examples : update to ggml-opt and ggml-backend changes (#0)

ggml-ci

diff --git a/examples/gpt-2/main-sched.cpp b/examples/gpt-2/main-sched.cpp
index 9f42aca92fb7c8766d578675d636ea7d5520a44d..63176029f8a9b719ebe40c1675d08657b1809295 100644 (file)
--- a/examples/gpt-2/main-sched.cpp
+++ b/examples/gpt-2/main-sched.cpp
@@ -947,7 +947,7 @@ int main(int argc, char ** argv) {
     ggml_backend_sched_t sched;
     {
         // initialize the scheduler
-        sched = ggml_backend_sched_new(model.backends.data(), NULL, model.backends.size(), GPT2_MAX_NODES, false);
+        sched = ggml_backend_sched_new(model.backends.data(), NULL, model.backends.size(), GPT2_MAX_NODES, false, true);
 
         // create the worst case graph for memory usage estimation
         int n_tokens = std::min(model.hparams.n_ctx, params.n_batch);

diff --git a/examples/mnist/mnist-common.cpp b/examples/mnist/mnist-common.cpp
index c848823b033c07b71565f169fc79ebe3069b19fc..6bf369f5c578e5b3ce6d460ac22aa92bf3c1e4fc 100644 (file)
--- a/examples/mnist/mnist-common.cpp
+++ b/examples/mnist/mnist-common.cpp
@@ -310,10 +310,10 @@ mnist_model mnist_model_init_random(const std::string & arch, const std::string
 
 void mnist_model_build(mnist_model & model) {
     if (model.arch == "mnist-fc") {
-        ggml_set_param(model.ctx_compute, model.fc1_weight);
-        ggml_set_param(model.ctx_compute, model.fc1_bias);
-        ggml_set_param(model.ctx_compute, model.fc2_weight);
-        ggml_set_param(model.ctx_compute, model.fc2_bias);
+        ggml_set_param(model.fc1_weight);
+        ggml_set_param(model.fc1_bias);
+        ggml_set_param(model.fc2_weight);
+        ggml_set_param(model.fc2_bias);
 
         ggml_tensor * fc1 = ggml_relu(model.ctx_compute, ggml_add(model.ctx_compute,
             ggml_mul_mat(model.ctx_compute, model.fc1_weight, model.images),
@@ -322,12 +322,12 @@ void mnist_model_build(mnist_model & model) {
             ggml_mul_mat(model.ctx_compute, model.fc2_weight, fc1),
             model.fc2_bias);
     } else if (model.arch == "mnist-cnn") {
-        ggml_set_param(model.ctx_compute, model.conv1_kernel);
-        ggml_set_param(model.ctx_compute, model.conv1_bias);
-        ggml_set_param(model.ctx_compute, model.conv2_kernel);
-        ggml_set_param(model.ctx_compute, model.conv2_bias);
-        ggml_set_param(model.ctx_compute, model.dense_weight);
-        ggml_set_param(model.ctx_compute, model.dense_bias);
+        ggml_set_param(model.conv1_kernel);
+        ggml_set_param(model.conv1_bias);
+        ggml_set_param(model.conv2_kernel);
+        ggml_set_param(model.conv2_bias);
+        ggml_set_param(model.dense_weight);
+        ggml_set_param(model.dense_bias);
 
         struct ggml_tensor * images_2D = ggml_reshape_4d(model.ctx_compute, model.images, MNIST_HW, MNIST_HW, 1, model.images->ne[1]);
 
@@ -384,7 +384,7 @@ void mnist_model_build(mnist_model & model) {
 ggml_opt_result_t mnist_model_eval(mnist_model & model, ggml_opt_dataset_t dataset) {
     ggml_opt_result_t result = ggml_opt_result_init();
 
-    ggml_opt_params params = ggml_opt_default_params(model.backend_sched, model.ctx_compute, model.images, model.logits, GGML_OPT_LOSS_TYPE_CROSS_ENTROPY);
+    ggml_opt_params params = ggml_opt_default_params(model.backend_sched, GGML_OPT_LOSS_TYPE_CROSS_ENTROPY);
     params.build_type = GGML_OPT_BUILD_TYPE_FORWARD;
     ggml_opt_context_t opt_ctx = ggml_opt_init(params);
 
@@ -453,7 +453,7 @@ extern "C" {
 int wasm_eval(uint8_t * digitPtr) {
     std::vector<float> digit(digitPtr, digitPtr + MNIST_NINPUT);
 
-    ggml_opt_dataset_t dataset = ggml_opt_dataset_init(MNIST_NINPUT, MNIST_NCLASSES, 1, 1);
+    ggml_opt_dataset_t dataset = ggml_opt_dataset_init(GGML_TYPE_F32, GGML_TYPE_F32, MNIST_NINPUT, MNIST_NCLASSES, 1, 1);
     struct ggml_tensor * data = ggml_opt_dataset_data(dataset);
     memcpy(data->data, digitPtr, ggml_nbytes(data));
     ggml_set_zero(ggml_opt_dataset_labels(dataset)); // The labels are not needed.

