owen/longhaul.cpp
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owen
2026-07-29 01:00:10 -05:00
commit 23e8ea90e4
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ggml/src/ggml-zdnn/.gitignore
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zdnn.h
ggml/src/ggml-zdnn/CMakeLists.txt
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if (DEFINED ZDNN_ROOT)
message(STATUS "zdnn: using ZDNN_ROOT override: ${ZDNN_ROOT}")
set(ZDNN_HINT "${ZDNN_ROOT}")
else()
set(ZDNN_HINT "")
endif()
find_path(ZDNN_INCLUDE
NAMES zdnn.h
HINTS ${ZDNN_HINT} /usr /usr/local
PATH_SUFFIXES include)
if (ZDNN_INCLUDE)
message(STATUS "zdnn: found include: ${ZDNN_INCLUDE}")
else()
message(FATAL_ERROR "zdnn: include directory not found, please set ZDNN_ROOT to the proper path if necessary")
endif()
find_library(ZDNN_LIB
NAMES zdnn
HINTS ${ZDNN_HINT} /usr /usr/local
PATH_SUFFIXES lib lib64)
if (ZDNN_LIB)
message(STATUS "zdnn: found library: ${ZDNN_LIB}")
else()
message(FATAL_ERROR "zdnn: library not found, please set ZDNN_ROOT to the proper path if necessary")
endif()
file(GLOB GGML_SOURCES_ZDNN "*.c" "*.cpp")
file(GLOB GGML_HEADERS_ZDNN "*.h" "*.hpp")
ggml_add_backend_library(ggml-zdnn ${GGML_HEADERS_ZDNN} ${GGML_SOURCES_ZDNN})
target_link_libraries(ggml-zdnn PRIVATE ${ZDNN_LIB})
target_include_directories(ggml-zdnn PRIVATE ${ZDNN_INCLUDE})
target_link_directories(ggml-zdnn PRIVATE ${ZDNN_LIB})
target_compile_definitions(ggml-zdnn PRIVATE GGML_USE_ZDNN)
ggml/src/ggml-zdnn/common.hpp
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#ifndef GGML_ZDNN_COMMON_HPP
#define GGML_ZDNN_COMMON_HPP
#include "ggml.h"
#include "ggml-impl.h"
#include "zdnn.h"
#include <vector>
#include <memory>
#define GGML_ZDNN_NAME "zDNN"
#define GGML_ZDNN_VERSION ZDNN_VERNUM
#define ZDNN_CHECK(stmt) \
do { \
zdnn_status status = (stmt); \
GGML_ASSERT(status == ZDNN_OK); \
} while (0);
struct ggml_backend_zdnn_device_context {
int zdnn_device;
int zdnn_device_ref_count;
bool has_parmblkformat_0;
bool has_parmblkformat_1; // checks for z17
size_t max_size;
char name[128];
};
struct ggml_backend_zdnn_context {
int device;
ggml_cgraph * gf;
};
struct ggml_backend_zdnn_buffer {
void * data;
ggml_backend_zdnn_buffer * extra; // for bias, etc.
size_t size;
zdnn_tensor_desc pre_tfm_desc;
zdnn_tensor_desc tfm_desc;
zdnn_ztensor ztensor;
char name[GGML_MAX_NAME];
};
struct ggml_backend_zdnn_buffer_context {
void * all_data;
size_t all_size;
bool owned;
int n_buffers;
std::vector<std::unique_ptr<ggml_backend_zdnn_buffer>> buffers;
};
#endif // GGML_ZDNN_COMMON_HPP
ggml/src/ggml-zdnn/ggml-zdnn.cpp
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#include "ggml-zdnn.h"
#include "ggml-impl.h"
#include "ggml-backend-impl.h"
#include "ggml-zdnn/common.hpp"
#include "ggml-zdnn/mmf.hpp"
#include "ggml-zdnn/utils.hpp"
#include "ggml.h"
#include <vector>
#include <memory>
#include <csignal> // raise(SIGTRAP)
#include <unistd.h>
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__);
#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 (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)
ggml/src/ggml-zdnn/mmf.cpp
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#include "ggml.h"
#include "mmf.hpp"
void ggml_zdnn_mul_mat_f(
const 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;
ggml_backend_zdnn_buffer * bias_extra = (ggml_backend_zdnn_buffer *)output_extra->extra;
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;
// 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, &bias_extra->ztensor,
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));
GGML_UNUSED(ctx);
GGML_UNUSED(weights_rows);
GGML_UNUSED(weights_cols);
GGML_UNUSED(inputs_rows);
GGML_UNUSED(inputs_cols);
GGML_UNUSED(output_rows);
GGML_UNUSED(output_cols);
}
ggml/src/ggml-zdnn/mmf.hpp
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#ifndef GGML_ZDNN_MMF_HPP
#define GGML_ZDNN_MMF_HPP
#include "common.hpp"
void ggml_zdnn_mul_mat_f(
const ggml_backend_zdnn_context * ctx,
const ggml_tensor * src0,
const ggml_tensor * src1,
ggml_tensor * dst);
#endif // GGML_ZDNN_MMF_HPP
ggml/src/ggml-zdnn/utils.cpp
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#include "ggml.h"
#include "utils.hpp"
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_Q8_0:
return INT8;
case GGML_TYPE_I8:
return INT8;
case GGML_TYPE_I32:
return INT32;
default:
GGML_ABORT("%s: fatal: unable to determine zTensor data type",
__func__);
break;
}
}
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));
}
void ggml_zdnn_load_tensor(zdnn_ztensor & ztensor, void * buffer) {
ZDNN_CHECK(zdnn_transform_ztensor(&ztensor, buffer));
}
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));
}
ggml/src/ggml-zdnn/utils.hpp
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#ifndef GGML_ZDNN_UTILITIES_HPP
#define GGML_ZDNN_UTILITIES_HPP
#include "common.hpp"
zdnn_data_types ggml_zdnn_type_mapping(ggml_type type);
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);
void ggml_zdnn_load_tensor(zdnn_ztensor & ztensor, void * buffer);
void ggml_zdnn_init_tensor(ggml_backend_zdnn_buffer * buffer, const ggml_tensor * tensor);
#endif // GGML_ZDNN_UTILITIES_HPP