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https://github.com/huggingface/candle.git
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Cleanup some todos. (#226)
* Cleanup some todos. * Fix more todo. * Optimize for the contiguous case. * Add the IntDType trait. * Handle the intdtype trait for more ops. * Remove a todo. * Remove a todo.
This commit is contained in:
Laurent Mazare
@ -1,26 +1,20 @@
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// TODO: Use a proper distributed reduction rather than atomicAdd.
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// https://people.maths.ox.ac.uk/gilesm/cuda/prac4/reduction.pdf
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#include "cuda_utils.cuh"
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#include<stdint.h>
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#include<cmath>
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#include <cmath>
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#include <stdint.h>
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const int BLOCK_SIZE = 1024;
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// TODO: Maybe add some fast_sum_f16_f32 variant that not only accumulate in f32 but
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// also expect a f32 output so that this can be used for normalization e.g. in softmax.
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// TODO: Maybe add some fast_sum_f16_f32 variant that not only accumulate in f32
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// but also expect a f32 output so that this can be used for normalization e.g.
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// in softmax.
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// Fast reduce sum kernel, this assumes that the dimensions to loop over are at
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// the end, each block is responsible for populating one value in the output array.
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// There are at most 1024 threads per block.
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// the end, each block is responsible for populating one value in the output
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// array. There are at most 1024 threads per block.
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template <typename T>
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__device__ void fast_sum(
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const size_t src_numel,
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const size_t el_to_sum_per_block,
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const size_t num_dims,
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const size_t *info,
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const T *src,
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T *dst
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) {
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__device__ void
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fast_sum(const size_t src_numel, const size_t el_to_sum_per_block,
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const size_t num_dims, const size_t *info, const T *src, T *dst) {
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const size_t *dims = info;
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const size_t *strides = info + num_dims;
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@ -47,21 +41,18 @@ __device__ void fast_sum(
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// https://stackoverflow.com/questions/66078814/is-cuda-atomicadd-operation-faster-than-launch-another-kernel-when-we-do-reduce
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for (int s = blockDim.x / 2; s > 0; s >>= 1) {
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__syncthreads();
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if (tid < s) shr[tid] += shr[tid + s];
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if (tid < s)
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shr[tid] += shr[tid + s];
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}
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if (tid == 0) dst[dst_id] = shr[0];
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if (tid == 0)
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dst[dst_id] = shr[0];
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}
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template <typename T>
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__device__ void fast_max(
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const size_t src_numel,
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const size_t el_to_sum_per_block,
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const size_t num_dims,
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const size_t *info,
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const T *src,
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T *dst
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) {
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__device__ void
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fast_max(const size_t src_numel, const size_t el_to_sum_per_block,
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const size_t num_dims, const size_t *info, const T *src, T *dst) {
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const size_t *dims = info;
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const size_t *strides = info + num_dims;
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@ -88,21 +79,18 @@ __device__ void fast_max(
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// https://stackoverflow.com/questions/66078814/is-cuda-atomicadd-operation-faster-than-launch-another-kernel-when-we-do-reduce
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for (int s = blockDim.x / 2; s > 0; s >>= 1) {
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__syncthreads();
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if (tid < s) shr[tid] = maxg(shr[tid], shr[tid + s]);
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if (tid < s)
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shr[tid] = maxg(shr[tid], shr[tid + s]);
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}
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if (tid == 0) dst[dst_id] = shr[0];
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if (tid == 0)
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dst[dst_id] = shr[0];
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}
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template <typename T>
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__device__ void fast_min(
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const size_t src_numel,
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const size_t el_to_sum_per_block,
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const size_t num_dims,
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const size_t *info,
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const T *src,
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T *dst
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) {
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__device__ void
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fast_min(const size_t src_numel, const size_t el_to_sum_per_block,
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const size_t num_dims, const size_t *info, const T *src, T *dst) {
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const size_t *dims = info;
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const size_t *strides = info + num_dims;
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@ -129,83 +117,69 @@ __device__ void fast_min(
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// https://stackoverflow.com/questions/66078814/is-cuda-atomicadd-operation-faster-than-launch-another-kernel-when-we-do-reduce
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for (int s = blockDim.x / 2; s > 0; s >>= 1) {
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__syncthreads();
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if (tid < s) shr[tid] = ming(shr[tid], shr[tid + s]);
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if (tid < s)
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shr[tid] = ming(shr[tid], shr[tid + s]);
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}
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if (tid == 0) dst[dst_id] = shr[0];
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if (tid == 0)
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dst[dst_id] = shr[0];
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}
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#define FAST_OP(TYPENAME, MIN_NAME, MAX_NAME, SUM_NAME) \
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extern "C" __global__ void MIN_NAME( \
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const size_t src_numel, \
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const size_t el_to_sum_per_block, \
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const size_t num_dims, \
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const size_t *info, \
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const TYPENAME *src, \
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TYPENAME *dst \
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) { \
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fast_min(src_numel, el_to_sum_per_block, num_dims, info, src, dst); \
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} \
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extern "C" __global__ void MAX_NAME( \
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const size_t src_numel, \
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const size_t el_to_sum_per_block, \
