--- /dev/null
+#include "ggml.h"
+#include "ggml-impl.h"
+#include "ggml-backend.h"
+#include "ggml-backend-impl.h"
+#include "ggml-alloc.h"
+#include "ggml-cpp.h"
+
+// TODO: tmp
+#include "ggml-ext.h"
+
+#include <algorithm>
+#include <cassert>
+#include <cmath>
+#include <cstddef>
+#include <cstdint>
+#include <cstring>
+#include <map>
+#include <memory>
+#include <string>
+#include <tuple>
+#include <utility>
+#include <vector>
+
+struct ggml_backend_meta_device;
+struct ggml_backend_meta_buffer_type;
+struct ggml_backend_meta_buffer;
+struct ggml_backend_meta;
+
+const char * ggml_backend_meta_split_axis_name(enum ggml_backend_meta_split_axis split_axis) {
+ switch (split_axis) {
+ case GGML_BACKEND_SPLIT_AXIS_0:
+ return "0";
+ case GGML_BACKEND_SPLIT_AXIS_1:
+ return "1";
+ case GGML_BACKEND_SPLIT_AXIS_2:
+ return "2";
+ case GGML_BACKEND_SPLIT_AXIS_3:
+ return "3";
+ case GGML_BACKEND_SPLIT_AXIS_MIRRORED:
+ return "MIRRORED";
+ case GGML_BACKEND_SPLIT_AXIS_PARTIAL:
+ return "PARTIAL";
+ case GGML_BACKEND_SPLIT_AXIS_NONE:
+ return "NONE";
+ case GGML_BACKEND_SPLIT_AXIS_UNKNOWN:
+ return "UNKNOWN";
+ default:
+ GGML_ABORT("fatal error");
+ }
+}
+
+//
+// meta backend device
+//
+
+struct ggml_backend_meta_device_context {
+ std::vector<ggml_backend_dev_t> simple_devs;
+ ggml_backend_meta_get_split_state_t get_split_state;
+ void * get_split_state_ud;
+
+ std::string name;
+ std::string description;
+
+ ggml_backend_meta_device_context(
+ std::vector<ggml_backend_dev_t> simple_devs, ggml_backend_meta_get_split_state_t get_split_state, void * get_split_state_ud) :
+ simple_devs(std::move(simple_devs)), get_split_state(get_split_state), get_split_state_ud(get_split_state_ud) {
+ name = std::string("Meta(");
+ description = std::string("Meta(");
+ for (size_t i = 0; i < simple_devs.size(); i++) {
+ if (i > 0) {
+ name += ",";
+ description += ",";
+ }
+ name += ggml_backend_dev_name (simple_devs[i]);
+ description += ggml_backend_dev_description(simple_devs[i]);
+ }
+ name += ")";
+ description += ")";
+ }
+
+ bool operator<(const ggml_backend_meta_device_context & other) const {
+ return std::tie(simple_devs, get_split_state, get_split_state_ud)
+ < std::tie(other.simple_devs, other.get_split_state, other.get_split_state_ud);
+ }
+};
+
+static bool ggml_backend_dev_is_meta(ggml_backend_dev_t dev);
+
+static const char * ggml_backend_meta_device_get_name(ggml_backend_dev_t dev) {
+ GGML_ASSERT(ggml_backend_dev_is_meta(dev));
+ const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) dev->context;
+ return meta_dev_ctx->name.c_str();
+}
+
+static const char * ggml_backend_meta_device_get_description(ggml_backend_dev_t dev) {
+ GGML_ASSERT(ggml_backend_dev_is_meta(dev));
+ const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) dev->context;
+ return meta_dev_ctx->description.c_str();
+}
+
+static void ggml_backend_meta_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
+ GGML_ASSERT(ggml_backend_dev_is_meta(dev));
+ const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) dev->context;
+ *free = 0;
+ *total = 0;
+ for (ggml_backend_dev_t dev : meta_dev_ctx->simple_devs) {
+ size_t tmp_free, tmp_total;
+ ggml_backend_dev_memory(dev, &tmp_free, &tmp_total);
+ *free += tmp_free;
+ *total += tmp_total;
+ }
+}
+
+static enum ggml_backend_dev_type ggml_backend_meta_device_get_type(ggml_backend_dev_t dev) {
+ return GGML_BACKEND_DEVICE_TYPE_META;
+
+ GGML_UNUSED(dev);
+}
+
+static void ggml_backend_meta_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) {
+ GGML_ASSERT(ggml_backend_dev_is_meta(dev));
+ const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) dev->context;
+
+ // TODO replace placeholders
+ props->name = ggml_backend_meta_device_get_name(dev);
+ props->description = ggml_backend_meta_device_get_description(dev);
+ props->type = ggml_backend_meta_device_get_type(dev);
+ props->device_id = 0;
+
+ ggml_backend_meta_device_get_memory(dev, &props->memory_free, &props->memory_total);
+
+ props->caps = {
+ /* .async = */ true,
+ /* .host_buffer = */ false, // Not implemented.
+ /* .buffer_from_host_ptr = */ false, // Not implemented.
+ /* .events = */ false, // Not implemented.
+ };
+ for (ggml_backend_dev_t simple_dev : meta_dev_ctx->simple_devs) {
+ ggml_backend_dev_props tmp_props;
+ ggml_backend_dev_get_props(simple_dev, &tmp_props);
+ props->caps.async = props->caps.async && tmp_props.caps.async;
+ props->caps.host_buffer = props->caps.host_buffer && tmp_props.caps.host_buffer;
+ props->caps.buffer_from_host_ptr = props->caps.buffer_from_host_ptr && tmp_props.caps.buffer_from_host_ptr;
+ props->caps.events = props->caps.events && tmp_props.caps.events;
+ }
+}
+
+static ggml_backend_t ggml_backend_meta_device_init_backend(ggml_backend_dev_t dev, const char * params);
+
+static ggml_backend_buffer_type_t ggml_backend_meta_device_get_buffer_type(ggml_backend_dev_t dev);
+
+static ggml_backend_buffer_type_t ggml_backend_meta_device_get_host_buffer_type(ggml_backend_dev_t dev);
+
+static bool ggml_backend_meta_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
+ GGML_ASSERT(ggml_backend_dev_is_meta(dev));
+ const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) dev->context;
+ return std::all_of(meta_dev_ctx->simple_devs.begin(), meta_dev_ctx->simple_devs.end(),
+ [op](ggml_backend_dev_t simple_dev) { return ggml_backend_dev_supports_op(simple_dev, op); });
+}
+
+static bool ggml_backend_meta_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
+ GGML_ASSERT(ggml_backend_dev_is_meta(dev));
+ ggml_backend_dev_t dev_buft = ggml_backend_buft_get_device(buft);
+ if (!ggml_backend_dev_is_meta(dev_buft)) {
+ return false;
+ }
+ const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) dev->context;
+ const ggml_backend_meta_device_context * meta_buft_dev_ctx = (const ggml_backend_meta_device_context *) dev_buft->context;
+ if (meta_dev_ctx->simple_devs.size() != meta_buft_dev_ctx->simple_devs.size()) {
+ return false;
+ }
+ for (size_t i = 0; i < meta_dev_ctx->simple_devs.size(); i++) {
+ if (meta_dev_ctx->simple_devs[i] != meta_buft_dev_ctx->simple_devs[i]) {
+ return false;
+ }
+ }
+ return true;
+}
+
+static const ggml_backend_device_i ggml_backend_meta_device_iface = {
+ /* .get_name = */ ggml_backend_meta_device_get_name,
+ /* .get_description = */ ggml_backend_meta_device_get_description,
+ /* .get_memory = */ ggml_backend_meta_device_get_memory,
+ /* .get_type = */ ggml_backend_meta_device_get_type,
+ /* .get_props = */ ggml_backend_meta_device_get_props,
+ /* .init_backend = */ ggml_backend_meta_device_init_backend,
+ /* .get_buffer_type = */ ggml_backend_meta_device_get_buffer_type,
+ /* .get_host_buffer_type = */ ggml_backend_meta_device_get_host_buffer_type,
+ /* .buffer_from_host_ptr = */ nullptr,
+ /* .supports_op = */ ggml_backend_meta_device_supports_op,
+ /* .supports_buft = */ ggml_backend_meta_device_supports_buft,
+ /* .offload_op = */ nullptr,
+ /* .event_new = */ nullptr,
+ /* .event_free = */ nullptr,
+ /* .event_synchronize = */ nullptr,
+};
+
+static bool ggml_backend_dev_is_meta(ggml_backend_dev_t dev) {
+ return dev != nullptr && dev->iface.get_name == ggml_backend_meta_device_iface.get_name;
+}
+
+static size_t ggml_backend_meta_dev_n_devs(ggml_backend_dev_t meta_dev) {
+ GGML_ASSERT(ggml_backend_dev_is_meta(meta_dev));
+ const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) meta_dev->context;
+ return meta_dev_ctx->simple_devs.size();
+}
+
+static ggml_backend_dev_t ggml_backend_meta_dev_simple_dev(ggml_backend_dev_t meta_dev, size_t index) {
+ GGML_ASSERT(ggml_backend_dev_is_meta(meta_dev));
+ const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) meta_dev->context;
+ GGML_ASSERT(index < meta_dev_ctx->simple_devs.size());
+ return meta_dev_ctx->simple_devs[index];
+}
+
+ggml_backend_dev_t ggml_backend_meta_device(
+ ggml_backend_dev_t * devs, size_t n_devs, ggml_backend_meta_get_split_state_t get_split_state, void * get_split_state_ud) {
+ GGML_ASSERT(n_devs <= GGML_BACKEND_META_MAX_DEVICES);
+ // TODO: this is not thread-safe - needs to be fixed
+ static std::vector<std::unique_ptr<ggml_backend_meta_device_context>> ctxs;
+ static std::map<ggml_backend_meta_device_context, struct ggml_backend_device> meta_devs;
+
+ std::vector<ggml_backend_dev_t> simple_devs;
+ simple_devs.reserve(n_devs);
+ for (size_t i = 0; i < n_devs; i++) {
+ simple_devs.push_back(devs[i]);
+ }
+ ggml_backend_meta_device_context ctx(simple_devs, get_split_state, get_split_state_ud);
+
+ {
+ auto it = meta_devs.find(ctx);
+ if (it != meta_devs.end()) {
+ return &it->second;
+ }
+ }
+ ctxs.push_back(std::make_unique<ggml_backend_meta_device_context>(ctx));
+
+ struct ggml_backend_device meta_dev = {
+ /*iface =*/ ggml_backend_meta_device_iface,
+ /*reg =*/ nullptr,
+ /*ctx =*/ ctxs.back().get(),
+ };
+
+ auto result = meta_devs.emplace(*ctxs.back(), meta_dev);
+ return &result.first->second;
+}
+
+//
+// meta backend buffer type
+//
+
+struct ggml_backend_meta_buffer_type_context {
+ std::vector<ggml_backend_buffer_type_t> simple_bufts;
+
+ std::string name;
+
+ ggml_backend_meta_buffer_type_context(std::vector<ggml_backend_buffer_type_t> simple_bufts) : simple_bufts(std::move(simple_bufts)) {
+ name = "Meta(";
+ for (size_t i = 0; i < simple_bufts.size(); i++) {
+ if (i > 0) {
+ name += ",";
+ }
+ name += ggml_backend_buft_name(simple_bufts[i]);
+ }
+ name += ")";
+ }
+
+ bool operator<(const ggml_backend_meta_buffer_type_context & other) const {
+ return simple_bufts < other.simple_bufts;
+ }
+};
+
+static size_t ggml_backend_meta_buft_n_bufts(ggml_backend_buffer_type_t meta_buft) {
+ GGML_ASSERT(ggml_backend_buft_is_meta(meta_buft));
+ const ggml_backend_meta_buffer_type_context * meta_buft_ctx = (const ggml_backend_meta_buffer_type_context *) meta_buft->context;
+ return meta_buft_ctx->simple_bufts.size();
+}
+
+static const char * ggml_backend_meta_buffer_type_get_name(ggml_backend_buffer_type_t buft) {
+ GGML_ASSERT(ggml_backend_buft_is_meta(buft));
+ const ggml_backend_meta_buffer_type_context * meta_buft_ctx = (const ggml_backend_meta_buffer_type_context *) buft->context;
+ return meta_buft_ctx->name.c_str();
+}
+
+static ggml_backend_buffer_type_t ggml_backend_meta_buft_simple_buft(ggml_backend_buffer_type_t meta_buft, size_t index) {
+ GGML_ASSERT(ggml_backend_buft_is_meta(meta_buft));
+ const ggml_backend_meta_buffer_type_context * meta_buft_ctx = (const ggml_backend_meta_buffer_type_context *) meta_buft->context;
