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 +1,196 @@
+/* ******************************************************************************
+ *
+ *
+ * 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 04.06.2018
+//
+
+#include <ops/declarable/CustomOperations.h>
+#include <ops/declarable/helpers/axis.h>
+#include <ops/declarable/helpers/reductions.h>
+#if NOT_EXCLUDED(OP_reduce_variance)
+namespace sd {
+namespace ops {
+
+//////////////////////////////////////////////////////////////////////////
+CUSTOM_OP_IMPL(reduce_variance, -1, 1, false, 0, 0) {
+  auto input = INPUT_VARIABLE(0);
+  auto output = OUTPUT_VARIABLE(0);
+
+  bool biasCorrected = false;  // block.getTArguments()->size() > 1 ? (bool)T_ARG(1) : false;
+
+  auto dimensions = *block.getIArguments();
+  if (block.width() > 1) {
+    auto axesVector = INPUT_VARIABLE(1);
+    helpers::adjustAxis(input->rankOf(), axesVector, dimensions);
+  }
+
+  if (block.getBArguments()->size()) {
+    if (block.getBArguments()->size() > 1) biasCorrected = B_ARG(1);
+  } else if (block.getTArguments()->size()) {
+    if (block.getTArguments()->size() > 1) biasCorrected = (bool)T_ARG(1);
+  }
+
+  REQUIRE_TRUE(
+      dimensions.size() <= static_cast<size_t>(input->rankOf()), 0,
+      "REDUCE_VARIANCE 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_VARIANCE OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
+      input->rankOf(), input->rankOf(), item);
+
+  sd::ops::helpers::variance(*input, *output, dimensions, biasCorrected);
+
+  return sd::Status::OK;
+}
+
+DECLARE_SHAPE_FN(reduce_variance) {
+  bool keepDims = false;  // block.getTArguments()->size() > 0 ? (bool)T_ARG(0) : false;
+  auto dimensions = *block.getIArguments();
+  if (block.width() > 1) {
+    auto axesVector = INPUT_VARIABLE(1);
+    helpers::adjustAxis(INPUT_VARIABLE(0)->rankOf(), axesVector, dimensions);
+  }
+
+  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_VARIABLE(0)->rankOf()), 0,
+      "REDUCE_VARIANCE 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_VARIANCE 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(inputShape->at(0)), &dimensions, inputShape->at(0),
+                                                      keepDims, false, block.getWorkspace());
+
+  return SHAPELIST(outShapeInfo);
+}
+
+DECLARE_TYPES(reduce_variance) {
+  getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
+}
+
+//////////////////////////////////////////////////////////////////////////
+CUSTOM_OP_IMPL(reduce_variance_bp, -1, 1, false, 0, 0) {
+  auto input = INPUT_VARIABLE(0);
+  auto gradO = INPUT_VARIABLE(1);
+
+  auto gradI = OUTPUT_VARIABLE(0);
+  bool keepDims = true;
+  bool biasCorrected = false;
+
+
+  auto dimensions = *block.getIArguments();
+  if (block.width() > 2) {
+    auto axesVector = INPUT_VARIABLE(2);
+    helpers::adjustAxis(input->rankOf(), axesVector, dimensions);
+  }
+
+  if (block.getBArguments()->size()) {
+    keepDims = B_ARG(0);
+    if (block.getBArguments()->size() > 1) biasCorrected = B_ARG(1);
+  } else if (block.getTArguments()->size()) {
+    keepDims = (bool)T_ARG(0);
+    if (block.getTArguments()->size() > 1) biasCorrected = (bool)T_ARG(1);
+  }
+
+  REQUIRE_TRUE(
+      dimensions.size() <= static_cast<size_t>(input->rankOf()), 0,
+      "REDUCE_VARIANCE_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 >= -input->rankOf() && item < input->rankOf(), 0,
+        "REDUCE_VARIANCE_BP OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
+        input->rankOf(), input->rankOf(), item);
+    sd_debug("Dimension item is %d\n", item);
+  }
+
+  auto inputLen = input->lengthOf();
+  //avoid divide by zero
+  auto grad0Length = gradO->isScalar() || gradO->lengthOf() < 1 ? 1  :  gradO->lengthOf();
+  const sd::LongType N = inputLen / grad0Length;
+  const sd::LongType NminusOne = biasCorrected ? N - 1 : N;
+  
+  // Break down: (*input - mean) * (2.0f / NminusOne)
+  auto* mean = input->reduceAlongDimension(reduce::Mean, &dimensions, true);
+  auto* inputMinusMean = (*input) - (*mean);
+  delete mean;
+  auto* assign = (*inputMinusMean) * (2.0f / NminusOne);
+  delete inputMinusMean;
+  
+  gradI->assign(assign);  // automatic broadcasting happens here
+  delete assign;
+  
+  if (!keepDims) {
+    auto gradOShapeKeepDims = ShapeUtils::evalReduceShapeInfo(gradO->ordering(), &dimensions, *input, true, false, block.getWorkspace());
+    auto grad0Shape = ShapeUtils::pullShapeFromShapeInfo(gradOShapeKeepDims);
+    auto* reshaped = !gradO->isScalar() ? gradO->reshape(gradO->ordering(), grad0Shape) : gradO;  // for example could be something like [a,b] -> [1,a,1,b];
+    *gradI *= (*reshaped);  // for example could be something like [a,b] -> [1,a,1,b]
+    //reshape can vary and may have the same buffer as the original
+    if(reshaped != gradO && reshaped->buffer() != gradO->buffer() && reshaped->specialBuffer() != gradI->specialBuffer())
+      delete reshaped;
+
+  } else {
+    *gradI *= (*gradO);  // automatic broadcasting happens here
+  }
+  return sd::Status::OK;
+}
+
+DECLARE_SHAPE_FN(reduce_variance_bp) {
+  auto in = inputShape->at(0);
+  auto rank = shape::rank(in);
+  auto dimensions = *block.getIArguments();
+  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_VARIANCE_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 >= -inputShape->at(0)[0] && item < inputShape->at(0)[0], 0,
+      "REDUCE_VARIANCE_BP 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);
+  return SHAPELIST(CONSTANT(in));
+}
+
+DECLARE_TYPES(reduce_variance_bp) {
+  getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
+}
+
+}  // namespace ops
+}  // namespace sd
+#endif