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talk-llama : sync llama.cpp

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Georgi Gerganov 2024-02-25 20:00:10 +02:00
parent 7a6e385c1b
commit 3170841ed9
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2 changed files with 89 additions and 61 deletions
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120
examples/talk-llama/llama.cpp
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@ -509,7 +509,6 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
@ -851,9 +850,9 @@ struct LLM_TN {
//
static std::map<int32_t, const char *> LLAMA_ROPE_SCALING_TYPES = {
{ LLAMA_ROPE_SCALING_NONE, "none" },
{ LLAMA_ROPE_SCALING_LINEAR, "linear" },
{ LLAMA_ROPE_SCALING_YARN, "yarn" },
{ LLAMA_ROPE_SCALING_TYPE_NONE, "none" },
{ LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" },
{ LLAMA_ROPE_SCALING_TYPE_YARN, "yarn" },
};
static int32_t llama_rope_scaling_type_from_string(const std::string & name) {
@ -863,7 +862,7 @@ static int32_t llama_rope_scaling_type_from_string(const std::string & name) {
}
}
return LLAMA_ROPE_SCALING_UNSPECIFIED;
return LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
}
static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) {
@ -1581,7 +1580,7 @@ struct llama_hparams {
bool causal_attn = true;
bool need_kq_pos = false;
uint32_t pooling_type = LLAMA_POOLING_NONE;
uint32_t pooling_type = LLAMA_POOLING_TYPE_NONE;
bool operator!=(const llama_hparams & other) const {
if (this->vocab_only != other.vocab_only) return true;
@ -2346,9 +2345,9 @@ namespace GGUFMeta {
static const char * override_type_to_str(const llama_model_kv_override_type ty) {
switch (ty) {
case LLAMA_KV_OVERRIDE_BOOL: return "bool";
case LLAMA_KV_OVERRIDE_INT: return "int";
case LLAMA_KV_OVERRIDE_FLOAT: return "float";
case LLAMA_KV_OVERRIDE_TYPE_BOOL: return "bool";
case LLAMA_KV_OVERRIDE_TYPE_INT: return "int";
case LLAMA_KV_OVERRIDE_TYPE_FLOAT: return "float";
}
return "unknown";
}
@ -2359,13 +2358,13 @@ namespace GGUFMeta {
LLAMA_LOG_INFO("%s: Using metadata override (%5s) '%s' = ",
__func__, override_type_to_str(override->tag), override->key);
switch (override->tag) {
case LLAMA_KV_OVERRIDE_BOOL: {
case LLAMA_KV_OVERRIDE_TYPE_BOOL: {
LLAMA_LOG_INFO("%s\n", override->bool_value ? "true" : "false");
} break;
case LLAMA_KV_OVERRIDE_INT: {
case LLAMA_KV_OVERRIDE_TYPE_INT: {
LLAMA_LOG_INFO("%" PRId64 "\n", override->int_value);
} break;
case LLAMA_KV_OVERRIDE_FLOAT: {
case LLAMA_KV_OVERRIDE_TYPE_FLOAT: {
LLAMA_LOG_INFO("%.6f\n", override->float_value);
} break;
default:
@ -2384,7 +2383,7 @@ namespace GGUFMeta {
template<typename OT>
static typename std::enable_if<std::is_same<OT, bool>::value, bool>::type
try_override(OT & target, const struct llama_model_kv_override *override) {
if (validate_override(LLAMA_KV_OVERRIDE_BOOL, override)) {
if (validate_override(LLAMA_KV_OVERRIDE_TYPE_BOOL, override)) {
target = override->bool_value;
return true;
}
@ -2394,7 +2393,7 @@ namespace GGUFMeta {
template<typename OT>
static typename std::enable_if<!std::is_same<OT, bool>::value && std::is_integral<OT>::value, bool>::type
try_override(OT & target, const struct llama_model_kv_override *override) {
if (validate_override(LLAMA_KV_OVERRIDE_INT, override)) {
if (validate_override(LLAMA_KV_OVERRIDE_TYPE_INT, override)) {
target = override->int_value;
return true;
}
@ -2404,7 +2403,7 @@ namespace GGUFMeta {
template<typename OT>
static typename std::enable_if<std::is_floating_point<OT>::value, bool>::type
try_override(T & target, const struct llama_model_kv_override *override) {
if (validate_override(LLAMA_KV_OVERRIDE_FLOAT, override)) {
if (validate_override(LLAMA_KV_OVERRIDE_TYPE_FLOAT, override)) {
target = override->float_value;
return true;
}
@ -2546,6 +2545,7 @@ struct llama_model_loader {
case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break;
case GGML_TYPE_IQ1_S: ftype = LLAMA_FTYPE_MOSTLY_IQ1_S; break;
case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break;
case GGML_TYPE_IQ3_S: ftype = LLAMA_FTYPE_MOSTLY_IQ3_S; break;
default:
{
LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
@ -2891,6 +2891,8 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw";
case LLAMA_FTYPE_MOSTLY_IQ1_S :return "IQ1_S - 1.5625 bpw";
case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw";
case LLAMA_FTYPE_MOSTLY_IQ3_S: return "IQ3_S - 3.4375 bpw";
case LLAMA_FTYPE_MOSTLY_IQ3_M: return "IQ3_S mix - 3.66 bpw";
default: return "unknown, may not work";
}
@ -2997,7 +2999,7 @@ static void llm_load_hparams(
std::string rope_scaling("linear");
ml.get_key(LLM_KV_ROPE_SCALING_TYPE, rope_scaling, false);
hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling);
GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_UNSPECIFIED);
GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED);
// rope_freq_scale (inverse of the kv) is optional
float ropescale = 0.0f;
@ -3641,7 +3643,7 @@ static bool llm_load_tensors(
model.buft_layer[i] = llama_default_buffer_type_cpu(true);
}
if (split_mode == LLAMA_SPLIT_LAYER) {
if (split_mode == LLAMA_SPLIT_MODE_LAYER) {
// calculate the split points
int device_count = llama_get_device_count();
bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; });
@ -3680,10 +3682,10 @@ static bool llm_load_tensors(
}
} else {
ggml_backend_buffer_type_t split_buft;
if (split_mode == LLAMA_SPLIT_ROW) {
if (split_mode == LLAMA_SPLIT_MODE_ROW) {
split_buft = llama_default_buffer_type_split(main_gpu, tensor_split);
} else {
// LLAMA_SPLIT_NONE or LLAMA_SPLIT_LAYER in backends where it is not supported
// LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_LAYER in backends where it is not supported
split_buft = llama_default_buffer_type_offload(main_gpu);
}
// assign the repeating layers
@ -4056,7 +4058,10 @@ static bool llm_load_tensors(
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, false);
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
// same as tok_embd, duplicated to allow offloading
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
ml.n_created--; // artificial tensor
ml.size_data += ggml_nbytes(model.output);
}
for (int i = 0; i < n_layer; ++i) {
@ -5065,7 +5070,7 @@ struct llm_build_context {
kv_head (worst_case ? n_ctx - n_tokens : kv_self.head),
n_orig_ctx (cparams.n_yarn_orig_ctx),
do_rope_shift (worst_case || kv_self.has_shift),
pooling_type (cparams.do_pooling ? hparams.pooling_type : (uint32_t)LLAMA_POOLING_NONE),
pooling_type (cparams.do_pooling ? hparams.pooling_type : (uint32_t)LLAMA_POOLING_TYPE_NONE),
cb (cb),
buf_compute_meta (lctx.buf_compute_meta) {
// all initializations should be done in init()
@ -6045,12 +6050,12 @@ struct llm_build_context {
cur = inpL;
// pooling layer
if (pooling_type == LLAMA_POOLING_MEAN) {
if (pooling_type == LLAMA_POOLING_TYPE_MEAN) {
cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean);
} else if (pooling_type == LLAMA_POOLING_CLS) {
} else if (pooling_type == LLAMA_POOLING_TYPE_CLS) {
cur = ggml_get_rows(ctx0, cur, inp_cls);
} else {
GGML_ASSERT(pooling_type == LLAMA_POOLING_NONE && "Invalid pooling type");
GGML_ASSERT(pooling_type == LLAMA_POOLING_TYPE_NONE && "Invalid pooling type");
}
cb(cur, "result_embd", -1);
@ -7749,7 +7754,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
}
}
if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_MEAN) {
if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) {
const int64_t n_tokens = batch.n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer));
@ -7777,7 +7782,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
}
}
if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_CLS) {
if (cparams.do_pooling && hparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
const int64_t n_tokens = batch.n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
@ -10542,6 +10547,12 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_Q3_K : GGML_TYPE_IQ3_XXS;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_S && qs.model.hparams.n_gqa() >= 4) {
new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
}
@ -10573,13 +10584,17 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
new_type = GGML_TYPE_Q8_0;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) {
new_type = GGML_TYPE_Q2_K;
new_type = GGML_TYPE_IQ3_XXS;
}
} else if (name.find("attn_q.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) {
new_type = GGML_TYPE_IQ3_XXS;
}
} else if (name.find("ffn_down") != std::string::npos) {
auto info = layer_info(qs.i_ffn_down, qs.n_ffn_down, name.c_str());
int i_layer = info.first, n_layer = info.second;
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) {
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) {
if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) {
@ -10590,6 +10605,10 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
: arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K
: GGML_TYPE_Q3_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M && (i_layer < n_layer/8 ||
(qs.model.hparams.n_expert == 8 && use_more_bits(i_layer, n_layer)))) {
new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
new_type = arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
}
@ -10621,37 +10640,41 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
if (qs.model.hparams.n_expert == 8) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL ||
ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S ||
ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
new_type = GGML_TYPE_Q5_K;
}
} else {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K;
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ) new_type = GGML_TYPE_Q4_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ) new_type = GGML_TYPE_Q5_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M ) new_type = GGML_TYPE_Q4_K;
}
} else {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
}
}
else if (name.find("attn_qkv.weight") != std::string::npos) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K;
}
else if (name.find("ffn_gate") != std::string::npos) {
auto info = layer_info(qs.i_ffn_gate, qs.n_ffn_gate, name.c_str());
int i_layer = info.first, n_layer = info.second;
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS && !use_more_bits(i_layer, n_layer)) {
new_type = GGML_TYPE_Q2_K;
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
new_type = GGML_TYPE_IQ3_XXS;
}
++qs.i_ffn_gate;
}
else if (name.find("ffn_up") != std::string::npos) {
auto info = layer_info(qs.i_ffn_up, qs.n_ffn_up, name.c_str());
int i_layer = info.first, n_layer = info.second;
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS && !use_more_bits(i_layer, n_layer)) {
new_type = GGML_TYPE_Q2_K;
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
new_type = GGML_TYPE_IQ3_XXS;
}
++qs.i_ffn_up;
}
@ -10671,7 +10694,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K ||
new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS ||
new_type == GGML_TYPE_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) {
new_type == GGML_TYPE_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || new_type == GGML_TYPE_IQ3_S) {
int nx = tensor->ne[0];
int ny = tensor->ne[1];
if (nx % QK_K != 0) {
@ -10686,6 +10709,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
case GGML_TYPE_IQ2_XXS:
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K: new_type = GGML_TYPE_IQ4_NL; break;
@ -10717,7 +10741,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
// K-quants
case LLAMA_FTYPE_MOSTLY_Q2_K_S:
case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break;
case LLAMA_FTYPE_MOSTLY_Q3_K_XS:
case LLAMA_FTYPE_MOSTLY_Q3_K_XS: quantized_type = GGML_TYPE_IQ3_S; break;
case LLAMA_FTYPE_MOSTLY_Q3_K_S:
case LLAMA_FTYPE_MOSTLY_Q3_K_M:
case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break;
@ -10731,6 +10755,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
case LLAMA_FTYPE_MOSTLY_IQ3_XXS: quantized_type = GGML_TYPE_IQ3_XXS; break;
case LLAMA_FTYPE_MOSTLY_IQ1_S: quantized_type = GGML_TYPE_IQ1_S; break;
case LLAMA_FTYPE_MOSTLY_IQ4_NL: quantized_type = GGML_TYPE_IQ4_NL; break;
case LLAMA_FTYPE_MOSTLY_IQ3_S: quantized_type = GGML_TYPE_IQ3_S; break;
case LLAMA_FTYPE_MOSTLY_IQ3_M: quantized_type = GGML_TYPE_IQ3_S; break;
default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