+ GGML_ASSERT(index < meta_buft_ctx->simple_bufts.size());
+ return meta_buft_ctx->simple_bufts[index];
+}
+
+static ggml_backend_buffer_t ggml_backend_meta_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size);
+
+static size_t ggml_backend_meta_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+ const size_t n_simple_bufts = ggml_backend_meta_buft_n_bufts(buft);
+ size_t max_alignment = 1;
+ for (size_t i = 0; i < n_simple_bufts; i++) {
+ const size_t alignment = ggml_backend_buft_get_alignment(ggml_backend_meta_buft_simple_buft(buft, i));
+ max_alignment = std::max(max_alignment, alignment);
+ GGML_ASSERT(max_alignment % alignment == 0);
+ }
+ return max_alignment;
+}
+
+static size_t ggml_backend_meta_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
+ const size_t n_simple_bufts = ggml_backend_meta_buft_n_bufts(buft);
+ size_t max_size = SIZE_MAX;
+ for (size_t i = 0; i < n_simple_bufts; i++) {
+ max_size = std::min(max_size, ggml_backend_buft_get_max_size(ggml_backend_meta_buft_simple_buft(buft, i)));
+ }
+ return max_size;
+}
+
+static size_t ggml_backend_meta_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+ const size_t n_simple_bufts = ggml_backend_meta_buft_n_bufts(buft);
+ size_t max_alloc_size = 0;
+ for (size_t i = 0; i < n_simple_bufts; i++) {
+ const size_t alloc_size = ggml_backend_buft_get_alloc_size(ggml_backend_meta_buft_simple_buft(buft, i), tensor);
+ max_alloc_size = std::max(max_alloc_size, alloc_size);
+ }
+ return max_alloc_size;
+}
+
+static bool ggml_backend_meta_buffer_type_is_host(ggml_backend_buffer_type_t buft) {
+ const size_t n_simple_bufts = ggml_backend_meta_buft_n_bufts(buft);
+ for (size_t i = 0; i < n_simple_bufts; i++) {
+ if (!ggml_backend_buft_is_host(ggml_backend_meta_buft_simple_buft(buft, i))) {
+ return false;
+ }
+ }
+ return true;
+}
+
+static const struct ggml_backend_buffer_type_i ggml_backend_meta_buffer_type_iface = {
+ /* .get_name = */ ggml_backend_meta_buffer_type_get_name,
+ /* .alloc_buffer = */ ggml_backend_meta_buffer_type_alloc_buffer,
+ /* .get_alignment = */ ggml_backend_meta_buffer_type_get_alignment,
+ /* .get_max_size = */ ggml_backend_meta_buffer_type_get_max_size,
+ /* .get_alloc_size = */ ggml_backend_meta_buffer_type_get_alloc_size,
+ /* .is_host = */ ggml_backend_meta_buffer_type_is_host,
+};
+
+bool ggml_backend_buft_is_meta(ggml_backend_buffer_type_t buft) {
+ return buft != nullptr && buft->iface.get_name == ggml_backend_meta_buffer_type_iface.get_name;
+}
+
+static ggml_backend_buffer_type_t ggml_backend_meta_device_get_buffer_type(ggml_backend_dev_t dev) {
+ static std::map<ggml_backend_dev_t, struct ggml_backend_buffer_type> meta_bufts;
+ GGML_ASSERT(ggml_backend_dev_is_meta(dev));
+ {
+ auto it = meta_bufts.find(dev);
+ if (it != meta_bufts.end()) {
+ return &it->second;
+ }
+ }
+
+ const size_t n_devs = ggml_backend_meta_dev_n_devs(dev);
+ std::vector<ggml_backend_buffer_type_t> simple_bufts;
+ simple_bufts.reserve(n_devs);
+ for (size_t i = 0; i < n_devs; i++) {
+ simple_bufts.push_back(ggml_backend_dev_buffer_type(ggml_backend_meta_dev_simple_dev(dev, i)));
+ }
+ ggml_backend_meta_buffer_type_context * buft_ctx = new ggml_backend_meta_buffer_type_context(simple_bufts);
+
+ struct ggml_backend_buffer_type meta_buft = {
+ /*iface =*/ ggml_backend_meta_buffer_type_iface,
+ /*device =*/ dev,
+ /*ctx =*/ buft_ctx,
+ };
+ auto result = meta_bufts.emplace(dev, meta_buft);
+ return &result.first->second;
+}
+
+static ggml_backend_buffer_type_t ggml_backend_meta_device_get_host_buffer_type(ggml_backend_dev_t dev) {
+ GGML_ASSERT(ggml_backend_dev_is_meta(dev));
+ const ggml_backend_meta_device_context * meta_dev_ctx = (const ggml_backend_meta_device_context *) dev->context;
+
+ ggml_backend_buffer_type_t host_buft = nullptr;
+ for (ggml_backend_dev_t simple_dev : meta_dev_ctx->simple_devs) {
+ ggml_backend_buffer_type_t simple_host_buft = ggml_backend_dev_host_buffer_type(simple_dev);
+ if (simple_host_buft == nullptr) {
+ return nullptr;
+ }
+ if (host_buft == nullptr) {
+ host_buft = simple_host_buft;
+ } else if (host_buft != simple_host_buft) {
+ // if different simple devices have different host buffer types,
+ // we cannot provide a single host buffer type for the meta device
+ return nullptr;
+ }
+ }
+ return host_buft;
+}
+
+//
+// meta backend buffer
+//
+
+struct ggml_backend_meta_buffer_context {
+ static constexpr size_t nbtc = GGML_TENSOR_SIZE - sizeof(ggml_tensor::padding);
+
+ std::map<std::pair<const ggml_tensor *, bool>, std::pair<ggml_backend_meta_split_state, char[nbtc]>> split_state_cache;
+ std::map< const ggml_tensor *, std::vector<ggml_tensor *>> simple_tensors;
+
+ struct buffer_config {
+ ggml_context * ctx;
+ ggml_backend_buffer_t buf;
+
+ buffer_config(ggml_context * ctx, ggml_backend_buffer_t buf) : ctx(ctx), buf(buf) {}
+ };
+ std::vector<buffer_config> buf_configs;
+
+ int debug;
+
+ ggml_backend_meta_buffer_context() {
+ const char * GGML_META_DEBUG = getenv("GGML_META_DEBUG");
+ debug = GGML_META_DEBUG ? atoi(GGML_META_DEBUG) : 0;
+ }
+};
+
+static void ggml_backend_meta_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+ GGML_ASSERT(ggml_backend_buffer_is_meta(buffer));
+ ggml_backend_meta_buffer_context * buf_ctx = (ggml_backend_meta_buffer_context *) buffer->context;
+ for (auto & [ctx, buf] : buf_ctx->buf_configs) {
+ ggml_backend_buffer_free(buf);
+ ggml_free(ctx);
+ }
+ delete buf_ctx;
+}
+
+static size_t ggml_backend_meta_buffer_n_bufs(ggml_backend_buffer_t meta_buf) {
+ GGML_ASSERT(ggml_backend_buffer_is_meta(meta_buf));
+ ggml_backend_meta_buffer_context * buf_ctx = (ggml_backend_meta_buffer_context *) meta_buf->context;
+ return buf_ctx->buf_configs.size();
+}
+
+static ggml_backend_buffer_t ggml_backend_meta_buffer_simple_buffer(ggml_backend_buffer_t meta_buf, size_t index) {
+ GGML_ASSERT(ggml_backend_buffer_is_meta(meta_buf));
+ ggml_backend_meta_buffer_context * buf_ctx = (ggml_backend_meta_buffer_context *) meta_buf->context;
+ GGML_ASSERT(index < buf_ctx->buf_configs.size());
+ return buf_ctx->buf_configs[index].buf;
+}
+
+static struct ggml_tensor * ggml_backend_meta_buffer_simple_tensor(const struct ggml_tensor * tensor, size_t index) {
+ GGML_ASSERT(ggml_backend_buffer_is_meta(tensor->buffer));
+ ggml_backend_meta_buffer_context * buf_ctx = (ggml_backend_meta_buffer_context *) tensor->buffer->context;
+ GGML_ASSERT(index < buf_ctx->buf_configs.size());
+
+ auto it = buf_ctx->simple_tensors.find(tensor);
+ if (it == buf_ctx->simple_tensors.end()) {
+ return nullptr;
+ }
+ return it->second[index];
+}
+
+static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(const struct ggml_tensor * tensor, bool assume_sync) {
+ const size_t n_bufs = ggml_backend_meta_buffer_n_bufs(tensor->buffer);
+ ggml_backend_meta_buffer_context * buf_ctx = (ggml_backend_meta_buffer_context *) tensor->buffer->context;
+
+ auto split_states_equal = [&](const ggml_backend_meta_split_state & a, const ggml_backend_meta_split_state & b) -> bool {
+ if (a.axis != b.axis) {
+ return false;
+ }
+ for (size_t j = 0; j < n_bufs; j++) {
+ int64_t sum_a = 0;
+ for (size_t s = 0; s < a.n_segments; s++) {
+ sum_a += a.ne[s*n_bufs + j];
+ }
+ int64_t sum_b = 0;
+ for (size_t s = 0; s < b.n_segments; s++) {
+ sum_b += b.ne[s*n_bufs + j];
+ }
+ if (sum_a != sum_b) {
+ return false;
+ }
+ }
+ return true;
+ };
+
+ auto handle_generic = [&](const std::vector<ggml_backend_meta_split_state> & src_ss, bool scalar_only) -> ggml_backend_meta_split_state {
+ ggml_backend_meta_split_state ret = {GGML_BACKEND_SPLIT_AXIS_NONE, {0}, 1};
+ for (size_t i = 0; i < GGML_MAX_SRC; i++) {
+ if (tensor->src[i] == nullptr || tensor->src[i] == tensor) {
+ continue;
+ }
+ if (ret.axis == GGML_BACKEND_SPLIT_AXIS_NONE) {
+ ret = src_ss[i];
+ } else if (!split_states_equal(src_ss[i], ret)) {
+ ret = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+ break;
+ }
+ }
+ if (ret.axis == GGML_BACKEND_SPLIT_AXIS_NONE) {
+ ret = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+ }
+ if (scalar_only && ret.axis >= 0 && ret.axis < GGML_MAX_DIMS) {
+ ret = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+ }
+ GGML_ASSERT(ret.axis != GGML_BACKEND_SPLIT_AXIS_UNKNOWN);
+ return ret;
+ };
+
+ // Some ops process data on a per-row bases:
+ auto handle_per_row = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+ GGML_ASSERT(src_ss[0].axis != GGML_BACKEND_SPLIT_AXIS_0);
+ return src_ss[0];
+ };
+
+ // Some ops broadcast the src1 data across src0:
+ auto handle_bin_bcast = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+ if (src_ss[0].axis >= 0 && src_ss[0].axis < GGML_MAX_DIMS &&
+ tensor->src[1]->ne[src_ss[0].axis] == 1 && src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
+ return src_ss[0];
+ }
+ if (src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && (src_ss[0].axis == src_ss[1].axis ||
+ (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && (src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_PARTIAL)))) {
+ return src_ss[0]; // GGML_OP_ADD_ID
+ }
+ GGML_ASSERT(tensor->src[2] == nullptr || src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
+ return handle_generic(src_ss, /*scalar_only =*/ false);
+ };
+
+ auto handle_concat = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+ const ggml_backend_meta_split_axis concat_axis = ggml_backend_meta_split_axis(ggml_get_op_params_i32(tensor, 0));
+ if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[1].axis >= 0 && src_ss[1].axis < GGML_MAX_DIMS) {
+ GGML_ASSERT(concat_axis != src_ss[1].axis);
+ return src_ss[1];
+ }
+ if (src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[0].axis >= 0 && src_ss[0].axis < GGML_MAX_DIMS) {
+ GGML_ASSERT(concat_axis != src_ss[0].axis);
+ return src_ss[0];
+ }
+ if (src_ss[0].axis == src_ss[1].axis && src_ss[0].axis != concat_axis) {
+ return src_ss[0];
+ }
+ return handle_generic(src_ss, /*scalar_only =*/ true);
+ };
+
+ auto handle_mul_mat = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+ if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
+ return {GGML_BACKEND_SPLIT_AXIS_MIRRORED, {0}, 1};
+ }
+ if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_1 && src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
+ ggml_backend_meta_split_state ret = src_ss[0];
+ ret.axis = GGML_BACKEND_SPLIT_AXIS_0;
+ ret.n_segments = 1;
+ return ret;
+ }
+ if (src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_1 && src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
