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ggml-zdnn: inital backend impl

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Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

ggml-zdnn: temp change z17 to arch15

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

ggml-zdnn: fix build bugs

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
This commit is contained in:
Aaron Teo
2025-07-18 19:59:24 +08:00
parent 01612b7409
commit e084821a3f
9 changed files with 1405 additions and 1 deletions
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1
ggml/CMakeLists.txt
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@@ -183,6 +183,7 @@ option(GGML_VULKAN_VALIDATE "ggml: enable Vulkan validation"
option(GGML_VULKAN_RUN_TESTS "ggml: run Vulkan tests" OFF) option(GGML_VULKAN_RUN_TESTS "ggml: run Vulkan tests" OFF)
option(GGML_WEBGPU "ggml: use WebGPU" OFF) option(GGML_WEBGPU "ggml: use WebGPU" OFF)
option(GGML_WEBGPU_DEBUG "ggml: enable WebGPU debug output" OFF) option(GGML_WEBGPU_DEBUG "ggml: enable WebGPU debug output" OFF)
option(GGML_ZDNN "ggml: use zDNN" OFF)
option(GGML_METAL "ggml: use Metal" ${GGML_METAL_DEFAULT}) option(GGML_METAL "ggml: use Metal" ${GGML_METAL_DEFAULT})
option(GGML_METAL_USE_BF16 "ggml: use bfloat if available" OFF) option(GGML_METAL_USE_BF16 "ggml: use bfloat if available" OFF)
option(GGML_METAL_NDEBUG "ggml: disable Metal debugging" OFF) option(GGML_METAL_NDEBUG "ggml: disable Metal debugging" OFF)

16
ggml/include/ggml-zdnn.h Normal file
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@@ -0,0 +1,16 @@
#pragma once
#include "ggml.h"
#include "ggml-backend.h"
#ifdef __cplusplus
extern "C" {
#endif
GGML_BACKEND_API ggml_backend_t ggml_backend_zdnn_init(void);
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_zdnn_reg(void);
#ifdef __cplusplus
}
#endif

1
ggml/src/CMakeLists.txt
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@@ -371,6 +371,7 @@ ggml_add_backend(RPC)
ggml_add_backend(SYCL) ggml_add_backend(SYCL)
ggml_add_backend(Vulkan) ggml_add_backend(Vulkan)
ggml_add_backend(WebGPU) ggml_add_backend(WebGPU)
ggml_add_backend(zDNN)
ggml_add_backend(OpenCL) ggml_add_backend(OpenCL)
foreach (target ggml-base ggml) foreach (target ggml-base ggml)

7
ggml/src/ggml-backend-reg.cpp
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@@ -49,6 +49,10 @@
#include "ggml-webgpu.h" #include "ggml-webgpu.h"
#endif #endif
#ifdef GGML_USE_ZDNN
#include "ggml-zdnn.h"
#endif
#ifdef GGML_USE_OPENCL #ifdef GGML_USE_OPENCL
#include "ggml-opencl.h" #include "ggml-opencl.h"
#endif #endif
@@ -180,6 +184,9 @@ struct ggml_backend_registry {
#ifdef GGML_USE_WEBGPU #ifdef GGML_USE_WEBGPU
register_backend(ggml_backend_webgpu_reg()); register_backend(ggml_backend_webgpu_reg());
#endif #endif
#ifdef GGML_USE_ZDNN
register_backend(ggml_backend_zdnn_reg());
#endif
#ifdef GGML_USE_OPENCL #ifdef GGML_USE_OPENCL
register_backend(ggml_backend_opencl_reg()); register_backend(ggml_backend_opencl_reg());
#endif #endif

2
ggml/src/ggml-cpu/CMakeLists.txt
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@@ -457,7 +457,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
elseif (${S390X_M} MATCHES "9175|9176") elseif (${S390X_M} MATCHES "9175|9176")
# NOTE: Only available from GCC 15.1.0 onwards. Any z17 machine with compile issues must first verify their GCC version. # NOTE: Only available from GCC 15.1.0 onwards. Any z17 machine with compile issues must first verify their GCC version.
message(STATUS "z17 target") message(STATUS "z17 target")
list(APPEND ARCH_FLAGS -march=z17) list(APPEND ARCH_FLAGS -march=arch15)
else() else()
message(STATUS "Unknown target") message(STATUS "Unknown target")
message(WARNING "Unknown target. If you are compiling for z14 and earlier, you might have to add -DGGML_VXE=OFF.") message(WARNING "Unknown target. If you are compiling for z14 and earlier, you might have to add -DGGML_VXE=OFF.")

38
ggml/src/ggml-zdnn/CMakeLists.txt Normal file
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@@ -0,0 +1,38 @@
if (GGML_ZDNN)
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_ZDNN GGML_USE_ZDNN)
endif()

59
ggml/src/ggml-zdnn/ggml-zdnn-impl.h Normal file
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@@ -0,0 +1,59 @@
#ifndef GGML_ZDNN_IMPL
#define GGML_ZDNN_IMPL
#include "zdnn.h"
#include "ggml.h"
#include "ggml-zdnn.h"
#include <memory>
#include <vecintrin.h>
#define GGML_ZDNN_NAME "zDNN"
#define GGML_ZDNN_VERSION ZDNN_VERNUM
#define vec_neg(a) (-(a)) // Vector Negate
#define vec_add(a, b) ((a) + (b)) // Vector Add
#define vec_sub(a, b) ((a) - (b)) // Vector Subtract
#define vec_mul(a, b) ((a) * (b)) // Vector Multiply
#define vec_div(a, b) ((a) / (b)) // Vector Divide
#define vec_sl(a, b) ((a) << (b)) // Vector Shift Left
#define vec_sra(a, b) ((a) >> (b)) // Vector Shift Right
#define vec_sr(a, b) ((a) >> (b)) // Vector Shift Right Algebraic
#define vec_slo(a, b) vec_slb(a, (b) << 64) // Vector Shift Left by Octet
#define vec_sro(a, b) vec_srb(a, (b) << 64) // Vector Shift Right by Octet
#ifndef vec_and
#define vec_and(a, b) ((a) & (b)) // Vector AND
#endif
#ifndef vec_or
#define vec_or(a, b) ((a) | (b)) // Vector OR
#endif
#ifndef vec_xor
#define vec_xor(a, b) ((a) ^ (b)) // Vector XOR
#endif
typedef signed char char8x16_t __attribute__((vector_size(16)));
typedef unsigned char uchar8x16_t __attribute__((vector_size(16)));
typedef int8_t int8x16_t __attribute__((vector_size(16)));
typedef int16_t int16x8_t __attribute__((vector_size(16)));
typedef int32_t int32x4_t __attribute__((vector_size(16)));
typedef uint8_t uint8x16_t __attribute__((vector_size(16)));
typedef uint16_t uint16x8_t __attribute__((vector_size(16)));
typedef uint32_t uint32x4_t __attribute__((vector_size(16)));
typedef float float32x4_t __attribute__((vector_size(16)));
typedef double double64x2_t __attribute__((vector_size(16)));
typedef signed long long long64x2_t __attribute__((vector_size(16)));
typedef unsigned long long ulong64x2_t __attribute__((vector_size(16)));
#define ZDNN_CHECK(stmt) \
do { \
zdnn_status status = (stmt); \
GGML_ASSERT(status == ZDNN_OK); \
} while (0);
#endif // GGML_ZDNN_IMPL

622
ggml/src/ggml-zdnn/ggml-zdnn.cpp Normal file
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@@ -0,0 +1,622 @@
#include "zdnn.h"
#include "ggml-zdnn.h"
#include "ggml-zdnn-impl.h"
#include "ggml-impl.h"
#include "ggml-backend-impl.h"
#include <csignal>
#include <unistd.h>
struct zdnn_extra {
zdnn_tensor_desc pre_tfm_desc;
zdnn_tensor_desc tfm_desc;
zdnn_ztensor ztensor;
struct zdnn_extra * extra; // for bias, etc.
