Support multi-outputs feature for broadcast ops

paddle/fluid/operators/kernel_primitives/datamover_primitives.h

@@ -254,8 +254,8 @@ __device__ __forceinline__ void ReadData(T* dst, const T* __restrict__ src,
  }
  }
  } else { // blockDim,x * NX < num
- const int kVectorSize = (NX % 4 == 0) ? 4 : (NX % 2 == 0) ? 2 : 1;
- const int kVectorsPerThread = NX / kVectorSize;
+ constexpr int kVectorSize = (NX % 4 == 0) ? 4 : (NX % 2 == 0) ? 2 : 1;
+ constexpr int kVectorsPerThread = NX / kVectorSize;
  int thread_offset = threadIdx.x * kVectorsPerThread;
 
  using VecType = details::VectorType<T, kVectorSize>;
@@ -441,8 +441,8 @@ __device__ __forceinline__ void WriteData(T* dst, T* __restrict__ src,
  }
  } else {
  // Vector type
- const int kVectorSize = (NX % 4 == 0) ? 4 : (NX % 2 == 0) ? 2 : 1;
- const int kVectorsPerThread = NX / kVectorSize;
+ constexpr int kVectorSize = (NX % 4 == 0) ? 4 : (NX % 2 == 0) ? 2 : 1;
+ constexpr int kVectorsPerThread = NX / kVectorSize;
 
  int thread_offset = threadIdx.x * kVectorsPerThread;
  using VecType = details::VectorType<T, kVectorSize>;

paddle/pten/kernels/hybird/cuda/elementwise/elementwise_broadcast.cu.h

@@ -193,12 +193,13 @@ template <typename InT,
  typename OutT,
  typename Functor,
  int Arity,
+ int NumOuts,
  int VecSize,
  int Rank,
  bool IsBoundary = false>
 __device__ void ElementwiseBroadcastKernelImpl(
  const paddle::framework::Array<const InT *__restrict__, Arity> &ins,
- OutT *out,
+ paddle::framework::Array<OutT *, NumOuts> outs,
  const paddle::framework::Array<bool, Arity> &use_broadcast,
  uint32_t numel,
  const paddle::framework::Array<kps::details::BroadcastConfig<Rank>, Arity>
@@ -207,7 +208,7 @@ __device__ void ElementwiseBroadcastKernelImpl(
  int block_offset,
  Functor func) {
  InT args[Arity][VecSize];
- OutT result[VecSize];
+ OutType<OutT, NumOuts> result[VecSize];
 
 #pragma unroll
  for (int i = 0; i < Arity; i++) {
@@ -220,28 +221,29 @@ __device__ void ElementwiseBroadcastKernelImpl(
  num,
  use_broadcast[i]);
  }
-
- const bool kCallElementwiseAny =
+ constexpr bool kCallElementwiseAny =
  paddle::platform::FunctionTraits<Functor>::has_pointer_args;
  ElementwisePrimitiveCaller<InT,
- OutT,
+ OutType<OutT, NumOuts>,
  VecSize,
  Functor,
  Arity,
  kCallElementwiseAny>()(func, args, result);
- kps::WriteData<OutT, VecSize, 1, 1, IsBoundary>(
- out + block_offset, result, num);
+
+ ElementwiseWriteDataCaller<OutT, VecSize, IsBoundary, NumOuts>()(
+ outs, result, block_offset, num);
 }
 
