vbatts/llama.cpp
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style : minor fixes, mostly indentations

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Georgi Gerganov 2023-07-15 09:57:35 +03:00
parent da730c53bf
commit d0b6c942fc
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GPG key ID: 449E073F9DC10735
4 changed files with 65 additions and 53 deletions
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43
ggml-metal.m
View file

@ -885,32 +885,33 @@ void ggml_metal_graph_compute(
const int n_past = ((int32_t *)(src1->data))[0]; const int n_past = ((int32_t *)(src1->data))[0];
float freq_base, freq_scale; float freq_base;
memcpy(&freq_base, (int32_t *) src1->data + 4, sizeof(float)); float freq_scale;
memcpy(&freq_base, (int32_t *) src1->data + 4, sizeof(float));
memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float));
[encoder setComputePipelineState:ctx->pipeline_rope]; [encoder setComputePipelineState:ctx->pipeline_rope];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3];
[encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4];
[encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5];
[encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7];
[encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8];
[encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9];
[encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10];
[encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11];
[encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12];
[encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13];
[encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14];
[encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15];
[encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16];
[encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17];
[encoder setBytes:&n_past length:sizeof( int) atIndex:18]; [encoder setBytes:&n_past length:sizeof( int) atIndex:18];
[encoder setBytes:&n_dims length:sizeof( int) atIndex:19]; [encoder setBytes:&n_dims length:sizeof( int) atIndex:19];
[encoder setBytes:&mode length:sizeof( int) atIndex:20]; [encoder setBytes:&mode length:sizeof( int) atIndex:20];
[encoder setBytes:&freq_base length:sizeof(float) atIndex:21]; [encoder setBytes:&freq_base length:sizeof(float) atIndex:21];
[encoder setBytes:&freq_scale length:sizeof(float) atIndex:22]; [encoder setBytes:&freq_scale length:sizeof(float) atIndex:22];

16
ggml.c
View file

@ -6975,7 +6975,7 @@ struct ggml_tensor * ggml_rope_impl(
((int32_t *) b->data)[1] = n_dims; ((int32_t *) b->data)[1] = n_dims;
((int32_t *) b->data)[2] = mode; ((int32_t *) b->data)[2] = mode;
((int32_t *) b->data)[3] = n_ctx; ((int32_t *) b->data)[3] = n_ctx;
memcpy((int32_t *) b->data + 4, &freq_base, sizeof(float)); memcpy((int32_t *) b->data + 4, &freq_base, sizeof(float));
memcpy((int32_t *) b->data + 5, &freq_scale, sizeof(float)); memcpy((int32_t *) b->data + 5, &freq_scale, sizeof(float));
ggml_scratch_load(ctx); ggml_scratch_load(ctx);
@ -12084,12 +12084,14 @@ static void ggml_compute_forward_rope_f32(
return; return;
} }
float freq_base;
float freq_scale;
const int n_past = ((int32_t *) src1->data)[0]; const int n_past = ((int32_t *) src1->data)[0];
const int n_dims = ((int32_t *) src1->data)[1]; const int n_dims = ((int32_t *) src1->data)[1];
const int mode = ((int32_t *) src1->data)[2]; const int mode = ((int32_t *) src1->data)[2];
const int n_ctx = ((int32_t *) src1->data)[3]; const int n_ctx = ((int32_t *) src1->data)[3];
float freq_base, freq_scale; memcpy(&freq_base, (int32_t *) src1->data + 4, sizeof(float));
memcpy(&freq_base, (int32_t *) src1->data + 4, sizeof(float));
memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float));
assert(n_past >= 0); assert(n_past >= 0);
@ -12214,12 +12216,14 @@ static void ggml_compute_forward_rope_f16(
return; return;
} }
float freq_base;
float freq_scale;
const int n_past = ((int32_t *) src1->data)[0]; const int n_past = ((int32_t *) src1->data)[0];
const int n_dims = ((int32_t *) src1->data)[1]; const int n_dims = ((int32_t *) src1->data)[1];
const int mode = ((int32_t *) src1->data)[2]; const int mode = ((int32_t *) src1->data)[2];
const int n_ctx = ((int32_t *) src1->data)[3]; const int n_ctx = ((int32_t *) src1->data)[3];
float freq_base, freq_scale; memcpy(&freq_base, (int32_t *) src1->data + 4, sizeof(float));
memcpy(&freq_base, (int32_t *) src1->data + 4, sizeof(float));
memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float));
assert(n_past >= 0); assert(n_past >= 0);
@ -12322,7 +12326,7 @@ static void ggml_compute_forward_rope_f16(
const float x0 = GGML_FP16_TO_FP32(src[0]); const float x0 = GGML_FP16_TO_FP32(src[0]);
const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]);
dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta);
dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
} }
} }

