Merge pull request #18 from desy-ml/pytorch-histogramdd

Replace custom histogramdd() with torch.histogramdd()

Author: jank324

benchmark/cheetah/cheetah.ipynb

@@ -745,7 +745,7 @@ def reading(self):
             image = dist.pdf(pos)
             image = np.flipud(image.T)
         elif isinstance(self.read_beam, ParticleBeam):
-            image, _ = utils.histogramdd(
+            image, _ = torch.histogramdd(
                 torch.stack((self.read_beam.xs, self.read_beam.ys)),
                 bins=self.pixel_bin_edges,
             )

cheetah/accelerator.py

@@ -113,7 +113,8 @@ def ocelot2cheetah(element, warnings=True):
     elif isinstance(element, oc.Monitor) and "BSC" in element.id:
         if warnings:
             print(
-                "WARNING: Diagnostic screen was converted with default screen properties."
+                "WARNING: Diagnostic screen was converted with default screen"
+                " properties."
             )
         return acc.Screen((2448, 2040), (3.5488e-6, 2.5003e-6), name=element.id)
     elif isinstance(element, oc.Monitor) and "BPM" in element.id:
@@ -141,127 +142,3 @@ def subcell_of_ocelot(cell, start, end):
             break
 
     return subcell
-
-
-_range = range
-
-
-def histogramdd(sample, bins=None, range=None, weights=None, remove_overflow=True):
-    """
-    Pytorch version of n-dimensional histogram.
-
-    Taken from https://github.com/miranov25/RootInteractive/blob/b54446e09072e90e17f3da72d5244a20c8fdd209/RootInteractive/Tools/Histograms/histogramdd.py
-    """
-    edges = None
-    device = None
-    custom_edges = False
-    D, N = sample.shape
-    if device == None:
-        device = sample.device
-    if bins == None:
-        if edges == None:
-            bins = 10
-            custom_edges = False
-        else:
-            try:
-                bins = edges.size(1) - 1
-            except AttributeError:
-                bins = torch.empty(D)
-                for i in _range(len(edges)):
-                    bins[i] = edges[i].size(0) - 1
-                bins = bins.to(device)
-            custom_edges = True
-    try:
-        M = bins.size(0)
-        if M != D:
-            raise ValueError(
-                "The dimension of bins must be equal to the dimension of sample x."
-            )
-    except AttributeError:
-        # bins is either an integer or a list
-        if type(bins) == int:
-            bins = torch.full([D], bins, dtype=torch.long, device=device)
-        elif torch.is_tensor(bins[0]):
-            custom_edges = True
-            edges = bins
-            bins = torch.empty(D, dtype=torch.long)
-            for i in _range(len(edges)):
-                bins[i] = edges[i].size(0) - 1
-            bins = bins.to(device)
-        else:
-            bins = torch.as_tensor(bins)
-    if bins.dim() == 2:
-        custom_edges = True
-        edges = bins
-        bins = torch.full([D], bins.size(1) - 1, dtype=torch.long, device=device)
-    if custom_edges:
-        use_old_edges = False
-        if not torch.is_tensor(edges):
-            use_old_edges = True
-            edges_old = edges
-            m = max(i.size(0) for i in edges)
-            tmp = torch.empty([D, m], device=edges[0].device)
-            for i in _range(D):
-                s = edges[i].size(0)
-                tmp[i, :] = edges[i][-1]
-                tmp[i, :s] = edges[i][:]
-            edges = tmp.to(device)
-        k = torch.searchsorted(edges, sample)
-        k = torch.min(k, (bins + 1).reshape(-1, 1))
-        if use_old_edges:
-            edges = edges_old
-        else:
-            edges = torch.unbind(edges)
-    else:
-        if range == None:  # range is not defined
-            range = torch.empty(2, D, device=device)
-            if N == 0:  # Empty histogram
-                range[0, :] = 0
-                range[1, :] = 1
-            else:
-                range[0, :] = torch.min(sample, 1)[0]
-                range[1, :] = torch.max(sample, 1)[0]
-        elif not torch.is_tensor(range):  # range is a tuple
-            r = torch.empty(2, D)
-            for i in _range(D):
-                if range[i] is not None:
-                    r[:, i] = torch.as_tensor(range[i])
-                else:
-                    if N == 0:  # Edge case: empty histogram
-                        r[0, i] = 0
-                        r[1, i] = 1
-                    r[0, i] = torch.min(sample[:, i])[0]
-                    r[1, i] = torch.max(sample[:, i])[0]
-            range = r.to(device=device, dtype=sample.dtype)
-        singular_range = torch.eq(
-            range[0], range[1]
-        )  # If the range consists of only one point, pad it up.
-        range[0, singular_range] -= 0.5
-        range[1, singular_range] += 0.5
-        edges = [
-            torch.linspace(range[0, i], range[1, i], bins[i] + 1)
-            for i in _range(len(bins))
-        ]
-        tranges = torch.empty_like(range)
-        tranges[1, :] = bins / (range[1, :] - range[0, :])
-        tranges[0, :] = 1 - range[0, :] * tranges[1, :]
-        k = torch.addcmul(
-            tranges[0, :].reshape(-1, 1), sample, tranges[1, :].reshape(-1, 1)
-        ).long()  # Get the right index
-        k = torch.max(
-            k, torch.zeros([], device=device, dtype=torch.long)
-        )  # Underflow bin
-        k = torch.min(k, (bins + 1).reshape(-1, 1))
-
-    multiindex = torch.ones_like(bins)
-    multiindex[1:] = torch.cumprod(torch.flip(bins[1:], [0]) + 2, -1).long()
-    multiindex = torch.flip(multiindex, [0])
-    l = torch.sum(k * multiindex.reshape(-1, 1), 0)
-    hist = torch.bincount(
-        l, minlength=(multiindex[0] * (bins[0] + 2)).item(), weights=weights
-    )
-    hist = hist.reshape(tuple(bins + 2))
-    if remove_overflow:
-        core = D * (slice(1, -1),)
-        hist = hist[core]
-    return hist, edges

cheetah/utils.py

@@ -7,7 +7,7 @@
 
 setup(
     name="cheetah-accelerator",
-    version="0.5.16",
+    version="0.5.17",
     author="Jan Kaiser & Oliver Stein",
     author_email="[email protected]",
     url="https://github.com/desy-ml/cheetah",

setup.py

@@ -17,8 +17,9 @@
      "output_type": "stream",
      "text": [
       "[INFO    ] : : \u001b[0mbeam.py: module NUMBA is not installed. Install it to speed up calculation\u001b[0m\n",
-      "[INFO    ] : : \u001b[0mhigh_order.py: module NUMBA is not installed. Install it to speed up calculation\u001b[0m\n",
-      "[INFO    ] : : : : : : : : : : : \u001b[0mradiation_py.py: module NUMBA is not installed. Install it to speed up calculation\u001b[0m\n",
+      "[INFO    ] : : : : : : : : \u001b[0mhigh_order.py: module NUMBA is not installed. Install it to speed up calculation\u001b[0m\n",
+      "[INFO    ] \u001b[0mradiation_py.py: module NUMBA is not installed. Install it to speed up calculation\u001b[0m\n",
+      "[INFO    ] \u001b[0mradiation_py.py: module NUMBA is not installed. Install it to speed up calculation\u001b[0m\n",
       "[INFO    ] \u001b[0mcsr.py: module NUMBA is not installed. Install it to speed up calculation\u001b[0m\n",
       "[INFO    ] \u001b[0mcsr.py: module PYFFTW is not installed. Install it to speed up calculation.\u001b[0m\n",
       "[INFO    ] \u001b[0mcsr.py: module NUMEXPR is not installed. Install it to speed up calculation\u001b[0m\n",
@@ -48,8 +49,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "beam1 = cheetah.ParameterBeam.from_astra(\"../distributions/ACHIP_EA1_2021.1351.001\")\n",
-    "beam2 = cheetah.ParticleBeam.from_astra(\"../distributions/ACHIP_EA1_2021.1351.001\")"
+    "beam1 = cheetah.ParameterBeam.from_astra(\"../benchmark/cheetah/ACHIP_EA1_2021.1351.001\")\n",
+    "beam2 = cheetah.ParticleBeam.from_astra(\"../benchmark/cheetah/ACHIP_EA1_2021.1351.001\")"
    ]
   },
   {
@@ -77,7 +78,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "91.7 µs ± 221 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
+      "86.6 µs ± 816 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
      ]
     }
    ],
@@ -95,7 +96,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "902 µs ± 21.7 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
+      "955 µs ± 15.4 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
      ]
     }
    ],
@@ -127,7 +128,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "z = 42.34949999999999 / 42.34949999999999 : applied:   3.36 ms ± 1.63 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "z = 42.34949999999999 / 42.34949999999999. Applied: 2.82 ms ± 1.33 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -146,7 +147,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.13 ('cheetah-test')",
+   "display_name": "rl39",
    "language": "python",
    "name": "python3"
   },
@@ -160,12 +161,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.9.15"
   },
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "947d9ee3b458d99f0eb80dac10135f6d6a35887bd0ce9fb941e727a4631c373a"
+    "hash": "343fe3b89e2d7877d61a0509fd880204236e5c07449e4c121f53f2530ef83fc9"
    }
   }
  },