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b7340443d4cf6f89e8616de87ec6b3557055e581
llama_cpp/ggml/src/ggml-hexagon/ggml-hexagon.cpp
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Pranav DhinakarGitHubMax Krasnyansky
 b7340443d4 ggml-hexagon: add PAD op HVX kernel (#23078) 
* ggml-hexagon: add PAD op HVX kernel

Implements GGML_OP_PAD on the Hexagon HTP backend using HVX vectorized
kernels. Supports zero-padding and circular padding across all 4 tensor
dimensions.

* hex-ggml: remove duplicate op cases (merge conflict)

* hex-pad: fix editorconfig checks and macro alignment

---------

Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
2026-05-18 13:39:36 -07:00

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#include <assert.h>
#include <inttypes.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>
#include <atomic>
#include <chrono>
#include <mutex>
#include <thread>
#include <cstddef>
#include <stdexcept>
#include <string>
#include <sstream>
#include <iomanip>
#include <unordered_set>
#include <unordered_map>
#include <regex>
#include <queue>
#ifdef _WIN32
# include <sal.h>
#else
# include <semaphore.h>
# include <unistd.h>
#endif
#pragma clang diagnostic ignored "-Wnested-anon-types"
#pragma clang diagnostic ignored "-Wgnu-anonymous-struct"
#include <AEEStdErr.h>
#include <dspqueue.h>
#include <rpcmem.h>
#define GGML_COMMON_IMPL_CPP
#include "ggml-backend-impl.h"
#include "ggml-common.h"
#include "ggml-hexagon.h"
#include "ggml-impl.h"
#include "ggml-quants.h"
#include "op-desc.h"
#include "htp-ops.h"
#include "htp_iface.h"
#include "htp-drv.h"
using intvec = std::vector<int>;
using uintvec = std::vector<unsigned int>;
using u32vec = std::vector<uint32_t>;
static int opt_arch = 0; // autodetect
static size_t opt_ndev = 1;
static size_t opt_nhvx = 0; // use all
static int opt_use_hmx = 1; // when set, enable HMX; when 0, use HVX only
static size_t opt_vmem = HTP_OP_MAX_VMEM_DEFAULT; // max available va space for buffer mappings
static size_t opt_mbuf = 1ul * 1024 * 1024 * 1024; // max buffer size
static int opt_etm = 0;
static int opt_verbose = 0;
static int opt_profile = 0; // profiling mode (0-disabled, 1-basic, 2-pmu)
static int opt_hostbuf = 1; // hostbuf ON by default
// Default PMU events, if profiling with PMU (mode=2) is enabled
// See https://docs.qualcomm.com/doc/80-N2040-60/topic/pmu-events.html
// https://docs.qualcomm.com/doc/80-N2040-61/topic/hvx-pmu-events.html
static u32vec opt_pmu_evt { 0x3, 0x111, 0x100, 0x105, 0x240, 0x256, 0x7D, 0x8C };
// Enable all stages by default
static int opt_opstage = HTP_OPSTAGE_QUEUE | HTP_OPSTAGE_COMPUTE;
static int opt_opbatch = 1024; // max number of ops in a batch
static int opt_opqueue = 16; // max number of pending batches
static std::regex* opt_opfilter = NULL; // regex of ops to not claim
#define HEX_VERBOSE(...) \
if (opt_verbose) GGML_LOG_DEBUG(__VA_ARGS__)
static inline uint64_t hex_is_aligned(void * addr, uint32_t align) {
return ((size_t) addr & (align - 1)) == 0;
}
static inline size_t hex_round_up(size_t n, size_t m) {
return m * ((n + m - 1) / m);
}
static const char * status_to_str(uint32_t status) {
switch (status) {
case HTP_STATUS_OK:
return "OK";
case HTP_STATUS_NO_SUPPORT:
return "NO-SUPPORT";
case HTP_STATUS_INVAL_PARAMS:
return "INVAL-PARAMS";
case HTP_STATUS_VTCM_TOO_SMALL:
return "VTCM-TOO-SMALL";
case HTP_STATUS_INTERNAL_ERR:
return "INTERNAL-ERROR";
default:
return "UNKNOWN";
}
}
// ** debug helpers
static void ggml_hexagon_dump_op_exec(const std::string &sess_name, const ggml_tensor * op, const uint32_t req_flags) {
if (!opt_verbose) return;
op_desc desc(op);
GGML_LOG_DEBUG("ggml-hex: %s execute-op %s: %s : %s : %s : %s : %s : flags 0x%x\n", sess_name.c_str(),
ggml_op_desc(op), desc.names, desc.dims, desc.types, desc.strides, desc.buffs, req_flags);
}
static void ggml_hexagon_dump_op_supp(const std::string &sess_name, const struct ggml_tensor * op, bool supp) {
if (!opt_verbose) return;
op_desc desc(op);
GGML_LOG_DEBUG("ggml-hex: %s supports-op %s: %s : %s : %s : %s : %s : %s\n", sess_name.c_str(),
ggml_op_desc(op), desc.names, desc.dims, desc.types, desc.strides, desc.buffs, supp ? "yes" : "no");
}
static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const ggml_tensor * op,
uint32_t op_usec, uint32_t op_cycles, const uint32_t pmu[]) {
if (!opt_profile) return;
char pmu_str[256] = "";
if (opt_profile > 1) {
static_assert(HTP_PROF_PMU_NCNT == 8, "current implementation assumes 8 PMU counters");
sprintf(pmu_str, " pmu [%u,%u,%u,%u,%u,%u,%u,%u]",
pmu[0], pmu[1], pmu[2], pmu[3], pmu[4], pmu[5], pmu[6], pmu[7]);
}
op_desc desc(op);
GGML_LOG_DEBUG("ggml-hex: %s profile-op %s: %s : %s : %s : %s : usec %u cycles %u%s\n", sess_name.c_str(),
ggml_op_desc(op), desc.names, desc.dims, desc.types, desc.strides, op_usec, op_cycles, pmu_str);
}
// ** backend sessions
struct ggml_hexagon_opbatch;
struct ggml_hexagon_opqueue;
struct ggml_hexagon_session {
std::string name;
remote_handle64 handle;
dspqueue_t queue;
uint32_t session_id;
uint32_t domain_id;
uint64_t queue_id;
int dev_id;
bool valid_session;
bool valid_handle;
bool valid_queue;
bool valid_iface;
std::atomic<int> op_pending;
ggml_hexagon_opbatch* op_batch;
ggml_hexagon_opqueue* op_queue;
ggml_backend_buffer_type buffer_type = {};
ggml_backend_buffer_type repack_buffer_type = {};
ggml_hexagon_session(int dev_id, ggml_backend_dev_t dev) noexcept(false);
~ggml_hexagon_session() noexcept(true);
const char* c_name() const { return name.c_str(); }
void allocate(int dev_id) noexcept(false);
void release() noexcept(true);
void enqueue_op(htp_op_code opcode, const ggml_tensor *op);
void flush(bool all = true);
void flush_pending(bool all = false);
void flush_batch();
};
// ** backend buffers
struct ggml_backend_hexagon_buffer_type_context {
ggml_backend_hexagon_buffer_type_context(const std::string & name, ggml_hexagon_session * sess) {
this->sess = sess;
this->name = name;
}
ggml_hexagon_session * sess;
std::string name;
};
struct ggml_hexagon_shared_buffer {
ggml_hexagon_session * sess;
uint8_t * base;
size_t size;
int fd;
bool mapped;
bool pinned;
void mmap() {
fastrpc_map_flags flags = this->pinned ? FASTRPC_MAP_FD : FASTRPC_MAP_FD_DELAYED;
int err = fastrpc_mmap(sess->domain_id, this->fd, (void *) this->base, 0, this->size, flags);
if (err != 0) {
GGML_LOG_ERROR("ggml-hex: %s buffer mapping failed : domain_id %d size %zu fd %d error 0x%08x\n", sess->c_name(),
sess->domain_id, this->size, this->fd, (unsigned) err);
throw std::runtime_error("ggml-hex: fastrpc_mmap failed (see log for details)");
}
HEX_VERBOSE("ggml-hex: %s mapped buffer: base %p size %zu fd %d pinned %u\n",
sess->c_name(), (void *) this->base, this->size, this->fd, pinned);
this->mapped = true;
}
void unmap() {
if (!this->mapped) return;
if (!this->pinned) {
// HTP might still hold a reference, tell it drop it
htp_iface_munmap(sess->handle, this->fd);
}
fastrpc_munmap(sess->domain_id, this->fd, (void *) this->base, this->size);
HEX_VERBOSE("ggml-hex: %s unmapped buffer: base %p size %zu fd %d\n", sess->c_name(),
(void *) this->base, size, this->fd);
this->mapped = false;
this->fd = -1;
}
void alloc(size_t size) {
if (this->base) return;
this->base = (uint8_t *) rpcmem_alloc2(RPCMEM_HEAP_ID_SYSTEM, RPCMEM_DEFAULT_FLAGS, size);
if (!this->base) {
GGML_LOG_ERROR("ggml-hex: %s failed to allocate buffer : size %zu\n", sess->c_name(), size);
throw std::runtime_error("ggml-hex: rpcmem_alloc failed (see log for details)");
}
this->fd = rpcmem_to_fd(this->base);
if (this->fd < 0) {
GGML_LOG_ERROR("ggml-hex: %s failed to get FD for buffer %p\n", sess->c_name(), (void *) this->base);
throw std::runtime_error("ggml-hex: rpcmem_to_fd failed (see log for details)");
}
this->size = size;
HEX_VERBOSE("ggml-hex: %s allocated buffer: base %p size %zu fd %d pinned %d\n", sess->c_name(),
(void *) this->base, this->size, this->fd, (int) pinned);
mmap();
}
void free() {
if (!this->base) return;
unmap();
rpcmem_free(this->base);
HEX_VERBOSE("ggml-hex: %s freed buffer: base %p size %zu fd %d\n", sess->c_name(),
(void *) this->base, size, this->fd);
this->base = NULL;
}
ggml_hexagon_shared_buffer(ggml_hexagon_session * sess, size_t size, bool pinned = false) {
this->sess = sess;
this->size = 0;
this->base = nullptr;
this->fd = -1;
this->mapped = false;
this->pinned = pinned;
alloc(size);
}
~ggml_hexagon_shared_buffer() {
free();
}
};
static ggml_hexagon_session * ggml_backend_hexagon_buffer_get_sess(ggml_backend_buffer_t buffer) {
return static_cast<ggml_backend_hexagon_buffer_type_context *>(buffer->buft->context)->sess;
}
static void ggml_backend_hexagon_buffer_free_buffer(ggml_backend_buffer_t buffer) {
auto sbuf = static_cast<ggml_hexagon_shared_buffer *>(buffer->context);
delete sbuf;
}
static void * ggml_backend_hexagon_buffer_get_base(ggml_backend_buffer_t buffer) {
auto sbuf = static_cast<ggml_hexagon_shared_buffer *>(buffer->context);
return sbuf->base;
}
static enum ggml_status ggml_backend_hexagon_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
auto sbuf = static_cast<ggml_hexagon_shared_buffer *>(buffer->context);
auto sess = sbuf->sess;
HEX_VERBOSE("ggml-hex: %s init-tensor %s : base %p data %p nbytes %zu usage %d\n", sess->c_name(),
tensor->name, (void *) sbuf->base, tensor->data, ggml_nbytes(tensor), (int) buffer->usage);
if (tensor->view_src != NULL && tensor->view_offs == 0) {
return GGML_STATUS_SUCCESS; // nothing to do for the view
}
return GGML_STATUS_SUCCESS;
}
// ======== Q4x4x2 ====================
struct x2_q4 {
int v[2];
};
static x2_q4 unpack_q4(uint8_t v) {
x2_q4 x = { (int) (v & 0x0f) - 8, (int) (v >> 4) - 8 };
return x;
}
static void dump_block_q4_0(const block_q4_0 * b, int i) {
HEX_VERBOSE("ggml-hex: repack q4_0 %d: %d %d %d %d ... %d %d %d %d : %.6f\n", i, unpack_q4(b->qs[0]).v[0],
unpack_q4(b->qs[1]).v[0], unpack_q4(b->qs[2]).v[0], unpack_q4(b->qs[3]).v[0], unpack_q4(b->qs[12]).v[1],
unpack_q4(b->qs[13]).v[1], unpack_q4(b->qs[14]).v[1], unpack_q4(b->qs[15]).v[1],
GGML_FP16_TO_FP32(b->d));
}
static void dump_packed_block_q4x4x2(const uint8_t * v, unsigned int i, size_t k) {
static const int qk = QK_Q4_0x4x2;
const int dblk_size = 8 * 2; // 8x __fp16
const int qblk_size = qk / 2; // int4
const int qrow_size = k / 2; // int4 (not padded)
const uint8_t * v_q = v + 0; // quants first
const uint8_t * v_d = v + qrow_size; // then scales
const uint8_t * q = v_q + i * qblk_size;
const ggml_half * d = (const ggml_half *) (v_d + i * dblk_size);
HEX_VERBOSE("ggml-hex: repack q4x4x2-%d: %d %d %d %d ... %d %d %d %d ... %d %d %d %d : %.6f %.6f %.6f %.6f\n", i,
unpack_q4(q[0]).v[0], unpack_q4(q[1]).v[0], unpack_q4(q[2]).v[0], unpack_q4(q[3]).v[0],
unpack_q4(q[60]).v[0], unpack_q4(q[61]).v[0], unpack_q4(q[62]).v[0], unpack_q4(q[63]).v[0],
unpack_q4(q[124]).v[0], unpack_q4(q[125]).v[0], unpack_q4(q[126]).v[0], unpack_q4(q[127]).v[0],
GGML_FP16_TO_FP32(d[0]), GGML_FP16_TO_FP32(d[1]), GGML_FP16_TO_FP32(d[2]), GGML_FP16_TO_FP32(d[3]));
HEX_VERBOSE("ggml-hex: repack q4x4x2-%d: %d %d %d %d ... %d %d %d %d ... %d %d %d %d : %.6f %.6f %.6f %.6f\n",
i + 1, unpack_q4(q[0]).v[1], unpack_q4(q[1]).v[1], unpack_q4(q[2]).v[1], unpack_q4(q[3]).v[1],
unpack_q4(q[60]).v[1], unpack_q4(q[61]).v[1], unpack_q4(q[62]).v[1], unpack_q4(q[63]).v[1],
unpack_q4(q[124]).v[1], unpack_q4(q[125]).v[1], unpack_q4(q[126]).v[1], unpack_q4(q[127]).v[1],
GGML_FP16_TO_FP32(d[4]), GGML_FP16_TO_FP32(d[5]), GGML_FP16_TO_FP32(d[6]), GGML_FP16_TO_FP32(d[7]));
}
static void unpack_q4_0_quants(uint8_t * qs, const block_q4_0 * x, unsigned int bi) {
static const int qk = QK4_0;
for (unsigned int i = 0; i < qk / 2; ++i) {
const int x0 = (x->qs[i] & 0x0F);
const int x1 = (x->qs[i] >> 4);
qs[bi * qk + i + 0] = x0;
qs[bi * qk + i + qk / 2] = x1;
}
}
static void pack_q4_0_quants(block_q4_0 * x, const uint8_t * qs, unsigned int bi) {
static const int qk = QK4_0;
for (unsigned int i = 0; i < qk / 2; ++i) {
const uint8_t x0 = qs[bi * qk + i + 0];
const uint8_t x1 = qs[bi * qk + i + qk / 2];
x->qs[i] = x0 | (x1 << 4);
}
}
static void repack_row_q4x4x2(uint8_t * y, const block_q4_0 * x, int64_t k) {
static const int qk = QK_Q4_0x4x2;
const int nb = (k + qk - 1) / qk; // number of blocks (padded)
const int nloe = k % qk; // leftovers
const int dblk_size = 8 * 2; // 8x __fp16
const int qblk_size = qk / 2; // int4
const int qrow_size = k / 2; // int4 (not padded to blocks)
uint8_t * y_q = y + 0; // quants first
uint8_t * y_d = y + qrow_size; // then scales
if (opt_verbose > 2) {
for (int i = 0; i < nb; i++) {
dump_block_q4_0(&x[i * 8 + 0], 0);
dump_block_q4_0(&x[i * 8 + 1], 1);
dump_block_q4_0(&x[i * 8 + 2], 2);
dump_block_q4_0(&x[i * 8 + 3], 3);
dump_block_q4_0(&x[i * 8 + 4], 4);
dump_block_q4_0(&x[i * 8 + 5], 5);
dump_block_q4_0(&x[i * 8 + 6], 6);
dump_block_q4_0(&x[i * 8 + 7], 7);
}
}
// Repack the quants
for (int i = 0; i < nb; i++) {
uint8_t qs[QK_Q4_0x4x2]; // unpacked quants
unpack_q4_0_quants(qs, &x[i * 8 + 0], 0);
unpack_q4_0_quants(qs, &x[i * 8 + 1], 1);
unpack_q4_0_quants(qs, &x[i * 8 + 2], 2);
unpack_q4_0_quants(qs, &x[i * 8 + 3], 3);
unpack_q4_0_quants(qs, &x[i * 8 + 4], 4);
unpack_q4_0_quants(qs, &x[i * 8 + 5], 5);
unpack_q4_0_quants(qs, &x[i * 8 + 6], 6);
unpack_q4_0_quants(qs, &x[i * 8 + 7], 7);
bool partial = (nloe && i == nb-1);
uint8_t * q = y_q + (i * qblk_size);
for (int j = 0; j < qk / 2; j++) {
q[j] = partial ? (qs[j*2+1] << 4) | qs[j*2+0] : (qs[j+128] << 4) | qs[j+000];
}
}
// Repack the scales
// Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q4_0x4x2)
// the last block is truncated and overridden by the scales.
