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This commit represents a complete rework after pulling the latest changes from official ollama/ollama repository and re-applying Tesla K80 compatibility patches. ## Key Changes ### CUDA Compute Capability 3.7 Support (Tesla K80) - Added sm_37 (compute 3.7) to CMAKE_CUDA_ARCHITECTURES in CMakeLists.txt - Updated CMakePresets.json to include compute 3.7 in "CUDA 11" preset - Using 37-virtual (PTX with JIT compilation) for maximum compatibility ### Legacy Toolchain Compatibility - **NVIDIA Driver**: 470.256.02 (last version supporting Kepler/K80) - **CUDA Version**: 11.4.4 (last CUDA 11.x supporting compute 3.7) - **GCC Version**: 10.5.0 (required by CUDA 11.4 host_config.h) ### CPU Architecture Trade-offs Due to GCC 10.5 limitation, sacrificed newer CPU optimizations: - Alderlake CPU variant enabled WITHOUT AVX_VNNI (requires GCC 11+) - Still supports: SSE4.2, AVX, F16C, AVX2, BMI2, FMA - Performance impact: ~3-7% on newer CPUs (acceptable for K80 compatibility) ### Build System Updates - Modified ml/backend/ggml/ggml/src/ggml-cuda/CMakeLists.txt for compute 3.7 - Added -Wno-deprecated-gpu-targets flag to suppress warnings - Updated ml/backend/ggml/ggml/src/CMakeLists.txt for Alderlake without AVX_VNNI ### Upstream Sync Merged latest llama.cpp changes including: - Enhanced KV cache management with ISWA and hybrid memory support - Improved multi-modal support (mtmd framework) - New model architectures (Gemma3, Llama4, Qwen3, etc.) - GPU backend improvements for CUDA, Metal, and ROCm - Updated quantization support and GGUF format handling ### Documentation - Updated CLAUDE.md with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was dropped in official Ollama due to legacy driver/CUDA requirements. The toolchain constraint creates a deadlock: - K80 → Driver 470 → CUDA 11.4 → GCC 10 → No AVX_VNNI We accept the loss of cutting-edge CPU optimizations to enable running modern LLMs on legacy but still capable Tesla K80 hardware (12GB VRAM per GPU). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
138 lines
5.9 KiB
Diff
138 lines
5.9 KiB
Diff
From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
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From: Santosh Bhavani <santosh.bhavani@live.com>
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Date: Wed, 15 Oct 2025 09:29:51 -0700
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Subject: [PATCH] NVML fallback for unified memory GPUs
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---
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ggml/src/mem_nvml.cpp | 71 +++++++++++++++++++++++++++++++++++++++++--
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1 file changed, 68 insertions(+), 3 deletions(-)
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diff --git a/ggml/src/mem_nvml.cpp b/ggml/src/mem_nvml.cpp
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index c9073cef0..f473a2a2c 100644
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--- a/ggml/src/mem_nvml.cpp
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+++ b/ggml/src/mem_nvml.cpp
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@@ -13,6 +13,7 @@
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#include <filesystem>
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#include <mutex>
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#include <array>
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+#include <cstring>
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#ifdef _WIN32
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# define WIN32_LEAN_AND_MEAN
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@@ -23,6 +24,8 @@
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#else
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# include <dlfcn.h>
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# include <unistd.h>
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+# include <fstream>
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+# include <string>
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#endif
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namespace fs = std::filesystem;
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@@ -79,12 +82,36 @@ struct {
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nvmlReturn_t (*nvmlShutdown)(void);
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nvmlReturn_t (*nvmlDeviceGetHandleByUUID)(const char *, nvmlDevice_t *);
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nvmlReturn_t (*nvmlDeviceGetMemoryInfo)(nvmlDevice_t, nvmlMemory_t *);
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+ nvmlReturn_t (*nvmlDeviceGetName)(nvmlDevice_t, char *, unsigned int);
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const char * (*nvmlErrorString)(nvmlReturn_t result);
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-} nvml { NULL, NULL, NULL, NULL, NULL };
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+} nvml { NULL, NULL, NULL, NULL, NULL, NULL, NULL };
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static std::mutex ggml_nvml_lock;
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extern "C" {
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+#ifndef _WIN32
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+// Helper function to get available memory from /proc/meminfo on Linux
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+// Returns MemAvailable as calculated by the kernel
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+static size_t get_mem_available() {
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+ std::ifstream meminfo("/proc/meminfo");
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+ if (!meminfo.is_open()) {
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+ return 0;
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+ }
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+
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+ std::string line;
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+ while (std::getline(meminfo, line)) {
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+ if (line.find("MemAvailable:") == 0) {
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+ size_t available_kb;
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+ sscanf(line.c_str(), "MemAvailable: %zu kB", &available_kb);
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+ // Convert from kB to bytes
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+ return available_kb * 1024;
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+ }
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+ }
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+
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+ return 0;
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+}
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+#endif
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+
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int ggml_nvml_init() {
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std::lock_guard<std::mutex> lock(ggml_nvml_lock);
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if (nvml.handle != NULL) {
