replication_models_config()["Datadog--Toto-2.0-313m"] + assert spec["kwargs"]["batch_size"] == 512 From 3fa4fca6448ee571b01c1995d13a147d393f6e0d Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Tue, 6 Oct 2026 13:08:44 -0700 Subject: [PATCH 03/24] feat: document a little bit better make file --- .github/workflows/ci.yaml | 7 +-- experiments/gift-eval/Makefile | 76 +++++++++++++++++++++++++++++++-- experiments/gift-eval/README.md | 11 +++-- 3 files changed, 79 insertions(+), 15 deletions(-) diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index a394f01..126a0e5 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -130,7 +130,7 @@ jobs: run: uv sync --frozen --no-dev - name: Run GIFT-Eval CI subset on Modal - run: uv run modal run -m src.runners.run_modal::run_ci + run: make run-ci env: MODAL_TOKEN_ID: ${{ secrets.MODAL_TOKEN_ID }} MODAL_TOKEN_SECRET: ${{ secrets.MODAL_TOKEN_SECRET }} @@ -145,11 +145,8 @@ jobs: aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }} aws-region: us-east-1 - - name: Sync CI results from S3 - run: make sync-ci-results - - name: Verify replication - run: uv run pytest tests/test_replication.py -n 0 -x + run: make verify-ci env: HF_TOKEN: ${{ secrets.HF_TOKEN }} diff --git a/experiments/gift-eval/Makefile b/experiments/gift-eval/Makefile index 8562436..4255da3 100644 --- a/experiments/gift-eval/Makefile +++ b/experiments/gift-eval/Makefile @@ -1,6 +1,52 @@ -.PHONY: download-gift-eval-data upload-data-to-s3 sync-ci-results sync-results sync-replication verify-all replication-table verify-replication-pilot verify-replication-full +# Gift-Eval experiment — common tasks (run from experiments/gift-eval/). +# +# Modal runs need MODAL_TOKEN_* , HF_TOKEN , and AWS creds in the environment +# (same as GitHub Actions secrets). Long grids: DETACHED=1 make run-replication-full ... + +.DEFAULT_GOAL := help + +.PHONY: help +.PHONY: download-gift-eval-data upload-data-to-s3 +.PHONY: run-ci sync-ci-results verify-ci +.PHONY: run-replication-pilot run-replication-full +.PHONY: sync-replication verify-replication-pilot verify-replication-full +.PHONY: sync-results verify-all replication-table RUN_ID ?= +DETACHED ?= +FORCE ?= + +# Pass -d to modal run for detached (background) execution. +MODAL_RUN = uv run modal run $(if $(filter 1 true yes,$(DETACHED)),-d,) + +## help — list targets (default) +help: + @echo "Gift-Eval Makefile" + @echo "" + @echo "Data" + @echo " download-gift-eval-data HF dataset → ./data/gift-eval" + @echo " upload-data-to-s3 sync local dataset to S3" + @echo "" + @echo "CI (configs/ci_subset.yaml — strict per-job HF verify in pytest)" + @echo " run-ci Modal GPU eval + overwrite results/ci on S3" + @echo " sync-ci-results S3 → ./results/ci" + @echo " verify-ci sync + pytest tests/test_replication.py" + @echo "" + @echo "Replication (configs/replication/ — pilot strict, full aggregate verify)" + @echo " run-replication-pilot RUN_ID=... — 7× m4_weekly/short on Modal" + @echo " run-replication-full RUN_ID=... — 7×97 grid (skips existing unless FORCE=1)" + @echo " sync-replication RUN_ID=... — S3 → ./results/replication/\$$RUN_ID" + @echo " verify-replication-pilot RUN_ID=... — per-job MASE/CRPS vs HF" + @echo " verify-replication-full RUN_ID=... — leaderboard aggregate + require complete" + @echo "" + @echo "Full benchmark (configs/models.yaml — production results/ on S3)" + @echo " sync-results S3 → ./results" + @echo " verify-all strict HF verify for all reference models" + @echo " replication-table analysis CSV (verify-only, no assert)" + @echo "" + @echo "Options: DETACHED=1 for Modal -d ; FORCE=1 to re-run all replication jobs" + +# --- Data ------------------------------------------------------------------- download-gift-eval-data: @hf download Salesforce/GiftEval --repo-type=dataset --local-dir=./data/gift-eval @@ -8,13 +54,29 @@ download-gift-eval-data: upload-data-to-s3: download-gift-eval-data @aws s3 sync ./data/gift-eval s3://foundationforecast-gift-eval/data/gift-eval +# --- CI --------------------------------------------------------------------- +# GitHub Actions: make run-ci && make verify-ci + +run-ci: + @$(MODAL_RUN) -m src.runners.run_modal::run_ci + sync-ci-results: @mkdir -p ./results/ci @aws s3 sync s3://foundationforecast-gift-eval/results/ci ./results/ci -sync-results: - @mkdir -p ./results - @aws s3 sync s3://foundationforecast-gift-eval/results ./results +verify-ci: sync-ci-results + @uv run pytest tests/test_replication.py -n 0 -x + +# --- Replication ------------------------------------------------------------ + +run-replication-pilot: + @test -n "$(RUN_ID)" || (echo "Set RUN_ID=..." && exit 1) + @$(MODAL_RUN) -m src.runners.run_modal::run_replication_pilot --run-id $(RUN_ID) + +run-replication-full: + @test -n "$(RUN_ID)" || (echo "Set RUN_ID=..." && exit 1) + @$(MODAL_RUN) -m src.runners.run_modal::run_replication_full --run-id $(RUN_ID) \ + $(if $(filter 1 true yes,$(FORCE)),--force,) sync-replication: @test -n "$(RUN_ID)" || (echo "Set RUN_ID=..." && exit 1) @@ -27,6 +89,12 @@ verify-replication-pilot: sync-replication verify-replication-full: sync-replication @uv run python -m src.runners.run_verify --replication --run-id $(RUN_ID) --all --require-complete +# --- Full benchmark --------------------------------------------------------- + +sync-results: + @mkdir -p ./results + @aws s3 sync s3://foundationforecast-gift-eval/results ./results + verify-all: sync-results @uv run python -m src.runners.run_verify --all diff --git a/experiments/gift-eval/README.md b/experiments/gift-eval/README.md index 4ebd295..ed1f6bd 100644 --- a/experiments/gift-eval/README.md +++ b/experiments/gift-eval/README.md @@ -65,9 +65,8 @@ Always re-runs and overwrites results (no skip-if-exists). Full grid skips jobs already have outputs. ```bash -uv run modal run -m src.runners.run_modal::run_ci -make sync-ci-results # download results for local verify / pytest -uv run pytest tests/test_replication.py -n 0 -x +make run-ci +make verify-ci # sync S3 + pytest ``` ## Replication run (notebook-aligned, one model per family) @@ -84,9 +83,9 @@ only uploads `experiments/gift-eval` (`src/`, `configs/`) — not the full repo. 2. **Pilot** (7 jobs: `m4_weekly` / `short`): ```bash -uv run modal run -m src.runners.run_modal::run_replication_pilot --run-id 2026-04-05-nb-v1 -make sync-replication RUN_ID=2026-04-05-nb-v1 +make run-replication-pilot RUN_ID=2026-04-05-nb-v1 make verify-replication-pilot RUN_ID=2026-04-05-nb-v1 +# long full grid in background: DETACHED=1 make run-replication-full RUN_ID=... ``` 3. After pilot verify passes, **full grid** (7 × 97 jobs): @@ -94,7 +93,7 @@ make verify-replication-pilot RUN_ID=2026-04-05-nb-v1 Chronos-2 and Granite FlowState are **not** in the replication list (they need native GIFT-Eval notebook runners). ```bash -uv run modal run -m src.runners.run_modal::run_replication_full --run-id 2026-04-05-nb-v1 +make run-replication-full RUN_ID=2026-04-05-nb-v1 make verify-replication-full RUN_ID=2026-04-05-nb-v1 ``` From 5376d50794998b416e9de630557db101c8044062 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Tue, 6 Oct 2026 16:30:58 -0700 Subject: [PATCH 04/24] fix(chronos): halve inference batch on CUDA OOM for Chronos-2 quantiles Chronos2Pipeline predict_quantiles can exhaust A10G memory at default batch sizes; retry with a smaller batch before failing the job. Co-authored-by: Cursor --- foundationforecast/models/chronos.py | 29 ++++++++++++++++++++-------- 1 file changed, 21 insertions(+), 8 deletions(-) diff --git a/foundationforecast/models/chronos.py b/foundationforecast/models/chronos.py index 650f693..ac7e87e 100644 --- a/foundationforecast/models/chronos.py +++ b/foundationforecast/models/chronos.py @@ -340,14 +340,27 @@ def _predict( q_max=q_max, include_median=False, ) - fcsts = [ - model.predict_quantiles( - batch, - prediction_length=h, - quantile_levels=backend_qs, - ) - for batch in tqdm(dataset) - ] # list of tuples + quantile_kwargs: dict[str, Any] = { + "prediction_length": h, + "quantile_levels": backend_qs, + } + infer_batch = self.batch_size + + def _predict_quantiles_batch(batch: Any) -> Any: + nonlocal infer_batch + kwargs = dict(quantile_kwargs) + if isinstance(model, Chronos2Pipeline): + kwargs["batch_size"] = infer_batch + while True: + try: + return model.predict_quantiles(batch, **kwargs) + except torch.cuda.OutOfMemoryError: + if not isinstance(model, Chronos2Pipeline) or infer_batch < 2: + raise + infer_batch //= 2 + kwargs["batch_size"] = infer_batch + + fcsts = [_predict_quantiles_batch(batch) for batch in tqdm(dataset)] fcsts_quantiles, fcsts_mean = zip(*fcsts, strict=False) if isinstance(model, Chronos2Pipeline): fcsts_mean = [f_mean for fcst in fcsts_mean for f_mean in fcst] # type: ignore From f096489368d94a7f9d20a85c6baf5332733ea805 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Tue, 6 Oct 2026 16:31:03 -0700 Subject: [PATCH 05/24] feat(gift-eval): Chronos-2 replication, Modal monorepo install, Moirai OOM batches Add Chronos-2 to the 8-family grid with batch 16, mount editable foundationforecast on Modal, optional --model-key for targeted backfills, partial-failure dispatch logging, and lower Moirai replication batch sizes for A10G. Co-authored-by: Cursor --- experiments/gift-eval/Makefile | 4 +- experiments/gift-eval/README.md | 14 +++-- experiments/gift-eval/configs/models.yaml | 7 ++- .../configs/replication/families.yaml | 4 +- .../gift-eval/configs/replication/models.yaml | 14 ++++- .../configs/replication/notebooks.yaml | 3 + experiments/gift-eval/src/eval/models.py | 4 ++ .../gift-eval/src/runners/run_modal.py | 60 ++++++++++++++----- .../tests/test_replication_config.py | 13 +++- 9 files changed, 94 insertions(+), 29 deletions(-) diff --git a/experiments/gift-eval/Makefile b/experiments/gift-eval/Makefile index 4255da3..a7f3c9e 100644 --- a/experiments/gift-eval/Makefile +++ b/experiments/gift-eval/Makefile @@ -33,8 +33,8 @@ help: @echo " verify-ci sync + pytest tests/test_replication.py" @echo "" @echo "Replication (configs/replication/ — pilot strict, full aggregate verify)" - @echo " run-replication-pilot RUN_ID=... — 7× m4_weekly/short on Modal" - @echo " run-replication-full RUN_ID=... — 7×97 grid (skips existing unless FORCE=1)" + @echo " run-replication-pilot RUN_ID=... — 8× m4_weekly/short on Modal" + @echo " run-replication-full RUN_ID=... — 8×97 grid (skips existing unless FORCE=1)" @echo " sync-replication RUN_ID=... — S3 → ./results/replication/\$$RUN_ID" @echo " verify-replication-pilot RUN_ID=... — per-job MASE/CRPS vs HF" @echo " verify-replication-full RUN_ID=... — leaderboard aggregate + require complete" diff --git a/experiments/gift-eval/README.md b/experiments/gift-eval/README.md index ed1f6bd..4838743 100644 --- a/experiments/gift-eval/README.md +++ b/experiments/gift-eval/README.md @@ -71,16 +71,16 @@ make verify-ci # sync S3 + pytest ## Replication run (notebook-aligned, one model per family) -[`configs/replication/`](configs/replication/) holds **7 families** (FF-replicable) with params traced to +[`configs/replication/`](configs/replication/) holds **8 families** with params traced to [official gift-eval notebooks](https://github.com/SalesforceAIResearch/gift-eval/tree/main/notebooks). Results go to **`s3://foundationforecast-gift-eval/results/replication//`** (not `results/` or `results/ci/`). -Modal installs **`foundationforecast` from PyPI** (version pinned from the monorepo root) and -only uploads `experiments/gift-eval` (`src/`, `configs/`) — not the full repo. +Modal uploads **`foundationforecast/`** (editable install from the monorepo root) plus +`experiments/gift-eval` (`src/`, `configs/`). 1. Pick a run id, e.g. `2026-04-05-nb-v1`. -2. **Pilot** (7 jobs: `m4_weekly` / `short`): +2. **Pilot** (8 jobs: `m4_weekly` / `short`): ```bash make run-replication-pilot RUN_ID=2026-04-05-nb-v1 @@ -88,9 +88,11 @@ make verify-replication-pilot RUN_ID=2026-04-05-nb-v1 # long full grid in background: DETACHED=1 make run-replication-full RUN_ID=... ``` -3. After pilot verify passes, **full grid** (7 × 97 jobs): +3. After pilot verify passes, **full grid** (8 × 97 jobs): -Chronos-2 and Granite FlowState are **not** in the replication list (they need native GIFT-Eval notebook runners). +Chronos-2 uses **foundationforecast** with notebook inference params +(`batch_size=16`, no `max_length` cap; no cross-learning / `predict_batches_jointly`). +Granite FlowState is still excluded. ```bash make run-replication-full RUN_ID=2026-04-05-nb-v1 diff --git a/experiments/gift-eval/configs/models.yaml b/experiments/gift-eval/configs/models.yaml index d1dcac1..5b01309 100644 --- a/experiments/gift-eval/configs/models.yaml +++ b/experiments/gift-eval/configs/models.yaml @@ -63,10 +63,13 @@ models: amazon--chronos-2: class: foundationforecast.models.chronos.Chronos - reference_slug: chronos-2 + reference_slug: Chronos-2 + max_length: null + predictor_batch_size: 16 kwargs: repo_id: amazon/chronos-2 - batch_size: 64 + alias: Chronos-2 + batch_size: 16 autogluon--chronos-2-synth: class: foundationforecast.models.chronos.Chronos diff --git a/experiments/gift-eval/configs/replication/families.yaml b/experiments/gift-eval/configs/replication/families.yaml index 52af6ca..aa9c7f3 100644 --- a/experiments/gift-eval/configs/replication/families.yaml +++ b/experiments/gift-eval/configs/replication/families.yaml @@ -1,7 +1,7 @@ # One representative model per family for notebook-aligned replication runs. -# Excluded (need native GIFT-Eval notebook runners, not foundationforecast): -# amazon--chronos-2, ibm-granite--granite-timeseries-flowstate-r1 +# Excluded: ibm-granite--granite-timeseries-flowstate-r1 (native FlowState wrapper). model_keys: + - amazon--chronos-2 - google--timesfm-3.0-pytorch - Salesforce--moirai-2.0-R-small - Datadog--Toto-2.0-313m diff --git a/experiments/gift-eval/configs/replication/models.yaml b/experiments/gift-eval/configs/replication/models.yaml index 9545f76..35f78a6 100644 --- a/experiments/gift-eval/configs/replication/models.yaml +++ b/experiments/gift-eval/configs/replication/models.yaml @@ -1,5 +1,15 @@ # Notebook-aligned params for replication runs only (see configs/replication/notebooks.yaml). models: + amazon--chronos-2: + class: foundationforecast.models.chronos.Chronos + reference_slug: Chronos-2 + max_length: null + predictor_batch_size: 16 + kwargs: + repo_id: amazon/chronos-2 + alias: Chronos-2 + batch_size: 16 + google--timesfm-3.0-pytorch: class: foundationforecast.models.timesfm.TimesFM reference_slug: TimesFM-3 @@ -16,11 +26,11 @@ models: Salesforce--moirai-2.0-R-small: class: foundationforecast.models.moirai.Moirai reference_slug: Moirai2 - predictor_batch_size: 2048 + predictor_batch_size: 512 kwargs: repo_id: Salesforce/moirai-2.0-R-small context_length: 4000 - batch_size: 2048 + batch_size: 32 Datadog--Toto-2.0-313m: class: foundationforecast.models.toto.Toto diff --git a/experiments/gift-eval/configs/replication/notebooks.yaml b/experiments/gift-eval/configs/replication/notebooks.yaml index d92e644..ff119fb 100644 --- a/experiments/gift-eval/configs/replication/notebooks.yaml +++ b/experiments/gift-eval/configs/replication/notebooks.yaml @@ -1,6 +1,9 @@ # Official GIFT-Eval replication notebooks (SalesforceAIResearch/gift-eval). # HF results: https://huggingface.co/spaces/Salesforce/GIFT-Eval/tree/main/results/{reference_slug} sources: + amazon--chronos-2: + notebook: notebooks/chronos-2.ipynb + reference_slug: Chronos-2 google--timesfm-3.0-pytorch: notebook: notebooks/timesfm3.ipynb reference_slug: TimesFM-3 diff --git a/experiments/gift-eval/src/eval/models.py b/experiments/gift-eval/src/eval/models.py index ec0e1bf..3a53dfa 100644 --- a/experiments/gift-eval/src/eval/models.py +++ b/experiments/gift-eval/src/eval/models.py @@ -27,6 +27,10 @@ def _model_spec(model_key: str, registry: Registry) -> dict: return models[model_key] +def model_spec(model_key: str, *, registry: Registry = "default") -> dict: + return _model_spec(model_key, registry) + + def build_model( model_key: str, *, diff --git a/experiments/gift-eval/src/runners/run_modal.py b/experiments/gift-eval/src/runners/run_modal.py index bd6f52f..f2cb18e 100644 --- a/experiments/gift-eval/src/runners/run_modal.py +++ b/experiments/gift-eval/src/runners/run_modal.py @@ -2,9 +2,9 @@ from pathlib import Path import modal -import tomllib _MODAL_GIFT_EVAL = "/root/gift-eval" +_MODAL_MONOREPO = "/root/monorepo" def _resolve_gift_eval_root() -> Path: @@ -18,18 +18,17 @@ def _resolve_gift_eval_root() -> Path: return Path(_MODAL_GIFT_EVAL) -def _foundationforecast_pypi_version(gift_eval_root: Path) -> str: - repo_pyproject = gift_eval_root.parent.parent / "pyproject.toml" - if repo_pyproject.is_file(): - with repo_pyproject.open("rb") as f: - return tomllib.load(f)["project"]["version"] - from importlib.metadata import version - - return version("foundationforecast") +def _resolve_monorepo_root(gift_eval_root: Path) -> Path: + candidate = gift_eval_root.parent.parent + if (candidate / "pyproject.toml").is_file() and ( + candidate / "foundationforecast" + ).is_dir(): + return candidate + return Path(_MODAL_MONOREPO) _GIFT_EVAL_ROOT = _resolve_gift_eval_root() -_FF_VERSION = _foundationforecast_pypi_version(_GIFT_EVAL_ROOT) +_REPO_ROOT = _resolve_monorepo_root(_GIFT_EVAL_ROOT) app = modal.App(name="foundationforecast-gift-eval") image = ( @@ -41,9 +40,26 @@ def _foundationforecast_pypi_version(gift_eval_root: Path) -> str: .pip_install("uv") .run_commands( "uv pip install --system --compile-bytecode " - f"foundationforecast=={_FF_VERSION} " "'timecopilot-gift-eval>=0.3.1' modal pyyaml s3fs typer", ) + .add_local_file( + _REPO_ROOT / "pyproject.toml", + remote_path=f"{_MODAL_MONOREPO}/pyproject.toml", + copy=True, + ) + .add_local_file( + _REPO_ROOT / "README.md", + remote_path=f"{_MODAL_MONOREPO}/README.md", + copy=True, + ) + .add_local_dir( + _REPO_ROOT / "foundationforecast", + remote_path=f"{_MODAL_MONOREPO}/foundationforecast", + copy=True, + ) + .run_commands( + "uv pip install --system --compile-bytecode -e /root/monorepo", + ) .add_local_file( _GIFT_EVAL_ROOT / "pyproject.toml", remote_path=f"{_MODAL_GIFT_EVAL}/pyproject.toml", @@ -158,12 +174,22 @@ def _dispatch_jobs( run_gift_eval_modal.starmap( args, return_exceptions=True, - wrap_returned_exceptions=False, ) ) errors = [result for result in results if isinstance(result, Exception)] if errors: - raise RuntimeError(f"Modal jobs failed: {errors}") + for exc in errors[:10]: + logging.error("Job failed: %s", exc) + if len(errors) > 10: + logging.error("... and %s more failures", len(errors) - 10) + logging.info( + "Modal batch finished: ok=%s failed=%s total=%s", + len(results) - len(errors), + len(errors), + len(results), + ) + if errors and len(errors) == len(results): + raise RuntimeError(f"All Modal jobs failed ({len(errors)} jobs)") def run_ci_modal( @@ -297,11 +323,17 @@ def run_replication_pilot(run_id: str, force: bool = True) -> None: @app.local_entrypoint() -def run_replication_full(run_id: str, force: bool = False) -> None: +def run_replication_full( + run_id: str, + force: bool = False, + model_key: str = "", +) -> None: from src.eval.jobs import load_replication_matrix prefix = replication_s3_prefix(run_id) jobs = load_replication_matrix() + if model_key: + jobs = [job for job in jobs if job.model_key == model_key] if force: selected = jobs else: diff --git a/experiments/gift-eval/tests/test_replication_config.py b/experiments/gift-eval/tests/test_replication_config.py index 686379b..8768be5 100644 --- a/experiments/gift-eval/tests/test_replication_config.py +++ b/experiments/gift-eval/tests/test_replication_config.py @@ -32,7 +32,18 @@ def test_moirai2_replication_context_and_batch() -> None: spec = load_replication_models_config()["Salesforce--moirai-2.0-R-small"] assert spec["kwargs"]["context_length"] == 4000 - assert spec["kwargs"]["batch_size"] == 2048 + assert spec["kwargs"]["batch_size"] == 32 + assert spec["predictor_batch_size"] == 512 + + +def test_chronos2_replication_notebook_runner_params() -> None: + from src.eval.jobs import load_replication_models_config + + spec = load_replication_models_config()["amazon--chronos-2"] + assert spec["reference_slug"] == "Chronos-2" + assert spec["predictor_batch_size"] == 16 + assert spec["kwargs"]["batch_size"] == 16 + assert spec["max_length"] is None def test_toto_replication_batch_size() -> None: From 0cb029ae3983e94bce1118683298008c29fc7ea3 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Wed, 7 Oct 2026 18:23:59 -0700 Subject: [PATCH 06/24] feat(gift-eval): expand replication registry to 16 families and index S3 jobs in one walk - Add Chronos Bolt base/small, Chronos-2 small, Toto 2 4M, PatchTST-FM r1, TiRex 1.1, Moirai 1.1 large and T0 alpha to configs/replication with notebook-aligned params; add autogluon--chronos-2-small to models.yaml. - Replace per-job S3 exists() calls with a single fs.find() index (filter_jobs_by_s3_mode / index_s3_job_artifacts) and parse dataset names that contain '/'. - Add --max-containers to run_replication_full and cap Modal fan-out. - Skip aggregate verify for families whose HF reference is not published yet (REPLICATION_PENDING_HF_REFERENCE). - Use google/timesfm-1.0-200m-pytorch repo id for TimesFM 1.0. Co-authored-by: Cursor --- experiments/gift-eval/configs/models.yaml | 12 +- .../configs/replication/families.yaml | 17 ++- .../gift-eval/configs/replication/models.yaml | 126 ++++++++++++++---- .../configs/replication/notebooks.yaml | 48 +++++-- experiments/gift-eval/src/eval/jobs.py | 74 ++++++++++ .../gift-eval/src/runners/run_modal.py | 61 +++------ .../src/verify/replication_aggregate.py | 11 +- 7 files changed, 263 insertions(+), 86 deletions(-) diff --git a/experiments/gift-eval/configs/models.yaml b/experiments/gift-eval/configs/models.yaml index 5b01309..faa0722 100644 --- a/experiments/gift-eval/configs/models.yaml +++ b/experiments/gift-eval/configs/models.yaml @@ -79,12 +79,22 @@ models: alias: Chronos-2-Synth batch_size: 64 + autogluon--chronos-2-small: + class: foundationforecast.models.chronos.Chronos + reference_slug: Chronos-2-Small + max_length: null + predictor_batch_size: 16 + kwargs: + repo_id: autogluon/chronos-2-small + alias: Chronos-2-Small + batch_size: 16 + # --- TimesFM --- google--timesfm-1.0-200m: class: foundationforecast.models.timesfm.TimesFM reference_slug: timesfm kwargs: - repo_id: google/timesfm-1.0-200m + repo_id: google/timesfm-1.0-200m-pytorch alias: TimesFM batch_size: 64 diff --git a/experiments/gift-eval/configs/replication/families.yaml b/experiments/gift-eval/configs/replication/families.yaml index aa9c7f3..01e5747 100644 --- a/experiments/gift-eval/configs/replication/families.yaml +++ b/experiments/gift-eval/configs/replication/families.yaml @@ -1,11 +1,18 @@ # One representative model per family for notebook-aligned replication runs. -# Excluded: ibm-granite--granite-timeseries-flowstate-r1 (native FlowState wrapper). model_keys: - amazon--chronos-2 - - google--timesfm-3.0-pytorch - - Salesforce--moirai-2.0-R-small + - amazon--chronos-bolt-base + - amazon--chronos-bolt-small + - autogluon--chronos-2-small - Datadog--Toto-2.0-313m - - NX-AI--TiRex-2-gifteval-zs + - Datadog--Toto-2.0-4m + - google--timesfm-3.0-pytorch + - ibm-granite--granite-timeseries-patchtst-fm-r1 - ibm-granite--granite-timeseries-patchtst-fm-r2 - - theforecastingcompany--t0-beta + - NX-AI--TiRex-1.1-gifteval + - NX-AI--TiRex-2-gifteval-zs + - Salesforce--moirai-1.1-R-large--Moirai_large + - Salesforce--moirai-2.0-R-small - tafsut-univariate-base + - theforecastingcompany--t0-alpha + - theforecastingcompany--t0-beta diff --git a/experiments/gift-eval/configs/replication/models.yaml b/experiments/gift-eval/configs/replication/models.yaml index 35f78a6..d9e8908 100644 --- a/experiments/gift-eval/configs/replication/models.yaml +++ b/experiments/gift-eval/configs/replication/models.yaml @@ -10,6 +10,53 @@ models: alias: Chronos-2 batch_size: 16 + amazon--chronos-bolt-base: + class: foundationforecast.models.chronos.Chronos + reference_slug: chronos_bolt_base + max_length: null + predictor_batch_size: 1024 + kwargs: + repo_id: amazon/chronos-bolt-base + alias: chronos_bolt_base + batch_size: 32 + + amazon--chronos-bolt-small: + class: foundationforecast.models.chronos.Chronos + reference_slug: chronos_bolt_small + max_length: null + predictor_batch_size: 1024 + kwargs: + repo_id: amazon/chronos-bolt-small + alias: chronos_bolt_small + batch_size: 64 + + autogluon--chronos-2-small: + class: foundationforecast.models.chronos.Chronos + # HF GIFT-Eval results/{slug} not published yet; enable aggregate verify when live. + reference_slug: Chronos-2-Small + max_length: null + predictor_batch_size: 16 + kwargs: + repo_id: autogluon/chronos-2-small + alias: Chronos-2-Small + batch_size: 16 + + Datadog--Toto-2.0-313m: + class: foundationforecast.models.toto.Toto + reference_slug: Toto-2.0-313m + kwargs: + repo_id: Datadog/Toto-2.0-313m + context_length: 4096 + batch_size: 512 + + Datadog--Toto-2.0-4m: + class: foundationforecast.models.toto.Toto + reference_slug: Toto-2.0-4m + kwargs: + repo_id: Datadog/Toto-2.0-4m + context_length: 4096 + batch_size: 512 + google--timesfm-3.0-pytorch: class: foundationforecast.models.timesfm.TimesFM reference_slug: TimesFM-3 @@ -23,21 +70,31 @@ models: use_variate_attention: true use_sdpa: true - Salesforce--moirai-2.0-R-small: - class: foundationforecast.models.moirai.Moirai - reference_slug: Moirai2 - predictor_batch_size: 512 + ibm-granite--granite-timeseries-patchtst-fm-r1: + class: foundationforecast.models.patchtst_fm.PatchTSTFM + reference_slug: Granite-PatchTST-FM-r1 + predictor_batch_size: 1024 kwargs: - repo_id: Salesforce/moirai-2.0-R-small - context_length: 4000 + repo_id: ibm-granite/granite-timeseries-patchtst-fm-r1 + alias: Granite-PatchTST-FM-r1 batch_size: 32 - Datadog--Toto-2.0-313m: - class: foundationforecast.models.toto.Toto - reference_slug: Toto-2.0-313m + ibm-granite--granite-timeseries-patchtst-fm-r2: + class: foundationforecast.models.patchtst_fm.PatchTSTFM + reference_slug: Granite-PatchTST-FM-r2 + max_length: 8192 kwargs: - repo_id: Datadog/Toto-2.0-313m - context_length: 4096 + repo_id: ibm-granite/granite-timeseries-patchtst-fm-r2 + context_length: 8192 + batch_size: 32 + + NX-AI--TiRex-1.1-gifteval: + class: foundationforecast.models.tirex.TiRex + reference_slug: TiRex + predictor_batch_size: 512 + kwargs: + repo_id: NX-AI/TiRex-1.1-gifteval + alias: TiRex batch_size: 512 NX-AI--TiRex-2-gifteval-zs: @@ -48,23 +105,24 @@ models: repo_id: NX-AI/TiRex-2-gifteval-zs batch_size: 512 - ibm-granite--granite-timeseries-patchtst-fm-r2: - class: foundationforecast.models.patchtst_fm.PatchTSTFM - reference_slug: Granite-PatchTST-FM-r2 - max_length: 8192 + Salesforce--moirai-1.1-R-large--Moirai_large: + class: foundationforecast.models.moirai.Moirai + reference_slug: Moirai_large + predictor_batch_size: 512 kwargs: - repo_id: ibm-granite/granite-timeseries-patchtst-fm-r2 - context_length: 8192 - batch_size: 32 + repo_id: Salesforce/moirai-1.1-R-large + alias: Moirai_large + context_length: 4000 + batch_size: 16 - theforecastingcompany--t0-beta: - class: foundationforecast.models.t0.T0 - reference_slug: t0-beta + Salesforce--moirai-2.0-R-small: + class: foundationforecast.models.moirai.Moirai + reference_slug: Moirai2 + predictor_batch_size: 512 kwargs: - repo_id: theforecastingcompany/t0-beta - alias: t0-beta - context_length: 8192 - batch_size: 64 + repo_id: Salesforce/moirai-2.0-R-small + context_length: 4000 + batch_size: 32 tafsut-univariate-base: class: foundationforecast.models.tafsut.Tafsut @@ -73,3 +131,21 @@ models: repo_id: Tafsut-FM/tafsut-univariate-base alias: tafsut batch_size: 128 + + theforecastingcompany--t0-alpha: + class: foundationforecast.models.t0.T0 + reference_slug: t0-alpha + kwargs: + repo_id: theforecastingcompany/t0-alpha + alias: t0-alpha + context_length: 8192 + batch_size: 64 + + theforecastingcompany--t0-beta: + class: foundationforecast.models.t0.T0 + reference_slug: t0-beta + kwargs: + repo_id: theforecastingcompany/t0-beta + alias: t0-beta + context_length: 8192 + batch_size: 64 diff --git a/experiments/gift-eval/configs/replication/notebooks.yaml b/experiments/gift-eval/configs/replication/notebooks.yaml index ff119fb..33460ea 100644 --- a/experiments/gift-eval/configs/replication/notebooks.yaml +++ b/experiments/gift-eval/configs/replication/notebooks.yaml @@ -4,24 +4,48 @@ sources: amazon--chronos-2: notebook: notebooks/chronos-2.ipynb reference_slug: Chronos-2 - google--timesfm-3.0-pytorch: - notebook: notebooks/timesfm3.ipynb - reference_slug: TimesFM-3 - Salesforce--moirai-2.0-R-small: - notebook: notebooks/moirai2.ipynb - reference_slug: Moirai2 + amazon--chronos-bolt-base: + notebook: notebooks/chronos.ipynb + reference_slug: chronos_bolt_base + amazon--chronos-bolt-small: + notebook: notebooks/chronos.ipynb + reference_slug: chronos_bolt_small + autogluon--chronos-2-small: + notebook: notebooks/chronos-2.ipynb + reference_slug: Chronos-2-Small Datadog--Toto-2.0-313m: notebook: notebooks/toto_2_0.ipynb reference_slug: Toto-2.0-313m - NX-AI--TiRex-2-gifteval-zs: - notebook: notebooks/tirex-2.ipynb - reference_slug: TiRex-2-Zeroshot + Datadog--Toto-2.0-4m: + notebook: notebooks/toto_2_0.ipynb + reference_slug: Toto-2.0-4m + google--timesfm-3.0-pytorch: + notebook: notebooks/timesfm3.ipynb + reference_slug: TimesFM-3 + ibm-granite--granite-timeseries-patchtst-fm-r1: + notebook: notebooks/patchtst_fm.ipynb + reference_slug: Granite-PatchTST-FM-r1 ibm-granite--granite-timeseries-patchtst-fm-r2: notebook: notebooks/patchtst_fm_r2.ipynb reference_slug: Granite-PatchTST-FM-r2 - theforecastingcompany--t0-beta: - notebook: notebooks/t0-beta.ipynb - reference_slug: t0-beta + NX-AI--TiRex-1.1-gifteval: + notebook: notebooks/tirex.ipynb + reference_slug: TiRex + NX-AI--TiRex-2-gifteval-zs: + notebook: notebooks/tirex-2.ipynb + reference_slug: TiRex-2-Zeroshot + Salesforce--moirai-1.1-R-large--Moirai_large: + notebook: notebooks/moirai.ipynb + reference_slug: Moirai_large + Salesforce--moirai-2.0-R-small: + notebook: notebooks/moirai2.ipynb + reference_slug: Moirai2 tafsut-univariate-base: notebook: notebooks/tafsut.ipynb reference_slug: tafsut + theforecastingcompany--t0-alpha: + notebook: notebooks/t0-alpha.ipynb + reference_slug: t0-alpha + theforecastingcompany--t0-beta: + notebook: notebooks/t0-beta.ipynb + reference_slug: t0-beta diff --git a/experiments/gift-eval/src/eval/jobs.py b/experiments/gift-eval/src/eval/jobs.py index f630cbc..e23edbd 100644 --- a/experiments/gift-eval/src/eval/jobs.py +++ b/experiments/gift-eval/src/eval/jobs.py @@ -13,6 +13,9 @@ REPLICATION_PILOT_DATASET = "m4_weekly" REPLICATION_PILOT_TERM = "short" +# Families in the replication grid before GIFT-Eval HF results/{slug} exists. +REPLICATION_PENDING_HF_REFERENCE = frozenset({"autogluon--chronos-2-small"}) + @dataclass(frozen=True) class Job: @@ -103,6 +106,77 @@ def ci_output_root() -> Path: return DEFAULT_RESULTS_ROOT / "ci" +def job_key(job: Job) -> tuple[str, str, str]: + return (job.model_key, job.dataset_name, job.term) + + +def parse_job_dir_suffix(relative_dir: str) -> tuple[str, str, str] | None: + """Parse ``model_key/dataset/term`` (dataset may contain ``/``).""" + parts = relative_dir.strip("/").split("/") + if len(parts) < 3: + return None + term = parts[-1] + model_key = parts[0] + dataset_name = "/".join(parts[1:-1]) + return (model_key, dataset_name, term) + + +def index_s3_job_artifacts( + *, + bucket: str, + prefix: str, +) -> tuple[set[tuple[str, str, str]], set[tuple[str, str, str]]]: + """List ``all_results.csv`` / ``timing.json`` under prefix in one S3 walk.""" + import fsspec + + fs = fsspec.filesystem("s3") + base = f"{bucket}/{prefix.strip('/')}/" + has_results: set[tuple[str, str, str]] = set() + has_timing: set[tuple[str, str, str]] = set() + try: + paths = fs.find(base) + except FileNotFoundError: + return has_results, has_timing + for path in paths: + if not path.startswith(base): + continue + rel = path[len(base) :] + if rel.endswith("/all_results.csv"): + key = parse_job_dir_suffix(rel[: -len("/all_results.csv")]) + if key is not None: + has_results.add(key) + elif rel.endswith("/timing.json"): + key = parse_job_dir_suffix(rel[: -len("/timing.json")]) + if key is not None: + has_timing.add(key) + return has_results, has_timing + + +def filter_jobs_by_s3_mode( + jobs: list[Job], + *, + bucket: str, + prefix: str, + mode: str, +) -> list[Job]: + if mode == "all": + return list(jobs) + if mode not in ("missing", "missing_timing"): + raise ValueError(f"Unknown job selection mode: {mode!r}") + has_results, has_timing = index_s3_job_artifacts(bucket=bucket, prefix=prefix) + selected: list[Job] = [] + for job in jobs: + key = job_key(job) + has_r = key in has_results + has_t = key in has_timing + if mode == "missing": + if not has_r: + selected.append(job) + elif has_r and not has_t: + selected.append(job) + return selected + + def jobs_missing_timing(jobs: list[Job], output_root: Path) -> list[Job]: missing: list[Job] = [] for job in jobs: diff --git a/experiments/gift-eval/src/runners/run_modal.py b/experiments/gift-eval/src/runners/run_modal.py index f2cb18e..fc7bc40 100644 --- a/experiments/gift-eval/src/runners/run_modal.py +++ b/experiments/gift-eval/src/runners/run_modal.py @@ -161,6 +161,7 @@ def _dispatch_jobs( output_root: str, force: bool, registry: str = "default", + max_containers: int | None = None, ) -> None: logging.basicConfig(level=logging.INFO) if not jobs: @@ -170,8 +171,12 @@ def _dispatch_jobs( (*job_tuple, storage_path, output_root, force, registry) for job_tuple in _job_tuples(jobs) ] + run_fn = run_gift_eval_modal + if max_containers is not None: + run_fn = run_gift_eval_modal.with_options(max_containers=max_containers) + logging.info("Modal max_containers=%s", max_containers) results = list( - run_gift_eval_modal.starmap( + run_fn.starmap( args, return_exceptions=True, ) @@ -201,31 +206,6 @@ def run_ci_modal( _dispatch_jobs(jobs, storage_path=storage_path, output_root=output_root, force=True) -def _s3_job_paths( - job, - *, - bucket: str, - prefix: str, -) -> tuple[str, str]: - base = f"s3://{bucket}/{prefix}/{job.model_key}/{job.dataset_name}/{job.term}" - return f"{base}/all_results.csv", f"{base}/timing.json" - - -def _job_matches_mode( - *, - mode: str, - has_results: bool, - has_timing: bool, -) -> bool: - if mode == "missing": - return not has_results - if mode == "missing_timing": - return has_results and not has_timing - if mode == "all": - return True - raise ValueError(f"Unknown job selection mode: {mode!r}") - - def _jobs_from_s3( jobs: list, *, @@ -233,21 +213,14 @@ def _jobs_from_s3( prefix: str, mode: str, ) -> list: - import fsspec - - fs = fsspec.filesystem("s3") - selected = [] - for job in jobs: - results_path, timing_path = _s3_job_paths(job, bucket=bucket, prefix=prefix) - has_results = fs.exists(results_path) - has_timing = fs.exists(timing_path) - if _job_matches_mode( - mode=mode, - has_results=has_results, - has_timing=has_timing, - ): - selected.append(job) - return selected + from src.eval.jobs import filter_jobs_by_s3_mode + + return filter_jobs_by_s3_mode( + jobs, + bucket=bucket, + prefix=prefix, + mode=mode, + ) @app.local_entrypoint() @@ -327,6 +300,7 @@ def run_replication_full( run_id: str, force: bool = False, model_key: str = "", + max_containers: int = 20, ) -> None: from src.eval.jobs import load_replication_matrix @@ -343,10 +317,12 @@ def run_replication_full( prefix=prefix, mode="missing", ) + logging.basicConfig(level=logging.INFO) logging.info( - "Replication full grid: %s jobs (force=%s) → s3://%s/%s", + "Replication full grid: %s jobs (force=%s, max_containers=%s) → s3://%s/%s", len(selected), force, + max_containers, S3_BUCKET, prefix, ) @@ -356,4 +332,5 @@ def run_replication_full( output_root=f"/s3-bucket/{prefix}", force=force, registry="replication", + max_containers=max_containers, ) diff --git a/experiments/gift-eval/src/verify/replication_aggregate.py b/experiments/gift-eval/src/verify/replication_aggregate.py index f5a3e21..bea63db 100644 --- a/experiments/gift-eval/src/verify/replication_aggregate.py +++ b/experiments/gift-eval/src/verify/replication_aggregate.py @@ -11,7 +11,12 @@ from numpy.testing import assert_allclose from scipy import stats -from src.eval.jobs import Job, load_replication_matrix, result_csv +from src.eval.jobs import ( + REPLICATION_PENDING_HF_REFERENCE, + Job, + load_replication_matrix, + result_csv, +) from src.eval.models import Registry, reference_slug from src.verify.reference import CRPS_COL, MASE_COL, load_reference_results from src.verify.verify import ReplicationSkip, load_actual_results @@ -169,6 +174,10 @@ def verify_replication_aggregates( failed: list[tuple[str, str]] = [] for key in model_keys: + if key in REPLICATION_PENDING_HF_REFERENCE: + skipped.append((key, "GIFT-Eval HF reference results not published yet")) + logger.warning("Skipped %s: pending HF reference", key) + continue try: verify_replication_aggregate( key, From f26d2a5129abf1b971abcbf7a7d4b6ab113a3ead Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Wed, 7 Oct 2026 18:23:59 -0700 Subject: [PATCH 07/24] test(gift-eval): cover S3 job selection and new replication families Add tests for parse_job_dir_suffix / filter_jobs_by_s3_mode, the 16-family count, Chronos-2 small and Moirai 1.1 large replication params, and skip the HF alias check for families pending a published reference. Co-authored-by: Cursor --- .../tests/test_replication_config.py | 33 ++++++++++++++++++- .../gift-eval/tests/test_s3_job_selection.py | 21 ++++++++++++ 2 files changed, 53 insertions(+), 1 deletion(-) create mode 100644 experiments/gift-eval/tests/test_s3_job_selection.py diff --git a/experiments/gift-eval/tests/test_replication_config.py b/experiments/gift-eval/tests/test_replication_config.py index 8768be5..441c315 100644 --- a/experiments/gift-eval/tests/test_replication_config.py +++ b/experiments/gift-eval/tests/test_replication_config.py @@ -1,11 +1,17 @@ import pytest -from src.eval.jobs import load_replication_families, load_replication_pilot_jobs +from src.eval.jobs import ( + REPLICATION_PENDING_HF_REFERENCE, + load_replication_families, + load_replication_pilot_jobs, +) from src.eval.models import build_model, reference_slug from src.verify.reference import load_reference_results @pytest.mark.parametrize("model_key", load_replication_families()) def test_replication_alias_matches_hf_reference_model_column(model_key: str) -> None: + if model_key in REPLICATION_PENDING_HF_REFERENCE: + pytest.skip("GIFT-Eval HF reference results not published yet") slug = reference_slug(model_key, registry="replication") assert slug is not None, f"missing reference_slug for {model_key}" expected_name = load_reference_results(slug)["model"].iloc[0] @@ -51,3 +57,28 @@ def test_toto_replication_batch_size() -> None: spec = load_replication_models_config()["Datadog--Toto-2.0-313m"] assert spec["kwargs"]["batch_size"] == 512 + + +def test_replication_families_count() -> None: + assert len(load_replication_families()) == 16 + + +def test_chronos2_small_replication_params() -> None: + from src.eval.jobs import load_replication_models_config + + spec = load_replication_models_config()["autogluon--chronos-2-small"] + assert spec["kwargs"]["repo_id"] == "autogluon/chronos-2-small" + assert spec["kwargs"]["batch_size"] == 16 + assert spec["max_length"] is None + assert spec["predictor_batch_size"] == 16 + + +def test_moirai11_replication_context_and_batch() -> None: + from src.eval.jobs import load_replication_models_config + + large = load_replication_models_config()[ + "Salesforce--moirai-1.1-R-large--Moirai_large" + ] + assert large["kwargs"]["context_length"] == 4000 + assert large["kwargs"]["batch_size"] == 16 + assert large["predictor_batch_size"] == 512 diff --git a/experiments/gift-eval/tests/test_s3_job_selection.py b/experiments/gift-eval/tests/test_s3_job_selection.py new file mode 100644 index 0000000..fcddc37 --- /dev/null +++ b/experiments/gift-eval/tests/test_s3_job_selection.py @@ -0,0 +1,21 @@ +from src.eval.jobs import parse_job_dir_suffix + + +def test_parse_job_dir_suffix_simple_dataset() -> None: + assert parse_job_dir_suffix("amazon--chronos-2/m4_weekly/short") == ( + "amazon--chronos-2", + "m4_weekly", + "short", + ) + + +def test_parse_job_dir_suffix_slash_in_dataset() -> None: + assert parse_job_dir_suffix("amazon--chronos-2/electricity/15T/short") == ( + "amazon--chronos-2", + "electricity/15T", + "short", + ) + + +def test_parse_job_dir_suffix_too_short() -> None: + assert parse_job_dir_suffix("only/two") is None From e3d01dc6bce1b962ae92e0406662854abcb9f5ad Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Wed, 7 Oct 2026 18:23:59 -0700 Subject: [PATCH 08/24] docs(gift-eval): document 16-family replication grid and MAX_CONTAINERS Co-authored-by: Cursor --- experiments/gift-eval/Makefile | 5 ++++- experiments/gift-eval/README.md | 12 ++++++------ 2 files changed, 10 insertions(+), 7 deletions(-) diff --git a/experiments/gift-eval/Makefile b/experiments/gift-eval/Makefile index a7f3c9e..4beb6d1 100644 --- a/experiments/gift-eval/Makefile +++ b/experiments/gift-eval/Makefile @@ -15,6 +15,7 @@ RUN_ID ?= DETACHED ?= FORCE ?= +MAX_CONTAINERS ?= 20 # Pass -d to modal run for detached (background) execution. MODAL_RUN = uv run modal run $(if $(filter 1 true yes,$(DETACHED)),-d,) @@ -34,7 +35,7 @@ help: @echo "" @echo "Replication (configs/replication/ — pilot strict, full aggregate verify)" @echo " run-replication-pilot RUN_ID=... — 8× m4_weekly/short on Modal" - @echo " run-replication-full RUN_ID=... — 8×97 grid (skips existing unless FORCE=1)" + @echo " run-replication-full RUN_ID=... — 16×97 grid (skips existing unless FORCE=1)" @echo " sync-replication RUN_ID=... — S3 → ./results/replication/\$$RUN_ID" @echo " verify-replication-pilot RUN_ID=... — per-job MASE/CRPS vs HF" @echo " verify-replication-full RUN_ID=... — leaderboard aggregate + require complete" @@ -45,6 +46,7 @@ help: @echo " replication-table analysis CSV (verify-only, no assert)" @echo "" @echo "Options: DETACHED=1 for Modal -d ; FORCE=1 to re-run all replication jobs" + @echo " MAX_CONTAINERS=N (default 20) caps parallel GPUs on replication full" # --- Data ------------------------------------------------------------------- @@ -76,6 +78,7 @@ run-replication-pilot: run-replication-full: @test -n "$(RUN_ID)" || (echo "Set RUN_ID=..." && exit 1) @$(MODAL_RUN) -m src.runners.run_modal::run_replication_full --run-id $(RUN_ID) \ + --max-containers $(MAX_CONTAINERS) \ $(if $(filter 1 true yes,$(FORCE)),--force,) sync-replication: diff --git a/experiments/gift-eval/README.md b/experiments/gift-eval/README.md index 4838743..aefd78c 100644 --- a/experiments/gift-eval/README.md +++ b/experiments/gift-eval/README.md @@ -71,7 +71,7 @@ make verify-ci # sync S3 + pytest ## Replication run (notebook-aligned, one model per family) -[`configs/replication/`](configs/replication/) holds **8 families** with params traced to +[`configs/replication/`](configs/replication/) holds **16 families** with params traced to [official gift-eval notebooks](https://github.com/SalesforceAIResearch/gift-eval/tree/main/notebooks). Results go to **`s3://foundationforecast-gift-eval/results/replication//`** (not `results/` or `results/ci/`). @@ -80,7 +80,7 @@ Modal uploads **`foundationforecast/`** (editable install from the monorepo root `experiments/gift-eval` (`src/`, `configs/`). 1. Pick a run id, e.g. `2026-04-05-nb-v1`. -2. **Pilot** (8 jobs: `m4_weekly` / `short`): +2. **Pilot** (16 jobs: `m4_weekly` / `short`): ```bash make run-replication-pilot RUN_ID=2026-04-05-nb-v1 @@ -88,11 +88,11 @@ make verify-replication-pilot RUN_ID=2026-04-05-nb-v1 # long full grid in background: DETACHED=1 make run-replication-full RUN_ID=... ``` -3. After pilot verify passes, **full grid** (8 × 97 jobs): +3. After pilot verify passes, **full grid** (16 × 97 jobs): -Chronos-2 uses **foundationforecast** with notebook inference params -(`batch_size=16`, no `max_length` cap; no cross-learning / `predict_batches_jointly`). -Granite FlowState is still excluded. +Chronos-2 and Chronos Bolt use **foundationforecast** with notebook-aligned inference +(`max_length: null` where the notebook does not cap context; no cross-learning / +`predict_batches_jointly`). ```bash make run-replication-full RUN_ID=2026-04-05-nb-v1 From 392e31cebf5d6ba9f2d1426309547c15956974b5 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Wed, 7 Oct 2026 18:33:31 -0700 Subject: [PATCH 09/24] refactor(chronos): extract CUDA OOM batch back-off into _QuantileBatchPredictor Lift the per-batch predict_quantiles retry closure out of Chronos._predict so the halving logic is a plain callable that can be exercised without a GPU. No behavior change. Co-authored-by: Cursor --- foundationforecast/models/chronos.py | 70 +++++++++++++++++++--------- 1 file changed, 49 insertions(+), 21 deletions(-) diff --git a/foundationforecast/models/chronos.py b/foundationforecast/models/chronos.py index ac7e87e..7ffd1e5 100644 --- a/foundationforecast/models/chronos.py +++ b/foundationforecast/models/chronos.py @@ -64,6 +64,47 @@ class ChronosFinetuningConfig: save_path: str | Path | None = None +class _QuantileBatchPredictor: + """Call ``model.predict_quantiles`` per batch, backing off on CUDA OOM. + + When ``adaptive_batch_size`` is set (Chronos-2 pipelines accept a + ``batch_size`` inference argument), a ``torch.cuda.OutOfMemoryError`` halves + the inference batch size and retries. The reduced size is kept for later + batches. Other pipelines, or a batch size that cannot be halved further, + re-raise the error. + """ + + def __init__( + self, + model: Any, + *, + prediction_length: int, + quantile_levels: list[float], + batch_size: int, + adaptive_batch_size: bool, + ) -> None: + self.model = model + self.kwargs: dict[str, Any] = { + "prediction_length": prediction_length, + "quantile_levels": quantile_levels, + } + self.batch_size = batch_size + self.adaptive_batch_size = adaptive_batch_size + + def __call__(self, batch: Any) -> Any: + kwargs = dict(self.kwargs) + if self.adaptive_batch_size: + kwargs["batch_size"] = self.batch_size + while True: + try: + return self.model.predict_quantiles(batch, **kwargs) + except torch.cuda.OutOfMemoryError: + if not self.adaptive_batch_size or self.batch_size < 2: + raise + self.batch_size //= 2 + kwargs["batch_size"] = self.batch_size + + class Chronos(ExogCapableForecaster): """ Chronos models are large pre-trained models for time series forecasting, @@ -340,27 +381,14 @@ def _predict( q_max=q_max, include_median=False, ) - quantile_kwargs: dict[str, Any] = { - "prediction_length": h, - "quantile_levels": backend_qs, - } - infer_batch = self.batch_size - - def _predict_quantiles_batch(batch: Any) -> Any: - nonlocal infer_batch - kwargs = dict(quantile_kwargs) - if isinstance(model, Chronos2Pipeline): - kwargs["batch_size"] = infer_batch - while True: - try: - return model.predict_quantiles(batch, **kwargs) - except torch.cuda.OutOfMemoryError: - if not isinstance(model, Chronos2Pipeline) or infer_batch < 2: - raise - infer_batch //= 2 - kwargs["batch_size"] = infer_batch - - fcsts = [_predict_quantiles_batch(batch) for batch in tqdm(dataset)] + predict_quantiles = _QuantileBatchPredictor( + model, + prediction_length=h, + quantile_levels=backend_qs, + batch_size=self.batch_size, + adaptive_batch_size=isinstance(model, Chronos2Pipeline), + ) + fcsts = [predict_quantiles(batch) for batch in tqdm(dataset)] fcsts_quantiles, fcsts_mean = zip(*fcsts, strict=False) if isinstance(model, Chronos2Pipeline): fcsts_mean = [f_mean for fcst in fcsts_mean for f_mean in fcst] # type: ignore From 664b54d9e3a42aedb5841648fae72e96de866832 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Wed, 7 Oct 2026 18:33:32 -0700 Subject: [PATCH 10/24] test(chronos): cover CUDA OOM batch-size back-off Fake pipeline raises torch.cuda.OutOfMemoryError to check that Chronos-2 halves batch_size until it fits, keeps the reduced size for later batches, re-raises when it cannot halve further, and that non-Chronos-2 pipelines re-raise without a batch_size kwarg. Co-authored-by: Cursor --- tests/models/test_chronos_oom_backoff.py | 75 ++++++++++++++++++++++++ 1 file changed, 75 insertions(+) create mode 100644 tests/models/test_chronos_oom_backoff.py diff --git a/tests/models/test_chronos_oom_backoff.py b/tests/models/test_chronos_oom_backoff.py new file mode 100644 index 0000000..345f4c0 --- /dev/null +++ b/tests/models/test_chronos_oom_backoff.py @@ -0,0 +1,75 @@ +import pytest +import torch + +from foundationforecast.models.chronos import _QuantileBatchPredictor + + +class _FakePipeline: + """Raises CUDA OOM while ``batch_size`` is above ``fits_at``.""" + + def __init__(self, fits_at: int | None = None, fail_times: int = 0) -> None: + self.fits_at = fits_at + self.fail_times = fail_times + self.calls: list[dict] = [] + + def predict_quantiles(self, batch, **kwargs): + self.calls.append(kwargs) + if self.fits_at is not None and kwargs.get("batch_size", 0) > self.fits_at: + raise torch.cuda.OutOfMemoryError("fake OOM") + if self.fail_times > 0: + self.fail_times -= 1 + raise torch.cuda.OutOfMemoryError("fake OOM") + return ("quantiles", "mean", batch) + + +def _predictor(model, *, batch_size: int, adaptive: bool) -> _QuantileBatchPredictor: + return _QuantileBatchPredictor( + model, + prediction_length=3, + quantile_levels=[0.1, 0.5, 0.9], + batch_size=batch_size, + adaptive_batch_size=adaptive, + ) + + +def test_adaptive_halves_batch_size_until_it_fits_and_keeps_it(): + model = _FakePipeline(fits_at=4) + predict = _predictor(model, batch_size=16, adaptive=True) + + assert predict("b0") == ("quantiles", "mean", "b0") + assert [c["batch_size"] for c in model.calls] == [16, 8, 4] + assert predict.batch_size == 4 + + # Later batches start from the reduced size without retrying. + assert predict("b1") == ("quantiles", "mean", "b1") + assert model.calls[-1]["batch_size"] == 4 + assert len(model.calls) == 4 + + +def test_adaptive_passes_prediction_kwargs(): + model = _FakePipeline() + predict = _predictor(model, batch_size=8, adaptive=True) + predict("b0") + assert model.calls == [ + {"prediction_length": 3, "quantile_levels": [0.1, 0.5, 0.9], "batch_size": 8} + ] + + +def test_adaptive_reraises_when_batch_size_cannot_be_halved(): + model = _FakePipeline(fits_at=0) + predict = _predictor(model, batch_size=2, adaptive=True) + with pytest.raises(torch.cuda.OutOfMemoryError): + predict("b0") + # 2 → 1, then OOM at 1 is fatal. + assert [c["batch_size"] for c in model.calls] == [2, 1] + assert predict.batch_size == 1 + + +def test_non_adaptive_pipeline_reraises_and_does_not_pass_batch_size(): + model = _FakePipeline(fail_times=1) + predict = _predictor(model, batch_size=16, adaptive=False) + with pytest.raises(torch.cuda.OutOfMemoryError): + predict("b0") + assert len(model.calls) == 1 + assert "batch_size" not in model.calls[0] + assert predict.batch_size == 16 From 9579bf069eaa724dc7c11a29a9f6cd95d43f5cd8 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Wed, 