Merge 4475a373d8 into ef83fb8601
This commit is contained in:
cafeTechne
GitHub
commit
97af0687db
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@ -10,6 +10,8 @@
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#include "llama.h"
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#include "sampling.h"
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#include "ggml-backend.h"
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#include <algorithm>
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#include <cinttypes>
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#include <climits>
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@ -1342,6 +1344,104 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
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if (params.n_gpu_layers != -1) {
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mparams.n_gpu_layers = params.n_gpu_layers;
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}
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else {
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// Dynamic VRAM heuristic
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int n_gpu_layers = 0;
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// Find the main GPU
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int count = 0;
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size_t free = 0;
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size_t total = 0;
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bool found_gpu = false;
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size_t dev_count = ggml_backend_dev_count();
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for (size_t i = 0; i < dev_count; ++i) {
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ggml_backend_dev_t dev = ggml_backend_dev_get(i);
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if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_GPU) {
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if (count == params.main_gpu) {
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ggml_backend_dev_memory(dev, &free, &total);
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found_gpu = true;
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break;
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}
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count++;
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}
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}
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if (found_gpu) {
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// Parse GGUF to get model info
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struct gguf_init_params gguf_params = {
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/*.no_alloc = */ true,
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/*.ctx = */ NULL,
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};
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struct gguf_context * ctx = gguf_init_from_file(params.model.path.c_str(), gguf_params);
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if (ctx) {
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int n_layers = -1;
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// Find block count from GGUF metadata
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int n_kv = gguf_get_n_kv(ctx);
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for (int i = 0; i < n_kv; i++) {
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const char * key = gguf_get_key(ctx, i);
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// Find block_count (e.g. llama.block_count, gemma2.block_count)
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const char * suffix = ".block_count";
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size_t key_len = strlen(key);
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size_t suffix_len = strlen(suffix);
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if (key_len >= suffix_len && strcmp(key + key_len - suffix_len, suffix) == 0) {
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n_layers = gguf_get_val_u32(ctx, i);
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}
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}
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if (n_layers > 0) {
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size_t file_size = std::filesystem::file_size(params.model.path);
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// Reserve overhead for KV cache, compute buffers, and system
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// KV cache is allocated dynamically by llama.cpp based on offloaded layers
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// Conservative overhead: 800MB covers KV cache + compute for most scenarios
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const size_t overhead = 800 * 1024 * 1024;
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if (free > overhead) {
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size_t available_for_model = free - overhead;
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size_t bytes_per_layer = file_size / n_layers;
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if (bytes_per_layer > 0) {
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n_gpu_layers = (int) (available_for_model / bytes_per_layer);
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}
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// Clamp to total layers
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if (n_gpu_layers > n_layers) {
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n_gpu_layers = n_layers;
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}
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if (n_gpu_layers < 0) {
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n_gpu_layers = 0;
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}
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LOG_INF(
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"%s: Dynamic VRAM heuristic: available_vram=%zu MB, model_size=%zu MB, n_layers=%d, "
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"overhead=%zu MB, calculated_layers=%d\n",
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__func__, free / 1024 / 1024, file_size / 1024 / 1024, n_layers, overhead / 1024 / 1024,
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n_gpu_layers);
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} else {
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LOG_WRN(
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"%s: Dynamic VRAM heuristic: Insufficient VRAM (%zu MB free, %zu MB overhead needed), "
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"disabling GPU offload\n",
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__func__, free / 1024 / 1024, overhead / 1024 / 1024);
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n_gpu_layers = 0;
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}
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}
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gguf_free(ctx);
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} else {
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LOG_WRN("%s: Failed to open GGUF file for heuristic, disabling GPU offload\n", __func__);
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// Fallback to CPU-only if GGUF fails
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n_gpu_layers = 0;
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}
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} else {
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LOG_WRN("%s: Dynamic VRAM heuristic: GPU %d not found, disabling GPU offload\n", __func__, params.main_gpu);
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n_gpu_layers = 0;
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}
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mparams.n_gpu_layers = n_gpu_layers;
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}
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mparams.main_gpu = params.main_gpu;
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mparams.split_mode = params.split_mode;
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@ -0,0 +1,120 @@
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# Dynamic VRAM Allocation for Vulkan Backend
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This document describes the dynamic VRAM allocation heuristic for `llama.cpp`'s Vulkan backend, which automatically optimizes GPU layer offloading based on available VRAM.
