Files
ik_llama.cpp/src/llama-impl.h
Kawrakow fc06bc9d27 Enable CUDA graphs for MoE models + GPT-OSS support (#689)
* gmp-oss: common

* gpt-oss: attnetion sinks, swiglu_oai

* gpt-oss: WIP llama

Model loads and runs (CPU only), but PPL is much to high
(~1500 for 1st batch vs ~200 in mainline).
Is it because of SWA, because of vocab, or did I introduce a bug somewhere?

* gpt-oss: CPU seems to be working

It was the SWA thta was missing in the previous commit.

There are issues with EOG tokens, so this still needs to be added.

* CUDA: ADD_ID

Just a copy from mainline

* gpt-oss: Seems to be working on CUDA

* gpt-oss: add sinks to the attn-vec kernels

* CUDA: add head size of 64 to new mma

Haven't turned it on yet, but observe slightly better PP and slightly
worse TG performance with that.

* gpt-oss: add ability to use -fmoe (only CUDA for now)

* Move row sums to the write place

* Add sinks to iqk flash attention

* gpt_oss: Implement -fmoe on the CPU

* Simdify swiglu_oai

Turning it off for now as performance becomes more variable,
so perhaps I'm running into thermal trottling imore often
because of making the CPU work too hard.

* llama: factor out model loader

* Builds successfully

* It runs, but mmap does not work

* Fix llama_mmap so mmap works

* Minor

* Fix CUDA after latest changes

* Attempt to use CUDA graphs with MoE models - not working

* CUDA graphs WIP - still not working

* CUDA graphs - seems to be working

Likely not all MLA variants are working.
I no longer remember why I added the q8_0 cpy that
transposes the tensor, but if really needed, this is now
missing. Also missing is q6_0.

* Make q8_0 cache work for DeepSeek models with CUDA graphs

* cuda: cpy for q6_0

* Fix llama_mmap on non-Linux platforms

* Adding forgotten file

* Iterating on Windows build failures

* cuda: re-add q8_0 -> q8_0 transpose

so mla = 2 can be used with CUDA graphs and q8_0 cache.

* Disable graphs without -fmoe

* Minor

* Turn graphs on by default

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2025-08-15 09:18:07 +03:00

221 lines
5.5 KiB
C++

//
// Copyright (C) 2023-2025 The llama.cpp authors
// Copyright (C) 2024-2025 Iwan Kawrakow
// MIT license
// SPDX-License-Identifier: MIT
//
#pragma once
#define LLAMA_API_INTERNAL
#include "llama.h"
#include <stdexcept>
#include <climits>
#include <cstdarg>
#include <vector>
#include <cinttypes>
#include <cstring>
#ifdef __GNUC__
#ifdef __MINGW32__
#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
#else
#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
#endif
#else
#define LLAMA_ATTRIBUTE_FORMAT(...)
#endif
//
// logging
//
LLAMA_ATTRIBUTE_FORMAT(2, 3)
void llama_log_internal (ggml_log_level level, const char * format, ...);
void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data);
#define LLAMA_LOG_INFO(...) llama_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
#define LLAMA_LOG_WARN(...) llama_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
#define LLAMA_LOG_ERROR(...) llama_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
#define LLAMA_LOG_DEBUG(...) llama_log_internal(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
//
// helpers
//
static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
if (search.empty()) {
return;
}
std::string builder;
builder.reserve(s.length());
size_t pos = 0;
size_t last_pos = 0;
while ((pos = s.find(search, last_pos)) != std::string::npos) {
builder.append(s, last_pos, pos - last_pos);
builder.append(replace);
last_pos = pos + search.length();
}
builder.append(s, last_pos, std::string::npos);
s = std::move(builder);
}
// the ring buffer works similarly to std::deque, but with a fixed capacity
template<typename T>
struct ring_buffer {
ring_buffer(size_t cap) : capacity(cap), data(cap) {}
T& front() {
if (sz == 0) {
throw std::runtime_error("ring buffer is empty");
}
return data[first];
}
const T& front() const {
if (sz == 0) {
throw std::runtime_error("ring buffer is empty");
}
return data[first];
}
T& back() {
if (sz == 0) {
throw std::runtime_error("ring buffer is empty");
}
return data[pos];
}
const T& back() const {
if (sz == 0) {
throw std::runtime_error("ring buffer is empty");
}
return data[pos];
}
void push_back(const T& value) {
if (capacity == 0) {
throw std::runtime_error("ring buffer: capacity is zero");
}
if (sz == capacity) {
// advance the start when buffer is full
first = (first + 1) % capacity;
}
else {
sz++;
}
data[pos] = value;
pos = (pos + 1) % capacity;
}
T pop_front() {
if (sz == 0) {
throw std::runtime_error("ring buffer is empty");
}
T value = data[first];
first = (first + 1) % capacity;
sz--;
return value;
}
//T & operator[](size_t i) {
// if (i >= sz) {
// throw std::runtime_error("ring buffer: index out of bounds");
// }
// return data[(first + i) % capacity];
//}
//const T & at(size_t i) const {
// if (i >= sz) {
// throw std::runtime_error("ring buffer: index out of bounds");
// }
// return data[(first + i) % capacity];
//}
const T& rat(size_t i) const {
if (i >= sz) {
throw std::runtime_error("ring buffer: index out of bounds");
}
return data[(first + sz - i - 1) % capacity];
}
std::vector<T> to_vector() const {
std::vector<T> result;
result.reserve(sz);
for (size_t i = 0; i < sz; i++) {
result.push_back(data[(first + i) % capacity]);
}
return result;
}
void clear() {
// here only reset the status of the buffer
sz = 0;
first = 0;
pos = 0;
}
bool empty() const {
return sz == 0;
}
size_t size() const {
return sz;
}
size_t capacity = 0;
size_t sz = 0;
size_t first = 0;
size_t pos = 0;
std::vector<T> data;
};
LLAMA_ATTRIBUTE_FORMAT(1, 2)
static std::string format(const char * fmt, ...) {
va_list ap;
va_list ap2;
va_start(ap, fmt);
va_copy(ap2, ap);
int size = vsnprintf(NULL, 0, fmt, ap);
GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
std::vector<char> buf(size + 1);
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
GGML_ASSERT(size2 == size);
va_end(ap2);
va_end(ap);
return std::string(buf.data(), size);
}
static std::string llama_format_tensor_shape(const std::vector<int64_t> & ne) {
char buf[256];
snprintf(buf, sizeof(buf), "%5" PRId64, ne.at(0));
for (size_t i = 1; i < ne.size(); i++) {
snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, ne.at(i));
}
return buf;
}
static std::string llama_format_tensor_shape(const struct ggml_tensor * t) {
char buf[256];
snprintf(buf, sizeof(buf), "%5" PRId64, t->ne[0]);
for (int i = 1; i < GGML_MAX_DIMS; i++) {
snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, t->ne[i]);
}
return buf;
}
template <typename T>
struct no_init {
T value;
no_init() { /* do nothing */ }
};
struct gguf_context;
std::string gguf_kv_to_str(const gguf_context * ctx_gguf, int i);
ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer);