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llama: implement NTK-By-Parts (NTKv2) RoPE scaling

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This commit is contained in:
Cebtenzzre 2023-07-17 20:07:15 -04:00
parent 93356bdb7a
commit 8dec38c35c
7 changed files with 189 additions and 50 deletions
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16
examples/common.cpp
View file

@ -194,6 +194,18 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break;
}
params.rope_freq_scale = std::stof(argv[i]);
} else if (arg == "--rope-ntk-factor") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.rope_ntk_factor = std::stof(argv[i]);
} else if (arg == "--rope-ext-factor") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.rope_ext_factor = std::stof(argv[i]);
} else if (arg == "--memory-f32") {
params.memory_f16 = false;
} else if (arg == "--top-p") {
@ -566,6 +578,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stdout, " --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale);
fprintf(stdout, " --rope-freq-base N RoPE base frequency (default: %.1f)\n", params.rope_freq_base);
fprintf(stdout, " --rope-freq-scale N RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale);
fprintf(stdout, " --rope-ntk-factor N RoPE NTK mix factor (default: %.1f)\n", params.rope_ntk_factor);
fprintf(stdout, " --rope-ext-factor N RoPE extrapolation mix factor (default: %.1f)\n", params.rope_ext_factor);
fprintf(stdout, " --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
fprintf(stdout, " --no-penalize-nl do not penalize newline token\n");
fprintf(stdout, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
@ -657,6 +671,8 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
lparams.embedding = params.embedding;
lparams.rope_freq_base = params.rope_freq_base;
lparams.rope_freq_scale = params.rope_freq_scale;
lparams.rope_ntk_factor = params.rope_ntk_factor;
lparams.rope_ext_factor = params.rope_ext_factor;
return lparams;
}

2
examples/common.h
View file

@ -32,6 +32,8 @@ struct gpt_params {
float rms_norm_eps = LLAMA_DEFAULT_RMS_EPS; // rms norm epsilon
float rope_freq_base = 10000.0f; // RoPE base frequency
float rope_freq_scale = 1.0f; // RoPE frequency scaling factor
float rope_ntk_factor = 0.0f; // RoPE NTK mix factor
float rope_ext_factor = 0.0f; // RoPE extrapolation mix factor
// sampling parameters
std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens

18
examples/server/server.cpp
View file

@ -612,6 +612,8 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
fprintf(stdout, " -eps N, --rms-norm-eps N rms norm eps (TEMP!!! use 1e-5 for LLaMAv2) (default: %.1e)\n", params.rms_norm_eps);
fprintf(stdout, " --rope-freq-base N RoPE base frequency (default: %.1f)\n", params.rope_freq_base);
fprintf(stdout, " --rope-freq-scale N RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale);
fprintf(stdout, " --rope-ntk-factor N RoPE NTK mix factor (default: %.1f)\n", params.rope_ntk_factor);
fprintf(stdout, " --rope-ext-factor N RoPE extrapolation mix factor (default: %.1f)\n", params.rope_ext_factor);
fprintf(stdout, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
fprintf(stdout, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
fprintf(stdout, " not recommended: doubles context memory required and no measurable increase in quality\n");
@ -764,6 +766,22 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
}
params.rope_freq_scale = std::stof(argv[i]);
}
else if (arg == "--rope-ntk-factor")
{
if (++i >= argc) {
invalid_param = true;
break;
}
params.rope_ntk_factor = std::stof(argv[i]);
}
else if (arg == "--rope-ext-factor")
{
if (++i >= argc) {
invalid_param = true;
break;
}
params.rope_ext_factor = std::stof(argv[i]);
}
else if (arg == "--memory-f32" || arg == "--memory_f32")
{
params.memory_f16 = false;

