ggml-opt: fix data corruption (ggml/1022)
This commit is contained in:
Johannes Gäßler
Georgi Gerganov
parent
9abe9eeae9
commit
02e4eaf22f
5 changed files with 129 additions and 118 deletions
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@ -14,51 +14,51 @@
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#include <vector>
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struct ggml_opt_dataset {
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struct ggml_context * ctx;
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ggml_backend_buffer_t buf;
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struct ggml_tensor * data;
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struct ggml_tensor * labels;
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struct ggml_context * ctx = nullptr;
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ggml_backend_buffer_t buf = nullptr;
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struct ggml_tensor * data = nullptr;
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struct ggml_tensor * labels = nullptr;
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int64_t ndata;
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int64_t ndata_shard;
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size_t nbs_data;
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size_t nbs_labels;
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int64_t ndata = -1;
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int64_t ndata_shard = -1;
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size_t nbs_data = -1;
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size_t nbs_labels = -1;
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std::vector<int64_t> permutation;
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};
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struct ggml_opt_context {
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ggml_backend_sched_t backend_sched;
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ggml_cgraph * allocated_graph;
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ggml_cgraph * allocated_graph_copy;
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struct ggml_context * ctx_static;
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struct ggml_context * ctx_static_cpu;
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struct ggml_context * ctx_compute;
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struct ggml_context * ctx_copy;
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ggml_backend_buffer_t buf_static;
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ggml_backend_buffer_t buf_static_cpu;
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ggml_backend_sched_t backend_sched = nullptr;
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ggml_cgraph * allocated_graph = nullptr;
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ggml_cgraph * allocated_graph_copy = nullptr;
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struct ggml_context * ctx_static = nullptr;
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struct ggml_context * ctx_static_cpu = nullptr;
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struct ggml_context * ctx_compute = nullptr;
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struct ggml_context * ctx_copy = nullptr;
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ggml_backend_buffer_t buf_static = nullptr;
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ggml_backend_buffer_t buf_static_cpu = nullptr;
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std::mt19937 rng;
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struct ggml_tensor * inputs;
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struct ggml_tensor * outputs;
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struct ggml_tensor * labels;
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struct ggml_tensor * inputs = nullptr;
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struct ggml_tensor * outputs = nullptr;
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struct ggml_tensor * labels = nullptr;
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struct ggml_tensor * loss;
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struct ggml_tensor * pred;
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struct ggml_tensor * ncorrect;
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struct ggml_tensor * loss = nullptr;
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struct ggml_tensor * pred = nullptr;
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struct ggml_tensor * ncorrect = nullptr;
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struct ggml_cgraph * gf;
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struct ggml_cgraph * gb_grad;
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struct ggml_cgraph * gb_opt;
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struct ggml_cgraph * gf = nullptr;
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struct ggml_cgraph * gb_grad = nullptr;
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struct ggml_cgraph * gb_opt = nullptr;
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int64_t iter;
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int32_t opt_period;
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int32_t opt_i;
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bool loss_per_datapoint;
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int64_t iter = 1;
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int32_t opt_period = 1;
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int32_t opt_i = 0;
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bool loss_per_datapoint = false;
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ggml_opt_get_optimizer_params get_opt_pars;
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void * get_opt_pars_ud;
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struct ggml_tensor * adamw_params;
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ggml_opt_get_optimizer_params get_opt_pars = nullptr;
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void * get_opt_pars_ud = nullptr;
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struct ggml_tensor * adamw_params = nullptr;
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};
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struct ggml_opt_result {
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@ -67,8 +67,8 @@ struct ggml_opt_result {
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std::vector<int32_t> pred;
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int64_t ncorrect = 0;
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bool loss_per_datapoint = false;
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int64_t opt_period = -1;
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int64_t opt_period = -1;
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bool loss_per_datapoint = false;
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};
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// ====== Dataset ======
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@ -188,11 +188,11 @@ struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * us
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}
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struct ggml_opt_params ggml_opt_default_params(
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ggml_backend_sched_t backend_sched,
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struct ggml_context * ctx_compute,
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struct ggml_tensor * inputs,
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struct ggml_tensor * outputs,
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enum ggml_opt_loss_type loss_type) {
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ggml_backend_sched_t backend_sched,
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struct ggml_context * ctx_compute,
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struct ggml_tensor * inputs,
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struct ggml_tensor * outputs,
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enum ggml_opt_loss_type loss_type) {
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return {
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/*backend_sched =*/ backend_sched,
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/*ctx_compute =*/ ctx_compute,
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@ -237,25 +237,33 @@ static ggml_tensor * map_tensor(std::map<ggml_tensor *, ggml_tensor *> & tensor_
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return new_tensor;
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}
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static ggml_cgraph * dup_graph(ggml_context * ctx, ggml_cgraph * graph) {
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static ggml_cgraph * dup_graph(ggml_context * ctx, ggml_cgraph * src) {
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std::map<ggml_tensor *, ggml_tensor *> tensor_map;
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ggml_cgraph * new_graph = ggml_new_graph_custom(ctx, GGML_DEFAULT_GRAPH_SIZE, /*grads =*/ true);
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ggml_cgraph * dst = ggml_new_graph_custom(ctx, src->size, /*grads =*/ true);
