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https://github.com/ggerganov/llama.cpp.git
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Capture CUDA logging output (#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>
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fraxy-v
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059031b8c4
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f5bf761747
100
ggml-cuda.cu
100
ggml-cuda.cu
@ -43,19 +43,59 @@
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#include <mutex>
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#include <stdint.h>
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#include <stdio.h>
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#include <stdarg.h>
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#include <stdlib.h>
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#include <string>
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#include <vector>
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static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
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static void ggml_cuda_default_log_callback(enum ggml_log_level level, const char * msg, void * user_data) {
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GGML_UNUSED(level);
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GGML_UNUSED(user_data);
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fprintf(stderr, "%s", msg);
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}
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ggml_log_callback ggml_cuda_log_callback = ggml_cuda_default_log_callback;
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void * ggml_cuda_log_user_data = NULL;
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GGML_API void ggml_backend_cuda_log_set_callback(ggml_log_callback log_callback, void * user_data) {
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ggml_cuda_log_callback = log_callback;
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ggml_cuda_log_user_data = user_data;
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}
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#define GGML_CUDA_LOG_INFO(...) ggml_cuda_log(GGML_LOG_LEVEL_INFO, __VA_ARGS__)
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#define GGML_CUDA_LOG_WARN(...) ggml_cuda_log(GGML_LOG_LEVEL_WARN, __VA_ARGS__)
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#define GGML_CUDA_LOG_ERROR(...) ggml_cuda_log(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
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GGML_ATTRIBUTE_FORMAT(2, 3)
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static void ggml_cuda_log(enum ggml_log_level level, const char * format, ...) {
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if (ggml_cuda_log_callback != NULL) {
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va_list args;
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va_start(args, format);
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char buffer[128];
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int len = vsnprintf(buffer, 128, format, args);
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if (len < 128) {
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ggml_cuda_log_callback(level, buffer, ggml_cuda_log_user_data);
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} else {
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std::vector<char> buffer2(len + 1); // vsnprintf adds a null terminator
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va_end(args);
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va_start(args, format);
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vsnprintf(&buffer2[0], buffer2.size(), format, args);
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ggml_cuda_log_callback(level, buffer2.data(), ggml_cuda_log_user_data);
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}
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va_end(args);
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}
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}
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[[noreturn]]
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void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg) {
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int id = -1; // in case cudaGetDevice fails
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cudaGetDevice(&id);
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fprintf(stderr, "CUDA error: %s\n", msg);
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fprintf(stderr, " current device: %d, in function %s at %s:%d\n", id, func, file, line);
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fprintf(stderr, " %s\n", stmt);
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GGML_CUDA_LOG_ERROR("CUDA error: %s\n", msg);
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GGML_CUDA_LOG_ERROR(" current device: %d, in function %s at %s:%d\n", id, func, file, line);
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GGML_CUDA_LOG_ERROR(" %s\n", stmt);
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// abort with GGML_ASSERT to get a stack trace
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GGML_ASSERT(!"CUDA error");
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}
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@ -91,7 +131,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
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cudaError_t err = cudaGetDeviceCount(&info.device_count);
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if (err != cudaSuccess) {
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fprintf(stderr, "%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err));
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GGML_CUDA_LOG_ERROR("%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err));
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return info;
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}
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@ -99,16 +139,16 @@ static ggml_cuda_device_info ggml_cuda_init() {
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int64_t total_vram = 0;
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#if defined(GGML_CUDA_FORCE_MMQ)
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fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__);
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GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__);
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#else
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fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: no\n", __func__);
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GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: no\n", __func__);
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#endif
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#if defined(CUDA_USE_TENSOR_CORES)
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fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: yes\n", __func__);
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GGML_CUDA_LOG_INFO("%s: CUDA_USE_TENSOR_CORES: yes\n", __func__);
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#else
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fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: no\n", __func__);
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GGML_CUDA_LOG_INFO("%s: CUDA_USE_TENSOR_CORES: no\n", __func__);
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#endif
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fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count);
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GGML_CUDA_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count);
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for (int id = 0; id < info.device_count; ++id) {
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int device_vmm = 0;
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@ -129,7 +169,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
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cudaDeviceProp prop;
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CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
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fprintf(stderr, " Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no");
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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");
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info.default_tensor_split[id] = total_vram;
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total_vram += prop.totalGlobalMem;
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@ -235,8 +275,8 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
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*actual_size = look_ahead_size;
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pool_size += look_ahead_size;
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#ifdef DEBUG_CUDA_MALLOC
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fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, device, nnz,
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(uint32_t)(max_size/1024/1024), (uint32_t)(pool_size/1024/1024), (uint32_t)(size/1024/1024));
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GGML_CUDA_LOG_INFO("%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, device, nnz,
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(uint32_t)(max_size / 1024 / 1024), (uint32_t)(pool_size / 1024 / 1024), (uint32_t)(size / 1024 / 1024));
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#endif
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return ptr;
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}
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@ -250,7 +290,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
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return;
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}
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}
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fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n");
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GGML_CUDA_LOG_WARN("Cuda buffer pool full, increase MAX_CUDA_BUFFERS\n");
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ggml_cuda_set_device(device);
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CUDA_CHECK(cudaFree(ptr));
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pool_size -= size;
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@ -499,7 +539,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffe
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void * dev_ptr;
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cudaError_t err = cudaMalloc(&dev_ptr, size);
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if (err != cudaSuccess) {
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fprintf(stderr, "%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size/1024.0/1024.0, buft_ctx->device, cudaGetErrorString(err));
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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));
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return nullptr;
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}
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@ -1002,8 +1042,8 @@ static void * ggml_cuda_host_malloc(size_t size) {
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if (err != cudaSuccess) {
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// clear the error
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cudaGetLastError();
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fprintf(stderr, "%s: warning: failed to allocate %.2f MiB of pinned memory: %s\n", __func__,
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size/1024.0/1024.0, cudaGetErrorString(err));
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GGML_CUDA_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__,
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size / 1024.0 / 1024.0, cudaGetErrorString(err));
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return nullptr;
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}
