chore: import upstream snapshot with attribution

paddle/phi/kernels/gpu/logsumexp_function.cu.h

@@ -0,0 +1,486 @@
+// Copyright (c) 2023 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.
+#pragma once
+
+#include <assert.h>
+#include "paddle/phi/kernels/funcs/aligned_vector.h"
+#include "paddle/phi/kernels/primitive/functor_primitives.h"
+
+#define CUDART_INF __longlong_as_double(0x7ff0000000000000ULL)
+#define CUDART_INF_F __int_as_float(0x7f800000)
+
+namespace phi {
+namespace funcs {
+
+constexpr int kWarpSize = 32;
+
+template <typename T>
+__inline__ __device__ T Inf();
+
+template <>
+__inline__ __device__ float Inf<float>() {
+  return CUDART_INF_F;
+}
+
+template <>
+__inline__ __device__ double Inf<double>() {
+  return CUDART_INF;
+}
+
+template <typename T,
+          template <typename>
+          class Functor,
+          int ThreadGroupWidth = kWarpSize>
+__inline__ __device__ T WarpAllReduce(T val) {
+  for (int mask = ThreadGroupWidth / 2; mask > 0; mask /= 2) {
+#if PADDLE_WITH_HIP
+    val = Functor<T>()(val, __shfl_xor(0xffffffff, val, mask));
+#else
+    val = Functor<T>()(val, __shfl_xor_sync(0xffffffff, val, mask));
+#endif
+  }
+  return val;
+}
+
+#if PADDLE_WITH_HIP
+inline void GetNumBlocks(int64_t block_size,
+                         int64_t max_blocks,
+                         int64_t waves,
+                         int* num_blocks) {
+  int dev;
+  PADDLE_ENFORCE_GPU_SUCCESS(hipGetDevice(&dev));
+  int sm_count;
+  PADDLE_ENFORCE_GPU_SUCCESS(hipDeviceGetAttribute(
+      &sm_count, hipDeviceAttributeMultiprocessorCount, dev));
+  int tpm;
+  PADDLE_ENFORCE_GPU_SUCCESS(hipDeviceGetAttribute(
+      &tpm, hipDeviceAttributeMaxThreadsPerMultiProcessor, dev));
+  *num_blocks = std::max<int>(
+      1, std::min<int64_t>(max_blocks, sm_count * tpm / block_size * waves));
+}
+#else
+inline void GetNumBlocks(int64_t block_size,
+                         int64_t max_blocks,
+                         int64_t waves,
+                         int* num_blocks) {
+  int dev;
+  PADDLE_ENFORCE_GPU_SUCCESS(cudaGetDevice(&dev));
+  int sm_count;
+  PADDLE_ENFORCE_GPU_SUCCESS(
+      cudaDeviceGetAttribute(&sm_count, cudaDevAttrMultiProcessorCount, dev));
+  int tpm;
+  PADDLE_ENFORCE_GPU_SUCCESS(cudaDeviceGetAttribute(
+      &tpm, cudaDevAttrMaxThreadsPerMultiProcessor, dev));
+  *num_blocks = std::max<int>(
+      1, std::min<int64_t>(max_blocks, sm_count * tpm / block_size * waves));
+}
+#endif
+
+template <typename T,
+          typename SourceType,
+          typename Context,
+          int VecSize,
+          int ColsPerThread,
+          int RowsPerThread,
+          int ThreadGroupWidth,
+          bool NeedPadding>
+__global__ void LogsumexpWarpImpl(const Context& dev_ctx,
+                                  const int64_t num_row,
+                                  const int64_t num_col,
+                                  const SourceType* in,
+                                  SourceType* out) {
+  static_assert(ColsPerThread % VecSize == 0, "");
+  static_assert(ThreadGroupWidth <= kWarpSize, "");
+  static_assert(kWarpSize % ThreadGroupWidth == 0, "");
+  constexpr int num_read = ColsPerThread / VecSize;
+  assert(num_col <= ColsPerThread * ThreadGroupWidth);
+  const int group_id = blockIdx.x * blockDim.y + threadIdx.y;
+  const int num_thread_group = gridDim.x * blockDim.y;
+  const int thread_id = threadIdx.x;
+  const int step = num_thread_group * RowsPerThread;
+
+  using LoadType = AlignedVector<SourceType, VecSize>;
+  using StoreType = AlignedVector<SourceType, RowsPerThread>;
+
+  LoadType load_vec;
+  StoreType store_vec;
+
+  T buffer[RowsPerThread][ColsPerThread];
+
+  for (int64_t cur_row = group_id * RowsPerThread; cur_row < num_row;
+       cur_row += step) {
+    T thread_max[RowsPerThread];
+// Read data
+#pragma unroll
+    for (int row_id = 0; row_id < RowsPerThread; row_id++) {
+      thread_max[row_id] = -Inf<T>();
+      T* row_buffer = buffer[row_id];
+#pragma unroll
+      for (int read_id = 0; read_id < num_read; read_id++) {
+        const int offset = read_id * VecSize;
+        const int cur_col = (read_id * ThreadGroupWidth + thread_id) * VecSize;
+        if (!NeedPadding || cur_col < num_col) {
+          int64_t load_offset = ((cur_row + row_id) * num_col + cur_col);
+          Load<SourceType, VecSize>(in + load_offset, &load_vec);
+#pragma unroll
+          for (int i = 0; i < VecSize; i++) {
