chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/cumprod_grad_kernel.cc

@@ -0,0 +1,173 @@
+// Copyright (c) 2022 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.
+
+#include "paddle/phi/kernels/cumprod_grad_kernel.h"
+
+#include "paddle/common/ddim.h"
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/allocator.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/complex_functors.h"
+#include "paddle/phi/kernels/funcs/cumprod.h"
+#include "paddle/phi/kernels/funcs/for_range.h"
+
+// NOTE(@xiongkun): use of IsComplex<>
+#include "paddle/phi/core/utils/data_type.h"
+
+namespace phi {
+template <typename T, typename Context>
+void CumprodGradKernel(const Context& dev_ctx,
+                       const DenseTensor& x,
+                       const DenseTensor& out,
+                       const DenseTensor& d_out,
+                       int dim,
+                       bool exclusive,
+                       bool reverse,
+                       DenseTensor* d_x) {
+  const DDim& shape = x.dims();
+
+  auto* d_out_data = d_out.data<T>();
+  auto* x_data = x.data<T>();
+  auto* out_data = out.data<T>();
+  auto* d_x_data = dev_ctx.template Alloc<T>(d_x);
+
+  size_t outer_dim = 1;
+  size_t mid_dim = 1;
+  size_t inner_dim = 1;
+  GetCumprodDimInfo(shape, dim, &outer_dim, &mid_dim, &inner_dim);
+  if (shape.size() == 0) {
+    Copy<Context>(dev_ctx, d_out, dev_ctx.GetPlace(), false, d_x);
+    return;
+  }
+  size_t numel = outer_dim * mid_dim * inner_dim;
+
+  // deal with complex
+  const T* x_data_deal = nullptr;
+  const T* out_data_deal = nullptr;
+  Allocator::AllocationPtr x_conj;
+  Allocator::AllocationPtr out_conj;
+  if (IsComplexType(x.dtype())) {
+    x_conj = const_cast<Allocator&>(dev_ctx.GetAllocator())  // NOLINT
+                 .Allocate(numel * sizeof(T));
+    auto* x_data_conj = reinterpret_cast<T*>(x_conj->ptr());
+    out_conj = const_cast<Allocator&>(dev_ctx.GetAllocator())  // NOLINT
+                   .Allocate(numel * sizeof(T));
+    auto* out_data_conj = reinterpret_cast<T*>(out_conj->ptr());
+
+    funcs::ForRange<Context> for_range_x(dev_ctx, numel);
+    funcs::ConjFunctor<T> functor_x(x_data, numel, x_data_conj);
+    for_range_x(functor_x);
+
+    funcs::ForRange<Context> for_range_out(dev_ctx, numel);
+    funcs::ConjFunctor<T> functor_out(out_data, numel, out_data_conj);
+    for_range_out(functor_out);
+
+    x_data_deal = x_data_conj;
+    out_data_deal = out_data_conj;
+  } else {
+    x_data_deal = x_data;
+    out_data_deal = out_data;
+  }
+  if (!reverse) {
+    for (size_t i = 0; i < outer_dim; i++) {
+      for (size_t k = 0; k < inner_dim; k++) {
+        for (size_t j = 0; j < mid_dim; j++) {
+          size_t index = i * mid_dim * inner_dim + j * inner_dim + k;
+          d_x_data[index] = 0;
+          for (size_t n = 0; n < mid_dim; n++) {
+            size_t pos = i * mid_dim * inner_dim + n * inner_dim + k;
+            T elem;
+            if (exclusive) {
+              if (pos > index) {
+                elem = d_out_data[pos] * out_data_deal[index];
+                for (size_t m = index + inner_dim; m <= pos - inner_dim;
+                     m += inner_dim) {
+                  elem *= x_data_deal[m];
+                }
+              } else {
+                elem = static_cast<T>(0);
+              }
+            } else {
+              if (j == 0) {
+                elem = d_out_data[pos];
+              } else {
+                elem = d_out_data[pos] * out_data_deal[index - inner_dim];
+              }
+              if (pos > index) {
+                for (size_t m = index + inner_dim; m <= pos; m += inner_dim) {
+                  elem *= x_data_deal[m];
+                }
+              } else if (pos < index) {
+                elem = static_cast<T>(0);
+              }
+            }
+            d_x_data[index] += elem;
+          }
+        }
+      }
+    }
+  } else {
+    for (size_t i = 0; i < outer_dim; i++) {
+      for (size_t k = 0; k < inner_dim; k++) {
+        for (size_t j = mid_dim; j > 0; j--) {
+          size_t index = i * mid_dim * inner_dim + (j - 1) * inner_dim + k;
+          d_x_data[index] = 0;
+          for (size_t n = mid_dim; n > 0; n--) {
+            size_t pos = i * mid_dim * inner_dim + (n - 1) * inner_dim + k;
+            T elem;
+            if (exclusive) {
+              if (pos < index) {
+                elem = d_out_data[pos] * out_data_deal[index];
+                for (size_t m = index - inner_dim; m >= pos + inner_dim;
+                     m -= inner_dim) {
+                  elem *= x_data_deal[m];
+                }
+              } else {
+                elem = static_cast<T>(0);
+              }
+            } else {
+              if (j == mid_dim) {
+                elem = d_out_data[pos];
+              } else {
+                elem = d_out_data[pos] * out_data_deal[index + inner_dim];
+              }
+              if (pos < index) {
+                for (size_t m = index - inner_dim + inner_dim;
+                     m >= pos + inner_dim;
+                     m -= inner_dim) {  // both m and pos should + inner_dim to
+                                        // avoid 0-a=MAX_SIZET-a
+                  elem *= x_data_deal[m - inner_dim];
+                }
+              } else if (pos > index) {
+                elem = static_cast<T>(0);
+              }
+            }
+            d_x_data[index] += elem;
+          }
+        }
+      }
+    }
+  }
+}
+}  // namespace phi
+PD_REGISTER_KERNEL(cumprod_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::CumprodGradKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t,
+                   phi::complex64,
+                   phi::complex128) {}