Modified sigmoid by the elementwise interface. (#39898)

* Modified sigmoid by elementwise interface.

* using TensorReduceImpl to repalce Sum function

* using reduceimpl to calculate the norm variable

* Removed useless code

paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.cu

@@ -20,9 +20,11 @@ namespace cub = hipcub;
 #endif
 #include "paddle/fluid/memory/malloc.h"
 #include "paddle/fluid/operators/math.h"
+#include "paddle/fluid/operators/reduce_ops/reduce_op.cu.h"
 #include "paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.h"
 #include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
 #include "paddle/phi/core/hostdevice.h"
+#include "paddle/phi/kernels/funcs/elementwise_base.h"
 
 namespace paddle {
 namespace operators {
@@ -42,71 +44,86 @@ static inline int NumBlocks(const int N) {
 }
 
 template <typename T>
-__global__ void GPUSigmoidForward(const T *x_data, const T *label_data,
-                                  const int ignore_index, const int limit,
-                                  T *out_data, T *counts) {
-  CUDA_KERNEL_LOOP(i, limit) {
-    T x = x_data[i];
-    T label = label_data[i];
+struct NonzeroFunctor {
+  HOSTDEVICE explicit inline NonzeroFunctor() {}
+  HOSTDEVICE inline T operator()(const T x) const {
+    return static_cast<T>(static_cast<double>(x) != 0);
+  }
+};
+
+template <typename T>
+struct SigmoidFwdFunctor {
+  T ignore_index_;
   T eps = static_cast<T>(1e-5);
-    T diff = label - static_cast<T>(ignore_index);
+
+  HOSTDEVICE inline SigmoidFwdFunctor(const T ignore_index)
+      : ignore_index_(ignore_index) {}
+
+  HOSTDEVICE inline phi::Array<T, 2> operator()(const T x, const T label) {
+    T counts;
+    T out_data;
+
+    T diff = label - static_cast<T>(ignore_index_);
     if ((diff > -eps) && (diff < eps)) {
-      out_data[i] = static_cast<T>(0.);
-      counts[i] = 0;
+      out_data = static_cast<T>(0.);
+      counts = 0;
     } else {
       T term1 = (x > 0) ? x : 0;
       T term2 = x * label;
       T term3 = real_log(static_cast<T>(1) + real_exp(static_cast<T>(-abs(x))));
-      out_data[i] = term1 - term2 + term3;
-      counts[i] = 1;
-    }
-  }
-}
 
-template <typename T, int BlockDim>
-__global__ void Sum(const T *counts, int num, const T eps, T *sum) {
-  typedef cub::BlockReduce<double, BlockDim> BlockReduce;
-  __shared__ typename BlockReduce::TempStorage temp_storage;
-  T in = 0;
-  for (int i = threadIdx.x; i < num; i += BlockDim) {
-    in += counts[i];
-  }
-  __syncthreads();
-  auto out =
-      BlockReduce(temp_storage).Reduce(static_cast<double>(in), cub::Sum());
-  __syncthreads();
-  if (threadIdx.x == 0) {
-    T a = out > eps ? out : eps;
-    sum[0] = a;
+      out_data = term1 - term2 + term3;
+      counts = 1;
     }
-}
+    phi::Array<T, 2> outs;
 
-template <typename T>
-__global__ void Div(T *loss, const int num, const T *norm) {
-  CUDA_KERNEL_LOOP(i, num) { loss[i] /= norm[0]; }
-}
+    outs[0] = out_data;
+    outs[1] = counts;
+    return outs;
+  }
+};
 
