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llama.cpp/ggml/src/ggml-cuda/ssm-scan.cu

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#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11070
#define USE_CUB
#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11070
#ifdef USE_CUB
#include <cub/cub.cuh>
using namespace cub;
#endif // USE_CUB
#include "ssm-scan.cuh"
// We would like to keep pragma unroll for cases where L_template is not 0,
// so we suppress the clang transformation warning.
#ifdef __clang__
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wpass-failed"
#endif // __clang__
template <size_t splitD, size_t N, size_t L_template>
__global__ void __launch_bounds__(splitD, 1)
ssm_scan_f32(const float *__restrict__ src0, const float *__restrict__ src1, const float *__restrict__ src2,
const float *__restrict__ src3, const float *__restrict__ src4, const float *__restrict__ src5,
const int32_t * __restrict__ src6, float * __restrict__ dst,
const int src0_nb2, const int src0_nb3, const int src1_nb2, const int src1_nb3,
const int src2_nb1, const int src2_nb2, const int src3_nb1,
const int src4_nb2, const int src4_nb3, const int src5_nb2, const int src5_nb3,
const int64_t s_off, const int64_t d_inner, const int64_t L_param)
{
const size_t L = L_template == 0 ? L_param : L_template;
const float *s0_block = (const float *)((const char *)src0 + src6[blockIdx.x] * src0_nb3 + blockIdx.y * splitD * src0_nb2);
const float *x_block = (const float *)((const char *)src1 + (blockIdx.x * src1_nb3) + blockIdx.y * splitD * sizeof(float));
const float *dt_block = (const float *)((const char *)src2 + (blockIdx.x * src2_nb2) + blockIdx.y * splitD * sizeof(float));
const float *A_block = (const float *)((const char *)src3 + blockIdx.y * splitD * src3_nb1);
const float *B_block = (const float *)((const char *)src4 + (blockIdx.x * src4_nb3));
const float *C_block = (const float *)((const char *)src5 + (blockIdx.x * src5_nb3));
float *y_block = (float *)((char *)dst + (blockIdx.x * d_inner * L * sizeof(float)) + blockIdx.y * splitD * sizeof(float));
float *s_block = (float *)((char *)dst + s_off + blockIdx.x * src0_nb3 + blockIdx.y * splitD * src0_nb2);
const int stride_x = src1_nb2 / sizeof(float);
const int stride_dt = src2_nb1 / sizeof(float);
const int stride_B = src4_nb2 / sizeof(float);
const int stride_C = src5_nb2 / sizeof(float);
const int stride_y = d_inner;
float regA[N];
float regs0[N];
__shared__ float smemB[N];
__shared__ float smemC[N];
#ifdef USE_CUB
using BlockLoad = cub::BlockLoad<float, splitD, N, cub::BLOCK_LOAD_WARP_TRANSPOSE>;
using BlockStore = cub::BlockStore<float, splitD, N, cub::BLOCK_STORE_WARP_TRANSPOSE>;
union CubTempStorage {
typename BlockLoad::TempStorage load_temp;
typename BlockStore::TempStorage store_temp;
};
__shared__ CubTempStorage cub_temp_storage;
BlockLoad(cub_temp_storage.load_temp).Load(A_block, regA);
BlockLoad(cub_temp_storage.load_temp).Load(s0_block, regs0);
#else
const int stride_s0 = src0_nb2 / sizeof(float);
const int stride_A = src3_nb1 / sizeof(float);
#pragma unroll
for (size_t n = 0; n < N; ++n)
{
regA[n] = A_block[threadIdx.x * stride_A + n];
regs0[n] = s0_block[threadIdx.x * stride_s0 + n];
}
#endif
#pragma unroll
for (size_t i = 0; i < L; i++)
{
if (threadIdx.x < N)
{
smemB[threadIdx.x] = B_block[i * stride_B + threadIdx.x];
smemC[threadIdx.x] = C_block[i * stride_C + threadIdx.x];
}
__syncthreads();
float dt_soft_plus = dt_block[i * stride_dt + threadIdx.x];
if (dt_soft_plus <= 20.0f)
{
dt_soft_plus = log1pf(expf(dt_soft_plus));
}
float x_dt = x_block[i * stride_x + threadIdx.x] * dt_soft_plus;
float sumf = 0.0f;
#pragma unroll
for (size_t n = 0; n < N; n++)
{
float state = regs0[n] * expf(dt_soft_plus * regA[n]) + smemB[n] * x_dt;
sumf += state * smemC[n];
regs0[n] = state;
}
y_block[i * stride_y + threadIdx.x] = sumf;
}
#ifdef USE_CUB
BlockStore(cub_temp_storage.store_temp).Store(s_block, regs0);
#else
const int stride_s = stride_s0;
#pragma unroll
for (size_t n = 0; n < N; ++n)
{
s_block[threadIdx.x * stride_s + n] = regs0[n];
}
#endif
}
#ifdef __clang__
#pragma clang diagnostic pop
#endif // __clang__
// assumes as many threads as d_state
template <int c_factor, int d_state>
__global__ void __launch_bounds__(d_state, 1)
ssm_scan_f32_group(
const float * __restrict__ src0, const float * __restrict__ src1, const float * __restrict__ src2,
const float * __restrict__ src3, const float * __restrict__ src4, const float * __restrict__ src5,
const int32_t * __restrict__ src6, float * __restrict__ dst,
const int src0_nb2, const int src0_nb3, const int src1_nb2, const int src1_nb3,
const int src2_nb1, const int src2_nb2, const int src3_nb1,
const int src4_nb2, const int src4_nb3, const int src5_nb2, const int src5_nb3,
const int64_t s_off, const int64_t n_head, const int64_t d_head, const int64_t n_group, const int64_t n_tok) {
const int warp = threadIdx.x / WARP_SIZE;
const int lane = threadIdx.x % WARP_SIZE;
const int warp_idx = blockIdx.x * c_factor + warp;
const int head_idx = warp_idx / d_head;
const int head_off = (warp_idx % d_head) * sizeof(float);
const int seq_idx = blockIdx.y;
const int group_off = (head_idx / (n_head / n_group)) * d_state * sizeof(float);
// TODO: refactor strides to be in elements/floats instead of bytes to be cleaner and consistent with the rest of the codebase
const float * s0_warp = (const float *) ((const char *) src0 + src6[seq_idx] * src0_nb3 + head_idx * src0_nb2 + head_off * d_state);
const float * x_warp = (const float *) ((const char *) src1 + (seq_idx * src1_nb3) + (warp_idx * sizeof(float)));
const float * dt_warp = (const float *) ((const char *) src2 + (seq_idx * src2_nb2) + head_idx * sizeof(float));
const float * A_warp = (const float *) ((const char *) src3 + head_idx * src3_nb1);
const float * B_warp = (const float *) ((const char *) src4 + (seq_idx * src4_nb3) + (group_off));
const float * C_warp = (const float *) ((const char *) src5 + (seq_idx * src5_nb3) + (group_off));
float * y_warp = dst + (seq_idx * n_tok * n_head * d_head) + warp_idx;
float * s_warp = (float *) ((char *) dst + s_off + seq_idx * src0_nb3 + head_idx * src0_nb2 + head_off * d_state);
// strides across n_seq_tokens
const int stride_x = src1_nb2 / sizeof(float);
const int stride_dt = src2_nb1 / sizeof(float);
const int stride_B = src4_nb2 / sizeof(float);
const int stride_C = src5_nb2 / sizeof(float);
const int stride_y = n_head * d_head;
float state[c_factor];
float state_sum = 0.0f;
#pragma unroll
for (int j = 0; j < c_factor; j++) {
state[j] = s0_warp[WARP_SIZE * j + lane];
}
for (int64_t i = 0; i < n_tok; i++) {
// NOTE: dt_soft_plus, dA and x_dt have the same value for a warp here.
