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llama.cpp/llama.cpp
xaedes f954edda93
ggml : implement backward pass for llama + small training-llama-from-scratch example (#1360) 
* implement 8 of 14 missing backward pass operations used by llama

- GGML_OP_ADD_AT
- GGML_OP_CPY
- GGML_OP_MUL_MAT (src0.grad)
- GGML_OP_PERMUTE
- GGML_OP_RESHAPE
- GGML_OP_SCALE
- GGML_OP_TRANSPOSE
- GGML_OP_VIEW

implement additional ggml operation GGML_OP_ADD_AT, which is necessary for backward pass of GGML_OP_VIEW.

this operation adds src1 to src0 with data offset, i.e. to view(src0, ..., offset).
the values are return in a tensor size of src0. values outside of [data+offset:data+offset+nbytes(src1)] are just the original values from src0.

still missing backward passes for llama:

- GGML_OP_DIAG_MASK_INF
- GGML_OP_GET_ROWS
- GGML_OP_RMS_NORM
- GGML_OP_ROPE
- GGML_OP_SILU
- GGML_OP_SOFT_MAX

* implement 5 of 6 missing backward pass operations used by llama

- GGML_OP_DIAG_MASK_INF
- GGML_OP_GET_ROWS
- GGML_OP_RMS_NORM
- GGML_OP_SILU
- GGML_OP_SOFT_MAX

add necessary ggml operations GGML_OP_ADD1, GGML_OP_SILU_BACK, GGML_OP_RMS_NORM_BACK, GGML_OP_DIAG_MASK_ZERO, and GGML_OP_ROPE_BACK

GGML_OP_ADD1 is necessary to add a scalar value in the backward pass of GGML_OP_SOFT_MAX
GGML_OP_ADD1 could also be replaced by using GGML_OP_ADD and GGML_OP_REPEAT, but the performance would be worse. additionally GGML_OP_REPEAT will return unexpected value when the the input to GGML_OP_SOFT_MAX contains only a single scalar. in this case GGML_OP_REPEAT will not return the value that should be repeated (src1) but the value which shape the result should take (src0). So in this case it can not replace GGML_OP_ADD1.

GGML_OP_SILU_BACK, GGML_OP_RMS_NORM_BACK and GGML_OP_ROPE_BACK are necessary for backward pass of GGML_OP_SILU, GGML_OP_RMS_NORM and GGML_OP_ROPE. The backward pass for these functions cannot be easily composed of existing operations. Since the backward pass builds a computation graph we need operations forward pass implementations of the the required backward passes. Sounds a bit confusing at first, I know...

GGML_OP_DIAG_MASK_ZERO is necessary for backward pass of GGML_OP_DIAG_MASK_INF.

Some operations where previously inplace-only. for backward pass there needs to be non-inplace variants.
staying consistent with other operations that have non-inplace and inplace variants, the operations are changed to non-inplace and
functions with "_inplace" are added which are inplace.
in llama we need to call the inplace variants so that it is implemented as before.
for llama backward pass we need to use the non-inplace variants.

still not completely implemented backward passes for llama:

- GGML_OP_ROPE: needs forward pass for GGML_OP_ROPE_BACK
- GGML_OP_GET_ROWS: only necessary for tokenizer

* norm & rms_norm can not be threaded:

after investigation rms norm for quite some time I come to the conclusion that neither norm, nor rms_norm can be threaded, because we need mean over all items, not just of the slices each thread sees.

* remove already resolved TODO

* implement backward pass of ggml_rope and ggml_rope_back

* implement backward pass for ggml_get_rows and for new operation ggml_get_rows_back

* add test-grad0.c

* use GGML_PRINT_DEBUG for debug messages which will otherwise flood the console

* test both gradients of mul_mat

* disable graph dot export as it floods console

* bug fixes for silu_back

* successfully test silu backward

* bug fix for scale backward pass

use sum instead of mean for gradient of scalar scale parameter

* successfully test scale backward

* improve performance of sum backward pass

use add1(x,y) instead of add(x,repeat(y,x))

* improve performance of sqr backward pass

use scale(x,y) instead of mul(x,repeat(y,x))

* successfully test rope backward

* bug fix for cpy backward pass

* successfully test cpy backward

* bug fix for reshape backward pass

* successfully test reshape backward

* add test-opt.c

this uses ggml_opt to train a,b for minimal e=sum(sqr(c - a*b)) for random initial a,b,c

* correctly implement softmax backward pass using new operation ggml_diag

ggml_diag constructs diagonal matrices with entries.
ggml_diag(shape[a,1,c,d]) -> shape[a,a,c,d]

* successfully test soft_max backward

* align shape annotations

* add shape annotations for llama

* de-duplicate ggml_forward_dup code taking care of contiguous tensors of same type.

with this we can duplicate tensor of any typ as long as they are contiguous.

* fix ggml_compute_forward_dup_same_cont for when nelements < nthreads

when more threads are used than elements exist ie1 was less than ie0, resulting in invalid negative byte count argument in memcpy

* bug fix for add_at forward

required for view backward pass

src0 values must be copied to dst, because during addition we don't touch all dst elements in contrast to the normal add function.

* successfully test view backward

* minor code format improvement

* fix ggml_forward_add functions to work correctly with transposed tensors

uses the same logic as in ggml_compute_forward_add_q_f32, but make it consistent across all ggml_compute_forward_add_... functions.
this also slightly changes the mem access pattern of the different threads to works as in ggml_compute_forward_add_q_f32.

* fix ggml_forward_add1 functions to work correctly with transposed tensors

uses the same logic as in ggml_compute_forward_add1_q_f32, but make it consistent across all ggml_compute_forward_add1_... functions.
this also slightly changes the mem access pattern of the different threads to works as in ggml_compute_forward_add1_q_f32.

* test-grad0.c : add print_elements to help with debugging

* successfully test permute backward

* some minor test-grad0 fixes

* fix sub, mul and div functions to work correctly with transposed tensors

uses the same logic as in add

* implement ggml_cont backward pass

* successfully test transpose backward and permute for all permutations

also test sub, mul and div up to max n_dims

* test-grad0.c add TODO for view_2d and view_3d

add_at (required for view backward pass) is a bit tricky for n_dims > 1.

* fix comments

* successfully test diag_mask_inf and diag_mask_zero backward

* test-grad0 : fix test for div

nargs and ndims was swapped, corrupting the stack

* fix diag_mask to work with non-inplace input

* move dup call into the actual add_at functions

* fix get rows backward pass

* successfully test get_rows backward

* fix view backward pass

add nb parameters to add_at like in view.
together with offset they define how to view dst and src0 during the add_at operation.

* successfully test backward pass of view_1d, view_2d and view_3d

* fix backward pass for rms_norm

I would have used formulas from other frameworks, but they differed so I could not decide which is correct.
Instead it was derived here in comment using manual forward-backward automatic differention of rms_norm and simplification.

* successfully test backward pass of rms_norm

some tests may fail when gradients are large.
could not find a satisfying configuration to check for abs error and relative error that passes all tests while still actually testing the results with tight enough error bounds.
when looking at the values the "failed" tests look actually ok. for example:

rms_norm: ndims=2, i=0, k=2, x0=0.000153, xm=0.000053, xp=0.000253, f0=0.278594, f1=0.086213, g0=961.905457, g1=966.064941, eps=0.000100, error_abs=4.159485, error_rel=0.004324

it is due to the test logic in check_gradients that they fail.

* add todos for llama backward pass

- implementation for ADD1 backward pass should probably use sum instead of mean (but this backward pass is not required)
- repeat is not yet tested and looks like it only works for single element src0 inputs.

* add operation ggml_sum_rows

ggml_sum_rows(shape[a,b,c,d]) -> shape[1,b,c,d]

* add missing GGML_OP_SUM_ROWS

* fix backward pass for repeat

requires ggml_sum_rows

* successfully test backward pass of repeat

* update quantization types in switch-case of add_at and add1

* add baby-llama example training a very small llama model from scratch to output a sinusoidal wave.

had to increase maximum number of optimization parameters to train from scratch.

* fix softmax in baby-llama example

* switching from training with adam to lbfgs produces much better results in the baby-llama example

* train with two examples, creating new tensors each time..

* fix bug when using ggml_opt to optimize params in one context and use a renewable context for eval and opt

when not keeping gradients of model parameters they are overwritten by tensors created by opt, which may be invalid after opt context is renewed.
so we need to keep the original gradients and make dups for opt

* train on multiple examples, generate & print tokens with trained model afterwards

ctx0 for evaluation and optimization is renewed for each sample

* add ggml_reshape_1d, ggml_reshape_4d and ggml_view_4d

* fix soft_max backward pass for input->ne[1] != 1

* add ggml_log operation necessary for cross entropy loss

* add test for ggml_log gradients

* implement backward pass for ggml_sum_rows, necessary for cross entropy loss

* implement ggml_repeat support for rank > 2 tensors

* add test for ggml_sum_rows gradients

* fix training get_example_targets

predict the next token, not the current token!

* add square_error_loss and cross_entropy_loss functions

* optimize loss over multiple samples

this increases computation graph, need parallel batched forward for more efficiency.

* fix backward pass for add_at and change arguments to have same order as in view

* add ggml_set(ctx, a, b) to set b in view of a and return modified a

necessary to set values into kv_self cache and properly propagate the gradients

* fix kv_self gradients for training

use ggml_set instead of ggml_cpy to set kv_self cache with properly propagating gradients

* replace inplace operations for training with copying operations to allow gradient propagation

* add GGML_ASSERT to catch ggml_rope and back value errors

* add trainable lora-only model with all big matrices C split into A,B with A*B=C

this is not a lora-finetune, but the whole model changed to have only low-rank "lora" matrices.

training this instead of the normal model resulted in much worse results though...

* vastly improve training results

instead of logit targets 0 and 1 use -1 and +1.

* shorten code using a variable

* change name of GGML_OP_ADD_AT to GGML_OP_ACC

* smaller default values for baby llama model parameters

* update static assert of GGML_OP_COUNT

* remove shape annotations in llama_eval_internal

* revert disabling of threading for rms_norm and norm

* rename print functions in baby-llama example

* fix call to ggml_set_name

* add missing include for strcmp, etc

* remove trailing whitespace

* reduce number of test-grad0 iterations

avoid exceeding timeout of automated tests

* remove busy loop that was used as sleep for slower sinus wave generation

* disable slow tests grad0 and opt to avoid exceeding timeouts

* c++ in baby-llama example

use c++ includes instead of c includes
use std::min, std::max instead of MIN, MAX macros

* c++ in baby-llama example

use c++ includes instead of c includes
use std::min, std::max instead of MIN, MAX macros

* ggml : fix compiler warnings + cosmetic changes

* ggml : fix nullptr derefs in GGML_OP_CONT and GGML_OP_RESHAPE back

* swap arguments to vDSP_vdiv call

documentation for vDSP_vdiv states: "Note that B comes before A!"

* swap arguments to vDSP_vdiv call

documentation for vDSP_vdiv states: "Note that B comes before A!"