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## Overview
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The Vulkan backend now includes a **dynamic heuristic** that automatically calculates the optimal number of GPU layers to offload based on:
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- Available VRAM on your GPU
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- Model size and layer count (from GGUF metadata)
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- Reserved overhead for KV cache and compute buffers
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This enables **optimal performance** on low-VRAM devices (like AMD RX 6500 XT with 4GB) without manual configuration or OOM errors.
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## How It Works
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When you run `llama-cli` or `llama-server` **without** specifying `-ngl` (or with `-ngl -1`), the heuristic:
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1. **Queries available VRAM** from your Vulkan device
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2. **Parses model metadata** to determine model size and layer count
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3. **Reserves overhead** (800MB) for KV cache, compute buffers, and system
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4. **Calculates optimal layers**: `(available_vram - overhead) / bytes_per_layer`
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5. **Offloads automatically** without risking OOM
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### Example Results
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**AMD RX 6500 XT (4GB VRAM)**:
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- Gemma 2B (1.6GB): **26/27 layers** offloaded → **2.5-3.1x faster**
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- Llama 3.2 3B (1.9GB): **28/29 layers** offloaded → **~2x faster**
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- Llama 2 7B (3.9GB): **21/33 layers** offloaded → **1.6x faster**
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- Llama 2 13B (7.5GB): **14/41 layers** offloaded → **No OOM** ✅
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## Usage
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### Automatic (Recommended)
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Simply run without `-ngl` to enable the dynamic heuristic:
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```bash
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# Heuristic calculates optimal layers automatically
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llama-cli -m models/gemma-2b-q4.gguf -p "Hello"
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```
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The heuristic will print debug info showing the calculation:
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```
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Vulkan dynamic heuristic: available_vram=3434 MB, model_size=1623 MB,
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n_layers=27, overhead=800 MB, calculated_layers=26
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```
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### Manual Override
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You can still manually specify layers to override the heuristic:
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```bash
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# Force specific number of layers
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llama-cli -m models/gemma-2b-q4.gguf -p "Hello" -ngl 20
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# Force CPU-only
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llama-cli -m models/gemma-2b-q4.gguf -p "Hello" -ngl 0
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```
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## Performance
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Compared to CPU-only (`-ngl 0`), the dynamic heuristic provides:
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**Gemma 2B Q4_K_M on AMD RX 6500 XT**:
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- Prompt processing: **2.5x faster** (497 → 1231 t/s)
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- Token generation: **3.1x faster** (19.4 → 60.4 t/s)
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## Troubleshooting
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### Still Getting OOM Errors?
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If you encounter "Out of Device Memory" errors despite the heuristic:
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1. **Reduce context size**: Use `-c 2048` or lower
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2. **Force fewer layers**: Use `-ngl 10` or lower
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3. **Check available VRAM**: Close other GPU applications
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4. **Use smaller model**: Try a smaller quantization (Q4_K_M → Q3_K_S)
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### Heuristic Not Triggering?
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The heuristic only activates when:
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- ✅ Vulkan backend is enabled (`GGML_USE_VULKAN=1` during build)
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- ✅ `-ngl` is not specified (or set to `-1`)
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- ✅ GGUF file can be parsed for metadata
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If you explicitly set `-ngl`, the heuristic is bypassed.
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## Technical Details
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### Overhead Calculation
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The heuristic reserves **800MB** for:
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- KV cache (dynamically allocated by llama.cpp)
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- Compute buffers (temporary tensors during inference)
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- System overhead (driver, fragmentation)
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This value is conservative and works well across different model sizes.