7 Oct 2026 18:39:39 -0700 Subject: [PATCH 11/24] fix(gift-eval): honor pending HF reference skip in replication table verify-replication-full passed the aggregate check but then crashed with HTTP 404 while writing the analysis table: build_replication_table fetched the GIFT-Eval reference for autogluon--chronos-2-small, whose results are not published yet. Skip REPLICATION_PENDING_HF_REFERENCE keys like the aggregate verify does, and treat a remote HTTPError like a missing local reference (warn and skip) instead of aborting the run. Co-authored-by: Cursor --- .../gift-eval/src/verify/replication_table.py | 7 +- tests/models/test_chronos_oom_backoff.py | 75 ------------------- 2 files changed, 6 insertions(+), 76 deletions(-) delete mode 100644 tests/models/test_chronos_oom_backoff.py diff --git a/experiments/gift-eval/src/verify/replication_table.py b/experiments/gift-eval/src/verify/replication_table.py index 9b264f2..6bdd862 100644 --- a/experiments/gift-eval/src/verify/replication_table.py +++ b/experiments/gift-eval/src/verify/replication_table.py @@ -3,9 +3,11 @@ import json import logging from pathlib import Path +from urllib.error import HTTPError import pandas as pd +from src.eval.jobs import REPLICATION_PENDING_HF_REFERENCE from src.eval.models import Registry, reference_slug from src.verify.reference import CRPS_COL, MASE_COL, load_reference_results @@ -55,6 +57,9 @@ def build_replication_table( rows: list[dict] = [] for model_key in model_keys: + if model_key in REPLICATION_PENDING_HF_REFERENCE: + logger.warning("Skipping %s: pending HF reference", model_key) + continue slug = reference_slug(model_key, registry=registry) if slug is None: logger.warning("Skipping %s: no reference_slug", model_key) @@ -63,7 +68,7 @@ def build_replication_table( try: actual = load_actual_results(model_key, output_root) expected = load_reference_results(slug) - except FileNotFoundError as exc: + except (FileNotFoundError, HTTPError) as exc: logger.warning("Skipping %s: %s", model_key, exc) continue diff --git a/tests/models/test_chronos_oom_backoff.py b/tests/models/test_chronos_oom_backoff.py deleted file mode 100644 index 345f4c0..0000000 --- a/tests/models/test_chronos_oom_backoff.py +++ /dev/null @@ -1,75 +0,0 @@ -import pytest -import torch - -from foundationforecast.models.chronos import _QuantileBatchPredictor - - -class _FakePipeline: - """Raises CUDA OOM while ``batch_size`` is above ``fits_at``.""" - - def __init__(self, fits_at: int | None = None, fail_times: int = 0) -> None: - self.fits_at = fits_at - self.fail_times = fail_times - self.calls: list[dict] = [] - - def predict_quantiles(self, batch, **kwargs): - self.calls.append(kwargs) - if self.fits_at is not None and kwargs.get("batch_size", 0) > self.fits_at: - raise torch.cuda.OutOfMemoryError("fake OOM") - if self.fail_times > 0: - self.fail_times -= 1 - raise torch.cuda.OutOfMemoryError("fake OOM") - return ("quantiles", "mean", batch) - - -def _predictor(model, *, batch_size: int, adaptive: bool) -> _QuantileBatchPredictor: - return _QuantileBatchPredictor( - model, - prediction_length=3, - quantile_levels=[0.1, 0.5, 0.9], - batch_size=batch_size, - adaptive_batch_size=adaptive, - ) - - -def test_adaptive_halves_batch_size_until_it_fits_and_keeps_it(): - model = _FakePipeline(fits_at=4) - predict = _predictor(model, batch_size=16, adaptive=True) - - assert predict("b0") == ("quantiles", "mean", "b0") - assert [c["batch_size"] for c in model.calls] == [16, 8, 4] - assert predict.batch_size == 4 - - # Later batches start from the reduced size without retrying. - assert predict("b1") == ("quantiles", "mean", "b1") - assert model.calls[-1]["batch_size"] == 4 - assert len(model.calls) == 4 - - -def test_adaptive_passes_prediction_kwargs(): - model = _FakePipeline() - predict = _predictor(model, batch_size=8, adaptive=True) - predict("b0") - assert model.calls == [ - {"prediction_length": 3, "quantile_levels": [0.1, 0.5, 0.9], "batch_size": 8} - ] - - -def test_adaptive_reraises_when_batch_size_cannot_be_halved(): - model = _FakePipeline(fits_at=0) - predict = _predictor(model, batch_size=2, adaptive=True) - with pytest.raises(torch.cuda.OutOfMemoryError): - predict("b0") - # 2 → 1, then OOM at 1 is fatal. - assert [c["batch_size"] for c in model.calls] == [2, 1] - assert predict.batch_size == 1 - - -def test_non_adaptive_pipeline_reraises_and_does_not_pass_batch_size(): - model = _FakePipeline(fail_times=1) - predict = _predictor(model, batch_size=16, adaptive=False) - with pytest.raises(torch.cuda.OutOfMemoryError): - predict("b0") - assert len(model.calls) == 1 - assert "batch_size" not in model.calls[0] - assert predict.batch_size == 16 From 2d72a192c2784ba4b53c4a2712f4b03fad1968db Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Wed, 7 Oct 2026 18:39:48 -0700 Subject: [PATCH 12/24] test: cover replication table skips; move Chronos OOM tests into test_chronos - test_replication_table.py: pending HF reference keys never hit the network; a 404 on the remote reference is skipped instead of raised. - Fold the _QuantileBatchPredictor OOM back-off tests into tests/models/test_chronos.py next to the other Chronos tests. Co-authored-by: Cursor --- .../gift-eval/tests/test_replication_table.py | 47 +++++++++++ tests/models/test_chronos.py | 77 ++++++++++++++++++- 2 files changed, 123 insertions(+), 1 deletion(-) create mode 100644 experiments/gift-eval/tests/test_replication_table.py diff --git a/experiments/gift-eval/tests/test_replication_table.py b/experiments/gift-eval/tests/test_replication_table.py new file mode 100644 index 0000000..7c95deb --- /dev/null +++ b/experiments/gift-eval/tests/test_replication_table.py @@ -0,0 +1,47 @@ +from pathlib import Path +from urllib.error import HTTPError + +import pytest +from src.eval.jobs import REPLICATION_PENDING_HF_REFERENCE +from src.verify import replication_table +from src.verify.replication_table import ( + REPLICATION_TABLE_COLS, + build_replication_table, +) + + +def test_build_replication_table_skips_pending_hf_reference( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + pending_key = next(iter(REPLICATION_PENDING_HF_REFERENCE)) + + def _no_network(slug: str): + raise AssertionError(f"reference download attempted for {slug}") + + monkeypatch.setattr(replication_table, "load_reference_results", _no_network) + monkeypatch.setattr( + "src.verify.verify.load_actual_results", lambda key, root: object() + ) + + table = build_replication_table([pending_key], tmp_path, registry="replication") + + assert table.empty + assert list(table.columns) == REPLICATION_TABLE_COLS + + +def test_build_replication_table_skips_missing_remote_reference( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + model_key = "amazon--chronos-2" + + def _not_found(slug: str): + raise HTTPError("https://example/results", 404, "Not Found", {}, None) # type: ignore[arg-type] + + monkeypatch.setattr(replication_table, "load_reference_results", _not_found) + monkeypatch.setattr( + "src.verify.verify.load_actual_results", lambda key, root: object() + ) + + table = build_replication_table([model_key], tmp_path, registry="replication") + + assert table.empty diff --git a/tests/models/test_chronos.py b/tests/models/test_chronos.py index 9eb8183..316aa9a 100644 --- a/tests/models/test_chronos.py +++ b/tests/models/test_chronos.py @@ -2,7 +2,11 @@ import torch from tests.helpers import generate_series -from foundationforecast.models.chronos import Chronos, ChronosFinetuningConfig +from foundationforecast.models.chronos import ( + Chronos, + ChronosFinetuningConfig, + _QuantileBatchPredictor, +) pytestmark = pytest.mark.models @@ -95,3 +99,74 @@ def test_chronos_lora_finetuning_save_and_reuse(tmp_path): assert not fcst_reuse.empty assert "Chronos" in fcst_reuse.columns assert len(fcst_reuse) == n_series * 2 + + +class _FakeOOMPipeline: + """Raises CUDA OOM while ``batch_size`` is above ``fits_at``.""" + + def __init__(self, fits_at: int | None = None, fail_times: int = 0) -> None: + self.fits_at = fits_at + self.fail_times = fail_times + self.calls: list[dict] = [] + + def predict_quantiles(self, batch, **kwargs): + self.calls.append(kwargs) + if self.fits_at is not None and kwargs.get("batch_size", 0) > self.fits_at: + raise torch.cuda.OutOfMemoryError("fake OOM") + if self.fail_times > 0: + self.fail_times -= 1 + raise torch.cuda.OutOfMemoryError("fake OOM") + return ("quantiles", "mean", batch) + + +def _oom_predictor(model, *, batch_size: int, adaptive: bool): + return _QuantileBatchPredictor( + model, + prediction_length=3, + quantile_levels=[0.1, 0.5, 0.9], + batch_size=batch_size, + adaptive_batch_size=adaptive, + ) + + +def test_oom_backoff_halves_batch_size_until_it_fits_and_keeps_it(): + model = _FakeOOMPipeline(fits_at=4) + predict = _oom_predictor(model, batch_size=16, adaptive=True) + + assert predict("b0") == ("quantiles", "mean", "b0") + assert [c["batch_size"] for c in model.calls] == [16, 8, 4] + assert predict.batch_size == 4 + + # Later batches start from the reduced size without retrying. + assert predict("b1") == ("quantiles", "mean", "b1") + assert model.calls[-1]["batch_size"] == 4 + assert len(model.calls) == 4 + + +def test_oom_backoff_passes_prediction_kwargs(): + model = _FakeOOMPipeline() + predict = _oom_predictor(model, batch_size=8, adaptive=True) + predict("b0") + assert model.calls == [ + {"prediction_length": 3, "quantile_levels": [0.1, 0.5, 0.9], "batch_size": 8} + ] + + +def test_oom_backoff_reraises_when_batch_size_cannot_be_halved(): + model = _FakeOOMPipeline(fits_at=0) + predict = _oom_predictor(model, batch_size=2, adaptive=True) + with pytest.raises(torch.cuda.OutOfMemoryError): + predict("b0") + # 2 → 1, then OOM at 1 is fatal. + assert [c["batch_size"] for c in model.calls] == [2, 1] + assert predict.batch_size == 1 + + +def test_oom_backoff_non_adaptive_pipeline_reraises_without_batch_size(): + model = _FakeOOMPipeline(fail_times=1) + predict = _oom_predictor(model, batch_size=16, adaptive=False) + with pytest.raises(torch.cuda.OutOfMemoryError): + predict("b0") + assert len(model.calls) == 1 + assert "batch_size" not in model.calls[0] + assert predict.batch_size == 16 From c695ece70a8748d5ad2c0ae14a8ae1776bde7d39 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Wed, 7 Oct 2026 18:48:46 -0700 Subject: [PATCH 13/24] docs(gift-eval): add replication results table for run 2026-10-06-nb-v2 Per-model official vs replicated leaderboard aggregates (MASE, CRPS), their relative difference, the per-config standard deviation of ours/official - 1, and A10G wall time / cost. Markdown table plus a collapsible LaTeX source (booktabs) for papers. Co-authored-by: Cursor --- experiments/gift-eval/README.md | 77 +++++++++++++++++++++++++++++++++ 1 file changed, 77 insertions(+) diff --git a/experiments/gift-eval/README.md b/experiments/gift-eval/README.md index aefd78c..f8ef319 100644 --- a/experiments/gift-eval/README.md +++ b/experiments/gift-eval/README.md @@ -108,6 +108,83 @@ Individual job drift is allowed if the aggregate still matches (see `src/verify/replication_aggregate.py`). CI pytest (`tests/test_replication.py`) still enforces strict per-job replication on the CI subset only. +### Replication results (run `2026-10-06-nb-v2`) + +FoundationForecast reproduces the **leaderboard aggregates** of every official +submission it wraps: across 15 models with a published reference, the aggregate +MASE and CRPS land within **±1.8%** of the official values (tolerance is 2.5%). +Per-config results are noisier (σ is the standard deviation, over the 97 dataset +configurations, of the per-config relative difference `ours / official − 1`), but +the noise averages out in the geometric mean the leaderboard ranks on. The whole +grid (16 models × 97 configs) took **33.7 GPU-hours ≈ $37** on a single A10G. + +MASE and CRPS are the leaderboard aggregates: geometric mean over the 97 configs of +the metric normalized by Seasonal Naive (lower is better). *Official* is recomputed +from each model's submitted CSV in the GIFT-Eval Hugging Face space; *ours* from this +run. Δ is the relative difference of the aggregates. Compute is wall time on an A10G +(Modal) at $1.10/GPU-h. Chronos-2 small has no official submission yet. + +| Model | Org. | MASE official | MASE ours | Δ (%) | σ (%) | CRPS official | CRPS ours | Δ (%) | σ (%) | GPU-h | USD | +|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| +| TimesFM 3 | Google | 0.667 | 0.675 | +1.2 | 3.2 | 0.456 | 0.462 | +1.3 | 3.1 | 6.73 | 7.41 | +| PatchTST-FM r2 | IBM | 0.685 | 0.684 | -0.1 | 3.4 | 0.467 | 0.467 | +0.0 | 3.9 | 3.31 | 3.64 | +| T0 beta | TFC | 0.687 | 0.689 | +0.3 | 1.4 | 0.474 | 0.475 | +0.4 | 2.8 | 1.41 | 1.55 | +| Tafsut base | Huawei | 0.693 | 0.694 | +0.2 | 0.4 | 0.481 | 0.482 | +0.2 | 1.1 | 1.42 | 1.56 | +| Chronos-2 | Amazon | 0.698 | 0.704 | +0.9 | 3.6 | 0.485 | 0.483 | -0.6 | 7.1 | 1.62 | 1.78 | +| TiRex 2 | NX-AI | 0.697 | 0.704 | +1.0 | 2.2 | 0.478 | 0.485 | +1.6 | 5.3 | 1.29 | 1.42 | +| Toto 2 313M | Datadog | 0.703 | 0.705 | +0.3 | 2.7 | 0.481 | 0.485 | +0.7 | 4.0 | 1.68 | 1.84 | +| PatchTST-FM r1 | IBM | 0.717 | 0.716 | -0.2 | 0.9 | 0.488 | 0.487 | -0.1 | 1.2 | 1.25 | 1.37 | +| TiRex 1.1 | NX-AI | 0.716 | 0.724 | +1.1 | 8.8 | 0.488 | 0.494 | +1.1 | 10.1 | 1.57 | 1.73 | +| Chronos-2 small | Amazon | — | 0.724 | — | — | — | 0.496 | — | — | 0.95 | 1.04 | +| T0 alpha | TFC | 0.724 | 0.729 | +0.7 | 2.1 | 0.494 | 0.495 | +0.3 | 2.7 | 0.94 | 1.03 | +| Moirai 2 small | Salesforce | 0.728 | 0.736 | +1.1 | 5.8 | 0.516 | 0.521 | +0.8 | 5.2 | 2.65 | 2.92 | +| Toto 2 4M | Datadog | 0.757 | 0.761 | +0.6 | 2.2 | 0.524 | 0.531 | +1.3 | 4.6 | 0.55 | 0.61 | +| Chronos Bolt base | Amazon | 0.808 | 0.813 | +0.7 | 2.5 | 0.574 | 0.566 | -1.4 | 4.9 | 4.66 | 5.13 | +| Chronos Bolt small | Amazon | 0.822 | 0.829 | +0.8 | 2.7 | 0.577 | 0.566 | -1.8 | 4.4 | 1.69 | 1.86 | +| Moirai 1.1 large | Salesforce | 0.875 | 0.886 | +1.2 | 12.5 | 0.599 | 0.602 | +0.5 | 14.0 | 1.98 | 2.18 | +| **Total** (16 models) | | | | | | | | | | **33.7** | **37.07** | + +
+LaTeX source (needs booktabs) + +```latex +\begin{table}[t] +\centering +\small +\setlength{\tabcolsep}{4pt} +\begin{tabular}{l l rrrr rrrr rr} +\toprule + & & \multicolumn{4}{c}{MASE$^\dagger$} & \multicolumn{4}{c}{CRPS$^\dagger$} & \multicolumn{2}{c}{Compute} \\ +\cmidrule(lr){3-6} \cmidrule(lr){7-10} \cmidrule(lr){11-12} +Model & Org. & Official & Ours & $\Delta$ (\%) & $\sigma$ (\%) & Official & Ours & $\Delta$ (\%) & $\sigma$ (\%) & GPU-h & USD \\ +\midrule +TimesFM 3 & Google & 0.667 & 0.675 & +1.2 & 3.2 & 0.456 & 0.462 & +1.3 & 3.1 & 6.73 & 7.41 \\ +PatchTST-FM r2 & IBM & 0.685 & 0.684 & -0.1 & 3.4 & 0.467 & 0.467 & +0.0 & 3.9 & 3.31 & 3.64 \\ +T0 beta & TFC & 0.687 & 0.689 & +0.3 & 1.4 & 0.474 & 0.475 & +0.4 & 2.8 & 1.41 & 1.55 \\ +Tafsut base & Huawei & 0.693 & 0.694 & +0.2 & 0.4 & 0.481 & 0.482 & +0.2 & 1.1 & 1.42 & 1.56 \\ +Chronos-2 & Amazon & 0.698 & 0.704 & +0.9 & 3.6 & 0.485 & 0.483 & -0.6 & 7.1 & 1.62 & 1.78 \\ +TiRex 2 & NX-AI & 0.697 & 0.704 & +1.0 & 2.2 & 0.478 & 0.485 & +1.6 & 5.3 & 1.29 & 1.42 \\ +Toto 2 313M & Datadog & 0.703 & 0.705 & +0.3 & 2.7 & 0.481 & 0.485 & +0.7 & 4.0 & 1.68 & 1.84 \\ +PatchTST-FM r1 & IBM & 0.717 & 0.716 & -0.2 & 0.9 & 0.488 & 0.487 & -0.1 & 1.2 & 1.25 & 1.37 \\ +TiRex 1.1 & NX-AI & 0.716 & 0.724 & +1.1 & 8.8 & 0.488 & 0.494 & +1.1 & 10.1 & 1.57 & 1.73 \\ +Chronos-2 small & Amazon & -- & 0.724 & -- & -- & -- & 0.496 & -- & -- & 0.95 & 1.04 \\ +T0 alpha & TFC & 0.724 & 0.729 & +0.7 & 2.1 & 0.494 & 0.495 & +0.3 & 2.7 & 0.94 & 1.03 \\ +Moirai 2 small & Salesforce & 0.728 & 0.736 & +1.1 & 5.8 & 0.516 & 0.521 & +0.8 & 5.2 & 2.65 & 2.92 \\ +Toto 2 4M & Datadog & 0.757 & 0.761 & +0.6 & 2.2 & 0.524 & 0.531 & +1.3 & 4.6 & 0.55 & 0.61 \\ +Chronos Bolt base & Amazon & 0.808 & 0.813 & +0.7 & 2.5 & 0.574 & 0.566 & -1.4 & 4.9 & 4.66 & 5.13 \\ +Chronos Bolt small & Amazon & 0.822 & 0.829 & +0.8 & 2.7 & 0.577 & 0.566 & -1.8 & 4.4 & 1.69 & 1.86 \\ +Moirai 1.1 large & Salesforce & 0.875 & 0.886 & +1.2 & 12.5 & 0.599 & 0.602 & +0.5 & 14.0 & 1.98 & 2.18 \\ +\midrule +\multicolumn{10}{l}{Total (16 models, 97 dataset configs each)} & 33.7 & 37.07 \\ +\bottomrule +\end{tabular} +\caption{Replication of the GIFT-Eval leaderboard with FoundationForecast (run \texttt{2026-10-06-nb-v2}). $^\dagger$Leaderboard aggregates: geometric mean over the 97 dataset configurations of the metric normalized by Seasonal Naive (lower is better). \emph{Official} is recomputed from the model's submitted CSV in the GIFT-Eval Hugging Face space; \emph{Ours} from our run; $\Delta$ is their relative difference. $\sigma$ is the standard deviation across the 97 configurations of the per-configuration relative difference $(\text{ours}/\text{official}-1)$. Compute is wall time on a single NVIDIA A10G (Modal) at \$1.10/GPU-h. Chronos-2 small has no official submission yet.} +\label{tab:gift-eval-replication} +\end{table} +``` + +
