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https://github.com/ggerganov/whisper.cpp.git
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llama/ggml: add LLM training support (llama/10544)
* llama/ggml: add LLM training support more compact progress bar llama_save_model_to_file llama_opt_param_filter ggml_graph_dup force_grads refactor ggml_opt, fix test-opt * remove logits_all * refactor CUDA implementation for ACC * reset graph at beginning of opt period
This commit is contained in:
Johannes Gäßler
Georgi Gerganov
parent
93ef22657e
commit
5d8b068249
@ -37,13 +37,16 @@ extern "C" {
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// ====== Dataset ======
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GGML_API ggml_opt_dataset_t ggml_opt_dataset_init(
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int64_t ne_datapoint, // number of elements per datapoint
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int64_t ne_label, // number of elements per label
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int64_t ndata, // total number of datapoints/labels
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int64_t ndata_shard); // number of datapoints/labels per shard (unit at which the dataset is shuffled/copied)
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enum ggml_type type_data, // the type for the internal data tensor
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enum ggml_type type_label, // the type for the internal labels tensor
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int64_t ne_datapoint, // number of elements per datapoint
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int64_t ne_label, // number of elements per label
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int64_t ndata, // total number of datapoints/labels
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int64_t ndata_shard); // number of datapoints/labels per shard (unit at which the dataset is shuffled/copied)
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GGML_API void ggml_opt_dataset_free(ggml_opt_dataset_t dataset);
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// get underlying tensors that store the data
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GGML_API int64_t ggml_opt_dataset_ndata (ggml_opt_dataset_t dataset);
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GGML_API struct ggml_tensor * ggml_opt_dataset_data (ggml_opt_dataset_t dataset); // shape = [ne_datapoint, ndata]
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GGML_API struct ggml_tensor * ggml_opt_dataset_labels(ggml_opt_dataset_t dataset); // shape = [nd_label, ndata]
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@ -56,13 +59,19 @@ extern "C" {
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struct ggml_tensor * data_batch, // shape = [ne_datapoint, ndata_batch]
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struct ggml_tensor * labels_batch, // shape = [ne_label, ndata_batch]
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int64_t ibatch);
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GGML_API void ggml_opt_dataset_get_batch_host(
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ggml_opt_dataset_t dataset,
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void * data_batch,
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size_t nb_data_batch,
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void * labels_batch,
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int64_t ibatch);
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// ====== Model / Context ======
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enum ggml_opt_build_type {
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GGML_OPT_BUILD_TYPE_FORWARD,
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GGML_OPT_BUILD_TYPE_GRAD,
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GGML_OPT_BUILD_TYPE_OPT,
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GGML_OPT_BUILD_TYPE_FORWARD = 10,
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GGML_OPT_BUILD_TYPE_GRAD = 20,
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GGML_OPT_BUILD_TYPE_OPT = 30,
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};
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// parameters that control which optimizer is used and how said optimizer tries to find the minimal loss
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@ -81,20 +90,22 @@ extern "C" {
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// userdata can be used to pass arbitrary data
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typedef struct ggml_opt_optimizer_params (*ggml_opt_get_optimizer_params)(void * userdata);
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// returns the default optimizer params (constant)
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// returns the default optimizer params (constant, hard-coded values)
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// userdata is not used
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GGML_API struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * userdata);
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// casts userdata to ggml_opt_optimizer_params and returns it
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GGML_API struct ggml_opt_optimizer_params ggml_opt_get_constant_optimizer_params(void * userdata);
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// parameters for initializing a new optimization context
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struct ggml_opt_params {
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ggml_backend_sched_t backend_sched; // defines which backends are used to construct the compute graphs
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struct ggml_context * ctx_compute; // created in user code, holds non-static tensors
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// the forward graph is defined by inputs and outputs
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// those tensors and all tensors inbetween are not intended to be reusable between multiple optimization contexts
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struct ggml_tensor * inputs;
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struct ggml_tensor * outputs;
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// by default the forward graph needs to be reconstructed for each eval
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// if ctx_compute, inputs, and outputs are set the graphs are instead allocated statically
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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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enum ggml_opt_build_type build_type;
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@ -107,12 +118,9 @@ extern "C" {
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// get parameters for an optimization context with defaults set where possible
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// parameters for which no sensible defaults exist are supplied as arguments to this function
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GGML_API 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_API struct ggml_opt_params ggml_opt_default_params(
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ggml_backend_sched_t backend_sched,
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enum ggml_opt_loss_type loss_type);
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GGML_API ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params);
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GGML_API void ggml_opt_free(ggml_opt_context_t opt_ctx);
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@ -121,6 +129,7 @@ extern "C" {
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GGML_API void ggml_opt_reset(ggml_opt_context_t opt_ctx, bool optimizer);
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// get underlying tensors that store data
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// if not using static graphs these pointers become invalid with the next call to ggml_opt_alloc
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GGML_API struct ggml_tensor * ggml_opt_inputs( ggml_opt_context_t opt_ctx); // forward graph input tensor
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GGML_API struct ggml_tensor * ggml_opt_outputs( ggml_opt_context_t opt_ctx); // forward graph output tensor
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GGML_API struct ggml_tensor * ggml_opt_labels( ggml_opt_context_t opt_ctx); // labels to compare outputs against
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@ -128,11 +137,12 @@ extern "C" {
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GGML_API struct ggml_tensor * ggml_opt_pred( ggml_opt_context_t opt_ctx); // predictions made by outputs
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GGML_API struct ggml_tensor * ggml_opt_ncorrect(ggml_opt_context_t opt_ctx); // number of matching predictions between outputs and labels
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// get the gradient accumulator for a node from the forward graph
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GGML_API struct ggml_tensor * ggml_opt_grad_acc(ggml_opt_context_t opt_ctx, struct ggml_tensor * node);
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// ====== Optimization Result ======
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GGML_API ggml_opt_result_t ggml_opt_result_init();
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GGML_API ggml_opt_result_t ggml_opt_result_init(void);
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GGML_API void ggml_opt_result_free(ggml_opt_result_t result);
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GGML_API void ggml_opt_result_reset(ggml_opt_result_t result);
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@ -144,11 +154,20 @@ extern "C" {
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// ====== Computation ======
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// do forward pass, increment result if not NULL
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GGML_API void ggml_opt_forward(ggml_opt_context_t opt_ctx, ggml_opt_result_t result);
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// if not using static graphs, this function must be called prior to ggml_opt_alloc
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GGML_API void ggml_opt_prepare_alloc(
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ggml_opt_context_t opt_ctx,
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struct ggml_context * ctx_compute,
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struct ggml_cgraph * gf,
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struct ggml_tensor * inputs,
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struct ggml_tensor * outputs);
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// do forward pass, increment result if not NULL, do backward pass
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GGML_API void ggml_opt_forward_backward(ggml_opt_context_t opt_ctx, ggml_opt_result_t result);
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// allocate the next graph for evaluation, either forward or forward + backward
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// must be called exactly once prior to calling ggml_opt_eval
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GGML_API void ggml_opt_alloc(ggml_opt_context_t opt_ctx, bool backward);
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// do forward pass, increment result if not NULL, do backward pass if allocated
