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softmax.cuh
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softmax.cuh
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/******************************************************************************
* Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
******************************************************************************/
#ifndef __SOFTMAX_HXX__
#define __SOFTMAX_HXX__
__device__ __forceinline__ void namedBarrierSync(int name, int numThreads) {
asm volatile("bar.sync %0, %1;" : : "r"(name), "r"(numThreads) : "memory");
}
template <typename T, int NUM_THREADS, int NUM_ROWS, int UNROLL>
__device__ __inline__ void softmax_select(int first_col, int num_cols, T* input, T* output, float* selector, int* selection, int barrierName, int numThreads, int thread_id = threadIdx.x) {
const int ROWS_PER_THREAD = NUM_ROWS / NUM_THREADS;
for (int col=first_col; col<first_col+num_cols; col+=UNROLL) {
// Load activations
float activations_in[UNROLL][ROWS_PER_THREAD];
for (int u=0; u<UNROLL; u++) {
for (int r=0; r<ROWS_PER_THREAD; r++) {
activations_in[u][r] = input[(col+u)*NUM_ROWS + thread_id*ROWS_PER_THREAD + r];
}
}
float local_max[UNROLL];
float local_sum[UNROLL];
for (int u=0; u<UNROLL; u++) {
local_max[u] = activations_in[u][0];
local_sum[u] = 0.f;
}
// Compute the max first so we can subtract it from the inputs and prevent explosions
#pragma unroll
for (int u=0; u<UNROLL; u++) {
#pragma unroll
for (int r=1; r<ROWS_PER_THREAD; r++) {
if (activations_in[u][r] > local_max[u]) local_max[u] = activations_in[u][r];
}
}
// Each warp computes its max
__shared__ float warp_max[UNROLL][NUM_THREADS/32];
#pragma unroll
for (int u=0; u<UNROLL; u++) {
float wmax = local_max[u];
for (int offset=16; offset>0; offset /= 2) {
float v = __shfl_down_sync(0xFFFFFFFF,wmax,offset);
if (v > wmax) wmax = v;
}
if ((thread_id%32)==0) {
warp_max[u][thread_id/32] = wmax;
}
}
namedBarrierSync(barrierName, numThreads);
// Now each thread computes max across warps
#pragma unroll
for (int u=0; u<UNROLL; u++) {
#pragma unroll
for (int w=0; w<NUM_THREADS/32; w++) {
float wmax = warp_max[u][w];
if (wmax > local_max[u]) local_max[u] = wmax;
}
}
// Subtract the max from the input
#pragma unroll
for (int u=0; u<UNROLL; u++) {
#pragma unroll
for (int r=0; r<ROWS_PER_THREAD; r++) {
activations_in[u][r] -= local_max[u];
}
}
// Exponentiate and sum
#pragma unroll
for (int u=0; u<UNROLL; u++) {
#pragma unroll
for (int r=0; r<ROWS_PER_THREAD; r++) {
float act_exp = expf(activations_in[u][r]);
activations_in[u][r] = act_exp;
local_sum[u] += activations_in[u][r];
}
}
__shared__ float thread_sums[UNROLL][NUM_ROWS];
__shared__ float warp_sums[UNROLL][NUM_THREADS/32];
for (int u=0; u<UNROLL; u++) {
thread_sums[u][thread_id] = local_sum[u];
}
#pragma unroll
for (int u=0; u<UNROLL; u++) {
float accum = local_sum[u];
for (int offset=16; offset>0; offset /= 2) {
accum += __shfl_down_sync(0xFFFFFFFF,accum,offset);
}
if ((thread_id&0x1f)==0) {
warp_sums[u][thread_id/32] = accum;
}
}
namedBarrierSync(barrierName, numThreads);
float sum[UNROLL];
#pragma unroll
for (int u=0; u<UNROLL; u++) {
sum[u] = 0.f;
for (int w=0; w<NUM_THREADS/32;w++) {
sum[u] += warp_sums[u][w];
}
}
if (output != NULL) {
#pragma unroll
for (int u=0; u<UNROLL; u++) {
for (int r=0; r<ROWS_PER_THREAD; r++) {
int row = thread_id*ROWS_PER_THREAD+r;
output[(col+u)*NUM_ROWS + row] = activations_in[u][r] / sum[u];
}
}
}
for (int u=0; u<UNROLL; u++) {
// Now do the weighted selection
float sel = selector[col+u] * sum[u];
float wsum = 0.f;
// Write out a value in case scan fails
selection[col+u] = 128;
namedBarrierSync(barrierName, numThreads);
int warp_id = thread_id/32;
for (int w=0; w<NUM_THREADS/32; w++) {
float wsum_next = wsum + warp_sums[u][w];
if (sel <= wsum_next) {
if ( warp_id == w) {
// We're in the right warp
float tsum = wsum;
for (int i=0; i<32; i++) {
float tsum_next = tsum + thread_sums[u][w*32+i];
if (sel <= tsum_next) {
if ((thread_id&0x1f) == i) {
// We're in the right thread
float s = tsum;
for (int r=0; r<ROWS_PER_THREAD; r++) {
float s_next = s + activations_in[u][r];
if (sel <= s_next) {
// Found it!
selection[col+u] = thread_id*ROWS_PER_THREAD + r;
break;
}
s = s_next;
}
}
break;
}
tsum = tsum_next;
}
}
break;
}
wsum = wsum_next;
}
}
}
}
#endif