52
llama.cpp
View file

@ -190,8 +190,10 @@ struct llama_hparams {
uint32_t n_head = 32; uint32_t n_head = 32;
uint32_t n_layer = 32; uint32_t n_layer = 32;
uint32_t n_rot = 64; uint32_t n_rot = 64;
float rope_freq_base = 10000.0f; float rope_freq_base = 10000.0f;
float rope_freq_scale = 1.0f; float rope_freq_scale = 1.0f;
enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16; enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;
bool operator!=(const llama_hparams & other) const { bool operator!=(const llama_hparams & other) const {
@ -843,12 +845,12 @@ struct llama_context_params llama_context_default_params() {
struct llama_context_params result = { struct llama_context_params result = {
/*.seed =*/ LLAMA_DEFAULT_SEED, /*.seed =*/ LLAMA_DEFAULT_SEED,
/*.n_ctx =*/ 512, /*.n_ctx =*/ 512,
/*.rope_freq_base =*/ 10000.0f,
/*.rope_freq_scale =*/ 1.0f,
/*.n_batch =*/ 512, /*.n_batch =*/ 512,
/*.gpu_layers =*/ 0, /*.gpu_layers =*/ 0,
/*.main_gpu =*/ 0, /*.main_gpu =*/ 0,
/*.tensor_split =*/ {0}, /*.tensor_split =*/ {0},
/*.rope_freq_base =*/ 10000.0f,
/*.rope_freq_scale =*/ 1.0f,
/*.progress_callback =*/ nullptr, /*.progress_callback =*/ nullptr,
/*.progress_callback_user_data =*/ nullptr, /*.progress_callback_user_data =*/ nullptr,
/*.low_vram =*/ false, /*.low_vram =*/ false,
@ -968,12 +970,12 @@ static void llama_model_load_internal(
llama_model & model, llama_model & model,
llama_vocab & vocab, llama_vocab & vocab,
int n_ctx, int n_ctx,
float rope_freq_base,
float rope_freq_scale,
int n_batch, int n_batch,
int n_gpu_layers, int n_gpu_layers,
int main_gpu, int main_gpu,
const float * tensor_split, const float * tensor_split,
float rope_freq_base,
float rope_freq_scale,
bool low_vram, bool low_vram,
ggml_type memory_type, ggml_type memory_type,
bool use_mmap, bool use_mmap,
@ -1008,26 +1010,27 @@ static void llama_model_load_internal(
} }
hparams.n_ctx = n_ctx; hparams.n_ctx = n_ctx;
hparams.rope_freq_base = rope_freq_base;
hparams.rope_freq_base = rope_freq_base;
hparams.rope_freq_scale = rope_freq_scale; hparams.rope_freq_scale = rope_freq_scale;
} }
const uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult; const uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
{ {
fprintf(stderr, "%s: format = %s\n", __func__, llama_file_version_name(file_version)); 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_vocab = %u\n", __func__, hparams.n_vocab);
fprintf(stderr, "%s: n_ctx = %u\n", __func__, hparams.n_ctx); 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_embd = %u\n", __func__, hparams.n_embd);
fprintf(stderr, "%s: n_mult = %u\n", __func__, hparams.n_mult); 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_head = %u\n", __func__, hparams.n_head);
fprintf(stderr, "%s: n_layer = %u\n", __func__, hparams.n_layer); 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: n_rot = %u\n", __func__, hparams.n_rot);
fprintf(stderr, "%s: freq_base = %.1f\n", __func__, hparams.rope_freq_base); 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: freq_scale = %g\n", __func__, hparams.rope_freq_scale);
fprintf(stderr, "%s: ftype = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype)); 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_ff = %u\n", __func__, n_ff);
fprintf(stderr, "%s: model size = %s\n", __func__, llama_model_type_name(model.type)); fprintf(stderr, "%s: model size = %s\n", __func__, llama_model_type_name(model.type));
} }
if (file_version < LLAMA_FILE_VERSION_GGJT_V2) { if (file_version < LLAMA_FILE_VERSION_GGJT_V2) {
@ -1278,12 +1281,12 @@ static bool llama_model_load(
llama_model & model, llama_model & model,
llama_vocab & vocab, llama_vocab & vocab,
int n_ctx, int n_ctx,
float rope_freq_base,
float rope_freq_scale,
int n_batch, int n_batch,
int n_gpu_layers, int n_gpu_layers,
int main_gpu, int main_gpu,
