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ggml-zendnn : add ZenDNN backend for AMD CPUs (#17690) 

* ggml-zennn: add ZenDNN backend support

* ggml-zendnn : address ZenDNN backend review fixes and suggestions

* docs : apply blockquote syntax to ZenDNN docs

---------

Co-authored-by: Manoj Kumar <mkumar@zettabolt.com>
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1
README.md
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@ -276,6 +276,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
| [MUSA](docs/build.md#musa) | Moore Threads GPU | | [MUSA](docs/build.md#musa) | Moore Threads GPU |
| [CUDA](docs/build.md#cuda) | Nvidia GPU | | [CUDA](docs/build.md#cuda) | Nvidia GPU |
| [HIP](docs/build.md#hip) | AMD GPU | | [HIP](docs/build.md#hip) | AMD GPU |
| [ZenDNN](docs/build.md#zendnn) | AMD CPU |
| [Vulkan](docs/build.md#vulkan) | GPU | | [Vulkan](docs/build.md#vulkan) | GPU |
| [CANN](docs/build.md#cann) | Ascend NPU | | [CANN](docs/build.md#cann) | Ascend NPU |
| [OpenCL](docs/backend/OPENCL.md) | Adreno GPU | | [OpenCL](docs/backend/OPENCL.md) | Adreno GPU |

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# llama.cpp for AMD ZenDNN
> [!WARNING]
> **Note:** ZenDNN is **not** the same as zDNN.
> - **ZenDNN** (this page): AMD's deep learning library for AMD EPYC CPUs
> - **zDNN**: IBM's Deep Neural Network acceleration library for IBM Z & LinuxONE Mainframes ([see zDNN documentation](zDNN.md))
- [Background](#background)
- [OS](#os)
- [Hardware](#hardware)
- [Supported Operations](#supported-operations)
- [DataType Supports](#datatype-supports)
- [Linux](#linux)
- [Environment Variable](#environment-variable)
- [Performance Optimization](#performance-optimization)
- [Known Issues](#known-issues)
- [TODO](#todo)
## Background
**ZenDNN** (Zen Deep Neural Network Library) is AMD's high-performance deep learning inference library optimized for AMD EPYC™ CPUs. It provides optimized implementations of key deep learning primitives and operations, delivering significant performance improvements for neural network workloads on AMD Zen-based processor architectures.
**Llama.cpp + ZenDNN**
The llama.cpp ZenDNN backend leverages AMD's optimized matrix multiplication primitives to accelerate inference on AMD CPUs. It utilizes ZenDNN's **LowOHA (Low Overhead Hardware Accelerated)** MatMul operator for efficient GEMM operations with minimal execution overhead, built-in weight caching, and direct access to backend libraries (AOCL BLIS, LibXSMM, OneDNN).
For more information about ZenDNN, visit: https://www.amd.com/en/developer/zendnn.html
## OS
| OS | Status | Verified |
|:-------:|:-------:|:----------------------------------------------:|
| Linux | Support | Ubuntu 20.04, 22.04, 24.04 |
For the latest list of supported operating systems, see the [ZenDNN Supported OS](https://github.com/amd/ZenDNN/blob/zendnnl/README.md#15-supported-os).
## Hardware
### AMD CPUs
**Recommended Processors**
ZenDNN is optimized for AMD EPYC™ processors and AMD Ryzen™ processors based on "Zen" microarchitecture and newer.
| CPU Family | Status | Notes |
|:-----------------------------:|:-------:|:----------------------------------:|
| AMD EPYC™ 9005 Series (Turin)| Support | 5th Gen - Zen 5 architecture |
| AMD EPYC™ 9004 Series (Genoa)| Support | 4th Gen - Zen 4 architecture |
| AMD EPYC™ 7003 Series (Milan)| Support | 3rd Gen - Zen 3 architecture |
| AMD Ryzen™ AI MAX (Strix Halo)| Support | High-performance mobile processors |
*Notes:*
- Best performance is achieved on AMD EPYC™ processors with high core counts (e.g., EPYC 9005 series).
- ZenDNN leverages AMD's advanced CPU features including AVX2 and AVX-512 instruction sets.
- For optimal performance, ensure your system has sufficient memory bandwidth.
## Supported Operations
The ZenDNN backend currently accelerates **matrix multiplication (MUL_MAT)** operations only. Other operations are handled by the standard CPU backend.
| Operation | Status | Notes |
|:-------------|:-------:|:----------------------------------------------:|
| MUL_MAT | ✓ | Accelerated via ZenDNN LowOHA MatMul |
*Note:* Since only MUL_MAT is accelerated, models will benefit most from ZenDNN when matrix multiplications dominate the computational workload (which is typical for transformer-based LLMs).
## DataType Supports
| DataType | Status | Notes |
|:----------------------:|:-------:|:---------------------------------------------:|
| FP32 | Support | Full precision floating point |
| BF16 | Support | BFloat16 (best performance on Zen 4/Zen 5) |
*Notes:*
- **BF16** provides best performance on Zen 4 and Zen 5 EPYC™ processors (Genoa, Turin).
## Linux
### I. Setup Environment
You have two options to set up ZenDNN:
#### Option 1: Automatic Download and Build (Recommended)
CMake will automatically download and build ZenDNN for you:
```sh
# Build llama.cpp - ZenDNN will be automatically downloaded and built
cmake -B build -DGGML_ZENDNN=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build --config Release -j $(nproc)
```
No manual ZenDNN installation required. CMake will handle everything automatically.
#### Option 2: Use Custom ZenDNN Installation
If you want to build ZenDNN yourself or use a specific version:
**Step 1: Build ZenDNN from source**
```sh
# Clone ZenDNN repository
git clone https://github.com/amd/ZenDNN.git
cd ZenDNN
git checkout zendnnl
# Build and install (requires CMake >= 3.25)
mkdir build && cd build
cmake ..
cmake --build . --target all
```
Default installation path: `ZenDNN/build/install`
**For detailed build instructions**, refer to the [ZenDNN README](https://github.com/amd/ZenDNN/blob/zendnnl/README.md).
**Step 2: Build llama.cpp with custom ZenDNN path**
```sh
# Using environment variable
export ZENDNN_ROOT=/path/to/ZenDNN/build/install
cmake -B build -DGGML_ZENDNN=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build --config Release -j $(nproc)
# OR specify path directly in CMake
cmake -B build -DGGML_ZENDNN=ON -DZENDNN_ROOT=/path/to/ZenDNN/build/install -DCMAKE_BUILD_TYPE=Release
cmake --build build --config Release -j $(nproc)
```
### II. Run the Server
#### 1. Download Model
Download LLaMA 3.1 8B Instruct BF16 model:
```sh
# Download from Hugging Face
huggingface-cli download meta-llama/Llama-3.1-8B-Instruct-GGUF --local-dir models/
```
#### 2. Start Server
Run llama.cpp server with ZenDNN acceleration:
```sh
# Set optimal configuration
export OMP_NUM_THREADS=64 # Adjust to your CPU core count
export ZENDNNL_MATMUL_ALGO=2 # Blocked AOCL BLIS for best performance
# Start server
./build/bin/llama-server \
-m models/Llama-3.1-8B-Instruct.BF16.gguf \
--host 0.0.0.0 \
--port 8080 \
-t 64
```
Access the server at `http://localhost:8080`.
**Performance tips**:
- Set `OMP_NUM_THREADS` to match your physical core count
- Use `ZENDNNL_MATMUL_ALGO=2` for optimal performance
- For NUMA systems: `numactl --cpunodebind=0 --membind=0 ./build/bin/llama-server ...`
## Environment Variable
### Build Time
| Name | Value | Function |
|--------------------|---------------------------------------|---------------------------------------------|
| GGML_ZENDNN | ON/OFF | Enable ZenDNN backend support |
| ZENDNN_ROOT | Path to ZenDNN installation | Set ZenDNN installation directory |
| GGML_OPENMP | ON/OFF (recommended: ON) | Enable OpenMP for multi-threading |
### Runtime
| Name | Value | Function |
|-------------------------|--------------------------|-------------------------------------------------------------------|
| OMP_NUM_THREADS | Number (e.g., 64) | Set number of OpenMP threads (recommended: physical core count) |
| ZENDNNL_MATMUL_ALGO | 0-5 | Select MatMul backend algorithm (see Performance Optimization) |
| ZENDNNL_PROFILE_LOG_LEVEL | 0-4 | Profiling log level (0=disabled, 4=verbose) |
| ZENDNNL_ENABLE_PROFILER | 0 or 1 | Enable detailed profiling (1=enabled) |
| ZENDNNL_API_LOG_LEVEL | 0-4 | API log level (0=disabled, 4=verbose) |
**Example**:
```sh
export OMP_NUM_THREADS=64
export ZENDNNL_MATMUL_ALGO=2 # Use Blocked AOCL BLIS for best performance
./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Test" -n 100
```
## Performance Optimization
### MatMul Algorithm Selection
ZenDNN's LowOHA MatMul supports multiple backend algorithms. For **best performance**, use the **Blocked AOCL BLIS** algorithm:
```sh
export ZENDNNL_MATMUL_ALGO=2 # Blocked AOCL BLIS (recommended)
```
**Available algorithms**:
| Value | Algorithm | Description |
|:-----:|:-----------------------|:----------------------------------------------|
| 0 | Dynamic Dispatch | Automatic backend selection (default) |
| 1 | AOCL BLIS | AOCL BLIS backend |
| 2 | AOCL BLIS Blocked | **Blocked AOCL BLIS (recommended)** |
| 3 | OneDNN | OneDNN backend |
| 4 | OneDNN Blocked | Blocked OneDNN |
| 5 | LibXSMM | LibXSMM backend |
### Profiling and Debugging
For detailed profiling and logging options, refer to the [ZenDNN Logging Documentation](https://github.com/amd/ZenDNN/blob/zendnnl/docs/logging.md).
## Known Issues
- **Limited operation support**: Currently only matrix multiplication (MUL_MAT) is accelerated via ZenDNN. Other operations fall back to the standard CPU backend.
- **BF16 support**: BF16 operations require AMD Zen 4 or Zen 5 architecture (EPYC 9004/9005 series). On older CPUs, operations will use FP32.
- **NUMA awareness**: For multi-socket systems, manual NUMA binding may be required for optimal performance.
## Q&A
**Q: How do I verify that ZenDNN backend is being used?**
A: Check the log output when running llama.cpp. You should see messages indicating the ZenDNN backend is initialized. You can also check the backend name in the output.
**Q: What performance improvement can I expect?**
A: Performance gains vary depending on the model size, batch size, and CPU architecture. On AMD EPYC processors, you can typically expect 1.1x-2x speedup compared to standard CPU inference for matrix multiplication operations.
**Q: Can I use ZenDNN on non-AMD processors?**
A: ZenDNN is optimized specifically for AMD processors. While it may work on other x86-64 CPUs, performance benefits are only guaranteed on AMD Zen-based architectures.
**Q: Does ZenDNN support quantized models?**
A: Currently, ZenDNN primarily supports FP32 and BF16 data types. Quantized model support is not available at this time.
**Q: Why is my inference not faster with ZenDNN?**
A: Ensure:
1. You're using an AMD EPYC or Ryzen processor (Zen 2 or newer)
2. `OMP_NUM_THREADS` is set appropriately (physical core count)
3. `ZENDNNL_MATMUL_ALGO=2` is set for best performance (Blocked AOCL BLIS)
4. You're using a sufficiently large model (small models may not benefit as much)
5. Enable profiling to verify ZenDNN MatMul is being called
### **GitHub Contribution**:
Please add the **[ZenDNN]** prefix/tag in issues/PRs titles to help the ZenDNN-team check/address them without delay.
## TODO
- Expand operation support beyond MUL_MAT (attention operations, activations, etc.)

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# llama.cpp for IBM zDNN Accelerator # llama.cpp for IBM zDNN Accelerator
> [!WARNING]
> **Note:** zDNN is **not** the same as ZenDNN.
> - **zDNN** (this page): IBM's Deep Neural Network acceleration library for IBM Z & LinuxONE Mainframes
> - **ZenDNN**: AMD's deep learning library for AMD EPYC CPUs ([see ZenDNN documentation](ZenDNN.md))
## Background ## Background
IBM zDNN (Z Deep Neural Network) is a hardware acceleration library designed specifically to leverage the IBM NNPA (Neural Network Processor Assist) accelerator located within IBM Telum I and II processors. It provides significant performance improvements for neural network inference operations. IBM zDNN (Z Deep Neural Network) is a hardware acceleration library designed specifically to leverage the IBM NNPA (Neural Network Processor Assist) accelerator located within IBM Telum I and II processors. It provides significant performance improvements for neural network inference operations.

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docs/build.md
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@ -495,6 +495,38 @@ llama_new_context_with_model: CANN compute buffer size = 1260.81 MiB
For detailed info, such as model/device supports, CANN install, please refer to [llama.cpp for CANN](./backend/CANN.md). For detailed info, such as model/device supports, CANN install, please refer to [llama.cpp for CANN](./backend/CANN.md).
## ZenDNN
ZenDNN provides optimized deep learning primitives for AMD EPYC™ CPUs. It accelerates matrix multiplication operations for inference workloads.
### Compilation
- Using `CMake` on Linux (automatic build):
```bash
cmake -B build -DGGML_ZENDNN=ON
cmake --build build --config Release
```
The first build will automatically download and build ZenDNN, which may take 5-10 minutes. Subsequent builds will be much faster.
- Using `CMake` with custom ZenDNN installation:
```bash
cmake -B build -DGGML_ZENDNN=ON -DZENDNN_ROOT=/path/to/zendnn/install
cmake --build build --config Release
```
### Testing
You can test with:
```bash
./build/bin/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -n 50
```
For detailed information about hardware support, setup instructions, and performance optimization, refer to [llama.cpp for ZenDNN](./backend/ZenDNN.md).
## Arm® KleidiAI™ ## Arm® KleidiAI™
KleidiAI is a library of optimized microkernels for AI workloads, specifically designed for Arm CPUs. These microkernels enhance performance and can be enabled for use by the CPU backend. KleidiAI is a library of optimized microkernels for AI workloads, specifically designed for Arm CPUs. These microkernels enhance performance and can be enabled for use by the CPU backend.

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@ -12,111 +12,111 @@ Legend:
- 🟡 Partially supported by this backend - 🟡 Partially supported by this backend
- ❌ Not supported by this backend - ❌ Not supported by this backend
| Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan | WebGPU | zDNN | | Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan | WebGPU | ZenDNN | zDNN |
|-----------|------|------|------|------|------|------|------|------|------|------| |-----------|------|------|------|------|------|------|------|------|------|------|------|
| ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | | ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
| ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | | ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | | ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
| ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | | ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | | ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | | ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | | ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | | CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | | CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | | CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
| CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | | CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
| CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | | CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
| CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | | CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
| CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | | CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | | CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | | COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | | COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | | CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
| CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
| CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
| CUMSUM | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | | CUMSUM | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | | DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
| DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | | DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
| DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | | DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
| ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | | ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
| EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | | EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
| EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | | EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
| FILL | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | | FILL | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | | FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
| FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | | FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
| GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | | GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
| GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | | GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
| GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | | GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
| GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | | GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
| GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | | GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
| GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | | GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
| GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | | GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
| GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
| GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
| HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | | HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
| HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | | HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
| IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | | IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | | L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | | LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
| LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ❌ | ❌ | | LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
| MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | | MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | | MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
| MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | | MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
| MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | | MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
| NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | | NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
| NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ | | NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ |
| NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
| OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | | OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | | OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | | OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ |
| PAD | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | | PAD | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | ❌ |
| PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | | PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
| POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | | POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | | REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
| RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | | RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
| REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ❌ | ❌ | | REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ❌ | ❌ | ❌ |
| REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | | REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| RMS_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | | RMS_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
| RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | | RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| RMS_NORM_MUL_ADD | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | | RMS_NORM_MUL_ADD | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
| ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | | ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | | ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
| ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | | ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| ROUND | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | | ROUND | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | | RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | | RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | | SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
| SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | | SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ |
| SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | | SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
| SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | | SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
| SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | | SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
| SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | | SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
| SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | | SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | | SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
| SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | | SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
| SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | | SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
| SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | | SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
| SOLVE_TRI | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | | SOLVE_TRI | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
| SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | | SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | | SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | | SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
| SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | | SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
| STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | | STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
| SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | | SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
| SUM | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | | SUM | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | ❌ |
| SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | | SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
| SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | | SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ |
| TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | | TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
| TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
| TOP_K | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | | TOP_K | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
| TRI | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | | TRI | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
| TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | | TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | | UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
| XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | | XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |

18741
docs/ops/ZenDNN.csv Normal file
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4
ggml/CMakeLists.txt
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@ -253,6 +253,9 @@ option(GGML_HEXAGON "ggml: enable Hexagon backend"
# toolchain for vulkan-shaders-gen # toolchain for vulkan-shaders-gen
set (GGML_VULKAN_SHADERS_GEN_TOOLCHAIN "" CACHE FILEPATH "ggml: toolchain file for vulkan-shaders-gen") set (GGML_VULKAN_SHADERS_GEN_TOOLCHAIN "" CACHE FILEPATH "ggml: toolchain file for vulkan-shaders-gen")
option(GGML_ZENDNN "ggml: use ZenDNN" OFF)
option(ZENDNN_ROOT "ggml: path to ZenDNN installation" "")
# extra artifacts # extra artifacts
option(GGML_BUILD_TESTS "ggml: build tests" ${GGML_STANDALONE}) option(GGML_BUILD_TESTS "ggml: build tests" ${GGML_STANDALONE})
option(GGML_BUILD_EXAMPLES "ggml: build examples" ${GGML_STANDALONE}) option(GGML_BUILD_EXAMPLES "ggml: build examples" ${GGML_STANDALONE})
@ -314,6 +317,7 @@ set(GGML_PUBLIC_HEADERS
include/ggml-sycl.h include/ggml-sycl.h
include/ggml-vulkan.h include/ggml-vulkan.h
include/ggml-webgpu.h include/ggml-webgpu.h
include/ggml-zendnn.h
include/gguf.h) include/gguf.h)
set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}") set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}")

22
ggml/include/ggml-zendnn.h Normal file
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@ -0,0 +1,22 @@
#pragma once
#include "ggml-backend.h"
#include "ggml.h"
#ifdef __cplusplus
extern "C" {
#endif
// backend API
GGML_BACKEND_API ggml_backend_t ggml_backend_zendnn_init(void);
GGML_BACKEND_API bool ggml_backend_is_zendnn(ggml_backend_t backend);
// number of threads used for zendnn operations
GGML_BACKEND_API void ggml_backend_zendnn_set_n_threads(ggml_backend_t backend_zendnn, int n_threads);
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_zendnn_reg(void);
#ifdef __cplusplus
}
#endif

1
ggml/src/CMakeLists.txt
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@ -440,6 +440,7 @@ ggml_add_backend(WebGPU)
ggml_add_backend(zDNN) ggml_add_backend(zDNN)
ggml_add_backend(OpenCL) ggml_add_backend(OpenCL)
ggml_add_backend(Hexagon) ggml_add_backend(Hexagon)
ggml_add_backend(ZenDNN)
foreach (target ggml-base ggml) foreach (target ggml-base ggml)
target_include_directories(${target} PUBLIC $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/../include> $<INSTALL_INTERFACE:include>) target_include_directories(${target} PUBLIC $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/../include> $<INSTALL_INTERFACE:include>)

8
ggml/src/ggml-backend-reg.cpp
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@ -73,6 +73,10 @@
#include "ggml-cann.h" #include "ggml-cann.h"
#endif #endif
#ifdef GGML_USE_ZENDNN
#include "ggml-zendnn.h"
#endif
// disable C++17 deprecation warning for std::codecvt_utf8 // disable C++17 deprecation warning for std::codecvt_utf8
#if defined(__clang__) #if defined(__clang__)
# pragma clang diagnostic push # pragma clang diagnostic push
@ -203,6 +207,9 @@ struct ggml_backend_registry {
#ifdef GGML_USE_OPENCL #ifdef GGML_USE_OPENCL
register_backend(ggml_backend_opencl_reg()); register_backend(ggml_backend_opencl_reg());
#endif #endif
#ifdef GGML_USE_ZENDNN
register_backend(ggml_backend_zendnn_reg());
#endif
#ifdef GGML_USE_HEXAGON #ifdef GGML_USE_HEXAGON
register_backend(ggml_backend_hexagon_reg()); register_backend(ggml_backend_hexagon_reg());
#endif #endif
@ -605,6 +612,7 @@ void ggml_backend_load_all_from_path(const char * dir_path) {
#endif #endif
ggml_backend_load_best("blas", silent, dir_path); ggml_backend_load_best("blas", silent, dir_path);
ggml_backend_load_best("zendnn", silent, dir_path);
ggml_backend_load_best("cann", silent, dir_path); ggml_backend_load_best("cann", silent, dir_path);
ggml_backend_load_best("cuda", silent, dir_path); ggml_backend_load_best("cuda", silent, dir_path);
ggml_backend_load_best("hip", silent, dir_path); ggml_backend_load_best("hip", silent, dir_path);

92
ggml/src/ggml-zendnn/CMakeLists.txt Normal file
View File

@ -0,0 +1,92 @@
ggml_add_backend_library(ggml-zendnn
ggml-zendnn.cpp)
# Get ZenDNN path
if (NOT DEFINED ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "")
set(ZENDNN_ROOT "$ENV{ZENDNN_ROOT}")
endif()
# Check if path is still empty or OFF
if (NOT ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "" OR ZENDNN_ROOT STREQUAL "OFF")
message(STATUS "ZENDNN_ROOT not set. Automatically downloading and building ZenDNN...")
message(STATUS "This will take several minutes on first build...")
include(ExternalProject)
set(ZENDNN_PREFIX ${CMAKE_BINARY_DIR}/_deps/zendnn-prefix)
set(ZENDNN_SOURCE_DIR ${ZENDNN_PREFIX}/src/zendnn)
set(ZENDNN_BUILD_DIR ${ZENDNN_PREFIX}/build)
set(ZENDNN_INSTALL_DIR ${ZENDNN_BUILD_DIR}/install)
ExternalProject_Add(
zendnn
GIT_REPOSITORY https://github.com/amd/ZenDNN.git
GIT_TAG zendnnl
PREFIX ${ZENDNN_PREFIX}
SOURCE_DIR ${ZENDNN_SOURCE_DIR}
BINARY_DIR ${ZENDNN_BUILD_DIR}
CMAKE_ARGS
-DCMAKE_BUILD_TYPE=Release
-DCMAKE_INSTALL_PREFIX=${ZENDNN_INSTALL_DIR}
-DZENDNNL_BUILD_EXAMPLES=OFF
-DZENDNNL_BUILD_DOXYGEN=OFF
-DZENDNNL_BUILD_GTEST=OFF
-DZENDNNL_BUILD_BENCHDNN=OFF
# Enable ALL matmul algorithm backends
-DZENDNNL_DEPENDS_AOCLDLP=ON
-DZENDNNL_DEPENDS_ONEDNN=ON
-DZENDNNL_DEPENDS_LIBXSMM=ON
BUILD_COMMAND ${CMAKE_COMMAND} --build ${ZENDNN_BUILD_DIR} --target zendnnl
INSTALL_COMMAND ${CMAKE_COMMAND} --build ${ZENDNN_BUILD_DIR} --target install
BUILD_ALWAYS OFF
LOG_DOWNLOAD ON
LOG_CONFIGURE ON
LOG_BUILD ON
LOG_INSTALL ON
)
# Add dependency so ZenDNN builds before our library
add_dependencies(ggml-zendnn zendnn)
# Set ZENDNN_ROOT to the installation directory
set(ZENDNN_ROOT ${ZENDNN_INSTALL_DIR})
message(STATUS "ZenDNN will be built to: ${ZENDNN_ROOT}")
else()
message(STATUS "Using custom ZenDNN installation at: ${ZENDNN_ROOT}")
endif()
# ZenDNN headers + libs
target_include_directories(ggml-zendnn PRIVATE
${ZENDNN_ROOT}/zendnnl/include
${ZENDNN_ROOT}/deps/aocldlp/include
${ZENDNN_ROOT}/deps/aoclutils/include
${ZENDNN_ROOT}/deps/json/include
${ZENDNN_ROOT}/deps/libxsmm/include
${ZENDNN_ROOT}/deps/onednn/include
)
target_link_directories(ggml-zendnn PRIVATE
${ZENDNN_ROOT}/zendnnl/lib
${ZENDNN_ROOT}/deps/aocldlp/lib
${ZENDNN_ROOT}/deps/aoclutils/lib
${ZENDNN_ROOT}/deps/libxsmm/lib
${ZENDNN_ROOT}/deps/onednn/lib
)
target_link_libraries(ggml-zendnn PRIVATE
zendnnl_archive # ZenDNN main
aocl-dlp # AOCL libraries
aoclutils
au_cpuid
dnnl # OneDNN
xsmm # libxsmm small matrix math
xsmmext
xsmmnoblas
m
pthread
)
if (GGML_OPENMP)
target_link_libraries(ggml-zendnn PRIVATE OpenMP::OpenMP_CXX)
endif()

466
ggml/src/ggml-zendnn/ggml-zendnn.cpp Normal file
View File

@ -0,0 +1,466 @@
#include "ggml-zendnn.h"
#include "ggml-backend-impl.h"
#include "ggml-impl.h"
#include "ggml-cpu.h"
#include "zendnnl.hpp"
#include <cstring>
struct ggml_backend_zendnn_context {
int n_threads = GGML_DEFAULT_N_THREADS;
std::unique_ptr<char[]> work_data;
size_t work_size = 0;
};
template<typename T>
zendnnl::common::data_type_t ggml_to_zendnn_type() {
if constexpr (std::is_same_v<T, float>) {
return zendnnl::common::data_type_t::f32;
} else if constexpr (std::is_same_v<T, ggml_bf16_t>) {
return zendnnl::common::data_type_t::bf16;
} else {
return zendnnl::common::data_type_t::none;
}
}
/**
* ZenDNN matmul: computes C = B * A.
*
* - A: weights, shape (k, m), column-major (each column is a weight vector for one output).
* - B: input, shape (n, k), row-major (each row is an input sample).
* - C: output, shape (n, m), row-major.
*
* Dimensions:
* m = output features (columns of C, columns of A)
* n = batch size (rows of C, rows of B)
* k = inner dimension (columns of B, rows of A)
*/
template <typename TA, typename TB, typename TC>
static bool ggml_zendnn_matmul(ggml_backend_zendnn_context * ctx, int64_t m, int64_t n, int64_t k,
const TA * A, int64_t lda, const TB * B, int64_t ldb, TC * C,
int64_t ldc) {
zendnnl::lowoha::lowoha_params params;
params.dtypes.src = ggml_to_zendnn_type<TB>();
params.dtypes.wei = ggml_to_zendnn_type<TA>();
params.dtypes.dst = ggml_to_zendnn_type<TC>();
params.num_threads = ctx->n_threads;
zendnnl::lowoha::status_t status = zendnnl::lowoha::matmul_direct(
'r', false, true, // row-major, don't transpose B, transpose A (because it's column-major)
n, // M: rows of B and C
m, // N: cols of A^T and C
k, // K: cols of B, rows of A
1.0f, // alpha
B, ldb, // src: B[n,k]
A, lda, // weight: A[k,m] column-major (transposed)
nullptr, // bias
0.0f, // beta
C, ldc, // output C[n,m]
true, // is_weights_const
{}, // batch_params
params // params
);
if (status != zendnnl::lowoha::status_t::success) {
GGML_LOG_ERROR("%s, ZenDNN matmul failed: status=%d\n", __func__, static_cast<int>(status));
return false;
}
return true;
}
static bool ggml_zendnn_sgemm(ggml_backend_zendnn_context * ctx, int64_t m, int64_t n, int64_t k,
const void * A, int64_t lda, const void * B, int64_t ldb, void * C,
int64_t ldc, int Atype, int Btype, int Ctype) {
assert(m >= 0);
assert(n >= 0);
assert(k >= 0);
assert(lda >= k);
assert(ldb >= k);
assert(ldc >= m);
// categorize types
switch (Atype) {
case GGML_TYPE_F32:
if (Btype != GGML_TYPE_F32 || Ctype != GGML_TYPE_F32)
return false;
return ggml_zendnn_matmul<float, float, float>(
ctx, m, n, k,
(const float *)A, lda,
(const float *)B, ldb,
(float *)C, ldc);
case GGML_TYPE_BF16:
if (Btype != GGML_TYPE_BF16)
return false;
if (Ctype == GGML_TYPE_BF16)
return ggml_zendnn_matmul<ggml_bf16_t, ggml_bf16_t, ggml_bf16_t>(
ctx, m, n, k,
(const ggml_bf16_t *)A, lda,
(const ggml_bf16_t *)B, ldb,
(ggml_bf16_t *)C, ldc);
if (Ctype == GGML_TYPE_F32)
return ggml_zendnn_matmul<ggml_bf16_t, ggml_bf16_t, float>(
ctx, m, n, k,
(const ggml_bf16_t *)A, lda,
(const ggml_bf16_t *)B, ldb,
(float *)C, ldc);
return false;
default:
return false; // unsupported type
}
}
static void ggml_zendnn_compute_forward_mul_mat(
ggml_backend_zendnn_context * ctx,
ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0]; // weights
const ggml_tensor * src1 = dst->src[1]; // inputs
GGML_TENSOR_BINARY_OP_LOCALS
ggml_type const vec_dot_type = ggml_get_type_traits_cpu(src0->type)->vec_dot_type;
ggml_from_float_t const from_float = ggml_get_type_traits_cpu(vec_dot_type)->from_float;
GGML_ASSERT(ne0 == ne01);
GGML_ASSERT(ne1 == ne11);
GGML_ASSERT(ne2 == ne12);
GGML_ASSERT(ne3 == ne13);
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == ggml_type_size(src0->type));
GGML_ASSERT(nb10 == ggml_type_size(src1->type));
// dst cannot be transposed or permuted
GGML_ASSERT(nb0 == sizeof(float));
GGML_ASSERT(nb0 <= nb1);
GGML_ASSERT(nb1 <= nb2);
GGML_ASSERT(nb2 <= nb3);
// broadcast factors
const int64_t r2 = ne12/ne02;
const int64_t r3 = ne13/ne03;
void * work_data = ctx->work_data.get();
if (src1->type != vec_dot_type) {
const size_t nbw1 = ggml_row_size(vec_dot_type, ne10);
const size_t nbw2 = nbw1 * ne11;
const size_t nbw3 = nbw2 * ne12;
const size_t desired_wsize = ne13 * nbw3;
if (ctx->work_size < desired_wsize) {
ctx->work_data.reset(new char[desired_wsize]);
ctx->work_size = desired_wsize;
}
work_data = ctx->work_data.get();
// #pragma omp parallel for num_threads(ctx->n_threads)
#pragma omp parallel for collapse(3) num_threads(ctx->n_threads) schedule(static)
for (int64_t i13 = 0; i13 < ne13; ++i13) {
for (int64_t i12 = 0; i12 < ne12; ++i12) {
for (int64_t i11 = 0; i11 < ne11; ++i11) {
const float * src1_f32 = (float *)((char *)src1->data + i11*nb11 + i12*nb12 + i13*nb13);
void * src1_conv = (char *)work_data + i11*nbw1 + i12*nbw2 + i13*nbw3;
from_float(src1_f32, src1_conv, ne10);
}
}
}
}
for (int64_t i13 = 0; i13 < ne13; i13++) {
for (int64_t i12 = 0; i12 < ne12; i12++) {
const void* wdata = src1->type == vec_dot_type ? src1->data : work_data;
const size_t row_size = ggml_row_size(vec_dot_type, ne10);
if (!ggml_zendnn_sgemm(ctx,
ne01, // m
ne11, // n
ne10, // k
static_cast<const char *>(src0->data) + (i12/r2)*nb02 + (i13/r3)*nb03,
ne00, // lda
static_cast<const char *>(wdata) + (i12*ne11 + i13*ne12*ne11)*row_size,
ne10, // ldb
static_cast<char *>(dst->data) + i12*nb2 + i13*nb3,
ne01, // ldc
src0->type,
vec_dot_type,
dst->type))
GGML_ABORT("%s: ZenDNN sgemm failed\n", __func__);
}
}
}
// backend interface
static const char * ggml_backend_zendnn_get_name(ggml_backend_t backend) {
return "ZenDNN";
GGML_UNUSED(backend);
}
static void ggml_backend_zendnn_free(ggml_backend_t backend) {
ggml_backend_zendnn_context * ctx = (ggml_backend_zendnn_context *)backend->context;
delete ctx;
delete backend;
}
static ggml_status ggml_backend_zendnn_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
ggml_backend_zendnn_context * ctx = (ggml_backend_zendnn_context *)backend->context;
for (int i = 0; i < cgraph->n_nodes; i++) {
struct ggml_tensor * node = cgraph->nodes[i];
switch (node->op) {
case GGML_OP_MUL_MAT:
ggml_zendnn_compute_forward_mul_mat(ctx, node);
break;
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
break;
default:
GGML_ABORT("%s: unsupported op %s\n", __func__, ggml_op_desc(node));
}
}
return GGML_STATUS_SUCCESS;
GGML_UNUSED(backend);
}
static struct ggml_backend_i ggml_backend_zendnn_i = {
/* .get_name = */ ggml_backend_zendnn_get_name,
/* .free = */ ggml_backend_zendnn_free,
/* .set_tensor_async = */ NULL,
/* .get_tensor_async = */ NULL,
/* .cpy_tensor_async = */ NULL,
/* .synchronize = */ NULL,
/* .graph_plan_create = */ NULL,
/* .graph_plan_free = */ NULL,
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_zendnn_graph_compute,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
/* .graph_optimize = */ NULL,
};
static ggml_guid_t ggml_backend_zendnn_guid(void) {
static const char * guid_str = "AMD-ZENDNN-ACCEL";
return reinterpret_cast<ggml_guid_t>(const_cast<char*>(guid_str));
}
ggml_backend_t ggml_backend_zendnn_init(void) {
ggml_backend_zendnn_context * ctx = new ggml_backend_zendnn_context;
ggml_backend_t backend = new ggml_backend {
/* .guid = */ ggml_backend_zendnn_guid(),
/* .iface = */ ggml_backend_zendnn_i,
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_zendnn_reg(), 0),
/* .context = */ ctx,
};
return backend;
}
bool ggml_backend_is_zendnn(ggml_backend_t backend) {
return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_zendnn_guid());
}
void ggml_backend_zendnn_set_n_threads(ggml_backend_t backend_zendnn, int n_threads) {
GGML_ASSERT(ggml_backend_is_zendnn(backend_zendnn));
ggml_backend_zendnn_context * ctx = (ggml_backend_zendnn_context *)backend_zendnn->context;
ctx->n_threads = n_threads;
}
// device interface
static const char * ggml_backend_zendnn_device_get_name(ggml_backend_dev_t dev) {
return "ZenDNN";
GGML_UNUSED(dev);
}
/**
* ZenDNN is AMD's performance library providing optimized primitives and implementations
* for deep learning workloads on AMD CPUs. It targets improved performance for common
* neural network operations on AMD architectures. For more information, see:
* https://www.amd.com/en/developer/zendnn.html
*/
static const char * ggml_backend_zendnn_device_get_description(ggml_backend_dev_t dev) {
return "ZenDNN: AMD optimized primitives backend for GGML (optimized for AMD CPUs)";
GGML_UNUSED(dev);
}
static void ggml_backend_zendnn_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
*free = 0;
*total = 0;
GGML_UNUSED(dev);
}
static enum ggml_backend_dev_type ggml_backend_zendnn_device_get_type(ggml_backend_dev_t dev) {
return GGML_BACKEND_DEVICE_TYPE_ACCEL;
GGML_UNUSED(dev);
}
static void ggml_backend_zendnn_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
props->name = ggml_backend_zendnn_device_get_name(dev);
props->description = ggml_backend_zendnn_device_get_description(dev);
props->type = ggml_backend_zendnn_device_get_type(dev);
ggml_backend_zendnn_device_get_memory(dev, &props->memory_free, &props->memory_total);
props->caps = {
/* .async = */ false,
/* .host_buffer = */ false,
