Feature/profile sfno no changes

.pre-commit-config.yaml

@@ -12,7 +12,7 @@ repos:
       args: [--maxkb=250]
       exclude: |
         (?x)^(
-        fme/ace/aggregator/inference/testdata/.*-regression.pt
+        fme/ace/aggregator/inference/testdata/.*-regression\.pt |
         )$
     - id: trailing-whitespace
     - id: file-contents-sorter

conftest.py

@@ -1,10 +1,23 @@
+import os
+
+os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"  # required for determinism
+
 import gc
 import signal
 from unittest import mock
 
 import pytest
 import torch
 
+from fme.core.rand import set_seed
+
+
+@pytest.fixture(autouse=True, scope="session")
+def deterministic_pytorch():
+    torch.use_deterministic_algorithms(True)
+    torch.backends.cudnn.benchmark = False
+    set_seed(0)
+
 
 def pytest_addoption(parser):
     parser.addoption(

fme/core/__init__.py

@@ -1,3 +1,4 @@
+from . import models as _  # to trigger registrations
 from .atmosphere_data import AtmosphereData
 from .device import get_device, using_gpu
 from .gridded_ops import GriddedOperations
@@ -14,6 +15,8 @@
 from .rand import set_seed
 from .registry import Registry
 
+del _
+
 __all__ = [
     "spherical_area_weights",
     "weighted_mean",

fme/core/benchmark/.gitignore

@@ -0,0 +1 @@
+results

fme/core/benchmark/benchmark.py

@@ -0,0 +1,305 @@
+import abc
+import dataclasses
+import pathlib
+from collections.abc import Callable
+from typing import Self, TypeVar
+
+import dacite
+import matplotlib.pyplot as plt
+import torch
+
+from fme.core.benchmark.memory import MemoryResult, benchmark_memory
+from fme.core.benchmark.timer import CUDATimer, NullTimer, Timer, TimerResult
+from fme.core.typing_ import TensorDict
+
+
+@dataclasses.dataclass
+class BenchmarkResult:
+    memory: MemoryResult
+    timer: TimerResult
+
+    def __repr__(self) -> str:
+        return f"BenchmarkResult(memory={self.memory}, timer={self.timer})"
+
+    def asdict(self) -> dict:
+        return dataclasses.asdict(self)
+
+    @classmethod
+    def from_dict(cls, d: dict) -> "BenchmarkResult":
+        return dacite.from_dict(cls, d, config=dacite.Config(strict=True))
+
+    def assert_close(
+        self, other: "BenchmarkResult", rtol=0.02, children_rtol=0.02
+    ) -> None:
+        try:
+            self.timer.assert_close(other.timer, rtol=rtol, children_rtol=children_rtol)
+        except AssertionError as e:
+            raise AssertionError(f"Timer results differ: {e}") from e
+        try:
+            self.memory.assert_close(other.memory, rtol=rtol)
+        except AssertionError as e:
+            raise AssertionError(f"Memory results differ: {e}") from e
+
+    def to_png(
+        self, path: str | pathlib.Path, label: str, child: str | None = None
+    ) -> None:
+        # note this function was generated with AI
+        def avg_time(t: TimerResult) -> float:
+            return float(t.avg_time)
+
+        def self_time(t: TimerResult) -> float:
+            t_avg = avg_time(t)
+            c_avg = sum(avg_time(c) for c in t.children.values())
+            return max(t_avg - c_avg, 0.0)
+
+        def fmt_time(ms: float) -> str:
+            if ms >= 1000.0:
+                return f"{ms/1000.0:.2f}s"
+            if ms >= 10.0:
+                return f"{ms:.1f}ms"
+            return f"{ms:.2f}ms"
+
+        def label_ok(name: str, ms: float, frac_of_root: float) -> bool:
+            if not name:
+                return False
+            return frac_of_root >= 0.05
+
+        def sorted_children(t: TimerResult) -> list[tuple[str, TimerResult]]:
+            return sorted(
+                t.children.items(), key=lambda kv: avg_time(kv[1]), reverse=True
+            )
+
+        def blend_with_white(
+            rgb: tuple[float, float, float], amount: float
+        ) -> tuple[float, float, float]:
+            # amount in [0,1]: 0 -> original, 1 -> white
+            return (
+                rgb[0] + (1.0 - rgb[0]) * amount,
+                rgb[1] + (1.0 - rgb[1]) * amount,
+                rgb[2] + (1.0 - rgb[2]) * amount,
+            )
+
+        root = self.timer
+        if child is not None:
+            for part in child.split("."):
+                if part not in root.children:
+                    raise ValueError(f"Child '{child}' not found in timer results.")
+                root = root.children[part]
+        root_avg = avg_time(root)
+
+        max_alloc_mb = self.memory.max_alloc / (1024.0 * 1024.0)
+
+        fig = plt.figure(figsize=(8, 6), constrained_layout=True)
+        if root_avg <= 0.0:
+            fig.suptitle(
+                f"Benchmark for {label}\ntotal=0.00s, max_alloc={max_alloc_mb:.1f} MB",
+                fontsize=14,
+            )
+            ax0 = fig.add_subplot(1, 1, 1)
+            ax0.text(0.5, 0.5, "No timing data", ha="center", va="center")
+            ax0.axis("off")
+            fig.savefig(path, dpi=200)
+            plt.close(fig)
+            return
+
+        fig.suptitle(
+            f"Benchmark for {label}\ntotal={fmt_time(root_avg)}, "
+            f"max_alloc={max_alloc_mb:.1f} MB",
+            fontsize=14,
+        )
+
+        ax = fig.add_subplot(1, 1, 1)
+        ax.set_xlim(0, 2)
+        ax.set_ylim(0, root_avg)
+        ax.set_xticks([0.5, 1.5])
+        ax.set_xticklabels(["Level 1", "Level 2"])
+        ax.set_ylabel("Avg time")
+        ax.set_yticks([])
+        ax.spines["top"].set_visible(False)
+        ax.spines["right"].set_visible(False)
+
+        gray = (0.85, 0.85, 0.85, 1.0)
+        cmap = plt.get_cmap("tab20")
+
+        lvl1 = sorted_children(root)
+        lvl1_names = [n for n, _ in lvl1]
+        lvl1_index = {n: i for i, n in enumerate(lvl1_names)}
+
+        # Level 1 stack (root children + root self in gray, unlabeled)
+        lvl1_segments: list[tuple[str, float, tuple[float, float, float, float]]] = []
+        for n1, t1 in lvl1:
+            base = cmap(lvl1_index[n1] % cmap.N)
+            lvl1_segments.append((n1, avg_time(t1), base))
+        r_self = self_time(root)
+        if r_self > 0.0:
+            lvl1_segments.append(("", r_self, gray))
+
+        def draw_stack(
+            x_center: float,
+            segments: list[tuple[str, float, tuple[float, float, float, float]]],
+        ) -> None:
+            width = 0.86
+            y = 0.0
+            for name, sec, color in segments:
+                if sec <= 0.0:
+                    continue
+                ax.bar(
+                    x_center,
+                    sec,
+                    bottom=y,
+                    width=width,
+                    align="center",
+                    color=color,
+                    edgecolor="white",
+                    linewidth=1.0,
+                )
+                frac = sec / root_avg
+                if label_ok(name, sec, frac):
+                    ax.text(
+                        x_center,
+                        y + sec / 2.0,
+                        f"{name}\n{fmt_time(sec)}",
+                        ha="center",
+                        va="center",
+                        fontsize=9,
+                        rotation=0,  # keep horizontal to avoid cross-column overlap
+                        clip_on=True,
+                    )
+                y += sec
+            if y < root_avg:
+                ax.bar(
+                    x_center,
+                    root_avg - y,
+                    bottom=y,
+                    width=width,
+                    align="center",
+                    color=gray,
+                    edgecolor="white",
+                    linewidth=1.0,
+                )
+
+        draw_stack(0.5, lvl1_segments)
+
+        # Level 2 stack:
+        # For each level-1 slice, stack its children
+        # (colored as parent hue variants) + self in gray.
+        lvl2_segments: list[tuple[str, float, tuple[float, float, float, float]]] = []
