ggml : change ggml_graph_compute() API to not require context

.github/workflows/build.yml

@@ -16,7 +16,9 @@ on:
  paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu']
 
 env:
- BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
+ BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
+ GGML_NLOOP: 3
+ GGML_NITER: 1
 
 jobs:
  ubuntu-focal-make:
@@ -64,7 +66,7 @@ jobs:
  id: cmake_test
  run: |
  cd build
- ctest --verbose
+ ctest --verbose --timeout 900
 
  ubuntu-latest-cmake-sanitizer:
  runs-on: ubuntu-latest
@@ -99,7 +101,7 @@ jobs:
  id: cmake_test
  run: |
  cd build
- ctest --verbose
+ ctest --verbose --timeout 900
 
  macOS-latest-make:
  runs-on: macos-latest
@@ -147,10 +149,11 @@ jobs:
  id: cmake_test
  run: |
  cd build
- ctest --verbose
+ ctest --verbose --timeout 900
 
  windows-latest-cmake:
  runs-on: windows-latest
+
  env:
  OPENBLAS_VERSION: 0.3.23
  OPENCL_VERSION: 2023.04.17
@@ -249,7 +252,7 @@ jobs:
  if: ${{ matrix.build != 'clblast' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} # Test AVX-512 only when possible
  run: |
  cd build
- ctest -C Release --verbose
+ ctest -C Release --verbose --timeout 900
 
  - name: Get commit hash
  id: commit

examples/baby-llama/baby-llama.cpp

@@ -31,6 +31,17 @@ float frand_normal(struct random_normal_distribution * rnd) {
  return ((r < rnd->min) ? (rnd->min) : (r > rnd->max) ? (rnd->max) : r);
 }
 
+void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
+ struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
+
+ if (plan.work_size > 0) {
+ buf.resize(plan.work_size);
+ plan.work_data = buf.data();
+ }
+
+ ggml_graph_compute(graph, &plan);
+}
+
 struct ggml_tensor * randomize_tensor(
  struct ggml_tensor * tensor,
  int ndims,
@@ -1569,6 +1580,8 @@ int main(int argc, char ** argv) {
  int n_tokens = model.hparams.n_ctx;
  int n_vocab = model.hparams.n_vocab;
 
+ std::vector<uint8_t> work_buffer;
+
  for (int ex=0; ex<n_examples; ++ex) {
  struct ggml_init_params params = {
  /*.mem_size =*/ compute_size,
@@ -1586,7 +1599,6 @@ int main(int argc, char ** argv) {
  int n_past = 0;
 
  ggml_cgraph gf = {};
- gf.n_threads = 1;
 
  get_example_targets_batch(ctx0, 64*ex+0, tokens_input, targets);
 
@@ -1595,7 +1607,7 @@ int main(int argc, char ** argv) {
  struct ggml_tensor * e = square_error_loss(ctx0, targets, logits);
 
  ggml_build_forward_expand(&gf, e);
- ggml_graph_compute(ctx0, &gf);
+ ggml_graph_compute_helper(work_buffer, &gf, /*n_threads*/ 1);
 
  float error_before_opt = ggml_get_f32_1d(e, 0);
 
@@ -1611,7 +1623,7 @@ int main(int argc, char ** argv) {
  ggml_opt(ctx0, opt_params_lbfgs, e);
  //
  ggml_build_forward_expand(&gf, e);
- ggml_graph_compute(ctx0, &gf);
+ ggml_graph_compute_helper(work_buffer, &gf, /*n_threads*/ 1);
 
  float error_after_opt = ggml_get_f32_1d(e, 0);
 
@@ -1659,13 +1671,12 @@ int main(int argc, char ** argv) {
  struct ggml_context * ctx0 = ggml_init(params);
 
  ggml_cgraph gf = {};
- gf.n_threads = 1;
 
  int n_past = 0;
  struct ggml_tensor * logits = forward(&model, &kv_self, ctx0, &gf, tokens_input, sample_ctx, n_past);
 
  ggml_build_forward_expand(&gf, logits);
- ggml_graph_compute(ctx0, &gf);
+ ggml_graph_compute_helper(work_buffer, &gf, /*n_threads*/ 1);
 
  struct ggml_tensor * best_samples = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, sample_ctx);
  struct ggml_tensor * probs = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_vocab, sample_ctx);
@@ -1687,10 +1698,11 @@ int main(int argc, char ** argv) {
  }
 
  print_matrix(model.tok_embeddings);
-
  printf("done\n");
+
  // ggml_free(kv_self.ctx);
  // ggml_free(model_lora.ctx);
  ggml_free(model.ctx);
+
  return 0;
 }

examples/benchmark/benchmark-matmult.cpp

@@ -20,6 +20,17 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
+void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
+ struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
+
+ if (plan.work_size > 0) {
+ buf.resize(plan.work_size);
+ plan.work_data = buf.data();
+ }
+
+ ggml_graph_compute(graph, &plan);
+}
+
 float tensor_sum_elements(const ggml_tensor * tensor) {
  float sum = 0;
  if (tensor->type==GGML_TYPE_F32) {
@@ -159,13 +170,14 @@ int main(int argc, char ** argv) {
  // printf("Creating compute graph\n");
  struct ggml_cgraph gf = ggml_build_forward(m11xm2);
 
