perf: try using fori_loop but unrolling everything

.github/workflows/python_package.yaml

@@ -20,8 +20,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10", "3.11", "3.12"]
-        group: [1, 2, 3, 4]
+        python-version: ["3.12"]
 
     steps:
       - uses: actions/checkout@v4
@@ -34,18 +33,15 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          python -m pip install pytest pytest-codspeed pytest-split pytest-randomly
+          python -m pip install pytest pytest-codspeed pytest-randomly
           python -m pip install .
           # temp pin until 0.5 is on conda
           python -m pip install "jax<0.5.0"
 
       - name: Test with pytest
         run: |
           git submodule update --init --recursive
-          pytest -v --durations=0 \
-            -k "not test_fpack" \
-            --randomly-seed=42 \
-            --splits=4 --group=${{ matrix.group }} --splitting-algorithm least_duration
+          pytest -vv --durations=100 --randomly-seed=42
 
   build-status:
     needs: build

.pre-commit-config.yaml

@@ -15,6 +15,6 @@ repos:
     hooks:
       - id: ruff
         args: [ --fix ]
-        exclude: tests/GalSim/|tests/Coord/|tests/jax/galsim/
+        exclude: tests/GalSim/|tests/Coord/|tests/jax/galsim/|dev/notebooks/
       - id: ruff-format
-        exclude: tests/GalSim/|tests/Coord/|tests/jax/galsim/
+        exclude: tests/GalSim/|tests/Coord/|tests/jax/galsim/|dev/notebooks/

dev/notebooks/spergel_fixed_point.ipynb

@@ -0,0 +1,174 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d7b8bc37-8799-433c-9399-de95a21a1727",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import jax\n",
+    "jax.config.update(\"jax_enable_x64\", True)\n",
+    "\n",
+    "import galsim\n",
+    "import numpy as np\n",
+    "\n",
+    "import jax.numpy as jnp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "774101b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from jax_galsim.spergel import (\n",
+    "    fz_nup1, _gammap1, _spergel_hlr_pade,\n",
+    "    fluxfractionFunc, fz_nu, calculateFluxRadius,\n",
+    ")\n",
+    "\n",
+    "@jax.jit\n",
+    "def _calculateFluxRadius_newtons_kernel(i, args):\n",
+    "    \"\"\"Newton's method kernel for calculateFluxRadius\n",
+    "\n",
+    "    Returns\n",
+    "\n",
+    "        lnz - fluxfractionFunc(z, nu, alpha) / dfluxfractionFunc(z, nu, alpha)_dz / z\n",
+    "\n",
+    "    which is Newton's kernel but in log space.\n",
+    "    \"\"\"\n",
+    "    lnz, alpha, nu = args\n",
+    "    z = jnp.exp(lnz)\n",
+    "    dn = (jnp.power(2.0, nu) * _gammap1(nu))\n",
+    "    fz = 1.0 - fz_nup1(z, nu) / dn - alpha\n",
+    "    dfzdz = z * fz_nu(z, nu) / dn\n",
+    "\n",
+    "    # we clip the result to avoid numerical issues near bounds\n",
+    "    lnz = jnp.clip(\n",
+    "        lnz - fz / dfzdz / z,\n",
+    "        min=-100,\n",
+    "        max=100,\n",
+    "    )\n",
+    "\n",
+    "    return lnz, alpha, nu\n",
+    "\n",
+    "\n",
+    "@jax.jit\n",
+    "def calculateFluxRadiusNewton(alpha, nu):\n",
+    "    \"\"\"Return radius R enclosing flux fraction alpha in unit of the scale radius r0\n",
+    "\n",
+    "    Method: Solve  F(R/r0=z)/Flux - alpha = 0 using Netwon's method\n",
+    "\n",
+    "    We can integrate the profile to get\n",
+    "\n",
+    "        F(R)/F =  int( 1/(2^nu Gamma(nu+1)) (r/r0)^(nu+1) K_nu(r/r0) dr/r0; r=0..R) = alpha\n",
+    "\n",
+    "    So if we define z = R/r0 and f(z) = F(z * r0)/F - alpha, then Newton's method is\n",
+    "\n",
+    "        z -> z - f(z) / f'(z)\n",
+    "\n",
+    "    We actually run the method for ln(z) which is\n",
+    "\n",
+    "        ln(z) -> ln(z) - f(z) / f'(z) / z\n",
+    "\n",
+    "    Typical use cases include:\n",
+    "\n",
+    "      - alpha = 1/2 => R = Half-Light-Radius,\n",
+    "      - alpha = 1 - folding-thresold => R used for stepk computation\n",
+    "    \"\"\"\n",
+    "    # seed the iteration with the Pade approximation to the HLR\n",
+    "    # scaled by the fraction of flux to some power\n",
+    "    zalpha = _spergel_hlr_pade(nu) * jnp.sqrt(alpha / 0.5)\n",
+    "    return jnp.exp(jax.lax.fori_loop(\n",
+    "        0, 100,\n",
+    "        _calculateFluxRadius_newtons_kernel,\n",
+    "        (jnp.log(zalpha), alpha, nu),\n",
+    "    )[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1be23e1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "eps, nu, log10(alpha): 1e-12 -0.84 -12.0\n",
+      "3.5138887102897e-38 -2.2121720121483927e-17 1.0587911840678754e-21 1.8761616702453412e-07 1.000534100015216e-12\n",
+      "\n",
+      "eps, nu, log10(alpha): 1e-12 3.999 -12.0\n",
+      "3.9966817649384216e-06 -1.576433954596703e-15 3.999832106175669e-06 3.1094518726606304e-16 9.984622740022494e-13\n",
+      "\n",
+      "eps, nu, log10(alpha): 1e-12 -0.84 -4.3428487456249e-13\n",
+      "25.572845945758726 0.0 25.572509765625 -3.3306690738754696e-16 0.999999999999\n",
+      "\n",
+      "eps, nu, log10(alpha): 1e-12 3.999 -4.3428487456249e-13\n",
+      "38.6677767503012 0.0 38.6676025390625 -1.1102230246251565e-16 0.999999999999\n",
+      "\n",
+      "eps, nu, log10(alpha): 0.1 -0.84 -1.0\n",
+      "0.0008333666650951336 6.38378239159465e-16 0.0008333666650951221 3.0531133177191805e-16 0.10000000000000096\n",
+      "\n",
+      "eps, nu, log10(alpha): 0.1 3.999 -1.0\n",
+      "1.3092245672406861 6.38378239159465e-16 1.3092245672406833 8.326672684688674e-17 0.10000000000000037\n",
+      "\n",
+      "eps, nu, log10(alpha): 0.1 -0.84 -0.045757490560675115\n",
+      "1.2147258941802845 0.0 1.214725894180284 -1.1102230246251565e-16 0.9000000000000001\n",
+      "\n",
+      "eps, nu, log10(alpha): 0.1 3.999 -0.045757490560675115\n",
+      "6.899340112339111 -1.1102230246251565e-16 6.899340112339113 -1.1102230246251565e-16 0.8999999999999999\n"
+     ]
+    }
+   ],
+   "source": [
+    "for eps in [1e-12, 0.1]:\n",
+    "    for alpha in [eps, 1.0 - eps]:\n",
+    "        for nu in [-0.84, 3.999]:\n",
+    "\n",
+    "            print(\"\\neps, nu, log10(alpha):\", eps, nu, np.log10(alpha))\n",
+    "            zfp = calculateFluxRadiusNewton(alpha, nu)\n",
+    "            zbs = calculateFluxRadius(alpha, nu)\n",
+    "            print(\n",
+    "                zfp,\n",
+    "                fluxfractionFunc(zfp, nu, alpha),\n",
+    "                zbs,\n",
+    "                fluxfractionFunc(zbs, nu, alpha),\n",
+    "                galsim.Spergel(nu, scale_radius=1.0).calculateIntegratedFlux(zfp),\n",
+    "            )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29cd9aa2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "jax-galsim",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

jax_galsim/core/utils.py

@@ -254,7 +254,7 @@ def _func(i, args):
     flow = func(low)
     fhigh = func(high)
     args = (func, low, flow, high, fhigh)
-    return jax.lax.fori_loop(0, niter, _func, args)[-2]
+    return jax.lax.fori_loop(0, niter, _func, args, unroll=15)[-2]
 
 
 # start of code from https://github.com/google/jax/blob/main/jax/_src/numpy/util.py #

jax_galsim/fitswcs.py

@@ -1052,10 +1052,47 @@ def _invert_ab_noraise(u, v, ab, abp=None):
     dvdxcoef = (jnp.arange(nab)[:, None] * ab[1])[1:, :-1]
     dvdycoef = (jnp.arange(nab) * ab[1])[:-1, 1:]
 
