diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu index fd5a5cac..12da791f 100644 --- a/ggml/src/ggml-cuda.cu +++ b/ggml/src/ggml-cuda.cu @@ -3173,7 +3173,16 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg ggml_cuda_op_relu(ctx, dst); break; case GGML_UNARY_OP_SIGMOID: - ggml_cuda_op_sigmoid(ctx, dst); + if (i + 4 < cgraph->n_nodes && + cgraph->nodes[i+1]->op == GGML_OP_RESHAPE && + cgraph->nodes[i+2]->op == GGML_OP_ADD && + cgraph->nodes[i+3]->op == GGML_OP_GROUPED_TOPK && + cgraph->nodes[i+4]->op == GGML_OP_GET_ROWS) { + cuda_bailingmoev2_experts(ctx, cgraph->nodes[i+4], cgraph->nodes[i+3]); + i += 4; + } else { + ggml_cuda_op_sigmoid(ctx, dst); + } break; case GGML_UNARY_OP_HARDSIGMOID: ggml_cuda_op_hardsigmoid(ctx, dst); diff --git a/ggml/src/ggml-cuda/argsort.cu b/ggml/src/ggml-cuda/argsort.cu index 25c61588..3976c9d2 100644 --- a/ggml/src/ggml-cuda/argsort.cu +++ b/ggml/src/ggml-cuda/argsort.cu @@ -25,25 +25,8 @@ struct store { constexpr static bool has_thresh = false; }; -template -static __global__ void k_argsort_f32_T(const float * x, dst_t * dst, const int ncols, int ncols_pad, int ntop, Store s) { -// int min_experts, float thresh_experts) { - // bitonic sort - int col = threadIdx.x; - int row = blockIdx.y; - - if (col >= ncols_pad) { - return; - } - - const float * x_row = x + row * ncols; - extern __shared__ int dst_row[]; - - // initialize indices - dst_row[col] = col; - - __syncthreads(); - +template +static __device__ __forceinline__ void sort(int ncols_pad, int ncols, int col, const float * x_row, int * dst_row) { for (int k = 2; k <= ncols_pad; k *= 2) { for (int j = k / 2; j > 0; j /= 2) { int ixj = col ^ j; @@ -69,6 +52,28 @@ static __global__ void k_argsort_f32_T(const float * x, dst_t * dst, const int n __syncthreads(); } } +} + +template +static __global__ void k_argsort_f32_T(const float * x, dst_t * dst, const int ncols, int ncols_pad, int ntop, Store s) { +// int min_experts, float thresh_experts) { + // bitonic sort + int col = threadIdx.x; + int row = blockIdx.y; + + if (col >= ncols_pad) { + return; + } + + const float * x_row = x + row * ncols; + extern __shared__ int dst_row[]; + + // initialize indices + dst_row[col] = col; + + __syncthreads(); + + sort(ncols_pad, ncols, col, x_row, dst_row); if constexpr (Store::has_thresh) { __syncthreads(); @@ -92,7 +97,8 @@ static __global__ void k_argsort_f32_T(const float * x, dst_t * dst, const int n } template -static __global__ void k_topk_sum(const float * x, float * dst, const int ncols, int ncols_pad, int n_top_k) { +static __global__ void k_argsort_f32_f32_i32(const float * x_biased, const float * x, float * weights, int * ids, const int ncols, int ncols_pad, int ntop, + size_t nb_ids) { // bitonic sort int col = threadIdx.x; int row = blockIdx.y; @@ -101,7 +107,7 @@ static __global__ void k_topk_sum(const float * x, float * dst, const int ncols, return; } - const float * x_row = x + row * ncols; + const float * x_row = x_biased + row * ncols; extern __shared__ int dst_row[]; // initialize indices @@ -109,31 +115,42 @@ static __global__ void k_topk_sum(const float * x, float * dst, const int ncols, __syncthreads(); - for (int k = 2; k <= ncols_pad; k *= 2) { - for (int j = k / 2; j > 0; j /= 2) { - int ixj = col ^ j; - if (ixj > col) { - if ((col & k) == 0) { - if (dst_row[col] >= ncols || - (dst_row[ixj] < ncols && (order == GGML_SORT_ORDER_ASC ? - x_row[dst_row[col]] > x_row[dst_row[ixj]] : - x_row[dst_row[col]] < x_row[dst_row[ixj]])) - ) { - ggml_cuda_swap(dst_row[col], dst_row[ixj]); - } - } else { - if (dst_row[ixj] >= ncols || - (dst_row[col] < ncols && (order == GGML_SORT_ORDER_ASC ? - x_row[dst_row[col]] < x_row[dst_row[ixj]] : - x_row[dst_row[col]] > x_row[dst_row[ixj]])) - ) { - ggml_cuda_swap(dst_row[col], dst_row[ixj]); - } - } - } - __syncthreads(); - } + sort(ncols_pad, ncols, col, x_row, dst_row); + + if (col < ntop) { + weights[row * ntop + col] = x[row * ncols + dst_row[col]]; + auto row_ids = (int *)((char *)ids + row*nb_ids); + row_ids[col] = dst_row[col]; } +} + +template +static __global__ void k_topk_sum(float * x, const float * bias, float * x_p, float * dst, const int ncols, int ncols_pad, int n_top_k) { + // bitonic sort + int col = threadIdx.x; + int row = blockIdx.y; + + if (col >= ncols_pad) { + return; + } + + float * x_row = x + row * ncols; + extern __shared__ int dst_row[]; + + // initialize indices + dst_row[col] = col; + if (bias && x_p) { + float * x_p_row = x_p + row * ncols; + if (col < ncols) { + x_row[col] = 1/(1 + expf(-x_row[col])); + x_p_row[col] = x_row[col] + bias[col]; + } + x_row = x_p_row; + } + + __syncthreads(); + + sort(ncols_pad, ncols, col, x_row, dst_row); float val = col < n_top_k ? x_row[dst_row[col]] : 0; val = warp_reduce_sum(val); @@ -208,6 +225,29 @@ static void argsort_f32_T_cuda(const float * x, dst_t * dst, const int ncols, co } } +static void argsort_f32_f32_i32_cuda(const float * x_biased, const float * x, float * weights, int * ids, const int ncols, const int nrows, int ntop, + size_t nb_ids, ggml_sort_order order, cudaStream_t stream) { + // bitonic sort requires ncols to be power of 2 + const int ncols_pad = next_power_of_2(ncols); + + const dim3 block_dims(ncols_pad, 1, 1); + const dim3 block_nums(1, nrows, 1); + const size_t shared_mem = ncols_pad * sizeof(int); + + // FIXME: this limit could be raised by ~2-4x on Ampere or newer + GGML_ASSERT(shared_mem <= ggml_cuda_info().devices[ggml_cuda_get_device()].smpb); + + if (order == GGML_SORT_ORDER_ASC) { + k_argsort_f32_f32_i32<<>>(x_biased, x, weights, ids, + ncols, ncols_pad, ntop, nb_ids); + } else if (order == GGML_SORT_ORDER_DESC) { + k_argsort_f32_f32_i32<<>>(x_biased, x, weights, ids, + ncols, ncols_pad, ntop, nb_ids); + } else { + GGML_ABORT("fatal error"); + } +} + void ggml_cuda_op_argsort(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const float * src0_d = (const float *)src0->data; @@ -246,7 +286,8 @@ void ggml_cuda_op_argsort_thresh(ggml_backend_cuda_context & ctx, ggml_tensor * argsort_f32_T_cuda(src0_d, (int *)dst_d, ncols, nrows, ncols, GGML_SORT_ORDER_DESC, min_experts, thresh, stream); } -static void ggml_cuda_op_topk_sum(ggml_backend_cuda_context & ctx, const float * src, float * dst, int ncols, int nrows, int n_top_k) { +static void ggml_cuda_op_topk_sum(ggml_backend_cuda_context & ctx, float * src, const float * bias, float * src_p, float * dst, + int ncols, int nrows, int n_top_k) { GGML_ASSERT(n_top_k <= ncols); @@ -257,7 +298,7 @@ static void ggml_cuda_op_topk_sum(ggml_backend_cuda_context & ctx, const float * const size_t shared_mem = std::max(ncols_pad, WARP_SIZE) * sizeof(int); GGML_ASSERT(shared_mem <= ggml_cuda_info().devices[ggml_cuda_get_device()].smpb); - k_topk_sum<<>>(src, dst, ncols, ncols_pad, n_top_k); + k_topk_sum<<>>(src, bias, src_p, dst, ncols, ncols_pad, n_top_k); } void ggml_cuda_op_grouped_topk(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { @@ -291,7 +332,7 @@ void ggml_cuda_op_grouped_topk(ggml_backend_cuda_context & ctx, ggml_tensor * ds CUDA_CHECK(cudaGetLastError()); #else ggml_cuda_pool_alloc group_scores(ctx.pool(), nrows*n_groups); - ggml_cuda_op_topk_sum(ctx, (const float *)src->data, group_scores.get(), n_per_group, nrows*n_groups, nk); + ggml_cuda_op_topk_sum(ctx, (float *)src->data, nullptr, nullptr, group_scores.get(), n_per_group, nrows*n_groups, nk); CUDA_CHECK(cudaGetLastError()); #endif @@ -310,3 +351,49 @@ void ggml_cuda_op_grouped_topk(ggml_backend_cuda_context & ctx, ggml_tensor * ds argsort_f32_T_cuda((const float *)src->data, (int *)dst->data, ne00, nrows, ne0, GGML_SORT_ORDER_DESC, -1, 0.0f, ctx.stream()); } + +void cuda_bailingmoev2_experts(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * topk) { + auto topk_src = topk->src[0]; + auto probs = topk_src->src[0]->src[0]; + auto bias = topk_src->src[1]; + + auto nrows = ggml_nrows(probs); + + int n_groups = topk->op_params[0]; + int n_top_groups = topk->op_params[1]; + int nk = topk->op_params[2]; + + int ne00 = probs->ne[0]; + int ne0 = topk->ne[0]; + GGML_ASSERT(ggml_is_contiguous(probs)); + GGML_ASSERT(bias->ne[1] == 1); + GGML_ASSERT(bias->ne[0] == probs->ne[0]); + GGML_ASSERT(ne0 == dst->ne[1]); + GGML_ASSERT(ne0 <= ne00); + GGML_ASSERT(ne00%n_groups == 0); + int n_per_group = ne00/n_groups; + GGML_ASSERT(nk <= n_per_group); + GGML_ASSERT(n_top_groups <= n_groups); + int n_discarded_groups = n_groups - n_top_groups; + + ggml_cuda_pool_alloc group_scores(ctx.pool(), nrows*n_groups); + ggml_cuda_op_topk_sum(ctx, (float *)probs->data, (const float *)bias->data, (float *)topk_src->data, group_scores.get(), + n_per_group, nrows*n_groups, nk); + CUDA_CHECK(cudaGetLastError()); + + ggml_cuda_pool_alloc discarded_groups(ctx.pool(), nrows*n_discarded_groups); + argsort_f32_T_cuda(group_scores.get(), discarded_groups.get(), n_groups, nrows, n_discarded_groups, GGML_SORT_ORDER_ASC, -1, 0.0f, ctx.stream()); + CUDA_CHECK(cudaGetLastError()); + + { + const dim3 block_dims(WARP_SIZE, 1, 1); + const dim3 block_nums(1, nrows, 1); + cudaStream_t stream = ctx.stream(); + k_apply_mask<<>>((float *)topk_src->data, discarded_groups.get(), n_discarded_groups, n_per_group, ne00); + CUDA_CHECK(cudaGetLastError()); + } + + argsort_f32_f32_i32_cuda((const float *)topk_src->data, (const float *)probs->data, (float *)dst->data, (int *)topk->data, ne00, nrows, ne0, + topk->nb[1], GGML_SORT_ORDER_DESC, ctx.stream()); + +} diff --git a/ggml/src/ggml-cuda/argsort.cuh b/ggml/src/ggml-cuda/argsort.cuh index 7bd28a1f..e467abf0 100644 --- a/ggml/src/ggml-cuda/argsort.cuh +++ b/ggml/src/ggml-cuda/argsort.cuh @@ -11,3 +11,5 @@ void ggml_cuda_op_argsort(ggml_backend_cuda_context & ctx, ggml_tensor * dst); void ggml_cuda_op_argsort_thresh(ggml_backend_cuda_context & ctx, ggml_tensor * dst); void ggml_cuda_op_grouped_topk(ggml_backend_cuda_context & ctx, ggml_tensor * dst); + +void cuda_bailingmoev2_experts(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * topk); diff --git a/ggml/src/iqk/iqk_cpu_ops.cpp b/ggml/src/iqk/iqk_cpu_ops.cpp index 5d0adcba..f823d282 100644 --- a/ggml/src/iqk/iqk_cpu_ops.cpp +++ b/ggml/src/iqk/iqk_cpu_ops.cpp @@ -41,47 +41,58 @@ inline std::vector> & get_work_buffer(size_t size) { } #ifdef __ARM_NEON -inline float32x4_t v_biased_sigmoid(float32x4_t x, float32x4_t b) { +inline float32x4_t v_sigmoid(float32x4_t x) { const float32x4_t one = vdupq_n_f32(1.0f); const float32x4_t zero = vdupq_n_f32(0.0f); const float32x4_t neg_x = vsubq_f32(zero, x); const float32x4_t exp_neg_x = v_expf(neg_x); const float32x4_t one_plus_exp_neg_x = vaddq_f32(one, exp_neg_x); - return vaddq_f32(b, vdivq_f32(one, one_plus_exp_neg_x)); + return vdivq_f32(one, one_plus_exp_neg_x); } #endif #ifdef __AVX2__ -inline __m256 v_biased_sigmoid(__m256 x, __m256 b) { +inline __m256 v_sigmoid(__m256 x) { const __m256 one = _mm256_set1_ps(1); const __m256 zero = _mm256_setzero_ps(); const __m256 neg_x = _mm256_sub_ps(zero, x); const __m256 exp_neg_x = v_expf(neg_x); const __m256 one_plus_exp_neg_x = _mm256_add_ps(one, exp_neg_x); - return _mm256_add_ps(b, _mm256_div_ps(one, one_plus_exp_neg_x)); + return _mm256_div_ps(one, one_plus_exp_neg_x); } #endif #if defined __AVX512F__ && defined __AVX512DQ__ -inline __m512 v_biased_sigmoid(__m512 x, __m512 b) { +inline __m512 v_sigmoid(__m512 x) { const __m512 one = _mm512_set1_ps(1); const __m512 zero = _mm512_setzero_ps(); const __m512 neg_x = _mm512_sub_ps(zero, x); const __m512 exp_neg_x = v_expf(neg_x); const __m512 one_plus_exp_neg_x = _mm512_add_ps(one, exp_neg_x); - return _mm512_add_ps(b, _mm512_div_ps(one, one_plus_exp_neg_x)); + return _mm512_div_ps(one, one_plus_exp_neg_x); } #endif -inline void biased_sigmoid(int n, const float * x, const float * bias, float * y) { +inline void biased_sigmoid(int n, const float * x, const float * bias, float * y, float * z) { int i = 0; #if defined __AVX512F__ && defined __AVX512DQ__ - for (; i + 15 < n; i += 16) _mm512_storeu_ps(y + i, v_biased_sigmoid(_mm512_loadu_ps(x + i), _mm512_loadu_ps(bias + i))); + for (; i + 15 < n; i += 16) { + auto v = v_sigmoid(_mm512_loadu_ps(x + i)); + _mm512_storeu_ps(y + i, _mm512_add_ps(v, _mm512_loadu_ps(bias + i))); + _mm512_storeu_ps(z + i, v); + } #endif #if defined __AVX2__ && defined __FMA__ - for (; i + 7 < n; i += 8) _mm256_storeu_ps(y + i, v_biased_sigmoid(_mm256_loadu_ps(x + i), _mm256_loadu_ps(bias + i))); + for (; i + 7 < n; i += 8) { + auto v = v_sigmoid(_mm256_loadu_ps(x + i)); + _mm256_storeu_ps(y + i, _mm256_add_ps(v, _mm256_loadu_ps(bias + i))); + _mm256_storeu_ps(z + i, v); + } #endif #ifdef __ARM_NEON for (; i + 3 < n; i += 4) vst1q_f32(y + i, v_biased_sigmoid(vld1q_f32(x + i), vld1q_f32(bias + i))); #endif - for (; i < n; ++i) y[i] = 1/(1 + expf(-x[i])) + bias[i]; + for (; i < n; ++i) { + z[i] = 1/(1 + expf(-x[i])); + y[i] = y[i] + bias[i]; + } } } @@ -214,17 +225,18 @@ void iqk_bailingmoev2_experts(struct ggml_tensor * dst, struct ggml_tensor * top GGML_ASSERT(nk <= n_per_group); GGML_ASSERT(n_top_groups <= n_groups); - size_t work_size = n_groups + n_per_group*n_top_groups + (ne00 + 1)/2; + size_t work_size = n_groups + n_per_group*n_top_groups + ne00; auto& aux = get_work_buffer(work_size); auto groups = aux.data() + n_per_group*n_top_groups; - auto values = (float *)(groups + n_groups); + auto biased_values = (float *)(groups + n_groups); + auto values = biased_values + ne00; auto bias = (const float *)t_bias->data; for (int ir = first; ir < last; ++ir) { auto data = (const float *)((const char *)probs->data + ir*probs->nb[1]); - biased_sigmoid(ne00, data, bias, values); + biased_sigmoid(ne00, data, bias, biased_values, values); //for (int j = 0; j < ne00; ++j) values[j] = 1/(1 + expf(-data[j])) + bias[j]; auto weights = (float *)((char *)dst->data + ir*dst->nb[2]); auto ids = (int32_t *)((char *)topk->data + ir*topk->nb[1]); @@ -237,21 +249,21 @@ void iqk_bailingmoev2_experts(struct ggml_tensor * dst, struct ggml_tensor * top } if (n_top_groups < n_groups) { for (int ig = 0; ig < n_groups; ++ig) { - groups[ig] = { group_score(n_per_group, nk, values + ig*n_per_group, (float *)aux.data()), ig }; + groups[ig] = { group_score(n_per_group, nk, biased_values + ig*n_per_group, (float *)aux.data()), ig }; } std::partial_sort(groups, groups + n_top_groups, groups + n_groups, std::greater>{}); for (int ig = 0; ig < n_top_groups; ++ig) { int i0 = n_per_group * ig; int j0 = n_per_group * groups[ig].second; - for (int j = 0; j < n_per_group; ++j) aux[i0 + j] = { values[j0 + j], j0 + j }; + for (int j = 0; j < n_per_group; ++j) aux[i0 + j] = { biased_values[j0 + j], j0 + j }; } } else { - for (int j = 0; j < ne00; ++j) aux[j] = { values[j], j }; + for (int j = 0; j < ne00; ++j) aux[j] = { biased_values[j], j }; } std::partial_sort(aux.begin(), aux.begin() + ne0, aux.begin() + n_top_groups*n_per_group, std::greater>{}); for (int j = 0; j < ne0; ++j) { - weights[j] = aux[j].first; + weights[j] = values[aux[j].second]; ids[j] = aux[j].second; }