};
struct ggml_backend_zdnn_context {
int n_threads = GGML_DEFAULT_N_THREADS;
};
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));
}
static void ggml_backend_zdnn_mul_mat(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;
zdnn_tensor_desc pre_tfm_desc_weights, tfm_desc_weights;
zdnn_tensor_desc pre_tfm_desc_inputs, tfm_desc_inputs;
zdnn_tensor_desc pre_tfm_desc_bias, tfm_desc_bias;
zdnn_tensor_desc pre_tfm_desc_output, tfm_desc_output;
zdnn_ztensor ztensor_weights, ztensor_inputs, ztensor_bias, ztensor_output;
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 = dst->ne[1];
const int64_t output_cols = dst->ne[0];
const int64_t inputs_dim [GGML_MAX_DIMS] = { 1, 1, inputs_cols, inputs_rows };
const int64_t weights_dim[GGML_MAX_DIMS] = { 1, 1, weights_cols, weights_rows };
const int64_t bias_dim [GGML_MAX_DIMS] = { 1, 1, 1, output_cols };
const int64_t output_dim [GGML_MAX_DIMS] = { 1, 1, output_cols, output_rows };
ggml_zdnn_create_tensor(pre_tfm_desc_inputs, tfm_desc_inputs, ztensor_inputs, src1, inputs_dim, ZDNN_2D);
ggml_zdnn_create_tensor(pre_tfm_desc_weights, tfm_desc_weights, ztensor_weights, src0, weights_dim, ZDNN_2D);
ggml_zdnn_create_tensor(pre_tfm_desc_bias, tfm_desc_bias, ztensor_bias, dst, bias_dim, ZDNN_1D);
ggml_zdnn_create_tensor(pre_tfm_desc_output, tfm_desc_output, ztensor_output, dst, output_dim, ZDNN_2D);
const size_t weights_size = ggml_element_size(src0);
void * bias_data = (void *)calloc(output_cols, sizeof(ggml_element_size(dst)));
ZDNN_CHECK(zdnn_transform_ztensor(&ztensor_weights, weights->data));
ZDNN_CHECK(zdnn_transform_ztensor(&ztensor_inputs, inputs->data));
ZDNN_CHECK(zdnn_transform_ztensor(&ztensor_bias, bias_data));
ZDNN_CHECK(zdnn_matmul_transpose_op(&ztensor_inputs, &ztensor_weights, &ztensor_bias,
false, true, MATMUL_OP_ADDITION, &ztensor_output));
ZDNN_CHECK(zdnn_transform_origtensor(&ztensor_output, output->data));
ZDNN_CHECK(zdnn_free_ztensor_buffer(&ztensor_weights));
ZDNN_CHECK(zdnn_free_ztensor_buffer(&ztensor_inputs));
ZDNN_CHECK(zdnn_free_ztensor_buffer(&ztensor_bias));
ZDNN_CHECK(zdnn_free_ztensor_buffer(&ztensor_output));
free(bias_data);
}
static void ggml_backend_zdnn_mul_mat_dispatch(ggml_backend_zdnn_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_UNUSED(ctx);
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_backend_zdnn_mul_mat(ctx, src0, src1, dst);
}
}
static bool ggml_backend_zdnn_compute_forward(ggml_backend_zdnn_context * ctx, ggml_tensor * dst) {
switch (dst->op) {
case GGML_OP_MUL_MAT:
ggml_backend_zdnn_mul_mat_dispatch(ctx, dst->src[0], dst->src[1], dst);
break;
default:
return false;
}
return true;
}
static const char * ggml_backend_zdnn_get_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;
delete ctx;
delete backend;
}
static ggml_status ggml_backend_zdnn_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
ggml_backend_zdnn_context * ctx = (ggml_backend_zdnn_context *)backend->context;
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->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_backend_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(backend);
}
static ggml_backend_i ggml_backend_zdnn_i = {
/* .get_name = */ ggml_backend_zdnn_get_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) {
// guid spells out IBM-NNPA-ACCELER
static ggml_guid guid = { 0x49, 0x42, 0x4D, 0x2D, 0x4E, 0x4E, 0x50, 0x41,
0x2D, 0x41, 0x43, 0x43, 0x45, 0x4C, 0x45, 0x52 };
return &guid;
}
ggml_backend_t ggml_backend_zdnn_init(void) {
ggml_backend_zdnn_context * ctx = new ggml_backend_zdnn_context;
ggml_backend_t backend = new ggml_backend {
/* .guid = */ ggml_backend_zdnn_guid(),
/* .iface = */ ggml_backend_zdnn_i,
/* .device = */ ggml_backend_reg_dev_get(ggml_backend_zdnn_reg(), 0),
/* .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());
}
void ggml_backend_zdnn_set_n_threads(ggml_backend_t backend_zdnn, int n_threads) {
GGML_ASSERT(ggml_backend_is_zdnn(backend_zdnn));
ggml_backend_zdnn_context * ctx = (ggml_backend_zdnn_context *)backend_zdnn->context;
ctx->n_threads = n_threads;
}
static const char * ggml_backend_zdnn_device_get_name(ggml_backend_dev_t dev) {
return GGML_ZDNN_NAME;
}
static const char * ggml_backend_zdnn_device_get_description(ggml_backend_dev_t dev) {
return GGML_ZDNN_NAME;
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 = {
/* .async = */ false,
/* .host_buffer = */ false,
/* .buffer_from_host_ptr = */ true,
/* .events = */ false,
};
}
static ggml_backend_t ggml_backend_zdnn_device_init_backend(ggml_backend_dev_t dev, const char * params) {
return ggml_backend_zdnn_init();
GGML_UNUSED(dev);
GGML_UNUSED(params);
}
static void * ggml_backend_zdnn_buffer_get_base(ggml_backend_buffer_t buffer) {
uintptr_t data = (uintptr_t)buffer->context;
if (data % 256 != 0) {
data = GGML_PAD(data, 256);
}
return (void *)data;
}
static 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;
}
zdnn_extra * extra = (zdnn_extra *)malloc(sizeof(zdnn_extra));
const int64_t dims[GGML_MAX_DIMS] = { 1, 1, tensor->ne[0], tensor->ne[1] };
zdnn_init_pre_transformed_desc(
ZDNN_2D,
ggml_zdnn_type_mapping(tensor->type),
&extra->pre_tfm_desc,
dims[3], dims[2], dims[1], dims[0]
);
ZDNN_CHECK(zdnn_generate_transformed_desc(&extra->pre_tfm_desc, &extra->tfm_desc));
ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&extra->pre_tfm_desc, &extra->tfm_desc, &extra->ztensor));
if (tensor->op == GGML_OP_MUL_MAT) {
zdnn_extra * bias_extra = (zdnn_extra *)malloc(sizeof(zdnn_extra));
const int64_t bias_dims[GGML_MAX_DIMS] = { 1, 1, 1, tensor->ne[0] };
zdnn_init_pre_transformed_desc(
ZDNN_1D,
ggml_zdnn_type_mapping(tensor->type),
&bias_extra->pre_tfm_desc,
bias_dims[3], bias_dims[2], bias_dims[1], bias_dims[0]
);
ZDNN_CHECK(zdnn_generate_transformed_desc(&bias_extra->pre_tfm_desc, &bias_extra->tfm_desc));
ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&bias_extra->pre_tfm_desc, &bias_extra->tfm_desc, &bias_extra->ztensor));
extra->extra = bias_extra;
}
tensor->extra = extra;
return GGML_STATUS_SUCCESS;
}
static void ggml_backend_zdnn_buffer_free_buffer(ggml_backend_buffer_t buffer) {
ggml_aligned_free(buffer->context, buffer->size);
}
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) {
memset(buffer->context, value, buffer->size);
}
static const ggml_backend_buffer_i ggml_backend_zdnn_buffer_i = {
/* .free_buffer = */ ggml_backend_zdnn_buffer_free_buffer, // zdnn buffers are not owned by the backend
/* .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,
};
static const ggml_backend_buffer_i ggml_backend_zdnn_buffer_from_ptr_i = {
/* .free_buffer = */ NULL, // ptr is not owned by the 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,
};
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) {
void * data = ggml_aligned_malloc(size);
if (data == NULL) {
GGML_LOG_ERROR("%s: failed to allocate %zu bytes\n", __func__, size);
return NULL;
}
return ggml_backend_buffer_init(buft, ggml_backend_zdnn_buffer_i, data, 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);
}
static ggml_backend_buffer_type_t ggml_backend_zdnn_device_get_buffer_type(ggml_backend_dev_t dev) {
static ggml_backend_buffer_type ggml_backend_zdnn_buffer_type = {
/* .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, // defaults to SIZE_MAX
/* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
/* .is_host = */ ggml_backend_zdnn_buffer_type_is_host,
},
/* .device = */ NULL,
/* .context = */ NULL,
};
return &ggml_backend_zdnn_buffer_type;
}
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_zdnn_buffer_type = {
/* .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, // defaults to SIZE_MAX
/* .get_alloc_size = */ NULL, // defaults to ggml_nbytes
/* .is_host = */ ggml_backend_zdnn_buffer_type_is_host,
},
/* .device = */ NULL,
/* .context = */ NULL,
};
return &ggml_backend_zdnn_buffer_type;
}
static ggml_backend_buffer_t ggml_backend_zdnn_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
GGML_ASSERT((uintptr_t)ptr % 256 == 0 && "buffer pointer must be aligned");
return ggml_backend_buffer_init(ggml_backend_zdnn_buffer_from_ptr_type(), ggml_backend_zdnn_buffer_from_ptr_i, ptr, size);
}
static bool ggml_backend_zdnn_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
const ggml_tensor * src0 = op->src[0];
const ggml_tensor * src1 = op->src[1];
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 * 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 = zdnn_get_nnpa_max_dim_idx_size();
return ggml_is_contiguous(src0) &&
ggml_is_contiguous(src1) &&
src1->type == GGML_TYPE_F32 &&
(ne0 <= max_batch && ne1 <= max_batch && ne10 <= max_batch) &&
(src0->type == GGML_TYPE_F32 || ggml_get_type_traits(src0->type)->to_float != NULL);
}
default:
return false;
}
GGML_UNUSED(dev);
}
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_backend,
/* .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_host_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_get_device_count(ggml_backend_reg_t reg) {
return 1;
GGML_UNUSED(reg);
}
static ggml_backend_dev_t ggml_backend_zdnn_reg_get_device(ggml_backend_reg_t reg, size_t index) {
GGML_ASSERT(index == 0);
static ggml_backend_device ggml_backend_zdnn_device = {
/* .iface = */ ggml_backend_zdnn_device_i,
/* .reg = */ reg,
/* .context = */ nullptr,
};
return &ggml_backend_zdnn_device;
GGML_UNUSED(reg);
GGML_UNUSED(index);
}
static void * ggml_backend_zdnn_get_proc_address(ggml_backend_reg_t reg, const char * name) {
if (strcmp(name, "ggml_backend_set_n_threads") == 0) {
return (void *)ggml_backend_zdnn_set_n_threads;
}
return NULL;
GGML_UNUSED(reg);
}
static const ggml_backend_reg_i ggml_backend_zdnn_reg_i = {
/* .get_name = */ ggml_backend_zdnn_reg_get_name,
/* .get_device_count = */ ggml_backend_zdnn_reg_get_device_count,
/* .get_device = */ ggml_backend_zdnn_reg_get_device,
/* .get_proc_address = */ ggml_backend_zdnn_get_proc_address,
};
ggml_backend_reg_t ggml_backend_zdnn_reg(void) {
static ggml_backend_reg ggml_backend_zdnn_reg = {
/* .api_version = */ GGML_ZDNN_VERSION,
/* .iface = */ ggml_backend_zdnn_reg_i,
/* .context = */ NULL,
};
return &ggml_backend_zdnn_reg;
}
GGML_BACKEND_DL_IMPL(ggml_backend_zdnn_reg)

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// SPDX-License-Identifier: Apache-2.0
/*
* Copyright IBM Corp. 2021, 2024
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef ZDNN_ZDNN_H_
#define ZDNN_ZDNN_H_
#include <inttypes.h>
#include <stdbool.h>
#include <stddef.h>
#include <stdint.h>
#ifdef __cplusplus
extern "C" {
#endif
// -----------------------------------------------------------------------------
// NOTE:
// Ensure that symbols in zdnn.h and zdnn.map are in sync!