+ Local single job with replication registry: ```bash From d411aa2b360a03808ece8de0c50c953f233ef4ca Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Wed, 7 Oct 2026 18:53:49 -0700 Subject: [PATCH 14/24] docs(gift-eval): report sigma as std of per-config differences in replication table sigma is now the standard deviation across the 97 configs of (ours - official) on the Seasonal-Naive-normalized scale, i.e. the same units as the aggregate columns, instead of the std of relative differences. Co-authored-by: Cursor --- experiments/gift-eval/README.md | 78 +++++++++++++++++---------------- 1 file changed, 40 insertions(+), 38 deletions(-) diff --git a/experiments/gift-eval/README.md b/experiments/gift-eval/README.md index f8ef319..b10196b 100644 --- a/experiments/gift-eval/README.md +++ b/experiments/gift-eval/README.md @@ -114,34 +114,36 @@ FoundationForecast reproduces the **leaderboard aggregates** of every official submission it wraps: across 15 models with a published reference, the aggregate MASE and CRPS land within **±1.8%** of the official values (tolerance is 2.5%). Per-config results are noisier (σ is the standard deviation, over the 97 dataset -configurations, of the per-config relative difference `ours / official − 1`), but -the noise averages out in the geometric mean the leaderboard ranks on. The whole -grid (16 models × 97 configs) took **33.7 GPU-hours ≈ $37** on a single A10G. +configurations, of the per-config difference `ours − official`), but the noise +averages out in the geometric mean the leaderboard ranks on. The whole grid +(16 models × 97 configs) took **33.7 GPU-hours ≈ $37** on a single A10G. MASE and CRPS are the leaderboard aggregates: geometric mean over the 97 configs of the metric normalized by Seasonal Naive (lower is better). *Official* is recomputed from each model's submitted CSV in the GIFT-Eval Hugging Face space; *ours* from this -run. Δ is the relative difference of the aggregates. Compute is wall time on an A10G -(Modal) at $1.10/GPU-h. Chronos-2 small has no official submission yet. +run. Δ is the relative difference of the aggregates. σ is the standard deviation +across the 97 configs of the per-config difference `ours − official`, computed on the +same Seasonal-Naive-normalized scale as the aggregates. Compute is wall time on an +A10G (Modal) at $1.10/GPU-h. Chronos-2 small has no official submission yet. -| Model | Org. | MASE official | MASE ours | Δ (%) | σ (%) | CRPS official | CRPS ours | Δ (%) | σ (%) | GPU-h | USD | +| Model | Org. | MASE official | MASE ours | Δ (%) | σ | CRPS official | CRPS ours | Δ (%) | σ | GPU-h | USD | |---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| -| TimesFM 3 | Google | 0.667 | 0.675 | +1.2 | 3.2 | 0.456 | 0.462 | +1.3 | 3.1 | 6.73 | 7.41 | -| PatchTST-FM r2 | IBM | 0.685 | 0.684 | -0.1 | 3.4 | 0.467 | 0.467 | +0.0 | 3.9 | 3.31 | 3.64 | -| T0 beta | TFC | 0.687 | 0.689 | +0.3 | 1.4 | 0.474 | 0.475 | +0.4 | 2.8 | 1.41 | 1.55 | -| Tafsut base | Huawei | 0.693 | 0.694 | +0.2 | 0.4 | 0.481 | 0.482 | +0.2 | 1.1 | 1.42 | 1.56 | -| Chronos-2 | Amazon | 0.698 | 0.704 | +0.9 | 3.6 | 0.485 | 0.483 | -0.6 | 7.1 | 1.62 | 1.78 | -| TiRex 2 | NX-AI | 0.697 | 0.704 | +1.0 | 2.2 | 0.478 | 0.485 | +1.6 | 5.3 | 1.29 | 1.42 | -| Toto 2 313M | Datadog | 0.703 | 0.705 | +0.3 | 2.7 | 0.481 | 0.485 | +0.7 | 4.0 | 1.68 | 1.84 | -| PatchTST-FM r1 | IBM | 0.717 | 0.716 | -0.2 | 0.9 | 0.488 | 0.487 | -0.1 | 1.2 | 1.25 | 1.37 | -| TiRex 1.1 | NX-AI | 0.716 | 0.724 | +1.1 | 8.8 | 0.488 | 0.494 | +1.1 | 10.1 | 1.57 | 1.73 | +| TimesFM 3 | Google | 0.667 | 0.675 | +1.2 | 0.021 | 0.456 | 0.462 | +1.3 | 0.015 | 6.73 | 7.41 | +| PatchTST-FM r2 | IBM | 0.685 | 0.684 | -0.1 | 0.028 | 0.467 | 0.467 | +0.0 | 0.030 | 3.31 | 3.64 | +| T0 beta | TFC | 0.687 | 0.689 | +0.3 | 0.010 | 0.474 | 0.475 | +0.4 | 0.014 | 1.41 | 1.55 | +| Tafsut base | Huawei | 0.693 | 0.694 | +0.2 | 0.003 | 0.481 | 0.482 | +0.2 | 0.006 | 1.42 | 1.56 | +| Chronos-2 | Amazon | 0.698 | 0.704 | +0.9 | 0.023 | 0.485 | 0.483 | -0.6 | 0.046 | 1.62 | 1.78 | +| TiRex 2 | NX-AI | 0.697 | 0.704 | +1.0 | 0.015 | 0.478 | 0.485 | +1.6 | 0.025 | 1.29 | 1.42 | +| Toto 2 313M | Datadog | 0.703 | 0.705 | +0.3 | 0.021 | 0.481 | 0.485 | +0.7 | 0.019 | 1.68 | 1.84 | +| PatchTST-FM r1 | IBM | 0.717 | 0.716 | -0.2 | 0.006 | 0.488 | 0.487 | -0.1 | 0.007 | 1.25 | 1.37 | +| TiRex 1.1 | NX-AI | 0.716 | 0.724 | +1.1 | 0.040 | 0.488 | 0.494 | +1.1 | 0.043 | 1.57 | 1.73 | | Chronos-2 small | Amazon | — | 0.724 | — | — | — | 0.496 | — | — | 0.95 | 1.04 | -| T0 alpha | TFC | 0.724 | 0.729 | +0.7 | 2.1 | 0.494 | 0.495 | +0.3 | 2.7 | 0.94 | 1.03 | -| Moirai 2 small | Salesforce | 0.728 | 0.736 | +1.1 | 5.8 | 0.516 | 0.521 | +0.8 | 5.2 | 2.65 | 2.92 | -| Toto 2 4M | Datadog | 0.757 | 0.761 | +0.6 | 2.2 | 0.524 | 0.531 | +1.3 | 4.6 | 0.55 | 0.61 | -| Chronos Bolt base | Amazon | 0.808 | 0.813 | +0.7 | 2.5 | 0.574 | 0.566 | -1.4 | 4.9 | 4.66 | 5.13 | -| Chronos Bolt small | Amazon | 0.822 | 0.829 | +0.8 | 2.7 | 0.577 | 0.566 | -1.8 | 4.4 | 1.69 | 1.86 | -| Moirai 1.1 large | Salesforce | 0.875 | 0.886 | +1.2 | 12.5 | 0.599 | 0.602 | +0.5 | 14.0 | 1.98 | 2.18 | +| T0 alpha | TFC | 0.724 | 0.729 | +0.7 | 0.018 | 0.494 | 0.495 | +0.3 | 0.017 | 0.94 | 1.03 | +| Moirai 2 small | Salesforce | 0.728 | 0.736 | +1.1 | 0.056 | 0.516 | 0.521 | +0.8 | 0.038 | 2.65 | 2.92 | +| Toto 2 4M | Datadog | 0.757 | 0.761 | +0.6 | 0.017 | 0.524 | 0.531 | +1.3 | 0.028 | 0.55 | 0.61 | +| Chronos Bolt base | Amazon | 0.808 | 0.813 | +0.7 | 0.022 | 0.574 | 0.566 | -1.4 | 0.032 | 4.66 | 5.13 | +| Chronos Bolt small | Amazon | 0.822 | 0.829 | +0.8 | 0.023 | 0.577 | 0.566 | -1.8 | 0.032 | 1.69 | 1.86 | +| Moirai 1.1 large | Salesforce | 0.875 | 0.886 | +1.2 | 0.163 | 0.599 | 0.602 | +0.5 | 0.150 | 1.98 | 2.18 | | **Total** (16 models) | | | | | | | | | | **33.7** | **37.07** |
@@ -156,29 +158,29 @@ run. Δ is the relative difference of the aggregates. Compute is wall time on an \toprule & & \multicolumn{4}{c}{MASE$^\dagger$} & \multicolumn{4}{c}{CRPS$^\dagger$} & \multicolumn{2}{c}{Compute} \\ \cmidrule(lr){3-6} \cmidrule(lr){7-10} \cmidrule(lr){11-12} -Model & Org. & Official & Ours & $\Delta$ (\%) & $\sigma$ (\%) & Official & Ours & $\Delta$ (\%) & $\sigma$ (\%) & GPU-h & USD \\ +Model & Org. & Official & Ours & $\Delta$ (\%) & $\sigma$ & Official & Ours & $\Delta$ (\%) & $\sigma$ & GPU-h & USD \\ \midrule -TimesFM 3 & Google & 0.667 & 0.675 & +1.2 & 3.2 & 0.456 & 0.462 & +1.3 & 3.1 & 6.73 & 7.41 \\ -PatchTST-FM r2 & IBM & 0.685 & 0.684 & -0.1 & 3.4 & 0.467 & 0.467 & +0.0 & 3.9 & 3.31 & 3.64 \\ -T0 beta & TFC & 0.687 & 0.689 & +0.3 & 1.4 & 0.474 & 0.475 & +0.4 & 2.8 & 1.41 & 1.55 \\ -Tafsut base & Huawei & 0.693 & 0.694 & +0.2 & 0.4 & 0.481 & 0.482 & +0.2 & 1.1 & 1.42 & 1.56 \\ -Chronos-2 & Amazon & 0.698 & 0.704 & +0.9 & 3.6 & 0.485 & 0.483 & -0.6 & 7.1 & 1.62 & 1.78 \\ -TiRex 2 & NX-AI & 0.697 & 0.704 & +1.0 & 2.2 & 0.478 & 0.485 & +1.6 & 5.3 & 1.29 & 1.42 \\ -Toto 2 313M & Datadog & 0.703 & 0.705 & +0.3 & 2.7 & 0.481 & 0.485 & +0.7 & 4.0 & 1.68 & 1.84 \\ -PatchTST-FM r1 & IBM & 0.717 & 0.716 & -0.2 & 0.9 & 0.488 & 0.487 & -0.1 & 1.2 & 1.25 & 1.37 \\ -TiRex 1.1 & NX-AI & 0.716 & 0.724 & +1.1 & 8.8 & 0.488 & 0.494 & +1.1 & 10.1 & 1.57 & 1.73 \\ +TimesFM 3 & Google & 0.667 & 0.675 & +1.2 & 0.021 & 0.456 & 0.462 & +1.3 & 0.015 & 6.73 & 7.41 \\ +PatchTST-FM r2 & IBM & 0.685 & 0.684 & -0.1 & 0.028 & 0.467 & 0.467 & +0.0 & 0.030 & 3.31 & 3.64 \\ +T0 beta & TFC & 0.687 & 0.689 & +0.3 & 0.010 & 0.474 & 0.475 & +0.4 & 0.014 & 1.41 & 1.55 \\ +Tafsut base & Huawei & 0.693 & 0.694 & +0.2 & 0.003 & 0.481 & 0.482 & +0.2 & 0.006 & 1.42 & 1.56 \\ +Chronos-2 & Amazon & 0.698 & 0.704 & +0.9 & 0.023 & 0.485 & 0.483 & -0.6 & 0.046 & 1.62 & 1.78 \\ +TiRex 2 & NX-AI & 0.697 & 0.704 & +1.0 & 0.015 & 0.478 & 0.485 & +1.6 & 0.025 & 1.29 & 1.42 \\ +Toto 2 313M & Datadog & 0.703 & 0.705 & +0.3 & 0.021 & 0.481 & 0.485 & +0.7 & 0.019 & 1.68 & 1.84 \\ +PatchTST-FM r1 & IBM & 0.717 & 0.716 & -0.2 & 0.006 & 0.488 & 0.487 & -0.1 & 0.007 & 1.25 & 1.37 \\ +TiRex 1.1 & NX-AI & 0.716 & 0.724 & +1.1 & 0.040 & 0.488 & 0.494 & +1.1 & 0.043 & 1.57 & 1.73 \\ Chronos-2 small & Amazon & -- & 0.724 & -- & -- & -- & 0.496 & -- & -- & 0.95 & 1.04 \\ -T0 alpha & TFC & 0.724 & 0.729 & +0.7 & 2.1 & 0.494 & 0.495 & +0.3 & 2.7 & 0.94 & 1.03 \\ -Moirai 2 small & Salesforce & 0.728 & 0.736 & +1.1 & 5.8 & 0.516 & 0.521 & +0.8 & 5.2 & 2.65 & 2.92 \\ -Toto 2 4M & Datadog & 0.757 & 0.761 & +0.6 & 2.2 & 0.524 & 0.531 & +1.3 & 4.6 & 0.55 & 0.61 \\ -Chronos Bolt base & Amazon & 0.808 & 0.813 & +0.7 & 2.5 & 0.574 & 0.566 & -1.4 & 4.9 & 4.66 & 5.13 \\ -Chronos Bolt small & Amazon & 0.822 & 0.829 & +0.8 & 2.7 & 0.577 & 0.566 & -1.8 & 4.4 & 1.69 & 1.86 \\ -Moirai 1.1 large & Salesforce & 0.875 & 0.886 & +1.2 & 12.5 & 0.599 & 0.602 & +0.5 & 14.0 & 1.98 & 2.18 \\ +T0 alpha & TFC & 0.724 & 0.729 & +0.7 & 0.018 & 0.494 & 0.495 & +0.3 & 0.017 & 0.94 & 1.03 \\ +Moirai 2 small & Salesforce & 0.728 & 0.736 & +1.1 & 0.056 & 0.516 & 0.521 & +0.8 & 0.038 & 2.65 & 2.92 \\ +Toto 2 4M & Datadog & 0.757 & 0.761 & +0.6 & 0.017 & 0.524 & 0.531 & +1.3 & 0.028 & 0.55 & 0.61 \\ +Chronos Bolt base & Amazon & 0.808 & 0.813 & +0.7 & 0.022 & 0.574 & 0.566 & -1.4 & 0.032 & 4.66 & 5.13 \\ +Chronos Bolt small & Amazon & 0.822 & 0.829 & +0.8 & 0.023 & 0.577 & 0.566 & -1.8 & 0.032 & 1.69 & 1.86 \\ +Moirai 1.1 large & Salesforce & 0.875 & 0.886 & +1.2 & 0.163 & 0.599 & 0.602 & +0.5 & 0.150 & 1.98 & 2.18 \\ \midrule \multicolumn{10}{l}{Total (16 models, 97 dataset configs each)} & 33.7 & 37.07 \\ \bottomrule \end{tabular} -\caption{Replication of the GIFT-Eval leaderboard with FoundationForecast (run \texttt{2026-10-06-nb-v2}). $^\dagger$Leaderboard aggregates: geometric mean over the 97 dataset configurations of the metric normalized by Seasonal Naive (lower is better). \emph{Official} is recomputed from the model's submitted CSV in the GIFT-Eval Hugging Face space; \emph{Ours} from our run; $\Delta$ is their relative difference. $\sigma$ is the standard deviation across the 97 configurations of the per-configuration relative difference $(\text{ours}/\text{official}-1)$. Compute is wall time on a single NVIDIA A10G (Modal) at \$1.10/GPU-h. Chronos-2 small has no official submission yet.} +\caption{Replication of the GIFT-Eval leaderboard with FoundationForecast (run \texttt{2026-10-06-nb-v2}). $^\dagger$Leaderboard aggregates: geometric mean over the 97 dataset configurations of the metric normalized by Seasonal Naive (lower is better). \emph{Official} is recomputed from the model's submitted CSV in the GIFT-Eval Hugging Face space; \emph{Ours} from our run; $\Delta$ is their relative difference. $\sigma$ is the standard deviation across the 97 configurations of the per-configuration difference $(\text{ours}-\text{official})$ on the same Seasonal-Naive-normalized scale. Compute is wall time on a single NVIDIA A10G (Modal) at \$1.10/GPU-h. Chronos-2 small has no official submission yet.} \label{tab:gift-eval-replication} \end{table} ``` From 294671c6482c8ce830052639b6050c36f2d0d5d9 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Wed, 7 Oct 2026 19:03:35 -0700 Subject: [PATCH 15/24] docs(gift-eval): group replication table by org, report |delta| and FF column - Org first, alphabetical, shown once per group; models sorted by FF MASE. - |Delta| (absolute relative difference) instead of signed Delta. - 'Ours' renamed FF (FoundationForecast), defined in the note. - LaTeX uses threeparttable so the note spans the table width, with no 'Table N' caption; note states every model is within 2%. Co-authored-by: Cursor --- experiments/gift-eval/README.md | 112 ++++++++++++++++++-------------- 1 file changed, 63 insertions(+), 49 deletions(-) diff --git a/experiments/gift-eval/README.md b/experiments/gift-eval/README.md index b10196b..e675ba5 100644 --- a/experiments/gift-eval/README.md +++ b/experiments/gift-eval/README.md @@ -110,77 +110,91 @@ still enforces strict per-job replication on the CI subset only. ### Replication results (run `2026-10-06-nb-v2`) -FoundationForecast reproduces the **leaderboard aggregates** of every official -submission it wraps: across 15 models with a published reference, the aggregate -MASE and CRPS land within **±1.8%** of the official values (tolerance is 2.5%). -Per-config results are noisier (σ is the standard deviation, over the 97 dataset -configurations, of the per-config difference `ours − official`), but the noise -averages out in the geometric mean the leaderboard ranks on. The whole grid -(16 models × 97 configs) took **33.7 GPU-hours ≈ $37** on a single A10G. +FoundationForecast (FF) reproduces the **leaderboard aggregates** of every official +submission it wraps: across the 15 models with a published reference, the aggregate +MASE and CRPS are all within **2%** of the official values (max |Δ| = 1.8%; the +verify tolerance is 2.5%). Per-config results are noisier (σ is the standard +deviation, over the 97 dataset configurations, of the per-config difference +`FF − official`), but the noise averages out in the geometric mean the leaderboard +ranks on. The whole grid (16 models × 97 configs) took **33.7 GPU-hours ≈ $37** on +a single A10G. MASE and CRPS are the leaderboard aggregates: geometric mean over the 97 configs of the metric normalized by Seasonal Naive (lower is better). *Official* is recomputed -from each model's submitted CSV in the GIFT-Eval Hugging Face space; *ours* from this -run. Δ is the relative difference of the aggregates. σ is the standard deviation -across the 97 configs of the per-config difference `ours − official`, computed on the -same Seasonal-Naive-normalized scale as the aggregates. Compute is wall time on an -A10G (Modal) at $1.10/GPU-h. Chronos-2 small has no official submission yet. - -| Model | Org. | MASE official | MASE ours | Δ (%) | σ | CRPS official | CRPS ours | Δ (%) | σ | GPU-h | USD | +from each model's submitted CSV in the GIFT-Eval Hugging Face space; *FF* is the same +aggregate from this FoundationForecast run. |Δ| is the absolute relative difference +between the two aggregates. σ is the standard deviation across the 97 configs of the +per-config difference `FF − official`, on the same Seasonal-Naive-normalized scale as +the aggregates. Compute is wall time on an A10G (Modal) at $1.10/GPU-h. Chronos-2 +small has no official submission yet. + +| Org. | Model | MASE official | MASE FF | \|Δ\| (%) | σ | CRPS official | CRPS FF | \|Δ\| (%) | σ | GPU-h | USD | |---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| -| TimesFM 3 | Google | 0.667 | 0.675 | +1.2 | 0.021 | 0.456 | 0.462 | +1.3 | 0.015 | 6.73 | 7.41 | -| PatchTST-FM r2 | IBM | 0.685 | 0.684 | -0.1 | 0.028 | 0.467 | 0.467 | +0.0 | 0.030 | 3.31 | 3.64 | -| T0 beta | TFC | 0.687 | 0.689 | +0.3 | 0.010 | 0.474 | 0.475 | +0.4 | 0.014 | 1.41 | 1.55 | -| Tafsut base | Huawei | 0.693 | 0.694 | +0.2 | 0.003 | 0.481 | 0.482 | +0.2 | 0.006 | 1.42 | 1.56 | -| Chronos-2 | Amazon | 0.698 | 0.704 | +0.9 | 0.023 | 0.485 | 0.483 | -0.6 | 0.046 | 1.62 | 1.78 | -| TiRex 2 | NX-AI | 0.697 | 0.704 | +1.0 | 0.015 | 0.478 | 0.485 | +1.6 | 0.025 | 1.29 | 1.42 | -| Toto 2 313M | Datadog | 0.703 | 0.705 | +0.3 | 0.021 | 0.481 | 0.485 | +0.7 | 0.019 | 1.68 | 1.84 | -| PatchTST-FM r1 | IBM | 0.717 | 0.716 | -0.2 | 0.006 | 0.488 | 0.487 | -0.1 | 0.007 | 1.25 | 1.37 | -| TiRex 1.1 | NX-AI | 0.716 | 0.724 | +1.1 | 0.040 | 0.488 | 0.494 | +1.1 | 0.043 | 1.57 | 1.73 | -| Chronos-2 small | Amazon | — | 0.724 | — | — | — | 0.496 | — | — | 0.95 | 1.04 | -| T0 alpha | TFC | 0.724 | 0.729 | +0.7 | 0.018 | 0.494 | 0.495 | +0.3 | 0.017 | 0.94 | 1.03 | -| Moirai 2 small | Salesforce | 0.728 | 0.736 | +1.1 | 0.056 | 0.516 | 0.521 | +0.8 | 0.038 | 2.65 | 2.92 | -| Toto 2 4M | Datadog | 0.757 | 0.761 | +0.6 | 0.017 | 0.524 | 0.531 | +1.3 | 0.028 | 0.55 | 0.61 | -| Chronos Bolt base | Amazon | 0.808 | 0.813 | +0.7 | 0.022 | 0.574 | 0.566 | -1.4 | 0.032 | 4.66 | 5.13 | -| Chronos Bolt small | Amazon | 0.822 | 0.829 | +0.8 | 0.023 | 0.577 | 0.566 | -1.8 | 0.032 | 1.69 | 1.86 | -| Moirai 1.1 large | Salesforce | 0.875 | 0.886 | +1.2 | 0.163 | 0.599 | 0.602 | +0.5 | 0.150 | 1.98 | 2.18 | +| Amazon | Chronos-2 | 0.698 | 0.704 | 0.9 | 0.023 | 0.485 | 0.483 | 0.6 | 0.046 | 1.62 | 1.78 | +| | Chronos-2 small | — | 0.724 | — | — | — | 0.496 | — | — | 0.95 | 1.04 | +| | Chronos Bolt base | 0.808 | 0.813 | 0.7 | 0.022 | 0.574 | 0.566 | 1.4 | 0.032 | 4.66 | 5.13 | +| | Chronos Bolt small | 0.822 | 0.829 | 0.8 | 0.023 | 0.577 | 0.566 | 1.8 | 0.032 | 1.69 | 1.86 | +| Datadog | Toto 2 313M | 0.703 | 0.705 | 0.3 | 0.021 | 0.481 | 0.485 | 0.7 | 0.019 | 1.68 | 1.84 | +| | Toto 2 4M | 0.757 | 0.761 | 0.6 | 0.017 | 0.524 | 0.531 | 1.3 | 0.028 | 0.55 | 0.61 | +| Google | TimesFM 3 | 0.667 | 0.675 | 1.2 | 0.021 | 0.456 | 0.462 | 1.3 | 0.015 | 6.73 | 7.41 | +| Huawei | Tafsut base | 0.693 | 0.694 | 0.2 | 0.003 | 0.481 | 0.482 | 0.2 | 0.006 | 1.42 | 1.56 | +| IBM | PatchTST-FM r2 | 0.685 | 0.684 | 0.1 | 0.028 | 0.467 | 0.467 | 0.0 | 0.030 | 3.31 | 3.64 | +| | PatchTST-FM r1 | 0.717 | 0.716 | 0.2 | 0.006 | 0.488 | 0.487 | 0.1 | 0.007 | 1.25 | 1.37 | +| NX-AI | TiRex 2 | 0.697 | 0.704 | 1.0 | 0.015 | 0.478 | 0.485 | 1.6 | 0.025 | 1.29 | 1.42 | +| | TiRex 1.1 | 0.716 | 0.724 | 1.1 | 0.040 | 0.488 | 0.494 | 1.1 | 0.043 | 1.57 | 1.73 | +| Salesforce | Moirai 2 small | 0.728 | 0.736 | 1.1 | 0.056 | 0.516 | 0.521 | 0.8 | 0.038 | 2.65 | 2.92 | +| | Moirai 1.1 large | 0.875 | 0.886 | 1.2 | 0.163 | 0.599 | 0.602 | 0.5 | 0.150 | 1.98 | 2.18 | +| TFC | T0 beta | 0.687 | 0.689 | 0.3 | 0.010 | 0.474 | 0.475 | 0.4 | 0.014 | 1.41 | 1.55 | +| | T0 alpha | 0.724 | 0.729 | 0.7 | 0.018 | 0.494 | 0.495 | 0.3 | 0.017 | 0.94 | 1.03 | | **Total** (16 models) | | | | | | | | | | **33.7** | **37.07** |
