ggml: new optimization interface (ggml/988)
This commit is contained in:
parent
5c9a8b22b1
commit
8a43e940ab
15 changed files with 2663 additions and 1633 deletions
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@ -207,9 +207,11 @@ add_library(ggml-base
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../include/ggml-alloc.h
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../include/ggml-backend.h
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../include/ggml-cpp.h
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../include/ggml-opt.h
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ggml.c
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ggml-alloc.c
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ggml-backend.cpp
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ggml-opt.cpp
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ggml-threading.cpp
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ggml-threading.h
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ggml-quants.c
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@ -466,18 +466,12 @@ static bool ggml_gallocr_is_own(ggml_gallocr_t galloc, struct ggml_tensor * t) {
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return ggml_gallocr_hash_get(galloc, t)->allocated;
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}
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static void ggml_gallocr_set_node_offset(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id, size_t offset) {
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struct hash_node * hn = ggml_gallocr_hash_get(galloc, node);
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hn->buffer_id = buffer_id;
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hn->offset = offset;
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hn->allocated = true;
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}
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static bool ggml_gallocr_is_allocated(ggml_gallocr_t galloc, struct ggml_tensor * t) {
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return t->data != NULL || ggml_gallocr_hash_get(galloc, t)->allocated;
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}
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static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id) {
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GGML_ASSERT(buffer_id >= 0);
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struct hash_node * hn = ggml_gallocr_hash_get(galloc, node);
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if (!ggml_gallocr_is_allocated(galloc, node) && !ggml_is_view(node)) {
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@ -816,7 +810,11 @@ static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor *
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}
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static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_tensor * node, struct tensor_alloc * talloc) {
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size_t node_size = (node->data || node->view_src) ? 0 : ggml_backend_buft_get_alloc_size(galloc->bufts[talloc->buffer_id], node);
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size_t node_size = 0;
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if (!node->data && !node->view_src) {
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GGML_ASSERT(talloc->buffer_id >= 0); // prevent segfault when misusing the API
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node_size = ggml_backend_buft_get_alloc_size(galloc->bufts[talloc->buffer_id], node);
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}
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return talloc->size_max >= node_size;
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}
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@ -279,7 +279,7 @@ void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, siz
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buf->iface.get_tensor(buf, tensor, data, offset, size);
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}
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GGML_API void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
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void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
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ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
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if (size == 0) {
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@ -12216,11 +12216,16 @@ static void ggml_compute_forward_opt_step_adamw_f32(
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const struct ggml_compute_params * params,
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struct ggml_tensor * dst) {
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const struct ggml_tensor * src0 = dst->src[0];
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const struct ggml_tensor * src0_grad = dst->src[1];
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const struct ggml_tensor * src0_grad_m = dst->src[2];
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const struct ggml_tensor * src0_grad_v = dst->src[3];
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const struct ggml_tensor * src0 = dst->src[0];
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const struct ggml_tensor * src0_grad = dst->src[1];
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const struct ggml_tensor * src0_grad_m = dst->src[2];
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const struct ggml_tensor * src0_grad_v = dst->src[3];
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const struct ggml_tensor * adamw_params = dst->src[4];
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GGML_ASSERT(ggml_are_same_shape(src0, src0_grad));
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GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_m));
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GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_v));
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GGML_ASSERT(ggml_nelements(adamw_params) == 7);
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const int ith = params->ith;
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const int nth = params->nth;
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@ -12237,16 +12242,14 @@ static void ggml_compute_forward_opt_step_adamw_f32(
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const int ir0 = dr*ith;
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const int ir1 = MIN(ir0 + dr, nr);
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/* const float gnorm = 1.0f; */
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int64_t iter; memcpy(&iter, &dst->op_params[0], sizeof(int64_t));
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const float alpha = ggml_get_op_params_f32(dst, 2);
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const float beta1 = ggml_get_op_params_f32(dst, 3);
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const float beta2 = ggml_get_op_params_f32(dst, 4);
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const float eps = ggml_get_op_params_f32(dst, 5);
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const float wd = ggml_get_op_params_f32(dst, 6);
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const float beta1h = alpha/(1.0f - powf(beta1, iter));
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const float beta2h = 1.0f/(1.0f - powf(beta2, iter));
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const float * adamw_params_ptr = ggml_get_data_f32(adamw_params);
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const float alpha = adamw_params_ptr[0];
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const float beta1 = adamw_params_ptr[1];
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const float beta2 = adamw_params_ptr[2];
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const float eps = adamw_params_ptr[3];
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const float wd = adamw_params_ptr[4];
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const float beta1h = adamw_params_ptr[5];
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const float beta2h = adamw_params_ptr[6];
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for (int ir = ir0; ir < ir1; ++ir) {
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const int64_t i03 = ir/(ne02*ne01);
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@ -12270,17 +12273,9 @@ static void ggml_compute_forward_opt_step_adamw_f32(
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// The weight decay is applied independently of the Adam momenta m and v.
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// This is NOT equivalent to l2 regularization that adds w[i00]*w[i00] to the loss.