// Please also have a look at zdnn.map how to add, update or remove a symbol.
// -----------------------------------------------------------------------------
// -----------------------------------------------------------------------------
// Initializer and global variables
// -----------------------------------------------------------------------------
void zdnn_init();
// -----------------------------------------------------------------------------
// zDNN Status
// -----------------------------------------------------------------------------
// -----------------------------------------------------------------------------
// NOTE:
// Update status.c and zdnn_private.h after any status modification!
// -----------------------------------------------------------------------------
// Status categories
#define ZDNN_WARNING 0x00020000
#define ZDNN_PARAMETER_ERROR 0x00040000
#define ZDNN_DATA_ERROR 0x00100000
#define ZDNN_HW_ERROR 0x000c0000
// clang-format off
typedef enum zdnn_status {
// ----------------------------------------------------------------
ZDNN_OK = 0x00000000, // Success.
// ----------------------------------------------------------------
ZDNN_ELEMENT_RANGE_VIOLATION = ZDNN_WARNING + 0x0001, // zAIU operation resulted in data that was out of the normal range.
// ----------------------------------------------------------------
ZDNN_INVALID_SHAPE = ZDNN_PARAMETER_ERROR + 0x0001, // Invalid shape information in one (or more) of the input/output tensor(s).
ZDNN_INVALID_LAYOUT, // Invalid layout information in one (or more) of the input/output tensor(s).
ZDNN_INVALID_TYPE, // Invalid type information in one (or more) of the input/output tensor(s).
ZDNN_INVALID_FORMAT, // Invalid format information in one (or more) of the input/output tensor(s).
ZDNN_INVALID_DIRECTION, // Invalid RNN direction.
ZDNN_INVALID_CONCAT_INFO, // Invalid concatenation info.
ZDNN_INVALID_STRIDE_PADDING, // Invalid padding type parameter for current strides
ZDNN_INVALID_STRIDES, // Invalid stride height or width parameter.
ZDNN_MISALIGNED_PARMBLOCK, // NNPA parameter block is not on double word boundary.
ZDNN_INVALID_CLIPPING_VALUE, // Invalid clipping for the specified operation.
ZDNN_INVALID_ADJUSTMENT_FACTOR, // Invalid adjustment for the specified operation.
ZDNN_INVALID_EPSILON, // Invalid epsilon for the specified operation.
ZDNN_INVALID_TRANSFORM_TYPE, // Invalid transformation type
ZDNN_INVALID_BETA, // Invalid beta value for the specified operation.
ZDNN_INVALID_GAMMA, // Invalid gamma value for the specified operation.
ZDNN_INVALID_BESSEL_CORRECTION, // Invalid bessel correction value for the specified operation.
ZDNN_INVALID_SCALE, // Invalid scale value for the specified operation.
ZDNN_INVALID_OFFSET, // Invalid offset value for the specified operation.
// ----------------------------------------------------------------
ZDNN_ALLOCATION_FAILURE = ZDNN_DATA_ERROR + 0x0001, // Can not allocate storage.
ZDNN_INVALID_BUFFER, // Buffer address is NULL or not on 4K-byte boundary, or insufficient buffer size.
ZDNN_CONVERT_FAILURE, // Floating point data conversion failure.
ZDNN_INVALID_STATE, // Invalid zTensor state.
ZDNN_UNSUPPORTED_AIU_EXCEPTION, // zAIU operation returned an unexpected exception.
// ----------------------------------------------------------------
ZDNN_UNSUPPORTED_PARMBLOCK = ZDNN_HW_ERROR + 0x0001, // NNPA parameter block format is not supported by the model.
ZDNN_UNAVAILABLE_FUNCTION, // Specified NNPA function is not defined or installed on the machine.
ZDNN_UNSUPPORTED_FORMAT = ZDNN_HW_ERROR + 0x0010, // Specified tensor data layout format is not supported.
ZDNN_UNSUPPORTED_TYPE, // Specified tensor data type is not supported.
ZDNN_EXCEEDS_MDIS, // Tensor dimension exceeds maximum dimension index size (MDIS).
ZDNN_EXCEEDS_MTS, // Total number of elements in tensor exceeds maximum tensor size. (MTS).
ZDNN_MISALIGNED_TENSOR, // Tensor address is not on 4K-byte boundary.
ZDNN_MISALIGNED_SAVEAREA, // Function specific save area address is not on 4K-byte boundary.
// ----------------------------------------------------------------
// Function specific response code (F00x)
ZDNN_FUNC_RC_F000 = ZDNN_HW_ERROR + 0xF000, // Function specific response code (F000).
ZDNN_FUNC_RC_F001, // Function specific response code (F001).
ZDNN_FUNC_RC_F002, // Function specific response code (F002).
ZDNN_FUNC_RC_F003, // Function specific response code (F003).
ZDNN_FUNC_RC_F004, // Function specific response code (F004).
ZDNN_FUNC_RC_F005, // Function specific response code (F005).
ZDNN_FUNC_RC_F006, // Function specific response code (F006).
ZDNN_FUNC_RC_F007, // Function specific response code (F007).
ZDNN_FUNC_RC_F008, // Function specific response code (F008).
ZDNN_FUNC_RC_F009, // Function specific response code (F009).