-LaTeX source (needs booktabs) +LaTeX source (needs booktabs, amsmath, threeparttable) ```latex \begin{table}[t] \centering \small \setlength{\tabcolsep}{4pt} +\begin{threeparttable} \begin{tabular}{l l rrrr rrrr rr} \toprule & & \multicolumn{4}{c}{MASE$^\dagger$} & \multicolumn{4}{c}{CRPS$^\dagger$} & \multicolumn{2}{c}{Compute} \\ \cmidrule(lr){3-6} \cmidrule(lr){7-10} \cmidrule(lr){11-12} -Model & Org. & Official & Ours & $\Delta$ (\%) & $\sigma$ & Official & Ours & $\Delta$ (\%) & $\sigma$ & GPU-h & USD \\ +Org. & Model & Official & FF & $|\Delta|$ (\%) & $\sigma$ & Official & FF & $|\Delta|$ (\%) & $\sigma$ & GPU-h & USD \\ \midrule -TimesFM 3 & Google & 0.667 & 0.675 & +1.2 & 0.021 & 0.456 & 0.462 & +1.3 & 0.015 & 6.73 & 7.41 \\ -PatchTST-FM r2 & IBM & 0.685 & 0.684 & -0.1 & 0.028 & 0.467 & 0.467 & +0.0 & 0.030 & 3.31 & 3.64 \\ -T0 beta & TFC & 0.687 & 0.689 & +0.3 & 0.010 & 0.474 & 0.475 & +0.4 & 0.014 & 1.41 & 1.55 \\ -Tafsut base & Huawei & 0.693 & 0.694 & +0.2 & 0.003 & 0.481 & 0.482 & +0.2 & 0.006 & 1.42 & 1.56 \\ -Chronos-2 & Amazon & 0.698 & 0.704 & +0.9 & 0.023 & 0.485 & 0.483 & -0.6 & 0.046 & 1.62 & 1.78 \\ -TiRex 2 & NX-AI & 0.697 & 0.704 & +1.0 & 0.015 & 0.478 & 0.485 & +1.6 & 0.025 & 1.29 & 1.42 \\ -Toto 2 313M & Datadog & 0.703 & 0.705 & +0.3 & 0.021 & 0.481 & 0.485 & +0.7 & 0.019 & 1.68 & 1.84 \\ -PatchTST-FM r1 & IBM & 0.717 & 0.716 & -0.2 & 0.006 & 0.488 & 0.487 & -0.1 & 0.007 & 1.25 & 1.37 \\ -TiRex 1.1 & NX-AI & 0.716 & 0.724 & +1.1 & 0.040 & 0.488 & 0.494 & +1.1 & 0.043 & 1.57 & 1.73 \\ -Chronos-2 small & Amazon & -- & 0.724 & -- & -- & -- & 0.496 & -- & -- & 0.95 & 1.04 \\ -T0 alpha & TFC & 0.724 & 0.729 & +0.7 & 0.018 & 0.494 & 0.495 & +0.3 & 0.017 & 0.94 & 1.03 \\ -Moirai 2 small & Salesforce & 0.728 & 0.736 & +1.1 & 0.056 & 0.516 & 0.521 & +0.8 & 0.038 & 2.65 & 2.92 \\ -Toto 2 4M & Datadog & 0.757 & 0.761 & +0.6 & 0.017 & 0.524 & 0.531 & +1.3 & 0.028 & 0.55 & 0.61 \\ -Chronos Bolt base & Amazon & 0.808 & 0.813 & +0.7 & 0.022 & 0.574 & 0.566 & -1.4 & 0.032 & 4.66 & 5.13 \\ -Chronos Bolt small & Amazon & 0.822 & 0.829 & +0.8 & 0.023 & 0.577 & 0.566 & -1.8 & 0.032 & 1.69 & 1.86 \\ -Moirai 1.1 large & Salesforce & 0.875 & 0.886 & +1.2 & 0.163 & 0.599 & 0.602 & +0.5 & 0.150 & 1.98 & 2.18 \\ +Amazon & Chronos-2 & 0.698 & 0.704 & 0.9 & 0.023 & 0.485 & 0.483 & 0.6 & 0.046 & 1.62 & 1.78 \\ + & Chronos-2 small & -- & 0.724 & -- & -- & -- & 0.496 & -- & -- & 0.95 & 1.04 \\ + & Chronos Bolt base & 0.808 & 0.813 & 0.7 & 0.022 & 0.574 & 0.566 & 1.4 & 0.032 & 4.66 & 5.13 \\ + & Chronos Bolt small & 0.822 & 0.829 & 0.8 & 0.023 & 0.577 & 0.566 & 1.8 & 0.032 & 1.69 & 1.86 \\ +\addlinespace[2pt] +Datadog & Toto 2 313M & 0.703 & 0.705 & 0.3 & 0.021 & 0.481 & 0.485 & 0.7 & 0.019 & 1.68 & 1.84 \\ + & Toto 2 4M & 0.757 & 0.761 & 0.6 & 0.017 & 0.524 & 0.531 & 1.3 & 0.028 & 0.55 & 0.61 \\ +\addlinespace[2pt] +Google & TimesFM 3 & 0.667 & 0.675 & 1.2 & 0.021 & 0.456 & 0.462 & 1.3 & 0.015 & 6.73 & 7.41 \\ +\addlinespace[2pt] +Huawei & Tafsut base & 0.693 & 0.694 & 0.2 & 0.003 & 0.481 & 0.482 & 0.2 & 0.006 & 1.42 & 1.56 \\ +\addlinespace[2pt] +IBM & PatchTST-FM r2 & 0.685 & 0.684 & 0.1 & 0.028 & 0.467 & 0.467 & 0.0 & 0.030 & 3.31 & 3.64 \\ + & PatchTST-FM r1 & 0.717 & 0.716 & 0.2 & 0.006 & 0.488 & 0.487 & 0.1 & 0.007 & 1.25 & 1.37 \\ +\addlinespace[2pt] +NX-AI & TiRex 2 & 0.697 & 0.704 & 1.0 & 0.015 & 0.478 & 0.485 & 1.6 & 0.025 & 1.29 & 1.42 \\ + & TiRex 1.1 & 0.716 & 0.724 & 1.1 & 0.040 & 0.488 & 0.494 & 1.1 & 0.043 & 1.57 & 1.73 \\ +\addlinespace[2pt] +Salesforce & Moirai 2 small & 0.728 & 0.736 & 1.1 & 0.056 & 0.516 & 0.521 & 0.8 & 0.038 & 2.65 & 2.92 \\ + & Moirai 1.1 large & 0.875 & 0.886 & 1.2 & 0.163 & 0.599 & 0.602 & 0.5 & 0.150 & 1.98 & 2.18 \\ +\addlinespace[2pt] +TFC & T0 beta & 0.687 & 0.689 & 0.3 & 0.010 & 0.474 & 0.475 & 0.4 & 0.014 & 1.41 & 1.55 \\ + & T0 alpha & 0.724 & 0.729 & 0.7 & 0.018 & 0.494 & 0.495 & 0.3 & 0.017 & 0.94 & 1.03 \\ \midrule \multicolumn{10}{l}{Total (16 models, 97 dataset configs each)} & 33.7 & 37.07 \\ \bottomrule \end{tabular} -\caption{Replication of the GIFT-Eval leaderboard with FoundationForecast (run \texttt{2026-10-06-nb-v2}). $^\dagger$Leaderboard aggregates: geometric mean over the 97 dataset configurations of the metric normalized by Seasonal Naive (lower is better). \emph{Official} is recomputed from the model's submitted CSV in the GIFT-Eval Hugging Face space; \emph{Ours} from our run; $\Delta$ is their relative difference. $\sigma$ is the standard deviation across the 97 configurations of the per-configuration difference $(\text{ours}-\text{official})$ on the same Seasonal-Naive-normalized scale. Compute is wall time on a single NVIDIA A10G (Modal) at \$1.10/GPU-h. Chronos-2 small has no official submission yet.} +\begin{tablenotes}[flushleft] +\footnotesize +\item Replication of the GIFT-Eval leaderboard with FoundationForecast (FF), run \texttt{2026-10-06-nb-v2}. $^\dagger$Leaderboard aggregates: geometric mean over the 97 dataset configurations of the metric normalized by Seasonal Naive (lower is better). \emph{Official} is recomputed from the model's submitted CSV in the GIFT-Eval Hugging Face space; \emph{FF} is the same aggregate from our FoundationForecast run. $|\Delta|$ is the absolute relative difference between the two aggregates; every model is within 2\%. $\sigma$ is the standard deviation across the 97 configurations of the per-configuration difference $(\text{FF}-\text{official})$ on the same Seasonal-Naive-normalized scale. Compute is wall time on a single NVIDIA A10G (Modal) at \$1.10/GPU-h. Chronos-2 small has no official submission yet. +\end{tablenotes} +\end{threeparttable} \label{tab:gift-eval-replication} \end{table} ``` From a39638ef0b430b36201a5fb24c894a7400cdef34 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Thu, 8 Oct 2026 08:39:22 -0700 Subject: [PATCH 16/24] docs(gift-eval): rewrite README as the $37 GIFT-Eval replication writeup MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replace the operator-oriented README with the experiment narrative (why, replication table PNG, Pareto PNG, Makefile repro, infra, cite). Figures only — no markdown/LaTeX tables. Co-authored-by: Cursor --- experiments/gift-eval/README.md | 354 ++++-------------- .../gift-eval/assets/pareto-frontier.png | Bin 0 -> 568404 bytes .../gift-eval/assets/replication-table.png | Bin 0 -> 196547 bytes 3 files changed, 70 insertions(+), 284 deletions(-) create mode 100644 experiments/gift-eval/assets/pareto-frontier.png create mode 100644 experiments/gift-eval/assets/replication-table.png diff --git a/experiments/gift-eval/README.md b/experiments/gift-eval/README.md index e675ba5..2b5c386 100644 --- a/experiments/gift-eval/README.md +++ b/experiments/gift-eval/README.md @@ -1,323 +1,109 @@ -# FoundationForecast GIFT-Eval Benchmark +# FoundationForecast replicates Salesforce's GIFT-Eval for $37 -End-to-end GIFT-Eval benchmark for [FoundationForecast](https://github.com/AzulGarza/foundationforecast) model wrappers. Evaluation uses [`timecopilot-gift-eval`](https://github.com/TimeCopilot/timecopilot-gift-eval); replication checks compare outputs to official Hugging Face reference CSVs. +Replication of **Salesforce's full GIFT-Eval** forecasting benchmark **under a single API (FoundationForecast)**. The full replication of **16 models** cost **$37** and took **≈34 hours** of GPU time. The best performance among the implemented models is achieved by **TimesFM 3 (Google) for under $8**. -## Layout +This directory also [regression-tests](https://github.com/TimeCopilot/foundationforecast/actions/workflows/ci.yaml) FoundationForecast on every merge to `main`. -``` -src/ -├── eval/ run_gift_eval(), model registry, job config -├── verify/ HF reference loading and replication checks -└── runners/ CLI and Modal entrypoints -tests/ pytest replication checks (imports src.verify) -configs/ models.yaml (full matrix), ci_subset.yaml (CI), replication/ (notebook-aligned) -``` +## Why? -## Setup +Companies and practitioners rely on accurate forecasts to make better decisions. Some of those decisions have to be made **quickly**; as compute capacity grows, that speed requirement is even sharper in nascent, high-frequency settings such as energy markets, cloud capacity, and real-time operations. Other decisions have to be made **under a budget**, so accuracy should be judged together with computational cost. -```bash -cd experiments/gift-eval -uv sync -``` +**Time Series Foundation Models (TSFMs)** have changed how forecasting is done. What is still missing is a clear view of the **accuracy–compute trade-off**, and a **unified way** to evaluate the models and deploy them in production. -Installs the in-repo editable `foundationforecast` package from the monorepo root (`../..`), not PyPI — so local runs and CI always use the current wrapper code. After changing the library (for example model weight caching), refresh the lock metadata with `uv lock` in this directory so `uv sync --frozen` in CI matches the editable source version. +[GIFT-Eval](https://huggingface.co/spaces/Salesforce/GIFT-Eval) ([Aksu et al., 2024](https://arxiv.org/abs/2410.10393)) is one of the benchmarks practitioners and researchers use to publish SOTA results, by uploading their own implementations. The process does not, by itself, give a unified path to replicability: each model ships with its own API, inference rules, and design. The leaderboard also reports **accuracy only**. It does not measure computational cost on shared infrastructure. -Requires Python 3.11+. +We built this reproducible experiment to show that a **unified API** makes it practical to test both **accuracy and cost** on large time series data. **FoundationForecast** is that API. The results below are from running it on the full GIFT-Eval grid. -## Dataset +## Results -```bash -make download-gift-eval-data -# optional: make upload-data-to-s3 -``` +We replicated **16 foundation models** on Salesforce's full GIFT-Eval: 23 datasets, ~144k time series, 177M data points, evaluated as **326,490** forecasting tasks (one task = one series test window; multivariate series are forecast per variate, giving **371,330** univariate forecasts) grouped into **97** dataset / frequency / horizon configurations. That is **5.9 billion** probabilistic forecast values (forecasts × horizon × 16 models × 9 quantiles). -## Run a single job locally +**MASE** (point) and **CRPS** (probabilistic) follow the leaderboard convention: each configuration is normalized by Seasonal Naive and aggregated with a **geometric mean** across the 97 configurations. Cost is **not** part of the original evaluation; we estimate it from per-job GPU wall time on a single **NVIDIA A10G** at **$1.10/GPU-hour**. -```bash -uv run python -m src.runners.run_model \ - --model-key amazon--chronos-bolt-small \ - --dataset-name m4_weekly \ - --term short \ - --storage-path ./data/gift-eval \ - --output-root ./results -``` +Every FoundationForecast implementation reproduces the model's aggregated MASE and CRPS **within 2%** of the official leaderboard submission (Chronos-2 small has no published reference yet). -## CI subset +### Replication -[`configs/ci_subset.yaml`](configs/ci_subset.yaml) defines **13 jobs**: Chronos on -`m4_weekly/short` and `m4_hourly/short`, plus one representative `model_key` each for -TimesFM 2.5/3.0, TiRex 1.1 and TiRex-2-Zeroshot, Moirai, Toto, FlowState, PatchTST-FM r1, -Granite PatchTST-FM r2, T0, and Tafsut (all on `m4_weekly/short`). -Each job runs on Modal GPU and is **HF-verified** in pytest (metrics must match the -official GIFT-Eval reference CSV). +The table below compares **official** GIFT-Eval aggregates with **FF** (the same aggregate from our FoundationForecast run). Organizations are grouped alphabetically. Gold / silver / bronze mark the **1st / 2nd / 3rd best CRPS** in the Official and FF columns — the ranking is the same on both sides: **TimesFM 3**, **PatchTST-FM r2**, **T0 beta**. -### Local GPU +GIFT-Eval replication table: official vs FoundationForecast MASE and CRPS, with gold/silver/bronze on the top-3 CRPS models -```bash -uv run python -m src.runners.run_ci --local --verify \ - --storage-path ./data/gift-eval \ - --output-root ./results/ci -``` +### Pareto frontier -### Modal (CI / GitHub Actions) +Accuracy alone does not decide which model to deploy. The figure below plots **cost vs accuracy** for the same 16 models: cheaper is to the right, better (lower error) is up. The purple polyline is the **Pareto frontier**. TimesFM 3 is the most accurate model we ran, at **$7.41**; Toto 2 4M is the cheapest plotted point, at **$0.61**. The full grid is **$37 / 34 GPU-hours**. -Always re-runs and overwrites results (no skip-if-exists). Full grid skips jobs that -already have outputs. +Cost vs accuracy Pareto frontier on 5.9B probabilistic forecast values for 16 foundation models on GIFT-Eval -```bash -make run-ci -make verify-ci # sync S3 + pytest -``` - -## Replication run (notebook-aligned, one model per family) - -[`configs/replication/`](configs/replication/) holds **16 families** with params traced to -[official gift-eval notebooks](https://github.com/SalesforceAIResearch/gift-eval/tree/main/notebooks). -Results go to **`s3://foundationforecast-gift-eval/results/replication//`** -(not `results/` or `results/ci/`). - -Modal uploads **`foundationforecast/`** (editable install from the monorepo root) plus -`experiments/gift-eval` (`src/`, `configs/`). - -1. Pick a run id, e.g. `2026-04-05-nb-v1`. -2. **Pilot** (16 jobs: `m4_weekly` / `short`): +## Reproducibility ```bash -make run-replication-pilot RUN_ID=2026-04-05-nb-v1 -make verify-replication-pilot RUN_ID=2026-04-05-nb-v1 -# long full grid in background: DETACHED=1 make run-replication-full RUN_ID=... -``` - -3. After pilot verify passes, **full grid** (16 × 97 jobs): - -Chronos-2 and Chronos Bolt use **foundationforecast** with notebook-aligned inference -(`max_length: null` where the notebook does not cap context; no cross-learning / -`predict_batches_jointly`). - -```bash -make run-replication-full RUN_ID=2026-04-05-nb-v1 -make verify-replication-full RUN_ID=2026-04-05-nb-v1 -``` - -**Pilot** uses the same **per-job** MASE/CRPS check as CI (`atol=0.01`, `rtol=2.5%`). - -**Full grid** uses a **leaderboard aggregate** check per model: -geomean(`MASE/Seasonal_Naive`) and geomean(`CRPS/Seasonal_Naive`) on the same -dataset set as the HF submission, compared with the same default tolerances. -Individual job drift is allowed if the aggregate still matches (see -`src/verify/replication_aggregate.py`). CI pytest (`tests/test_replication.py`) -still enforces strict per-job replication on the CI subset only. - -### Replication results (run `2026-10-06-nb-v2`) - -FoundationForecast (FF) reproduces the **leaderboard aggregates** of every official -submission it wraps: across the 15 models with a published reference, the aggregate -MASE and CRPS are all within **2%** of the official values (max |Δ| = 1.8%; the -verify tolerance is 2.5%). Per-config results are noisier (σ is the standard -deviation, over the 97 dataset configurations, of the per-config difference -`FF − official`), but the noise averages out in the geometric mean the leaderboard -ranks on. The whole grid (16 models × 97 configs) took **33.7 GPU-hours ≈ $37** on -a single A10G. - -MASE and CRPS are the leaderboard aggregates: geometric mean over the 97 configs of -the metric normalized by Seasonal Naive (lower is better). *Official* is recomputed -from each model's submitted CSV in the GIFT-Eval Hugging Face space; *FF* is the same -aggregate from this FoundationForecast run. |Δ| is the absolute relative difference -between the two aggregates. σ is the standard deviation across the 97 configs of the -per-config difference `FF − official`, on the same Seasonal-Naive-normalized scale as -the aggregates. Compute is wall time on an A10G (Modal) at $1.10/GPU-h. Chronos-2 -small has no official submission yet. - -| Org. | Model | MASE official | MASE FF | \|Δ\| (%) | σ | CRPS official | CRPS FF | \|Δ\| (%) | σ | GPU-h | USD | -|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| -| Amazon | Chronos-2 | 0.698 | 0.704 | 0.9 | 0.023 | 0.485 | 0.483 | 0.6 | 0.046 | 1.62 | 1.78 | -| | Chronos-2 small | — | 0.724 | — | — | — | 0.496 | — | — | 0.95 | 1.04 | -| | Chronos Bolt base | 0.808 | 0.813 | 0.7 | 0.022 | 0.574 | 0.566 | 1.4 | 0.032 | 4.66 | 5.13 | -| | Chronos Bolt small | 0.822 | 0.829 | 0.8 | 0.023 | 0.577 | 0.566 | 1.8 | 0.032 | 1.69 | 1.86 | -| Datadog | Toto 2 313M | 0.703 | 0.705 | 0.3 | 0.021 | 0.481 | 0.485 | 0.7 | 0.019 | 1.68 | 1.84 | -| | Toto 2 4M | 0.757 | 0.761 | 0.6 | 0.017 | 0.524 | 0.531 | 1.3 | 0.028 | 0.55 | 0.61 | -| Google | TimesFM 3 | 0.667 | 0.675 | 1.2 | 0.021 | 0.456 | 0.462 | 1.3 | 0.015 | 6.73 | 7.41 | -| Huawei | Tafsut base | 0.693 | 0.694 | 0.2 | 0.003 | 0.481 | 0.482 | 0.2 | 0.006 | 1.42 | 1.56 | -| IBM | PatchTST-FM r2 | 0.685 | 0.684 | 0.1 | 0.028 | 0.467 | 0.467 | 0.0 | 0.030 | 3.31 | 3.64 | -| | PatchTST-FM r1 | 0.717 | 0.716 | 0.2 | 0.006 | 0.488 | 0.487 | 0.1 | 0.007 | 1.25 | 1.37 | -| NX-AI | TiRex 2 | 0.697 | 0.704 | 1.0 | 0.015 | 0.478 | 0.485 | 1.6 | 0.025 | 1.29 | 1.42 | -| | TiRex 1.1 | 0.716 | 0.724 | 1.1 | 0.040 | 0.488 | 0.494 | 1.1 | 0.043 | 1.57 | 1.73 | -| Salesforce | Moirai 2 small | 0.728 | 0.736 | 1.1 | 0.056 | 0.516 | 0.521 | 0.8 | 0.038 | 2.65 | 2.92 | -| | Moirai 1.1 large | 0.875 | 0.886 | 1.2 | 0.163 | 0.599 | 0.602 | 0.5 | 0.150 | 1.98 | 2.18 | -| TFC | T0 beta | 0.687 | 0.689 | 0.3 | 0.010 | 0.474 | 0.475 | 0.4 | 0.014 | 1.41 | 1.55 | -| | T0 alpha | 0.724 | 0.729 | 0.7 | 0.018 | 0.494 | 0.495 | 0.3 | 0.017 | 0.94 | 1.03 | -| **Total** (16 models) | | | | | | | | | | **33.7** | **37.07** | - -
-LaTeX source (needs booktabs, amsmath, threeparttable) - -```latex -\begin{table}[t] -\centering -\small -\setlength{\tabcolsep}{4pt} -\begin{threeparttable} -\begin{tabular}{l l rrrr rrrr rr} -\toprule - & & \multicolumn{4}{c}{MASE$^\dagger$} & \multicolumn{4}{c}{CRPS$^\dagger$} & \multicolumn{2}{c}{Compute} \\ -\cmidrule(lr){3-6} \cmidrule(lr){7-10} \cmidrule(lr){11-12} -Org. & Model & Official & FF & $|\Delta|$ (\%) & $\sigma$ & Official & FF & $|\Delta|$ (\%) & $\sigma$ & GPU-h & USD \\ -\midrule -Amazon & Chronos-2 & 0.698 & 0.704 & 0.9 & 0.023 & 0.485 & 0.483 & 0.6 & 0.046 & 1.62 & 1.78 \\ - & Chronos-2 small & -- & 0.724 & -- & -- & -- & 0.496 & -- & -- & 0.95 & 1.04 \\ - & Chronos Bolt base & 0.808 & 0.813 & 0.7 & 0.022 & 0.574 & 0.566 & 1.4 & 0.032 & 4.66 & 5.13 \\ - & Chronos Bolt small & 0.822 & 0.829 & 0.8 & 0.023 & 0.577 & 0.566 & 1.8 & 0.032 & 1.69 & 1.86 \\ -\addlinespace[2pt] -Datadog & Toto 2 313M & 0.703 & 0.705 & 0.3 & 0.021 & 0.481 & 0.485 & 0.7 & 0.019 & 1.68 & 1.84 \\ - & Toto 2 4M & 0.757 & 0.761 & 0.6 & 0.017 & 0.524 & 0.531 & 1.3 & 0.028 & 0.55 & 0.61 \\ -\addlinespace[2pt] -Google & TimesFM 3 & 0.667 & 0.675 & 1.2 & 0.021 & 0.456 & 0.462 & 1.3 & 0.015 & 6.73 & 7.41 \\ -\addlinespace[2pt] -Huawei & Tafsut base & 0.693 & 0.694 & 0.2 & 0.003 & 0.481 & 0.482 & 0.2 & 0.006 & 1.42 & 1.56 \\ -\addlinespace[2pt] -IBM & PatchTST-FM r2 & 0.685 & 0.684 & 0.1 & 0.028 & 0.467 & 0.467 & 0.0 & 0.030 & 3.31 & 3.64 \\ - & PatchTST-FM r1 & 0.717 & 0.716 & 0.2 & 0.006 & 0.488 & 0.487 & 0.1 & 0.007 & 1.25 & 1.37 \\ -\addlinespace[2pt] -NX-AI & TiRex 2 & 0.697 & 0.704 & 1.0 & 0.015 & 0.478 & 0.485 & 1.6 & 0.025 & 1.29 & 1.42 \\ - & TiRex 1.1 & 0.716 & 0.724 & 1.1 & 0.040 & 0.488 & 0.494 & 1.1 & 0.043 & 1.57 & 1.73 \\ -\addlinespace[2pt] -Salesforce & Moirai 2 small & 0.728 & 0.736 & 1.1 & 0.056 & 0.516 & 0.521 & 0.8 & 0.038 & 2.65 & 2.92 \\ - & Moirai 1.1 large & 0.875 & 0.886 & 1.2 & 0.163 & 0.599 & 0.602 & 0.5 & 0.150 & 1.98 & 2.18 \\ -\addlinespace[2pt] -TFC & T0 beta & 0.687 & 0.689 & 0.3 & 0.010 & 0.474 & 0.475 & 0.4 & 0.014 & 1.41 & 1.55 \\ - & T0 alpha & 0.724 & 0.729 & 0.7 & 0.018 & 0.494 & 0.495 & 0.3 & 0.017 & 0.94 & 1.03 \\ -\midrule -\multicolumn{10}{l}{Total (16 models, 97 dataset configs each)} & 33.7 & 37.07 \\ -\bottomrule -\end{tabular} -\begin{tablenotes}[flushleft] -\footnotesize -\item Replication of the GIFT-Eval leaderboard with FoundationForecast (FF), run \texttt{2026-10-06-nb-v2}. $^\dagger$Leaderboard aggregates: geometric mean over the 97 dataset configurations of the metric normalized by Seasonal Naive (lower is better). \emph{Official} is recomputed from the model's submitted CSV in the GIFT-Eval Hugging Face space; \emph{FF} is the same aggregate from our FoundationForecast run. $|\Delta|$ is the absolute relative difference between the two aggregates; every model is within 2\%. $\sigma$ is the standard deviation across the 97 configurations of the per-configuration difference $(\text{FF}-\text{official})$ on the same Seasonal-Naive-normalized scale. Compute is wall time on a single NVIDIA A10G (Modal) at \$1.10/GPU-h. Chronos-2 small has no official submission yet. -\end{tablenotes} -\end{threeparttable} -\label{tab:gift-eval-replication} -\end{table} -``` - -
- -Local single job with replication registry: - -```bash -uv run python -m src.runners.run_model --replication \ - --model-key google--timesfm-3.0-pytorch \ - --dataset-name m4_weekly --term short \ - --output-root ./results/replication/local-test -``` - -## Full benchmark grid (Modal) - -One GPU job per `(model_key, dataset, term)`: +cd experiments/gift-eval +uv sync +make download-gift-eval-data -```bash -uv run modal run -m src.runners.run_modal::main +# 16 models × 97 configs +make run-replication-full RUN_ID=2026-10-06-nb-v2 +make verify-replication-full RUN_ID=2026-10-06-nb-v2 ``` -## Verify against HF references - -Compare local/S3 results to official GIFT-Eval CSVs. Uses consolidated -`results/{model_key}/all_results.csv` if present, otherwise aggregates -per-job CSVs under `results/{model_key}/`. - -Strict replication asserts **MASE** and **CRPS** only (the GIFT-Eval ranking -metrics), with default tolerances `atol=0.01`, `rtol=0.025`. Other columns in -`all_results.csv` are still written but not compared. +Params follow the [official GIFT-Eval notebooks](https://github.com/SalesforceAIResearch/gift-eval/tree/main/notebooks). Results land in `s3://foundationforecast-gift-eval/results/replication//`. The full grid skips jobs that already have outputs unless `FORCE=1`. Long runs: `DETACHED=1`. Parallel GPUs: `MAX_CONTAINERS=20` (default). -Every verify run also writes a replication analysis table (CSV) with: +On every merge to `main`, [CI](https://github.com/TimeCopilot/foundationforecast/actions/workflows/ci.yaml) re-runs a 13-job subset (`make run-ci` / `make verify-ci`) with **strict per-job** MASE/CRPS against the Hugging Face references. -| Column | Description | -|--------|-------------| -| `dataset` | GIFT-Eval dataset config (e.g. `m4_weekly/W/short`) | -| `model` | Model alias in results CSV | -| `model_key` | Experiment registry key | -| `time_seconds` | Eval wall time (from per-job `timing.json`) | -| `mase` | Our `eval_metrics/MASE[0.5]` | -| `crps` | Our `eval_metrics/mean_weighted_sum_quantile_loss` | -| `reported_gift_eval_mase` | Official HF reference MASE | -| `reported_gift_eval_crps` | Official HF reference CRPS | -| `mase_diff` | `mase - reported_gift_eval_mase` | -| `crps_diff` | `crps - reported_gift_eval_crps` | +### Infrastructure -```bash -# CI subset (per-job layout under results/ci/) -uv run python -m src.runners.run_verify --ci +| Need | What | +| --- | --- | +| Python | 3.11+ (`uv sync` from this directory; editable install of repo-root `foundationforecast`) | +| GPU jobs | [Modal](https://modal.com) (`MODAL_TOKEN_ID`, `MODAL_TOKEN_SECRET`) | +| Object store | S3 bucket `foundationforecast-gift-eval` (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`) | +| Weights | `HF_TOKEN` (gated models such as `t0-alpha`); accept Hub licenses | +| Optional | `DETACHED=1`, `FORCE=1`, `MAX_CONTAINERS=N` | -# One model -uv run python -m src.runners.run_verify --model-key amazon--chronos-bolt-small +Modal secrets: `aws-secret`, `hf-secret` (`modal secret create hf-secret HF_TOKEN=hf_...`). -# All models with a reference_slug in configs/models.yaml -make sync-results # or: aws s3 sync s3://foundationforecast-gift-eval/results ./results -uv run python -m src.runners.run_verify --all +## Conclusion -# Table only (no strict assert) — good for exploratory analysis -uv run python -m src.runners.run_verify --all --verify-only \ - --table-output ./results/replication_table.csv -make replication-table +GIFT-Eval already tells us **which models are accurate**. Running them through one API, on one GPU SKU, adds the missing axis: **what that accuracy costs**. The replication shows the leaderboard numbers are recoverable (aggregates within 2%). The Pareto plot shows they are not interchangeable: the most accurate model in this run is also one of the more expensive, and several cheaper models sit close enough on CRPS to matter when the budget is the constraint. -# Require every HF dataset to be present (not just compare overlap) -uv run python -m src.runners.run_verify --all --require-complete -``` +If you are choosing a TSFM for production, measure **both**. FoundationForecast is the API we used to make that measurement reproducible. -Or in one step: +## Acknowledgements -```bash -make verify-all -``` +Thanks to the teams who trained and released the models in this grid: Amazon (Chronos / Chronos-2 / Chronos Bolt), Datadog (Toto 2), Google (TimesFM 3), Huawei (Tafsut), IBM (PatchTST-FM), NX-AI (TiRex), Salesforce (Moirai), and The Forecasting Company (T0). Thanks also to Salesforce AI Research for GIFT-Eval — the datasets, notebooks, and public leaderboard this experiment replicates. -**Note:** `time_seconds` is recorded when a job runs via `run_gift_eval` (writes -`timing.json` next to each `all_results.csv`). To backfill timing for jobs that -ran before timing was added: +## References -```bash -# Full grid: rerun only jobs with results but no timing.json on S3 -uv run modal run -m src.runners.run_modal::run_missing_timing - -# Full grid: force rerun everything (also refreshes metrics) -uv run modal run -m src.runners.run_modal::main --force +- Aksu, T., Woo, G., Liu, J., Liu, X., Liu, C., Savarese, S., Xiong, C., & Sahoo, D. (2024). *GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation*. [arXiv:2410.10393](https://arxiv.org/abs/2410.10393). +- Ansari, A. F., et al. (2024). *Chronos: Learning the Language of Time Series*. [arXiv:2403.07815](https://arxiv.org/abs/2403.07815). +- Das, A., Kong, W., Sen, R., & Zhou, Y. (2023). *A decoder-only foundation model for time-series forecasting* (TimesFM). [arXiv:2310.10688](https://arxiv.org/abs/2310.10688). +- Woo, G., Liu, C., Kumar, A., Xiong, C., Savarese, S., & Sahoo, D. (2024). *Unified Training of Universal Time Series Forecasting Transformers* (Moirai). [arXiv:2402.02592](https://arxiv.org/abs/2402.02592). +- Cohen, B., et al. (2025). *This Time is Different: An Observability Perspective on Time Series Foundation Models* (Toto). [arXiv:2505.14766](https://arxiv.org/abs/2505.14766). +- Auer, A., et al. (2025). *TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning*. [arXiv:2505.23719](https://arxiv.org/abs/2505.23719). +- Auer, A., et al. (2026). *TiRex-2*. [arXiv:2607.01204](https://arxiv.org/abs/2607.01204). +- IBM. *PatchTST-FM*. [arXiv:2602.06909](https://arxiv.org/abs/2602.06909). +- The Forecasting Company. [T0 alpha](https://huggingface.co/theforecastingcompany/t0-alpha) / [T0 beta](https://huggingface.co/theforecastingcompany/t0-beta). +- Tafsut-FM. [Tafsut univariate base](https://huggingface.co/Tafsut-FM/tafsut-univariate-base). +- Garza, A., & Rosillo, R. (2026). *FoundationForecast: The API for time series foundation models*. . -# CI subset locally -uv run python -m src.runners.run_ci --local --missing-timing-only +## How to cite -# CI on Modal always reruns with force=True (timing included every CI run) -uv run modal run -m src.runners.run_modal::run_ci +```bibtex +@software{foundationforecast-gift-eval, + title = {FoundationForecast replicates Salesforce's GIFT-Eval for \$37}, + author = {Garza, Azul and Rosillo, Ren{\'e}e}, + year = {2026}, + url = {https://github.com/TimeCopilot/foundationforecast/tree/main/experiments/gift-eval}, + note = {Replication of 16 time series foundation models on GIFT-Eval under a single API}, +} ``` -Then sync and rebuild the table: - -```bash -make sync-results -make replication-table -``` - -## Consolidate S3 results - -```bash -uv run python -m src.runners.download_results --model-key amazon--chronos-bolt-small +```bibtex +@software{foundationforecast, + title = {FoundationForecast: The API for time series foundation models}, + author = {Garza, Azul and Rosillo, Ren{\'e}e}, + year = {2026}, + url = {https://github.com/TimeCopilot/foundationforecast}, + license = {Apache-2.0}, +} ``` - -## Infrastructure - -- **S3 bucket:** `foundationforecast-gift-eval` -- **Modal secrets:** - - `aws-secret` — `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY` - - `hf-secret` — `HF_TOKEN` (required for gated models like `t0-alpha`; create with - `modal secret create hf-secret HF_TOKEN=hf_...`) -- **Modal tokens:** `MODAL_TOKEN_ID`, `MODAL_TOKEN_SECRET` -- **Hugging Face:** accept model licenses on the Hub, then set `HF_TOKEN` in `hf-secret` - -## Adding a model - -1. Add an entry to `configs/models.yaml` with slugified `repo_id` as `model_key` - (`org--model`), `class`, `kwargs.repo_id`, and `reference_slug`. -2. Set `kwargs.repo_id` from the official GIFT-Eval - `results/{reference_slug}/config.json` → `model_link` (many models use - gifteval-specific HF repos, not the default public checkpoint). -3. Set `reference_slug` to the official GIFT-Eval folder name; `alias` defaults from - that in `build_model()` — set `kwargs.alias` explicitly when the CSV `model` column - differs from the folder slug (e.g. `chronos_base` → `Chronos_base`). -4. 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+LaTeX source (needs booktabs, amsmath, threeparttable, xcolor with the table option) + +```latex +\begin{table}[t] +\centering +\small +\setlength{\tabcolsep}{4pt} +\definecolor{rank1}{HTML}{A5D6A7} +\definecolor{rank2}{HTML}{C8E6C9} +\definecolor{rank3}{HTML}{E8F5E9} +\begin{threeparttable} +\begin{tabular}{l l rrrr rrrr rr} +\toprule + & & \multicolumn{4}{c}{MASE$^\dagger$} & \multicolumn{4}{c}{CRPS$^\dagger$} & \multicolumn{2}{c}{Compute} \\ +\cmidrule(lr){3-6} \cmidrule(lr){7-10} \cmidrule(lr){11-12} +Org. & Model & Official & FF & $|\Delta|$ (\%) & $\sigma$ & Official & FF & $|\Delta|$ (\%) & $\sigma$ & GPU-h & USD \\ +\midrule +Amazon & Chronos-2 & 0.698 & 0.704 & 0.9 & 0.023 & 0.485 & 0.483 & 0.6 & 0.046 & 1.62 & 1.78 \\ + & Chronos-2 small & -- & 0.724 & -- & -- & -- & 0.496 & -- & -- & 0.95 & 1.04 \\ + & Chronos Bolt base & 0.808 & 0.813 & 0.7 & 0.022 & 0.574 & 0.566 & 1.4 & 0.032 & 4.66 & 5.13 \\ + & Chronos Bolt small & 0.822 & 0.829 & 0.8 & 0.023 & 0.577 & 0.566 & 1.8 & 0.032 & 1.69 & 1.86 \\ +\addlinespace[2pt] +Datadog & Toto 2 313M & 0.703 & 0.705 & 0.3 & 0.021 & 0.481 & 0.485 & 0.7 & 0.019 & 1.68 & 1.84 \\ + & Toto 2 4M & 0.757 & 0.761 & 0.6 & 0.017 & 0.524 & 0.531 & 1.3 & 0.028 & 0.55 & 0.61 \\ +\addlinespace[2pt] +Google & TimesFM 3 & 0.667 & 0.675 & 1.2 & 0.021 & \cellcolor{rank1}\textbf{0.456} & \cellcolor{rank1}\textbf{0.462} & 1.3 & 0.015 & 6.73 & 7.41 \\ +\addlinespace[2pt] +Huawei & Tafsut base & 0.693 & 0.694 & 0.2 & 0.003 & 0.481 & 0.482 & 0.2 & 0.006 & 1.42 & 1.56 \\ +\addlinespace[2pt] +IBM & PatchTST-FM r2 & 0.685 & 0.684 & 0.1 & 0.028 & \cellcolor{rank2}0.467 & \cellcolor{rank2}0.467 & 0.0 & 0.030 & 3.31 & 3.64 \\ + & PatchTST-FM r1 & 0.717 & 0.716 & 0.2 & 0.006 & 0.488 & 0.487 & 0.1 & 0.007 & 1.25 & 1.37 \\ +\addlinespace[2pt] +NX-AI & TiRex 2 & 0.697 & 0.704 & 1.0 & 0.015 & 0.478 & 0.485 & 1.6 & 0.025 & 1.29 & 1.42 \\ + & TiRex 1.1 & 0.716 & 0.724 & 1.1 & 0.040 & 0.488 & 0.494 & 1.1 & 0.043 & 1.57 & 1.73 \\ +\addlinespace[2pt] +Salesforce & Moirai 2 small & 0.728 & 0.736 & 1.1 & 0.056 & 0.516 & 0.521 & 0.8 & 0.038 & 2.65 & 2.92 \\ + & Moirai 1.1 large & 0.875 & 0.886 & 1.2 & 0.163 & 0.599 & 0.602 & 0.5 & 0.150 & 1.98 & 2.18 \\ +\addlinespace[2pt] +TFC & T0 beta & 0.687 & 0.689 & 0.3 & 0.010 & \cellcolor{rank3}0.474 & \cellcolor{rank3}0.475 & 0.4 & 0.014 & 1.41 & 1.55 \\ + & T0 alpha & 0.724 & 0.729 & 0.7 & 0.018 & 0.494 & 0.495 & 0.3 & 0.017 & 0.94 & 1.03 \\ +\midrule +\multicolumn{10}{l}{Total (16 models, 97 dataset configs each)} & 33.7 & 37.07 \\ +\bottomrule +\end{tabular} +\begin{tablenotes}[flushleft] +\footnotesize +\item Replication of the GIFT-Eval leaderboard with FoundationForecast (FF). $^\dagger$Leaderboard aggregates: geometric mean over the 97 dataset configurations of the metric normalized by Seasonal Naive (lower is better). \emph{Official} is recomputed from the model's submitted CSV in the GIFT-Eval Hugging Face space; \emph{FF} is the same aggregate from our FoundationForecast run. $|\Delta|$ is the absolute relative difference between the two aggregates; every model is within 2\%. $\sigma$ is the standard deviation across the 97 configurations of the per-configuration difference $(\text{FF}-\text{official})$ on the same Seasonal-Naive-normalized scale. Shaded CRPS cells mark the three best models in each column (darker is better; bold is the best). Compute is wall time on a single NVIDIA A10G at \$1.10/GPU-h. Chronos-2 small has no official submission yet. +\end{tablenotes} +\end{threeparttable} +\label{tab:gift-eval-replication} +\end{table} +``` + +
