diff --git a/common/train.h b/common/train.h index d86c93cc4..263d940c0 100644 --- a/common/train.h +++ b/common/train.h @@ -9,6 +9,8 @@ #include "ggml.h" #include "llama.h" +#define LLAMA_TRAIN_MAX_NODES 16384 + typedef std::string mt19937_state; struct train_state { diff --git a/examples/benchmark/benchmark-matmult.cpp b/examples/benchmark/benchmark-matmult.cpp index 76e3f57cc..284733b10 100644 --- a/examples/benchmark/benchmark-matmult.cpp +++ b/examples/benchmark/benchmark-matmult.cpp @@ -171,7 +171,8 @@ int main(int argc, char ** argv) { struct ggml_tensor * m11xm2 = ggml_mul_mat(ctx, m11, m2); // printf("Creating compute graph\n"); - struct ggml_cgraph gf = ggml_build_forward(m11xm2); + struct ggml_cgraph * gf = ggml_new_graph(ctx); + ggml_build_forward_expand(gf, m11xm2); printf("n_threads=%i\n", benchmark_params.n_threads); @@ -180,9 +181,9 @@ int main(int argc, char ** argv) { std::vector work_buffer; - ggml_graph_compute_helper(work_buffer, &gf, benchmark_params.n_threads); + ggml_graph_compute_helper(work_buffer, gf, benchmark_params.n_threads); - TENSOR_DUMP(gf.nodes[0]); + TENSOR_DUMP(gf->nodes[0]); printf("\n------ Test 2 - Matrix Mult via %s code\n", ggml_type_name(qtype)); @@ -200,7 +201,8 @@ int main(int argc, char ** argv) { struct ggml_tensor * q31 = ggml_mul_mat(ctx, q11, m2); // printf("Creating compute graph\n"); - struct ggml_cgraph gf31 = ggml_build_forward(q31); + struct ggml_cgraph * gf31 = ggml_new_graph(ctx); + ggml_build_forward_expand(gf31, q31); // Set up a second graph computation to make sure we override the CPU cache lines // printf("Creating new tensor q12 & Running quantize\n"); @@ -211,7 +213,8 @@ int main(int argc, char ** argv) { struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2); //printf("Creating compute graph\n"); - struct ggml_cgraph gf32 = ggml_build_forward(q32); + struct ggml_cgraph * gf32 = ggml_new_graph(ctx); + ggml_build_forward_expand(gf32, q32); printf("n_threads=%i\n", benchmark_params.n_threads); const int dimx = sizex; @@ -223,7 +226,7 @@ int main(int argc, char ** argv) { // Let's use the F32 result from above as a reference for the quantized multiplication - float sum_of_F32_reference = tensor_sum_elements(gf.nodes[0]); + float sum_of_F32_reference = tensor_sum_elements(gf->nodes[0]); printf("Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_u_Seconds; gigaFLOPS\n"); printf("=====================================================================================\n"); @@ -233,7 +236,7 @@ int main(int argc, char ** argv) { long long int start = ggml_time_us(); //printf("Running ggml_graph_compute\n"); - ggml_graph_compute_helper(work_buffer, &gf31, benchmark_params.n_threads); + ggml_graph_compute_helper(work_buffer, gf31, benchmark_params.n_threads); long long int stop = ggml_time_us(); long long int usec = stop-start; @@ -251,7 +254,7 @@ int main(int argc, char ** argv) { // Check that the matrix multiplication result is in the right ballpark // We cannot use the exact value from the F32 multiplication because the quantizuation will be slightly different - float sum_of_Q4_result = tensor_sum_elements(gf31.nodes[0]); + float sum_of_Q4_result = tensor_sum_elements(gf31->nodes[0]); float delta = std::abs(sum_of_Q4_result - sum_of_F32_reference); float allowed_delta = (sum_of_F32_reference) / 1000 / 1000; // Let's accept an epsilon of 10^-6 @@ -266,7 +269,7 @@ int main(int argc, char ** argv) { } // Running a different graph computation to make sure we override the CPU cache lines - ggml_graph_compute_helper(work_buffer, &gf32, benchmark_params.n_threads); + ggml_graph_compute_helper(work_buffer, gf32, benchmark_params.n_threads); } printf("\n"); printf("Average%78.2f\n",gflops_sum/((double)benchmark_params.n_iterations)); diff --git a/examples/export-lora/export-lora.cpp b/examples/export-lora/export-lora.cpp index d803cfd5c..c8754ce70 100644 --- a/examples/export-lora/export-lora.cpp +++ b/examples/export-lora/export-lora.cpp @@ -240,7 +240,7 @@ static struct lora_data * load_lora(struct lora_info * info) { } struct ggml_init_params params_ggml; - params_ggml.mem_size = ggml_tensor_overhead() * GGML_MAX_NODES; + params_ggml.mem_size = ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE; params_ggml.mem_buffer = NULL; params_ggml.no_alloc = true; result->ctx = ggml_init(params_ggml); @@ -334,7 +334,7 @@ static bool apply_lora(struct ggml_tensor * tensor, struct lora_data * lora, int float scaling = lora->info.scale * (float)lora->lora_alpha / (float)lora->lora_r; struct ggml_init_params params; - params.mem_size = GGML_OBJECT_SIZE + GGML_GRAPH_SIZE + ggml_tensor_overhead()*4 + GGML_MEM_ALIGN*5; + params.mem_size = GGML_OBJECT_SIZE + ggml_graph_overhead() + ggml_tensor_overhead()*4 + GGML_MEM_ALIGN*5; params.mem_buffer = NULL; params.no_alloc = true; struct ggml_context * ctx = NULL; diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 649a3b7c1..248927966 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -1742,8 +1742,8 @@ int main(int argc, char ** argv) { // context for compute tensors without their data size_t estimated_compute_size_wo_data = ( - ggml_tensor_overhead()*GGML_MAX_NODES*2 - + (GGML_OBJECT_SIZE+GGML_GRAPH_SIZE)*( + ggml_tensor_overhead()*LLAMA_TRAIN_MAX_NODES*2 + + (GGML_OBJECT_SIZE+ggml_graph_overhead())*( params.common.use_checkpointing ? 3 : 2 ) ); diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 61932e659..877eb834b 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -664,7 +664,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { // measure mem requirement and allocate { static const size_t tensor_alignment = 32; - new_clip->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead()); + new_clip->buf_compute.resize(ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead()); new_clip->alloc = ggml_allocr_new_measure(tensor_alignment); clip_image_f32_batch batch; batch.size = 1; diff --git a/examples/metal/metal.cpp b/examples/metal/metal.cpp index c05a4fa93..16c1146f9 100644 --- a/examples/metal/metal.cpp +++ b/examples/metal/metal.cpp @@ -34,7 +34,7 @@ int main(int argc, char ** argv) { struct ggml_context * ctx_data = NULL; struct ggml_context * ctx_eval = NULL; - struct ggml_cgraph gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval); + struct ggml_cgraph * gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval); // this allocates all Metal resources and memory buffers auto * ctx_metal = ggml_metal_init(1); @@ -46,13 +46,13 @@ int main(int argc, char ** argv) { // main { - struct ggml_tensor * input = ggml_graph_get_tensor(&gf, "embd"); + struct ggml_tensor * input = ggml_graph_get_tensor(gf, "embd"); *(int32_t *) input->data = 1; // BOS ggml_metal_set_tensor(ctx_metal, input); // warmup - ggml_metal_graph_compute(ctx_metal, &gf); + ggml_metal_graph_compute(ctx_metal, gf); const int n_iter = 16; @@ -60,7 +60,7 @@ int main(int argc, char ** argv) { // the actual inference happens here for (int i = 0; i < n_iter; ++i) { - ggml_metal_graph_compute(ctx_metal, &gf); + ggml_metal_graph_compute(ctx_metal, gf); } const int64_t t1 = ggml_time_us(); @@ -70,7 +70,7 @@ int main(int argc, char ** argv) { // debug output { - struct ggml_tensor * logits = gf.nodes[gf.n_nodes - 1]; + struct ggml_tensor * logits = gf->nodes[gf->n_nodes - 1]; ggml_metal_get_tensor(ctx_metal, logits); float * ptr = (float *) ggml_get_data(logits); diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 2a257e632..8e5eb4230 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -1109,8 +1109,8 @@ int main(int argc, char ** argv) { // context for compute tensors without their data size_t estimated_compute_size_wo_data = ( - ggml_tensor_overhead()*GGML_MAX_NODES*2 - + (GGML_OBJECT_SIZE+GGML_GRAPH_SIZE)*( + ggml_tensor_overhead()*LLAMA_TRAIN_MAX_NODES*2 + + (GGML_OBJECT_SIZE+ggml_graph_overhead())*( params.common.use_checkpointing ? 