+ ggml_backend_meta_split_state ret = src_ss[1];
+ ret.n_segments = 1;
+ return ret;
+ }
+ if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_0 && src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_0) {
+ GGML_ASSERT(split_states_equal(src_ss[0], src_ss[1]));
+ return {assume_sync ? GGML_BACKEND_SPLIT_AXIS_MIRRORED : GGML_BACKEND_SPLIT_AXIS_PARTIAL, {0}, 1};
+ }
+ GGML_ABORT("fatal error");
+ //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+ };
+
+ auto handle_cpy = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+ if (src_ss[0].axis >= 0 && src_ss[0].axis < GGML_MAX_DIMS) {
+ int64_t ne_split_src = tensor->src[0]->ne[0];
+ for (int dim = 1; dim <= src_ss[0].axis; dim++) {
+ ne_split_src *= tensor->src[0]->ne[dim];
+ }
+ int64_t ne_split_dst = 1;
+ for (int dim = 0; dim < GGML_MAX_DIMS; dim++) {
+ ne_split_dst *= tensor->ne[dim];
+ if (ne_split_dst == ne_split_src) {
+ return {ggml_backend_meta_split_axis(dim), {0}, 1};
+ }
+ }
+ }
+ return handle_generic(src_ss, /*scalar_only =*/ false);
+ };
+
+ auto handle_reshape = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+ switch (src_ss[0].axis) {
+ case GGML_BACKEND_SPLIT_AXIS_0:
+ case GGML_BACKEND_SPLIT_AXIS_1:
+ case GGML_BACKEND_SPLIT_AXIS_2:
+ case GGML_BACKEND_SPLIT_AXIS_3: {
+ GGML_ASSERT(!ggml_is_permuted(tensor) && !ggml_is_permuted(tensor->src[0]));
+ if (src_ss[0].axis == ggml_n_dims(tensor->src[0]) - 1) {
+ return {ggml_backend_meta_split_axis(ggml_n_dims(tensor) - 1), {0}, 1};
+ }
+ std::vector<int64_t> base_ne_in;
+ base_ne_in.reserve(GGML_MAX_DIMS - src_ss[0].axis);
+ {
+ base_ne_in.push_back(1);
+ int dim = 0;
+ for (; dim <= src_ss[0].axis; dim++) {
+ base_ne_in[0] *= tensor->src[0]->ne[dim];
+ }
+ for (; dim <= GGML_MAX_DIMS; dim++) {
+ base_ne_in.push_back(base_ne_in.back() * tensor->src[0]->ne[dim]);
+ }
+ }
+ int64_t base_ne_out = 1;
+ for (int dim = 0; dim < GGML_MAX_DIMS; dim++) {
+ const int64_t base_ne_out_next = base_ne_out *= tensor->ne[dim];
+ for (const int64_t & bni : base_ne_in) {
+ if (bni == base_ne_out_next) {
+ return {ggml_backend_meta_split_axis(dim), {0}, 1};
+ }
+ }
+ if (base_ne_out_next > base_ne_in[0]) {
+ GGML_ASSERT(dim + 1 < GGML_MAX_DIMS);
+ return {ggml_backend_meta_split_axis(dim + 1), {0}, 1};
+ }
+ base_ne_out = base_ne_out_next;
+ }
+ GGML_ABORT("shape mismatch for %s", ggml_op_name(tensor->op));
+ }
+ case GGML_BACKEND_SPLIT_AXIS_MIRRORED:
+ case GGML_BACKEND_SPLIT_AXIS_PARTIAL: {
+ return src_ss[0];
+ }
+ default: {
+ GGML_ABORT("fatal error");
+ //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+ }
+ }
+ };
+
+ auto handle_view = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+ if (ggml_is_contiguous(tensor) && ggml_is_contiguous(tensor->src[0])) {
+ return handle_reshape(src_ss);
+ }
+ const int axis = src_ss[0].axis;
+ {
+ bool all_strides_the_same = true;
+ for (int dim = 0; dim < GGML_MAX_DIMS; dim++) {
+ if (tensor->ne[dim] == 1 && tensor->src[0]->ne[dim] == 1) {
+ continue;
+ }
+ if (tensor->nb[dim] != tensor->src[0]->nb[dim]) {
+ all_strides_the_same = false;
+ break;
+ }
+ }
+ if (all_strides_the_same) {
+ return src_ss[0];
+ }
+ }
+ if (!ggml_is_permuted(tensor) && !ggml_is_permuted(tensor->src[0]) && axis >= 0 && axis < GGML_MAX_DIMS-1) {
+ for (int dim = 0; dim < GGML_MAX_DIMS-1; dim++) {
+ if (tensor->nb[dim+1] == tensor->src[0]->nb[axis+1]) {
+ return {ggml_backend_meta_split_axis(dim), {0}, 1};
+ }
+ }
+ GGML_ABORT("fatal error");
+ }
+ if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED || src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_PARTIAL) {
+ return src_ss[0];
+ }
+ GGML_ABORT("view of permuted tensor not implemented");
+ //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+ };
+
+ auto handle_permute = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+ switch (src_ss[0].axis) {
+ case GGML_BACKEND_SPLIT_AXIS_0:
+ case GGML_BACKEND_SPLIT_AXIS_1:
+ case GGML_BACKEND_SPLIT_AXIS_2:
+ case GGML_BACKEND_SPLIT_AXIS_3: {
+ return {ggml_backend_meta_split_axis(tensor->op_params[src_ss[0].axis]), {0}, 1};
+ }
+ case GGML_BACKEND_SPLIT_AXIS_MIRRORED:
+ case GGML_BACKEND_SPLIT_AXIS_PARTIAL: {
+ return src_ss[0];
+ }
+ default: {
+ GGML_ABORT("fatal error");
+ //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+ }
+ }
+ };
+
+ auto handle_transpose = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+ switch (src_ss[0].axis) {
+ case GGML_BACKEND_SPLIT_AXIS_0:
+ case GGML_BACKEND_SPLIT_AXIS_1: {
+ return {ggml_backend_meta_split_axis(int(src_ss[0].axis) ^ 1), {0}, 1};
+ }
+ case GGML_BACKEND_SPLIT_AXIS_2:
+ case GGML_BACKEND_SPLIT_AXIS_3:
+ case GGML_BACKEND_SPLIT_AXIS_MIRRORED:
+ case GGML_BACKEND_SPLIT_AXIS_PARTIAL: {
+ return src_ss[0];
+ }
+ default: {
+ GGML_ABORT("fatal error");
+ //return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+ }
+ }
+ };
+
+ auto handle_get_rows = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+ if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_0 && src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
+ return src_ss[0];
+ }
+ return handle_generic(src_ss, /*scalar_only =*/ true);
+ };
+
+ auto handle_set_rows = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+ GGML_ASSERT(src_ss[0].axis != GGML_BACKEND_SPLIT_AXIS_1);
+ GGML_ASSERT(src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
+ GGML_ASSERT(split_states_equal(src_ss[0], src_ss[2]));
+ return src_ss[0];
+ };
+
+ auto handle_rope = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+ GGML_ASSERT(src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
+ return src_ss[0];
+ };
+
+ auto handle_pad = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+ if (src_ss[0].axis >= 0 && src_ss[0].axis < GGML_MAX_DIMS) {
+ GGML_ASSERT(tensor->op_params[2*src_ss[0].axis + 0] == 0);
+ GGML_ASSERT(tensor->op_params[2*src_ss[0].axis + 1] == 0);
+ }
+ return src_ss[0];
+ };
+
+ auto handle_flash_attn_ext = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+ GGML_ASSERT( src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_2);
+ GGML_ASSERT( src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_2);
+ GGML_ASSERT( src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_2);
+ GGML_ASSERT(tensor->src[4] == nullptr || src_ss[3].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED);
+ GGML_ASSERT(tensor->src[4] == nullptr || src_ss[4].axis == GGML_BACKEND_SPLIT_AXIS_0);
+ return {GGML_BACKEND_SPLIT_AXIS_1, {0}, 1};
+ };
+
+ auto handle_ssm_conv = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+ if (src_ss[0].axis == src_ss[1].axis) {
+ if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_0) {
+ return {GGML_BACKEND_SPLIT_AXIS_1, {0}, 1};
+ }
+ if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_1) {
+ return {GGML_BACKEND_SPLIT_AXIS_0, {0}, 1};
+ }
+ }
+ return handle_generic(src_ss, /*scalar_only =*/ false);
+ };
+
+ auto handle_gated_delta_net = [&](const std::vector<ggml_backend_meta_split_state> & src_ss) -> ggml_backend_meta_split_state {
+ if (src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED &&
+ src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[3].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED &&
+ src_ss[4].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED && src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
+ return src_ss[0];
+ }
+ GGML_ASSERT(src_ss[0].axis == GGML_BACKEND_SPLIT_AXIS_1);
+ GGML_ASSERT(src_ss[1].axis == GGML_BACKEND_SPLIT_AXIS_1);
+ GGML_ASSERT(src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_1);
+ GGML_ASSERT(src_ss[3].axis == GGML_BACKEND_SPLIT_AXIS_1);
+ GGML_ASSERT(src_ss[4].axis == GGML_BACKEND_SPLIT_AXIS_1);
+ GGML_ASSERT(src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_2);
+ return {GGML_BACKEND_SPLIT_AXIS_0, {0}, 1};
+ };
+
+ auto calculate_split_state = [&]() -> ggml_backend_meta_split_state {
+ if (ggml_nelements(tensor) == 0) {
+ return {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+ }
+ if (ggml_backend_buffer_get_usage(tensor->buffer) != GGML_BACKEND_BUFFER_USAGE_COMPUTE && tensor->view_src == nullptr) {
+ ggml_backend_dev_t dev = ggml_backend_buft_get_device(ggml_backend_buffer_get_type(tensor->buffer));
+ const ggml_backend_meta_device_context * dev_ctx = (const ggml_backend_meta_device_context *) dev->context;
+ ggml_backend_meta_split_state ret = dev_ctx->get_split_state(tensor, dev_ctx->get_split_state_ud);
+ if (ret.axis >= 0 && ret.axis <= GGML_MAX_DIMS) {
+ const int64_t granularity = ret.axis == GGML_BACKEND_SPLIT_AXIS_0 ? ggml_blck_size(tensor->type) : 1;
+ int64_t ne_sum = 0;
+ for (size_t sj = 0; sj < ret.n_segments*n_bufs; sj++) {
+ GGML_ASSERT(ret.ne[sj] % granularity == 0);
+ ne_sum += ret.ne[sj];
+ }
+ GGML_ASSERT(ne_sum == tensor->ne[ret.axis]);
+ }
+ return ret;
+ }
+
+ std::vector<ggml_backend_meta_split_state> src_ss(GGML_MAX_SRC, {GGML_BACKEND_SPLIT_AXIS_NONE, {0}, 1});
+ for (size_t i = 0; i < GGML_MAX_SRC; i++) {
+ if (tensor->src[i] == nullptr || tensor->src[i] == tensor) {
+ src_ss[i] = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+ continue;
+ }
+ src_ss[i] = ggml_backend_meta_get_split_state(tensor->src[i], /*assume_sync =*/ true);
+ GGML_ASSERT(src_ss[i].axis != GGML_BACKEND_SPLIT_AXIS_UNKNOWN);
+ }
+
+ ggml_backend_meta_split_state split_state;
+ switch (tensor->op) {
+ case GGML_OP_NONE: {
+ split_state = {GGML_BACKEND_SPLIT_AXIS_MIRRORED, {0}, 1};
+ } break;
+ case GGML_OP_DUP: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ true);
+ } break;
+ case GGML_OP_ADD:
+ case GGML_OP_ADD_ID: {
+ split_state = handle_bin_bcast(src_ss);
+ } break;
+ case GGML_OP_ADD1:
+ case GGML_OP_ACC: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ true);
+ } break;
+ case GGML_OP_SUB:
+ case GGML_OP_MUL:
+ case GGML_OP_DIV: {
+ split_state = handle_bin_bcast(src_ss);
+ } break;
+ case GGML_OP_SQR:
+ case GGML_OP_SQRT:
+ case GGML_OP_LOG:
+ case GGML_OP_SIN:
+ case GGML_OP_COS: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ false);
+ } break;
+ case GGML_OP_SUM: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ true);
+ } break;
+ case GGML_OP_SUM_ROWS:
+ case GGML_OP_CUMSUM:
+ case GGML_OP_MEAN:
+ case GGML_OP_ARGMAX:
+ case GGML_OP_COUNT_EQUAL: {
+ split_state = handle_per_row(src_ss);
+ } break;
+ case GGML_OP_REPEAT:
+ case GGML_OP_REPEAT_BACK: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ false);
+ } break;
+ case GGML_OP_CONCAT: {
+ split_state = handle_concat(src_ss);
+ } break;