+            row_buffer[offset + i] = static_cast<T>(load_vec[i]);
+            thread_max[row_id] =
+                max(thread_max[row_id], row_buffer[offset + i]);
+          }
+        } else {
+#pragma unroll
+          for (int i = 0; i < VecSize; i++) {
+            row_buffer[offset + i] = -Inf<T>();
+          }
+        }
+      }
+    }
+    T warp_max[RowsPerThread];
+// Get warp max
+#pragma unroll
+    for (int row_id = 0; row_id < RowsPerThread; row_id++) {
+      warp_max[row_id] = WarpAllReduce<T, kps::MaxFunctor, ThreadGroupWidth>(
+          thread_max[row_id]);
+    }
+    T thread_sum[RowsPerThread];
+// Calculate
+#pragma unroll
+    for (int row_id = 0; row_id < RowsPerThread; row_id++) {
+      thread_sum[row_id] = 0;
+      T* row_buffer = buffer[row_id];
+#pragma unroll
+      for (int i = 0; i < ColsPerThread; i++) {
+        thread_sum[row_id] += exp(row_buffer[i] - warp_max[row_id]);
+      }
+    }
+// Get warp sum and write
+#pragma unroll
+    for (int row_id = 0; row_id < RowsPerThread; row_id++) {
+      T res = log(WarpAllReduce<T, kps::AddFunctor, ThreadGroupWidth>(
+          thread_sum[row_id]));
+      store_vec[row_id] = static_cast<SourceType>(res + warp_max[row_id]);
+    }
+    if (thread_id == 0 && cur_row < num_row) {
+      Store<SourceType, RowsPerThread>(store_vec, out + cur_row);
+    }
+  }
+}
+
+template <typename T,
+          typename SourceType,
+          typename Context,
+          int VecSize,
+          int ColsPerThread,
+          int RowsPerThread,
+          int ThreadGroupWidth,
+          bool NeedPadding>
+#if PADDLE_WITH_HIP
+inline hipError_t LaunchLogsumexpWarp(const Context& dev_ctx,
+                                      const int64_t num_row,
+                                      const int64_t num_col,
+                                      const SourceType* in,
+                                      SourceType* out) {
+#else
+inline cudaError_t LaunchLogsumexpWarp(const Context& dev_ctx,
+                                       const int64_t num_row,
+                                       const int64_t num_col,
+                                       const SourceType* in,
+                                       SourceType* out) {
+#endif
+  constexpr int block_size = 128;
+  constexpr int waves = 32;
+  static_assert(block_size % ThreadGroupWidth == 0, "");
+  constexpr int thread_groups_per_block = block_size / ThreadGroupWidth;
+  dim3 block_dim(ThreadGroupWidth, thread_groups_per_block);
+  const int64_t num_blocks =
+      (num_row / RowsPerThread + thread_groups_per_block - 1) /
+      thread_groups_per_block;
+  int grid_dim_x;
+  { GetNumBlocks(block_size, num_blocks, waves, &grid_dim_x); }
+  LogsumexpWarpImpl<T,
+                    SourceType,
+                    Context,
+                    VecSize,
+                    ColsPerThread,
+                    RowsPerThread,
+                    ThreadGroupWidth,
+                    NeedPadding>
+      <<<grid_dim_x, block_dim, 0, dev_ctx.stream()>>>(
+          dev_ctx, num_row, num_col, in, out);
+#if PADDLE_WITH_HIP
+  return hipPeekAtLastError();
+#else
+  return cudaPeekAtLastError();
+#endif
+}
+
+template <typename T,
+          typename SourceType,
+          typename Context,
+          int VecSize,
+          int ColsPerThread,
+          int RowsPerThread,
+          int ThreadGroupWidth>
+#if PADDLE_WITH_HIP
+inline hipError_t DispatchLogsumexpWarpWithPadding(const Context& dev_ctx,
+                                                   const int64_t num_row,
+                                                   const int64_t num_col,
+                                                   const SourceType* in,
+                                                   SourceType* out) {
+#else
+inline cudaError_t DispatchLogsumexpWarpWithPadding(const Context& dev_ctx,
+                                                    const int64_t num_row,
+                                                    const int64_t num_col,
+                                                    const SourceType* in,
+                                                    SourceType* out) {
+#endif
+  if (num_col == ColsPerThread * ThreadGroupWidth) {
+    return LaunchLogsumexpWarp<T,
+                               SourceType,
+                               Context,
+                               VecSize,
+                               ColsPerThread,
+                               RowsPerThread,
