Safetensors
GGUF
Turkish
llama
Llama-3
instruct
finetune
chatml
gpt4
synthetic data
distillation
function calling
json mode
axolotl
roleplaying
chat
Instructions to use tda45/TdAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use tda45/TdAI with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tda45/TdAI", filename="llama.cpp/models/ggml-vocab-aquila.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tda45/TdAI with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf tda45/TdAI # Run inference directly in the terminal: ./llama-cli -hf tda45/TdAI
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf tda45/TdAI # Run inference directly in the terminal: ./build/bin/llama-cli -hf tda45/TdAI
Use Docker
docker model run hf.co/tda45/TdAI
- LM Studio
- Jan
- Ollama
How to use tda45/TdAI with Ollama:
ollama run hf.co/tda45/TdAI
- Unsloth Studio
How to use tda45/TdAI with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tda45/TdAI to start chatting
- Atomic Chat new
- Docker Model Runner
How to use tda45/TdAI with Docker Model Runner:
docker model run hf.co/tda45/TdAI
- Lemonade
How to use tda45/TdAI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tda45/TdAI
Run and chat with the model
lemonade run user.TdAI-{{QUANT_TAG}}List all available models
lemonade list
| static void ggml_zdnn_compute_forward_mul_mat( | |
| const ggml_backend_zdnn_context * ctx, | |
| ggml_tensor * dst) { | |
| const ggml_tensor * src0 = dst->src[0]; // weights | |
| const ggml_tensor * src1 = dst->src[1]; // inputs | |
| // TODO: implement support for quantized types | |
| // we currently only support f32, f16, and bf16 | |
| ggml_zdnn_mul_mat_f(ctx, src0, src1, dst); | |
| } | |
| static bool ggml_zdnn_compute_forward( | |
| ggml_backend_zdnn_context * ctx, | |
| ggml_tensor * dst) { | |
| switch (dst->op) { | |
| case GGML_OP_MUL_MAT: | |
| { | |
| ggml_zdnn_compute_forward_mul_mat(ctx, dst); | |
| } break; | |
| default: | |
| return false; | |
| } | |
| return true; | |
| } | |
| static enum ggml_status ggml_zdnn_graph_compute(ggml_backend_t backend, ggml_cgraph * gf) { | |
| ggml_backend_zdnn_context * ctx = ( ggml_backend_zdnn_context *)backend->context; | |
| ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *)backend->device->context; | |
| ctx->gf = gf; | |
| for (int i = 0; i < gf->n_nodes; i++) { | |
| ggml_tensor * node = gf->nodes[i]; | |
| if (ggml_is_empty(node) | |
| || node->op == GGML_OP_NONE | |
| || node->op == GGML_OP_RESHAPE | |
| || node->op == GGML_OP_VIEW | |
| || node->op == GGML_OP_PERMUTE | |
| || node->op == GGML_OP_TRANSPOSE) { | |
| continue; | |
| } | |
| if ((node->flags & GGML_TENSOR_FLAG_COMPUTE) == 0) { | |
| continue; | |
| } | |
| bool ok = ggml_zdnn_compute_forward(ctx, node); | |
| if (!ok) { | |
| GGML_LOG_ERROR("%s: unsupported op %s (%s)\n", | |
| __func__, node->name, ggml_op_name(node->op)); | |
| } | |
| GGML_ASSERT(ok); | |
| } | |
| return GGML_STATUS_SUCCESS; | |
| GGML_UNUSED(ctx_dev); | |
| } | |
| static bool ggml_zdnn_supports_op(const ggml_backend_zdnn_device_context * ctx_dev, const ggml_tensor * op) { | |
| switch (op->op) { | |
| case GGML_OP_NONE: | |
| case GGML_OP_RESHAPE: | |
| case GGML_OP_VIEW: | |
| case GGML_OP_TRANSPOSE: | |
| case GGML_OP_PERMUTE: | |
| 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 max_batch = ctx_dev->max_size; | |
| if (!ggml_is_matrix(weights) || !ggml_is_matrix(inputs) || | |
| !ggml_is_contiguous(weights) || !ggml_is_contiguous(inputs) || | |
