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https://github.com/LostRuins/koboldcpp.git
synced 2025-09-11 09:34:37 +00:00
All samplers moved to kcpp side
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Concedo
commit
b63158005f
54 changed files with 3765 additions and 2577 deletions
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@ -787,6 +787,9 @@ static vk_submission ggml_vk_create_submission(vk_device& device, vk_queue& q, s
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static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) {
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if (ctx->seqs.empty()) {
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if (fence) {
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ctx->q->queue.submit({}, fence);
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}
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return;
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}
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VK_LOG_DEBUG("ggml_vk_submit(" << ctx << ", " << fence << ")");
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@ -4616,7 +4619,7 @@ static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context& subctx, const
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}, dryrun);
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}
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static void ggml_vk_sin(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
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static void ggml_vk_sin(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
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const uint32_t src0_type_size = ggml_type_size(src0->type);
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const uint32_t dst_type_size = ggml_type_size(dst->type);
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@ -4626,10 +4629,10 @@ static void ggml_vk_sin(ggml_backend_vk_context * ctx, vk_context& subctx, const
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(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size,
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0,
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0.0f, 0.0f,
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});
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}, dryrun);
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}
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static void ggml_vk_cos(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
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static void ggml_vk_cos(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
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const uint32_t src0_type_size = ggml_type_size(src0->type);
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const uint32_t dst_type_size = ggml_type_size(dst->type);
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@ -4639,7 +4642,7 @@ static void ggml_vk_cos(ggml_backend_vk_context * ctx, vk_context& subctx, const
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(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size,
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0,
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0.0f, 0.0f,
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});
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}, dryrun);
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}
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static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
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@ -5658,11 +5661,15 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) {
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}
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}
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static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, int node_idx, bool last_node, bool dryrun){
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static bool ggml_vk_compute_forward(ggml_backend_vk_context* ctx, ggml_tensor* tensor, int tensor_idx, bool use_fence);
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// Returns true if node has enqueued work into the queue, false otherwise
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// If submit is true the current all operations queued so far are being submitted to Vulkan to overlap cmdlist creation and GPU execution.
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static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, int node_idx, ggml_tensor *node_begin, int node_idx_begin, bool dryrun, bool last_node, bool submit){
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ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra;
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if (ggml_is_empty(node) || extra == nullptr) {
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return;
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return false;
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}
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VK_LOG_DEBUG("ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")");
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@ -5679,7 +5686,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
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case GGML_OP_PERMUTE:
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case GGML_OP_TRANSPOSE:
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case GGML_OP_NONE:
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return;
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return false;
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case GGML_OP_UNARY:
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switch (ggml_get_unary_op(node)) {
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case GGML_UNARY_OP_SILU:
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@ -5689,7 +5696,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
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case GGML_UNARY_OP_TANH:
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break;
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default:
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return;
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return false;
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}
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break;
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case GGML_OP_REPEAT:
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@ -5726,7 +5733,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
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default:
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std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(node->op) << std::endl;
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GGML_ABORT("fatal error");
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return;
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return false;
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}
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vk_context compute_ctx;
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@ -5783,11 +5790,11 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
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break;
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case GGML_OP_SIN:
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ggml_vk_sin(ctx, compute_ctx, src0, node);
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ggml_vk_sin(ctx, compute_ctx, src0, node, dryrun);
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break;
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case GGML_OP_COS:
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ggml_vk_cos(ctx, compute_ctx, src0, node);
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ggml_vk_cos(ctx, compute_ctx, src0, node, dryrun);
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break;
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case GGML_OP_CLAMP:
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@ -5826,7 +5833,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
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ggml_vk_unary(ctx, compute_ctx, src0, node, dryrun);
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break;
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default:
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return;
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return false;
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}
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break;
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case GGML_OP_DIAG_MASK_INF:
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@ -5870,11 +5877,11 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
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break;
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default:
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return;
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return false;
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}
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if (dryrun) {
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return;
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return false;
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}
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ctx->tensor_ctxs[node_idx] = compute_ctx;
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@ -5885,14 +5892,34 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
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last_node = true;
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#endif
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if (last_node) {
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if (submit || last_node) {
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ggml_vk_ctx_end(compute_ctx);
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compute_ctx->exit_tensor_idx = node_idx;
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// TODO probably it'd be better to pass a exit_node flag to ggml_vk_compute_forward
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if (last_node) {
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compute_ctx->exit_tensor_idx = node_idx_begin;
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}
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else {
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compute_ctx->exit_tensor_idx = -1;
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}
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ctx->compute_ctx.reset();
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bool ok = ggml_vk_compute_forward(ctx, node_begin, node_idx_begin, false);
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if (!ok) {
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if (node->op == GGML_OP_UNARY) {
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std::cerr << __func__ << ": error: op not supported UNARY " << node->name << " (" << ggml_unary_op_name(static_cast<ggml_unary_op>(node->op_params[0])) << ")" << std::endl;
