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 Yurii Shyrma (iuriish@yahoo.com), created on 29.08.2018
+//
+
+#include <system/op_boilerplate.h>
+#if NOT_EXCLUDED(OP_sparse_softmax_cross_entropy_loss_with_logits)
+
+#include <ops/declarable/CustomOperations.h>
+#include <ops/declarable/generic/helpers/ScatterHelper.h>
+
+namespace sd {
+namespace ops {
+
+//////////////////////////////////////////////////////////////////////////
+CUSTOM_OP_IMPL(sparse_softmax_cross_entropy_loss_with_logits, 2, 1, false, 0, 0) {
+  auto labels = INPUT_VARIABLE(0);
+  auto logits = INPUT_VARIABLE(1);
+
+  auto output = OUTPUT_VARIABLE(0);
+
+  const int labelsRank = labels->rankOf();
+  const int logitsRank = logits->rankOf();
+
+  // input validation
+  REQUIRE_TRUE(labelsRank == logitsRank - 1, 0,
+               "SPARSE_SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS OP: input arrays should satisfy relation (labels_rank = "
+               "logits_rank - 1), but got labels_rank = %i and logits_rank = %i instead !",
+               labelsRank, logitsRank);
+
+  auto* labelsShapePtr = labels->getShapeAsVector();
+  std::vector<LongType> labelsShape = *labelsShapePtr;
+  delete labelsShapePtr;
+  auto* logitsShapePtr = logits->getShapeAsVector();
+  std::vector<LongType> logitsShape = *logitsShapePtr;
+  delete logitsShapePtr;
+  logitsShape.pop_back();
+  bool equalSoft = logitsShape == labelsShape;
+
+  REQUIRE_TRUE(
+      equalSoft, 0,
+      "SPARSE_SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS OP: wrong shape of labels array, its shape should be the same as "
+      "logits shape with last dimension excluded, however got labels_shape = %s and logits_shape = %s instead !",
+      ShapeUtils::shapeAsString(labelsShape).c_str(), ShapeUtils::shapeAsString(logitsShape).c_str());
+
+  std::vector<LongType> dimension = {-1};
+
+  // Compute log softmax: -log(exp(logits - max) / sum(exp(logits - max)))
+  NDArray* maxAlongDim_ptr = logits->reduceAlongDimension(reduce::Max, &dimension, true);
+  NDArray maxAlongDim = *maxAlongDim_ptr;
+  delete maxAlongDim_ptr;
+  
+  NDArray* shiftedLogits_ptr = (*logits) - maxAlongDim;
+  NDArray* logitsExp_ptr = shiftedLogits_ptr->transform(transform::Exp, nullptr);
+  delete shiftedLogits_ptr;
+  NDArray logitsExp = *logitsExp_ptr;
+  delete logitsExp_ptr;
+  
+  NDArray* sumLogitsExp_ptr = logitsExp.reduceAlongDimension(reduce::Sum, &dimension, true);
+  NDArray sumLogitsExp = *sumLogitsExp_ptr;
+  delete sumLogitsExp_ptr;
+  
+  NDArray* softmaxRatio_ptr = logitsExp / sumLogitsExp;
+  NDArray* logSoftmax_ptr = softmaxRatio_ptr->transform(transform::Log);
+  delete softmaxRatio_ptr;
+  
+  // Apply negation: -log(softmax)
+  NDArray negLogSoftmax = -(*logSoftmax_ptr);  // unary negation returns value
+  delete logSoftmax_ptr;
+  
+  NDArray logSoftMax = negLogSoftmax;
+
+  helpers::scatterForLoss(block.launchContext(), *labels, logSoftMax, *output, false);
+
+  return Status::OK;
+}
+
+//////////////////////////////////////////////////////////////////////////
+DECLARE_TYPES(sparse_softmax_cross_entropy_loss_with_logits) {
+  getOpDescriptor()
+      ->setAllowedInputTypes(0, {ALL_INTS})
+      ->setAllowedInputTypes(1, {ALL_FLOATS})
+      ->setAllowedOutputTypes({ALL_FLOATS});
+}
+
+//////////////////////////////////////////////////////////////////////////
+DECLARE_SHAPE_FN(sparse_softmax_cross_entropy_loss_with_logits) {
+  auto labelsShapeInfo = inputShape->at(0);
+  auto logitsShapeInfo = inputShape->at(1);
+
+  REQUIRE_TRUE(labelsShapeInfo[0] == logitsShapeInfo[0] - 1, 0,
+               "SPARSE_SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS OP: input arrays should satisfy relation (labels_rank = "
+               "logits_rank - 1), but got labels_rank = %i and logits_rank = %i instead !",
+               labelsShapeInfo[0], logitsShapeInfo[0]);
+
+  bool equalSoft = true;
+  for (int i = 1; i < labelsShapeInfo[0]; ++i)
+    if (labelsShapeInfo[i] != logitsShapeInfo[i]) {
+      equalSoft = false;
+      break;
+    }
+
+  REQUIRE_TRUE(
+      equalSoft, 0,
+      "SPARSE_SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS OP: wrong shape of labels array, its shape should be the same as "
+      "logits shape with last dimension excluded, however got labels_shape = %s and logits_shape = %s instead !",
+      ShapeUtils::shapeAsString(labelsShapeInfo).c_str(), ShapeUtils::shapeAsString(logitsShapeInfo).c_str());
+
+  auto outShapeInfo =
