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cuda : supports running on CPU for GGML_USE_CUBLAS=ON build (#3946)

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* protyping the idea that supports running on CPU for a GGML_USE_CUBLAS=on build

* doc: add comments to ggml_cublas_loaded()

* fix defined(...)
This commit is contained in:
Meng Zhang
2023-11-06 22:49:08 -08:00
committed by GitHub
parent 381efbf480
commit 46876d2a2c
3 changed files with 127 additions and 76 deletions
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17
ggml-cuda.cu
View File

@@ -5790,6 +5790,11 @@ static void ggml_cuda_pool_free(void * ptr, size_t size) {
CUDA_CHECK(cudaFree(ptr));
}
static bool g_cublas_loaded = false;
bool ggml_cublas_loaded(void) {
return g_cublas_loaded;
}
void ggml_init_cublas() {
static bool initialized = false;
@@ -5803,7 +5808,12 @@ void ggml_init_cublas() {
CUDA_CHECK(cudaDeviceSynchronize());
#endif
CUDA_CHECK(cudaGetDeviceCount(&g_device_count));
if (cudaGetDeviceCount(&g_device_count) != cudaSuccess) {
initialized = true;
g_cublas_loaded = false;
return;
}
GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES);
int64_t total_vram = 0;
#if defined(GGML_CUDA_FORCE_MMQ)
@@ -5851,6 +5861,7 @@ void ggml_init_cublas() {
// CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr));
initialized = true;
g_cublas_loaded = true;
}
}
@@ -7158,6 +7169,8 @@ static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src
}
bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
if (!g_cublas_loaded) return false;
const int64_t ne10 = src1->ne[0];
const int64_t ne0 = dst->ne[0];
@@ -7843,6 +7856,8 @@ void ggml_cuda_free_scratch() {
}
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
if (!g_cublas_loaded) return false;
ggml_cuda_func_t func;
const bool any_on_device = tensor->backend == GGML_BACKEND_GPU
|| (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT))

5
ggml-cuda.h
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@@ -17,7 +17,12 @@ extern "C" {
#define GGML_CUDA_MAX_DEVICES 16
// Always success. To check if CUDA is actually loaded, use `ggml_cublas_loaded`.
GGML_API void ggml_init_cublas(void);
// Returns `true` if there are available CUDA devices and cublas loads successfully; otherwise, it returns `false`.
GGML_API bool ggml_cublas_loaded(void);
GGML_API void * ggml_cuda_host_malloc(size_t size);
GGML_API void ggml_cuda_host_free(void * ptr);

