forked from mindspore-Ecosystem/mindspore
the performance optimization of logsoftmax operator on gpu device. 50+ times better
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@ -36,43 +36,48 @@ constexpr int ALIGN_BYTES = 16;
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} \
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}
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inline dim3 SpatialSoftMax_getGridSize(dim3 *block, uint32_t max_active_blocks, uint64_t outer_size, uint64_t dim_size,
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inline dim3 SpatialSoftMaxGetGridSize(dim3 *block, uint32_t activate_block, uint64_t outer_size, uint64_t dim_size,
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uint64_t inner_size) {
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// First, tile as many blocks as we can over the y axis
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uint32_t inner_blocks = (inner_size + block->y - 1) / block->y;
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if (inner_blocks > max_active_blocks) inner_blocks = max_active_blocks;
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// Fill the x axis with as many blocks as we can fit (a little more is ok too)
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uint32_t outer_blocks = (max_active_blocks + inner_blocks - 1) / inner_blocks;
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if (outer_blocks > outer_size) outer_blocks = outer_size;
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return dim3(outer_blocks, inner_blocks);
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uint32_t inner = (inner_size + block->y - 1) / block->y;
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if (inner > activate_block) {
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inner = activate_block;
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}
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uint32_t outer = (activate_block + inner - 1) / inner;
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if (outer > outer_size) {
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outer = outer_size;
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}
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return dim3(outer, inner);
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}
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inline dim3 SpatialSoftMax_getBlockSize(uint64_t outer_size, uint64_t dim_size, uint64_t inner_size) {
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uint32_t inner_threads = inner_size;
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inner_threads = std::min(inner_threads, static_cast<uint32_t>(max_threads));
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inline dim3 SpatialSoftMaxGetBlockSize(uint64_t outer_size, uint64_t dim_size, uint64_t inner_size) {
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uint32_t inner_ths = inner_size;
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inner_ths = std::min(inner_ths, static_cast<uint32_t>(max_threads));
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uint32_t dim_threads = 1;
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if (inner_threads <= 64 && dim_size >= 64) {
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while (inner_threads * dim_threads <= max_threads && dim_threads <= dim_size) dim_threads *= 2;
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if (inner_ths <= 64 && dim_size >= 64) {
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while ((inner_ths * dim_threads <= max_threads) && (dim_threads <= dim_size)) {
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dim_threads *= 2;
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}
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dim_threads /= 2;
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}
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return dim3(dim_threads, inner_threads);
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return dim3(dim_threads, inner_ths);
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}
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template <typename accumulate_t, typename Kernel>
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void SpatialSoftMax_getLaunchSizes(Kernel k, uint64_t outer_size, uint64_t dim_size, uint64_t inner_size, dim3 *grid,
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void SpatialSoftMaxGetLaunchSizes(Kernel k, uint64_t outer_size, uint64_t dim_size, uint64_t inner_size, dim3 *grid,
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dim3 *block, uint32_t *smem_size, uint32_t device_id) {
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*block = SpatialSoftMax_getBlockSize(outer_size, dim_size, inner_size);
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uint32_t block_threads = block->x * block->y;
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*smem_size = block->x == 1 ? 0 : block_threads * sizeof(accumulate_t);
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int max_active_blocks;
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cudaOccupancyMaxActiveBlocksPerMultiprocessor(&max_active_blocks, k, block_threads, *smem_size);
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*block = SpatialSoftMaxGetBlockSize(outer_size, dim_size, inner_size);
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uint32_t block_ths = block->x * block->y;
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if (block->x == 1) {
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*smem_size = 0;
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} else {
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*smem_size = block_ths * sizeof(accumulate_t);
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}
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int activate_size;
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cudaOccupancyMaxActiveBlocksPerMultiprocessor(&activate_size, k, block_ths, *smem_size);
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cudaDeviceProp prop;
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(void)cudaGetDeviceProperties(&prop, device_id);
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max_active_blocks *= prop.multiProcessorCount;
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*grid = SpatialSoftMax_getGridSize(block, max_active_blocks, outer_size, dim_size, inner_size);
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activate_size *= prop.multiProcessorCount;
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*grid = SpatialSoftMaxGetGridSize(block, activate_size, outer_size, dim_size, inner_size);
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}
