mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2024-12-19 04:37:51 +00:00
Capture CUDA logging output (llama/7298)
* logging: output capture in cuda module * fix compile error * fix: vsnprintf terminates with 0, string use not correct * post review * Update llama.cpp Co-authored-by: slaren <slarengh@gmail.com> * Update llama.cpp Co-authored-by: slaren <slarengh@gmail.com> --------- Co-authored-by: slaren <slarengh@gmail.com>
This commit is contained in:
fraxy-v
Georgi Gerganov
parent
705fe30a02
commit
7f257b210f
100
ggml-cuda.cu
100
ggml-cuda.cu
@ -43,19 +43,59 @@
|
||||
#include <mutex>
|
||||
#include <stdint.h>
|
||||
#include <stdio.h>
|
||||
#include <stdarg.h>
|
||||
#include <stdlib.h>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
|
||||
|
||||
static void ggml_cuda_default_log_callback(enum ggml_log_level level, const char * msg, void * user_data) {
|
||||
GGML_UNUSED(level);
|
||||
GGML_UNUSED(user_data);
|
||||
fprintf(stderr, "%s", msg);
|
||||
}
|
||||
|
||||
ggml_log_callback ggml_cuda_log_callback = ggml_cuda_default_log_callback;
|
||||
void * ggml_cuda_log_user_data = NULL;
|
||||
|
||||
GGML_API void ggml_backend_cuda_log_set_callback(ggml_log_callback log_callback, void * user_data) {
|
||||
ggml_cuda_log_callback = log_callback;
|
||||
ggml_cuda_log_user_data = user_data;
|
||||
}
|
||||
|
||||
#define GGML_CUDA_LOG_INFO(...) ggml_cuda_log(GGML_LOG_LEVEL_INFO, __VA_ARGS__)
|
||||
#define GGML_CUDA_LOG_WARN(...) ggml_cuda_log(GGML_LOG_LEVEL_WARN, __VA_ARGS__)
|
||||
#define GGML_CUDA_LOG_ERROR(...) ggml_cuda_log(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
|
||||
|
||||
GGML_ATTRIBUTE_FORMAT(2, 3)
|
||||
static void ggml_cuda_log(enum ggml_log_level level, const char * format, ...) {
|
||||
if (ggml_cuda_log_callback != NULL) {
|
||||
va_list args;
|
||||
va_start(args, format);
|
||||
char buffer[128];
|
||||
int len = vsnprintf(buffer, 128, format, args);
|
||||
if (len < 128) {
|
||||
ggml_cuda_log_callback(level, buffer, ggml_cuda_log_user_data);
|
||||
} else {
|
||||
std::vector<char> buffer2(len + 1); // vsnprintf adds a null terminator
|
||||
va_end(args);
|
||||
va_start(args, format);
|
||||
vsnprintf(&buffer2[0], buffer2.size(), format, args);
|
||||
ggml_cuda_log_callback(level, buffer2.data(), ggml_cuda_log_user_data);
|
||||
}
|
||||
va_end(args);
|
||||
}
|
||||
}
|
||||
|
||||
[[noreturn]]
|
||||
void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg) {
|
||||
int id = -1; // in case cudaGetDevice fails
|
||||
cudaGetDevice(&id);
|
||||
|
||||
fprintf(stderr, "CUDA error: %s\n", msg);
|
||||
fprintf(stderr, " current device: %d, in function %s at %s:%d\n", id, func, file, line);
|
||||
fprintf(stderr, " %s\n", stmt);
|
||||
GGML_CUDA_LOG_ERROR("CUDA error: %s\n", msg);
|
||||
GGML_CUDA_LOG_ERROR(" current device: %d, in function %s at %s:%d\n", id, func, file, line);
|
||||
GGML_CUDA_LOG_ERROR(" %s\n", stmt);
|
||||
// abort with GGML_ASSERT to get a stack trace
|
||||
GGML_ASSERT(!"CUDA error");
|
||||
}
|
||||
@ -91,7 +131,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
|
||||
|
||||
cudaError_t err = cudaGetDeviceCount(&info.device_count);
|
||||
if (err != cudaSuccess) {
|
||||
fprintf(stderr, "%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err));
|
||||
GGML_CUDA_LOG_ERROR("%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err));
|
||||
return info;
|
||||
}
|
||||
|
||||
@ -99,16 +139,16 @@ static ggml_cuda_device_info ggml_cuda_init() {
|
||||
|
||||
int64_t total_vram = 0;
|
||||
#if defined(GGML_CUDA_FORCE_MMQ)
|
||||
fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__);
|
||||
GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__);
|
||||
#else
|
||||
fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: no\n", __func__);
|
||||
GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: no\n", __func__);
|
||||
#endif
|
||||
#if defined(CUDA_USE_TENSOR_CORES)
|
||||
fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: yes\n", __func__);
|
||||
GGML_CUDA_LOG_INFO("%s: CUDA_USE_TENSOR_CORES: yes\n", __func__);
|
||||
#else
|
||||
fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: no\n", __func__);
|
||||
GGML_CUDA_LOG_INFO("%s: CUDA_USE_TENSOR_CORES: no\n", __func__);
|
||||
#endif
|
||||
fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count);
|
||||
GGML_CUDA_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count);
|
||||
for (int id = 0; id < info.device_count; ++id) {
|
||||
int device_vmm = 0;
|
||||
|
||||
@ -129,7 +169,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
|
||||
|
||||
cudaDeviceProp prop;
|
||||
CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
|
||||
fprintf(stderr, " Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no");
|
||||
GGML_CUDA_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no");
|
||||
|
||||
info.default_tensor_split[id] = total_vram;
|
||||
total_vram += prop.totalGlobalMem;
|
||||
@ -235,8 +275,8 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
|
||||
*actual_size = look_ahead_size;
|
||||
pool_size += look_ahead_size;
|
||||
#ifdef DEBUG_CUDA_MALLOC
|
||||
fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, device, nnz,
|
||||
(uint32_t)(max_size/1024/1024), (uint32_t)(pool_size/1024/1024), (uint32_t)(size/1024/1024));
|
||||
GGML_CUDA_LOG_INFO("%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, device, nnz,
|
||||
(uint32_t)(max_size / 1024 / 1024), (uint32_t)(pool_size / 1024 / 1024), (uint32_t)(size / 1024 / 1024));
|
||||
#endif
|
||||
return ptr;
|
||||
}
|
||||
@ -250,7 +290,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
|
||||
return;
|
||||
}
|
||||
}
|
||||
fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n");
|
||||
GGML_CUDA_LOG_WARN("Cuda buffer pool full, increase MAX_CUDA_BUFFERS\n");
|
||||
ggml_cuda_set_device(device);
|
||||
CUDA_CHECK(cudaFree(ptr));
|
||||
pool_size -= size;
|
||||
@ -499,7 +539,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffe
|
||||
void * dev_ptr;
|
||||
cudaError_t err = cudaMalloc(&dev_ptr, size);
|
||||
if (err != cudaSuccess) {
|
||||
fprintf(stderr, "%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size/1024.0/1024.0, buft_ctx->device, cudaGetErrorString(err));
|
||||
GGML_CUDA_LOG_ERROR("%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size / 1024.0 / 1024.0, buft_ctx->device, cudaGetErrorString(err));
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
@ -1002,8 +1042,8 @@ static void * ggml_cuda_host_malloc(size_t size) {
|
||||
if (err != cudaSuccess) {
|
||||
// clear the error
|
||||
cudaGetLastError();
|
||||
fprintf(stderr, "%s: warning: failed to allocate %.2f MiB of pinned memory: %s\n", __func__,
|
||||
size/1024.0/1024.0, cudaGetErrorString(err));
|
||||
GGML_CUDA_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__,
|
||||
size / 1024.0 / 1024.0, cudaGetErrorString(err));
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
@ -2246,7 +2286,7 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
|
||||
break;
|
||||
case GGML_OP_MUL_MAT:
|
||||
if (dst->src[0]->ne[3] != dst->src[1]->ne[3]) {
|
||||
fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, dst->name, dst->src[0]->ne[3], dst->src[1]->ne[3]);
|
||||
GGML_CUDA_LOG_ERROR("%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, dst->name, dst->src[0]->ne[3], dst->src[1]->ne[3]);
|
||||
return false;
|
||||
} else {
|
||||
ggml_cuda_mul_mat(ctx, dst->src[0], dst->src[1], dst);
|
||||
@ -2300,7 +2340,7 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
|
||||
|
||||
cudaError_t err = cudaGetLastError();
|
||||
if (err != cudaSuccess) {
|
||||
fprintf(stderr, "%s: %s failed\n", __func__, ggml_op_desc(dst));
|
||||
GGML_CUDA_LOG_ERROR("%s: %s failed\n", __func__, ggml_op_desc(dst));
|
||||
CUDA_CHECK(err);
|
||||
}
|
||||
|
||||
@ -2476,7 +2516,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
|
||||
if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) {
|
||||
cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true;
|
||||
#ifndef NDEBUG
|
||||
fprintf(stderr, "%s: disabling CUDA graphs due to GPU architecture\n", __func__);
|
||||
GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to GPU architecture\n", __func__);
|
||||
#endif
|
||||
}
|
||||
}
|
||||
@ -2523,14 +2563,14 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
|
||||
if (node->src[0] && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) {
|
||||
use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture
|
||||
#ifndef NDEBUG
|
||||
fprintf(stderr, "%s: disabling CUDA graphs due to split buffer\n", __func__);
|
||||
GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to split buffer\n", __func__);
|
||||
#endif
|
||||
}
|
||||
|
||||
if (node->op == GGML_OP_MUL_MAT_ID) {
|
||||
use_cuda_graph = false; // This node type is not supported by CUDA graph capture
|
||||
#ifndef NDEBUG
|
||||
fprintf(stderr, "%s: disabling CUDA graphs due to mul_mat_id\n", __func__);
|
||||
GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to mul_mat_id\n", __func__);
|
||||
#endif
|
||||
}
|
||||
|
||||
@ -2539,7 +2579,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
|
||||
// Changes in batch size or context size can cause changes to the grid size of some kernels.