181
llama.cpp
View File

@@ -596,19 +596,37 @@ static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph *
// llama helpers
//
inline void * llama_host_malloc(size_t n) {
#ifdef GGML_USE_CUBLAS
# define llama_host_malloc(n) ggml_cuda_host_malloc(n)
# define llama_host_free(data) ggml_cuda_host_free(data)
if (ggml_cublas_loaded()) {
return ggml_cuda_host_malloc(n);
} else {
return malloc(n);
}
#elif GGML_USE_METAL
# define llama_host_malloc(n) ggml_metal_host_malloc(n)
# define llama_host_free(data) ggml_metal_host_free(data)
return ggml_metal_host_malloc(n);
#elif GGML_USE_CPU_HBM
# define llama_host_malloc(n) hbw_malloc(n)
# define llama_host_free(data) if (data != NULL) hbw_free(data)
return hbw_malloc(n);
#else
# define llama_host_malloc(n) malloc(n)
# define llama_host_free(data) free(data)
return malloc(n);
#endif
}
inline void llama_host_free(void * ptr) {
#ifdef GGML_USE_CUBLAS
if (ggml_cublas_loaded()) {
return ggml_cuda_host_free(ptr);
} else {
return free(ptr);
}
#elif GGML_USE_METAL
return ggml_metal_host_free(ptr);
#elif GGML_USE_CPU_HBM
return hbw_free(ptr);
#else
return free(ptr);
#endif
}
#if defined(_WIN32)
static std::string llama_format_win_err(DWORD err) {
@@ -1200,9 +1218,11 @@ struct llama_kv_cache {
}
#ifdef GGML_USE_CUBLAS
ggml_cuda_free_data(k);
ggml_cuda_free_data(v);
#endif // GGML_USE_CUBLAS
if (ggml_cublas_loaded()) {
ggml_cuda_free_data(k);
ggml_cuda_free_data(v);
}
#endif
}
};
@@ -1302,11 +1322,15 @@ struct llama_model {
}
#ifdef GGML_USE_CUBLAS
for (size_t i = 0; i < tensors_by_name.size(); ++i) {
ggml_cuda_free_data(tensors_by_name[i].second);
if (ggml_cublas_loaded()) {
for (size_t i = 0; i < tensors_by_name.size(); ++i) {
ggml_cuda_free_data(tensors_by_name[i].second);
}
ggml_cuda_free_scratch();
}
ggml_cuda_free_scratch();
#elif defined(GGML_USE_CLBLAST)
#endif
#if defined(GGML_USE_CLBLAST)
for (size_t i = 0; i < tensors_by_name.size(); ++i) {
ggml_cl_free_data(tensors_by_name[i].second);
}
@@ -1418,23 +1442,26 @@ static bool llama_kv_cache_init(
ggml_set_name(cache.v, "cache_v");
(void) n_gpu_layers;
#ifdef GGML_USE_CUBLAS
size_t vram_kv_cache = 0;
if (n_gpu_layers > (int)n_layer + 1) {
ggml_cuda_assign_buffers_no_scratch(cache.v);
LLAMA_LOG_INFO("%s: offloading v cache to GPU\n", __func__);
vram_kv_cache += ggml_nbytes(cache.v);
#ifdef GGML_USE_CUBLAS
if (ggml_cublas_loaded()) {
size_t vram_kv_cache = 0;
if (n_gpu_layers > (int)n_layer + 1) {
ggml_cuda_assign_buffers_no_scratch(cache.v);
LLAMA_LOG_INFO("%s: offloading v cache to GPU\n", __func__);
vram_kv_cache += ggml_nbytes(cache.v);
}
if (n_gpu_layers > (int)n_layer + 2) {
ggml_cuda_assign_buffers_no_scratch(cache.k);
LLAMA_LOG_INFO("%s: offloading k cache to GPU\n", __func__);
vram_kv_cache += ggml_nbytes(cache.k);
}
if (vram_kv_cache > 0) {
LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0);
}
}
if (n_gpu_layers > (int)n_layer + 2) {
ggml_cuda_assign_buffers_no_scratch(cache.k);
LLAMA_LOG_INFO("%s: offloading k cache to GPU\n", __func__);
vram_kv_cache += ggml_nbytes(cache.k);
}
if (vram_kv_cache > 0) {
LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0);
}
#endif // GGML_USE_CUBLAS
#endif
return true;
}
@@ -2521,18 +2548,22 @@ static void llm_load_tensors(
}
(void) main_gpu;
enum ggml_backend_type llama_backend_offload = GGML_BACKEND_CPU;
enum ggml_backend_type llama_backend_offload_split = GGML_BACKEND_CPU;
#ifdef GGML_USE_CUBLAS
LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__);
ggml_cuda_set_main_device(main_gpu);
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU_SPLIT
if (ggml_cublas_loaded()) {
LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__);
ggml_cuda_set_main_device(main_gpu);
llama_backend_offload = GGML_BACKEND_GPU;
llama_backend_offload_split = GGML_BACKEND_GPU_SPLIT;
}
#elif defined(GGML_USE_CLBLAST)
LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__);
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU
#else
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CPU
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_CPU
LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__);
llama_backend_offload = GGML_BACKEND_GPU;
llama_backend_offload_split = GGML_BACKEND_GPU;
#endif
// prepare memory for the weights
@@ -2559,12 +2590,12 @@ static void llm_load_tensors(
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = LLAMA_BACKEND_OFFLOAD;
backend_norm = llama_backend_offload;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32
backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
@@ -2588,8 +2619,8 @@ static void llm_load_tensors(
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
@@ -2625,12 +2656,12 @@ static void llm_load_tensors(
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = LLAMA_BACKEND_OFFLOAD;
backend_norm = llama_backend_offload;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32
backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
@@ -2654,8 +2685,8 @@ static void llm_load_tensors(
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
@@ -2695,12 +2726,12 @@ static void llm_load_tensors(
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = LLAMA_BACKEND_OFFLOAD;
backend_norm = llama_backend_offload;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32
backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
@@ -2726,8 +2757,8 @@ static void llm_load_tensors(
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
@@ -2772,12 +2803,12 @@ static void llm_load_tensors(
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = LLAMA_BACKEND_OFFLOAD;
backend_norm = llama_backend_offload;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32
backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
@@ -2803,8 +2834,8 @@ static void llm_load_tensors(
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
@@ -2849,12 +2880,12 @@ static void llm_load_tensors(
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = LLAMA_BACKEND_OFFLOAD;
backend_norm = llama_backend_offload;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32
backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
@@ -2877,8 +2908,8 @@ static void llm_load_tensors(
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT;
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload;
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split;
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
@@ -2915,12 +2946,12 @@ static void llm_load_tensors(
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = LLAMA_BACKEND_OFFLOAD;
backend_norm = llama_backend_offload;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32
backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
@@ -2946,8 +2977,8 @@ static void llm_load_tensors(
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
@@ -2993,12 +3024,12 @@ static void llm_load_tensors(
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = LLAMA_BACKEND_OFFLOAD;
backend_norm = llama_backend_offload;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32
backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
@@ -3022,8 +3053,8 @@ static void llm_load_tensors(
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];