vbatts/llama.cpp
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OpenCL: Fix duplication of layers in VRAM and RAM, add GPU mul kernel (#1653)

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* Use events instead of clFinish, where possible

* OpenCL: Don't load gpu layers into RAM, add mul_f32 kernel

* Reduce queueing overhead for contiguous tensors by using single mul kernel call

* Adapt to #1612 cl_mem malloc changes

* Reduce code duplication between cuda and opencl branches

* Improve implementation
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0cc4m 2023-06-04 08:12:05 +02:00 • committed by GitHub
parent d8bd0013e8
commit dcb2ed4826
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4 changed files with 210 additions and 40 deletions
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llama.cpp
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@ -1010,8 +1010,12 @@ static void llama_model_load_internal(
}
}
#ifdef GGML_USE_CUBLAS
#if defined(GGML_USE_CUBLAS)
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CUDA
fprintf(stderr, "%s: using CUDA for GPU acceleration\n", __func__);
#elif defined(GGML_USE_CLBLAST)
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CL
fprintf(stderr, "%s: using OpenCL for GPU acceleration\n", __func__);
#else
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CPU
#endif
@ -1063,7 +1067,7 @@ static void llama_model_load_internal(
layer.w2 = ml->get_tensor(layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}, backend);
layer.w3 = ml->get_tensor(layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}, backend);
if (backend == GGML_BACKEND_CUDA) {
if (backend == LLAMA_BACKEND_OFFLOAD) {
vram_total +=
ggml_nbytes(layer.attention_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.attention_norm) +
@ -1093,15 +1097,15 @@ static void llama_model_load_internal(
fprintf(stderr, "%s: mem required = %7.2f MB (+ %7.2f MB per state)\n", __func__,
mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0);
#ifdef GGML_USE_CUBLAS
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
fprintf(stderr, "%s: [cublas] offloading %d layers to GPU\n", __func__, n_gpu);
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
fprintf(stderr, "%s: offloading %d layers to GPU\n", __func__, n_gpu);
if (n_gpu_layers > (int) hparams.n_layer) {
fprintf(stderr, "%s: [cublas] offloading output layer to GPU\n", __func__);
fprintf(stderr, "%s: offloading output layer to GPU\n", __func__);
}
fprintf(stderr, "%s: [cublas] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
#elif !defined(GGML_USE_CLBLAST)
fprintf(stderr, "%s: total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
#else
(void) n_gpu_layers;
#endif
}
@ -1113,7 +1117,7 @@ static void llama_model_load_internal(
ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &lctx.model.mlock_mmap : NULL);
#ifdef GGML_USE_CUBLAS
#if defined(GGML_USE_CUBLAS)
{
size_t done_size = 0;
size_t data_size = 0;
@ -1136,29 +1140,24 @@ static void llama_model_load_internal(
}
#elif defined(GGML_USE_CLBLAST)
{
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
fprintf(stderr, "ggml_opencl: offloading %d layers to GPU\n", n_gpu);
size_t vram_total = 0;
for (int i = 0; i < n_gpu; ++i) {
const auto & layer = model.layers[i];
ggml_cl_transform_tensor(layer.wq); vram_total += ggml_nbytes(layer.wq);
ggml_cl_transform_tensor(layer.wk); vram_total += ggml_nbytes(layer.wk);
ggml_cl_transform_tensor(layer.wv); vram_total += ggml_nbytes(layer.wv);
ggml_cl_transform_tensor(layer.wo); vram_total += ggml_nbytes(layer.wo);
ggml_cl_transform_tensor(layer.w1); vram_total += ggml_nbytes(layer.w1);
ggml_cl_transform_tensor(layer.w2); vram_total += ggml_nbytes(layer.w2);
ggml_cl_transform_tensor(layer.w3); vram_total += ggml_nbytes(layer.w3);
size_t done_size = 0;
size_t data_size = 0;
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
data_size += lt.size;
if (lt.ggml_tensor->backend == GGML_BACKEND_CPU) {
done_size += lt.size;
}
}
if (n_gpu_layers > (int) hparams.n_layer) {
fprintf(stderr, "ggml_opencl: offloading output layer to GPU\n");
ggml_cl_transform_tensor(model.output); vram_total += ggml_nbytes(model.output);
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
if (lt.ggml_tensor->backend != GGML_BACKEND_CL) {
continue;
}
if (progress_callback) {
progress_callback((float) done_size / data_size, progress_callback_user_data);
}
ggml_cl_load_data(fname.c_str(), lt.ggml_tensor, lt.shards.at(0).file_off);
done_size += lt.size;
}
fprintf(stderr, "ggml_opencl: total VRAM used: %zu MB\n", vram_total / 1024 / 1024);
}
#endif