- gf.n_threads=benchmark_params.n_threads;
- printf("cgraph->n_threads=%i\n",gf.n_threads);
+ printf("n_threads=%i\n", benchmark_params.n_threads);
 
  TENSOR_DUMP(m11);
  TENSOR_DUMP(m2);
 
- ggml_graph_compute(ctx, &gf);
+ std::vector<uint8_t> work_buffer;
+
+ ggml_graph_compute_helper(work_buffer, &gf, benchmark_params.n_threads);
 
  TENSOR_DUMP(gf.nodes[0]);
 
@@ -187,7 +199,6 @@ int main(int argc, char ** argv) {
 
  // printf("Creating compute graph\n");
  struct ggml_cgraph gf31 = ggml_build_forward(q31);
- gf31.n_threads=benchmark_params.n_threads;
 
  // Set up a second graph computation to make sure we override the CPU cache lines
  // printf("Creating new tensor q12 & Running quantize\n");
@@ -199,8 +210,7 @@ int main(int argc, char ** argv) {
 
  //printf("Creating compute graph\n");
  struct ggml_cgraph gf32 = ggml_build_forward(q32);
- gf32.n_threads=benchmark_params.n_threads;
- printf("cgraph->n_threads=%i\n",gf31.n_threads);
+ printf("n_threads=%i\n", benchmark_params.n_threads);
 
  const int dimx = sizex;
  const int dimy = sizey;
@@ -221,14 +231,15 @@ int main(int argc, char ** argv) {
 
  long long int start = ggml_time_us();
  //printf("Running ggml_graph_compute\n");
- ggml_graph_compute(ctx, &gf31);
+ ggml_graph_compute_helper(work_buffer, &gf31, benchmark_params.n_threads);
+
  long long int stop = ggml_time_us();
  long long int usec = stop-start;
  double gflops = (double)(flops_per_matrix)/usec/1000.0;
  gflops_sum += gflops;
  printf("%9i;%8i;%6i;%6i;%6i;%15lli;%18lli;%10.2f\n",
  i,
- gf31.n_threads,
+ benchmark_params.n_threads,
  sizex, sizey, sizez, flops_per_matrix,
  usec,gflops);
 
@@ -253,7 +264,7 @@ int main(int argc, char ** argv) {
  }
 
  // Running a different graph computation to make sure we override the CPU cache lines
- ggml_graph_compute(ctx, &gf32);
+ 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));

examples/metal/metal.cpp

@@ -35,10 +35,9 @@ int main(int argc, char ** argv) {
  struct ggml_context * ctx_eval = NULL;
 
  struct ggml_cgraph gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval);
- gf.n_threads = 1;
 
  // this allocates all Metal resources and memory buffers
- auto * ctx_metal = ggml_metal_init();
+ auto * ctx_metal = ggml_metal_init(1);
 
  const size_t max_size_data = ggml_get_max_tensor_size(ctx_data);
  const size_t max_size_eval = ggml_get_max_tensor_size(ctx_eval);

examples/train-text-from-scratch/train-text-from-scratch.cpp

@@ -60,6 +60,17 @@ float frand_uniform(struct random_uniform_distribution * rnd) {
  return rnd->rd(rnd->gen);
 }
 
+void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
+ struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
+
+ if (plan.work_size > 0) {
+ buf.resize(plan.work_size);
+ plan.work_data = buf.data();
+ }
+
+ ggml_graph_compute(graph, &plan);
+}
+
 struct ggml_tensor * randomize_tensor_normal(struct ggml_tensor * tensor, struct random_normal_distribution * rnd) {
  float scale = 1.0f; // xavier
  switch (tensor->n_dims) {
@@ -1426,11 +1437,9 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
 
  gf->n_nodes = 0;
  gf->n_leafs = 0;
- gf->work_size = 0;
  gf->perf_runs = 0;
  gf->perf_cycles = 0;
  gf->perf_time_us = 0;
- gf->work = NULL;
 
  const auto & hparams = model->hparams;
  //const int n_ctx = hparams.n_ctx;
@@ -3162,6 +3171,7 @@ int main(int argc, char ** argv) {
  printf("used_mem model+cache: %zu bytes\n", ggml_used_mem(model.ctx));
  // ggml_print_tensor_objects(model.ctx);
 