+ case GGML_OP_SILU_BACK: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ false);
+ } break;
+ case GGML_OP_NORM:
+ case GGML_OP_RMS_NORM:
+ case GGML_OP_RMS_NORM_BACK:
+ case GGML_OP_GROUP_NORM:
+ case GGML_OP_L2_NORM: {
+ split_state = handle_per_row(src_ss);
+ } break;
+ case GGML_OP_MUL_MAT:
+ case GGML_OP_MUL_MAT_ID: {
+ split_state = handle_mul_mat(src_ss);
+ } break;
+ case GGML_OP_OUT_PROD: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ true);
+ } break;
+ case GGML_OP_SCALE: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ false);
+ } break;
+ case GGML_OP_SET: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ true);
+ } break;
+ case GGML_OP_CPY: {
+ split_state = handle_cpy(src_ss);
+ } break;
+ case GGML_OP_CONT:
+ case GGML_OP_RESHAPE: {
+ split_state = handle_reshape(src_ss);
+ } break;
+ case GGML_OP_VIEW: {
+ split_state = handle_view(src_ss);
+ } break;
+ case GGML_OP_PERMUTE: {
+ split_state = handle_permute(src_ss);
+ } break;
+ case GGML_OP_TRANSPOSE: {
+ split_state = handle_transpose(src_ss);
+ } break;
+ case GGML_OP_GET_ROWS: {
+ split_state = handle_get_rows(src_ss);
+ } break;
+ case GGML_OP_GET_ROWS_BACK: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ true);
+ } break;
+ case GGML_OP_SET_ROWS: {
+ split_state = handle_set_rows(src_ss);
+ } break;
+ case GGML_OP_DIAG:
+ case GGML_OP_DIAG_MASK_INF:
+ case GGML_OP_DIAG_MASK_ZERO: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ true);
+ } break;
+ case GGML_OP_SOFT_MAX:
+ case GGML_OP_SOFT_MAX_BACK: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ false);
+ } break;
+ case GGML_OP_ROPE: {
+ split_state = handle_rope(src_ss);
+ } break;
+ case GGML_OP_ROPE_BACK: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ true);
+ } break;
+ case GGML_OP_CLAMP: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ false);
+ } break;
+ case GGML_OP_CONV_TRANSPOSE_1D:
+ case GGML_OP_IM2COL:
+ case GGML_OP_IM2COL_BACK:
+ case GGML_OP_IM2COL_3D:
+ case GGML_OP_CONV_2D:
+ case GGML_OP_CONV_3D:
+ case GGML_OP_CONV_2D_DW:
+ case GGML_OP_CONV_TRANSPOSE_2D:
+ case GGML_OP_POOL_1D:
+ case GGML_OP_POOL_2D:
+ case GGML_OP_POOL_2D_BACK:
+ case GGML_OP_UPSCALE: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ true);
+ } break;
+ case GGML_OP_PAD: {
+ split_state = handle_pad(src_ss);
+ } break;
+ case GGML_OP_PAD_REFLECT_1D:
+ case GGML_OP_ROLL:
+ case GGML_OP_ARANGE:
+ case GGML_OP_TIMESTEP_EMBEDDING: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ true);
+ } break;
+ case GGML_OP_ARGSORT:
+ case GGML_OP_TOP_K: {
+ split_state = handle_per_row(src_ss);
+ } break;
+ case GGML_OP_LEAKY_RELU: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ false);
+ } break;
+ case GGML_OP_TRI: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ true);
+ } break;
+ case GGML_OP_FILL: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ false);
+ } break;
+ case GGML_OP_FLASH_ATTN_EXT: {
+ split_state = handle_flash_attn_ext(src_ss);
+ } break;
+ case GGML_OP_FLASH_ATTN_BACK: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ true);
+ } break;
+ case GGML_OP_SSM_CONV: {
+ split_state = handle_ssm_conv(src_ss);
+ } break;
+ case GGML_OP_SSM_SCAN:
+ case GGML_OP_WIN_PART:
+ case GGML_OP_WIN_UNPART:
+ case GGML_OP_GET_REL_POS:
+ case GGML_OP_ADD_REL_POS:
+ case GGML_OP_RWKV_WKV6:
+ case GGML_OP_GATED_LINEAR_ATTN:
+ case GGML_OP_RWKV_WKV7:
+ case GGML_OP_SOLVE_TRI: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ true);
+ } break;
+ case GGML_OP_GATED_DELTA_NET: {
+ split_state = handle_gated_delta_net(src_ss);
+ } break;
+ case GGML_OP_UNARY: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ false);
+ } break;
+ case GGML_OP_MAP_CUSTOM1:
+ case GGML_OP_MAP_CUSTOM2:
+ case GGML_OP_MAP_CUSTOM3:
+ case GGML_OP_CUSTOM: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ true);
+ } break;
+ case GGML_OP_CROSS_ENTROPY_LOSS:
+ case GGML_OP_CROSS_ENTROPY_LOSS_BACK: {
+ split_state = handle_per_row(src_ss);
+ } break;
+ case GGML_OP_OPT_STEP_ADAMW:
+ case GGML_OP_OPT_STEP_SGD:
+ case GGML_OP_GLU: {
+ split_state = handle_generic(src_ss, /*scalar_only =*/ false);
+ } break;
+ default: {
+ GGML_ABORT("ggml op not implemented: %s", ggml_op_name(tensor->op));
+ split_state = {GGML_BACKEND_SPLIT_AXIS_UNKNOWN, {0}, 1};
+ } break;
+ }
+ if (split_state.axis >= 0 && split_state.axis < GGML_MAX_DIMS) {
+ bool first_src_split_by_axis = true;
+ const size_t n_bufs = ggml_backend_meta_buffer_n_bufs(tensor->buffer);
+
+ for (size_t i = 0; i < GGML_MAX_SRC; i++) {
+ if (tensor->src[i] == nullptr || src_ss[i].axis < 0 || src_ss[i].axis >= GGML_MAX_DIMS) {
+ continue;
+ }
+ if (first_src_split_by_axis) {
+ for (size_t j = 0; j < n_bufs; j++) {
+ // Take over ratio from src:
+ for (size_t s = 0; s < src_ss[i].n_segments; s++) {
+ split_state.ne[s*n_bufs + j] = 0;
+ }
+ for (size_t s = 0; s < src_ss[i].n_segments; s++) {
+ split_state.ne[j] += src_ss[i].ne[s*n_bufs + j];
+ }
+ split_state.ne[j] *= tensor->ne[split_state.axis];
+ if (split_state.ne[j] != 0 || tensor->src[i]->ne[src_ss[i].axis] != 0) {
+ GGML_ASSERT(split_state.ne[j] % tensor->src[i]->ne[src_ss[i].axis] == 0);
+ split_state.ne[j] /= tensor->src[i]->ne[src_ss[i].axis];
+ }
+ }
+ } else {
+ for (size_t j = 0; j < n_bufs; j++) {
+ int64_t sum = 0;
+ for (size_t s = 0; s < src_ss[i].n_segments; s++) {
+ sum += src_ss[i].ne[s*n_bufs + j];
+ }
+ // Assert that ratio is consistent:
+ GGML_ASSERT(split_state.ne[j] * tensor->src[i]->ne[src_ss[i].axis]
+ == sum * tensor->ne[split_state.axis]);
+ }
+ }
+ first_src_split_by_axis = false;
+ }
+ GGML_ASSERT(!first_src_split_by_axis);
+ }
+ return split_state;
+ };
+
+ const std::pair key = std::make_pair(tensor, assume_sync);
+ auto it = buf_ctx->split_state_cache.find(key);
+ if (it != buf_ctx->split_state_cache.end() && memcmp(it->second.second, (const char *) tensor, sizeof(it->second.second)) != 0) {
+ buf_ctx->split_state_cache.clear();
+ it = buf_ctx->split_state_cache.end();
+ }
+
+ if (it == buf_ctx->split_state_cache.end()) {
+ buf_ctx->split_state_cache[key].first = calculate_split_state();
+ memcpy(buf_ctx->split_state_cache[key].second, tensor, sizeof(buf_ctx->split_state_cache[key].second));
+ if (buf_ctx->debug > 0) {
+ std::string srcs_info;
+ for (size_t i = 0; i < GGML_MAX_SRC; i++) {
+ if (tensor->src[i] == nullptr) {
+ continue;
+ }
+ if (!srcs_info.empty()) {
+ srcs_info += ", ";
+ }
+ const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor->src[0], true);
+ const char * axis_name = ggml_backend_meta_split_axis_name(split_state.axis);
+ std::string ne_info;
+ for (size_t j = 0; j < n_bufs; j++) {
+ if (!ne_info.empty()) {
+ ne_info += ", ";
+ }
+ ne_info += std::to_string(split_state.ne[j]);
+ }
+ srcs_info += std::string(tensor->src[i]->name) + "[" + ggml_op_name(tensor->src[i]->op) + ", " + axis_name + ", {" + ne_info + "}]";
+ }
+ std::string ne_info;
+ for (size_t j = 0; j < n_bufs; j++) {
+ if (!ne_info.empty()) {
+ ne_info += ", ";
+ }
+ ne_info += std::to_string(buf_ctx->split_state_cache[key].first.ne[j]);
+ }
+ GGML_LOG_DEBUG("SPLIT_STATE: {%s} -> %s[%s, %s, {%s}]\n", srcs_info.c_str(), tensor->name, ggml_op_name(tensor->op),
+ ggml_backend_meta_split_axis_name(buf_ctx->split_state_cache[key].first.axis), ne_info.c_str());
+ }
+ }
+
+ ggml_backend_meta_split_state ret = buf_ctx->split_state_cache[key].first;
+ GGML_ASSERT(ret.axis != GGML_BACKEND_SPLIT_AXIS_NONE);
+#ifndef NDEBUG
+ if (ret.axis >= 0 && ret.axis < GGML_MAX_DIMS) {
+ int64_t ne_ret = 0;
+ for (size_t sj = 0; sj < ret.n_segments*n_bufs; sj++) {
+ ne_ret += ret.ne[sj];
+ }
+ assert(ne_ret == tensor->ne[int(ret.axis)]);
+ }
+#endif // NDEBUG
+ return ret;
+}
+
+static void * ggml_backend_meta_buffer_get_base(ggml_backend_buffer_t buffer) {
+ GGML_UNUSED(buffer);
+ return (void *) 0x1000000000000000; // FIXME
+}
+
+static enum ggml_status ggml_backend_meta_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
+ GGML_ASSERT(ggml_backend_buffer_is_meta(buffer));
+ ggml_backend_meta_buffer_context * buf_ctx = (ggml_backend_meta_buffer_context *) buffer->context;
+ const size_t n_simple_bufs = ggml_backend_meta_buffer_n_bufs(buffer);
+
+ const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ true);
+ GGML_ASSERT(ggml_nelements(tensor) == 0 || split_state.axis != GGML_BACKEND_SPLIT_AXIS_UNKNOWN);
+ GGML_ASSERT(split_state.n_segments <= 16);
+
+ int split_dim = split_state.axis;
+ int64_t ne[GGML_MAX_DIMS];
+ size_t nb[GGML_MAX_DIMS];
+ for (size_t k = 0; k < GGML_MAX_DIMS; k++) {
+ ne[k] = tensor->ne[k];
+ nb[k] = tensor->nb[k];
+ }
+
+ std::vector<ggml_tensor *> simple_tensors;
+ simple_tensors.reserve(n_simple_bufs);
+ for (size_t j = 0; j < n_simple_bufs; j++) {
+ ggml_context * simple_ctx = buf_ctx->buf_configs[j].ctx;
+ ggml_backend_buffer_t simple_buf = buf_ctx->buf_configs[j].buf;
+
+ if (split_dim >= 0 && split_dim < GGML_MAX_DIMS) {
+ // TODO: the following assert fails for llama-parallel even though the results are correct:
+ // GGML_ASSERT(ggml_is_contiguously_allocated(tensor));
+ ne[split_dim] = 0;
+ for (size_t s = 0; s < split_state.n_segments; s++) {
+ ne[split_dim] += split_state.ne[s*n_simple_bufs + j];
+ }
+ for (int i = 0; i < GGML_MAX_DIMS; i++) {
+ if (tensor->nb[i] > tensor->nb[split_dim]) {
+ nb[i] = tensor->nb[i] * ne[split_dim]/tensor->ne[split_dim];
+ }
+ }
+ }
+
+ ggml_tensor * t_ij = ggml_new_tensor(simple_ctx, tensor->type, GGML_MAX_DIMS, ne);
+ t_ij->op = tensor->op;
+ for (int i = 0; i < GGML_MAX_DIMS; i++) {
+ t_ij->nb[i] = nb[i];
+ }
+ t_ij->flags = tensor->flags;
+ memcpy(t_ij->op_params, tensor->op_params, sizeof(tensor->op_params));
+ ggml_set_name(t_ij, tensor->name);
+ t_ij->buffer = simple_buf;
+ t_ij->view_src = tensor->view_src;
+ t_ij->view_offs = tensor->view_offs;
+ if (t_ij->view_src != nullptr && ggml_backend_buffer_is_meta(t_ij->view_src->buffer)) {
+ t_ij->view_src = ggml_backend_meta_buffer_simple_tensor(tensor->view_src, j);
+ if (t_ij->view_offs > 0 && split_dim >= 0 && split_dim < GGML_MAX_DIMS) {
+ GGML_ASSERT(ne[split_dim] != 0 && tensor->ne[split_dim] != 0);
+ const int split_dim_view_src = ggml_backend_meta_get_split_state(tensor->view_src, /*assume_sync =*/ true).axis;
+ GGML_ASSERT(split_dim_view_src >= 0 && split_dim_view_src < GGML_MAX_DIMS);
+
+ // The offset can be internal to the data split, in those cases the view offset should not be scaled.
+ // If however, the offset is larger than the data split then it needs to be scaled proportionally.