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int log2_ceil(int val) {
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@ -135,63 +140,58 @@ __forceinline__ __device__ T SpatialBlockReduceX(T *memsha, T val) {
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return memsha[0];
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}
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template <typename input_t, typename accumulate_t, typename output_t>
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template <typename input_t, typename accumulate_t, typename output_t, bool is_log_softmax>
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__global__ void SpatialSoftMaxForward(output_t *output, input_t *input, uint32_t outer_size, uint32_t dim_size,
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uint32_t inner_size) {
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extern __shared__ unsigned char smem[];
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auto sdata = reinterpret_cast<accumulate_t *>(smem);
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const uint32_t outer_stride = inner_size * dim_size;
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const uint32_t dim_stride = inner_size;
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for (uint32_t outer_index = blockIdx.x; outer_index < outer_size; outer_index += gridDim.x) {
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const uint32_t outer_offset = outer_index * outer_stride;
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for (uint32_t inner_index = blockIdx.y * blockDim.y + threadIdx.y; inner_index < inner_size;
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inner_index += blockDim.y * gridDim.y) {
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const uint32_t data_offset = outer_offset + inner_index;
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const uint32_t offset = outer_offset + inner_index;
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if (blockDim.x > 1) {
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accumulate_t max_input = std::numeric_limits<accumulate_t>::lowest();
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accumulate_t max_data_input = std::numeric_limits<accumulate_t>::lowest();
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for (uint32_t d = threadIdx.x; d < dim_size; d += blockDim.x) {
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const accumulate_t value = static_cast<accumulate_t>(input[data_offset + d * dim_stride]);
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max_input = atomic::Max()(max_input, value);
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const accumulate_t value = static_cast<accumulate_t>(input[offset + d * dim_stride]);
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max_data_input = atomic::Max()(max_data_input, value);
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}
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max_input = SpatialBlockReduceX<accumulate_t, atomic::Max>(sdata, max_input);
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max_data_input = SpatialBlockReduceX<accumulate_t, atomic::Max>(sdata, max_data_input);
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accumulate_t sum = 0;
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for (uint32_t d = threadIdx.x; d < dim_size; d += blockDim.x)
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sum += std::exp(static_cast<accumulate_t>(input[data_offset + d * dim_stride]) - max_input);
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sum += std::exp(static_cast<accumulate_t>(input[offset + d * dim_stride]) - max_data_input);
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sum = SpatialBlockReduceX<accumulate_t, atomic::Add>(sdata, sum);
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SoftMaxForwardEpilogue<input_t, accumulate_t, output_t> epilogue(max_input, sum);
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SoftMaxForwardEpilogue<input_t, accumulate_t, output_t, is_log_softmax> epilogue(max_data_input, sum);
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for (uint32_t d = threadIdx.x; d < dim_size; d += blockDim.x)
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output[data_offset + d * dim_stride] = epilogue(input[data_offset + d * dim_stride]);
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output[offset + d * dim_stride] = epilogue(input[offset + d * dim_stride]);
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} else {
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accumulate_t max_input = std::numeric_limits<accumulate_t>::lowest();
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accumulate_t max_data_input = std::numeric_limits<accumulate_t>::lowest();
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for (uint32_t d = threadIdx.x; d < dim_size; d += blockDim.x) {
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const accumulate_t value = static_cast<accumulate_t>(input[data_offset + d * dim_stride]);
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max_input = atomic::Max()(max_input, value);
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const accumulate_t value = static_cast<accumulate_t>(input[offset + d * dim_stride]);
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max_data_input = atomic::Max()(max_data_input, value);
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}
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accumulate_t sum = 0;
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for (uint32_t d = threadIdx.x; d < dim_size; d += blockDim.x)
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sum += std::exp(static_cast<accumulate_t>(input[data_offset + d * dim_stride]) - max_input);
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SoftMaxForwardEpilogue<input_t, accumulate_t, output_t> epilogue(max_input, sum);
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sum += std::exp(static_cast<accumulate_t>(input[offset + d * dim_stride]) - max_data_input);
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SoftMaxForwardEpilogue<input_t, accumulate_t, output_t, is_log_softmax> epilogue(max_data_input, sum);
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for (uint32_t d = threadIdx.x; d < dim_size; d += blockDim.x)
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output[data_offset + d * dim_stride] = epilogue(input[data_offset + d * dim_stride]);
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output[offset + d * dim_stride] = epilogue(input[offset + d * dim_stride]);
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}
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}
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}
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}