for (int i = 0; i < nb; i++) {
// Repack the scales
ggml_half * d = (ggml_half *) (y_d + i * dblk_size);
d[0] = x[i * 8 + 0].d;
d[1] = x[i * 8 + 1].d;
d[2] = x[i * 8 + 2].d;
d[3] = x[i * 8 + 3].d;
d[4] = x[i * 8 + 4].d;
d[5] = x[i * 8 + 5].d;
d[6] = x[i * 8 + 6].d;
d[7] = x[i * 8 + 7].d;
}
if (opt_verbose > 2) {
for (int i = 0; i < nb; i++) {
dump_packed_block_q4x4x2(y, i, k);
}
}
}
static void unpack_row_q4x4x2(block_q4_0 * x, const uint8_t * y, int64_t k) {
static const int qk = QK_Q4_0x4x2;
const int nb = (k + qk - 1) / qk; // number of blocks (padded)
const int nloe = k % qk; // leftovers
const int dblk_size = 8 * 2; // 8x __fp16
const int qblk_size = qk / 2; // int4
const int qrow_size = k / 2; // int4 (not padded to blocks)
const uint8_t * y_q = y + 0; // quants first
const uint8_t * y_d = y + qrow_size; // then scales
if (opt_verbose > 2) {
for (int i = 0; i < nb; i++) {
dump_packed_block_q4x4x2(y, i, k);
}
}
// Unpack the quants
for (int i = 0; i < nb; i++) {
uint8_t qs[QK_Q4_0x4x2]; // unpacked quants
bool partial = (nloe && i == nb-1);
const uint8_t * q = y_q + (i * qblk_size);
for (int j = 0; j < qk / 2; j++) {
if (partial) {
qs[j*2+0] = q[j] & 0xf;
qs[j*2+1] = q[j] >> 4;
} else {
qs[j+000] = q[j] & 0xf;
qs[j+128] = q[j] >> 4;
}
}
pack_q4_0_quants(&x[i * 8 + 0], qs, 0);
pack_q4_0_quants(&x[i * 8 + 1], qs, 1);
pack_q4_0_quants(&x[i * 8 + 2], qs, 2);
pack_q4_0_quants(&x[i * 8 + 3], qs, 3);
pack_q4_0_quants(&x[i * 8 + 4], qs, 4);
pack_q4_0_quants(&x[i * 8 + 5], qs, 5);
pack_q4_0_quants(&x[i * 8 + 6], qs, 6);
pack_q4_0_quants(&x[i * 8 + 7], qs, 7);
}
// Repack the scales
// Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q4_0x4x2)
// the last block is truncated and overridden by the scales.
for (int i = 0; i < nb; i++) {
// Unpack the scales
const ggml_half * d = (const ggml_half *) (y_d + i * dblk_size);
x[i * 8 + 0].d = d[0];
x[i * 8 + 1].d = d[1];
x[i * 8 + 2].d = d[2];
x[i * 8 + 3].d = d[3];
x[i * 8 + 4].d = d[4];
x[i * 8 + 5].d = d[5];
x[i * 8 + 6].d = d[6];
x[i * 8 + 7].d = d[7];
}
if (opt_verbose > 2) {
for (int i = 0; i < nb; i++) {
dump_block_q4_0(&x[i * 8 + 0], 0);
dump_block_q4_0(&x[i * 8 + 1], 1);
dump_block_q4_0(&x[i * 8 + 2], 2);
dump_block_q4_0(&x[i * 8 + 3], 3);
dump_block_q4_0(&x[i * 8 + 4], 4);
dump_block_q4_0(&x[i * 8 + 5], 5);
dump_block_q4_0(&x[i * 8 + 6], 6);
dump_block_q4_0(&x[i * 8 + 7], 7);
}
}
}
static void init_row_q4x4x2(block_q4_0 * x, int64_t k) {
static const int qk = QK_Q4_0x4x2;
const int nb = (k + qk - 1) / qk; // number of blocks (padded)
// Init the quants such that they unpack into zeros
uint8_t qs[QK_Q4_0x4x2]; // unpacked quants
memset(qs, 8, sizeof(qs));
for (int i = 0; i < nb; i++) {
pack_q4_0_quants(&x[i * 8 + 0], qs, 0);
pack_q4_0_quants(&x[i * 8 + 1], qs, 1);
pack_q4_0_quants(&x[i * 8 + 2], qs, 2);
pack_q4_0_quants(&x[i * 8 + 3], qs, 3);
pack_q4_0_quants(&x[i * 8 + 4], qs, 4);
pack_q4_0_quants(&x[i * 8 + 5], qs, 5);
pack_q4_0_quants(&x[i * 8 + 6], qs, 6);
pack_q4_0_quants(&x[i * 8 + 7], qs, 7);
}
// Init the scales
// Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q4_0x4x2)
// the last block is truncated and overridden by the scales.
for (int i = 0; i < nb; i++) {
// Unpack the scales
x[i * 8 + 0].d = 0;
x[i * 8 + 1].d = 0;
x[i * 8 + 2].d = 0;
x[i * 8 + 3].d = 0;
x[i * 8 + 4].d = 0;
x[i * 8 + 5].d = 0;
x[i * 8 + 6].d = 0;
x[i * 8 + 7].d = 0;
}
}
// repack q4_0 data into q4x4x2 tensor
static void repack_q4_0_q4x4x2(ggml_tensor * t, const void * data, size_t size) {
int64_t nrows = ggml_nrows(t);
size_t row_size = ggml_row_size(t->type, t->ne[0]);
size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q4_0x4x2)); // extra elements for the pad
size_t row_size_rp = row_size * 2; // extra space for tmp pad (if any)
// Ensure we don't try to read more data than is available in the source buffer 'data'
// or write more than the tensor can hold.
const size_t total_tensor_size = (size_t)nrows * row_size;
const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
// Calculate how many full rows and how many remaining bytes we need to process.
const int64_t n_full_rows = n_bytes_to_copy / row_size;
const size_t n_rem_bytes = n_bytes_to_copy % row_size;
void * buf_pd = ggml_aligned_malloc(row_size_pd);
GGML_ASSERT(buf_pd != NULL);
void * buf_rp = ggml_aligned_malloc(row_size_rp);
GGML_ASSERT(buf_rp != NULL);
HEX_VERBOSE("ggml-hex: repack-q4_0-q4x4x2 %s : data %p size %zu dims %ldx%ld row-size %zu\n", t->name, data, size,
t->ne[0], nrows, row_size);
init_row_q4x4x2((block_q4_0 *) buf_pd, t->ne[0]); // init padded buffer to make sure the tail is all zeros
// 1. Process all the full rows
for (int64_t i = 0; i < n_full_rows; i++) {
const uint8_t * src = (const uint8_t *) data + (i * row_size);
uint8_t * dst = (uint8_t *) t->data + (i * row_size);
memcpy(buf_pd, src, row_size);
repack_row_q4x4x2((uint8_t *) buf_rp, (const block_q4_0 *) buf_pd, t->ne[0]);
memcpy(dst, buf_rp, row_size);
}
// 2. Process the final, potentially partial, row
if (n_rem_bytes > 0) {
const int64_t i = n_full_rows;
const uint8_t * src = (const uint8_t *) data + (i * row_size);
uint8_t * dst = (uint8_t *) t->data + (i * row_size);
// re-init the row because we are potentially copying a partial row
init_row_q4x4x2((block_q4_0 *) buf_pd, t->ne[0]);
// Copy only the remaining bytes from the source.
memcpy(buf_pd, src, n_rem_bytes);
// Repack the entire buffer
repack_row_q4x4x2((uint8_t *) buf_rp, (const block_q4_0 *) buf_pd, t->ne[0]);
// Write only the corresponding remaining bytes to the destination tensor.
memcpy(dst, buf_rp, n_rem_bytes);
}
ggml_aligned_free(buf_pd, row_size_pd);
ggml_aligned_free(buf_rp, row_size_rp);
}
// repack q4x4x2 tensor into q4_0 data
static void repack_q4x4x2_q4_0(void * data, const ggml_tensor * t, size_t size) {
int64_t nrows = ggml_nrows(t);
size_t row_size = ggml_row_size(t->type, t->ne[0]);
size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q4_0x4x2)); // extra elements for the pad
size_t row_size_rp = row_size * 2; // extra space for tmp pad (if any)
// Ensure we don't try to copy more data than the tensor actually contains.
const size_t total_tensor_size = (size_t)nrows * row_size;
const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
// Calculate how many full rows and how many remaining bytes we need to process.
const int64_t n_full_rows = n_bytes_to_copy / row_size;
const size_t n_rem_bytes = n_bytes_to_copy % row_size;
void * buf_pd = ggml_aligned_malloc(row_size_pd);
GGML_ASSERT(buf_pd != NULL);
void * buf_rp = ggml_aligned_malloc(row_size_rp);
GGML_ASSERT(buf_rp != NULL);
HEX_VERBOSE("ggml-hex: repack-q4x4x2-q4_0 %s : data %p size %zu dims %ldx%ld row-size %zu\n", t->name, data, size,
t->ne[0], nrows, row_size);
memset(buf_pd, 0, row_size_pd); // clear-out padded buffer to make sure the tail is all zeros
// 1. Process all the full rows
for (int64_t i = 0; i < n_full_rows; i++) {
const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
uint8_t * dst = (uint8_t *) data + (i * row_size);
memcpy(buf_pd, src, row_size);
unpack_row_q4x4x2((block_q4_0 *) buf_rp, (const uint8_t *) buf_pd, t->ne[0]);
memcpy(dst, buf_rp, row_size);
}
// 2. Process the final, potentially partial, row
if (n_rem_bytes > 0) {
const int64_t i = n_full_rows;
const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
uint8_t * dst = (uint8_t *) data + (i * row_size);
// We still need to read and unpack the entire source row because quantization is block-based.
memcpy(buf_pd, src, row_size);
unpack_row_q4x4x2((block_q4_0 *) buf_rp, (const uint8_t *) buf_pd, t->ne[0]);
// But we only copy the remaining number of bytes to the destination.
memcpy(dst, buf_rp, n_rem_bytes);
}
ggml_aligned_free(buf_pd, row_size_pd);
ggml_aligned_free(buf_rp, row_size_rp);
}
// ======== Q8x4x2 ====================
static void dump_block_q8_0(const block_q8_0 * b, int i) {
HEX_VERBOSE("ggml-hex: repack q8_0 %d: %d %d %d %d ... %d %d %d %d : %.6f\n", i, b->qs[0], b->qs[1], b->qs[2],
b->qs[3], b->qs[28], b->qs[29], b->qs[30], b->qs[31], GGML_FP16_TO_FP32(b->d));
}
static void dump_packed_block_q8x4x2(const uint8_t * v, unsigned int i, size_t k) {
static const int qk = QK_Q8_0x4x2;
const int dblk_size = 8 * 2; // 8x __fp16
const int qblk_size = qk; // int8
const int qrow_size = k; // int8 (not padded)
const uint8_t * v_q = v + 0; // quants first
const uint8_t * v_d = v + qrow_size; // then scales
const uint8_t * q = v_q + i * qblk_size;
const ggml_half * d = (const ggml_half *) (v_d + i * dblk_size);
HEX_VERBOSE("ggml-hex: repack q8x4x2-%d: %d %d %d %d ... %d %d %d %d ... %d %d %d %d : %.6f %.6f %.6f %.6f\n", i,
q[0], q[1], q[2], q[3], q[60], q[61], q[62], q[63], q[124], q[125], q[126], q[127],
GGML_FP16_TO_FP32(d[0]), GGML_FP16_TO_FP32(d[1]), GGML_FP16_TO_FP32(d[2]), GGML_FP16_TO_FP32(d[3]));
HEX_VERBOSE("ggml-hex: repack q8x4x2-%d: %d %d %d %d ... %d %d %d %d ... %d %d %d %d : %.6f %.6f %.6f %.6f\n",
i + 1, q[128], q[129], q[130], q[131], q[192], q[193], q[194], q[195], q[252], q[253], q[254], q[255],
GGML_FP16_TO_FP32(d[4]), GGML_FP16_TO_FP32(d[5]), GGML_FP16_TO_FP32(d[6]), GGML_FP16_TO_FP32(d[7]));
}
static void unpack_q8_0_quants(uint8_t * qs, const block_q8_0 * x, unsigned int bi) {
static const int qk = QK8_0;
for (unsigned int i = 0; i < qk; ++i) {
qs[bi * qk + i] = x->qs[i];
}
}
static void pack_q8_0_quants(block_q8_0 * x, const uint8_t * qs, unsigned int bi) {
static const int qk = QK8_0;
for (unsigned int i = 0; i < qk; ++i) {
x->qs[i] = qs[bi * qk + i];
}
}
static void repack_row_q8x4x2(uint8_t * y, const block_q8_0 * x, int64_t k) {
static const int qk = QK_Q8_0x4x2;
const int nb = (k + qk - 1) / qk; // number of blocks (padded)
const int dblk_size = 8 * 2; // 8x __fp16
const int qblk_size = qk; // int8
const int qrow_size = k; // int8 (not padded to blocks)
uint8_t * y_q = y + 0; // quants first
uint8_t * y_d = y + qrow_size; // then scales
if (opt_verbose > 2) {
for (int i = 0; i < nb; i++) {
dump_block_q8_0(&x[i * 8 + 0], 0);
dump_block_q8_0(&x[i * 8 + 1], 1);
dump_block_q8_0(&x[i * 8 + 2], 2);
dump_block_q8_0(&x[i * 8 + 3], 3);
dump_block_q8_0(&x[i * 8 + 4], 4);
dump_block_q8_0(&x[i * 8 + 5], 5);
dump_block_q8_0(&x[i * 8 + 6], 6);
dump_block_q8_0(&x[i * 8 + 7], 7);
}
}
// Repack the quants
for (int i = 0; i < nb; i++) {
uint8_t qs[QK_Q8_0x4x2]; // unpacked quants
unpack_q8_0_quants(qs, &x[i * 8 + 0], 0);
unpack_q8_0_quants(qs, &x[i * 8 + 1], 1);
unpack_q8_0_quants(qs, &x[i * 8 + 2], 2);
unpack_q8_0_quants(qs, &x[i * 8 + 3], 3);
unpack_q8_0_quants(qs, &x[i * 8 + 4], 4);
unpack_q8_0_quants(qs, &x[i * 8 + 5], 5);
unpack_q8_0_quants(qs, &x[i * 8 + 6], 6);
unpack_q8_0_quants(qs, &x[i * 8 + 7], 7);
uint8_t * q = y_q + (i * qblk_size);
for (int j = 0; j < qk; j++) {
q[j] = qs[j];
}
}
// Repack the scales
// Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q4_0x4x2)
// the last block is truncated and overridden by the scales.
for (int i = 0; i < nb; i++) {
// Repack the scales
ggml_half * d = (ggml_half *) (y_d + i * dblk_size);
d[0] = x[i * 8 + 0].d;
d[1] = x[i * 8 + 1].d;
d[2] = x[i * 8 + 2].d;
d[3] = x[i * 8 + 3].d;
d[4] = x[i * 8 + 4].d;
d[5] = x[i * 8 + 5].d;
d[6] = x[i * 8 + 6].d;
d[7] = x[i * 8 + 7].d;
}
if (opt_verbose > 2) {
for (int i = 0; i < nb; i++) {
dump_packed_block_q8x4x2(y, i, k);
}
}
}
static void unpack_row_q8x4x2(block_q8_0 * x, const uint8_t * y, int64_t k) {
static const int qk = QK_Q8_0x4x2;
const int nb = (k + qk - 1) / qk; // number of blocks (padded)
const int dblk_size = 8 * 2; // 8x __fp16
const int qblk_size = qk; // int8
const int qrow_size = k; // int8 (not padded to blocks)
const uint8_t * y_q = y + 0; // quants first
const uint8_t * y_d = y + qrow_size; // then scales
if (opt_verbose > 2) {
for (int i = 0; i < nb; i++) {
dump_packed_block_q8x4x2(y, i, k);
}
}
// Unpack the quants
for (int i = 0; i < nb; i++) {
uint8_t qs[QK_Q4_0x4x2]; // unpacked quants
const uint8_t * q = y_q + (i * qblk_size);
for (int j = 0; j < qk; j++) {
qs[j] = q[j];
}
pack_q8_0_quants(&x[i * 8 + 0], qs, 0);
pack_q8_0_quants(&x[i * 8 + 1], qs, 1);
pack_q8_0_quants(&x[i * 8 + 2], qs, 2);
pack_q8_0_quants(&x[i * 8 + 3], qs, 3);
pack_q8_0_quants(&x[i * 8 + 4], qs, 4);
pack_q8_0_quants(&x[i * 8 + 5], qs, 5);
pack_q8_0_quants(&x[i * 8 + 6], qs, 6);
pack_q8_0_quants(&x[i * 8 + 7], qs, 7);
}
// Repack the scales
// Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q4_0x4x2)
// the last block is truncated and overridden by the scales.
for (int i = 0; i < nb; i++) {
// Unpack the scales
const ggml_half * d = (const ggml_half *) (y_d + i * dblk_size);
x[i * 8 + 0].d = d[0];
x[i * 8 + 1].d = d[1];
x[i * 8 + 2].d = d[2];
x[i * 8 + 3].d = d[3];
x[i * 8 + 4].d = d[4];
x[i * 8 + 5].d = d[5];
x[i * 8 + 6].d = d[6];
x[i * 8 + 7].d = d[7];
}
if (opt_verbose > 2) {
for (int i = 0; i < nb; i++) {
dump_block_q8_0(&x[i * 8 + 0], 0);
dump_block_q8_0(&x[i * 8 + 1], 1);
dump_block_q8_0(&x[i * 8 + 2], 2);
dump_block_q8_0(&x[i * 8 + 3], 3);
dump_block_q8_0(&x[i * 8 + 4], 4);
dump_block_q8_0(&x[i * 8 + 5], 5);
dump_block_q8_0(&x[i * 8 + 6], 6);
dump_block_q8_0(&x[i * 8 + 7], 7);
}
}
}
static void init_row_q8x4x2(block_q8_0 * x, int64_t k) {
static const int qk = QK_Q8_0x4x2;
const int nb = (k + qk - 1) / qk; // number of blocks (padded)
// Init the quants such that they unpack into zeros
uint8_t qs[QK_Q8_0x4x2]; // unpacked quants
memset(qs, 0, sizeof(qs));
for (int i = 0; i < nb; i++) {
pack_q8_0_quants(&x[i * 8 + 0], qs, 0);
pack_q8_0_quants(&x[i * 8 + 1], qs, 1);
pack_q8_0_quants(&x[i * 8 + 2], qs, 2);
pack_q8_0_quants(&x[i * 8 + 3], qs, 3);
pack_q8_0_quants(&x[i * 8 + 4], qs, 4);
pack_q8_0_quants(&x[i * 8 + 5], qs, 5);
pack_q8_0_quants(&x[i * 8 + 6], qs, 6);
pack_q8_0_quants(&x[i * 8 + 7], qs, 7);
}
// Init the scales
// Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_Q8_0x4x2)
// the last block is truncated and overridden by the scales.
for (int i = 0; i < nb; i++) {
// Unpack the scales
x[i * 8 + 0].d = 0;
x[i * 8 + 1].d = 0;
x[i * 8 + 2].d = 0;
x[i * 8 + 3].d = 0;
x[i * 8 + 4].d = 0;
x[i * 8 + 5].d = 0;
x[i * 8 + 6].d = 0;
x[i * 8 + 7].d = 0;
}
}
// repack q8_0 data into q8x4x2 tensor
static void repack_q8_0_q8x4x2(ggml_tensor * t, const void * data, size_t size) {
int64_t nrows = ggml_nrows(t);
size_t row_size = ggml_row_size(t->type, t->ne[0]);
size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q8_0x4x2)); // extra elements for the pad
size_t row_size_rp = row_size * 2; // extra space for tmp pad (if any)
// Ensure we don't try to read more data than is available in the source buffer 'data'
// or write more than the tensor can hold.
const size_t total_tensor_size = (size_t)nrows * row_size;
const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
// Calculate how many full rows and how many remaining bytes we need to process.
const int64_t n_full_rows = n_bytes_to_copy / row_size;
const size_t n_rem_bytes = n_bytes_to_copy % row_size;
void * buf_pd = ggml_aligned_malloc(row_size_pd);
GGML_ASSERT(buf_pd != NULL);
void * buf_rp = ggml_aligned_malloc(row_size_rp);
GGML_ASSERT(buf_rp != NULL);
HEX_VERBOSE("ggml-hex: repack-q8_0-q8x4x2 %s : data %p size %zu dims %ldx%ld row-size %zu\n", t->name, data, size,
t->ne[0], nrows, row_size);
init_row_q8x4x2((block_q8_0 *) buf_pd, t->ne[0]); // init padded buffer to make sure the tail is all zeros
// 1. Process all the full rows
for (int64_t i = 0; i < n_full_rows; i++) {
const uint8_t * src = (const uint8_t *) data + (i * row_size);
uint8_t * dst = (uint8_t *) t->data + (i * row_size);
memcpy(buf_pd, src, row_size);
repack_row_q8x4x2((uint8_t *) buf_rp, (const block_q8_0 *) buf_pd, t->ne[0]);
memcpy(dst, buf_rp, row_size);
}
// 2. Process the final, potentially partial, row
if (n_rem_bytes > 0) {
const int64_t i = n_full_rows;
const uint8_t * src = (const uint8_t *) data + (i * row_size);
uint8_t * dst = (uint8_t *) t->data + (i * row_size);
// re-init the row because we are potentially copying a partial row
init_row_q8x4x2((block_q8_0 *) buf_pd, t->ne[0]);
// Copy only the remaining bytes from the source.
memcpy(buf_pd, src, n_rem_bytes);
// Repack the entire buffer
repack_row_q8x4x2((uint8_t *) buf_rp, (const block_q8_0 *) buf_pd, t->ne[0]);
// Write only the corresponding remaining bytes to the destination tensor.
memcpy(dst, buf_rp, n_rem_bytes);
}
ggml_aligned_free(buf_pd, row_size_pd);
ggml_aligned_free(buf_rp, row_size_rp);
}
// repack q8x4x2 tensor into q8_0 data
static void repack_q8x4x2_q8_0(void * data, const ggml_tensor * t, size_t size) {
int64_t nrows = ggml_nrows(t);
size_t row_size = ggml_row_size(t->type, t->ne[0]);
size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q8_0x4x2)); // extra elements for the pad
size_t row_size_rp = row_size * 2; // extra space for tmp pad (if any)
// Ensure we don't try to copy more data than the tensor actually contains.
const size_t total_tensor_size = (size_t)nrows * row_size;
const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
// Calculate how many full rows and how many remaining bytes we need to process.
const int64_t n_full_rows = n_bytes_to_copy / row_size;
const size_t n_rem_bytes = n_bytes_to_copy % row_size;
void * buf_pd = ggml_aligned_malloc(row_size_pd);
GGML_ASSERT(buf_pd != NULL);
void * buf_rp = ggml_aligned_malloc(row_size_rp);
GGML_ASSERT(buf_rp != NULL);
HEX_VERBOSE("ggml-hex: repack-q8x4x2-q8_0 %s : data %p size %zu dims %ldx%ld row-size %zu\n", t->name, data, size,
t->ne[0], nrows, row_size);
memset(buf_pd, 0, row_size_pd); // clear-out padded buffer to make sure the tail is all zeros
// 1. Process all the full rows
for (int64_t i = 0; i < n_full_rows; i++) {
const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
uint8_t * dst = (uint8_t *) data + (i * row_size);
memcpy(buf_pd, src, row_size);
unpack_row_q8x4x2((block_q8_0 *) buf_rp, (const uint8_t *) buf_pd, t->ne[0]);
memcpy(dst, buf_rp, row_size);
}
// 2. Process the final, potentially partial, row
if (n_rem_bytes > 0) {
const int64_t i = n_full_rows;
const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
uint8_t * dst = (uint8_t *) data + (i * row_size);
// We still need to read and unpack the entire source row because quantization is block-based.
memcpy(buf_pd, src, row_size);
unpack_row_q8x4x2((block_q8_0 *) buf_rp, (const uint8_t *) buf_pd, t->ne[0]);
// But we only copy the remaining number of bytes to the destination.
memcpy(dst, buf_rp, n_rem_bytes);
}
ggml_aligned_free(buf_pd, row_size_pd);
ggml_aligned_free(buf_rp, row_size_rp);
}
// ======== MXFP4x4x2 ====================
struct x2_mxfp4 {
int v[2];
};
static x2_mxfp4 unpack_mxfp4(uint8_t v) {
x2_mxfp4 x;
x.v[0] = kvalues_mxfp4[(v & 0x0f)];
x.v[1] = kvalues_mxfp4[(v >> 4)];
return x;
}
static void dump_block_mxfp4(const block_mxfp4 * b, int i) {
HEX_VERBOSE("ggml-hex: repack mxfp4 %d: %d %d %d %d ... %d %d %d %d : %.6f\n", i, unpack_mxfp4(b->qs[0]).v[0],
unpack_mxfp4(b->qs[1]).v[0], unpack_mxfp4(b->qs[2]).v[0], unpack_mxfp4(b->qs[3]).v[0],
unpack_mxfp4(b->qs[12]).v[1], unpack_mxfp4(b->qs[13]).v[1], unpack_mxfp4(b->qs[14]).v[1],
unpack_mxfp4(b->qs[15]).v[1], GGML_E8M0_TO_FP32_HALF(b->e));
}
static void dump_packed_block_mxfp4x4x2(const uint8_t * v, unsigned int i, size_t k) {
static const int qk = QK_MXFP4x4x2;
const int eblk_size = 8 * 1; // 8x E8M0
const int qblk_size = qk / 2; // int4
const int qrow_size = k / 2; // int4 (not padded)
const uint8_t * v_q = v + 0; // quants first
const uint8_t * v_e = v + qrow_size; // then scales
const uint8_t * q = v_q + i * qblk_size;
const uint8_t * e = (const uint8_t *) (v_e + i * eblk_size);
HEX_VERBOSE("ggml-hex: repack mxfp4x4x2-%d: %d %d %d %d ... %d %d %d %d ... %d %d %d %d : %.6f %.6f %.6f %.6f\n", i,
unpack_mxfp4(q[0]).v[0], unpack_mxfp4(q[1]).v[0], unpack_mxfp4(q[2]).v[0], unpack_mxfp4(q[3]).v[0],
unpack_mxfp4(q[60]).v[0], unpack_mxfp4(q[61]).v[0], unpack_mxfp4(q[62]).v[0], unpack_mxfp4(q[63]).v[0],
unpack_mxfp4(q[124]).v[0], unpack_mxfp4(q[125]).v[0], unpack_mxfp4(q[126]).v[0],
unpack_mxfp4(q[127]).v[0], GGML_E8M0_TO_FP32_HALF(e[0]), GGML_E8M0_TO_FP32_HALF(e[1]),
GGML_E8M0_TO_FP32_HALF(e[2]), GGML_E8M0_TO_FP32_HALF(e[3]));
HEX_VERBOSE("ggml-hex: repack mxfp4x4x2-%d: %d %d %d %d ... %d %d %d %d ... %d %d %d %d : %.6f %.6f %.6f %.6f\n",
i + 1, unpack_mxfp4(q[0]).v[1], unpack_mxfp4(q[1]).v[1], unpack_mxfp4(q[2]).v[1],
unpack_mxfp4(q[3]).v[1], unpack_mxfp4(q[60]).v[1], unpack_mxfp4(q[61]).v[1], unpack_mxfp4(q[62]).v[1],
unpack_mxfp4(q[63]).v[1], unpack_mxfp4(q[124]).v[1], unpack_mxfp4(q[125]).v[1],
unpack_mxfp4(q[126]).v[1], unpack_mxfp4(q[127]).v[1], GGML_E8M0_TO_FP32_HALF(e[4]),
GGML_E8M0_TO_FP32_HALF(e[5]), GGML_E8M0_TO_FP32_HALF(e[6]), GGML_E8M0_TO_FP32_HALF(e[7]));
}
static void unpack_mxfp4_quants(uint8_t * qs, const block_mxfp4 * x, unsigned int bi) {
static const int qk = QK_MXFP4;
for (unsigned int i = 0; i < qk / 2; ++i) {
const uint8_t x0 = (x->qs[i] & 0x0F);
const uint8_t x1 = (x->qs[i] >> 4);
qs[bi * qk + i + 0] = x0;
qs[bi * qk + i + qk / 2] = x1;
}
}
static void pack_mxfp4_quants(block_mxfp4 * x, const uint8_t * qs, unsigned int bi) {
static const int qk = QK4_0;
for (unsigned int i = 0; i < qk / 2; ++i) {
const uint8_t x0 = qs[bi * qk + i + 0];
const uint8_t x1 = qs[bi * qk + i + qk / 2];
x->qs[i] = x0 | (x1 << 4);
}
}
static void repack_row_mxfp4x4x2(uint8_t * y, const block_mxfp4 * x, int64_t k) {
static const int qk = QK_MXFP4x4x2;
const int nb = (k + qk - 1) / qk; // number of blocks (padded)
const int nloe = k % qk; // leftovers
const int eblk_size = 8 * 1; // 8x E8M0
const int qblk_size = qk / 2; // int4
const int qrow_size = k / 2; // int4 (not padded to blocks)
uint8_t * y_q = y + 0; // quants first
uint8_t * y_e = y + qrow_size; // then scales
if (opt_verbose > 2) {
for (int i = 0; i < nb; i++) {
dump_block_mxfp4(&x[i * 8 + 0], 0);
dump_block_mxfp4(&x[i * 8 + 1], 1);
dump_block_mxfp4(&x[i * 8 + 2], 2);
dump_block_mxfp4(&x[i * 8 + 3], 3);
dump_block_mxfp4(&x[i * 8 + 4], 4);
dump_block_mxfp4(&x[i * 8 + 5], 5);
dump_block_mxfp4(&x[i * 8 + 6], 6);
dump_block_mxfp4(&x[i * 8 + 7], 7);
}
}
// Repack the quants
for (int i = 0; i < nb; i++) {
uint8_t qs[QK_MXFP4x4x2]; // unpacked quants
unpack_mxfp4_quants(qs, &x[i * 8 + 0], 0);
unpack_mxfp4_quants(qs, &x[i * 8 + 1], 1);
unpack_mxfp4_quants(qs, &x[i * 8 + 2], 2);
unpack_mxfp4_quants(qs, &x[i * 8 + 3], 3);
unpack_mxfp4_quants(qs, &x[i * 8 + 4], 4);
unpack_mxfp4_quants(qs, &x[i * 8 + 5], 5);
unpack_mxfp4_quants(qs, &x[i * 8 + 6], 6);
unpack_mxfp4_quants(qs, &x[i * 8 + 7], 7);
bool partial = (nloe && i == nb-1);
uint8_t * q = y_q + (i * qblk_size);
for (int j = 0; j < qk / 2; j++) {
q[j] = partial ? (qs[j*2+1] << 4) | qs[j*2+0] : (qs[j+128] << 4) | qs[j+000];
}
}
// Repack the scales
// Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_MXFP4x4x2)
// the last block is truncated and overridden by the scales.
for (int i = 0; i < nb; i++) {
// Repack the scales
uint8_t * e = (uint8_t *) (y_e + i * eblk_size);
e[0] = x[i * 8 + 0].e;
e[1] = x[i * 8 + 1].e;
e[2] = x[i * 8 + 2].e;
e[3] = x[i * 8 + 3].e;
e[4] = x[i * 8 + 4].e;
e[5] = x[i * 8 + 5].e;
e[6] = x[i * 8 + 6].e;
e[7] = x[i * 8 + 7].e;
}
if (opt_verbose > 2) {
for (int i = 0; i < nb; i++) {
dump_packed_block_mxfp4x4x2(y, i, k);
}
}
}
static void unpack_row_mxfp4x4x2(block_mxfp4 * x, const uint8_t * y, int64_t k) {
static const int qk = QK_MXFP4x4x2;
const int nb = (k + qk - 1) / qk; // number of blocks (padded)
const int nloe = k % qk; // leftovers
const int eblk_size = 8 * 1; // 8x E8M0
const int qblk_size = qk / 2; // int4
const int qrow_size = k / 2; // int4 (not padded to blocks)
const uint8_t * y_q = y + 0; // quants first
const uint8_t * y_e = y + qrow_size; // then scales
if (opt_verbose > 2) {
for (int i = 0; i < nb; i++) {
dump_packed_block_mxfp4x4x2(y, i, k);
}
}
// Unpack the quants
for (int i = 0; i < nb; i++) {
uint8_t qs[QK_MXFP4x4x2]; // unpacked quants
bool partial = (nloe && i == nb-1);
const uint8_t * q = y_q + (i * qblk_size);
for (int j = 0; j < qk / 2; j++) {
if (partial) {
qs[j*2+0] = q[j] & 0xf;
qs[j*2+1] = q[j] >> 4;
} else {
qs[j+000] = q[j] & 0xf;
qs[j+128] = q[j] >> 4;
}
}
pack_mxfp4_quants(&x[i * 8 + 0], qs, 0);
pack_mxfp4_quants(&x[i * 8 + 1], qs, 1);
pack_mxfp4_quants(&x[i * 8 + 2], qs, 2);
pack_mxfp4_quants(&x[i * 8 + 3], qs, 3);
pack_mxfp4_quants(&x[i * 8 + 4], qs, 4);
pack_mxfp4_quants(&x[i * 8 + 5], qs, 5);
pack_mxfp4_quants(&x[i * 8 + 6], qs, 6);
pack_mxfp4_quants(&x[i * 8 + 7], qs, 7);
}
// Repack the scales
// Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_MXFP4_0x4x2)
// the last block is truncated and overridden by the scales.
for (int i = 0; i < nb; i++) {
// Unpack the scales
const uint8_t * e = (const uint8_t *) (y_e + i * eblk_size);
x[i * 8 + 0].e = e[0];
x[i * 8 + 1].e = e[1];
x[i * 8 + 2].e = e[2];
x[i * 8 + 3].e = e[3];
x[i * 8 + 4].e = e[4];
x[i * 8 + 5].e = e[5];
x[i * 8 + 6].e = e[6];
x[i * 8 + 7].e = e[7];
}
if (opt_verbose > 2) {
for (int i = 0; i < nb; i++) {
dump_block_mxfp4(&x[i * 8 + 0], 0);
dump_block_mxfp4(&x[i * 8 + 1], 1);
dump_block_mxfp4(&x[i * 8 + 2], 2);
dump_block_mxfp4(&x[i * 8 + 3], 3);
dump_block_mxfp4(&x[i * 8 + 4], 4);
dump_block_mxfp4(&x[i * 8 + 5], 5);
dump_block_mxfp4(&x[i * 8 + 6], 6);
dump_block_mxfp4(&x[i * 8 + 7], 7);
}
}
}
static void init_row_mxfp4x4x2(block_mxfp4 * x, int64_t k) {
static const int qk = QK_MXFP4x4x2;
const int nb = (k + qk - 1) / qk; // number of blocks (padded)
// Init the quants such that they unpack into zeros
uint8_t qs[QK_MXFP4x4x2]; // unpacked quants
memset(qs, 0, sizeof(qs));
for (int i = 0; i < nb; i++) {
pack_mxfp4_quants(&x[i * 8 + 0], qs, 0);
pack_mxfp4_quants(&x[i * 8 + 1], qs, 1);
pack_mxfp4_quants(&x[i * 8 + 2], qs, 2);
pack_mxfp4_quants(&x[i * 8 + 3], qs, 3);
pack_mxfp4_quants(&x[i * 8 + 4], qs, 4);
pack_mxfp4_quants(&x[i * 8 + 5], qs, 5);
pack_mxfp4_quants(&x[i * 8 + 6], qs, 6);
pack_mxfp4_quants(&x[i * 8 + 7], qs, 7);
}
// Init the scales
// Note: Do not combine with the loop above. For tensor sizes not multiple of 256 (QK_MXFP4x4x2)
// the last block is truncated and overridden by the scales.
for (int i = 0; i < nb; i++) {
// Unpack the scales
x[i * 8 + 0].e = 0;
x[i * 8 + 1].e = 0;
x[i * 8 + 2].e = 0;
x[i * 8 + 3].e = 0;
x[i * 8 + 4].e = 0;
x[i * 8 + 5].e = 0;
x[i * 8 + 6].e = 0;
x[i * 8 + 7].e = 0;
}
}
// repack mxfp4 data into mxfp4x4x2 tensor
static void repack_mxfp4_mxfp4x4x2(ggml_tensor * t, const void * data, size_t size) {
int64_t nrows = ggml_nrows(t);
size_t row_size = ggml_row_size(t->type, t->ne[0]);
size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_MXFP4x4x2)); // extra elements for the pad
size_t row_size_rp = row_size * 2; // extra space for tmp pad (if any)
// Ensure we don't try to read more data than is available in the source buffer 'data'
// or write more than the tensor can hold.
const size_t total_tensor_size = (size_t)nrows * row_size;
const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
// Calculate how many full rows and how many remaining bytes we need to process.
const int64_t n_full_rows = n_bytes_to_copy / row_size;
const size_t n_rem_bytes = n_bytes_to_copy % row_size;
void * buf_pd = ggml_aligned_malloc(row_size_pd);
GGML_ASSERT(buf_pd != NULL);
void * buf_rp = ggml_aligned_malloc(row_size_rp);
GGML_ASSERT(buf_rp != NULL);
HEX_VERBOSE("ggml-hex: repack-mxfp4-mxfp4x4x2 %s : data %p size %zu dims %ldx%ld row-size %zu\n", t->name, data,
size, t->ne[0], nrows, row_size);
init_row_mxfp4x4x2((block_mxfp4 *) buf_pd, t->ne[0]); // init padded buffer to make sure the tail is all zeros
// 1. Process all the full rows
for (int64_t i = 0; i < n_full_rows; i++) {
const uint8_t * src = (const uint8_t *) data + (i * row_size);
uint8_t * dst = (uint8_t *) t->data + (i * row_size);
memcpy(buf_pd, src, row_size);
repack_row_mxfp4x4x2((uint8_t *) buf_rp, (const block_mxfp4 *) buf_pd, t->ne[0]);
memcpy(dst, buf_rp, row_size);
}
// 2. Process the final, potentially partial, row
if (n_rem_bytes > 0) {
const int64_t i = n_full_rows;
const uint8_t * src = (const uint8_t *) data + (i * row_size);
uint8_t * dst = (uint8_t *) t->data + (i * row_size);
// re-init the row because we are potentially copying a partial row
init_row_mxfp4x4x2((block_mxfp4 *) buf_pd, t->ne[0]);
// Copy only the remaining bytes from the source.
memcpy(buf_pd, src, n_rem_bytes);
// Repack the entire buffer (partial data + zero padding).
repack_row_mxfp4x4x2((uint8_t *) buf_rp, (const block_mxfp4 *) buf_pd, t->ne[0]);
// Write only the corresponding remaining bytes to the destination tensor.
memcpy(dst, buf_rp, n_rem_bytes);
}
ggml_aligned_free(buf_pd, row_size_pd);
ggml_aligned_free(buf_rp, row_size_rp);
}
// repack mxfp4x4x2 tensor into mxfp4 data
static void repack_mxfp4x4x2_mxfp4(void * data, const ggml_tensor * t, size_t size) {
int64_t nrows = ggml_nrows(t);
size_t row_size = ggml_row_size(t->type, t->ne[0]);
size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_MXFP4x4x2)); // extra elements for the pad
size_t row_size_rp = row_size * 2; // extra space for tmp pad (if any)
// Ensure we don't try to copy more data than the tensor actually contains.
const size_t total_tensor_size = (size_t)nrows * row_size;
const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
// Calculate how many full rows and how many remaining bytes we need to process.
const int64_t n_full_rows = n_bytes_to_copy / row_size;
const size_t n_rem_bytes = n_bytes_to_copy % row_size;
void * buf_pd = ggml_aligned_malloc(row_size_pd);
GGML_ASSERT(buf_pd != NULL);
void * buf_rp = ggml_aligned_malloc(row_size_rp);
GGML_ASSERT(buf_rp != NULL);
HEX_VERBOSE("ggml-hex: repack-mxfp4x4x2-mxfp4 %s : data %p size %zu dims %ldx%ld row-size %zu\n", t->name, data,
size, t->ne[0], nrows, row_size);
memset(buf_pd, 0, row_size_pd); // clear-out padded buffer to make sure the tail is all zeros
// 1. Process all the full rows
for (int64_t i = 0; i < n_full_rows; i++) {
const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
uint8_t * dst = (uint8_t *) data + (i * row_size);
memcpy(buf_pd, src, row_size);
unpack_row_mxfp4x4x2((block_mxfp4 *) buf_rp, (const uint8_t *) buf_pd, t->ne[0]);
memcpy(dst, buf_rp, row_size);
}
// 2. Process the final, potentially partial, row
if (n_rem_bytes > 0) {
const int64_t i = n_full_rows;
const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
uint8_t * dst = (uint8_t *) data + (i * row_size);
// We still need to read and unpack the entire source row because the format is block-based.
memcpy(buf_pd, src, row_size);
unpack_row_mxfp4x4x2((block_mxfp4 *) buf_rp, (const uint8_t *) buf_pd, t->ne[0]);
// But we only copy the remaining number of bytes to the destination to respect the size limit.
memcpy(dst, buf_rp, n_rem_bytes);
}
ggml_aligned_free(buf_pd, row_size_pd);
ggml_aligned_free(buf_rp, row_size_rp);
}
static void ggml_backend_hexagon_buffer_set_tensor(ggml_backend_buffer_t buffer,
ggml_tensor * tensor,
const void * data,
size_t offset,
size_t size) {
auto sbuf = (ggml_hexagon_shared_buffer *) buffer->context;
auto sess = sbuf->sess;
HEX_VERBOSE("ggml-hex: %s set-tensor %s : data %p offset %zu size %zu\n", sess->c_name(), tensor->name, data, offset, size);
switch (tensor->type) {
case GGML_TYPE_Q4_0:
GGML_ASSERT(offset == 0);
GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
repack_q4_0_q4x4x2(tensor, data, size);
break;
case GGML_TYPE_Q8_0:
GGML_ASSERT(offset == 0);
GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
repack_q8_0_q8x4x2(tensor, data, size);
break;
case GGML_TYPE_IQ4_NL:
GGML_ASSERT(offset == 0);
GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
// IQ4_NL has identical block layout to Q4_0 (ggml_half d + uint8_t qs[16])
repack_q4_0_q4x4x2(tensor, data, size);
break;
case GGML_TYPE_MXFP4:
GGML_ASSERT(offset == 0);
GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
repack_mxfp4_mxfp4x4x2(tensor, data, size);
break;
default:
memcpy((char *) tensor->data + offset, data, size);
break;
}
}
static void ggml_backend_hexagon_buffer_get_tensor(ggml_backend_buffer_t buffer,
const ggml_tensor * tensor,
void * data,
size_t offset,
size_t size) {
auto sbuf = (ggml_hexagon_shared_buffer *) buffer->context;
auto sess = sbuf->sess;
HEX_VERBOSE("ggml-hex: %s get-tensor %s : data %p offset %zu size %zu\n", sess->c_name(), tensor->name, data, offset, size);
switch (tensor->type) {
case GGML_TYPE_Q4_0:
GGML_ASSERT(offset == 0);
GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
repack_q4x4x2_q4_0(data, tensor, size);
break;
case GGML_TYPE_Q8_0:
GGML_ASSERT(offset == 0);
GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
repack_q8x4x2_q8_0(data, tensor, size);
break;
case GGML_TYPE_IQ4_NL:
GGML_ASSERT(offset == 0);
GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
repack_q4x4x2_q4_0(data, tensor, size);
break;
case GGML_TYPE_MXFP4:
GGML_ASSERT(offset == 0);
GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
repack_mxfp4x4x2_mxfp4(data, tensor, size);
break;
default:
memcpy(data, (const char *) tensor->data + offset, size);
break;
}
}
static bool ggml_backend_hexagon_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
const struct ggml_tensor * src,
struct ggml_tensor * dst) {
GGML_UNUSED(buffer);
GGML_UNUSED(src);
GGML_UNUSED(dst);
// we might optimize this later, for now take the slow path (ie get/set_tensor)
return false;
}
static void ggml_backend_hexagon_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
auto sbuf = (ggml_hexagon_shared_buffer *) buffer->context;
auto sess = sbuf->sess;
HEX_VERBOSE("ggml-hex: %s clear-buff base %p size %zu\n", sess->c_name(), (void *) sbuf->base, sbuf->size);
memset(sbuf->base, value, sbuf->size);
}
static ggml_backend_buffer_i ggml_backend_hexagon_buffer_interface = {
/* .free_buffer = */ ggml_backend_hexagon_buffer_free_buffer,
/* .get_base = */ ggml_backend_hexagon_buffer_get_base,
/* .init_tensor = */ ggml_backend_hexagon_buffer_init_tensor,
/* .memset_tensor = */ NULL,
/* .set_tensor = */ ggml_backend_hexagon_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_hexagon_buffer_get_tensor,
/* .set_tensor_2d = */ NULL,
/* .get_tensor_2d = */ NULL,
/* .cpy_tensor = */ ggml_backend_hexagon_buffer_cpy_tensor,
/* .clear = */ ggml_backend_hexagon_buffer_clear,
/* .reset = */ NULL,
};
// ** backend buffer type
static const char * ggml_backend_hexagon_buffer_type_name(ggml_backend_buffer_type_t buffer_type) {
return static_cast<ggml_backend_hexagon_buffer_type_context *>(buffer_type->context)->name.c_str();
}
static ggml_backend_buffer_t ggml_backend_hexagon_buffer_type_alloc_buffer(
ggml_backend_buffer_type_t buffer_type, size_t size) {
auto sess = static_cast<ggml_backend_hexagon_buffer_type_context *>(buffer_type->context)->sess;
try {
size += 4 * 1024; // guard page
ggml_hexagon_shared_buffer * sbuf = new ggml_hexagon_shared_buffer(sess, size);
return ggml_backend_buffer_init(buffer_type, ggml_backend_hexagon_buffer_interface, sbuf, size);
} catch (const std::exception & exc) {
GGML_LOG_ERROR("ggml-hex: %s failed to allocate buffer context (host): %s\n", sess->c_name(), exc.what());
return nullptr;
}
}
static ggml_backend_buffer_t ggml_backend_hexagon_repack_buffer_type_alloc_buffer(
ggml_backend_buffer_type_t buffer_type, size_t size) {
auto sess = static_cast<ggml_backend_hexagon_buffer_type_context *>(buffer_type->context)->sess;
try {
size += 4 * 1024; // guard page
ggml_hexagon_shared_buffer * sbuf = new ggml_hexagon_shared_buffer(sess, size);
return ggml_backend_buffer_init(buffer_type, ggml_backend_hexagon_buffer_interface, sbuf, size);
} catch (const std::exception & exc) {
GGML_LOG_ERROR("ggml-hex: %s failed to allocate buffer context (repack): %s\n", sess->c_name(), exc.what());
return nullptr;
}
}
static size_t ggml_backend_hexagon_buffer_type_get_alignment(ggml_backend_buffer_type_t buffer_type) {
return 128; // HVX alignment
GGML_UNUSED(buffer_type);
}
static size_t ggml_backend_hexagon_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const struct ggml_tensor * t) {
return ggml_nbytes(t);
}
static size_t ggml_backend_hexagon_buffer_type_get_max_size(ggml_backend_buffer_type_t buffer_type) {
return opt_mbuf; // typically 1GB per buffer
GGML_UNUSED(buffer_type);
}
static bool ggml_backend_hexagon_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
return opt_hostbuf;
GGML_UNUSED(buft);
}
static bool ggml_backend_hexagon_repack_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
return false;
GGML_UNUSED(buft);
}
static ggml_backend_buffer_type_i ggml_backend_hexagon_buffer_type_interface = {
/* .get_name = */ ggml_backend_hexagon_buffer_type_name,
/* .alloc_buffer = */ ggml_backend_hexagon_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_hexagon_buffer_type_get_alignment,
/* .get_max_size = */ ggml_backend_hexagon_buffer_type_get_max_size,
/* .get_alloc_size = */ ggml_backend_hexagon_buffer_type_get_alloc_size,
/* .is_host = */ ggml_backend_hexagon_buffer_type_is_host,
};
static ggml_backend_buffer_type_i ggml_backend_hexagon_repack_buffer_type_interface = {
/* .get_name = */ ggml_backend_hexagon_buffer_type_name,
/* .alloc_buffer = */ ggml_backend_hexagon_repack_buffer_type_alloc_buffer,
/* .get_alignment = */ ggml_backend_hexagon_buffer_type_get_alignment,
/* .get_max_size = */ ggml_backend_hexagon_buffer_type_get_max_size,
/* .get_alloc_size = */ ggml_backend_hexagon_buffer_type_get_alloc_size,
/* .is_host = */ ggml_backend_hexagon_repack_buffer_type_is_host,
};
// Backend session implementation
struct ggml_hexagon_opbatch {
ggml_hexagon_session* sess;
std::vector<const ggml_tensor*> ops; // pointers to original ops
std::vector<htp_buf_desc> h_bufs; // htp buffer descriptors
std::vector<htp_tensor> h_tens; // htp tensor descriptors
std::vector<htp_op_desc> h_ops; // htp op descriptors
std::unordered_map<int, int> b_map; // buffer fd to index
std::unordered_map<const ggml_tensor*, int> t_map; // tensor ptr to index
std::unordered_multimap<void*, int> d_map; // tensor data to index
unsigned int n_bufs; // num buffers in the batch
unsigned int n_tens; // num tensors ...
unsigned int n_ops; // num ops ...
size_t b_vmem; // sum of all buffer sizes
unsigned int n_bufs_max;
unsigned int n_tens_max;
unsigned int n_ops_max;
size_t b_vmem_max;
void reset() {
n_bufs = 0;
n_tens = 0;
n_ops = 0;
b_vmem = 0;
b_map.clear();
t_map.clear();
d_map.clear();
}
ggml_hexagon_opbatch(ggml_hexagon_session *sess, size_t batch_size, size_t max_vmem) {
this->sess = sess;
n_bufs_max = HTP_OP_MAX_BUFS;
n_ops_max = batch_size;
n_tens_max = n_ops_max + n_ops_max * HTP_OP_MAX_INPUTS;
b_vmem_max = max_vmem;
ops.resize(n_ops_max);
h_bufs.resize(n_bufs_max);
h_tens.resize(n_tens_max);
h_ops.resize(n_ops_max);
b_map.reserve(n_bufs_max);
t_map.reserve(n_tens_max);
d_map.reserve(n_tens_max);
GGML_LOG_INFO("ggml-hex: %s op batching: n-bufs %u n-tensors %u n-ops %u vmem %zu\n",
sess->c_name(), n_bufs_max, n_tens_max, n_ops_max, b_vmem_max);
reset();
}
bool empty() const { return n_ops == 0; }
// add buffer and return its index
int add_buffer(ggml_hexagon_shared_buffer * sbuf) {
// Lookup by fd
auto it = b_map.find(sbuf->fd);
if (it != b_map.end()) { return it->second; }
// Add new buffer to the batch
int bi = n_bufs++;
GGML_ASSERT(n_bufs < HTP_OP_MAX_BUFS);
b_map.insert({sbuf->fd, bi});
htp_buf_desc &b = h_bufs[bi];
b.base = (uint64_t) sbuf->base;
b.fd = sbuf->fd;
b.size = sbuf->size;
b_vmem += b.size;
HEX_VERBOSE("ggml-hex: add-buffer #%u : fd %d base %p size %zu : vmem %zu\n", bi, b.fd, (void*) sbuf->base, (size_t) b.size, b_vmem);
return bi;
}
bool same_shape(const htp_tensor * h, const ggml_tensor * t) const {
return (h->ne[0] == t->ne[0]) && (h->ne[1] == t->ne[1]) && (h->ne[2] == t->ne[2]) && (h->ne[3] == t->ne[3]) &&
(h->nb[0] == t->nb[0]) && (h->nb[1] == t->nb[1]) && (h->nb[2] == t->nb[2]) && (h->nb[3] == t->nb[3]);
}
// add tensor and return its index
int add_tensor(const ggml_tensor * t) {
auto sbuf = static_cast<ggml_hexagon_shared_buffer *>(t->buffer->context);
// First lookup by tensor data
auto range = d_map.equal_range(t->data);
for (auto it = range.first; it != range.second; ++it) {
htp_tensor * h = &h_tens[it->second];
if (same_shape(h, t)) { return it->second; }
}
// Lookup by tensor ptr
auto it = t_map.find(t);
if (it != t_map.end()) { return it->second; }
// Add new tensor to the batch
int ti = n_tens++;
GGML_ASSERT(n_tens <= n_tens_max);
t_map.insert({t, ti});
d_map.insert({t->data, ti});
uint64_t t_offset = (uint8_t *) t->data - sbuf->base;
size_t t_size = ggml_nbytes(t);
htp_tensor &h = h_tens[ti];
h.bi = add_buffer(sbuf);
h.data = t_offset;
h.size = t_size;
h.type = t->type;
h.ne[0] = t->ne[0]; h.ne[1] = t->ne[1]; h.ne[2] = t->ne[2]; h.ne[3] = t->ne[3];
h.nb[0] = t->nb[0]; h.nb[1] = t->nb[1]; h.nb[2] = t->nb[2]; h.nb[3] = t->nb[3];
h.flags = 0;
if (ggml_backend_buffer_get_usage(t->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE) {
h.flags |= HTP_TENSOR_COMPUTE;
}
HEX_VERBOSE("ggml-hex: add-tensor #%u %s : bi %d data %p offset %zu size %zu flags 0x%x : %zu:%zu:%zu:%zu\n",
ti, t->name, h.bi, (void*) t->data, (size_t) t_offset, t_size, h.flags,
(size_t) t->ne[0], (size_t) t->ne[1], (size_t) t->ne[2], (size_t) t->ne[3]);
return ti;
}
bool fit_op(const struct ggml_tensor *t) const {
if (n_ops >= n_ops_max ) return false;
// check how much extras we will need
size_t extra_bufs = 0;
size_t extra_vmem = 0;
size_t extra_tens = 0;
auto fit_tensor = [&](const ggml_tensor *t) {
if (!t_map.count(t)) {
extra_tens++;
auto sbuf = static_cast<ggml_hexagon_shared_buffer *>(t->buffer->context);
if (!b_map.count(sbuf->fd)) {
extra_vmem += sbuf->size;
extra_bufs += 1;
}
}
};
for (unsigned int i=0; i < HTP_OP_MAX_INPUTS && t->src[i]; i++) {
fit_tensor(t->src[i]);
}
fit_tensor(t);
if ((extra_bufs + n_bufs) > n_bufs_max) return false;
if ((extra_tens + n_tens) > n_tens_max) return false;
if ((extra_vmem + b_vmem) > b_vmem_max) return false;
return true;
}
// assumes that fit_op() was called first and returned true
void add_op(htp_op_code opcode, const struct ggml_tensor * t) {
// Add new op
unsigned int n = n_ops++;
GGML_ASSERT(n_ops <= n_ops_max);
ops[n] = t;
htp_op_desc &o = h_ops[n];
memcpy(&o.params, &t->op_params, sizeof(t->op_params));
o.opcode = opcode;
o.flags = 0;
if (!(opt_opstage & HTP_OPSTAGE_COMPUTE)) {
o.flags |= HTP_OPFLAGS_SKIP_COMPUTE;
}
ggml_hexagon_dump_op_exec(sess->c_name(), t, o.flags);
for (unsigned int i=0; i < HTP_OP_MAX_INPUTS; i++) {
o.src[i] = t->src[i] ? add_tensor(t->src[i]) : 0xffff;
}
o.dst = add_tensor(t);
}
};
struct ggml_hexagon_opqueue {
// Shared buffer for storing batches
ggml_hexagon_shared_buffer *shm_buf;
size_t shm_blk_size;
using opvec = std::vector<const ggml_tensor*>;
std::queue<unsigned int> done; // completed batch ids
std::vector<opvec> op_cache; // per batch op cache
std::vector<uint64_t> start_usec; // per batch start time
ggml_hexagon_opqueue(ggml_hexagon_session *sess, size_t batch_size, size_t depth) {
size_t n_bufs = HTP_OP_MAX_BUFS;
size_t n_ops = batch_size;
size_t n_tensors = n_ops + n_ops * HTP_OP_MAX_INPUTS;
shm_blk_size = sizeof(htp_buf_desc) * n_bufs +
sizeof(htp_tensor) * n_tensors +
sizeof(htp_op_desc) * n_ops +
sizeof(htp_prof_desc) * n_ops;
shm_buf = new ggml_hexagon_shared_buffer(sess, shm_blk_size * depth, true /* pinned */);
op_cache.resize(depth);
start_usec.resize(depth, 0);
// init done queue
for (unsigned int i = 0; i < depth; i++) { done.push(i); }
if (opt_verbose) {
GGML_LOG_INFO("ggml-hex: %s allocated op-queue : batch-size %zu depth %zu shm-size %zu shm-block-size %zu\n",
sess->c_name(), batch_size, depth, shm_buf->size, shm_blk_size);
}
}
~ggml_hexagon_opqueue() {
delete shm_buf;
}
// push new batch
bool push(htp_opbatch_req& req, dspqueue_buffer& dbuf, ggml_hexagon_opbatch* op_batch) {
static_assert(sizeof(htp_opbatch_req) % 8 == 0, "sizeof(htp_opbatch_req) must be multiple of 8");
static_assert(sizeof(htp_opbatch_rsp) % 8 == 0, "sizeof(htp_opbatch_rsp) must be multiple of 8");
static_assert(sizeof(htp_buf_desc) % 8 == 0, "sizeof(htp_buf_desc) must be multiple of 8");
static_assert(sizeof(htp_tensor) % 8 == 0, "sizeof(htp_tensor) must be multiple of 8");
static_assert(sizeof(htp_op_desc) % 8 == 0, "sizeof(htp_op_desc) must be multiple of 8");
static_assert(sizeof(htp_prof_desc) % 8 == 0, "sizeof(htp_prof_desc) must be multiple of 8");
if (done.empty()) { return false; }
req.id = done.front(); done.pop(); // batch id
req.n_bufs = op_batch->n_bufs;
req.n_tensors = op_batch->n_tens;
req.n_ops = op_batch->n_ops;
op_cache[req.id] = op_batch->ops;
start_usec[req.id] = ggml_time_us();
const size_t b_size = sizeof(htp_buf_desc) * req.n_bufs;
const size_t t_size = sizeof(htp_tensor) * req.n_tensors;
const size_t o_size = sizeof(htp_op_desc) * req.n_ops;
const size_t p_size = sizeof(htp_prof_desc) * req.n_ops;
dbuf.ptr = shm_buf->base + (req.id * shm_blk_size);
dbuf.fd = shm_buf->fd;
dbuf.flags = DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT;
dbuf.offset = (uint8_t*) dbuf.ptr - (uint8_t*) shm_buf->base;
dbuf.size = b_size + t_size + o_size + p_size;
GGML_ASSERT(dbuf.size <= shm_blk_size);
uint8_t * m_ptr = (uint8_t*) dbuf.ptr;
uint8_t * b_ptr = m_ptr; m_ptr += b_size;
uint8_t * t_ptr = m_ptr; m_ptr += t_size;
uint8_t * o_ptr = m_ptr;
memcpy(b_ptr, (void *) op_batch->h_bufs.data(), b_size);
memcpy(t_ptr, (void *) op_batch->h_tens.data(), t_size);
memcpy(o_ptr, (void *) op_batch->h_ops.data(), o_size);
HEX_VERBOSE("ggml-hex: %s op-queue push batch #%u : n-bufs %u n-tensors %u n-ops %u vmem %zu : b-size %zu t-size %zu o-size %zu m-size %zu\n",
shm_buf->sess->c_name(), req.id, req.n_bufs, req.n_tensors, req.n_ops, op_batch->b_vmem,
b_size, t_size, o_size, (size_t) dbuf.size);
op_batch->reset();
if (opt_verbose > 1) {
htp_buf_desc *b = (htp_buf_desc*) b_ptr;
for (unsigned int i=0; i < req.n_bufs; i++) {
GGML_LOG_DEBUG("ggml-hex: %s htp-buf #%u : fd %d base %p size %zu\n", shm_buf->sess->c_name(), i,
b[i].fd, (void *) b[i].base, (size_t) b[i].size);
}
htp_tensor *t = (htp_tensor*) t_ptr;
for (unsigned int i=0; i < req.n_tensors; i++) {
GGML_LOG_DEBUG("ggml-hex: %s htp-tensor #%u : bi %u offset %u size %u : %zu:%zu:%zu:%zu\n",
shm_buf->sess->c_name(), i, t[i].bi, t[i].data, t[i].size,
(size_t) t[i].ne[0], (size_t) t[i].ne[1], (size_t) t[i].ne[2], (size_t) t[i].ne[3]);
}
}
return true;
}
void pop(htp_opbatch_rsp rsp, dspqueue_buffer dbuf) {
GGML_ASSERT(rsp.id < op_cache.size());
done.push(rsp.id);
const size_t b_size = sizeof(htp_buf_desc) * rsp.n_bufs;
const size_t t_size = sizeof(htp_tensor) * rsp.n_tensors;
const size_t o_size = sizeof(htp_op_desc) * rsp.n_ops;
const size_t p_size = sizeof(htp_prof_desc) * rsp.n_ops;
const size_t m_size = b_size + t_size + o_size + p_size;
GGML_ASSERT(m_size <= shm_blk_size);
HEX_VERBOSE("ggml-hex: %s op-queue pop batch #%u : n-bufs %u n-tensors %u n-ops %u : m-size %zu b-size %zu t-size %zu o-size %zu\n",
shm_buf->sess->c_name(), rsp.id, rsp.n_bufs, rsp.n_tensors, rsp.n_ops,
(size_t) dbuf.size, b_size, t_size, o_size);
uint8_t * m_ptr = (uint8_t*) dbuf.ptr;
uint8_t * p_ptr = m_ptr + (b_size + t_size + o_size);
if (opt_profile && rsp.n_ops > 0) {
auto & ops = op_cache[rsp.id];
uint64_t batch_usec = ggml_time_us() - start_usec[rsp.id];
uint32_t htp_usec = 0;
GGML_ASSERT(rsp.n_ops <= ops.size());
const htp_prof_desc * pd = (const htp_prof_desc *) p_ptr;
for (uint32_t i = 0; i < rsp.n_ops; i++) {
htp_usec += pd[i].usecs;
ggml_hexagon_dump_op_prof(shm_buf->sess->name, ops[i], pd[i].usecs, pd[i].cycles, pd[i].pmu);
}
GGML_LOG_DEBUG("ggml-hex: %s profile-batch n-ops %u batch-dur-usec %lld htp-ops-usec %u\n",
shm_buf->sess->c_name(), rsp.n_ops, (long long) batch_usec, htp_usec);
}
}
};
// Flush HTP response queue i.e wait for all outstanding requests to complete
void ggml_hexagon_session::flush_pending(bool all) {
while (this->op_pending) {
struct htp_opbatch_rsp rsp;
uint32_t rsp_size;
uint32_t flags;
struct dspqueue_buffer dbuf;
uint32_t n_dbufs;
// Read response packet from queue
int err = dspqueue_read(this->queue, &flags, 1, &n_dbufs, &dbuf, sizeof(rsp), &rsp_size, (uint8_t *) &rsp, DSPQUEUE_TIMEOUT);
if (err == AEE_EEXPIRED) {
continue;
}
if (err != 0) {
GGML_ABORT("ggml-hex: dspqueue_read failed: 0x%08x\n", (unsigned) err);
}
// Basic sanity checks
if (rsp_size != sizeof(rsp) || n_dbufs != 1) {
GGML_ABORT("ggml-hex: %s dspcall : bad response : size %u dspbufs %u\n", this->c_name(), rsp_size, n_dbufs);
}
if (rsp.status != HTP_STATUS_OK) {
GGML_LOG_ERROR("ggml-hex: %s dspcall : dsp-rsp: %s\n", this->c_name(), status_to_str(rsp.status));
// TODO: handle errors
}
op_queue->pop(rsp, dbuf);
this->op_pending--; // atomic dec
if (!all) break;
}
}
void ggml_hexagon_session::flush_batch() {
if (op_batch->empty()) { return; }
htp_opbatch_req req {};
dspqueue_buffer dbuf{};
if (!op_queue->push(req, dbuf, op_batch)) {
flush_pending(false);
op_queue->push(req, dbuf, op_batch);
}
// Bump pending flag (cleared in the session::flush once we get the response)
this->op_pending++; // atomic inc
HEX_VERBOSE("ggml-hex: %s queue-opbatch: %p size %u\n", this->c_name(), dbuf.ptr, dbuf.size);
int err = dspqueue_write(this->queue, 0, 1, &dbuf, sizeof(req), (const uint8_t*) &req, DSPQUEUE_TIMEOUT);
if (err != 0) {
GGML_ABORT("ggml-hex: %s dspqueue_write failed: 0x%08x\n", this->c_name(), (unsigned) err);
}
}
void ggml_hexagon_session::enqueue_op(htp_op_code opcode, const ggml_tensor *op) {
if (!op_batch->fit_op(op)) {
flush_batch();
}
op_batch->add_op(opcode, op);
}
// Flush HTP response queue i.e wait for all outstanding requests to complete
void ggml_hexagon_session::flush(bool all) {
flush_batch();
flush_pending(all);
}
static size_t ggml_hexagon_measure_max_vmem(ggml_hexagon_session *sess) {
// Allocate a bunch pinned buffers till failure.
// This is kind of expensive but handy for figuring out exactly how much we can mmap on a specific device.
// Typically we're going to allocate all/most of these buffers anyway for the model weights.