+ bool split_internal_offset = t_ij->view_offs <= tensor->view_src->nb[split_dim_view_src];
+ for (int i = 0; i < GGML_MAX_DIMS; i++) {
+ const size_t dim_size = tensor->ne[i] * tensor->nb[i];
+ if (tensor->view_offs <= dim_size && dim_size < tensor->nb[split_dim]) {
+ split_internal_offset = true;
+ break;
+ }
+ }
+ if (!split_internal_offset) {
+ t_ij->view_offs = t_ij->view_offs * ne[split_dim]/tensor->ne[split_dim];
+ }
+ }
+ }
+ if (t_ij->view_src != nullptr) {
+ t_ij->data = (char *) t_ij->view_src->data + t_ij->view_offs;
+ } else if (simple_buf != nullptr) {
+ t_ij->data = (char *) ggml_backend_buffer_get_base(simple_buf)
+ + size_t(tensor->data) - size_t(ggml_backend_buffer_get_base(buffer));
+ }
+ t_ij->extra = tensor->extra;
+ for (int i = 0; i < GGML_MAX_SRC; i++) {
+ t_ij->src[i] = tensor->src[i];
+ if (tensor->src[i] == tensor) {
+ t_ij->src[i] = t_ij;
+ } else if (t_ij->src[i] != nullptr && ggml_backend_buffer_is_meta(t_ij->src[i]->buffer)) {
+ t_ij->src[i] = ggml_backend_meta_buffer_simple_tensor(tensor->src[i], j);
+ }
+ }
+
+ simple_tensors.push_back(t_ij);
+ }
+ buf_ctx->simple_tensors[tensor] = simple_tensors;
+
+ return GGML_STATUS_SUCCESS;
+}
+
+static void ggml_backend_meta_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+ const size_t n_bufs = ggml_backend_meta_buffer_n_bufs(buffer);
+ GGML_ASSERT(ggml_is_contiguous(tensor));
+
+ const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ false);
+
+ if (split_state.n_segments != 1) {
+ GGML_ASSERT(split_state.axis >= 0 && split_state.axis < GGML_MAX_DIMS);
+ GGML_ASSERT(offset == 0);
+ GGML_ASSERT(size == ggml_nbytes(tensor));
+ GGML_ASSERT(tensor->ne[3] == 1);
+ size_t offset_data = 0;
+ std::vector<size_t> simple_offsets(n_bufs, 0);
+ if (split_state.axis == GGML_BACKEND_SPLIT_AXIS_0) {
+ GGML_ASSERT(tensor->ne[2] == 1);
+ const int64_t blck_size = ggml_blck_size(tensor->type);
+ for (size_t s = 0; s < split_state.n_segments; s++) {
+ for (size_t j = 0; j < n_bufs; j++) {
+ ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
+ GGML_ASSERT(split_state.ne[s*n_bufs + j] % blck_size == 0);
+ const size_t nbytes = split_state.ne[s*n_bufs + j]/blck_size * tensor->nb[0];
+ ggml_backend_tensor_set_2d(simple_tensor, (const char *) data + offset_data, simple_offsets[j], nbytes,
+ tensor->ne[1], simple_tensor->nb[1], tensor->nb[1]);
+ offset_data += nbytes;
+ simple_offsets[j] += nbytes;
+ }
+ }
+ GGML_ASSERT(offset_data*tensor->ne[1] == size);
+ return;
+ }
+ GGML_ASSERT(split_state.axis == GGML_BACKEND_SPLIT_AXIS_1);
+ for (size_t s = 0; s < split_state.n_segments; s++) {
+ for (size_t j = 0; j < n_bufs; j++) {
+ ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
+ const size_t nbytes = split_state.ne[s*n_bufs + j] * tensor->nb[1];
+ ggml_backend_tensor_set_2d(simple_tensor, (const char *) data + offset_data, simple_offsets[j], nbytes,
+ tensor->ne[2], simple_tensor->nb[2], tensor->nb[2]);
+ offset_data += nbytes;
+ simple_offsets[j] += nbytes;
+ }
+ }
+ GGML_ASSERT(offset_data*tensor->ne[2] == size);
+ return;
+ }
+
+ switch (split_state.axis) {
+ case GGML_BACKEND_SPLIT_AXIS_0:
+ case GGML_BACKEND_SPLIT_AXIS_1:
+ case GGML_BACKEND_SPLIT_AXIS_2: {
+ // Exploit that tensors are contiguous to splice it with simple tensors as "chunks".
+ const size_t chunk_size_full = tensor->nb[split_state.axis + 1];
+ GGML_ASSERT(offset % chunk_size_full == 0);
+ GGML_ASSERT(size % chunk_size_full == 0);
+ const int64_t i_start = offset /chunk_size_full;
+ const int64_t i_stop = (offset + size)/chunk_size_full;
+ size_t offset_j = 0;
+ for (size_t j = 0; j < n_bufs; j++) {
+ ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
+ const size_t chunk_size_j = simple_tensor->nb[split_state.axis + 1];
+ const size_t simple_offset = i_start * chunk_size_j;
+ ggml_backend_tensor_set_2d(simple_tensor, (const char *) data + offset_j, simple_offset, chunk_size_j, i_stop - i_start, chunk_size_j, chunk_size_full);
+ offset_j += chunk_size_j;
+ }
+ GGML_ASSERT(offset_j == chunk_size_full);
+ } break;
+ case GGML_BACKEND_SPLIT_AXIS_MIRRORED: {
+ for (size_t j = 0; j < n_bufs; j++) {
+ ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
+ ggml_backend_tensor_set(simple_tensor, data, offset, size);
+ }
+ } break;
+ case GGML_BACKEND_SPLIT_AXIS_PARTIAL: {
+ GGML_ASSERT(tensor->type == GGML_TYPE_F32);
+ const int64_t ne = ggml_nelements(tensor);
+ std::vector<float> tmp;
+ tmp.reserve(ne);
+ for (int64_t i = 0; i < ne; i++) {
+ tmp.push_back(((const float *) data)[i] / n_bufs);
+ }
+ for (size_t j = 0; j < n_bufs; j++) {
+ ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
+ ggml_backend_tensor_set(simple_tensor, tmp.data(), offset, size);
+ }
+ } break;
+ default: {
+ GGML_ABORT("fatal error");
+ }
+ }
+}
+
+static void ggml_backend_meta_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+ const size_t n_bufs = ggml_backend_meta_buffer_n_bufs(buffer);
+ GGML_ASSERT(ggml_is_contiguous(tensor));
+
+ const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ false);
+ GGML_ASSERT(split_state.n_segments == 1);
+
+ switch (split_state.axis) {
+ case GGML_BACKEND_SPLIT_AXIS_0:
+ case GGML_BACKEND_SPLIT_AXIS_1:
+ case GGML_BACKEND_SPLIT_AXIS_2: {
+ // Exploit that tensors are contiguous to splice it with simple tensors as "chunks".
+ const size_t chunk_size_full = tensor->nb[split_state.axis + 1];
+ GGML_ASSERT(offset % chunk_size_full == 0);
+ GGML_ASSERT(size % chunk_size_full == 0);
+ const int64_t i_start = offset /chunk_size_full;
+ const int64_t i_stop = (offset + size)/chunk_size_full;
+ size_t offset_j = 0;
+ for (size_t j = 0; j < n_bufs; j++){
+ const ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
+ const size_t chunk_size_j = simple_tensor->nb[split_state.axis + 1];
+ const size_t simple_offset = i_start * chunk_size_j;
+ ggml_backend_tensor_get_2d(simple_tensor, (char *) data + offset_j, simple_offset, chunk_size_j, i_stop - i_start, chunk_size_j, chunk_size_full);
+ offset_j += chunk_size_j;
+ }
+ GGML_ASSERT(offset_j == chunk_size_full);
+ } break;
+ case GGML_BACKEND_SPLIT_AXIS_MIRRORED: {
+ // TODO other simple backend may be better
+ const ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, 0);
+ ggml_backend_tensor_get(simple_tensor, data, offset, size);
+ } break;
+ default: {
+ GGML_ABORT("fatal error");
+ }
+ }
+}
+
+static void ggml_backend_meta_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+ const size_t n_buffers = ggml_backend_meta_buffer_n_bufs(buffer);
+ for (size_t i = 0; i < n_buffers; i++) {
+ ggml_backend_buffer_clear(ggml_backend_meta_buffer_simple_buffer(buffer, i), value);
+ }
+}
+
+static void ggml_backend_meta_buffer_reset(ggml_backend_buffer_t buffer) {
+ const size_t n_buffers = ggml_backend_meta_buffer_n_bufs(buffer);
+ for (size_t i = 0; i < n_buffers; i++) {
+ ggml_backend_buffer_reset(ggml_backend_meta_buffer_simple_buffer(buffer, i));
+ }
+}
+
+static const ggml_backend_buffer_i ggml_backend_meta_buffer_iface = {
+ /* .free_buffer = */ ggml_backend_meta_buffer_free_buffer,
+ /* .get_base = */ ggml_backend_meta_buffer_get_base,
+ /* .init_tensor = */ ggml_backend_meta_buffer_init_tensor,
+ /* .memset_tensor = */ nullptr, // TODO implement
+ /* .set_tensor = */ ggml_backend_meta_buffer_set_tensor,
+ /* .get_tensor = */ ggml_backend_meta_buffer_get_tensor,
+ /* .set_tensor_2d = */ nullptr,
+ /* .get_tensor_2d = */ nullptr,
+ /* .cpy_tensor = */ nullptr,
+ /* .clear = */ ggml_backend_meta_buffer_clear,
+ /* .reset = */ ggml_backend_meta_buffer_reset,
+};
+
+bool ggml_backend_buffer_is_meta(ggml_backend_buffer_t buf) {
+ return buf != nullptr && buf->iface.free_buffer == ggml_backend_meta_buffer_iface.free_buffer;
+}
+
+static ggml_backend_buffer_t ggml_backend_meta_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+ const size_t n_simple_bufts = ggml_backend_meta_buft_n_bufts(buft);
+
+ ggml_init_params params = {
+ /*.mem_size =*/ 1024*1024*1024, // FIXME
+ /*.mem_buffer =*/ nullptr,
+ /*.no_alloc =*/ true,
+ };
+
+ ggml_backend_meta_buffer_context * buf_ctx = new ggml_backend_meta_buffer_context();
+ size_t max_size = 0;
+ buf_ctx->buf_configs.reserve(n_simple_bufts);
+ for (size_t i = 0; i < n_simple_bufts; i++) {
+ ggml_backend_buffer_t simple_buf = ggml_backend_buft_alloc_buffer(ggml_backend_meta_buft_simple_buft(buft, i), size);
+ max_size = std::max(max_size, ggml_backend_buffer_get_size(simple_buf));
+ buf_ctx->buf_configs.emplace_back(ggml_init(params), simple_buf);
+ }
+
+ return ggml_backend_buffer_init(buft, ggml_backend_meta_buffer_iface, buf_ctx, max_size);
+}
+
+struct ggml_backend_buffer * ggml_backend_meta_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) {
+ const size_t n_simple_bufts = ggml_backend_meta_buft_n_bufts(buft);
+
+ ggml_init_params params = {
+ /*.mem_size =*/ 1024*1024*1024, // FIXME
+ /*.mem_buffer =*/ nullptr,
+ /*.no_alloc =*/ true,
+ };
+
+ ggml_backend_meta_buffer_context * meta_buf_ctx = new ggml_backend_meta_buffer_context();
+ meta_buf_ctx->buf_configs.reserve(n_simple_bufts);
+ for (size_t i = 0; i < n_simple_bufts; i++) {
+ meta_buf_ctx->buf_configs.emplace_back(ggml_init(params), nullptr);
+ }
+
+ ggml_backend_buffer_t meta_buf = ggml_backend_buffer_init(buft, ggml_backend_meta_buffer_iface, meta_buf_ctx, 0);
+ for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
+ t->buffer = meta_buf;
+ ggml_backend_meta_buffer_init_tensor(meta_buf, t);
+ t->data = (void *) 0x2000000000000000; // FIXME
+ }
+ for (size_t i = 0; i < n_simple_bufts; i++) {
+ meta_buf_ctx->buf_configs[i].buf = ggml_backend_alloc_ctx_tensors_from_buft(
+ meta_buf_ctx->buf_configs[i].ctx, ggml_backend_meta_buft_simple_buft(buft, i));
+ meta_buf->size = std::max(meta_buf->size, ggml_backend_buffer_get_size(meta_buf_ctx->buf_configs[i].buf));
+ }
+ return meta_buf;
+}
+
+//
+// meta backend
+//
+
+static ggml_guid_t ggml_backend_meta_guid() {
+ static ggml_guid guid = {0xf1, 0x0e, 0x34, 0xcf, 0x9c, 0x6f, 0x43, 0xcb, 0x96, 0x92, 0xbe, 0x8e, 0xbb, 0x71, 0x3f, 0xda};
+ return &guid;
+}
+
+struct ggml_backend_meta_context {
+ struct cgraph_config {
+ ggml_cgraph * cgraph_main = nullptr;
+ int offset = 0; // Node offset vs. original graph
+
+ std::vector<ggml_cgraph *> cgraphs_aux;
+ };
+ struct backend_config {
+ ggml_backend_t backend;
+
+ std::vector<cgraph_config> cgraphs;
+ std::vector<ggml_tensor *> nodes;
+ ggml_backend_buffer_ptr buf;
+
+ backend_config(ggml_backend_t backend) : backend(backend) {}
+ };
+ std::string name;
+ std::vector<backend_config> backend_configs;
+ ggml_context_ptr ctx;
+ std::vector<ggml_cgraph *> cgraphs_aux;
+ std::vector<ggml_tensor *> nodes_aux;
+ int max_nnodes = 0;
+ size_t max_tmp_size = 0;
+ size_t max_subgraphs = 0;
+
+ ggml_backend_meta_context(ggml_backend_dev_t meta_dev, const char * params) {
+ const size_t n_devs = ggml_backend_meta_dev_n_devs(meta_dev);
+ name = "Meta(";
+ backend_configs.reserve(n_devs);
+ for (size_t i = 0; i < n_devs; i++) {
+ ggml_backend_dev_t simple_dev = ggml_backend_meta_dev_simple_dev(meta_dev, i);
+ if (i > 0) {
+ name += ",";
+ }
+ name += ggml_backend_dev_name(simple_dev);
+ backend_configs.emplace_back(ggml_backend_dev_init(simple_dev, params));
+ }
+ name += ")";
+ }
+
+ ~ggml_backend_meta_context() {
+ for (auto & bc : backend_configs) {
+ ggml_backend_free(bc.backend);
+ }
+ }
+
+ size_t n_reduce_steps() const {
+ return std::ceil(std::log2(backend_configs.size()));
+ }
+};
+
+static const char * ggml_backend_meta_get_name(ggml_backend_t backend) {
+ GGML_ASSERT(ggml_backend_is_meta(backend));
+ const ggml_backend_meta_context * backend_ctx = (const ggml_backend_meta_context *) backend->context;
+ return backend_ctx->name.c_str();
+}
+
+static void ggml_backend_meta_free(ggml_backend_t backend) {
+ GGML_ASSERT(ggml_backend_is_meta(backend));
+ ggml_backend_meta_context * backend_ctx = (ggml_backend_meta_context *) backend->context;
+ delete backend_ctx;
+ delete backend;
+}
+
+static void ggml_backend_meta_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+ const size_t n_backends = ggml_backend_meta_n_backends(backend);
+ GGML_ASSERT(offset == 0);
+ GGML_ASSERT(ggml_is_contiguous(tensor));
+
+ const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ false);
+ GGML_ASSERT(split_state.n_segments == 1);
+
+ switch (split_state.axis) {
+ case GGML_BACKEND_SPLIT_AXIS_0:
+ case GGML_BACKEND_SPLIT_AXIS_1:
+ case GGML_BACKEND_SPLIT_AXIS_2: {
+ // Exploit that tensors are contiguous to splice it with simple tensors as "chunks".
