faster ssm_scan
This commit is contained in:
lihan
parent
9f912511bc
commit
65180fbaaa
5 changed files with 431 additions and 1 deletions
|
|
@ -31,6 +31,8 @@
|
|||
#include "ggml-cuda/rope.cuh"
|
||||
#include "ggml-cuda/scale.cuh"
|
||||
#include "ggml-cuda/softmax.cuh"
|
||||
#include "ggml-cuda/ssm_conv.cuh"
|
||||
#include "ggml-cuda/ssm_scan.cuh"
|
||||
#include "ggml-cuda/sum.cuh"
|
||||
#include "ggml-cuda/sumrows.cuh"
|
||||
#include "ggml-cuda/tsembd.cuh"
|
||||
|
|
@ -2155,6 +2157,12 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
|
|||
case GGML_OP_SUM_ROWS:
|
||||
ggml_cuda_op_sum_rows(ctx, dst);
|
||||
break;
|
||||
case GGML_OP_SSM_CONV:
|
||||
ggml_cuda_op_ssm_conv(ctx, dst);
|
||||
break;
|
||||
case GGML_OP_SSM_SCAN:
|
||||
ggml_cuda_op_ssm_scan(ctx, dst);
|
||||
break;
|
||||
case GGML_OP_ARGSORT:
|
||||
ggml_cuda_op_argsort(ctx, dst);
|
||||
break;
|
||||
|
|
@ -2989,7 +2997,9 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
|
|||
case GGML_OP_SIN:
|
||||
case GGML_OP_COS:
|
||||
case GGML_OP_CLAMP:
|
||||
return true;
|
||||
case GGML_OP_SSM_SCAN:
|
||||
case GGML_OP_SSM_CONV:
|
||||
return true;
|
||||
case GGML_OP_CONT:
|
||||
return op->src[0]->type != GGML_TYPE_BF16;
|
||||
case GGML_OP_DIAG_MASK_INF:
|
||||
|
|
|
|||
94
ggml/src/ggml-cuda/ssm_conv.cu
Normal file
94
ggml/src/ggml-cuda/ssm_conv.cu
Normal file
|
|
@ -0,0 +1,94 @@
|
|||
#include "ssm_conv.cuh"
|
||||
|
||||
template <int block_size>
|
||||
static __global__ void ssm_conv_f32(const float *__restrict__ src0,
|
||||
const float *__restrict__ src1,
|
||||
const int src0_nb0, const int src0_nb1,
|
||||
const int src0_nb2, const int src1_nb1,
|
||||
float *__restrict__ dst, const int dst_nb0,
|
||||
const int dst_nb1, const int dst_nb2,
|
||||
const int nc, const int ncs, const int nr,
|
||||
const int n_t, const int n_s) {
|
||||
const int tid = blockIdx.y;
|
||||
const int i3 = blockIdx.x;
|
||||
const int i2 = threadIdx.x;
|
||||
|
||||
const int ith = tid;
|
||||
const int nth = WARP_SIZE;
|
||||
|
||||
// rows per thread
|
||||
const int dr = (nr + nth - 1) / nth;
|
||||
|
||||
// row range for this thread
|
||||
const int ir0 = dr * ith;
|
||||
const int ir1 = min(ir0 + dr, nr);
|
||||
const int ir = ir1 - ir0;
|
||||
|
||||
// {d_conv - 1 + n_t, d_inner, n_seqs}
|
||||
// sliding window
|
||||
const float *s =
|
||||
(const float *)((const char *)src0 + ir0 * src0_nb1 + i2 * src0_nb0 +
|
||||
i3 * src0_nb2); // {d_conv, d_inner, n_s}
|
||||
const float *c = (const float *)((const char *)src1 +
|
||||
ir0 * src1_nb1); // {d_conv, d_inner}
|
||||
float *x = (float *)((char *)dst + ir0 * dst_nb0 + i2 * dst_nb1 +
|
||||
i3 * dst_nb2); // {d_inner, n_t, n_s}
|
||||
|
||||
// TODO: transpose the output for smaller strides for big batches?