 template <typename T>
-__global__ void GPUSigmoidBackward(const T *x_data, const T *label_data,
-                                   const int ignore_index, const T *dout_data,
-                                   const int limit, T *dx_data, T *counts) {
-  CUDA_KERNEL_LOOP(i, limit) {
-    T x = x_data[i];
-    T label = label_data[i];
-    T dout = dout_data[i];
+struct SigmoidBwdFunctor {
+  T ignore_index_;
   T eps = static_cast<T>(1e-5);
-    T diff = label - static_cast<T>(ignore_index);
+
+  HOSTDEVICE inline SigmoidBwdFunctor(const T ignore_index)
+      : ignore_index_(ignore_index) {}
+
+  HOSTDEVICE inline phi::Array<T, 2> operator()(const T x, const T label,
+                                                const T dout) {
+    T counts;
+    T dx_data;
+
+    T diff = label - static_cast<T>(ignore_index_);
     if ((diff > -eps) && (diff < eps)) {
-      dx_data[i] = static_cast<T>(0.);
-      counts[i] = 0;
+      dx_data = static_cast<T>(0.);
+      counts = 0;
     } else {
       T simoid_x = static_cast<T>(1) / (static_cast<T>(1) + real_exp(-x));
       T diff = simoid_x - label;
-      dx_data[i] = dout * diff;
-      counts[i] = 1;
+      dx_data = dout * diff;
+      counts = 1;
     }
+    phi::Array<T, 2> outs;
+
+    outs[0] = dx_data;
+    outs[1] = counts;
+    return outs;
   }
-}
+};
+
+template <typename T>
+struct DivFunctor {
+  const T norm_;
+  HOSTDEVICE inline DivFunctor(const T norm) : norm_(norm) {}
+
+  HOSTDEVICE inline T operator()(T loss) {
+    loss /= norm_;
+    return loss;
+  }
+};
 
 // Out = max(X, 0) - X * Labels + log(1 + exp(-abs(X)))
 template <typename DeviceContext, typename T>
@@ -123,20 +140,48 @@ class GPUSigmoidCrossEntropyWithLogitsKernel : public framework::OpKernel<T> {
     bool normalize = context.Attr<bool>("normalize");
 
     // Temporary memory
-    auto cnt_ptr = memory::Alloc(dev_ctx, Labels->numel() * sizeof(T));
-    T *counts = reinterpret_cast<T *>(cnt_ptr->ptr());
-
+    Tensor *counts_tensor = new Tensor();
+    counts_tensor->mutable_data<T>(context.GetPlace(),
+                                   Labels->numel() * sizeof(T));
+    counts_tensor->Resize(Out->dims());
     int limit = Out->numel();
     int blocks = NumBlocks(limit);
     int threads = kNumCUDAThreads;
-    GPUSigmoidForward<T><<<blocks, threads, 0, dev_ctx.stream()>>>(
-        X->data<T>(), Labels->data<T>(), ignore_index, limit, out_data, counts);
+    std::vector<const framework::Tensor *> ins = {X, Labels};
+    std::vector<framework::Tensor *> outs = {Out, counts_tensor};
+    auto functor = SigmoidFwdFunctor<T>(ignore_index);
+    constexpr int Size = 2;
+    phi::funcs::ElementwiseKernel<T, decltype(functor), Size>(dev_ctx, ins,
+                                                              &outs, functor);
     if (normalize) {
-      auto norm_ptr = memory::Alloc(dev_ctx, sizeof(T));
-      T *norm = reinterpret_cast<T *>(norm_ptr->ptr());
-      Sum<T, kNumCUDAThreads><<<1, kNumCUDAThreads, 0, dev_ctx.stream()>>>(
-          counts, limit, static_cast<T>(1e-5), norm);
-      Div<T><<<blocks, threads, 0, dev_ctx.stream()>>>(out_data, limit, norm);
+      T *counts = counts_tensor->mutable_data<T>(context.GetPlace());
+      Tensor *norm_tensor = new Tensor();
+      norm_tensor->mutable_data<T>(context.GetPlace(), sizeof(T));
+      auto dims = phi::vectorize(counts_tensor->dims());
+      std::vector<int> reduce_dim = {};
+      for (int i = 0; i < dims.size(); i++) {
+        reduce_dim.push_back(i);
+      }
+
+      TensorReduceImpl<T, T, kps::AddFunctor, NonzeroFunctor<T>>(
+          context.cuda_device_context(), *counts_tensor, norm_tensor,
+          NonzeroFunctor<T>(), reduce_dim, dev_ctx.stream());
+      T *norm = norm_tensor->mutable_data<T>(context.GetPlace());
+      auto norm_cpu_mem = memory::Alloc(platform::CPUPlace(), sizeof(T));
+      T *norm_cpu_ptr = reinterpret_cast<T *>(norm_cpu_mem->ptr());
+      memory::Copy(platform::CPUPlace(), norm_cpu_ptr, dev_ctx.GetPlace(), norm,
+                   sizeof(T), dev_ctx.stream());
+      auto eps = static_cast<T>(1e-5);
+      *norm_cpu_ptr = *norm_cpu_ptr > eps ? *norm_cpu_ptr : eps;
+
+      std::vector<const framework::Tensor *> div_ins = {Out};
+      std::vector<framework::Tensor *> div_outs = {Out};
+      auto div_functor = DivFunctor<T>(*norm_cpu_ptr);
+      phi::funcs::ElementwiseKernel<T>(dev_ctx, div_ins, &div_outs,
+                                       div_functor);
+
+      delete norm_tensor;
+      delete counts_tensor;
     }
   }
 };
@@ -157,22 +202,48 @@ class GPUSigmoidCrossEntropyWithLogitsGradKernel
 