155
ggml.c
View file

@ -1,5 +1,6 @@
#define _GNU_SOURCE // Defines CLOCK_MONOTONIC on Linux
#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows
#define _USE_MATH_DEFINES // For M_PI on MSVC
#include "ggml.h"
@ -6711,6 +6712,8 @@ static struct ggml_tensor * ggml_rope_impl(
int n_ctx,
float freq_base,
float freq_scale,
float ntk_factor,
float ext_factor,
bool inplace) {
GGML_ASSERT(n_past >= 0);
bool is_node = false;
@ -6721,9 +6724,11 @@ static struct ggml_tensor * ggml_rope_impl(
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
int32_t params[6] = { n_past, n_dims, mode, n_ctx };
int32_t params[8] = { n_past, n_dims, mode, n_ctx };
memcpy(params + 4, &freq_base, sizeof(float));
memcpy(params + 5, &freq_scale, sizeof(float));
memcpy(params + 6, &ntk_factor, sizeof(float));
memcpy(params + 7, &ext_factor, sizeof(float));
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_ROPE;
@ -6740,7 +6745,7 @@ struct ggml_tensor * ggml_rope(
int n_dims,
int mode,
int n_ctx) {
return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, false);
return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, 0.0f, false);
}
struct ggml_tensor * ggml_rope_inplace(
@ -6750,7 +6755,7 @@ struct ggml_tensor * ggml_rope_inplace(
int n_dims,
int mode,
int n_ctx) {
return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, true);
return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, 0.0f, true);
}
struct ggml_tensor * ggml_rope_custom(
@ -6761,8 +6766,10 @@ struct ggml_tensor * ggml_rope_custom(
int mode,
int n_ctx,
float freq_base,
float freq_scale) {
return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, false);
float freq_scale,
float ntk_factor,
float ext_factor) {
return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, ntk_factor, ext_factor, false);
}
struct ggml_tensor * ggml_rope_custom_inplace(
@ -6773,8 +6780,10 @@ struct ggml_tensor * ggml_rope_custom_inplace(
int mode,
int n_ctx,
float freq_base,
float freq_scale) {
return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, true);
float freq_scale,
float ntk_factor,
float ext_factor) {
return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, ntk_factor, ext_factor, true);
}
// ggml_rope_back
@ -12003,6 +12012,52 @@ static void ggml_compute_forward_clamp(
// ggml_compute_forward_rope
// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get
// `corr_fac(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))`
#define NTKV2_MAX_POS_EMB 2048
#define NTKV2_CORRECTION_FACTOR(n_rot) (__builtin_logf(NTKV2_MAX_POS_EMB / ((n_rot) * 2 * (float)M_PI)) / 2)
static inline float rope_ntkv2_ramp(const float low, const float high, const int i0) {
const float y = (i0 / 2 - low) / MIN(0.001f, high - low);
return 1 - MIN(1, MAX(0, y));
}
// NTKv2 algorithm based on LlamaPartNTKScaledRotaryEmbedding.py from https://github.com/jquesnelle/scaled-rope
// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
static float rope_ntkv2(
const float theta_base,
const float theta_ntk,
const float dims_over_base,
const float freq_scale,
const int64_t i0,
const float ntk_factor,
const float ext_factor,
const int n_dims) {
// Interpolation constants found experimentally for LLaMA (might not be totally optimal though)
// Do not change unless there is a good reason for doing so!
static const float BETA_0 = 1.75f;
static const float BETA_1 = 1.25f;
static const float GAMMA_0 = 16.0f;
static const float GAMMA_1 = 2.0f;
static const float low_1p = NTKV2_CORRECTION_FACTOR(BETA_0);
static const float high_1p = NTKV2_CORRECTION_FACTOR(BETA_1);
static const float low_2p = NTKV2_CORRECTION_FACTOR(GAMMA_0);
static const float high_2p = NTKV2_CORRECTION_FACTOR(GAMMA_1);
// start and end correction factors
const float low_1 = MAX(0, floorf(low_1p * dims_over_base));
const float high_1 = MIN(n_dims - 1, ceilf(high_1p * dims_over_base));
const float low_2 = MAX(0, floorf(low_2p * dims_over_base));
const float high_2 = MIN(n_dims - 1, ceilf(high_2p * dims_over_base));
const float theta_linear = freq_scale * theta_base;