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GGML_API void ggml_opt_eval(ggml_opt_context_t opt_ctx, ggml_opt_result_t result);
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// ############################################################################
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// ## The high-level functions start here. They do not depend on any private ##
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@ -200,9 +219,9 @@ extern "C" {
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// fit model defined by inputs and outputs to dataset
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GGML_API void ggml_opt_fit(
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ggml_backend_sched_t backend_sched, // backend scheduler for constructing the compute graphs
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ggml_context * ctx_compute, // context with temporarily allocated tensors to calculate the outputs
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ggml_tensor * inputs, // input tensor with shape [ne_datapoint, ndata_batch]
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ggml_tensor * outputs, // output tensor, must have shape [ne_label, ndata_batch] if labels are used
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struct ggml_context * ctx_compute, // context with temporarily allocated tensors to calculate the outputs
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struct ggml_tensor * inputs, // input tensor with shape [ne_datapoint, ndata_batch]
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struct ggml_tensor * outputs, // output tensor, must have shape [ne_label, ndata_batch] if labels are used
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ggml_opt_dataset_t dataset, // dataset with data and optionally also labels
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enum ggml_opt_loss_type loss_type, // loss to minimize
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ggml_opt_get_optimizer_params get_opt_pars, // callback to get optimizer params, userdata is pointer to epoch (of type int64_t)
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@ -768,7 +768,7 @@ extern "C" {
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// Tensor flags
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GGML_API void ggml_set_input(struct ggml_tensor * tensor);
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GGML_API void ggml_set_output(struct ggml_tensor * tensor);
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GGML_API void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor);
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GGML_API void ggml_set_param(struct ggml_tensor * tensor);
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GGML_API void ggml_set_loss(struct ggml_tensor * tensor);
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//
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@ -938,7 +938,7 @@ extern "C" {
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GGML_API struct ggml_tensor * ggml_repeat_back(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b);
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struct ggml_tensor * b); // sum up values that are adjacent in dims > 0 instead of repeated with same stride
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// concat a and b along dim
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// used in stable-diffusion
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@ -2049,15 +2049,14 @@ extern "C" {
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GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
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GGML_API void ggml_build_backward_expand(
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struct ggml_context * ctx_static, // context for static gradients (loss + gradient accumulation)
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struct ggml_context * ctx_compute, // context for gradient computation
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struct ggml_cgraph * cgraph,
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bool accumulate); // whether or not gradients should be accumulated, requires static allocation of tensors in ctx_static
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struct ggml_context * ctx, // context for gradient computation
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struct ggml_cgraph * cgraph,
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struct ggml_tensor ** grad_accs);
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// graph allocation in a context
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GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false
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GGML_API struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads);
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GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph);
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GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph, bool force_grads);
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GGML_API void ggml_graph_cpy (struct ggml_cgraph * src, struct ggml_cgraph * dst);
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GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // set regular grads + optimizer momenta to 0, set loss grad to 1
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GGML_API void ggml_graph_clear (struct ggml_cgraph * cgraph);
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@ -1111,7 +1111,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
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const int node_backend_id = tensor_backend_id(node);
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assert(node_backend_id != -1); // all nodes should be assigned by now
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assert(node_backend_id != -1); // all nodes should be assigned by now, this can happen if there is no CPU fallback
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// check if we should start a new split based on the sources of the current node
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bool need_new_split = false;
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@ -1,47 +1,61 @@
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#include "acc.cuh"
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static __global__ void acc_f32(const float * x, const float * y, float * dst, const int ne,
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const int ne10, const int ne11, const int ne12,
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const int nb1, const int nb2, int offset) {
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const int i = blockDim.x * blockIdx.x + threadIdx.x;
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static __global__ void acc_f32(const float * x, const float * y, float * dst, const int64_t ne,
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const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13,
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const int64_t s11, const int64_t s12, const int64_t s13, const int64_t offset) {
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const int64_t i = blockDim.x * blockIdx.x + threadIdx.x;
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if (i >= ne) {
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return;
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}
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int src1_idx = i - offset;
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int oz = src1_idx / nb2;
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int oy = (src1_idx - (oz * nb2)) / nb1;
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int ox = src1_idx % nb1;
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if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) {
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dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11];
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} else {
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dst[i] = x[i];
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int64_t src1_idx = i - offset;
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int64_t tmp = src1_idx;
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const int64_t i13 = tmp / s13;
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tmp -= i13 * s13;
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const int64_t i12 = tmp / s12;
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tmp -= i12 * s12;
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const int64_t i11 = tmp / s11;
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tmp -= i11 * s11;
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const int64_t i10 = tmp;
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float val = x[i];
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if (src1_idx >= 0 && i10 < ne10 && i11 < ne11 && i12 < ne12 && i13 < ne13) {
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val += y[((i13*ne12 + i12) * ne11 + i11) * ne10 + i10];
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}
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dst[i] = val;
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}
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static void acc_f32_cuda(const float * x, const float * y, float * dst, const int n_elements,
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const int ne10, const int ne11, const int ne12,
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const int nb1, const int nb2, const int offset, cudaStream_t stream) {
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int num_blocks = (n_elements + CUDA_ACC_BLOCK_SIZE - 1) / CUDA_ACC_BLOCK_SIZE;
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acc_f32<<<num_blocks, CUDA_ACC_BLOCK_SIZE, 0, stream>>>(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset);
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static void acc_f32_cuda(const float * x, const float * y, float * dst, const int64_t n_elements,
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const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13,
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const int64_t s1, const int64_t s2, const int64_t s3, const int64_t offset, cudaStream_t stream) {
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const int num_blocks = (n_elements + CUDA_ACC_BLOCK_SIZE - 1) / CUDA_ACC_BLOCK_SIZE;
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acc_f32<<<num_blocks, CUDA_ACC_BLOCK_SIZE, 0, stream>>>(x, y, dst, n_elements, ne10, ne11, ne12, ne13, s1, s2, s3, offset);
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}
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void ggml_cuda_op_acc(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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const ggml_tensor * src0 = dst->src[0];
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const ggml_tensor * src1 = dst->src[1];
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const float * src0_d = (const float *)src0->data;
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const float * src1_d = (const float *)src1->data;
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float * dst_d = (float *)dst->data;
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const float * src0_d = (const float *) src0->data;
