vbatts/llama.cpp
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fix order of deepseek and deepseek2, move gigachat temlate to the end of func

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Valentin Mamedov 2024-12-15 13:42:33 +07:00
parent 66e59b0155
commit b32159c8a7
1 changed files with 74 additions and 74 deletions
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148
src/llama.cpp
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@ -238,8 +238,8 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_OLMOE, "olmoe" }, { LLM_ARCH_OLMOE, "olmoe" },
{ LLM_ARCH_OPENELM, "openelm" }, { LLM_ARCH_OPENELM, "openelm" },
{ LLM_ARCH_ARCTIC, "arctic" }, { LLM_ARCH_ARCTIC, "arctic" },
{ LLM_ARCH_DEEPSEEK2, "deepseek2" },
{ LLM_ARCH_DEEPSEEK, "deepseek" }, { LLM_ARCH_DEEPSEEK, "deepseek" },
{ LLM_ARCH_DEEPSEEK2, "deepseek2" },
{ LLM_ARCH_CHATGLM, "chatglm" }, { LLM_ARCH_CHATGLM, "chatglm" },
{ LLM_ARCH_BITNET, "bitnet" }, { LLM_ARCH_BITNET, "bitnet" },
{ LLM_ARCH_T5, "t5" }, { LLM_ARCH_T5, "t5" },
@ -1290,6 +1290,33 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
}, },
}, },
{
LLM_ARCH_DEEPSEEK,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
{ LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
{ LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" },
{ LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
},
},
{ {
LLM_ARCH_DEEPSEEK2, LLM_ARCH_DEEPSEEK2,
{ {
@ -1319,33 +1346,6 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" }, { LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
}, },
}, },
{
LLM_ARCH_DEEPSEEK,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
{ LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
{ LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" },
{ LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
},
},
{ {
LLM_ARCH_CHATGLM, LLM_ARCH_CHATGLM,
{ {
@ -6088,6 +6088,19 @@ static void llm_load_hparams(
model.type = e_model::MODEL_UNKNOWN; model.type = e_model::MODEL_UNKNOWN;
} }
} break; } break;
case LLM_ARCH_DEEPSEEK:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
switch (hparams.n_layer) {
case 28: model.type = e_model::MODEL_20B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_DEEPSEEK2: case LLM_ARCH_DEEPSEEK2:
{ {
bool is_lite = (hparams.n_layer == 27); bool is_lite = (hparams.n_layer == 27);
@ -6108,19 +6121,6 @@ static void llm_load_hparams(
default: model.type = e_model::MODEL_UNKNOWN; default: model.type = e_model::MODEL_UNKNOWN;
} }
} break; } break;
case LLM_ARCH_DEEPSEEK:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
switch (hparams.n_layer) {
case 28: model.type = e_model::MODEL_20B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_CHATGLM: case LLM_ARCH_CHATGLM:
{ {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@ -7099,6 +7099,13 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
LLAMA_LOG_INFO("%s: max token length = %d\n", __func__, vocab.max_token_len); LLAMA_LOG_INFO("%s: max token length = %d\n", __func__, vocab.max_token_len);
if (model.arch == LLM_ARCH_DEEPSEEK) {
LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
LLAMA_LOG_INFO("%s: n_expert_shared = %d\n", __func__, hparams.n_expert_shared);
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
}
if (model.arch == LLM_ARCH_DEEPSEEK2) { if (model.arch == LLM_ARCH_DEEPSEEK2) {
LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead); LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
LLAMA_LOG_INFO("%s: n_lora_q = %d\n", __func__, hparams.n_lora_q); LLAMA_LOG_INFO("%s: n_lora_q = %d\n", __func__, hparams.n_lora_q);
@ -7109,13 +7116,6 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
LLAMA_LOG_INFO("%s: rope_yarn_log_mul = %.4f\n", __func__, hparams.rope_yarn_log_mul); LLAMA_LOG_INFO("%s: rope_yarn_log_mul = %.4f\n", __func__, hparams.rope_yarn_log_mul);
} }
if (model.arch == LLM_ARCH_DEEPSEEK) {
LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
LLAMA_LOG_INFO("%s: n_expert_shared = %d\n", __func__, hparams.n_expert_shared);
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
}
if (model.arch == LLM_ARCH_QWEN2MOE) { if (model.arch == LLM_ARCH_QWEN2MOE) {
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp); LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp); LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp);
@ -22121,32 +22121,6 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) { if (add_ass) {
ss << "<|im_start|>assistant\n"; ss << "<|im_start|>assistant\n";
} }
} else if (tmpl == LLM_CHAT_TEMPLATE_GIGACHAT) {
// GigaChat template
bool has_system = !chat.empty() && std::string(chat[0]->role) == "system";
// Handle system message if present
if (has_system) {
ss << "<s>" << chat[0]->content << "<|message_sep|>";
} else {
ss << "<s>";
}
// Process remaining messages
for (size_t i = has_system ? 1 : 0; i < chat.size(); i++) {
std::string role(chat[i]->role);
if (role == "user") {
ss << "user<|role_sep|>" << chat[i]->content << "<|message_sep|>"
<< "available functions<|role_sep|>[]<|message_sep|>";
} else if (role == "assistant") {
ss << "assistant<|role_sep|>" << chat[i]->content << "<|message_sep|>";
}
}
// Add generation prompt if needed
if (add_ass) {
ss << "assistant<|role_sep|>";
}
} else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V7) { } else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V7) {
// Official mistral 'v7' template // Official mistral 'v7' template
// See: https://huggingface.co/mistralai/Mistral-Large-Instruct-2411#basic-instruct-template-v7 // See: https://huggingface.co/mistralai/Mistral-Large-Instruct-2411#basic-instruct-template-v7
@ -22450,6 +22424,32 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) { if (add_ass) {
ss << "<|start_of_role|>assistant<|end_of_role|>\n"; ss << "<|start_of_role|>assistant<|end_of_role|>\n";
} }
} else if (tmpl == LLM_CHAT_TEMPLATE_GIGACHAT) {
// GigaChat template
bool has_system = !chat.empty() && std::string(chat[0]->role) == "system";
// Handle system message if present
if (has_system) {
ss << "<s>" << chat[0]->content << "<|message_sep|>";
} else {
ss << "<s>";
}
// Process remaining messages
for (size_t i = has_system ? 1 : 0; i < chat.size(); i++) {
std::string role(chat[i]->role);
if (role == "user") {
ss << "user<|role_sep|>" << chat[i]->content << "<|message_sep|>"
<< "available functions<|role_sep|>[]<|message_sep|>";
} else if (role == "assistant") {
ss << "assistant<|role_sep|>" << chat[i]->content << "<|message_sep|>";
}
}
// Add generation prompt if needed
if (add_ass) {
ss << "assistant<|role_sep|>";
}
} else { } else {
// template not supported // template not supported
return -1; return -1;