# Copy all required headers (common for all platforms)
cp include/llama.h ${header_path}
cp ggml/include/ggml.h ${header_path}
+ cp ggml/include/ggml-opt.h ${header_path}
cp ggml/include/ggml-alloc.h ${header_path}
cp ggml/include/ggml-backend.h ${header_path}
cp ggml/include/ggml-metal.h ${header_path}
return result;
}
+
+ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector<llama_token> & tokens, int64_t stride) {
+ const int64_t ne_datapoint = llama_n_ctx(ctx);
+ const int64_t ndata = (tokens.size() - ne_datapoint - 1) / stride;
+ ggml_opt_dataset_t result = ggml_opt_dataset_init(
+ GGML_TYPE_I32, GGML_TYPE_I32, ne_datapoint, ne_datapoint, ndata, /*ndata_shard =*/ 1);
+
+ llama_token * data = (llama_token *) ggml_opt_dataset_data(result)->data;
+ llama_token * labels = (llama_token *) ggml_opt_dataset_labels(result)->data;
+
+ for (int64_t idata = 0; idata < ndata; ++idata) {
+ memcpy(data + idata*ne_datapoint, tokens.data() + idata*stride + 0, ne_datapoint*sizeof(llama_token));
+ memcpy(labels + idata*ne_datapoint, tokens.data() + idata*stride + 1, ne_datapoint*sizeof(llama_token));
+ }
+
+ return result;
+}
const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
}
+
+//
+// training utils
+//
+
+ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector<llama_token> & tokens, int64_t stride);
add_subdirectory(speculative)
add_subdirectory(speculative-simple)
add_subdirectory(gen-docs)
+ add_subdirectory(training)
if (NOT GGML_BACKEND_DL)
add_subdirectory(convert-llama2c-to-ggml)
# these examples use the backends directly and cannot be built with dynamic loading
--- /dev/null
+set(TARGET llama-finetune)
+add_executable(${TARGET} finetune.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
--- /dev/null
+# llama.cpp/examples/training
+
+This directory contains examples related to language model training using llama.cpp/GGML.
+So far finetuning is technically functional (for FP32 models and limited hardware setups) but the code is very much WIP.
+Finetuning of Stories 260K and LLaMA 3.2 1b seems to work with 24 GB of memory.
+**For CPU training, compile llama.cpp without any additional backends such as CUDA.**
+**For CUDA training, use the maximum number of GPU layers.**
+
+Proof of concept:
+
+``` sh
+export model_name=llama_3.2-1b && export quantization=f32
+./build/bin/finetune --file wikitext-2-raw/wiki.test.raw -ngl 999 --model models/${model_name}-${quantization}.gguf -c 512 -b 512 -ub 512
+./build/bin/perplexity --file wikitext-2-raw/wiki.test.raw -ngl 999 --model finetuned-model.gguf
+```
+
+The perplexity value of the finetuned model should be lower after training on the test set for 2 epochs.
--- /dev/null
+#include "arg.h"
+#include "common.h"
+#include "log.h"
+#include "llama.h"
+
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <ctime>
+#include <vector>
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+int main(int argc, char ** argv) {
+ common_params params;
+
+ params.escape = false;
+
+ if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PERPLEXITY)) {
+ return 1;
+ }
+
+ if (params.use_mmap) {
+ LOG_INF("%s: force disabling memory mapping because it would result in-read-only pointers to the weights\n", __func__);
+ params.use_mmap = false;
+ }
+ if (params.cache_type_k != GGML_TYPE_F32) {
+ LOG_INF("%s: force changing k cache type to f32 due to a lack of f16 support for OUT_PROD\n", __func__);
+ params.cache_type_k = GGML_TYPE_F32;
+ }
+ if (params.cache_type_v != GGML_TYPE_F32) {
+ LOG_INF("%s: force changing v cache type to f32 due to a lack of f16 support for OUT_PROD\n", __func__);
+ params.cache_type_v = GGML_TYPE_F32;
+ }
+
+ common_init();
+ llama_backend_init();
+ llama_numa_init(params.numa);
+
+ // load the model and apply lora adapter, if any
+ common_init_result llama_init = common_init_from_params(params);
+ llama_model_ptr & model = llama_init.model;
+ llama_context_ptr & ctx = llama_init.context;
+
+ if (model == NULL) {
+ LOG_ERR("%s: unable to load model\n", __func__);
+ return 1;
+ }
+
+ // print system information
+ {
+ LOG_INF("\n");
+ LOG_INF("%s\n", common_params_get_system_info(params).c_str());
+ }
+
+ constexpr float val_split = 0.05f;
+
+ std::vector<llama_token> tokens = common_tokenize(ctx.get(), params.prompt, true);
+ ggml_opt_dataset_t dataset = common_opt_dataset_init(ctx.get(), tokens, llama_n_ctx(ctx.get())/2);
+
+ struct ggml_opt_optimizer_params optimizer_params = ggml_opt_get_default_optimizer_params(nullptr);
+ optimizer_params.adamw.alpha = 1e-7f; // learning rate
+
+ struct llama_opt_params lopt_params {
+ /*n_ctx_train =*/ 0,
+ /*param_filter =*/ llama_opt_param_filter_all,
+ /*param_filter_ud =*/ nullptr,
+ /*get_opt_pars =*/ ggml_opt_get_constant_optimizer_params,
+ /*get_opt_pars_ud =*/ &optimizer_params,
+ };
+ llama_opt_init(ctx.get(), model.get(), lopt_params);
+
+ const int64_t idata_split = ggml_opt_dataset_ndata(dataset) * (1.0f - val_split);
+
+ ggml_opt_result_t result_train = ggml_opt_result_init();
+ ggml_opt_result_t result_eval = ggml_opt_result_init();
+
+ for (int epoch = 0; epoch < 2; ++epoch) {
+ llama_opt_epoch(ctx.get(), dataset, result_train, result_eval, idata_split,
+ ggml_opt_epoch_callback_progress_bar, ggml_opt_epoch_callback_progress_bar);
+ fprintf(stderr, "\n");
+
+ ggml_opt_result_reset(result_train);
+ ggml_opt_result_reset(result_eval);
+ }
+ ggml_opt_result_free(result_train);
+ ggml_opt_result_free(result_eval);
+
+ llama_model_save_to_file(model.get(), "finetuned-model.gguf");
+
+ llama_backend_free();
+
+ return 0;
+}
// ====== Dataset ======
GGML_API ggml_opt_dataset_t ggml_opt_dataset_init(
- int64_t ne_datapoint, // number of elements per datapoint
- int64_t ne_label, // number of elements per label
- int64_t ndata, // total number of datapoints/labels
- int64_t ndata_shard); // number of datapoints/labels per shard (unit at which the dataset is shuffled/copied)
+ enum ggml_type type_data, // the type for the internal data tensor
+ enum ggml_type type_label, // the type for the internal labels tensor
+ int64_t ne_datapoint, // number of elements per datapoint
+ int64_t ne_label, // number of elements per label
+ int64_t ndata, // total number of datapoints/labels
+ int64_t ndata_shard); // number of datapoints/labels per shard (unit at which the dataset is shuffled/copied)
GGML_API void ggml_opt_dataset_free(ggml_opt_dataset_t dataset);
// get underlying tensors that store the data
+ GGML_API int64_t ggml_opt_dataset_ndata (ggml_opt_dataset_t dataset);
GGML_API struct ggml_tensor * ggml_opt_dataset_data (ggml_opt_dataset_t dataset); // shape = [ne_datapoint, ndata]
GGML_API struct ggml_tensor * ggml_opt_dataset_labels(ggml_opt_dataset_t dataset); // shape = [nd_label, ndata]
struct ggml_tensor * data_batch, // shape = [ne_datapoint, ndata_batch]
struct ggml_tensor * labels_batch, // shape = [ne_label, ndata_batch]
int64_t ibatch);
+ GGML_API 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);
// ====== Model / Context ======
enum ggml_opt_build_type {
- GGML_OPT_BUILD_TYPE_FORWARD,
- GGML_OPT_BUILD_TYPE_GRAD,
- GGML_OPT_BUILD_TYPE_OPT,
+ GGML_OPT_BUILD_TYPE_FORWARD = 10,
+ GGML_OPT_BUILD_TYPE_GRAD = 20,
+ GGML_OPT_BUILD_TYPE_OPT = 30,
};
// parameters that control which optimizer is used and how said optimizer tries to find the minimal loss
// userdata can be used to pass arbitrary data
typedef struct ggml_opt_optimizer_params (*ggml_opt_get_optimizer_params)(void * userdata);
- // returns the default optimizer params (constant)
+ // returns the default optimizer params (constant, hard-coded values)
// userdata is not used
GGML_API struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * userdata);
+ // casts userdata to ggml_opt_optimizer_params and returns it
+ GGML_API struct ggml_opt_optimizer_params ggml_opt_get_constant_optimizer_params(void * userdata);
+
// parameters for initializing a new optimization context
struct ggml_opt_params {
ggml_backend_sched_t backend_sched; // defines which backends are used to construct the compute graphs
- struct ggml_context * ctx_compute; // created in user code, holds non-static tensors
-
- // the forward graph is defined by inputs and outputs
- // those tensors and all tensors inbetween are not intended to be reusable between multiple optimization contexts
- struct ggml_tensor * inputs;
- struct ggml_tensor * outputs;
+ // by default the forward graph needs to be reconstructed for each eval
+ // if ctx_compute, inputs, and outputs are set the graphs are instead allocated statically
+ struct ggml_context * ctx_compute;
+ struct ggml_tensor * inputs;
+ struct ggml_tensor * outputs;
enum ggml_opt_loss_type loss_type;
enum ggml_opt_build_type build_type;
// get parameters for an optimization context with defaults set where possible
// parameters for which no sensible defaults exist are supplied as arguments to this function
- GGML_API 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);
+ GGML_API struct ggml_opt_params ggml_opt_default_params(
+ ggml_backend_sched_t backend_sched,
+ enum ggml_opt_loss_type loss_type);
GGML_API ggml_opt_context_t ggml_opt_init(struct ggml_opt_params params);
GGML_API void ggml_opt_free(ggml_opt_context_t opt_ctx);
GGML_API void ggml_opt_reset(ggml_opt_context_t opt_ctx, bool optimizer);
// get underlying tensors that store data
+ // if not using static graphs these pointers become invalid with the next call to ggml_opt_alloc
GGML_API struct ggml_tensor * ggml_opt_inputs( ggml_opt_context_t opt_ctx); // forward graph input tensor
GGML_API struct ggml_tensor * ggml_opt_outputs( ggml_opt_context_t opt_ctx); // forward graph output tensor
GGML_API struct ggml_tensor * ggml_opt_labels( ggml_opt_context_t opt_ctx); // labels to compare outputs against
GGML_API struct ggml_tensor * ggml_opt_pred( ggml_opt_context_t opt_ctx); // predictions made by outputs
GGML_API struct ggml_tensor * ggml_opt_ncorrect(ggml_opt_context_t opt_ctx); // number of matching predictions between outputs and labels
+ // get the gradient accumulator for a node from the forward graph
GGML_API struct ggml_tensor * ggml_opt_grad_acc(ggml_opt_context_t opt_ctx, struct ggml_tensor * node);
// ====== Optimization Result ======
- GGML_API ggml_opt_result_t ggml_opt_result_init();
+ GGML_API ggml_opt_result_t ggml_opt_result_init(void);
GGML_API void ggml_opt_result_free(ggml_opt_result_t result);
GGML_API void ggml_opt_result_reset(ggml_opt_result_t result);
// ====== Computation ======
- // do forward pass, increment result if not NULL
- GGML_API void ggml_opt_forward(ggml_opt_context_t opt_ctx, ggml_opt_result_t result);
