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llama.cpp
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Uncached model read

This commit is contained in:
JTischbein 2025-12-13 20:10:21 +01:00
parent 5266379bca
commit 3074b500a5
5 changed files with 255 additions and 7 deletions
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7
common/arg.cpp
View File

@ -1984,6 +1984,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.use_mmap = value;
}
).set_env("LLAMA_ARG_MMAP"));
add_opt(common_arg(
{"--mmap"},
"memory-map model",
[](common_params & params) {
params.use_mmap = true;
}
).set_env("LLAMA_ARG_MMAP"));
add_opt(common_arg(
{"--numa"}, "TYPE",
"attempt optimizations that help on some NUMA systems\n"

2
common/common.h
View File

@ -413,7 +413,7 @@ struct common_params {
bool kv_unified = false; // enable unified KV cache
bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix
bool use_mmap = true; // use mmap for faster loads
bool use_mmap = false; // use uncached reads for faster loads
bool use_mlock = false; // use mlock to keep model in memory
bool verbose_prompt = false; // print prompt tokens before generation
bool display_prompt = true; // print prompt before generation

133
src/llama-mmap.cpp
View File

@ -13,9 +13,10 @@
#ifdef __has_include
#if __has_include(<unistd.h>)
#include <unistd.h>
#include <fcntl.h>
#include <sys/stat.h>
#if defined(_POSIX_MAPPED_FILES)
#include <sys/mman.h>
#include <fcntl.h>
#endif
#if defined(_POSIX_MEMLOCK_RANGE)
#include <sys/resource.h>
@ -158,6 +159,129 @@ struct llama_file::impl {
std::fclose(fp);
}
}
#elif defined(__linux__)
impl(const char * fname, const char * mode) : impl(fname, mode, false) {}
impl(const char * fname, const char * mode, bool uncached_read) {
if (uncached_read) {
fd = open(fname, O_RDONLY | O_DIRECT);
if (fd == -1) {
throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
}
struct stat file_stats{};
fstat(fd, &file_stats);
size = file_stats.st_size;
off_t ret = lseek(fd, 0, SEEK_SET);
if (ret == -1) {
throw std::runtime_error(format("seek error: %s", strerror(errno)));
}
} else {
fp = ggml_fopen(fname, mode);
if (fp == NULL) {
throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
}
seek(0, SEEK_END);
size = tell();
seek(0, SEEK_SET);
}
}
size_t tell() const {
if (fd == -1) {
long ret = std::ftell(fp);
if (ret == -1) {
throw std::runtime_error(format("ftell error: %s", strerror(errno)));
}
return (size_t) ret;
}
off_t pos = lseek(fd, 0, SEEK_CUR);
if (pos == -1) {
throw std::runtime_error(format("lseek error: %s", strerror(errno)));
}
return (size_t) pos;
}
void seek(size_t offset, int whence) const {
off_t ret = 0;
if (fd == -1) {
ret = std::fseek(fp, (long) offset, whence);
} else {
ret = lseek(fd, offset, whence);
}
if (ret == -1) {
throw std::runtime_error(format("seek error: %s", strerror(errno)));
}
}
void read_raw(void * ptr, size_t len) const {
if (len == 0) {
return;
}
if (fd == -1) {
errno = 0;
std::size_t ret = std::fread(ptr, len, 1, fp);
if (ferror(fp)) {
throw std::runtime_error(format("read error: %s", strerror(errno)));
}
if (ret != 1) {
throw std::runtime_error("unexpectedly reached end of file");
}
} else {
bool successful = false;
while (!successful) {
off_t ret = read(fd, ptr, len);
if (ret == -1) {
if (errno == EINTR) {
continue; // Interrupted by signal, retry
}
throw std::runtime_error(format("read error: %s", strerror(errno)));
}
if (ret == 0) {
throw std::runtime_error("unexpectedly reached end of file");
}
successful = true;
}
}
}
uint32_t read_u32() const {
uint32_t ret;
read_raw(&ret, sizeof(ret));
return ret;
}
void write_raw(const void * ptr, size_t len) const {
if (len == 0) {
return;
}
errno = 0;
size_t ret = std::fwrite(ptr, len, 1, fp);
if (ret != 1) {
throw std::runtime_error(format("write error: %s", strerror(errno)));
}
}
void write_u32(uint32_t val) const {
write_raw(&val, sizeof(val));
}
~impl() {
if (fp) {
std::fclose(fp);
} else if (fd != -1) {
close(fd);
}
}
int fd = -1;
#else
impl(const char * fname, const char * mode) {
fp = ggml_fopen(fname, mode);
@ -237,11 +361,14 @@ struct llama_file::impl {
}
#endif
FILE * fp;
size_t size;
FILE * fp{};
size_t size{};
};
llama_file::llama_file(const char * fname, const char * mode) : pimpl(std::make_unique<impl>(fname, mode)) {}
#if defined(__linux__)
llama_file::llama_file(const char * fname, const char * mode, bool uncached_read) : pimpl(std::make_unique<impl>(fname, mode, uncached_read)) {}
#endif
llama_file::~llama_file() = default;
size_t llama_file::tell() const { return pimpl->tell(); }

