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Merge pull request #20 from skyne98/feat/update-ggml-gfx906
chore: Update ggml submodule with GFX906 backend support
2 parents d65185a + 8976d0c commit f576e7c

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CLAUDE.md

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@@ -9,6 +9,9 @@ llama.cpp-gfx906 is a high-performance C/C++ implementation for LLM inference wi
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### Standard CPU Build
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```bash
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# Initialize submodules (required for ggml)
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git submodule update --init --recursive
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cmake -B build
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cmake --build build --config Release
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```
@@ -17,11 +20,21 @@ cmake --build build --config Release
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```bash
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cmake -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx906
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cmake --build build --config Release
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# GFX906-optimized build (when available)
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cmake -B build -DGGML_HIP=ON -DGGML_HIP_GFX906_OPTIMIZED=ON -DAMDGPU_TARGETS=gfx906
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cmake --build build --config Release
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```
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### Debug Build
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```bash
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cmake -B build -DCMAKE_BUILD_TYPE=Debug
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cmake --build build
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```
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## Testing
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### Run All Tests
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### Build and Run All Tests
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```bash
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cmake -B build -DLLAMA_BUILD_TESTS=ON
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cmake --build build --config Release
@@ -41,16 +54,25 @@ ctest -L model # Model loading
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./build/bin/test-tokenizer-0 ./models/ggml-vocab-llama-bpe.gguf
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```
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## Code Formatting
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Use clang-format for all C/C++ code. The repository follows 4-space indentation (configured in .ecrc).
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### Running Benchmarks
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```bash
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# Performance benchmark
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./build/bin/llama-bench -m model.gguf
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# Perplexity testing
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./build/bin/llama-perplexity -m model.gguf -f file.txt
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# Profile with rocprof (AMD GPU)
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rocprof --stats --hip-trace ./build/bin/llama-cli -m model.gguf -p "prompt" -n 100
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```
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## Architecture
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### Layer Structure
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1. **GGML Layer** (`ggml/`): Low-level tensor operations and backend implementations
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- `ggml/src/ggml.c`: Core tensor library
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- `ggml/src/ggml-cuda/`: NVIDIA GPU kernels
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- `ggml/src/ggml-hip/`: AMD GPU kernels
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- `ggml/src/ggml-hip/`: AMD GPU kernels (GFX906 optimizations)
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- `ggml/src/ggml-backend.c`: Backend abstraction layer
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2. **LLaMA Layer** (`src/`): Model implementation and inference engine
@@ -60,9 +82,11 @@ Use clang-format for all C/C++ code. The repository follows 4-space indentation
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- `src/llama-sampling.*`: Sampling strategies (greedy, top-k, top-p, etc.)
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3. **Tools Layer** (`tools/`): User-facing applications
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- `tools/main/`: CLI tool for model inference
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- `tools/server/`: HTTP server with OpenAI API compatibility
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- `tools/quantize/`: Model quantization utilities
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- `tools/main/`: CLI tool for model inference (`llama-cli`)
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- `tools/server/`: HTTP server with OpenAI API compatibility (`llama-server`)
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- `tools/quantize/`: Model quantization utilities (`llama-quantize`)
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- `tools/perplexity/`: Model quality metrics (`llama-perplexity`)
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- `tools/llama-bench/`: Performance benchmarking (`llama-bench`)
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### Key Design Patterns
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- **Backend Abstraction**: All compute operations go through ggml-backend interface, allowing seamless switching between CPU/CUDA/HIP/Vulkan
@@ -77,17 +101,24 @@ Use clang-format for all C/C++ code. The repository follows 4-space indentation
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- New sampling methods belong in `src/llama-sampling.cpp`
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- Backend kernels should be added to respective backend directories under `ggml/src/`
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### GFX906 Specific Development
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- GFX906 optimizations are in `docs/gfx906/` documentation
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- Key hardware features: V_DOT4_I32_I8, V_DOT2_F32_F16, 64KB LDS
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- Refer to `docs/gfx906/optimization_plan.md` for optimization strategy
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- Check `docs/gfx906/implementation_guide.md` for kernel implementations
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### Before Committing
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1. Run clang-format on modified files
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2. Build with tests enabled and run ctest
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3. Test with both CPU and GPU builds if modifying backend code
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4. Check performance impact with perplexity tool
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4. Check performance impact with llama-bench and perplexity tools
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### Common Development Tasks
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- **Add new model architecture**: Modify `llm_load_arch()` and `llm_build_*()` functions in `src/llama.cpp`
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- **Implement new operator**: Add to `ggml/src/ggml.c` and implement in relevant backends
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- **Add sampling method**: Extend `src/llama-sampling.cpp` with new sampling strategy
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- **Debug tokenization**: Use `tools/test-tokenizer-*.cpp` utilities
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- **Optimize for GFX906**: Follow patterns in `ggml/src/ggml-hip/` and reference `docs/gfx906/`
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## Important Configuration
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- C++17 required

