vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Merge branch 'master' into llava-lib

Browse source
This commit is contained in:
M. Yusuf Sarıgöz 2023-11-05 23:40:51 +03:00
commit 71ea278ad8
4 changed files with 38 additions and 12 deletions
Download patch file Download diff file Expand all files Collapse all files

13
common/common.cpp
View file

@ -90,6 +90,19 @@ void process_escapes(std::string& input) {
case '\'': input[output_idx++] = '\''; break;
case '\"': input[output_idx++] = '\"'; break;
case '\\': input[output_idx++] = '\\'; break;
case 'x':
// Handle \x12, etc
if (input_idx + 2 < input_len) {
const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 };
char *err_p = nullptr;
const long val = std::strtol(x, &err_p, 16);
if (err_p == x + 2) {
input_idx += 2;
input[output_idx++] = char(val);
break;
}
}
// fall through
default: input[output_idx++] = '\\';
input[output_idx++] = input[input_idx]; break;
}

2
examples/server/README.md
View file

@ -7,7 +7,7 @@ Command line options:
- `--threads N`, `-t N`: Set the number of threads to use during generation.
- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation.
- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`).
- `-m ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
- `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
- `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.

24
ggml-cuda.cu
View file

@ -6893,6 +6893,8 @@ static void ggml_cuda_op_mul_mat(
int64_t row_low[GGML_CUDA_MAX_DEVICES];
int64_t row_high[GGML_CUDA_MAX_DEVICES];
int used_devices = 0;
for (int64_t id = 0; id < g_device_count; ++id) {
// by default, use all rows
row_low[id] = 0;
@ -6920,6 +6922,8 @@ static void ggml_cuda_op_mul_mat(
continue;
}
used_devices++;
const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device;
const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device;
@ -6958,12 +6962,12 @@ static void ggml_cuda_op_mul_mat(
// if multiple devices are used they need to wait for the main device
// here an event is recorded that signals that the main device has finished calculating the input data
if (split && g_device_count > 1) {
if (split && used_devices > 1) {
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
CUDA_CHECK(cudaEventRecord(src0_extra->events[g_main_device][0], g_cudaStreams[g_main_device][0]));
}
const int64_t src1_col_stride = split && g_device_count > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11;
const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11;
for (int64_t src1_col_0 = 0; src1_col_0 < ne11; src1_col_0 += src1_col_stride) {
const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0;
const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride;
@ -7079,6 +7083,9 @@ static void ggml_cuda_op_mul_mat(
}
for (int64_t id = 0; id < g_device_count; ++id) {
if ((!split && id != g_main_device) || row_low[id] == row_high[id]) {
continue;
}
CUDA_CHECK(ggml_cuda_set_device(id));
// free buffers again when done
@ -7103,6 +7110,9 @@ static void ggml_cuda_op_mul_mat(
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
for (int64_t id = 0; id < g_device_count; ++id) {
if (row_low[id] == row_high[id]) {
continue;
}
for (int64_t is = 0; is < is_max; ++is) {
CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[g_main_device][0], src0_extra->events[id][is], 0));
}
@ -7400,10 +7410,12 @@ static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const
static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const bool all_on_device =
(src0->backend == GGML_BACKEND_GPU) &&
(src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) &&
(src1->backend == GGML_BACKEND_GPU) &&
( dst->backend == GGML_BACKEND_GPU);
const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
int64_t min_compute_capability = INT_MAX;
for (int64_t id = 0; id < g_device_count; ++id) {
if (min_compute_capability > g_compute_capabilities[id] && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) {
@ -7425,13 +7437,13 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
//printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
//printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
if (all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
// KQ single-batch
ggml_cuda_mul_mat_vec_p021(src0, src1, dst);
} else if (all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
} else if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
// KQV single-batch
ggml_cuda_mul_mat_vec_nc(src0, src1, dst);
} else if (all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
} else if (!split && all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
// KQ + KQV multi-batch
ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst);
} else if (src0->type == GGML_TYPE_F32) {

3
llama.cpp
View file

@ -5168,7 +5168,8 @@ static int llama_decode_internal(
model.arch == LLM_ARCH_BAICHUAN ||
model.arch == LLM_ARCH_FALCON ||
model.arch == LLM_ARCH_REFACT ||
model.arch == LLM_ARCH_MPT;
model.arch == LLM_ARCH_MPT ||
model.arch == LLM_ARCH_STARCODER;
const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {