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
+ ******************************************************************************/
+
+//
+// Created by sgazeos@gmail.com on 26.01.2018.
+//
+
+#include <system/op_boilerplate.h>
+#if NOT_EXCLUDED(OP_moments)
+
+#include <ops/declarable/CustomOperations.h>
+#include <ops/declarable/helpers/axis.h>
+
+namespace sd {
+namespace ops {
+CUSTOM_OP_IMPL(moments, 1, 2, false, 0, -2) {
+  auto input = INPUT_VARIABLE(0);
+  auto means = OUTPUT_VARIABLE(0);
+  auto variances = OUTPUT_VARIABLE(1);
+
+  std::vector<LongType> axis = *block.getIArguments();
+  const bool keepDims = block.getBArguments()->size() > 0 ? (bool)B_ARG(0) : false;
+  reduce_variance varianceOp;
+
+  // axis might be dynamic (i.e. tf mode)
+  if (block.width() > 1) {
+    auto axisVector = INPUT_VARIABLE(1);
+    helpers::adjustAxis(input->rankOf(), axisVector, axis);
+    varianceOp.execute({input, axisVector}, {variances}, {}, {}, {keepDims}, {}, false);
+  } else {
+    std::vector<LongType>& dims = axis;
+    std::vector<LongType> axes;
+    for (size_t i = 0; i < dims.size(); i++) {
+      axes.push_back(dims[i]);
+    }
+
+    varianceOp.execute({input}, {variances}, {}, axes, {keepDims}, {}, false);
+  }
+
+  input->reduceAlongDimension(reduce::Mean, means, &axis, keepDims);
+
+  return Status::OK;
+}
+
+DECLARE_SHAPE_FN(moments) {
+  auto axis = *block.getIArguments();
+  auto input = INPUT_VARIABLE(0);
+
+  // axis might be dynamic (i.e. tf mode)
+  if (block.width() > 1 && axis.size() == 0) {
+    auto axisVector = INPUT_VARIABLE(1);
+
+    for (int e = 0; e < axisVector->lengthOf(); e++) {
+      int ca = axisVector->e<int>(e);
+      if (ca < 0) ca += input->rankOf();
+
+      axis.emplace_back(ca);
+    }
+  }
+  const bool keepDims = block.getBArguments()->size() > 0 ? (bool)B_ARG(0) : false;
+
+  auto meanShape = ShapeUtils::evalReduceShapeInfo('c', &axis, *input, keepDims, false, block.workspace());
+  auto varianceShape = ShapeUtils::evalReduceShapeInfo('c', &axis, *input, keepDims, false, block.workspace());
+  return SHAPELIST(meanShape, varianceShape);
+}
+
+DECLARE_TYPES(moments) {
+  getOpDescriptor()->setAllowedInputTypes(ANY)->setAllowedOutputTypes({ALL_FLOATS});
+}
+}  // namespace ops
+
+}  // namespace sd
+
+#endif