     auto &dev_ctx = context.cuda_device_context();
     // Temporary memory
-    auto cnt_ptr = memory::Alloc(dev_ctx, X->numel() * sizeof(T));
-    T *counts = reinterpret_cast<T *>(cnt_ptr->ptr());
+    Tensor *counts_tensor = new Tensor();
+    counts_tensor->mutable_data<T>(context.GetPlace(),
+                                   Labels->numel() * sizeof(T));
+    counts_tensor->Resize(dX->dims());
 
     int limit = dX->numel();
     int blocks = NumBlocks(limit);
     int threads = kNumCUDAThreads;
-    GPUSigmoidBackward<T><<<blocks, threads, 0, dev_ctx.stream()>>>(
-        X->data<T>(), Labels->data<T>(), ignore_index, dOut->data<T>(), limit,
-        dx_data, counts);
+    std::vector<const framework::Tensor *> ins = {X, Labels, dOut};
+    std::vector<framework::Tensor *> outs = {dX, counts_tensor};
+    auto functor = SigmoidBwdFunctor<T>(ignore_index);
+    constexpr int Size = 2;
+    phi::funcs::ElementwiseKernel<T, decltype(functor), Size>(dev_ctx, ins,
+                                                              &outs, functor);
     bool normalize = context.Attr<bool>("normalize");
     if (normalize) {
-      auto norm_ptr = memory::Alloc(dev_ctx, sizeof(T));
-      T *norm = reinterpret_cast<T *>(norm_ptr->ptr());
-      Sum<T, kNumCUDAThreads><<<1, kNumCUDAThreads, 0, dev_ctx.stream()>>>(
-          counts, limit, static_cast<T>(1e-5), norm);
-      Div<T><<<blocks, threads, 0, dev_ctx.stream()>>>(dx_data, limit, norm);
+      T *counts = counts_tensor->mutable_data<T>(context.GetPlace());
+      Tensor *norm_tensor = new Tensor();
+      norm_tensor->mutable_data<T>(context.GetPlace(), sizeof(T));
+      auto dims = phi::vectorize(counts_tensor->dims());
+      std::vector<int> reduce_dim = {};
+      for (int i = 0; i < dims.size(); i++) {
+        reduce_dim.push_back(i);
+      }
+
+      TensorReduceImpl<T, T, kps::AddFunctor, NonzeroFunctor<T>>(
+          context.cuda_device_context(), *counts_tensor, norm_tensor,
+          NonzeroFunctor<T>(), reduce_dim, dev_ctx.stream());
+      T *norm = norm_tensor->mutable_data<T>(context.GetPlace());
+      auto norm_cpu_mem = memory::Alloc(platform::CPUPlace(), sizeof(T));
+      T *norm_cpu_ptr = reinterpret_cast<T *>(norm_cpu_mem->ptr());
+      memory::Copy(platform::CPUPlace(), norm_cpu_ptr, dev_ctx.GetPlace(), norm,
+                   sizeof(T), dev_ctx.stream());
+      auto eps = static_cast<T>(1e-5);
+      *norm_cpu_ptr = *norm_cpu_ptr > eps ? *norm_cpu_ptr : eps;
+
+      std::vector<const framework::Tensor *> div_ins = {dX};
+      std::vector<framework::Tensor *> div_outs = {dX};
+      auto div_functor = DivFunctor<T>(*norm_cpu_ptr);
+      phi::funcs::ElementwiseKernel<T>(dev_ctx, div_ins, &div_outs,
+                                       div_functor);
+      delete norm_tensor;
     }
   }
 };