chore: import upstream snapshot with attribution

paddle/phi/kernels/impl/cvm_kernel_impl.h

@@ -0,0 +1,135 @@
+// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// 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.
+
+#pragma once
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+
+namespace phi {
+
+template <typename T>
+void CvmComputeKernel(const bool use_cvm,
+                      const int64_t item_width,
+                      const T** X,
+                      T** Y) {
+  const auto cvm_offset = use_cvm ? 0 : 2;
+
+  std::memcpy(*Y, *X + cvm_offset, (item_width - cvm_offset) * sizeof(T));
+
+  if (use_cvm) {
+    (*Y)[0] = log((*Y)[0] + 1);
+    (*Y)[1] = log((*Y)[1] + 1) - (*Y)[0];
+  }
+
+  (*X) += item_width;
+  (*Y) += item_width - cvm_offset;
+}
+
+template <typename T>
+void CvmGradComputeKernel(const bool use_cvm,
+                          const int64_t item_width,
+                          const T& CVM,
+                          const T** DY,
+                          T** DX) {
+  const auto cvm_offset = use_cvm ? 0 : 2;
+
+  std::memcpy(*DX + cvm_offset, *DY, (item_width - cvm_offset) * sizeof(T));
+
+  (*DX)[0] = (&CVM)[0];
+  (*DX)[1] = (&CVM)[1];
+
+  (*DX) += item_width;
+  (*DY) += item_width - cvm_offset;
+}
+
+template <typename T, typename Context>
+void CVMOpKernel(const Context& dev_ctx,
+                 const DenseTensor& x_in,
+                 const DenseTensor& cvm,
+                 bool use_cvm,
+                 DenseTensor* out) {
+  const auto* x = &x_in;
+  const T* x_data = x->data<T>();
+
+  auto batch_size = x->dims()[0];
+  auto item_size = x->numel() / batch_size;
+
+  auto* y = out;
+  T* y_data = dev_ctx.template Alloc<T>(y);
+
+  // for Input X do not have Lod Information.
+  if (x->NumLevels() == 0) {
+    if (use_cvm) {
+      for (int i = 0; i < batch_size; i++) {
+        int64_t cursor = i * item_size;
+        y_data[cursor] = log(x_data[cursor] + 1);
+        y_data[cursor + 1] = log(x_data[cursor + 1] + 1) - y_data[cursor];
+        for (int j = 2; j < item_size; j++) {
+          y_data[cursor + j] = x_data[cursor + j];
+        }
+      }
+    } else {
+      for (int i = 0; i < batch_size; i++) {
+        CvmComputeKernel(use_cvm, item_size, &x_data, &y_data);
+      }
+    }
+  } else {
+    auto lod = x->lod()[0];
+    for (size_t i = 0; i < lod.size() - 1; ++i) {
+      for (size_t j = 0; j < lod[i + 1] - lod[i]; ++j) {
+        CvmComputeKernel(use_cvm, item_size, &x_data, &y_data);
+      }
+    }
+  }
+}
+
+template <typename T, typename Context>
+void CVMGradOpKernel(const Context& dev_ctx,
+                     const DenseTensor& x_in,
+                     const DenseTensor& cvm_in,
+                     const DenseTensor& out_grad,
+                     bool use_cvm,
+                     DenseTensor* x_grad) {
+  auto* dx = x_grad;
+  T* dx_data = dev_ctx.template Alloc<T>(dx);
+
+  const DenseTensor* cvm = &cvm_in;
+  const T* cvm_data = cvm->data<T>();
+
+  const auto* dOut = &out_grad;
+  const T* dout_data = dOut->data<T>();
+
+  auto offset = 2;
+  auto batch_size = dx->dims()[0];
+  auto item_size = dx->numel() / batch_size;
+
+  // for Input X do not have Lod Information.
+  if (dx->NumLevels() == 0) {
+    for (int x = 0; x < batch_size; ++x) {
+      CvmGradComputeKernel(use_cvm, item_size, *cvm_data, &dout_data, &dx_data);
+      cvm_data += offset;
+    }
+  } else {
+    auto lod = dx->lod()[0];
+    int seq_num = static_cast<int>(lod.size()) - 1;
+    for (int i = 0; i < seq_num; ++i) {
+      for (size_t j = 0; j < lod[i + 1] - lod[i]; ++j) {
+        CvmGradComputeKernel(
+            use_cvm, item_size, *cvm_data, &dout_data, &dx_data);
+      }
+      cvm_data += offset;
+    }
+  }
+}
+}  // namespace phi