llama.cpp/ggml-cuda/softmax.cu
Georgi Gerganov 9cb317f77e
ggml : full ALiBi support (#7192)
* ggml : full ALiBi support

* ggml : update ggml_soft_max_ext() CUDA, SYCL

* ggml : ggml_flash_attn_ext() support ALiBi (CPU)

* ggml : ggml_flash_attn_ext() support ALiBi (Metal)

* ggml : fix warning

* ggml : ggml_flash_attn_ext() support ALiBi (CUDA)

ggml-ci

* ggml : fix assert message

* vulkan : add dev notes

* ggml : require mask when using ALiBi

ggml-ci

* convert : fix convert for refact models
2024-05-11 10:32:41 +03:00

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#include "softmax.cuh"
template <typename T>
static __device__ __forceinline__ float t2f32(T val) {
return (float) val;
}
template <>
__device__ float __forceinline__ t2f32<half>(half val) {
return __half2float(val);
}
template <bool vals_smem, int ncols_template, int block_size_template, typename T>
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) {
const int ncols = ncols_template == 0 ? ncols_par : ncols_template;
const int tid = threadIdx.x;
const int rowx = blockIdx.x;
const int rowy = rowx % nrows_y; // broadcast the mask in the row dimension
const int block_size = block_size_template == 0 ? blockDim.x : block_size_template;
const int warp_id = threadIdx.x / WARP_SIZE;
const int lane_id = threadIdx.x % WARP_SIZE;
float slope = 1.0f;
// ALiBi
if (max_bias > 0.0f) {
const int h = rowx/nrows_y; // head index
const float base = h < n_head_log2 ? m0 : m1;
const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
slope = powf(base, exph);
}
extern __shared__ float data_soft_max_f32[];
float * buf_iw = data_soft_max_f32; // shared memory buffer for inter-warp communication
// shared memory buffer to cache values between iterations:
float * vals = vals_smem ? buf_iw + WARP_SIZE : dst + (int64_t)rowx*ncols;
float max_val = -INFINITY;
#pragma unroll
for (int col0 = 0; col0 < ncols; col0 += block_size) {
const int col = col0 + tid;
if (ncols_template == 0 && col >= ncols) {
break;
}
const int64_t ix = (int64_t)rowx*ncols + col;
const int64_t iy = (int64_t)rowy*ncols + col;
const float val = x[ix]*scale + (mask ? slope*t2f32(mask[iy]) : 0.0f);
vals[col] = val;
max_val = max(max_val, val);
}
// find the max value in the block
max_val = warp_reduce_max(max_val);
if (block_size > WARP_SIZE) {
if (warp_id == 0) {
buf_iw[lane_id] = -INFINITY;
}
__syncthreads();
if (lane_id == 0) {
buf_iw[warp_id] = max_val;
}
__syncthreads();
max_val = buf_iw[lane_id];
max_val = warp_reduce_max(max_val);
}
float tmp = 0.0f; // partial sum
#pragma unroll
for (int col0 = 0; col0 < ncols; col0 += block_size) {
const int col = col0 + tid;
if (ncols_template == 0 && col >= ncols) {
break;
}
const float val = expf(vals[col] - max_val);
tmp += val;
vals[col] = val;
}
// find the sum of exps in the block
tmp = warp_reduce_sum(tmp);
if (block_size > WARP_SIZE) {
__syncthreads();
if (warp_id == 0) {
buf_iw[lane_id] = 0.0f;
}
__syncthreads();
if (lane_id == 0) {
buf_iw[warp_id] = tmp;
}
__syncthreads();
tmp = buf_iw[lane_id];
tmp = warp_reduce_sum(tmp);
}
const float inv_sum = 1.0f / tmp;
#pragma unroll
for (int col0 = 0; col0 < ncols; col0 += block_size) {
const int col = col0 + tid;
if (ncols_template == 0 && col >= ncols) {
return;
}
const int64_t idst = (int64_t)rowx*ncols + col;
dst[idst] = vals[col] * inv_sum;
}
}
template<typename T>
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) {
int nth = WARP_SIZE;
while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2;
const dim3 block_dims(nth, 1, 1);
const dim3 block_nums(nrows_x, 1, 1);
const size_t shmem = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE)*sizeof(float);
static_assert(CUDA_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted.");
const uint32_t n_head = nrows_x/nrows_y;
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
if (shmem < ggml_cuda_info().devices[ggml_cuda_get_device()].smpb) {
switch (ncols_x) {
case 32:
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);
break;
case 64:
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);
break;
case 128:
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);
break;
case 256:
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);
break;
case 512:
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);
break;
case 1024:
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);
break;
case 2048:
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);
break;
case 4096:
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);
break;
default:
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);
break;
}
} else {
const size_t shmem_low = WARP_SIZE*sizeof(float);
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);
}
}
void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
const float * src0_d = (const float *)src0->data;
const void * src1_d = src1 ? (const void *)src1->data : nullptr;
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
const int64_t ne00 = src0->ne[0];
const int64_t nrows_x = ggml_nrows(src0);
const int64_t nrows_y = src0->ne[1];
float scale = 1.0f;
float max_bias = 0.0f;
memcpy(&scale, (float *) dst->op_params + 0, sizeof(float));
memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float));
const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16);
if (use_f16) {
const half * src1_dd = (const half *)src1_d;
soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
} else {
const float * src1_dd = (const float *)src1_d;
soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
}
}