std::vector<ggml_hexagon_shared_buffer *> sbufs;
const size_t MiB = 1024 * 1024;
const size_t GiB = MiB * 1024;
size_t vmem = 0;
size_t step = 256u * MiB;
try {
sbufs.push_back(new ggml_hexagon_shared_buffer(sess, GiB, true)); vmem += GiB;
sbufs.push_back(new ggml_hexagon_shared_buffer(sess, GiB, true)); vmem += GiB;
sbufs.push_back(new ggml_hexagon_shared_buffer(sess, GiB, true)); vmem += GiB;
while (1) {
sbufs.push_back(new ggml_hexagon_shared_buffer(sess, step, true));
vmem += step;
}
} catch (...) { }
for (auto b : sbufs) { delete b; }
return vmem - step; // backoff to account for overhead from internal mappings
}
void ggml_hexagon_session::allocate(int dev_id) noexcept(false) {
this->valid_session = false;
this->valid_handle = false;
this->valid_queue = false;
this->valid_iface = false;
this->domain_id = 3; // Default for CDSP, updated after the session is created
this->session_id = 0; // Default for CDSP, updated after the session is created
this->dev_id = dev_id;
this->name = std::string("HTP") + std::to_string(dev_id);
this->op_pending = 0;
GGML_LOG_DEBUG("ggml-hex: %s allocating new session\n", this->name.c_str());
domain * my_domain = get_domain(this->domain_id);
if (my_domain == NULL) {
GGML_LOG_ERROR("ggml-hex: unable to get domain struct for CDSP\n");
throw std::runtime_error("ggml-hex: failed to get CDSP domain (see log for details)");
}
// Create new session
if (dev_id != 0) {
struct remote_rpc_reserve_new_session n;
n.domain_name_len = strlen(CDSP_DOMAIN_NAME);
n.domain_name = const_cast<char *>(CDSP_DOMAIN_NAME);
n.session_name = const_cast<char *>(this->name.c_str());
n.session_name_len = this->name.size();
int err = remote_session_control(FASTRPC_RESERVE_NEW_SESSION, (void *) &n, sizeof(n));
if (err != AEE_SUCCESS) {
GGML_LOG_ERROR("ggml-hex: failed to reserve new session %d : error 0x%x\n", dev_id, err);
throw std::runtime_error("ggml-hex: remote_session_control(new-sess) failed (see log for details)");
}
// Save the IDs
this->session_id = n.session_id;
this->domain_id = n.effective_domain_id;
this->valid_session = true;
}
// Get session URI
char session_uri[256];
{
char htp_uri[256];
snprintf(htp_uri, sizeof(htp_uri), "file:///libggml-htp-v%u.so?htp_iface_skel_handle_invoke&_modver=1.0", opt_arch);
struct remote_rpc_get_uri u = {};
u.session_id = this->session_id;
u.domain_name = const_cast<char *>(CDSP_DOMAIN_NAME);
u.domain_name_len = strlen(CDSP_DOMAIN_NAME);
u.module_uri = const_cast<char *>(htp_uri);
u.module_uri_len = strlen(htp_uri);
u.uri = session_uri;
u.uri_len = sizeof(session_uri);
int err = remote_session_control(FASTRPC_GET_URI, (void *) &u, sizeof(u));
if (err != AEE_SUCCESS) {
// fallback to single session uris
int htp_URI_domain_len = strlen(htp_uri) + MAX_DOMAIN_NAMELEN;
snprintf(session_uri, htp_URI_domain_len, "%s%s", htp_uri, my_domain->uri);
GGML_LOG_WARN("ggml-hex: failed to get URI for session %d : error 0x%x. Falling back to single session URI: %s\n", dev_id, err, session_uri);
}
}
// Enable Unsigned PD
{
struct remote_rpc_control_unsigned_module u;
u.domain = this->domain_id;
u.enable = 1;
int err = remote_session_control(DSPRPC_CONTROL_UNSIGNED_MODULE, (void *) &u, sizeof(u));
if (err != AEE_SUCCESS) {
GGML_LOG_ERROR("ggml-hex: failed to enable unsigned PD for session %d : error 0x%x\n", dev_id, err);
throw std::runtime_error("ggml-hex: remote_session_control(unsign) failed (see log for details)");
}
}
// Open session
int err = htp_iface_open(session_uri, &this->handle);
if (err != AEE_SUCCESS) {
GGML_LOG_ERROR("ggml-hex: failed to open session %d : error 0x%x\n", dev_id, err);
throw std::runtime_error("ggml-hex: failed to open session (see log for details)");
}
this->valid_handle = true;
// Enable FastRPC QoS mode
{
struct remote_rpc_control_latency l;
l.enable = 1;
int err = remote_handle64_control(this->handle, DSPRPC_CONTROL_LATENCY, (void *) &l, sizeof(l));
if (err != 0) {
GGML_LOG_WARN("ggml-hex: failed to enable fastrpc QOS mode: 0x%08x\n", (unsigned) err);
}
}
GGML_LOG_INFO("ggml-hex: %s new session : session-id %d domain-id %d uri %s handle 0x%lx\n", this->c_name(),
this->session_id, this->domain_id, session_uri, (unsigned long) this->handle);
const size_t req_q_size = (sizeof(htp_opbatch_req) * opt_opqueue * 2) + 1024;
const size_t rsp_q_size = (sizeof(htp_opbatch_rsp) * opt_opqueue * 2) + 1024;
// Now let's setup the DSP queue
err = dspqueue_create(this->domain_id,
0, // Flags
req_q_size, // Request queue size (in bytes)
rsp_q_size, // Response queue size (in bytes)
nullptr, // Read packet callback (we handle reads explicitly)
nullptr, // Error callback (we handle errors during reads)
(void *) this, // Callback context
&queue);
if (err != 0) {
GGML_LOG_ERROR("ggml-hex: %s dspqueue_create failed: 0x%08x\n", this->name.c_str(), (unsigned) err);
throw std::runtime_error("ggml-hex: failed to create dspqueue (see log for details)");
}
this->valid_queue = true;
// Export queue for use on the DSP
err = dspqueue_export(queue, &this->queue_id);
if (err != 0) {
GGML_LOG_ERROR("ggml-hex: dspqueue_export failed: 0x%08x\n", (unsigned) err);
throw std::runtime_error("ggml-hex: dspqueue export failed (see log for details)");
}
if (opt_etm) {
err = htp_iface_etm(this->handle, 1);
if (err != 0) {
GGML_LOG_ERROR("ggml-hex: failed to enable ETM tracing: 0x%08x\n", (unsigned) err);
}
}
if (opt_profile) {
htp_iface_pmu_conf pmu_conf{};
std::copy(opt_pmu_evt.begin(), opt_pmu_evt.end(), pmu_conf.events);
err = htp_iface_profiler(this->handle, opt_profile, &pmu_conf);
if (err != 0) {
GGML_LOG_ERROR("ggml-hex: failed to enable profiling: 0x%08x\n", (unsigned) err);
}
}
// Allocate buffers and state for op batching
this->op_queue = new ggml_hexagon_opqueue(this, opt_opbatch, opt_opqueue);
if (!opt_vmem) {
opt_vmem = ggml_hexagon_measure_max_vmem(this);
GGML_LOG_INFO("ggml-hex: %s measured max vmem %zu\n", this->c_name(), opt_vmem);
}
this->op_batch = new ggml_hexagon_opbatch(this, opt_opbatch, opt_vmem);
// Start dspqueue/opbatch processing
err = htp_iface_start(this->handle, dev_id, this->queue_id, opt_nhvx, opt_use_hmx, opt_vmem);
if (err != 0) {
GGML_LOG_ERROR("ggml-hex: %s failed to start session: 0x%08x\n", this->c_name(), (unsigned) err);
throw std::runtime_error("ggml-hex: iface start failed (see log for details)");
}
this->valid_iface = true;
}
void ggml_hexagon_session::release() noexcept(true) {
GGML_LOG_INFO("ggml-hex: releasing session: %s\n", this->name.c_str());
int err;
if (this->valid_iface) {
// Stop dspqueue/opbatch processing
err = htp_iface_stop(this->handle);
if (err != 0) {
GGML_ABORT("ggml-hex: htp_iface_stop failed: 0x%08x\n", (unsigned) err);
}
}
delete this->op_batch;
delete this->op_queue;
if (opt_etm) {
err = htp_iface_etm(this->handle, 0);
if (err != 0) {
GGML_LOG_ERROR("ggml-hex: warn : failed to disable ETM tracing: 0x%08x\n", (unsigned) err);
}
}
if (opt_profile) {
htp_iface_pmu_conf pmu_conf{};
err = htp_iface_profiler(this->handle, 0, &pmu_conf);
if (err != 0) {
GGML_LOG_ERROR("ggml-hex: warn : failed to disable profiling: 0x%08x\n", (unsigned) err);
}
}
if (this->valid_queue) {
err = dspqueue_close(queue);
if (err != 0) {
GGML_ABORT("ggml-hex: dspqueue_close failed: 0x%08x\n", (unsigned) err);
}
}
if (this->valid_handle) {
htp_iface_close(this->handle);
}
}
ggml_hexagon_session::ggml_hexagon_session(int dev_id, ggml_backend_dev_t dev) noexcept(false) {
buffer_type.device = dev;
repack_buffer_type.device = dev;
op_batch = nullptr;
op_queue = nullptr;
try {
allocate(dev_id);
buffer_type.iface = ggml_backend_hexagon_buffer_type_interface;
buffer_type.context = new ggml_backend_hexagon_buffer_type_context(this->name, this);
repack_buffer_type.iface = ggml_backend_hexagon_repack_buffer_type_interface;
repack_buffer_type.context = new ggml_backend_hexagon_buffer_type_context(this->name + "-REPACK", this);
} catch (const std::exception & exc) {
release();
throw;
}
}
ggml_hexagon_session::~ggml_hexagon_session() noexcept(true) {
release();
delete static_cast<ggml_backend_hexagon_buffer_type_context *>(buffer_type.context);
delete static_cast<ggml_backend_hexagon_buffer_type_context *>(repack_buffer_type.context);
}
// ** backend interface
static bool ggml_backend_buffer_is_hexagon(const struct ggml_backend_buffer * b) {
return b->buft->iface.get_alignment == ggml_backend_hexagon_buffer_type_get_alignment;
}
static inline bool ggml_backend_buffer_is_hexagon_repack(const struct ggml_backend_buffer * b) {
if (!opt_hostbuf) {
return ggml_backend_buffer_is_hexagon(b);
}
return b->buft->iface.alloc_buffer == ggml_backend_hexagon_repack_buffer_type_alloc_buffer;
}
static bool ggml_hexagon_supported_flash_attn_ext(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
const struct ggml_tensor * src2 = op->src[2];
const struct ggml_tensor * src3 = op->src[3];
const struct ggml_tensor * src4 = op->src[4];
const struct ggml_tensor * dst = op;
// Check for F16 support only as requested
if ((src0->type != GGML_TYPE_F16 && src0->type != GGML_TYPE_F32) || src1->type != GGML_TYPE_F16 || src2->type != GGML_TYPE_F16) {
return false;
}
if (src3 && src3->type != GGML_TYPE_F16) { // mask
return false;
}
if (src4 && src4->type != GGML_TYPE_F32) { // sinks
return false;
}
// For now we support F32 or F16 output as htp backend often converts output on the fly if needed,
// but the op implementation writes to F16 or F32.
// Let's assume dst can be F32 or F16.
if (dst->type != GGML_TYPE_F32 && dst->type != GGML_TYPE_F16) {
return false;
}
if (dst->ne[3] != 1) {
return false;
}
return true;
}
static bool ggml_hexagon_supported_gated_delta_net(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * q = op->src[0];
const struct ggml_tensor * k = op->src[1];
const struct ggml_tensor * v = op->src[2];
const struct ggml_tensor * g = op->src[3];
const struct ggml_tensor * beta = op->src[4];
const struct ggml_tensor * state = op->src[5];
const struct ggml_tensor * dst = op;
if (!q || !k || !v || !g || !beta || !state) {
return false;
}
if (q->type != GGML_TYPE_F32 || k->type != GGML_TYPE_F32 || v->type != GGML_TYPE_F32 ||
g->type != GGML_TYPE_F32 || beta->type != GGML_TYPE_F32 || state->type != GGML_TYPE_F32 ||
dst->type != GGML_TYPE_F32) {
return false;
}
if (!ggml_is_contiguous_rows(q) || !ggml_is_contiguous_rows(k) || !ggml_is_contiguous_rows(v) ||
!ggml_is_contiguous(g) || !ggml_is_contiguous(beta) || !ggml_is_contiguous(state) ||
!ggml_is_contiguous(dst)) {
return false;
}
const int64_t S_v = v->ne[0];
const int64_t H = v->ne[1];
const int64_t n_tokens = v->ne[2];
const int64_t n_seqs = v->ne[3];
if (S_v <= 0 || S_v > 128 || H <= 0 || n_tokens <= 0 || n_seqs <= 0) {
return false;
}
if (q->ne[0] != S_v || k->ne[0] != S_v || q->ne[1] <= 0 || k->ne[1] <= 0 ||
q->ne[2] != n_tokens || k->ne[2] != n_tokens || q->ne[3] <= 0 || k->ne[3] <= 0 ||
(n_seqs % q->ne[3]) != 0 || (n_seqs % k->ne[3]) != 0) {
return false;
}
if ((g->ne[0] != 1 && g->ne[0] != S_v) || beta->ne[0] != 1) {
return false;
}
if (ggml_nelements(state) != S_v * S_v * H * n_seqs) {
return false;
}
if (dst->ne[0] != S_v * H || dst->ne[1] != n_tokens * n_seqs + S_v * n_seqs) {
return false;
}
GGML_UNUSED(sess);
return true;
}
static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * sess, const struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
if (dst->type != GGML_TYPE_F32) {
return false;
}
if (src1->type != GGML_TYPE_F32 && src1->type != GGML_TYPE_F16) {
return false;
}
switch (src0->type) {
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q8_0:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_MXFP4:
if (src0->ne[0] % 32) {
return false;
}
if (ggml_nrows(src0) > 16 * 1024) {
return false; // typically the lm-head which would be too large for VTCM
}
if (ggml_nrows(src1) > 1024 || src1->ne[2] != 1 || src1->ne[3] != 1) {
return false; // no huge batches or broadcasting (for now)
}
// src0 (weights) must be repacked
if (src0->buffer && !ggml_backend_buffer_is_hexagon_repack(src0->buffer)) {
return false;
}
break;
case GGML_TYPE_F16:
if (src0->nb[1] < src0->nb[0]) {
GGML_LOG_DEBUG("ggml_hexagon_supported_mul_mat: permuted F16 src0 not supported\n");
return false;
}
if (ggml_nrows(src1) > 1024) {
return false; // no huge batches (for now)
}
break;
default:
return false;
}
return true;
}
static bool ggml_hexagon_supported_mul_mat_id(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
const struct ggml_tensor * src2 = op->src[2];
const struct ggml_tensor * dst = op;
if (src1->type != GGML_TYPE_F32 || dst->type != GGML_TYPE_F32 || src2->type != GGML_TYPE_I32) {
return false;
}
switch (src0->type) {
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q8_0:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_MXFP4:
if ((src0->ne[0] % 32)) {
return false;
}
// src0 (weights) must be repacked
if (src0->buffer && !ggml_backend_buffer_is_hexagon_repack(src0->buffer)) {
return false;
}
break;
default:
return false;
}
return true;
}
static bool ggml_hexagon_supported_binary(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
const struct ggml_tensor * dst = op;
if (src0->type == GGML_TYPE_F32) {
if (src1->type != GGML_TYPE_F32) {
return false;
}
if (dst->type != GGML_TYPE_F32) {
return false;
}
}
else if (src0->type == GGML_TYPE_F16) {
if (src1->type != GGML_TYPE_F16) {
return false;
}
if (dst->type != GGML_TYPE_F16) {
return false;
}
}
else {
return false;
}
if (!ggml_are_same_shape(src0, dst)) {
return false;
}
if (!ggml_can_repeat(src1, src0) || ggml_is_permuted(src1)) {
return false;
}
return true;
}
static bool ggml_hexagon_supported_add_id(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
const struct ggml_tensor * dst = op;
if (src0->type != GGML_TYPE_F32) {
return false;
}
if (src1->type != GGML_TYPE_F32) {
return false;
}
if (dst->type != GGML_TYPE_F32) {
return false;
}
if (!ggml_are_same_shape(src0, dst)) {
return false;
}
// REVISIT: add support for non-contigiuos tensors
if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1) || !ggml_is_contiguous(dst)) {
return false;
}
return true;
}
static bool ggml_hexagon_supported_unary(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * dst = op;
if (src0->type != GGML_TYPE_F32) {
return false;
}
if (dst->type != GGML_TYPE_F32) {
return false;
}
if (!ggml_are_same_shape(src0, dst)) {
return false;
}
// dst must be contiguous; src0 may be non-contiguous
if (!ggml_is_contiguous(dst)) {
return false;
}
return true;
}
static bool ggml_hexagon_supported_sum_rows(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * dst = op;
if (src0->type != GGML_TYPE_F32) {
return false;
}
if (dst->type != GGML_TYPE_F32) {
return false;
}
// TODO: add support for non-contigiuos tensors
if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(dst)) {
return false;
}
return true;
}
static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session * sess,
const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
const struct ggml_tensor * dst = op;
if (src0->type != GGML_TYPE_F32) {
return false;
}
if (dst->type != GGML_TYPE_F32) {
return false;
}
if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(dst)) {
return false;
}
if (src1) {
if (src1->type != GGML_TYPE_F32) {
return false;
}
if (!ggml_are_same_shape(src0, src1)) {
return false;
}
if (!ggml_is_contiguous(src1)) {
return false;
}
