use LOG_TEE for stderr in ggml-cuda.cu
This commit is contained in:
Yann Follet
parent
799fc22689
commit
35a1e44e44
1 changed files with 23 additions and 22 deletions
45
ggml-cuda.cu
45
ggml-cuda.cu
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@ -89,6 +89,7 @@
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#include "ggml-cuda.h"
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#include "ggml.h"
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#include "ggml-backend-impl.h"
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#include "common/log.h"
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#define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
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#define CC_VOLTA 700
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@ -193,9 +194,9 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
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if (err_ != cudaSuccess) { \
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int id; \
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cudaGetDevice(&id); \
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fprintf(stderr, "\nCUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \
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LOG_TEE("\nCUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \
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cudaGetErrorString(err_)); \
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fprintf(stderr, "current device: %d\n", id); \
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LOG_TEE("current device: %d\n", id); \
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GGML_ASSERT(!"CUDA error"); \
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} \
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} while (0)
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@ -207,9 +208,9 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
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if (err_ != CUBLAS_STATUS_SUCCESS) { \
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int id; \
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cudaGetDevice(&id); \
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fprintf(stderr, "\ncuBLAS error %d at %s:%d: %s\n", \
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LOG_TEE("\ncuBLAS error %d at %s:%d: %s\n", \
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err_, __FILE__, __LINE__, cublasGetStatusString(err_)); \
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fprintf(stderr, "current device: %d\n", id); \
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LOG_TEE("current device: %d\n", id); \
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GGML_ASSERT(!"cuBLAS error"); \
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} \
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} while (0)
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@ -220,8 +221,8 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
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if (err_ != CUBLAS_STATUS_SUCCESS) { \
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int id; \
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cudaGetDevice(&id); \
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fprintf(stderr, "\ncuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__); \
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fprintf(stderr, "current device: %d\n", id); \
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LOG_TEE("\ncuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__); \
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LOG_TEE("current device: %d\n", id); \
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GGML_ASSERT(!"cuBLAS error"); \
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} \
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} while (0)
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@ -6591,7 +6592,7 @@ static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
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return ptr;
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}
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#ifdef DEBUG_CUDA_MALLOC
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fprintf(stderr, "%s: %d buffers, max_size = %u MB, tot_size = %u MB, requested %u MB\n", __func__, nnz,
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LOG_TEE("%s: %d buffers, max_size = %u MB, tot_size = %u MB, requested %u MB\n", __func__, nnz,
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(uint32_t)(max_size/1024/1024), (uint32_t)(tot_size/1024/1024), (uint32_t)(size/1024/1024));
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#endif
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void * ptr;
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@ -6615,7 +6616,7 @@ static void ggml_cuda_pool_free(void * ptr, size_t size) {
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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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LOG_TEE("WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n");
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CUDA_CHECK(cudaFree(ptr));
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}
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@ -6646,20 +6647,20 @@ void ggml_init_cublas() {
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GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES);
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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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LOG_TEE("%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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LOG_TEE("%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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LOG_TEE("%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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LOG_TEE("%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__, g_device_count);
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LOG_TEE("%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, g_device_count);
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for (int id = 0; id < g_device_count; ++id) {
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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\n", id, prop.name, prop.major, prop.minor);
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LOG_TEE(" Device %d: %s, compute capability %d.%d\n", id, prop.name, prop.major, prop.minor);
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g_tensor_split[id] = total_vram;
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total_vram += prop.totalGlobalMem;
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@ -6729,7 +6730,7 @@ void * ggml_cuda_host_malloc(size_t size) {
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// The allocation error can be bypassed. A null ptr will assigned out of this function.
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// This can fixed the OOM error in WSL.
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cudaGetLastError();
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fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
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LOG_TEE("WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
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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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@ -6844,7 +6845,7 @@ inline void ggml_cuda_op_bin_bcast(
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} else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
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op()(src0, src1, dst, (const half *) src0_dd, src1_dd, dst_dd, main_stream);
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} else {
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fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__,
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LOG_TEE("%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__,
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ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type));
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GGML_ASSERT(false);
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}
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@ -8958,7 +8959,7 @@ static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, gg
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} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
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ggml_cpy_f16_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
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} else {
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fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
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LOG_TEE("%s: unsupported type combination (%s to %s)\n", __func__,
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ggml_type_name(src0->type), ggml_type_name(src1->type));
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GGML_ASSERT(false);
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}
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@ -9259,7 +9260,7 @@ void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor) {
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void ggml_cuda_set_main_device(const int main_device) {
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if (main_device >= g_device_count) {
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fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n",
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LOG_TEE("warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n",
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main_device, g_device_count, g_main_device);
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return;
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}
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@ -9268,7 +9269,7 @@ void ggml_cuda_set_main_device(const int main_device) {
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g_main_device = main_device;
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cudaDeviceProp prop;
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CUDA_CHECK(cudaGetDeviceProperties(&prop, g_main_device));
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fprintf(stderr, "%s: using device %d (%s) as main device\n", __func__, g_main_device, prop.name);
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LOG_TEE("%s: using device %d (%s) as main device\n", __func__, g_main_device, prop.name);
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}
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}
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@ -9305,7 +9306,7 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
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if (tensor->op == GGML_OP_MUL_MAT) {
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if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) {
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#ifndef NDEBUG
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fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = " PRId64 ", src1->ne[3] = " PRId64 " - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]);
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LOG_TEE("%s: cannot compute %s: src0->ne[3] = " PRId64 ", src1->ne[3] = " PRId64 " - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]);
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#endif
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return false;
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}
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@ -9787,7 +9788,7 @@ static void ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph
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bool ok = ggml_cuda_compute_forward(¶ms, 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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LOG_TEE("%s: error: 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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@ -9945,7 +9946,7 @@ ggml_backend_t ggml_backend_cuda_init(int device) {
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ggml_init_cublas(); // TODO: remove from ggml.c
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if (device < 0 || device >= ggml_cuda_get_device_count()) {
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fprintf(stderr, "%s: error: invalid device %d\n", __func__, device);
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LOG_TEE("%s: error: invalid device %d\n", __func__, device);
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return nullptr;
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}
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