+ // if not using static graphs, this function must be called prior to ggml_opt_alloc
+ GGML_API 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);
+
+ // allocate the next graph for evaluation, either forward or forward + backward
+ // must be called exactly once prior to calling ggml_opt_eval
+ GGML_API void ggml_opt_alloc(ggml_opt_context_t opt_ctx, bool backward);
- // do forward pass, increment result if not NULL, do backward pass
- GGML_API void ggml_opt_forward_backward(ggml_opt_context_t opt_ctx, ggml_opt_result_t result);
+ // do forward pass, increment result if not NULL, do backward pass if allocated
+ GGML_API void ggml_opt_eval(ggml_opt_context_t opt_ctx, ggml_opt_result_t result);
// ############################################################################
// ## The high-level functions start here. They do not depend on any private ##
// fit model defined by inputs and outputs to dataset
GGML_API void ggml_opt_fit(
ggml_backend_sched_t backend_sched, // backend scheduler for constructing the compute graphs
- ggml_context * ctx_compute, // context with temporarily allocated tensors to calculate the outputs
- ggml_tensor * inputs, // input tensor with shape [ne_datapoint, ndata_batch]
- ggml_tensor * outputs, // output tensor, must have shape [ne_label, ndata_batch] if labels are used
+ struct ggml_context * ctx_compute, // context with temporarily allocated tensors to calculate the outputs
+ struct ggml_tensor * inputs, // input tensor with shape [ne_datapoint, ndata_batch]
+ struct ggml_tensor * outputs, // output tensor, must have shape [ne_label, ndata_batch] if labels are used
ggml_opt_dataset_t dataset, // dataset with data and optionally also labels
enum ggml_opt_loss_type loss_type, // loss to minimize
ggml_opt_get_optimizer_params get_opt_pars, // callback to get optimizer params, userdata is pointer to epoch (of type int64_t)
// Tensor flags
GGML_API void ggml_set_input(struct ggml_tensor * tensor);
GGML_API void ggml_set_output(struct ggml_tensor * tensor);
- GGML_API void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor);
+ GGML_API void ggml_set_param(struct ggml_tensor * tensor);
GGML_API void ggml_set_loss(struct ggml_tensor * tensor);
//
GGML_API struct ggml_tensor * ggml_repeat_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
- struct ggml_tensor * b);
+ struct ggml_tensor * b); // sum up values that are adjacent in dims > 0 instead of repeated with same stride
// concat a and b along dim
// used in stable-diffusion
GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
GGML_API void ggml_build_backward_expand(
- struct ggml_context * ctx_static, // context for static gradients (loss + gradient accumulation)
- struct ggml_context * ctx_compute, // context for gradient computation
- struct ggml_cgraph * cgraph,
- bool accumulate); // whether or not gradients should be accumulated, requires static allocation of tensors in ctx_static
+ struct ggml_context * ctx, // context for gradient computation
+ struct ggml_cgraph * cgraph,
+ struct ggml_tensor ** grad_accs);
// graph allocation in a context
GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false
GGML_API struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads);
- GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph);
+ GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph, bool force_grads);
GGML_API void ggml_graph_cpy (struct ggml_cgraph * src, struct ggml_cgraph * dst);
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // set regular grads + optimizer momenta to 0, set loss grad to 1
GGML_API void ggml_graph_clear (struct ggml_cgraph * cgraph);
const int node_backend_id = tensor_backend_id(node);
- assert(node_backend_id != -1); // all nodes should be assigned by now
+ assert(node_backend_id != -1); // all nodes should be assigned by now, this can happen if there is no CPU fallback
// check if we should start a new split based on the sources of the current node
bool need_new_split = false;
#include "acc.cuh"
-static __global__ void acc_f32(const float * x, const float * y, float * dst, const int ne,
- const int ne10, const int ne11, const int ne12,
- const int nb1, const int nb2, int offset) {
- const int i = blockDim.x * blockIdx.x + threadIdx.x;
+static __global__ void acc_f32(const float * x, const float * y, float * dst, const int64_t ne,
+ const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13,
+ const int64_t s11, const int64_t s12, const int64_t s13, const int64_t offset) {
+ const int64_t i = blockDim.x * blockIdx.x + threadIdx.x;
+
if (i >= ne) {
return;
}
- int src1_idx = i - offset;
- int oz = src1_idx / nb2;
- int oy = (src1_idx - (oz * nb2)) / nb1;
- int ox = src1_idx % nb1;
- if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) {
- dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11];
- } else {
- dst[i] = x[i];
+
+ int64_t src1_idx = i - offset;
+
+ int64_t tmp = src1_idx;
+ const int64_t i13 = tmp / s13;
+ tmp -= i13 * s13;
+ const int64_t i12 = tmp / s12;
+ tmp -= i12 * s12;
+ const int64_t i11 = tmp / s11;
+ tmp -= i11 * s11;
+ const int64_t i10 = tmp;
+
+ float val = x[i];
+ if (src1_idx >= 0 && i10 < ne10 && i11 < ne11 && i12 < ne12 && i13 < ne13) {
+ val += y[((i13*ne12 + i12) * ne11 + i11) * ne10 + i10];
}
+ dst[i] = val;
}
-static void acc_f32_cuda(const float * x, const float * y, float * dst, const int n_elements,
- const int ne10, const int ne11, const int ne12,
- const int nb1, const int nb2, const int offset, cudaStream_t stream) {
- int num_blocks = (n_elements + CUDA_ACC_BLOCK_SIZE - 1) / CUDA_ACC_BLOCK_SIZE;
- acc_f32<<<num_blocks, CUDA_ACC_BLOCK_SIZE, 0, stream>>>(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset);
+static void acc_f32_cuda(const float * x, const float * y, float * dst, const int64_t n_elements,
+ const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13,
+ const int64_t s1, const int64_t s2, const int64_t s3, const int64_t offset, cudaStream_t stream) {
+ const int num_blocks = (n_elements + CUDA_ACC_BLOCK_SIZE - 1) / CUDA_ACC_BLOCK_SIZE;
+ acc_f32<<<num_blocks, CUDA_ACC_BLOCK_SIZE, 0, stream>>>(x, y, dst, n_elements, ne10, ne11, ne12, ne13, s1, s2, s3, offset);
}
void ggml_cuda_op_acc(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
- const float * src0_d = (const float *)src0->data;
- const float * src1_d = (const float *)src1->data;
- float * dst_d = (float *)dst->data;
+
+ const float * src0_d = (const float *) src0->data;
+ const float * src1_d = (const float *) src1->data;
+ float * dst_d = (float *) dst->data;
+
cudaStream_t stream = ctx.stream();
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
- GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported
- int nb1 = dst->op_params[0] / 4; // 4 bytes of float32
- int nb2 = dst->op_params[1] / 4; // 4 bytes of float32
- // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused
- int offset = dst->op_params[3] / 4; // offset in bytes
+ GGML_ASSERT(ggml_is_contiguous(src1));
+ GGML_ASSERT(dst->nb[0] == ggml_element_size(dst));
+ GGML_ASSERT(ggml_is_contiguously_allocated(dst));
+
+ const int64_t s1 = dst->op_params[0] / sizeof(float);
+ const int64_t s2 = dst->op_params[1] / sizeof(float);
+ const int64_t s3 = dst->op_params[2] / sizeof(float);
+ const int64_t offset = dst->op_params[3] / sizeof(float);
- 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);
+ 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);
}
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
- GGML_ASSERT(ggml_is_contiguous(src0));
+ GGML_ASSERT(ggml_is_contiguously_allocated(src0));
const float * src0_d = (const float *) src0->data;
float * dst_d = (float *) dst->data;
};
struct ggml_opt_context {
- ggml_backend_sched_t backend_sched = nullptr;
- ggml_cgraph * allocated_graph = nullptr;
- ggml_cgraph * allocated_graph_copy = nullptr;
- struct ggml_context * ctx_static = nullptr;
- struct ggml_context * ctx_static_cpu = nullptr;
- struct ggml_context * ctx_compute = nullptr;
- struct ggml_context * ctx_copy = nullptr;
- ggml_backend_buffer_t buf_static = nullptr;
- ggml_backend_buffer_t buf_static_cpu = nullptr;
- std::mt19937 rng;
+ ggml_backend_sched_t backend_sched = nullptr;
+ ggml_cgraph * allocated_graph = nullptr;
+ ggml_cgraph * allocated_graph_copy = nullptr;
+ struct ggml_context * ctx_static = nullptr;
+ struct ggml_context * ctx_cpu = nullptr;
+ struct ggml_context * ctx_compute = nullptr;
+ struct ggml_context * ctx_copy = nullptr;
+ ggml_backend_buffer_t buf_static = nullptr;
+ ggml_backend_buffer_t buf_cpu = nullptr;
+ std::mt19937 rng;
+ enum ggml_opt_loss_type loss_type;
+ enum ggml_opt_build_type build_type;
+ enum ggml_opt_build_type build_type_alloc;
struct ggml_tensor * inputs = nullptr;
struct ggml_tensor * outputs = nullptr;
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;
// ====== 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);
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;
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;
}
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);
}
}
+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) {
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,
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;
- }
-
- ggml_backend_sched_reset(opt_ctx->backend_sched); // clear allocation of previous graph
-
- {
- 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);
+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_set_input(result->inputs);
- ggml_set_output(result->outputs);
+ 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);
- 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);
-
- 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);
+ ggml_set_output(opt_ctx->loss);
+ ggml_set_loss(opt_ctx->loss);
+ ggml_build_forward_expand(opt_ctx->gf, opt_ctx->loss);
+
+ 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);
+
+ 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 (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;
+ 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;
}
- 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->grad_accs.empty()) {
+ GGML_ASSERT(opt_ctx->build_type_alloc >= GGML_OPT_BUILD_TYPE_GRAD);
+
+ 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;
+ }
+ }
+
+ 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;
+ }
+ }
+ }
}
// 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);
+ 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());
- 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;
+ 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_ASSERT(params.build_type == GGML_OPT_BUILD_TYPE_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.
