vbatts/llama.cpp
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update grok model loading

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slaren 2024-04-02 03:08:04 +02:00
parent 8f84ca3cd9
commit 5de4a5da07
1 changed files with 34 additions and 7 deletions
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39
llama.cpp
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@ -4556,12 +4556,39 @@ static bool llm_load_tensors(
GGML_ASSERT(hparams.n_expert > 0);
GGML_ASSERT(hparams.n_expert_used > 0);
// MoE branch
layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, hparams.n_expert}, false);
if (layer.ffn_gate_exps) {
layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, hparams.n_expert});
layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, hparams.n_expert});
} else {
// merge split expert into a single tensor
// requires disabling mmap
ml.use_mmap = false;
ggml_type type_gate = ml.get_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, 0).c_str())->type;
ggml_type type_down = ml.get_tensor_meta(tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, 0).c_str())->type;
ggml_type type_up = ml.get_tensor_meta(tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, 0).c_str())->type;
layer.ffn_gate_exps = ggml_new_tensor_3d(ctx_split, type_gate, n_embd, n_ff, hparams.n_expert);
layer.ffn_down_exps = ggml_new_tensor_3d(ctx_split, type_down, n_ff, n_embd, hparams.n_expert);
layer.ffn_up_exps = ggml_new_tensor_3d(ctx_split, type_up, n_embd, n_ff, hparams.n_expert);
ggml_set_name(layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i).c_str());
ggml_set_name(layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i).c_str());
ggml_set_name(layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i).c_str());
for (uint32_t x = 0; x < hparams.n_expert; ++x) {
GGML_ASSERT(!"not implemented");
//layer.ffn_gate_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), {n_embd, n_ff});
//layer.ffn_down_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd});
//layer.ffn_up_exp[x] = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), {n_embd, n_ff});
// the individual experts are loaded into a view of the merged tensor
ggml_tensor * ffn_gate_exp = ggml_view_2d(ctx_split, layer.ffn_gate_exps, n_embd, n_ff, layer.ffn_gate_exps->nb[1], layer.ffn_gate_exps->nb[2]*x);
ggml_tensor * ffn_down_exp = ggml_view_2d(ctx_split, layer.ffn_down_exps, n_ff, n_embd, layer.ffn_down_exps->nb[1], layer.ffn_down_exps->nb[2]*x);
ggml_tensor * ffn_up_exp = ggml_view_2d(ctx_split, layer.ffn_up_exps, n_embd, n_ff, layer.ffn_up_exps->nb[1], layer.ffn_up_exps->nb[2]*x);
ggml_set_name(ffn_gate_exp, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x).c_str());
ggml_set_name(ffn_down_exp, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x).c_str());
ggml_set_name(ffn_up_exp, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x).c_str());
ml.n_created += 3;
}
}
layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
@ -13322,7 +13349,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
kv_overrides = v->data();
}
llama_model_loader ml(fname_inp, use_mmap, kv_overrides);
ml.init_mappings(false); // no prefetching?
ml.init_mappings(false); // no prefetching
llama_model model;
llm_load_arch(ml, model);