Optimize of backward of log_softmax when axis is -1 and dim_size <= 1…

…024 (#32180)

paddle/fluid/operators/log_softmax_op.cu

@@ -65,19 +65,14 @@ __global__ void ComputeLogSoftmaxForwardInWarp(T *dst, const T *src,
   constexpr int warp_iter = near_greater_power_of_two / kernel_warp_size;
   int batch_id = blockDim.y * blockIdx.x + threadIdx.y;
 
-  // set effective_warp_id as 1 when warps do effective work,
-  // when warps do ineffective work, effective_warp_id remains unchanged.
-  int effective_warp_id = batch_size - batch_id;
-  if (effective_warp_id > 1) effective_warp_id = 1;
-
   int thread_in_warp_idx = threadIdx.x;
 
   // 1.read data from global memory to registers
   AccT elements[warp_iter];
   // set effective_element_count as the num of elements when warps do effective
   // work
   // set effective_element_count as 0, when warps do ineffective work
-  int effective_element_count = (effective_warp_id <= 0) ? 0 : element_count;
+  int effective_element_count = (batch_id < batch_size) ? element_count : 0;
   for (int it = 0; it < warp_iter; ++it) {
     int element_index = thread_in_warp_idx + it * kernel_warp_size;
     if (element_index < effective_element_count) {
@@ -181,6 +176,131 @@ class LogSoftmaxKernel<platform::CUDADeviceContext, T>
   }
 };
 
+// Backward below
+#define LAUNCH_WARP_BACKWARD_COMPUTE(near_greater_power_of_two)              \
+  case near_greater_power_of_two:                                            \
+    ComputeLogSoftmaxBackwardInWarp<                                         \
+        T, AccT, near_greater_power_of_two><<<blocks, threads, 0, stream>>>( \
+        output, grad_output, grad_input, outer_size, dim_size);              \
+    break;
+
+template <typename T, typename AccT, int NearGreaterPowerOfTwo>
+__global__ void ComputeLogSoftmaxBackwardInWarp(const T *output,
+                                                const T *grad_output,
+                                                T *grad_input, int batch_size,
+                                                int element_count) {
+  constexpr int near_greater_power_of_two = NearGreaterPowerOfTwo;
+  constexpr int kernel_warp_size =
+      (near_greater_power_of_two < 32) ? near_greater_power_of_two : 32;
+  constexpr int warp_iter = near_greater_power_of_two / kernel_warp_size;
+  int batch_id = blockDim.y * blockIdx.x + threadIdx.y;
+
+  int thread_in_warp_idx = threadIdx.x % kernel_warp_size;
+
+  // 1.read data from global memory to registers
+  AccT output_register[warp_iter];
+  AccT grad_output_register[warp_iter];
+  int effective_element_count = (batch_id < batch_size) ? element_count : 0;
+  for (int iter = 0; iter < warp_iter; ++iter) {
+    int element_index = thread_in_warp_idx + iter * kernel_warp_size;
+    if (element_index < effective_element_count) {
+      output_register[iter] =
+          static_cast<AccT>(output[batch_id * element_count + element_index]);
+      grad_output_register[iter] = static_cast<AccT>(
+          grad_output[batch_id * element_count + element_index]);
+    } else {
+      output_register[iter] = AccT(0);
+      grad_output_register[iter] = AccT(0);
+    }
+  }
+
+  // 2. For each warp, accumulate all thread registers
+  AccT sum = grad_output_register[0];
+#pragma unroll
+  for (int iter = 1; iter < warp_iter; ++iter) {
+    sum += grad_output_register[iter];
+  }
+  sum = WarpReduceSum<AccT, kernel_warp_size>(sum);
+
+// 3. write result in grad_input
+#pragma unroll
+  for (int iter = 0; iter < warp_iter; ++iter) {
+    int element_index = thread_in_warp_idx + iter * kernel_warp_size;
+    if (element_index < element_count) {
+      grad_input[batch_id * element_count + element_index] = static_cast<T>(
+          (grad_output_register[iter] - std::exp(output_register[iter]) * sum));
+    }
+  }
+}
+
+template <typename T, typename AccT>
+void LaunchSoftmaxBackwardForLastAxis(T *grad_input, const T *grad_output,
+                                      const T *output, int dim_size,
+                                      int outer_size, gpuStream_t stream) {
+  int threads_per_block = 128;
+  int near_greater_power_of_two = GetNearGreaterPowerOfTwo(dim_size);
+  int kernel_warp_size =
+      (near_greater_power_of_two < 32) ? near_greater_power_of_two : 32;
+  int warps_per_block = (threads_per_block / kernel_warp_size);
+  int blocks = (outer_size + warps_per_block - 1) / warps_per_block;
+  dim3 threads(kernel_warp_size, warps_per_block, 1);
+
+  switch (near_greater_power_of_two) {
+    LAUNCH_WARP_BACKWARD_COMPUTE(1);     // dim_size: 1
+    LAUNCH_WARP_BACKWARD_COMPUTE(2);     // dim_size: 2
+    LAUNCH_WARP_BACKWARD_COMPUTE(4);     // dim_size: 3~4
+    LAUNCH_WARP_BACKWARD_COMPUTE(8);     // dim_size: 5~8
+    LAUNCH_WARP_BACKWARD_COMPUTE(16);    // dim_size: 9~16
+    LAUNCH_WARP_BACKWARD_COMPUTE(32);    // dim_size: 17~32
+    LAUNCH_WARP_BACKWARD_COMPUTE(64);    // dim_size: 33~64
+    LAUNCH_WARP_BACKWARD_COMPUTE(128);   // dim_size: 65~128
+    LAUNCH_WARP_BACKWARD_COMPUTE(256);   // dim_size: 129~256
+    LAUNCH_WARP_BACKWARD_COMPUTE(512);   // dim_size: 257~512
+    LAUNCH_WARP_BACKWARD_COMPUTE(1024);  // dim_size: 513~1024
+
+    default:
+      break;
+  }
+}
+
+template <typename T>
+class LogSoftmaxGradKernel<platform::CUDADeviceContext, T>
+    : public framework::OpKernel<T> {
+  using MPDType = typename details::MPTypeTrait<T>::Type;
+
+ public:
+  void Compute(const framework::ExecutionContext &context) const override {
+    const auto *out = context.Input<framework::Tensor>("Out");
+    const auto *g_out =
+        context.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto *g_x = context.Output<framework::Tensor>(framework::GradVarName("X"));
+
+    const auto *out_data = out->data<T>();
+    const auto *g_out_data = g_out->data<T>();
+    auto *g_x_data = g_x->mutable_data<T>(context.GetPlace());
+
+    const int rank = out->dims().size();
+    const int axis = CanonicalAxis(context.Attr<int>("axis"), rank);
+
+    int dim_size = out->dims()[axis];
+    int inner_size = 1;
+    for (int i = axis + 1; i < out->dims().size(); ++i) {
+      inner_size *= out->dims()[i];
+    }
+    int outer_size = SizeToAxis(axis, out->dims());
+    gpuStream_t stream = context.cuda_device_context().stream();
+
+    if (inner_size == 1 && dim_size <= 1024 && dim_size * sizeof(T) <= 4096) {
+      LaunchSoftmaxBackwardForLastAxis<T, MPDType>(
+          g_x_data, g_out_data, out_data, dim_size, outer_size, stream);
+    } else {
+      LogSoftmaxGradFunctor<platform::CUDADeviceContext, T>()(
+          context.template device_context<platform::CUDADeviceContext>(), out,
+          g_out, g_x, axis);
+    }
+  }
+};
+
 }  // operators
 }  // paddle