// ----------------------------------------------------------------
} zdnn_status;
// clang-format on
// -----------------------------------------------------------------------------
// NNPA hardware defined values as described in
// z/Architecture - Principles of Operation
// -----------------------------------------------------------------------------
typedef enum nnpa_function_code {
NNPA_QAF = 0,
NNPA_ADD = 16,
NNPA_SUB = 17,
NNPA_MUL = 18,
NNPA_DIV = 19,
NNPA_MIN = 20,
NNPA_MAX = 21,
NNPA_LOG = 32,
NNPA_EXP = 33,
NNPA_SQRT = 34,
NNPA_INVSQRT = 35,
// reserved = 48
NNPA_RELU = 49,
NNPA_TANH = 50,
NNPA_SIGMOID = 51,
NNPA_SOFTMAX = 52,
NNPA_GELU = 53,
NNPA_BATCHNORMALIZATION = 64,
NNPA_MOMENTS = 65,
NNPA_LAYERNORM = 66,
NNPA_NORM = 67,
NNPA_MAXPOOL2D = 80,
NNPA_AVGPOOL2D = 81,
NNPA_LSTMACT = 96,
NNPA_GRUACT = 97,
NNPA_CONVOLUTION = 112,
NNPA_MATMUL_OP = 113,
NNPA_MATMUL_OP_BCAST23 = 114,
NNPA_MATMUL_OP_BCAST1 = 115,
NNPA_TRANSFORM = 240,
NNPA_REDUCE = 241
} nnpa_function_code;
typedef enum nnpa_parmblk_format {
NNPA_PARMBLKFORMAT_0 = 0,
NNPA_PARMBLKFORMAT_1 = 1,
} nnpa_parmblk_format;
typedef enum nnpa_data_type {
NNPA_DATATYPE_1 = 0,
NNPA_32_BIT_BINARY_FP_SHORT = 6,
NNPA_8_BIT_BINARY_INT = 8,
NNPA_32_BIT_BINARY_INT = 10
} nnpa_data_type;
typedef enum nnpa_layout_format {
NNPA_LAYOUTFMT_4DFEATURE = 0,
NNPA_LAYOUTFMT_4DKERNEL = 1,
NNPA_LAYOUTFMT_4DWEIGHTS = 2,
NNPA_LAYOUTFMT_4DGENERIC = 31
} nnpa_layout_format;
typedef enum nnpa_bfp_format {
// 0 is reversed
NNPA_BFPFMT_TINY = 1,
NNPA_BFPFMT_SHORT = 2
} nnpa_bfp_format;
// NNPA_SOFTMAX, NNPA_REDUCE, and NNPA_TRANSFORM require 8K work area
#define ZDNN_SOFTMAX_SAVEAREA_SIZE 8 * 1024
#define ZDNN_8K_SAVEAREA_SIZE 8 * 1024
// NNPA Hardware defined values for Function Specific Parameters
typedef enum nnpa_matmul_operations {
NNPA_MATMUL_OP_ADDITION = 0,
NNPA_MATMUL_OP_COMP_HIGH = 1,
NNPA_MATMUL_OP_COMP_NOT_LOW = 2,
NNPA_MATMUL_OP_COMP_EQUAL = 3,
NNPA_MATMUL_OP_COMP_NOT_EQUAL = 4,
NNPA_MATMUL_OP_COMP_NOT_HIGH = 5,
NNPA_MATMUL_OP_COMP_LOW = 6,
} nnpa_matmul_operations;
typedef enum nnpa_matmul_bcast_operations {
NNPA_MATMUL_BCAST_OP_ADDITION = 0,
NNPA_MATMUL_BCAST_OP_COMP_HIGH = 1,
NNPA_MATMUL_BCAST_OP_COMP_NOT_LOW = 2,
NNPA_MATMUL_BCAST_OP_COMP_EQUAL = 3,
NNPA_MATMUL_BCAST_OP_COMP_NOT_EQUAL = 4,
NNPA_MATMUL_BCAST_OP_COMP_NOT_HIGH = 5,
NNPA_MATMUL_BCAST_OP_COMP_LOW = 6
} nnpa_matmul_bcast_operations;
typedef enum nnpa_softmax_act {
NNPA_SOFTMAX_NONE = 0,
NNPA_SOFTMAX_LOG = 1
} nnpa_softmax_act;
typedef enum nnpa_reduce_operations {
NNPA_REDUCE_OP_MINIMUM = 0,
NNPA_REDUCE_OP_MINIMUM_IDX = 1,
NNPA_REDUCE_OP_MAXIMUM = 2,
NNPA_REDUCE_OP_MAXIMUM_IDX = 3
} nnpa_reduce_operations;
// -----------------------------------------------------------------------------
// zdnn_query_*() bit-field enums
// -----------------------------------------------------------------------------
// pos is counting from left to right
#define MSB_BITMASK(field_size, pos) 1u << ((field_size - 1) - pos)
typedef enum zdnn_query_datatypes {
QUERY_DATATYPE_INTERNAL1 = MSB_BITMASK(16, NNPA_DATATYPE_1),
QUERY_DATATYPE_BINARY_FP32 = MSB_BITMASK(16, NNPA_32_BIT_BINARY_FP_SHORT),
QUERY_DATATYPE_BINARY_INT8 = MSB_BITMASK(16, NNPA_8_BIT_BINARY_INT),
QUERY_DATATYPE_BINARY_INT32 = MSB_BITMASK(16, NNPA_32_BIT_BINARY_INT)
} zdnn_query_datatypes;
typedef enum zdnn_query_layoutfmts {
QUERY_LAYOUTFMT_4DFEATURE = MSB_BITMASK(32, NNPA_LAYOUTFMT_4DFEATURE),
QUERY_LAYOUTFMT_4DKERNEL = MSB_BITMASK(32, NNPA_LAYOUTFMT_4DKERNEL),
QUERY_LAYOUTFMT_4DWEIGHTS = MSB_BITMASK(32, NNPA_LAYOUTFMT_4DWEIGHTS),
QUERY_LAYOUTFMT_4DGENERIC = MSB_BITMASK(32, NNPA_LAYOUTFMT_4DGENERIC)
} zdnn_query_layoutfmts;
typedef enum zdnn_query_bfpfmts {
QUERY_BFPFMT_TINY = MSB_BITMASK(16, NNPA_BFPFMT_TINY),
QUERY_BFPFMT_SHORT = MSB_BITMASK(16, NNPA_BFPFMT_SHORT)
} zdnn_query_bfpfmts;
// -----------------------------------------------------------------------------
// ZDNN enums
// -----------------------------------------------------------------------------
typedef enum zdnn_data_types {
ZDNN_DLFLOAT16 = NNPA_DATATYPE_1, // 16-bit deep learning format
ZDNN_BINARY_FP32 =
NNPA_32_BIT_BINARY_FP_SHORT, // 32-bit binary-floating-point format
ZDNN_BINARY_INT8 =
NNPA_8_BIT_BINARY_INT, // 8-bit signed or unsigned binary integer
ZDNN_BINARY_INT32 =
NNPA_32_BIT_BINARY_INT, // 32-bit signed or unsigned binary integer
INT8 = 251, // 8-bit signed or unsigned binary integer format
INT32 = 252, // 32-bit signed or unsigned binary integer format
BFLOAT = 253, // Brain floating point format