- result->gb_opt = ggml_graph_dup(result->ctx_compute, result->gb_grad);
+ opt_ctx->gb_opt = ggml_graph_dup(opt_ctx->ctx_compute, opt_ctx->gb_grad, /*force_grads =*/ true);
- 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");
+ 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 = 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);
+ 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 (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 (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);
}
}
- result->buf_static = ggml_backend_alloc_ctx_tensors(
- result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0));
+ 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);
+ }
- result->buf_static_cpu = ggml_backend_alloc_ctx_tensors_from_buft(result->ctx_static_cpu, ggml_backend_cpu_buffer_type());
+ opt_ctx->buf_cpu = ggml_backend_alloc_ctx_tensors_from_buft(opt_ctx->ctx_cpu, ggml_backend_cpu_buffer_type());
+}
- ggml_graph_reset(result->gb_opt);
+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;
}
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;
}
// ====== 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);
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;
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;
}
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(
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);
}
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, " ");
}
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) {
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;
// 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
}
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);
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);
}
}
-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;
}
}
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++) {
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;
}
#include "ggml.h"
#include "ggml-cpu.h"
#include "ggml-backend.h"
+#include "ggml-opt.h"
#include <stddef.h>
#include <stdint.h>
size_t n_paths,
struct llama_model_params params);
+ LLAMA_API void llama_model_save_to_file(
+ const struct llama_model * model,
+ const char * path_model);
+
DEPRECATED(LLAMA_API void llama_free_model(struct llama_model * model),
"use llama_model_free instead");
LLAMA_API void llama_perf_sampler_print(const struct llama_sampler * chain);
LLAMA_API void llama_perf_sampler_reset( struct llama_sampler * chain);
+ //
+ // training
+ //
+
+ // function that returns whether or not a given tensor contains trainable parameters
+ typedef bool (*llama_opt_param_filter)(const struct ggml_tensor * tensor, void * userdata);
+
+ // always returns true
+ LLAMA_API bool llama_opt_param_filter_all(const struct ggml_tensor * tensor, void * userdata);
+
+ struct llama_opt_params {
+ uint32_t n_ctx_train; // assumed context size post training, use context size specified in llama_context if 0
+
+ llama_opt_param_filter param_filter; // callback for determining which tensors contain trainable parameters
+ void * param_filter_ud; // userdata for determining which tensors contain trainable parameters
+
+ ggml_opt_get_optimizer_params get_opt_pars; // callback for calculating optimizer parameters
+ void * get_opt_pars_ud; // userdata for calculating optimizer parameters
+ };
+
+ LLAMA_API void llama_opt_init(struct llama_context * lctx, struct llama_model * model, struct llama_opt_params lopt_params);
+
+ LLAMA_API void llama_opt_epoch(
+ struct llama_context * lctx,
+ ggml_opt_dataset_t dataset,
+ ggml_opt_result_t result_train,
+ ggml_opt_result_t result_eval,
+ int64_t idata_split,
+ ggml_opt_epoch_callback callback_train,
+ ggml_opt_epoch_callback callback_eval);
+
#ifdef __cplusplus
}
#endif
llama-memory.cpp
llama-mmap.cpp
llama-model-loader.cpp
+ llama-model-saver.cpp
llama-model.cpp
llama-quant.cpp
llama-sampling.cpp
}
}
-llama_context::~llama_context() = default;
+llama_context::~llama_context() {
+ ggml_opt_free(opt_ctx);
+}
void llama_context::synchronize() {
ggml_backend_sched_synchronize(sched.get());
t_p_eval_us = n_p_eval = 0;
}
+//
+// training
+//
+
+static void llama_set_param(struct ggml_tensor * tensor, llama_opt_param_filter param_filter, void * userdata) {
+ if (!tensor || tensor->type != GGML_TYPE_F32) {
+ return;
+ }
+ if (!param_filter(tensor, userdata)) {
+ return;
+ }
+ if (strcmp(tensor->name, "token_embd.weight") == 0) {
+ return; // FIXME
+ }
+ if (strcmp(tensor->name, "rope_freqs.weight") == 0) {
+ return; // FIXME
+ }
+ ggml_set_param(tensor);
+}
+
+void llama_context::opt_init(struct llama_model * model, struct llama_opt_params lopt_params) {
+ GGML_ASSERT(!opt_ctx);
+ model->hparams.n_ctx_train = lopt_params.n_ctx_train > 0 ? lopt_params.n_ctx_train : n_ctx();
+ const uint32_t n_batch = std::min(this->n_batch(), model->hparams.n_ctx_train);
+ const uint32_t n_ubatch = std::min(this->n_ubatch(), n_batch);
+ GGML_ASSERT(model->hparams.n_ctx_train % n_batch == 0);
+ GGML_ASSERT(n_batch % n_ubatch == 0);
+
+ ggml_opt_params opt_params = ggml_opt_default_params(sched.get(), GGML_OPT_LOSS_TYPE_CROSS_ENTROPY);
+ opt_params.opt_period = n_batch / n_ubatch;
+ opt_params.get_opt_pars = lopt_params.get_opt_pars;
+ opt_params.get_opt_pars_ud = lopt_params.get_opt_pars_ud;
+
+ opt_ctx = ggml_opt_init(opt_params);
+
+ llama_opt_param_filter param_filter = lopt_params.param_filter;
+ void * param_filter_ud = lopt_params.param_filter_ud;
+
+ //llama_set_param(model->tok_embd, param_filter, param_filter_ud); // FIXME
+ llama_set_param(model->type_embd, param_filter, param_filter_ud);
+ llama_set_param(model->pos_embd, param_filter, param_filter_ud);
+ llama_set_param(model->tok_norm, param_filter, param_filter_ud);
+ llama_set_param(model->tok_norm_b, param_filter, param_filter_ud);
+ llama_set_param(model->output_norm, param_filter, param_filter_ud);
+ llama_set_param(model->output_norm_b, param_filter, param_filter_ud);
+ llama_set_param(model->output, param_filter, param_filter_ud);
+ llama_set_param(model->output_b, param_filter, param_filter_ud);
+ llama_set_param(model->output_norm_enc, param_filter, param_filter_ud);
+ llama_set_param(model->cls, param_filter, param_filter_ud);
+ llama_set_param(model->cls_b, param_filter, param_filter_ud);
+ llama_set_param(model->cls_out, param_filter, param_filter_ud);
+ llama_set_param(model->cls_out_b, param_filter, param_filter_ud);
+
+ for (struct llama_layer & layer : model->layers) {
+ for (size_t i = 0; i < sizeof(layer)/sizeof(struct ggml_tensor *); ++i) {
+ llama_set_param(reinterpret_cast<struct ggml_tensor **>(&layer)[i], param_filter, param_filter_ud);
+ }
+ }
+}
+
+void llama_context::opt_epoch_iter(
+ ggml_opt_dataset_t dataset,
+ ggml_opt_result_t result,
+ const std::vector<llama_token> & tokens,
+ const std::vector<llama_token> & labels_sparse,
+ llama_batch & batch,
+ ggml_opt_epoch_callback callback,
+ bool train,
+ int64_t idata_in_loop,
+ int64_t ndata_in_loop,
+ int64_t t_loop_start) {
+ GGML_ASSERT(opt_ctx);
+ const uint32_t n_ctx = llama_model_n_ctx_train(&model);
+ const uint32_t n_batch = std::min(this->n_batch(), n_ctx);
+ const uint32_t n_ubatch = std::min(this->n_ubatch(), n_batch);
+
+ llama_kv_cache * kv_self = static_cast<llama_kv_cache *>(memory.get());
+
+ kv_self->clear();
+ llama_kv_cache_guard kv_guard(kv_self);
+
+ for (uint32_t pos_ctx = 0; pos_ctx < n_ctx; pos_ctx += n_batch) {
+ batch.n_tokens = n_batch;
+ for (uint32_t pos_batch = 0; pos_batch < n_batch; ++pos_batch) {
+ batch.token [pos_batch] = tokens[pos_ctx + pos_batch];
+ batch.pos [pos_batch] = pos_ctx + pos_batch;
+ batch.n_seq_id[pos_batch] = 1;
+ batch.seq_id [pos_batch][0] = 0;
+ batch.logits [pos_batch] = true;
+ }
+