 template <typename InT,
  typename OutT,
  typename Functor,
  int Arity,
+ int NumOuts,
  int VecSize,
  int Rank>
 __global__ void ElementwiseBroadcastKernel(
  paddle::framework::Array<const InT *__restrict__, Arity> ins,
- OutT *out,
+ paddle::framework::Array<OutT *, NumOuts> outs,
  paddle::framework::Array<bool, Arity> use_broadcast,
  uint32_t numel,
  paddle::framework::Array<kps::details::BroadcastConfig<Rank>, Arity>
@@ -251,16 +253,18 @@ __global__ void ElementwiseBroadcastKernel(
  Functor func) {
  int block_offset = BLOCK_ID_X * BLOCK_NUM_X * VecSize;
  int stride = BLOCK_NUM_X * GRID_NUM_X * VecSize;
+
 #ifdef PADDLE_WITH_XPU2
  for (; block_offset < main_offset; block_offset += stride) {
  ElementwiseBroadcastKernelImpl<InT,
  OutT,
  Functor,
  Arity,
+ NumOuts,
  VecSize,
  Rank,
  false>(ins,
- out,
+ outs,
  use_broadcast,
  numel,
  configs,
@@ -273,22 +277,23 @@ __global__ void ElementwiseBroadcastKernel(
  OutT,
  Functor,
  Arity,
+ NumOuts,
  VecSize,
  Rank,
  true>(
- ins, out, use_broadcast, numel, configs, tail_tid, block_offset, func);
+ ins, outs, use_broadcast, numel, configs, tail_tid, block_offset, func);
  }
-
 #else
  if (block_offset < main_offset) {
  ElementwiseBroadcastKernelImpl<InT,
  OutT,
  Functor,
  Arity,
+ NumOuts,
  VecSize,
  Rank,
  false>(ins,
- out,
+ outs,
  use_broadcast,
  numel,
  configs,
@@ -300,10 +305,11 @@ __global__ void ElementwiseBroadcastKernel(
  OutT,
  Functor,
  Arity,
+ NumOuts,
  VecSize,
  Rank,
  true>(
- ins, out, use_broadcast, numel, configs, tail_tid, block_offset, func);
+ ins, outs, use_broadcast, numel, configs, tail_tid, block_offset, func);
  }
 #endif
 }
@@ -312,25 +318,30 @@ template <typename InT,
  typename OutT,
  typename Functor,
  int Arity,
+ int NumOuts,
  int VecSize,
  int Rank>
 void LaunchKernel(const paddle::platform::CUDADeviceContext &ctx,
  const std::vector<const DenseTensor *> &ins,
- DenseTensor *out,
+ std::vector<DenseTensor *> *outs,
  Functor func,
  DimensionsTransform merge_dims) {
- int numel = out->numel();
+ int numel = (*outs)[0]->numel();
  const int threads = 256;
  int blocks = ((numel + VecSize - 1) / VecSize + threads - 1) / threads;
 
  int main_offset = (numel / (VecSize * threads)) * VecSize * threads;
  int tail_tid = numel % (VecSize * threads);
  auto stream = ctx.stream();
- OutT *out_data = out->mutable_data<OutT>();
 
  paddle::framework::Array<kps::details::BroadcastConfig<Rank>, Arity> configs;
  paddle::framework::Array<bool, Arity> use_broadcast;
  paddle::framework::Array<const InT *__restrict__, Arity> ins_data;
+ paddle::framework::Array<OutT *, NumOuts> outs_data;
+
+ for (int i = 0; i < NumOuts; ++i) {
+ outs_data[i] = (*outs)[i]->mutable_data<OutT>();
+ }
 
  for (int i = 0; i < Arity; i++) {
  use_broadcast[i] = (ins[i]->numel() != numel);
@@ -343,6 +354,7 @@ void LaunchKernel(const paddle::platform::CUDADeviceContext &ctx,
  merge_dims.out_dims, merge_dims.in_dims[i], merge_dims.dim_size);
  }
  }
+
 #ifdef PADDLE_WITH_XPU2
  threads = 128;
  blocks = 8;
@@ -352,9 +364,10 @@ void LaunchKernel(const paddle::platform::CUDADeviceContext &ctx,
  OutT,
  Functor,
  Arity,
+ NumOuts,
  VecSize,
  Rank><<<blocks, threads, stream>>>(ins_data,
- out_data,
+ outs_data,
  use_broadcast,
  numel,
  configs,
@@ -366,10 +379,11 @@ void LaunchKernel(const paddle::platform::CUDADeviceContext &ctx,
  OutT,
  Functor,
  Arity,
+ NumOuts,
  VecSize,
  Rank><<<blocks, threads, 0, stream>>>(
  ins_data,
- out_data,
+ outs_data,
  use_broadcast,
  numel,
  configs,
@@ -379,19 +393,24 @@ void LaunchKernel(const paddle::platform::CUDADeviceContext &ctx,
 #endif
 }
 