| weights->view_src != nullptr || inputs->view_src != nullptr || | |
| ne0 > max_batch || ne1 > max_batch || ne10 > max_batch) { | |
| return false; | |
| } | |
| switch (weights->type) { | |
| case GGML_TYPE_F32: | |
| case GGML_TYPE_F16: | |
| case GGML_TYPE_BF16: | |
| return true; | |
| default: | |
| return false; | |
| } | |
| } break; | |
| default: | |
| return false; | |
| } | |
| } | |
| //////////////////////////////////////////////////////////////////////////////// | |
| // | |
| // globals | |
| // | |
| // initialised in ggml_backend_zdnn_reg | |
| static ggml_backend_reg g_ggml_backend_zdnn_reg; | |
| static ggml_backend_device g_ggml_backend_zdnn_device; | |
| static ggml_backend_zdnn_device_context g_ggml_ctx_dev_main = { | |
| /* .zdnn_device = */ 0, | |
| /* .zdnn_device_ref_count = */ 0, | |
| /* .has_parmblkformat_0 = */ false, | |
| /* .has_parmblkformat_1 = */ false, | |
| /* .max_size = */ 0, | |
| /* .name = */ "", | |
| }; | |
| static int ggml_backend_zdnn_device_acq(ggml_backend_zdnn_device_context * ctx) { | |
| assert(ctx != NULL); | |
| if (ctx->zdnn_device == 0) { | |
| ctx->zdnn_device = 1; | |
| } | |
| if (ctx->zdnn_device >= 1) { | |
| ctx->has_parmblkformat_0 = zdnn_is_nnpa_parmblk_fmt_installed(1, NNPA_PARMBLKFORMAT_0); | |
| ctx->has_parmblkformat_1 = zdnn_is_nnpa_parmblk_fmt_installed(1, NNPA_PARMBLKFORMAT_1); | |
| ctx->max_size = zdnn_get_nnpa_max_dim_idx_size(); | |
| strncpy(ctx->name, GGML_ZDNN_NAME, sizeof(ctx->name) - 1); | |
| } | |
| ctx->zdnn_device_ref_count++; | |
| return ctx->zdnn_device; | |
| } | |
| static void ggml_backend_zdnn_device_rel(ggml_backend_zdnn_device_context * ctx) { | |
| assert(ctx != NULL); | |
| assert(ctx->zdnn_device_ref_count > 0); | |
| ctx->zdnn_device_ref_count--; | |
| if (ctx->zdnn_device_ref_count == 0) { | |
| if (ctx->zdnn_device >= 0) { | |
| ctx->zdnn_device = 0; | |
| } | |
| } | |
| } | |
| static ggml_backend_zdnn_context * ggml_zdnn_init(ggml_backend_dev_t dev) { | |
| GGML_LOG_INFO("%s: allocating\n", __func__); | |
| GGML_LOG_INFO("%s: found 1 device\n", __func__); | |
| zdnn_init(); | |
| ggml_backend_zdnn_context * ctx = new ggml_backend_zdnn_context(); | |
| ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *)dev->context; | |
| int device = 1; | |
| GGML_LOG_INFO("%s: picking default device: %s\n", __func__, ctx_dev->name); | |
| ctx->device = device; | |
| GGML_LOG_INFO("%s: NNPA name: %s\n", __func__, ctx_dev->name); | |
| GGML_LOG_INFO("%s: NNPA_PARMBLKFORMAT_0 = %s\n", __func__, ctx_dev->has_parmblkformat_0 ? "true" : "false"); | |
| GGML_LOG_INFO("%s: NNPA_PARMBLKFORMAT_1 = %s\n", __func__, ctx_dev->has_parmblkformat_1 ? "true" : "false"); | |
| ctx->gf = nullptr; | |
| return ctx; | |
| } | |
| static void ggml_zdnn_free(ggml_backend_zdnn_context * ctx) { | |
| GGML_LOG_INFO("%s: deallocating\n", __func__); | |
| delete ctx; | |
| } | |
| // | |
| // backend interface | |
| // | |
| static void ggml_backend_zdnn_buffer_free_buffer(ggml_backend_buffer_t buffer) { | |
| ggml_backend_zdnn_buffer_context * ctx = (ggml_backend_zdnn_buffer_context *)buffer->context; | |
| for (const auto & buf_ptr : ctx->buffers) { | |
| ggml_backend_zdnn_buffer * buf = buf_ptr.get(); | |
| // Free any extra buffer allocated for the tensor. E.g., bias for GGML_OP_MUL_MAT | |
| if (buf->extra != nullptr) free(buf->extra->data); | |
| if (buf->ztensor.buffer_size > 0) ZDNN_CHECK(zdnn_free_ztensor_buffer(&buf->ztensor)); | |
| } | |
| delete ctx; | |
| } | |
| static void * ggml_backend_zdnn_buffer_get_base(ggml_backend_buffer_t buffer) { | |
| ggml_backend_zdnn_buffer_context * ctx = (ggml_backend_zdnn_buffer_context *)buffer->context; | |
| return ctx->all_data; | |
| } | |
| static enum ggml_status ggml_backend_zdnn_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { | |
| if (tensor->view_src != NULL) { | |
| assert(tensor->view_src->buffer->buft == buffer->buft); | |
| return GGML_STATUS_SUCCESS; | |
| } | |
| ggml_backend_zdnn_buffer_context * ctx = (ggml_backend_zdnn_buffer_context *)buffer->context; | |
| const int64_t tsize = ggml_nbytes(tensor); | |
| int buffer_idx = ctx->n_buffers; | |
| std::unique_ptr<ggml_backend_zdnn_buffer> zdnn_buffer = std::make_unique<ggml_backend_zdnn_buffer>(); | |
| zdnn_buffer->data = tensor->data; | |
| zdnn_buffer->size = tsize; | |
| zdnn_buffer->extra = nullptr; | |
| snprintf(zdnn_buffer->name, GGML_MAX_NAME, "%s", tensor->name); | |
| ggml_zdnn_init_tensor(zdnn_buffer.get(), tensor); | |
| tensor->extra = zdnn_buffer.get(); | |
| switch (tensor->op) { | |
| case GGML_OP_MUL_MAT: | |
| { | |
| std::unique_ptr<ggml_backend_zdnn_buffer> zdnn_bias_buffer = std::make_unique<ggml_backend_zdnn_buffer>(); | |
| zdnn_bias_buffer->data = (void *)calloc(tensor->ne[0], ggml_element_size(tensor)); | |
| zdnn_bias_buffer->size = ggml_element_size(tensor) * tensor->ne[0]; | |
| snprintf(zdnn_bias_buffer->name, GGML_MAX_NAME, "%.*s (bias)", | |
| GGML_MAX_NAME - (int)sizeof(" (bias)"), tensor->name); | |
| const int64_t bias_dim[GGML_MAX_DIMS] = { 1, 1, 1, tensor->ne[0] }; | |
| ggml_zdnn_create_tensor(zdnn_bias_buffer->pre_tfm_desc, | |
| zdnn_bias_buffer->tfm_desc, | |
| zdnn_bias_buffer->ztensor, | |
| tensor, bias_dim, ZDNN_1D); | |
| ggml_zdnn_load_tensor(zdnn_bias_buffer->ztensor, zdnn_bias_buffer->data); | |
| zdnn_buffer->extra = zdnn_bias_buffer.get(); | |
| ctx->buffers.push_back(std::move(zdnn_bias_buffer)); | |
| ctx->n_buffers++; | |
| } break; | |
| default: | |
| break; | |
| } | |
| ctx->buffers.push_back(std::move(zdnn_buffer)); | |
| ctx->n_buffers++; | |
| // GGML_LOG_INFO("%s: initialised tensor '%s' in buffer %d, size = %8.2f MiB\n", | |
| // __func__, tensor->name, buffer_idx, tsize); | |
| return GGML_STATUS_SUCCESS; | |
| GGML_UNUSED(buffer_idx); | |
| } | |
| static void ggml_backend_zdnn_buffer_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { | |
| memset((char *)tensor->data + offset, value, size); | |
| GGML_UNUSED(buffer); | |
| } | |
| static void ggml_backend_zdnn_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { | |
| memcpy((char *)tensor->data + offset, data, size); | |
| ggml_backend_zdnn_buffer * extra = (ggml_backend_zdnn_buffer *)tensor->extra; | |
| // Fixes the LLAMA_SET_ROWS bug | |
| // see: https://github.com/ggml-org/llama.cpp/issues/15414 | |
| if (tensor->buffer->usage == GGML_BACKEND_BUFFER_USAGE_COMPUTE && extra->ztensor.is_transformed) zdnn_reset_ztensor(&extra->ztensor); | |
| if (extra->ztensor.is_transformed == false) ggml_zdnn_load_tensor(extra->ztensor, tensor->data); | |
| GGML_UNUSED(buffer); | |
| } | |
| static void ggml_backend_zdnn_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { | |
| memcpy(data, (const char *)tensor->data + offset, size); | |
| GGML_UNUSED(buffer); | |
| } | |
| static void ggml_backend_zdnn_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { | |
| ggml_backend_zdnn_buffer_context * ctx = (ggml_backend_zdnn_buffer_context *)buffer->context; | |
| memset(ctx->all_data, value, ctx->all_size); | |
| } | |
| static ggml_backend_buffer_i ggml_backend_zdnn_buffer_i = { | |
| /* .free_buffer = */ ggml_backend_zdnn_buffer_free_buffer, | |
| /* .get_base = */ ggml_backend_zdnn_buffer_get_base, | |
| /* .init_tensor = */ ggml_backend_zdnn_buffer_init_tensor, | |
| /* .memset_tensor = */ ggml_backend_zdnn_buffer_memset_tensor, | |
| /* .set_tensor = */ ggml_backend_zdnn_buffer_set_tensor, | |
| /* .get_tensor = */ ggml_backend_zdnn_buffer_get_tensor, | |
| /* .set_tensor_2d = */ NULL, | |
| /* .get_tensor_2d = */ NULL, | |
| /* .cpy_tensor = */ NULL, | |
| /* .clear = */ ggml_backend_zdnn_buffer_clear, | |
| /* .reset = */ NULL, | |
| }; | |
| // | |
| // default buffer type | |
| // | |
| static const char * ggml_backend_zdnn_buffer_type_get_name(ggml_backend_buffer_type_t buft) { | |
| return GGML_ZDNN_NAME; | |
| GGML_UNUSED(buft); | |
| } | |
| static ggml_backend_buffer_t ggml_backend_zdnn_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { | |
| ggml_backend_zdnn_buffer_context * ctx = new ggml_backend_zdnn_buffer_context(); | |
| const size_t size_page = sysconf(_SC_PAGESIZE); | |
| size_t size_aligned = size; | |
| if ((size_aligned % size_page) != 0) { | |
| size_aligned += size_page - (size_aligned % size_page); | |
| } | |
| ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *)buft->device->context; | |
| GGML_ASSERT(ctx_dev->zdnn_device >= 0); | |
| int device = ctx_dev->zdnn_device; GGML_UNUSED(device); | |
| ctx->all_data = ggml_aligned_malloc(size_aligned); | |
| ctx->all_size = size_aligned; | |
| ctx->owned = true; | |
| ctx->n_buffers = 1; | |
| if (ctx->all_data != NULL) { | |
| std::unique_ptr<ggml_backend_zdnn_buffer> zdnn_buffer = std::make_unique<ggml_backend_zdnn_buffer>(); | |
| zdnn_buffer->data = ctx->all_data; | |
| zdnn_buffer->size = size_aligned; | |
| ctx->buffers.push_back(std::move(zdnn_buffer)); | |
| } | |
| if (size_aligned > 0 && (ctx->all_data == NULL)) { | |
| GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f\n", | |
| __func__, size_aligned / 1024.0 / 1024.0); | |
| delete ctx; | |
| return NULL; | |
| } | |
| return ggml_backend_buffer_init(buft, ggml_backend_zdnn_buffer_i, ctx, size); | |
| } | |
| static size_t ggml_backend_zdnn_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { | |
| return 256; | |
| GGML_UNUSED(buft); | |
| } | |
| static bool ggml_backend_zdnn_buffer_type_is_host(ggml_backend_buffer_type_t buft) { | |
| /* while it resides in host memory, additional transformation is needed */ | |
| return false; | |
| GGML_UNUSED(buft); | |
| } | |
| ggml_backend_buffer_type_t ggml_backend_zdnn_buffer_type(void) { | |
| static ggml_backend_buffer_type ggml_backend_buffer_type_zdnn = { | |
| /* .iface = */ { | |
| /* .get_name = */ ggml_backend_zdnn_buffer_type_get_name, | |
| /* .alloc_buffer = */ ggml_backend_zdnn_buffer_type_alloc_buffer, | |
| /* .get_alignment = */ ggml_backend_zdnn_buffer_type_get_alignment, | |
| /* .get_max_size = */ NULL, | |
| /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes | |
| /* .is_host = */ ggml_backend_zdnn_buffer_type_is_host, | |
| }, | |
| /* .device = */ &g_ggml_backend_zdnn_device, | |
| /* .context = */ NULL, | |
| }; | |
| return &ggml_backend_buffer_type_zdnn; | |
| } | |
| // | |
| // backend | |
| // | |
| static const char * ggml_backend_zdnn_name(ggml_backend_t backend) { | |
| return GGML_ZDNN_NAME; | |
| GGML_UNUSED(backend); | |
| } | |
| static void ggml_backend_zdnn_free(ggml_backend_t backend) { | |
| ggml_backend_zdnn_context * ctx = (ggml_backend_zdnn_context *)backend->context; | |
| ggml_zdnn_free(ctx); | |
| free(backend); | |
| } | |
| static enum ggml_status ggml_backend_zdnn_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { | |
| return ggml_zdnn_graph_compute(backend, cgraph); | |
| } | |
| static ggml_backend_i ggml_backend_zdnn_i = { | |
| /* .get_name = */ ggml_backend_zdnn_name, | |
| /* .free = */ ggml_backend_zdnn_free, | |
| /* .set_tensor_async = */ NULL, | |
| /* .get_tensor_async = */ NULL, | |
| /* .set_tensor_2d_async = */ NULL, | |
| /* .get_tensor_2d_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_zdnn_graph_compute, | |
| /* .event_record = */ NULL, | |
| /* .event_wait = */ NULL, | |
| /* .graph_optimize = */ NULL, | |
| }; | |
| static ggml_guid_t ggml_backend_zdnn_guid(void) { | |
| static const char * guid_str = "IBM-ZDNN-ACCELER"; | |
| return reinterpret_cast<ggml_guid_t>((void *)guid_str); | |
| } | |
| bool ggml_backend_is_zdnn(ggml_backend_t backend) { | |
| return backend != NULL && | |
| ggml_guid_matches(backend->guid, ggml_backend_zdnn_guid()); | |
| GGML_UNUSED(backend); | |
| } | |
| // | |
| // backend device | |
| // | |
| static const char * ggml_backend_zdnn_device_get_name(ggml_backend_dev_t dev) { | |
| return GGML_ZDNN_NAME; | |
| GGML_UNUSED(dev); | |
| } | |
| static const char * ggml_backend_zdnn_device_get_description(ggml_backend_dev_t dev) { | |
| return "IBM Z Neural Network Processing Assist (NNPA)"; | |
| GGML_UNUSED(dev); | |
| } | |
| static void ggml_backend_zdnn_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_zdnn_device_get_type(ggml_backend_dev_t dev) { | |
| return GGML_BACKEND_DEVICE_TYPE_ACCEL; | |
| GGML_UNUSED(dev); | |
| } | |
| static void ggml_backend_zdnn_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) { | |
| props->name = ggml_backend_zdnn_device_get_name(dev); | |
| props->description = ggml_backend_zdnn_device_get_description(dev); | |
| props->type = ggml_backend_zdnn_device_get_type(dev); | |
| ggml_backend_zdnn_device_get_memory(dev, &props->memory_free, &props->memory_total); | |
| props->caps = (ggml_backend_dev_caps) { | |
| /* .async = */ false, | |
| /* .host_buffer = */ false, | |
| /* .buffer_from_host_ptr = */ false, | |
| /* .events = */ false | |
| }; | |
| } | |
| static ggml_backend_t ggml_backend_zdnn_device_init(ggml_backend_dev_t dev, const char * params) { | |
| ggml_backend_zdnn_context * ctx = ggml_zdnn_init(dev); | |
| if (ctx == NULL) { | |
| GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__); | |
| return NULL; | |
| } | |
| ggml_backend_t backend = (ggml_backend *)malloc(sizeof(ggml_backend)); | |
| *backend = (ggml_backend) { | |
| /* .guid = */ ggml_backend_zdnn_guid(), | |
| /* .iface = */ ggml_backend_zdnn_i, | |
| /* .device = */ dev, | |
| /* .context = */ ctx | |
| }; | |
| return backend; | |
| GGML_UNUSED(params); | |
| } | |
| static ggml_backend_buffer_type_t ggml_backend_zdnn_device_get_buffer_type(ggml_backend_dev_t dev) { | |
| return ggml_backend_zdnn_buffer_type(); | |
| GGML_UNUSED(dev); | |
| } | |
| static bool ggml_backend_zdnn_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { | |
| ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *) dev->context; | |
| return ggml_zdnn_supports_op(ctx_dev, op); | |
| } | |
| static bool ggml_backend_zdnn_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { | |
| return | |
| buft->iface.get_name == ggml_backend_zdnn_buffer_type_get_name; | |
| GGML_UNUSED(dev); | |
| } | |
| static ggml_backend_device_i ggml_backend_zdnn_device_i = { | |
| /* .get_name = */ ggml_backend_zdnn_device_get_name, | |
| /* .get_description = */ ggml_backend_zdnn_device_get_description, | |
| /* .get_memory = */ ggml_backend_zdnn_device_get_memory, | |
| /* .get_type = */ ggml_backend_zdnn_device_get_type, | |
| /* .get_props = */ ggml_backend_zdnn_device_get_props, | |
| /* .init_backend = */ ggml_backend_zdnn_device_init, | |
| /* .get_buffer_type = */ ggml_backend_zdnn_device_get_buffer_type, | |
| /* .get_host_buffer_type = */ NULL, | |
| /* .buffer_from_host_ptr = */ NULL, | |
| /* .supports_op = */ ggml_backend_zdnn_device_supports_op, | |
| /* .supports_buft = */ ggml_backend_zdnn_device_supports_buft, | |
| /* .offload_op = */ NULL, | |
| /* .event_new = */ NULL, | |
| /* .event_free = */ NULL, | |
| /* .event_synchronize = */ NULL, | |
| }; | |
| // | |
| // backend registry | |
| // | |
| static const char * ggml_backend_zdnn_reg_get_name(ggml_backend_reg_t reg) { | |
| return GGML_ZDNN_NAME; | |
| GGML_UNUSED(reg); | |
| } | |
| static size_t ggml_backend_zdnn_reg_device_count(ggml_backend_reg_t reg) { | |
| if (!zdnn_is_nnpa_installed()) { | |
| return 0; | |
| } | |
| return 1; | |
| GGML_UNUSED(reg); | |
| } | |
| static ggml_backend_dev_t ggml_backend_zdnn_reg_device_get(ggml_backend_reg_t reg, size_t index) { | |
| GGML_ASSERT(index == 0); | |
| return &g_ggml_backend_zdnn_device; | |
| GGML_UNUSED(reg); | |
| GGML_UNUSED(index); | |
| } | |
| static ggml_backend_feature g_ggml_backend_zdnn_features[] = { | |
| { "NNPA", zdnn_is_nnpa_installed() ? "1" : "0" }, | |
| { "NNPA_PARMBLKFORMAT_0", zdnn_is_nnpa_parmblk_fmt_installed(1, NNPA_PARMBLKFORMAT_0) ? "1" : "0" }, | |
| { "NNPA_PARMBLKFORMAT_1", zdnn_is_nnpa_parmblk_fmt_installed(1, NNPA_PARMBLKFORMAT_1) ? "1" : "0" }, | |
| { NULL, NULL }, | |
| }; | |
| static ggml_backend_feature * ggml_backend_zdnn_get_features(ggml_backend_reg_t reg) { | |
| return g_ggml_backend_zdnn_features; | |
| GGML_UNUSED(reg); | |
| } | |
| static void * ggml_backend_zdnn_get_proc_address(ggml_backend_reg_t reg, const char * name) { | |
| if (strcmp(name, "ggml_backend_get_features") == 0) { | |
| return (void *) ggml_backend_zdnn_get_features; | |
| } | |
| return NULL; | |
| GGML_UNUSED(reg); | |
| } | |
| static ggml_backend_reg_i ggml_backend_zdnn_reg_i = { | |
| /* .get_name = */ ggml_backend_zdnn_reg_get_name, | |
| /* .get_device_count = */ ggml_backend_zdnn_reg_device_count, | |
| /* .get_device = */ ggml_backend_zdnn_reg_device_get, | |
| /* .get_proc_address = */ ggml_backend_zdnn_get_proc_address | |
| }; | |
| static void ggml_zdnn_cleanup(void) { | |
| ggml_backend_zdnn_device_rel(&g_ggml_ctx_dev_main); | |
| } | |
| // TODO: make thread-safe | |
| ggml_backend_reg_t ggml_backend_zdnn_reg(void) { | |
| ggml_backend_zdnn_device_acq(&g_ggml_ctx_dev_main); | |
| // register cleanup callback | |
| atexit(ggml_zdnn_cleanup); | |
| { | |
| g_ggml_backend_zdnn_reg = (ggml_backend_reg) { | |
| /* .api_version = */ GGML_ZDNN_VERSION, | |
| /* .iface = */ ggml_backend_zdnn_reg_i, | |
| /* .context = */ NULL | |
| }; | |
| g_ggml_backend_zdnn_device = (ggml_backend_device) { | |
| /* .iface = */ ggml_backend_zdnn_device_i, | |
| /* .reg = */ &g_ggml_backend_zdnn_reg, | |
| /* .context = */ &g_ggml_ctx_dev_main | |
| }; | |
| return &g_ggml_backend_zdnn_reg; | |
| } | |
| } | |
| GGML_BACKEND_DL_IMPL(ggml_backend_zdnn_reg) | |