233 lines
8.7 KiB
Text
233 lines
8.7 KiB
Text
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/**
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* llama.cpp - commit 8962422b1c6f9b8b15f5aeaea42600bcc2d44177 - do not edit this file
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*
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* MIT License
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*
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* Copyright (c) 2023-2024 The ggml authors
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "common.cuh"
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#include "softmax.cuh"
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template <typename T>
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static __device__ __forceinline__ float t2f32(T val) {
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return (float) val;
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}
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template <>
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__device__ float __forceinline__ t2f32<half>(half val) {
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return __half2float(val);
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}
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template <bool vals_smem, int ncols_template, int block_size_template, typename T>
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static __global__ void soft_max_f32(const float * x, const T * mask, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) {
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const int ncols = ncols_template == 0 ? ncols_par : ncols_template;
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const int tid = threadIdx.x;
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const int rowx = blockIdx.x;
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const int rowy = rowx % nrows_y; // broadcast the mask in the row dimension
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const int block_size = block_size_template == 0 ? blockDim.x : block_size_template;
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const int warp_id = threadIdx.x / WARP_SIZE;
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const int lane_id = threadIdx.x % WARP_SIZE;
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const float slope = get_alibi_slope(max_bias, rowx/nrows_y, n_head_log2, m0, m1);
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extern __shared__ float data_soft_max_f32[];
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float * buf_iw = data_soft_max_f32; // shared memory buffer for inter-warp communication
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// shared memory buffer to cache values between iterations:
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float * vals = vals_smem ? buf_iw + WARP_SIZE : dst + (int64_t)rowx*ncols;
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float max_val = -INFINITY;
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#pragma unroll
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for (int col0 = 0; col0 < ncols; col0 += block_size) {
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const int col = col0 + tid;
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if (ncols_template == 0 && col >= ncols) {
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break;
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}
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const int64_t ix = (int64_t)rowx*ncols + col;
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const int64_t iy = (int64_t)rowy*ncols + col;
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const float val = x[ix]*scale + (mask ? slope*t2f32(mask[iy]) : 0.0f);
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vals[col] = val;
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max_val = max(max_val, val);
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}
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// find the max value in the block
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max_val = warp_reduce_max(max_val);
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if (block_size > WARP_SIZE) {
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if (warp_id == 0) {
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buf_iw[lane_id] = -INFINITY;
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}
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__syncthreads();
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if (lane_id == 0) {
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buf_iw[warp_id] = max_val;
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}
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__syncthreads();
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max_val = buf_iw[lane_id];
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max_val = warp_reduce_max(max_val);
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}
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float tmp = 0.0f; // partial sum
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#pragma unroll
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for (int col0 = 0; col0 < ncols; col0 += block_size) {
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const int col = col0 + tid;
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if (ncols_template == 0 && col >= ncols) {
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break;
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}
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const float val = expf(vals[col] - max_val);
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tmp += val;
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vals[col] = val;
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}
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// find the sum of exps in the block
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tmp = warp_reduce_sum(tmp);
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if (block_size > WARP_SIZE) {
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__syncthreads();
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if (warp_id == 0) {
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buf_iw[lane_id] = 0.0f;
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}
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__syncthreads();
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if (lane_id == 0) {
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buf_iw[warp_id] = tmp;
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}
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__syncthreads();
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tmp = buf_iw[lane_id];
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tmp = warp_reduce_sum(tmp);
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}
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const float inv_sum = 1.0f / tmp;
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#pragma unroll
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for (int col0 = 0; col0 < ncols; col0 += block_size) {
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const int col = col0 + tid;
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if (ncols_template == 0 && col >= ncols) {
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return;
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}
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const int64_t idst = (int64_t)rowx*ncols + col;
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dst[idst] = vals[col] * inv_sum;
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}
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}
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template<typename T>
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static void soft_max_f32_cuda(const float * x, const T * mask, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) {
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int nth = WARP_SIZE;
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while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2;
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const dim3 block_dims(nth, 1, 1);
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const dim3 block_nums(nrows_x, 1, 1);
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const size_t shmem = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE)*sizeof(float);
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static_assert(CUDA_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted.");
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const uint32_t n_head = nrows_x/nrows_y;
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const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
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const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
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const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
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// FIXME: this limit could be raised by ~2-4x on Ampere or newer
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if (shmem < ggml_cuda_info().devices[ggml_cuda_get_device()].smpb) {
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switch (ncols_x) {
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case 32:
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soft_max_f32<true, 32, 32><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
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break;
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case 64:
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soft_max_f32<true, 64, 64><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
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break;
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case 128:
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soft_max_f32<true, 128, 128><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
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break;
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case 256:
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soft_max_f32<true, 256, 256><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
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break;
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case 512:
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soft_max_f32<true, 512, 512><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
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break;
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case 1024:
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soft_max_f32<true, 1024, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
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break;
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case 2048:
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soft_max_f32<true, 2048, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
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break;
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case 4096:
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soft_max_f32<true, 4096, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
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break;
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default:
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soft_max_f32<true, 0, 0><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
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break;
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}
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} else {
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const size_t shmem_low = WARP_SIZE*sizeof(float);
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soft_max_f32<false, 0, 0><<<block_nums, block_dims, shmem_low, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2);
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}
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}
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void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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const ggml_tensor * src0 = dst->src[0];
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const ggml_tensor * src1 = dst->src[1];
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const float * src0_d = (const float *)src0->data;
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const void * src1_d = src1 ? (const void *)src1->data : nullptr;
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float * dst_d = (float *)dst->data;
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cudaStream_t stream = ctx.stream();
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GGML_ASSERT(src0->type == GGML_TYPE_F32);
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GGML_ASSERT( dst->type == GGML_TYPE_F32);
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GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
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const int64_t ne00 = src0->ne[0];
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const int64_t nrows_x = ggml_nrows(src0);
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const int64_t nrows_y = src0->ne[1];
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float scale = 1.0f;
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float max_bias = 0.0f;
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memcpy(&scale, (float *) dst->op_params + 0, sizeof(float));
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memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float));
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const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16);
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if (use_f16) {
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const half * src1_dd = (const half *)src1_d;
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soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
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} else {
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const float * src1_dd = (const float *)src1_d;
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soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
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}
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}
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