+ const auto n_tokens_all = batch.n_tokens;
+
+ n_queued_tokens += n_tokens_all;
+
+ // this indicates we are doing pooled embedding, so we ignore batch.logits and output all tokens
+ const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE;
+
+ embd_seq.clear();
+
+ int64_t n_outputs_all = n_tokens_all;
+
+ llama_sbatch sbatch = kv_self->sbatch_init(batch, /*logits_all =*/ true);
+
+ // reserve output buffer
+ if (output_reserve(n_outputs_all) < n_outputs_all) {
+ LLAMA_LOG_ERROR("%s: could not reserve space for batch with %" PRId64 " outputs\n", __func__, n_outputs_all);
+ GGML_ABORT("TODO: handle this error");
+ };
+
+ for (uint32_t pos_batch = 0; pos_batch < n_batch; pos_batch += n_ubatch) {
+ llama_ubatch ubatch = kv_self->ubatch_next(sbatch, cparams.n_ubatch, embd_pooled);
+
+ n_outputs = ubatch.n_tokens;
+
+ // TODO: not sure if this is needed
+ if (!kv_self->find_slot(ubatch)) {
+ LLAMA_LOG_WARN("%s: failed to find KV cache slot for ubatch of size %d\n", __func__, ubatch.n_tokens);
+
+ GGML_ABORT("TODO: handle this error");
+ }
+
+ auto * gf = graph_init();
+ auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT);
+
+ struct ggml_context * ctx_compute_opt;
+ {
+ const size_t size_gf = ggml_graph_size(gf);
+ const size_t size_meta = 4*size_gf*ggml_tensor_overhead() + 2*ggml_graph_overhead_custom(size_gf, /*grads = */ true);
+ struct ggml_init_params params = {
+ /*.mem_size =*/ size_meta,
+ /*.mem_buffer =*/ nullptr,
+ /*.no_alloc =*/ true,
+ };
+ ctx_compute_opt = ggml_init(params);
+ }
+ ggml_opt_prepare_alloc(opt_ctx, ctx_compute_opt, gf, res->get_tokens(), res->get_logits());
+ ggml_opt_alloc(opt_ctx, train);
+ res->set_inputs(&ubatch);
+ {
+ struct ggml_tensor * labels = ggml_opt_labels(opt_ctx);
+ GGML_ASSERT(labels->ne[1] == n_ubatch);
+ ggml_set_zero(labels);
+ const float onef = 1.0f;
+ for (uint32_t pos_ubatch = 0; pos_ubatch < n_ubatch; ++pos_ubatch) {
+ const uint32_t ilabel = pos_ctx + pos_batch + pos_ubatch;
+ GGML_ASSERT(labels_sparse[ilabel] < labels->ne[0]);
+ ggml_backend_tensor_set(labels, &onef, (pos_ubatch*labels->ne[0] + labels_sparse[ilabel])*sizeof(float), sizeof(float));
+ }
+ }
+ ggml_opt_eval(opt_ctx, result);
+ if (callback) {
+ callback(train, opt_ctx, dataset, result, idata_in_loop + (pos_ctx + pos_batch)/n_ubatch + 1, ndata_in_loop, t_loop_start);
+ }
+ ggml_free(ctx_compute_opt);
+ }
+ }
+
+ kv_guard.commit();
+}
+
+void llama_context::opt_epoch(
+ ggml_opt_dataset_t dataset,
+ ggml_opt_result_t result_train,
+ ggml_opt_result_t result_eval,
+ int64_t idata_split,
+ ggml_opt_epoch_callback callback_train,
+ ggml_opt_epoch_callback callback_eval) {
+ const uint32_t n_ctx = this->n_ctx();
+ const uint32_t n_batch = std::min(cparams.n_batch, n_ctx);
+ const uint32_t n_ubatch = std::min(cparams.n_ubatch, n_batch);
+ const int64_t ndata = ggml_opt_dataset_ndata(dataset);
+
+ GGML_ASSERT(idata_split >= 0);
+ GGML_ASSERT(idata_split <= ndata);
+
+ const uint32_t ubatch_per_ctx = n_ctx / n_ubatch;
+
+ struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
+ std::vector<llama_token> tokens(n_ctx);
+ std::vector<llama_token> labels_sparse(n_ctx);
+
+ int64_t idata = 0;
+
+ int64_t t_loop_start = ggml_time_us();
+ int64_t ndata_in_loop = idata_split*ubatch_per_ctx;
+ for (; idata < idata_split; ++idata) {
+ constexpr bool train = true;
+ const int64_t idata_in_loop = idata*ubatch_per_ctx;
+
+ ggml_opt_dataset_get_batch_host(dataset, tokens.data(), n_ctx*sizeof(llama_token), labels_sparse.data(), idata);
+ opt_epoch_iter(dataset, result_train, tokens, labels_sparse, batch,
+ callback_train, train, idata_in_loop, ndata_in_loop, t_loop_start);
+ }
+
+ t_loop_start = ggml_time_us();
+ ndata_in_loop = (ndata - idata_split)*ubatch_per_ctx;
+ for (; idata < ndata; ++idata) {
+ constexpr bool train = false;
+ const int64_t idata_in_loop = (idata - idata_split)*ubatch_per_ctx;
+
+ ggml_opt_dataset_get_batch_host(dataset, tokens.data(), n_ctx*sizeof(llama_token), labels_sparse.data(), idata);
+ opt_epoch_iter(dataset, result_eval, tokens, labels_sparse, batch,
+ callback_eval, train, idata_in_loop, ndata_in_loop, t_loop_start);
+ }
+
+ llama_batch_free(batch);
+}
+
//
// interface implementation
//
void llama_perf_context_reset(llama_context * ctx) {
ctx->perf_reset();
}
+
+//
+// training
+//
+
+bool llama_opt_param_filter_all(const struct ggml_tensor * tensor, void * userdata) {
+ GGML_UNUSED(tensor);
+ GGML_UNUSED(userdata);
+ return true;
+}
+
+void llama_opt_init(struct llama_context * ctx, struct llama_model * model, struct llama_opt_params lopt_params) {
+ ctx->opt_init(model, lopt_params);
+}
+
+void llama_opt_epoch(
+ struct llama_context * ctx,
+ ggml_opt_dataset_t dataset,
+ ggml_opt_result_t result_train,
+ ggml_opt_result_t result_eval,
+ int64_t idata_split,
+ ggml_opt_epoch_callback callback_train,
+ ggml_opt_epoch_callback callback_eval) {
+ ctx->opt_epoch(
+ dataset,
+ result_train,
+ result_eval,
+ idata_split,
+ callback_train,
+ callback_eval);
+}
#include "llama-adapter.h"
#include "ggml-cpp.h"
+#include "ggml-opt.h"
#include <map>
#include <vector>
llama_perf_context_data perf_get_data() const;
void perf_reset();
+ //
+ // training
+ //
+
+ void opt_init(struct llama_model * model, struct llama_opt_params lopt_params);
+
+ void opt_epoch(
+ ggml_opt_dataset_t dataset,
+ ggml_opt_result_t result_train,
+ ggml_opt_result_t result_eval,
+ int64_t idata_split,
+ ggml_opt_epoch_callback callback_train,
+ ggml_opt_epoch_callback callback_eval);
+
+ void opt_epoch_iter(
+ ggml_opt_dataset_t dataset,
+ ggml_opt_result_t result,
+ const std::vector<llama_token> & tokens,
+ const std::vector<llama_token> & labels_sparse,
+ llama_batch & batch,
+ ggml_opt_epoch_callback callback,
+ bool train,
+ int64_t idata_in_loop,
+ int64_t ndata_in_loop,
+ int64_t t_loop_start);
+
private:
//
// output
ggml_context_ptr ctx_compute;
+ // training
+ ggml_opt_context_t opt_ctx = nullptr;
+
ggml_threadpool_t threadpool = nullptr;
ggml_threadpool_t threadpool_batch = nullptr;
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
//cb(inp->tokens, "inp_tokens", -1);
ggml_set_input(inp->tokens);
+ res->t_tokens = inp->tokens;
cur = ggml_get_rows(ctx0, tok_embd, inp->tokens);
public:
virtual ~llm_graph_result_i() = default;
+ virtual ggml_tensor * get_tokens() = 0;
virtual ggml_tensor * get_logits() = 0;
virtual ggml_tensor * get_embd() = 0;
virtual ggml_tensor * get_embd_pooled() = 0;
public:
virtual ~llm_graph_result() = default;
+ ggml_tensor * get_tokens() override { return t_tokens; }
ggml_tensor * get_logits() override { return t_logits; }
ggml_tensor * get_embd() override { return t_embd; }
ggml_tensor * get_embd_pooled() override { return t_embd_pooled; }
}
// important graph nodes
+ ggml_tensor * t_tokens = nullptr;
ggml_tensor * t_logits = nullptr;
ggml_tensor * t_embd = nullptr;
ggml_tensor * t_embd_pooled = nullptr;
GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta.get(), kid);
switch (arr_info.gt) {
- case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
- case GGUF_TYPE_INT32: GGML_ASSERT(
- (std::is_same<T, int32_t>::value) ||
- (std::is_same<T, uint32_t>::value)); break;
+ case GGUF_TYPE_UINT32:
+ case GGUF_TYPE_INT32: GGML_ASSERT((std::is_same<T, int32_t>::value) ||
+ (std::is_same<T, uint32_t>::value)); break;
+ case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
default:
- throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str()));
+ throw std::runtime_error(format("%s is not a float32/uint32/int32 array", key.c_str()));
}
result.resize(arr_info.length);
GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta.get(), kid);
switch (arr_info.gt) {
- case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
