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 +1,86 @@
+/* ******************************************************************************
+ *
+ *
+ * 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 18.09.2018
+//
+#include <execution/Threads.h>
+#include <ops/declarable/helpers/convolutions.h>
+
+namespace sd {
+namespace ops {
+
+//////////////////////////////////////////////////////////////////////////
+template <typename T>
+static void upsampling2dBP_(NDArray& gradO, NDArray& gradI, const bool isNCHW) {
+  // gradO has shape [bS, iC, factorH*iH, factorW*iW ] (NCHW) or [bS, factorH*iH, factorW*iW, iC] (NHWC)
+  // gradI has shape [bS, iC, iH, iW] (NCHW) or [bS, iH, iW, iC] (NHWC)
+
+  const T* x = gradO.bufferAsT<T>();
+  T* z = gradI.bufferAsT<T>();
+
+  const sd::LongType dimIH = isNCHW ? 2 : 1;
+  const sd::LongType dimIC = isNCHW ? 1 : 3;
+
+  const sd::LongType bS = gradI.sizeAt(0);
+  const sd::LongType iC = gradI.sizeAt(dimIC);
+  const sd::LongType iH = gradI.sizeAt(dimIH);
+  const sd::LongType iW = gradI.sizeAt(dimIH + 1);
+
+  const sd::LongType factorH = gradO.sizeAt(dimIH) / iH;
+  const sd::LongType factorW = gradO.sizeAt(dimIH + 1) / iW;
+
+  const sd::LongType xStride0 = gradO.stridesOf()[0];
+  const sd::LongType xStride1 = gradO.stridesOf()[dimIC];
+  const sd::LongType xStride2 = gradO.stridesOf()[dimIH];
+  const sd::LongType xStride3 = gradO.stridesOf()[dimIH + 1];
+
+  const sd::LongType zStride0 = gradI.stridesOf()[0];
+  const sd::LongType zStride1 = gradI.stridesOf()[dimIC];
+  const sd::LongType zStride2 = gradI.stridesOf()[dimIH];
+  const sd::LongType zStride3 = gradI.stridesOf()[dimIH + 1];
+
+  // loop through output array
+  auto func = PRAGMA_THREADS_FOR_3D {
+    for (sd::LongType b = start_x; b < stop_x; b += inc_x) {
+      for (sd::LongType c = start_y; c < stop_y; c += inc_y) {
+        for (sd::LongType h = start_z; h < stop_z; h += inc_z) {
+          for (sd::LongType w = 0; w < iW; ++w) {
+            const auto zOffset = b * zStride0 + c * zStride1 + h * zStride2 + w * zStride3;
+
+            z[zOffset] = 0;
+
+            for (sd::LongType xh = h * factorH; xh < h * factorH + factorH; ++xh)
+              for (sd::LongType xw = w * factorW; xw < w * factorW + factorW; ++xw)
+                z[zOffset] += x[b * xStride0 + c * xStride1 + xh * xStride2 + xw * xStride3];
+          }
+        }
+      }
+    }
+  };
+
+  samediff::Threads::parallel_for(func, 0, bS, 1, 0, iC, 1, 0, iH, 1);
+}
+
+void ConvolutionUtils::upsampling2dBP(sd::graph::Context& block, NDArray& gradO, NDArray& gradI,
+                                      const bool isNCHW) {
+  BUILD_SINGLE_SELECTOR(gradO.dataType(), upsampling2dBP_, (gradO, gradI, isNCHW), SD_FLOAT_TYPES);
+}
+
+}  // namespace ops
+}  // namespace sd