--- /dev/null
+#include "zdnn.h"
+#include "ggml-zdnn.h"
+#include "ggml-zdnn-impl.h"
+
+#include "ggml-impl.h"
+#include "ggml-backend-impl.h"
+
+#include <vector>
+#include <memory>
+#include <csignal>
+#include <unistd.h>
+
+inline zdnn_data_types ggml_zdnn_type_mapping(ggml_type type) {
+ switch (type) {
+ case GGML_TYPE_F32:
+ return FP32;
+ case GGML_TYPE_F16:
+ return FP16;
+ case GGML_TYPE_BF16:
+ return BFLOAT;
+ case GGML_TYPE_I8:
+ return INT8;
+ case GGML_TYPE_I32:
+ return INT32;
+ case GGML_TYPE_Q8_0:
+ return INT8;
+ default:
+ GGML_ABORT("%s: fatal: unable to determine zTensor data type",
+ __func__);
+ break;
+ }
+}
+
+inline void ggml_zdnn_create_tensor(zdnn_tensor_desc & pre_tfm_desc,
+ zdnn_tensor_desc & tfm_desc,
+ zdnn_ztensor & ztensor,
+ const ggml_tensor * src,
+ const int64_t * ne,
+ const zdnn_data_layouts layout) {
+ zdnn_init_pre_transformed_desc(
+ layout,
+ ggml_zdnn_type_mapping(src->type),
+ &pre_tfm_desc,
+ ne[3], ne[2], ne[1], ne[0]
+ );
+
+ ZDNN_CHECK(zdnn_generate_transformed_desc(&pre_tfm_desc, &tfm_desc));
+ ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&pre_tfm_desc, &tfm_desc, &ztensor));
+}
+
+inline void ggml_zdnn_load_tensor(zdnn_ztensor & ztensor,
+ void * buffer) {
+ ZDNN_CHECK(zdnn_transform_ztensor(&ztensor, buffer));
+}
+
+inline void ggml_zdnn_init_tensor(ggml_backend_zdnn_buffer * buffer, const ggml_tensor * tensor) {
+ switch (tensor->op) {
+ case GGML_OP_MUL_MAT:
+ {
+ zdnn_init_pre_transformed_desc(
+ ZDNN_2D,
+ ggml_zdnn_type_mapping(tensor->type),
+ &buffer->pre_tfm_desc,
+ tensor->ne[1], tensor->ne[0]
+ );
+ } break;
+
+ default:
+ {
+ // For 4D tensors, GGML uses NCHW layout. However, because zDNN
+ // automatically transforms everything to NHWC, we will use it
+ // directly to avoid the performance penalty changing the
+ // layout and reshaping the tensor.
+ zdnn_init_pre_transformed_desc(
+ ZDNN_NHWC,
+ ggml_zdnn_type_mapping(tensor->type),
+ &buffer->pre_tfm_desc,
+ tensor->ne[3], tensor->ne[2], tensor->ne[1], tensor->ne[0]
+ );
+
+ // TODO: Consider adding a ggml check.
+ // TODO: If tensor = 4D, use ZDNN_NCHW by default.
+ // TODO: If tensor = 2D, use ZDNN_NHWC by default.
+ } break;
+ }
+
+ ZDNN_CHECK(zdnn_generate_transformed_desc(&buffer->pre_tfm_desc, &buffer->tfm_desc));
+ ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&buffer->pre_tfm_desc, &buffer->tfm_desc, &buffer->ztensor));
+}
+
+static void ggml_zdnn_mul_mat_op(ggml_backend_zdnn_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+ GGML_TENSOR_BINARY_OP_LOCALS;
+
+ const enum ggml_type type = src0->type;
+
+ 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(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);
+
+ const ggml_tensor * weights = src0;
+ const ggml_tensor * inputs = src1;
+ ggml_tensor * output = dst;
+
+ ggml_backend_zdnn_buffer * weights_extra = (ggml_backend_zdnn_buffer *)weights->extra;
+ ggml_backend_zdnn_buffer * inputs_extra = (ggml_backend_zdnn_buffer *)inputs->extra;
+ ggml_backend_zdnn_buffer * output_extra = (ggml_backend_zdnn_buffer *)output->extra;
+
+ zdnn_tensor_desc ptd_bias, td_bias;
+ zdnn_ztensor zt_bias;
+
+ const int64_t weights_rows = ne01;
+ const int64_t weights_cols = ne00;
+ const int64_t inputs_rows = ne11;
+ const int64_t inputs_cols = ne10;
+
+ assert(inputs_cols == weights_cols);
+
+ const int64_t output_rows = ne1;
+ const int64_t output_cols = ne0;
+
+ const int64_t bias_dim [GGML_MAX_DIMS] = { 1, 1, 1, output_cols };
+ ggml_zdnn_create_tensor(ptd_bias, td_bias, zt_bias, output, bias_dim, ZDNN_1D);
+
+ void * bias_data = (void *)calloc(ne0, ggml_element_size(output));
+ if (weights_extra->ztensor.is_transformed == false) ggml_zdnn_load_tensor(weights_extra->ztensor, weights->data);
+ if (inputs_extra->ztensor.is_transformed == false) ggml_zdnn_load_tensor(inputs_extra->ztensor, inputs->data);
+ ggml_zdnn_load_tensor(zt_bias, bias_data);
+
+ // GGML_LOG_INFO("%s: tensor '%s' tensor dimensions: [%ld, %ld, %ld, %ld] pre_tfm_desc dimensions: [%ld, %ld, %ld, %ld]\n",
+ // __func__, weights_extra->name,
+ // weights->ne[3], weights->ne[2], weights->ne[1], weights->ne[0],
+ // weights_extra->pre_tfm_desc.dim1,
+ // weights_extra->pre_tfm_desc.dim2,
+ // weights_extra->pre_tfm_desc.dim3,
+ // weights_extra->pre_tfm_desc.dim4);
+
+ // GGML_LOG_INFO("%s: tensor '%s' tensor dimensions: [%ld, %ld, %ld, %ld] pre_tfm_desc dimensions: [%ld, %ld, %ld, %ld]\n",
+ // __func__, inputs_extra->name,
+ // inputs->ne[3], inputs->ne[2], inputs->ne[1], inputs->ne[0],
+ // inputs_extra->pre_tfm_desc.dim1,
+ // inputs_extra->pre_tfm_desc.dim2,
+ // inputs_extra->pre_tfm_desc.dim3,
+ // inputs_extra->pre_tfm_desc.dim4);
+
+ GGML_ASSERT(weights_extra->pre_tfm_desc.dim1 == weights->ne[0] && "weights_extra->pre_tfm_desc.dim1 must match weights->ne[0]");
+ GGML_ASSERT(weights_extra->pre_tfm_desc.dim2 == weights->ne[1] && "weights_extra->pre_tfm_desc.dim2 must match weights->ne[1]");
+ GGML_ASSERT(inputs_extra->pre_tfm_desc.dim1 == inputs->ne[0] && "inputs_extra->pre_tfm_desc.dim1 must match inputs->ne[0]");
+ GGML_ASSERT(inputs_extra->pre_tfm_desc.dim2 == inputs->ne[1] && "inputs_extra->pre_tfm_desc.dim2 must match inputs->ne[1]");
+
+ ZDNN_CHECK(zdnn_matmul_transpose_op(&inputs_extra->ztensor, &weights_extra->ztensor, &zt_bias,
+ false, true, MATMUL_OP_ADDITION, &output_extra->ztensor));
+ // TODO: Remove in the future as we are currently DLF16 -> FP32 then in the next op, FP32 -> DLF16 again. Inefficient.
+ ZDNN_CHECK(zdnn_transform_origtensor(&output_extra->ztensor, output->data));
+
+ ZDNN_CHECK(zdnn_free_ztensor_buffer(&zt_bias));
+ free(bias_data);
+}
+
+static void ggml_zdnn_mul_mat_dispatch(ggml_backend_zdnn_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+ bool use_mul_mat_vec =
+ (src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_F16)
+ && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
+ && src0->ne[0] % 2 == 0 && src1->ne[1] == 1;
+
+ bool use_mul_mat_vec_q =
+ ggml_is_quantized(src0->type)
+ && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
+
+ bool use_mul_mat_q =
+ ggml_is_quantized(src0->type)
+ && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
+
+ // debug helpers
+ // GGML_LOG_INFO("%s: use_mul_mat_vec = %d\n", __func__, use_mul_mat_vec);
+ // GGML_LOG_INFO("%s: use_mul_mat_vec_q = %d\n", __func__, use_mul_mat_vec_q);
+ // GGML_LOG_INFO("%s: use_mul_mat_q = %d\n", __func__, use_mul_mat_q);
+ // GGML_LOG_INFO("%s: src0: %8d %8d %8d %8d\n", __func__, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
+ // GGML_LOG_INFO("%s: %8d %8d %8d %8d\n", __func__, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]);
+ // GGML_LOG_INFO("%s: src1: %8d %8d %8d %8d\n", __func__, src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]);
+ // GGML_LOG_INFO("%s: %8d %8d %8d %8d\n", __func__, src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]);
+ // GGML_LOG_INFO("%s: src0 is contiguous %d, transposed %d, type = %s, name = %s\n", __func__, ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
+ // GGML_LOG_INFO("%s: src1 is contiguous %d, transposed %d, type = %s, name = %s\n", __func__, ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
+
+ if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16
+ && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)
+ && src1->ne[2] * src1->ne[3] > 1) {
+ // general KQ + KQV multi-batch
+ GGML_LOG_INFO("%s: using zdnn_mul_mat_batched for KQ + KQV multi-batch\n", __func__);
+ // ggml_zdnn_mul_mat_batched(ctx, src0, src1, dst);
+ } else if (use_mul_mat_vec) {
+ GGML_LOG_INFO("%s: using zdnn_op_mul_mat_vec for vector multiplication\n", __func__);
+ // ggml_zdnn_op_mul_mat(ctx, src0, src1, dst, ggml_zdnn_op_mul_mat_vec, nullptr);
+ } else if (use_mul_mat_vec_q) {
+ GGML_LOG_INFO("%s: using zdnn_op_mul_mat_vec_q for quantized vector multiplication\n", __func__);
+ // ggml_zdnn_op_mul_mat(ctx, src0, src1, dst, ggml_zdnn_op_mul_mat_vec_q, ggml_zdnn_quantize_row_q8_1);
+ } else if (use_mul_mat_q) {
+ GGML_LOG_INFO("%s: using zdnn_op_mul_mat_q for quantized matrix multiplication\n", __func__);
+ // ggml_zdnn_op_mul_mat(ctx, src0, src1, dst, ggml_zdnn_op_mul_mat_q, ggml_zdnn_quantize_mmq_q8_1);
+ } else {
+ // GGML_LOG_INFO("%s: using zdnn_op_mul_mat for general matrix multiplication\n", __func__);
+ ggml_zdnn_mul_mat_op(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_mul_mat_dispatch(ctx, dst->src[0], dst->src[1], 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;
+ }
+
+ 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;
+}
+
+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 * src0 = op->src[0];
+ const ggml_tensor * src1 = op->src[1];
+
+ const int64_t ne10 = src1->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;
+
+ return ggml_is_matrix(src0) &&
+ ggml_is_matrix(src1) &&
+ ggml_is_contiguous(src0) &&
+ ggml_is_contiguous(src1) &&
+ src0->view_src == nullptr && src1->view_src == nullptr &&
+ src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 &&
+ (ne0 <= max_batch && ne1 <= max_batch && ne10 <= max_batch);
+ } 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__);
+
+ #ifdef STATIC_LIB
+ zdnn_init();
+ #endif
+
+ 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 (int i = 0; i < ctx->n_buffers; i++) {
+ if (ctx->buffers[i]->ztensor.buffer != NULL && ctx->buffers[i]->ztensor.is_transformed) {
+ ZDNN_CHECK(zdnn_free_ztensor_buffer(&ctx->buffers[i]->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;
+ strncpy(zdnn_buffer->name, tensor->name, GGML_MAX_NAME - 1);
+
+ ggml_zdnn_init_tensor(zdnn_buffer.get(), tensor);
+ tensor->extra = zdnn_buffer.get();
+
+ 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;
+}
+
+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_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,
+ /* .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) {
+ return true;
+
+ 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;
+}
+
+static const char * ggml_backend_zdnn_buffer_from_ptr_type_get_name(ggml_backend_buffer_type_t buft) {
+ return GGML_ZDNN_NAME "_Mapped";
+
+ GGML_UNUSED(buft);
+}
+
+static ggml_backend_buffer_type_t ggml_backend_zdnn_buffer_from_ptr_type(void) {
+ static ggml_backend_buffer_type ggml_backend_buffer_from_ptr_type_zdnn = {
+ /* .iface = */ {
+ /* .get_name = */ ggml_backend_zdnn_buffer_from_ptr_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_from_ptr_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,
+ /* .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,
+};
+
+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);
+}
+
+// TODO: remove in the future
+ggml_backend_t ggml_backend_zdnn_init(void) {
+ ggml_backend_dev_t dev = ggml_backend_reg_dev_get(ggml_backend_zdnn_reg(), 0);
+
+ 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_t)malloc(sizeof(ggml_backend));
+ *backend = (ggml_backend) {
+ /* .guid = */ ggml_backend_zdnn_guid(),
+ /* .iface = */ ggml_backend_zdnn_i,
+ /* .device = */ dev,
+ /* .context = */ ctx,
+ };
+
+ return backend;
+}
+
+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)";
+}
+
+static void ggml_backend_zdnn_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
+ *free = 0;
+ *total = 0;
+}
+
+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 = */ true,
+ /* .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 ggml_backend_buffer_t ggml_backend_zdnn_device_buffer_from_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
+ ggml_backend_zdnn_buffer_context * ctx = new ggml_backend_zdnn_buffer_context();
+
+ ctx->all_data = ptr;
+ ctx->all_size = size;
+ ctx->owned = false;
+ ctx->n_buffers = 0;
+
+ const size_t size_page = sysconf(_SC_PAGESIZE);
+
+ // page-align the data ptr
+ {
+ const uintptr_t offs = (uintptr_t) ptr % size_page;
+ ptr = (void *)((char *)ptr - offs);
+ size += offs;
+ }
+
+ 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 *)dev->context;
+
+ GGML_ASSERT(ctx_dev->zdnn_device >= 0);
+ int device = ctx_dev->zdnn_device; GGML_UNUSED(device);
+
+ std::unique_ptr<ggml_backend_zdnn_buffer> zdnn_buffer = std::make_unique<ggml_backend_zdnn_buffer>();
+ zdnn_buffer->data = ptr;
+ zdnn_buffer->size = size;
+ ctx->buffers.push_back(std::move(zdnn_buffer));
+
+ GGML_LOG_INFO("%s: allocated buffer, size = %8.2f MiB\n",
+ __func__, size_aligned / 1024.0 / 1024.0);
+
+ ++ctx->n_buffers;
+
+ return ggml_backend_buffer_init(ggml_backend_zdnn_buffer_from_ptr_type(), ggml_backend_zdnn_buffer_i, ctx, size);
+}
+
+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 ||
+ buft->iface.get_name == ggml_backend_zdnn_buffer_from_ptr_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 = */ ggml_backend_zdnn_device_buffer_from_ptr,
+ /* .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)
overview / pkg / ggml / sources / ggml / commitdiff