|
||||
// d_inner
|
||||
for (int i1 = 0; i1 < ir; ++i1) {
|
||||
// rowwise dot product
|
||||
// NOTE: not using ggml_vec_dot_f32, because its sum is in double precision
|
||||
float sumf = 0.0f;
|
||||
|
||||
// d_conv
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < nc; ++i0) {
|
||||
sumf += s[i0 + i1 * ncs] * c[i0 + i1 * nc];
|
||||
}
|
||||
x[i1] = sumf;
|
||||
}
|
||||
}
|
||||
|
||||
static void ssm_conv_f32_cuda(const float *src0, const float *src1,
|
||||
const int src0_nb0, const int src0_nb1,
|
||||
const int src0_nb2, const int src1_nb1,
|
||||
float *dst, const int dst_nb0, const int dst_nb1,
|
||||
const int dst_nb2, const int nc, const int ncs,
|
||||
const int nr, const int n_t, const int n_s,
|
||||
cudaStream_t stream) {
|
||||
const dim3 block_dims(n_t, 1, 1);
|
||||
// const int nblocks = n_s; // TODO
|
||||
const dim3 grid_dims(n_s, WARP_SIZE, 1);
|
||||
|
||||
ssm_conv_f32<WARP_SIZE><<<grid_dims, block_dims, 0, stream>>>(
|
||||
src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1, dst, dst_nb0, dst_nb1,
|
||||
dst_nb2, nc, ncs, nr, n_t, n_s);
|
||||
}
|
||||
|
||||
void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context &ctx, ggml_tensor *dst) {
|
||||
const struct ggml_tensor *src0 = dst->src[0]; // conv_x
|
||||
const struct ggml_tensor *src1 = dst->src[1]; // conv1d.weight
|
||||
|
||||
const int nc = src1->ne[0]; // d_conv
|
||||
const int ncs = src0->ne[0]; // d_conv - 1 + n_t
|
||||
const int nr = src0->ne[1]; // d_inner
|
||||
const int n_t = dst->ne[1]; // tokens per sequence
|
||||
const int n_s = dst->ne[2]; // number of sequences in the batch
|
||||
|
||||
GGML_ASSERT(dst->ne[0] == nr);
|
||||
GGML_ASSERT(src0->nb[0] == sizeof(float));
|
||||
GGML_ASSERT(src1->nb[0] == sizeof(float));
|
||||
GGML_ASSERT(src0->nb[1] == src0->ne[0] * sizeof(float));
|
||||
|
||||
const float *src0_d = (const float *)src0->data;
|
||||
const float *src1_d = (const float *)src1->data;
|
||||
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);
|
||||
ssm_conv_f32_cuda(src0_d, src1_d, src0->nb[0], src0->nb[1], src0->nb[2],
|
||||
src1->nb[1], dst_d, dst->nb[0], dst->nb[1], dst->nb[2], nc,
|
||||
ncs, nr, n_t, n_s, stream);
|
||||
}
|
||||
3
ggml/src/ggml-cuda/ssm_conv.cuh
Normal file
3
ggml/src/ggml-cuda/ssm_conv.cuh
Normal file
|
|
@ -0,0 +1,3 @@
|
|||
#include "common.cuh"
|
||||
|
||||
void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context& ctx, ggml_tensor* dst);
|
||||
320
ggml/src/ggml-cuda/ssm_scan.cu
Normal file
320
ggml/src/ggml-cuda/ssm_scan.cu
Normal file
|
|
@ -0,0 +1,320 @@
|
|||
#include "ssm_scan.cuh"
|
||||
|
||||
// #include <cuda_runtime.h>
|
||||