}
return true;
}
static bool ggml_hexagon_supported_softmax(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
const struct ggml_tensor * src2 = op->src[2];
const struct ggml_tensor * dst = op;
if (src2) {
return false; // FIXME: add support for sinks
}
if (src0->type != GGML_TYPE_F32) {
return false;
}
if (dst->type != GGML_TYPE_F32) {
return false;
}
if (src1) {
if (src1->type != GGML_TYPE_F32 && src1->type != GGML_TYPE_F16) {
return false;
}
if (src0->ne[0] != src1->ne[0]) {
return false;
}
if (src1->ne[1] < src0->ne[1]) {
return false;
}
if (src0->ne[2] % src1->ne[2] != 0) {
return false;
}
if (src0->ne[3] % src1->ne[3] != 0) {
return false;
}
}
if (src1) {
if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1) || !ggml_is_contiguous(dst)) {
return false;
}
} else {
if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(dst)) {
return false;
}
}
// Reject non-HVX-aligned sizes when ne[0] > HVX_F32_LANES
// The HVX softmax implementation has issues with tail handling for larger non-aligned sizes
// Small sizes (ne[0] <= 32) work correctly with tail-only processing
const int64_t ne0 = src0->ne[0];
if (ne0 > 32 && (ne0 & (32 - 1)) != 0) {
return false;
}
// HVX vector size constraints for softmax
#define SOFTMAX_MAX_ROW_SIZE 131072 // 128K elements max for numerical precision
// Reject very large row sizes to avoid numerical precision issues
// Softmax accumulation over many elements can lead to precision loss
if (ne0 > SOFTMAX_MAX_ROW_SIZE) {
return false;
}
return true;
}
static bool ggml_hexagon_supported_set_rows(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0]; // values
const struct ggml_tensor * src1 = op->src[1]; // indices
const struct ggml_tensor * dst = op;
if (src0->type != GGML_TYPE_F32) {
return false;
}
if (src1->type != GGML_TYPE_I32 && src1->type != GGML_TYPE_I64) {
return false;
}
if (dst->type != GGML_TYPE_F16) {
return false;
}
return true;
}
static bool ggml_hexagon_supported_get_rows(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0]; // values
const struct ggml_tensor * src1 = op->src[1]; // indices
const struct ggml_tensor * dst = op;
if (src0->type != GGML_TYPE_F32) {
return false;
}
if (src1->type != GGML_TYPE_I32 && src1->type != GGML_TYPE_I64) {
return false;
}
if (dst->type != GGML_TYPE_F32) {
return false;
}
return true;
}
static bool ggml_hexagon_supported_argsort(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0]; // values
const struct ggml_tensor * dst = op; // indices
if (src0->type != GGML_TYPE_F32) {
return false;
}
if (dst->type != GGML_TYPE_I32) {
return false;
}
if (src0->ne[0] > (16*1024)) {
// reject tensors with huge rows for now
return false;
}
return true;
}
static bool ggml_hexagon_supported_rope(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const int32_t * op_params = &op->op_params[0];
int mode = op_params[2];
if ((mode & GGML_ROPE_TYPE_MROPE) || (mode & GGML_ROPE_TYPE_VISION)) {
return false;
}
if (mode & 1) {
return false;
}
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
const struct ggml_tensor * src2 = op->src[2];
const struct ggml_tensor * dst = op;
if (src0->type != GGML_TYPE_F32) {
return false; // FIXME: add support for GGML_TYPE_F16 for src0
}
if (dst->type != GGML_TYPE_F32) {
return false;
}
if (src1->type != GGML_TYPE_I32) {
return false;
}
if (src2) {
if (src2->type != GGML_TYPE_F32) {
return false;
}
int n_dims = op_params[1];
if (src2->ne[0] < (n_dims / 2)) {
return false;
}
}
if (src2) {
if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1) || !ggml_is_contiguous(src2) ||
!ggml_is_contiguous(dst)) {
return false;
}
} else {
if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1) || !ggml_is_contiguous(dst)) {
return false;
}
}
return true;
}
static bool ggml_hexagon_supported_ssm_conv(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * src1 = op->src[1];
const struct ggml_tensor * dst = op;
// Only support FP32 for now
if (src0->type != GGML_TYPE_F32 || src1->type != GGML_TYPE_F32 || dst->type != GGML_TYPE_F32) {
return false;
}
// Check IO tensor shapes and dims
if (src0->ne[3] != 1 || src1->ne[2] != 1 || src1->ne[3] != 1 || dst->ne[3] != 1) {
return false; // src0 should be effectively 3D
}
const int d_conv = src1->ne[0];
const int d_inner = src0->ne[1];
const int n_t = dst->ne[1];
const int n_s = dst->ne[2];
if (src0->ne[0] != d_conv - 1 + n_t || src0->ne[1] != d_inner || src0->ne[2] != n_s) {
return false;
}
if (src1->ne[0] != d_conv || src1->ne[1] != d_inner) {
return false;
}
if (dst->ne[0] != d_inner || dst->ne[1] != n_t || dst->ne[2] != n_s) {
return false;
}
// TODO: add support for non-contiguous tensors
if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(src1) || !ggml_is_contiguous(dst)) {
return false;
}
return true;
}
static bool ggml_hexagon_supported_pad(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * dst = op;
if (src0->type != GGML_TYPE_F32 || dst->type != GGML_TYPE_F32) {
return false;
}
GGML_UNUSED(sess);
return true;
}
static bool ggml_hexagon_supported_cumsum(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * dst = op;
if (src0->type != GGML_TYPE_F32 || dst->type != GGML_TYPE_F32) {
return false;
}
if (!ggml_is_contiguous(src0) || !ggml_is_contiguous(dst)) {
return false;
}
GGML_UNUSED(sess);
return true;
}
static bool ggml_hexagon_supported_diag(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * dst = op;
// diag only supports F32 currently
if (src0->type != GGML_TYPE_F32 || dst->type != GGML_TYPE_F32) {
return false;
}
// Input must have ne[1] == 1 (vector input)
if (src0->ne[1] != 1) {
return false;
}
// Output must be square in first two dimensions
if (dst->ne[0] != dst->ne[1] || dst->ne[0] != src0->ne[0]) {
return false;
}
GGML_UNUSED(sess);
return true;
}
static bool ggml_hexagon_supported_solve_tri(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0]; // A
const struct ggml_tensor * src1 = op->src[1]; // B
const struct ggml_tensor * dst = op; // X
if (!src0 || !src1) {
return false;
}
if (src0->type != GGML_TYPE_F32 || src1->type != GGML_TYPE_F32 || dst->type != GGML_TYPE_F32) {
return false;
}
if (src0->ne[0] != src0->ne[1]) {
return false;
}
if (src0->ne[1] != src1->ne[1]) {
return false;
}
if (src0->ne[2] != src1->ne[2] || src0->ne[3] != src1->ne[3]) {
return false;
}
if (dst->ne[0] != src1->ne[0] || dst->ne[1] != src1->ne[1] || dst->ne[2] != src1->ne[2] || dst->ne[3] != src1->ne[3]) {
return false;
}
GGML_UNUSED(sess);
return true;
}
static const char * ggml_backend_hexagon_name(ggml_backend_t backend) {
auto sess = static_cast<ggml_hexagon_session *>(backend->context);
return sess->c_name();
}
static void ggml_backend_hexagon_free(ggml_backend_t backend) {
// we just need to delete the backend here
// the sessions are allocated & freed as part of the registry
delete backend;
}
static htp_op_code op_remap_to_htp(const ggml_tensor * t) {
switch (t->op) {
case GGML_OP_FLASH_ATTN_EXT: return HTP_OP_FLASH_ATTN_EXT;
case GGML_OP_MUL_MAT: return HTP_OP_MUL_MAT;
case GGML_OP_MUL_MAT_ID: return HTP_OP_MUL_MAT_ID;
case GGML_OP_MUL: return HTP_OP_MUL;
case GGML_OP_ADD: return HTP_OP_ADD;
case GGML_OP_ADD_ID: return HTP_OP_ADD_ID;
case GGML_OP_SUB: return HTP_OP_SUB;
case GGML_OP_DIV: return HTP_OP_DIV;
case GGML_OP_CPY: return HTP_OP_CPY;
case GGML_OP_CONT: return HTP_OP_CPY;
case GGML_OP_GET_ROWS: return HTP_OP_GET_ROWS;
case GGML_OP_SET_ROWS: return HTP_OP_SET_ROWS;
case GGML_OP_SUM_ROWS: return HTP_OP_SUM_ROWS;
case GGML_OP_ARGSORT: return HTP_OP_ARGSORT;
case GGML_OP_L2_NORM: return HTP_OP_L2_NORM;
case GGML_OP_RMS_NORM: return HTP_OP_RMS_NORM;
case GGML_OP_SCALE: return HTP_OP_SCALE;
case GGML_OP_SQR: return HTP_OP_SQR;
case GGML_OP_SQRT: return HTP_OP_SQRT;
case GGML_OP_SOFT_MAX: return HTP_OP_SOFTMAX;
case GGML_OP_SSM_CONV: return HTP_OP_SSM_CONV;
case GGML_OP_GATED_DELTA_NET: return HTP_OP_GATED_DELTA_NET;
case GGML_OP_ROPE: return HTP_OP_ROPE;
case GGML_OP_REPEAT: return HTP_OP_REPEAT;
case GGML_OP_CUMSUM: return HTP_OP_CUMSUM;
case GGML_OP_FILL: return HTP_OP_FILL;
case GGML_OP_DIAG: return HTP_OP_DIAG;
case GGML_OP_SOLVE_TRI: return HTP_OP_SOLVE_TRI;
case GGML_OP_PAD: return HTP_OP_PAD;
case GGML_OP_UNARY:
switch (ggml_get_unary_op(t)) {
case GGML_UNARY_OP_SILU: return HTP_OP_UNARY_SILU;
case GGML_UNARY_OP_GELU: return HTP_OP_UNARY_GELU;
case GGML_UNARY_OP_SIGMOID: return HTP_OP_UNARY_SIGMOID;
case GGML_UNARY_OP_NEG: return HTP_OP_UNARY_NEG;
case GGML_UNARY_OP_EXP: return HTP_OP_UNARY_EXP;
case GGML_UNARY_OP_SOFTPLUS: return HTP_OP_UNARY_SOFTPLUS;
case GGML_UNARY_OP_TANH: return HTP_OP_UNARY_TANH;
default:
break;
}
break;
case GGML_OP_GLU:
switch (ggml_get_glu_op(t)) {
case GGML_GLU_OP_SWIGLU: return HTP_OP_GLU_SWIGLU;
case GGML_GLU_OP_SWIGLU_OAI: return HTP_OP_GLU_SWIGLU_OAI;
case GGML_GLU_OP_GEGLU: return HTP_OP_GLU_GEGLU;
default: break;
}
break;
default:
GGML_ABORT("\nggml-hex: graph-compute %s is not supported\n", ggml_op_desc(t));
}
return HTP_OP_INVALID;
}
static inline bool op_is_compute(ggml_tensor *node)
{
return !ggml_op_is_empty(node->op) && !ggml_is_empty(node) && (node->flags & GGML_TENSOR_FLAG_COMPUTE);
}
static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) {
auto sess = static_cast<ggml_hexagon_session *>(backend->context);
HEX_VERBOSE("ggml-hex: %s graph-compute n_nodes %d\n", sess->c_name(), graph->n_nodes);
for (int i = 0; i < graph->n_nodes; ++i) {
ggml_tensor * n = graph->nodes[i];
if (op_is_compute(n) && (opt_opstage & HTP_OPSTAGE_QUEUE)) {
sess->enqueue_op(op_remap_to_htp(n), n);
}
}
// Wait until all pending ops complete
sess->flush();
return GGML_STATUS_SUCCESS;
}
static void ggml_backend_hexagon_synchronize(ggml_backend_t backend) {
auto sess = static_cast<ggml_hexagon_session *>(backend->context);
HEX_VERBOSE("ggml-hex: %s synchronize\n", sess->c_name());
// Wait until all pending ops complete
sess->flush();
}
struct node_info {
ggml_tensor * node;
std::vector<ggml_tensor *> fused;
ggml_op op() const {
return node->op;
}
const ggml_tensor * dst() const {
return fused.empty() ? node : fused.back();
}
const ggml_tensor * src0() const {
return node->src[0];
}
const ggml_tensor * src1() const {
return node->src[1];
}
bool is_empty() const {
return ggml_op_is_empty(node->op);
}
void add_fused(ggml_tensor * t) {
fused.push_back(t);
}
bool stackable() const {
switch (this->op()) {
case GGML_OP_MUL_MAT:
case GGML_OP_MUL_MAT_ID:
return ggml_is_quantized(this->src0()->type);
default:
return false;
}
}
bool same_input(const node_info& n) const {
return n.src1() == this->src1();
}
};
static std::vector<int> ggml_hexagon_graph_optimize_reorder(const std::vector<node_info> & nodes) {
const int n = nodes.size();
std::vector<int> res;
res.reserve(n);
std::vector<bool> used(n, false);
// The main goal here is to stack the MUL_MAT ops with the same src1 input.
// This allows use to reuse dynamically quantized src1 in VTCM.
// TODO: the current version might do incorrect reordering in cases where quantized src0
// input is an output of another Op.
for (int i0 = 0; i0 < n; i0++) {
if (used[i0]) {
continue;
}
res.push_back(i0);
const auto & node0 = nodes[i0];
if (!node0.stackable()) {
continue;
}
// that many nodes forward to search for stackable nodes that can reuse VTCM
constexpr int N_FORWARD = 16;
for (int i1 = i0 + 1; i1 < i0 + N_FORWARD && i1 < n; i1++) {
if (used[i1]) {
continue;
}
const auto & node1 = nodes[i1];
if (node1.stackable() && node1.same_input(node0)) {
res.push_back(i1);
used[i1] = true;
}
}
}
return res;
}
static void ggml_backend_hexagon_graph_optimize(ggml_backend_t backend, ggml_cgraph * gf) {
const int n = gf->n_nodes;
constexpr int MAX_FUSE = 16;
enum ggml_op ops[MAX_FUSE];
std::vector<node_info> nodes;
nodes.reserve(gf->n_nodes);
// fuse nodes:
// we don't want to make reorders that break fusing, so we first pack all fusable tensors
// and perform the reorder over the fused nodes. after the reorder is done, we unfuse
for (int i = 0; i < n; i++) {
node_info node = {
/*.node =*/gf->nodes[i],
/*.fused =*/{},
};
// fuse only ops that start with these operations
// can be expanded when needed
if (node.op() == GGML_OP_ADD ||
node.op() == GGML_OP_NORM ||
node.op() == GGML_OP_RMS_NORM) {
ops[0] = node.op();
int f = i + 1;
while (f < n && f < i + MAX_FUSE) {
// conservatively allow fusing only these ops
// can be expanded when needed
if (gf->nodes[f]->op != GGML_OP_ADD &&
gf->nodes[f]->op != GGML_OP_MUL &&
gf->nodes[f]->op != GGML_OP_NORM &&
gf->nodes[f]->op != GGML_OP_RMS_NORM) {
break;
}
ops[f - i] = gf->nodes[f]->op;
f++;
}
f -= i;
for (; f > 1; f--) {
if (ggml_can_fuse(gf, i, ops, f)) {
break;
}
}
// add the fused tensors into the node info so we can unfuse them later
for (int k = 1; k < f; k++) {
++i;
// the .dst() becomes the last fused tensor
node.add_fused(gf->nodes[i]);
}
}
nodes.push_back(std::move(node));
}
const auto order = ggml_hexagon_graph_optimize_reorder(nodes);
// unfuse
{
int j = 0;
for (const auto i : order) {
const auto & node = nodes[i];
gf->nodes[j++] = node.node;
for (auto * fused : node.fused) {
gf->nodes[j++] = fused;
}
}
}
}
static struct ggml_backend_i hexagon_backend_i = {
/* .get_name = */ ggml_backend_hexagon_name,
/* .free = */ ggml_backend_hexagon_free,
/* .set_tensor_async = */ NULL,
/* .get_tensor_async = */ NULL,
/* .set_tensor_2d_async = */ NULL,
/* .get_tensor_2d_async = */ NULL,
/* .cpy_tensor_async = */ NULL,
/* .synchronize = */ ggml_backend_hexagon_synchronize,
/* .graph_plan_create = */ NULL,
/* .graph_plan_free = */ NULL,
/* .graph_plan_update = */ NULL,
/* .graph_plan_compute = */ NULL,
/* .graph_compute = */ ggml_backend_hexagon_graph_compute,
/* .event_record = */ NULL,
/* .event_wait = */ NULL,
/* .graph_optimize = */ ggml_backend_hexagon_graph_optimize,
};
static ggml_guid_t ggml_backend_hexagon_guid() {
static ggml_guid guid = { 0x7b, 0x57, 0xdc, 0xaf, 0xde, 0x12, 0x1d, 0x49,
0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11, 0x11 };
return &guid;
}
bool ggml_backend_is_hexagon(ggml_backend_t backend) {
return backend && backend->iface.get_name == ggml_backend_hexagon_name;
}
// device interface
static ggml_backend_t ggml_backend_hexagon_device_init(ggml_backend_dev_t dev, const char * params) {
auto sess = static_cast<ggml_hexagon_session *>(dev->context);
return new ggml_backend{
/* .guid = */ ggml_backend_hexagon_guid(),
/* .interface = */ hexagon_backend_i,
/* .device = */ dev,
/* .context = */ sess,
};
GGML_UNUSED(params);
}
static const char * ggml_backend_hexagon_device_get_name(ggml_backend_dev_t dev) {
auto sess = static_cast<ggml_hexagon_session *>(dev->context);
return sess->c_name();
GGML_UNUSED(dev);
}
static const char * ggml_backend_hexagon_device_get_description(ggml_backend_dev_t dev) {
return "Hexagon";
GGML_UNUSED(dev);
}
static void ggml_backend_hexagon_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
*free = 0;
*total = *free;
GGML_UNUSED(dev);
}
static enum ggml_backend_dev_type ggml_backend_hexagon_device_get_type(ggml_backend_dev_t dev) {
return GGML_BACKEND_DEVICE_TYPE_GPU;
GGML_UNUSED(dev);
}
static void ggml_backend_hexagon_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
props->name = ggml_backend_hexagon_device_get_name(dev);
props->description = ggml_backend_hexagon_device_get_description(dev);
props->type = ggml_backend_hexagon_device_get_type(dev);