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const float * src1_d = (const float *) src1->data;
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float * dst_d = (float *) dst->data;
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cudaStream_t stream = ctx.stream();
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GGML_ASSERT(src0->type == GGML_TYPE_F32);
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GGML_ASSERT(src1->type == GGML_TYPE_F32);
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GGML_ASSERT( dst->type == GGML_TYPE_F32);
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GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported
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int nb1 = dst->op_params[0] / 4; // 4 bytes of float32
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int nb2 = dst->op_params[1] / 4; // 4 bytes of float32
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// int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused
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int offset = dst->op_params[3] / 4; // offset in bytes
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GGML_ASSERT(ggml_is_contiguous(src1));
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GGML_ASSERT(dst->nb[0] == ggml_element_size(dst));
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GGML_ASSERT(ggml_is_contiguously_allocated(dst));
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acc_f32_cuda(src0_d, src1_d, dst_d, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, stream);
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const int64_t s1 = dst->op_params[0] / sizeof(float);
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const int64_t s2 = dst->op_params[1] / sizeof(float);
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const int64_t s3 = dst->op_params[2] / sizeof(float);
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const int64_t offset = dst->op_params[3] / sizeof(float);
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acc_f32_cuda(src0_d, src1_d, dst_d, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], s1, s2, s3, offset, stream);
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}
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@ -31,7 +31,7 @@ void ggml_cuda_op_sum(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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GGML_ASSERT(src0->type == GGML_TYPE_F32);
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GGML_ASSERT( dst->type == GGML_TYPE_F32);
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GGML_ASSERT(ggml_is_contiguous(src0));
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GGML_ASSERT(ggml_is_contiguously_allocated(src0));
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const float * src0_d = (const float *) src0->data;
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float * dst_d = (float *) dst->data;
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@ -28,16 +28,19 @@ struct ggml_opt_dataset {
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};
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struct ggml_opt_context {
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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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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_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_cpu = nullptr;
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std::mt19937 rng;
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enum ggml_opt_loss_type loss_type;
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enum ggml_opt_build_type build_type;
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enum ggml_opt_build_type build_type_alloc;
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struct ggml_tensor * inputs = nullptr;
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struct ggml_tensor * outputs = nullptr;
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@ -50,6 +53,11 @@ struct ggml_opt_context {
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||||
struct ggml_cgraph * gf = nullptr;
|
||||
struct ggml_cgraph * gb_grad = nullptr;
|
||||
struct ggml_cgraph * gb_opt = nullptr;
|
||||
bool static_graphs = false;
|
||||
bool eval_ready = false;
|
||||
std::vector<struct ggml_tensor *> grad_accs;
|
||||
std::vector<struct ggml_tensor *> grad_m;
|
||||
std::vector<struct ggml_tensor *> grad_v;
|
||||
|
||||
int64_t iter = 1;
|
||||
int32_t opt_period = 1;
|
||||
@ -73,7 +81,13 @@ struct ggml_opt_result {
|
||||
|
||||
// ====== Dataset ======
|
||||
|
||||
ggml_opt_dataset_t ggml_opt_dataset_init(int64_t ne_datapoint, int64_t ne_label, int64_t ndata, int64_t ndata_shard) {
|
||||
ggml_opt_dataset_t ggml_opt_dataset_init(
|
||||
enum ggml_type type_data,
|
||||
enum ggml_type type_label,
|
||||
int64_t ne_datapoint,
|
||||
int64_t ne_label,
|
||||
int64_t ndata,
|
||||
int64_t ndata_shard) {
|
||||
GGML_ASSERT(ne_datapoint > 0);
|
||||
GGML_ASSERT(ne_label >= 0);
|
||||
GGML_ASSERT(ndata > 0);
|
||||
@ -92,11 +106,11 @@ ggml_opt_dataset_t ggml_opt_dataset_init(int64_t ne_datapoint, int64_t ne_label,
|
||||
result->ctx = ggml_init(params);
|
||||
}
|
||||
|
||||
result->data = ggml_new_tensor_2d(result->ctx, GGML_TYPE_F32, ne_datapoint, ndata);
|
||||
result->data = ggml_new_tensor_2d(result->ctx, type_data, ne_datapoint, ndata);
|
||||
result->nbs_data = ggml_nbytes(result->data) * ndata_shard/ndata;
|
||||
|
||||
if (ne_label > 0) {
|
||||
result->labels = ggml_new_tensor_2d(result->ctx, GGML_TYPE_F32, ne_label, ndata);
|
||||
result->labels = ggml_new_tensor_2d(result->ctx, type_label, ne_label, ndata);
|
||||
result->nbs_labels = ggml_nbytes(result->labels) * ndata_shard/ndata;
|
||||
} else {
|
||||
result->labels = nullptr;
|
||||
@ -119,6 +133,10 @@ void ggml_opt_dataset_free(ggml_opt_dataset_t dataset) {
|
||||
delete dataset;
|
||||
}
|
||||
|
||||
int64_t ggml_opt_dataset_ndata(ggml_opt_dataset_t dataset) {
|
||||
return dataset->ndata;
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_opt_dataset_data(ggml_opt_dataset_t dataset) {
|
||||
return dataset->data;
|
||||
}
|
||||
@ -144,6 +162,8 @@ void ggml_opt_dataset_get_batch(ggml_opt_dataset_t dataset, struct ggml_tensor *
|
||||
GGML_ASSERT( data_batch && ggml_is_contiguous(data_batch));
|
||||
GGML_ASSERT(!labels_batch || ggml_is_contiguous(labels_batch));
|
||||
GGML_ASSERT((labels_batch == nullptr) == (dataset->labels == nullptr));
|
||||
GGML_ASSERT( data_batch->type == dataset->data->type);
|
||||
GGML_ASSERT(!labels_batch || labels_batch->type == dataset->labels->type);
|
||||
|
||||
const size_t nb_data_batch = ggml_nbytes(data_batch);
|
||||
GGML_ASSERT(nb_data_batch % dataset->nbs_data == 0);
|
||||
@ -171,6 +191,31 @@ void ggml_opt_dataset_get_batch(ggml_opt_dataset_t dataset, struct ggml_tensor *
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_opt_dataset_get_batch_host(ggml_opt_dataset_t dataset, void * data_batch, size_t nb_data_batch, void * labels_batch, int64_t ibatch) {
|
||||
GGML_ASSERT((labels_batch == nullptr) == (dataset->labels == nullptr));
|
||||
GGML_ASSERT(nb_data_batch % dataset->nbs_data == 0);
|
||||
|
||||
const int64_t shards_per_batch = nb_data_batch / dataset->nbs_data;
|
||||
|
||||
GGML_ASSERT((ibatch + 1)*shards_per_batch <= int64_t(dataset->permutation.size()));
|
||||
|
||||
for (int64_t ishard_batch = 0; ishard_batch < shards_per_batch; ++ishard_batch) {
|
||||
const int64_t ishard = dataset->permutation[ibatch*shards_per_batch + ishard_batch];
|
||||
|
||||
const char * ptr_data = (const char *) dataset->data->data + ishard *dataset->nbs_data;
|
||||
char * ptr_data_batch = (char *) data_batch + ishard_batch*dataset->nbs_data;
|
||||
memcpy(ptr_data_batch, ptr_data, dataset->nbs_data);
|
||||
|
||||
if (!labels_batch) {
|
||||
continue;
|
||||
}
|
||||
|
||||
const char * ptr_labels = (const char *) dataset->labels->data + ishard *dataset->nbs_labels;
|
||||
char * ptr_labels_batch = (char *) labels_batch + ishard_batch*dataset->nbs_labels;
|
||||
memcpy(ptr_labels_batch, ptr_labels, dataset->nbs_labels);
|
||||
}
|
||||
}
|
||||
|
||||
// ====== Model / Context ======
|
||||
|
||||
struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * userdata) {
|
||||
@ -187,17 +232,18 @@ struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * us
|
||||
return result;
|
||||
}
|
||||
|
||||
struct ggml_opt_optimizer_params ggml_opt_get_constant_optimizer_params(void * userdata) {
|
||||
return *((struct ggml_opt_optimizer_params *) userdata);
|
||||
}
|
||||
|
||||
struct ggml_opt_params ggml_opt_default_params(
|
||||
ggml_backend_sched_t backend_sched,
|
||||
struct ggml_context * ctx_compute,
|
||||
struct ggml_tensor * inputs,
|
||||
struct ggml_tensor * outputs,
|
||||
enum ggml_opt_loss_type loss_type) {
|
||||
return {
|
||||
/*backend_sched =*/ backend_sched,
|
||||
/*ctx_compute =*/ ctx_compute,
|
||||
/*inputs =*/ inputs,
|
||||
/*logits =*/ outputs,
|
||||
/*ctx_compute =*/ nullptr,
|
||||
/*inputs =*/ nullptr,
|
||||
/*logits =*/ nullptr,
|
||||
/*loss_type =*/ loss_type,
|
||||
/*build_type =*/ GGML_OPT_BUILD_TYPE_OPT,
|
||||
/*opt_period =*/ 1,
|
||||
@ -266,195 +312,246 @@ static ggml_cgraph * dup_graph(ggml_context * ctx, ggml_cgraph * src) {
|
||||
return dst;
|
||||
}
|
||||
|
||||
static void ggml_opt_alloc_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph) {
|
||||
GGML_ASSERT(graph);
|
||||
if (opt_ctx->allocated_graph == graph) {
|
||||
return;
|
||||
}
|
||||
static void ggml_opt_build(ggml_opt_context_t opt_ctx) {
|
||||
GGML_ASSERT(opt_ctx->ctx_compute && "no compute context set, either use static graphs or set one with ggml_opt_prepare_alloc");
|
||||
GGML_ASSERT((!opt_ctx->static_graphs || opt_ctx->inputs->data) && "when using static graphs the inputs must be allocated statically");
|
||||
|
||||
ggml_backend_sched_reset(opt_ctx->backend_sched); // clear allocation of previous graph
|
||||
const bool accumulate = opt_ctx->build_type_alloc >= GGML_OPT_BUILD_TYPE_GRAD &&
|
||||
!(opt_ctx->static_graphs && opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_OPT && opt_ctx->opt_period == 1);
|
||||
|
||||
{
|
||||
ggml_init_params params = {
|
||||
/*.mem_size =*/ ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE,
|
||||
/*.mem_buffer =*/ nullptr,
|
||||
/*.no_alloc =*/ true,
|
||||
};
|
||||
ggml_free(opt_ctx->ctx_copy);
|
||||
opt_ctx->ctx_copy = ggml_init(params);
|
||||
}
|
||||
|
||||
opt_ctx->allocated_graph_copy = dup_graph(opt_ctx->ctx_copy, graph);
|
||||
|
||||
ggml_backend_sched_alloc_graph(opt_ctx->backend_sched, opt_ctx->allocated_graph_copy);
|
||||
opt_ctx->allocated_graph = graph;
|
||||
}
|
||||
|
||||
ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
|
||||
ggml_opt_context_t result = new struct ggml_opt_context;
|
||||
result->backend_sched = params.backend_sched;
|
||||
result->ctx_compute = params.ctx_compute;
|
||||
result->inputs = params.inputs;
|
||||
result->outputs = params.outputs;
|
||||
result->opt_period = params.opt_period;
|
||||
result->get_opt_pars = params.get_opt_pars;
|
||||
result->get_opt_pars_ud = params.get_opt_pars_ud;
|
||||
|
||||
GGML_ASSERT(result->inputs->data && "the inputs must be allocated statically");
|
||||
GGML_ASSERT(result->opt_period >= 1);
|
||||
|
||||
const bool accumulate = params.build_type == GGML_OPT_BUILD_TYPE_GRAD ||
|
||||
(params.build_type == GGML_OPT_BUILD_TYPE_OPT && result->opt_period > 1);
|
||||
|
||||
ggml_set_input(result->inputs);
|
||||
ggml_set_output(result->outputs);
|
||||
|
||||
result->gf = ggml_new_graph_custom(result->ctx_compute, GGML_DEFAULT_GRAPH_SIZE, /*grads =*/ true); // Forward pass.