const float ramp_mix = rope_ntkv2_ramp(low_1, high_1, i0) * ntk_factor;
const float theta_mix = theta_linear * (1 - ramp_mix) + theta_ntk * ramp_mix;
const float ramp_final = rope_ntkv2_ramp(low_2, high_2, i0) * ext_factor;
return theta_mix * (1 - ramp_final) + theta_base * ramp_final;
}
static void ggml_compute_forward_rope_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
@ -12014,6 +12069,8 @@ static void ggml_compute_forward_rope_f32(
float freq_base;
float freq_scale;
float ntk_factor;
float ext_factor;
const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
@ -12021,6 +12078,8 @@ static void ggml_compute_forward_rope_f32(
const int n_ctx = ((int32_t *) dst->op_params)[3];
memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float));
memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
memcpy(&ntk_factor, (int32_t *) dst->op_params + 6, sizeof(float));
memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float));
assert(n_past >= 0);
@ -12050,6 +12109,8 @@ static void ggml_compute_forward_rope_f32(
int ir = 0;
const float theta_scale = powf(freq_base, -2.0f/n_dims);
const float theta_ntk_scale = powf(freq_base * powf(freq_scale, (n_dims / (n_dims - 2.0f))), -2.0f/n_dims);
const float dims_over_base = n_dims / logf(freq_base);
const bool is_neox = mode & 2;
const bool is_glm = mode & 4;
@ -12061,18 +12122,19 @@ static void ggml_compute_forward_rope_f32(
if (ir++ < ir0) continue;
if (ir > ir1) break;
float theta = freq_scale * (float)p;
float theta_base = (float)p;
float theta_ntk = theta_base;
if (is_glm) {
theta = MIN(p, n_ctx - 2);
theta_base = MIN(p, n_ctx - 2);
float block_theta = MAX(p - (n_ctx - 2), 0);
for (int64_t i0 = 0; i0 < ne0 / 4; i0++) {
const float cos_theta = cosf(theta);
const float sin_theta = sinf(theta);
const float cos_theta = cosf(theta_base);
const float sin_theta = sinf(theta_base);
const float cos_block_theta = cosf(block_theta);
const float sin_block_theta = sinf(block_theta);
theta *= theta_scale;
theta_base *= theta_scale;
block_theta *= theta_scale;
const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
@ -12090,10 +12152,13 @@ static void ggml_compute_forward_rope_f32(
}
} else if (!is_neox) {
for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
const float theta = rope_ntkv2(theta_base, theta_ntk, dims_over_base,
freq_scale, i0, ntk_factor, ext_factor, n_dims);
const float cos_theta = cosf(theta);
const float sin_theta = sinf(theta);
theta *= theta_scale;
theta_base *= theta_scale;
theta_ntk *= theta_ntk_scale;
const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
@ -12107,12 +12172,13 @@ static void ggml_compute_forward_rope_f32(
} else {
// TODO: this is probably wrong, but I can't figure it out ..
// ref: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28
theta_base *= freq_scale;
for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
for (int64_t ic = 0; ic < n_dims; ic += 2) {
const float cos_theta = cosf(theta);
const float sin_theta = sinf(theta);
const float cos_theta = cosf(theta_base);
const float sin_theta = sinf(theta_base);
theta *= theta_scale;
theta_base *= theta_scale;
const int64_t i0 = ib*n_dims + ic/2;
@ -12143,6 +12209,8 @@ static void ggml_compute_forward_rope_f16(
float freq_base;
float freq_scale;
float ntk_factor;
float ext_factor;
const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
@ -12150,6 +12218,8 @@ static void ggml_compute_forward_rope_f16(
const int n_ctx = ((int32_t *) dst->op_params)[3];
memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float));
memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
memcpy(&ntk_factor, (int32_t *) dst->op_params + 6, sizeof(float));
memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float));
assert(n_past >= 0);
@ -12179,6 +12249,8 @@ static void ggml_compute_forward_rope_f16(
int ir = 0;
const float theta_scale = powf(freq_base, -2.0f/n_dims);
const float theta_ntk_scale = powf(freq_base * powf(freq_scale, (n_dims / (n_dims - 2.0f))), -2.0f/n_dims);