ggml_backend_hexagon_device_get_memory(dev, &props->memory_free, &props->memory_total);
props->caps = {
/* .async = */ true,
/* .host_buffer = */ (bool) opt_hostbuf,
/* .buffer_from_host_ptr = */ false,
/* .events = */ false,
};
}
static ggml_backend_buffer_type_t ggml_backend_hexagon_device_get_buffer_type(ggml_backend_dev_t dev) {
auto sess = static_cast<ggml_hexagon_session *>(dev->context);
return &sess->buffer_type;
}
static ggml_backend_buffer_type_t ggml_backend_hexagon_device_get_repack_buffer_type(ggml_backend_dev_t dev) {
auto sess = static_cast<ggml_hexagon_session *>(dev->context);
return &sess->repack_buffer_type;
}
static bool ggml_hexagon_supported_buffer(ggml_hexagon_session *sess, const struct ggml_tensor * t) {
if (t && t->buffer) {
if (ggml_backend_buffer_is_hexagon(t->buffer) == false) return false; // not our buffer
if (ggml_backend_hexagon_buffer_get_sess(t->buffer) != sess) return false; // wrong session
}
return true;
}
static bool ggml_hexagon_supported_buffers(ggml_hexagon_session *sess, const struct ggml_tensor * t) {
// all srcs & dsts must be mapped to the same session
if (!ggml_hexagon_supported_buffer(sess, t)) {
return false;
}
for (int i = 0; i < GGML_MAX_SRC; i++) {
if (!ggml_hexagon_supported_buffer(sess, t->src[i])) {
return false;
}
}
return true;
}
static bool ggml_hexagon_supported_cpy(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * dst = op;
// for now we can do f32 -> f16 and f16 -> f32 (without reshaping)
if (src0->type != GGML_TYPE_F32 && src0->type != GGML_TYPE_F16) return false;
if ( dst->type != GGML_TYPE_F32 && dst->type != GGML_TYPE_F16) return false;
const bool sametype = (src0->type == dst->type);
const bool transposed = ggml_is_transposed(src0) || ggml_is_transposed(dst);
const bool sameshape = !transposed && ggml_are_same_shape(src0, dst);
// can handle any shape and any same-type (pretty slow if reshaping is required)
if (sametype) return true;
// cannot handle re-shaping and type conversion at the same time
if (!sameshape) return false;
return true;
}
static bool ggml_hexagon_supported_cont(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
GGML_UNUSED(sess);
const struct ggml_tensor * src0 = op->src[0];
// CONT is same-type only, supports f32 and f16
if (src0->type != GGML_TYPE_F32 && src0->type != GGML_TYPE_F16) return false;
return true;
}
static bool ggml_hexagon_supported_repeat(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
GGML_UNUSED(sess);
const struct ggml_tensor * src0 = op->src[0];
const struct ggml_tensor * dst = op;
// Support f32 and f16
if (src0->type != GGML_TYPE_F32 && src0->type != GGML_TYPE_F16) return false;
// src and dst must be the same type
if (src0->type != dst->type) return false;
// dst dims must be multiples of src dims
if (dst->ne[0] % src0->ne[0] != 0) return false;
if (dst->ne[1] % src0->ne[1] != 0) return false;
if (dst->ne[2] % src0->ne[2] != 0) return false;
if (dst->ne[3] % src0->ne[3] != 0) return false;
// require contiguous tensors (no transposition)
if (ggml_is_transposed(src0) || ggml_is_transposed(dst)) return false;
return true;
}
static bool ggml_hexagon_supported_fill(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) {
const struct ggml_tensor * dst = op;
if (dst->type != GGML_TYPE_F32 && dst->type != GGML_TYPE_F16) {
return false;
}
GGML_UNUSED(sess);
return true;
}
static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
auto sess = static_cast<ggml_hexagon_session *>(dev->context);
// reject ops that match the filter
if (opt_opfilter && std::regex_match(ggml_op_desc(op), *opt_opfilter)) {
return false;
}
// all srcs & dsts must be mapped to the same session
if (!ggml_hexagon_supported_buffers(sess, op)) {
ggml_hexagon_dump_op_supp(sess->name, op, false);
return false;
}
bool supp = false;
switch (op->op) {
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
supp = true;
break;
case GGML_OP_MUL:
case GGML_OP_ADD:
case GGML_OP_SUB:
case GGML_OP_DIV:
supp = ggml_hexagon_supported_binary(sess, op);
break;
case GGML_OP_MUL_MAT:
supp = ggml_hexagon_supported_mul_mat(sess, op);
break;
case GGML_OP_MUL_MAT_ID:
supp = ggml_hexagon_supported_mul_mat_id(sess, op);
break;
case GGML_OP_ADD_ID:
supp = ggml_hexagon_supported_add_id(sess, op);
break;
case GGML_OP_L2_NORM:
supp = ggml_hexagon_supported_unary(sess, op);
break;
case GGML_OP_RMS_NORM:
case GGML_OP_SCALE:
supp = ggml_hexagon_supported_unary(sess, op);
break;
case GGML_OP_SQR:
case GGML_OP_SQRT:
supp = ggml_hexagon_supported_unary(sess, op);
break;
case GGML_OP_SUM_ROWS:
supp = ggml_hexagon_supported_sum_rows(sess, op);
break;
case GGML_OP_SOFT_MAX:
supp = ggml_hexagon_supported_softmax(sess, op);
break;
case GGML_OP_UNARY:
switch (ggml_get_unary_op(op)) {
case GGML_UNARY_OP_NEG:
case GGML_UNARY_OP_EXP:
case GGML_UNARY_OP_SIGMOID:
case GGML_UNARY_OP_SOFTPLUS:
case GGML_UNARY_OP_TANH:
supp = ggml_hexagon_supported_unary(sess, op);
break;
case GGML_UNARY_OP_SILU:
case GGML_UNARY_OP_GELU:
supp = ggml_hexagon_supported_activations(sess, op);
break;
default:
break;
}
break;
case GGML_OP_GLU:
switch (ggml_get_glu_op(op)) {
case GGML_GLU_OP_SWIGLU:
case GGML_GLU_OP_SWIGLU_OAI:
case GGML_GLU_OP_GEGLU:
supp = ggml_hexagon_supported_activations(sess, op);
break;
default:
break;
}
break;
case GGML_OP_ROPE:
supp = ggml_hexagon_supported_rope(sess, op);
break;
case GGML_OP_FLASH_ATTN_EXT:
supp = ggml_hexagon_supported_flash_attn_ext(sess, op);
break;
case GGML_OP_SET_ROWS:
supp = ggml_hexagon_supported_set_rows(sess, op);
break;
case GGML_OP_GET_ROWS:
supp = ggml_hexagon_supported_get_rows(sess, op);
break;
case GGML_OP_CPY:
supp = ggml_hexagon_supported_cpy(sess, op);
break;
case GGML_OP_CONT:
supp = ggml_hexagon_supported_cont(sess, op);
break;
case GGML_OP_REPEAT:
supp = ggml_hexagon_supported_repeat(sess, op);
break;
case GGML_OP_ARGSORT:
supp = ggml_hexagon_supported_argsort(sess, op);
break;
case GGML_OP_SSM_CONV:
supp = ggml_hexagon_supported_ssm_conv(sess, op);
break;
case GGML_OP_GATED_DELTA_NET:
supp = ggml_hexagon_supported_gated_delta_net(sess, op);
break;
case GGML_OP_CUMSUM:
supp = ggml_hexagon_supported_cumsum(sess, op);
break;
case GGML_OP_FILL:
supp = ggml_hexagon_supported_fill(sess, op);
break;
case GGML_OP_DIAG:
supp = ggml_hexagon_supported_diag(sess, op);
break;
case GGML_OP_SOLVE_TRI:
supp = ggml_hexagon_supported_solve_tri(sess, op);
break;
case GGML_OP_PAD:
supp = ggml_hexagon_supported_pad(sess, op);
break;
default:
break;
}
ggml_hexagon_dump_op_supp(sess->name, op, supp);
return supp;
}
static bool ggml_backend_hexagon_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
if (buft->iface.get_alignment != ggml_backend_hexagon_buffer_type_get_alignment) {
return false;
}
auto s0 = static_cast<ggml_hexagon_session *>(dev->context);
auto s1 = static_cast<ggml_backend_hexagon_buffer_type_context *>(buft->context)->sess;
// Need session/domain-id for buffers to be compatible
bool supp = (s0->session_id == s1->session_id);
HEX_VERBOSE("ggml-hex: %s device-supports-buft %s (%d)\n", s0->name.c_str(), s1->name.c_str(), (int) supp);
return supp;
}
static ggml_backend_buffer_type_t * ggml_backend_hexagon_device_get_extra_buffers_type(ggml_backend_dev_t dev) {
auto s0 = static_cast<ggml_hexagon_session *>(dev->context);
HEX_VERBOSE("ggml-hex: device-get-extra-buft : %s \n", s0->name.c_str());
static ggml_backend_buffer_type_t bufts[2];
bufts[0] = ggml_backend_hexagon_device_get_repack_buffer_type(dev);
bufts[1] = NULL;
return bufts;
}
static const struct ggml_backend_device_i ggml_backend_hexagon_device_i = {
/* .get_name = */ ggml_backend_hexagon_device_get_name,
/* .get_description = */ ggml_backend_hexagon_device_get_description,
/* .get_memory = */ ggml_backend_hexagon_device_get_memory,
/* .get_type = */ ggml_backend_hexagon_device_get_type,
/* .get_props = */ ggml_backend_hexagon_device_get_props,
/* .init_backend = */ ggml_backend_hexagon_device_init,
/* .get_buffer_type = */ ggml_backend_hexagon_device_get_buffer_type,
/* .get_host_buffer_type = */ NULL, // ggml_backend_hexagon_device_get_host_buffer_type,
/* .buffer_from_host_ptr = */ NULL, // ggml_backend_hexagon_device_buffer_from_ptr,
/* .supports_op = */ ggml_backend_hexagon_device_supports_op,
/* .supports_buft = */ ggml_backend_hexagon_device_supports_buft,
/* .offload_op = */ NULL, // ggml_backend_hexagon_device_offload_op,
/* .event_new = */ NULL,
/* .event_free = */ NULL,
/* .event_synchronize = */ NULL,
};
//** backend registry
#define GGML_HEXAGON_MAX_SESSIONS 16
struct ggml_hexagon_registry {
ggml_hexagon_registry(ggml_backend_reg_t reg);
~ggml_hexagon_registry();
ggml_backend_device devices[GGML_HEXAGON_MAX_SESSIONS];
};
ggml_hexagon_registry::ggml_hexagon_registry(ggml_backend_reg_t reg) {
GGML_LOG_INFO("ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev %zu\n", opt_ndev);
GGML_LOG_INFO("ggml-hex: Hexagon Arch version v%d\n", opt_arch);
// Create devices / sessions
for (size_t i = 0; i < opt_ndev; i++) {
devices[i].iface = ggml_backend_hexagon_device_i;
devices[i].reg = reg;
try {
devices[i].context = new ggml_hexagon_session(i, &devices[i]);
} catch (const std::exception & exc) {
GGML_LOG_ERROR("ggml-hex: failed to create device/session %zu\n", i);
devices[i].context = nullptr;
}
}
}
ggml_hexagon_registry::~ggml_hexagon_registry() {
GGML_LOG_INFO("ggml-hex: releasing registry\n");
// Release devices / sessions
for (size_t i = 0; i < opt_ndev; i++) {
auto sess = static_cast<ggml_hexagon_session *>(devices[i].context);
delete sess;
}
}
static const char * ggml_backend_hexagon_reg_get_name(ggml_backend_reg_t reg) {
return "HTP";
GGML_UNUSED(reg);
}
static size_t ggml_backend_hexagon_reg_get_device_count(ggml_backend_reg_t reg) {
return opt_ndev;
GGML_UNUSED(reg);
}
static ggml_backend_dev_t ggml_backend_hexagon_reg_get_device(ggml_backend_reg_t reg, size_t index) {
auto hreg = static_cast<ggml_hexagon_registry *>(reg->context);
if (index >= opt_ndev || !hreg->devices[index].context) {
return nullptr;
}
return &hreg->devices[index];
}
static void * ggml_backend_hexagon_get_proc_address(ggml_backend_reg_t reg, const char * name) {
if (strcmp(name, "ggml_backend_dev_get_extra_bufts") == 0 && opt_hostbuf) {
ggml_backend_dev_get_extra_bufts_t fct = ggml_backend_hexagon_device_get_extra_buffers_type;
return (void *) fct;
}
return NULL;
}
template<typename T> std::vector<T> str_to_vec(const char* str) {
std::stringstream ss(str);
std::vector<T> v;
std::string t;
while (std::getline(ss, t, ',')) {
v.push_back(std::stoul(t, nullptr, 0));
}
return v;
}
template<typename T, int BASE=10> std::string vec_to_str(std::vector<T> v) {
std::stringstream ss;
ss << std::setbase(BASE) << std::showbase;
for (auto i : v) { ss << i << ','; }
auto str = ss.str(); str.pop_back(); // drop last comma
return str;
}
static void ggml_hexagon_init(ggml_backend_reg * reg) {
// Basic sanity checks to make sure definitions match
static_assert((unsigned int) HTP_TYPE_Q4_0 == (unsigned int) GGML_TYPE_Q4_0,
"please update hexagon_type to match ggml_type");
static_assert((unsigned int) HTP_TYPE_Q8_0 == (unsigned int) GGML_TYPE_Q8_0,
"please update hexagon_type to match ggml_type");
static_assert((unsigned int) HTP_TYPE_MXFP4 == (unsigned int) GGML_TYPE_MXFP4,
"please update hexagon_type to match ggml_type");
static_assert((unsigned int) HTP_TYPE_IQ4_NL == (unsigned int) GGML_TYPE_IQ4_NL,
"please update hexagon_type to match ggml_type");
const char * str_verbose = getenv("GGML_HEXAGON_VERBOSE");
const char * str_hostbuf = getenv("GGML_HEXAGON_HOSTBUF");
const char * str_opstage = getenv("GGML_HEXAGON_OPSTAGE");
const char * str_opbatch = getenv("GGML_HEXAGON_OPBATCH");
const char * str_opqueue = getenv("GGML_HEXAGON_OPQUEUE");
const char * str_opfilter = getenv("GGML_HEXAGON_OPFILTER");
const char * str_profile = getenv("GGML_HEXAGON_PROFILE");
const char * str_etm = getenv("GGML_HEXAGON_ETM");
const char * str_nhvx = getenv("GGML_HEXAGON_NHVX");
const char * str_use_hmx = getenv("GGML_HEXAGON_USE_HMX");
const char * str_ndev = getenv("GGML_HEXAGON_NDEV");
const char * str_arch = getenv("GGML_HEXAGON_ARCH");
const char * str_vmem = getenv("GGML_HEXAGON_VMEM");
const char * str_mbuf = getenv("GGML_HEXAGON_MBUF");
// Init Arch first since it affects other defaults
if (!str_arch) {
int err = get_hex_arch_ver(CDSP_DOMAIN_ID, &opt_arch);
if (err != 0) {
GGML_LOG_ERROR("ggml-hex: failed to query HTP version (err %d) defaulting to v73\n", err);
opt_arch = 73;
}
} else {
if (str_arch[0] == 'v' || str_arch[0] == 'V') {
str_arch++;
}
opt_arch = strtoul(str_arch, NULL, 0);
}
size_t MiB = 1024 * 1024;
// Update vmem default
opt_vmem = opt_arch >= 75 ? HTP_OP_MAX_VMEM_DEFAULT : 3000 * MiB;
auto RE_ICASE = std::regex_constants::icase;
opt_opfilter = str_opfilter ? new std::regex(str_opfilter, RE_ICASE) : NULL;
opt_verbose = str_verbose ? atoi(str_verbose) : 0;
opt_hostbuf = str_hostbuf ? atoi(str_hostbuf) : opt_hostbuf;
opt_opstage = str_opstage ? strtoul(str_opstage, NULL, 0) : opt_opstage;
opt_opbatch = str_opbatch ? strtoul(str_opbatch, NULL, 0) : opt_opbatch;
opt_opqueue = str_opqueue ? strtoul(str_opqueue, NULL, 0) : opt_opqueue;
opt_profile = str_profile ? atoi(str_profile) : 0;
opt_etm = str_etm ? atoi(str_etm) : 0;
opt_nhvx = str_nhvx ? strtoul(str_nhvx, NULL, 0) : opt_nhvx;
opt_use_hmx = str_use_hmx ? atoi(str_use_hmx) : opt_use_hmx;
opt_ndev = str_ndev ? strtoul(str_ndev, NULL, 0) : opt_ndev;
opt_hostbuf = str_hostbuf ? atoi(str_hostbuf) : opt_hostbuf;
opt_mbuf = str_mbuf ? strtoul(str_mbuf, NULL, 0) * MiB : opt_mbuf;
opt_vmem = str_vmem ? strtoul(str_vmem, NULL, 0) * MiB : opt_vmem;
if (opt_ndev > GGML_HEXAGON_MAX_SESSIONS) {
opt_ndev = GGML_HEXAGON_MAX_SESSIONS;
}
#if defined(__ANDROID__)
if (opt_arch < 75) {
opt_ndev = 1;
GGML_LOG_WARN("ggml-hex: forcing ndev to 1 for SoCs archs lower than v75.\n");
}
#endif
if (str_profile) {
opt_pmu_evt = [&]() -> std::vector<uint32_t> {
auto v = str_to_vec<uint32_t>(str_profile);
switch (v.size()) {
case 1: opt_profile = v[0]; return opt_pmu_evt; // mode with default pmu events
case 8: opt_profile = 2; return v; // mode with custom pmu events
default: opt_profile = 0; return {}; // garbage input
}}();
if (opt_profile == 1) opt_pmu_evt = {};
GGML_LOG_INFO("ggml-hex: Profiling mode %u : pmu-evt [ %s ]\n", opt_profile,
vec_to_str<uint32_t, 16>(opt_pmu_evt).c_str());
}
reg->context = new ggml_hexagon_registry(reg);
}
static const struct ggml_backend_reg_i ggml_backend_hexagon_reg_i = {
/* .get_name = */ ggml_backend_hexagon_reg_get_name,
/* .get_device_count = */ ggml_backend_hexagon_reg_get_device_count,
/* .get_device = */ ggml_backend_hexagon_reg_get_device,
/* .get_proc_address = */ ggml_backend_hexagon_get_proc_address,
};
ggml_backend_reg_t ggml_backend_hexagon_reg(void) {
static bool initialized = false;
static ggml_backend_reg reg = { /* .api_version = */ GGML_BACKEND_API_VERSION,
/* .iface = */ ggml_backend_hexagon_reg_i,
/* .context = */ NULL };
{
static std::mutex mutex;
std::lock_guard<std::mutex> lock(mutex);
if (!initialized) {
auto nErr = htpdrv_init();
if (nErr != AEE_SUCCESS) {
return NULL;
}
ggml_hexagon_init(&reg);
}
initialized = true;
}
return &reg;
}
GGML_BACKEND_DL_IMPL(ggml_backend_hexagon_reg)
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