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const size_t num_dims, \
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const size_t *info, \
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const TYPENAME *src, \
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TYPENAME *dst \
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) { \
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fast_max(src_numel, el_to_sum_per_block, num_dims, info, src, dst); \
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} \
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extern "C" __global__ void SUM_NAME( \
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const size_t src_numel, \
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const size_t el_to_sum_per_block, \
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const size_t num_dims, \
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const size_t *info, \
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const TYPENAME *src, \
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TYPENAME *dst \
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) { \
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fast_sum(src_numel, el_to_sum_per_block, num_dims, info, src, dst); \
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} \
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#define FAST_OP(TYPENAME, MIN_NAME, MAX_NAME, SUM_NAME) \
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extern "C" __global__ void MIN_NAME( \
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const size_t src_numel, const size_t el_to_sum_per_block, \
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const size_t num_dims, const size_t *info, const TYPENAME *src, \
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TYPENAME *dst) { \
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fast_min(src_numel, el_to_sum_per_block, num_dims, info, src, dst); \
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} \
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extern "C" __global__ void MAX_NAME( \
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const size_t src_numel, const size_t el_to_sum_per_block, \
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const size_t num_dims, const size_t *info, const TYPENAME *src, \
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TYPENAME *dst) { \
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fast_max(src_numel, el_to_sum_per_block, num_dims, info, src, dst); \
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} \
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extern "C" __global__ void SUM_NAME( \
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const size_t src_numel, const size_t el_to_sum_per_block, \
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const size_t num_dims, const size_t *info, const TYPENAME *src, \
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TYPENAME *dst) { \
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fast_sum(src_numel, el_to_sum_per_block, num_dims, info, src, dst); \
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}
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#define SUM_OP(TYPENAME, FN_NAME) \
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extern "C" __global__ void FN_NAME( \
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const size_t numel, \
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const size_t num_dims, \
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const size_t num_sum_dims, \
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const size_t *info, \
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const TYPENAME *inp, \
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TYPENAME *out \
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) { \
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const size_t *dims = info; \
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const size_t *strides = info + num_dims; \
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const size_t *sum_dims_l = info + 2*num_dims; \
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const size_t *sum_dims_s = info + 2*num_dims + num_sum_dims; \
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if (is_contiguous(num_dims, dims, strides)) { \
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for (unsigned int i = blockIdx.x * blockDim.x + threadIdx.x; i < numel; i += blockDim.x * gridDim.x) { \
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size_t dst_index = i; \
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for (unsigned int nd = 0; nd < num_sum_dims; ++nd) { \
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size_t stride = sum_dims_s[nd]; \
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size_t pre = dst_index / stride; \
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size_t post = dst_index % stride; \
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dst_index = (pre / sum_dims_l[nd]) * stride + post; \
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} \
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atomicAdd(out + dst_index, inp[i]); \
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} \
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} \
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else { \
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for (unsigned int i = blockIdx.x * blockDim.x + threadIdx.x; i < numel; i += blockDim.x * gridDim.x) { \
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unsigned strided_i = get_strided_index(i, num_dims, dims, strides); \
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size_t dst_index = i; \
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for (unsigned int nd = 0; nd < num_sum_dims; ++nd) { \
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size_t stride = sum_dims_s[nd]; \
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size_t pre = dst_index / stride; \
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size_t post = dst_index % stride; \
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dst_index = (pre / sum_dims_l[nd]) * stride + post; \
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} \
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atomicAdd(out + dst_index, inp[strided_i]); \
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} \
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} \
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} \
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#define SUM_OP(TYPENAME, FN_NAME) \
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extern "C" __global__ void FN_NAME( \
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const size_t numel, const size_t num_dims, const size_t num_sum_dims, \
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const size_t *info, const TYPENAME *inp, TYPENAME *out) { \
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const size_t *dims = info; \
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const size_t *strides = info + num_dims; \
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const size_t *sum_dims_l = info + 2 * num_dims; \
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const size_t *sum_dims_s = info + 2 * num_dims + num_sum_dims; \
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if (is_contiguous(num_dims, dims, strides)) { \
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for (unsigned int i = blockIdx.x * blockDim.x + threadIdx.x; i < numel; \
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i += blockDim.x * gridDim.x) { \
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size_t dst_index = i; \
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for (unsigned int nd = 0; nd < num_sum_dims; ++nd) { \
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size_t stride = sum_dims_s[nd]; \
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size_t pre = dst_index / stride; \
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size_t post = dst_index % stride; \
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dst_index = (pre / sum_dims_l[nd]) * stride + post; \
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} \
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atomicAdd(out + dst_index, inp[i]); \
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} \
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} else { \
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for (unsigned int i = blockIdx.x * blockDim.x + threadIdx.x; i < numel; \
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i += blockDim.x * gridDim.x) { \
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unsigned strided_i = get_strided_index(i, num_dims, dims, strides); \
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size_t dst_index = i; \
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for (unsigned int nd = 0; nd < num_sum_dims; ++nd) { \
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size_t stride = sum_dims_s[nd]; \
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size_t pre = dst_index / stride; \
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size_t post = dst_index % stride; \
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dst_index = (pre / sum_dims_l[nd]) * stride + post; \
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} \
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atomicAdd(out + dst_index, inp[strided_i]); \
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} \
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} \
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}
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#if __CUDA_ARCH__ >= 800
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SUM_OP(__nv_bfloat16, sum_bf16)
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