- case GGUF_TYPE_INT32: GGML_ASSERT(
- (std::is_same<T, int32_t>::value) ||
- (std::is_same<T, uint32_t>::value)); break;
+ case GGUF_TYPE_UINT32:
+ case GGUF_TYPE_INT32: GGML_ASSERT((std::is_same<T, int32_t>::value) ||
+ (std::is_same<T, uint32_t>::value)); break;
+ case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
default:
- throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str()));
+ throw std::runtime_error(format("%s is not a float32/uint32/int32 array", key.c_str()));
}
if (arr_info.length > N_MAX) {
--- /dev/null
+#include "llama-model-saver.h"
+
+#include "gguf.h"
+
+#include "llama.h"
+#include "llama-hparams.h"
+#include "llama-model.h"
+#include "llama-vocab.h"
+
+#include <string>
+
+llama_model_saver::llama_model_saver(const struct llama_model & model) : model(model), llm_kv(model.arch) {
+ gguf_ctx = gguf_init_empty();
+}
+
+llama_model_saver::~llama_model_saver() {
+ gguf_free(gguf_ctx);
+}
+
+void llama_model_saver::add_kv(const enum llm_kv key, const uint32_t value) {
+ gguf_set_val_u32(gguf_ctx, llm_kv(key).c_str(), value);
+}
+
+void llama_model_saver::add_kv(const enum llm_kv key, const int32_t value) {
+ gguf_set_val_i32(gguf_ctx, llm_kv(key).c_str(), value);
+}
+
+void llama_model_saver::add_kv(const enum llm_kv key, const float value) {
+ gguf_set_val_f32(gguf_ctx, llm_kv(key).c_str(), value);
+}
+
+void llama_model_saver::add_kv(const enum llm_kv key, const bool value) {
+ gguf_set_val_bool(gguf_ctx, llm_kv(key).c_str(), value);
+}
+
+void llama_model_saver::add_kv(const enum llm_kv key, const char * value) {
+ gguf_set_val_str(gguf_ctx, llm_kv(key).c_str(), value);
+}
+
+[[noreturn]]
+void llama_model_saver::add_kv(const enum llm_kv key, const char value) {
+ GGML_UNUSED(key);
+ GGML_UNUSED(value);
+ GGML_ABORT("fatal error"); // this should never be called, only needed to make the template below compile
+}
+
+template <typename Container>
+void llama_model_saver::add_kv(const enum llm_kv key, const Container & value, const bool per_layer) {
+ const size_t n_values = per_layer ? size_t(model.hparams.n_layer) : value.size();
+ GGML_ASSERT(n_values <= value.size());
+
+ if (n_values == 0) {
+ return;
+ }
+
+ if (per_layer) {
+ bool all_values_the_same = true;
+ for (size_t i = 1; i < n_values; ++i) {
+ if (value[i] != value[0]) {
+ all_values_the_same = false;
+ break;
+ }
+ }
+ if (all_values_the_same) {
+ add_kv(key, value[0]);
+ return;
+ }
+ }
+
+ if (std::is_same<typename Container::value_type, uint8_t>::value) {
+ gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_UINT8, value.data(), n_values);
+ } else if (std::is_same<typename Container::value_type, int8_t>::value) {
+ gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_INT8, value.data(), n_values);
+ } else if (std::is_same<typename Container::value_type, uint32_t>::value) {
+ gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_UINT32, value.data(), n_values);
+ } else if (std::is_same<typename Container::value_type, int32_t>::value) {
+ gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_INT32, value.data(), n_values);
+ } else if (std::is_same<typename Container::value_type, float>::value) {
+ gguf_set_arr_data(gguf_ctx, llm_kv(key).c_str(), GGUF_TYPE_FLOAT32, value.data(), n_values);
+ } else if (std::is_same<Container, std::string>::value) {
+ gguf_set_val_str(gguf_ctx, llm_kv(key).c_str(), reinterpret_cast<const char *>(value.data()));
+ } else {
+ GGML_ABORT("fatal error");
+ }
+}
+
+void llama_model_saver::add_kv(const enum llm_kv key, const std::vector<std::string> & value) {
+ std::vector<const char *> tmp(value.size());
+ for (size_t i = 0; i < value.size(); ++i) {
+ tmp[i] = value[i].c_str();
+ }
+ gguf_set_arr_str(gguf_ctx, llm_kv(key).c_str(), tmp.data(), tmp.size());
+}
+
+void llama_model_saver::add_tensor(const struct ggml_tensor * tensor) {
+ if (!tensor) {
+ return;
+ }
+ if (gguf_find_tensor(gguf_ctx, tensor->name) >= 0) {
+ GGML_ASSERT(std::string(tensor->name) == "rope_freqs.weight"); // FIXME
+ return;
+ }
+ gguf_add_tensor(gguf_ctx, tensor);
+}
+
+void llama_model_saver::add_kv_from_model() {
+ const llama_hparams & hparams = model.hparams;
+ const llama_vocab & vocab = model.vocab;
+
+ const int32_t n_vocab = vocab.n_tokens();
+ std::vector<std::string> tokens(n_vocab);
+ std::vector<float> scores(n_vocab);
+ std::vector<int32_t> token_types(n_vocab);
+
+ for (int32_t id = 0; id < n_vocab; ++id) {
+ const llama_vocab::token_data & token_data = vocab.get_token_data(id);
+
+ tokens[id] = token_data.text;
+ scores[id] = token_data.score;
+
+ switch(token_data.attr) {
+ case LLAMA_TOKEN_ATTR_UNKNOWN: token_types[id] = LLAMA_TOKEN_TYPE_UNKNOWN; break;
+ case LLAMA_TOKEN_ATTR_UNUSED: token_types[id] = LLAMA_TOKEN_TYPE_UNUSED; break;
+ case LLAMA_TOKEN_ATTR_NORMAL: token_types[id] = LLAMA_TOKEN_TYPE_NORMAL; break;
+ case LLAMA_TOKEN_ATTR_CONTROL: token_types[id] = LLAMA_TOKEN_TYPE_CONTROL; break;
+ case LLAMA_TOKEN_ATTR_USER_DEFINED: token_types[id] = LLAMA_TOKEN_TYPE_USER_DEFINED; break;
+ case LLAMA_TOKEN_ATTR_BYTE: token_types[id] = LLAMA_TOKEN_TYPE_BYTE; break;
+ case LLAMA_TOKEN_ATTR_UNDEFINED:
+ default: token_types[id] = LLAMA_TOKEN_TYPE_UNDEFINED; break;
+ }
+ }
+
+ // add_kv(LLM_KV_GENERAL_TYPE, ???);
+ add_kv(LLM_KV_GENERAL_ARCHITECTURE, model.arch_name());
+ // add_kv(LLM_KV_GENERAL_QUANTIZATION_VERSION, ???);
+ // add_kv(LLM_KV_GENERAL_ALIGNMENT, ???);
+ add_kv(LLM_KV_GENERAL_NAME, model.name);
+ // add_kv(LLM_KV_GENERAL_AUTHOR, ???);
+ // add_kv(LLM_KV_GENERAL_VERSION, ???);
+ // add_kv(LLM_KV_GENERAL_URL, ???);
+ // add_kv(LLM_KV_GENERAL_DESCRIPTION, ???);
+ // add_kv(LLM_KV_GENERAL_LICENSE, ???);
+ // add_kv(LLM_KV_GENERAL_SOURCE_URL, ???);
+ // add_kv(LLM_KV_GENERAL_SOURCE_HF_REPO, ???);
+
+ add_kv(LLM_KV_VOCAB_SIZE, vocab.n_tokens());
+ add_kv(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train);
+ add_kv(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd);
+ add_kv(LLM_KV_BLOCK_COUNT, hparams.n_layer);
+ add_kv(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
+ add_kv(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, true);
+ add_kv(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+ add_kv(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+ add_kv(LLM_KV_USE_PARALLEL_RESIDUAL, hparams.use_par_res);
+ // add_kv(LLM_KV_TENSOR_DATA_LAYOUT, ???);
+ add_kv(LLM_KV_EXPERT_COUNT, hparams.n_expert);
+ add_kv(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used);
+ add_kv(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
+ add_kv(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
+ add_kv(LLM_KV_POOLING_TYPE, uint32_t(hparams.pooling_type));
+ add_kv(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
+ add_kv(LLM_KV_DECODER_START_TOKEN_ID, hparams.dec_start_token_id);
+ add_kv(LLM_KV_ATTN_LOGIT_SOFTCAPPING, hparams.f_attn_logit_softcapping);
+ add_kv(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping);
+ add_kv(LLM_KV_SWIN_NORM, hparams.swin_norm);
+ add_kv(LLM_KV_RESCALE_EVERY_N_LAYERS, hparams.rescale_every_n_layers);
+ add_kv(LLM_KV_TIME_MIX_EXTRA_DIM, hparams.time_mix_extra_dim);
+ add_kv(LLM_KV_TIME_DECAY_EXTRA_DIM, hparams.time_decay_extra_dim);
+ add_kv(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale);
+ add_kv(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale);
+
+ add_kv(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, true);
+ add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv_arr, true);
+ add_kv(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
+ add_kv(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv);
+ add_kv(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k);
+ add_kv(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v);