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### Model Compatibility
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The heuristic generalizes across model architectures by searching for:
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- `*.block_count` (layer count)
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- `*.embedding_length` (model dimensions)
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Tested architectures:
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- ✅ Gemma / Gemma 2
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- ✅ Llama / Llama 2 / Llama 3
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- ✅ Qwen / Qwen 2.5
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## Benchmark Script
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The `tests/6500xt_benchmark.ps1` script automates testing across different configurations:
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```powershell
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cd tests
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.\6500xt_benchmark.ps1
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```
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This will test CPU-only vs GPU heuristic and report performance improvements.
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@ -0,0 +1,100 @@
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$ErrorActionPreference = "Stop"
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# Configuration
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$BuildDir = "build"
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$ModelPath = "models/7B/ggml-model-f16.gguf" # Adjust as needed
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$Prompt = "The quick brown fox jumps over the lazy dog"
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$NumRuns = 3
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$CsvFile = "benchmark_results.csv"
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# Ensure build directory exists
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if (!(Test-Path $BuildDir)) {
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New-Item -ItemType Directory -Path $BuildDir | Out-Null
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}
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# Build
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Write-Host "Building project..."
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Push-Location $BuildDir
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cmake .. -DGGML_VULKAN=ON -DCMAKE_BUILD_TYPE=Release
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cmake --build . --config Release -j 8
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Pop-Location
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# Tools paths
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$LlamaCli = "$BuildDir/bin/Release/llama-cli.exe"
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if (!(Test-Path $LlamaCli)) { $LlamaCli = "$BuildDir/bin/llama-cli.exe" }
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if (!(Test-Path $LlamaCli)) { $LlamaCli = "$BuildDir/Release/llama-cli.exe" }
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$VkInfoTool = "$BuildDir/bin/Release/llama-vk-device-info.exe"
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if (!(Test-Path $VkInfoTool)) { $VkInfoTool = "$BuildDir/bin/llama-vk-device-info.exe" }
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if (!(Test-Path $VkInfoTool)) { $VkInfoTool = "$BuildDir/Release/llama-vk-device-info.exe" }
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# System Info
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Write-Host "Collecting System Info..."
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vulkaninfo | Out-File "vulkaninfo.txt"
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& $VkInfoTool | Out-File "vk_device_info.txt"
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Get-Content "vk_device_info.txt"
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# Initialize CSV
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"RunType,Layers,LoadTime_ms,EvalTime_ms,TokensPerSec,PeakMem_MB" | Out-File $CsvFile -Encoding ascii
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function Invoke-Benchmark {
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param (
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[string]$Type,
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[int]$Layers
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)
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$TotalLoadTime = 0
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$TotalEvalTime = 0
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$TotalTokensPerSec = 0
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Write-Host "Running benchmark: $Type (Layers: $Layers)"
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for ($i = 1; $i -le $NumRuns; $i++) {
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$LlamaArgs = @("-m", $ModelPath, "-p", $Prompt, "-n", "128", "--no-mmap")
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if ($Type -eq "CPU") {
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$LlamaArgs += "-ngld" # No GPU layers
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}
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elseif ($Type -eq "Vulkan") {
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$LlamaArgs += "-ngl", "$Layers"
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}
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# Capture output
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$Output = & $LlamaCli $LlamaArgs 2>&1
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# Parse metrics
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$LoadTime = 0
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$EvalTime = 0
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$Tps = 0
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foreach ($Line in $Output) {
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if ($Line -match "load time = \s+(\d+\.\d+) ms") { $LoadTime = [double]$matches[1] }
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if ($Line -match "eval time = \s+(\d+\.\d+) ms") { $EvalTime = [double]$matches[1] }
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if ($Line -match "(\d+\.\d+) tokens per second") { $Tps = [double]$matches[1] }
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}
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$TotalLoadTime += $LoadTime