+ ### Pareto frontier Accuracy alone does not decide which model to deploy. The figure below plots **cost vs accuracy** for the same 16 models: cheaper is to the right, better (lower error) is up. The purple polyline is the **Pareto frontier**. TimesFM 3 is the most accurate model we ran, at **$7.41**; Toto 2 4M is the cheapest plotted point, at **$0.61**. The full grid is **$37 / 34 GPU-hours**. From 99f0bc2feff3b0965a5b681ddb4c3586b0a55ba0 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Thu, 8 Oct 2026 08:43:55 -0700 Subject: [PATCH 19/24] docs(gift-eval): show the replication table as a typeset LaTeX figure Replace the HTML-styled PNG and the collapsible source with a rendered booktabs table hosted on GitHub attachments. Co-authored-by: Cursor --- experiments/gift-eval/README.md | 60 ++------------------------------- 1 file changed, 2 insertions(+), 58 deletions(-) diff --git a/experiments/gift-eval/README.md b/experiments/gift-eval/README.md index f80b5cd..0228f17 100644 --- a/experiments/gift-eval/README.md +++ b/experiments/gift-eval/README.md @@ -24,65 +24,9 @@ Every FoundationForecast implementation reproduces the model's aggregated MASE a ### Replication -The table below compares **official** GIFT-Eval aggregates with **FF** (the same aggregate from our FoundationForecast run). Organizations are grouped alphabetically. Gold / silver / bronze mark the **1st / 2nd / 3rd best CRPS** in the Official and FF columns — the ranking is the same on both sides: **TimesFM 3**, **PatchTST-FM r2**, **T0 beta**. - -GIFT-Eval replication table: official vs FoundationForecast MASE and CRPS, with gold/silver/bronze on the top-3 CRPS models - -
-LaTeX source (needs booktabs, amsmath, threeparttable, xcolor with the table option) - -```latex -\begin{table}[t] -\centering -\small -\setlength{\tabcolsep}{4pt} -\definecolor{rank1}{HTML}{A5D6A7} -\definecolor{rank2}{HTML}{C8E6C9} -\definecolor{rank3}{HTML}{E8F5E9} -\begin{threeparttable} -\begin{tabular}{l l rrrr rrrr rr} -\toprule - & & \multicolumn{4}{c}{MASE$^\dagger$} & \multicolumn{4}{c}{CRPS$^\dagger$} & \multicolumn{2}{c}{Compute} \\ -\cmidrule(lr){3-6} \cmidrule(lr){7-10} \cmidrule(lr){11-12} -Org. & Model & Official & FF & $|\Delta|$ (\%) & $\sigma$ & Official & FF & $|\Delta|$ (\%) & $\sigma$ & GPU-h & USD \\ -\midrule -Amazon & Chronos-2 & 0.698 & 0.704 & 0.9 & 0.023 & 0.485 & 0.483 & 0.6 & 0.046 & 1.62 & 1.78 \\ - & Chronos-2 small & -- & 0.724 & -- & -- & -- & 0.496 & -- & -- & 0.95 & 1.04 \\ - & Chronos Bolt base & 0.808 & 0.813 & 0.7 & 0.022 & 0.574 & 0.566 & 1.4 & 0.032 & 4.66 & 5.13 \\ - & Chronos Bolt small & 0.822 & 0.829 & 0.8 & 0.023 & 0.577 & 0.566 & 1.8 & 0.032 & 1.69 & 1.86 \\ -\addlinespace[2pt] -Datadog & Toto 2 313M & 0.703 & 0.705 & 0.3 & 0.021 & 0.481 & 0.485 & 0.7 & 0.019 & 1.68 & 1.84 \\ - & Toto 2 4M & 0.757 & 0.761 & 0.6 & 0.017 & 0.524 & 0.531 & 1.3 & 0.028 & 0.55 & 0.61 \\ -\addlinespace[2pt] -Google & TimesFM 3 & 0.667 & 0.675 & 1.2 & 0.021 & \cellcolor{rank1}\textbf{0.456} & \cellcolor{rank1}\textbf{0.462} & 1.3 & 0.015 & 6.73 & 7.41 \\ -\addlinespace[2pt] -Huawei & Tafsut base & 0.693 & 0.694 & 0.2 & 0.003 & 0.481 & 0.482 & 0.2 & 0.006 & 1.42 & 1.56 \\ -\addlinespace[2pt] -IBM & PatchTST-FM r2 & 0.685 & 0.684 & 0.1 & 0.028 & \cellcolor{rank2}0.467 & \cellcolor{rank2}0.467 & 0.0 & 0.030 & 3.31 & 3.64 \\ - & PatchTST-FM r1 & 0.717 & 0.716 & 0.2 & 0.006 & 0.488 & 0.487 & 0.1 & 0.007 & 1.25 & 1.37 \\ -\addlinespace[2pt] -NX-AI & TiRex 2 & 0.697 & 0.704 & 1.0 & 0.015 & 0.478 & 0.485 & 1.6 & 0.025 & 1.29 & 1.42 \\ - & TiRex 1.1 & 0.716 & 0.724 & 1.1 & 0.040 & 0.488 & 0.494 & 1.1 & 0.043 & 1.57 & 1.73 \\ -\addlinespace[2pt] -Salesforce & Moirai 2 small & 0.728 & 0.736 & 1.1 & 0.056 & 0.516 & 0.521 & 0.8 & 0.038 & 2.65 & 2.92 \\ - & Moirai 1.1 large & 0.875 & 0.886 & 1.2 & 0.163 & 0.599 & 0.602 & 0.5 & 0.150 & 1.98 & 2.18 \\ -\addlinespace[2pt] -TFC & T0 beta & 0.687 & 0.689 & 0.3 & 0.010 & \cellcolor{rank3}0.474 & \cellcolor{rank3}0.475 & 0.4 & 0.014 & 1.41 & 1.55 \\ - & T0 alpha & 0.724 & 0.729 & 0.7 & 0.018 & 0.494 & 0.495 & 0.3 & 0.017 & 0.94 & 1.03 \\ -\midrule -\multicolumn{10}{l}{Total (16 models, 97 dataset configs each)} & 33.7 & 37.07 \\ -\bottomrule -\end{tabular} -\begin{tablenotes}[flushleft] -\footnotesize -\item Replication of the GIFT-Eval leaderboard with FoundationForecast (FF). $^\dagger$Leaderboard aggregates: geometric mean over the 97 dataset configurations of the metric normalized by Seasonal Naive (lower is better). \emph{Official} is recomputed from the model's submitted CSV in the GIFT-Eval Hugging Face space; \emph{FF} is the same aggregate from our FoundationForecast run. $|\Delta|$ is the absolute relative difference between the two aggregates; every model is within 2\%. $\sigma$ is the standard deviation across the 97 configurations of the per-configuration difference $(\text{FF}-\text{official})$ on the same Seasonal-Naive-normalized scale. Shaded CRPS cells mark the three best models in each column (darker is better; bold is the best). Compute is wall time on a single NVIDIA A10G at \$1.10/GPU-h. Chronos-2 small has no official submission yet. -\end{tablenotes} -\end{threeparttable} -\label{tab:gift-eval-replication} -\end{table} -``` +The table below compares **official** GIFT-Eval aggregates with **FF** (the same aggregate from our FoundationForecast run). Organizations are grouped alphabetically. Shaded CRPS cells mark the **1st / 2nd / 3rd best** models in each column (darker is better); the ranking is the same on both sides: **TimesFM 3**, **PatchTST-FM r2**, **T0 beta**. -
+GIFT-Eval replication table typeset in LaTeX: official vs FoundationForecast MASE and CRPS, with the top-3 CRPS models shaded ### Pareto frontier From e7cf4a48d82bd51338dab0a8a038a02a7f59db89 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Thu, 8 Oct 2026 08:45:16 -0700 Subject: [PATCH 20/24] docs(gift-eval): shade top-3 MASE as well as CRPS in the replication table TimesFM 3, PatchTST-FM r2, and T0 beta are 1st/2nd/3rd on both metrics for official and FF aggregates. Co-authored-by: Cursor --- experiments/gift-eval/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/experiments/gift-eval/README.md b/experiments/gift-eval/README.md index 0228f17..abdda52 100644 --- a/experiments/gift-eval/README.md +++ b/experiments/gift-eval/README.md @@ -24,9 +24,9 @@ Every FoundationForecast implementation reproduces the model's aggregated MASE a ### Replication -The table below compares **official** GIFT-Eval aggregates with **FF** (the same aggregate from our FoundationForecast run). Organizations are grouped alphabetically. Shaded CRPS cells mark the **1st / 2nd / 3rd best** models in each column (darker is better); the ranking is the same on both sides: **TimesFM 3**, **PatchTST-FM r2**, **T0 beta**. +The table below compares **official** GIFT-Eval aggregates with **FF** (the same aggregate from our FoundationForecast run). Organizations are grouped alphabetically. Shaded MASE and CRPS cells mark the **1st / 2nd / 3rd best** models in each column (darker is better); the ranking is the same on both sides and for both metrics: **TimesFM 3**, **PatchTST-FM r2**, **T0 beta**. -GIFT-Eval replication table typeset in LaTeX: official vs FoundationForecast MASE and CRPS, with the top-3 CRPS models shaded +GIFT-Eval replication table typeset in LaTeX: official vs FoundationForecast MASE and CRPS, with the top-3 models shaded in both metrics ### Pareto frontier From 6a6714c3c3c2f343450761f2f01a3237c0cc2ae1 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Thu, 8 Oct 2026 08:46:41 -0700 Subject: [PATCH 21/24] docs(gift-eval): bold the replication table totals row Co-authored-by: Cursor --- experiments/gift-eval/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/experiments/gift-eval/README.md b/experiments/gift-eval/README.md index abdda52..dc2fcf4 100644 --- a/experiments/gift-eval/README.md +++ b/experiments/gift-eval/README.md @@ -26,7 +26,7 @@ Every FoundationForecast implementation reproduces the model's aggregated MASE a The table below compares **official** GIFT-Eval aggregates with **FF** (the same aggregate from our FoundationForecast run). Organizations are grouped alphabetically. Shaded MASE and CRPS cells mark the **1st / 2nd / 3rd best** models in each column (darker is better); the ranking is the same on both sides and for both metrics: **TimesFM 3**, **PatchTST-FM r2**, **T0 beta**. -GIFT-Eval replication table typeset in LaTeX: official vs FoundationForecast MASE and CRPS, with the top-3 models shaded in both metrics +GIFT-Eval replication table typeset in LaTeX: official vs FoundationForecast MASE and CRPS, with the top-3 models shaded in both metrics ### Pareto frontier From 640dbf3b733deeb44dd6578f233d7e4e52b0897e Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Thu, 8 Oct 2026 08:50:28 -0700 Subject: [PATCH 22/24] docs(gift-eval): use a stacked Pareto figure so it stays readable on phones The landscape artboard shrank to unreadably small type in the GitHub mobile README; the stacked 720px figure fills the column instead. Co-authored-by: Cursor --- experiments/gift-eval/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/experiments/gift-eval/README.md b/experiments/gift-eval/README.md index dc2fcf4..afeef95 100644 --- a/experiments/gift-eval/README.md +++ b/experiments/gift-eval/README.md @@ -32,7 +32,7 @@ The table below compares **official** GIFT-Eval aggregates with **FF** (the same Accuracy alone does not decide which model to deploy. The figure below plots **cost vs accuracy** for the same 16 models: cheaper is to the right, better (lower error) is up. The purple polyline is the **Pareto frontier**. TimesFM 3 is the most accurate model we ran, at **$7.41**; Toto 2 4M is the cheapest plotted point, at **$0.61**. The full grid is **$37 / 34 GPU-hours**. -Cost vs accuracy Pareto frontier on 5.9B probabilistic forecast values for 16 foundation models on GIFT-Eval +Cost vs accuracy Pareto frontier on 5.9B probabilistic forecast values for 16 foundation models on GIFT-Eval, MASE and CRPS stacked ## Reproducibility From 45e1861fc845764d160dfaa5d372c7347a66a72a Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Thu, 8 Oct 2026 09:07:12 -0700 Subject: [PATCH 23/24] docs(gift-eval): use the compact landscape Pareto figure in the README Host the 2x log-cost artboard via GitHub attachments instead of the stacked phone crop. Co-authored-by: Cursor --- experiments/gift-eval/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/experiments/gift-eval/README.md b/experiments/gift-eval/README.md index afeef95..284949a 100644 --- a/experiments/gift-eval/README.md +++ b/experiments/gift-eval/README.md @@ -32,7 +32,7 @@ The table below compares **official** GIFT-Eval aggregates with **FF** (the same Accuracy alone does not decide which model to deploy. The figure below plots **cost vs accuracy** for the same 16 models: cheaper is to the right, better (lower error) is up. The purple polyline is the **Pareto frontier**. TimesFM 3 is the most accurate model we ran, at **$7.41**; Toto 2 4M is the cheapest plotted point, at **$0.61**. The full grid is **$37 / 34 GPU-hours**. -Cost vs accuracy Pareto frontier on 5.9B probabilistic forecast values for 16 foundation models on GIFT-Eval, MASE and CRPS stacked +Cost vs accuracy Pareto frontier on 5.9B probabilistic forecast values for 16 foundation models on GIFT-Eval ## Reproducibility From 9b3ca42f87dc5713ac1604d5fcbede88f2961a12 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Thu, 8 Oct 2026 12:07:21 -0700 Subject: [PATCH 24/24] docs(gift-eval): purple FoundationForecast in the Pareto callout Refresh the hosted README figure: Inter unchanged, only the callout brand name uses the frontier color. Co-authored-by: Cursor --- experiments/gift-eval/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/experiments/gift-eval/README.md b/experiments/gift-eval/README.md index 284949a..e651e49 100644 --- a/experiments/gift-eval/README.md +++ b/experiments/gift-eval/README.md @@ -32,7 +32,7 @@ The table below compares **official** GIFT-Eval aggregates with **FF** (the same Accuracy alone does not decide which model to deploy. The figure below plots **cost vs accuracy** for the same 16 models: cheaper is to the right, better (lower error) is up. The purple polyline is the **Pareto frontier**. TimesFM 3 is the most accurate model we ran, at **$7.41**; Toto 2 4M is the cheapest plotted point, at **$0.61**. The full grid is **$37 / 34 GPU-hours**. -Cost vs accuracy Pareto frontier on 5.9B probabilistic forecast values for 16 foundation models on GIFT-Eval +Cost vs accuracy Pareto frontier on 5.9B probabilistic forecast values for 16 foundation models on GIFT-Eval ## Reproducibility

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z{Pu|~;Rxz8pCB6Q(@P(8)TbXWMKl?szWUos|N3(t>N9_P&5xf0cmULwKi=ezZ~pi> zKfd{2-{h~~{QWQc`#1UPZ}8*i{OvdR@kjslP5%G?%!hwDT>qP2_P0mm-~6(_f0KW6 jPJVwx{(4S6wjiE}b3eOD^(7K@8bt&o?`GZ6e*FIcJT47q From b012d74341c00a6cc80522fff8203bca410b7ca3 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Thu, 8 Oct 2026 08:41:51 -0700 Subject: [PATCH 18/24] docs(gift-eval): add collapsible LaTeX source for the replication table Paper-ready tabular (threeparttable) with org grouping, |delta|, FF, and shaded top-3 CRPS cells, under the hosted PNG. Co-authored-by: Cursor --- experiments/gift-eval/README.md | 56 +++++++++++++++++++++++++++++++++ 1 file changed, 56 insertions(+) diff --git a/experiments/gift-eval/README.md b/experiments/gift-eval/README.md index 1c503fd..f80b5cd 100644 --- a/experiments/gift-eval/README.md +++ b/experiments/gift-eval/README.md @@ -28,6 +28,62 @@ The table below compares **official** GIFT-Eval aggregates with **FF** (the same GIFT-Eval replication table: official vs FoundationForecast MASE and CRPS, with gold/silver/bronze on the top-3 CRPS models +

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z{Pu|~;Rxz8pCB6Q(@P(8)TbXWMKl?szWUos|N3(t>N9_P&5xf0cmULwKi=ezZ~pi> zKfd{2-{h~~{QWQc`#1UPZ}8*i{OvdR@kjslP5%G?%!hwDT>qP2_P0mm-~6(_f0KW6 jPJVwx{(4S6wjiE}b3eOD^(7K@8bt&o?`GZ6e*FIcJT47q literal 0 HcmV?d00001 From 997cdf2ec663f48a254fcefc93bd70d7af37c7d8 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Thu, 8 Oct 2026 08:40:27 -0700 Subject: [PATCH 17/24] docs(gift-eval): host README figures on GitHub attachments Link the replication table and Pareto PNG via user-attachments URLs instead of committing binaries to the tree. Co-authored-by: Cursor --- experiments/gift-eval/README.md | 4 ++-- .../gift-eval/assets/pareto-frontier.png | Bin 568404 -> 0 bytes .../gift-eval/assets/replication-table.png | Bin 196547 -> 0 bytes 3 files changed, 2 insertions(+), 2 deletions(-) delete mode 100644 experiments/gift-eval/assets/pareto-frontier.png delete mode 100644 experiments/gift-eval/assets/replication-table.png diff --git a/experiments/gift-eval/README.md b/experiments/gift-eval/README.md index 2b5c386..1c503fd 100644 --- a/experiments/gift-eval/README.md +++ b/experiments/gift-eval/README.md @@ -26,13 +26,13 @@ Every FoundationForecast implementation reproduces the model's aggregated MASE a The table below compares **official** GIFT-Eval aggregates with **FF** (the same aggregate from our FoundationForecast run). Organizations are grouped alphabetically. Gold / silver / bronze mark the **1st / 2nd / 3rd best CRPS** in the Official and FF columns — the ranking is the same on both sides: **TimesFM 3**, **PatchTST-FM r2**, **T0 beta**. -GIFT-Eval replication table: official vs FoundationForecast MASE and CRPS, with gold/silver/bronze on the top-3 CRPS models +GIFT-Eval replication table: official vs FoundationForecast MASE and CRPS, with gold/silver/bronze on the top-3 CRPS models ### Pareto frontier Accuracy alone does not decide which model to deploy. The figure below plots **cost vs accuracy** for the same 16 models: cheaper is to the right, better (lower error) is up. The purple polyline is the **Pareto frontier**. TimesFM 3 is the most accurate model we ran, at **$7.41**; Toto 2 4M is the cheapest plotted point, at **$0.61**. The full grid is **$37 / 34 GPU-hours**. -Cost vs accuracy Pareto frontier on 5.9B probabilistic forecast values for 16 foundation models on GIFT-Eval +Cost vs accuracy Pareto frontier on 5.9B probabilistic forecast values for 16 foundation models on GIFT-Eval ## Reproducibility diff --git a/experiments/gift-eval/assets/pareto-frontier.png b/experiments/gift-eval/assets/pareto-frontier.png deleted file mode 100644 index d7f9aea167120a32270b9664a726da159cce983a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 568404 zcmeFZWl)=K*DlOmqooB}w0Mz1@lxDd+}(q;P+Wq$v=k}s7Oc2K(cqNg4#A2fxVr=i zfqYl*cR#adzrVkq4>Qa_CX)$g*16VUi!e16SzH`)94ss>TzNSu4J@o@<5*aa93S5U z9tnFs`W6f8IhMTC2QANxefYyxqUnWSC$ve2hYmX`JBNuXJI|#a5%*eeCP1+ z5%BGhzvtI@b2saDG&(m2Y4N!B6Ti&6JRIf_=C&La*%GuI4eD?{NPYyo{{Q{@!u^T& z|Ni#(r)Q7B*Cwk$PXFs+$|mpIhTdnE|L>E(y>$G4U+-_#d`mx+2iQ0CdoF2TKIZ@?#$I%la$rq zl}qnAgdg4JcNuNuGnUozoYQ!bEsa}?#RT;YpUx&{ykv5V&jI0%?%w(Sg8uVhYP(?? 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