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template <int InsP, typename input_t, typename accum_t, typename output_t>
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template <int InsP, typename input_t, typename accum_t, typename output_t, bool is_log_softmax>
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__device__ __forceinline__ void WriteResults(int clas, input_t *input, output_t *output, input_t max_k,
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input_t sum_all) {
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SoftMaxForwardEpilogue<input_t, accum_t, output_t> epilogue(max_k, sum_all);
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SoftMaxForwardEpilogue<input_t, accum_t, output_t, is_log_softmax> epilogue(max_k, sum_all);
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int offset = threadIdx.x;
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int last = clas % (InsP * blockDim.x);
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for (; offset < clas - last; offset += blockDim.x * InsP) {
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input_t tmp[InsP];
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#pragma unroll
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for (int j = 0; j < InsP; ++j) {
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tmp[j] = input[offset + j * blockDim.x];
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@ -201,54 +201,42 @@ __device__ __forceinline__ void WriteResults(int clas, input_t *input, output_t
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output[offset + j * blockDim.x] = epilogue(tmp[j]);
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}
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}
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for (; offset < clas; offset += blockDim.x) {
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output[offset] = epilogue(input[offset]);
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}
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}
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template <int InsP, typename input_t, typename accum_t, typename output_t>
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__device__ __forceinline__ void WriteResultsVectorized(int size, const int shift, input_t *input, output_t *output,
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template <int InsP, typename input_t, typename accum_t, typename output_t, bool is_log_softmax>
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__device__ __forceinline__ void WriteResultsVectorized(int size, const int deviate, input_t *input, output_t *output,
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input_t max_k, input_t sum_all) {
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SoftMaxForwardEpilogue<input_t, accum_t, output_t> epilogue(max_k, sum_all);
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using LoadT = aligned_vector<input_t>;
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using StoreT = aligned_vector<output_t>;
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SoftMaxForwardEpilogue<input_t, accum_t, output_t, is_log_softmax> epilogue(max_k, sum_all);
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using loadT = aligned_vector<input_t>;
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using storeT = aligned_vector<output_t>;
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int offset = threadIdx.x;
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if (shift > 0) {
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input -= shift;
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output -= shift;
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size += shift;
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if (threadIdx.x >= shift) {
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if (deviate > 0) {
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input -= deviate;
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output -= deviate;
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size += deviate;
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if (threadIdx.x >= deviate) {
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output[offset] = epilogue(input[offset]);
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}
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size -= blockDim.x;
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input += blockDim.x;
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output += blockDim.x;
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}
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const int last = size % (InsP * blockDim.x);
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input_t in_v[InsP];
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LoadT *in_value = reinterpret_cast<LoadT *>(&in_v);
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loadT *in_value = reinterpret_cast<loadT *>(&in_v);
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output_t out_v[InsP];
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StoreT *out_value = reinterpret_cast<StoreT *>(&out_v);
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storeT *out_value = reinterpret_cast<storeT *>(&out_v);
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for (; offset * InsP < (size - last); offset += blockDim.x) {
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*in_value = reinterpret_cast<LoadT *>(input)[offset];
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*in_value = reinterpret_cast<loadT *>(input)[offset];
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#pragma unroll
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for (int j = 0; j < InsP; ++j) {
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out_v[j] = epilogue(in_v[j]);
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}
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reinterpret_cast<StoreT *>(output)[offset] = *out_value;
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reinterpret_cast<storeT *>(output)[offset] = *out_value;
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}
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offset = size - last + threadIdx.x;
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for (; offset < size; offset += blockDim.x) {
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output[offset] = epilogue(input[offset]);
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@ -256,52 +244,43 @@ __device__ __forceinline__ void WriteResultsVectorized(int size, const int shift
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}
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template <typename Reduction, typename AccT>