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// See: https://arxiv.org/pdf/1711.05101v3.pdf
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w[i00] = w[i00]*(1.0f - alpha*wd) - mh/vh;
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w[i00] = w[i00]*(1.0f - alpha*wd) - alpha*mh/vh;
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}
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}
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ggml_barrier(params->threadpool);
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if (ith != 0) {
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return;
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}
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iter++;
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memcpy(&dst->op_params[0], &iter, sizeof(int64_t));
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}
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static void ggml_compute_forward_opt_step_adamw(
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@ -1,11 +1,11 @@
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#include "ggml-impl.h"
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#include "opt-step-adamw.cuh"
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#include <cstdint>
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static __global__ void opt_step_adamw_f32(
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float * __restrict__ x, const float * __restrict__ g, float * __restrict__ g_m, float * __restrict__ g_v, const int64_t k,
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const float alpha, const float beta1, const float beta2, const float eps, const float wd,
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const float beta1h, const float beta2h) {
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float * __restrict__ x, const float * __restrict__ g, float * __restrict__ g_m, float * __restrict__ g_v,
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const float * __restrict__ pars, const int64_t k) {
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const int64_t i = (int64_t) blockIdx.x*blockDim.x + threadIdx.x;
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@ -13,6 +13,14 @@ static __global__ void opt_step_adamw_f32(
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return;
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}
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const float alpha = pars[0];
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const float beta1 = pars[1];
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const float beta2 = pars[2];
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const float eps = pars[3];
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const float wd = pars[4];
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const float beta1h = pars[5];
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const float beta2h = pars[6];
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const float gi = g[i];
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const float gmi = g_m[i]*beta1 + gi*(1.0f - beta1);
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const float gvi = g_v[i]*beta2 + gi*gi*(1.0f - beta2);
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@ -23,58 +31,48 @@ static __global__ void opt_step_adamw_f32(
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const float mh = gmi*beta1h;
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const float vh = sqrtf(gvi*beta2h) + eps;
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x[i] = x[i]*(1.0f - alpha*wd) - mh/vh;
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x[i] = x[i]*(1.0f - alpha*wd) - alpha*mh/vh;
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}
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static void opt_step_adamw_f32_cuda(
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float * x, const float * g, float * g_m, float * g_v, const int64_t k,
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const float alpha, const float beta1, const float beta2, const float eps, const float wd,
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const float beta1h, const float beta2h, cudaStream_t stream) {
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float * x, const float * g, float * g_m, float * g_v, const float * pars, const int64_t k, cudaStream_t stream) {
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const dim3 block_dims(CUDA_OPT_STEP_ADAMW_BLOCK_SIZE, 1, 1);
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const dim3 block_nums((k + CUDA_OPT_STEP_ADAMW_BLOCK_SIZE - 1) / CUDA_OPT_STEP_ADAMW_BLOCK_SIZE, 1, 1);
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opt_step_adamw_f32<<<block_nums, block_dims, 0, stream>>>(x, g, g_m, g_v, k, alpha, beta1, beta2, eps, wd, beta1h, beta2h);
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opt_step_adamw_f32<<<block_nums, block_dims, 0, stream>>>(x, g, g_m, g_v, pars, k);
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}
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void ggml_cuda_opt_step_adamw(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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const ggml_tensor * src0 = dst->src[0];
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const ggml_tensor * src0_grad = dst->src[1];
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const ggml_tensor * src0_grad_m = dst->src[2];
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const ggml_tensor * src0_grad_v = dst->src[3];
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const ggml_tensor * src0 = dst->src[0];
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const ggml_tensor * src0_grad = dst->src[1];
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const ggml_tensor * src0_grad_m = dst->src[2];
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const ggml_tensor * src0_grad_v = dst->src[3];
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const ggml_tensor * adamw_params = dst->src[4];
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GGML_ASSERT(src0->type == GGML_TYPE_F32);
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GGML_ASSERT(src0_grad->type == GGML_TYPE_F32);
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GGML_ASSERT(src0_grad_m->type == GGML_TYPE_F32);
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GGML_ASSERT(src0_grad_v->type == GGML_TYPE_F32);
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GGML_ASSERT(src0->type == GGML_TYPE_F32);
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GGML_ASSERT(src0_grad->type == GGML_TYPE_F32);
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GGML_ASSERT(src0_grad_m->type == GGML_TYPE_F32);
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GGML_ASSERT(src0_grad_v->type == GGML_TYPE_F32);
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GGML_ASSERT(adamw_params->type == GGML_TYPE_F32);
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GGML_ASSERT(ggml_is_contiguous(src0));
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GGML_ASSERT(ggml_is_contiguous(src0_grad));
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GGML_ASSERT(ggml_is_contiguous(src0_grad_m));
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GGML_ASSERT(ggml_is_contiguous(src0_grad_v));