const float dims_over_base = n_dims / logf(freq_base);
const bool is_neox = mode & 2;
const bool is_glm = mode & 4;
@ -12190,18 +12262,19 @@ static void ggml_compute_forward_rope_f16(
if (ir++ < ir0) continue;
if (ir > ir1) break;
float theta = freq_scale * (float)p;
float theta_base = (float)p;
float theta_ntk = theta_base;
if (is_glm) {
theta = MIN(p, n_ctx - 2);
theta_base = MIN(p, n_ctx - 2);
float block_theta = MAX(p - (n_ctx - 2), 0);
for (int64_t i0 = 0; i0 < ne0 / 4; i0++) {
const float cos_theta = cosf(theta);
const float sin_theta = sinf(theta);
const float cos_theta = cosf(theta_base);
const float sin_theta = sinf(theta_base);
const float cos_block_theta = cosf(block_theta);
const float sin_block_theta = sinf(block_theta);
theta *= theta_scale;
theta_base *= theta_scale;
block_theta *= theta_scale;
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
@ -12219,10 +12292,13 @@ static void ggml_compute_forward_rope_f16(
}
} if (!is_neox) {
for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
const float theta = rope_ntkv2(theta_base, theta_ntk, dims_over_base,
freq_scale, i0, ntk_factor, ext_factor, n_dims);
const float cos_theta = cosf(theta);
const float sin_theta = sinf(theta);
theta *= theta_scale;
theta_base *= theta_scale;
theta_ntk *= theta_ntk_scale;
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
@ -12236,12 +12312,13 @@ static void ggml_compute_forward_rope_f16(
} else {
// TODO: this is probably wrong, but I can't figure it out ..
// ref: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py#LL251C1-L294C28
theta_base *= freq_scale;
for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
for (int64_t ic = 0; ic < n_dims; ic += 2) {
const float cos_theta = cosf(theta);
const float sin_theta = sinf(theta);
const float cos_theta = cosf(theta_base);
const float sin_theta = sinf(theta_base);
theta *= theta_scale;
theta_base *= theta_scale;
const int64_t i0 = ib*n_dims + ic/2;
@ -12335,14 +12412,14 @@ static void ggml_compute_forward_rope_back_f32(
if (ir++ < ir0) continue;
if (ir > ir1) break;
float theta = (float)p;
float theta_base = (float)p;
if (!is_neox) {
for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
const float cos_theta = cosf(theta);
const float sin_theta = sinf(theta);
const float cos_theta = cosf(theta_base);
const float sin_theta = sinf(theta_base);
theta *= theta_scale;
theta_base *= theta_scale;
const float * const dy = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
float * dx = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
@ -12356,10 +12433,10 @@ static void ggml_compute_forward_rope_back_f32(
} else {
for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
for (int64_t ic = 0; ic < n_dims; ic += 2) {
const float cos_theta = cosf(theta);
const float sin_theta = sinf(theta);
const float cos_theta = cosf(theta_base);
const float sin_theta = sinf(theta_base);
theta *= theta_scale;
theta_base *= theta_scale;
const int64_t i0 = ib*n_dims + ic/2;
@ -12431,14 +12508,14 @@ static void ggml_compute_forward_rope_back_f16(
if (ir++ < ir0) continue;
if (ir > ir1) break;
float theta = (float)p;
float theta_base = (float)p;
if (!is_neox) {
for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
const float cos_theta = cosf(theta);
const float sin_theta = sinf(theta);
const float cos_theta = cosf(theta_base);
const float sin_theta = sinf(theta_base);
theta *= theta_scale;
theta_base *= theta_scale;
const ggml_fp16_t * const dy = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
ggml_fp16_t * dx = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
@ -12452,10 +12529,10 @@ static void ggml_compute_forward_rope_back_f16(
} else {
for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
for (int64_t ic = 0; ic < n_dims; ic += 2) {
const float cos_theta = cosf(theta);
const float sin_theta = sinf(theta);
const float cos_theta = cosf(theta_base);
const float sin_theta = sinf(theta_base);
theta *= theta_scale;
theta_base *= theta_scale;
const int64_t i0 = ib*n_dims + ic/2;