+ // TODO: use std::vector<uint8_t> intead of "new"
  size_t compute_size = 1024ll*1024ll*1024ll*((size_t) params.mem_compute_gb);
  uint8_t * compute_addr = new uint8_t[compute_size];
 
@@ -3183,6 +3193,8 @@ int main(int argc, char ** argv) {
  GGML_ASSERT(train_samples[i]+n_tokens-1 < (int) train_tokens.size());
  }
 
+ std::vector<uint8_t> work_buffer;
+
  printf("%s: begin training\n", __func__);
 
  for (int ex = 0; ex < params.n_examples; ++ex) {
@@ -3217,9 +3229,6 @@ int main(int argc, char ** argv) {
  struct ggml_cgraph * gf = (struct ggml_cgraph *) gfbuf->data;
  struct ggml_cgraph * gb = (struct ggml_cgraph *) gbbuf->data;
 
- // ggml_cgraph gf = {};
- gf->n_threads = params.n_threads;
- gb->n_threads = params.n_threads;
 
  get_example_targets_batch(lctx, train_samples.data(), train_samples.size(), train_tokens.data(), train_tokens.size(), ex, tokens_input, target_logits, target_probs);
 
@@ -3248,7 +3257,7 @@ int main(int argc, char ** argv) {
  *gb = ggml_build_backward(ctx0, gf, true);
  }
 
- ggml_graph_compute(ctx0, gf);
+ ggml_graph_compute_helper(work_buffer, gf, params.n_threads);
 
  size_t used_mem_before_opt = ggml_used_mem(ctx0);
 
@@ -3272,7 +3281,7 @@ int main(int argc, char ** argv) {
  model.train_samples += n_batch;
  model.train_tokens += n_batch * n_tokens;
 
- ggml_graph_compute(ctx0, gf);
+ ggml_graph_compute_helper(work_buffer, gf, params.n_threads);
 
  float error_after_opt = ggml_get_f32_1d(loss, 0);
 
@@ -3354,13 +3363,12 @@ int main(int argc, char ** argv) {
  struct ggml_context * ctx0 = ggml_init(cparams);
 
  ggml_cgraph gf = {};
- gf.n_threads = params.n_threads;
 
  int n_past = 0;
  struct ggml_tensor * logits = forward(&model, &kv_self, ctx0, &gf, tokens_input, sample_ctx, n_past);
 
  ggml_build_forward_expand(&gf, logits);
- ggml_graph_compute(ctx0, &gf);
+ ggml_graph_compute_helper(work_buffer, &gf, params.n_threads);
 
  //struct ggml_tensor * best_samples = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, sample_ctx);
  //struct ggml_tensor * probs = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_vocab, sample_ctx);
@@ -3386,6 +3394,7 @@ int main(int argc, char ** argv) {
  delete[] compute_addr;
  delete[] compute_buf_0;
  delete[] compute_buf_1;
+
  llama_free(lctx);
  llama_free_model(lmodel);
  ggml_free(model.ctx);

ggml-metal.h

@@ -34,9 +34,13 @@ extern "C" {
 
 struct ggml_metal_context;
 
-struct ggml_metal_context * ggml_metal_init(void);
+// number of command buffers to use
+struct ggml_metal_context * ggml_metal_init(int n_cb);
 void ggml_metal_free(struct ggml_metal_context * ctx);
 
+// set the number of command buffers to use
+void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb);
+
 // creates a mapping between a host memory buffer and a device memory buffer
 // - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute
 // - the mapping is used during computation to determine the arguments of the compute kernels

ggml-metal.m

@@ -25,6 +25,8 @@
 };
 
 struct ggml_metal_context {
+ int n_cb;
+
  float * logits;
 
  id<MTLDevice> device;
@@ -86,11 +88,12 @@ @interface GGMLMetalClass : NSObject
 @implementation GGMLMetalClass
 @end
 
-struct ggml_metal_context * ggml_metal_init(void) {
+struct ggml_metal_context * ggml_metal_init(int n_cb) {
  fprintf(stderr, "%s: allocating\n", __func__);
 
  struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context));
 
+ ctx->n_cb = n_cb;
  ctx->device = MTLCreateSystemDefaultDevice();
  ctx->queue = [ctx->device newCommandQueue];
  ctx->n_buffers = 0;
@@ -208,6 +211,10 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
  free(ctx);
 }
 
+void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) {
+ ctx->n_cb = n_cb;
+}
+
 // finds the Metal buffer that contains the tensor data on the GPU device
 // the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the
 // Metal buffer based on the host memory pointer
@@ -354,7 +361,7 @@ void ggml_metal_graph_compute(
  // create multiple command buffers and enqueue them
  // then, we encode the graph into the command buffers in parallel
 
- const int n_cb = gf->n_threads;
+ const int n_cb = ctx->n_cb;
 
  NSMutableArray * command_buffers = [NSMutableArray arrayWithCapacity:n_cb];