}
@ -11325,7 +11351,7 @@ static int llama_apply_lora_from_file_internal(
struct llama_model_params llama_model_default_params() {
struct llama_model_params result = {
/*.n_gpu_layers =*/ 0,
/*.split_mode =*/ LLAMA_SPLIT_LAYER,
/*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER,
/*.main_gpu =*/ 0,
/*.tensor_split =*/ nullptr,
/*.progress_callback =*/ nullptr,
@ -11351,7 +11377,7 @@ struct llama_context_params llama_context_default_params() {
/*.n_batch =*/ 512,
/*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
/*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS,
/*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_UNSPECIFIED,
/*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
/*.rope_freq_base =*/ 0.0f,
/*.rope_freq_scale =*/ 0.0f,
/*.yarn_ext_factor =*/ -1.0f,
@ -11539,16 +11565,16 @@ struct llama_context * llama_new_context_with_model(
cparams.cb_eval_user_data = params.cb_eval_user_data;
auto rope_scaling_type = params.rope_scaling_type;
if (rope_scaling_type == LLAMA_ROPE_SCALING_UNSPECIFIED) {
if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED) {
rope_scaling_type = hparams.rope_scaling_type_train;
}
if (rope_scaling_type == LLAMA_ROPE_SCALING_NONE) {
if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_NONE) {
cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none
}
if (cparams.yarn_ext_factor < 0.0f) { // negative indicates 'not set'
cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_YARN ? 1.0f : 0.0f;
cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f;
}
if (params.seed == LLAMA_DEFAULT_SEED) {
@ -11582,8 +11608,8 @@ struct llama_context * llama_new_context_with_model(
}
#elif defined(GGML_USE_CUBLAS)
if (model->n_gpu_layers > 0) {
// with split_mode LLAMA_SPLIT_NONE or LLAMA_SPLIT_ROW, only the main GPU backend is used
if (model->split_mode == LLAMA_SPLIT_NONE || model->split_mode == LLAMA_SPLIT_ROW) {
// with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu);
if (backend == nullptr) {
LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu);
@ -11592,7 +11618,7 @@ struct llama_context * llama_new_context_with_model(
}
ctx->backends.push_back(backend);
} else {
// LLAMA_SPLIT_LAYER requires a backend for each GPU
// LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) {
ggml_backend_t backend = ggml_backend_cuda_init(device);
if (backend == nullptr) {

30
examples/talk-llama/llama.h
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@ -102,28 +102,30 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ1_S = 24, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ4_NL = 25, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ3_S = 26, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ3_M = 27, // except 1d tensors
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
};
enum llama_rope_scaling_type {
LLAMA_ROPE_SCALING_UNSPECIFIED = -1,
LLAMA_ROPE_SCALING_NONE = 0,
LLAMA_ROPE_SCALING_LINEAR = 1,
LLAMA_ROPE_SCALING_YARN = 2,
LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN,
LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1,
LLAMA_ROPE_SCALING_TYPE_NONE = 0,
LLAMA_ROPE_SCALING_TYPE_LINEAR = 1,
LLAMA_ROPE_SCALING_TYPE_YARN = 2,
LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_YARN,
};
enum llama_pooling_type {
LLAMA_POOLING_NONE = 0,
LLAMA_POOLING_MEAN = 1,
LLAMA_POOLING_CLS = 2,
LLAMA_POOLING_TYPE_NONE = 0,
LLAMA_POOLING_TYPE_MEAN = 1,
LLAMA_POOLING_TYPE_CLS = 2,
};
enum llama_split_mode {
LLAMA_SPLIT_NONE = 0, // single GPU
LLAMA_SPLIT_LAYER = 1, // split layers and KV across GPUs
LLAMA_SPLIT_ROW = 2, // split rows across GPUs
LLAMA_SPLIT_MODE_NONE = 0, // single GPU
LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs
LLAMA_SPLIT_MODE_ROW = 2, // split rows across GPUs
};
typedef struct llama_token_data {
@ -171,9 +173,9 @@ extern "C" {
} llama_batch;
enum llama_model_kv_override_type {
LLAMA_KV_OVERRIDE_INT,
LLAMA_KV_OVERRIDE_FLOAT,
LLAMA_KV_OVERRIDE_BOOL,
LLAMA_KV_OVERRIDE_TYPE_INT,
LLAMA_KV_OVERRIDE_TYPE_FLOAT,
LLAMA_KV_OVERRIDE_TYPE_BOOL,
};
struct llama_model_kv_override {