From 890638cf7dc47e101d4b78c1b58fa326fc4261c7 Mon Sep 17 00:00:00 2001
From: Roc <30228238+sljlp@users.noreply.github.com>
Date: Thu, 9 Dec 2021 12:05:44 +0800
Subject: [PATCH] optimize flip op, removing duplicated computation when dim
 size is one (#37825)

---
 paddle/fluid/operators/flip_op.cu | 41 -------------------------------
 1 file changed, 41 deletions(-)

diff --git a/paddle/fluid/operators/flip_op.cu b/paddle/fluid/operators/flip_op.cu
index 26b3d11bc6c..2391d4b907a 100644
--- a/paddle/fluid/operators/flip_op.cu
+++ b/paddle/fluid/operators/flip_op.cu
@@ -24,24 +24,6 @@ namespace operators {
 using Tensor = framework::Tensor;
 using CUDADeviceContext = paddle::platform::CUDADeviceContext;
 
-template <typename T>
-__global__ void kernel_pointwise_flip_apply(const int N, const T* in_data,
-                                            T* out_data, int dim0, int stride0,
-                                            int dim1, int flip_dim) {
-  for (int idx = blockIdx.x * blockDim.x + threadIdx.x; idx < N;
-       idx += gridDim.x * blockDim.x) {
-    int dst_offset = 0;
-    if (flip_dim == 0) {
-      // flip 1st dim
-      dst_offset = (dim0 - 1 - idx / stride0) * stride0 + idx % stride0;
-    } else {
-      // flip last dim
-      dst_offset = idx / stride0 * stride0 + (dim1 - 1 - idx % stride0);
-    }
-    out_data[dst_offset] = in_data[idx];
-  }
-}
-
 template <typename T>
 __global__ void flip_cuda_kernel(const int N, const T* in_data, T* out_data,
                                  int64_t* x_shape, int64_t* x_stride,
@@ -103,29 +85,6 @@ class FlipKernel<platform::CUDADeviceContext, T>
     std::vector<int64_t> x_dims_v = framework::vectorize(x_dims);
     std::vector<int64_t> x_stride_v = framework::vectorize(x_stride);
 
-    // wrap high-dims to 2-dims
-    if (flip_dims_size == 1 &&
-        (flip_dims[0] == 0 || flip_dims[0] == total_dims - 1)) {
-      int dim0 = 1, dim1 = 1;
-      int stride0 = 1;
-      if (flip_dims[0] == 0) {
-        dim0 = x_dims_v[0];
-        stride0 = x_stride_v[0];
-        for (size_t i = 1; i < total_dims; ++i) {
-          dim1 *= x_dims_v[i];
-        }
-      } else {
-        dim1 = x_dims_v[total_dims - 1];
-        for (size_t i = 0; i < total_dims - 1; ++i) {
-          dim0 *= x_dims_v[i];
-        }
-        stride0 *= x_dims_v[total_dims - 1];
-      }
-      kernel_pointwise_flip_apply<
-          T><<<dim_grid, dim_block, 0, ctx.cuda_device_context().stream()>>>(
-          N, in_data, out_data, dim0, stride0, dim1, flip_dims[0]);
-    }
-
     int bytes = total_dims * sizeof(int64_t);
     auto x_strides_array_tmp = memory::Alloc(dev_ctx, bytes);
     int64_t* x_strides_array_gpu =
-- 
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