|
||||
use_cuda_graph = false;
|
||||
#ifndef NDEBUG
|
||||
fprintf(stderr, "%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]);
|
||||
GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]);
|
||||
#endif
|
||||
}
|
||||
|
||||
@ -2567,7 +2607,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
|
||||
if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) {
|
||||
cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true;
|
||||
#ifndef NDEBUG
|
||||
fprintf(stderr, "%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
|
||||
GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
|
||||
#endif
|
||||
}
|
||||
}
|
||||
@ -2605,7 +2645,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
|
||||
|
||||
bool ok = ggml_cuda_compute_forward(*cuda_ctx, node);
|
||||
if (!ok) {
|
||||
fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
|
||||
GGML_CUDA_LOG_ERROR("%s: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
|
||||
}
|
||||
GGML_ASSERT(ok);
|
||||
}
|
||||
@ -2624,7 +2664,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
|
||||
use_cuda_graph = false;
|
||||
cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture = true;
|
||||
#ifndef NDEBUG
|
||||
fprintf(stderr, "%s: disabling CUDA graphs due to failed graph capture\n", __func__);
|
||||
GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to failed graph capture\n", __func__);
|
||||
#endif
|
||||
} else {
|
||||
graph_evaluated_or_captured = true; // CUDA graph has been captured
|
||||
@ -2691,7 +2731,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
|
||||
cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info);
|
||||
if (stat == cudaErrorGraphExecUpdateFailure) {
|
||||
#ifndef NDEBUG
|
||||
fprintf(stderr, "%s: CUDA graph update failed\n", __func__);
|
||||
GGML_CUDA_LOG_ERROR("%s: CUDA graph update failed\n", __func__);
|
||||
#endif
|
||||
// The pre-existing graph exec cannot be updated due to violated constraints
|
||||
// so instead clear error and re-instantiate
|
||||
@ -2948,13 +2988,13 @@ static ggml_guid_t ggml_backend_cuda_guid() {
|
||||
|
||||
GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) {
|
||||
if (device < 0 || device >= ggml_backend_cuda_get_device_count()) {
|
||||
fprintf(stderr, "%s: error: invalid device %d\n", __func__, device);
|
||||
GGML_CUDA_LOG_ERROR("%s: invalid device %d\n", __func__, device);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context(device);
|
||||
if (ctx == nullptr) {
|
||||
fprintf(stderr, "%s: error: failed to allocate context\n", __func__);
|
||||
GGML_CUDA_LOG_ERROR("%s: failed to allocate context\n", __func__);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
@ -2998,8 +3038,8 @@ GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size
|
||||
// clear the error
|
||||
cudaGetLastError();
|
||||
|
||||
fprintf(stderr, "%s: warning: failed to register %.2f MiB of pinned memory: %s\n", __func__,
|
||||
size/1024.0/1024.0, cudaGetErrorString(err));
|
||||
GGML_CUDA_LOG_WARN("%s: failed to register %.2f MiB of pinned memory: %s\n", __func__,
|
||||
size / 1024.0 / 1024.0, cudaGetErrorString(err));
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
|
||||
@ -38,6 +38,7 @@ GGML_API GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t *
|
||||
GGML_API GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size);
|
||||
GGML_API GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer);
|
||||
|
||||
GGML_API void ggml_backend_cuda_log_set_callback(ggml_log_callback log_callback, void * user_data);
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
||||
Loading…
Reference in New Issue
Block a user