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GGML_ASSERT(ggml_is_contiguous(adamw_params));
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GGML_ASSERT(ggml_are_same_shape(src0, src0_grad));
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GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_m));
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GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_v));
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GGML_ASSERT(ggml_nelements(adamw_params) == 7);
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float * src0_d = (float *) src0->data;
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const float * src0_grad_d = (const float *) src0_grad->data;
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float * src0_grad_m_d = (float *) src0_grad_m->data;
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float * src0_grad_v_d = (float *) src0_grad_v->data;
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float * src0_d = (float *) src0->data;
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const float * src0_grad_d = (const float *) src0_grad->data;
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float * src0_grad_m_d = (float *) src0_grad_m->data;
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float * src0_grad_v_d = (float *) src0_grad_v->data;
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const float * adamw_params_d = (const float *) adamw_params->data;
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cudaStream_t stream = ctx.stream();
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const int64_t ne = ggml_nelements(src0);
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int64_t iter; memcpy(&iter, &dst->op_params[0], sizeof(int64_t));
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float alpha; memcpy(&alpha, &dst->op_params[2], sizeof(float));
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float beta1; memcpy(&beta1, &dst->op_params[3], sizeof(float));
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float beta2; memcpy(&beta2, &dst->op_params[4], sizeof(float));
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float eps; memcpy(&eps, &dst->op_params[5], sizeof(float));
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float wd; memcpy(&wd, &dst->op_params[6], sizeof(float));
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const float beta1h = alpha/(1.0f - powf(beta1, iter));
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const float beta2h = 1.0f/(1.0f - powf(beta2, iter));
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opt_step_adamw_f32_cuda(src0_d, src0_grad_d, src0_grad_m_d, src0_grad_v_d, ne, alpha, beta1, beta2, eps, wd, beta1h, beta2h, stream);
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iter++;
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memcpy(&dst->op_params[0], &iter, sizeof(int64_t));
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opt_step_adamw_f32_cuda(src0_d, src0_grad_d, src0_grad_m_d, src0_grad_v_d, adamw_params_d, ne, stream);
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}
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@ -196,7 +196,7 @@ void ggml_hash_set_reset(struct ggml_hash_set * hash_set);
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static bool ggml_hash_contains(const struct ggml_hash_set * hash_set, struct ggml_tensor * key);
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// returns GGML_HASHSET_FULL if table is full, otherwise the current index of the key or where it should be inserted
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static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, struct ggml_tensor * key);
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static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, const struct ggml_tensor * key);
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// returns GGML_HASHSET_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full
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static size_t ggml_hash_insert(struct ggml_hash_set * hash_set, struct ggml_tensor * key);
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@ -210,7 +210,7 @@ static inline size_t ggml_hash(const struct ggml_tensor * p) {
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return (size_t)(uintptr_t)p >> 4;
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}
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static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, struct ggml_tensor * key) {
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static size_t ggml_hash_find(const struct ggml_hash_set * hash_set, const struct ggml_tensor * key) {
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size_t h = ggml_hash(key) % hash_set->size;
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// linear probing
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@ -281,13 +281,14 @@ enum ggml_cgraph_eval_order {
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};
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struct ggml_cgraph {
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int size;
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int n_nodes;
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int n_leafs;
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int size; // maximum number of nodes/leafs/grads/grad_accs
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int n_nodes; // number of nodes currently in use
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int n_leafs; // number of leafs currently in use
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struct ggml_tensor ** nodes;
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struct ggml_tensor ** grads;
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struct ggml_tensor ** leafs;
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struct ggml_tensor ** nodes; // tensors with data that can change if the graph is evaluated
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struct ggml_tensor ** grads; // the outputs of these tensors are the gradients of the nodes
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struct ggml_tensor ** grad_accs; // accumulators for node gradients
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struct ggml_tensor ** leafs; // tensors with constant data
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struct ggml_hash_set visited_hash_set;
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@ -3639,6 +3639,12 @@ static void * ggml_backend_metal_buffer_get_base(ggml_backend_buffer_t buffer) {
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return ctx->all_data;
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}
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static void ggml_backend_metal_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
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memset((char *)tensor->data + offset, value, size);
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UNUSED(buffer);
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}
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static void ggml_backend_metal_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
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memcpy((char *)tensor->data + offset, data, size);
