llama.cpp/ggml-cuda/fattn-common.cuh
Johannes Gäßler dc685be466
CUDA: add FP32 FlashAttention vector kernel (#7188)
* CUDA: add FP32 FlashAttention vector kernel

* fixup! CUDA: add FP32 FlashAttention vector kernel

* fixup! fixup! CUDA: add FP32 FlashAttention vector kernel

* fixup! fixup! fixup! CUDA: add FP32 FlashAttention vector kernel
2024-05-12 19:40:45 +02:00

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#define FATTN_KQ_STRIDE 256
#define HALF_MAX_HALF __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction.
#define SOFTMAX_FTZ_THRESHOLD -20.0f // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs.
template<int D, int parallel_blocks> // D == head size
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
__launch_bounds__(D, 1)
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
static __global__ void flash_attn_combine_results(
const float * __restrict__ VKQ_parts,
const float2 * __restrict__ VKQ_meta,
float * __restrict__ dst) {
VKQ_parts += parallel_blocks*D * gridDim.y*blockIdx.x;
VKQ_meta += parallel_blocks * gridDim.y*blockIdx.x;
dst += D * gridDim.y*blockIdx.x;
const int tid = threadIdx.x;
__builtin_assume(tid < D);
__shared__ float2 meta[parallel_blocks];
if (tid < 2*parallel_blocks) {
((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.y*(2*parallel_blocks) + tid];
}
__syncthreads();
float kqmax = meta[0].x;
#pragma unroll
for (int l = 1; l < parallel_blocks; ++l) {
kqmax = max(kqmax, meta[l].x);
}
float VKQ_numerator = 0.0f;
float VKQ_denominator = 0.0f;
#pragma unroll
for (int l = 0; l < parallel_blocks; ++l) {
const float diff = meta[l].x - kqmax;
const float KQ_max_scale = expf(diff);
const uint32_t ftz_mask = 0xFFFFFFFF * (diff > SOFTMAX_FTZ_THRESHOLD);
*((uint32_t *) &KQ_max_scale) &= ftz_mask;
VKQ_numerator += KQ_max_scale * VKQ_parts[l*gridDim.y*D + blockIdx.y*D + tid];
VKQ_denominator += KQ_max_scale * meta[l].y;
}
dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator;
}