// Recalculation is intentional; sharing via shuffles/smem proved slower due to sync overhead.
const float dt_soft_plus = (dt_warp[i * stride_dt] <= 20.0f ? log1pf(expf(dt_warp[i * stride_dt])) : dt_warp[i * stride_dt]);
state_sum = 0.0f;
const float dA = expf(dt_soft_plus * A_warp[0]);
const float x_dt = x_warp[i * stride_x] * dt_soft_plus;
#pragma unroll
for (int j = 0; j < c_factor; j++) {
const float B_val = B_warp[i * stride_B + WARP_SIZE * j + lane];
const float C_val = C_warp[i * stride_C + WARP_SIZE * j + lane];
state[j] = (state[j] * dA) + (B_val * x_dt);
state_sum += state[j] * C_val;
}
// parallel accumulation for output
state_sum = warp_reduce_sum(state_sum);
if (lane == 0) {
y_warp[i * stride_y] = state_sum;
}
}
// write back the state
#pragma unroll
for (int j = 0; j < c_factor; j++) {
s_warp[WARP_SIZE * j + lane] = state[j];
}
}
static void ssm_scan_f32_cuda(const float * src0, const float * src1, const float * src2, const float * src3,
const float * src4, const float * src5, const int32_t * src6, float * dst,
const int src0_nb2, const int src0_nb3, const int src1_nb2, const int src1_nb3, const int src2_nb1,
const int src2_nb2, const int src3_nb1, const int src4_nb2, const int src4_nb3, const int src5_nb2,
const int src5_nb3, const int64_t s_off, const int64_t d_state, const int64_t head_dim,
const int64_t n_head, const int64_t n_group, const int64_t n_tok, const int64_t n_seq,
cudaStream_t stream) {
// NOTE: if you change conditions here, be sure to update the corresponding supports_op condition!
if (src3_nb1 == sizeof(float)) {
// Mamba-2
if (d_state == 128) {
constexpr int threads = 128;
constexpr int num_warps = threads/WARP_SIZE;
const dim3 blocks((n_head * head_dim + (num_warps - 1)) / num_warps, n_seq, 1);
ssm_scan_f32_group<128/WARP_SIZE, 128><<<blocks, threads, 0, stream>>>(
src0, src1, src2, src3, src4, src5, src6, dst,
src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2, src3_nb1,
src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, head_dim, n_group, n_tok);
} else if (d_state == 256) { // Falcon-H1
constexpr int threads = 256;
constexpr int num_warps = threads/WARP_SIZE;
const dim3 blocks((n_head * head_dim + (num_warps - 1)) / num_warps, n_seq, 1);
ssm_scan_f32_group<256/WARP_SIZE, 256><<<blocks, threads, 0, stream>>>(
src0, src1, src2, src3, src4, src5, src6, dst,
src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2, src3_nb1,
src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, head_dim, n_group, n_tok);
} else {
GGML_ABORT("doesn't support d_state!=(128 or 256).");
}
} else {
// Mamba-1
constexpr int threads = 128;
GGML_ASSERT(n_head % threads == 0);
GGML_ASSERT(head_dim == 1);
GGML_ASSERT(n_group == 1);
const dim3 blocks(n_seq, (n_head + threads - 1) / threads, 1);
const int smem_size = (threads * (d_state + 1) * 2) * sizeof(float);
if (d_state == 16) {
switch (n_tok)
{
case 1:
ssm_scan_f32<threads, 16, 1><<<blocks, threads, smem_size, stream>>>(
src0, src1, src2, src3, src4, src5, src6, dst,
src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
break;
case 2:
ssm_scan_f32<threads, 16, 2><<<blocks, threads, smem_size, stream>>>(
src0, src1, src2, src3, src4, src5, src6, dst,
src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
break;
case 3:
ssm_scan_f32<threads, 16, 3><<<blocks, threads, smem_size, stream>>>(
src0, src1, src2, src3, src4, src5, src6, dst,
