vbatts/llama.cpp
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Bad indents and trailing whitespaces

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Nexesenex 2024-08-25 03:30:36 +02:00
parent f63860eaac
commit dd3df754b2
1 changed files with 9 additions and 9 deletions
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18
src/llama.cpp
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@ -16386,8 +16386,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
// original formula use_more_bits :
// return i_layer < n_layers/8 || i_layer >= 7*n_layers/8 || (i_layer - n_layers/8)%3 == 2;
// The intervals of 3 are replaced by a broad bump in the central layers.
// In the case of a 32 layers model, layers 5-7 and layers 12-16 are always skipped.
// In the case of a 40 layers model, layers 6-9 and layers 15-20 are always skipped.
// In the case of a 32 layers model, layers 5-7 and layers 12-16 are always skipped.
// In the case of a 40 layers model, layers 6-9 and layers 15-20 are always skipped.
// difquant_half_tensors replaces it and keeps the broad 50% bump to the upper quant. Ex : 16/32
auto difquant_half_tensors = [](int i_layer, int n_layers) -> bool {
return i_layer <= n_layers/8 || i_layer > 6*n_layers/8 || (i_layer >= 2*n_layers/8 && i_layer < 3*n_layers/8);
@ -16448,7 +16448,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
else new_type = GGML_TYPE_IQ4_XS;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_XL || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_S) {
ftype == LLAMA_FTYPE_MOSTLY_IQ2_S) {
if (qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q6_K;
else if (qs.model.hparams.n_head() <= 20) new_type = GGML_TYPE_IQ4_XS;
else new_type = GGML_TYPE_Q4_K;
@ -16478,7 +16478,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
if (qs.model.hparams.n_expert >= 4) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q2_K_L ||
ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ||
ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XL) {
ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XL) {
new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS ||
@ -16611,20 +16611,20 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_L) {
if (qs.model.hparams.n_gqa() >= 2 || qs.model.hparams.n_expert >= 2) new_type = GGML_TYPE_Q4_K;
else new_type = GGML_TYPE_Q3_K;
else new_type = GGML_TYPE_Q3_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) {
if (qs.model.hparams.n_gqa() >= 2 || qs.model.hparams.n_expert >= 2) new_type = GGML_TYPE_Q4_K;
else new_type = GGML_TYPE_Q3_K;
else new_type = GGML_TYPE_Q3_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 2) new_type = GGML_TYPE_Q5_K;
else if (qs.model.hparams.n_gqa() >= 2) new_type = GGML_TYPE_Q4_K;
else new_type = GGML_TYPE_Q3_K;
else new_type = GGML_TYPE_Q3_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XL) {
if (qs.model.hparams.n_gqa() >= 2 || qs.model.hparams.n_expert >= 2) new_type = GGML_TYPE_Q5_K;
else new_type = GGML_TYPE_Q4_K;
else new_type = GGML_TYPE_Q4_K;
}
else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S ||
ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) && (qs.model.hparams.n_gqa() >= 2 || qs.model.hparams.n_expert >= 2)) {
@ -16722,7 +16722,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) {
if (qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
else if (qs.model.hparams.n_gqa() >= 2 || qs.model.hparams.n_expert >= 2) new_type = GGML_TYPE_Q3_K;
else new_type = GGML_TYPE_Q2_K;
else new_type = GGML_TYPE_Q2_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XL) {
if (qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;