8
ggml.h
View file

@ -1194,7 +1194,9 @@ extern "C" {
int mode,
int n_ctx,
float freq_base,
float freq_scale);
float freq_scale,
float ntk_factor,
float ext_factor);
// in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
@ -1205,7 +1207,9 @@ extern "C" {
int mode,
int n_ctx,
float freq_base,
float freq_scale);
float freq_scale,
float ntk_factor,
float ext_factor);
// rotary position embedding backward, i.e compute dx from dy
// a - dy

38
llama.cpp
View file

@ -196,6 +196,8 @@ struct llama_hparams {
float rope_freq_base = 10000.0f;
float rope_freq_scale = 1.0f;
float rope_ntk_factor = 0.0f;
float rope_ext_factor = 0.0f;
enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;
@ -898,6 +900,8 @@ struct llama_context_params llama_context_default_params() {
/*.tensor_split =*/ nullptr,
/*.rope_freq_base =*/ 10000.0f,
/*.rope_freq_scale =*/ 1.0f,
/*.rope_ntk_factor =*/ 0.0f,
/*.rope_ext_factor =*/ 0.0f,
/*.progress_callback =*/ nullptr,
/*.progress_callback_user_data =*/ nullptr,
/*.low_vram =*/ false,
@ -1032,6 +1036,8 @@ static void llama_model_load_internal(
const bool mul_mat_q,
float rope_freq_base,
float rope_freq_scale,
float rope_ntk_factor,
float rope_ext_factor,
bool low_vram,
ggml_type memory_type,
bool use_mmap,
@ -1083,6 +1089,8 @@ static void llama_model_load_internal(
hparams.rope_freq_base = rope_freq_base;
hparams.rope_freq_scale = rope_freq_scale;
hparams.rope_ntk_factor = rope_ntk_factor;
hparams.rope_ext_factor = rope_ext_factor;
}
// ref: https://github.com/facebookresearch/llama/blob/6c7fe276574e78057f917549435a2554000a876d/llama/model.py#L194-L199
@ -1106,6 +1114,8 @@ static void llama_model_load_internal(
fprintf(stderr, "%s: n_ff = %u\n", __func__, n_ff);
fprintf(stderr, "%s: freq_base = %.1f\n", __func__, hparams.rope_freq_base);
fprintf(stderr, "%s: freq_scale = %g\n", __func__, hparams.rope_freq_scale);
fprintf(stderr, "%s: ntk_factor = %g\n", __func__, hparams.rope_ntk_factor);
fprintf(stderr, "%s: ext_factor = %g\n", __func__, hparams.rope_ext_factor);
fprintf(stderr, "%s: ftype = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype));
fprintf(stderr, "%s: model size = %s\n", __func__, llama_model_type_name(model.type));
}
@ -1374,6 +1384,8 @@ static bool llama_model_load(
const bool mul_mat_q,
float rope_freq_base,
float rope_freq_scale,
float rope_ntk_factor,
float rope_ext_factor,
bool low_vram,
ggml_type memory_type,
bool use_mmap,
@ -1382,9 +1394,10 @@ static bool llama_model_load(
llama_progress_callback progress_callback,
void *progress_callback_user_data) {
try {
llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gqa, rms_norm_eps, n_gpu_layers,
main_gpu, tensor_split, mul_mat_q, rope_freq_base, rope_freq_scale, low_vram, memory_type,
use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data);
llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gqa, rms_norm_eps, n_gpu_layers, main_gpu,
tensor_split, mul_mat_q, rope_freq_base, rope_freq_scale, rope_ntk_factor,