+ add_kv(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ add_kv(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
+ add_kv(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
+ add_kv(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
+ add_kv(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
+ add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
+ add_kv(LLM_KV_ATTENTION_SCALE, hparams.f_attention_scale);
+
+ const float rope_scaling_factor = hparams.rope_freq_scale_train == 1.0f ? 0.0f : 1.0f/hparams.rope_freq_scale_train;
+
+ add_kv(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot);
+ add_kv(LLM_KV_ROPE_FREQ_BASE, hparams.rope_freq_base_train);
+ // add_kv(LLM_KV_ROPE_SCALE_LINEAR, rope_scaling_factor); // old name
+ add_kv(LLM_KV_ROPE_SCALING_TYPE, llama_rope_scaling_type_name(hparams.rope_scaling_type_train));
+ add_kv(LLM_KV_ROPE_SCALING_FACTOR, rope_scaling_factor);
+ add_kv(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor);
+ add_kv(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_ctx_orig_yarn);
+ add_kv(LLM_KV_ROPE_SCALING_FINETUNED, hparams.rope_finetuned);
+ add_kv(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul);
+
+ // TODO: implement split file support
+ // add_kv(LLM_KV_SPLIT_NO, ???);
+ // add_kv(LLM_KV_SPLIT_COUNT, ???);
+ // add_kv(LLM_KV_SPLIT_TENSORS_COUNT, ???);
+
+ add_kv(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
+ add_kv(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
+ add_kv(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
+ add_kv(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+ add_kv(LLM_KV_SSM_DT_B_C_RMS, hparams.ssm_dt_b_c_rms);
+
+ add_kv(LLM_KV_WKV_HEAD_SIZE, hparams.wkv_head_size);
+
+ add_kv(LLM_KV_TOKENIZER_MODEL, vocab.get_tokenizer_model());
+ add_kv(LLM_KV_TOKENIZER_PRE, vocab.get_tokenizer_pre());
+ add_kv(LLM_KV_TOKENIZER_LIST, tokens);
+ add_kv(LLM_KV_TOKENIZER_TOKEN_TYPE, token_types);
+ add_kv(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, vocab.n_token_types());
+ add_kv(LLM_KV_TOKENIZER_SCORES, scores);
+ add_kv(LLM_KV_TOKENIZER_MERGES, vocab.get_bpe_merges());
+ // FIXME llama_token is type i32 but when reading in a GGUF file u32 is expected, not an issue for writing though
+ add_kv(LLM_KV_TOKENIZER_BOS_ID, uint32_t(vocab.token_bos()));
+ add_kv(LLM_KV_TOKENIZER_EOS_ID, uint32_t(vocab.token_eos()));
+ add_kv(LLM_KV_TOKENIZER_EOT_ID, uint32_t(vocab.token_eot()));
+ add_kv(LLM_KV_TOKENIZER_EOM_ID, uint32_t(vocab.token_eom()));
+ add_kv(LLM_KV_TOKENIZER_UNK_ID, uint32_t(vocab.token_unk()));
+ add_kv(LLM_KV_TOKENIZER_SEP_ID, uint32_t(vocab.token_sep()));
+ add_kv(LLM_KV_TOKENIZER_PAD_ID, uint32_t(vocab.token_pad()));
+ // add_kv(LLM_KV_TOKENIZER_CLS_ID, uint32_t(vocab.token_bos())); // deprecated
+ // add_kv(LLM_KV_TOKENIZER_MASK_ID, ???);
+ add_kv(LLM_KV_TOKENIZER_ADD_BOS, vocab.get_add_bos());
+ add_kv(LLM_KV_TOKENIZER_ADD_EOS, vocab.get_add_eos());
+ add_kv(LLM_KV_TOKENIZER_ADD_PREFIX, vocab.get_add_space_prefix());
+ add_kv(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, vocab.get_remove_extra_whitespaces());
+ add_kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, vocab.get_precompiled_charsmap());
+ // add_kv(LLM_KV_TOKENIZER_HF_JSON, ???);
+ // add_kv(LLM_KV_TOKENIZER_RWKV, ???);
+ add_kv(LLM_KV_TOKENIZER_FIM_PRE_ID, uint32_t(vocab.token_fim_pre()));
+ add_kv(LLM_KV_TOKENIZER_FIM_SUF_ID, uint32_t(vocab.token_fim_suf()));
+ add_kv(LLM_KV_TOKENIZER_FIM_MID_ID, uint32_t(vocab.token_fim_mid()));
+ add_kv(LLM_KV_TOKENIZER_FIM_PAD_ID, uint32_t(vocab.token_fim_pad()));
+ add_kv(LLM_KV_TOKENIZER_FIM_REP_ID, uint32_t(vocab.token_fim_rep()));
+ add_kv(LLM_KV_TOKENIZER_FIM_SEP_ID, uint32_t(vocab.token_fim_sep()));
+
+ // TODO: implement LoRA support
+ // add_kv(LLM_KV_ADAPTER_TYPE, ???);
+ // add_kv(LLM_KV_ADAPTER_LORA_ALPHA, ???);
+
+ // deprecated
+ // add_kv(LLM_KV_TOKENIZER_PREFIX_ID, ???);
+ // add_kv(LLM_KV_TOKENIZER_SUFFIX_ID, ???);
+ // add_kv(LLM_KV_TOKENIZER_MIDDLE_ID, ???);
+}
+
+void llama_model_saver::add_tensors_from_model() {
+ if (std::string(model.output->name) != std::string(model.tok_embd->name)) {
+ add_tensor(model.tok_embd); // some models use the same tensor for tok_embd and output
+ }
+ add_tensor(model.type_embd);
+ add_tensor(model.pos_embd);
+ add_tensor(model.tok_norm);
+ add_tensor(model.tok_norm_b);
+ add_tensor(model.output_norm);
+ add_tensor(model.output_norm_b);
+ add_tensor(model.output);
+ add_tensor(model.output_b);
+ add_tensor(model.output_norm_enc);
+ add_tensor(model.cls);
+ add_tensor(model.cls_b);
+ add_tensor(model.cls_out);
+ add_tensor(model.cls_out_b);
+
+ for (const struct llama_layer & layer : model.layers) {
+ for (size_t i = 0; i < sizeof(layer)/sizeof(struct ggml_tensor *); ++i) {
+ add_tensor(reinterpret_cast<const struct ggml_tensor * const *>(&layer)[i]);
+ }
+ }
+}
+
+void llama_model_saver::save(const std::string & path_model) {
+ gguf_write_to_file(gguf_ctx, path_model.c_str(), false);
+}
+
--- /dev/null
+#pragma once
+
+#include "llama.h"
+#include "llama-arch.h"
+
+#include <vector>
+
+struct llama_model_saver {
+ struct gguf_context * gguf_ctx = nullptr;
+ const struct llama_model & model;
+ const struct LLM_KV llm_kv;
+
+ llama_model_saver(const struct llama_model & model);
+ ~llama_model_saver();
+
+ void add_kv(enum llm_kv key, uint32_t value);
+ void add_kv(enum llm_kv key, int32_t value);
+ void add_kv(enum llm_kv key, float value);
+ void add_kv(enum llm_kv key, bool value);
+ void add_kv(enum llm_kv key, const char * value);
+
+ [[noreturn]]
+ void add_kv(enum llm_kv key, char value); // needed to make the template below compile
+
+ template <typename Container>
+ void add_kv(enum llm_kv key, const Container & value, bool per_layer = false);
+
+ void add_kv(enum llm_kv key, const std::vector<std::string> & value);
+
+ void add_tensor(const struct ggml_tensor * tensor);
+
+ void add_kv_from_model();
+
+ void add_tensors_from_model();
+
+ void save(const std::string & path_model);
+};
{ LLAMA_ROPE_SCALING_TYPE_LONGROPE, "longrope" },
};
+std::string llama_rope_scaling_type_name(llama_rope_scaling_type rope_scaling_type) {
+ return LLAMA_ROPE_SCALING_TYPES.at(rope_scaling_type);
+}
+
static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) {
for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
if (kv.second == name) {
}
void llama_model::print_info() const {
- const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
+ const std::string rope_scaling_type = llama_rope_scaling_type_name(hparams.rope_scaling_type_train);
auto print_f = [](const std::function<uint32_t(uint32_t)> & f, uint32_t n) {
bool is_var = false;
LLAMA_LOG_INFO("%s: causal attn = %d\n", __func__, hparams.causal_attn);
LLAMA_LOG_INFO("%s: pooling type = %d\n", __func__, hparams.pooling_type);
LLAMA_LOG_INFO("%s: rope type = %d\n", __func__, hparams.rope_type);
- LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type);
+ LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type.c_str());
LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train);
LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train);
LLAMA_LOG_INFO("%s: n_ctx_orig_yarn = %u\n", __func__, hparams.n_ctx_orig_yarn);
LLM_TYPE_235B_A22B,
};
+std::string llama_rope_scaling_type_name(llama_rope_scaling_type rope_scaling_type);
+
struct llama_layer_posnet {
// resnet
struct ggml_tensor * norm1 = nullptr;
nthread = std::thread::hardware_concurrency();
}
- // mmap consistently increases speed Linux, and also increases speed on Windows with
+ // mmap consistently increases speed on Linux, and also increases speed on Windows with
// hot cache. It may cause a slowdown on macOS, possibly related to free memory.