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for (int i = 0; i < graph->n_leafs; i++) {
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ggml_build_forward_expand(new_graph, map_tensor(tensor_map, ctx, graph->leafs[i]));
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for (int i = 0; i < src->n_leafs; i++) {
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ggml_build_forward_expand(dst, map_tensor(tensor_map, ctx, src->leafs[i]));
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}
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for (int i = 0; i < graph->n_nodes; i++) {
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ggml_build_forward_expand(new_graph, map_tensor(tensor_map, ctx, graph->nodes[i]));
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GGML_ASSERT(dst->n_leafs == src->n_leafs);
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for (int i = 0; i < src->n_nodes; i++) {
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ggml_build_forward_expand(dst, map_tensor(tensor_map, ctx, src->nodes[i]));
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}
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for (int i = 0; i < graph->n_nodes; ++i) {
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const size_t igrad_src = ggml_hash_find(&graph->visited_hash_set, graph->nodes[i]);
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const size_t igrad_dst = ggml_hash_find(&new_graph->visited_hash_set, new_graph->nodes[i]);
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graph->grads[igrad_dst] = new_graph->grads[igrad_src];
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graph->grad_accs[igrad_dst] = new_graph->grad_accs[igrad_src];
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GGML_ASSERT(dst->n_nodes == src->n_nodes);
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for (int i = 0; i < src->n_nodes; ++i) {
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const size_t igrad_src = ggml_hash_find(&src->visited_hash_set, src->nodes[i]);
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const size_t igrad_dst = ggml_hash_find(&dst->visited_hash_set, dst->nodes[i]);
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GGML_ASSERT(igrad_src != GGML_HASHSET_FULL);
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GGML_ASSERT(ggml_bitset_get(src->visited_hash_set.used, igrad_src));
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GGML_ASSERT(igrad_dst != GGML_HASHSET_FULL);
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GGML_ASSERT(ggml_bitset_get(dst->visited_hash_set.used, igrad_dst));
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dst->grads[igrad_dst] = src->grads[igrad_src];
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dst->grad_accs[igrad_dst] = src->grad_accs[igrad_src];
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}
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return new_graph;
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return dst;
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}
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static void ggml_opt_alloc_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph) {
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@ -284,18 +292,13 @@ static void ggml_opt_alloc_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph
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ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
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ggml_opt_context_t result = new struct ggml_opt_context;
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result->backend_sched = params.backend_sched;
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result->allocated_graph = nullptr;
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result->allocated_graph_copy = nullptr;
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result->ctx_compute = params.ctx_compute;
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result->ctx_copy = nullptr;
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result->inputs = params.inputs;
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result->outputs = params.outputs;
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result->iter = 1;
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result->opt_period = params.opt_period;
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result->opt_i = 0;
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result->get_opt_pars = params.get_opt_pars;
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result->get_opt_pars_ud = params.get_opt_pars_ud;
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result->backend_sched = params.backend_sched;
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result->ctx_compute = params.ctx_compute;
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result->inputs = params.inputs;
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result->outputs = params.outputs;
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result->opt_period = params.opt_period;
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result->get_opt_pars = params.get_opt_pars;
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result->get_opt_pars_ud = params.get_opt_pars_ud;
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GGML_ASSERT(result->inputs->data && "the inputs must be allocated statically");
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GGML_ASSERT(result->opt_period >= 1);
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@ -348,7 +351,6 @@ ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
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switch (params.loss_type) {
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case GGML_OPT_LOSS_TYPE_MEAN: {
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result->labels = nullptr;
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result->loss = ggml_sum(result->ctx_static, result->outputs);
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ggml_set_name(result->loss, "loss_sum");
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const float scale = 1.0f / (result->opt_period * ggml_nelements(result->outputs));
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@ -358,7 +360,6 @@ ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
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break;
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}
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case GGML_OPT_LOSS_TYPE_SUM: {
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result->labels = nullptr;
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result->loss = ggml_sum(result->ctx_static, result->outputs);
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ggml_set_name(result->loss, "loss_sum");
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result->loss_per_datapoint = false;
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@ -413,14 +414,7 @@ ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
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}
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if (params.build_type == GGML_OPT_BUILD_TYPE_FORWARD) {
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result->gb_grad = nullptr;
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result->gb_opt = nullptr;
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result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0));
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result->buf_static_cpu = nullptr;
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ggml_opt_alloc_graph(result, result->gf);
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return result;
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}
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@ -429,14 +423,8 @@ ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
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ggml_build_backward_expand(result->ctx_static, result->ctx_compute, result->gb_grad, accumulate);
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if (params.build_type == GGML_OPT_BUILD_TYPE_GRAD) {
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result->gb_opt = nullptr;
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result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0));
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result->buf_static_cpu = nullptr;
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ggml_opt_alloc_graph(result, result->gb_grad);
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ggml_graph_reset(result->gb_grad);
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return result;
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}
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@ -466,7 +454,6 @@ ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
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result->buf_static_cpu = ggml_backend_alloc_ctx_tensors_from_buft(result->ctx_static_cpu, ggml_backend_cpu_buffer_type());
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ggml_opt_alloc_graph(result, result->gb_opt);
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ggml_graph_reset(result->gb_opt);
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return result;
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