src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
break;
case 4:
ssm_scan_f32<threads, 16, 4><<<blocks, threads, smem_size, stream>>>(
src0, src1, src2, src3, src4, src5, src6, dst,
src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
break;
case 5:
ssm_scan_f32<threads, 16, 5><<<blocks, threads, smem_size, stream>>>(
src0, src1, src2, src3, src4, src5, src6, dst,
src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
break;
case 6:
ssm_scan_f32<threads, 16, 6><<<blocks, threads, smem_size, stream>>>(
src0, src1, src2, src3, src4, src5, src6, dst,
src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
break;
case 7:
ssm_scan_f32<threads, 16, 7><<<blocks, threads, smem_size, stream>>>(
src0, src1, src2, src3, src4, src5, src6, dst,
src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
break;
case 8:
ssm_scan_f32<threads, 16, 8><<<blocks, threads, smem_size, stream>>>(
src0, src1, src2, src3, src4, src5, src6, dst,
src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
break;
default:
ssm_scan_f32<threads, 16, 0><<<blocks, threads, smem_size, stream>>>(
src0, src1, src2, src3, src4, src5, src6, dst,
src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2,
src3_nb1, src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, n_tok);
break;
}
} else {
GGML_ABORT("doesn't support d_state!=16.");
}
}
}
void ggml_cuda_op_ssm_scan(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0]; // s
const struct ggml_tensor * src1 = dst->src[1]; // x
const struct ggml_tensor * src2 = dst->src[2]; // dt
const struct ggml_tensor * src3 = dst->src[3]; // A
const struct ggml_tensor * src4 = dst->src[4]; // B
const struct ggml_tensor * src5 = dst->src[5]; // C
const struct ggml_tensor * src6 = dst->src[6]; // ids
const int64_t nc = src0->ne[0]; // d_state
const int64_t nr = src0->ne[1]; // head_dim or 1
const int64_t nh = src1->ne[1]; // n_head
const int64_t ng = src4->ne[1]; // n_group
const int64_t n_t = src1->ne[2]; // number of tokens per sequence
const int64_t n_s = src1->ne[3]; // number of sequences in the batch
const int64_t s_off = ggml_nelements(src1) * sizeof(float);
GGML_ASSERT(ggml_nelements(src1) + nc*nr*nh*n_s == ggml_nelements(dst));
GGML_ASSERT(src0->nb[0] == sizeof(float));
GGML_ASSERT(src1->nb[0] == sizeof(float));
GGML_ASSERT(src2->nb[0] == sizeof(float));
GGML_ASSERT(src3->nb[0] == sizeof(float));
GGML_ASSERT(src4->nb[0] == sizeof(float));
GGML_ASSERT(src5->nb[0] == sizeof(float));
GGML_ASSERT(src6->nb[0] == sizeof(int32_t));
const float * src0_d = (const float *) src0->data;
const float * src1_d = (const float *) src1->data;
const float * src2_d = (const float *) src2->data;
const float * src3_d = (const float *) src3->data;
const float * src4_d = (const float *) src4->data;
const float * src5_d = (const float *) src5->data;
const int32_t * src6_d = (const int32_t *) src6->data;
float * dst_d = (float *) dst->data;
cudaStream_t stream = ctx.stream();
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src6->type == GGML_TYPE_I32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
ssm_scan_f32_cuda(src0_d, src1_d, src2_d, src3_d, src4_d, src5_d, src6_d, dst_d,
src0->nb[2], src0->nb[3], src1->nb[2], src1->nb[3], src2->nb[1], src2->nb[2],
src3->nb[1], src4->nb[2], src4->nb[3], src5->nb[2], src5->nb[3],
s_off, nc, nr, nh, ng, n_t, n_s, stream);
}
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