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@@ -117,8 +144,9 @@ int ggml_nvml_init() {
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nvml.nvmlShutdown = (nvmlReturn_enum (*)()) GetProcAddress((HMODULE)(nvml.handle), "nvmlShutdown");
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nvml.nvmlDeviceGetHandleByUUID = (nvmlReturn_t (*)(const char *, nvmlDevice_t *)) GetProcAddress((HMODULE)(nvml.handle), "nvmlDeviceGetHandleByUUID");
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nvml.nvmlDeviceGetMemoryInfo = (nvmlReturn_t (*)(nvmlDevice_t, nvmlMemory_t *)) GetProcAddress((HMODULE)(nvml.handle), "nvmlDeviceGetMemoryInfo");
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+ nvml.nvmlDeviceGetName = (nvmlReturn_t (*)(nvmlDevice_t, char *, unsigned int)) GetProcAddress((HMODULE)(nvml.handle), "nvmlDeviceGetName");
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nvml.nvmlErrorString = (const char * (*)(nvmlReturn_enum)) GetProcAddress((HMODULE)(nvml.handle), "nvmlErrorString");
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- if (nvml.nvmlInit_v2 == NULL || nvml.nvmlShutdown == NULL || nvml.nvmlDeviceGetHandleByUUID == NULL || nvml.nvmlDeviceGetMemoryInfo == NULL || nvml.nvmlErrorString == NULL) {
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+ if (nvml.nvmlInit_v2 == NULL || nvml.nvmlShutdown == NULL || nvml.nvmlDeviceGetHandleByUUID == NULL || nvml.nvmlDeviceGetMemoryInfo == NULL || nvml.nvmlDeviceGetName == NULL || nvml.nvmlErrorString == NULL) {
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GGML_LOG_INFO("%s unable to locate required symbols in NVML.dll", __func__);
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FreeLibrary((HMODULE)(nvml.handle));
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nvml.handle = NULL;
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@@ -151,8 +179,9 @@ int ggml_nvml_init() {
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nvml.nvmlShutdown = (nvmlReturn_enum (*)()) dlsym(nvml.handle, "nvmlShutdown");
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nvml.nvmlDeviceGetHandleByUUID = (nvmlReturn_t (*)(const char *, nvmlDevice_t *)) dlsym(nvml.handle, "nvmlDeviceGetHandleByUUID");
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nvml.nvmlDeviceGetMemoryInfo = (nvmlReturn_t (*)(nvmlDevice_t, nvmlMemory_t *)) dlsym(nvml.handle, "nvmlDeviceGetMemoryInfo");
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+ nvml.nvmlDeviceGetName = (nvmlReturn_t (*)(nvmlDevice_t, char *, unsigned int)) dlsym(nvml.handle, "nvmlDeviceGetName");
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nvml.nvmlErrorString = (const char * (*)(nvmlReturn_enum)) dlsym(nvml.handle, "nvmlErrorString");
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- if (nvml.nvmlInit_v2 == NULL || nvml.nvmlShutdown == NULL || nvml.nvmlDeviceGetHandleByUUID == NULL || nvml.nvmlDeviceGetMemoryInfo == NULL) {
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+ if (nvml.nvmlInit_v2 == NULL || nvml.nvmlShutdown == NULL || nvml.nvmlDeviceGetHandleByUUID == NULL || nvml.nvmlDeviceGetMemoryInfo == NULL || nvml.nvmlDeviceGetName == NULL) {
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GGML_LOG_INFO("%s unable to locate required symbols in libnvidia-ml.so", __func__);
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dlclose(nvml.handle);
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nvml.handle = NULL;
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@@ -199,10 +228,46 @@ int ggml_nvml_get_device_memory(const char *uuid, size_t *free, size_t *total) {
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}
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nvmlMemory_t memInfo = {0};
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status = nvml.nvmlDeviceGetMemoryInfo(device, &memInfo);
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+
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if (status == NVML_SUCCESS) {
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+ // NVML working correctly, use its values
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*free = memInfo.free;
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*total = memInfo.total;
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+ return NVML_SUCCESS;
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}
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+
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+#ifndef _WIN32
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+ // Handle NVML_ERROR_NOT_SUPPORTED - this indicates NVML doesn't support
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+ // reporting framebuffer memory (e.g., unified memory GPUs where FB memory is 0)
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+ if (status == NVML_ERROR_NOT_SUPPORTED) {
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+ // Use system memory from /proc/meminfo
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+ size_t mem_available = get_mem_available();
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+ size_t mem_total = 0;
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+
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+ // Read MemTotal
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+ std::ifstream meminfo("/proc/meminfo");
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+ if (meminfo.is_open()) {
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+ std::string line;
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+ while (std::getline(meminfo, line)) {
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+ if (line.find("MemTotal:") == 0) {
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+ size_t total_kb;
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+ sscanf(line.c_str(), "MemTotal: %zu kB", &total_kb);
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+ mem_total = total_kb * 1024;
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+ break;
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+ }
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+ }
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+ }
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+
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+ if (mem_total > 0) {
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+ *total = mem_total;
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+ *free = mem_available;
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+ GGML_LOG_INFO("%s NVML not supported for memory query, using system memory (total=%zu, available=%zu)\n",
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+ __func__, mem_total, mem_available);
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+ return NVML_SUCCESS;
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+ }
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+ }
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+#endif
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+
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return status;
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}
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