* ggml : swap vDSP_vsub args as per documentation

* add parallel batched forward function for baby-llama training

* cleanup code for batched training

* remove trailing whitespace

* minor : fix compiler warnings + indentation style

* ggml : fix null ptr deref in backward pass

* ggml : remove Q4_2 remnants

* ggml : fix clang-tidy warnings

* baby-llama : couple of clang-tidy warnings

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-05-13 15:56:40 +03:00

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// Defines fileno on msys:
#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#include <cstdint>
#include <cstdio>
#endif
#include "llama-util.h"
#include "llama.h"
#include "ggml.h"
#include <array>
#include <ctime>
#include <cinttypes>
#include <fstream>
#include <random>
#include <map>
#include <unordered_map>
#include <queue>
#include <cassert>
#include <cstring>
#include <climits>
#include <memory>
#include <algorithm>
#include <initializer_list>
#include <thread>
#include <atomic>
#include <mutex>
#include <sstream>
#include <numeric>
#define LLAMA_USE_SCRATCH
#define LLAMA_MAX_SCRATCH_BUFFERS 16
// available llama models
enum e_model {
MODEL_UNKNOWN,
MODEL_7B,
MODEL_13B,
MODEL_30B,
MODEL_65B,
};
static const size_t MB = 1024*1024;
// computed for n_ctx == 2048
// TODO: dynamically determine these sizes
// needs modifications in ggml
static const std::map<e_model, size_t> & MEM_REQ_SCRATCH0()
{
static std::map<e_model, size_t> k_sizes = {
{ MODEL_7B, 512ull * MB },
{ MODEL_13B, 512ull * MB },
{ MODEL_30B, 512ull * MB },
{ MODEL_65B, 1024ull * MB },
};
return k_sizes;
}
static const std::map<e_model, size_t> & MEM_REQ_SCRATCH1()
{
static std::map<e_model, size_t> k_sizes = {
{ MODEL_7B, 512ull * MB },
{ MODEL_13B, 512ull * MB },
{ MODEL_30B, 512ull * MB },
{ MODEL_65B, 1024ull * MB },
};
return k_sizes;
}
// 2*n_embd*n_ctx*n_layer*sizeof(float16)
static const std::map<e_model, size_t> & MEM_REQ_KV_SELF()
{
static std::map<e_model, size_t> k_sizes = {
{ MODEL_7B, 1026ull * MB },
{ MODEL_13B, 1608ull * MB },
{ MODEL_30B, 3124ull * MB },
{ MODEL_65B, 5120ull * MB },
};
return k_sizes;
}
// this is mostly needed for temporary mul_mat buffers to dequantize the data
// not actually needed if BLAS is disabled
static const std::map<e_model, size_t> & MEM_REQ_EVAL()
{
static std::map<e_model, size_t> k_sizes = {
{ MODEL_7B, 768ull * MB },
{ MODEL_13B, 1024ull * MB },
{ MODEL_30B, 1280ull * MB },
{ MODEL_65B, 1536ull * MB },
};
return k_sizes;
}
// default hparams (LLaMA 7B)
struct llama_hparams {
uint32_t n_vocab = 32000;
uint32_t n_ctx = 512; // this is provided as user input?
uint32_t n_embd = 4096;
uint32_t n_mult = 256;
uint32_t n_head = 32;
uint32_t n_layer = 32;
uint32_t n_rot = 64;
enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;
bool operator!=(const llama_hparams & other) const {
return memcmp(this, &other, sizeof(llama_hparams));
}
};
struct llama_layer {
// normalization
struct ggml_tensor * attention_norm;
// attention
struct ggml_tensor * wq;
struct ggml_tensor * wk;
struct ggml_tensor * wv;
struct ggml_tensor * wo;
// normalization
struct ggml_tensor * ffn_norm;
// ff
struct ggml_tensor * w1;
struct ggml_tensor * w2;
struct ggml_tensor * w3;
};
struct llama_kv_cache {
struct ggml_tensor * k;
struct ggml_tensor * v;
struct ggml_context * ctx = NULL;
llama_ctx_buffer buf;
int n; // number of tokens currently in the cache
~llama_kv_cache() {
if (ctx) {
ggml_free(ctx);
}
}
};
struct llama_model {
e_model type = MODEL_UNKNOWN;
llama_hparams hparams;
struct ggml_tensor * tok_embeddings;
struct ggml_tensor * norm;
struct ggml_tensor * output;
std::vector<llama_layer> layers;
// context
struct ggml_context * ctx = NULL;
// key + value cache for the self attention
// TODO: move to llama_state
struct llama_kv_cache kv_self;
// the model memory buffer
llama_ctx_buffer buf;
// model memory mapped file
std::unique_ptr<llama_mmap> mapping;
// objects representing data potentially being locked in memory
llama_mlock mlock_buf;
llama_mlock mlock_mmap;
// for quantize-stats only
std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;
~llama_model() {
if (ctx) {
ggml_free(ctx);
}
}
};
struct llama_vocab {
using id = int32_t;
using token = std::string;
struct token_score {
token tok;
float score;
};
std::unordered_map<token, id> token_to_id;
std::vector<token_score> id_to_token;
};
struct llama_context {
std::mt19937 rng;
int64_t t_load_us = 0;
int64_t t_start_us = 0;
bool has_evaluated_once = false;
int64_t t_sample_us = 0;
int64_t t_eval_us = 0;
int64_t t_p_eval_us = 0;
int32_t n_sample = 0; // number of tokens sampled
int32_t n_eval = 0; // number of eval calls
int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
llama_model model;
llama_vocab vocab;
size_t mem_per_token = 0;
// decode output (2-dimensional array: [n_tokens][n_vocab])
std::vector<float> logits;
bool logits_all = false;
// input embedding (1-dimensional array: [n_embd])
std::vector<float> embedding;
// memory buffers used to evaluate the model
// TODO: move in llama_state
llama_ctx_buffer buf_compute;
llama_ctx_buffer buf_scratch[LLAMA_MAX_SCRATCH_BUFFERS];
int buf_last = 0;
size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
void use_buf(struct ggml_context * ctx, int i) {
#if defined(LLAMA_USE_SCRATCH)
size_t last_size = 0;
if (i == -1) {
last_size = ggml_set_scratch(ctx, { 0, 0, nullptr, });
} else {
auto & buf = buf_scratch[i];
last_size = ggml_set_scratch(ctx, { 0, buf.size, buf.addr, });
}
if (buf_last >= 0) {
buf_max_size[buf_last] = std::max(buf_max_size[buf_last], last_size);
}
buf_last = i;
#else
(void) i;
(void) ctx;
#endif
}
size_t get_buf_max_mem(int i) const {
#if defined(LLAMA_USE_SCRATCH)
return buf_max_size[i];
#else
(void) i;
return 0;
#endif
}
};
template <typename T>
static T checked_mul(T a, T b) {
T ret = a * b;
if (a != 0 && ret / a != b) {
throw format("overflow multiplying %llu * %llu",
(unsigned long long) a, (unsigned long long) b);
}
return ret;
}
static size_t checked_div(size_t a, size_t b) {
if (b == 0 || a % b != 0) {
throw format("error dividing %zu / %zu", a, b);
}
return a / b;
}
static std::string llama_format_tensor_shape(const std::vector<uint32_t> & ne) {
char buf[256];
snprintf(buf, sizeof(buf), "%5u", ne.at(0));
for (size_t i = 1; i < ne.size(); i++) {
snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), " x %5u", ne.at(i));
}
return buf;
}
static size_t llama_calc_tensor_size(const std::vector<uint32_t> & ne, enum ggml_type type) {
size_t size = ggml_type_size(type);
for (uint32_t dim : ne) {
size = checked_mul<size_t>(size, dim);
}
return size / ggml_blck_size(type);
}
struct llama_load_tensor_shard {
std::vector<uint32_t> ne;
size_t size;
enum ggml_type type;
size_t file_idx;
size_t file_off;
void calc_size() {
size = llama_calc_tensor_size(ne, type);
}
};
enum llama_split_type {
SPLIT_NONE,
SPLIT_BY_COLUMNS,
SPLIT_BY_ROWS
};
struct llama_load_tensor {
std::vector<llama_load_tensor_shard> shards;
std::string name;
enum ggml_type type = GGML_TYPE_F32;
llama_split_type split_type = SPLIT_NONE;
std::vector<uint32_t> ne;
size_t size;
struct ggml_tensor * ggml_tensor = NULL;
uint8_t * data;
llama_load_tensor(const std::string & name) : name(name) {}
void calc_all() {
calc_type();
calc_split_type();
calc_ne();
calc_size();
}
void calc_type() {
const auto & first_shard = shards.at(0);
for (const auto & shard : shards) {
if (shard.type != first_shard.type) {
throw format("inconsistent tensor shard type in '%s'", name.c_str());
}
}
type = first_shard.type;
}
void calc_split_type() {
if (shards.at(0).ne.size() == 1 || // 1D tensors are just duplicated in every file
shards.size() == 1) { // only one file?
split_type = SPLIT_NONE;
} else if (name.find("tok_embeddings.") == 0 ||
name.find(".attention.wo.weight") != std::string::npos ||
name.find(".feed_forward.w2.weight") != std::string::npos) {
split_type = SPLIT_BY_COLUMNS;
} else {
split_type = SPLIT_BY_ROWS;
}
}
void calc_ne() {
const auto & first_shard = shards.at(0);
for (const auto & shard : shards) {
if (shard.ne != first_shard.ne) {
throw format("inconsistent tensor shard shape in '%s': first was %s, other was %s",
name.c_str(), llama_format_tensor_shape(first_shard.ne).c_str(), llama_format_tensor_shape(shard.ne).c_str());
}
}
ne = first_shard.ne;
LLAMA_ASSERT(shards.size() <= UINT32_MAX);
uint32_t n_shards = (uint32_t) shards.size();
switch (split_type) {
case SPLIT_NONE:
ne = first_shard.ne;
break;
case SPLIT_BY_COLUMNS:
ne = {checked_mul<uint32_t>(first_shard.ne[0], n_shards),
first_shard.ne[1]};
break;
case SPLIT_BY_ROWS:
ne = {first_shard.ne[0],
checked_mul<uint32_t>(first_shard.ne[1], n_shards)};
break;
}
}
void calc_size() {
size = llama_calc_tensor_size(ne, type);
}
};
struct llama_load_tensors_map {
// tensors is kept in a separate vector to preserve file order
std::vector<llama_load_tensor> tensors;
std::unordered_map<std::string, size_t> name_to_idx;
};
enum llama_file_version {
LLAMA_FILE_VERSION_GGML,
LLAMA_FILE_VERSION_GGMF_V1, // added version field and scores in vocab
LLAMA_FILE_VERSION_GGJT_V1, // added padding
LLAMA_FILE_VERSION_GGJT_V2, // changed quantization format
};
struct llama_file_loader {
llama_file file;
llama_file_version file_version;
llama_hparams hparams;
llama_vocab vocab;
llama_file_loader(const char * fname, size_t file_idx, llama_load_tensors_map & tensors_map)
: file(fname, "rb") {
fprintf(stderr, "llama.cpp: loading model from %s\n", fname);
read_magic();
read_hparams();
read_vocab();
read_tensor_metadata(file_idx, tensors_map);
}
void read_magic() {
uint32_t magic = file.read_u32();
uint32_t version = 0;
if (magic != 'ggml') {
version = file.read_u32();
}
if (magic == 'ggml' && version == 0) {
file_version = LLAMA_FILE_VERSION_GGML;
} else if (magic == 'ggmf' && version == 1) {
file_version = LLAMA_FILE_VERSION_GGMF_V1;
} else if (magic == 'ggjt' && version == 1) {
file_version = LLAMA_FILE_VERSION_GGJT_V1;
} else if (magic == 'ggjt' && version == 2) {
file_version = LLAMA_FILE_VERSION_GGJT_V2;
} else {
throw format("unknown (magic, version) combination: %08x, %08x; is this really a GGML file?",
magic, version);
}
}
void read_hparams() {
hparams.n_vocab = file.read_u32();
hparams.n_embd = file.read_u32();
hparams.n_mult = file.read_u32();
hparams.n_head = file.read_u32();
hparams.n_layer = file.read_u32();
hparams.n_rot = file.read_u32();
hparams.ftype = (enum llama_ftype) file.read_u32();
}
void read_vocab() {
vocab.id_to_token.resize(hparams.n_vocab);
for (uint32_t i = 0; i < hparams.n_vocab; i++) {
uint32_t len = file.read_u32();
std::string word = file.read_string(len);
float score = 0.0f;
if (file_version >= LLAMA_FILE_VERSION_GGMF_V1) {
file.read_raw(&score, sizeof(score));
}
vocab.token_to_id[word] = i;
auto & tok_score = vocab.id_to_token[i];
tok_score.tok = std::move(word);