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__device__ __forceinline__ AccT ReduceBlock(AccT *smem, AccT val, AccT defaultVal) {
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__device__ __forceinline__ AccT ReduceBlock(AccT *sharemen, AccT val, AccT initVal) {
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Reduction r = Reduction();
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__syncthreads();
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smem[threadIdx.x] = val;
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sharemen[threadIdx.x] = val;
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__syncthreads();
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AccT warpVal = defaultVal;
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AccT warpVal = initVal;
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uint32_t mask = (((uint64_t)1) << (blockDim.x / WARPSIZE)) - 1;
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if (threadIdx.x < WARPSIZE) {
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int lane = threadIdx.x % WARPSIZE;
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if (lane < blockDim.x / WARPSIZE) {
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#pragma unroll
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for (int i = 0; i < WARPSIZE; ++i) {
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warpVal = r(warpVal, smem[lane * WARPSIZE + i]);
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warpVal = r(warpVal, sharemen[lane * WARPSIZE + i]);
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}
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#ifndef __HIP_PLATFORM_HCC__
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__syncwarp(mask);
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#endif
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smem[lane] = warpVal;
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sharemen[lane] = warpVal;
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}
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}
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__syncthreads();
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AccT blockVal = defaultVal;
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AccT blockVal = initVal;
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if (threadIdx.x == 0) {
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for (int i = 0; i < blockDim.x / WARPSIZE; ++i) {
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blockVal = r(blockVal, smem[i]);
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blockVal = r(blockVal, sharemen[i]);
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}
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smem[0] = blockVal;
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sharemen[0] = blockVal;
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}
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__syncthreads();
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return smem[0];
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return sharemen[0];
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}
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template <template <typename, typename> class Reduction, int InsP, typename T, typename AccT>
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__device__ __forceinline__ AccT ILPReduce(int shift, T *data, int size, const Reduction<T, AccT> &r, AccT defaultVal) {
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using LoadT = aligned_vector<T>;
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AccT threadVal = defaultVal;
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__device__ __forceinline__ AccT ILPReduce(int shift, T *data, int size, const Reduction<T, AccT> &r, AccT initVal) {
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using loadT = aligned_vector<T>;
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AccT threadVal = initVal;
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int offset = threadIdx.x;
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if (shift > 0) {
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data -= shift;
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size += shift;
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@ -313,10 +292,9 @@ __device__ __forceinline__ AccT ILPReduce(int shift, T *data, int size, const Re
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}
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int last = size % (InsP * blockDim.x);
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T v[InsP];
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LoadT *value = reinterpret_cast<LoadT *>(&v);
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loadT *value = reinterpret_cast<loadT *>(&v);
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for (; offset * InsP < (size - last); offset += blockDim.x) {
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*value = reinterpret_cast<LoadT *>(data)[offset];
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*value = reinterpret_cast<loadT *>(data)[offset];
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#pragma unroll
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for (int j = 0; j < InsP; ++j) {
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threadVal = r(threadVal, v[j]);
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@ -324,7 +302,6 @@ __device__ __forceinline__ AccT ILPReduce(int shift, T *data, int size, const Re
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}
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offset = size - last + threadIdx.x;
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for (; offset < size; offset += blockDim.x) threadVal = r(threadVal, data[offset]);
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return threadVal;
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}
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@ -350,7 +327,6 @@ __global__ void SoftMaxWarpForward(output_t *dst, const input_t *src, int batch_
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constexpr int WARP_BATCH = (next_power_of_two <= 128) ? 2 : 1;
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int first_batch = (blockDim.y * blockIdx.x + threadIdx.y) * WARP_BATCH;
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int local_batches = batch_size - first_batch;
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if (local_batches > WARP_BATCH) local_batches = WARP_BATCH;
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int local_idx = threadIdx.x;
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@ -449,7 +425,6 @@ __global__ void SoftMaxWarpForward(output_t *dst, const input_t *src, int batch_