+ const size_t chunk_size_full = tensor->nb[split_state.axis + 1];
+ GGML_ASSERT(offset % chunk_size_full == 0);
+ GGML_ASSERT(size % chunk_size_full == 0);
+ const int64_t i_start = offset /chunk_size_full;
+ const int64_t i_stop = (offset + size)/chunk_size_full;
+ size_t offset_j = 0;
+ for (size_t j = 0; j < n_backends; j++){
+ ggml_backend_t simple_backend = ggml_backend_meta_simple_backend(backend, j);
+ ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
+ const size_t chunk_size_j = simple_tensor->nb[split_state.axis + 1];
+ ggml_backend_tensor_set_2d_async(simple_backend, simple_tensor, (const char *) data + offset_j, offset, chunk_size_j,
+ i_stop - i_start, chunk_size_j, chunk_size_full);
+ offset_j += chunk_size_j;
+ }
+ GGML_ASSERT(offset_j == chunk_size_full);
+ } break;
+ case GGML_BACKEND_SPLIT_AXIS_MIRRORED: {
+ for (size_t j = 0; j < n_backends; j++) {
+ ggml_backend_tensor_set_async(
+ ggml_backend_meta_simple_backend(backend, j), ggml_backend_meta_buffer_simple_tensor(tensor, j), data, offset, size);
+ }
+ } break;
+ default: {
+ GGML_ABORT("fatal error");
+ }
+ }
+}
+
+static void ggml_backend_meta_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+ const size_t n_backends = ggml_backend_meta_n_backends(backend);
+ GGML_ASSERT(offset == 0);
+ GGML_ASSERT(ggml_is_contiguous(tensor));
+
+ const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(tensor, /*assume_sync =*/ false);
+ GGML_ASSERT(split_state.n_segments == 1);
+
+ switch (split_state.axis) {
+ case GGML_BACKEND_SPLIT_AXIS_0:
+ case GGML_BACKEND_SPLIT_AXIS_1:
+ case GGML_BACKEND_SPLIT_AXIS_2: {
+ // Exploit that tensors are contiguous to splice it with simple tensors as "chunks".
+ const size_t chunk_size_full = tensor->nb[split_state.axis + 1];
+ GGML_ASSERT(offset % chunk_size_full == 0);
+ GGML_ASSERT(size % chunk_size_full == 0);
+ const int64_t i_start = offset /chunk_size_full;
+ const int64_t i_stop = (offset + size)/chunk_size_full;
+ size_t offset_j = 0;
+ for (size_t j = 0; j < n_backends; j++){
+ ggml_backend_t simple_backend = ggml_backend_meta_simple_backend(backend, j);
+ const ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, j);
+ const size_t chunk_size_j = simple_tensor->nb[split_state.axis + 1];
+ ggml_backend_tensor_get_2d_async(simple_backend, simple_tensor, (char *) data + offset_j, offset, chunk_size_j,
+ i_stop - i_start, chunk_size_j, chunk_size_full);
+ offset_j += chunk_size_j;
+ }
+ GGML_ASSERT(offset_j == chunk_size_full);
+ } break;
+ case GGML_BACKEND_SPLIT_AXIS_MIRRORED: {
+ // TODO other simple backend may be better
+ ggml_backend_t simple_backend = ggml_backend_meta_simple_backend(backend, 0);
+ const ggml_tensor * simple_tensor = ggml_backend_meta_buffer_simple_tensor(tensor, 0);
+ ggml_backend_tensor_get_async(simple_backend, simple_tensor, data, offset, size);
+ } break;
+ default: {
+ GGML_ABORT("fatal error");
+ }
+ }
+}
+
+static void ggml_backend_meta_synchronize(ggml_backend_t backend) {
+ const size_t n_backends = ggml_backend_meta_n_backends(backend);
+ for (size_t i = 0; i < n_backends; i++) {
+ ggml_backend_synchronize(ggml_backend_meta_simple_backend(backend, i));
+ }
+}
+
+static enum ggml_status ggml_backend_meta_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+ GGML_ASSERT(cgraph->grads == nullptr);
+ const size_t n_backends = ggml_backend_meta_n_backends(backend);
+ ggml_backend_meta_context * backend_ctx = (ggml_backend_meta_context *) backend->context;
+
+ bool max_nnodes_raised = false;
+ if (cgraph->n_nodes > backend_ctx->max_nnodes) {
+ for (size_t j = 0; j < n_backends; j++) {
+ auto & bcj = backend_ctx->backend_configs[j];
+ bcj.nodes.resize(cgraph->n_nodes);
+ bcj.cgraphs.resize(cgraph->n_nodes);
+ }
+ backend_ctx->max_nnodes = cgraph->n_nodes;
+ max_nnodes_raised = true;
+ }
+ for (size_t j = 0; j < n_backends; j++) {
+ auto & bcj = backend_ctx->backend_configs[j];
+
+ for (int i = 0; i < cgraph->n_nodes; i++) {
+ ggml_tensor * node = cgraph->nodes[i];
+ if (node->view_src != nullptr && node->view_src->op == GGML_OP_NONE && ggml_backend_buffer_is_host(node->view_src->buffer)) {
+ // FIXME s_copy_main is on the CPU and its view seems to be incorrectly added to the graph nodes.
+ // For regular usage this doesn't matter since it's a noop but trying to call ggml_backend_meta_buffer_simple_tensor results in a crash.
+ bcj.nodes[i] = node;
+ continue;
+ }
+ bcj.nodes[i] = ggml_backend_meta_buffer_simple_tensor(node, j);
+ GGML_ASSERT(bcj.nodes[i]);
+ }
+ }
+
+ size_t n_subgraphs = 0;
+ size_t max_tmp_size = 0;
+ {
+ // For MoE models it may make sense to delay the AllReduce in order to reduce I/O:
+ auto get_i_delayed = [&](const int i) -> int {
+ int id = i; // i_delayed
+ int idr = i; // i_delayed return, last safe return value
+
+ ggml_tensor * node = cgraph->nodes[id];
+ int32_t n_used = ggml_node_get_use_count(cgraph, id);
+ if (id + 1 >= cgraph->n_nodes) {
+ return idr;
+ }
+ {
+ ggml_tensor * next = cgraph->nodes[id+1];
+ if (next->op == GGML_OP_ADD_ID && next->src[0] == node &&
+ ggml_backend_meta_get_split_state(next->src[1], false).axis == GGML_BACKEND_SPLIT_AXIS_PARTIAL &&
+ ggml_backend_meta_get_split_state(next->src[2], false).axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
+ node = next;
+ id++;
+ idr = id;
+ n_used = ggml_node_get_use_count(cgraph, id);
+ }
+ }
+ if (id + 1 >= cgraph->n_nodes) {
+ return idr;
+ }
+ {
+ ggml_tensor * next = cgraph->nodes[id+1];
+ if (next->op == GGML_OP_MUL && next->src[0] == node &&
+ ggml_backend_meta_get_split_state(next->src[1], false).axis == GGML_BACKEND_SPLIT_AXIS_MIRRORED) {
+ node = next;
+ id++;
+ idr = id;
+ n_used = ggml_node_get_use_count(cgraph, id);
+ }
+ }
+
+ if (n_used != node->ne[1] || id + 2*n_used-1 >= cgraph->n_nodes) {
+ return idr;
+ }
+ for (int32_t k = 0; k < n_used; k++) {
+ ggml_tensor * next = cgraph->nodes[id+1];
+ if (next->op != GGML_OP_VIEW || next->view_src != node || next->view_offs != k*node->nb[1] ||
+ next->ne[0] != node->ne[0] || next->ne[1] != node->ne[2] || next->nb[1] != node->nb[2] ||
+ ggml_node_get_use_count(cgraph, id+1) != 1) {
+ return idr;
+ }
+ id++;
+ }
+ {
+ ggml_tensor * next = cgraph->nodes[id+1];
+ if (next->op != GGML_OP_ADD || next->src[0] != cgraph->nodes[id - (n_used-1)] ||
+ next->src[1] != cgraph->nodes[id - (n_used-2)] || ggml_node_get_use_count(cgraph, id+1) != 1) {
+ return idr;
+ }
+ id++;
+ }
+ for (int32_t k = 0; k < n_used - 2; k++) {
+ ggml_tensor * next = cgraph->nodes[id+1];
+ if (next->op != GGML_OP_ADD || next->src[0] != cgraph->nodes[id] ||
+ next->src[1] != cgraph->nodes[id - (n_used-2)] || ggml_node_get_use_count(cgraph, id+1) != 1) {
+ return idr;
+ }
+ id++;
+ }
+ idr = id;
+ return idr;
+ };
+
+ int i_start = 0;
+ for (int i = 0; i < cgraph->n_nodes; i++) {
+ ggml_tensor * node = cgraph->nodes[i];
+ if (node->view_src != nullptr && node->view_src->op == GGML_OP_NONE && ggml_backend_buffer_is_host(node->view_src->buffer)) {
+ continue;
+ }
+ const ggml_backend_meta_split_state split_state = ggml_backend_meta_get_split_state(node, /*assume_sync =*/ false);
+ if (split_state.axis == GGML_BACKEND_SPLIT_AXIS_PARTIAL) {
+ max_tmp_size = std::max(max_tmp_size, ggml_nbytes(node));
+ }
+ const bool new_subgraph = i + 1 == cgraph->n_nodes || split_state.axis == GGML_BACKEND_SPLIT_AXIS_PARTIAL;
+ if (!new_subgraph) {
+ continue;
+ }
+
+ i = get_i_delayed(i);
+
+ for (size_t j = 0; j < n_backends; j++) {
+ auto & bcj = backend_ctx->backend_configs[j];
+ bcj.cgraphs[n_subgraphs].offset = i_start;
+ }
+ n_subgraphs++;
+ i_start = i + 1;
+ }
+ GGML_ASSERT(i_start == cgraph->n_nodes);
+ }
+
+ if (max_tmp_size > backend_ctx->max_tmp_size) {
+ for (size_t j = 0; j < n_backends; j++) {
+ auto & bcj = backend_ctx->backend_configs[j];
+ bcj.buf.reset(ggml_backend_alloc_buffer(bcj.backend, max_tmp_size));
+ }
+ backend_ctx->max_tmp_size = max_tmp_size;
+ }
+
+
+ if (max_nnodes_raised || n_subgraphs > backend_ctx->max_subgraphs) {
+ backend_ctx->max_subgraphs = std::max(backend_ctx->max_subgraphs, n_subgraphs);
+ const size_t n_reduce_steps = backend_ctx->n_reduce_steps();
+ const size_t n_nodes_per_device = 2 * n_reduce_steps; // tmp + ADD per step
+ const size_t n_cgraphs_per_device = n_reduce_steps; // 1 ADD graph per step
+ const size_t mem_per_device_graphs_main = backend_ctx->max_subgraphs*ggml_graph_overhead_custom(backend_ctx->max_nnodes, cgraph->grads);
+ const size_t mem_per_device_graphs_aux = n_cgraphs_per_device*backend_ctx->max_subgraphs*ggml_graph_overhead_custom(1, cgraph->grads);
+ const size_t mem_per_device_nodes_aux = n_nodes_per_device*backend_ctx->max_subgraphs*ggml_tensor_overhead();
+ ggml_init_params params = {
+ /*.mem_size =*/ n_backends * (mem_per_device_graphs_main + mem_per_device_graphs_aux + mem_per_device_nodes_aux),
+ /*.mem_buffer =*/ nullptr,
+ /*.no_alloc =*/ true,
+ };
+ backend_ctx->ctx.reset(ggml_init(params));
+ for (size_t j = 0; j < n_backends; j++) {
+ auto & bcj = backend_ctx->backend_configs[j];
+ for (size_t i = 0; i < n_subgraphs; i++) {
+ bcj.cgraphs[i].cgraph_main = ggml_new_graph_custom(backend_ctx->ctx.get(), cgraph->n_nodes, /*grads =*/ false);
+ }
+ }
+ backend_ctx->cgraphs_aux.resize(n_backends*n_cgraphs_per_device*backend_ctx->max_subgraphs);
+ for (size_t k = 0; k < backend_ctx->cgraphs_aux.size(); k++) {
+ backend_ctx->cgraphs_aux[k] = ggml_new_graph_custom(backend_ctx->ctx.get(), 1, cgraph->grads);
+ }
+ backend_ctx->nodes_aux.resize(n_backends*n_nodes_per_device*backend_ctx->max_subgraphs);
+ for (size_t k = 0; k < backend_ctx->nodes_aux.size(); k++) {
+ backend_ctx->nodes_aux[k] = ggml_new_tensor_1d(backend_ctx->ctx.get(), GGML_TYPE_F32, 1);
+ }
+ }
+
+ for (size_t j = 0; j < n_backends; j++) {
+ auto & bcj = backend_ctx->backend_configs[j];
+ for (size_t i_graph = 0; i_graph < n_subgraphs; i_graph++) {
+ ggml_cgraph * cgraph_ij = bcj.cgraphs[i_graph].cgraph_main;
+ const size_t i_node_start = bcj.cgraphs[i_graph].offset;
+ const size_t i_node_stop = i_graph + 1 < n_subgraphs ? bcj.cgraphs[i_graph + 1].offset : cgraph->n_nodes;
+ cgraph_ij->n_nodes = i_node_stop - i_node_start;
+ ggml_hash_set_reset(&cgraph_ij->visited_hash_set);
+ for (size_t i_node = i_node_start; i_node < i_node_stop; i_node++) {
+ ggml_tensor * node_ij = bcj.nodes[i_node];
+ cgraph_ij->nodes[i_node - i_node_start] = node_ij;
+ const size_t hash_pos_orig = ggml_hash_find(&cgraph->visited_hash_set, cgraph->nodes[i_node]);
+ const size_t hash_pos_ij = ggml_hash_insert(&cgraph_ij->visited_hash_set, node_ij);
+ cgraph_ij->use_counts[hash_pos_ij] = cgraph->use_counts[hash_pos_orig];
+ }
+ }
+ }
+
+ size_t iga = 0; // i graph aux
+ size_t ina = 0; // i node aux
+
+ // FIXME usage_counts
+ auto get_cgraph_aux = [&]() -> ggml_cgraph * {
+ ggml_cgraph * ret = backend_ctx->cgraphs_aux[iga++];
+ return ret;
+ };
+ auto get_node_aux = [&](ggml_tensor * t) -> ggml_tensor * {
+ ggml_tensor * ret = backend_ctx->nodes_aux[ina++];
+ memset(ret, 0, sizeof(ggml_tensor));
+ ret->op = GGML_OP_NONE;
+ ret->type = t->type;
+ for (size_t k = 0; k < GGML_MAX_DIMS; k++) {