tok_score.score = score;
}
}
void read_tensor_metadata(size_t file_idx, llama_load_tensors_map & tensors_map) {
while (file.tell() < file.size) {
llama_load_tensor_shard shard;
uint32_t n_dims = file.read_u32();
uint32_t name_len = file.read_u32();
shard.type = (enum ggml_type) file.read_u32();
shard.ne.resize(n_dims);
file.read_raw(shard.ne.data(), sizeof(shard.ne[0]) * n_dims);
std::string name = file.read_string(name_len);
if (n_dims < 1 || n_dims > 2) {
throw format("llama.cpp: tensor '%s' should not be %u-dimensional", name.c_str(), n_dims);
}
switch (shard.type) {
case GGML_TYPE_F32:
case GGML_TYPE_F16:
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
break;
default: {
throw format("unrecognized tensor type %u\n", shard.type);
}
}
if (file_version >= LLAMA_FILE_VERSION_GGJT_V1) {
// skip to the next multiple of 32 bytes
file.seek(-file.tell() & 31, SEEK_CUR);
}
shard.file_idx = file_idx;
shard.file_off = file.tell();
shard.calc_size();
file.seek(shard.size, SEEK_CUR);
auto it = tensors_map.name_to_idx.find(name);
size_t idx;
if (it != tensors_map.name_to_idx.end()) {
idx = it->second;
} else {
tensors_map.tensors.emplace_back(name);
idx = tensors_map.tensors.size() - 1;
tensors_map.name_to_idx.emplace(name, idx);
}
tensors_map.tensors.at(idx).shards.push_back(shard);
}
}
};
struct llama_file_saver {
llama_file file;
llama_file_loader * any_file_loader;
llama_file_saver(const char * fname, llama_file_loader * any_file_loader, enum llama_ftype new_ftype)
: file(fname, "wb"), any_file_loader(any_file_loader) {
fprintf(stderr, "llama.cpp: saving model to %s\n", fname);
write_magic();
write_hparams(new_ftype);
write_vocab();
}
void write_magic() {
file.write_u32(LLAMA_FILE_MAGIC); // magic
file.write_u32(LLAMA_FILE_VERSION); // version
}
void write_hparams(enum llama_ftype new_ftype) {
const llama_hparams & hparams = any_file_loader->hparams;
file.write_u32(hparams.n_vocab);
file.write_u32(hparams.n_embd);
file.write_u32(hparams.n_mult);
file.write_u32(hparams.n_head);
file.write_u32(hparams.n_layer);
file.write_u32(hparams.n_rot);
file.write_u32(new_ftype);
}
void write_vocab() {
if (any_file_loader->file_version == LLAMA_FILE_VERSION_GGML) {
fprintf(stderr, "llama.cpp: WARNING: input is an old file that doesn't have scores; will add dummy scores\n");
}
uint32_t n_vocab = any_file_loader->hparams.n_vocab;
for (uint32_t i = 0; i < n_vocab; i++) {
const auto & token_score = any_file_loader->vocab.id_to_token.at(i);
file.write_u32((uint32_t) token_score.tok.size());
file.write_raw(token_score.tok.data(), token_score.tok.size());
file.write_raw(&token_score.score, sizeof(token_score.score));
}
}
void write_tensor(llama_load_tensor & tensor, enum ggml_type new_type, const void * new_data, size_t new_size) {
switch (new_type) {
case GGML_TYPE_F32:
case GGML_TYPE_F16:
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
break;
default: LLAMA_ASSERT(false);
}
file.write_u32((uint32_t) tensor.ne.size());
file.write_u32((uint32_t) tensor.name.size());
file.write_u32(new_type);
file.write_raw(tensor.ne.data(), sizeof(tensor.ne[0]) * tensor.ne.size());
file.write_raw(tensor.name.data(), tensor.name.size());
file.seek(-file.tell() & 31, SEEK_CUR);
LLAMA_ASSERT(new_size == llama_calc_tensor_size(tensor.ne, new_type));
file.write_raw(new_data, new_size);
}
};
struct llama_model_loader {
std::vector<std::unique_ptr<llama_file_loader>> file_loaders;
llama_load_tensors_map tensors_map;
bool use_mmap;
size_t num_ggml_tensors_created = 0;
struct ggml_context * ggml_ctx = NULL;
std::unique_ptr<llama_mmap> mapping;
llama_model_loader(const std::string & fname_base, bool use_mmap, bool vocab_only) {
auto * first_file = new llama_file_loader(fname_base.c_str(), 0, tensors_map);
file_loaders.emplace_back(first_file);
uint32_t n_parts = vocab_only ? 1 : guess_n_parts();
for (uint32_t i = 1; i < n_parts; i++) {
std::string fname = fname_base + "." + std::to_string(i);
auto * ith_file = new llama_file_loader(fname.c_str(), i, tensors_map);
file_loaders.emplace_back(ith_file);
if (ith_file->hparams != first_file->hparams) {
throw format("llama.cpp: hparams inconsistent between files");
}
}
if (!llama_mmap::SUPPORTED) {
use_mmap = false;
}
if (use_mmap && alignment_prevents_mmap()) {
fprintf(stderr, "llama.cpp: can't use mmap because tensors are not aligned; convert to new format to avoid this\n");
use_mmap = false;
}
this->use_mmap = use_mmap;
for (llama_load_tensor & lt : tensors_map.tensors) {
lt.calc_all();
}
}
bool alignment_prevents_mmap() {
for (const llama_load_tensor & lt : tensors_map.tensors) {
for (const llama_load_tensor_shard & shard : lt.shards) {
if (shard.file_off & 3) {
return true;
}
}
}
return false;
}
uint32_t guess_n_parts() const {
auto it = tensors_map.name_to_idx.find("tok_embeddings.weight");
if (it == tensors_map.name_to_idx.end()) {
throw std::string("missing tok_embeddings.weight");
}
const llama_load_tensor & lt = tensors_map.tensors.at(it->second);
return file_loaders.at(0)->hparams.n_embd / lt.shards.at(0).ne.at(0);
}
void calc_sizes(size_t * ctx_size_p, size_t * mmapped_size_p) const {
*ctx_size_p = *mmapped_size_p = 0;
for (const llama_load_tensor & lt : tensors_map.tensors) {
*ctx_size_p += sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE;
*(use_mmap ? mmapped_size_p : ctx_size_p) += lt.size;
}
}
struct ggml_tensor * get_tensor(const std::string & name, const std::vector<uint32_t> & ne) {
auto it = tensors_map.name_to_idx.find(name);
if (it == tensors_map.name_to_idx.end()) {
throw format("llama.cpp: tensor '%s' is missing from model", name.c_str());
}
llama_load_tensor & lt = tensors_map.tensors.at(it->second);
if (lt.ne != ne) {
throw format("llama.cpp: tensor '%s' has wrong shape; expected %s, got %s",
name.c_str(), llama_format_tensor_shape(ne).c_str(), llama_format_tensor_shape(lt.ne).c_str());
}
return get_tensor_for(lt);
}
struct ggml_tensor * get_tensor_for(llama_load_tensor & lt) {
struct ggml_tensor * tensor;
if (lt.ne.size() == 2) {
tensor = ggml_new_tensor_2d(ggml_ctx, lt.type, lt.ne.at(0), lt.ne.at(1));
} else {
LLAMA_ASSERT(lt.ne.size() == 1);
tensor = ggml_new_tensor_1d(ggml_ctx, lt.type, lt.ne.at(0));
}
ggml_set_name(tensor, lt.name.c_str());
LLAMA_ASSERT(lt.ggml_tensor == NULL); // if this fails, we called get_tensor twice on the same tensor
lt.ggml_tensor = tensor;
num_ggml_tensors_created++;
return tensor;
}
void done_getting_tensors() const {
if (num_ggml_tensors_created != tensors_map.tensors.size()) {
throw std::string("llama.cpp: file contained more tensors than expected");
}
}
void load_all_data(llama_progress_callback progress_callback, void * progress_callback_user_data, llama_mlock * lmlock) {
size_t data_size = 0;
for (const llama_load_tensor & lt : tensors_map.tensors) {
data_size += lt.size;
}
if (use_mmap) {
mapping.reset(new llama_mmap(&file_loaders.at(0)->file));
if (!lmlock) {
// Don't call the callback since the actual loading will be lazy
// and we can't measure it.
progress_callback = NULL;
}
if (lmlock) {
lmlock->init(mapping->addr);
}
}
size_t done_size = 0;
for (llama_load_tensor & lt : tensors_map.tensors) {
if (progress_callback) {
progress_callback((float) done_size / data_size, progress_callback_user_data);
}
LLAMA_ASSERT(lt.ggml_tensor); // unused tensors should have been caught by load_data already
lt.data = (uint8_t *) lt.ggml_tensor->data;
load_data_for(lt);
lt.ggml_tensor->data = lt.data;
done_size += lt.size;
if (use_mmap && lmlock) {
lmlock->grow_to(done_size);
}
}
if (progress_callback) {
progress_callback(1.0f, progress_callback_user_data);
}
}
void load_data_for(llama_load_tensor & lt) {
if (use_mmap) {
LLAMA_ASSERT(lt.shards.size() == 1);
lt.data = (uint8_t *) mapping->addr + lt.shards.at(0).file_off;
} else if (lt.split_type == SPLIT_NONE) {
llama_file & file = file_loaders.at(lt.shards.at(0).file_idx)->file;
file.seek(lt.shards.at(0).file_off, SEEK_SET);
file.read_raw(lt.data, lt.size);
} else if (lt.split_type == SPLIT_BY_ROWS) {
size_t offset = 0;
for (llama_load_tensor_shard & shard : lt.shards) {
llama_file & file = file_loaders.at(shard.file_idx)->file;
file.seek(shard.file_off, SEEK_SET);
file.read_raw(lt.data + offset, shard.size);
offset += shard.size;
}
LLAMA_ASSERT(offset == lt.size);
} else if (lt.split_type == SPLIT_BY_COLUMNS) {
// Let's load the data into temporary buffers to ensure the OS performs large loads.
std::vector<llama_buffer> tmp_bufs(lt.shards.size());
for (size_t i = 0; i < lt.shards.size(); i++) {
llama_load_tensor_shard & shard = lt.shards.at(i);
llama_file & file = file_loaders.at(shard.file_idx)->file;
file.seek(shard.file_off, SEEK_SET);
tmp_bufs.at(i).resize(shard.size);
file.read_raw(tmp_bufs.at(i).addr, shard.size);
}
// Then reshape.
size_t num_rows = lt.ne.at(1);
size_t per_shard_row_size = lt.shards.at(0).size / num_rows;
size_t out_offset = 0;
for (size_t row = 0; row < num_rows; row++) {
for (llama_buffer & tmp_buf : tmp_bufs) {
memcpy(lt.data + out_offset,
tmp_buf.addr + row * per_shard_row_size,
per_shard_row_size);
out_offset += per_shard_row_size;
}
}
LLAMA_ASSERT(out_offset == lt.size);
}
if (0) {
print_checksum(lt);
}
}
static void print_checksum(llama_load_tensor & lt) {
uint32_t sum = 0;
for (size_t i = 0; i < lt.size; i++) {
uint8_t byte = lt.data[i];
sum = byte + (sum << 6) + (sum << 16) - sum; // sdbm hash
}
fprintf(stderr, "%s checksum: %#08x (%s, size %zu)\n", lt.name.c_str(), sum,
llama_format_tensor_shape(lt.ne).c_str(), lt.size);
}
};
//
// kv cache
//
static bool kv_cache_init(
const struct llama_hparams & hparams,
struct llama_kv_cache & cache,
ggml_type wtype,
int n_ctx) {
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int64_t n_mem = n_layer*n_ctx;
const int64_t n_elements = n_embd*n_mem;
cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB);
struct ggml_init_params params;
params.mem_size = cache.buf.size;
params.mem_buffer = cache.buf.addr;
params.no_alloc = false;
cache.ctx = ggml_init(params);
if (!cache.ctx) {
fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
return false;
}
cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
ggml_set_name(cache.k, "cache_k");
ggml_set_name(cache.v, "cache_v");
return true;
}
struct llama_context_params llama_context_default_params() {
struct llama_context_params result = {
/*.n_ctx =*/ 512,
/*.n_parts =*/ -1,
/*.seed =*/ -1,
/*.f16_kv =*/ false,
/*.logits_all =*/ false,
/*.vocab_only =*/ false,
/*.use_mmap =*/ true,
/*.use_mlock =*/ false,
/*.embedding =*/ false,
/*.progress_callback =*/ nullptr,
/*.progress_callback_user_data =*/ nullptr,
};
return result;
}
bool llama_mmap_supported() {
return llama_mmap::SUPPORTED;
}
bool llama_mlock_supported() {
return llama_mlock::SUPPORTED;
}
//
// model loading
//
static const char *llama_file_version_name(llama_file_version version) {
switch (version) {