|
||||
ggml_build_forward_expand(result->gf, result->outputs);
|
||||
ggml_set_input(opt_ctx->inputs);
|
||||
ggml_set_output(opt_ctx->outputs);
|
||||
|
||||
int n_param = 0;
|
||||
for (int i = 0; i < result->gf->n_nodes; ++i) {
|
||||
if (result->gf->nodes[i]->flags & GGML_TENSOR_FLAG_PARAM) {
|
||||
for (int i = 0; i < opt_ctx->gf->n_nodes; ++i) {
|
||||
const struct ggml_tensor * node = opt_ctx->gf->nodes[i];
|
||||
if (node->flags & GGML_TENSOR_FLAG_PARAM) {
|
||||
n_param++;
|
||||
}
|
||||
GGML_ASSERT(!(node->flags & GGML_TENSOR_FLAG_LOSS) && "support for extra loss terms not implemented");
|
||||
}
|
||||
|
||||
{
|
||||
if (!opt_ctx->ctx_static) {
|
||||
// The static context is used for:
|
||||
// - gradients (1 tensor per param if using gradient accumulation)
|
||||
// - gradients (1 per loss, 1 tensor per param if using gradient accumulation)
|
||||
// - optimizer momenta (2 tensors per param)
|
||||
// - labels
|
||||
// - loss + its gradient (up to 5 tensors)
|
||||
// - pred
|
||||
// - ncorrect (2 tensors).
|
||||
const size_t tensors_per_param = (accumulate ? 1 : 0) + (params.build_type == GGML_OPT_BUILD_TYPE_OPT ? 2 : 0);
|
||||
const size_t size_meta = (tensors_per_param*n_param + 9) * ggml_tensor_overhead();
|
||||
// - labels (if using static graphs)
|
||||
// - loss (if using static graphs, up to 5 tensors)
|
||||
// - pred (if using static graphs)
|
||||
// - ncorrect (if using static graphs, 2 tensors).
|
||||
constexpr size_t n_loss = 1;
|
||||
const size_t tensors_per_param = (accumulate ? 1 : 0) +
|
||||
(opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_OPT ? 2 : 0);
|
||||
const size_t tensors_const = opt_ctx->static_graphs ? 9 : 0;
|
||||
const size_t size_meta = (n_loss + tensors_per_param*n_param + tensors_const) * ggml_tensor_overhead();
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ size_meta,
|
||||
/*.mem_buffer =*/ nullptr,
|
||||
/*.no_alloc =*/ true,
|
||||
};
|
||||
result->ctx_static = ggml_init(params);
|
||||
opt_ctx->ctx_static = ggml_init(params);
|
||||
}
|
||||
GGML_ASSERT(opt_ctx->build_type <= opt_ctx->build_type_alloc);
|
||||
|
||||
{
|
||||
// The static cpu context is used for:
|
||||
// - optimizer parameters (1 for the entire context)
|
||||
// The cpu context is allocated statically if using static graphs, dynamically otherwise.
|
||||
// It is used for:
|
||||
// - optimizer parameters (1 shared for all optimizer invocations)
|
||||
const size_t size_meta = 1 * ggml_tensor_overhead();
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ size_meta,
|
||||
/*.mem_buffer =*/ nullptr,
|
||||
/*.no_alloc =*/ true,
|
||||
};
|
||||
result->ctx_static_cpu = ggml_init(params);
|
||||
ggml_free(opt_ctx->ctx_cpu);
|
||||
opt_ctx->ctx_cpu = ggml_init(params);
|
||||
|
||||
ggml_backend_buffer_free(opt_ctx->buf_cpu);
|
||||
opt_ctx->buf_cpu = nullptr;
|
||||
}
|
||||
|
||||
struct ggml_context * ctx_results = opt_ctx->static_graphs ? opt_ctx->ctx_static : opt_ctx->ctx_compute;
|
||||
|
||||
switch (params.loss_type) {
|
||||
switch (opt_ctx->loss_type) {
|
||||
case GGML_OPT_LOSS_TYPE_MEAN: {
|
||||
result->loss = ggml_sum(result->ctx_static, result->outputs);
|
||||
ggml_set_name(result->loss, "loss_sum");
|
||||
const float scale = 1.0f / (result->opt_period * ggml_nelements(result->outputs));
|
||||
result->loss = ggml_scale(result->ctx_static, result->loss, scale);
|
||||
ggml_set_name(result->loss, "loss_mean");
|
||||
result->loss_per_datapoint = true;
|
||||
opt_ctx->loss = ggml_sum(ctx_results, opt_ctx->outputs);
|
||||
ggml_set_name(opt_ctx->loss, "loss_sum");
|
||||
const float scale = 1.0f / (opt_ctx->opt_period * ggml_nelements(opt_ctx->outputs));
|
||||
opt_ctx->loss = ggml_scale(ctx_results, opt_ctx->loss, scale);
|
||||
ggml_set_name(opt_ctx->loss, "loss_mean");
|
||||
opt_ctx->loss_per_datapoint = true;
|
||||
break;
|
||||
}
|
||||
case GGML_OPT_LOSS_TYPE_SUM: {
|
||||
result->loss = ggml_sum(result->ctx_static, result->outputs);
|
||||
ggml_set_name(result->loss, "loss_sum");
|
||||
result->loss_per_datapoint = false;
|
||||
opt_ctx->loss = ggml_sum(ctx_results, opt_ctx->outputs);
|
||||
ggml_set_name(opt_ctx->loss, "loss_sum");
|
||||
opt_ctx->loss_per_datapoint = false;
|
||||
break;
|
||||
}
|
||||
case GGML_OPT_LOSS_TYPE_CROSS_ENTROPY: {
|
||||
result->labels = ggml_dup_tensor(result->ctx_static, result->outputs);
|
||||
ggml_set_input(result->labels);
|
||||
ggml_set_name(result->labels, "labels");
|
||||
result->loss = ggml_cross_entropy_loss(result->ctx_static, result->outputs, result->labels);
|
||||
ggml_set_name(result->loss, "loss_cross_entropy");
|
||||
if (result->opt_period > 1) {
|
||||
result->loss = ggml_scale(result->ctx_static, result->loss, 1.0f / result->opt_period);
|
||||
ggml_set_name(result->loss, "loss_cross_entropy_scaled");
|
||||
opt_ctx->labels = ggml_dup_tensor(ctx_results, opt_ctx->outputs);
|
||||
ggml_set_input(opt_ctx->labels);
|
||||
ggml_set_name(opt_ctx->labels, "labels");
|
||||
opt_ctx->loss = ggml_cross_entropy_loss(ctx_results, opt_ctx->outputs, opt_ctx->labels);
|
||||
ggml_set_name(opt_ctx->loss, "loss_cross_entropy");
|
||||
if (opt_ctx->opt_period > 1) {
|
||||
opt_ctx->loss = ggml_scale(ctx_results, opt_ctx->loss, 1.0f / opt_ctx->opt_period);
|
||||
ggml_set_name(opt_ctx->loss, "loss_cross_entropy_scaled");
|
||||
}
|
||||
result->loss_per_datapoint = true;
|
||||
opt_ctx->loss_per_datapoint = true;
|
||||
break;
|
||||
}
|
||||
case GGML_OPT_LOSS_TYPE_MEAN_SQUARED_ERROR: {
|
||||
result->labels = ggml_dup_tensor(result->ctx_static, result->outputs);
|
||||
ggml_set_input(result->labels);
|
||||
ggml_set_name(result->labels, "labels");
|
||||
result->loss = ggml_sub(result->ctx_static, result->outputs, result->labels);
|
||||
ggml_set_name(result->loss, "loss_error");
|
||||
result->loss = ggml_sqr(result->ctx_static, result->loss);
|
||||
ggml_set_name(result->loss, "loss_squared_error");
|
||||
result->loss = ggml_sum(result->ctx_static, result->loss);
|
||||
ggml_set_name(result->loss, "loss_sum_squared_error");
|
||||