float * tensor_split, float * tensor_split,
float rope_freq_base,
float rope_freq_scale,
bool low_vram, bool low_vram,
ggml_type memory_type, ggml_type memory_type,
bool use_mmap, bool use_mmap,
@ -1292,7 +1295,7 @@ static bool llama_model_load(
llama_progress_callback progress_callback, llama_progress_callback progress_callback,
void *progress_callback_user_data) { void *progress_callback_user_data) {
try { try {
llama_model_load_internal(fname, model, vocab, n_ctx, rope_freq_base, rope_freq_scale, n_batch, n_gpu_layers, main_gpu, tensor_split, low_vram, memory_type, llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, rope_freq_base, rope_freq_scale, low_vram, memory_type,
use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data); use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data);
return true; return true;
} catch (const std::exception & err) { } catch (const std::exception & err) {
@ -1342,9 +1345,10 @@ static bool llama_eval_internal(
const int n_head = hparams.n_head; const int n_head = hparams.n_head;
const int n_vocab = hparams.n_vocab; const int n_vocab = hparams.n_vocab;
const int n_rot = hparams.n_embd/hparams.n_head; const int n_rot = hparams.n_embd/hparams.n_head;
const int n_gpu_layers = model.n_gpu_layers;
const float freq_base = hparams.rope_freq_base; const float freq_base = hparams.rope_freq_base;
const float freq_scale = hparams.rope_freq_scale; const float freq_scale = hparams.rope_freq_scale;
const int n_gpu_layers = model.n_gpu_layers;
auto & mem_per_token = lctx.mem_per_token; auto & mem_per_token = lctx.mem_per_token;
auto & buf_compute = lctx.buf_compute; auto & buf_compute = lctx.buf_compute;
@ -2689,9 +2693,9 @@ struct llama_model * llama_load_model_from_file(
ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32; 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.rope_freq_base, params.rope_freq_scale, if (!llama_model_load(path_model, *model, model->vocab, params.n_ctx, params.n_batch, params.n_gpu_layers,
params.n_batch, params.n_gpu_layers, params.main_gpu, params.tensor_split, params.low_vram, memory_type, params.main_gpu, params.tensor_split, params.rope_freq_base, params.rope_freq_scale,params.low_vram,
params.use_mmap, params.use_mlock, params.vocab_only, params.progress_callback, memory_type, params.use_mmap, params.use_mlock, params.vocab_only, params.progress_callback,
params.progress_callback_user_data)) { params.progress_callback_user_data)) {
delete model; delete model;
fprintf(stderr, "%s: failed to load model\n", __func__); fprintf(stderr, "%s: failed to load model\n", __func__);

7
llama.h
View file

@ -85,12 +85,15 @@ extern "C" {
struct llama_context_params { struct llama_context_params {
uint32_t seed; // RNG seed, -1 for random uint32_t seed; // RNG seed, -1 for random
int32_t n_ctx; // text context int32_t n_ctx; // text context
float rope_freq_base; // RoPE base frequency
float rope_freq_scale; // RoPE frequency scaling factor
int32_t n_batch; // prompt processing batch size int32_t n_batch; // prompt processing batch size
int32_t n_gpu_layers; // number of layers to store in VRAM int32_t n_gpu_layers; // number of layers to store in VRAM
int32_t main_gpu; // the GPU that is used for scratch and small tensors int32_t main_gpu; // the GPU that is used for scratch and small tensors
float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs
// ref: https://github.com/ggerganov/llama.cpp/pull/2054
float rope_freq_base; // RoPE base frequency
float rope_freq_scale; // RoPE frequency scaling factor
// called with a progress value between 0 and 1, pass NULL to disable // called with a progress value between 0 and 1, pass NULL to disable
llama_progress_callback progress_callback; llama_progress_callback progress_callback;
// context pointer passed to the progress callback // context pointer passed to the progress callback