[cherry-pick] Optimize sparse kernel and fix some bug (#50118)

cherry-pick some PR about optimize sparse kernel and fix some bug：
#47736 #47703 #47604 #46679 #48439 #49009 #49734

Author: zhangkaihuo

cmake/external/cutlass.cmake

@@ -0,0 +1,43 @@
+# 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(ExternalProject)
+
+set(CUTLASS_PREFIX_DIR ${THIRD_PARTY_PATH}/cutlass)
+
+set(CUTLASS_REPOSITORY https://github.com/NVIDIA/cutlass.git)
+set(CUTLASS_TAG v2.9.1)
+
+include_directories("${THIRD_PARTY_PATH}/cutlass/src/extern_cutlass/")
+include_directories("${THIRD_PARTY_PATH}/cutlass/src/extern_cutlass/include/")
+include_directories(
+  "${THIRD_PARTY_PATH}/cutlass/src/extern_cutlass/tools/util/include/")
+
+add_definitions("-DPADDLE_WITH_CUTLASS")
+
+ExternalProject_Add(
+  extern_cutlass
+  ${EXTERNAL_PROJECT_LOG_ARGS} ${SHALLOW_CLONE}
+  GIT_REPOSITORY ${CUTLASS_REPOSITORY}
+  GIT_TAG "${CUTLASS_TAG}"
+  PREFIX ${CUTLASS_PREFIX_DIR}
+  UPDATE_COMMAND ""
+  CONFIGURE_COMMAND ""
+  BUILD_COMMAND ""
+  INSTALL_COMMAND ""
+  TEST_COMMAND "")
+
+add_library(cutlass INTERFACE)
+
+add_dependencies(cutlass extern_cutlass)

cmake/third_party.cmake

@@ -492,4 +492,14 @@ if(WITH_CUSPARSELT)
   list(APPEND third_party_deps extern_cusparselt)
 endif()
 
+if(WITH_GPU
+   AND NOT WITH_ARM
+   AND NOT WIN32
+   AND NOT APPLE)
+  if(${CMAKE_CUDA_COMPILER_VERSION} GREATER_EQUAL 11.0)
+    include(external/cutlass) # download, build, install cusparselt
+    list(APPEND third_party_deps extern_cutlass)
+  endif()
+endif()
+
 add_custom_target(third_party ALL DEPENDS ${third_party_deps})

paddle/phi/kernels/funcs/norm_utils.h

@@ -18,6 +18,10 @@ limitations under the License. */
 
 namespace phi {
 namespace funcs {
+#define CUDNN_PER_ACTIVATION_THRESHOLD 10240
+#define CUDNN_SPATIAL_THRESHOLD_TRAIN 880801
+#define CUDNN_SPATIAL_THRESHOLD_EVAL 65535
+
 inline void ExtractNCWHD(const phi::DDim &dims,
                          const DataLayout &data_layout,
                          int *N,

paddle/phi/kernels/funcs/sparse/utils.cu.h

@@ -26,6 +26,19 @@ __global__ void DistanceKernel(const T* start, const T* end, T* distance) {
   }
 }
 
+inline __device__ bool SetBits(const int value, int* ptr) {
+  const int index = value >> 5;
+  const int mask = 1 << (value & 31);
+  const int old = atomicOr(ptr + index, mask);
+  return (mask & old) != 0;
+}
+
+inline __device__ bool TestBits(const int value, const int* ptr) {
+  const int index = value >> 5;
+  const int mask = 1 << (value & 31);
+  return (mask & ptr[index]) != 0;
+}
+
 }  // namespace sparse
 }  // namespace funcs
 }  // namespace phi

paddle/phi/kernels/gpu/batch_norm_grad_kernel.cu

@@ -852,15 +852,17 @@ void BatchNormGradRawKernel(const Context &ctx,
 //             ctx.GetPlace()),
 //         epsilon, saved_mean_data, saved_var_data));
 #else
-      // CUDNN only support small batch size
-      // const size_t CUDNN_PER_ACTIVATION_THRESHOLD = 131070;
-      const size_t CUDNN_PER_ACTIVATION_THRESHOLD = 10240;
-      const size_t CUDNN_SPATIAL_THRESHOLD = 880801;
-      const bool use_native_kernel =
-          ((x_dims.size() == 2 && N >= CUDNN_PER_ACTIVATION_THRESHOLD) ||
-           (x_dims.size() == 3 && N >= CUDNN_SPATIAL_THRESHOLD));
-      if (use_native_kernel) {
-        if (x_dims.size() == 2) {
+    }
+    // CUDNN only support small batch size
+    bool use_native_nhwc =
+        d_x ? (x_dims.size() == 4 && compute_format == DataLayout::kNHWC)
+            : false;
+    const bool use_native_kernel =
+        ((x_dims.size() == 2 && N >= CUDNN_PER_ACTIVATION_THRESHOLD) ||
+         (x_dims.size() == 3 && N >= CUDNN_SPATIAL_THRESHOLD_TRAIN));
+    if (use_native_nhwc || (d_x && d_scale && d_bias)) {
+      if (use_native_kernel || use_native_nhwc) {
+        if (x_dims.size() == 2 || use_native_nhwc) {
           dim3 block;
           dim3 grid;
           const int block_size = 512;