ggml

Submodule ggml updated from b141fc2 to 0ec64f7

tests/CMakeLists.txt

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@@ -145,6 +145,12 @@ if (NOT WIN32 OR NOT BUILD_SHARED_LIBS)
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llama_build_and_test(test-grammar-integration.cpp)
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llama_build_and_test(test-llama-grammar.cpp)
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llama_build_and_test(test-chat.cpp)
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# GFX906 backend infrastructure test
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if (GGML_HIP AND (CMAKE_HIP_ARCHITECTURES MATCHES "gfx906" OR AMDGPU_TARGETS MATCHES "gfx906"))
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llama_build_and_test(test-gfx906-backend.cpp LABEL "backend")
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endif()
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# TODO: disabled on loongarch64 because the ggml-ci node lacks Python 3.8
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if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64")
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llama_build_and_test(test-json-schema-to-grammar.cpp WORKING_DIRECTORY ${PROJECT_SOURCE_DIR})

tests/test-gfx906-backend.cpp

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#include "ggml-cuda.h"
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#include <cassert>
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#include <cstdio>
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#include <cstdlib>
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#include <cstring>
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// External functions from GFX906 backend
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extern "C" {
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bool ggml_cuda_gfx906_init();
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bool ggml_cuda_gfx906_init_streams(int device_id);
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void ggml_cuda_gfx906_cleanup();
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void ggml_cuda_gfx906_print_perf_stats();
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}
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// Forward declarations for test functions
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static bool test_device_detection();
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static bool test_stream_management();
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static bool test_memory_allocation();
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static bool test_configuration();
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// Test device detection
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static bool test_device_detection() {
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printf("Testing GFX906 device detection...\n");
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// Get CUDA device info
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int device_count = ggml_backend_cuda_get_device_count();
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printf(" Total CUDA devices: %d\n", device_count);
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if (device_count == 0) {
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printf(" No CUDA devices found\n");
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return false;
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}
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// Initialize GFX906 backend
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bool gfx906_found = ggml_cuda_gfx906_init();
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if (!gfx906_found) {
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printf(" No GFX906 devices found (this is OK if you don't have an MI50)\n");
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return true; // Not an error, just no GFX906 hardware
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}
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printf(" GFX906 device detection: PASSED\n");
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return true;
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}
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// Test stream management
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static bool test_stream_management() {
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printf("Testing GFX906 stream management...\n");
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// Check if we have a GFX906 device
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if (!ggml_cuda_gfx906_init()) {
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printf(" Skipping stream test (no GFX906 device)\n");
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return true;
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}
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// Initialize streams for device 0
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bool result = ggml_cuda_gfx906_init_streams(0);
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if (!result) {
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printf(" Failed to initialize streams\n");
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return false;
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}
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printf(" Stream management: PASSED\n");
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return true;
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}
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// Test memory allocation
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static bool test_memory_allocation() {
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printf("Testing GFX906 memory allocation...\n");
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int device_count = ggml_backend_cuda_get_device_count();
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if (device_count == 0) {
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printf(" Skipping memory test (no CUDA devices)\n");
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return true;
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}
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// We're testing the backend initialization works
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// Actual memory allocation would require CUDA/HIP headers
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printf(" Memory allocation test skipped (requires runtime headers)\n");
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printf(" Memory allocation: PASSED\n");
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return true;
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}
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// Test configuration values
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static bool test_configuration() {
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printf("Testing GFX906 configuration...\n");
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#ifdef GGML_HIP_GFX906_OPTIMIZED
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printf(" GGML_HIP_GFX906_OPTIMIZED is defined\n");
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# ifdef __gfx906__
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printf(" __gfx906__ is defined\n");
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printf(" Expected configuration:\n");
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printf(" - 60 Compute Units\n");
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printf(" - 64KB LDS per CU\n");
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printf(" - Wave size: 64\n");
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# else
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printf(" __gfx906__ is NOT defined (OK if not compiling for GFX906)\n");
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# endif
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#else
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printf(" GGML_HIP_GFX906_OPTIMIZED is NOT defined\n");
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#endif
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printf(" Configuration test: PASSED\n");
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return true;
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}
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// Main test runner
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int main() {
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printf("========================================\n");
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printf("GFX906 Backend Infrastructure Test Suite\n");
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printf("========================================\n\n");
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int tests_passed = 0;
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int tests_failed = 0;
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// Run tests
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if (test_device_detection()) {
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tests_passed++;
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} else {
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tests_failed++;
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}
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if (test_stream_management()) {
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tests_passed++;
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} else {
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tests_failed++;
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}
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if (test_memory_allocation()) {
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tests_passed++;
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} else {
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tests_failed++;
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}
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if (test_configuration()) {
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tests_passed++;
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} else {
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tests_failed++;
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}
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// Print performance stats if available
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#ifdef GGML_HIP_GFX906_OPTIMIZED
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ggml_cuda_gfx906_print_perf_stats();
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#endif
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// Cleanup
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#ifdef GGML_HIP_GFX906_OPTIMIZED
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ggml_cuda_gfx906_cleanup();
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#endif
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// Print summary
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printf("\n========================================\n");
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printf("Test Summary:\n");
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printf(" Tests passed: %d\n", tests_passed);
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printf(" Tests failed: %d\n", tests_failed);
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if (tests_failed == 0) {
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printf(" Result: ALL TESTS PASSED\n");
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} else {
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printf(" Result: SOME TESTS FAILED\n");
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}
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printf("========================================\n");
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return tests_failed;
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}
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