const float scale = 1.0f / (result->opt_period * ggml_nelements(result->outputs));
|
||||
result->loss = ggml_scale(result->ctx_static, result->loss, scale);
|
||||
ggml_set_name(result->loss, "loss_mean_squared_error");
|
||||
result->loss_per_datapoint = true;
|
||||
opt_ctx->labels = ggml_dup_tensor(ctx_results, opt_ctx->outputs);
|
||||
ggml_set_input(opt_ctx->labels);
|
||||
ggml_set_name(opt_ctx->labels, "labels");
|
||||
opt_ctx->loss = ggml_sub(ctx_results, opt_ctx->outputs, opt_ctx->labels);
|
||||
ggml_set_name(opt_ctx->loss, "loss_error");
|
||||
opt_ctx->loss = ggml_sqr(ctx_results, opt_ctx->loss);
|
||||
ggml_set_name(opt_ctx->loss, "loss_squared_error");
|
||||
opt_ctx->loss = ggml_sum(ctx_results, opt_ctx->loss);
|
||||
ggml_set_name(opt_ctx->loss, "loss_sum_squared_error");
|
||||
const float scale = 1.0f / (opt_ctx->opt_period * ggml_nelements(opt_ctx->outputs));
|
||||
opt_ctx->loss = ggml_scale(ctx_results, opt_ctx->loss, scale);
|
||||
ggml_set_name(opt_ctx->loss, "loss_mean_squared_error");
|
||||
opt_ctx->loss_per_datapoint = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
ggml_set_output(result->loss);
|
||||
ggml_set_loss(result->loss);
|
||||
ggml_build_forward_expand(result->gf, result->loss);
|
||||
ggml_set_output(opt_ctx->loss);
|
||||
ggml_set_loss(opt_ctx->loss);
|
||||
ggml_build_forward_expand(opt_ctx->gf, opt_ctx->loss);
|
||||
|
||||
result->pred = ggml_argmax(result->ctx_static, result->outputs);
|
||||
ggml_set_name(result->pred, "pred");
|
||||
ggml_set_output(result->pred);
|
||||
ggml_build_forward_expand(result->gf, result->pred);
|
||||
if (opt_ctx->loss_type == GGML_OPT_LOSS_TYPE_CROSS_ENTROPY) {
|
||||
opt_ctx->pred = ggml_argmax(ctx_results, opt_ctx->outputs);
|
||||
ggml_set_name(opt_ctx->pred, "pred");
|
||||
ggml_set_output(opt_ctx->pred);
|
||||
ggml_build_forward_expand(opt_ctx->gf, opt_ctx->pred);
|
||||
|
||||
if (result->labels) {
|
||||
result->ncorrect = ggml_count_equal(result->ctx_static, result->pred, ggml_argmax(result->ctx_static, result->labels));
|
||||
ggml_set_name(result->ncorrect, "ncorrect");
|
||||
ggml_set_output(result->ncorrect);
|
||||
ggml_build_forward_expand(result->gf, result->ncorrect);
|
||||
} else {
|
||||
result->ncorrect = nullptr;
|
||||
opt_ctx->ncorrect = ggml_count_equal(ctx_results, opt_ctx->pred, ggml_argmax(ctx_results, opt_ctx->labels));
|
||||
ggml_set_name(opt_ctx->ncorrect, "ncorrect");
|
||||
ggml_set_output(opt_ctx->ncorrect);
|
||||
ggml_build_forward_expand(opt_ctx->gf, opt_ctx->ncorrect);
|
||||
}
|
||||
|
||||
if (params.build_type == GGML_OPT_BUILD_TYPE_FORWARD) {
|
||||
result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0));
|
||||
return result;
|
||||
if (opt_ctx->buf_static) {
|
||||
if (opt_ctx->build_type == GGML_OPT_BUILD_TYPE_FORWARD) {
|
||||
return;
|
||||
}
|
||||
} else if (opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_FORWARD) {
|
||||
opt_ctx->buf_static = ggml_backend_alloc_ctx_tensors(
|
||||
opt_ctx->ctx_static, ggml_backend_sched_get_backend(opt_ctx->backend_sched, 0));
|
||||
return;
|
||||
}
|
||||
|
||||
// gb_grad == graph backward gradients, forward pass, then backward pass to calculate gradients.
|
||||
result->gb_grad = ggml_graph_dup(result->ctx_compute, result->gf);
|
||||
ggml_build_backward_expand(result->ctx_static, result->ctx_compute, result->gb_grad, accumulate);
|
||||
if (opt_ctx->grad_accs.empty()) {
|
||||
GGML_ASSERT(opt_ctx->build_type_alloc >= GGML_OPT_BUILD_TYPE_GRAD);
|
||||
|
||||
if (params.build_type == GGML_OPT_BUILD_TYPE_GRAD) {
|
||||
result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0));
|
||||
ggml_graph_reset(result->gb_grad);
|
||||
return result;
|
||||
}
|
||||
const int n_nodes = opt_ctx->gf->n_nodes;
|
||||
opt_ctx->grad_accs.resize(n_nodes);
|
||||
for (int i = 0; i < n_nodes; ++i) {
|
||||
ggml_tensor * node = opt_ctx->gf->nodes[i];
|
||||
if ((accumulate && (node->flags & GGML_TENSOR_FLAG_PARAM)) || (node->flags & GGML_TENSOR_FLAG_LOSS)) {
|
||||
opt_ctx->grad_accs[i] = ggml_new_tensor(opt_ctx->ctx_static, GGML_TYPE_F32, GGML_MAX_DIMS, node->ne);
|
||||
} else {
|
||||
opt_ctx->grad_accs[i] = nullptr;
|
||||
}
|
||||
}
|
||||
|
||||
GGML_ASSERT(params.build_type == GGML_OPT_BUILD_TYPE_OPT);
|
||||
|
||||
// gb_opt == graph backward optimize, forward pass, then backward pass to calculate gradients, then optimizer step.
|
||||
result->gb_opt = ggml_graph_dup(result->ctx_compute, result->gb_grad);
|
||||
|
||||
result->adamw_params = ggml_new_tensor_1d(result->ctx_static_cpu, GGML_TYPE_F32, 7);
|
||||
ggml_set_input(result->adamw_params);
|
||||
ggml_set_name(result->adamw_params, "adamw_params");
|
||||
|
||||
for (int i = result->gf->n_nodes-1; i >= 0; --i) {
|
||||
struct ggml_tensor * node = result->gb_opt->nodes[i];
|
||||
struct ggml_tensor * grad = ggml_graph_get_grad(result->gb_opt, node);
|
||||
|
||||
if (node->flags & GGML_TENSOR_FLAG_PARAM) {
|
||||
struct ggml_tensor * m = ggml_dup_tensor(result->ctx_static, node);
|
||||
struct ggml_tensor * v = ggml_dup_tensor(result->ctx_static, node);
|
||||
struct ggml_tensor * opt_step = ggml_opt_step_adamw(result->ctx_compute, node, grad, m, v, result->adamw_params);
|
||||
ggml_build_forward_expand(result->gb_opt, opt_step);
|
||||
if (opt_ctx->build_type_alloc >= GGML_OPT_BUILD_TYPE_OPT) {
|
||||
opt_ctx->grad_m.resize(n_nodes);
|
||||
opt_ctx->grad_v.resize(n_nodes);
|
||||
for (int i = 0; i < n_nodes; ++i) {
|
||||
ggml_tensor * node = opt_ctx->gf->nodes[i];
|
||||
if (node->flags & GGML_TENSOR_FLAG_PARAM) {
|
||||
opt_ctx->grad_m[i] = ggml_new_tensor(opt_ctx->ctx_static, GGML_TYPE_F32, GGML_MAX_DIMS, node->ne);
|
||||
opt_ctx->grad_v[i] = ggml_new_tensor(opt_ctx->ctx_static, GGML_TYPE_F32, GGML_MAX_DIMS, node->ne);
|
||||
} else {
|
||||
opt_ctx->grad_m[i] = nullptr;
|
||||
opt_ctx->grad_v[i] = nullptr;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
result->buf_static = ggml_backend_alloc_ctx_tensors(
|
||||
result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0));
|
||||
// gb_grad == graph backward gradients, forward pass, then backward pass to calculate gradients.