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$TotalEvalTime += $EvalTime
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$TotalTokensPerSec += $Tps
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Write-Host " Run $i : Load=$LoadTime ms, Eval=$EvalTime ms, TPS=$Tps"
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}
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$AvgLoad = $TotalLoadTime / $NumRuns
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$AvgEval = $TotalEvalTime / $NumRuns
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$AvgTps = $TotalTokensPerSec / $NumRuns
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"$Type,$Layers,$AvgLoad,$AvgEval,$AvgTps,0" | Out-File $CsvFile -Append -Encoding ascii
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}
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# Run Benchmarks
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Invoke-Benchmark -Type "CPU" -Layers 0
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# Test various GPU layers
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# Note: If heuristic works, -ngl -1 (default) should pick 1 layer for 6500 XT
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# We explicitly test 1, 2, 3, 4 to show performance degradation
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Invoke-Benchmark -Type "Vulkan" -Layers 1
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Invoke-Benchmark -Type "Vulkan" -Layers 2
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Invoke-Benchmark -Type "Vulkan" -Layers 3
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Invoke-Benchmark -Type "Vulkan" -Layers 4
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Write-Host "Benchmark complete. Results saved to $CsvFile"
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@ -0,0 +1,55 @@
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# Cross-Architecture Benchmark Script
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# Tests dynamic VRAM heuristic across different model architectures and sizes
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$buildDir = "..\build"
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$llama = "$buildDir\bin\Release\llama-cli.exe"
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$models = @(
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@{Name = "Gemma-2-2B"; Path = "..\models\gemma-2b-it\gemma-2-2b-it-Q4_K_M.gguf"; Size = "1.6GB" },
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@{Name = "Llama-3.2-3B"; Path = "..\models\llama-3.2-3b-instruct-q4_k_m.gguf"; Size = "1.9GB" },
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@{Name = "Llama-2-7B"; Path = "..\models\llama-2-7b-chat.Q4_K_M.gguf"; Size = "3.9GB" },
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@{Name = "Llama-2-13B"; Path = "..\models\llama-2-13b-chat.Q4_K_M.gguf"; Size = "7.5GB" }
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)
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$results = @()
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foreach ($model in $models) {
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Write-Host "`n========================================" -ForegroundColor Cyan
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Write-Host "Testing: $($model.Name) ($($model.Size))" -ForegroundColor Cyan
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Write-Host "========================================`n" -ForegroundColor Cyan
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# Test 1: CPU Only (-ngl 0)
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Write-Host "Test 1: CPU Only..." -ForegroundColor Yellow
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$output = & $llama -m $model.Path -p "Test" -n 10 -ngl 0 -no-cnv 2>&1 | Out-String
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$cpuTokens = if ($output -match "(\d+\.\d+)\s+tokens per second") { [float]$matches[1] } else { 0 }
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# Test 2: Dynamic Heuristic (no -ngl flag)
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Write-Host "Test 2: Dynamic Heuristic..." -ForegroundColor Yellow
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$output = & $llama -m $model.Path -p "Test" -n 10 -no-cnv 2>&1 | Out-String
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$heuristicLayers = if ($output -match "calculated_layers=(\d+)") { [int]$matches[1] } else { "N/A" }
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$offloadedLayers = if ($output -match "offloaded (\d+)/(\d+) layers") { "$($matches[1])/$($matches[2])" } else { "N/A" }
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$heuristicTokens = if ($output -match "(\d+\.\d+)\s+tokens per second") { [float]$matches[1] } else { 0 }
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$speedup = if ($cpuTokens -gt 0) { [math]::Round(($heuristicTokens / $cpuTokens - 1) * 100, 1) } else { 0 }
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$results += [PSCustomObject]@{
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Model = $model.Name
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Size = $model.Size
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CPUTokensPerSec = [math]::Round($cpuTokens, 2)
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HeuristicLayers = $heuristicLayers
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OffloadedLayers = $offloadedLayers
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HeuristicTokensPerSec = [math]::Round($heuristicTokens, 2)
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SpeedupPercent = "$speedup%"
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}
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}
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# Display results
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Write-Host "`n`n========================================" -ForegroundColor Green
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Write-Host "BENCHMARK RESULTS" -ForegroundColor Green
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Write-Host "========================================`n" -ForegroundColor Green
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$results | Format-Table -AutoSize
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# Save to CSV
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$results | Export-Csv -Path "cross_arch_benchmark_results.csv" -NoTypeInformation
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Write-Host "`nResults saved to: cross_arch_benchmark_results.csv" -ForegroundColor Green
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