vbatts/llama.cpp
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llama/ggml: add LLM training support

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more compact progress bar

refactor: llama_prepare_sbatch/ubatch

llama_save_model_to_file

gqa_mode arg for repeat_back

llama_opt_param_filter

ggml_graph_dup force_grads

refactor ggml_opt, fix test-opt
This commit is contained in:
Johannes Gäßler 2024-11-17 14:58:51 +01:00
parent a5203b4465
commit c25557362a
26 changed files with 1294 additions and 339 deletions
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51
tests/test-opt.cpp
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@ -57,7 +57,8 @@ static helper_ctx_data helper_get_ctx_data(
enum ggml_opt_loss_type loss_type = GGML_OPT_LOSS_TYPE_SUM) {
std::vector<ggml_opt_dataset_t> datasets(ndata);
for (int64_t ndata_shard = 1; ndata_shard <= ndata; ++ndata_shard) {
ggml_opt_dataset_t dataset = ggml_opt_dataset_init(ne_datapoint, ne_label, ndata, ndata_shard);
ggml_opt_dataset_t dataset = ggml_opt_dataset_init(
GGML_TYPE_F32, GGML_TYPE_F32, ne_datapoint, ne_label, ndata, ndata_shard);
float * data = ggml_get_data_f32(ggml_opt_dataset_data( dataset));
float * labels = ggml_get_data_f32(ggml_opt_dataset_labels(dataset));
@ -74,7 +75,8 @@ static helper_ctx_data helper_get_ctx_data(
datasets[ndata_shard-1] = dataset;
}
ggml_opt_dataset_t dataset_unsupervised = ggml_opt_dataset_init(1, 0, ndata, /*ndata_shard =*/ 1);
ggml_opt_dataset_t dataset_unsupervised = ggml_opt_dataset_init(
GGML_TYPE_F32, GGML_TYPE_F32, 1, 0, ndata, /*ndata_shard =*/ 1);
float * data = ggml_get_data_f32(ggml_opt_dataset_data(dataset_unsupervised));
@ -113,7 +115,7 @@ static helper_ctx_data helper_get_ctx_data(
struct ggml_tensor * weights = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1);
ggml_set_name(weights, "weights");
ggml_set_param(ctx_static, weights);
ggml_set_param(weights);
struct ggml_tensor * intermediary = ggml_add(ctx_compute, inputs, weights);
@ -127,8 +129,11 @@ static helper_ctx_data helper_get_ctx_data(
GGML_ASSERT(nbatch_logical % nbatch_physical == 0);
const int32_t opt_period = nbatch_logical / nbatch_physical;
struct ggml_opt_params opt_params = ggml_opt_default_params(backend_sched, ctx_compute, inputs, outputs, loss_type);
opt_params.opt_period = opt_period;
struct ggml_opt_params opt_params = ggml_opt_default_params(backend_sched, loss_type);
opt_params.ctx_compute = ctx_compute;
opt_params.inputs = inputs;
opt_params.outputs = outputs;
opt_params.opt_period = opt_period;
if (!optimizer_defaults) {
opt_params.get_opt_pars = helper_get_test_opt_pars;
}
@ -264,8 +269,9 @@ static std::pair<int, int> test_grad(ggml_backend_sched_t backend_sched, ggml_ba
for (int idata = 0; idata < ndata; ++idata) {
const float idataf = idata;
ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
ggml_opt_forward_backward(cd.opt_ctx, cd.result);
ggml_opt_eval(cd.opt_ctx, cd.result);
ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata, 0, sizeof(float));
}
@ -334,8 +340,9 @@ static std::pair<int, int> test_forward_backward(
} else {
for (int idata = 0; idata < ndata; ++idata) {
const float idataf = idata;
ggml_opt_alloc(cd.opt_ctx, /*backward =*/ false);
ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
ggml_opt_forward(cd.opt_ctx, cd.result);
ggml_opt_eval(cd.opt_ctx, cd.result);
ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float));
}
}
@ -367,7 +374,8 @@ static std::pair<int, int> test_forward_backward(
float w0;
ggml_backend_tensor_get(cd.weights, &w0, 0, sizeof(float));
for (int i = 0; i < 10; ++i) {
ggml_opt_forward_backward(cd.opt_ctx, nullptr);
ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
ggml_opt_eval(cd.opt_ctx, cd.result);
}
ggml_backend_tensor_set(cd.weights, &w0, 0, sizeof(float));
@ -387,8 +395,9 @@ static std::pair<int, int> test_forward_backward(
} else {
for (int idata = 0; idata < ndata; ++idata) {
const float idataf = idata;
ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
ggml_opt_forward_backward(cd.opt_ctx, cd.result);
ggml_opt_eval(cd.opt_ctx, cd.result);
ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float));
}
}
@ -492,14 +501,16 @@ static std::pair<int, int> test_idata_split(ggml_backend_sched_t backend_sched,
int idata = 0;
for (; idata < idata_split; ++idata) {
const float idataf = idata;
ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
ggml_opt_forward_backward(cd.opt_ctx, cd.result);
ggml_opt_eval(cd.opt_ctx, cd.result);
ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float));
}
for (; idata < ndata; ++idata) {
const float idataf = idata;
ggml_opt_alloc(cd.opt_ctx, /*backward =*/ false);
ggml_backend_tensor_set(cd.inputs, &idataf, 0, ggml_nbytes(cd.inputs));
ggml_opt_forward(cd.opt_ctx, cd.result2);
ggml_opt_eval(cd.opt_ctx, cd.result2);
ggml_backend_tensor_get(loss, loss_history.data() + idata, 0, sizeof(float));
}
}
@ -584,15 +595,17 @@ static std::pair<int, int> test_gradient_accumulation(
if (nbatch_physical == 1) {
for (int idata = 0; idata < ndata; ++idata) {
const float idataf = idata;
ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
ggml_backend_tensor_set(cd.inputs, &idataf, 0, 1*sizeof(float));
ggml_opt_forward_backward(cd.opt_ctx, cd.result);
ggml_opt_eval(cd.opt_ctx, cd.result);
ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata, 0, 1*sizeof(float));
}
} else if (nbatch_physical == 2) {
for (int idata = 0; idata < ndata; idata += 2) {
const float idataf[2] = {float(idata + 0), float(idata + 1)};
ggml_opt_alloc(cd.opt_ctx, /*backward =*/ true);
ggml_backend_tensor_set(cd.inputs, idataf, 0, 2*sizeof(float));
ggml_opt_forward_backward(cd.opt_ctx, cd.result);
ggml_opt_eval(cd.opt_ctx, cd.result);
grad_history[idata + 0] = 0.0f;
ggml_backend_tensor_get(ggml_opt_grad_acc(cd.opt_ctx, cd.weights), grad_history.data() + idata + 1, 0, 1*sizeof(float));
@ -617,7 +630,7 @@ static std::pair<int, int> test_gradient_accumulation(
}
subtest_ok = subtest_ok && almost_equal(grad_history[1], 2.0, atol);
subtest_ok = subtest_ok && almost_equal(grad_history[3], 4.0, atol);
subtest_ok = subtest_ok && almost_equal(grad_history[5], 0.0, atol);
subtest_ok = subtest_ok && almost_equal(grad_history[5], 6.0, atol);
} else if (loss_type == GGML_OPT_LOSS_TYPE_MEAN) {
if (nbatch_physical == 1) {
subtest_ok = subtest_ok && almost_equal(grad_history[0], 1.0/ndata, atol);
@ -630,7 +643,7 @@ static std::pair<int, int> test_gradient_accumulation(
}
subtest_ok = subtest_ok && almost_equal(grad_history[1], 2.0/ndata, atol);
subtest_ok = subtest_ok && almost_equal(grad_history[3], 4.0/ndata, atol);
subtest_ok = subtest_ok && almost_equal(grad_history[5], 0.0/ndata, atol);
subtest_ok = subtest_ok && almost_equal(grad_history[5], 6.0/ndata, atol);
} else {
GGML_ASSERT(false);
}
@ -692,7 +705,8 @@ static std::pair<int, int> test_regression(ggml_backend_sched_t backend_sched, g
std::mt19937 gen(12345);
std::normal_distribution<float> nd{0.0f, 0.1f};
ggml_opt_dataset_t dataset = ggml_opt_dataset_init(1, 1, ndata_regression, ndata_regression);
ggml_opt_dataset_t dataset = ggml_opt_dataset_init(
GGML_TYPE_F32, GGML_TYPE_F32, 1, 1, ndata_regression, ndata_regression);
float * data = ggml_get_data_f32(ggml_opt_dataset_data( dataset));
float * labels = ggml_get_data_f32(ggml_opt_dataset_labels(dataset));
@ -733,15 +747,14 @@ static std::pair<int, int> test_regression(ggml_backend_sched_t backend_sched, g
struct ggml_tensor * a = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1);
ggml_set_name(a, "a");
ggml_set_param(ctx_static, a);
ggml_set_param(a);
struct ggml_tensor * b = ggml_new_tensor_1d(ctx_static, GGML_TYPE_F32, 1);
ggml_set_name(b, "b");
ggml_set_param(ctx_static, b);
ggml_set_param(b);
struct ggml_tensor * f = ggml_add(ctx_compute, ggml_mul(ctx_compute, x, a), b);
ggml_set_name(f, "f");
ggml_set_param(ctx_static, f);
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx_static, backend);
const float a0 = 1.0f;