|
||||
opt_ctx->gb_grad = ggml_graph_dup(opt_ctx->ctx_compute, opt_ctx->gf, /*force_grads =*/ true);
|
||||
ggml_build_backward_expand(opt_ctx->ctx_compute, opt_ctx->gb_grad, opt_ctx->grad_accs.data());
|
||||
|
||||
result->buf_static_cpu = ggml_backend_alloc_ctx_tensors_from_buft(result->ctx_static_cpu, ggml_backend_cpu_buffer_type());
|
||||
if (opt_ctx->buf_static) {
|
||||
if (opt_ctx->build_type == GGML_OPT_BUILD_TYPE_GRAD) {
|
||||
return;
|
||||
}
|
||||
} else if (opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_GRAD) {
|
||||
opt_ctx->buf_static = ggml_backend_alloc_ctx_tensors(opt_ctx->ctx_static, ggml_backend_sched_get_backend(opt_ctx->backend_sched, 0));
|
||||
ggml_graph_reset(opt_ctx->gb_grad);
|
||||
}
|
||||
|
||||
ggml_graph_reset(result->gb_opt);
|
||||
GGML_ASSERT(opt_ctx->build_type_alloc == GGML_OPT_BUILD_TYPE_OPT);
|
||||
|
||||
// gb_opt == graph backward optimize, forward pass, then backward pass to calculate gradients, then optimizer step.
|
||||
opt_ctx->gb_opt = ggml_graph_dup(opt_ctx->ctx_compute, opt_ctx->gb_grad, /*force_grads =*/ true);
|
||||
|
||||
opt_ctx->adamw_params = ggml_new_tensor_1d(opt_ctx->ctx_cpu, GGML_TYPE_F32, 7);
|
||||
ggml_set_input(opt_ctx->adamw_params);
|
||||
ggml_set_name(opt_ctx->adamw_params, "adamw_params");
|
||||
|
||||
for (int i = opt_ctx->gf->n_nodes-1; i >= 0; --i) {
|
||||
struct ggml_tensor * node = opt_ctx->gb_opt->nodes[i];
|
||||
struct ggml_tensor * grad = ggml_graph_get_grad(opt_ctx->gb_opt, node);
|
||||
|
||||
if (grad && (node->flags & GGML_TENSOR_FLAG_PARAM)) {
|
||||
struct ggml_tensor * m = opt_ctx->grad_m[i];
|
||||
struct ggml_tensor * v = opt_ctx->grad_v[i];
|
||||
struct ggml_tensor * opt_step = ggml_opt_step_adamw(opt_ctx->ctx_compute, node, grad, m, v, opt_ctx->adamw_params);
|
||||
|
||||
ggml_set_name(m, (std::string("AdamW m for ") + std::string(node->name)).c_str());
|
||||
ggml_set_name(v, (std::string("AdamW v for ") + std::string(node->name)).c_str());
|
||||
ggml_set_name(opt_step, (std::string("AdamW step for ") + std::string(node->name)).c_str());
|
||||
|
||||
ggml_build_forward_expand(opt_ctx->gb_opt, opt_step);
|
||||
}
|
||||
}
|
||||
|
||||
if (!opt_ctx->buf_static) {
|
||||
opt_ctx->buf_static = ggml_backend_alloc_ctx_tensors(
|
||||
opt_ctx->ctx_static, ggml_backend_sched_get_backend(opt_ctx->backend_sched, 0));
|
||||
ggml_graph_reset(opt_ctx->gb_opt);
|
||||
}
|
||||
|
||||
opt_ctx->buf_cpu = ggml_backend_alloc_ctx_tensors_from_buft(opt_ctx->ctx_cpu, ggml_backend_cpu_buffer_type());
|
||||
}
|
||||
|
||||
ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params) {
|
||||
ggml_opt_context_t result = new struct ggml_opt_context;
|
||||
result->backend_sched = params.backend_sched;
|
||||
result->ctx_compute = params.ctx_compute;
|
||||
result->loss_type = params.loss_type;
|
||||
result->build_type = params.build_type;
|
||||
result->build_type_alloc = params.build_type;
|
||||
result->inputs = params.inputs;
|
||||
result->outputs = params.outputs;
|
||||
result->opt_period = params.opt_period;
|
||||
result->get_opt_pars = params.get_opt_pars;
|
||||
result->get_opt_pars_ud = params.get_opt_pars_ud;
|
||||
|
||||
GGML_ASSERT(result->opt_period >= 1);
|
||||
|
||||
result->static_graphs = result->ctx_compute;
|
||||
|
||||
if (!result->static_graphs) {
|
||||
GGML_ASSERT(!result->inputs);
|
||||
GGML_ASSERT(!result->outputs);
|
||||
return result;
|
||||
}
|
||||
|
||||
GGML_ASSERT(result->inputs);
|
||||
GGML_ASSERT(result->outputs);
|
||||
|
||||
result->gf = ggml_new_graph_custom(result->ctx_compute, GGML_DEFAULT_GRAPH_SIZE, /*grads =*/ true); // Forward pass.
|
||||
ggml_build_forward_expand(result->gf, result->outputs);
|
||||
|
||||
ggml_opt_build(result);
|
||||
|
||||
return result;
|
||||
}
|
||||
@ -464,9 +561,9 @@ void ggml_opt_free(ggml_opt_context_t opt_ctx) {
|
||||
return;
|
||||
}
|
||||
ggml_backend_buffer_free(opt_ctx->buf_static);
|
||||
ggml_backend_buffer_free(opt_ctx->buf_static_cpu);
|
||||
ggml_backend_buffer_free(opt_ctx->buf_cpu);
|
||||
ggml_free(opt_ctx->ctx_static);
|
||||
ggml_free(opt_ctx->ctx_static_cpu);
|
||||
ggml_free(opt_ctx->ctx_cpu);
|
||||
delete opt_ctx;
|
||||
}
|
||||
|
||||
@ -582,8 +679,79 @@ void ggml_opt_result_accuracy(ggml_opt_result_t result, double * accuracy, doubl
|
||||
|
||||
// ====== Computation ======
|
||||
|
||||
static void ggml_opt_eval_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph, ggml_opt_result * result) {
|
||||
if (graph != opt_ctx->gf) {
|
||||
void ggml_opt_prepare_alloc(
|
||||
ggml_opt_context_t opt_ctx,
|
||||
struct ggml_context * ctx_compute,
|
||||
struct ggml_cgraph * gf,
|
||||
struct ggml_tensor * inputs,
|
||||
struct ggml_tensor * outputs) {
|
||||
GGML_ASSERT(!opt_ctx->static_graphs);
|
||||
opt_ctx->ctx_compute = ctx_compute;
|
||||
opt_ctx->gf = gf;
|
||||
opt_ctx->inputs = inputs;
|
||||
opt_ctx->outputs = outputs;
|
||||
}
|
||||
|
||||
void ggml_opt_alloc(ggml_opt_context_t opt_ctx, bool backward) {
|
||||
GGML_ASSERT(!opt_ctx->eval_ready);
|
||||
if (opt_ctx->build_type == GGML_OPT_BUILD_TYPE_OPT && opt_ctx->opt_period > 1 && opt_ctx->opt_i == 0) {
|
||||
ggml_graph_reset(opt_ctx->gb_grad);
|
||||
}
|
||||
if (backward) {
|
||||
const int32_t opt_i_next = (opt_ctx->opt_i + 1) % opt_ctx->opt_period;
|
||||
opt_ctx->build_type = opt_i_next == 0 ? GGML_OPT_BUILD_TYPE_OPT : GGML_OPT_BUILD_TYPE_GRAD;
|
||||
} else {
|
||||
opt_ctx->build_type = GGML_OPT_BUILD_TYPE_FORWARD;
|
||||
}
|
||||
|
||||
if (!opt_ctx->static_graphs) {
|
||||
ggml_opt_build(opt_ctx);
|
||||
}
|
||||
|
||||
struct ggml_cgraph * graph = nullptr;
|
||||