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}
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else {
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std::cerr << __func__ << ": error: op not supported " << node->name << " (" << ggml_op_name(node->op) << ")" << std::endl;
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}
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}
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}
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return true;
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}
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static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor * tensor, int tensor_idx){
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static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor * tensor, int tensor_idx, bool use_fence = true){
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ggml_tensor_extra_gpu * extra = nullptr;
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switch (tensor->op) {
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@ -5960,40 +5987,38 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor *
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VK_LOG_DEBUG("ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")");
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#ifdef GGML_VULKAN_CHECK_RESULTS
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ggml_vk_check_results_0(tensor);
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#endif
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vk_context subctx = ctx->tensor_ctxs[tensor_idx].lock();
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#ifdef GGML_VULKAN_PERF
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std::chrono::steady_clock::time_point start;
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#endif // GGML_VULKAN_PERF
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// always wait for the GPU work to be done for the last submit
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if (tensor_idx == subctx->exit_tensor_idx) {
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use_fence = true;
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}
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// Only run if ctx hasn't been submitted yet
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if (!subctx->seqs.empty()) {
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#ifdef GGML_VULKAN_CHECK_RESULTS
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ggml_vk_check_results_0(tensor);
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use_fence = true;
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#endif
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// Do staging buffer copies
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for (auto& cpy : subctx->in_memcpys) {
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memcpy(cpy.dst, cpy.src, cpy.n);
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}
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#ifdef GGML_VULKAN_PERF
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start = std::chrono::steady_clock::now();
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#endif // GGML_VULKAN_PERF
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ggml_vk_submit(subctx, use_fence ? ctx->fence : vk::Fence{});
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ggml_vk_submit(subctx, ctx->fence);
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if (use_fence) {
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VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences");
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ctx->device->device.resetFences({ ctx->fence });
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}
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#ifdef GGML_VULKAN_CHECK_RESULTS
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ggml_vk_check_results_1(tensor);
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#endif
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}
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if (tensor_idx == subctx->exit_tensor_idx) {
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VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences");
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#ifdef GGML_VULKAN_PERF
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auto duration = std::chrono::duration_cast<std::chrono::nanoseconds>(std::chrono::steady_clock::now() - start);
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ctx->device->perf_logger->log_timing(tensor, duration.count());
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#endif // GGML_VULKAN_PERF
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ctx->device->device.resetFences({ ctx->fence });
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// Do staging buffer copies
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for (auto& cpy : subctx->out_memcpys) {
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memcpy(cpy.dst, cpy.src, cpy.n);
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@ -6482,7 +6507,7 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen
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ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
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for (int i = 0; i < cgraph->n_nodes; i++) {
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ggml_vk_build_graph(ctx, cgraph->nodes[i], i, 0, true);
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ggml_vk_build_graph(ctx, cgraph->nodes[i], i, nullptr, 0, true, false, false);
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}
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ggml_vk_preallocate_buffers(ctx);
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ggml_pipeline_allocate_descriptor_sets(ctx->device);
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@ -6497,31 +6522,36 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen
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// Reserve tensor context space for all nodes
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ctx->tensor_ctxs.resize(cgraph->n_nodes);
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for (int i = 0; i < cgraph->n_nodes; i++) {
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ggml_vk_build_graph(ctx, cgraph->nodes[i], i, i == last_node, false);
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}
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bool first_node_in_batch = true; // true if next node will be first node in a batch
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int submit_node_idx = 0; // index to first node in a batch
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// submit work every submit_count node to overlap CPU cmdbuffer generation with GPU execution
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constexpr int submit_count = 100;
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int submitted_nodes = 0;
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for (int i = 0; i < cgraph->n_nodes; i++) {
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ggml_tensor * node = cgraph->nodes[i];
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if (ggml_vk_is_empty(node)) {
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continue;
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if (first_node_in_batch) {
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submit_node_idx = i;
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}
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bool ok = ggml_vk_compute_forward(ctx, node, i);
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if (!ok) {
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if (node->op == GGML_OP_UNARY) {
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std::cerr << __func__ << ": error: op not supported UNARY " << node->name << " (" << ggml_unary_op_name(static_cast<ggml_unary_op>(node->op_params[0])) << ")" << std::endl;
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} else {
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std::cerr << __func__ << ": error: op not supported " << node->name << " (" << ggml_op_name(node->op) << ")" << std::endl;
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bool submit = (submitted_nodes >= submit_count) || (i == last_node);
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bool enqueued = ggml_vk_build_graph(ctx, cgraph->nodes[i], i, cgraph->nodes[submit_node_idx], submit_node_idx, false, i == last_node, submit);
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if (enqueued) {
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++submitted_nodes;
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#ifndef GGML_VULKAN_CHECK_RESULTS
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if (first_node_in_batch) {
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first_node_in_batch = false;
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}
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}
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#ifdef GGML_VULKAN_CHECK_RESULTS
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else {
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ggml_vk_check_results_1(node);
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}
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#endif
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GGML_ASSERT(ok);
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}
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if (submit) {
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first_node_in_batch = true;
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submitted_nodes = 0;
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}
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}
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#ifdef GGML_VULKAN_PERF
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@ -6602,6 +6632,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const
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return false;
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}
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} break;
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case GGML_OP_CONT:
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case GGML_OP_CPY:
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case GGML_OP_DUP:
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{
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@ -6642,7 +6673,6 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const
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case GGML_OP_COS:
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case GGML_OP_CLAMP:
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case GGML_OP_PAD:
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case GGML_OP_CONT:
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case GGML_OP_DIAG_MASK_INF:
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case GGML_OP_SOFT_MAX:
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case GGML_OP_ARGSORT:
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