+                               ThreadGroupWidth,
+                               false>(dev_ctx, num_row, num_col, in, out);
+  } else {
+    return LaunchLogsumexpWarp<T,
+                               SourceType,
+                               Context,
+                               VecSize,
+                               ColsPerThread,
+                               RowsPerThread,
+                               ThreadGroupWidth,
+                               true>(dev_ctx, num_row, num_col, in, out);
+  }
+}
+
+template <typename T, typename SourceType, typename Context, int VecSize>
+#if PADDLE_WITH_HIP
+typename std::enable_if<VecSize == 1, hipError_t>::type
+DispatchLogsumexpWarpCols(const Context& dev_ctx,
+                          const int64_t num_row,
+                          const int64_t num_col,
+                          const SourceType* in,
+                          SourceType* out) {
+#else
+typename std::enable_if<VecSize == 1, cudaError_t>::type
+DispatchLogsumexpWarpCols(const Context& dev_ctx,
+                          const int64_t num_row,
+                          const int64_t num_col,
+                          const SourceType* in,
+                          SourceType* out) {
+#endif
+  if (num_col <= 0) {
+#if PADDLE_WITH_HIP
+    return hipErrorInvalidValue;
+#else
+    return cudaErrorInvalidValue;
+#endif
+  }
+#define HANDLE_THREAD_GROUP(thread_group_width)                    \
+  if (num_col <= (thread_group_width)*VecSize) {                   \
+    if (num_row % 2 == 0) {                                        \
+      return DispatchLogsumexpWarpWithPadding<T,                   \
+                                              SourceType,          \
+                                              Context,             \
+                                              VecSize,             \
+                                              VecSize,             \
+                                              2,                   \
+                                              thread_group_width>( \
+          dev_ctx, num_row, num_col, in, out);                     \
+    } else {                                                       \
+      return DispatchLogsumexpWarpWithPadding<T,                   \
+                                              SourceType,          \
+                                              Context,             \
+                                              VecSize,             \
+                                              VecSize,             \
+                                              1,                   \
+                                              thread_group_width>( \
+          dev_ctx, num_row, num_col, in, out);                     \
+    }                                                              \
+  }
+  HANDLE_THREAD_GROUP(1)
+  HANDLE_THREAD_GROUP(2)
+  HANDLE_THREAD_GROUP(4)
+  HANDLE_THREAD_GROUP(8)
+  HANDLE_THREAD_GROUP(16)
+  HANDLE_THREAD_GROUP(32)
+#undef HANDLE_ROWS
+// if num_col > 32
+#define HANDLE_COL(col)                                 \
+  if (num_col <= (col)*kWarpSize) {                     \
+    return DispatchLogsumexpWarpWithPadding<T,          \
+                                            SourceType, \
+                                            Context,    \
+                                            VecSize,    \
+                                            col,        \
+                                            1,          \
+                                            kWarpSize>( \
+        dev_ctx, num_row, num_col, in, out);            \
+  }
+
+  HANDLE_COL(2)
+  HANDLE_COL(3)
+  HANDLE_COL(4)
+  HANDLE_COL(5)
+  HANDLE_COL(6)
+  HANDLE_COL(7)
+  HANDLE_COL(8)
+  HANDLE_COL(9)
+  HANDLE_COL(10)
+  HANDLE_COL(11)
+  HANDLE_COL(12)
+  HANDLE_COL(13)
+  HANDLE_COL(14)
+  HANDLE_COL(15)
+  HANDLE_COL(16)
+  HANDLE_COL(17)
+  HANDLE_COL(18)
+  HANDLE_COL(19)
+  HANDLE_COL(20)
+  HANDLE_COL(21)
+  HANDLE_COL(22)
+  HANDLE_COL(23)
+  HANDLE_COL(24)
+  HANDLE_COL(25)
+  HANDLE_COL(26)
+  HANDLE_COL(27)
+  HANDLE_COL(28)
+  HANDLE_COL(29)
+  HANDLE_COL(30)
+  HANDLE_COL(31)
+  HANDLE_COL(32)
+#undef HANDLE_COL
+#if PADDLE_WITH_HIP
+  return hipErrorInvalidValue;
+#else
+  return cudaErrorInvalidValue;
+#endif
+}
+
+template <typename T, typename SourceType, typename Context, int VecSize>
+#if PADDLE_WITH_HIP
+typename std::enable_if<VecSize == 2, hipError_t>::type