#if defined(__linux__) || defined(_WIN32)
constexpr bool use_mmap = true;
llama_model_kv_override * kv_overrides = nullptr;
if (params->kv_overrides) {
- auto v = (std::vector<llama_model_kv_override>*)params->kv_overrides;
+ auto * v = (std::vector<llama_model_kv_override>*)params->kv_overrides;
kv_overrides = v->data();
}
#include "llama-vocab.h"
+#include "ggml.h"
+#include "gguf.h"
#include "llama-impl.h"
#include "llama-model-loader.h"
struct llama_vocab::impl {
uint32_t n_token_types = 0; // for BERT-style token types
+ std::string tokenizer_model;
+ std::string tokenizer_pre;
+
enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM;
enum llama_vocab_pre_type pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
// determine vocab type
{
- std::string tokenizer_model;
- std::string tokenizer_pre;
-
ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_model);
ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false);
const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
if (precompiled_charsmap_keyidx != -1) {
- size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
+ const gguf_type pc_type = gguf_get_arr_type(ctx, precompiled_charsmap_keyidx);
+ GGML_ASSERT(pc_type == GGUF_TYPE_INT8 || pc_type == GGUF_TYPE_UINT8);
+
+ const size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
const char * pc = (const char *) gguf_get_arr_data(ctx, precompiled_charsmap_keyidx);
precompiled_charsmap.assign(pc, pc + n_precompiled_charsmap);
#ifdef IS_BIG_ENDIAN
pimpl->load(ml, kv);
}
+std::string llama_vocab::get_tokenizer_model() const {
+ return pimpl->tokenizer_model;
+}
+
+std::string llama_vocab::get_tokenizer_pre() const {
+ return pimpl->tokenizer_pre;
+}
+
enum llama_vocab_type llama_vocab::get_type() const {
return pimpl->type;
}
return it->second;
}
+std::vector<std::string> llama_vocab::get_bpe_merges() const {
+ std::vector<std::string> result(pimpl->bpe_ranks.size());
+
+ for (const auto & pair : pimpl->bpe_ranks) {
+ result[pair.second] = pair.first.first + " " + pair.first.second;
+ }
+
+ return result;
+}
+
+std::vector<char> llama_vocab::get_precompiled_charsmap() const {
+ return pimpl->precompiled_charsmap;
+}
+
int32_t llama_vocab::tokenize(
const char * text,
int32_t text_len,
void load(llama_model_loader & ml, const LLM_KV & kv);
+ std::string get_tokenizer_model() const;
+ std::string get_tokenizer_pre() const;
+
enum llama_vocab_type get_type() const;
enum llama_vocab_pre_type get_pre_type() const;
int max_token_len() const;
int find_bpe_rank(const std::string & token_left, const std::string & token_right) const;
+ std::vector<std::string> get_bpe_merges() const;
+
+ std::vector<char> get_precompiled_charsmap() const;
int32_t tokenize(
const char * text,
#include "llama-mmap.h"
#include "llama-vocab.h"
#include "llama-model-loader.h"
+#include "llama-model-saver.h"
#include "llama-model.h"
#include "ggml.h"
return llama_model_load_from_file_impl(splits.front(), splits, params);
}
+void llama_model_save_to_file(const struct llama_model * model, const char * path_model) {
+ llama_model_saver ms(*model);
+ ms.add_kv_from_model();
+ ms.add_tensors_from_model();
+ ms.save(path_model);
+}
+
//
// chat templates
//
return s.c_str();
}
+
ggml_build_forward_expand(gf, out);
ggml_graph_cpy(gf, gb);
- ggml_build_backward_expand(ctx.get(), ctx.get(), gb, false);
+ ggml_build_backward_expand(ctx.get(), gb, nullptr);
if (expect.size() != 1 || expect[0] != 0.0f) {
GGML_ASSERT(ggml_graph_n_nodes(gb) > ggml_graph_n_nodes(gf));
for (ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != NULL; t = ggml_get_next_tensor(ctx.get(), t)) {
// Step 3: return the output tensor.
return out;
}
- // In order to also check the gradients for your op, add calls like ggml_set_param(ctx, a)
+ // In order to also check the gradients for your op, add calls like ggml_set_param(a)
// immediately after you create the tensors.
// This is optional and only makes sense if a backward pass has actually been implemented for the new op.
};
auto ne = ne_a; ne[0] *= 3;
a = ggml_new_tensor(ctx, type, 4, ne.data());
if (grad_supported) {
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
}
ggml_set_name(a, "a");
} else {
a = ggml_new_tensor(ctx, type, 4, ne_a.data());
if (grad_supported) {
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
}
ggml_set_name(a, "a");
}
const bool grad_supported = ggml_is_matrix(in) && ggml_is_vector(rows);
if (grad_supported) {
- ggml_set_param(ctx, in);
+ ggml_set_param(in);
// rows is a constant input -> no gradients
}
ggml_set_name(target, "target");
ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_set_param(ctx, src);
+ ggml_set_param(src);
ggml_set_name(src, "src");
ggml_tensor * out = ggml_repeat(ctx, src, target);
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_set_param(ctx, src);
+ ggml_set_param(src);
ggml_set_name(src, "src");
if (_use_permute) {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne.data());
- ggml_set_param(ctx, src);
+ ggml_set_param(src);
ggml_set_name(src, "src");
auto ne_dst = ne;
ne_dst[i] *= 2;
}
ggml_tensor* dst = ggml_new_tensor(ctx, type_dst, 4, ne_dst.data());
- ggml_set_param(ctx, dst);
+ ggml_set_param(dst);
ggml_set_name(dst, "dst");
size_t offset = 0;
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne.data());
- ggml_set_param(ctx, src);
+ ggml_set_param(src);
ggml_set_name(src, "src");
if (_src_use_permute) {
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_set_param(ctx, src);
+ ggml_set_param(src);
ggml_set_name(src, "src");
src = ggml_transpose(ctx, src);
// The backward pass supports broadcasting only for GGML_ADD:
const bool grad_supported = op == ggml_add || ggml_are_same_shape(a, b);
if (grad_supported) {
- ggml_set_param(ctx, a);
- ggml_set_param(ctx, b);
+ ggml_set_param(a);
+ ggml_set_param(b);
}
ggml_tensor * out = op(ctx, a, b);
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * b = ggml_new_tensor_1d(ctx, type, 1);
- // ggml_set_param(ctx, b); // TODO: implement
+ // ggml_set_param(b); // TODO: implement
ggml_set_name(b, "b");
ggml_tensor * out = ggml_add1(ctx, a, b);
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_scale(ctx, a, scale);
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
ggml_set_name(a, "a");
if (v) {
b = ggml_new_tensor_4d(ctx, type_b, ne_b[per[0]], ne_b[per[1]], ne_b[per[2]], ne_b[per[3]]);
if (!ggml_is_quantized(type_a)) {
if (bs[1] == 1 && nr[1] == 1) {
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
}
- ggml_set_param(ctx, b);
+ ggml_set_param(b);
}
ggml_set_name(a, "a");
ggml_set_name(b, "b");
ggml_set_name(a, "a_permuted");
ggml_set_name(b, "b_permuted");
} else {
-
if (v) {
a = ggml_new_tensor_4d(ctx, type_a, k*2, m, bs[0], bs[1]);
b = ggml_new_tensor_4d(ctx, type_b, k*2, n, bs[0]*nr[0], bs[1]*nr[1]);
+ if (!ggml_is_quantized(type_a)) {
+ if (bs[1] == 1 && nr[1] == 1) {
+ ggml_set_param(a);
+ }
+ ggml_set_param(b);
+ }
+
a = ggml_view_4d(ctx, a, k, m, bs[0], bs[1], a->nb[1], a->nb[2], a->nb[3], 0);
b = ggml_view_4d(ctx, b, k, n, bs[0]*nr[0], bs[1]*nr[1], b->nb[1], b->nb[2], b->nb[3], 0);
} else {
a = ggml_new_tensor_4d(ctx, type_a, k, m, bs[0], bs[1]);
b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0]*nr[0], bs[1]*nr[1]);
- }
- if (!ggml_is_quantized(type_a)) {
- if (bs[1] == 1 && nr[1] == 1) {
- ggml_set_param(ctx, a);
+
+ if (!ggml_is_quantized(type_a)) {
+ if (bs[1] == 1 && nr[1] == 1) {
+ ggml_set_param(a);
+ }
+ ggml_set_param(b);
}
- ggml_set_param(ctx, b);
}
ggml_set_name(a, "a");
ggml_set_name(b, "b");
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_sqr(ctx, a);
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_sqrt(ctx, a);
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_log(ctx, a);
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_sin(ctx, a);
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_cos(ctx, a);
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_diag_mask_inf(ctx, a, n_past);
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * mask = nullptr;
auto ne = ne_a; ne[0] *= 2; ne[1] *= 4; ne[2] *= 3;
a = ggml_new_tensor(ctx, type, 4, ne.data());
if (forward) {
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
}
ggml_set_name(a, "a");
} else {
a = ggml_new_tensor(ctx, type, 4, ne_a.data());
if (forward) {
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
}
ggml_set_name(a, "a");
}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data());
- ggml_set_param(ctx, input);
+ ggml_set_param(input);
ggml_set_name(input, "input");
ggml_tensor * out = ggml_pool_2d(ctx, input, pool_type, k0, k1, s0, s1, p0, p1);
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data());
- ggml_set_param(ctx, input);
+ ggml_set_param(input);
ggml_set_name(input, "input");
ggml_tensor * kernel = ggml_new_tensor(ctx, type_kernel, 4, ne_kernel.data());
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_sum(ctx, a);
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_sum_rows(ctx, a);
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * out = ggml_mean(ctx, a);
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
- ggml_set_param(ctx, a);
+ ggml_set_param(a);
ggml_set_name(a, "a");
ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne_b.data());
- ggml_set_param(ctx, b);
+ ggml_set_param(b);
ggml_set_name(b, "b");
ggml_tensor * out = ggml_acc(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], b->nb[1]);
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * logits = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_set_param(ctx, logits);
+ ggml_set_param(logits);
ggml_set_name(logits, "logits");
ggml_tensor * labels = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]);
- ggml_set_param(ctx, a); // Despite tensor a having gradients the output tensor will not.