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@ -3671,7 +3677,7 @@ static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = {
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/* .free_buffer = */ ggml_backend_metal_buffer_free_buffer,
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/* .get_base = */ ggml_backend_metal_buffer_get_base,
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/* .init_tensor = */ NULL,
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/* .memset_tensor = */ NULL,
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/* .memset_tensor = */ ggml_backend_metal_buffer_memset_tensor,
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/* .set_tensor = */ ggml_backend_metal_buffer_set_tensor,
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/* .get_tensor = */ ggml_backend_metal_buffer_get_tensor,
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/* .cpy_tensor = */ ggml_backend_metal_buffer_cpy_tensor,
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867
ggml/src/ggml-opt.cpp
Normal file
867
ggml/src/ggml-opt.cpp
Normal file
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@ -0,0 +1,867 @@
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#include "ggml-opt.h"
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#include "ggml.h"
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#include "ggml-alloc.h"
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#include "ggml-backend.h"
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#include "ggml-impl.h"
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#include <algorithm>
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#include <cmath>
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#include <cstdint>
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#include <inttypes.h>
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#include <map>
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#include <random>
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#include <vector>
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struct ggml_opt_dataset {
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struct ggml_context * ctx;
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ggml_backend_buffer_t buf;
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struct ggml_tensor * data;
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struct ggml_tensor * labels;
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int64_t ndata;
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int64_t ndata_shard;
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size_t nbs_data;
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size_t nbs_labels;
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std::vector<int64_t> permutation;
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};
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struct ggml_opt_context {
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ggml_backend_sched_t backend_sched;
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ggml_cgraph * allocated_graph;
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ggml_cgraph * allocated_graph_copy;
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struct ggml_context * ctx_static;
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struct ggml_context * ctx_static_cpu;
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struct ggml_context * ctx_compute;
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struct ggml_context * ctx_copy;
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ggml_backend_buffer_t buf_static;
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ggml_backend_buffer_t buf_static_cpu;
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std::mt19937 rng;
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struct ggml_tensor * inputs;
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struct ggml_tensor * outputs;
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struct ggml_tensor * labels;
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struct ggml_tensor * loss;
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struct ggml_tensor * pred;
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struct ggml_tensor * ncorrect;
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struct ggml_cgraph * gf;
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struct ggml_cgraph * gb_grad;
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||||
struct ggml_cgraph * gb_opt;
|
||||
|
||||
int64_t iter;
|
||||
int32_t opt_period;
|
||||
int32_t opt_i;
|
||||
bool loss_per_datapoint;
|
||||
|
||||
ggml_opt_get_optimizer_params get_opt_pars;
|
||||
void * get_opt_pars_ud;
|
||||
struct ggml_tensor * adamw_params;
|
||||
};
|
||||
|
||||
struct ggml_opt_result {
|
||||
int64_t ndata = 0;
|
||||
std::vector<float> loss;
|
||||
std::vector<int32_t> pred;
|
||||
int64_t ncorrect = 0;
|
||||
|
||||
bool loss_per_datapoint = false;
|
||||
int64_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_ASSERT(ne_datapoint > 0);
|
||||
GGML_ASSERT(ne_label >= 0);
|
||||
GGML_ASSERT(ndata > 0);
|
||||
GGML_ASSERT(ndata_shard > 0);
|
||||
|
||||
ggml_opt_dataset_t result = new ggml_opt_dataset;
|
||||
result->ndata = ndata;
|
||||
result->ndata_shard = ndata_shard;
|
||||
|
||||
{
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ 2*ggml_tensor_overhead(),
|
||||
/*.mem_buffer =*/ nullptr,
|
||||
/*.no_alloc =*/ true,
|
||||
};
|
||||
result->ctx = ggml_init(params);
|
||||
}
|
||||
|
||||
result->data = ggml_new_tensor_2d(result->ctx, GGML_TYPE_F32, 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->nbs_labels = ggml_nbytes(result->labels) * ndata_shard/ndata;
|
||||
} else {
|
||||
result->labels = nullptr;
|
||||
result->nbs_labels = 0;
|
||||
}
|
||||
|
||||
result->buf = ggml_backend_alloc_ctx_tensors_from_buft(result->ctx, ggml_backend_cpu_buffer_type());
|
||||
|
||||
const int64_t nshards = ndata/ndata_shard;
|
||||
result->permutation.resize(nshards);
|
||||
for (int64_t i = 0; i < nshards; ++i) {
|
||||
result->permutation[i] = i;
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
void ggml_opt_dataset_free(ggml_opt_dataset_t dataset) {
|
||||
ggml_backend_buffer_free(dataset->buf);
|
||||
ggml_free(dataset->ctx);
|
||||
delete dataset;
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_opt_dataset_data(ggml_opt_dataset_t dataset) {
|
||||
return dataset->data;
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_opt_dataset_labels(ggml_opt_dataset_t dataset) {
|
||||
return dataset->labels;
|
||||
}
|
||||
|
||||
void ggml_opt_dataset_shuffle(ggml_opt_context_t opt_ctx, ggml_opt_dataset_t dataset, int64_t idata) {
|
||||
GGML_ASSERT(idata <= dataset->ndata);
|
||||
|
||||
if (idata < 0) {
|
||||
std::shuffle(dataset->permutation.begin(), dataset->permutation.end(), opt_ctx->rng);
|
||||
return;
|
||||
}
|
||||
|
||||
GGML_ASSERT(idata % dataset->ndata_shard == 0);
|
||||
const int64_t ishard_max = idata / dataset->ndata_shard;
|
||||
std::shuffle(dataset->permutation.begin(), dataset->permutation.begin() + ishard_max, opt_ctx->rng);
|
||||
}
|
||||
|
||||
void ggml_opt_dataset_get_batch(ggml_opt_dataset_t dataset, struct ggml_tensor * data_batch, struct ggml_tensor * labels_batch, int64_t ibatch) {
|
||||
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));
|
||||
|
||||
const size_t nb_data_batch = ggml_nbytes(data_batch);