switch (opt_ctx->build_type) {
|
||||
case GGML_OPT_BUILD_TYPE_FORWARD: {
|
||||
graph = opt_ctx->gf;
|
||||
} break;
|
||||
case GGML_OPT_BUILD_TYPE_GRAD: {
|
||||
graph = opt_ctx->gb_grad;
|
||||
} break;
|
||||
case GGML_OPT_BUILD_TYPE_OPT: {
|
||||
graph = opt_ctx->gb_opt;
|
||||
} break;
|
||||
}
|
||||
GGML_ASSERT(graph);
|
||||
|
||||
if (opt_ctx->allocated_graph == graph) {
|
||||
opt_ctx->eval_ready = true;
|
||||
return;
|
||||
}
|
||||
|
||||
ggml_backend_sched_reset(opt_ctx->backend_sched); // clear allocation of previous graph
|
||||
|
||||
if (opt_ctx->static_graphs) {
|
||||
ggml_init_params params = {
|
||||
/*.mem_size =*/ graph->size*ggml_tensor_overhead() + ggml_graph_overhead_custom(graph->size, graph->grads),
|
||||
/*.mem_buffer =*/ nullptr,
|
||||
/*.no_alloc =*/ true,
|
||||
};
|
||||
ggml_free(opt_ctx->ctx_copy);
|
||||
opt_ctx->ctx_copy = ggml_init(params);
|
||||
|
||||
opt_ctx->allocated_graph_copy = dup_graph(opt_ctx->ctx_copy, graph);
|
||||
} else {
|
||||
opt_ctx->allocated_graph_copy = graph;
|
||||
}
|
||||
|
||||
ggml_backend_sched_alloc_graph(opt_ctx->backend_sched, opt_ctx->allocated_graph_copy);
|
||||
opt_ctx->allocated_graph = graph;
|
||||
|
||||
opt_ctx->eval_ready = true;
|
||||
}
|
||||
|
||||
void ggml_opt_eval(ggml_opt_context_t opt_ctx, ggml_opt_result_t result) {
|
||||
GGML_ASSERT(opt_ctx->eval_ready);
|
||||
if (opt_ctx->allocated_graph == opt_ctx->gb_opt) {
|
||||
struct ggml_opt_optimizer_params opt_pars = opt_ctx->get_opt_pars(opt_ctx->get_opt_pars_ud);
|
||||
|
||||
GGML_ASSERT(opt_pars.adamw.alpha > 0.0f);
|
||||
@ -609,9 +777,19 @@ static void ggml_opt_eval_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph,
|
||||
adamw_par_data[6] = beta2h;
|
||||
}
|
||||
|
||||
ggml_opt_alloc_graph(opt_ctx, graph);
|
||||
ggml_backend_sched_graph_compute(opt_ctx->backend_sched, opt_ctx->allocated_graph_copy);
|
||||
opt_ctx->iter += opt_ctx->allocated_graph == opt_ctx->gb_opt;
|
||||
opt_ctx->opt_i = (opt_ctx->opt_i + 1) % opt_ctx->opt_period;
|
||||
|
||||
if (!opt_ctx->static_graphs) {
|
||||
opt_ctx->gf = nullptr;
|
||||
opt_ctx->gb_grad = nullptr;
|
||||
opt_ctx->gb_opt = nullptr;
|
||||
opt_ctx->allocated_graph = nullptr;
|
||||
opt_ctx->allocated_graph_copy = nullptr;
|
||||
}
|
||||
|
||||
opt_ctx->eval_ready = false;
|
||||
|
||||
if (!result) {
|
||||
return;
|
||||
@ -635,12 +813,14 @@ static void ggml_opt_eval_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph,
|
||||
ggml_backend_tensor_get(opt_ctx->loss, &loss, 0, ggml_nbytes(opt_ctx->loss));
|
||||
result->loss.push_back(loss);
|
||||
|
||||
GGML_ASSERT(opt_ctx->pred->type == GGML_TYPE_I32);
|
||||
std::vector<int32_t> pred(ndata);
|
||||
ggml_backend_tensor_get(opt_ctx->pred, pred.data(), 0, ggml_nbytes(opt_ctx->pred));
|
||||
result->pred.insert(result->pred.end(), pred.begin(), pred.end());
|
||||
if (opt_ctx->pred) {
|
||||
GGML_ASSERT(opt_ctx->pred->type == GGML_TYPE_I32);
|
||||
std::vector<int32_t> pred(ndata);
|
||||
ggml_backend_tensor_get(opt_ctx->pred, pred.data(), 0, ggml_nbytes(opt_ctx->pred));
|
||||
result->pred.insert(result->pred.end(), pred.begin(), pred.end());
|
||||
}
|
||||
|
||||
if (!opt_ctx->labels || result->ncorrect < 0) {
|
||||
if (!opt_ctx->ncorrect || result->ncorrect < 0) {
|
||||
result->ncorrect = -1;
|
||||
return;
|
||||
}
|
||||
@ -652,26 +832,6 @@ static void ggml_opt_eval_graph(ggml_opt_context_t opt_ctx, ggml_cgraph * graph,
|
||||
result->ncorrect += ncorrect;
|
||||
}
|
||||
|
||||
void ggml_opt_forward(ggml_opt_context_t opt_ctx, ggml_opt_result * result) {
|
||||
ggml_opt_eval_graph(opt_ctx, opt_ctx->gf, result);
|
||||
}
|
||||
|
||||
void ggml_opt_forward_backward(ggml_opt_context_t opt_ctx, ggml_opt_result * result) {
|
||||
if (opt_ctx->opt_period == 1) {
|
||||
ggml_opt_eval_graph(opt_ctx, opt_ctx->gb_opt, result);
|
||||
return;
|
||||
}
|
||||
|
||||
const int32_t opt_i_next = (opt_ctx->opt_i + 1) % opt_ctx->opt_period;
|
||||
if (opt_i_next == 0) {
|
||||
ggml_opt_eval_graph(opt_ctx, opt_ctx->gb_opt, result);
|
||||
ggml_opt_reset(opt_ctx, /*optimizer =*/ false);
|
||||
} else {
|
||||
ggml_opt_eval_graph(opt_ctx, opt_ctx->gb_grad, result);
|
||||
}
|
||||
opt_ctx->opt_i = opt_i_next;
|
||||
}
|
||||
|
||||
// ====== High-Level Functions ======
|
||||
|
||||
void ggml_opt_epoch(
|
||||
@ -700,16 +860,18 @@ void ggml_opt_epoch(
|
||||
int64_t ibatch = 0;
|
||||
int64_t t_loop_start = ggml_time_us();
|
||||
for (; ibatch < ibatch_split; ++ibatch) {
|
||||
ggml_opt_alloc(opt_ctx, /*backward =*/ true);
|
||||
ggml_opt_dataset_get_batch(dataset, inputs, labels, ibatch);
|
||||
ggml_opt_forward_backward(opt_ctx, result_train);
|
||||
ggml_opt_eval(opt_ctx, result_train);
|
||||
if (callback_train) {
|
||||
callback_train(true, opt_ctx, dataset, result_train, ibatch+1, ibatch_split, t_loop_start);
|
||||
}
|
||||
}
|
||||
t_loop_start = ggml_time_us();
|
||||
for (; ibatch < nbatches; ++ibatch) {
|
||||
ggml_opt_alloc(opt_ctx, /*backward =*/ false);
|
||||
ggml_opt_dataset_get_batch(dataset, inputs, labels, ibatch);
|
||||
ggml_opt_forward(opt_ctx, result_eval);
|
||||
ggml_opt_eval(opt_ctx, result_eval);
|
||||
if (callback_eval) {
|
||||
callback_eval(false, opt_ctx, dataset, result_eval, ibatch+1-ibatch_split, nbatches-ibatch_split, t_loop_start);
|
||||
}
|
||||
@ -726,13 +888,26 @@ void ggml_opt_epoch_callback_progress_bar(
|
||||
int64_t t_start_us) {
|
||||
fprintf(stderr, "%s[", train ? "train: " : "val: ");
|
||||
|
||||
constexpr int64_t bar_length = 25;
|
||||
// The progress bar consists of partially filled blocks, unicode has 8 separate fill levels.