-    for _ in range(10):
-        x, y, dx, dy = _invert_ab_noraise_loop_body(
-            x, y, u, v, ab, dudxcoef, dudycoef, dvdxcoef, dvdycoef
-        )
+    def _step(i, args):
+        x, y, _, _, u, v, ab, dudxcoef, dudycoef, dvdxcoef, dvdycoef = args
+
+        # Want Jac^-1 . du
+        # du
+        du = horner2d(x, y, ab[0], triangle=True) - u
+        dv = horner2d(x, y, ab[1], triangle=True) - v
+        # J
+        dudx = horner2d(x, y, dudxcoef, triangle=True)
+        dudy = horner2d(x, y, dudycoef, triangle=True)
+        dvdx = horner2d(x, y, dvdxcoef, triangle=True)
+        dvdy = horner2d(x, y, dvdycoef, triangle=True)
+        # J^-1 . du
+        det = dudx * dvdy - dudy * dvdx
+        duu = -(du * dvdy - dv * dudy) / det
+        dvv = -(-du * dvdx + dv * dudx) / det
+
+        x += duu
+        y += dvv
+
+        return x, y, duu, dvv, u, v, ab, dudxcoef, dudycoef, dvdxcoef, dvdycoef
+
+    x, y, dx, dy = jax.lax.fori_loop(
+        0,
+        10,
+        _step,
+        (
+            x,
+            y,
+            jnp.zeros_like(x),
+            jnp.zeros_like(y),
+            u,
+            v,
+            ab,
+            dudxcoef,
+            dudycoef,
+            dvdxcoef,
+            dvdycoef,
+        ),
+        unroll=True,
+    )[0:4]
 
     x, y = jax.lax.cond(
         jnp.maximum(jnp.max(jnp.abs(dx)), jnp.max(jnp.abs(dy))) > 2e-12,

jax_galsim/moffat.py

@@ -61,10 +61,15 @@ def _MoffatCalculateSRFromHLR(re, rm, beta, nitr=100):
     nb2. In GalSim definition rm = 0 (ex. no truncated Moffat) means in reality rm=+Inf.
          BUT the case rm==0 is already done, so HERE rm != 0
     """
-    xcur = re
-    for _ in range(nitr):
-        xcur = _bodymi(xcur, rm, re, beta)
-    return xcur
+
+    # fix loop iteration is faster and reach eps=1e-6 (single precision)
+    def body(i, xcur):
+        x = (1 + jnp.power(1 + (rm / xcur) ** 2, 1 - beta)) / 2
+        x = jnp.power(x, 1 / (1 - beta))
+        x = jnp.sqrt(x - 1)
+        return re / x
+
+    return jax.lax.fori_loop(0, 100, body, re, unroll=True)
 
 
 @implements(_galsim.Moffat)

jax_galsim/spergel.py

@@ -167,7 +167,7 @@ def reducedfluxfractionFunc(z, nu, norm):
 
 
 @jax.jit
-def calculateFluxRadius(alpha, nu, zmin=0.0, zmax=30.0):
+def calculateFluxRadius(alpha, nu, zmin=0.0, zmax=40.0):
     """Return radius R enclosing flux fraction alpha in unit of the scale radius r0
 
     Method: Solve  F(R/r0=z)/Flux - alpha = 0 using bisection algorithm
@@ -186,7 +186,10 @@ def calculateFluxRadius(alpha, nu, zmin=0.0, zmax=30.0):
      nb. it is supposed that nu is in [-0.85, 4.0] checked in the Spergel class init
     """
     return bisect_for_root(
-        partial(fluxfractionFunc, nu=nu, alpha=alpha), zmin, zmax, niter=75
+        partial(fluxfractionFunc, nu=nu, alpha=alpha),
+        zmin,
+        zmax,
+        niter=75,
     )
 
 

tests/jax/test_benchmarks.py

@@ -301,3 +301,17 @@ def test_benchmark_moffat_init(benchmark, kind):
         benchmark, kind, lambda: _run_benchmark_moffat_init().block_until_ready()
     )
     print(f"time: {dt:0.4g} ms", end=" ")
+
+
+def _run_benchmark_spergel_calcfluxrad():
+    return jgs.spergel.calculateFluxRadius(1e-10, 2.0)
+
+
+@pytest.mark.parametrize("kind", ["compile", "run"])
+def test_benchmark_spergel_calcfluxrad(benchmark, kind):
+    dt = _run_benchmarks(
+        benchmark,
+        kind,
+        lambda: _run_benchmark_spergel_calcfluxrad().block_until_ready(),
+    )
+    print(f"time: {dt:0.4g} ms", end=" ")