FP16 = 254, // 16-bit IEEE-754 floating point format
FP32 = 255, // 32-bit IEEE-754 floating point format
} zdnn_data_types;
typedef enum zdnn_data_layouts {
ZDNN_1D, // 1d tensor
ZDNN_2D, // 2d tensor
ZDNN_2DS, // represents special 2D tensors required by LSTM/GRU
ZDNN_3D, // 3d tensor
ZDNN_3DS, // represents special 3D tensors required by
// LSTM/GRU/Softmax/Matmul
ZDNN_ZRH, // represents (update, reset, hidden) used by GRU
ZDNN_4D, // 4d tensor
ZDNN_4DS, // represents special 4D tensors required by LSTM/GRU output
ZDNN_NHWC, // 4d feature tensor in NHWC
ZDNN_NCHW, // 4d feature tensor in NCHW
ZDNN_FICO, // represents (forget, input, cell, output) used by LSTM
ZDNN_HWCK, // 4d kernel CNN tensor
ZDNN_BIDIR_ZRH, // ZRH variant to work with bidirectional LSTM/GRU output
ZDNN_BIDIR_FICO // FICO variant to work with bidirectional LSTM/GRU output
} zdnn_data_layouts;
typedef enum zdnn_data_formats {
ZDNN_FORMAT_4DFEATURE =
NNPA_LAYOUTFMT_4DFEATURE, // tensor in zAIU data layout format 0
ZDNN_FORMAT_4DKERNEL =
NNPA_LAYOUTFMT_4DKERNEL, // tensor in zAIU data layout format 1
ZDNN_FORMAT_4DWEIGHTS =
NNPA_LAYOUTFMT_4DWEIGHTS, // tensor in zAIU data layout format 2
ZDNN_FORMAT_4DGENERIC =
NNPA_LAYOUTFMT_4DGENERIC, // tensor in zAIU data layout 31
} zdnn_data_formats;
typedef enum zdnn_quantized_transform_types {
QUANTIZED_DLFLOAT16 = 0, // quantized dlfloat16
QUANTIZED_INT8 = 1, // quantized int8
QUANTIZED_WEIGHTS_INT8 = 2 // quantized weights
} zdnn_quantized_transform_types;
// Supported padding types for use in pooling functions
typedef enum zdnn_pool_padding {
VALID_PADDING = 0,
SAME_PADDING = 1
} zdnn_pool_padding;
// Support operations for use in matmul functions
typedef enum zdnn_matmul_ops {
MATMUL_OP_ADDITION = NNPA_MATMUL_OP_ADDITION,
MATMUL_OP_GREATER = NNPA_MATMUL_OP_COMP_HIGH,
MATMUL_OP_GREATER_EQUAL = NNPA_MATMUL_OP_COMP_NOT_LOW,
MATMUL_OP_EQUAL = NNPA_MATMUL_OP_COMP_EQUAL,
MATMUL_OP_NOT_EQUAL = NNPA_MATMUL_OP_COMP_NOT_EQUAL,
MATMUL_OP_LESSER_EQUAL = NNPA_MATMUL_OP_COMP_NOT_HIGH,
MATMUL_OP_LESSER = NNPA_MATMUL_OP_COMP_LOW
} zdnn_matmul_ops;
// Support operations for use in matmul function
typedef enum zdnn_matmul_bcast_ops {
MATMUL_BCAST_OP_ADDITION = NNPA_MATMUL_BCAST_OP_ADDITION,
MATMUL_BCAST_OP_GREATER = NNPA_MATMUL_BCAST_OP_COMP_HIGH,
MATMUL_BCAST_OP_GREATER_EQUAL = NNPA_MATMUL_BCAST_OP_COMP_NOT_LOW,
MATMUL_BCAST_OP_EQUAL = NNPA_MATMUL_BCAST_OP_COMP_EQUAL,
MATMUL_BCAST_OP_NOT_EQUAL = NNPA_MATMUL_BCAST_OP_COMP_NOT_EQUAL,
MATMUL_BCAST_OP_LESSER_EQUAL = NNPA_MATMUL_BCAST_OP_COMP_NOT_HIGH,
MATMUL_BCAST_OP_LESSER = NNPA_MATMUL_BCAST_OP_COMP_LOW
} zdnn_matmul_bcast_ops;
typedef enum zdnn_softmax_act {
SOFTMAX_ACT_NONE = NNPA_SOFTMAX_NONE,
SOFTMAX_ACT_LOG = NNPA_SOFTMAX_LOG
} zdnn_softmax_act;
typedef enum zdnn_conv2d_act {
CONV2D_ACT_NONE,
CONV2D_ACT_RELU
} zdnn_conv2d_act;
// Support operations for use in reduce functions
typedef enum zdnn_reduce_ops {
REDUCE_OP_MINIMUM = NNPA_REDUCE_OP_MINIMUM,
REDUCE_OP_MINIMUM_IDX = NNPA_REDUCE_OP_MINIMUM_IDX,
REDUCE_OP_MAXIMUM = NNPA_REDUCE_OP_MAXIMUM,
REDUCE_OP_MAXIMUM_IDX = NNPA_REDUCE_OP_MAXIMUM_IDX
} zdnn_reduce_ops;
typedef enum zdnn_moments_bessel {
MOMENTS_BESSEL_POPULATION,
MOMENTS_BESSEL_SAMPLE,
} zdnn_moments_bessel;
// -----------------------------------------------------------------------------
// Structs
// ----------------------------------------------------------------------------
// describes general pre-transformed or transformed information (e.g. shape) of
// a tensor
typedef struct zdnn_tensor_desc {
zdnn_data_layouts layout; // data layout
zdnn_data_formats format; // internal use only
zdnn_data_types type; // data type
uint32_t dim4; // number of elements in outermost dimension
uint32_t dim3; // ... outer dimension
uint32_t dim2; // ... inner dimension
uint32_t dim1; // number of elements in innermost dimension
} zdnn_tensor_desc;
// struct for describing a ztensor
typedef struct zdnn_ztensor {
zdnn_tensor_desc
*pre_transformed_desc; // tensor's shape information before transformation
zdnn_tensor_desc *transformed_desc; // transformed tensor's shape information
uint64_t buffer_size; // tensor size in bytes
void *buffer; // pointer to the tensor in memory
bool is_transformed; // indicator if data in buffer has been transformed
char reserved[3]; // not currently used, should contain zeros.
float rec_scale; // the scale factor for quantization, stored as reciprocal
float offset; // the offset for quantization
char reserved2[20]; // not currently used, should contain zeros.
