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Merge remote-tracking branch 'origin/master' into cli-ui-update

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This commit is contained in:
Tomáš Pazdiora 2023-04-11 22:55:36 +02:00
commit 414b66fcc4
20 changed files with 485 additions and 176 deletions
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5
.ecrc Normal file
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@ -0,0 +1,5 @@
{
"Disable": {
"IndentSize": true
}
}

16
.editorconfig Normal file
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@ -0,0 +1,16 @@
# https://EditorConfig.org
# Top-most EditorConfig file
root = true
# Unix-style newlines with a newline ending every file, utf-8 charset
[*]
end_of_line = lf
insert_final_newline = true
trim_trailing_whitespace = true
charset = utf-8
indent_style = space
indent_size = 4
[Makefile]
indent_style = tab

17
.github/workflows/editorconfig.yml vendored Normal file
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@ -0,0 +1,17 @@
name: EditorConfig Checker
on:
push:
branches:
- master
pull_request:
branches:
- master
jobs:
editorconfig:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: editorconfig-checker/action-editorconfig-checker@main
- run: editorconfig-checker

1
README.md
View file

@ -42,6 +42,7 @@ New features will probably be added mostly through community contributions.
- [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
- [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
- [X] [Vicuna](https://github.com/ggerganov/llama.cpp/discussions/643#discussioncomment-5533894)
- [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
**Bindings:**

12
examples/Miku.sh
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@ -19,15 +19,15 @@ GEN_OPTIONS=(--batch_size 1024
--top_p 0.5)
if [ -n "$N_THREAD" ]; then
GEN_OPTIONS+=(--threads "$N_THREAD")
GEN_OPTIONS+=(--threads "$N_THREAD")
fi
./main "${GEN_OPTIONS[@]}" \
--model "$MODEL" \
--n_predict "$N_PREDICTS" \
--color --interactive \
--reverse-prompt "${USER_NAME}:" \
--prompt "
--model "$MODEL" \
--n_predict "$N_PREDICTS" \
--color --interactive \
--reverse-prompt "${USER_NAME}:" \
--prompt "
This is a transcript of a 1000 page, never ending conversation between ${USER_NAME} and the cute and helpful AI assistant ${AI_NAME}. ${AI_NAME} is a girl who is an AI running on the users computer.
${AI_NAME} can think for herself without the user seeing her thoughts by adding a /think prefix to her output. She uses this to reason about the world and to think about what she should say next.
${AI_NAME} is always coherent and makes sense, but if she isn't sure if what she is saying is correct she will ask the user for help.

8
examples/common.cpp
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@ -448,10 +448,10 @@ void win32_console_init(bool enable_color) {
// Convert a wide Unicode string to an UTF8 string
void win32_utf8_encode(const std::wstring & wstr, std::string & str) {
int size_needed = WideCharToMultiByte(CP_UTF8, 0, &wstr[0], (int)wstr.size(), NULL, 0, NULL, NULL);
std::string strTo(size_needed, 0);
WideCharToMultiByte(CP_UTF8, 0, &wstr[0], (int)wstr.size(), &strTo[0], size_needed, NULL, NULL);
str = strTo;
int size_needed = WideCharToMultiByte(CP_UTF8, 0, &wstr[0], (int)wstr.size(), NULL, 0, NULL, NULL);
std::string strTo(size_needed, 0);
WideCharToMultiByte(CP_UTF8, 0, &wstr[0], (int)wstr.size(), &strTo[0], size_needed, NULL, NULL);
str = strTo;
}
#endif

5
examples/main/main.cpp
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@ -1,3 +1,8 @@
// Defines sigaction on msys:
#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#endif
#include "common.h"
#include "llama.h"