3 : 2 ) ); diff --git a/ggml-metal.m b/ggml-metal.m index b33a3cb8f..9136a7cf6 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1,5 +1,6 @@ #import "ggml-metal.h" +#import "ggml-backend-impl.h" #import "ggml.h" #import @@ -23,7 +24,7 @@ #define UNUSED(x) (void)(x) -#define GGML_MAX_CONCUR (2*GGML_MAX_NODES) +#define GGML_MAX_CONCUR (2*GGML_DEFAULT_GRAPH_SIZE) struct ggml_metal_buffer { const char * name; @@ -744,6 +745,20 @@ void ggml_metal_graph_compute( struct ggml_tensor * src1 = gf->nodes[i]->src[1]; struct ggml_tensor * dst = gf->nodes[i]; + switch (dst->op) { + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_TRANSPOSE: + case GGML_OP_PERMUTE: + { + // noop -> next node + } continue; + default: + { + } break; + } + const int64_t ne00 = src0 ? src0->ne[0] : 0; const int64_t ne01 = src0 ? src0->ne[1] : 0; const int64_t ne02 = src0 ? src0->ne[2] : 0; @@ -797,14 +812,6 @@ void ggml_metal_graph_compute( //} switch (dst->op) { - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_TRANSPOSE: - case GGML_OP_PERMUTE: - { - // noop - } break; case GGML_OP_CONCAT: { const int64_t nb = ne00; diff --git a/ggml.h b/ggml.h index 1cbf3b9eb..98afb2033 100644 --- a/ggml.h +++ b/ggml.h @@ -1733,9 +1733,6 @@ extern "C" { 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, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep); - GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor); - GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep); - // 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); diff --git a/llama.cpp b/llama.cpp index eb3f5e4da..2c74e8b0e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8559,7 +8559,7 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat const size_t elt_size = ggml_element_size(kv_self.k); ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true }); - ggml_cgraph gf{}; + ggml_cgraph * gf = ggml_new_graph(cpy_ctx); ggml_tensor * kout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_head, n_layer); std::vector kout3d_data(ggml_nbytes(kout3d), 0); @@ -8577,9 +8577,9 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat kv_head, n_embd, n_layer, elt_size*n_ctx, elt_size*n_ctx*n_embd, 0); - ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, k3d, kout3d)); - ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, v3d, vout3d)); - ggml_graph_compute_helper(ctx->work_buffer, &gf, /*n_threads*/ 1); + ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, k3d, kout3d)); + ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, v3d, vout3d)); + ggml_graph_compute_helper(ctx->work_buffer, gf, /*n_threads*/ 1); ggml_free(cpy_ctx); @@ -8687,7 +8687,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { const size_t elt_size = ggml_element_size(kv_self.k); ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true }); - ggml_cgraph gf{}; + ggml_cgraph * gf = ggml_new_graph(cpy_ctx); ggml_tensor * kin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_head, n_layer); kin3d->data = (void *) inp; @@ -8705,9 +8705,9 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { kv_head, n_embd, n_layer, elt_size*n_ctx, elt_size*n_ctx*n_embd, 0); - ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, kin3d, k3d)); - ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, vin3d, v3d)); - ggml_graph_compute_helper(ctx->work_buffer, &gf, /*n_threads*/ 1); + ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, kin3d, k3d)); + ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, vin3d, v3d)); + ggml_graph_compute_helper(ctx->work_buffer, gf, /*n_threads*/ 1); ggml_free(cpy_ctx); }