+ ret->ne[k] = t->ne[k];
+ ret->nb[k] = t->nb[k];
+ }
+ return ret;
+ };
+
+ // Preferentially use backend-specific allreduce_tensor_async (e.g. NCCL for CUDA), use a generic fallback if unavailable:
+ auto allreduce_fallback = [&](size_t i) -> ggml_status {
+ std::vector<ggml_cgraph *> step_cgraphs(n_backends, nullptr);
+
+ for (size_t offset_j = 1; offset_j < n_backends; offset_j *= 2) {
+ std::fill(step_cgraphs.begin(), step_cgraphs.end(), nullptr);
+
+ for (size_t j = 0; j < n_backends; j++) {
+ const size_t j_other = j ^ offset_j;
+ if (j_other > j) {
+ continue;
+ }
+
+ auto & bcj1 = backend_ctx->backend_configs[j];
+ auto & bcj2 = backend_ctx->backend_configs[j_other];
+
+ ggml_tensor * node1 = bcj1.cgraphs[i].cgraph_main->nodes[bcj1.cgraphs[i].cgraph_main->n_nodes - 1];
+ ggml_tensor * node2 = bcj2.cgraphs[i].cgraph_main->nodes[bcj2.cgraphs[i].cgraph_main->n_nodes - 1];
+ GGML_ASSERT(ggml_is_contiguous(node1));
+ GGML_ASSERT(ggml_is_contiguous(node2));
+
+ // Tmp tensors to receive P2P copies
+ ggml_tensor * node_tmp_1 = get_node_aux(node1);
+ node_tmp_1->buffer = bcj1.buf.get();
+ node_tmp_1->data = ggml_backend_buffer_get_base(bcj1.buf.get());
+
+ ggml_tensor * node_tmp_2 = get_node_aux(node2);
+ node_tmp_2->buffer = bcj2.buf.get();
+ node_tmp_2->data = ggml_backend_buffer_get_base(bcj2.buf.get());
+
+ // 2 P2P copies: exchange full buffers
+ ggml_backend_tensor_copy_async(bcj1.backend, bcj2.backend, node1, node_tmp_2);
+ ggml_backend_tensor_copy_async(bcj2.backend, bcj1.backend, node2, node_tmp_1);
+
+ // Local ADD: node1 += tmp1 (in-place via view)
+ ggml_tensor * node_red_1 = get_node_aux(node1);
+ node_red_1->view_src = node1->view_src == nullptr ? node1 : node1->view_src;
+ node_red_1->view_offs = node1->view_offs;
+ node_red_1->op = GGML_OP_ADD;
+ node_red_1->src[0] = node1;
+ node_red_1->src[1] = node_tmp_1;
+ node_red_1->flags |= GGML_TENSOR_FLAG_COMPUTE;
+ ggml_backend_view_init(node_red_1);
+
+ // Local ADD: node2 += tmp2 (in-place via view)
+ ggml_tensor * node_red_2 = get_node_aux(node2);
+ node_red_2->view_src = node2->view_src == nullptr ? node2 : node2->view_src;
+ node_red_2->view_offs = node2->view_offs;
+ node_red_2->op = GGML_OP_ADD;
+ node_red_2->src[0] = node2;
+ node_red_2->src[1] = node_tmp_2;
+ node_red_2->flags |= GGML_TENSOR_FLAG_COMPUTE;
+ ggml_backend_view_init(node_red_2);
+
+ // Build 1-node cgraphs for the ADD ops
+ ggml_cgraph * cgraph_aux_1 = get_cgraph_aux();
+ cgraph_aux_1->nodes[0] = node_red_1;
+ cgraph_aux_1->n_nodes = 1;
+ step_cgraphs[j] = cgraph_aux_1;
+
+ ggml_cgraph * cgraph_aux_2 = get_cgraph_aux();
+ cgraph_aux_2->nodes[0] = node_red_2;
+ cgraph_aux_2->n_nodes = 1;
+ step_cgraphs[j_other] = cgraph_aux_2;
+ }
+
+ // Execute local ADDs for this step
+ for (size_t j = 0; j < n_backends; j++) {
+ if (step_cgraphs[j] == nullptr) {
+ continue;
+ }
+ auto & bcj = backend_ctx->backend_configs[j];
+ const ggml_status status = ggml_backend_graph_compute_async(bcj.backend, step_cgraphs[j]);
+ if (status != GGML_STATUS_SUCCESS) {
+ return status;
+ }
+ }
+ }
+ return GGML_STATUS_SUCCESS;
+ };
+
+
+ for (size_t i = 0; i < n_subgraphs; i++) {
+ for (size_t j = 0; j < n_backends; j++) {
+ auto & bcj = backend_ctx->backend_configs[j];
+ const ggml_status status = ggml_backend_graph_compute_async(bcj.backend, bcj.cgraphs[i].cgraph_main);
+ if (status != GGML_STATUS_SUCCESS) {
+ return status;
+ }
+ }
+
+ if (n_backends > 1 && i < n_subgraphs - 1) {
+ bool backend_allreduce_success = false;
+ ggml_backend_allreduce_tensor_t allreduce_tensor = (ggml_backend_allreduce_tensor_t) ggml_backend_reg_get_proc_address(
+ ggml_backend_dev_backend_reg(ggml_backend_get_device(backend_ctx->backend_configs[0].backend)), "ggml_backend_allreduce_tensor");
+ if (allreduce_tensor) {
+ std::vector<ggml_backend_t> backends;
+ backends.reserve(n_backends);
+ std::vector<ggml_tensor *> nodes;
+ nodes.reserve(n_backends);
+ for (size_t j = 0; j < n_backends; j++) {
+ auto & bcj = backend_ctx->backend_configs[j];
+ backends.push_back(bcj.backend);
+ ggml_cgraph * cgraph_ij = bcj.cgraphs[i].cgraph_main;
+ nodes.push_back(cgraph_ij->nodes[cgraph_ij->n_nodes-1]);
+ }
+ backend_allreduce_success = allreduce_tensor(backends.data(), nodes.data(), n_backends);
+ }
+
+ if (!backend_allreduce_success) {
+ const ggml_status status = allreduce_fallback(i);
+ if (status != GGML_STATUS_SUCCESS) {
+ return status;
+ }
+ }
+ }
+ }
+ return GGML_STATUS_SUCCESS;
+}
+
+static const ggml_backend_i ggml_backend_meta_i = {
+ /* .get_name = */ ggml_backend_meta_get_name,
+ /* .free = */ ggml_backend_meta_free,
+ /* .set_tensor_async = */ ggml_backend_meta_set_tensor_async,
+ /* .get_tensor_async = */ ggml_backend_meta_get_tensor_async,
+ /* .get_tensor_2d_async = */ nullptr,
+ /* .set_tensor_2d_async = */ nullptr,
+ /* .cpy_tensor_async = */ nullptr,
+ /* .synchronize = */ ggml_backend_meta_synchronize,
+ /* .graph_plan_create = */ nullptr,
+ /* .graph_plan_free = */ nullptr,
+ /* .graph_plan_update = */ nullptr,
+ /* .graph_plan_compute = */ nullptr,
+ /* .graph_compute = */ ggml_backend_meta_graph_compute,
+ /* .event_record = */ nullptr,
+ /* .event_wait = */ nullptr,
+ /* .graph_optimize = */ nullptr,
+};
+
+bool ggml_backend_is_meta(ggml_backend_t backend) {
+ return backend != nullptr && backend->iface.get_name == ggml_backend_meta_i.get_name;
+}
+
+static ggml_backend_t ggml_backend_meta_device_init_backend(ggml_backend_dev_t dev, const char * params) {
+ ggml_backend_meta_context * backend_ctx = new ggml_backend_meta_context(dev, params);
+
+ ggml_backend_t backend = new struct ggml_backend;
+ backend->guid = ggml_backend_meta_guid();
+ backend->iface = ggml_backend_meta_i;
+ backend->device = dev;
+ backend->context = backend_ctx;
+ return backend;
+}
+
+size_t ggml_backend_meta_n_backends(ggml_backend_t meta_backend) {
+ GGML_ASSERT(ggml_backend_is_meta(meta_backend));
+ const ggml_backend_meta_context * backend_ctx = (const ggml_backend_meta_context *) meta_backend->context;
+ return backend_ctx->backend_configs.size();
+}
+
+ggml_backend_t ggml_backend_meta_simple_backend(ggml_backend_t meta_backend, size_t index) {
+ GGML_ASSERT(ggml_backend_is_meta(meta_backend));
+ const ggml_backend_meta_context * backend_ctx = (const ggml_backend_meta_context *) meta_backend->context;
+ return backend_ctx->backend_configs[index].backend;
+}
+
// configure logging to stdout
// CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr));
+ for (int id = 0; id < info.device_count; ++id) {
+ ggml_cuda_set_device(id);
+ for (int id_other = 0; id_other < info.device_count; ++id_other) {
+ if (id == id_other) {
+ continue;
+ }
+ int can_access_peer;
+ CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, id, id_other));
+ if (can_access_peer) {
+ CUDA_CHECK(cudaDeviceEnablePeerAccess(id_other, 0));
+ }
+ }
+ }
+
+#ifdef GGML_USE_NCCL
+ int dev_ids[GGML_CUDA_MAX_DEVICES];
+ for (int id = 0; id < info.device_count; ++id) {
+ dev_ids[id] = id;
+ }
+ NCCL_CHECK(ncclCommInitAll(info.comms, info.device_count, dev_ids));
+#endif // GGML_USE_NCCL
+
return info;
}
}
static void ggml_backend_cuda_buffer_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
- ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+ ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *) buffer->context;
ggml_cuda_set_device(ctx->device);
- CUDA_CHECK(cudaMemsetAsync((char *)tensor->data + offset, value, size, cudaStreamPerThread));
+ CUDA_CHECK(cudaMemsetAsync((char *) tensor->data + offset, value, size, cudaStreamPerThread));
CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
}
static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
- ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+ ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *) buffer->context;
ggml_cuda_set_device(ctx->device);
- CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, cudaStreamPerThread));
+ CUDA_CHECK(cudaMemcpyAsync((char *) tensor->data + offset, data, size, cudaMemcpyHostToDevice, cudaStreamPerThread));
CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
}
static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+ ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *) buffer->context;
+
+ ggml_cuda_set_device(ctx->device);
+ CUDA_CHECK(cudaMemcpyAsync(data, (const char *) tensor->data + offset, size, cudaMemcpyDeviceToHost, cudaStreamPerThread));
+ CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
+}
+
+static void ggml_backend_cuda_buffer_set_tensor_2d(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data,
+ size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) {
+ ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *) buffer->context;
+
+ ggml_cuda_set_device(ctx->device);
+ CUDA_CHECK(cudaMemcpy2DAsync(
+ (char *) tensor->data + offset, stride_tensor, data, stride_data, size, n_copies, cudaMemcpyHostToDevice, cudaStreamPerThread));
+ CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
+}
+
+static void ggml_backend_cuda_buffer_get_tensor_2d(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data,
+ size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) {
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
ggml_cuda_set_device(ctx->device);
- CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, cudaStreamPerThread));
+ CUDA_CHECK(cudaMemcpy2DAsync(
+ data, stride_data, (const char *) tensor->data + offset, stride_tensor, size, n_copies, cudaMemcpyDeviceToHost, cudaStreamPerThread));
CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
}
/* .memset_tensor = */ ggml_backend_cuda_buffer_memset_tensor,
/* .set_tensor = */ ggml_backend_cuda_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_cuda_buffer_get_tensor,
+ /* .set_tensor_2d = */ ggml_backend_cuda_buffer_set_tensor_2d,
+ /* .get_tensor_2d = */ ggml_backend_cuda_buffer_get_tensor_2d,
/* .cpy_tensor = */ ggml_backend_cuda_buffer_cpy_tensor,
/* .clear = */ ggml_backend_cuda_buffer_clear,
/* .reset = */ NULL,
/* .memset_tensor = */ NULL,
/* .set_tensor = */ ggml_backend_cuda_split_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_cuda_split_buffer_get_tensor,
+ /* .set_tensor_2d = */ NULL,
+ /* .get_tensor_2d = */ NULL,
/* .cpy_tensor = */ NULL,
/* .clear = */ ggml_backend_cuda_split_buffer_clear,
/* .reset = */ NULL,
/* .is_host = */ ggml_backend_cuda_split_buffer_type_is_host,
};
+bool ggml_backend_cuda_allreduce_tensor(ggml_backend_t * backends, struct ggml_tensor ** tensors, size_t n_backends) {
+#ifdef GGML_USE_NCCL
+ const int64_t ne = ggml_nelements(tensors[0]);
+ // FIXME the input of llm_graph_context::build_in_out_ids can produce a tensor with 0 elements if n_outputs == 0
+ // This then causes a crash in this function
+ if (ne == 0) {
+ return true;
+ }
+ for (size_t i = 0; i < n_backends; ++i) {
+ GGML_ASSERT(tensors[i] != nullptr);
+ GGML_ASSERT(ggml_nelements(tensors[i]) == ne);
+ GGML_ASSERT(ggml_is_contiguously_allocated(tensors[i]));
+ }
+
+ const ggml_cuda_device_info info = ggml_cuda_info();
+
+ // For small tensors, simply reduce them as FP32.