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@ -2246,7 +2286,7 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
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break;
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case GGML_OP_MUL_MAT:
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if (dst->src[0]->ne[3] != dst->src[1]->ne[3]) {
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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]);
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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]);
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return false;
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} else {
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ggml_cuda_mul_mat(ctx, dst->src[0], dst->src[1], dst);
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@ -2300,7 +2340,7 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
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cudaError_t err = cudaGetLastError();
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if (err != cudaSuccess) {
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fprintf(stderr, "%s: %s failed\n", __func__, ggml_op_desc(dst));
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GGML_CUDA_LOG_ERROR("%s: %s failed\n", __func__, ggml_op_desc(dst));
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CUDA_CHECK(err);
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}
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@ -2476,7 +2516,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
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if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) {
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cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true;
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#ifndef NDEBUG
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fprintf(stderr, "%s: disabling CUDA graphs due to GPU architecture\n", __func__);
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GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to GPU architecture\n", __func__);
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#endif
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}
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}
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@ -2523,14 +2563,14 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
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if (node->src[0] && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) {
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use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture
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#ifndef NDEBUG
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fprintf(stderr, "%s: disabling CUDA graphs due to split buffer\n", __func__);
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GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to split buffer\n", __func__);
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#endif
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}
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if (node->op == GGML_OP_MUL_MAT_ID) {
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use_cuda_graph = false; // This node type is not supported by CUDA graph capture
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#ifndef NDEBUG
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fprintf(stderr, "%s: disabling CUDA graphs due to mul_mat_id\n", __func__);
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GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to mul_mat_id\n", __func__);
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#endif
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}
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@ -2539,7 +2579,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
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// Changes in batch size or context size can cause changes to the grid size of some kernels.
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use_cuda_graph = false;
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#ifndef NDEBUG
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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]);
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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]);
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#endif
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}
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@ -2567,7 +2607,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
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if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) {
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cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true;
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#ifndef NDEBUG
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fprintf(stderr, "%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
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GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__);
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#endif
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}
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}
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@ -2605,7 +2645,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
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bool ok = ggml_cuda_compute_forward(*cuda_ctx, node);
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if (!ok) {
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fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
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GGML_CUDA_LOG_ERROR("%s: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
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}
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GGML_ASSERT(ok);
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}
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@ -2624,7 +2664,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
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use_cuda_graph = false;
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cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture = true;
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#ifndef NDEBUG
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fprintf(stderr, "%s: disabling CUDA graphs due to failed graph capture\n", __func__);
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GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to failed graph capture\n", __func__);
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#endif
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} else {
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graph_evaluated_or_captured = true; // CUDA graph has been captured
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@ -2691,7 +2731,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
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cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info);
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if (stat == cudaErrorGraphExecUpdateFailure) {
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#ifndef NDEBUG
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fprintf(stderr, "%s: CUDA graph update failed\n", __func__);
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GGML_CUDA_LOG_ERROR("%s: CUDA graph update failed\n", __func__);
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#endif
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// The pre-existing graph exec cannot be updated due to violated constraints
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// so instead clear error and re-instantiate
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@ -2948,13 +2988,13 @@ static ggml_guid_t ggml_backend_cuda_guid() {
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GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) {
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if (device < 0 || device >= ggml_backend_cuda_get_device_count()) {
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fprintf(stderr, "%s: error: invalid device %d\n", __func__, device);
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GGML_CUDA_LOG_ERROR("%s: invalid device %d\n", __func__, device);
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return nullptr;
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}
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ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context(device);
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if (ctx == nullptr) {
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fprintf(stderr, "%s: error: failed to allocate context\n", __func__);
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GGML_CUDA_LOG_ERROR("%s: failed to allocate context\n", __func__);
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return nullptr;
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}
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@ -2998,8 +3038,8 @@ GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size
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// clear the error
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cudaGetLastError();
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fprintf(stderr, "%s: warning: failed to register %.2f MiB of pinned memory: %s\n", __func__,
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size/1024.0/1024.0, cudaGetErrorString(err));
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GGML_CUDA_LOG_WARN("%s: failed to register %.2f MiB of pinned memory: %s\n", __func__,
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size / 1024.0 / 1024.0, cudaGetErrorString(err));
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return false;
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}
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return true;
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@ -38,6 +38,7 @@ GGML_API GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t *
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GGML_API GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size);
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GGML_API GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer);
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GGML_API void ggml_backend_cuda_log_set_callback(ggml_log_callback log_callback, void * user_data);
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#ifdef __cplusplus
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}
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#endif
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@ -1697,6 +1697,8 @@ struct llama_state {
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llama_state() {
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#ifdef GGML_USE_METAL
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ggml_backend_metal_log_set_callback(log_callback, log_callback_user_data);
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#elif defined(GGML_USE_CUDA)
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ggml_backend_cuda_log_set_callback(log_callback, log_callback_user_data);
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#endif
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}
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@ -18174,6 +18176,8 @@ void llama_log_set(ggml_log_callback log_callback, void * user_data) {
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g_state.log_callback_user_data = user_data;
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#ifdef GGML_USE_METAL
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ggml_backend_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
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#elif defined(GGML_USE_CUDA)
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ggml_backend_cuda_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
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#endif
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}
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