10
examples/quantize/quantize.cpp
View file

@ -5,15 +5,15 @@
#include <string>
// usage:
// ./llama-quantize models/llama/ggml-model.bin models/llama/ggml-model-quant.bin type
// ./quantize models/llama/ggml-model.bin models/llama/ggml-model-quant.bin type
//
int main(int argc, char ** argv) {
ggml_time_init();
if (argc != 4) {
fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type\n", argv[0]);
fprintf(stderr, " type = 2 - q4_0\n");
fprintf(stderr, " type = 3 - q4_1\n");
fprintf(stderr, " type = %d - q4_0\n", LLAMA_FTYPE_MOSTLY_Q4_0);
fprintf(stderr, " type = %d - q4_1\n", LLAMA_FTYPE_MOSTLY_Q4_1);
return 1;
}
@ -27,7 +27,7 @@ int main(int argc, char ** argv) {
const std::string fname_inp = argv[1];
const std::string fname_out = argv[2];
const int itype = atoi(argv[3]);
const enum llama_ftype ftype = (enum llama_ftype)atoi(argv[3]);
const int64_t t_main_start_us = ggml_time_us();
@ -37,7 +37,7 @@ int main(int argc, char ** argv) {
{
const int64_t t_start_us = ggml_time_us();
if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), itype)) {
if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), ftype)) {
fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
return 1;
}

342
ggml.c
View file

@ -1,4 +1,4 @@
// Defines CLOCK_MONOTONIC and asprintf on Linux
// Defines CLOCK_MONOTONIC on Linux
#define _GNU_SOURCE
#include "ggml.h"
@ -26,14 +26,9 @@
#define static_assert(cond, msg) struct global_scope_noop_trick
#endif
#if defined _MSC_VER || defined(__MINGW32__)
#if defined(_WIN32)
#if !defined(__MINGW32__)
#include <Windows.h>
#else
// ref: https://github.com/ggerganov/whisper.cpp/issues/168
#include <windows.h>
#endif
typedef volatile LONG atomic_int;
typedef atomic_int atomic_bool;
@ -55,6 +50,7 @@ typedef HANDLE pthread_t;
typedef DWORD thread_ret_t;
static int pthread_create(pthread_t* out, void* unused, thread_ret_t(*func)(void*), void* arg) {
(void) unused;
HANDLE handle = CreateThread(NULL, 0, (LPTHREAD_START_ROUTINE) func, arg, 0, NULL);
if (handle == NULL)
{
@ -66,6 +62,7 @@ static int pthread_create(pthread_t* out, void* unused, thread_ret_t(*func)(void
}
static int pthread_join(pthread_t thread, void* unused) {
(void) unused;
return (int) WaitForSingleObject(thread, INFINITE);
}
@ -231,12 +228,12 @@ static inline float fp32_from_bits(uint32_t w) {
}
static inline uint32_t fp32_to_bits(float f) {
union {
float as_value;
uint32_t as_bits;
} fp32;
fp32.as_value = f;
return fp32.as_bits;
union {
float as_value;
uint32_t as_bits;
} fp32;
fp32.as_value = f;
return fp32.as_bits;
}
static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
@ -599,10 +596,7 @@ static void quantize_row_q4_0(const float * restrict x, void * restrict vy, int
for (int l = 0; l < 2; l++) amaxv[4*l] = vmaxq_f32(amaxv[4*l], amaxv[4*l+2]);
for (int l = 0; l < 1; l++) amaxv[8*l] = vmaxq_f32(amaxv[8*l], amaxv[8*l+4]);
// absolute max
const float amax = MAX(
MAX(vgetq_lane_f32(amaxv[0], 0), vgetq_lane_f32(amaxv[0], 1)),
MAX(vgetq_lane_f32(amaxv[0], 2), vgetq_lane_f32(amaxv[0], 3)));
const float amax = vmaxvq_f32(amaxv[0]);
const float d = amax / ((1 << 3) - 1);
const float id = d ? 1.0f/d : 0.0f;
@ -924,7 +918,7 @@ static void quantize_row_q4_1(const float * restrict x, void * restrict vy, int
float32x4_t minv[8];
float32x4_t maxv[8];
for (int l = 0; l < 8; l++) srcv[l] = vld1q_f32(x + i*32 + 4*l);
for (int l = 0; l < 8; l++) srcv[l] = vld1q_f32(x + i*QK + 4*l);
for (int l = 0; l < 4; l++) minv[2*l] = vminq_f32(srcv[2*l], srcv[2*l + 1]);
for (int l = 0; l < 2; l++) minv[4*l] = vminq_f32(minv[4*l], minv[4*l + 2]);
@ -947,7 +941,8 @@ static void quantize_row_q4_1(const float * restrict x, void * restrict vy, int
for (int l = 0; l < 8; l++) {
const float32x4_t v = vmulq_n_f32(vsubq_f32(srcv[l], minv0), id);
const int32x4_t vi = vcvtq_s32_f32(v);
const float32x4_t vf = vaddq_f32(v, vdupq_n_f32(0.5f)); // needed to round to nearest
const int32x4_t vi = vcvtq_s32_f32(vf);
y[i].qs[2*l + 0] = vgetq_lane_s32(vi, 0) | (vgetq_lane_s32(vi, 1) << 4);
y[i].qs[2*l + 1] = vgetq_lane_s32(vi, 2) | (vgetq_lane_s32(vi, 3) << 4);
@ -1886,7 +1881,7 @@ static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * rest
sum1 += x1->d * y1->d * (vgetq_lane_s32(p_1, 0) + vgetq_lane_s32(p_1, 1) + vgetq_lane_s32(p_1, 2) + vgetq_lane_s32(p_1, 3));
#endif
#else
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0ls), vget_low_s8 (v1_0ls));
const int16x8_t pl0l = vmull_s8(vget_low_s8 (v0_0ls), vget_low_s8 (v1_0ls));
const int16x8_t pl0h = vmull_s8(vget_high_s8(v0_0ls), vget_high_s8(v1_0ls));
const int16x8_t ph0l = vmull_s8(vget_low_s8 (v0_0hs), vget_low_s8 (v1_0hs));
@ -1961,7 +1956,6 @@ static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * rest
// Main loop
for (int i = 0; i < nb; i+=UNROLL_COUNT) {
// This loop will be unrolled by the compiler
for (int u=0;u<UNROLL_COUNT;u++) {
/* Compute combined scale for the block */
@ -2014,7 +2008,6 @@ static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * rest
/* Multiply q with scale and accumulate */
acc = _mm256_fmadd_ps( scale, q, acc );
}
}
// Return horizontal sum of the acc vector
@ -2076,18 +2069,18 @@ static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void * rest
float sum1 = 0.0f;
for (int i = 0; i < nb; i += 2) {
const block_q4_0 * restrict x0 = &px[i + 0];
const block_q4_0 * restrict y0 = &py[i + 0];