|
||||
GGML_ASSERT(nb_data_batch % dataset->nbs_data == 0);
|
||||
const int64_t shards_per_batch = nb_data_batch / dataset->nbs_data;
|
||||
|
||||
if (labels_batch) {
|
||||
const size_t nb_labels_batch = ggml_nbytes(labels_batch);
|
||||
GGML_ASSERT(nb_labels_batch == shards_per_batch*dataset->nbs_labels);
|
||||
}
|
||||
|
||||
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;
|
||||
ggml_backend_tensor_set(data_batch, ptr_data, ishard_batch*dataset->nbs_data, dataset->nbs_data);
|
||||
|
||||
if (!labels_batch) {
|
||||
continue;
|
||||
}
|
||||
|
||||
const char * ptr_labels = (const char *) dataset->labels->data + ishard*dataset->nbs_labels;
|
||||
ggml_backend_tensor_set(labels_batch, ptr_labels, ishard_batch*dataset->nbs_labels, dataset->nbs_labels);
|
||||
}
|
||||
}
|
||||
|
||||
// ====== Model / Context ======
|
||||
|
||||
struct ggml_opt_optimizer_params ggml_opt_get_default_optimizer_params(void * userdata) {
|
||||
GGML_UNUSED(userdata);
|
||||
|
||||
ggml_opt_optimizer_params result;
|
||||
|
||||
result.adamw.alpha = 0.001f;
|
||||
result.adamw.beta1 = 0.9f;
|
||||
result.adamw.beta2 = 0.999f;
|
||||
result.adamw.eps = 1e-8f;
|
||||
result.adamw.wd = 0.0f;
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
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,
|
||||
/*loss_type =*/ loss_type,
|
||||
/*build_type =*/ GGML_OPT_BUILD_TYPE_OPT,
|
||||
/*opt_period =*/ 1,
|
||||
/*get_opt_pars =*/ ggml_opt_get_default_optimizer_params,
|
||||
/*get_opt_pars_ud =*/ nullptr,
|
||||
};
|
||||
}
|
||||
|
||||
static ggml_tensor * map_tensor(std::map<ggml_tensor *, ggml_tensor *> & tensor_map, ggml_context * ctx, ggml_tensor * tensor) {
|
||||
if (!tensor) {
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
if (tensor_map.find(tensor) != tensor_map.end()) {
|
||||
return tensor_map[tensor];
|
||||
}
|
||||
|
||||
ggml_tensor * new_tensor = ggml_dup_tensor(ctx, tensor);
|
||||
tensor_map[tensor] = new_tensor;
|
||||
|
||||
new_tensor->op = tensor->op;
|
||||
for (int i = 0; i < GGML_MAX_DIMS; i++) {
|
||||
new_tensor->nb[i] = tensor->nb[i];
|
||||
}
|
||||
new_tensor->flags = tensor->flags;
|
||||
memcpy(new_tensor->op_params, tensor->op_params, sizeof(tensor->op_params));
|
||||
strcpy(new_tensor->name, tensor->name);
|
||||
new_tensor->data = tensor->data;
|
||||
new_tensor->buffer = tensor->buffer;
|
||||
new_tensor->extra = tensor->extra;
|
||||
new_tensor->view_offs = tensor->view_offs;
|
||||
new_tensor->view_src = map_tensor(tensor_map, ctx, tensor->view_src);
|
||||
for (int i = 0; i < GGML_MAX_SRC; i++) {
|
||||
new_tensor->src[i] = map_tensor(tensor_map, ctx, tensor->src[i]);
|
||||
}
|
||||
|
||||
return new_tensor;
|
||||
}
|
||||
|
||||
static ggml_cgraph * dup_graph(ggml_context * ctx, ggml_cgraph * graph) {
|
||||
std::map<ggml_tensor *, ggml_tensor *> tensor_map;
|
||||
|
||||
ggml_cgraph * new_graph = ggml_new_graph_custom(ctx, GGML_DEFAULT_GRAPH_SIZE, /*grads =*/ true);
|
||||
|
||||
for (int i = 0; i < graph->n_leafs; i++) {
|
||||
ggml_build_forward_expand(new_graph, map_tensor(tensor_map, ctx, graph->leafs[i]));
|
||||
}
|
||||
for (int i = 0; i < graph->n_nodes; i++) {
|
||||
ggml_build_forward_expand(new_graph, map_tensor(tensor_map, ctx, graph->nodes[i]));
|
||||
}
|
||||
for (int i = 0; i < graph->n_nodes; ++i) {
|
||||
const size_t igrad_src = ggml_hash_find(&graph->visited_hash_set, graph->nodes[i]);
|
||||
const size_t igrad_dst = ggml_hash_find(&new_graph->visited_hash_set, new_graph->nodes[i]);
|
||||
graph->grads[igrad_dst] = new_graph->grads[igrad_src];
|
||||
graph->grad_accs[igrad_dst] = new_graph->grad_accs[igrad_src];
|
||||
}
|
||||
|
||||
return new_graph;
|
||||
}
|
||||
|
||||
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->allocated_graph = nullptr;
|
||||
result->allocated_graph_copy = nullptr;
|
||||
result->ctx_compute = params.ctx_compute;
|
||||
result->ctx_copy = nullptr;
|
||||
result->inputs = params.inputs;
|
||||
result->outputs = params.outputs;
|
||||
result->iter = 1;
|
||||
result->opt_period = params.opt_period;
|
||||
result->opt_i = 0;
|
||||
result->get_opt_pars = params.get_opt_pars;
|
||||
result->get_opt_pars_ud = params.get_opt_pars_ud;
|
||||
|
||||
GGML_ASSERT(result->inputs->data && "the inputs must be allocated statically");
|
||||
GGML_ASSERT(result->opt_period >= 1);
|
||||
|
||||
const bool accumulate = params.build_type == GGML_OPT_BUILD_TYPE_GRAD ||
|
||||
(params.build_type == GGML_OPT_BUILD_TYPE_OPT && result->opt_period > 1);
|
||||
|
||||
ggml_set_input(result->inputs);
|
||||
ggml_set_output(result->outputs);
|
||||
|
||||
result->gf = ggml_new_graph_custom(result->ctx_compute, GGML_DEFAULT_GRAPH_SIZE, /*grads =*/ true); // Forward pass.
|
||||
ggml_build_forward_expand(result->gf, result->outputs);
|
||||
|
||||
int n_param = 0;
|
||||
for (int i = 0; i < result->gf->n_nodes; ++i) {
|
||||
if (result->gf->nodes[i]->flags & GGML_TENSOR_FLAG_PARAM) {
|
||||
n_param++;
|
||||
}
|
||||
}
|
||||
|
||||
{
|
||||
// The static context is used for:
|
||||
// - gradients (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();
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ size_meta,
|
||||
/*.mem_buffer =*/ nullptr,
|
||||
/*.no_alloc =*/ true,
|
||||
};
|
||||
result->ctx_static = ggml_init(params);
|
||||
}
|
||||
{
|
||||
// The static cpu context is used for:
|
||||
// - optimizer parameters (1 for the entire context)
|
||||
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);
|
||||
}
|
||||
|
||||
|
||||
switch (params.loss_type) {
|
||||
case GGML_OPT_LOSS_TYPE_MEAN: {
|
||||
result->labels = nullptr;
|
||||
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;
|
||||
break;
|
||||
}
|
||||
case GGML_OPT_LOSS_TYPE_SUM: {
|
||||
result->labels = nullptr;
|
||||
result->loss = ggml_sum(result->ctx_static, result->outputs);
|
||||
ggml_set_name(result->loss, "loss_sum");
|
||||
result->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");
|
||||
}
|
||||
result->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;
|
||||
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);
|
||||
|
||||
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 (params.build_type == GGML_OPT_BUILD_TYPE_FORWARD) {
|
||||
result->gb_grad = nullptr;
|
||||
result->gb_opt = nullptr;
|
||||
|
||||
result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0));
|
||||
result->buf_static_cpu = nullptr;
|
||||
|
||||
ggml_opt_alloc_graph(result, result->gf);
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
// gb_grad == graph backward gradients, forward pass, then backward pass to calculate gradients.
|
||||
result->gb_grad = ggml_graph_dup(result->ctx_compute, result->gf);
|
||||
ggml_build_backward_expand(result->ctx_static, result->ctx_compute, result->gb_grad, accumulate);
|
||||
|
||||
if (params.build_type == GGML_OPT_BUILD_TYPE_GRAD) {
|
||||
result->gb_opt = nullptr;
|
||||
|
||||
result->buf_static = ggml_backend_alloc_ctx_tensors(result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0));
|
||||
result->buf_static_cpu = nullptr;
|
||||
|
||||
ggml_opt_alloc_graph(result, result->gb_grad);
|
||||
ggml_graph_reset(result->gb_grad);
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
GGML_ASSERT(params.build_type == GGML_OPT_BUILD_TYPE_OPT);
|
||||
|
||||
// gb_opt == graph backward optimize, forward pass, then backward pass to calculate gradients, then optimizer step.