+ ggml_set_param(a); // Despite tensor a having gradients the output tensor will not.
ggml_set_name(a, "a");
ggml_tensor * grad = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]);
enum ggml_opt_loss_type loss_type = GGML_OPT_LOSS_TYPE_SUM) {
std::vector<ggml_opt_dataset_t> datasets(ndata);
for (int64_t ndata_shard = 1; ndata_shard <= ndata; ++ndata_shard) {
- ggml_opt_dataset_t dataset = ggml_opt_dataset_init(ne_datapoint, ne_label, ndata, ndata_shard);
+ ggml_opt_dataset_t dataset = ggml_opt_dataset_init(
+ GGML_TYPE_F32, GGML_TYPE_F32, ne_datapoint, ne_label, ndata, ndata_shard);
float * data = ggml_get_data_f32(ggml_opt_dataset_data( dataset));
float * labels = ggml_get_data_f32(ggml_opt_dataset_labels(dataset));
datasets[ndata_shard-1] = dataset;
}
- ggml_opt_dataset_t dataset_unsupervised = ggml_opt_dataset_init(1, 0, ndata, /*ndata_shard =*/ 1);
+ ggml_opt_dataset_t dataset_unsupervised = ggml_opt_dataset_init(
+ GGML_TYPE_F32, GGML_TYPE_F32, 1, 0, ndata, /*ndata_shard =*/ 1);
float * data = ggml_get_data_f32(ggml_opt_dataset_data(dataset_unsupervised));
struct ggml_tensor * weights = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1);
ggml_set_name(weights, "weights");
- ggml_set_param(ctx_static, weights);
+ ggml_set_param(weights);
struct ggml_tensor * intermediary = ggml_add(ctx_compute, inputs, weights);
GGML_ASSERT(nbatch_logical % nbatch_physical == 0);
const int32_t opt_period = nbatch_logical / nbatch_physical;
- struct ggml_opt_params opt_params = ggml_opt_default_params(backend_sched, ctx_compute, inputs, outputs, loss_type);
- opt_params.opt_period = opt_period;
+ struct ggml_opt_params opt_params = ggml_opt_default_params(backend_sched, loss_type);
+ opt_params.ctx_compute = ctx_compute;
+ opt_params.inputs = inputs;
+ opt_params.outputs = outputs;
+ opt_params.opt_period = opt_period;
if (!optimizer_defaults) {
opt_params.get_opt_pars = helper_get_test_opt_pars;
}
for (int idata = 0; idata < ndata; ++idata) {
const float idataf = idata;
+ ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
- ggml_opt_forward_backward(cd.opt_ctx, cd.result);
+ ggml_opt_eval(cd.opt_ctx, cd.result);
ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata, 0, sizeof(float));
}
} else {
for (int idata = 0; idata < ndata; ++idata) {
const float idataf = idata;
+ ggml_opt_alloc(cd.opt_ctx, /*backward =*/ false);
ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
- ggml_opt_forward(cd.opt_ctx, cd.result);
+ ggml_opt_eval(cd.opt_ctx, cd.result);
ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float));
}
}
float w0;
ggml_backend_tensor_get(cd.weights, &w0, 0, sizeof(float));
for (int i = 0; i < 10; ++i) {
- ggml_opt_forward_backward(cd.opt_ctx, nullptr);
+ ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
+ ggml_opt_eval(cd.opt_ctx, cd.result);
}
ggml_backend_tensor_set(cd.weights, &w0, 0, sizeof(float));
} else {
for (int idata = 0; idata < ndata; ++idata) {
const float idataf = idata;
+ ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
- ggml_opt_forward_backward(cd.opt_ctx, cd.result);
+ ggml_opt_eval(cd.opt_ctx, cd.result);
ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float));
}
}
int idata = 0;
for (; idata < idata_split; ++idata) {
const float idataf = idata;
+ ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
- ggml_opt_forward_backward(cd.opt_ctx, cd.result);
+ ggml_opt_eval(cd.opt_ctx, cd.result);
ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float));
}
for (; idata < ndata; ++idata) {
const float idataf = idata;
+ ggml_opt_alloc(cd.opt_ctx, /*backward =*/ false);
ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
- ggml_opt_forward(cd.opt_ctx, cd.result2);
+ ggml_opt_eval(cd.opt_ctx, cd.result2);
ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float));
}
}
struct helper_ctx_data cd = helper_get_ctx_data(
backend_sched, backend, /*init_opt_ctx =*/ true, /*optimizer_defaults =*/ false, /*nbatch_logical =*/ 6, nbatch_physical, loss_type);
- struct ggml_tensor * loss = ggml_opt_loss(cd.opt_ctx);
std::vector<float> grad_history(ndata);
for (int64_t idata = 0; idata < ndata; ++idata) {
if (nbatch_physical == 1) {
for (int idata = 0; idata < ndata; ++idata) {
const float idataf = idata;
+ ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
ggml_backend_tensor_set(cd.inputs, &idataf, 0, 1*sizeof(float));
- ggml_opt_forward_backward(cd.opt_ctx, cd.result);
+ ggml_opt_eval(cd.opt_ctx, cd.result);
ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata, 0, 1*sizeof(float));
}
} else if (nbatch_physical == 2) {
for (int idata = 0; idata < ndata; idata += 2) {
const float idataf[2] = {float(idata + 0), float(idata + 1)};
+ ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
ggml_backend_tensor_set(cd.inputs, idataf, 0, 2*sizeof(float));
- ggml_opt_forward_backward(cd.opt_ctx, cd.result);
+ ggml_opt_eval(cd.opt_ctx, cd.result);
grad_history[idata + 0] = 0.0f;
ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata + 1, 0, 1*sizeof(float));
}
subtest_ok = subtest_ok && almost_equal(grad_history[1], 2.0, atol);
subtest_ok = subtest_ok && almost_equal(grad_history[3], 4.0, atol);
- subtest_ok = subtest_ok && almost_equal(grad_history[5], 0.0, atol);
+ subtest_ok = subtest_ok && almost_equal(grad_history[5], 6.0, atol);
} else if (loss_type == GGML_OPT_LOSS_TYPE_MEAN) {
if (nbatch_physical == 1) {
subtest_ok = subtest_ok && almost_equal(grad_history[0], 1.0/ndata, atol);
}
subtest_ok = subtest_ok && almost_equal(grad_history[1], 2.0/ndata, atol);
subtest_ok = subtest_ok && almost_equal(grad_history[3], 4.0/ndata, atol);
- subtest_ok = subtest_ok && almost_equal(grad_history[5], 0.0/ndata, atol);
+ subtest_ok = subtest_ok && almost_equal(grad_history[5], 6.0/ndata, atol);
} else {
GGML_ASSERT(false);
}
std::mt19937 gen(12345);
std::normal_distribution<float> nd{0.0f, 0.1f};
- ggml_opt_dataset_t dataset = ggml_opt_dataset_init(1, 1, ndata_regression, ndata_regression);
+ ggml_opt_dataset_t dataset = ggml_opt_dataset_init(
+ GGML_TYPE_F32, GGML_TYPE_F32, 1, 1, ndata_regression, ndata_regression);
float * data = ggml_get_data_f32(ggml_opt_dataset_data( dataset));
float * labels = ggml_get_data_f32(ggml_opt_dataset_labels(dataset));
struct ggml_tensor * a = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1);
ggml_set_name(a, "a");
- ggml_set_param(ctx_static, a);
+ ggml_set_param(a);
struct ggml_tensor * b = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1);
ggml_set_name(b, "b");
- ggml_set_param(ctx_static, b);
+ ggml_set_param(b);
struct ggml_tensor * f = ggml_add(ctx_compute, ggml_mul(ctx_compute, x, a), b);
ggml_set_name(f, "f");
- ggml_set_param(ctx_static, f);
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx_static, backend);
const float a0 = 1.0f;
overview / pkg / ggml / sources / llama.cpp / commitdiff