const block_q4_0 * restrict x1 = &px[i + 1];
const block_q4_0 * restrict y1 = &py[i + 1];
const block_q4_0 * restrict x0 = &x[i + 0];
const block_q4_0 * restrict y0 = &y[i + 0];
const block_q4_0 * restrict x1 = &x[i + 1];
const block_q4_0 * restrict y1 = &y[i + 1];
const v128_t m4b = wasm_u8x16_splat(0xf);
const v128_t s8b = wasm_i8x16_splat(0x8);
const v128_t v0_0 = wasm_v128_load(x0.qs);
const v128_t v0_1 = wasm_v128_load(y0.qs);
const v128_t v1_0 = wasm_v128_load(x1.qs);
const v128_t v1_1 = wasm_v128_load(y1.qs);
const v128_t v0_0 = wasm_v128_load(x0->qs);
const v128_t v0_1 = wasm_v128_load(y0->qs);
const v128_t v1_0 = wasm_v128_load(x1->qs);
const v128_t v1_1 = wasm_v128_load(y1->qs);
// 4-bit -> 8-bit
const v128_t v0_0l = wasm_v128_and(v0_0, m4b);
@ -2567,29 +2560,26 @@ inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x
//
static const int GGML_BLCK_SIZE[GGML_TYPE_COUNT] = {
QK,
QK,
1,
1,
1,
1,
1,
[GGML_TYPE_F32] = 1,
[GGML_TYPE_F16] = 1,
[GGML_TYPE_Q4_0] = QK,
[GGML_TYPE_Q4_1] = QK,
[GGML_TYPE_I8] = 1,
[GGML_TYPE_I16] = 1,
[GGML_TYPE_I32] = 1,
};
static_assert(GGML_TYPE_COUNT == 7, "GGML_TYPE_COUNT != 5");
static_assert(GGML_TYPE_COUNT == 7, "GGML_BLCK_SIZE is outdated");
static const size_t GGML_TYPE_SIZE[GGML_TYPE_COUNT] = {
sizeof(block_q4_0),
sizeof(block_q4_1),
sizeof(int8_t ),
sizeof(int16_t),
sizeof(int32_t),
sizeof(ggml_fp16_t),
sizeof(float ),
[GGML_TYPE_F32] = sizeof(float),
[GGML_TYPE_F16] = sizeof(ggml_fp16_t),
[GGML_TYPE_Q4_0] = sizeof(block_q4_0),
[GGML_TYPE_Q4_1] = sizeof(block_q4_1),
[GGML_TYPE_I8] = sizeof(int8_t),
[GGML_TYPE_I16] = sizeof(int16_t),
[GGML_TYPE_I32] = sizeof(int32_t),
};
// don't forget to update the array above when adding new types
static_assert(GGML_TYPE_COUNT == 7, "GGML_TYPE_COUNT != 5");
static_assert(GGML_TYPE_COUNT == 7, "GGML_TYPE_SIZE is outdated");
static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
"NONE",
@ -2618,6 +2608,7 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
"SCALE",
"CPY",
"CONT",
"RESHAPE",
"VIEW",
"PERMUTE",
@ -2633,7 +2624,7 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
"FLASH_FF",
};
static_assert(GGML_OP_COUNT == 35, "GGML_OP_COUNT != 35");
static_assert(GGML_OP_COUNT == 36, "GGML_OP_COUNT != 36");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@ -2662,6 +2653,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"x*v",
"x-\\>y",
"cont(x)",
"reshape(x)",
"view(x)",
"permute(x)",
@ -2677,7 +2669,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"flash_ff(x)",
};
static_assert(GGML_OP_COUNT == 35, "GGML_OP_COUNT != 35");
static_assert(GGML_OP_COUNT == 36, "GGML_OP_COUNT != 36");
static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
@ -4310,6 +4302,41 @@ struct ggml_tensor * ggml_cpy_inplace(
return ggml_cpy_impl(ctx, a, b, true);
}
// ggml_cont
struct ggml_tensor * ggml_cont_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
bool inplace) {
bool is_node = false;
if (!inplace && a->grad) {
GGML_ASSERT(false); // TODO: implement backward
is_node = true;
}
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
result->op = GGML_OP_CONT;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = NULL;
return result;
}
struct ggml_tensor * ggml_cont(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_cont_impl(ctx, a, false);
}
struct ggml_tensor * ggml_cont_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_cont_impl(ctx, a, true);
}
// ggml_reshape
struct ggml_tensor * ggml_reshape(
@ -4852,6 +4879,85 @@ static void ggml_compute_forward_dup_f16(
// TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy
if (ggml_is_contiguous(dst)) {
if (src0->nb[0] == sizeof(ggml_fp16_t)) {
if (dst->type == GGML_TYPE_F16) {
size_t id = 0;
const size_t rs = ne00*nb00;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
char * dst_ptr = (char *) dst->data + id*rs;
memcpy(dst_ptr, src0_ptr, rs);
id++;
}
}
}
} else if (dst->type == GGML_TYPE_F32) {
size_t id = 0;
float * dst_ptr = (float *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr);
id++;
}
}
}
}
} else {
GGML_ASSERT(false); // TODO: implement
}
} else {
//printf("%s: this is not optimal - fix me\n", __func__);
if (dst->type == GGML_TYPE_F32) {
size_t id = 0;
float * dst_ptr = (float *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = GGML_FP16_TO_FP32(*src0_ptr);
id++;
}
}
}
}
} else if (dst->type == GGML_TYPE_F16) {
size_t id = 0;
ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const ggml_fp16_t * src0_ptr = (ggml_fp16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = *src0_ptr;
id++;
}
}
}
}
} else {
GGML_ASSERT(false); // TODO: implement
}
}
return;
}
// dst counters
int64_t i10 = 0;
int64_t i11 = 0;
@ -4946,6 +5052,105 @@ static void ggml_compute_forward_dup_f32(
return;
}
if (src0->type == dst->type &&
src0->ne[0] == dst->ne[0] &&
src0->nb[0] == GGML_TYPE_SIZE[src0->type] && dst->nb[0] == GGML_TYPE_SIZE[dst->type]) {
// copy by rows
const size_t rs = ne00*nb00;
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
for (int64_t i01 = 0; i01 < ne01; i01++) {
memcpy(
((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3),
((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03),
rs);
}
}
}
return;
}
if (ggml_is_contiguous(dst)) {
// TODO: simplify
if (src0->nb[0] == sizeof(float)) {
if (dst->type == GGML_TYPE_F32) {
size_t id = 0;
const size_t rs = ne00*nb00;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
char * dst_ptr = (char *) dst->data + id*rs;
memcpy(dst_ptr, src0_ptr, rs);
id++;
}
}
}
} else if (dst->type == GGML_TYPE_F16) {
size_t id = 0;
ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr);
id++;
}
}
}
}
} else {
GGML_ASSERT(false); // TODO: implement
}
} else {
//printf("%s: this is not optimal - fix me\n", __func__);
if (dst->type == GGML_TYPE_F32) {
size_t id = 0;
float * dst_ptr = (float *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = *src0_ptr;
id++;
}
}
}
}
} else if (dst->type == GGML_TYPE_F16) {
size_t id = 0;
ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
for (int i03 = 0; i03 < ne03; i03++) {
for (int i02 = 0; i02 < ne02; i02++) {
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr);
id++;
}
}
}
}
} else {
GGML_ASSERT(false); // TODO: implement
}
}
return;
}
// dst counters
int64_t i10 = 0;
int64_t i11 = 0;
@ -5066,14 +5271,18 @@ static void ggml_compute_forward_add_f32(
GGML_ASSERT(nb00 == sizeof(float));
if (nb10 == sizeof(float)) {
const int j0 = (n/nth)*ith;
const int j1 = ith == nth - 1 ? n : (n/nth)*(ith + 1);
for (int j = j0; j < j1; j++) {
for (int j = ith; j < n; j += nth) {
#ifdef GGML_USE_ACCELERATE
vDSP_vadd(
(float *) ((char *) src0->data + j*nb01), 1,
(float *) ((char *) src1->data + j*nb11), 1,
(float *) ((char *) dst->data + j*nb1), 1, nc);
#else
ggml_vec_add_f32(nc,
(float *) ((char *) dst->data + j*nb1),
(float *) ((char *) src0->data + j*nb01),
(float *) ((char *) src1->data + j*nb11));
#endif
}
} else {
// src1 is not contiguous
@ -6821,6 +7030,15 @@ static void ggml_compute_forward_cpy(
ggml_compute_forward_dup(params, src0, dst);
}
// ggml_compute_forward_cont
static void ggml_compute_forward_cont(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
struct ggml_tensor * dst) {
ggml_compute_forward_dup(params, src0, dst);
}
// ggml_compute_forward_reshape
static void ggml_compute_forward_reshape(
@ -8651,6 +8869,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_cpy(params, tensor->src0, tensor);
} break;
case GGML_OP_CONT:
{
ggml_compute_forward_cont(params, tensor->src0, tensor);
} break;
case GGML_OP_RESHAPE:
{
ggml_compute_forward_reshape(params, tensor->src0, tensor);
@ -8895,8 +9117,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src1->grad =
ggml_add_impl(ctx,
src1->grad,
// TODO: fix transpose, the node will break the graph connections
ggml_mul_mat(ctx, ggml_transpose(ctx, src0), tensor->grad),
ggml_mul_mat(ctx,
ggml_cont(ctx, ggml_transpose(ctx, src0)),
tensor->grad),
inplace);
}
} break;
@ -8908,6 +9131,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_CONT:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_RESHAPE:
{
GGML_ASSERT(false); // TODO: not implemented
@ -9362,6 +9589,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
node->n_tasks = n_threads;
} break;
case GGML_OP_CPY:
case GGML_OP_CONT:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:

15
ggml.h
View file

@ -198,13 +198,14 @@ struct ggml_object;
struct ggml_context;
enum ggml_type {
GGML_TYPE_Q4_0,
GGML_TYPE_Q4_1,
// explicitly numbered values are used in llama.cpp files
GGML_TYPE_F32 = 0,
GGML_TYPE_F16 = 1,
GGML_TYPE_Q4_0 = 2,
GGML_TYPE_Q4_1 = 3,
GGML_TYPE_I8,
GGML_TYPE_I16,
GGML_TYPE_I32,
GGML_TYPE_F16,
GGML_TYPE_F32,
GGML_TYPE_COUNT,
};
@ -236,6 +237,7 @@ enum ggml_op {
GGML_OP_SCALE,
GGML_OP_CPY,
GGML_OP_CONT,
GGML_OP_RESHAPE,
GGML_OP_VIEW,
GGML_OP_PERMUTE,
@ -525,6 +527,11 @@ struct ggml_tensor * ggml_cpy(
struct ggml_tensor * a,
struct ggml_tensor * b);
// make contiguous
struct ggml_tensor * ggml_cont(
struct ggml_context * ctx,
struct ggml_tensor * a);
// return view(a), b specifies the new shape
// TODO: when we start computing gradient, make a copy instead of view
struct ggml_tensor * ggml_reshape(

72
llama.cpp
View file

@ -1,3 +1,8 @@
// Defines fileno on msys:
#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#endif
#include "llama_util.h"
#include "llama.h"
#include "llama_internal.h"
@ -77,7 +82,7 @@ struct llama_hparams {
uint32_t n_head = 32;
uint32_t n_layer = 32;
uint32_t n_rot = 64;
uint32_t f16 = 1;
enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;
bool operator!=(const llama_hparams & other) const {
return memcmp(this, &other, sizeof(llama_hparams));
@ -427,7 +432,7 @@ struct llama_file_loader {
hparams.n_head = file.read_u32();
hparams.n_layer = file.read_u32();
hparams.n_rot = file.read_u32();
hparams.f16 = file.read_u32();
hparams.ftype = (enum llama_ftype) file.read_u32();
}
void read_vocab() {
vocab.id_to_token.resize(hparams.n_vocab);
@ -453,20 +458,21 @@ struct llama_file_loader {
llama_load_tensor_shard shard;
uint32_t n_dims = file.read_u32();
uint32_t name_len = file.read_u32();
uint32_t ftype = 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 (ftype) {
case 0: shard.type = GGML_TYPE_F32; break;
case 1: shard.type = GGML_TYPE_F16; break;
case 2: shard.type = GGML_TYPE_Q4_0; break;
case 3: shard.type = GGML_TYPE_Q4_1; break;
switch (shard.type) {
case GGML_TYPE_F32:
case GGML_TYPE_F16:
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
break;
default: {
throw format("unrecognized ftype %u\n", ftype);
throw format("unrecognized tensor type %u\n", shard.type);
}
}
@ -497,18 +503,18 @@ struct llama_file_loader {
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, uint32_t new_f16)
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_f16);
write_hparams(new_ftype);
write_vocab();
}
void write_magic() {
file.write_u32('ggjt'); // magic
file.write_u32(1); // version
}
void write_hparams(uint32_t new_f16) {
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);
@ -516,7 +522,7 @@ struct llama_file_saver {
file.write_u32(hparams.n_head);
file.write_u32(hparams.n_layer);
file.write_u32(hparams.n_rot);
file.write_u32(new_f16);
file.write_u32(new_ftype);
}
void write_vocab() {
if (any_file_loader->file_version == LLAMA_FILE_VERSION_GGML) {
@ -531,17 +537,17 @@ struct llama_file_saver {
}
}
void write_tensor(llama_load_tensor & tensor, enum ggml_type new_type, const void * new_data, size_t new_size) {
uint32_t ftype;
switch (new_type) {
case GGML_TYPE_F32: ftype = 0; break;
case GGML_TYPE_F16: ftype = 1; break;
case GGML_TYPE_Q4_0: ftype = 2; break;
case GGML_TYPE_Q4_1: ftype = 3; break;
case GGML_TYPE_F32:
case GGML_TYPE_F16:
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
break;
default: LLAMA_ASSERT(false);
}
file.write_u32((uint32_t) tensor.ne.size());
file.write_u32((uint32_t) tensor.name.size());
file.write_u32(ftype);
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);
@ -815,6 +821,16 @@ static const char *llama_file_version_name(llama_file_version version) {
}
}
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";
default: LLAMA_ASSERT(false);
}
}
static const char *llama_model_type_name(e_model type) {
switch (type) {
case MODEL_7B: return "7B";
@ -867,7 +883,7 @@ static void llama_model_load_internal(
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: f16 = %u\n", __func__, hparams.f16);
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));
@ -1539,17 +1555,17 @@ static llama_vocab::id llama_sample_top_p_top_k(
// quantization
//
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, int itype) {
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, enum llama_ftype ftype) {
ggml_type quantized_type;
switch (itype) {
case 2: quantized_type = GGML_TYPE_Q4_0; break;
case 3: quantized_type = GGML_TYPE_Q4_1; break;
default: throw format("invalid quantization type %d\n", itype);
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;
default: throw format("invalid output file type %d\n", ftype);
};
std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp.c_str(), /*use_mmap*/ false,
/*vocab_only*/ false));
llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), (uint32_t) itype);
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;
@ -1740,9 +1756,9 @@ void llama_free(struct llama_context * ctx) {
int llama_model_quantize(
const char * fname_inp,
const char * fname_out,
int itype) {
enum llama_ftype ftype) {
try {
llama_model_quantize_internal(fname_inp, fname_out, itype);
llama_model_quantize_internal(fname_inp, fname_out, ftype);
return 0;
} catch (const std::string & err) {
fprintf(stderr, "%s: failed to quantize: %s\n", __func__, err.c_str());

10
llama.h
View file

@ -65,6 +65,14 @@ extern "C" {
void * progress_callback_user_data;
};
// model file types
enum llama_ftype {
LLAMA_FTYPE_ALL_F32 = 0,
LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
};
LLAMA_API struct llama_context_params llama_context_default_params();
LLAMA_API bool llama_mmap_supported();
@ -85,7 +93,7 @@ extern "C" {
LLAMA_API int llama_model_quantize(
const char * fname_inp,
const char * fname_out,
int itype);
enum llama_ftype ftype);
// Returns the KV cache that will contain the context for the
// ongoing prediction with the model.

12
llama_util.h
View file

@ -26,7 +26,9 @@
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#define NOMINMAX
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#include <io.h>
#include <stdio.h> // for _fseeki64
@ -209,6 +211,7 @@ struct llama_mmap {
throw format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str());
}
#if _WIN32_WINNT >= _WIN32_WINNT_WIN8
// Advise the kernel to preload the mapped memory
WIN32_MEMORY_RANGE_ENTRY range;
range.VirtualAddress = addr;
@ -217,6 +220,9 @@ struct llama_mmap {
fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
}
#else
#pragma message("warning: You are building for pre-Windows 8; prefetch not supported")
#endif // _WIN32_WINNT >= _WIN32_WINNT_WIN8
}
~llama_mmap() {
@ -338,8 +344,8 @@ struct llama_mlock {
// Hopefully a megabyte is enough overhead:
size_t increment = size + 1048576;
// The minimum must be <= the maximum, so we need to increase both:
min_ws_size += size;
max_ws_size += size;
min_ws_size += increment;
max_ws_size += increment;
if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
fprintf(stderr, "warning: SetProcessWorkingSetSize failed: %s\n",
llama_format_win_err(GetLastError()).c_str());