// static __device__ void global_to_shared(const float *src, float *dst) {
|
||||
// asm volatile("cp.async.");
|
||||
// }
|
||||
|
||||
template <size_t splitD, size_t N>
|
||||
__global__ void __launch_bounds__(splitD, 2)
|
||||
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 int src0_nb1, const int src0_nb2, const int src1_nb0,
|
||||
const int src1_nb1, const int src1_nb2, const int src1_nb3,
|
||||
const int src2_nb0, const int src2_nb1, const int src2_nb2,
|
||||
const int src3_nb1, const int src4_nb1, const int src4_nb2,
|
||||
const int src5_nb1, const int src5_nb2,
|
||||
float *__restrict__ dst, const int D, const int L,
|
||||
const int B) {
|
||||
const int bidx = blockIdx.x; // split along B
|
||||
const int bidy = blockIdx.y; // split along D
|
||||
const int tid = threadIdx.x;
|
||||
const int wid = tid / 32;
|
||||
const int wtid = tid % 32;
|
||||
|
||||
extern __shared__ float smem[];
|
||||
const int stride_sA = N + 1;
|
||||
const int stride_ss0 = N + 1;
|
||||
float *smem_A = smem;
|
||||
float *smem_s0 = smem_A + splitD * stride_sA;
|
||||
|
||||
const float *s0_block = (const float *)((char *)src0 + bidx * src0_nb2 +
|
||||
bidy * splitD * src0_nb1);
|
||||
const float *x_block = (const float *)((char *)src1 + (bidx * src1_nb2) +
|
||||
bidy * splitD * sizeof(float));
|
||||
const float *dt_block = (const float *)((char *)src2 + (bidx * src2_nb2) +
|
||||
bidy * splitD * sizeof(float));
|
||||
const float *A_block =
|
||||
(const float *)((char *)src3 + bidy * splitD * src3_nb1);
|
||||
const float *B_block = (const float *)((char *)src4 + (bidx * src4_nb2));
|
||||
const float *C_block = (const float *)((char *)src5 + (bidx * src5_nb2));
|
||||
float *y_block = (float *)((char *)dst + (bidx * src1_nb2) +
|
||||
bidy * splitD * sizeof(float));
|
||||
float *s_block = (float *)((char *)dst + src1_nb3 + bidx * src0_nb2 +
|
||||
bidy * splitD * src0_nb1);
|
||||
|
||||
const int stride_s0 = src0_nb1 / sizeof(float);
|
||||
const int stride_x = src1_nb1 / sizeof(float);
|
||||
const int stride_dt = src2_nb1 / sizeof(float);
|
||||
const int stride_A = src3_nb1 / sizeof(float);
|
||||
const int stride_B = src4_nb1 / sizeof(float);
|
||||
const int stride_C = src5_nb1 / sizeof(float);
|
||||
const int stride_s = stride_s0;
|
||||
const int stride_y = stride_x;
|
||||
|
||||
// can N not be 16? for example 32?
|
||||
if (N == 16) {
|
||||
#pragma unroll
|
||||
for (int i = 0; i < splitD / 4; i += 2) {
|
||||
float value = A_block[(wid * warpSize + i) * stride_A + wtid];
|
||||
// todo: bank conflict
|
||||
// I am always confused with how to use the swizzling method to solve
|
||||
// bank conflit. Hoping somebody can tell me.
|
||||
smem_A[(wid * warpSize + i) * stride_sA + wtid +
|
||||
((wtid / 16) > 0 ? 1 : 0)] = value;
|
||||
}
|
||||
#pragma unroll
|
||||
for (int i = 0; i < splitD / 4; i += 2) {
|
||||
float value = s0_block[(wid * warpSize + i) * stride_s0 + wtid];
|
||||
smem_s0[(wid * warpSize + i) * stride_ss0 + wtid +
|
||||
((wtid / 16) > 0 ? 1 : 0)] = value;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
for (int i = 0; i < L; i++) {
|
||||
float dt_soft_plus = dt_block[i * stride_dt + wid * warpSize + wtid];
|
||||
if (dt_soft_plus <= 20.0f) {
|
||||
dt_soft_plus = log1pf(exp(dt_soft_plus));
|
||||
}
|
||||
float x_dt = x_block[i * stride_x + wid * warpSize + wtid] * dt_soft_plus;
|
||||
float sumf = 0.0f;
|
||||
#pragma unroll
|
||||
for (int j = 0; j < N; j++) {
|
||||
float state = (smem_s0[(wid * warpSize + wtid) * stride_ss0 + j] *
|
||||
expf(dt_soft_plus *
|
||||
smem_A[(wid * warpSize + wtid) * stride_sA + j])) +
|
||||
(B_block[i * stride_B + j] * x_dt);
|
||||
sumf += state * C_block[i * stride_C + j];
|
||||
if (i == L - 1) {
|
||||
s_block[(wid * warpSize + wtid) * stride_s + j] = state;
|
||||
} else {
|
||||
smem_s0[(wid * warpSize + wtid) * stride_ss0 + j] = state;
|
||||
}
|
||||
}
|
||||
__syncthreads();
|
||||
y_block[i * stride_y + wid * warpSize + wtid] = sumf;
|
||||
}
|
||||
}
|
||||
|
||||
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 int src0_nb1,
|
||||
const int src0_nb2, const int src1_nb0, const int src1_nb1,
|
||||
const int src1_nb2, const int src1_nb3, const int src2_nb0,
|
||||
const int src2_nb1, const int src2_nb2, const int src3_nb1,
|
||||
const int src4_nb1, const int src4_nb2, const int src5_nb1,
|
||||
const int src5_nb2, float *dst, const int N, const int D, const int L,
|
||||
const int B, cudaStream_t stream) {
|
||||
const int threads = 128;
|
||||
// todo: consider D cannot be divided,does this situation exist?
|
||||
GGML_ASSERT(D % threads == 0);
|
||||
const dim3 blocks(B, (D + threads - 1) / threads, 1);
|
||||
const int smem_size = (threads * (N + 1) * 2) * sizeof(float);
|
||||
if (N == 16) {
|
||||
ssm_scan_f32<128, 16><<<blocks, threads, smem_size, stream>>>(
|
||||
src0, src1, src2, src3, src4, src5, src0_nb1, src0_nb2, src1_nb0,
|
||||
src1_nb1, src1_nb2, src1_nb3, src2_nb0, src2_nb1, src2_nb2, src3_nb1,
|
||||
src4_nb1, src4_nb2, src5_nb1, src5_nb2, dst, D, L, B);
|
||||
} else {
|
||||
GGML_ABORT("doesn't support N!=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 int64_t d_state = src0->ne[0];
|
||||
// const int64_t d_inner = src0->ne[1];
|
||||
// const int64_t l = src1->ne[1];
|
||||
// const int64_t b = src0->ne[2];
|
||||
|
||||
const int64_t nc = src0->ne[0]; // d_state
|
||||
const int64_t nr = src0->ne[1]; // d_inner
|
||||
const int64_t n_t = src1->ne[1]; // number of tokens per sequence
|
||||
const int64_t n_s = src0->ne[2]; // number of sequences in the batch
|
||||
|
||||
GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) ==
|
||||
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));
|
||||
// required for the dot product between s and C
|
||||
GGML_ASSERT(src0->nb[1] == src0->ne[0] * sizeof(float));
|
||||
// required for per-sequence offsets for states