-template <typename InT, typename OutT, typename Functor, int Arity, int VecSize>
+template <typename InT,
+ typename OutT,
+ typename Functor,
+ int Arity,
+ int NumOuts,
+ int VecSize>
 void LaunchBroadcastKernelForDifferentVecSize(
  const paddle::platform::CUDADeviceContext &ctx,
  const std::vector<const DenseTensor *> &ins,
- DenseTensor *out,
+ std::vector<DenseTensor *> *outs,
  int axis,
  Functor func) {
- const auto merge_dims = DimensionsTransform(ins, out->dims(), axis);
+ const auto merge_dims = DimensionsTransform(ins, (*outs)[0]->dims(), axis);
 
-#define CALL_BROADCAST_FOR_DIM_SIZE(rank) \
- case rank: { \
- LaunchKernel<InT, OutT, Functor, Arity, VecSize, rank>( \
- ctx, ins, out, func, merge_dims); \
+#define CALL_BROADCAST_FOR_DIM_SIZE(rank)  \
+ case rank: {  \
+ LaunchKernel<InT, OutT, Functor, Arity, NumOuts, VecSize, rank>( \
+ ctx, ins, outs, func, merge_dims);  \
  } break;
 
  switch (merge_dims.dim_size) {
@@ -414,7 +433,11 @@ void LaunchBroadcastKernelForDifferentVecSize(
 #undef CALL_BROADCAST_FOR_DIM_SIZE
 }
 
-template <ElementwiseType ET, typename InT, typename OutT, typename Functor>
+template <ElementwiseType ET,
+ typename InT,
+ typename OutT,
+ typename Functor,
+ int NumOuts = 1>
 void LaunchBroadcastElementwiseCudaKernel(
  const paddle::platform::CUDADeviceContext &ctx,
  const std::vector<const DenseTensor *> &ins,
@@ -438,32 +461,68 @@ void LaunchBroadcastElementwiseCudaKernel(
  "Currently only broadcast of binary is supported and "
  "verified, but received %d.",
  kArity));
-
+ PADDLE_ENFORCE_EQ(
+ outs->size(),
+ NumOuts,
+ paddle::platform::errors::InvalidArgument(
+ "Number of outputs shall equal to number of functions, "
+ "but number of outputs is %d, number of functions is %d.",
+ outs->size(),
+ NumOuts));
  int in_vec_size = 4;
- DenseTensor *out = (*outs)[0];
+ int out_vec_size = 4;
+ if (NumOuts > 1) {
+ for (int i = 0; i < NumOuts; ++i) {
+ PADDLE_ENFORCE_EQ(
+ (*outs)[i]->dims(),
+ (*outs)[0]->dims(),
+ paddle::platform::errors::InvalidArgument(
+ "The shape of each output tensor shall be identical yet, but "
+ "%dth output tensor`s shape is not.",
+ i));
+ out_vec_size = std::min(
+ paddle::platform::GetVectorizedSize<OutT>((*outs)[i]->data<OutT>()),
+ out_vec_size);
+ }
+ } else {
+ out_vec_size =
+ paddle::platform::GetVectorizedSize<OutT>((*outs)[0]->data<OutT>());
+ }
+
  for (auto *in : ins) {
  auto temp_size = paddle::platform::GetVectorizedSize<InT>(in->data<InT>());
- in_vec_size = in->dims() == out->dims() ? std::min(temp_size, in_vec_size)
- : in_vec_size;
+ in_vec_size = in->dims() == (*outs)[0]->dims()
+ ? std::min(temp_size, in_vec_size)
+ : in_vec_size;
  }
- int out_vec_size =
- paddle::platform::GetVectorizedSize<OutT>(out->data<OutT>());
  int vec_size = std::min(out_vec_size, in_vec_size);
 
