Merge branch 'main' into patch-1

.github/workflows/tests.yml

@@ -35,7 +35,7 @@ jobs:
         run: |
           ./scripts/coverage.sh --xml
       - name: Upload coverage reports to Codecov
-        if: ${{ github.event.push && (github.event.push.ref == github.event.repository.default_branch) }}$
+        if: ${{ github.repository == 'april-tools/cirkit' && github.event.push && (github.event.push.ref == github.event.repository.default_branch) }}$
         uses: codecov/codecov-action@v4
         with:
           token: ${{ secrets.CODECOV_TOKEN }}

cirkit/backend/torch/layers/input.py

@@ -296,9 +296,6 @@ def __init__(
                 raise ValueError(f"The number of folds and shape of 'probs' must match the layer's")
         self.probs = probs
         self.logits = logits
-        self.idx_mode = (
-            len(torch.unique(self.scope_idx)) > 4096 or self.num_categories > 256
-        )
 
     def _valid_parameter_shape(self, p: TorchParameter) -> bool:
         if p.num_folds != self.num_folds:
@@ -326,30 +323,17 @@ def params(self) -> Mapping[str, TorchParameter]:
     def log_unnormalized_likelihood(self, x: Tensor) -> Tensor:
         if x.is_floating_point():
             x = x.long()  # The input to Categorical should be discrete
+        # x: (F, C, B, 1) -> (F, C, B)
+        x = x.squeeze(dim=3)
+        # logits: (F, K, C, N)
         logits = torch.log(self.probs()) if self.logits is None else self.logits()
-        if self.idx_mode:
-            if self.num_channels == 1:
-                x = (
-                    logits[:, :, 0, :]
-                    .transpose(1, 2)[range(self.num_folds), x[:, 0, :, 0].t()]
-                    .transpose(0, 1)
-                )
-            else:
-                x = x[..., 0].permute(2, 0, 1)
-                x = (
-                    logits[
-                        torch.arange(self.num_folds).unsqueeze(1),
-                        :,
-                        torch.arange(self.num_channels).unsqueeze(0),
-                        x,
-                    ]
-                    .sum(2)
-                    .transpose(0, 1)
-                )
+        if self.num_channels == 1:
+            idx_fold = torch.arange(self.num_folds, device=logits.device)
+            x = logits[:, :, 0][idx_fold[:, None], :, x[:, 0]]
         else:
-            x = F.one_hot(x, self.num_categories)  # (F, C, B, 1, num_categories)
-            x = x.squeeze(dim=3)  # (F, C, B, num_categories)
-            x = torch.einsum("fcbi,fkci->fbk", x.to(logits.dtype), logits)
+            idx_fold = torch.arange(self.num_folds, device=logits.device)[:, None, None]
+            idx_channel = torch.arange(self.num_channels)[None, :, None]
+            x = torch.sum(logits[idx_fold, :, idx_channel, x], dim=1)
         return x
 
     def log_partition_function(self) -> Tensor:

notebooks/compilation-options.ipynb

@@ -203,8 +203,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 4.57 s, sys: 950 ms, total: 5.52 s\n",
-      "Wall time: 5.45 s\n"
+      "CPU times: user 4.56 s, sys: 1.09 s, total: 5.66 s\n",
+      "Wall time: 5.56 s\n"
      ]
     }
    ],
@@ -273,7 +273,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.2 s ± 14.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "1.17 s ± 14.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -338,8 +338,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 4.48 s, sys: 991 ms, total: 5.47 s\n",
-      "Wall time: 5.39 s\n"
+      "CPU times: user 4.65 s, sys: 967 ms, total: 5.62 s\n",
+      "Wall time: 5.52 s\n"
      ]
     }
    ],
@@ -420,7 +420,7 @@
    "id": "f074e168-dee4-4234-8eae-afd28fae317f",
    "metadata": {},
    "source": [
-    "As we see in the next code snippet, enabling folding provided an (approximately) **20x speed-up** for feed-forward circuit evaluations."
+    "As we see in the next code snippet, enabling folding provided an (approximately) **19.9x speed-up** for feed-forward circuit evaluations."
    ]
   },
   {
@@ -433,7 +433,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "60 ms ± 15.5 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "58.9 ms ± 6.43 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
@@ -527,8 +527,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 4.81 s, sys: 876 ms, total: 5.68 s\n",
-      "Wall time: 5.61 s\n"
+      "CPU times: user 5.02 s, sys: 929 ms, total: 5.95 s\n",
+      "Wall time: 5.85 s\n"
      ]
     }
    ],
@@ -591,7 +591,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "26.7 ms ± 15.2 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "25.6 ms ± 26.2 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
@@ -608,7 +608,7 @@
    "id": "11d95c02-2c66-4414-b676-0dec303f2aa9",
    "metadata": {},
    "source": [
-    "Note that, we achieved an (approximately) **2.2x speed-up**, when compared to the folded circuit compiled above, and an (approximately) **44.9x speed-up**, when compared to the circuit compiled with no folding and no optimizations."
+    "Note that, we achieved an (approximately) **2.3x speed-up**, when compared to the folded circuit compiled above, and an (approximately) **45.7x speed-up**, when compared to the circuit compiled with no folding and no optimizations."
    ]
   },
   {