+DispatchLogsumexpWarpCols(const Context& dev_ctx,
+                          const int64_t num_row,
+                          const int64_t num_col,
+                          const SourceType* in,
+                          SourceType* out) {
+#else
+typename std::enable_if<VecSize == 2, cudaError_t>::type
+DispatchLogsumexpWarpCols(const Context& dev_ctx,
+                          const int64_t num_row,
+                          const int64_t num_col,
+                          const SourceType* in,
+                          SourceType* out) {
+#endif
+  if (num_col <= 0) {
+#if PADDLE_WITH_HIP
+    return hipErrorInvalidValue;
+#else
+    return cudaErrorInvalidValue;
+#endif
+  }
+#define HANDLE_THREAD_GROUP(thread_group_width)                    \
+  if (num_col <= (thread_group_width)*VecSize) {                   \
+    if (num_row % 2 == 0) {                                        \
+      return DispatchLogsumexpWarpWithPadding<T,                   \
+                                              SourceType,          \
+                                              Context,             \
+                                              VecSize,             \
+                                              VecSize,             \
+                                              2,                   \
+                                              thread_group_width>( \
+          dev_ctx, num_row, num_col, in, out);                     \
+    } else {                                                       \
+      return DispatchLogsumexpWarpWithPadding<T,                   \
+                                              SourceType,          \
+                                              Context,             \
+                                              VecSize,             \
+                                              VecSize,             \
+                                              1,                   \
+                                              thread_group_width>( \
+          dev_ctx, num_row, num_col, in, out);                     \
+    }                                                              \
+  }
+  HANDLE_THREAD_GROUP(1)
+  HANDLE_THREAD_GROUP(2)
+  HANDLE_THREAD_GROUP(4)
+  HANDLE_THREAD_GROUP(8)
+  HANDLE_THREAD_GROUP(16)
+  HANDLE_THREAD_GROUP(32)
+#undef HANDLE_THREAD_GROUP
+// if num_col > 32
+#define HANDLE_COL(col)                                 \
+  if (num_col <= (col)*kWarpSize) {                     \
+    return DispatchLogsumexpWarpWithPadding<T,          \
+                                            SourceType, \
+                                            Context,    \
+                                            VecSize,    \
+                                            col,        \
+                                            1,          \
+                                            kWarpSize>( \
+        dev_ctx, num_row, num_col, in, out);            \
+  }
+
+  HANDLE_COL(4)
+  HANDLE_COL(6)
+  HANDLE_COL(8)
+  HANDLE_COL(10)
+  HANDLE_COL(12)
+  HANDLE_COL(14)
+  HANDLE_COL(16)
+  HANDLE_COL(18)
+  HANDLE_COL(20)
+  HANDLE_COL(22)
+  HANDLE_COL(24)
+  HANDLE_COL(26)
+  HANDLE_COL(28)
+  HANDLE_COL(30)
+  HANDLE_COL(32)
+#undef HANDLE_COL
+#if PADDLE_WITH_HIP
+  return hipErrorInvalidValue;
+#else
+  return cudaErrorInvalidValue;
+#endif
+}
+
+template <typename T, typename SourceType, typename Context>
+#if PADDLE_WITH_HIP
+inline hipError_t DispatchLogsumexpWarp(const Context& dev_ctx,
+                                        const int64_t num_row,
+                                        const int64_t num_col,
+                                        const SourceType* in,
+                                        SourceType* out) {
+#else
+inline cudaError_t DispatchLogsumexpWarp(const Context& dev_ctx,
+                                         const int64_t num_row,
+                                         const int64_t num_col,
+                                         const SourceType* in,
+                                         SourceType* out) {
+#endif
+  // dispatch logsumexp warp with vecsize
+  if (num_col % 2 == 0) {
+    return DispatchLogsumexpWarpCols<T, SourceType, Context, 2>(
+        dev_ctx, num_row, num_col, in, out);
+  } else {
+    return DispatchLogsumexpWarpCols<T, SourceType, Context, 1>(
+        dev_ctx, num_row, num_col, in, out);
+  }
+}
+}  // namespace funcs
+}  // namespace phi