3
src/llama-mmap.h
View File

@ -14,6 +14,9 @@ using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
struct llama_file {
llama_file(const char * fname, const char * mode);
#if defined(__linux__)
llama_file(const char * fname, const char * mode, bool uncached_read);
#endif
~llama_file();
size_t tell() const;

117
src/llama-model-loader.cpp
View File

@ -502,8 +502,12 @@ llama_model_loader::llama_model_loader(
get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
llm_kv = LLM_KV(llm_arch_from_string(arch_name));
#if defined(__linux__)
files.emplace_back(new llama_file(fname.c_str(), "rb", !use_mmap));
#else
files.emplace_back(new llama_file(fname.c_str(), "rb"));
#endif
contexts.emplace_back(ctx);
// Save tensors data offset of the main file.
@ -571,7 +575,11 @@ llama_model_loader::llama_model_loader(
}
}
#if defined(__linux__)
files.emplace_back(new llama_file(fname_split, "rb", !use_mmap));
#else
files.emplace_back(new llama_file(fname_split, "rb"));
#endif
contexts.emplace_back(ctx);
// Save tensors data offset info of the shard.
@ -933,7 +941,14 @@ bool llama_model_loader::load_all_data(
// 4 staging buffers for async uploads, each sized 1MB seems to be a good default for single NVMe drives.
// NVMe raid configurations might require more / larger buffers.
constexpr size_t n_buffers = 4;
#if defined(__linux__)
constexpr size_t alignment = 4 * 1024; // 4 KiB for Direct I/O
// Buffer size: balance between memory usage and I/O efficiency
// 64MB works well for NVMe drives
constexpr size_t buffer_size = 64 * 1024 * 1024; // 64 MiB
#else
constexpr size_t buffer_size = 1 * 1024 * 1024; // 1MB
#endif
std::vector<ggml_backend_buffer_t> host_buffers;
std::vector<ggml_backend_event_t> events;
@ -982,7 +997,11 @@ bool llama_model_loader::load_all_data(
// If the backend is supported, create pinned memory buffers and events for synchronisation.
for (size_t idx = 0; idx < n_buffers; ++idx) {
#if defined(__linux__)
auto * buf = ggml_backend_buft_alloc_buffer(host_buft, buffer_size + 2 * alignment);
#else
auto * buf = ggml_backend_buft_alloc_buffer(host_buft, buffer_size);
#endif
if (!buf) {
LLAMA_LOG_DEBUG("%s: failed to allocate host buffer for async uploads for device %s\n", func,
ggml_backend_dev_name(dev));
@ -1019,6 +1038,35 @@ bool llama_model_loader::load_all_data(
ggml_backend_name(upload_backend));
}
#if defined(__linux__)
auto read_aligned_chunk = [](const llama_file * file,
size_t offset,
void * dest,
size_t size,
size_t alignment) {
off_t aligned_offset = offset & ~(alignment - 1);
off_t offset_from_alignment = offset - aligned_offset;
size_t bytes_to_read = (offset_from_alignment + size + alignment - 1) & ~(alignment - 1);
void * raw_buffer = nullptr;
int ret = posix_memalign(&raw_buffer, alignment, bytes_to_read);
if (ret != 0) {
throw std::runtime_error(format("posix_memalign failed with error %d", ret));
}
struct aligned_buffer_deleter {
void operator()(void * p) const { free(p); }
};
std::unique_ptr<void, aligned_buffer_deleter> buffer(raw_buffer);
file->seek(aligned_offset, SEEK_SET);
file->read_raw(buffer.get(), bytes_to_read);
uintptr_t actual_data = reinterpret_cast<uintptr_t>(buffer.get()) + offset_from_alignment;
memcpy(dest, reinterpret_cast<void *>(actual_data), size);
};
#endif
for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
