chore: import upstream snapshot with attribution

2026-07-13 12:47:05 +08:00
commit 4f3b7da785
7394 changed files with 2005594 additions and 0 deletions
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+/* ******************************************************************************
+ *
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ *  See the NOTICE file distributed with this work for additional
+ *  information regarding copyright ownership.
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
+//
+//  @author raver119@gmail.com
+//
+
+#include <system/op_boilerplate.h>
+#if NOT_EXCLUDED(OP_norm)
+
+#include <ops/declarable/CustomOperations.h>
+#include <ops/declarable/helpers/axis.h>
+
+namespace sd {
+namespace ops {
+REDUCTION_OP_IMPL(norm, 1, 1, false, 1, -2) {
+  auto input = INPUT_VARIABLE(0);
+  NDArray *output = OUTPUT_VARIABLE(0);
+
+  auto mode = (int)T_ARG(0);
+  std::vector<sd::LongType> dims = *block.getIArguments();
+  bool overwrite = false;
+
+  if (block.width() == 1) {
+    output = OUTPUT_VARIABLE(0);
+  } else {
+    auto axisVector = INPUT_VARIABLE(1);
+    dims.resize(axisVector->lengthOf());
+    helpers::adjustAxis(input->rankOf(), axisVector, dims);
+    auto shape = ShapeUtils::evalReduceShapeInfo(input->ordering(), &dims, *input, false, false);
+    if (!shape::equalsStrict(shape, output->shapeInfo())) {
+      output = new NDArray(shape, false, block.launchContext());
+      overwrite = true;
+    }
+  }
+  switch (mode) {
+    case 0: {
+      REQUIRE_TRUE(dims.size() == 2 || (input->rankOf() == 2 && dims.size() == 0), 0,
+                   "Norm: Frobenius is defined for 2D matrices or TADS only");
+      // fro
+      input->reduceAlongDimension(reduce::NormFrobenius, output, &dims, false, output->rankOf() == 2);
+    } break;
+    case 1: {
+      // euclidean
+      if ((input->rankOf() == 2 && dims.size() == 0) || dims.size() == 2) {
+        input->reduceAlongDimension(reduce::NormFrobenius, output, &dims, false, output->rankOf() == 2);
+      } else {
+        input->reduceAlongDimension(reduce::Norm2, output, &dims, false, output->rankOf() == 2);
+      }
+    } break;
+    case 2: {
+      // 1
+      input->reduceAlongDimension(reduce::Norm1, output, &dims, false, output->rankOf() == 2);
+    } break;
+    case 3: {
+      // 2
+      input->reduceAlongDimension(reduce::Norm2, output, &dims, false, output->rankOf() == 2);
+    } break;
+    case 4: {
+      // inf-norm
+      input->reduceAlongDimension(reduce::NormMax, output, &dims, false, output->rankOf() == 2);
+    } break;
+    default: {
+      // p-norm
+      REQUIRE_TRUE(block.getIArguments()->size() > 1, 0,
+                   "P-Norm reductions requires 2 TArguments, but only 1 was provided");
+      // FIXME: p is required here
+      // T p = T_ARG(1);
+      input->reduceAlongDimension(reduce::NormP, output, &dims, false, output->rankOf() == 2);
+    }
+  }
+
+  if (overwrite) {
+    OVERWRITE_RESULT(output);
+  }
+
+  return sd::Status::OK;
+};
+
+DECLARE_TYPES(norm) { getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS}); }
+}  // namespace ops
+}  // namespace sd
+
+#endif