/* .buffer_from_host_ptr = */ true,
/* .events = */ false
};
}
static ggml_backend_t ggml_backend_zendnn_device_init_backend(ggml_backend_dev_t dev, const char * params) {
ggml_backend_t backend = ggml_backend_zendnn_init();
if (backend == NULL) {
GGML_LOG_ERROR("%s: error: failed to initialize ZenDNN backend\n", __func__);
return NULL;
}
return backend;
GGML_UNUSED(dev);
GGML_UNUSED(params);
}
static ggml_backend_buffer_type_t ggml_backend_zendnn_device_get_buffer_type(ggml_backend_dev_t dev) {
return ggml_backend_cpu_buffer_type();
GGML_UNUSED(dev);
}
static ggml_backend_buffer_t ggml_backend_zendnn_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
return ggml_backend_cpu_buffer_from_ptr(ptr, size);
GGML_UNUSED(dev);
GGML_UNUSED(max_tensor_size);
}
static bool ggml_backend_zendnn_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
switch (op->op) {
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
return true;
case GGML_OP_MUL_MAT:
{
const ggml_tensor * weights = op->src[0];
const ggml_tensor * inputs = op->src[1];
const int64_t ne10 = inputs->ne[0];
const int64_t ne0 = op->ne[0];
const int64_t ne1 = op->ne[1];
const int64_t min_batch = 1;
if (!ggml_is_contiguous(weights) || !ggml_is_contiguous(inputs) ||
ne0 < min_batch || ne1 < min_batch || ne10 < min_batch) {
return false;
}
switch (weights->type) {
case GGML_TYPE_F32:
case GGML_TYPE_BF16:
return true;
default:
return false;
}
} break;
default:
return false;
}
GGML_UNUSED(dev);
}
static bool ggml_backend_zendnn_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
return ggml_backend_buft_is_host(buft);
GGML_UNUSED(dev);
}
static const struct ggml_backend_device_i ggml_backend_zendnn_device_i = {
/* .get_name = */ ggml_backend_zendnn_device_get_name,
/* .get_description = */ ggml_backend_zendnn_device_get_description,
/* .get_memory = */ ggml_backend_zendnn_device_get_memory,
/* .get_type = */ ggml_backend_zendnn_device_get_type,
/* .get_props = */ ggml_backend_zendnn_device_get_props,
/* .init_backend = */ ggml_backend_zendnn_device_init_backend,
/* .get_buffer_type = */ ggml_backend_zendnn_device_get_buffer_type,
/* .get_host_buffer_type = */ NULL,
/* .buffer_from_host_ptr = */ ggml_backend_zendnn_device_buffer_from_host_ptr,
/* .supports_op = */ ggml_backend_zendnn_device_supports_op,
/* .supports_buft = */ ggml_backend_zendnn_device_supports_buft,
/* .offload_op = */ NULL,
/* .event_new = */ NULL,
/* .event_free = */ NULL,
/* .event_synchronize = */ NULL,
};
// backend reg interface
static const char * ggml_backend_zendnn_reg_get_name(ggml_backend_reg_t reg) {
return "ZenDNN";
GGML_UNUSED(reg);
}
static size_t ggml_backend_zendnn_reg_get_device_count(ggml_backend_reg_t reg) {
return 1;
GGML_UNUSED(reg);
}
static ggml_backend_dev_t ggml_backend_zendnn_reg_get_device(ggml_backend_reg_t reg, size_t index) {
GGML_ASSERT(index == 0);
static ggml_backend_device ggml_backend_zendnn_device = {
/* .iface = */ ggml_backend_zendnn_device_i,
/* .reg = */ reg,
/* .context = */ nullptr,
};
return &ggml_backend_zendnn_device;
}
static void * ggml_backend_zendnn_get_proc_address(ggml_backend_reg_t reg, const char * name) {
if (std::strcmp(name, "ggml_backend_set_n_threads") == 0) {
return (void *) ggml_backend_zendnn_set_n_threads;
}
return NULL;
GGML_UNUSED(reg);
GGML_UNUSED(name);
}
static const struct ggml_backend_reg_i ggml_backend_zendnn_reg_i = {
/* .get_name = */ ggml_backend_zendnn_reg_get_name,
/* .get_device_count = */ ggml_backend_zendnn_reg_get_device_count,
/* .get_device = */ ggml_backend_zendnn_reg_get_device,
/* .get_proc_address = */ ggml_backend_zendnn_get_proc_address,
};
ggml_backend_reg_t ggml_backend_zendnn_reg(void) {
static struct ggml_backend_reg ggml_backend_zendnn_reg = {
/* .api_version = */ GGML_BACKEND_API_VERSION,
/* .iface = */ ggml_backend_zendnn_reg_i,
/* .context = */ NULL,
};
return &ggml_backend_zendnn_reg;
}
GGML_BACKEND_DL_IMPL(ggml_backend_zendnn_reg)

3
tools/llama-bench/llama-bench.cpp
View File

@ -1737,7 +1737,8 @@ struct markdown_printer : public printer {
fields.emplace_back("params"); fields.emplace_back("params");
fields.emplace_back("backend"); fields.emplace_back("backend");
bool is_cpu_backend = test::get_backend().find("CPU") != std::string::npos || bool is_cpu_backend = test::get_backend().find("CPU") != std::string::npos ||
test::get_backend().find("BLAS") != std::string::npos; test::get_backend().find("BLAS") != std::string::npos ||
test::get_backend().find("ZenDNN") != std::string::npos;
if (!is_cpu_backend) { if (!is_cpu_backend) {
fields.emplace_back("n_gpu_layers"); fields.emplace_back("n_gpu_layers");
} }