+      ShapeBuilders::copyShapeInfoAndType(labelsShapeInfo, logitsShapeInfo, false, block.getWorkspace());
+
+  return SHAPELIST(CONSTANT(outShapeInfo));
+}
+
+//////////////////////////////////////////////////////////////////////////
+CUSTOM_OP_IMPL(sparse_softmax_cross_entropy_loss_with_logits_grad, 2, 1, false, 0, 0) {
+  auto labels = INPUT_VARIABLE(0);
+  auto logits = INPUT_VARIABLE(1);
+
+  auto dLdp = OUTPUT_VARIABLE(0);  // dL/dlogits
+
+  const int labelsRank = labels->rankOf();
+  const int logitsRank = logits->rankOf();
+
+  // input validation
+  REQUIRE_TRUE(labelsRank == logitsRank - 1, 0,
+               "SPARSE_SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS_GRAD OP: input arrays should satisfy relation "
+               "(labels_rank = logits_rank - 1), but got labels_rank = %i and logits_rank = %i instead !",
+               labelsRank, logitsRank);
+
+  auto* labelsShapePtr = labels->getShapeAsVector();
+  std::vector<LongType> labelsShape = *labelsShapePtr;
+  delete labelsShapePtr;
+  auto* logitsShapePtr = logits->getShapeAsVector();
+  std::vector<LongType> logitsShape = *logitsShapePtr;
+  delete logitsShapePtr;
+  logitsShape.pop_back();
+  bool equalSoft = logitsShape == labelsShape;
+
+  REQUIRE_TRUE(equalSoft, 0,
+               "SPARSE_SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS_GRAD OP: wrong shape of labels array, its shape should "
+               "be the same as logits shape with last dimension excluded, however got labels_shape = %s and "
+               "logits_shape = %s instead !",
+               ShapeUtils::shapeAsString(labelsShape).c_str(), ShapeUtils::shapeAsString(logitsShape).c_str());
+
+  std::vector<LongType> dimension = {-1};
+
+  // Compute softmax
+  NDArray* maxAlongDim_ptr = logits->reduceAlongDimension(reduce::Max, &dimension, true);
+  NDArray maxAlongDim = *maxAlongDim_ptr;
+  delete maxAlongDim_ptr;
+  
+  NDArray* shiftedLogits_ptr = (*logits) - maxAlongDim;
+  NDArray* softmax_ptr = shiftedLogits_ptr->transform(transform::Exp);
+  delete shiftedLogits_ptr;
+  NDArray softmax = *softmax_ptr;
+  delete softmax_ptr;
+  
+  NDArray* sumSoftmax_ptr = softmax.reduceAlongDimension(reduce::Sum, &dimension, true);
+  NDArray sumSoftmax = *sumSoftmax_ptr;
+  delete sumSoftmax_ptr;
+  
+  softmax /= sumSoftmax;
+
+  // dEdp = softmax - 1 (or 0)
+  dLdp->assign(&softmax);
+
+  // subtract unities at appropriate indexes of dLdp array
+  helpers::scatterForLoss(block.launchContext(), *labels, *dLdp,
+                          *labels /*actually third array is unnecessary for gradient calculation*/, true);
+
+  return Status::OK;
+}
+
+//////////////////////////////////////////////////////////////////////////
+DECLARE_TYPES(sparse_softmax_cross_entropy_loss_with_logits_grad) {
+  getOpDescriptor()
+      ->setAllowedInputTypes(0, {ALL_INTS})
+      ->setAllowedInputTypes(1, {ALL_FLOATS})
+      ->setAllowedOutputTypes({ALL_FLOATS});
+}
+
+//////////////////////////////////////////////////////////////////////////
+DECLARE_SHAPE_FN(sparse_softmax_cross_entropy_loss_with_logits_grad) {
+  auto labelsShapeInfo = inputShape->at(0);
+  auto logitsShapeInfo = inputShape->at(1);
+
+  REQUIRE_TRUE(labelsShapeInfo[0] == logitsShapeInfo[0] - 1, 0,
+               "SPARSE_SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS_GRAD OP: input arrays should satisfy relation "
+               "(labels_rank = logits_rank - 1), but got labels_rank = %i and logits_rank = %i instead !",
+               labelsShapeInfo[0], logitsShapeInfo[0]);
+
+  bool equalSoft = true;
+  for (int i = 1; i < labelsShapeInfo[0]; ++i)
+    if (labelsShapeInfo[i] != logitsShapeInfo[i]) {
+      equalSoft = false;
+      break;
+    }
+
+  REQUIRE_TRUE(equalSoft, 0,
+               "SPARSE_SOFTMAX_CROSS_ENTROPY_LOSS_WITH_LOGITS_GRAD OP: wrong shape of labels array, its shape should "
+               "be the same as logits shape with last dimension excluded, however got labels_shape = %s and "
+               "logits_shape = %s instead !",
+               ShapeUtils::shapeAsString(labelsShapeInfo).c_str(), ShapeUtils::shapeAsString(logitsShapeInfo).c_str());
+
+  DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(logitsShapeInfo));
+
+  LongType *dLdpShapeInfo =
+      ShapeBuilders::copyShapeInfoAndType(logitsShapeInfo, outType, false, block.getWorkspace());
+
+  return SHAPELIST(CONSTANT(dLdpShapeInfo));
+}
+
+}  // namespace ops
+}  // namespace sd
+
+#endif