case LLAMA_FILE_VERSION_GGML: return "'ggml' (old version with low tokenizer quality and no mmap support)";
case LLAMA_FILE_VERSION_GGMF_V1: return "ggmf v1 (old version with no mmap support)";
case LLAMA_FILE_VERSION_GGJT_V1: return "ggjt v1 (pre #1405)";
case LLAMA_FILE_VERSION_GGJT_V2: return "ggjt v2 (latest)";
}
return "unknown";
}
static const char *llama_ftype_name(enum llama_ftype ftype) {
switch (ftype) {
case LLAMA_FTYPE_ALL_F32: return "all F32";
case LLAMA_FTYPE_MOSTLY_F16: return "mostly F16";
case LLAMA_FTYPE_MOSTLY_Q4_0: return "mostly Q4_0";
case LLAMA_FTYPE_MOSTLY_Q4_1: return "mostly Q4_1";
case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16:
return "mostly Q4_1, some F16";
case LLAMA_FTYPE_MOSTLY_Q5_0: return "mostly Q5_0";
case LLAMA_FTYPE_MOSTLY_Q5_1: return "mostly Q5_1";
case LLAMA_FTYPE_MOSTLY_Q8_0: return "mostly Q8_0";
default: return "unknown, may not work";
}
}
static const char *llama_model_type_name(e_model type) {
switch (type) {
case MODEL_7B: return "7B";
case MODEL_13B: return "13B";
case MODEL_30B: return "30B";
case MODEL_65B: return "65B";
default: LLAMA_ASSERT(false);
}
}
static void llama_model_load_internal(
const std::string & fname,
llama_context & lctx,
int n_ctx,
ggml_type memory_type,
bool use_mmap,
bool use_mlock,
bool vocab_only,
llama_progress_callback progress_callback,
void * progress_callback_user_data) {
lctx.t_start_us = ggml_time_us();
std::unique_ptr<llama_model_loader> ml(new llama_model_loader(fname, use_mmap, vocab_only));
lctx.vocab = std::move(ml->file_loaders.at(0)->vocab);
auto & model = lctx.model;
model.hparams = ml->file_loaders.at(0)->hparams;
llama_file_version file_version = ml->file_loaders.at(0)->file_version;
auto & hparams = model.hparams;
uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
{
switch (hparams.n_layer) {
case 32: model.type = e_model::MODEL_7B; break;
case 40: model.type = e_model::MODEL_13B; break;
case 60: model.type = e_model::MODEL_30B; break;
case 80: model.type = e_model::MODEL_65B; break;
}
hparams.n_ctx = n_ctx;
}
{
fprintf(stderr, "%s: format = %s\n", __func__, llama_file_version_name(file_version));
fprintf(stderr, "%s: n_vocab = %u\n", __func__, hparams.n_vocab);
fprintf(stderr, "%s: n_ctx = %u\n", __func__, hparams.n_ctx);
fprintf(stderr, "%s: n_embd = %u\n", __func__, hparams.n_embd);
fprintf(stderr, "%s: n_mult = %u\n", __func__, hparams.n_mult);
fprintf(stderr, "%s: n_head = %u\n", __func__, hparams.n_head);
fprintf(stderr, "%s: n_layer = %u\n", __func__, hparams.n_layer);
fprintf(stderr, "%s: n_rot = %u\n", __func__, hparams.n_rot);
fprintf(stderr, "%s: ftype = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype));
fprintf(stderr, "%s: n_ff = %u\n", __func__, n_ff);
fprintf(stderr, "%s: n_parts = %zu\n", __func__, ml->file_loaders.size());
fprintf(stderr, "%s: model size = %s\n", __func__, llama_model_type_name(model.type));
}
if (file_version != LLAMA_FILE_VERSION_GGJT_V2) {
if (hparams.ftype != LLAMA_FTYPE_ALL_F32 &&
hparams.ftype != LLAMA_FTYPE_MOSTLY_F16 &&
hparams.ftype != LLAMA_FTYPE_MOSTLY_Q8_0) {
throw format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1305)");
}
}
if (vocab_only) {
return;
}
auto & ctx = model.ctx;
size_t ctx_size;
size_t mmapped_size;
ml->calc_sizes(&ctx_size, &mmapped_size);
fprintf(stderr, "%s: ggml ctx size = %6.2f KB\n", __func__, ctx_size/1024.0);
// print memory requirements
{
const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1;
// this is the total memory required to run the inference
const size_t mem_required =
ctx_size +
mmapped_size +
MEM_REQ_SCRATCH0().at(model.type) +
MEM_REQ_SCRATCH1().at(model.type) +
MEM_REQ_EVAL().at(model.type);
// this is the memory required by one llama_state
const size_t mem_required_state =
scale*MEM_REQ_KV_SELF().at(model.type);
fprintf(stderr, "%s: mem required = %7.2f MB (+ %7.2f MB per state)\n", __func__,
mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0);
}
// create the ggml context
{
lctx.model.buf.resize(ctx_size);
if (use_mlock) {
lctx.model.mlock_buf.init(lctx.model.buf.addr);
lctx.model.mlock_buf.grow_to(lctx.model.buf.size);
}
struct ggml_init_params params = {
/*.mem_size =*/ lctx.model.buf.size,
/*.mem_buffer =*/ lctx.model.buf.addr,
/*.no_alloc =*/ ml->use_mmap,
};
model.ctx = ggml_init(params);
if (!model.ctx) {
throw format("ggml_init() failed");
}
}
// prepare memory for the weights
{
const uint32_t n_embd = hparams.n_embd;
const uint32_t n_layer = hparams.n_layer;
const uint32_t n_vocab = hparams.n_vocab;
ml->ggml_ctx = ctx;
model.tok_embeddings = ml->get_tensor("tok_embeddings.weight", {n_embd, n_vocab});
model.norm = ml->get_tensor("norm.weight", {n_embd});
model.output = ml->get_tensor("output.weight", {n_embd, n_vocab});
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
auto & layer = model.layers[i];
std::string layers_i = "layers." + std::to_string(i);
layer.attention_norm = ml->get_tensor(layers_i + ".attention_norm.weight", {n_embd});
layer.wq = ml->get_tensor(layers_i + ".attention.wq.weight", {n_embd, n_embd});
layer.wk = ml->get_tensor(layers_i + ".attention.wk.weight", {n_embd, n_embd});
layer.wv = ml->get_tensor(layers_i + ".attention.wv.weight", {n_embd, n_embd});
layer.wo = ml->get_tensor(layers_i + ".attention.wo.weight", {n_embd, n_embd});
layer.ffn_norm = ml->get_tensor(layers_i + ".ffn_norm.weight", {n_embd});
layer.w1 = ml->get_tensor(layers_i + ".feed_forward.w1.weight", {n_embd, n_ff});
layer.w2 = ml->get_tensor(layers_i + ".feed_forward.w2.weight", { n_ff, n_embd});
layer.w3 = ml->get_tensor(layers_i + ".feed_forward.w3.weight", {n_embd, n_ff});
}
}
ml->done_getting_tensors();
// populate `tensors_by_name`
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
model.tensors_by_name.emplace_back(lt.name, lt.ggml_tensor);
}
ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &lctx.model.mlock_mmap : NULL);
model.mapping = std::move(ml->mapping);
// loading time will be recalculate after the first eval, so
// we take page faults deferred by mmap() into consideration
lctx.t_load_us = ggml_time_us() - lctx.t_start_us;
}
static bool llama_model_load(
const std::string & fname,
llama_context & lctx,
int n_ctx,
ggml_type memory_type,
bool use_mmap,
bool use_mlock,
bool vocab_only,
llama_progress_callback progress_callback,
void *progress_callback_user_data) {
try {
llama_model_load_internal(fname, lctx, n_ctx, memory_type, use_mmap, use_mlock,
vocab_only, progress_callback, progress_callback_user_data);
return true;
} catch (const std::string & err) {
fprintf(stderr, "error loading model: %s\n", err.c_str());
return false;
}
}
// evaluate the transformer
//
// - lctx: llama context
// - tokens: new batch of tokens to process
// - n_past: the context size so far
// - n_threads: number of threads to use
//
static bool llama_eval_internal(
llama_context & lctx,
const llama_token * tokens,
const int n_tokens,
const int n_past,
const int n_threads) {
// enforce that the first token is BOS
if (n_past == 0 && tokens[0] != llama_token_bos()) {
fprintf(stderr, "%s: first token must be BOS\n", __func__);
return false;
}
const int64_t t_start_us = ggml_time_us();
const int N = n_tokens;
const auto & model = lctx.model;
const auto & hparams = model.hparams;
const auto & kv_self = model.kv_self;
LLAMA_ASSERT(!!kv_self.ctx);
const int n_embd = hparams.n_embd;
const int n_layer = hparams.n_layer;
const int n_ctx = hparams.n_ctx;
const int n_head = hparams.n_head;
const int n_vocab = hparams.n_vocab;
const int n_rot = hparams.n_embd/hparams.n_head;
auto & mem_per_token = lctx.mem_per_token;
auto & buf_compute = lctx.buf_compute;
struct ggml_init_params params = {
/*.mem_size =*/ buf_compute.size,
/*.mem_buffer =*/ buf_compute.addr,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx0 = ggml_init(params);
// for big prompts, if BLAS is enabled, it is better to use only one thread
// otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
ggml_cgraph gf = {};
gf.n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
ggml_set_name(embd, "embd");
memcpy(embd->data, tokens, N*ggml_element_size(embd));
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
struct ggml_tensor * cur;
lctx.use_buf(ctx0, 0);
// norm
{
cur = ggml_rms_norm(ctx0, inpL);
// cur = attention_norm*cur
cur = ggml_mul(ctx0,
ggml_repeat(ctx0, model.layers[il].attention_norm, cur),
cur);
}
// self-attention
{
// compute Q and K and RoPE them
struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
ggml_set_name(Qcur, "Qcur");
ggml_set_name(Kcur, "Kcur");
// store key and value to memory
{
// compute the transposed [N, n_embd] V matrix
struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), n_embd, N));
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,
( n_ctx)*ggml_element_size(kv_self.v),
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v));
// important: storing RoPE-ed version of K in the KV cache!
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
}
struct ggml_tensor * Q =
ggml_permute(ctx0,
Qcur,
0, 2, 1, 3);
ggml_set_name(Q, "Q");
struct ggml_tensor * K =
ggml_permute(ctx0,
ggml_reshape_3d(ctx0,
ggml_view_1d(ctx0, kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kv_self.k)*n_embd),
n_embd/n_head, n_head, n_past + N),
0, 2, 1, 3);
ggml_set_name(K, "K");
// K * Q
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
ggml_set_name(KQ, "KQ");
// KQ_scaled = KQ / sqrt(n_embd/n_head)
struct ggml_tensor * KQ_scale = ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head));
ggml_set_name(KQ_scale, "1/sqrt(n_embd/n_head)");
// KQ_scaled shape [n_past + N, N, n_head, 1]
struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
ggml_set_name(KQ_scaled, "KQ_scaled");
// KQ_masked = mask_past(KQ_scaled)
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
ggml_set_name(KQ_masked, "KQ_masked");
// KQ = soft_max(KQ_masked)
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
ggml_set_name(KQ_soft_max, "KQ_soft_max");
// split cached V into n_head heads
struct ggml_tensor * V =
ggml_view_3d(ctx0, kv_self.v,
n_past + N, n_embd/n_head, n_head,
n_ctx*ggml_element_size(kv_self.v),
n_ctx*ggml_element_size(kv_self.v)*n_embd/n_head,
il*n_ctx*ggml_element_size(kv_self.v)*n_embd);
ggml_set_name(V, "V");
#if 1
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
ggml_set_name(KQV, "KQV");
#else
// make V contiguous in memory to speed up the matmul, however we waste time on the copy
// on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation
// is there a better way?
struct ggml_tensor * V_cont = ggml_cpy(ctx0, V, ggml_new_tensor_3d(ctx0, kv_self.v->type, n_past + N, n_embd/n_head, n_head));
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_cont, KQ_soft_max);
#endif
// KQV_merged = KQV.permute(0, 2, 1, 3)
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
ggml_set_name(KQV_merged, "KQV_merged");
// cur = KQV_merged.contiguous().view(n_embd, N)
cur = ggml_cpy(ctx0,
KQV_merged,
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
ggml_set_name(cur, "KQV_merged_contiguous");
// projection (no bias)
cur = ggml_mul_mat(ctx0,
model.layers[il].wo,
cur);
}
lctx.use_buf(ctx0, 1);
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
// feed-forward network
{
// norm
{
cur = ggml_rms_norm(ctx0, inpFF);
// cur = ffn_norm*cur
cur = ggml_mul(ctx0,
ggml_repeat(ctx0, model.layers[il].ffn_norm, cur),
cur);
}
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
model.layers[il].w3,
cur);
cur = ggml_mul_mat(ctx0,
model.layers[il].w1,
cur);
// SILU activation
cur = ggml_silu(ctx0, cur);
cur = ggml_mul(ctx0, cur, tmp);
cur = ggml_mul_mat(ctx0,
model.layers[il].w2,
cur);
}
cur = ggml_add(ctx0, cur, inpFF);
// input for next layer
inpL = cur;
}
lctx.use_buf(ctx0, 0);
// used at the end to optionally extract the embeddings
struct ggml_tensor * embeddings = NULL;
// norm
{
inpL = ggml_rms_norm(ctx0, inpL);
// inpL = norm*inpL
inpL = ggml_mul(ctx0,
ggml_repeat(ctx0, model.norm, inpL),
inpL);
embeddings = inpL;
}
// lm_head
inpL = ggml_mul_mat(ctx0, model.output, inpL);
lctx.use_buf(ctx0, -1);
// logits -> probs
//inpL = ggml_soft_max_inplace(ctx0, inpL);
// run the computation
ggml_build_forward_expand(&gf, inpL);
ggml_graph_compute (ctx0, &gf);
#ifdef GGML_PERF
// print timing information per ggml operation (for debugging purposes)
// requires GGML_PERF to be defined
ggml_graph_print(&gf);
#endif
// plot the computation graph in dot format (for debugging purposes)
//if (n_past%100 == 0) {
// ggml_graph_dump_dot(&gf, NULL, "llama.dot");
//}
//embd_w.resize(n_vocab*N);
//memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
// update kv token count
lctx.model.kv_self.n = n_past + N;
// extract logits
{
auto & logits_out = lctx.logits;
if (lctx.logits_all) {
logits_out.resize(n_vocab * N);
memcpy(logits_out.data(), (float *) ggml_get_data(inpL), sizeof(float)*n_vocab*N);
} else {
// return result for just the last token
logits_out.resize(n_vocab);
memcpy(logits_out.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
}
}
// extract embeddings
if (!lctx.embedding.empty()) {
auto & embedding_out = lctx.embedding;
embedding_out.resize(n_embd);
memcpy(embedding_out.data(), (float *) ggml_get_data(embeddings) + (n_embd*(N - 1)), sizeof(float)*n_embd);
}
if (mem_per_token == 0) {
mem_per_token = ggml_used_mem(ctx0)/N;
}
#if 0
printf("\n%s: used_mem = %.3f MB, scratch -- %.3f MB %.3f MB\n", __func__,
ggml_used_mem(ctx0)/1024.0/1024.0,
lctx.get_buf_max_mem(0)/1024.0/1024.0,
lctx.get_buf_max_mem(1)/1024.0/1024.0);
#endif
ggml_free(ctx0);
// measure the performance only for the single-token evals
if (N == 1) {
lctx.t_eval_us += ggml_time_us() - t_start_us;
lctx.n_eval++;
}
else if (N > 1) {
lctx.t_p_eval_us += ggml_time_us() - t_start_us;
lctx.n_p_eval += N;
}
return true;
}
//
// tokenizer
//
static size_t utf8_len(char src) {
const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
uint8_t highbits = static_cast<uint8_t>(src) >> 4;
return lookup[highbits];
}
struct llama_sp_symbol {
using index = int;
index prev;
index next;
const char * text;
size_t n;
};
static_assert(std::is_trivially_copyable<llama_sp_symbol>::value, "llama_sp_symbol is not trivially copyable");
struct llama_sp_bigram {
struct comparator {
bool operator()(llama_sp_bigram & l, llama_sp_bigram & r) {
return (l.score < r.score) || (l.score == r.score && l.left > r.left);
}
};
using queue_storage = std::vector<llama_sp_bigram>;
using queue = std::priority_queue<llama_sp_bigram, queue_storage, comparator>;
llama_sp_symbol::index left;
llama_sp_symbol::index right;
float score;
size_t size;
};
// original implementation:
// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
struct llama_tokenizer {
llama_tokenizer(const llama_vocab & vocab): vocab_(vocab) {}
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
// split string into utf8 chars
int index = 0;
size_t offs = 0;
while (offs < text.size()) {
llama_sp_symbol sym;
size_t char_len = std::min(text.size() - offs, utf8_len(text[offs]));
sym.text = text.c_str() + offs;
sym.n = char_len;
offs += char_len;
sym.prev = index - 1;
sym.next = offs == text.size() ? -1 : index + 1;
index++;
symbols_.emplace_back(sym);
}
// seed the work queue with all possible 2-character tokens.
for (size_t i = 1; i < symbols_.size(); ++i) {
try_add_bigram(i - 1, i);
}
// keep substituting the highest frequency pairs for as long as we can.
while (!work_queue_.empty()) {
auto bigram = work_queue_.top();
work_queue_.pop();
auto & left_sym = symbols_[bigram.left];
auto & right_sym = symbols_[bigram.right];
// if one of the symbols already got merged, skip it.
if (left_sym.n == 0 || right_sym.n == 0 ||
left_sym.n + right_sym.n != bigram.size) {
continue;
}
// merge the right sym into the left one
left_sym.n += right_sym.n;
right_sym.n = 0;
//printf("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
// remove the right sym from the chain
left_sym.next = right_sym.next;
if (right_sym.next >= 0) {
symbols_[right_sym.next].prev = bigram.left;
}
// find more substitutions
try_add_bigram(left_sym.prev, bigram.left);
try_add_bigram(bigram.left, left_sym.next);
}
for (int i = 0; i != -1; i = symbols_[i].next) {
auto & symbol = symbols_[i];
auto token = vocab_.token_to_id.find(std::string(symbol.text, symbol.n));
if (token == vocab_.token_to_id.end()) {
// output any symbols that did not form tokens as bytes.
for (int j = 0; j < (int) symbol.n; ++j) {
llama_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
output.push_back(token_id);
}
} else {
output.push_back((*token).second);
}
}
}
private:
void try_add_bigram(int left, int right) {
if (left == -1 || right == -1) {
return;
}
const std::string text = std::string(symbols_[left].text, symbols_[left].n + symbols_[right].n);
auto token = vocab_.token_to_id.find(text);
if (token == vocab_.token_to_id.end()) {
return;
}
if (static_cast<size_t>((*token).second) >= vocab_.id_to_token.size()) {
return;
}
const auto &tok_score = vocab_.id_to_token[(*token).second];
llama_sp_bigram bigram;
bigram.left = left;
bigram.right = right;
bigram.score = tok_score.score;
bigram.size = text.size();
work_queue_.push(bigram);
}
const llama_vocab & vocab_;
std::vector<llama_sp_symbol> symbols_;
llama_sp_bigram::queue work_queue_;
};
static std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, const std::string & text, bool bos) {
llama_tokenizer tokenizer(vocab);
std::vector<llama_vocab::id> output;
if (text.empty()) {
return output;
}
if (bos) {
output.push_back(llama_token_bos());
}
tokenizer.tokenize(text, output);
return output;
}
//
// sampling
//
void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates) {
assert(candidates->size > 0);
const int64_t t_start_sample_us = ggml_time_us();
// Sort the logits in descending order
if (!candidates->sorted) {
std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
return a.logit > b.logit;
});
candidates->sorted = true;
}
float max_l = candidates->data[0].logit;
float cum_sum = 0.0f;
for (size_t i = 0; i < candidates->size; ++i) {
float p = expf(candidates->data[i].logit - max_l);
candidates->data[i].p = p;
cum_sum += p;
}
for (size_t i = 0; i < candidates->size; ++i) {
candidates->data[i].p /= cum_sum;
}
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep) {
const int64_t t_start_sample_us = ggml_time_us();
k = std::max(k, (int) min_keep);
k = std::min(k, (int) candidates->size);
// Sort scores in descending order
if (!candidates->sorted) {
auto comp = [](const llama_token_data & a, const llama_token_data & b) {
return a.logit > b.logit;
};
if (k == (int) candidates->size) {
std::sort(candidates->data, candidates->data + candidates->size, comp);
} else {
std::partial_sort(candidates->data, candidates->data + k, candidates->data + candidates->size, comp);
}
candidates->sorted = true;
}
candidates->size = k;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
if (p >= 1.0f) {
return;
}
const int64_t t_start_sample_us = ggml_time_us();
llama_sample_softmax(ctx, candidates);
// Compute the cumulative probabilities
float cum_sum = 0.0f;
size_t last_idx = candidates->size;
for (size_t i = 0; i < candidates->size; ++i) {
cum_sum += candidates->data[i].p;
// Check if the running sum is greater than p or if we have kept at least min_keep tokens
if (cum_sum > p && i >= min_keep) {
last_idx = i;
break;
}
}
// Resize the output vector to keep only the top-p tokens
candidates->size = last_idx;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) {
if (z >= 1.0f || candidates->size <= 2) {
return;
}
const int64_t t_start_sample_us = ggml_time_us();
llama_sample_softmax(nullptr, candidates);
// Compute the first and second derivatives
std::vector<float> first_derivatives(candidates->size - 1);
std::vector<float> second_derivatives(candidates->size - 2);
for (size_t i = 0; i < first_derivatives.size(); ++i) {
first_derivatives[i] = candidates->data[i].p - candidates->data[i + 1].p;
}
for (size_t i = 0; i < second_derivatives.size(); ++i) {
second_derivatives[i] = first_derivatives[i] - first_derivatives[i + 1];
}
// Calculate absolute value of second derivatives
for (size_t i = 0; i < second_derivatives.size(); ++i) {
second_derivatives[i] = abs(second_derivatives[i]);
}
// Normalize the second derivatives
float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);
for (float & value : second_derivatives) {
value /= second_derivatives_sum;
}
float cum_sum = 0.0f;
size_t last_idx = candidates->size;
for (size_t i = 0; i < second_derivatives.size(); ++i) {
cum_sum += second_derivatives[i];
// Check if the running sum is greater than z or if we have kept at least min_keep tokens
if (cum_sum > z && i >= min_keep) {
last_idx = i;
break;
}
}
// Resize the output vector to keep only the tokens above the tail location
candidates->size = last_idx;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
// Reference implementation:
// https://github.com/huggingface/transformers/compare/main...cimeister:typical-sampling:typical-pr
if (p >= 1.0f) {
return;
}
const int64_t t_start_sample_us = ggml_time_us();
// Compute the softmax of logits and calculate entropy
llama_sample_softmax(nullptr, candidates);
float entropy = 0.0f;
for (size_t i = 0; i < candidates->size; ++i) {
entropy += -candidates->data[i].p * logf(candidates->data[i].p);
}
// Compute the absolute difference between negative log probability and entropy for each candidate
std::vector<float> shifted_scores;
for (size_t i = 0; i < candidates->size; ++i) {
float shifted_score = fabsf(-logf(candidates->data[i].p) - entropy);
shifted_scores.push_back(shifted_score);
}
// Sort tokens based on the shifted_scores and their corresponding indices
std::vector<size_t> indices(candidates->size);
std::iota(indices.begin(), indices.end(), 0);
std::sort(indices.begin(), indices.end(), [&](size_t a, size_t b) {
return shifted_scores[a] < shifted_scores[b];
});
// Compute the cumulative probabilities
float cum_sum = 0.0f;
size_t last_idx = indices.size();
for (size_t i = 0; i < indices.size(); ++i) {
size_t idx = indices[i];
cum_sum += candidates->data[idx].p;
// Check if the running sum is greater than typical or if we have kept at least min_keep tokens
if (cum_sum > p && i >= min_keep - 1) {
last_idx = i + 1;
break;
}
}
// Resize the output vector to keep only the locally typical tokens
std::vector<llama_token_data> new_candidates;
for (size_t i = 0; i < last_idx; ++i) {
size_t idx = indices[i];
new_candidates.push_back(candidates->data[idx]);
}
// Replace the data in candidates with the new_candidates data
std::copy(new_candidates.begin(), new_candidates.end(), candidates->data);
candidates->size = new_candidates.size();
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
const int64_t t_start_sample_us = ggml_time_us();
for (size_t i = 0; i < candidates_p->size; ++i) {
candidates_p->data[i].logit /= temp;
}
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty) {
if (last_tokens_size == 0 || penalty == 1.0f) {
return;
}
const int64_t t_start_sample_us = ggml_time_us();
for (size_t i = 0; i < candidates->size; ++i) {
const auto * token_iter = std::find(last_tokens, last_tokens + last_tokens_size, candidates->data[i].id);
if (token_iter == last_tokens + last_tokens_size) {
continue;
}
// The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong.
// This is common fix for this problem, which is to multiply by the penalty instead of dividing.
if (candidates->data[i].logit <= 0) {
candidates->data[i].logit *= penalty;
} else {
candidates->data[i].logit /= penalty;
}
}
candidates->sorted = false;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens_p, size_t last_tokens_size, float alpha_frequency, float alpha_presence) {
if (last_tokens_size == 0 || (alpha_frequency == 0.0f && alpha_presence == 0.0f)) {
return;
}
const int64_t t_start_sample_us = ggml_time_us();
// Create a frequency map to count occurrences of each token in last_tokens
std::unordered_map<llama_token, int> token_count;
for (size_t i = 0; i < last_tokens_size; ++i) {
token_count[last_tokens_p[i]]++;
}
// Apply frequency and presence penalties to the candidates
for (size_t i = 0; i < candidates->size; ++i) {
auto token_iter = token_count.find(candidates->data[i].id);
if (token_iter == token_count.end()) {
continue;
}
int count = token_iter->second;
candidates->data[i].logit -= float(count) * alpha_frequency + float(count > 0) * alpha_presence;
}
candidates->sorted = false;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu) {
assert(ctx);
auto N = float(llama_n_vocab(ctx));
int64_t t_start_sample_us;
t_start_sample_us = ggml_time_us();
llama_sample_softmax(nullptr, candidates);
// Estimate s_hat using the most probable m tokens
float s_hat = 0.0;
float sum_ti_bi = 0.0;
float sum_ti_sq = 0.0;
for (size_t i = 0; i < size_t(m - 1) && i < candidates->size - 1; ++i) {
float t_i = logf(float(i + 2) / float(i + 1));
float b_i = logf(candidates->data[i].p / candidates->data[i + 1].p);
sum_ti_bi += t_i * b_i;
sum_ti_sq += t_i * t_i;
}
s_hat = sum_ti_bi / sum_ti_sq;
// Compute k from the estimated s_hat and target surprise value
float epsilon_hat = s_hat - 1;
float k = powf((epsilon_hat * powf(2, *mu)) / (1 - powf(N, -epsilon_hat)), 1 / s_hat);
// Sample the next word X using top-k sampling
llama_sample_top_k(nullptr, candidates, int(k), 1);
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
llama_token X = llama_sample_token(ctx, candidates);
t_start_sample_us = ggml_time_us();
// Compute error as the difference between observed surprise and target surprise value
size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
return candidate.id == X;
}));
float observed_surprise = -log2f(candidates->data[X_idx].p);
float e = observed_surprise - tau;
// Update mu using the learning rate and error
*mu = *mu - eta * e;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
ctx->n_sample++;
}
return X;
}
llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) {
assert(ctx);
int64_t t_start_sample_us;
t_start_sample_us = ggml_time_us();
llama_sample_softmax(ctx, candidates);
// Truncate the words with surprise values greater than mu
candidates->size = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
return -log2f(candidate.p) > *mu;
}));
// Normalize the probabilities of the remaining words
llama_sample_softmax(ctx, candidates);
// Sample the next word X from the remaining words
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
llama_token X = llama_sample_token(ctx, candidates);
t_start_sample_us = ggml_time_us();
// Compute error as the difference between observed surprise and target surprise value
size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
return candidate.id == X;
}));
float observed_surprise = -log2f(candidates->data[X_idx].p);
float e = observed_surprise - tau;
// Update mu using the learning rate and error
*mu = *mu - eta * e;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
return X;
}
llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates) {
const int64_t t_start_sample_us = ggml_time_us();
// Find max element
auto * max_iter = std::max_element(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
return a.logit < b.logit;
});
llama_token result = max_iter->id;
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
ctx->n_sample++;
}
return result;
}
llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) {
assert(ctx);
const int64_t t_start_sample_us = ggml_time_us();
llama_sample_softmax(nullptr, candidates);
std::vector<float> probs;
probs.reserve(candidates->size);
for (size_t i = 0; i < candidates->size; ++i) {
probs.push_back(candidates->data[i].p);
}
std::discrete_distribution<> dist(probs.begin(), probs.end());
auto & rng = ctx->rng;
int idx = dist(rng);
llama_token result = candidates->data[idx].id;
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
ctx->n_sample++;
return result;
}
//
// quantization
//
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, enum llama_ftype ftype, int nthread) {
ggml_type quantized_type;
switch (ftype) {
case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break;
case LLAMA_FTYPE_MOSTLY_Q4_1: quantized_type = GGML_TYPE_Q4_1; break;
case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break;
case LLAMA_FTYPE_MOSTLY_Q5_1: quantized_type = GGML_TYPE_Q5_1; break;
case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break;
default: throw format("invalid output file type %d\n", ftype);
};
if (nthread <= 0) {
nthread = std::thread::hardware_concurrency();
}
std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp, /*use_mmap*/ false,
/*vocab_only*/ false));
llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), ftype);
size_t total_size_org = 0;
size_t total_size_new = 0;
std::vector<int64_t> hist_all(1 << 4, 0);
std::vector<std::thread> workers;
std::mutex mutex;
size_t idx = 0;
for (llama_load_tensor & tensor : model_loader->tensors_map.tensors) {
llama_buffer read_data;
read_data.resize(tensor.size);
tensor.data = read_data.addr;
model_loader->load_data_for(tensor);
printf("[%4zu/%4zu] %36s - %16s, type = %6s, ",
++idx, model_loader->tensors_map.tensors.size(),
tensor.name.c_str(), llama_format_tensor_shape(tensor.ne).c_str(),
ggml_type_name(tensor.type));
// This used to be a regex, but <regex> has an extreme cost to compile times.
bool quantize = tensor.name.rfind("weight") == tensor.name.size() - 6; // ends with 'weight'?
// quantize only 2D tensors
quantize &= (tensor.ne.size() == 2);
// uncomment this to keep the output layer in FP16
//if (tensor.name == "output.weight") {
// quantize = false;
//}
enum ggml_type new_type;
void * new_data;
size_t new_size;
llama_buffer work;
if (!quantize) {
new_type = tensor.type;
new_data = tensor.data;
new_size = tensor.size;
printf("size = %8.3f MB\n", tensor.size/1024.0/1024.0);
} else {
new_type = quantized_type;
float * f32_data;
size_t nelements = tensor.ne.at(0) * tensor.ne.at(1);
llama_buffer f32_conv_buf;
if (tensor.type == GGML_TYPE_F32) {
f32_data = (float *) tensor.data;
} else if (tensor.type == GGML_TYPE_F16) {
f32_conv_buf.resize(nelements * sizeof(float));
f32_data = (float *) f32_conv_buf.addr;
const auto * f16_data = (const ggml_fp16_t *) tensor.data;
for (size_t i = 0; i < nelements; i++) {
f32_data[i] = ggml_fp16_to_fp32(f16_data[i]);
}
} else {
throw format("type %s unsupported for integer quantization", ggml_type_name(tensor.type));
}
printf("quantizing .. ");
fflush(stdout);
work.resize(nelements * 4); // upper bound on size
new_data = work.addr;
std::vector<int64_t> hist_cur(1 << 4, 0);
int chunk_size = 32 * 512;
const int nchunk = (nelements + chunk_size - 1)/chunk_size;
const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1;
if (nthread_use < 2) {
new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nelements, hist_cur.data());
} else {
size_t counter = 0;
new_size = 0;
auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, nelements, chunk_size] () {
std::vector<int64_t> local_hist;
size_t local_size = 0;
while (true) {
std::unique_lock<std::mutex> lock(mutex);
size_t first = counter; counter += chunk_size;
if (first >= nelements) {
if (!local_hist.empty()) {
for (int j=0; j<int(local_hist.size()); ++j) {
hist_cur[j] += local_hist[j];
}
new_size += local_size;
}
break;
}
lock.unlock();
size_t last = std::min(nelements, first + chunk_size);
if (local_hist.empty()) {
local_hist.resize(hist_cur.size(), 0);
}
local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first, last - first, local_hist.data());
}
};
if ((int) workers.size() < nthread_use - 1) {
workers.resize(nthread_use - 1);
}
for (int it = 0; it < nthread_use - 1; ++it) {
workers[it] = std::thread(compute);
}
compute();
for (int it = 0; it < nthread_use - 1; ++it) {
workers[it].join();
}
}
printf("size = %8.2f MB -> %8.2f MB | hist: ", tensor.size/1024.0/1024.0, new_size/1024.0/1024.0);
for (size_t i = 0; i < hist_cur.size(); i++) {
hist_all[i] += hist_cur[i];
}
for (size_t i = 0; i < hist_cur.size(); i++) {
printf("%5.3f ", hist_cur[i] / float(nelements));
}
printf("\n");
}
total_size_org += tensor.size;
total_size_new += new_size;
file_saver.write_tensor(tensor, new_type, new_data, new_size);
}
printf("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
printf("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
{
int64_t sum_all = 0;
for (size_t i = 0; i < hist_all.size(); i++) {
sum_all += hist_all[i];
}
printf("%s: hist: ", __func__);
for (size_t i = 0; i < hist_all.size(); i++) {
printf("%5.3f ", hist_all[i] / float(sum_all));
}
printf("\n");
}
}
//
// interface implementation
//
struct llama_context * llama_init_from_file(
const char * path_model,
struct llama_context_params params) {
ggml_time_init();
llama_context * ctx = new llama_context;
if (params.seed < 0) {
params.seed = time(NULL);
}
unsigned cur_percentage = 0;
if (params.progress_callback == NULL) {
params.progress_callback_user_data = &cur_percentage;
params.progress_callback = [](float progress, void * ctx) {
unsigned * cur_percentage_p = (unsigned *) ctx;
unsigned percentage = (unsigned) (100 * progress);
while (percentage > *cur_percentage_p) {
++*cur_percentage_p;
fprintf(stderr, ".");
fflush(stderr);
if (percentage >= 100) {
fprintf(stderr, "\n");
}
}
};
}
ctx->rng = std::mt19937(params.seed);
ctx->logits_all = params.logits_all;
ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
if (!llama_model_load(path_model, *ctx, params.n_ctx, memory_type,
params.use_mmap, params.use_mlock, params.vocab_only,
params.progress_callback, params.progress_callback_user_data)) {
fprintf(stderr, "%s: failed to load model\n", __func__);
llama_free(ctx);
return nullptr;
}
// reserve memory for context buffers
if (!params.vocab_only) {
if (!kv_cache_init(ctx->model.hparams, ctx->model.kv_self, memory_type, ctx->model.hparams.n_ctx)) {
fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
llama_free(ctx);
return nullptr;
}
{
const size_t memory_size = ggml_nbytes(ctx->model.kv_self.k) + ggml_nbytes(ctx->model.kv_self.v);
fprintf(stderr, "%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
}
const auto & hparams = ctx->model.hparams;
// resized during inference
if (params.logits_all) {
ctx->logits.reserve(hparams.n_ctx*hparams.n_vocab);
} else {
ctx->logits.reserve(hparams.n_vocab);
}
if (params.embedding){
ctx->embedding.resize(hparams.n_embd);
}
ctx->buf_compute.resize(MEM_REQ_EVAL().at(ctx->model.type));
ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0().at(ctx->model.type));
ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1().at(ctx->model.type));
}
return ctx;
}
void llama_free(struct llama_context * ctx) {
delete ctx;
}
int llama_model_quantize(
const char * fname_inp,
const char * fname_out,
enum llama_ftype ftype,
int nthread) {
try {
llama_model_quantize_internal(fname_inp, fname_out, ftype, nthread);
return 0;
} catch (const std::string & err) {
fprintf(stderr, "%s: failed to quantize: %s\n", __func__, err.c_str());
return 1;
}
}
int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char * path_lora, const char * path_base_model, int n_threads) {
fprintf(stderr, "%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);
auto & model = ctx->model;
const int64_t t_start_lora_us = ggml_time_us();
auto fin = std::ifstream(path_lora, std::ios::binary);
if (!fin) {
fprintf(stderr, "%s: failed to open '%s'\n", __func__, path_lora);
return 1;
}
// verify magic and version
{
uint32_t magic;
fin.read((char *) &magic, sizeof(magic));
if (magic != 'ggla') {
fprintf(stderr, "%s: bad file magic\n", __func__);
return 1;
}
uint32_t format_version;
fin.read((char *) &format_version, sizeof(format_version));
if (format_version != 1) {
fprintf(stderr, "%s: unsupported file version\n", __func__ );
return 1;
}
}
int32_t lora_r;
int32_t lora_alpha;
fin.read((char *) &lora_r, sizeof(lora_r));
fin.read((char *) &lora_alpha, sizeof(lora_alpha));
float scaling = (float)lora_alpha / (float)lora_r;
fprintf(stderr, "%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
// create a temporary ggml context to store the lora tensors
// todo: calculate size from biggest possible tensor
std::vector<uint8_t> lora_buf(1024ull * 1024ull * 1024ull);
struct ggml_init_params params;
params.mem_size = lora_buf.size();
params.mem_buffer = lora_buf.data();
params.no_alloc = false;
ggml_context * lora_ctx = ggml_init(params);
std::unordered_map<std::string, struct ggml_tensor *> lora_tensors;
// create a name -> tensor map of the model to accelerate lookups
std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
for (auto & kv: model.tensors_by_name) {
model_tensors.insert(kv);
}
// load base model
std::unique_ptr<llama_model_loader> model_loader;
ggml_context * base_ctx = NULL;
llama_buffer base_buf;
if (path_base_model) {
fprintf(stderr, "%s: loading base model from '%s'\n", __func__, path_base_model);
model_loader.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*vocab_only*/ false));
size_t ctx_size;
size_t mmapped_size;
model_loader->calc_sizes(&ctx_size, &mmapped_size);
base_buf.resize(ctx_size);
ggml_init_params base_params;
base_params.mem_size = base_buf.size;
base_params.mem_buffer = base_buf.addr;
base_params.no_alloc = model_loader->use_mmap;
base_ctx = ggml_init(base_params);
model_loader->ggml_ctx = base_ctx;
// maybe this should in llama_model_loader
if (model_loader->use_mmap) {
model_loader->mapping.reset(new llama_mmap(&model_loader->file_loaders.at(0)->file, /* prefetch */ false));
}
}
// read tensors and apply
bool warned = false;
int n_tensors = 0;
while (true) {
int32_t n_dims;
int32_t length;
int32_t ftype;
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
if (fin.eof()) {
break;
}
int32_t ne[2] = { 1, 1 };
for (int i = 0; i < n_dims; ++i) {
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
}
std::string name;
{
char buf[1024];
fin.read(buf, length);
name = std::string(buf, length);
}
// check for lora suffix and get the type of tensor
const std::string lora_suffix = ".lora";
size_t pos = name.rfind(lora_suffix);
if (pos == std::string::npos) {
fprintf(stderr, "%s: error: '%s' is not a lora tensor\n", __func__, name.c_str());
return 1;
}
std::string lora_type = name.substr(pos + lora_suffix.length());
std::string base_name = name;
base_name.erase(pos);
// fprintf(stderr, "%s: %s => %s (lora type %s) ", __func__, name.c_str(),base_name.c_str(), lora_type.c_str());
if (model_tensors.find(base_name) == model_tensors.end()) {
fprintf(stderr, "%s: unknown tensor '%s' in lora adapter\n", __func__, name.data());
return 1;
}
// create ggml tensor
ggml_type wtype;
switch (ftype) {
case 0: wtype = GGML_TYPE_F32; break;
case 1: wtype = GGML_TYPE_F16; break;
default:
{
fprintf(stderr, "%s: invalid tensor data type '%d'\n",
__func__, ftype);
return false;
}
}
ggml_tensor* lora_tensor;
if (n_dims == 2) {
lora_tensor = ggml_new_tensor_2d(lora_ctx, wtype, ne[0], ne[1]);
}
else {
fprintf(stderr, "%s: unsupported tensor dimension %d\n", __func__, n_dims);
return 1;
}
// load tensor data
size_t offset = fin.tellg();
size_t tensor_data_size = ggml_nbytes(lora_tensor);
offset = (offset + 31) & -32;
fin.seekg(offset);
fin.read((char*)lora_tensor->data, tensor_data_size);
lora_tensors[name] = lora_tensor;
// check if we have both A and B tensors and apply
if (lora_tensors.find(base_name + ".loraA") != lora_tensors.end() &&
lora_tensors.find(base_name + ".loraB") != lora_tensors.end()) {
ggml_tensor * dest_t = model_tensors[base_name];
ggml_tensor * base_t;
if (model_loader) {
// load from base model
if (model_loader->tensors_map.name_to_idx.find(base_name) == model_loader->tensors_map.name_to_idx.end()) {
fprintf(stderr, "%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
return 1;
}
size_t idx = model_loader->tensors_map.name_to_idx[base_name];
llama_load_tensor & lt = model_loader->tensors_map.tensors[idx];
base_t = model_loader->get_tensor(base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] });
lt.data = (uint8_t *) lt.ggml_tensor->data;
model_loader->load_data_for(lt);
lt.ggml_tensor->data = lt.data;
}
else {
base_t = dest_t;
}
if (ggml_is_quantized(base_t->type)) {
if (!warned) {
fprintf(stderr, "%s: warning: using a lora adapter with a quantized model may result in poor quality, "
"use a f16 or f32 base model with --lora-base\n", __func__);
warned = true;
}
}
ggml_tensor * loraA = lora_tensors[base_name + ".loraA"];
ggml_tensor * loraB = lora_tensors[base_name + ".loraB"];
if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
fprintf(stderr, "%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
" are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
return 1;
}
// w = w + BA*s
ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
if (scaling != 1.0f) {
ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, scaling);
BA = ggml_scale_inplace(lora_ctx, BA, scale_tensor);
}
ggml_tensor * r;
if (base_t == dest_t) {
r = ggml_add_inplace(lora_ctx, dest_t, BA);
}
else {
r = ggml_add(lora_ctx, base_t, BA);
r = ggml_cpy(lora_ctx, r, dest_t);
}
struct ggml_cgraph gf = ggml_build_forward(r);
gf.n_threads = n_threads;
ggml_graph_compute(lora_ctx, &gf);
// we won't need these tensors again, reset the context to save memory
ggml_free(lora_ctx);
lora_ctx = ggml_init(params);
lora_tensors.clear();
n_tensors++;
if (n_tensors % 4 == 0) {
fprintf(stderr, ".");
}
}
}
// TODO: this should be in a destructor, it will leak on failure
ggml_free(lora_ctx);
if (base_ctx) {
ggml_free(base_ctx);
}
const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
fprintf(stderr, " done (%.2f ms)\n", t_lora_us / 1000.0);
return 0;
}
int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, const char * path_base_model, int n_threads) {
try {
return llama_apply_lora_from_file_internal(ctx, path_lora, path_base_model, n_threads);
} catch (const std::string & err) {
fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.c_str());
return 1;
}
}
int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
return ctx->model.kv_self.n;
}
#define LLAMA_MAX_RNG_STATE (64*1024)
void llama_set_rng_seed(struct llama_context * ctx, int seed) {
if (seed < 0) {
seed = time(NULL);
}
ctx->rng.seed(seed);
}
// Returns the *maximum* size of the state
size_t llama_get_state_size(const struct llama_context * ctx) {
// we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
// for reference, std::mt19937(1337) serializes to 6701 bytes.
const size_t s_rng_size = sizeof(size_t);
const size_t s_rng = LLAMA_MAX_RNG_STATE;
const size_t s_logits_capacity = sizeof(size_t);
const size_t s_logits_size = sizeof(size_t);
const size_t s_logits = ctx->logits.capacity() * sizeof(float);
const size_t s_embedding_size = sizeof(size_t);
const size_t s_embedding = ctx->embedding.size() * sizeof(float);
const size_t s_kv_size = sizeof(size_t);
const size_t s_kv_ntok = sizeof(int);
const size_t s_kv = ctx->model.kv_self.buf.size;
const size_t s_total = (
+ s_rng_size
+ s_rng
+ s_logits_capacity
+ s_logits_size
+ s_logits
+ s_embedding_size
+ s_embedding
+ s_kv_size
+ s_kv_ntok
+ s_kv
);
return s_total;
}
// Copies the state to the specified destination address
size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
uint8_t * out = dst;
// copy rng
{
std::stringstream rng_ss;
rng_ss << ctx->rng;
const size_t rng_size = rng_ss.str().size();
char rng_buf[LLAMA_MAX_RNG_STATE];
memset(&rng_buf[0], 0, LLAMA_MAX_RNG_STATE);
memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size());
memcpy(out, &rng_size, sizeof(rng_size)); out += sizeof(rng_size);
memcpy(out, &rng_buf[0], LLAMA_MAX_RNG_STATE); out += LLAMA_MAX_RNG_STATE;
}
// copy logits
{
const size_t logits_cap = ctx->logits.capacity();
const size_t logits_size = ctx->logits.size();
memcpy(out, &logits_cap, sizeof(logits_cap)); out += sizeof(logits_cap);
memcpy(out, &logits_size, sizeof(logits_size)); out += sizeof(logits_size);
if (logits_size) {
memcpy(out, ctx->logits.data(), logits_size * sizeof(float));
}
out += logits_cap * sizeof(float);
}
// copy embeddings
{
const size_t embedding_size = ctx->embedding.size();
memcpy(out, &embedding_size, sizeof(embedding_size)); out += sizeof(embedding_size);
if (embedding_size) {
memcpy(out, ctx->embedding.data(), embedding_size * sizeof(float));
out += embedding_size * sizeof(float);
}
}
// copy kv cache
{
const auto & kv_self = ctx->model.kv_self;
const auto & hparams = ctx->model.hparams;
const int n_layer = hparams.n_layer;
const int n_embd = hparams.n_embd;
const int n_ctx = hparams.n_ctx;
const size_t kv_size = kv_self.buf.size;
const int kv_ntok = llama_get_kv_cache_token_count(ctx);
memcpy(out, &kv_size, sizeof(kv_size)); out += sizeof(kv_size);
memcpy(out, &kv_ntok, sizeof(kv_ntok)); out += sizeof(kv_ntok);
if (kv_size) {
const size_t elt_size = ggml_element_size(kv_self.k);
char buffer[4096];
ggml_context * cpy_ctx = ggml_init({ sizeof(buffer), buffer, /* no_alloc */ true });
ggml_cgraph gf{};
gf.n_threads = 1;
ggml_tensor * kout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_ntok, n_layer);
kout3d->data = out;
out += ggml_nbytes(kout3d);
ggml_tensor * vout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_ntok, n_embd, n_layer);
vout3d->data = out;
out += ggml_nbytes(vout3d);
ggml_tensor * k3d = ggml_view_3d(cpy_ctx, kv_self.k,
n_embd, kv_ntok, n_layer,
elt_size*n_embd, elt_size*n_embd*n_ctx, 0);
ggml_tensor * v3d = ggml_view_3d(cpy_ctx, kv_self.v,
kv_ntok, n_embd, n_layer,
elt_size*n_ctx, elt_size*n_ctx*n_embd, 0);
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, k3d, kout3d));
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, v3d, vout3d));
ggml_graph_compute(cpy_ctx, &gf);
ggml_free(cpy_ctx);
}
}
const size_t written = out - dst;
const size_t max_size = llama_get_state_size(ctx);
LLAMA_ASSERT(written <= max_size);
return written;
}
// Sets the state reading from the specified source address
size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
const uint8_t * inp = src;
// set rng
{
size_t rng_size;
char rng_buf[LLAMA_MAX_RNG_STATE];
memcpy(&rng_size, inp, sizeof(rng_size)); inp += sizeof(rng_size);
memcpy(&rng_buf[0], inp, LLAMA_MAX_RNG_STATE); inp += LLAMA_MAX_RNG_STATE;
std::stringstream rng_ss;
rng_ss.str(std::string(&rng_buf[0], rng_size));
rng_ss >> ctx->rng;
LLAMA_ASSERT(rng_ss.fail() == false);
}
// set logits
{
size_t logits_cap;
size_t logits_size;
memcpy(&logits_cap, inp, sizeof(logits_cap)); inp += sizeof(logits_cap);
memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size);
LLAMA_ASSERT(ctx->logits.capacity() == logits_cap);
if (logits_size) {
ctx->logits.resize(logits_size);
memcpy(ctx->logits.data(), inp, logits_size * sizeof(float));
}
inp += logits_cap * sizeof(float);
}
// set embeddings
{
size_t embedding_size;
memcpy(&embedding_size, inp, sizeof(embedding_size)); inp += sizeof(embedding_size);
LLAMA_ASSERT(ctx->embedding.capacity() == embedding_size);
if (embedding_size) {
memcpy(ctx->embedding.data(), inp, embedding_size * sizeof(float));
inp += embedding_size * sizeof(float);
}
}
// set kv cache
{
const auto & kv_self = ctx->model.kv_self;
const auto & hparams = ctx->model.hparams;
const int n_layer = hparams.n_layer;
const int n_embd = hparams.n_embd;
const int n_ctx = hparams.n_ctx;
size_t kv_size;
int kv_ntok;
memcpy(&kv_size, inp, sizeof(kv_size)); inp += sizeof(kv_size);
memcpy(&kv_ntok, inp, sizeof(kv_ntok)); inp += sizeof(kv_ntok);
if (kv_size) {
LLAMA_ASSERT(kv_self.buf.size == kv_size);
const size_t elt_size = ggml_element_size(kv_self.k);
char buffer[4096];
ggml_context * cpy_ctx = ggml_init({ sizeof(buffer), buffer, /* no_alloc */ true });
ggml_cgraph gf{};
gf.n_threads = 1;
ggml_tensor * kin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_ntok, n_layer);
kin3d->data = (void *) inp;
inp += ggml_nbytes(kin3d);
ggml_tensor * vin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_ntok, n_embd, n_layer);
vin3d->data = (void *) inp;
inp += ggml_nbytes(vin3d);
ggml_tensor * k3d = ggml_view_3d(cpy_ctx, kv_self.k,
n_embd, kv_ntok, n_layer,
elt_size*n_embd, elt_size*n_embd*n_ctx, 0);
ggml_tensor * v3d = ggml_view_3d(cpy_ctx, kv_self.v,
kv_ntok, n_embd, n_layer,
elt_size*n_ctx, elt_size*n_ctx*n_embd, 0);
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, kin3d, k3d));
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, vin3d, v3d));
ggml_graph_compute(cpy_ctx, &gf);
ggml_free(cpy_ctx);
}
ctx->model.kv_self.n = kv_ntok;
}
const size_t nread = inp - src;
const size_t max_size = llama_get_state_size(ctx);
LLAMA_ASSERT(nread <= max_size);
return nread;
}
bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
llama_file file(path_session, "rb");
// sanity checks
{
const uint32_t magic = file.read_u32();
const uint32_t version = file.read_u32();
if (magic != LLAMA_SESSION_MAGIC || version != LLAMA_SESSION_VERSION) {
fprintf(stderr, "%s : unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
return false;
}
llama_hparams session_hparams;
file.read_raw(&session_hparams, sizeof(llama_hparams));
if (session_hparams != ctx->model.hparams) {
fprintf(stderr, "%s : model hparams didn't match from session file!\n", __func__);
return false;
}
}
// load the prompt
{
const uint32_t n_token_count = file.read_u32();
if (n_token_count > n_token_capacity) {
fprintf(stderr, "%s : token count in session file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
return false;
}
file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
*n_token_count_out = n_token_count;
}
// restore the context state
{
const size_t n_state_size_cur = file.size - file.tell();
const size_t n_state_size_max = llama_get_state_size(ctx);
if (n_state_size_cur > n_state_size_max) {
fprintf(stderr, "%s : the state size in session file is too big! max %zu, got %zu\n", __func__, n_state_size_max, n_state_size_cur);
return false;
}
std::vector<uint8_t> state_data(n_state_size_max);
file.read_raw(state_data.data(), n_state_size_cur);
llama_set_state_data(ctx, state_data.data());
}
return true;
}
bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
llama_file file(path_session, "wb");
file.write_u32(LLAMA_SESSION_MAGIC);
file.write_u32(LLAMA_SESSION_VERSION);
file.write_raw(&ctx->model.hparams, sizeof(llama_hparams));
// save the prompt
file.write_u32((uint32_t) n_token_count);
file.write_raw(tokens, sizeof(llama_token) * n_token_count);
// save the context state
{
const size_t n_state_size_max = llama_get_state_size(ctx);
std::vector<uint8_t> state_data(n_state_size_max);
const size_t n_state_size_cur = llama_copy_state_data(ctx, state_data.data());
file.write_raw(state_data.data(), n_state_size_cur);
}
return true;
}
int llama_eval(
struct llama_context * ctx,
const llama_token * tokens,
int n_tokens,
int n_past,
int n_threads) {
if (!llama_eval_internal(*ctx, tokens, n_tokens, n_past, n_threads)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
return 1;
}
// get a more accurate load time, upon first eval
// TODO: fix this
if (!ctx->has_evaluated_once) {
ctx->t_load_us = ggml_time_us() - ctx->t_start_us;
ctx->has_evaluated_once = true;
}
return 0;
}
int llama_tokenize(
struct llama_context * ctx,
const char * text,
llama_token * tokens,
int n_max_tokens,
bool add_bos) {
auto res = llama_tokenize(ctx->vocab, text, add_bos);
if (n_max_tokens < (int) res.size()) {
fprintf(stderr, "%s: too many tokens\n", __func__);
return -((int) res.size());
}
for (size_t i = 0; i < res.size(); i++) {
tokens[i] = res[i];
}
return res.size();
}
int llama_n_vocab(const struct llama_context * ctx) {
return ctx->vocab.id_to_token.size();
}
int llama_n_ctx(const struct llama_context * ctx) {
return ctx->model.hparams.n_ctx;
}
int llama_n_embd(const struct llama_context * ctx) {
return ctx->model.hparams.n_embd;
}
float * llama_get_logits(struct llama_context * ctx) {
return ctx->logits.data();
}
float * llama_get_embeddings(struct llama_context * ctx) {
return ctx->embedding.data();
}
const char * llama_token_to_str(const struct llama_context * ctx, llama_token token) {
if (token >= llama_n_vocab(ctx)) {
return nullptr;
}
return ctx->vocab.id_to_token[token].tok.c_str();
}
llama_token llama_token_bos() {
return 1;
}
llama_token llama_token_eos() {
return 2;
}
llama_token llama_token_nl() {
return 13;
}
void llama_print_timings(struct llama_context * ctx) {
const int64_t t_end_us = ggml_time_us();
const int32_t n_sample = std::max(1, ctx->n_sample);
const int32_t n_eval = std::max(1, ctx->n_eval);
const int32_t n_p_eval = std::max(1, ctx->n_p_eval);
fprintf(stderr, "\n");
fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0);
fprintf(stderr, "%s: sample time = %8.2f ms / %5d runs (%8.2f ms per token)\n", __func__, 1e-3 * ctx->t_sample_us, n_sample, 1e-3 * ctx->t_sample_us / n_sample);
fprintf(stderr, "%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token)\n", __func__, 1e-3 * ctx->t_p_eval_us, n_p_eval, 1e-3 * ctx->t_p_eval_us / n_p_eval);
fprintf(stderr, "%s: eval time = %8.2f ms / %5d runs (%8.2f ms per token)\n", __func__, 1e-3 * ctx->t_eval_us, n_eval, 1e-3 * ctx->t_eval_us / n_eval);
fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0);
}
void llama_reset_timings(struct llama_context * ctx) {
ctx->t_start_us = ggml_time_us();
ctx->t_sample_us = ctx->n_sample = 0;
ctx->t_eval_us = ctx->n_eval = 0;
ctx->t_p_eval_us = ctx->n_p_eval = 0;
}
const char * llama_print_system_info(void) {
static std::string s;
s = "";
s += "AVX = " + std::to_string(ggml_cpu_has_avx()) + " | ";
s += "AVX2 = " + std::to_string(ggml_cpu_has_avx2()) + " | ";
s += "AVX512 = " + std::to_string(ggml_cpu_has_avx512()) + " | ";
s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | ";
s += "AVX512_VNNI = " + std::to_string(ggml_cpu_has_avx512_vnni()) + " | ";
s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " | ";
s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " | ";
s += "ARM_FMA = " + std::to_string(ggml_cpu_has_arm_fma()) + " | ";
s += "F16C = " + std::to_string(ggml_cpu_has_f16c()) + " | ";
s += "FP16_VA = " + std::to_string(ggml_cpu_has_fp16_va()) + " | ";
s += "WASM_SIMD = " + std::to_string(ggml_cpu_has_wasm_simd()) + " | ";
s += "BLAS = " + std::to_string(ggml_cpu_has_blas()) + " | ";
s += "SSE3 = " + std::to_string(ggml_cpu_has_sse3()) + " | ";
s += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | ";
return s.c_str();
}
// For internal test use
std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx) {
return ctx->model.tensors_by_name;
}
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