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only use vmm for the main device

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slaren 2023-12-25 22:49:06 +01:00
parent 0dcc1a77d7
commit 23c6dd677b
1 changed files with 11 additions and 11 deletions
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22
ggml-cuda.cu
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@ -6741,7 +6741,7 @@ static void ggml_cuda_pool_free_vmm(int device, void * ptr, size_t size) {
} }
static void * ggml_cuda_pool_malloc(int device, size_t size, size_t * actual_size) { static void * ggml_cuda_pool_malloc(int device, size_t size, size_t * actual_size) {
if (g_device_caps[device].vmm) { if (device == g_main_device && g_device_caps[device].vmm) {
return ggml_cuda_pool_malloc_vmm(device, size, actual_size); return ggml_cuda_pool_malloc_vmm(device, size, actual_size);
} else { } else {
return ggml_cuda_pool_malloc_leg(device, size, actual_size); return ggml_cuda_pool_malloc_leg(device, size, actual_size);
@ -6749,7 +6749,7 @@ static void * ggml_cuda_pool_malloc(int device, size_t size, size_t * actual_siz
} }
static void ggml_cuda_pool_free(int device, void * ptr, size_t size) { static void ggml_cuda_pool_free(int device, void * ptr, size_t size) {
if (g_device_caps[device].vmm) { if (device == g_main_device && g_device_caps[device].vmm) {
ggml_cuda_pool_free_vmm(device, ptr, size); ggml_cuda_pool_free_vmm(device, ptr, size);
} else { } else {
ggml_cuda_pool_free_leg(device, ptr, size); ggml_cuda_pool_free_leg(device, ptr, size);
@ -7365,7 +7365,7 @@ static void ggml_cuda_op_mul_mat_q(
static int64_t get_row_rounding(ggml_type type) { static int64_t get_row_rounding(ggml_type type) {
int64_t min_compute_capability = INT_MAX; int64_t min_compute_capability = INT_MAX;
int64_t max_compute_capability = INT_MIN; int64_t max_compute_capability = INT_MIN;
for (int64_t id = 0; id < g_device_count; ++id) { for (int id = 0; id < g_device_count; ++id) {
if (g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { if (g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) {
if (min_compute_capability > g_device_caps[id].cc) { if (min_compute_capability > g_device_caps[id].cc) {
min_compute_capability = g_device_caps[id].cc; min_compute_capability = g_device_caps[id].cc;
@ -8078,7 +8078,7 @@ static void ggml_cuda_op_mul_mat(
int used_devices = 0; int used_devices = 0;
for (int64_t id = 0; id < g_device_count; ++id) { for (int id = 0; id < g_device_count; ++id) {
// by default, use all rows // by default, use all rows
dev[id].row_low = 0; dev[id].row_low = 0;
dev[id].row_high = ne01; dev[id].row_high = ne01;
@ -8158,7 +8158,7 @@ static void ggml_cuda_op_mul_mat(
const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0; 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; const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride;
for (int64_t id = 0; id < g_device_count; ++id) { for (int id = 0; id < g_device_count; ++id) {
if ((!split && id != g_main_device) || dev[id].row_low == dev[id].row_high) { if ((!split && id != g_main_device) || dev[id].row_low == dev[id].row_high) {
continue; continue;
} }
@ -8209,7 +8209,7 @@ static void ggml_cuda_op_mul_mat(
} }
} else if (src1->backend == GGML_BACKEND_CPU || (src1_on_device && !src1_is_contiguous)) { } else if (src1->backend == GGML_BACKEND_CPU || (src1_on_device && !src1_is_contiguous)) {
CUDA_CHECK(ggml_cuda_cpy_tensor_2d( CUDA_CHECK(ggml_cuda_cpy_tensor_2d(
src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream)); src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream));
} else { } else {
GGML_ASSERT(false); GGML_ASSERT(false);
} }
@ -8255,7 +8255,7 @@ static void ggml_cuda_op_mul_mat(
// cudaMemcpy2DAsync may fail with copies between vmm pools of different devices // cudaMemcpy2DAsync may fail with copies between vmm pools of different devices
cudaMemcpy3DPeerParms p = {}; cudaMemcpy3DPeerParms p = {};
p.dstDevice = g_main_device; p.dstDevice = g_main_device;
p.dstPtr = make_cudaPitchedPtr(dhf_dst_i, ne0*sizeof(float), ne0, src1_ncols); p.dstPtr = make_cudaPitchedPtr(dhf_dst_i, ne0*sizeof(float), row_diff, src1_ncols);
p.srcDevice = id; p.srcDevice = id;
p.srcPtr = make_cudaPitchedPtr(dst_dd_i, row_diff*sizeof(float), row_diff, src1_ncols); p.srcPtr = make_cudaPitchedPtr(dst_dd_i, row_diff*sizeof(float), row_diff, src1_ncols);
p.extent = make_cudaExtent(row_diff*sizeof(float), src1_ncols, 1); p.extent = make_cudaExtent(row_diff*sizeof(float), src1_ncols, 1);
@ -8290,7 +8290,7 @@ static void ggml_cuda_op_mul_mat(
is_max = is_max <= MAX_STREAMS ? is_max : MAX_STREAMS; is_max = is_max <= MAX_STREAMS ? is_max : MAX_STREAMS;
ggml_cuda_set_device(g_main_device); ggml_cuda_set_device(g_main_device);
for (int64_t id = 0; id < g_device_count; ++id) { for (int id = 0; id < g_device_count; ++id) {
if (dev[id].row_low == dev[id].row_high) { if (dev[id].row_low == dev[id].row_high) {
continue; continue;
} }
@ -8655,7 +8655,7 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT; const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
int64_t min_compute_capability = INT_MAX; int64_t min_compute_capability = INT_MAX;
for (int64_t id = 0; id < g_device_count; ++id) { for (int id = 0; id < g_device_count; ++id) {
if (min_compute_capability > g_device_caps[id].cc && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { if (min_compute_capability > g_device_caps[id].cc && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) {
min_compute_capability = g_device_caps[id].cc; min_compute_capability = g_device_caps[id].cc;
} }
@ -9162,7 +9162,7 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
ggml_tensor_extra_gpu * extra = new struct ggml_tensor_extra_gpu; ggml_tensor_extra_gpu * extra = new struct ggml_tensor_extra_gpu;
memset(extra, 0, sizeof(*extra)); memset(extra, 0, sizeof(*extra));
for (int64_t id = 0; id < g_device_count; ++id) { for (int id = 0; id < g_device_count; ++id) {
if (backend == GGML_BACKEND_GPU && id != g_main_device) { if (backend == GGML_BACKEND_GPU && id != g_main_device) {
continue; continue;
} }
@ -9233,7 +9233,7 @@ void ggml_cuda_free_data(struct ggml_tensor * tensor) {
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
for (int64_t id = 0; id < g_device_count; ++id) { for (int id = 0; id < g_device_count; ++id) {
ggml_cuda_set_device(id); ggml_cuda_set_device(id);
if (extra->data_device[id] != nullptr) { if (extra->data_device[id] != nullptr) {
CUDA_CHECK(cudaFree(extra->data_device[id])); CUDA_CHECK(cudaFree(extra->data_device[id]));