From 1e645678e766205a237422edd8039e052a1ef4fd Mon Sep 17 00:00:00 2001 From: lihan <1091770049@qq.com> Date: Thu, 28 Nov 2024 17:16:08 +0800 Subject: [PATCH] clang format --- ggml/src/ggml-cuda/ssm_conv.cu | 134 +++++++--------- ggml/src/ggml-cuda/ssm_conv.cuh | 2 +- ggml/src/ggml-cuda/ssm_scan.cu | 275 +++++++++++++++----------------- ggml/src/ggml-cuda/ssm_scan.cuh | 2 +- 4 files changed, 190 insertions(+), 223 deletions(-) diff --git a/ggml/src/ggml-cuda/ssm_conv.cu b/ggml/src/ggml-cuda/ssm_conv.cu index ad7c67611..ca0089cd3 100644 --- a/ggml/src/ggml-cuda/ssm_conv.cu +++ b/ggml/src/ggml-cuda/ssm_conv.cu @@ -1,94 +1,82 @@ #include "ssm_conv.cuh" template -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; +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; + const int ith = tid; + const int nth = WARP_SIZE; - // rows per thread - const int dr = (nr + nth - 1) / nth; + // 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; + // 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} + // {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; + // 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]; + for (int i0 = 0; i0 < nc; ++i0) { + sumf += s[i0 + i1 * ncs] * c[i0 + i1 * nc]; + } + x[i1] = sumf; } - 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); +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<<>>( - src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1, dst, dst_nb0, dst_nb1, - dst_nb2, nc, ncs, nr, n_t, n_s); + ssm_conv_f32<<>>( + 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 +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 + 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)); + 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(); + 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); -} \ No newline at end of file + 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); +} diff --git a/ggml/src/ggml-cuda/ssm_conv.cuh b/ggml/src/ggml-cuda/ssm_conv.cuh index f4b23776e..8e6c1f00b 100644 --- a/ggml/src/ggml-cuda/ssm_conv.cuh +++ b/ggml/src/ggml-cuda/ssm_conv.cuh @@ -1,3 +1,3 @@ #include "common.cuh" -void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context& ctx, ggml_tensor* dst); \ No newline at end of file +void ggml_cuda_op_ssm_conv(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/ssm_scan.cu b/ggml/src/ggml-cuda/ssm_scan.cu index f95b34629..357d3eba5 100644 --- a/ggml/src/ggml-cuda/ssm_scan.cu +++ b/ggml/src/ggml-cuda/ssm_scan.cu @@ -7,171 +7,150 @@ template __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; + 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; + 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 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; + 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) { + // 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; - } + 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; + 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; - } + + 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><<>>( - 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."); - } +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><<>>( + 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 +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 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 + 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)); + 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(); + 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); + 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); + 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); } diff --git a/ggml/src/ggml-cuda/ssm_scan.cuh b/ggml/src/ggml-cuda/ssm_scan.cuh index 3d07ef0ce..ee078f5eb 100644 --- a/ggml/src/ggml-cuda/ssm_scan.cuh +++ b/ggml/src/ggml-cuda/ssm_scan.cuh @@ -1,3 +1,3 @@ #include "common.cuh" -void ggml_cuda_op_ssm_scan(ggml_backend_cuda_context& ctx, ggml_tensor* dst); \ No newline at end of file +void ggml_cuda_op_ssm_scan(ggml_backend_cuda_context & ctx, ggml_tensor * dst);