+ // The following heuristic for how "small" a tensor should be is based on RTX 4090s connected via 16x PCIe 4.0.
+ if ((n_backends <= 2 && ne < 32768) || (n_backends == 3 && ne < 131072) || (n_backends >= 4 && ne < 262144)) {
+ NCCL_CHECK(ncclGroupStart());
+ for (size_t i = 0; i < n_backends; ++i) {
+ ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backends[i]->context;
+ NCCL_CHECK(ncclAllReduce(tensors[i]->data, tensors[i]->data, ne, ncclFloat, ncclSum, info.comms[cuda_ctx->device], cuda_ctx->stream()));
+ }
+ NCCL_CHECK(ncclGroupEnd());
+
+ return true;
+ }
+
+ // For large tensors it's faster to compress them to BF16 for the reduction:
+ to_bf16_cuda_t to_bf16 = ggml_get_to_bf16_cuda(GGML_TYPE_F32);
+ to_fp32_cuda_t to_fp32 = ggml_get_to_fp32_cuda(GGML_TYPE_BF16);
+
+ ggml_cuda_pool_alloc<nv_bfloat16> tmp[GGML_CUDA_MAX_DEVICES];
+ for (size_t i = 0; i < n_backends; ++i) {
+ ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backends[i]->context;
+ tmp[i].pool = &cuda_ctx->pool();
+ tmp[i].alloc(ne);
+
+ ggml_cuda_set_device(i);
+ to_bf16(tensors[i]->data, tmp[i].get(), ne, cuda_ctx->stream());
+ CUDA_CHECK(cudaGetLastError());
+ }
+
+ NCCL_CHECK(ncclGroupStart());
+ for (size_t i = 0; i < n_backends; ++i) {
+ ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backends[i]->context;
+ NCCL_CHECK(ncclAllReduce(tmp[i].get(), tmp[i].get(), ne, ncclBfloat16, ncclSum, info.comms[cuda_ctx->device], cuda_ctx->stream()));
+ }
+ NCCL_CHECK(ncclGroupEnd());
+
+ for (size_t i = 0; i < n_backends; ++i) {
+ ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backends[i]->context;
+
+ ggml_cuda_set_device(i);
+ to_fp32(tmp[i].get(), (float *) tensors[i]->data, ne, cuda_ctx->stream());
+ CUDA_CHECK(cudaGetLastError());
+ }
+
+ return true;
+#else
+ // If NCCL is installed it is used by default for optimal performance.
+ // However, NVIDIA does not distribute NCCL with CUDA so users may be unwittingly missing this package.
+ // RCCL is disabled by default, users are explicitly opting in.
+ // Therefore print no warning for RCCL.
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+ static bool warning_printed = false;
+ if (!warning_printed) {
+ GGML_LOG_WARN("%s: NVIDIA Collective Communications Library (NCCL) is unavailable, multi GPU performance will be suboptimal\n", __func__);
+ warning_printed = true;
+ }
+#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+ GGML_UNUSED_VARS(backends, tensors, n_backends);
+ return false;
+#endif // GGML_USE_NCCL
+}
+
ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(int main_device, const float * tensor_split) {
static std::mutex mutex;
std::lock_guard<std::mutex> lock(mutex);
GGML_UNUSED_VARS(dst, src1_ddq_i, src1_padded_row_size);
}
-static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) {
- static bool peer_access_enabled = false;
-
- const bool enable_peer_access = n_tokens <= GGML_CUDA_PEER_MAX_BATCH_SIZE;
-
- if (peer_access_enabled == enable_peer_access) {
- return;
- }
-
-#ifdef NDEBUG
- for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
- ggml_cuda_set_device(id);
- CUDA_CHECK(cudaDeviceSynchronize());
- }
-
- for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
- ggml_cuda_set_device(id);
-
- for (int id_other = 0; id_other < ggml_backend_cuda_get_device_count(); ++id_other) {
- if (id == id_other) {
- continue;
- }
- if (id != main_device && id_other != main_device) {
- continue;
- }
-
- int can_access_peer;
- CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, id, id_other));
- if (can_access_peer) {
- if (enable_peer_access) {
- cudaError_t err = cudaDeviceEnablePeerAccess(id_other, 0);
- if (err != cudaErrorPeerAccessAlreadyEnabled) {
- CUDA_CHECK(err);
- } else {
- // reset the error
- (void)cudaGetLastError();
- }
- } else {
- cudaError_t err = cudaDeviceDisablePeerAccess(id_other);
- if (err != cudaErrorPeerAccessNotEnabled) {
- CUDA_CHECK(err);
- } else {
- // reset the error
- (void)cudaGetLastError();
- }
- }
- }
- }
- }
-
- ggml_cuda_set_device(main_device);
-#endif // NDEBUG
-
- peer_access_enabled = enable_peer_access;
-
- GGML_UNUSED(main_device);
-}
-
static cudaError_t ggml_cuda_Memcpy2DPeerAsync(
void * dst, int dstDevice, size_t dpitch, void * src, int srcDevice, size_t spitch, size_t width, size_t height, cudaStream_t stream) {
}
static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct ggml_tensor * dst) {
- // why is this here instead of mul_mat?
- if (dst->src[0] != nullptr && ggml_backend_buft_is_cuda_split(dst->src[0]->buffer->buft)) {
- ggml_cuda_set_peer_access(dst->src[1]->ne[1], ctx.device);
- }
-
switch (dst->op) {
case GGML_OP_ARGMAX:
ggml_cuda_argmax(ctx, dst);
}
static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
- ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+ ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context;
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
- CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, cuda_ctx->stream()));
+ CUDA_CHECK(cudaMemcpyAsync((char *) tensor->data + offset, data, size, cudaMemcpyHostToDevice, cuda_ctx->stream()));
}
static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
- ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+ ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context;
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
- CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, cuda_ctx->stream()));
+ CUDA_CHECK(cudaMemcpyAsync(data, (const char *) tensor->data + offset, size, cudaMemcpyDeviceToHost, cuda_ctx->stream()));
+}
+
+static void ggml_backend_cuda_set_tensor_2d_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data,
+ size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) {
+ ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context;
+ ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+
+ GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
+
+ CUDA_CHECK(cudaMemcpy2DAsync(
+ (char *) tensor->data + offset, stride_tensor, data, stride_data, size, n_copies, cudaMemcpyHostToDevice, cuda_ctx->stream()));
+}
+
+static void ggml_backend_cuda_get_tensor_2d_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data,
+ size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) {
+ ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context;
+ ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+
+ GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
+
+ CUDA_CHECK(cudaMemcpy2DAsync(
+ data, stride_data, (const char *) tensor->data + offset, stride_tensor, size, n_copies, cudaMemcpyDeviceToHost, cuda_ctx->stream()));
}
static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) {
return false;
}
- if (!ggml_backend_buffer_is_cuda(src->buffer) || !ggml_backend_buffer_is_cuda(dst->buffer)) {
+ if (!ggml_backend_buffer_is_cuda(buf_src) || !ggml_backend_buffer_is_cuda(buf_dst)) {
return false;
}
// device -> device copy
- ggml_backend_cuda_context * cuda_ctx_src = (ggml_backend_cuda_context *)backend_src->context;
- ggml_backend_cuda_context * cuda_ctx_dst = (ggml_backend_cuda_context *)backend_dst->context;
+ ggml_backend_cuda_context * cuda_ctx_src = (ggml_backend_cuda_context *) backend_src->context;
+ ggml_backend_cuda_context * cuda_ctx_dst = (ggml_backend_cuda_context *) backend_dst->context;
- ggml_backend_cuda_buffer_context * buf_ctx_src = (ggml_backend_cuda_buffer_context *)buf_src->context;
- ggml_backend_cuda_buffer_context * buf_ctx_dst = (ggml_backend_cuda_buffer_context *)buf_dst->context;
+ ggml_backend_cuda_buffer_context * buf_ctx_src = (ggml_backend_cuda_buffer_context *) buf_src->context;
+ ggml_backend_cuda_buffer_context * buf_ctx_dst = (ggml_backend_cuda_buffer_context *) buf_dst->context;
if (cuda_ctx_src->device != buf_ctx_src->device || cuda_ctx_dst->device != buf_ctx_dst->device) {
#ifndef NDEBUG
GGML_LOG_DEBUG("%s: backend and buffer devices do not match\n", __func__);
-#endif
+#endif // NDEBUG
return false;
}
return false;
#else
CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, cuda_ctx_dst->device, src->data, cuda_ctx_src->device, ggml_nbytes(dst), cuda_ctx_src->stream()));
-#endif
+#endif // GGML_CUDA_NO_PEER_COPY
}
// record event on src stream after the copy
/* .free = */ ggml_backend_cuda_free,
/* .set_tensor_async = */ ggml_backend_cuda_set_tensor_async,
/* .get_tensor_async = */ ggml_backend_cuda_get_tensor_async,
+ /* .get_tensor_2d_async = */ ggml_backend_cuda_set_tensor_2d_async,
+ /* .set_tensor_2d_async = */ ggml_backend_cuda_get_tensor_2d_async,
/* .cpy_tensor_async = */ ggml_backend_cuda_cpy_tensor_async,
/* .synchronize = */ ggml_backend_cuda_synchronize,
/* .graph_plan_create = */ NULL,
static void * ggml_backend_cuda_reg_get_proc_address(ggml_backend_reg_t reg, const char * name) {
GGML_UNUSED(reg);
+ if (strcmp(name, "ggml_backend_allreduce_tensor") == 0) {
+ return (void *)ggml_backend_cuda_allreduce_tensor;
+ }
if (strcmp(name, "ggml_backend_split_buffer_type") == 0) {
return (void *)ggml_backend_cuda_split_buffer_type;
}
overview / pkg / ggml / sources / whisper.cpp / commitdiff