|
||||
result->gb_opt = ggml_graph_dup(result->ctx_compute, result->gb_grad);
|
||||
|
||||
result->adamw_params = ggml_new_tensor_1d(result->ctx_static_cpu, GGML_TYPE_F32, 7);
|
||||
ggml_set_input(result->adamw_params);
|
||||
ggml_set_name(result->adamw_params, "adamw_params");
|
||||
|
||||
for (int i = result->gf->n_nodes-1; i >= 0; --i) {
|
||||
struct ggml_tensor * node = result->gb_opt->nodes[i];
|
||||
struct ggml_tensor * grad = ggml_graph_get_grad(result->gb_opt, node);
|
||||
|
||||
if (node->flags & GGML_TENSOR_FLAG_PARAM) {
|
||||
struct ggml_tensor * m = ggml_dup_tensor(result->ctx_static, node);
|
||||
struct ggml_tensor * v = ggml_dup_tensor(result->ctx_static, node);
|
||||
struct ggml_tensor * opt_step = ggml_opt_step_adamw(result->ctx_compute, node, grad, m, v, result->adamw_params);
|
||||
ggml_build_forward_expand(result->gb_opt, opt_step);
|
||||
}
|
||||
}
|
||||
|
||||
result->buf_static = ggml_backend_alloc_ctx_tensors(
|
||||
result->ctx_static, ggml_backend_sched_get_backend(result->backend_sched, 0));
|
||||
|
||||
result->buf_static_cpu = ggml_backend_alloc_ctx_tensors_from_buft(result->ctx_static_cpu, ggml_backend_cpu_buffer_type());
|
||||
|
||||
ggml_opt_alloc_graph(result, result->gb_opt);
|
||||
ggml_graph_reset(result->gb_opt);
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
void ggml_opt_free(ggml_opt_context_t opt_ctx) {
|
||||
if (opt_ctx == nullptr) {
|
||||
return;
|
||||
}
|
||||
ggml_backend_buffer_free(opt_ctx->buf_static);
|
||||
ggml_backend_buffer_free(opt_ctx->buf_static_cpu);
|
||||
ggml_free(opt_ctx->ctx_static);
|
||||
ggml_free(opt_ctx->ctx_static_cpu);
|
||||
delete opt_ctx;
|
||||
}
|
||||
|
||||
void ggml_opt_reset(ggml_opt_context_t opt_ctx, bool optimizer) {
|
||||
if (optimizer) {
|
||||
ggml_graph_reset(opt_ctx->gb_opt);
|
||||
opt_ctx->iter = 1;
|
||||
} else {
|
||||
ggml_graph_reset(opt_ctx->gb_grad);
|
||||
}
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_opt_inputs(ggml_opt_context_t opt_ctx) {
|
||||
return opt_ctx->inputs;
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_opt_outputs(ggml_opt_context_t opt_ctx) {
|
||||
return opt_ctx->outputs;
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_opt_labels(ggml_opt_context_t opt_ctx) {
|
||||
return opt_ctx->labels;
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_opt_loss(ggml_opt_context_t opt_ctx) {
|
||||
return opt_ctx->loss;
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_opt_pred(ggml_opt_context_t opt_ctx) {
|
||||
return opt_ctx->pred;
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_opt_ncorrect(ggml_opt_context_t opt_ctx) {
|
||||
return opt_ctx->ncorrect;
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_opt_grad_acc(ggml_opt_context_t opt_ctx, struct ggml_tensor * node) {
|
||||
return ggml_graph_get_grad_acc(opt_ctx->gb_opt, node);
|
||||
}
|
||||
|
||||
// ====== Optimization Result ======
|
||||
|
||||
ggml_opt_result_t ggml_opt_result_init() {
|
||||
return new ggml_opt_result;
|
||||
}
|
||||
|
||||
void ggml_opt_result_free(ggml_opt_result_t result) {
|
||||
delete result;
|
||||
}
|
||||
|
||||
void ggml_opt_result_reset(ggml_opt_result_t result) {
|
||||
result->ndata = 0;
|
||||
result->loss.clear();
|
||||
result->pred.clear();
|
||||
result->ncorrect = 0;
|
||||
}
|
||||
|
||||
void ggml_opt_result_ndata(ggml_opt_result_t result, int64_t * ndata) {
|
||||
*ndata = result->ndata;
|
||||
}
|
||||
|
||||
void ggml_opt_result_loss(ggml_opt_result_t result, double * loss, double * unc) {
|
||||
const int64_t nbatches = result->loss.size(); // Number of physical batches.
|
||||
|
||||
if (nbatches == 0) {
|
||||
*loss = 0.0;
|
||||
*unc = NAN;
|
||||
return;
|
||||
}
|
||||
|
||||
double sum = 0.0;
|
||||
double sum_squared = 0.0;
|
||||
|
||||
for (const float & loss : result->loss) {
|
||||
// If the loss is per datapoint it was scaled by 1.0f/opt_period for each physical batch.
|
||||
const float loss_scaled = result->loss_per_datapoint ? loss*result->opt_period : loss;
|
||||
sum += loss_scaled;
|
||||
sum_squared += loss_scaled*loss_scaled;
|
||||
}
|
||||
|
||||
const double mean = sum/nbatches;
|
||||
*loss = result->loss_per_datapoint ? mean : sum;
|
||||
|
||||
if (!unc) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (nbatches < 2) {
|
||||
*unc = NAN;
|
||||
return;
|
||||
}
|
||||
|
||||
const double var_sum = sum_squared/nbatches - mean*mean; // variance without Bessel's correction, i.e. nbatches/(nbatches-1)
|
||||
*unc = result->loss_per_datapoint ? sqrt(var_sum / (nbatches - 1)) : sqrt(var_sum * nbatches/(nbatches - 1));
|
||||
}
|
||||
|
||||
void ggml_opt_result_pred(ggml_opt_result_t result, int32_t * pred) {
|
||||
for (size_t i = 0; i < result->pred.size(); ++i) {
|
||||
pred[i] = result->pred[i];
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_opt_result_accuracy(ggml_opt_result_t result, double * accuracy, double * unc) {
|
||||
*accuracy = result->ncorrect >= 0 ? double(result->ncorrect) / double(result->ndata) : NAN;
|
||||
|
||||
if (!unc) {
|
||||
return;
|
||||
}
|
||||
|
||||
*unc = result->ncorrect >= 0 && result->ndata >= 2 ?