} zdnn_ztensor;
#define ZDNN_VERSION "1.2.0"
#define ZDNN_VERNUM 0x010200 // 0x[major][minor][patch]
#define ZDNN_VER_MAJOR 1
#define ZDNN_VER_MINOR 2
#define ZDNN_VER_PATCH 0
// -----------------------------------------------------------------------------
// External Tensor Functions
// -----------------------------------------------------------------------------
// Concatenation information is encoded into a 32-bit word:
// [RNN_TYPE: 8][PREV_LAYER_TYPE: 8][USAGE: 8][8]
typedef uint32_t zdnn_concat_info;
#define BITSHIFT_RNN_TYPE 24
#define BITSHIFT_PREV_LAYER 16
#define BITSHIFT_USAGE 8
#define RNN_TYPE_LSTM (0 << BITSHIFT_RNN_TYPE)
#define RNN_TYPE_GRU (1 << BITSHIFT_RNN_TYPE)
#define PREV_LAYER_UNI (0 << BITSHIFT_PREV_LAYER)
#define PREV_LAYER_NONE PREV_LAYER_UNI
#define PREV_LAYER_BIDIR (1 << BITSHIFT_PREV_LAYER)
#define USAGE_WEIGHTS (0 << BITSHIFT_USAGE)
#define USAGE_HIDDEN_WEIGHTS (1 << BITSHIFT_USAGE)
#define USAGE_BIASES (2 << BITSHIFT_USAGE)
#define USAGE_HIDDEN_BIASES (3 << BITSHIFT_USAGE)
#define CONCAT_RNN_TYPE(info) (info & (0xFFu << BITSHIFT_RNN_TYPE))
#define CONCAT_PREV_LAYER(info) (info & (0xFFu << BITSHIFT_PREV_LAYER))
#define CONCAT_USAGE(info) (info & (0xFFu << BITSHIFT_USAGE))
void zdnn_init_pre_transformed_desc(zdnn_data_layouts layout,
zdnn_data_types type,
zdnn_tensor_desc *pre_tfrmd_desc, ...);
zdnn_status
zdnn_generate_transformed_desc(const zdnn_tensor_desc *pre_tfrmd_desc,
zdnn_tensor_desc *tfrmd_desc);
zdnn_status zdnn_generate_quantized_transformed_desc(
const zdnn_tensor_desc *pre_tfrmd_desc,
zdnn_quantized_transform_types transform_type,
zdnn_tensor_desc *tfrmd_desc);
zdnn_status zdnn_generate_transformed_desc_concatenated(
const zdnn_tensor_desc *pre_tfrmd_desc, zdnn_concat_info info,
zdnn_tensor_desc *tfrmd_desc);
zdnn_status zdnn_allochelper_ztensor(zdnn_ztensor *ztensor);
zdnn_status zdnn_free_ztensor_buffer(const zdnn_ztensor *ztensor);
void zdnn_init_ztensor(zdnn_tensor_desc *pre_tfrmd_desc,
zdnn_tensor_desc *tfrmd_desc, zdnn_ztensor *output);
void zdnn_init_quantized_ztensor(zdnn_tensor_desc *pre_tfrmd_desc,
zdnn_tensor_desc *tfrmd_desc, float scale,
float offset, zdnn_ztensor *output);
zdnn_status zdnn_init_ztensor_with_malloc(zdnn_tensor_desc *pre_tfrmd_desc,
zdnn_tensor_desc *tfrmd_desc,
zdnn_ztensor *output);
zdnn_status zdnn_init_quantized_ztensor_with_malloc(
zdnn_tensor_desc *pre_tfrmd_desc, zdnn_tensor_desc *tfrmd_desc, float scale,
float offset, zdnn_ztensor *output);
bool zdnn_is_quantized_ztensor(zdnn_ztensor *ztensor);
void zdnn_reset_ztensor(zdnn_ztensor *ztensor);
uint64_t zdnn_getsize_ztensor(const zdnn_tensor_desc *tfrmd_desc);
zdnn_status zdnn_getrange_ztensor(const zdnn_ztensor *ztensor, float *min,
float *max);
// -----------------------------------------------------------------------------
// External Query Functions
// -----------------------------------------------------------------------------
bool zdnn_is_nnpa_installed();
bool zdnn_is_nnpa_function_installed(int count, ...);
bool zdnn_is_nnpa_parmblk_fmt_installed(int count, ...);
bool zdnn_is_nnpa_datatype_installed(uint16_t types_bitmask);
bool zdnn_is_nnpa_layout_fmt_installed(uint32_t layout_bitmask);
bool zdnn_is_nnpa_conversion_installed(nnpa_data_type type,
uint16_t format_bitmask);
uint32_t zdnn_get_nnpa_max_dim_idx_size();
uint32_t zdnn_get_max_for_dim(uint8_t dimension);
uint64_t zdnn_get_nnpa_max_tensor_size();
zdnn_status zdnn_refresh_nnpa_query_result();
// -----------------------------------------------------------------------------
// Versioning Functions
// -----------------------------------------------------------------------------
bool zdnn_is_version_runnable(uint32_t ver_num);
uint32_t zdnn_get_max_runnable_version();
// -----------------------------------------------------------------------------
// External Elementwise Operations
// -----------------------------------------------------------------------------
zdnn_status zdnn_add(const zdnn_ztensor *input_a, const zdnn_ztensor *input_b,
zdnn_ztensor *output);
zdnn_status zdnn_sub(const zdnn_ztensor *input_a, const zdnn_ztensor *input_b,
zdnn_ztensor *output);
zdnn_status zdnn_mul(const zdnn_ztensor *input_a, const zdnn_ztensor *input_b,
zdnn_ztensor *output);
zdnn_status zdnn_div(const zdnn_ztensor *input_a, const zdnn_ztensor *input_b,
zdnn_ztensor *output);
zdnn_status zdnn_min(const zdnn_ztensor *input_a, const zdnn_ztensor *input_b,
zdnn_ztensor *output);
zdnn_status zdnn_max(const zdnn_ztensor *input_a, const zdnn_ztensor *input_b,
zdnn_ztensor *output);
zdnn_status zdnn_log(const zdnn_ztensor *input, zdnn_ztensor *output);
zdnn_status zdnn_exp(const zdnn_ztensor *input, zdnn_ztensor *output);
zdnn_status zdnn_sqrt(const zdnn_ztensor *input, zdnn_ztensor *output);
zdnn_status zdnn_invsqrt(const zdnn_ztensor *input, float epsilon,
zdnn_ztensor *output);
// -----------------------------------------------------------------------------
// External Activation Operations
// -----------------------------------------------------------------------------
zdnn_status zdnn_relu(const zdnn_ztensor *input, const void *clipping_value,
zdnn_ztensor *output);
zdnn_status zdnn_leaky_relu(const zdnn_ztensor *input,
const void *clipping_value, float adjustment_factor,
zdnn_ztensor *output);
zdnn_status zdnn_tanh(const zdnn_ztensor *input, zdnn_ztensor *output);
zdnn_status zdnn_sigmoid(const zdnn_ztensor *input, zdnn_ztensor *output);
zdnn_status zdnn_softmax(const zdnn_ztensor *input, void *save_area,
zdnn_softmax_act act_func, zdnn_ztensor *output);
zdnn_status zdnn_softmax_mask(const zdnn_ztensor *input, void *save_area,
zdnn_softmax_act act_func, uint32_t softmax_mask,
zdnn_ztensor *output);
zdnn_status zdnn_gelu(const zdnn_ztensor *input, zdnn_ztensor *output);
// -----------------------------------------------------------------------------
// Recurrent Neural Network (RNN) Operations
// -----------------------------------------------------------------------------
typedef enum lstm_gru_direction { FWD, BWD, BIDIR } lstm_gru_direction;
zdnn_status zdnn_lstm(const zdnn_ztensor *input, const zdnn_ztensor *h0,
const zdnn_ztensor *c0, const zdnn_ztensor *weights,
const zdnn_ztensor *biases,
const zdnn_ztensor *hidden_weights,
const zdnn_ztensor *hidden_biases,
lstm_gru_direction direction, void *work_area,
zdnn_ztensor *hn_output, zdnn_ztensor *cf_output);
zdnn_status zdnn_gru(const zdnn_ztensor *input, const zdnn_ztensor *h0,
const zdnn_ztensor *weights, const zdnn_ztensor *biases,
const zdnn_ztensor *hidden_weights,
const zdnn_ztensor *hidden_biases,
lstm_gru_direction direction, void *work_area,
zdnn_ztensor *hn_output);
// -----------------------------------------------------------------------------
// Matrix Multiplication Operations
// -----------------------------------------------------------------------------
zdnn_status zdnn_matmul_op(const zdnn_ztensor *input_a,
const zdnn_ztensor *input_b,
const zdnn_ztensor *input_c, zdnn_matmul_ops op_type,
zdnn_ztensor *output);
zdnn_status zdnn_matmul_bcast_op(const zdnn_ztensor *input_a,
const zdnn_ztensor *input_b,
const zdnn_ztensor *input_c,
zdnn_matmul_bcast_ops op_type,
zdnn_ztensor *output);
zdnn_status zdnn_matmul_transpose_op(const zdnn_ztensor *input_a,
const zdnn_ztensor *input_b,
const zdnn_ztensor *input_c,
bool transpose_a, bool transpose_b,
zdnn_matmul_ops op_type,
zdnn_ztensor *output);
zdnn_status zdnn_quantized_matmul_op(
const zdnn_ztensor *input_a, const zdnn_ztensor *input_b,
const zdnn_ztensor *input_c, zdnn_matmul_ops op_type, const int8_t clip_min,
const int8_t clip_max, const bool disable_clipping, const bool dequantize,
const bool pre_computed, void *work_area, zdnn_ztensor *output);
// -----------------------------------------------------------------------------
// External Norm Operations
// -----------------------------------------------------------------------------
zdnn_status zdnn_batchnorm(const zdnn_ztensor *input_a,
const zdnn_ztensor *input_b,
const zdnn_ztensor *input_c, zdnn_ztensor *output);
zdnn_status zdnn_norm(const zdnn_ztensor *input_a, const zdnn_ztensor *input_b,
zdnn_ztensor *output);
zdnn_status zdnn_moments(const zdnn_ztensor *input,
zdnn_moments_bessel bessel_correction_type,
zdnn_ztensor *output_a, zdnn_ztensor *output_b);
zdnn_status zdnn_layernorm(const zdnn_ztensor *input_a,
const zdnn_ztensor *input_b,
const zdnn_ztensor *input_c, const float beta_value,
const float gamma_value, const float epsilon_value,
zdnn_ztensor *output);
zdnn_status zdnn_meanreduce2d(const zdnn_ztensor *input, zdnn_ztensor *output);
zdnn_status zdnn_reduce(const zdnn_ztensor *input, void *save_area,
zdnn_reduce_ops op_type, zdnn_ztensor *output);
// -----------------------------------------------------------------------------
// External Pool Operations
// -----------------------------------------------------------------------------
zdnn_status zdnn_avgpool2d(const zdnn_ztensor *input,
zdnn_pool_padding padding_type,
uint32_t kernel_height, uint32_t kernel_width,
uint32_t stride_height, uint32_t stride_width,
zdnn_ztensor *output);
zdnn_status zdnn_maxpool2d(const zdnn_ztensor *input,
zdnn_pool_padding padding_type,
uint32_t kernel_height, uint32_t kernel_width,
uint32_t stride_height, uint32_t stride_width,
zdnn_ztensor *output);
// -----------------------------------------------------------------------------
// External Convolution Operations
// -----------------------------------------------------------------------------
zdnn_status zdnn_conv2d(const zdnn_ztensor *input, const zdnn_ztensor *kernel,
const zdnn_ztensor *bias,
zdnn_pool_padding padding_type, uint32_t stride_height,
uint32_t stride_width, zdnn_conv2d_act act_func,
const void *clipping_value, zdnn_ztensor *output);
// -----------------------------------------------------------------------------
// External Tensor Transform Operations
// -----------------------------------------------------------------------------
zdnn_status zdnn_transform_ztensor(zdnn_ztensor *ztensor, ...);
zdnn_status zdnn_transform_ztensor_with_saturation(zdnn_ztensor *ztensor, ...);
zdnn_status zdnn_transform_quantized_ztensor(zdnn_ztensor *ztensor,
bool saturation_control,
int8_t clip_min, int8_t clip_max,
const void *data);
zdnn_status zdnn_transform_origtensor(const zdnn_ztensor *ztensor,
void *out_buf);
zdnn_status zdnn_reshape_ztensor(const zdnn_ztensor *src, zdnn_ztensor *dest);
// -----------------------------------------------------------------------------
// External Version Related Functions
// -----------------------------------------------------------------------------
char *zdnn_get_library_version_str();
uint32_t zdnn_get_library_version();
// -----------------------------------------------------------------------------
// zDNN Status Related Functions
// -----------------------------------------------------------------------------
const char *zdnn_get_status_message(zdnn_status status);
// -----------------------------------------------------------------------------
// zDNN Data Type Limit Functions
// -----------------------------------------------------------------------------
zdnn_status zdnn_get_max_limit(zdnn_data_types transformed_type,
zdnn_data_types pre_transformed_type,
void *limit);
zdnn_status zdnn_get_min_limit(zdnn_data_types transformed_type,
zdnn_data_types pre_transformed_type,
void *limit);
#ifdef __cplusplus
}
#endif /* __cplusplus */
#endif /* ZDNN_ZDNN_H_ */