paddle/phi/kernels/gpu/batch_norm_kernel.cu

@@ -72,6 +72,40 @@ static __global__ void BNForwardInference(const T *x,
   }
 }
 
+template <typename T>
+static __global__ void InverseVariance(const BatchNormParamType<T> *variance,
+                                       const double epsilon,
+                                       const int C,
+                                       BatchNormParamType<T> *inv_variance) {
+  int tid = threadIdx.x + blockIdx.x * blockDim.x;
+  if (tid < C) {
+    inv_variance[tid] = 1 / sqrt(variance[tid] + epsilon);
+  }
+}
+
+template <typename T, phi::DataLayout layout>
+static __global__ void BN1DForwardInference(
+    const T *x,
+    const BatchNormParamType<T> *mean,
+    const BatchNormParamType<T> *inv_variance,
+    const BatchNormParamType<T> *scale,
+    const BatchNormParamType<T> *bias,
+    const int C,
+    const int N,
+    const int HxW,
+    const double epsilon,
+    T *y) {
+  int gid = blockIdx.x * blockDim.x + threadIdx.x;
+  int stride = blockDim.x * gridDim.x;
+  int num = N * C * HxW;
+  for (int i = gid; i < num; i += stride) {
+    const int c = layout == phi::DataLayout::kNCHW ? i / HxW % C : i % C;
+    BatchNormParamType<T> x_sub_mean =
+        static_cast<BatchNormParamType<T>>(x[i]) - mean[c];
+    y[i] = static_cast<T>(scale[c] * x_sub_mean * inv_variance[c] + bias[c]);
+  }
+}
+
 template <typename T, int BlockDim, phi::DataLayout layout>
 static __global__ LAUNCH_BOUNDS(BlockDim) void BNForwardTraining(
     const T *x,
@@ -691,9 +725,6 @@ void BatchNormKernel(const Context &ctx,
 
   auto handle = ctx.cudnn_handle();
 
-  const size_t CUDNN_PER_ACTIVATION_THRESHOLD = 10240;
-  const size_t CUDNN_SPATIAL_THRESHOLD = 880801;
-
   // Now, depending on whether we are running test or not, we have two paths.
   // It is training mode when it's not reference AND not using pre-trained
   // model.
@@ -797,8 +828,8 @@ void BatchNormKernel(const Context &ctx,
 //         epsilon));
 #else
     const bool use_native_kernel =
-        ((x_dims.size() == 2 && N >= CUDNN_PER_ACTIVATION_THRESHOLD) ||
-         (x_dims.size() == 3 && N >= CUDNN_SPATIAL_THRESHOLD));
+        (x_dims.size() == 2 ||
+         (x_dims.size() == 3 && N >= CUDNN_SPATIAL_THRESHOLD_EVAL));
     if (use_native_kernel) {
       const int block_size = 256;
       const int grid_size = (N * C * H * W * D + block_size - 1) / block_size;
@@ -816,18 +847,43 @@ void BatchNormKernel(const Context &ctx,
                 epsilon,
                 transformed_y.template data<T>());
       } else {
-        BNForwardInference<T, DataLayout::kNHWC>
-            <<<grid_size, block_size, 0, ctx.stream()>>>(
-                transformed_x.template data<T>(),
-                est_mean->template data<BatchNormParamType<T>>(),
-                est_var->template data<BatchNormParamType<T>>(),
-                scale.template data<BatchNormParamType<T>>(),
-                bias.template data<BatchNormParamType<T>>(),
-                C,
-                N,
-                H * W * D,
-                epsilon,
-                transformed_y.template data<T>());
+        if (x_dims.size() == 2) {
+          DenseTensor inv_var = phi::Empty<BatchNormParamType<T>>(ctx, {C});
+          auto *inv_var_ptr = inv_var.data<BatchNormParamType<T>>();
+          const int threads = 512 > C ? C : 512;
+          const int blocks = (C + 511) / 512;
+          InverseVariance<T><<<blocks, threads>>>(
+              est_var->template data<BatchNormParamType<T>>(),
+              epsilon,
+              C,
+              inv_var_ptr);
+          BN1DForwardInference<T, DataLayout::kNHWC>
+              <<<grid_size, block_size, 0, ctx.stream()>>>(
+                  transformed_x.template data<T>(),
+                  est_mean->template data<BatchNormParamType<T>>(),
+                  // est_var->template data<BatchNormParamType<T>>(),
+                  inv_var_ptr,
+                  scale.template data<BatchNormParamType<T>>(),
+                  bias.template data<BatchNormParamType<T>>(),
+                  C,
+                  N,
+                  H * W * D,
+                  epsilon,
+                  transformed_y.template data<T>());
+        } else {
+          BNForwardInference<T, DataLayout::kNHWC>
+              <<<grid_size, block_size, 0, ctx.stream()>>>(
+                  transformed_x.template data<T>(),
+                  est_mean->template data<BatchNormParamType<T>>(),
+                  est_var->template data<BatchNormParamType<T>>(),
+                  scale.template data<BatchNormParamType<T>>(),
+                  bias.template data<BatchNormParamType<T>>(),
+                  C,
+                  N,
+                  H * W * D,
+                  epsilon,
+                  transformed_y.template data<T>());
+        }
       }
     } else {
       PADDLE_ENFORCE_GPU_SUCCESS(
@@ -949,7 +1005,7 @@ void BatchNormKernel(const Context &ctx,
       // const size_t CUDNN_PER_ACTIVATION_THRESHOLD = 131070;
       const bool use_native_kernel =
           ((x_dims.size() == 2 && N >= CUDNN_PER_ACTIVATION_THRESHOLD) ||
-           (x_dims.size() == 3 && N >= CUDNN_SPATIAL_THRESHOLD));
+           (x_dims.size() == 3 && N >= CUDNN_SPATIAL_THRESHOLD_TRAIN));
       if (use_native_kernel) {
         dim3 block;
         dim3 grid;