|
||||
sqrt((*accuracy) * (1.0 - (*accuracy)) / double(result->ndata - 1)) : NAN;
|
||||
}
|
||||
|
||||
// ====== 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) {
|
||||
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);
|
||||
GGML_ASSERT(opt_pars.adamw.beta1 >= 0.0f);
|
||||
GGML_ASSERT(opt_pars.adamw.beta1 <= 1.0f);
|
||||
GGML_ASSERT(opt_pars.adamw.beta2 >= 0.0f);
|
||||
GGML_ASSERT(opt_pars.adamw.beta2 <= 1.0f);
|
||||
GGML_ASSERT(opt_pars.adamw.eps >= 0.0f);
|
||||
GGML_ASSERT(opt_pars.adamw.wd >= 0.0f);
|
||||
GGML_ASSERT(opt_pars.adamw.wd <= 1.0f);
|
||||
|
||||
// beta1, beta2 after applying warmup
|
||||
const float beta1h = 1.0f/(1.0f - powf(opt_pars.adamw.beta1, opt_ctx->iter));
|
||||
const float beta2h = 1.0f/(1.0f - powf(opt_pars.adamw.beta2, opt_ctx->iter));
|
||||
|
||||
float * adamw_par_data = ggml_get_data_f32(opt_ctx->adamw_params);
|
||||
adamw_par_data[0] = opt_pars.adamw.alpha;
|
||||
adamw_par_data[1] = opt_pars.adamw.beta1;
|
||||
adamw_par_data[2] = opt_pars.adamw.beta2;
|
||||
adamw_par_data[3] = opt_pars.adamw.eps;
|
||||
adamw_par_data[4] = opt_pars.adamw.wd;
|
||||
adamw_par_data[5] = beta1h;
|
||||
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;
|
||||
|
||||
if (!result) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (result->ndata == 0) {
|
||||
result->loss_per_datapoint = opt_ctx->loss_per_datapoint;
|
||||
result->opt_period = opt_ctx->opt_period;
|
||||
} else {
|
||||
GGML_ASSERT(result->loss_per_datapoint == opt_ctx->loss_per_datapoint);
|
||||
GGML_ASSERT(result->opt_period == opt_ctx->opt_period);
|
||||
}
|
||||
|
||||
const int64_t ndata = opt_ctx->outputs->ne[1];
|
||||
GGML_ASSERT(result->ndata == ndata*int64_t(result->loss.size()) && "varying batch size not supported");
|
||||
result->ndata += ndata;
|
||||
|
||||
GGML_ASSERT(ggml_is_scalar(opt_ctx->loss));
|
||||
GGML_ASSERT(opt_ctx->loss->type == GGML_TYPE_F32);
|
||||
float loss;
|
||||
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->labels || result->ncorrect < 0) {
|
||||
result->ncorrect = -1;
|
||||
return;
|
||||
}
|
||||
|
||||
GGML_ASSERT(ggml_is_scalar(opt_ctx->ncorrect));
|
||||
GGML_ASSERT(opt_ctx->ncorrect->type == GGML_TYPE_I64);
|
||||
int64_t ncorrect;
|
||||
ggml_backend_tensor_get(opt_ctx->ncorrect, &ncorrect, 0, ggml_nbytes(opt_ctx->ncorrect));
|
||||
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(
|
||||
ggml_opt_context_t opt_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) {
|
||||
struct ggml_tensor * inputs = ggml_opt_inputs(opt_ctx);
|
||||
struct ggml_tensor * labels = ggml_opt_labels(opt_ctx);
|
||||
struct ggml_tensor * data = ggml_opt_dataset_data(dataset);
|
||||
GGML_ASSERT(data->ne[0] == inputs->ne[0]);
|
||||
|
||||
const int64_t ndata = data->ne[1];
|
||||
const int64_t ndata_batch = inputs->ne[1];
|
||||
|
||||
GGML_ASSERT(data->ne[1] % inputs->ne[1] == 0);
|
||||
const int64_t nbatches = ndata/ndata_batch;
|
||||
|
||||
idata_split = idata_split < 0 ? ndata : idata_split;
|
||||
GGML_ASSERT(idata_split % ndata_batch == 0);
|
||||
const int64_t ibatch_split = idata_split / ndata_batch;
|
||||
|
||||
int64_t ibatch = 0;
|
||||
int64_t t_loop_start = ggml_time_us();
|
||||
for (; ibatch < ibatch_split; ++ibatch) {
|
||||
ggml_opt_dataset_get_batch(dataset, inputs, labels, ibatch);
|
||||
ggml_opt_forward_backward(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_dataset_get_batch(dataset, inputs, labels, ibatch);
|
||||
ggml_opt_forward(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);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_opt_epoch_callback_progress_bar(
|
||||
bool train,
|
||||
ggml_opt_context_t opt_ctx,
|
||||
ggml_opt_dataset_t dataset,
|
||||
ggml_opt_result_t result,
|
||||
int64_t ibatch,
|
||||
int64_t ibatch_max,
|
||||
int64_t t_start_us) {
|
||||
fprintf(stderr, "%s[", train ? "train: " : "val: ");
|
||||
|
||||
constexpr int64_t bar_length = 25;
|
||||
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, ">");
|
||||
} else {
|
||||
fprintf(stderr, " ");
|
||||
}
|
||||
}
|
||||
|
||||
const int64_t batch_size = ggml_opt_inputs(opt_ctx)->ne[1];
|
||||
const int64_t idata = ibatch*batch_size;
|
||||
const int64_t idata_max = ibatch_max*batch_size;