diff --git a/examples/mnist/mnist-common.h b/examples/mnist/mnist-common.h
index 0ecca3f6b8dcdf7b7ecfb723370e98d6f5aa4eb9..a663b3b4527beeca5ab25263d2eafa4ec2d8e7f2 100644 (file)
--- a/examples/mnist/mnist-common.h
+++ b/examples/mnist/mnist-common.h
@@ -108,7 +108,7 @@ struct mnist_model {
         }
 
         // The order of the backends passed to ggml_backend_sched_new determines which backend is given priority.
-        backend_sched = ggml_backend_sched_new(backends.data(), nullptr, backends.size(), GGML_DEFAULT_GRAPH_SIZE, false);
+        backend_sched = ggml_backend_sched_new(backends.data(), nullptr, backends.size(), GGML_DEFAULT_GRAPH_SIZE, false, true);
         fprintf(stderr, "%s: using %s (%s) as primary backend\n",
                 __func__, ggml_backend_name(backends[0]), ggml_backend_dev_description(devices[0]));
         if (backends.size() >= 2) {

diff --git a/examples/mnist/mnist-eval.cpp b/examples/mnist/mnist-eval.cpp
index 218742cfedd2f446d847e44f60887bd27e22d884..3a45233406f20841c2b8f9a7284294fb2d5db806 100644 (file)
--- a/examples/mnist/mnist-eval.cpp
+++ b/examples/mnist/mnist-eval.cpp
@@ -25,7 +25,7 @@ int main(int argc, char ** argv) {
         exit(1);
     }
 
-    ggml_opt_dataset_t dataset = ggml_opt_dataset_init(MNIST_NINPUT, MNIST_NCLASSES, MNIST_NTEST, MNIST_NBATCH_PHYSICAL);
+    ggml_opt_dataset_t dataset = ggml_opt_dataset_init(GGML_TYPE_F32, GGML_TYPE_F32, MNIST_NINPUT, MNIST_NCLASSES, MNIST_NTEST, MNIST_NBATCH_PHYSICAL);
 
     if (!mnist_image_load(argv[2], dataset)) {
         return 1;

diff --git a/examples/mnist/mnist-train.cpp b/examples/mnist/mnist-train.cpp
index a61dd05b0aff1d58a634d82b4df7ec2e18257e4b..b9df47b962ec4d696ee5d8274fb9016327658612 100644 (file)
--- a/examples/mnist/mnist-train.cpp
+++ b/examples/mnist/mnist-train.cpp
@@ -20,7 +20,7 @@ int main(int argc, char ** argv) {
     // The MNIST model is so small that the overhead from data shuffling is non-negligible, especially with CUDA.
     // With a shard size of 10 this overhead is greatly reduced at the cost of less shuffling (does not seem to have a significant impact).
     // A batch of 500 images then consists of 50 random shards of size 10 instead of 500 random shards of size 1.
-    ggml_opt_dataset_t dataset = ggml_opt_dataset_init(MNIST_NINPUT, MNIST_NCLASSES, MNIST_NTRAIN, /*ndata_shard =*/ 10);
+    ggml_opt_dataset_t dataset = ggml_opt_dataset_init(GGML_TYPE_F32, GGML_TYPE_F32, MNIST_NINPUT, MNIST_NCLASSES, MNIST_NTRAIN, /*ndata_shard =*/ 10);
 
     if (!mnist_image_load(argv[3], dataset)) {
         return 1;

diff --git a/tests/test-mul-mat0.c b/tests/test-mul-mat0.c
index 6e561efe3073adabaf29844d19e83c8f5bfcbb8d..d254a161fe583cb6e3e6d648e6406eca56f57312 100644 (file)
--- a/tests/test-mul-mat0.c
+++ b/tests/test-mul-mat0.c
@@ -101,8 +101,8 @@ bool check_gradient(
 
     struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, GGML_DEFAULT_GRAPH_SIZE, true);
     ggml_build_forward_expand(gf, f);
-    struct ggml_cgraph * gb = ggml_graph_dup(ctx0, gf);
-    ggml_build_backward_expand(ctx0, ctx0, gb, false);
+    struct ggml_cgraph * gb = ggml_graph_dup(ctx0, gf, false);
+    ggml_build_backward_expand(ctx0, gb, false);
 
     ggml_graph_compute_with_ctx(ctx0, gf, n_threads);
     ggml_graph_reset(gb);
@@ -266,7 +266,7 @@ int main(int argc, const char ** argv) {
                 ne[1] = rand()%4 + 1;
                 x[1] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
 
-                ggml_set_param(ctx0, x[0]);
+                ggml_set_param(x[0]);
 
                 struct ggml_tensor * m = ggml_mul_mat(ctx0, x[1], x[0]);
                 struct ggml_tensor * f = ggml_sum(ctx0, m);
@@ -303,7 +303,7 @@ int main(int argc, const char ** argv) {
                 ne[0] = rand()%4 + 1;
                 x[1] = ggml_cont(ctx0, ggml_transpose(ctx0, get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f)));
 
-                ggml_set_param(ctx0, x[0]);
+                ggml_set_param(x[0]);
 
                 struct ggml_tensor * m = ggml_mul_mat(ctx0, x[1], x[0]);
                 struct ggml_tensor * f = ggml_sum(ctx0, m);