|
||||
GGML_ASSERT(src0->nb[2] == src0->ne[0] * src0->ne[1] * sizeof(float));
|
||||
// required to get correct offset for state destination (i.e. src1->nb[3])
|
||||
GGML_ASSERT(src1->nb[3] ==
|
||||
src1->ne[0] * src1->ne[1] * src1->ne[2] * sizeof(float));
|
||||
|
||||
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;
|
||||
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);
|
||||
|
||||
ssm_scan_f32_cuda(src0_d, src1_d, src2_d, src3_d, src4_d, src5_d, src0->nb[1],
|
||||
src0->nb[2], src1->nb[0], src1->nb[1], src1->nb[2],
|
||||
src1->nb[3], src2->nb[0], src2->nb[1], src2->nb[2],
|
||||
src3->nb[1], src4->nb[1], src4->nb[2], src5->nb[1],
|
||||
src5->nb[2], dst_d, nc, nr, n_t, n_s, stream);
|
||||
}
|
||||
|
||||
// #include "ssm_scan.cuh"
|
||||
|
||||
// template <int block_size>
|
||||
// static __global__ void 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 int src0_nb1, const int src0_nb2, const int src1_nb0,
|
||||
// const int src1_nb1, const int src1_nb2, const int src1_nb3,
|
||||
// const int src2_nb0, const int src2_nb1, const int src2_nb2,
|
||||
// const int src3_nb1, const int src4_nb1, const int src4_nb2,
|
||||
// const int src5_nb1, const int src5_nb2, float *__restrict__ dst,
|
||||
// const int nc, const int nr, const int n_t, const int n_s) {
|
||||
// // const int row = blockIdx.x*blockDim.y + threadIdx.y;
|
||||
// const int tid = threadIdx.x;
|
||||
// const int i3 = threadIdx.y;
|
||||
|
||||
// const int ith = tid;
|
||||
// const int nth = WARP_SIZE;
|
||||
|
||||
// // rows per thread
|
||||
// const int dr = (nr + nth - 1) / nth;
|
||||
|
||||
// // row range for this thread
|
||||
// const int ir0 = dr * ith;
|
||||
// const int ir1 = min(ir0 + dr, nr);
|
||||
// const int ir = ir1 - ir0;
|
||||
// for (int i2 = 0; i2 < n_t; ++i2) {
|
||||
// const float *s0 =
|
||||
// (const float *)((const char *)src0 + ir0 * src0_nb1 +
|
||||
// i3 * src0_nb2); // {d_state, d_inner, n_s}
|
||||
// const float *x =
|
||||
// (const float *)((const char *)src1 + ir0 * src1_nb0 + i2 * src1_nb1 +
|
||||
// i3 * src1_nb2); // {d_inner, n_t, n_s}
|
||||
// const float *dt =
|
||||
// (const float *)((const char *)src2 + ir0 * src2_nb0 + i2 * src2_nb1 +
|
||||
// i3 * src2_nb2); // {d_inner, n_t, n_s}
|
||||
// const float *A = (const float *)((const char *)src3 +
|
||||
// ir0 * src3_nb1); // {d_state, d_inner}
|
||||
// const float *B = (const float *)((const char *)src4 + i2 * src4_nb1 +
|
||||
// i3 * src4_nb2); // {d_state, n_t, n_s}
|
||||
// const float *C = (const float *)((const char *)src5 + i2 * src5_nb1 +
|
||||
// i3 * src5_nb2); // {d_state, n_t, n_s}
|
||||
// float *y = (float *)((char *)dst + ir0 * src1_nb0 + i2 * src1_nb1 +
|
||||
// i3 * src1_nb2); // {d_inner, n_t, n_s}
|
||||
// float *s = (float *)((char *)dst + ir0 * src0_nb1 + i3 * src0_nb2 +
|
||||
// src1_nb3); // {d_state, d_inner, n_s}
|
||||
|
||||
// // use the output as the source for the next token-wise iterations
|
||||
// if (i2 > 0) {
|
||||
// s0 = s;
|
||||
// }
|
||||
|
||||
// // d_inner
|
||||
// for (int i1 = 0; i1 < ir; ++i1) {
|
||||
// // ref:
|
||||
// //
|
||||
// https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78
