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backend : offload large batches to GPU (#6083)

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* backend : offload large batches to GPU

* fix hip

* code cleanup

* fix CUDA split buffers

* Update ggml-backend-impl.h

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* cuda : fix memset without set_device

* imatrix : remove sched affix from weight names

* sched : add a new split if the current one has too many inputs
reduce max inputs per split
more cleanup

* update backends

ggml-ci

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
This commit is contained in:
slaren
2024-03-18 11:03:04 +01:00
committed by GitHub
parent 496bc79bc2
commit 2bf8d0f7c4
14 changed files with 349 additions and 396 deletions
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67
llama.cpp
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@@ -2040,6 +2040,11 @@ struct llama_model {
ggml_free(ctx);
}
for (ggml_backend_buffer_t buf : bufs) {
#ifdef GGML_USE_CUBLAS
if (ggml_backend_buffer_get_type(buf) == ggml_backend_cpu_buffer_type()) {
ggml_backend_cuda_unregister_host_buffer(ggml_backend_buffer_get_base(buf));
}
#endif
ggml_backend_buffer_free(buf);
}
}
@@ -5033,6 +5038,13 @@ static bool llm_load_tensors(
size_t first, last;
ml.get_mapping_range(&first, &last, ctx);
buf = ggml_backend_cpu_buffer_from_ptr((char *) ml.mapping->addr + first, last - first);
#ifdef GGML_USE_CUBLAS
if (n_layer >= n_gpu_layers) {
ggml_backend_cuda_register_host_buffer(
ggml_backend_buffer_get_base(buf),
ggml_backend_buffer_get_size(buf));
}
#endif
}
#ifdef GGML_USE_METAL
else if (ml.use_mmap && buft == ggml_backend_metal_buffer_type()) {
@@ -8231,7 +8243,6 @@ struct llm_build_context {
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
@@ -8601,12 +8612,15 @@ static struct ggml_cgraph * llama_build_graph(
}
// norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends
// to fix this, we assign the norm layer manually to the backend of its layer
if (il != -1 && strcmp(name, "norm") == 0) {
for (auto * backend : lctx.backends) {
if (ggml_backend_buft_supports_backend(lctx.model.buft_layer[il].buft, backend)) {
ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend);
break;
// FIXME: fix in ggml_backend_sched
const bool full_offload = lctx.model.n_gpu_layers > (int)lctx.model.hparams.n_layer;
if (batch.n_tokens < 32 || full_offload) {
if (il != -1 && strcmp(name, "norm") == 0) {
for (auto * backend : lctx.backends) {
if (ggml_backend_buft_supports_backend(lctx.model.buft_layer[il].buft, backend)) {
ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend);
break;
}
}
}
}
@@ -13107,27 +13121,25 @@ struct llama_context * llama_new_context_with_model(
ctx->backends.push_back(ctx->backend_metal);
}
#elif defined(GGML_USE_CUBLAS)
if (model->n_gpu_layers > 0) {
if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
// with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu);
ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu);
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu);
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(backend);
} else {
// LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) {
ggml_backend_t backend = ggml_backend_cuda_init(device);
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu);
LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, device);
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(backend);
} else {
// LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) {
ggml_backend_t backend = ggml_backend_cuda_init(device);
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, device);
llama_free(ctx);
return nullptr;
}
ctx->backends.push_back(backend);
}
}
}
#elif defined(GGML_USE_VULKAN)
@@ -13285,14 +13297,17 @@ struct llama_context * llama_new_context_with_model(
ggml_backend_t backend = ctx->backends[i];
ggml_backend_buffer_type_t buft = backend_buft[i];
size_t size = ggml_backend_sched_get_buffer_size(ctx->sched, backend);
LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
ggml_backend_buft_name(buft),
size / 1024.0 / 1024.0);
if (size > 1) {
LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
ggml_backend_buft_name(buft),
size / 1024.0 / 1024.0);
}
}
// note: the number of splits during measure is higher than during inference due to the kv shift
int n_splits = ggml_backend_sched_get_n_splits(ctx->sched);
LLAMA_LOG_INFO("%s: graph splits: %d\n", __func__, n_splits);
LLAMA_LOG_INFO("%s: graph nodes = %d\n", __func__, gf->n_nodes);
LLAMA_LOG_INFO("%s: graph splits = %d\n", __func__, n_splits);
}
}