|
||||
|
||||
double loss;
|
||||
double loss_unc;
|
||||
ggml_opt_result_loss(result, &loss, &loss_unc);
|
||||
|
||||
double accuracy;
|
||||
double accuracy_unc;
|
||||
ggml_opt_result_accuracy(result, &accuracy, &accuracy_unc);
|
||||
|
||||
const int64_t t_ibatch_us = ggml_time_us() - t_start_us;
|
||||
int64_t t_ibatch_s = t_ibatch_us / 1000000;
|
||||
const int64_t t_ibatch_h = t_ibatch_s / 3600;
|
||||
t_ibatch_s -= t_ibatch_h * 3600;
|
||||
const int64_t t_ibatch_m = t_ibatch_s / 60;
|
||||
t_ibatch_s -= t_ibatch_m * 60;
|
||||
|
||||
const int64_t t_eta_us = t_ibatch_us * (ibatch_max - ibatch)/ibatch;
|
||||
int64_t t_eta_s = t_eta_us / 1000000;
|
||||
const int64_t t_eta_h = t_eta_s / 3600;
|
||||
t_eta_s -= t_eta_h * 3600;
|
||||
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",
|
||||
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) {
|
||||
fprintf(stderr, "\n");
|
||||
}
|
||||
fflush(stderr);
|
||||
|
||||
GGML_UNUSED(dataset);
|
||||
}
|
||||
|
||||
void ggml_opt_fit(
|
||||
ggml_backend_sched_t backend_sched,
|
||||
ggml_context * ctx_compute,
|
||||
ggml_tensor * inputs,
|
||||
ggml_tensor * outputs,
|
||||
ggml_opt_dataset_t dataset,
|
||||
enum ggml_opt_loss_type loss_type,
|
||||
ggml_opt_get_optimizer_params get_opt_pars,
|
||||
int64_t nepoch,
|
||||
int64_t nbatch_logical,
|
||||
float val_split,
|
||||
bool silent) {
|
||||
ggml_time_init();
|
||||
const int64_t t_start_us = ggml_time_us();
|
||||
|
||||
const int64_t ndata = ggml_opt_dataset_data(dataset)->ne[1];
|
||||
const int64_t nbatch_physical = inputs->ne[1];
|
||||
GGML_ASSERT(ndata % nbatch_logical == 0);
|
||||
GGML_ASSERT(nbatch_logical % nbatch_physical == 0);
|
||||
|
||||
const int64_t opt_period = nbatch_logical / nbatch_physical;
|
||||
const int64_t nbatches_logical = ndata / nbatch_logical;
|
||||
|
||||
GGML_ASSERT(val_split >= 0.0f);
|
||||
GGML_ASSERT(val_split < 1.0f);
|
||||
const int64_t ibatch_split = int64_t(((1.0f - val_split) * nbatches_logical)) * opt_period; // train <-> val split index (physical)
|
||||
const int64_t idata_split = ibatch_split * nbatch_physical;
|
||||
|
||||
int64_t epoch = 1;
|
||||
|
||||
ggml_opt_params params = ggml_opt_default_params(backend_sched, ctx_compute, inputs, outputs, loss_type);
|
||||
params.opt_period = opt_period;
|
||||
params.get_opt_pars = get_opt_pars;
|
||||
params.get_opt_pars_ud = &epoch;
|
||||
ggml_opt_context_t opt_ctx = ggml_opt_init(params);
|
||||
|
||||
// Shuffling the data is generally useful but there is only a point if not all data is used in a single batch.
|
||||
if (nbatch_logical < ndata) {
|
||||
ggml_opt_dataset_shuffle(opt_ctx, dataset, -1); // Shuffle all data (train + validation).
|
||||
}
|
||||
|
||||
ggml_opt_result_t result_train = ggml_opt_result_init();
|
||||
ggml_opt_result_t result_val = ggml_opt_result_init();
|
||||
|
||||
ggml_opt_epoch_callback epoch_callback = silent ? nullptr : ggml_opt_epoch_callback_progress_bar;
|
||||
|
||||
for (; epoch <= nepoch; ++epoch) {
|
||||
if (nbatch_logical < idata_split) {
|
||||
ggml_opt_dataset_shuffle(opt_ctx, dataset, idata_split);
|
||||
}
|
||||
|
||||
ggml_opt_result_reset(result_train);
|
||||
ggml_opt_result_reset(result_val);
|
||||
|
||||
if (!silent) {
|
||||
fprintf(stderr, "%s: epoch %04" PRId64 "/%04" PRId64 ":\n", __func__, epoch, nepoch);
|
||||
}
|
||||
ggml_opt_epoch(opt_ctx, dataset, result_train, result_val, idata_split, epoch_callback, epoch_callback);
|
||||
if (!silent) {
|
||||
fprintf(stderr, "\n");
|
||||
}
|
||||
}
|
||||
|
||||
if (!silent) {
|
||||
int64_t t_total_s = (ggml_time_us() - t_start_us) / 1000000;
|
||||
const int64_t t_total_h = t_total_s / 3600;
|
||||
t_total_s -= t_total_h * 3600;
|
||||
const int64_t t_total_m = t_total_s / 60;
|
||||
t_total_s -= t_total_m * 60;
|
||||
fprintf(stderr, "%s: training took %02" PRId64 ":%02" PRId64 ":%02" PRId64 "\n", __func__, t_total_h, t_total_m, t_total_s);
|
||||
}
|
||||
|
||||
ggml_opt_free(opt_ctx);
|
||||
ggml_opt_result_free(result_train);
|
||||
ggml_opt_result_free(result_val);
|
||||
}
|
1750
ggml/src/ggml.c
1750
ggml/src/ggml.c
File diff suppressed because it is too large
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