|
||||
constexpr int64_t bar_length = 8;
|
||||
const int64_t ibatch8 = 8 * ibatch;
|
||||
for (int64_t j = 0; j < bar_length; ++j) {
|
||||
const int64_t ibatch_j = ibatch_max * j/bar_length;
|
||||
if (ibatch_j < ibatch) {
|
||||
fprintf(stderr, "=");
|
||||
} else if (ibatch_max * (j - 1)/bar_length < ibatch) {
|
||||
fprintf(stderr, ">");
|
||||
if (ibatch_max * (8*j + 8) / bar_length < ibatch8) {
|
||||
fprintf(stderr, "\u2588"); // full block
|
||||
} else if (ibatch_max * (8*j + 7) / bar_length < ibatch8) {
|
||||
fprintf(stderr, "\u2589"); // 7/8 filled
|
||||
} else if (ibatch_max * (8*j + 6) / bar_length < ibatch8) {
|
||||
fprintf(stderr, "\u258A"); // 6/8 filled
|
||||
} else if (ibatch_max * (8*j + 5) / bar_length < ibatch8) {
|
||||
fprintf(stderr, "\u258B"); // 5/8 filled
|
||||
} else if (ibatch_max * (8*j + 4) / bar_length < ibatch8) {
|
||||
fprintf(stderr, "\u258C"); // 4/8 filled
|
||||
} else if (ibatch_max * (8*j + 3) / bar_length < ibatch8) {
|
||||
fprintf(stderr, "\u258D"); // 3/8 filled
|
||||
} else if (ibatch_max * (8*j + 2) / bar_length < ibatch8) {
|
||||
fprintf(stderr, "\u258E"); // 2/8 filled
|
||||
} else if (ibatch_max * (8*j + 1) / bar_length < ibatch8) {
|
||||
fprintf(stderr, "\u258F"); // 1/8 filled
|
||||
} else {
|
||||
fprintf(stderr, " ");
|
||||
}
|
||||
@ -764,8 +939,8 @@ void ggml_opt_epoch_callback_progress_bar(
|
||||
const int64_t t_eta_m = t_eta_s / 60;
|
||||
t_eta_s -= t_eta_m * 60;
|
||||
|
||||
fprintf(stderr, "| data=%06" PRId64 "/%06" PRId64 ", loss=%.6lf+-%.6lf, accuracy=%.2lf+-%.2lf%%, "
|
||||
"t=%02" PRId64 ":%02" PRId64 ":%02" PRId64 ", ETA=%02" PRId64 ":%02" PRId64 ":%02" PRId64 "]\r",
|
||||
fprintf(stderr, "] data=%07" PRId64 "/%07" PRId64 " loss=%.5lf±%.5lf acc=%.2lf±%.2lf%% "
|
||||
"t=%02" PRId64 ":%02" PRId64 ":%02" PRId64 " ETA=%02" PRId64 ":%02" PRId64 ":%02" PRId64 " \r",
|
||||
idata, idata_max, loss, loss_unc, 100.0*accuracy, 100.0*accuracy_unc,
|
||||
t_ibatch_h, t_ibatch_m, t_ibatch_s, t_eta_h, t_eta_m, t_eta_s);
|
||||
if (ibatch == ibatch_max) {
|
||||
@ -806,7 +981,10 @@ void ggml_opt_fit(
|
||||
|
||||
int64_t epoch = 1;
|
||||
|
||||
ggml_opt_params params = ggml_opt_default_params(backend_sched, ctx_compute, inputs, outputs, loss_type);
|
||||
ggml_opt_params params = ggml_opt_default_params(backend_sched, loss_type);
|
||||
params.ctx_compute = ctx_compute;
|
||||
params.inputs = inputs;
|
||||
params.outputs = outputs;
|
||||
params.opt_period = opt_period;
|
||||
params.get_opt_pars = get_opt_pars;
|
||||
params.get_opt_pars_ud = &epoch;
|
||||
|
||||
@ -5499,7 +5499,7 @@ static void ggml_compute_backward(
|
||||
// tensor = src0 * 1 + src1 * 0
|
||||
if (src0_needs_grads) {
|
||||
// dsrc0 = dtensor * 1
|
||||
ggml_add_or_set(ctx, cgraph, isrc0, grad);
|
||||
ggml_add_or_set(ctx, cgraph, isrc0, ggml_reshape(ctx, grad, src0));
|
||||
}
|
||||
if (src1_needs_grads) {
|
||||
// dsrc1 = dtensor * 0 -> noop
|
||||
@ -5780,10 +5780,9 @@ void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor *
|
||||
}
|
||||
|
||||
void ggml_build_backward_expand(
|
||||
struct ggml_context * ctx_static,
|
||||
struct ggml_context * ctx_compute,
|
||||
struct ggml_cgraph * cgraph,
|
||||
bool accumulate) {
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_cgraph * cgraph,
|
||||
struct ggml_tensor ** grad_accs) {
|
||||
GGML_ASSERT(cgraph->n_nodes > 0);
|
||||
GGML_ASSERT(cgraph->grads);
|
||||
GGML_ASSERT(cgraph->grad_accs);
|
||||
@ -5856,21 +5855,24 @@ void ggml_build_backward_expand(
|
||||
GGML_ASSERT(!node->view_src || node->op == GGML_OP_CPY || node->op == GGML_OP_VIEW ||
|
||||
node->op == GGML_OP_RESHAPE || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_TRANSPOSE);
|
||||
|
||||
const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node);
|
||||
GGML_ASSERT(igrad != GGML_HASHSET_FULL);
|
||||
GGML_ASSERT(ggml_bitset_get(cgraph->visited_hash_set.used, igrad));
|
||||
if ((accumulate && (node->flags & GGML_TENSOR_FLAG_PARAM)) || (node->flags & GGML_TENSOR_FLAG_LOSS)) {
|
||||
cgraph->grad_accs[igrad] = ggml_dup_tensor(ctx_static, node);
|
||||
cgraph->grads[igrad] = cgraph->grad_accs[igrad];
|
||||
ggml_format_name(cgraph->grad_accs[igrad], "grad acc for %s", node->name);
|
||||
const size_t ihash = ggml_hash_find(&cgraph->visited_hash_set, node);
|
||||
GGML_ASSERT(ihash != GGML_HASHSET_FULL);
|
||||
GGML_ASSERT(ggml_bitset_get(cgraph->visited_hash_set.used, ihash));
|
||||
if (grad_accs && grad_accs[i]) {
|
||||
cgraph->grad_accs[ihash] = grad_accs[i];
|
||||
cgraph->grads[ihash] = cgraph->grad_accs[ihash];
|
||||
} else if (node->flags & GGML_TENSOR_FLAG_LOSS) {
|
||||
// loss tensors always need a gradient accumulator
|
||||
cgraph->grad_accs[ihash] = ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, node->ne);
|
||||
cgraph->grads[ihash] = cgraph->grad_accs[ihash];
|
||||
}
|
||||
grads_needed[igrad] = true;
|
||||
grads_needed[ihash] = true;
|
||||
}
|
||||
|
||||
for (int i = n_nodes_f - 1; i >= 0; --i) {
|
||||
// inplace operations to add gradients are not created by ggml_compute_backward except for gradient accumulation
|
||||
// use allocator to automatically make inplace operations
|
||||
ggml_compute_backward(ctx_compute, cgraph, i, grads_needed);
|
||||
ggml_compute_backward(ctx, cgraph, i, grads_needed);
|
||||
}
|
||||
|
||||
free(grads_needed);
|
||||
@ -6016,8 +6018,8 @@ void ggml_graph_cpy(struct ggml_cgraph * src, struct ggml_cgraph * dst) {
|
||||
}
|
||||
}
|
||||
|
||||
struct ggml_cgraph * ggml_graph_dup(struct ggml_context * ctx, struct ggml_cgraph * cgraph) {
|
||||
struct ggml_cgraph * result = ggml_new_graph_custom(ctx, cgraph->size, cgraph->grads != NULL);
|
||||
struct ggml_cgraph * ggml_graph_dup(struct ggml_context * ctx, struct ggml_cgraph * cgraph, bool force_grads) {
|
||||
struct ggml_cgraph * result = ggml_new_graph_custom(ctx, cgraph->size, cgraph->grads || force_grads);
|
||||
ggml_graph_cpy(cgraph, result);
|
||||
return result;
|
||||
}
|
||||
@ -6036,6 +6038,9 @@ struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) {
|
||||
}
|
||||
|
||||
void ggml_graph_reset(struct ggml_cgraph * cgraph) {
|
||||
if (!cgraph) {
|
||||
return;
|
||||
}
|
||||
GGML_ASSERT(cgraph->grads != NULL);
|
||||
|
||||
for (int i = 0; i < cgraph->n_nodes; i++) {
|
||||
@ -6345,8 +6350,8 @@ void ggml_set_output(struct ggml_tensor * tensor) {
|
||||
tensor->flags |= GGML_TENSOR_FLAG_OUTPUT;
|
||||
}
|
||||
|
||||
void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor) {
|
||||
GGML_UNUSED(ctx); // TODO: remove this parameter
|
||||
void ggml_set_param(struct ggml_tensor * tensor) {
|
||||
GGML_ASSERT(tensor->op == GGML_OP_NONE);
|
||||
tensor->flags |= GGML_TENSOR_FLAG_PARAM;
|
||||
}
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user