  switch (vec_size) {
  case 4: {
- LaunchBroadcastKernelForDifferentVecSize<InT, OutT, Functor, kArity, 4>(
- ctx, ins, out, axis, func);
+ LaunchBroadcastKernelForDifferentVecSize<InT,
+ OutT,
+ Functor,
+ kArity,
+ NumOuts,
+ 4>(ctx, ins, outs, axis, func);
  break;
  }
  case 2: {
- LaunchBroadcastKernelForDifferentVecSize<InT, OutT, Functor, kArity, 2>(
- ctx, ins, out, axis, func);
+ LaunchBroadcastKernelForDifferentVecSize<InT,
+ OutT,
+ Functor,
+ kArity,
+ NumOuts,
+ 2>(ctx, ins, outs, axis, func);
  break;
  }
  case 1: {
- LaunchBroadcastKernelForDifferentVecSize<InT, OutT, Functor, kArity, 1>(
- ctx, ins, out, axis, func);
+ LaunchBroadcastKernelForDifferentVecSize<InT,
+ OutT,
+ Functor,
+ kArity,
+ NumOuts,
+ 1>(ctx, ins, outs, axis, func);
  break;
  }
  default: {

paddle/pten/kernels/hybird/cuda/elementwise/elementwise_common.cu.h

@@ -24,6 +24,12 @@ namespace pten {
 namespace kps = paddle::operators::kernel_primitives;
 enum ElementwiseType { kUnary = 1, kBinary = 2, kTernary = 3, kAny = -1 };
 
+/* Packing scalar type T(float, int etc.) into Array<T, NumOuts> type
+ for supporting multiple-output feature in elementwise system.*/
+template <class T, int Num>
+using OutType =
+ typename std::conditional_t<Num == 1, T, paddle::framework::Array<T, Num>>;
+
 template <typename InT,
  typename OutT,
  int VecSize,
@@ -76,4 +82,39 @@ struct ElementwisePrimitiveCaller<InT, OutT, VecSize, Functor, 3, false> {
  }
 };
 
+template <typename OutT, int VecSize, bool IsBoundary, int NumOuts>
+struct ElementwiseWriteDataCaller {
+ __device__ __forceinline__ void operator()(
+ paddle::framework::Array<OutT *, NumOuts> outs,
+ OutType<OutT, NumOuts> src[VecSize],
+ int block_offset,
+ int num) {
+ OutT dst[NumOuts][VecSize];
+#pragma unroll
+ for (int i = 0; i < VecSize; ++i) {
+#pragma unroll
+ for (int j = 0; j < NumOuts; ++j) {
+ dst[j][i] = (src[i])[j];
+ }
+ }
+#pragma unroll
+ for (int i = 0; i < NumOuts; ++i) {
+ kps::WriteData<OutT, VecSize, 1, 1, IsBoundary>(
+ outs[i] + block_offset, dst[i], num);
+ }
+ }
+};
+
+template <typename OutT, int VecSize, bool IsBoundary>
+struct ElementwiseWriteDataCaller<OutT, VecSize, IsBoundary, 1> {
+ __device__ __forceinline__ void operator()(
+ paddle::framework::Array<OutT *, 1> outs,
+ OutT src[VecSize],
+ int block_offset,
+ int num) {
+ kps::WriteData<OutT, VecSize, 1, 1, IsBoundary>(
+ outs[0] + block_offset, src, num);
+ }
+};
+
 } // namespace pten