const auto * weight = get_weight(ggml_get_name(cur));
if (weight == nullptr) {
@ -1064,9 +1112,18 @@ bool llama_model_loader::load_all_data(
}
} else {
const auto & file = files.at(weight->idx);
#if defined(__linux__)
auto offset = (off_t) weight->offs;
off_t aligned_offset = offset & ~(alignment - 1);
off_t offset_from_alignment = offset - aligned_offset;
#endif
if (ggml_backend_buffer_is_host(cur->buffer)) {
#if defined(__linux__)
read_aligned_chunk(file.get(), weight->offs, cur->data, n_size, alignment);
#else
file->seek(weight->offs, SEEK_SET);
file->read_raw(cur->data, n_size);
#endif
if (check_tensors) {
validation_result.emplace_back(std::async(std::launch::async, [cur, n_size] {
return std::make_pair(cur, ggml_validate_row_data(cur->type, cur->data, n_size));
@ -1075,6 +1132,55 @@ bool llama_model_loader::load_all_data(
} else {
// If upload_backend is valid load the tensor in chunks to pinned memory and upload the buffers asynchronously to the GPU.
if (upload_backend) {
#if defined(__linux__)
// Calculate aligned read boundaries
size_t read_start = aligned_offset;
size_t read_end = (offset + n_size + alignment - 1) & ~(alignment - 1);
size_t bytes_read = 0;
size_t data_read = 0; // Actual tensor data copied (excluding padding)
file->seek(aligned_offset, SEEK_SET);
while (bytes_read < read_end - read_start) {
size_t read_size = std::min<size_t>(buffer_size, read_end - read_start - bytes_read);
// Align the destination pointer within the pinned buffer
uintptr_t ptr_dest_aligned = (reinterpret_cast<uintptr_t>(host_ptrs[buffer_idx]) + alignment - 1) & ~(alignment - 1);
// Wait for previous upload to complete before reusing buffer
ggml_backend_event_synchronize(events[buffer_idx]);
// Read aligned chunk from file
file->read_raw(reinterpret_cast<void *>(ptr_dest_aligned), read_size);
// Calculate actual data portion (excluding alignment padding)
uintptr_t ptr_data = ptr_dest_aligned;
size_t data_to_copy = read_size;
// Skip alignment padding at start of first chunk
if (bytes_read == 0) {
ptr_data += offset_from_alignment;
data_to_copy -= offset_from_alignment;
}
// Trim alignment padding at end of last chunk
if (aligned_offset + bytes_read + read_size > offset + n_size) {
data_to_copy -= (read_end - (offset + n_size));
}
// Async upload actual data to GPU
ggml_backend_tensor_set_async(upload_backend, cur,
reinterpret_cast<void *>(ptr_data), data_read, data_to_copy);
ggml_backend_event_record(events[buffer_idx], upload_backend);
data_read += data_to_copy;
bytes_read += read_size;
++buffer_idx;
buffer_idx %= n_buffers;
}
#else
file->seek(weight->offs, SEEK_SET);
size_t bytes_read = 0;
@ -1091,11 +1197,16 @@ bool llama_model_loader::load_all_data(
++buffer_idx;
buffer_idx %= n_buffers;
}
#endif
} else {
read_buf.resize(n_size);
#if defined(__linux__)
read_aligned_chunk(file.get(), weight->offs, read_buf.data(), n_size, alignment);
#else
file->seek(weight->offs, SEEK_SET);
file->read_raw(read_buf.data(), n_size);
ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size);
file->read_raw(read_buf.data(), n_size);
#endif
ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size);
if (check_tensors && !ggml_validate_row_data(cur->type, read_buf.data(), n_size)) {
throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
}