+        for n1, t1 in lvl1:
+            parent_rgba = cmap(lvl1_index[n1] % cmap.N)
+            parent_rgb = (parent_rgba[0], parent_rgba[1], parent_rgba[2])
+
+            children = sorted_children(t1)
+            k = len(children)
+            for i, (n2, t2) in enumerate(children):
+                # Same “type” of color as parent: lighten progressively per child.
+                # First child is closest to parent; later children are lighter.
+                lighten = 0.10 + (0.55 * (i / max(k - 1, 1)))
+                rgb = blend_with_white(parent_rgb, lighten)
+                lvl2_segments.append((n2, avg_time(t2), (rgb[0], rgb[1], rgb[2], 1.0)))
+
+            s1 = self_time(t1)
+            if s1 > 0.0:
+                lvl2_segments.append(("", s1, gray))
+
+        draw_stack(1.5, lvl2_segments)
+
+        fig.tight_layout(rect=(0.02, 0.02, 0.98, 0.98))
+        fig.savefig(path, dpi=200, bbox_inches="tight")
+        plt.close(fig)
+
+
+T = TypeVar("T")
+
+
+class BenchmarkABC(abc.ABC):
+    @classmethod
+    def new_from_fn(
+        cls,
+        fn: Callable[[Timer], TensorDict],
+    ) -> "BenchmarkABC":
+        class FnBenchmark(BenchmarkABC):
+            @classmethod
+            def new(cls) -> "FnBenchmark":
+                return FnBenchmark()
+
+            def run_instance(self, timer: Timer) -> TensorDict:
+                return fn(timer)
+
+        return FnBenchmark()
+
+    @classmethod
+    @abc.abstractmethod
+    def new(cls: type[Self]) -> Self:
+        """
+        Initialize any state needed for the benchmark.
+        This will be called once before the benchmark is run.
+        """
+        pass
+
+    @classmethod
+    def new_for_regression(cls: type[Self]) -> Self | None:
+        """
+        Initialize any state needed for regression testing.
+        This will be called once before regression tests are run.
+
+        If regression testing is not needed, this can return None,
+        and regression testing will not be run.
+
+        This exists as a separate method from new so that it can
+        use small data sizes more conducive to storing regression targets in git.
+        """
+        return None
+
+    @classmethod
+    def run_benchmark(cls, iters=10, warmup=1) -> BenchmarkResult:
+        if not torch.cuda.is_available():
+            raise RuntimeError("CUDA is not available, cannot run benchmark.")
+        null_timer = NullTimer()
+        benchmark = cls.new()
+        for _ in range(warmup):
+            benchmark.run_instance(null_timer)
+        timer = CUDATimer()
+        with benchmark_memory() as bm:
+            for _ in range(iters):
+                with timer:
+                    benchmark.run_instance(timer)
+        return BenchmarkResult(
+            timer=timer.result,
+            memory=bm.result,
+        )
+
+    @classmethod
+    def run_regression(cls) -> TensorDict | None:
+        benchmark = cls.new_for_regression()
+        if benchmark is None:
+            return None
+        null_timer = NullTimer()
+        return benchmark.run_instance(null_timer)
+
+    @abc.abstractmethod
+    def run_instance(self: Self, timer: Timer) -> TensorDict:
+        """
+        Run the benchmark. This will be called multiple times,
+        and should return a TensorDict of results.
+
+        This must not mutate any state on self, since the same instance may be
+        used across multiple iterations.
+        """
+        pass
+
+
+_BENCHMARKS: dict[str, type[BenchmarkABC]] = {}
+
+
+def register_benchmark(name: str) -> Callable[[type[BenchmarkABC]], type[BenchmarkABC]]:
+    def _register(fn: type[BenchmarkABC]) -> type[BenchmarkABC]:
+        if name in _BENCHMARKS:
+            raise ValueError(f"Benchmark with name '{name}' is already registered.")
+        _BENCHMARKS[name] = fn
+        return fn
+
+    return _register
+
+
+def get_benchmarks() -> dict[str, type[BenchmarkABC]]:
+    return _BENCHMARKS.copy()