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}
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}
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warp_reduce<acc_t, WARP_BATCH, WARP_SIZE, atomic::Add>(sum);
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#pragma unroll
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for (int i = 0; i < WARP_BATCH; ++i) {
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if (i >= local_batches) break;
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@ -472,17 +447,15 @@ __global__ void SoftMaxWarpForward(output_t *dst, const input_t *src, int batch_
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}
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}
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template <int InsP, typename T, typename accumulate_t>
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template <int InsP, typename T, typename accumulate_t, bool is_log_softmax>
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__global__ void cunn_SoftMaxForward(T *output, T *input, int classes) {
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extern __shared__ unsigned char smem[];
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auto sdata = reinterpret_cast<accumulate_t *>(smem);
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using LoadT = aligned_vector<T>;
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using StoreT = aligned_vector<T>;
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using loadT = aligned_vector<T>;
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using storeT = aligned_vector<T>;
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input += blockIdx.x * classes;
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output += blockIdx.x * classes;
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const int shift = ((uint64_t)input) % ALIGN_BYTES / sizeof(T);
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const int output_shift = ((uint64_t)output) % ALIGN_BYTES / sizeof(T);
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@ -496,14 +469,13 @@ __global__ void cunn_SoftMaxForward(T *output, T *input, int classes) {
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accumulate_t sumAll = ReduceBlock<atomic::Add, accumulate_t>(sdata, threadExp, static_cast<accumulate_t>(0));
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if (shift == output_shift) {
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WriteResultsVectorized<InsP, T, accumulate_t, T>(classes, shift, input, output, max_k, sumAll);
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WriteResultsVectorized<InsP, T, accumulate_t, T, is_log_softmax>(classes, shift, input, output, max_k, sumAll);
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} else {
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WriteResults<InsP, T, accumulate_t, T>(classes, input, output, max_k, sumAll);
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WriteResults<InsP, T, accumulate_t, T, is_log_softmax>(classes, input, output, max_k, sumAll);
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}
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}
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// end of kernel function
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template <typename input_t, typename output_t, typename acc_t, bool is_log_softmax, bool is_masked>
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void dispatch_softmax_forward(output_t *dst, const input_t *src, int softmax_elements, int softmax_elements_stride,
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int batch_count, cudaStream_t stream, const bool *mask = nullptr, int chunk_size = -1,
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@ -567,16 +539,16 @@ void Softmax(T *input_, T *output_, size_t dim_size_, size_t outer_size_, size_t
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} else {
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constexpr int InsP = sizeof(float4) / sizeof(T);
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dim3 block = SoftMaxGetBlockSize(InsP, dim_size_);
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cunn_SoftMaxForward<InsP, T, accumulate_t>
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cunn_SoftMaxForward<InsP, T, accumulate_t, is_log_softmax>
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<<<grid, block, block.x * sizeof(T), cuda_stream>>>(output_, input_, dim_size_);
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CUDA_CHECK();
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}
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} else {
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||||
uint32_t smem_size;
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||||
dim3 grid, block;
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||||
SpatialSoftMax_getLaunchSizes<T>(&SpatialSoftMaxForward<T, accumulate_t, T>, outer_size_, dim_size_, inner_size_,
|
||||
&grid, &block, &smem_size, device_id);
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||||
SpatialSoftMaxForward<T, accumulate_t, T>
|
||||
SpatialSoftMaxGetLaunchSizes<T>(&SpatialSoftMaxForward<T, accumulate_t, T, is_log_softmax>, outer_size_, dim_size_,
|
||||
inner_size_, &grid, &block, &smem_size, device_id);
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||||
SpatialSoftMaxForward<T, accumulate_t, T, is_log_softmax>
|
||||
<<<grid, block, smem_size, cuda_stream>>>(output_, input_, outer_size_, dim_size_, inner_size_);
|
||||
CUDA_CHECK();
|
||||
}
|
||||
|
|
|
|||
|
|
@ -37,14 +37,14 @@ struct AccumulateType<double, true> {
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|||
template <typename T, bool is_cuda>
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||||
using acc_type = typename AccumulateType<T, is_cuda>::type;
|
||||
|
||||
template <typename T, typename AccumT, typename OutT>
|
||||
template <typename T, typename AccumT, typename OutT, bool is_log_softmax>
|
||||
struct SoftMaxForwardEpilogue {
|
||||
__device__ __forceinline__ SoftMaxForwardEpilogue(AccumT max_input, AccumT sum) : max_input(max_input), sum(sum) {}
|
||||
|
||||
__device__ __forceinline__ SoftMaxForwardEpilogue(AccumT max_input, AccumT sum)
|
||||
: max_input(max_input), sum(is_log_softmax == true ? std::log(sum) : sum) {}
|
||||
__device__ __forceinline__ OutT operator()(T input) const {
|
||||
return static_cast<OutT>(std::exp((AccumT)input - max_input) / sum);
|
||||
return is_log_softmax == true ? static_cast<OutT>((AccumT)input - max_input - sum)
|
||||
: static_cast<OutT>(std::exp((AccumT)input - max_input) / sum);
|
||||
}
|
||||
|
||||
const AccumT max_input;
|
||||
const AccumT sum;
|
||||
};
|
||||
|
|
|
|||
Loading…
Reference in New Issue