diff --git a/ggml/src/ggml-cuda/gated_delta_net.cu b/ggml/src/ggml-cuda/gated_delta_net.cu index a547360eb0..1b431a724d 100644 --- a/ggml/src/ggml-cuda/gated_delta_net.cu +++ b/ggml/src/ggml-cuda/gated_delta_net.cu @@ -10,6 +10,7 @@ gated_delta_net_cuda(const float * q, const float * beta, const float * curr_state, float * dst, + float * state, int64_t H, int64_t n_tokens, int64_t n_seqs, @@ -25,6 +26,7 @@ gated_delta_net_cuda(const float * q, const uint3 neqk1_magic, const uint3 rq3_magic, float scale, + int64_t state_slot_stride, int K) { const uint32_t h_idx = blockIdx.x; const uint32_t sequence = blockIdx.y; @@ -35,9 +37,7 @@ gated_delta_net_cuda(const float * q, const uint32_t iq1 = fastmodulo(h_idx, neqk1_magic); const uint32_t iq3 = fastdiv(sequence, rq3_magic); - const int64_t attn_score_elems = S_v * H * n_tokens * n_seqs; float * attn_data = dst; - float * state = dst + attn_score_elems; // input state holds s0 only: [S_v, S_v, H, n_seqs] — seq stride is D = H * S_v * S_v. // output state layout (per-slot D * n_seqs) — same per-(seq,head) offset as before. @@ -145,10 +145,9 @@ gated_delta_net_cuda(const float * q, if constexpr (keep_rs_t) { // snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back. // When n_tokens < K only slots 0..n_tokens-1 are written; older slots are caller-owned. - const int64_t state_size_per_token = S_v * S_v * H * n_seqs; // per-slot stride in output const int target_slot = (int) n_tokens - 1 - t; if (target_slot >= 0 && target_slot < K) { - float * curr_state = (dst + attn_score_elems) + target_slot * state_size_per_token + state_out_offset; + float * curr_state = state + target_slot * state_slot_stride; #pragma unroll for (int r = 0; r < rows_per_lane; r++) { const int i = r * warp_size + lane; @@ -171,13 +170,13 @@ template static void launch_gated_delta_net( const float * q_d, const float * k_d, const float * v_d, const float * g_d, const float * b_d, const float * s_d, - float * dst_d, + float * dst_d, float * state_d, int64_t S_v, int64_t H, int64_t n_tokens, int64_t n_seqs, int64_t sq1, int64_t sq2, int64_t sq3, int64_t sv1, int64_t sv2, int64_t sv3, int64_t sb1, int64_t sb2, int64_t sb3, int64_t neqk1, int64_t rq3, - float scale, int K, cudaStream_t stream) { + float scale, int64_t state_slot_stride, int K, cudaStream_t stream) { //TODO: Add chunked kernel for even faster pre-fill const int warp_size = ggml_cuda_info().devices[ggml_cuda_get_device()].warp_size; const int num_warps = 4; @@ -187,34 +186,32 @@ static void launch_gated_delta_net( const uint3 neqk1_magic = init_fastdiv_values(neqk1); const uint3 rq3_magic = init_fastdiv_values(rq3); - int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; - const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(grid_dims, block_dims, 0, stream); switch (S_v) { case 16: ggml_cuda_kernel_launch(gated_delta_net_cuda<16, KDA, keep_rs_t>, launch_params, - q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H, + q_d, k_d, v_d, g_d, b_d, s_d, dst_d, state_d, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, - sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, K); + sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, state_slot_stride, K); break; case 32: ggml_cuda_kernel_launch(gated_delta_net_cuda<32, KDA, keep_rs_t>, launch_params, - q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H, + q_d, k_d, v_d, g_d, b_d, s_d, dst_d, state_d, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, - sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, K); + sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, state_slot_stride, K); break; case 64: { ggml_cuda_kernel_launch(gated_delta_net_cuda<64, KDA, keep_rs_t>, launch_params, - q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H, + q_d, k_d, v_d, g_d, b_d, s_d, dst_d, state_d, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, - sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, K); + sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, state_slot_stride, K); break; } case 128: { ggml_cuda_kernel_launch(gated_delta_net_cuda<128, KDA, keep_rs_t>, launch_params, - q_d, k_d, v_d, g_d, b_d, s_d, dst_d, H, + q_d, k_d, v_d, g_d, b_d, s_d, dst_d, state_d, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, - sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, K); + sb1, sb2, sb3, neqk1_magic, rq3_magic, scale, state_slot_stride, K); break; } default: @@ -223,7 +220,8 @@ static void launch_gated_delta_net( } } -void ggml_cuda_op_gated_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { +static void ggml_cuda_op_gated_delta_net_impl( + ggml_backend_cuda_context & ctx, ggml_tensor * dst, const ggml_cuda_gated_delta_net_fused_cache * cache) { ggml_tensor * src_q = dst->src[0]; ggml_tensor * src_k = dst->src[1]; ggml_tensor * src_v = dst->src[2]; @@ -288,25 +286,42 @@ void ggml_cuda_op_gated_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor * const int K = ggml_get_op_params_i32(dst, 0); const bool keep_rs = K > 1; + // recurrent state -> gdn_out tail (after attention scores), or the cache when fusing + float * state_d = dst_d + S_v * H * n_tokens * n_seqs; + int64_t state_slot_stride = S_v * S_v * H * n_seqs; + if (cache != nullptr) { + state_d = cache->data; + state_slot_stride = cache->slot_stride; + } + if (kda) { if (keep_rs) { - launch_gated_delta_net(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, + launch_gated_delta_net(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, state_d, S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, - sb1, sb2, sb3, neqk1, rq3, scale, K, stream); + sb1, sb2, sb3, neqk1, rq3, scale, state_slot_stride, K, stream); } else { - launch_gated_delta_net(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, + launch_gated_delta_net(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, state_d, S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, - sb1, sb2, sb3, neqk1, rq3, scale, K, stream); + sb1, sb2, sb3, neqk1, rq3, scale, state_slot_stride, K, stream); } } else { if (keep_rs) { - launch_gated_delta_net(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, + launch_gated_delta_net(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, state_d, S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, - sb1, sb2, sb3, neqk1, rq3, scale, K, stream); + sb1, sb2, sb3, neqk1, rq3, scale, state_slot_stride, K, stream); } else { - launch_gated_delta_net(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, + launch_gated_delta_net(q_d, k_d, v_d, g_d, b_d, s_d, dst_d, state_d, S_v, H, n_tokens, n_seqs, sq1, sq2, sq3, sv1, sv2, sv3, - sb1, sb2, sb3, neqk1, rq3, scale, K, stream); + sb1, sb2, sb3, neqk1, rq3, scale, state_slot_stride, K, stream); } } } + +void ggml_cuda_op_gated_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + ggml_cuda_op_gated_delta_net_impl(ctx, dst, nullptr); +} + +void ggml_cuda_op_gated_delta_net_fused_cache( + ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_cuda_gated_delta_net_fused_cache cache) { + ggml_cuda_op_gated_delta_net_impl(ctx, dst, &cache); +} diff --git a/ggml/src/ggml-cuda/gated_delta_net.cuh b/ggml/src/ggml-cuda/gated_delta_net.cuh index 7375e81c0c..f9bf437067 100644 --- a/ggml/src/ggml-cuda/gated_delta_net.cuh +++ b/ggml/src/ggml-cuda/gated_delta_net.cuh @@ -1,4 +1,14 @@ #include "common.cuh" #include "ggml.h" +// fused-kernel recurrent-state output; strides in elements (per-seq stride is always D, set in-kernel) +struct ggml_cuda_gated_delta_net_fused_cache { + float * data; // rollback slot 0 + int64_t slot_stride; // between rollback slots (0 when K==1) +}; + void ggml_cuda_op_gated_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor * dst); + +// same op, but writes the snapshot(s) into the cache instead of dst (see ggml_cuda_try_gdn_cache_fusion) +void ggml_cuda_op_gated_delta_net_fused_cache(ggml_backend_cuda_context & ctx, ggml_tensor * dst, + ggml_cuda_gated_delta_net_fused_cache cache); diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index cca70592f8..78d2218e55 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -3251,6 +3251,11 @@ static void ggml_backend_cuda_synchronize(ggml_backend_t backend) { GGML_UNUSED(backend); } +static bool ggml_cuda_is_view_or_noop(const ggml_tensor * t) { + return ggml_is_empty(t) || t->op == GGML_OP_RESHAPE || t->op == GGML_OP_TRANSPOSE || + t->op == GGML_OP_VIEW || t->op == GGML_OP_PERMUTE || t->op == GGML_OP_NONE; +} + #ifdef USE_CUDA_GRAPH static bool ggml_cuda_graph_check_compability(ggml_cgraph * cgraph) { @@ -3260,7 +3265,7 @@ static bool ggml_cuda_graph_check_compability(ggml_cgraph * cgraph) { for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; - if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { + if (ggml_cuda_is_view_or_noop(node)) { continue; } @@ -3403,6 +3408,70 @@ static bool ggml_cuda_should_fuse_rope_set_rows(const ggml_tensor * rope, return true; } +// match gated_delta_net + the strided cpy that scatters its state snapshots into the cache +// (slot i -> rollback group i, slot 0 newest), so the kernel can write them and skip the cpy. +static int ggml_cuda_try_gdn_cache_fusion( + const ggml_cgraph * cgraph, int node_idx, ggml_cuda_gated_delta_net_fused_cache & fused_state_cpy) { + const ggml_tensor * gdn = cgraph->nodes[node_idx]; + // the kernel skips the snapshot tail, so the gdn output must not be a graph output + if (gdn->op != GGML_OP_GATED_DELTA_NET || gdn->type != GGML_TYPE_F32 || + (gdn->flags & GGML_TENSOR_FLAG_OUTPUT)) { + return 0; + } + + const ggml_tensor * src_v = gdn->src[2]; + const int64_t S_v = src_v->ne[0]; + const int64_t H = src_v->ne[1]; + const int64_t n_tokens = src_v->ne[2]; + const int64_t n_seqs = src_v->ne[3]; + const int64_t D = S_v * S_v * H; + const int64_t K = ggml_get_op_params_i32(gdn, 0); // snapshot slot count + const int64_t n_written = std::min(n_tokens, K); // newest n_written slots are written + + // snapshot tail starts right after the attention scores + const size_t tail_off = ggml_row_size(GGML_TYPE_F32, S_v * H * n_tokens * n_seqs); + + // snapshot cpy is the first real node after the gdn (skip views/no-ops) + const ggml_tensor * cpy = nullptr; + int skip = 0; + for (int j = node_idx + 1; j < cgraph->n_nodes && cpy == nullptr; ++j) { + const ggml_tensor * n = cgraph->nodes[j]; + if (ggml_cuda_is_view_or_noop(n)) { + continue; + } + if (n->op != GGML_OP_CPY || (n->flags & GGML_TENSOR_FLAG_OUTPUT)) { + return 0; + } + cpy = n; + skip = j - node_idx; + } + if (cpy == nullptr) { + return 0; + } + + const ggml_tensor * src = cpy->src[0]; // view of the gdn snapshot tail + const ggml_tensor * dst = cpy->src[1]; // cache view the kernel writes to + + // src must be this gdn's snapshot tail (contiguous, at the tail offset) + if (src->op != GGML_OP_VIEW || src->view_src != gdn || src->view_offs != tail_off || + !ggml_is_contiguous(src)) { + return 0; + } + + // dst is the [D, n_seqs, n_written] cache view; require nb[1] == D (the per-seq stride the kernel + // assumes). ggml_cpy pins src to the same element count. + const std::array expected_ne = { D, n_seqs, n_written, 1 }; + if (dst->op != GGML_OP_VIEW || dst->type != GGML_TYPE_F32 || dst->data == nullptr || + !std::equal(expected_ne.begin(), expected_ne.end(), dst->ne) || + dst->nb[0] != ggml_type_size(GGML_TYPE_F32) || dst->nb[1] != (size_t) ggml_row_size(GGML_TYPE_F32, D)) { + return 0; + } + + fused_state_cpy.data = (float *) dst->data; // rollback group 0 (newest) + fused_state_cpy.slot_stride = K > 1 ? (int64_t) (dst->nb[2] / sizeof(float)) : 0; + return skip; +} + static bool ggml_cuda_topk_moe_fusion(const struct ggml_cgraph * cgraph, int node_idx, ggml_cuda_topk_moe_args & args) { args.sigmoid = false; args.softmax = false; @@ -3844,6 +3913,20 @@ static int ggml_cuda_try_fuse(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph ggml_tensor * node = cgraph->nodes[i]; + // gated_delta_net -> cpy: scatter recurrent-state snapshots into the cache + if (node->op == GGML_OP_GATED_DELTA_NET) { + ggml_cuda_gated_delta_net_fused_cache fused_state_cpy; + const int nodes_to_skip = ggml_cuda_try_gdn_cache_fusion(cgraph, i, fused_state_cpy); + if (nodes_to_skip > 0) { +#ifdef GGML_CUDA_DEBUG + GGML_LOG_INFO("%s: fused gated_delta_net snapshot copies for %s (skipped %d nodes)\n", + __func__, node->name, nodes_to_skip); +#endif + ggml_cuda_op_gated_delta_net_fused_cache(*cuda_ctx, node, fused_state_cpy); + return nodes_to_skip; + } + } + //topk-moe if (cgraph->nodes[i]->op == GGML_OP_UNARY || cgraph->nodes[i]->op == GGML_OP_SOFT_MAX || cgraph->nodes[i]->op == GGML_OP_ARGSORT) { @@ -4372,7 +4455,7 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud #endif prev_i = i; - if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { + if (ggml_cuda_is_view_or_noop(node)) { continue; }