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
@@ -0,0 +1,193 @@
+/* ******************************************************************************
+ *
+ *
+ * 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 Yurii Shyrma (iuriish@yahoo.com), created on 01.06.2018
+//
+#include <ops/declarable/CustomOperations.h>
+#include <system/op_boilerplate.h>
+
+#if NOT_EXCLUDED(OP_reduce_mean)
+#include <ops/declarable/helpers/axis.h>
+namespace sd {
+namespace ops {
+
+//////////////////////////////////////////////////////////////////////////
+CUSTOM_OP_IMPL(reduce_mean, -1, 1, false, 0, 0) {
+  auto input = INPUT_VARIABLE(0);
+  auto output = OUTPUT_VARIABLE(0);
+  auto dimensions = *block.getIArguments();
+  if (block.width() > 1) {
+    auto axesVector = INPUT_VARIABLE(1);
+    helpers::adjustAxis(input->rankOf(), axesVector, dimensions);
+  }
+
+  bool keepDims = false;
+  if (block.getBArguments()->size())
+    keepDims = B_ARG(0);
+  else if (block.getTArguments()->size())
+    keepDims = (bool)T_ARG(0);
+
+  REQUIRE_TRUE(
+      dimensions.size() <= static_cast<size_t>(input->rankOf()), 0,
+      "REDUCE_MEAN OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead",
+      dimensions.size());
+
+  for (const auto &item : dimensions) {
+    REQUIRE_TRUE(item >= -input->rankOf() && item < input->rankOf(), 0,
+                 "REDUCE_MEAN OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
+                 input->rankOf(), input->rankOf(), item);
+  }
+
+  input->reduceAlongDimension(reduce::Mean, output, &dimensions, keepDims);
+  return sd::Status::OK;
+}
+
+DECLARE_SHAPE_FN(reduce_mean) {
+  auto dimensions = *block.getIArguments();
+  auto in = inputShape->at(0);
+  if (block.width() > 1) {
+    auto axesVector = INPUT_VARIABLE(1);
+    helpers::adjustAxis(shape::rank(in), axesVector, dimensions);
+  }
+
+  bool keepDims = false;
+  if (block.getBArguments()->size())
+    keepDims = B_ARG(0);
+  else if (block.getTArguments()->size())
+    keepDims = (bool)T_ARG(0);
+
+  REQUIRE_TRUE(
+      dimensions.size() <= static_cast<size_t>(in[0]), 0,
+      "REDUCE_MEAN OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead",
+      dimensions.size());
+
+  for (const auto &item : dimensions)
+  REQUIRE_TRUE(item >= -inputShape->at(0)[0] && item < inputShape->at(0)[0], 0,
+               "REDUCE_MEAN OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
+               inputShape->at(0)[0], inputShape->at(0)[0], item);
+
+  auto outShapeInfo =
+      ShapeUtils::evalReduceShapeInfo(shape::order(in), &dimensions, in, keepDims, false, block.getWorkspace());
+
+  return SHAPELIST(outShapeInfo);
+}
+
+DECLARE_TYPES(reduce_mean) {
+  getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
+}
+
+//////////////////////////////////////////////////////////////////////////
+CUSTOM_OP_IMPL(reduce_mean_bp, -2, 1, false, 0, 0) {
+  auto input = INPUT_VARIABLE(0);
+  auto gradO = INPUT_VARIABLE(1);
+  auto gradI = OUTPUT_VARIABLE(0);
+  auto dimensions = *block.getIArguments();
+  if (block.width() > 2) {
+    auto axesVector = INPUT_VARIABLE(2);
+    helpers::adjustAxis(input->rankOf(), axesVector, dimensions);
+  }
+
+  bool keepDims = false;
+  if (block.getBArguments()->size())
+    keepDims = B_ARG(0);
+  else if (block.getTArguments()->size())
+    keepDims = (bool)T_ARG(0);
+  REQUIRE_TRUE(
+      dimensions.size() <= static_cast<size_t>(input->rankOf()), 0,
+      "REDUCE_MEAN_BP OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead",
+      dimensions.size());
+
+  auto dimLength = 1.0;
+  for (const auto &item : dimensions) {
+    REQUIRE_TRUE(
+        item >= -input->rankOf() && item < input->rankOf(), 0,
+        "REDUCE_MEAN_BP OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
+        input->rankOf(), input->rankOf(), item);
+    dimLength *= input->sizeAt(item);
+  }
+
+  if (gradO->isScalar()) {
+    if (dimensions.size() > 0) {
+      NDArray *assign = gradO->e(0) / (static_cast<double>(dimLength));
+      gradI->assign(assign);
+      delete assign;
+    } else {
+      NDArray *assign = gradO->e(0) / (static_cast<double>(input->lengthOf()));
+      gradI->assign(assign);
+      delete assign;
+    }
+
+  } else {
+    auto val = (static_cast<double>(gradO->lengthOf() < 1 ? 1.0 : gradO->lengthOf()) )
+               / (static_cast<double>(input->lengthOf() < 1 ? 1.0 : input->lengthOf()));
+    if(val == 0.0)
+      val = SD_EPSILON;
+    gradI->assign(val);
+    if (!keepDims) {
+      auto gradOShapeKeepDims =
+          ShapeUtils::evalReduceShapeInfo(gradO->ordering(), &dimensions, *input, true, false, block.getWorkspace());
+
+      std::vector<sd::LongType> shape =  ShapeUtils::pullShapeFromShapeInfo(
+          gradOShapeKeepDims);
+      NDArray *reshapedGradO = gradO->reshape(gradO->ordering(), shape);
+      *gradI *= *reshapedGradO;
+      delete reshapedGradO;
+    } else {
+      gradI->applyTrueBroadcast(sd::BroadcastOpsTuple::Multiply(), gradO, gradI);
+    }
+  }
+
+
+  return sd::Status::OK;
+}
+
+DECLARE_SHAPE_FN(reduce_mean_bp) {
+  auto in = inputShape->at(0);
+  auto dimensions = *block.getIArguments();
+  auto rank = shape::rank(in);
+
+  if (block.width() > 2) {
+    auto axesVector = INPUT_VARIABLE(2);
+    helpers::adjustAxis(rank, axesVector, dimensions);
+  }
+  REQUIRE_TRUE(
+      dimensions.size() <= static_cast<size_t>(rank), 0,
+      "REDUCE_MEAN_BP OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead",
+      dimensions.size());
+
+  for (const auto &item : dimensions)
+  REQUIRE_TRUE(
+      item >= -rank || item < rank, 0,
+      "REDUCE_MEAN_BP OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !", rank,
+      rank, item);
+
+  sd::LongType *gradIshapeInfo = new sd::LongType[shape::shapeInfoLength(rank)];
+  memcpy(gradIshapeInfo, in, shape::shapeInfoByteLength(in));
+  auto ret =  SHAPELIST(CONSTANT(gradIshapeInfo));
+  delete[] gradIshapeInfo;
+  return ret;
+}
+
+DECLARE_TYPES(reduce_mean_bp) {
+  getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
+}
+
+}  // namespace ops
+}  // namespace sd
+#endif