|
||||
// float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1];
|
||||
// float x_dt = x[i1] * dt_soft_plus;
|
||||
// float sumf = 0.0f;
|
||||
// // d_state
|
||||
// #pragma unroll
|
||||
// for (int i0 = 0; i0 < nc; ++i0) {
|
||||
// int i = i0 + i1 * nc;
|
||||
// // state = prev_state * dA + dB * x
|
||||
// float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt);
|
||||
// // y = rowwise_dotprod(state, C)
|
||||
// sumf += state * C[i0];
|
||||
// s[i] = state;
|
||||
// }
|
||||
// y[i1] = sumf;
|
||||
// }
|
||||
// }
|
||||
// }
|
||||
|
||||
// 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 int src0_nb1, const
|
||||
// int src0_nb2, const int src1_nb0, const int src1_nb1, const int src1_nb2,
|
||||
// const int src1_nb3, const int src2_nb0, const int src2_nb1, const int
|
||||
// src2_nb2, const int src3_nb1, const int src4_nb1, const int src4_nb2,
|
||||
// const int src5_nb1, const int src5_nb2, float *dst, const int nc, const
|
||||
// int nr, const int n_t, const int n_s, cudaStream_t stream) {
|
||||
// const dim3 block_dims(WARP_SIZE, n_s, 1);
|
||||
// const int nblocks = 1; // TODO
|
||||
|
||||
// ssm_scan_f32<WARP_SIZE><<<nblocks, block_dims, 0, stream>>>(
|
||||
// src0, src1, src2, src3, src4, src5, src0_nb1, src0_nb2, src1_nb0,
|
||||
// src1_nb1, src1_nb2, src1_nb3, src2_nb0, src2_nb1, src2_nb2, src3_nb1,
|
||||
// src4_nb1, src4_nb2, src5_nb1, src5_nb2, dst, nc, nr, n_t, n_s);
|
||||
// }
|
||||
|
||||
// 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 int64_t nc = src0->ne[0]; // d_state
|
||||
// const int64_t nr = src0->ne[1]; // d_inner
|
||||
// const int64_t n_t = src1->ne[1]; // number of tokens per sequence
|
||||
// const int64_t n_s = src0->ne[2]; // number of sequences in the batch
|
||||
|
||||
// GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) ==
|
||||
// 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));
|
||||
// // required for the dot product between s and C
|
||||
// GGML_ASSERT(src0->nb[1] == src0->ne[0] * sizeof(float));
|
||||
// // required for per-sequence offsets for states
|
||||
// GGML_ASSERT(src0->nb[2] == src0->ne[0] * src0->ne[1] * sizeof(float));
|
||||
// // required to get correct offset for state destination (i.e. src1->nb[3])
|
||||
// GGML_ASSERT(src1->nb[3] ==
|
||||
// src1->ne[0] * src1->ne[1] * src1->ne[2] * sizeof(float));
|
||||
|
||||
// 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;
|
||||
// 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);
|
||||
|
||||
// ssm_scan_f32_cuda(src0_d, src1_d, src2_d, src3_d, src4_d, src5_d,
|
||||
// src0->nb[1],
|
||||
// src0->nb[2], src1->nb[0], src1->nb[1], src1->nb[2],
|
||||
// src1->nb[3], src2->nb[0], src2->nb[1], src2->nb[2],
|
||||
// src3->nb[1], src4->nb[1], src4->nb[2], src5->nb[1],
|
||||
// src5->nb[2], dst_d, nc, nr, n_t, n_s, stream);
|
||||
// }
|
||||
3
ggml/src/ggml-cuda/ssm_scan.cuh
Normal file
3
ggml/src/ggml-cuda/ssm_scan.cuh
Normal file
|
|
@ -0,0 +1,3 @@
|
|||
#include "common.cuh"
|
||||
|
||||
void ggml_cuda_op_ssm_scan(ggml_backend_cuda_context& ctx, ggml_tensor* dst);
|
||||
Loading…
Add table
Add a link
Reference in a new issue