rope_ext_factor, low_vram, memory_type, use_mmap, use_mlock, vocab_only,
progress_callback, progress_callback_user_data);
return true;
} catch (const std::exception & err) {
fprintf(stderr, "error loading model: %s\n", err.what());
@ -1422,6 +1435,8 @@ static struct ggml_cgraph * llama_build_graph(
const float freq_base = hparams.rope_freq_base;
const float freq_scale = hparams.rope_freq_scale;
const float ntk_factor = hparams.rope_ntk_factor;
const float ext_factor = hparams.rope_ext_factor;
const float rms_norm_eps = hparams.f_rms_norm_eps;
const int n_gpu_layers = model.n_gpu_layers;
@ -1551,11 +1566,15 @@ static struct ggml_cgraph * llama_build_graph(
offload_func_kq(tmpq);
ggml_set_name(tmpq, "tmpq");
struct ggml_tensor * Kcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, N), n_past, n_embd_head, 0, 0, freq_base, freq_scale);
struct ggml_tensor * Kcur = ggml_rope_custom_inplace(
ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, N), n_past, n_embd_head, 0, 0, freq_base,
freq_scale, ntk_factor, ext_factor);
offload_func_kq(Kcur);
ggml_set_name(Kcur, "Kcur");
struct ggml_tensor * Qcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, N), n_past, n_embd_head, 0, 0, freq_base, freq_scale);
struct ggml_tensor * Qcur = ggml_rope_custom_inplace(
ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, N), n_past, n_embd_head, 0, 0, freq_base,
freq_scale, ntk_factor, ext_factor);
offload_func_kq(Qcur);
ggml_set_name(Qcur, "Qcur");
@ -3197,10 +3216,11 @@ struct llama_model * llama_load_model_from_file(
ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
if (!llama_model_load(path_model, *model, model->vocab, params.n_ctx, params.n_batch, params.n_gqa, params.rms_norm_eps, params.n_gpu_layers,
params.main_gpu, params.tensor_split, params.mul_mat_q, params.rope_freq_base, params.rope_freq_scale,params.low_vram,
memory_type, params.use_mmap, params.use_mlock, params.vocab_only, params.progress_callback,
params.progress_callback_user_data)) {
if (!llama_model_load(path_model, *model, model->vocab, params.n_ctx, params.n_batch, params.n_gqa,
params.rms_norm_eps, params.n_gpu_layers, params.main_gpu, params.tensor_split, params.mul_mat_q,
params.rope_freq_base, params.rope_freq_scale, params.rope_ntk_factor, params.rope_ext_factor,
params.low_vram, memory_type, params.use_mmap, params.use_mlock, params.vocab_only,
params.progress_callback, params.progress_callback_user_data)) {
delete model;
fprintf(stderr, "%s: failed to load model\n", __func__);
return nullptr;

2
llama.h
View file

@ -100,6 +100,8 @@ extern "C" {
// ref: https://github.com/ggerganov/llama.cpp/pull/2054
float rope_freq_base; // RoPE base frequency
float rope_freq_scale; // RoPE frequency scaling factor
float rope_ntk_factor; // RoPE NTK mix factor
float rope_ext_factor; // RoPE extrapolation mix factor
// called with a progress value between 0 and 1, pass NULL to disable
llama_progress_callback progress_callback;